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Presentation type:

ITS1 – Digital Geosciences

EGU24-1101 | ECS | Orals | ITS1.1/CL0.1.17

Monitoring The Development Of Land Heatwaves Using Spatiotemporal Models 

Swarnalee Mazumder, Sebastian Hahn, and Wolfgang Wagner

This study introduces an approach for land heatwave forecasting, using spatiotemporal machine learning models trained with ERA5 reanalysis data. We focused on key environmental variables like soil moisture, vegetation, and meteorological factors for modelling. The study utilized linear regression as a base model, augmented by more complex algorithms such as Random Forest (RF), XGBoost, and Graph Neural Networks (GNN). We defined heatwaves using temperature data from 1970-2000, and the training phase involved data from 2000 to 2020, focusing on predictive accuracy for 2021-2023. This methodology enabled a detailed exploration of heatwave trends and dynamics over an extended period. Finally, we used explainable AI methods to further deepen our understanding of the complex interplay between environmental variables and heatwave occurrences.

How to cite: Mazumder, S., Hahn, S., and Wagner, W.: Monitoring The Development Of Land Heatwaves Using Spatiotemporal Models, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1101, https://doi.org/10.5194/egusphere-egu24-1101, 2024.

Deep learning methods have emerged as a potential alternative for the complex problem of climate data downscaling. Precipitation downscaling is challenging due to its stochasticity, skewness, and sparse extreme values. Also, the extreme values are essential to preserve during downscaling and extrapolating future climate projections, as they serve as trivial signals for impact assessments. This research looks into the usefulness of a deep learning method designed for gridded precipitation downscaling, focusing on how well it can generalize and transfer what it learns. This study configures and evaluates a deep learning-based super-resolution neural network called the Super-Resolution Deep Residual Network (SRDRN). Several synthetic experiments are designed to assess its performance over four geographically and climatologically distinct domain boxes over the Indian subcontinent. Domain boxes over Central India (CI), Southern Peninsula (SP), Northwest (NW), and Northeast (NE), exhibiting diverse geographical and climatological characteristics, are chosen to assess the generalization and transferability of SRDRN. Following the training on a set of samples from CI, SP and NW, the performance of the models is evaluated in comparison to the Bias Correction and Spatial Disaggregation (BCSD), a renowned statistical downscaling method. NE is a transfer domain where the trained SRDRN models are directly applied without additional training or fine-tuning. Several objective evaluation metrics, like the Kling-Gupta Efficiency (KGE) score, root mean squared error, mean absolute relative error, and percentage bias, are chosen for the evaluation of SRDRN. The systematic assessment of SRDRN models (KGE~0.9) across these distinct regions reveals a substantial superiority of SRDRN over the BCSD method (KGE~0.7) in downscaling and reconstructing precipitation rates during the test period, along with preserving extreme values with high precision. In conclusion, SRDRN proves to be a promising alternative for the statistical downscaling of gridded precipitation.

Keywords: Precipitation, Statistical downscaling, Deep learning, Transfer learning, SRDRN

How to cite: Murukesh, M. and Kumar, P.: Downscaling and reconstruction of high-resolution precipitation fields using a deep residual neural network: An assessment over Indian subcontinent, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-2552, https://doi.org/10.5194/egusphere-egu24-2552, 2024.

EGU24-2819 | ECS | Orals | ITS1.1/CL0.1.17

Hybrid-Modeling of Land-Atmosphere Fluxes Using Integrated Machine Learning in the ICON-ESM Modeling Framework 

Reda ElGhawi, Christian Reimers, Reiner Schnur, Markus Reichstein, Marco Körner, Nuno Carvalhais, and Alexander J. Winkler

The exchange of water and carbon between the land-surface and the atmosphere is regulated by meteorological conditions as well as plant physiological processes. Accurate modeling of the coupled system is not only crucial for understanding local feedback loops but also for global-scale carbon and water cycle interactions. Traditional mechanistic modeling approaches, e.g., the Earth system model ICON-ESM with the land component JSBACH4, have long been used to study the land-atmosphere coupling. However, these models are hampered by relatively rigid functional representations of terrestrial biospheric processes, e.g., semi-empirical parametrizations for stomatal conductance.

Here, we develop data-driven, flexible parametrizations controlling terrestrial carbon-water coupling based on eddy-covariance flux measurements using machine learning (ML). Specifically, we introduce a hybrid modeling approach (integration of data-driven and mechanistic modeling), that aims to replace specific empirical parametrizations of the coupled photosynthesis (GPP ) and transpiration (Etr ) modules with ML models pre-trained on observations. First, as a proof-of-concept, we train parametrizations based on original JSBACH4 output to showcase that our approach succeeds in reconstructing the original parametrizations, namely latent dynamic features for stomatal (gs) and aerodynamic (ga) conductance, the carboxylation rate of RuBisCO (Vcmax), and the photosynthetic electron transport rate for RuBisCO regeneration (Jmax). Second, we replace JSBACH4’s original parametrizations by dynamically calling the emulator parameterizations trained on the original JSBACH4 output using a Python-FORTRAN bridge. This allows us to assess the impact of data-driven parametrizations on the output in the coupled land-surface model. In the last step, we adopt the approach to infer these parametrizations from FLUXNET observations to construct an observation-informed model of water and carbon fluxes in JSBACH4.

Preliminary results in emulating JSBACH4 parametrizations reveal R2 ranging between 0.91-0.99 and 0.92-0.97 for GPP, Etr, and the sensible heat flux QH  at half-hourly scale for forest and grassland sites, respectively. JSBACH4 with the plugged-in ML-emulator parametrizations provides very similar, but not identical predictions as the original JSBACH4. For example, R2 for Etr (gs) amounts to 0.91 (0.84) and 0.93 (0.86) at grassland and forest sites, respectively. These differences in the transpiration flux between original predictions and JSBACH4 with emulating parametrizations only result in minor changes in the system, e.g., the soil-water budget in the two models is almost the same (R2 of ~0.99). Based on these promising results of our proof-of-concept, we are now preparing the hybrid JSBACH4 model with parametrizations trained on FLUXNET observations.

This modeling framework will then serve as the foundation for coupled land-atmosphere simulations using ICON-ESM, where key biospheric processes are represented by our hybrid observation-informed land-surface model.

How to cite: ElGhawi, R., Reimers, C., Schnur, R., Reichstein, M., Körner, M., Carvalhais, N., and Winkler, A. J.: Hybrid-Modeling of Land-Atmosphere Fluxes Using Integrated Machine Learning in the ICON-ESM Modeling Framework, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-2819, https://doi.org/10.5194/egusphere-egu24-2819, 2024.

EGU24-3272 | ECS | Orals | ITS1.1/CL0.1.17

Reconstructing total water storage changes in the Yangtze River Basin based on deep learning models 

Jielong Wang, Yunzhong Shen, Joseph Awange, Ling Yang, and Qiujie Chen

Understanding long-term total water storage (TWS) changes in the Yangtze River Basin (YRB) is essential for optimizing water resource management and mitigating hydrological extremes. While the Gravity Recovery and Climate Experiment (GRACE) and its follow-on (GRACE-FO) mission have provided valuable observations for investigating global or regional TWS changes, the approximately one-year data gap between these missions and their relatively short 20-year data record limits our ability to study the continuous and long-term variability of YRB's TWS. In this study, two deep learning models are employed to bridge the data gap and reconstruct the historical TWS changes within YRB, respectively. For the data gap filling task, a noise-augmented u-shaped network (NA-UNet) is presented to address UNet's overfitting issues associated with training on limited GRACE observations. Results show that NA-UNet can accurately bridge the data gap, exhibiting favourable and stable performance at both the basin and grid scales. Subsequently, we introduce another deep learning model named RecNet, specifically designed to reconstruct the climate-driven TWS changes in YRB from 1923 to 2022. RecNet is trained on precipitation, temperature, and GRACE observations using a weighted mean square error (WMSE) loss function. We show that RecNet can successfully reconstruct the historical TWS changes, achieving strong correlations with GRACE, water budget estimates, hydrological models, drought indices, and existing reconstruction datasets. We also observe superior performance in RecNet when trained with WMSE compared to its non-weighted counterpart. In addition, the reconstructed datasets reveal a recurring occurrence of diverse hydrological extremes over the past century within YRB, influenced by major climate patterns. Together, NA-UNet and RecNet provide valuable observations for studying long-term climate variability and projecting future hydrological extremes in YRB, which can inform effective water resource management and contribute to the development of adaptive strategies for climate change.

How to cite: Wang, J., Shen, Y., Awange, J., Yang, L., and Chen, Q.: Reconstructing total water storage changes in the Yangtze River Basin based on deep learning models, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-3272, https://doi.org/10.5194/egusphere-egu24-3272, 2024.

EGU24-3307 | ECS | Posters virtual | ITS1.1/CL0.1.17

Comparative Study of Supervised Learning Algorithms on Rainfall Prediction using NEX-GDDP-CMIP6 Data 

Ratih Prasetya, Adhi Harmoko Saputro, Donaldi Sukma Permana, and Nelly Florida Riama

This study explores the transformative potential of supervised machine learning algorithms in improving rainfall prediction models for Indonesia. Leveraging the NEX-GDDP-CMIP6 dataset's high-resolution, global, and bias-corrected data, we compare various machine learning regression algorithms. Focusing on the EC Earth3 model, our approach involves an in-depth analysis of five weather variables closely tied to daily rainfall. We employed a diverse set of algorithms, including linear regression, K-nearest neighbor regression (KNN), random forest regression, decision tree regression, AdaBoost, extra tree regression, extreme gradient boosting regression (XGBoost), support vector regression (SVR), gradient boosting decision tree regression (GBDT), and multi-layer perceptron. Performance evaluation highlights the superior predictive capabilities of Gradient Boosting Decision Tree and KNN, achieving an impressive RMSE score of 0.04 and an accuracy score of 0.99. In contrast, XGBoost exhibits lower performance metrics, with an RMSE score of 5.1 and an accuracy score of 0.49, indicating poor rainfall prediction. This study contributes in advancing rainfall prediction models, hence emphasizing the improvement of methodological choices in harnessing machine learning for climate research.

How to cite: Prasetya, R., Harmoko Saputro, A., Sukma Permana, D., and Florida Riama, N.: Comparative Study of Supervised Learning Algorithms on Rainfall Prediction using NEX-GDDP-CMIP6 Data, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-3307, https://doi.org/10.5194/egusphere-egu24-3307, 2024.

EGU24-3499 | ECS | Orals | ITS1.1/CL0.1.17 | Highlight

A Hybrid Machine Learning Climate Simulation Using High Resolution Convection Modelling 

James Briant, Dan Giles, Cyril Morcrette, and Serge Guillas

Underrepresentation of cloud formation is a known failing in current climate simulations. The coarse grid resolution required by the computational constraint of integrating over long time scales does not permit the inclusion of underlying cloud generating physical processes. This work employs a multi-output Gaussian Process (MOGP) trained on high resolution Unified Model (UM) simulation data to predict the variability of temperature and specific humidity fields within the climate model. A proof-of-concept study has been carried out where a trained MOGP model is coupled in-situ with a simplified Atmospheric General Circulation Model (AGCM) named SPEEDY. The temperature and specific humidity profiles of the SPEEDY model outputs are perturbed at each timestep according to the predicted high resolution informed variability. 10-year forecasts are generated for both default SPEEDY and ML-hybrid SPEEDY models and output fields are compared ensuring hybrid model predictions remain representative of Earth's atmosphere. Some changes in the precipitation, outgoing longwave and shortwave radiation patterns are observed indicating modelling improvements in the complex region surrounding India and the Indian sea.

How to cite: Briant, J., Giles, D., Morcrette, C., and Guillas, S.: A Hybrid Machine Learning Climate Simulation Using High Resolution Convection Modelling, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-3499, https://doi.org/10.5194/egusphere-egu24-3499, 2024.

EGU24-3614 | Orals | ITS1.1/CL0.1.17

From climate to weather reconstruction with inexpensive neural networks 

Martin Wegmann and Fernando Jaume-Santero

Understanding atmospheric variability is essential for adapting to future climate extremes. Key ways to do this are through analysing climate field reconstructions and reanalyses. However, producing such reconstructions can be limited by high production costs, unrealistic linearity assumptions, or uneven distribution of local climate records. 

Here, we present a machine learning-based non-linear climate variability reconstruction method using a Recurrent Neural Network that is able to learn from existing model outputs and reanalysis data. As a proof-of-concept, we reconstructed more than 400 years of global, monthly temperature anomalies based on sparse, realistically distributed pseudo-station data.

Our reconstructions show realistic temperature patterns and magnitude reproduction costing about 1 hour on a middle-class laptop. We highlight the method’s capability in terms of mean statistics compared to more established methods and find that it is also suited to reconstruct specific climate events. This approach can easily be adapted for a wide range of regions, periods and variables. As additional work-in-progress we show output of this approach for reconstructing European weather in 1807, including the extreme summer heatwave of that year.

How to cite: Wegmann, M. and Jaume-Santero, F.: From climate to weather reconstruction with inexpensive neural networks, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-3614, https://doi.org/10.5194/egusphere-egu24-3614, 2024.

EGU24-3640 | ECS | Posters on site | ITS1.1/CL0.1.17

Exploiting Pseudo Wells in a Synthetic Sedimentary Basin: a simulation in the Santos Off-Shore Basin in the Southeast Atlantic portion of Brazil, using synthetic TOC for k-means classification. 

Victor Carreira, Milena Silva, Igor Venancio, André Belem, Igor Viegas, André Spigolon, Ana Luiza Albuquerque, and Pedro Vitor

Shales are important rocks that store a significant amount of Organic Content. In this work, we present applications of realistic synthetic simulations using real-scaled geological sections. The case of the study is Santos Sedimentary Basin, a well-known and well-studied Geologic Basin. This synthetic data improves the performance of our IA for TOC estimators. Besides, it reduces costs and resources concerning data acquisition for IA simulations. The work consists of reconstructing a pseudo-well formed in a fracture zone modelled through an accurate 2D geological section. To simulate the effects of a fracture zone on geophysical logging data, we present the law of mixtures based on well-drilling concepts, whose objective is to impose geometric conditions on the set of subsurface rock packages. We generated four rock packs belonging to two mixed classes. Tests with noisy synthetic data produced by an accurate geological section were developed and classified using the proposed method (Carreira et al., 2024). Firstly, we go for a more controlled problem and simulate well-log data directly from an interpreted geologic cross-section. We then define two specific training data sets composed of density (RHOB), sonic (DT), spontaneous potential (SP) and gamma-ray (GR) logs,  and  Total Organic Carbon (TOC), spontaneous potential (SP), density (RHOB) and photoelectric effect (PE) all simulated through a Gaussian distribution function per lithology. Acquiring the sonic profile is essential not only for estimating the porosity of the rocks but also for in-depth simulations of the Total Organic Content (TOC) with the geological units cut by the synthetic wells. Since most wells Exploitation does not have this profile well and it is not economically viable to make a new acquisition, resorting to the nonlinear regression models to estimate the sonic profile showed that it is an important feature. We estimate the observed Total Organic Carbon (TOC) measurements using Passey and Wang's (2016) methodology to input data into the k-means classification model. The synthetic model proposed showed promissory results indicating that linear dependency may underscore k-means shale classification. 

How to cite: Carreira, V., Silva, M., Venancio, I., Belem, A., Viegas, I., Spigolon, A., Albuquerque, A. L., and Vitor, P.: Exploiting Pseudo Wells in a Synthetic Sedimentary Basin: a simulation in the Santos Off-Shore Basin in the Southeast Atlantic portion of Brazil, using synthetic TOC for k-means classification., EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-3640, https://doi.org/10.5194/egusphere-egu24-3640, 2024.

EGU24-4460 | Orals | ITS1.1/CL0.1.17 | Highlight

Huge Ensembles of Weather Extremes using the Fourier Forecasting Neural Network 

William Collins, Michael Pritchard, Noah Brenowitz, Yair Cohen, Peter Harrington, Karthik Kashinath, Ankur Mahesh, and Shashank Subramanian

Studying low-likelihood high-impact extreme weather and climate events in a warming world requires massive
ensembles to capture long tails of multi-variate distributions. In combination, it is simply impossible to generate
massive ensembles, of say 10,000 members, using traditional numerical simulations of climate models at high
resolution. We describe how to bring the power of machine learning (ML) to replace traditional numerical
simulations for short week-long hindcasts of massive ensembles, where ML has proven to be successful in terms of
accuracy and fidelity, at five orders-of-magnitude lower computational cost than numerical methods. Because
the ensembles are reproducible to machine precision, ML also provides a data compression mechanism to
avoid storing the data produced from massive ensembles. The machine learning algorithm FourCastNet (FCN) is
based on Fourier Neural Operators and Transformers, proven to be efficient and powerful in modeling a wide
range of chaotic dynamical systems, including turbulent flows and atmospheric dynamics. FCN has already been
proven to be highly scalable on GPU-based HPC systems. 

We discuss our progress using statistics metrics for extremes adopted from operational NWP centers to show
that FCN is sufficiently accurate as an emulator of these phenomena. We also show how to construct huge
ensembles through a combination of perturbed-parameter techniques and a variant of bred vectors to generate a
large suite of initial conditions that maximize growth rates of ensemble spread. We demonstrate that these
ensembles exhibit a ratio of ensemble spread relative to RMSE that is nearly identical to one, a key metric of
successful near-term NWP systems. We conclude by applying FCN to severe heat waves in the recent climate
record.

How to cite: Collins, W., Pritchard, M., Brenowitz, N., Cohen, Y., Harrington, P., Kashinath, K., Mahesh, A., and Subramanian, S.: Huge Ensembles of Weather Extremes using the Fourier Forecasting Neural Network, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-4460, https://doi.org/10.5194/egusphere-egu24-4460, 2024.

As communities observe recurring regional weather patterns they will often ascribe colloquial names to them such as the Meiyu in East Asia or the Santa Ana winds of California. However, attaching quantitative characterizations to these same names often proves challenging. Classically heuristics have been developed for particular locations and climate phenomena, but their inherent subjectivity undermine the robustness of any subsequent quantitative analysis. To develop a neutral universal mesoscale metric we start by observing that the spatial distribution of rain in a given region is controlled by the interplay between the meteorological parameters (humidity, wind, pressure etc.) and the Earth’s topography. As a result, each recurring climactic phenomena exhibits a unique regional signature/distribution. Unlike at the synoptic scale, mesoscale climate patterns are largely stationary and an accumulation of two decades of high resolution satellite observations means that these patterns can now be reliably numerically extracted. The key additional observation is that at the mesoscale climate phenomena typically have either one or two non-co-occurring stationary states. This allows us to isolate patterns by a simple bifurcating of the subspace of the first two singular vectors. The end result behaves like a trivial Empirical Orthogonal Function (EOF) rotation that has a clear interpretation. It isolates the climate patterns as basis vectors and allows us to subsequently estimate the presence of the climate phenomena at arbitrary timescales. As a case study we use gridded precipitation data from NASA’s Global Precipitation Measurement (GPM) mission (compiled in to the IMERG dataset) in several regions and timescales of particular interest

How to cite: Kontsevich, G. and Löwemark, L.: Using IMERG precipitation patterns to index climate at the mesoscale: A basis rotation method based on climate bistability - an update, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-4507, https://doi.org/10.5194/egusphere-egu24-4507, 2024.

EGU24-5033 | ECS | Posters on site | ITS1.1/CL0.1.17

Causal inference of the CO2 fertilisation effect from ecosystem flux measurements 

Samantha Biegel, Konrad Schindler, and Benjamin Stocker

Land ecosystems play an important role in the carbon cycle, and hence the climate system. The engine of this cycle is Gross Primary Production (GPP), the assimilation of CO2 via photosynthesis at the ecosystem scale. Photosynthesis is directly affected by rising CO2 levels which, in turn, is expected to increase GPP and alter the dynamics of the carbon cycle. However, there is substantial uncertainty about the magnitude and geographical variability of the CO2 fertilisation effect (CFE) on GPP.

We use a large collection of eddy covariance measurements (317 sites, 2226 site-years), paired with remotely sensed information of vegetation greenness to estimate the effect of rising CO2 levels on GPP. We propose a hybrid modelling architecture, combining a physically-grounded process model based on eco-evolutionary optimality theory and a deep learning model. The intuition is that the process model represents the current understanding of the CFE, whereas the deep learning model does not implement explicit physical relations but has a higher capacity to learn effects of large and fast variations in the light, temperature, and moisture environment. The hybrid model is set up to learn a correction on the theoretically expected CFE. This makes it more effective in distilling the relatively small and gradual CFE. 

Our study investigates inherent limitations of different models when it comes to drawing conclusions about the CO2 fertilisation effect. Often, these limitations are due to the presence of latent confounders that give rise to spurious correlations. A promising avenue to address them is therefore the use of causal inference techniques. We show that one way to investigate causality is to test whether the trained hybrid model and its estimate of the CFE is stable across different ecosystems, as expected for a causal physical relation. 

In summary, we study how causal inference, based on a combination of physics-informed and statistical modelling, can contribute to more reliable estimates of the CO2 fertilisation effect, derived from ecosystem flux measurements.

How to cite: Biegel, S., Schindler, K., and Stocker, B.: Causal inference of the CO2 fertilisation effect from ecosystem flux measurements, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5033, https://doi.org/10.5194/egusphere-egu24-5033, 2024.

EGU24-5103 | ECS | Orals | ITS1.1/CL0.1.17

Reconstructing Historical Climate Fields With Deep Learning 

Nils Bochow, Anna Poltronieri, Martin Rypdal, and Niklas Boers

Historical records of climate fields are often sparse due to missing measurements, especially before the introduction of large-scale satellite missions. Several statistical and model-based methods have been introduced to fill gaps and reconstruct historical records. Here, we employ a recently introduced deep-learning approach based on Fourier convolutions, trained on numerical climate model output, to reconstruct historical climate fields. Using this approach we are able to realistically reconstruct large and irregular areas of missing data, as well as reconstruct known historical events such as strong El Niño and La Niña with very little given information. Our method outperforms the widely used statistical kriging method as well as other recent machine learning approaches. The model generalizes to higher resolutions than the ones it was trained on and can be used on a variety of climate fields. Moreover, it allows inpainting of masks never seen before during the model training.

How to cite: Bochow, N., Poltronieri, A., Rypdal, M., and Boers, N.: Reconstructing Historical Climate Fields With Deep Learning, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5103, https://doi.org/10.5194/egusphere-egu24-5103, 2024.

EGU24-5611 | ECS | Orals | ITS1.1/CL0.1.17 | Highlight

Advances and Prospects of Deep Learning for Medium-Range Extreme Weather Forecasting 

Leonardo Olivetti and Gabriele Messori

In recent years, deep learning models have rapidly emerged as a standalone alternative to physics-based numerical models for medium-range weather forecasting. Several independent research groups claim to have developed deep learning weather forecasts which outperform those from state-of-the-art physics-basics models, and operational implementation of data-driven forecasts appears to be drawing near. Yet, questions remain about the capabilities of deep learning models to provide robust forecasts of extreme weather.

Our current work aims to provide an overview of recent developments in the field of deep learning weather forecasting, and highlight the challenges that extreme weather events pose to leading deep learning models. Specifically, we problematise the fact that predictions generated by many deep learning models appear to be oversmooth, tending to underestimate the magnitude of wind and temperature extremes. To address these challenges, we argue for the need to tailor data-driven models to forecast extreme events, and develop models aiming to maximise the skill in the tails rather than in the mean of the distribution. Lastly, we propose a foundational workflow to develop robust models for extreme weather, which may function as a blueprint for future research on the topic.

How to cite: Olivetti, L. and Messori, G.: Advances and Prospects of Deep Learning for Medium-Range Extreme Weather Forecasting, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5611, https://doi.org/10.5194/egusphere-egu24-5611, 2024.

EGU24-5616 | ECS | Posters on site | ITS1.1/CL0.1.17

Rethinking Tropical Cyclone Genesis Potential Indices via Feature Selection 

Filippo Dainelli, Guido Ascenso, Enrico Scoccimarro, Matteo Giuliani, and Andrea Castelletti

Tropical Cyclones (TCs) are synoptic-scale, rapidly rotating storm systems primarily driven by air-sea heat and moisture exchanges. They are among the deadliest geophysical hazards, causing substantial economic losses and several fatalities due to their associated strong winds, heavy precipitation, and storm surges, leading to coastal and inland flooding. Because of the severe consequences of their impacts, accurately predicting the occurrence, intensity, and trajectory of TCs is of crucial socio-economic importance. Over the past few decades, advancements in Numerical Weather Prediction models, coupled with the availability of high-quality observational data from past events, have increased the accuracy of short-term forecasts of TC tracks and intensities. However, this level of improvement has not yet been mirrored in long-term climate predictions and projections. This can be attributed to the substantial computational resources required for running high-resolution climate models with numerous ensemble members over long periods. Additionally, the physical processes underlying TC formation are still poorly understood. To overcome these challenges, the future occurrence of TCs can instead be studied using indices, known as Genesis Potential Indices (GPIs), which correlate the likelihood of Tropical Cyclone Genesis (TCG) with large-scale environmental factors instrumental in their formation. GPIs are generally constructed as a product of atmospheric and oceanic variables accounting both for dynamic and thermodynamic processes. The variables are combined with coefficients and exponents numerically determined from past TC observations. Despite reproducing the spatial pattern and the seasonal cycle of observed TCs, GPIs fail to capture the inter-annual variability and exhibit inconsistent long-term trends.

In this work, we propose a new way to formulate these indices by using Machine Learning. Specifically, we forego all previously empirically determined coefficients and exponents and consider all the dynamic and thermodynamic factors incorporated into various indices documented in the literature. Then, using feature selection algorithms, we identify the most significant variables to explain TCG. Our analysis incorporates atmospheric variables as candidate factors to discern whether they inherently possess predictive signals for TCG. Furthermore, we also consider several climate indices that have been demonstrated to be related to TCG at the ocean basin scale. Recognizing that each factor and teleconnection has a distinct impact on TCG, we tailored our analysis to individual ocean basins. Consequently, our final model comprises a series of sub-models, each corresponding to a different tropical region. These sub-models estimate the distribution of TCG using distinct inputs, which are determined based on the outcomes of the basin-specific feature selection process. Preliminary findings indicate that the feature selection process yields distinct inputs for each ocean basin.

How to cite: Dainelli, F., Ascenso, G., Scoccimarro, E., Giuliani, M., and Castelletti, A.: Rethinking Tropical Cyclone Genesis Potential Indices via Feature Selection, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5616, https://doi.org/10.5194/egusphere-egu24-5616, 2024.

In the context of global warming, changes in extreme weather events may pose a larger threat to society. Therefore, it is particularly important to improve our climatological understanding of high impact precipitation types (PTs), and how their frequency may change under warming. In this study, we use MIDAS (the Met Office Integrated Data Archive System) observational data to provide our best estimate of historical PTs (e.g. liquid rain, freezing rain, snow etc.) over China. We use machine learning (ML) techniques and meteorological analysis methods applied to data from the ERA5 historical climate reanalysis data to find the best variables for diagnosing PTs, and formed training and testing sets, which were input into ML training. We evaluate the diagnostic ability of the Random Forest Classifier (RFC) for different PTs. The results show that using meteorological variables such as temperature, relative humidity, and winds to determine different PTs, ERA5 grid data and MIDAS station data have good matching ability. Comparing the feature selection results with Kernel Density Estimation, it was found that the two methods have consistent results in evaluating the ability of variables to distinguish different PTs. RFC shows strong robustness in predicting different PTs by learning the differences in meteorological variables between 1990 and 2014. It can capture the frequency and spatial distribution of different PTs well, but this capture ability is sensitive to the training methods of the algorithm. In addition, the algorithm finds it difficult to identify events such as hail that are very low frequency in observations. According to the results of testing for different regions and seasons in China, models trained using seasonal data samples have relatively good performance, especially in winter. These results show the potential for combining a RFC with state-of-the-art climate models to effectively project the possible response of different PT frequencies to climate warming in the future. However, the training method of ML algorithm should be selected with caution.

How to cite: Wang, Y.: Identifying precipitation types over China using a machine learning algorithm, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-6282, https://doi.org/10.5194/egusphere-egu24-6282, 2024.

EGU24-6655 | ECS | Orals | ITS1.1/CL0.1.17

Detecting spatio-temporal dynamics of western European heatwaves using deep learning 

Tamara Happe, Jasper Wijnands, Miguel Ángel Fernández-Torres, Paolo Scussolini, Laura Muntjewerf, and Dim Coumou

Heatwaves over western Europe are increasing faster than elsewhere, which recent studies have attributed at least partly to changes in atmospheric dynamics. To increase our understanding of the dynamical drivers of western European heatwaves, we developed a heatwave classification method taking into account the spatio-temporal atmospheric dynamics. Our deep learning approach consists of several steps: 1) heatwave detection using the Generalized Density-based Spatial Clustering of Applications with Noise (GDBSCAN) algorithm; 2) dimensionality reduction of the spatio-temporal heatwave samples using a 3D Variational Autoencoder (VAE); and 3) a clustering of heatwaves using K-means, a Gaussian Mixture Model, and opt-SNE. We show that a VAE can extract meaningful features from high-dimensional climate data. Furthermore, we find four physically distinct clusters of heatwaves that are interpretable with known circulation patterns, i.e. UK High, Scandinavian High, Atlantic High, and Atlantic Low. Our results indicate that the heatwave phase space, as found with opt-SNE, is continuous with soft boundaries between these circulation regimes, indicating that heatwaves are best categorized in a probabilistic way.

How to cite: Happe, T., Wijnands, J., Fernández-Torres, M. Á., Scussolini, P., Muntjewerf, L., and Coumou, D.: Detecting spatio-temporal dynamics of western European heatwaves using deep learning, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-6655, https://doi.org/10.5194/egusphere-egu24-6655, 2024.

The tropical Pacific experienced triple La Nina conditions during 2020-22, and the future evolution of the climate condition in the region has received extensive attention. Recent observations and studies indicate that an El Nino condition is developing with its peak stage in late 2023, but large uncertainties still exist. Here, a transformer-based deep learning model is adopted to make predictions of the 2023-24 climate condition in the tropical Pacific. This purely data driven model is configured in such a way that upper-ocean temperature at seven depths and zonal and meridional wind stress fields are used as input predictors and output predictands, representing ocean-atmosphere interactions that participate in the form of the Bjerknes feedback and providing physical basis for predictability. In the same way as dynamical models, the prediction procedure is executed in a rolling manner; multi-month 3D temperature fields as well as surface winds are simultaneously preconditioned as input predictors in the prediction. This transformer model has been demonstrated to outperform other state-of-the-art dynamical models in retrospective prediction cases. Real-time predictions indicate that El Nino conditions in the tropical Pacific peak in late 2023. The underlying processes are further analyzed by conducting sensitivity experiments using this transformer model, in which initial fields of surface winds and upper-ocean temperature fields can be purposely adjusted to illustrate the changes to prediction skills. A comparison with other dynamical coupled model is also made.

How to cite: Zhang, R.: A purely data-driven transformer model for real-time predictions of the 2023-24 climate condition in the tropical Pacific, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-6924, https://doi.org/10.5194/egusphere-egu24-6924, 2024.

EGU24-8010 | ECS | Posters on site | ITS1.1/CL0.1.17

Statistical Downscaling for urban meteorology at hectometric scale 

Julia Garcia Cristobal, Jean Wurtz, and Valéry Masson

Predicting the weather in urban environments is a complex task because of the highly heterogeneous nature of the urban structure. However, there are many issues inherent in urban meteorology, such as thermal comfort and building’s energy consumption. Those stakes are linked to highly heterogeneous meteorological variables within the city such as temperature, humidity, wind, net radiative flux and city characteristics such as building uses and characteristics. State-of-the-art meteorological models with hectometric resolution, such as the Meso-NH (Lac et al. 2018) research model, can provide accurate forecasts of urban meteorology. However, they require too much computing power to be deployed operationally. Statistical downscaling techniques are machine learning methods enabling the estimation of a fine resolution field based on one or several lower resolution fields. ARPEGE is the operational planetary model of Météo-France and operates at a resolution of 5km on France. Using Meso-NH simulations covering Paris and the Île-de-France region, a statistical downscaling has been carried out to obtain a temperature field at 300m resolution using simulation outputs from the ARPEGE planetary model at 5km. The deduced temperature reproduces the urban heat island and the temperature heterogeneity simulated in Meso-NH. The estimated temperature field is able to represent the links between temperature and topography as well as the sharp gradients between the city and the urban parks.

 

Lac et al. 2018 : https://doi.org/10.5194/gmd-11-1929-2018

How to cite: Garcia Cristobal, J., Wurtz, J., and Masson, V.: Statistical Downscaling for urban meteorology at hectometric scale, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-8010, https://doi.org/10.5194/egusphere-egu24-8010, 2024.

EGU24-8955 | ECS | Posters on site | ITS1.1/CL0.1.17

A Systematic Framework for Data Augmentation for Tropical Cyclone Intensity Estimation Using Deep Learning 

Guido Ascenso, Giulio Palcic, Enrico Scoccimarro, Matteo Giuliani, and Andrea Castelletti

Tropical cyclones (TCs) are among the costliest and deadliest natural disasters worldwide. The destructive potential of a TC is usually modelled as a power of its maximum sustained wind speed, making the estimation of the intensity of TCs (TCIE) an active area of research. Indeed, TCIE has improved steadily in recent years, especially as researchers moved from subjective methods based on hand-crafted features to methods based on deep learning, which are now solidly established as the state of the art.

However, the datasets used for TCIE, which are typically collections of satellite images of TCs, often have two major issues: they are relatively small (usually ≤ 40,000 samples), and they are highly imbalanced, with orders of magnitude more samples for weak TCs than for intense ones. Together, these issues make it hard for deep learning models to estimate the intensity of the strongest TCs. To mitigate these issues, researchers often use a family of Computer Vision techniques known as “data augmentation”—transformations (e.g., rotations) applied to the images in the dataset that create similar, synthetic samples. The way these techniques have been used in TCIE studies has been largely unexamined and potentially problematic. For instance, some authors flip images horizontally to generate new samples, while others avoid doing so because it would cause images from the Northern Hemisphere to look like images from the Southern Hemisphere, which they argue would confuse the model. The effectiveness or potentially detrimental effects of this and other data augmentation techniques for TCIE have never been examined, as authors typically borrow their data augmentation strategies from established fields of Computer Vision. However, data augmentation techniques are highly sensitive to the task for which they are used and should be optimized accordingly. Furthermore, it remains unclear how to properly use data augmentation for TCIE to alleviate the imbalance of the datasets.

In our work, we explore how best to perform data augmentation for TCIE using an off-the-shelf deep learning model, focusing on two objectives:

  • Determining how much augmentation is needed and how to distribute it across the various classes of TC intensity. To do so, we use a modified Gini coefficient to guide the amount of augmentation to be done. Specifically, we aim to augment the dataset more for more intense (and therefore less represented) TCs. Our goal is to obtain a dataset that, when binned according to the Saffir Simpson scale, is as close to a normal distribution as possible (i.e., all classes of intensity are equally represented). 
  • Evaluating which augmentation techniques are best for deep learning-based TCIE. To achieve this, we use a simple feature selection algorithm called backwards elimination, which leads us to find an optimal set of data augmentations to be used. Furthermore, we explore the optimal parameter space for each augmentation technique (e.g., by what angles images should be rotated).

Overall, our work provides the first in-depth analysis of the effects of data augmentation for deep learning-based TCIE, establishing a framework to use these techniques in a way that directly addresses highly imbalanced datasets.

How to cite: Ascenso, G., Palcic, G., Scoccimarro, E., Giuliani, M., and Castelletti, A.: A Systematic Framework for Data Augmentation for Tropical Cyclone Intensity Estimation Using Deep Learning, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-8955, https://doi.org/10.5194/egusphere-egu24-8955, 2024.

EGU24-9110 | ECS | Orals | ITS1.1/CL0.1.17 | Highlight

Explainable AI for distinguishing future climate change scenarios 

Zachary Labe, Thomas Delworth, Nathaniel Johnson, and William Cooke

To account for uncertainties in future projections associated with the level of greenhouse gas emissions, most climate models are run using different forcing scenarios, like the Shared Socioeconomic Pathways (SSPs). Although it is possible to compare real-world greenhouse gas concentrations with these hypothetical scenarios, it is less clear how to determine whether observed patterns of weather and climate anomalies align with individual scenarios, especially at the interannual timescale. As a result, this study designs a data-driven approach utilizing artificial neural networks (ANNs) that learn to classify global maps of annual-mean temperature or precipitation with a matching emission scenario using a high-resolution, single model initial-condition large ensemble. Here we construct our ANN framework to consider whether a climate map is from SSP1-1.9, SSP2-4.5, SSP5-8.5, a historical forcing scenario, or a natural forcing scenario using the Seamless System for Prediction and EArth System Research (SPEAR) by the NOAA Geophysical Fluid Dynamics Laboratory. A local attribution technique from explainable AI is then applied to identify the most relevant temperature and precipitation patterns used for each ANN prediction. The explainability results reveal that some of the most important geographic regions for distinguishing each climate scenario include anomalies over the subpolar North Atlantic, Central Africa, and East Asia. Lastly, we evaluate data from two overshoot simulations that begin in either 2031 or 2040, which are a set of future simulations that were excluded from the ANN training process. For the rapid mitigation experiment that starts a decade earlier, we find that the ANN links its climate maps to the lowest emission scenario by the end of the 21st century (SSP1-1.9) in comparison to the more moderate scenario (SSP2-4.5) that is selected for the later mitigation experiment. Overall, this framework suggests that explainable machine learning could provide one possible strategy for assessing observations with future climate change pathways.

How to cite: Labe, Z., Delworth, T., Johnson, N., and Cooke, W.: Explainable AI for distinguishing future climate change scenarios, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-9110, https://doi.org/10.5194/egusphere-egu24-9110, 2024.

EGU24-10129 | ECS | Orals | ITS1.1/CL0.1.17

Subseasonal to seasonal forecasts using Masked Autoencoders 

Jannik Thümmel, Jakob Schlör, Felix Strnad, and Bedartha Goswami

Subseasonal to seasonal (S2S) weather forecasts play an important role as a decision making tool in several sectors of modern society. However, the time scale on which these forecasts are skillful is strongly dependent on atmospheric and oceanic background conditions. While deep learning-based weather prediction models have shown impressive results in the short- to medium range, S2S forecasts from such models are currently limited, partly due to fewer available training data and larger fluctuations in predictability. In order to develop more reliable S2S predictions we leverage Masked Autoencoders, a state-of-the-art deep learning framework, to extract large-scale representations of tropical precipitation and sea-surface temperature data.  We show that the learned representations are highly predictive for the El Niño Southern Oscillation and the Madden-Julian Oscillation, and can thus serve as a foundation for identifying windows of opportunity and generating skillful S2S forecasts.

How to cite: Thümmel, J., Schlör, J., Strnad, F., and Goswami, B.: Subseasonal to seasonal forecasts using Masked Autoencoders, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-10129, https://doi.org/10.5194/egusphere-egu24-10129, 2024.

EGU24-10156 | Posters on site | ITS1.1/CL0.1.17

Heat wave vulnerability maps of Naples (Italy) from Landsat images and machine learning 

Daniela Flocco, Ester Piegari, and Nicola Scafetta

Maps of land surface temperature of the area of Naples (Southern Italy) show large spatial variation of temperature anomalies. In particular, the metropolitan area of Naples is generally characterized by higher temperatures than the rest of the area considered.

Since heat waves have become more frequent in the last decade, the creation of heat maps helps to understand the location where a town’s population may be more affected by them. Ideally, this kind of maps would provide residents with accurate information about the health problems they may face.

Large temperature anomalies variations are caused by multiple or competing factors, leaving uncertainty in identifying vulnerable areas at this time.

To overcome this limitation and identify areas more vulnerable to the effects of heat waves, not only in the city of Naples but also in its suburbs, we combine the use of Landsat data with unsupervised machine learning algorithms to provide detailed heat wave vulnerability maps. In particular, we develop a procedure based on a combined use of hierarchical and partitional cluster analyses that allows us to better identify areas characterized by temperature anomalies that are more similar to each other than to any other all over the year. This has important implications allowing discrimination between locations that potentially would be impacted higher or lower energy consumption.

How to cite: Flocco, D., Piegari, E., and Scafetta, N.: Heat wave vulnerability maps of Naples (Italy) from Landsat images and machine learning, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-10156, https://doi.org/10.5194/egusphere-egu24-10156, 2024.

EGU24-10262 | ECS | Orals | ITS1.1/CL0.1.17 | Highlight

Machine learning-based emulation of a km-scale UK climate model 

Henry Addison, Elizabeth Kendon, Suman Ravuri, Laurence Aitchison, and Peter Watson

High resolution projections are useful for planning climate change adaptation [1] but are expensive to produce using physical simulations. We make use of a state-of-the-art generative machine learning (ML) method, a diffusion model [2], to predict variables from a km-scale model over England and Wales. This is trained to emulate daily mean output from the Met Office 2.2km UK convection-permitting model (CPM) [3], averaged to 8.8km scale for initial testing, given coarse-scale (60km) weather states from the Met Office HadGEM3 general circulation model. This achieves downscaling at much lower computational cost than is required to run the CPM and when trained to predict precipitation the emulator produces samples with realistic spatial structure [4, 5]. We show the emulator learns to represent climate change over the 21st century. We present some diagnostics indicating that there is skill for extreme events with ~100 year return periods, as is necessary to inform decision-making. This is made possible by training the model on ~500 years of CPM data (48 years from each of 12 ensemble members). We also show the method can be useful in scenarios with limited high-resolution data. The method is stochastic and we find that it produces a well-calibrated spread of high resolution precipitation samples for given large-scale conditions, which is highly important for correctly representing extreme events.

Furthermore, we are extending this method to generate coherent multivariate samples including other impact-relevant variables (e.g. 2m temperature, 2m humidity and 10m wind). We will show the model’s performance at producing samples with coherent structure across all the different variables and its ability to represent extremes in multivariate climate impact indices.

References

[1] Kendon, E. J. et al. (2021). Update to the UKCP Local (2.2km) projections. Science report, Met Office Hadley Centre, Exeter, UK. [Online]. Available: https://www.metoffice.gov.uk/pub/data/weather/uk/ukcp18/science-reports/ukcp18_local_update_report_2021.pdf

[2] Song, Y. et al. (2021). Score-Based Generative Modeling through Stochastic Differential Equations. ICLR.

[3] Kendon EJ, E Fischer, CJ Short (2023) Variability conceals emerging trend in 100yr projections of UK local hourly rainfall extremes, Nature Comms, doi: 10.1038/s41467-023-36499-9

[4] Addison, Henry, Elizabeth Kendon, Suman Ravuri, Laurence Aitchison, and Peter AG Watson. (2022). Machine learning emulation of a local-scale UK climate model. arXiv preprint arXiv:2211.16116.

[5] Addison, H., Kendon, E., Ravuri, S., Aitchison, L., and Watson, P. (2023). Downscaling with a machine learning-based emulator of a local-scale UK climate model, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-14253, https://doi.org/10.5194/egusphere-egu23-14253

How to cite: Addison, H., Kendon, E., Ravuri, S., Aitchison, L., and Watson, P.: Machine learning-based emulation of a km-scale UK climate model, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-10262, https://doi.org/10.5194/egusphere-egu24-10262, 2024.

EGU24-10298 | ECS | Orals | ITS1.1/CL0.1.17 | Highlight

Downscaling precipitation simulations from Earth system models with generative deep learning 

Philipp Hess, Maximilian Gelbrecht, Michael Aich, Baoxiang Pan, Sebastian Bathiany, and Niklas Boers

Accurately assessing precipitation impacts due to anthropogenic global warming relies on numerical Earth system model (ESM) simulations. However, the discretized formulation of ESMs, where unresolved small-scale processes are included as semi-empirical parameterizations, can introduce systematic errors in the simulations. These can, for example, lead to an underestimation of spatial intermittency and extreme events.
 Generative deep learning has recently been shown to skillfully bias-correct and downscale precipitation fields from numerical simulations [1,2]. Using spatial context, these methods can jointly correct spatial patterns and summary statistics, outperforming established statistical approaches.
However, these approaches require separate training for each Earth system model individually, making corrections of large ESM ensembles computationally costly. Moreover, they only allow for limited control over the spatial scale at which biases are corrected and may suffer from training instabilities.
Here, we follow a novel diffusion-based generative approach [3, 4] by training an unconditional foundation model on the high-resolution target ERA5 dataset only. Using fully coupled ESM simulations of precipitation, we investigate the controllability of the generative process during inference to preserve spatial patterns of a given ESM field on different spatial scales.

[1] Hess, P., Drüke, M., Petri, S., Strnad, F. M., & Boers, N. (2022). Physically constrained generative adversarial networks for improving precipitation fields from Earth system models. Nature Machine Intelligence, 4(10), 828-839.

[2] Harris, L., McRae, A. T., Chantry, M., Dueben, P. D., & Palmer, T. N. (2022).A generative deep learning approach to stochastic downscaling of precipitation forecasts. Journal of Advances in Modeling Earth Systems, 14(10), e2022MS003120.

[3] Meng, C., He, Y., Song, Y., Song, J., Wu, J., Zhu, J. Y., & Ermon, S. (2021).  Sdedit: Guided image synthesis and editing with stochastic differential equations. arXiv preprint arXiv:2108.01073.

[4] Bischoff, T., & Deck, K. (2023). Unpaired Downscaling of Fluid Flows with Diffusion Bridges. arXiv preprint arXiv:2305.01822.

How to cite: Hess, P., Gelbrecht, M., Aich, M., Pan, B., Bathiany, S., and Boers, N.: Downscaling precipitation simulations from Earth system models with generative deep learning, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-10298, https://doi.org/10.5194/egusphere-egu24-10298, 2024.

EGU24-10325 | ECS | Orals | ITS1.1/CL0.1.17 | Highlight

Interpretable multiscale Machine Learning-Based Parameterizations of Convection for ICON 

Helge Heuer, Mierk Schwabe, Pierre Gentine, Marco A. Giorgetta, and Veronika Eyring

In order to improve climate projections, machine learning (ML)-based parameterizations have been developed for Earth System Models (ESMs) with the goal to better represent subgrid-scale processes or to accelerate computations by emulating existent parameterizations. These data-driven models have shown success in approximating subgrid-scale processes based on high-resolution storm-resolving simulations. However, most studies have used a particular machine learning method such as simple Multilayer Perceptrons (MLPs) or Random Forest (RFs) to parameterize the subgrid tendencies or fluxes originating from the compound effect of various small-scale processes (e.g., turbulence, radiation, convection, gravity waves). Here, we use a filtering technique to explicitly separate convection from these processes in data produced by the Icosahedral Non-hydrostatic modelling framework (ICON) in a realistic setting. We use a method improved by incorporating density fluctuations for computing the subgrid fluxes and compare a variety of different machine learning algorithms on their ability to predict the subgrid fluxes. We further examine the predictions of the best performing non-deep learning model (Gradient Boosted Tree regression) and the U-Net. We discover that the U-Net can learn non-causal relations between convective precipitation and convective subgrid fluxes and develop an ablated model excluding precipitating tracer species. We connect the learned relations of the U-Net to physical processes in contrast to non-deep learning-based algorithms. Our results suggest that architectures such as a U-Net are particularly well suited to parameterize multiscale problems like convection, paying attention to the plausibility of the learned relations, thus providing a significant advance upon existing ML subgrid representation in ESMs.

How to cite: Heuer, H., Schwabe, M., Gentine, P., Giorgetta, M. A., and Eyring, V.: Interpretable multiscale Machine Learning-Based Parameterizations of Convection for ICON, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-10325, https://doi.org/10.5194/egusphere-egu24-10325, 2024.

EGU24-10328 | ECS | Posters on site | ITS1.1/CL0.1.17 | Highlight

Emulating Land-Processes in Climate Models Using Generative Machine Learning 

Graham Clyne

Recent advances in climate model emulation have been shown to accurately represent atmospheric variables from large general circulation models, but little investigation has been done into emulating land-related variables. The land-carbon sink absorbs around a third of the fossil fuel anthropogenic emissions every year, yet there is significant uncertainty around this prediction. We aim to reduce this uncertainty by first investigating the predictability of several land-related variables that drive land-atmospheric carbon exchange. We use data from the IPSL-CM6A-LR submission to the Decadal Climate Prediction Project (DCPP). The DCPP is initialized from observed data and explores decadal trends in relationships between various climatic variables. The land-component of the IPSL-CM6A-LR, ORCHIDEE, represents various land-carbon interactions and we target these processes for emulation. As a first step, we attempt to predict the target land variables from ORCHIDEE using a vision transformer. We then investigate the impacts of different feature selection on the target variables - by including atmospheric and oceanic variables, how does this improve the short and medium term predictions of land-related processes? In a second step, we apply generative modeling (with diffusion models) to emulate land processes. The diffusion model can be used to generate several unseen scenarios based on the DCPP and provides a tool to investigate a wider range of climatic scenarios that would be otherwise computationally expensive. 

How to cite: Clyne, G.: Emulating Land-Processes in Climate Models Using Generative Machine Learning, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-10328, https://doi.org/10.5194/egusphere-egu24-10328, 2024.

EGU24-10692 | ECS | Posters on site | ITS1.1/CL0.1.17

Down-scaling and bias correction of precipitation with generative machine learning models  

Michael Aich, Baoxiang Pan, Philipp Hess, Sebastian Bathiany, Yu Huang, and Niklas Boers

Earth system models (ESMs) are crucial for understanding and predicting the behaviour of the Earth’s climate system. Understanding and accurately simulating precipitation is particularly important for assessing the impacts of climate change, predicting extreme weather events, and developing sustainable strategies to manage water resources and mitigate associated risks. However, earth system models are prone to large precipitation biases because the relevant processes occur on a large range of scales and involve substantial uncertainties. In this work, we aim to correct such model biases by training generative machine learning models that map between model data and observational data. We address the challenge that the datasets are not paired, meaning that there is no sample-related ground truth to compare the model output to, due to the chaotic nature of geophysical flows. This challenge renders many machine learning approach unsuitable, and also implies a lack of performance metrics.

Our main contribution is the construction of a proxy variable that overcomes this problem and allows for supervised training and evaluation of a bias correction model. We show that a generative model is then able to correct spatial patterns and remove statistical biases in the South American domain. The approach successfully preserves large scale structures in the climate model fields while correcting small scale biases in the model data’s spatio-temporal structure and frequency distribution.

How to cite: Aich, M., Pan, B., Hess, P., Bathiany, S., Huang, Y., and Boers, N.: Down-scaling and bias correction of precipitation with generative machine learning models , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-10692, https://doi.org/10.5194/egusphere-egu24-10692, 2024.

EGU24-10759 | ECS | Orals | ITS1.1/CL0.1.17 | Highlight

Is linear regression all you need? Clarifying use-cases for deep learning in climate emulation 

Björn Lütjens, Noelle Selin, Andre Souza, Gosha Geogdzhayev, Dava Newman, Paolo Giani, Claudia Tebaldi, Duncan Watson-Parris, and Raffaele Ferrari

Motivation. Climate models are computationally so expensive that each model is only run for a very selected set of assumptions. In policy making, this computational complexity makes it difficult to rapidly explore the comparative impact of climate policies, such as quantifying the projected difference of local climate impacts with a 30 vs. 45€ price on carbon (Lütjens et al., 2023). Recently however, machine learning (ML) models have been used to emulate climate models that can rapidly interpolate within existing climate dataset.

Related Works. Several deep learning models have been developed to emulate the impact of greenhouse gas emissions onto climate variables such as temperature and precipitation. Currently, the foundation model ClimaX with O(100M-1B) parameters is considered the best performer according to the benchmark datasets, ClimateSet and ClimateBenchv1.0 (Kaltenborn et al., 2023; Nguyen et al., 2023; Watson-Parris et al., 2022).

Results. We show that linear pattern scaling, a simple method with O(10K) parameters, is at least on par with the best models for some climate variables, as shown in Fig 1. In particular, the ClimateBenchv1.0 annually-averaged and locally-resolved surface temperatures, precipitation, and 90th percentile precipitation can be well estimated with linear pattern scaling. Our research resurfaces that temperature-dependent climate variables have a mostly linear relationship to cumulative CO2 emissions.

As a next step, we will identify the complex climate emulation tasks that are not addressed by linear models and might benefit from deep learning research. To do so, we will plot the data complexity per climate variable and discuss the ML difficulties in multiple spatiotemporal scales, irreversible dynamics, and internal variability. We will conclude with a list of tasks that demand more advanced ML models.

Conclusion. Most of the ML-based climate emulation efforts have focused on variables that can be well approximated by linear regression models. Our study reveals the solved and unsolved problems in climate emulation and provides guidance for future research directions.

Data and Methods. We use the ClimateBenchv1.0 dataset and will show additional results on ClimateSet and a CMIP climate model that contains many ensemble members. Our model fits one linear regression to map cumulative CO2 emissions, co2(t), to globally- and annually-averaged surface temperature, tas(t). Our model then fits one linear regression model per grid cell to map tas(t) onto 2.5° local surface temperature. Our model is time-independent and uses only co2(t) as input. Our analysis will be available at github.com/blutjens/climate-emulator-tutorial

References.

Kaltenborn, J. et al., (2023). ClimateSet: A Large-Scale Climate Model Dataset for Machine Learning, in NeurIPS Datasets and Benchmarks

Lütjens, B. (2023). Deep Learning Emulators for Accessible Climate Projections, Thesis, Massachusetts Institute of Technology.

Nguyen, T. et al., (2023). ClimaX: A foundation model for weather and climate, in ICML

Watson-Parris, D. et al. (2022). ClimateBenchv1.0: A Benchmark for Data-Driven Climate Projections, in JAMES

How to cite: Lütjens, B., Selin, N., Souza, A., Geogdzhayev, G., Newman, D., Giani, P., Tebaldi, C., Watson-Parris, D., and Ferrari, R.: Is linear regression all you need? Clarifying use-cases for deep learning in climate emulation, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-10759, https://doi.org/10.5194/egusphere-egu24-10759, 2024.

EGU24-10876 | ECS | Posters on site | ITS1.1/CL0.1.17 | Highlight

Physics-aware Machine Learning to Estimate Ice Thickness of Glaciers in West Svalbard 

Viola Steidl, Jonathan Bamber, and Xiao Xiang Zhu

Glacier ice thickness is a fundamental variable required for modelling flow and mass balance. However, direct measurements of ice thickness are scarce. Physics-based and data-driven approaches aim to reconstruct glacier ice thicknesses from the limited in-situ data. Farinotti et al. compared 17 models and found that their ice thickness estimates differ considerably on test glaciers.[1] Following these results, Farinotti et al. created an ensemble of models to develop the so-called consensus estimate of the ice thickness for the world’s glaciers in 2019.[2] Later, Millan et al. derived ice thickness estimates for the world’s glaciers using ice motion as the primary constraint. However, these results differ considerably from existing estimates and the 2019 consensus estimates.[3] It is evident, therefore, that significant uncertainty remains in ice thickness estimates.

Deep learning approaches are flexible and adapt well to complex structures and non-linear behaviour. However, they do not guarantee physical correctness of the predicted quantities. Therefore, we employ a physics-informed neural network (PINN), which integrates physical laws into their training process and is not purely data-driven. We include, for example, the conservation of mass in the loss function and estimate the depth-averaged flow velocity. Teisberg et al. also employed a mass-conserving PINN to interpolate the ice thickness of the well-studied Byrd glacier in Antarctica.[4] In this work, we extend the methodology by integrating the ratio between slope and surface flow velocities in estimating the depth-averaged flow velocity and mapping the coordinate variables to higher dimensional Fourier Features.[5] This allows to encompass glaciers in western Svalbard, addressing challenges posed by basal sliding, surface melting, and complex glacier geometries. Using surface velocity data from Millan et al. and topographical data from Copernicus DEM GLO-90[6] gathered through OGGM[7],  the model predicts ice thickness on glaciers with limited measurements. We are extending it to perform as a predictor of thickness for glaciers with no observations. Here, we present the machine learning pipeline, including the physical constraints employed and preliminary results for glaciers in western Svalbard.


[1] Daniel Farinotti et al., ‘How Accurate Are Estimates of Glacier Ice Thickness? Results from ITMIX, the Ice Thickness Models Intercomparison eXperiment’, The Cryosphere 11, no. 2 (April 2017): 949–70, https://doi.org/10.5194/tc-11-949-2017.

[2] Daniel Farinotti et al., ‘A Consensus Estimate for the Ice Thickness Distribution of All Glaciers on Earth’, Nature Geoscience 12, no. 3 (March 2019): 168–73, https://doi.org/10.1038/s41561-019-0300-3.

[3] Romain Millan et al., ‘Ice Velocity and Thickness of the World’s Glaciers’, Nature Geoscience 15, no. 2 (February 2022): 124–29, https://doi.org/10.1038/s41561-021-00885-z.

[4] Thomas O. Teisberg, Dustin M. Schroeder, and Emma J. MacKie, ‘A Machine Learning Approach to Mass-Conserving Ice Thickness Interpolation’, in 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, 2021, 8664–67, https://doi.org/10.1109/IGARSS47720.2021.9555002.

[5] Matthew Tancik et al., ‘Fourier Features Let Networks Learn High Frequency Functions in Low Dimensional Domains’, (arXiv, 18 June 2020), https://doi.org/10.48550/arXiv.2006.10739.

[6] {https://doi.org/10.5270/ESA-c5d3d65}

[7] Fabien Maussion et al., ‘The Open Global Glacier Model (OGGM) v1.1’, Geoscientific Model Development 12, no. 3 (March 2019): 909–31, https://doi.org/10.5194/gmd-12-909-2019.

How to cite: Steidl, V., Bamber, J., and Zhu, X. X.: Physics-aware Machine Learning to Estimate Ice Thickness of Glaciers in West Svalbard, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-10876, https://doi.org/10.5194/egusphere-egu24-10876, 2024.

EGU24-10922 | ECS | Orals | ITS1.1/CL0.1.17

Graph Neural Networks for Atmospheric Transport Modeling of CO2  

Vitus Benson, Ana Bastos, Christian Reimers, Alexander J. Winkler, Fanny Yang, and Markus Reichstein

Large deep neural network emulators are poised to revolutionize numerical weather prediction (NWP). Recent models like GraphCast or NeuralGCM can now compete and sometimes outperform traditional NWP systems, all at much lower computational cost. Yet to be explored is the applicability of large deep neural network emulators to other dense prediction tasks such as the modeling of 3D atmospheric composition. For instance the inverse modeling of carbon fluxes essential for estimating carbon budgets relies on fast CO2 transport models.

Here, we present a novel approach to atmospheric transport modeling of CO2 and other inert trace gases. Existing Eulerian transport modeling approaches rely on numerical solvers applied to the continuity equation, which are expensive: short time steps are required for numerical stability at the poles, and the loading of driving meteorological fields is IO-intensive. We learn high-fidelity transport in latent space by training graph neural networks, analogous to approaches used in weather forecasting, including an approach that conserves the CO2 mass. For this, we prepare the CarbonBench dataset, a deep learning ready dataset based on Jena Carboscope CO2 inversion data and NCEP NCAR meteorological reanalysis data together with ObsPack station observations for model evaluation.

Qualitative and quantitative experiments demonstrate the superior performance of our approach over a baseline U-Net for short-term (<40 days) atmospheric transport modeling of carbon dioxide. While the original GraphCast architecture achieves a similar speed to the TM3 transport model used to generate the training data, we show how various architectural changes introduced by us contribute to a reduced IO load (>4x) of our model, thereby speeding up forward runs. This is especially useful when applied multiple times with the same driving wind fields, e.g. in an inverse modeling framework. Thus, we pave the way towards integrating not only atmospheric observations (as is done in current CO2 inversions), but also ecosystem surface fluxes (not yet done) into carbon cycle inversions. The latter requires backpropagating through a transport operator to optimize a flux model with many more parameters (e.g. a deep neural network) than those currently used in CO2 inversions – which becomes feasible if the transport operator is fast enough. To the best of our knowledge, this work presents the first emulator of global Eulerian atmospheric transport, thereby providing an initial step towards next-gen inverse modeling of the carbon cycle with deep learning.

 

How to cite: Benson, V., Bastos, A., Reimers, C., Winkler, A. J., Yang, F., and Reichstein, M.: Graph Neural Networks for Atmospheric Transport Modeling of CO2 , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-10922, https://doi.org/10.5194/egusphere-egu24-10922, 2024.

EGU24-11831 | ECS | Orals | ITS1.1/CL0.1.17

Analyzing Climate Scenarios Using Dynamic Mode Decomposition With Control 

Nathan Mankovich, Shahine Bouabid, and Gustau Camps-Valls

Analyzing climate scenarios is crucial for quantifying uncertainties, identifying trends, and validating models. Objective statistical methods provide decision support for policymakers, optimize resource allocation, and enhance our understanding of complex climate dynamics. These tools offer a systematic and quantitative framework for effective decision-making and policy formulation amid climate change, including accurate projections of extreme events—a fundamental requirement for Earth system modeling and actionable future predictions. 

This study applies dynamic mode decomposition with control (DMDc) to assess temperature and precipitation variability in climate model projections under various future shared socioeconomic pathways (SSPs). We leverage global greenhouse gas emissions and local aerosol emissions as control parameters to unveil nuanced insights into climate dynamics.Our approach involves fitting distinct DMDc models over a high-ambition/low-forcing scenario (SSP126), a medium-forcing scenario (SSP245) and a high-forcing scenario (SSP585). By scrutinizing the eigenvalues and dynamic modes of each DMDc model, we uncover crucial patterns and trends that extend beyond traditional climate analysis methods. Preliminary findings reveal that temporal modes effectively highlight variations in global warming trends under different emissions scenarios. Moreover, the spatial modes generated by DMDc offer a refined understanding of temperature disparities across latitudes, effectively capturing large-scale oscillations such as the El Niño Southern Oscillation. 

The proposed data-driven analytical framework not only enriches our comprehension of climate dynamics but also enhances our ability to anticipate and adapt to the multifaceted impacts of climate change. Integrating DMDc into climate scenario analysis may help formulate more effective strategies for mitigation and adaptation.

References

Allen, Myles R., et al. "Warming caused by cumulative carbon emissions towards the trillionth tonne." Nature 458.7242 (2009): 1163-1166.

Zelinka, Mark D., et al. "Causes of higher climate sensitivity in CMIP6 models." Geophysical Research Letters 47.1 (2020): e2019GL085782.

Proctor, Joshua L., Steven L. Brunton, and J. Nathan Kutz. "Dynamic mode decomposition with control." SIAM Journal on Applied Dynamical Systems 15.1 (2016): 142-161.

How to cite: Mankovich, N., Bouabid, S., and Camps-Valls, G.: Analyzing Climate Scenarios Using Dynamic Mode Decomposition With Control, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-11831, https://doi.org/10.5194/egusphere-egu24-11831, 2024.

This study focuses on the application of machine learning techniques to better characterize predictability of the spatiotemporal variability of sea surface temperature (SST) on the basin scale. Both, sub-seasonal variability including extreme events (cf. marine heatwaves) and interannual variability are considered. 

We rely on dimensionality reduction techniques---linear principal component analysis (PCA)  and nonlinear autoencoders and their variants---to then perform the actual prediction tasks in the corresponding latent space using disparate methodologies ranging from linear inverse modeling (LIM) to reservoir computing (RC), and attention-based transformers. 

After comparing performance, we examine various issues including the role of generalized synchronization in RC and implicit memory of RC vs. explicit long-term memory of transformers with the broad aim of shedding light on the effectiveness of these techniques in the context of data-driven climate prediction.

How to cite: Nadiga, B. and Srinivasan, K.: Climate Prediction in Reduced Dimensions: A Comparative Analysis of Linear Inverse Modeling, Reservoir Computing and Attention-based Transformers, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12141, https://doi.org/10.5194/egusphere-egu24-12141, 2024.

EGU24-12495 | Orals | ITS1.1/CL0.1.17

Hybrid neural differential equation models for atmospheric dynamics 

Maximilian Gelbrecht and Niklas Boers

Combining process-based models in Earth system science with data-driven machine learning methods holds tremendous promise. Can we harness the best of both approaches? In our study, we integrate components of atmospheric models into artificial neural networks (ANN). The resulting hybrid atmospheric model can learn atmospheric dynamics from short trajectories while ensuring robust generalization and stability. We achieve this using the neural differential equations framework, combining ANNs with a differentiable, GPU-enabled version of the well-studied Marshall Molteni Quasigeostrophic Model (QG3). Similar to the approach of many atmospheric models, part of the model is computed in the spherical harmonics domain, and other parts in the grid domain. In our model, ANNs are used as parametrizations in both domains, and form together with the components of the QG3 model the right-hand side of our hybrid model. We showcase the capabilities of our model by demonstrating how it generalizes from the QG3 model to the significantly more complex primitive equation model of SpeedyWeather.jl. 

How to cite: Gelbrecht, M. and Boers, N.: Hybrid neural differential equation models for atmospheric dynamics, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12495, https://doi.org/10.5194/egusphere-egu24-12495, 2024.

EGU24-12600 | Posters on site | ITS1.1/CL0.1.17

Autoencoder-based model for improving  reconstruction of heat waves using the analogue method 

Jorge Pérez-Aracil, Cosmin M. Marina, Pedro Gutiérrez, David Barriopedro, Ricardo García-Herrera, Matteo Giuliani, Ronan McAdam, Enrico Scoccimarro, Eduardo Zorita, Andrea Castelletti, and Sancho Salcedo-Sanz

The Analogue Method (AM) is a classical statistical downscaling technique applied to field reconstruction. It is widely used for prediction and attribution tasks. The method is based on the principle that two similar atmospheric states cause similar local effects. The core of the AM method is a K-nearest neighbor methodology. Thus, two different states have similarities according to the analogy criterion. The method has remained unchanged since its definition, although some attempts have been made to improve its performance. Machine learning (ML) techniques have recently been used to improve AM performance, however, it remains very similar. An ML-based hybrid approach for heatwave (HW) analysis based on the AM is presented here. It is based on a two-step procedure: in the first step, a non-supervised task is developed, where an autoencoder (AE) model is trained to reconstruct the predictor variable, i.e. the pressure field. Second, an HW event is selected, and then the AM method is applied to the latent space of the trained AE. Thus, the analogy between the fields is searched in the encoded data of the input variable, instead of on the original field. Experiments show that the meaningful features extracted by the AE lead to a better reconstruction of the target field when pressure variables are used as input. In addition, the analysis of the latent space allows for interpreting the results, since HW occurrence can be easily distinguished. Further research can be done on including multiple input variables. 

How to cite: Pérez-Aracil, J., Marina, C. M., Gutiérrez, P., Barriopedro, D., García-Herrera, R., Giuliani, M., McAdam, R., Scoccimarro, E., Zorita, E., Castelletti, A., and Salcedo-Sanz, S.: Autoencoder-based model for improving  reconstruction of heat waves using the analogue method, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12600, https://doi.org/10.5194/egusphere-egu24-12600, 2024.

EGU24-12826 | ECS | Orals | ITS1.1/CL0.1.17

Comparing Machine Learning Methods for Dynamical Systems 

Christof Schötz, Alistair White, and Niklas Boers

We explore the task of learning the dynamics of a system from observed data without prior knowledge of the laws governing the system. Our extensive simulation study focuses on ordinary differential equation (ODE) problems that are specifically designed to reflect key aspects of various machine learning tasks for dynamical systems - namely, chaos, complexity, measurement uncertainty, and variability in measurement intervals. The study evaluates a variety of methods, including neural ODEs, transformers, Gaussian processes, echo state networks, and spline-based estimators. Our results show that the relative performance of the methods tested varies widely depending on the specific task, highlighting that no single method is universally superior. Although our research is predominantly in low-dimensional settings, in contrast to the high-dimensional nature of many climate science challenges, it provides insightful comparisons and understanding of how different approaches perform in learning the dynamics of complex systems.

How to cite: Schötz, C., White, A., and Boers, N.: Comparing Machine Learning Methods for Dynamical Systems, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12826, https://doi.org/10.5194/egusphere-egu24-12826, 2024.

EGU24-13138 | ECS | Posters on site | ITS1.1/CL0.1.17

Neural Network Driven Early Warning System for Groundwater Flooding: A Comprehensive Approach in Lowland Karst Areas 

Ruhhee Tabbussum, Bidroha Basu, and Laurence Gill

Enhancing flood prediction is imperative given the profound socio-economic impacts of flooding and the projected increase in its frequency due to the impacts of climate change. In this context, artificial intelligence (AI) models have emerged as valuable tools, offering enhanced accuracy and cost-effective solutions to simulate physical flood processes. This study addresses the development of an early warning system for groundwater flooding in the lowland karst area of south Galway, Ireland, employing neural network models with Bayesian regularization and scaled conjugate gradient training algorithms. The lowland karst area is characterised by several groundwater fed, intermittent lakes, known as turloughs that fill when the underlying karst system becomes surcharged during periods of high rainfall. The training datasets incorporate several years of field data from the study area and outputs from a highly calibrated semi-distributed hydraulic/hydrological model of the karst network. Inputs for training the models include flood volume data from the past 5 days, rainfall data, and tidal amplitude data over the preceding 4 days. Both daily and hourly models were developed to facilitate real-time flood predictions. Results indicate strong performance by both Bayesian and Scaled Conjugate Gradient models in real-time flood forecasting. The Bayesian model shows forecasting capabilities extending up to 45 days into the future, with a Nash-Sutcliffe Efficiency (NSE) of 1.00 up to 7 days ahead and 0.95 for predictions up to 45 days ahead. The Scaled Conjugate Gradient model offers the best performance up to 60 days into the future with NSE of 0.98 up to 20 days ahead and 0.95 for predictions up to 60 days ahead, coupled with the advantage of significantly reduced training time compared to the Bayesian model. Additionally, both models exhibit a Co-efficient of Correlation (r) value of 0.98 up to 60 days ahead. Evaluation measures such as Kling Gupta Efficiency reveal high performance, with values of 0.96 up to 15 days ahead for both Bayesian and Scaled Conjugate Gradient models, and 0.90 up to 45 days ahead in the future. The integration of diverse data sources and consideration of both daily and hourly models enhance the resilience and reliability of such an early warning system. In particular, the Scaled Conjugate Gradient model emerges as a versatile tool. It balances predictive accuracy with reduced computational demands, thereby offering practical insights for real-time flood prediction, and aiding in proactive flood management and response efforts.

How to cite: Tabbussum, R., Basu, B., and Gill, L.: Neural Network Driven Early Warning System for Groundwater Flooding: A Comprehensive Approach in Lowland Karst Areas, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-13138, https://doi.org/10.5194/egusphere-egu24-13138, 2024.

EGU24-15144 | ECS | Orals | ITS1.1/CL0.1.17 | Highlight

A Graph Neural Network emulator for greenhouse gas emissions inference 

Elena Fillola, Raul Santos-Rodriguez, and Matt Rigby

Inverse modelling systems relying on Lagrangian Particle Dispersion Models (LPDMs) are a popular way to quantify greenhouse gas emissions using atmospheric observations, providing independent evaluation of countries' self-reported emissions. For each GHG measurement, the LPDM performs backward-running simulations of particle transport in the atmosphere, calculating source-receptor relationships (“footprints”). These reflect the upwind areas where emissions would contribute to the measurement. However, the increased volume of satellite measurements from high-resolution instruments like TROPOMI cause computational bottlenecks, limiting the amount of data that can be processed for inference. Previous approaches to speed up footprint generation revolve around interpolation, therefore still requiring expensive new runs. In this work, we present the first machine learning-driven LPDM emulator that once trained, can approximate satellite footprints using only meteorology and topography. The emulator uses Graph Neural Networks in an Encode-Process-Decode structure, similar to Google’s Graphcast [1], representing latitude-longitude coordinates as nodes in a graph. We apply the model for GOSAT measurements over Brazil to emulate footprints produced by the UK Met Office’s NAME LPDM, training on data for 2014 and 2015 on a domain of size approximately 1600x1200km at a resolution of 0.352x0.234 degrees. Once trained, the emulator can produce footprints for a domain of up to approximately 6500x5000km, leveraging the flexibility of GNNs. We evaluate the emulator for footprints produced across 2016 on the 6500x5000km domain size, achieving intersection-over-union scores of over 40% and normalised mean absolute errors of under 30% for simulated CH4 concentrations. As well as demonstrating the emulator as a standalone AI application, we show how to integrate it with the full GHG emissions pipeline to quantify Brazil’s emissions. This method demonstrates the potential of GNNs for atmospheric dispersion applications and paves the way for large-scale near-real time emissions emulation.

 [1] Remi Lam et al.,Learning skillful medium-range global weather forecasting. Science 382,1416-1421 (2023). DOI:10.1126/science.adi2336

How to cite: Fillola, E., Santos-Rodriguez, R., and Rigby, M.: A Graph Neural Network emulator for greenhouse gas emissions inference, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15144, https://doi.org/10.5194/egusphere-egu24-15144, 2024.

EGU24-15174 | ECS | Orals | ITS1.1/CL0.1.17 | Highlight

Using spatio-temporal neural networks to investigating teleconnections and enhance S2S forecasts of european extreme weather  

Philine L. Bommer, Marlene Kretschmer, Paul Boehnke, and Marina M.-C. Hoehne née Vidovic

Decision making and efficient early warning systems for extreme weather rely on subseasonal-to-seasonal (S2S) forecasts. However, the chaotic nature of the atmosphere impedes predictions by dynamical forecast systems on the S2S time scale. Improved predictability may arise due to remote drivers and corresponding teleconnections in so-called windows of opportunities, but using knowledge of such drivers to boost S2S forecast skill is challenging. Here, we present a spatio-temporal deep neural network (DNN), predicting a time series of weekly North Atlantic European (NAE) weather regimes on lead-times of one to six weeks during boreal winter. The spatio-temporal architecture combines a convolutional Long-short-term-memory (convLSTM) encoder with an Long-short-term-memory (LSTM) decoder and was built to consider both short and medium-range variability as information. As predictors it uses 2D (image) time series input data of expected drivers of European winter weather, including the stratospheric polar vortex  and tropical sea surface temperatures, alongside the 1D time series of NAE regimes. Our results indicate that additional information provided in the image time series yield a skill score improvement for longer lead times. In addition, by analysing periods of enhanced or decreased predictability of the DNN, we can infer further information regarding prevalent teleconnections.

How to cite: Bommer, P. L., Kretschmer, M., Boehnke, P., and Hoehne née Vidovic, M. M.-C.: Using spatio-temporal neural networks to investigating teleconnections and enhance S2S forecasts of european extreme weather , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15174, https://doi.org/10.5194/egusphere-egu24-15174, 2024.

EGU24-15586 | ECS | Posters on site | ITS1.1/CL0.1.17 | Highlight

Identifying Windows of Opportunity in Deep Learning Weather Models 

Daniel Banciu, Jannik Thuemmel, and Bedartha Goswami

Deep learning-based weather prediction models have gained popularity in recent years and are effective in forecasting weather over short to medium time scales with models such as FourCastNet being competitive with Numerical Weather Prediction models. 
However, on longer timescales, the complexity and interplay of different weather and climate variables leads to increasingly inaccurate predictions. 

Large-scale climate phenomena, such as the active periods of the Madden-Julian Oscillation (MJO), are known to provide higher predictability for longer forecast times.
These so called Windows of Opportunity thus hold promise as strategic tools for enhancing S2S forecasts.

In this work, we evaluate the capability of FourCastNet to represent and utilize the presence of (active) MJO phases.
First, we analyze the correlation between the feature space of FourCastNet and different MJO indices.
We further conduct a comparative analysis of prediction accuracy within the South East Asia region during active and inactive MJO phases.

How to cite: Banciu, D., Thuemmel, J., and Goswami, B.: Identifying Windows of Opportunity in Deep Learning Weather Models, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15586, https://doi.org/10.5194/egusphere-egu24-15586, 2024.

EGU24-16513 | ECS | Orals | ITS1.1/CL0.1.17

Hybrid Modelling: Bridging Neural Networks and Physics-Based Approaches in Terrestrial Biogeochemical Ecosystems 

Lazaro Alonso, Sujan Koirala, Nuno Carvalhais, Fabian Gans, Bernhard Ahrens, Felix Cremer, Thomas Wutzler, Mohammed Ayoub Chettouh, and Markus Reichstein

The application of automatic differentiation and deep learning approaches to tackle current challenges is now a widespread practice. The biogeosciences community is no stranger to this trend; however, quite often, previously known physical model abstractions are discarded.

In this study, we model the ecosystem dynamics of vegetation, water, and carbon cycles adopting a hybrid approach. This methodology involves preserving the physical model representations for simulating the targeted processes while utilizing neural networks to learn the spatial variability of their parameters. These models have historically posed challenges due to their complex process representations, varied spatial scales, and parametrizations.

We show that a hybrid approach effectively predicts model parameters with a single neural network, compared with the site-level optimized set of parameters. This approach demonstrates its capability to generate predictions consistent with in-situ parameter calibrations across various spatial locations, showcasing its versatility and reliability in modelling coupled systems.
Here, the physics-based process models undergo evaluation across several FLUXNET sites. Various observations—such as gross primary productivity, net ecosystem exchange, evapotranspiration, transpiration, the normalized difference vegetation index, above-ground biomass, and ecosystem respiration—are utilized as targets to assess the model's performance. Simultaneously, a neural network (NN) is trained to predict the model parameters, using input features(to the NN) such as plant functional types, climate types, bioclimatic variables, atmospheric nitrogen and phosphorus deposition, and soil properties. The model simulation is executed within our internal framework Sindbad.jl (to be open-sourced), designed to ensure compatibility with gradient-based optimization methods.

This work serves as a stepping stone, demonstrating that incorporating neural networks into a broad collection of physics-based models holds significant promise and has the potential to leverage the abundance of current Earth observations, enabling the application of these methods on a larger scale.

How to cite: Alonso, L., Koirala, S., Carvalhais, N., Gans, F., Ahrens, B., Cremer, F., Wutzler, T., Ayoub Chettouh, M., and Reichstein, M.: Hybrid Modelling: Bridging Neural Networks and Physics-Based Approaches in Terrestrial Biogeochemical Ecosystems, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-16513, https://doi.org/10.5194/egusphere-egu24-16513, 2024.

EGU24-17165 | ECS | Posters on site | ITS1.1/CL0.1.17

Conditioning Deep Learning Weather Prediction Models On Exogenous Fields 

Sebastian Hoffmann, Jannik Thümmel, and Bedartha Goswami

Deep learning weather prediction (DLWP) models have recently proven to be a viable alternative to classical numerical integration. Often, the skill of these models can be improved further by providing additional exogenous fields such as time of day, orography, or sea surface temperatures stemming from an independent ocean model. These merely serve as information sources and are not predicted by the model.

In this study, we explore how such exogenous fields can be utilized by DLWP models most optimally and find that the de facto standard way of concatenating them to the input is suboptimal. Instead, we suggest leveraging existing conditioning techniques from the broader deep learning community that modulate the mean and variance of normalized feature vectors in latent space. These, so called, style-based techniques lead to consistently smaller forecast errors and, at the same time, can be integrated with relative ease into existing forecasting architectures. This makes them an attractive avenue to improve deep learning weather prediction in the future.

How to cite: Hoffmann, S., Thümmel, J., and Goswami, B.: Conditioning Deep Learning Weather Prediction Models On Exogenous Fields, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17165, https://doi.org/10.5194/egusphere-egu24-17165, 2024.

EGU24-17389 | ECS | Orals | ITS1.1/CL0.1.17

Analyzing Spatio-Temporal Machine Learning Models through Input Perturbation 

Claire Robin, Vitus Benson, Christan Requena-Mesa, Lazaro Alonso, Jeran Poehls, Marc Russwurm, Nuno Carvalhais, and Markus Reichstein

The biogeoscience community has increasingly embraced the application of machine learning models across various domains from fire prediction to vegetation forecasting. Yet, as these models become more widely used, there is sometimes a gap in understanding between what we assume the model learns and what the model actually learns. For example, Long-short Term Memory (LSTM) models are applied to long time series, hoping they benefit from access to more information, despite their tendency to rapidly forget information. This can lead to erroneous conclusions, misinterpretation of results, and an overestimation of the models, ultimately eroding trust in their reliability. 

To address this issue, we employ an explainable artificial intelligence (XAI) post hoc perturbation technique that is task-agnostic and model-agnostic. We aim to examine the extent to which the model leverages information for its predictions, both in terms of time and space. In other words, we want to observe the actual receptive field utilized by the model. We introduce a methodology designed to quantify both the spatial impact of neighboring pixels on predicting a specific pixel and the temporal periods contributing to predictions in time series models. The experiments take place after training the model, during inference. In the spatial domain, we define ground-truth pixels to predict, then examine the increase in prediction error, caused by shuffling their neighboring pixels at various distances from the selection. In the temporal domain, we investigate how shuffling a sequence of frames within the context period at different intervals relative to the target period affects the increase in prediction loss. This method can be applied across a broad spectrum of spatio-temporal tasks. Importantly, the method is easy-to-implement, as it only relies on the inference of predictions at test time and the shuffling of the perturbation area. 

For our experiments, we focus on the vegetation forecasting task, i.e., forecasting the evolution of the Vegetation Index (VI) based on Sentinel-2 imagery using previous Sentinel-2 sequences and weather information to guide the prediction. This task involves both spatial non-linear dependencies arising from the spatial context (e.g., the surrounding area, such as a river or a slope, directly influencing the VI) and non-linear temporal dependencies such as the gradual onset of drought conditions and the rapid influence of precipitation events. We compare several models for spatio-temporal tasks, including ConvLSTM and transformer-based architectures on their usage of neighboring pixels in space, and context period in time. We demonstrate that the ConvLSTM relies on a  restricted spatial area in its predictions, indicating a limited utilization of the spatial context up to 50m (5 pixels). Furthermore, it utilizes the global order of the time series sequence to capture the seasonal cycle but loses sensitivity to the local order after 15 days (3 frames). The introduced XAI method allows us to quantify spatial and temporal behavior exhibited by machine learning methods.

How to cite: Robin, C., Benson, V., Requena-Mesa, C., Alonso, L., Poehls, J., Russwurm, M., Carvalhais, N., and Reichstein, M.: Analyzing Spatio-Temporal Machine Learning Models through Input Perturbation, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17389, https://doi.org/10.5194/egusphere-egu24-17389, 2024.

EGU24-17554 | ECS | Posters on site | ITS1.1/CL0.1.17

Using Cascaded Diffusion Models and Multi-Channel Data Integration for High-Resolution Statistical Downscaling of ERA5 over Denmark 

Thea Quistgaard, Peter L. Langen, Tanja Denager, Raphael Schneider, and Simon Stisen

Central to understanding climate change impacts and mitigation strategies is the generation of high-resolution, local-scale projections from global climate models. This study focuses on Danish hydrology, developing models finely tuned to generate essential climate fields such as temperature, precipitation, evaporation, and water vapor flux.

Employing advancements in computer science and deep learning, we introduce a pioneering Cascaded Diffusion Model for high-resolution image generation. This model utilizes our understanding of climate dynamics in a hydrological context by integrating multiple climate variable fields across an expanded North Atlantic domain to produce a model for stable and realistic generation. In our approach, 30 years of low-resolution daily conditioning data (ERA5) are re-gridded to match the 2.5x2.5 km 'ground truth' data (30 years of DANRA), and preprocessed by shifting a 128x128 image within a larger 180x180 pixel area, ensuring varied geographic coverage. This data, along with land-sea masks and topography, is fed as channels into the model. A novel aspect of our model is its specialized loss function, weighted by a signed distance function to reduce the emphasis on errors over sea areas, aligning with our focus on land-based hydrological modeling.

This research is part of a larger project aimed at bridging the gap between CMIP data models and ERA5 and DANRA analysis. It represents the first phase in a three-step process, with future stages focusing on downscaling from CMIP6 to CORDEX-EUROPE models, and ultimately integrating model and analysis work to form a complete pipeline from global projections to localized daily climate statistics.

How to cite: Quistgaard, T., Langen, P. L., Denager, T., Schneider, R., and Stisen, S.: Using Cascaded Diffusion Models and Multi-Channel Data Integration for High-Resolution Statistical Downscaling of ERA5 over Denmark, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17554, https://doi.org/10.5194/egusphere-egu24-17554, 2024.

EGU24-17601 | ECS | Orals | ITS1.1/CL0.1.17

Machine learning aerosol impacts on regional climate change. 

Maura Dewey, Annica Ekman, Duncan Watson-Parris, and Hans Christen Hansson

Here we develop a machine learning emulator based on the Norwegian Earth System Model (NorESM) to predict regional climate responses to aerosol emissions and use it to study the sensitivity of surface temperature to anthropogenic emission changes in key policy regions. Aerosol emissions have both an immediate local effect on air quality, and regional effects on climate in terms of changes to temperature and precipitation distributions via direct radiative impacts and indirect cloud-aerosol interactions. Regional climate change depends on a balance between aerosol and greenhouse gas forcing, and in particular extreme events are very sensitive to changes in aerosol emissions. Our goal is to provide a tool which can be used to test the impacts of policy-driven emission changes efficiently and accurately, while retaining the spatio-temporal complexity of the larger physics-based Earth System Model.

How to cite: Dewey, M., Ekman, A., Watson-Parris, D., and Hansson, H. C.: Machine learning aerosol impacts on regional climate change., EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17601, https://doi.org/10.5194/egusphere-egu24-17601, 2024.

EGU24-17694 | ECS | Orals | ITS1.1/CL0.1.17

Spatio-temporal Nonlinear Quantile Regression for Heatwave Prediction and Understanding 

Deborah Bassotto, Emiliano Diaz, and Gustau Camps-Valls

In recent years, the intersection of machine learning (ML) and climate science has yielded profound insights into understanding and predicting extreme climate events, particularly heatwaves and droughts. Various approaches have been suggested to define and model extreme events, including extreme value theory (Sura, 2011), random forests (e.g., (Weirich-Benet et al., 2023) and, more recently, deep learning (e.g., (Jacques-Dumas et al., 2022)). Within this context, quantile regression (QR) is valuable for modelling the relationship between variables by estimating the conditional quantiles of the response variable. This provides insights into the entire distribution rather than just the mean but also aids in unravelling the complex relationships among climate variables (Barbosa et al., 2011; Franzke, 2015). QR has been extended in many ways to address critical issues such as nonlinear relations, nonstationary processes, compound events, and the complexities of handling spatio-temporal data. 

This study presents a novel approach for predicting and better understanding heatwaves. We introduce an interpretable, nonlinear, non-parametric, and structured Spatio-Temporal Quantile Regression (STQR) method that incorporates the QR check function, commonly known as pinball loss, into machine learning models. We focus on analysing how the importance of predictors changes as the quantile being modelled increases. This allows us to circumvent arbitrary definitions of what constitutes a heatwave and instead observe if a natural definition of a heatwave emerges in predictor space. By analysing European heatwaves over recent decades using reanalysis and weather data, we demonstrate the advantages of our methodology over traditional extreme event modelling methods.

References

Barbosa, S.M., Scotto, M.G., Alonso, A.M., 2011. Summarising changes in air temperature over Central Europe by quantile regression and clustering. Nat. Hazards Earth Syst. Sci. 11, 3227–3233. https://doi.org/10.5194/nhess-11-3227-2011

Franzke, C.L.E., 2015. Local trend disparities of European minimum and maximum temperature extremes. Geophys. Res. Lett. 42, 6479–6484. https://doi.org/10.1002/2015GL065011

Jacques-Dumas, V., Ragone, F., Borgnat, P., Abry, P., Bouchet, F., 2022. Deep Learning-based Extreme Heatwave Forecast. Front. Clim. 4, 789641. https://doi.org/10.3389/fclim.2022.789641

Sura, P., 2011. A general perspective of extreme events in weather and climate. Atmospheric Res. 101, 1–21. https://doi.org/10.1016/j.atmosres.2011.01.012

Weirich-Benet, E., Pyrina, M., Jiménez-Esteve, B., Fraenkel, E., Cohen, J., Domeisen, D.I.V., 2023. Subseasonal Prediction of Central European Summer Heatwaves with Linear and Random Forest Machine Learning Models. Artif. Intell. Earth Syst. 2. https://doi.org/10.1175/AIES-D-22-0038.1

How to cite: Bassotto, D., Diaz, E., and Camps-Valls, G.: Spatio-temporal Nonlinear Quantile Regression for Heatwave Prediction and Understanding, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17694, https://doi.org/10.5194/egusphere-egu24-17694, 2024.

EGU24-19460 | ECS | Orals | ITS1.1/CL0.1.17 | Highlight

Earth Observation Applications through Neural Embedding Compression from Foundation Models 

Carlos Gomes and Thomas Brunschwiler

Earth observation (EO) repositories comprise Petabytes of data. Due to their widespread use, these repositories experience extremely large volumes of data transfers. For example, users of the Sentinel Data Access System downloaded 78.6 PiB of data in 2022 alone. The transfer of such data volumes between data producers and consumers causes substantial latency and requires significant amounts of energy and vast storage capacities. This work introduces Neural Embedding Compression (NEC), a method that transmits compressed embeddings to users instead of raw data, greatly reducing transfer and storage costs. The approach uses general purpose embeddings from Foundation Models (FM), which can serve multiple downstream tasks and neural compression, which balances between compression rate and the utility of the embeddings. We implemented the method by updating a minor portion of the FM’s parameters (approximately 10%) for a short training period of about 1% of the original pre-training iterations. NEC’s effectiveness is assessed through two EO tasks: scene classification and semantic segmentation. When compared to traditional compression methods applied to raw data, NEC maintains similar accuracy levels while reducing data by 75% to 90%. Notably, even with a compression rate of 99.7%, there’s only a 5% decrease in accuracy for scene classification. In summary, NEC offers a resource-efficient yet effective solution for multi-task EO modeling with minimal transfer of data volumes.

How to cite: Gomes, C. and Brunschwiler, T.: Earth Observation Applications through Neural Embedding Compression from Foundation Models, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-19460, https://doi.org/10.5194/egusphere-egu24-19460, 2024.

EGU24-20342 | ECS | Posters on site | ITS1.1/CL0.1.17

Building A Machine Learning Model To Predict Sample Pesticide Content Utilizing Thermal Desorption MION-CIMS Analysis 

Federica Bortolussi, Hilda Sandström, Fariba Partovi, Joona Mikkilä, Patrick Rinke, and Matti Rissanen

Pests significantly impact crop yields, leading to food insecurity. Pesticides are substances, or a mixture of substances, made to eliminate or control pests, or to regulate the growth of crops.
Currently, more than 1000 pesticides are available in the market. However, their long-lasting environmental impact necessitates strict regulation, especially regarding their presence in food (FAO, 2022). Pesticides play also a role in the atmosphere as their volatilization can produce oxidized products through photolysis or OH reactions and they can be transported over large distances.
The fundamental properties and behaviours of these compounds are still not well understood. Because of their complex structure, even low DFT level computations can be extremely expensive. 
This project applies machine learning (ML) tools to chemical ionization mass spectra to ultimately develop a technique capable of predicting spectra’s peak intensities and the chemical ionization mass spectrometry (CIMS) sensitivity to pesticides. The primary challenge is to develop a ML model that comprehensively explains ion-molecule interactions while minimizing computational costs.

Our data set comprises different standard mixtures containing, in total, 716 pesticides measured with an orbitrap atmospheric pressure CIMS, with a multi-scheme chemical ionization inlet (MION) and five different concentrations (Rissanen et al, 2019; Partovi et al, 2023). The reagents of the ionization methods are CH2Br2, H2O, O2 and (CH3)2CO, generating respectively Br- , H3O+, O2- and [(CH3)2 CO + H]+ ions.

The project follows a general ML workflow: after an exploratory analysis, the data are preprocessed and fed to the ML algorithm, which classifies the ionization method able to detect the molecule, and, therefore, predicts the peak intensity of each pesticide; the accuracy of the prediction can be retrieved after measuring the performance of the model.
A random forest classifier was chosen to perform the classification of the ionization methods, to predict which one was able to detect each pesticide. The regression was performed with a kernel ridge regressor. Each algorithm was run with different types of molecular descriptors (topological fingerprint, MACCS keys and many-body tensor representation), to test which one was able to represent the molecular structure most accurately.

The results of the exploratory analysis highlight different trends between the positive and negative ionization methods, suggesting that different ion-molecule mechanisms are involved (Figure 1). The classification reaches around 80% accuracy for each ionization method with all four molecular descriptors tested, while the regression can predict fairly well the logarithm of the intensities of each ionization method, reaching 0.5 of error with MACCS keys for (CH3)2CO reagent (Figure 2).

Figure 1: Distribution of pesticide peak intensities for each reagent ion at five different concentrations.

Figure 2: Comparison of the KRR performance on (CH3)2CO reagent data with four different molecular descriptors.

 

 

How to cite: Bortolussi, F., Sandström, H., Partovi, F., Mikkilä, J., Rinke, P., and Rissanen, M.: Building A Machine Learning Model To Predict Sample Pesticide Content Utilizing Thermal Desorption MION-CIMS Analysis, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-20342, https://doi.org/10.5194/egusphere-egu24-20342, 2024.

Leveraging Machine Learning (ML) models, particularly Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs) like Long-Short Term Memory (LSTM), and Artificial Neural Networks (ANN), has become pivotal in addressing the escalating frequency and severity of extreme events such as heatwaves, hurricanes, floods, and droughts. In climate modeling, ML proves invaluable for analyzing diverse datasets, including climate data and satellite imagery, outperforming traditional methods by adeptly handling vast information and identifying intricate patterns. Focusing on the study's emphasis on extreme precipitation events, the urgency arises from climate change, demanding more accurate and timely methods to predict and manage the impacts of these events.

In this study, we completed two main experiments to understand if ML algorithms can detect the extreme events. In both experiment the predictors that have been used are eastern and northern wind (u,v), geopotential height (z), specific humidity (q) and temperature (t) at four pressure levels, which are 1000hpa, 850hpa, 700hpa, and 500hpa. The frequency for the predictors is 3 hours, while the predictand being the precipitation accumulated over 3 hours. The data used in this study are the Re-Analysis -5th generation- (ERA5) produced by European Center for Medium-Range Weather Forecast (ECMWF), which provides global hourly estimates of large number of atmospheric, land and oceanic climate variables with a resolution of 25 km at different pressure levels and for the surface (precipitation in our case).

In this study, two main architectures have been applied. The first emulator, ERA-Emulator, contains 14 layers, divided in 4 blocks (input, convolutional, dense, output). In the convolutional block we have 6 convolutional layers, one layer of type ConvLSTM2D, that combines a 2D Convolutional layer and an LSTM layer, and five simple 2D convolutional layers, with two of them followed by a MaxPooling layer. In the Dense block there are three fully connected Dense layers followed by one Flatten layer and one Dropout layer. Then, we have the output layer, also a Dense layer. We used the same architecture for the second emulator, GRIPHO-Emulator, with one extra MaxPooling in the convolutional block, for a total of 15 layers. The first emulator uses variables from ERA5 both as input and output at 25 km resolution, while the second one uses variables from ERA5 as input, and the Gridded Italian Precipitation Hourly Observations dataset (GRIPHO) as output at 3 km resolution.

The ERA-Emulator is designed to approximate the downscaling function by utilizing low-resolution simulations to generate equivalent low resolution precipitation fields. ERA-Emulator resulted in a viable approach to address this challenge. The emulator demonstrates the capability to derive precipitation fields that align with ERA5 low-resolution simulations.  GRIPHO-emulator aims to downscale high resolution precipitation from low-resolution large-scale predictors. The emulator aims to estimate the downscaling function. GRIPHO-Emulator is able to create realistic high-resolution precipitation fields that well represent the observed precipitation distribution from the high resolution GRIPHO dataset.

How to cite: Abed, W. and Coppola, E.: Detection of High Convective Precipitation Events Using Machine Learning Methods, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-21760, https://doi.org/10.5194/egusphere-egu24-21760, 2024.

Ocean regional climate variability is a part of the Earth's complex system that can influence the occurrence and intensity of extreme weather events. Variability in ocean temperature can either amplify or mitigate the impact of these events. For example, the El Niño phenomena affect weather conditions in various parts of the world, leading to droughts, floods, and altered precipitation patterns. Furthermore, regional climate variability is also linked to changes in sea level. Understanding regional variability is crucial for predicting how sea level changes will vary in different parts of the world, which has profound implications for coastal communities and infrastructure. To contribute to this understanding, we have developed a novel method that combines K-means clustering and Principal Component Analysis to extract ocean climate modes at a regional scale worldwide. This integrated approach automatically identifies regions of variability, allowing for the emulation of coastal and regional sea level variations across multiple timescales. It also has the potential to offer valuable insights into the significance of temperature across multiple depth layers extending up to 700 meters. The produced set of regional sea-level emulators are a complementary source of information in coastal areas, especially in situations where satellite altimetry encounters challenges and/or tide-gauge sensor records are incomplete, thereby supporting well-informed decision-making.

How to cite: Radin, C. and Nieves, V.: Exploring Regional Ocean Climate Variability: Insights from Integrated Clustering and Principal Component Analysis., EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-120, https://doi.org/10.5194/egusphere-egu24-120, 2024.

EGU24-2297 | ECS | Posters on site | ITS1.2/OS4.10

Parameterizing ocean vertical mixing using deep learning trained from high-resolution simulations 

Rin Irie, Helen Stewart, Tsuneko Kura, Masaki Hisada, and Takaharu Yaguchi

Ocean vertical mixing plays a fundamental role in phenomena such as upwelling of nutrient-rich deep waters, and is crucial for determining net primary productivity in the ocean [1]. Simulating vertical mixing requires careful consideration and ingenuity for stable execution, as vertical mixing is often turbulent. Direct Numerical Simulations, in which the Navier-Stokes equations are solved without a turbulence model, are not realistic due to the enormous computational complexity. Ocean General Circulation Models (OGCMs) have low resolution and cannot directly resolve small-scale turbulence such as vertical mixing. Consequently, OGCMs based on the Reynolds Averaged Navier-Stokes equations use turbulence parameterizations to model the effect of unresolved motions on the mean flow [2]. Although K-Profile Parameterization (KPP) is widely recognized as a method for parameterizing vertical mixing [3], recent advancements in machine learning have triggered active exploration of data-driven approaches to parameterization [4, 5]. This study aims to develop a novel vertical mixing parameterization method using deep learning. High-resolution simulation results (O(103) m) are used as training data for a neural network to estimate vertical diffusion and viscosity. These estimates are then used to parameterize fine-scale dynamics in a low-resolution simulation (O(104) m).

The input parameters of the neural network are the state variables RL = (vL, θL, SL)T, where vL is the flow velocity field, θL is the potential temperature, and SL is the salinity. Here, the L and H subscripts will be used to indicate the low and high-resolution simulations. The output parameters are P = (κh, Ah)T, where κh and Ah are the estimated vertical diffusion and viscosities respectively. The loss function is defined as the mean squared error between the state variables of the high and low-resolution simulations RLRH. Verification experiments for the proposed parameterization method are conducted for an idealized double-gyre configuration, which models western boundary currents such as the Gulf Stream in the North Atlantic Ocean. We confirm the performance and efficiency of the proposed method compared to traditional KPP for conducting high-resolution simulations at low computational cost.

Acknowledgements
This work used computational resources of supercomputer Fugaku provided by the RIKEN Center for Computational Science through the HPCI System Research Project (Project ID: hp230382).

References
[1] D. Couespel et. al (2021), Oceanic primary production decline halved in eddy-resolving simulations of global warming, Biogeosciences, 18(14), 4321-4349.
[2] M. Solano, and Y. Fan (2022), A new K-profile parameterization for the ocean surface boundary layer under realistic forcing conditions, Ocean Modelling, 171, 101958.
[3] W. G. Large et. al (1994), Oceanic vertical mixing: A review and a model with a nonlocal boundary layer parameterization, Reviews of geophysics, 32(4), 363–403.
[4] Y. Han et. al (2020), A moist physics parameterization based on deep learning, Journal of Advances in Modeling Earth Systems, 12(9), e2020MS002076.
[5] Y. Zhu et. al (2022), Physics-informed deep-learning parameterization of ocean vertical mixing improves climate simulations, National Science Review, 9(8), nwac044. 

How to cite: Irie, R., Stewart, H., Kura, T., Hisada, M., and Yaguchi, T.: Parameterizing ocean vertical mixing using deep learning trained from high-resolution simulations, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-2297, https://doi.org/10.5194/egusphere-egu24-2297, 2024.

EGU24-2934 | Posters on site | ITS1.2/OS4.10

Accelerating Marine UAV Drone Image Analysis with Sliced Detection and Clustering (MBARI SDCAT) 

Duane R. Edgington, Danelle E. Cline, Thomas O'Reilly, Steven H.D. Haddock, John Phillip Ryan, Bryan Touryan-Schaefer, William J. Kirkwood, Paul R. McGill, and Rob S. McEwen

Uncrewed Aerial Vehicles (UAVs) can be a cost-effective solution for capturing a comprehensive view of surface ocean phenomena to study marine population dynamics and ecology. UAVs have several advantages, such as quick deployment from shore, low operational costs, and the ability to be equipped with various sensors, including visual imaging systems and thermal imaging sensors. However, analyzing high-resolution images captured from UAVs can be challenging and time-consuming, especially when identifying small objects or anomalies. Therefore, we developed a method to quickly identify a diverse range of targets in UAV images.

We will discuss our workflow for accelerating the analysis of high-resolution visual images captured from a Trinity F90+ Vertical Take-Off and Landing (VTOL) drone in near-shore habitats around the Monterey Bay region in California at approximately 60 meters altitude. Our approach uses a state-of-the-art self-distillation with knowledge (DINO) transformer foundation model and multi-scale, sliced object detection (SAHI) methods to locate a wide range of objects, from small to large, such as schools or individual jellyfish, flocks of birds, kelp forests or kelp fragments, small debris, occasional cetaceans, and pinnipeds. To make the data analysis more efficient, we create clusters of similar objects based on visual similarity, which can be quickly examined through a web-based interface. This approach eliminates the need for previously labeled objects to train a model, optimizing limited human resources. Our work demonstrates the useful application of state-of-the-art techniques to assist in the rapid analysis of images and how this can be used to develop a recognition system based upon machine-learning for the rapid detection and classification of UAV images. All of our work is freely available as open-source code.

How to cite: Edgington, D. R., Cline, D. E., O'Reilly, T., Haddock, S. H. D., Ryan, J. P., Touryan-Schaefer, B., Kirkwood, W. J., McGill, P. R., and McEwen, R. S.: Accelerating Marine UAV Drone Image Analysis with Sliced Detection and Clustering (MBARI SDCAT), EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-2934, https://doi.org/10.5194/egusphere-egu24-2934, 2024.

El Niño-Southern Oscillation (ENSO) events have significant impacts on global climate change, and the research on their accurate forecasting and dynamic predictability holds remarkable scientific and engineering values. Recent years, we have constructed two ENSO deep learning forecasting models, ENSO-ASC and ENSO-GTC, which are both incorporated with prior ENSO dynamic mechanisms. Specifically, the former possesses the multivariate air-sea coupler (ASC), which can simulate the occurrence and decay of ENSO events, accompanied by concurrent energy interactions among multiple physical variables in the Pacific Ocean. The latter possesses the global teleconnection coupler (GTC), which can modulate the significant teleconnections of global ocean basins rather than the isolated interactions in the Pacific Ocean. From the perspective of forecasting skill, the Niño 3.4 index correlation skills of these two models can reach 0.78/0.65/0.50 (0.79/0.66/0.51) in 6/12/18 lead-month prediction, which means they exhibit an effective forecasting lead month of more than 18, outperforming the Ham et al.'s Nature-published ENSO forecasting model. The test of the past year's (2022) forecast results shows that the average forecast error of these two models is 0.156, which is less than 10% of the actual ENSO amplitudes. It is worth noting that these two models also encounter the spring presistence barrier (SPB), but indicates a profound improvement compared to the numerical models. From the perspective of ENSO predictability, zonal and meridional winds are two sensitive predictors for ENSO forecasting besides sea surface temperature (SST), which greatly contribute to the Bjerknes positive feedback mechanism and WES mechanism. Walker circulation, acting as the "atmpsphric bridge", induces the teleconnections of the three oceans, which can derive the easterly wind anomalies in the equatorial western Pacific from the Indian Ocean and North Pacific meridional mode in the northeastern Pacific from the Atlantic Ocean, promoting ENSO event development and decay.

How to cite: Qin, B.: Two Physics-informed Enso Deep Learning Forecasting Models: ENSO-ASC and ENSO-GTC, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-3372, https://doi.org/10.5194/egusphere-egu24-3372, 2024.

The assessment and monitoring of microbial plankton biodiversity are essential to obtain a robust evaluation of the health status of marine environments. The PETRI-MED project addresses this imperative by developing novel strategies to monitor the microbial plankton community composition and function, based on satellite observations. PETRI-MED will focus on the Mediterranean Sea as a global biodiversity hotspot with profound ecological and cultural importance. The primary objectives of PETRI-MED project encompass (i) the development of innovative satellite-based indicators to determine the biodiversity status and trends of microbial plankton community, (ii) the identification of spatio-temporal patterns in microbial plankton distribution and diversity, and (iii) the elucidation of key controls of biodiversity patterns, including ecological connectivity, natural and human-related forcings, by focusing on key indicators of ocean’s health and/or biogeochemical state. To do so, PETRI-MED will largely rely on satellite optical radiometric measurements (i.e, Ocean Colour, OC), exploiting the combined temporal and spatial characteristics of latest OC European datasets (i.e., Copernicus Sentinel-3 and European Space Agency OC-CCI) with state-of-the-art remote sensing observations and biogeochemical models (as provided by Copernicus Marine), marine currents modelling, and genomic techniques. To achieve the ambitious goal of merging remote sensing, biogeochemical/physical modelling, and in situ omics measurements, PETRI-MED will rely on Artificial Intelligence (AI). The overarching goal of PETRI-MED is to empower policymakers and stakeholders with the necessary knowledge to adopt prioritization approaches for ecosystem management based on quantitative, real-time metrics. This includes the design and implementation of protection strategies and policies to safeguard biodiversity, quantifying the impact of implemented actions at various levels, and enabling systematic, fact-supported management of Marine Protected Areas (MPAs), Key Biodiversity Areas, and Ecologically or Biologically Significant Marine Areas. Furthermore, PETRI-MED seeks to evaluate the viability of MPA management in response to climate change, ensuring adaptive strategies for the conservation of marine ecosystems in the face of environmental challenges. In summary, PETRI-MED represents a comprehensive and innovative approach to advancing our understanding of microbial plankton biodiversity in the Mediterranean Sea. Through the integration of satellite technology, omics techniques and AI, the project contributes valuable insights and tools for effective marine ecosystem management and conservation strategies.

How to cite: Tinta, T. and the PETRI-MED: PETRI-MED: Advancing Satellite-Based Monitoring for Microbial Plankton Biodiversity in the Mediterranean Sea, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-3523, https://doi.org/10.5194/egusphere-egu24-3523, 2024.

The development of the world economy in recent years has been accompanied by a significant increase in maritime traffic. Accordingly, numerous ship collision incidents, especially in dense maritime traffic zones, have been reported with damage, including oil spills, transportation interruption, etc. To improve maritime surveillance and minimize incidents over the seas, satellite imagery provided by synthetic aperture radar (SAR) and optical sensors has become one of the most effective and economical solutions in recent years. Indeed, both SAR and optical images can be used to detect vessels of different sizes and categories, thanks to their high spatial resolutions and wide swath.

To process a mass of satellite data, Deep Learning (DL) has become an indispensable solution to detect ships with a high accuracy rate. However, the DL models require time and effort for implementation, especially for training, validating, and testing with big datasets. This issue is more significant if we use different satellite imagery datasets for ship detection because data preparation tasks will be multiplied. Therefore, this paper aims to investigate various approaches for applying the DL models trained and tested on different datasets with various spatial resolution and radiometric features. Concretely, we focus on two aspects of ship detection from multi-source satellite imagery that have not been attentively discussed in the literature. First, we compare the performance of DL models trained on one HR or MR dataset and those trained on the combined HR and MR datasets. Second, we compare the performance of DL models trained on an optical or SAR dataset and tested on another. Likewise, we evaluate the performance of DL models trained on the combined SAR and optical dataset. The objective of this work is to answer a practical question of ship detection in maritime surveillance, especially for emergency cases if we can directly apply the DL models trained on one dataset to others having differences in spatial resolution and radiometric features without the supplementary steps such as data preparation and DL models retraining.

When dealing with a limited number of training images, the performance of DL models via the approaches proposed in this study was satisfactory. They could improve 5–20% of average precision, depending on the optical images tested. Likewise, DL models trained on the combined optical and radar dataset could be applied to both optical and radar images. Our experiments showed that the models trained on an optical dataset could be used for radar images, while those trained on a radar dataset offered very poor scores when applied to optical images.

How to cite: La, T.-V., Pham, M.-T., and Chini, M.: Collocation of multi-source satellite imagery for ship detection based on Deep Learning models, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-3954, https://doi.org/10.5194/egusphere-egu24-3954, 2024.

EGU24-4126 | ECS | Posters on site | ITS1.2/OS4.10

Revealing Machine Learning's potential for morphotectonic analysis of marine faults: Application to the North-South faults in the Alboran Sea (Westernmost Mediterranean) 

Ariadna Canari, Léa Pousse-Beltran, Sophie Giffard-Roisin, Hector Perea, and Sara Martínez – Loriente

Seismic hazard assessment requires a detailed understanding of the evolution of fault systems, rupture processes, and linkage between segments. Identifying and characterizing Quaternary surface faulting features, such as fault scarps, provide valuable morphotectonic data on cumulative displacement over time, especially in regions with moderate to low seismic activity. Although fault cumulative vertical surface offsets have been traditionally measured using topographic profiles, these profiles are unevenly spread along the faults and may not reflect all the morphological changes along them. To address this situation, expanding the analysis to encompass a larger number of profiles is a viable option; nevertheless, manually executing this task would prove significantly time-consuming. Machine Learning (ML) has shown unprecedented capacities to evaluate large datasets in reduced time and provide a wealth valuable information with their related uncertainties. With this in mind, we propose a ML algorithm called ScarpLearn based on Convolutional Neural Networks (CNN) to compute the vertical cumulative displacement and its uncertainty for normal fault scarps. Despite ScarpLearn being initially developed to characterize simple scarps in onshore areas, we have enhanced its capabilities so that it can also be used in offshore areas subject to oceanic processes. This includes, among others, more intense diffusion, or the presence of seabed features such as pockmarks. Additionally, we have improved the code's versatility by providing a method modification that allows it to better characterization of scarps in more complex areas where multiple faults offset the seafloor. To this, we have trained the algorithm using a large database of realistic synthetic bathymetric profiles, including different parameters such as fault dip, slip velocity, scarp spread, scarp diffusion coefficient, and variable resolutions to ensure adaptability to all datasets. These modifications have resulted in the improvement of the ScarpLearn algorithm’s adaptability, elevating its accuracy and reliability in capturing the complexity of marine fault systems, but also applicable to terrestrial systems. We have applied the new ScarpLearn version to the North-South faults of the northern Alboran Sea, contributing to the accurate analysis of this Plio-Quaternary transtensional system and its complex geological structures. This innovative approach has allowed us to refine the morphotectonic analysis of the area and to understand better the geodynamics of the North-South faults system. In this research, we have explored the advances of the CNN method achieved in oceanic environments, considering intensive data compilation, computational time, accuracy, uncertainties, and current limitations. Our advances demonstrate the ScarpLearn ML potential, specifically tailored to analyze marine environments and multiple fault segments both onshore and offshore. Our research results contribute to the progress of marine geosciences by improving morphotectonic analysis using ML algorithms.

 

Keywords: Convolutional Neural Networks (CNN), Oceanic processes, Normal faults, Multiple scarps.

 

How to cite: Canari, A., Pousse-Beltran, L., Giffard-Roisin, S., Perea, H., and Martínez – Loriente, S.: Revealing Machine Learning's potential for morphotectonic analysis of marine faults: Application to the North-South faults in the Alboran Sea (Westernmost Mediterranean), EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-4126, https://doi.org/10.5194/egusphere-egu24-4126, 2024.

Prediction of sea surface current is essential for various marine activities, such as tourist industry, commercial transportation, fishing industries, search and rescue operations, and so on. Numerical forecast models make it possible to predict a realistic ocean with the help of data-assimilation and fine spatial resolution. Nevertheless, complicated numerical prediction model requires heavy power and time for computation, which initiated development of novel approaches with efficient computational costs. In that sense, artificial neural networks could be one of the solutions because they need low computational power for prediction thanks to pre-trained networks. Here, we present a prediction framework applicable to the surface current prediction in the seas around the Korean peninsula using three-dimensional (3-D) convolutional neural networks. The network is based on the 3-D U-net structure and modified to predict sea surface currents using oceanic and atmospheric variables. In the forecast procedure, it is optimized to minimize the error of the next day’s sea surface current field and its recursively predicting structure allows more days to be predicted. The network’s performance is evaluated by changing input days and variables to find the optimal surface-current-prediction artificial neural network model, which demonstrates its strong potential for practical uses near future.

How to cite: Park, J.-H., Chae, J.-Y., and Kim, Y. T.: Surface current prediction in the seas around the Korean peninsula using a CNN-based deep-learning model , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-4488, https://doi.org/10.5194/egusphere-egu24-4488, 2024.

The Indonesian ThroughfFow (ITF) plays a vital role in the global ocean circulation and climate system. The intricate labyrinth of passages around the Indonesian Seas poses a grand challenge in monitoring and understanding the throughflow in the region. In this study, we employ the deep-learning approach to examine to what degree known sea level variations can determine main in- and outflows through the Indonesian Seas. The approach is first validated using the simulated environment from a regional circulation model. Our results show that the Recurrent Neural Network (RNN) models can well represent the temporal variations of throughflows across the Indonesian Seas. Moreover, the skills can be significantly improved if aided by time series of transport from a small number of passages. We also apply the trained model to the satellite derived sea surface height in design of more effective allocations of observation assets.

How to cite: Xue, H., Wang, Z., and Wang, Y.: Applying Deep-learning Models in Observation Simulation Experiments of Throughflows Across the Indonesian Seas, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-4587, https://doi.org/10.5194/egusphere-egu24-4587, 2024.

EGU24-5552 | ECS | Posters on site | ITS1.2/OS4.10

Scalable 3D Semantic Mapping of Coral Reefs with Deep Learning 

Jonathan Sauder, Guilhem Banc-Prandi, Gabriela Perna, Anders Meibom, and Devis Tuia

Coral reefs, which host more than a third of the ocean’s biodiversity on less than 0.1% of its surface, are existentially threatened by climate change and other human activities. This necessitates methods for evaluating the state of coral reefs that are efficient, scalable, and low-cost. Current digital reef monitoring tools typically rely on conventional Structure-from-Motion photogrammetry, which can limit the scalability, and current datasets for training semantic segmentation systems are either sparsely labeled, domain-specific, or very small. We describe the first deep-learning-based 3D semantic mapping approach, which enables rapid mapping of coral reef transects by leveraging the synergy between self-supervised deep learning SLAM systems and neural network-based semantic segmentation, even when using low-cost underwater cameras. The 3D mapping component learns to tackle the challenging lighting effects of underwater environments from a large dataset of reef videos. The transnational data-collection initiative was carried out in Djibouti, Sudan, Jordan, and Israel, with over 150 hours of collected video footage for training the neural network for 3D reconstruction. The semantic segmentation component is a neural network trained on a dataset of video frames with over 80’000 annotated polygons from 36 benthic classes, down to the resolution of prominent visually identifiable genera found in the shallow reefs of the Red Sea. This research paves the way for affordable and widespread deployment of the method in analysis of video transects in conservation and ecology, highlighting a promising intersection with machine learning for tangible impact in understanding these oceanic ecosystems. 

How to cite: Sauder, J., Banc-Prandi, G., Perna, G., Meibom, A., and Tuia, D.: Scalable 3D Semantic Mapping of Coral Reefs with Deep Learning, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5552, https://doi.org/10.5194/egusphere-egu24-5552, 2024.

EGU24-5926 | ECS | Posters on site | ITS1.2/OS4.10

Reconstructing Global Ocean Deoxygenation Over a Century with Deep Learning 

Bin Lu, Ze Zhao, Luyu Han, Xiaoying Gan, Yuntao Zhou, Lei Zhou, Luoyi Fu, Xinbing Wang, Jing Zhang, and Chenghu Zhou

Oxygen is fundamentally essential for all life. Unfortunately, recent research has shown that global ocean deoxygenation has significantly increased over the past 50 years, and the stock of dissolved oxygen (DO) in the ocean has been continuously decreasing. Breathless ocean has led to large-scale death of fish, seriously affecting the marine ecosystem. Moreover, global warming and human activities have further intensified the expansion of dead zones (low-oxygen area) in the ocean.

Hence, it is of vital importance to quantitatively understand and predict the trend of global ocean deoxygenation. However, despite of the accumulation of in-situ DO observation in recent years, global and long-term observation data is still severely sparse, leading to a critical challenge in reconstructing global ocean deoxygenation over a century. Existing works can be categorized into two ways: (1) Physics-informed numerical models. These methods simulate the DO concentration based on climate models without utilizing in-situ observations, e.g., Coupled Model Intercomparison Project Phase 6 (CMIP6). However, these models fail to adjust biased simulation results based on temporal DO observations and cause error propagation. (2) Spatial interpolation methods. These methods reconstruct the global deoxygenation through available observations by geostatistical regression, Kriging, etc. But these ways are unable to capture the complex spatiotemporal heterogeneity and physical-biogeochemical properties, showing inconsistent performance in different areas.

To this end, we propose a knowledge-infused deep graph learning method called 4D Spatio-Temporal Graph HyperNetwork (4D-STGHN) to reconstruct four-dimensional (including time, latitude, longitude, and depth) global ocean deoxygenation from 1920 till now. To capture the spatio-temporal heterogeneity in different regions, 4D-STGHN utilize hypernetwork to generate non-shared parameters by fusing 4D geographic information and observations. Moreover, we design a chemistry-informed gradient norm mechanism as the loss function by integrating the observation of nitrate and phosphate, hereby further improving the performance of DO reconstruction. 4D-STGHN shows promising reconstruction with mean absolute percentage error (MAPE) of only 5.39%, largely outperforming three CMIP6 experiments (CESM2-omip1, CESM2-omip2 and GFDL-ESM4-historical) on dissolved oxygen and other machine learning methods. Further analysis on the global oxygen minimum zones, as well as regional analysis are conducted to evaluate the effectiveness of our proposed methods.

How to cite: Lu, B., Zhao, Z., Han, L., Gan, X., Zhou, Y., Zhou, L., Fu, L., Wang, X., Zhang, J., and Zhou, C.: Reconstructing Global Ocean Deoxygenation Over a Century with Deep Learning, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5926, https://doi.org/10.5194/egusphere-egu24-5926, 2024.

EGU24-6735 | Orals | ITS1.2/OS4.10

Analyzing Zooplankton grazing spatial variability in the Southern Ocean using deep learning 

Gian Giacomo Navarra, Aakash Sane, and Curtis Deutsch

To elucidate the complex dynamics of zooplankton grazing and its impact on the organic carbon pump, we leveraged machine learning algorithms to analyze extensive datasets encompassing zooplankton behavior, environmental variables, and carbon flux measurements. Specifically, we employed regression models to establish predictive relationships between zooplankton grazing rates and key environmental factors, such as Potential Temperature, Sea Ice extension and iron availability.

The results demonstrate the potential of machine learning in discerning patterns and nonlinear relationships within the data, offering insights into the factors influencing zooplankton grazing dynamics. Additionally, the models provide a predictive framework to estimate the contribution of zooplankton to the organic carbon pump under varying environmental conditions. We have further analyzed the results by using two explainable AI methods, the Layer Wise Relevance Propagation and Integrated Gradients that informs which physical variables contribute to the prediction.

This research contributes to our understanding of the intricate processes governing carbon sequestration in the ocean, with implications for climate change mitigation and marine ecosystem management. Machine learning techniques assists to unravel the complexities of zooplankton-mediated carbon flux, to unravel the complexities of zooplankton-mediated carbon flux, paving the way for more accurate predictions and proactive conservation strategies in the face of global environmental changes.

How to cite: Navarra, G. G., Sane, A., and Deutsch, C.: Analyzing Zooplankton grazing spatial variability in the Southern Ocean using deep learning, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-6735, https://doi.org/10.5194/egusphere-egu24-6735, 2024.

EGU24-6927 | ECS | Posters on site | ITS1.2/OS4.10 | Highlight

A Deep Learning Model for Tropical Cyclone Center Localization Based on SAR Imagery 

Shanshan Mu, Haoyu Wang, and Xiaofeng Li

Tropical cyclones (TCs) are natural disasters originating over tropical or subtropical oceans. Their landfall is generally accompanied by extensive high winds and persistent precipitation, causing severe economic losses and human casualties yearly. Consequently, conducting effective TC landfall intensity forecasts for disaster risk reduction is imperative. The calm center of a TC, known as the TC eye, serves as a vital indicator of its intensity. Hence, precisely locating TC centers is crucial for determining TC intensity. In this study, a deep-learning model was developed to extract TC centers from satellite remote-sensing images automatically.
Space-borne synthetic aperture radar (SAR) imagery plays a critical role in monitoring natural hazards owing to its high spatial resolution, wide coverage, and day-night imaging capabilities. A total of 110 Sentinel SAR images spanning from 2016 to 2019 were used for TC center localization in this paper. They were acquired in interferometric-wide (IW) mode with a pixel size of 10 m and extra-wide (EW) mode with a pixel size of 40 m. They were resampled by spatial averaging to maintain the same pixel size of 80 m. Additionally, we manually annotated the central area of tropical cyclone images as ground truth data.
For the dataset, we initially divided 110 SAR images and the corresponding truth data into training, validation, and testing sets in an 8:1:1 ratio. Subsequently, we partitioned the SAR images into 256 × 256 pixel-sized slices as the model inputs. 32151/4611/3900 samples were extracted as the training/validation/testing dataset. Considering the target samples containing the center position are far less than compared background samples in TCs, we retained all center-containing samples and randomly selected 1.2 times the number of background samples for each image. Consequently, we obtained a final dataset of 2388/257/245 samples for training, validation, and testing.
As is known, deep learning technology excels in learning non-linear relationships and is good at automatically extracting intricate patterns from SAR imagery. The Res-UNet, a U-Net-like model with the weighted attention mechanism and the skip connection scheme that has been proven effective in solving the problem of contrast reduction caused by signal interference, was ultimately determined as the deep learning model for the automatic positioning of tropical cyclone centers in our study.
We calculated the centroid of the central region and compared the model results with ground truth. Our model outputs agreed well with the visually located TC center with a mean intersection over union (IOU) and a mean TC center location error of 0.71/0.70/0.67 and 3.59/2.24/2.20 km for the training/validation/testing dataset. Moreover, our model greatly simplifies the complexity of traditional methods such as using spiral rainbands and background wind fields for center positioning. At the same time, our method can not only obtain the position of the TC center but also extract the central area, thereby obtaining the morphological characteristics of TCs, which is conducive to better monitoring and intensity determination of TC.

How to cite: Mu, S., Wang, H., and Li, X.: A Deep Learning Model for Tropical Cyclone Center Localization Based on SAR Imagery, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-6927, https://doi.org/10.5194/egusphere-egu24-6927, 2024.

EGU24-8207 | ECS | Posters on site | ITS1.2/OS4.10

Unveiling the Ocean’s Rhythms: Blending Deep Learning and Spectral Analysis Together to Gain Insights into Sunda Shelf Surface Currents using AIS Data 

Jun Yu Puah, Ivan D. Haigh, David Lallemant, Ronan Fablet, Kyle Morgan, and Adam D. Switzer

Surface currents influence ship navigation, coastal heat transfer and sediment transport, and thus necessitate robust models that can reliably predict surface current behaviour. However, our ability to make predictions over long time scales are commonly hampered by a lack of long observational datasets. Remote sensing technologies, which include satellite altimetry and high-frequency radar, are often used to measure global surface currents. However, their ability to reveal insights on ocean dynamics at a regional scale remain limited by restrictions related to space-time sampling. Here, we explore the use of AIS data as a means to derive surface currents in the Sunda Shelf Region in equatorial southeast Asia. Firstly, we apply nearest-neighbour interpolation to map relevant AIS information, that includes the ship’s speed over ground, course over ground and heading, onto a grid with a spatial resolution of 100m and an hourly temporal resolution. Next, we applied a gradient descent approach to derive surface currents at the positions of the ships. We then implement a generative model on PyTorch to reconstruct surface currents in the region. The model performance is evaluated by comparing to observational data from drifters and drifting buoys. Lastly, we employed wavelet analysis, a type of nonstationary spectral analysis, to examine the dominant frequencies or periods where surface currents are strong. Our pilot study highlights the potential of AIS data as a credible alternative to traditional methods of measuring surface currents in data scarce areas.

How to cite: Puah, J. Y., Haigh, I. D., Lallemant, D., Fablet, R., Morgan, K., and Switzer, A. D.: Unveiling the Ocean’s Rhythms: Blending Deep Learning and Spectral Analysis Together to Gain Insights into Sunda Shelf Surface Currents using AIS Data, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-8207, https://doi.org/10.5194/egusphere-egu24-8207, 2024.

EGU24-8942 | ECS | Orals | ITS1.2/OS4.10

Chlorophyll-a satellite climate time series: How machine learning can help distinguish between bias and consistency 

Etienne Pauthenet, Elodie Martinez, Thomas Gorgues, Joana Roussillon, Lucas Drumetz, Ronan Fablet, and Maïlys Roux

Phytoplankton sustains marine ecosystems and influences global carbon dioxide levels through photosynthesis. To grow, phytoplankton rely on nutrient availability in the upper sunlit layer, closely related to ocean dynamics and specifically ocean stratification. Human-caused climate change is responsible, among others, for an increase in global temperature and regional modifications of winds, thus affecting the stratification of the ocean's surface. Consequently, phytoplankton biomass is expected to be impacted by these environmental changes. While most existing studies focus on one or two satellite products to investigate phytoplankton trends in the global ocean, in this study, we analyze surface chlorophyll-a concentration (chl-a), a proxy for phytoplankton biomass, using six merged satellite products from January 1998 to December 2020. Significant regional discrepancies are observed among the different products, displaying opposing trends. To distinguish trends arising from changes in the physical ocean from those potentially resulting from sensor biases, a convolutional neural network is employed to examine the relationship between chl-a and physical ocean variables (sea surface temperature, sea surface height, sea surface currents, wind, and solar radiation). The training is conducted over 2002-2009 when the number of merged sensors is constant, and chl-a is reconstructed over 2010-2020. Our results suggest that the merging algorithm of the Globcolour Garver, Siegel, Maritorena (GSM) bio-optical model is not reliable for trend detection. Specifically, changes in chl-a after 2016 are not supported by changes in the physical ocean but rather by the introduction of the VIIRS sensor. These results emphasize the need for a careful interpretation of chl-a trends and highlight the potential of machine learning to study the evolution of marine ecosystems.

How to cite: Pauthenet, E., Martinez, E., Gorgues, T., Roussillon, J., Drumetz, L., Fablet, R., and Roux, M.: Chlorophyll-a satellite climate time series: How machine learning can help distinguish between bias and consistency, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-8942, https://doi.org/10.5194/egusphere-egu24-8942, 2024.

EGU24-11061 | Posters on site | ITS1.2/OS4.10

Machine-learning-based analysis and reconstruction of high-resolution sea-surface temperatures for the North Sea and Baltic Sea 

Tim Kruschke, Christopher Kadow, Johannes Meuer, and Claudia Hinrichs

The Federal Maritime and Hydrographic Agency of Germany performs weekly analyses of sea surface temperatures (SST) for the North Sea and Baltic Sea on an operational basis. The analysis is based on in-situ observations and satellite retrievals. Existing procedures require manual quality control and subjective decisions on plausibility of measurements combined with simple interpolation techniques. This study presents ongoing work to develop new procedures based on a machine learning approach, designed to fill in gaps in observational data fields. The employed inpainting technique makes use of a convolutional neural network (CNN) that is trained with complete SST-fields from high-resolution (~3 km) ocean model simulations and masks derived from satellite retrievals to ignore regions covered by clouds on respective days.

First validation efforts for the North Sea based on reconstructing modelled fields that were excluded from training data indicate very promising results, that is an RMSE of ~ 0.5 K or less for most regions of the North Sea. However, areas with high variance such as waters very close to the coast and the Norwegian Channel exhibit larger errors up to 1 K. Additionally, we can show that errors tend to be larger when less observational data are available, e.g. on days with lots of clouds.

It will be tested if optional features of the algorithm may help to improve results in these cases. Especially the possibility to use “memory” of preceding days – potentially featuring less clouds – seems promising in this respect. Furthermore, it will be elaborated if the option of overwriting existing observational data with values better fitting the patterns learned by the CNN is suitable to improve the overall results and hence may be an alternative to external (manual) quality control and plausibility checking.

The final aim of this study is to establish an approach suitable for the operational analysis of daily SSTs with a horizontal resolution of approx. 5 km and the production of an SST reanalysis of North Sea and Baltic Sea SSTs starting in 1990.

How to cite: Kruschke, T., Kadow, C., Meuer, J., and Hinrichs, C.: Machine-learning-based analysis and reconstruction of high-resolution sea-surface temperatures for the North Sea and Baltic Sea, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-11061, https://doi.org/10.5194/egusphere-egu24-11061, 2024.

EGU24-12271 | Posters on site | ITS1.2/OS4.10

Harnessing Machine Learning and Principal Components Techniques for Atmospheric and Glint Correction to Retrieve Ocean Color from Geostationary Satellites 

Zachary Fasnacht, Joanna Joiner, Matthew Bandel, David Haffner, Alexander Vassilkov, Patricia Castellanos, and Nickolay Krotkov

Retrievals of ocean color (OC) properties from space are important for better understanding the ocean ecosystem and carbon cycle. The launch of atmospheric hyperspectral instruments such as the geostationary Tropospheric Emissions: Monitoring of Pollution (TEMPO) and GEMS, provide a unique opportunity to examine the diurnal variability in ocean ecology across various waters in North America and prepare for the future suite of hyperspectral OC sensors. While TEMPO does not have as high spatial resolution or full spectral coverage as planned coastal ocean sensors such as the Geosynchronous Littoral Imaging and Monitoring Radiometer (GLIMR) or GeoXO OC instrument (OCX), it provides hourly coverage of US coastal regions and great lakes, such as Lake Erie and the Gulf of Mexico at spatial scales of approximately 5 km. We will apply our newly developed machine learning (ML) based atmospheric correction approach for OC retrievals to TEMPO data. Our approach begins by decomposing measured hyperspectral radiances into spectral features that explain the variability in atmospheric scattering and absorption as well as the underlying surface reflectance. The coefficients of the principal components are then used to train a neural network to predict OC properties such as chlorophyll concentration derived from collocated MODIS/VIIRS physically-based retrievals. This ML approach compliments the standard radiative transfer-based OC retrievals by providing gap-filling over cloudy regions where the standard algorithms are limited. Previously, we applied our approach using blue and UV wavelengths with the Ozone Monitoring Instrument (OMI) and TROPOspheric Monitoring Instrument (TROPOMI) to show that it can estimate OC properties in less-than-ideal conditions such as lightly to moderately cloudy conditions as well as sun glint and thus improve the spatial coverage of ocean color measurements. TEMPO provides an opportunity to improve on this approach since it provides extended spectral measurements at green and red wavelengths which are important particularly for coastal waters. Additionally, our ML technique can be applied to provisional data early in the mission and has potential to demonstrate the value of near real time OC products that are important for monitoring of harmful algae blooms and transient oceanic phenomena.   

 

How to cite: Fasnacht, Z., Joiner, J., Bandel, M., Haffner, D., Vassilkov, A., Castellanos, P., and Krotkov, N.: Harnessing Machine Learning and Principal Components Techniques for Atmospheric and Glint Correction to Retrieve Ocean Color from Geostationary Satellites, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12271, https://doi.org/10.5194/egusphere-egu24-12271, 2024.

EGU24-13571 | ECS | Orals | ITS1.2/OS4.10

Application of a Neural Network Algorithm to Estimate the Nutrients Concentration in the Peruvian Upwelling System 

Cristhian Asto, Anthony Bosse, Alice Pietri, François Colas, Raphaëlle Sauzède, and Dimitri Gutiérrez

The Peruvian coastal upwelling system (PCUS) is one of the most productive in the world ocean. The Peruvian Marine Research Institute (IMARPE) has been monitoring the PCUS  since the 1960’s with an increase in the frequency and spatial distribution of measurements since the early 2000’s focusing on temperature, salinity and oxygen. In recent years, autonomous gliders have started to be routinely deployed by IMARPE, collecting a large amount of profiles. However, there is still a gap for the high-resolution  sampling of biogeochemical parameters such as nutrients (nitrate, phosphate and silicate).

New  methods using machine learning to reconstruct missing data have been developed recently with promising results (Sauzède et al, 2017; Bittig et al., 2018; Fourrier et al., 2020). In particular, a recent global approach using neural networks (NN) named CANYON-B (CArbonate system and Nutrientes concentration from hYdrological properties and Oxygen using a Neural network) was developed in order to fill those gaps and infer nutrients’ concentrations from the more sampled variables of temperature, salinity and oxygen (Bittig et al., 2018).

In this work we show the application of this global CANYON-B algorithm to the PCUS using all the historical IMARPE’s CTD casts. Moreover, we trained a new NN and compared its outputs with the ones from CANYON-B, demonstrating the benefits of training the NN with the extensive regional data set collected by IMARPE.

How to cite: Asto, C., Bosse, A., Pietri, A., Colas, F., Sauzède, R., and Gutiérrez, D.: Application of a Neural Network Algorithm to Estimate the Nutrients Concentration in the Peruvian Upwelling System, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-13571, https://doi.org/10.5194/egusphere-egu24-13571, 2024.

EGU24-14839 | ECS | Orals | ITS1.2/OS4.10

Near-real-time monitoring of global ocean carbon sink based on CNN 

Piyu Ke, Xiaofan Gui, Wei Cao, Dezhi Wang, Ce Hou, Lixing Wang, Xuanren Song, Yun Li, Biqing Zhu, Jiang Bian, Stephen Sitch, Philippe Ciais, Pierre Friedlingstein, and Zhu Liu

The ocean plays a critical role in modulating climate change by absorbing atmospheric CO2. Timely and geographically detailed estimates of the global ocean-atmosphere CO2 flux provide an important constraint on the global carbon budget, offering insights into temporal changes and regional variations in the global carbon cycle. However, previous estimates of this flux have a 1 year delay and cannot monitor the very recent changes in the global ocean carbon sink. Here we present a near-real-time, monthly grid-based dataset of global surface ocean fugacity of CO2 and ocean-atmosphere CO2 flux data from January 2022 to July 2023, which is called Carbon Monitor Ocean (CMO-NRT). The data have been derived by updating the estimates from 10 Global Ocean Biogeochemical Models and 8 data products in the Global Carbon Budget 2022 to a near-real-time framework. This is achieved by employing Convolutional Neural Networks and semi-supervised learning methods to learn the non-linear relationship between the estimates from models or products and the observed predictors. The goal of this dataset is to offer a more immediate, precise, and comprehensive understanding of the global ocean-atmosphere CO2 flux. This advancement enhances the capacity of scientists and policymakers to monitor and respond effectively to alterations in the ocean's CO2 absorption, thereby contributing significantly to climate change management.

How to cite: Ke, P., Gui, X., Cao, W., Wang, D., Hou, C., Wang, L., Song, X., Li, Y., Zhu, B., Bian, J., Sitch, S., Ciais, P., Friedlingstein, P., and Liu, Z.: Near-real-time monitoring of global ocean carbon sink based on CNN, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-14839, https://doi.org/10.5194/egusphere-egu24-14839, 2024.

EGU24-15508 | Posters on site | ITS1.2/OS4.10

Data-driven short-term forecast of suspended inorganic matter as seen by ocean colour remote sensing. 

Jean-Marie Vient, Frédéric Jourdin, Ronan Fablet, and Christophe Delacourt

Short-term forecasting (several days in advance) of underwater visibility range is needed for marine and maritime operations involving divers or optical sensors, as well as for recreational activities such as scuba diving (e.g. Chang et al 2013). Underwater visibility mainly depends on water turbidity, which is caused by small suspended particles of organic and mineral origin (Preisendorfer 1986). Modelling the fate of these particles can be complex, encouraging the development of machine learning methods based on satellite data and hydrodynamic simulations (e.g. Jourdin et al 2020). In the field of forecasting visibility, deep learning methods are emerging (Prypeshniuk 2023). Here, in continuation of Vient et al (2022) on the interpolation purpose, this work deals with forecasting subsurface mineral turbidity levels over the French continental shelf of the Bay of Biscay using the deep learning method entitled 4DVarNet (Fablet et al 2021) applied to ocean colour satellite data, with additional data such as bathymetry (ocean depths) and time series of main forcing statistical parameters like wave significant heights and tidal coefficients. Using satellite data alone, results show that 2-day forecasts are accurate enough. When adding bathymetry and forcing parameters in the process, forecasts can go up to 6 days in advance.

References

Chang, G., Jones, C., and Twardowski, M. (2013), Prediction of optical variability in dynamic nearshore environments, Methods in Oceanography, 7, 63-78, https://doi.org/10.1016/j.mio.2013.12.002

Fablet, R., Chapron, B., Drumetz, L., Mémin, E., Pannekoucke, O., and Rousseau, F. (2021), Learning variational data assimilation models and solvers, Journal of Advances in Modeling Earth Systems, 13, e2021MS002572, https://doi.org/10.1029/2021MS002572

Jourdin, F., Renosh, P.R., Charantonis, A.A., Guillou, N., Thiria, S., Badran, F. and Garlan, T. (2021), An Observing System Simulation Experiment (OSSE) in Deriving Suspended Sediment Concentrations in the Ocean From MTG/FCI Satellite Sensor, IEEE Transactions on Geoscience and Remote Sensing, 59(7), 5423-5433, https://doi.org/10.1109/TGRS.2020.3011742

Preisendorfer, R. W. (1986), Secchi disk science: Visual optics of natural waters, Limnology and Oceanography, 31(5), 909-926, https://doi.org/10.4319/lo.1986.31.5.0909

Prypeshniuk, V. (2023), Ocean surface visibility prediction, Master thesis, Ukrainian Catholic University, Faculty of Applied Sciences, Department of Computer Sciences, Lviv, Ukraine, 39 pp, https://er.ucu.edu.ua/handle/1/3948?locale-attribute=en

Vient, J.-M., Fablet, R.;, Jourdin, F. and Delacourt, C. (2022), End-to-End Neural Interpolation of Satellite-Derived Sea Surface Suspended Sediment Concentrations, Remote Sens., 14(16), 4024, https://doi.org/10.3390/rs14164024

How to cite: Vient, J.-M., Jourdin, F., Fablet, R., and Delacourt, C.: Data-driven short-term forecast of suspended inorganic matter as seen by ocean colour remote sensing., EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15508, https://doi.org/10.5194/egusphere-egu24-15508, 2024.

EGU24-15594 | Posters on site | ITS1.2/OS4.10 | Highlight

Conditional Generative Models for OceanBench Sea Surface Height Interpolation 

Nils Lehmann, Jonathan Bamber, and Xiaoxiang Zhu

Rising sea levels are one of many consequences of anthropogenic climate
change. Over the past few decades, several global observational records have
become available that give a more detailed picture of the increasing
impacts. Nevertheless, there continue to be data challenges, such as
sparsity or signal to noise ratio, that need to be dealt with. Machine Learning (ML)
and specifically, Deep Learning (DL) approaches have presented themselves as valuable
tools for such large-scale and complex data sources. To this end, the OceanBench
Benchmark suite was recently developed to provide a
standardized pre-processing and evaluation framework for Sea Surface Height
(SSH) interpolation tasks involving nadir and Surface Water and Ocean Topography
(SWAT) Altimetry Tracks. From the methodological perspective, a reoccurring
issue is the lack of uncertainty quantification for DL applications in Earth
Observation. Therefore, we extend the suite of metrics provided by OceanBench
to probabilistic evaluation metrics and test state-of-the-art uncertainty
quantification models from the DL community. Specifically, we focus on
Conditional Convolutional Neural Processes (ConvCNP) and
Inpainting Diffusion models as methodologies to quantify
uncertainty for the interpolation task and demonstrate their viability and
advantages over other ML methods for both accuracy and probabilistic metrics.

How to cite: Lehmann, N., Bamber, J., and Zhu, X.: Conditional Generative Models for OceanBench Sea Surface Height Interpolation, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15594, https://doi.org/10.5194/egusphere-egu24-15594, 2024.

EGU24-16166 | ECS | Orals | ITS1.2/OS4.10

A global daily gap-filled chlorophyll-a dataset in open oceans during 2001–2021 from multisource information using convolutional neural networks 

Zhongkun Hong, Di Long, Xingdong Li, Yiming Wang, Jianmin Zhang, Mohamed Hamouda, and Mohamed Mohamed

Ocean color data are essential for developing our understanding of biological and ecological phenomena and processes and also of important sources of input for physical and biogeochemical ocean models. Chlorophyll-a (Chl-a) is a critical variable of ocean color in the marine environment. Quantitative retrieval from satellite remote sensing is a main way to obtain large-scale oceanic Chl-a. However, missing data are a major limitation in satellite remote-sensing-based Chl-a products due mostly to the influence of cloud, sun glint contamination, and high satellite viewing angles. The common methods to reconstruct (gap fill) missing data often consider spatiotemporal information of initial images alone, such as Data Interpolating Empirical Orthogonal Functions, optimal interpolation, Kriging interpolation, and the extended Kalman filter. However, these methods do not perform well in the presence of large-scale missing values in the image and overlook the valuable information available from other datasets for data reconstruction. Here, we developed a convolutional neural network (CNN) named Ocean Chlorophyll-a concentration reconstruction by convolutional neural NETwork (OCNET) for Chl-a concentration data reconstruction in open-ocean areas, considering environmental variables that are associated with ocean phytoplankton growth and distribution. Sea surface temperature (SST), salinity (SAL), photosynthetically active radiation (PAR), and sea surface pressure (SSP) from reanalysis data and satellite observations were selected as the input of OCNET to correlate with the environment and phytoplankton biomass. The developed OCNET model achieves good performance in the reconstruction of global open ocean Chl-a concentration data and captures spatiotemporal variations of these features. The reconstructed Chl-a data are available online at https://doi.org/10.5281/zenodo.10011908. This study also shows the potential of machine learning in large-scale ocean color data reconstruction and offers the possibility of predicting Chl-a concentration trends in a changing environment.

How to cite: Hong, Z., Long, D., Li, X., Wang, Y., Zhang, J., Hamouda, M., and Mohamed, M.: A global daily gap-filled chlorophyll-a dataset in open oceans during 2001–2021 from multisource information using convolutional neural networks, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-16166, https://doi.org/10.5194/egusphere-egu24-16166, 2024.

EGU24-17159 | Posters on site | ITS1.2/OS4.10 | Highlight

Exploring Pretrained Transformers for Ocean State Forecasting 

Clemens Cremer, Henrik Anderson, and Jesper Mariegaard

Traditional physics-based numerical models have served and are serving as reliable tools to gain insights into spatiotemporal behavior of ocean states such as water levels and currents. However, they have significant computational demand that often translates to slower forecasting capabilities. Additionally, these models can encounter difficulties in capturing certain physical processes and struggle to effectively bridge various spatial and temporal scales.

Considering these challenges, machine learning-based surrogate models emerge as a promising alternative. Physical surrogate models that learn multiple physics (on different spatial and temporal scales) from large datasets during extensive pretraining (Multiple physics pretraining, MPP) can enable later applications to poorly observed data domains which are common in ocean sciences. Hence, transfer learning capabilities can help improve the oceanographic forecasting, especially in data-limited regimes.

In this work, we explore the capabilities of pretrained transformer models for prediction on a test case for the North Sea. The results from two-dimensional simulations are used for training and fine-tuning. We utilize 2D datasets from publicly available PDEBench together with domain-specific datasets from DHI’s historical records of simulated 2D metocean data. We forecast water levels and currents with pretrained models and evaluate MPP forecast results against in-situ point observations and numerical model results.

Initial findings suggest that pretraining poses potential for generalizing and transferring knowledge to novel regions and relevance in practical application. A challenge is posed by model interpretability, highlighting an area for further development.

How to cite: Cremer, C., Anderson, H., and Mariegaard, J.: Exploring Pretrained Transformers for Ocean State Forecasting, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17159, https://doi.org/10.5194/egusphere-egu24-17159, 2024.

EGU24-17199 | ECS | Orals | ITS1.2/OS4.10 | Highlight

A Multi-Fidelity Ensemble Kalman Filter with a machine learned surrogate model 

Jeffrey van der Voort, Martin Verlaan, and Hanne Kekkonen

One of the disadvantages of oceanographic models is that they can be very computationally expensive. When combined with data assimilation, dynamical approaches such as the EnKF become expensive as they need a large number of ensemble members and thus model runs. In this work we investigate the use of a Multi-Fidelity Ensemble Kalman Filter (MF-EnKF), where the lower fidelity model is a machine learned surrogate model and the high fidelity model is the original full model. The idea behind this is to use an ensemble of a few but expensive full model runs, combined with an ensemble of many cheap but less accurate surrogate model runs. In this way we can reach similar or increased accuracy with less full model runs and thus less computational time. We investigate the performance by testing the approach on a simple atmospheric model, namely the Lorenz-96 model, and an oceanographic model, namely the Quasi-Geostrophic model. Results show that the MF-EnKF outperforms the EnKF for the same number of full model runs and that the MF-EnKF can reach similar or improved accuracy with less full model runs.

How to cite: van der Voort, J., Verlaan, M., and Kekkonen, H.: A Multi-Fidelity Ensemble Kalman Filter with a machine learned surrogate model, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17199, https://doi.org/10.5194/egusphere-egu24-17199, 2024.

EGU24-17320 | ECS | Posters on site | ITS1.2/OS4.10

Assessing data assimilation techniques with deep learning-based eddy detection 

Issam El Kadiri, Simon Van Gennip, Marie Drevillon, Anass El Aouni, Daria Botvinko, and Ronan Fablet

Mesoscale eddies significantly influence ocean circulation, nutrient distribution, and climate patterns globally.  A thorough reconstruction of the eddy field is therefore important, yet classical eddy detection algorithms based on sea level anomaly (SLA) suffer from the low coverage of the current altimetry network.

In this work, we evaluate the efficacy of deep learning techniques in enhancing the oceanic eddy field reconstruction of an operational ocean forecasting system. We use two ocean models from an Observing System Simulation Experiments (OSSE): a free-run high-resolution ocean circulation model representing the ‘truth’ and a second one constrained by synthetic observations mimicking the altimetry network through assimilation techniques to approximate the state of the ’truth’ model. 

We train a neural network model that takes sea surface temperature, sea surface height, and ocean surface currents inputs from the data-assimilation model to recover eddies identified in the ‘truth’ model, which are generated with py-eddy-tracker, a sea surface height-based eddy detection algorithm.

Our investigation centers on a semantic segmentation problem using the U- Net architecture to classify pixels for a given map into non-eddy, cyclonic eddy, and anticyclonic eddy. Our study focuses on the Gulf Stream region, an area renowned for its dynamic oceanic conditions. We find a higher detection rate of eddies and reduced inter-class misclassification when compared to eddy fields reconstructed from the data-assimilated model using the traditional SLA-based algorithm. 

Our data-driven method improves the detection of ‘true’ eddies from degraded data in an OSSE framework, and shows potential for application in operational analysis and forecasting systems.

How to cite: El Kadiri, I., Van Gennip, S., Drevillon, M., El Aouni, A., Botvinko, D., and Fablet, R.: Assessing data assimilation techniques with deep learning-based eddy detection, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17320, https://doi.org/10.5194/egusphere-egu24-17320, 2024.

EGU24-17465 | Orals | ITS1.2/OS4.10

Deep Sea Surface Height Multivariate Interpolation 

Théo Archambault, Pierre Garcia, Anastase Alexandre Charantonis, and Dominique Béréziat

The Sea Surface Height (SSH) is an important variable of the ocean state. It is currently estimated by satellites measuring the return time of a radar pulse. Due to this remote sensing technology, nadir-pointing altimeters take measures vertically, only along their ground tracks. Recovering fully gridded SSH fields involves a challenging spatiotemporal interpolation. The most widely used operational product, the Data Unification and Altimeter Combination System (DUACS), combines data from several satellites through linear optimal interpolation to estimate the SSH field. However several studies demonstrate that DUACS does not resolve mesoscale structures, motivating our interest in improving interpolation methods. Recently, Deep Learning has emerged as one of the leading methods to solve ill-posed inverse imaging problems. Deep Neural Networks can use multi-variate information to constrain the interpolation. Among them, Sea Surface Temperature (SST) data is based on a different remote-sensing technology, which leads to higher data coverage and resolution. Deep Learning methods have been proposed to interpolate SSH from track measurements, efficiently using SST contextual information. However, training neural networks usually requires either a realistic simulation of the problem on which we have access to SSH ground truth or a loss function that does not require it. Both solutions present limitations: the first is likely to suffer from domain gap issues once applied to real-world data, and training on observations only leads to lower performance than supervision on complete fields. We propose a hybrid method: a supervised pretraining on a realistic simulation, and an unsupervised fine-tuning on real-world observations. This approach was performed using a deep Attention-based Encoder-Decoder architecture. We compare the performances of the same neural network architecture trained in the three described settings: simulation-based training, observation-based training, and our hybrid approach. Preliminary results show an improvement of approximately 25% over DUACS in the interpolation task on the Ocean Data Challenge 2021 dataset. We further explore the ability of the architecture proposed to produce near real-time forecasts of SSH.

How to cite: Archambault, T., Garcia, P., Charantonis, A. A., and Béréziat, D.: Deep Sea Surface Height Multivariate Interpolation, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17465, https://doi.org/10.5194/egusphere-egu24-17465, 2024.

Global ocean numerical models typically have their first vertical level about 0.5m below the sea surface. However, a key physical quantity like the sea surface temperature (SST) can be retrieved from satellites at a reference depth of a few microns or millimeters below the sea surface. Assimilating such temperatures can lead to bias in the ocean models and it is thus necessary to project the satellite retrievals to the first model level to safely use them in the assimilation process. This projection is non-trivial, since it depends on several factors (e.g., daily cycle, winds, latitude) and it is usually performed either with computationally expensive numerical models or with too simple statistical methods.  

In this work we present an attempt to construct the projection operator with machine learning techniques. We consider three different networks: a convolutional neural network architecture called U-Net, which was first introduced in the field of computer vision and image segmentation, and it is thus optimal to process satellite retrievals; a pix2pix network, which is a U-Net trained in an adversarial way against a patch-classifier discriminator; a random forest model, which is a more traditional machine learning technique. We train the networks with L3 global subskin SST from AVHRR’s infrared channels on MetOp satellites produced by OSISAF and wind speed analysis at 10m by ECMWF to reproduce the ESA SST CCI and C3S global SST reprocessed product by CMEMS, that we take as ground truth during training and validation. The pix2pix network is the most effective in the projection and we thus choose it to shape an observation operator for the CMCC’s OceanVar assimilation system.

Finally, we compare several one-year-long reanalysis-like experiments, based on the CMCC reanalysis system, that assimilate the SST in different ways, e.g. nudging, unbiased approach, as observation operator. We discuss the potential impact of such new scheme in providing the best surface ocean state estimate.

How to cite: Broccoli, M., Cipollone, A., and Masina, S.: Towards an Observation Operator for Satellite Retrievals of Sea Surface Temperature with Convolutional Neural Network, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17731, https://doi.org/10.5194/egusphere-egu24-17731, 2024.

EGU24-18493 | ECS | Posters on site | ITS1.2/OS4.10 | Highlight

Leveraging recent advances in Large Language Models for the ocean science community 

Redouane Lguensat

Large Language Models (LLMs) have made significant strides in language understanding, including natural language processing, summarization, and translation, and they have the potential to be applied to a range of climate-related challenges. For instance, LLMs can be leveraged for data cleaning and transformation, and also assisting scientists/engineers in their daily work tasks.

For the machine learning community, the year 2023 was arguably the year of breakthroughts in LLM use in production. I present in this work the exciting potential for recent advances in LLMs to revolutionize how the ocean science community can interact with computer code, information gathering, dataset finding, etc. Specifically, I will present simple applications of how these advancements in Natural Language Processing (NLP) can assist the NEMO ocean model community. Examples range from using question answering systems for browsing efficiently NEMO documentation to creating conversational agents or chatbots that can assist not only new members wanting to learn about the NEMO model but also confirmed users. 

An important aspect of this work is relying only on open source LLMs, evaluating the performances of several models and discussing the ethical implications of these tools. I also discuss the question of whether using these LLMs blindly without domain knowledge is a good idea, as an important chunk of this work can arguably be easily done by anyone with good computer science skills thanks to the democratization of data science tools and learning materials.

 

How to cite: Lguensat, R.: Leveraging recent advances in Large Language Models for the ocean science community, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18493, https://doi.org/10.5194/egusphere-egu24-18493, 2024.

EGU24-18627 | Posters on site | ITS1.2/OS4.10

Prediction of sill fjord basin water renewals and oxygen levels 

João Bettencourt

The water in the basin of sill fjords is renewed occasionally. In some fjords, this renewal occurs irregularly while in others it has a more regular character. Independently of the renewal period, the renewal mechanism is thought to be common to all sill fjords: subsurface water outside of the fjord mouth lifted above the sill depth will trigger a renewal, provided that the lifted water mass is denser than the water in the basin. In Western Norway, the northerly, upwelling favorable winds that occur during Spring/Summer, provide a forcing for the uplifting of the isopycnals and bring dense, subsurface water to the upper water column, thereby creating the conditions for renewals to occur. The renewal of sill fjord basins is an important aspect of the fjord ecological condition because it supplies oxygen rich water to the fjord basin, whose oxygen is consumed by the degradation of organic matter during the stagnant periods. Byfjorden is the urban fjord in Bergen, Norway. It is heavily urbanized and has been consistently showing lower oxygen levels in its basin, which has ecological implications.

Byfjorden’s basin water is regularly renewed between the months of March and August and a strong link to coastal and atmospheric variability is well known, which makes it an attractive choice for the application of Deep Learning to predict basin water renewal in sill fjords, in the context of the atmospheric and hydrographic setting of the Norwegian coast.

In this work, the prediction of deep water renewal in Byfjorden and the basin’s oxygen levels is investigated with deep learning techniques. After a statistical study of oxygen variability correlation with wind forcing along the Norwegian coast, we develop and test a model to predict renewals and fill gaps in Byfjorden’s oxygenatio record.

How to cite: Bettencourt, J.: Prediction of sill fjord basin water renewals and oxygen levels, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18627, https://doi.org/10.5194/egusphere-egu24-18627, 2024.

EGU24-18663 | ECS | Orals | ITS1.2/OS4.10

Linking Satellite and physics-informed Data with Phytoplankton communities Using Deep Learning 

Luther Ollier, Roy El Hourany, and Marina Levy

Understanding Phytoplankton community dynamics in response to environmental shifts is crucial for assessing the impact of climate change on marine biology. To this end, satellite observations offer a dataset spanning two decades, capturing diverse sea surface parameters, including temperature, ocean color, and surface height. Notably, ocean color data is processed to derive sea surface chlorophyll-a concentration, widely acknowledged as a reliable proxy for phytoplankton biomass. 

Lately, advances in ocean color observation allow us to describe the phytoplankton community structure in terms of groups (broad functional or taxonomic groups) or size classes. Although these advances provide more detailed information on phytoplankton diversity and structure, these datasets suffer from spatial and temporal coverage limitations due to strict quality control in the presence of atmospheric aerosols, clouds, sea ice, etc... As a result, studies examining phytoplankton trends over the past two decades and future projections rely on incomplete chlorophyll-a and ocean color data. Therefore this compromises the identification of consistent trends within phytoplankton datasets.

In this study, we address this issue using a deep-learning approach. Our method constructs an attention network that learns from the available satellite dataset of Chla and phytoplankton size classes images (weekly and one-degree-degraded spatial resolution) while assimilating information from gap-free sea surface physics data originating from satellite observations and assimilated numerical models). The primary objective is to estimate the phytoplankton dataset based on the knowledge of physical factors, while filling the gaps within this dataset

The trained deep-learning model allows us to discern patterns and correlations between chlorophyll concentration and the phytoplankton size classes on one hand, and the physics-based data on the other hand. From a phytoplankton weekly database spanning from 1997 to 2020, with 50% missing pixels, our approach demonstrates promising results in replicating chlorophyll concentration and accurately inferring phytoplankton size classes.

The methodology shows the potential of deep-learning for robust ecological applications but mainly lays the groundwork for future trend studies on phytoplankton communities.

How to cite: Ollier, L., El Hourany, R., and Levy, M.: Linking Satellite and physics-informed Data with Phytoplankton communities Using Deep Learning, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18663, https://doi.org/10.5194/egusphere-egu24-18663, 2024.

EGU24-18688 | Posters on site | ITS1.2/OS4.10

Spatial Generalization of 4DVarNet in ocean colour Remote Sensing 

Clément Dorffer, Thi Thuy Nga Nguyen, Fréderic Jourdin, and Ronan Fablet

4DVarNet algorithm is an AI based variational approach that performs spatiotemporal time-series interpolation. It has been used with success on Ocean Color satellite images to fill in the blank of missing data due to e.g., the satellites trajectories or the clouds covering. 4DVarNet has shown impressive interpolation performances compare to other classical approaches such as DInEOF.
We propose to show that 4DVarNet is a flexible model that learns global dynamics instead of local patterns, thus enabling it to interpolate different type of data, i.e., data from different spatio-temporal domain and/or representing different variables, using the same pre-trained model.

The core of our technique involves extrapolating the learned models to other, somewhat larger geographical areas, including the entire Mediterranean and other regions like the North Sea. We achieve this by segmenting larger areas into smaller and manageable sections, and then choosing a section to train the model. Finally the trained model is applied to each segment and seamlessly integrating the prediction results. This method ensures detailed and accurate coverage over extensive areas, significantly enhancing the predictive power of our models while maintaining low computational costs.

Our results demonstrate that this approach not only outperforms traditional methods in terms of accuracy but also provides a scalable solution, adaptable to various geographical contexts. By leveraging localized training and strategic extrapolation, we offer a robust framework for ocean monitoring, paving the way for advanced satellite image applications in diverse settings.

How to cite: Dorffer, C., Nguyen, T. T. N., Jourdin, F., and Fablet, R.: Spatial Generalization of 4DVarNet in ocean colour Remote Sensing, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18688, https://doi.org/10.5194/egusphere-egu24-18688, 2024.

EGU24-18759 | Posters on site | ITS1.2/OS4.10

Detection and identification of environmental faunal proxies in digital images and video footage from northern Norwegian fjords and coastal waters using deep learning object detection algorithms 

Steffen Aagaard Sørensen, Eirik Myrvoll-Nielsen, Iver Martinsen, Fred Godtliebsen, Stamatia Galata, Juho Junttila, and Tone Vassdal

The ICT+ project:” Transforming ocean surveying by the power of DL and statistical methods” hosted by UiT The Artic University of Norway, aims at employing machine learning techniques in improving and streamlining methods currently used in ocean surveying by private sector partners to the project, MultiConsult and Argeo. The tasks include detection and identification of µm (e.g. foraminifera, microplastics) to m (e.g. boulders, shipwrecks) sized objects and elements at and in the seabed in data that presently is processed manually by skilled workers, but ideally could be wholly or partially processed using an automated approach.

Here we present preliminary work and results related to application of the YOLO (You Only Look Once) algorithms in detection and identification of meio fauna (foraminifera) in - and macro (mollusc) fauna at the seabed. Both proxies are used in evaluation of the environmental state of the seabed. YOLO is a real-time object detection deep learning algorithm that efficiently identifies and locates objects in images or videos in a single pass through the neural network.

Presently the year on year growth or shrinkage of protected mollusc banks in northern Norwegian fjords is manually evaluated via video observation in seabed video sequences annually captured via remotely operated vehicles. The preliminary results suggest that upon moderate training the YOLO algorithm can identify presence/absence of mollusc bank formations in set video sequences, thus supporting and eventually minimizing the task of inspecting the video footage manually.      

Foraminifera are abundant marine meiofauna living in the water column or at and in the seabed. Foraminifera are utilized in research into both modern and past marine environments as they have high turnover rates and individual shells have high preservation potential. Foraminiferal shells are accumulated in the sediments and after sample processing, they subsequently can be manually detected and identified via microscope. This work is very labour-intensive and demands skilled expertise but suffers from errors by and bias of the individual expert.

Preliminary results show that a YOLO network, trained on ca 4100 individuals (20 subgroups; benthic calcareous foraminifera (n=19), Planktic foraminifera (n=1)) in 346 images have model performances of up to 0.96 mAP (mean average precision) when trained, validated and tested on the training set. These promising results will be tested on real world samples. This testing is complicated by real world samples containing many more foraminiferal species/groups that were not trained upon, overlapping or closely set specimens and presence of non-foraminiferal material (e.g. sediment grains, other meio-fauna or –flora, etc.). Thus, additional training with focus on set complicating aspects will likely be necessary and most recent result will be presented.

How to cite: Aagaard Sørensen, S., Myrvoll-Nielsen, E., Martinsen, I., Godtliebsen, F., Galata, S., Junttila, J., and Vassdal, T.: Detection and identification of environmental faunal proxies in digital images and video footage from northern Norwegian fjords and coastal waters using deep learning object detection algorithms, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18759, https://doi.org/10.5194/egusphere-egu24-18759, 2024.

EGU24-18857 | ECS | Posters on site | ITS1.2/OS4.10

A two-phase Neural Model for CMIP6 bias correction 

Abhishek Pasula and Deepak Subramani

The Coupled Model Intercomparison Project, now in its sixth phase (CMIP6), is a global effort to project future climate scenarios on following certain shared socioeconomic pathways (SSP). For the period 1950-2014, CMIP6 provides a historical model output. From 2015 future projections with four different SSP scenarios, viz. SSP126, 245, 370 and 585 are available. From 2015-2023, we also have reanalysis of the actual ocean and atmosphere variables in these years. From this data, it is observed that CMIP6 future projections of ocean variables have a root mean square error (RMSE) of 1.22 psu in sea surface salinity, 1.24 °C in sea surface temperature, 2.23 m/s in the zonal ocean velocity component, 1.34 m/s in the meridional ocean velocity component. Similarly, the atmospheric variables have a RMSE of 1.34 °C in temperature at 2-meter height, 2.12 m/s in the zonal, and 1.321 m/s meridional wind component. Our goal is to develop an accurate method to correct this bias and provide updated future projections for scientific analysis. To this end, we developed a two phase deep neural network model that accepts monthly fields from the CMIP6 projections (all four SSP scenarios), and outputs a bias corrected field. In the first phase, a deep neural model, which we call as Atmospheric-Ocean Network 1 (AONet1) is used to obtain bias corrected fields for each of the four SSP separately. The AONet1 is trained and validated using the historical CMIP6 data (1950-2014) as input and ORAS5 and ERA5 data as the output (the bias corrected field). In the second phase, the four bias-corrected SSP fields are fed to AONet2 and the final bias corrected single field is produced. The AONet2 is trained and validated using future projection data from 2015-2021 as input and ORAS5 and ERA5 from the same period as output. The testing of the two phase model is performed for years 2022 and 2023, before bias corrected future fields are produced. Results are compared to the statistical EDCDF method using different Image Quality Assessment metrics such as Data structural similarity index measure (DSSIM), Multi-Scale SSIM, and Visual information fidelity. On test data, the RMSE after bias reduction using the two phase AONet model is 40% lower. Image assessment metric values surpassed the EDCDF approach as well.

How to cite: Pasula, A. and Subramani, D.: A two-phase Neural Model for CMIP6 bias correction, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18857, https://doi.org/10.5194/egusphere-egu24-18857, 2024.

EGU24-19104 | Orals | ITS1.2/OS4.10 | Highlight

Fast data-driven reduced order models for emulating physics-based flexible mesh coastal-ocean models  

Jesper Sandvig Mariegaard, Emil Siim Larsen, and Allan Peter Engsig-Karup

Physics-based coastal ocean models provide vital insights into local and regional coastal dynamics but require significant computational resources to solve numerically. In this work, we develop data-driven reduced order models (ROMs) using machine learning techniques to emulate a 2D flexible mesh hydrodynamic model of Øresund, the Straight between Denmark and Sweden, achieving orders of magnitude speedup while retaining good accuracy. This Øresund model has complex spatio-temporal dynamics driven by time-varying boundary conditions. Two different approaches to generate ROMs offline are developed and compared. Our objective is to assess the advantage of generating such models offline to enable real-time analysis in the online setting.

The first approach extracts patterns in space and time using principal component analysis and learn mappings from previous states and boundary conditions to future states using gradient boosting. The second approach employs Dynamic Mode Decomposition with control (DMDc) to account for boundary forcing. The reduced models are trained offline on a part of the available 12 months of 30-minute resolution snapshots of surface elevation, and u- and v-components of the depth-averaged currents. In both cases a very low number O(100) of latent space dimensions are necessary to get accurate results at the order of 2-4 cm RMSE compared to the full high-fidelity model.

The emulators provide state estimates online in seconds rather than hours, enabling new applications like uncertainty quantification, data assimilation and parameter optimization that require fast model evaluations. Further developments could look to condition the ROMs on a wider range of potential boundary forcings for scenario exploration. This demonstrates machine learning's potential for accelerating coastal simulations for real-time decision support and planning systems facing long-term change and uncertainty.

How to cite: Mariegaard, J. S., Larsen, E. S., and Engsig-Karup, A. P.: Fast data-driven reduced order models for emulating physics-based flexible mesh coastal-ocean models , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-19104, https://doi.org/10.5194/egusphere-egu24-19104, 2024.

EGU24-19157 | ECS | Posters on site | ITS1.2/OS4.10

Estimating global POC fluxes using ML and data fusion on heterogeneous and sparse in situ observations 

Abhiraami Navaneethanathan, Bb Cael, Chunbo Luo, Peter Challenor, Adrian Martin, and Sabina Leonelli

The ocean biological carbon pump, a significant set of processes in the global carbon cycle, drives the sinking of particulate organic carbon (POC) towards the deep ocean. Global estimates of POC fluxes and an improved understanding of how environmental factors influence organic ocean carbon transport can help quantify how much carbon is sequestered in the ocean and how this can change in different environmental conditions, in addition to improving global carbon and marine ecosystem models. POC fluxes can be derived from observations taken by a variety of in situ instruments such as sediment traps, 234-Thorium tracers and Underwater Vision Profilers. However, the manual and time-consuming nature of data collection leads to limitations of spatial data sparsity on a global scale, resulting in large estimate uncertainties in under-sampled regions.

This research takes an observation-driven approach with machine learning and statistical models trained to estimate POC fluxes on a global scale using the in situ observations and well-sampled environmental driver datasets, such as temperature and nutrient concentrations. This approach holds two main benefits: 1) the ability to fill observational gaps on both a spatial and temporal scale and 2) the opportunity to interpret the importance of each environmental factor for estimating POC fluxes, and therefore exposing their relationship to organic carbon transport processes. The models built include random forests, neural networks and Bayesian hierarchical models, where their global POC flux estimates, feature importance and model performances are studied and compared. Additionally, this research explores the use of data fusion methods to combine all three heterogeneous in situ POC flux data sources to achieve improved accuracy and better-informed inferences about organic carbon transport than what is possible using a single data source. By treating the heterogeneous data sources differently, accounting for their biases, and introducing domain knowledge into the models, our data fusion method can not only harness the information from all three data sources, but also gives insights into their key differences.

How to cite: Navaneethanathan, A., Cael, B., Luo, C., Challenor, P., Martin, A., and Leonelli, S.: Estimating global POC fluxes using ML and data fusion on heterogeneous and sparse in situ observations, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-19157, https://doi.org/10.5194/egusphere-egu24-19157, 2024.

EGU24-20454 | ECS | Posters on site | ITS1.2/OS4.10

Arctic Processes Under Ice: Structures in a Changing Climate 

Owen Allemang

The Arctic region is undergoing unprecedented transformations due to Arctic amplification, warming at twice the global average rate. This warming has led to a drastic reduction in sea ice, with predictions of ice-free Arctic summers before 2050. Such profound changes signal a shift to a new climatic regime, posing significant risks to regional communities, industries, and ecosystems.

This research addresses the urgent need to understand the evolving Arctic environment by harnessing machine learning (ML) to analyse sparse oceanic data. Utilising nearly two decades of Ice Tethered Profilers (ITP) data, complemented by ship-based (U-DASH), and ARGO profiles, this study aims to investigate the structure and dynamics of the Arctic Ocean.

We fit a Gaussian Mixture Model (GMM) to our observations, assigning each data point into a different cluster or class. Despite no spatial information being provided to the model, we find coherent classes emerge. We analyse the properties of each class, compare them with standard water masses from the literature, and look at decadal trends in properties such as oxygen saturation. This approach promises to enhance our understanding of Arctic water masses and their evolving role in a changing environment.

How to cite: Allemang, O.: Arctic Processes Under Ice: Structures in a Changing Climate, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-20454, https://doi.org/10.5194/egusphere-egu24-20454, 2024.

EGU24-20799 | ECS | Posters virtual | ITS1.2/OS4.10

Size classification of particulate organic carbon concentration and its link to the ecosystem based on Machine Learning techniques. 

Anna Denvil-Sommer, Corinne Le Quere, Rainer Kiko, Erik Buitenhuis, Marie-Fanny Racault, and Fabien Lombard

Biogeochemical ocean models are usually based on two size classes for particulate organic matter: small classes (1-100 𝜇m) and large classes (100-5000 𝜇m). Based on the measurements of particulate organic carbon (POC) concentration from UVP5 profiles and observations of environmental and ecosystem conditions we estimated an optimal number of size classes for POC that can be introduced in biogeochemical ocean models. 

We identified four size classes based on the correlation between POC concentration and environmental and ecosystem variables. It gives us information on the relationship between POC and surrounding temperature, chlorophyll-a concentration, nitrate, phosphate and oxygen levels as well as plankton functional types (PFTs). 

Further, we applied Machine Learning methods to reconstruct size classes of POC concentration and identify the most important drivers for each class. We showed that the concentration of POC smaller than 0.3 mm mostly depends on environmental characteristics while concentration of POC bigger than 0.3 mm strongly depends on PFTs.  

How to cite: Denvil-Sommer, A., Le Quere, C., Kiko, R., Buitenhuis, E., Racault, M.-F., and Lombard, F.: Size classification of particulate organic carbon concentration and its link to the ecosystem based on Machine Learning techniques., EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-20799, https://doi.org/10.5194/egusphere-egu24-20799, 2024.

EGU24-21554 | ECS | Posters on site | ITS1.2/OS4.10

A deep learning pipeline for automatic microfossil analysis and classification 

Iver Martinsen, David Wade, Benjamin Ricaud, and Fred Godtliebsen

Microfossils are important in climate analysis and in exploration of subsea energy resources. The abundance and distribution of species found in sediment cores provide valuable information, but the analysis is difficult and time consuming as it is based on manual work by human experts. It is also a challenge to have enough labelled data to train a standard deep learning classifier on microfossil images. We propose an efficient pipeline for processing and grouping fossils by species from microscope slides using self-supervised learning. First we show how to efficiently extract crops from whole slide images by adapting previously trained object detection algorithms. Second, we provide a comparison of a range of contrastive self-supervised learning methods to classify and identify microfossil from very few labels. We obtain excellent results with convolutional neural networks and vision transformers fine-tuned by self-supervision.

How to cite: Martinsen, I., Wade, D., Ricaud, B., and Godtliebsen, F.: A deep learning pipeline for automatic microfossil analysis and classification, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-21554, https://doi.org/10.5194/egusphere-egu24-21554, 2024.

The Southern Ocean closes the global overturning circulation and is key to the regulation of carbon, heat, biological production, and sea level. However, the dynamics of the general circulation and its leading order controls remain poorly understood, in part because of the challenge of characterizing and tracking changes in ocean physics in complex models. This gap in understanding is especially problematic in the face of climate change. Here, we wish to understand changes in the dynamics of the Southern Ocean under climate change, specifically how bathymetric controls on the general circulation could impact the location of major currents and impact upwelling. We use a suite of CMIP models for our analysis. A physics-informed equation discovery framework guided by machine learning is used to partition and interpret dynamics is used to understand spatial structures, and a supervised learning framework that quantifies its uncertainty and provides explanations of its predictions is leveraged to track change. The method, called Tracking global Heating with Ocean Regimes (THOR). A region undergoing a profound shift is where the Antarctic Circumpolar Current intersects with bathymetry, for example, the Pacific-Antarctic Ridge. We see major changes in areas associated with upwelling between the CMIP models, suggesting the changes in wind stress allow the control bathymetry has in the historical scenario to change. For example, we find that as the Antarctic Circumpolar Current shifts north under intensifying wind stress, when meeting the Pacific-Antarctic Ridge. We note associated change in the regions where gyre circulation favors upwelling, with spatial distributions varying between models. Our efforts go towards a better understanding of what dynamics are driving changes, and could allow reduction of bias between models and decrease uncertainties in future projections.

How to cite: Sonnewald, M., Yik, W., Clare, M. C., and Lguensat, R.: Discovering Dominant Controls on Southern Ocean Dynamics Under Climate Change: New Knowledge Through Physics-Guided Machine Learning , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-21905, https://doi.org/10.5194/egusphere-egu24-21905, 2024.

EGU24-22070 | ECS | Posters on site | ITS1.2/OS4.10 | Highlight

Pushing the Limits of Subseasonal-to-Seasonal Sea Ice Forecasting with Deep Generative Modelling  

Andrew McDonald, Jonathan Smith, Peter Yatsyshin, Tom Andersson, Ellen Bowler, Louisa van Zeeland, Bryn Ubald, James Byrne, María Pérez-Ortiz, Richard E. Turner, and J. Scott Hosking

Conventional studies of subseasonal-to-seasonal sea ice variability across scales have relied upon computationally expensive physics-based models solving systems of differential equations. IceNet, a deep learning-based sea ice forecasting model under development since 2021, has proven competitive to such state-of-the-art physics-based models, capable of generating daily 25 km resolution forecasts of sea ice concentration across the Arctic and Antarctic at a fraction of the computational cost once trained. Yet, these IceNet forecasts leave room for improvement through three main weaknesses. First, the forecasts exhibit physically unrealistic spatial and temporal blurring characteristic of deep learning methods trained under mean loss objectives. Second, the use of 25 km scale OSISAF data renders local forecasts along coastal regions and in regions surrounding maritime vessels inconclusive. Third, the sole provision of sea ice concentration in forecasts leaves questions about other critical ice properties such as thickness unanswered. We present preliminary results addressing these three challenges, turning to deep generative models to capture forecast uncertainty and improve spatial sharpness; leveraging 3 and 6 km scale AMSR-2 sea ice products to improve spatial resolution; and incorporating auxiliary datasets, chiefly thickness, into the training and inference pipeline to produce multivariate forecasts of sea ice properties beyond simple sea ice concentration. We seek feedback for improvement and hope continued development of IceNet can help answer key scientific questions surrounding the state of sea ice in our changing polar climates.

How to cite: McDonald, A., Smith, J., Yatsyshin, P., Andersson, T., Bowler, E., van Zeeland, L., Ubald, B., Byrne, J., Pérez-Ortiz, M., Turner, R. E., and Hosking, J. S.: Pushing the Limits of Subseasonal-to-Seasonal Sea Ice Forecasting with Deep Generative Modelling , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-22070, https://doi.org/10.5194/egusphere-egu24-22070, 2024.

EGU24-2443 | ECS | Posters on site | ITS1.3/CL0.1.18

Deep learning generative strategies to enhance 3D physics-based seismic wave propagation: from diffusive super-resolution to 3D Fourier Neural Operators. 

Filippo Gatti, Fanny Lehmann, Hugo Gabrielidis, Michaël Bertin, Didier Clouteau, and Stéphane Vialle

Estimating the seismic hazard in earthquake-prone regions, in order to assess the risk associated to nuclear facilities, must take into account a large number of uncertainties, and in particular our limited knowledge of the geology. And yet, we know that certain geological features can create site effects that considerably amplify earthquake ground motion. In this work, we provide a quantitative assessment of how largely can earthquake ground motion simulation benefit from deep learning approaches, quantifying the influence of geological heterogeneities on the spatio-temporal nature of the earthquake-induced site response. Two main frameworks are addressed: conditional generative approaches with diffusion models and neural operators. On one hand, generative adversarial learning and diffusions models are compared in a time-series super-resolution context [1]. The main task is to improve the outcome of 3D fault-to-site earthquake numerical simulations (accurate up to 5 Hz [2, 3]) at higher frequencies (5-30 Hz), by learning the low-to-high frequency mapping from seismograms recorded worldwide [1]. The generation is conditioned by the numerical simulation synthetic time-histories, in a one-to-many setup that enables site-specific probabilistic hazard assessment. On the other hand, the successful use of Factorized Fourier Neural Operator (F-FNO) to entirely replace cumbersome 3D elastodynamic numerical simulations is described [4], showing how this approach can pave the way to real-time large-scale digital twins of earthquake prone regions. The trained neural operator learns the relationship between 3D heterogeneous geologies and surface ground motions generated by the propagation of seismic wave through these geologies. The F-FNO is trained on the HEMEW-3D (https://github.com/lehmannfa/HEMEW3D/releases) database, comprising 30000 high-fidelity numerical simulations of earthquake ground motion through generic geologies, performed by employing the high-performance code SEM3D [4]. Next, a smaller database was built specifically for the Teil region (Ardèche, France), where a MW 4.9 moderate shallow earthquake occurred in November 2019 [4]. The F-FNO is then specialized on this database database with just 250 examples. Transfer learning improved the prediction error by 22 %. According to seismological Goodness-of-Fit (GoF) metrics, 91% of predictions have an excellent GoF for the phase (and 62% for the envelope). Ground motion intensity measurements are, on average, slightly underestimated.

[1] Gatti, F.; Clouteau, D. Towards Blending Physics-Based Numerical Simulations and Seismic Databases Using Generative Adversarial Network. Computer Methods in Applied Mechanics and Engineering 2020, 372, 113421.
https://doi.org/10.1016/j.cma.2020.113421.

[2] Touhami, S.; Gatti, F.; Lopez-Caballero, F.; Cottereau, R.; de Abreu Corrêa, L.;Aubry, L.; Clouteau, D. SEM3D: A 3D High-Fidelity Numerical Earthquake Sim-ulator for Broadband (0–10 Hz) Seismic Response Prediction at a Regional Scale.Geosciences 2022, 12 (3), 112. https://doi.org/10.3390/geosciences12030112. https://github.com/sem3d/SEM

[3] Gatti, F.; Carvalho Paludo, L. D.; Svay, A.; Lopez-Caballero, F.-; Cottereau, R.;Clouteau, D. Investigation of the Earthquake Ground Motion Coherence in Het-erogeneous Non-Linear Soil Deposits. Procedia Engineering 2017, 199, 2354–2359.https://doi.org/10.1016/j.proeng.2017.09.232.[4] Lehmann, F.; Gatti, F.; Bertin, M.; Clouteau, D. Machine Learning Opportunities to Conduct High-Fidelity Earthquake Simulations in Multi-Scale Heterogeneous Geology.Front. Earth Sci. 2022, 10, 1029160. https://doi.org/10.3389/feart.2022.1029160.

[4] Lehmann, F.; Gatti, F.; Bertin, M.; Clouteau, D. Fourier Neural Operator Sur-rogate Model to Predict 3D Seismic Waves Propagation. arXiv April 20, 2023.http://arxiv.org/abs/2304.10242 (accessed 2023-04-21).

How to cite: Gatti, F., Lehmann, F., Gabrielidis, H., Bertin, M., Clouteau, D., and Vialle, S.: Deep learning generative strategies to enhance 3D physics-based seismic wave propagation: from diffusive super-resolution to 3D Fourier Neural Operators., EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-2443, https://doi.org/10.5194/egusphere-egu24-2443, 2024.

EGU24-2691 | Orals | ITS1.3/CL0.1.18 | Highlight

Grand designs: quantifying many kinds of model uncertainty to improve projections of sea level rise  

Tamsin Edwards, Fiona Turner, Jonathan Rougier, and Jeremy Rohmer and the EU PROTECT project

In the EU Horizon 2020 project PROTECT, we have performed around 5000 simulations of the Greenland and Antarctic ice sheets and the world’s glaciers to predict the land ice contribution to sea level rise up to 2300. Unlike previous international model intercomparison projects (Edwards et al., 2021; IPCC Sixth Assessment Report, 2021), this is a "grand ensemble" sampling every type of model uncertainty – plausible structures, parameters and initial conditions – and is performed under many possible boundary conditions (climate change projected by multiple global and regional climate models). The simulations also start in the past, unlike the previous projects, to assess the impact of these uncertainties on historical changes.

We use probabilistic machine learning to emulate the relationships between model inputs (climate change; ice sheet and glacier model choices) and outputs (sea level contribution), so we can make predictions for any climate scenario and sample model uncertainties more thoroughly than with the original physical models. We try multiple machine learning methods that have different strengths in terms of speed, smoothness, interpretability, and performance for categorical uncertainties (Gaussian Processes, random forests).

The design of the grand ensemble allows the influence of all these uncertainties to be captured explicitly, rather than treating them as simple noise, and the earlier start date allows formal calibration (Bayesian or history matching) with observed ice sheet and glacier changes, to improve confidence (and typically reduce uncertainties) in the projections. Here we show preliminary projections for global mean sea level rise up to 2300 using these advances, and describe challenges and solutions found along the way.

How to cite: Edwards, T., Turner, F., Rougier, J., and Rohmer, J. and the EU PROTECT project: Grand designs: quantifying many kinds of model uncertainty to improve projections of sea level rise , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-2691, https://doi.org/10.5194/egusphere-egu24-2691, 2024.

EGU24-3520 | Orals | ITS1.3/CL0.1.18

Machine Learning for Nonorographic Gravity Waves in a Climate Model 

Steven Hardiman, Adam Scaife, Annelize van Niekerk, Rachel Prudden, Aled Owen, Samantha Adams, Tom Dunstan, Nick Dunstone, and Sam Madge

Use of machine learning algorithms in climate simulations requires such algorithms to replicate certain aspects of the physics in general circulation models.  In this study, a neural network is used to mimic the behavior of one of the subgrid parameterization schemes used in global climate models, the nonorographic gravity wave scheme.  Use of a one-dimensional mechanistic model is advocated, allowing neural network hyperparameters to be chosen based on emergent features of the coupled system with minimal computational cost, and providing a testbed prior to coupling to a climate model. A climate model simulation, using the neural network in place of the existing parameterization scheme, is found to accurately generate a quasi-biennial oscillation of the tropical stratospheric winds, and correctly simulate the nonorographic gravity wave variability associated with the El Niño–Southern Oscillation and stratospheric polar vortex variability. These internal sources of variability are essential for providing seasonal forecast skill, and the gravity wave forcing associated with them is reproduced without explicit training for these patterns.

How to cite: Hardiman, S., Scaife, A., van Niekerk, A., Prudden, R., Owen, A., Adams, S., Dunstan, T., Dunstone, N., and Madge, S.: Machine Learning for Nonorographic Gravity Waves in a Climate Model, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-3520, https://doi.org/10.5194/egusphere-egu24-3520, 2024.

EGU24-5048 | Orals | ITS1.3/CL0.1.18

Emulators for Predicting Tsunami Inundation Maps at High Resolution 

Steven J. Gibbons, Erlend Briseid Storrøsten, Naveen Ramalingam, Stefano Lorito, Manuela Volpe, Carlos Sánchez-Linares, and Finn Løvholt

Predicting coastal tsunami impact requires the computation of inundation metrics such as maximum inundation height or momentum flux at all locations of interest. The high computational cost of inundation modelling, in both long term tsunami hazard assessment and urgent tsunami computing, comes from two major factors: (1) the high number of simulations needed to capture the source uncertainty and (2) the need to solve the nonlinear shallow water equations on high-resolution grids. We seek to mitigate the second of these factors using machine learning. The offshore tsunami wave is far cheaper to calculate than the full inundation map, and an emulator able to predict an inundation map with acceptable accuracy from simulated offshore wave height time-series would allow both more rapid hazard estimates and the processing of greater numbers of scenarios. The procedure would necessarily be specific to one stretch of coastline and a complete numerical simulation is needed for each member of the training set. Success of an inundation emulator would demand an acceptable reduction in time-to-solution, a modest number of training scenarios, an acceptable accuracy in inundation predictions, and good performance for high impact, low probability, scenarios. We have developed a convolutional encoder-decoder based neural network and applied it to a dataset of high-resolution inundation simulations for the Bay of Catania in Sicily, calculated for almost 28000 subduction earthquake scenarios in the Mediterranean Sea. We demonstrate encouraging performance in this case study for relatively small training sets (of the order of several hundred scenarios) provided that appropriate choices are made in the setting of model parameters, the loss function, and training sets. Scenarios with severe inundation need to be very well represented in the training sets for the ML-models to perform sufficiently well for the most tsunamigenic earthquakes. The importance of regularization and model parameter choices increases as the size of the training sets decrease.

How to cite: Gibbons, S. J., Briseid Storrøsten, E., Ramalingam, N., Lorito, S., Volpe, M., Sánchez-Linares, C., and Løvholt, F.: Emulators for Predicting Tsunami Inundation Maps at High Resolution, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5048, https://doi.org/10.5194/egusphere-egu24-5048, 2024.

EGU24-5852 | Posters on site | ITS1.3/CL0.1.18

CROMES - A fast and efficient machine learning emulator pipeline for gridded crop models 

Christian Folberth, Artem Baklanov, Nikolay Khabarov, Thomas Oberleitner, Juraj Balkovic, and Rastislav Skalsky

Global gridded crop models (GGCMs) have become state-of-the-art tools in large-scale climate impact and adaptation assessments. Yet, these combinations of large-scale spatial data frameworks and plant growth models have limitations in the volume of scenarios they can address due to computational demand and complex software structures. Emulators mimicking such models have therefore become an attractive option to produce reasonable predictions of GGCMs’ crop productivity estimates at much lower computational costs. However, such emulators’ flexibility is thus far typically limited in terms of crop management flexibility and spatial resolutions among others. Here we present a new emulator pipeline CROp model Machine learning Emulator Suite (CROMES) that serves for processing climate features from netCDF input files, combining these with site-specific features (soil, topography), and crop management specifications (planting dates, cultivars, irrigation) to train machine learning emulators and subsequently produce predictions. Presently built around the GGCM EPIC-IIASA and employing a boosting algorithm, CROMES is capable of producing predictions for EPIC-IIASA’s crop yield estimates with high accuracy and very high computational efficiency. Predictions require for a first used climate dataset about 45 min and 10 min for any subsequent scenario based on the same climate forcing in a single thread compared to approx. 14h for a GGCM simulation on the same system.

Prediction accuracy is highest if modeling the case when crops receive sufficient nutrients and are consequently most sensitive to climate. When training an emulator on crop model simulations for rainfed maize and a single global climate model (GCM), the yield prediction accuracy for out-of-bag GCMs is R2=0.93-0.97, RMSE=0.5-0.7, and rRMSE=8-10% in space and time. Globally, the best agreement between predictions and crop model simulations occurs in (sub-)tropical regions, the poorest is in cold, arid climates where both growing season length and water availability limit crop growth. The performance slightly deteriorates if fertilizer supply is considered, more so at low levels of nutrient inputs than at the higher end.

Importantly, emulators produced by CROMES are virtually scale-free as all training samples, i.e., pixels, are pooled and hence treated as individual locations solely based on features provided without geo-referencing. This allows for applications on increasingly available high-resolution climate datasets or in regional studies for which more granular data may be available than at global scales. Using climate features based on crop growing seasons and cardinal growth stages enables also adaptation studies including growing season and cultivar shifts. We expect CROMES to facilitate explorations of comprehensive climate projection ensembles, studies of dynamic climate adaptation scenarios, and cross-scale impact and adaptation assessments.

 

How to cite: Folberth, C., Baklanov, A., Khabarov, N., Oberleitner, T., Balkovic, J., and Skalsky, R.: CROMES - A fast and efficient machine learning emulator pipeline for gridded crop models, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5852, https://doi.org/10.5194/egusphere-egu24-5852, 2024.

EGU24-6622 | ECS | Posters virtual | ITS1.3/CL0.1.18

Comparison of SWAT and a deep learning model in nitrate load simulation at the Tuckahoe creek watershed in the United States 

Jiye Lee, Dongho Kim, Seokmin Hong, Daeun Yun, Dohyuck Kwon, Robert Hill, Yakov Pachepsky, Feng Gao, Xuesong Zhang, Sangchul Lee, and KyungHwa Cho

Simulating nitrate fate and transport in freshwater is an essential part in water quality management. Numerical and data-driven models have been used for it. The numerical model SWAT simulates daily nitrate loads using simulated flow rate. Data-driven models are more flexible compared to SWAT as they can simulate nitrate load and flow rate independently. The objective of this work was evaluating the performance of SWAT and a deep learning model in terms of nutrient loads in cases when deep learning model is used in (a) simulating flow rate and nitrate concentration independently and (b) simulating both flow rate and nitrate concentration. The deep learning model was built using long-short-term-memory and three-dimensional convolutional networks. The input data, weather data and image data including leaf area index and land use, were acquired at the Tuckahoe Creek watershed in Maryland, United States. The SWAT model was calibrated with data over the training period (2014-2017) and validated with data over the testing period (2019) to simulate flow rate and nitrate load. The Nash-Sutcliffe efficiency was 0.31 and 0.40 for flow rate and -0.26 and -0.18 for the nitrate load over training and testing periods, respectively. Three data-driven modeling scenarios were generated for nitrate load. Scenario 1 included the flow rate observation and nitrate concentration simulation, scenario 2 included the flow rate simulation and nitrate concentration observation, and scenario 3 included the flow rate and nitrate concentration simulations. The deep learning model outperformed SWAT in all three scenarios with NSE from 0.49 to 0.58 over the training period and from 0.28 to 0.80 over the testing period. Scenario 1 showed the best results for nitrate load. The performance difference between SWAT and the deep learning model was most noticeable in fall and winter seasons. The deep learning modeling can be an efficient alternative to numerical watershed-scale models when the regular high frequency data collection is provided.

How to cite: Lee, J., Kim, D., Hong, S., Yun, D., Kwon, D., Hill, R., Pachepsky, Y., Gao, F., Zhang, X., Lee, S., and Cho, K.: Comparison of SWAT and a deep learning model in nitrate load simulation at the Tuckahoe creek watershed in the United States, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-6622, https://doi.org/10.5194/egusphere-egu24-6622, 2024.

EGU24-7455 | ECS | Orals | ITS1.3/CL0.1.18

Assessment of ARPEGE-Climat using a neural network convection parameterization based upon data from SPCAM 5 

Blanka Balogh, David Saint-Martin, Olivier Geoffroy, Mohamed Aziz Bhouri, and Pierre Gentine

Interfacing challenges continue to impede the implementation of neural network-based parameterizations into numerical models of the atmosphere, particularly those written in Fortran. In this study, we leverage a specialized interfacing tool to successfully implement a neural network-based parameterization for both deep and shallow convection within the General Circulation Model, ARPEGE-Climat. Our primary objective is to not only evaluate the performance of this data-driven parameterization but also assess the numerical stability of ARPEGE-Climat when coupled with a convection parameterization trained on data from a different high-resolution model, namely SPCAM 5. 

The performance evaluation encompasses both offline and online assessments of the data-driven parameterization within this framework. The data-driven parameterization for convection is designed using a multi-fidelity approach and is adaptable for use in a stochastic configuration. Challenges associated with this approach include ensuring consistency between variables in ARPEGE-Climat and the parameterization based on data from SPCAM 5, as well as managing disparities in geometry (e.g., horizontal and vertical resolutions), which are crucial factors affecting the intermodel parameterization transferability.

How to cite: Balogh, B., Saint-Martin, D., Geoffroy, O., Bhouri, M. A., and Gentine, P.: Assessment of ARPEGE-Climat using a neural network convection parameterization based upon data from SPCAM 5, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-7455, https://doi.org/10.5194/egusphere-egu24-7455, 2024.

EGU24-7581 | Posters on site | ITS1.3/CL0.1.18

Blending machine-learning and mesoscale numerical weather prediction models to quantify city-scale heat mitigation 

Yongling Zhao, Zhi Wang, Dominik Strebel, and Jan Carmeliet

Urban warming in cities is increasingly exacerbated by the escalation of more frequent and severe heat extremes. Effectively mitigating overheating necessitates the adoption of a comprehensive, whole-system approach that integrates various heat mitigation measures to generate rapid and sustained efficacy in mitigation efforts. However, there remains a significant gap in the exploration of how to quantify the efficacy of mitigation strategies at the city-scale.

We address this research question by leveraging mesoscale Weather Research Forecasting (WRF) models alongside machine-learning (ML) techniques. As a showcase, ML models have been established for Zurich and Basel, Switzerland, utilizing seven WRF-output-based features, including shortwave downward radiation (SWDNB), hour of the day (HOUR), zenith angle (COSZEN), rain mix ratio (QRAIN), longwave downward radiation (LWDNB), canopy water content (CANWAT), and planetary boundary layer height (PBLH). Impressively, the resultant median R2 values for T2 (2m temperature) predictions during heatwave and non-heatwave periods are notably high at 0.94 and 0.91 respectively.

Within the perspective of the whole-system approach, we quantify the impacts of reducing shortwave radiation absorption at ground surfaces, a potential result of a combination of both shading and reflective coating-based mitigation measures, through the utilization of ML models. Remarkably, a 5% reduction in the absorption of radiation at ground surfaces in Zurich could lead to a reduction in T2 by as much as 3.5 °C in the city center. During a heatwave in Basel, the potential for cooling is even more pronounced, with temperature decreases of up to 5 °C. These case studies in Zurich and Basel underscore the efficacy of utilizing WRF feature-trained ML models to quantify heat mitigation strategies at the city-scale.

How to cite: Zhao, Y., Wang, Z., Strebel, D., and Carmeliet, J.: Blending machine-learning and mesoscale numerical weather prediction models to quantify city-scale heat mitigation, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-7581, https://doi.org/10.5194/egusphere-egu24-7581, 2024.

EGU24-7681 | ECS | Posters on site | ITS1.3/CL0.1.18

Multi-scale hydraulic-based graph neural networks: generalizing spatial flood mapping to irregular meshes and time-varying boundary condition 

Roberto Bentivoglio, Elvin Isufi, Sebastian Nicolaas Jonkman, and Riccardo Taormina

Deep learning models emerged as viable alternatives to rapid and accurate flood mapping, overcoming the computational burden of numerical methods. In particular, hydraulic-based graph neural networks present a promising avenue, offering enhanced transferability to domains not used for the model training. These models exploit the analogy between finite-volume methods and graph neural networks to describe how water moves in space and time across neighbouring cells. However, existing models face limitations, having been exclusively tested on regular meshes and necessitating initial conditions from numerical solvers. This study proposes an extension of hydraulic-based graph neural networks to accommodate time-varying boundary conditions, showcasing its efficacy on irregular meshes. For this, we employ multi-scale methods that jointly model the flood at different scales. To remove the necessity of initial conditions, we leverage ghost cells that enforce the solutions at the boundaries. Our approach is validated on a dataset featuring irregular meshes, diverse topographies, and varying input hydrograph discharges. Results highlight the model's capacity to replicate flood dynamics across unseen scenarios, without any input from the numerical model, emphasizing its potential for realistic case studies.

How to cite: Bentivoglio, R., Isufi, E., Jonkman, S. N., and Taormina, R.: Multi-scale hydraulic-based graph neural networks: generalizing spatial flood mapping to irregular meshes and time-varying boundary condition, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-7681, https://doi.org/10.5194/egusphere-egu24-7681, 2024.

EGU24-10087 | ECS | Orals | ITS1.3/CL0.1.18

Contribution of latent variables to emulate the physics of the IPSL model 

Ségolène Crossouard, Masa Kageyama, Mathieu Vrac, Thomas Dubos, Soulivanh Thao, and Yann Meurdesoif

Atmospheric general circulation models include two main distinct components: the dynamical one solves the Navier-Stokes equations to provide a mathematical representation of atmospheric movements while the physical one includes parameterizations representing small-scale phenomena such as turbulence and convection (Balaji et al., 2022). However, computational demands of the parameterizations limit the numerical efficiency of the models. The burgeoning field of machine learning techniques opens new horizons by producing accurate, robust and fast emulators of parts of a climate model. In particular, they can reliably reproduce physical processes, thus providing an efficient alternative to traditional process representation. Indeed, some pioneering studies (Gentine et al., 2018; Rasp et al., 2018) have shown that these emulators can replace one or more parameterizations that are computationally expensive and so, have the potential to enhance numerical efficiency.

Our research work aligns with these perspectives, since it involves exploiting the potential of developing an emulator of the physical parameterizations of the IPSL climate model, and more specifically of the ICOLMDZOR atmospheric model (for DYNAMICO, the dynamic solver using an icosahedral grid - LMDZ, the atmospheric component - ORCHIDEE, the surface component). The emulator could improve performance, as currently almost half of the total computing time is given to the physical part of the model.

We have developed two initial offline emulators of the physical parameterizations of our standard model, in an idealized aquaplanet configuration, to reproduce profiles of tendencies of the key variables - zonal wind, meridional wind, temperature, humidity and water tracers - for each atmospheric column. The results of these emulators, based on a dense neural network or a convolutional neural network, have begun to show their potential for use, since we easily obtain good performances in terms of the mean of the predicted tendencies. Nevertheless, their variability is not well captured, and the variance is underestimated, posing challenges for our application. A study of physical processes has revealed that turbulence was at the root of the problem. Knowing how turbulence is parameterized in the model, we show that incorporating physical knowledge through latent variables as predictors into the learning process, leading to a significant improvement of the variability.

Future plans involve an online physics emulator, coupled with the atmospheric model to provide a better assessment of the learning process (Yuval et al., 2021).

How to cite: Crossouard, S., Kageyama, M., Vrac, M., Dubos, T., Thao, S., and Meurdesoif, Y.: Contribution of latent variables to emulate the physics of the IPSL model, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-10087, https://doi.org/10.5194/egusphere-egu24-10087, 2024.

EGU24-10749 | ECS | Orals | ITS1.3/CL0.1.18

Replacing parametrisations of melt ponds on sea ice with machine learning emulators 

Simon Driscoll, Alberto Carrassi, Julien Brajard, Laurent Bertino, Marc Bocquet, Einar Olason, and Amos Lawless

Sea ice plays an essential role in global ocean circulation and in regulating Earth's climate and weather, and melt ponds that form on the ice have a profound impact on the Arctic's climate by altering the ice albedo. Melt pond evolution is complex, sub grid scale and poorly understood - and melt ponds are represented in sea ice models as parametrisations. Parametrisations of these physical processes are based on a number of assumptions and can include many uncertain parameters that have a substantial effect on the simulated evolution of the melt ponds. 

We have shown, using Sobol sensitivity analysis and through investigating perturbed parameter ensembles (PPEs), that a state-of-the-art sea ice column model, Icepack, demonstrates substantial sensitivity to its uncertain melt pond parameters. These PPEs demonstrate that perturbing melt pond parameters (within known ranges of uncertainty) cause predicted sea ice thickness over the Arctic Ocean to differ by many metres after only a decade of simulation. Understanding the sources of uncertainty, improving parametrisations and fine tuning the parameters is a paramount, but usually very complex and difficult task. Given this uncertainty, we propose to replace the sub grid scale melt pond parametrisation (MPP) in Icepack with a machine learning emulator. 

Building and replacing the MPP with a machine learning emulator has been done in two broad steps that contain multiple computational challenges. The first is generating a melt pond emulator using 'perfect' or 'model' data. Here we demonstrate a proof of concept and show how we achieve numerically stable simulations of Icepack when embedding an emulator in place of the MPP - with Icepack running stably for the whole length of the simulations (over a decade) across the Arctic. 

Secondly, we develop offline an emulator from observational data that faithfully predicts observed sea ice albedo and melt pond fraction given climatological input variables. Embedding an observational emulator can require different challenges as compared to using model data, such as not all variables needed by the host model being observable/observed for an emulator to predict. We discuss how we achieve online simulations interfacing this emulator with the Icepack model.

Our focus on using column models ensures that our observational emulator of sea ice albedo and melt pond fraction can readily be used in sea ice models around the world, irrespective of grid resolutions and mesh specifications, and offers one approach for creating general emulators that can be used by many climate models. 

How to cite: Driscoll, S., Carrassi, A., Brajard, J., Bertino, L., Bocquet, M., Olason, E., and Lawless, A.: Replacing parametrisations of melt ponds on sea ice with machine learning emulators, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-10749, https://doi.org/10.5194/egusphere-egu24-10749, 2024.

EGU24-11880 | ECS | Posters on site | ITS1.3/CL0.1.18

Understanding geoscientific system behaviour from machine learning surrogates 

Oriol Pomarol Moya, Derek Karssenberg, Walter Immerzeel, Madlene Nussbaum, and Siamak Mehrkanoon

Machine learning (ML) models have become popular in the Earth Sciences for improving predictions based on observations. Beyond pure prediction, though, ML has a large potential to create surrogates that emulate complex numerical simulation models, considerably reducing run time, hence facilitating their analysis.

The behaviour of eco-geomorphological systems is often examined using minimal models, simple equation-based expressions derived from expert knowledge. From them, one can identify complex system characteristics such as equilibria, tipping points, and transients. However, model formulation is largely subjective, thus disputable. Here, we propose an alternative approach where a ML surrogate of a high-fidelity numerical model is used instead, conserving suitability for analysis while incorporating the higher-order physics of its parent model. The complexities of developing such an ML surrogate for understanding the co-evolution of vegetation, hydrology, and geomorphology on a geological time scale are presented, highlighting the potential of this approach to capture novel, data-driven scientific insights.

To obtain the surrogate, the ML models were trained on a data set simulating a coupled hydrological-vegetation-soil system. The rate of change of the two variables describing the system, soil depth and biomass, was used as output, taking their value at the previous time step and the pre-defined grazing pressure as inputs. Two popular ML methods, random forest (RF) and fully connected neural network (NN), were used. As proof of concept and to configure the model setup, we first trained the ML models on the output of the minimal model described in [1], comparing the ML responses at gridded inputs with the derivative values predicted by the minimal model. While RF required less tuning to achieve competitive results, a relative root mean squared error (rRMSE) of 5.8% and 0.04% for biomass and soil depth respectively, NN produced better-behaved outcome, reaching a rRMSE of 2.2% and 0.01%. Using the same setup, the ML surrogates were trained on a high-resolution numerical model describing the same system. The study of the response from this surrogate provided a more accurate description of the dynamics and equilibria of the hillslope ecosystem, depicting, for example, a much more complex process of hillslope desertification than captured by the minimal model.

It is thus concluded that the use of ML models instead of expert-based minimal models may lead to considerably different findings, where ML models have the advantage that they directly rely on system functioning embedded in their parent numerical simulation model.

How to cite: Pomarol Moya, O., Karssenberg, D., Immerzeel, W., Nussbaum, M., and Mehrkanoon, S.: Understanding geoscientific system behaviour from machine learning surrogates, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-11880, https://doi.org/10.5194/egusphere-egu24-11880, 2024.

EGU24-14744 | ECS | Orals | ITS1.3/CL0.1.18 | Highlight

End-to-end Learning in Hybrid Modeling Systems: How to Deal with Backpropagation Through Numerical Solvers 

Said Ouala, Bertrand Chapron, Fabrice Collard, Lucile Gaultier, and Ronan Fablet

Artificial intelligence and deep learning are currently reshaping numerical simulation frameworks by introducing new modeling capabilities. These frameworks are extensively investigated in the context of model correction and parameterization where they demonstrate great potential and often outperform traditional physical models. Most of these efforts in defining hybrid dynamical systems follow offline learning strategies in which the neural parameterization (called here sub-model) is trained to output an ideal correction. Yet, these hybrid models can face hard limitations when defining what should be a relevant sub-model response that would translate into a good forecasting performance. End-to-end learning schemes, also referred to as online learning, could address such a shortcoming by allowing the deep learning sub-models to train on historical data. However, defining end-to-end training schemes for the calibration of neural sub-models in hybrid systems requires working with an optimization problem that involves the solver of the physical equations. Online learning methodologies thus require the numerical model to be differentiable, which is not the case for most modeling systems. To overcome this difficulty and bypass the differentiability challenge of physical models, we present an efficient and practical online learning approach for hybrid systems. The method, called EGA for Euler Gradient Approximation, assumes an additive neural correction to the physical model, and an explicit Euler approximation of the gradients. We demonstrate that the EGA converges to the exact gradients in the limit of infinitely small time steps. Numerical experiments are performed on various case studies, including prototypical ocean-atmosphere dynamics. Results show significant improvements over offline learning, highlighting the potential of end-to-end online learning for hybrid modeling.

How to cite: Ouala, S., Chapron, B., Collard, F., Gaultier, L., and Fablet, R.: End-to-end Learning in Hybrid Modeling Systems: How to Deal with Backpropagation Through Numerical Solvers, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-14744, https://doi.org/10.5194/egusphere-egu24-14744, 2024.

EGU24-14957 | ECS | Posters on site | ITS1.3/CL0.1.18

Exploring data-driven emulators for snow on sea ice  

Ayush Prasad, Ioanna Merkouriadi, and Aleksi Nummelin

Snow is a crucial element of the sea ice system, impacting various environmental and climatic processes. SnowModel is a numerical model that is developed to simulate the evolution of snow depth and density, blowing-snow redistribution and sublimation, snow grain size, and thermal conductivity, in a spatially distributed, multi-layer snowpack framework. However, SnowModel faces challenges with slow processing speeds and the need for high computational resources. To address these common issues in high-resolution numerical modeling, data-driven emulators are often used. They aim to replicate the output of complex numerical models like SnowModel but with greater efficiency. However, these emulators often face their own set of problems, primarily a lack of generalizability and inconsistency with physical laws. A significant issue related to this is the phenomenon of concept drift, which may arise when an emulator is used in a region or under conditions that differ from its training environment. For instance, an emulator trained on data from one Arctic region might not yield accurate results if applied in another region with distinct snow properties or climatic conditions. In our study, we address these challenges with a physics-guided approach in developing our emulator. By integrating physical laws that govern changes in snow density due to compaction, we aim to create an emulator that is efficient while also adhering to essential physical principles. We evaluated this approach by comparing four machine learning models: Long Short-Term Memory (LSTM), Physics-Guided LSTM, Gradient Boosting Machines, and Random Forest, across five distinct Arctic regions. Our evaluations indicate that all models achieved high accuracy, with the Physics-Guided LSTM model demonstrating the most promising results in terms of accuracy and generalizability. This approach offers a computationally faster way to emulate the SnowModel with high fidelity. 

How to cite: Prasad, A., Merkouriadi, I., and Nummelin, A.: Exploring data-driven emulators for snow on sea ice , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-14957, https://doi.org/10.5194/egusphere-egu24-14957, 2024.

EGU24-15914 | Posters on site | ITS1.3/CL0.1.18

Machine Learning Estimator for Ground-Shaking maps 

Marisol Monterrubio-Velasco, Rut Blanco, Scott Callaghan, Cedric Bhihe, Marta Pienkowska, Jorge Ejarque, and Josep de la Puente

The Machine Learning Estimator for Ground Shaking Maps (MLESmaps) harnesses the ground shaking inference capability of Machine Learning (ML) models trained on physics-informed earthquake simulations. It infers intensity measures, such as RotD50, seconds after a significant earthquake has occurred given its magnitude and location. 

Our methodology incorporates both offline and online phases in a comprehensive workflow. It begins with the generation of a synthetic training data set, progresses through the extraction of predictor characteristics, proceeds to the validation and learning stages, and yields a learned inference model. 

MLESmap results can complement empirical Ground Motion Models (GMMs), in particular in data-poor areas, to assess post-earthquake hazards rapidly and accurately, potentially improving disaster response in earthquake-prone regions. Learned models incorporate physical features such as directivity, topography, or resonance at a speed comparable to that of the empirical GMMs. 

In this work, we present an overview of the MLESmap methodology and its application to two distinct study areas: southern California and southern Iceland

 

How to cite: Monterrubio-Velasco, M., Blanco, R., Callaghan, S., Bhihe, C., Pienkowska, M., Ejarque, J., and de la Puente, J.: Machine Learning Estimator for Ground-Shaking maps, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15914, https://doi.org/10.5194/egusphere-egu24-15914, 2024.

The combination of Machine Learning (ML) with geoscientific models is an active area of research with a wide variety of applications. A key practical question for those models is to define how high level languages ML components can be encoded and maintained into pre-existing legacy solvers, written in low level abstraction languages (as Fortran). We address this question through the strategy of creating pipes between a geoscientific code and ML components executed in their own separate scripts. The main advantage of this approach is the possibility to easily share the inference models within the community without keeping them bound to one code with its specific numerical methods. Here, we chose to focus on OASIS (https://oasis.cerfacs.fr/en/), which is a Fortran coupling library that performs field exchanges between coupled executables. It is commonly used in the numerical geoscientific community to couple different codes and assemble earth-system models. Last releases of OASIS provided C and Python APIs, which enable coupling between non-homogeneously written codes. We seek to take advantage of those new features and the presence of OASIS in the community codes, and propose a Python library (named Eophis) that facilitates the deployment of inference models for coupled execution. Basically, Eophis allows to: (i) wrap an OASIS interface to exchange data with a coupled earth-system code, (ii) wrap inference models into a simple in/out interface, and (iii) emulate time evolution to synchronize connexions between earth-system and models. We set up a demonstration case with the European numerical code NEMO in which the pre-existing OASIS interface has been slightly modified. A forced global ocean model simulation is performed with regular exchanges of 2D and 3D fields with Eophis. Received data are then sent to inference models that are not implemented in NEMO. Performances of the solution will finally be assessed with references.

How to cite: Barge, A. and Le Sommer, J.: Online deployment of pre-trained machine learning components within Earth System models via OASIS, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-16148, https://doi.org/10.5194/egusphere-egu24-16148, 2024.

EGU24-16149 | ECS | Orals | ITS1.3/CL0.1.18

Two Methods for Constraining Neural Differential Equations 

Alistair White, Niki Kilbertus, Maximilian Gelbrecht, and Niklas Boers

Neural differential equations (NDEs) provide a powerful and general framework for interfacing machine learning with numerical modeling. However, constraining NDE solutions to obey known physical priors, such as conservation laws or restrictions on the allowed state of the system, has been a challenging problem in general. We present stabilized NDEs (SNDEs) [1], the first method for imposing arbitrary explicit constraints in NDE models. Alongside robust theoretical guarantees, we demonstrate the effectiveness of SNDEs across a variety of settings and using diverse classes of constraints. In particular, SNDEs exhibit vastly improved generalization and stability compared to unconstrained baselines. Building on this work, we also present constrained NDEs (CNDEs), a novel and complementary method with fewer hyperparameters and stricter constraints. We compare and contrast the two methods, highlighting their relative merits and offering an intuitive guide to choosing the best method for a given application.

[1] Alistair White, Niki Kilbertus, Maximilian Gelbrecht, Niklas Boers. Stabilized neural differential equations for learning dynamics with explicit constraints. In Advances in Neural Information Processing Systems, 2023.

How to cite: White, A., Kilbertus, N., Gelbrecht, M., and Boers, N.: Two Methods for Constraining Neural Differential Equations, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-16149, https://doi.org/10.5194/egusphere-egu24-16149, 2024.

EGU24-17852 | Orals | ITS1.3/CL0.1.18 | Highlight

FTorch - lowering the technical barrier of incorporating ML into Fortran models 

Dominic Orchard, Elliott Kasoar, Jack Atkinson, Thomas Meltzer, Simon Clifford, and Athena Elafrou

Across geoscience, numerical models are used for understanding, experimentation, and prediction of complex systems. Many of these models are computationally intensive and involve sub-models for certain processes, often known as parameterisations. Such parameterisations may capture unresolved sub-grid processes, such as turbulence, or represent fast-moving dynamics, such as gravity waves, or provide a combination of the two, such as microphysics schemes.

Recently there has been significant interest in incorporating machine learning (ML) methods
into these parameterisations. Two of the main drivers are the emulation of computationally intensive processes, thereby reducing computational resources required, and the development of data-driven parameterisation schemes that could improve accuracy through capturing ‘additional physics’.

Integrating ML sub-models in the context of numerical modelling brings a number of challenges, some of which are scientific, others computational. For example, many numerical models are written in Fortran, whilst the majority of machine learning is conducted using Python-based frameworks such as PyTorch that provide advanced ML modelling capabilities. As such there is a need to leverage ML models developed externally to Fortran, rather than the error-prone approach of writing neural networks directly in Fortran, missing the benefits of highly-developed libraries.

Interoperation of the two languages requires care, and increases the burden on researchers and developers. To reduce these barriers we have developed the open-source FTorch library [1] for coupling PyTorch models to Fortran. The library is designed to streamline the development process, offering a Fortran interface mimicking the style of the Python library whilst abstracting away the complex details of interoperability to provide a computationally efficient interface.

A significant benefit of this approach is that it enables inference to be performed on either CPU or GPU, enabling deployment on a variety of architectures with low programmer effort. We will report on the performance characteristics of our approach, both in the CPU and GPU settings and include a comparison with alternative approaches.

This approach has been deployed on two relevant case studies in the geoscience context: a gravity-wave parameterisation in an intermediate complexity atmospheric model (MiMA) based on Espinosa et al. [2], and a convection parameterisation in a GCM (CAM/CESM) based on Yuval et al. [3]. We will report on these applications and lessons learned from their development. 

[1] FTorch https://github.com/Cambridge-ICCS/FTorch
[2] Espinosa et al., Machine Learning Gravity Wave Parameterization Generalizes to Capture the QBO and Response to Increased CO2, GRL 2022 https://doi.org/10.1029/2022GL098174
[3] Yuval et al., Use of Neural Networks for Stable, Accurate and Physically Consistent Parameterization of Subgrid Atmospheric Processes With Good Performance at Reduced Precision, GRL 2021 https://doi.org/10.1029/2020GL091363

How to cite: Orchard, D., Kasoar, E., Atkinson, J., Meltzer, T., Clifford, S., and Elafrou, A.: FTorch - lowering the technical barrier of incorporating ML into Fortran models, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17852, https://doi.org/10.5194/egusphere-egu24-17852, 2024.

EGU24-18444 | ECS | Posters on site | ITS1.3/CL0.1.18

Rapid Computation of Physics-Based Ground Motions in the Spectral Domain using Neural Networks 

Fatme Ramadan, Bill Fry, and Tarje Nissen-Meyer

Physics-based simulations of earthquake ground motions prove invaluable, particularly in regions where strong ground motion recordings remain scarce. However, the computational demands associated with these simulations limit their applicability in tasks that necessitate large-scale computations of a wide range of possible earthquake scenarios, such as those required in physics-based probabilistic seismic hazard analyses. To address this challenge, we propose a neural-network approach that enables the rapid computation of earthquake ground motions in the spectral domain, alleviating a significant portion of the computational burden. To illustrate our approach, we generate a database of ground motion simulations in the San Francisco Bay Area using AxiSEM3D, a 3D seismic wave simulator. The database includes 30 double-couple sources with varying depths and horizontal locations. Our simulations explicitly incorporate the effects of topography and viscoelastic attenuation and are accurate up to frequencies of 0.5 Hz. Preliminary results demonstrate that the trained neural network almost instantaneously produces estimates of peak ground displacements as well as displacement waveforms in the spectral domain that align closely with those obtained from the wave propagation simulations. Our approach also extends to predicting ground motions for ‘unsimulated’ source locations, ultimately providing a comprehensive resolution of the source space in our chosen physical domain. This advancement paves the way for a cost-effective simulation of numerous seismic sources, and enhances the feasibility of physics-based probabilistic seismic hazard analyses. 

How to cite: Ramadan, F., Fry, B., and Nissen-Meyer, T.: Rapid Computation of Physics-Based Ground Motions in the Spectral Domain using Neural Networks, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18444, https://doi.org/10.5194/egusphere-egu24-18444, 2024.

EGU24-19255 | Posters on site | ITS1.3/CL0.1.18

A digital twin for volcanic deformation merging 3D numerical simulations and AI 

Chiara P Montagna, Deepak Garg, Martina Allegra, Flavio Cannavò, Gilda Currenti, Rebecca Bruni, and Paolo Papale

At active volcanoes, surface deformation is often a reflection of subsurface magma activity that is associated with pressure variations in magma sources. Magma dynamics cause a change of stress in the surrounding rocks. Consequently, the deformation signals propagate through the rocks and arrive at the surface where the monitoring network records them.

It is invaluable to have an automated tool that can instantly analyze the surface signals and give information about the evolution of the location and magnitude of pressure variations in case of volcanic unrest. Inverse methods employed for this often suffer from ill-posedness of the problem and non-uniqueness of solutions.

To this end, we are developing a digital twin to use on Mount Etna volcano, combining the capability of numerical simulations and AI. Our digital twin is composed of two AI models: the first AI model (AI1) will be trained on multi-parametric data to recognize unrest situations, and the second AI model (AI2) will be trained on a large number (order 10^5 - 10^6) of 3D elastostatic numerical simulations for dike intrusions with the real topography and best available heterogeneous elastic rock properties of Mount Etna Volcano using a forward modeling approach. Numerical simulations will be performed on Fenix HPC resources using the advanced open-source multi-physics finite element software Gales.

Both AI modules will be developed and trained independently and then put into use together. After activation, AI1 will analyze the streaming of monitoring data and activate AI2 in case of a volcanic crisis. AI2 will provide information about the acting volcanic source.

The software will be provided as an open-source package to allow replication on other volcanoes. The tool will serve as an unprecedented prototype for civil protection authorities to manage volcanic crises.

How to cite: Montagna, C. P., Garg, D., Allegra, M., Cannavò, F., Currenti, G., Bruni, R., and Papale, P.: A digital twin for volcanic deformation merging 3D numerical simulations and AI, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-19255, https://doi.org/10.5194/egusphere-egu24-19255, 2024.

EGU24-19352 | ECS | Posters on site | ITS1.3/CL0.1.18

Learning phytoplankton bloom patterns - A long and rocky road from data to equations  

Pascal Nieters, Maximilian Berthold, and Rahel Vortmeyer-Kley

Non-linear, dynamic patterns are the rule rather than the exception in ecosystems. Predicting such patterns would allow an improved understanding of energy and nutrient flows in such systems. The Scientific Machine Learning approach Universal Differential Equation (UDE) by Rackauckas et al. (2020) tries to extract the underlying dynamical relations of state variables directly from their time series in combination with some knowledge on the dynamics of the system. This approach makes this kind of tool a promising approach to support classical modeling when precise knowledge of dynamical relationships is lacking, but measurement data of the phenomenon to be modeled is available.

We applied the UDE approach to a 22-year data set of the southern Baltic Sea coast, which constituted six different phytoplankton bloom types. The data set contained the state variables chlorophyll and different dissolved and total nutrients. We learned the chlorophyll:nutrient interactions from the data with additional forcing of external temperature, salinity and light attenuation dynamics as drivers. We used a neural network as a universal function approximator that provided time series of the state variables and their derivatives.

Finally, we recovered algebraic relationships between the variables chlorophyll, dissolved and total nutrients and the external drivers temperature, salinity and light attenuation using Sparse Identification of nonlinear Dynamics (SinDy) by Brunton et al. (2016).

The gained algebraic relationships differed in their importance of the different state variables and drivers for the six phytoplankton bloom types in accordance with general mechanisms reported in literature for the southern Baltic Sea coast. Our approach may be a viable option to guide ecosystem management decisions based on those algebraic relationships.

Rackauckas et al. (2020), arXiv preprint arXiv:2001.04385.

Brunton et al. (2016), PNAS 113.15: 3932-3937.

How to cite: Nieters, P., Berthold, M., and Vortmeyer-Kley, R.: Learning phytoplankton bloom patterns - A long and rocky road from data to equations , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-19352, https://doi.org/10.5194/egusphere-egu24-19352, 2024.

EGU24-19502 | ECS | Posters on site | ITS1.3/CL0.1.18

SAIPy: A Python Package for single station Earthquake Monitoring using Deep Learning 

Nishtha Srivastava, Wei Li, Megha Chakraborty, Claudia Quinteros Cartaya, Jonas Köhler, Johannes Faber, and Georg Rümpker

Seismology has witnessed significant advancements in recent years with the application of deep
learning methods to address a broad range of problems. These techniques have demonstrated their
remarkable ability to effectively extract statistical properties from extensive datasets, surpassing the
capabilities of traditional approaches to an extent. In this study, we present SAIPy, an open-source
Python package specifically developed for fast data processing by implementing deep learning.
SAIPy offers solutions for multiple seismological tasks, including earthquake detection, magnitude
estimation, seismic phase picking, and polarity identification. We introduce upgraded versions
of previously published models such as CREIME_RT capable of identifying earthquakes with an
accuracy above 99.8% and a root mean squared error of 0.38 unit in magnitude estimation. These
upgraded models outperform state-of-the-art approaches like the Vision Transformer network. SAIPy
provides an API that simplifies the integration of these advanced models, including CREIME_RT,
DynaPicker_v2, and PolarCAP, along with benchmark datasets. The package has the potential to be
used for real-time earthquake monitoring to enable timely actions to mitigate the impact of seismic
events.

How to cite: Srivastava, N., Li, W., Chakraborty, M., Cartaya, C. Q., Köhler, J., Faber, J., and Rümpker, G.: SAIPy: A Python Package for single station Earthquake Monitoring using Deep Learning, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-19502, https://doi.org/10.5194/egusphere-egu24-19502, 2024.

EGU24-20863 | ECS | Posters on site | ITS1.3/CL0.1.18

Partial land surface emulator forecasts ecosystem states at verified horizons 

Marieke Wesselkamp, Matthew Chantry, Maria Kalweit, Ewan Pinnington, Margarita Choulga, Joschka Boedecker, Carsten Dormann, Florian Pappenberger, and Gianpaolo Balsamo

While forecasting of climate and earth system processes has long been a task for numerical models, the rapid development of deep learning applications has recently brought forth competitive AI systems for weather prediction. Earth system models (ESMs), even though being an integral part of numerical weather prediction have not yet caught that same attention. ESMs forecast water, carbon and energy fluxes and in the coupling with an atmospheric model, provide boundary and initial conditions. We set up a comparison of different deep learning approaches for improving short-term forecasts of land surface and ecosystem states on a regional scale. Using simulations from the numerical model and combining them with observations, we will partially emulate an existing land surface scheme, conduct a probabilistic forecasts of core ecosystem processes and determine forecast horizons for all variables.

How to cite: Wesselkamp, M., Chantry, M., Kalweit, M., Pinnington, E., Choulga, M., Boedecker, J., Dormann, C., Pappenberger, F., and Balsamo, G.: Partial land surface emulator forecasts ecosystem states at verified horizons, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-20863, https://doi.org/10.5194/egusphere-egu24-20863, 2024.

Thanks to the recent progress in numerical methods, the application fields of artificial intelligence (AI) and machine learning methods (ML) are growing at a very fast pace. The EURAD (European Joint Programme on Radioactive Waste Management) community has recently started using ML for a) acceleration of numerical simulations, b) improvement of multiscale and multiphysics couplings efficiency, c) uncertainty quantification and sensitivity analysis. A number of case studies indicate that use of ML based approaches leads to overall acceleration of geochemical and reactive transport simulations from one to four orders of magnitude. The achieved speed-up depends on the chemical system, simulation code, problem formulation and the research question to be answered. Within EURAD-DONUT (Development and Improvement Of Numerical methods and Tools for modelling coupled processes), a benchmark is on-going to coordinate the relevant activities and to test a variety of ML techniques for geochemistry and reactive transport simulations in the framework of radioactive waste disposal. It aims at benchmarking several widely used geochemical codes, at generating high-quality geochemical data for training/validation of existing/new methodologies, and at providing basic guidelines about the benefits, drawbacks, and current limitations of using ML techniques.

A joint effort has resulted in the definition of benchmarks of which one is presented here. The benchmark system is relevant to the sorption of U in claystone formations (e.g. Callovo-Oxfordian, Opalinus or Boom clay). Regarding the chemical complexity, a system containing Na-Cl-U-H-O is considered as the base case, and a more complex system with the addition of calcium and carbonate (CO2) to change aqueous speciation of U. Parameters of interest, among others, are the resulting concentrations of U sorbed on edges (surface complexes), of U on ion exchange sites, and the amount of metaSchoepite, with the resulting Kd’s. Following aspects are discussed: (i) Streamline the production of high-quality consistent training datasets, using the most popular geochemical solvers (PHREEQC, ORCHESTRA and GEMS). (ii) The use of different methods (e.g. Deep Neural Networks, Polynomial Chaos Expansion, Gaussian Processes, Active Learning, and other techniques to learn from the generated data. (iii) Setup appropriate metrics for the critical evaluation of the accuracy of ML models. (iv) Testing the accuracy of predictions for geochemical and reactive transport calculations. 

How to cite: Laloy, E. and Montoya, V. and the EURAD-DONUT Team: Machine learning based metamodels for geochemical calculations in reactive transport models: Benchmark within the EURAD Joint Project, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-21545, https://doi.org/10.5194/egusphere-egu24-21545, 2024.

EGU24-444 | ECS | Posters on site | ITS1.5/NP8.6

Multifractal analysis of recent precipitation projections in the context of climate change 

Pedro Henrique Dias Kovalczuk, Daniel Schertzer, and Ioulia Tchiguirinskaia

Despite efforts to obtain consistent results, the prediction of patterns in the behavior of geophysical fields still faces many uncertainties. However, these analyses are important for studying the effects of human action on the environment and the effects reflected in climate change. There is much evidence that Multifractals are capable of describing intermittent behavior and statistical data of all orders and over a wide range of scales. Therefore, this work consists of using the multifractal framework to analyze recent precipitation projection data in France, verifying the evolution of its parameters over a relatively long period of time (from 1951 to 2100) and over space, using 12 points on French territory with a resolution of 2.8º x 2.8º. For this, the Double Trace Moment technique was applied to determine the mean intermittency codimensions, the multifractality indexes and the maximum probability singularities. These results were compared to the article by J.-F. Royer et al., C. R. Geoscience 340 (2008) to verify if projections remained consistent with changes in data and economic scenarios. Despite the differences found in the range of parameter values ​​and scaling behavior, recent data also indicated an increase in intermittency over time and presented spatial behavior similar to old projections, which reinforces the expectation of an increase in precipitation extremes in the coming decades.

How to cite: Dias Kovalczuk, P. H., Schertzer, D., and Tchiguirinskaia, I.: Multifractal analysis of recent precipitation projections in the context of climate change, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-444, https://doi.org/10.5194/egusphere-egu24-444, 2024.

EGU24-531 | ECS | Posters on site | ITS1.5/NP8.6

Combining Generative Adversarial Networks with Multifractals for Urban Precipitation Nowcasting  

Hai Zhou, Daniel Schertzer, and Ioulia Tchiguirinskaia

Precipitation nowcasting, referring to short-term forecasting ahead for 0 to 6 hours, is an important aspect of many urban meteorological and hydrological studies. This is due to the fact that reliable nowcasting can serve as an early warning of massive flooding and a guide for water-related risk management, making it highly significant in urban areas from a socio-economic perspective. Precipitation exhibits extreme variability over a wide range of space-time scales, so nowcasting is essentially a spatiotemporal sequence forecasting. Convolutional long short-term memory (ConvLSTM) models are frequently used to capture the spatiotemporal correlation, but they often struggle with an issue that produces blurry predictions. Therefore, generative adversarial network (GAN) architecture is employed to achieve more detailed and realistic predictions. The framework of universal multifractal (UM) with only three scale-independent parameters (α, C1, H) is also introduced in the deep learning model to characterize the extreme variability of precipitation. The developed hybrid approach using stochastic models physically based on the cascade paradigm ensures that intermittency is directly taken into account, including in the generation of uncertainty. In addition to the common evaluating metrics, like mean absolute error (MAE), root mean squared error (RMSE), critical success index (CSI), probability of detection (POD), power spectral density (PSD) and UM are also introduced to evaluate nowcasting performance in the spectrum space. This ongoing work is based on the previous research about combining recurrent neural networks with variational mode decomposition and multifractals to predict rainfall time series in Paris area.

How to cite: Zhou, H., Schertzer, D., and Tchiguirinskaia, I.: Combining Generative Adversarial Networks with Multifractals for Urban Precipitation Nowcasting , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-531, https://doi.org/10.5194/egusphere-egu24-531, 2024.

Urbanization induced carbon dioxide (CO2) emissions have attracted widespread attention.

A comprehensive attribution analysis model is designed to understand the inherent uncertainties in diagnosing the effects of urban expansion dynamics and modes on carbon dioxide (CO2) emissions. First, 68 selected cities across China are categorized into three types, including expanding, contracting, and staying cities, through developing an evaluation indicator system by integrating population, economy, construction, and social information. Next, the carbon dioxide (CO2) emissions of the cities were quantified. The Lasso method was employed to select the factors influencing CO2 emissions. For cities with different development modes, the XGBoost regression model with SHAP algorithm was employed to calculate the contribution rate of various factors to carbon emissions in different types of cities. Additionally, the analysis considered the temporal changes of these factors.

The main conclusions are as follows:

(i)Comparing urban built-up areas extracted from the nighttime light dataset with China's national land use and cover change dataset, the results reveal a minimum correlation of 0.72-0.82 and an average overall accuracy of 78%.

(ii)The urbanization process of 68 cities exhibits a predominant pattern of normal fluctuations, with a coexistence of expansion and contraction. The results indicate that over the past 20 years, expanding cities have been concentrated mainly in coastal regions such as the Yangtze River Delta and the Pearl River Delta, while contracting cities are primarily found in inland areas characterized by traditional industrial cities. It is observed that the development processes of most cities involve an initial phase of intensive expansion (or contraction), followed by a gradual trend towards stability in the later stages.

(iii)The factors influencing carbon emissions in expanding and contracting cities share commonalities and differences. Population and energy efficiency both have significant impacts on carbon emissions in different types of cities. For expanding cities, the impact of green area on carbon emissions is more pronounced. Conversely, in contracting cities, the influence of foreign trade is more significant.

How to cite: Qian, J. and Cai, D.: The impact of the expansion and contraction of China’s cities on CO2 emissions,2002-2021,evidence from integrated nighttime light data, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-821, https://doi.org/10.5194/egusphere-egu24-821, 2024.

EGU24-1003 | ECS | Orals | ITS1.5/NP8.6

A Transformer-Based Model for Effective Representation of Geospatial Data and Context 

Rui Deng, Ziqi Li, and Mingshu Wang

Machine learning (ML) and Artificial Intelligence (AI) models have been increasingly adopted for geospatial tasks. However, geospatial data (such as points and raster cells) are often influenced by underlying spatial effects, and current model designs often lack adequate consideration of these effects. Determining the efficient model structure for representing geospatial data and capturing the underlying complex spatial and contextual effects still needs to be explored. To address this gap, we propose a Transformer-like encoder-decoder architecture to first represent geospatial data with respect to their corresponding geospatial context, and then decode the representation for task-specific inferences. The encoder consists of embedding layers that transform the input location and attributes of geospatial data into meaningful embedding vectors. The decoder comprises task-specific neural network layers that map the encoder outputs to the final output. Spatial contextual effects are measured using explainable artificial intelligence (XAI) methods. We evaluate and compare the performance of our model with other model structures on both synthetic and real-world datasets for spatial regression and interpolation tasks. This work proposes a generalizable approach to better modeling and measuring complex spatial contextual effects, potentially contribute to efficient and reliable urban analytic applications that require geo-context information.

How to cite: Deng, R., Li, Z., and Wang, M.: A Transformer-Based Model for Effective Representation of Geospatial Data and Context, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1003, https://doi.org/10.5194/egusphere-egu24-1003, 2024.

EGU24-1223 | ECS | Orals | ITS1.5/NP8.6

Spatial and Temporal Analysis for Identifying the Movement of Chronic Kidney Disease (CKDu) Hotspots; in Reference to River Basins in North Central Province, Sri Lanka 

Charunika Sandamini Arambegedara, Yu Lijun, Danlu Cai, Jianfeng Zhu, Asanga Venura Ranasinghe, and Ambepitiyawaduge Pubudu De Silva

In recent years, Sri Lanka has experienced a high prevalence of chronic kidney disease (CKDu) in certain regions, especially in the North Central Province (NCP). The etiology of this disease is not yet clearly understood, although several hypotheses involving environmental and occupational factors have been proposed. To better understand the patterns of CKDu incidence and its potential relationship to environmental factors, a spatial and temporal analysis was conducted using geographic information system (GIS) tools. In this study, we identified the geographical hotspots of CKDu incidence over a period of eleven years (from 2010 to 2020) in the NCP, of Sri Lanka. The analysis was done for the districts of Anuradhapura and Polonnaruwa in NCP. Furthermore, we analysed the temporal trends of CKDu incidence by comparing the disease burden between different years. Finally, we examined the association between river basins and CKDu incidence by overlaying the spatial layers of the disease incidence and river basins. Our results showed that there were significant spatial and temporal variations in CKDu incidence in the region over the study period. The disease is characterized by a fluctuating trend. Also, the number of hotspots has decreased over time, and the number of CKDu-affected patients has also decreased. Similarly found that CKDu hotspots were concentrated around the mainly 4 river basins in the region, indicating a possible link between water resources and the disease. By identifying CKDu hotspots and understanding the disease's movement over time, public health officials can target their efforts more effectively, reducing the disease's impact on affected communities. This study provides important insights into the spatial and temporal patterns of CKDu and suggests the need for further research to investigate the potential environmental risk factors contributing to this disease.

 

Key Words: Chronic Kidney Disease of Unknown Etiology (CKDu), Hotspots Analysis, Spatial and Temporal Variation, Geographical Information System (GIS)

How to cite: Arambegedara, C. S., Lijun, Y., Cai, D., Zhu, J., Ranasinghe, A. V., and Silva, A. P. D.: Spatial and Temporal Analysis for Identifying the Movement of Chronic Kidney Disease (CKDu) Hotspots; in Reference to River Basins in North Central Province, Sri Lanka, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1223, https://doi.org/10.5194/egusphere-egu24-1223, 2024.

Cities play a crucial role in climate neutrality because although they occupy only 4% of the EU land area, they host 75% of its population. In addition, they consume over 65% of global energy and account for more than 70% of global CO2 emissions. As climate change mitigation depends on urban action, the EU has decided to support cities in accelerating their green and digital transformation. The EU Mission on Climate-Neutral and Smart Cities aims to make the participating cities climate neutral and smart by 2030, in areas such as energy, waste management, transport, and buildings, to improve the quality of life. A WEBGIS Smart City Geospatial Framework has been developed for the Limassol Municipality in Cyprus. The establishment of a Smart City Geospatial Framework is imperative for several reasons. Firstly, it enables data-driven decision-making, allowing city officials to make informed choices about urban planning and resource allocation. Secondly, it enhances the efficiency of public services, such as transportation and emergency response, by leveraging real-time spatial data. Moreover, the framework promotes sustainability by providing insights into environmental factors, contributing to eco-friendly urban development. Lastly, the integration of geospatial technologies fosters citizen engagement, transparency, and overall improvement in the quality of life for urban residents. Under this WEBGIS smart city framework, the authors explore the importance of supporting the Limassol Municipality under the EU Mission for climate-neutral and smart cities by 2030 initiative, using the proposed WEBGIS smart city framework.  Results are presented using the GIS dashboard.

How to cite: Papantoniou, A., Danezis, C., and Hadjimitsis, D.: Exploring the importance of using a novel Smart City Geospatial Integrated Framework for supporting Cities participating in EU Mission for climate-neutral and smart cities by 2030: the case study of Limassol in Cyprus., EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1502, https://doi.org/10.5194/egusphere-egu24-1502, 2024.

Information on urban groundwater in Vienna is important not only to secure a sustainable use and supply but also to protect groundwater quality. Here, we provide a compilation of available information and data to cover all relevant aspects of hydrogeology within the city in order to improve planning and policy making with regard to water extraction, geothermal use and groundwater protection.

We propose a grouping of the Quaternary and Neogene sediments as well as of the underlying sedimentary rocks of the Flysch zone and the Calcareous Alps, into hydrogeological units with distinct properties. Each unit is described regarding lithology, aquifer type, groundwater occurrence and yield. Additionally, the area percentage of sealed ground surface and the conditions of groundwater recharge are defined. Finally, the types of groundwater use, withdrawal rates, hydrochemical signatures and heavy metal contents are characterized.

Limestones and dolomites of the Calcareous Alps represent high yield karst aquifers with calcium-magnesium-bicarbonate-type hydrochemistry, used as spa water drawn from 800 m deep, artesian wells.

Within the Flysch zone, clay- and marlstones act as aquitards while sandstones constitute fractured or double-porosity aquifers which are partially confined, of low yield and used locally for drinking water, industrial water and irrigation. At the surface, the zone occurs in the Vienna Woods, where groundwater recharge through rain water can be high within sandstone areas.

Where Neogene silts and clays contain sand and gravel layers, these represent porous aquifers of low to medium yield, used mainly for irrigation, industrial water and geothermal purposes. Groundwater recharge from the surface is impeded by a thick loess cover. In the eastern part of the city, groundwater in a conglomerate layer of 300 m thickness and 3000 m below ground, reaches temperatures of up to 100°C and is considered Vienna’s future geo-energy reservoir.

Pleistocene terraces are made of gravel and, with decreasing age, show decreasing amounts of sand and silt intercalations, while the groundwater shows increasing yield, increasing mineralisation and major ion contents shifting from Ca and Mg dominance towards more Na and K. The terraces’ occurrence coincides with intense urban land use, sealing of the ground surface, low recharge and potential infiltration of leaking sewage water.

Within the Danube plain, 60 % of the land is used for agriculture and recreation where rain water can infiltrate easily into Holocene gravel. Recharge also happens partially through river bank filtrate of the Danube, partially through artificial recharge. Among all groundwater units in Vienna, this continuous aquifer shows the highest yield and the most intense use for irrigation and groundwater heat pumps. During peak periods of water demand, groundwater is also used as drinking water.

Vienna’s water consumption amounts to 200 litres per person per day approximatively. In periods of normal demand, drinking water is provided exclusively by Alpine karst springs captured up to 120 km southwest of the city.

How to cite: Pfleiderer, S.: The hydrogeological units of Vienna - land use, groundwater use and groundwater chemistry, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1818, https://doi.org/10.5194/egusphere-egu24-1818, 2024.

EGU24-2965 | Orals | ITS1.5/NP8.6

How do urban river networks regulate city climate? A case study in Shanghai, China 

Jiyun Song, Dachuan Shi, and Qilong Zhong

Urban blue (water) and green (vegetation) spaces are natural refuges of cool spots for citizens to escape from the extreme heat outdoors and have been widely used in traditional and modern urban designs called ‘water towns’ (i.e., buildings are sited along rivers and trees), particularly in Southern China with rich water resources. This study represents the first comprehensive investigation into the cooling effect of urban river networks at different climatic scales in Shanghai, a Chinese megacity characterized by a significant presence of water towns. At the neighborhood scale, we conducted fine-resolution street-level monitoring of microclimatic data along various rivers during the 2022 heatwave periods in central Shanghai and applied an advanced spatial regression algorithm to quantify the synergistic effect of river and vegetation. At the city scale, we quantified the cooling buffer zones and cooling intensities of urban river networks by integrating fine-resolution urban river network maps with multi-source remotely sensed datasets. We found that the width of rivers, coverage ratio, density, and morphology of river networks are the key factors affecting the cooling potential. The confluence or proximity of river tributaries can also bring an enhanced cooling effect than standalone ones. In a diurnal cycle, rivers can lead to an averaged cooling intensity of 0.4–0.8 °C in air temperature with a maximum value of 3.5 °C in the afternoon, as well as a cooling distance ranging from 100 m to 700 m at various riverside neighborhoods. On the other hand, city-scale results show that river networks can provide a considerable cooling buffer zones covering 36.9% of Shanghai and a maximum cooling intensity of 5.5 °C in surface temperature. Our study implies that urban river networks cannot be neglected in urban climatic studies and should be incorporated into a new conceptualization of water-included urban local climate zone classifications in the world urban database.

How to cite: Song, J., Shi, D., and Zhong, Q.: How do urban river networks regulate city climate? A case study in Shanghai, China, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-2965, https://doi.org/10.5194/egusphere-egu24-2965, 2024.

EGU24-3246 | Posters on site | ITS1.5/NP8.6

WRF-SUEWS Coupled System: Development and Prospect 

Ting Sun, Hamidreza Omidvar, Zhenkun Li, Ning Zhang, Wenjuan Huang, Simone Kotthaus, Helen Ward, Zhiwen Luo, and Sue Grimmond

We present the coupling of the Surface Urban Energy and Water Scheme (SUEWS) into the Weather Research and Forecasting (WRF) model, which includes pre-processing to capture spatial variability in surface characteristics. Fluxes and mixed layer height observations from southern UK were utilised to assess the WRF-SUEWS system over two-week periods across different seasons. Mean absolute errors are lower in residential Swindon compared to central London for turbulent sensible and latent heat fluxes (QH, QE), with increased accuracy on clear days at both locations. The model's performance exhibits clear seasonality, showing enhanced precision for QH and QE during autumn and winter due to more frequent clear days than in spring and summer. Using the coupled system, we explored how anthropogenic heat flux emissions affect boundary layer dynamics by contrasting areas with varying human activities within Greater London; higher emissions not only raise mixed layer heights but also create a warmer, drier near-surface atmosphere. Future updates will align the coupled system with the latest SUEWS version, focusing on detailed surface-layer diagnostics that can support various urban climate applications such as building energy modelling and human thermal comfort assessments.

How to cite: Sun, T., Omidvar, H., Li, Z., Zhang, N., Huang, W., Kotthaus, S., Ward, H., Luo, Z., and Grimmond, S.: WRF-SUEWS Coupled System: Development and Prospect, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-3246, https://doi.org/10.5194/egusphere-egu24-3246, 2024.

Urban overheating is becoming an increasingly pressing concern under the dual challenges of global warming and urban heat island effect. One effective way to mitigate urban overheating problems is to create urban cool spots via urban blue-green spaces (BGS).  To investigate the synergistic cooling effect of urban BGS, we proposed a new urban BGS coupling system by integrating a new urban water module with the state-of-the-art urban vegetation module in the framework of an urban canopy model (UCM). This coupled BGS system can represent complicated radiative exchanges between building, tree, and water, and simulate dynamic variations of shadow length, temperature, humidity, as well as energy and water fluxes within the urban street canyon. The new urban BGS model has been evaluated in typical neighborhoods with building and trees siting along rivers (also named ‘water towns’) in two Chinese megacities, i.e., Shanghai and Hong Kong. Based on this model, we investigated the synergistic cooling effect of BGS in different ‘water town’ design scenarios with different combinations of BGS characteristics (e.g., tree crown radius and height, river width, the distance between tree and river) and street canyon characteristics (e.g., geometries and orientations). Our study emphasizes the importance of optimizing 'water town' design to offer more effective cool spots for urban citizens facing escalating heat stress.

How to cite: Shi, D. and Song, J.: Investigating the synergistic cooling effect of urban blue and green spaces via an advanced urban canopy model, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-4483, https://doi.org/10.5194/egusphere-egu24-4483, 2024.

EGU24-6045 | Orals | ITS1.5/NP8.6 | Highlight

Improved representation of anthropogenic deposits in 3D urban geological subsurface models 

Jeroen Schokker and Joris Dijkstra

The urban subsurface is increasingly disturbed by human activity and/or covered by anthropogenic deposits. This is particularly true for city centres, with thick and heterogeneous subsurface archives related to historical urban development, as well as for modern residential and industrial areas, that are often built on extensive sheets of filling sand. The anthropogenic deposits may be very diverse in nature, ranging from natural aggregates (crushed rock, gravel, sand or clay) to various types of novel anthropogenic materials (e.g. steelworks slags, concrete and rubble), as well as mixtures of these.

Although anthropogenic deposits could be represented on subsurface maps and in 3D models, these deposits are often omitted. Their lateral extent and thickness are not well constrained and relevant information on the lithological properties of the deposits is generally lacking. At the same time, the demand for complete and detailed subsurface information in the built environment is increasing and relates to anything from building stability and ground heat extraction to preserving cultural heritage and mitigating the effects of climate change.

This presentation therefore focusses on the lithological characterisation and stratigraphical subdivision of anthropogenic deposits in order to improve their representation in 3D geological subsurface models. We will evaluate current lithological standards and stratigraphic approaches and present the principles of the approach that we are developing in the Netherlands. We will discuss the practical consequences and give examples of bringing our approach into practice. Ultimately, a well-thought lithological description and classification system of anthropogenic deposits is a prerequisite to produce reliable subsurface and coupled surface-subsurface models. In that way, we can address the many challenges related to the ever-increasing use of  urban space and thus improve the wellbeing of our citizens.

How to cite: Schokker, J. and Dijkstra, J.: Improved representation of anthropogenic deposits in 3D urban geological subsurface models, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-6045, https://doi.org/10.5194/egusphere-egu24-6045, 2024.

EGU24-7877 | ECS | Orals | ITS1.5/NP8.6

Multiscale characterisation of varied risks for transportation infrastructures under climate change 

Yangzi Qiu, Pierre-Antoine Versini, Nathanaël Mifsud-Couchaux, and Ioulia Tchiguirinskaia

The infrastructures of Régie Autonome des Transports Parisiens (RATP) system are significant for the transportation of the Île-de-France region, providing essential social and economic services. In order to assess and mitigate the negative impact of climate change, this study aims to characterise the flood and heat wave risks of RATP infrastructures under climate change on multiple scales. Extreme flood events and heat wave events may result in the functional disruptions to the RATP infrastructures by interrupting circulation for more or less long periods. Therefore, a better understanding of the multi-scales risk (combining hazard, exposure and vulnerability indicators) of RATP infrastructures could enhance their resilience to climate change. With this respect, a multi-scale analysis of flood and heat wave risks of RATP infrastructures is presented by integrating the Universal Multifractal (UM) framework and analytic hierarchy process (AHP). The UM framework is a stochastic method that allows analysis of the natural hazards (extreme precipitation and temperature) and risks under three future climate scenarios (RCP2.6, RCP4.5, RCP8.5) across a range of scales. The AHP method is applied for quantifying the various risks by weighting hazard, exposure and vulnerability indicators based on experts’ knowledge. The results show that a certain number of RATP stations and lines are prone to flood and heat waves under climate change, especially in the RCP8.5 scenario. By undertaking the multiple scales of flood and heat wave risks of RATP infrastructures, this study seeks to contribute valuable insights that will inform strategic planning and resilience-building initiatives for RATP infrastructures under climate change (adaptation measures). It provides a theoretical basis for multiple risk assessments in other metropolitan areas worldwide.

How to cite: Qiu, Y., Versini, P.-A., Mifsud-Couchaux, N., and Tchiguirinskaia, I.: Multiscale characterisation of varied risks for transportation infrastructures under climate change, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-7877, https://doi.org/10.5194/egusphere-egu24-7877, 2024.

EGU24-8391 | Orals | ITS1.5/NP8.6

Urban hydrogeologic uncertainty characterisation to evaluate risk of groundwater flooding 

Charalampos Ntigkakis, Stephen Birkinshaw, Ross Stirling, and Brian Thomas

Groundwater flooding within the urban infrastructure can play a major role in determining the resilience of urban environments. Urban groundwater models can be used to simulate the complex interactions between surface water and groundwater within the urban system and can be developed to jointly account for groundwater-surface water processes and subsurface characterization. They can be used to simulate potential groundwater flooding and help understand the role of groundwater in urban resilience to climate change. However, urban groundwater is a component of the wider urban water system that has traditionally been overlooked, and the complex interactions between surface water and groundwater may obscured by urban infrastructure and its influence on groundwater flow. Furthermore, the subsurface characterisation is an integral part of any groundwater model, however it’s influence on model performance is not yet fully understood. Therefore, the inherent complexities of the urban environment, combined with the scarcity of appropriate groundwater and subsurface data, can lead to increased model uncertainty. It is argued that robust urban groundwater modelling depends on a strong conceptual understanding of the groundwater system, and constraining the uncertainty in the subsurface characterisation.

This project aims to assess model sensitivity to the geological interpretation in simulating groundwater dynamics that represent regions of groundwater flooding. It accounts for uncertainty in the subsurface information to develop an ensemble of different geological interpretations and evaluate the influence of the subsurface characterisation on groundwater flow model performance, within the Ouseburn watershed in the greater Newcastle upon Tyne area.

How to cite: Ntigkakis, C., Birkinshaw, S., Stirling, R., and Thomas, B.: Urban hydrogeologic uncertainty characterisation to evaluate risk of groundwater flooding, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-8391, https://doi.org/10.5194/egusphere-egu24-8391, 2024.

EGU24-8476 | Orals | ITS1.5/NP8.6

Subsurface in territorial soil desealing strategies 

Cecile Le Guern, Fabien Prézeau, Pierre Chrétien, and Blandine Clozel

Desealing appears as an option to disartificialise soils. It embraces several territorial issues like water management, adaptation to climate change, the well-being of inhabitants and biodiversity. In practice, many desealing operations are carried out. The areas to be desealed are most often linked to opportunities such as development projects or target actions (like school playgrounds). There are in fact few potential maps to support desealing strategies. Existing methods systematically take certain criteria into account (e.g. water infiltration). Environmental criteria are however more or less considered.

The DésiVille project (2021-2024) aims to provide decision-making tools to support desealing strategies. In particular, it is preparing a methodological guide to map the potential for desealing, in order to propose a harmonized and concerted framework. The methodology considers 4 thematics: i) the characteristics of the sealed surfaces, ii) the potential of infiltration of soils, iii) the environmental risks and the protection of resources, and iv) the benefits of desealing.

The thematics linked to the potential of infiltration of soils and to the environmental risks consider information on the subsurface. In particular the presence of clay and the groundwater depth feed the potential of infiltration. The environmental risks and protection of resources integrate the presence of soluble rocks, the risk of soil pollution, the risk of flooding due to groundwater rise, the geotechnical risk, area of protection of the water resource. A multicriteria spatial analysis crosses the information per thematic on one side, and among thematics on the other side. The study case of Nantes Métropole (France) illustrates the influence of the potential of infiltration and of the environmental risks and protection of resource on the global potential of desealing maps.

The subsurface needs to be considered to build desealing strategies. More generally, it is essential to consider it in urban planning and development. Although out of sight, it must not be out of mind.

How to cite: Le Guern, C., Prézeau, F., Chrétien, P., and Clozel, B.: Subsurface in territorial soil desealing strategies, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-8476, https://doi.org/10.5194/egusphere-egu24-8476, 2024.

EGU24-8492 | Posters on site | ITS1.5/NP8.6

The subsoil of the city of Naples: accomplishment of a digital platform for its representation, management and protection 

Paolo Maria Guarino, Antonino Barba, Fausto Marra, Fabio Pascarella, and Mauro Roma

Naples is the third largest Italian city by size and population. Over 75% of its area is urbanized and the development of the city, often disorderly over the centuries, have occurred despite that the city is exposed to numerous geological hazards, namely: the volcanic and seismic hazard associated with a possible reactivation of Vesuvius and Phlegraean Fields volcanic centres; the seismic hazard connected with the  Apennine seismic activity; the landslide hazard due to the geologically immature landscape and the sinkhole hazard associated with the anthropic use of the subsoil. The studies undertaken and commissioned in the past by the Municipal Administration of Naples, starting from those aimed at facing the so-called Naples’ Subsoil Emergency in the early 2000s, have allowed the acquisition of a large amount of geological information relating to the subsoil, which requires a new and more modern data management structure. For this purpose, the Ufficio Servizio Difesa Idrogeologica del Territorio of the Municipality of Naples has started a project aimed at valorising and updating the enormous amount of data in its possession, through the creation of an digital platform aimed at representing the subsoil of the municipal territory. In this work the preliminary results of the project are presented. The objective of the project is to build a dataset of the geological subsoil information, structured by means of a system of coherent and organic relationships, which will concern not only the geological features (stratigraphic logs, geotechnical parameters etc.) but also the anthropic features (man-made cavities, underground services, tunnels etc.) and that will be included, in the future, within a broader digital  platform concerning the housing and underground public facilities. ISPRA, via the Department for the Geological Survey of Italy, has carried out numerous studies in the Neapolitan area in recent years, also in collaboration with the Municipality of Naples. In this context, ISPRA will provide scientific support and data in its possession for the construction of an updated geological model of the subsoil and the revision of the city’s geological map. With the accomplishment of the project, the digital platform of the subsoil of the city of Naples will become the reference geo-informatics tool of the municipal GIS; it will also have a strong participatory value open to all stakeholders, with the possibility of activating exchanges between citizens and institutions aimed at a continuously updating the acquired knowledge.

How to cite: Guarino, P. M., Barba, A., Marra, F., Pascarella, F., and Roma, M.: The subsoil of the city of Naples: accomplishment of a digital platform for its representation, management and protection, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-8492, https://doi.org/10.5194/egusphere-egu24-8492, 2024.

EGU24-8678 | ECS | Posters on site | ITS1.5/NP8.6

Remotely sensed monitoring of urban greening in China from 1990-2019 to support SDG11 

Ping Zhang, Hao Wu, Hao Chen, and Qiangqiang Sun

Understanding and accurate identification of long-term urban greening dynamics in China are critical for the sustainable urban management (Sustainable Development Goals, SDG11) and living environment of humans. But it was often challenging because a lack of continuous high-frequent data at high spatial resolution and over large time scales. Here, we proposed a framework for identifying detailed evolution processes and regime shifts in relation to urban greening based on characterization of urban greenness in continuous fields over space and time. We utilized annual, fractional estimates of urban green vegetation (GV) endmember time series from per-pixel Landsat composites, using a standardized spectral mixture Vegetation-Impervious surface-Soil (VIS) model in China over the past three decades. A Google Earth Engine platform-based non-linear model (logistic curves) was developed to derive the magnitude, timing and duration of urban greening at a per-pixel basis during these time series records. These parameters were combined to characterize heterogeneous pattern of urban greening throughout the entire China in 1990-2019. We found that the unmixed fractions of urban GV exhibited a generally consistent agreement with estimated fractions from high-spatial-resolution Google earth images (RMSE =11.30%), demonstrating its high suitability and reliability. Using detailed geographic process model with logistic trajectory fitting curves, our findings indicate that the ratio of the area with significant greening trends during 1990-2019 account for nearly 3.0% to the overall urbanized area in China. These greening changes are predominantly distributed in eastern coastal region and northeast Plain. In particular, the Jing-jin-ji, Ha-Chang and Middle-Southern Liaoning are the top three urban agglomerations contributing the greening for this period. Notably, Urumqi, the capital city in north-western China, has the highest ratio of the area with significant increasing GV relative to the urbanized space of the entire city, due to great achievements of urban green construction (i.e., the newly established parks or street plants), and relatively low greenness before 1990. Based on the derived change parameters, our results also reveal the economic impacts on the timing of urban greening are prevalent. For instance, the timing of turning points for urban greening in three major highly-urbanized and developed urban agglomerations, that is, the Jing-jin-ji, Yangtze River Delta, Pearl River Delta showed 2-3 years earlier than other regions. Compared to the state-of-the-art approaches, this framework has the potential to detect high-frequent urban greening process as continuous spatial and time fields with multi-dimensional thematic, thus could help support sustainable urban management practices.

How to cite: Zhang, P., Wu, H., Chen, H., and Sun, Q.: Remotely sensed monitoring of urban greening in China from 1990-2019 to support SDG11, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-8678, https://doi.org/10.5194/egusphere-egu24-8678, 2024.

EGU24-9413 | ECS | Orals | ITS1.5/NP8.6

Enhancing Building Height Estimation through Occlusion Reduction with Advanced Deep Learning Models 

Yizhen Yan, Bo Huang, Weixi Wang, Linfu Xie, Renzhong Guo, and Yunxiang Zhao

Building heights play a crucial role in various urban research fields, including 3D modeling, urban environmental analysis, sustainable development, and urban planning and management. Numerous methods have been developed to derive building heights from different data sources, including street view imagery, which offers detailed, ground-level perspectives of buildings. However, occlusions from street elements such as trees and vehicles present significant challenges, especially in densely built or complex urban areas. To address this challenge, we propose the use of advanced deep learning models for occlusion reduction, enhancing building height estimation from street view images. As trees typically cause the most occlusion, we employ an open-set detector and a large segmentation deep neural network to create tree masks in the images. Subsequently, we use a stable diffusion model for image inpainting, restoring parts of buildings occluded by trees. These inpainted images are then processed through building instance segmentation, yielding clearer building boundaries for height estimation. Moreover, we integrate a single-view metrology-based height estimation method with a building footprint auxiliary approach, leveraging their respective strengths and mitigating the impact of varying distances between street view cameras and buildings. Our methodology is validated using a dataset comprising 954 buildings and 3814 images. Experimental results demonstrate that our approach increases the percentage of height estimates within a two-meter error margin by approximately 7%, confirming its effectiveness. This work offers a cost-effective solution for large-scale building height mapping and updating, and it opens new avenues for urban research requiring accurate building height data.

How to cite: Yan, Y., Huang, B., Wang, W., Xie, L., Guo, R., and Zhao, Y.: Enhancing Building Height Estimation through Occlusion Reduction with Advanced Deep Learning Models, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-9413, https://doi.org/10.5194/egusphere-egu24-9413, 2024.

EGU24-9711 | Orals | ITS1.5/NP8.6

GeoSciences IR: a geological research infrastructure for land management in urban areas 

Luca Guerrieri, Marzia Rizzo, and Roberto Passaquieti

A full access to high-quality geological data is fundamental to address all different aspects of land management, such as adapting to existing geohazard and ensuring the availability of georesources (e.g. critical raw materials and geothermal energy). This is particularly relevant in urban areas, where a multidisciplinary and integrated approach to diverse geological issues is imperative.

GeoSciences IR is a geological research infrastructure currently being implemented through NextGenerationEurope funds, with the aim of meeting the needs of Regional Geological Surveys (RGS), the local technical offices having a specific mandate on geological topics at regional and local level, including the urban environment.

Through the GeoSciences IR platform, it will be possible to access data, services, tools, and training modules developed in accordance with the FAIR principles and the INSPIRE Directive, which require fully open accessibility, interoperability, and reusability.

The priority topics of GeoSciences IR have been selected according to the RGS'needs and encompass various geological themes, including 2D and 3D geological mapping, marine geology, geoheritage conservation, geohazard mapping and monitoring, sustainable mining, and land consumption.

Among datasets under preparation, some will be of more specific interest for the urban environment, including i) stratigraphies from boreholes; ii) characterization of local geohazard related to landslides, sinkholes, active and capable faulting; iii) structural works for the mitigation of hydrogeological risk; iv) ground motion mapping and monitoring for low-velocity slope movements and subsidence; v) soil sealing and land consumption monitoring.

Users will also benefit from the full interoperability among services and will be able to access innovative tools based on specific algorithms available for cloud data processing.

Furthermore, a specific section of GeoSciences IR will be dedicated to e-learning modules built to increase the transfer of knowledge from scientists to end-users of GeoSciences IR. These modules have mainly focused on the methodological approach for data collection and on the use of available datasets and tools.

GeoSciences IR is under implementation by a large consortium composed by 13 Italian universities and 3 research institutes, coordinated by ISPRA, Geological Survey of Italy. The infrastructure will open to the public in 2025 and will be maintained for at least 10 years.

In this long-term perspective, a dialogue with external stakeholders (from institutions and the private sector) has already started with the aim of building a reference infrastructure for geological data in Italy, taking into account also their feedback and, in some cases, including additional contributions in terms of data, services and tools. Meanwhile, a constant interaction has been established with other existing research infrastructures available at European level (e.g. EPOS ERIC, EGDI) to ensure their complementarity and identify eventual gaps and overlaps.

How to cite: Guerrieri, L., Rizzo, M., and Passaquieti, R.: GeoSciences IR: a geological research infrastructure for land management in urban areas, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-9711, https://doi.org/10.5194/egusphere-egu24-9711, 2024.

EGU24-11021 | ECS | Orals | ITS1.5/NP8.6

Simulating Temperature and Evapotranspiration using a Universal Multifractal approach 

Arun Ramanathan, Pierre-Antoine Versini, Daniel Schertzer, Ioulia Tchiguirinskaia, Remi Perrin, and Lionel Sindt

Abstract

Temporal structure functions are usually defined as the q-th order statistical moment of the absolute fluctuation in a time series over a temporal lag at a given resolution. However, applying this in analyzing a temperature time series results in the possibility of simulating only a similar fluctuation over a temporal lag at a resolution and not the temperature directly. Since the aim is to simulate a temperature time series this simulated fluctuation series can be added to an assumed mean temperature to obtain a temperature time series. However, proceeding this way seems to necessitate some ad-hoc moving average technique that seems difficult to be physically reasoned. Secondly but more importantly both diurnal and seasonal periodicity have to be forcibly introduced once again in a non-rigorous manner. A drastic yet reasonably useful alternative would be to modify the definition of the structure-function instead. For order of statistical moment q  the modified structure function is now defined here as

Sq(Δt)=⟨ΙTλ - Tλ/2,2Ιq

Where the scale ratio λ∝1/ΙΔtΙ; 2m/2m=1≤λ≤Λ=2m/20 and ΙΔtΙ is the time lag, whereas 2m is the largest possible scale out of the scales analyzed that can be represented as a power of 2. While Tλ is the temperature at scale ratio λ or scale l, Tλ/2,2 is the upscaled (by a scale ratio of 2) temperature at scale ratio λ/2 or scale 2l, and the subscript ‘2’ indicates that each element of  Tλ/2 (upscaled temperature) is repeated twice consecutively. It should be noted that Tλ/2,2 is not the same as Tλ because the former is an upscaled series, twice repeated (consecutively) of the latter. The largest scale ratio considered in the analysis is Λ. By defining the structure-function in this way temperature at a larger scale after being repeated a sufficient number of times can be directly added to the fluctuation at a smaller scale to result in the temperature at a smaller scale. The universal multifractal parameters obtained from the modified structure-function analysis are not necessarily equal to those obtained from the usual structure-function analysis (i.e. the two different structure functions follow two different scaling laws). An iterative curve fitting technique is used to estimate the values of Universal Multifractal (UM) parameters C1, H, and a  while the value of α  is estimated using a normalized form of the modified structure function along with the un-normalized one. A simulation procedure that utilizes the aforementioned modified structure function definition is proposed here to generate temperature scenarios. Finally, reference evapotranspiration is estimated based on the simulated temperature using a simple empirical power law function. The actual evapotranspiration is estimated using the reference evapotranspiration and water content via a different, simpler empirical function. The tentative methodology proposed here when used along with simulated reference rainfall scenarios could help design zero-emission green roof solutions.

 

Keywords

Multifractals, Non-linear geophysical systems, Cascade dynamics, Scaling, Hydrology, Meteorology.

How to cite: Ramanathan, A., Versini, P.-A., Schertzer, D., Tchiguirinskaia, I., Perrin, R., and Sindt, L.: Simulating Temperature and Evapotranspiration using a Universal Multifractal approach, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-11021, https://doi.org/10.5194/egusphere-egu24-11021, 2024.

EGU24-13309 | ECS | Orals | ITS1.5/NP8.6 | Highlight

Urban energy futures: Unraveling the dynamics of city-scale building energy use and CO2 emissions under mid-century scenarios 

Chenghao Wang, Janet Reyna, Henry Horsey, and Robert Jackson

Residential and commercial buildings jointly account for 39% of energy consumption and 28% of greenhouse gas emissions in the U.S. In densely populated urban areas, the share of energy use and emissions attributable to buildings can be even higher. The future evolution of building energy use and associated carbon emissions is uncertain, with potentially substantial variations in climate conditions, socioeconomic development, and power sector trajectories; accounting for these in future projections is often compounded by limited data availability and resolution of conventional modeling approaches. To address these challenges, in this study, we employed a bottom-up, high-resolution modeling approach and evaluated city-scale building energy consumption and CO2 emissions across 277 urban areas in the U.S. under various mid-21st century scenarios. Our findings reveal substantial spatial and temporal variations in future changes in building energy use and CO2 emissions among U.S. cities under a variety of climate, socioeconomic, and power sector evolution scenarios. On average, a 1°C warming at the city scale projects a 13.8% increase in building energy use intensity for cooling, accompanied by an approximately 11% decrease in energy use intensity for heating, albeit with notable spatial disparities. Collectively, driven by global warming and socioeconomic development, mid-century city-level building energy use is projected to rise on average by 17.5–39.8% under all scenarios except for SSP3-7.0 when compared with the last decade. In contrast, city-level building CO2 emissions are projected to decrease in most urban areas (averaging from 10.6% to 66.0% under different scenarios), with spatial variations primarily influenced by climate change and power sector decarbonization.

How to cite: Wang, C., Reyna, J., Horsey, H., and Jackson, R.: Urban energy futures: Unraveling the dynamics of city-scale building energy use and CO2 emissions under mid-century scenarios, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-13309, https://doi.org/10.5194/egusphere-egu24-13309, 2024.

EGU24-14334 | Orals | ITS1.5/NP8.6

Realtime monitoring of urban flooding by ensemble Kalman filters 

Le Duc, Juyoung Jo, and Yohei Sawada

Urban drainage models have been used in many cities for analysis, prediction, and control related to urban flooding. Many sources of uncertainties exist in these models comprising model parameters, meteorological forcings, and surface conditions. Thus, it is necessary to calibrate models before using them in reality. A common choice in calibration is to fit the model outputs with observations through many cases. This strategy is known as the offline mode in calibration and works on the stationary assumption of model parameters. If parameters vary in time, this method usually yields the climatological range of the parameters, which are not necessarily optimal in specific cases. In this study, instead of the offline model we follow the online mode in estimating model parameters by using an ensemble Kalman filter (EnKF). Furthermore, we estimate not only model parameters but also model states simultaneously utilizing the EnKF. Note that originally, EnKF is a data assimilation technique that is based on sampling in estimating any system states given observations, and later is used for the purpose of parameter estimation. The combination of EnKF and an urban drainage model is expected to lead to a real-time monitoring system for urban flooding similar to reanalysis systems in numerical weather prediction.

How to cite: Duc, L., Jo, J., and Sawada, Y.: Realtime monitoring of urban flooding by ensemble Kalman filters, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-14334, https://doi.org/10.5194/egusphere-egu24-14334, 2024.

EGU24-14606 | ECS | Orals | ITS1.5/NP8.6

A cross-scale methodological framework for the quantification of the impact of urban features on intra-city microclimate 

Xiaotian Ding, Yongling Zhao, Dominik Strebel, Yifan Fan, Jian Ge, and Jan Carmeliet

Evaluation of the outdoor thermal comfort and comprehension of the impact of urban morphology are essential for assessing heat-related risks and implementing urban planning strategies that enhance the resilience of urban populations to extreme heat events. However, the challenge lies in achieving city-wide thermal comfort mapping at high spatial and temporal resolutions, which requires consideration of the complex urban morphology (urban geometry and land cover) at a microscale, as well as the background meteorological factors at larger scale. Here, we introduce an effective framework for city-scale thermal comfort mapping at high spatial-temporal resolution that integrates WRF-UCM and SOLWEIG model, aiming to achieve fine-grained thermal comfort mapping at the city scale and to explore the impact of urban morphology on these thermal conditions.

In the proposed framework, we employ the WRF-UCM model (The Weather Research and Forecasting model coupled with the urban canopy model) to establish the background meteorological condition at local-scale (500m resolution). Additionally, we utilize the SOLWEIG (Solar and Longwave Environmental Irradiance Geometry) model for the simulation of mean radiant temperature at a finer micro-scale (10m resolution), a critical determinant of thermal comfort. These simulations are performed using detailed 3D urban morphological data and land cover information. Subsequently, the Universal Thermal Climate Index (UTCI) is calculated on hourly basis, integrating the aforementioned factors.

A case study conducted for a Chinese city with a population of 15 million demonstrates a significant correction between the rise in the UTCI during daytime and an increase in impervious surface area, evidenced by a maximum correlation coefficient of 0.80. Furthermore, our findings emphasize the significance of tree canopy coverage in mitigating heat, demonstrating that an implementation of 40% tree cover could diminish daytime UTCI by approximately 1.5 to 2.0 ºC. This methodological framework is not only instrumental in assessing heat-related risks and human thermal discomfort within intricate urban environments but also offers pivotal insights for the adoption of climate-resilient urban planning strategies.

How to cite: Ding, X., Zhao, Y., Strebel, D., Fan, Y., Ge, J., and Carmeliet, J.: A cross-scale methodological framework for the quantification of the impact of urban features on intra-city microclimate, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-14606, https://doi.org/10.5194/egusphere-egu24-14606, 2024.

Urban areas are major contributors to climate change, accounting for 71 to 76% of CO2 emissions from global final energy use [1]. Nevertheless, cities are growing in both size and number. By 2030, it is projected that 730 million people will live in megacities (cities with at least 10 million inhabitants) compared to 500 million people in 2016 [2]. The number of megacities will also increase from 29 to 43 [3]. On the other side, solar radiation is an important component in the energy balance of urban areas. Urban form impacts the production of building-integrated photovoltaics, solar heat gains and heating/cooling demand of buildings. Relevant urban form characteristics include urban layout, population density, and individual building characteristics, such as height, wall orientation, roof slope, and construction material. Optimization of the urban form design can contribute to better energy performance of buildings. However, optimization is a large multivariable problem that is computationally intensive. A good understanding of the urban form impact can guide the optimization. In this work, the influence of shadow from surrounding buildings on solar radiation incident on buildings is studied provided a three-dimensional (3D) model of an area.

Open Access 3D models for many cities are made available by local authorities. Standardized data formats for 3D modelling are well-established. The scientific community has been working towards understanding urban forms, their impact on energy demand, and the potential for realizing sustainable urban forms. So far, the available work relied on different tools to analyze the impact of urban form on space heating/cooling demand for a specific city making reproducibility difficult. 

This work shows the advantage of using the standardized CityJSON format to establish an open-source Python-based framework to calculate hourly solar irradiance on building facades, considering the shadow of surrounding buildings, generate a thermal model of building envelopes, and calculate heat losses, gains, and the heating load of a building. The proposed methodology involves three phases. First is data collection and pre-processing. Second is the calculation of direct solar radiation on building facades and roofs. For that, hourly sun positions have been determined.  Maximum shadow length is calculated for each sun position. The geometry of buildings is analyzed, shared walls are excluded, and exemplary window vertices are allocated on the free walls such that the window-to-wall ratio ranges between 15% and 25%. Orientations of walls and slopes of tilted roofs were identified. Hyper-points are deployed on each surface in a 0.5m grid. With that, shadow height at each hyper-point and direct solar radiation were calculated. Third is the estimation of the heating or cooling load.

An exemplary neighborhood in Munich is presented as a real case study. Preliminarily results confirm that urban form is influencing the energy performance of buildings. Less shadowing on a building implies higher solar exposure but not necessarily reduced heating demand despite identical thermal properties of buildings’ envelope.

 

 

References:

[1] United Nations. (2017). Urban Environment. https://unfccc.int/resource/climateaction2020/media/1308/Urban_Environment_17.pdf

[2] United Nations. (2016). The World’s cities in 2016: data booklet. http://digitallibrary.un.org/record/1634928

[3] European Commission. (2020). Urbanisation worldwide. https://knowledge4policy.ec.europa.eu/foresight/topic/continuing-urbanisation/urbanisation-worldwide_en

How to cite: Alfouly, M. and Hamacher, T.: Evaluating Urban Form Influence on Solar Exposure and Corresponding Building Energy Demands, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15532, https://doi.org/10.5194/egusphere-egu24-15532, 2024.

EGU24-15561 | Orals | ITS1.5/NP8.6

How do geological surveys respond to evolving uses and interaction in the urban subsurface? 

Tim Kearsey, Stephanie Bricker, Ricky Terrington, Holger Kessler, Helen Burke, and Steve Thorpe

The UK Government Office for Science has recently commissioned a Foresight Project on the ‘Future of the Subsurface’. The project draws on experts across different government departments and industry - including representative from the geological and environmental community, planning specialists, infrastructure and service providers, city authorities and energy specialists - to understand the future demands that will be placed on the subsurface to deliver our sustainable development goals; What are the high-value future societal subsurface uses? What climatic and environmental pressures are expected? What policy interventions will be required to protect and enhance the value of the subsurface in the longer-term? We present outcomes from the Foresight project's subsurface issues paper, alongside recommendations from the National and Regional level expert elicitation. Drawing on our research in urban geosciences and subsurface assessment we highlight how geological surveys can, and are, responding to the issues and recommendations highlighted by the Foresight project.  Some common themes emerge for which the geological survey has a role, for example, ensuring coordinated and interdisciplinary approaches to planning; Assessing opportunities to update or streamline subsurface governance and regulation; Improving the coverage, quality, availability and interoperability of data.

In addition to these overarching principles, the variability of regional geology in the UK and its impact on subsurface issues is a prominent outcome of the Foresight project and necessitates place-based approaches, tailored to distinct geologies and geographies, to define a hierarchy of subsurface need.  The UK has a particularly varied geology spanning the whole Phanerozoic this means that there are very different geological problems in different cities. Taking this placed-based approach we show how the evolution of 3D geology mapping and geospatial tools at the British Geological Survey (BGS), has shifted towards multi-assessment to appraise the diverse integrated and competing subsurface uses. We highlight the practical applications of 3D models in improving data availability and accessibility e.g. by updating geological maps, enhancing data products, and facilitating user accessibility through tools like model viewers. The paper concludes by emphasizing the importance of geological information to help facilitate dialogue and stakeholder consultation, and support evidence-based policymaking.

How to cite: Kearsey, T., Bricker, S., Terrington, R., Kessler, H., Burke, H., and Thorpe, S.: How do geological surveys respond to evolving uses and interaction in the urban subsurface?, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15561, https://doi.org/10.5194/egusphere-egu24-15561, 2024.

EGU24-16662 | Orals | ITS1.5/NP8.6 | Highlight

Different Approaches to the Impacts of Climate Change, with a Common Goal: a Healthy Planet 

Ioulia Tchiguirinskaia, Yangzi Qiu, and Daniel Schertzer

This work has benefited from a multidisciplinary scientific and technical contributions geared by the HM&Co Lab of the Ecole des Ponts ParisTech (hmco.enpc.fr) towards the sustainable, desirable, and resilient city. The deepening of the Universal Multifractal (UM) concepts and the encouragement of their operational applications have been linked to several initiatives launched in recent years to better integrate the heterogeneity/intermittency into public policy practices. Considering the complex, dynamic interactions between geophysical and anthropogenic fields within a conurbation such as the Ile de France region, a transition towards the shared value economy has been considered to best stimulate sober and collaborative development, and there exist at least 3 ways to approach today’s discussions about future transformations. Their intercomparison is the core of this presentation.

Following the United Nations 2030 Agenda, the first most conventional approach is based on notions of sustainable development, supported by appropriate adaptation and mitigation of climate change.

Combining the notions of extreme variability and complexity would require linking together geophysical and urban scales within extreme variability, and therefore considering geosciences, and not just geophysics! Such a synergistic and integrative approach would help move beyond traditional silo thinking, addressing the complexity of data- and/or theory-driven urban geosciences.

Finaly, combining the notions of scaling and nonlinear variability would ultimately require linking cascades, multiplicative chaos, and multifractals. This would initiate a break with linear stochastic models towards stronger heterogeneity / intermittency, which would in turn lead to a plausible clustering of field and activity fluctuations. The appearance of multifractal phase transitions then becomes possible, considerably amplifying the impact of any action, and would make future transformations fully efficient, effectively imitating the way in which Nature acts. This will be finally illustrated using several examples of so-called Nature Based Solutions (NBS).

How to cite: Tchiguirinskaia, I., Qiu, Y., and Schertzer, D.: Different Approaches to the Impacts of Climate Change, with a Common Goal: a Healthy Planet, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-16662, https://doi.org/10.5194/egusphere-egu24-16662, 2024.

EGU24-17225 | Orals | ITS1.5/NP8.6

Geophysics for urban subsurface characterization: Two case studies from Spain 

Beatriz Benjumea, Carlos Marín-Lechado, Beatriz Gaite, Ana Ruíz-Constán, Martin Schimmel, Fernando Bohoyo, and Zack J. Spica

This work focuses on two case studies carried out in Spain, where urban geophysics plays an important role in subsurface characterization. The application of geophysical methods in urban scenarios faces several challenges related to environmental noise (seismic or electromagnetic) or logistical constraints (lack of open space, complexity of instrumentation setup). In order to overcome these problems, research efforts are needed on both acquisition and processing aspects. The first case study presents the use of an innovative technology to acquire seismic data in the city of Granada. Distributed Acoustic Sensing (DAS) is based on the measurement of strain rate along a buried optical fiber that provides seismic measurements in a dense array of sensors. In our study, the fiber is a pre-existing underground telecommunications cable that crosses the city from northwest to southeast. We used 10 hours of ambient noise recordings to obtain subsurface reflection images that provide critical information for ground motion studies and seismic hazards in the metropolitan area. The second case study is located in the autonomous city of Melilla (North Africa). In this work, a gravimetric survey was carried out over the urban area with the aim of delineating the bedrock using 3D gravimetric inversion. We integrated the resulting geophysical model with surface geological observations, electrical resistivity tomography sections and borehole data to produce a 3D geological model of the city. Both studies highlight the suitability of geophysical information to complement the urban geological and geotechnical dataset to characterize and image the city underground.

How to cite: Benjumea, B., Marín-Lechado, C., Gaite, B., Ruíz-Constán, A., Schimmel, M., Bohoyo, F., and Spica, Z. J.: Geophysics for urban subsurface characterization: Two case studies from Spain, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17225, https://doi.org/10.5194/egusphere-egu24-17225, 2024.

EGU24-18514 | ECS | Posters on site | ITS1.5/NP8.6

Modeling the Interplay between Urban Environmental Characteristics and Cyclist Route Preferences 

Pranav Pandya, Maider Llaguno-Munitxa, Martin Edwards, Emilie Lacroix, and Gabriele Manoli

As cities grapple with the multifaceted challenges posed by climate change, the Brussels Capital Region (BCR) stands at the forefront of fostering sustainable urban mobility, particularly through the development of cycling infrastructure aimed at bolstering public health and well-being. Policy initiatives implemented in BCR such as 'Good Move' and 'Ville 30' have acted as catalysts, prompting a paradigm shift towards specialized cycling lanes and facilities, thereby enhancing the safety and convenience of cycling as a viable transportation alternative. However, the growing recognition of urban heat stress and thermal discomfort as significant public health concerns, particularly for users of urban soft mobility means, highlights the pressing need for immediate and targeted interventions from urban stakeholders. While it is widely recognized that weather conditions, especially during very hot and cold days, influence cycling behavior, as do urban environmental features like the urban fabric and the presence of green infrastructure in a street, there remains a need to establish quantifiable metrics for assessing the impact of thermal comfort on cycling behavior. This study aims to address this gap, offering a nuanced examination of the cycling routes and cycling behavior of the BCR. We propose a multidisciplinary approach that integrates geospatial, psychological, and environmental sciences to examine the complex interplay between cycling path planning, urban design, micrometeorology, and thermal comfort. Data spanning from 2019 to 2022 has been sourced from multiple channels, including Brussel Mobility, Google Street View (GSV) with semantic image classification, Local Climate Zone (LCZ) maps, and meteorological stations. Geospatial data for Elsene and Etterbeek has been collected. The initial findings reveal that creating green pathways in urban areas can lessen heat stress and enhance comfort for cyclists. Moreover, cyclists are inclined to steer clear of extremely hot or cold weather conditions. Integrating urban microclimatological conditions into the framework of urban cycling design, this research aims to steer policy development towards creating urban soft mobility solutions that are more comfortable, climate-adaptive, and prioritize health considerations.

How to cite: Pandya, P., Llaguno-Munitxa, M., Edwards, M., Lacroix, E., and Manoli, G.: Modeling the Interplay between Urban Environmental Characteristics and Cyclist Route Preferences, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18514, https://doi.org/10.5194/egusphere-egu24-18514, 2024.

EGU24-18610 | Orals | ITS1.5/NP8.6 | Highlight

From paper reports to 3D models for all – Irish geodata in urban settings 

Sophie O'Connor and Beatriz Mozo Lopez

Communicating the subsurface is a challenge. Geoscientists are trained to visualise what is underneath them and to see the subsurface in 3D, whereas planners, policy makers and the people impacted by both (i.e., the public) are not.

Over many years, Geological Survey Ireland has developed several services in different formats to help pull together information about the subsurface, to present it in an organised manner and to portray it in three dimensions. Underpinned by the organisation’s commitment to open data and re-use of public sector information, these services are:

  • National Geotechnical Borehole Database
  • Geotechnical Viewer
  • 3D models and model viewer

Assembled over several decades, the National Geotechnical Borehole Database has expanded with the submission of ground investigations that have been carried out ahead of development projects by the private and public sectors. It acts as a secure, national repository and is a valuable resource for:

  • planning and optimising future ground investigations;
  • understanding the subsurface and urban geology;
  • for helping construct 2D and 3D models.

For ease of access, data and reports from the National Geotechnical Borehole Database are published on the Geotechnical Viewer, freely available to all.  The online Geotechnical Viewer displays ground investigations as digitised, georeferenced polygons, with an associated downloadable report in .pdf format. Several thousands of ground investigations projects are presented.

With time and technical and software advances, Geological Survey Ireland has produced urban 3D geological models using the National Geotechnical Borehole Database. A primary function of these models is visual communication of the subsurface to geoscientists, professionals from other disciplines, researchers, students and members of the public.

Our urban 3D models can assist with:

  • Resource (water and geothermal) mapping;
  • Understanding and characterising urban geology, with potential relevance for basement impact assessment, Sustainable Drainage Systems (SuDS), flooding and, subsurface management;
  • Optimising geotechnical investigation, design and construction;
  • De-risking human activities from impact of our subsurface environment;
  • Investigating impact of human activities on environment around and beneath us, e.g., dewatering;
  • and informing policy, planning, protective and climate adaptation measures.

3D geological models allow everyone to visualise the subsurface and can be used to communicate the geoscience behind policy, thereby making defensible decisions visible. To ensure the 3D models are easily accessible by all, Geological Survey Ireland have a 3D model viewer where no software or zip file downloads are needed. The 3D model viewer has Interactive and Augmented Reality functionality.

Recognising the importance of freely available, accessible data for non-geoscientists, Geological Survey Ireland has created and smoothed pathways for stakeholders to access and visualise geological data in urban settings.

How to cite: O'Connor, S. and Mozo Lopez, B.: From paper reports to 3D models for all – Irish geodata in urban settings, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18610, https://doi.org/10.5194/egusphere-egu24-18610, 2024.

EGU24-18749 | Posters on site | ITS1.5/NP8.6

Tackling practical challenges in anomaly detection for real-time monitoring of urban waste water networks 

Lennart Schmidt, Felix Weiske, Manfred Schütze, Phillip Grimm, Julius Polz, and Jan Bumberger

Waste water networks constitute a crucial element of urban infrastructure that are influenced by an observed increase in urban flooding events. To ensure regular network operation and minimal environmental impact, anomaly detection of urban waste water networks timeseries can serve as a real-time monitoring tool to detect a) sensor defects and b) system anomalies such as leaks or blockages. However, setting up such a monitoring system in practice can face significant challenges. These include limited amounts of labeled anomalies, heterogenous data quality, inconsistent measurement frequencies as well as instationarity of the system (sensor displacement and drop-out, changes in network layout). For the waste water network of a medium-sized German city, we set up machine learning based anomaly detection and present strategies to tackle aforementioned challenges. Our results show that autoencoder-based model architectures are valuable tools in such a context where only a minimal fraction (<0.01%) of the data is labeled. Both a well-parametrized interpolation strategy and a model architecture that is largely robust to missing values are essential prerequisites for adequate model performance. Based on our results, we derive general strategies to aid in setting up anomaly detection systems in real-world use cases.

How to cite: Schmidt, L., Weiske, F., Schütze, M., Grimm, P., Polz, J., and Bumberger, J.: Tackling practical challenges in anomaly detection for real-time monitoring of urban waste water networks, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18749, https://doi.org/10.5194/egusphere-egu24-18749, 2024.

The urban heat island effect is a well-documented phenomenon in cities, particularly in metropolitan areas, with recognized environmental consequences. Mitigating this effect through urban green space planting strategies has been widely acknowledged. However, the extent of the spatial heterogeneity of the cooling effect across different urban functional zones remains insufficiently explored at a fine scale of urban green space.

In this study, we employed a robust semi-supervised deep learning method to precisely segment urban green spaces from high-resolution remote sensing images and developed a 0.5 m fine-scale urban green space product tailored for the Beijing metropolitan area. Leveraging the fine-grained urban green space segmentation results, we modeled cooling efficiency through a nonlinear relationship, quantified as the temperature reduction for a 1% urban green space cover increase. We also conducted a comprehensive assessment of differential cooling efficacy, considering both reference temperature and urban green space cover levels, across diverse urban functional zones at the scale of 300 m × 300 m urban grids.

The results revealed substantial disparities in cooling efficiency among different urban functional zones and different levels of urban green space coverage in Beijing. To be specific, with a 1% increase in urban green space, the commercial zone, residential zone, industrial zone, transportation zone, and public zone can achieve a cooling effect with a mean of 0.095 ± 0.075°C, 0.075 ±0.065°C, 0.075±0.065°C, 0.070±0.060°C and 0.055±0.045°C respectively. By uncovering spatial variations and heterogeneity in cooling effects, our study underscores the critical need for customized strategies in urban green space planning based on functional zone characteristics and offers valuable insights into urban planning and sustainable development practices.

How to cite: Zeng, Y., Guo, J., and Zhu, X. X.: Differential Cooling Efficacy of Fine-Grained Urban Green Spaces Across Diverse Functional Zones: A Case Study in the Beijing Metropolitan Area, China, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18781, https://doi.org/10.5194/egusphere-egu24-18781, 2024.

EGU24-20304 | ECS | Orals | ITS1.5/NP8.6

Asssessing the impacts of extreme rainfall on urban transport: a complex systems approach 

Alistair Ford, Yimeng Liu, Richard Dawson, and Saini Yang

Extreme rainfall causes disruption and damage to urban transport networks through flooding, resulting in economic impacts for residents and businesses. The impact of such extreme weather events is the result of a complex interaction between the hazard (shaped by the nature of the rainfall and urban characteristics such as topography and land-use), exposure (the spatial and temporal intersection of the flood footprint with urban infrastructure and assets), and vulnerability (the ability of those assets and their users to cope with the level of flooding).

This paper demonstrates a complex systems approach to understand the role of these three components of the impact on urban transport systems by dynamically coupling a hydrodynamic flood model (such as CADDIES 2D or CityCAT) with an agent-based transport model (SUMO). By simulating a range of extreme rainfall events at a range of times of day, the modelling approach allows quantification of the scale of the impact (both direct and indirect) and assessment of adaptation options to reduce the disruption. Inclusion of coupled dynamic models allows the exploration of both hard, including engineered and nature-based approaches, and soft measures such as early warning and home working. This allows for a more-complete cost-benefit analysis of interventions and understanding of their effectiveness.

The modelling approach is demonstrated for a range of extreme rainfall events on commuting journeys on the road network in the city of Beijing, China. The results show that whilst grey and green approaches to adaptation can reduce the impact of extreme rainfall on the transport network, the benefits of soft measures, such as demand reduction by increased home working, are greater. Such soft measures also have additional co-benefits for reduction in emissions from transport, and potentially a lower implementation cost. Only by considering these interactions in a complex systems approach can such an assessment be undertaken.

 

How to cite: Ford, A., Liu, Y., Dawson, R., and Yang, S.: Asssessing the impacts of extreme rainfall on urban transport: a complex systems approach, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-20304, https://doi.org/10.5194/egusphere-egu24-20304, 2024.

EGU24-20495 | Posters on site | ITS1.5/NP8.6

Giasone: a method to assess sustainability of georesources cultivation 

Gabriele Leoni, Giovanni De Caterini, Marco D'Antona, Stefano De Corso, Claudia Delfini, Marco Di Leginio, Massimo Diaco, Giovanni Finocchiaro, Fiorenzo Fumanti, Luca Guerrieri, Mauro Lucarini, Ines Marinosci, Michele Munafo', Nicolo' Giovanni Tria, and Daniele Spizzichino

The concept of georesources, within the framework of the new environmental strategies of the European Union's (EU) Green Deal, has gained an expanded perspective, beyond the traditional approach linked to the mining industry. Georesources are defined as natural resources or elements of the landscape, physical space, and territory, to which economic, environmental, or social value is attributed. This definition encompasses raw materials, water resources, soil conservation, as well as intangible elements such as geoheritage, natural landscape, and ecosystem balance.

The concept of sustainability integrates with a technical principle that promotes the improvement of land conditions in natural, ecological, social, economic, and cultural terms. This perspective acknowledges that the European territory is the result of millennia of transformations by humans, with activities such as agriculture, land exploitation, and the use of natural resources that have altered environments.

The EU action plan aims to promote sustainability as a central element of economic growth, guiding capital flows towards a more sustainable economy. A priority is to define a classification of sustainability for georesources cultivation, based on technical-scientific and industrial standards, to which the sustainability of investments in the sector can be referred.

The Green Deal aims to address challenges related to climate change by promoting a new economy based on sustainable development, ecosystem protection, biodiversity conservation, and climate change mitigation. EU economic strategies are oriented towards assigning 'value' to environmental aspects, stimulating innovation and competitiveness in a dynamic market.

The concept of environmental value extends to various areas such as energy efficiency, renewable energy, sustainable agriculture, green mobility, and new technologies. This includes the creation of green jobs to ensure a fair transition to a new sustainable economy and reduced inequalities.

In the context of georesources, traditionally associated with the exploitation of non-renewable and renewable resources, an analytical approach is proposed to assess sustainability not only in the extractive field but also in the context of land planning within a broader geographic context.

For the quantitative assessment of the value of georesources in the policies outlined in the Green Deal, a parametric method based on the integrated analysis of the following themes is proposed: Geography, Hydrography, Environment, Sociology, Nature, and Economics to characterize the intrinsic value of georesources.

The use of GIS as a multidisciplinary analysis tool for integrating environmental and socio-economic data allows for a dynamic approach in identifying the intricate relationships of various themes, simplifying the representation of land status.

For each area identified through the comparison of indicators, a "georesource sustainability" index - the GIASONE index - is calculated by a weighted sum of the indices related to each theme. The use of the parametric method also allows for the comparison of different scenarios under varying environmental and socioeconomic conditions, useful for planning decisions.

How to cite: Leoni, G., De Caterini, G., D'Antona, M., De Corso, S., Delfini, C., Di Leginio, M., Diaco, M., Finocchiaro, G., Fumanti, F., Guerrieri, L., Lucarini, M., Marinosci, I., Munafo', M., Tria, N. G., and Spizzichino, D.: Giasone: a method to assess sustainability of georesources cultivation, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-20495, https://doi.org/10.5194/egusphere-egu24-20495, 2024.

Recently, we have seen an increase in models that combine powerful technical simulations with efficient visualizations and user interfaces that support decision-making in environmental and urban policies. These tools, known as Digital Twins (DTs) have been currently applied to water management and cities, however, their use tends to be limited to reduced groups of technical experts, policymakers and city officials, with the models behind these tools not being openly available, even though they may be publicly funded. Simultaneously developers, who may be interested in using these models to assess their proposals, cannot access them and must develop their local models, in many cases trying to catch up with new legislation.  A more efficient and open method could be implemented based on sharing evidence-based models through the planning application process. We call this an Integrated Water Planning Portal (IWPP), which consists of a web platform that gives developers access to a water systems model to test their proposals and use this work in the planning application process, which can be done through the same platform. In parallel to this, planners can use the portal to review this work, comment on it or give a final planning verdict. For such a system to work, robust data-sharing and model deployment protocols need to be implemented to strike a balance between accuracy, understandability and data protection. We present work on the feasibility of IWPP, based on prototype development and semi-structured interviews with stakeholders in the UK water management field. Evidence from this work suggests a targeted approach to modelling and data collection which is presented in a model framework. This approach satisfies the requirements of different stakeholders and provides a robust base for further development of tools such as IWPP.

How to cite: Rico Carranza, E.: Integrated Water Planning Portal: Feasibility study for a development-oriented digital twin to facilitate integrated water management through targeted data and model sharing., EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-20502, https://doi.org/10.5194/egusphere-egu24-20502, 2024.

EGU24-20648 | ECS | Orals | ITS1.5/NP8.6 | Highlight

Current status of the Urban Geo-climate Footprint project 

Azzurra Lentini, Jorge Pedro Galve, Moreno Beatriz Benjumea, Stephanie Bricker, Xavier Devleeschouwer, Paolo Maria Guarino, Timothy Kearsey, Gabriele Leoni, Romeo Saverio, Guri Venvik, and Francesco La Vigna

The Urban Geo-climate Footprint (UGF) project has been developed in the context of the Urban Geology Expert Group of Euro Geo Surveys, aimed to define a new methodology to classify and cluster cities by geological and climatic point of view.

The basic assumption of the UGF approach is that cities with similar geological-geographical settings should have similar challenges to manage, due to both common geological issues and climate change subsoil-related effects. Following this approach, a holistic tool consisting in a complex spreadsheet has been developed and applied to more than 40 European cities, in collaboration with several Geological Surveys of Europe.

It is demonstrated as the Urban Geo-climate Footprint tool is currently capable of providing a semi-quantitative quick representation of the pressures driven by geological and climatic complexity in the analysed cities, providing for the first time such classification for the urban environment.

Through the wide application of this methodology several benefits could be reached as the general awareness increase of non-experts and the enhanced reading-the-landscape capacity of decision makers about the link between geological setting and the increase in pressures due to climate change and anthropogenic activity.

Furthermore, the UGF approach would facilitate the possibility to exchange best practices among similar cities for planning purposes, and it would support the decision processes to define and differentiate policies and actions, also supporting policy and cooperative geoscience and climate justice.

 

How to cite: Lentini, A., Galve, J. P., Benjumea, M. B., Bricker, S., Devleeschouwer, X., Guarino, P. M., Kearsey, T., Leoni, G., Saverio, R., Venvik, G., and La Vigna, F.: Current status of the Urban Geo-climate Footprint project, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-20648, https://doi.org/10.5194/egusphere-egu24-20648, 2024.

EGU24-20734 | Orals | ITS1.5/NP8.6

UNDER: Geosystem services underneath for sustainable communities and improved spatial planning practices 

Fredrik Mossmark, Jenny Norrman, Paula Lindgren, Emrik Lundin Frisk, Lorena Melgaço, Marilu Melo Zurita, Victoria Svahn, Tore Söderqvist, Olof Taromi Sandström, and Yevheniya Volchko

Geosystem services (GS) can be defined as the contributions humans derive from the subsurface: the use of the subsurface to build and construct within and on top, groundwater, energy and material extraction, storing of e.g. water, energy and carbon dioxide, providing habitats for diverse species and support for surface life, and serving as an archive of cultural and geological heritage. Sectorial management and lack of consequent consideration of subsurface geosystem services and competing or complementary subsurface uses promote the first-come-first-served principle, potentially hindering a sustainable management of the subsurface and compromising inter- and intra-generational equity. The research project “UNDER: Geosystem services underneath for sustainable communities and improved spatial planning practices” has the overall goal to develop a framework for systematic and structured consideration of geosystem services in Swedish planning practices that can support a path towards sustainable cities and communities. The specific objectives of the UNDER project are to: i) advance the concept of GS by identifying and mapping associated societal values (social, environmental and economic), ii) identify methods to assess societal values and investigate possibilities for integration in existing tools, iii) identify structures of governance and develop a broader and practice-informed understanding of the different societal actors in subsurface planning, and iv) create a participative learning environment, extended beyond the project implementation period leading to transformative processes in planning practice. The project is case study driven and works in collaboration with Swedish municipalities. Four ongoing spatial planning processes in Swedish municipalities have been selected as case studies, which will provide a variety of spatial planning contexts and objectives. The project is a multi-disciplinary, international project with funding from the Swedish research council Formas, running during 2021 - 2025. The presentation of the project will focus on the project activities, preliminary results, and future work.

How to cite: Mossmark, F., Norrman, J., Lindgren, P., Lundin Frisk, E., Melgaço, L., Melo Zurita, M., Svahn, V., Söderqvist, T., Taromi Sandström, O., and Volchko, Y.: UNDER: Geosystem services underneath for sustainable communities and improved spatial planning practices, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-20734, https://doi.org/10.5194/egusphere-egu24-20734, 2024.

EGU24-22240 | Orals | ITS1.5/NP8.6

Urban geology as part of 3D city models - challenges and solutions 

Rouwen Lehne, Sonu Roy, Heiner Heggemann, and Christoph Schueth

While 3D city models are now available for many large and medium-sized cities and are increasingly being used, the urban subsurface (= urban geology) continues to be neglected in such models in most cases. The reasons for this are both inhomogeneous and complex geological/hydrogeological information, which at the same time is not assembled in a context-specific way, as well as a lack of standards, interfaces and exchange formats.

To overcome these barriers, geological and hydrogeological 2D and 3D content is currently being elaborated for several urban areas in the federal state of Hesse in close cooperation with the municipal cooperation partners using all available input data (in particular, however, boreholes, geological cross sections and groundwater level measurements), which are being assembled with a view to defined "urban geoparameters".

In addition, an attempt will be made to visualize the urban underground infrastructure (man-made objects) in 3D space and thus bring it into a synopsis with the geological and hydrogeological 2D and 3D content.

The synopsis, in turn, should be carried out in the respective working environments as far as possible, i.e. using the software solutions operated by the cooperation partners. To ensure this, both suitable interfaces and a suitable exchange format are required in the 3D data management systems for geological/hydrogeological models. The OGC API 3D GeoVolume and Styles interfaces and the 3D Tiles exchange format are considered to be the solution here.

With this presentation, we would like to present the current state of work with a focus on the parameterisation and packaging of geological and hydrogeological 2D and 3D data for urban areas.

How to cite: Lehne, R., Roy, S., Heggemann, H., and Schueth, C.: Urban geology as part of 3D city models - challenges and solutions, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-22240, https://doi.org/10.5194/egusphere-egu24-22240, 2024.

EGU24-1283 | Orals | ITS1.6/BG1.18

Present and future importance of protected areas as carbon sinks and storages in Finland 

Martin Forsius, Virpi Junttila, Heini Kujala, Mikko Savolahti, and Torsti Schulz

The EU aims at reaching carbon neutrality by 2050 and Finland by 2035. Net negative greenhouse gas emissions are needed to comply with the targets of the Paris climate agreement. We integrated results of three spatially distributed model systems (FRES, PREBAS, Zonation) to evaluate the potential to reach this goal at both national and regional scale in Finland, by simultaneously considering protection targets of the EU biodiversity strategy. Modelling of both anthropogenic emissions and forestry measures were carried out, and forested areas important for biodiversity protection were identified based on spatial prioritization. We used scenarios until 2050 based on mitigation measures of the national climate and energy strategy, forestry policies and predicted climate change, and evaluated how implementation of these scenarios would affect greenhouse gas fluxes, carbon storages, and the possibility to reach the carbon neutrality target. Potential new forested areas for biodiversity protection according to the EU 10% strict protection target provided a significant carbon storage (426-452 TgC) and sequestration potential (-12 to -17.5 TgCO2eq a-1) by 2050, indicating complementarity of emission mitigation and conservation measures. Assuming a price of ca. 80 € ton-1 CO2eq according to the current level of the EU emission trading system (EU ETS), the economic value of the carbon sequestration of the current protected areas in Finland would be about 500 million € per year. These areas thus provide ecosystem services of significant economic value. The results of our study can be utilized for integrating climate and biodiversity policies, accounting of ecosystem services for climate regulation, and delimitation of areas for conservation.

How to cite: Forsius, M., Junttila, V., Kujala, H., Savolahti, M., and Schulz, T.: Present and future importance of protected areas as carbon sinks and storages in Finland, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1283, https://doi.org/10.5194/egusphere-egu24-1283, 2024.

EGU24-3894 | ECS | Orals | ITS1.6/BG1.18 | Highlight

Impact of clouds on the forest albedo measured at the Leipzig Canopy Crane - A pilot study 

Kevin Wolf, Michael Schäfer, Sudhanshu Shekhar Jha, Alexandra Weigelt, Ronny Richter, Tom Kühne, André Ehrlich, Evelyn Jäkel, and Manfred Wendisch

Albedo, defined as the ratio between reflected radiation and total incoming radiation, is a key variable in the Earth radiative budget. In a fast changing climate with more frequent extreme events, such as droughts and excessive heat, vegetation is under constant stress. Such stress factors might modify the tree physiology, the reflectivity of individual leaves, and, eventually, the forest albedo as an entity. This might alter the local radiative budget and contribute to changes in the local climate, e.g., intensifying drought - a potential feedback loop. The understating of those effects might be further complicated by the occurrence of clouds. Therefore, this study presents spectral solar measurements of upward and downward irradiance that are used to determine the spectral albedo over a forest canopy. Since June 2021, ongoing measurements are performed on top of the Leipzig Canopy Crane located in the Leipzig floodplain forest. The measurements are separated for illumination geometries, i.e., the solar zenith angle, as well as for different cloud conditions. The interpretation of the measurements is aided and validated by coupled radiative transfer simulations using the library for radiative transfer model (libRadtran) and the Soil Canopy Observation of Photosynthesis and Energy fluxes (SCOPE2.0) model. Both models allow for simulations in the visible, near- and far-infrared wavelength range. By that, the impact of clouds on the spectral and broad band albedo, as well as the net radiative budget can be investigated. First simulations revealed that the presence of clouds enhance the spectral forest albedo. The magnitude of the effect is controlled by the cloud optical thickness, i.e., the ratio of direct and diffuse radiation. The enhancement is more pronounced for small solar zenith angles. However, the effect from clouds appears to be smaller than influences of variations in the surface properties. The presentation aims to outline the measurement set-up and strategy, and to discuss preliminary results. Furthermore, the new, iterative coupling of the atmosphere and soil-vegetation model is presented, which aims to improve the understating of cloud-vegetation radiation interactions.

How to cite: Wolf, K., Schäfer, M., Shekhar Jha, S., Weigelt, A., Richter, R., Kühne, T., Ehrlich, A., Jäkel, E., and Wendisch, M.: Impact of clouds on the forest albedo measured at the Leipzig Canopy Crane - A pilot study, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-3894, https://doi.org/10.5194/egusphere-egu24-3894, 2024.

EGU24-3958 | Posters on site | ITS1.6/BG1.18

Modelling the effects of forest use change on brownification of Finnish rivers under pressures of acidification and climate change 

Katri Rankinen, José Enrique Cano Bernal, Maria Holmberg, Magnus Nordling, Torsti Schulz, Annikki Mäkelä, Ninni Mikkonen, Heini Kujala, Leah Jackson-Blake, Heleen De Wit, and Martin Forsius

Browning of surface waters due to increased terrestrial loading of dissolved organic matter is observed across the Northern Hemisphere. Brownification directly influences freshwater productivity and ecosystem services like water purification. Brownification often is explained by changes in large-scale anthropogenic pressures and ecosystem functioning, including acidification and climate change. Land use or cover changes and forestry measures have recently been observed to be one reason for the increase in brownification. Climate change influences brownification by increasing temperatures and thus stimulating the decay of dissolved organic carbon in soils, and by changing the timing and intensity of precipitation and snowmelt. A decrease in sulphur deposition is assumed to increase soil organic matter solubility. In Finland, productive forests cover about 66% of the land area. This study aimed to examine the effect of forest use changes on water browning in Finland under pressure of acidification and climate change. EU land use policies (Biodiversity Strategy, LULUCF Policy) influence land use but also forestry practices. Finland is committed to the EU's goal of protecting 30% of land and sea areas, and 10% of them strictly. The LULUCF regulation agrees how carbon sinks and greenhouse gas emissions from the land use sector are considered in the EU's climate goals until 2030. Finland aims to keep forests as carbon sinks. When studying the environmental effects of land use/cover changes due to these policies, environmental influence on biodiversity, and ecosystem services (sustainability of forestry, and water quality) should be simultaneously considered. We modelled organic carbon loading from river basins under changes in global pressures (climate and deposition) by mathematical models. We combined the watershed scale model (Simply-C) with scenarios of climate change, atmospheric deposition, and forest use change (1985-2060). We used daily data from five global climate models (CMIP5) under representative concentration pathway (RCP) scenarios RCP4.5 and RCP8.5. For atmospheric sulphur deposition, we used the chemical transport model results that are based on the EMEP MSC-W model (v4.4) and the MATCH model results. We explored two forest use scenarios that focus on potential changes taking place in the forested areas in Finland: 1) forest management, and 2) forest protection. The forest management scenario was based on simulations of clear-cut following Finnish national recommendations with the PREBAS forest growth and carbon balance model. Forest protection scenarios were based on spatial data of forests with high conservation value, optimized by Zonation programme. Modelling results indicated that global influence (atmospheric deposition, climate change) seemed to weaken in southern Finland after 2016. That gave more space for the effect of local forest use change due to different EU land use policies. Forest use change was more influential in river basins dominated by organic soils than in mineral soils. In northern Finland brownification seemed to continue, mainly driven by climate change.

How to cite: Rankinen, K., Cano Bernal, J. E., Holmberg, M., Nordling, M., Schulz, T., Mäkelä, A., Mikkonen, N., Kujala, H., Jackson-Blake, L., De Wit, H., and Forsius, M.: Modelling the effects of forest use change on brownification of Finnish rivers under pressures of acidification and climate change, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-3958, https://doi.org/10.5194/egusphere-egu24-3958, 2024.

The loss of biodiversity from human activities on land is a widely-recognized, worldwide problem. Since the advent of the industrial revolution the loss of plant and animal species has increased dramatically, with 25% of species now at risk of extinction. Conventions and targets to protect biodiversity have been implemented, but with limited success. The Aichi targets for 2020, for example, were almost all missed, with worsening trends for 12 out of the 20 targets. One reason for this failure is the ineffective application of broad-scale measures that are not tailored to the underlying causes of biodiversity loss. Knowledge on the spatial and temporal distribution of anthropogenic drivers of biodiversity loss would therefore enable targeted interventions that address location-specific stressors and thus would be better-adapted measures to protect biodiversity.

The Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES) has identified five main drivers of anthropogenic origin as the causes of biodiversity loss: land use, natural resource extraction, climate change, pollution, and invasive alien species. However, when seeking to quantify impacts on biodiversity, these drivers are still usually treated separately. We develop a Biodiversity Pressure Index (BPI) by quantifying and mapping data for nine indicators of the five drivers into a single, annually changing index with a spatial resolution of 0.1° at global scale covering the period 1990-2020.

We find that large areas (approximately 86%, including Antarctica, Greenland) are under major human pressure and that almost all areas have experienced an increase (about 96% of land) in pressure over the past thirty years. Industrialised regions had high pressure levels already in 1990 and continue to do so in 2020, whereas regions with rapid economic growth setting in after 2000 where low in pressure in 1990, but show high pressure levels today. Whilst areas impacted by human activities are increasing, areas of wilderness are decreasing to a point that in 2020, only 0.02% of the terrestrial land are entirely free from human influence. (Sub-) tropical wetlands and temperate grasslands are the biomes with the highest pressures today. And whilst land use is still one of the main factors, climate change - especially increasing temperature - is one of the major recent and future threats to biodiversity.

How to cite: Ramm, K., Brown, C., Arneth, A., and Rounsevell, M.: Human pressure on global land ecosystems and biodiversity increases notably from 1990-2020 - Development of a spatially explicit Biodiversity Pressure Index (BPI), EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5708, https://doi.org/10.5194/egusphere-egu24-5708, 2024.

EGU24-7473 | ECS | Posters on site | ITS1.6/BG1.18

Developing Restoration Strategies for Dynamic Population Changes of Plant-Pollinator Networks in a Warming Climate 

Adrija Datta, Sarth Dubey, and Udit Bhatia

Ensuring robust pollination service is vital for sustainable food production, as three-quarters of crops require insect pollinators to reproduce, but many insect populations are rapidly declining.  Yet, it is widely reported that insect pollinators face increased extinction risk due to habitat loss and warming climate. The biological impact of global mean temperature projections on individual terrestrial ectotherms is often predicted to increase with the rate of warming. However, it also depends on the interdependence of the plant-pollinator network and the physiological sensitivity of ectotherms to temperature change over time. Here, we have used sampled plant-pollinator network data from different climatic zones and the Earth system model projected temperature data of different future projection scenarios. In this study, we present a mathematical framework for modeling species population dynamics using the Lotka-Volterra model, where parameters are integrated from empirical fitness curves of terrestrial insects at different latitudes. This approach also investigates how species abundance evolves in the twenty-first century with and without species management, focusing on maintaining a constant abundance of generalist species to avert sudden ecosystem collapses over declining environmental health. The results show that tropical networks are more sensitive in abundance and extinction to future temperature increase as they live very close to their optimal temperature. In contrast, species of temperate regions have broader thermal tolerance, so the warming may increase their abundance. This study offers insights into how different future temperature projections influence species management, thereby restoring the functional integrity of the entire ecosystem. Also, this study provides region-specific restoration guidelines, offers insights for agro-advisory services, informs sustainable cropping patterns, and optimizes resource allocation. 

How to cite: Datta, A., Dubey, S., and Bhatia, U.: Developing Restoration Strategies for Dynamic Population Changes of Plant-Pollinator Networks in a Warming Climate, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-7473, https://doi.org/10.5194/egusphere-egu24-7473, 2024.

EGU24-10432 | Orals | ITS1.6/BG1.18 | Highlight

Plant diversity-climate interactions from a modeling perspective 

Pin-hsin Hu, Christian H. Reick, Axel Kleidon, and Martin Claussen

Mounting evidence from field observations has shown that high functional diversity is associated with strong ecosystem resilience and stability. However, plant ecology studies have focused on the passive response of global ecosystems to climatic changes while the impacts of plant-functional diversity on climate including its feedback are seldom addressed. Moreover, state-of-the-art climate models are insufficient to address such topics. Their land component models cover only a restricted range of present-day plant features, so that adaptation at the sub-grid scale is ignored. Based on a process-based plant functional trade-off scheme developed by Kleidon and Mooney (2000), we have set up a new vegetation model JeDi-BACH into the land component of the ICON-Earth System Model (ICON-ESM). The advantage of this new model is that the representation of global vegetation is an emergent outcome of environmental filtering following several well-known fundamental functional trade-offs that link plant functions to abiotic and biotic attributes. In such a way, plants dynamically adjust to the changing environment and meanwhile modify climate. With this new model, we present a series of sensitivity studies investigating the effect of plant trait diversity on the coupled vegetation-climate system in a coupled land-atmosphere setup. We found that high plant diversity ecosystems tend to stabilize terrestrial climate in a high water-turnover state, leading to a wet and cool climate. The enhancement in evapotranspiration with increasing diversity found in our study is consistent with the BEF (Biodiversity-Ecosystem Functioning) relationship derived from the field studies. Our modeling results demonstrate the importance of the "biodiversity-climate feedback" and highlight the role of plant functional diversity in shaping a robust climate.

Kleidon, A. and Mooney, H. A.: A global distribution of biodiversity inferred from climatic constraints: Results from a process-based modelling study, Glob. Chang. Biol., 6(5), 507–523, doi:10.1046/j.1365-2486.2000.00332.x, 2000.

 

How to cite: Hu, P., Reick, C. H., Kleidon, A., and Claussen, M.: Plant diversity-climate interactions from a modeling perspective, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-10432, https://doi.org/10.5194/egusphere-egu24-10432, 2024.

EGU24-10869 | Orals | ITS1.6/BG1.18

A vertical RothC model for simulating the Soil Organic Carbon  dynamics in coastal wetland environments 

Carmela Marangi, Vsevolod Bohaienko, Fasma Diele, Angela Martiradonna, and Antonello Provenzale

The significance of considering vertical layers in studying soil organic carbon (SOC) dynamics within wetlands arises from the interplay of hydrological and ecological factors across various soil depths, where anaerobic conditions prevail in the deeper layers. This anaerobic environment significantly influences microbial processes, leading to methane production rather than carbon dioxide. Factors such as the accumulation of organic material, temperature gradients, and fluctuations in the water table contribute to diverse SOC dynamics across different vertical strata. Understanding these variations in vertical layers is crucial for accurate assessments of carbon stocks, greenhouse gas emissions, and the overall role of wetlands in the global carbon cycle. Such understanding is essential for devising effective conservation and management strategies, particularly in the face of climate change and land-use modifications impacting wetlands.  To model these dynamics, a vertical extension of the Rothamsted Carbon (RothC) model can be successfully employed in conjunction with the Richardson equation. This combined approach simulates the influence of soil moisture flux on the transport of carbon throughout the soil column. The specific scenario examined is focused on the growth of rice in the Ebro Delta lands and on the carbon flux emissions in the Ria de Aveiro Coastal lagoon, both sites being part of the Long-Term Ecological Research (LTER) network and the eLTER RI community.  This work contributes to the research activities carried out by the authors within the projects H2020 eLTER PLUS, HE RESTORE4Cs, and PNRR - “National Biodiversity Future Centre”, funded by the European Union – NextGenerationEU.

 

References

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F. Diele, C. Marangi, A. Martiradonna, "Non-Standard Discrete RothC Models for Soil Carbon Dynamics." Axioms 10.2 (2021): 56.  

F. Diele, I. Luiso, C. Marangi, A. Martiradonna, E. Wozniakk, "Evaluating the impact of increasing temperatures on changes in soil organic carbon stocks: sensitivity analysis and non-standard discrete approximation", Computational Geosciences 26 (2022) 1345–1366.

 J. Smith, P. Gottschalk, J. Bellarby, M. Richards, D. Nayak, K. Coleman, J. Hillier, H. Flynn, M. Wattenbach, M. Aitkenhead, et al., "Model to estimate carbon in organic soils–sequestration and emissions (ecosse)", Carbon 44 (2010) 1–73.

Y. Zhang, C. Li, C. C. Trettin, H. Li, G. Sun, "An integrated model of soil, hydrology, and vegetation for carbon dynamics in wetland ecosystems", Global biogeochemical cycles 16 (2002) 9–1.

 

How to cite: Marangi, C., Bohaienko, V., Diele, F., Martiradonna, A., and Provenzale, A.: A vertical RothC model for simulating the Soil Organic Carbon  dynamics in coastal wetland environments, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-10869, https://doi.org/10.5194/egusphere-egu24-10869, 2024.

EGU24-12455 | ECS | Orals | ITS1.6/BG1.18

Identification of socio-economic variables to implement advanced artificial intelligence models to manage climate change risk 

David Jesús Felibert Álvarez, Manuel Enrique Guineme Baracaldo, Jhon Alexander Triana Forero, Johanna Karina Solano Meza, and Javier Rodrigo-Ilarri

To develop climate change mitigation strategies, it is necessary to identify variables that facilitate the modeling of prospective scenarios. There are a large number of variables that must be analyzed in an integrated manner in order for scenarios to be proposed that include the particularities of a given area, measuring the possible effects of this phenomenon in terms of productivity. Identifying and analyzing variables and their variations over time enables fundamental predictions to understand the potential environmental impacts on ecosystems and human activity. Understanding these variables is important to support decision-making, policy development and implementing actions that help reduce greenhouse gas emissions and guarantee food security. This research study not only seeks to determine the technical variables, which are fundamental in predictive models, but also sets out to emphasize the importance of integrating social and economic aspects that can become decisive factors.

Rural areas in Colombia, with the department of Cundinamarca used as a case study, have been affected in various ways by climate change [1]. This scenario represents a challenge that needs to be addressed in a prioritized manner to ensure food security and independence, economic development, sustainability, livestock and human health, among other aspects that precisely relate to the development of a region. To propose solutions, artificial intelligence (AI) is emerging as an innovative alternative that makes it possible to process large amounts of data and find patterns, correlations and trends that can provide an understanding of the variables’ behavior, as well as develop systems to adapt to climate change. Therefore, identifying variables to apply advanced AI models to forecast the effects of climate change in a given region is a fundamental step towards generating an efficient and accurate tool to establish mitigation actions in a region that, together with the implementation of policies and actions that promote sustainability, will strengthen communities’ current capacity for action.

The variables identified include economic structure, access to technological resources, governance models, education levels, access to public services, poverty rate, demographics and crop price references. Through AI models and an in-depth analysis of available information, these types of models will become more precise for the implementation of early warning systems (EWS) and sustainable practices, as well as strengthen infrastructure. Historically in Colombia, rural areas are the most vulnerable to climate change given that they have fewer economic and technological resources that enable them to adapt to its impacts, with the most frequent phenomena being torrential rainfall, extreme flooding and forest fires; events associated with climate change.

  • Peña Q, Andrés J, Arce B, Blanca A, Boshell V, J. Francisco, Paternina Q, María J, Ayarza M, Miguel A, & Rojas B, Edwin O. (2011). Trend analysis to determine hazards related to climate change in the Andean agricultural areas of Cundinamarca and Boyacá. Agronomía Colombiana, 29(2), 467-478. Retrieved January 09, 2024, from http://www.scielo.org.co/scielo.php?script=sci_arttext&pid=S0120-99652011000200014&lng=en&tlng=en.

How to cite: Felibert Álvarez, D. J., Guineme Baracaldo, M. E., Triana Forero, J. A., Solano Meza, J. K., and Rodrigo-Ilarri, J.: Identification of socio-economic variables to implement advanced artificial intelligence models to manage climate change risk, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12455, https://doi.org/10.5194/egusphere-egu24-12455, 2024.

In the temperate region, inter-annual variation of air temperature affects leaf phenology, i.e., timings of leaf emergence and growth in spring and defoliation in autumn. These changes have significant impacts not only on the canopy of dominant trees of forest ecosystems, but also on the seasonal light environment within the forest understory which further influences the growth and survival of tree seedlings, shrubs, and herbaceous species. Consequently, global warming is expected to influence biodiversity by altering species-specific growth responses to the environmental shifts, affecting primary production and hence the progress of vegetation succession. Therefore, in order to comprehensively monitor and assess the state and changes in forest ecosystems across wide geographical and decadal scales, it is important to observe leaf phenology at both the species and ecosystem scales, which is considered one of Essential Biodiversity Variables (EBVs).

The objective of this study is to investigate the decadal-scale change of the leaf phenology in deciduous forest in Japan. We examined 20-year changes of the dates of leaf emergence, leaf area index (LAI) reached its maximum, and defoliation by using in-situ and satellite data. The in-situ remote sensing has been conducted by a spectroradiometer and automated digital cameras on a canopy tower since 2003 at a deciduous forest in Takayama site, located in the cool-temperate region in the central Japan. The system is part of the Phenological Eyes Network (PEN). We estimated the dates of leaf emergence, maximum LAI, and defoliation based on the seasonal pattern on the Green-Red Vegetation Index (GRVI). These dates exhibit notable inter-annual variations, and notably, the date of maximum LAI occurrence tended to shift earlier over the 20-years period from 2004 to 2023. Those inter-annual variations in the leaf phenology were strongly related to the air temperature. Based on the knowledge gained at the Takayama site, we then examined the spatial distribution and annual changes of phenology of the deciduous forests in Honshu Island with satellite-GRVI. We will discuss the spatial and temporal changes in phenology along the environmental gradient and rising air temperature due to global warming, and evaluate the sensitivity or tolerance of these forests by focusing on species composition and geographical characteristics.

The authors thank PEN for sharing the data of spectral reflectance and canopy images.

How to cite: Noda, H., Takeuchi, Y., and Muraoka, H.: Assessing the 20-Year Changes in Leaf Phenology of Temperate Deciduous Forests in Japan Using in-situ and Satellite-GRVI, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15339, https://doi.org/10.5194/egusphere-egu24-15339, 2024.

Forest carbon sequestration is a key part of the European transition to carbon neutrality. Quantification of forest carbon sequestration rates relies on relies on successful integration of high volumes of remote sensing and in-situ data arriving at ever increasing velocities with a bewildering variety of “long tail” and legacy data. Research Infrastructures (RIs) can add value to these data by supporting their harmonised, cross-site collection, curation and publication and by providing a platform for assessing data veracity. Integration of RI networks through site co-location and standardised observation methods has been proposed as one way of dealing with the Big Data needed to quantify societally relevant environmental processes including those related to the carbon cycle. However, the full potential of RI network integration as a tool to improve environmental understanding has yet to be realised.

Here, we review current successes, identify challenges to better integration, and suggest ways forward. We provide recommendations for scientists, site managers and policy makers that will support the transition to a Big Data approach to quantifying and communicating forest carbon sequestration using the Swedish situation as an example.

How to cite: Futter, M., Högbom, L., Moldan, F., Peacock, M., and Villwock, H.: Challenges and Opportunities for Research Infrastructure Co-location to Improve Understanding of Terrestrial Carbon Cycling in Northern European Forests, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15707, https://doi.org/10.5194/egusphere-egu24-15707, 2024.

EGU24-16679 | ECS | Posters on site | ITS1.6/BG1.18

Biodiversity changes atmospheric chemistry through plant volatiles and particles 

Anvar Sanaei, Hartmut Herrmann, Loreen Alshaabi, Jan Beck, Olga Ferlian, Khanneh Wadinga Fomba, Sylvia Haferkorn, Manuela van Pinxteren, Johannes Quaas, Julius Quosh, René Rabe, Christian Wirth, Nico Eisenhauer, and Alexandra Weigelt

Given the significant human-induced changes in biodiversity and climate, the link between atmospheric and biological measurements is crucial to improve our understanding of atmosphere-biosphere feedbacks. Changes in climate and biodiversity influence the emission of biogenic volatile organic compounds (BVOCs) from plants, leading to the formation of biogenic secondary organic aerosols (BSOA). These BSOA can have diverse effects, including influencing Earth's radiative balance and impacting cloud and precipitation formation. However, at present, it is unclear how changing biodiversity will lead to changes in BVOC emissions, BSOA and their corresponding effects. We present a conceptual framework of the relationships between biodiversity and BVOC emissions based on our current mechanistic understanding and combining knowledge from the fields of biology and atmospheric chemistry. In this framework, first, we hypothesized that mixed forests enable resource partitioning, often leading to higher stand productivity and leaf area index, thus emitting higher amounts of BVOC. Second, given the significant difference in biotic and abiotic stress in monoculture and mixture plots, we hypothesized that increasing tree diversity would decrease BVOC emissions. We tested the effect of tree diversity on BVOC emission and BSOA formation in this framework by varying tree species richness, including monocultures, two- and four-species mixtures at the MyDiv experimental site in Germany. We quantified nine different BVOCs from the investigated plots, i.e., α-pinene, camphene, β-pinene, 3-carene, p-cymene, limonene, α-terpinene, isophorone, and acetophenone. The relative differences in tree monocultures and mixtures show that the overall concentration of BVOC decreases with increasing biodiversity. For BSOA, a total of fifteen BSOA compounds have been quantified, including diaterpenylic acid acetate [DTAA], 3-methyl-1,2,3-butanetricarboxylic acid [MBTCA], norpinonic acid, pinonic acid, terebic acid, terpenylic acid, pinic acid, adipic acid, pimelic acid, azelaic acid, suberic acid, succinic acid, glutaric acid, salicylic acid, and sebacic acid. The relative differences in tree monocultures and mixtures for BSOA showed mixed and overall non-significant results. A deeper understanding of how changing biodiversity influences biogenic organic compound emissions and biogenic secondary organic aerosol formation requires in-depth investigations of microclimate conditions, accurate monitoring of above- and below-ground biotic and abiotic stress, and manipulating stress conditions across long-term biodiversity experiments. Our findings highlight the need for multidisciplinary work at the interface between the biosphere and the atmosphere to better understand the reciprocal effects of biodiversity and climate change.

How to cite: Sanaei, A., Herrmann, H., Alshaabi, L., Beck, J., Ferlian, O., Fomba, K. W., Haferkorn, S., van Pinxteren, M., Quaas, J., Quosh, J., Rabe, R., Wirth, C., Eisenhauer, N., and Weigelt, A.: Biodiversity changes atmospheric chemistry through plant volatiles and particles, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-16679, https://doi.org/10.5194/egusphere-egu24-16679, 2024.

EGU24-16765 | Orals | ITS1.6/BG1.18

A comprehensive tool for prioritising ecologically sensitive locations and driving nature-positive actions 

Thea Piovano, Rhosanna Jenkins, Lorna Burnell, Claire Burke, and Beccy Wilebore

There exists an urgent need to address the ongoing nature crisis, and businesses must play a pivotal role in fostering positive change. As a result, there has been a significant increase in corporate attention on biodiversity. In response to this attention, several frameworks for companies to report their impacts on nature have emerged, including the EU’s Corporate Sustainability Reporting Directive (CSRD) and the Taskforce on Nature-related Financial Disclosures (TNFD). These frameworks set out steps for companies wanting to make a positive impact and include nature in business, particularly through determining their proximity to ecologically sensitive locations.

Our advanced prioritisation tool enables screening of any site in the world (both terrestrial and marine assets) for its proximity to ecologically sensitive locations. This tool incorporates metrics including Ecological Integrity, Decline in Ecological Integrity, Areas of High Physical Water Stress, Areas of High Potential Ecosystem Services and Biodiversity Sensitive Areas. Our tool aligns with best practices and with reporting guidance and standards (TNFD and CSRD).

By leveraging our screening tool, businesses can turn data-driven insights into responsible nature-positive actions.

How to cite: Piovano, T., Jenkins, R., Burnell, L., Burke, C., and Wilebore, B.: A comprehensive tool for prioritising ecologically sensitive locations and driving nature-positive actions, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-16765, https://doi.org/10.5194/egusphere-egu24-16765, 2024.

EGU24-18370 | ECS | Orals | ITS1.6/BG1.18 | Highlight

Drone4Tree: A cloud-based geospatial platform for large-scale UAV data processing and tree canopy detection 

Sharad Kumar Gupta, Franz Schulze, Ralf Gründling, and Ulf Mallast

Forests cover approximately 31% of the global land area and are home to 80% of the Earth's terrestrial biodiversity. Humans depend on forests for countless ecosystem services, but these ecosystems are highly vulnerable to human-induced climate change. As our climate undergoes dynamic changes, it is imperative to implement automated monitoring systems to quantify canopy growth and assess changes occurring within forest structures, especially at the level of individual trees, to determine the response of forests to climate anomalies. In this context, tree canopy detection can be considered one of the most important applications using Unmanned Aerial Vehicles (UAVs) as it can be used to obtain information on numerous essential ecosystem variables (EEVs) such as gross primary productivity, leaf area index, etc. for individual trees or shed light on essential biodiversity variables (EBVs) such as ecosystem structure and function. However, due to the plethora of information available, users may find it challenging to apply UAVs and algorithms to their specific projects. Hence, an integrated, seamless platform that can process UAV-acquired images to generate ortho-mosaics, detect individual trees, and monitor specific traits (including ecosystem structure and function) is the need of the hour.

In this study, a platform, Drone4Tree, has been developed using Streamlit and Flask to provide an end-to-end solution for generating orthomosaics and delineating individual tree crowns from UAV images. Users simply upload raw UAV survey data and receive the final results. The complete processing chain is carried out on our high-end servers, which is an advantage for users with limited computing resources. The developed web application uses open-source algorithms, models, and frameworks for easy implementation of components such as orthomosaic (structure from motion in OpenDroneMap), tree canopy detection (DeepForest and U-Net segmentation), and downloading of results. The platform offers two processing modes: standard and advanced. The standard mode comes with default parameters for orthomosaic generation and tree canopy detection, benefiting users with no experience in UAV image processing. The advanced mode allows users to customize the processes, such as the scale of the generated canopy boundary or patch size for large images. It also extends its functionality towards analysis-ready drone image time series (incl. a co-registration of orthomosaics to a reference image using the AROSICS method and reprojection using the geospatial data abstraction library (GDAL)). Finally, the processing outcomes can be easily downloaded using the generated links. 

The web app was used to generate a time series of individual tree canopies, which provided a deeper understanding of changes in EEVs during a phenological cycle. The canopy boundaries can also be used to generate spectral libraries for tree species from high spatial resolution hyperspectral images, which has several applications in species detection and mapping. This platform can guide other users wishing to efficiently produce individual tree canopy boundaries for large areas without investing substantial time tailoring imagery acquisition and processing parameters. The resulting tree canopy boundaries can provide opportunities to characterize individual trees' species, size, condition, and location and are critical resources for advancing ecological theory and informing forest management.

How to cite: Gupta, S. K., Schulze, F., Gründling, R., and Mallast, U.: Drone4Tree: A cloud-based geospatial platform for large-scale UAV data processing and tree canopy detection, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18370, https://doi.org/10.5194/egusphere-egu24-18370, 2024.

EGU24-19043 | ECS | Orals | ITS1.6/BG1.18 | Highlight

Exploring climate-biodiversity interactions in observational data and models 

Petra Sieber, Jonas Schwaab, Dirk Karger, and Sonia Seneviratne and the FeedBaCks consortium

Climate change and biodiversity loss are increasingly considered jointly, particularly to find optimal solutions for both crises and to avoid negative side-effects and feedbacks. Much research has been devoted to predicting the effects of climatic changes on the distribution of species, but the consequences of biodiversity changes for the climate system are less understood. For instance, what are the main aspects (species richness, functional diversity, land cover patchiness) and mechanisms through which biodiversity interacts with the climate? Do landscapes with different levels of diversity contribute differently to climate regulation or feedbacks? How do human choices such as nature conservation or natural resources production affect the climate? To address these questions, we combine observational and modelling approaches in a collaborative effort of ecologists and climate scientists.

First, we present how ecosystem diversity affects forests’ climate response (indicated by interannual variability in summer NDVI) and climate effect (indicated by interannual variability in summer LST), using 20 years (2003-2022) of remote sensing data at 1 km resolution over Europe. We consider different diversity levels (taxonomic, functional, structural) together with various ecosystem, topography, soil, and climate predictors in a multiple linear regression with Ridge regularisation. This approach allows isolating the effects of specific biodiversity aspects (e.g. tree species richness, forest edge density), functional properties (e.g. leaf type, leaf traits), and structure (e.g. canopy height, tree cover density), and determining the sign and magnitude of their contribution. We show which aspects and scales of biodiversity are relevant for ecosystem stability and climate regulation, respectively, and classify forests into response and effect types that could be considered in coupled biosphere-atmosphere models.

Second, we discuss how biodiversity aspects can be integrated into the coupled biosphere-atmosphere regional climate model COSMO-CLM2 to quantify their effects on land-atmosphere interactions and feedbacks over Europe. We demonstrate one approach, utilising future land cover scenarios derived from the Nature Futures Framework that represent different value perspectives on nature (intrinsic, instrumental, and relational), habitat types from EUNIS (European Nature Information System), and species abundances from EVA (European Vegetation Archive). Our results show temperature differences of up to several degrees locally, with enhanced temperature sensitivities under hot and dry conditions. Such findings can help identify synergies between biodiversity conservation, climate change mitigation, and adaptation, and support the development of effective policy solutions.

Finally, this presentation will provide perspectives for research at the interface of biodiversity and climate change.

How to cite: Sieber, P., Schwaab, J., Karger, D., and Seneviratne, S. and the FeedBaCks consortium: Exploring climate-biodiversity interactions in observational data and models, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-19043, https://doi.org/10.5194/egusphere-egu24-19043, 2024.

EGU24-19803 | ECS | Posters on site | ITS1.6/BG1.18

Exploring the carbon dynamics and epiphytic lichen diversity of boreal old-growth forests  

Anu Akujärvi, Aleksi Nirhamo, Risto K. Heikkinen, Juha Pykälä, Otto Saikkonen, Timothy Green, Mikko Peltoniemi, and Annikki Mäkelä

The loss of pristine old-growth boreal forest landscapes due to the intensive management for timber production has caused both a severe decline of forest biodiversity in Northern Europe as well as significantly altered their carbon stocks and dynamics. Understanding of the dynamics of old-growth forests is needed to evaluate the consequences of different forest management and conservation strategies on climate change mitigation and biodiversity conservation. It is increasingly suggested that integrated forest management and conservation planning is required to secure both biodiversity and carbon storage values. However, it is insufficiently known how closely these values coincide at the local level, i.e., whether the same structural and quality features in old-growth forests support both high biodiversity and carbon stock.

The objectives of this study were, first, to explore the dynamics of stand growth and carbon sequestration in boreal old-growth forests and second, to investigate whether the occurrence of red-listed epiphytic forest lichens coincides with high carbon stock and structural features related to it. The study was based on an extensive repeated forest inventory dataset collected between 1990 and 2019 in southern Finland and a lichen inventory conducted during 2020 – 2021 at the same sites.

The estimated volume of standing trees and deadwood were higher in the studied forest stands than in managed forests on average. Estimates of net primary production showed varying trends of carbon sequestration among the study plots. Stand gross growth increased by 50% during the study period. The standing volume remained stable because a large proportion of the biomass increment was allocated to deadwood. The study sites showed a high occurrence of red-listed epiphytic lichens. No relationship was found between the species richness of red-listed lichens and the aboveground carbon stock. However, a significant negative relationship was found between the number of red-listed lichen occurrences and carbon stock.  The species richness of red-listed lichens showed a strong unimodal response to the aboveground carbon stock change: the highest species richness was associated with intermediate carbon sinks.

Our results highlight the major role of tree mortality driving the carbon dynamics of old-growth forests, with simultaneous benefits for deadwood-associated species. However, more research is needed on the stability of carbon stocks of forests in the face of shifting disturbance regimes due to climate change. While the species richness of red-listed epiphytic lichens had a neutral relationship with the aboveground carbon stock size, we observed fewer occurrences in carbon-rich forests, and lower species richness and occurrences in plots with large carbon sinks. Therefore, if climate benefits are sought with methods that increase stand density, negative impacts may be expected on lichen species that fare poorly in dense stands with low light. Additionally, high carbon sequestration in fast-growing stands may come at the expense of reduced biodiversity.

In summary, this study supports the idea that old-growth forests provide considerable benefits regarding both climate change mitigation and biodiversity. Therefore, increasing the area of old-growth forests would simultaneously support these key goals.

How to cite: Akujärvi, A., Nirhamo, A., Heikkinen, R. K., Pykälä, J., Saikkonen, O., Green, T., Peltoniemi, M., and Mäkelä, A.: Exploring the carbon dynamics and epiphytic lichen diversity of boreal old-growth forests , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-19803, https://doi.org/10.5194/egusphere-egu24-19803, 2024.

EGU24-20282 | ECS | Posters on site | ITS1.6/BG1.18 | Highlight

The Leipzig Canopy Crane experiment: DNA metabarcoding of air samples to monitor seasonal variations in airborne fungal and plant communities composition 

Ettore Fedele, Birgit Gemeinholzer, Ronny Richter, Christian Wirth, and Beatriz Sánchez-Parra

Rapid and accurate assessments of ecological responses to environmental changes are key to the development of effective measures aimed at the mitigation of detrimental effects on the integrity of ecosystems and the provision of services that support the livelihoods of billions of people worldwide. Traditionally, however, the study of ecological communities has relied on laborious and complex taxonomic work, that undermines the feasibility and practicality of urgent monitoring programmes.

In the last two decades, the emerging field of environmental DNA analysis has opened to the possibility to study complex systems at a fraction of the original time and financial costs, hence producing vast amounts of vital information. Here, we utilised DNA metabarcoding analysis of bioaerosol samples collected during 2019 at the Leipzig Canopy Crane to study seasonal variations in airborne fungal and plant species composition, in relation to changes in humidity, wind, and temperature. Preliminary results show significant differences in both plant and fungal communities. Specifically, climatic differences between the coldest and warmest months significantly affect the taxa Ascomycota and Basidiomycota, whereas the period between March and April reportedly displayed an increase in the abundance of anemophilous plants and members of the genus Salix. Lastly, with this study we intend to showcase the importance of long-term monitoring programmes of environmental DNA for investigating the implications of climate change.

How to cite: Fedele, E., Gemeinholzer, B., Richter, R., Wirth, C., and Sánchez-Parra, B.: The Leipzig Canopy Crane experiment: DNA metabarcoding of air samples to monitor seasonal variations in airborne fungal and plant communities composition, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-20282, https://doi.org/10.5194/egusphere-egu24-20282, 2024.

EGU24-2270 | ECS | Posters on site | ITS1.8/TS9.1

Geologic formation database for Africa with projections onto plate reconstructions 

Wen Du, James Ogg, Gabriele Ogg, Rebecca Bobick, Jacques LeBlanc, Monica Juvane, Dércio José Levy, Aditya Sivathanu, Suyash Mishra, Yuzheng Qian, and Sabrina Chang

It is a challenge to obtain information about the geologic formations and their succession in Africa due to lack of on-line lexicons for most regions.  Therefore, we established AfricaLex as a free public online database that includes details on the geologic formations in all major basins, onshore and offshore, of Africa.

AfricaLex (https://africalex.geolex.org/) offers search for geologic formations in the database by standard search criteria (name, partial name, age, region, lithology keywords, or any combination), and a map-based graphic user interface with stratigraphic-column navigation. The returned entries can be displayed by-age or in alphabetical order. Each formation is color-coded based on the Geologic Time Scale 2020, and with digitized regional extent in GeoJSON format. These enable plotting of the individual formations or time-slices of all formations across Africa of a user-selected age, with each regional-extent filled with their appropriate lithologic facies pattern, onto any of three proposed plate reconstruction models with a single click.

The aim is to make information on Africa geology and its component geologic formations more to accessible to geologists and the general public from the world and for improving paleogeographic maps.  Users can obtain a view of the sediments and volcanics that were accumulating at any time across the ancient land of Africa.These lexicon systems will be interlinked to other stratigraphic and paleogeographic databases through the lUGS Deep-Time Digital Earth platform.

How to cite: Du, W., Ogg, J., Ogg, G., Bobick, R., LeBlanc, J., Juvane, M., Levy, D. J., Sivathanu, A., Mishra, S., Qian, Y., and Chang, S.: Geologic formation database for Africa with projections onto plate reconstructions, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-2270, https://doi.org/10.5194/egusphere-egu24-2270, 2024.

EGU24-2425 | ECS | Posters on site | ITS1.8/TS9.1

South East Asia and Middle East Geologic Formation Databases with Visualizations on Plate Reconstructions 

ONeil Mamallapalli, Raju DSN Datla, Hongfei Hou, Bruno Granier, Nallapa Reddy Addula, Jacques LeBlanc, James Ogg, Nusrat Kamal Siddiqui, Cecilia Shafer, Gabriele Ogg, and Wen Du

In a successful collaboration with numerous regional experts on the stratigraphy of Southeast Asia and the Middle East, our international team developed cloud-based stratigraphic lexicons with graphical user-interfaces. These databases consist of the Indian Plate (indplex.geolex.org) of nearly 1000 onshore and offshore sedimentary and volcanic formations across India, Pakistan, Nepal, Bhutan, Sri Lanka, Bangladesh, and Myanmar, of southeast Asian regions (chinalex.geolex.org; thailex.geolex.org; vietlex.geolex.org; japanlex.geolex.org) with ca. 5000 formations as of January 2024), and of Middle East regions (mideastlex.geolex.org; qatarlex.geolex.org). The entries for each formation contain details on the succession of lithology, as well as the fossils present, age range, regional distribution and associated images. APIs enable easy access and integration with other applications. A comprehensive search system allows users to retrieve information on all geologic formations for a specific date or geologic stage from multiple databases. The cloud-based databases and websites can be explored through user-friendly map and stratigraphic-column interfaces generated from TimeScale Creator software.

Regional extents of each formation in GeoJSON format enables visualization as facies-pattern-filled polygons projected onto three proposed plate reconstructions of its corresponding time interval; or as time slices of regional paleogeography. These lexicon systems will be interlinked to other stratigraphic and paleogeographic databases through the lUGS Deep-Time Digital Earth platform. This comprehensive approach allows one better comprehend deep-time dynamics and gain valuable insights into the evolution of the different regions of our planet Earth.

How to cite: Mamallapalli, O., Datla, R. D., Hou, H., Granier, B., Addula, N. R., LeBlanc, J., Ogg, J., Siddiqui, N. K., Shafer, C., Ogg, G., and Du, W.: South East Asia and Middle East Geologic Formation Databases with Visualizations on Plate Reconstructions, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-2425, https://doi.org/10.5194/egusphere-egu24-2425, 2024.

EGU24-2473 | Orals | ITS1.8/TS9.1

Online databases of the geologic formations of Asia and Africa with display onto plate reconstructions 

James Ogg, Wen Du, Aditya Sivathanu, Sabrina Chang, Suyash Mishra, Sabin Zahirovic, Aaron Ault, O'Neil Mamallapalli, Haipeng Li, Mingcai Hou, and Gabriele Ogg

Building paleogeographic maps that are projected onto different tectonic plate reconstruction models requires team efforts to compile extensive interlinked databases of regional sedimentary and volcanic facies, data sharing standards, and computer projection methods. Two goals of the Deep-Time Digital Earth (DDE) program of the International Union of Geological Sciences (IUGS) Paleogeography Working Group are: (1) to interconnect online national databases for all geologic formations, and to compile these online "lexicons" for countries that currently lack these; (2) to project the combined paleogeographic output of these distributed databases for any time interval onto appropriate plate tectonic reconstructions.

Therefore, we have worked with regional experts to compile and interlink cloud-based lexicons for different regions of the world that are enhanced by graphic user-interfaces. Online lexicons with map-based and stratigraphic-column navigation are currently completed for the Indian Plate (ca. 800 formations), China (ca. 3200), Vietnam-Thailand-Malaysia (ca. 600), and all major basins in Africa (ca. 700) and in the Middle East (ca. 700 formations). These will soon be joined by Japan (ca. 600 formations) and basins in South America (ca. 700 formations). A multi-database search system (age, region, lithology keywords, etc.) enables all returned entries be displayed by-age or in alphabetical order. The genera in the "fossil" field are auto-linked to their entries and images in the online Treatise of Invertebrate Paleontology. With a single click, a user can plot the original extent of the geologic formation (or an array of regional formations of a specified age) onto different plate reconstruction models with the polygon(s) filled with the appropriate lithologic facies pattern(s). Our team collaborated with the Macrostrat team at Univ. Wisconsin (Madison) to interlink with their extensive regional facies-time compilations for North America and the ocean basins to enable a near-global coverage. Following the lead of Macrostrat's ROCKD app, this project is in partnership with UNESCO's Commission for the Geologic Map of the World and other geological surveys to enable linking online geologic map units for direct access to the lexicon details on that geologic formation and its former paleogeographic setting. Essentially, goal is to create a view of the sediments and volcanics that were accumulating onto the Earth's surface at any time in the past.

The main website (https://geolex.org) has links to the growing array of regional lexicons.

How to cite: Ogg, J., Du, W., Sivathanu, A., Chang, S., Mishra, S., Zahirovic, S., Ault, A., Mamallapalli, O., Li, H., Hou, M., and Ogg, G.: Online databases of the geologic formations of Asia and Africa with display onto plate reconstructions, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-2473, https://doi.org/10.5194/egusphere-egu24-2473, 2024.

EGU24-3558 | ECS | Posters on site | ITS1.8/TS9.1

Semi-Supervised Machine Learning for Predicting Lacustrine Carbonate Facies in theBarra Velha Formation, Santos Basin 

Pedro Vitor Abreu Affonso, Ana Luiza Spadano Albuquerque, and André Luiz Durante Spigolon

There is an increasing availability of geoscientific exploration data for the oil and gas industry. Supporting data-driven tools have become important for the optimization and geoscientific information gain from this kind of data and thus allowing a fastest and more trustable decision making. Nonetheless, the development of this kind of technology depends on the standardization of the data and its descriptive methodologies, that many times diverges between the geoscientists and its many data sources, that recurrently comes from different scales of samples. The complexity of non-conventional reservoir, like the ones from brazilian pre-salt, increases those pre-existing difficulties. In this sense, this work evaluates the results of a semi-supervised Machine-learning methodology that was applied to the aptian carbonates of Barra Velha formation, from the Santos Basin pre-salt. This methodology follows a PU-learning approach with the utilization of the Random-forest algorithm based on public data from geological cores, side samples and geophysical data from the corresponding depths of the Barra Velha carbonates. A team of geoscientists provided a carbonate facies grouping, and this work regrouped it based on quantitative and qualitative descriptions, and in depositional criteria related for those samples, aiming to better utilize this data for Machine-learning. To deal with the fact that the samples belong for different scales and data-sources, the classified samples from geological cores were select as “labeled”, and the rest of it was defined as “unlabeled”, establishing a criteria for description of the samples and that fits the workflow for semi-supervisioned Machine-learning. Model evaluation metric were analyzed and compared to results of a regular supervisioned model approach. The results show that the overall precision of the semi-supervisioned model has increased significantly by 10% in relation to the supersivioned methodology, and critical suggestions were made based on the results for motivation of future researches from this topic.

How to cite: Abreu Affonso, P. V., Spadano Albuquerque, A. L., and Durante Spigolon, A. L.: Semi-Supervised Machine Learning for Predicting Lacustrine Carbonate Facies in theBarra Velha Formation, Santos Basin, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-3558, https://doi.org/10.5194/egusphere-egu24-3558, 2024.

EGU24-4485 | ECS | Posters on site | ITS1.8/TS9.1

WebGPlates: A Unity-based Tool For Enhancing Paleogeographic Research 

Haipeng Li, Han Cheng, Sabin Zahirovic, and Yisa Wang
GPlates, an open-source, cross-platform GIS software, has been pivotal in plate tectonics and paleogeography. The recent browser-based implementation of GPlates, facilitated by pyGPlates and Cesium, offers real-time rotation of online datasets. Yet, this approach encounters limitations in data rotation efficiency and integration with diverse datasets. To address this issue, we introduce the Unity-based WebGPlates (https://dplanet.deep-time.org/DPlanet/), which harnesses the capabilities of the Web Assembly and Unity framework for enhanced computing efficiency and browser-based rendering. More importantly, WebGPlates integrates with the Deep-time Digital Earth Platform, ensuring comprehensive data access and services. Our preliminary results highlight the potential of WebGPlates as an indispensable tool in paleogeographic research. We extend an invitation to the whole community to engage and collaborate utilizing this enhanced platform.

How to cite: Li, H., Cheng, H., Zahirovic, S., and Wang, Y.: WebGPlates: A Unity-based Tool For Enhancing Paleogeographic Research, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-4485, https://doi.org/10.5194/egusphere-egu24-4485, 2024.

EGU24-5551 | Posters on site | ITS1.8/TS9.1

Intercomparison and Definition of Uncertainties of Deep-Time Global Earth Reconstructions: What’s the problem? 

Christian Vérard, Florian Franziskakis, and Grégory Giuliani

Global Earth reconstruction maps are used as baseline information for many studies, with high-level impacts and large implications. Yet, virtually no study fundamentally question the reliability of those reconstructions. In many cases, the model the study uses is not even credited. The reason for the absence of such discussion probably lies in the fact that none of the plate tectonic / palæogeographic modellers themselves have been able so far to assess the reliability of their own maps.

Why? First, because actually, there are ‘palæo-continental’, ‘plate tectonics’, ‘palæo-environmental’, and ‘palæogeographic’-types of reconstruction and it is difficult to compare apples and oranges. Second, because the workflow, definition, standard and vocabulary used to by the modellers can be quite different. And third and overall, because data, which reconstructions are based upon, may be contradictory and modellers must make choices.

If, for example, 4 data suggest a collision at a given time and a fifth does not, can we state that the model should display a collision zone at the 80% confidence level? What geological information is undoubtedly a proof of a collision? If among the 5 data, 2 corresponds to flysch-series, 1 corresponds to S-type granite, the 4th to tectonic unconformities and structural deformation, and the 5th is the definition of a retrograde path of metamorphic P – T conditions, is it sufficient to talk about collision, and do the 5 data have the same weight in terms of uncertainties? What about if the model does not display the collision zone at time the 4 first data suggest collision, but does display collision at the next time slice in agreement with the 5th information?

Contradictory data and debatable choices will always exist, and the existence of numerous global Earth reconstruction models is thus a wealth. However, in order to talk about uncertainties and to allow some intercomparison, the modellers of the Earth reconstruction community must collaborate, form an International Panel for Earth Reconstruction (IPER), and lay the foundation for shared definitions, concepts, vocabulary, and FAIR principles. A quantification of uncertainty on past reconstructions may then possibly be achieved by intercomparison between various models.

How to cite: Vérard, C., Franziskakis, F., and Giuliani, G.: Intercomparison and Definition of Uncertainties of Deep-Time Global Earth Reconstructions: What’s the problem?, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5551, https://doi.org/10.5194/egusphere-egu24-5551, 2024.

EGU24-5938 | ECS | Posters on site | ITS1.8/TS9.1

Dynamic interaction between thermal insulation by cratonic keels and asthenospheric convection: insights from numerical experiments 

João Pedro Macedo Silva, Victor Sacek, and Gianreto Manatschal

The conductive heat transport in the lithosphere is less efficient than the convective heat transport in the asthenospheric mantle. Therefore, the lithosphere behaves as a thermal insulation above the asthenospheric mantle. As a consequence, the temperature in the mantle can increase, also affecting the rheological structure of the mantle, both in the asthenosphere and at the base of the lithosphere. As the thickness of the thermal lithosphere can vary laterally from less than 100 km to more than 200 km under cratonic domains, the impact of thermal insulation can vary geographically. Therefore, the variation of lithospheric thickness may affect the efficiency of the heat transport from the asthenosphere to the lithospheric mantle. Using thermo-mechanical numerical models, we investigate how lateral variation of lithospheric thickness affects the heat flow to the surface, the convective pattern inside the asthenospheric mantle and the impacts of thermal evolution of cratonic keel over time scales of hundreds of million years. We test scenarios considering different lateral positions for the cratonic keel, scenarios with relative movement between lithosphere and asthenospheric mantle to emulate lateral movement over geological time. We also test the impacts of assuming different mantle potential temperatures for the asthenosphere. Additionally, yield strength envelopes are calculated in different portions of the lithosphere in the numerical domain to assess the impact of the thermal insulation to the rheological structure of the lithosphere. The preliminary results indicate that rising/hot thermal anomalies tend to concentrate at the base of cratonic keels, which may eventually act as a weakening effect in the lithosphere. In scenarios with relative movement, we observe a systematic shift in the location of hot thermal anomalies in the opposite direction of the relative movement.

How to cite: Macedo Silva, J. P., Sacek, V., and Manatschal, G.: Dynamic interaction between thermal insulation by cratonic keels and asthenospheric convection: insights from numerical experiments, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5938, https://doi.org/10.5194/egusphere-egu24-5938, 2024.

EGU24-6149 | ECS | Orals | ITS1.8/TS9.1

Robust estimation of seismogenic depths and their uncertainties 

Álvaro González

Earthquakes occur in a depth range where the physical conditions allow rocks to behave as brittle and to deform in a stick-slip fashion. This range is limited by the so-called upper and lower seismogenic depths, which are input parameters for bounding seismogenic ruptures in models of seismic hazard assessment.

Usually, such limits are estimated from the observed depth distribution of hypocenters. An exact estimation is not possible, because earthquake locations (and particularly hypocentral depths) are uncertain. Also, the sample of observed earthquakes is finite, and shallower or deeper earthquakes than those so far observed at a site could eventually happen. For these reasons, the extreme values of the distribution (the shallowest and the deepest earthquakes in the sample) are weak estimators, especially if a small sample (with few earthquakes) is used.

A common, more statistically robust, proxy to those limits is a given percentile of the distribution of earthquake depths. For example, the 90%, 95% or 99% percentiles (named D90, D95 or D99, respectively) are frequently used as proxies to the lower seismogenic depth. But the actual uncertainties of such estimates are, so far, not properly assessed.

Here I present a method for calculating such percentiles with an unbiased estimator and quantifying their uncertainties in detail.

Earthquakes are more easily missed (more difficult to detect) the deeper they are. So earthquake catalogues preferentially contain shallow events. To avoid this bias, only those events with magnitude at least equal to the magnitude of completeness of the sample are regarded.

A mapping procedure is used in order to highlight spatial variations of seismogenic depths, considering, for each point in the map, the subsample of its closest earthquakes. Uncertainties arising from the finite sample size are dealt with by using bootstrap.

Each hypocentral location is randomized in space in a Monte Carlo simulation, to take into account the reported location uncertainties. Also, crustal earthquakes can be considered separately from deeper ones, by truncating the hypocentral depth distribution with a Moho model for which the uncertainty can also be taken into account.

This procedure allows calculating statistically robust maps of the seismogenic depths with a realistic treatment of their uncertainties, as exemplified with the analysis of a regional seismic catalogue.

How to cite: González, Á.: Robust estimation of seismogenic depths and their uncertainties, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-6149, https://doi.org/10.5194/egusphere-egu24-6149, 2024.

EGU24-6992 | ECS | Orals | ITS1.8/TS9.1

Streamlining Multi-Data Geophysical Inference with BayesBridge 

Fabrizio Magrini, Jiawen He, and Malcolm Sambridge

The Earth's interior structure must be inferred from geophysical observations collected at the surface. Compared to just a few decades ago, the amount of geophysical data available today is voluminous and growing exponentially. Dense seismic networks like USArray, AlpArray, and AusArray now enable joint inversions of various geophysical data types to maximise subsurface resolution at scales ranging from local to continental. However, the practical application of joint inversions faces several challenges:

  • Various geophysical techniques typically probe different scales and depths, complicating the choice of an appropriate discretisation for the Earth's interior.
  • Different geophysical observables may respond to physical properties that are not directly related (e.g., density and electrical conductivity), making the construction of self-consistent parameterisations a non-trivial task.
  • Without a comprehensive understanding of noise characteristics, standard methods require assigning weights to different data sets, yet robust choices remain elusive.

Capable of overcoming these recognised challenges and allowing estimates of model uncertainty, probabilistic inversions have grown in popularity in the geosciences over the last few decades, and have been successfully applied to specific modelling problems. Here, we present BayesBridge, a user-friendly Python package for generalised transdimensional and hierarchical Bayesian inference. Computationally optimised through Cython, our software offers multi-processing capabilities and runs smoothly on both standard computers and computer clusters. As opposed to existing software libraries, BayesBridge provides high-level functionalities to define complex parameterisations, with prior probabilities (defined by uniform, Gaussian, or custom density functions) that may or may not be dependent on depth and/or geographic coordinates. By default, BayesBridge employs reversible-jump Markov chain Monte Carlo for sampling the posterior probability, with the option of parallel tempering, but its low-level features enable effortless implementations of arbitrary sampling criteria. Utilising object-oriented programming principles, BayesBridge ensures that each component of the inversion -- such as the discretisation, the physical properties to be inferred, and the data noise -- is a self-contained unit. This design facilitates the seamless integration of various forward solvers and data sets, promoting the use of multiple data types in geophysical inversions.

How to cite: Magrini, F., He, J., and Sambridge, M.: Streamlining Multi-Data Geophysical Inference with BayesBridge, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-6992, https://doi.org/10.5194/egusphere-egu24-6992, 2024.

EGU24-7570 | ECS | Posters on site | ITS1.8/TS9.1

Navigating the Academic Landscape: Intelligent Retrieval Systems for Geoscience Exploration 

Yi Xu, Cheng Deng, Shuchen Cai, Bo Xue, and Xinbing Wang

The surge in academic publications mirrors the evolutionary strides of human civilization, marked by an exponential growth in their numbers. Addressing the lacuna in well-organized academic retrieval systems for geoscientists, the Geo-Literature system emerges as a transformative tool. This system, boasting a vast repository of over seven million papers and information on four million scholars, employs cutting-edge technology to reshape the landscape of academic search, analysis, and visualization within the geoscience domain.

Driven by the necessity to bridge the gap between modeling frameworks and geological constraints, Geo-Literature incorporates geoscience knowledge mining and representation technologies. Through its intelligent update and fusion system, it not only integrates new publications but also analyzes language, space, and time relationships, effectively overcoming challenges posed by knowledge ambiguity. The platform's geoscience knowledge interaction and presentation technology facilitate intelligent retrieval, recommendation systems, and the creation of comprehensive scholarly portraits.

The impact of Geo-Literature transcends conventional academic boundaries. Establishing associations, mapping key attributes, and providing hierarchical visualizations, the system assists researchers in uncovering knowledge and forming a nuanced understanding of the academic space in geosciences. Consequently, Geo-Literature not only enhances the efficiency of paper retrieval but also contributes to broader scientific goals by fostering interdisciplinary collaboration and advancing our comprehension of Earth's deep-time processes.

How to cite: Xu, Y., Deng, C., Cai, S., Xue, B., and Wang, X.: Navigating the Academic Landscape: Intelligent Retrieval Systems for Geoscience Exploration, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-7570, https://doi.org/10.5194/egusphere-egu24-7570, 2024.

EGU24-7977 | Orals | ITS1.8/TS9.1

Reconstructing the Earth in Deep-Time: A New and Open Framework for the PANALESIS Model 

Florian Franziskakis, Christian Vérard, and Gregory Giuliani

The Panalesis model (Vérard, 2019) was developed in a preliminary version according to concepts, methods and tools that follow the work carried out for more than 20 years at the University of Lausanne (Stampfli & Borel, 2002; Hochard, 2008). Although the techniques are relevant, development under ArcGIS® does not allow visibility and easy accessibility of the model to the scientific community.

A major effort is therefore underway to migrate the entire model to an open source version using a FAIR approach for research software (Chue Hong et al., 2021). This migration concerns both the plate tectonic maps covering all the world over the entire Phanerozoic and part of the Neoproterozoic, but also the creation of paleoDEMs (global quantified topographies).

The Panalesis model and its entire architecture is therefore currently migrated to QGIS (a free and open source geographic information system). TopographyMaker, the software designed to convert polylines from the reconstruction map into a points grid with elevation values is now working as a plugin on QGIS. The output palaeoDEMS will also be published according to the FAIR principles for scientific data management and stewardship (Wilkinson et al., 2016).

The development and future refinements of TopographyMaker will enhance the Earth system modelling, especially coupling between models of different shells of the Earth such as atmospheric circulation, climatic evolution, and mantle dynamics. The topography is, for instance, considered a first order controlling factor for CO2 evolution over geological timescales, through silicate weathering (MacDonald et al., 2019).

How to cite: Franziskakis, F., Vérard, C., and Giuliani, G.: Reconstructing the Earth in Deep-Time: A New and Open Framework for the PANALESIS Model, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-7977, https://doi.org/10.5194/egusphere-egu24-7977, 2024.

EGU24-8215 | ECS | Orals | ITS1.8/TS9.1

Physically Structured Variational Inference for Bayesian Full Waveform Inversion 

Xuebin Zhao and Andrew Curtis

Full waveform inversion (FWI) has become a commonly used tool to obtain high resolution subsurface images from seismic waveform data. Typically, FWI is solved using a local optimisation method which finds one model that best fits observed data. Due to the high non-linearity and non-uniqueness of FWI problems, finding globally best-fitting solutions is not necessarily desirable since they fit noise in the data, and quantifying uncertainties in the solution is challenging. In principle, Bayesian FWI calculates a posterior probability distribution function (pdf), which describes all possible model solutions and their probabilities. However, characterising the posterior pdf by sampling alone is often intractably expensive due to the high dimensionality of FWI problems and the computational expense of their forward functions. Alternatively, variational inference solves Bayesian FWI problems efficiently by minimising the difference between a predefined (variational) family of distributions and the true posterior distribution, requiring optimisation rather than random sampling. We propose a new variational methodology called physically structured variational inference (PSVI), in which a physics-based structure is imposed on the variational family. In a simple example motivated by prior information from past FWI solutions, we include parameter correlations between pairs of spatial locations within a dominant wavelength of each other, and set other correlations to zero. This makes the method far more efficient in terms of both memory requirements and computational cost. We demonstrate the proposed method with a 2D acoustic FWI scenario, and compare the results with those obtained using three other variational methods. This verifies that the method can produce accurate statistical information of the posterior distribution with significantly improved efficiency.

How to cite: Zhao, X. and Curtis, A.: Physically Structured Variational Inference for Bayesian Full Waveform Inversion, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-8215, https://doi.org/10.5194/egusphere-egu24-8215, 2024.

EGU24-13715 | ECS | Posters on site | ITS1.8/TS9.1

Accelerating Geoscience Research: An Advanced Platform for Efficient Multimodal Data Integration from Geoscience Literature 

Zhixin Guo, Jianping Zhou, Guanjie Zheng, Xinbing Wang, and Chenghu Zhou

In the era of big data science, geoscience has experienced a significant paradigm shift, moving towards a data-driven approach to scientific discovery. This shift, however, presents a considerable challenge due to the plethora of geoscience data scattered across various sources. These challenges encompass data collection and collation and the intricate database construction process. Addressing this issue, we introduce a comprehensive, publicly accessible platform designed to facilitate extracting multimodal data from geoscience literature, encompassing text, visual, and tabular formats. Furthermore, our platform streamlines the search for targeted data and enables effective knowledge fusion. A distinctive feature of it is its capability to enhance the generalizability of Deep-Time Digital Earth data processing. It achieves this by customizing standardized target data and keyword mapping vocabularies for each specific domain. This innovative approach successfully overcomes the constraints typically imposed by a need for domain-specific knowledge in data processing. The platform has been effectively applied in processing diverse data sets, including mountain disaster data, global orogenic belt isotope data, and environmental pollutant data. This has facilitated substantial academic research, evidenced by developing knowledge graphs based on mountain disaster data, establishing a global Sm-Nd isotope database, and meticulous detection and analysis of environmental pollutants. The utility of our platform is further enhanced by its sophisticated network of models, which offer a cohesive multimodal understanding of text, images, and tabular data. This functionality empowers researchers to curate and regularly update their databases meticulously with enhanced efficiency. To demonstrate the platform's practical application, we highlight a case study involving compiling Sm-Nd isotope data to create a specialized database and subsequent geographic analysis. The compilation process in this scenario is comprehensive, encompassing tasks such as PDF pre-processing, recognition of target elements, human-in-the-loop annotation, and integrating multimodal knowledge. The results obtained consistently mirror patterns found in manually compiled data, thereby reinforcing the reliability and accuracy of our automated data processing tool. As a core component of the Deep-Time Digital Earth (DDE) program, our platform has significantly contributed to the field, supporting forty geoscience research teams in their endeavors and processing over 40,000 documents. This accomplishment underscores the platform's capacity for handling large-scale data and its pivotal role in advancing geoscience research in the age of big data.

How to cite: Guo, Z., Zhou, J., Zheng, G., Wang, X., and Zhou, C.: Accelerating Geoscience Research: An Advanced Platform for Efficient Multimodal Data Integration from Geoscience Literature, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-13715, https://doi.org/10.5194/egusphere-egu24-13715, 2024.

EGU24-13884 | ECS | Posters virtual | ITS1.8/TS9.1

Bayesian network based evaluation and comparison of the urban flood risk factors for the 2016 flood and a 100-year return period flood event in Baton Rouge, Louisiana  

Fuad Hasan, Sabarethinam Kameshwar, Rubayet Bin Mostafiz, and Carol Friedland

The study focuses on evaluating and comparing different flood risk factors that correlate with each other and affect the probability of flooding. Previous research is limited to identifying these factors’ influence on specific flood events. In contrast, buildings are constructed based on design flood maps, such as the 100/500-year return period flood map in the United States. Therefore, it is important to compare risk factors obtained from historical events and flood maps to identify any missing flood risk factors. To this end, a study was conducted to determine the difference between the probability of flooding and associated factors from a historic 2016 flood event in Baton Rouge, Louisiana, with the 100-year return period Federal Emergency Management Agency (FEMA)  flood map using a Bayesian network. The Bayesian network approach was used for this study due to its transparent forward and backward inference capabilities. The potential flood risk factors (population, household income, land cover, race, rainfall, river, and road proximity, and topography) were identified and corresponding data was preprocessed in ArcGIS to convert them as raster files of the same extent, and coordinate system. The factors were also classified based on different approaches (i.e., equalization, percentile, k-means clustering, etc.) to identify the most suitable classification method. A likelihood maximization-based parameter learning approach was used to obtain the conditional probability tables in the Bayesian network. This approach was used to develop separate Bayesian networks for the 2016 flood and the 100-year flood map. After setting up the Bayesian networks, sensitivity analysis, influential strength, and correlation matrix were generated, which were used to identify the most important flood risk factors. E.g., it was observed that land cover,topography, and river proximity are highly influential to the probability of flooding.

How to cite: Hasan, F., Kameshwar, S., Mostafiz, R. B., and Friedland, C.: Bayesian network based evaluation and comparison of the urban flood risk factors for the 2016 flood and a 100-year return period flood event in Baton Rouge, Louisiana , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-13884, https://doi.org/10.5194/egusphere-egu24-13884, 2024.

EGU24-14483 | Posters on site | ITS1.8/TS9.1

CoFI - Linking geoscience inference problems with tools for their solution 

Jiawen He, Juerg Hauser, Malcolm Sambridge, Fabrizio Magrini, Andrew Valentine, and Augustin Marignier

Inference problems within the geosciences vary significantly in size and scope, ranging from the detection of data trends through simple linear regressions, to the construction of complex 3D models representing the Earth’s interior structure. Successfully solving an inverse problem typically requires combining various types of data sets, each associated with its own forward solver. In the absence of established software, many researchers and practitioners resort to developing bespoke inversion and parameter estimation algorithms tailored to their specific needs. However, this practice does not promote reproducibility and necessitates a substantial amount of work that is frequently beyond the primary objectives of the research.

Our aim with CoFI (pronounced: coffee), the Common Framework for Inference, is to capture inherent commonalities present in all types of inverse problems, independent of the specific methods employed to solve them. CoFI is an open-source Python package that provides a link to reliable and sophisticated third-party packages, such as SciPy and PyTorch, to tackle inverse problems of a broad range. The modular and object-oriented design of CoFI, supplemented by our comprehensive suite of tutorials and practical examples, ensures its accessibility to users of all skill levels, from experts to novices. This not only has the potential to streamline research but also to support education and STEM training.

This poster presentation aims to give an overview of CoFI’s main features and usage through practical examples. Moreover, we hope to foster collaboration and invite contributions on inference algorithms and domain-relevant examples.

How to cite: He, J., Hauser, J., Sambridge, M., Magrini, F., Valentine, A., and Marignier, A.: CoFI - Linking geoscience inference problems with tools for their solution, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-14483, https://doi.org/10.5194/egusphere-egu24-14483, 2024.

EGU24-14517 | Orals | ITS1.8/TS9.1

Multiproxy investigation of secular changes in tectonic regimes and crustal recycling in Earth history 

N. Ryan McKenzie, Hangyu Liu, Cody Colleps, and Adam Nordsvan

Tectonic processes influence numerous biogeochemical cycles. Accordingly, the evolution of the continental curst and changes in tectonic styles are inherently linked with secular chages in Earth’s surface environment. Here we present multiproxy mineralogical and geochronologic data to evaluate compositional changes in the upper crust along with variations in tectonic regimes and crustal recycling.  Our data indicate transitions from dominantly mafic to volumetrically extensive felsic upper crust occurred from the Archean into the Paleoproterozoic, which corresponds with evidence for enhanced crustal reworking. That later Paleoproterozoic through the Mesoproterozoic is characterized by a general reduction in crustal recycling and assimilatory tectonics with relatively limited active crustal thickening. Finally, the Neoproterozoic–Phanerozoic represents an interval with of increased juvenile magmatism and extensional tectonics, corresponding with deep and steep subduction and slab-rollback. This leads to enhanced island arc and back-arc basin formation, and subsequent arc collision.  These major shifts in composition and tectonic regimes that broadly bookended the Proterozoic have profound effects on numerous biogeochemical cycles particularly carbon, oxygen, and phosphorous cycles, and are thus likely linked to changes in the oxidative state and climate of Earth’s surface system observed during these times.

How to cite: McKenzie, N. R., Liu, H., Colleps, C., and Nordsvan, A.: Multiproxy investigation of secular changes in tectonic regimes and crustal recycling in Earth history, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-14517, https://doi.org/10.5194/egusphere-egu24-14517, 2024.

EGU24-15087 | ECS | Orals | ITS1.8/TS9.1

A web-based and data-driven approach to paleogeographic reconstructions 

Jovid Aminov, Guillaume Dupont-Nivet, Nozigul Tirandozova, Fernando Poblete, Ibragim Rakhimjanov, Loiq Amonbekov, and Ruslan Rikamov

Paleogeographic maps illustrate the distribution of land and sea, as well as the topography of the Earth’s surface during different geological periods based on the compilation of a wide range of geological and geophysical datasets. These maps provide boundary conditions for various models of the Earth’s systems, including climate, mantle convection, and land surface evolution. A number of software programs and computer algorithms have been developed in the past three decades to reconstruct either the past positions of continents and oceans or their elevation and depth. We recently developed the open-source and user friendly "Terra Antiqua", allowing users to create digital paleogeographic maps in a Geographic Information System (GIS) environment (QGIS), using various tools that are easy to operate in combination with Gplates, a widely used software for plate tectonic reconstructions. The next step is to develop a comprehensive and integrated solution easily accessible on the web that can automate most of the steps involved in reconstructing past plate configurations and topography. We present here a web application ("Terra Antiqua online") that we are developing for the creation of digital paleogeographic maps. The web application has two parts: (1) The front-end uses CesiumJS, an open-source JavaScript library for making 3D globes and maps, to visualize the databases and let the users interact with it.  (2) The back-end uses Python algorithms and libraries such as GDAL and pyGPlates to process the data and perform tectonic and hypsometric reconstructions.  Terra Antiqua online uses pyGplates API to access existing tectonic models and apply them to rotate plate positions and datasets to their past position. New developments are allowing it to estimate the elevation, depth and distribution of the land and sea by automatically processing various geological proxy data (e.g. paleofacies maps, paleo-elevation proxies, fossils databases etc…) according to physically based algorithms. The project further aims to incorporate web-based landscape modeling tools and develop a community around a geological database and paleogeographic reconstruction methods and standards.

How to cite: Aminov, J., Dupont-Nivet, G., Tirandozova, N., Poblete, F., Rakhimjanov, I., Amonbekov, L., and Rikamov, R.: A web-based and data-driven approach to paleogeographic reconstructions, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15087, https://doi.org/10.5194/egusphere-egu24-15087, 2024.

EGU24-15385 | ECS | Orals | ITS1.8/TS9.1

Probabilistic Approach toward Seismic Exploration with Autonomous Robotic Swarms 

Kai Nierula, Dmitriy Shutin, Ban-Sok Shin, Heiner Igel, Sabrina Keil, Felix Bernauer, Philipp Reiss, Rok Sesko, and Fabian Lindner

This research introduces a novel approach to seismic exploration on the Moon and Mars, employing autonomous robotic swarms equipped with seismic sensing and processing hardware. By relying on probabilistic inference methods, we aim to survey large surface areas to both autonomously identify and map subsurface features such as lava tubes and ice deposits. These are crucial for future human habitats and potential in-situ resource utilization.

This endeavor presents unique challenges due to the communication limitations and uncertainties inherent in remote, autonomous operations. To address these challenges, we adopt a distributed approach with robotic swarms, where each rover processes seismic data and shares the results with other rovers in its vicinity, contending with imperfect communication links. Thus, the swarm is used as a distributed computing network. The decisions made within the network are based on probabilistic modeling of the underlying seismic inference problem. A key innovation in this respect is the use of factor graphs to integrate uncertainties and manage inter-rover communications. This framework enables each rover to generate a localized subsurface map and autonomously decide on strategic changes in the seismic network topology, either exploring new areas or repositioning to enhance measurement accuracy of targeted underground regions.

The vision is to implement this approach on a distributed factor graph, allowing for a coordinated, probabilistic analysis of seismic data across the swarm. This strategy represents a significant departure from traditional static seismic sensor arrays, offering a dynamic and adaptable solution for planetary exploration. The first step towards realizing this vision involves implementing a Kalman filter for the one-dimensional linear heterogeneous wave equation. This has been achieved by reformulating finite difference schemes for wave propagation simulation into a state-space description. The resulting linear continuous n-th order system can be explicitly solved and rewritten into a discrete state space model that can be used in the standard Kalman filter recursion. However, the standard Kalman filter is limited due to its assumption that both model and process noise are Gaussian. With factor graphs, this limitation can be overcome, enabling a more robust and versatile analysis. Several simulation results will be shown to demonstrate the performance of these approaches.

We intend to extend the approach to higher-dimensional problems, implementing distributed versions of the Kalman filter and factor graph with simulated, non-perfect communication links. Eventually, the seismic inverse problems will be solved in these frameworks. Successfully achieving these objectives could greatly enhance our capabilities in extraterrestrial exploration, paving the way for more informed and efficient future space missions.

How to cite: Nierula, K., Shutin, D., Shin, B.-S., Igel, H., Keil, S., Bernauer, F., Reiss, P., Sesko, R., and Lindner, F.: Probabilistic Approach toward Seismic Exploration with Autonomous Robotic Swarms, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15385, https://doi.org/10.5194/egusphere-egu24-15385, 2024.

EGU24-15712 | ECS | Orals | ITS1.8/TS9.1

GeoBUS - A Probabilistic Workflow Combining ERT Inverse Modeling and Implicit Geological Modeling  

Christin Bobe, Jan von Harten, Nils Chudalla, and Florian Wellmann

The interface between different rock units is usually described as a sharp boundary in geological models. Such geological interfaces are often a main target of geological as well as geophysical investigations. In the inverse images derived from electrical resistivity tomography (ERT), geological interfaces are typically represented by a continuous, smooth change in the electrical resistivity. This smoothing of interfaces is often unwanted since it deviates significantly from typical geological features where the exact location of the interface can be precisely determined.

The proposed GeoBUS workflow (Geological modeling by Bayesian Updating of Scalar fields) aims to generate probabilistic geological models which include the information from probabilistic ERT inversion results using Bayesian updates. The GeoBUS workflow consists of three main steps. The method Kalman ensemble generator (KEG), a numerical implementation for computing Bayesian updates, plays an important role in this workflow.

In the first step of the GeoBUS workflow, the KEG is used for inversion of ERT data. The KEG generates probabilistic, yet smooth images of the subsurface in terms of electrical resistivity.

In the second step of the GeoBUS workflow, we perform implicit geological modeling of the subsurface creating an ensemble of scalar fields. For the geological modeling, we use point information, i.e. the location and orientation of present geological units, along with the uncertainty associated to both location and orientation. The resulting ensemble consists of scalar fields that are defined everywhere in space and build the basis of the geological model. Drawing contours into each scalar field for the scalar field values for which geological interfaces are confirmed, we create an ensemble of geological models.

For the third and final step of the GeoBUS workflow, we adopt the subsurface discretization used for the ERT inverse modeling and use the ensemble of geological models from step two to assign a probabilistic scalar field value to each cell of the discretized subsurface. This discrete version of the scalar field is used as the prior for a second KEG application. Based on literature values, we assign a probability density function for electrical resistivity values to each geological unit of the geological model to formulate a corresponding likelihood. Using the KEG, we derive a Bayesian update of the discretized scalar field combining the petrophysical likelihood and the information from the ERT inversion. This results in a posterior scalar field which again can be used to generate an ensemble of geological models that now includes the information from the geophysical measurements.

We demonstrate this novel workflow for simple and synthetic two-dimensional subsurface models, generating both synthetic geological and geophysical data. This way we aim to (1) create simple benchmark examples, and (2) give a first evaluation of the performance of the GeoBUS workflow. 

How to cite: Bobe, C., von Harten, J., Chudalla, N., and Wellmann, F.: GeoBUS - A Probabilistic Workflow Combining ERT Inverse Modeling and Implicit Geological Modeling , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15712, https://doi.org/10.5194/egusphere-egu24-15712, 2024.

EGU24-16036 | Orals | ITS1.8/TS9.1

An Independent State sampler for Trans-dimensional Bayesian Inference 

Malcolm Sambridge, Andrew Valentine, and Juerg Hauser

Over the past twenty years, Trans-dimensional Bayesian Inference has become a popular approach for Bayesian sampling. It has been applied widely in the geosciences when the best class of model representation, e.g. of the subsurface, is not obvious in advance, or the number of free variables undecided. Making arbitrary choices in these areas may result in sub-optimal inferences from data. In trans-D, one typically defines a finite number of model states, with differing numbers of unknowns, over which Bayesian Inference is to be performed using the data.

A key attraction of Trans-D Bayesian Inference is that it is designed to let the data decide which state, as well as which configurations of parameters within each state, are preferred by the data, in a probabilistic manner. Trans-D algorithms may hence be viewed as a combination of fixed dimensional within-state sampling and simultaneous between-state sampling where Markov chains visit each state in proportion to their support from the data.

In theory, each state may be completely independent, involving different classes of model parameterization, with different numbers of unknowns, data noise levels, and even different assumptions about the data-model relationship. Practical considerations, such as convergence of the finite length Markov chains between states, usually mean that each state must be closely related to each other, e.g. differing by a single layer in a 1-D seismic Earth model. In addition, since the form of the necessary Metropolis-Hastings balance condition depends on the mathematical relationship between the unknowns in each state, then implementations are often bespoke to each class of model parameterization and data type. To our knowledge there exists no automatic trans-D sampler where one can define arbitrary independent states, together with a prior and Likelihood, and simply pass to a generalised sampling algorithm, as is common with many fixed dimensional MCMC algorithms and software packages. 

A second limitation in trans-D sampling is that since implementations are bespoke within a class of model parameterizations, within-state sampling is typically performed with simplistic and often dated algorithms, e.g. Metropolis-Hastings or Gibbs samplers, thereby limiting convergence rates. Over the past 30 years fixed dimensional sampling has advanced considerably with numerous efficient algorithms available and many conveniently translated into user friendly software packages, almost all of which have not been used within a trans-D framework due to a lack of a way to conveniently deploy them in a trans-D setting.

In this presentation we will address all of these issues by describing the theory under-pinning an ‘Independent State’ (IS) Trans-D sampler, together with some illustrative examples. In this algorithm class, sampling may be performed across states that are completely independent, containing arbitrary numbers of unknowns and parameter classes. In addition, the IS-sampler can conveniently take advantage of any fixed dimensional sampler without the need to derive and re-code bespoke Markov chain balance conditions, or specify mechanisms for transitions between model parameters within different states. In this sense it represents a general purpose automatic trans-D sampler.

How to cite: Sambridge, M., Valentine, A., and Hauser, J.: An Independent State sampler for Trans-dimensional Bayesian Inference, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-16036, https://doi.org/10.5194/egusphere-egu24-16036, 2024.

EGU24-16101 | Posters virtual | ITS1.8/TS9.1

Utilizing Large Language Models for Geoscience Literature Information Extraction 

Peng Yu, Cheng Deng, Huawei Ji, and Ying Wen
Extracting information from unstructured and semi-structured geoscience literature is a crucial step in conducting geological research. The traditional machine learning extraction paradigm requires a substantial amount of high-quality manually annotated data for model training, which is time-consuming, labor-intensive, and not easily transferable to new fields. Recently, large language models (LLMs) (e.g., ChatGPT, GPT-4, and LLaMA), have shown great performance in various natural language processing (NLP) tasks, such as question answering, machine translation, and text generation. A substantial body of work has demonstrated that LLMs possess strong in-context learning (ICL) and even zero-shot learning capabilities to solve downstream tasks without specifically designed supervised fine-tuning.
In this paper, we propose utilizing LLMs for geoscience literature information extraction. Specifically, we design a hierarchical PDF parsing pipeline and an automated knowledge extraction process, which can significantly reduce the need for manual data annotation, assisting geoscientists in literature data mining. For the hierarchical PDF parsing pipeline, firstly, a document layout detection model fine-tuned on geoscience literature is employed for layout detection, obtaining layout detection information for the document. Secondly, based on the document layout information, an optical character content parsing model is used for content parsing, obtaining the text structure and plain text corresponding to the content. Finally, the text structure and plain text are combined and reconstructed to ultimately obtain the parsed structured data. For the automated knowledge extraction process, firstly, the parsed long text is segmented into paragraphs to adapt to the input length limit of LLMs. Subsequently, a few-shot prompting method is employed for structured knowledge extraction, encompassing two tasks: attribute value extraction and triplet extraction. In attribute value extraction, prompts are generated automatically by the LLMs based on the subdomain and attribute names, facilitating the location and extraction of values related to subdomain attribute names in the text. For triplet extraction, the LLMs employ a procedural approach to entity extraction, entity type extraction, and relation extraction, following the knowledge graph structure pattern. Finally, the extracted structured knowledge is stored in the form of knowledge graphs, facilitating further analysis and integration of various types of knowledge from the literature.
Our proposed approach turns out to be simple, flexible, and highly effective in geoscience literature information extraction. Demonstrations of information extraction in subdomains such as radiolarian fossils and fluvial facies have yielded satisfactory results. The extraction efficiency has significantly improved, and feedback from domain experts indicates a relatively high level of accuracy in the extraction process. The extracted results can be used to construct a foundational knowledge graph for geoscience literature, supporting the comprehensive construction and efficient application of a geoscience knowledge graph.

How to cite: Yu, P., Deng, C., Ji, H., and Wen, Y.: Utilizing Large Language Models for Geoscience Literature Information Extraction, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-16101, https://doi.org/10.5194/egusphere-egu24-16101, 2024.

EGU24-16434 | ECS | Posters on site | ITS1.8/TS9.1

Probabilistic inversion of geoelectric and induced polarization measurements on reduced model spaces using Hamiltonian Monte Carlo 

Joost Hase, Florian M. Wagner, Maximilian Weigand, and Andreas Kemna

The probabilistic formulation of geoelectric and induced polarization inverse problems using Bayes’ theorem inherently accounts for data errors and uncertainties in the prior assumptions, both of which are propagated naturally into the solution. Due to the non-linearity of the physics underlying the geoelectric forward calculation, the inverse problem must be solved numerically. Markov chain Monte Carlo (MCMC) methods provide the capability to create a sample of the corresponding posterior distribution, based on which statistical estimators of interest can be approximated. In a typical geoelectric imaging application, the subsurface is discretized as a 2-D mesh with the model parameters representing the averaged values of the imaged electrical conductivity within the individual cells. The resulting model space is often of high dimensionality and usually insufficiently resolved by the measurements, posing a challenge to the efficient application of MCMC methods. In our work, we use the Hamiltonian Monte Carlo (HMC) method to sample from the posterior distribution and operate on a reduced model space to enhance the efficiency of the inversion. The basis of the reduced model space is constructed via a principal component analysis of the model prior term. We consider different resolution measures to ensure that the information lost by operating in the reduced model space is negligible. In addition to the inversion of electrical resistivity tomography measurements in real variables, we also demonstrate the model space reduction and subsequent application of HMC for the solution of the complex resistivity tomography inverse problem in complex variables, imaging the distribution of the complex electrical conductivity in the subsurface. This study contributes to the needed increase of uncertainty quantification in the inversion of geoelectric and induced polarization measurements, aiming to provide a reliable basis for the processing and interpretation of geophysical imaging results.

How to cite: Hase, J., Wagner, F. M., Weigand, M., and Kemna, A.: Probabilistic inversion of geoelectric and induced polarization measurements on reduced model spaces using Hamiltonian Monte Carlo, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-16434, https://doi.org/10.5194/egusphere-egu24-16434, 2024.

EGU24-17157 | ECS | Orals | ITS1.8/TS9.1

Impact modeling with Bayesian inference for crop yield assessment and prediction 

Odysseas Vlachopoulos, Niklas Luther, Andrej Ceglar, Andrea Toreti, and Elena Xoplaki

It is common knowledge that climate variability and change have a profound impact on crop production. From the principle that “it is green and it grows” to the assessment of the actual impacts of major weather drivers and their extremes on crop growth through the adoption of agro-management strategies informed by tailored and effective climate services, there is a well documented scientific and operational gap. This work focuses on the development, implementation and testing of an AI-based methodology that aims to reproduce a crop growth model informing on grain maize yield in the European domain. A surrogate AI model based on Bayesian deep learning and inference is compared for its efficiency against the process-based deterministic ECroPS model developed by the Joint Research Centre of the European Commission. The rationale behind this effort is that such mechanistic crop models rely on multiple input meteorological variables and are relatively costly in terms of computing resources and time, crucial aspects for a scalable and widely adopted solution. Such approaches make it possible to run very large ensembles of simulations based, for instance, on ensembles of climate predictions and projections and/or a perturbed parametrization (e.g. on the atmospheric CO2 concentration effects). Our surrogate crop model relies on three weather input variables: daily minimum and maximum temperatures and daily precipitation, where the training was performed with the ECMWF-ERA5 reanalysis. 

How to cite: Vlachopoulos, O., Luther, N., Ceglar, A., Toreti, A., and Xoplaki, E.: Impact modeling with Bayesian inference for crop yield assessment and prediction, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17157, https://doi.org/10.5194/egusphere-egu24-17157, 2024.

EGU24-17939 | ECS | Posters on site | ITS1.8/TS9.1

Bayesian optimal experimental design for fracture imaging 

Zhi Yuan, Chen Gu, Yichen Zhong, Peng Wu, Zhuoyu Chen, and Borui Kang

Fracture imaging is a pivotal technique in a variety of fields including Carbon Capture, Utilization, and Storage (CCUS), geothermal exploration, and wasterwater disposal, essential for the success of the field operation and seismic hazard mitigation. However, accurate fracture imaging is challenging due to accurate fracture imaging is challenging due to the complex nature of subsurface geology, the presence of multiple overlapping signals, and the variability of fracture sizes and orientations. Additionally, limitations in the resolution of current imaging technologies and the need for high-quality data acquisition further complicate the process.

To address these challenges, we have conducted fracture imaging experiments utilizing acoustic sensors in laboratory-scale specimens with varied fracture geometries.A dynamic acquisition system involving robotic arms have been developed, enabling the flexible positioning of sensors on any part of the specimen's surface.This not only significantly reduces the time and resources required for experiments but also increases the adaptability of the process to different surface topography of specimens and fracture geometries.

In addition, we employ Bayesian optimization algorithms to enhance the efficiency of sensor placement in laboratory-scale specimens, aiming to achieve precise fracture imaging with the least number of measurements necessary. This algorithmic approach optimizes the data collection process, ensuring that we gather the most relevant and accurate information with minimal intrusion. The collected data is then rigorously compared and calibrated against findings from numerical simulations, which helps in refining the algorithm for broader applications.

How to cite: Yuan, Z., Gu, C., Zhong, Y., Wu, P., Chen, Z., and Kang, B.: Bayesian optimal experimental design for fracture imaging, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17939, https://doi.org/10.5194/egusphere-egu24-17939, 2024.

EGU24-18486 | ECS | Orals | ITS1.8/TS9.1 | Highlight

Towards a community platform for paleoclimate data and temperature gradients over the last 540 million years  

Sebastian Steinig, Helen Johnson, Stuart Robinson, Paul J. Valdes, and Daniel J. Lunt

Earth’s climate shows a remarkable variability on geological timescales, ranging from widespread glaciation to ice-free greenhouse conditions over the course of the Phanerozoic, i.e. the last 540 million years. Earth system modelling allows us to better understand and constrain the drivers of these changes and provides valuable reference data for other paleoclimate disciplines (e.g., chemistry, geology, hydrology). However, the sheer volume and complexity of these datasets often prevents direct access and use by non-modellers, limiting their benefits for large parts of our community.

We present the online platform “climatearchive.org” to break down these barriers and provide intuitive access to paleoclimate data for everyone. More than 100 global coupled climate model simulations covering the entire Phanerozoic at the stage level build the backbone of the web application. Key climate variables (e.g. temperature, precipitation, vegetation and circulation) are displayed on a virtual globe in an intuitive three-dimensional environment and on a continuous time axis throughout the Phanerozoic. The software runs in any web browser — including smartphones — and promotes visual data exploration, streamlines model-data comparisons, and supports public outreach efforts. We discuss the current proof of concept and outline the future integration of new sources of model and geochemical proxy data to streamline and advance interdisciplinary paleoclimate research.

We also present ongoing efforts for an integrated model-data synthesis to quantify changes in meridional and zonal temperature gradients throughout the Phanerozoic and to address the relative roles of individual forcings (greenhouse gases, solar, geography). While substantial effort has been made to quantify the evolution of global mean temperatures over the last 540 million years, changes in the large-scale temperature gradients and their causes are comparably less constrained. As a fundamental property of the climate system, changes in the spatial patterns of surface temperature play a critical role in controlling large-scale atmospheric and ocean circulation and influence hydrological, ecological, and land surface processes. The resulting best estimate product of meridional and zonal temperature gradients over the last 540 million years will represent a step change in our understanding of the drivers and consequences of past temperature gradient changes and will provide the community with a valuable resource for future climatological, geological, and ecological research.

How to cite: Steinig, S., Johnson, H., Robinson, S., Valdes, P. J., and Lunt, D. J.: Towards a community platform for paleoclimate data and temperature gradients over the last 540 million years , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18486, https://doi.org/10.5194/egusphere-egu24-18486, 2024.

EGU24-19320 | Posters on site | ITS1.8/TS9.1

Paleogeographic evolution of Asia in the Cenozoic reconstructed with the Terra Antiqua software 

Guillaume Dupont-Nivet, Jovid Aminov, Fernando Poblete, Diego Ruiz, and Haipeng Li

The ability to reconstruct the geologic evolution of the Earth as a system including the geosphere, atmosphere and biosphere interactions, is essential to understand the fate of our environment in the context of the Climate, Life and Energy crises of the new Anthropocene era. Scientists of tomorrow working on environmental changes require ever more detailed databases and maps to access and correlate the overwhelming mass of information stemming from the ongoing surge of environmental data and models. Earth System reconstructions are fundamental assets to assess potential sources and locations of key geo-resources that are now vital for the energy transition (e.g. raw materials, rare earth elements, subsurface storage, geothermal sites). Earth System reconstructions are also the best means to communicate past and future Life and Environmental evolutions, while providing consciousness of our role and situation in the immensity of Time and Nature. They convey these essential lessons in a didactic fashion for teachers and students, museums, or for governments and NGOs to make decisions and promote public awareness. Although Earth System reconstructions have long been recognized as essential, they have yet to deliver their full breakthrough potential combining various booming disciplines. As part of a large project over Asia, we review here the case of the intensely studied, yet still extremely controversial India-Asia collision with major implications on regional environmental, depositional and global climate transitions. Ongoing debates argue for radically different end-member models of the collision timing and its configuration, and of associated topographic growth in the collision zone. We present here new Asian paleogeographic reconstructions at 50 and 30 Ma that complement an existing set at 60, 40 and 20 Ma with updates. These integrate various end-members models of the India-Asia collision and associated topographic patterns and land-sea masks with implications on the locus, source and generation of resources. Results are provided online (https://map.paleoenvironment.eu/) in various model-relevant formats with associated database and discussion forums to comment an contribute to the amelioration of these maps and databases. We also present the latest developments of the user-friendly and open-source Terra Antiqua Q-GIS plugin (https://paleoenvironment.eu/terra-antiqua/) that has been used and specifically developed with new tools including data-driven and web-based applications

How to cite: Dupont-Nivet, G., Aminov, J., Poblete, F., Ruiz, D., and Li, H.: Paleogeographic evolution of Asia in the Cenozoic reconstructed with the Terra Antiqua software, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-19320, https://doi.org/10.5194/egusphere-egu24-19320, 2024.

Geological models can be constructed with a variety of mathematical methods. Generally, we can describe the modeling process in a formal way as a functional relationship between input parameters (geological observations, orientations, interpolation parameters) and an output in space (lithology, stratigraphy, rock property, etc.). However, in order to obtain a suitable implementation in geophysical inverse frameworks, we have to consider specific requirements. In recent years, a substantial amount of work focused on low-dimensional parameterizations and efficient automation of geological modeling methods, as well as their combination with suitable geophysical forward simulations. In this contribution, we focus on differential geomodelling approaches, which allow for an integration of geological modeling methods into gradient-based inverse approaches.

In this work, we emphasize differential geomodelling approaches. These approaches seamlessly integrate geological modeling methods into gradient-based inverse approaches. To achieve this integration, we actively employ modern machine learning frameworks, specifically TensorFlow and PyTorch. We then incorporate these geometric geological modeling methods into a Stein Variational Gradient Descent (SVGD) algorithm. SVGD is adept at addressing the challenges of multimodality in probabilistic inversion. Moreover, we demonstrate the implementation of these methods in a Hamiltonian Monte Carlo approach.

Our results are promising, showing that treating geological modeling as a differentiable approach unlocks new possibilities. This method facilitates novel applications in the integration of geological modeling with geophysical inversion, paving the way for advanced research in this field.

How to cite: Wellmann, F., de la Varga, M., and Liang, Z.: Differentiable Geomodeling: towards a tighter implementation of structural geological models into geophysical inverse frameworks, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-20508, https://doi.org/10.5194/egusphere-egu24-20508, 2024.

EGU24-21753 | ECS | Orals | ITS1.8/TS9.1

Basin evolution and Paleo reconstruction of the Mesoproterozoic South Nicholson Region, NE Australia 

Harikrishnan Nalinakumar and Stuart Raymond Clark

This study explores the geological complexity of the South Nicholson Region, an area spanning the Northern Territory and Queensland in Australia, from the newly drilled NDI Carrara 1 well, thus exposing the burial history of the Carrara sub-basin. Formed before the formation of the Nuna supercontinent, this region is positioned near resource-abundant basins and boasts a complex geological history. It has undergone significant tectonic shifts, orogenic activities, and the development of sedimentary basins over 1.6 billion of years while the world was developing as we see it in present. Despite its potential for mineral and petroleum resources, the South Nicholson Region was previously under-explored, lacking in-depth seismic, well, and geophysical data. Recently acquired data from the region includes five seismic lines and a new well, offering invaluable insights into the region's subsurface geology, including the identification of a new sub-basin, the Carrara Sub-basin. Characterised by a gravity low on its southeast side, the Carrara Sub-basin encompasses thick sequences of Proterozoic rocks from the Northern Lawn Hill Platform, Mount Isa Province and McArthur Basin. The primary objective of this study is to examine the burial history, tectonic subsidence and paleo-reconstruction of the South Nicholson region.

Our results indicate that the South Nicholson Region has undergone multiple cycles of sedimentation, tectonic uplift and erosion. Between ~1640 Ma and 1580 Ma, the region experienced increasing deposition rates. The presence of an unconformity obscures the sedimentation and tectonic history from 1600 to 500 Ma. However, by 500 Ma, significant subsidence had occurred, indicating that subsidence was the predominant geological force during this period. After this interval, an uplift event is evident, exhuming the layers until 400 Ma. From 400 Ma until today, little to no subsidence has been briefly interrupted by minor uplift events. Our calculated tectonic subsidence curve closely aligns with the regional deposition patterns, highlighting the intricate relationship between sediment deposition and tectonic activities, thereby providing valuable insights into the interplay between sedimentary and tectonic processes in the region.

How to cite: Nalinakumar, H. and Clark, S. R.: Basin evolution and Paleo reconstruction of the Mesoproterozoic South Nicholson Region, NE Australia, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-21753, https://doi.org/10.5194/egusphere-egu24-21753, 2024.

EGU24-111 | ECS | Orals | ITS1.10/CL0.1.9

CMIP6 precipitation and temperature projections for Chile 

Álvaro Salazar, Marcus Thatcher, Katerina Goubanova, Patricio Bernal, Julio Guitérrez, and Francisco Squeo

Precipitation and near-surface temperature from an ensemble of 36 new state‐of‐the‐art climate models under the Coupled Model Inter‐comparison Project phase 6 (CMIP6) are evaluated over Chile´s climate. The analysis is focused on four distinct climatic subregions: Northern Chile, Central Chile, Northern Patagonia, and Southern Patagonia. Over each of the subregions, first, we evaluate the performance of individual global climate models (GCMs) against a suit of precipitation and temperature observation-based gridded datasets over the historical period (1986-2014) and then we analyze the models’ projections for the end of the century (2080-2099) for four different shared socioeconomic pathways scenarios (SSP). Although the models are characterized by general wet and warm mean bias, they reproduce realistically the main spatiotemporal climatic variability over different subregions. However, none of the models is best across all subregions for both precipitation and temperature. Moreover, among the best performing models defined based on the Taylor skill score, one finds the so-called “hot models” likely exhibiting an overestimated climate sensitivity, which suggests caution in using these models for accessing future climate change in Chile. We found robust (90% of models agree in the direction of change) projected end-of-the-century reductions in mean annual precipitation for Central Chile (~-20% to ~-40%) and Northern Patagonia (~-10% to ~-30%) under scenario SSP585, but changes are strong from scenario SSP245 onwards, where precipitation is reduced by 10-20%. Northern Chile and Southern Patagonia show non-robust changes in precipitation across the models. Yet, future near-surface temperature warming presented high inter-model agreement across subregions, where the greatest increments occurred along the Andes Mountains. Northern Chile displays the strongest increment of up to ~6°C in SSP585, followed by Central Chile (up to ~5°C). Both Northern and Southern Patagonia show a corresponding increment by up to ~4°C. We also briefly discuss about the environmental and socio-economic implications of these future changes for Chile.

How to cite: Salazar, Á., Thatcher, M., Goubanova, K., Bernal, P., Guitérrez, J., and Squeo, F.: CMIP6 precipitation and temperature projections for Chile, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-111, https://doi.org/10.5194/egusphere-egu24-111, 2024.

EGU24-1411 | Posters on site | ITS1.10/CL0.1.9

The North Atlantic climate variability in single-forcing large ensemble simulations with MPI-ESM-LR 

Holger Pohlmann and Wolfgang A. Müller

The origin of multi-decadal climate variability in the North Atlantic is under debate. The variability could be caused by oceanic internal variability or by external anthropogenic or natural forcing. We have produced a set of single-forcing historical simulations with the Max Planck Institute - Earth System Model (MPI-ESM) in low resolution (LR). The historical-like simulations consists of 30 ensemble members and the external forcing is from the Coupled Model Intercomparison phase 6 (CMIP6). Each set of simulation is forced by either only greenhouse-gases, total ozone, solar insolation, anthropogenic aerosols or volcanic aerosols. We present first results of our attribution of the climate signals in the North Atlantic region to the different single forcings.

How to cite: Pohlmann, H. and Müller, W. A.: The North Atlantic climate variability in single-forcing large ensemble simulations with MPI-ESM-LR, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1411, https://doi.org/10.5194/egusphere-egu24-1411, 2024.

EGU24-1657 | Orals | ITS1.10/CL0.1.9

Climatological Evaluation of the Mei-yu Front Representation in CMIP6 

Gregor C. Leckebusch, Kelvin S. Ng, and Kevin I. Hodges

Given the significant socioeconomic impact of the East Asian Summer Monsoon (EASM), a critical area of investigation involves comprehending how the EASM and, consequently, the hydrological cycle over East Asia might change in future climates. To address this inquiry, reliable climate models must be employed. While assessments of model performance commonly concentrate on the generated precipitation amounts during the EASM period, it is important to note that the representation of dynamical components such as the Mei-yu front (MYF) are not frequently investigated. As model outputs may be correct for incorrect reasons, the dynamical components of the EASM might be misrepresented.
In this investigation, we scrutinized the representation of the MYF in historical simulations of 38 CMIP6 models from May to August, comparing them to ERA5. Our findings reveal that numerous CMIP6 models encounter difficulties in reproducing the climatology of the MYF similar to observations. By sub-sampling models based on the meridional position bias of the MYF in May, we identified distinct monthly variations within these groupings. Additionally, the origins of these biases were examined. Our study stresses the link between the misrepresentation of MYF climatology in CMIP6 models and the depiction of the North Pacific High, particularly its western edge. The implications of these discoveries are also explored. 

How to cite: Leckebusch, G. C., Ng, K. S., and Hodges, K. I.: Climatological Evaluation of the Mei-yu Front Representation in CMIP6, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1657, https://doi.org/10.5194/egusphere-egu24-1657, 2024.

EGU24-1711 | Orals | ITS1.10/CL0.1.9

Recognizing distinctiveness of SSP3-7.0 for use in impact assessments 

Hideo Shiogama, Shinichiro Fujimori, Tomoko Hasegawa, Michiya Hayashi, Yukiko Hirabayashi, Tomoo Ogura, Toshichika Iizumi, Kiyoshi Takahashi, and Toshihiko Takemura

Because recent mitigation efforts have made the upper-end scenario of the future GHG concentration (SSP5-8.5) highly unlikely, SSP3-7.0 has received attention as an alternative high-end scenario for impacts, adaptation, and vulnerability (IAV) studies. However, the ‘distinctiveness’ of SSP3-7.0 may not be well-recognized by the IAV community. When the integrated assessment model (IAM) community developed the SSP-RCPs, they did not anticipate the limelight on SSP3-7.0 for IAV studies because SSP3-7.0 was the ‘distinctive’ scenario regarding to aerosol emissions (and land-use land cover changes). Aerosol emissions increase or change little in SSP3-7.0 due to the assumption of a lenient air quality policy, while they decrease in the other SSP-RCPs of CMIP6 and all the RCPs of CMIP5. This distinctive high-aerosol-emission design of SSP3-7.0 was intended to enable climate model (CM) researchers to investigate influences of extreme aerosol emissions on climate. Here we show that large aerosol emissions in SSP3-7.0 significantly suppress future increases in precipitation. We recommend IAV researchers to compare impact simulations at the same warming level between SSP3-7.0 and SSP5-8.5 to examine the effects of aerosols in the case that such analyses are adequate. We also recommend ScenarioMIP for CMIP7 to exclude scenarios with extreme policies of aerosols (and land-use land-cover changes) from Tier 1 experiments and instead include them in Tier 2.

 

Reference: Shiogama, H., et al. Nat. Clim. Chang. 13, 1276–1278 (2023). https://doi.org/10.1038/s41558-023-01883-2

How to cite: Shiogama, H., Fujimori, S., Hasegawa, T., Hayashi, M., Hirabayashi, Y., Ogura, T., Iizumi, T., Takahashi, K., and Takemura, T.: Recognizing distinctiveness of SSP3-7.0 for use in impact assessments, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1711, https://doi.org/10.5194/egusphere-egu24-1711, 2024.

The IPCC’s 2021 assessment suggested that substantial emissions reduction and limiting global temperature rise to well below 2.0°C could prevent the complete loss of Arctic sea ice in this century. However, these assessments come with large uncertainties. Recent research projects a summer ice-free Arctic by the 2050s even under a low emission scenario by constraining future sea ice area with satellite-derived sea ice concentration (SIC) since 1979. Notably, the climate models in these assessments commonly underestimate the accelerated Arctic warming and the pace of sea ice melting, particularly over the last two decades. Moreover, recent studies indicate that in a warming climate, the thinning of sea ice and snow over sea ice may intensify surface warming, thereby accelerating the melting.

In this study, we leverage the increasing availability of observations and recent reanalysis data for Arctic-wide sea ice to investigate the link between changes in sea ice thickness (SIT), sea ice concentration (SIC), and Arctic warming. We employ these datasets to evaluate biases in historical periods and uncertainties in future scenarios within the CMIP6 multi-model ensemble for SIT and SIC. We further investigate the relationship between the thinning of sea ice and the snow layer on sea ice and surface temperature changes on a basin or regional scale. The findings are then used to constrain projected Arctic changes. Our study aims to gain some insights into the impact of model biases in the Arctic on projected climate projections, crucial for decision-making in a changing climate.

How to cite: Tian, T. and Yang, S.: The impact of sea ice thickness biases on the projected summer sea ice-free Arctic in CMIP6 ensemble experiments , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1855, https://doi.org/10.5194/egusphere-egu24-1855, 2024.

The southeastern U.S. is frequently impacted by severe thunderstorms, which are known for producing damaging winds, hail, and tornadoes. The National Oceanic and Atmospheric Administration (NOAA) reports that this region experiences the highest frequency of thunderstorms in the country. In recent decades, these storms have shown a trend of increasing both in frequency and intensity. Moreover, the southeastern states are susceptible to hurricanes and tropical storms, which have been intensifying due to warmer ocean temperatures. The escalating severity of these weather events poses significant risks to public safety, infrastructure, and the economy in the southeast. Our proposed study uses advanced satellite technology, specifically Interferometric Synthetic Aperture Radar (InSAR), to map storm-induced flooding and damage from October 2019 to August 2021. This period includes Hurricane Sally, which caused significant destruction in Alabama on September 16, 2020. By analyzing satellite images taken before and after hurricanes, we aim to identify affected areas and assess infrastructural damage. The study employs Sentinel-1 InSAR data processed by the COMET-LiCSAR system and the LiCSBAS processing package, generating surface deformation time series. We also integrate optical images to examine soil moisture and climate changes, correlating them with displacement and radar coherence data from SAR images. This research will classify and discuss the impact of hurricanes on infrastructure and roadways, providing critical information to prioritize emergency response and inform repair and reconstruction planning.

How to cite: Khosravi, A., Ghorbani, Z., and Maghsoudi, Y.: Monitoring Severe Storm Impacts and Climate Trends in the Southeastern US using Satellite-Based Proxy Indicators: A Case Study of Hurricane Sally, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-2173, https://doi.org/10.5194/egusphere-egu24-2173, 2024.

EGU24-2368 | Posters on site | ITS1.10/CL0.1.9

Changes in Day-to-day temperature variability in United States driven by cleaner air 

Guzailinuer Yasen, Qi Liu, and Weidong Guo

Day-to-day (DTD) temperature variability is an important characteristic of air temperature, which significantly affects human health and ecosystems. However, the changing trend of DTD under recent climate warming and its causes need to be further explored. Here, Using daily temperature observations, we examine the spatial heterogeneity of DTD and its long-term trends in the United States (US) over the last 26 years and find a significant increase in winter DTD in the central and eastern United States during the study period. In addition, by using the observed data and The Coupled Model Intercomparison Project Phase 6 (CMIP6) multi-model simulations, we further demonstrate that cleaner air leads to significant changes in DTD. Specifically, by comparing the contributions of greenhouse gases, anthropogenic aerosols, natural forcing, and total forcing, it is concluded that the reduction of anthropogenic aerosol concentrations in the United States after 1997 led to enhanced DTD . Of the 32 members used in this study, nearly 60% show positive trends in the DTD index during 1997–2022 in the historical simulations. The trends for the ensemble members range from -0.06 to 0.08 °C ·decade-1  with an ensemble mean of 0.008°C· decade-1 which encompasses the trend derived from the observations (0.08 °C·decade-1 ) . The historical simulations reasonably capture the observed DTD trends except with a weaker magnitude. The increasing trend is also evident in the anthropogenic-aerosol-only historical simulations, where about 56% of the 32members show positive trends, with an ensemble mean of 0.01 °C·decade-1. While contrary to the results of the anthropogenic-aerosol-only historical simulations (hist-aer), there was negative trends In the natural-only historical (hist-nat) and the greenhouse-gas-only historical (hist-GHG) simulations, only about 44% and 47% of the members showed the positive trends, The trend for the ensemble mean is -0.013/-0.015°C·decade-1 for the hist-nat / hist-GHG simulations. Therefore, the positive trend of the DTD index can be attributed to the anthropogenic aerosols , while the negative trend of which can be attributed to the natural forcing and greenhouse gas forcing. The observed DTD enhancement over 1997-2022 is dominated by the effect of anthropogenic aerosols, while natural forcing and GHGS partially counteract the effect of anthropogenic aerosols. That is, Based on climate modeling experiments, we demonstrate that the reduced aerosol emissions in US can contribute to the enhanced trend of DTD in USA.

How to cite: Yasen, G., Liu, Q., and Guo, W.: Changes in Day-to-day temperature variability in United States driven by cleaner air, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-2368, https://doi.org/10.5194/egusphere-egu24-2368, 2024.

Despite the early warnings of the scientific community in general and of the IPCC in particular, we have entered decades in which climate models are no longer black boxes as the consequences of past emissions of greenhouse gases are emerging rapidly in multiple climate records. This unprecedented situation is likely to change our methods and our view of the respective roles of models and observations in understanding recent and predicting future climate change, regardless of the considered emission scenario. Among the key questions raised are the role of observations in model tuning versus projection constraining and the design of future model intercomparison projects. These questions will be illustrated by several recent studies aimed at constraining CMIP6 projections and, hopefully, with a fresh although critical look on the forthcoming CMIP7 project.

How to cite: Douville, H.: Confronting Earth System Model Trends with Observations: The Good, the Bad, and the Ugly, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-2903, https://doi.org/10.5194/egusphere-egu24-2903, 2024.

The reversibility of a wide range of components of the earth system was investigated by comparing forward and time-reversed 
historical and future simulations of a coupled earth system model known as the Beijing Normal University earth system 
model. Many characteristics of the climate system, including the surface temperature, ocean heat content (OHC), convective 
precipitation, total runof, ground evaporation, soil moisture, sea ice extent, and Atlantic Meridional Overturning Circulation, 
did not fully return to their initial values when the historical or future natural and anthropogenic forcing agents were reversed. 
The surface temperature and OHC declines lagged behind the decline in greenhouse gases (GHGs). Reverses in other variables occurred in direct response to the decline in GHGs. The sea level increased, even after all of the forces returned to the 
original values. Furthermore, most of the climate variables did not return to their original values because of thermal inertial. 
The end states of variables, other than those related to thermal storage, mainly depended on the original state of the natural 
and anthropogenic forces, and were unafected by the future growth rate of the GHGs. The climate policy implication of this 
study is that climate change cannot be completely reversed even if all the external forces are returned to their initial values

How to cite: Yang, S.: Reversibility of historical and future climate change with a complex earth system model, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-2964, https://doi.org/10.5194/egusphere-egu24-2964, 2024.

Untangling the impact of anthropogenic forcing on drought is particularly essential for climate change mitigation. Previous studies have indicated that anthropogenic forcing exacerbates drought, raising concerns about global drought evolution, yet little is known about the impact of anthropogenic forcing on drought evolution through anthropogenic greenhouse gases (GHGs) and aerosol (AER). We integrated standardized precipitation evapotranspiration index (SPEI) data under different experiments to study drought development with Coupled Model Intercomparison Project Phase 6 (CMIP6) global climate models (GCMs). Subsequently, we conducted sensitivity analyses to quantify the changes in drought sensitivity to anthropogenic greenhouse gas (DSG) and aerosol (DSA) from 1900 to 2014. Our findings reveal different effects of AER and GHGs on drought trends during three periods. Specifically, GHGs slightly increased global drought severity in the early 20th century. Conversely, from 1956 to 1982, the drought-mitigating effects of AER surpassed the drought-enhancing effects of GHGs, and the global was humidified. Then, from 1982 to 2014, the trends of increasing DSG and decreasing DSA suggest that an important global shift is taking place. GHG re-emerged as the primary driver, thus leading to increased drought severity. Taken together, these findings elucidate how anthropogenic forcing impacts global drought severity through drought-enhancing effects of GHGs and drought-mitigating effects of AER, which provides new insights into understanding the risk of anthropogenic impacts on global drought.

How to cite: Li, H.: Anthropogenic forcing inconsistently exacerbates global drought, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-4371, https://doi.org/10.5194/egusphere-egu24-4371, 2024.

EGU24-5200 | Orals | ITS1.10/CL0.1.9

Anthropogenic aerosol forcing in CMIP from prescribed optical and cloud microphysical properties 

Stephanie Fiedler, Sabine Bischof, Natalia Sudarchikova, Rachel M. Hoesly, and Steven J. Smith

Anthropogenic aerosol forcing is quantitatively uncertain affecting the ability to constrain the climate response to anthropogenic perturbations. Climate models participating in the Coupled Model Intercomparison Project (CMIP) use different methods to incorporate direct and cloud-mediated aerosol effects. Some models in CMIP6 used prescribed anthropogenic aerosol optical properties and associated effects on cloud droplet number concentrations from the Simple Plumes parameterization fitted to the Max-Planck-Institute for Meteorology’s Aerosol Climatology version 2 (MACv2-SP). MACv2-SP was originally designed for the use in a subset of experiments for the Radiative Forcing Model Intercomparison Project to better understand the model diversity in aerosol forcing (Fiedler et al., 2023). The final uptake of MACv2-SP for research was, however, much broader. In the context of CMIP, the implementation of MACv2-SP in several climate models led to the request for new MACv2-SP input data that are consistent with updated emissions, e.g., in the framework of CovidMIP (Fiedler et al., 2021) and now in preparation for CMIP7 via the CMIP Climate Forcings Task Team. Moreover, MACv2-SP also serves in creating seasonal and decadal predictions, and satellite products.

We will therefore derive and freely provide new data for the anthropogenic aerosol optical properties and their cloud-mediated effects based on newly available emissions. The next data version of MACv2-SP is currently in preparation for interests in using CMIP6plus compliant boundary data. It will use the historical emission data for aerosols and their precursors from the new release of the Community Emission Data System (CEDS), which will be published at the beginning of 2024. The new emissions will allow us to revise and extent the historical data for MACv2-SP to include years after 2014. Expected changes compared to the MACv2-SP data used in CMIP6 are improved aerosol optical depth over some land regions in recent years, where the observations developed differently compared to assumptions in the scenarios. We will further translate uncertainty in the emission data to expected differences in the aerosol forcing. In addition to the new data for CMIP6plus, a new development of the simple plumes approach will be made for an assessment of the radiative forcing and climate response to aerosols from severe wild fires in recent years that are not represented by CMIP6 models.

Fiedler, S., Wyser, K., Rogelj, J. and van Noije, T. (2021) Radiative effects of reduced aerosol emissions during the COVID-19 pandemic and the future recovery.  Atmospheric Research, 264 . Art.Nr. 105866. DOI 10.1016/j.atmosres.2021.105866.

Fiedler, S., van Noije, T., Smith, C. J., Boucher, O., Dufresne, J., Kirkevåg, A., Olivié, D., Pinto, R., Reerink, T., Sima, A. and Schulz, M. (2023) Historical Changes and Reasons for Model Differences in Anthropogenic Aerosol Forcing in CMIP6. Geophysical Research Letters, 50 (15). Art.Nr. e2023GL104848. DOI 10.1029/2023GL104848.

How to cite: Fiedler, S., Bischof, S., Sudarchikova, N., Hoesly, R. M., and Smith, S. J.: Anthropogenic aerosol forcing in CMIP from prescribed optical and cloud microphysical properties, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5200, https://doi.org/10.5194/egusphere-egu24-5200, 2024.

EGU24-5527 | Orals | ITS1.10/CL0.1.9

Regional impacts poorly constrained by climate sensitivity  

Ranjini Swaminathan, Jacob Schewe, Jeremy Walton, Klaus Zimmermann, Richard Betts, Chantelle Burton, Chris Jones, Colin Jones, Matthias Mengel, Christopher Reyer, Andrew Turner, and Katja Weigel

Climate risk assessments must account for a wide range of possible future changes, so scientists often use many climate models in order to fully explore the range of potential changes in regional climates and their impacts. Many of the latest-generation global climate models have high values of effective climate sensitivity (EffCS), which are unlikely according to independent estimates of EffCS. It has been argued that these “hot” models are unrealistic and should therefore be excluded from analyses of climate change impacts. However, whether this would really improve regional impact assessments, or actually make them worse, is unclear. Here we show that there is no universal relationship between EffCS and projected changes in important climatic impact drivers. Analysing three different impacts - heavy rainfall, meteorological drought, and fire weather in important world regions, we find a significant correlation with EffCS only in some regions and for some metrics. Moreover, even in those cases, internal variability has a larger effect on projected changes than has EffCS. This means that impact studies should not select climate models based solely on their EffCS, which does not help constrain projections and may potentially neglect realistic impacts in models deemed “unrealistic” on the basis of their sensitivity. We recommend that model selection or filtering must be based on a more specific evaluation of models vis-à-vis the impact of interest.

How to cite: Swaminathan, R., Schewe, J., Walton, J., Zimmermann, K., Betts, R., Burton, C., Jones, C., Jones, C., Mengel, M., Reyer, C., Turner, A., and Weigel, K.: Regional impacts poorly constrained by climate sensitivity , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5527, https://doi.org/10.5194/egusphere-egu24-5527, 2024.

EGU24-5895 | ECS | Orals | ITS1.10/CL0.1.9

Pathways for avoiding ocean biogeochemical damage: Mitigation targets, mitigation options, and projections 

Timothée Bourgeois, Olivier Torres, Friederike Fröb, Aurich Jeltsch-Thömmes, Giang T. Tran, Jörg Schwinger, Thomas L. Frölicher, Fortunat Joos, David Keller, Andreas Oschlies, and Laurent Bopp

Tipping points are thresholds beyond which large, abrupt and possibly irreversible changes in the climate system or in large scale ecosystems would occur. The crossing of such tipping points under anthropogenic forcing poses a threat to biodiversity, food security, and human societies. However, due to the complexity of the processes involved, it remains notoriously difficult to determine exact thresholds that need to be avoided to stay within a “safe operating space” for humanity. Here, we map, for a variety of mitigation metrics, the crossing of thresholds, which we define to represent a wide range of deviations from the unperturbed state. We assess the crossing of these thresholds in a wide range of plausible future emission pathways: two climate mitigation scenarios (one with a strong overshoot) and one no-mitigation high-emissions scenario. These scenarios are simulated by the latest generation of Earth system models and by two Earth system models of intermediate complexity, for which we created large perturbed-parameter ensembles. Using this comprehensive model database we provide estimates of when and at which warming level 4 mitigation targets (thresholds) for 14 different impact metrics are exceeded along with an assessment of uncertainties. We find that under the high-emissions scenario, even the highest thresholds for many of the impact metrics are exceeded with high confidence, such as the expansion of ocean areas that are undersaturated with respect to aragonite, decreases in plankton biomass, Arctic summer sea ice extent, strength of the Atlantic meridional overturning circulation (AMOC), and subsurface oxygen concentration. The risk of exceeding a given mitigation target decreases under low-emissions and overshoot scenarios. Yet, exceedance of ambitious targets for aragonite undersaturation, Arctic summer sea ice extent, and steric sea level rise (SSLR) are projected to be difficult to avoid (high confidence) even under the low-emissions scenario. The overshoot scenario reduces the risk of exceeding mitigation targets related to Arctic summer sea ice extent, SSLR, AMOC and plankton biomass compared to the high-emissions scenario, particularly in the long-term. Uncertainties in Earth system model projections of net primary production prevent us from concluding on the risk of mitigation target exceedance for this impact metric.

How to cite: Bourgeois, T., Torres, O., Fröb, F., Jeltsch-Thömmes, A., Tran, G. T., Schwinger, J., Frölicher, T. L., Joos, F., Keller, D., Oschlies, A., and Bopp, L.: Pathways for avoiding ocean biogeochemical damage: Mitigation targets, mitigation options, and projections, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5895, https://doi.org/10.5194/egusphere-egu24-5895, 2024.

Over four decades, CMIP has driven massive improvements in the modelled representation of the Earth system, whilst also seeing huge growth in its scope and complexity. In its most recent phase, CMIP6, a broad spectrum of questions continues to be answered across twenty-four individual model intercomparison projects (MIPs). This science improves process understanding and assesses the climate’s response to forcing, systematic biases, variability, and predictability in line with WCRP Scientific Objectives. CMIP and its associated data infrastructure have become essential to the Intergovernmental Panel on Climate Change (IPCC) and other international and national climate assessments, increasingly including the downstream mitigation, impacts, and adaptation communities.

However, despite the invaluable science produced from CMIP6 data, many challenges were still faced by the model data providers, the data delivery infrastructure, and users, which need to be addressed moving forwards. A specific challenge in CMIP6 was the burden placed on the modelling centres, in part due to the large number of requested experiments and delays in the preparation of the CMIP6 forcing datasets and climate data request.

The CMIP structure is evolving into a continuous, community-based climate modelling programme to tackle key and timely climate science questions and facilitate delivery of relevant multi-model simulations. This activity will be supported by the design of experimental protocols, an infrastructure that supports data publication and access, and quasi-operational extension of historical forcings.  A subset of experiments is proposed to be fast-tracked to deliver climate information for national and international climate assessments and informing policy and decision making. The CMIP governing panels are coordinating community activities to reduce the burden placed on modelling centres, continue to enhance novel and innovative scientific activities, and maximise computational efficiencies, whilst continuing to deliver impactful climate model data.

How to cite: Hewitt, H. and Dunne, J. and the CMIP Panel and IPO: Evolving The Coupled Model Intercomparison Project (CMIP) To Better Support The Climate Community And Future Climate Assessments, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-6364, https://doi.org/10.5194/egusphere-egu24-6364, 2024.

Global climate change projections, such as those from the Coupled Model Intercomparison Project phase 6 (CMIP6), are still subject to substantial modelling uncertainties. A variety of Emergent Constraints (ECs) have been suggested to address these uncertainties, but remain heavily debated in the scientific community. Still, the central idea behind ECs to relate future projections to already observable quantities has no real substitute.

Here we discuss machine learning (ML) approaches for new types of controlling factor analyses (CFA) as a promising alternative. The principal idea is to use ML to find climate-invariant relationships in historical data, which also hold approximately under strong climate change scenarios. On the basis of existing big data archives such as those from the CMIPs, these climate-invariant relationships can be validated in perfect-climate-model frameworks.

From a ML perspective, we argue that CFA are promising for three reasons: (a) they can be objectively validated both for present-day data and future data and (b) they provide more direct - by design physically-plausible - links between historical observations and potential future climates compared to ECs and (c) they can take higher dimensional relationships into account that better characterize the still complex nature of large-scale emerging relationships. We highlight these advantages for three examples in the form of constraints on climate feedback mechanisms (clouds [1], stratospheric water vapour [2]) and forcings (aerosol-cloud interactions).

References:

1. Ceppi P. and Nowack P. Observational evidence that cloud feedback amplifies global warming, Proceedings of the National Academy of Sciences 118 (30), e2026290118 (2021). https://doi.org/10.1073/pnas.2026290118

2. Nowack P., Ceppi P., Davis S.M., Chiodo G., Ball W., Diallo M.A., Hassler B., Jia Y., Keeble J., and Joshi M. Response of stratospheric water vapour to warming constrained by satellite observations, Nature Geoscience 16, 577-583 (2023). https://doi.org/10.1038/s41561-023-01183-6

How to cite: Nowack, P. and Watson-Parris, D.: Why all emergent constraints are wrong but some are useful - a machine learning perspective, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-6750, https://doi.org/10.5194/egusphere-egu24-6750, 2024.

The solar forcing dataset prepared for the 6th round of the Coupled Model Intercomparison Project (CMIP6) has been used extensively in climate model experiments. Recently, an International Space Science Institute (ISSI) Working Group was established to revisit the solar forcing recommendations in order to define a roadmap for building a revised solar forcing dataset for the upcoming 7th round of CMIP (Funke et al., 2023). This new dataset will introduce changes in the radiative forcing of climate either directly, or indirectly via changes in atmospheric composition. In CMIP6, the solar forcing consisted of both a total solar irradiance (TSI), along with a spectrally resolved solar irradiance (SSI). The TSI for solar minimum was set to 1360.8±0.5Wm-2 and the SSI covered the 10nm to 100mm spectral region. A similar approach is proposed for CMIP7 except for two major aspects of the reconstruction: 1) the definition of the reference spectrum for the quite Sun; 2) the temporal variability. The major difference between the proposed CMIP7 SSI quite sun reference spectrum and that used for CMIP6 is the spectral shape. The new SSI spectrum has an irradiance that is 1-5% higher in the visible band and lower by 1-2% in the Near-IR wavelength range (1000-2000nm). The solar temporal variability in the CMIP6 and CMIP7 reconstructions are based on both the NRLSSI2 and SATIRE reconstructions. These reconstructions have been improved in preparation for CMIP7 and the aim is for both reconstructions to use the same reference spectrum and be driven by the same solar proxies. In this work we used the Whole Atmosphere Community Climate Model (WACCM) to examine the chemical and climate implications of the proposed CMIP7 solar forcing updates compared to the CMIP6 approach. WACCM is a chemistry-climate model that extends from the surface to 140km. The horizontal resolution is ~1degree. WACCM has a detailed representation of chemical and dynamical processes from the troposphere through the lower thermosphere. We examined the “chemical only” impacts of the solar forcing choice by running WACCM in the specified dynamics mode using NASA Modern-Era Retrospective analysis for Research and Applications Version 2 (MERRA2). The “climate” impacts were derived by running the model with interactive dynamics coupled to a deep ocean. Conclusions from this work will support the development of the next version of WACCM for participation in the CMIP7 assessment.

Funke, B., Dudok de Wit, T., Ermolli, I., Haberreiter, M., Kinnison, D., Marsh, D., Nesse, H., Seppälä, A., Sinnhuber, M., and Usoskin, I.: Towards the definition of a solar forcing dataset for CMIP7, Geosci. Model Dev. Discuss. https://doi.org/10.5194/gmd-2023-100.

 

How to cite: Kinnison, D., Marsh, D., and Tilmes, S.: Evaluation of the chemistry and climate impact of the new solar forcing dataset for CMIP7 using the Whole Atmosphere Community Climate Model, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-6774, https://doi.org/10.5194/egusphere-egu24-6774, 2024.

EGU24-7042 | ECS | Orals | ITS1.10/CL0.1.9

The Competition Between Anthropogenic Aerosol and Greenhouse Gas Forcing is Revealed by North Pacific Water-mass Changes 

Jia-Rui Shi, Susan Wijffels, Young-Oh Kwon, Lynne Talley, and Sarah Gille

Modelled water-mass changes in the North Pacific thermocline from CMIP6, both in the subsurface and at the surface, reveal the impact of the competition between anthropogenic aerosols and greenhouse gases (GHGs) over the past 6 decades. The aerosol effect overwhelms the GHG effect during 1950-1985 in driving salinity changes on density surfaces, while after 1985 the GHG effect dominates. These subsurface water-mass changes are traced back to changes at the surface, of which ~70% stems from the migration of density surface outcrops, equatorward due to regional cooling by anthropogenic aerosols and subsequent poleward due to warming by GHGs. Ocean subduction connects these surface outcrop changes to the main thermocline. Both observations and models reveal this transition in climate forcing around 1985 and highlight the important role of anthropogenic aerosol climate forcing on our oceans’ water masses.

How to cite: Shi, J.-R., Wijffels, S., Kwon, Y.-O., Talley, L., and Gille, S.: The Competition Between Anthropogenic Aerosol and Greenhouse Gas Forcing is Revealed by North Pacific Water-mass Changes, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-7042, https://doi.org/10.5194/egusphere-egu24-7042, 2024.

EGU24-7159 | Orals | ITS1.10/CL0.1.9

Unveiling the Subjectivity in Ranking of NEX-GDDP-CMIP6 Climate Models Over Munneru River Basin, India 

Venkata Reddy Keesara, Eswar Sai Buri, and Loukika Kotapati Narayanaswamy

Regional climate modelling has evolved significantly, offering versatile applications across various scales and resolutions. This study aims to provide a comprehensive framework for selecting top five Climate Models at each grid for climate variables in the Munneru River Basin, comes under Lower Krishna River Basin, India. Employing the NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP) datasets, which are derived from General Circulation Model (GCM) runs under the Coupled Model Intercomparison Project Phase 6 (CMIP6), is compared with the observed precipitation, maximum, and minimum temperature datasets obtained from the Indian Meteorological Department (IMD). These datasets have a spatial resolution of (0.25° × 0.25°) and available from 1970 to 2014. The methodology adopted in this study uses advanced statistical techniques to evaluate the performance of the CMIP6 models. The study incorporates Multicriterion Decision-Making Techniques (MCDM) and Group Decision-Making (GDM) methodologies within the Reliable-Ensemble Averaging (REA) framework. MIROC-ES2L, GISS-E2-1-G and TaiESM1 are the top ranked models for precipitation data. Whereas, BCC-CSM2-MR, ACCESS-ESM1-5 and GFDL-CM4_gr2 obtained as most suitable RCMs for maximum temperature data. For minimum temperature data, MIROC-ES2L, KIOST-ESM and MIROC6 obtained as top ranked CMIP6 models. The projected climate variables, including precipitation, maximum temperature and minimum temperatures, under three distinct Shared Socioeconomic Pathways (SSP) scenarios: SSP 245, SSP 370 and SSP 585 extending up to the year 2100. The spatio-temporal analysis encompasses key climate parameters, identifying trends, variations, and potential anomalies in the Munneru River Basin. This study contributes to the broader context of regional climate modelling research and enhances our understanding of the Munneru River Basin's climate dynamics. The research findings presented in this study aim to understand the methodological advancements in regional climate modelling, performance assessments of CMIP6 models and the application of CMIP6 models in regional process studies.

How to cite: Keesara, V. R., Buri, E. S., and Kotapati Narayanaswamy, L.: Unveiling the Subjectivity in Ranking of NEX-GDDP-CMIP6 Climate Models Over Munneru River Basin, India, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-7159, https://doi.org/10.5194/egusphere-egu24-7159, 2024.

The influence of anthropogenic (ANT) activity and the other external factors on extreme temperature changes over the mid–high latitudes of Asia are analysed using the different forcing simulations from the Coupled Model Intercomparison Project Phase 6 (CMIP6) models. The optimal fingerprinting technique and the probability ratio (PR) are employed to detect and quantify the influences of the external forcings on extreme temperature changes, which include annual maximum daily maximum temperature (TXx), annual minimum daily minimum temperature (TNn). Results indicate that TXx and TNn have increased from 1979 to 2014, and the simulations from historical (anthropogenic plus natural; ALL), greenhouse gas (GHG), and anthropogenic (ANT) experiments reasonably reproduce the spatiotemporal characteristics of extreme temperatures. Based on the optimal fingerprinting method, the impact of ANT forcing, in which GHG forcing is critical, can be detected in the changes of warm extremes and cold extremes. ANT and NAT forcings are separately detectable for warm extremes. GHG forcing can be separated from other ANT forcings for cold extremes but not warm extremes. Furthermore, the analysis applying the PR method shows that the probability of observed warm extremes that occur once in 20 years over the mid–high latitudes of Asia has risen by approximately three times owing to the anthropogenic influence, whereas the cold extremes became once in 50 years. Briefly, the increased anthropogenic activity has exacerbated the warm extremes and soothed the cold extremes over the mid–high latitudes of Asia during the past decades.

How to cite: Jiang, W. and Chen, H.: Anthropogenic influence on extreme temperature changes over the mid–high latitudes of Asia, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-7881, https://doi.org/10.5194/egusphere-egu24-7881, 2024.

EGU24-8659 | ECS | Posters on site | ITS1.10/CL0.1.9

Studying the pre-industrial to present-day radiative forcing from wildfire aerosols using EC-Earth 

Rafaila Nikola Mourgela, Eirini Boleti, Konstantinos Seiradakis, Klaus Wyser, Phillipe Le Sager, Angelos Gkouvousis, and Apostolos Voulgarakis

The occurrence of more frequent and extensive wildfires is a widely discussed potential consequence of climate change, stemming from a vicious cycle of cause and effect in which wildfires are taking part. Global and regional wildfire patterns and changes are driven by climate-related factors such as land cover, heat waves, and rainfall patterns. Wildfires can, in turn, cause climate perturbations through the emissions of greenhouse gases and aerosols, and through the alteration of landscapes. For these reasons, understanding wildfires and their interactions with the Earth’s atmosphere is crucial for assessing a potentially important climate feedback.

The current study focuses on the interconnection between wildfires and the atmosphere, and more precisely on the radiative effect of wildfire emissions on a global scale. To achieve this, simulations using the EC-Earth Earth System Model (ESM) were employed. More specifically, a 30-year atmosphere-only (fixed-SST) control simulation was performed for the pre-industrial period, and repeated with the wildfire aerosol emissions set to present-day values. Using the output of these simulations, we estimate the global effective radiative forcing (ERF) of wildfire-emitted aerosols from pre-industrial times to the present day. We also identify which regions experience stronger forcing from wildfire emissions, and separate the role of black carbon and organic carbon in driving this forcing. Finally, we identify mechanisms that lead to fast atmospheric adjustments following wildfire emissions, including changes in temperatures, humidity, precipitation, and clouds. This analysis contributes to the better understanding of the historical evolution of radiative forcing and the role of wildfires in the climate system.

 

How to cite: Mourgela, R. N., Boleti, E., Seiradakis, K., Wyser, K., Le Sager, P., Gkouvousis, A., and Voulgarakis, A.: Studying the pre-industrial to present-day radiative forcing from wildfire aerosols using EC-Earth, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-8659, https://doi.org/10.5194/egusphere-egu24-8659, 2024.

EGU24-8690 | ECS | Posters on site | ITS1.10/CL0.1.9

How your aerosol implementation choices affect your model’s climate system response 

Estela Monteiro and Nadine Mengis

Anthropogenic activities have disrupted the energy balance of the planet since preindustrial era through, among other drivers, the emission of various greenhouse gases and aerosols. The largest uncertainty to current climate forcing and future projections relates to the effect of aerosols. Their different impacts on the planet’s radiative balance, that is, with direct radiative and indirect cloud interaction forcing, need to be considered accurately in simple policy-informing climate models. Especially in the context of high ambition mitigation scenarios, variability in the future development of spatiotemporal aerosol forcing will have a relatively large impact on climate projections. Accordingly, an accurate inclusion of the relevant processes onto the modeling scheme, such as the spatiotemporal level of detail chosen when accounting for aerosol forcing in simple(r) climate models must be carefully considered.

Here we explore the impact of different aerosols implementation schemes in an intermediate complexity Earth system model configuration with an energy moisture balance model (UVic ESCM, version 2.10). While the global mean forcing is the same for all scenarios, we vary spatial and temporal resolution of optical depth maps or implement aerosol forcing as direct radiative forcing to the Earth system. These schemes are applied to relevant ambitious mitigation scenarios aiming at temperature stabilization, which will become especially relevant in the upcoming CMIP exercises. Using a newly developed assessment framework, we will provide insights into the impacts of this model implementation choice onto future temperature development, the carbon cycle and heat uptake processes. Ultimately these insights aim to improve, constrain and design better scenario simulations that are both applicable and relevant to the scientific and decision-making communities.

How to cite: Monteiro, E. and Mengis, N.: How your aerosol implementation choices affect your model’s climate system response, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-8690, https://doi.org/10.5194/egusphere-egu24-8690, 2024.

EGU24-9312 | ECS | Posters on site | ITS1.10/CL0.1.9

Historical volcanic sulfur emissions and stratospheric sulfate aerosol optical properties for CMIP7 

Thomas Aubry, Anja Schmidt, Mahesh Kovilakam, Matthew Toohey, and Michael Sigl

Explosive volcanic eruptions injecting gases and aerosols into the stratosphere are a key natural driver of climate variability at annual to centennial timescales. They are thus one of the forcings considered by the Coupled Model Intercomparison Project (CMIP) Climate Forcings Task Team, in charge of identifying and implementing the next generation forcings for current and future generations of Earth System models. This presentation will provide an overview of ongoing work to produce volcanic forcing datasets for phase 7 of CMIP (CMIP7).

The datasets we produce will cover the period from 1750 to 2022 at version 1 to meet to the need of modelling groups who might run extended historical simulations starting in 1750 instead of 1850. We are producing one volcanic stratospheric sulfur emission dataset catering for the needs of models which have a prognostic interactive stratospheric aerosol scheme, as well as a stratospheric sulfate aerosol optical property dataset required by models that cannot interactively simulate stratospheric sufate aerosols. For the satellite era (from 1979 onwards), sulfur emissions and sufate aerosol optical properties are based on NASA’s MSVOLSO2L4 and GloSSAC datasets, respectively. For the pre-satellite era (1750-1978), the emission dataset is based on ice-core datasets complemented by the geological record, whereas the aerosol optical property dataset is directly derived from emissions using the latest version of the Easy Volcanic Aerosol (EVA) model. This ensures methodological consistency between our emission and optical property datasets, further enhanced by the fact that EVA is calibrated using the same datasets we use for the satellite era. Our choice of methods aims to maximize consistency with methodologies used in individual model intercomparison projects (e.g. PMIP and VolMIP). A major focus of our task team is to produce well-documented datasets, which includes extensive meta-data and flags, detailed documentation, and provision of open-access scripts used to create the datasets, which should facilitate future development and operationalization by the community. We also discuss the most critical challenges for providing accurate volcanic forcing datasets, including the under-recording of small-to-moderate magnitude eruptions before the satellite era, and the Hunga Tonga-Hunga Ha'apai 2022 eruptions, which injected relatively small amounts of sulfur, but 150 Tg of water into the stratosphere.

How to cite: Aubry, T., Schmidt, A., Kovilakam, M., Toohey, M., and Sigl, M.: Historical volcanic sulfur emissions and stratospheric sulfate aerosol optical properties for CMIP7, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-9312, https://doi.org/10.5194/egusphere-egu24-9312, 2024.

EGU24-9994 | Orals | ITS1.10/CL0.1.9 | Highlight

Biomass burning emissions since the pre-industrial and into the future; progress and challenges 

Guido van der Werf and Margreet van Marle

Fires impact a suite of radiative forcing agents but fire is one of the most challenging sources of emissions to model due to a large degree of stochasticism and a wide range of climatic and human influences that can both increase and decrease the occurrence of fires. Although many Earth system models now account for fires, there is still a need for a coherent and consistent community dataset to intercompare and constrain models. We developed a historic dataset combining satellite data over the past two decades with proxy data and fire models for use in CMIP6. Since then, new satellite data has indicated that global burned area may be much higher than previously thought and several regional datasets have shed light on the question whether fire emissions are now higher or not than in the pre-industrial era. We show how the latest insight and developments will be used to construct an updated fire emissions dataset for CMIP7, and show which fire categories carry the largest uncertainty, both for the past and into the future.

How to cite: van der Werf, G. and van Marle, M.: Biomass burning emissions since the pre-industrial and into the future; progress and challenges, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-9994, https://doi.org/10.5194/egusphere-egu24-9994, 2024.

EGU24-10136 | ECS | Orals | ITS1.10/CL0.1.9

CMIP6 models evaluation using multi-resolution analysis and satellite observations : study of the atmospheric water vapor  

Cedric Gacial Ngoungue Langue, Hélène Brogniez, and Philippe Naveau

Water vapor is one of the fundamental atmospheric components, and as such, is one  Essential Climate Variable  (ECV) monitored by the Global Climate Observing System. In this work, the global water vapor Climate Data Record (CDR) generated within the ESA Water Vapor climate change initiative project (WV_cci) is used as reference (daily, 0.1°, 2003-2014) to evaluate a sample of the Coupled Model Intercomparison Project phase 6 (CMIP6) as well as the fifth generation ECMWF reanalysis (ERA5), with a focus on temporal signal decomposition. This temporal decomposition is performed using multi-resolution analysis (MRA). MRA is a mathematical tool which consists of decomposing a signal into its subcomponents on different time scales. Using this tool, the representation of the total column water vapor over the tropics in the CMIP6 models and ERA5 can be assessed separately from daily to annual and decadal time scales, including monthly and seasonal time scales. This approach is powerful for the  identification of  the relevant time scales for which CMIP6 predictions are most reliable. Hence, at the global-tropical scale, the MRA decomposition of the water vapor signal shows a good correlation between CMIP6 and WV_cci on both seasonal (2 - 8 months) and annual (1 - 1.4 year) time scales. Using a linear regression, we attempt to reconstruct the WV_cci signal using the CMIP6 models and ERA5 as explanatory variables based on the correlation found between the products and WV_cci at each level of decomposition. Such reconstruction highlights the scales of variability that are closest to the observed one. The presentation will detail the MRA approach and the most prominent results, as well as an extension to other parameters linked to atmospheric water vapor distribution, namely cloud cover and types and sea surface temperature. 

How to cite: Ngoungue Langue, C. G., Brogniez, H., and Naveau, P.: CMIP6 models evaluation using multi-resolution analysis and satellite observations : study of the atmospheric water vapor , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-10136, https://doi.org/10.5194/egusphere-egu24-10136, 2024.

EGU24-10382 | ECS | Orals | ITS1.10/CL0.1.9

A key role of surface atmospheric circulation changes in setting global ocean warming magnitude 

Kwatra Sadhvi, Matthieu Lengaigne, Jérôme Vialard, Vincent Danielli, Gopika Suresh, and Suresh Iyyappan

Surface air-sea feedbacks play a pivotal role in modulating the amplitude of global ocean warming. Zhang and Li (2014, ZL14) introduced a simple theoretical framework to identify the driving processes responsible for the Sea Surface Temperature (SST) increase under global warming. This method involves decomposing changes in latent and upwelling longwave surface heat fluxes into two parts: one tied to direct atmospheric forcing and the other directly associated with local (SST) changes, termed feedback. Applying this heat budget equation across 53 CMIP5 and 6 models underscores the pivotal role of increased surface downwelling longwave radiation (DLR) in steering the amplitude of future global ocean warming. However, ZL14 solely considered DLR as a direct forcing, overlooking its substantial feedback response to surface warming.

In this study, we employ a novel methodology from Shakespeare and Roderick (2022, SR22) to decompose DLR changes into a direct radiative forcing and SST-related feedbacks, evaluating the implications of integrating the DLR feedback in the ZL14 framework. Our analysis is in line with SR22’s findings across 5 CMIP5 models, our results across 53 models indicate that roughly 90% of DLR increase emerges from feedbacks associated with the rising SST. The large ocean heat capacity transfers warming to the overlying atmosphere, increasing its DLR primarily through direct air temperature increase and the increasing greenhouse effect associated with increased water vapour.

Incorporating the DLR feedback in ZL14 framework yields a dominant effect of latent heat flux forcing on global ocean warming for both multi-model mean and intermodel diversity. This latent heat flux forcing is related to the evaporative cooling modulation associated with projected changes in the surface atmospheric circulation, and is highly correlated with the magnitude of the global average warming. This underscores the substantial influence of projected atmospheric circulation changes on the level of global average warming.

How to cite: Sadhvi, K., Lengaigne, M., Vialard, J., Danielli, V., Suresh, G., and Iyyappan, S.: A key role of surface atmospheric circulation changes in setting global ocean warming magnitude, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-10382, https://doi.org/10.5194/egusphere-egu24-10382, 2024.

Large uncertainty in model predictions of land carbon responses to climate change has been ubiquitously demonstrated in model intercomparison projects (MIPs). The large uncertainty become a major impediment in advancing climate change prediction. Thus, it is imperative to identify sources of the uncertainty before we can fully understand and address the uncertainty issue. In this presentation, I show a novel matrix approach to analytically pin down the sources of model uncertainty in predicting carbon dynamics in response to rising atmospheric CO2 concentration and increasing temperature. We developed a matrix-based MIP by converting the carbon cycle module of eight land models (i.e., TEM, CENTURY4, DALEC2, TECO, FBDC, CASA, CLM5 and ORCHIDEE) into eight matrix models. In response to rising atmospheric CO2 concentration and increasing temperature, predicted ecosystem net primary production (NPP), net ecosystem production (NEP), and net ecosystem carbon storage spread among the eight models as simulations go over time. We applied the traceability analysis method to decompose simulated carbon dynamics to their traceable components according to the matrix equations. Our analysis indicates that the uncertainty among the eight models was mainly due to inter-model difference in baseline carbon residence time and environmental scalar. Once the sources of model uncertainty were identified, we sequentially standardized model parameters to shrink simulated ecosystem carbon storage and NEP to almost none. Our study demonstrates that the sources of uncertainty in carbon cycle modeling can be precisely traced to model structures and parameters, regardless of their complexity, so that the uncertainty issue for MIPs can be precisely understood and well addressed.

How to cite: Luo, Y.: Uncertainty spreading and shrinking among eight land carbon cycle models in response to climate change, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-10567, https://doi.org/10.5194/egusphere-egu24-10567, 2024.

So-called “radiative" or "rapid" adjustments describe the surface-temperature-independent response of the climate state to an instantaneous radiative forcing. However, the term “rapid” can be misleading since various processes are considered adjustments, which appear on timescales of hours (e.g. aerosol-cloud-interactions) to month (e.g. stratospheric temperature change) or even longer timescales (e.g. adjustments of biosphere and cryosphere). On time scales of months and longer, differentiating between adjustments and feedbacks becomes increasingly difficult. Depending on the scientific method the definition of “adjustments” and which processes are considered can vary. Nevertheless, a good understanding of these processes is crucial for improving climate models and advance our general understanding of how the Earth climate system reacts to a forcing.

The abrupt-solm4p experiment from CFMIP (Cloud Forcing Model Intercomparison Project) from CMIP6 (Coupled Model Intercomparison Project phase 6) simulates an instantaneous reduction of the solar constant by 4% branching from a pre-industrial control run on 01/01/1850. We analysed changes in geographical distribution as well as global mean temporal development of various climate variables (e.g. surface and atmospheric temperature, precipitation, humidity), different cloud properties (e.g. cloud cover, column integrated liquid and ice water), as well as radiative fluxes at top of atmosphere and the cloud radiative effect. The different variables were evaluated on timescales of hours, days, months and up to 150 years after the onset of forcing, in order to learn more about the timing of different adjustment processes. Four different models participated in the abrupt-solm4p experiment. Their outputs were compared and possible source of differences discussed. During the first hours all models unanimously simulate decreasing surface and atmospheric temperature, especially strong in the Antarctica, which experiences 24hr irradiation at the onset of forcing. In the beginning, the stratospheric cooling is strongest. The moderate cooling of the troposphere leads to increased condensation and thereby increased cloud cover, even in Northern latitudes, that do not directly experience the forcing, and strengthened precipitation in the tropics. 

In a next step, we plan to compare the results from abrupt-solm4p (CFMIP) to simulations of a homogeneous stratospheric sulfate scattering-layer and to the volc-pinatubo-full-experiment (VolMIP). We expect some similarities between the simulated adjustments in these experiments, because in all three cases, incoming solar radiation is reduced in the troposphere and at surface level. However, more realistic experiments, like the volc-pinatubo experiment are expected to show more complex adjustments and the comparison to more simplified experiments like abrupt-solm4p might provide valuable insights to adjustment processes after volcanic eruptions.

How to cite: Lange, C. and Quaas, J.: Radiative adjustments after a 4%-reduction of the solar constant, based on data from the abrupt-solm4p experiment (CFMIP from CMIP6), EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-11255, https://doi.org/10.5194/egusphere-egu24-11255, 2024.

EGU24-12768 | Posters on site | ITS1.10/CL0.1.9

Revisiting the ‘transfer function’ of stratospheric sulfur loading from volcanic sulfate deposited on polar ice sheets 

Andrea Burke, Herman Fuglestvedt, Liz Thomas, Lauren Marshall, and Kirstin Krüger

Records of the volcanic forcing of climate prior to the satellite era depend on scaling the flux of sulfate deposited on polar ice sheets­ using a ‘transfer function’, a number calibrated based on radioactivity in Greenland from thermonuclear testing as well as Antarctic sulfate flux records from the 1991 Pinatubo eruption (e.g. Gao et al., 2007). For high latitude eruptions, this transfer function is based solely on model simulations of sulfate flux to Greenland from the Icelandic Laki eruption in 1783 and the Alaskan Katmai/Novarupta eruption in 1912 (Gao et al., 2007).  Since the initial determination of this transfer function, the number of ice cores containing sulfate from the Pinatubo eruption has increased eight-fold, and sulfur isotope measurements at high resolution over sulfate peaks in the ice has allowed for discrimination between stratospheric sulfate and sulfate transported at lower levels in the atmosphere from different sources (e.g. Burke et al., 2023). Here we revisit the estimation of the transfer function in light of these new data-based constraints from eruptions in the 20th century, and we reassess the uncertainty associated with the application of a single transfer function across volcanic eruptions in the past.

 

Gao, C., Oman, L., Robock, A. and Stenchikov, G.L., 2007. Atmospheric volcanic loading derived from bipolar ice cores: Accounting for the spatial distribution of volcanic deposition. Journal of Geophysical Research: Atmospheres112(D9).

Burke, A., Innes, H.M., Crick, L., Anchukaitis, K.J., Byrne, M.P., Hutchison, W., McConnell, J.R., Moore, K.A., Rae, J.W., Sigl, M. and Wilson, R., 2023. High sensitivity of summer temperatures to stratospheric sulfur loading from volcanoes in the Northern Hemisphere. Proceedings of the National Academy of Sciences120(47), p.e2221810120.

How to cite: Burke, A., Fuglestvedt, H., Thomas, L., Marshall, L., and Krüger, K.: Revisiting the ‘transfer function’ of stratospheric sulfur loading from volcanic sulfate deposited on polar ice sheets, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12768, https://doi.org/10.5194/egusphere-egu24-12768, 2024.

EGU24-14596 | ECS | Orals | ITS1.10/CL0.1.9

Rapid development of systematic trend errors in seasonal forecasts and their connection to CMIP6 trend errors 

Jonathan Beverley, Matthew Newman, and Andrew Hoell

Questions regarding the uncertainty of trends in both historical and projected climate model simulations have been limited by uncertainty about the relative importance of internal variability and external forcing to trends over the relatively short observational record. For example, is the discrepancy between historically simulated tropical Pacific trends (El Niño-like) and observations (broadly, La Niña-like) over recent decades a reflection of sampling issues or model error in internal variability and/or forced global responses (either locally or remotely, such as from the Southern Ocean)? At the same time, it is known that systematic operational seasonal forecast errors (e.g., westward shift of ENSO) are dominated by model errors that develop quite quickly, on the order of a few months of forecast lead time.

Here, we suggest that climate model trend errors can be usefully investigated by examining their rapid development within seasonal hindcast datasets. We show that many apparent climate simulation trend discrepancies are evident in trends computed from monthly seasonal hindcasts over the 1994-2016 period for a suite of operational initialised forecast models from C3S and NMME, and in many cases are well developed even at short lead times. These hindcasts use models similar to CMIP-class models and include the same CMIP historical external forcings, but critically are initialised with observations, removing uncertainty related to internal variability. We find these trend errors in many different regions worldwide for several key variables, including sea surface temperature, precipitation and sea level pressure, and investigate their seasonal dependence as well. Notably, we find tropical Pacific "El Niño-like" SST trend errors in all seasons but spring, and related surface pressure, temperature, and precipitation errors in autumn and spring, especially in the North America region. We also find errors in Southern Ocean SSTs, which develop less rapidly than the tropical Pacific SST errors or their global teleconnections.

We suggest that these hindcast trend errors reflect sensitivity of the model mean biases to the changing radiative forcing, rather than a forced response. That is, similarity between errors in free running simulations and hindcasts is a result of the seasonal forecast models quickly transitioning from nature’s attractor to the climate model attractor, particularly in the atmospheric model component. This suggests that we might be able to better diagnose the climate model trend errors by looking at the early development of the forecast trend error in the seasonal forecast models.

How to cite: Beverley, J., Newman, M., and Hoell, A.: Rapid development of systematic trend errors in seasonal forecasts and their connection to CMIP6 trend errors, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-14596, https://doi.org/10.5194/egusphere-egu24-14596, 2024.

EGU24-14986 | ECS | Posters on site | ITS1.10/CL0.1.9

Assessing the Impact of Changing Warming Patterns on Transient Global Warming: A Multivariate Energy Budget Approach 

Robin Guillaume-Castel, Benoit Meyssignac, and Rémy Roca

The pattern of surface warming plays a significant role in determining the Earth's response to radiative forcing. Indeed, the Earth's radiative response is intricately linked to the intensity of climate feedbacks, which, in turn, are influenced by the regional distribution of surface warming. Distinct patterns of surface warming lead to divergent equilibrium and transient responses to identical forcing, emphasizing the need to analyse this pattern effect to understand the climate responses to external forcing.

While existing studies have primarily focused on assessing the influence of warming patterns on long-term warming, such as equilibrium climate sensitivity or committed warming, the role of warming patterns in shaping the transient trajectory of global warming remains poorly understood. In this study, we introduce a novel analytical method to quantify the importance of evolving warming patterns on transient global warming.

Our approach involves developing a multivariate global energy budget, which provides a unified framework for interpreting the sensitivity of the radiative response of the Earth to the warming pattern. This framework explicitly separates the radiative response caused by the global mean temperature increase, from the additional response induced by changing temperature patterns.

Using this new energy balance model, we assess the relative contributions of the direct radiative forcing and changing temperature patterns to the global mean temperature change in linearly increasing forcing experiments (1pctCO2) from nine CMIP6 models. We show that the pattern effect consistently dampens global warming in the first 100 years of all simulations studied. Specifically, we quantify that the transient climate response, reached after 70 years of simulations, would be 0.4±0.2K higher (equivalent to a 20±15% increase) if the warming was uniformly distributed (i.e. in the absence of changing warming patterns).

Furthermore, our study demonstrates that distinct models exhibit significantly divergent transient global warming patterns solely due to variations in the pattern effect. Overall, our results highlight the importance of changing warming patterns, specifically through the pattern effect, in influencing decadal-scale transient warming. These findings notably support recent suggestions to incorporate warming pattern uncertainties in future climate projections.

How to cite: Guillaume-Castel, R., Meyssignac, B., and Roca, R.: Assessing the Impact of Changing Warming Patterns on Transient Global Warming: A Multivariate Energy Budget Approach, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-14986, https://doi.org/10.5194/egusphere-egu24-14986, 2024.

EGU24-17153 | Posters on site | ITS1.10/CL0.1.9

NIMS/KMA Plans for Climate Change Projection Production and Utilization on CMIP7 

Chu-Yong Chung, Young-Hwa Byun, Hyun Min Sung, Jin-Uk Kim, and Sungbo Shim

The National Institute of Meteorological Sciences in the Korea Meteorological Administration (NIMS/KMA) has been actively contributing to the CMIP program since CMIP3. NIMS participated in CMIP6 through a collaborative effort with the UK Met Office Hadley Centre as part of a mutually agreed scientific plan. Within this collaboration, NIMS utilized the Earth System Model developed by the UK Met Office (UKESM) to generate future climate change scenarios for four distinct Shared Socio-economic Pathways (SSPs). NIMS also employed the KMA Advanced Community Earth (K-ACE) model, a modified version of HadGEM2-AO developed through in-house research, to analyze global climate projections. Five different regional climate models were used for the regional climate simulations: HadGEM3-RA, RegCM4, CCLM, GRIMs, and WRF, organized under the CORDEX-EA (East Asia) program. Furthermore, for the South Korean area, NIMS produced 1km resolution climate change scenario data using the statistical downscaling technique, the Parameter-elevation Relationships on Independent Slopes Model (PRISM)-based Dynamic downscaling Error correction (PRIDE). These projections played a pivotal role in contributing to the preparation of the Sixth Assessment Report (AR6) by the Intergovernmental Panel on Climate Change (IPCC) and provided crucial foundational data for national climate change adaptation efforts.

Currently, NIMS has initiated preparations for CMIP7 participation. In this program, K-ACE will be employed for producing global climate projections, having undergone improvements such as coupling with an ocean-biogeochemistry model, TOPAZ, and modifications to the cloud-aerosol process, among other enhancements. NIMS plans to use a reduced number of RCMs compared to the CMIP6 phase but intends to increase the ensemble members by combining physical processes. Currently under consideration as RCM candidates are WRF and WRF-ROMS. To comprehend the impact of climate change on local-scale heavy rain, a Convection Permitting Model (CPM) with a spatial resolution of about 2.5km can be employed. For the South Korean region, our objective is to produce more high-resolution, detailed climate scenarios through sensitivity experiments and reliability verification studies.

This presentation aims to introduce KMA's Earth System Models, aligning with recent trends and developments outlined in CMIP7, and presenting the overall plans for the generation and utilization of global-regional-local climate projections in line with CMIP7.

How to cite: Chung, C.-Y., Byun, Y.-H., Sung, H. M., Kim, J.-U., and Shim, S.: NIMS/KMA Plans for Climate Change Projection Production and Utilization on CMIP7, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17153, https://doi.org/10.5194/egusphere-egu24-17153, 2024.

Given the fact that many Icelandic volcanic systems are on the verge of an eruption, producing some of the largest volcanic eruptions over the past millennia, e.g., Öræfajökull, Bárðabunga, Grímsvötn and the Katla system, it is important to be able to predict potential changes in Northern Hemisphere (NH) climate variability in the following years after an NH eruption in due time. Recent volcanic activity in Iceland, e.g., Holuhraun 2014-2015 and Reykjanes/Geldingadalur 2021-2023, further demonstrates this urgency.

With the aim to contribute to improving the forcasting and adaptation strategies for the North Atlantic region we, as a first step, forced an Earth System Model (CESM1.2.2) with an idealized long-lasting high-latitude volcanic eruption to quantify i) the response within the stratospheric polar vortex and ii) the resulting response within the coupled climate system in the Northern Hemisphere (NH) by assessing the first 15 years following the eruption focusing on the winter (DJF) response. Here results will be presented showing evidence of sudden stratospheric warming events and a deceleration of the stratospheric polar vortex occurring in the second and third post-volcanic winter. This is identified in the temperature and zonal winds at 50hPa as a result of the large modelled surface cooling in the NH where Eliassen-Palmer wave flux calculations further support these findings. The strong stratospheric response identified further influences surface climate throughout the continental NH in the first 5 years following this event via the NAO. Our result suggest that two competing mechanisms are at work during these first years, partly explaining this long-lasting short-term response. The long-term impact is identified as a change in regional surface temperature and sea ice variability as well as a general strengthening of the AMOC, reaching a maximum in winter 2 and remaining positive throughout the run.

How to cite: Guðlaugsdóttir, H., Peings, Y., Zanchettin, D., and Magnúsdóttir, G.: Modelling the climate response following idealized long-lasting high latitude volcanic eruptions: The stratospheric response and resulting implications for North Atlantic surface weather, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-19839, https://doi.org/10.5194/egusphere-egu24-19839, 2024.

EGU24-20254 | Orals | ITS1.10/CL0.1.9

Uncertainties of past volcanic forcing - Modelling the impacts of eruption parameters and atmospheric background conditions 

Kirstin Krüger, Herman Fuglestvedt, Zhihong Zhuo, and Andrea Burke

Reconstructions of past volcanic forcing rely on the assumption that the stratospheric sulphur loading from eruptions in the pre-satellite era is directly proportional to the sulphate flux recorded in polar ice sheets. The scaling factors, known as "transfer functions," used for this calculation are currently based on the Antarctic sulphate flux following the 1991 Pinatubo eruption, radioactivity in Greenland ice from nuclear weapon tests, and model simulations of two high-latitude eruptions. However, recent studies have shown that ice sheet deposition of volcanic sulphate varies significantly as a function of both eruptive parameters and the background atmospheric state, presenting an opportunity to enhance the accuracy and reliability of volcanic forcing reconstructions through improving the use of transfer functions.

 

Here, we investigate how the transfer function depends on eruption parameters and background conditions. Using simulations with the Earth system model CESM2-WACCM6, we explore a wide range of parameters, including eruption magnitude, latitude, plume composition, season, and plume height. By understanding the relationships between eruption parameters and resulting polar sulphate fluxes, we aim to improve the transfer function estimate used in the volcanic forcing for CMIP6 and shed light on the associated uncertainties.

How to cite: Krüger, K., Fuglestvedt, H., Zhuo, Z., and Burke, A.: Uncertainties of past volcanic forcing - Modelling the impacts of eruption parameters and atmospheric background conditions, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-20254, https://doi.org/10.5194/egusphere-egu24-20254, 2024.

EGU24-971 | ECS | Posters on site | ITS1.11/NP4.2

Terrestrial Water Storage Reconstruction: A Causal Inference Approach 

Vivek Kumar Yadav and Bramha Dutt Vishwakarma

The water availability in a region is driven by the water cycle, which is changing quickly in response to climate change and direct human interventions. The water cycle is defined and controlled by the variation in water fluxes such as Precipitation (P), Evapotranspiration (Et), Runoff (R), and Storage change (ΔS). Out of these water fluxes, ΔS is a key variable for ecosystem habitability and surviving droughts. It is an important parameter in drafting water management policy, but due to lack of long and reliable data the impact of climate change on ΔS is yet to be understood. The only Global observations of Terrestrial water storage (TWS) are available from GRACE satellite mission since 2002 at monthly scale.

Although GRACE data has transformed hydrological science significantly, its short time series limits usage of GRACE for climate change analysis of hydrological fluxes (closing the multidecadal water budget and sea level budget, understanding the spatiotemporal evolution of water availability, and so on). To tackle this, several studies have attempted reconstructing ΔS prior to GRACE period. These studies employ either hydrological modelling of ΔS, statistical regression,  or machine learning techniques. While machine learning methods have been assessed superior, they suffer from issues such as a lack of explainability, failure to identify causal drivers of TWS change, and use of short time series for feature extraction and training leading to poor or no representation of decadal natural variability.

Furthermore, in all the studies till now, representation of local human activities, such as ground water extraction or reservoir operation,  was either absent or assumed to be a linear trend. Here we revisit a reconstruction method by Humphrey et al., 2017 and show that these approximations have a considerable impact on the quality of reconstruction. Then we propose a multivariate regression model that relates selected hydrometeorological variables with TWS. These variables are identified from causal analysis of JULES model outputs. We show that temperature has a very weak relation with TWS compared to precipitation. The causal inference based model is able to capture realistic variability in reconstructed TWS. Our TWS reconstruction for the Ganges basin outperforms the contemporary attempts and is able to identify the drivers for interannual changes in TWS . The results bring historical perspective to the current state of water resources in the basin and provide context for design of future water resources policy.

How to cite: Yadav, V. K. and Vishwakarma, B. D.: Terrestrial Water Storage Reconstruction: A Causal Inference Approach, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-971, https://doi.org/10.5194/egusphere-egu24-971, 2024.

EGU24-1838 | Posters on site | ITS1.11/NP4.2

A comparison of two causal methods in the context of climate analyses 

David Docquier, Giorgia Di Capua, Reik V. Donner, Carlos A. L. Pires, Amélie Simon, and Stéphane Vannitsem

Correlation does not necessarily imply causation, and this is why causal methods have been developed to try to disentangle true causal links from spurious relationships. In our study, we use two causal methods, namely the Liang-Kleeman information flow (LKIF) and the Peter and Clark momentary conditional independence (PCMCI) algorithm, and apply them to four different artificial models of increasing complexity and one real-case study based on climate indices in the North Atlantic and North Pacific. We show that both methods are superior to the classical correlation analysis, especially in removing spurious links. LKIF and PCMCI display some strengths and weaknesses for the three simplest models, with LKIF performing better with a smaller number of variables, and PCMCI being best with a larger number of variables. Detecting causal links from the fourth model is more challenging as the system is nonlinear and chaotic. For the real-case study with climate indices, both methods present some similarities and differences at monthly time scale. One of the key differences is that LKIF identifies the Arctic Oscillation (AO) as the largest driver, while El Niño-Southern Oscillation (ENSO) is the main influencing variable for PCMCI. More research is needed to confirm these links, in particular including nonlinear causal methods.

How to cite: Docquier, D., Di Capua, G., Donner, R. V., Pires, C. A. L., Simon, A., and Vannitsem, S.: A comparison of two causal methods in the context of climate analyses, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1838, https://doi.org/10.5194/egusphere-egu24-1838, 2024.

Causality analysis is an important and old problem lying at the heart of scientific research. Causality analysis based on data, however, is a relatively recent development. Traditionally causal inference has been classified as a field in statistics. Motivated by the predictability problem in physical science, it is found that causality in terms of information flow/transfer is actually a real notion in physics that can be derived ab initio, rather than axiomatically proposed as an ansatz, and, moreover, can be quantified. A comprehensive study with generic systems (both deterministic and stochastic) has just been fulfilled, with explicit formulas attained in closed form (Liang, 2016). These formulas are invariant upon nonlinear coordinate transformation, indicating that the so-obtained information flow should be an intrinsic physical property. The principle of nil causality that reads, an event is not causal to another if the evolution of the latter is independent of the former, which all formalisms seek to verify in their respective applications, turns out to be a proven theorem here. In the linear limit, its maximum likelihood estimator is concise in form, involving only the commonly used statistics, i.e., sample covariances. An immediate corollary is that causation implies correlation, but the converse does not hold, expressing the long standing philosophical debate ever since Berkeley (1710) in a transparent mathematical expression.

The above rigorous formalism has been validated with benchmark systems like baker transformation, Hénon map, stochastic gradient system, and with causal networks in extreme situations such as those buried in heavy noises and those with nodes almost synchronized (e.g., Liang, 2021), to name a few. They have also been applied to real world problems in the diverse disciplines such as climate science, dynamic meteorology, turbulence, neuroscience, financial economics, quantum mechanics, etc., with interesting new findings. For example, Stips et al. (216) found that, while CO2 emission does drive the recent global warming, on a paleoclimate scale, it is global warming that drives the CO2 emission; PNA, a teleconnection pattern related to the inclement weather in North America, may trace a part of its origin to a rather limited local marginal sea far away in Asia. Besides, with the above causality analysis, pollution sourcing (particularly PM2.5) may be conducted in a rather effective way via causal graph reconstruction. If time permits, I will also present an ongoing application to the development of causal AI algorithms to overcome the interpretability crisis, and a recent remarkable exercise with such an algorithm in the forecasting of El Niño Modoki, a climate mode linked to hazards in far-flung regions of the globe.

 

References:

Liang, 2014: Unraveling the cause-effect relation between time series. Phy. Rev. E,  90, 052150.

Liang, 2016: Information flow and causality as rigorous notions ab initio. Phys. Rev. E, 94, 052201.

Liang, 2021: Normalized multivariate time series causality analysis and causal graph reconstruction. Entropy, 23, 679.

Liang et al., 2021: El Niño Modoki can be mostly predicted more than 10 years ahead of time. Nature Sci. Rep. 11:17860

 

How to cite: Liang, X. S.: Causality as a real physical notion ab initio, and its applications in Earth system sciences, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-2618, https://doi.org/10.5194/egusphere-egu24-2618, 2024.

EGU24-2948 | Orals | ITS1.11/NP4.2 | Highlight

Causal methods for climate extremes 

Sebastian Engelke

The talk discusses a critical topic in climate science: understanding how interventions on our climate system influence the likelihood of extreme events. The focus is on methodologies that enable causal attribution of such events to specific drivers, rather than merely predicting their occurrence. We discuss common practices and highlight the use of recent statistical methods that are applicable when only observational data is available, as opposed to model-based data. The talk defines the concept of a causal effect of a treatment (such as changes in flood infrastructure or increased CO2 emissions) on extreme outcomes (like a one in 100 year flood). We also cover the estimation of these effects amidst confounding factors and the assessment of associated uncertainties. Finally, we discuss the inherent challenges of applying causal inference to extreme climate events. 

How to cite: Engelke, S.: Causal methods for climate extremes, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-2948, https://doi.org/10.5194/egusphere-egu24-2948, 2024.

EGU24-4191 | ECS | Posters on site | ITS1.11/NP4.2

Causal effects of teleconnection patterns on soil moisture through different climate paths over the Greater Horn of Africa 

Wen Zhuo, Shibo Fang, Xinran Gao, Ricardo B. Lourenco, Yanru Yu, Jiahao Han, and Alemu Gonsamo

Soil moisture is undoubtably a vital variable of the climate system. Understanding the interactions among atmosphere, climate, and soil is necessary for water resource management, drought monitoring, and disaster prevention. However, evaluation of those interactions so far primarily focused on typical correlation analysis which often fail to imply causal relationship due to autocorrelation and high dimensionality within time series variables. Here, we used a data driven causal inference method called PCMCI+ to discover causal relationships among teleconnection patterns (El Niño Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD)), climate variables (precipitation and temperature) and soil moisture during 1980-2022 over Great Horn of Africa (GHOA), where is a susceptible region to suffer from severe drought. Further, we quantitative calculated the causal effects of teleconnection patterns on SM through different climate paths. Results suggest that IOD generally presents higher causal effects on climate variables (temperature and precipitation) or on soil moisture through both precipitation and temperature paths than ENSO over most parts of GHOA. Moreover, precipitation performs shorter lag effect and greater causal effect on soil moisture in GHOA. Our study provides the first attempt to quantitatively analyze the causal effects of teleconnection patterns on SM through both precipitation path and temperature path, and it highlights the causal relationships within atmosphere-climate-soil interactions, which could help for better understanding of climate change impact on drought over GHOA.

How to cite: Zhuo, W., Fang, S., Gao, X., Lourenco, R. B., Yu, Y., Han, J., and Gonsamo, A.: Causal effects of teleconnection patterns on soil moisture through different climate paths over the Greater Horn of Africa, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-4191, https://doi.org/10.5194/egusphere-egu24-4191, 2024.

EGU24-4315 | Orals | ITS1.11/NP4.2

Evaluation of Shannon Entropy-based Information transfer in nonlinear systems  

Carlos Pires, Stéphane Vannitsem, and David Docquier

We present a general theory for computing and estimating Shannon entropy-based information transfer in nonlinear stochastic systems driven by deterministic forcings and additive and/or multiplicative noises, by extending the Liang-Kleeman framework of causality inference to nonlinear cases. The method presents effective and computable formulas of the rates of information transfer between sets of causal and consequential system variables, relying on the evaluation of conditional expectations of the deterministic and stochastic forcings (Causal Sensitivity Method: CSM). The CSM can work with a) ensemble model runs, b) system time series in ergodic conditions and c) time series without a priori knowledge of model equations. The CSM also allows to express the information transfer parcels, which are attributable either to one-to-one interactions or to synergies across groups of variables and assess where the information is more relevant in the state space. The CSM is tested in two proof-of-concept low-order models: 1) a nonlinear model derived from a potential function and 2) the classical chaotic Lorenz model, both forced by additive and/or multiplicative noises. The CSM is also tested with a nonlinear regression model of the ice-cover time evolution, forced by radiation. The CSM estimation is much more robust and efficient than methods using the stochastic model’s full probability density function and its derivatives, whose estimation is rather unreliable in case of short data availability. The analysis also demonstrates that the CSM estimation is computationally cheap in the different experiments, providing evidence of the possibilities and generalizations offered by the method, thus opening new perspectives on real-world applications. This work was funded by the Portuguese Fundação para a Ciência e a Tecnologia (FCT) I.P./MCTES through national funds (PIDDAC) – UIDB/50019/2020(https://doi.org/10.54499/UIDB/50019/2020),UIDP/50019/2020(https://doi.org/10.54499/UIDP/50019/2020) and LA/P/0068/2020 (https://doi.org/10.54499/LA/P/0068/2020) and the project  JPIOCEANS/0001/2019 (ROADMAP).

 

How to cite: Pires, C., Vannitsem, S., and Docquier, D.: Evaluation of Shannon Entropy-based Information transfer in nonlinear systems , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-4315, https://doi.org/10.5194/egusphere-egu24-4315, 2024.

EGU24-4693 | ECS | Posters on site | ITS1.11/NP4.2

Granger causality in tail 

Juraj Bodik

Granger causality plays a pivotal role in uncovering directional relationships among time-varying variables and enhancing decision-making in complex systems. While this notion gains heightened importance during extreme events in highly volatile periods,
state-of-the-art methods primarily focus on causality within the body of the distribution. We introduce a new rigorous mathematical framework for “Granger causality in tail,” designed to evaluate whether an extreme event in one time series causes a corresponding extreme event in another. Moreover, we describe how we can quantify the magnitude of the causal impact of an extreme event on other variables. 

We establish equivalences between our Granger causality in tail and other causal concepts, including “classical Granger causality,” “Sims causality,” and “structural causality.” By proving the key properties of Granger causality in tail, we assert its usefulness in high-dimensional complex systems with potential hidden confounders. Here, to model the tails of the variables, we utilize the “extreme value theory” framework. We also propose an inference method for detecting the presence of Granger causality in tail and provide insights into the asymptotic properties of our estimator within the framework of a stochastic recurrence equation (SRE) model.

How to cite: Bodik, J.: Granger causality in tail, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-4693, https://doi.org/10.5194/egusphere-egu24-4693, 2024.

EGU24-6535 | Orals | ITS1.11/NP4.2

Unfolding the Manifold Flavours of Causality 

Rui A. P. Perdigão

The present communication provides a contribution to an overarching cross-methodological causality investigation, encompassing a methodological synergy among physical, analytical, information-theoretic and systems intelligence approaches to causal discovery and quantification in complex system dynamics. These efforts methodologically lead to the emergence of a broader causal framework, valid not only in classical recurrence-based dynamical systems, but also on the generalized information physics of non-ergodic coevolutionary spatiotemporal complexity.

This study begins with a comprehensive cross-examination of causality metrics derived from these diverse domains, by synthesizing causality insights from information theory, which enables the quantification of information flow among variables; differential geometry, which captures the curvature and structure of causal relationships; dynamical systems, which analyze the temporal evolution of systems and associated kinematic geometric properties; and fundamental physical metrics, which elucidate causal connections in the physical world from fundamental thermodynamic principles. Through this analysis, we aim to deepen our understanding of causality in complex systems, with physical process understanding and geophysical applications in mind.

Having laid out some of the key methodological flavours of causality, the present communication introduces new metrics further contributing to a broader non-Shannonian information theoretic causality pool of methods, along with additional advances on quantum thermodymamical, nonlinear statistical mechanical, differential geometric and topologic approaches on causality. Positioning ourselves in a broader nonlinear non-Gaussian non-ergodic setting to tackle far-from-equilibrium structural-functional coevolution and synergistic emergence in complex system dynamics, our derivations further contribute to a new generation of information theoretic, dynamical systems and non-equilibrium thermodynamic causality approaches, along with their synergistic articulation towards a unified framework. This brings out further cross-methodological comparability, portability and complementary insights on dealing with the intricate causality of complex multiscale far-from-equilibrium Earth system dynamic phenomena.

By unveiling manifold flavours of causality and their interconnections, this study brings out their commonalities, synergies, and further potential synergistic applications across disciplines. This interdisciplinary approach not only enhances our theoretical understanding of causality but also provides practical implications for applications in fields such as data science, network theory, and complex systems analysis, with direct relevance across the Earth system sciences and beyond.

How to cite: Perdigão, R. A. P.: Unfolding the Manifold Flavours of Causality, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-6535, https://doi.org/10.5194/egusphere-egu24-6535, 2024.

EGU24-6584 | Posters on site | ITS1.11/NP4.2

Causal discovery among wind-related variables in a wind farm under extreme wind speed scenarios: Comparison of results using Granger causality and interactive k-means clustering 

Katerina Schindlerova (Hlavackova-Schindler), Kejsi Hoxhallari, Luis Caumel Morales, Irene Schicker, and Claudia Plant

Using the era5 meteorological reanalysis data from 2000 to 2020 [1], we investigate temporal effects of ten wind related processes in time intervals of extreme wind speed values, extracted and corrected towards wind turbine locations for a wind farm in Andau, Austria.  We approach the problem by two ways, by the Granger causal inference, namely by the heterogeneous Graphical Granger model (HMML) [2] and by clustering, namely by the interactive k-means clustering (IKM) [3].

We investigate six scenarios based on the hydrological half-year, a moderate wind speed and time intervals of low or high extreme wind speed in the farm. In case of HMML, we discover causal variables and their values for each scenario.  Regarding the method IKM, it is used for three clusters (clusters for a moderate wind speed and for a low and high extreme wind speed) to find coefficient representations of each interacting variable with respect to the wind speed in each of the six scenarios.   We compare the results of both methods in terms of the values of causal variables and of the values of the coefficients of representation and evaluate the interpretability of the discovered causal connections with the expert meteorological knowledge.

 [1]  https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-pressure levels?tab=overview   

[2] Hlaváčková-Schindler, K., Plant, C. (2020) Heterogeneous graphical Granger causality by minimum message length, Entropy, 22(1400). pp. 1-21 ISSN 1099-4300 MDPI (2020).

[3] Plant, C., Zherdin, A., Sorg, C., Meyer-Baese, A., Wohlschläger, A. M. Mining interaction patterns among brain regions by clustering. IEEE Transactions on Knowledge and Data Engineering, 26(9):2237–2249, 2014.

How to cite: Schindlerova (Hlavackova-Schindler), K., Hoxhallari, K., Caumel Morales, L., Schicker, I., and Plant, C.: Causal discovery among wind-related variables in a wind farm under extreme wind speed scenarios: Comparison of results using Granger causality and interactive k-means clustering, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-6584, https://doi.org/10.5194/egusphere-egu24-6584, 2024.

EGU24-7546 | Posters on site | ITS1.11/NP4.2

Quantifying the influence of cloud controlling factors with causal inference 

Lisa Bock, Adrian McDonalds, Axel Lauer, and Jakob Runge

As a key component of the hydrological cycle and the Earth’s radiation budget, clouds play an important role in both weather and climate. Our incomplete understanding of clouds and their role in cloud-climate feedbacks leads to large uncertainties in climate simulations. Using causal discovery as an unsupervised machine learning method we aim to systematically analyse and quantify causal interdependencies and dynamical links between cloud properties and their controlling factors. This approach goes beyond correlation-based measures by systematically excluding common drivers and indirect links. By estimating the causal effect of each of the cloud controlling factors for different cloud regimes we expect to be able to better understand the dominant processes which determine the micro- and macro-physical properties of clouds.

Specifically, causal inference is used to investigate the links between cloud properties such as cloud cover, cloud water path, cloud top height and cloud radiative effects and so-called cloud controlling factors, i.e., quantities that impact cloud formation and temporal evolution of the cloud (e.g., sea surface temperature, water vapour path and lower tropospheric stability). For this, causal networks are calculated from time series of these variables from satellite and reanalysis datasets averaged over different geographical regions and cloud regimes in order to quantify the strength of the individual links in the resulting causal graph by applying causal effect estimation.

How to cite: Bock, L., McDonalds, A., Lauer, A., and Runge, J.: Quantifying the influence of cloud controlling factors with causal inference, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-7546, https://doi.org/10.5194/egusphere-egu24-7546, 2024.

Many approaches to infer causal relations from time series in Earth sciences have been proposed and applied in order to identify diverse interactions, such as the influence of large-scale circulation modes on local temperature and precipitation, variability of Euroasian winters due to changing Arctic Sea ice cover, or interactions of solar activity and interplanetary medium conditions with the Earth’s magnetosphere-ionosphere systems. The methods usually depend on “dimensions” in which the understanding of underlying phenomena is located: The phenomena or processes can be linear or nonlinear; deterministic, or random. The third abstract “dimension” is the actual dimensionality of the problem, given either by the dimension of the state space of the underlying mechanism or the number of involved variables. We will conduct a short flight inside these “dimensions,” shedding light on some of the shades, comparing some of the causality inference methods using model and real data from the Earth sciences.

This study was supported by the Czech Academy of Sciences, Praemium Academiae awarded to M. Paluš and the Czech–Chinese Academies of Sciences Mobility Plus Project NSFC-23-08.

How to cite: Palus, M.: Many shades in three dimensions and parallel universes of causality analysis, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-8450, https://doi.org/10.5194/egusphere-egu24-8450, 2024.

EGU24-10448 | Posters on site | ITS1.11/NP4.2

Exploring Global and Local Water Scarcity Dynamics through Causal Discovery and Structural Causal Models 

Myrthe Leijnse, Marc F.P. Bierkens, and Niko Wanders

Water scarcity is driven by diverse natural and anthropogenic factors and represents a critical global challenge. Structural Causal Models are powerful tools to reveal the intricate interactions among social, ecological and hydrological components within human-water systems affected by water scarcity. This study integrates causal thinking into statistical and data-driven hydrological modelling, offering a different perspective on understanding system dynamics affecting water resources in water-scarce regions, the so-called water scarcity hotspots.

In this work we apply causal discovery methods to independent timeseries of sectoral water demand, social-economic variables, meteorological drivers and groundwater depletion to obtain a causal network representing human-water system interactions at global water scarcity hotpots. To derive this network we use global datasets and advanced causal network learning algorithms, specifically (Joint-)PCMCI (Runge et al., 2023). Recognizing the importance of large data sample sizes for a robust global causal network, we further extend our approach to construct a causal network specific to one of the water scarcity hotspots (California), using more detailed local data. Therefore, our framework provides a comprehensive understanding of water scarcity dynamics including both global and local scales. Through a comparative analysis of network outcomes derived from global datasets with those specific to California, we evaluate the effectiveness of our causal inference modelling framework.

After conducting and evaluating the causal networks at global and local scale, we applied methods from structural causal modelling and statistical machine learning to estimate causal effects of anthropogenic or natural system changes on water availability at water scarcity hotspots. This framework allows us to answer important (counterfactual) questions, such as understanding how the rate of unsustainable groundwater abstraction is affected by shifts in water management practices e.g., a reduction in irrigated cropland area.

As such, this work contributes to understanding how using causal inference methods are valuable to modelling of water scarcity, ultimately providing input to informed decision-making in water resource management and finding strategies to mitigate water scarcity impacts.

Runge, J., Gerhardus, A., Varando, G., Eyring, V., & Camps-Valls, G. (2023). Causal inference for time series. Nature Reviews Earth & Environment4(7), 487-505.

How to cite: Leijnse, M., Bierkens, M. F. P., and Wanders, N.: Exploring Global and Local Water Scarcity Dynamics through Causal Discovery and Structural Causal Models, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-10448, https://doi.org/10.5194/egusphere-egu24-10448, 2024.

EGU24-11714 | ECS | Posters on site | ITS1.11/NP4.2

Subseasonal prediction of heatwaves in the Iberian Peninsula using causality-based transformer networks. 

Cas Decancq, Daniel Hagan, Victoria Deman, Akash Koppa, and Diego Miralles

Subseasonal prediction of heatwaves, although highly valuable for risk reduction, is challenging because heatwave onsets and propagation are complex processes with both fast and slow drivers from local to global scale. Traditionally, subseasonal forecasting relies heavily on dynamical model ensembles, which are complex and of high computational cost. As an alternative, machine learning provides potentially performant solutions that may match or even outperform these physical-based models. Transformers, in particular, are the current state-of-the-art deep learning infrastructures, and using multi-head-attention allows them to keep track of long-term complex dependencies in timeseries data. However, to better forecast heatwaves subseasonally, it is essential to move beyond purely predictor-to-target associative measures when identifying the sources of predictability. Such endeavours require causal frameworks that provide directionality and explainable power for the predictor-to-target relationships.

This study seeks to implement the PCMCI+ (Runge, 2020) framework to identify causal drivers of heatwaves on the Iberian Peninsula on a subseasonal scale. Causally-selected predictors are employed to forecast the occurence of heatwaves up to six weeks in advance using transformer networks, both for different seasons and sub-regions in the Iberian Peninsula. Preliminary results reveal heatwaves can be predicted with reasonable accuracy with a forecast window of six weeks, particularly in water limited regions, using causality-based machine learning.


Reference:

Runge, J. (2020). Discovering contemporaneous and lagged causal relations in autocorrelated nonlinear time series datasets. In Conference on Uncertainty in Artificial Intelligence, pages 1388–1397. PMLR.

How to cite: Decancq, C., Hagan, D., Deman, V., Koppa, A., and Miralles, D.: Subseasonal prediction of heatwaves in the Iberian Peninsula using causality-based transformer networks., EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-11714, https://doi.org/10.5194/egusphere-egu24-11714, 2024.

EGU24-12204 | Orals | ITS1.11/NP4.2

Some alternative metods for causal discovery 

András Telcs

Causal inference is indeed a challenging endeavor, particularly when applied to observational studies of interacting systems. Perl's theory, along with the PC algorithm on directed acyclic graphs, and its extensions PCMCI and FCI, are powerful tools. However, their application to time series is time-consuming, and they still struggle to distinguish Markov-equivalent scenarios.

In our talk, we will present some methods based on principles that are partly or fully different from those underlying the aforementioned tools. Due to time constraints, we will focus on the main principles that allow the discovery of causal relations between a pair of systems, including hidden common causes (referred to as common drivers or confounders in different schools of thought). We won't delve into the numerous technical challenges due to the time limit.

How to cite: Telcs, A.: Some alternative metods for causal discovery, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12204, https://doi.org/10.5194/egusphere-egu24-12204, 2024.

In 2022, La Niña and negative Indian Ocean Dipole (IOD) coincided, causing abnormally warm sea surface conditions in the eastern Indian Ocean (near Indonesia). This provided additional moisture to feed monsoon depressions, resulting in heavy rainfall in Pakistan. El Niño-Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD) are two modes of sea surface temperature variability that can significantly impact precipitation in Pakistan's Upper Indus Basin. The current study used in situ observations and reanalysis ERA 5 precipitation data to determine the causal influence of ENSO and IOD on precipitation variability using an information-theoretic generalization of Granger causality. The predicted causal effect and causal delay obtained using conditional mutual information, a.k.a. transfer entropy, were further validated using conditional means ("composites") - precipitation means computed for different ENSO states; El Niño (positive), La Niña (negative), and neutral. Uncovering the causal and delayed effects of ENSO and IOD, as well as associated mechanisms, on subsequent precipitation in the UIB could provide a stronger foundation for improving seasonal climate predictions with a longer lead time, as well as understanding how regional and large-scale drivers affect regional precipitation.

This study was supported by the Czech Academy of Sciences, Praemium Academiae awarded to M. Paluš and the Czech–Chinese Academies of Sciences Mobility Plus Project NSFC-23-08.

How to cite: Latif, Y. and Palus, M.: Causal information flow and information transfer delay from ENSO and IOD to precipitation variability in the Upper Indus Basin, Pakistan, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12884, https://doi.org/10.5194/egusphere-egu24-12884, 2024.

EGU24-13220 | Posters on site | ITS1.11/NP4.2

Scaling properties of irreversibility indices in turbulence 

François G. Schmitt

In 3D turbulence there is a flux of energy from large to small scales in the inertial range, associated with irreversibility, i.e. a breaking of the time reversal symmetry (Pumir, 2016). Such turbulent flows are characterized by scaling properties and we consider here how irreversibility depends on the scale. Two indicators of irreversibility for time series are tested involving triple correlations in a non-symmetric way. The first one proposed by Pomeau (1982, 2004) is: Po(r)=<X(t)X(t+r)X(t+3r)>-<X(t)X(t+2r)X(t+3r)>, where r is an increment and X(t) is the turbulent velocity which is stationary with zero mean. The second indicator has been proposed in the finance literature (Ramsey and Rothman, 1996), and was called symmetric bicovariance function: γ(r) = <X2(t)X(t+r)>-<X(t)X2(t+r)>. For time reversible processes, both indicators are zero, whereas their departure from 0 is an indicator of irreversibility.

We study these indicators applied to fully developed turbulence time series, from flume tank, wind tunnel and atmospheric turbulence databases. It is found that irreversibility occurs in the inertial range and has scaling properties with slopes close to one. A maximum value is found around the injection scale. This confirms that the irreversibility is associated with the turbulent cascade in the inertial range and shows that the irreversibility is maximal at the injection scale, the largest scale of the turbulent cascade.

This is published in Schmitt, F.G., Scaling analysis of time-reversal asymmetries in fully developed turbulence, Fractal and Fractional, 7(8), 630, 2023.  https://doi.org/10.3390/fractalfract7080630

Cited references: Pumir et al., Phys. Rev. Lett.. 116, 124502 (2016); Pomeau, J. de Physique 43, 859 (1982); Pomeau, Lect. Notes Phys. 644, 425 (2004); Ramsey and Rothman, J. Money Credit Bank. 28, 1 (1996).

How to cite: Schmitt, F. G.: Scaling properties of irreversibility indices in turbulence, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-13220, https://doi.org/10.5194/egusphere-egu24-13220, 2024.

EGU24-14429 | ECS | Posters on site | ITS1.11/NP4.2

Compression-complexity based estimation of Causality: Applications in Earth and Climate Sciences 

Aditi Kathpalia, Pouya Manshour, and Milan Paluš

Many approaches to time series causality exist and have been inspired from fields such as statistics, information theory, physics and topology. We have proposed a method called compression-complexity causality (CCC) [1] inspired from the field of data compression in computer science. It is based on the idea that the compressibility of the ‘effect’ time series changes when the ‘cause’ time series is considered in the evolution of the future dynamics of the effect. Compressibility is estimated using compression-complexity estimator for time series called ‘effort-to-compress’, which employs a lossless data compression algorithm for complexity estimation. CCC makes minimal assumptions on given time series data and has been seen to work well for short length data, irregularly sampled data as well as data with low temporal resolution. We have also introduced a multidimensional version of CCC, called Permutation CCC (PCCC) [2], which uses Takens’ embedding for appropriate high dimensional representation of time series. This representation is subsequently encoded using ordinal patterns before computation of CCC. PCCC formulation retains the original robustness of CCC. This is demonstrated with its application on simulated multidimensional systems. We apply this formulation to infer causality between CO2 emissions – temperature recordings on three different time scales, El Niño–Southern Oscillation phenomena – South Asian Summer Monsoon on two different time scales, as well as North Atlantic Oscillations – European temperature recordings on two different time scales. These paleoclimate and climate datasets suffer from the issues of missing samples, low temporal resolution and short length data and so a reliable inference of these climatic interactions requires a robust causality estimator.  
Finally, we explore another variation of CCC which can help to infer causality in the multivariate cases. This variation helps to infer the existence of causal influences to a particular variable (from each other variable considered) while conditioning out the additional variables present. The presence of causal influences to each variable is decided by choosing the model of least compression-complexity which can help to explain the evolution of the future of that particular variable. In case more than one model has least complexity, the smallest model is chosen. We apply this formulation to understand interactions in space-weather system, particularly the solar wind-magnetosphere-ionosphere system interactions, which manifest as geomagnetic storms and substorms. We compare the performance of CCC formulations with existing methods in case of simulations as well as real data applications. 

This study is supported by the Czech Academy of Sciences, Praemium Academiae awarded to M. Paluš.

References:
[1] Kathpalia, A., & Nagaraj, N. (2019). Data-based intervention approach for Complexity-Causality measure. PeerJ Computer Science, 5, e196.
[2] Kathpalia, A., Manshour, P., & Paluš, M. (2022). Compression complexity with ordinal patterns for robust causal inference in irregularly sampled time series. Scientific Reports, 12(1), 14170.

How to cite: Kathpalia, A., Manshour, P., and Paluš, M.: Compression-complexity based estimation of Causality: Applications in Earth and Climate Sciences, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-14429, https://doi.org/10.5194/egusphere-egu24-14429, 2024.

EGU24-15830 | ECS | Posters on site | ITS1.11/NP4.2

Validating ENSO Feedbacks in Climate Models Using a Causal Discovery Method 

Emma Schultz, Dim Coumou, and Michael Massmann

The El Niño-Southern Oscillation (ENSO) stands out as the dominant driver of climate fluctuations on interannual timescales. As ENSO causes extreme weather events in the Pacific region and beyond, it has wide ranging socio-economic impacts. Over the past decades, a strengthening in the temperature gradient is observed between the Western and Eastern Pacific. However, climate model simulations do not depict this strengthening trend. Here we explore if the Bjerknes feedback is well represented in climate models, and if not whether this could explain the discrepancy between the observed and modeled trends. The Bjerkness feedback represents the dominant feedback processes between atmosphere and ocean that drive ENSO variability. A causal discovery method, the PCMCI algorithm, is used to construct causal networks of key variables in the Bjerknes feedback: near surface temperatures, sea level pressure and trade winds across the Pacific Ocean. Causal networks are constructed for time periods 1950-1982 and 1982-2014, based on both reanalysis data and climate model simulations. The observed changes between causal networks based on the early and later period are examined. The analysis reveals a strengthening causal influence of trade winds on sea level pressure and temperatures in networks based on reanalysis data. This significant strengthening trend is absent in networks based on climate model simulations. As an increased influence of the trade winds would have a cooling effect on Central and Eastern Pacific, this might explain why there is no observed warming in the Central and Eastern Pacific over the past decades, and thus a strengthened temperature gradient. The lack of this strengthening causal influence of trade winds in climate models might thus explain why the models do show a warming over the Eastern Pacific, weakening the temperature gradient.

How to cite: Schultz, E., Coumou, D., and Massmann, M.: Validating ENSO Feedbacks in Climate Models Using a Causal Discovery Method, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15830, https://doi.org/10.5194/egusphere-egu24-15830, 2024.

EGU24-15950 | ECS | Posters on site | ITS1.11/NP4.2

Leveraging Information Flow for Data-Driven Subseasonal Forecasting of Sahelian Hot Extremes 

Victoria M. H. Deman, Daniel F. T. Hagan, Damián Insua-Costa, Akash Koppa, and Diego G. Miralles

The semi-arid Sahel region has witnessed an increase in extreme weather conditions such as repeated drought cycles, desertification, heatwaves and floods in recent decades. These events pose existential threats to the already vulnerable population and natural ecosystem. Addressing the underexplored potential of subseasonal forecasting in the Sahel, data-driven models offer an alternative to traditional dynamical approaches. These models – distinguished by enhanced computational efficiency, reduced sensitivity to initial conditions, the ability to learn intricate relationships from data, and the ability to capture nonlinear dynamics – represent an asset in building resilience in the region. 

This study investigates the potential of employing a rigorous causality framework based on the Liang–Kleeman information flow for predictor selection. Previous research has underscored the pitfalls of using correlations for predictor selection when forecasting using machine learning models, as spurious correlations may lead to the selection of predictors without any physical connection. In response, our research investigates the potential of this information flow causality to select predictors within a vast array of predefined variables, including coupled ocean–atmospheric oscillation indices, sea-surface temperatures, vegetation indices and soil moisture. Subsequently, our focus is directed towards predicting summer maximum temperature extremes with lead times of 2, 4, 8 and 16 weeks using the selected predictors and a variety of deep learning techniques. Despite the challenge of predicting short-lived heatwaves in a region characterised by the high baseline temperatures, our results indicate that the information flow causality effectively reduces dimensionality, and enables a selection of features with causal relationships that facilitates subsequent forecasting. In the following, the causal knowledge from the predictor selection step will be quantitatively transferred into the machine learning models themselves, thereby providing an interpretable framework for the prediction of the hot extremes in the region. 

How to cite: Deman, V. M. H., Hagan, D. F. T., Insua-Costa, D., Koppa, A., and Miralles, D. G.: Leveraging Information Flow for Data-Driven Subseasonal Forecasting of Sahelian Hot Extremes, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15950, https://doi.org/10.5194/egusphere-egu24-15950, 2024.

EGU24-17312 | ECS | Orals | ITS1.11/NP4.2

Causal evaluation of humanitarian aid on food security 

Jordi Cerdà-Bautista, José María Tárraga, Vasileios Sitokonstantinou, and Gustau Camps-Valls

In a world where climate change is rapidly accelerating, droughts are becoming more frequent and severe, posing a serious challenge to food security in the most vulnerable regions. The Horn of Africa has witnessed a rise in acute malnutrition, affecting 6.5 million people in 2022 [1]. Prolonged dry spells significantly contribute to this crisis [2], yet it is crucial to recognize that droughts are not the sole driver. Various factors, including hydrological conditions, food production capabilities, market access, insufficient humanitarian aid, conflicts, and displacement, play a significant role [3,4]. Understanding the underlying causes of food insecurity is pivotal for improving the effectiveness of humanitarian actions, yet in this context, the study proves to be complex, involving multiple variables, scales, and non-linear relationships. Predictive Machine Learning (ML) techniques are not suited to understanding the causes and estimating the causal effect by default [5,6], instead, this study focuses on causal inference to quantify the impacts of climate and socioeconomic factors on food insecurity. Our key contributions involve discerning causal relationships within the intricate food security system, integrating a comprehensive database including socio-economic, weather and remote sensing data, and estimating the causal effect of humanitarian interventions on the food security index, the outcome of interest. The causal discovery task is performed via time series methods accounting for nonlinear and nonstationary relations, like the PCMCI algorithm and nonlinear Granger causality [7,8], identifying the drivers in the data that are causally linked to the outcome. Besides, the causal effect estimation task is performed via a Conditional Average Treatment Effect (CATE), gaining insights into the spatiotemporal heterogeneity of the impact of humanitarian interventions on the outcome [9]. Such endeavors are crucial for facilitating more efficient future interventions and policies, thereby improving transparency and accountability in humanitarian aid.

References

[1] WFP, “Impacts of the Cost of Inaction on WFP Food Assistance in Eastern Africa (2021 & 2022),” https://docs.wfp.org/api/documents/WFP-0000148305/download/, 2023.

[2] Coughlan de Perez E., et al, “From rain to famine: assessing the utility of rainfall observations and seasonal forecasts to anticipate food insecurity in East Africa,” Food Secur., vol. 11, no. 1, pp. 57–68, 2019.

[3] Maxwell D. et al, “Viewpoint: Determining famine: Multi-dimensional analysis for the twenty-first century,” Food Policy, vol. 92, 2020.

[4] Guy A. J. et al, “Climate, conflict and forced migration” Global Environmental Change, vol. 54, no. 4, 2019.

[5] Pearl J., “Causality: Models, reasoning, and inference,” Cambridge University Press, vol. 19, 2000.

[6] Peters J., Janzing D., and Schlkopf B., Elements of Causal Inference: Foundations and Learning Algorithms, The MIT Press, 2017.

[7] Runge, J.. "Discovering contemporaneous and lagged causal relations in autocorrelated nonlinear time series datasets." Conference on Uncertainty in Artificial Intelligence. PMLR, 2020.

[8] Camps-Valls, G. et al, “Discovering causal relations and equations from data”, Physics Reports 1044 :1--68, 2023

[9] Giannarakis, G. et al, (2022). Personalizing sustainable agriculture with causal machine learning. arXiv preprint arXiv:2211.03179.

How to cite: Cerdà-Bautista, J., Tárraga, J. M., Sitokonstantinou, V., and Camps-Valls, G.: Causal evaluation of humanitarian aid on food security, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17312, https://doi.org/10.5194/egusphere-egu24-17312, 2024.

EGU24-19242 | ECS | Orals | ITS1.11/NP4.2

Multi-model comparison of causal relationships between atmospheric and marine biogeochemical variables 

Germain Bénard, Marion Gehlen, and Mathieu Vrac

Time series of in situ observations and remote sensing data suggest variability in epipelagic ecosystems at seasonal to multiannual time scales. These go along with changes in physical-biogeochemical conditions. While a consensus exists on the proximate causes of observed ecosystem variability (e.g. mixed layer variability, availability of nutrients, grazing pressure), the role of large-scale drivers (e.g. natural climate modes) still needs to be better understood. Moreover, differences in the implementation of marine ecosystem processes exist among Earth System Models, and it is important to understand the uncertainty around the representation of specific interactions via inter-model comparison.

We use output from 5 multi-centennial Earth system model simulations under pre-industrial climate to identify modes of low-frequency biogeochemical properties and the importance of individual drivers. The study focuses on the North Atlantic subpolar gyre (NASPG), a region of high primary productivity and considerable observed natural variability in physical and biogeochemical conditions. We explore causality between modes of climate variability, ocean physics and biogeochemistry by applying a Knowledge-Data-Discovery method, PCMCI. This method enables causal links with a potential time lag to be established between different domains. It proposes a novel way for the comparison of differences between model dynamics.

First, six geographic subregions are identified, based on their physical-biogeochemical characteristics (e.g. deep convection zones, intensity of spring bloom), followed by by the selection of physical and biogeochemical variables. These variables are the maximum winter mixed layer depth due to the role in supplying nutrients to the surface fueling the spring bloom, the North Atlantic Oscillation (NAO), a dominant natural mode climate variability, for its contribution to sea surface temperature (SST) and nutrient variability in the subpolar gyre, and the Gyre Strength, an index reflecting the response of the NASPG to wind forcing. We focus on one micronutrient (Iron) and one macronutrient (Nitrate). They were chosen because both can limit the primary production in this region. 

Next, PCMCI is applied to search for the temporal relationships (potentially lagged) between different regions and variables. These relationships are computed from partial correlations which, for gaussian distributed data, is equivalent to a causal link. The application of this method allows networks of causality to be identified, highlighting drivers of nutrient variability under varying natural climate forcing. The approach enables the quantification of intermodel differences either by analyzing one link after another or by looking directly at the entire causal graphs with a newly proposed method to quantify the dissimilarity between two models.

This method verified expected interactions such as the role of mixed layer depth for nutrient supply and quantified the strength of this interaction across the models. It also highlighted model-specific dynamics such as the role of temperature (via sea-ice formation) for iron in two biogeochemical models out of 5. 



 

How to cite: Bénard, G., Gehlen, M., and Vrac, M.: Multi-model comparison of causal relationships between atmospheric and marine biogeochemical variables, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-19242, https://doi.org/10.5194/egusphere-egu24-19242, 2024.

EGU24-20089 | ECS | Posters on site | ITS1.11/NP4.2

Causal Discovery of Stochastic Dynamical Systems: A Markov Chain Approach 

Marcell Stippinger, Attila Bencze, Ádám Zlatniczki, Zoltán Somogyvári, and András Telcs

Exploring causal relationships among stochastic dynamic systems based solely on observed time series of their states poses a challenging problem. In this context, we present a novel method for causal discovery within stochastic dynamic systems, specifically designed to overcome the limitations of existing methods, particularly in detecting hidden and common drivers. Our proposed approach is based on a straightforward observation: a process generated by a stochastic dynamical system follows a Markov chain if and only if all external influences are independent and identically distributed (i.i.d.). Consequently, the primary tool in our proposed causal discovery scheme involves testing whether the process generates a Markov chain, as opposed to relying on the "classical" causal Markov property or d-separation.

Our method is nonparametric, requiring no intervention, and is built on a reasonably small number of assumptions. We tested our model both on simulated Markov chains of finite state space and structural vector autoregressive processes. To demonstrate the efficacy of our model, we apply it to weather data consisting of solar irradiation and daily average air temperature. Through our method, we successfully identify the ground truth, revealing that irradiation drives temperature. Furthermore, we adeptly pinpoint the true lag while eliminating spurious lags in the autocorrelation function.

How to cite: Stippinger, M., Bencze, A., Zlatniczki, Á., Somogyvári, Z., and Telcs, A.: Causal Discovery of Stochastic Dynamical Systems: A Markov Chain Approach, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-20089, https://doi.org/10.5194/egusphere-egu24-20089, 2024.

EGU24-20353 | Orals | ITS1.11/NP4.2

A Reply to “On Spurious Causality, CO2, and Global Temperature” 

Adolf Stips, San Liang, and Diego Macias-Moy

Stips et al (2016) demonstrated the existing causal relationship between Green House Gases (GHG) concentrations and Global Mean Surface Temperature (GMTA) based on the Information Flow (IF) methodology. Critics on the application of the Information Flow concept as developed by Liang (2008, 2016) has focused on the underlying assumption of uncorrelated residuals (noise) between the time series. However, this assumption can only make sense for a system with two components, as for a multi-dimensional system unobserved noise may well exist. Fundamentally, there can be no such thing like correlated noise at all. It can seemingly only appear because of some hidden process(es). For investigating this in detail a multivariate information flow analysis has been developed. We will show that in our tests using processes with correlated noises, the preset causalities can be well reproduced. Further, it will be demonstrated that reducing autocorrelation within the time series by pre-whitening, confirms the achieved causality directions. Finally, we question the validity of the proposed alternative measure using forecast error variance decomposition based on vector autoregression by Goulet and Goebel (2021), because in their method causal directions can be simply reversed by reordering.  A physically faithful causal measure should be generally independent of ordering.

 

Coulombe, P. G. and Goebel, M. 2021. On Spurious Causality, CO2, and Global Temperature.  Econometrics9(3), 33.

Liang, X. S. 2008. Information Flow within Stochastic Dynamical System. Phys. Rev. E 78: 031113.

Liang, X. S. 2016. Information Flow and Causality as rigorous Notions ab initio. Physical Review E 94: 05220.

Stips, A., D. Macias, C. Coughlan, E. Garcia-Gorriz, and X. S. Liang. 2016. On the Causal Structure between CO2 and Global Temperature. Scientific Reports 6: 21691.

How to cite: Stips, A., Liang, S., and Macias-Moy, D.: A Reply to “On Spurious Causality, CO2, and Global Temperature”, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-20353, https://doi.org/10.5194/egusphere-egu24-20353, 2024.

EGU24-20548 | Orals | ITS1.11/NP4.2 | Highlight

Some Thoughts on Causal Inference, the Scientific Method, and Data Assimilation 

Michael Ghil, Alberto Carrassi, and Olivier de Viron

Causal inference is at the heart of the scientific method as usually practiced. Still, Karl Popper (The Logic of Scientific Discovery, 1935/1959)  tells us that a theory in the empirical sciences can never be proven: it can only be falsified, meaning that it can, and should, be scrutinized with decisive experiments. Even so, nobody that I know writes or publishes papers to disprove one’s own theory, only an opposing theory. And the debate rages on.

At the heart of this session lies the question of whether, and how, one can prove, rather than just disprove, a causal link between phenomena in the empirical sciences. The session deals specifically with statistical, as opposed to dynamical methods. These methods have the advantage that they are essentially indifferent to any laws of, or other accumulated heuristic ideas on, the field to which they are being applied: whether the time series one considers are from the environmental sciences, biology or medicine does not matter, only their length and accuracy does.

Judea Pearl (e.g., Stat. Surveys, 2009) made an important observation on how to transcend the saying that “Correlation is not causation” by pointing out that standard methods of statistical analysis rely on the stationarity hypothesis of the phenomena being examined. Crucial questions, however, like the causal role of anthropogenic forcing in climate change, deal precisely with the causes of nonstationarity. In particular, Pearl suggested counterfactual analysis as an essential approach in establishing criteria for the necessary and sufficient character of a given cause for a given phenomenon. Thus, the common approach of detection and attribution in the climate sciences only covers the sufficiency aspect of anthropogenic forcing, and more can be done (Hannart et al., BAMS, 2016; Clim. Change, 2016).

The present talk will cover four specific aspects of these broad issues: (i) the distinction between information transfer, including both linear correlations and nonlinear extensions thereof, and true causation; (ii) the divergent results of some widely, and not so widely, used methods of studying information transfer (Krakovska et al., PRE, 2018; Kossakowski et al., Psychol. Methods, 2021; Delforge et al., HESS, 2022); (iii) shared variability of climatic time series (De Viron, GRL, 2013; ); and (iv) the uses of data assimilation in applying counterfactual theory to nonstationary phenomena (Carrassi, QJRMS, 2017; Metref et al., QJRMS, 2019).

Conclusions will include the obvious one that statistical studies of causal inference have to be complemented by dynamical ones.

How to cite: Ghil, M., Carrassi, A., and de Viron, O.: Some Thoughts on Causal Inference, the Scientific Method, and Data Assimilation, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-20548, https://doi.org/10.5194/egusphere-egu24-20548, 2024.

EGU24-21883 | Orals | ITS1.11/NP4.2 | Highlight

Large Language Models for Causal Discovery in the Earth Sciences 

Gustau Camps-Valls, Kai-Hendrik Cohrs, Emiliano Diaz, Vasileios Sitokonstantinou, and Gherardo Varando

Causality is essential for understanding complex systems like the Earth and climate, where a plethora of intertwined variables and processes happen in the wild. Constructing causal graphs often relies on either data-driven or expert-driven approaches, both fraught with challenges. The former methods, like the celebrated Peter-Clark (PC) algorithm, face issues with data requirements and assumptions of causal sufficiency, while the latter demand substantial time and expertise.

This work explores the capabilities of Large Language Models (LLMs) as an alternative to domain experts for causal graph generation. We frame conditional independence queries as prompts to LLMs and employ the PC algorithm with the answers. The performances of the LLM-based conditional independence oracle on systems with known causal graphs show a high degree of variability. We improve the performance through a proposed statistical-inspired voting schema that allows control over false-positives and false-negatives rates. We apply our chatPC algorithm to understand the causal relations between complex sets of variables (social, economic, conflicts, environmental, and climatic factors) in two pressing problems: population displacement and food insecurity in Africa. We find plausible graphs as corroborated by experts in the humanitarian sector, finding traces of causal reasoning in the model's answers. We posit that LLM-based causality is a new, promising, alternative avenue for automated causality, especially indicated for rapid response and data-scarce regimes.

How to cite: Camps-Valls, G., Cohrs, K.-H., Diaz, E., Sitokonstantinou, V., and Varando, G.: Large Language Models for Causal Discovery in the Earth Sciences, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-21883, https://doi.org/10.5194/egusphere-egu24-21883, 2024.

EGU24-22158 | ECS | Posters on site | ITS1.11/NP4.2

Spatiotemporal Causal Effect Estimation 

Rebecca Herman and Jakob Runge

Causal discovery and effect estimation for time series provide scientists with a way to extract causal information from observational studies when possible. But the high dimensionality of raw climate data causes computational problems for most analysis methods, and causal inference is no exception. To address this problem, climate scientists usually pre-process climate data using dimension reduction techniques (including seasonal and regional averaging and principle component analysis) that may result in the loss of valuable information before the true analysis even begins. For example, climate scientists often represent El Niño Southern Oscillation variability (ENSO) using the uni-variate Nino3.4 index, which cannot distinguish between central Pacific and eastern Pacific El Niño events, which are believed to impact global climate varaibility in different ways. This study introduces a method for avoiding premature data dimension reduction in causal effect estimation, implemented in tigramite. The method allows the researcher to define multi-variate climate indices, reducing the dimensionality of the causal inference problem via the causal assumptions instead of losing information from the data itself. To investigate the performance of this approach on climate data, we examine the effect of ENSO on the North Atlantic Oscillation (NAO) in simulated data from the Coupled Model Intercomparison Project, phase 6. We choose this as our case study because different types of El Nino are believed to have very different effects on NAO, to the extent that the impact may be completely undetectable in observations when no distinction between the types of ENSO is made. By comparing estimated effects using uni- and multi-variate climate indices, we demonstrate that this method retains valuable information that would be lost in uni-variate analysis, and make recommendations for best practices when using multi-variate climate indices in causal effect estimation.

How to cite: Herman, R. and Runge, J.: Spatiotemporal Causal Effect Estimation, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-22158, https://doi.org/10.5194/egusphere-egu24-22158, 2024.

EGU24-2034 | ECS | Posters on site | ITS1.12/AS5.15

Probabilistic Wind Speed Downscaling for Future Wind Power Assessment 

Nina Effenberger, Marvin Pförtner, Philipp Hennig, and Nicole Ludwig

Wind power and other renewable energy sources are essential for the energy supply. However, due to their dependence on both climate and highly local, variable weather conditions, they are less reliable and challenging to forecast.

Recent projections of climate models indicate that the mean annual energy density will change in the future [Pryor et al., 2020]. To avoid costly planning mistakes and improve return on investment, predictions of wind conditions with adequate spatial and temporal resolution are thus indispensable, to facilitate efficient planning of renewables. Recent research regarding the temporal resolution of wind speed data shows that inter-daily wind speed variability can be accounted for by instantaneous data of six-hourly resolution [Effenberger et al., 2024]. However, as wind is a very local phenomenon, the spatial resolution of climate and weather data is paramount in wind power forecasting.

Simulated climate data generally lacks the spatial resolution needed for highly localized wind power forecasts and needs to be downscaled. The downscaled data is subject to mainly two types of predictive uncertainty that are often ignored, yet non-negligible for decision-making. Firstly, climate projections depend on unknown physical processes, like the evolution of atmospheric CO2 concentration, and are thus inherently uncertain. We account for this uncertainty by ensembling across different climate models and scenarios. The second source of uncertainty, which is the main focus of this work, is that the coarse resolution of the simulated data introduces additional uncertainty, since interpolating wind speeds spatially is non-trivial. By downscaling different wind speed projections using a probabilistic Gaussian process simulation method, we can model the uncertainty that stems from interpolating wind speed data to unseen locations. Leveraging techniques from physics-informed machine learning, e.g. conditioning on partial differential equations [Pförtner et al., 2022], allows for a more realistic model, consistent with the actual dynamics of the atmosphere.

The resulting, physics-informed Gaussian process models, provide uncertainty-aware, location-specific wind speed predictions on multi-decadal scales. When planning new turbine locations, these wind speed projections based on climate model data can serve as a proxy for expected future wind power generation.

References:

Effenberger, N., Ludwig, N., and White, R. H. (2024). Mind the (spectral) gap: how the temporal resolution of wind data affects multi-decadal wind power forecasts. Environmental Research Letters, 19.
Pförtner, M., Steinwart, I., Hennig, P., and Wenger, J. (2022). Physics-informed Gaussian process regression generalizes linear PDE solvers. arXiv preprint arXiv:2212.12474.
Pryor, S. C., Barthelmie, R. J., Bukovsky, M. S., Leung, L. R., and Sakaguchi, K. (2020). Climate change impacts on wind power generation. Nature Reviews Earth & Environment, 1(12):627–643.
 

 

How to cite: Effenberger, N., Pförtner, M., Hennig, P., and Ludwig, N.: Probabilistic Wind Speed Downscaling for Future Wind Power Assessment, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-2034, https://doi.org/10.5194/egusphere-egu24-2034, 2024.

EGU24-2693 | Orals | ITS1.12/AS5.15

HRGEN: A stochastic generator of hourly rainfall 

Wenting Wang, Shuiqing Yin, and Bofu Yu

Rainfall data are needed as input to drive hydrological and soil erosion models. Daily rainfall data are commonly used and widely accessible, whether sourced from meteorological observations or simulated by Global Climate Models (GCMs). However, daily data cannot capture intensity variations during a storm event, and may not be sufficient to capture the changes during extreme weather events under climate change scenarios. Weather generators (WGs) are statistical models that can generate random sequences of meteorological variables that exhibit statistical characteristics that are similar to observations. However, the low accuracy of generated sub-daily rainfall intensities motivated this study to stochastically disaggregate daily precipitation total at hourly intervals so that observed or GCM generated daily rainfall can be downscaled into hourly scale stochastically. To achieve this, we developed a model, HRGEN, based on long-term hourly precipitation data from 1971 to 2020 from 2405 meteorological stations across mainland China. The major improvement of this model over CLIGEN includes: (1) HRGEN significantly enhances the simulation accuracy of maximum peak intensities on an hourly basis (Hmax). The average Hmax over 2405 stations of hourly observations and HRGEN-generated are 4.0 mm h-1 and 4.2 mm h-1, respectively, while that generated by CLImate GENerator (CLIGEN) is 6.5 mm h-1. The mean absolute relative error (MARE) over 2405 stations is 8.2%. This improvement is critical for accurately estimating daily EI30 values, a key index in soil erosion models and soil loss prediction; (2) HRGEN preserves the relationship between total daily precipitation and storm duration and peak intensity; (3) The model has only six parameters, markedly simplifying the calibration and simulation processes. The HRGEN-simulated hourly rainfall data can be used to estimate rainfall erosivity for erosion prediction. The R-factor estimated using HRGEN-generated hourly data agrees well with the observed R-factor values, with a high Nash-Sutcliffe efficiency coefficient (NSE) of 0.92. The average R-factor estimated from hourly observations and HRGEN-generated hourly observations over 2405 stations are 3699.2 and 3720.7 MJ mm ha-1 h-1 a-1, respectively. In comparison, R-factor estimated by CLIGEN-generated rainfall is 9100.7 MJ mm ha-1 h-1 a-1. This study highlights HRGEN’s potential as a robust tool for stochastic generation of sub-daily rainfall as input to hydrologic and soil erosion models.

How to cite: Wang, W., Yin, S., and Yu, B.: HRGEN: A stochastic generator of hourly rainfall, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-2693, https://doi.org/10.5194/egusphere-egu24-2693, 2024.

EGU24-3205 | ECS | Posters on site | ITS1.12/AS5.15

A data fusion uncertainty-enabled method to map street-scale hourly NO2: a case study in Barcelona 

Alvaro Criado, Jan Mateu Armengol, Hervé Petetin, Daniel Rodríguez-Rey, Jaime Benavides, Cristina Carnerero, Marc Guevara, Carlos Pérez García-Pando, Albert Soret, and Oriol Jorba

Considering that air pollution is the leading global environmental risk factor according to the WHO,  characterizing NO2 levels holds crucial significance, particularly in heavily trafficked urban areas where NO2 legal limits and health guidelines are frequently exceeded. Obtaining accurate and comprehensive NO2 datasets on a city level is especially challenging due to the inherent uncertainties associated with urban air quality models, and the scarcity of air quality monitoring stations. An alternative method to describe NO2 levels involves developing short-term experimental campaigns using indicative measurements, although they report period-averaged results and do not have full spatial coverage. 

Taking advantage of the three mentioned approaches,  this work proposes a data-fusion method that combines i) near-real-time hourly observations obtained from the official air quality monitoring network, ii) the output of an urban air quality model (CALIOPE-Urban) that operates at high spatial (up to 20m x 20m) and temporal (hourly) resolutions, and iii) a microscale Land-Use-Regression (LUR) model based on machine learning. The microscale-LUR model includes different urban datasets such as traffic flow or average building density and two NO2 experimental campaigns. 

While the hourly observations enable the temporal variability adjustment in the dispersion model, the microscale-LUR model provides additional insights into the spatial characteristics of NO2 distribution. Our data-fusion approach was implemented on an hourly basis over the metropolitan area of Barcelona in 2019. Besides the bias-corrected NO2 hourly maps, this method also computes the uncertainty associated with the variance of the estimated error during the correction process. By integrating both corrected NO2 values and their associated uncertainty, it produces maps that show the probability of exceeding the hourly 200 µg/m3 and the annual 40 µg/m3 NO2 legal thresholds over Barcelona. 

Cross-validated results at the monitoring stations demonstrate that the spatial bias correction increases the correlation coefficient (r) by +46 % and decreases the root mean square error (RMSE) by −48 %, compared to the model output. This research emphasizes the importance of highly detailed spatial data within data-fusion techniques, enhancing the accuracy of predicting exceedances at the street level.

How to cite: Criado, A., Mateu Armengol, J., Petetin, H., Rodríguez-Rey, D., Benavides, J., Carnerero, C., Guevara, M., Pérez García-Pando, C., Soret, A., and Jorba, O.: A data fusion uncertainty-enabled method to map street-scale hourly NO2: a case study in Barcelona, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-3205, https://doi.org/10.5194/egusphere-egu24-3205, 2024.

EGU24-3540 | ECS | Posters virtual | ITS1.12/AS5.15

Testing the use of deep learning techniques for emulating regional reanalysis 

Antonio Pérez, Mario Santa Cruz, Javier Diez-Sierra, Matthew Chantry, András Horányi, Mariana Clare, and Cornel Soci

Reanalysis datasets serve as essential components for contemporary climate monitoring, integrating historical weather observations with predictive models to create extensive climate data records for the last decades. The fifth generation ECMWF atmospheric global climate reanalysis (ERA5) dataset from the European Centre for Medium-Range Weather Forecasts (ECMWF) represents the latest update, providing a broad temporal scope and improved spatial granularity. However, its resolution may fall short for detailed local-scale analysis required in critical sectors such as agriculture, energy, and disaster response, among others. Even though more detailed regional information for Europe like the Copernicus European Regional ReAnalysis (CERRA) do exist, its high computational costs and the lack of very near real-time data updates create limitations to conducting analyses close to real time.

To solve some of these limitations, a deep learning model has been developed to mirror CERRA's 2m temperature field utilising ERA5 as input. This approach aims to replicate the details of CERRA, ensuring rapid and efficient emulation without surpassing its original quality, i.e. treating CERRA as the ground truth. Central to this model is the Swin2SRModel component (Swin v2), which has effectively demonstrated the ability to downscale the resolution of inputs by a factor of 8. This capability aligns well with the intended task of downscaling the grid from 0.25º (ERA5) to 0.05º (CERRA). To achieve this, a Convolutional Neural Network (CNN) pre-processes the data, reshaping it to the necessary feature map size. The model training is focused on the specific region of interest of the Iberian Peninsula, instead of the entire European CERRA domain. The training, lasting 100 epochs, takes approximately 3.6 days using small batch processing. It employs the Adam optimizer, starting with a learning rate of 0.0001 that decreases following a cosine curve, integrating a warm-up phase to mitigate training instability. It utilises 32 years of data, spanning from 1985 to 2016, and its performance is validated against the independent dataset of 2017 to 2021.

A comprehensive post-training evaluation of the model shows a marked improvement – 35% reduction in Mean Absolute Error (MAE) and a nearly 30% enhancement in Root Mean Square Error (RMSE) – compared to the bicubic interpolation method. This leap in accuracy is especially notable in complex landscapes. Validation on specific locations, such as the Aneto mountain, have demonstrated a dramatic refinement in the mean error, dropping from -6.3°C to 0.06°C – 99% improvement. Similar improvements have been observed in Cantabrian Mountains such as Peña Vieja (94%) and Peña Labra (88%), illustrating the model's superior performance in areas where previous errors were substantial, highlighting its ability in areas that most require it.

In conclusion, the project shows promising results in enhancing reanalysis data with AI, demonstrating potential in both computational efficiency and near real-time application. While initial results are encouraging, indicating reduced errors compared to the bicubic interpolation, comprehensive validation against CERRA using independent observations and expansion to broader domains and variables remain crucial for confirming the method's effectiveness and reliability.

How to cite: Pérez, A., Santa Cruz, M., Diez-Sierra, J., Chantry, M., Horányi, A., Clare, M., and Soci, C.: Testing the use of deep learning techniques for emulating regional reanalysis, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-3540, https://doi.org/10.5194/egusphere-egu24-3540, 2024.

EGU24-4810 | Orals | ITS1.12/AS5.15

Dynamical Downscaling Simulation of Asian Climate with a Bias-Corrected CMIP6 Dataset: Evaluation  

Zhongfeng Xu, Ying Han, Meng-Zhuo Zhang, Chi-Yung Tam, Zong-Liang Yang, Ahmed EL Kenawy, and Congbin Fu

    In this study, we aim to assess the impacts of GCM bias correction on dynamical downscaling simulation over the Asia-western North Pacific region. Three simulations were conducted with a 25-km grid spacing for the period 1980–2014. The first simulation (WRF_ERA5) was driven by the European Centre for Medium-Range Weather Forecasts Reanalysis 5 (ERA5) dataset and served as the validation dataset. The original GCM dataset (MPI-ESM1-2-HR model) was used to drive the second simulation (WRF_GCM), while the third simulation (WRF_GCMbc) was driven by the bias-corrected GCM dataset. The bias-corrected GCM data has an ERA5-based mean and interannual variance but the long-term trends are derived from the ensemble mean of 18 CMIP6 models. Results demonstrate that the WRF_GCMbc significantly reduced the root-mean-square errors (RMSEs) of the climatological mean of downscaled variables, including temperature, precipitation, snow, wind, relative humidity, and planetary boundary layer height by 50%–90% compared to the WRF_GCM. Similarly, the RMSEs of interannual-to-interdecadal variances of downscaled variables were reduced by 30%–60%. Furthermore, the WRF_GCMbc better captured the annual cycle of the monsoon circulation and intraseasonal and day-to-day variabilities. The leading empirical orthogonal function (EOF) shows a monopole precipitation mode in the WRF_GCM. In contrast, the WRF_GCMbc successfully reproduced the observed tri-pole mode of summer precipitation over eastern China. This improvement could be attributed to a better-simulated location of the western North Pacific subtropical high in the WRF_GCMbc after GCM bias correction.

How to cite: Xu, Z., Han, Y., Zhang, M.-Z., Tam, C.-Y., Yang, Z.-L., EL Kenawy, A., and Fu, C.: Dynamical Downscaling Simulation of Asian Climate with a Bias-Corrected CMIP6 Dataset: Evaluation , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-4810, https://doi.org/10.5194/egusphere-egu24-4810, 2024.

EGU24-5980 | ECS | Posters on site | ITS1.12/AS5.15

Benchmarking Deep Learning based Downscaling of Wind Speed 

Luca Schmidt and Nicole Ludwig

The efficient placement of wind turbines relies on strategic assessment of local wind speed. Recent
studies highlight the crucial role of spatial resolution in accurately forecasting wind speed and
estimating the associated wind energy potential [1].

However, climate models typically fail to provide the spatial data resolution necessary for precise
energy resource assessment. To address this challenge, various downscaling methods have been
proposed to infer high-resolution data from coarser resolution data. Notably, image super-resolution
methods, a class of image processing techniques originally developed in computer vision to enhance
the resolution of natural images, have emerged as a promising approach for statistical downscaling.
By interpreting gridded data as images, these techniques are amenable to increasing the spatial resolution
of climate [3] and weather data [2].

We provide a comprehensive benchmark to compare the performance of various state-of-the-art image
superresolution models on weather data, such as ERA5 reanalysis data. The benchmark ranges from
interpolation baselines to all prominent deep learning based models, including a CNN-based model,
an attention-based model and a spatio-temporal model.

 

[1] Jung, C. and Schindler, D. [2022], ‘On the influence of wind speed model resolution on the global technical
wind energy potential’, Renewable and Sustainable Energy Reviews 156, 112001.
[2] Kurinchi-Vendhan, R., Lütjens, B., Gupta, R., Werner, L. and Newman, D. [2021], ‘Wisosuper: Bench-
marking super-resolution methods on wind and solar data’, arXiv preprint arXiv:2109.08770 .
[3] Stengel, K., Glaws, A., Hettinger, D. and King, R. N. [2020], ‘Adversarial super-resolution of climatological
wind and solar data’, Proceedings of the National Academy of Sciences 117(29), 16805–16815.

 

How to cite: Schmidt, L. and Ludwig, N.: Benchmarking Deep Learning based Downscaling of Wind Speed, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5980, https://doi.org/10.5194/egusphere-egu24-5980, 2024.

Global climate models (GCMs) or Earth system models (ESMs) exhibit biases, with resolutions too coarse to capture local variability for fine-scale, reliable drought and climate impact assessment. However, conventional bias correction approaches may cause implausible climate change signals due to unrealistic representations of spatial and intervariable dependences. While purely data-driven deep learning has achieved significant progress in improving climate and earth system simulations and predictions, they cannot reliably learn the circumstances (e.g., extremes) that are largely unseen in historical climate but likely becoming more frequent in the future climate (i.e., climate non-stationarity).  This study shows an integrated trend-preserving deep learning approach can address the spatial and intervariable dependences and climate non-stationarity issues for downscaling and bias correcting GCMs/ESMs. Here we combine the super-resolution deep residual network (SRDRN) with the trend-preserving quantile delta mapping (QDM) to downscale and bias correct six primary climate variables at once (including daily precipitation, maximum temperature, minimum temperature, relative humidity, solar radiation, and wind speed) from five state-of-the-art GCMs/ESMs in the Coupled Model Intercomparison Project Phase 6 (CMIP6). We found that the SRDRN-QDM approach greatly reduced GCMs/ESMs biases in spatial and intervariable dependences while significantly better reducing biases in extremes compared to deep learning. The estimated drought based on the six bias-corrected and downscaled variables captured the observed drought intensity and frequency, which outperformed the state-of-the-art multivariate bias correction approach, demonstrating its capability for correcting GCMs/ESMs biases in spatial and multivariable dependences and extremes.

How to cite: Tian, D. and Wang, F.: Trend-Preserving Deep Learning for Multivariate Bias Correction and Downscaling of Climate Models, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-6408, https://doi.org/10.5194/egusphere-egu24-6408, 2024.

EGU24-7111 | ECS | Orals | ITS1.12/AS5.15

Revisiting Tabular Machine Learning and Sequential Models to Advance Climate Downscaling 

Sanaa Hobeichi, Yawen Shao, Neelesh Rampal, Matthias Bittner, and Gab Abramowitz

Recent advancements in the empirical downscaling of climate fields using Machine Learning have predominantly leveraged computer vision approaches. These methods treat a climate field as an image channel, applying image processing techniques to automatically extract features for the downscaling model from its latent space embeddings. In contrast, this work aims to revisit and validate the potential of tabular and sequential models in the context of grid-by-grid downscaling, where each grid cell in a map is individually downscaled and input features for the downscaling model are selected manually by a climate expert. We present downscaling results for precipitation and evapotranspiration using three distinct models: Long Short-Term Memory (LSTM), Multi-layer Perceptron (MLP), and a hybrid approach that combines Linear Regression with Random Forest. Our discussion includes the setup and optimization strategies for these models to enhance their ability to capture extremes. The merits of this grid-by-grid approach are highlighted, focusing not only on performance and effectiveness in preserving spatial features but also on its flexibility, spatial transferability, ease of model fine-tuning, and training efficiency.

How to cite: Hobeichi, S., Shao, Y., Rampal, N., Bittner, M., and Abramowitz, G.: Revisiting Tabular Machine Learning and Sequential Models to Advance Climate Downscaling, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-7111, https://doi.org/10.5194/egusphere-egu24-7111, 2024.

EGU24-8464 | ECS | Orals | ITS1.12/AS5.15

Machine Learning for Multivariate Downscaling: A Generative Model Inspired by Forecast Evaluation 

Maybritt Schillinger, Xinwei Shen, Maxim Samarin, and Nicolai Meinshausen

To complement computationally expensive regional climate model (RCM) simulations, machine learning methods can predict the high-resolution RCM data from low-resolution global climate model (GCM) input. Instead of merely targeting the conditional mean of the RCM field given the GCM data, more recent works are based on generative adversarial networks or diffusion models and aim to learn the full conditional distribution. In this spirit, we present a novel generative model that relies on statistical tools from forecast evaluation. The model can sample several plausible RCM realisations and enables assessing their variability. To achieve this goal, we use a simple neural network architecture that predicts Fourier coefficients of the high-resolution fields for multiple variables jointly (temperature, precipitation, solar radiation and wind). The loss function of our model is a proper scoring rule that measures the discrepancy between the model’s predictive distribution and the RCM’s true distribution. The score is minimised if both distributions agree. Our generative model is trained on multiple GCM-RCM combinations from the Euro-Cordex project. Furthermore, we show how the framework can be augmented to perform a bias-correction task: With a modified loss function, it is possible to generate data from the observational distribution, for example resembling gridded E-OBS data. To summarise, our work presents a machine learning method that allows us to generate multivariate high-resolution climate data, and can be extended flexibly to include further variables or downscale and bias-correct future projections.

How to cite: Schillinger, M., Shen, X., Samarin, M., and Meinshausen, N.: Machine Learning for Multivariate Downscaling: A Generative Model Inspired by Forecast Evaluation, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-8464, https://doi.org/10.5194/egusphere-egu24-8464, 2024.

EGU24-8821 | Orals | ITS1.12/AS5.15

Downscaling statistical information: a statistical approach 

Rasmus Benestad, Kajsa M. Parding, Abdelkader Mezghani, Andreas Dobler, Oskar A. Landgren, and Julia Lutz

If the shape of mathematical curves describing local weather statistics are systematically influenced by large-scale conditions and geographical factors, then it may be possible to downscale this kind of information directly. Such curves may include probability density functions (pdfs) for daily temperature/precipitation or intensity-duration-frequency (IDF) curves for estimating return values of intense sub-daily rainfall. Downscaling the shape of such curves may be referred to as ‘downscaling climate’ if we regard ‘local climate’ as the statistical description of various weather parameters. This approach is distinct from the more traditional approach ‘downscaling weather’, where one seeks to estimate particular local states for instance on a day-by-day basis. We present work on downscaling the shapes of pdfs and IDFs involving large multi-model ensembles for the application in climate change adaptation efforts. Our efforts also include an evaluation of both methodology and the global climate models' (GCMs) ability to reproduce observed large-scale climatic variability in terms of the salient spatio-temporal covariance structure. We emphasise that it’s important to combine different strategies for downscaling, e.g. regional climate models (RCMs) and empirical-statistical downscaling (ESD) that are based on different assumptions, for getting robust future regional climate projections.

How to cite: Benestad, R., Parding, K. M., Mezghani, A., Dobler, A., Landgren, O. A., and Lutz, J.: Downscaling statistical information: a statistical approach, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-8821, https://doi.org/10.5194/egusphere-egu24-8821, 2024.

EGU24-9083 | ECS | Orals | ITS1.12/AS5.15

A Novel Bias-Adjustment Methodology for Streaming Global Climate Models 

Ehsan Sharifi, Katherine Grayson, Sebastian Müller, and Stephan Thober

Projections generated by global climate models (GCMs) are increasingly utilized to inform climate adaptation policies. It is known that climate models simplify the real climate system, leading to biases between simulated and observed climates. The spatial and temporal resolution of GCMs is ever increasing to provide a better representation of the Earth system and in turn, also provide higher quality information for users. To effectively handle the substantial climate data produced by these models, which can reach Terabytes to Petabytes, the Destination Earth (DestinE) initiative is exploring data streaming—a new approach that enables user applications to run Earth system models in an end-to-end workflow directly downstream of the climate simulations, eliminating the need to store entire time-series of variables to disk.

Traditional methods for quantile or percentile calculation typically involve sorting the data and directly computing the specific value corresponding to the desired quantile. These methods can be computationally intensive, especially for large datasets, as it necessitates storing and processing the entire dataset. While traditional bias-adjustment (BA) algorithms rely on data being fully available, a further challenge lies in developing bias-adjustment procedures capable of accommodating streamed data on-the-fly. In the DestinE Climate Digital Twin (CDT), we extend the quantile-mapping technique used in the ISI-MIP project (isimip.org) because it is a well-established method and preserves the trend of the original data. The technique involves aligning the CDFs of the model data with those of the observed data by adjusting the model's cumulative distribution to match that of the observed data. The enhancements of the BA method in DestinE-CDT is making use of the T-Digest algorithm, a sophisticated strategy that dynamically clusters data points into small groups, which is used to generate a summarized representation of the data distribution from streamed data and accurately calculate percentiles. This clustering technique offers an accurate estimate of percentiles while efficiently managing large and unbounded data streams where new data points are continuously added.

We apply the developed quantile-mapping BA for different variables on a global scale and compare it with the parametric distribution functions used in quantile-mapping BA from the ISI-MIP project.

How to cite: Sharifi, E., Grayson, K., Müller, S., and Thober, S.: A Novel Bias-Adjustment Methodology for Streaming Global Climate Models, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-9083, https://doi.org/10.5194/egusphere-egu24-9083, 2024.

Downscaling techniques are one of the most prominent applications of Deep Learning (DL) in Earth System Modeling. A robust DL downscaling model can generate high-resolution fields from coarse-scale numerical model simulations, saving the timely and resourceful applications of regional/local models. Moreover, specific DL models can generate uncertainty information and provide ensemble-like pool scenarios, hardly achievable using traditional numerical simulations due to their high computational requirements. In this work, we present the application of deep generative models, namely a Generative Adversarial Network (GAN) and a Latent Diffusion model (LDCast, Leinonen et al., 2023), to perform the downscaling of ERA5 (Hersbach et al., 2018) data over Italy up to a resolution of 2km. The target high-resolution data used for training consists in the Italian high-resolution dynamical reanalyses obtained with COSMO-CLM (Raffa et al., 2021). The goal of the study is to show that recent advancements in generative modeling can learn to provide comparable results with numerical dynamical downscaling models, such as the COSMO-CLM model, given the same input data (i.e., ERA5 data), preserving the realism of fine-scale features and flow characteristics. The training and testing database is composed of hourly data from 2000 to 2020 (~184000 timestamps), and the target variables of the study are 2-m temperature and horizontal wind components. A selection of predictand variables from ERA5 is used as input to the DL models (e.g., 850hPa temperature, specific humidity, and wind). The generative models are compared with reference baselines, both DL-based (e.g., UNET) and statistical methods. Preliminary results are presented, highlighting the improvements introduced with this architecture with respect to the baselines. The results are evaluated by different quantitative verification scores: RMSE, predicted spectra, frequency distributions, and spatial distribution of errors. 

How to cite: Tomasi, E., Franch, G., and Cristoforetti, M.: Can AI be enabled to dynamical downscaling? Training Deep Generative Models to downscale ERA5 to high-resolution COSMO-CLM dynamical reanalyses over Italy , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-10091, https://doi.org/10.5194/egusphere-egu24-10091, 2024.

EGU24-10376 | ECS | Posters on site | ITS1.12/AS5.15

Spatial downscaling of climate projections of temperature and precipitation over complex mountain terrain: A case study in the north-eastern Italian Alps 

Michael Matiu, Anna Napoli, Dino Zardi, Alberto Bellin, and Bruno Majone

Mountain regions are particularly sensitive to climatic change. In these areas the complex topography modulates meteorological and climatic patterns with the elevation playing the strongest influence on temperature and precipitation. However, most regional climate models used in climate change assessments are too coarse to capture the relevant elevation gradients for impact studies, such as in hydrology, which require detailed spatial information on water availability, either in liquid or in solid state.

Focusing as a case study on Trentino-Alto Adige region in the north-eastern Italian Alps, we compare several statistical approaches for downscaling regional climate models to the spatial scale needed for impact studies in mountain areas. In particular, we propose a comparison between a novel method, based solely on climate model output using generalized additive models (GAM), and quantile mapping (QM) methods using an interpolated observational dataset as reference. We then evaluate and discuss the effectiveness of  downscaling approaches, relying on both spatial and temporal metrics and taking into account the possible elevation dependency.

Preliminary results show that the approach using GAMs offers spatial fields consistent with the large-scale climate model, while the QM methods have artificial breaks at grid cell boundaries. On the other hand, the GAM approach inherits the biases from the climate model, while QM also simultaneously performs bias adjustment using the observational dataset.

How to cite: Matiu, M., Napoli, A., Zardi, D., Bellin, A., and Majone, B.: Spatial downscaling of climate projections of temperature and precipitation over complex mountain terrain: A case study in the north-eastern Italian Alps, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-10376, https://doi.org/10.5194/egusphere-egu24-10376, 2024.

EGU24-11216 | ECS | Posters virtual | ITS1.12/AS5.15

Refining Regional Climate Projections for Louisiana and Mississippi: Dynamical Downscaling with WRF Model in the Face of Projected Sea Level Rise 

Zuhayr Shahid Ishmam, Paul Miller, Robert Rohli, and Rubayet Bin Mostafiz

Global climate models (GCMs) lack the necessary spatial resolution to accurately depict the atmospheric and land surface processes that define the regional climate of any particular location. In contrast, regional climate models (RCMs) explicitly capture the interactions between the broad-scale weather patterns simulated by global models and the specific characteristics of the local terrain. In this work, the Weather Research and Forecasting (WRF) model is used for dynamical downscaling simulations for a historical period (2001-2005) and the future (2095-2099) forced by the NCAR’s Community Earth System Model, version 1 (CESM1), for Louisiana and Mississippi, United States. The future RCM was run with both a present-day and future land-sea mask, considering model projections of sea level rise along the Gulf of Mexico coast. The convection-permitting, high-resolution (4 km) model performs more satisfactorily for temperature than rainfall when validated against observations from meteorological stations and gridded rainfall data. The future RCM runs demonstrate significant projected changes in average and extreme temperatures and rainfall from the current climate over the model domain. The probable retreat of the coastline shifts the sea breeze landward from its present-day area, which generates heavier rainfall and more moderate temperatures at places presently relatively distant from the Gulf of Mexico. This study enhances the existing dynamical downscaling methodology by incorporating the impacts of anticipated sea level rise on the regional climate.

How to cite: Ishmam, Z. S., Miller, P., Rohli, R., and Mostafiz, R. B.: Refining Regional Climate Projections for Louisiana and Mississippi: Dynamical Downscaling with WRF Model in the Face of Projected Sea Level Rise, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-11216, https://doi.org/10.5194/egusphere-egu24-11216, 2024.

EGU24-12144 | Posters on site | ITS1.12/AS5.15 | Highlight

Reconstruction of the atmosphere over the European Alps from 1850 to present using dynamical downscaling  

Madlene Pfeiffer, Ben Marzeion, and Inga Labuhn

The Alps are very sensitive to climate change and have experienced a strong increase in temperatures since the end of the Little Ice Age (1850 AD). This in turn influences the alpine glaciers, which are experiencing strong melting, further impacting geomorphological and hydrological processes in the high Alpine catchments. The combined change in climate and in prevalence of ice then has further impacts on erosional processes, biosphere, including local flora, and societies (e.g. by changes in the seasonal cycle of river runoff). In order to better understand small-scale processes, which are not well represented in climate observations and reanalysis products, as well as feedbacks and system interactions within the high Alpine Earth system, we have reconstructed atmospheric conditions over the European Alps from 1850 to present by dynamically downscaling global reanalysis data with the advanced research version of the Weather Research and Forecasting model (WRF-ARW) in a nested grid configuration with domains of 18-, 6-, and 2-km spatial resolution, respectively. To account for uncertainty introduced by the reanalysis, we have forced WRF with an ensemble of global reanalysis products. To quantify the errors, we compare our datasets to in-situ observations. In comparison to the reanalysis products that act as a forcing, we find an improvement in spatial correlation between the simulated and observed temperatures, as well as a better representation of precipitation patterns and amounts in the high-resolution domain. We present the first dynamically downscaled dataset over Europe (18 km), the entire Alps (6 km), and parts of central Alps (2 km), at high temporal resolution (3, 1, and 1 hour, respectively) that spans the entire period from 1850 to present.

How to cite: Pfeiffer, M., Marzeion, B., and Labuhn, I.: Reconstruction of the atmosphere over the European Alps from 1850 to present using dynamical downscaling , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12144, https://doi.org/10.5194/egusphere-egu24-12144, 2024.

EGU24-12446 | Orals | ITS1.12/AS5.15

Evaluating CMIP6 models under different statistical downscaling methods for climate assessments in the north of Chile 

Catalina Jerez, Miguel Lagos-Zuñiga, and Santiago Montserrat

Statistical Downscaling Methods (SDMs) play a pivotal role in climate change assessments at local and regional scales, as they can efficiently reproduce historical climate observations, overcoming the limitation of Global Climate Models (GCMs) in capturing fine-scale climatic features. However, the evaluation of GCMs and SDMs often focuses on historical climatology, neglecting extreme events representation and climate change signal preservation. In response, this paper proposes a methodological guideline for GCMs and SDMs selection, incorporating three key criteria: representation of historical climatology (Past Performance Index - PPI), representation of extreme wet climate indices (Climate Integrated Impact Index - CI3), and preservation of climate signal change (Climate Signal Performance Criteria - SCPI). Satisfactory GCM and SDM performance during the historical period is defined by meeting conditions such as PPI ≥ 0.5 for each climatic variable (precipitation, minimum and maximum temperature) and CI3 ≥ 0.4. For future projections, SCPI guides the selection process, considering short (2015 – 2040), medium (2041 – 2070), and long-term (2071 – 2100) projections across different Shared Socioeconomic Pathways (SSPs) (see step d) in Figure 1).

 

The study evaluates 18 GCMs from Sixth Model Intercomparison Phase (CMIP6), interpolated to the gridded meteorological product CR2METv2.0 (0.05° x 0.05°) for the northern region of Chile (17ºS – 32º). Ten SDMs are applied to short, medium, and long-term periods under SSP2-4.5 and SSP5-8.5 scenarios. Results indicate that no single SDM corrects all criteria for all GCMs. Climate projection groups are established based on the number of criteria met, distinguishing models that satisfy two or three criteria. The historical evaluation shows that interannual variability is the most influential in the PPI results, both for precipitation and temperatures (min and max). Better historical performance is also observed for multivariate methods family over quantile mapping family or hybrid methods family (combination of analogs, resampling, climate fingerprinting and quantile mapping). In the case of CI3, all SDMs for all the GCMs show a similar bias for maximum precipitation magnitude and their mean temperature, meanwhile the consecutive wet days, days with precipitation over 50 mm and snow process indices present a bias of less than 10%. For this metric, no SDM family has a better performance over another SDM family. Finally, the preserving of climate signal change (for each SSP scenario and projection period) is not observed with the hybrid method. For quantile methods, we observed a tendency of modification of the signal climate change, and the multivariate methods has the best performance in these criteria. This proposed methodology facilitates the selection of GCM subsets based on study objectives (climatology, extreme events, or climate change signals). Future work should focus on advancing additional statistical downscaling methods capable of representing diverse criteria, including natural variability and climate change signals.

Figure 1. Methodological scheme for the selection of suitable GCMs and SDMs.

How to cite: Jerez, C., Lagos-Zuñiga, M., and Montserrat, S.: Evaluating CMIP6 models under different statistical downscaling methods for climate assessments in the north of Chile, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12446, https://doi.org/10.5194/egusphere-egu24-12446, 2024.

EGU24-13235 | Orals | ITS1.12/AS5.15 | Highlight

A Process-Informed Determination of Credibility Across Different Downscaling Methods 

Melissa Bukovsky, Seth McGinnis, Rachel McCrary, and Linda Mearns

Despite the ongoing advancements in Earth system simulation, the results from Global Climate Models (GCMs) are still not refined enough to be directly applied to numerous climate impact issues. There are many techniques available to downscale GCM outputs to finer resolutions, from basic statistical adjustments to more complex methods like dynamical downscaling and machine learning. However, these methods often yield different results, making it difficult to assess their relative reliability, particularly when comparing statistical versus dynamical downscaling methods.

We consider downscaled results to be credible when the phenomena and processes producing it are consistent; for instance, if it’s raining, the necessary conditions for rain (such as lift and atmospheric moisture) should be present. To assess various downscaling techniques, and demonstrate this technique, we examine the occurrence of rainfall at a location the Southern Great Plains, specifically near the DOE ARRM site in Oklahoma during May, the rainiest month. In this scenario, we are looking for an atmospheric setup that produces uplift at this location and corresponds with the northward movement of moisture from the Gulf of Mexico.

By comparing the composite synoptic-scale meteorological conditions on days with and without rain from the GCM being downscaled or from the downscaling method, as appropriate, we can verify if the outcomes of downscaling GCM precipitation align with the processes that drive them. This method offers a process-based added-value analysis strategy for all kinds of downscaling techniques, which extends beyond basic measures of statistical resemblance.

We’ve used two regional climate models (RegCM4 & WRF), a machine learning technique (U-Net CNN), and four statistical methods of different complexities to downscale precipitation from three distinct GCMs. By using this method to compare them with each other and the raw GCM results, we’ve discovered that all downscaling methods can yield plausible outcomes when the GCM performs well, as they inherit its credibility. However, when the GCM’s performance is subpar, only dynamical methods can rectify regional circulation errors, unlike the other methods. Interestingly, we also found that simpler statistical methods outperform more complex non-dynamical methods when dealing with poor GCM inputs.

How to cite: Bukovsky, M., McGinnis, S., McCrary, R., and Mearns, L.: A Process-Informed Determination of Credibility Across Different Downscaling Methods, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-13235, https://doi.org/10.5194/egusphere-egu24-13235, 2024.

EGU24-14630 | ECS | Posters on site | ITS1.12/AS5.15

Machine learning-based downscaling of coarse resolution temperature and its application for potential frost identification over complex terrain. 

Sudheer Bhakare, Sara Dal Gesso, Marco Venturini, and Dino Zardi

The precise representation of spatial temperature is important for practical applications like agriculture where they require local information at very high resolution for managing agricultural activities. In recent times, statistical downscaling methods, specifically those utilizing machine learning methods are gaining importance due to their computational of time efficiency over dynamic downscaling.

This study focuses on enhancing the downscaling of spatial temperature over complex terrain using machine learning algorithms, particularly Random Forest (RF), Artificial Neural Networks (ANN), and Convolutional Neural Networks (CNN). The primary aim of this study is to identify the most promising machine learning model for downscaling gridded temperature at 2 meters from 9 km to 1 km over Non and Adige valleys. Additionally, we aim to apply these models for potential frost identification for the months of March, April, and May. We used static predictors such as Shutter Radar Topography Mission (SRTM) elevation which plays an important role in complex terrains to improve the performance of models. In addition to that, dynamic predictors such as zonal and meridional winds (U, V), windspeed, surface pressure (SP), etc. are used as auxiliary inputs. The study’s methodology includes training and evaluating the performance of three machine learning models using statistical metrics such as Root Mean Square Error (RMSE), Mean Absolute Error (MAE), R square (R2), and Mean Bias Error (MBE). Furthermore, we used other metrics such as recall, precision, and F1 score for assessing model performance for frost identification.

Our results show CNN models outperform other models across all the seasons with the best performance in summer (RMSE=1, MAE= 0.78, R2=0.94) and the least in winter (RMSE=1.3, MAE=1, R2=0.87).  All These models exhibit a consistent pattern of having good performance in summer and least in winter. The superiority of the CNN model can be attributed to its ability to capture spatial patterns in temperature data which makes it more reliable for complex terrains. Additionally, for frost identification, CNN models show better performance with the highest F1 score across March, April, and May.

How to cite: Bhakare, S., Dal Gesso, S., Venturini, M., and Zardi, D.: Machine learning-based downscaling of coarse resolution temperature and its application for potential frost identification over complex terrain., EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-14630, https://doi.org/10.5194/egusphere-egu24-14630, 2024.

EGU24-15468 | ECS | Posters on site | ITS1.12/AS5.15

Spatio-temporal AI downscaling of ERA5-land precipitation estimates 

Luca Glawion, Julius Polz, Harald Kunstmann, Benjamin Fersch, and Christian Chwala

Generative deep learning models have been proven to have great potential for precipitation nowcasting and downscaling applications. spateGAN [1] is a conditional generative neural network that we initially developed for spatio-temporal superresolution of radar-rainfall in Germany. Here, we apply the model for downscaling of ERA5-land precipitation estimates and discuss the specific challenges that arise in such an application.

 

While ERA5 data are vital in climate science, their limited grid size and temporal resolution (1 hour and 0.1°, ERA5 global: 0.25°) hinder accurate representation of e.g. convective rainfall events. To address these limitations, we trained a physical constraint spateGAN to enhance the resolution of time sequences of ERA5 land precipitation patches towards the resolution of RADKLIM-YW, a high-resolution (5 minutes and 1 km) rain-gauge-adjusted radar product tailored for Germany which we used as a training target. Additionally, for comprehensive validation, we assessed the Multi-Radar/Multi-Sensor (MRMS) radar product for the United States. The downscaled rainfields produced by spateGAN exhibit coherent spatio-temporal patterns and an improved representation of extreme values. Employing an ensemble approach, by generating multiple high-resolution solutions by shifting model input patches both pixel- and timewise, further enhances the quality of the downscaling product, quantified by Continuous Ranked Probability Score (CRPS), ensemble Fractions Skill Score (FSS), and rank histograms. Furthermore, our analysis of downscaled MRMS data highlights spateGAN's applicability for global downscaling applications and beyond its original training region.

 

In summary, our findings show the feasibility of generating a global  high-resolution precipitation product based on ERA5. Such a product holds significant promise for various environmental applications, including in-depth analyses of rainfall variability on a fine-scaled global grid, impact assessments of extreme rainfall events, expanded possibilities for climate and hydrological model calibration and evaluation and as training data for AI weather forecasting models.

 

[1] Glawion, L., Polz, J., Kunstmann, H., Fersch, B., Chwala, C. (2023): spateGAN: Spatio-Temporal Downscaling of Rainfall Fields Using a cGAN Approach. Earth and Space Science. 10(10). e2023EA002906. https://doi.org/10.1029/2023EA002906.

 

How to cite: Glawion, L., Polz, J., Kunstmann, H., Fersch, B., and Chwala, C.: Spatio-temporal AI downscaling of ERA5-land precipitation estimates, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15468, https://doi.org/10.5194/egusphere-egu24-15468, 2024.

Accurate downscaling of daily precipitation is crucial for hydrological and climate modeling, especially in regions with complex terrain and a lack of observational data. In such regions, climate reanalysis are not reliable and thus accurate downscaling is usually limited to those locations captured by a (discrete) network of in-situ measurements instead. For this reason, learning to downscale in ungauged locations, whilst maintaining the spatial structure of precipitation, is crucial to effectively downscale (gridded) climate simulations. 

This study introduces a Gaussian Process - Multi-Layer Perceptron (GP-MLP) latent variable model tailored for the probabilistic downscaling of daily precipitation in ungauged locations. By generating spatially coherent precipitation fields, this model addresses key challenges in regional climate impact assessments and water resource management.

The GP-MLP model consists of an MLP that performs non-linear regression, mapping a set of inputs to distributional parameters of a given probability distribution for each spatio-temporal locations, and we induce spatial correlation between locations with a latent variable modelled by a GP  We jointly learn the GP and MLP parameters using variational inference, which critically allows us to model non-Gaussian probability distributions. 

We test our approach in two geographically and climatologically diverse regions: the Swiss Alps and the Langtang Valley in Nepal. The Swiss Alps, with their complex terrain and relatively dense observational network, serve as an ideal region for the initial training of our model. In the Langtang Valley, a high-mountain region with limited ground-based observations, we employ a transfer learning strategy on the model pre-trained in the Swiss Alps. This process involves fine-tuning the model parameters to the unique climatic and topographical features of the Himalayas, thereby enhancing its performance in predicting daily precipitation in this data-sparse region.

Our preliminary findings demonstrate the model's strong capability in producing accurate and spatially coherent predictions of daily precipitation for ungauged locations. The probabilistic nature of the model's outputs is particularly valuable, providing not only predictions of daily precipitation but also quantifying the associated uncertainties - a crucial aspect for risk management in hydrology and agriculture in areas where the paucity of data has traditionally limited detailed climate impact analysis.

How to cite: Girona-Mata, M., Orr, A., and Turner, R. E.: Spatially-Coherent Probabilistic Downscaling of Daily Precipitation in Ungauged Mountain Locations: a Transfer Learning Study in the Swiss Alps and the Langtang Valley, Nepal., EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15911, https://doi.org/10.5194/egusphere-egu24-15911, 2024.

EGU24-16245 | ECS | Posters on site | ITS1.12/AS5.15

Stochastic simulation of high space-time resolution precipitation fields in Beijing 

Tinghui Li, Shuiqing Yin, Zeqi Li, Maoqing Wang, and Nadav Peleg

Precipitation is closely related to many earth surface processes, for some of them, such as urban flooding, high-resolution precipitation fields data are required. However, those high-resolution precipitation fields are often not available for a long enough period to be used for flood estimates. Stochastic models attempting to simulate precipitation at single or multiple sites face challenges in capturing the high spatial heterogeneity inherent in precipitation. We calibrated the Advanced WEather GENerator for a two-dimensional grid (AWE-GEN-2d) to simulate continuous 2-D precipitation fields and evaluated its performance based on CMA Multi-source merged Precipitation Analysis System Product (CMPAS) for the period from 2015 to 2020, with a spatial resolution of 0.01°×0.01° and a temporal resolution of hourly. Characteristics of spatiotemporal precipitation fields for 486 events were analyzed and monthly parameters in AWE-GEN-2d were obtained. AWE-GEN-2d was utilized to stochastically simulate hourly spatiotemporal precipitation fields at a resolution of 0.01°×0.01° for 30 years and its simulation accuracy was subsequently assessed by comparing with the observations. The results showed precipitation fields simulated by AWE-GEN-2d demonstrated consistency with the observed fields in terms of annual and monthly precipitation, the number and duration of precipitation events, and the average hourly precipitation intensity. For extreme hourly precipitation, the 95th and 99th percentiles of hourly precipitation were underestimated by 12.6% and 11.2%, respectively, compared to the observations. In terms of spatial pattern, we calculated the spatial autocorrelation function and spatial variation coefficient of the precipitation fields. The AWE-GEN-2d captured the general pattern but the spatial coefficient of variation was underestimated (spring to winter observations were 0.81, 1.16, 1.05, and 0.70; while the simulated were 0.57, 0.81, 0.74, and 0.49). The temporal autocorrelations were also underestimated, resulting in discontinuity jumps in rainfall centers. Future research work will focus on collecting sub-hourly observation interval data, such as 5 min or 10 min, and improve the simulation of the evolution of precipitation events, especially those with short durations and heavy intensities, which may bring high risks in urban flooding.

How to cite: Li, T., Yin, S., Li, Z., Wang, M., and Peleg, N.: Stochastic simulation of high space-time resolution precipitation fields in Beijing, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-16245, https://doi.org/10.5194/egusphere-egu24-16245, 2024.

EGU24-17936 | Orals | ITS1.12/AS5.15 | Highlight

An interactive climate atlas for northern Europe 

Kajsa Parding, Andreas Dobler, Rasmus Benestad, Julia Lutz, Abdelkader Mezghani, and Anita Verpe Dybdal

We present an interactive climate atlas providing visualisations of future regional climate projections of temperature and precipitation in northern Europe from multiple sources. It is based on results of both empirical-statistical and dynamical downscaling of multi-model ensembles from CMIP5 and CMIP6 including several emission scenarios. Displayed alongside each other, the projected climate change estimated from different model ensembles can be compared and contrasted. The comparison can be useful to evaluate the robustness of the climate change information and the influence of methodological choices such as the downscaling method and the selection of global climate models, and to explore how the level of greenhouse gas emissions may affect the future climate. The application is developed by researchers at the Norwegian Meteorological Institute and is freely available at the website futureclimate.met.no/dse4KSS.

How to cite: Parding, K., Dobler, A., Benestad, R., Lutz, J., Mezghani, A., and Dybdal, A. V.: An interactive climate atlas for northern Europe, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17936, https://doi.org/10.5194/egusphere-egu24-17936, 2024.

Land Surface Temperature (LST) is crucial in many areas; but seamless LST data are difficult to obtain due to limitations in thermal infrared sensor technologies. Numerical modeling, which is based on physics-driven process, can simulate continuous spatial and temporal data. Simultaneously, machine learning, a typical data-driven approach, has been effective in remotely-sensed data reconstruction. In this study, we designed a fusion framework that combines the strengths of numerical modeling and machine learning. The framework includes the following steps: 1) Optimization of the numerical model: We use the urbanized High-Resolution Land Data Assimilation System (u-HRLDAS) model. Various spatio-temporal data sources are used to refine and optimize the model's simulations. 2) Database creation for LST reconstruction: This database incorporates forcing variables like 2-meter temperature, relative humidity, air pressure, wind speed, downward longwave and shortwave radiation for the u-HRLDAS model, along with the model's simulated LST outputs. Additional remotely-sensed data such as the Digital Elevation Model (DEM), Normalized Difference Vegetation Index (NDVI), latitude, longitude, land use and cover, and slope are also included. The datasets span the summer months (June to August) from 2011 to 2014. Daily LST data from MOD11A1 and MYD11A1 are used as label data. 3) Optimal model identification via automatic machine learning framework: The MODIS LST data in the database serves as training labels, with a 70/30 split for training and validation. Evaluation metrics like RMSE, MAE, and R² guide the selection. We chose the AutoGluon-Tabular framework, developed by Amazon, for its superior performance, which is achieved through bagging and stacking methods.  Finally, the 1-km seamless LST is reconstructed based on the model with the highest accuracy in validation.

Taking Xi’an city in China as the study region, nine models (Weightensemble_L2, LightGBMLarge, XGBoost, LightGBM, CatBoost, LightGBM, ExtraTree, NeuralNetTorch, and NeuralNetFastAI) were trained within the Autogluon-Tabular framework. These models displayed RMSE values ranging from 0.737 to 1.417 K, MAE spanning 0.522 to 1.031 K, and R² from 0.967 to 0.991. Notably, the Weightedensemble_L2 model excelled, with the lowest RMSE (0.737) and MAE (0.522), and the highest R² (0.991), closely followed by the LightBGMlarge model. with RMSE, MAE, and R² values of 0.739, 0.526, and 0.991, respectively. Furthermore, we conducted supplementary testing using four reserved MODIS LST images. Employing the previously trained WeightedEnsemble_L2 model, seamless predictions of MODIS LST were generated at four overpass timestamps: 02:30, 05:30, 14:30, and 17:30. The resulting spatial distributions is similar with the observed LST, validating our method's capability to capture LST's spatial characteristics and ensure spatial continuity compared to the original MODIS LST data.

In conclusion, the proposed fusion framework which integrates numerical modeling and automatic machine learning, successfully reconstructed LST with high accuracy and strong spatial similarities. There are still shortcomings of this method, such as the predicted images losing some spatial details compared to the observations, which need to be improved in the future.

How to cite: Yumin, L., Meiling, G., and Zhenhong, L.: Retrieving gapless 1-km land surface temperature based on numerical model and auto machine learning approach, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18741, https://doi.org/10.5194/egusphere-egu24-18741, 2024.

EGU24-19266 | ECS | Posters on site | ITS1.12/AS5.15

Application of novel generative diffusion models to precipitation downscaling 

Alex Saoulis, Sebastian Moraga, Natalie Lord, Peter Uhe, and Nans Addor

Machine Learning (ML) is playing an increasingly valuable role in statistical downscaling. Capable of leveraging complex, non-linear relationships latent in the training data, the community has demonstrated significant potential for ML to learn a downscaling mapping. Following the perfect-prognosis (PP) approach, ML models can be trained on historical reanalysis data to learn a relationship between coarse predictors and higher resolution (i.e. downscaled) predictands. Once trained, the models can then be evaluated on general circulation model (GCM) outputs to generate regional downscaled results. Due to the relatively low computational cost of training and utilising these models, they can be used to efficiently downscale large ensembles of climate models over regional to global domains.

This work employs a novel diffusion algorithm to downscale climate data. Diffusion models have proven highly successful in applications such as natural image generation and super-resolution (the natural image analogue to climate downscaling). Diffusion models have been shown to significantly outperform earlier generative ML models such as Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs); they can produce highly diverse samples, emulate fine details with high fidelity, and exhibit much more stable training than alternative ML models. 

This work trains and evaluates diffusion models on the Multi-Source Weighted-Ensemble Precipitation (MSWEP) observational dataset over the Colorado River Basin (USA). High resolution (10km x 10km) MSWEP fields are artificially coarsened to generate training data. Once trained, the models are applied to bias-corrected climate model outputs to evaluate their ability to generate realistic downscaled precipitation fields. Performance is compared with several benchmarks, including classical regression techniques as well as alternative ML models.

How to cite: Saoulis, A., Moraga, S., Lord, N., Uhe, P., and Addor, N.: Application of novel generative diffusion models to precipitation downscaling, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-19266, https://doi.org/10.5194/egusphere-egu24-19266, 2024.

EGU24-19303 | Orals | ITS1.12/AS5.15 | Highlight

NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP-CMIP6) 

Bridget Thrasher, Weile Wang, Andrew Michaelis, Ian Brosnan, and Sepideh Khajehei

The NASA Earth Exchange Global Daily Downscaled Projections CMIP6 archive (NEX-GDDP-CMIP6) contains daily climate projections of nine variables derived from thirty-five CMIP6 GCMs and four SSP scenarios (SSP2-4.5, SSP5-8.5, SSP1-2.6 and SSP3-7.0) for the period 2015-2100. Each of these climate projections was downscaled to a spatial resolution of 0.25 degrees x 0.25 degrees using the daily version of the BCSD statistical downscaling method. The purpose of this archive is to provide a set of global, high-resolution, bias-corrected climate change projections that can be used to evaluate climate change impacts on processes that are sensitive to finer-scale climate gradients and the effects of local topography on climate conditions. In this session, we will provide an overview of the methodology, as well as the details of its execution on the NASA Advanced Supercomputing (NAS) facility. In addition, we will discuss the various considerations, assumptions, and limitations of the downscaled data. Lastly, we will illustrate the various modes of accessing the archive, including examples using the NASA Regional Climate Model Evaluation System (RCMES) and cloud computing resources.

How to cite: Thrasher, B., Wang, W., Michaelis, A., Brosnan, I., and Khajehei, S.: NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP-CMIP6), EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-19303, https://doi.org/10.5194/egusphere-egu24-19303, 2024.

EGU24-19311 | ECS | Posters on site | ITS1.12/AS5.15

Statistical downscaling of seasonal forecast temperature using a climate-informed AI approach 

Yanet Díaz Esteban, Qing Lin, Arthur Hrast Essenfelder, Andrea Toreti, Fatemeh Heidari, Edgar Fabián Espitia Sarmiento, and Elena Xoplaki

Climate predictions on seasonal timescales are of major importance for the scientific, planning and policy communities to understand the impacts of climate variability and change and emergent risks, and thus develop appropriate adaptation and mitigation strategies. Nevertheless, the coarse spatial scale of that data limits its use in decision making. Downscaling is therefore emerging as a solution to transfer the climate information to a scale suitable for impact studies and climate-related risk assessments. In this study, a method for downscaling seasonal forecast temperature is presented, that integrates a Deep Residual Neural Network (DRNN) with an analog-based approach to increase the information from climate predictors. The advantage of the proposed approach is the incorporation of relevant large-scale variables, such as the geopotential height from different ensemble members, which supplies the model with varied information from the atmospheric circulation instead of using only a single input field as a predictor. This allows the model to capture the complex relationships between climate drivers and local scale variables such as temperature, that provides a great potential to reduce the large biases in climate model outputs. The DRNN based downscaling is applied to minimum and maximum temperature from ECMWF seasonal forecast at 1° resolution, downscaled to a resolution of 1 arcminute (~1.2 km), in a region that covers Germany and surrounding areas. The results are assessed against observations using both deterministic and probabilistic metrics and show an overall agreement between the downscaled product and the ground truth. This work demonstrates the added value of post-processing of seasonal forecasts, especially for applications of early warnings of extreme events and the associated hazards on a sub-seasonal to seasonal scale.  

How to cite: Díaz Esteban, Y., Lin, Q., Hrast Essenfelder, A., Toreti, A., Heidari, F., Espitia Sarmiento, E. F., and Xoplaki, E.: Statistical downscaling of seasonal forecast temperature using a climate-informed AI approach, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-19311, https://doi.org/10.5194/egusphere-egu24-19311, 2024.

EGU24-19420 | ECS | Orals | ITS1.12/AS5.15

Climate Services Downscale (CSDownscale): A statistical downscaling tool for (sub)seasonal to decadal climate predictions 

Eren Duzenli, Jaume Ramon Gamon, Alba Llabres, Verónica Torralba, Lluis Palma Garcia, Sara Moreno Montes, Carlos Delgado-Torres, Nuria Perez-Zanon, Javier Corvillo Guerra, and Raul Marcos

Statistical downscaling is a technique that allows to obtain high-resolution climate information from the coarse-resolution Global Climate Model (GCM) outputs through the long-term relationship between the GCM output and a reference dataset such as in-situ observations. The key benefit of employing statistical downscaling (SD) methods over the dynamical approaches is their significantly less computational costs. The cost-effectiveness of these methods enables the processing of large hindcasts, including multi-model systems with numerous ensemble members, which is highly relevant for the users. Thus, a comprehensive tool that allows users to apply state-of-the-art statistical downscaling methods on climate variables is crucial. CSDownscale is a new generation R package that has been  developed to statistically downscale subseasonal to seasonal to decadal climate predictions in the context of climate services, including its use in operational applications. The tool produces a downscaled field or time series using several bias correction, regression (i.e., linear and logistic) and analogs methods. Additionally, the package contains various interpolation methods such as nearest neighbor or bilinear approaches, which are used for regridding purposes. Users can easily combine these with bias correction and regression methods to perform downscaling. When applying these combined operations, the GCM data is initially interpolated to the resolution of the reference dataset, then the selected bias correction or regression method is implemented on the regridded data. However, the package also incorporates a method that infers the high-resolution values using a multi-linear regression with the four closest coarse-scale grid points, which skips the step of interpolation. Furthermore, the CSDownscale package includes an analogs based method, which looks for fields with similar conditions to the one being predicted and returns the high-resolution outcome of past conditions that are most similar, a certain number of similar fields or a combination of them. Finally, the CSDownscale package allows for the GCM data to be downscaled to either a reference grid space or a specific point location. All the methods are conceived to be done in cross-validation, that is, by excluding data from the specific time step being post-processed to avoid overfitting and, consequently, the overestimation of the actual prediction skill.

How to cite: Duzenli, E., Ramon Gamon, J., Llabres, A., Torralba, V., Palma Garcia, L., Moreno Montes, S., Delgado-Torres, C., Perez-Zanon, N., Corvillo Guerra, J., and Marcos, R.: Climate Services Downscale (CSDownscale): A statistical downscaling tool for (sub)seasonal to decadal climate predictions, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-19420, https://doi.org/10.5194/egusphere-egu24-19420, 2024.

EGU24-19461 | Orals | ITS1.12/AS5.15

Stochastic Spatial Weather Generator SPAGETTA: Development and Applications 

Martin Dubrovsky, Radan Huth, Petr Stepanek, Ondrej Lhotka, Jiri Miksovsky, Jan Meitner, Jan Balek, Adam Vizina, and Mirek Trnka

Stochastic weather generators are one of the most frequently used methodologies for producing input weather series for various process-based models (especially agricultural crop growth models and hydrological rainfall-runoff models) used e.g. in assessing impacts of climate change/variability on weather-dependent processes.

SPAGETTA (Dubrovsky et al., 2020, Theor. Appl. Climatol.) is a parametric multi-variable spatial weather generator run commonly (but not only) with daily time step. It is based on applying the spatialisation approach developed by Wilks (1998, J. Hydrol.) to our older single-site weather generator M&Rfi. Similarly to M&Rfi, SPAGETTA is designed for agricultural and hydrological modeling. Until recently, the stress was put on finetuning and validating the generator. Now, when the generator performs reasonably well, it is being used in various experiments.

In the first part of the presentation, the main results of the validation tests will be shown, focusing on the ability of the generator to reproduce spatial-temporal variability of multi-site temperature and precipitation series. Concerning the temporal variability, both high-frequency (interdiurnal) and low-frequency (intermonthly and interannual) variability was considered. The performance of the generator was compared with the performance of 19 RCMs taken from the CORDEX database.

In the second-part, to demonstrate different applications of the generators, we show results obtained in four experiments: (1) Assessment of separate effects of changes in the WG parameters, which represent the means, variability and lag-0 & lag-1 spatial correlations of temperature and precipitation, on a set of temperature and precipitation indices. The generator parameters were calibrated using the observational E-OBS data from 8 European regions and then modified with the changes (2070-99 vs. 1971-2000) derived from 19 RCM climate simulations (this experiment was already presented in EGU 2023). (2) To show the generator's performance in hydrological modeling, we applied the rainfall–runoff model to the watershed of Dyje river. The model outputs obtained using the synthetic weather series were compared with outputs obtained with the observed weather series (we call this type of experiment “indirect validation of WG”. (3) The generator was used to develop a new test for examining the collective significance of local trends at multiple sites (Huth and Dubrovsky, 2021, J. Clim.). This was made by applying large ensembles of realizations of synthetic multi-site weather series for user-defined lag-0 and lag-1 spatial correlation matrices, (4) The generator was used to assess the statistical significance of climate change scenarios produced by Regional Climate Models. The significance of the RCM-based changes (future vs. baseline) in individual WG parameters is based on comparing their values with the spread of the changes of these parameters based on ensembles of synthetic weather series, i.e. the pairs of synthetic series representing future and present climate; the generator was calibrated by RCM simulations for the corresponding time slices.

How to cite: Dubrovsky, M., Huth, R., Stepanek, P., Lhotka, O., Miksovsky, J., Meitner, J., Balek, J., Vizina, A., and Trnka, M.: Stochastic Spatial Weather Generator SPAGETTA: Development and Applications, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-19461, https://doi.org/10.5194/egusphere-egu24-19461, 2024.

EGU24-19640 | ECS | Orals | ITS1.12/AS5.15

Application of Machine Learning Statistical Downscaling to Seasonal Climate Forecasts for the Alpine Region 

Dhinakaran Suriyah, Crespi Alice, Jacob Alexander, and Pebesma Edzer

Climate change is a pressing global challenge, notably impacting sensitive regions like the Alpine area. Its diverse terrain and ecology make it vulnerable to heightened climate risks, including intensified weather extremes due to global warming. Precise local climate predictions are vital for managing risks in vulnerable areas like the Alpine region, necessitating reliable high-resolution climate data and forecasts. Global products often fall short in providing the fine-grained information needed for accurate localized assessments. This work aims to address the critical need for refined, high-resolution seasonal climate forecasts in the Alpine region as a tool to increase the ability to manage and anticipate climate variability and hazardous conditions. The study endeavors to utilize Perfect Prognosis (PP) within Statistical Downscaling (SD), leveraging regression-based Machine Learning (ML) algorithms to enhance the spatial resolution of daily temperature and total precipitation of ECMWF (European Centre for Medium Range Weather Forecasts) SEAS5 (Seasonal Forecast System 5) seasonal forecasts. Four ensemble learning methods — random forest, light gradient-boosting machine (LGBM), Adaptive Boosting (AdaBoost) and Extreme Gradient Boosting (XGBoost) are considered, while CERRA (Copernicus European Regional Reanalysis) reanalysis (5.5 km) is used as reference target. In order to define the optimal ML model and configuration, a preparatory phase is performed in which ML methods are implemented, optimized and inter-compared by considering ERA5 reanalysis predictor fields (~ 31 km) for the training period 1985-2015 and validation period 2016-2020. Initial findings show that LGBM reports better performance in training and validation, demonstrating superior computational speed and efficiency with respect to the others. LGBM reconstructs daily variability with R2 scores of 0.95 for mean temperature and 0.67 for precipitation. Remaining bias as yearly average is -0.05°C fo daily mean temperature and 5.34% for daily precipitation. Other error metrics, e.g., mean absolute error (MAE) and root mean squared error (RMSE) suggest room for improvements, especially in extreme value predictions and annual precipitation averages. LGBM is thus applied and further optimized on SEAS5. The contribution will further elaborate the inter-comparison of ML models and their downscaling skills for seasonal forecasts will be presented and discussed. The expected outcomes of this study in particular will serve as a crucial input of a drought prediction module in the framework of the EU-funded interTwin project. This research has been funded by the European Union through the interTwin project (101058386).

How to cite: Suriyah, D., Alice, C., Alexander, J., and Edzer, P.: Application of Machine Learning Statistical Downscaling to Seasonal Climate Forecasts for the Alpine Region, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-19640, https://doi.org/10.5194/egusphere-egu24-19640, 2024.

EGU24-1315 | ECS | PICO | ITS1.14/ERE6.11

Soil data quality and resolution matter when predicting woody plant species in temperate forests 

Francesco Rota, Daniel Scherrer, Ariel Bergamini, Bronwyn Price, Lorenz Walthert, and Andri Baltensweiler

Soil properties influence plant physiology and growth, playing a fundamental role in shaping species niches in forest ecosystems. Here, we investigated the impact of soil data quality on the performance of climate-topography species distribution models (SDMs) of temperate forest woody plants. We compared models based on measured soil properties with those based on digitally mapped soil properties at different spatial resolutions (25m and 250m). We first calibrated SDMs with measured soil data and plant species presences and absences from plots in mature temperate forest stands. Then, we developed models using the same soil predictors, but extracted from digital soil maps at the nearest neighbouring plots of the Swiss National Forestry Inventory. Our approach enabled a comprehensive assessment of the significance of soil data quality for 41 Swiss forest woody plant species. The predictive power of SDMs without soil information compared to those with soil information, as well as those with measured vs digitally mapped soil information at different spatial resolutions was evaluated with metrics of model performance and variable contribution. On average, performance of models with measured and digitally mapped soil properties was significantly improved over those without soil information. SDMs based on measured and high-resolution soil maps showed a higher performance, especially for species with an ‘extreme’ niche position (e.g. preference for high or low pH), compared to those using coarse-resolution (250m) soil information. Nevertheless, globally available soil maps can provide important predictors if no high-resolution soil maps are available. Moreover, among the tested soil predictors,  pH and clay content of the topsoil layers improved the predictive power of SDMs for forest woody plants the most. Such improved model performance informs biodiversity modelling about the relevance of soil data quality in SDMs for species of temperate forest ecosystems. In conclusion, the incorporation of accurate soil information into SDMs becomes indispensable for making well-informed forecasts for guiding decisions in forest management, also when addressing the potential distribution shifts of woody plant species due to climate change.

How to cite: Rota, F., Scherrer, D., Bergamini, A., Price, B., Walthert, L., and Baltensweiler, A.: Soil data quality and resolution matter when predicting woody plant species in temperate forests, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1315, https://doi.org/10.5194/egusphere-egu24-1315, 2024.

EGU24-2788 | PICO | ITS1.14/ERE6.11 | Highlight

Altitudinal shifting of major forest tree species in Italian mountains under climate change 

Sergio Noce, Cristina Cipriano, and Monia Santini

Climate change has profound implications for global ecosystems, particularly in mountainous regions where species distribution and composition are highly sensitive to changing environmental conditions. Understanding the potential impacts of climate change on native forest species is crucial for effective conservation and management strategies. Despite numerous studies on climate change impacts, there remains a need to investigate the future dynamics of climate suitability for key native forest species, especially in specific mountainous sections. This study aims to address this knowledge gap by examining the potential shifts in altitudinal range and suitability for forest species in Italy's mountainous regions. By using species distribution models, through MaxEnt we show the divergent impacts among species and scenarios, with most species experiencing a contraction in their altitudinal range of suitability whereas others show the potential to extend beyond the current tree line. The Northern and North-Eastern Apennines exhibit the greatest and most widespread impacts on all species, emphasizing their vulnerability. Our findings highlight the complex and dynamic nature of climate change impacts on forest species in Italy. While most species are projected to experience a contraction in their altitudinal range, the European larch in the Alpine region and the Turkey oak in the Apennines show potential gains and could play significant roles in maintaining wooded populations. The tree line is generally expected to shift upward, impacting the European beech, a keystone species in the Italian mountain environment, negatively in the Alpine arc and Northern Apennines, while showing good future suitability above 1,500 meters in the Central and Southern Apennines. Instead, the Maritime pine emerges as a promising candidate for the future of the Southern Apennines. The projected impacts on mountain biodiversity, particularly in terms of forest population composition, suggest the need for comprehensive conservation and management strategies. The study emphasizes the importance of using high-resolution climate data and considering multiple factors and scenarios when assessing species vulnerability. The findings have implications at the local, regional, and national levels, emphasizing the need for continued efforts in producing reliable datasets and forecasts to inform targeted conservation efforts and adaptive management strategies in the face of climate change.

How to cite: Noce, S., Cipriano, C., and Santini, M.: Altitudinal shifting of major forest tree species in Italian mountains under climate change, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-2788, https://doi.org/10.5194/egusphere-egu24-2788, 2024.

EGU24-3570 | PICO | ITS1.14/ERE6.11 | Highlight

Future Forest: A Decision Support System for Smart and Sustainable Forest Management. 

Flaminia Catalli, Fabian Faßnacht, Jonas Kerber, Jonathan Költzow, Johannes Mohr, Werner Rammer, Thorsten Reitz, and Christopher Schiller

Future Forest is an “AI Lighthouse” project funded by the German Ministry of the Environment that has two main objectives: develop a decision support system for forest management and build the foundations for a forest transformation data space.

The Future Forest decision support system is based on a chain of AI/numerical models. The information used to analyse the best alternatives in an area of interest comes from state-of-the-art process-based forest simulations of specific forest management scenarios, AI-based upscaling techniques, and remotely sensed data on current forest composition and health. This data will cover Germany’s forests wall-to-wall with an unprecedented resolution of 100m for the management scenarios and climate data, and up to 10m for other variables.

Creating such a system is impossible without having an accessible pool of data. Since much of the needed information is not freely available, data is collected and organized as an IDSA-compliant data space. Such a data space serves as a platform where various data holders and users converge, exchanging information and analytical applications within a structured data governance framework. This arrangement empowers platform users to retain comprehensive control over their data and enables them to share information with third parties in a controlled and secure environment.

 

Future Forest is one year away from completion, and we can now present the first results on our way towards a forest management 2.0 system. This system is designed to offer a spectrum of alternatives for effectively managing local forest stands in response to climate change. Considering the forest owner's management objectives, such as timber production or biodiversity, the system proposes alternatives using various ecosystem indicators, encompassing wood production, carbon storage, and biodiversity considerations. The final ranking of the alternatives is based on a multi-criteria decision analysis algorithm, which incorporates also a comprehensive robustness and sensitivity analysis.

In this contribution, we outline the tools utilized to make informed decisions, from the neuronal networks for forest classification to the forest dynamic simulations, and the decision support system. We discuss the constraints encountered and highlight the innovations incorporated in each of these tools. We will discuss the attempt made to offer an explainable or even interpretable model, as far as this was possible. 

How to cite: Catalli, F., Faßnacht, F., Kerber, J., Költzow, J., Mohr, J., Rammer, W., Reitz, T., and Schiller, C.: Future Forest: A Decision Support System for Smart and Sustainable Forest Management., EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-3570, https://doi.org/10.5194/egusphere-egu24-3570, 2024.

EGU24-5097 | ECS | PICO | ITS1.14/ERE6.11 | Highlight

Development of continuous cover forests under different levels of global warming – a methodological case study in Southern Germany 

Marc Djahangard, Maximiliano Costa, Harald Bugmann, and Rasoul Yousefpour

Informing forest decision makers about the impacts of climate change on forests is challenging because the representative concentration pathway scenarios (RCPs) impose deep uncertainty and complexity that is difficult to integrate in management planning. A user-oriented translation of the RCPs would facilitate the integration of climate change impacts in forest decisions and improve the understanding of how different climate policy actions would affect forests.

We applied a translation of the RCPs by analyzing how three global warming scenarios related to climate policy actions – the Paris targets (1.5°C and 2°C warming) and a higher warming level without climate policy (3°C) – would impact forest dynamics. We developed indices of forest processes (e.g., species succession, biomass, harvest) that capture changes induced by the global warming scenarios relative to a reference period (1981 – 2010). The methodology was adapted from the JRC PESETA IV project, where climate indices had been developed and impacts on forest vulnerability was explored.

We applied this method with a large-scale forest model (LandClim) on a complex and highly diverse 5000 ha forest landscape over an elevation gradient from lowland deciduous to high montane conifer forests in the area of Freiburg, Southern Germany. Simulations started from the state of the forest in the year 2010, and both no-management and a business-as-usual management (BAU) was simulated. For the initial state of the forest, we applied a state-of-the-art initialization procedure that makes use of a detailed inventory network (over 2000 inventory points in the study area) to depict the current forest conditions (e.g., species distribution, stem numbers, tree ages, stem diameters at breast height) at high resolution. BAU was applied in the form of close-to-nature management based on the guidelines by the State Forestry Department. It includes >10 forest types with both younger and older stands.

Simulation results indicate reductions of biomass and species richness at lower elevations, including both lowland and submontane zones, connected to an upslope shift of species. As these changes intensify with increasing global warming, the largest impacts are observed under the 3°C warming scenario, leading to the loss of biodiversity associated with dominant species capitalizing on the changing ecological conditions.

In summary, by applying this method for a diversity in continuous cover forests over a large elevation gradient, our study outlines important forest dynamics representative for temperate forests in Central Europe under three global warming scenarios. Moreover, the evaluation of close-to-nature management can give important insights for forest decision making.

How to cite: Djahangard, M., Costa, M., Bugmann, H., and Yousefpour, R.: Development of continuous cover forests under different levels of global warming – a methodological case study in Southern Germany, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5097, https://doi.org/10.5194/egusphere-egu24-5097, 2024.

Boreal forests play an important role in climate change mitigation, biodiversity conservation and the provision of vital ecosystem services. Changing climate is likely to increase the frequency and the severity of forest disturbances. Hence, increasing disturbances may offset the past and ongoing efforts to increase forest-based mitigation and halt biodiversity loss. Therefore, understanding the dynamics of forest ecosystems and predicting their responses to management, changing climate and disturbance regimes is vital.  While forest disturbance risk prevention measures i.e., adaptive management, offer solutions to safeguard future timber yields, the effects of adaptive management on biodiversity, climate change mitigation potential of forests and other ecosystem services have received little attention. In addition, it remains unknown whether climate change alters disturbance regimes in a way that cancels out efforts to increase and preserve carbon stocks and protect forest biodiversity. In this study we contrast the effects of mitigation versus adaptation forest management on the resilience of boreal forest ecosystems in a changing climate. We address the following questions: i) How timber harvests, forest carbon stocks and disturbed volumes develop in different forest management and land-use options that emphasize either adaptation or mitigation under different climate scenarios? ii) What are the synergies and trade-offs in ecosystem service and biodiversity indicators in adaptation and mitigation options? To address these questions, we used the process-based forest landscape and disturbance model iLand to dynamically simulate interactions of forest management, climate change and disturbances. We simulated combinations of seven forest management scenarios and three climate scenarios with ten replicate runs for 80 years in Finland. The forest management scenarios included a business-as-usual scenario and mitigation and adaptation scenarios with changes in rotation lengths and in the shares of deciduous trees in regeneration. Mitigation managements resulted in on average 6 to 15% higher carbon stocks over the simulation period compared to business-as-usual even when disturbances were accounted for but even halved the annual harvests. Mitigation management generally increased the amount of deadwood (3-21%) and large diameter trees (10-52%) compared to business-as-usual management but the severity of climate change reduced the positive trend on large diameter trees. Adaptive management reduced especially the bark beetle disturbances but, in some cases, the disturbed volumes were even higher than business-as-usual management because of increased wind damages. Generally, over the simulation period, adaptive management had a small positive impact on deadwood and mixed effects on large diameter trees.  Scenic beauty was impacted very little by climate change or management. Our findings highlight the complex interactions between disturbance risk prevention, biodiversity, carbon storage and sequestration and other ecosystem services. The results help to guide forest managers and policymakers in planning conservation and mitigation efforts, maximizing multiple benefits and enhancing forest resilience under a changing climate.

How to cite: Repo, A., Albrich, K., Jantunen, A., Aalto, J., Lehtonen, I., and Honkaniemi, J.: Contrasting mitigation and adaptation forest management strategies: unraveling the effects on biodiversity and ecosystem services in changing climate and disturbance regimes, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-8520, https://doi.org/10.5194/egusphere-egu24-8520, 2024.

EGU24-8664 | ECS | PICO | ITS1.14/ERE6.11

Stand age diversity affects forests' resilience and stability, although unevenly. 

Elia Vangi, Daniela Dalmonech, Elisa Cioccolo, Gina Marano, Leonardo Bianchini, Paulina Puchi, Elisa Grieco, Alessandro Cescatti, Gherardo Chirici, and Alessio Collalti

Tree age plays an essential role in forest ecosystems' functioning by affecting structural and physiological plant traits that modulate the water and carbon budgets. On the other hand, tree age distribution in forests depends on population dynamics and, therefore, on the balance between tree mortality and regeneration events, which are ultimately controlled by natural and anthropogenic disturbances. Therefore, the human-induced modulation of the tree age distribution in forests represents a significant and not fully explored pathway to optimize the stability and resilience of forests.

To examine the influence of age distribution on the stability and resilience of forest carbon budget under current and future climate conditions, we applied a biogeochemically process-based model to three past-managed forest stands and modeled their stability and resilience in terms of Net Primary Production (NPP) in the future as undisturbed systems. The model was forced with climate outputs of five Earth System Models under four representative climate scenarios plus one baseline climate scenario over a matrix of 11 age classes for each forest. We found that the NPP peak was reached in the young and middle-aged class (16- to 50-year-old) regardless of the climate scenario, as ecological theories postulate. Under climate change scenarios, the beech forest showed an increasing NPP as well as stability with increasing atmospheric CO2 and temperature across all age classes, while resilience remained stable. Conversely, in the spruce and Scots pine-dominated sites, NPP decreased under climate change scenarios. In coniferous stands, stability and resilience seem to be controlled mainly by age rather than the climate, with the older stands being more stable and resilient under all scenarios.

These findings highlight the importance of considering age classes and species-specific responses when assessing the impacts of climate change on forest stability and resilience, calling for tailored management strategies to enhance the adaptability of forests in the face of changing climatic conditions, reflecting the different species and age-dependent responses to climate.

How to cite: Vangi, E., Dalmonech, D., Cioccolo, E., Marano, G., Bianchini, L., Puchi, P., Grieco, E., Cescatti, A., Chirici, G., and Collalti, A.: Stand age diversity affects forests' resilience and stability, although unevenly., EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-8664, https://doi.org/10.5194/egusphere-egu24-8664, 2024.

EGU24-9634 | PICO | ITS1.14/ERE6.11

Incorporating nitrogen effects in a management and environment sensitive forest model at regional scale 

Annikki Mäkelä, Francesco Minunno, Ritika Srinet, and Mikko Peltoniemi

Regional and national level projections of forest growth, productivity and carbon sequestration are in high demand for policy makers to understand the impacts of climate change and forest management on ecosystem services. The rapid environmental change has accentuated the need of environmentally sensitive forest models that are simultaneously capable of simulating the development of forests under different management regimes and from an initial state defined in terms of standard forest mensuration variables. Efforts to make environmentally sensitive process models more management oriented have been supported by recent developments in model-data assimilation, allowing for quantitatively reliable, policy-relevant projections. However, while the processes related to forest C balance are quite well understood, possible future changes of nitrogen availability still remain a challenge for modelling, as empirical results are few and theories have not converged to a consensus. This is particularly important for the boreal zone where forests are generally regarded as N limited.

PREBAS is a management-sensitive carbon-balance model that has been calibrated to forest mensuration type data in Finland. In the calibration, N availability was assumed to be derivable from empirical site quality classification. Following empirical observations and predictions from theoretical models, site quality influences fine-root foliage ratio and stand carrying capacity in PREBAS. The model has been linked with a soil C balance model, Yasso. The combined model incorporates environmental impacts on photosynthesis, respiration, litter fall and soil organic matter decomposition. The model system has been found to produce a spatial distribution of national forest growth and C balance levels in Finland that are well comparable with forest statistics and the Finnish national greenhouse gas inventory, and it has also been evaluated more widely in Northern Europe.

The objective of this study was to examine the implications of different future N availabilities on PREBAS projections under climate change. For this, we carried out simulations in a set of 35 sites across a climatic transect and with variable site quality. For these sites we first estimated stand nitrogen requirement on the basis of growth, litter fall and tissue N concentration under maximum canopy cover and in current climate. We then postulated that N uptake depends on N availability and fine root biomass, and estimated N availability by demanding that N uptake should match the N requirement. Based on the results, we developed a method for estimating carrying capacity and below-ground allocation on the basis of changes in the relative availabilities of carbon and nitrogen.

We tested the method by simulating growth in a hypothetical FACE experiment, which showed results qualitatively consistent with the literature of ectomycorrhiza-dominated forests. We then compared three different assumptions of changing nitrogen availability under climate change: 1) no change, 2) change is derivable from changing SOM decomposition rate, and 3) N availability increases in pace with N requirement. These were applied in country-wide simulations under different climate scenarios. The plausibility of the scenarios and results are discussed in the light of previous literature.

 

How to cite: Mäkelä, A., Minunno, F., Srinet, R., and Peltoniemi, M.: Incorporating nitrogen effects in a management and environment sensitive forest model at regional scale, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-9634, https://doi.org/10.5194/egusphere-egu24-9634, 2024.

EGU24-9929 | ECS | PICO | ITS1.14/ERE6.11

Realistic representation of forest harvesting for large-scale models – integrating harvest information from national forest inventories to LPJ-GUESS 

Susanne Suvanto, Mats Lindeskog, Stefan Olin, Karl Piltz, and Thomas A. M. Pugh

Harvesting of wood is one of the key processes of forest management, strongly impacting the structure and dynamics of European forests. This makes accounting for it crucial in any large-scale analysis of forest ecosystems. Yet, the representation of forest harvests in large-scale models is typically far from realistic, as the actual management regimes are not well described by simple rules or even by formal management guidelines.

Here, we show an implementation of national forest inventory (NFI) -based forest harvesting regimes in a demographic vegetation model, LPJ-GUESS. In our approach, the probability of harvest in the model simulation is based on frequency of harvest events in the NFI data in forests with similar structure and geospatial location. Similarly, the characteristics of the harvest event (the percentage of the removed tree basal area and, in case of partial harvests, the tree size targeted in the harvest) are based on the observed harvest events in the data, and depend in the simulation on forest structure and location. This means that model simulations are dynamic, responding to the real state of the forest. We demonstrate this with several countries in Europe, for which we have earlier created NFI-based harvest regimes based on analysis of more than 180 000 forest plots. Forests are simulated with LPJ-GUESS with different forest harvesting set-ups, allowing us to compare the outcome of the suggested NFI-based harvest implementation to other approaches, including simplified clear-cut rules and density-based thinning (based on Reineke’s rule). In addition, the simulation results are compared to observational evaluation data.

Moving from simple rule-based approaches to observed NFI-based harvest regimes can bring the model simulations closer to how forests are actually currently managed. Our approach blending big data and dynamics modelling has potential to both enable improved assessments of continental-scale carbon dynamics and provide a realistic reference to which potential future forest management changes can be compared to.

How to cite: Suvanto, S., Lindeskog, M., Olin, S., Piltz, K., and Pugh, T. A. M.: Realistic representation of forest harvesting for large-scale models – integrating harvest information from national forest inventories to LPJ-GUESS, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-9929, https://doi.org/10.5194/egusphere-egu24-9929, 2024.

EGU24-15673 | ECS | PICO | ITS1.14/ERE6.11

Evaluating the UK forest demography and carbon cycle using a process-based Land Surface Model, JULES-RED 

Hsi-Kai Chou, Anna Harper, Arthur Argles, Carolina Duran-Rojas, Emma Littleton, and Peter Cox

Global warming and climate change caused by greenhouse gas (GHG) emission is projected to have multiple impacts on the forest ecosystems. To mitigate these impacts, the UK Government has set a goal of net zero emissions of GHG by 2050. One core strategy is to use afforestation and forestry management to implement large-scale Greenhouse Gas Removal (GGR). However, the effectiveness of afforestation as a GGR strategy is difficult to fully evaluate with standard empirical models due to the complexities of environmental conditions under a changing climate. Alternatively, process-based land surface models (LSM), such as the Joint UK Land Environment Simulator (JULES), are increasingly being used to evaluate forest growth within a national GGR context as they are driven by environmental drivers. By coupling the Robust Ecosystem Demography (RED) model with JULES, we model the forest dynamic and carbon sequestration among a set of Representative Concentration Pathway (RCP) projections geographically across the UK up to 2080. Our results demonstrate the capability of mapping the potential GGR across the UK while also accounting for the changing environment and risks of climate change. The results show that JULES-RED can provide an effective tool for national-scale afforestation evaluation toward the 2050 net-zero targets.

How to cite: Chou, H.-K., Harper, A., Argles, A., Duran-Rojas, C., Littleton, E., and Cox, P.: Evaluating the UK forest demography and carbon cycle using a process-based Land Surface Model, JULES-RED, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15673, https://doi.org/10.5194/egusphere-egu24-15673, 2024.

EGU24-16274 | ECS | PICO | ITS1.14/ERE6.11 | Highlight

Predicting trajectories of temperate forest understorey vegetation responses to global change 

Bingbin Wen and the forestREplot and PASTFORWARD

Predicting forest understorey community responses to global change and forest management is vital given the importance of the understorey for biodiversity conservation and forest functioning. Though substantial effort has gone into disentangling how global change will impact the understorey community, the scarcity of information on site-specific environmental drivers together with large temporal and spatial drivers has limited our understanding of how global change drivers affect understorey characteristics at specific forest sites. Here, using understorey resurvey data collected from 1363 plots across temperate Europe and applying a machine learning approach, we used Gradient Boosting Regression Models (GBM) to model and predict trajectories of four understorey characteristics (species richness, total understorey vegetation cover, proportion of woody species and proportion of forest specialists) to global-change and site-specific drivers (e.g. soil, overstory conditions). We applied the final GBM models to 8 forest sites in Austria to evaluate the effect of future scenarios for nitrogen deposition, climate change and forest management on the forest understory in the year 2030, and project the trajectory of understorey properties from year 1993 to 2030.  The trajectory results showed that increasing nitrogen deposition decreased species richness and proportion of woody species, but increased total understorey vegetation cover and proportion of forest specialists. The effect of climate warming on the proportion of forest specialists appeared to be limited but led to a decrease in species richness, total vegetation cover and proportion of woody species. Finally, a closed canopy could shift the community towards more woody species and forest specialists but may lower species richness and total vegetation cover. Our presented model allows the prediction of trajectories of understorey vegetation responses to global change and management interventions at specific forest sites. 

How to cite: Wen, B. and the forestREplot and PASTFORWARD: Predicting trajectories of temperate forest understorey vegetation responses to global change, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-16274, https://doi.org/10.5194/egusphere-egu24-16274, 2024.

EGU24-17088 | ECS | PICO | ITS1.14/ERE6.11

Modelling water balance components in a temperate forest in Germany: A comparative analysis of pine, oak, and beech 

Angela Morales-Santos, Michael Köhler, Stefan Fleck, Birte Scheler, Markus Wagner, and Henning Meesenburg

Understanding the water balance components in forests is crucial for sustainable land and water management. The Frankfurt Rhine-Main metropolitan region in Germany is heavily dependent on groundwater, with the Hessian Ried forest being one of the main sources. However, climate change, population growth, continuous land sealing, and the expansion of farmland and irrigation in the region, have increased the pressure on water resources, exacerbating conflicts over water use between the affected sectors. Therefore, the region requires comprehensive solutions for a sustainable and flexible water management.

This study focuses on modelling the water balance components in three monitoring plots located in the Hessian Ried. Each plot is dominated by a different tree species — pine, oak, and beech. The aim of the study is to assess the impact of tree species and soil physical properties on water dynamics and availability. We employed the LWF-Brook90R package for the implementation of the LWF-Brook90 model considering climatic boundary conditions, vegetation parameters and soil physical parameters at different depths. The study covers the period of 2005 to 2023 allowing the assessment of seasonal variations over several years. Moreover, we performed the assessment of different parameter sets and a Bayesian calibration in order to analyse the variations in the resulting water balance components for each plot. We compared our simulations to throughfall and soil water content observations.

Our findings revealed complex interplays between tree species and water balance components, highlighting the importance of species-specific considerations when modelling forests. We obtained a good agreement between our results and observed throughfall, indicated by an R2 ≥ 0.7. The different parameter sets and the calibration delivered highly similar statistical indicators of observed versus simulated throughfall. However, the calibration did not improve the throughfall simulations in all cases. Regarding actual transpiration and interception rates, the pine plot exhibited larger variations depending on the parameter set used, compared to oak and beech. Both deciduous stands presented a larger transpiration deficit as water stress indicator compared to the pine plot. The transpiration deficit increased considerably in the three plots after calibrating interception and soil physical parameters, compared to default datasets. Additionally, the simulations of the pine plot resulted in the lowest drainage rates among the plots, due to a combination of factors including the evergreen canopy and predominant sandy soil texture along the entire rooting depth. We achieved a more comprehensive and improved estimation of the soil water content — and consequently soil water storage in the root zone — after calibrating the soil physical parameters in contrast to pre-established soil datasets. This allowed for uncertainties in the estimation of soil water content in the unsaturated zone, which is a key consideration when modelling water balance components.

The insights gained from this research have implications for climate change adaptation and mitigation. As climate patterns shift, understanding how different tree species influence water availability and utilization becomes paramount. The presented models serve as a valuable tool for predicting and managing water resources in diverse forested landscapes, supporting the development of adaptive strategies for sustainable forest management.

How to cite: Morales-Santos, A., Köhler, M., Fleck, S., Scheler, B., Wagner, M., and Meesenburg, H.: Modelling water balance components in a temperate forest in Germany: A comparative analysis of pine, oak, and beech, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17088, https://doi.org/10.5194/egusphere-egu24-17088, 2024.

Forest ecosystems play a well established role in providing a multitude of ecosystem services. It is imperative to maintain the health of forests to ensure a continuous supply of these services. However, increasing pressures such as growing demand of wood products and forest overexploitation, climate change, land use change, etc. are compromising their resilience and services provision.
To address this challenge, various European and national policies are directed on either expanding natural and unmanaged forests (e.g. EU Biodiversity Strategy; European Climate Law ) or improving forest management practices (e. EU Forest Strategy, EU Bioeconomy Strategy). In the former case, the goal is to contain or exclude direct and indirect human intervention and disturbances. In the latter case, while human presence and management are allowed, they must adhere to sustainable and respectful practices
The burden to provide a better balanced array of ecosystem services, ensuring the maintenance of forest resilience in the future, falls largely on the shoulders of forest owners and managers who will face opportunity costs and a deviation from their profit maximization objective.
Nevertheless, achieving policy targets will be made more efficient and realistic with an active involvement of the entire community in a collective endeavour. Individuals may be encouraged and required to contribute to mitigating private economic effort by acknowledging the economic value of market and non market ecosystem services other than provisioning and facilitating payments for these services through a mechanism commonly referred as payments for ecosystem services (PES).

Employing a Choice Experiment methodology, we contribute to the existing knowledge regarding the economic value assigned to forest ecosystem services by assessing the willingness to pay of European citizens under future scenarios, which differ in policy ambition and forest management
Interestingly, as we explore alternative options, also based on outcomes of a project stakeholder workshop, we draw attention to emerging paradoxes within EU strategies. For instance, while provisioning services are generally perceived as undermining regulation services, the substitution of fossil fuels with wood biomass may indeed help reduc ing greenhouse gases emissions and supporting EU climate mitigation targets. Moreover, unlike many studies that treat cultural services as a n undistinguished bundle, we highlight potential conflicts arising from the increase in recreational opportunities and facilities, which may contrast with the desire to enjoy a more natural forest environment and wild biodiversity.
This research is conducted within the project ForestNavigator, involving multidisciplinary scientists dedicated to shape the future EU forests. The result of the economic assessment will be used to enhance the models employed within the project to help support both private and public actors in making well informed decisions on forests management and the preservation of their ecosystem services.

How to cite: eboli, F. and michetti, M.: Navigating Sustainable Forest Futures: Balancing Ecosystem Services in the EU, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17423, https://doi.org/10.5194/egusphere-egu24-17423, 2024.

 Heat and drought stress events have a significant impact on plant phenology. Changes in phenology can alter the length of the growing season and affect carbon, water, and energy fluxes. Some of these changes can persist for several years, especially in response to successive stress events. In this work, we combine remote sensing data and process-based modeling to investigate the effect of different heat and drought stress events on land surface phenology (LSP) and water and carbon fluxes in a deciduous and coniferous forest in southwest Germany. We used climate data to characterize different stress events for selected forest sites and as input for the process-based model LandscapeDNDC (LDNDC). For the determination of different LSP metrics we used time series of the Enhanced Vegetation Index (EVI) covering the last two decades. The evaluation of the model simulations was done using remote sensing data. The results indicated that different EVI and LSP trajectories exist for deciduous and coniferous sites. The model simulations also demonstrated that significant variations in water and carbon fluxes exist for the period during and after the stress events, and that leaf area recovery was linked to gas exchange. Since the overall forest development strongly depends on stress response strategy as well as stress frequency and intensity, combining climate projections and process-based models is needed to explore the suitability of forest response types under expected climate changes

How to cite: Moutahir, H., Petrik, P., Grote, R., and Kiese, R.: Changes in land surface phenology and gas exchange of deciduous and coniferous forests in response to heat and drought stress, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-20048, https://doi.org/10.5194/egusphere-egu24-20048, 2024.

EGU24-2543 | ECS | Posters on site | ITS1.15/GI1.3

Comparative Analysis of Ground-Based and Satellite-Derived UV Index: Variability and Reliability from Three South American Mid-Latitudes Sites 

Gabriela Reis, Hassan Bencherif, Marco Reis, Bibiana Lopes, Marcelo de Paula Corrêa, Damaris Kirsch Pinheiro, Lucas Vaz Peres, Rodrigo da Silva, and Thierry Portafaix

Solar Ultraviolet Radiation (UV) corresponds to electromagnetic waves with wavelengths of 100-400 nm, constituting approximately 5% of the energy emitted by the sun. The risks and benefits of exposure to UV for life on Earth have been known for many years and include impacts on human health, materials, terrestrial and aquatic ecosystems, and biogeochemical cycles. Climate change, influenced by land use change and other factors, can increase or decrease the intensity of the incident UV depending on location, seasons, and changes in the atmospheric composition. UV intensity reaching the surface can be informed as the UV index. This dimensionless indicator often makes it easier for people to assess their UV levels and understand how to protect themselves from excessive sun exposure. In middle-income countries like Brazil and Argentina, networks, and instruments for monitoring UV are often sparse and poorly supported with both capacity and funding, and thus, obtaining reliable UV data is difficult. With only a few stations reporting long-term UV measurements, which significantly restricts its extrapolations to all populated areas, a way to continuous monitoring UV globally is through satellites. Similar to ground-based observations, satellite measurements are affected by instrument errors and are subject to uncertainties in the algorithms used to derive surface UV radiation. Therefore, evaluation of satellite-based estimates of surface UV against available ground measurements at many locations around the world is needed to characterize the errors toward further refinement of the surface UV estimates, especially in the Southern Hemisphere, where there has been relatively limited work to compare ground-based and satellite-derived UV. This study compares ground-based and satellite-derived UV Index levels from OMI (Ozone Monitoring Instrument) at overpass time during clear sky conditions, which are determined using LER (Lambertian Equivalent Reflectivity). A characterization of the diurnal and seasonal variability of the ground-based UV index levels will also be reported. The study period will be from 2005 to 2022, varying according to each data source, and comprises data from two Brazilian cities – Itajubá (22.41ºS, 45.44ºW, 885 m, Davis 6490 UV sensor), Santa Maria (29.4°S, 53.8°W, 476 m, Brewer Spectrophotometer MKIII #167), and from Buenos Aires in Argentina (34.58º S, 58.48°W, 25 m, Solar Light UV Biometer – Radiometer model 501). Comparing satellite-derived data with ground-based measurements helps validate the accuracy of satellite data, which can help identify any discrepancies and improve the satellite data retrieval algorithms, leading to more accurate satellite-derived UV products. Also, such a process of data verification is necessary should these data be used for long-term trend analysis or the monitoring of UV exposure risk and possible impacts on human health, as we intend to do in a future study, to understand better the dynamics of the space-temporal variability of the surface UV in South America. 

How to cite: Reis, G., Bencherif, H., Reis, M., Lopes, B., de Paula Corrêa, M., Kirsch Pinheiro, D., Vaz Peres, L., da Silva, R., and Portafaix, T.: Comparative Analysis of Ground-Based and Satellite-Derived UV Index: Variability and Reliability from Three South American Mid-Latitudes Sites, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-2543, https://doi.org/10.5194/egusphere-egu24-2543, 2024.

A multi-channel brightness temperature (TB) Fundamental Climate Data Record (FCDR) for the period 1991-present has been developed in this study using measurements from two Special Sensor Microwave Imagers (SSM/I) onboard the F11 and F13 satellites and one Special Sensor Microwave Imager/Sounder (SSMIS) onboard the F17 satellite of the US Defense Meteorological Satellite Program (DMSP). Hardware differences among these instruments were corrected using a combination of techniques including Principal Component Analysis (PCA), using the third instrument as an intermediate, and weighted averaging, which accounts for interchannel covariability and observation matching issues. After intercalibration, all imagers were standardized using SSMIS as the observation reference. The average biases of the recalibrated TBs for almost all channels between any two instruments are globally less than 0.2 K, with standard deviations (STDs) of less than 1.2 K. This resulted in a 30-year continuous and stable FCDR. Based on this FCDR, a long time series of column water vapour (CWV) over the global oceans was retrieved. Validation of this retrieved moisture product against reanalysis, in-situ radiosonde, and Global Navigation Satellite System (GNSS) measurements showed reasonable accuracy, suggesting that the presented FCDR has high potential for climate applications. In the future, this research method will be applied to more satellites to create an expanding dataset of satellite observations that could enhance the accuracy of climate model assessments and improve the reliability of climate predictions.

How to cite: Liu, S. and Wang, Y.: Highly consistent brightness temperature fundamental climate data record from SSM/I and SSMIS, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-4525, https://doi.org/10.5194/egusphere-egu24-4525, 2024.

The National Oceanic and Atmospheric Administration’s (NOAA) Joint Polar Satellite System (JPSS) provides critical observations of the Earth and its atmosphere from the ultraviolet region to the microwave region in Leo Earth Orbit (LEO). The mission now has three satellites in the same orbit: NOAA20 the primary satellite, NOAA21 as secondary and Suomi National Polar-orbiting Partnership (Suomi NPP) as the tertiary satellite. The primary and secondary satellite provide redundancy since measurements from the mission provide critical inputs to global numerical weather prediction. Since 2011, the multi-mission series of Low Earth Orbit (LEO) polar-orbiting environmental satellites is serving as one of the most important sources of continuous state-of-the-art observations of the Earth’s land, oceans, and atmosphere to protect lives and property, and support the global economy by providing accurate and timely environmental information. The Visible Infrared Imaging Radiometer Suite (VIIRS), the Cross-track Infrared Sounder (CrIS), the Advanced Technology Microwave Sounder (ATMS), the Ozone Mapping and Profiler Suite (OMPS), and the Clouds and the Earth’s Radiant Energy System (CERES) observe a large part of the electromagnetic spectrum from the UV region to the microwave region. All the sensors have state of the art onboard calibration sources and the data undergo extensive pre and post launch calibration and validation activities before the data are declared operational. Additionally, NOAA/NESDIS center for satellite applications and research maintains an integrated calibration and validation system to continuously monitor and track the performance of the sensors through the mission life cycle. NOAA also co-leads the Global Space-based Inter-Calibration Sytem (GSICS) which is an international collaborative effort initiated in 2005 by the World Meteorological Organization (WMO) and the Coordination Group for Meteorological Satellites (CGMS) to monitor, improve and harmonize the quality of observations from operational weather and environmental satellites of the Global Observing System (GOS). The level 2 geophysical measurements and products also go through extensive verification and validation through comparison of satellite products with surface-based, airborne, and/or space-based observations that are extensively documented and shared with users. This presentation will highlight the calibration activities and the performance of JPSS sensors and products.

How to cite: Kalluri, S. and Cao, C.: Calibration and Validation of Low Earth Orbit Observations From NOAA to Support Global Environmental Monitoring, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-6427, https://doi.org/10.5194/egusphere-egu24-6427, 2024.

EGU24-6605 | Posters on site | ITS1.15/GI1.3

Utilizing Libya-4 to intercalibrate overlapping sensors in the same sun-synchronous orbit 

David Doelling, Conor Haney, Prathana Khakurel, Rajendra Bhatt, Benjamin Scarino, and Arun Gopalan

The NASA CERES observed SW and LW broadband fluxes are utilized by the climate community for monitoring the Earth’s energy imbalance and for climate model validation. The SNPP and NOAA20 CERES instruments and associated VIIRS imagers were launched into the same 1:30 PM mean local time sun-sun-synchronous orbits as well as the future NOAA22 Libera broadband instrument and VIIRS imager. The overlapping sensor records need to be intercalibrated to enable consistent broadband fluxes and imager cloud retrievals. The overlapping satellites are typically placed a half an orbit apart, thus preventing any simultaneous nadir overpass (SNO) events required for time-matched inter-calibration strategies. A Pseudo Invariant Calibration Site (PICS), such as Libya-4, can provide overlapping sensor radiometric scaling factors without the use of SNOs. 

The clear-sky Libya-4 observed radiances were characterized both spectrally and angularly and corrected for atmospheric effects. The Libya-4 natural variability was found to be consistent across the CERES and VIIRS records. This fact reveals that the sensor onboard calibration anomalies are smaller than the Libya-4 natural variability. By mitigating the Libya-4 natural variability will reduce the radiometric scaling factor uncertainty needed to provide both broadband flux and cloud retrieval continuity across the overlapping sensor records.

How to cite: Doelling, D., Haney, C., Khakurel, P., Bhatt, R., Scarino, B., and Gopalan, A.: Utilizing Libya-4 to intercalibrate overlapping sensors in the same sun-synchronous orbit, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-6605, https://doi.org/10.5194/egusphere-egu24-6605, 2024.

EGU24-6849 | Orals | ITS1.15/GI1.3

Validation and simulation of existing and future satellite mid and thermal infrared sensors using a combination of automated validation sites and airborne datasets 

Simon Hook, Bjorn Eng, Gerardo Rivera, Robert Freepartner, Brenna Hatch, William Johnson, Dirk Schüttemeyer, Mary Langsdale, and Martin Wooster

Post-launch calibration and validation over the lifetime of missions is needed to ensure that any long-term variation in an observation, e.g. an area getting hotter, can be unambiguously assigned to a change in the Earth system, rather than a change in calibration. Such activities enable measurements from different satellites to be inter-compared and used seamlessly to create long-term multi-instrument/multi-platform data records, which serve as the basis for large-scale international science investigations into topics with high societal or environmental importance. In order to help address this need we have established a set of automated validation sites where the necessary measurements for validating mid and thermal infrared data from spaceborne and airborne sensors are made every few minutes on a continuous basis. We have also conducted multi-agency airborne campaigns with thermal infrared sensors to develop precursor datasets for future NASA and ESA missions to acquire mid and thermal infrared data as well as characterize variability within the automated validation sties.

We have established automated validation sites at several locations including Lake Tahoe CA/NV, Salton Sea CA and La Crau, France. The Lake Tahoe site was established in 1999, the Salton Sea site was established in 2008 and the La Crau site was established in 2023. Each site has one or more custom-built highly accurate (50mK) radiometers measuring the surface skin temperature. All the measurements are made every few minutes and downloaded hourly via a cellular modem.

Data from the sites have been used to validate numerous satellite instruments including the Advanced Very High Resolution Radiometer (AVHRR) series, the Along Track Scanning Radiometer (ATSR) series, the Advanced Spaceborne Thermal Emission and Reflectance Radiometer (ASTER), the Landsat series, the Moderate Resolution Imaging Spectroradiometer (MODIS) on both the Terra and Aqua platforms, the Visible Infrared Imaging Radiometer Suite (VIIRS) and the ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS). In all cases the standard products have been validated including the standard radiance at sensor, radiance at surface, surface temperature and surface emissivity products.

Over the last several years NASA and ESA have conducted multiple joint airborne campaigns to obtain data at high spatial and spectral resolutions to simulate future satellite sensors as well as characterize potential validation sites, such as the La Crau validation site. These data are currently being used to simulate the ASI/NASA Surface Biology and Geology (SBG) thermal infrared (TIR) mission, the ESA Land Surface Temperature Monitoring (LSTM) mission and the ISRO/CNES Thermal infraRed Imaging Satellite for High-resolution Natural resource Assessment (TRISHNA) mission.

We will present results from the validation of the mid and thermal infrared data using the automated validation sites as well as results from the recent airborne campaigns.

How to cite: Hook, S., Eng, B., Rivera, G., Freepartner, R., Hatch, B., Johnson, W., Schüttemeyer, D., Langsdale, M., and Wooster, M.: Validation and simulation of existing and future satellite mid and thermal infrared sensors using a combination of automated validation sites and airborne datasets, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-6849, https://doi.org/10.5194/egusphere-egu24-6849, 2024.

EGU24-9248 | ECS | Posters on site | ITS1.15/GI1.3

Monitoring Metop ASCAT backscatter stability over tropical rainforests 

Clay Harrison, Sebastian Hahn, and Wolfgang Wagner

The Advanced Scatterometer (ASCAT) on-board the series of Metop satellites is a microwave radar instrument operating in C-band (5.255 GHz). ASCAT has been designed to measure wind speed and wind direction over open ocean, but the instrument has also shown its capabilities to observe changes of sea ice extent and surface soil moisture over land. While two Metop satellites (Metop-B launched in September 2012 and Metop-C launched in November 2018) are operational at the moment, the first Metop mission (Metop-A launched in October 2006) has been successfully completed in November 2021. Regular calibration campaigns based on active transponders located in Turkey ensure a continuous quality monitoring, but natural targets (e.g. tropical rainforests) have also been used in the past. Previous analyses have shown that ASCAT is an extremely stable instrument providing high quality Level 1b backscatter products. Any small changes are evaluated in detail and accounted for if necessary. However, the investigation of calibration anomalies detected by active transponders typically takes time. Monitoring natural targets has the advantage that data is continuously available rather than incremental (as is the case when using active transponders) allowing an earlier detection of anomalies. In any case, calibration problems can only be fully resolved retrospectively during a reprocessing of historic data and not entirely in Near Real-Time (NRT).

The upcoming EUMETSAT H SAF ASCAT Surface Soil Moisture (SSM) products sampled at 6.25 km and 12.5 km are divided into three product categories depending on their timeliness: (i) historic data are available as a Climate Data Record (CDR), (ii) a continuous and consistent extension of the CDR, also known as Intermediate CDR (ICDR) and (iii) Near Real- Time (NRT). It is important to note that NRT products could be subject to intentional (e.g. algorithmic updates) or unintentional (e.g. instrument drifts) changes at any given point in time, which would compromise the consistency compared to historic data. Therefore, ICDR products are introduced in order to fill this gap and maintain a consistency as best as possible. For this reason the ICDR products will be distributed with a one-week delay and ASCAT Level 1b backscatter will be continuously monitored using data over tropical rainforests.

In this study we present our strategy to monitor ASCAT Level 1b backscatter stability over tropical rainforests and show results based on historic ASCAT data for all three Metop satellites. We will also discuss the practical implementation of the monitoring methodology and its application as an early-warning system in case of the ASCAT SSM ICDR product. An anomaly detection should trigger a warning for the users until a more in-depth analysis determines whether it is advisable to continue the product distribution or stop. Discovering problems that undermine the coherence between CDR and ICDR products is of critical importance, since applications like drought monitoring or climate studies rely on consistent time series data.

How to cite: Harrison, C., Hahn, S., and Wagner, W.: Monitoring Metop ASCAT backscatter stability over tropical rainforests, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-9248, https://doi.org/10.5194/egusphere-egu24-9248, 2024.

EGU24-9252 | ECS | Posters on site | ITS1.15/GI1.3

Using hyperspectral sensors on the ground for satellite validation. A focus on the Fluorescence Explorer mission 

Paul Naethe, Andreas Burkart, Matthias Drusch, Dirk Schuettemeyer, Marin Tudoroiu, Roberto Colombo, Mitchell Kennedy, and Tommaso Julitta

The validation of optical satellite data products is a central but challenging component of the space missions. In order to validate the satellite images, ground data is used for reference and allows also the assessment of the associated total uncertainty budget. Overall, when comparing ground data and satellite measurements three main uncertainty sources need to be considered: i) instrument characterisation, ii) algorithm retrieval performances and iii) spatial representativeness. These key components affect the proper comparison of ground measurements with satellite data and, thus, have to be carefully examined. 

JB devices (FloX and RoX) are hyperspectral instruments acquiring optical field data with standardized hardware and routines. They have collected a legacy of data for over half of a decade using a comprehensive and readily implemented open-source data processing chain, considering the individual laboratory characterization of each instrument’s optical performance. Thus, the instruments are capable of providing valuable data products for the purpose of satellite validation. In particular, the FloX (Fluorescence BoX, JB Hyperspectral Devices GmbH) is the first commercially available device for the measurement of solar-induced chlorophyll fluorescence (SIF). The instrument was developed with the support of the scientific community following the specification of the Fluorescence Explorer mission (FLEX) by the European Space Agency (ESA), expected to be launched in 2024. The FloX features a high performing spectrometer (FWHM: 0.3 nm, SSI: 0.15, SNR: 1000) and allows stand-alone measurement of SIF emission at canopy level on the ground. Furthermore, the FloX enables the continuous measurements of spectral down-welling and up-welling radiance in the VIS-NIR range using an additional spectrometer to cover a larger spectral range and allows the automatic computation of reflectance as well as various vegetation indices (VIs). The instrument synchronously acquires upwelling and downwelling radiance during each measurement cycle, automatically optimizes the integration time according to light conditions and acquires the dark current and internal quality flags to ensure high quality data products. In addition to SIF and VIs, the FloX produces time series of high-resolution radiometric parameters, suitable for the investigation of the optical properties from the monitored targets. In the last years over 60 FloX units have been deployed worldwide.

Within a current ESA project, we are investigating the instrument uncertainty sources, with the final aim of defining a preliminary version of the FLEX validation plan. At the same time, currently deployed instruments in 10 location around the world were used to examine the agreement of the ground measurements with available satellite product (i.e. Sentinel-2). This approach reversed the common practice of validating satellite data with ground measurements by using the globally available, standardized L2A products of Sentiel-2 evaluating the conformance of ground-measured data products across a network of standardized instruments. An unprecedented alignment of satellite and ground data was achieved, confirming high validity of data products from the network of automated field spectrometers around the globe.

In summary, in this contribution we provide an overview of how field spectroscopy systems can be used in the framework of specific activities with the purpose of satellite validation.

How to cite: Naethe, P., Burkart, A., Drusch, M., Schuettemeyer, D., Tudoroiu, M., Colombo, R., Kennedy, M., and Julitta, T.: Using hyperspectral sensors on the ground for satellite validation. A focus on the Fluorescence Explorer mission, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-9252, https://doi.org/10.5194/egusphere-egu24-9252, 2024.

EGU24-10447 | Orals | ITS1.15/GI1.3

GBOV (Copernicus Ground-Based Observation for Validation) service: latest product updates and evolutions for EO data Cal/Val 

Christophe Lerebourg, Rémi Grousset, Thomas Vidal, Gabriele Bai, Marco Clerici, Nadine Gobron, Jadu Dash, Somnath Bar, Finn James, Luke Brown, Ernesto Lopez-baeza, Ana Perez-hoyos, Darren Ghent, Jasdeep Anand, Jan-Peter Muller, and Rui Song

GBOV (Copernicus Ground-Based Observation for Validation), is an element of CLMS (Copernicus Land Monitoring Service). Its initial purpose was to support yearly validation effort of core CLMS product (TOC-R, Albedo, LAI, FAPAR, FCOVER, SSM and LST), five of whom are listed among GCOS Essential Climate Variables (ECV). GBOV has however reached a much larger community with about 1200 users, including ESA optical MPC. There is a large variety of ground data publicly available through numerous networks including ICOS, BSRN, NEON, TERN, SurfRad … For GBOV service, the choice was made to focus on data from permanent deployment, i.e. long-term datasets, rather than field campaign data. Indeed, this reduces the number of available ground variables, but long-term deployments ensure the maximum of ground to satellite data matchups as well as measurement protocols consistency.

GBOV provides ground measurement (the so-called “Reference Measurements”) to the community, but its fundamental interest is that up-scaling procedures are applied to these ground measurements in order to provide ARVD (Analysis Ready Validation Data) to the community, the so-called “Land Products”. GBOV service is freely accessible on https://land.copernicus.eu/global/gbov and provides data over 112 sites. Available ground data variables include: Top of Canopy Reflectance (ToC-R), surface albedo, Leaf Area Index (LAI), Fraction of Absorbed Photosynthetically Available Radiation (FAPAR), Fraction of Covered ground (FCover), Surface Soil Moisture (SSM) and Land Surface Temperature (LST).

The networks providing GBOV initial input data are unfortunately not evenly distributed. In an attempt to reduce the thematic and geographical gap, GBOV is developing its own network as part of collaboration with the existing networks. In GOBV phase 1, six ground stations have been upgraded with additional instrumentation. In GBOV phase 2, a ground station has been deployed in August 2023 on Fuji Hokuroku research station in Japan for vegetation variables monitoring. This is part of a collaboration with NIES (National Institute of Environmental Studies). In 2024, a vegetation station will be installed over Fontainebleau research station (France) as part of a GBOV/ICOS collaboration. Fuji Hokuroku and Litchfield (TERN network Australia) will receive a GBOV LST station in 2024.

Over the past year, several updates have been implemented in GBOV database to better respond to CLMS and general users requirements. This includes improved uncertainty estimates for vegetation products, improved procedure for Soil Moisture and LST products. More effort is being made for the end-to-end uncertainty budget computation.

This presentation will emphasis product status and recent product evolutions.

How to cite: Lerebourg, C., Grousset, R., Vidal, T., Bai, G., Clerici, M., Gobron, N., Dash, J., Bar, S., James, F., Brown, L., Lopez-baeza, E., Perez-hoyos, A., Ghent, D., Anand, J., Muller, J.-P., and Song, R.: GBOV (Copernicus Ground-Based Observation for Validation) service: latest product updates and evolutions for EO data Cal/Val, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-10447, https://doi.org/10.5194/egusphere-egu24-10447, 2024.

EGU24-10864 | Orals | ITS1.15/GI1.3

Calibration and Validation Activities in the Context of the 2023 GABONX Airborne SAR Campaign for Tropical Forest Height and Change Analysis over Gabon 

Marc Jaeger, Irena Hajnsek, Matteo Pardini, Roman Guliaev, Kostas Papathanassiou, Markus Limbach, Martin Keller, Andreas Reigber, Temilola Fatoyinbo, Marc Simard, Michele Hofton, Bryan Blair, Ralph Dubayah, Aboubakar Mambimba Ndjoungui, Larissa Mengue, Ulrich Vianney Mpiga Assele, and Tania Casal

Tropical forests are of great ecological and climatological importance. Although they only cover about 6% of Earth’s surface, they are home to approx. 50% of the world’s animal and plant species. Their trees store 50% more carbon than trees outside the tropics. At the same time, they are one of the most endangered ecosystems on Earth: about 6 million of hectares per year are felled for timber or cleared for farming. Compared to the other components of the carbon cycle (i.e. the ocean as a sink and the burning of fossil fuels as a source), the uncertainties in the local land carbon stocks and the carbon fluxes are particularly large. This is especially true for tropical forests: more than 98% of the carbon flux generated by changes in land-use may be due to tropical deforestation, which converts carbon stored as biomass into emissions.

In this context, the AfriSAR 2015/16 campaign, supported by ESA, was carried out over four forest sites in Gabon by ONERA (July 2015) during the dry season and by DLR (February 2016) during the wet season. From the data collected the innovative techniques applied to estimate forest height and biomass could be improved significantly and are summarized in a special issue ‘Forest Structure Estimation in Remote Sensing’ of IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

The motivation of the AfriSAR campaign was to acquire demonstration data for the soon to be launched ESA BIOMASS mission, that was selected as the 7th Earth Explorer mission in May 2013 in order to meet the pressing need for information on tropical carbon sinks and sources by providing estimates of forest height and biomass. AfriSAR focused on African tropical and savannah forest types (with biomass in the 100-300 t/ha range) and complements previous ESA campaigns over Indonesian and Amazonian forest types in 2004 (INDREX-II) and 2009 (TropiSAR).

The present contribution concerns the GABONX campaign, the ESA supported successor to AfriSAR, which took place in May to July 2023. GABONX aims to detect and quantify changes that have occurred since the DLR acquisitions in February 2016. To this end, DLR’s F-SAR sensor acquired interferometric stacks of fully polarimetric L- and P-Band data over the same forest sites in the same flight geometry as in 2016. The results presented give an overview of campaign activities with particular emphasis on the calibration of the SAR instrument as well as the validation of forest parameters derived from polarimetric interferometry. The SAR sensor calibration is based on an innovative approach that leverages state-of-the-art EM simulation to accurately characterize the 5m trihedral reference target deployed for the campaign in Gabon. The validation of derived forest parameters uses lidar measurements obtained in the time frame of the GABONX campaign by NASA’s LVIS sensor. As an outlook, further collaborative calibration and validation activities will hopefully include the cross-calibration of DLR’s F-SAR and NASA’s UAVSAR, which is set to acquire L- and P-Band data over the GABONX sites in 2024.

How to cite: Jaeger, M., Hajnsek, I., Pardini, M., Guliaev, R., Papathanassiou, K., Limbach, M., Keller, M., Reigber, A., Fatoyinbo, T., Simard, M., Hofton, M., Blair, B., Dubayah, R., Mambimba Ndjoungui, A., Mengue, L., Vianney Mpiga Assele, U., and Casal, T.: Calibration and Validation Activities in the Context of the 2023 GABONX Airborne SAR Campaign for Tropical Forest Height and Change Analysis over Gabon, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-10864, https://doi.org/10.5194/egusphere-egu24-10864, 2024.

EGU24-11905 | ECS | Orals | ITS1.15/GI1.3

Multi-angular airborne observations for simulating thermal directionality at the satellite scale 

Mary Langsdale, Martin Wooster, Dirk Schuettemeyer, Simon Hook, Callum Middleton, Mark Grosvenor, Bjorn Eng, Roberto Colombo, Franco Miglietta, Lorenzo Genesio, Jose Sobrino, Gerardo Rivera, Daniel Beeden, and William Jay

Viewing and illumination geometry are known to have significant impacts on remotely sensed retrieval of land surface temperature (LST), particularly for heterogeneous regions with mixed components. Disregarding directional effects can have significant impacts on both the stability and accuracy of satellite datasets, for example when harmonising datasets from different sensors with different viewing geometries. However, it is difficult to accurately quantify these impacts, in part due to the challenges of retrieving high-quality data for the different components in a scene at a variety of different viewing and illumination geometries over a time period where the real surface temperature and sun-sensor geometries are invariant. With LST an Essential Climate Variable and the development of high resolution future thermal infrared missions (e.g. LSTM, SBG, TRISHNA), it is essential that further work is done to redress this.

With this in mind, a joint NASA-ESA airborne campaign focused on directionality was conducted in Italy in the summer of 2023, led by the National Centre for Earth Observation at King’s College London. This campaign involved concurrent acquisition across longwave infrared (LWIR) wavelengths at both nadir and off-nadir viewing angles through the deployment of two aircraft flying simultaneously, each equipped with state-of-the-art LWIR hyperspectral instrumentation. Data was collected to enable simulation of angular effects at the satellite scale over both agricultural and urban surfaces, with the aim of understanding and potentially developing adjustments for wide view angle satellite-based LST retrievals and remotely sensed evapotranspiration estimates. In-situ observations were collected additionally to enable accuracy assessment of the airborne datasets.

This presentation first details the airborne campaign, including the unique and novel data collection strategies and design modifications to enable evaluation of directional effects for thermal satellites. Preliminary results from the campaign are then presented as well as plans for further analysis related to future satellite thermal missions. 

How to cite: Langsdale, M., Wooster, M., Schuettemeyer, D., Hook, S., Middleton, C., Grosvenor, M., Eng, B., Colombo, R., Miglietta, F., Genesio, L., Sobrino, J., Rivera, G., Beeden, D., and Jay, W.: Multi-angular airborne observations for simulating thermal directionality at the satellite scale, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-11905, https://doi.org/10.5194/egusphere-egu24-11905, 2024.

EGU24-12167 | Posters on site | ITS1.15/GI1.3

The Cross-track Infrared Sounder Level 1B Product: NASA’s Accurate and Stable Infrared Hyperspectral Radiance Record 

David Tobin, Joe Taylor, Larrabee Strow, Hank Revercomb, Graeme Martin, Sergio DeSouza-Machado, Jess Braun, Daniel DeSlover, Ray Garcia, Michelle Loveless, Robert Knuteson, Howard Motteler, Greg Quinn, and William Roberts

The Cross-track Infrared Sounder (CrIS) is an infrared Fourier Transform Spectrometer onboard the Suomi-NPP (SNPP), JPSS-1, and JPSS-2 satellites. The CrIS instrument was designed to provide an optimum combination of optical performance, high radiometric accuracy, and compact packaging. While CrIS was developed primarily as a temperature and water vapor profiling instrument for weather forecasting, its high accuracy and extensive information about trace gases, clouds, dust, and surface properties make it a powerful tool for climate applications.

The goal of the NASA CrIS Level 1B project is to support NASA climate research by providing a climate quality Level 1B (geolocation and calibration) algorithm and create long-term measurement records for the CrIS instruments currently on-orbit on the SNPP, JPSS-1, and JPSS-2 satellites, and for those to be launched on JPSS-3 and JPSS-4. The long-term objectives of the project include:

  • Create well-documented and transparent software that produces climate quality CrIS Level 1B data to continue or improve on EOS-like data records, and to provide this software and associated documentation to the NASA Sounder Science Investigator-led Processing System (SIPS).
  • Provide long-term monitoring and validation of the CrIS Level 1B data record from SNPP and JPSS-1 through JPSS-4, and long-term maintenance and refinement of the Level 1B software to enable full mission reprocessing as often as needed.
  • Provide a homogeneous radiance product across all CrIS sensors through the end of the CrIS series lifetime, with rigorous radiance uncertainty estimates.
  • Develop and support of the CrIS/VIIRS IMG software and datasets, which provide a subset of Visible Infrared Imaging Radiometer Suite (VIIRS) products that are co-located to the CrIS footprints.
  • Develop and support of the Climate Hyperspectral Infrared Product (CHIRP) for the AIRS and CrIS sounders. The CHIRP product converts the parent instrument's radiances to a common Spectral Response Function (SRF) and removes inter-satellite biases, providing a consistent inter-satellite radiance record.

The NASA CrIS products are available via the NASA Goddard Earth Sciences (GES) Data and Information Services Center (DISC) at https://www.earthdata.nasa.gov/sensors/cris. This presentation will include (1) an overview of the NASA Level 1B calibration algorithm and product, (2) example post-launch calibration/validation results demonstrating the accuracy and stability of the CrIS Level 1B data, and (3) example science results.

How to cite: Tobin, D., Taylor, J., Strow, L., Revercomb, H., Martin, G., DeSouza-Machado, S., Braun, J., DeSlover, D., Garcia, R., Loveless, M., Knuteson, R., Motteler, H., Quinn, G., and Roberts, W.: The Cross-track Infrared Sounder Level 1B Product: NASA’s Accurate and Stable Infrared Hyperspectral Radiance Record, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12167, https://doi.org/10.5194/egusphere-egu24-12167, 2024.

EGU24-12346 | Posters on site | ITS1.15/GI1.3

Multi-frequency SAR measurements to advance snow water equivalent algorithm development 

Chris Derksen, Richard Kelly, Benoit Montpetit, Julien Meloche, Vincent Vionnet, Nicolas Leroux, Courtney Bayer, Aaron Thompson, and Anna Wendleder

Snow mass (commonly expressed as snow water equivalent – SWE) is the only component of the water cycle without a dedicated Earth Observation mission. A number of missions currently under development, however, will provide previously unachieved coverage and resolution at frequencies ideal for retrieving SWE. These missions include a Ku-band synthetic aperture radar (SAR) mission (presently named the ‘Terrestrial Snow Mass Mission’ – TSMM) under development in Canada, and two Copernicus Expansion Missions: the Radar Observing System for Europe at L-band (ROSE-L) and the Copernicus Imaging Microwave Radiometer (CIMR). Airborne measurements are required to support SWE algorithm development for all three of these missions. In this presentation, we will present analysis of measurements from the ‘CryoSAR’ instrument, an InSAR capable L- (1.3 GHz) and Ku-band (13.5 GHz) SAR installed on a Cessna-208 aircraft.

A time series of CryoSAR measurements were acquired over open, forested, and lake sites in central Ontario, Canada during the 2022/23 winter season. These measurements were used to evaluate a new computationally efficient SWE retrieval technique based on the use of physical snow model simulations to initialize snow microstructure information in forward model simulations for prediction of snow volume scattering at Ku-band. A primary challenge is the treatment of different layers within the snowpack. We show that a k-means classifier based on snow layer properties can effectively reduce a complex snowpack to three ‘radar-relevant’ layers which conserve SWE but simplify calculation of the snow volume radar extinction coefficient. Estimation of the background contribution is based on soil information derived from lower frequency radar measurements (X-, C-, and L-band). Our collective analysis of satellite and airborne radar observations, snow physical modeling, and SWE retrievals is facilitated by the recently developed TSMM simulator, which incorporates outputs from the Environment and Climate Change Canada land surface prediction system to produce synthetic dual-frequency (13.5 and 17.25 GHz) Ku-band radar data products.

The acquisition of multi-frequency airborne radar measurements from the CryoSAR, and the integration of these observation into the TSMM simulator, provides a fundamental new capability to provide pre-cursor datasets to advance SWE algorithms in preparation for upcoming missions.

How to cite: Derksen, C., Kelly, R., Montpetit, B., Meloche, J., Vionnet, V., Leroux, N., Bayer, C., Thompson, A., and Wendleder, A.: Multi-frequency SAR measurements to advance snow water equivalent algorithm development, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12346, https://doi.org/10.5194/egusphere-egu24-12346, 2024.

EGU24-12428 | Orals | ITS1.15/GI1.3

ESA/NASA Quality Assurance Framework for Earth Observation Products 

Samuel Hunt, Clément Albinet, Jaime Nickeson, Batuhan Osmanoglu, Alfreda Hall, Guoqing Lin, Leonardo De Laurentiis, Philippe Goryl, Frederick Policelli, Dana Ostrenga, and Nigel Fox

Across the broad potential user base for Earth Observation (EO) data, confidence in the quality of the available products is vital, particularly for users requiring quantitative measured outputs they can rely on. Particularly as the commercial EO sector rapidly expands, however, it is an increasing challenge for the user community to discern between the wide variety of product offerings in a reliable manner, especially in terms of product quality.

 

In response to this ESA and NASA, through their Joint Program Planning Group (JPPG) Subgroup, have developed a common EO product Quality Assurance (QA) Framework to provide comprehensive assessments of product quality. The evaluation is primarily aimed at verifying that the data has achieved its claimed performance levels, and, reviews the extent to which the products have been prepared following community best practice in a manner that is “fit for purpose”. A Cal/Val maturity matrix provides a high-level colour-coded a simple summary of the quality assessment results for users. The matrix contains a column for each section of analysis (e.g., metrology), and cells for each subsection of analysis (e.g., sensor calibration). Subsection grades are indicated by the colour of the respective grid cell, which are defined in the key.

 

Both ESA and NASA have on-going activities supporting the procurement of commercial EO data that make use of the joint QA Framework – to ensure decisions on data acquisition are made with confidence. On the ESA side, the Earthnet Data Assessment Project (EDAP) project performs data assessments on EO missions in optical, atmospheric and SAR domains. Similarly, the NASA Earth Science Division (ESD) Commercial Smallsat Data Acquisition (CSDA) Program, completed a pilot study in 2020, and has since entered sustainment use phase for some of the commercial data sets.

 

In this presentation the joint ESA/NASA QA Framework is described, with some examples of its application to commercial EO products.

How to cite: Hunt, S., Albinet, C., Nickeson, J., Osmanoglu, B., Hall, A., Lin, G., De Laurentiis, L., Goryl, P., Policelli, F., Ostrenga, D., and Fox, N.: ESA/NASA Quality Assurance Framework for Earth Observation Products, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12428, https://doi.org/10.5194/egusphere-egu24-12428, 2024.

EGU24-12444 | Posters on site | ITS1.15/GI1.3

Validation of the Radiometric Scales of GLAMR and Grande 

Julia Barsi, Brendan McAndrew, Boryana Efremova, Andrei Sushkov, Nathan Kelley, and Brian Cairns

The NASA/GSFC Code 618 Calibration Laboratories include the Radiometric Calibration Lab (RCL) and the Goddard Laser for Absolute Measurement of Radiance (GLAMR) facility.  Both have large integrating sphere sources with NIST-traceable radiometric calibration.

The workhorse of the RCL is a 1-m integrating sphere with a 25.4-cm port, called Grande, illuminated by nine 150W halogen lamps, providing a broad-band radiance source (300 nm to 2400 nm).  The radiometric calibration of Grande is NIST-traceable through calibrated FEL lamps and a transfer spectroradiometer.

GLAMR is a tunable-laser based system fiber coupled to a large integrating sphere, providing a full-aperture, uniform, monochromatic radiance source. The GLAMR system has two spheres; the one used for this study was a 50-cm sphere with a 20-cm port.  The radiometric calibration is NIST-traceable through a set of calibrated transfer radiometers.

The Research Scanning Polarimeter was calibrated by both sources in 2023.  There was a 3% discrepancy in the absolute radiometric calibration between the two systems.  In order to investigate the discrepancy, a full wavelength scan of the GLAMR system was run, with the Grande spectroradiometer in front of the GLAMR sphere, along with two other spectoradiometers that are used to monitor Grande in real time.  The analysis of this dataset should establish the source of the discrepancy between the two systems and bring the two radiometric calibration systems, Grande and GLAMR, within the combined uncertainties of the methods and instruments.

How to cite: Barsi, J., McAndrew, B., Efremova, B., Sushkov, A., Kelley, N., and Cairns, B.: Validation of the Radiometric Scales of GLAMR and Grande, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12444, https://doi.org/10.5194/egusphere-egu24-12444, 2024.

EGU24-12761 | Posters on site | ITS1.15/GI1.3

Simulated Sea Surface Salinity Data from a 1/48° Ocean Model  

Frederick Bingham, Séverine Fournier, Susannah Brodnitz, Akiko Hayashi, Mikael Kuusela, Elizabeth Westbrook, Karly Carlin, Cristina González-Haro, and Verónica González-Gambau

In order to study the validation process for sea surface salinity (SSS) we have generated a year (November 2011- October 2012) of simulated satellite and in situ “ground truth” data. This was done using the ECCO (Estimating the Circulation and Climate of the Oceans) 1/48° simulation, the highest resolution ocean model currently available. The ground tracks of three satellites, Aquarius, SMAP (Soil Moisture Active Passive) and SMOS (Soil Moisture and Ocean Salinity) were extracted and used to sample the model with a gaussian weighting similar to that of the satellites. This produced simulated level 2 (L2) data. Simulated level 3 (L3) data were then produced by averaging L2 data onto a regular grid. The model was sampled to produce simulated Argo and tropical mooring SSS datasets. The Argo data were combined into a simulated gridded monthly 1° Argo product. The simulated data produced from this effort have been used to study sampling errors, matchups, subfootprint variability and the validation process for SSS at L2 and L3.

How to cite: Bingham, F., Fournier, S., Brodnitz, S., Hayashi, A., Kuusela, M., Westbrook, E., Carlin, K., González-Haro, C., and González-Gambau, V.: Simulated Sea Surface Salinity Data from a 1/48° Ocean Model , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12761, https://doi.org/10.5194/egusphere-egu24-12761, 2024.

EGU24-12907 | Orals | ITS1.15/GI1.3

Advancing Sea Surface Salinity R&D: The Pi-MEP Initiative for Satellite Salinity Data Validation and Exploitation 

Sébastien Guimbard, Nicolas Reul, Roberto sabia, Raul Díez-García, Sylvain Herlédan, Ziad El Khoury Hanna, Tong Lee, Julian Schanze, Frederic Bingham, and Klaus Scipal

The Pilot-Mission Exploitation Platform (Pi-MEP) for salinity (https://www.salinity-pimep.org/) is an initiative originally meant to support and widen the uptake of ESA Soil Moisture and Ocean Salinity (SMOS) mission data over the ocean. Since its beginning in 2017, the project aims at setting up a computational web-based platform focusing on satellite sea surface salinity data validation, supporting also process studies over the ocean. It has been designed in close collaboration with a dedicated science advisory group in order to achieve three main objectives: 1) gathering all the data required to exploit satellite sea surface salinity data, 2) systematically producing a wide range of metrics for comparing and monitoring sea surface salinity products’ quality, and 3) providing user-friendly tools to explore, visualize and exploit both the collected products and the results of the automated analyses. 

Over the years, the Pi-MEP has become a reference hub for the validation of satellite sea surface salinity missions products (SMOS, Aquarius, SMAP), being collocated with an extensive in situ database (e.g. Argo float, thermosalinographs, moorings, surface drifters, saildrones and equipped marine mammals) and additional thematic datasets (precipitation, evaporation, currents, sea level anomalies, sea surface temperature, etc. ). Co-localized databases between satellite products and in situ datasets are systematically generated together with validation analysis reports for 30 predefined regions. The data and reports are made fully accessible through the web interface of the platform. The datasets, validation metrics and tools of the platform are described in detail in Guimbard et al., 2021. Several dedicated scientific case studies involving satellite SSS data are also systematically investigated by the platform, such as major river plumes monitoring, mesoscale signatures in boundary currents, or spatio-temporal evolution in challenging regions (high latitudes, semi-enclosed seas, and the high-precipitation region of the eastern tropical Pacific).

Since 2019, a partnership to sustain the Salinity Pi-MEP project has been agreed between ESA and NASA, encompassing R&D and validation over the entire set of satellite salinity sensors. The two Agencies are now working together to widen the platform features on several technical aspects, such as triple-collocation software implementation, additional match-up collocation criteria and sustained exploitation of data from dedicated in-situ field campaigns (e.g., SPURS, EUREC4A).

In this talk, we will showcase the main results of the latest phase of the project, with the recent distinctive focus on the representation errors characterization of the various satellite salinity missions. 

Guimbard, S.; Reul, N.; Sabia, R.; Herlédan, S.; Khoury Hanna, Z.E.; Piollé, J.-F.; Paul, F.; Lee, T.; Schanze, J.J.; Bingham, F.M.; Le Vine, D.; Vinogradova-Shiffer, N.; Mecklenburg, S.; Scipal, K. & Laur, H. (2021) The Salinity Pilot-Mission Exploitation Platform (Pi-MEP): A Hub for Validation and Exploitation of Satellite Sea Surface Salinity Data Remote Sensing 13(22):4600 https://doi.org/10.3390/rs13224600

How to cite: Guimbard, S., Reul, N., sabia, R., Díez-García, R., Herlédan, S., El Khoury Hanna, Z., Lee, T., Schanze, J., Bingham, F., and Scipal, K.: Advancing Sea Surface Salinity R&D: The Pi-MEP Initiative for Satellite Salinity Data Validation and Exploitation, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12907, https://doi.org/10.5194/egusphere-egu24-12907, 2024.

EGU24-13262 | Orals | ITS1.15/GI1.3

Intercomparison of Landsat OLI and Sentinel 2 MSI performance 

Esad Micijevic, Cody Anderson, Julia Barsi, Rajagopalan Rengarajan, MD. Obaidul Haque, and Joshua Mann

For Landsat 8 and Landsat 9 (L8 and L9), the radiometric stability of the Operational Land Imager (OLI) is monitored using two solar diffusers, three sets of stimulation lamps, and regular lunar collects. Consistent response to the multiple calibrators provides high confidence in the radiometric characterization of the imagers over time and calibration parameters needed to maintain the stability of image products. After 11 years on orbit, all spectral bands in Landsat 8 OLI are stable within 1.5%, while Landsat 9 OLI degradation over its 2.5 years of life remains within 0.3% across all bands.

The MultiSpectral Instruments (MSIs) onboard Sentinel 2A and 2B (S2A and S2B) satellites were designed with 8 similar spectral bands (out of 13) as the OLIs, which created opportunities to combine data from both types of instruments and obtain higher temporal frequency of Earth observations. To ensure proper interoperability among the different instruments, they need to be radiometrically cross-calibrated and consistently georeferenced. We use coincident acquisitions over Pseudo Invariant Calibration Sites (PICS) to monitor the radiometric calibration consistency and stability of the instruments over time. For geometry, Landsat and Sentinel 2 images acquired within a month of each other over the same ground targets were used to assess the co-registration accuracy between the sensor products.

Our results show a general agreement in radiometry of all four instruments over their lifetimes to within 1%. Following the launch of MSI instruments, the initial geometric co-registration assessment between the MSI instruments and the Landsat 8 OLI instrument showed more than 12 m Circular Error (CE90), larger than a Sentinel 2, 10m, pixel. To further improve co-registration and, thus, interoperability of the four instruments, Landsat Collection-2 products use a geometric reference that was harmonized using the Global Reference Image (GRI). The GRI is a dataset consisting of geometrically refined Sentinel 2 images with an absolute accuracy better than 6 m globally. After adopting a common geometric reference in the generation of Landsat and Sentinel 2 products, our assessment of geometric co-registration of the Landsat and Sentinel terrain-corrected products shows a CE90 error of less than 6 m.

Multiple efforts have also been made to validate the accuracy of surface reflectance products from both Landsat and Sentinel 2. In-situ measurements have been made during overpasses of L8, L9, S2A, and S2B using various methods. These measurements also show consistency between all the sensors and can also be used for other missions.

How to cite: Micijevic, E., Anderson, C., Barsi, J., Rengarajan, R., Haque, MD. O., and Mann, J.: Intercomparison of Landsat OLI and Sentinel 2 MSI performance, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-13262, https://doi.org/10.5194/egusphere-egu24-13262, 2024.

The fifth FengYun satellite (FY-3E) was successfully launched into orbit on 5 July, 2021. It carries the third-generation microwave temperature sounder (MWTS-III) and the second-generation microwave humidity sounder (MWHS-II), providing the global atmospheric temperature and humidity measurements. It is important to assess the in-orbit performance of MWTS-III and MWHS-II and understand their calibration accuracy before applications in numerical weather prediction. Since atmospheric profiles from Global Positioning System (GPS) radio occultation (RO) are stable and accurate, they are very valuable for assessing the microwave sounder performance in orbit as demonstrated by many previous studies. This study aims at quantifying the calibration biases of FY-3E MWTS-III and MWHS-II sounding channels of interest using the collocated GPS RO data during January 1st to September 30th, 2023. The MWTS-III channels inherit most of the second-generation MWTS features and have frequencies near the oxygen absorption band (50-60 GHz), and channels at the frequencies of 23.8 and 31.4 GHz were added. Considering that the GPS RO data are more stable and accurate in the mid-troposphere to lower stratosphere and the atmospheric radiative transfer model is accurate in the upper troposphere and lower stratosphere, the mid- to upper-level sounding channels of the MWTS-III, i.e. channels 7-14 are of interest in this study. The cross-tracking scanning instrument MWHS-II provides 15 channels, at frequencies near 89, 118.75, 150 and 183.31 GHz. Of interest to this study are MWHS-II channels 2-6 and 11-15. Using the collocated COSMIC RO data in clear-sky conditions as inputs to the Advanced Radiative Transfer Modeling System (ARMS), brightness temperatures and viewing angles are simulated for FY-3E MWTS-III and MWHS-II. The collocation criterion between the radio-occultation data and the MWTS-III/MWHS-II measurements is defined such that the spatial and temporal difference is less than 50 km and 3 h, respectively. To simulate more accurate bright temperatures, the RO data should be obtained under clear sky conditions over oceans. To determine the clear sky for MWTS-III, the cloud liquid water path algorithm developed by Grody et al. (2001) was used for MWTS-III. While for MWHS-II, the cloud detection algorithm developed by Hou et al. (2019) was used. The initial analysis shows that for the upper sounding channels, the mean biases of the MWTS-III observations relative to the GPS RO simulations are negative for channels 7-8 and 10-13, with absolute values <2 K, and positive for channels 9 and 14, with values <1 K. For the MWHS, the mean biases in brightness temperature are negative for channels 2–6, with absolute values < 2 K and relatively small standard deviations. The mean biases are also negative for MWHS-II channels 11–15 with absolute values <1 K, but with relatively large standard deviations. The biases of both MWTS-III and MWHS-II show scan-angle dependence and are almost symmetrical across the scan line. The long-term mean bias shows only a weak dependence on latitude, which suggests that biases do not vary systematically with brightness temperature. The evaluation results indicate very good prospects for the assimilation application of FY-3E microwave sounding data.

How to cite: Hou, X. and Han, Y.: Verification of FengYun-3E MWTS and MWHS Calibration Accuracy Using GPS Radio Occultation Data, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-13926, https://doi.org/10.5194/egusphere-egu24-13926, 2024.

EGU24-14759 | Orals | ITS1.15/GI1.3

Sea Surface Salinity in the Arctic Ocean - Results from the NASA SASSIE Field Campaign, Calibration-Validation of Satellite Observations, and Data Outreach 

Julian Schanze, Peter Gaube, Jessica Anderson, Frederick Bingham, Kyla Drushka, Sebastien Guimbard, Tong Lee, Nicolas Reul, Roberto Sabia, and Elizabeth Westbrook and the NASA Salinity and Stratification at the Sea Ice Edge Field Campaign Team


The National Aeronautical and Space Administration (NASA) Salinity and Stratification at the Sea Ice Edge (SASSIE) field campaign took place in the Arctic Ocean between August and October of 2022. The scientific aim is to understand the relationship between both haline and thermal stratification and sea-ice advance, and to test the hypothesis that a significant fresh layer at the surface can accelerate the formation of sea ice by limiting convective processes. With the advent of satellite-derived sea surface salinity (SSS) observations from SMOS, Aquarius/SAC-D, and SMAP in the last decade, such observations could provide insights into sea ice formation rates and extent. With the sensitivity of L-Band radiometry for SSS being low at the temperatures prevalent in the Arctic Ocean (-2°C – 5°C) and additional problems with sea ice contamination in the satellite footprint, careful calibration and validation is needed to determine the quality of satellite-derived SSS in this region, particularly near the ice-edge.


Here, we present three components that have resulted from this NASA Field Campaign.


1.) An overview of data gathered is presented, including an unprecedented density of near-surface salinity measurements from diverse platforms. These were measured during a one-month shipboard hydrographic and atmospheric survey in the Beaufort Sea and include continuous observations at radiometric depth (1-2cm) from the salinity snake instrument, more than 3000 high-resolution uCTD profiles, and air-sea flux measurements. Concurrent with the shipborne observations, an airborne campaign to observe ocean salinity, temperature, and other parameters from a low-flying aircraft was performed. Finally, we discuss the deployment and results of autonomous assets, buoys, and floats that were able to observe both the melt season and the sea ice advance. We combine these in situ observations with satellite SSS data to examine the effects of stratification on ocean dynamics in the Beaufort Sea near the sea ice edge and discuss the quality of SSS data in this region.


2.) NASA Physical Oceanography Programs has affirmed its commitment to Open Science and reproducibility of results. For the SASSIE field campaign, we have created a unique web portal that showcases the datasets gathered during the campaign, giving video overviews as well as written summaries of the available data and motivations for their collection. We have also created repositories that contain processing code used in the creation of these datasets, as well as example processing scripts in the form of Jupyter notebooks, which allow end users to execute a live download of datasets from NASA's Physical Oceanography Distributed Active Archive Center (PO.DAAC) as well as processing and plotting these data in Python.


3.) We show the active integration of these tools into the salinity pilot mission exploitation platform (Salinity Pi-MEP), operated by the European Space Agency (ESR) in collaboration with NASA. We demonstrate how such an integration leverages access to other datasets, and facilitates calibration-validation efforts for Level-2 and Level-3 satellite data from multiple satellites. 

How to cite: Schanze, J., Gaube, P., Anderson, J., Bingham, F., Drushka, K., Guimbard, S., Lee, T., Reul, N., Sabia, R., and Westbrook, E. and the NASA Salinity and Stratification at the Sea Ice Edge Field Campaign Team: Sea Surface Salinity in the Arctic Ocean - Results from the NASA SASSIE Field Campaign, Calibration-Validation of Satellite Observations, and Data Outreach, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-14759, https://doi.org/10.5194/egusphere-egu24-14759, 2024.

EGU24-14920 | Posters on site | ITS1.15/GI1.3

Sentinel-3 Land Ice Thematic Product: Evaluation of Greenland surface elevation and elevation change.  

Sebastian B. Simonsen, Louise Sandberg Sørensen, Stine K. Rose, and Jérémie Aublanc

The Sentinel-3 satellite series, developed by the European Space Agency as part of the Copernicus Programme, currently comprises two satellites, Sentinel-3A and Sentinel-3B, launched on 16th February 2016 and 25th April 2018, respectively. These satellites are equipped with various instruments, including a radar altimeter, enabling them to conduct operational topography measurements of the Earth's surface. The primary objective of the Sentinel-3 constellation concerning land ice is to provide highly accurate topographic measurements of polar ice sheets. This data is crucial in supporting, e.g., ice sheet mass balance studies. Unlike previous missions that utilized conventional pulse-limited altimeters, Sentinel-3 employs an advanced SAR Radar ALtimeter (SRAL) with delay-doppler capabilities, resulting in significantly enhanced spatial resolution for surface topography measurements. The Sentinel-3 Mission Performance Cluster (MPC) is tasked with monitoring the stability and accuracy of the mission. Here, we report on the latest findings on the Greenland ice sheet.

ESA and the MPC recently developed a specialized delay-Doppler Level-2 processing chain (thematic products) over three dedicated surfaces: Inland Waters, sea ice, and Land Ice. For land ice, delay-Doppler processing with an extended window has been implemented to enhance the coverage of the ice sheet margins. With the improved coverage at the ice sheet margins, we can now access and monitor the fastest-changing regions of the Greenland ice sheet. Hence, the essential climate variable surface elevation change (SEC) can directly be derived solely from Sentinel-3 and, due to the operational concept of the Sentinel program, is ensured to provide continuous observations until at least 2030. Here, we present the latest SEC results based on the land ice thematic product and compare it to the other polar altimetric missions (CryoSat-2 and ICESat-2) to provide a benchmark for the performance of the Sentinel-3 mission for the time to come with less abundant polar radar altimeters.   

How to cite: Simonsen, S. B., Sandberg Sørensen, L., Rose, S. K., and Aublanc, J.: Sentinel-3 Land Ice Thematic Product: Evaluation of Greenland surface elevation and elevation change. , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-14920, https://doi.org/10.5194/egusphere-egu24-14920, 2024.

EGU24-15137 | Orals | ITS1.15/GI1.3

Utilizing surface-based observations from the Micro Pulse Lidar Network (MPLNET) for validation of space-based satellite missions 

Jasper Lewis, James Campbell, Erica Dolinar, Simone Lolli, Sebastian Stewart, Larry Belcher, and Ellsworth Welton

Starting with the Lidar In-Space Technology Experiment (LITE) in 1994, spaceborne lidars have provided highly detailed global views of the vertical structure of clouds and aerosols. And since that time, surface-based lidar, well as aircraft lidar, have been used for validation through correlative measurements. While the validation of space-based lidar systems by surface-based lidar observations is not straightforward, protocols for doing so are well-established and have shown good agreement in many instances.     

The Micro Pulse Lidar Network (MPLNET) is a federated, global network of Micro Pulse Lidar systems deployed worldwide to measure aerosol and cloud vertical structure, and mixed layer heights. The data have been collected continuously, day and night, for more than 20 years from sites around the world with multiple sites containing 5+ or 10+ years of data. MPLNET is also a contributing network to the World Meteorological Organization (WMO) Global Atmospheric Watch (GAW) Aerosol Lidar Observation Network (GALION). The use of common instrumentation and processing algorithms within MPLNET allow for direct comparisons between sites. Thus, long-term MPLNET measurements can be used to verify the fidelity of geophysical parameters measured throughout the lifetime of individual satellite missions (e.g. CALIPSO, CATS, EarthCARE, CALIGOLA, and AOS) and provide a metric for intercomparisons between different space-based lidar missions when gaps between satellite missions occur.

In this presentation, we use multiple years of comparisons between MPLNET and the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) flown aboard CALIPSO. For these comparisons, we use newly developed Level 3 MPLNET products consisting of monthly, diurnal statistics for cloud and aerosol retrievals covering a representative range of conditions and locations. Furthermore, we compare top-of-the-atmosphere cirrus cloud radiative forcing derived from these two complementary platforms. Finally, using results from an upcoming validation rehearsal, we demonstrate how these procedures will be utilized during the EarthCARE mission, scheduled to launch in May 2024.    

How to cite: Lewis, J., Campbell, J., Dolinar, E., Lolli, S., Stewart, S., Belcher, L., and Welton, E.: Utilizing surface-based observations from the Micro Pulse Lidar Network (MPLNET) for validation of space-based satellite missions, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15137, https://doi.org/10.5194/egusphere-egu24-15137, 2024.

Fundamental climate data records (FCDRs) play a vital role in monitoring climate change. In this article, we develop a spaceborne passive microwave-based FCDR byrecalibrating the Advanced Microwave Scanning Radiometerfor Earth Observing System (AMSR-E) on the Aqua satellite,the microwave radiometer imager (MWRI) onboard the FengYun-3B (FY3B) satellite, and the Advanced Microwave ScanningRadiometer-2 (AMSR2) onboard the JAXA’s Global ChangeObservation Mission first-Water (GCOM-W1) satellite. Beforerecalibration, it is found that AMSR-E and AMSR2 observations are stable over time, but MWRI drifted colder beforeMay 2015 and had nonnegligible errors in geolocation formost channels. In addition, intersensor differences of brightnesstemperatures (TBs) are as large as 5–10 K. To improve dataconsistency and continuity, several intersensor calibration methods are applied by using AMSR2 as a reference while usingMWRI to bridge the data gap between AMSR2 and AMSRE. The double difference method is used to provide intersensordifference time series for correcting calibration biases, such asscene temperature-dependent bias, solar-heating-induced bias,and systematic constant bias. Hardware differences betweensensors are corrected using principal component analysis. Afterrecalibration, the mean biases of both MWRI and AMSR-Eare less than 0.3 K compared to the AMSR2 reference andtheir standard deviations are less than 1 K for all channels.Under oceanic rain-free conditions, the TB biases are less than0.2 K for all channels and no significant relative bias driftswere found between sensors for overlapping observations. Thesestatistics suggest that the consistency between these instrumentswas significantly improved and the derived FCDR could be usefulto obtain long-term water cycle-related variables for climateresearch. 

How to cite: Wu, B. and Wang, Y.:  A Fundamental Climate Data Record Derived fromAMSR-E, MWRI, and AMSR2 , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15316, https://doi.org/10.5194/egusphere-egu24-15316, 2024.

EGU24-15804 | ECS | Orals | ITS1.15/GI1.3

A multi frequency altimetry snow depth product over the Arctic sea ice 

Alice Carret, Sara Fleury, Alessandro Di Bella, Jack Landy, Isobel Lawrence, Antoine Laforge, Nathan Kurtz, and Florent Garnier

Since more than 10 years, CryoSat-2 (CS2) has observed and monitored the Arctic Ocean, providing unprecedented spatial and temporal coverage. Satellite altimetry enables to measure sea ice thickness, one essential variable to understand the sea ice dynamics. Numerous sea-ice products developed by the community showed the skills of CS2 to retrieve sea-ice thickness. Nevertheless, several questions remain to better quantify the quality of the measurements. One of them is to better assess the snow depth, a key parameter to obtain the sea ice thickness. In 2018, ICESat-2 mission was launched carrying a LIDAR altimeter. We took advantage of the difference of penetration in the snow layer of laser and Ku-Band altimetry to compute a snow depth product covering the ICESat-2 period. This product is then validated and compared to in situ datasets, reanalysis, models and other snow depth products from satellite missions such as SARAL. Results are quite good concerning the comparison to in situ datasets giving us confidence in the product reliability. In July 2020, the orbit of CryoSat-2 was raised, as part of the CRYO2ICE project, to coincide in space and time to tracks from NASA high resolution altimeter ICESat-2 over the Arctic ocean. This is a unique opportunity to benefit from along-track colocalised data. We present here a methodology to compare ICESat-2 and CryoSat-2 along coincident tracks and compare the resulting snow depth product to gridded products. The lack of in situ measurements is one of the main limitations to analyze the along-track product contribution. Finally we focus on the advantages of combining laser and Ku-band altimetry to lower the uncertainties. The snow depth uncertainties of our product are about 6 cm on average. This ESA-supported study should help prepare the Copernicus CRISTAL mission, which will include a Ka/Ku dual-frequency altimeter for the first time.

How to cite: Carret, A., Fleury, S., Di Bella, A., Landy, J., Lawrence, I., Laforge, A., Kurtz, N., and Garnier, F.: A multi frequency altimetry snow depth product over the Arctic sea ice, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15804, https://doi.org/10.5194/egusphere-egu24-15804, 2024.

EGU24-15810 | Posters on site | ITS1.15/GI1.3

Building a comprehensive picture of sea surface, troposphere and ionosphere contributions in precise GNSS reflectometry from space 

Maximilian Semmling, Weiqiang Li, Florian Zus, Mostafa Hoseini, Mario Moreno, Mainul Hoque, Jens Wickert, Estel Cardellach, Andreas Dielacher, and Hossein Nahavandchi

Signals of Global Navigation Satellite Systems (GNSS) are subjected to propagation effects, like reflection, refraction and scintillation. Twenty years ago, a first dedicated payload has been launched on a satellite mission (UK-DMC) to study Earth-reflected GNSS signals and their potential for Earth observations. It was a milestone in the research field of satellite-based reflectometry. The altimetric use of reflectometry is of particular interest for the geoscience community. The permanent and global availability of GNSS signals, exploited in an altimetric reflectometry concept, can help to improve the rather sparse coverage of today’s altimetric products.

Studies on altimetric reflectometry concepts started already thirty years ago. However, the sea surface roughness, the limited GNSS signal bandwidth, orbit uncertainties and the sub-mesoscale variability (we assume here a horizontal scale < 50 km) of troposphere and ionosphere pose a persistent challenge for the altimetric interpretation and application of reflectometry data.

The ESA nano-satellite mission PRETTY (Passive REflecTometry and dosimeTrY) will investigate the altimetric application of reflectometry. It concentrates on a grazing-angle geometry. A mitigation of roughness-induced signal disturbance can be expected under these angles. On the other hand, at grazing angles tropospheric and ionospheric variability will rise in importance. The PRETTY satellite and payload have been developed by an Austrian consortium and successfully launched on 9th October 2023 into the dedicated polar orbit (roughly 550 km in orbit height). We formed a science consortium (among the here listed partners) to merge competences in the field of altimetry and GNSS signal propagation effects.

Based on the mission’s ATBD (Algorithm Theoretical Baseline Document), we conducted simulations and case studies of existing satellite data. They allow a first quantification of expected roughness and sea surface topography effects, as well as, tropospheric and ionospheric biases in grazing-angle geometry. The preliminary results show that, for calm ocean areas (significant wave height < 1 m) and over sea ice, altimetric retrievals reach centimeter level precision. In these specific cases, the residual Doppler shift is small (mHz range) which indicates moderate variability of tropospheric and ionospheric contributions. New observation data of the PRETTY mission is expected early in 2024. Then, we will extend our picture for a more general altimetric use of precise reflectometry data.

How to cite: Semmling, M., Li, W., Zus, F., Hoseini, M., Moreno, M., Hoque, M., Wickert, J., Cardellach, E., Dielacher, A., and Nahavandchi, H.: Building a comprehensive picture of sea surface, troposphere and ionosphere contributions in precise GNSS reflectometry from space, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15810, https://doi.org/10.5194/egusphere-egu24-15810, 2024.

The US National Aeronautics and Space Administration (NASA) and the European Space Agency (ESA) created a Joint Program Planning Group (JPPG) in 2010 to enhance coordination between NASA and ESA on current and future space Earth Observation missions. One of the three sub-groups of the JPPG is dedicated to collaboration in field measurement campaigns, mission and product calval and more recent collaborative EO community science projects.

Since 2010 the JPPG has initiated or informed numerous airborne field campaigns to help develop and document the scientific objectives, develop geophysical retrieval algorithms and provide calibration and/or validation for present and/or future satellites to be operated by NASA, ESA, and its partners. The activities address an underlying need to demonstrate unambiguously that space-based measurements, which are typically based on engineering measurements by the detectors (e.g. photons), are sensitive to and can be used to reliably retrieve the geophysical and/or biogeochemical parameters of interest across the Earth and validate mission design. Such campaigns have included as diverse subjects as atmospheric trace gas composition over the western US, solar induced fluorescence over the Eastern United States, wind profiles over the north Atlantic, vegetation canopy profiles in Gabon, and sea ice and ice sheet properties in the Arctic and Antarctic. The collaborative field campaign and calval activities have helped use of surface-based, airborne, and/or space-based observations to develop precursor data sets and support both pre- and post- launch calibration/validation and retrieval algorithm development for space-based satellite missions measuring our Earth system.

The generation of consistent, inclusive, community-based assessments of Earth system change through integrated analyses of these different data sets is also a critically important process in the challenge of documenting Earth system change. To assist in this process the JPPG has supported collaborative community efforts including three installments of the Ice Mass Balance Intercomparison Experiment (IMBIE; two completed, one ongoing), the NASA-ESA Snow on Sea Ice Intercomparison (NESOSI), and the Arctic Methane and Permafrost Challenge (AMPAC).

In this talk a review of JPPG activities and their results, as well current plans for future collaborations including campaigns will be provided. 

How to cite: Davidson, M. W. J., Drinkwater, M., and Kaye, J.: An overview of collaborative field campaigns, calval and community science activities enabled through the ESA-NASA Joint Program Planning Group, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-16893, https://doi.org/10.5194/egusphere-egu24-16893, 2024.

EGU24-17973 | Orals | ITS1.15/GI1.3

Post-launch Validation of the Copernicus Atmospheric Composition Satellites: Outcomes of the CCVS Gap Analysis 

Tijl Verhoelst, Jean-Christopher Lambert, Martine De Mazière, Bavo Langerock, Steven Compernolle, Folkert Boersma, Daan Hubert, Arno Keppens, Clémence Pierangelo, Gaia Pinardi, Mahesh Kumar Sha, Frederik Tack, Nicolas Theys, Gijsbert Tilstra, Michel Van Roozendael, Corinne Vigouroux, Angelika Dehn, Philippe Goryl, Thierry Marbach, and Sébastien Clerc

The European Earth Observation programme Copernicus is implementing the next-generation system for atmospheric composition monitoring: after the success of the Sentinel-5 Precursor TROPOMI, a constellation of Sentinel-4 geostationary and Sentinel-5 Low-Earth orbiting missions will be launched in 2025 and beyond for air quality, ozone and climate variables monitoring, while the CO2M missions will observe greenhouse gases emissions and related proxies.  Post-launch validation of the data products is essential to determine their quality and enable users to judge their fitness-for-purpose.  Therefore, in 2021-2022 the European Union funded the H2020 Copernicus Cal/Val Solution (CCVS) project with the aim to review the status of existing validation infrastructures and methods for all Sentinel missions and to define a holistic solution to overcome limitations (https://ccvs.eu).  In this contribution we report on the maturity assessment and gap analysis performed in this project.  This assessment synthesizes lessons learned from earlier work in FP7 and H2020 projects, and from the operational/routine validation services run in the ESA/Copernicus Atmosphere Mission Performance Cluster (ATM-MPC), the EUMETSAT Atmospheric Composition Satellite Application Facility (AC SAF), the Copernicus Atmosphere Monitoring Service (CAMS) and the Copernicus Climate Change Service (C3S).  The CCVS assessment includes feedback from space agencies, Copernicus stakeholders and the CEOS Working Group on Calibration and Validation (WGCV).  

The validation means, such as the precursor data sets and comparison methods, have evolved significantly in the past decade: (1) New ground-based instruments have been developed and networks have expanded  in geographical coverage and in capabilities, (2) traceability to metrological standards and uncertainty characterization of the (Fiducial) Reference Measurements (FRM) has improved considerably, (3) rapid provision of FRM through data distribution services is becoming commonplace, (4)  the advantages of advanced comparison methods have been demonstrated, and (5) all of this has facilitated the development of operational, near-real-time validation systems such as the Validation Data Analysis Facility (VDAF-AVS) of the ATM-MPC for the Sentinel-5P mission. 

On the other hand, a list of remaining challenges still restrain the scope and quality of the validation of several atmospheric data products: (1) Station-to-station differences in ground-based validation results suggest (poorly understood) intra-network and inter-network inhomogeneity, (2) the coverage offered by ground-based networks (of the full range of the measurand values and of the influence quantities affecting the retrieval) can have important gaps, (3) timeliness of ground-based data provision remains poor for several products, (4) comparability (representativeness) between ground-based and satellite measurements requires further methodological advances and supporting measurement campaigns, (5) the accuracy and breadth of scope of the latest generation of satellite sounders puts correspondingly tight and difficult-to-meet requirements on the FRM data quality, (6) cross-validation of the different satellites requires a coordinated approach, and (7) some networks and activities experience increased/recurrent funding difficulties. 

We conclude this overview of the CCVS gap analysis for atmospheric composition data with illustrations of concrete actions undertaken recently to address some of the validation challenges highlighted by the project.

The CCVS project has received funding from the European Union’s Horizon 2020 programme under grant agreement No 101004242 (Project title: “Copernicus Cal/Val Solution). 

How to cite: Verhoelst, T., Lambert, J.-C., De Mazière, M., Langerock, B., Compernolle, S., Boersma, F., Hubert, D., Keppens, A., Pierangelo, C., Pinardi, G., Kumar Sha, M., Tack, F., Theys, N., Tilstra, G., Van Roozendael, M., Vigouroux, C., Dehn, A., Goryl, P., Marbach, T., and Clerc, S.: Post-launch Validation of the Copernicus Atmospheric Composition Satellites: Outcomes of the CCVS Gap Analysis, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17973, https://doi.org/10.5194/egusphere-egu24-17973, 2024.

EGU24-19307 | Orals | ITS1.15/GI1.3

Four decades of cryosphere albedo from spaceborne observations - assessment with field data 

Jason Box, Rasmus Bahbah, Andreas Ahlstrøm, Adrien Wehrlé, Alexander Kokhanovsky, Ghislain Picard, and Laurent Arnaud

Snow and ice albedo plays a fundamental role in climate change amplification. Its importance is by modulating absorbed sunlight; the largest average melt energy source. Further, the presence or lack of light absorbing impurities including living matter and meltwater effects can strongly influence snow and ice heating rates. Through multiple consecutive satellite missions, cryosphere albedo has been mapped globally and continuously for more than four decades now.
This work examines a 42 year record of cryosphere albedo by joining the satellite climate records of snow and ice albedo from AVHRR 1982 to present, NASA MODIS 1999 to present, and EU Copernicus Sentinel-3 2017 to present. The long-term stability of the climate records is examined using independent field data from Greenland and Antarctica. Additionally, the work presents long term trends in snow and ice albedo in relation to the competing effects of surface melting, snowfall and rainfall.

How to cite: Box, J., Bahbah, R., Ahlstrøm, A., Wehrlé, A., Kokhanovsky, A., Picard, G., and Arnaud, L.: Four decades of cryosphere albedo from spaceborne observations - assessment with field data, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-19307, https://doi.org/10.5194/egusphere-egu24-19307, 2024.

EGU24-19918 | Posters on site | ITS1.15/GI1.3

CryoSat Mission: CalVal, Science and International Cooperation Activities 

Alessandro Di Bella and Tommaso Parrinello

Launched in 2010, the European Space Agency’s (ESA) CryoSat mission was the first polar-orbiting satellite flying a SAR Interferometric altimeter dedicated to the cryosphere, with the objectives to monitor precise changes in the thickness of polar ice sheets and floating sea ice. After 14 years in orbit, CryoSat remains one of the most innovative radar altimeters in space and continues to deliver high-quality data, providing unique contributions to several Earth Science and application domains. The mission has been extended until the end of 2025 with the scope to achieve important scientific objectives and to extend the synergy with other missions by further strengthening international cooperation.

Routine CalVal activities are fundamental to evaluate the accuracy of CryoSat measurements, to monitor the long-term stability of the altimeter, and to characterise uncertainties on the final geophysical retrievals. In this talk, we present the CryoSat mission status and show results from some of the several CalVal activities currently in place, e.g., acquisition over transponders, comparison of sea level at tide gauges and exploitation of data collected during polar field campaigns. We also highlight the importance of international cooperation in CalVal and Science activities from the perspective of the ESA-NASA CRYO2ICE campaign, aligning CryoSat orbit to the one of ICESat-2, and the Sea Ice Thickness Intercomparison Exercise (SIN’XS) project, aiming to provide reconciled sea ice thickness estimates in both hemispheres. Finally, we discuss how current and future CryoSat activities are crucial to prepare for the upcoming Copernicus CRISTAL mission which will provide coincident measurements at Ka and Ku bands.

How to cite: Di Bella, A. and Parrinello, T.: CryoSat Mission: CalVal, Science and International Cooperation Activities, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-19918, https://doi.org/10.5194/egusphere-egu24-19918, 2024.

EGU24-20394 | Orals | ITS1.15/GI1.3

Validation and support of space-based measurements with the Pandonia Global Network of ground-based spectrometers 

Thomas Hanisco, Nader Abuhassan, Stefano Casadio, Alexander Cede, Limseok Chang, Angelika Dehn, Barry Lefer, Elena Lind, Apoorva Pandey, Bryan Place, Alberto Redondas, James Szykman, Martin Tiefengraber, Luke Valin, Michel van Roozendael, and Jonas von Bismarck

Since 2019 the NASA Pandora and ESA Pandonia projects have been collaborating to coordinate and facilitate the expansion of a global network of ground-based spectrometers to support space-based measurements of trace gases relevant to air quality (NO2, O3, HCHO, SO2, …). This network of standardized, calibrated Pandora instruments, the Pandonia Global Network (PGN, https://www.pandonia-global-network.org), is focused on providing data needed to help validate satellite measurements and to contribute to scientific studies of air quality.  As of January 2024, the PGN is comprised of 158 official sites in 34 countries. This presentation will describe recent efforts to expand and improve the network to support the increased capability and complexity of space-based measurements. Collaborative efforts by partner agencies, especially the US Environmental Protection Agency (EPA) and the Korean National Institute of Environmental Research (NIER), and new programs such as the Increasing Participation in Minority Serving Institutions (IPMSI) and Satellite Needs Working Group (SNWG) have accelerated the growth of the PGN, providing greater global coverage and allowing improved data products.  With these improvements and continued input from other suborbital assets, the PGN is well positioned to facilitate the interpretation and validation of high spatial resolution and diurnal measurements provided by the newest orbiting and geostationary satellite instruments. 

How to cite: Hanisco, T., Abuhassan, N., Casadio, S., Cede, A., Chang, L., Dehn, A., Lefer, B., Lind, E., Pandey, A., Place, B., Redondas, A., Szykman, J., Tiefengraber, M., Valin, L., van Roozendael, M., and von Bismarck, J.: Validation and support of space-based measurements with the Pandonia Global Network of ground-based spectrometers, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-20394, https://doi.org/10.5194/egusphere-egu24-20394, 2024.

EGU24-20397 | Orals | ITS1.15/GI1.3

Integration of ACIX-III Land Atmospheric Correction Inter-comparison eXercise within the Copernicus Expansion Mission Product Algorithm Laboratory to Support Surface Reflectance Cal/Val 

Kevin Alonso, Noelle Cremer, Valentina Boccia, Philip G. Brodrick, Adam Chlus, Georgia Doxani, Ferran Gascon, Sander Niemeijer, David R. Thompson, Philip Townsend, and Nikhil Ulahannan

Atmospheric Correction Inter-comparison eXercise (ACIX) was initiated in 2016 in the frame of the Committee on Earth Observation Satellites (CEOS) Working Group on Calibration & Validation (WGCV) and it is co-organised by ESA and NASA. The aim of ACIX is to compare the state-of-the-art atmospheric correction (AC) processors. ACIX is a voluntary and open-access initiative to which every AC processor’s developer is invited to participate. In the current third edition, ACIX-III Land, the focus is on imaging spectrometer data, also called hyperspectral data. Data from two spectrometers in orbit (PRISMA and EnMAP) will be used in a suite of test sites. These sites were selected based on the availability of ground-based measurements and flight campaign data with coincident acquisitions, i.e., RadCalNet and CHIME-AVIRIS-NG campaigns.

 The ACIX-III Land exercise will intercompare the performances of several AC software suits capable of retrieving Surface Reflectance (SR), Water Vapour (WV) and Aerosols Optical Depth (AOD). The original datasets along with the participant results will be catalogued, intercompared, and analysed within the Copernicus Expansion Mission - Product Algorithm Laboratory (CEM-PAL). The CEM-PAL is a virtual environment aiming to facilitate efficient prototyping of algorithms used to generate and test Expansion Missions Level-2 products, including algorithm modification, hosted processing, qualification functionalities and scientific validation environment. Once the ACIX-III results are published, the dataset will be repurposed to initially support the CHIME L2 developments with plans to extent the support to other missions (e.g., SBG, LSTM).

This contribution will present the ACIX-III Land, and CEM-PAL initiatives, highlighting the main implementation points, latest status, and future developments to support related Cal/Val activities.

How to cite: Alonso, K., Cremer, N., Boccia, V., Brodrick, P. G., Chlus, A., Doxani, G., Gascon, F., Niemeijer, S., Thompson, D. R., Townsend, P., and Ulahannan, N.: Integration of ACIX-III Land Atmospheric Correction Inter-comparison eXercise within the Copernicus Expansion Mission Product Algorithm Laboratory to Support Surface Reflectance Cal/Val, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-20397, https://doi.org/10.5194/egusphere-egu24-20397, 2024.

EGU24-20612 | Posters on site | ITS1.15/GI1.3

Using In Situ Airborne Measurements to Evaluate Pandora Ground-based Remote Sensing Formaldehyde Data Products  

Jason St. Clair, Glenn Wolfe, and Thomas Hanisco

Measurements of boundary layer formaldehyde (HCHO) are valuable for air quality monitoring, both because HCHO is classified as an air toxic by the US EPA and because HCHO concentrations directly reflect recent VOC oxidation and therefore are a diagnostic for ozone production. The Pandora network, with instruments deployed across the US and around the world, is a promising source of boundary layer HCHO data but previous evaluation of Pandora HCHO data was limited to total column HCHO at two sites during one campaign. Here we extend the evaluation to include Pandora tropospheric column and profiling data products derived from differential optical absorption spectroscopy (DOAS) operation. NASA’s SARP-East program provided a unique opportunity to evaluate the Pandora DOAS data products with profiling spirals by an airborne in situ payload that included the NASA Goddard CAFE HCHO instrument. Comparison of CAFE and Pandora data will be presented with the goal of better informing the Pandora data community of its performance.

How to cite: St. Clair, J., Wolfe, G., and Hanisco, T.: Using In Situ Airborne Measurements to Evaluate Pandora Ground-based Remote Sensing Formaldehyde Data Products , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-20612, https://doi.org/10.5194/egusphere-egu24-20612, 2024.

EGU24-20665 | Orals | ITS1.15/GI1.3

Using Pandora direct sun and MAX-DOAS formaldehyde columns for evaluating satellite retrievals 

Apoorva Pandey, Bryan Place, Jin Liao, Nader Abuhassan, Alexander Cede, Thomas Hanisco, and Elena Lind

Atmospheric formaldehyde (HCHO) is a short-lived but ubiquitous product of hydrocarbon oxidation. It is a tracer of hydrocarbon emissions and reactivity. HCHO has been observed from satellite-based instruments for over two decades. Retrievals typically involve (1) fitting slant columns to the observed UV/IR radiances and (2) deriving vertical columns from the slant columns using air mass factors. Air mass factors are calculated using radiative modeling and a-priori vertical HCHO distributions from a chemical transport model. The Pandora instruments form a ground-based remote sensing network that is valuable for validating satellite retievals. Pandora provides total and tropospheric columns of HCHO via direct sun (DS) and Multi-Axis Differential Optical Absorption Spectroscopy (MAX-DOAS) observations in the UV, respectively. Here, we discuss conversion of slant columns to vertical columns for DS and MAX-DOAS Pandora measurements, neither of which involves radiative modeling and a-priori assumptions. We intercompare daily and seasonal variations in Pandora HCHO columns from these two distinct measurement techniques for ‘hotspot’ and ‘background’ sites to demonstrate their robustness and complementary strengths, as well as to estimate their uncertainties. We further examine the inter-site and seasonal variability in satellite (e.g., OMI, OMPS) retrievals relative to Pandora HCHO columns.     

How to cite: Pandey, A., Place, B., Liao, J., Abuhassan, N., Cede, A., Hanisco, T., and Lind, E.: Using Pandora direct sun and MAX-DOAS formaldehyde columns for evaluating satellite retrievals, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-20665, https://doi.org/10.5194/egusphere-egu24-20665, 2024.

EGU24-20707 | ECS | Posters on site | ITS1.15/GI1.3

Intercomparison of Pandora surface and vertical profile NO2 retrievals with in-situ network measurements and airborne observations across the Eastern USA 

Bryan Place, Apoorva Pandey, Lukas Valin, Jason St. Clair, Thomas Hanisco, Nader Abuhassan, Alexander Cede, and Elena Spinei

Trace gas total and tropospheric/stratospheric column retrievals from the Pandora instruments across the Pandonia Global Network (PGN) have played a key role in satellite validation. With the addition of multi-axis differential optical absorption spectroscopy (MAX-DOAS) retrievals to the latest Pandora processing software (Blick v1.8), the PGN now generates surface and vertically-resolved trace gas measurements that will further aid in future satellite product validation. The MAX-DOAS retrievals developed for the Pandora instrument rely upon simple assumptions and measurements and do not require complex radiative transfer calculations, allowing for the columns to be retrieved at a sub-hourly timescale. In this presentation, we give a brief overview of the theory and measurements behind the Pandora MAX-DOAS retrievals and provide an evaluation of the MAX-DOAS NO2 products. For the evaluation we show an intercomparison of PGN NO2 surface products with co-located surface network measurements taken from the US Environmental Protection Agency Air Quality System (EPA AQS) database.  We also compare Pandora NO2 vertical profiles with profiles collected from both sonde and aircraft measurements in the Eastern United States.

How to cite: Place, B., Pandey, A., Valin, L., St. Clair, J., Hanisco, T., Abuhassan, N., Cede, A., and Spinei, E.: Intercomparison of Pandora surface and vertical profile NO2 retrievals with in-situ network measurements and airborne observations across the Eastern USA, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-20707, https://doi.org/10.5194/egusphere-egu24-20707, 2024.

EGU24-180 | ECS | Orals | ITS1.23/SSS0.1.4

Developing a Rangeland Carbon Tracking and Monitoring System Using Remote Sensing Imagery Coupled With a Modeling Approach 

Yushu Xia, Jonathan Sanderman, Jennifer Watts, Megan Machmuller, Stephanie Ewing, Andrew Mullen, Charlotte Rivard, and Haydee Hernandez

Rangelands play a crucial role in providing various ecosystem services and have significant potential for carbon sequestration. However, monitoring soil organic carbon (SOC) stocks in rangelands is challenging due to the large size of ranches and the high spatial variability influenced by climate and management factors. To address these challenges, we have developed the Rangeland Carbon Tracking and Management (RCTM) system, which integrates remote sensing inputs, survey data sources, and both empirical and process-based SOC models. In this work, we will introduce the structure of RCTM v1.0, its data input requirements, data processing pipelines, and the resulting data outputs. Additionally, we will discuss the high-resolution soil moisture data layers, baseline SOC maps, and the targeted field sampling plan generated through an empirical digital soil mapping approach. The Bayesian calibration and validation scheme for obtaining grassland plant functional type (PFT)-specific parameters using flux tower network data will also be explained. After calibration, the RCTM system generated estimates of rangeland carbon fluxes across PFTs (R2 between 0.6 and 0.7) and surface depth SOC stocks (R2 = 0.6) with moderate accuracy at the regional scale. The visualization of modeling results associated with long-term rangeland C dynamics at different scales will be demonstrated using the Google Earth Engine platform to inform management decisions and policymaking.

How to cite: Xia, Y., Sanderman, J., Watts, J., Machmuller, M., Ewing, S., Mullen, A., Rivard, C., and Hernandez, H.: Developing a Rangeland Carbon Tracking and Monitoring System Using Remote Sensing Imagery Coupled With a Modeling Approach, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-180, https://doi.org/10.5194/egusphere-egu24-180, 2024.

Soil erosion is a widespread environmental challenge with far-reaching implications for agricultural productivity, water quality and ecosystem health. Addressing this complex issue requires the use of modelling tools that empower diverse stakeholders, such as researchers and decision-makers, to simulate soil erosion systems under different scenarios. For these tools to be effective, not only they need to make good predictions, but they need to be accessible and educational, so users, regardless of their technical skills and modelling expertise, can understand and even more importantly, trust the model. In traditional soil erosion modelling, the primary emphasis to build trust is by demonstrating the model’s ability to replicate past observations, and less attention is given to build trust by providing an educational and exploratory experience. We introduce a project that aims at democratizing soil erosion modelling, making it more accessible and trustworthy to researchers, educators, decision-makers, and local communities. Leveraging the versatility and accessibility of Jupyter Notebooks, we are developing iMPACT-erosion, a soil erosion modelling toolbox to support education, land management and informed decision making. A series of dedicated Notebooks not only explain and simulate the main soil erosion processes but guides users through the main steps to enhance the credibility of the model results, i.e. sensitivity analysis, model calibration, uncertainty analysis, model evaluation and scenario analysis. The integration of interactive visualization enhances this experience by facilitating exploration of both the model configuration and the soil erosion system's response under different scenarios/decisions. This model development approach is not confined to the field of soil erosion and offers the potential to facilitate knowledge transfer and collaboration between model developers and decision makers in various domains.

How to cite: Peñuela, A.: Democratizing soil erosion modelling: A Jupyter Notebook approach, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1907, https://doi.org/10.5194/egusphere-egu24-1907, 2024.

EGU24-3186 | ECS | Orals | ITS1.23/SSS0.1.4

Prediction of soil phosphorus sorption capacity in agricultural soils using mid-infrared spectroscopy.  

Sifan Yang, Blánaid White, Fiona Regan, Nigel Kent, Rebecca Hall, Felipe de Santana, and Karen Daly

             Advice for phosphorus (P) fertilisation based on soil testing using extractive methods but does not consider P sorption processes. Traditional soil P sorption capacity examined from a Langmuir isotherm batch experimental design, which is time-consuming, labour intensive and expensive. Mid-infrared (MIR) spectroscopy is a rapid analysis technique that can potentially replace the extractive technique traditionally used in soil analysis. The objective of this work was to predict the isothermal parameter of P sorption maximum capacity (Smax, mg·kg-1) from MIR spectroscopy.

              This study created spectral libraries from benchtop (Bruker) and handheld (Agilent) MIR spectrometers by scanning samples in two particle sizes, < 0.100 mm (ball-milled) and < 2 mm. The four spectral libraries created used an archive of samples with a database of sorption parameters where soils were classified into low and high sorption capacities using a threshold value of Smax = 450.03 mg·kg-1. To assess the optimal algorithmic method with highest Smax prediction accuracy, regression models were based on the partial least squares (PLS) regression, Cubist, support vector machine (SVM) regression and random forest (RF) regression algorithms. After the first derivative Savitzky-Golay smoothing, Bruker spectroscopies with both soil particle sizes yielded ‘excellent models’, with SVM predicting Smax values with high accuracy (RPIQVal = 4.50 and 4.25 for the spectral libraries of the ball-milled and <2mm samples, respectively). In comparison, the Agilent handheld spectrometer produced spectra with more noise and less resolution than the Bruker benchtop spectrometer. Unlike Bruker, for Agilent MIR spectroscopy, more homogeneous samples after ball-milling resulted in a higher accurate Smax prediction. For Agilent spectroscopy of ball-milled samples, an ‘approximate quantitative model’ (RPIQVal = 2.74) was obtained from the raw spectra using the Cubist algorithm. However, for Agilent spectroscopy of < 2 mm samples, the best performing Cubist algorithm can only achieve a ‘fair model’ (RPIQVal = 2.23) with the potential to discriminate between high and low Smax values.

              The results suggest that the Bruker bench-top spectrometer can predict the Langmuir Smax value with high accuracy without the need to ball-mill samples, highlighting the availability of the MIR spectrometer as a rapid alternative method for understanding soil P sorption capacity. However, for handheld spectrometers, the Agilent instruments can only make approximate quantitative predictions of Smax for ball milled samples. For <2mm samples, Agilent can only be used to classify low and high sorption capacity soils.

How to cite: Yang, S., White, B., Regan, F., Kent, N., Hall, R., de Santana, F., and Daly, K.: Prediction of soil phosphorus sorption capacity in agricultural soils using mid-infrared spectroscopy. , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-3186, https://doi.org/10.5194/egusphere-egu24-3186, 2024.

This study employs the PHYGROW simulation model to assess the 40-year dynamics of arid grassland in Jordan, focusing on the Leaf Area Index (LAI) as a pivotal indicator of vegetation health. The observed results reveal a notable decline in LAI over the study period, with the highest recorded value in 2005 (2.27) and a subsequent reduction to 1.68 in 2021. Rigorous statistical analyses, including regression analysis, confirm the significance of this downward trend, prompting further investigation into potential contributing factors such as changes in climate, land use, and soil conditions.

 

Interannual variability analysis identifies specific years marked by noteworthy LAI fluctuations, providing insights into the dynamic responses of the arid grassland ecosystem. Comparison with concurrent climate data underscores the intricate relationship between LAI trends and environmental variables. The study emphasizes the importance of continuous monitoring and understanding the underlying drivers of vegetation dynamics in arid regions.

The observed decrease in LAI holds implications for the overall health and resilience of the ecosystem, highlighting the need for informed decision-making in sustainable land management practices. These findings contribute significantly to the broader understanding of arid land dynamics, guiding future research and collaborative efforts with experts in related fields. Such collaborations are essential for enhancing the robustness and applicability of the results, ultimately informing conservation and resource management strategies tailored to the unique challenges of arid environments.

How to cite: Alhamad, M. N. and Abdullah, S.: Simulation Modeling of Arid Grassland Dynamics in Jordan: A 40-Year Analysis of Leaf Area Index Trends, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-4117, https://doi.org/10.5194/egusphere-egu24-4117, 2024.

In the Sahel region, landscape configuration is closely linked to factors such as climate, ecology, soil composition, agronomy, livestock, and biology. Over the past decades, significant changes in these factors have been observed, including shorter rainy seasons, irregular precipitation, a decrease in biomass productivity, and rapid population growth, negatively impacting local agricultural and pastoral systems. In response to this pressure, mitigation strategies have been implemented to contribute to the improvement of local food, nutritional, and economic security. Agroforestry systems, involving a combination of trees, shrubs, crops, and animals in the same plot, represent one of these strategies. Therefore, characterizing these systems in the current context of climate change is crucial for sustainable natural resource management.

In this study, three agroforestry landscapes of the Senegalese Sahel were described, spanning a bioclimatic gradient from the Louga region (Ouarkhokh) in the north to the Fatick region (Niakhar) in the center, and the Tambacounda region (Koussanar) in the south. The data utilized included satellite imagery synthesis (Sentinel-2 and Spot), landscape variables (rainfall, evapotranspiration, biomass, and vegetation), spectral indices (NDVI, NDRE, GNDVI), and field data on land use and woody cover. The methodology consisted of three main approaches: (i) landscape stratification involving Sentinel image segmentation in 2021, selection of relevant landscape variables, and mixed discriminant factor analysis to establish landscape heterogeneity; (ii) land use and land cover mapping through supervised pixel-based classification using a Random Forest (RF) machine learning classifier with 500 trees; (iii) floristic diversity analysis by assessing floristic composition and calculating diversity indices (i.e., Shannon, Pielou, and Simpson indices).

Landscape stratification identified seven classes with distinct landscape characteristics. Classes (1, 2, and 4) in the Ouarkhokh site had lower average biomass, rainfall, and actual evapotranspiration values than classes (3 and 4) in the Niakhar site. Similarly, classes (5, 6, and 7) in the Koussanar area had higher average biomass, rainfall, and actual evapotranspiration values than the first two sites. Land use mapping showed vegetation predominance in the Ouarkhokh site, significance in the Koussanar site, and low presence in the Niakhar area. Other identified units (cultivated areas, built-up areas, water, and bare land) were dominant in the Niakhar area, present in the Koussanar site, and low in the Ouarkhokh area. Likewise, vegetation dominated in classes 1, 5, 6, and 7. Class 1 was exclusively found in Ouarkhokh, while classes 5, 6, and 7 were located in the Koussanar site. The majority of cultivated surfaces were in class 3, exclusively located in the Niakhar area. Species richness was higher in the Niakhar area (60 species, 21 families) and lower in the Koussanar area (56 species, 16 families) and Ouarkhokh area (31 species, 13 families). This landscape distribution of land use, landscape classes, and identified species highlights the influence of anthropogenic, soil-related, and climatic factors specific to each site.

How to cite: sylla, D., Diouf, A. A., and Ndao, B.: Variation of woody plants diversity and land use along a bioclimatic gradient of agroforestry landscapes in Senegalese Sahel, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5133, https://doi.org/10.5194/egusphere-egu24-5133, 2024.

The landscape-scale evaluation and modeling of the impact of agricultural management and climate change on soil-derived ecosystem services requires soil information at a spatial resolution addressing individual agricultural fields. A pattern recognition approach is presented that generates a nationwide data product. It agglomerates the multivariate soil parameter space into a limited number of functional soil process units (SPUs) that facilitate operating agricultural process models. Each SPU is defined by a multivariate parameter distribution along its depth profile from 0 to 100 cm. It has a depth resolution of 1 cm and a spatial resolution of 100 m. The methodological approach is based on an unsupervised classification procedure involving remote sensing, cluster analysis, and machine learning. It accounts for differences in variable types and distributions and involves genetic algorithm optimization to identify those SPUs with the lowest internal variability and maximum inter-unit difference with regards to both, their soil characteristics and landscape setting. The high potential of the method is demonstrated for the agricultural soil landscape of Germany. It can be applied to other landscapes and ecosystem contexts.

How to cite: Ließ, M.: A pattern recognition approach to generate soil process units for ecosystem modeling, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5461, https://doi.org/10.5194/egusphere-egu24-5461, 2024.

EGU24-5750 | ECS | Orals | ITS1.23/SSS0.1.4

Identifying landscape hot and cold spots of soil greenhouse gas fluxes by combining field measurements and remote sensing data 

Elizabeth Wangari, Ricky Mwanake, Tobias Houska, David Kraus, Gretchen Gettel, Ralf Kiese, Lutz Breuer, and Klaus Butterbach-Bahl

Upscaling chamber measurements of soil greenhouse gas (GHG) fluxes from point scale to landscape scale remain challenging due to the high variability in the fluxes in space and time. This study measured GHG fluxes and soil parameters at selected point locations (n = 268), thereby implementing a stratified sampling approach on a mixed-landuse landscape (∼ 5.8 km2). Based on these field-based measurements and remotely sensed data on landscape and vegetation properties, we used random forest (RF) models to predict GHG fluxes at a landscape scale (1 m resolution) in summer and autumn. The RF models, combining field-measured soil parameters and remotely sensed data, outperformed those with field-measured predictors or remotely sensed data alone. Available satellite data products from Sentinel-2 on vegetation cover and water content played a more significant role than those attributes derived from a digital elevation model, possibly due to their ability to capture both spatial and seasonal changes in the ecosystem parameters within the landscape. Similar seasonal patterns of higher soil/ecosystem respiration (SR/ER–CO2) and nitrous oxide (N2O) fluxes in summer and higher methane (CH4) uptake in autumn were observed in both the measured and predicted landscape fluxes. Based on the upscaled fluxes, we also assessed the contribution of hot spots to the total landscape fluxes. The identified emission hot spots occupied a small landscape area (7 % to 16 %) but accounted for up to 42 % of the landscape GHG fluxes. Our study showed that combining remotely sensed data with chamber measurements and soil properties is a promising approach for identifying spatial patterns and hot spots of GHG fluxes across heterogeneous landscapes. Such information may be used to inform targeted mitigation strategies at the landscape scale.

How to cite: Wangari, E., Mwanake, R., Houska, T., Kraus, D., Gettel, G., Kiese, R., Breuer, L., and Butterbach-Bahl, K.: Identifying landscape hot and cold spots of soil greenhouse gas fluxes by combining field measurements and remote sensing data, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5750, https://doi.org/10.5194/egusphere-egu24-5750, 2024.

EGU24-5949 | ECS | Posters on site | ITS1.23/SSS0.1.4

The Joint FAO/IAEA Center and the Soil Fertility Project: Integrating Nuclear and Related Techniques for Modelling to Support Practical Decision Management Support 

Magdeline Vlasimsky, Gerd Dercon, Hami Said Ahmed, Sarata Daraboe, Yusuf Yigini, Yuxin Tong, Yi Peng, Franck Albinet, Maria Heiling, and Christian Resch

The Soil Fertility (SoilFer) project, led by the Land and Water Division at FAO, seeks to enhance agricultural practices and resilience globally, starting with five countries (Guatemala, Honduras, Zambia, Kenya, and Ghana). The project collaborates with governments and relevant national partners to establish comprehensive national monitoring and mapping systems for soil management, catering to the diverse needs of agriculture stakeholders. The Soil and Water Management Laboratory at the Joint FAO/IAEA Center serves as a crucial hub for advancing research and technical expertise in soil and water management using nuclear and related techniques. Through its multifaceted approach in collaboration with the Land and Water Division, the laboratory contributes significantly to the SoilFer project, through the development and implementation of technical training programs for and expert advising on the application of Mid-Infrared Spectroscopy (MIRS), Cosmic Ray Neutron Sensor (CRNS), and Gamma Ray Spectroscopy (GRS) to soil monitoring and mapping.

The integration of MIRS, CRNS, and GRS technologies within the SoilFer project forms a robust framework for soil monitoring and mapping, as MIRS has been shown to provide detailed insights into soil composition and carbon content, CRNS offers real-time data on soil moisture dynamics, and GRS contributes to the analysis of radioactive isotopes and elemental composition. Given the integrated nature of landscape processes, the adoption of technological approaches must mirror this complexity. Interconnected ecological, hydrological, and geological processes within landscapes necessitate a holistic and integrated technological framework. This approach ensures that diverse data streams, derived from technologies such as remote sensing, geographic information systems (GIS), and advanced sensor networks, can be harmoniously synthesized. Only through such integration can a comprehensive understanding of landscape dynamics be achieved, facilitating informed decision-making and sustainable management practices across multifaceted environmental systems. The project emphasizes the seamless integration of these advanced technologies with soil monitoring and mapping systems, ensuring a comprehensive and effective approach to soil management practices, while improving national capacity and stakeholder engagement in data-based decision making. 

The key objectives of the SoilFer project encompass the development of robust national soil information systems, the implementation of decision support systems targeting soil health, and the promotion of sustainable soil management practices. By fostering collaboration and knowledge exchange, the project aspires to build technical, increase agricultural resilience and ensure food security in the participating countries.

How to cite: Vlasimsky, M., Dercon, G., Said Ahmed, H., Daraboe, S., Yigini, Y., Tong, Y., Peng, Y., Albinet, F., Heiling, M., and Resch, C.: The Joint FAO/IAEA Center and the Soil Fertility Project: Integrating Nuclear and Related Techniques for Modelling to Support Practical Decision Management Support, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5949, https://doi.org/10.5194/egusphere-egu24-5949, 2024.

EGU24-6126 | Posters on site | ITS1.23/SSS0.1.4

NewLife4Drylands Protocol for dryland restoration in Protected Areas: an innovative tool to support restoration activities. 

Serena D'Ambrogi, Francesca Assennato, Rocco Labadessa, Paolo Mazzetti, Valentina Rastelli, Nicola Riitano, and Cristina Tarantino

Land degradation processes have experienced a significant increase in recent decades, a trend that is projected to escalate further in the absence of any intervention. The need of adopting practices to contain, mitigate and restore degraded land have been stressed also by the new European Mission 'A Soil Deal for Europe'. To guide and support restoration actions, through a common and effective framework, an efficient monitoring approach and an adaptive ecological restoration process is needed. 

The NewLife4Drylands LIFE project provides a Protocol for design, implementation, and maintenance of restoration activities based on Nature-Based Solutions (NBS) within drylands. The Protocol, developed following the principles and inputs of some international restoration standards (SER, IUCN), is based on the identification and monitoring of degradation processes exploiting remote sensing capabilities, with the aim to integrate data derivation procedures into ecological restoration and maintenance activities. The Protocol is supported by a Decision-Making web-tool guiding trough the degradation processes, NBS along with indices/indicators with the aim to reduce the knowledge effort and helps in prioritizing options. 

The Newlife4drylands experience highlighted the heterogeneity and complexity of degradation processes, as resulted from a selected set of degraded pilot sites within Mediterranean Protected Areas, together with the issue for harmonization and standardization of ecological/physical indicators, especially those derived from satellite observations, when used as proxies of land degradation. The integrated use of both available field data (for short-term monitoring) and satellite data (for medium and long-term monitoring) have been explored to identify indicators for evaluating the effectiveness of planned restoration actions. This approach is geared, towards fostering adaptive and collaborative management of the ecological restoration process. 

Therefore, the Protocol acts as support tool for decision-makers, including public administration of Protected Areas, as well as technicians and planners. The proposed approach aims to raise awareness about the needs of drylands and opportunities provided by NBS. It serves as a guide for the identification of specific/local NBS for the restoration of drylands, beginning with the identification of degradation processes.

How to cite: D'Ambrogi, S., Assennato, F., Labadessa, R., Mazzetti, P., Rastelli, V., Riitano, N., and Tarantino, C.: NewLife4Drylands Protocol for dryland restoration in Protected Areas: an innovative tool to support restoration activities., EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-6126, https://doi.org/10.5194/egusphere-egu24-6126, 2024.

EGU24-6475 | Posters on site | ITS1.23/SSS0.1.4

Introducing the ’miniRECgap’ package with GUI-supported R-scripts for simple gap-filling of Eddy Covariance CO2 flux data 

Alina Premrov, Jagadeesh Yeluripati, and Matthew Saunders

The Eddy covariance (EC) is a well-known technique used (among others) to investigate the ecosystem exchange of greenhouse gasses (GHGs) between the biosphere and the atmosphere (Burba et al., 2007), often required in studies on soil-plant-atmosphere interactions and GHG emissions/removals from different soil systems. The long data records from EC measurements often experience data gaps due to various reasons (BaldocchiI, 2003) resulting in  many gap-filling methods being developed over the past decades. This study is introducing the new ’miniRECgap’ (Premrov, 2024) computational tool, which is using so-called ‘classic’, traditional robust and validated modelling approaches for gap-filling the missing EC CO2 flux measurements,  based on the application of environmental temperature and light response functions (Lloyd and Taylor, 1994; Rabinowitch, 1951) in combination with empirical/semi-empirical parameter-optimisation. ‘miniRECgap’ is a very small R package that operates in a user-friendly way via GUI (Graphical User Interface) supported scripts. It is purposely designed to be simple, operating in only 5 steps. The application of ‘miniRECgap’ will be demonstrated using EC CO2 flux data from an Irish peatland site Clara Bog. Due to its simplicity, it is thought that the new tool may be beneficial for new R users and that it may allow for easier and less time-consuming testing of the potential suitability of ‘classic’ empirical/semi-empirical gap-filling on different datasets.

 

Acknowledgements

The authors are grateful to the Irish Environmental Protection Agency (EPA) for funding the CO2PEAT project (2022-CE-1100) under the EPA Research Programme 2021-2030.

 

References

BaldocchiI, D.D. (2003) Assessing the eddy covariance technique for evaluating carbon dioxide exchange rates of ecosystems: past, present and future.  9, 479-492.

Burba, G., Anderson, D., Amen, J., (2007) Eddy Covariance Method: Overview of General Guidelines and Conventional Workflow, AGU Fall Meeting Abstracts, pp. B33D-1575.

Lloyd, J., Taylor, J.A. (1994) On the temperature dependence of soil respiration. Functional Ecology 8, 315-323.

Premrov, A., (2024) miniRECgap. R package  with GUI suported scripts for gap-filling the of Eddy Covariance CO2 flux data.  Copyright: Trinity College Dublin. URL:  'miniRECgap package will be uploaded on GitHub in near future'.

Rabinowitch, E.I. (1951) Photosynthesis and Related Processes. Interscience Publishers.

How to cite: Premrov, A., Yeluripati, J., and Saunders, M.: Introducing the ’miniRECgap’ package with GUI-supported R-scripts for simple gap-filling of Eddy Covariance CO2 flux data, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-6475, https://doi.org/10.5194/egusphere-egu24-6475, 2024.

EGU24-6832 | ECS | Posters on site | ITS1.23/SSS0.1.4

A Comprehensive Assessment of the AquaCrop Model in drylands: Performance Examination and Sensitivity Analysis 

Ahmed S. Almalki, Marcel M. El Hajj, Kasper Johansen, and Matthew F. McCabe

The AquaCrop model is a powerful tool for crop monitoring, providing a daily estimation of soil-crop-atmosphere dynamics. The model requires a substantial number of input variables and parameters, highlighting the need for identifying those that significantly influence model outputs. Sensitivity analysis is a vital method for this purpose. A key objective of this study is to examine the performance of the AquaCrop model in simulating wheat yield and irrigation water requirement in drylands under two scenarios: first running the model employing a minimal amount of in situ data, and second using all available in situ data. A second focus is to analyze the sensitivity to all crop and soil related input variables and parameters. To do this, a pilot-scale study was undertaken, focusing on a commercial farm in the Al-Jouf province of Saudi Arabia. The farm comprised 200 center-pivot fields of mainly wheat crops. In situ data was collected to calibrate the model for two consecutive growing seasons (2019-2020 and 2020-2021). Using the variance-based Sobol technique, the sensitivity of the AquaCrop model outputs, particularly wheat yield and irrigation water requirement, to crop and soil related input variables and parameters was examined, as were the influential and non-influential inputs on these outputs. Results showed that the second scenario (all data) outperformed the first (minimal data), demonstrating more accurate wheat yield predictions with rRMSE values of 17% and 21% for the 2019-2020 and 2020-2021 growing seasons, respectively. Regarding irrigation water requirement estimations, the second scenario also exhibited lower rRMSE values of 20% and 19% for the same growing seasons. Results also demonstrated that the sensitivity indices of variables and parameters varied with model outputs and growing seasons. By synthesizing inputs sensitivities under different conditions, the influential input variables and parameters were distinguished. Overall, six variables and parameters held significant influence on the analyzed model outputs based on their total-order sensitivity indices. These included duration from sowing to senescence (senescence), duration from sowing to harvesting (maturity), duration from sowing to yield formation (HIstart), base temperature below which growth does not progress (Tbase), minimum air temperature below which pollination failure begins (Tmin_up), and shape factor describing reduction in biomass production (fshabe_b). It was revealed that most variables and parameters were non-influential, which might allow them to be fixed within their ranges to optimize model calibration. The research represents the performance assessment and sensitivity analysis of the AquaCrop model over a desert farming system and offers guidelines for model calibration by delivering information on influential and non-influential input variables and parameters.

How to cite: Almalki, A. S., El Hajj, M. M., Johansen, K., and McCabe, M. F.: A Comprehensive Assessment of the AquaCrop Model in drylands: Performance Examination and Sensitivity Analysis, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-6832, https://doi.org/10.5194/egusphere-egu24-6832, 2024.

EGU24-8466 | Posters on site | ITS1.23/SSS0.1.4

Metamodel simulation of carbon fluxes across an eroding and pristine blanket bog in Scotland 

Bhaskar Mitra, Jagadeesh Yeluripati, James Cash, Linda Toca, Mhairi Coyle, and Rebekka Artz

Accurately quantifying carbon dynamics in peatlands is critical to assess their role in regulating global climate. Within hotspots of peatland degradation, such as in Europe and South-east Asia, skilful assessment of the spatial and temporal impacts of climate change and different land management options is required to meet emissions reductions targets and improve regional management planning.

To address this challenge, a random forest-based metamodel was evaluated to assess its utility in simulating various greenhouse gas (CO2) emission components, including Net Ecosystem Exchange (NEE), Gross Primary Productivity (GPP), and Ecosystem Respiration (ER) across two Scottish peatlands. The metamodel mimicked the complex Wetland-DNDC model at a higher level of abstraction with increased efficiency and lower computational time.

While Wetland-DNDC also simulates NEE, GPP and ER, it typically involves a considerable number of parameters related to soil properties, climate data, vegetation characteristics, biogeochemical processes, hydrology, nutrient cycling, and microbial activity. Many of these parameters (more than 100) are challenging to measure in the field, and literature values are often adopted, which may not necessarily reflect local site conditions. In essence, this multidimensional parameter space introduces high uncertainties in modelling carbon fluxes.

In contrast, random forest-based metamodel preserved the key relationships between NEE and input variables (air and soil temperature, water table, precipitation, vegetation, and soil properties) as described in the Wetland-DNDC model with lower parameter requirements (less than 20) and increased accuracy. Similar unique relationships were established for GPP and ER. The random forest-based metamodel represented the Wetland-DNDC model  within the spectrum of input values and parameters across which it was simulated.

The simulation was conducted in two locations across Scotland with contrasting contemporary carbon dynamics: a near natural blanket bog in Cross Lochs, Forsinard, currently functioning as a resilient net carbon dioxide sink (UK-CLS; Lat. = 58.37, Long. = -3.96; altitude = 207 m) and an eroding oceanic blanket bog located in the Cairngorms, currently net emitting carbon dioxide (UK-BAM; Lat. = 56.92, Long. = -3.15, altitude = 642 m). The simulation was validated against eddy covariance flux measurements under varying climate conditions.

In contrast to Wetland-DNDC (R2 = 0.43), the metamodel provided a much-improved fit to the 1:1 line for NEE (R2 = 0.83). Model accuracy was slightly lower for the former (RMSE = 0.72) compared to its metamodel version (RMSE = 0.699). Similar trends were observed for GPP and ER simulations. At a monthly resolution, Wetland-DNDC-derived NEE, GPP, and ER consistently deviated by more than 20% from the eddy covariance-derived estimates, whereas its metamodel version showed deviations of less than 10%. Currently, work is in progress to incorporate management and drought simulation within a metamodel framework, as well as to upscale carbon fluxes from tower to landscape resolution.

The simulation of carbon fluxes using the metamodel-based approach holds the promise of enhancing emission reporting to Tier 3 standards and offers a hopeful avenue for modelling carbon dynamics in peatlands.

How to cite: Mitra, B., Yeluripati, J., Cash, J., Toca, L., Coyle, M., and Artz, R.: Metamodel simulation of carbon fluxes across an eroding and pristine blanket bog in Scotland, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-8466, https://doi.org/10.5194/egusphere-egu24-8466, 2024.

EGU24-10001 | Orals | ITS1.23/SSS0.1.4

MIR spectroscopy combined with meteorological data can estimate soil compaction risks in top and subsoils. 

Felipe de Santana, Rebecca Hall, Longnan Shi, Victoria Lowe, Jim Hodgson, and Karen Daly

Soil compaction is an important physical characteristic that affects agricultural productivity by increasing soil density, which reduces the volume of a given soil mass. Due to the higher compaction, plant roots find resistance in penetrating deeply into the soil, limiting their access to essential nutrients and moisture, impacting the plant health with lower levels of N, P and K, resulting in lower productivity. Soil compaction can also reduce soil porosity, aeration, carbon mineralisation/sequestration and increasing the production of greenhouse gases through denitrification in anaerobic sites. Besides that, soil compaction can cause surface runoff and erosion, increasing the risk of flooding and soil loss. A partial recuperation of compacted soils is an expensive and labour-intensive task. In addition, agricultural land expansion for crops is limited. Hence, mapping agricultural areas at risk of soil compaction is essential to implement strategies to mitigate the adverse effects of soil compaction.

Soil particle size and soil drainage were used to classify topsoil's (T) compaction risk class. For subsoil (S) soils (after horizon A), the subsoil particle size, packing density (bulk density + 0.009 * clay (%)), soil drainage and field capacity days were used to estimate the compaction risks. The main problem of this strategy is that these analyses are expensive and time-consuming, i.e., soil particle size analysis requires an average time of 1 month per 100 samples and costs ~ 40.00 per sample. Bulk density analysis costs ~ € 7.00 per sample and is also time-consuming; consequently, bulk density values are mainly predicted using pedo-transfer functions in mapping studies.

To speed up the analysis and minimise the costs, vibrational spectroscopy combined with chemometrics was used to determine soil particle size and bulk density. Both parameters were combined with field capacity days (obtained from 104 national wide meteorological stations) and drainage class (obtained from Irish - Environmental Protection Agency) to map soil compaction risk areas in the northern half of the Republic of Ireland with a resolution of 4 km2 (2x2km) and 1 km2 grid for regional and periurban regions, respectively (Tellus achieve). To confidentially map these regions, spectral control charts based on PCA were used to identify unrepresentative sample spectra based on the spectral models used. Only samples classified as representative were predicted by the spectral models. Using this strategy, we could predict ~ 90% (T) and ~66% (S) compaction risks in non-peat soils. The prediction results showed that ~33% (T) and ~43% (S) were classified as high risks of compaction, ~19% (T) and ~23% (S) as moderate, and ~37% (T) and <1% (S) as low risks or other classes.

How to cite: de Santana, F., Hall, R., Shi, L., Lowe, V., Hodgson, J., and Daly, K.: MIR spectroscopy combined with meteorological data can estimate soil compaction risks in top and subsoils., EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-10001, https://doi.org/10.5194/egusphere-egu24-10001, 2024.

EGU24-10906 | Posters virtual | ITS1.23/SSS0.1.4

SDG 15.3.1 indicator at local scale for monitoring land degradation in protected areas 

Cristina Tarantino, Mariella Aquilino, Saverio Vicario, Rocco Labadessa, Vito Emanuele Cambria, Christos Georgiadis, Marcello Vitale, Francesca Assennato, and Paolo Mazzetti

In the framework of the NewLife4Drylands LIFE Preparatory project (LIFE20 PRE/IT/000007, 2021-2024) the estimation of SDG 15.3.1 indicator [1], adopted in the UNCCD’s Good Practice Guidance [2], was applied for evaluating Land Degradation (LD) in different Mediterranean Protected Areas (PA). To effectively support PAs managers, joint effort was made in the evaluation of SDG 15.3.1 indicator at the local scale by using satellite Remote Sensing data in the computation of the three main sub-indicators as trend in Land Cover (LC), Primary Production (PP) and Soil Organic Carbon (SOC) stock. Where feasible, local scale sub-indicators were not sourced from open-access global/European databases due to their lack of accuracy at the site scale [3]. LD was estimated not only for the whole PA but also for specific LC classes of interest, considering additional sub-indicators related to pressures and threats affecting the class. This study focuses on the dryland Alta Murgia (IT9120007) PA, in southern Italy, and the wetland Nestos River Delta (GR1150001) PA, in Greece. For Alta Murgia site, featuring semi-natural dry grassland habitats of community interest that are frequently subjected to fire events during the summer season, the Burn Severity (BS) index was included. BS trends were measured by assessing the difference in pre/post–fire Normalized Burn Ratio (NBR) index from Landsat data during summer. Baseline data from 2004, coinciding with the establishment of a National Park within PA, was compared with 2018 for validating field data availability. Nestos River Delta hosts the largest natural riparian forest in Greece and is frequently subjected to hydrological cycle modifications, involving water scarcity due to both inappropriate river management and climate change, in turn hampering the transport of nutrient-rich sediments and the enrichment of soils being at risk of aridification. Within this framework, Hydroperiod and Soil Salinity indices were considered for LD and specific impacts on aquatic vegetation LC. Baseline data from 2017, after the dry climate conditions of 2016-2017, was compared with 2021 for validating field data availability. Both in Alta Murgia and Nestos, LC mappings were obtained by a data-driven pixel-based approach considering Landsat/Sentinel-2, respectively, multi-seasonal imagery and a multi-class Support Vector Machine (SVM) classifier trained with data from in-field campaigns and historical orthophotos interpretation. Time series of MSAVI from Landsat (which replaced standard NDVI for its soil correction benefits [4]) and PPI from Sentinel-2 by Copernicus services, respectively, were used to track grassland PP trends. Lastly, for SOC stock trends, the open-source Trends.Earth QGIS plugin [5], incorporating customized LC data and global SoilGrids product, was adopted to supplement local data limitations. According to its specification, the SDG 15.3.1 indicator was computed by integrating all the sub-indicators according to the principle “one out, all out” obtaining the 3-classes output mapping (Degradation, Improvement, Stable). The findings can support the monitoring and evaluation of LD, guiding protective measures aligned with the Agenda 2030 for Sustainable Development. They, also, highlight the importance of the integration of local scale data and sub-indicators within the UNCCD methodology.

References

[1] https://unstats.un.org/sdgs/metadata/files/Metadata-15-03-01.pdf

[2]https://www.unccd.int/publications/good-practice-guidance-sdg-indicator-1531-proportion-land-degraded-over-total-land

[3] https://doi.org/10.3390/rs13020277

[4] https://doi.org/10.3390/rs12010083

[5] http://trends.earth/docs/en

 

How to cite: Tarantino, C., Aquilino, M., Vicario, S., Labadessa, R., Cambria, V. E., Georgiadis, C., Vitale, M., Assennato, F., and Mazzetti, P.: SDG 15.3.1 indicator at local scale for monitoring land degradation in protected areas, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-10906, https://doi.org/10.5194/egusphere-egu24-10906, 2024.

EGU24-11219 | ECS | Posters on site | ITS1.23/SSS0.1.4

Exploring soil organic carbon dynamics through a multi-model simulation of multiple long-term experiments  

Matteo Longo, Ilaria Piccoli, Antonio Berti, Michela Farneselli, Vincenzo Tabaglio, Domenico Ventrella, Samuele Trestini, and Francesco Morari

Agricultural system models are widely recognized as valuable tools for identifying best management practices and addressing the challenges posed by climate change. In this context, the use of model ensembles has been recently recommended for their enhanced performance and accuracy. However, assessing their effectiveness over a large geographical area, such as national scale is often currently lacking. This study focuses on simulating soil organic carbon (SOC) dynamics using an ensemble of models comprising DSSAT, CropSyst, EPIC, and APSIM models, utilizing data derived from five Long-Term Experiments (LTEs) spread across a north-to-south pedoclimatic range transect in Italy. This region is of particular importance as it represents a significant hotspot for climate change. The LTEs featured a robust array of 63 unique experimental protocols, incorporating variation effect in fertilization rates, cropping rotations, and tillage prescriptions. This resulted in a total of 2184 years of simulated data for each model. The dataset employed included SOC stocks and crop yield and biomass. Models underwent independent calibration, with crop and SOC parameters selected based on expert knowledge. Main crop cultivars, such as maize, soybean, sugarbeet, and wheat, were further categorized and calibrated by maturity classes. A similar approach was used for cover crops. The extensive dataset enabled a nuanced exploration of the models’ performance across varied agro-ecological contexts. The models proved capable of accurately reproducing the varied pedo-climatic conditions of the Italian peninsula, contributing to the advancement of our understanding of SOC dynamics.

How to cite: Longo, M., Piccoli, I., Berti, A., Farneselli, M., Tabaglio, V., Ventrella, D., Trestini, S., and Morari, F.: Exploring soil organic carbon dynamics through a multi-model simulation of multiple long-term experiments , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-11219, https://doi.org/10.5194/egusphere-egu24-11219, 2024.

EGU24-11605 | Orals | ITS1.23/SSS0.1.4

A Geospatial Overview of Agricultural Long-Term Field Experiments across Europe 

Cenk Donmez, Carsten Hoffmann, Nikolai Svoboda, Tommy D'Hose, Xenia Specka, and Katharina Helming

Long-Term Field Experiments (LTEs) are agricultural infrastructures for studying the long-term effects of different management practices and soil and crop properties in changing climate conditions. These experiments are essential to examine the impact of management and environment on crop production and soil resources on different soil textures and types. Some of those LTEs have average times of 20-50 years, even more than 100 years. These infrastructures are thus scientific heritages with high values of agricultural data; however, LTE-related information was difficult to find since it was scattered. To close this gap, we developed a geospatial data infrastructure, including an LTE overview map to compile and analyze the meta-information of the LTEs across Europe. The map provides a spatial representation of LTEs and the meta-information, collected by extensive literature review and factsheets in collaboration with BonaRes and EJPSoil projects, clustered in different categories (management operations, land use, duration, status, etc.) (Grosse et al. 2021; Donmez et al., 2022; Blanchy et al., 2023; Donmez et al., 2023). A threshold filter with a minimum duration of 20 years was applied, which results in a total of 500 LTEs across Europe and included into the map. The clusters of LTEs were geospatially analyzed to provide inputs for the agricultural sector, scientists, farmers and policy-makers. The fertilization treatment was the major research theme of collected and studied LTEs, followed by crop rotation and tillage trials. Bringing the meta information of dispersed LTEs through the development of the LTE overview map is expected to help developing a mutual management framework of efficient agricultural production by revealing the LTE potential internationally. This will contribute to scaling up the agricultural practices from site to landscape level for increasing the climate change adaptation to agricultural yield and management.

References

Donmez C., Schmidt M., Cilek A., Grosse M., Paul C., Hierold W., Helming K., (2023): Climate Change Impacts on Long-Term Field Experiments in Germany. https://doi.org/10.1016/j.agsy.2022.103578. Vol.205, 103578. Agricultural Systems.

Blanchy G., D’Hose T., Donmez C., Hoffmann C., Makoschitz L., Murugan R., O’Sullivan L., Sanden T., Spiegel A., Svoboda N., Boltenstern S.Z., Klummp K., (2023): An open-source database of European long-term field experiments. https://doi.org/10.1111/sum.12978  Soil Use and Management

Donmez C., Blanchy G., Svoboda N., D’Hose T., Hoffmann C., Hierold W., Klummp K., (2022): Provision of the metadata of European Agricultural Long-Term Experiments through BonaRes and EJP SOIL Collaboration. Data in Brief. https://doi.org/10.1016/j.dib.2022.108226.

Grosse, M., Ahlborn, M.C., Hierold, W. (2021): Metadata of agricultural long-term experiments in Europe exclusive of Germany. Data in Brief 38, https://doi.org/10.1016/j.dib.2021.107322

How to cite: Donmez, C., Hoffmann, C., Svoboda, N., D'Hose, T., Specka, X., and Helming, K.: A Geospatial Overview of Agricultural Long-Term Field Experiments across Europe, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-11605, https://doi.org/10.5194/egusphere-egu24-11605, 2024.

EGU24-12129 | Posters on site | ITS1.23/SSS0.1.4

Soil water holding capacity as descriptor of soil health at district scale – a sensitivity study 

Thomas Weninger, Irene Schwaighofer, Florian Darmann, Thomas Brunner, and Peter Strauss

The proposal for the European Soil Monitoring Law includes an integrated value of soil water holding capacity to be determined as a proxy for soil quality for whole soil districts. As this is a relatively new but interesting approach, a number of details of the assessment procedure remain open at the current stage of formulation. The aim of this study is to quantify the effects of the choice of different options on the overall result, focusing on the delineation of soil districts in different sizes, the detailed definition of the respective soil property, and the treatment of sealed areas.

High-resolution data for soil hydrological properties for two Austrian provinces are used as a basis, including different approaches to calculate soil water holding capacity. The size of the study area corresponds to the maximum size of a soil district as proposed. Thus, a variation of three size levels is possible, namely the whole area, major river catchments, and agro-geographical sub-units. The term soil water holding capacity is basically defined in the proposed EU Directive, but several options for its determination are possible. We used two different pedotransfer functions to derive soil water holding capacity values and an additional method based on averaging results from randomly located sampling points. Soil sealing is a major threat to hydrological soil functionality, and its assessment over large areas is still not standardized. Here, the European LUISA land use/land cover dataset for 2020 (1 km resolution) and a national dataset with higher resolution are used. Both datasets are optionally overlaid with the Copernicus imperviousness layer involves gradual information about surface imperviousness.

By combining all these factors with each other, different ways were evaluated to determine the target value of soil water holding capacity integrated on a regional scale. Differences in the results and their sensitivity to input variations are quantified to inform policy decisions in the implementation of the European Soil Monitoring Law in the member states.

How to cite: Weninger, T., Schwaighofer, I., Darmann, F., Brunner, T., and Strauss, P.: Soil water holding capacity as descriptor of soil health at district scale – a sensitivity study, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12129, https://doi.org/10.5194/egusphere-egu24-12129, 2024.

Soil erosion constitutes an increasing threat to soil productivity and food security. This work describes the potential of using Artificial Neural Networks (ANN) for upscaling soil loss outputs from medium to low scale. The Revised Universal Soil Loss Equation (RUSLE) model was implemented to calculate soil loss rates in two scales in Crete, Greece. Specifically, the RUSLE model was applied in six (6) watersheds across the island using medium spatial resolution satellite images (5m), namely Planetscope. These results were used to feed an ANN model to upscale the mesoscale outputs (5m) to regional outputs (30m-island level). The ANN system was trained using spatial environmental parameters such as the Normalized Difference Vegetation Index, Digital Elevation Model, and topographical slope angle. This "optimized" soil loss derivative later made it possible to compare it with the corresponding final derivative of Crete (regional spatial scale), which emerged from the straightforward processing of RUSLE model with the more "coarse" and generalized data as estimated from the  Landsat-8 satellite images (30m). The statistics revealed that the detailed and high-quality soil loss data, as derived from the upscaling process, provide more precise and reliable results.

How to cite: Alexakis, D. D. and Polykretis, C.: Using Artificial Neural Networks to upscale soil erosion model results from local to regional scale. An example from Crete, Greece., EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-14678, https://doi.org/10.5194/egusphere-egu24-14678, 2024.

EGU24-15537 | ECS | Orals | ITS1.23/SSS0.1.4

Integrating UAV data and soil-crop modelling for Enhanced Soil Health Monitoring 

Nikolaos-Christos Vavlas, Lammert Kooistra, Fenny van Egmond, and Gerlinde De Deyn

The necessity of soil health monitoring is paramount in reversing soil degradation and promoting sustainable farming. Including cover crops in the crop rotation is one of the sustainable soil management practices contributing to soil health. Cover crops contribute to soil health by nutrient retention and carbon accumulation during their growth and return of organic matter to the soil upon their incorporation. During monitoring, the sampling frequency can change from annual in the case of SOC to weekly or daily for fertilization and irrigation. Remote sensing techniques offer a solution, enabling the monitoring of vegetation over time and space, thereby enhancing our understanding of the impact of cover crops on the main crop. However, this technology makes it possible to see the surface of the field which can assist with the above-ground changes of the system. Process-based modelling and data assimilation can subsequently link the above-ground component with soil functions. In-situ data collection that includes crop characteristics such as biomass and N-uptake is essential both for transforming remote sensing data into crop characteristics and for calibrating models. Using Unmanned Aerial Vehicles (UAVs) can potentially collect data at high frequency, which can be used to enhance soil process modelling. The development of this UAV-based method has the potential to be scaled up to a satellite level in the future.

In our research, we have combined the study of nutrient cycling and the effect of cover crops on soil health. To achieve this, we have used the WOFOST-SWAP-ANIMO model to simulate the varying influence of cover crop monocultures and mixtures on Soil Organic Carbon (SOC) and Nitrogen cycling in a 7-year crop rotation on sandy soil. The model simulates vegetation characteristics such as biomass, leaf area index, and yield, as well as soil moisture and mineral Nitrogen concentrations. This will give us a good estimation of the vegetation input into the soil as well as the nutrient uptake from both cover crops and main crops. Soil sampling is also important to model calibration/validation to be able to simulate the N dynamics of biological activity under the surface. Our findings suggest that the model, in conjunction with UAV data and field sensors, can effectively monitor soil health indicators crucial for field management practice selection, such as the Carbon cycle and Nitrogen use efficiency.

How to cite: Vavlas, N.-C., Kooistra, L., van Egmond, F., and De Deyn, G.: Integrating UAV data and soil-crop modelling for Enhanced Soil Health Monitoring, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15537, https://doi.org/10.5194/egusphere-egu24-15537, 2024.

EGU24-18038 | Orals | ITS1.23/SSS0.1.4

Empowering soil health in Mediterranean environments through collaborative stakeholder engagement: insights from Sardinian Living Lab of the InBestSoil project 

Valentina Mereu, Gianluca Carboni, Alessio Menini, Marta Canu, Marco Dettori, Giulia Urracci, and Serena Marras

Preserving soil health and enhancing the ecosystem services that soil produces is of primary importance in European strategies and policies. More than 60% of the European soils are unhealthy due to unsustainable land use, pollution, climate change, and extreme events. This causes loss of ecosystem services, costing the EU at least €50 billion annually. Collaboration among businesses, policymakers, public administration, and the scientific community is crucial to develop practices that recognize the essential role of soils in sustaining livelihoods, biodiversity, and climate regulation.

In this framework, the Horizon Europe funded project InBestSoil (https://inbestsoil.eu/) aims to co-create a framework for investments in soil health preservation and restoration by developing a system for the economic valuation of the ecosystem services provided by healthy soil and the impacts of soil interventions, and its incorporation into business models and incentives. To achieve this, InBestSoil has selected 7 existing Soil Health Lighthouses (LHs) and 2 Soil Health Living Labs (LLs, in different maturity stages) covering four land uses (agricultural, forestry, urban, mining) across four biogeographic regions over Europe. The LLs are collaborative initiatives focused on co-creating knowledge and innovations, while LHs represent individual sites known for exemplary performance. The LL1, located in Sardinia (Italy), is coordinated by the CMCC Foundation and Agris Sardegna Research Agency. It focuses on Mediterranean agricultural soils and aims addressing the challenges related to climate change and extreme events, soil pollution, land abandonment, and water scarcity. It includes 2 LHs on conservation agriculture managed by Agris and 9 Living Lab Experimental Sites (LLES), which evaluate the introduction of sustainable soil practices. The LHs included in the LL are two Long-Term Experiments (>20 years) on conservation agriculture (reduced and no tillage versus conventional tillage) on durum wheat in rotation with legumes, in soils with different fertility levels that are representative of Mediterranean cereal farming conditions. Conservation agriculture is among the most promising climate-smart agricultural practices because it contributes to both climate change mitigation and adaptation objectives while helping to maintain and increase farmers' incomes. However, it is important both to acquire additional information to assess the medium- to long-term effects of these practices in different environments and cropping systems as well as to disseminate the scientific evidence and support the wider application of these practices in the Mediterranean region.

The LHs aim to provide scientific evidence and disseminate knowledge and experience gained in the long-term application of conservation agriculture in Mediterranean agricultural systems.  Moreover, in the selected 9 LLES, located in different areas and including cereal, olive tree and vineyard farms, soil samplings and analyses are being conducted to measure soil indicators and provide information to assess the economic evaluation of ecosystem services provided by soils managed with sustainable agricultural practices, primarily including conservation agriculture.

We aim to create a permanent space of discussion on the topic of soil health, involving all relevant actors, from farmers to researchers to policy makers, in order to identify common solutions and innovations that can face the economic and environmental challenges the Mediterranean agriculture is facing.

How to cite: Mereu, V., Carboni, G., Menini, A., Canu, M., Dettori, M., Urracci, G., and Marras, S.: Empowering soil health in Mediterranean environments through collaborative stakeholder engagement: insights from Sardinian Living Lab of the InBestSoil project, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18038, https://doi.org/10.5194/egusphere-egu24-18038, 2024.

Mountain grasslands play a pivotal role in delivering both economic and cultural ecosystem services, including food production, carbon sequestration, the provision of clean water, and preserving local traditions. However, these ecosystems are facing increasing threats from climate change around the world. Among the main challenges is the intensification of extreme precipitation events. They can aggravate the process of soil erosion and trigger landslides in mountain grasslands, with possible negative consequences on both ecosystems and human activities. However, the high variability of these ecosystems, as well as their wide distribution, makes it complex to adequately map their locations and investigate possible soil erosion hotspots, especially under future scenarios with varied rainfall regimes. In this context, the use of remote sensing technologies and modeling approach could open new frontiers to investigate critical areas and therefore guide mitigation solutions. The satellite Earth Observation (EO) through international space missions, coupled with cloud-based data analysis platforms like Google Earth Engine (GGE), facilitates ecosystem mapping at a resolution and frequency previously inaccessible. Furthermore, the utilization of multi-temporal models for potential soil erosion analysis in present and future scenarios can enhance our understanding of erosion dynamics attributed to climate change. In this research, we first map at high resolution the global mountain grasslands distribution taking advantage of Sentinel-based EO’s products. In such locations, we evaluate the multi-temporal soil erosion dynamics caused by water employing diverse climate scenarios (RUSLE model; 2015 vs. 2070-RCP8.5). Our findings indicate a potential global escalation in soil erosion within mountain grasslands, notably in South America and Africa, alongside identifiable localized hotspots. Remote sensing-based research paired with a modeling approach aimed at mapping critical areas and analyzing environmental challenges in ecosystems is therefore imperative. Such investigations not only delineate vulnerable regions but also guide targeted solutions crucial for safeguarding these ecosystems and their ecosystem services in the face of climate change.

How to cite: Straffelini, E., Luo, J., and Tarolli, P.: Satellite-based remote sensing and multitemporal modeling approach for mapping soil erosion hotspots in global mountain grasslands under climate change, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18161, https://doi.org/10.5194/egusphere-egu24-18161, 2024.

Peatland restoration and rehabilitation action has become more widely acknowledged as a necessary response to mitigating climate change risks and improving global carbon storage. Peatland ecosystems require restoration timespans on the order of decades and thus cannot be dependent upon the shorter-term monitoring often carried out in research projects. Hydrological assessments using geospatial tools provide the basis for planning restoration works as well as analysing associated environmental influences. “Restoration” encompasses applications to pre- and post-restoration scenarios for both bogs and fens, across a range of environmental impact fields. A scoping review was carried out to identify, describe, and categorise current process-based modelling uses in peatlands in order to investigate the applicability and appropriateness of eco- and/or hydrological models for northern peatland restoration. Two literature searches were conducted using the Web of Science entire database in September 2022 and August 2023. Of the final 211 papers included in the review, models and their applications were categorised according to this review’s research interests in 7 distinct categories aggregating the papers’ research themes and model outputs. Key themes emerging from topics covered by papers in the database included: modelling restoration development from a bog growth perspective; the prioritisation of modelling GHG emissions dynamics as a part of policymaking; the importance of spatial connectivity within or alongside process-based models to represent heterogeneous systems; and the emerging prevalence of remote sensing and machine learning techniques to predict restoration progress with little physical site intervention. Based on this assessment, CoupModel, DigiBog, and MPeat2D were calibrated for the case of Abbeyleix Bog, Co. Laois, Ireland (ongoing with results expected before April 2024). The exploration of subsequent simulations to represent varying peatland restoration conditions is discussed from an ecohydrological lens.

How to cite: Silva, M.: Ecohydrological modelling on peatlands: scoping review and application of three process-based models to Irish raised bog restoration, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18533, https://doi.org/10.5194/egusphere-egu24-18533, 2024.

Carbon use efficiency has recently been proposed as a central parameter that promotes soil organic carbon storage based on data assimilation with a global soil organic carbon database and a vertical, microbial explicit soil organic carbon model (Tao et al., 2023). In this research, we present a sensitivity study with a vertical soil organic carbon model, COMISSION v2.0 (Ahrens et al., 2020), that not only models microbial interactions explicitly but also represents organo-mineral interactions with a maximum capacity, Qmax, to form mineral-associated organic carbon (MAOC).

The COMISSION model represents the formation of MAOC from microbial necromass and dissolved organic carbon analogous to Langmuir sorption. Empirical studies have provided Qmax parameterizations derived from quantile or boundary line regressions with clay and silt content. For the sensitivity study, we vary Qmax along the full range of observed Qmax values while simultaneously varying carbon use efficiency (CUE). Our results highlight that CUE and Qmax promote soil organic carbon storage to similar degrees along their respective observed ranges. The remaining parameters of the COMISSION model were kept at their calibrated values from a multi-site calibration with soil organic carbon, mineral-associated organic carbon, and radiocarbon profiles (Ahrens et al., 2020). While Qmax and CUE are of similar importance for promoting soil organic carbon storage, they also interact in promoting SOC storage. Higher Qmax values strengthen the promotion of soil organic carbon storage with higher CUE. This positive interaction results from higher microbial necromass with higher CUE and the subsequent association of microbial necromass on mineral surfaces mediated through Qmax. The sensitivity study revealed that CUE is the dominant driver for microbial biomass levels. Qmax affects microbial biomass only to a small degree through 'competition' between mineral surfaces and microbial biomass for dissolved organic carbon. While the effect of Qmax on microbial biomass is small, the relationship between Qmax and microbial biomass is generally negative. At the lower end of the tested range of carbon use efficiencies (CUE < 0.15), further model experiments reveal that imposing a stronger microbial limitation of depolymerization can lead to a negative relationship between CUE and soil organic carbon storage.

Overall, our results highlight that in soil organic carbon models with microbial interactions and a limited capacity to form organo-mineral associations, both processes can be of similar importance in promoting soil organic carbon storage. The current debate in the observational realm, whether there is indeed an upper limit for mineral-associated organic carbon formation, can spark a similar debate in the modeling realm on how to represent mineral-associated organic carbon formation in models mechanistically.

 

References

Ahrens B, Guggenberger G, Rethemeyer J et al. (2020) Combination of energy limitation and sorption capacity explains 14C depth gradients. Soil Biology and Biochemistry, 148, 107912.

Tao F, Huang Y, Hungate BA et al. (2023) Microbial carbon use efficiency promotes global soil carbon storage. Nature, 618, 981-985.

Funding acknowledgment: Bernhard Ahrens has received funding through the AI4SoilHealth project. The AI4SoilHealth project has received funding from the European Union's Horizon Europe research and innovation programme under grant agreement No. 101086179.

How to cite: Ahrens, B. and Chettouh, M. A.: Carbon use efficiency and mineralogical capacity are of similar importance for promoting soil organic carbon stocks, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18844, https://doi.org/10.5194/egusphere-egu24-18844, 2024.

EGU24-19814 | ECS | Posters on site | ITS1.23/SSS0.1.4

Performance of the DNDC in Estimating CO 2 and N 2 O emissions of Integrated Crop-Livestock Systems 

Priscila S Matos, Johnny R Soares, Maria C S Carvalho, Beata E Madari, Bruno J R Alves, Claudia P Jantalia, Antônio C R Freitas, Bhaskar Mitraa, and Jagadeesh Yelupirati

Integrated crop-livestock (ICL) systems can have a complex of effects on soil properties that can influence greenhouse gas emissions (GHG). The ICL aim to capture atmospheric CO2 and sequester it in the soil, holding promise for reducing GHG emission intensity from livestock products. Moreover, modeling N2O emissions can help assess the potential impact of N management on the ICL system to optimize the sustainability of agriculture production. Field data were obtained from an ICL experiment of EMBRAPA-Rice and Beans, located on Capivara farm, Santo Antônio de Goiás/GO, Brazil (16°28´S; 49°17´W; 823 m alt.). The ICL experiment was evaluated for four years (2013-2016) with the following crop rotation sequence: pasture-fallow-maize, fallow-soybean, maize-fallow-maize, and beans-fallow. The N2O data was obtained from the 2013-14 season, which was measured in a static chamber during maize cultivation. The experiment consisted of 9 treatments (N sources and rates) with 5 replicates. The N2O was measured in 30 sampling events over almost 100 days. The daily N2O fluxes from the treatments control (No N), urea (UR), calcium ammonium nitrate (CAN), and ammonium sulfate (AS) at an N rate of 150 kg/ha were used to parametrize the DNDC. Model crop and soil parameters were adjusted to better simulate maize production and N2O emission according to observed data. DNDC simulated CO2 emissions, quantified as Net Ecosystem Exchange (NEE), were validated against CO2 emissions derived from eddy-covariance data, using statistical parameters such as R2, RMSE, MAE, and Bias. While data refinement is ongoing, preliminary findings indicate that DNDC shows promise for estimating CO2 emissions IPS under tropical conditions The DNDC had a satisfactory performance in predicting N2O emission in the ICL system, resulting in a significant correlation with the observed data (r = 0.63, p < 0.001), MAE of 0.024, and RMSE of 0.036. The average daily N2O-N emission observed was 0.026 kg ha-1 day-1 and simulated was 0.025 kg ha-1 day-1. The UR, CAN and AS applications showed a peak of N2O emission on 31th day after sowing (2 days after fertilization) corresponding to 0.175, 0.217, and 0.163 kg ha-1 day-1, respectively, where the model simulated N2O peaks of 0.151, 0.123, and 0.173 kg ha-1 day-1. The accumulated N2O emissions were 0.513, 1.148 1.738, and 0.890 kg ha-1 for control, UR, CAN, and AS respectively, in which the simulated by DNDC were 0. 778, 1.612, 1.391, and 1.755 kg ha-1. In general, the model had a good fit with daily N2O emissions, but it tended to overestimate the N2O emission from UR and AS, and underestimate from CAN. Further model parametrization and calibration may be necessary to better predict N2O and CO2 emissions. The DNDC satisfactory simulated the N2O emissions from different N sources applied to ICL system, which can be used to evaluate the potential emissions and mitigation according to N management in ICL.

How to cite: Matos, P. S., Soares, J. R., Carvalho, M. C. S., Madari, B. E., Alves, B. J. R., Jantalia, C. P., Freitas, A. C. R., Mitraa, B., and Yelupirati, J.: Performance of the DNDC in Estimating CO 2 and N 2 O emissions of Integrated Crop-Livestock Systems, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-19814, https://doi.org/10.5194/egusphere-egu24-19814, 2024.

EGU24-19820 | ECS | Posters on site | ITS1.23/SSS0.1.4

The LOESS project to boost soil health literacy across Europe: The case of Italy 

Marco Peli, Arianna Dada, Francesca Barisani, Vera Ventura, Michèle Pezzagno, Stefano Barontini, and Giovanna Grossi

The Horizon Europe project LOESS ‘Literacy boost through an Operational Educational Ecosystem of Societal actors on Soil health’ officially started in June 2023 involving twenty partner organizations in fifteen countries across Europe, lead by the WILA Bonn Science Shop. The final goal of the project is to raise awareness on the importance of soil and of its functions and to increase soil literacy across Europe. To do so, the first step of the project activity is designed to map and connect multiple actors in Communities of Practice (CoPs) at the national level, and engage them to provide an overview of the current level of soil related knowledge and teaching programmes and materials, in order to identify the gap between this material and the educational needs amongst different levels of the society (from pupils to students to citizens).

The Italian chapter is led by two university research groups with different expertise (civil and environmental engineering at the University of Brescia on one hand and social sciences at the University of Sassari on the other) and one NGO (Controvento) whose mission is children not-formal education. The Italian CoP, led by the University of Brescia, is composed of 62 members from both the higher education and the research community, as well as from the primary and secondary education levels (teachers and pupils), from the productive sectors (farmers and spatial planners), from the politics world (local administrators) and from the civil society (NGOs and associations).

This contribution presents the activities performed so far, viz the stakeholder mapping, the creation of the CoP and its first meetings and the community-based participatory activity which was organized on the World Soil Day 2023.

How to cite: Peli, M., Dada, A., Barisani, F., Ventura, V., Pezzagno, M., Barontini, S., and Grossi, G.: The LOESS project to boost soil health literacy across Europe: The case of Italy, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-19820, https://doi.org/10.5194/egusphere-egu24-19820, 2024.

EGU24-20664 | Posters on site | ITS1.23/SSS0.1.4

A framework for setting soil health targets and thresholds in agricultural soils  

Amanda Matson, Maria Fantappiè, Grant A. Campbell, Jorge F. Miranda-Vélez, Jack H. Faber, Lucas Carvalho Gomes, Rudi Hessel, Marcos Lana, Stefano Mocali, Pete Smith, David Robinson, Antonio Bispo, Fenny van Egmond, Saskia Keesstra, Nicolas P.A. Saby, Bozena Smreczak, Claire Froger, Azamat Suleymanov, and Claire Chenu

Soil health is a key concept in worldwide efforts to reverse soil degradation, but to be used as a tool to improve soils, it must be definable at a policy level and quantifiable in some way. Soil indicators can be used to define soil health and quantify the degree to which soils fulfil expected functions. Indicators are assessed using target and/or threshold values, which define achievable levels of the indicators or associated soil functions. However, defining robust targets and thresholds is not a trivial task, as they should account for differences in soil type, climate, land-use, management, and history, among other factors.

We assessed (through theory and stakeholder feedback) four approaches to setting targets and thresholds: fixed values based on research, fixed proportions of natural reference values, values based on the existing range (e.g. lower quartile of the observed distribution), and targets based on relative change (e.g. a 20% increase of the indicator’s value). Three approaches (not including relative change) were then further explored using case study examples from Denmark, Italy, and France, which highlighted key strengths and weaknesses of each approach. Here, we present a selection of the assessment and case study results, as well as a framework, which facilitates both choosing the most appropriate target/threshold method for a given context, and using targets/thresholds to trigger follow-up actions to promote soil health.  

How to cite: Matson, A., Fantappiè, M., Campbell, G. A., Miranda-Vélez, J. F., Faber, J. H., Gomes, L. C., Hessel, R., Lana, M., Mocali, S., Smith, P., Robinson, D., Bispo, A., van Egmond, F., Keesstra, S., Saby, N. P. A., Smreczak, B., Froger, C., Suleymanov, A., and Chenu, C.: A framework for setting soil health targets and thresholds in agricultural soils , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-20664, https://doi.org/10.5194/egusphere-egu24-20664, 2024.

EGU24-21616 | Posters on site | ITS1.23/SSS0.1.4

Searching for pedotransfer functions to predict sorption of pharmaceuticals from soil properties 

Pierre Benoit, Charline Godard, Marjolaine Deschamps, Nathalie Bernet, Ghislaine Delarue, Valenti Serre, and Claire-Sophie Haudin

In the context of recycling organic waste products or irrigation by treated wastewaters (re-use), the fate of human and veterinary pharmaceuticals in agricultural soils and consequent ground-water contamination are influenced by many factors, including soil properties controlling sorption and dissipation processes (Verlicchi et al., 2015, Mejías et al., 2021, Rietra et al., 2022). Sorption coefficients are among the most sensitive parameters in models used for risk assessment. However, for different classes of pharmaceuticals, the variations in sorption among different soil types are poorly described and understood (Kodesova et al., 2015). Here we reviewed sorption parameters for different classes of pharmaceuticals and their variation with selected soil properties. We also evaluated the sorption isotherms for three pharmaceuticals, ofloxacin, tetracycline, diclofenac and a bactericide,  riclocarban and ten soils from temperate and tropical regions, and assessed the impact of soil properties on Freundlich equation parameters Kf and n. Batch experiments were set up adapting OECD protocol and using initial concentration ranges from 5 to 1000 μg/L. For strongly sorbed molecules, namely ofloxacin, tetracycline and triclocarban, there were strong technical constraints for the quantification of equilibrium concentrations by LC-MS-MS. We used this knowledge from both literature review and experimental data to build pedotransfer functions that allow predicting sorption parameters for a wide range of soils. Sorption of ionizable pharmaceuticals was, in many cases, highly affected by soil pH and CEC whereas soil organic matter content remained a driving factor of sorption for neutral molecular forms.


References:
Kodesova, R., et al. (2015) Science of the Total Environment 511, 435–443.
Mejías, C. et al. (2021) Trends in Environmental Analytical Chemistry 30, e00125.
Rietra, R.P.P.J., et al. (2024) Heliyon 10 (2024) e23718.
Verlicchi, P. & Zambello, E., (2015) Science of The Total Environment 538, 750–767

How to cite: Benoit, P., Godard, C., Deschamps, M., Bernet, N., Delarue, G., Serre, V., and Haudin, C.-S.: Searching for pedotransfer functions to predict sorption of pharmaceuticals from soil properties, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-21616, https://doi.org/10.5194/egusphere-egu24-21616, 2024.

EGU24-22341 | Posters on site | ITS1.23/SSS0.1.4

Transport and bioaccessibility of nano-contaminants in Brazilian latosol through pore water evaluation 

Aline de Andrade, Marco A. Z. Arruda, Sophie Miguel, Stéphanie Reynaud, and Javier Jiménez-Lamana

Plastic production worldwide has increased from 1.5 million tons in 1950 to 390.7 million tons in 2021.1 Nanoplastics (NPTs) have been considered an emergent contaminant entering the environment without any control since they can be formed by the degradation of large-sized plastic inadequately disposed of and considering that only 9% are effectively recycled.2 Just as the NPTs, nanoparticles (NPs) are considered emergent contaminants, and their application in different industrial products raises concern regarding the NPs entering the environment matrices.3 The soil bioaccessibility is an important parameter when considering the contaminants assessment evaluation with biological soil phase, and the study of soil liquid solution, which is called the soil pore water, can elucidate not only the bioaccessibility but also NPTs and NPs mobility, fate, and stability.4 The NPTs’ and NPs’ concentrations in the range of ng L-1 might be a limitation for their evaluation. However, spICP-MS can provide information on size, number concentration, and mass concentration, even in environmental conditions.5 In this study, a typical Brazilian soil used for plant cultivation (Latosol) was employed, and the soil moisture was controlled according to the field capacity determined in advance. Polystyrene (PS) nanoparticles with gold core and silver NPs (AgNPs), considering their abundance in different goods, were used as model nano-contaminants. The soil pore water was collected in two sampling points through a low-pressure lysimetric method using Rhizon® samplers once a week for 45 days of the experiment. In addition, the soil moisture was controlled by monitoring and adding more water to maintain the soil humidity, considering the three field capacity percentages studied. Results showed a downward trend in the number of particles detected in successive collections over time for both nano-contaminants. However, they also demonstrated different behaviours between them. The NPTs were bioaccessible in the pore water after the first days from the beginning of the experiments, and their concentration decreased constantly. At the same time, the NPs presented an inconstant transport through the soil column, gradually becoming bioaccessible. Finally, the concentration proved to be an important and decisive parameter, bringing essential discussion regarding the nano-contaminant's increasing concentration and behaviour in an environmental matrix, demonstrating the necessity to comprehend their interactions with the soil and between each other.

 

1 S. Maity, R. Guchhait, M. B. Sarkar and K. Pramanick, Plant. Cell Environ., 2022, 45, 1011–1028.
2 P. Zhou, L. Wang, J. Gao, Y. Jiang, M. Adeel and D. Hou, Soil Use Manag., 2023, 39, 13–42.
3 Q. Abbas, B. Yousaf, Amina, M. U. Ali, M. A. M. Munir, A. El-Naggar, J. Rinklebe and M. Naushad, Environ. Int., 2020, 138, 105646.
4 M. Di Bonito, N. Breward, N. Crout, B. Smith and S. Young, in Environmental Geochemistry, Elsevier, 2008, pp. 213–249.
5 J. Jiménez-Lamana, L. Marigliano, J. Allouche, B. Grassl, J. Szpunar and S. Reynaud, Anal. Chem., 2020, 92, 11664–11672.

How to cite: de Andrade, A., Arruda, M. A. Z., Miguel, S., Reynaud, S., and Jiménez-Lamana, J.: Transport and bioaccessibility of nano-contaminants in Brazilian latosol through pore water evaluation, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-22341, https://doi.org/10.5194/egusphere-egu24-22341, 2024.

ITS2 – Impacts of Climate and Weather in an Inter-and Transdisciplinary context

EGU24-2668 | ECS | Posters virtual | ITS2.1/CL0.1.2

Hominin response to oscillations in climate and local environments during the Mid-Pleistocene Climate Transition in northern China 

Zhe Wang, Bin Zhou, Xiangchun Xu, Yang Pang, Michael Bird, Bin Wang, Michael Meadows, and David Taylor

Long-term climate trends superimposed on climate variability changes are recognized to manipulate the living environments, and ultimately ecological resources for hominins, which in turn affect hominin activities. Archaeological evidence from loess sediments from Shangchen on the southeastern Chinese Loess Plateau indicates a suspension of hominin occupation around the time of the early mid-Pleistocene climate transition (MPT), prompting a re-assessment of climate-vegetation-hominin interactions. Our research generated magnetic susceptibility, total organic carbon cotent and its carbon isotope compositions, black carbon content and brGDGTs-derived mean annual temperatue and precipitation records in loess deposits with in situ lithic records covering the period of hominin occupation (~2.1–0.6 Ma). The results reveal four distinct climate-vegetation periods (2.1–1.8 Ma, 1.8–1.26 Ma, 1.26–0.9 Ma and 0.9–0.6 Ma). During the early MPT (1.26–0.9 Ma), unprecendently high variability in climate-environment and a long-term aridification with C4 vegetation expansion trend may have driven early humans to move to more hospitable locations in the region. Comparison with the record at Nihewan indicates that large-scale climate oscillations induced disparate hominin responses due to distinctive local environmental conditions.

How to cite: Wang, Z., Zhou, B., Xu, X., Pang, Y., Bird, M., Wang, B., Meadows, M., and Taylor, D.: Hominin response to oscillations in climate and local environments during the Mid-Pleistocene Climate Transition in northern China, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-2668, https://doi.org/10.5194/egusphere-egu24-2668, 2024.

Community assembly principles driving microbial biogeography have been studied in many environments, but rarely in the Arctic deep biosphere. The sea-level rise during the Holocene (11–0 ky BP) and its resulting sedimentation and biogeochemical processes can control microbial life in the Arctic sediments. We investigated subsurface sediments from the Arctic Ocean using metabarcoding-based sequencing to characterize bacterial 16S rRNA gene composition, respectively. We found enriched cyanobacterial sequences in methanogenic sediments, suggesting past cyanobacterial blooms in the Arctic Mid-Holocene (7–8 ky BP). Bacterial assemblage profiles with a sedimentary history of Holocene sea-level rise in the Arctic Ocean enabled a better understanding of the ecological processes governing community assembly across Holocene sedimentary habitats. The Arctic subsurface sediments deposited during the Holocene harbour distinguishable bacterial communities reflecting geochemical and paleoclimate separations. These local bacterial communities were phylogenetically influenced by interactions between biotic (symbiosis–competition or immigration–emigration) and abiotic (habitat specificity) factors governing community assembly under paleoclimate conditions. We conclude that bacterial profiles integrated with geological records seem useful for tracking microbial habitat preference, which reflects climate-triggered changes from the paleodepositional environment (the so-called ‘ancient DNAs’).

How to cite: Dukki, H. and Seung-Il, N.: Ancient DNAs: Influence of Sedimentary Deposition on Bacterial Communities in Arctic Holocene Sediments, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-2955, https://doi.org/10.5194/egusphere-egu24-2955, 2024.

EGU24-4247 | ECS | Posters on site | ITS2.1/CL0.1.2

Modeling and future prediction of spring phenology in grassland on the Qinghai-Tibetan Plateau 

Lei Wang, Xinyi Zhao, Haobo Yin, and Guoying Zhu

The Qinghai-Tibet Plateau (QTP) is an important ecological barrier in China and even East Asia, and its main vegetation cover type is grassland. With the global climate change, the phenological period of grassland on the QTP is constantly changing, which affects the climate and ecosystem through carbon cycle, hydrothermal cycle, etc. The influencing factors of phenology and its future change trend have become the key issues. In this paper, the spring phenological model of the QTP grassland was constructed by using the start of growing season (SOS) extracted from MODIS NDVI, air temperature and soil moisture data from 2000 to 2020. Combined with CMIP6 climate data, the future phenological changes of the QTP grassland under the SSP245 scenario were predicted. The results showed that: (1) The cumulative temperature and cumulative soil water threshold model was effective in simulating spring phenology of grassland on the QTP, and the root-mean-square error was only about 8 days. (2) The climatic thresholds at SOS of different vegetation types are closely related to their spatial distribution locations. Vegetation growth in the eastern and southern parts of the QTP requires higher hydrothermal conditions. (3) The QTP showed an overall warming and wetting trend in the future, with greater changes in the first half of the 21st century than those in the second half of the 21st century. (4) The advance of SOS in the northwest grassland was significantly higher than that in the southeast grassland. By the end of the 21st century, most grasslands on the QTP began to grow before mid-June.

How to cite: Wang, L., Zhao, X., Yin, H., and Zhu, G.: Modeling and future prediction of spring phenology in grassland on the Qinghai-Tibetan Plateau, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-4247, https://doi.org/10.5194/egusphere-egu24-4247, 2024.

EGU24-4791 * | Orals | ITS2.1/CL0.1.2 | Highlight

Climate, culture and population size 

Axel Timmermann, Abdul Wasay, Pasquale Raia, and Jiaoyang Ruan

Human history is full of examples documenting that cultural innovations played a key role in reducing the impact of environmental stress on early populations. Over the past 1 million years this type of adaptation (e.g., clothing, shelter, hunting techniques, social behaviour) likely also increased human population size. Humans are cumulative cultural learners, who can integrate knowledge and culture from one generation to the next. The larger the number of interacting people, the faster the rate of innovation.  Here we introduce a stochastic consumer-resource modeling framework, that simulates the dynamics of cultural transmission, learning, and innovation, population size, and resource depletion in a changing environment. Culture is introduced as a booster to carrying capacity. A zero-dimensional version of the model simulates nonlinear phase-synchronization between culture, population and external climate forcings. We will also present the first results of the model in 2 dimensions with full global resolution and 3 interacting hominin species to assess which role differences in cultural innovation played in the extinction of Neanderthals and Denisovans.

 

 

How to cite: Timmermann, A., Wasay, A., Raia, P., and Ruan, J.: Climate, culture and population size, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-4791, https://doi.org/10.5194/egusphere-egu24-4791, 2024.

EGU24-5928 | Posters on site | ITS2.1/CL0.1.2

Snapshots of Ireland’s Holocene climate and fauna from stalagmites 

Claire Ansberque, Anna Linderholm, Chris Mark, Malin Kylander, and Frank McDermott

Stalagmites are well-known as paleoclimatic archives, but recent work [e.g., 1,2] has also demonstrated their paleobiological potential as archives of ancient animal and plant DNA. Because of this property, stalagmites have the potential to provide information on how past climatic fluctuations have impacted land fauna, specifically cave fauna of which bats are key ecosystem services providers. The aim of this work is to use stalagmites to gain precisely such knowledge. With this endeavour, we acquired geochemical data (Sr/Ca, δ18O, δ13C) along the growth axis of three early Holocene stalagmites from Ireland, which we used for climatic and environmental reconstruction. In addition, we acquired ancient DNA data in stalagmite laminae, including those where climatic and environmental shifts were observed. Results of these analyses are presented here and include new U-Th-dated stable isotopic curves and ancient DNA data chronologically anchored to stalagmite-derived climatic records. We also discuss our analytical workflow and the pros and cons we faced while combining geological and biological data on stalagmites such as data acquisition resolution, stalagmite chemistry, and DNA data quality.

[1] Stahlschmidt et al. (2019) Scientific Reports, 9, 6628. [2] Marchesini et al. (2023) Quaternary Research, 112, 180-188

How to cite: Ansberque, C., Linderholm, A., Mark, C., Kylander, M., and McDermott, F.: Snapshots of Ireland’s Holocene climate and fauna from stalagmites, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5928, https://doi.org/10.5194/egusphere-egu24-5928, 2024.

EGU24-6756 | ECS | Posters on site | ITS2.1/CL0.1.2

Assessing Model Relevance: Agroclimatic Indices Across Different CORDEX Domains for Enhanced Climate Projections in the Houceima-Tanger-Tétouan Region 

Meryem Qacami, Marc-André Bourgault, Mohamed Chikhaoui, Thierry Badard, Mélanie Trudel, and Bhiry Najat

Understanding the intricacies of climate behavior is paramount for regions like Houceima-Tanger-Tétouan, where agroclimatic phenomena directly influence socio-economic stability. This study rigorously evaluates the performance of climate models against the ERA5-Land reanalysis data, focusing on two pivotal agroclimatic indices: dry spell and heat wave frequencies. Such indices are integral for regional drought risk management, agricultural planning, and environmental policy formulation.

Our approach integrates a dual comparison framework—comparing model outputs against each other (inter-model) and against multiple runs of the same model (intra-model). We also validate the ERA5-Land data against 16 years of in-situ measurements to confirm its aptitude as a benchmark dataset, particularly examining its representation of temperature and precipitation.

Findings indicate a strong temperature data correlation with in-situ measurements, affirming the ERA5-Land's reliability for temperature-related indices. However, precipitation data showed considerable variability, necessitating cautious application and potential model adjustments. Among the models, the MOHC-HadGEM2-ES demonstrated notable accuracy in dry spell predictions for selected domains, while the MPI-M-MPI-ESM-MR model stood out for its heat wave frequency projections, especially in the EUR-44 domain.

Our results pave the way for selecting the most appropriate models for regional climate projections. They also highlight the necessity of model calibration, especially for precipitation indices, to ensure the precision of climate-related predictions. The study contributes to the field by providing a clear pathway for the utilization of tailored climate models in developing robust adaptive strategies to climate variability in the Houceima-Tanger-Tétouan region.

How to cite: Qacami, M., Bourgault, M.-A., Chikhaoui, M., Badard, T., Trudel, M., and Najat, B.: Assessing Model Relevance: Agroclimatic Indices Across Different CORDEX Domains for Enhanced Climate Projections in the Houceima-Tanger-Tétouan Region, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-6756, https://doi.org/10.5194/egusphere-egu24-6756, 2024.

EGU24-6896 | ECS | Orals | ITS2.1/CL0.1.2

Climatic and ecological responses to medium-sized asteroid collision 

Lan Dai and Axel Timmermann

There is a chance of 1 in 2,700 that asteroid Bennu will hit Earth in 2182 CE. The collision of such medium-sized asteroids (~0.3-1 km in diameter) with our planet can inject massive amounts of dust into the atmosphere, with unknown consequences for terrestrial and marine ecosystems. Here, we use the coupled high-top Community Earth System Model Version 2 (CESM2) with interactive chemistry to investigate how medium-sized asteroid strikes would impact climate, vegetation, and marine productivity. Our idealized simulations show that globally dispersed dust layers of up to 400 Tg in mass block shortwave radiation to the surface for nearly two years, resulting in rapid global cooling and delayed weakening of the hydrological cycle for up to four years after the impact. The combined effects of reduced sunlight, cold temperature, and decreased precipitation significantly inhibit photosynthesis in the terrestrial ecosystem for almost nineteen months. Marine phytoplankton production decreases moderately within five months due to reduced sunlight. Subsequently, however, and depending on the iron amount of the asteroid, large diatom blooms occur over the eastern equatorial Pacific and Southern Ocean due to iron fertilization from strong upwelling and dust deposition, respectively.

How to cite: Dai, L. and Timmermann, A.: Climatic and ecological responses to medium-sized asteroid collision, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-6896, https://doi.org/10.5194/egusphere-egu24-6896, 2024.

EGU24-9488 | ECS | Posters virtual | ITS2.1/CL0.1.2

Enhancing Climate Resilience in IoT Devices: Challenges, innovations, and best practices.  

Dinara Zhunissova, Professor David Topping, and Professor James Evans

With growing concern about climate change and the increasing importance of Internet of Things (IoT) devices, the interaction between these two topics has been a focus of increased research. The purpose of this research paper, "Enhancing Climate Change Resilience in IoT Devices: Qualitative Analysis of Problems, Innovations, and Best Practises of IoT Devices," is to conduct a comprehensive qualitative analysis of the relation between IoT technology and climate resilience. This paper details the findings, providing contribution to the departments by offering solutions and recommendations that organisations can consider for improving the resilience of IoT devices in a severe weather condition. The paper includes an in-depth analysis of the present condition of IoT device usage, showing the broad and diverse areas of their application in many sectors, such as smart infrastructure, industrial manufacturing, agriculture, healthcare and more. This analysis highlights that many companies in both, the public and private sectors, are using sensors, actuators, cameras, routers and other devices. It then conducts a qualitative analysis of the particular problems that these devices deal with when subjected to challenging climatic conditions, with a focus on the impact of the environment on their performance. The paper illustrates IoT devices that have shown great climate resilience through real-world examples and in-depth qualitative evaluations of effective situations, delivering useful quality lessons for both developers and consumers. Furthermore, the study conducts a qualitative analysis of the elements that manufacturers and developers should consider while developing climate resistant IoT devices.

The evaluation of the importance of quality aspects, such as standards and certifications, in assuring the reliability of IoT devices in various climatic situations is a key aspect of this qualitative study. The paper conducts deep research of these parameters and their influence on device performance, it also emphasises the significance of subjective components of maintenance and protection practises, providing organisations with practical qualitative to overcome severe weather conditions and secure their IoT devices. By looking more closely at these factors, the study aims to find the deeper fundamental factors that affect how resilient and durable devices are. Bringing up the importance of qualitative aspects of maintenance and protection practises shows how important it is to think about not only technological aspects but also subjective features that make IoT devices more durable and make sure they work well even in extreme weather conditions. Over this research, comprehensive interviews with IT professionals from a variety of companies were used to gather data for this study. Open-ended questions were used to get rich and detailed insights. Along with the descriptive information, reports from the sector, case studies, and best practises were also analysed analytically. This created a complete narrative framework for learning about the problems and chances that come with those devices that are resilient to climate change. Besides that, includes qualitative analysis of predicted quality improvements and IoT device applications, taking into consideration changing climatic challenges and technology developments. Remote tracking and predictive maintenance are critical for maintaining the reliability and resilience of IoT devices.  

 

How to cite: Zhunissova, D., Topping, P. D., and Evans, P. J.: Enhancing Climate Resilience in IoT Devices: Challenges, innovations, and best practices. , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-9488, https://doi.org/10.5194/egusphere-egu24-9488, 2024.

EGU24-10113 | ECS | Posters on site | ITS2.1/CL0.1.2

Microbial evidences of abrupt shifts in dunes ecosystems after passing an aridity threshold 

Shuai Wu, Manuel Delgado-Baquerizo, and Aidong Ruan

Dune ecosystems are among the most vulnerable regions to climate change worldwide. However, studies on how crossing critical aridity thresholds influence the microbiome of these ecosystems remains scarce. These microbes play a pivotal role in shaping terrestrial ecosystem traits and functions.

In this study, we collected 1.4-meter sediment cores at 5 cm intervals from deserts in Xinjiang, China, in two study sites before and after crossing a previously described aridity threshold. We conducted a comprehensive analysis of community diversity and spatial structure, in light of the changes in environmental heterogeneity and autocorrelation, further exploring the community’s differential sensitivity to fluctuations and evidence of state transitions under various states.

The results demonstrate that microbial communities in sand dunes before and after crossing aridity thresholds exhibit distinct vertical ecological niche differentiation patterns under spatial effects. This includes variations in their beta diversity, rarity mode, assembly process, topological properties, and the stability of their networks. This offers new insights into the possible evidence of microbial community state transitions and potential mechanisms in deserts crossing aridity thresholds.

How to cite: Wu, S., Delgado-Baquerizo, M., and Ruan, A.: Microbial evidences of abrupt shifts in dunes ecosystems after passing an aridity threshold, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-10113, https://doi.org/10.5194/egusphere-egu24-10113, 2024.

Climate variations during the last glacial period had major impacts on plant and animal populations including humans. Yet, relationships between human population levels and climate through time and across space remain elusive. Here, we used the archaeological radiocarbon dates spanning 50 to 10 ka BP in China to indicate fluctuations in human population sizes, and investigated their correlations with climate variables from paleoclimate proxies and climate model outputs using a Bayesian radiocarbon‐dated event count (REC) statistical model. We find that temperature has a significant positive effect on population in China during 50 – 10 ka, while the sensitivity of population size to temperature exhibits a declining trend over time, suggesting a potential gradual adaptation to cold climates. We further used a global ecosystem model that explicitly simulates human population dynamics, the ORCHIDEE-FOEGE model, to reconstruct human densities during the LGM, and investigated the roles of climate and atmospheric CO2 levels in shaping the distribution of human populations in China.

How to cite: Zhu, D., Lin, Z., and Zhou, J.: Spatiotemporal relationships between human population and climate during the last glacial period in China, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-10236, https://doi.org/10.5194/egusphere-egu24-10236, 2024.

EGU24-12518 | ECS | Posters on site | ITS2.1/CL0.1.2

Strontium isotope turnover event mapped onto an elephant molar: implications for movement reconstructions 

Deming Yang, Katya Podkovyroff, Kevin Uno, Gabriel Bowen, Diego Fernandez, and Thure Cerling

Strontium isotope ratios (⁸⁷Sr/⁸⁶Sr) of incrementally grown tissues have been used to study movement and migration of animals. Despite advances in characterizing ⁸⁷Sr/⁸⁶Sr turnover [1], the 2-D geometry of turnover in the tooth enamel is still poorly understood. The relocation of a zoo elephant (Loxodonta africana) named Misha provided an exceptional case study for understanding this pattern [1]. We documented the ⁸⁷Sr/⁸⁶Sr turnover in Misha’s molar using high-resolution in situ measurements with laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS).

We prepared a longitudinally-cut thick section from Misha’s molar plate for LA-ICP-MS analysis. Within the tooth enamel, we measured 10 LA-ICP-MS transects parallel to the enamel dentine junction (EDJ), to map the 2-D pattern of ⁸⁷Sr/⁸⁶Sr turnover. Within the dentine, we measured a transect adjacent to the EDJ to document the unattenuated ⁸⁷Sr/⁸⁶Sr turnover sequence. We also analyzed conventionally drilled enamel samples from the same molar plate using the solution method for ⁸⁷Sr/⁸⁶Sr to document any turnover signal attenuation.

Molar dentine data are consistent with the published Sr turnover pattern in Misha’s tusk dentine. The inner half of the molar enamel preserves the turnover features in high fidelity, with a 2-D turnover geometry closely following that of enamel apposition. By contrast, the middle to outer surface of the enamel shows progressively more elevated ⁸⁷Sr/⁸⁶Sr values than those of the dentine. Data from drilled enamel samples show an attenuated turnover pattern due to averaging during drilling, as well as more elevated ⁸⁷Sr/⁸⁶Sr. We attribute these elevated Sr ratios to post-relocation Sr overprinting primarily on the outer enamel surface during enamel maturation.

Our results suggest that in situ LA-ICP-MS analysis of the inner half of enamel best recovers the time scale and magnitude of the ⁸⁷Sr/⁸⁶Sr turnover in an elephant molar. By contrast, the attenuated and overprinted turnover sequence from conventionally drilled enamel samples may lead to biased interpretations of the timing and geospatial scale of the animal’s movement history. To properly interpret conventionally drilled enamel sequences, future work would benefit from a modeling framework that can account for attenuation, overprint, and turnover of Sr, to quantitatively reconstruct movement or life history of extant and extinct animals. 

References:

[1] Yang, D.Bowen, G. J.Uno, K. T.Podkovyroff, K.Carpenter, N. A.Fernandez, D. P., & Cerling, T. E. (2023). BITS: A Bayesian Isotope Turnover and Sampling model for strontium isotopes in proboscideans and its potential utility in movement ecologyMethods in Ecology and Evolution1428002813. https://doi.org/10.1111/2041-210X.14218

How to cite: Yang, D., Podkovyroff, K., Uno, K., Bowen, G., Fernandez, D., and Cerling, T.: Strontium isotope turnover event mapped onto an elephant molar: implications for movement reconstructions, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12518, https://doi.org/10.5194/egusphere-egu24-12518, 2024.

EGU24-12629 | ECS | Posters on site | ITS2.1/CL0.1.2

The impact of protected areas on biodiversity conservation under different climate and land use change projections 

Chantal Hari, Markus Fischer, and Édouard Davin

Increasing conservation efforts are required to avert biodiversity decline caused by climate and land use changes.

In a recent study (Hari et al. in prep), we combined climate change scenarios (RCP2.6 and RCP6.0) and land use change projections to assess their impact on future species distribution for a large number of mammals, birds and amphibians. Future projections of land use change were derived from the Land Use Harmonization dataset v2 (LUH2), which does not make any explicit assumptions about the area under protection in these scenarios.

Here, we extend the scope of our future biodiversity projections by adding new land use scenarios explicitly accounting for different “Nature Futures” in the sense of different levels of biodiversity conservation (i.e., current protected areas or 30x30 target). In the first conservation scenario, we fix the protected areas based on the World Database on Protected Areas (WDPA), thereby assuming that protected areas will remain the same in the future as it is today. In a second category of scenarios, we create land use scenarios compatible with the Global Biodiversity Framework’s “30x30” target based on the spatially optimized dataset by Jung et al. (2021) combined with LUH2.

We then quantify how incorporating different levels of protected areas for conservation change the future species richness based on our land use filtering approach. We also analyze how these two scenarios of land management for conservation interfere with different levels of global warming and what are the implications for the climate resilience of different biodiversity conservation choices.

How to cite: Hari, C., Fischer, M., and Davin, É.: The impact of protected areas on biodiversity conservation under different climate and land use change projections, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12629, https://doi.org/10.5194/egusphere-egu24-12629, 2024.

EGU24-13260 | ECS | Orals | ITS2.1/CL0.1.2 | Highlight

Human adaptation to diverse biomes over the past 3 million years 

Elke Zeller, Axel Timmermann, Kyung-Sook Yun, Pasquale Raia, Karl Stein, and Jiaoyang Ruan

We identify past human habitat preferences over time to investigate the role of vegetation and ecosystem diversity on hominin adaptation and migration. Using a transient 3-million-year earth system-biome model simulation and an extensive hominin fossil and archaeological database we distinguish in what habitat previous Hominin lived. Our analysis shows that early African hominins predominantly lived in open environments such as grassland and dry shrubland. Hominins adapted to a broader range of biomes by migrating into Eurasia. By linking the location and age of hominin sites with corresponding simulated regional biomes, we also find a preference for spatially diverse environments. Suggesting our ancestors actively sought out mosaic landscapes.

How to cite: Zeller, E., Timmermann, A., Yun, K.-S., Raia, P., Stein, K., and Ruan, J.: Human adaptation to diverse biomes over the past 3 million years, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-13260, https://doi.org/10.5194/egusphere-egu24-13260, 2024.

EGU24-14207 | Orals | ITS2.1/CL0.1.2 | Highlight

Decoding Cryptic Population Structures using Stable Isotope Markers 

Gabriel Bowen, Kyle Brennan, Sean Brennan, and Timothy Cline

Life-history diversity has been shown to contribute to the resilience of species but can be challenging to quantify, particularly where intra-population genetic structure is lacking. Such is the case for salmon within many fisheries of the North American Pacific Northwest, where the resolution of genetic markers is variable and limited. For Sockeye salmon (Oncorhynchus nerka) within the U.S.-Canada transboundary Taku Watershed, for example, single-nucleotide polymorphisms have successfully distinguished populations associated with specific inland lakes but allocates many individuals to an undifferentiated “River Type” stock. The extent and dynamics of geographic structure within this stock, and thus its potential contribution to the fishery’s resilience, remain unresolved.

In such cases, intrinsic non-genetic markers that record key aspects of life history, such as the isotope ratios of body tissues, can provide valuable information on population structure and diversity. We combined a recently published stream network model for strontium stable isotopes (87Sr/86Sr) with otolith (ear stone) microchemistry data to infer the geographic natal origins of 45 adult fish captured during the 2019 run. Our analysis was implemented in a Bayesian framework and leveraged radio tag data as a source of prior information. We distinguish 4 previously undifferentiated sub-populations within the River Type stock, characterized by groups of fish with distinct natal 87Sr/86Sr values and, by inference, natal habitat locations. Although data from additional years will be needed to assess the persistence of these patterns, the result implies potential for previously unrecognized geographic structure within the River Type stock as a contributor to resilience within the population. The lack of genetic differentiation among the subpopulations may suggest that plasticity of habitat use is prevalent and contributes to adaptation. Alternatively, individuals may exhibit strong site fidelity, but differentiation of these sub-populations may be relatively recent or obscured by gene flow. Distinction between these hypotheses should be resolvable by applying the Sr-isotope method to fish recovered across multiple years.

How to cite: Bowen, G., Brennan, K., Brennan, S., and Cline, T.: Decoding Cryptic Population Structures using Stable Isotope Markers, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-14207, https://doi.org/10.5194/egusphere-egu24-14207, 2024.

EGU24-14448 | Posters on site | ITS2.1/CL0.1.2

Concurrent Asian monsoon strengthening and early modern human dispersal to East Asia during the last interglacial 

Jiaoyang Ruan, Hong Ao, María Martinón-Torrese, Mario Krapp, Diederik Liebrandh, Mark J. Dekkers, Thibaut Caley, Tara N. Jonell, Zongmin Zhu, Chunju Huang, Xinxia Li, Ziyun Zhang, Qiang Sun, Pingguo Yang, Jiali Jiang, Xinzhou Li, Yougui Song, Xiaoke Qiang, Peng Zhang, and Zhisheng An

The relationship between initial Homo sapiens dispersal from Africa to East Asia and the orbitally paced evolution of the Asian summer monsoon (ASM)—currently the largest monsoon system—remains underexplored due to lack of coordinated synthesis of both Asianpaleoanthropological and paleoclimatic data. Here, we investigate orbital-scale ASM dynamics during the last 280 thousand years (kyr) and their likely influences on early H. sapiens dispersal to East Asia, through a unique integration of i) new centennial-resolution ASM records from the Chinese Loess Plateau, ii) model-basedEast Asian hydroclimatic reconstructions, iii) paleoanthropological data compilations, and iv) global H. sapiens habitat suitability simulations. Our combined proxy- and model-based reconstructions suggest that ASM precipitation responded to a combination of Northern Hemisphere ice volume, greenhouse gas, and regional summer insolation forcing, with cooccurring primary orbital cycles of ~100-kyr,41-kyr, and ~20-kyr. Between ~125 and 70 kyr ago, summer monsoon rains and temperatures increased in vast areas across Asia. This episode coincides with the earliest H. sapiens fossil occurrence at multiple localities in East Asia. Following the transcontinental increase in simulated habitat suitability, we suggest that ASM strengthening together with Southeast African climate deterioration may have promoted the initial H. sapiens dispersal from their African homeland to remote East Asia during the last interglacial.

How to cite: Ruan, J., Ao, H., Martinón-Torrese, M., Krapp, M., Liebrandh, D., Dekkers, M. J., Caley, T., Jonell, T. N., Zhu, Z., Huang, C., Li, X., Zhang, Z., Sun, Q., Yang, P., Jiang, J., Li, X., Song, Y., Qiang, X., Zhang, P., and An, Z.: Concurrent Asian monsoon strengthening and early modern human dispersal to East Asia during the last interglacial, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-14448, https://doi.org/10.5194/egusphere-egu24-14448, 2024.

EGU24-18466 | Orals | ITS2.1/CL0.1.2

Using long-term remote sensing series to upscale the vegetation shifts along elevation in the GLORIA network Italian peaks 

Marco Vuerich, Francesco Boscutti, Davide Mosanghini, and Giacomo Trotta and the GLORIA Italian Network team

Plant species and communities’ distribution are remarkably affected by the climate change, particularly in arctic and alpine biomes. In alpine ecosystems, species and communities are shifting upwards due to the temperature increase, seeking for the optimum growth conditions. As a prominent effect, a progressive increase of vegetation cover is leading an alpine greening, with important consequences for the overall plant diversity. Nonetheless, little is known about how this trend may produce different effects along elevation gradients. Innovative upscaling approaches able to link field monitoring evidence to remote sensing data represent a promising tool to get new insights into the ecological mechanisms involved in these changes, and to produce reliable projections over time. This study aimed at parsing the long-term trends of remote sensing-derived vegetation indices in five GLORIA (Global Observation Research Initiative in Alpine Environments) network target regions, located across the Italian Alps and Apennines. Normalized Difference Vegetation Index (NDVI) was calculated for each growing season (June-September) in the period 1985-2022, using Landsat 5 and 8 multispectral satellite images of each mountain summit. Linear mixed-effects models were used to analyze the relationships between NDVI, time and climate variables, in different elevation belts. NDVI linearly increased over the last 37 years, but with significant higher increase rates and values at the treeline, lower alpine and alpine zones, compared to the upper alpine, subnival and nival belts. Moreover, NDVI was significantly affected by temperature at lower altitudes, with a significant interaction with rain precipitations, while climate variables were not determinant at high elevations. These results provided further evidence of the ongoing alpine greening and showed that vegetation at the treeline is responding faster than the other communities to a warmer and drier climate. Therefore, future scenarios depicting the fate of alpine plant community communities should not neglect for the interplay of temperature and precipitation regimes. Our finding opens future perspectives on the interpretation of GLORIA field evidence, in a continental upscaling perspective.

How to cite: Vuerich, M., Boscutti, F., Mosanghini, D., and Trotta, G. and the GLORIA Italian Network team: Using long-term remote sensing series to upscale the vegetation shifts along elevation in the GLORIA network Italian peaks, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18466, https://doi.org/10.5194/egusphere-egu24-18466, 2024.

EGU24-18850 | ECS | Posters on site | ITS2.1/CL0.1.2

Testing the climate-niche paradigm for species extinction risk 

Claus Sarnighausen, Maximilian Kotz, Leonie Wenz, and Sanam Vardag

The increasing relevance of climate change as a threat of species extinction is a pressing concern, as highlighted by the recent IUCN Red List accessment for amphibians (Luedtke et al., 2023). Despite the reported threats of climate change, measuring its influence across species remains complex and lacking the appropiate tools (Cazalis et al., 2022). Changes in "climate niche", referring to the environmental conditions necessary for a species to thrive, have long been discussed and used to predict species distributions and extinctions. Here, we utilize the recently available Red List classifications to test this paradigm within state-of-the-art predictive models of comparative extinction risk. Using historical weather data from the ERA-5 reanalysis, we explore the predictive significance of a wide range of potential definitions of climate niche exceedance. Extinction risk models have consistently identified geographic range size and human population density as important correlates to extinction risk. Also controling for factors such as habitat fragmentation, land use, human preassures, biogeographical realms and biological traits, we use a random forest model to predict the transitions between Red List categories for over 5.000 amphibian species and evaluate results against the official accessments. This approach tests the evidence base of the climate niche paradigm and evaluates its effectiveness as a tool for incorporating climate change into extinction risk models.


Luedtke, J.A., Chanson, J., Neam, K. et al. Ongoing declines for the world’s amphibians in the face of emerging threats. Nature 622, 308–314 (2023). https://doi.org/10.1038/s41586-023-06578-4

Cazalis, V., Di Marco, M., Butchart, S. H. et al., Bridging the research-implementation gap in iucn red list assessments, Trends in Ecology & Evolution (2022).
https://doi.org/10.1016/j.tree.2021.12.002

How to cite: Sarnighausen, C., Kotz, M., Wenz, L., and Vardag, S.: Testing the climate-niche paradigm for species extinction risk, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18850, https://doi.org/10.5194/egusphere-egu24-18850, 2024.

EGU24-19008 | ECS | Orals | ITS2.1/CL0.1.2 | Highlight

Anthropogenic intensification of climate extremes has altered vertebrate species abundance 

Maximilian Kotz, Tatsuya Amano, James Watson, and Leonie Wenz

Assessments of the effects of climate change on terrestrial biodiversity typically rely on species distribution models [1] which neither exploit data on historical abundance changes nor consider the potentially important role of climate extremes. Here, we combine global data on the abundance of vertebrate species populations [2] with metrics of exposure to local climate conditions to demonstrate that historical warming and increased exposure to heat, heavy precipitation extremes and drought have had significant impacts on abundance, even after controlling for changing human pressures. Fixed-effects models reveal plausibly causal impacts which vary by species class and habitat system, as well as by latitude and the extent of human pressure. Results indicate that warming and intensified heat extremes have negative impacts at low latitudes for freshwater fish and terrestrial birds. By contrast, warming can bring benefits to freshwater birds and terrestrial mammals. Heavy precipitation extremes and drought appear to have had mainly negative impacts on abundance across species’ and habitats. We then combine these empirical results with estimates of the changes in climate conditions and extremes which are attributable to anthropogenic influence, using an established impact-attribution framework [3]. This approach reveals that anthropogenic climate change has caused considerable alterations to the abundance of terrestrial life, for example by reducing the abundance of terrestrial birds and freshwater fish by up to 40% at low latitudes.

 

[1] Thomas, Chris D., et al. "Extinction risk from climate change." Nature 427.6970 (2004): 145-148.

 

[2] Loh, Jonathan, et al. "The Living Planet Index: using species population time series to track trends in biodiversity." Philosophical Transactions of the Royal Society B: Biological Sciences 360.1454 (2005): 289-295.

 

[3] Mengel, Matthias, et al. "ATTRICI v1. 1–counterfactual climate for impact attribution." Geoscientific Model Development 14.8 (2021): 5269-5284.

How to cite: Kotz, M., Amano, T., Watson, J., and Wenz, L.: Anthropogenic intensification of climate extremes has altered vertebrate species abundance, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-19008, https://doi.org/10.5194/egusphere-egu24-19008, 2024.

EGU24-21192 | ECS | Orals | ITS2.1/CL0.1.2 | Highlight

29 million years of diverse mammalian enamel proteomes from Turkana in the East African Rift System 

Daniel Green, Kevin Uno, Ellen Miller, Craig Feibel, Eipa Aoron, Catherine Beck, Aryeh Grossman, Francis Kirera, Martin Kirinya, Louise Leakey, Cynthia Liutkus-Pierce, Fredrick Manthi, Emmanuel Ndiema, Cyprian Nyete, John Rowan, Gabrielle Russo, William Sanders, Tara Smiley, Patricia Princehouse, Natasha Vitek, and Timothy Cleland

Exploration of the paleobiology of extinct taxa through ancient DNA and proteomics has been largely limited to Plio-Pleistocene fossils due to molecular breakdown over time, a problem exacerbated in tropical settings. Here, we report small proteomes from the interior enamel of fossils deposited at paleontological sites dating between 29–1.5 Ma in the Turkana Basin, Kenya, which has produced the richest record of Cenozoic mammal evolution in eastern Africa. We recovered enamel protein fragments in all sampled fossils, including a ~ 29 Ma Arsinoitherium specimen belonging to an extinct mammalian order, Embrithopoda. Identified proteins include the classical structural enamel proteins amelogenin, enamelin, and ameloblastin, but also less abundant enamel proteins including collagens and proteases. Protein fragment counts decline in progressively older fossils, but we observe significant variability in Early Miocene preservation across sites, with ~17 Ma deinothere and elephantimorph proboscidean fossils from Buluk preserving substantially more proteins than rhinocerotid and anthracotheriid fossils from ~18 Ma Locherangan and hippopotamids from younger localities at Napudet (< 11 Ma). Most specimens yield known clade-specific diagenetiforms that support morphology-based taxonomic identifications. Matches to clade-specific proteins suggest the future potential of paleoproteomics to contribute to the systematic placement of extinct taxa, but should be approached with caution due to sometimes sparse fragment identification and the possibility of sequence diagenesis. We identify likely modifications that support the ancient age of these proteins, and the oldest examples of advanced glycation end-products and carbamylation yet known. The discovery of protein sequences within dense enamel tissues in one of the persistently warmest regions on Earth promises the discovery of far older proteomes that will aid in the study of the biology and evolutionary relationships of extinct taxa.

How to cite: Green, D., Uno, K., Miller, E., Feibel, C., Aoron, E., Beck, C., Grossman, A., Kirera, F., Kirinya, M., Leakey, L., Liutkus-Pierce, C., Manthi, F., Ndiema, E., Nyete, C., Rowan, J., Russo, G., Sanders, W., Smiley, T., Princehouse, P., Vitek, N., and Cleland, T.: 29 million years of diverse mammalian enamel proteomes from Turkana in the East African Rift System, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-21192, https://doi.org/10.5194/egusphere-egu24-21192, 2024.

Issues related to whether climate change have caused great calamites in human society are of fundamental importance to current climate change research. The causes and ecological consequences of climate change can, of course, be measured at different levels according to different scales because the natural sciences have long understood the verification of causality and importance of scale. Research regarding human responses to climate change in the humanities and social sciences has been less explicit, less precise, and more variable. The growing need for interdisciplinary work in the issues across the natural/social science boundary (gap), however, demands some common understandings about the causality and scaling issues on climate impact. We seek to facilitate the dialogue between natural and social scientists by reviewing some of the fundamental aspects of the philosophical concepts of causality and scale that can be employed in the climate change/human response study, especially as they relate to large scales of the human responses to ever-changing global climate in history. Here we present the common philosophical concepts of causality and scale in natural sciences and social sciences, examine how researchers in the field employ the philosophical concepts to verify the relationship between human societies and climate change using various samples with multiple scales and explore how to connect and break the links between climate change, human calamites and resilience at different levels of hierarchies. 

How to cite: Zhang, D. D.: Scale and Causal inference: from philosophical concepts to empirical verification in relationship between climate change and social responses., EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-21871, https://doi.org/10.5194/egusphere-egu24-21871, 2024.

EGU24-22198 | Orals | ITS2.1/CL0.1.2

Variable enamel growth rates in hippopotamid canines: Implications for seasonality reconstructions using inverse modeling of intra-tooth isotope data 

Antoine Souron, Maëlle Couvrat, Éric Pubert, Frédéric Santos, Deming Yang, Delphine Frémondeau, Clarisse Nékoulnang, and Olga Otero

Seasonal variations in climatic variables, and the resulting changes in vegetation, are strong factors governing ecosystem dynamics in modern and ancient times. Stable isotope ratios recorded in tooth enamel document isotopic variations in the environment at the time of enamel formation and thus reveal the intensity and duration of seasonal dietary and climatic variations. However, the long and multi-phased process of enamel mineralization causes a dampening of the original input signal. An inverse model previously developed for ever-growing canines of Hippopotamus amphibius proposes to recover the original input signal and assumes constant enamel growth rate, appositional angle, and maturation length. The present study aims to test these assumptions. To do so, we integrated data from histological thin sections, microtomodensitometric analyses, and stable isotope analyses on teeth of extant H. amphibius specimens (3 upper canines, 1 lower canine, 1 third molar) to quantify the geometric and temporal patterns of enamel mineralization. To estimate enamel extension rates (EER, in µm/increment), we counted the number of increments representing the position of appositional front for each segment of 5 mm along the enamel-dentine junction in thin sections made along the growth axis of each tooth. We used microtomodensitometry to determine the pattern of enamel maturation using grey values profiles of X-ray radiographies as a proxy for enamel mineralization degree. Serial sampling along one upper canine of an individual from Chad, coming from an environment with one rainy season per year, allowed us to document the intra-tooth d13C and d18O variations over 6 years and thus provided an independent temporal control on histological variations. The histological study showed that the enamel apposition phase is strongly irregular over time within the canines, with no clear temporal trend. EERs vary strongly among teeth and within each tooth (50-200 µm/increment, 100-350 µm/increment, and 80-200 µm/increment for the 3 upper canines; 150-550 µm/increment for the lower canine; 70-130 µm/increment for the third molar). The median EER value from the upper canine of the juvenile individual (ca. 180 µm/increment) is significantly higher than median EER values from the upper canines of two adult individuals (ca. 110 µm/increment). Similar variations are also observed in apposition angles (3°-8°, 2.5°-4.5°, 3°-7° for the 3 upper canines; 2°-8° for the lower canine; 6°-18° for the third molar). The enamel mineralization parameters vary with age and tooth type (canine vs. molar). Based on strongly correlated seasonal variations in d13C and d18O, we also confirm cyclic dietary variations with higher proportions of C4 plants consumed during the dry seasons. Using the range of enamel mineralization parameters observed within one single hippo canine, we conducted sensitivity tests on the inverse modeling method, producing different modeled input signals that suggest a wider range of uncertainty. In conclusion, the documented intra-canine variability of EER, as well as other histological parameters (apposition angle, maturation length), reveals challenges when applying the current inverse model to wild populations. Future work would benefit from a systematic histological investigation into the sources of variation of enamel growth and mineralization patterns. 

How to cite: Souron, A., Couvrat, M., Pubert, É., Santos, F., Yang, D., Frémondeau, D., Nékoulnang, C., and Otero, O.: Variable enamel growth rates in hippopotamid canines: Implications for seasonality reconstructions using inverse modeling of intra-tooth isotope data, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-22198, https://doi.org/10.5194/egusphere-egu24-22198, 2024.

 

Weather Compound Events (WCE), broadly defined as “the combination of multiple drivers and/or hazards that contributes to societal or environmental risk” [1], contribute to important societal impacts and widespread economical damages. However, the underlying mechanisms and complete storylines of these events are complex and not well understood yet.

In this study, we build an 25-year database of co-occurrent hot and dry compound events (HDCE) including heatwaves, droughts, dust storms and wildfires affecting Europe and the Mediterranean Basin from 2003 to 2020. based on Earth Observation exclusively. Individual natural hazards were systematically identified by a spatial and temporal matching algorithm applied on consistent ESA CCI Earth Observation datasets. The resulting individual natural hazard masks were then overlayed over Europe and permitted to identify regions simultaneously affected by two or more natural hazards on a daily basis. The climatology revealed HDCE hotspots among others in Northern Italy, Balkans and Caucasus regions.

Characteristics of HDCE such as their duration, intensity and spatial extension are stored in the database. HDCE could also be associated with a severity index to aid comparison across events.

Long-term statistics of the generated HDCE have shown a high interannual variability with HDCE being more frequent during the 5 last years rather than two decades ago.

The large-scale preconditions preceding and occurring during HDCE are investigated as well in this study and revealed systematic patterns in the atmospheric dynamics.

 

[1] Zscheischler, J., Martius, O., Westra, S., Bevacqua, E., Raymond, C., Horton, R.M., van den Hurk, B., AghaKouchak, A., Jézéquel, A., Mahecha, M.D. and Maraun, D., 2020. A typology of compound weather and climate events. Nature reviews earth & environment1(7), pp.333-347.

How to cite: Fluck, E.: A 25-year assessment of Hot and Dry Weather Compound Events in Europe using Earth Observation , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-109, https://doi.org/10.5194/egusphere-egu24-109, 2024.

EGU24-134 | ECS | Orals | ITS2.3/CL0.1.1 | Highlight

Revealing the role of long-term drought in the record-shattering April 2023 heatwave in the Western Mediterranean 

Marc Lemus-Canovas, Damian Insua-Costa, Ricardo M. Trigo, and Diego G. Miralles

In April 2023, the Western Mediterranean region was hit by an exceptional and unprecedented heatwave that broke several temperature records. In Cordoba (Spain), the previous April maximum temperature record was exceeded by almost 5ºC. In this study, we investigated the interaction between soil moisture and the extreme temperatures reached during this event, using the latest available observational data and several statistical techniques capable of quantifying this relationship. Our results revealed that soil moisture deficit preconditions, concurring with a strong subtropical ridge as a synoptic driver, had a key contribution to the amplification of this record-breaking heatwave. Specifically, we estimated that the most extreme temperature records would have been 4.53 times less likely and 2.19°C lower if the soils had been wet. These findings indicated that soil moisture content may be a crucial variable for seasonal forecasting of early HW in this region and other Mediterranean climate regimes that already suffering an increment in the frequency of compound drought–heatwave events. 

How to cite: Lemus-Canovas, M., Insua-Costa, D., Trigo, R. M., and Miralles, D. G.: Revealing the role of long-term drought in the record-shattering April 2023 heatwave in the Western Mediterranean, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-134, https://doi.org/10.5194/egusphere-egu24-134, 2024.

Attribution of compound events informs preparedness for emerging hazards. However, the task remains challenging because of complex space-time interactions amongst extremes, climate models’ deficiency in reproducing dynamics of various scales, and uncertainties in dynamic aspects of climate change. 
During June-July 2020, a historic flood hit the Yangtze River Valley and to its south the hottest summer since 1961 was observed, leading to disproportionate socioeconomic and environmental impacts to southern China. For attributing the recording-breaking spatially compounding event, we conduct a storyline attribution analysis by designing a series of simulation experiments via a weather forecast model, with large-scale dynamics equally constrained and thermodynamics of the climate system modified. We report that given the large-scale dynamic setup, anthropogenic influence has exacerbated the 2020 extreme Mei-yu rainfall by ~6.5% and warmed the southern co-occurring seasonal heat by ~1℃. The framework further details human influence on key elements to the two extremes individually and their coupling in space. If the same compound event unfolds in the 2090s, it is plausible to expect the monsoonal rainfall extremes ~14% wetter and the accompanying South China heat ~2.1°C warmer than observed.
This method opens an avenue for attribution of low-likelihood, dynamically-driven, spatially and temporally compounding events.

How to cite: Chen, Y.: Storyline attribution and projection of the 2020 spatially compounding flood-heat event in southern China, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-185, https://doi.org/10.5194/egusphere-egu24-185, 2024.

Extreme droughts and pluvials are recurrent natural hazards that often lead to disastrous socio-economic impacts. These hydroclimatic extremes are generally characterized by large-scale spatial-temporal patterns spanning thousands of kilometres with time-evolving features of expansion or shrinkage. The spatial-temporal dynamics of these hydroclimatic extremes can pose compound impacts across multiple locations. Understanding the propagation behaviour, including movement and propagation, is crucial for disaster response and mitigation. The spatial propagation dynamics of droughts/pluvials are inherently complex as they are often associated with and modulated by natural climate variability, such as El Niño-Southern Oscillation (ENSO), North Atlantic Oscillation (NAO), and atmospheric dynamics like Rossby waves. However, the specific influences of these drivers on the spatial propagation pathways of droughts and pluvials remain elusive. Here, we conduct a multi-layer complex network-based analysis to explore the interactions between drought/pluvial propagation pathways and potential modulating mechanisms with a focus on the conterminous United States. We first identify extreme drought and pluvial occurrences using self-calibrated Palmer Drought Severity Index (scPDSI) and Standardized Precipitation Index (SPI) during 1948–2016. We then apply event coincidence analysis (ECA) for all location pairs to construct fully-connected drought and pluvial complex networks, based on which we identify the spatial-temporal propagation pathways through community analysis. Subsequently, partial event coincidence analysis is carried out to elucidate the direct links from potential climate modulators (e.g., ENSO, NAO, and Rossby waves) to extreme event propagation. Our results provide insights into how climate variability and large-scale circulation patterns affect the spatial propagation of droughts and pluvials, offering valuable information for pre-emptive actions to mitigate remotely synchronized extreme events.

How to cite: Wang, H.-M. and He, X.: Lagged Synchronizations of Hydroclimatic Extremes and Their Propagation Dynamics Revealed by Complex Event Coincidence Networks, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-842, https://doi.org/10.5194/egusphere-egu24-842, 2024.

EGU24-955 | ECS | Posters on site | ITS2.3/CL0.1.1 | Highlight

A European Perspective on Joint Probabilities of Multi-Hazards 

Judith Claassen, Philip Ward, Wiebke Jäger, Elco Koks, and Marleen de Ruiter

Natural hazards rarely occur in isolation. Frequently, one hazard triggers another, such as an earthquake triggering a tsunami. Likewise, the likelihood of a hazardous event can be amplified by the occurrence of a previous event, such as a drought amplifying the likelihood of a wildfire to occur. However, two extremes can also co-occur as a compound event, leading to even higher combined impacts.

While the field of compound events is advancing rapidly, studies often focus solely on climatic extremes occurring at the same time, excluding non-climate-related hazards or previous triggering and amplifying conditions. Therefore, this research aims to better understand the dependencies between different (pre-conditioning) hazard magnitudes, geographic features, and historic natural hazard footprints accounting for both climatic and geological hazards.

With the use of statistical tools, such as vine copulas, we model the relationships within two different hazard groups. The first group consists of drought, heatwave, and fuel indicators to calculate the risk of wildfires. The second group includes earthquakes, precipitation, and slope data to calculate the risk of landslides. While the first group is considered a compound event, the second group can be classified as a multi-hazard, with different triggering or amplifying relationships. For both groups, we attempt to use the same method to model stochastic events that include a potential hazard footprint for wildfires and landslides on a local to European scale. This model allows users to evaluate potential hazard combinations and footprints in their regions, enabling better preparedness for potential multi-hazard events.

How to cite: Claassen, J., Ward, P., Jäger, W., Koks, E., and de Ruiter, M.: A European Perspective on Joint Probabilities of Multi-Hazards, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-955, https://doi.org/10.5194/egusphere-egu24-955, 2024.

The emergency of global‐scale hydroclimatic extremes (i.e., meteorological droughts, extreme precipitations, heat waves and cold surges) and associated compound events has recently drawn much attention. A global‐scale unified and comprehensive event set with accurate information on spatiotemporal evolutions is necessary for better mechanism understanding and reliable predictions in sequential studies. Accordingly, this manuscript describes the first‐generation global event‐based database of hydroclimatic extremes produced with the newly proposed 3D (longitude–latitude–time) DBSCAN‐based workflow of event detection. The short name of this database is Glo3DHydroClimEventSet(v1.0) , which is obtained from the FigsharePlus webpage ( https://doi.org/10.25452/figshare.plus.23564517 ). The 1951–2022 ERA5‐based multiscale and multi‐threshold daily running datasets of precipitation and near‐surface air temperature are calculated and employed as the input data. A comprehensive event set of hydroclimate extremes is the output of the 3D DBSCAN‐based workflow. From perspectives of spatiotemporal evolutions, this event‐based database is also measured and attached with metric information. For case‐based validation, some recently reported hydroclimatic extremes (e.g., the 2020 summertime flood‐inducing Yangtze River extreme precipitation event) are employed and accurately detected in the Glo3DHydroClimEventSet(v1.0) database. Meanwhile, global‐scale spatiotemporal distributions are preliminarily analysed. For example, global‐scale event counts of extreme heatwaves displayed an increasing tendency since 2005, with a rapid increase after 2010. To sum up, this Glo3DHydroClimEventSet(v1.0) database may facilitate new scientific achievements concerning event‐based hydroclimatic extremes, especially in communities of atmosphere, hydrology, natural hazards and associated socioeconomics. The DOI-based linkage is  https://doi.org/10.1002/joc.8289 .

How to cite: Liu, Z. and Zhou, W.: Glo3DHydroClimEventSet(v1.0) : A global‐scale event set of hydroclimatic extremes detected with the 3D DBSCAN ‐based workflow (1951–2022), EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-2346, https://doi.org/10.5194/egusphere-egu24-2346, 2024.

This study investigates the coupled variability of temperature and precipitation in eastern China during summer using empirical orthogonal function (EOF) analysis to better understand and mitigate simultaneous occurrences of extreme events,such as compound droughts and heat waves. Two dominant modes are identified: the first exhibits a strong warming and drying trend in the region north of the Yangtze River, with the opposite occurring in the south; the second illustrates decadal oscillations in temperature and precipitation, alternating between cool-wet conditions and warm-dry conditions in southern China. The underlying mechanisms for these leading modes are revealed through correlation, composite analysis,and model simulations. The first mode is associated with a negative Pacific-Japan teleconnection in the lower atmosphere and a stationary Rossby wave train across Eurasia in the upper troposphere, which are influenced by global warming and sea surface temperature anomalies in the western North Atlantic. The second mode is linked to alternating active periods of the North Atlantic Oscillation (NAO) and Pacific Decadal Oscillation (PDO). The NAO exerts a significant influence on the summer climate in eastern China during its active phases, while the PDO shows an opposite effect when the NAO is less active. These findings provide valuable implications for long-term planning and adaptation strategies to better cope with compound extreme events in eastern China.

How to cite: Zhang, Y. and Zhou, W.: Long-term coupled variability of temperature and precipitationin eastern China and the underlying mechanisms, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-2743, https://doi.org/10.5194/egusphere-egu24-2743, 2024.

EGU24-2962 | Orals | ITS2.3/CL0.1.1 | Highlight

Intensification and Poleward Shift of Compound Wind and Precipitation Extremes in a Warmer Climate 

Delei Li, Jakob Zscheischler, Yang Chen, Baoshu Yin, Jianlong Feng, Mandy Freund, Jifeng Qi, Yuchao Zhu, and Emanuele Bevacqua

Compound wind and precipitation extremes (CWPEs) can severely impact natural and socioeconomic systems. However, our understanding of CWPE future changes, drivers, and uncertainties under a warmer climate is limited. Here, analyzing the event both on oceans and landmasses via state-of-the-art climate model simulations, we reveal a poleward shift of CWPE occurrences by the late 21st century, with notable increases at latitudes exceeding 50° in both hemispheres and decreases in the subtropics around 25°. CWPE intensification occurs across approximately 90% of global landmasses, especially under a high-emission scenario. Most changes in CWPE frequency and intensity (about 70% and 80%, respectively) stem from changes in precipitation extremes. We further identify large uncertainties in CWPE changes, which can be understood at the regional level by considering climate model differences in trends of CWPE drivers. These results provide insights into understanding CWPE changes under a warmer climate, aiding robust regional adaptation strategy development.

How to cite: Li, D., Zscheischler, J., Chen, Y., Yin, B., Feng, J., Freund, M., Qi, J., Zhu, Y., and Bevacqua, E.: Intensification and Poleward Shift of Compound Wind and Precipitation Extremes in a Warmer Climate, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-2962, https://doi.org/10.5194/egusphere-egu24-2962, 2024.

EGU24-3151 | Orals | ITS2.3/CL0.1.1

Usable Compound Event Research 

Kai Kornhuber

High impact events are often compound events with relevance for a wide range of societal sectors: Infrastructure and Urban Resilience, Agricultural Adaptation and Food Security, Public Health and Healthcare Preparedness, Insurance and Financial Risk Management, Energy Systems, Natural Systems, Globally interconnected Networks: Food Networks, Supply chains, transport systems.

 Consequently, compound events and associated physical risks have been prominently acknowledged in recent high-level reports such as the sixth assessment report of the IPCC, fifth US National Climate Assessment, numerous UNDRR briefing notes and the Risk report of the world economic forum among others.

Driven by the need to enhance our physical and statistical understanding of high impact climate events, compound event research has made substantial progress and has emerged as a new inter/trans/multi-disciplinary field of study over the past decade, bridging climate, environmental science as well as statistics and data science. To be fully usable for solving real world problems substantial challenges remain, these include lack of high-resolution data, model biases in tail risks, and impact relevant event definition. This talk will provide an overview of current challenges in accurately projecting and predicting risks from compound events for various societal sectors and points towards potential solutions to address these.

How to cite: Kornhuber, K.: Usable Compound Event Research, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-3151, https://doi.org/10.5194/egusphere-egu24-3151, 2024.

EGU24-3395 | Posters on site | ITS2.3/CL0.1.1

Global Warming Determines Future Increase in Compound Dry and Hot Days within Wheat Growing Seasons Worldwide 

Yan He, Yanxia Zhao, Yihong Duan, Xiaokang Hu, and Peijun Shi

Compound dry and hot extremes are proved to be the most damaging climatic stressor to wheat thereby with grave implications for food security, thus it is critical to systematically reveal their changes under unabated global warming. This study provides a comprehensive analysis of the changes in compound dry and hot days (CDHD) occurring within dynamic wheat growing seasons of 2015-2100 over dynamic wheat planting regions worldwide under SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5, including CDHD’s frequency and severity. This study sought to fill the gap in knowledge by identifying the CDHD occurring within dynamic wheat growing seasons, clarifying the correlations between droughts and heats as well as their impacts on CDHD, and revealing the driven mechanism of global warming for the increase of CDHD.

Our results demonstrate a notable increase in CDHD’s frequency and severity worldwide under all SSPs, such increase is sharper over southern Asia in winter wheat growing season, and southern Canada, northern America, Ukraine, Turkey and northern Kazakhstan in spring wheat growing season. As the top 10 wheat producer, India and America will suffer much more detrimental CDHD in their wheat growing season. Adopting a low forcing pathway will mitigate CDHD risks in up to 93.3% of wheat areas. Positive dependence between droughts and heats in wheat growing season is found over more than 74.2% of wheat areas, which will effectively promote the frequency and severity of CDHD. Global warming will dominate the increase of CDHD directly by increasing hot days and indirectly by enhancing potential evapotranspiration thereby aggravating droughts. This study helps to optimize adaptation strategies for mitigating CDHD risks on wheat production, and provides new insights and analysis paradigm for investigating future variations in compound extremes occurring within dynamic crops growing seasons.

How to cite: He, Y., Zhao, Y., Duan, Y., Hu, X., and Shi, P.: Global Warming Determines Future Increase in Compound Dry and Hot Days within Wheat Growing Seasons Worldwide, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-3395, https://doi.org/10.5194/egusphere-egu24-3395, 2024.

EGU24-3689 | ECS | Posters on site | ITS2.3/CL0.1.1

Abrupt transitions between drought and pluvial events becoming more widespread and intense 

Yuheng Yang, Xixi Lu, and Xue Xiao

Droughts and floods, as individual hazards, pose significant challenges, but their consecutive occurrence can trigger catastrophic cascades of disasters. Therefore, it is crucial to understand these extreme events, known as drought-pluvial (DPAT) and pluvial-drought abrupt transitions (PDAT), to mitigate their risks and potential impacts effectively. Our study utilizes historical records spanning from 1940 to 2022 to identify DPAT and PDAT events, investigating their frequencies, durations, intensities, and underlying causes. Additionally, we analyzed the frequency, duration, and intensity of these events under projected future scenarios. Globally, there has been an increasing trend in the frequency of DPAT and PDAT events, with significant upticks observed in Eastern North America, South Asia, East Asia, the Middle East, Africa, and Australia. In the 2010s, these disasters impacted over 100 million people, predominantly in less economically developed countries. Our findings enhance the current understanding of DPAT and PDAT, thereby contributing to the development of more effective mitigation and adaptation strategies against their impacts.

How to cite: Yang, Y., Lu, X., and Xiao, X.: Abrupt transitions between drought and pluvial events becoming more widespread and intense, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-3689, https://doi.org/10.5194/egusphere-egu24-3689, 2024.

EGU24-4229 | Orals | ITS2.3/CL0.1.1 | Highlight

Human influences on spatially compounding flooding and heatwave events in China and future increasing risks 

Cheng Qian, Yangbo Ye, Emanuele Bevacqua, and Jakob Zscheischler

Attribution of high-impact weather events to anthropogenic climate change is important for disentangling long-term trends from natural variability and estimating potential future impacts. Up to this point, most attribution studies have focused on univariate drivers, despite the fact that many impacts are related to multiple compounding weather and climate drivers. For instance, co-occurring climate extremes in neighbouring regions can lead to very large combined impacts. Yet, attribution of spatially compounding events with different hazards poses a great challenge. Here, we present a comprehensive framework for compound event attribution to disentangle the effects of natural variability and anthropogenic climate change on the event. Taking the 2020 spatially compounding heavy precipitation and heatwave event in China as a showcase, we find that the respective dynamic and thermodynamic contributions to the intensity of this event are 51% (35–67%) and 39% (18–59%), and anthropogenic climate change has increased the occurrence probability of similar events at least 10-fold. We estimate that compared to the current climate, such events will become 10 times and 14 times more likely until the middle and end of the 21st century, respectively, under a high-emissions scenario. This increase in likelihood can be substantially reduced (to seven times more likely) under a low-emissions scenario. Our study demonstrates the effect of anthropogenic climate change on high-impact compound extreme events and highlights the urgent need to reduce greenhouse gas emissions.

How to cite: Qian, C., Ye, Y., Bevacqua, E., and Zscheischler, J.: Human influences on spatially compounding flooding and heatwave events in China and future increasing risks, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-4229, https://doi.org/10.5194/egusphere-egu24-4229, 2024.

EGU24-4582 | Orals | ITS2.3/CL0.1.1

Increasing occurrence of sudden turns from drought to flood over China 

Hao Wang, Shanshan Wang, Xinya Shu, Yongli He, and Jianping Huang

This study focuses on a new compounding concern, the sudden turn from drought to flood (STDF), that is becoming increasingly prominent. Droughts usually end due to increased precipitation, but if excessive rainfall occurs, it can lead to secondary impacts on already barren land, increasing the likelihood of landslides and making farmland flooding significantly costlier than it would have been if only flooding had occurred. Therefore, we must pay more attention to compound disasters that increase the vulnerability of populations and ecosystems. Most studies on rapid drought-to-flood transitions have analyzed individual cases, whereas few have studied the STDF characteristics in China or even globally or the long-term changes in the STDF trend. In this study, we selected an STDF screening method that is accurate on a daily scale.

In this study we calculated the SPEI on a 1-month scale, sliding a 30-day window in order to obtain the SPEI values for each day. Second, we used a relative threshold rather than an absolute threshold to define a flood in consideration of regional precipitation differences. A definition of STDF as follows:

,where to is the drought start time, td is the drought end time, and tp is the time when flooding starts. Here, a drought is said to have occurred when the SPEI ≤-0.5 for more than 40 consecutive days. Our reference method considers drought duration to be more than 20 days, which is based on the persistence of the drought. And the main reason for our choice of 40 days is mainly to exclude the effect of flash droughts, although that type of event proved not to have a significant impact on our results in the subsequent discussion. PREt represents the t-d precipitation (for example, t=3, PRE3 is the 3d cumulative precipitation), when PREt is greater than the 99.5th (for PRE3)/99.3th (for PRE5)/98.7th (for PRE10) percentile precipitation for each reference period (1961-2020) as the flood threshold. (Based on the natural disasters released by the Emergency Management Department and the China’s Yearbook of Meteorological Disasters , 234 floods events were obtained for the period of 2010-2020, and so a threshold of 99.5th, 99.3th, and 98.7th percentile (corresponding to 3d/5d/10d continuous precipitation) was determined for their ranking in the rainfall series from 1961 to 2020.)

The results show that STDFs have been increasing more frequently in China at a rate of average 2.8 events per decade. The most significant increases occurred in May and June, resulting in an advance of one month for the STDF peak. The STDF hotspots are concentrated in north and northeast China and YRD. Nearly 35% of droughts in northern and northeast China have been immediately followed by a flood rather than a gradual drought mitigation or a drought alone. STDFs have become more prevalent in northern China as a result of increased flood frequency and precipitation volatility, while in southern China, the increase in STDF frequency is primarily due to an increase in drought frequency.

How to cite: Wang, H., Wang, S., Shu, X., He, Y., and Huang, J.: Increasing occurrence of sudden turns from drought to flood over China, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-4582, https://doi.org/10.5194/egusphere-egu24-4582, 2024.

In the context of global climate change, extreme climate events are becoming increasingly frequent.  Extreme climate events constitute major risks to global food security. The simultaneous occurrence of multiple extreme climate events may have a much greater impact than individual extreme events in isolation. Here we quantitatively analyzed the impact of individual and combined extreme climate indices, including cold days (CD), warm degree days (WDD), precipitation, and compound hot – windy - dry (HWD), on the yields of three major crops (winter wheat, soybeans, and maize) globally by establishing a linear mixed-effects model. CD, HWD, and WDD are identified as the most significant driving factors causing yield losses in winter wheat, soybeans, and maize, respectively. During the planting to the jointing stage, per 10 days of CD account for a 3.2% reduction in winter wheat yield. During the jointing to heading stage, per 10 h of HWD and per 10 °C day-1 WDD result in a 7.5% reduction in soybean yield and a 2.7% reduction in maize yield, respectively. We quantified "yield shocks" and found that the regions experiencing yield shocks exhibit a similar spatial distribution to extreme climate indices. These extreme climate indices are likely to be the driving factors behind yield shocks for the three crops. Our findings indicate that multiple individual extreme climate factors, as well as compound heat-drought-wind (HDW) indices that have been overlooked in traditional risk assessments, impact the yield of the three major crops globally.

How to cite: kun, X. and Xin, Q. C. X.: Investigate the Effects of Compound Extreme Climate Events on Global  crop Yield from 1982 to 2016, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-4843, https://doi.org/10.5194/egusphere-egu24-4843, 2024.

EGU24-5030 | ECS | Orals | ITS2.3/CL0.1.1 | Highlight

Projecting Changes in the Drivers of Compound Flooding in Europe Using CMIP6 Models 

Tim Hermans, Julius Busecke, Thomas Wahl, Víctor Malagón-Santos, Michael Tadesse, Robert Jane, and Roderik van de Wal

When different flooding drivers co-occur, they can cause compound floods. Despite the potential impact of compound flooding, few studies have projected how the joint probability of flooding drivers may change. Furthermore, existing projections are based on only 5 to 6 climate model simulations because flooding drivers such as storm surges and river run-off need to be simulated offline using computationally expensive hydrodynamic and hydrological models. Here, we use a large ensemble of simulations from the Coupled Model Intercomparison Project 6 to project changes in the joint probability of extreme storm surges and precipitation in Europe under a medium and high emissions scenario. To compute storm surges for so many simulations, we apply a statistical storm surge model trained with tide gauge observations and atmospheric forcing from the ERA5 reanalysis. We find that the joint probability of extreme storm surges and precipitation will increase in the northwest and decrease in most of the southwest of Europe. On average, the absolute magnitude of these changes is 36% to 49% by 2080, depending on the scenario. We show that due to internal climate variability and inter-model differences, projections based on small climate model ensembles can differ qualitatively depending on the specific simulations included. Therefore, our results provide a more robust and less uncertain representation of changes in the potential for compound flooding in Europe than previous projections.

How to cite: Hermans, T., Busecke, J., Wahl, T., Malagón-Santos, V., Tadesse, M., Jane, R., and van de Wal, R.: Projecting Changes in the Drivers of Compound Flooding in Europe Using CMIP6 Models, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5030, https://doi.org/10.5194/egusphere-egu24-5030, 2024.

EGU24-5210 | ECS | Orals | ITS2.3/CL0.1.1

Compounding preconditions leading to wildfires differ across European climate regions 

Julia Miller, Michaela Macakova, Danielle Touma, and Manuela Brunner

Recent wildfire seasons broke records in terms of severity and damage in different regions of the world, e.g. in California in 2021 and in Southern Europe in 2022. The  probability of such severe and large wildfires is enhanced by compounding meteorological conditions of hot, dry and windy weather, which lead to dry fuels supporting the spread of fires. Drivers of low-frequency but high-impact fire events operate on different spatio-temporal scales and are difficult to identify with classical regression methods. Here, we use causal inference methods to describe the relationships between different variables driving fires and quantify their effect on the occurrence of fire events. We examine hydro-meteorological and land-surface drivers of wildfires in different European climate regions by leveraging ESAs’ FireCCI burnt area product together with CERRA reanalysis data from 2002 to 2022. Our results show region-specific patterns of the different variables prior to the wildfire events, which allow us to identify different wildfire pre-condition types. Highlighting the spatial variability of different wildfire drivers in various climate regions of Europe provides valuable insights for the development of targeted fire prevention measures and management. 

How to cite: Miller, J., Macakova, M., Touma, D., and Brunner, M.: Compounding preconditions leading to wildfires differ across European climate regions, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5210, https://doi.org/10.5194/egusphere-egu24-5210, 2024.

Hot extremes impose severe effects on human health and the ecosystem, especially when high-temperature extremes sequentially occur in both daytime and nighttime within 1 day, known as Compound Hot Extremes (CHEs). Although a number of studies have focused on independent hot extremes, not enough work is devoted to compound ones, not to mention the coupling strength in covariations between the two variables (daytime and nighttime temperature: Tmax and Tmin) over a given region. The instantaneous coupling strength can be derived by Dynamical System (DS) approach from covariations between Tmax and Tmin over a given region, and used to classify CHEs into coupled and decoupled types. Results show that coupled CHEs tend to be more intense with prolonged duration and extensive spatial extent compared with decoupled CHEs. Also, the mechanisms behind these two types of CHEs are largely different. Coupled CHEs are accompanied by a significant intensification and westward extension of the western North Pacific subtropical high (WNPSH), and the extremely high-temperature is mainly caused by receiving more solar radiation under the corresponding anticyclone. It is found that barotropic structure, weak jet stream and developing La Niña are conducive to the enhancement and persistence of WNPSH, in favor of the occurrence of long-lasting CHEs. Decoupled CHEs are associated with strong sea-land breeze (SLB), whose diurnal cycle could weaken the persistent large-scale circulation and suppress covariations between Tmax and Tmin. This kind of decoupled hot extremes are attributed to the combined effect of receiving more solar radiation during the day and trapping more long-wave radiation at night, where moisture and cloud cover play an important role.

How to cite: Guo, Y. and Fu, Z.: Regional coupled and decoupled day-night compound hot extremes over the mid-lower reaches of the Yangtze River: characteristics and mechanisms, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5420, https://doi.org/10.5194/egusphere-egu24-5420, 2024.

EGU24-5617 | ECS | Posters on site | ITS2.3/CL0.1.1

Preconditioned biosphere flux extremes in terrestrial carbon cycle models and reanalyses in the recent past, present, and future 

Björn Riebandt, Moritz Adam, Elisa Ziegler, and Kira Rehfeld

The increasing frequency and severity of climate extremes pose a multifaceted threat to health, economic stability, and both natural and human-made environments. Potential overlap and accumulation of extremes as compound extremes poses further challenges. Ongoing climate change intensifies these challenges, underscoring the importance of a better understanding of the causes and drivers for compound events. Earth system model projections suggest that more frequent climatic compound extremes affect terrestrial biosphere fluxes, potentially reducing the land’s CO2 storage potential. However, whether models are able to represent such interactions like the priming of the biosphere towards extremes accurately remains to be shown.

Here, we focus on the role of concurrent precipitation and temperature as drivers of biosphere flux extremes and investigate their change in frequency and intensity based on their occurrence in historical simulations, reanalyses, and future projections. We use thresholds to define concurrent extremes and Monte Carlo randomization to constrain uncertainties. Further, we examine the association of climatic compound events with anomalies in biosphere carbon fluxes to ascertain their mutual relation, aiming to establish how these climatic compound events contribute to preconditioning extremes in the biosphere. Given this assessment of the occurrence change of climatic compound events and their connection to extremes in biosphere carbon fluxes, we infer how climatic compound events may precondition the biosphere for extremes. Lagged overlaps show significant seasonality and spatial heterogeneity in preconditioning. Comparing reanalyses and historical simulations in a model of the terrestrial carbon cycle and a comprehensive Earth System Model, we examine how well primed biosphere extremes agree in different data sources. Leveraging these findings, we evaluate if model projections show signs of stronger climatic priming of the biosphere in the next century.

How to cite: Riebandt, B., Adam, M., Ziegler, E., and Rehfeld, K.: Preconditioned biosphere flux extremes in terrestrial carbon cycle models and reanalyses in the recent past, present, and future, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5617, https://doi.org/10.5194/egusphere-egu24-5617, 2024.

EGU24-5986 | Orals | ITS2.3/CL0.1.1

Compound events increase the ground-level tropospheric ozone concentrations worldwide. 

Pedro Jimenez-Guerrero, Ivana Cvijanovic, Xavier Rodó, and Patricia Tarín-Carrasco

Compound extreme weather events (CE), characterized by the concurrent influence of multiple weather and climate drivers, have the potential to exacerbate the concentration of air pollution on the atmosphere. Attributing specific extreme weather events directly to climate change is challenging; however, it is widely acknowledged that climate change will intensify different extreme events by changing their frequency, intensity, spatial extent, duration, and timing. Several types of weather extremes, such as stagnation conditions and heatwaves (HW), can lead to hazardous air quality situations by allowing some pollutants, such as ozone (O3), to accumulate and persist in the near-surface environment. O3 is in general more pronounced in the summer due to the photochemical nature of the source. Given its highly heterogeneous distribution across both space and time, combined with a relatively short life-time, it becomes imperative to gain insights into the patterns governing the global spatial data distribution related to this complex phenomenon. This study aims to evaluate the amplifying effects of CE (concurrence of stagnation and heatwaves) on O3 peak levels globally during the summer season.

The study utilizes the simulations of historical 1980-2009) and future (2050-2079) climate under the Shared Socio-economic Pathways (SSP) SSP2-4.5 and SSP5-8.5. Using a model from the Coupled Model Intercomparison Project Phase 6 (CMIP6), the investigation explores the global temporo-spatial trends and disparities in compound-event occurrences across countries.

We find that O3 concentrations during the summer are higher in the center of North America and the center of the Asian continent compare with the other parts in the world (surpassing the 85 pbb during summer). A significant disparity in ozone concentrations was observed between the SSP2-4.5 and SSP5-8.5 scenarios. The SSP5-8.5 scenario demonstrates notably higher concentrations of peak O3 compared to the historical period, with increase of up to 20 ppb in certain regions, such as the Asian continent. Furthermore, it is noteworthy that O3 concentrations are expected to decrease in the future in the central part of North America in both scenarios up to 15 ppb during the summer season.

Focusing on CE throughout the summer season and under all scenarios studied, elevated O3 concentrations are observed worldwide during CE compared to non-event conditions, particularly during heatwaves, with an increase of 40, 35 and 40 ppb during summer in the historical, SSP2-4.5 and SSP5-8.5 scenarios in comparison with non-event conditions. These heatwave events generally dominate the formation of O3 peak concentrations during CE.

Comparatively, during stagnation events, the highest peak O3 concentrations undergo a substantial increase in the mid-to-late century scenario, notably in the Asian continent, with a projected increase of nearly 12% in Ofor the SSP2-4.5 scenario and a 25% increase for the SSP5-8.5 scenario. Conversely, during combined heatwave and stagnation events in the SSP2-4.5 scenario, a decrease in average concentrations is expected in the future across all continents.

These results underscore the imperative need to further mitigate air pollutant emissions during weather extremes to minimize the adverse impacts of these events on air quality and human health.

How to cite: Jimenez-Guerrero, P., Cvijanovic, I., Rodó, X., and Tarín-Carrasco, P.: Compound events increase the ground-level tropospheric ozone concentrations worldwide., EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5986, https://doi.org/10.5194/egusphere-egu24-5986, 2024.

EGU24-6635 | Posters on site | ITS2.3/CL0.1.1

Temporal Analysis of Large-Scale Winds in Austral Chile 

Ana Maria Cordova, Pablo Andrade, Diana Pozo, Deniz Bozkurt, and Jorge Arevalo

Austral Chile, characterized by its intricate topography of small islands, channels, and fiords, relies heavily on navigation for local economic activities, security, and societal functions. Wind-related hazards pose a significant safety threat to navigation, with the complex topography exerting a profound influence on local wind patterns. This study undertakes a comprehensive examination of large-scale winds in the region as an initial step toward understanding the intricate dynamics of local wind systems. This study is part of a larger research project that aims to produce a very high-resolution wind forecasting system, based on the downscaling of WRF simulations by using Deep learning techniques (SiVAR-Austral, funded by ANID ID22I10206).

Utilizing 50 years of ERA 5 reanalysis daily wind fields, we employ a self-organizing map (SOM) approach, with four distinct SOMs corresponding to each season, to unveil seasonal wind patterns. Furthermore, a cluster algorithm is applied to establish relationships between these patterns, elucidating the various stages of synoptic conditions associated with different wind patterns. Through an in-depth analysis, we explore the frequencies of these patterns across different decades, providing insights into their temporal evolution.

Our findings reveal the complex interplay between the region's topography and wind patterns, offering a better understanding of the seasonal variations in large-scale winds. The identification of distinct synoptic conditions associated with specific wind patterns enhances our ability to predict and mitigate navigation-related safety threats. Additionally, the temporal evolution of these patterns across decades contributes valuable information for long-term planning and risk assessment. This research lays the foundation for a more robust comprehension of wind dynamics in Austral Chile, with potential applications in enhancing navigation safety protocols and supporting sustainable coastal development.

How to cite: Cordova, A. M., Andrade, P., Pozo, D., Bozkurt, D., and Arevalo, J.: Temporal Analysis of Large-Scale Winds in Austral Chile, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-6635, https://doi.org/10.5194/egusphere-egu24-6635, 2024.

EGU24-7651 | Orals | ITS2.3/CL0.1.1

Climatology and Trends in Concurrent Temperature Extremes in the Global Extratropics 

Antonio Segalini, Gabriele Messori, and Alexandre M. Ramos

Simultaneous occurrences of multiple heatwaves or cold spells in remote geographical regions have drawn considerable attention in the literature, due to their potentially far-reaching impacts. These include widespread crop failures, increased mortality, wildfires, power supply disruptions and more. We introduce a flexible toolbox to identify and study such concurrent temperature extremes, with adjustable parameters that different users can tailor to their specific needs and impacts of interest. We then use the toolbox to present a climatological analysis of concurrent heatwaves and cold spells in the global midlatitudes. Specific geographical areas, such as Western Russia, Central Europe, Southwestern Eurasia and Western North America, emerge as hotspots for concurrent temperature extremes. Concurrent heatwaves are becoming more frequent, longer-lasting and more extended in the Northern Hemisphere, while the opposite holds for concurrent cold spells. Concurrent heatwaves in the Southern Hemisphere are comparatively rare. However, their sharp increase in recent decades means that they are becoming an emerging hazard in the Southern midlatitudes. Notably, trends in concurrent temperature extremes are significantly stronger than the corresponding trends in all temperature extremes. This suggests that concurrent heatwaves will be an increasingly important climatic hazard in both absolute and relative terms in a future, warmer, climate.

How to cite: Segalini, A., Messori, G., and Ramos, A. M.: Climatology and Trends in Concurrent Temperature Extremes in the Global Extratropics, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-7651, https://doi.org/10.5194/egusphere-egu24-7651, 2024.

EGU24-8078 | ECS | Posters on site | ITS2.3/CL0.1.1

Characteristics of compound flooding along the Indian coastline: Seasonal and interannual variability 

Diljit Dutta, Venkata Vemavarapu Srinivas, and Govindasamy Bala

The Indian coastline, flanked by the Bay of Bengal and the Arabian Sea, is prone to the impact of intense low-pressure systems, specifically tropical cyclones and monsoon depressions and lows, which are accompanied by extreme rainfall and storm surges. The vulnerability of the Indian coastline to compound flooding, characterized by concurrent occurrence of extreme rainfall with extreme storm surge (SS-RF) or extreme rainfall with extreme sea level (SL-RF), poses a significant challenge in the face of changing climatic conditions. Analysing the past changes in the characteristics of compound flood events is essential to understanding the changing flood risks associated with concurrent extremes along the Indian coastline. This study utilises hourly sea level data from 8 tide gauge stations operated by Survey of India and daily rainfall data at those stations prepared from 0.25° gridded rainfall product of the India Meteorological Department (IMD). The skew surge time series corresponding to the stations are prepared by harmonic analysis of sea level data, and daily maxima of the time series which represent storm surge are analyzed. The concurrent extremes are identified as events where extremes of rainfall, sea level, and skew surge exceeded their respective 95th percentile thresholds concurrently. Our findings reveal distinct seasonal patterns, with higher occurrences of extreme sea level-rainfall (SL-RF) and extreme storm surge-rainfall (SS-RF) events during the summer monsoon (June to September) and post-monsoon (October to December) seasons along the east coast. Conversely, along the west coast, there are negligible SL-RF events throughout the year and the SS-RF events are clustered in the summer monsoon season only. The variability in frequency and intensity of concurrent extremes is higher in the post-monsoon than in the summer monsoon season along the east coast. The interannual variability of compound extremes on the east coast is primarily influenced by the El Niño Southern Oscillation (ENSO). During El Niño conditions, a decreasing trend in the frequency and intensity of concurrent extremes is observed, while La Niña conditions contribute to an increasing trend. ENSO impact also extends to the frequency and intensity of tropical cyclones during the post-monsoon season, also contributing to the interannual variability of concurrent extremes. The findings underscore the complex dynamics of the compound flood risk along the Indian coastline and provide valuable insights for assessing and managing flood risk under changing climate.

Figure 1: The number of compound extremes witnessed at typical locations along the east-coast of India during (a) the summer monsoon (JJAS) and (b) post-monsoon (OND) seasons. The El Nino and La Nina composite of the frequency of compound extremes are plotted for JJAS in (c), (d) and for OND in (e), (f).

How to cite: Dutta, D., Srinivas, V. V., and Bala, G.: Characteristics of compound flooding along the Indian coastline: Seasonal and interannual variability, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-8078, https://doi.org/10.5194/egusphere-egu24-8078, 2024.

EGU24-8594 | ECS | Orals | ITS2.3/CL0.1.1

Reconstructing compound events from crop variability in Europe 

Niklas Luther, Arthur Hrast Essenfelder, Andrej Ceglar, Andrea Toreti, Odysseas Vlachopoulos, and Elena Xoplaki

Many studies have shown that compounding extreme events are likely to exacerbate socio-economic risks compared to single extremes. Despite this important fact, studies focussing on the connectivity of extreme events and their associated impacts frequently have some shortcomings. First, extreme events such as droughts and heat waves are often predefined through thresholds, restricting the class of meteorological events leading to the observed impacts. The choice of threshold for defining these extreme events is also often of meteorological and/or statistical nature and thus potentially unsuitable for the holistic identification of the associated impacts. Furthermore, impacts can arise from combinations of non-extreme events that might fall short of the threshold-based identification, thereby limiting the ability to account for key dynamics that determine the risk associated with compound events. Our study aims to overcome those shortcomings by linking climate events with their observed impacts in agriculture. We analyse wet and warm late winters followed by dry and hot springs, and the associated agricultural damages in Europe with the aim of reconstructing these compound events based on the observed impact. A first analysis is conducted for winter wheat impacts in France, the largest European winter wheat producer. We identify agro-climatic zones based on multivariate time series clustering and employ a regularized generalized canonical correlation analysis to identify the large-scale drivers of crop variability for these regions. The patterns that emerge from the analysis are characterized by wet and warm conditions in January and February linked to a positive North Atlantic Oscillation (NAO) state, followed by warm and dry conditions in April induced by a tripole with a blocking high over Central Europe. Using imbalanced random forests, we construct objective bounds and define thresholds to identify which temperatures are warm enough or which water balances are low enough to be associated with significant effect on crop yield reduction. Our results indicate that imbalanced random forests can predict these types of events reasonably well at the local scale, and that the derived thresholds are mostly lower than the commonly used thresholds for detecting similar extreme events. The latter illustrates that the combination of non-extreme climate events can indeed be detrimental to agricultural production in Europe, which is also crucial as the analysed types of events are predicted to occur more often in the future as a result of climate change. 

How to cite: Luther, N., Essenfelder, A. H., Ceglar, A., Toreti, A., Vlachopoulos, O., and Xoplaki, E.: Reconstructing compound events from crop variability in Europe, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-8594, https://doi.org/10.5194/egusphere-egu24-8594, 2024.

EGU24-9036 | ECS | Orals | ITS2.3/CL0.1.1

Temporal clustering of rainfall for landslides detection 

Fabiola Banfi, Emanuele Bevacqua, Pauline Rivoire, Sérgio C. Oliveira, Joaquim G. Pinto, Alexandre M. Ramos, and Carlo De Michele

Landslides are impactful and complex natural hazards, causing important damages in vulnerable areas. They can be related to several pre-existing conditions and triggering factors. The former are variables that do not directly cause the event but that increase its likelihood in the presence of a triggering variable. Example of the former are the slope or the aspect, of the latter precipitation, earthquakes, snowmelt, or human disturbances. Among the triggering factors the most important is rainfall. Usually deep-seated movement, characterized by a slip surface deeper than 1.5 m, are related to repeated moderate precipitation episodes while shallow landslides, characterized by a slip surface less deep than 1.5 m, to single and more intense episodes. Landslide detection is usually performed with the use of precipitation thresholds, either process-based or empirical ones. Here we introduce a new methodology to detect landslides based on temporal clustering of precipitation. Temporal clustering is a particular typology of compound event falling inside the category of temporal compounding events and it is defined as the occurrence of multiple events of the same type in close succession. The new method is compared with the use of empirical rainfall threhsolds considering as case study two landslide inventories in the Lisbon region, Portugal. The method shows a better sensitivity with respect to empirical rainfall thresholds and a performance in terms of precision variable dependending on the site. In general, the detection of deep landslides is better than of shallow landslide. The method requires only precipitation data and the selection of a precipitation quantile to identify events and it could help to improve the detection of rainfall-triggered landslides.

How to cite: Banfi, F., Bevacqua, E., Rivoire, P., Oliveira, S. C., Pinto, J. G., Ramos, A. M., and De Michele, C.: Temporal clustering of rainfall for landslides detection, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-9036, https://doi.org/10.5194/egusphere-egu24-9036, 2024.

EGU24-9167 | ECS | Posters on site | ITS2.3/CL0.1.1

Avoided impacts of climate change on compound hot-dry events under sustainable development versus fossil-fueled development 

Parisa Hosseinzadehtalaei, Piet Termonia, and Hossein Tabari

Climate change is expected to increase the frequency and intensity of compound hot-dry events, which can have significant impacts on human life, economic systems, and agriculture. The extent of this impact depends on the socioeconomic pathway we adopt in the future. While sustainable development aspires to reconcile economic growth, environmental protection, and social equity, thereby ensuring a more sustainable future for all, fossil-fueled development may drive economic growth at the expense of exacerbating climate change, pollution, and resource depletion. This study employs a CMIP6 multi-model ensemble to scrutinize the global-scale potential for mitigating climate change impacts on compound hot-dry events under sustainable development versus fossil-fueled development. These events are quantified by analyzing the joint distribution probability between temperature and soil moisture extremes through bivariate copula functions. The results show that although the likelihood of compound hot-dry events is expected to increase under both scenarios, the increase under fossil-fueled development is anticipated to be twice larger than that under sustainable development. The results show that although the likelihood of compound hot-dry events is expected to increase under both scenarios, the increase under fossil-fueled development is anticipated to be twice as large as that under sustainable development. The mitigated impact through sustainable development is not regionally uniform, with the largest mitigation, up to one-third, expected in the Mediterranean region.

How to cite: Hosseinzadehtalaei, P., Termonia, P., and Tabari, H.: Avoided impacts of climate change on compound hot-dry events under sustainable development versus fossil-fueled development, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-9167, https://doi.org/10.5194/egusphere-egu24-9167, 2024.

EGU24-9271 | Posters on site | ITS2.3/CL0.1.1

Monitoring compound drought-heat events over Brazil’s Pantanal wetland 

Ana Paula Martins do Amaral Cunha

Brazil’s Pantanal wetland is one of the most threatened Brazilian ecosystems from direct anthropogenic pressures and climate change. In this study, the overarching research question is to explore whether compound drought-heat events (CDHEs) have become more recurrent, intense, and widespread over Brazil’s Pantanal wetland in recent decades. For this, two different approaches were proposed and tested using validated long-term time series of monthly precipitation, temperature, and the satellite-based Vegetation Health Index (VHI) to characterize the spatiotemporal pattern of CDHEs over Pantanal. The Standardized Precipitation Index (SPI), Standardized Temperature Index (STI), and Standardized Precipitation Evapotranspiration Index (SPEI) from 1981 to 2021 were calculated. The results showed that using both approaches, the frequency of events is higher in the moderate category, which is expected since the criteria are less restrictive. In addition, the highest frequency of CDHE events occurs at the end of the dry season. The results also indicated that CDHE events have been more recurrent and widespread since 2000 in Pantanal. Besides, considering all methods for identifying the CDHEs, the probability density function indicates a shift pattern to warmer and drier conditions in the last 40 years. The Mann-Kendall tests also confirmed the assumption that there is a significantly increasing trend in the compound drought-heat events in the Pantanal. Developing methodologies for monitoring compound climate events is crucial for assessing climate risks in a warming climate. Besides, it is expected that the results contribute to convincing the urgent need for environmental protection strategies and disaster risk reduction plans for the Pantanal.

How to cite: Martins do Amaral Cunha, A. P.: Monitoring compound drought-heat events over Brazil’s Pantanal wetland, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-9271, https://doi.org/10.5194/egusphere-egu24-9271, 2024.

EGU24-10748 | ECS | Orals | ITS2.3/CL0.1.1

Changes in the causal effect networks of single and compound extreme hot and dry events in Central Europe 

Giorgia Di Capua, Yinglin Tian, Domenico Giaquinto, Judith Claassen, Javed Ali, Hao Li, and Carlo De Michele

Hot and dry extreme events in Europe have become more frequent and pose serious threats to human health, agriculture, infrastructure, and ecology. Single and compound hot and dry extremes in Europe have been attributed to synoptic atmospheric circulation variations and land-atmosphere interactions. However, the exact causal pathways and their strength, as well as their historical trends, have not been quantified. An accurate understanding of the mechanisms behind these land-atmosphere extremes is crucial to improving S2S forecasts and implementing appropriate adaptation measures. Here, we use the Peter and Clark momentary conditional independence (PCMCI) based Causal Effect Networks (CENs) to detect and quantify dynamic and thermodynamic causal precursors of extremely high 2m temperature (T2m) and extremely low soil water deficit and surplus (WSD) in central Europe (CEU).

Our analysis reveals that the single hot events are driven mainly by anomalous atmospheric patterns and soil water deficiency, while single dry events are mainly driven by the soil moisture memory, and anomalous atmospheric patterns, and only marginally by temperature changes. The atmospheric circulation patterns preceding both single hot and dry events show a high-pressure system over central Europe, with a low-pressure system over the Atlantic Ocean, and partly explain the occurrence of the compound events. This atmospheric pattern is also linked to an anomalous zonal cold-warm-cold SST pattern over the Atlantic Ocean and a warmer eastern Pacific Ocean.

The identified causal links vary with temperature and humidity conditions, that is, the impact of soil moisture memory on the WSD variation is sensitive to T2m and WSD, while the influence of soil moisture condition on T2m changes is strengthened by reduced WSD. Moreover, during compound hot and dry extremes, the effect of reduced soil moisture on temperature is significantly higher than during single events, reaching twice the magnitude under moderate conditions. When historical trends are analyzed, we show that the impact of dry soil on temperature is amplified by 42% (46%) for single (compound) extremes during 1979-2020, while the influence of atmospheric drivers on soil moisture is intensified by 28% (43%).

This work emphasizes (i) the intensification of the strength of the thermodynamic causal pathways for warmer and dryer CEU over time and (ii) the stress on the varying forcing strength of the drivers, which can lead to non-linear variations of weather stressors under climate changes and thus add extra challenges to extreme adaptations.

 

 

How to cite: Di Capua, G., Tian, Y., Giaquinto, D., Claassen, J., Ali, J., Li, H., and De Michele, C.: Changes in the causal effect networks of single and compound extreme hot and dry events in Central Europe, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-10748, https://doi.org/10.5194/egusphere-egu24-10748, 2024.

EGU24-11331 | ECS | Posters on site | ITS2.3/CL0.1.1

Time and period of emergence of compound events in France 

Joséphine Schmutz, Mathieu Vrac, and Bastien François

Compound events (CE) are the combination of climate phenomena which, taken individually, are not necessarily extreme but whose (concurrent or sequential) composition can cause very strong impacts and damages. Hence, the understanding of their potential past and future changes and evolutions are of great importance and, thus, more and more research is being carried out on this issue ([1], [2]). However, these questions are still rarely addressed over France, especially at high spatial resolution, even though they are necessary for the development of adaptation strategies. The present study focuses on historical multivariate compound events (several events occurring at the same time and same location), like hot and dry events or extreme wind and precipitation events, and aims to detect past changes in probability of such events over France. ERA5 reanalyses [3] are then used on the 1950-2022 period.

The first question that arises is: Where and when did these signals emerge in France? Are patterns forming? This issue is addressed through the analysis of “times” and “periods” of emergence, corresponding to moments when the change in probability of a specific CE is out of its natural variability [4].  The second question that comes up is: “What drives the emergence? What are the contributions of the changes in the marginal distributions and in the dependence structure to the change of compound events probability?” The study tries to answer this question thanks to the copula theory, allowing to decompose these different contributions. Copula functions are used to model bivariate joint probabilities, and are increasingly applied to hydroclimatic variables ([5], [6]).

Depending on the intensity and the type of the compound, the results indicate that (1) maps of time of emergence show clear spatial patterns and (2) that the changes in marginal distributions play a much more significant role than the changes in dependence during the emergence. This work opens perspectives for future projects, such as investigating physical phenomena driving these patterns and more deeply understanding changes in dependence between the different climate variables. Then analyzing climate model ability to reproduce the results would enable the application of the methodology to attribution framework and a better assessment of the risks associated with past and future climate change. 

References
[1] Singh, Harsimrenjit, Mohammad Reza Najafi, and Alex J. Cannon. "Characterizing non-stationary compound extreme events in a changing climate based on large-ensemble climate simulations." Climate Dynamics 56 (2021): 1389-1405.
[2] Ridder, N. N., et al. "Increased occurrence of high impact compound events under climate change." Npj Climate and Atmospheric Science 5.1 (2022): 3.
[3] Hersbach, Hans, et al. "The ERA5 global reanalysis." Quarterly Journal of the Royal Meteorological Society 146.730 (2020): 1999-2049.
[4] François, Bastien, and Mathieu Vrac. "Time of emergence of compound events: contribution of univariate and dependence properties." Natural Hazards and Earth System Sciences 23.1 (2023): 21-44.
[5] Zscheischler, Jakob, and Sonia I. Seneviratne. "Dependence of drivers affects risks associated with compound events." Science advances 3.6 (2017): e1700263.
[6] Tootoonchi, Faranak, et al. "Copulas for hydroclimatic analysis: A practice‐oriented overview." Wiley Interdisciplinary Reviews: Water 9.2 (2022): e1579.

How to cite: Schmutz, J., Vrac, M., and François, B.: Time and period of emergence of compound events in France, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-11331, https://doi.org/10.5194/egusphere-egu24-11331, 2024.

EGU24-11345 | Orals | ITS2.3/CL0.1.1

Understanding the association between global teleconnections and concurrent drought and heatwaves events over India 

Rajarshi Das Bhowmik, Ruhhee Tabbussum, and Pradeep Mujumdar

The variability in the occurrence of concurrent extremes like droughts and heatwaves is often attributed to climate change and anthropogenic factors, neglecting its connection with large-scale global teleconnections. The current study investigates the temporal and spatial connections between concurrent droughts and heatwaves (CDHW) in India to large scale global teleconnections like El Nino Southern Oscillation, North Atlantic Oscillation, Pacific Decadal Oscillation, and Indian Ocean Dipole. Utilizing composite and wavelet coherence analyses, we conduct a univariate assessment of droughts and heatwaves, quantified with the standardized precipitation index and standardized heat index, respectively, in association with large-scale global teleconnections (referred as climate drivers). Further, a novel attribution table framework proposed to quantify the conditional probability of CDHW given the onset of climate drivers. We found that the probability of CDHW preceeding the onset of climate drivers is much higher compared to the probability of CDHW occuring without the onset of climate drivers. The insights from this study suggest the potential use of global teleconnections for issuing season-ahead forecasts of CDHW.

How to cite: Das Bhowmik, R., Tabbussum, R., and Mujumdar, P.: Understanding the association between global teleconnections and concurrent drought and heatwaves events over India, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-11345, https://doi.org/10.5194/egusphere-egu24-11345, 2024.

EGU24-11560 | Orals | ITS2.3/CL0.1.1

Compound Flood Potential from Co-occurrence of River Discharge and Storm Surge in Croatia 

Nino Krvavica, Marta Marija Bilić, and Igor Ružić

Coastal areas are becoming increasingly vulnerable due to climate change. These regions are exposed to various sources of flooding, such as high sea levels, river discharge and heavy rainfall. Our study focuses on understanding compound flooding from storm surges and river discharge in Croatia. This is the first study on compound floods in this country. For this purpose, we analysed the time series of water levels and discharges from hydrological stations located along ten major coastal rivers. Since there are only a limited number of tide gauges in Croatia, we combined measured data with numerical reanalyses. The sea level data for the entire Adriatic Sea were obtained from the Copernicus Marine Service (Mediterranean Sea Physics Reanalysis) and were then corrected using machine learning and measured data.

Previous studies have shown that neglecting seasonal variations in river discharge and storm surges could lead to a significant underestimation of the expected annual damage from compound floods. Different seasons bring distinct weather and river discharge patterns that influence the probability and severity of compound floods. To address this, our study investigated seasonal correlation and co-occurrence by analysing the monthly maximum values. By examining each season in detail, we uncovered the variations in the compound flood potential index.

This analysis provides a more comprehensive understanding of compound floods in Croatia, which is crucial for risk assessment and risk management. Finally, we mapped the correlation coefficients, the number of co-occurrences and the compound flood potential index along the Croatian coast and organised the results in a GIS database. These maps will improve our ability to systematically select the most vulnerable areas where the risk of compound flooding should be analysed at the local level.

How to cite: Krvavica, N., Bilić, M. M., and Ružić, I.: Compound Flood Potential from Co-occurrence of River Discharge and Storm Surge in Croatia, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-11560, https://doi.org/10.5194/egusphere-egu24-11560, 2024.

Changes in wind speed and temperature significantly co-alter soil erosion climatic erosivity. However, knowledge on compound climatic elements of soil erosion to climate change is limited. Here, we quantify long-term climatic erosivity based on the wind erosion climatic erovisity and freeze-thaw climatic index, and analyze the contributions of single and compound factors using the slope change ratio of accumulative quantity methods. Our results show frequency of compound events is gradually decreasing as a result of climate change. Compound climatic erosivity exhibits large spatial variability and decreases with the wind erosion climatic erosivity and freeze-thaw climatic index. Moreover, a negative temporal trend of compound climatic erosivity is found in 61.28% of the study area from 1981 to 2020, which is largely attributed to declining wind speed. One unanticipated finding was that the frequency of compound erosion has shown a decreasing trend at some sites, but the intensity has shown an increasing trend. A possible explanation for this might be the extreme wind speeds and temperatures. Our findings highlight compounding effects of climatic conditions have a more severe impact on soil erosion.

How to cite: Yang, W.: Compound variation in freeze-thaw index and wind climatic erosivity in the agro-pastoral ecotone in northern China , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12712, https://doi.org/10.5194/egusphere-egu24-12712, 2024.

EGU24-12824 | Orals | ITS2.3/CL0.1.1

Local climate change impacts - new insights for mountain regions of Salzburg based on high resolution climate simulations 

Marianne Bügelmayer-Blaschek, Kristofer Hasel, Johann Züger, Robert Monjo, and César Paradinas

Climate change impacts are accelerating and intensifying, as observed over the past years, especially in the past year 2023.The current CMIP6 global climate simulations (GCMs) show higher climate sensitivity resulting in stronger warming and related impacts than previous simulations. Mountain regions are especially vulnerable as the warming climate relates to thawing of permafrost destabilising slopes and the emerging risk of heat and altered precipitation pattern that cause (extreme) flooding. Furthermore, the occurrence of compound events has gained increased attention as those pose substantial threat to the prevailing settlements and infrastructure.

Nevertheless, the available GCM simulations are spatially too coarse to investigate the mentioned extreme events in complex terrain. Therefore, statistical and dynamical downscaling is performed within the ICARIA project (Russo et al., 2023) to better analyse future climate impacts for the mountain regions of Salzburg. For the dynamical downscaling two regional climate models (RCMs), the WRF and COSMO-CLM (CCLM) are used to simulate the future climate conditions for the SSP126, SSP585 at spatial resolution of 2-5 km2 until 2100.

The verification of the two RCMs with respect to CHELSA (Karger et al., 2017) display that the 5km² WRF model simulations overestimate the precipitation intensities, especially in mountainous regions, the same goes for CCLM. With respect to temperature, WRF and CCLM display an underestimation of temperature in higher altitudes (above 600m) and a good representation below.

Additionally, statistical downscaling has also been performed within ICARIA following the FICLIMA method. For this procedure, a set of 59 weather observations were used together with 10 CMIP6 GCMs. ERA5-Land and statistics such as MAE, Bias or Kolmogorov-Smirnov test were used for verification purposes of the methodology for each spot and model. Those that passed filters of quality and performance in the representation of past climate produced local downscaled climate projections at daily resolution for each location for the Tier 1 SSPs (1.26, 2.45, 3.70 and 5.85). Both the statistical and dynamical downscaling methods' outputs will serve to compare results and better assess the inherent uncertainties of climate projections.

Since the focus is on extreme events, the prevailing simulations are analysed with respect to the global warming levels (1.5°C, 2°C, 3°C and 4°C) and their related local impacts. To investigate extreme events related to precipitation and wind, as well as their compound occurrence, suitable indicators are investigated, such as precipitation intensity estimates through future IDF curves and wind gust events with return periods of 1, 2, 5, 10, 20, 50, 100, 500 years. Further, consecutive events, that have a compound impact on the system, are considered through investigating the region and hazard specific time period before and after the occurrence of the extreme event.

 

Russo, B., de la Cruz Coronas, À., Leone, M., Evans, B., Brito, R. S., Havlik, D., ... & Sfetsos, A. (2023). Improving Climate Resilience of Critical Assets: The ICARIA Project. Sustainability, 15(19), 14090

Karger, D. N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R. W., ... & Kessler, M. (2017). Climatologies at high resolution for the earth’s land surface areas. Scientific data, 4(1), 1-20.

How to cite: Bügelmayer-Blaschek, M., Hasel, K., Züger, J., Monjo, R., and Paradinas, C.: Local climate change impacts - new insights for mountain regions of Salzburg based on high resolution climate simulations, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12824, https://doi.org/10.5194/egusphere-egu24-12824, 2024.

EGU24-12906 | ECS | Orals | ITS2.3/CL0.1.1 | Highlight

Summers full of extreme heat: using ensemble boosting storylines to quantify the drivers of heatwave clusters 

Laura Suarez-Gutierrez, Urs Beyerle, Magdalena Mittermeier, Robert Vautard, and Erich M. Fischer

We investigate the most extreme but physically plausible heat-loaded European summers in current and near future climate conditions using ensemble boosting. With this approach, we identify the most extreme summers in an initial-condition large ensemble with the model CESM2 and boost them, creating a large ensemble of re-initialized simulations with slightly perturbed atmospheric initial conditions. This allows us to efficiently generate storylines for summers that are even more extreme than the original simulations, either due to a higher number of days or grid cells exceeding extreme heat thresholds, or original heatwave clusters exceeding such thresholds by larger margins.

We compare these storylines of summer heat clusters to the most extreme European summers in the observational record, and determine the necessary and exacerbating mechanisms behind these clusters of extreme heat. We quantify how factors such as the intensity and persistence of atmospheric patterns as well as sea surface temperatures and terrestrial water budgets contribute to the most extreme simulated summers. Furthermore, we disentangle the effects of extreme early heat in May-June acting as a preconditioning factor in driving more extreme conditions during the rest of the summer, due to it causing more heat-prone conditions such as warmer oceanic basins and dryer soils, versus the effects of large-scale preconditioning factors that may lead to more persistent and intense heat through the summer, regardless of if it starts early in the season or not.

Ensemble boosting is a computationally efficient approach that allows us to sample extreme rare events, now over time scales of several months, while preserving physical consistency both in time, space and across variables. This is an ideal setup for disentangling contributions from different driving factors, and the generated boosting storylines can be used in impact studies that require physical consistency, a prolonged simulation time, and successive or compounding hazard exposure.

How to cite: Suarez-Gutierrez, L., Beyerle, U., Mittermeier, M., Vautard, R., and Fischer, E. M.: Summers full of extreme heat: using ensemble boosting storylines to quantify the drivers of heatwave clusters, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12906, https://doi.org/10.5194/egusphere-egu24-12906, 2024.

Global coffee production is at risk from synchronous crop failures, characterised by widespread reductions in yield occurring in multiple regions at the same time. For other crops, we know that these synchronous failures can be forced by spatially compounding climate anomalies, which in turn may be driven by large-scale climate modes like the El Niño Southern Oscillation (ENSO).

This talk will discuss the extent to which climate hazards occur and co-occur across the world’s major coffee-growing regions. These climate hazards include temperature and rainfall anomalies and are selected to cover two coffee species and different periods of the crop growing cycle. The talk will show that regional and global risk posed from spatially compounding hazards has increased over recent decades. There is a clear shift in the profile of this risk. Temperature-based hazards are now much more likely to exceed thresholds for optimal growing conditions, rather than being overly cold as observed during the 1980s.

Through multiple lines of evidence we find relationships between spatially compounding hazards and six tropical climate modes such as ENSO and the Madden Julian Oscillation. Individual regions exhibit differing relationships with these modes. ENSO is found to have the strongest links with multiple regions during the same crop cycle, posing implications for ENSO-driven global impacts to supply.

How to cite: Richardson, D.: The risk to global coffee supply from synchronous climate hazards, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-13620, https://doi.org/10.5194/egusphere-egu24-13620, 2024.

EGU24-14082 | Orals | ITS2.3/CL0.1.1

Translating Flood Insurance Claims in the Coastal CONUS within the Spectrum of Compound Flood Risk 

Mahjabeen Fatema Mitu, Giulia Sofia, Xinyi Shen, and Emmanouil N. Anagnostou

The intricate physical complexity of compound coastal flooding—resulting from the combination of river floods and storm surges—is known for often leading to more severe consequences than independent-driver floods. Damages from this type of flooding are expected to increase due to the impact of climate change on precipitation patterns and coastal storms, coupled with the increasing trends in population growth and economic activities along coastal regions. In the United States, the Federal Emergency Management Agency’s (FEMA) National Flood Insurance Program (NFIP) is the largest provider of flood insurance policies, and currently, more than two million NFIP flood claim transactions (1978 to present) are available to the public for analysis. However, there is a lack of studies that analyze how compound events reflect on insurance claims.

In this study, we focus on over 60,000 counties across the entire coastline of the United States to provide an exhaustive analysis of the distribution of economic losses in areas subject to river flooding, coastal flooding, and regions susceptible to compound events.

To identify the relative importance of the driving mechanisms (inland vs. coastal flows) for a particular location, we apply a published index [D-Index, readers are referred to the article, https://doi.org/10.1016/j.jhydrol.2023.130278 for details] that is capable of physically attributing the cause of flood depth to either river or coastal drivers, or a combination of both rainfall and storm surge.

We focus the analysis on the number of damages reported in the claims, comparing and contrasting claims in counties physically labeled as coastal, river, or compound. By calculating the quantile weight distance (QWD) of the damages from claims in the ‘compound’ counties and claims in the ‘independent-driver’ counties, we further investigate how rainfall and tide characteristics of storm events relate to the NFIP flood claims in the case of compound events. We further quantify differences in QWD by comparing and contrasting FEMA’s high-risk flood zones (identifying the 1-percent annual chance floodplain), where insurance is required for homes financed through federally backed or federally-regulated lenders, and FEMA’s low and moderate-risk flood zones, where flood insurance is not required.

In conclusion, this study furnishes invaluable insights into the intricate challenges of assessing compound coastal flooding impacts on insurance claims. The proposed methodology, integrating a flood type-specific mapping system and considering spatial variabilities of inundation characteristics, establishes a robust foundation for a comprehensive and improved flood risk assessment in coastal CONUS.

These findings empower coastal communities to proactively manage concealed risks and fortify their resilience against the compounding impacts of environmental forcings. This research offers a proactive and informed strategy to mitigate the potentially disastrous consequences of compound coastal flooding in a changing climate and socio-economic landscape.

How to cite: Mitu, M. F., Sofia, G., Shen, X., and Anagnostou, E. N.: Translating Flood Insurance Claims in the Coastal CONUS within the Spectrum of Compound Flood Risk, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-14082, https://doi.org/10.5194/egusphere-egu24-14082, 2024.

EGU24-14205 | ECS | Orals | ITS2.3/CL0.1.1

Fast and Accurate Calculation of Wet-bulb Temperature for Humid-Heat Extremes 

Cassandra Rogers and Robert Warren

It is well known that heat extremes have increased in frequency, intensity, and duration over recent decades. However, since extreme heat is typically examined using dry-bulb temperature, the reported changes do not fully reflect the impacts these events may have on human health. By accounting for humidity in measures of extreme heat, we can gain a better understanding of the health risk associated with these events in current and future climates.  

  

A variety of indices are used to examine humid heat. One of the simplest is wet-bulb temperature (Tw), which is defined as the temperature of a parcel of air cooled to saturation by the evaporation of water into it. Tw is typically calculated using empirical equations (e.g., Stull 2011, Davies-Jones 2008); however, these can be inaccurate for extreme values or slow due to the need for iterations in the solution. Here, we present a fast and highly accurate calculation of Tw, which we call NEWT (Noniterative Evaluation of Wet-bulb Temperature). This method follows the diagrammatic approach to evaluating Tw, where a parcel is lifted dry adiabatically to its lifting condensation level (LCL) and then brought pseudoadiabatically back to its original level. To avoid the need for iterations, NEWT uses exact equations for the LCL from Romps (2017) and a modified version of the high-order polynomial fits to pseudoadiabats from Moisseeva and Stull (2017).  

  

A comparison of NEWT with three other methods for calculating Tw (Stull, MetPy, and Davies-Jones) reveals a marked improvement in accuracy, with maximum errors of ~0.01°C (cf. ~1.3°C for Stull, ~0.4°C for MetPy, and ~0.05°C for Davies-Jones). The accuracy of each method is further assessed using Automatic Weather Station data from the Bureau of Meteorology, with a focus on extreme values. 

How to cite: Rogers, C. and Warren, R.: Fast and Accurate Calculation of Wet-bulb Temperature for Humid-Heat Extremes, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-14205, https://doi.org/10.5194/egusphere-egu24-14205, 2024.

EGU24-14358 | ECS | Posters on site | ITS2.3/CL0.1.1

Compound occurrence of heat waves and drought in the Northern Hemisphere, atmospheric circulation patterns and impacts. 

Natalia Castillo, Marco Gaetani, and Mario Martina

The compound occurrence of heatwaves and droughts (COHWD) may result in disastrous impacts and losses across various socioeconomic sectors. Therefore, it is important to understand and predict these phenomena to support decision makers and stakeholders in implementing preparedness and adaptation measures. However, questions concerning the underlying physics that drive and potentially exacerbate these extremes in the future still remain open. 

This study focuses on identifying COHWD and their characteristics during the lasts 62 summers through the analysis of atmospheric variables from the ERA5, GPCC and CRU datasets in the northern hemisphere (NH). Three regions, as categorized in the latest IPCC report, are analyzed: Western & Central Europe (WCE), the Mediterranean (MED) and Eastern Asia (EAS). These regions are selected because they account for the main breadbaskets in the NH.

Results show that WCE and MED have witnessed an increase in the area affected by COHWD over . In contrast, EAS does not exhibit a clear trend over the past six decades.  Moreover, by analyzing the variability of large atmospheric circulation patterns and climate oscillations, such as the North Atlantic Oscillation and the El Niño/Southern Oscillation, the dynamical drivers of COHWDs are identified. This research aims at providing new insights into the dynamical mechanisms driving COHWDs, to improve the identification, understanding, prediction and management of such events in the future. 

How to cite: Castillo, N., Gaetani, M., and Martina, M.: Compound occurrence of heat waves and drought in the Northern Hemisphere, atmospheric circulation patterns and impacts., EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-14358, https://doi.org/10.5194/egusphere-egu24-14358, 2024.

EGU24-14371 | ECS | Posters virtual | ITS2.3/CL0.1.1

Pathways to temperature variability in South Asia 

Hardik Shah and Joy Monteiro

For improving climate projections, there is a need to understand the physical processes governing the variability of dynamically driven variables, like near-surface temperature. Studies have shown that some features like surface drying and anticyclonic upper level conditions are associated with enhanced surface warming. However, the different ways in which surface, radiative and atmospheric variables compound to cause a heatwave, and the relative magnitudes of these variables and their relationship with heatwave intensity has not been well understood. Further, the large scale dynamics governing such conditions, and the effects of slowly varying climate features like ENSO and AO, are unresolved.

Using the ERA5 reanalysis dataset, we are studying the drivers of variability of daily mean 2 meter temperature (T2m) anomaly over the northwest Indian heatwave hotspot region, in the entire premonsoon season (March to June). Our approach is to develop an interaction framework which identifies governing surface and weather regimes active during different months, and quantify how large-scale climate patterns modulate their frequency of occurrence. We are leveraging the decision tree classification framework to identify the dominant weather patterns explaining different quartiles of T2m anomaly, owing to its non-linear modeling capability. 

During March and April, the T2m anomalies are accompanied by a vertically coherent temperature anomaly field, and typically last only for a day or two. The decision tree classification algorithm suggests that anomalous surface warming during this period is preceded by increased shortwave radiation corresponding to subsidence across the tropospheric extent. The decay of such an anomaly is marked by decreased downward shortwave radiation fluxes and increased downward longwave radiation fluxes, indicating the role of ventilation and cloud formation. The direction of sensible flux anomaly also changes between the two phases, directed from the atmosphere to the surface in the warming phase, and from the surface of the atmosphere in the decay phase. During May and June, the warming anomalies last for more than three days, and the sensible heat flux anomalies are directed toward the surface. Although shortwave anomalies peak along with T2m anomalies, there is also an increased convergence of dry static energy in the lower troposphere, between 600–900 hPa, in the region. Geopotential anomalies on the 350 K isentropic surface are anti-correlated with potential vorticity anomaly, establishing the role of Rossby wave packets as the dynamical drivers of temperature variability in this region. 

Thus, we show how an interpretable machine learning algorithm like the decision tree could potentially identify proximal drivers and compounding factors of heatwaves, provide a way to rank them by their importance, and eventually lead to a multiscale framework by incorporating longer term signals such as ENSO. 

How to cite: Shah, H. and Monteiro, J.: Pathways to temperature variability in South Asia, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-14371, https://doi.org/10.5194/egusphere-egu24-14371, 2024.

EGU24-14461 | ECS | Orals | ITS2.3/CL0.1.1

Accelerating Heatwaves Intensify Spatial Synchronization of Compound Drought and Heatwave Events 

Waqar ul Hassan, Md Saquib Saharwardi, Hari Prasad Dasari, Harikishan Gandham, Ibrahim Hoteit, and Yasser Abualnaja

Compound droughts and heatwaves (CDHWs) exert substantial socio-economic and ecological impacts, with their impacts reach epidemic proportions when CDHWs manifest simultaneously across multiple locations. Recent studies have begun to understand CDHWs, but their spatial compounding effects are not yet explored. This study utilizes weekly precipitation and temperature data to investigate the spatial synchronization of CDHWs and its changes. We define drought and heatwave weeks using the Standardized Precipitation Index (SPI 3-weekly) and the 90th percentile threshold of weekly temperatures. Our analysis reveals an unprecedented increase in the global land area and the number of regions experiencing concurrent CDHWs, particularly notable post-2000. The frequency of globally synchronized CDHWs (more than 5 regions affected simultaneously) has surged from 3 weeks (1982-1992) to 18 weeks (2012-2022), which is primarily attributed to a simultaneous global rise in temperatures driven by climate change. Analyzing CDHWs from observed data and counterfactual scenarios, where temperature data is detrended, we noted significantly higher likelihood of synchronization in observations due to intensified heatwaves in a warmer world. Notably, certain region pairs exhibit a higher likelihood of CDHW synchronization regardless being geographically distant. Spearman correlation and Granger causality analyses highlight major climatic modes, including El-Nino Southern Oscillation, Atlantic Multidecadal Oscillation, Western Tropical Indian Ocean, and Mode-2 of global Sea Surface Temperature, influencing changes in the areal extent of CDHWs globally as well as regionally. These insights are useful to predict the CDHWs and to quantify their socio-ecological impacts.

How to cite: ul Hassan, W., Saharwardi, M. S., Prasad Dasari, H., Gandham, H., Hoteit, I., and Abualnaja, Y.: Accelerating Heatwaves Intensify Spatial Synchronization of Compound Drought and Heatwave Events, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-14461, https://doi.org/10.5194/egusphere-egu24-14461, 2024.

EGU24-14796 | ECS | Orals | ITS2.3/CL0.1.1

Compound Coastal Flooding Drivers in the Pacific Northwest: Understanding Precipitation-Surge-Wave Interactions and Projected Changes 

Mohammad Fereshtehpour, Mohammad Reza Najafi, and Mercè Casas-Prat

Coastal regions face escalating threats under climate change, necessitating a comprehensive understanding of compound flooding dynamics. This study aims to investigate the interplay between precipitation, wind waves, and meteorologically-driven storm surge, assessing their joint behavior leading to compound coastal flood risks in the Pacific Northwest. We examined two approaches to capture all possible drivers leading to compound events, which may not necessarily result from the extreme conditions of individual marginal variables. First, we used a conditional approach and assessed the block maxima (BM) of each variable in conjunction with the corresponding values of the other variables. Second, a peak-over-threshold (POT) investigation was conducted to generate datasets where all variables exceed their 95th percentiles. To calculate the joint return period of coastal flooding drivers, we used the most appropriate marginal distributions commonly used in coastal engineering, including the Generalized Pareto Distribution (GPD) for the POT-based approach and the Generalized Extreme Value (GEV) distribution for the BM. Subsequently, we computed the joint probability distribution by fitting the best-suited copula to the datasets to capture the interdependencies between the drivers. Moreover, as meteorological drivers may change under global warming, we extended our analysis to consider future projections of surge, waves, and precipitation. This enabled us to examine changes in the aforementioned dependencies and return periods. Sub-daily time series of surge and wave heights were obtained from the Canadian Coastal Climate Risk Information System (CCCRIS) (https://cccris.ca/), which provides high-resolution (~250 m along coastlines) simulations driven by ERA5 reanalysis and future projections until 2100 under the RCP8.5 emission scenario driven by four different combinations of global and regional models, namely, CanESM2.CanRCM4, CanESM2.CRCM5-QUAM, MPI-ESM-MR.CRCM5-QUAM, and GFDL-ESM2M.WRF. For each grid point, the corresponding precipitation data is obtained from the nearest grid point of the respective climate models. We assessed the degree to which each driver contributed to the overall change in the joint return period of concurring extremes in coastal flooding. We also conducted an analysis to quantify the respective contributions of each driver’s projection and their dependence structure to the uncertainty in changes of return periods. This study leveraged high-resolution data that encapsulated the regional dynamic responses, which is pivotal for precisely evaluating climatic hazards and developing efficient adaptation schemes, thereby ensuring a more informed decision-making process for coastal management and engineering applications.

How to cite: Fereshtehpour, M., Najafi, M. R., and Casas-Prat, M.: Compound Coastal Flooding Drivers in the Pacific Northwest: Understanding Precipitation-Surge-Wave Interactions and Projected Changes, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-14796, https://doi.org/10.5194/egusphere-egu24-14796, 2024.

EGU24-15681 | Posters on site | ITS2.3/CL0.1.1

Climate change impact on inland flood risks due to compound storm tide and precipitation events for managed low-lying coastal areas. 

Lidia Gaslikova, Helge Bormann, Jenny Kebschull, Ralf Weisse, and Elke Meyer

Many coastal low-lying areas prone to coastal floods are protected by defense constructions. This often entails the establishing of artificial drainage systems to keep the hinterlands from flooding during heavy rain events. The coincidence of storm tide and heavy precipitation events may considerably limit the technical drainage capacity and lead to flooding. This situation can be exacerbated in the future due to changing conditions of both single drivers as well and their combinations. To assess the risks of inland flooding, a model based approach, combining the results from regional climate models with hydrological model for hinterlands and hydrodynamic model for coastal areas is established and applied. As a focus area, the water board Emden (Germany) and the gauge Knock are selected, which is a low-lying artificially drained area between the Ems river and the North Sea. For historical events, the main drivers leading to diminished drainage capacity and system overload were moderate storm series combined with the large-scale heavy precipitations. Whereas extreme storm tides or heavy precipitations alone posed no significant challenge for the system. The combinations of future emission scenarios (RCP2.6 and RCP8.5) and regionalized climate models (MPI-ESM and HadGEM2) together with local sea level rise projections are used to estimate the system overload and flood risk under the climate change conditions. For control period, the main cause of moderate system overload appears to be heavy precipitations rather than storm tides. For future projections, the importance and intensity of compound events will increase, reflecting changes in mean sea level and thus storm tides as well as intensification of heavy rain events.

How to cite: Gaslikova, L., Bormann, H., Kebschull, J., Weisse, R., and Meyer, E.: Climate change impact on inland flood risks due to compound storm tide and precipitation events for managed low-lying coastal areas., EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15681, https://doi.org/10.5194/egusphere-egu24-15681, 2024.

EGU24-15746 | ECS | Posters on site | ITS2.3/CL0.1.1

Determining the frequency of unfavorable conditions for sailing in Adriatic Sea channels  

Ena Kožul, Iris Odak Plenković, and Ines Muić

The intricate coastline of the Adriatic Sea presents challenges for sailing, especially through narrow island channels in severe weather conditions. To plan construction work, an assessment was requested to determine the most favorable period for conducting maritime activities in two channels in the first half of the year, the Hvar Channel and the Korčula Channel. Motivated by that request, climatological analysis using available measurements of several meteorological parameters was conducted.

Favorable conditions for sailing usually include weak or moderate wind intensity, often generated by island or coastal circulation. To determine the unfavorable conditions for maritime transport several meteorological parameters are examined with emphasis on wind, wave height, and thunderstorms, as these might contribute to the most hazardous sailing conditions in this region. The eastern coast of the Adriatic Sea is exposed to the strong winds blowing during the colder part of the year: the bora (northeast wind) and the jugo (southeast wind). Due to the orientation of the Adriatic Sea and analyzed sea channels, the jugo usually generates larger waves than the bora thus endangering maritime transport. However, navigating in strong bora conditions poses different risks due to its typically turbulent nature and strong intensity.

With these considerations in mind, unfavorable navigation conditions are defined using three criteria: (i) wind strength reaching or exceeding Force 5 (Beaufort scale) and at least a moderate wave height, (ii) wind strength reaching or exceeding Force 8 regardless of the sea state, and (iii) the presence of thunderstorm conditions involving hail, thunder, and showers.

In the analysis, it is concluded that the number of days with unfavorable conditions decreases from January to June, as expected. The most unfavorable conditions are most likely to occur in January, while June proves to be the most suitable month for conducting work with an average of 5.7 days with unfavorable conditions. Throughout all considered months, there should be at least 10 days with favorable conditions. Moreover, in June of any year, the number of days with unfavorable conditions did not exceed 7.

How to cite: Kožul, E., Odak Plenković, I., and Muić, I.: Determining the frequency of unfavorable conditions for sailing in Adriatic Sea channels , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15746, https://doi.org/10.5194/egusphere-egu24-15746, 2024.

Various countries around the world have been experiencing coastal disasters caused by coastal flooding, and Korean Peninsula is no exception. Most coastal flooding occurs during extreme sea level conditions which is comprised astronomical tides, nontidal residuals, wind wave, and mean sea level. To respond to coastal flooding disasters, it is important to understand the characteristics of extreme sea levels. Therefore, this study analyzed the spatiotemporal patterns of extreme sea levels along the Korean Peninsula and evaluated the effects of the astronomical tides and nontidal residuals represented by storm surges on extreme sea levels among the components constituting extreme sea levels. At this time, when analyzing the impact of the storm surge, it was evaluated whether the storm surge was caused by tropical cyclones or extra-tropical cyclones, and what storm condition were more dangerous in the Korean Peninsula. This study collected observed tidal data from 1979 to 2021 at 48 tide stations which are installed along the coast of the KP and performed a hormonic analysis to distinguish them into astronomical and storm surge components. In this case, storm surges occurring in summer and winter were considered to be caused by tropical cyclones and continental cyclones, respectively. In addition, to more accurately analyze the regional characteristics, the Korea’s coast was divided in the three zones: the East Sea, the West Sea, and the South Sea. As a result of the study, it was found that the extreme sea levels along the Korean Peninsula showed regional differences, and in the case of the south coast, storm surges generated by tropical cyclones were the main drive of extreme sea levels.

How to cite: Yang, J.-A.: Spatio-temporal analysis of extreme sea level in the Korean Peninsula, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-16044, https://doi.org/10.5194/egusphere-egu24-16044, 2024.

EGU24-17562 | ECS | Posters on site | ITS2.3/CL0.1.1

Drivers of compound drought-heat extremes across recent decades 

Josephin Kroll, Ruth Stephan, Harald Rieder, Jens Hesselbjerg Christensen, and Rene Orth

The joint occurrence of droughts and heat waves is expected to change with advancing climate change. While drought and heat themselves can already have major impacts on ecosystems and society, their compound occurrence can lead to amplified effects. Previous studies have analyzed changes in the occurrences frequency of compound drought-heat events and found increasing trends in some regions. In this study, we revisit these occurrence trends and additionally analyze the mechanisms that couple drought and heat as well as their changes in space and time. Considering drought as deficit of soil moisture and heat as an extreme temperature, evapotranspiration (ET) is the main physical process connecting both extremes. Therefore, we focus particularly on ET anomalies, because higher-than-normal ET during drought-heat events indicates that heat is inducing drought (heat → drought) as high temperatures lead to high vapor pressure deficit which increases ET that in turn depletes soil moisture. Vice versa, lower-than-normal ET suggests drought is triggering hot temperatures (drought → heat) as low soil moisture limits ET such that more of the incoming radiation is partitioned to sensible heat flux and hence warming the air. To better understand the underlying controls of these ET anomalies, we analyze their drivers by considering anomalies of precipitation, radiation, vapor pressure deficit and Leaf Area Index, which are in turn linked to anomalies in atmospheric circulation. Finally, we compare the relevance of these drivers, and of the drought → heat vs. heat → drought mechanisms in space, and link them with aridity and land cover type. In our analysis, we employ weekly data from the ERA5 reanalysis alongside gridded products derived with machine learning methods which were trained with in-situ observations. We define drought and heat with a percentile based approach filtering the lowest (< 5th percentile) absolute soil moisture values and highest (> 95th percentile) absolute temperatures at each grid cell. Understanding the mechanisms behind compound drought-heat extremes can help improve related forecasts, and to validate and constrain model projections of trends in these events. 

How to cite: Kroll, J., Stephan, R., Rieder, H., Hesselbjerg Christensen, J., and Orth, R.: Drivers of compound drought-heat extremes across recent decades, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17562, https://doi.org/10.5194/egusphere-egu24-17562, 2024.

EGU24-18239 | ECS | Posters on site | ITS2.3/CL0.1.1

Changes in extreme precipitation patterns over the Greater Antilles and teleconnection with large-scale sea surface temperature 

Carlo Destouches, Arona Diedhiou, Sandrine Anquetin, Benoit Hingray, Armand Pierre, Adermis Joseph, and Dominique Boisson

This study investigates the evolution of extreme precipitation over the Greater Antilles and its relationship with large-scale sea surface temperature (SST) during the period 1985-2015. The data used are derived from two satellite datasets, CHIRPS (Climate Hazards Group InfraRed Precipitation, Funk et al. (2015)) and NOAA (OI V2 Sea Surface Temperature, Huang et al. (2021)), at resolution of 5km and 25km respectively.  Changes in the characteristics of six indices of precipitation extremes (Precipitation total; number of rainy days;  contribution of heavy rainfall, R95p, maximum duration of consecutive rainy and dry days) defined by the WMO ETCCDI (World Meteorological Organization Expert Team on Climate Change Detection and Indices, Peterson et al. (2001)) are described and the influence of four large-scale SST indices (Northern Oscillation Index, NAO; Southern Oscillation Index, SOI; Tropical South Atlantic, TSA; Caribbean Sea Surface Temperature, SST-CAR) is investigated using Spearman's correlation coefficient. The results show that at regional scale, a positive phase of the TSA index contributes to an increase of the rainfall intensity while a positive phase of NAO is significantly associated with a decrease of total precipitation, of daily rainfall intensity, and of heavy rainfall. At country level, in southeastern Cuba and Puerto Rico, the increase in heavy precipitation and rainfall intensity is linked to a positive phase of the SOI, TSA and SST-CAR, while in Jamaica and northern Haiti, they are associated with positive phase of TSA and SST-CAR. Increases in the number of rainy days and the maximum duration of consecutive rainy days over the southern Haiti and the Dominican Republic are significantly associated with positive phase of the Southern Oscillation (SOI) and warming of SST over the east of the Caribbean Sea. The results of this study show that, in the Caribbean, particularly in the Greater Antilles, large-scale SST have had a strong influence on extreme precipitation over the past 30 years.

 

Keywords: Caribbean region; Greater Antilles; Extreme precipitation; Climate variability; Sea surface temperature

How to cite: Destouches, C., Diedhiou, A., Anquetin, S., Hingray, B., Pierre, A., Joseph, A., and Boisson, D.: Changes in extreme precipitation patterns over the Greater Antilles and teleconnection with large-scale sea surface temperature, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18239, https://doi.org/10.5194/egusphere-egu24-18239, 2024.

EGU24-18528 | ECS | Orals | ITS2.3/CL0.1.1

Interconnections and decadal predictability of global hot, dry and compound hot-dry events 

Alvise Aranyossy, Markus Donat, Paolo Deluca, Carlos Delgado-Torres, and Balakrishnan Solaraju-Murali

We investigate the representation of compound hot-dry events in decadal predictions and their relationship with their univariate hot and dry components. We use a CMIP6 multi-model ensemble (MME) of 125 members from the Decadal Climate Prediction Project (DCPP) hindcast simulations and compare it with different observational references. Our analysis focuses on the first five lead years of the simulations, with the different ensemble members initialised every year from 1960 to 2014. We analyse the skill of predicting hot, dry and hot-dry events in the multi-model ensemble. Specifically, we select the days above the 90th percentile of the daily maximum temperature for hot events. For dry events, we use two indicators, the Standardised Precipitation Index (SPI) and the Standardised Precipitation Evapotranspiration Index (SPEI), with accumulation periods of 3, 6 and 12 months, and we consider a dry event a month that shows an SPI or an SPEI value ≤1. Finally, we identify days that present both hot and dry conditions according to these criteria as compound hot-dry days.

Preliminary results for the observations show a strong correlation between precipitation and the occurrence of compound events, especially for long accumulation periods, suggesting the importance of dryness as a driver for compound hot-dry events. In the DCPP hindcasts, the hot events show some robust predictive skill, mainly as a consequence of the increasing trend in temperature. On the other hand, dry events show sparse skill, concentrated in dry areas of the world and especially for extended accumulation periods. Further analysis of the skill of compound events and their relationship to their univariate counterparts in DCPP hindcasts will shed light on the representation of such events in decadal forecasts. However, these initial results underline the importance of precipitation in both the occurrence of present hot-dry compound events and the prediction of such events in the future.

How to cite: Aranyossy, A., Donat, M., Deluca, P., Delgado-Torres, C., and Solaraju-Murali, B.: Interconnections and decadal predictability of global hot, dry and compound hot-dry events, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18528, https://doi.org/10.5194/egusphere-egu24-18528, 2024.

EGU24-18959 | Orals | ITS2.3/CL0.1.1

Bayesian Network Approach for Assessing Probability of Multi-Hazard Climate Driven Events 

Barry Evans, Albert Chen, Alex De La Cruz Coronas, Beniamino Russo, Agnese Turchi, Mattia Leone, and Marianne Büegelmayer

With the intensity and frequency of climate driven disasters increasing as result of climate change, there is ever more need to plan for such events and develop means to mitigate against them (UNDRR, 2015). Traditionally, the assessment of risks and impacts to regions posed by climate extreme events have been carried out in a “one at a time” approach, where the effects of each hazard, are assessed individually (Russo et al., 2023). However, it is recognised that  a transition to a more multi-hazard and multisectoral approach  is needed to be more efficient and effective in mitigating the risks/impacts posed to society, infrastructures, or the environment (Sendai Framework, 2015), (Russo et al. 2023). Whilst risk/impact assessment modelling can be complex, the derivation of risk/impacts is complicated further within a multi-hazard assessment due to the interdependent relationships between hazard, exposure and vulnerability, and that these vary over time in response to a preceding hazard (Gill et al. 2021).

The European Funded ICARIA project seeks to create an asset level modelling framework for understanding the potential risks/impacts posed by multi-hazard climate driven hazards, whilst also providing insight into cost-effective means of mitigating against them through the application of suitable adaptation measures. Two of the key challenges when transitioning from a single to a multi-hazard modelling approach are that (1) hazards are not directly comparable due differences in their processes and metrics, and (2) the effects of one hazard can influence the behaviour/characteristics of another hazard (Forzieri et al., 2016). To simulate the potential risks/impacts that could result from the modelled range of compound and consecutive hazards, a two-stage approach is being adopted that consists of (1) a deterministic physical modelling approach for quantifying the risks/impacts that can arise through simulation of various compound and consecutive hazard scenarios, along with (2) a stochastic Bayesian Network (BN) method for defining the probability distribution of such events. The BN will consider historical data for defining the probability distribution of modelled, multi-hazard scenarios for both current and future scenarios whilst data from the physical modelling will be used for defining the distribution of parameters relating to exposure, vulnerability, and impacts for the business as usual (no adaptation) and future adaptation scenarios.

 

Acknowledgement

The ICARIA project (Improving Climate Resilience of Critical Assets) is funded by the European Commission through the Horizon Europe Programme, grant number 101093806. https://cordis.europa.eu/project/id/101093806.

 

References

Forzieri, G., Feyen, L., Russo, S., Vousdoukas, M., Alfieri, L., Outten, S., Migliavacca, M., Bianchi, A., Rojas, R., & Cid, A. (2016). Multi-hazard assessment in Europe under climate change. Climatic Change, 137(1), 105–119. https://doi.org/10.1007/s10584-016-1661-x

Gill, J. C., Hussain, E., & Malamud, B. D. (2021). Workshop Report: Multi-Hazard Risk Scenarios for Tomorrow’s Cities.

Russo, B., de la Cruz Coronas, À., Leone, M., Evans, B., Brito, R. S., Havlik, D., Bügelmayer-Blaschek, M., Pacheco, D., & Sfetsos, A. (2023). Improving Climate Resilience of Critical Assets: The ICARIA Project. Sustainability, 15(19). https://doi.org/10.3390/su151914090

“United Nations - Headquarters United Nations Office for Disaster Risk Reduction.” (2015). Sendai Framework for Disaster Risk Reduction 2015-2030.

How to cite: Evans, B., Chen, A., De La Cruz Coronas, A., Russo, B., Turchi, A., Leone, M., and Büegelmayer, M.: Bayesian Network Approach for Assessing Probability of Multi-Hazard Climate Driven Events, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18959, https://doi.org/10.5194/egusphere-egu24-18959, 2024.

EGU24-20174 | ECS | Orals | ITS2.3/CL0.1.1

Extreme and compounding events in Pakistan 

aamir imran

Globally, climate change is a vital issue which exacerbates many severe consequences and causes the increasing frequency and severity of extreme weather events. Extreme climatic events, such as flash flooding, heatwaves, and droughts, pose severe impacts on societies and ecosystems, due to their large spatial coverage and high intensity. These extreme climatic events often occur simultaneously or sequentially as so-called compound events (CEs), causing high economic and societal losses as compared to the losses due to individual climatic extreme events. In the last two decades, Pakistan was ranked among the top ten countries which are most vulnerable to climate change and disasters, such as intense flooding, extreme heat, and droughts, among others. This paper presents case studies of extreme and compounding events in the last two decades with severe devastating impacts on people, infrastructure, and ecosystems. Specifically, two worst-case studies have been focused such as a flood in 2010 followed by a drought and a flood in 2022 followed by the heatwave. The post-disaster analysis shows that major part of the country was severely affected by these two CEs as a result of damaging the standing crops, destroying land, and causing displacement of millions of people along with losses and damages in fatalities and monetary terms. Therefore, this study is very vital for decision-making authorities to perceive the expected risk for human life, environment, and infrastructure in the future. So that pre and post-disaster mitigation policies and strategies could be formulated at local and national levels. The paper concludes with a discussion of the implications for CE adaptation in Pakistan. Key recommendations are provided to mitigate the impacts of future CEs.

How to cite: imran, A.: Extreme and compounding events in Pakistan, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-20174, https://doi.org/10.5194/egusphere-egu24-20174, 2024.

EGU24-20589 | ECS | Posters virtual | ITS2.3/CL0.1.1

On the use of probabilistic network models to assess spatially compound events in a warmer world 

Catharina Elisabeth Graafland, Ana Casanueva, Rodrigo Manzanas, and José Manuel Gutierrez

Probabilistic network models (PNMs) have established themselves as a data-driven modeling and machine learning prediction technique utilized across various disciplines, including climate analysis. Learning algorithms efficiently extract the underlying spatial dependency structure in a graph and a consistent probabilistic model from data (e.g. gridded reanalysis or climate model outputs for particular variables). The graph and probabilistic model together constitute a truly probabilistic backbone of the system underlying the data. The complex dependency structure between the variables in the dataset is encoded using both pairwise and conditional dependencies and can be explored and characterized using network and probabilistic metrics. When applied to climate data, PNMs have been demonstrated to faithfully uncover the various long‐range teleconnections relevant in temperature datasets, in particular those emerging in El Niño periods (Graafland, 2020).

The combination of multiple climate drivers and/or hazards that contribute to societal or environmental risk are the so-called compound weather and climate events. These compound events can be the result of a combination of factors over different dimensions: temporal, spatial, multi-variable, etc. (Zscheischler et al. 2020). In particular, spatially compound events take place when hazards in multiple connected locations cause an aggregated impact. In this work we apply PNMs to extract and characterize most essential spatial dependencies of compound events resulting from concurrent temperature and precipitation hazards, either in the same location or spatially connected, which can be relevant for agriculture. Furthermore, PNMs are used to propagate evidence of different levels of observed and projected global warming to assess the possible evolution of compound events in a changing climate.

References

Graafland, C.E., Gutiérrez, J.M., López, J.M. et al. The probabilistic backbone of data-driven complex networks: an example in climate. Sci Rep 10, 11484 (2020). DOI: 10.1038/s41598-020-67970-y

Zscheischler, J., Martius, O., Westra, S. et al.  (2020). A typology of compound weather and climate events. Nat Rev Earth Environ 1, 333–347, doi: 10.1038/s43017-020-0060-z.

Acknowledgement

This work is part of Project COMPOUND (TED2021-131334A-I00) funded by MCIN/AEI/10.13039/501100011033 and by the European Union NextGenerationEU/PRTR. 



How to cite: Graafland, C. E., Casanueva, A., Manzanas, R., and Gutierrez, J. M.: On the use of probabilistic network models to assess spatially compound events in a warmer world, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-20589, https://doi.org/10.5194/egusphere-egu24-20589, 2024.

EGU24-20600 | ECS | Orals | ITS2.3/CL0.1.1

Assessing Multidimensional Climate Extremes and Associated Vulnerabilities Across the United States  

Saurav Bhattarai, Sanjib Sharma, and Rocky Talchabhadel

Climate change is intensifying the occurrence of various extreme weather events across different geographic regions. While most research tends to concentrate on individual extremes, such as heatwaves, droughts, or floods, there’s been minimal exploration into how multiple, diverse extremes interact and compound impact social vulnerability. This study analyzes the overlapping spatial and temporal impact of temperature, precipitation, and hydroclimatic extremes across the US in the context of climate change.

 

Using data and predictions from global and regional climate models for present (including historical) and future emissions scenarios, we compute several indices of different extremes related to heatwaves, floods, and droughts. The aim is to categorize regions, or states or counties, based on their exposure to simultaneous extremes, incorporating social vulnerability and socioeconomic factors. The combination of exposure to multiple hazards and social vulnerability reveals regions in the US that face the highest risks from climate change.

 

Understanding the likelihood of compound climatic extremes occurring in areas with vulnerable populations can significantly aid in planning for adaptation and reducing the risk of disasters. By employing machine learning techniques to predict both multidimensional extremes and social vulnerability, policymakers can tailor evidence-based strategies to enhance community resilience. The methodology and findings provide a framework for evaluating multidimensional climate risks, applicable not just in the US but also in other countries and regions worldwide.



How to cite: Bhattarai, S., Sharma, S., and Talchabhadel, R.: Assessing Multidimensional Climate Extremes and Associated Vulnerabilities Across the United States , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-20600, https://doi.org/10.5194/egusphere-egu24-20600, 2024.

EGU24-270 | ECS | Posters virtual | ITS2.4/NH13.7

Domino effects of Climate Change on Financial Capital of India under CMIP6 Projections 

Vivek Ganesh, Santonu Goswami, and Harini Nagendra

Climate change is a major driver of increased flood risk, which is causing economic meltdown in many parts of the world. Globally, economic losses incurred by floods are estimated at around 453 billion USD. In the Asian region, India experienced the third highest economic loss of 4.2 billion USD due to flooding. Mumbai, India’s financial capital, faces climate change threats due to rising sea level, increased rainfall, and intense cyclones, posing risks to infrastructures, economy, and population, especially in low-lying areas. The Mithi river which overflows during monsoon season, plays a crucial role in carrying storm water to the sea in Mumbai. As it flows through an international airport, major industrial complexes and densely populated residentials, these areas became more vulnerable to flooding. This study demonstrates the domino effects of climate change on Mithi River watershed by utilising CMIP6 13 GCM ensembled daily mean precipitation model data for the near future 2030 under shared socio-economic pathways (SSP) 245 and 585 scenarios. Using the Hydrodynamic model GeoHECRAS, the flood inundation depth and extent were estimated. Under both projections, July 25-26, 2030, observed maximum rainfall and exhibited maximum streamflow with a peak discharge of 51.2 m3/sec (SSP245) and 38.5 m3/sec (SSP585). A quantitative risk assessment conducted based on the domino effects triggered by flooding to determine the projected impacted population and economic losses. The annual projected impacted population under both scenarios is observed as SSP245: Very High (0.24M), High (0.74M), Moderate (0.80M), Low (2.90M), and SSP585: Very High (0.68M), High (0.70M), Moderate (0.86M), Low (2.45M). The annual expected amount of urban property damaged due to this effect will range from $157 billion to $535 billion, with a projected affected GDP of more than $84 billion. This cascading effect is likely to disrupt Mumbai's million-dollar trade, affecting global financial flows. This study will be useful to understand the domino effect and raising the flood risk awareness for the development of sustainable policies.

Keywords: Domino effects, CMIP6, economic loss, hydrodynamic, flood depth and extent

How to cite: Ganesh, V., Goswami, S., and Nagendra, H.: Domino effects of Climate Change on Financial Capital of India under CMIP6 Projections, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-270, https://doi.org/10.5194/egusphere-egu24-270, 2024.

Energy systems across the world are rapidly evolving to meet climate mitigation targets. This requires a rapid transition to electricity systems lower reliance on fossil fuels and greater weather-dependent renewable generation (such as wind power, solar power, and hydropower). This increased weather dependence adds a new set of challenges for balancing supply and demand due to the inherent variability of weather, increasing the need for investment in storage and flexible technologies. The impacts of climate variability and climate change on national energy systems is a topic of current academic interest. Both in terms of security of supply risks from system level challenges (e.g., energy shortfall events, where existing generation is insufficient to meet demand) or from smaller-scale infrastructure challenges (e.g., extreme weather impacting the operability of energy system components).

This talk will discuss a programme of work on energy sector impacts using the UK Climate projections data (UKCP18). This is a suite of state-of-the-art climate model projections available at 60km resolution globally, 12km spatial resolution over Europe, and 2.2km resolution over the UK. Electricity demand, wind power, and solar photovoltaic power timeseries are developed for the period 1980-2080 using the regional climate model outputs. Climate data of this high spatial and temporal resolution is critical for the accurate quantification of meteorological hazards of relevance to the energy sector. The UK energy sector will be used as a case study in this talk due to its large share of variable renewables and commitments to reach net-zero emissions by 2050 and decarbonising the electricity system by 2035.

This talk will highlight weather-driven risks to the energy sector in both a present and future climate, with a particular focus on compound events. At short timescales examples of these risks could be periods of high demand combined with low wind power generation, or weather patterns extending over a very large area of Europe (therefore creating a spatial compound event) or sequences of extreme weather (such as several storms happening in quick succession, which could damage energy infrastructure). At longer timescales these types of compound events could be years with low renewable energy production relative to demand, or as successive years with low production. Future work will use the years containing extreme events highlighted in this talk as inputs within high resolution power system modelling simulations.

 

How to cite: Bloomfield, H.: Using high resolution climate data to help prepare future energy systems for weather-driven extremes., EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-8794, https://doi.org/10.5194/egusphere-egu24-8794, 2024.

EGU24-8945 | Orals | ITS2.4/NH13.7

Bringing high-resolution climate data into action: Experiences from the transdisciplinary funding measure RegIKlim 

Kevin Sieck, Joaquim Pinto, Jan-Albrecht Harrs, Bente Tiedje, Astrid Ziemann, Elena Xoplaki, Beate Geyer, Hendrik Feldmann, Julia Mömken, Heiko Paeth, Katja Trachte, Christopher Kadow, and Laura Dalitz

In the RegIKlim funding measure (Regional Information for Action on Climate Change, https://www.fona.de/en/measures/funding-measures/regional-information-for-action-on-climate-change.php), the cross-sectional project NUKLEUS (Actionable Local Climate Information for Germany) is concerned with the provision of useful, actionable, and high-resolution climate information for Germany and the improvement of the interface between climate data and subsequent use, e.g. in impact models for adaptation to climate change, in six pilot regions distributed across Germany.   

Climate simulations on the convection-permitting scale were hardly available at the beginning of the project and their simulation areas generally did not cover all model regions or longer time periods. Based on the requirements of the users from the model regions, the prototype of an ensemble with simulations of three regional climate models was generated and thus the first multi-decadal multi-climate model ensemble on a convection-permitting scale (approx. 3 km horizontal resolution) for Germany. It can be shown that the model results are within the expected deviations compared to measured values and that the high-resolution data of the 3 km simulations on short time and spatial scales offer added value compared to the EURO-CORDEX simulations. 

In order to improve the interface between climate data and impact models for application, a data and analysis portal (Freva) was set up in NUKLEUS, which facilitates users from the model regions to find suitable data and generate customized data sets using small programs (plugins). The first user-driven plugins have been developed and their application will be presented.  

The improvement of the interface also includes information on the uncertainties of certain influencing variables in the impact modeling and the reduction of systematic deviations of the simulations from the observed climate by e.g. bias correction methods. An important result of the uncertainty analysis of the model chain is that the range of climate information is not always the most important variable. Insufficient or outdated land use information can also have a decisive influence on the climate signal. The testing of different bias correction methods shows that the bias correction in principle leads to a reduction in systematic errors, but that the availability of high-resolution observational data for the correction is a major challenge in s. With the statistical refinement approach, good results were achieved for precipitation at a very high resolution of 300-500 m, especially in geographically highly structured regions. 

To ensure the translation of the modeling-based information into practical application, the cross-sectional project WIRKsam (Scientific Coordination for the Development of a Regional Climate Register) has developed a set of best practices based on transdisciplinary working group discussions.  To specifically address public spatial planning, it is important to exemplify the utilization potential of the data in pilot application (e.g. development plans) and develop user-oriented capacity-building modules and interpretations guidelines. Through surveys and workshops, transdisciplinary research projects can identify crucial municipal administrative processes, develop information tools for decision support and learn how they could benefit from the new data. This might involve facilitating a cross-departmental understanding of roles and responsibilities.

How to cite: Sieck, K., Pinto, J., Harrs, J.-A., Tiedje, B., Ziemann, A., Xoplaki, E., Geyer, B., Feldmann, H., Mömken, J., Paeth, H., Trachte, K., Kadow, C., and Dalitz, L.: Bringing high-resolution climate data into action: Experiences from the transdisciplinary funding measure RegIKlim, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-8945, https://doi.org/10.5194/egusphere-egu24-8945, 2024.

EGU24-9098 | ECS | Posters on site | ITS2.4/NH13.7

Climate risk analysis for adaptation planning in Zambia’s agricultural sector 

Rahel Laudien, Abel Chemura, Carla Cronauer, Tim Heckmann, Stephanie Gleixner, Christoph Gornott, Lisa Murken, and Julia Tomalka

Climate change and climate extremes increasingly threaten agricultural production and thereby pose a serious risk to agricultural livelihoods, particularly in the Global South. In support of adaptation planning, science-based information on projected climate impacts and sound information on the suitability of adaptation options is needed.

This study provides a comprehensive analysis of current and future climate-related risks in Zambia – a country that is highly vulnerable to climate change due to its geographic location and the strong socio-economic dependency on agriculture. Using data from ten Global Climate Models (GCMs) under two climate change scenarios (SSP1-RCP2.6 and SSP3-RCP7.0), we analyze future trends in climatic conditions and model their impacts on agricultural yields and crop suitability. Moreover, the study evaluates two adaptation options to promote climate-resilience in the agricultural system i.e. 1) conservation agriculture and 2) a climate and agricultural extension service called PICSA (Participatory Integrated Climate Services for Agriculture). The evaluation includes biophysical, economic, financial and gender aspects to provide comprehensive and usable information that can inform adaptation policies on the ground. The study was co-designed together with stakeholders from Zambian governmental institutions, civil society, academia, the private sector, practitioners and development partners.

Results show the strongest negative impacts of climate change in South Western Zambia where the strongest increases in temperature and dry conditions are projected. The projected impacts underline the need for strong adaptation efforts: 1) Conservation agriculture can buffer climate impacts in the near term and even increase sorghum yields by 25 to 31% in drought-prone areas in Zambia. It can play a vital role in adapting to increasingly extreme and dry climatic conditions. 2) The PICSA approach proved to be a highly economically beneficial adaptation option with each USD invested generating between 3.6 and 3.8 USD in benefits.

In addition, the study reflects on lessons learned from interdisciplinary and stakeholder-driven research – focusing not only on the Zambian context, but also on climate risk analyses that were conducted in Burkina Faso, Cameroon, Ethiopia, Ghana, Madagascar, Niger and Uganda.

How to cite: Laudien, R., Chemura, A., Cronauer, C., Heckmann, T., Gleixner, S., Gornott, C., Murken, L., and Tomalka, J.: Climate risk analysis for adaptation planning in Zambia’s agricultural sector, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-9098, https://doi.org/10.5194/egusphere-egu24-9098, 2024.

EGU24-9297 | ECS | Orals | ITS2.4/NH13.7 | Highlight

From Stakeholder Engagement to Inclusivity: Advancing Participatory Modeling for Net-Zero Sustainable Development 

Victoria Herbig, Stephanie Briers, and Bianca Vienni-Baptista

Despite the significant advancements of Integrated Assessment Models [IAMs] in recent years, criticisms underscore their limitations in effectively responding to questions on climate change adaptation and mitigation (4). Such critiques highlight the need for IAMs to be not only technologically advanced but also transparently accessible to both the modeling community and stakeholders (1).

The Horizon Europe project “Delivering the next generation of Open Integrated Assessment Models for net-zero, sustainable development” [DIAMOND] seeks to bridge these gaps. By leveraging participatory and transdisciplinary approaches, DIAMOND aims to enhance, extend, and open up IAMs, aligning them more closely with climate action and sustainable development objectives through open and responsible stakeholder engagement.

Engaging a broad range of stakeholders and working collaboratively with them stands out as pivotal in bolstering the credibility and effectiveness of modeling results (5; 6). Acknowledging policymakers’ inputs further strengthens the potential integration of modeling results into policy-making processes (1). This paper presents co-created comprehensive good practice guidelines for inclusive stakeholder engagement, grounded in a case study of the DIAMOND project. The focus is on establishing an inclusive modeling environment that ensures representation and decision making embody diverse stakeholders’ perspectives, knowledge, and interests, including those of policymakers. Utilizing a transdisciplinary approach facilitates a move towards genuine inclusivity, ensuring all relevant parties, regardless of their background or expertise, are given the opportunity to participate, contribute, and have their voices heard in the decision-making process (2). Employing a mixed-methods approach that combines a literature review, stakeholder elicitation, an online survey, and semi-structured interviews, this study triangulates these methods to comprehensively assess collaborative dynamics, adaptive strategies, and the operational context, providing a nuanced understanding of the complex interactions at play.

This paper endeavors to guide modelers, irrespective of their modeling background, towards producing relevant and actionable results that are aligned with real-world implications and policy needs (3). Through assessing and integrating the dimension of “inclusivity” within participatory modeling processes and demonstrating its integration within a transdisciplinary framework, this study aspires to offer valuable insights to the broader modeling community. The insights derived can empower modelers across disciplines to provide policymakers with evidence-based approaches for designing effective climate change adaptation measures and informing mitigation decisions, paving the way for better-informed policies guiding society towards a sustainable and net-zero future.

References:

(1) Doukas, H., Nikas, A. (2019). European Journal of Operational Research, 280, 1-24. https://doi.org/10.1016/j.ejor.2019.01.017
(2) Ernst, A., Fischer-Hotzel, A., Schumann, D. (2017). Energy Research & Social Science, 29, 23-35. http://dx.doi.org/10.1016/j.erss.2017.04.006
(3) Jordan, R., Gray, S., Zellner, M., Glynn, P. D., Voinov, A., et al. (2018). Earth’s Future, 6, 1046–1057. https://doi.org/10.1029/2018EF000841
(4) Keppo, I., Butnar, I., Bauer, N., Caspani, M., Edelenbosch, O., et al. (2021). Environmental Research Letters, 16, 053006. https://doi.org/10.1088/1748-9326/abe5d8
(5) McGookin C., Gallachóir B., Byrne, E. (2021). Renewable and Sustainable Energy Review, 151, 111504. https://doi.org/10.1016/j.rser.2021.111504
(6) Pisano, U., Lange, L., Lepuschitz, K., Berger, G. (2015). European Sustainable Development Network. ESDN Quarterly Report, 39.

How to cite: Herbig, V., Briers, S., and Vienni-Baptista, B.: From Stakeholder Engagement to Inclusivity: Advancing Participatory Modeling for Net-Zero Sustainable Development, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-9297, https://doi.org/10.5194/egusphere-egu24-9297, 2024.

Climate change poses a significant threat to communities on regional scale as well as worldwide, and the urgency for adaptation is particularly crucial for small- and medium-sized communities and cities. However, a pervasive knowledge gap exists in these regions, hindering their ability to adapt effectively. The lack of accessible and tailored climate information and services exacerbates the vulnerability of these communities. Therefore, this study focuses on addressing this knowledge gap and developing effective science communication strategies, emphasizing the regional scale through the implementation of Regional Climate Information Platforms.

The chosen case study location, Oberland (Upper Bavaria, Germany), is characterized by complex terrain, encompassing Alpine and Pre-Alpine regions, with three distinct climate zones in close proximity. The diverse topography of Oberland presents unique challenges, as climate change impacts may manifest differently across the region, particularly for hydro-meteorological extremes. Moreover, the region heavily depends on tourism, making it economically susceptible to changing climate conditions and increasing extreme events, such as extreme precipitation, flooding, summer heatwaves and decreasing snowfall affecting tourism activities (e.g. skiing, hiking, climbing, etc.).

Thus, the study aims to follow a comprehensive workflow, starting with the collection of climate data, followed by bias correction and regionalization for Oberland. High-resolution rainfall statistics will be developed and integrated into hydrodynamic simulations and cluster analyses of flood triggering mechanisms. The outcome will be the creation of risk maps for hydro-meteorological extremes, providing crucial information for stakeholders and decision-makers. Finally, these risk maps will be then incorporated into the digital decision support system, Platform Oberland within the KARE (Klimawandelanpassung auf regionaler Ebene) Project.

In addition to the scientific aspects, the study emphasizes the importance of stakeholder interaction and co-design in the development of Platform Oberland. The collaboration between scientists and stakeholders ensures that the information generated is relevant and usable for decision-making. With this study, it is also aimed to identify "best-practice" approaches for transferring scientific workflows and results into actionable climate-related measures for small- and medium-sized communities.

This case study in Oberland could serve as a regional model for effective science communication and adaptation strategies at the regional level for hydro-meteorological extremes, offering insights into the development of climate indicators and the integration of scientific findings into practical, community-centered climate adaptation.

How to cite: Koc, G., Lorenz, C., Feldmann, D., and Böker, B.: Effective Science Communication for Climate Change Adaptation on Regional Scale – Regional Climate Information Platforms: A Case Study in Oberland (Upper Bavaria – Germany), EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-10592, https://doi.org/10.5194/egusphere-egu24-10592, 2024.

EGU24-10602 | ECS | Posters on site | ITS2.4/NH13.7

Bias correction of SMILEs: A bulk approach to preserve internal variability 

Jorge Sebastian Moraga, Sabine Undorf, Peter Uhe, Natalie Lord, and Nans Addor

Single Model Initial-condition Large Ensembles (SMILEs) represent a pivotal progress in climate modeling, offering multiple simulations from a single model to address the inherent uncertainties in climate projections (Maher et al., 2021). However, biases intrinsic to climate models can distort SMILEs' outputs, potentially misrepresenting climate risks and uncertainties.

In climate impact studies, bias correction of Earth System Models (ESMs) typically aligns model outputs with observed historical data, using statistical methods to adjust climatic variables. While essential, this correction may suppress the range of climatic conditions, particularly when applied individually to each ensemble member, thus diminishing the ensemble's diversity and its ability to represent varied climate futures. Instead, we explore whether a bulk approach to bias correction is more appropriate for SMILEs. This method involves applying a consistent correction across the entire ensemble, thereby maintaining the relative differences and natural variability among the ensemble members and preserving the unique capacity of SMILEs to represent a broad spectrum of climatic conditions, in particular under current and near-future climate.

Our analysis used the 100-member dataset from the Community Earth System Model Large Ensemble Project Phase 2 (CESM-LENS2, Rodgers et al., 2021), covering historical and future climate simulations. We adjusted key climate variables—precipitation, temperature, relative humidity, and surface pressure within the CONUS domain—using the ISIMIP3basd algorithm (Lange, 2019), with MSWX reanalysis data as the historical reference (Beck et al., 2022). Our experiment involved a twofold comparison: We first evaluated the results after adjusting the entire ensemble at once using (the bulk approach) and, secondly, after adjusting each individual ensemble member separately (member-by-member approach). This comparative analysis allowed us to discern the effects of these two different bias correction methodologies on the ensemble's ability to represent climate variability and extremes.

Our results show the effect of both bias correction approaches on the variability of crucial climate extreme statistics and the correlation between ENSO and climate variables. Additionally, we discuss how the choice of bias adjustment method can influence the magnitude of projected changes under future climate scenarios, a key consideration in climate impact studies.

References:

  • Beck, H. E., Van Dijk, A. I., Larraondo, P. R., McVicar, T. R., Pan, M., Dutra, E., & Miralles, D. G. (2022). MSWX: Global 3-hourly 0.1 bias-corrected meteorological data including near-real-time updates and forecast ensembles. Bulletin of the American Meteorological Society, 103(3), E710-E732.
  • Lange, S. (2019). Trend-preserving bias adjustment and statistical downscaling with ISIMIP3BASD (v1. 0). Geoscientific Model Development, 12(7), 3055-3070.
  • Maher, N., Milinski, S., & Ludwig, R. (2021). Large ensemble climate model simulations: introduction, overview, and future prospects for utilising multiple types of large ensemble. Earth System Dynamics, 12(2), 401-418.
  • Rodgers, K. B., Lee, S. S., Rosenbloom, N., Timmermann, A., Danabasoglu, G., Deser, C., ... & Yeager, S. G. (2021). Ubiquity of human-induced changes in climate variability. Earth System Dynamics, 12(4), 1393-1411.



How to cite: Moraga, J. S., Undorf, S., Uhe, P., Lord, N., and Addor, N.: Bias correction of SMILEs: A bulk approach to preserve internal variability, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-10602, https://doi.org/10.5194/egusphere-egu24-10602, 2024.

EGU24-12154 | Posters on site | ITS2.4/NH13.7

Assessing and explaining future changes on sub-daily precipitation extremes using an ensemble of convection-permitting models 

Eleonora Dallan, Francesco Marra, Giorgia Fosser, Marco Marani, and Marco Borga

Anticipating and understanding the future evolution of intense precipitation events is crucial for improved risk management, especially in regions with mountainous terrain and urban areas vulnerable to natural disasters from extreme weather. Convection-permitting climate models (CPMs) operating at kilometer scales realistically depict convective precipitation mechanisms and complex terrain, enhancing the description of sub-daily extreme precipitation. However, their computational demands restrict simulations to short time periods (10-20 years), and limit the availability of ensemble members, hindering the evaluation of extreme event change and associated uncertainty.

This study employs an innovative non-asymptotic extreme value approach, proven effective in estimating rare return levels with reduced stochastic uncertainty even from short datasets, and which can help in providing insights on the changing processes. We apply the Simplified Metastatistical Extreme Value distribution (SMEV) to estimate the projected changes in future extreme sub-daily precipitation in a region characterized by complex terrain—specifically, the North Italy area encompassing both lowlands and the Italian Alps. Our analysis focuses on an ensemble of 9 CPMs from the CORDEX-FPS project, with a spatial resolution of 3 kilometers. We investigate three time periods: historical (1996-2005), near future (2041-2050), and far future (2090-2099) under the RCP8.5 emission scenario. We estimate return levels up to a 1% yearly exceedance probability (100-year return time) for precipitation durations from 1 to 24 hours. Their future change is evaluated at each grid point, conducting a permutation test to assess the statistical significance of the changes.

Results indicate a general increase in extreme precipitation across the domain and all durations, with spatial patterns of significant changes varying with durations, time period, and location. A pronounced increase occurs in some of the mountainous areas: at short durations in Eastern Alps, and across all durations in the northern Apennines. The western Alps and surroundings show moderate and not-significant change. Leveraging SMEV's ability to separate precipitation intensity distribution from event occurrence, we also examine the change in distribution parameters to interpret the shift in return levels in term of changes in thermodynamics (linked to temperature and water vapor content) and atmospheric dynamics controls. Interestingly, thermodynamics seems to be driving significant changes at short durations, while small-scale local dynamics contribute across all durations. Differences emerge between the Eastern Alps and Northern Apennines, with the latter showing a stronger intensification of intense versus moderate extreme events.

These findings provide valuable insights towards quantifying and understanding the future changes in precipitation extremes, benefiting stakeholders involved in risk management and design of adaptation measures.

This study was carried out within the RETURN Extended Partnership and received funding from the European Union Next-GenerationEU (National Recovery and Resilience Plan – NRRP, Mission 4, Component 2, Investment 1.3 – D.D. 1243 2/8/2022, PE0000005).

How to cite: Dallan, E., Marra, F., Fosser, G., Marani, M., and Borga, M.: Assessing and explaining future changes on sub-daily precipitation extremes using an ensemble of convection-permitting models, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12154, https://doi.org/10.5194/egusphere-egu24-12154, 2024.

EGU24-12378 | ECS | Posters on site | ITS2.4/NH13.7

Comparing extreme sub-daily rainfall projections from temperature-scaling and convection-permitting climate models across an Alpine gradient 

Rashid Akbary, Marco Marani, Eleonora Dallan, and Marco Borga

Understanding projected changes in sub-daily extreme rainfall in mountainous basins can help increase our capability to adapt to and mitigate against flash floods and debris flows. Here we compare the changes in extreme rainfall projections from apparent Clausius-Clapeyron (CC) temperature scaling against those obtained from convection-permitting climate model simulations. Temperature and precipitation projections are obtained from an ensemble of convection-permitting climate models (CPM), which are suitable to the task given their ability to explicitly represent deep convection and to resolve the mountainous topography. The CPM data provided by the CORDEX-FPS Convection project at 1-hour temporal and remapped to 3 km spatial resolution, cover historical and far-future (2090-2099) time periods under the extreme climate change scenario (RCP8.5). Due to the computational demands however, CPM simulations are still too short (typically 10-20 years) for analyzing extremes using conventional methods. We use a non-asymptotic statistical approach (the Metastatistical Extreme Value, MEVD, Marani and Ignaccolo, 2015) for the analysis of extremes from short time periods, such as the ones of CPM simulations. We use hourly precipitation and temperature data from 174 stations in an orographically complex area in northeastern Italy as a benchmark.

Results from our analysis reveal that the apparent CC temperature scaling method demonstrates effective performance when applied to 1-hour extreme rainfall projections and for high return periods. However, its accuracy decreases as the precipitation duration increases, highlighting potential limitations in accurately predicting changes in longer-duration extreme rainfall. Variations in performance are also noted when considering different return periods, as we find CPM changes depending on them, contradicting traditional CC-scaling. Furthermore, we show that elevation is a key factor influencing temperature variations, with higher elevation locations experiencing more pronounced temperature increases with respect to lowland areas. This affects more the results for 1 hr extreme rainfall projections, whereas it is less relevant for 24-h duration. These findings identify some serious limitations of traditional CC scaling and emphasize the need for a nuanced understanding of the scaling method's applicability under various conditions.

How to cite: Akbary, R., Marani, M., Dallan, E., and Borga, M.: Comparing extreme sub-daily rainfall projections from temperature-scaling and convection-permitting climate models across an Alpine gradient, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12378, https://doi.org/10.5194/egusphere-egu24-12378, 2024.

EGU24-12806 | ECS | Posters on site | ITS2.4/NH13.7

The effect of terrestrial water storage anomalies on regional economic growth 

Anna Reckwitz, Maximilian Kotz, Christian Voigt, and Leonie Wenz

Terrestrial water storage (TWS) is an essential resource for agriculture, urban development, and energy production, as well as ecosystem health and climate change mitigation. Through satellite gravimetry methods, GRACE and GRACE-FO measurements enable the assessment of TWS anomalies globally, revealing significant alterations over the past two decades due to natural variability, climate change impacts, and direct human influence. Existing studies focus on the impacts of TWS changes on the production of specific crops or agricultural output in specific countries, yet the effects on agro-economic output on a more global scale are not yet well understood. 

To address this gap in our understanding of the macroeconomic impacts of TWS changes, we combine GRACE measurements with data on economic growth from more than 1600 subnational regions worldwide over the last 60 years. We then empirically assess the impact of TWS anomalies on regional economic growth, employing a long-difference model and fixed-effects panel regression, following recent work on temperature and precipitation impacts. We find that negative groundwater anomalies are associated with reductions in economic growth in a majority of regions. This highlights the critical role of freshwater availability, in particular in low-income regions. Furthermore, we observe that the relationship between TWS and economic growth depends on both meteorological and socioeconomic factors. These heterogeneous relations reflect the complex interplay between water resources and economic development, and indicate potential endogeneity therein. We therefore further discuss instrumental variable approaches for isolating the meteorological drivers of water storage and their causal impact on economic output. These findings contribute valuable insights to the ongoing discourse on sustainable water management and its implications for economic prosperity.

How to cite: Reckwitz, A., Kotz, M., Voigt, C., and Wenz, L.: The effect of terrestrial water storage anomalies on regional economic growth, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12806, https://doi.org/10.5194/egusphere-egu24-12806, 2024.

EGU24-13127 | ECS | Posters on site | ITS2.4/NH13.7 | Highlight

Climate adaptive urban extreme weather risk assessment and management 

Lingyan Kang, Jiang Wu, and Fengting Li

Climate change, an escalating global predicament, is intricately linked with the uncertainties surrounding urban development, a process that is intricately tied to economic growth and social progress. This interconnectedness gives rise to new interconnected risks that present significant social and economic challenges, threatening the sustainability of our urban centers. This study takes into account climate change risk mitigation and adaptation strategies, focuses on urban climate risk identification and establishment of climate adaptive city risk assessment index system. Through the sorting of historical data, the improvement of disaster statistics and other interconnection to clarify regional risks in different fields, and discusses methods to achieve efficient risk management and governance of climate-resilient cities under the dual background of urbanization and climate change. By adopting a perspective centered on climate risk management, this research provides forward-thinking guidance for long-term perspectives on urban planning and construction.

How to cite: Kang, L., Wu, J., and Li, F.: Climate adaptive urban extreme weather risk assessment and management, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-13127, https://doi.org/10.5194/egusphere-egu24-13127, 2024.

The IPCC AR6 assessment of the impacts and risks associated with projected climate changes for the 21st century is both alarming and ambiguous. According to computer projections, global surface may warm from 1.3 to 8.0 °C by 2100, depending on the global climate model (GCM) and the shared socioeconomic pathway (SSP) scenario used for the simulations. Actual climate-change hazards are estimated to be high and very high if the global surface temperature rises, respectively, more than 2.0 °C and 3.0 °C above pre-industrial levels. Recent studies, however, showed that a substantial number of CMIP6 GCMs run “too hot” because they appear to be too sensitive to radiative forcing, and that the high/extreme emission scenarios SSP3-7.0 and SSP5-8.5 must be rejected because judged to be "unlikely" and "highly unlikely", respectively. Yet, the IPCC AR6 mostly focused on such alarmistic scenarios for risk assessments. This paper examines the impacts and risks of “realistic” climate change projections for the 21st century generated by assessing the theoretical models and integrating them with the existing empirical knowledge on global warming and the various natural cycles of climate change that have been recorded by a variety of scientists and historians. This is achieved by combining the "realistic" SSP2-4.5 scenario and empirically optimized climate modeling. The GCM macro-ensemble that best hindcast the global surface warming observed from 1980–1990 to 2012–2022 is found to be made up of models that are characterized by a low equilibrium climate sensitivity (ECS) (1.5<ECS<3.0 °C), in contrast to the IPCC AR6 likely and very likely ECS ranges of 2.5-4.0 °C and 2.0-5.0 °C, respectively. This GCM macro-ensemble projects a global surface temperature warming of 1.68-3.09 °C by 2080–2100 instead of 1.98-3.82 °C obtained with the 2.5-4.0 °C ECS GCMs. However, if the global surface temperature records are affected by significant non-climatic warm biases — as suggested by satellite-based lower troposphere temperature records and current studies on urban heat island effects — the same climate simulations should be scaled down by about 30%, resulting in a warming of about 1.18-2.16 °C by 2080–2100. Furthermore, similar moderate warming estimates (1.15-2.52 °C) are also projected by alternative empirically derived models that aim to recreate the decadal-to-millennial natural climatic oscillations, which the GCMs do not reproduce. The obtained climate projections show that the expected global surface warming for the 21st century will likely be mild, that is, no more than 2.5-3.0 °C and, on average, likely below the 2.0 °C threshold. This should allow for the mitigation and management of the most dangerous climate-change-related hazards through appropriate low-cost adaptation policies. In conclusion, enforcing expensive decarbonization and net-zero emission scenarios, such as SSP1-2.6, is not required because the Paris Agreement temperature target of keeping global warming below 2 °C throughout the 21st century should be compatible also with moderate and pragmatic shared socioeconomic pathways such as the SSP2-4.5.

Reference: Scafetta, N.: 2024. Impacts and risks of “realistic” global warming projections for the 21st century. Geoscience Frontiers 15(2), 101774. https://doi.org/10.1016/j.gsf.2023.101774

How to cite: Scafetta, N.: Impacts and risks of “realistic” global warming projections for the 21st century, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-16297, https://doi.org/10.5194/egusphere-egu24-16297, 2024.

The correct representation of fine-scale atmospheric processes, like convection, is vital for predicting extreme weather events and CPMs have already shown to provide more reliable representation of extreme precipitation. However, in most cases their validation is limited to the precipitation field and based on sparse in-situ observations or coarser resolution observational gridded dataset. In this study, we first explore whether high-resolution (i.e., grid spacing 2.2km) reanalysis product SPHERA provides a realistic representation of the in-situ observations, thus offering  a comprehensive overview of the atmosphere at fine scale and functioning as a reliable reference dataset for CPMs evaluation. Then the sub-daily precipitation and wind fields of the CPMs ensemble from the CORDEX Flagship Pilot project on Convective Phenomena over Europe and the Mediterranean (FPS Convection) is validated against both in-situ observation and SPHERA. The validation focuses on extreme quantiles, spatial variability and event representation with a quantile based approach (i.e., the event starts when atmospheric variables are above a certain quantile, and ends when it goes below). Results show a general good agreement between in-situ observations and SPHERA, that is found to be a good reference dataset to evaluate the CPM models. When looking at the extreme quantiles, the CPMs well represent  both wind and precipitation fields, although they underestimate heavy precipitation in summer (i.e., June-July-August). Similarly, the spatial distribution of precipitation and wind is well represented for all the season, with a decrease in the spatial variability and spatial correlation for the heavy precipitation in the summer. Finally the CPMs underestimate the number of the events when precipitation and wind are treated singularly, while they substantially overestimate the number of compound events of rainfall and winds. The analysis shows the capability of CPMs to represent the precipitation and wind fields and highlights the possibility of using high-resolution reanalysis into the evaluation of convection-permitting models. Moving from point-based measurements to high-resolution gridded observational datasets opens the path to the use of SPHERA for advanced bias correction methods that could take into account the full 3D dimension of the atmosphere and the processes within it.

How to cite: Cesarini, L. and Fosser, G.: Validation of CPM’s wind and precipitation field against observations and the highresolution reanalysis dataset SPHERA, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17774, https://doi.org/10.5194/egusphere-egu24-17774, 2024.

EGU24-17775 | Posters on site | ITS2.4/NH13.7

Assessment of convection-permitting sub-daily extreme precipitation simulations over Italy 

Marco Borga, Paola Mazzoglio, Marco Lompi, Francesco Marra, Eleonora Dallan, Roberto Deidda, Pieluigi Claps, Salvatore Manfreda, Leonardo Noto, Alberto Viglione, Mario Raffa, and Enrica Caporali

Convection-permitting climate models have the potential to capture crucial processes in the climate system, presenting an opportunity to significantly enhance climate projections by providing more accurate representations of precipitation extremes. In this work, we conduct an evaluation of the accuracy of sub-daily precipitation extremes obtained from VHR-PRO_IT (Very High-Resolution PROjections for Italy, Raffa et al., 2023) over the Italian peninsula,. VHR-PRO_IT is generated through dynamic downscaling of the Italy 8km-CM climate projection at approximately 2.2 km resolution under the IPCC RCP4.5 and RCP8.5 scenarios, employing the Regional Climate Model COSMO-CLM.

Gauged locations are used to assess the accuracy of VHR-PRO_IT in reproducing observed extremes. More specifically, the observed dataset used as ground truth for the comparison is I2-RED (Improved Italian – Rainfall Extreme Dataset; Mazzoglio et al., 2020). For this work, 742 rain gauges covering the entire country with a minimum of 30 years of short-duration (1, 3, 6, 12, 24 h) annual maximum rainfall depths recorded from 1980 to 2022 are used. Conversely, the dataset derived from the VHR-PRO_IT climate projections includes annual maxima from a 30-year time series, connecting the historical period (1981-2005) with 5 years of the RCP8.5 scenario (2006-2010) of the CPM. Return levels are obtained for both dataset by means of a GEV distribution and inform the assessment of the CPM simulations. 

Preliminary results outline the quality of the CPM simulations, especially at 24 hours duration, and show the impacts of return period, seasonality, elevation, latitude and proximity to the sea on the CPM model deviations. The results from this work are expected to have implications for both water resources management and adaptation measures.

References

Mazzoglio P., Butera I., Claps P. (2020). I2-RED: a massive update and quality control of the Italian annual extreme rainfall dataset. Water, 12, 3308.

Raffa M., Adinolfi M., Reder A., Marras G.F., Mancini M., Scipione G., Santini M., Mercogliano P.  (2023). Very High Resolution Projections over Italy under different CMIP5 IPCC scenarios. Scientific Data, 10, 238.

How to cite: Borga, M., Mazzoglio, P., Lompi, M., Marra, F., Dallan, E., Deidda, R., Claps, P., Manfreda, S., Noto, L., Viglione, A., Raffa, M., and Caporali, E.: Assessment of convection-permitting sub-daily extreme precipitation simulations over Italy, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17775, https://doi.org/10.5194/egusphere-egu24-17775, 2024.

In the contemporary landscape, the aftermath of each weather-related disaster triggers swift estimations of economic losses, often accompanied by attributions of increased frequency or intensity of such events. The prompt assignment of blame for weather-related disaster losses is a complex endeavor, as discerning the precise role of climate change proves challenging due to the intricacy arising from intertwining climate alterations with societal transformations, contributing to the evolving dynamics of disaster impacts. In parallel, assessing disaster loss and damage is crucial, especially in vulnerable areas prone to natural disasters, such as the Himalayan region, as it is highly susceptible to climate-induced events and potentially severe consequences for the environment and human settlements. The study focuses on the state of Uttarakhand in India, aiming to comprehensively understand the interplays between climate change, societal shifts, and economic repercussions following weather-related calamities. The primary objective is to develop a detailed loss inventory for Uttarakhand, specifically focusing on past events, types of losses, and their spatial distribution. The methodology thoroughly examines secondary sources, data from the Em-Dat database, government reports, and relevant research articles. This comprehensive approach enables understanding of weather-related disaster losses, considering the impacts of climate change and societal changes in the region. The study also employs a robust time-series analysis methodology to unravel the temporal and spatial distribution of disasters due to extreme events, recognizing their significance in shaping disaster dynamics. The analysis aims to identify vulnerable rural and urban clusters within Uttarakhand, provide valuable insights into the spatial patterns of specific loss types, and map high-risk areas within Uttarakhand, contributing to proactive disaster mitigation strategies. This information is crucial for adapting disaster response and recovery strategies, allowing for the effective allocation of resources based on the unique needs of affected regions by integrating loss inventory creation, time-series analysis, and vulnerability mapping. The findings are expected to not only deepen our understanding of the complex interplays between climate change, societal shifts, and disaster losses but also provide actionable insights for mitigating the impact of future weather-related calamities in the Himalayan region, particularly in the state of Uttarakhand.

How to cite: Goyal, S. and Mukherjee, M.: Comprehensive Assessment of Climate-Induced Disaster Losses in Uttarakhand: A Time-Series Analysis and Vulnerability Mapping Approach , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18267, https://doi.org/10.5194/egusphere-egu24-18267, 2024.

EGU24-19847 | ECS | Orals | ITS2.4/NH13.7 | Highlight

Challenges in quantifying physical risk to assets globally 

Joe Stables, Graham Reverly, James Brennan, Sally Woodhouse, Nicholas Leach, Laura Ramsamy, Patricia Sullivan, and Jonathan Davies

As the physical processes of our world change, the landscape of risk has changed with it. At Climate X, we provide high-quality data to the financial sector so that evolving risks to global portfolios can be quantified. A crucial element of this is the physical risk from events, including extreme weather events.

Traditionally risk assessments have been carried out at an asset level on small scales, with a dedicated team spending days on tens of assets. The high price and slow turnaround makes this unfeasible for large scale operations. We provide an alternative, leveraging open source datasets and research to estimate the physical risk to over half a billion buildings worldwide. This talk will highlight some challenges of working at this scale, and illustrate our approaches to resolving them.

How to cite: Stables, J., Reverly, G., Brennan, J., Woodhouse, S., Leach, N., Ramsamy, L., Sullivan, P., and Davies, J.: Challenges in quantifying physical risk to assets globally, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-19847, https://doi.org/10.5194/egusphere-egu24-19847, 2024.

EGU24-20739 | Orals | ITS2.4/NH13.7 | Highlight

Incorporating Ethics into Climate Intervention Research, Experimentation, and Potential Deployment 

Mark Shimamoto, Janice Lachance, and Billy Williams

Climate change requires urgent action. Aggressive actions toward carbon emissions reduction must remain the primary strategy for reversing and addressing climate change. However, increasingly the world is considering technology-based climate intervention approaches—often called climate engineering. There are major practical and ethical questions about the significant risks and potential trade-offs some of these approaches would bring and how they would be measured against the risks of our warming world. Recognizing the need for guiding principles in this fast-moving, dynamic space and building on AGU’s longstanding history of advancing and advocating for strong scientific ethics, AGU is facilitating the development of a draft Ethical Framework for Climate Intervention Research, Experimentation, and Deployment. The ethical framework will be released in 2024 and will serve as a resource to help governments, researchers, NGOs, and the private sector make responsible decisions when engaging in climate intervention research or policy. In 2023, the draft framework completed a rigorous three-month public comment period and consultation process to include more holistic input from other scientists and ethicists, as well as community voices, youth advocates, and many more. This presentation will highlight the ethical principles and how the science community can incorporate and advocate for ethics in climate intervention research.

How to cite: Shimamoto, M., Lachance, J., and Williams, B.: Incorporating Ethics into Climate Intervention Research, Experimentation, and Potential Deployment, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-20739, https://doi.org/10.5194/egusphere-egu24-20739, 2024.

Considerable risk is involved in the use of climate models or their products (i.e., simulations and data) when there is a lack of adequacy or fitness for one’s purpose. Of specific concern is the risk of generating information in response to an actionable or applied question or aim that is irrelevant, misleading, inappropriate, inconsistent, or highly inaccurate, as this can lead to downstream harms such as maladaptation. This form of “misuse” is innocent or unintentional, and is largely a function of a user’s misunderstanding or misinterpretation of the intended purposes of a model and/or modeling exercise and the applicability of the model’s products. Ineffective communication and lack of transparency into the intended purposes, assumptions, representational features, adequacies, as well as inadequacies and limitations of a model, can lead to this form of inappropriate and unjustified repurposing. Currently, there is an increase in the demand for open and accessible data, and an increase in the use of climate data, especially data from high-resolution modeling efforts, for applied and actionable purposes (contexts in which derived products are used to inform decision-making). Given both conditions, the reduction and management of possible inappropriate repurposing, i.e., misuse, has become a highly salient consideration for any modeling effort. Producers of models and their products have a moral duty to implement mechanisms to aid users in the identification, understanding, and control of this risk. This can happen by way of the distribution of expert guidance, increase in intentional transparency, and instantiation of systematic norms for clearly and plainly communicating the fitness of purpose and inadequacies of models and their products. This would provide a large step forward toward the reduction of misuse of information in climate science that could lead to harmful consequences, and pave the way for the development of an ethics of scientific practice for the climate science community.

How to cite: Morrison, M.: Towards an Ethics of Modeling and Data Use for Actionable Climate Science, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-20838, https://doi.org/10.5194/egusphere-egu24-20838, 2024.

EGU24-22469 | Posters on site | ITS2.4/NH13.7

Dynamic agricultural weather indicators for extreme weather prediction in agriculture 

Timm Waldau, Pedro Batista, Peter Baumann, Thorsten Behrens, Peter Fiener, Jens Foeller, Markus Moeller, Ingrid Noehles, Karsten Schmidt, and Burkhard Golla

The project “DynAWI – dynamische Agararwetterindikatoren” (dynamic agriculture weather indices) aims to develop a process chain for data integration and real-time analysis for extreme weather. Extreme weather events have a major impact on agriculture and horticulture and cause significant economic costs. The damage depends not only on the type of extreme weather event (e.g. heat wave, drought stress or heavy precipitation), but also on the ontogenetic development of the crops. Previously, farmers calculated their risk with fixed weather indicators and because of the multi-dimensionality of the source data and it was difficult to calculate using traditional relational databases in an acceptable time.

We have developed a web application for real-time calculation of dynamic weather indicators by linking a back-end infrastructure of Datacube servers and a Vue front-end infrastructure with a machine learning model in an R environment. The web application can perform real-time analyses based on multi-dimensional spatio-temporal data. Future plans include enriching the web application with additional agricultural weather indicators and linking it to weather forecasts to provide an in-season risk assessment for crop losses.

How to cite: Waldau, T., Batista, P., Baumann, P., Behrens, T., Fiener, P., Foeller, J., Moeller, M., Noehles, I., Schmidt, K., and Golla, B.: Dynamic agricultural weather indicators for extreme weather prediction in agriculture, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-22469, https://doi.org/10.5194/egusphere-egu24-22469, 2024.

EGU24-22471 | Orals | ITS2.4/NH13.7

From National Climate Scenarios to National Climate Information 

Carol McSweeney, Jason Lowe, and Neha Mittal

National Climate Scenarios provide a common basis for national risk assessment and adaptation planning. Recent examples include the UK’s UKCP18, the Netherlands’ KNMI23 and the Australian ‘Climate Change in Australia’ (2015).

Advances in climate modelling approaches provide the potential for a step change in the quality, and type of national climate scenarios that will likely be produced over coming years. While these advances include improvements in the traditional approaches employed in the provision of future climate projections for adaptation planning (updated global model ensembles, various downscaling approaches including convective permitting regional projections, improvements in constraining model ensembles), developments in a wider range techniques are increasingly being used in the assessment of climate resilience. These include large initial-condition ensembles, event attribution, the exploration of ‘High Impact Low Likelihood’ (HILL) scenarios, as well as the potential to exploit enhanced skill in initialised seasonal and decadal forecasts.

Here we will share what we are learning through parallel activities which seek to (a) develop our understanding of the needs of the diverse user community in the UK through an extensive user consultation to enhance usefulness and usability and (b) scope the opportunities emerging from the climate science community may have to address the gaps in existing information, and their readiness to contribute to a National Climate Information package.

How to cite: McSweeney, C., Lowe, J., and Mittal, N.: From National Climate Scenarios to National Climate Information, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-22471, https://doi.org/10.5194/egusphere-egu24-22471, 2024.

Gender vulnerabilities to climate change are increasingly recognized in the global arena; however, attention to gender in the context of climate change in India is relatively recent. Agriculture is a crucial part of the country’s economy and the agricultural practices in the Indian Himalaya are highly influenced by gender dynamics due to traditional gender roles and various social and cultural constraints. This study provides empirical evidence on how gender plays a role in the susceptibility to climate change from a district of Central Himalaya in Uttarakhand. The study identifies the key indicators that affect vulnerability both within and between genders. Additionally, the gender data is categorized based on caste (social segregation) and lower and higher elevation in the hills (geographical segregation) for investigating gender-specific vulnerabilities - both inter and intra-gender - in agricultural households. The primary data were collected in the months, April - June 2022 from 298 sample households based on stratified sampling selected from 20 villages in the district, Almora, Uttarakhand. Categorical principal component analysis (Cat-PCA) was used to develop weights for adaptive capacity and sensitivity indicators. Based on the Inter-governmental Panel on Climate Change (IPCC) framework 2014 and the theory of intersectionality, an intrinsic gender vulnerability index is developed. A sensitivity analysis approach is further adopted to pinpoint the major indicators influencing gender intersectional vulnerabilities. The expected results go beyond the conventional gender paradigms by exploring the intersectional nature of vulnerability and recognizing the complex interplay of various socioeconomic factors such as caste, education, income, and access to resources that contribute to differential gender vulnerabilities.

Keywords: Gender vulnerability, intersectionality, climate change, Cat-PCA, Sensitivity analysis.

How to cite: Choudhary, A., Sam, A. S., Kaechele, H., and Joshi, P. K.: Identifying key indicators and exploring gender intersectional vulnerabilities to climate change in agricultural households: A study of Central Himalaya, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1005, https://doi.org/10.5194/egusphere-egu24-1005, 2024.

EGU24-1695 | ECS | Orals | ITS2.5/NH13.5

Emergent vulnerabilities: exploring the role of drought for increasingly diverse groundwater conflicts in Germany  

Jan Sodoge, Giuliano Di Baldassarre, Christian Kuhlicke, and Mariana Madruga de Brito

Historically, groundwater resources have been perceived as inexhaustible in Central Europe by policy-makers and the general public. However, recently increasing drought periods and user groups with competing interests caused conflicts about the usage of and access to groundwater resources. Groundwater-related conflicts, defined here as social issues resulting from divergent viewpoints among diverse stakeholders, have been extensively examined in regions with an extended history of water scarcity. Yet, there is limited research on the emergence of groundwater-related conflicts in Central Europe and the role of recent drought events in shaping these. Here, we study the emergence of groundwater-related conflicts in Germany since 2000 using a text-mining approach. Specifically, we investigate four research questions: (i) how are groundwater-related conflicts characterized, (ii) which influential stakeholders are shaping these conflicts, (iii) what are the spatio-temporal patterns of these conflicts and (iv) how do drought events and different socio-economic factors influence their occurrence? To address these questions, we use machine learning and text-mining techniques on more than one million newspaper articles to develop a spatio-temporal database of conflicts. We also extract and categorize involved stakeholders using a named entity recognition algorithm. Then, we use statistical modeling to link the occurrences of groundwater conflicts with drought indices and other additional explanatory variables. Our results reveal the growing diversity and geographical spread of groundwater-related conflicts in Germany. Also, our results shed light on the role of the recent drought events’ influence on conflicts. Our findings contribute to mapping the evolving landscape of groundwater-related conflicts in Germany and the effects of drought events. The proposed methods have the potential to enable large-scale studies of environmental conflicts using vastly available text data.

How to cite: Sodoge, J., Di Baldassarre, G., Kuhlicke, C., and Madruga de Brito, M.: Emergent vulnerabilities: exploring the role of drought for increasingly diverse groundwater conflicts in Germany , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1695, https://doi.org/10.5194/egusphere-egu24-1695, 2024.

EGU24-2160 | ECS | Posters on site | ITS2.5/NH13.5

The Tail End of Migration: Assessing the Climate Resilience of Migrant Households in Ethiopia 

Ann-Christine Link and Roman Hoffmann

Climate change is associated with increasing frequencies and intensities of extreme weather events. These can, directly and indirectly, shape human (im)mobility. While most research on migration in the context of climate change focuses on climate as a migration driver in origin areas, there is a gap in knowledge on the role of migration for climate resilience in the destination areas. This paper studies differences in resilience (resistance and recovery) to climatic shocks between migrant and non-migrant households in Ethiopia, a country that is highly exposed and vulnerable to climate change. We use longitudinal data from the Living Standards Measurement Study (LSMS) conducted by the World Bank to construct a comprehensive Well-Being Index, which is used to analyze the impacts of climatic shocks and identify households that are more or less able to resist and recover from shocks. We use fixed effect panel regression approaches to model the impacts of climatic shocks on well-being over time for migrant and non-migrant households. Further explorative mediation analyses yield insights into mechanisms explaining differences between households. We find that migrant households have an overall lower climate resistance as they experience double as high well-being impacts when exposed to climatic shocks compared to non-migrant households. Climatic shocks significantly reduce the food security of all affected households and, in addition, negatively impact access to basic infrastructures and health for migrant households. Mediation analyses suggest that these differential climatic impacts are mainly driven by characteristics of migrant-origin regions, including poverty. Migrant households originating from less prosperous regions still face disadvantages even if they now reside in more prosperous regions. This contrasts the experience of non-migrant households whose resilience benefits from increased prosperity in their region of residence. While migrant households show a lower resistance to climate shocks, they recover faster from climatic shocks, which can be associated with diversified livelihoods and remittances that take time to unfold. This research is highly relevant to policy as it improves the understanding of underlying factors shaping differential vulnerability to climate change impacts and supports targeted interventions to increase the resilience of affected households.

How to cite: Link, A.-C. and Hoffmann, R.: The Tail End of Migration: Assessing the Climate Resilience of Migrant Households in Ethiopia, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-2160, https://doi.org/10.5194/egusphere-egu24-2160, 2024.

EGU24-3460 | ECS | Posters on site | ITS2.5/NH13.5

Using data and findings from natural and social sciences to assess urban heat vulnerability: a comparison of different methodologies. 

Karina Löffler, Andrea Damm, Heinz Gallaun, Judith Köberl, Dominik Kortschak, Petra Miletich, Lena Oberhuber, and Manuel Strohmaier

Climate change is causing temperatures around the globe to rise, leading to an increase in the frequency and intensity of hot days and heatwaves. In urban areas, this trend is further exacerbated by urban characteristics, such as the high building density and degree of sealing, the high concentration of anthropogenic heat sources or the reduced outgoing radiation. Extreme heat puts a strain on health, especially for elders and people with pre-existing illnesses. For effective and targeted prevention of heat-related morbidity and mortality, information on the spatial variance of people’s exposure and sensitivity, but also their adaptability towards heat can be of great importance.

A common practice for determining the distribution of vulnerable population groups within a city or an area is to construct a spatial Heat Vulnerability Index (HVI) based on findings and data from natural and social sciences, including e.g. socio-economic data, health data, remote sensing data, and climate data. However, there is no standardized workflow but a variety of approaches for the construction of an HVI, which may lead to significant differences in the calculated index ranks. In order to assess the impact of changes in the method design on the resulting index, we test different input data sets, weighting methods and spatial scales for the construction of a spatial HVI for the city of Graz (Austria). The input parameters for the HVI include temperature data, derived from satellite data and weather stations, as well as spatial socio-economic data that describe the population’s sensitivity towards heat and the capability to adapt to high temperatures. By conducting an uncertainty analysis and a global variance-based sensitivity analysis, the partial contribution of changing input variables, chosen weighting methods and different spatial scales to the output’s variance is determined. In addition, a local sensitivity analysis compares the application of land surface temperature derived from thermal satellite imagery to the use of station temperature data for the construction of an HVI.

How to cite: Löffler, K., Damm, A., Gallaun, H., Köberl, J., Kortschak, D., Miletich, P., Oberhuber, L., and Strohmaier, M.: Using data and findings from natural and social sciences to assess urban heat vulnerability: a comparison of different methodologies., EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-3460, https://doi.org/10.5194/egusphere-egu24-3460, 2024.

EGU24-3565 | ECS | Posters on site | ITS2.5/NH13.5

The lethal grip of heat: mapping the heat wave-mortality nexus in Spain (1975-2019) 

Dariya Ordanovich, Ana Casanueva, Aurelio Tobías, and Diego Ramiro

Nowadays, the rise in the global temperatures are a source of concern, particularly in the Mediterranean region, where Spain is already witnessing notable consequences for its aging population. Predictions for the end of the XXI century reveal a persistent increase in air temperatures along with an increment of extreme episodes. Abnormal heat, once considered an 'environmental accident', is now a serious public threat. This contribution endeavors to quantify the added effects of heat wave exposure on mortality by demographic and socioeconomic strata during the period of 45 years in Spain at the provincial level. Moreover, we aim to explore the temporal evolution in these effects and variations in its spatial patterns, especially focusing on the inequality aspects that shape the health outcomes in an increasingly aging population.

Here we leverage daily individual mortality data and other contextual data on population from the National Institute of Statistics of Spain and air temperature estimates from the ERA5 global reanalysis. We also use the historical settlement data as a proxy for population distribution from 1975 onward. To estimate the main and added effects of heat waves we fit a quasi-Poisson time-series regression model using a distributed lag non-linear model with 10 days of lag, controlling for trends and day of the week.

We analyze approximately 15.8 million of deaths registered in Spain between 1975 and 2019. During the selected time window, we expect to see a shift in the temperature-mortality association from a V-shape in the first decades of the observation to a U-shape by the end of the period all across the provinces, thus revealing a progressive flattening of the exposure-response curve. We also expect to observe an overall reduction in the mortality burden associated with the temperatures. In particular, we anticipate more significant and rapid decline in the cold-related risks and attributable fractions in comparison with the heat-related ones, with some latitudinal variations across the country.

On the other hand, we witness a steady increase in the incidence of the heat wave episodes with time all over the country. We expect to see a positive added effect of heat wave on mortality, however this effect is assumed to be smaller than the primary effect. In addition, we anticipate observing variations in the effect depending on the heat wave order, duration, intensity, geographic location and demographic strata. The largest added effects are expected for the longest and strongest heat waves in the oldest-old population in the less accustomed to extreme heat areas.

How to cite: Ordanovich, D., Casanueva, A., Tobías, A., and Ramiro, D.: The lethal grip of heat: mapping the heat wave-mortality nexus in Spain (1975-2019), EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-3565, https://doi.org/10.5194/egusphere-egu24-3565, 2024.

EGU24-4065 | ECS | Posters on site | ITS2.5/NH13.5

Quantifying the Stability of Refugee Populations: A Case Study in Austria 

Ola Ali, Elma Dervic, Rainer Stütz, Ljubica Nedelkoska, and Rafael Prieto-Curiel

The global surge in displacement, with nearly 110 million people uprooted due to violence, underscores the pressing need to comprehend the challenges faced by refugees. Population growth, environmental crises, and political instability contribute to this crisis, projecting an escalating trend in the decades ahead. While hosting countries strive to address concerns related to labour markets, state provisions, and cultural integration, understanding the well-being of refugees upon entry needs to be more adequately explored. This study focuses on refugee stability and integration, employing Austria as a case study. Utilising comprehensive administrative data spanning November 2022 to November 2023, we examine residence movements as a proxy for stability. Our findings reveal a stark contrast in the stability of refugees compared to other migrant groups. Analysing movement profiles, we establish that refugees exhibit significantly higher rates of residential mobility than their counterparts, especially among male refugees. This imbalance persists even when comparing refugees to migrants from top refugee-sending countries without official refugee status. This study contributes valuable insights into the intricate dynamics of refugee stability, shedding light on the enduring challenges faced by this population. By examining movement patterns as a key indicator, we provide a nuanced understanding of the residential experiences of refugees, that can inform targeted policies and interventions for enhanced refugee well-being and integration.

How to cite: Ali, O., Dervic, E., Stütz, R., Nedelkoska, L., and Prieto-Curiel, R.: Quantifying the Stability of Refugee Populations: A Case Study in Austria, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-4065, https://doi.org/10.5194/egusphere-egu24-4065, 2024.

Drought, flood, hail and low temperature frost (LTF) are the main agrometeorological disasters in China. However, a comprehensive and quantitative study on the long-term trend of farmland and economic damage across the country is still lacking and needs to be carried out urgently. Based on historical statistical data from yearbooks and bulletins, the overall characteristics of the impacts of provincial meteorological disasters on population, economy and farmland during 1989-2022 were analyzed by using Mann-Kendall trend test at yearly and provincial scales in China. The results showed that the proportion of direct economic losses caused by meteorological disasters to GDP showed a decreasing trend. The SGD13.1 index, based on the number of deaths and the value of disaster losses, shows that there are abrupt years on the time scale under the Mann-Kendall trend test. In the past 30 years, crop loss in China has increased first and then decreased under natural disasters, and drought is the most serious type of disaster that causes farmland loss. The Person correlation analysis combining disaster intensity index and multiple factors shows that agricultural economic output has a significant negative correlation with disaster intensity, SDG13.1 and total precipitation, and a positive correlation with average annual temperature. There was a significant positive correlation between SDG13.1 and disaster intensity index. The results of this study systematically reveal the damage characteristics of meteorological disasters to socio-economic system in China, which are critical and necessary for disaster risk reduction and adaptive strategy development.

How to cite: airiken, M. and Li, S.: Spatiotemporal variations in damages to socio-economic system from meteorological disasters in mainland China during 1989–2022, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-4576, https://doi.org/10.5194/egusphere-egu24-4576, 2024.

EGU24-4863 | ECS | Posters on site | ITS2.5/NH13.5

Influence of Extreme Weather and Climate Events on Crop Yields in China 

Dezhen Yin and Fang Li

Extreme weather and climate events, such as extreme temperatures, droughts, and floods, cause significant yield losses and threaten global food security. Their frequency and intensity have increased in recent decades, a trend expected to continue. China is the world's largest grain producer and also a country where extreme events occur frequently. Nevertheless, the influence of extreme weather and climate events on crop yields in China is not yet well understood. This study quantified the impact of heat waves, frost, droughts, and floods on the yields of wheat, maize, rice, and soybean in China from 1970 to 2019, using the superposed epoch analysis (SEA) method, agricultural statistics collected from the National Bureau of Statistics of China, and crop calendar reanalysis dataset. Furthermore, the performance of 13 global gridded crop models (GGCMs) in simulating these impacts is evaluated. The results show that heat waves, frost events, droughts, and floods significantly decrease crop yields by 2.1%, 1.0%, 2.2%, and 1.7% for wheat, maize, rice, and soybean, respectively, accounting for 23.6%, 10.5%, 21.4%, and 18.9% of the interannual variability. Yields of different crop types in China are sensitive to specific extreme weather events. The GGCMs effectively capture the impact of droughts, with nine out of thirteen models detecting a significant effect, yet they struggle to accurately simulate the effects of heat waves, frost events, and floods, with only five, two, and two models detecting these impacts, respectively.

How to cite: Yin, D. and Li, F.: Influence of Extreme Weather and Climate Events on Crop Yields in China, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-4863, https://doi.org/10.5194/egusphere-egu24-4863, 2024.

Event attribution science quantifies the influence of anthropogenic climate change on the occurrence of extreme weather events. One incentive for such research is an assumed motivational effect on people’s climate change mitigation and adaptation efforts, but little empirical evidence exists regarding this. While subjective attribution has been shown to matter, the few studies concerned with scientific attribution were gathered in societies polarised above average. Moreover, scientists and stakeholders have suggested that intellectual and communicative obstacles hinder motivational effects. They also questioned any effect on adaptation (rather than mitigation) intentions.

Here, we present results using the high-impact flood in July 2021 in Germany to empirically test the motivational effect of scientific attribution on mitigation and adaptation intentions. Data from a nationally representative sample and oversamples from the two flood-affected federal states in a control (n=663) and an attribution (n=611) group were collected in March 2022. Both groups learned about the consequences and immediate causes of the flood. The attribution group additionally learned about the World Weather Attribution's result that climate change to date had made the associated heavy rainfall more likely and more intense and that this influence would increase further in future. Groups did not differ in socioeconomic factors; mediation analyses and ordinary least squares linear regressions were applied.

Results showed that learning about event attribution results increased people’s subjective attribution of the event to climate change and their mitigation and adaptation intentions. It also increased their belief that the climate is changing and that this is due to human activities. Subjective attribution, but not personal flooding experience, mediated these effects. The effect on adaptation but not mitigation intentions was positively related to low education and to far-right political orientation. We set the results in the context of related evidence, highlight methodological caveats, and discuss implications for climate/impact attribution science.

How to cite: Undorf, S. and Undorf, M.: Increased climate change mitigation and adaptation intentions through learning about an event attribution result for the 2021 European floods, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5601, https://doi.org/10.5194/egusphere-egu24-5601, 2024.

Migration is one of human’s most drastic adaptation strategies against unfavorable conditions. In this work, we developed a minimalistic mechanistic model for human migration, dubbed CHASE, is developed. The model is named after the factors it includes to capture human migration, namely CH = Changing mindset, A = Agglomeration, S = Social ties, and E = the Environment.  Numerical experiments were conducted by subjecting the human agents in the model to two different kinds of disturbances: sudden shocks and gradual changes. Model results revealed highly nonlinear interplay among diversity, distance barrier, and social ties. The results also showed distinct responses to sudden shocks and gradual changes, both in terms of dynamics of the populations and diversity patterns.  Some ongoing and future work will also be briefly discussed.

How to cite: Muneepeerakul, R.: Modeling human migration: a minimalistic mechanistic modelling approach, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-7400, https://doi.org/10.5194/egusphere-egu24-7400, 2024.

EGU24-8637 | ECS | Posters on site | ITS2.5/NH13.5

An inclusive assessment framework for exploring climate-resilient nutrition security in sub-Saharan Africa 

Stewart Jennings, Andrew Challinor, Jennie Macdiarmid, Edward Pope, Thomas Crocker, Weston Anderson, Richard King, Stephen Whitfield, Rebecca Sarku, Christian Chomba, Masiye Nawiko, Lucas Rutting, and Marieke Veeger

Achieving climate-smart nutrition security in sub-Saharan Africa is an urgent challenge due to increasing climate risks to agricultural production, population growth and food price volatility This necessitates an integrated evidence base that takes into account not only future food system modelling but wider academic expertise and stakeholder knowledge and the plausible and desirable transformations that these information streams can provide. Accordingly, we use the integrated Future Estimator for Emissions and Diets (iFEED) to explore scenarios of food system transformation towards nutrition security. iFEED integrates climate, crop and land use modelling to explore scenarios of relevance to the policy landscape, as informed by stakeholders, assessing the adequacy of energy and nutrient supplies to meet dietary requirements at a population level. Our results show that calories are not always sufficient at the population level in extremely hot and dry years by mid-century in Zambia, even when maximising food production on available land. The majority of micronutrients also remain below population requirements. An alternative scenario where crops for population level nutrition security are prioritised shows that there are larger calorie shortfalls in extremely hot and dry years, although more micronutrient requirements are met than in the production-focused scenario. Both scenarios show benefits, and we point to ways forward that address the challenges to achieving climate-resilient nutrition security in the region. We also introduce our latest thinking on a new inclusive assessment framework that aims to expand iFEED to incorporate bottom-up disruptive seeds work and top-down modelling across spatial scales to deliver socially-equitable nutrition security in Kenya.

How to cite: Jennings, S., Challinor, A., Macdiarmid, J., Pope, E., Crocker, T., Anderson, W., King, R., Whitfield, S., Sarku, R., Chomba, C., Nawiko, M., Rutting, L., and Veeger, M.: An inclusive assessment framework for exploring climate-resilient nutrition security in sub-Saharan Africa, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-8637, https://doi.org/10.5194/egusphere-egu24-8637, 2024.

Hurricanes are among the most frequent and devastating natural disasters in tropical regions. These events often necessitate massive evacuations when warnings are issued, which often place a significant burden on transportation systems. The situation becomes even more complex and challenging when hurricanes coincide with other disruptive events, such as pandemics or compounded infrastructure damages. These compound scenarios not only dramatically increase community vulnerability but also add layers of complexity to emergency management, particularly in coastal communities with direct impacts. Understanding individual responses to such emergencies is vital for developing effective emergency management strategies. The focus of this study is to enhance our understanding of how individuals react and respond to emergencies in the face of such compound hazards. We concentrated specifically on the evacuation behaviors of residents in the state of Florida, U.S., during a major hurricane event. To this end, an activity-based model was developed. The model employs the Metropolis-Hastings algorithm, to generate a simulated population. The simulated population, characterized by diverse socioeconomic attributes, is designed to reflect the demographics and behaviors of the actual population in the study area. We integrated information from a local household hurricane evacuation survey and aggregated evacuation data to measure the evacuation decisions, timing, and destinations of individuals. We then applied the model to examine three distinct evacuation scenarios: a standalone hurricane, a hurricane coinciding with a pandemic, and a hurricane combined with storm surge flooding on the transportation systems. Our findings underscore the profound impact that compound hazards on transportation systems. We observed that the average travel time for evacuation could potentially double under compound hazard conditions. This highlights the potential inadequacy of current infrastructure resilience in handling complex emergency situations under compound hazards. This developed model offers valuable insights for assessing system-wide impacts of natural disasters in coastal regions and can be adapted for various scenarios to aid in disaster preparedness and response planning.

How to cite: Han, Y. and van Westen, C.: Modeling Evacuation Strategies in Response to Compound Hazards: Lessons Learned from a Major Hurricane Event in the US, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-9975, https://doi.org/10.5194/egusphere-egu24-9975, 2024.

EGU24-13023 | Posters on site | ITS2.5/NH13.5

Water Footprints of Growing Maize Crops in the Danube Plain (Bulgaria) 

Nina Nikolova and Simeon Matev

The water footprint of maize production is an indicator that provides information not only about direct water use for crop yields but also about indirect water use and virtual water trade. The general aim of the present research is to enlarge the knowledge about climate variability's impact on agriculture concerning improving sustainable water use for crop production. The accent of the proposed work will be on the assessment and analysis of green (rainfed production) and blue (irrigation water) water used for growing maize crops in the Danube Plain (Bulgaria).

The investigation is based on the following data: climatic data (air temperature, precipitation, wind speed, relative humidity); statistical data from agriculture, local authorities, and farmers (data about crop parameters and yields, and irrigation), and geographical data (climatic maps, maps about land use, soil maps, maps of main agricultural plants dissemination). The calculation and assessment of the water footprint of growing maize is done by the application of Cropwat software. The water needed for irrigation under various crop management options is determined. The main investigated period is 1961-2022 but special attention is given to water footprints of maize production during the extreme dry and extreme wet years. The results of the present work allow us to identify the hotspots regarding water use and water scarcity. The knowledge about the water footprint and climate-agriculture relationship could be used in water resources management and for effectively coping with the environmental and economic problems related to water scarcity and drought.

Acknowledgments: This study has been carried out in the framework of the project “The Nexus Approach in Agriculture. The water-food nexus in the context of climate change”, supported by the Ministry of Education and Science (MES) of Bulgaria (Agreement № КП-06-КОСТ-2/17.05.2022 and is based upon work from COST Action NEXUSNET, CA20138, supported by COST (European Cooperation in Science and Technology).

How to cite: Nikolova, N. and Matev, S.: Water Footprints of Growing Maize Crops in the Danube Plain (Bulgaria), EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-13023, https://doi.org/10.5194/egusphere-egu24-13023, 2024.

An increasing number of organizations are providing climate risk information for real estate properties in the form of climate risk scores. We investigate individuals' attitudes toward the accuracy of such information and whether this information impacts participants' willingness to buy properties. In a series of online experiments, participants (N=612) were asked to rate the desirability of a range of properties based on different attributes, including price, size, and year built. These properties were paired with high, low, or no climate risk scores. Following these tasks, participants completed surveys measuring their beliefs and perceptions regarding climate risk. Experiment 1 manipulated risk-level between subjects and found that participants were less willing to buy high-risk properties than low-risk properties or properties with no risk information, with no significant differences between the last two. Experiment 2, manipulated risk scores within-subject and found that not only were the high-risk properties rated lower than no risk and low-risk ones, but participants were also more willing to buy the low-risk properties than those with no risk information. In Experiment 3, the same tendency to buy low-risk properties compared to high-risk ones was found among a sample of homeowners, regardless of the timeframe (12 months vs. 30 years) and the granularity (risk at the property-level vs. postcode-level) of the risk information. The findings also revealed that individual beliefs and perceptions of climate change did not impact willingness ratings for any of the property types, except in Experiment 3, in which the higher expected risk due to climate change was negatively related to willingness to buy high-risk properties. Together, the findings suggest that climate risk scores impact individuals' assessments of properties, regardless of their beliefs and experience with climate-related events. 

How to cite: Newell, B. and Ghasemi, O.: Evaluating the Impact of Climate Risk Scores on Property Purchase Decisions, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-13476, https://doi.org/10.5194/egusphere-egu24-13476, 2024.

EGU24-14015 | ECS | Orals | ITS2.5/NH13.5 | Highlight

Urban Residents’ Justice Preferences in the Design of Climate Adaptation Flood Policy 

Melissa Tier, Elke Weber, and Michael Oppenheimer

There is an increasing need for ex-ante climate adaptation policy planning and design. Moreover, meeting robust standards to minimize harm and environmental inequities will require innovative practices and foresight, but little is currently known regarding how such standards influence residents’ preferences for or against climate policies. One set of climate adaptation strategies ripe for such consideration is urban risk management for worsening flooding. These strategies are often complex and controversial (e.g., choices between protection, retreat, and relocation), and can vary widely in structure with regard to key justice components (e.g., types of distributive, procedural, and corrective justice).

 

This presentation will share results from a large-scale, international survey that examined a comprehensive set of justice values underlying residents’ urban flood policy preferences. The online survey was translated and administered in 5 cities globally (n=650 residents per city): Buenos Aires (Spanish), Johannesburg (Zulu & English), London (English), New York City (English, Spanish, & Korean), and Seoul (Korean). The survey explores which urban climate adaptation flood policies are generally preferred by residents, whether certain categories of policies are preferred over others, and whether certain characteristics of residents best predict their preferences. More specifically, analysis of survey data considers which variables are best predictors of differences in policy preferences: a) self-perceived vulnerability to flood risk; b) city of residence; c) political, economic, and psychological worldviews; or d) other common demographics. Preliminary analysis of survey results suggests that residents with higher self-perceived vulnerability to flood risk also have an increased likelihood of preferring more expansive adaptation strategies (i.e., not just homeowner-focused policies, not just protection strategies, and more reparative actions).

 

This survey was designed to integrate contemporary topics in environmental justice, climate adaptation, and urban planning. The hypothesis was that people who self-identify as more vulnerable to flood risk prefer policies that focus more on other vulnerable people – in other words, an empathy effect caused by higher salience of vulnerability. Moreover, this effect was expected to be stronger than that of city of residence, worldviews (e.g., political identities), and other demographic characteristics. The presentation will both review detailed statistical analysis of the survey data, as well as discuss recommendations for how to best frame risk management policies in order to increase support for policies aimed at minimizing environmental inequities.

 

This dissertation thesis project has been supported by the Princeton School of Public & International Affairs and the 2023 Young Scientists Summer Program at the International Institute of Applied Systems Analysis.

How to cite: Tier, M., Weber, E., and Oppenheimer, M.: Urban Residents’ Justice Preferences in the Design of Climate Adaptation Flood Policy, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-14015, https://doi.org/10.5194/egusphere-egu24-14015, 2024.

Despite feeding the majority of the global population, small (<2 ha) farmers are among the poorest and disproportionately vulnerable to climate changes. Their ability to improve yields amid increasingly severe and frequent climate shocks will largely determine the success of the UN’s Sustainable Development Goals (SDGs) to eliminate poverty and hunger. Because smallholder farmers play a central role in efforts to achieve global food security, many governmental and private institutions have influenced smallholders’ on-farm management practices through interventions. However, interventions led by different institutions have pushed communities of smallholders to adopt divergent adaptation strategies: Some communities have taken proactive measures by diversifying their crop rotations or implementing tree-based systems as natural climate solutions, while others have primarily used reactive measures, implementing adaptations that were directly informed by their recent experiences with extreme weather events (e.g., altering sow and harvest dates to avoid a period of extreme heat). Despite the deadly consequences of food shortages in smallholder communities, very little research has quantified the impact of specific adaptations on their sensitivity to inter-annual climate variability. Fortunately, the recent influx of satellite sensors has enabled us to remotely monitor changes in smallholder field-level cultivation practices and tree-based systems, and with high performance computing, we can scale these analyses across landscapes. Here, we integrated administrative yield data, multi-source satellite and weather data, and household and field survey data across India, Nepal, and Bangladesh in mixed-effect models to answer: Where, and how have smallholder communities adapted their cultivation practices? And, how have these adaptations impacted their resilience to weather shocks? The results of these findings were contextualized using household survey data of 2,000 smallholder farmers to understand the drivers of farmers’ decisions and their perspectives on climate-induced adaptations. Our findings can inform future interventions in the region, and the algorithms will be directly transferable to other regions of smallholder agriculture where farmers adopt distinct adaptations and experience other climate threats.

How to cite: Hinks, I. and Gray, J.: From Satellites to Soil: Integrating Satellite and Household Survey Data to Assess the Impacts of Adaptations on Smallholder Farmers’ Climate Resilience, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-14059, https://doi.org/10.5194/egusphere-egu24-14059, 2024.

It is well known that the impacts of climate change to health and well-being are exacerbated by existing social inequality. Throughout the world, women face heightened vulnerability to climate stress due to pervasive power imbalances, gender norms, and economic marginalization. Interdisciplinary collaborations that carefully integrate social and physical data are critically needed to foster a deeper understanding of the processes that increase women’s exposure. In this talk, I share findings from recent work examining the effects of extreme weather on early and forced marriage, intimate partner violence, and social isolation of girls and women. I will discuss these trends in relation to recent progress in the opportunities available to women, and offer insights into the conditions that might support women’s well-being in the face of climate risk.

How to cite: Carrico, A.: Gendered Responses to Climate Change and the Well-Being of Girls and Women , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-14376, https://doi.org/10.5194/egusphere-egu24-14376, 2024.

Social network plays a critical role in risk communication diffusing information in near real time. Disaster-affected communities utilize their social network to report catastrophic damages and increase the perceived risk of the ongoing disaster by non-affected communities, which enhance their willingness to donate and support emergency aids to the affected communities. Previous studies have focused on social network structure or information diffusion separately. This study strives to reproduce the social response to natural disasters aims integrating the two aspects of social network structure and information diffusion. This study focuses on two classical and catastrophic U.S. disasters, such as 2012 flash drought and wildfire, to establish the social network during these two disasters and understand difference in the patterns of the risk communication within the data-driven social network and random social network (e.g., (the equal chance/importance of a nodes). Random social network is made from the LFR benchmark algorithm using the properties of the data-driven network, including node number, degree distribution, community distribution, and average degree. This study leverages over 120,000 (53,000) tweets that contains a term, drought (wildfire). In this study, a Susceptible-Infected-Recovered (SIR) model is employed to simulate the information diffusion patterns using the data-driven and random social network. After fitting SIR model with the Twitter data using these two social network-based simulations, this study aims to assess 1) the impact of the structure difference on risk communication and 2) the impact of influential users in different social network structures. Result shows that the trained SIR model using the data-driven social network reproduced the observed information diffusion patterns for the 2012 drought and wildfires but with relatively higher uncertainty in the information diffusion pattern for wildfires. The SIR model simulation with data-driven social network shows a faster information diffusion pattern with a higher information reach rate than that with the random social network. In closing, this study discusses limitations and opportunities of next-generation social dynamic modeling for natural disaster risk communication. This study highlights the value of an interdisciplinary approach in improving risk communication and developing a more efficient and effective mitigation policies for not only droughts and wildfires and other natural disasters.

How to cite: Song, J. and Kam, J.: Understanding the dynamics of information diffusion through data-driven social network modeling for the 2012 U.S. drought and wildfire, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-14972, https://doi.org/10.5194/egusphere-egu24-14972, 2024.

EGU24-14994 | Posters on site | ITS2.5/NH13.5

Calibrating Displacement Curves to Forecast Forced Migration due to Sea-Level Rise and Tropical Storms 

David Lallemant, Sonali Manimaran, Thannaletchimy Housset, and Sylvain Ponserre

Coastal communities are expected to be highly exposed to rising sea levels and more frequent and intense tropical storms in the coming decades, with forced migration (or displacement) highly likely in many of these places. The exposure to these hazards is driven not just by climate change, but also by growing populations and rapid urbanisation of coastal cities. However, the extent of forced migration will be highly variable, and will be dependent on pre-existing physical and social vulnerabilities present in each location. Therefore, in order to reliably forecast future forced migration due to sea-level rise and tropical storms, it is necessary to construct spatially explicit displacement curves that link hazard levels to the migratory response of communities. This study has calibrated displacement curves through regression analysis for the Philippines based on historical internal migratory movements due to coastal flooding and tropical storms. The data for calibration was obtained from the Internal Displacement Monitoring Centre and governmental disaster reports, and the calibration was performed at the level 3 administrative boundaries. With the displacement curves, critical thresholds of flood and wind damage, at which point forced migration occurs, are identified. Subsequently, these displacement curves are combined with projections of future sea-levels and tropical storms in order to forecast the forced migration of communities under climate change. The displacement curves can be used by researchers, planners and policymakers to understand the varied migratory response of communities to sea-level rise and its associated hazards. This will allow for effective adaptation plans to be devised in advance in order to manage such forced migration in a manner that allows communities, including vulnerable ones, to relocate and avoid the adverse impacts of a changing climate.

How to cite: Lallemant, D., Manimaran, S., Housset, T., and Ponserre, S.: Calibrating Displacement Curves to Forecast Forced Migration due to Sea-Level Rise and Tropical Storms, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-14994, https://doi.org/10.5194/egusphere-egu24-14994, 2024.

EGU24-16735 | ECS | Posters on site | ITS2.5/NH13.5

Modeling human displacement in the 2022 Pakistan floods: Current gaps and opportunities. 

Steffen Lohrey, Pui Man Kam, Bianca Biess, Tabea Cache, Sabrina Di Vincenzo, Radley M. Horton, and Lisa Thalheimer

The 2022 Pakistan floods have been unprecedented in their extent. They affected around 33 million people, caused about 15 billion USD in damages, and took the lives of more than 1,800 persons, dominantly in the southern parts of the country.

Effective disaster response requires fast assessments of likely impacts from hazardous weather to inform decision-makers and guide relief efforts for early action. Displacement modeling is a key technique towards these goals. However, displacement modeling which accounts for socio-economic components and uncertainties is methodologically challenging, and quantitative evidence largely remains limited and fragmented. Much work is needed to resolve these.

This study aims at providing a case study for disaster displacement modeling by using the open-source impact assessment platform CLIMADA to investigate the extent by which flood-related hazards can be used to quantify displacement numbers in a data-limited region. Here, we estimate displacement from the 2022 Pakistan floods in Sindh province as a case study. We combine data on flood depth, exposed population, and provide impact functions that relate vulnerability of people likely to be displaced. We further use published numbers of affected people as target data for our model. The centerpiece of our analysis is the choice of impact functions. We test different forms of impact functions as well as assumptions about critical flood depths to proxy the number of displaced people, first using ex-ante assumptions, and then a numerically optimized version.

With ex-ante assumptions, our model predicts a range of 1.94 to 5.65 million of displaced people in Sindh province, as compared to a total number of 6.76 million as reported by government sources. When we apply numerically optimized impact functions, the results closely resemble those obtained using the ex-ante assumptions, indicating that the current methods underestimate the extent of displacement. Additionally, we have evaluated the relationship between local vulnerability and the level of urbanization, and our findings reveal a negative correlation.

We use this model to explain different displacement estimates for the 2022 floods across Pakistan and thereby contribute a case study to the growing field of displacement models, and towards the development of more refined ones. It highlights opportunities as well as limitations, and is a quantitative contribution to an existing discussion on how much disaster-related displacement can be modelled, and in how far assumptions can be generalized. These insights also support a better understanding of displacement and migration from future climate risks.

How to cite: Lohrey, S., Kam, P. M., Biess, B., Cache, T., Di Vincenzo, S., Horton, R. M., and Thalheimer, L.: Modeling human displacement in the 2022 Pakistan floods: Current gaps and opportunities., EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-16735, https://doi.org/10.5194/egusphere-egu24-16735, 2024.

EGU24-16971 | Orals | ITS2.5/NH13.5

Regional probabilistic flood displacement risk assessment: the Horn of Africa case study 

Eva Trasforini, Lorenzo Campo, Tatiana Ghizzoni, Andrea Libertino, Daria Ottonelli, Sylvain Ponserre, Lauro Rossi, and Roberto Rudari

The risk of displacement caused by natural hazards has been increasingly impactful and emerges as a topical issue point in the field of disaster risk management. Given the potential escalation of this phenomenon due to climate change, population growth and urbanization, enhancing displacement risk assessment through reliable models and data has become increasingly crucial. Different applications require approaches that can be adapted at different spatial scales, from local to global scale. In pursuit of this goal, we have devised a probabilistic procedure for estimating the potential displacement of individuals due to riverine floods. The methodology is based on a novel approach to vulnerability assessment which considers that people’s vulnerability depends on several physical and social factors such as direct impacts on houses, livelihoods and critical facilities (such as schools and hospitals). These concepts are seamlessly woven into a comprehensive probabilistic risk assessment. A modelling chain that incorporates climatic, hydrological, and hydraulic and exposure/vulnerability models can be run different resolution to predict impacts at different special scales, from local to global scale.

This approach already demonstrated its validity for in Fiji and Vanuatu, where the small size of the countries allows for the definition of a building scale exposure model. In the present study, our focus turns towards adjusting the methodology for large countries, where using a high-resolution exposure model becomes impractical.

For our case study, we selected three countries in the Horn of Africa—Ethiopia, Somalia, and Sudan—acknowledging their particular vulnerability to the challenges posed by recurrent floods and the resulting internal displacement.

To properly match the 90m resolution of riverine flood hazard maps and avoid distortions in the final risk computations, a specific procedure for downscaling global exposure dataset, such as the 1-km resolution Global Exposure Socio-Economic and Building Layer (GESEBL), was implemented using high-resolution population distribution products. The resulting exposure layers are a set of population distributions associated to different sectorial assets (residential, industrial and agricultural production, services), characterized in terms of physical vulnerability to floods.

Impacts of current and future flood scenarios on those assets may render them unable to provide their function, thus causing people to forcedly move. In this procedure we took special care to avoid double counting, i.e. those cases where people lose both habitual place of residence and livelihoods.

Displacement risk expressed in annual average displacement and probable maximum displacement was evaluated under current and future climate conditions with optimistic and pessimistic scenarios. The results indicate a potential 2 to 4 times increase in average annual displacement for optimistic scenarios compared to current conditions, with even higher risk for pessimistic scenarios.

The application of this methodology in larger countries paves the way for its implementation on a global scale.

How to cite: Trasforini, E., Campo, L., Ghizzoni, T., Libertino, A., Ottonelli, D., Ponserre, S., Rossi, L., and Rudari, R.: Regional probabilistic flood displacement risk assessment: the Horn of Africa case study, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-16971, https://doi.org/10.5194/egusphere-egu24-16971, 2024.

EGU24-17120 | ECS | Posters on site | ITS2.5/NH13.5

Investigating the Effects of Extreme Weather and their Interactions with Farm Management on Crop Yields in the Netherlands 

Sinne van der Veer, Raed Hamed, Hande Karabiyik, and Jamal Roskam

Recent studies that address the impacts of extreme weather on crop yields, are predominantly focused on expansive geographical scales and generally ignore the role of management practices in modulating the dynamics of weather-crop sensitivities. In our study, a unique dataset containing data from the Dutch Minerals Policy Monitoring Program and the Farm Accountancy Data Network (FADN) is used to explore the relationship between extreme weather and crop yields at farm level in the Netherlands. The dataset consists of unbalanced panel data from the years 2006 to 2021 including an average of about 1,500 farms. The Standardized Precipitation Evapotranspiration Index (SPEI) is used to reflect weather anomalies, both extreme wet and dry conditions. The climatological variables necessary to compute the SPEI are estimated at field-level using data gathered by the Royal Netherlands Meteorological Institute from 277 precipitation stations and 18 climate stations. In total, ten types of crops are covered and the role of soil type, irrigation and nutrient application in modulating the relationship between extreme weather and crops is elucidated. Distinction is made between drought and excessive precipitation during the planting-, growing- and harvesting period. The results show substantial impacts from drought during the growing- and harvesting period and excessive precipitation during the planting- and growing period. Severe droughts show statistically significant (p≤0.05) reductions in yield for nine crops, and lead to yield reductions ranging from 10 to 25 percent when only occurring during the growing period. Meanwhile, eight crops show statistically significant (p≤0.05) reductions in yield due to severe precipitation excess, with reductions ranging from 5 to 20 percent from excessive precipitation during the planting period. Soils such as sand or loess amplify the negative impact of drought on crop yield, while softening the impact of excessive precipitation. Furthermore, irrigation and nutrient application (both nitrogen and phosphate) are shown to moderately decrease the impact of extreme weather on crop yield, with substantial differences depending on crop type and the period in which the extreme weather event occurred. The findings of this study provide valuable insights to guide local adaptation priorities which are critical given the projected increase in the intensity and frequency of extreme weather under climate change.

How to cite: van der Veer, S., Hamed, R., Karabiyik, H., and Roskam, J.: Investigating the Effects of Extreme Weather and their Interactions with Farm Management on Crop Yields in the Netherlands, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17120, https://doi.org/10.5194/egusphere-egu24-17120, 2024.

EGU24-18164 | ECS | Orals | ITS2.5/NH13.5

Can we understand the variability in flood-induced displacement using process-based global flood modelling?  

Sandra Zimmermann, Katja Frieler, and Jacob Schewe and the ISIMIP Team

Every year, disasters force millions of people around the world to leave their homes. Disaster-induced displacement often leads to humanitarian hardship and imposes substantial costs on vulnerable, low-income societies in the Global South. With anthropogenic climate change increasing the intensity and number of extreme events in many regions globally, understanding and projecting disaster-induced displacement becomes increasingly important. Floods are among the main causes of disaster-induced displacements. However, the causes of variability in flood displacement over time and space are not well understood. Therefore, it is not known to what extent climate change has already affected displacement in the past, making it difficult to produce reliable estimates of future displacement risk.

In our study, we address the question of how much of the observed variability can be explained on the basis of process-based flood hazard modeling. We use the output of state-of-the-art global hydrological models forced with observational climate and direct human forcings to derive flood extents from the global hydrodynamic model CaMa-Flood. We first assess how well modelled flood hazards can explain annual variations in past displacement as recorded by the Internal Displacement Monitoring Center at a global as well as national scale, before also accounting for different vulnerabilities of communities by applying spatially-disaggregated vulnerability factors derived from comparing the simulated number of people affected by flooding to observational displacement data. We hence provide a comprehensive assessment of the explanatory power of the process-based fluvial flood hazard component concerning displacement.

How to cite: Zimmermann, S., Frieler, K., and Schewe, J. and the ISIMIP Team: Can we understand the variability in flood-induced displacement using process-based global flood modelling? , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18164, https://doi.org/10.5194/egusphere-egu24-18164, 2024.

EGU24-18203 | ECS | Orals | ITS2.5/NH13.5 | Highlight

Temporal Dynamics of Internal Mobility in Response to Climate Extremes: A Global Analysis. 

Kristina Petrova, Karim Zantout, Sandra Zimmermann, Katja Frieler, and Jacob Schewe and the the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP)

This study presents a novel approach to understanding the impact of climate extremes on human mobility by examining not only the immediate response to the occurrence of such events per se but also the effect of their duration and frequency over time. Utilizing the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) climate data in combination with recently released geo-located sub-national net migration data provided by Niva et al. 2023, we assess the influence of various climate-related events, including droughts, floods, crop failures, and tropical cyclones. Our analysis goes beyond the traditional binary assessment of whether climate extremes affect mobility, delving into the nuanced ways these recurrent events shape migration patterns in areas with different levels of socio-economic development and political inclusivity over time. We aim to capture the shifts in net migration at a granular level, providing insights into how populations respond to environmental stressors over short, medium, and long-term periods. This temporal aspect is crucial in understanding the resilience and adaptability of communities in the face of climate change. Our findings reveal significant variations in mobility responses depending on the nature and duration of climate extremes.  This study contributes to the broader discourse on climate change and human mobility by highlighting the importance of considering temporal dynamics in policy development and planning for climate resilience.

How to cite: Petrova, K., Zantout, K., Zimmermann, S., Frieler, K., and Schewe, J. and the the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP): Temporal Dynamics of Internal Mobility in Response to Climate Extremes: A Global Analysis., EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18203, https://doi.org/10.5194/egusphere-egu24-18203, 2024.

EGU24-18416 | ECS | Orals | ITS2.5/NH13.5

An agent-based model for testing the impact of policy options on flood displacement in Sudan 

Eleonora Panizza, Yared Abayneh Abebe, Roberto Rudari, and Mauro Spotorno

The IGAD region in East Africa has experienced a rise in the occurrence and severity of floods over time, as a consequence of climate variability and change. Among member states, Sudan stands out as one of the most affected by recurrent floods, suffering significant damage to houses, livelihoods, infrastructure, and economic activities. Areas along the River Nile, in particular, are often affected by riverine flooding. These events continue to displace thousands of people annually in the country, while immobility in the face of disasters is also an issue. In response to this challenge, the design and implementation of effective flood risk mitigation policies have become paramount, addressing both physical and socio-economic perspectives. 

The aim of this research was to develop an agent-based model (ABM) to simulate human behavior and assess the impact of policies on flood displacement patterns in seven locations in Khartoum State, Sudan. To lay the groundwork for the ABM, a household survey was conducted to collect information about the socioeconomic characteristics, flood displacement experience, and risk perceptions of the resident population. The ABM operates as a tool for modeling the behavior of autonomous household entities in various 30-year hazard and policy scenarios. Policies, tested both individually and in combination, include the Early Warning System, the Awareness Programme, the Basic Income Programme, the House Repair Programme and the Build Back Better Programme. 

In the model, households’ actions and decisions within the different flood and policy scenarios depend on their personal characteristics. Elements that influence the decision to move or stay include risk perception, socioeconomic characteristics, and flood damage. This innovative model serves as an instrument for estimating the volume of displacement, evacuation, and immobility across different scenarios. It supports the identification of the most effective intervention strategy for the context under consideration. 

The focus of the presentation is on the results of the comparative policy analysis derived from the ABM simulations. These findings are also instrumental in supporting local and national decision-makers in mitigating the risk of flood displacement and immobility, thereby strengthening the resilience of communities to flood challenges.

How to cite: Panizza, E., Abebe, Y. A., Rudari, R., and Spotorno, M.: An agent-based model for testing the impact of policy options on flood displacement in Sudan, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18416, https://doi.org/10.5194/egusphere-egu24-18416, 2024.

EGU24-18736 | ECS | Posters on site | ITS2.5/NH13.5

Abandoned villages in the Catalan and Aranese Pyrenees during the Little Ice Age and the 20th Century: exploration of climate forcings through historical documents 

Mercè Cisneros, Josep Barriendos, Mariano Barriendos, Agustí Esteban i Amat, Cristina Simó, Claudi Aventín-Boya, and Javier Sigró

The unequivocal global warming of the climate system and the clear influence of human activities underscore the urgency of addressing the present challenge of Earth's warming. The exploration of past climate patterns presents significant opportunities in this regard.

Past climate information in high-mountain-areas, such as the Catalan or Aranese Pyrenees, is often still scarce. This is attributed to various reasons. On one hand, instrumental data series for these regions during the 20th century are not abundant and/or frequently start only from the 1960s. On the other hand, concerning climate information derived from historical documents for the past centuries in some of these regions, although its potential has been demonstrated in previous studies, it remains largely unexplored. Given all of this, it is not difficult to realize that these high-mountain-regions may exhibit a particular vulnerability in the face of current conditions of global warming. At the same time, its reactivity allows for the swift documentation of changes, as observed in the rapid regression of permanent Pyrenean glaciers over the past 50 years.

It is important to note that, given the strategic position of many of these locations as passages and border areas, especially from the mid-17th century onward, with the consolidation of European nation-states, there comes the implementation of the concept of political borders, various events throughout history (such as fires, wars, etc.) have led to the total or partial destruction of numerous documents. Frequently, the history of certain events is only preserved through oral accounts passed down from generation to generation.

Life in the Pyrenees has often been challenging, sustained by those individuals who have remained faithful, resisted, and persevered. The people of the Pyrenees have relied on the forest, pastures, and rather lean lands for their livelihood, and transportation has consistently posed difficulties. Additionally, sporadic phenomena of various kinds, whether historical, economic, or natural (avalanches, floods, earthquakes...), the latter strongly impacting the natural hazards in mountainous areas, have triggered changes in the villages or, in the worst cases, their abandonment and/or disappearance. The impact on these communities has often resulted from a combination of phenomena that is challenging to disentangle.

Here, we present an initial exploration of abandoned villages in the Catalan and Aranese Pyrenees during the Little Ice Age and the 20th century. The developed methodology includes the classification of depopulated areas based on various attributes: moment of disappearance, cause, altitude, and location. We have examined the climatic trends that could have affected the regions of the depopulated areas at different times. Causes include natural phenomena such as avalanches and landslides, as well as other factors like epidemics or plagues. The combination of these physical and biological factors can produce strong economic crisis at different scales. In extreme cases, this deterioration leds to the abandonment of specific villages. It is worth noting the centrifugal effect of large industrial and service agglomerations located in proximity, which have significantly contributed to the depopulation of Pyrenean settlements, whether seasonally (especially in the 19th century) or permanently (particularly in the 20th century).

How to cite: Cisneros, M., Barriendos, J., Barriendos, M., Esteban i Amat, A., Simó, C., Aventín-Boya, C., and Sigró, J.: Abandoned villages in the Catalan and Aranese Pyrenees during the Little Ice Age and the 20th Century: exploration of climate forcings through historical documents, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18736, https://doi.org/10.5194/egusphere-egu24-18736, 2024.

EGU24-19158 | ECS | Posters on site | ITS2.5/NH13.5 | Highlight

A data-driven approach to predict water security and societal impacts: the risk of drought-induced internal displacement in the Horn of Africa. 

Marthe Wens, Hans de Moel, Anne van Loon, Michel Isabellon, Daria Ottonelli, Sylvain Ponserre, and Lauro Rossi

The characterization of drought hazards remains a complex endeavor, primarily due to the absence of a universally accepted definition for a "drought event." Different deficits across various parts of the water cycle contribute to a spectrum of drought consequences, rendering the definition contingent upon the impacts incurred. Moreover, quantifying drought vulnerability poses challenges given the intricate interplay among socioeconomic, political, and environmental factors that influence the relationship between a drought event and its impacts on exposed production systems, people and nature. 
Our work addresses these challenges by introducing a novel data-driven methodology employing an array of drought indices and several datasets on observed drought impacts. Applying decision tree-based AI techniques, this method identifies combinations of hydrometeorological conditions known to generate societal consequences, and as such is able to estimate probabilistic drought disaster risk.

The presented impact-based approach is generalizable and impacts evaluated include energy production losses, internal displacement, crop and livestock damage, malnutrition, ecosystem health degradation, and strains on drinking water utilities. Illustrated through a case study in the Horn of Africa, this contribution exemplifies the quantification of expected annual drought impact, whereby impact is measured as the number of drought-induced internally displaced persons (IDPs). Drawing on the latest IDMC Displacement Tracking Matrix data, we assessed drought displacement risks under current and projected climate scenarios for Somalia and Ethiopia. Both countries grapple with complex human mobility dynamics, driven by a multitude of push and pull factors. Our findings reveal average annual IDPs up to 2% in some regions in Ethiopia, rising to 3% with unmitigated climate change. In Somalia, the majority of regions are anticipated to experience on average >10,000 drought-induced IDPs annually, under all future projections. Our model demonstrates proficiency in distinguishing prolonged and flash droughts as drivers for displacement. Furthermore, it facilitates the identification of hotspot areas, thereby supporting drought disaster risk reduction decisions and proactive policies.

How to cite: Wens, M., de Moel, H., van Loon, A., Isabellon, M., Ottonelli, D., Ponserre, S., and Rossi, L.: A data-driven approach to predict water security and societal impacts: the risk of drought-induced internal displacement in the Horn of Africa., EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-19158, https://doi.org/10.5194/egusphere-egu24-19158, 2024.

Climate change interacts with a multitude of socioeconomic characteristics (i.e. income, age, employment), determining individual risk and coping capacities. However, existing impact assessments of climate risk commonly focus on aggregate levels, leaving blind spots with respect to within-country distributional effects. Adhering to the concept of intersectionality, this study examines differential vulnerabilities and factors determining heterogeneities on a household level in the context of heat and flood related risks in Austria. 

We extend upon previous research by identifying differential vulnerabilities and the patterns determining heterogeneities among agents. To this end, we develop a mixed-methods approach, bringing together two ends of the spectrum: the generic representation of a single representative household and highly context specific individual risk determinants. Building on stakeholder involvement at different governance levels, qualitative insights from workshops and interviews are developed into narratives and storylines. These are vital for identifying key drivers of vulnerability and later integrated and combined with multivariate statistical analysis. Using the K-modes clustering algorithm, we combine geocoded socioeconomic data (e.g. age, sector and type of employment and income) with climate impact data (flood inundation level for different return periods, kysely days) on a 1kmx1km scale. Such development of archetypes aligns quantitative clusters with qualitative narratives, fostering mutual validation and a profound understanding of differential climate risk. Thus, the iterative exchange between quantitative and qualitative methods constitutes the backbone of this study. 

Through this approach, we identify reoccurring indicator combinations to disentangle the socioeconomic drivers of differential vulnerabilities and coping capacities in the context of flood- and heat-related climate risk. This sheds light on the within-country distributional implications of climate change, characterizing archetypical patterns of vulnerability and the constraints underlying adaptive capacities. Our findings contribute towards a more nuanced representation of society in climate impact assessments and enhance the understanding of the individual constraints limiting adaptive capacities, informing the development of targeted and just adaptation. 

How to cite: Beier, J., Preinfalk, E., and Hanger-Kopp, S.: Identifying archetypes of climate vulnerability: A mixed-methods approach for heat and flood related risk in Austria , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-19508, https://doi.org/10.5194/egusphere-egu24-19508, 2024.

EGU24-20205 | Posters on site | ITS2.5/NH13.5

An integrated assessment of future risks of climate change for Austria: spatio-temporal trends of ozone, heat, and social vulnerability  

Michael Friesenecker, Thomas Thaler, Monika Mayer, Harald Rieder, Herbert Formayr, Christian Schmidt, and Lehner Fabian

Assessing the spatio-temporality of risks associated with climate change have become dominant in disaster risk research. However, integrated assessments of spatio-temporal aspects combing hazard, exposure and social vulnerability is still under-researched, especially in the fields extreme heat events and heightened ozone concentrations. Studies frequently tend to concentrate either solely on the hazard dimension, such as heatwaves and ozone exceedances, neglecting their interactions (Feron et al. 2023), or solely on isolated spatio-temporal assessments of social vulnerability and exposure (Santos et al. 2022). Using the recent risk conception of the latest IPCC report, we analyze risk as the cumulative interaction of hazard, exposure and vulnerability for historical trends and near future scenarios.

A novel data set allows for an integrated assessment of historic spatio-temporal trends as well as near-future trends using different SSP-RCP combinations (SSP2-4.5 & SSP3-8.5) at census tract level. To assess the combined impact of temperature and ozone extremes, we utilize bias-corrected model fields from high resolution runs of the coupled chemistry-climate model WRF-Chem. Population data was projected until 2050 by combining historical growth rates for selected indicators with national change rates from the Shared Socio-economic Pathways (SSP) database by IIASA (Riahi et al. 2017). Regional variations in national SSP change rates are weighted with regionalized projections for population and age groups, and historic data on income and education from the Eurostat Database.

Methodologically, we use the Adjusted Mazziotta-Pareto Index (AMPI) normalization method to overcome the limitations of comparing z-scored values over time as reported by Santos et al. (2022). This has the advantaged that all values across all periods of time are considered in normalization (Mazziota & Pareto 2022). Bases on the integration into a composite indicator, we, first, performed a multivariate analysis of how sub-indicators for hazard, exposure and social vulnerability relate to each other for Austria. Second, we applied global and local Moran’s I statistics to analyze if the spatial patterns have changed in terms of spatial heterogeneity or spatial clustering over time.

The paper concludes by highlighting the needs of integrated risk assessments and discusses the potentials and limitations of our assessment approach. Finally, possible benefits of the interdisciplinary and small-scale use of SSP-RCP combinations for a more comprehensive formulation of informed policy guidelines.

 

Feron, S., Cordero, R. R., Damiani, A., Oyola, P., Ansari, T., Pedemonte, J. C., ... & Gallo, V. (2023). Compound climate-pollution extremes in Santiago de Chile. Scientific Reports13(1), 6726.

Mazziotta, M., & Pareto, A. (2022). Normalization methods for spatio‐temporal analysis of environmental performance: Revisiting the Min–Max method. Environmetrics33(5), e2730.

Riahi, K., Van Vuuren, D. P., Kriegler, E., Edmonds, J., O’neill, B. C., Fujimori, S., ... & Tavoni, M. (2017). The Shared Socioeconomic Pathways and their energy, land use, and greenhouse gas emissions implications: An overview. Global environmental change42, 153-168.

Santos, P. P., Zêzere, J. L., Pereira, S., Rocha, J., & Tavares, A. O. (2022). A novel approach to measuring spatiotemporal changes in social vulnerability at the local level in Portugal. International Journal of Disaster Risk Science13(6), 842-861.

How to cite: Friesenecker, M., Thaler, T., Mayer, M., Rieder, H., Formayr, H., Schmidt, C., and Fabian, L.: An integrated assessment of future risks of climate change for Austria: spatio-temporal trends of ozone, heat, and social vulnerability , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-20205, https://doi.org/10.5194/egusphere-egu24-20205, 2024.

EGU24-20639 | ECS | Orals | ITS2.5/NH13.5

Integrating adaptive approaches in addressing climate-induced stresses: Evidence of a mixed-method study in coastal Bangladesh  

Md Abdullah Al Mamun, Jianfeng Li, Aihong Cui, Raihana Chowdhury, and Md Lokman Hossain

The coastal regions of Bangladesh have been struggling with extreme weather events, including frequent storm surges, heatwaves, droughts, and rising sea levels. These coastal regions provide the majority of the produced agricultural crops and sustain the lives and livelihoods of marginalized people of the country. Given the increasing frequency and intensity of extreme weather events, understanding the existing challenges in agriculture and the adaptive mechanisms in crop production is critical for ensuring agricultural sustainability and ensuring livelihoods in smallholder farmers in the coastal region. In this study, using qualitative and quantitative methods, we assessed the challenges and adaptive techniques in agriculture and the trajectory of climatic conditions in two agriculture-dominated but climate-vulnerable sub-districts in the southeastern coastal region of Bangladesh.

Using focus group discussions (FGDs) and key informant interviews (KIIs), we explored (i) the challenges faced by the farmers, and (ii) adaptive techniques farmers have adopted in addressing climate-induced stresses in two highly climate-vulnerable sub-districts in the southeastern coastal region of Bangladesh. Two drought indices (Standardized Precipitation Evapotranspiration Index: SPEI, and the Standardized Terrestrial water storage Index: STI) were used to assess the temporal trends of climatic conditions in the studied sub-districts. Qualitative information was analyzed by thematic and content analyses, while quantitative information was analyzed by the Kendall test.

Respondents in FGDs and KIIs identified untimely precipitation, droughts in crop growing seasons, limited irrigation water, and outbreaks of pests during flowering time are the major challenges in agriculture. Farmers have adopted resilient crop varieties to address these challenges. The prominent crop varieties are heat- and salt-tolerant rice, drought-tolerant vegetables, and pest-resistant crops. Notably, qualitative findings show that farmers are utilizing organic fertilizers (vermicompost) to improve soil health, mulching to keep the soil moist, storing rainwater in ponds to irrigate winter and summer crops, and growing shallow-rooted and short-rotation crops to better adjust to changing weather conditions. The study highlights the growing popularity of vermicompost in improving soil fertility and improving soil water holding capacity, indicating its potential as a nature-based solution in agricultural sustainability. Examination of the temporal trend of climatic conditions obtained from SPEI and STI values, we found that both of our studied sub-districts experienced increasing dry climatic conditions. The observed increasing growing season dry climatic conditions (obtained from 3- and 6-month SPEI and STI values) in both sub-districts support the documented responses of the respondents in FGDs and KIIs.

This study highlights the extensive problem of climate-induced stresses in coastal Bangladesh and its impact on crop production. It emphasizes the significance of adaptive practices, like stress-tolerant crop varieties, bio-fertilizers, rainwater harvesting, mulching, and cultivating short rotation and shallow-rooted crops to address the adverse impacts of climate change. The findings are of practical importance for the government, NGOs, and stakeholders for ensuring sustainable agriculture and food security in coastal Bangladesh.

How to cite: Mamun, M. A. A., Li, J., Cui, A., Chowdhury, R., and Hossain, M. L.: Integrating adaptive approaches in addressing climate-induced stresses: Evidence of a mixed-method study in coastal Bangladesh , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-20639, https://doi.org/10.5194/egusphere-egu24-20639, 2024.

EGU24-21029 | Orals | ITS2.5/NH13.5 | Highlight

Global Evidence of the Impacts of Natural Disasters on Economic Preferences  

Sara M. Constantino, Giovanna d’Adda, Milica Vranic, and Elke U. Weber

Extreme weather events are increasing in frequency and severity, directly affecting economic growth and development, especially in low-income countries. Disasters may also have indirect effects through their impacts on economic preferences, including risk, time, and social preferences, which shape individual investment decisions and economic relationships. Using experimentally validated measures of six economic preferences in 64 countries, we find that recent exposure to natural disasters makes individuals on average more risk averse, less patient and less prosocial. The effects are strongest among individuals who are less resilient to shocks because they (a) live in countries with limited resources and inadequate social and institutional safety nets; or b) are in cultural contexts with “looser” social norms and lower social cohesion; or (c) are exposed to shocks against which it is hard to prepare. We also find that short- term exposure to natural disasters may hamper interpersonal relationships by decreasing negative reciprocity and social trust, but that higher lifetime exposure may actually increase trust and positive reciprocity over the long-term. Our results point to the importance of climate adaptation and mitigation policies and robust and rapid post-disaster relief measures that reduce the negative impacts of natural disasters, mitigating their indirect as well as direct impacts on economic growth and human development.

How to cite: Constantino, S. M., d’Adda, G., Vranic, M., and Weber, E. U.: Global Evidence of the Impacts of Natural Disasters on Economic Preferences , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-21029, https://doi.org/10.5194/egusphere-egu24-21029, 2024.

EGU24-3624 | ECS | Orals | ITS2.8/AS4.10

Discriminators of Antarctic Atmospheric River Environments             

Rebecca Baiman, Andrew C. Winters, Benjamin Pohl, Vincent Favier, Jonathan D. Wille, and Kyle R. Clem

Although rare, atmospheric rivers (ARs) substantially influence the interannual variability of Antarctic surface mass balance. We identify characteristics unique to AR environments by comparing (1) AR, (2) Analog (environments that feature high-low pressure couplets, similar to AR environments, but no AR), and (3) Top AR (high-precipitation AR timesteps) during 1980–2019 around Antarctica. We find significant differences between AR and Analog environments including more intense and poleward-shifted mid-tropospheric geopotential height couplets as well as larger atmospheric moisture anomalies. We find similar significant enhancement in synoptic-scale dynamic drivers of Top ARs compared to AR environments, but no significant difference in local integrated water vapor anomalies. Instead, our results highlight the importance of large-scale dynamic drivers of Top AR timesteps, including connections between high-precipitation ARs and Rossby waves excited by tropical convection. This deeper understanding of Antarctic AR environments provides context for interpreting future changes to the Antarctic surface mass balance.

How to cite: Baiman, R., Winters, A. C., Pohl, B., Favier, V., Wille, J. D., and Clem, K. R.: Discriminators of Antarctic Atmospheric River Environments            , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-3624, https://doi.org/10.5194/egusphere-egu24-3624, 2024.

EGU24-3880 | ECS | Orals | ITS2.8/AS4.10 | Highlight

Future Atmospheric Rivers in Antarctica using CMIP6-IPSL model : intensity and impacts 

Léonard Barthelemy, Francis Codron, Vincent Favier, and Jonathan Wille

Atmospheric Rivers (AR) are extreme hydrological events that have strong impacts on the different components of the Antarctic ice sheet surface mass balance (SMB), through both snow accumulation and surface melt due to heating and rain. Their evolving characteristics are therefore important to understand for an accurate prediction of future SMB changes.

We use here an ensemble of simulations of the mid-21st century climate using the IPSL-CM6 model. The future Antarctic ARs are identified using a detection algorithm adapted to the region, and taking into account in the detection threshold (based on moisture fluxes) the rising background moisture in a warmer climate. While a constant detection threshold leads to a continuous increase of the number of ARs detected, the use of this adaptative threshold leads instead to a relatively stable frequency of occurence, but with a larger penetration over Antarctica (+5% occurence over the continent). In addition, a wave number 3 component appears in the future change in frequency, as well as in AR-related snowfall.

While the number of ARs does not change much, their intensity, as measured by the associated water vapor transport, increases in line with the Clausius-Clapeyron relation. Their different impacts on the SMB also become larger, with both increasing snowfall, and surface melt and rainfall in the coastal regions. The direct effect on the SMB is however dominated by the increase in snow accumulation.

How to cite: Barthelemy, L., Codron, F., Favier, V., and Wille, J.: Future Atmospheric Rivers in Antarctica using CMIP6-IPSL model : intensity and impacts, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-3880, https://doi.org/10.5194/egusphere-egu24-3880, 2024.

EGU24-6344 | Orals | ITS2.8/AS4.10

Characteristics of surface melt potential over Antarctic ice shelves based on regional atmospheric model simulations of summer air temperature extremes from 1979/80 to 2018/19 

Andrew Orr, Pranab Deb, Kyle Clem, Ella Gilbert, David Bromwich, Fredrik Boberg, Steve Colwell, Nicolaj Hansen, Matthew Lazzara, Priscilla Mooney, Ruth Mottram, Masashi Niwano, Tony Phillips, Denis Pishniak, Carleen Reijmer, Willem Jan van de Berg, Stuart Webster, and Xun Zou

We calculate a regional surface “melt potential” index (MPI) over Antarctic ice shelves that describes the frequency (MPI-freq, %) and intensity (MPI-int, K) of daily maximum summer temperatures exceeding a melt threshold of 273.15 K. This is used to determine which ice shelves are vulnerable to melt-induced hydrofracture and is calculated using near-surface temperature output for each summer from 1979/80 to 2018/19 from two high-resolution regional atmospheric model hindcasts (using the MetUM and HIRHAM5). MPI is highest for Antarctic Peninsula ice shelves (MPI-freq 23-35%, MPI-int 1.2-2.1 K), lowest (2-3%, < 0 K) for Ronne-Filchner and Ross ice shelves, and around 10-24% and 0.6-1.7 K for the other West and East Antarctic ice shelves. Hotspots of MPI are apparent over many ice shelves, and they also show a decreasing trend in MPI-freq. The regional circulation patterns associated with high MPI values over West and East Antarctic ice shelves are remarkably consistent for their respective region but tied to different large-scale climate forcings. The West Antarctic circulation resembles the central Pacific El Niño pattern with a stationary Rossby wave and a strong anticyclone over the high-latitude South Pacific. By contrast, the East Antarctic circulation comprises a zonally symmetric negative Southern Annular Mode pattern with a strong regional anticyclone on the plateau and enhanced coastal easterlies/weakened Southern Ocean westerlies. Values of MPI are 3-4 times larger for a lower temperature/melt threshold of 271.15 K used in a sensitivity test, as melting can occur at temperatures lower than 273.15 K depending on snowpack properties.

How to cite: Orr, A., Deb, P., Clem, K., Gilbert, E., Bromwich, D., Boberg, F., Colwell, S., Hansen, N., Lazzara, M., Mooney, P., Mottram, R., Niwano, M., Phillips, T., Pishniak, D., Reijmer, C., van de Berg, W. J., Webster, S., and Zou, X.: Characteristics of surface melt potential over Antarctic ice shelves based on regional atmospheric model simulations of summer air temperature extremes from 1979/80 to 2018/19, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-6344, https://doi.org/10.5194/egusphere-egu24-6344, 2024.

EGU24-6416 | ECS | Posters on site | ITS2.8/AS4.10 | Highlight

Modelling the Impacts of Summer Extreme Precipitation Events on Surface Mass Balance in Southern Greenland 

Nicole Loeb, Alex Crawford, and Julienne Stroeve

The warming Arctic climate drives an increased potential for extreme precipitation events. Here, extreme precipitation is defined as the top 5% of daily accumulations where at least 1 mm occurred. Case studies have shown that these events can have substantial impacts on the regional surface mass balance (SMB) of the Greenland Ice Sheet. Depending on the precipitation phase and timing, mass may be added via the precipitation, or melt may be enhanced from rainfall, driving increased runoff and ice discharge. Southern Greenland is an area undergoing substantial change in terms of both intense precipitation occurrence and SMB, so it is essential to understand their relationship as the climate warms.

Observations of extreme precipitation are limited due to its rare nature and sparse observational networks. Modelling studies can shed light on broader changes by filling in data gaps and providing future projections, allowing for a deeper look into physical linkages and changes. Here, historical and future simulations of the Regional Atmospheric Climate Model (RACMO) and Variable-Resolution Community Earth System Model (VR-CESM) are used. Representation of summer extreme precipitation events in southern Greenland in VR-CESM and RACMO is explored and compared through case studies. Key variables, including precipitation phase, runoff, and overall SMB are evaluated to discern potential impacts in each model. Events in the historical and future (following SSP5-8.5) periods are investigated to determine whether the response to events of similar magnitude and seasonal timing differs in a warmer climate.

Furthermore, an approximation of how these extreme precipitation events influence seasonal SMB is presented by assessing the ratio of the event-related anomaly to the cumulative seasonal SMB anomalies. Comparisons of event-specific contributions with broader seasonal variations shed light on the connection between short-term meteorological events and longer-term climatic shifts in shaping Greenland's SMB.

How to cite: Loeb, N., Crawford, A., and Stroeve, J.: Modelling the Impacts of Summer Extreme Precipitation Events on Surface Mass Balance in Southern Greenland, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-6416, https://doi.org/10.5194/egusphere-egu24-6416, 2024.

EGU24-7689 | ECS | Posters on site | ITS2.8/AS4.10

Quantification of the Greenland ice sheet surface mass balance using high-resolution CARRA data and in-situ observations 

Verena Mülder, Maurice van Tiggelen, and Carleen Tijm-Reijmer

This project contributes to the understanding of the surface mass and energy balance of the Greenland ice sheet, by evaluating the accuracy of the Copernicus Arctic Regional Reanalysis (CARRA) dataset against in-situ observations collected from automatic weather stations (AWS) positioned along the K-transect on the Greenland ice sheet.  Additionally, the results are compared with the Regional Atmospheric Climate Model 2.3p2 (RACMO2.3p2), containing a spatial resolution of 11 km against CARRA’s 2.5 km horizontal resolution. This research thereby emphasizes the improvements and shortcomings of the new CARRA dataset for reproducing the near surface climatology on the Greenland ice sheet.

The validated CARRA dataset is then used as forcing in a surface energy balance model, enabling the determination of the surface mass and energy balance components of the Greenland ice sheet at higher spatial resolution. The modelled surface mass balance is evaluated against in-situ measurements along the K-transect, and to other regions where in situ measurements are available. 

Preliminary results show that the CARRA dataset accurately reproduces radiative fluxes, such as short- and longwave radiation components, as well as turbulent fluxes, including temperature and wind gradients. These accurate representations provide updated, high-resolution gridded fields of the Greenland ice sheet’s climate, and are crucial for precise modelling of the melt and runoff dynamics of the Greenland ice sheet through the surface energy balance model.

This research thereby presents an updated high-resolution depiction of the Greenland ice sheet climate and energy balance, which can be used as a foundation for future projections of the Greenland Ice Sheet in forthcoming studies.

How to cite: Mülder, V., van Tiggelen, M., and Tijm-Reijmer, C.: Quantification of the Greenland ice sheet surface mass balance using high-resolution CARRA data and in-situ observations, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-7689, https://doi.org/10.5194/egusphere-egu24-7689, 2024.

EGU24-10419 | Posters on site | ITS2.8/AS4.10

Unraveling the Forcings behind West Antarctic Summer Melt: CMIP6 Perspectives on Remote Climate Drivers 

Yingfei Fang, James Screen, Song Yang, Xiaoming Hu, and Shuheng Lin

The circulation pattern conducive to summer surface melt over the Ross Ice Shelf in West Antarctica is intricately linked to sea surface temperature anomalies in the tropical central-eastern Pacific associated with El Niño, along with atmospheric heating anomalies over western Australia. Our study utilizes 61 models within the Coupled Model Intercomparison Project (CMIP6) and reveals their ability to effectively simulate these primary drivers that influence the circulation pattern over West Antarctica.

El Niño emerges as a crucial force shaping atmospheric circulation anomalies over the Ross Sea, inducing two distinct wave trains toward West Antarctica—one originating from the central Pacific and the other from the Maritime Continent. Furthermore, irrespective of El Niño, anomalous atmospheric heating over western Australia emerges as another significant forcing, initiating a Rossby wave train that extends from subtropical Australia to the Ross Sea.

This comprehensive assessment advances our understanding of the remote forcings steering climate variability in West Antarctica during the austral summer. Moreover, it instills confidence in the predictability of future climate changes in this region.

How to cite: Fang, Y., Screen, J., Yang, S., Hu, X., and Lin, S.: Unraveling the Forcings behind West Antarctic Summer Melt: CMIP6 Perspectives on Remote Climate Drivers, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-10419, https://doi.org/10.5194/egusphere-egu24-10419, 2024.

EGU24-10663 | ECS | Orals | ITS2.8/AS4.10 | Highlight

Contribution of blowing snow sublimation to the surface mass balance of Antarctica 

Srinidhi Gadde and Willem Jan van de Berg

Blowing snow transport is an essential polar boundary layer process and constitutes the major ablation term in the Antarctic ice sheet's surface mass balance (SMB). Here, we present an update to the blowing snow model in the Regional Atmospheric Climate Model (RACMO), version 2.3p3, to include the effect of blowing snow sublimation and transport in the prognostic equations for temperature and water vapour. Updates rectify the numerical artefacts in the modelled blowing snow flux variation with wind speed. Updates include the replacement of uniformly distributed ice particle radius, which limited the maximum ice particle radius to ≤ 50 μm, with an exponentially increasing ice particle radius distribution to include all the relevant range of radii between 2 to 300 μm without any additional computational overhead. We compare the model results against the observations from site D47 in Adèlie Land, East Antarctica. These updates correct the numerical artefacts observed in the previous model results, and RACMO successfully predicts the power-law variation of the blowing snow transport flux with wind speed. Updates also improve the prediction of the magnitude of the blowing snow fluxes. In addition, at site D47, we obtain an average blowing snow layer depth of 230±116 μm, which falls within the range of values obtained from satellite observations. A qualitative comparison of the simulated blowing snow frequency from RACMO with CALIPSO satellite observations shows that the simulated frequency matches well with the satellite product. Compared to the previous model version for the period 2000–2010, the contribution of integrated blowing snow sublimation is increased by 30%, with a yearly average of 176±4 Gt yr-1. The increase amounts to 1.2% reduction in the integrated SMB of the Antarctic ice sheet. The updates also introduce changes in the climatology of blowing snow in Antarctica. Specifically, we observe significant changes in the sublimation of interior regions of the escarpment zone of Antarctica.

How to cite: Gadde, S. and van de Berg, W. J.: Contribution of blowing snow sublimation to the surface mass balance of Antarctica, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-10663, https://doi.org/10.5194/egusphere-egu24-10663, 2024.

EGU24-11814 | Posters on site | ITS2.8/AS4.10 | Highlight

Large-scale drivers of the exceptionally low winter Antarctic Sea Ice Extent in 2023 

Monica Ionita-Scholz

The year 2023 marked a turning point for the Antarctic region, as the Southern Hemisphere experienced a significant reduction in its sea ice cover, with a record-breaking sea ice minimum in July 2023 of ~2.4 million square kilometers below the long-term mean. This study investigates the drivers behind this exceptional event, by combining observational, satellite and reanalysis data. Throughout the year, the Antarctic Sea ice extent broke record after record, ranking as the lowest sea ice on record from January to September, with the exception of March and April. The exceptionally low sea ice extent from May to August was mainly driven by the prevalence of a zonal wave number 3 pattern, with alternating surface high- and low-pressure systems, which favored the advection of heat and moisture, especially over the Ross Sea (RS), Weddell Sea (WS), and Indian Ocean (IO). From May 2023 to August 2023, record-breaking low sea ice extent and high temperatures were recorded, and the most affected regions were RS, WS, and IO. Over the Weddell Sea, temperature anomalies of up to 10°C have been observed from May to July, whereas over the Ross Sea, temperature anomalies of up to 10°C have been observed, especially in July and August. A regime shift in the Antarctic Sea ice, as well as in the average mean air temperature and subsurface ocean temperature over the Weddell Sea, was detected around 2015. The analysis revealed complex interactions between atmospheric circulation patterns, oceanic processes, and their implications for variability and change in Antarctic Sea ice. Understanding the underlying mechanisms of these extreme events provides crucial insights into the changing dynamics of Antarctic Sea ice and its broader climatic significance.

How to cite: Ionita-Scholz, M.: Large-scale drivers of the exceptionally low winter Antarctic Sea Ice Extent in 2023, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-11814, https://doi.org/10.5194/egusphere-egu24-11814, 2024.

EGU24-12356 | ECS | Orals | ITS2.8/AS4.10

Understanding local and large-scale changes in the Arctic and the effect on Cyclone activity 

Chelsea Parker, Melinda Webster, Priscilla Mooney, Elina Valkonen, and Linette Boisvert

The Arctic is warming four times faster than the rest of the globe, with a concurrent rapid loss of sea ice extent and thickness. Cyclones are synoptic weather events that transport heat and moisture into the Arctic, and have complex impacts on sea ice, marine ecosystems, and socio-economic activities. However, the effect of a changing climate on Arctic cyclone behavior remains poorly understood. This study uses a combination of reanalysis data, cyclone tracking techniques, and high-resolution numerical modeling to explore the effect of recent and future climate change on Arctic cyclone behavior across seasons.

This work first examines the relative importance of changes in local surface conditions and turbulent fluxes and broader changes in pressure patterns, steering flow, and baroclinicity with recent climate change in governing cyclone frequency, intensity, and trajectories. Our analysis suggests that cyclone activity is shifting throughout the autumn with competing effects of turbulent fluxes and large-scale conditions. With recent climate change, sea ice is declining, and surface temperatures and turbulent fluxes are increasing, resulting in slight increases in Autumn cyclone intensity. In early autumn, cyclone frequency and trajectories are strongly governed by the large-scale flow despite increases in surface turbulent fluxes and baroclinicity. By late autumn, land-sea temperature contrast is increasing with sea ice loss, and changes in baroclinicity and large-scale flow work in concert to increase cyclone activity in the Arctic.

This work then uses regional, high resolution, convection-permitting Weather Research and Forecasting (WRF) model simulations to demonstrate the sensitivity of cyclone characteristics to recent and future climate change. Simulations with downscaled CMIP6 global climate projections reveal that future sea ice loss and increasing surface temperatures by the year 2100 drive large increases in the near-surface vertical temperature gradient, sensible and latent heat fluxes into the atmosphere, and deep convection during spring cyclone events. The changes in the future (warmer) climate alter cyclone trajectories and increase and prolong intensity, with significantly increased wind speeds, temperatures, and precipitation. Such changes in cyclone lifecycles and characteristics may exacerbate sea ice loss and Arctic warming through positive feedback mechanisms. The increasing extreme nature of weather events such as Arctic cyclones has important implications for atmosphere-ice-ocean interactions in the new Arctic.

How to cite: Parker, C., Webster, M., Mooney, P., Valkonen, E., and Boisvert, L.: Understanding local and large-scale changes in the Arctic and the effect on Cyclone activity, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12356, https://doi.org/10.5194/egusphere-egu24-12356, 2024.

EGU24-12942 | Orals | ITS2.8/AS4.10 | Highlight

Abrupt increase in Greenland melt governed by atmospheric wave change 

Rune Grand Graversen, Tuomas Heiskanen, Richard Bintanja, and Heiko Goelzer

Recent Greenland ice-sheet melt constitutes an alarming contribution to global sea-level rise. Observations indicate an approximate balance of the ice sheet until the late 1990s, after which a strong increase in melting occurred. This cannot be attributed linearly to gradually-increasing global warming. Instead the abrupt shift is suggested to be linked to atmospheric circulation changes, although causality is not fully understood. Here we show that changes of atmospheric waves over Greenland have significantly contributed to the shift into a strong melting state. This is evident from applying a newly-developed methodology effectively decomposing atmospheric flow patterns into parts associated with Rossby waves and smaller perturbations. A westerly-flow reduction, consistent with anthropogenic Arctic warming, affected transports by atmospheric waves and led to a decrease in precipitation and an increase in surface warming, contributing to ice-sheet mass loss, in particular over the southwestern regions. Hence the Greenland ice-sheet melt is an example of a climate response non-linearly coupled to global warming.

How to cite: Graversen, R. G., Heiskanen, T., Bintanja, R., and Goelzer, H.: Abrupt increase in Greenland melt governed by atmospheric wave change, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12942, https://doi.org/10.5194/egusphere-egu24-12942, 2024.

EGU24-13437 | Orals | ITS2.8/AS4.10

Atmospheric river brings warmth and rainfall to the northern Antarctic Peninsula during the mid-austral winter of 2023 

Deniz Bozkurt, Jorge F. Carrasco, Raul R. Cordero, Francisco Fernandoy, Alvaro Gómez, Benjamin Carillo, and Bin Guan

Recent research has extensively analyzed summertime atmospheric river (AR) events in the Antarctic Peninsula (AP) using ground-based and atmospheric observations, yet a significant gap remains in understanding the occurrence and impacts of ARs during the Antarctic winter. This study focuses on an extraordinary warming event in the AP between 1 and 3 July 2023, utilizing data from recent wintertime field campaigns and ERA5 reanalysis. On 2 July, the Frei station in northern AP recorded a remarkable daily maximum near-surface air temperature of 2.7°C, significantly higher than the mean winter value of -3.8°C and surpassing the winter 99th percentile of 1.8°C. On 2-3 July, at least 6 hours of liquid precipitation were recorded, as corroborated by ERA5 data, leading to notable rain-on-snow and melt events. This occurrence challenges conventional expectations, as liquid precipitation during the depths of the southern winter is exceedingly rare in Antarctica. Radiosonde observations indicated a substantial elevation of the freezing level to about 650 meters, a stark contrast to the 20 meters observed before the event. These observations also revealed a moist and nearly saturated atmospheric profile. The event was synoptically characterized by a distinct trough over the Bellingshausen Sea and a pronounced northwest-southeast oriented blocking ridge from the southwestern Atlantic to the Weddell Sea, resulting in a dipole-like pressure pattern around the AP. These conditions were instrumental in the development of an AR with a north-to-south flow. This flow was marked by maximum integrated vapor transport values exceeding 500 kg m-1 s-1, channeling warm, moisture-laden air from continental South America towards the AP. A long-term winter trend analysis reveals a significant strengthening of the dipole pattern, which correlates with increased frequencies of ARs and consequently leads to notable warm temperature anomalies over the northern AP. The study underscores the importance of understanding the complex relationship between local, synoptic conditions, and the dynamics of ARs in influencing winter climate patterns in the AP. This study's ongoing high-resolution simulations and isotope analysis aim to uncover the detailed characteristics and isotopic signatures of this extraordinary warming event, enhancing our understanding of its origins and impacts.

How to cite: Bozkurt, D., Carrasco, J. F., Cordero, R. R., Fernandoy, F., Gómez, A., Carillo, B., and Guan, B.: Atmospheric river brings warmth and rainfall to the northern Antarctic Peninsula during the mid-austral winter of 2023, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-13437, https://doi.org/10.5194/egusphere-egu24-13437, 2024.

EGU24-14236 | ECS | Posters on site | ITS2.8/AS4.10

Visibility and Fog Synoptic and Mesoscale Variability over Marambio Base, Antarctic Peninsula 

Mauricio Jimenez Garcia, John Mejia, Juan Jose Henao, Noemi Troche, Alvaro Rafael Martinez, and Kevin Alexander Chicaeme

Summertime aviation, research, and field campaigns in Marambio Base, Antarctic Peninsula (AP), and surrounding areas, are frequently affected by low visibility and fog.  Additionally, upper-air soundings in the area are launched weekly, limiting the study of the synoptic time scale variability of these hazards. A special field campaign was designed to fill this observational gap, and to examine the drivers of fog events.  A three week-long intensive observation campaign during February 2023 successfully captured the evolution and vertical structure of two multiday fog episodes that were later interrupted by westerly Foehn winds, favoring sudden warming, drying, and clear skies over eastern flank of the AP.  This dataset is also used to evaluate and assess the skill of regional climate simulations using the Global Forecasting Systems data and the Polar-WRF model.  We carried out the later modeling activities to examine the mesoscale characteristics of the interplay between the fog episodes and the Foehn winds.  This study shows the analyses of the special upper-air observations and modeling simulations, with emphasis in the description of the observable and predictable mesoscale ingredients and their relationship with synoptic forcings. We found a cycle that modulates visibility and fog: (i) low visibility ahead of the synoptic trough bringing a deep northerly moistening and warming dominating warm advection fog on the northeastern side of the AP; (ii) an enhanced mid-level inversion is formed by adiabatic warming due to westerly winds on the lee side of the AP limiting mixing; (iii) visibility increases as Foehn winds warm up and dry out the low-level atmosphere west of the AP; (iii) a meso-low (heat-low) developed on the lee side of the AP that later moved eastward with the synoptic trough, bringing cooler southerly air masses that lower visibility and favoring cold advection fog; finally (iv) cooling is maintained ahead of the synoptic ridge sustaining cold advection fog.  Polar-WRF helped us diagnose the mechanistic nature of the fog events, while providing intricate multiscale connections modulating visibility in the region.

How to cite: Jimenez Garcia, M., Mejia, J., Henao, J. J., Troche, N., Martinez, A. R., and Chicaeme, K. A.: Visibility and Fog Synoptic and Mesoscale Variability over Marambio Base, Antarctic Peninsula, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-14236, https://doi.org/10.5194/egusphere-egu24-14236, 2024.

EGU24-15041 | ECS | Posters on site | ITS2.8/AS4.10

Comparison of Atmospheric Large-scale Patterns during two Warming Periods in Greenland in the last 100 years  

Florina Roana Schalamon, Jakob Abermann, Sebastian Scher, Andreas Trügler, and Wolfgang Schöner

The air temperature (AT) increased during the Early 20th Century Warming (ETCW), especially in the Arctic, with a similar trend as during the present warming period. This AT increase is observed while investigating the annual AT anomaly of historic observations provided by the Danish Meteorological Institute (DMI) and of the zonal average of Greenland based on reanalysis data (NOAA 20CRv3). 

We define two distinct warming periods (1922–1932 and 1993–2007) for Greenland with a continuous increase in the AT anomaly. The increase is the largest at the northernmost observations in Upernavik and the smallest at the easternmost observations in Tasiilaq. The zonal average trend (Sen's slope) of AT increase in Greenland is 0.1°C/year in both periods, exceeding the global AT trend. Examining the spatial distribution of the AT trend in the reanalysis data during the warming periods reveals a warming hotspot in the sea in front of the West Coast of Greenland, which is more dominant in the second period. Nonetheless, the positive trend is rather homogeneous over Greenland, indicative of large-scale influences rather than localized phenomena. This motivates our study to analyse and compare the structure of atmospheric large-scale patterns (LSP) during these two warming periods. 

To do this, we use an unsupervised self-organizing maps (SOM) algorithm to highlight prevalent LSPs based on the reanalysis of the geopotential height of 500hPa. SOM is an artificial neural network used for clustering data into distinct groups, so-called nodes, by reducing its dimensionality. In the first approach to compare both periods, the frequency of the nodes is evaluated, meaning comparing how often a specific prevalent LSP defined by SOM occurs in the one and the other warming periods. A preliminary result is that there are significant differences in the occurrence of the nodes. Further exploration of the difference in node frequency and setting them into a meteorological context are the primary objectives of this study. 

Additionally, we aim to establish links between LSP and anomalies of atmospheric variables (such as air temperature) to investigate whether similar LSP are accountable for similar deviations. This will deepen our understanding of the atmospheric dynamics during Greenland's warming periods, which affect the cryosphere.  

How to cite: Schalamon, F. R., Abermann, J., Scher, S., Trügler, A., and Schöner, W.: Comparison of Atmospheric Large-scale Patterns during two Warming Periods in Greenland in the last 100 years , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15041, https://doi.org/10.5194/egusphere-egu24-15041, 2024.

EGU24-16074 | ECS | Posters on site | ITS2.8/AS4.10

Correcting uncertainty estimations of  20th-century reanalysis with independent historic datasets in the arctic 

Sebastian Scher, Florina Schalamon, Jakob Abermann, and Andreas Trügler

20th-century reanalysis datasets are an invaluable tool for understanding the climate from the beginning of the last century up to the present. They provide a best guess of the atmospheric state, based on a combination of observations and numerical modeling. Contrary to other reanalysis datasets, however, 20th-century reanalysis uses solely surface observations and is thus much less constrained. Consequently, the uncertainty of the analysis is high compared to reanalysis datasets for the satellite era. In the Arctic, where observations are even more sparse than in other parts of the globe, this issue is especially severe. Therefore, a robust estimation of the uncertainty of the reanalysis product is essential. While state of the art 20th-century reanalysis datasets provide some measures of uncertainty, they do not cover the whole uncertainty. We test whether historic independent measurements – that were not assimilated in the reanalysis – can be used to get a more reliable uncertainty estimation of temperature time-series over the last century. For this aim, we use recently digitized in-situ measurements from Alfred Wegener’s last Greenland expedition.  Finally, we assess how the outcome of testing typical hypotheses – such as warming trends or comparison of different periods - is affected when considering the new uncertainty estimations 

How to cite: Scher, S., Schalamon, F., Abermann, J., and Trügler, A.: Correcting uncertainty estimations of  20th-century reanalysis with independent historic datasets in the arctic, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-16074, https://doi.org/10.5194/egusphere-egu24-16074, 2024.

EGU24-16268 | ECS | Orals | ITS2.8/AS4.10

Atmospheric drivers of the rapid decline of Novaya Zemlya's glaciers 

Jan Haacker, Bert Wouters, Xavier Fettweis, Jason Box, and Isolde Glissenaar
The glaciers on the High Russian Arctic archipielago Novaya Zemlya have been losing roughly 10 Gt/yr over the past decade, 5 Gt/yr more than in the one before. While earlier research pointed to ocean discharge as driver of the acceleration, we present new results that show that foehn events, triggered by atmospheric rivers, led to the most severe melt events in the recent times. We use output of the regional atmospheric model MAR, together with geodetic observations from CryoSat-2, and reanalysis data (CARRA, ERA5, MERRA-2) to show that roughly 70 % of the melt occurs during atmospheric rivers episodes. Between 1990 and 2022, 45 of the 54 days with more than 1 Gt melt were accompanied by foehn winds. We conclude that the representation of atmospheric rivers and foehn winds in models is crucial for accurate projections of the future glacier evolution.

How to cite: Haacker, J., Wouters, B., Fettweis, X., Box, J., and Glissenaar, I.: Atmospheric drivers of the rapid decline of Novaya Zemlya's glaciers, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-16268, https://doi.org/10.5194/egusphere-egu24-16268, 2024.

EGU24-18137 | ECS | Orals | ITS2.8/AS4.10 | Highlight

Melt ponds and atmosphere-ice-ocean exchange in the UK Met Office Unified Model during the Arctic Summertime Cyclones field campaign 

Christopher Barrell, Ian Renfrew, John Methven, and Andrew Elvidge

Melt ponds play a key role in the Arctic sea-ice surface energy budget. Their reduced albedo compared to the surrounding ice and snow surfaces increases the absorption of short-wave radiation and enhances ice melt. Further, melt ponds affect atmosphere-ice-ocean surface turbulent exchanges of heat, moisture and momentum, which influence the structure of the overlying boundary layer. 

Simulation of melt ponds and surface exchange over sea ice in coupled numerical weather prediction models depends on parameterization schemes that need further development. However, the relationship between sea ice surface conditions and the overlying boundary layer is difficult to constrain due to the lack of in-situ observations in Arctic regions. 

We carried out the Arctic Summertime Cyclones project field campaign in July-August 2022 to make observations of sea-ice surface exchange and cyclone dynamics. Using the British Antarctic Survey MASIN Twin Otter aircraft we observed a range of sea ice surface types, some with a very high melt pond fraction during warm melt conditions, and the overlying atmospheric boundary layer. 

Using these observations to evaluate forecasts from the UK Met Office Unified Model, we show that a combination of deficiencies in the model sea ice field, melt pond representation and surface exchange parameterizations are linked to errors in the simulated boundary layer structure. In particular, the model consistently exhibits surface temperature and albedo biases over sea ice with melt ponds that act as sources of error in the surface energy budget.

How to cite: Barrell, C., Renfrew, I., Methven, J., and Elvidge, A.: Melt ponds and atmosphere-ice-ocean exchange in the UK Met Office Unified Model during the Arctic Summertime Cyclones field campaign, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18137, https://doi.org/10.5194/egusphere-egu24-18137, 2024.

EGU24-18598 | ECS | Posters on site | ITS2.8/AS4.10

Projection of near-surface winds in Antarctica using ESMs downscaled by a regional atmospheric model (MAR) 

Cécile Davrinche, Cécile Agosta, Charles Amory, Christoph Kittel, and Anaïs Orsi

Antarctica's climate is unique, partly due to strong westerlies on the ocean and strong easterlies at the ice sheet margins. On the continent, near-surface winds play a major role in shaping the climate of the continent as they influence sea-ice formation, the amount of precipitation reaching the ground or the stability of the boundary layer. They result from both large-scale and surface forcings, whose relative magnitude and future evolution is yet uncertain.

We show an evaluation at present day of a selection of Earth System Models (ESMs) from CMIP6 and their downscalings by the regional atmospheric model MAR. The ESMs have been selected based on their demonstrated ability to represent fairly well the southern hemisphere general atmospheric circulation. They are thus expected to have a good representation of the large-scale forcing of near-surface wind. We present a framework for evaluating against field observations how accurately different CMIP6 products are able to represent near-surface winds over Antarctica. We also present the selection process for the automatic weather stations to use and the metrics for the evaluation.

Then, we investigate the future evolution of near-surface winds on the Antarctic continent as projected by the ESMs and their downscalings. We show maps of their projected changes up to 2100 and investigate whether these changes are significant with regards to the internal variability of the ESMs and their historical biases. This evaluation provides us with a first step towards characterizing the future evolution of near-surface winds in Antarctica. Further work will then be undertaken to provide a more comprehensive analysis of their potential drivers, including the evolution of both large-scale and surface forcings.

How to cite: Davrinche, C., Agosta, C., Amory, C., Kittel, C., and Orsi, A.: Projection of near-surface winds in Antarctica using ESMs downscaled by a regional atmospheric model (MAR), EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18598, https://doi.org/10.5194/egusphere-egu24-18598, 2024.

EGU24-18912 | Posters on site | ITS2.8/AS4.10

Evaluating a state of the art, internationally coordinated pan-Arctic regional climate model ensemble 

Priscilla Mooney, Alok Samantaray, Chiara De Falco, and Ruth Mottram and the PolarRES regional climate modellers

Within the Horizon 2020 project PolarRES, a new ensemble of regional climate simulations has been developed using the latest generation of regional climate models (RCMs) for the Arctic. These state-of-the-art RCMs downscale the ERA5 reanalysis over the period 2001-2020, covering the entire Arctic region at a grid spacings of approximately 12km. Furthermore, all simulations follow the Polar CORDEX protocol for the next generation of regional climate projections of the polar regions. This new ensemble of high-resolution climate simulations offers considerable opportunities to advance our understanding of the present-day climate of the Arctic. However, a first step to realising this potential is to evaluate the performance of the regional climate models, highlighting their strengths and limitations. This is also necessary for understanding and interpreting the future projections that will be generated by these RCMs using a novel storylines approach to downscale CMIP6 models.

The work presented here will focus on the simulations of the present-day climate driven by the ERA5 reanalysis. As part of the evaluation process, a clustering technique is applied to reanalysis data to identify regions with similar annual and seasonal characteristics of surface temperature and precipitation. This approach allows for a better understanding of the regional climates of the Arctic, provides a more physically consistent basis for model evaluation, and eases the investigation of model deficiencies in simulating regional scale forcings. This work will focus on the regionalisation of the Arctic for model evaluation and present preliminary results of the application of this regionalisation to the aforementioned Arctic climate simulations.

How to cite: Mooney, P., Samantaray, A., De Falco, C., and Mottram, R. and the PolarRES regional climate modellers: Evaluating a state of the art, internationally coordinated pan-Arctic regional climate model ensemble, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18912, https://doi.org/10.5194/egusphere-egu24-18912, 2024.

EGU24-20816 | Orals | ITS2.8/AS4.10

Surface melt over the Antarctic Peninsula: targeted observations capturing recent extreme events 

Irina V. Gorodetskaya, Claudio Durán-Alarcón, Penny Rowe, Xun Zou, Sangjong Park, and Vincent Favier

The recent two years have been marked by many regional climate-state extremes particularly over the southern polar region including record-high surface melt over the Antarctic Peninsula in February 2022 (Gorodetskaya et al., 2023; Zou et al., 2023), the strongest heatwave ever recorded over East Antarctica bringing extreme inland snowfall and coastal surface melt in March 2022 (Wille et al., 2024), and an extremely low Antarctic sea ice area observed in winter 2022 outpaced by the lowest record in winter 2023 (Purich and Doddridge, 2023). Increased magnitude and probability of occurrence of extreme events, along with their high impacts on the Antarctic surface mass balance require detailed understanding of the underlying large-scale, regional and local drivers, using comprehensive and high-resolution observations and modeling. Here we will present analysis of extreme surface melt events and their drivers based on targeted observations conducted during 2022-2023 over the northern Antarctic Peninsula, including two austral summer campaigns and the winter Year of Polar Prediction in the Southern Hemisphere (YOPP-SH) enhanced observational period. Cloud and precipitation profiles using radar and lidar measurements are analyzed together with thermodynamic state of the troposphere from radiosonde observations and surface radiative fluxes with a specific focus on the extreme warm events characterized by surface melt and/or rainfall. In particular, the February 2022 extreme warm event showed very high downwelling longwave flux (up to 350 W/m2) due to the low warm-base liquid-containing clouds. Frequent occurrence of supercooled liquid water with low and warm cloud-bases is characteristic of the site during both summer and winter seasons and plays an important role in surface melt events. Another key factor during warm events is the transition from snowfall to rainfall (both with height in the vertical column, indicated by melt layer height derived from the precipitation radar measurements, and with time over the course of the event). Using radiosonde profiling, we identify layers of maximum moisture and heat transport into the Antarctic Peninsula, which showed an outstanding magnitude during the hot spell in February 2022 associated with an intense atmospheric river and which we further compare to other observed warm events. Significant differences are found for cloud and precipitation properties between ground-based measurements and ERA5 reanalysis, prompting the use of state-of-art high-resolution observations to improve representation of relevant processes in the models particularly during surface melt events.

Funding acknowledgements: Portuguese Polar Program projects APMAR/TULIP/APMAR2; FCT projects MAPS and ATLACE; ANR project ARCA; KOPRI; NSF awards 2127632 and 2229392.

References:

Gorodetskaya et al. (2023): Record-high Antarctic Peninsula temperatures and surface melt in February 2022: a compound event with an intense atmospheric river. npj Clim Atmos Sci, https://doi.org/10.1038/s41612-023-00529-6

Purich and Doddridge (2023): Record low Antarctic sea ice coverage indicates a new sea ice state. Commun Earth Environ, https://doi.org/10.1038/s43247-023-00961-9

Wille et al (2024): The Extraordinary March 2022 East Antarctica “Heat” Wave. Part I: Observations and Meteorological Drivers. J. Climate, https://doi.org/10.1175/JCLI-D-23-0175.1.

Zou et al (2023): Strong warming over the Antarctic Peninsula during combined atmospheric River and foehn events: Contribution of shortwave radiation and turbulence. J. Geophys. Res. Atmos., https://doi. org/10.1029/2022JD038138 

 

How to cite: Gorodetskaya, I. V., Durán-Alarcón, C., Rowe, P., Zou, X., Park, S., and Favier, V.: Surface melt over the Antarctic Peninsula: targeted observations capturing recent extreme events, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-20816, https://doi.org/10.5194/egusphere-egu24-20816, 2024.

EGU24-365 | Orals | ITS2.9/CL0.1.10

Spatio-temporal patterns of hydrological processes on non-floodplain wetlands in an upstream basin of Pampa Plain (Argentina) during present wet conditions  

Pablo Augusto Cello, Daniela M. Kröhling, Ernesto Brunetto, María Cecilia Zalazar, Reinaldo García, Mauro Nalesso, Jacinto Artigas, and José Rafaél Córdova

This work aims at deepening the knowledge of the mechanisms that govern the response of small temporary non-floodplain wetlands (NFWs) of neotectonic origin in the North Pampa under wet conditions. The study focuses on the Vila-Cululú upstream sub-basin (973 km2), a tributary of the Salado River belonging to the Paraná River basin. The Pampa Plain has been affected by more frequent high-intensity rainfall events during the last five decades giving rise to a steady increase in the water table and a decrease in soil infiltration, leading to flood events that impact both rural and urban environments. Under wet conditions, a flat landscape alters the surface runoff and favors the development of temporary NFWs, increasing flood vulnerability and jeopardizing human activities. Structural depressions with polygonal patterns and a network of Late Pleistocene (ca. 100 ka. BP) parallel ENE-trending fluvial palaeochannels characterize the study area. These palaeochannels were deactivated by neotectonics and covered by loess, Last Glacial Maximum in age. In some sectors, the palaeochannels intercept the small tectonic depressions and significantly restrict the present drainage network (low-order streams and artificial channels).  The research involved an integrated approach, including geomorphic and morphometric analyses based on remotely sensed satellite imagery in a GIS platform and fieldworks, and 2D hydrologic-hydraulic simulations using HydroBID Flood (hydrobidlac.org) to capture the system behavior for an extraordinary rainfall event (December 2016-March 2017). Simulation results show that the model represents hydrodynamics fairly well. The flooded areas were comparable to those obtained from the analysis of satellite images. The dendritic runoff pattern towards the tectonic depressions, the water storage evolution, and the hydraulic connectivity were numerically replicated. In particular, the Vila-Cululú sub-basin points out a significant delay in the hydraulic response downstream since the system must first satisfy groundwater and surface water storage. Once storage capacity is exceeded, the hydraulic behavior results in a dynamic process that involves the spilling and merging of ponds generated in small deflation hollows, generally nested within fluvial palaeochannels. Such a hierarchical structure controls surface runoff towards the shallow tectonic depressions. This mechanism gives rise to the development of NFWs as simulation time evolves. Besides, the surface runoff flow pattern also highlights the poor capacity of both natural and artificial drainage networks, displaying highly lateral mobility and scarce connectivity downstream. However, these NFWs eventually might connect to a more hierarchical drainage network downstream at the final stage of the storm event. The dense network of artificial channels started to develop in the 1940s to evacuate water excess to the outlet. Despite the anthropic interventions, geomorphologic thresholds finally control hydrodynamics adding to surface water storage and limiting channel conveyance. This work is one of the first studies in North Pampa that combines hydrological and geomorphological data to explain the present hydrodynamics. These could be applied to palaeoflood hydrology. Identifying critical geomorphological thresholds adds to the knowledge of different levels of hydrologic connectivity, providing a better assessment of flood hazards on large plains.

How to cite: Cello, P. A., Kröhling, D. M., Brunetto, E., Zalazar, M. C., García, R., Nalesso, M., Artigas, J., and Córdova, J. R.: Spatio-temporal patterns of hydrological processes on non-floodplain wetlands in an upstream basin of Pampa Plain (Argentina) during present wet conditions , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-365, https://doi.org/10.5194/egusphere-egu24-365, 2024.

EGU24-3989 | Orals | ITS2.9/CL0.1.10

Groundwater effects on flood dynamics  

Wouter Berghuijs, Louise Slater, Ross Woods, and Markus Hrachowitz

Fluvial floods are typically the result of large precipitation or snowmelt events, often conditioned by high pre-event soil moisture levels. However, soil moisture represents only a small fraction of the water stored in landscapes. Groundwater, often a much larger water store, may also contribute a significant proportion of river flow but its role in large-scale flood assessments often remains understudied. Here I discuss how (ground)water storage conditions can shape multi-year variability and long-term trends of river flow and flooding across thousands of catchments worldwide. Since often relatively slow groundwater dynamics can affect the much faster and more erratic flood responses, incorporating groundwater may be important to accurately model and analyze these hydrological extremes.

How to cite: Berghuijs, W., Slater, L., Woods, R., and Hrachowitz, M.: Groundwater effects on flood dynamics , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-3989, https://doi.org/10.5194/egusphere-egu24-3989, 2024.

EGU24-4382 | ECS | Posters on site | ITS2.9/CL0.1.10

Effects of Long-Term Wetland Variations on Flood Risks in the Yangtze River Basin  

Ziying Guo, Xiaogang Shi, and Qunshan Zhao

In the Yangtze River Basin (YRB), flooding is the most frequent natural disaster with enormous socio-economic damages. As a critical component in the hydrological cycle, the wetlands along the YRB have been changing during recent decades because of urbanization, intensive farming (e.g., aquaculture and agriculture) and climate change. Due to the lack of a long-term wetland classification dataset with comprehensive wetland categories, however, there’s a noticeable gap in the YRB water management regarding the relative roles of different wetland categories on flood resilience. Therefore, this study aimed to generate a long-term wetland classification dataset for the YRB and further investigate the long-term wetland variations on the YRB flood risk assessments for the period from 1985 to 2021. The dataset named Long-Term Wetland Classification Dataset for YRB (LTWCD_YRB) was created using a Random Forest machine learning classifier on Google Earth Engine with 30m resolution Landsat 5, 7, 8 muti-spectral images. The maps of LTWCD_YRB demonstrated the spatial distribution, annual variability, and seasonal cycle of nine wetland categories in the extent, and the total validation accuracy can reach 85%. The LTWCD_YRB indicated that the total wetland area of the YRB in 2021 was larger than that in 1985, with constantly increased human-made wetlands and fluctuated natural wetlands. Aquaculture ponds expanded the most (4,987 km2); inland marsh in the source region was the wetland category with the most fluctuations. Seasonal changes in wetlands were prominent in the Poyang Lake Basin, Dongting Lake Basin, and YRB source region. The LTWCD_YRB can offer a consistent agreement of wetland area variations with the other satellite-based wetland datasets in the YRB, which is valuable for researchers and stakeholders to better understand the YRB wetlands and would support sustainable wetland management practices. With the LTWCD_YRB data as modelling inputs, a GIS-based spatial multi-index flooding risk assessment model was applied for investigating the long-term implications of wetland variations on flood risks in the YRB. The model results indicate that in the year with large floods and extremely high precipitation, flood risk level increased obviously after adding the wetland factor. For the years with normal precipitation, flood risk level decreased with wetlands expansion and increased with wetlands shrinkage in the YRB. The long-term expansion of aquaculture ponds contributed to a lower flood risk in the Taihu Lake Basin. In contrast, the Poyang Lake Basin experienced an increasing flood risk due to the long-term shrinkage in lake areas resulting from soil erosion and urbanization along the lakeside. This study would be helpful for stakeholders to develop feasible wetland management practices, and to improve flood risk resilience in the YRB.

How to cite: Guo, Z., Shi, X., and Zhao, Q.: Effects of Long-Term Wetland Variations on Flood Risks in the Yangtze River Basin , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-4382, https://doi.org/10.5194/egusphere-egu24-4382, 2024.

EGU24-4543 | ECS | Posters on site | ITS2.9/CL0.1.10

Flood frequency elasticity to extreme precipitation: a practical approach for Climate Change projection of flood probabilities 

Luigi Cafiero, Paola Mazzoglio, Alberto Viglione, and Francesco Laio

Flood risk management institutions and practitioners need  innovative and easy-to-use approaches that incorporate the changing climate conditions into flood predictions in ungauged basins. The traditional approach to regional flood frequency analysis enables the estimation of hydrological variables under stationary conditions. However, it is nowadays crucial to develop innovative techniques that consider the non-stationarity of climate variables. The present work aims at implementing an operative procedure to include the expected variation in precipitation extremes into regional analysis. We compare the Flood Frequency Curves (FFC) and the Intensity-Duration-Frequency (IDF) curves defining a relation between them through the elasticity, an indication of the sensitivity of floods to precipitation extremes. Under the assumption that this relation does not change in time, we obtain modified FFC according to the projections of an ensemble mean of 25 Cordex simulations of CMIP5. This methodology was applied to 227 catchments of the Po River basin in northern Italy. Elasticity values range between 0.5 and 2: the lowest values were found in Valle d'Aosta region, and the highest in the south-western part of Piemonte. Over the Po river basin, the percentage increase of the 100-year floods ranges between 15% and 40%. The most relevant increase of flood discharge is found in the area between Liguria and Emilia-Romagna in the southern part of the Po River basin, where the projected increase of precipitation extremes is the highest.

How to cite: Cafiero, L., Mazzoglio, P., Viglione, A., and Laio, F.: Flood frequency elasticity to extreme precipitation: a practical approach for Climate Change projection of flood probabilities, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-4543, https://doi.org/10.5194/egusphere-egu24-4543, 2024.

Existing research has provided evidence on how culture mediates disasters and exacerbates or mitigates their impact in various contexts but is often concentrated among popular cultural heritage or large scale culture phenomena. The significance of culture belonging to indigenous communities is less studied in mainstream climate change adaptation, despite its importance in helping build local social resilience to climate impacts. An Achang indigenous settlement located in the western part of China's Yunnan Province, where intense flash floods occurred frequently in its history, was used as a case study. The study aims to excavate the flood culture within the Achang community and examine how culture, particularly religion, blood-related organization, indigenous knowledge, and customary law have helped Achang communities for generations to build coping strategies to flood events. Data was gathered using participant observations in community activities, semi-structured interviews, more open thematic conversations, and document review in July 2023. Respondents included survivors for the storytelling, households for the semi-structured interview, and officers of the local authorities for the key informant interviews. The study found that the Achang community has a rich flood culture, which profoundly influences the behavior of the local people during flood events. First, the Achang people are culturally rooted in Buddhist tradition of nature worship and an equanimity view of living, forming an environmentally friendly community and providing a refuge for the spirit. Second, self-organization forms based on geography and kinship plays an important role in responding swiftly and maintaining long-term collaboration in times of flood. Thirdly, the Achang people's acquisition of ecological knowledge from nature has heightened their sensitivity to natural phenomena, enabling them to skillfully leverage their environment for home transformation and effective flood response. Finally, The Achang community is governed by a number of customary laws concerning flood prevention, which call on villagers to preserve forests, conserve soil and water, and contribute to post-disaster reconstruction for the common good. All of above provides an adaptable culture system from values-knowledge-institutions-practice with a strong ecological view and that is flexible enough to accommodate the adjustments needed to respond to changes. The relocation case in the Achang community illustrates that scientific disaster reduction decisions need to consider local flood culture to establish effective interventions in indigenous flood hotspots, further becoming the foundation for community resilience. As such, greater effort should be made by the State to full-scale investigations of these cultural, and the participation of indigenous flood culture in the planning and implementation of disaster risk reduction intervention.

How to cite: Ai, M., Yang, L. E., and Zhou, Q.: Culture system and social resilience to flood impacts - An investigation of Achang communities in Yunnan, China, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-4720, https://doi.org/10.5194/egusphere-egu24-4720, 2024.

EGU24-5195 | Orals | ITS2.9/CL0.1.10 | Highlight

The unique 1432–2013 flood marks from the Děčín Castle Rock, Czech Republic, are scanned in 3D and utilized 

Libor Elleder, Tomáš Kabelka, and Jolana Šírová

Our contribution presents an example of archiving of an invaluable collection of flood marks. With respect to the height of the object carrying these flood marks exceeding 12 metres it is not possible to explore all flood marks in detail in situ. 3D scan, however, offers an excellent possibility how to solve this task. We have analysed the Děčín Castle Rock (further DCR) flood marks in context of their importance, history, recent scanning, reliability check and utilization.  The DCR ranks amongst the most important epigraphic hydrological objects in Europe. Three major reasons for that can be listed as follows: (i) the Děčín town geographical position represents the outflow of the whole Bohemia concentrating the water volume from the upper part of the Elbe river catchment, (ii) the presence of ancient flood marks (the oldest one representing the 1432 flood event) engraved in the sandstone Castle Rock, (iii) the striking relation between the DCR flood marks and the Děčín Hungerstone drought marks situated in its close vicinity  (only some 200 metres apart). It is not the number of flood marks but joint placement of both the flood and drought (low) marks which makes Děčín truly a unique place in European context. The whole flood and drought mark system served as a tool for ancient safe navigation for boats and rafts, and later ships and steamers. We place all these Děčín flood and drought marks in context of other important records in Prague, Litoměřice, and German Pirna, Dresden and Meissen. Furthermore, the oldest water level gauge – estimated to be at least 200 years old - is situated in the same place allowing for direct and easy reading of flood mark heights. Altogether, the Hungerstone drought marks and  DCR flood marks with the old water level  gauge in the Czech town of Děčín  represent an unparalleled complementary system of centennial information for extremely  low and extremely high water levels. Our Map of Extreme Floods (MEF, 2024) application currently offers selected floods the culmination water levels of which are engraved on the DCR, such as July 1432, August 1501, February 1595, February 1682, August 2002 and June 2013, the other will be available sooner (1824, 1890) or later (1771, 1784, 1799, 1830, 1845 and 1862).

 

Reference:

MEF, 2024.  Available at:

https://chmi.maps.arcgis.com/apps/MapSeries/index.html?appid=dc50b65b4483465cb98c50d4b55df75d.

 

How to cite: Elleder, L., Kabelka, T., and Šírová, J.: The unique 1432–2013 flood marks from the Děčín Castle Rock, Czech Republic, are scanned in 3D and utilized, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5195, https://doi.org/10.5194/egusphere-egu24-5195, 2024.

EGU24-9242 | Posters on site | ITS2.9/CL0.1.10

Contextualizing recent extreme floods in the Western Mediterranean region: insights from historical records and paleoflood hydrology 

Juan Antonio Ballesteros-Canovas, Tamir Grodek, Carlos Naharro, Josep Barriendos, Mariano Barriendos, Alicia Medialdea, Alberto Muñoz-Torrero, and Gerardo Benito

The Mediterranean region is expected to experience more extreme rainfall events due to climate change. These extreme weather events, together with the ever-increasing human occupation, could lead to an increase in the risk of flash floods. This situation could be worrying, as wildfires may occur during hotter and drier summers, which might increase the hydrological response. Adaptation and mitigation strategies need to be put in place at the level of water and civil protection authorities. However, this is challenging due to the widely recognised lack of data, the high variability of the Mediterranean hydroclimate, and previous shortcomings in the performance of climate-based models for the region. Here, we combine historical, geological and tree-ring data to provide a compressive multi-century reconstruction of flood frequency and magnitude for the Clariano River, a medium-sized (265 km2) Mediterranean catchment in the Province of Alicante (Spain). A historical flood database was collected from published compilations, documentary sources, photographic archives and newspapers. The Municipal Archive at Ontinyent provided flood evidence since CE 1320 with a continuous flood record since 1500. Slackwater flood deposits were studied in ten stratigraphic profiles on three river reaches, and flood units were dated by radiocarbon and optically stimulated luminescence. Finally, thirty-five scarred trees growing on floodplains in three different river reaches were sampled to record the occurrence of recent floods. In three river reaches, 1D and 2D hydraulic models were implemented on high-resolution topographies to convert palaeostages and historical levels into flood discharge. The multi-source data compilation provides evidence of at least 47 major floods since the 13th Century. Apart from the flood caused by the dam break in 1689, the magnitude of the most recent floods caused by mesoscale convective cells in 2016 and 2019 were similar to or slightly below in magnitude to those experienced during the rich flood period (1850-1895) following the end of the Little Ice Age. This implies that the information on past extreme floods could be used as a scenario-based approach to quantify expectations of recent extreme floods under climate change scenarios. Furthermore, our records have allowed a more accurate estimation of flood frequency in Ontinyent city, which could be used to provide a more robust flood hazard zonation. Throughout this comprehensive study, we show that quantitative historical and palaeoflood hydrology allows the determination of past and recent flood magnitude response to climate variability, reducing the uncertainties in flood hazard and risk assessment in the Mediterranean region.

How to cite: Ballesteros-Canovas, J. A., Grodek, T., Naharro, C., Barriendos, J., Barriendos, M., Medialdea, A., Muñoz-Torrero, A., and Benito, G.: Contextualizing recent extreme floods in the Western Mediterranean region: insights from historical records and paleoflood hydrology, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-9242, https://doi.org/10.5194/egusphere-egu24-9242, 2024.

EGU24-11182 | ECS | Posters on site | ITS2.9/CL0.1.10

Spatial signatures of flooding and blocking are related on the long-term scale 

Diego Hernandez, David Lun, Miriam Bertola, Bodo Ahrens, and Günter Blöschl

Process-based explanations of flood controls have increasingly advanced in the last years along with comprehensive datasets availability. However, the relationship on the long-term scale between floods and large-scale atmospheric drivers remains unclear, hindering the understanding of flood-prone periods and the projections of flood change. The translation of atmospheric blocking (i.e., a persistent mid-latitude high-pressure system that blocks westerly flows) into flooding has not been raised for large samples due to the spatiotemporal complexity of the atmospheric and hydrological response. For the 1950-2010 period, this study analyzes summer flood events from a pan-European database, a gridded binary blocking index derived from ERA20C, and hemispheric fields of four meteorological variables from ERA5. By defining a window of days with flooding (dF) related to precipitation surpluses in central Europe, days with blocking (dB) at three different regions namely North Atlantic (NATL), Europe (EU) and Scandinavia (SCAN), and days with simultaneous flooding and blocking (dFxB), our results indicate spatially similar meteorological signatures for dF and dFxB at NATL, but different patterns between dB and dFxB at NATL, suggesting there is a subset of blocking events at NATL controlling the meteorological signature of flood events in central Europe. Patterns for dB and dFxB at SCAN are similar implying that blocking in the SCAN region has the most direct effect on floods in central Europe. Hence, this research could provide new insights into large-scale atmospheric controls and sources of predictability regarding floods.

How to cite: Hernandez, D., Lun, D., Bertola, M., Ahrens, B., and Blöschl, G.: Spatial signatures of flooding and blocking are related on the long-term scale, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-11182, https://doi.org/10.5194/egusphere-egu24-11182, 2024.

EGU24-11746 | Posters on site | ITS2.9/CL0.1.10

The October 1787 Ebro flood: the biggest flood event of NE Iberian Peninsula in the last 500 years 

Josep Carles Balasch Solanes, Josep Barriendos, Mariano Barriendos, Jordi Tuset, and David Pino

The reconstruction of past flood episodes is of vital importance in the study of river dynamics for assessing the impact of climatic and environmental changes, and evaluating the risk of these disasters on current populations. The main objective of this study is to present a multidisciplinary analysis of the catastrophic flood episode that occurred in the Ebro River basin (85,000 km2) on 8th-9th October 1787.

The methodology includes an extensive research from documentary sources of the damaged locations. By using this data, maps of the extent of the affected area and the temporal evolution of the event have been reconstructed. Then, utilizing the maximum water height (3 flood marks), numerical simulations of hydraulic and hydrological reconstructions have been carried out to obtain the peak flows and the amount of precipitation. The meteorological reconstruction utilizes daily barometric information collected at that time from different observatories in Western Europe to plot surface pressure maps to estimate wind direction and the location of the cyclonic centers.

The results show that this is the most serious episode that has occurred in the northeast of the Iberian Peninsula the last 500 years. There were more than 500 fatalities in the Lower Ebro area, numerous homes and structures were destroyed and the regional economy was damaged for several decades. The affected area was mainly the eastern Ebro basin (with 31 documented points), but it also extended to small areas of coastal basins of the Llobregat and Júcar Rivers (9 affected points). After about 10-12 consecutive days of rain caused by two active low-pressure centers combined with an influx of moist air from the Mediterranean Sea, some of the largest peak flows that the Ebro River has experienced since the beginning of the 16th century occurred. These flows reach to 12,900 m3·s-1 of the Ebro River in Tortosa (mean flow: 428 m3·s-1), 4,500 m3·s-1 of the Ebro in Zaragoza (mean flow: 231 m3·s-1), 4,500 m3·s-1 of the Segre River in Lleida (mean flow: 80 m3·s-1) and about 2,500 m3·s-1 of the Cinca River in Fraga (mean flow: 78 m3·s-1). According to historical accounts, the origin of the flood is purely pluvial without contributions of snow melting in the Pyrenees.

The specific peak flow of the Ebro in Tortosa (0.15 m3·s-1·km-2) exceed the flows of any large European river of the same basin size (Po, Danube, Rhine, Rhône). Therefore, we are facing an event of extreme magnitude that is essential to study and to explain fluvial variability and risk analysis.

How to cite: Balasch Solanes, J. C., Barriendos, J., Barriendos, M., Tuset, J., and Pino, D.: The October 1787 Ebro flood: the biggest flood event of NE Iberian Peninsula in the last 500 years, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-11746, https://doi.org/10.5194/egusphere-egu24-11746, 2024.

EGU24-13047 | Posters on site | ITS2.9/CL0.1.10

Storm Daniel and the timing and magnitude of floods in Northeast Libya 

Chris Hunt, Hwedi El-Rishi, David Brown, and Jon Dick

Storm Daniel caused major flooding throughout much of the Jebel al-Akhdar massif in Northeast Libya, leading to huge damage and loss of life in the city of Derna and widespread damage to infrastructure through the region in September 2023. There is little historical record of significant floods in the region. We conducted dendrogeomorphological and palaeohydrological research in the wadis Kouf and Bottamsa in the Jebel al-Akhdar. Radiocarbon- and tree-ring dated flood return and flood magnitude sequences suggest three major floods during the 17th to 19th centuries AD in the Wadi Kouf and one major flood during the 18th Century in the Wadi Bottamsa, with major flood return intervals of about one per 100 years. The timing of the major floods in these two catchments seem to be different, suggesting the storms that caused them were localised. The major floods in the Wadi Kouf would have been large enough to have caused considerable damage to modern infrastructure, which seems to have been designed to cope with the much smaller floods of the mid-20th Century. Storm Daniel, however, was the product of a much larger weather system than the storms that gave rise to the earlier floods and it caused the largest floods in these wadis in the last 400 years.

How to cite: Hunt, C., El-Rishi, H., Brown, D., and Dick, J.: Storm Daniel and the timing and magnitude of floods in Northeast Libya, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-13047, https://doi.org/10.5194/egusphere-egu24-13047, 2024.

Streamflow has a crucial role in the global water cycle. The demand for long-term daily streamflow observations becomes essential for robust water resources planning, hydroclimatic extremes analysis, and informed ecological assessments. However, there is a lack of availability of this type of dataset, particularly concerning the river basins of South Asia daily. The hydrologic-hydrodynamic model can simulate the streamflow over the domain. However, these models are not well calibrated to provide the locally relevant streamflow simulation daily. In response to this crucial knowledge deficit, in this study, we developed a state-of-the-art hydrological-hydrodynamic model to simulate daily streamflow spanning the years 1949 to 2022 across river basins South Asia by calibrating the model with observed daily streamflow. Leveraging meteorological observations meticulously gathered by the India Meteorological Department (IMD) inside India, and Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA2) outside domain, our model integrates the Noah MP as the land surface model and the HyMAP routing model to generate intricate daily streamflow dynamics within the South Asian sub-continental river basins. We calibrated the model at the 173-gauge stations against observed streamflow over South Asia. The calibration and validation time periods were 3 and 5 years respectively. This process ensures the adaptability and relevance to the local nuances of Basins in the model, aligning the simulated daily streamflow patterns with observed data. A comprehensive examination of the model's performance provides good results, with key metrics such as Kling-Gupta Model Efficiency (KGE), coefficient of determination (R2), and Nash-Sutcliffe efficiency (NSE) consistently exceeding a median threshold of 0.34. Taking our analysis further, we calculated the KGE skill score of the dataset, we found that 83/173 in calibration and 72/173 in validation showed KGE skill score more than 0.08. This extensive reconstruction and evaluation of streamflow dynamics not only contribute significantly to filling the knowledge gap but also lay the foundation for more precise and informed water management strategies in the dynamic landscape of South Asia's river basins.

How to cite: Prakash, V. and Saharia, M.: India Water Model: A Transboundary Water Modeling System Over South Asia and a 75-year Daily Streamflow Reanalysis, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15063, https://doi.org/10.5194/egusphere-egu24-15063, 2024.

Fluvial ecosystems are among the environments most significantly modified by human activities. Channelization, levee construction, floodplain disconnection from the riverbed, alteration of the fluvial regime and ecosystem, interruption of the sediment dynamics and alteration or destruction of the shape and morphology of the riverbed, are among the main effects of such interventions. Restoring or rehabilitating fluvial environments, including hydrological and geomorphological processes, is currently being undertaken in many river systems of the world given the benefits that these environments provide to mankind. However, depending on the magnitude of the human interventions and their impacts on the river system, reaching a restoration stage before human intervention cannot be fully achieved. In this context, the Congost River is a representative example of the evolution of the morphology of a river channel in the metropolitan area of Barcelona during the 20th and 21st century. The river flows through Granollers, a city of 60,000 inhabitants exposed to flood risk. During the 70s and 80’s the Congost river was channelized, narrowed and disconnected from its floodplain to promote urban and industrial growth.  The river channel was then fixed to avoid lateral migration by constructing sleepers (transversal structures), and fluvial landforms such as secondary channels and gravel bars were intentionally removed from the riverbed to create a drainage channel. However, to recover green riverine areas, sleepers in the peri-urban area of Granollers were demolished, whereas in the urban core area sleepers were conserved.

Analysis of aerial images of 1945, 1956, 1986, 1998, 2009 and 2022 shows the following transformation: the natural braided channel, adapted to slope, flood frequency and sediment load changed after the human intervention to a restrained channel. The result of the restored river stretches showed higher hydro-morphological characteristics than the urban section, but they are still far from the expected outcomes of a fully successful restoration of a braided river. Yet, the channel morphology improves natural river processes. At this point, however, it is not known how the riverbed will evolve in terms of incision or avulsion, and whether further river management measures will be necessary to implement. Monitoring of channel evolution is required to fully understand the human impacts on partially restored urban fluvial systems through time. 

How to cite: Farguell, J., Ferreira, F., Moreno, M., Barriocanal, C., and Schulte, L.: Human-induced alterations to the morphology of an urban Mediterranean watercourse from 1945 to 2022: transitioning from its natural state to phases of correction and restoration. , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-16354, https://doi.org/10.5194/egusphere-egu24-16354, 2024.

EGU24-17027 | ECS | Posters on site | ITS2.9/CL0.1.10

A comprehensive framework for the application of IF and TCIF theoretically derived distributions in Southern Italy 

Martina Ciccone, Andrea Gioia, Vincenzo Totaro, Federica Mesto, Maria Rosaria Margiotta, Salvatore Manfreda, Mauro Fiorentino, and Vito Iacobellis

An increasing amount of evidence is now available for demonstrating how flood series often incorporate data coming from different populations, thus emphasizing the need to understand the physical nature of floods before carrying out their probabilistic analysis. Theoretically derived distributions of floods were introduced by Eagleson (1972) as an alternative, probabilistic and physically based modelling of processes responsible for flood generation. Based on this framework, Iacobellis and Fiorentino (2000) proposed the IF probability model in which the direct contribution to peak flow is obtained as the product of partial contributing area and the discharge per unit of area, both considered as random mutually dependent variables. Moving from the consideration that floods can be triggered by different runoff productions mechanisms, Gioia et al. (2008) introduced the TCIF probability model.  IF and TCIF distributions were successfully applied on a wide area of Southern Italy, which includes Puglia, Basilicata and Calabria regions, providing advances in the understanding of physical phenomenology of flood generation in these areas. In our research we revisited the parametric structure of these theoretically derived distributions applied in the entire Southern Italy, exploiting, among other, the availability of updated rainfall data and previous knowledge developed within the framework of VAPI project. Results showed the good performances of both distributions in fitting annual maxima of flood data, highlighting how IF and TCIF distributions possess a solid background for interpreting the actual underlying flood generation processes. Findings of the study can represent a reliable source of information for supporting model selection activities at both local and regional scales.

How to cite: Ciccone, M., Gioia, A., Totaro, V., Mesto, F., Margiotta, M. R., Manfreda, S., Fiorentino, M., and Iacobellis, V.: A comprehensive framework for the application of IF and TCIF theoretically derived distributions in Southern Italy, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17027, https://doi.org/10.5194/egusphere-egu24-17027, 2024.

EGU24-17145 | Orals | ITS2.9/CL0.1.10 | Highlight

Can reservoirs and dams effectively reduce flood runoff in river basins? A case study of the Rhine basin 

Ralf Merz, Gustavo Andrei Speckhann, Viet Dung Nguyen, and Bruno Merz

Flood retention basins constitute a pivotal component of flood protection measures. Local studies have unequivocally demonstrated their efficacy in significantly mitigating flood discharges, thereby minimizing potential downstream damage. However, the impact of these retention basins on the reduction of flood discharges at the large river basin scale remains ambiguous.

This study delves into the assessment of the influence wielded by reservoirs and dams on the reduction of flood discharges within the Rhine basin. Employing a spatially distributed version of the HBV model and Nash-cascade routing, daily discharges from 912 sub-catchments spanning the period 1951-2020 were simulated. The modeling approach comprehensively incorporates the influence of 192 reservoirs in the Rhine catchment on daily runoff volumes. Calibration at 200 gauging stations, facilitates a regional parameterization of the model, based on the PASS method.

Through various scenarios, the study explores how large-scale flood discharges would evolve in the absence of reserves for flood protection or if there were alterations to the storage capacity and function of individual reservoirs. Beyond merely assessing the reduction of runoff peaks, the research scrutinizes alterations in the duration of individual flood events and their spatial expansion, taking into account the intricate network of the 192 reservoirs.

In essence, this study not only contributes to the ongoing discourse on the efficacy of flood retention basins but also sheds light on the nuanced dynamics of reservoirs and dams in shaping the hydrological landscape of the Rhine basin. The findings provide valuable insights for optimizing flood protection strategies, encompassing considerations of storage capacities, operational functions, and the broader spatial and temporal dimensions of flood events.

How to cite: Merz, R., Speckhann, G. A., Nguyen, V. D., and Merz, B.: Can reservoirs and dams effectively reduce flood runoff in river basins? A case study of the Rhine basin, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17145, https://doi.org/10.5194/egusphere-egu24-17145, 2024.

EGU24-17170 | ECS | Posters on site | ITS2.9/CL0.1.10

Decoding spatiotemporal pattern of flood episodes and climatic variability in western and eastern catchments of the Southern Alps, New Zealand. 

Alexander Schulte, Lothar Schulte, Juan Carlos Peña, Ian C. Fuller, Filipe Carvalho, and Sebastian Schulte

In the Northern Hemisphere, the PAGES Floods Working Group database documents 345 paleoflood studies, while in the humid temperate zones of the Southern Hemisphere, studies are limited due to differences in i) continent and ocean distribution, ii) population density, iii) settlement history, and iv) documentary sources. Assessing Southern Hemisphere flood trends becomes a significant goal in the context of Global Change. Our study focuses on spatial-temporal reconstruction and climatic characterization of floods in New Zealand's southern regions (43° – 47°S) from 1862 to 2020 CE.

Due to limitations in generating continuous flood series from the number of flood fatalities or economic losses over the past 160 years, we opted to reconstruct regional indices of historical flood severity and spatial incidence. To accomplish this, we compiled three regional synthetic flood databases from the New Zealand National Institute of Water and Atmospheric Research's catalogue of historical meteorological events. The flood severity matrix integrates various parameters, including numbers of fatalities, witness descriptions of peak flows, flooded areas, geomorphological impacts, losses of livestock, properties, and infrastructure, as well as information on evacuation and mitigation measures. We reanalyzed information from more than 8,000 data entries and reviewed 903 impact points to characterize a total of 295 floods. Additionally, the influence of climatic variability, as inferred from the Principal EOF of the Sea Level Pressure monthly anomalies, was reconstructed using data from the 20th Century Reanalysis Project.

The three flood damage series, comprising 295 floods, reveal several synchronous flood pulses around the years 1878, 1905, 1913, 1957, 1968, 1978, 1999, and 2008 CE. However, other flood pulses are out of phase due to different physiographic settings, catchment size, location on the western (West Coast) or eastern slope of the Southern Alps (Otago and Southland), and exposure to oceans and paths of weather systems.

Notably, in the West Coast Region with very high relief and steep slopes, the most severe floods occurred in spring and summer. Seven out of ten flood pulses recorded from 1862 to 2020 correlate with positive Southern Annular Mode, higher sea surface temperatures (SST), blocking weather types in summer, and lows over the Tasman Sea, resulting in increased humid airflows from the north and northwest.

The larger Otago catchments, comprising humid alpine relief in the northwest, dry basins and ranges in the central area, and humid lowlands in the east, experienced the maximum number of severe floods during summer. Ten out of fourteen pulses occurred during the positive phase of the Southern Oscillation Index (La Niña), characterized by higher SST, blocking types in summer and autumn, and an increase in northeasterly winds.

In contrast, the landforms of Southland, featuring lower ridges, gentler slopes, and large floodplains, saw floods primarily in summer and autumn. Ten out of fourteen pulses in this region correlated with negative phases of the Southern Oscillation Index (El Niño), characterized by lower sea surface temperatures, more zonal flow, and troughs with stronger and more frequent winds from the west in summer and the south in winter.

How to cite: Schulte, A., Schulte, L., Peña, J. C., Fuller, I. C., Carvalho, F., and Schulte, S.: Decoding spatiotemporal pattern of flood episodes and climatic variability in western and eastern catchments of the Southern Alps, New Zealand., EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17170, https://doi.org/10.5194/egusphere-egu24-17170, 2024.

Written mainly in German and partly in Latin, the chamberlain accounts of historical Pozsony/Pressburg (present-day Bratislava), almost continuously available between 1434 and 1595 and 1595, contain daily/weekly resolution data on Danube floods, low flows, ice cover and various weather phenomena. Analysed and presented for the first time, the 176 volumes of the accounts provide systematic, annual accounts of incomes and expenses, with only occasional gaps: flood- and weather-related reports are mainly included in the bridge masters’, the ferrymen’s, the ice-cutters’, the town messengers’, and the road and wall maintenance accounts. Furthermore, water-level related information occasionally was also identified in other sections of the accounts, regarding smaller bridges, river transportation, fishing, meadows and hayfields, woods, and other utilities of the nearby island area. With applying additional information available in the broader Bratislava area and the Carpathian Basin in other contemporary sources such as charters, letters, diaries and other narratives, it is possible to provide unusually high resolution, (quasi-)systematic three-scaled index-based quantitative reconstructions of the frequency, intensity, types (incl. ice-jam floods) and seasonality of Danube floods, and occasionally also of low water-levels.

The greatest floods usually occurred during flood-rich periods; unique great (ice-jam) floods outside of the flood-rich decades happened, for example, in 1454 and 1458. Flood-rich periods were identified during the 1430s-1440s, around the 1480s-1510s and in the mid- and late 16th century – while the first anomaly was also a period of a more frequent water-level variability including memorable low flows, the latter three periods coincide with major European flood-rich periods identified in the last 500 years (see Blöschl et al. 2020). As floods in Bratislava mainly reflect on the hydroclimatic conditions of the Upper-Danube and partly those of the Middle-Danube area, the dataset also provides exceptionally valuable, systematic information to the analysis of 15th-16th century (covering the famous, long Spörer solar minimum) climate variability in Central Europe. Furthermore, major groups of contemporary flood response, prevention and mitigation methods, especially detectable during flood-rich and low-flow periods, are also presented and analysed in the paper in comparison with the available other Middle-Danube (documentary and archaeological data based) evidence, in a broader Danube and Central European context.

How to cite: Kiss, A.: Danube floods, low flows and flood resilience at Bratislava in 1435-1595:Analysis of daily/weekly resolution flood-related evidence in a European context, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18900, https://doi.org/10.5194/egusphere-egu24-18900, 2024.

EGU24-19193 | Posters virtual | ITS2.9/CL0.1.10 | Highlight

Shaping long-term human-environmental dynamics in a floodplain landscape of the Pannonian Plain (Central Europe) over the last millennium 

Zsolt Pinke, Balázs Pal, Beatrix F. Romhanyi, Csilla Zatyko, and Zsolt Kozma

Aiming at a deeper understanding of long-term feedback and interactions, here we reconstructed the changing socio-ecological system of a 9931 km2 wetland landscape over the last millennium. The study area is situated in the steppe-forest zone representing a major part of World Heritage inland salt grasslands in Europe.

Merging GIS-based historico-geographical and archaeo-topographical records from the 11th–mid-16th centuries, detailed spatiotemporal dynamics of settlement patterns, and random information on vegetation and economic activities were reconstructed. Testing the mean elevation of archaeological remains of settlements (sites) and the average soil agro-suitability in their buffer zones by non-parametric t-tests we found an extensive dispersion of settlements in the fertile deep floodplains at the turn of the 11th and 12th centuries but this reclaimed flood zone had been abandoned by the early 14th century. Statistical test results also suggested that the late medieval (LMA) (14th–mid-16th centuries) group was situated significantly higher than the high medieval (HMA) group (late 10th–13th centuries), and the deserted settlements were situated lower than the permanently settled group. Certain geomorphological formations, floodplain islands, and low fluvial ridges became scenes of settlement abandonment, while a dynamic concentration took place on high ridges. These outcomes suggest that the settlement pattern shrunk and vertically displaced significantly by the 14th-century beginning of the Little Ice Age (LIA) when hydrological challenges emerged all over Europe.

Testing the statistical-based settlement-indicated-flood-zone method in a 237 km2 area by an integrated hydrological model concerning the elevation of sites, we simulated the HMA, LMA, and late 18th-century extension of flood zones.

However, not only climatic conditions but anthropogenic transformation in runoff conditions of the upper catchment may also have triggered hydrological challenges in the low-lying plains. The reconstructed transformation of medieval settlement patterns in the Tisza basin (157000 km²) suggests that tens of thousands of square kilometers of virgin forests could have been destroyed in that age. Adapting to a changing hydro-climatic and socio-economic environment a complex community-based ‘livestock-water-crop farming’ trinity evolved, and livestock breeding and export became the strategic sector in the plain over the next centuries.

The socio-economic basis of mixed farming collapsed by the 18th century. As a response to chronic socio-economic backwardness and emerging hydro-climatic challenges, the aristocratic elite began the biggest river regulation in 19th-century Europe, which transformed the plain into a homogenous agricultural area (1950s cropland covering ~70 %).  However, this adaptation strategy failed, and the land use regime of the plain has fallen into a longstanding crisis today. To demonstrate this transformation between the late 18th century (water cover ~30 %) and today (water cover <5 %), we present a series of land cover reconstructions based on digitalized military maps (1782–1785, 1858, 1940–1944 and 1953–1959) and the Corine2018 dataset. Finally, we digitalized the first known flood map (2246 km²) of the region presenting the inundated areas during the catastrophic flood of 1879, the turning point of the century-long wetland reclamation, when the extension of inundated areas was essentially similar to that of the late 18th-century wetlands.

How to cite: Pinke, Z., Pal, B., F. Romhanyi, B., Zatyko, C., and Kozma, Z.: Shaping long-term human-environmental dynamics in a floodplain landscape of the Pannonian Plain (Central Europe) over the last millennium, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-19193, https://doi.org/10.5194/egusphere-egu24-19193, 2024.

EGU24-19865 | Orals | ITS2.9/CL0.1.10 | Highlight

What contradictory signals in flood trends can tell us about drivers of hydrological change 

Gregor Laaha, Johannes Laimighofer, Nur Banu Özcelik, and Juray Parajka

Flood trends are commonly assessed based on instantaneous peak flows on an hourly timescale, as these are most relevant for flood management. However, when hourly data are missing, it has been suggested to perform flood statistics on daily flood values instead, assuming a scaling relationship that depends on the shape of the flood hydrograph and applies over the entire observation period (e.g. Bartens & Haberlandt, 2021).

In an Austria-wide assessment, recent flood trends show diverging spatial patterns that contradict such a stationarity assumption. Interestingly, an aggravation of the flood situation is mainly observed for the peak flow (IPF), while the high values of the mean daily discharge (MDF) show much smaller and, importantly, less significant trends.

Rather than applying flood statistics corrections (e.g. Beylich et al. 2021), the aim of this contribution is to use flood divergence at different timescales as a mean of inferring likely drivers of flood trends. To this end, we combine several established and innovative indicators, such as a trend divergence index (peak versus daily flood scale), a seasonal trend index (to infer information about flood generation processes), and a seasonal shift index (to infer changes in the relevance of these processes). We show the extent to which these indices can inform us about likely drivers of change, i.e. climate-related vs. anthropogenic changes in the catchment. We discuss how these indicators perform in the light of existing flood scale indices, such as the flood timescale (Gaál et al., 2012) and the peak-volume ratio (Bartens & Haberlandt, 2021). The results suggest that the conflicting space-time patterns contain a wealth of information that is highly informative about changes in flood controls under global change.

References:

Bartens, A. and Haberlandt, U.: Flood frequency analysis using mean daily flows vs. instantaneous peak flows, HESS Discussions, https://doi.org/10.5194/hess-2021-466, 2021.

Beylich, M., Haberlandt, U., and Reinstorf, F.: Daily vs. hourly simulation for estimating future flood peaks in mesoscale catchments, Hydrology Research, 52, 821–833, https://doi.org/10.2166/nh.2021.152, 2021.

Gaál, L., Szolgay, J., Kohnová, S., Parajka, J., Merz, R., Viglione, A., and Blöschl, G.: Flood timescales: Understanding the interplay of climate and catchment processes through comparative hydrology, Water Resources Research, 48, W04511, https://doi.org/doi:10.1029/2011WR011509, 2012.

How to cite: Laaha, G., Laimighofer, J., Özcelik, N. B., and Parajka, J.: What contradictory signals in flood trends can tell us about drivers of hydrological change, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-19865, https://doi.org/10.5194/egusphere-egu24-19865, 2024.

Based on monthly resolved temperature and precipitation indices for Central Europe since 1500, which are derived from the virtual research environment tambora.org, statistical methods are presented to use the drought and moisture indices derived from tree ring data such as the scPDSI by Cook et al. (2015), long historical indexed flood series (Bloeschl et al (2020) as well as local and regional wine quality series to improve and refine periods of high and low water levels. Additionally, it will be demonstrated, how this approach can be used to interpolate climate parameters not only temporally but also spatially.

Therefore Bayesian methods are used to mutually verify and derive existing indices that are available on different scales. Furthermore, the references of indices to text quotes are mapped automatically. This not only makes the direct weather, weather and climate descriptions accessible, but also their immediate causes as well as the consequences and effects on the environment and societies. Overall, with this approach, new text quotes can be automatically analysed and integrated into the data pool. This also creates a bridge between historical and recent data and information.

How to cite: Kahle, M. and Glaser, R.: Statistical approaches to the integration of multi-proxy data for the reconstruction of high and low water episodes in Central Europe of the last millennium, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-20506, https://doi.org/10.5194/egusphere-egu24-20506, 2024.

EGU24-20773 | Orals | ITS2.9/CL0.1.10

Nationwide flood risk assessment using large ensemble climate change dataset and the Rainfall-Runoff-Inundation model 

Takahiro Sayama, Jiachao Chen, Yoshito Sugawara, and Masafumi Yamada

Floods pose significant threats, particularly in the context of climate change. This research focuses on a comprehensive analysis of river flooding nationwide in Japan. We utilize the latest dynamic downscaling data, d4PDF-5km, for the entire country, feeding this information into the Rainfall-Runoff-Inundation (RRI) model with a spatial resolution of 150 meters. The objective is to efficiently estimate the probability discharge of all rivers by developing a new method for extracting rainfall events from long-term ensemble data.

 The proposed method involves extracting heavy rainfall events from 720 years (12 ensembles of 60-year records) of downscaled data for each present, 2K and 4K scenarios and inputting them into the RRI model. This approach allows for the estimation of quantiles by analyzing peak flow as non-annual data with the peak-over-threshold method. When applied to the Shikoku region, the results demonstrate the effectiveness of the method, with the ability to estimate probability flows exhibiting a bias of 10% or less compared to a comprehensive calculation of all rainfall events.

 Furthermore, the research identifies variations in the increase of peak flow under climate change, particularly emphasizing differences between the main river and its tributaries. Notably, smaller rivers in the upper reaches are more significantly influenced by changes in rainfall patterns than the lower reaches of the main river.

 The implications of this research extend beyond hydrologic science. The estimated probability flows and corresponding hydrographs serve as crucial boundary conditions for assessing local flood risk. This information is fundamental for informed river management by governments and local authorities. Additionally, private companies, residents, and other stakeholders can utilize this data for robust risk assessments. In conclusion, our research provides valuable insights and a practical methodology for understanding and mitigating flood risks in Japan, taking into account the complexities introduced by climate change.

How to cite: Sayama, T., Chen, J., Sugawara, Y., and Yamada, M.: Nationwide flood risk assessment using large ensemble climate change dataset and the Rainfall-Runoff-Inundation model, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-20773, https://doi.org/10.5194/egusphere-egu24-20773, 2024.

EGU24-21588 | Posters on site | ITS2.9/CL0.1.10

A 1500-year flood history in Romania using multi-archive reconstructions 

Maria Rădoane, Ioana Perşoiu, Gabriela Florescu, and Aurel Perșoiu

This study integrates documentary, instrumental, archaeological and sedimentological data to reconstruct periods of increased flooding in present-day Romania over the last 1500 years.

We identified 22 flood-rich periods between AD 600-650, 830-930, 990 – 1020, 1060 – 1110, 1136 – 1165, 1195 - 1245, 1304 - 1317 and 1340 – 1373, 1400 – 1440, 1460 – 1470, 1490 – 1540, 1560 – 1580, 1592 – 1622, 1635 – 1657, 1667 - 1675, 1699 - 1731, 1771 - 1793, 1831 – 1864, 1890 - 1920, 1930s, 1970s - 1980s, 1990s – present. Our reconstructions show an increase in the incidence of floods during the Medieval Climate Anomaly and towards the end of the Little Ice Age.

In order to understand the potential causes behind these flooding events, we have used reconstructions of seasonally-distinct air temperature, precipitation amount and atmospheric circulation patterns based on an array of proxy records (e.g., cave ice and speleothem stable isotopes, tree ring-based proxies).

The most extensive floods were recorded between AD 1050-1250, mostly in the extra-Carpathian region, attributed to the advance of humid Eastern Mediterranean air masses. Currently, there is no conclusive information about their magnitude during the Migration Period, although the limited information of fluvial origin supports a reduced flood magnitude compared to the Medieval Climate Anomaly. Over the last 500 years, floods with maximum geomorphological effects occurred at the end of the 18th and 19th centuries (1770 – 1800 and 1880 – 1920) across the entire study area, against the background of an unstable climate, marked by the intensification of westerly Atlantic circulation and frequent northward incursions of Eastern Mediterranean cyclones. These were followed in magnitude by recent events (1990 - present), favored predominantly by warm and humid Eastern Mediterranean air masses, and the intensification of the westerly circulation of Atlantic origin at the onset of the Little Ice Age (1460 – 1470 and 1490 – 1530).

Alongside the climate signal, floods in the last 500 years also exhibit a strong anthropogenic component, accentuated in the last 250 years.

How to cite: Rădoane, M., Perşoiu, I., Florescu, G., and Perșoiu, A.: A 1500-year flood history in Romania using multi-archive reconstructions, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-21588, https://doi.org/10.5194/egusphere-egu24-21588, 2024.

EGU24-21845 | Orals | ITS2.9/CL0.1.10

Reconstructing historical flash flood events in South-Eastern Spain: An integrated approach with multiproxy records and hydrological modeling 

Filipe Carvalho, Lothar Schulte, Carlos Sánchez-García, Antonio Gómez-Bolea, and Juan Carlos Peña

Flash floods in Mediterranean catchments are a significant threat. Over the last decades, research in this area has normally focus on recent events, largely due to the absence of long-range instrumental data. However, alternative sources like historical records and natural archives can offer valuable insights and improve our knowledge of past events. In this study, we conduct a reconstruction of major flash flood events over the past century that have impacted several catchments in the South-Eastern Spain, specifically in the Almanzora, Antas and Aguas catchments.

Our study adopts a multidisciplinary approach for the reconstruction of flash floods. We integrate a variety of instrumental gauge data, historic water level indicators on buildings and bridges, and descriptions of inundated areas and flood heights from historical documents. Additionally, we incorporate biomarkers indicative of flood levels, identified through lichenometric analysis of rock surfaces affected by water flow. This combination of diverse proxy records enables us to estimate the peak flow heights at several crucial locations within the study area. For the reconstruction of the maximum flood discharge, we perform a one-dimensional hydrological model across all study sites and in select smaller areas requiring a detailed understanding of the hydraulic behavior, we apply two-dimensional models.

The findings of this study reveal that, despite the region's characteristic low annual precipitation (less than 300 mm), it is occasionally subjected to extreme rainfall events leading to significantly high peak discharges. Typically, these meteorological episodes are associated with atmospheric circulation patterns involving blocking systems along the Mediterranean coast. Hydraulic modeling has identified peak discharges exceeding 5000 m3 s-1 during a major flash flood event in October 1973. This event stands as the most devastating in the past century, resulting in loss of human lives and extensive damage to numerous settlements in all the studied catchments. While other notable flash flood events occurred in 1924 and 2012, they were of lesser magnitude compared to the 1973 flood. Post the 1973 disaster, various hydraulic modifications to the river system were implemented. These included for instance a channelization of significant portions of the Almanzora's main channel and some tributaries, as well as the construction of a large dam. These interventions have contributed to a reduced flood risk in certain areas of the catchment, particularly in the lower sections near the Mediterranean Sea outlet. Nevertheless, recent land use changes such as extensive agriculture and tourism could contribute to changes in flow regime and increased flood vulnerability.

How to cite: Carvalho, F., Schulte, L., Sánchez-García, C., Gómez-Bolea, A., and Peña, J. C.: Reconstructing historical flash flood events in South-Eastern Spain: An integrated approach with multiproxy records and hydrological modeling, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-21845, https://doi.org/10.5194/egusphere-egu24-21845, 2024.

EGU24-21886 | Posters on site | ITS2.9/CL0.1.10

Wetland restoration and its effects on the hydrological conditions and provisioning ecosystem services – a model-based case study at a Hungarian lowland catchment 

Zsolt Kozma, Tamás Ács, Bence Decsi, Máté Krisztián Kardos, Dóra Hidy, Mátyás Árvai, Péter Kalicz, Zoltán Kern, and Zsolt Pinke

The alluvial character of the Great Hungarian Plain has long determined its land use. Human-environmental interactions and landscale patterns were characterised by adaptation to frequent floods and high water availability. Different socio-economical factors in the 18-19th centuries initiated major drainage works and river regulations. These works aimed to adjust hydrological conditions in the floodplains to meet the demands of a new land use approach. This focused on maximizing crop production as the dominant provisioning ecosystem service (ES) instead of the previous land use practice (e.g utilization a broader range of various ES by adaptition).

Over time, this new land use-water management strategy led to a trajectory of constrains: 1) Water demands of the agricultural landscape are restricted to a much narrower range than natural hydrological conditions, leading to damages during extremely dry or wet conditions; 2) Artificial drainage attempts to ensure this narrow range during wet periods in the protected former floodplain areas; 3) However, drainage increases water scarcity and drought damage during consecutive dry periods, which cannot be compensated by the irrigation system due to its limited capacity.

As a result of this outdated strategy and contemporary processes, Hungarian landscape management is facing a crisis. Climate and hydrological changes, the aging farmer community, agricultural sector profitability, alterations in the land use subsities, preferring greening and afforestation are among the leading factors of this crisis. These factors are likely to drive current land use conditions into a significantly altered riverine landscape scenario in the coming decades. Among the current environmental-economic-regulatory conditions, one of the most feasible alternative scenario focuses on water retention and the corresponding adaptive land use. However, the hydrological impacts of such alternative water management-land use on crop yield remain poorly understood.

We examined this by using hydrological simulations at a 272 km2 study site located next to the River Tisza. Here, the morphology of the heterogeneous terrain offers a remarkable semi-natural storage capacity as it encompasses a deep floodplain area.

We defined six different water governance-land use scenarios. First, three water management scenarios were defined and simulated: reference, excess water retention, and flood retention. Along these scenarios inland excess water (a specific type of flooding) hazard maps were used as an indicator for potentially reclaimable floodplains. Next, an alternative land use map was derived following the prevailing Hungarian landscape planning logic, considering factors such as present location and proportion of current agricultural croplands, grasslands, forests, settlement; soil conditions, water availability (agricultural suitability), and nature conservation status.

An integrated hydrological model was set up with the MIKE SHE software to depict spatio-temporal variations in water resources under present conditions (with an operational drainage system) and for all described alternative cases (without a drainage system). Simulated groundwater levels were a key output used to estimate changes in crop yields at selected pointwise locations. The results indicate significant potential for nature-based hydrological adaptation and co-benefits for provisioning ES.

The project FK20-134547 has been implemented with the support provided from the National Research, Development and Innovation Fund of Hungary.

How to cite: Kozma, Z., Ács, T., Decsi, B., Kardos, M. K., Hidy, D., Árvai, M., Kalicz, P., Kern, Z., and Pinke, Z.: Wetland restoration and its effects on the hydrological conditions and provisioning ecosystem services – a model-based case study at a Hungarian lowland catchment, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-21886, https://doi.org/10.5194/egusphere-egu24-21886, 2024.

EGU24-1285 | ECS | PICO | ITS2.11/NH13.2

The contribution of participatory decision making in the planning of ecosystem-based adaptation 

Mar Riera Spiegelhalder and Luís Campos Rodrigues

Inland and coastal floods are becoming more frequent and severe, affecting natural and socioeconomic systems. Coastal urban areas, where population and economic activity are highly concentrated, appear as particularly vulnerable to these events. Local adaptation to climate change benefits from the integration of opinions from different stakeholders in the design and decision process, helping practitioners, planners, and policy makers to address climate change. This process can be operated under the umbrella of Living Labs, where innovative solutions to specific problems can be defined, designed and created through a social-iterative approach. Multicriteria analysis (MCA) is a suitable decision-making tool to develop within the context of Living Labs and climate change adaptation as it allows to capture perceptions from different actors about adaptation measures characterised though various criteria. This study presents the results of an MCA applied to the evaluation of Ecosystem-based Adaptation (EbA) to flooding in three Coastal City Living Labs of the Iberian Peninsula: An ex-ante analysis in Vilanova i la Geltrú (Spain) focused on potential measures to be implemented in an intermittent river-stream; Benidorm (Spain) followed an interim evaluation of planned EbA to address flooding in different city areas; and an ex-post analysis was performed in Oeiras (Metropolitan area of Lisbon; Portugal) to assess the perception of different stakeholders about the performance of already implemented measures along a river stream.

How to cite: Riera Spiegelhalder, M. and Campos Rodrigues, L.: The contribution of participatory decision making in the planning of ecosystem-based adaptation, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1285, https://doi.org/10.5194/egusphere-egu24-1285, 2024.

Due to the particularity of geographical location, coastal areas are not only affected by climate change and urbanization, but also affected by the lower boundary jacking caused by sea level rise, so it is easier to form a flood process with "high peak, large quantity and short duration". This study comprehensively considered future climate change, land use change, and sea level change, combined with hydrological model, simulated the flood process of the Qianshan River Basin in the future, and explored the effects of multiple future environmental changes on flooding in the coastal area. The results show that the flood characteristics of Qianshan River Basin will increase due to multiple future environmental changes, and the increase rate will increase with the increase of future scenario level. Among them, the increase of peak discharge is the largest in Dachong; The increase of peak water depth is the largest in Hongwanchong under normal conditions and Guangchangchong under extreme conditions; The location of the inundation has not changed obviously, and it is mainly concentrated in the southern part of the basin; The high risk areas showed a significant increase trend, and concentrated in Tanzhou Town and outlet of Qianshanshuidao. The increase pattern of these flood characteristics basically follows: In the future SSP126, SSP245, SPP370, and SSP585 scenarios, the flood characteristics produced by a design rainfall of grade n correspond to those produced by a design rainfall of grade (n+1), (n+2), (n+3), and (n+4) in the current period, respectively.

How to cite: Yao, Z. and Huang, G.: Effects of multiple future environmental changes on flooding in coastal area: A case study of Qianshan River Basin, South China, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-2240, https://doi.org/10.5194/egusphere-egu24-2240, 2024.

EGU24-4725 | PICO | ITS2.11/NH13.2

Coastal Dynamics of Thua Thien Huế, Vietnam: Insights from 35 Years of Earth Observation Data 

Felix Bachofer, Ronja Lappe, Hoang Khanh Linh Nguyen, Dang Giang Chau Nguyen, Patrick Sogno, Tobias Ullmann, and Claudia Kuenzer

For the entire shoreline of Vietnam, a comprehensive analysis spanning from 1984 to 2021 was conducted. The study employed a cloud-based processing strategy on Google Earth Engine, utilizing Landsat-derived annual composites based on the Modified Normalized Difference Water Index (MNDWI). Coastline change rates were quantified using linear regressions along shore-normal transects, and hotspots were identified based on erosion and accretion rates. Notable erosion hotspots were observed in the Mekong Delta and Nam Dinh province, while accretion was prominent near Hai Phong city.

The coastal region of Vietnam, including Thua Thien Hue province, is exceptionally susceptible to sea level rise, storm surges and changing sedimentation patterns due to urbanization, agriculture, aquaculture, tourism, and industrial activities competing for limited and attractive coastal zones. Thua Thien Hue, home to the largest lagoon in Southeast Asia, the Tam Giang-Cau Hai lagoon, emerged as a unique case emphasizing the significance of understanding and monitoring coastline dynamics. An extensive dune, stretching across approximately 70 km, acts as a natural barrier, separating the lagoon from the sea. This region encompasses a distinctive ecosystem, agricultural expanses, aquaculture ventures, and the culturally rich City of Hue, once the imperial capital boasting numerous heritage sites. The hinterland, sheltering this amalgamation of natural and cultural treasures, faces the recurrent challenge of compound flooding events. These events are intensified by the interplay of storm surges from the sea and associated backwater effects. Given this, comprehending the historical dynamics becomes imperative, serving as a cornerstone for informed decisions on future adaptation strategies in the realms of coastal and flood protection.

More than half of Thua Thien Hue's coast was classified as predominantly stable, but localized erosion and accretion patterns revealed varying dynamics. The central finding was the identification of five local hotspots with strong coastline change rates. These hotspots exhibited dynamic patterns of erosion and accretion, notably at the Thuan An inlet and in Tu Hien in the south of Hue province.

The Thuan An inlet showcased an erosion hotspot with an average erosion rate of -4 m/yr over 900 meters. This erosion intensified in the 2000s, stabilizing after 2014, illustrating the temporal variability of coastal dynamics. Conversely, on the opposite side of the lagoon inlet, a headland was identified as an accretion hotspot with an average rate of +3 m/yr and alternating phases of erosion and accretion. Severe erosion hotspots were also noted north and south of the lagoon inlet in Tu Hien.

Thua Thien Hue's coastline changes are multifaceted but understudied. They are probably influenced by sediment redistribution, reduced coastal sediment availability, and direct human interventions. Despite the overall stability of most parts of the coastline, the localized changes underscore the intricate interplay of natural and anthropogenic factors shaping the coastal dynamics of Thua Thien Hue over the past three and a half decades.

 

How to cite: Bachofer, F., Lappe, R., Nguyen, H. K. L., Nguyen, D. G. C., Sogno, P., Ullmann, T., and Kuenzer, C.: Coastal Dynamics of Thua Thien Huế, Vietnam: Insights from 35 Years of Earth Observation Data, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-4725, https://doi.org/10.5194/egusphere-egu24-4725, 2024.

EGU24-5640 | ECS | PICO | ITS2.11/NH13.2

Integrated Hydrological Modeling of Climate Change Scenarios on Future Flood Estimations: A Case Study of Bafra Subbasin in the Black Sea Region, Türkiye 

Şule Haliloğlu, Neslihan Beden, Vahdettin Demir, Sema Arıman, Nazire Göksu Soydan Oksal, and Bahtiyar Efe

A primary concern about climate change is the possible rise in the frequency and severity of extreme meteorological/climatological events, like heat waves, intense storms, severe flooding, or droughts. Extreme precipitation events are predicted to increase in size and frequency due to climate change, which could result in more frequent and severe river flooding. Hydrological modeling is integral to accurately deriving flow hydrographs, which is crucial for hydraulic models. This study employs various statistical distributions to assess future simulations' rainfall-runoff relationship and project flow hydrographs under climate change scenarios in the Bafra subbasin of the Black Sea Region. The investigation centers on obtaining flow hydrographs for the Bafra subbasin in the Black Sea Region. The annual maximum precipitation value for the relevant year is determined from daily total precipitation values, and its compatibility with statistical distributions is systematically evaluated. The modeling process considers two climate change scenarios, a moderate radiative forcing scenario (RCP 4.5) and a warming scenario (RCP 8.5), extending projections from 2006 to 2100. The RCP 4.5 and RCP 8.5 scenarios’ data sets are sourced from the Coordinated Regional Climate Downscaling Experiment (CORDEX) data for future estimations. MNA-44 domain that covers Türkiye with a horizontal resolution of 0.44 degrees and 232 points in longitude and 118 points in latitude is used. An accurate determination of flow hydrographs is essential in hydrological modeling. Various statistical distributions, such as Normal Distribution, Log-Normal (2 Parameters), Log-Normal (3 Parameters), Pearson Type-3 (Gamma Type-3), Log-Pearson Type-3, and Gumbel distributions, are employed to identify the most suitable distribution, and the base flow is taken as the current 95% of the time for flow hydrographs. The goodness of fit tests using the Kolmogorov-Smirnov test are conducted to assess distribution types.

As a result of the conducted analyses, in the RCP4.5 flow hydrograph, the Q50 value is determined as 334.7 m3/s, the Q100 value as 350.5 m3/s, and the Q500 value as 382.3 m3/s. In contrast, in the RCP8.5 flow hydrograph, these values are obtained as 395.5 m3/s, 429.4 m3/s, and 506.1 m3/s, respectively. Accordingly, in the pessimistic scenario, the discharge amount that would lead to flooding is 18% higher at Q50, 22% higher at Q100, and 32% higher at Q500. The integration of statistical analyses and climate scenarios enhances the accuracy and reliability of flood estimations, contributing to a comprehensive understanding of the potential impacts of climate change on hydrological processes in the Black Sea Region. In further studies, hydraulic modeling of the flood will be carried out using the Hydrologic Engineering Center - River Analysis System (HEC-RAS) with the most appropriate hydrographs that are obtained from future simulations (RCP 4.5, RCP 8.5). The inundation area of the flood will be computed employing this model, and the hydrological impacts resulting from diverse climate simulations will be acquired through two-dimensional modeling, thereby enhancing comprehension.

How to cite: Haliloğlu, Ş., Beden, N., Demir, V., Arıman, S., Soydan Oksal, N. G., and Efe, B.: Integrated Hydrological Modeling of Climate Change Scenarios on Future Flood Estimations: A Case Study of Bafra Subbasin in the Black Sea Region, Türkiye, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5640, https://doi.org/10.5194/egusphere-egu24-5640, 2024.

EGU24-8109 | ECS | PICO | ITS2.11/NH13.2

Investigating Extreme Wave-Induced Runup in Villanova, Spain: A Comparative Analysis of Extreme Value Models 

Iulia Anton, Roberta Paranunzio, Michele Bendoni, Sudha-Rani Nalakurthi, Salem Gharbia, and Luca Baldini

Coastal cities are increasingly vulnerable to the impacts of extreme wave-induced runup (ssh-runup), which can cause significant damage to infrastructure, ecosystems, and human life. A comprehensive understanding of the characteristics and future trends of extreme ssh-runup is crucial for effective coastal risk management and adaptation strategies. This study employs extreme value analysis (EVA) to investigate wave-induced runup (ssh-runup) in Villanova, Spain, a coastal community participating in the SCORE project's Coastal City Living Labs initiative.

Historical (1956-2005), evaluation run (1980-2018), and future (2015-2094) ssh-runup data are analyzed under two representative concentration pathways (RCP 4.5 and 8.5). Four statistical models are applied for EVA: Block Maxima Generalized Extreme Value (GEV) with L-moments using Gumbel and Peak Over Threshold (POT) Generalized Pareto Distribution (GPD) with a 98% threshold and a constant threshold (0.82). Model performance is evaluated using the Akaike Information Criterion (AIC) and the Bayesian Information Criterion (BIC), as well as different plots (e.g., QQ plot). Results indicate that the GPD model performs consistently better than the other methods in all datasets. The GPD model exhibits a slight improvement over GEV and other models in the historical and evaluation runs, while it outperforms GEV and other models significantly in future projections. This suggests that the GPD model is better suited for capturing the increasing trend in extreme ssh-runup under climate change scenarios.

The findings of this study provide valuable insights into the characteristics and future trends of wave-induced runup in Villanova, aiding in coastal risk assessment and adaptation planning. Applying different EVA techniques highlights the importance of selecting the most appropriate model for the specific data and context. These findings contribute to the understanding of coastal hazards and inform the development of effective adaptation strategies to mitigate the risks associated with extreme wave-induced runup.

How to cite: Anton, I., Paranunzio, R., Bendoni, M., Nalakurthi, S.-R., Gharbia, S., and Baldini, L.: Investigating Extreme Wave-Induced Runup in Villanova, Spain: A Comparative Analysis of Extreme Value Models, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-8109, https://doi.org/10.5194/egusphere-egu24-8109, 2024.

One of the most significant consequences of climate change that is already felt today and will be felt even more in the future is the frequency and severity of natural disasters. Of those, sea-caused floods and storm surges will have the biggest impact on coastal communities, which will be further potentiated on one hand by the sea level rise and on the other by increasing coastal population and economic activity which will make those communities even more vulnerable. As the underlying causes of extreme weather events cannot be circumvented, alternatively it is feasible to decrease the flood vulnerability of most affected areas and implement the right flood control measures. But before any steps can be taken in this direction it is of the utmost importance to analyse the patterns of such events and to establish an early warning system that will allow the local community to respond to such events in a timely manner. Slovenia keeps records on natural disasters to inform civil protection services for performing mobilizing actions during calamity interventions, and an environmental agency that keeps records on past weather conditions through various stationary land and sea sensors. In the case of coastal storms, the latter informs the first, as a matter of public safety during potentially emerging extreme weather conditions giving rise to coastal flooding. Piran, a coastal historic town situated on a narrow peninsula surrounded by North Adriatic Sea waters, is especially vulnerable to coastal floods with 7.3 floods per year on average occurring generally from October through March. Low-lying parts are especially flood-prone, of which the areas below 2.3 m above sea level cover a large percentage of the town covering a mixture of residential, commercial and cultural heritage buildings. With no long-term preventative sustainable measures yet in place and urban sensors 4 to 15 km away from the town, the early warning system does not rely on local climate services but uses general national forecasts. Here we combine the historic records on past flooding events and environmental data to understand the local flood patterns in Piran. This study aims to offer a more nuanced understanding of flood patterns in Piran through the combination of localized field-report and sensor systems from national databases to reliably enhance the precision of flood predictions. The study underscores the pivotal role of accurate, localized data to be extracted from national or regional registries where available that aid in fortifying coastal towns against the escalating impacts of climate change, safeguarding both the inhabitants and the invaluable architectural heritage of historic areas.

 

How to cite: Kralj, E., Kumer, P., and Meulenberg, C.: Insight into temporal and spatial coastal flooding through databases with historic meteorological data and national registry-reported natural disaster events, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12104, https://doi.org/10.5194/egusphere-egu24-12104, 2024.

EGU24-13455 | PICO | ITS2.11/NH13.2

Creating the world’s first Weather Risk Free & Climate Resilient area: WeRISE Project  

Michail Elaiopoulos, Ciro Borrelli, Takehiro Oyama, Hiroki Watanabe, Michelle Boella, Emanuele Giorgi, Antonino Caliri, Roberto Minerdo, and Federico Ottavio Pescetto

“WeRise” is a medium scale, applied research, cooperative project, initiated in the coastal communities of the central east Italian peninsula, in the coasts of Abruzzi Region. The project aims in evaluating an holistic approach to address existing and future weather and climate risks. The central pillars of the proposed solutions consist in providing hyper-localized, high accuracy weather alerts and climate analysis (50, 100 and 150 years), integrated with all civic activity, from infrastructural project design to urban planning and economic development of the whole region. From an architectural and IT point of view, the project consists in a digital comunication platform that, from one side enables citizens to access high accuracy weather alerts and climatic projections, while give to local governments a power tool to stay connected with the citizens and coordinate activities in cases of extreme weather events and disasters. Of course the system represents also a powerfull approach to disaster preparedness and prevention. WeRise employs a two-phased strategy - an initial pilot application that involves 12 comunities in the cities of Lanciano, San Vito Chietino, Ortona and Francavilla al Mare, followed by a regional scale up designed to integrate around 100K citizens. The pilot phase focuses on deploying and testing technology in a controlled environment, assessing its effectiveness in real-world settings. The project aims to bring a new level of precision to weather alerts and risk management, directly benefiting both infrastructure planning and communities’ safety. Primary goals include enhancing weather resilience at the local level, improving emergency response mechanisms, and supporting informed decision-making in urban planning and economic activities. Initial findings from the pilot phase indicate a significant impact on community preparedness and risk mitigation, promising for broader applications. The project’s next steps involve expanding the tested approach to larger, more diverse regions, with an aim to evaluate and develop a national-scale model to organically manage weather and climate risks in Italy.

How to cite: Elaiopoulos, M., Borrelli, C., Oyama, T., Watanabe, H., Boella, M., Giorgi, E., Caliri, A., Minerdo, R., and Pescetto, F. O.: Creating the world’s first Weather Risk Free & Climate Resilient area: WeRISE Project , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-13455, https://doi.org/10.5194/egusphere-egu24-13455, 2024.

Climate change and sea level rise is expected to increase the flood risk in coastal regions. These areas will not only suffer from more frequent and severe storm surges, it will also become increasingly challenging to naturally discharge the excess water from rivers and precipitation. Large pumping stations along the coast can contribute in discharging excess water if high sea levels prevent the natural outflow. A large pumping station is already employed in the Netherlands at IJmuiden, which is responsible for the drainage of a large area in the western Netherlands, including cities as Amsterdam and Utrecht. Pumping stations will often not function at full capacity due to failures, maintenance, or high sea water levels that may reduce the operational pump capacity or even exceed the operational threshold.  Pump reliability can have a significant effect on the flood risk in a water system and thereby strongly influence the optimal investment strategy. Nevertheless, the influence of pump reliability is not considered when designing pumping-sluice stations.  Two separate approaches (graphical and computational modelling) were developed in this study to include pump reliability in when determining the required buffer and pump capacity in a water system. The graphical approach is most suitable for comprehensive visualizations and sensitivity analysis of the water system, while the computational modelling approach allows for a more detailed analysis. Including pump reliability in the design can lead to an increase in required buffer capacity or pumping capacity. However, it can also optimize the mitigation strategy and prevent unnecessary investments as the sensitivity of water systems depends on the system’s characteristics such as water storage capacity.

How to cite: Van Gijzen, L. and Bakker, A.: The effect of the reliability of pumping stations on coastal flood risk under a changing climate , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-16618, https://doi.org/10.5194/egusphere-egu24-16618, 2024.

EGU24-18125 | ECS | PICO | ITS2.11/NH13.2

Are the physical barriers sustainable to saltwater intrusion under changing climatic conditions? 

Rajagopal Sadhasivam, Venkatraman Srinivasan, and Indumathi Nambi

Physical barriers such as subsurface dams (SSD) and cutoff walls (COW) and hydraulic barriers such as freshwater recharge and saltwater pumping are some of the widely studied control measures to mitigate saltwater intrusion (SWI) in coastal aquifers. Past studies have focused on optimizing the design of these control measures, including installation location, depth, pumping, and injection rates under the specified hydraulic and boundary conditions of the aquifer. On the other hand, sea-level rise (SLR) and freshwater flux reduction (FFR) (caused by groundwater pumping and/or reduced aquifer recharge) alter the hydraulic conditions and can potentially change the optimum design of these control measures as well as their performances. Unlike hydraulic barriers with some potential to adapt to these altered hydraulic conditions (by modifying pumping and injection rates), physical barriers are fixed and not easily modifiable. Hence, the performances of physical barriers are highly subjected to changing climate conditions (SLR and FFR), and systematic vulnerability assessment of physical barriers is lacking. Here, we use a widely studied field-scale problem to assess the vulnerability of SSD and COW under SLR and FFR scenarios using constant flux inland boundary conditions. Our results indicate that SSD and COW are resilient to SLR, with SSD being more effective compared to COW. Furthermore, SSD and COW are highly vulnerable to FFR. While SSD is more effective than COW under small declines in FFR, COW outperforms SSD under large FFR. Using sensitivity simulations, we show that our results are valid across a range of aquifer and barrier parameters. These results add insights to the design of physical barriers, taking into account future climatic conditions. Also, our analysis aids in selecting appropriate mitigation measures to address the changing climatic conditions.

How to cite: Sadhasivam, R., Srinivasan, V., and Nambi, I.: Are the physical barriers sustainable to saltwater intrusion under changing climatic conditions?, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18125, https://doi.org/10.5194/egusphere-egu24-18125, 2024.

EGU24-18909 | ECS | PICO | ITS2.11/NH13.2

Assessment of Coastal Concrete Structures Exposed to Extreme Weather Conditions using Concrete Petrography (ASTM C856) 

Audrei Anne Ybañez, Nancy Aguda, Kate Cuyno, Jeremy James Jimenez, Chelly Mei Tanpoco, Reyno Antonio, and Carlo Arcilla

Concrete is used worldwide; however, it is susceptible to fluctuations in temperature and exposure to moisture. Coastal concrete structures, in particular, are exposed to extreme conditions brought about by hydrometeorological processes. The Philippines, as a maritime country, is highly dependent on its coastal structures for its economic development, mobility, and national defense. The country is exposed to the impacts of extreme conditions and natural hazards by virtue of its geologic setting.

In this study, concrete assessment is applied to three major ports using concrete petrography complemented by standard physical tests. Petrography offers information on concrete composition, distribution of air voids, water-cement ratio used, depth of carbonation, and the presence and degree of cracking and concrete deterioration phases. The use of petrography in concert with physical testing greatly expands the understanding of the impacts of extreme coastal conditions to these port structures. Structures assessed exhibited carbonation of the cement paste and the presence of cracking, alkali-silica reaction, and delayed ettringite formation. The researchers investigated further, the possible causes of the concrete degradation including the material sources, the existing coastal and climatological conditions on site, and past extreme weather events such as tropical storms and high waves. These technical findings will contribute to the formulation of standards and recommendations on appropriate concrete cover thickness and mix designs for the assurance of resilient coastal concrete structures in the face of extreme weather conditions.

How to cite: Ybañez, A. A., Aguda, N., Cuyno, K., Jimenez, J. J., Tanpoco, C. M., Antonio, R., and Arcilla, C.: Assessment of Coastal Concrete Structures Exposed to Extreme Weather Conditions using Concrete Petrography (ASTM C856), EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18909, https://doi.org/10.5194/egusphere-egu24-18909, 2024.

EGU24-18919 | ECS | PICO | ITS2.11/NH13.2

Impacts of Climate Change on Small Island Nations: A Data Science Framework using Remote Sensing and Observational Time Series 

Myriam Prasow-Émond, Yves Plancherel, Philippa J. Mason, Matthew D. Piggott, and Jonas Wahl

Small Island Developing States (SIDS) comprise a group of 58 nations identified by the United Nations as facing unique sustainability challenges. These challenges include high exposure to climate change and a lack of data and limited resources. The effects of climate change are already observed in SIDS, notably an increase in the magnitude and frequency of natural disasters, biodiversity loss, ocean acidification, coral bleaching, sea-level rise, and coastal erosion. The coastal zone is considered to be the main economic, environmental, and cultural resource of SIDS, making them particularly vulnerable to the adverse effects of climate change. This project focuses on quantifying and disentangling coastal changes, including erosion, accretion and coastline stability. Existing literature lacks a comprehensive understanding of the patterns of coastal changes, as well as the main anthropogenic and environmental drivers involved. We address this research gap by quantifying the challenges that SIDS encounter, with a particular emphasis on coastal changes.

The approach is data-driven, relying on observational time series extracted from remote sensing (e.g., Sentinel-2, Planet Scope, Landsat missions), in situ measurements (e.g., tide gauge data), and open-access databases. We have developed a robust method based on image segmentation to extract the island's shape over time, enabling us to illustrate the island's dynamics and obtain reliable time series of the coastline position.

 The main drivers of coastal changes are then identified and quantified using time series analysis methods, including causal inference and discovery methods, for SIDS worldwide. We place a specific focus on the Maldives (Indian Ocean) due to its low elevation and high human activity. Additionally, the methodology expands to investigate a spectrum of issues, including the impacts of human activities (e.g., land reclamation, sand mining, shoreline armouring) on the natural responses of coastlines, as well as the effects of confounding factors or common drivers (e.g., Indian monsoon, tropical cyclones, and El Niño/Southern Oscillation). The ultimate goal is to develop a spatiotemporal variable coastline vulnerability index by integrating socioeconomic and environmental time series data, facilitating the assessment of environmental policies in SIDS.

How to cite: Prasow-Émond, M., Plancherel, Y., Mason, P. J., Piggott, M. D., and Wahl, J.: Impacts of Climate Change on Small Island Nations: A Data Science Framework using Remote Sensing and Observational Time Series, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18919, https://doi.org/10.5194/egusphere-egu24-18919, 2024.

EGU24-19086 | ECS | PICO | ITS2.11/NH13.2

Evaluation of atmospheric forces induced by extreme Bora wind on a high-rise hospital in the coastal city of Trieste, Italy 

Petros Ampatzidis, Carlo Cintolesi, Andrea Petronio, and Silvana Di Sabatino

Extreme weather events dominate the disaster landscape of the 21st century and disaster risk is becoming systemic with one event overlapping and influencing another in ways that are testing our resilience to the limit. This is particularly true for critical infrastructure, such as hospitals, that are vital to the functioning of society but have received limited attention in terms of investment in prevention, climate change adaptation and risk reduction. One of the most severe weather events, present in mountainous coastal areas is the Bora wind, a strong and often gusty regional katabatic wind generated by cold and dry air spilling down from a mountain range. The Bora wind has been studied extensively from a meteorological point of view. However, there is limited research on its consequences on the critical infrastructure of coastal urban areas, particularly tall buildings that are susceptible to high wind and wind-driven rain. In Europe, strong Bora winds are encountered on the east coast of the Adriatic Sea. The scope of this study is to assess the Bora-wind-induced atmospheric forces exerted on the high-rise Cattinara hospital in Trieste, Italy, a location where strong Bora winds often occur during the autumn and winter seasons and an increased risk of functionality loss is present. High-resolution RANS simulations are performed for the hospital and the surrounding buildings over the complex and mountainous topography of the area. The imposed boundary conditions approximate the extreme February 2012 Bora wind event that saw gusts of more than 40 m/s in the region. The results provide an evaluation of the methodological framework, assess the inherent complexities of atmospheric simulations over intricate landscapes and demonstrate that a comprehensive understanding of the aerodynamic loads is imperative for mitigating potential vulnerabilities in critical infrastructure subjected to such extreme meteorological phenomena. The study is conducted within the remit of the HORIZON-EU project RISKADAPT (Asset Level Modelling of RISKs in the Face of Climate-Induced Extreme Events and ADAPtation) that seeks to provide solutions to support systemic, risk-informed decisions regarding adaptation to climate change induced compound events at the asset level.

How to cite: Ampatzidis, P., Cintolesi, C., Petronio, A., and Di Sabatino, S.: Evaluation of atmospheric forces induced by extreme Bora wind on a high-rise hospital in the coastal city of Trieste, Italy, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-19086, https://doi.org/10.5194/egusphere-egu24-19086, 2024.

In the intricate tapestry of coastal urban areas, the realities of climate change unfold with discernible impacts across regions like Nigeria, Chad, Cameroon, Rwanda, Somalia, and Kenya. Experiencing a spectrum of climate-related challenges, from extreme weather patterns to rising sea levels, these areas underscore the pressing need for proactive measures. The Lake Chad Basin, encompassing Nigeria, Chad, and Cameroon, grapples with heightened climate upheavals, exacerbating existing insecurities. Simultaneously, nations in East Africa, such as Rwanda, Somalia, and Kenya, navigate the repercussions of unpredictable weather patterns affecting agriculture, water resources, and community livelihoods. The humanitarian community, entrenched in its response, often finds itself constrained by the reactive nature of interventions. Here, the transformative potential of predictive analysis and artificial intelligence (AI) shines a light on proactive measures. Consider the INFORM Climate Change Index1, a forward-looking projection providing quantified estimates of climate change impacts on the future risk of humanitarian crises and disasters. Developed through collaboration between the Euro-Mediterranean Center on Climate Change and the Joint Research Centre of the European Commission, this innovative index modifies indicators in the hazard and exposure dimensions based on projected climate and socio-economic trends. The link between anticipatory humanitarian action and predictive analysis becomes more apparent when we delve into the numbers. Incorporating digital solutions, especially AI, significantly boosts the effectiveness of anticipatory measures. Recent initiatives show that when predictive analysis, AI-driven solutions, and innovative indices are integrated, a substantial percentage of climate-related events can be avoided. These digital tools empower coastal urban communities to construct preemptive barriers, devise effective mitigation strategies, and navigate challenges with resilience. The transformative impact is not just theoretical; it's quantifiable, with numbers indicating that a significant portion of potential crises can be averted through proactive measures informed by predictive analytics. This groundbreaking approach, where digital solutions are seamlessly integrated into anticipatory humanitarian action, transforms coastal urban communities from mere responders to architects of their climate destinies. The narrative, rooted in real-world examples and bolstered by numerical evidence, showcases the tangible benefits of technology. The path forward involves AI, predictive analysis, and innovative indices as indispensable tools in scripting resilience stories. As we explore the depths of climate-induced insecurities across diverse regions, the abstract underscores the pivotal role of AI, coupled with innovative indices like INFORM Climate Change, in guiding coastal urban communities towards a future where climate challenges are met with informed, proactive, and resilient responses.

1https://drmkc.jrc.ec.europa.eu/inform-index/INFORM-Climate-Change

How to cite: Ndatabaye, S., Dabiri, Z., Lang, S., and Wendt, L.: Anticipatory Climate Resilience in Coastal Urban Areas: Transformative Impact of Predictive Analysis, AI Solutions, and Innovative Indices, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-20281, https://doi.org/10.5194/egusphere-egu24-20281, 2024.

EGU24-21671 | PICO | ITS2.11/NH13.2

Does everyone speak English? 

Julian Mühle, Julie Ann Ewald, and Robert Eyres Kenward

Now, more than ever, the ‘eye-in-the-sky’ needs to work with the ‘grunt-on-the-ground’. This is
not just a matter of ground-truth checks on accuracy of remote mapping. For biodiversity
forecasts, of abundance, threats and restoration for species and systems, one needs to map not
only ground cover, but soil and water quality and content, not to mention individuals of small
species. Beneficial activities at local community and citizen level are needed too, as well as
guidance and motivation from above. This will require engagement and love of nature as well as
the support of governments that enable services from nature and do not ignore climate change.
Encouraging benefits at local level, and linkage with guidance or imagery from above, requires
simple communication and for conservation chores to become fun. It requires conservation
communication networks for the 80% in the world who do not speak English. Ideas for
transacting local knowledge as an enjoyable engagement were developed in a Framework 7
project to design a Transactional Environmental Support System but considered too challenging
socially. This verdict stimulated multilingual networking in the civic sector, leading to 10-
language www.sakernet.org (2014) and 23-language www.perdixnet.org (2017) for UNEP and
NGOs, before 43-language www.naturalliance.org was launched for IUCN in 2019. A new
Horizon project is now addressing issues of social motivation for engagement with such systems
in a project for A PROactive approach for COmmunities to enAble Societal Transformation which
is running from November 2023 for 3 years. PRO-COAST (project 101082327) brings together 20
partners from 14 countries to develop, apply and validate an innovative socio-ecological
framework for the study of coastal ecosystem dynamics for the benefit of the people most
exposed to risk deriving from biodiversity loss. Starting in 9 case studies across Europe, it will
develop scaled-up multilingual networking for much wider areas along coasts and inland, using
the global-with-local information networking developed by European Sustainable Use Group.

How to cite: Mühle, J., Ewald, J. A., and Kenward, R. E.: Does everyone speak English?, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-21671, https://doi.org/10.5194/egusphere-egu24-21671, 2024.

With the evolution in climate, heat waves are occurring more commonly which leads to imply indoor temperatures. Several temperature thresholds have been suggested in diverse environments for the indication of indoor overheating. In this study, threshold values for perceived heat stress are evaluated and differentiated between susceptible households and non-susceptible households for the residents of Faisalabad in Pakistan. Data from 52 low to middle-income households were analyzed with the help of regression analysis, t-tests, and analysis of variances to discover characteristics associated with perceived heat stress during the nighttime period in the selected houses. We considered socio-demographic characteristics, health-related queries, heat-related health problems, and house/building material variables from the selected households. The results suggest that the health status during heat stress, age factor, climate zone, and high indoor temperature were the key attributes for the perceived heat stress. The threshold limit advised by the WHO for indoor is 24°C and most of the dwellers in case study live in 36-38°C. People appeared to be at risk for perceived heat stress without knowing to be at risk, particularly when numerous people live in one room (threshold limit 34.8C), suffering from disease (35.6 C) and below 60 (39.8 oC); therefore they do not take it seriously, to take adaption measures.

How to cite: Ibrahim, M., Ehsan, S., and Abbas, F.: Estimate Temperature Threshold for Low to Middle-Income Dwellers of Faisalabad City during Hot Summer Days, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-384, https://doi.org/10.5194/egusphere-egu24-384, 2024.

Air pollution, the largest global environmental health threat, associated with millions of premature death each year, is getting worse with climate change. To protect their health from air pollution, governments encourage people to stay indoors and avoid high pollution episodes. Moving indoors to reduce exposure to outdoor air is a form of avoidance adaptation. The frequency of this adaptive action can affect the amount of time people spend inside buildings. In Europe and North America, people already spend 90% of their time indoors. Air pollution from outdoors can infiltrate the building envelope, exposing people to pollution of outdoor origin at all times, and reducing the value of avoidance adaptation. To better understand the effect of this infiltration on human health, we examine the impact of building standards on the value of avoidance adaptation. This involves considering the costs of improving building envelopes and ventilation, and associated benefits due to avoided premature death from air pollution exposure. We conduct a historical study in the United States from 1980 to 2010 to examine the spatial and temporal patterns of costs and benefits associated with improving building standards to enhance adaptation to air pollution. This includes investigating past missed opportunities in reducing mortality and laying the foundation for future studies on existing long-term opportunities, all within the context of a changing climate. To achieve this, we establish baseline levels of exposure to the most harmful air pollutant, fine particulate matter, under this historical building stock across the United States. Subsequently, we assess the benefits and costs realized under each building standard improvement scenario (Improved Building Envelope and Improved Ventilation). This study will identify the demographics that can benefit the most from these improvements, quantifying, for example, the potential net gains of improving housing quality for low-income communities. It will address open questions on the value of adaptation in protecting human health under increasing risks from a changing climate.

How to cite: Salehi, A. R., Sparks, M., and Saari, R.: Hidden Health Opportunities: The Role of Building Standards in Adapting to Air Pollution in a Changing Climate, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-783, https://doi.org/10.5194/egusphere-egu24-783, 2024.

EGU24-1878 | PICO | ITS2.12/CL0.1.4

Combined Impacts of Weather Conditions and COPD on the Risk for Community-Acquired Pneumonia 

Thomas Brenner, Ann-Christine Link, Christoph Reudenbach, Jörg Bendix, Barbara Weckler, Hendrik Pott, Jan Rupp, Martin Witzenrath, Gernot Rohde, Mathias Pletz, Wilhelm Bertrams, and Bernd Schmeck

Community-acquired pneumonia (CAP) is one of the most frequent causes of death among infectious diseases worldwide. Analyzing a dataset of 5,223 CAP patients in a German multicenter cohort study, our research uniquely explores the twofold combined impact of meteorological conditions, air quality conditions, and pre-existing chronic obstructive pulmonary disease (COPD) on CAP admissions. Both the twofold compound effect of absolute values of meteorological and air quality conditions and, even more, their day-to-day changes significantly influence CAP admissions. Our study emphasizes the important role of air quality conditions over meteorological conditions in contributing to increased CAP admissions, with these weather conditions exerting their influence with a lag time of approximately three to four days. Individuals with pre-existing COPD face the highest risk of CAP admission in the general cohort. The implications of our findings extend to supporting at-risk individuals through protective measures and providing healthcare providers with valuable insights for resource planning during pneumonia-inducing weather conditions.

How to cite: Brenner, T., Link, A.-C., Reudenbach, C., Bendix, J., Weckler, B., Pott, H., Rupp, J., Witzenrath, M., Rohde, G., Pletz, M., Bertrams, W., and Schmeck, B.: Combined Impacts of Weather Conditions and COPD on the Risk for Community-Acquired Pneumonia, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1878, https://doi.org/10.5194/egusphere-egu24-1878, 2024.

EGU24-2325 | PICO | ITS2.12/CL0.1.4

The influence of humid heat on morbidity of megacity Shanghai in China 

Chen Liang, Jiacan Yuan, Xu Tang, Haidong Kan, Wenjia Cai, and Jianmin Chen

Background: Increased attention has been paid to humid-heat extremes as they are projected to increase in both frequency and intensity. However, it remains unclear how compound extremes of heat and humidity affects morbidity when the climate is projected to continue warming in the future, in particular for a megacity with a large population.

Methods: We chose the Wet-Bulb Globe Temperature (WBGT) index as the metric to characterize the humid-heat exposure. The historical associations between daily outpatient visits and daily mean WBGT was established using a Distributed Lag Non-linear Model (DLNM) during the warm season (June to September) from 2013 to 2015 in Shanghai, a prominent megacity of China. Future morbidity burden related to the combined effect of high temperature and humidity were projected under four greenhouse gases (GHGs) emission scenarios (SSP126, SSP245, SSP370 and SSP585).

Results: The humid-heat weather was significantly associated with a higher risk of outpatient visits in Shanghai than the high-temperature conditions. Relative to the baseline period (2010–2019), the morbidity burden due to humid-heat weather was projected to increase 4.4% (95% confidence interval (CI): 1.1% –10.1%) even under the strict emission control scenario (SSP126) by 2100. Under the high-GHGs emission scenario (SSP585), this burden was projected to be 25.4% (95% CI: 15.8% –38.4%), which is 10.1% (95% CI: 6.5% –15.8%) more than that due to high-temperature weather. Our results also indicate that humid-hot nights could cause large morbidity risks under high-GHGs emission scenarios particularly in heat-sensible diseases such as the respiratory and cardiovascular disease by the end of this century.

Conclusions: Humid heat exposures significantly increased the all-cause morbidity risk in the megacity Shanghai, especially in humid-hot nights. Our findings suggest that the combined effect of elevated temperature and humidity is projected to have more substantial impact on health compared to high temperature alone in a warming climate.

How to cite: Liang, C., Yuan, J., Tang, X., Kan, H., Cai, W., and Chen, J.: The influence of humid heat on morbidity of megacity Shanghai in China, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-2325, https://doi.org/10.5194/egusphere-egu24-2325, 2024.

Climate change is expected to substantially alter biodiversity, leading to alterations in phenology, genetic composition, and species distribution while also affecting species interactions and ecosystem. Invasive alien species (IAS) have threatened the integrity of ecosystems throughout the world. They affect the species diversity of native ecosystems and threaten their biological integrity. Due to increasing movement of people and goods around the world, and with new trade routes opening and enhanced transportation, the number of species being introduced into new areas is rising. IAS reduce agricultural yields, irrigated croplands, grazing areas, and water availability, and contribute to the spread of mosquito-borne diseases. Mosquitoes are widely spread Mosquitoes are widely spread and transmit malaria and several arthropod-borne viruses. A particular example of IAS is Parthenium hysterophorus (Asteraceae). It is one of the world's most serious invasive plants that is able to thrive and spread aggressively outside its original geographical areas. Native to the subtropics and tropics of North and South America, Parthenium has negative effects on human, livestock, agriculture and the environment. The aim of this study is to determine the abundance and diversity of mosquito vectors at sites with different degrees of invasive plant infestations in the Rift valley area in Kenya. Currently, the spread of invasive plant species is a major problem in Kenya, where indigenous flora is replaced. The study sites are located in Baringo county. A total of 50000 mosquitoes were captured using a combination of different trapping techniques from six sites, three of them with IAS (Parthenium) and three without. We identified 48 species. A subset of 1000 mosquitoes was analyzed for evidence of recent plant feeding using cold anthrone test. An overall low fructose positivity rate (10.9%) was found. Barcode technique was applied to identify plant food source using specific primers for a locus from the chloroplast genome, ribulose diphosphate carboxylase. The DNA from all trees or shrubs within a 100m radius from the trap was collected to build a barcode reference library. Plant DNA with 55.3% (n = 553) success rate was identified. Sequences were successfully generated from samples, indicating Parthenium plants as the predominant plant fed by mosquito vectors. This survey is an inventory of the mosquito population composition and of the abundance and richness of arboviruses. It provides an insight into how changes in community ecology interact with the main types of land-use change and influence the dynamics of relevant arboviruses in Kenya. Thus, it provides a beneficial knowledge for targeted control.

Keywords

Climate change, land-use changes, agricultural expansion, infectious diseases, mosquito ecology, invasive plants, Parthenium hysterophorus

How to cite: Osman, T., Fevre, E., and Borgemeister, C.: Land-use management of invasive species could help prevent spread of mosquitoes borne diseases: Evidence from Kenya  , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-3257, https://doi.org/10.5194/egusphere-egu24-3257, 2024.

EGU24-3476 | ECS | PICO | ITS2.12/CL0.1.4 | Highlight

Real-time forecast of temperature-related excess mortality at small-area level: A conceptual framework 

Malcolm N. Mistry and Antonio Gasparrini

Development of innovative tools for real-time monitoring and forecast of environmental health impacts is central to effective public health interventions and resource allocation strategies. Though a need for such generic tools has been previously echoed by public health planners and regional authorities responsible for issuing anticipatory alerts, a comprehensive, robust and scalable real-time operational framework for predicting temperature-related excess deaths at local scale has not been developed yet. Filling this gap, we propose a flexible conceptual framework for coupling publicly available operational weather forecasts with temperature-mortality risk functions specific to small census-based zones, the latter derived using state-of-the-art environmental epidemiological models. Utilising high-resolution temperature data forecast by a leading European meteorological centre, we demonstrate a real-time application to forecast the excess mortality during the July 2022 heatwave over England and Wales. The output by way of expected temperature-related excess deaths at small geographic areas on different lead times, can be automated to generate maps at various spatio-temporal scales, thus facilitating preventive action and allocation of public-health resources in advance. While the real-case example discussed here demonstrates an application for predicting (expected) heat-related excess deaths, the framework can also be adapted to other weather-related health risks and to different geographical areas, provided data on both meteorological exposure and the underlying health outcomes are available to calibrate the associated risk functions. The proposed framework addresses an urgent need for predicting the short-term environmental health burden on public health systems globally, especially in low- and middle-income regions, where rapid response to mitigate adverse exposures and impacts to extreme temperatures are often constrained by available resources.

How to cite: Mistry, M. N. and Gasparrini, A.: Real-time forecast of temperature-related excess mortality at small-area level: A conceptual framework, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-3476, https://doi.org/10.5194/egusphere-egu24-3476, 2024.

EGU24-3881 | ECS | PICO | ITS2.12/CL0.1.4

How seasonal flooding affects diets in Bangladesh during a nutrition-sensitive agriculture intervention. 

Claudia Offner, Thalia M Sparling, Claire Dooley, Jillian Waid, Sabine Gabrysch, and Suneetha Kadiyala

Background and aims: Climate change is expected to increase the frequency and severity of monsoon floods in south-east Asia and will severely impact food and nutrition security. The Food and Agricultural Approaches to Reducing Malnutrition (FAARM) cluster-randomized controlled trial in rural Bangladesh, aimed to improve nutrition outcomes through a Nutrition-Sensitive Agriculture (NSA) intervention. We evaluated the role of the intervention in moderating the impact of seasonal flood exposures on women’s dietary diversity (WDD) and food group consumption.

Description and recommendations: Using Bayesian interaction models, we paired a time series measure of seasonal flooding with high-frequency dietary data collected bi-monthly from 2,701 women throughout the trial (2015-2019). We found that for a 1% increase in flooding in Mar/Apr, subsequent WDD decreased by 18% of a food group in the control-arm, with no detrimental effect observed in the treatment-arm. Of the food groups, vitamin-A-rich foods (VA) was most influenced by seasonal flooding. The odds of consuming VA are normally 41% higher in the May/June months. However, for every 1% increase in flooding in Mar/Apr, the odds of consuming VA in May/June only increases by 13% for the control-arm, and by 27% for the treatment group.

Significance: Flooding has a variable impact on WDD and food consumption, and the NSA intervention appeared to offset the detrimental effects of flooding on WDD in the most volatile season. This study highlights the sensitivity of diets to changing monsoon patterns and provides an approach to evaluating the impacts of interventions on these intricate pathways.

How to cite: Offner, C., Sparling, T. M., Dooley, C., Waid, J., Gabrysch, S., and Kadiyala, S.: How seasonal flooding affects diets in Bangladesh during a nutrition-sensitive agriculture intervention., EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-3881, https://doi.org/10.5194/egusphere-egu24-3881, 2024.

EGU24-5480 | ECS | PICO | ITS2.12/CL0.1.4 | Highlight

Health Benefits of Meeting 2-degree Warming Scenario in India 

Debajit Sarkar, Sagnik Dey, Pallav Purohit, and Sourangsu Chowdhury

Anthropogenic emissions are responsible for deteriorated air quality and accelerated climate change in developing countries like India. The current trajectory of emissions is expected to further degrade air quality, potentially leading to increased warming levels by the end of the century, posing severe consequences for public health. In this study, we analyzed two scenarios using the GAINS-model framework - the business-as-usual (BAU), relying on existing air pollution control policies and measures, and the sustainable development scenario (SDS), integrating advanced air pollution control policies and measures, aiming to contain the global temperature increase below 2°C by 2100. We estimated the health burden attributable to ambient air pollution in BAU and SDS scenarios, segregated into regional and sectoral emissions in India for the years 2030 and 2050. Under the BAU scenario, premature mortality and disability-adjusted life-years (DALYs) are projected to increase from 0.72 million (95% CI: 0.53-0.89) and 24.8 million (15.4-30.5) in 2015 by 9.7% and 2.4% in 2030, respectively. In 2050, mortality and DALYs are projected to further increase to 0.88 million (0.75-1.01) and 26.2 million (22.8-29.6). At the sub-national level, states with a low Socio-demographic Index (SDI) are expected to possess majority (49-53%) of the health burden. However, if India follows the SDS scenario, 0.16 million (0.14-0.18) lives and 3.7 million (3.2-4.3) DALYs can be avoided in 2030. The corresponding benefits in 2050 will be 0.34 million (0.29-0.39) lives and 8.4 million (7.1-9.7) DALYs, respectively, relative to the BAU scenario. Our results reveal that states with a high SDI would experience the most significant benefits (15% and 26% for mortality & 26% and 44% for DALYs in 2030 and 2050), as compared to middle and low SDI states. The findings underscore the importance of immediate adoption of cost-effective and advanced technologies driven by sustainable development policies is imperative to mitigate air pollution and climate change simultaneously. A stronger mandate to revise the environmental standards and health policies is necessary to maximize health benefits in India. 

How to cite: Sarkar, D., Dey, S., Purohit, P., and Chowdhury, S.: Health Benefits of Meeting 2-degree Warming Scenario in India, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5480, https://doi.org/10.5194/egusphere-egu24-5480, 2024.

The concurrent rise in global temperatures and air pollution levels has raised concerns regarding their joint effects on human health. Heatwaves, exacerbated by climate change, have become more frequent and intense, posing significant health risks to vulnerable populations. Concurrently, air pollution, stemming from anthropogenic activities and environmental factors, contributes to respiratory and cardiovascular ailments, amplifying the health burden.

 

It becomes important to utilize multifaceted data from climate models, demographic and socioeconomic projections like the Shared Socioeconomic Pathways (SSPs), geographical information and other pertinent datasets in exploring the complex relationship between climate change, exposure to air pollution, extreme heat and related health outcomes. Using various data sets including climate, demographic, and socioeconomic information at different scales (cohort, city, and small area levels), the recently concluded EU Horizon 2020 EXHAUSTION project quantified the synergetic effects of exposure to extreme heat and air pollution on mortality risks for respiratory and cardiovascular diseases. The project also investigated the influence of various vulnerability factors (e.g. socioeconomic conditions, access to green space) on the health risks. The heat-health burden was projected under future scenarios until 2100, taking into account shifting demographic patterns and baseline health status in various scenarios.

 

We advocate for the extension of methodologies employed in EXHAUSTION to encompass low- and middle-income countries in South Asia and sub-Saharan Africa, where extreme occurrences of heat and air pollution prevail. The assessment of climate change impacts on human health in these regions is notably challenging due to the scarcity of data across various domains, encompassing health, climate, and socio-demographic information. We advocate for enhanced accessibility and availability of this data to deepen our understanding of the effects of climate change-induced extreme heat and air pollution on mortality and morbidity in LMICs. This improved access will better equip health officials to strategize interventions and bolster adaptation responses. Furthermore, there is a need for more detailed emission and socio-demographic projections in LMICs, underpinned by data and reflective of current trends.

How to cite: Aunan, K.: Connecting climate change and health to protect the most vulnerable, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-6687, https://doi.org/10.5194/egusphere-egu24-6687, 2024.

EGU24-6696 | ECS | PICO | ITS2.12/CL0.1.4

The role of shading on biometeorological conditions in the historic centre of Prague, Czech Republic. 

Lucie Chlapcová, Aleš Urban, and Jan Kyselý

Prague is the capital and the largest city of the Czech Republic and its historic centre near the Vltava river is a popular tourist destination. Especially the area along the right bank of the Vltava river, called Náplavka, is one of the most popular locations to visit during the summer months due to many social and cultural events that take place here. However, given the north-south orientation of the Vltava river and the lack of greenery and shade in this area, the question arises as to what extent thermal conditions are comfortable during hot summer days at Náplavka. Many previous studies have shown that the presence of greenery and shade is essential for reducing the heat stress in the streets.

In this study we assessed the effect of shading on biometeorological conditions at eight different measuring sites located along a loop between Charles Square and the Náplavka riverbank. Meteorological parameters (including air temperature, relative humidity, wind speed, Heat Index, Wet-Bulb Globe Temperature) were measured and recorded using the Kestrel 5400 portable tool, every two hours between 8:00 a.m. and 6:00 p.m. CEST on 9 days during summer in 2019 and on 5 days in 2022. In addition, fisheye photographs were taken at each location to quantify the effect of shading. From these data, we calculated advanced thermal comfort indices (Physiologically Equivalent Temperature, Universal Thermal Climate Index) and Sky View Factor (SVF) in the RayMan Pro program. We compared measured data from all sites under different weather conditions between 2019 and 2022, and assessed the evolution of heat stress during the day as a function of shading at each site.

Our results showed that while in the morning Náplavka’s biometeorological conditions were most comfortable among all measurement sites, they became most stressful in the afternoon. The analysis of the fisheye images showed that the lack of greenery and shading at Náplavka contributed significantly to the high heat stress levels. Our results suggest that the relocation of day-long events from Náplavka to other locations (e.g. a park at Charles Square) should be considered and/or adequate sun protection should be provided on hot summer days.

How to cite: Chlapcová, L., Urban, A., and Kyselý, J.: The role of shading on biometeorological conditions in the historic centre of Prague, Czech Republic., EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-6696, https://doi.org/10.5194/egusphere-egu24-6696, 2024.

Heat stroke is a serious heat-related health outcome that can eventually lead to death. Due to the poor accessibility of heat stroke data, the large-scale relationship between heat stroke and meteorological factors is still unclear. We collected daily heat stroke search index and meteorological data for the period 2013–2020 in 333 Chinese cities to quantify the threshold of people may suffer from heat stroke by Random Forest model. When the daily mean temperature exceeded 23.5°C, heat stroke cases may occur in China. Then, we calculated the total heatwave duration exceeding the threshold quantified aforementioned and population exposure to heatwave in China using four scenario combinations, namely SSP1SSP1-2.6, SSP2SSP2-4.5, SSP3SSP3-7.0, SSP5SSP5-8.5, for 1986-2005, 2041-2060 and 2081-2100 periods.

How to cite: Han, Q.: Heat stroke risk in China quantified by web-based data, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-7407, https://doi.org/10.5194/egusphere-egu24-7407, 2024.

EGU24-7794 | PICO | ITS2.12/CL0.1.4 | Highlight

Weather, influenza epidemics and mortality patterns in central Europe 

Hana Hanzlíková, Aleš Urban, Eva Plavcová, Jan Kynčl, and Jan Kyselý

In temperate climates, influenza follows a seasonal pattern with peak incidence in winter and contributes significantly to excess winter mortality. The relationship between weather variability, influenza and human health is complex and the underlying mechanisms remain unclear. This study investigated the links between meteorological variables, influenza epidemics, and mortality in the Czech Republic over the 1982/83 to 2019/20 epidemics seasons. Results showed that severe influenza outbreaks with largest mortality impacts, primarily driven by A/H3N2 viruses, were preceded by falling temperatures, increasing relative humidity and cloud cover, and low air temperatures, high cloud cover and high relative humidity prevailed for their duration. In contrast, A/H1N1-related epidemics with lower mortality impacts occurred usually during periods of average or above-average temperatures, accompanied by elevated relative humidity and cloud cover. Influenza epidemics peaking later in winter or in early spring were associated with high excess mortality, usually lasted longer and were accompanied by prolonged periods of low temperatures. The results highlight the importance of ambient temperature and other weather variables in the transmission of influenza virus and course and severity of the epidemics. Prolonged periods of low temperatures in winter, together with the prevalence of influenza A/H3N2 in the population, were identified as an important contributing factors to the significant excess mortality in the temperate climate of central Europe.

How to cite: Hanzlíková, H., Urban, A., Plavcová, E., Kynčl, J., and Kyselý, J.: Weather, influenza epidemics and mortality patterns in central Europe, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-7794, https://doi.org/10.5194/egusphere-egu24-7794, 2024.

EGU24-8031 | PICO | ITS2.12/CL0.1.4

Inequality in the exposure to air pollution and temperature through the century 

Andrea Pozzer, Sourangsu Chowdhury, Lin Ma, and Brendan Steffens

Air quality and surface temperature exert significant influences on human health. However, the impact of air pollution and non-optimal temperature is not uniformly experienced across the population. In this study, we employ the "Gini" coefficient, a commonly used concept in economics. While traditionally applied to represent wealth inequality, we adapt this coefficient to gauge spatial inequality in population exposure to air pollutants and temperature, irrespective of the economic income of the population. As pollution and temperature are dynamic and subject to change in the future due to varying climate change and socioeconomic scenarios, our analysis extends to potential scenarios projected by the Coupled Model Intercomparison Project (CMIP6). We show changes of the Gini coefficient both at global, regional and country scale for the present century (2000-2100) covered by the model simulations. Our findings indicate that at global level, air quality inequality has peaked around the present time, with a trend towards decreasing inequality in most projections, reaching a minimum by the end of the century. Conversely, temperature exposure inequality will fluctuate based on the scenario, primarily showing an increasing inequality trend over time in alignment with anticipated climate change impacts. Importantly, the Gini coefficient estimation provides a complementary view to air quality and climate change assessment, indicating exposure disparities among the population in a specific region. Our study shows the unequal distribution of air quality and temperature exposure among populations, emphasizing the need for targeted interventions and policies to address these disparities, especially considering the projected changes in climate and socioeconomic factors.

How to cite: Pozzer, A., Chowdhury, S., Ma, L., and Steffens, B.: Inequality in the exposure to air pollution and temperature through the century, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-8031, https://doi.org/10.5194/egusphere-egu24-8031, 2024.

EGU24-9746 | ECS | PICO | ITS2.12/CL0.1.4

Residential green space and summer heat stress: a repeated cross-sectional study 

Eva Beele, Raf Aerts, Maarten Reyniers, and Ben Somers

Urbanization and global warming have led to the emergence of urban heat islands, profoundly impacting the liveability and long-term well-being of people living in cities. This study investigates the impact of urban green space composition and configuration on stress and sleep quality in Leuven, Belgium, during the summers of 2021 and 2022.

Utilizing three validated stress questionnaires (PSS, PSQI, and HSSI), we assessed mental health, sleep quality and heat stress during 4 heat and 4 control events for 785 respondents. Concurrently, we recorded risk and vulnerability factors related to physical sensitivity, socio-economic sensitivity and personal living space for each respondent. Urban land cover data at 50m and 250m buffer scales were analysed using composition and configuration metrics. Structural equation models were employed to investigate the impact of urban green space on stress and sleep quality during both heat and non-heat control events. Models were adjusted for risk and vulnerability factors, and effectively dealt with spatial autocorrelation inherent in our data.

During control events, mental health, sleep quality and heat stress were predominantly associated with risk and vulnerability factors. High physical sensitivity, elevated socio-economic sensitivity and suboptimal personal living spaces were associated with higher physiological stress, poor sleep quality, and higher heat stress. Conversely, during heat events, stress indicators were predominantly associated with the surrounding green space, while associations with risk and vulnerability factors were limited. Augmenting high green relative cover may mitigate heat stress, while increasing low green cover may alleviate both heat stress and enhance sleep quality. Stratified analyses for socio-economic status and distinct urban-rural regions revealed notable differences among subgroups.

In conclusion, this study emphasizes the importance of incorporating both low and high green spaces to mitigate heat stress and improve sleep quality and therefore, human health, during heat events.

How to cite: Beele, E., Aerts, R., Reyniers, M., and Somers, B.: Residential green space and summer heat stress: a repeated cross-sectional study, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-9746, https://doi.org/10.5194/egusphere-egu24-9746, 2024.

EGU24-10908 | ECS | PICO | ITS2.12/CL0.1.4

Reacting to climate change and temperature extremes: A case study on the tiger mosquito in Italy­ 

Miguel Garrido Zornoza, Cyril Caminade, and Adrian Tompkins

Native to tropical and subtropical regions of Southeast Asia, Aedes albopictus, commonly known as the tiger mosquito, has been spreading worldwide with the aid of human activity. The geographical distribution and temporal dynamics of this mosquito are of special interest, given its role as a vector for arboviruses such as dengue (DENV) and chikungunya (CHIKV). Climate change, and its consequent increase in ­­both mean surface temperatures and the frequency and intensity of heat waves, has the potential to affect the behavior and seasonal activity of this mosquito, thereby posing a significant risk to human health. Understanding the impact of mean temperature changes and extremes on potential vector-borne disease risk is paramount to forecasting future trends as well as developing meaningful intervention strategies.

 

In this work, we study the dynamics of Ae. albopictus over three decades, spanning 1990-2019, with a particular emphasis on the Italian Peninsula, which has remained a significant hotspot in Europe, since its introduction in the 1990s. We employed and adapted VECTRI, a climate-sensitive dynamical model that was originally designed for malaria. The model has been modified to parameterize Ae. albopictus and successfully calibrated to reproduce the seasonality of the vector using ovitrap data from various locations in Italy. Driving the model using high resolution EOBS gridded observation data, we perform various experiments to isolate the impact of temperature trends and late-spring to summer temperature extremes. Our results show a temperature-driven linear increase in the length of the mosquito season, with larger increases over the southern regions. Overall, temperature extremes tend to increase the bulk egg population across the country, although different spatial trends are highlighted: warm events tend to reduce vector populations in the Po valley and southern regions of Italy,already subject to the highest temperatures, while they tend to increase vector abundance over fringe highland areas. Our results indicate that 10-day temperature forecasts could be utilized to predict mosquito activity and consequently guide vector control intervention strategies such as insecticide spraying in the higher altitude regions identified in this study.

How to cite: Garrido Zornoza, M., Caminade, C., and Tompkins, A.: Reacting to climate change and temperature extremes: A case study on the tiger mosquito in Italy­, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-10908, https://doi.org/10.5194/egusphere-egu24-10908, 2024.

EGU24-11803 | PICO | ITS2.12/CL0.1.4 | Highlight

Copernicus Health Hub: Health community accessing environmental information from the Copernicus Programme 

Julie Letertre, Christian Borger, Cristina Ananasso, and Vincent-Henri Peuch

Copernicus is the Earth observation component of the European Union’s Space programme, looking at our planet and its environment to benefit all European citizens.

The Copernicus services transform a wealth of satellite and ground-based measurements into value-added information by processing and analysing the products.

All the information is provided with an open and free data policy to help public national and European authorities, policy makers, international organisations, and service providers to improve European citizens' quality of life.

There are six operational Copernicus Services covering the whole Earth System including ocean, land, atmosphere, and more horizontal domains such as climate change, emergency and security.

To facilitate the use of these information by the different user communities, some Thematic Hubs have been created and are under development. One of the first hubs is the Copernicus Health Hub (CHH) and it is focusing on the health community.

The CHH collects and provides all the Copernicus environmental information that are pertinent to Health, following the WHO definition: Physical, Mental and Well-being. The Health Hub is also supporting the users in better exploiting and uptake Copernicus data and products (via documentation, access to catalogues, inspirational use case stories, …). In addition, the CHH should collect new requirements for the evolution of the Copernicus programme.

In this presentation, the CHH will be introduced in more details, the different types of environmental information will be presented accompanied by some use cases to inspire further developments and new applications for the health community.

How to cite: Letertre, J., Borger, C., Ananasso, C., and Peuch, V.-H.: Copernicus Health Hub: Health community accessing environmental information from the Copernicus Programme, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-11803, https://doi.org/10.5194/egusphere-egu24-11803, 2024.

EGU24-12490 | ECS | PICO | ITS2.12/CL0.1.4

Digital thermal 3D model for thermal comfort analysis at district scale. 

Chaimaa Delasse, Rafika Hajji, Tania Landes, Hélène Macher, Pierre Kastendeuch, and Georges Najjar

Today’s cities face many challenges, including those related to climate change, energy efficiency, and human well-being. These issues are closely linked to the thermal dynamics of the built environment. Sub-optimal solutions and increased vulnerability often result from a lack of deep understanding of the spatial and temporal variations of thermal interactions in the urban context, particularly in data-limited regions. The primary objective of this thesis is to develop a methodology for creating "as-built" digital thermal models through 3D reconstruction of urban scene objects such as buildings, trees, and pavements. The coupling of 3D geometry and TIR (Thermal Infra-Red) acquisitions at different periods enhances the semantic richness of the model and facilitates the study of building-tree thermal interactions. This, in turn, enables the calculation and the monitoring of the evolution of thermal comfort indices at a micro-scale (<2km). To this end, the TRIO team has developed LASER/F (Latent And Sensible Radiation Fluxes), a microclimate simulation software that can replicate the effect of buildings and trees on the urban microclimate. The buildings and trees of interest are modeled with a high level of detail (LOD3) to improve the accuracy of the simulations. The simulated thermal model will be evaluated using "real" thermal and eco-physiological data collected in the field. The validated model will be used to simulate various scenarios for improving thermal comfort, making it a valuable decision-making tool for urban planning. The study will be conducted at two sites, one in Strasbourg (France) and the other in Rabat (Morocco). This study aims to analyze, compare, and improve LASER/F simulations at two sites, in two different countries and climates. The goal is to assess the impact of existing vegetation configurations and propose scenarios for improving thermal comfort. This may include changes to tree species or positions and the modification of urban geometry. Measurement campaigns have been carried out at the Strasbourg site during the summer of 2023. Fixed environmental measurements such as wind speed, relative humidity, global radiation, and sap flow were carried out. 3D geometry acquisitions were performed using laser scanners. TIR data was also acquired thanks to thermal cameras at fixed positions and thermobuttons located on facades. Moreover, a mobile system composed of RGB (Red Green Blue) cameras and a TIR camera has been specifically designed. Similar campaigns are planned for the Rabat site in 2024.

How to cite: Delasse, C., Hajji, R., Landes, T., Macher, H., Kastendeuch, P., and Najjar, G.: Digital thermal 3D model for thermal comfort analysis at district scale., EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12490, https://doi.org/10.5194/egusphere-egu24-12490, 2024.

EGU24-12795 | ECS | PICO | ITS2.12/CL0.1.4

The association between tropical sea surface temperature variability and sentinel reporting of travel-related dengue  

Stella Dafka, Michael Libman, Davidson H. Hamer, Joacim Rocklöv, and Ralph Huits

Oceanic-atmospheric interactions play a crucial role in the modulation of monsoon rainfall. This is the first study that directly investigates the impact of tropical sea surface temperature (SST) variability on the frequency of sentinel reporting of travel-related dengue from the Geosentinel global emerging infectious disease surveillance network, by using the latest climate reanalysis ERA-5 produced by the European Center for Medium-Range Weather Forecasts, for the period 2007 to 2019. More specifically, we explore lag structures and the associated spatial correlation patterns between travel-related dengue cases, SSTs, and total precipitation over the tropics. We found that the Indo-Pacific and Atlantic Ocean SSTs have a remote influence on dengue risk in global regions that exhibit distinct monsoon characteristics. The coupling between SST variations and rainfall is an important driver of travel-related dengue cases and could act as an early warning signal for outbreak preparedness and travel medicine preventive advice. Finally, our findings highlight the need to better understand the large-scale and local circulation response to changes in the pattern of tropical ocean warming, to be able to better predict extreme events such as droughts and floods and devise adaptation measures against dengue outbreaks.

How to cite: Dafka, S., Libman, M., Hamer, D. H., Rocklöv, J., and Huits, R.: The association between tropical sea surface temperature variability and sentinel reporting of travel-related dengue , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12795, https://doi.org/10.5194/egusphere-egu24-12795, 2024.

The city of Belgrade has experienced a rise in temperatures during summers, marked by an increased frequency and intensity of heat waves. A concerning element is the escalation of overnight temperatures, which fail to cool down adequately. This phenomenon is particularly prevalent in urban areas due to the urban heat island effect. This study aims to provide evidence of the summer discomfort experienced in Belgrade during tropical nights over the past two decades and its impact on health. To achieve this, it is compiled a dataset containing daily weather information recorded at 9 pm (CET) spanning the years 2000 to 2020.

How to cite: Pecelj, M.: Summer Discomfort During Tropical Nights in Belgrade (Serbia), EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-13063, https://doi.org/10.5194/egusphere-egu24-13063, 2024.

EGU24-13800 | ECS | PICO | ITS2.12/CL0.1.4 | Highlight

The exceedance of physiologically relevant thresholds in South Asia 

Joy Monteiro, Jenix Justine, Hardik Shah, and Neethi Rao

Since the pioneering work in the early 2000s, there has been interest in the climate science community in using the compounding effects of heat and humidity (in the form of wet-bulb temperatures or other meteorological indices such as heat index) to understand health risks due to thermal stress on humans. For instance it has been suggested that the combination of high heat and humidity was responsible for the high mortality observed during the 2015 heatwaves in South Asia. However, assessing health impacts of temperature and humidity is challenging in South Asia since the health data required for epidemiological work is rarely available or reliable for robust analyses.

Using quality-controlled surface observations, we show that the humidity (or equivalently, wet-bulb temperatures) was in fact lower during most high impact heatwaves in South Asia -- the daily maximum was very close to its monthly mean value whereas the daily minimum dropped to much lower values. We show that this is due to a deeper boundary layer which dilutes the near-surface water vapour concentrations. Therefore, our analysis suggests that one-dimensional indices such as wet-bulb temperature may not be accurate in predicting health risks across the wide variety of meteorological conditions that South Asia experiences.

Using recent experimental results that demonstrate that hazardous conditions can occur at lower humidity values, we show that thresholds derived from these experiments produce a more realistic spatial and temporal distribution of hazardous conditions in South Asia as compared to wet-bulb temperatures alone. Furthermore, we show that hazardous exposure during the day extends to times not usually considered hazardous in public health messaging. Our results suggest that physiological thresholds provide a complementary way to assess health risk due to heat along with epidemiological regression studies.

How to cite: Monteiro, J., Justine, J., Shah, H., and Rao, N.: The exceedance of physiologically relevant thresholds in South Asia, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-13800, https://doi.org/10.5194/egusphere-egu24-13800, 2024.

EGU24-13976 | ECS | PICO | ITS2.12/CL0.1.4

Quantifying future risk of South Pacific Hospitals from climate change 

Michelle McCrystall, Chris Horvat, Liz McLeod, Madelyn Stewart, Lydia Stone, Subhashni Taylor, Callum Forbes, Eileen Natuzzi, and Berlin Kafoa

Health facilities in Pacific Island Countries are under threat due to ongoing climate change, namely from extreme weather events such as tropical cyclones. However, obtaining accurate projections of risks are inhibited due to the size and complex geometries of these islands which are not accurately or sometimes even entirely represented in the current resolution of global climate models.  Using higher resolution models and the Synthetic Tropical cyclOne geneRation Model (STORM) to generate 10,000 synthentic tropical cyclones, this study takes a greater in-depth analysis of extreme weather events and tropical cyclones at hospitals in Fiji, Vanuatu, Solomon Islands and Tonga.

Preliminary results show an approximately 150% increase in the frequency of extreme cyclones of category 4 or 5 at hospitals across the Pacific, with Vanuatu and Tonga projected to experience a 200% increase in extreme storms. Projected increases in extreme rainfall days (number of days where rainfall exceeds 95th percentile) ranges between 14-161% and extreme heat days are expected to increase between 43-303 days per year by the end of the century. Mitigating against the impacts of climate change on medical care in these islands is hugely important, and so future aims of this work are to use statistical downscaling and AI-driven model acceleration, as part of our project EMPIRIC2 (EMulation of Pacific Island Risk to Infrastructure from Climate), to provide robust, time-variant facility risks statistics directly to policymakers who are working to improve health infrastructure resilience across the South Pacific.

How to cite: McCrystall, M., Horvat, C., McLeod, L., Stewart, M., Stone, L., Taylor, S., Forbes, C., Natuzzi, E., and Kafoa, B.: Quantifying future risk of South Pacific Hospitals from climate change, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-13976, https://doi.org/10.5194/egusphere-egu24-13976, 2024.

EGU24-15152 | PICO | ITS2.12/CL0.1.4 | Highlight

Health-relevant compound ground-level ozone and temperature events in Europe 

Elke Hertig and Irena Kaspar-Ott

Ground-level ozone is a major air pollutant harmful for human health and there are concerns that ground-level ozone will increase over Europe under climate change despite efforts for a rigorous air pollution control. In addition, high levels of ground-level ozone often occur in combination with high air temperatures, for instance under persistent anticyclonic conditions in summer. Due to climate change heat events such as hot days and heat waves are also increasing. Thus, ground-level ozone health risks could combine with increased health risks from heat exposure.

Changes in the atmospheric chemistry from increased biogenic volatile organic compound emissions, faster chemistry kinetics, and faster peroxyacetyl nitrate decomposition as well as enhanced stratosphere-troposphere exchange, changes of the large-scale atmospheric circulation and synoptic patterns, increased stagnancy, and changes of atmospheric humidity may lead to increases of ground-level ozone in the scope of climate change. For Europe regional differences exist. For instance, over central Europe there is a strong relationship with meteorological conditions, while over southern and northern Europe the influence of ozone persistence and hence precursor emissions is comparably strong on ozone exceedances.

The present contribution comprises relationships of ground-level ozone and temperature with the atmospheric circulation, changes of health-relevant ground-level ozone and temperature events under future climate change as well as the connection of ground-level ozone and temperature with human health outcomes.  

How to cite: Hertig, E. and Kaspar-Ott, I.: Health-relevant compound ground-level ozone and temperature events in Europe, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15152, https://doi.org/10.5194/egusphere-egu24-15152, 2024.

EGU24-15296 | PICO | ITS2.12/CL0.1.4

A computational framework for personal multi-exposure assessment using space-time activity and socio-economic data 

Oliver Schmitz, Kees de Hoogh, Nicole Probst-Hensch, Ayoung Jeong, Benjamin Flückiger, Danielle Vienneau, Gerard Hoek, Kalliopi Kyriakou, Roel C. H. Vermeulen, and Derek Karssenberg

The construction of simulation models for personal exposure analysis requires the integration of field-based data representing spatially distributed values (e.g. air pollution, noise, temperatures), agent-based data (e.g. daily activities, residential and work locations) and socio-economic data (e.g. age, social economic status, mode of commute) to fully cover the space-time activity patterns of cohort participants. In addition, evaluating the associated uncertainty is necessary as potentially not all required input variables are known.

We developed a modelling framework implemented in Python providing modules for 1) the specification of agents' activity diaries including the durations of activities and their spatial contexts, i.e. the location of a person during that activity, commute trips between residential and work location are thereby routed using OpenStreetMap data; 2) incorporating multiple environmental factors potentially on different temporal and spatial scales; 3) personal exposure assessment by calculating, for each time step and environmental factor, average exposure values within the spatial contexts. The modules can be combined in a Python script for exposure assessment of all agents in a cohort, including Monte Carlo simulations.

We show results from a modelling study conducted for the province of Utrecht, the Netherlands. The study area covers about 500000 residential address locations covering urban and rural areas. We used cadastral and census data to define characteristic diurnal activity profiles describing different characteristics such as social economic status and commute type (e.g. car, bicycle, on foot). We calculated individual exposures to NO2, PM2.5 and noise in Monte Carlo mode and demonstrate the spatial variability of exposures per activity profile and the associated uncertainty. The personal exposures for commuter profiles show more contrast across addresses compared to the homemaker profiles.

Our activity-based mobility simulation provides a representative description of space-time activities of individuals. The calculated personal exposures can be used for further epidemiological analysis to investigate the relationship between air pollution exposure and chronic diseases such as diabetes or cardiovascular disease.

How to cite: Schmitz, O., de Hoogh, K., Probst-Hensch, N., Jeong, A., Flückiger, B., Vienneau, D., Hoek, G., Kyriakou, K., Vermeulen, R. C. H., and Karssenberg, D.: A computational framework for personal multi-exposure assessment using space-time activity and socio-economic data, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15296, https://doi.org/10.5194/egusphere-egu24-15296, 2024.

EGU24-16105 | ECS | PICO | ITS2.12/CL0.1.4 | Highlight

Increasing climate change changes household medical expenditures 

Dianyu Zhu, Miaomiao Liu, Ruoqi Li, Yuli Shan, Haofan Zhang, Jun Bi, and Klaus Hubacek

Climate change is exacerbating global disease risks, which will change household medical expenditures. Employing machine learning techniques and fine-scale bank transaction data, this study explores the changing household medical expenditures in 290 Chinese cities under four SSP scenarios (SSP1-2.6、SSP2-4.5、SSP3-7.0、SSP5-8.5) and further evaluates the adaptive impacts from socio-economic and physiological adaptations. The results show that the increasing temperature is projected to decrease future medical expenses in China by 5.24% (SSP1-2.6) to 5.60% (SSP5-8.5) in 2060. Cities exhibit differentiated sensitivity to increasing temperatures. Richer cities have enhanced resilience to high temperatures, and cold regions demonstrate less vulnerability to extreme cold weather. Physiological adaptation to climate change can significantly reduce medical expenditures by 27.6% by 2060. Meanwhile, socio-economic adaptation is expected to amplify national total medical expenses by 22.5% in 2060 under the SSP5-8.5 scenario. Our study incorporates adaptation into the prediction of future medical expenditures in China, aiming to assist cities in devising tailored climate adaptation strategies to alleviate the household economic strain induced by climate change. 

How to cite: Zhu, D., Liu, M., Li, R., Shan, Y., Zhang, H., Bi, J., and Hubacek, K.: Increasing climate change changes household medical expenditures, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-16105, https://doi.org/10.5194/egusphere-egu24-16105, 2024.

EGU24-16469 | PICO | ITS2.12/CL0.1.4

Health protection from heat waves in Croatia - today and in the future 

Lidija Srnec, Vjeran Magjarević, and Renata Sokol Jurković

Some recent research shows that the average annual excess of deaths is higher due to cold than warm events. Despite that fact, the last two decades are the warmest in history of air temperature monitoring so the long term series analyses show the increase in the frequency but also the severity of the heat waves. 

A heat wave early warning system is a very useful way of protecting human health. This system in Croatia has been operational since 2012 and thanks to it vulnerable groups of people are timely warned about the level of possible risk. In this work, we will briefly explain how Croatian early warning system works nowadays and show the change of number and level of heat wave risks through the past.  

The possible change in heat wave risk in the future will be analysed by using regional climate simulations from the EURO-CORDEX data set. Simulations will cover a set of projections on 12.5 km horizontal resolution, taking into account moderate and high RCP scenarios. The future climate will be considered for three 30-year time slices.  

The operational criteria currently used in the Croatian heat wave early warning system will be applied to the projected daily minimum and maximum air temperatures. The modelled data will be bias-corrected according to the measured data at Croatian meteorological stations. Original outputs and bias-corrected data will be analysed and compared to see which data sets approach closer to the measured data set. Historical climate risk simulated by models will be compared with issued warnings to evaluate simulations. The difference between projected and historical climate risk will be analysed by level of risk, duration, and spatial distribution.              

How to cite: Srnec, L., Magjarević, V., and Sokol Jurković, R.: Health protection from heat waves in Croatia - today and in the future, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-16469, https://doi.org/10.5194/egusphere-egu24-16469, 2024.

Although the World Health Organization has declared that the COVID-19 pandemic no longer qualifies as a global public health emergency, it still needs to review the response of society to the COVID-19 pandemic. Previous studies indicated that socio-economic status (SES) was linearly associated with the COVID-19 pandemic. However, this relationship may be more complex due to regional differences. Meanwhile, it needs to analyze the nonlinear impact of multiple factors on the infection rate. In the study, we analyzed the differences in infections among low, lower-middle, upper-middle and high SES group (LSG, LMSG, UMSG, and HSG, respectively), and considered the social and meteorological factors, revealing the effect and mechanisms of SES on infections. The results showed that the relationship between SES and infection rate was inverted U-shaped, especially in the first three phases. The contribution of meteorological factors to the infection rate first increased and then decreased. In the first phase, mask usage was the most important factor affecting the change in infection rate, with the contribution of 23.17%. In the second phase, temperature was the most important factor affecting the change in infection rate. In the third and fourth phases, vaccination was the most important factor. Furthermore, the nonlinear impact of multiple factors related to SES on the infections explains the complex relationship between SES and infections. The study argues for greater attention to countries with medium SES and the need for future targeted measures to cope with infectious diseases.

How to cite: Sun, Y. and Shi, P.: Multiple factors drive the infection rate in the progress of the COVID-19 pandemic, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18312, https://doi.org/10.5194/egusphere-egu24-18312, 2024.

EGU24-19453 | ECS | PICO | ITS2.12/CL0.1.4

Assessing health risks in Croatia for cases of severe weather via UTCI and PET 

Ines Muić, Iris Odak Plenković, Lidija Srnec, and Kristian Horvath

As our climate is changing due to global warming, severe weather is expected to increase in frequency and it's intensity. Out of many examples of severe weather, we are focusing on cold and heat waves which greatly affect people causing increased mortality and morbidity. Also, some of the most important climate modifiers in Croatia are the Adriatic, the Mediterranean, the Dinarides orography, and the Pannonian plain. Because of this, the strongest winds in the Adriatic coast of Croatia are jugo and bora which can sometimes reach gale strength. They are associated with different weather conditions and can also have an impact on morbidity. For example, people describe a favorable impact on health and mood during most cases of moderate bora and unfavorable during moderate jugo episodes.

 In this work, we are exploring the potential of the Universal Thermal Climate Index (UTCI) and Potential Equivalent Temperature (PET) as severe weather-related health risk indicators in Croatia. The UTCI and PET are bioclimate indices that use human heat balance models to represent the thermal stress and comfort that is induced in the human body by meteorological conditions. For a couple of continental, maritime, and mountain stations in Croatia UTCI and PET are calculated from measurements. The exception is the mean radiant temperature which is estimated from the Rayman model based again on the measurements of global radiation, air temperature, and relative humidity. The distribution of all-cause death counts at different UTCI and PET values is investigated to determine a more appropriate measure of health risk.

The UTCI and PET are calculated for the domain over Croatia for the selected cases of a heat wave, a cold wave, and strong wind episodes. The meteorological data used for the calculation of UTCI and PET are hourly NWP model ALADIN-HR output values of air temperature, relative humidity, wind speed, and mean radiant temperature. The UTCI and PET are compared and show good agreement. Results for the cases of strong wind show UTCI sensitivity to the wind but depend on the air temperature primarily.

How to cite: Muić, I., Odak Plenković, I., Srnec, L., and Horvath, K.: Assessing health risks in Croatia for cases of severe weather via UTCI and PET, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-19453, https://doi.org/10.5194/egusphere-egu24-19453, 2024.

EGU24-20406 | ECS | PICO | ITS2.12/CL0.1.4

Regional Features of Long-Term Exposure to PM2.5 Air Quality over Asia under SSP Scenarios Based on CMIP6 Models 

Hyun Min Sung, Sungbo Shim, Jisun Kim, Jae-Hee Lee, Min-Ah Sun, Chu-Yong Chung, and Young-Hwa Byun

This study investigates changes in fine particulate matter (PM2.5) concentration and air-quality index (AQI) in Asia using nine different Coupled Model Inter-Comparison Project 6 (CMIP6) climate model ensembles from historical and future scenarios under shared socioeconomic pathways (SSPs). The results indicated that the estimated present-day PM2.5 concentrations were comparable to satellite-derived data. Overall, the PM2.5 concentrations of the analyzed regions exceeded the WHO air-quality guidelines, particularly in East Asia and South Asia. In future SSP scenarios that consider the implementation of significant air-quality controls (SSP1-2.6, SSP5-8.5) and medium air-quality controls (SSP2-4.5), the annual PM2.5 levels were predicted to substantially reduce (by 46% to around 66% of the present-day levels) in East Asia, resulting in a significant improvement in the AQI values in the mid-future. Conversely, weak air pollution controls considered in the SSP3-7.0 scenario resulted in poor AQI values in China and India. Moreover, a predicted increase in the percentage of aged populations (>65 years) in these regions, coupled with high AQI values, may increase the risk of premature deaths in the future. This study also examined the regional impact of PM2.5 mitigations on downward shortwave energy and surface air temperature. Our results revealed that, although significant air pollution controls can reduce long-term exposure to PM2.5, it may also contribute to the warming of near- and mid-future climates.

How to cite: Sung, H. M., Shim, S., Kim, J., Lee, J.-H., Sun, M.-A., Chung, C.-Y., and Byun, Y.-H.: Regional Features of Long-Term Exposure to PM2.5 Air Quality over Asia under SSP Scenarios Based on CMIP6 Models, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-20406, https://doi.org/10.5194/egusphere-egu24-20406, 2024.

EGU24-20465 | ECS | PICO | ITS2.12/CL0.1.4 | Highlight

Health integration in climate-related policies: evidence and gaps in the EU policy context   

Claudia de Luca, Benedetta Cavalieri, Benedetta Baldassarre, Joy Ommer, and Milan Kalas

Climate change represents the greatest threat to human health, with both direct and indirect effects. 

The direct increase of deaths, due to extreme weather and climate events, the emergence and spread of infectious diseases related to changing temperature, habitat and precipitation patterns, and eventually climate shocks and growing stress and anxiety that are affecting mental health. Moreover, extreme weather events cause issues on our health systems and infrastructures, reducing capacity to provide health coverage.  

An increasing awareness on adverse effects of climate change is leading to an update of the EU policy framework through the introduction of  the EU Green Deal, a ‘package’ of directive, policies and strategies to ensure planning, monitoring and reporting of progress towards responsive climate adaptation and climate neutrality; however, a clear demonstration of the health-relevant outcomes of climate policies and actions is still missing, and current policies do not properly consider human health protection.  

The study is developed within the Horizon Europe-funded project TRIGGER, aimed at deepening the understanding of the linkage between climate change and health and advancing society uptake at policy level. 

Starting from mapping and screening the existing climate-related policies and measures at European level, this study assesses the integration of health in such documents. Specifically, through a keyword-based content analysis, it evaluates the integration of health-relevant considerations in 11 European plans and strategies, referring to climate mitigation and adaptation, environmental sustainability and biodiversity conservation. To establish to what extent they consider the direct and indirect impacts of climate change on human health, a qualitative assessment of health integration is performed, exploring also, when available, cost-benefits estimation to possible health impacts and health-related indicators developed.  

The results show that extreme events, such as heat waves and droughts, heavy precipitation and flooding, are the climate-related hazards mostly mentioned in relation to health, even though the policy integration remains limited. Indeed, just few policies contain references to physical health impacts determined by climate change, such as infectious and vector borne diseases, injuries from extreme weather events and cardiovascular and respiratory diseases, while social and mental health effects are even less considered.  

 

How to cite: de Luca, C., Cavalieri, B., Baldassarre, B., Ommer, J., and Kalas, M.: Health integration in climate-related policies: evidence and gaps in the EU policy context  , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-20465, https://doi.org/10.5194/egusphere-egu24-20465, 2024.

EGU24-20786 | PICO | ITS2.12/CL0.1.4

Smart information system based on RS and GIS as an adaptation strategy for reducing mortality from heat waves  

Fabiola D. Yépez-Rincón, Alicia Avendaño, Sergio Fernández Delgadillo, Adrían L. Ferriño Fierro, Víctor H. Guerra Cobián, Roberto E. Huerta García, Bárbara González Méndez, Nelly L. Ramírez Serrato, Carlos J. Ábrego Góngora, Rebeca Pérez Ruiz, and Rogelio Aguilar Cruz

Multiple factors influence the risk of heat stroke and that, collectively, define the vulnerability of the population. This vulnerability can be physiologically differentiated by older adults and children, by gender, or due to the level of exposure to sporting activities or labor, among others. During the last two decades, hot extreme events are drastically increasing related to climate change and other climate phenomena such as El Niño event. The World Health Organization estimates that more than 70,000 heat-related deaths occurred in Europe during the last two weeks of August 2003 and almost 62,000 deaths during summer 2022. In Mexico, the record of heat-related deaths was set during the summer of 2023 when the Health Secretariat reported 373 deaths due to extreme heat events. The five ranking states were Nuevo León (27% of the cases), Sonora (20%), Baja California (14%), Tamaulipas and Veracruz (8% respectively), and 80% of them are located between the 25 to 31°Latitude North. To understand which the most influential factors for heat-related deaths are, this study analyzes the interaction between land surface temperature, spatial population dynamics, and the exposure-response relationship to urban form and the concentration of air pollution in the Monterrey Metropolitan Area. The paper will present the operational structure of a smart information system based on RS and GIS for planning a better and safer city life in San Nicolás de los Garza, the municipality that ranked first on heat-related deaths. In summary, results indicate the next highlights: (1) extreme heat waves are increasing every year in the metropolitan area, (2) urban heat islands are spatially and temporally located, therefore, (3) risk reduction and civil protection actions must include a holistic approach including warning early systems, social, labor and health care actions, (4) preventive policies must be implemented such as sustainable urban planning for population climate justice, (5) and adopting nature-based solutions. 

How to cite: Yépez-Rincón, F. D., Avendaño, A., Fernández Delgadillo, S., Ferriño Fierro, A. L., Guerra Cobián, V. H., Huerta García, R. E., González Méndez, B., Ramírez Serrato, N. L., Ábrego Góngora, C. J., Pérez Ruiz, R., and Aguilar Cruz, R.: Smart information system based on RS and GIS as an adaptation strategy for reducing mortality from heat waves , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-20786, https://doi.org/10.5194/egusphere-egu24-20786, 2024.

EGU24-20931 | ECS | PICO | ITS2.12/CL0.1.4

How agricultural droughts are contributing to child undernutrition in sub-Saharan Africa 

Anna Dimitrova, Alexander Gershunov, and Tarik Benmarhnia

Countries in sub-Saharan Africa (SSA) have some of the highest levels of child malnutrition, with more than one-third of children under five in the region characterized as chronically undernourished. High reliance on subsistence farming, poor adoption of irrigation technologies, and variable climate conditions make populations in SSA highly vulnerable to malnutrition during droughts. We use anthropometric data for 520,734 children under the age of five from 34 countries in SSA collected between 1990 and 2022 in combination with high-resolution agricultural and climate data to estimate the association between agricultural droughts and child undernutrition in the region. We use global gridded data on the geographical distribution of crop areas for 15 major crops. Data on crop planting and harvesting dates are also collected for each crop. The Standardized Precipitation Evapotranspiration Index (SPEI), a multi-scalar drought index, is used to measure the intensity and spatial distribution of droughts during key periods of agricultural production (planting, growth, and harvesting) and of different duration (seasonal and long-lasting droughts). Our analysis shows that droughts during the crop-growing seasons are associated with an increased risk of child undernutrition in SSA. The findings presented in this study call for urgent action to improve drought monitoring and response in SSA where the risks to child health posed by global warming are considerable. Under climate change, the severity and frequency of extreme weather and climate events, including droughts, are projected to increase, which will place millions of children at risk of hunger unless timely action plans are taken to improve food security in the region.

How to cite: Dimitrova, A., Gershunov, A., and Benmarhnia, T.: How agricultural droughts are contributing to child undernutrition in sub-Saharan Africa, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-20931, https://doi.org/10.5194/egusphere-egu24-20931, 2024.

ITS3 – Environment and Society in Geosciences

EGU24-478 | Orals | ITS3.2/ERE6.12 | Highlight

Environmental consequences resulted from the oil depots’ deterioration by the RF’s missile attacks 

Viktor Karamushka, Svitlana Boychenko, and Ruslan Havryliuk

Since the beginning of the full-scale aggression on 24 February, 2022, primary targets for missiles attacks of Russian Federation were the objects of energy sector of Ukraine.  According to the reports of the State Environmental Inspection of Ukraine, more than 30 units comprising oil depots, product warehouses, refineries, gas stations were destroyed during the March 2022 only. Most of these objects were oil depots.  The purpose of this investigation was an environmental impact assessment of the missile attacks on the petroleum depots. We analysed the cases of destruction of oil depots in Okhtyrka (Okhtyrkanaftogaz), Chernihiv (Aystra), Kalynivka (KLO) and Kryachki (AS Investment), which were completely or partially destroyed. The results of field research, satellite monitoring data, data of the State Environmental Inspection and other state bodies were used for the analysis.

As a result of the attacks, a significant part of petroleum products burned, which caused atmospheric air pollution by combustion products (carbon monoxide (CO), carbon dioxide (CO2), soot (C), nitrogen dioxide (NO2), sulphur dioxide (SO2), marginal hydrocarbons (С12-C19)). The estimated volume of emissions at the Kalynivka oil depot alone is more than 30 metric tons of carbon dioxide equivalent (mt CO2e). Spills of oil products caused pollution and partial burning of the surface layer of soils (at all bases) and penetration of oil products into groundwater with further migration over considerable distances (Kalynivka oil depot). The article presents the results of the monitoring and quantitative assessment of the soil and ground water pollution by oil derivatives as well as plant biodiversity assessment on the territory suffered from the incidents.

How to cite: Karamushka, V., Boychenko, S., and Havryliuk, R.: Environmental consequences resulted from the oil depots’ deterioration by the RF’s missile attacks, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-478, https://doi.org/10.5194/egusphere-egu24-478, 2024.

Recently, rock fibers have gained attention as versatile and promising substitutes for traditional carbon and glass fibers in a range of industries, including aerospace, defence, construction, and healthcare. Shifting to the use of rock fibers represents a more sustainable and environmentally considerate approach to using natural resources. This transition likely reflects efforts to reduce reliance on less sustainable materials (such as traditional carbon and glass fibers), thereby aligning with broader goals of sustainable resource management and environmental protection. Additionally, their asbestos-free nature in construction materials makes them a healthier industrial raw material, avoiding the health hazards associated with asbestos exposure. As a result, there has been a growing interest in research initiatives aimed at evaluating the potential of volcanic rocks from diverse geographic regions for fiber production. This trend reflects an increased emphasis on understanding the geochemical properties and commercial viability of these rocks in the context of sustainable material development. Turkey's abundant volcanic rock resources offer substantial opportunities for the production of rock fibers. Recent preliminary investigations into the volcanic rocks of Central Anatolia have indicated their suitability for rock fiber production. Within the scope of this study, it is aimed to specifically evaluate the potential of Western Anatolian volcanoes for rock fiber production. Geochemical data obtained in previous studies from volcanic rocks of Western Anatolia (Afyonkarahisar, Denizli, Eskişehir, İzmir, Kütahya, Manisa, Muğla, Uşak) were used. The chemical compositions of 241 rock samples with SiO2 content of less than 63% by weight were evaluated. Using this data, key descriptive coefficients relevant to rock fiber production were calculated, including the total acidity coefficient (Ktotal), total acidity modulus (Mtotal), acidity modulus (Ma), and viscosity modulus (Mv). These metrics were then compared with those derived from rocks from Ukraine, Georgia, and Russia, currently deemed suitable for rock fiber production. Conclusively, this research highlights the potential of Western Anatolian volcanoes as viable sources for rock fiber production.

How to cite: Ünal, B. C., Kaya, S., Atalay, C., Aydar, E., and Ersoy, O.: Evaluation of the Geochemical Compositions of Western Anatolia (Turkey) Volcanic Rocks and Their Suitability for Rock Fiber Production with the Help of Fiber Modules , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-518, https://doi.org/10.5194/egusphere-egu24-518, 2024.

EGU24-541 | ECS | Posters on site | ITS3.2/ERE6.12 | Highlight

Archaeometry in geosciences: the study of ancient geomaterials for archaeological investigations. 

Francesca Gambino, Lorenzo Appolonia, Alessandro Borghi, Sylvie Cheney, Roberto Cossio, Stefano Marco De Bernardi, Giovanna Antonella Dino, Stefano Ghignone, and Gabriele Sartorio

The term "archaeometry" was first used in 1958 as the title of a special volume published by the Research Laboratory for Archaeology and Art History at Oxford University.  Archaeometry is a scientific discipline that employs various techniques primarily for the identification of sites, settlement patterns, archaeological stratigraphy, and the production and analysis of found artefacts.

Ancient buildings, artifacts, and finds consist predominantly of natural and artificial resources obtained from geological sources. Geosciences techniques are optimal for obtaining information on the origin and technological properties of archaeological artefacts and materials used in cultural heritage from geological sources.

This study conducted a petrographic and geochemical analysis of historical mortars from the Roman Theatre of Aosta and the Medieval Sarriod de la Tour Castel located in the Aosta Valley in North-West Italy. Mineralogical phase-specific distribution of elements in mortar samples was calculated using a semi-automated method of image analysis incorporating multivariate statistical analysis of X-ray spectral images. Based on SEM backscattering, a cluster image analysis was conducted to determine the ratio of aggregate, binder, and porosity. Additionally, simple algebraic operations were utilized to fully quantify the oxides in every EDS spectrum, and to compute the distribution of Hydraulicity Index (HI) within the examined domains.

This study provided many answers about supply areas, variation of raw materials over time, network/transport systems, development and production processes. The petrographic analysis has enabled identification of both the binder and aggregate type. Specifically, it has afforded information on the type of raw material used to produce the lime, the ratio of binder to aggregate, the origin of the aggregate (sedimentary or crushed rock) and its composition.

These investigations were conducted in close collaboration with archaeologists to reconstruct the exchanges between ancient civilizations and evaluate their technological progress.

Ultimately, the progress of geosciences within the field of Cultural Heritage highlights how this type of study is essential for the dissemination and museology of what represents the culture of materials from  archaeological, historical and scientific point of view.

How to cite: Gambino, F., Appolonia, L., Borghi, A., Cheney, S., Cossio, R., De Bernardi, S. M., Dino, G. A., Ghignone, S., and Sartorio, G.: Archaeometry in geosciences: the study of ancient geomaterials for archaeological investigations., EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-541, https://doi.org/10.5194/egusphere-egu24-541, 2024.

In the last decade, the “storyline” approach has been developed in the field of attribution and detection of extreme climate events. Despite its merits, the storyline approach has been met with harsh criticism, especially from advocates of probabilistic (or risk-based) approaches. This reaction is amplified by the conflicting conclusions to which storylines and probabilistic approaches often lead. However, this conflict is only apparent, given that probabilistic and storyline approaches typically pursue different research concerns. Accordingly, one way to foster the legitimation of the storyline approach is by conceptualizing its epistemic contributions as a distinctive form of genuine “scientific understanding” under deep uncertainty.

The burgeoning philosophical literature on scientific understanding affords promising resources to undertake the endeavour mentioned above. However, given the recency and diversity of this philosophical field, there is still broad dissent on elementary matters, such as the nature of scientific understanding, its value, and its varieties. Following the school of "philosophy of science in practice", an informative strategy to advance philosophical debates on scientific understanding is to attend to the scientific debates between advocates of probabilistic and storyline approaches, inspect their specific practices, and assess how they should advise philosophical accounts of scientific understanding.

In this sense, there is a twofold problem. On the one hand, storylines require legitimation as an approach that affords a distinct but genuine scientific understanding. On the other hand, the very notion of genuine scientific understanding requires further philosophical elaboration, informed by scientific practices. Accordingly, this paper aims to display the synergies between the storyline approach and the philosophy of scientific understanding to foster the legitimation of the former and advance internal philosophical debates in the latter.

Three axes for synergies are identified and briefly discussed. First, the “factivity” of storyline-based understanding: Philosophers of science disagree on whether scientific understanding is solely grounded on facts or may involve non-factive representations. Storylines are a relevant method to inform these debates as they are not intended to represent factual unfoldings of extreme events. Second, the “effectiveness” of storyline-based understanding: Some philosophers of science argue that scientific understanding is not grounded on particular epistemic credentials (whether factive or non-factive) but rather on its effectiveness. However, it is unclear how untethered the effectiveness of scientific understanding can be from its epistemic credentials. The employment of storylines for decision-making under deep uncertainty affords relevant cases in which to assess the relation effectiveness and factivity of scientific understanding. And third, the “transdisciplinarity” of storyline-based understanding: An overlooked subject in the philosophical literature on scientific understanding is its relations to non-academic epistemic endeavours. This subject is relevant because i) non-academic epistemic agents and endeavours may contribute to scientific understanding, and ii) the integration of non-academic epistemic agents and endeavours into scientific research advances epistemic justice, which is critical to warrant trust in scientists and legitimize scientific understanding across stakeholders. The storyline approach is tailor-made for pondering over local knowledge and experiences, reported qualitatively, thus offering valuable opportunities for civil society to contribute to the scientific understanding of climate uncertainties.

How to cite: Bobadilla, H.: Synergies Between the Storyline Approach and the Philosophy of Scientific Understanding, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-6518, https://doi.org/10.5194/egusphere-egu24-6518, 2024.

The war in Ukraine, which has been going on since February 2022, has dealt a severe blow to the country's agricultural sector. Millions of hectares of agricultural land have been destroyed by shelling, explosions, and landmines. This has raised concerns about food security in the international community, as Ukraine was a leading producer and exporter of wheat, maize, barley, and sunflower oil before the war. In order to determine the extent of the damage and develop the necessary recovery measures, as well as to formulate effective resource management strategies to ensure the sustainability of the agricultural sector, it is critical to accurately assess and locate the damaged agricultural areas.

Remote sensing, with its advantages in speed, coverage, and objectivity over ground-based methods, combined with machine learning, offers opportunities for the automatic detection of damaged fields across the entire territory of Ukraine and tracking the dynamics of damage development almost in real-time. This research demonstrates the potential of remote sensing and machine learning in detecting and analyzing damaged agricultural fields in Ukraine because of the military conflict.

We utilize freely available two-week composites from the Sentinel-2 satellite with a spatial resolution of 10 meters. The search for damaged fields is conducted in the cloud environment of Google Earth Engine using a random forest binary classifier trained on a manually collected sample by three independent experts. The input parameters for the classifier include static indicators (minimum, average, maximum, variance) of two spectral bands (B2, B3) and two vegetation indices (NDVI and GCI), which have been experimentally found to be the most informative for detecting field damage. Additionally, within the classified damaged fields, we identify local damages using an anomaly detection method. This involves measuring the deviation of values of individual pixels from the mean value of all pixels within a specific field in the spectra of the above-mentioned bands and vegetation indices.

The developed classifier achieves an accuracy of 0.9 for both recall and precision. The anomaly analysis method proves sensitive to the vegetation period and the geographical location of the study area. However, with careful selection of the threshold coefficient, the developed method demonstrates sufficiently accurate results and allows the recognition of craters with an estimated area >50 m².

The results highlight substantial losses to Ukraine's agricultural sector due to the war. It was determined that from the beginning of the conflict until December 4, 2023, more than 1.5 million agricultural fields in Ukraine were damaged, constituting approximately 5.65% of the total sown area. The most affected crops were wheat (489,529 ha or 5.78% of the total cultivated area for this crop), sunflower (115,358 ha or 1.56% of the cultivated area), maize (61,123 ha or 1.2%), and rapeseed (42,783 ha or 2.65%).

Our methods are applicable to large territories for detecting damages to various agricultural crops. The research will be valuable for assessing and restoring damaged lands, as well as for developing strategies for adaptation and resilience of the agricultural sector to other similar crisis situations.

How to cite: Drozd, S., Kussul, N., and Yailymova, H.: Evaluating the Impact of Armed Conflict on Agricultural Sector in Ukraine through Remote Sensing and Machine Learning, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-10494, https://doi.org/10.5194/egusphere-egu24-10494, 2024.

EGU24-12062 | ECS | Orals | ITS3.2/ERE6.12

Long-term Monitoring of Environmental and Geophysical Impacts in Conflict-Endured Zones: A Landscape Perspective on Kivu Lake 

Ivan Lizaga, Borja Latorre, Montfort Bagalwa, Bossissi Nkuba, Samuel Bodé, Karume Katcho, Honoré Ciraba, Olivier Evrard, Karen Büscher, Koen Vlassenroot, Kristof Van Oost, William Blake, Ana Navas, and Pascal Boeckx

Human displacements, especially those driven by violent conflicts forcing sudden population migrations, wield profound and enduring impacts on landscapes, instigating substantial disruptions to the natural environment. Beyond immediate destruction, these consequences pose challenges to ecosystem health, food security, and biodiversity conservation, particularly exacerbated in the absence of effective governance. Traditional land management practices, agriculture, and conservation efforts are disrupted, constraining the implementation of long or medium-term conservation practices in agriculture. These disruptions may contribute to increased erosion and sediment transport, depleting soil nutrients and resulting in natural disasters such as flash floods, landslides, and water quality degradation. This phenomenon is particularly pronounced in regions experiencing high rainfall intensity, coupled with inadequate land use and agricultural management practices. Understanding the primary factors behind the last decades escalation in land degradation and subsequent sediment export is crucial to prevent further ecosystem degradation and heightened instability in conflict-affected areas. To address this, we have developed an integrated approach involving core sampling, sediment fingerprinting techniques, high-resolution sediment sampling, and automated remote sensing routines to pinpoint hotspot areas and track conservation efforts. Using the Lake Kivu region as a case study, situated on the border between Rwanda and the Democratic Republic of the Congo, an area marked by prolonged violent conflict since the early 1990s, we evaluate the applicability of this combined approach.

The preliminary results from the multiple techniques independently suggest an increasing trend in exported sediment over the last decade. This trend is particularly pronounced in areas characterized by high instability and economic challenges. In contrast, relatively more stable regions exhibit a stabilization in sedimentation rates. This stability is attributed primarily to the implementation of conservation practices and the presence of robust transport infrastructures, both playing crucial roles in landscape conservation. Results underscore the method's effectiveness in elucidating lasting effects on landscapes impacted by 'polycrisis', necessitating consolidated and comprehensive responses over mere technical solutions. The research objective is to target specific areas within conflict-affected regions, with a focus on mitigating environmental degradation and associated challenges.

How to cite: Lizaga, I., Latorre, B., Bagalwa, M., Nkuba, B., Bodé, S., Katcho, K., Ciraba, H., Evrard, O., Büscher, K., Vlassenroot, K., Van Oost, K., Blake, W., Navas, A., and Boeckx, P.: Long-term Monitoring of Environmental and Geophysical Impacts in Conflict-Endured Zones: A Landscape Perspective on Kivu Lake, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12062, https://doi.org/10.5194/egusphere-egu24-12062, 2024.

A particular challenge threatening global food security is the threat of armed conflict. In particular, the Panjshir valley of northeastern Afghanistan continues to experience acute food insecurity due to intense armed conflict. In this rural valley, conflict driven displacement leads to agricultural land abandonment and decreases in crop yields. These decreases in local food production have an outsized impact on food security, due to the region’s dependence on subsistence agriculture. Despite the consensus that armed conflict has a significant negative impact on the population’s food security, the exact mechanics of how conflict impacts food security remains unclear. 

 

To quantify armed conflict’s impact on local food production, I compare trends in vegetation health between agricultural plots in high-conflict and no-conflict landscapes with similar altitudinal gradients. I focus on the period during the Soviet occupation of Afghanistan from 1980-1989, which saw nine major military offensives occur in the Panjshir valley. I use Landsat 5 (1984-2012) to obtain the Normalised Difference Vegetation Index (NDVI) values for agricultural plots that have been designated as control (no conflict) and treatment (high conflict). These plots are delineated using HEXAGON KH-9 declassified spy imagery, and assigned conflict intensity designations based on explosive ordnance disposal (EOD) data from The HALO Trust, a non-governmental organisation which carries out unexploded ordnance clearance in Afghanistan. Residual Trend analysis (RESTREND) is applied to Landsat NDVI values to distinguish between the shifts in vegetation health that are anthropogenically and climatically driven. 

 

This research provides a deeper understanding of how past conflict has acted as a driver of food insecurity in the region. Additionally, it allows for future work to build off of these findings and predict how current and future conflict might have an impact. These findings can inform humanitarian and development aid policy, while the methodology can be applied to other contexts where conflict is present. 

How to cite: Allen, J.: Detecting Disturbance to Agricultural Productivity from Historical Armed Conflict in Afghanistan: The Panjshir Offensives, 1980-1985, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-13525, https://doi.org/10.5194/egusphere-egu24-13525, 2024.

The considerable research on the effects of the 1815 Tambora eruption (Behringer 2015) has shown not only to what extent large tropical volcanic eruptions can transform a society but also how advantageous it is for research when geosciences and humanities interlink.

While single eruption events such as Parker in 1640/1641 have already been analysed (Stoffel et al. 2022), there has been less focus on the potential teleconnections of multiple eruptions on one single study area. This paper looks at the climatic and societal impacts of three tropical volcanic eruptions – Huaynaputina (1600), Komaga-take/Parker (1640/1641) and the 1690s unknown event – on Fribourg, a region in the western part of the Swiss Confederation.

To answer this research question meticulously, a transdisciplinary approach is required – both in method and sources. Daring to bridge geosciences and humanities, as part of the VICES research project we developed a data processing tool called ClimeApp, which facilitates the usage of climate data and makes transdisciplinary interaction more accessible, especially for researchers from the humanities (http://mode-ra.unibe.ch/climeapp).

Using ClimeApp, the climatological impact of these 17th century eruptions will be assessed with modern climate reconstruction data from the state-of-the-art ModE-RA project (Valler et al. 2024). Novel archive material from municipal institutions – such as the Hôpital des bourgeois de Fribourg – allows us subsequently to determine the annually recorded harvest yields especially of the viti- and caseiculture. Additionally, essential archival sources, such as the Ratsmanuale (protocols) and the Mandatenbücher (regulations), depict whether the municipality of Fribourg deployed any measures or coping mechanisms in the wake of these volcanic eruptions. This combination of climatological data and historical sources enables us to look for potential interrelations between these climate anomalies and the effect they had on society.

The paper exemplary highlights on one side the advantages of research collaboration between two disciplines and on the other side sheds light on the possible impacts of multiple volcanic eruptions spanned over the period of almost hundred years on the same study region.

How to cite: Bartlome, N. E. and Warren, R. M.: Visible or negligible? Impacts of the 17th century volcanic eruption on climate and society in early modern Switzerland, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17186, https://doi.org/10.5194/egusphere-egu24-17186, 2024.

EGU24-17349 | ECS | Orals | ITS3.2/ERE6.12

Embracing the pitfalls and triumphs in interdisciplinary research. 

Rhonda McGovern, Dr. Francis Ludlow, Dr. Conor Kostick, and Dr. Selga Medeneiks

The Astronomical Diaries and Related Texts from Babylonia is a seven volume transliteration and translation of collected cuneiform texts, originally written on clay tablets, from Babylon (modern day Hillah in Iraq). For many centuries in the first millennium BCE, trained scribes positioned themselves night and day to watch and record the skies. It is the compilation of this work that embodies what are known today as the “astronomical diaries”. These texts provide a wealth of data ranging from sub-daily resolution to monthly summaries including: astronomical features and the movement of stars and planets; market prices for six commodities; river level heights for the Euphrates river; information regarding contemporary events; and meteorological data, which was systematically recorded using specific terminology for particular weather phenomena. So precise is this terminology that a few terms remain untranslated. To date, much work has been conducted on these diaries with the exception of the meteorological data. Doctoral research of the presenting author has involved extracting this into a large dataset to facilitate future analysis.

This research is conducted in an interdisciplinary context, within the wider Climates of Conflict in Ancient Babylonia project, where colleagues explore the potential impact of climate on conflict. The team is comprised of a climate historian with a background in geography, a historian, a geographer and a classicist, who interact with historical linguistic experts, climate modellers, climate scientists, and palaeoscientists. As in this project, the application of historical research is becoming increasingly prevalent in the geosciences. Historic texts have the potential to reveal implicit clues to climatic investigations. The Astronomical Diaries and Related Texts from Babylonia provide, for example, intriguing descriptions of events in which “the disk of the sun looked like that of the moon”, identified as volcanic dust veils and already utilised in updating ice-core chronologies of volcanic eruptions over the last 2,500 years.[1]

This paper will narrate the process of extracting climatic data from historical sources; highlight the pitfalls and triumphs in terms of the practicalities of this interdisciplinary research; and provide a volcanic impacts case study, continuing the scientific endeavour instigated by Babylonian scribes over 2,000 years ago.


[1] Sigl, M., M. Winstrup, J. R. McConnell, K. C. Welten, G. Plunkett, F. Ludlow, U. Büntgen, et al., ‘Timing and climate forcing of volcanic eruptions for the past 2,500 years’ in Nature, dxxiii, no. 7562 (2015), pp. 543–549.

How to cite: McGovern, R., Ludlow, Dr. F., Kostick, Dr. C., and Medeneiks, Dr. S.: Embracing the pitfalls and triumphs in interdisciplinary research., EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17349, https://doi.org/10.5194/egusphere-egu24-17349, 2024.

EGU24-19686 | Posters on site | ITS3.2/ERE6.12 | Highlight

Climate variability and food (in)security in medieval and early modern Europe: synthesising the state-of-the-art 

Fredrik Charpentier Ljungqvist, Andrea Seim, and Dominik Collet

On the basis of our new state-of-the-art research review article “Famines in medieval and early modern Europe – Connecting climate and society”, published in WIREs Climate Change this year, we provide an overview of recent scholarship on food insecurity and famines in Europe during the medieval and early modern periods (c. 700–1800). Focus is placed on how, and to what extent, climatic change and variability can explain the occurrence and severity of food shortages and famines during these periods. Current research, supported by recent advances in palaeoclimatology, has revealed that anomalous cold conditions were the main environmental backdrop for severe food production crises that could result in famines in pre-industrial Europe. Such food crises occurred most frequently between c. 1550 and 1710 during the climax of the Little Ice Age cooling. They can, to a large extent, be connected to the strong dependency on grain in Europe during this period and the limited possibility for long-distance transportation of bulk goods in inland regions. The available body of research demonstrates that famines in medieval and early modern Europe can be best understood as the result of the interactions of climatic and societal stressors responding to pre-existing societal vulnerabilities. We provide some recommendations for future studies on historical food shortages and famines in connection to climatic stress on food production.

How to cite: Charpentier Ljungqvist, F., Seim, A., and Collet, D.: Climate variability and food (in)security in medieval and early modern Europe: synthesising the state-of-the-art, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-19686, https://doi.org/10.5194/egusphere-egu24-19686, 2024.

EGU24-20528 | ECS | Posters on site | ITS3.2/ERE6.12

Investigating Impacts of Climate Change and War on the Green Cover Area in Northeast Syria Between 2000 and 2023 

Abdullah Sukkar, Sara Essoussi, Omar Alqaysi, Enes Hisam, and Dursun Zafer Seker

During the 20th century and continuing into the present, significant warming was observed due to the emission of greenhouse gases, primarily CO2 and CH4, into the atmosphere. The sixth assessment report of the Intergovernmental Panel on Climate Change estimates a warming of 1.1°C above 1850-1900 in 2011-2020. As climate warming continues to reshape atmospheric conditions and trigger extreme weather events such as drought, forest fires, and floods. The intricate relationship between these changes and vegetation dynamics becomes increasingly evident, profoundly affecting ecological systems, agriculture, and politics. Vegetation is an essential component in ecological systems since it serves as a connection between soil, atmosphere, and water; and plays a crucial role in maintaining the balance of carbon and water, facilitating the exchange of materials and energy, ensuring climate stability, and reducing greenhouse gas emissions. Generally, changes in vegetation are analyzed to assess the environmental conditions at both regional and global levels. The normalized difference vegetation index (NDVI) is a commonly employed tool for analyzing variations in vegetation dynamics. Examining these changes and their triggers is crucial for comprehending the relationships between vegetation and ecosystems. Syria, located at the intersection of Asia and the Mediterranean, is an area with a high level of water scarcity and is susceptible to extreme droughts, especially in the northeastern region, where temperature and evaporation have significant impacts. The land cover in the northeastern region has undergone significant alterations in recent decades due to the armed conflict, which its effects on the land use and land cover (LULC) are neither unidirectional nor spatially uniform. Research and policy alike have given careful consideration to the relationship between conflict and climate change. Extreme weather events, like droughts, have been shown to correspond with the start of armed conflicts occasionally. The most widely proposed mechanism between climate change and violent conflict is the relationship between shocks to agricultural productivity and the degradation of vegetation. In this study, the ERA5-Land data has been used to analyze the climatic conditions in northeast Syria between 2000 and 2023. In addition, the satellite images of Landsat 5, 7, 8, and 9 have been used to generate NDVI maps. Then, a correlation between the meteorological parameters and the NDVI was established to examine how climate change and drought have affected the green cover in the study area, especially after 2011, when the armed conflict started. Meteorological parameters such as temperature, soil temperature, precipitation, and evaporation on an hourly scale have been applied. The drought events have been addressed by the number of precipitation events, precipitation accumulation, and precipitation intensity. Moreover, the Standardized Precipitation Index (SPI), which is considered as a global standard for evaluating the severity of drought, has also been used for various time scales (3, 6, 9, and 12 months). The study highlighted how climate change had affected the vegetation areas in the northeastern region of Syria. The results emphasized different drought events and mapped the change in the LULC through the time period of the study.

How to cite: Sukkar, A., Essoussi, S., Alqaysi, O., Hisam, E., and Seker, D. Z.: Investigating Impacts of Climate Change and War on the Green Cover Area in Northeast Syria Between 2000 and 2023, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-20528, https://doi.org/10.5194/egusphere-egu24-20528, 2024.

EGU24-20730 | Orals | ITS3.2/ERE6.12 | Highlight

Detecting the undecteable: transhumant nomads in palynological data 

Adam Izdebski and Georgios Liakopoulos

Nomadic communities are difficult to detect in the written and material record historians and archaeologists traditionally use to study the past. Contrary to settled grain cultivators, who were easy target for state taxation and were often recorded in a variety of documents, or who left easy to detect traces of permanent villages, nomads often remained outside of the radar of the traditional sources. Nomadic communities, however, profoundly transformed landscapes they lived in. These landscapes, in turn, produced different environmental signals that are preserved in the sedimentary records. Pollen data, in particular, make it possible to reconstruct the presence and activities of nomads in a given area, filling in the gaps in the historical and archaeological record. In our short presentation, we will look at high resolution pollen evidence from Macedonia (Northern Greece) that could be used to trace the presence of transhumant nomads in this region in the last two millennia. We will show how the paleoenvironmental reconstruction can be connected to otherwise fragmentary and problematic written information to create a consilient reconstruction of the past, recovering the presence of diverse groups that inhabited the Northern Greek landscape in the medieval and early modern times.

How to cite: Izdebski, A. and Liakopoulos, G.: Detecting the undecteable: transhumant nomads in palynological data, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-20730, https://doi.org/10.5194/egusphere-egu24-20730, 2024.

EGU24-21661 | Posters virtual | ITS3.2/ERE6.12

Post-war rewilding as a decision-making influence-factor 

Yuliia Spinova and Oleksii Vasyliuk

Military actions create a number of destructive effects on natural and agricultural landscapes. These influences, which can be seen now during Russia's war in Ukraine, are short-term and their role in the future will consist more in how exactly they will change the territories usage regime. Long-term inaccessibility of territories due to occupation, mining and land pollution are the cause of large-scale spontaneous restoration of semi-natural ecosystems. While Ukraine does not have the opportunity to implement restoration projects on the occupied lands, there continue natural transformation processes that will determine the content of decisions to be made.

For example, the former Kakhovske Reservoir, which HPP and dam were blown up by retreating Russian troops on June 6, 2023, and water flooded out of it. Our research showed almost immediately recovering of native vegetation there. By the end of the year, this recovery led to the natural young forest appearance on a large area freed from the artificial reservoir. This process will allow to restore up to 1,800 km2 of natural ecosystems (of which at least 1,000 km2 will be climate-resistant forests) and about 250 km of the free-flow Dnipro river. Such a large ecosystem restoration can become a decisive Ukrainian contribution to the European Union ecosystems revival by 2030.

On the other hand, if the project of the Kakhovske Reservoir restoration, which requires the destruction of all the mentioned square kilometers of natural ecosystems, will be implemented, this is categorically not in line with the ideas of sustainable development. Therefore, the natural processes of recovery will significantly influence the decision-making in Ukraine and its support by the partner states.

In fact, the scale of these processes is already impressive. A comparison of MODIS thermal imaging data for the year 2023 with similar ones of previous years shows that all areas where hostilities were/are being conducted, as well as mined, have turned into large-scale overgrowth with vegetation. Thus, intensive spontaneous vegetation overgrowth, caused by the local population outflow local population and the economic influence cessation of economic influence, including plowing and pesticides use, is already taking place on an area about 1 million hectares. In the short-term perspective undoubtedly there are significant component of invasive plant species, but native perennial species will gradually displace them over time.

Currently, it is not known how long the occupation will last, let alone its demining. According to preliminary estimates of the Cabinet of Ministers of Ukraine, announced in 2022 - more than 70 years. In this case carrying out demining, there may already grow a 70-year-old forest on the aftermost territories, and mines will be buried deep in the ground under tree roots. So already now the expediency of complete demining can be questioned and we offer not to plan it for the most injured territories and around protected areas. Spontaneous ecosystem restoration there can become a powerful contribution of Ukraine into state tasks on preservation of degraded lands, as well as international obligations in the field struggle from climate change.

How to cite: Spinova, Y. and Vasyliuk, O.: Post-war rewilding as a decision-making influence-factor, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-21661, https://doi.org/10.5194/egusphere-egu24-21661, 2024.

EGU24-21994 | Posters virtual | ITS3.2/ERE6.12

Study of the military actions impact on the Buchansky district soils (Kyiv Region, Ukraine) 

Yuliia Spinova, Iryna Vyshenska, Anastasia Sakva, and Oleksii Vasyliuk

Soil damage as a result of military actions is primarily associated with shell bursts, as well as with the movement of heavy equipment, fortifications construction and related processes, such as fires caused by them and following changes in the phytodiversity.

The impact on the physical condition of soils occurs through a pneumatic effect - that is, upturning the soil by explosions, and changes due to the equipment movement, digging trenches and other fortifications.

Changes in the chemical and biological characteristics of the soil occur as a result of animal and human corpses decomposition products, of fuels and lubricants leakage from heavy equipment, and also a large amount of abandoned equipment, or its remnants, chemicals from projectiles and debris contamination.

Shelling accompanied by explosions, as well as the use of lighting and incendiary projectiles led to large-scale fires, which deplete soil nutrients (particularly humus and minerals such as magnesium and phosphorus). In addition to changing the chemical composition, high temperatures destroy pedobionts, which have a direct impact on both the chemical and physical condition of the soil, and are responsible for its fertility and stability. In the future, such changes may lead to accelerated erosion. Oil products from heavy machinery are quickly absorbed, especially into dry and sandy soils. As a result, physical and chemical characteristics change, water and air permeability and microbiological processes are disturbed, so soil degradation occurs. Besides the direct impact, the process of soil contamination with fuel substances can lead to easy ignition and large-scale fires.

The most widely used weapons in this war are 82 mm and 120 mm high-explosive shells, 125 mm high-explosive and cumulative shells, 122 mm, 152 mm, 203.3 mm, 240 mm high-explosive, incendiary and illuminating shells.

We made calculations of the burnings spread, identified correlations between them and shellings using QGis and NASA data on the Buchansky district for February 24, 2022 – June 26, 2022. In total, 2.712 burnings were detected for that 4 months of Russia's full-scale invasion on the territory of Buchansky district (area is 2.558 km²). Most of them took place within the settlements where battles were fought.

With satellite images from the open database of Maxar Technologies we analyzed the most affected soil surfaces in this district.

One of the chosen plots is represented by the Irpin River meadows and is the Emerald network site. 18 impact marks were counted here in total: 4 bursts from 82-mm shells, 8 bursts from 122-mm and 6 – from 152-mm shells. An additional external analysis of a young pine forest area showed the vegetation overgrowth is no more than 10% after 2.5 months of succession process and represented by ruderal and segetal plant species.

Moreover, 8 objects of the Nature Reserve Fund of Ukraine with particularly valuable ecosystems in the Buchansky district were affected by military operations and their stability is currently significantly reduced.

How to cite: Spinova, Y., Vyshenska, I., Sakva, A., and Vasyliuk, O.: Study of the military actions impact on the Buchansky district soils (Kyiv Region, Ukraine), EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-21994, https://doi.org/10.5194/egusphere-egu24-21994, 2024.

EGU24-1074 | ECS | Posters on site | ITS3.3/ESSI4.1

Pan-India analysis of relationship between Spatial Attributes of Urban Area and Population 

Ravi Verma and Pradeep Kumar Garg

Urban structures in any city needs to be analyzed in conjunction to Urban Green Spaces (UGS). The relations between spatial attributes of built-up and UGS Land use/ Land Cover (LULC) can help analyzing various ecosystem services like micro-climate problems in aspects of increasing Land Surface Temperature (LST) patterns causing Urban Heat Island (UHI) inside the city. These relations between both LULC can also improve aesthetic structure of city. India, a magnanimous country comprising of 36 administrative boundaries, shows a range of diversity in population and culture inhabited by its dwellers. These large population centres have different settlement characteristics at different administrative levels (States/Union Territories, Districts, Sub-Districts, Villages/Towns and Wards/Blocks etc.) of India. These settlements can affect climate and development of country in longer duration. As such spatio-temporal analysis of urban population dynamics over different constituent land use/land cover (LU/LC) is performed in this study using open source data and software programs only. The study derives a pattern of Landscape Metrics (LSM) of built-up LULC over a period of 30 years in 7 zones of India comprising of 694 districts in total of various 28 states and 8 UTs. Landscape Metrics are one of the efficient ways to analyze the patterns of LULC in a study area. Publically available data such as Pan India Decadal LULC by ORNL DAAC for year 2005 and Copernicus Global Land service LULC for year 2015 at 100m resolution has been used as classified maps in study. These decadal LULC maps are predominantly classified using multi-temporal Landsat series data for Pan India coverage giving annual LULC classification maps consisting 19 classes with overall classification accuracy of 0.94 for all 3 year data. Built-up class present in both classified maps are used for analysis as urban patches. Landscape metric analysis is done through landscapemetrics library in RStudio® and 34 of the class level landscape metrics were calculated for urban area using multi-patch analysis for multi-year data. Significance of metrics was determined through calculation of coefficient of determination and establishment of variable importance between all 34 landscape metrics for urban and Population averaged over states and UTs containing 694 districts units of India. "Number of Patches (NP)","Total Class Area (CA)", "Total Core Area (TCA)" and "Total Edge (TE)" stood out as most viable metrics showing relation as high as R2 of 0.82 between spatial attributes of urban patches and population in the Indian administrative units. Spatial relation in terms of zones of India is much more existent than temporal as yearly variation for relation between urban patches and population. North, West and North East Zone of India are showing most consistent and highest values of correlation whereas South zone and UTs lowest with Central zone being most inconsistent. Such high relations between spatial patterns of urban patches and population suggest a significant need to prioritize configuration and optimization of population in cities, which can not only affect urbanization pattern inside the city boundary but also help achieving the sustainability causes of ecosystem services in city boundary.

How to cite: Verma, R. and Garg, P. K.: Pan-India analysis of relationship between Spatial Attributes of Urban Area and Population, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1074, https://doi.org/10.5194/egusphere-egu24-1074, 2024.

This study scrutinizes the impact of an anomalous early summer Land Surface Temperature (LST) surge on food security, energy dynamics, and human health in India's National Capital Region (NCR), and its implications for Sustainable Development Goals (SDGs). By analyzing MODIS images and employing Standard Anomaly (StA), monthly diurnal LST ranges were assessed. Results reveal March temperatures peaking from 23.11 to 41.57 °C, 3.5 °C above the average 21.78 to 39.41 °C range. Notably, contrary to conventional patterns, prolonged rain deficits drive this early summer warming rather than Sea Surface Temperature (SST). This warming adversely affects SDGs, significantly reducing crop yields, jeopardizing SDG-2's Zero Hunger target, impeding indicator SDG-2.4.1, and disrupting target 3.4.1 for health. Moreover, heightened energy consumption due to early summer warming disrupts SDG-6 on clean energy, directly impacting target 7.1 for electricity access. The findings underscore the urgency of addressing early summer warming's impact to progress toward achieving SDGs in India's NCR. Understanding and mitigating these effects are imperative for sustainable development initiatives in the region.

How to cite: Mahato, S. and Joshi, P. K.: Rising Temperatures, Rising Concerns: Early Summer and Sustainable Development in National Capital Regions of India, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1207, https://doi.org/10.5194/egusphere-egu24-1207, 2024.

EGU24-4493 | ECS | Posters on site | ITS3.3/ESSI4.1

Building Height Estimation at 10m across multiple countries using Sentinel Time-Series 

Ritu Yadav, Andrea Nascetti, and Yifang Ban

With the rapid shift of urban population to cities, urbanization monitoring has become essential to ensure sustainable development. In the last decade, 2D urban monitoring such as building footprint extraction has received considerable attention resulting in multiple high and low-resolution products. But despite being the essential component of urbanization, the vertical dimension (height) has not been studied at a large scale. Accurate estimation of building height plays an important role in urban planning, as it is correlated with energy consumption, population, transportation, city planning, urban climate and many other monitoring and planning required for sustainable development.

Airborne LiDAR or high-resolution orthophotos can be used for accurate building height estimation but for large-scale monitoring applications, the data collection itself is extremely expensive. With a compromise of resolution, Earth observation data, especially free-of-cost data can be used for large-scale monitoring. Existing large-scale building height estimation methods operate at low resolution (1km to 100m). A few of the recent studies improved the resolution to 10m while operating in a few cities to few states of the country. In this study, we estimate building heights across four countries. We propose a DL model that operates on a time series of Sentinel-1 SAR and Sentinel-2 MSI data and estimates building height at 10m spatial resolution. Our model estimates building height with 1.89m RMSE (Root Mean Square Error) surpassing the best score of 3.73m reported in previous studies. 

To demonstrate the effectiveness of our approach, we tested it on data from four countries and compared it with a baseline and four recent DL networks. We evaluate the impact of time series input and individual input modality i.e., SAR and optical data on the performance of the proposed model. The model is also tested for generalizability. Furthermore, the predicted building heights are downsampled and compared with GHSL-Built-H R2023A, a state-of-the-art product at 100m spatial resolution. The results show an improvement of 0.3m RMSE.

References

[1] Yadav, R., Nascetti, A., & Ban, Y. (2022). BUILDING CHANGE DETECTION USING MULTI-TEMPORAL AIRBORNE LIDAR DATA. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLIII-B3-2022, 1377–1383. https://doi.org/10.5194/isprs-archives-xliii-b3-2022-1377-2022

[2] Yang, C., &; Zhao, S. (2022). A building height dataset across China in 2017 estimated by the spatially-informed approach. Scientific Data, 9(1). https://doi.org/10.1038/s41597-022-01192-x

[3] Cai, B., Shao, Z., Huang, X., Zhou, X., & Fang, S. (2023). Deep learning-based building height mapping using Sentinel-1 and Sentinel-2 data. International Journal of Applied Earth Observation and Geoinformation, 122, 103399. https://doi.org/10.1016/j.jag.2023.103399

[4] Yadav, Ritu, Andrea Nascetti, and Yifang Ban. "A CNN regression model to estimate buildings height maps using Sentinel-1 SAR and Sentinel-2 MSI time series." arXiv preprint arXiv:2307.01378 (2023)

[5] Pesaresi, M., and P. Politis. "GHS-BUILT-H R2023A—GHS Building Height, Derived from AW3D30, SRTM30, and Sentinel2 Composite (2018)." (2018)

 

How to cite: Yadav, R., Nascetti, A., and Ban, Y.: Building Height Estimation at 10m across multiple countries using Sentinel Time-Series, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-4493, https://doi.org/10.5194/egusphere-egu24-4493, 2024.

EGU24-4584 | Orals | ITS3.3/ESSI4.1

Solar Park Detection Based On Machine Learning 

Shivam Basant, Jayaluxmi Indu, and Biplab Banerjee

Solar energy shall be an indispensable part in India’s clean energy transition. As renewable energy requires large amount of space considerations, policy makers often question the land based targets for deploying solar parks. A robust geospatial information on existing solar parks shall be crucial for both the governments and policy makers.

This study presents a novel method to detect solar parks using a synergy of satellite imagery from Sentinel-2 and convolutional neural networks (CNN). For the work, a total of nearly 2000 satellite images from Sentinel-2 were chosen over ten number of solar parks situated in India. Case study results are presented for the solar parks in India namely Bhadla Solar Park, Rajasthan, and Pavagada Solar Park, Karnataka. This dataset measures solar footprint over India and examines environmental impacts of solar parks over nearby ecosystem.

How to cite: Basant, S., Indu, J., and Banerjee, B.: Solar Park Detection Based On Machine Learning, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-4584, https://doi.org/10.5194/egusphere-egu24-4584, 2024.

EGU24-8575 | ECS | Posters on site | ITS3.3/ESSI4.1

Optimizing UAV seaweed mapping through algorithm comparison across RGB, multispectral, and combined datasets 

damir akhmetshin, Owen Naughton, Leon Cavanagh, and Dean Callaghan

The use of unmanned aerial vehicles (UAVs) with off-the-shelf RGB and multispectral sensors has expanded for environmental monitoring. While multispectral data enables analysis impossible with RGB, visible range cameras have benefits for large-scale habitat mapping. This research compared RGB, multispectral, and fused RGB-multispectral data from UAVs for seaweed mapping along the Irish coast. Three classification algorithms – Random Forest, Maximum Likelihood Classifier and Support Vector Machines – were tested on the three datasets to compare accuracies for seaweed species delineation and percent cover estimation. The RGB sensor effectively classified broad intertidal classes, but struggled differentiating some seaweed species. Multispectral data significantly improved species-level classification accuracy but tended to overestimate the presence of red and green algae. Fusing the RGB and multispectral data improved species classification accuracy over multispectral and RGB images. The results demonstrate the benefits of RGB sensors for broad habitat mapping and cover estimation, and multispectral for detailed species delineation. Fusion of the two sensor types enhances the strengths of both. This highlights the potential for UAVs paired with off-the-shelf visible range and multispectral cameras to provide detailed, accurate, and affordable change monitoring of intertidal seaweed habitats.

How to cite: akhmetshin, D., Naughton, O., Cavanagh, L., and Callaghan, D.: Optimizing UAV seaweed mapping through algorithm comparison across RGB, multispectral, and combined datasets, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-8575, https://doi.org/10.5194/egusphere-egu24-8575, 2024.

EGU24-12274 | ECS | Posters on site | ITS3.3/ESSI4.1

MOSMIN: Multiscale observation services for mining related deposits 

Sandra Lorenz, Moritz Kirsch, René Booysen, and Richard Gloaguen

The transition towards a green economy has led to an increased demand for raw materials, which are mainly sourced by mining. Mining activities generate residues such as rock wastes, tailings and stockpiles. These materials are associated with environmental and safety risks that need to be carefully managed throughout their life cycle, with an emphasis on stability and the prevention of water and soil pollution. Earth-observation (EO)-based techniques are seldom used for monitoring these deposits, and multi-sensor field data is commonly not integrated despite recent technological advances. We will develop holistic, full-site services for the geotechnical and environmental monitoring as well as valorisation of mining-related deposits based on a combination of EO and in situ geophysical data. The work will be accomplished under the “Multiscale Observation Services for Mining related deposits” project (MOSMIN for short), and funded by the European Union Agency for the Space Programme (EUSPA) with project number 101131740. MOSMIN services will use Copernicus EO data for time-resolved, spatially extensive, remote monitoring of ground deformation and surface composition. Innovative change detection algorithms will highlight displacements and identify environmental hazards. Satellite data will be integrated with real-time, high-resolution data obtained from unoccupied aerial vehicles and sensors installed at the site, leveraging the power of machine learning for fusion and resolution enhancement of multi-scale, multi-source data. Novel, non-invasive geophysical techniques such as distributed fibre-optic sensing will provide subsurface information to identify potential risks such as internal deformation and seepage. In collaboration with international mining companies, MOSMIN will use pilot sites in the EU, Chile and Zambia to develop and trial comprehensive monitoring services, which are calculated to have a Total Available Market of €1.2bn and expect to be commercialised shortly after project completion by three industry partners. The MOSMIN integrative service and tools will improve the efficiency and reliability of monitoring, maximise resource utilisation and help mitigate environmental risks and the impact of mining operations. - On behalf of the MOSMIN consortium.

How to cite: Lorenz, S., Kirsch, M., Booysen, R., and Gloaguen, R.: MOSMIN: Multiscale observation services for mining related deposits, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12274, https://doi.org/10.5194/egusphere-egu24-12274, 2024.

EGU24-12810 | ECS | Posters on site | ITS3.3/ESSI4.1

Urban deprivation and enhanced inequality in sub-Saharan Africa 

Chengxiu Li and Le Yu

Globally, 1.2 billion urban dwellers live in slums facing essential service deficiencies and heightened vulnerability, thereby challenging the United Nations' commitment to "Leave no one behind" in achieving Sustainable Development Goals (SDGs). We investigated availability of key urban services (water, sanitation, housing, living spaces) that define slums, revealing that 58.9% of households in 27 African countries lack access to at least one of above service based on household surveys, leading to their categorization as slums households. While slum proportion has decreased over the past two decades, however inequality has rose in countries with a high prevalence of slums.

Through the integration of household surveys, geospatial data, and machine learning algorithms, we estimated the wealth level and key service availability across sub-Saharan Africa. This approach revealed that 53.4% of urban population resides in slums, surpassing the UN's estimate of 44.9%. This study revealed that poor urban service in slums exacerbate inequality, however current aggregated statistics underestimate the extent of under-serviced urban slums, leading to ineffective efforts in building prosperity for all.

How to cite: Li, C. and Yu, L.: Urban deprivation and enhanced inequality in sub-Saharan Africa, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12810, https://doi.org/10.5194/egusphere-egu24-12810, 2024.

Accounting of the hydrologic process of evapotranspiration (ET) or consumptive use of water is important for water resources allocation, irrigation management, drought early warning, climate change impact assessment as well as in agro-water-climate nexus modeling. In fact, monitoring the United Nations' sustainable development goals (SDGs) that emphasize on improved food security, access to clean water, promotion of sustainable habitats and mitigation of natural disasters (droughts) hinge upon access to better quality data of ET. Though numerous studies have targeted accurate estimation of potential evapotranspiration (PET) using earth observation (EO) data; hydrologists are yet to reach consensus on the best set of predictor variables that can be used irrespective of spatio-temporal scale. This can be attributed to the nonlinear and complex nature of the process of ET. When it comes to the estimation of actual ET (AET), studies employing Eddy Covariance (EC) towers have been successful in different regions of the world. However, the developing countries of the world lack access to EC observations, requiring viable economical methods for accurate ET measurement, even using reliable estimates of PET. The proposed study explored fusion of regional climate reanalysis data, EO data, and machine learning techniques for high-resolution PET estimation. In this analysis, owing to the documented success of data-driven models in hydrological studies, performance of two machine learning models- tree based Random Forest (RF) and regressor Multivariate Adaptive Regression Splines (MARS), are evaluated for estimating monthly PET. A suite of input predictors are chosen to describe three model categories: meteorological-, EO- and hybrid-based predictor models. There are about 10 input combinations that can be generated for the PET model development, particularly for an agriculture-dominated study region - Dhenkanal district, located in Odisha in eastern part of India. In this study, reanalysis-based (meteorological) inputs at a grid resolution of 0.12° and Sentinel 2A (EO) products at spatial resolution of 20 m have been used. Results of the analysis indicate that solar radiation is the most important meteorological variable that controls PET estimation. Among the vegetation indices obtained from remote sensing data, we find that the Normalized Difference Water Index (NDWI) that represents availability of water in plants and soil, is particularly useful. The best PET estimation model that uses only solar radiation and few vegetation indices (NDVI, NDWI) gave coefficient of determination (R2) 0.88 and root mean square error (RMSE) of 0.14 during validation stage, whereas the use of hybrid predictor model that utilize temperature and vegetation indices information further reduced the error and increased the prediction accuracy (6.86%). When the meteorological inputs: precipitation and wind speed are only used, model did not perform well. Mapping the ET using the proposed models can facilitate reporting of progress in SDG with regard to water use, crop water stress, adaptation to agricultural droughts and food security. In this context, the Evaporative Demand Drought Index (EDDI) is computed across the study region to understand the drought patterns in the region.

Keywords: Potential Evapotranspiration, Agricultural Drought, Food Security, EO Data, Random Forest, Machine Learning, Vegetation Indices

How to cite: Tripathy, S. S. and Ramadas, M.: Data Fusion of Regional Reanalysis- and Sentinel (Earth Observation)-based Products with Machine Learning Tools for Monitoring Evapotranspiration and Drought, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15184, https://doi.org/10.5194/egusphere-egu24-15184, 2024.

EGU24-15509 | ECS | Orals | ITS3.3/ESSI4.1 | Highlight

Advancing Forest Cover and Forest Cover Change Mapping for SDG 15: A Novel Approach Using Copernicus Data Products 

Chiara Aquino, Manuela Balzarolo, Maria Vincenza Chiriacò, and Monia Santini

Forests are the major component of the terrestrial ecosystem and provide an essential source of livelihood to local communities. Nevertheless, forests worldwide are increasingly threatened by natural and human-driven activities, such as extensive logging for the extractive industries, severe weather, pests and wildfires. A responsible forest management substantially contribute to the protection and conservation of forest ecosystem and services. The United Nations’ Sustainable Development Goals (UN SDGs) 15 “Life on land” – and specifically indicator 15.1.1 “Forest area as a proportion of total land area” – is concerned with mapping and protecting forest ecosystems.  At the European Union (EU) level, the UN indicator 15.1.1 is translated into EUROSTAT indicator “Share of forest area”.  Monitoring of this indicator enhance compliance with EU policies of land use and land cover, supporting the EU forest strategy for 2030 and helping to implement the regulation on deforestation-free products.

The SDGs-EYES project is a major EU-wide initiative aiming at exploiting data and information coming from the European Copernicus Programme to develop, implement and deploy a new service for monitoring SDG targets. It will provide novel and robust workflows to consistently assess SDG indicators across EU countries, with potential for global upscaling. In recent years, the release of frequent and high-resolution satellite data from the Copernicus Sentinel missions has opened new frontiers for consistently mapping global forest cover.  Nevertheless, detecting small-scale forest disturbance - also known as forest degradation - remains a challenging task. Studies aiming at quantifying the carbon emissions and extent of forest degradation show that it affects land portions similar to, or even larger, than deforestation. It is clear that accurate forest cover maps are urgently needed to avoid underestimating the loss of forest habitats, thereby preventing further carbon emissions, land degradation and biodiversity decline.

In this study, we apply a cumulative sum change detection algorithm on Sentinel-1 and Sentinel-2 time-series data to estimate forest cover and forest cover change in the Olt River basin, Romania, for the 2020-2022 period. Romania hosts the largest share (218,000 ha) of the EU's temperate primary and old-growth forests, many of which have been logged, both legally and illegally, although officially under protection by national parks or Natura 2000 sites. Through the integration of multi-sensor information (e.g. Sentinel-1 and 2, ESA CCI WorldCover), the resulting maps are able to detect hotspots of forest cover change at 20 m resolution, while also providing exact timing of the disturbance events. The suggested approach, hosted on the SDGs-EYES platform, provides a scalable methodology that can be systematically used in other geographical areas and for selected periods of interest. In this way, we enhance monitoring and evaluation of indicator 15.1.1, in agreement with the UN and EU indicators while improving the current weaknesses of the two frameworks.

 

How to cite: Aquino, C., Balzarolo, M., Chiriacò, M. V., and Santini, M.: Advancing Forest Cover and Forest Cover Change Mapping for SDG 15: A Novel Approach Using Copernicus Data Products, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15509, https://doi.org/10.5194/egusphere-egu24-15509, 2024.

EGU24-16115 | ECS | Orals | ITS3.3/ESSI4.1

The Potential of Remote Sensing for Enhancing a Sustainable Agricultural Intensification under a Changing Climate in West Africa 

Jonas Meier, Frank Thonfeld, Verena Huber Garcia, Kenneth Aidoo, Niklas Heiss, and Ursula Gessner

The challenges of climate change in West Africa are closely linked to food security in the region. Rising temperatures and increasingly variable precipitation threaten traditional rain-fed agriculture relying on the rainy season. Climate change is affecting the rainy season in West Africa in multiple ways, e.g., by shifting the onset, shortening its duration and increasingly interrupting the growing period by dry spells. An increase of extreme weather events such as heavy precipitation or storms add another risk to agriculture. The risk of crop failures hits an already vulnerable system. Since a large portion of food is imported the West African countries are vulnerable to external economic shocks. Furthermore, West Africa has one of the highest population growth rates in the world, its population will increase to 1.2 billion people by 2050. To guarantee sufficient food supply and to achieve the Sustainable Development Goals (SDG), a sustainable intensification of agriculture is needed (i.e., increasing yields without additional land consumption and without adverse effects on climate change) and mitigation and adaption strategies against the negative effects of climate change are required. Remote sensing has proven to be a suitable instrument to measure and evaluate both, mitigation and adaptation actions in a reliable and cost-effective way. Depending on the method of cultivation, agriculture causes different amounts of greenhouse gas (GHG) emissions. Remote sensing can provide information about biophysical development as input and reference data for land surface models to assess the produced GHG under different cultivation practices. Since the negative impact of climate change on agriculture is already measurable and visible, adaptation measures are highly important. They differ in terms of their complexity, their technical feasibility and their costs. Adaptation measures can be for example a change in land management, the choice of crop variety or technical innovation like weather forecast or irrigation systems. In various interdisciplinary research projects (CONCERT, COINS, AgRAIN), we selected adaptation measures of varying complexity and monitor and evaluate them using remote sensing-based analysis, mainly on Sentinel-1, Sentinel-2 and Planet data. The analyses range from land cover and land use mapping to crop classification, crop suitability modeling, field boundary delineation, identification of management events, and site-specific productivity measurements. We employ a range of methodologies, including random forest regression, convolutional neural networks (CNN), fuzzy logic approaches, and time series analysis. The results serve as a basis for local stakeholders and decision-makers, enabling the implementation of proven adaption measures to enhance resilience against climate change and promote sustainable agricultural intensification.

How to cite: Meier, J., Thonfeld, F., Huber Garcia, V., Aidoo, K., Heiss, N., and Gessner, U.: The Potential of Remote Sensing for Enhancing a Sustainable Agricultural Intensification under a Changing Climate in West Africa, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-16115, https://doi.org/10.5194/egusphere-egu24-16115, 2024.

Natural ecosystems, especially primary forests, are impacted by the rapid expansion of human land use and global climate change, putting the most bio-diverse areas of our planet under threat. Large amounts of Earth Observation and analysis-ready data sets are made available (almost) for free. Yet, the usage of such data in conservation finance and policy making does currently not live up to its full potential. It is a complex endeavor to access relevant portions of Big Geospatial Datasets efficiently due to the high number of different data providers, formats and interfaces. Even more important, we need to generate information in an open and reproducible way to take informed decisions to allocate funds responsibly and maximize public goods and benefits

MAps for Planning, Monitoring and Evaluation (MAPME) is an collaborative initiative based on OpenScience principles to leverage the potential of geospatial data for relevant actors in the development cooperation sector. The initiative is driven by Free and Open Software (FOSS) enthusiasts within German (KfW, GIZ) and French (AFD, IRD) development institutions. Together with our partner countries we are key decision makers in the allocation of the so-called Official Development Assistance (ODA). To bridge the “last-mile” gap between vast amounts of openly available geospatial data sets and productive monitoring applications, we have developed an OpenSource software used within our institutions.

The software is written in R and relies on the Geospatial Data Abstraction Library (GDAL) bindings provided by the `sf` and `terra` packages. It allows efficient analysis of large data collections on deforestation and greenhouse gas emissions such as Global Forest Watch (GFW). Focusing on expandability, everyone can include new in-house or open data sets, and custom analysis routines. Thus, the functionality can be extended to other sectors beyond forest monitoring. It opens the way to deliver crucial information on the state of ecosystems around the globe in a timely and reproducible way, allowing our institutions to make better allocation decisions.

We will present the MAPME Initiative and shed a light on our approach to developing applications based on FOSS. We will showcase first data solutions build by our partners on top of the framework, such as a geospatial impact evaluation of preventing deforestation and a dashboard for continuous monitoring of protected areas of the German development cooperation portfolio.

How to cite: Görgen, D. and Schielein, J.: MAPME – Versatile analysis tool for big geospatial data in the context of sustainable development, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18584, https://doi.org/10.5194/egusphere-egu24-18584, 2024.

EGU24-18869 | Orals | ITS3.3/ESSI4.1

Wildfire as an interplay between water deficiency, manipulated tree species composition and bark beetle. A remote sensing approach 

Jana Müllerová, Jan Pacina, Martin Adámek, Dominik Brétt, and Bobek Přemysl

During 2022, Bohemian Switzerland NP was affected by the largest wildfire in the Czech Republic throughout its modern history. The NP is formed by sandstone towers, deep narrow valleys and dense forests. From the 19th century onwards, Norway spruce and non-native Weymouth pine were massively planted here. A series of weather extremes in the last years caused an exceptional drought and consequent massive bark beetle outbreak and spruce die off, followed by the catastrophic event. Wildfires of such a dimension are rather uncommon in Central Europe, and this event therefore serves as a perfect model situation to study the role of species composition, bark beetle and water availability on the fire dynamics as well as the changes in biodiversity and natural succession after the disaster. Before the fire, the area was dominated by conifers, mostly standing dry after the bark beetle attack except along the water courses, and further formed of clear cuts, healthy deciduous beech forests and rocky outcrops. 

Pre-fire vegetation state, fire severity and post-fire regeneration were assessed using a combination of remote sensing sources. In particular, we used pre- and post-fire series of Sentinel-2 satellite MSS imagery, and acquisition of multispectral (MSS) and LIDAR data. The whole area was sampled from small aircraft TL232 Condor by three sensors - photogrammetric camera Hasselblad A6D-100c (ground sampling distance - GSD - 5 cm), MSS sensor MicaSense Altum (GSD 32 cm) and LIDAR RIEGL VUX 1-LR (13 points/m), and detailed sites were sampled using drone mounted sensors - MSS (MicaSense Altum, GSD 5 cm) and LIDAR (DJI L1). Forest composition and changes in health status were derived using a range of spectral indices and supervised classification. Fire severity and forest structure were derived using a combination of Lidar and optical point cloud, fisheye camera, ground sampling, and analysis of optical data (supervised classification, vegetation indices). 

Our research revealed that fire disturbance was low or none at native deciduous tree stands and waterlogged sites. On the opposite, it was more severe at dry bark-beetle clearings covered by a thick layer of litter as compared to standing dead spruce. We can infer that in places where many stems were only partly burned or the trees postponed the die-off, the fire went faster and the severity of disturbance was lower. In some cases, we could see patterns formed by ground fire, such as burned circles around trees or tree stools surrounded by unburned areas. Post-fire regeneration is very fast, and even after one year, vegetation growth can be detected using LIDAR and photogrammetric point clouds. Derived information on fire severity, detailed 3D stand structure and health status are to be used as a proxy of the fire disturbance impact on biodiversity and patterns of regeneration.

How to cite: Müllerová, J., Pacina, J., Adámek, M., Brétt, D., and Přemysl, B.: Wildfire as an interplay between water deficiency, manipulated tree species composition and bark beetle. A remote sensing approach, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18869, https://doi.org/10.5194/egusphere-egu24-18869, 2024.

EGU24-19568 | ECS | Orals | ITS3.3/ESSI4.1

Ocean Acidification: Weaves to be tied on European and global scale 

Elisabeth Kubin, Marina Lipizer, Maria Eugenia Molina Jack, Megan Anne French, and Alessandra Giorgetti

Oceanic uptake of anthropogenic CO2 is altering the seawater chemistry of the oceans, leading to a decrease in pH and thus to ocean acidification (OA). This has multiple consequences not only for marine biogeochemistry, but also for marine biota and ecosystems. Therefore, the Sustainable Development Goal SDG Target 14.3 addresses OA and the SDG 14.3.1 calls for the average marine acidity (pH) and on guidance on monitoring and reporting OA data.

Here we want to present the international collaboration between the European Marine Observation and Data Network (EMODnet Chemistry), NOAA and UNESCO on how to observe and report OA data, following the FAIR (Findable, Accessible, Interoperable and Reusable) principles. The final aim is to enable global comparisons of the changes in ocean chemistry and to provide a unified, globally coordinated, sustained, long-term observation network and database. Detailed vocabularies and the according metadata will guarantee the correct description of the carbonate system and thus also the long term usability of the data, including reliable trend calculations.

This global collaboration will provide more accurate and detailed OA data and will help policy and decision makers to communicate more clearly and precisely about the impacts of climate change on marine ecosystems and resources, enabling holistic approaches.

How to cite: Kubin, E., Lipizer, M., Molina Jack, M. E., French, M. A., and Giorgetti, A.: Ocean Acidification: Weaves to be tied on European and global scale, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-19568, https://doi.org/10.5194/egusphere-egu24-19568, 2024.

EGU24-19821 | Orals | ITS3.3/ESSI4.1

Remote-sensing based tools to monitor grassland ecosystem services 

Maria P. González-Dugo, Maria José Muñoz-Gómez, Cristina Gálvez, Ángel Blázquez-Carrasco, M. Dolores Carbonero, Francisco S. Tortosa, Juan Carlos Castro, José Guerrero-Casado, Juan Castro, Sergio Colombo, Manuel Arriaza, and Anastasio Villanueva

The provision of ecosystem services (ES) by agricultural systems is a shared objective of agricultural policies in most developed countries in response to an increasing demand from society. Sustainable management of grassland ecosystems leads to enhanced soil fertility, ensures food security, acts as natural filters and purifiers of water, and functions as carbon sinks, sequestering carbon dioxide and mitigating climate change. All of these goals are deeply interconnected with several SDGs. The Common Agricultural Policy (CAP) of the European Union is environmentally oriented. However, a broad consensus indicates that the current policy instruments are not effectively promoting the provision of ES. Thus, it is essential to develop efficient and innovative policy instruments to enhance ES's agricultural provision. One of the challenges for applying new policy instruments, such as results-based payments (OECD, 2015), is the quantification of ES supply, usually involving intensive and specialized field data. Therefore, there is a need to create quantitative indicators for ES based on reliable and affordable data. Remote sensing data can be an effective tool, especially if the data are easily accessible, available at an appropriate scale, and provided free of cost.

Olive groves and Mediterranean oak savanna were used in this work as case studies to examine the herbaceous layer's contribution to the provision of ecosystem services. In both ecosystems, grasslands play a relevant role in supplying provisioning (such as forage, freshwater or genetic library), regulating (carbon sequestration, soil conservation, climate, and air quality regulation) and cultural services (aesthetic appreciation, cultural identity). The biomass or above-ground net primary production (ANPP) and biodiversity are essential integrators of ecosystem functioning. Biomass is responsible for the input level of various ecosystem services, and it is directly connected to carbon sequestration and soil conservation. Biodiversity, on the other hand, contributes to the processes that underpin other ecosystem services and constitutes an ecosystem good that humans directly value. This work describes the general scheme to measure several grassland ES (GES) in olive groves and oak savannas, including ANPP, biodiversity, carbon sequestration, and aesthetic appreciation, and preliminary results about the ANPP and biodiversity are presented. 

How to cite: González-Dugo, M. P., Muñoz-Gómez, M. J., Gálvez, C., Blázquez-Carrasco, Á., Carbonero, M. D., Tortosa, F. S., Castro, J. C., Guerrero-Casado, J., Castro, J., Colombo, S., Arriaza, M., and Villanueva, A.: Remote-sensing based tools to monitor grassland ecosystem services, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-19821, https://doi.org/10.5194/egusphere-egu24-19821, 2024.

EGU24-20511 | Orals | ITS3.3/ESSI4.1

Using Copernicus High Resolution Layer Imperviousness Density to monitor soil sealing in agricultural areas (SDG 2: Zero Hunger) 

Wendy Fjellstad, Svein Olav Krøgli, Jonathan Rizzi, and Agata Hościło

Many countries have goals and strategies to reduce soil sealing of agricultural land to preserve food production capacity. This is essential in relation to Sustainable Development Goal 2: Zero Hunger. To monitor progress, reliable data are needed to quantify soil sealing and changes over time. We examined the potential of the Copernicus High Resolution Layer Imperviousness Density (HRL IMD) to assess soil sealing in agricultural areas in Poland and Norway.

We quantified the accuracy and reliability of the products Imperviousness Classified Change (IMCC) for the period 2015-2018 and Imperviousness degree (IMD) for the reference year 2018. We found a very high overall accuracy of IMCC 2015-2018 in both Poland and Norway. However, this was mainly due to the dominance of area with no change.  When we focused on the small areas where change does occur, we found low user accuracy, with an overestimation of soil sealing. The producer accuracy was generally much higher, meaning that real cases of soil sealing were captured. This is a much better result than if IMCC had under-estimated soil sealing. It suggests that IMCC can play a valuable role in detecting soil sealing, by highlighting areas where soil sealing may have occurred, allowing the user to carry out a further control of this much smaller area, without having to assess the great expanse of unchanged area.

We conclude that the datasets provide useful information for Europe. They are standardised and comparable across countries, which can enable comparison of the effects of policies intended to prevent soil sealing of agricultural land. We advise caution in using older versions of the change data. In particular, it is advisable to merge the closely related classes “1: new cover” and “11: increased cover” and the same for “2: loss of cover” and “12: decreased cover”. These distinctions are not reliable, but the general information about increase or decrease is much better. The transition to finer resolution (10 x 10 m) in the newer datasets represents a great improvement and will make the change data more reliable and useful in future versions.

How to cite: Fjellstad, W., Krøgli, S. O., Rizzi, J., and Hościło, A.: Using Copernicus High Resolution Layer Imperviousness Density to monitor soil sealing in agricultural areas (SDG 2: Zero Hunger), EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-20511, https://doi.org/10.5194/egusphere-egu24-20511, 2024.

Hydropower dams can lead to changes in the access and use of surrounding natural resources, such as land and water. However, in complex socio-ecological systems (SES), taking into consideration different temporal and spatial scales, dams can be just one of the shocks suffered by the SES. Changes in a SES are not linear and can be part of a cycle of causes and effects in a large chain of system processes. We explore these connected processes in the context of Colombia’s Andean region, one of the hotspots of hydropower expansion in the world. This area is also responsible for 70% of the Colombia's agricultural production. We investigated two large hydropower dams: El Quimbo (Huila Departament) and Hidrosogamoso (Santander Department). This study aims to analyze the changes in land-water systems related to cash crops production and the drivers of these change from the commissioning of the dams until recent years (2009 to 2020). Our goal is to understand how perceived changes in the land-water system are induced (or not) by the construction and operation of the dam and how this influence interacts with other global and regional shocks. We conducted 80 semi-structured interviews with representatives of the agricultural sector from the main food chains (palm oil, coffee, cocoa, and rice), and with government representatives responsible for managing the land and water systems. Regional land use and land cover change maps, national agricultural data and hydropower licenses were used to sample design. The influence of the dams in land use patterns regarding crops was different depending on the geographical location of the crops (downstream or upstream dams, and north or south of the Andes), and on the water and land demands for these crops. For example, in the case of rice, an irrigated crop, interviewees declared that the effects of the dam were minimal, unlike the case of coffee, which predominantly uses rainwater for production. In addition, there are some evidence that the influence of the dams in certain crops had indirect effects in some ecosystems, such as the case of oil palm and the wetlands ecosystems. These indirect changes also increased inequalities, as interviewees from large oil palm owners reported that they were switching to an irrigated system, while smallholders would keep relying on rainwater. We also found that global drivers might be able to mask the effect of local drivers, e.g., climatic variability and the variation in commodities prices in comparison to the influence of the dams. Another example are the changes in agricultural practices induced by the increase in prices of fertilizers due to the war in Ukraine, which illustrates the fact that several drivers, including external ones, are concomitantly influencing transformations in land-water system. This study highlights that the influence of certain shocks in SES, such as large infrastructures, cannot be analyzed separately from other concomitant processes, but in a broader perspective, investigating how these processes interact with each other. Different shocks, such as dams, can also aggravate disputes over land and water resources and increase inequalities.

How to cite: Salomão, C., Nascimento, N., and Lima, L.: Beyond energy production: A local perception about the drivers of change in land-water systems for cash crops production surrounding Colombian water reservoirs., EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1199, https://doi.org/10.5194/egusphere-egu24-1199, 2024.

EGU24-2446 | ECS | Orals | ITS3.4/NH13.4

Navigating uncertainties: an interdisciplinary approach to land use management in favour of the water cycle  

Itxaso Ruiz, Ambika Markanday, Noelia Zafra, and Marcela Brugnach

Exacerbated by climate change, water scarcity in the Mediterranean basin poses one of the most significant environmental challenges in the region, compromising adaptation capacities. Current knowledge of how forests contribute to rainwater recycling, i.e. by increasing evapotranspiration and promoting orographic precipitation, has led to the proposal of forest management strategies to mitigate desertification in the western Mediterranean basin. Focusing on a case study in eastern Spain, where formerly arable lands are today covered by dense forests, we reflect on the uncertainties that arise from this relation between land use changes and orographic precipitation at the watershed scale. We aim to transform the encountered uncertainties into actionable opportunities for adapting this territory to ongoing climate change. To support the development of intervention strategies that increase climate resilience, we use an interdisciplinary approach that integrates participatory processes for co-designing sustainable land management measures and a systematic literature review from which we identify the physical and biophysical uncertainties arising from the rainwater recycling hypothesis. In search of practical applications, we are developing a decision support game to test the implementation conditions of the management strategies. This game provides decision-makers with a tool to assess how the proposed measures align with the needs, capabilities, and willingness of local stakeholders, and it also enables reflecting on potential trade-offs. This research contributes to strengthening the water cycle through adaptive land management and, thus, promoting a more resilient western Mediterranean basin.

How to cite: Ruiz, I., Markanday, A., Zafra, N., and Brugnach, M.: Navigating uncertainties: an interdisciplinary approach to land use management in favour of the water cycle , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-2446, https://doi.org/10.5194/egusphere-egu24-2446, 2024.

Monitoring mulch-based solutions to reduce runoff and erosion in a variety of land uses in the Alentejo agro-silvo-pastoral systems

 

Canedo, J.1*, Coelho, L.1, Basch, G.1, Cabrita, M.J.1, Cachapa, F.1, Caldeira, F.1, Gonzalez-Pelayo, O.1,2, Marques, T.1,  Muñoz-Rojas, J.1,4, Palma, P.3, Pinto-Correia, T.1, Pinto-Cruz, C.1, Tomaz, A.3, Prats, S.A.1

1MED (Instituto Mediterrâneo para a Agricultura, Ambiente e Desenvolvimento) & CHANGE – Global Change and Sustainability Institute, Universidade de Évora, Pólo da Mitra, Ap. 94, 7002-554 Évora, Portugal.

2CESAM (Centro de Estudos do Ambiente e do Mar), Universidade de Aveiro, 3810 – 193 Aveiro, Portugal.

3Instituto Politécnico de Beja – Departamento de Tecnologias e Ciências Aplicadas, Edifício da Escola Superior Agrária, Campus do Instituto Politécnico de Beja, Rua Pedro Soares, 7800-295 Beja, Portugal.

4DPAO (Departamento de Paisagem, Ambiente e Ordenamento) – Universidade de Évora, Colégio Luis António Verney, Rua Romão Ramalho, 59 7000-671 Évora, Portugal.

 

*Corresponding author: joao.canedo@uevora.pt

 

Soil erosion is a critical socio-environmental problem for rural Mediterranean ecosystems and landscapes. Erosion inflicts multiple, serious damages in agro-ecosystems, including vineyards and olive groves, and also in other semi-natural ecosystems such as the Montado (cattle-sheep pastureland combined with Quercus sp. trees). In particular, erosion reduces the water storage capacity, soil organic matter, nutrients and valuable soil biota, which are transported off-site with runoff water. Nature-based solutions, such as the application of organic mulching, reduces runoff and soil erosion between 40% and 90%, respectively. Agri-forest residues such as olive and vineyard by-products can also be transformed to biochar and applied to the soil, increasing soil organic matter, soil moisture and, ultimately, improving the soil status and agronomic soil properties.

Our aim was to verify the effects of the application of combined mulch and biochar upon the mitigation of runoff and soil erosion. Runoff-erosion experimental plots were developed to independently measure runoff, by using pressure sensors, and erosion, by emptying, drying and weighing the sediments stored in sediment fences. A total of 60 plots were installed and monitored during 3 months in olive orchards, vineyards and Montado, which were consistently treated with mulch (2 Mg ha-1 straw/olive leaves) and mulch + biochar (2 Mg ha-1 straw + 10 Mg ha-1). All plots were located across Alentejo, the region of Portugal with a most marked Mediterranean climate.

Preliminary results showed that mulch reduced runoff peakflows in 7% and mulch + biochar reduced it in 28%. Soil erosion was reduced around 60 and 80%, respectively. There were important differences between olive orchards, vineyards and Montado systems. In general, the vineyards and olive orchards are much more prone to erosion when compared to the Montado. Further research is being carried out and will allow the assessment of the effects of mulch and mulch + biochar in other ecosystem services, such as water retention, carbon storage, soil habitat protection and soil fertility.

 

Keywords: Agriculture, climate change, sustainability, water storage, soil fertility

 

How to cite: Gomes Vicente Canedo, J. N.: Monitoring mulch-based solutions to reduce runoff and erosion in a variety of land uses in the Alentejo agro-silvo-pastoral systems, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5589, https://doi.org/10.5194/egusphere-egu24-5589, 2024.

EGU24-6526 | ECS | Posters on site | ITS3.4/NH13.4

Seasonal movement behavior of goats related to grazing intensity and environmental variability using Hidden Markov Models 

Hua Cheng, Kasper Johansen, Baocheng Jin, and Matthew Francis McCabe

Key research in movement ecology is investigating shifts in animal behavior and identifying the factors that induce alterations in movement behavior and mechanics. The impact of natural environments and human activities on the underlying behavioral processes of domestic goats are still being elucidated. We applied seasonal multivariate Hidden Markov Models (HMMs) to characterize the fine-scale movements (30- second intervals) of GPS-tracked Zhongwei goats for 124 days and determine how grazing intensity, seasonal food resources, terrain factors and daylight hours affect movement behavior in the mountain grassland in China. We classified the goats’ activities as two basic behavioral states of foraging (low step length, varied and undirected turning angle) and travelling (long step lengths, low and directed turning angles). Grazing intensity, a management factor, exerted the most significant influence on goats across different seasons. Additionally, factors such as daylight hour and slope had a more pronounced impact on their movement activities compared to the normalized difference vegetation index (NDVI). Elevation and solar radiation were found not explain much of the variability in movement behavior of goats. Their probability of foraging behavior was most likely to increase with grazing intensity, slope, diurnal hours and NDVI. In addition, the percentage time allocation of foraging was higher in spring and winter with lower food resources periods and shorten daylight hours, than summer and autumn with larger food resources and long daylight hours. The foraging percentage increased from morning to afternoon. HMMs are found useful for disentangling movement behavior and understanding how goats respond to seasonal grazing intensity, time of daylight, NDVI and slope. Our findings underscore the importance of accounting for interactions between movement behavior and gazing management, not only the environmental factors and behavioral rhythms, when assessing the movement characteristics and behavioral transitions of goats. These results are important for designing grazing management strategies that satisfy ecological and socioeconomic demands on mountain grassland ecosystems.

How to cite: Cheng, H., Johansen, K., Jin, B., and McCabe, M. F.: Seasonal movement behavior of goats related to grazing intensity and environmental variability using Hidden Markov Models, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-6526, https://doi.org/10.5194/egusphere-egu24-6526, 2024.

EGU24-7209 | ECS | Posters on site | ITS3.4/NH13.4

Synergies of Land Use Land Cover and Climate Change on Water Balance Components in SSP–RCP Scenarios over Munneru basin, India 

Loukika Kotapati Narayanaswamy, Venkata Reddy Keesara, and Eswar Sai Buri

The growing human population accelerates alterations in land use and land cover (LULC) over time, putting tremendous strain on natural resources. Rapid land use transformations, encompassing urbanization, intensive agriculture, and changes in natural landscapes, have a profound impact on water cycle. This necessitates the development and implementation of sustainable land management strategies to mitigate adverse effects on water resources. Anticipating future land use and cover (LU&LC) dynamics in the Munneru river basin is pivotal for modelling of hydrological processes. This study delves into the combined impact of Land Use and Land Cover Scenarios (LU&LC) which is based on Shared Socioeconomic Pathway (SSP2-45, SSP3-75 and SSP5-85) and climate change within the context of representative concentration pathway (RCP 4.5 & RCP 8.5) scenarios on water resources for Munneru river basin, India. Landsat data was employed for preparing LU&LC maps from the Google Earth Engine (GEE) using the random forest (RF) method for the period 2005-2020 with the accuracy of 91% and kappa coefficient of 0.89. The future scenarios of LU&LC’s were projected by integrating Global Change Assessment Model (GCAM) data and DynaCLUE model for 2030, 2050 and 2080. DynaCLUE model uses driving factors, Binary Logistic Regression analysis for past LU&LC maps for projecting future LU&LC maps. The SWAT model is calibrated and validated for the period 1983–2017 in SWAT-CUP using the SUFI2 algorithm for 2015 LU&LC map. The future projected LU&LC maps based on SSP’s are incorporated in SWAT model for future periods under both RCP 4.5 & 8.5 scenarios. The average monthly streamflow’s are simulated for the baseline period (1983–2005) and for three future periods, namely the near future (2021–2039), mid future (2040–2069) and far future (2070–2099) under both LU&LC and climate change scenarios. Results indicate that there is increase in surface runoff and water yield and decrease in evapotranspiration, groundwater and total aquifer for three SSP scenarios under both RCP’s. Assessing the impact on water balance components, provides the necessity for adaptive strategies in the face of shifting climate and land use dynamics.

How to cite: Kotapati Narayanaswamy, L., Keesara, V. R., and Buri, E. S.: Synergies of Land Use Land Cover and Climate Change on Water Balance Components in SSP–RCP Scenarios over Munneru basin, India, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-7209, https://doi.org/10.5194/egusphere-egu24-7209, 2024.

EGU24-10846 | Posters on site | ITS3.4/NH13.4

Canal use purposes impact the water quality: a case study within the Life Green4Blue project floodplain area  

Mauro De Feudis, Gloria Falsone, William Trenti, Andrea Morsolin, and Livia Vittori Antisari

Most of the floodplain ecosystems in the world have been reclaimed for allowing urbanization and agriculture. In reclaimed floodplains, water is addressed in artificial canals which could have several purposes such as irrigation, soil draining, hydraulic safety of the floodplain and source of biodiversity. In this context, the main aim of the present study was to evaluate the influence of artificial canal use (irrigation and receiving canals) crossing the Life Green4Blue project floodplain area on water quality. The study area is located within the Po plain (Italy) characterized by heavy reclamation activities for agricultural purposes in the last century. The irrigation canals, used for agricultural purposes, are fed during summer season (from April to September) by the Emiliano Romagnolo Canal which carries water from the Po River. The receiving canals, larger than irrigation canals, are mainly used as discharging canals for both irrigation and draining canal and to lesser extent for irrigation purposes. During the autumn and winter seasons (from October to March), both type of canals is used for hydraulic safety of the investigated floodplain area by keeping the water level of them low. The water survey was monthly conducted from the beginning of 2020 till December 2023. The cluster analysis (CA) showed a clear distinction between water of receiving canals and that from irrigation canals. According to the principal component analysis (PCA), the differences were mainly related to the amounts of nutrients and salts. In fact, water of receiving canals was characterized by higher amount of nutrients (e.g., N–NH4, Ca, K, Mg, P and S) and higher values of electrical conductivity (EC). The poorer water quality of receiving canals can be attributed both to the water origin, namely soil leachates and water of irrigation canals that already flowed for several kilometres the agricultural land, and the absence of freshwater inflow. Therefore, the water quality index (WQI) showed higher value for the irrigation canals (67) compared to the receiving ones (61). For both canals’ type the PCA highlighted the worsening of water quality during the autumn and winter (AW) seasons. Indeed, during AW seasons a greater loading of nutrients and EC were observed compared to spring and summer (SS) seasons. The higher load of nutrients in AW compared to SS might be due to the higher nutrient leaching from soils resulting from the higher rainfalls occurring in AW seasons. In addition, the lower water flow during AW seasons prevented a ‘dilution effect’ and allowed a greater exchange of both cations and anions from the bed sediments. However, it was interesting to observe that the water quality worsening during the AW seasons was marked for irrigation canals compared to receiving ones suggesting the major role of freshwater input on water quality of such type of canals. The present study highlighted the importance of canal use on water quality. Specifically, in a view of a sustainable conservation of floodplain ecosystem services, this study showed the needing to ensure the input of freshwater in all canals’ type and throughout the year.

How to cite: De Feudis, M., Falsone, G., Trenti, W., Morsolin, A., and Vittori Antisari, L.: Canal use purposes impact the water quality: a case study within the Life Green4Blue project floodplain area , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-10846, https://doi.org/10.5194/egusphere-egu24-10846, 2024.

EGU24-14569 | ECS | Posters on site | ITS3.4/NH13.4

Fine mapping of crop patterns in the North China Plain from 2013 ‒ 2022 

Liang Dong, Di Long, Caijin Zhang, Yingjie Cui, and Bridget R. Scanlon

A nuanced understanding of crop patterns is pivotal for accurate crop yield and irrigation water use calculations, holding profound implications for national food security and sustainable environmental development. In the water-scarce North China Plain (NCP), where agricultural intensity faces challenges due to groundwater suppression and ecological restoration, this study employs random forest classification on Sentinel-2 Multispectral Instrument (MSI) and Landsat 8 Operational Land Imagery (OLI) time series to reveal the spatial and temporal dynamics of crop patterns from 2013 to 2022. Our classification, featuring a finer scheme (nine categories), higher spatial resolution (10/30 m), and extensive field sampling points, aligns well with China's statistical yearbooks. The annual mapping exposes a shift towards economic forests, mainly from other food crops, across all NCP provinces. Distinct spatial patterns emerge, with wheat-maize rotation decreasing at higher latitudes, countered by an increase in single maize and economic forests. Despite these shifts, wheat-maize rotation remains dominant, and seasonal fallow is concentrated in regions with poor irrigation, notably in groundwater funnel areas. Overall, our crop pattern mapping provides a robust dataset for water conservation and land management, contributing to regional resilience planning.

How to cite: Dong, L., Long, D., Zhang, C., Cui, Y., and Scanlon, B. R.: Fine mapping of crop patterns in the North China Plain from 2013 ‒ 2022, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-14569, https://doi.org/10.5194/egusphere-egu24-14569, 2024.

EGU24-16027 | ECS | Posters on site | ITS3.4/NH13.4

Hypoxia exposure of short-term residents in the Qinghai-Tibet Plateau 

Wenyixin Huo and Peijun Shi

The unique habitat and plateau hypoxia in the Qinghai-Tibet Plateau have always troubled tourists. The study of plateau hypoxia is of great significance to improve tourists' well-being and formulate related policies. In this paper, based on the data of oxygen content and blood oxygen saturation of short-term residents in the Qinghai-Tibet Plateau, Qinghai Province was divided into severe hypoxia region, hypoxia region and non-hypoxia region according to the established relationship between blood oxygen saturation and oxygen content. Combined with the results of the spatialization of short-lived population, the exposure numbers of short-lived population under different hypoxic zones in summer and winter were calculated. The results show that: 1) The distribution of tourist population in Qinghai Province presents a distribution rule of "one center gathering", and the population is mainly concentrated in the eastern region. The population density is high in the main urban areas with dense POI, and very low in woodland, remote mountain and other areas. 2) With the decrease of oxygen content, blood oxygen saturation decreased exponentially. 3) Compared with winter, short-term residents is more suitable to travel to the plateau in summer.

How to cite: Huo, W. and Shi, P.: Hypoxia exposure of short-term residents in the Qinghai-Tibet Plateau, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-16027, https://doi.org/10.5194/egusphere-egu24-16027, 2024.

EGU24-16294 | Posters on site | ITS3.4/NH13.4

Surface oxygen concentration on the Qinghai-Tibet Plateau (2017–2022) 

Xiaokang Hu, Yanqiang Chen, Wenyixin Huo, Wei Jia, Heng Ma, Weidong Ma, Lu Jiang, Gangfeng Zhang, Yonggui Ma, Haiping Tang, and Peijun Shi

For the ecologically vulnerable Qinghai-Tibet Plateau (QTP), hypoxia is increasingly becoming an extremely important environmental risk factor that significantly affects the health of both humans and livestock in the plateau region, as well as hindering high-quality development. To focus on the problem of hypoxia, it is especially urgent to study the surface oxygen concentration (i.e., oxygen concentration). However, the existing research is not sufficient, and there is a lack of oxygen concentration data collected on the QTP. In this study, through the Second Tibetan Plateau Scientific Expedition and Research and field measurements, the oxygen concentration data and corresponding geographic environmental data were collected at 807 measurement points on the QTP from 2017 to 2022, and the spatiotemporal oxygen concentration patterns were estimated. This work filled the gaps in the measurement and research of oxygen concentrations on the QTP while providing data support for analyses of the influencing factors and spatiotemporal characteristics of oxygen concentrations, which is of great significance for promoting the construction of ecological civilization in the QTP region.

How to cite: Hu, X., Chen, Y., Huo, W., Jia, W., Ma, H., Ma, W., Jiang, L., Zhang, G., Ma, Y., Tang, H., and Shi, P.: Surface oxygen concentration on the Qinghai-Tibet Plateau (2017–2022), EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-16294, https://doi.org/10.5194/egusphere-egu24-16294, 2024.

High mountains are hotspots of climate and global environmental change. Mountain biodiversity is threatened by quickly rising temperatures which cause vegetation shifts, such as upslope migration. At the same time, natural hazards develop as mountain slopes become increasingly unstable due to permafrost degradation and changes in rain and snowfall regimes. Resulting slope movements, such as rockfalls and debris flows, can limit colonization by plants. However, plants that manage to colonize mountain slopes can stabilize them through their roots and above ground biomass.

Therefore, we believe that an interdisciplinary approach linking ecology and geomorphology is needed as a next step to better understand how climate change affects high mountain landscapes and ecosystems. Combining results from previous geomorphic, ecological and palaeoecological studies, we show that the response of high mountain environments to climate change can depend on the balance between slope movement intensity and the trait-dependent ability of plants to colonize and stabilize moving slopes. For this ‘biogeomorphic balance’ we envisage three possible scenarios: (1) Intensifying slope movements impede vegetation shifts, amplifying instability. (2) Ecosystem engineer species, adapted to moving slopes, stabilize slopes and facilitate shifts for less movement-adapted species. (3) Competitive trees and tall shrubs, shifting on stable slopes, reduce instability but potentially diminish biodiversity. Given the disparate rates of ecological and geomorphic responses to climate change, coupled with high environmental heterogeneity and elevational gradients in in mountains, we anticipate that future biogeomorphic balances will be variable and heterogeneous in both space and time.

To unravel these intricate biogeomorphic balances, we advocate for collaborative research between mountain geomorphologists and ecologists and propose three distinct future directions that combine advancing field measurement, remote sensing techniques and modeling approaches. We believe that by recognizing high mountains as 'biogeomorphic ecosystems', shaped by the interplay of geomorphic and ecological processes, we can improve our ability to safeguard people, infrastructure and ecosystems in mountain environments around the world.

 

References:

Eichel J, Stoffel M, Wipf S. 2023. Go or grow? Feedbacks between moving slopes and shifting plants in high mountain environments. Progress in Physical Geography: Earth and Environment 47 : 967–985. DOI: 10.1177/03091333231193844

How to cite: Eichel, J., Stoffel, M., and Wipf, S.: Go or grow? An interdisciplinary ‘biogeomorphic balance’ concept linking moving mountain slopes and shifting mountain plants, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-16482, https://doi.org/10.5194/egusphere-egu24-16482, 2024.

EGU24-16547 | Posters on site | ITS3.4/NH13.4

Exploring runoff sensitivity based on runoff ratio in the UK during 2000 to 2015 

Pei Xue, Dominick Spracklen, and Joseph Holden

The runoff ratio is important in hydrology and water resource management because it helps quantify the efficiency of a watershed or catchment area in handling precipitation. The runoff ratio can vary widely depending on factors such as land cover (e.g., urban, forested, agricultural), soil type and permeability, land slope, and climate.  Some previous research revealed that the number of days of precipitation is the major determinant of runoff ratio, while how runoff sensitivity changes at different ratio has been not fully understood. Here, we use runoff ratio as a hydrological indicator to explore the influencing factors of changes in runoff sensitivity. Since land cover types have not changed a lot in the UK after 2000. We calculated runoff ratio for catchments in the UK during 2000 to 2015 and its sensitivity to a range of controlling factors. This study will outline the key findings on runoff ratio controls, which will then be tested in other regions to determine the relative role of land cover change.

How to cite: Xue, P., Spracklen, D., and Holden, J.: Exploring runoff sensitivity based on runoff ratio in the UK during 2000 to 2015, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-16547, https://doi.org/10.5194/egusphere-egu24-16547, 2024.

EGU24-16826 | Posters virtual | ITS3.4/NH13.4

Restoration of pastures under tree canopy: effects of the undergrowth clearing and grazing in the maintenance of herbaceous species diversity and production  

Ana María Foronda Vazquez, Héctor Lafora, Olivia Barrantes, Yolanda Pueyo, Javier Ferrer, and Ramón Reiné

In a context of global change, the mountains of southern Europe have suffered in recent decades processes of land abandonment, leading to the loss of cultural mosaic landscapes, with negative effects on ecological and economic sustainability of agroecosystems. In the framework of the MIDMACC Project (LIFE18 CCA/ES/001099), landscape management measures to adapt marginal areas of Spanish mid-mountain to the impacts of global change have been tested. In this regard, experiences of forest management measures (thinning and undergrowth scrub clearing) followed by grazing with native cattle breeds have been conducted in two reforested areas in “La Garcipollera” valley (Huesca, Spain) to create and maintain herbaceous pastures under tree canopy (one area with Pinus nigra and the other with Populus x canadensis). The effects of forest management and grazing on the floristic composition and production of the herbaceous pasture were analysed in both areas separately. For this purpose, three replicates per each of three typologies of monitoring plots with a surface of 400 m2 were established: i) control plots (without neither forest management nor livestock), ii) managed plots without livestock and iii) managed plots with livestock (2 cows per plot for 48 hours and twice a year). Vegetation surveys were conducted every spring from 2020 in the pine area and 2021 in the poplar area to 2023. In those, the coverage of the bare soil and every plant species growing within four 1m2 subplots per plot were recorded. Additionally, in order to estimate dry biomass (production of the pasture) for the initial and final stage of the experiment, at each plot we collected the plants growing within four 0,5m2 subplots adjacent to the previous. Our results indicated that, after three years of experimentation, forest management decreased the bare soil cover, increased the cover, richness and biomass of herbaceous species and reduced the cover and richness of woody species compared to the control plots. This trend was common for both the pine and poplar areas. In the case of grazing effects, we found that the entry of livestock in the plots in the managed pine areas increased the bare soil cover and herbaceous plants cover and richness but reduced the biomass production and the woody species cover (thus controlling scrub encroachment). Regarding plots in the managed poplar areas, grazing affects differently from pine areas since no significant effects on herbaceous nor woody species cover and richness were found compared to control plots (shorter study period). Nevertheless, a positive effect of grazing was found since bare soil cover was reduced and herbaceous biomass production was increased compared to the plots with no livestock entry. Although in the mid-term (three/two years after the measures) the whole expected effects of grazing are not yet evident, the improvement of the herbaceous species and the control of scrub encroachment by cattle are apparent.

Acknowledgements: This research was supported by the LIFE MIDMACC (LIFE18 CCA/ES/001099), funded by the EC.

How to cite: Foronda Vazquez, A. M., Lafora, H., Barrantes, O., Pueyo, Y., Ferrer, J., and Reiné, R.: Restoration of pastures under tree canopy: effects of the undergrowth clearing and grazing in the maintenance of herbaceous species diversity and production , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-16826, https://doi.org/10.5194/egusphere-egu24-16826, 2024.

EGU24-17005 | ECS | Orals | ITS3.4/NH13.4

Alpine vegetation community patterns and implications to eco-hydrology in the Khumbu region, Nepalese Himalaya 

Ruolin Leng, Stephan Harrison, Elizabeth Byers, and Karen Anderson

The Himalayan alpine zone (HAZ) – a high altitude zone above approximately 4100 m.a.s.l., is projected to experience strong eco-environmental changes with climate change. As plants expand their range in this region, the plant-water functioning is likely to be impacted. Satellite remote sensing provides one means of understanding the distribution pattern of HAZ vegetation communities, but the often patchy distribution of alpine vegetation creates challenges when using coarse-grained satellite data whose pixels are typically coarser than the grain of vegetation pattern. Also, the lack of in-situ measurements limits the validation of remote sensing products, and our understanding to the eco-hydrological processes within this area. Here we use fine spatial resolution satellite imagery from WorldView-2 (2 m2 per pixel) coupled with elevation model data from the Copernicus GLO-30 product to produce a land cover classification for HAZ. Grassy meadows and dwarf shrubs belonging to the Rhododendron and Juniperus families dominate the ecology of HAZ in this region so we created three vegetation classes for mapping indicative major plant communities dominated by these species. Based on this land cover map, we compared in-situ measurements in shrubby and open area, to explore the impacts of Rhododendron spp. and Juniperus spp. on temperature under plant canopy. Afterwards, we coupled in-situ measurements with meteorological metrics derived from ERA5, to simulate the evapotranspiration (ET) of these two dominant plant communities. We found that altitude and aspect were dominant drivers of vegetation distribution in HAZ and that the average vegetation cover of Rhododendron spp. and Juniperus spp. reduced with increasing altitude, as expected. South- and east- facing slopes were dominated by Juniperus spp., while north- and west- facing slopes were dominated by Rhododendron spp., and the growth extent of Rhododendron spp. (between 4010 to 4820 m.a.s.l.) and meadow (between 4010 to 4680 m.a.s.l.) were vertically wider than Juniperus spp. (between 4010 to 4660 m.a.s.l.). In general, maximum temperatures under shrub canopies were lower and minimum temperatures were higher compared to unvegetated or open areas at the same location. Juniperus plants had more significant influences on temperature than Rhododendron. Results from this study demonstrate the present vegetation distribution pattern in HAZ at the plant community level, and the potential ET status relevant to the vegetation expansion trend within this area. This study provides an impetus for studies that seek further understanding to eco-hydrological interactions between dwarf plants and water flows and stores in HAZ.

How to cite: Leng, R., Harrison, S., Byers, E., and Anderson, K.: Alpine vegetation community patterns and implications to eco-hydrology in the Khumbu region, Nepalese Himalaya, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17005, https://doi.org/10.5194/egusphere-egu24-17005, 2024.

Groundwater is the primary reservoir of unfrozen freshwater, a critical element in the
water cycle. It is constantly extracted, which has resulted in irreversible depletion.
The significant extraction of groundwater led to a shift in the Earth's rotational pole
and has been attributed to global sea level rise, and disruption of the regional energy
budget. The extraction has influenced the soil quality and the interaction between
surface and subsurface water. The Hindon River basin, situated in the north-western
region of the Ganga plain in India, once witnessed the Indus Valley civilization, is
now facing adverse effects from anthropogenic activities. The groundwater level has
decreased by over a meter in recent decades, and the concentration of dissolved
nitrate, an indicator of pollution, has exceeded safe limits. The pollution in
groundwater has resulted in numerous severe health issues, including cancer and
liver disorders. Consequently, it is crucial to comprehend the human-induced
alterations in the water cycle, focusing on identifying pollutant sources and the
processes responsible for redistribution of water mass among different components
of the regional hydrological cycle. In this study, we have used remote sensing data in
the Soil and Water Assessment Tool (SWAT) to understand impact of crop patterns
on regional water budget. Chemical tracers such as stable water isotopes (δD-H2O,
δ18O-H2O), dissolved nitrate isotopes (δ15N-NO3 , δ18O-NO3 ), and ionic chemistry [NO3- ]
have been used to validate the model results. The initial output of the model
suggests that changes in existing cropping patterns can improve the discharge in the
river.

How to cite: Mandal, R., Sanyal, P., and Samantaray, S.: Agricultural impact on quality and quantity of groundwater in the north-western Ganga plain, India: A stable isotopes and remote sensing approach, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17140, https://doi.org/10.5194/egusphere-egu24-17140, 2024.

EGU24-17364 | Posters on site | ITS3.4/NH13.4

Sustainability of water transfers in the Crau plain 

Gilles Belaud, Kevin Daudin, Marielle Montginoul, François Charron, Pauline Igbui, Crystele Leauthaud, and Paul Vandôme

The Crau plain, 600 km2 located in South-East France, is mainly associated with the production of high quality hay (around 15,000 ha) irrigated from open-channel networks. Traditional irrigation practices consist in high discharge in order to reach the end of long plots, the excess of water being both drained by run-off to ditches and percolated to the so-called “Crau aquifer”. The aquifer recharge depends for around 70% on hay irrigation, the organization of its management thus relies on the sustainability of irrigation practices. However, hay production faces social and physical pressures from local to regional scales.

  • Socially, water management in the fields requires to be fine-tuned to balance working time dedicated to irrigation (difficult labor conditions with high workload and night shifts) with water flows throughout irrigated plots, farms and canals.
  • Physically, the low-performance hay irrigation is under tension because of local land-use changes due to the development of urban areas and other agricultural production (orchards and horticulture), in a context of hydraulic infrastructures requiring important rehabilitation works.
  • Locally, return flows provide a mix of interdependent services, the aquifer being used for the extraction of drinking water for 300,000 inhabitants, for other irrigated crops like orchards, and for industries.
  • Regionally, water comes from an historical inter-basin transfer, passing through a succession of hydraulic infrastructures and hydroelectric power plant before entering the plain. The climate change impacts on upstream precipitation make incoming water being less abundant, leading to water restrictions as experienced in 2022.

The sustainability of water transfers questions the integration of land and water planning. The aim of our research is to propose an original perspective coupling the characterization of water flows in relation to irrigation practices at the plot and scheme scales with the evaluation of farmers leeway in terms of economic and organizational constraints. The objective of this communication is to present each part of this work and to draw up further correspondences between the hydraulic and economic dimensions. First, an agrarian diagnosis revealed the lack of information on water flows, motivating in turn the original development of affordable measuring devices to track water in an irrigated block and automate parts of irrigation practices. Second, the context of water and land increasing scarcities motivated the characterization of the vulnerability of hay productions in terms of access to water, labor and markets. These studies aimed to directly contribute to water management in the Crau plain, respectively in the search for technical optimization to use water in the agricultural system more efficiently (contributing to reduce working flows) and for the definition and evaluation of strategies for adapting agriculture to meet the challenges of farm economics, groundwater recharge and water conservation. Finally, we will draw on both inputs to assess land cover scenarios and their impacts on aquifer recharge; we may also evaluate possible impacts of water restrictions on land uses.

How to cite: Belaud, G., Daudin, K., Montginoul, M., Charron, F., Igbui, P., Leauthaud, C., and Vandôme, P.: Sustainability of water transfers in the Crau plain, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17364, https://doi.org/10.5194/egusphere-egu24-17364, 2024.

EGU24-17956 | ECS | Posters on site | ITS3.4/NH13.4

Multi-temporal assessment of Groundwater Recharge Capacity in Protected Areas of Lithuania 

Marius Kalinauskas and Paulo Pereira

Groundwater recharge is one of the key Ecosystem Services (ES) supplied by protected areas (PAs). However, such drivers as biodiversity loss, climate, and land use change affect the capacity for groundwater recharge (GRC). National-scale PA studies focused on GRC ES are scarce, thus leaving a knowledge gap on a global scale. Therefore, it is critical to map and assess the groundwater recharge spatiotemporal dynamics in supporting human wellbeing. In this study we mapped and assessed GRC at different timeframes (1990, 2000, 2012, 2018, 2022) in the PAs of Lithuania at national scale. For the model we used 15 indicators such as annual average evapotranspiration and precipitation, topographic properties (slope inclination, topographic position index, topographic wetness index, roughness index, curvature index, drainage density, lineament density), lithology, geomorphology, soil (texture, depth, imperviousness), land use (Corine Land Cover, Esri Land Cover). The results show that the highest GRC is observed in PAs to the west of the country, closer to the Baltic Sea, and PAs located in the eastern part of Lithuania with dense network of lakes, less intensive agriculture, fewer impervious areas, and soil properties more suitable for water infiltration. Lesser GRC is observed in urban PAs with higher imperviousness (Vilnius city). PAs in south and southwest of Lithuania with more intense agriculture practices, higher drainage density, and less water bodies also show lower GRC, as well as coastal PAs with sandy soils, no freshwater bodies, and higher roughness. The Kruskal-Wallis test showed no significant difference between GRC spatial distribution through different years due to low variation of evapotranspiration and precipitation values, and lesser land use changes within the PAs. Our findings contribute to a better understanding the spatiotemporal dynamics of one of the key provisioning ES in the Lithuanian PAs – the GRC. Mapping and assessing groundwater recharge support better management of the PAs, and contributes to achieving global and regional (e.g., Sustainable Development Goals, EU Biodiversity Strategy for 2030) policy targets.

How to cite: Kalinauskas, M. and Pereira, P.: Multi-temporal assessment of Groundwater Recharge Capacity in Protected Areas of Lithuania, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17956, https://doi.org/10.5194/egusphere-egu24-17956, 2024.

EGU24-18023 | ECS | Posters on site | ITS3.4/NH13.4

Urbanization and River Health: Analyzing the Effects of Land Cover Change on the Upper Yamuna Basin 

Neenu Neenu and Mitthan Lal Kansal

Rapid urbanization and intensive agricultural practices have resulted in considerable changes in land use and land cover (LULC), underscoring the paramount significance of land cover analysis and change detection assessments for river ecosystems. The Yamuna River, a major tributary of the Ganges, is notably polluted, particularly in the Delhi region3. Thus, the compromised Yamuna River's health in Delhi necessitates an intricate exploration of land change intensity. In this context, the study seeks to enhance comprehension of landscape changes in the urbanized expanse of Delhi and scrutinize their repercussions on the Yamuna River. The Land Change Intensity (LCI) analysis, covering the period from 2016 to 2023, was conducted to examine the evolving dynamics of Delhi's temporal and spatial land use patterns. The LCI analysis assesses land use changes by examining the rate of overall change and the patterns of land transitions, determining their consistency across different time periods1. The findings of the study reveal prominent land use changes, with notable expansions into built-up and agricultural areas, resulting in encroachments upon barren land and green areas. During the period, an observable transformation in land cover was discerned, with 12% for built area and a concurrent 10% for crop area. The period also witnessed a 13% decrease in barren land alongside a 5% reduction in green spaces. The land use changes, particularly the expansion of urban areas, adversely affect the Yamuna River's health through a surge in water demand, reduction in capacity for pollutant absorption, extensive agricultural practices involving fertilizer use, and the occurrences of extreme events like floods2. Moreover, the visible and persistent foam formation in the Yamuna River is primarily attributed to urbanization and agricultural activities occurring in the Delhi stretch of the river4. Therefore, there is an urgent need to establish an equilibrium between developmental pursuits and environmental conservation for the holistic well-being of the river ecosystem. Through this study, we corroborate that the encroached floodplain of the Yamuna River in Delhi can be effectively utilized for phytoremediation. Such techniques would facilitate biotic absorption and neutralization of agricultural effluents and emerging pollutants like surfactants.

Keywords: Delhi, Land Change Intensity (LCI), LULC, Phytoremediation, Yamuna River

References

1. Aldwaik, S. Z., and R. G. Pontius. 2012. "Intensity analysis to unify measurements of size and stationarity of land changes by interval, category, and transition." Urban Plan., 106 (1): 103–114. Elsevier B.V. https://doi.org/10.1016/j.landurbplan.2012.02.010.

2. Kumar, M., M. Sharif, and S. Ahmed. 2020. "Impact of urbanization on the river Yamuna basin." J. River Basin Manag., 18 (4): 461–475. Taylor & Francis. https://doi.org/10.1080/15715124.2019.1613412.

3. Rajan, S., and J. R. Nandimandalam. 2024. "Environmental health risk assessment and source apportion of heavy metals using chemometrics and pollution indices in the upper Yamuna river basin, India." Chemosphere, 346 (May 2023): 140570. Elsevier Ltd. https://doi.org/10.1016/j.chemosphere.2023.140570.

4. Sejwal, G., and S. K. Singh. 2023. "Perspective: The unexplored dimensions behind the foam formation in River Yamuna, India." Sci. Pollut. Res., 30 (39): 90458–90470. Springer Berlin Heidelberg. https://doi.org/10.1007/s11356-023-28857-3.

How to cite: Neenu, N. and Kansal, M. L.: Urbanization and River Health: Analyzing the Effects of Land Cover Change on the Upper Yamuna Basin, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18023, https://doi.org/10.5194/egusphere-egu24-18023, 2024.

EGU24-18580 | Orals | ITS3.4/NH13.4

Terrain-Based Groundwater Potential and Groundwater Level Monitoring in Mountainous Regions of Central Taiwan 

Jung-Jun Lin, Feng-Mei Li, Nai-Chin Chen, Chien-Chung Ke, Yen-Tsu Lin, Chia-Hung Liang, Tzi-Hua Lai, and Chi-Chao Huang

The scarcity of freshwater has become a global issue in recent years, particularly in the plain regions of Taiwan. To address this challenge and enhance groundwater management for sustainable use, it is crucial to assess the groundwater resource potential in mountainous regions, as they serve as major recharge sources for the plains in Taiwan. To understand the relationship between groundwater potential and the geological settings of mountainous regions, various field investigation techniques were employed, including geological drilling, core logging, down-hole geophysical well logging, packer tests, and constant-rate pumping tests. This study focused on the main watershed in Central Taiwan, integrating all field investigation results to assess and analyze groundwater potential. Long-term groundwater monitoring wells were established to observe seasonal fluctuations.

Given the geological complexity of the mountainous region, a total of 75 boreholes with a depth of 100 meters were drilled in different geological units. Among the 48 selected sites with higher groundwater potential, groundwater monitoring stations were established, and constant-rate pumping tests were conducted to determine well yields and estimate the hydraulic properties of the rock aquifer. Integration of core and well logging revealed a composition of regolith and fractured bedrock. Geomorphological assessments, including slope analysis and the index of topographic position and wetness, categorized seven terrains: areas near the roof, at ridges, steep slopes, flat slopes, valleys or creek bottoms, alluvial fans downstream from valleys, and main riverbed deposits.

The results showed that the thickness of regolith ranged from 0.5 to 80.8 meters, with a geometric average of 14.7 meters, depending on different terrain types. Well yields ranged from 0.5 to 900 L/min, with an average of 134.4 L/min. Groundwater-level fluctuations ranged from 2.04 to 39.71 meters in shallow aquifers and 1.64 to 29.62 meters in deep aquifers, with outliers reaching 60.53 meters. Notably, higher average well yields and groundwater fluctuations were observed in main riverbed deposits and flat slopes. These findings highlight the observed terrain-based groundwater potential, emphasizing the pivotal role of groundwater-level fluctuation in recharge dynamics.

How to cite: Lin, J.-J., Li, F.-M., Chen, N.-C., Ke, C.-C., Lin, Y.-T., Liang, C.-H., Lai, T.-H., and Huang, C.-C.: Terrain-Based Groundwater Potential and Groundwater Level Monitoring in Mountainous Regions of Central Taiwan, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18580, https://doi.org/10.5194/egusphere-egu24-18580, 2024.

EGU24-19961 | ECS | Posters on site | ITS3.4/NH13.4

What if the deforestation stops: impact on water budget components in West Africa 

Francis E. Oussou, Souleymane Sy, Jan Bliefernicht, Harald Kunstmann, Thomas Rummler, Nicaise Yalo, and Yinusa Ayodele Asiwaju-Bello

The land cover degradation in the Anthropocene under a changing climate threat remains one of the significant concern for water resources preservation and planning. The reciprocal effects of land degradation and climate change is reported as a complex scenario with direct and indirect impact on land surface processes (IPCC, 2023). The purpose of this work is to simulate the water fluxes and states under the anthopogenic influence (control - CTRL) and natural evergreen (EBF) conditions using the hydrological model WRF-Hydro with NoahMP as the Land Surface Model (LSM). The change in the temporal and spatial patterns is evaluated in terms of the potential impact associated with preserving the natural land cover in WA. To achieve this, the offline mode of WRF-Hydro is forced with meteorological dataset from ERA5-land for the two land cover scenarios at ~11km spatial resolution between 2011 and 2023. The water budget outputs are post-processed with the R package “rwrfhydro” which computes the total precipitation partitioning into surface runoff, evaporation, and water storage in the surface and subsurface components. The water budget terms are analysed with Man-Kendall’s statistics and the difference between the two scenarios evaluated using multivariate techniques (Principal component analysis - PCA and Canonical correlation analysis - CCA), and Wavelet analysis.The results show that whatever the land cover scenario the leading temporal variations of the total precipitation (PC1) have a strong relationship with the water storage (groundwater, total soil moisture, and canopy water) while lags in the signals are more likely to have higher correlation with the surface and subsurface runoff. Further, the canonical loadings of the CCA modes of the water storage terms, evaporation terms and total precipitation indicate a shift towards the dry northern part (Sahel) of the study area. Compared to the CTRL simulation, the EBF scenario decreases the runoff fraction while increases the evaporation and storage change fractions. The natural land cover scenario simulated in this study provide considerable insight into the potential benefits of land reforestation actions in West Africa and offers opportunities for better decision making.

How to cite: Oussou, F. E., Sy, S., Bliefernicht, J., Kunstmann, H., Rummler, T., Yalo, N., and Asiwaju-Bello, Y. A.: What if the deforestation stops: impact on water budget components in West Africa, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-19961, https://doi.org/10.5194/egusphere-egu24-19961, 2024.

EGU24-20216 | ECS | Orals | ITS3.4/NH13.4

Agent-based modelling for understanding the socio-ecological resilience in alpine mountain communities 

Andreas Mayer, Claudine Egger, and Veronika Gaube

European mountain regions are becoming more vulnerable to natural hazards due to global change, climate change, and land-use change. Therefore, it is essential to understand their resilience. Currently, quantitative and dynamic models of coupled human-landscape interactions are in their infancy. However, agent-based modelling (ABM) approaches have high potential to advance the analysis of the interplay of natural and social factors affecting socio-ecological resilience in European mountain communities. The Socio-Ecological Land Agent-Based Model (SECLAND) integrates information from qualitative interviews and spatial data into a quantitative modelling environment. This enriches the diversity of scenario modelling beyond economic rationales by incorporating individual agent's motivations for land-use decisions. The outputs from this model have been used as input to hydrological or ecological models on multiple occasions.

SECLAND has been used to model the potential success of various adaptation strategies for coping with climate-induced natural hazards. In a study conducted in the department of Ariège, France, we analysed the potential impacts of intensified livestock grazing on mountain pastures under scenarios with strong climate change effects and increased extreme events. In this scenario, farmers use mountain pastures to seek additional forage resources in specific years. However, these grazing areas require considerate management in years when they are not needed for food provision. Our study also found that the utilization patterns of mountain pastures are strongly influenced by farm succession, vegetation regrowth on unused mountain pastures, and the search for cost-efficient forage resources. In a case study conducted in Eastern Austria, we found that adaptive learning moderates the decline in the number of active farms and farmland, regardless of the scenario conditions, compared to scenarios without adaptive learning. However, the results also indicate that adaptation increases the workload of farmers. This highlights the importance of considering more than just simplistic economic rationales when making land-use decisions. Agent-based models can be used to model socio-ecological responses and help cope with adaptation in complex socio-ecological systems.

Both studies emphasise that in the context of risk management and socio-ecological resilience, learning and managing additional workload are key factors for achieving adaptive success. To further improve, it is necessary to couple agent-based models with climatic and landscape models, allowing for bi-directional feedback between social and natural systems. SECLAND has been adapted to integrate adaptive learning processes, demonstrating the possibility of capturing mutual system dynamics and feedback loops. This allows the full capacity of agent-based models to be used to assess the resilience of mountain communities to cope with natural hazards, using a scenario approach that includes heterogeneous agents, different trajectories of socio-economic conditions, as well as global and climate change dynamics. This presentation outlines a conceptual framework for operationalizing an interdisciplinary effort within a modelling environment that integrates human decision-making, socio-economic conditions, and climatic and landscape dynamics.

How to cite: Mayer, A., Egger, C., and Gaube, V.: Agent-based modelling for understanding the socio-ecological resilience in alpine mountain communities, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-20216, https://doi.org/10.5194/egusphere-egu24-20216, 2024.

EGU24-1118 | ECS | PICO | ITS3.5/BG1.19

From electrical cable bacteria acidification to eelgrass colonisation: seasonal monitoring of foraminiferal ecology and shell preservation on estuarine mudflats. 

Maxime Daviray, Emmanuelle Geslin, Eric Beneteau, Sophie Quichard, Matéo Tougne, and Edouard Metzger

This study presents the seasonal monitoring of sediment acidification in two intertidal mudflat stations in the Auray river estuary (Atlantic coast, France). Sediment geochemistry and living benthic foraminifera and the preservation of their shells were investigated from April 2022 to July 2023. The development of eelgrass meadows was observed in both mudflats during Summer, something that had not happened for over ten years. Before these sprouts, the mudflats were bare, with seasonal algal deposits, and colonised by cable bacteria. Cable bacteria activity is characterised by electrogenic sulphide oxidation (e-SOx) measured by O2, H2S and pH microprofilings. e-SOx redesigns diagenetic processes generating strong pH gradients within the first few centimetres of sediment. The upstream mudflat showed seasonal dynamics of e-SOx. Cable bacteria appeared to be inactive in Winter (∆pH = 0.4) and led to intense pore water acidification during Fall (∆pH = 1.9) under meadow senescence. In the downstream mudflat, e-SOx remained continuous through the year with ∆pH from 0.9 in Winter to 2.3 in Fall. At both stations, the Ωcalc decreased from supersaturated to values well below 1 in the first few millimetres of sediment, excepted in Winter when Ωcalc was undersaturated due to freshwater flow. All year long, calcareous specimens, mostly dominated by Ammonia morphocomplex tepida and Haynesina germanica, showed test dissolution below the sedimentary oxic layer. During Fall, at both stations, calcareous specimens dwindled and tests were extremely corroded. In the meantime, the agglutinated species Ammobaculites balkwilli dominated the assemblage. During Spring, the upstream station was the setting for a H. germanica bloom after the cable bacteria seemed no longer active in Winter. During Summer, the upstream station showed a well-developed eelgrass meadow together with e-SOx (ΔpH = 1.3). Agglutinated species dominated the foraminiferal assemblage with A. balkwilli in the upper 5-mm and Eggerelloides scaber deeper down. The eelgrass colonisation has seemed to be beneficial to the foraminiferal community and stimulates its dynamism by encouraging a new species equilibrium in the assemblage. The most impacted species seemed to be A. morphocomplex tepida as between Summer 2022 and 2023 their density and relative abundance felt sharply in favour of Elphidium spp., Quiqueloculina spp. and A. balkwilli. These summery observations were quite different from those at the downstream station where cable bacteria were active all year long. Surprisingly, agglutinated species remained in minor proportions and A. morphocomplex tepida more or less constant. Moreover, dead assemblages showed important losses of calcareous tests where cable bacteria were active conducting to an organic lining enrichment with depth. To summarize, our study shows that foraminiferal ecology responds quickly to environmental changes in coastal sediments making them suitable for biomonitoring while the loss of their tests in acidic environments weakens their applicability for reconstructing temporal environmental chronicles.

How to cite: Daviray, M., Geslin, E., Beneteau, E., Quichard, S., Tougne, M., and Metzger, E.: From electrical cable bacteria acidification to eelgrass colonisation: seasonal monitoring of foraminiferal ecology and shell preservation on estuarine mudflats., EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1118, https://doi.org/10.5194/egusphere-egu24-1118, 2024.

EGU24-1401 | ECS | PICO | ITS3.5/BG1.19 | Highlight

The distribution pattern of vascular plant alpha diversity in the Qinghai-Tibet Plateau 

Yajie Zhang and Tao Zhou

Biodiversity plays a vital role in maintaining ecosystem functioning. Quantifying the impact of biotic and abiotic factors on plant diversity and creating a prediction map of biodiversity on the Qinghai-Tibet Plateau (QTP) can provide data and mechanism support for biodiversity conservation and restoration. Species richness (SR) serves as one of the indicators of biodiversity. In this study, we developed a SR estimation model based on the random forest algorithm, using 275 SR observation data, soil attribute data, meteorological data, topographical data, and human activity data. We assessed the pattern of SR on the QTP from 2000 to 2020, analyzed its spatiotemporal variation, and further evaluated significant environmental factors influencing vegetation alpha diversity. Our results showed that (1) Climate factor is the main influencing factor of SR spatial variation on the QTP, followed by terrain conditions. (2) Machine learning can account for 56% of SR and unveil distribution patterns showing a decrease in species richness from southeast to northwest on the QTP. (3) Over the past 20 years, there has been an increase in SR, particularly in the southeastern region.

How to cite: Zhang, Y. and Zhou, T.: The distribution pattern of vascular plant alpha diversity in the Qinghai-Tibet Plateau, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1401, https://doi.org/10.5194/egusphere-egu24-1401, 2024.

EGU24-1572 | ECS | PICO | ITS3.5/BG1.19

Simulating and analysing seabird flyways: An approach combining least-cost path modelling and machine learning 

Nomikos Skyllas, Mo Verhoeven, Maarten Loonen, and Richard Bintanja

Seabird migration is driven by general wind circulation and productive ocean regions. As a result, bird migration takes place along distinct corridors or "flyways” that have evolved by earth’s large-scale atmospheric circulation patterns. These flyways form a link between climate and bird migration, and by simulating their pattern we might better understand the present corridor and predict the potential future impacts of climate change. However, few studies have focused on modelling flyways (especially for multiple bird strategies, populations, seasons, species and oceans), with most of them simulating trajectories of individual birds.

We use climatic data in combination with a least-cost-path modelling approach to simulate and describe multiple seabird flyways. By combining bird tracking data and machine learning, we are able to infer whether the flyways used by the birds optimise time and/or energy. We focussed on five seabird flyways of arctic terns and sooty shearwaters, both spring and autumn migration either over the Atlantic or the Pacific Ocean. We will show that a bird's effort is influenced by tailwinds, crosswinds and food availability, and we use this to calculate how close to the theoretical optimal migration (time- or energy-minimising) these birds actually fly. Our findings show that it is possible to recreate observed flyways using environmental data and that these simulations can generate predictions about the effect of future climate change.

How to cite: Skyllas, N., Verhoeven, M., Loonen, M., and Bintanja, R.: Simulating and analysing seabird flyways: An approach combining least-cost path modelling and machine learning, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1572, https://doi.org/10.5194/egusphere-egu24-1572, 2024.

EGU24-2306 | PICO | ITS3.5/BG1.19

Global Potential Riparian Zones Estimation 

Ibrahim Mohammed, Kashif Shaad, John Bolten, and Maira Bezerra

The recently announced Freshwater Challenge (FWC) initiative (https://www.freshwaterchallenge.org/) at the United Nations Water conference, sets an ambitious goal of restoring 300,000 kilometers of degraded rivers and 350 million hectares of degraded wetlands across the globe by 2030. Central to moving towards this goal will be including tangible actions for freshwater and linked ecosystems into supporting country’s Nationally Determined Contributions (NDCs) and National Biodiversity Strategies and Action Plans (NBSAPs). This in turn relies on the availability and fidelity of geospatial information that can be the basis for planning. The currently available geospatial data that captures accurate delineation of riparian zones, i.e., the transitional semiterrestrial/semiaquatic areas regularly influenced by fresh water, usually extending from the edges of water bodies to the edges of upland communities, must be improved to address the needs highlighted in the Freshwater Challenge. This presentation gives a methodology for deriving a global potential riparian zones layer obtained by processing wetlands, riparian buffers, headwater catchments, layers, assets, and information. We process near real-time land cover dataset from dynamic World (https://dynamicworld.app/), global wetland maps (Tootchi et al., 2019), and High‐Resolution Global Hydrography Maps (Yamazaki et al., 2019; Amatulli et al., 2022) for our analysis. We further explore how this analysis will inform governments around the world on assessing the current state of Riparian Zones as well as estimating benefits from restoration effort, allowing movement towards the goals set by the Freshwater Challenge.

How to cite: Mohammed, I., Shaad, K., Bolten, J., and Bezerra, M.: Global Potential Riparian Zones Estimation, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-2306, https://doi.org/10.5194/egusphere-egu24-2306, 2024.

EGU24-2513 | PICO | ITS3.5/BG1.19 | Highlight

Overwintering and migration of sea turtles in Jeju Island of Korea: lessons from “SEAturtle” PICES special research project (2019-2023)  

Taewon Kim, Soojin Jang, Mi-Yeon Kim, Byung-Yeob Kim, Kyungsik Jo, Sookjin Jang, Jibin Im, George Balazs, Hideaki Nishizawa, Connie Ka Kan NG, George Shillinger, and Michelle María Early Capistrán

PICES special research project “SEAturtle” launched in 2019 to understand the ecology of sea turtles around Jeju Island in relation to environmental stressors. Though COVID 19 had interrupted the project, we had quite a successful outcome over the last 5 years. Until now (June 15, 2023), a total of 16 iridium transmitters were deployed on sea turtles (14 on green sea turtles and 2 on loggerhead sea turtles). Among them, we received the signals successfully from 15 sea turtles. We found that quite a proportion of green sea turtles released in Jeju Island (N = 4 out of 12, approx. 40%) overwintered nearby even in the cold sea where the temperature dropped to 15 °C. The diving duration increased to approx. 6 hrs with decreasing temperature. Most of migrating green sea turtles (N = 4) traveled toward southern Japan which suggests a strong link to the population in Japan. Our population genetics result on green sea turtles stranded suggests that a subunit of Jeju population also have an affinity to Japan population. On the other hand, one of our loggerhead sea turtles moved westward but the other moved southward from Jeju Island, suggesting that they may also have connectivity to both Japan and China. Our populations genetics and stable isotope analysis on the commensal barnacles support this. We also have actively worked on the threat of plastics on Jeju populations and found that derelict recreational fishing gears might cause more serious problems than commercial derelict fishing gears. Microplastics are other threats to them too. To conserve the population of sea turtles in Jeju Island, we need further extensive research and should keep up international cooperation.

How to cite: Kim, T., Jang, S., Kim, M.-Y., Kim, B.-Y., Jo, K., Jang, S., Im, J., Balazs, G., Nishizawa, H., Ka Kan NG, C., Shillinger, G., and María Early Capistrán, M.: Overwintering and migration of sea turtles in Jeju Island of Korea: lessons from “SEAturtle” PICES special research project (2019-2023) , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-2513, https://doi.org/10.5194/egusphere-egu24-2513, 2024.

EGU24-6353 | ECS | PICO | ITS3.5/BG1.19

Biodiversity Data Cubes for Cross-Cutting Science and Policy 

Lina M. Estupinan Suarez, Laura Abraham, Tim Adriaens, Lissa Breugelmans, David A. Clarke, Peter Desmet, Shawn Dove, Katelyn T. Faulkner, Miguel Fernandez, Louise A. Hendrickx, Cang Hui, Alexis Joly, Sabrina Kumschick, Ward Langeraert, Matilde Martini, Joe Miller, Damiano Oldoni, Henrique Pereira, Cristina Preda, and Quentin Groom and the Biodiversity Building Blocks for Policy Project

Biodiversity and the Earth climate system are coupled through multiple biotic and abiotic feedbacks. Although there are clear links between the two systems, there is a lack of integrative research to evaluate them. One reason is that both systems operate on different scales, impacting integration efforts. In addition, the state of the art for each has evolved at different rates over recent decades. The growing number of satellite missions has made it possible to measure Earth system variables on a global scale and with great frequency. This enormous amount of data, captured even on an hourly basis, in tandem with a network of gauging stations, and open-access policies have boosted Earth system modeling and projections, and thus increased our understanding of one of the Earth's components (i.e. climate). Biodiversity data has also increased, albeit at a slower rate. Citizen science, along with the application of different technologies such as camera traps, phenocams, bioacoustics and, more recently, eDNA, are enabling scientists to obtain data more efficiently. However, there are still large gaps in geographic and taxonomic coverage.This is partially related to abrupt biodiversity gradients and insufficient  explanatory variables that hinder modeling  biodiversity as smooth gradients in climate systems. Another reason is the difference between data formats and approaches among fields; for example, biodiversity data are often recorded as spatial points, in contrast to gridded satellite data. All these pose numerous challenges for a more coordinated and cross-cutting research. As a starting point, it is our task to reach other scientific communities and offer harmonized solutions for data integration and analysis. Specifically, in the Biodiversity Building Blocks for Policy project (B-Cubed) we are developing informatics workflows to facilitate the analysis of species occurrence information in a data cube format. We are using, though are not limited to, the world’s largest biodiversity database, the Global Biodiversity Information Facility (GBIF), to provide species occurrence information in a more interoperable format. Furthermore, we are also leveraging the concept of data cubes to standardise access to biodiversity data using the Essential Biodiversity Variables framework. Currently, the implementation of species occurrence cubes is aimed at analyzing invasive species, improving species distribution modeling techniques, and developing effective indicators for informing policy. We strongly believe that data cubes will facilitate both data sharing and processing, and the co-development of tools and approaches between biodiversity and Earth sciences, which will undoubtedly benefit cross-cutting research. Synergies between biodiversity and Earth system sciences are urgently needed for better informing decision makers about feedbacks in both systems that can respond to adopted and upcoming policies.

How to cite: Estupinan Suarez, L. M., Abraham, L., Adriaens, T., Breugelmans, L., Clarke, D. A., Desmet, P., Dove, S., Faulkner, K. T., Fernandez, M., Hendrickx, L. A., Hui, C., Joly, A., Kumschick, S., Langeraert, W., Martini, M., Miller, J., Oldoni, D., Pereira, H., Preda, C., and Groom, Q. and the Biodiversity Building Blocks for Policy Project: Biodiversity Data Cubes for Cross-Cutting Science and Policy, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-6353, https://doi.org/10.5194/egusphere-egu24-6353, 2024.

River corridors, i.e. channel and adjacent floodplains, are hotspots of biodiversity and provide manifold ecosystem services. Their functioning and thus their ability to maintain biodiversity and to provide ecosystem services is controlled by a complex interplay of hydrologic, geomorphic and ecologic processes. These processes both affect and depend on hydrologic, geomorphic and ecologic connectivity within the river system. Today, process regimes of most (large) rivers are affected by human activities such as the construction of dams and reservoirs, flood protection measures or the withdrawal of water for agricultural irrigation. Dams modify longitudinal connectivity and thus the natural flow and sediment regime, while flood protection dikes disconnect channel and floodplain. There is a growing body of research on how hydrology-geomorphology-ecology-interactions shape river corridors and how these interactions are disturbed by humans. However, these insights tend to arise from studies at either the small river system or the reach scale. Truly understanding the impact of human interventions on rivers requires a dynamic, system scale perspective on process regimes. In our contribution, we take the river network in the Aral Sea Basin in Central Asia as an example and demonstrate the use of satellite time series to make a functional assessment of the process regimes controlling riparian ecosystem development. This river network has a total length of 75.000 km draining a catchment of 1.2 million km². We start the assessment with the delineation of the river network and the riparian zone from digital elevation models. Then, we use a novel unsupervised approach to create a map of landcover and general habitat types within the river corridors. In a second step, we create a dam and reservoir database in order to assess river fragmentation. In a third step, we use time series of Landsat and MODIS satellite imagery to assess hydrologic and geomorphic dynamics as well as vegetation development. These time series are the basis to analyze the relationship of e.g. floodplain inundation dynamics and vegetation trends or the impact of flood pulses on morphological change triggering vegetation change. The results show that the Aral Sea Basin is highly fragmented and that this fragmentation influences downstream process regimes and initiates modifications in the riparian ecosystems. Our satellite time series approach is able to capture relevant process dynamics and their impact on ecosystem development (i) in data-scarce regions, (ii) at large spatial scales (large river basins) and (iii) at high temporal frequency as enabled by short revisit times of current satellite constellations and cloud computing. Thus, it is a promising way to generate system-scale knowledge on the interaction of hydrologic, geomorphic and ecologic processes being the basis for biodiversity maintenance and ecosystem service provision in river corridors.

How to cite: Betz, F., Lauermann, M., Schmitt, R., and Heckmann, T.: Towards system scale understanding of the complex interaction of hydrologic, geomorphic and ecologic processes controlling ecosystem functioning in river corridors: Using satellite time series to assess the river network in the Aral Sea Basin, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-11602, https://doi.org/10.5194/egusphere-egu24-11602, 2024.

EGU24-13341 | ECS | PICO | ITS3.5/BG1.19 | Highlight

Terrestrial land cover shapes fish diversity in major subtropical rivers 

Heng Zhang, Rosetta Blackman, Reinhard Furrer, Maslin Osathanunkul, Jeanine Brantschen, Cristina Di Muri, Lynsey Harper, Bernd Hänfling, Pascal Niklaus, Loïc Pellissier, Michael Schaepman, Shuo Zong, and Florian Altermatt

Freshwater biodiversity is critically affected by human modifications of terrestrial land use and land cover (LULC). Yet, knowledge of the spatial extent and magnitude of LULC-aquatic biodiversity linkages is still surprisingly limited, impeding the implementation of optimal management strategies. Here, we compiled fish diversity data across a 160,000-km2 subtropical river catchment in Thailand characterized by exceptional biodiversity yet intense anthropogenic alterations, and attributed fish species richness and community composition to contemporary terrestrial LULC across the catchment. We created a spatially explicit model and estimated a spatial range of LULC effects extending up to about 20 km upstream from sampling sites. The model explained nearly 60 % of the variance in the observed species richness, associated with major LULC categories including croplands, forest, and urban areas. We find that integrating both spatial range and magnitudes of LULC effects is needed to accurately predict fish species richness. Further, projected LULC changes showcase future gains and losses of fish species richness across the river network and offer a scalable basis for riverine biodiversity conservation and land management, allowing for potential mitigation of biodiversity loss in highly diverse yet data-deficient tropical to sub-tropical riverine habitats.

How to cite: Zhang, H., Blackman, R., Furrer, R., Osathanunkul, M., Brantschen, J., Di Muri, C., Harper, L., Hänfling, B., Niklaus, P., Pellissier, L., Schaepman, M., Zong, S., and Altermatt, F.: Terrestrial land cover shapes fish diversity in major subtropical rivers, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-13341, https://doi.org/10.5194/egusphere-egu24-13341, 2024.

EGU24-15186 | PICO | ITS3.5/BG1.19

Development of a Web-Responsive Analysis Tool for Tracking Sea Turtle Behavior and Habitat 

Kim Taehoon, Kim Bo ram, Hong Sang Hee, and Lee Chol young

  The environmental issues caused by marine debris and the problem of habitat pollution for marine organisms are pervasive worldwide. Both floating debris and sunken debris contaminate various habitats, including coastlines, coral reefs, and seaweed beds. Various marine organisms exposed to such marine debris ultimately suffer from entanglement and ingestion, with sea turtles, in particular, accounting for 66% of reported cases of harm among all marine mammals. In Korea, various cases of mortality due to entanglement and ingestion in sea turtles have been widely reported. To comprehend the correlation between the behavior, habitats, and marine debris associated with sea turtles, ecological research is being conducted through location tracking. it is essential to conduct habitat degradation research for sea turtles by analyzing their spatial behavior using location-based methods and understanding feeding patterns using various environmental information. To address these issues, it is crucial to accurately understand the movement routes and activity patterns of marine organisms. In the field of wildlife research, various studies are being conducted using geographic information systems to utilize diverse analytical methods.

  In this study, we aimed to develop a web-responsive analysis tool for continuous tracking of sea turtle behavior and habitat foraging. The analysis module comprises three parts: the preprocessing module, spatial analysis module, and exploratory analysis module. The preprocessing module functions to extract necessary data from Argos satellite-received location information and refine it into clean data. It extracts latitude, longitude, sea surface temperature, and depth information from multiple files, organizes them into a single table, and saves them in a analyzable file format. The analysis module includes functions for deriving sea turtle activity ranges and overlapping analyses of habitat within activity zones. The activity range analysis utilizes Kernel Density Estimation (KDE) based on sea turtle location point data. Bandwidth, defined automatically based on the distribution of accumulation and points, allows for efficient analysis. The habitat overlapping analysis integrates various biological occurrence information such as coral, algae, and jellyfish within the sea turtle's activity zone. This enables exploration of the sea turtle's habitat environment within dense areas. The exploratory analysis module offers visualization features for location information, received depth, and sea surface temperature derived from data received by Argos satellites. Depth and sea surface temperature details are presented alongside location information, utilizing color coding for enhanced comprehension.

  The analysis module and the platform it is implemented on were developed in the form of a responsive web application using the open-source R-shiny. The responsive web application allows researchers to input and analyze sea turtle location data directly from a web page in any internet-enabled environment. It is fast and efficient as the results can be promptly visualized on a map. The sea turtle behavioral analysis tool developed in this study enables researchers to obtain standardized information related to behavior and habitat using location-based sea turtle data received from various satellites. It establishes a systematic approach for researchers to easily utilize this information through the web.

How to cite: Taehoon, K., Bo ram, K., Sang Hee, H., and Chol young, L.: Development of a Web-Responsive Analysis Tool for Tracking Sea Turtle Behavior and Habitat, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15186, https://doi.org/10.5194/egusphere-egu24-15186, 2024.

EGU24-15721 | PICO | ITS3.5/BG1.19 | Highlight

Alternative migration strategies of fin whales in the Mediterranean sea : evidence of a lunar influence 

Clément Fontana, Hervé Glotin, and Carlo Brandini

Understanding migrational behavior of fin whales (Balaenoptera physalus) in the Mediterranean basin is of greatest importance in terms of research on cetaceans, but also in terms of conservation for a specie considered as ‘endangered’ based on the IUCN Red List criteria. We investigate in this study the migrational behavior of several individuals from this population. Several datasets (telemetry-tracking, satellite-estimated chlorophyll concentration and oceanic currents) are used to assess their long- and short-term behavioral adaptations to diverse biomes. We highlight the fact that meeting points with the North Atlantic population exist at strategical environmental locations. We prove that migrating fin whales show distinct swimming behaviors depending on the lunar phases by comparing their daily distances swam to the tortuosity of their paths. These distinct behaviors might be due to prey availability as well as acting as a temporal trigger to maximize chances of reproduction success. Indeed, this migration strategies of the Mediterranean population is also explained by reproductive constraints of an isolated population susceptible to inbreeding. We then focus the study on two fin whale paths in the Strait of Sicily showing that they are able to communicate between each others, adapt their foraging area to instantaneous moon-driven changes of oceanic conditions but also to follow cyclic seasonal variations of resources availability. We finally bring a new insight on an alternative pattern for migration strategies of fin whales in the Mediterranean sea.

How to cite: Fontana, C., Glotin, H., and Brandini, C.: Alternative migration strategies of fin whales in the Mediterranean sea : evidence of a lunar influence, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15721, https://doi.org/10.5194/egusphere-egu24-15721, 2024.

EGU24-18071 | PICO | ITS3.5/BG1.19

Impact of Hermodice carunculata (Pallas, 1766) (Polychaeta: Amphinomidae) on artisanal fishery: A case study from the Mediterranean Sea 

Emanuele Mancini, Riccardo Martellucci, Sebastiano Marino, Bianca Maria Lombardo, Umberto Scacco, and Francesco Tiralongo

Invasive species can cause severe economic damages, ecosystem alterations, and can even threat human health. In the global warming scenario, which can act as a driving force for the expansion of thermophilic species, we investigated for the first time the economic damage caused by the invasive bearded fireworm, Hermodice carunculata, to artisanal longline fishery in the Mediterranean Sea. We focused on bottom longline fishery targeting the highly prized white seabream Diplodus sargus, investigating catch composition of the fishing gear and Catch Per Unit Effort (CPUE) of species caught, with particular emphasis on the economic damage caused by the bearded fireworm, H. carunculata, in relation to water temperature. Our results clearly indicated direct and indirect economic damage to fishing activities practiced in the southeastern coast of Sicily (Ionian Sea). Type and extent of the damage caused by the invasive worm (H. carunculata) were discussed in relation to temporal scale and overall yields obtained by this traditional artisanal fishery, and some solutions are proposed. However, the actual situation requires special attention because it is expected to worsen in the context of the global warming future scenarios, such that further studies are urgently needed.

 

How to cite: Mancini, E., Martellucci, R., Marino, S., Lombardo, B. M., Scacco, U., and Tiralongo, F.: Impact of Hermodice carunculata (Pallas, 1766) (Polychaeta: Amphinomidae) on artisanal fishery: A case study from the Mediterranean Sea, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18071, https://doi.org/10.5194/egusphere-egu24-18071, 2024.

Opencast limestone mines, being oligotrophic environments characterized by harsh environmental conditions are considered as challenging habitat for colonization and growth of all life forms. These conditions include elevated temperatures, prolonged exposure to sunlight, and deficiencies in organic matter, moisture, and soil nutrients. In such environments, lithobionts may play an important role as the main sources of primary production and maintaining the ecosystem functioning. Unfortunately, our knowledge regarding the taxonomic diversity, potential functions, and ecology of limestone quarry/mines remains quite limited. Here, we explored the taxonomic composition and metabolic potential of lithobiontic microorganisms dwelling carbonate rocks of a limestone mine in Udaipur, Rajasthan, India by using high-throughput shotgun metagenomic sequencing. Community profile analysis revealed that the lithobiontic community was dominated by bacteria (98.94 %), with a minute fraction of the Eukaryota (0.77 %) and archaeal population (0.23 %). Microbes belonging to Phylum Cyanobacteria (39.74 %), Proteobacteria (35.21 %) and Actinobacteria (10.34 %) were predominant followed by a remarkable share of Chloroflexi (4.77 %) and Firmicutes (2.41 %). Metabolic potential analysis, based on six functional modules of the Kyoto Encyclopedia of Genes and Genomes (KEGG) database, revealed that functional genes involved in microbial metabolisms are highly represented in this community (59.68 %). Functional analysis of the carbonate microbiome indicated their capacity to influence carbon, nitrogen, and sulfur cycles. Results suggest that the oxygenic photosynthetic bacteria contribute significantly to primary productivity as well as carbonate precipitation in such arid and oligotrophic environments. Multi-omics level study on isolated cyanobacterial strains is underway to gain deeper insights into habitat adaptation and the functioning of lithobiontic niche of cyanobacteria in carbonate rocks.

How to cite: Singh, J. and Maharana, C.: Metagenomics of carbonate rocks from limestone mines, Udaipur, Rajasthan, India, reveal insight into lithobiontic microbial community and biogeochemical cycling., EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18368, https://doi.org/10.5194/egusphere-egu24-18368, 2024.

Agricultural land area is increasing globally despite the loss of productive agricultural lands in some world regions. The knowledge about major agricultural land changes and the impacts on the quality of land in both cropland and grassland in Africa is still very limited. We conducted an African continent-wide assessment of the dynamics of agricultural landscapes (i.e., gains, losses, and net change). With pressure mounting to halt biodiversity loss and stem land degradation in agricultural areas across all world regions, promoting sustainable agriculture requires not only an understanding of agricultural land-use change but also the impacts of such changes on land quality.
We identify influencing factors and model the quality of land associated with agricultural land gains and losses between 2000 and 2018. Land quality in gained and displaced croplands and grasslands was established using spatially-explicit analysis of changes in Net Primary Productivity, soil organic carbon content, crop suitability and percent yield change for five major crops of global importance grown across Africa. These are maize, rice, soybean, wheat, and alfalfa.
Influencing factors in each agricultural land change area (i.e., areas of cropland and grassland gains and losses) were examined. In cropland loss and gain areas, settlement development,
proximity to perennial rivers/water bodies, and access to a major road were important. For example, most land areas transitioning to cropland in Africa were associated with large distances away from major roads. The preceding finding suggests the remoteness of newly gained croplands. However, distances to a major road, waterbody, settlement, and elevation were important for explaining grassland dynamics. Land quality was better in gained
croplands than in those lost, whereas gained grasslands were of lesser quality compared to areas of grassland loss.
Five typologies of African countries were developed based on net yield and amount of land cultivated per crop in cropland change areas. Type 1 typifies net yield increase and cultivated land decrease, while type 2 is characterized by yield increase consequent upon cropland expansion. Net yield and land remain unchanged in type 3, while in type 4, cultivated land increased, but yield decreased for maize in 40% of African countries, and in type 5, yield and land area decreased. This study thus provides evidence about the quality of land in gained and lost agricultural areas and generalizable insights on their dynamics across Africa.

How to cite: Akinyemi, F. O. and Speranza, C. I.: Changes to agricultural landscapes impact the quality of land: An African continent-wide assessment in gained and displaced agricultural lands, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-21129, https://doi.org/10.5194/egusphere-egu24-21129, 2024.

Reversing the declines in biodiversity trends is a widely adopted goal, reflected in both the Kunming-Montreal Global Biodiversity Framework, and the EU 2030 Biodiversity Strategy. In this presentation, we will show two examples of how models and scenarios can be mobilized to provide support to achieving these goals in the context of the broader sustainable agenda. In a first example, multiple economic and biodiversity models are used to assess long-term, global scale, pathways aiming to explore whether—and how—humanity can reverse the declines in terrestrial biodiversity caused by habitat conversion reverse global biodiversity losses (Leclere et al, 2020). The results show that i) immediate efforts of unprecedented ambition and coordination could enable reversing the global terrestrial biodiversity trends caused by habitat conversion, and ii) that an integrated approach, combining increased protection and restoration efforts with sustainable production and consumption measures, is essential to not only enable a bending of global biodiversity trends before 2050, but also limit trade-offs and harness synergies with other sustainable goals. In a second example, we will demonstrate how models and scenarios are also mobilized to support policy design at the EU scale, with an application focusing on assessing the land use, LULUCF emissions and biodiversity implications of EU climate (e.g., Fitfor55 package and LULUCF regulation) and biodiversity (e.g., Nature Restoration Law) and their interactions.

How to cite: Havlík, P., Leclere, D., and Visconti, P.: Modeling of in support of long-term pathways and EU policies for bending the curve of biodiversity loss, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-22576, https://doi.org/10.5194/egusphere-egu24-22576, 2024.

Attitudes on climate change and the protection of the environment have been found to relate in different ways to the current economic and social situation of the respondents. This presentation will describe people's attitudes by analyzing surveys on the topic of climate change and the protection of the environment, including the recent International Social Survey Programme (ISSP) and the Swiss Environmental Panel Study. A closer look will be taken at the economic opinions and willingness to pay higher prices or taxes and their relationship to climate change attitudes. In addition, respondent's trust in people and different institutions will be analyzed. A structural equation analysis is performed to highlight the relations between those concepts. The results will show that support for a better economy and private enterprises are related to lower environmental and climate change concerns, support for paying higher prices or taxes is related to more environmental concerns and higher trust in people and institutions is related to deeper environmental concerns. After that, several demographic characteristics will be used to show if the results are stable when controlling for these. Demographic variables used are age, gender, education level, employment status, income, and political left-right placement. It can be shown that the factors of economic opinions, willingness to pay, and trust in people and institutions all relate to the environmental and climate change attitudes. 

How to cite: Zenk-Möltgen, W.: Attitudes on climate change and their relations to opinions about the economy, willingness to pay, and social trust, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-3191, https://doi.org/10.5194/egusphere-egu24-3191, 2024.

Payment for ecosystem services (PES) is a compensation concept used to incentivize landowners to improve land management practices in order to maintain and provide ecosystem services. Examples of such services include river basin protection, forest conservation, flood control, or carbon sequestration. Since the early 1990s, hundreds of PES schemes have been implemented worldwide, with varying degrees of success and has only become a new trend in Asia for the last decade. While analyzing PES cases can identify the factors that contribute to specific outcomes, given the high cost of implementing such schemes and the range of stakeholders involved, our study aims to compare PES cases in Europe where historically the human-nature relationship is more balanced and progressively protected with cases in Asia under rapid industrialization and urbanization. Methodologically, we employ a systematic literature review approach to include a total of 134 articles in Scopus database between 2009 and 2023 for systematic scrutiny. The study analyzes different aspects of the literature growth over the past decade, including project types, beneficiaries, who pays for activities (in USD), spatial scale and current size, and implementation barriers. Our analysis provides insights into the factors that contribute to the success of PES schemes for the goal of improving future research agenda and generating policy recommendations for Asian PES in the near future. In particular, we emphasize the importance of considering the environmental, socio-economic, political, and dynamic contexts of PES policies when designing and implementing such schemes.

How to cite: Jiang, T. and Chien, H.: Comparing Payments for Ecosystem Services in Europe and Asia: A Systematic Literature Review Approach, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-3312, https://doi.org/10.5194/egusphere-egu24-3312, 2024.

EGU24-3581 | ECS | Posters on site | ITS3.11/CL0.1.13 | Highlight

A Systems based approach to understanding the role of co-benefits in encouraging urban air quality interventions  

Nicole Cowell, Aoife Kirk, Gabriel Okello, Natasha O'Sullivan, Audrey de Nazelle, and Roderick Weller

There is untapped potential in urban planning behaviour change policies that can simultaneously improve air quality, support net-zero targets, and benefit communities and public and planetary health more broadly. There is evidence  siloed thinking restricts the policy making process in optimising air quality interventions for co-beneficial outcomes. Systems-based approaches create holistic insights and solutions which can address  complex cross-cutting issues by bringing together context-specific evidence, an array of expertise and perspectives whilst merging social and environmental sciences to engage in action. 

Horizon scanning academic and non-academic literature can  generate insight into the current state of play of air quality interventions, their related outcomes and co-benefits including pathways to healthier cities. It also allows  insight into the gaps between science and policy for an evaluation of how to  generate science-to-policy discussions.  Structured decision-making is a systems approach in which stakeholders are engaged throughout a decision-making process to identify and co-create shared objectives and values around a complex issue, such as urban air quality. 

This work brings together systems-based approaches to assess the state of play and optimal next steps for addressing urban air quality, investigating the role that co-benefits could play in inciting ambitious change for sustainable cities. The poster will present initial findings from horizon scanning air quality interventions, co-benefits and pathways to healthy cities, which will inform the next steps of generating a structured decision-making tool for assessing the opportunities and challenges of co-created and co-beneficial actions for air quality change.

This work is carried out in collaboration with the World Economic Forum Global Future Council on the Future of Clean Air, where academics and stakeholders are working together to address air pollution globally. 

 

How to cite: Cowell, N., Kirk, A., Okello, G., O'Sullivan, N., de Nazelle, A., and Weller, R.: A Systems based approach to understanding the role of co-benefits in encouraging urban air quality interventions , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-3581, https://doi.org/10.5194/egusphere-egu24-3581, 2024.

The role of cultural ecosystem services (CES) is vital to consider when developing ecological sustainable
development policies that can improve the well-being of humans. Research on CES has increased in recent years;
however, few studies have explored the complex mechanisms driving perceptions of CES and the factors influ
encing those perceptions. In areas with unique landforms and fragile ecological environments, this type of
research is difficult and rare. To address this research gap, this focuses on a typical karst area Guilin Xingping in
China, evaluating residents’ perceptions of local CES, and applying qualitative comparative analysis (QCA) to
explore the driving mechanism behind those perceptions. We found that the satisfaction of material needs is a
prerequisite and basis for further improving residents’ spiritual perceptions and pursuits. Residents’ socio
economic level, understanding of resource importance, and economic value determine whether residents can
fully perceive the value of CES. Optimizing the ability of managers, improving relevant systems, and improving
the experience with and understanding of ecosystems have a more than 50% probability of improving percep
tions related to CES. The research shows that the combination of multiple antecedents can achieve a high level of
perceptions related to CES. Managers can refer to the best path for policy regulation based on the actual situ
ation. Finally, this study provides a new policy scheme for promoting ecological sustainable development and
improving residents’ well-being, and can provide insights to inform the sustainable development of other karst
areas.

How to cite: Wang, Q.: Effectively enhancing perceptions of cultural ecosystem services: A case study of a karst cultural ecosystem , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-3787, https://doi.org/10.5194/egusphere-egu24-3787, 2024.

EGU24-6058 | ECS | Posters on site | ITS3.11/CL0.1.13

Impacts of receiving international industrial transfer on China’s air quality and health exceed those of export trade 

Lu Liu, Yu Zhao, Hongyan Zhao, Yifei Wang, and Chris P. Nielsen

Benefiting from international economic cooperation on income, technology diffusion, and employment, China also suffers its environmental and health impacts, from both international trade (IT), as is now widely understood, and international industrial transfer (IIT), which has been largely unrecognized. Here, we develop a comprehensive framework to estimate the impacts of exporting IT and receiving IIT. We find that China’s emissions of CO2 and almost all air pollutants associated with IIT and IT together grew after 1997 but then declined after 2010, with the peak shares of national total emissions ranging 18–31% for different species. These sources further accounted for 3.8% of nationwide PM2.5 concentrations and 94,610 (76,000–112,040) premature deaths in 2012, and the values declined to 2.6% and 67,370 (52,390–81,810), respectively, for 2017. Separated, the contribution of IIT to those impacts was more than twice that of IT. Scenario analyses suggest that improving emission controls in its less-developed regions would effectively reduce the impact of economic globalization, but such a benefit could be largely offset by strengthened international economic cooperation. The outcomes provide a scientific basis for adjusting China’s strategic roles in the international distribution of industrial production and its formulation of relevant environmental policies from a comprehensive perspective.

How to cite: Liu, L., Zhao, Y., Zhao, H., Wang, Y., and Nielsen, C. P.: Impacts of receiving international industrial transfer on China’s air quality and health exceed those of export trade, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-6058, https://doi.org/10.5194/egusphere-egu24-6058, 2024.

Science and society recognise the climate crisis as a serious problem; humankind is, nevertheless, still pursuing a path with high greenhouse gas, esp. carbon dioxide, CO2 emissions to the atmosphere. Barriers to effective reductions exist at political, institutional and individual levels. Incentives, trading and enforcement mechanisms are weak or not in place, and large-scale lifestyle changes towards sustainable development are out of sight. In such a wicked situation, the characteristics of carbon capture and storage, CCS seem attractive, negative emission paths even seem indispensable to reach the 1.5°C goal. In their “Special report on global warming of 1.5˚C”, the Intergovernmental Panel on Climate Change, IPCC found that three out of the four pathways to reaching net-zero by 2050 involve the use of CCS (IPCC 2018). It promises a – relatively – quick and technical, narrowly located but high-potential solution with no need for extensive efficiency improvement in dispersed facilities, equipment, appliances or “software” such as institutions and behaviour. The involved dimensions are manifold – there is no “one” method for analysis. Instead, cross-disciplinary investigations allow drawing lessons from various controversial long-term environmental issues – vital before fully embarking on this route. IPCC themselves admitted in their recent mitigation report in climate change that the “[i]mplementation of CCS currently faces technological, economic, institutional, ecological-environmental and socio-cultural barriers” (IPCC 2022, 28).

In order to become an efficient, effective and sustainable jigsaw piece of a low-carbon system transition, CCS has to prove its suitability. CCS embodies the tension between the advantage of a short-term “quick fix” and the disadvantages posed by the risk of long-term leakage and, from a technology policy perspective, the danger of perpetuating carbon lock-in. The present approach to scrutinise this question, laid out in Flüeler 2023, is a combination of disciplines and perspectives from systems theory, risk assessment, technology assessment and management. Six criteria address issues proven to be crucial in technology policy debates: 1. Need for deployment and benefits compared to competing technological options, 2. Total-system analysis and safety concept, 3. Internationally harmonised regulation and control, 4. Economic aspects, 5. Implementation along technology readiness levels, and 6. Societal issues. It conceptually and analytically serves to tackle the question raised 16 years ago whether CCS indeed is a “Trojan horse or a horn of plenty” (de Coninck 2008).

____________________

IPCC, 2018. Summary for policymakers [Masson-Delmotte, V. et al. (eds.)]. In: Global warming of 1.5°C. An IPCC special report. Cambridge Univ. Press, Cambridge, UK/New York, NY, USA. 24 pp. https://doi.org/10.1017/9781009157940.001.

IPCC 2022. Summary for policymakers [Shukla, P.R. et al. (eds.)]. In: Climate change 2022. Mitigation of climate change. Contribution of Working Group III to the Sixth Assessment Report. Cambridge Univ. Press, Cambridge, UK/New York, NY, USA. 48 pp. https://doi.org/10.1017/9781009157926.001.

Flüeler, T. 2023. Governance of radioactive waste, special waste and carbon storage. Literacy in dealing with long-term controversial sociotechnical issues. Springer Nature Switzerland, Cham. 145 pp. Chapter 2: https://doi.org/10.1007/978-3-031-03902-7_2.

de Coninck, H. 2008. Trojan horse or horn of plenty? Reflections on allowing CCS in the CDM. Energy Policy. 36/3. 929-936 https://doi.org/10.1016/j.enpol.2007.11.013.

How to cite: Flüeler, T.: “Trojan horse or horn of plenty”? Integrative technology assessment to analyse impacts, benefits and trade-offs of Carbon Capture and Storage, CCS, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-6674, https://doi.org/10.5194/egusphere-egu24-6674, 2024.

EGU24-6733 | ECS | Orals | ITS3.11/CL0.1.13

Interdisciplinary Insights into Urban Climate Governance: Navigating Complexities Through Collaborative Strategies 

Barbara Dias Carneiro, Ana María Isidoro Losada, Miranda Schreurs, and Kaayin Kee

The research presented in this paper underscores the profound importance of interdisciplinary collaboration, particularly between the realms of social sciences and natural sciences, in addressing the complex challenges of urban climate governance. The study, focused on the experiences of Paris, Munich, and Zurich, highlights the intricate multi-level governance structures inherent in these cities and the interactions between diverse stakeholders involved in shaping and implementing climate strategies. By employing a combination of interviews, document analysis, and event visits, the research not only illuminates the increasing complexity of interactions between different stakeholders but also accentuates the necessity for collaboration between social scientists and natural scientists. These collaborations extend beyond traditional relationships with higher levels of government, encompassing intra-city collaborations and engagements with science, businesses, and civil society.

In the context of the broader theme of environmental issues, the paper contributes to the discourse by emphasizing that effective solutions require a comprehensive and holistic understanding. It underscores that the integration of social science expertise with environmental research, and vice versa, is essential for developing innovative and sustainable solutions. The challenges faced by the cities in achieving ambitious climate goals stress the urgency of bridging the gap between disciplines.

In conclusion, this research contributes to the broader discourse on interdisciplinary collaboration by highlighting the evolving nature of urban climate governance and the importance of effective interaction among various stakeholders. It reaffirms the need for a comprehensive understanding of environmental problems and their solutions, emphasizing the significance of multi-level governance in contributing positively to the attainment of climate goals. The insights presented here align with the call for contributions that explore the synergy between social science and environmental research, fostering meaningful discussions and exchange of ideas across different perspectives and domains.

How to cite: Dias Carneiro, B., Isidoro Losada, A. M., Schreurs, M., and Kee, K.: Interdisciplinary Insights into Urban Climate Governance: Navigating Complexities Through Collaborative Strategies, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-6733, https://doi.org/10.5194/egusphere-egu24-6733, 2024.

EGU24-6958 | Posters on site | ITS3.11/CL0.1.13

Linking social and scientific efforts to address arsenic and heavy metals pollution in a mining area in Central Mexico 

M. Aurora Armienta, Luz Maria Del Razo, Juan Manuel Ledón, Israel Labastida, Margarita Beltrán, Antonio Sosa, Ivan Morales-Arredondo, Alejandra Aguayo, Olivia Cruz, and Omar Neri

For many years, the high concentration of arsenic (As) in deep groundwater, up to 1.2 mg/L, has posed a health risk to the residents of Zimapán, a mining town in Mexico with a population of about 40,000. Additionally, ore processing, mainly through selective flotation, has resulted in the production of thousands of tons of tailings, which have accumulated in the outskirts of the town, causing damage to soils and shallow wells. To address this environmental issue, Mexican and international scientists have conducted studies focused on various environmental compartments. Since the earliest studies, whose aim was to identify the source of As pollution, the local authorities and people of Zimapán have been involved in the research activities.

Three years ago, a collaborative working group was formed, including local authorities, scientific and social researchers from various universities, local social organizations, and individuals who were committed to the environment (Environmental Research Network, REA). Their participation has included support for field activities, communication and exchange of knowledge, and the promotion of alternatives identified by scientific and social efforts to high-level authorities.

The outcomes of their work have been significant. They have rehabilitated the As removal treatment plant, which was installed about 15 years earlier as a result of this science-social collaboration. Additionally, they have identified local limestone as an option to treat tainted water and acid mine drainage. They have also supported the municipality in building rain harvesting systems in two schools to provide safe water to students. Moreover, they have interacted with miners to propose alternatives to minimize the impact of the tailings, among other achievements. The quality of drinking water supplied to downtown Zimapán is not yet in line with the national As drinking water standards, which require the arsenic level to be below 0.025 mg/L. The current level of arsenic in the water varies between 0.2 and 0.4 mg/L, which is a significant improvement from the previous level of 1.2 mg/L. However, efforts are still underway to achieve a safe water supply that meets the national standards. The REA has been effective in reducing the arsenic concentration in the water and has proven to be a viable social-scientific method for creating a healthier environment in the locality. It is also a model for other areas in Mexico that are impacted by arsenic contamination.

How to cite: Armienta, M. A., Del Razo, L. M., Ledón, J. M., Labastida, I., Beltrán, M., Sosa, A., Morales-Arredondo, I., Aguayo, A., Cruz, O., and Neri, O.: Linking social and scientific efforts to address arsenic and heavy metals pollution in a mining area in Central Mexico, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-6958, https://doi.org/10.5194/egusphere-egu24-6958, 2024.

Idea and objectives: Urban citizens are key beneficiaries of benefits delivered by urban green-blue infrastructure as nature-based solutions. However, the delivery as well as the utilization of ecosystem services is tied to local context and therefore, depending, e.g., on the types of locally relevant societal or environmental issues, urban morphology, socio-demographic characteristics of potential beneficiaries and resulting demands for ecosystem services, or conditions of urban nature inclusive of the state of health of green elements. In this regard, citizens may not only act as beneficiaries of benefits provided by nature, but also as knowledge holders regarding local conditions in the broadest sense. Tapping into this body of knowledge, e.g., through citizen science and/or participatory mapping approaches, is considered crucial for achieving resilient, sustainable, and locally relevant as well as more widely accepted nature-based solutions that promote human health and well-being. From a set of diverse cases, the application of a trait-based framework showcases how citizen science and participatory mapping may support urban planning and the promotion, management and/or monitoring of urban green-blue infrastructure as nature-based solutions at the local level.

Background: Traits are understood as aggregate features of individual elements of the green-blue infrastructure, including, e.g., spatial, structural, functional, sensory, institutional or contextual qualities. In line with the social-ecological traits concept, these characteristics are seen to shape human experiences, knowledge and affordances, thus linking qualities of urban nature with ecosystem services and therefore, potential (co-)benefits. However, traits may also help to uncover local social-environmental issues including potentials and concerns, thus challenging urban policy-making. The implemented citizen science framework that is being presented adopts social-ecological traits as research theme-related boundary objects, e.g., to explore citizens’ awareness, perceptions and ideas of locally-specific traits. In so-doing, first, potential feedback loops that may shape compatibility of urban green-blue infrastructure elements for specific purposes, uses, and/or users may be uncovered. Second, potential pathways for local action may be identified to support a more holistic and more inclusive management and planning of nature-based solutions.

How to cite: Scheuer, S., Basnou, C., Sumfleth, L., and Haase, D.: How do we perceive green spaces? Trait-based citizen science to support the monitoring and management of nature-based solutions, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-8008, https://doi.org/10.5194/egusphere-egu24-8008, 2024.

Carbon emissions are closely related to climate change and sustainable development. Despite the existence of a large amount of research on carbon emissions, previous studies have focused more on regional analysis and lacked building-level research. When it comes to building-level carbon emissions, it usually involves a limited number of buildings, or collects a large amount of survey data within a specific region, which cannot be extended to large areas. This study takes buildings in Bao'an District, Shenzhen as the basic unit and uses statistical yearbooks, population density and nighttime light images to allocate total carbon emissions into each building through a top-down approach, to gain a more comprehensive understanding of the distribution of carbon emissions and their relationship with human activities. The findings of this study are expected to promote energy conservation and emission reduction and provide data support for achieving the goals of carbon peak and carbon neutrality.

How to cite: Lin, Z. and Huang, B.: Research on Building-level Operational Carbon Emissions in Shenzhen Based on Multi-Source Data, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-8236, https://doi.org/10.5194/egusphere-egu24-8236, 2024.

The aviation industry, among the transportation sector, has come under heightened scrutiny as it is a major contributor to global carbon emissions and one of the most challenging industries to decarbonize. In response to the overwhelming calls for climate actions, the aviation industry has turned to a market-based approach - voluntary carbon offsetting - showcasing their dedication to carbon reduction. Investing in high-quality carbon offset projects holds great significance and contributes to the global efforts aimed at reducing carbon emissions. However, the corporate communication of airlines, crucial in influencing public perception and comprehension regarding voluntary carbon offsetting, has faced criticism for its lack of transparency and accuracy. This research therefore investigates the communication practices of voluntary carbon offsetting in the aviation industry, focusing on accessibility, clarity and transparency, and operational aspects. The study employs a multi-faceted approach, including a literature review on greenwashing, a case study of five Asian-based airlines, and the development of a coding scheme for content analysis. By examining the airlines’ official websites and sustainability reports, we seek to identify patterns and variations in their communication strategies on voluntary carbon offsetting. Preliminary results from the literature review and ongoing case study analysis showcase the importance of accessibility, transparency, and clarity in voluntary carbon offsetting communication. As the research progresses, further content analysis will unveil the potential instances of misleading tactics and highlights of best practices, fostering a more informed and transparent approach to voluntary carbon offsetting communication in the aviation industry. 

How to cite: Tsoi, H. N.: Corporate Communication on Voluntary Carbon Offsetting in the Aviation Industry: A Case Study of Asian Airlines, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-8593, https://doi.org/10.5194/egusphere-egu24-8593, 2024.

EGU24-9999 | Orals | ITS3.11/CL0.1.13 | Highlight

FAIR to Enable Cross-Domain Research 

Simon Hodson

The major global scientific and human challenges of the 21st century (including climate mitigation and adaptation, environmental sustainability, biodiversity and ecosystem management, disaster risk reduction, the interplay of society, the economy and energy policy) can only be addressed through cross-domain research that seeks to understand complex systems through machine-assisted analysis at scale.  Our capacity for such analysis is currently constrained by the limitations in our ability to access and combine heterogenous data within and across domains.  The FAIR principles and the frameworks set by Open Science provide a significant part of the solution.  Attention needs to be paid to the interfaces where data is used between disciplines: the geosciences have a vital role to play in this work.

To help address these issues, CODATA has been entrusted by the International Science Council (ISC) to develop a programme of activity: ‘Making Data Work for Cross-Domain Grand Challenges’.  After some exploratory work, the flagship activity is the WorldFAIR project which focuses on the implementation of the FAIR principles both within and across 11 different domain and cross-domain case studies, with a central effort to understand and guide cross-domain FAIR. It is the first broad-based effort to understand the issues around cross-domain and cross-infrastructure FAIR implementation through a case study driven methodology. Ultimately, WorldFAIR will provide guidance for FAIR implementation both within specific domains and infrastructures and across them.  The necessity, affordances and opportunities for cross-domain research are often overlooked, partly due to entrenched academic disciplines.  This presentation will outline a number of concrete examples of work to advance cross-domain interoperability of relevance to the geosciences community.

The I and the R of FAIR pose considerable challenges but are fundamental to addressing complex issues where datasets need to be combined and in enhancing scientific rigour and reproducibility.  Consequently, increasing attention is being paid to semantics, the maintenance of referenceable vocabularies and ontologies and to metadata profiles—and to tools that facilitate the tracking of provenance and process, or that use variable level metadata and semantics to facilitate data integration.  The semantics of space are particularly important in data linking and combination.  WorldFAIR is also developing the Cross-Domain Interoperability Framework (CDIF) which identifies a set of functional requirements for interoperability, particularly for steps in data combination, and recommends good practices for each of these requirements, in relation to the use of existing or emerging standards and specifications.  The CDIF is categorically not a new standard, but is intended to act as a lingua franca across domain data practices and encourage the incorporation of a number of standards that perform important and specific functions across domains.  We are keen to test this approach with colleagues from as many disciplines and application areas as possible.

This talk will explore these developments in detail, make a case for the importance of further work on the I and the R of FAIR, and invite the geosciences research community to participate in the wider WorldFAIR initiative.

How to cite: Hodson, S.: FAIR to Enable Cross-Domain Research, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-9999, https://doi.org/10.5194/egusphere-egu24-9999, 2024.

EGU24-10601 | ECS | Orals | ITS3.11/CL0.1.13

The Climate Potential of Garden Management: A Socio-Ecological Perspective 

Janne Teerlinck, Kelly Wittemans, Valerie Dewaelheyns, Trui Steen, and Ben Somers

Despite being one of the most densely populated and urbanized regions of Europe, 84% of Flanders' citizens have a garden, covering 12% of its territory. Research has shown that the collective network of domestic gardens could make a substantial contribution to climate change adaptation and mitigation, emphasizing their spatial and ecological importance as an integral part of the urban green infrastructure. Nevertheless, these private outdoor spaces are autonomously managed by many individual gardeners, often prioritizing aesthetics rather than environmental considerations. Understanding how people manage their gardens, and why, is thus crucial for unlocking the climate potential of gardens. This understanding can shed light on the current situation and identify opportunities for change. Unfortunately, limited research has been conducted on both garden management practices and the social drivers behind the decision-making process of individual gardeners. Therefore, our research aimed at unveiling current management practices and examining their variations across the urban gradient of Flanders. Through an online citizen science survey with a substantial sample size (n = 827) of Flemish domestic garden owners, we assessed garden management practices, as well as, motivations and self-reported knowledge. Potential cofounding factors such as personal, socio-economic and spatial context were also taken into account. Using a mixed model approach, we researched to what extent motivations, self-reported knowledge and context influence garden management decisions. Simultaneously, our analysis focused on variations of garden management practices across different urbanization levels, highlighting the intricate relationship between local contexts and the diverse ecological and social drivers influencing individual gardeners' decisions. By recognizing this interconnectedness, our findings offer insights that can inform urban planning and policy strategies to harness the untapped potential within these private green spaces. Ultimately, integrating social science into environmental studies is crucial for a comprehensive approach to addressing climate change and encouraging individual gardeners to adopt more climate-resilient practices.

How to cite: Teerlinck, J., Wittemans, K., Dewaelheyns, V., Steen, T., and Somers, B.: The Climate Potential of Garden Management: A Socio-Ecological Perspective, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-10601, https://doi.org/10.5194/egusphere-egu24-10601, 2024.

Integrating social science into environmental research is essential in order to reduce people’s climate anxiety and assist human beings adapt in the low-carbon transition process. The concept of a just transition, emphasizing dignified work contributing to environmental sustainability, has gained prominence in socio-environmental discussions, seeking a balanced and equitable transition process that incorporates considerations of environmental justice, labor relations, and social inequality. However, the application of just transition in the commercial sphere remains underexplored.  This research aims to investigate decent work and just transition at the micro-level, centering on the financial industry in Taiwan, currently actively adopting advanced strategies for a low-carbon transition. In the transition to a low-carbon economy, workers frequently encounter the challenge of insufficient knowledge to shift towards more sustainable practices, along with the adverse effects of unemployment. The study emphasizes the pivotal role of social dialogue among corporate decision-makers and employees, urging the decision makers to consider the wider impact of their actions on stakeholders and society from a bottom-up perspective. The methodology involves a comprehensive investigation, including literature reviews on decent work, social dialogue, and just transition. A structured social dialogue framework is formulated to ensure the inclusion of workers' voices in decision-making. Social indicators, drawn from the literature review, are utilized to assess the effectiveness of corporate practices, labor conditions, and social sustainability. The initial findings highlight challenges in implementing environmental practices, gaps in salary ratios, inclusivity in decision-making, and the impact of extended working hours on employee well-being. These identified factors not only present alternative perspectives from workers in the decision-making process but also contribute to shaping inclusive adaptation strategies to enhance climate resilience during the low-carbon transition. As Taiwan progresses in this direction, the findings and approach outlined in this study could serve as a model for other nations with similiar systems, facilitating broader discussions on the adaptation of just transition into a sustainable society.

How to cite: Hsu, Y. and Tung, C.-P.: Socio-Environmental Integration in Taiwan's Financial Industry: A Path to Low-Carbon Transition, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-10989, https://doi.org/10.5194/egusphere-egu24-10989, 2024.

EGU24-11458 | Orals | ITS3.11/CL0.1.13

Developing Energy Communities with Intelligent and Sustainable Technologies – First Results 

Alexander Los, Rebecca Moody, Charalampos Andriotis, Seyran Khademi, and Pablo G. Morato

In a recently started scientific project aiming at “Developing Energy Communities with Intelligent and Sustainable Technologies” (DE-CIST), we combine physical data on buildings in Rotterdam (The Netherlands) with socio-economic data from neighbourhoods and input from citizens and communities. Individual building data, together with meteorological, air quality, and GHG emission data, are processed by a novel AI solution classifying neighbourhoods and buildings based on their current status of energy sustainability, and their energy saving and emission reduction potential. This, in turn, informs measures that fit best per building and per neighborhood. Yet, to reveal which buildings or neighbourhoods are the worst off, we approach the problem using a socio-technological transitions perspective, which takes into account the needs and concerns of all citizens, notably the ones of the most vulnerable populations to reveal energy poverty and injustice. Using this approach, we will show which neighbourhoods can benefit the most, technically as well as socially.

Our presentation will start with an overview of the DE-CIST project and demonstrate how the combination of environmental and social information can make the energy transition process more efficient, economically viable, equitable, and more human. From recent analysis we conclude that energy communities have a strong effect on trust and engagement, fostering environmental awareness and motivation to save energy. In our presentation we will provide further insights into energy efficiency and renovations of buildings, and into how we can realize a fair, coherent energy transition process using a combination of results from AI-based methods, environmental modelling (of air pollution), and our analysis of the interviews with stakeholders and survey data.

 

How to cite: Los, A., Moody, R., Andriotis, C., Khademi, S., and Morato, P. G.: Developing Energy Communities with Intelligent and Sustainable Technologies – First Results, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-11458, https://doi.org/10.5194/egusphere-egu24-11458, 2024.

The use of data across disiplinary boundaries is a challenge at many levels, but in order for researchers to make sense of often-unfamiliar data, they must be provided with a wealth of information regarding the provenance, methodology, structure, and semantics of data. Historically, such information has been modelled and implemented in different ways within different scientific domains. Approaches to geo-spatial data are especially problematic when we consider disiplines such as Environmental Science and Social Science. Recent work on cross-domain exchange of such metadata suggests that there are ways to improve this situation, making it far easier to support collaborative research. 

The EOSC "Climate Neutral and Smart Cities" project has demonstrated how improved tools for describing provenance and data processing could be developed for researchers, based on existing metadata standards such as DDI Lifecycle and DDI Cross-Domain Integration (DDI-CDI). Some of the same standards - notably DDI-CDI - are also at the core of an emerging framework designed to address the needs of cross-domain FAIR data exchange. This framework, the Cross-Domain Inteoperability Framework (CDIF) , is being developed through the WorldFAIR project, which looks at eleven different domain use cases. It exemplifies the kind of interoperability framework recommended by the EC's "Turning FAIR into Reality" report (doi: 10.2777/1524).

Collaborative research involving environmental, climate, and social data is increasingly relevant as we try to understand how our world is changing, and what policies will best help us to address these changes. Aligning our data management and documentation systems on emerging best practice will make this collaborative research easier and more effective, helping us to understand the issues we face. 

How to cite: Gregory, A.: Cross-Domain Standards, Tools, and Technical Approaches: EOSC "Climate Neutral and Smart Cities" and the WorldFAIR CDIF Framework, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-11938, https://doi.org/10.5194/egusphere-egu24-11938, 2024.

EGU24-15639 | ECS | Orals | ITS3.11/CL0.1.13

Transdisciplinary assessment of social-ecological vulnerability to Climate Change in Southwest Madagascar 

Estelle Razanatsoa, Lindsey Gillson, and Malika Virah-Sawmy

Climate models have shown that there will be an increasing susceptibility to drought in the future for semi-arid regions. However, the impact of these droughts depends on the sensitivity of landscapes and the adaptive capacity of communities. Using a vulnerability framework, and a mixed-methods approach, this paper assesses the vulnerability of the social-ecological systems along a rainfall gradient transect in southwest (SW) Madagascar at multiple timescales. We used a transdisciplinary approach, that combines synthesized regional climate records to assess the exposure to drought, and fossil pollen data from four sites ranging from wetter to drier areas to assess the sensitivity of landscapes over the last 2000 years. Local ecological knowledge (LEK) from household surveys from the driest sites in the Plateau Mahafaly was then conducted to infer adaptive capacity of local communities. Results show that over time, changes in climate linked to drought increase the vulnerability of the social ecological systems in Southwestern Madagascar particularly to the communities’ livelihoods in the driest regions, where there were fewer adaptation options, their need to migrate, and also on biodiversity. Although some coping and adaptation strategies including migration are in place for the communities, these might create feedback loop leading to further degradation and impacts on biodiversity and its conservation, especially in the driest regions where degradation is most likely to occur due to lower adaptive capacity. 

How to cite: Razanatsoa, E., Gillson, L., and Virah-Sawmy, M.: Transdisciplinary assessment of social-ecological vulnerability to Climate Change in Southwest Madagascar, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15639, https://doi.org/10.5194/egusphere-egu24-15639, 2024.

We delve into the comprehensive approach employed by the CAMS PL - Copernicus Atmosphere Monitoring Service National Collaboration Program for disseminating air quality knowledge to society. The initiative encompasses outreach through various channels, primarily leveraging social media platforms and the organization's website. A crucial aspect of this dissemination strategy is rooted in insights from surveys conducted among diverse stakeholders, including non-profit organizations, local administration units, the scientific community, secondary school teachers and students.

Specifically, this presentation sheds light on the program's utilization of Instagram and Facebook profiles as dynamic tools for engagement. The nuances of connecting with various demographics through these popular social media platforms are explored, emphasizing the adaptability and responsiveness required to convey air quality information effectively.

This presentation aims to contribute to the broader discourse on effective science communication strategies, particularly in environmental awareness and education.

How to cite: Drzewiecki, P. and Gienibor, A.: Disemination of air quality knowledge to the society through CAMS PL - Copernicus Atmosphere Monitoring Service National Colaboration Program., EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-16646, https://doi.org/10.5194/egusphere-egu24-16646, 2024.

EGU24-17770 | Orals | ITS3.11/CL0.1.13 | Highlight

Bringing the human dimension into the measurement of greenhouse gases emissions 

Diana Zavala-Rojas and Agustin Blanco Bosco

Given the growing concern about climate change and its impact on the lives of citizens, it is more necessary than ever to study their attitudes towards the environment and policies to mitigate it, especially in more polluted places such as cities. The Pilot Application in Urban Landscapes (PAUL) project, within the Integrated Carbon Observation System (ICOS Cities) network and in collaboration with the European Social Survey European Research Infrastructure Consortium (ESS ERIC), aims to introduce the social aspect of pollution measurement by conducting a three-wave panel survey in Paris and Munich to explore citizens' attitudes towards public policies to mitigate climate change, urban air quality, energy use and transport, among other topics. The presentation will cover the design of the survey, preliminary results from the first two waves, and how survey data can be mixed with environmental data to improve the findings and help understand social perceptions of climate change in cities.

How to cite: Zavala-Rojas, D. and Blanco Bosco, A.: Bringing the human dimension into the measurement of greenhouse gases emissions, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17770, https://doi.org/10.5194/egusphere-egu24-17770, 2024.

EGU24-18487 | Orals | ITS3.11/CL0.1.13 | Highlight

Real-time Data Using Services: A Co-design Opportunity in ICOS Cities Project  

Tatu Marttila, Idil Gaziulusoy, Katie Berns, and Liisa Ikonen

ICOS Cities is an EU-funded project that aims to develop a systematic greenhouse gas measurement system for urban areas. The Work Package (WP) 1 of the project investigates economic, societal, and political dimensions that influence how city decision-makers use and will use emission data. The two main aims of WP1 are to: 1) Collect, unlock and harmonise prior information on city climate infrastructures and emissions, and 2) Investigate relevant services the city observatory should provide to answer the needs of cities in terms of estimation of their GHG emissions and implementation of their climate policies. Stakeholder engagement is facilitated in WP1 to map the information, service and policy needs of the city administrations, as well as by conducting social surveys and semi-structured interviews with the citizens. The authors are responsible for WP1 Task 1.4, which aims to co-design a number of service prototypes demonstrating the potential of the project in the pilot cities context and develop a general methodology for service development for the use of other cities. 

In the initial phase of our research, we conducted a benchmarking study to develop an in-depth understanding of existing services used by the cities to display and make sense of emission data and feed into policy processes from the perspective of their intended users. To achieve this, first, a number of stakeholders in different European cities have been surveyed to collect data on the existing services. Then, the technological constraints and the situation in three selected pilot cities of the project (Zurich, Munich, Paris) have been further explored in selected in-depth interviews with pilot city representatives or other topical experts. As a result, we developed an initial typology of existing services targeting different users of GHG emissions data, including but not limited to city-level policymakers. Several service types related to GHG monitoring were found, focusing on interactive carbon impact data, emission reduction monitoring, and services for estimating emissions of different types. These services have also been targeted at different actor groups and geographical resolutions and have different design realisations. 

Our findings indicate that services that connect real-time measurements (or even periodic measurements) to activities in municipal planning currently do not exist. Despite the availability of real-time data, the practices and standards on how such data is processed and used are only emerging, and the data is scattered amongst several actors. There also exist major challenges to moving assessments further from scope 1 (the direct impacts of energy and fuel use), and the process depends on many types of supplementary data. These gaps, amongst other elements of the service system, indicate significant opportunities for new service development, which we will focus on in the next phase of the project.

How to cite: Marttila, T., Gaziulusoy, I., Berns, K., and Ikonen, L.: Real-time Data Using Services: A Co-design Opportunity in ICOS Cities Project , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18487, https://doi.org/10.5194/egusphere-egu24-18487, 2024.

Ambiguity is a unique form of uncertainty that goes beyond merely indicating knowledge deficits or gaps; rather, it represents a state of confusion among decision actors. This confusion arises within a group due to the coexistence of diverse, and at times, conflicting meanings and interpretations concerning a situation. In the presence of ambiguity, it may not be clear what the main issues of concern are, who hold responsibility over them, what needs to be done. As an inherent characteristic of a collective, ambiguity is tightly linked with diversity and plurality, and the processes and procedures that underlie group dynamics. Here, I argue that ambiguity plays a pivotal role in adapting to climate change.

To investigate the functioning of ambiguity, I draw upon (uncertainty) relational theory and analyse different study cases of water management. The results suggest that ambiguity can yield significant benefits in adaptation. It enhances flexibility in managing unknown conditions, enables the anticipation of conflicts and avoids maladaptation, and creates opportunities for establishing new supportive relationships and alternative solutions. These insights contribute to a nuanced understanding of the role of ambiguity in climate change adaptation, offering valuable guidance for policymakers, water managers, and stakeholders engaged in crafting resilient and sustainable water management strategies.

How to cite: Brugnach, M.: Ambiguity: Why does it hold a key role in the adaptation to climate change?, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18518, https://doi.org/10.5194/egusphere-egu24-18518, 2024.

Through our work in Science Project 9 of EOSC Future WP6.3 we demonstrate that relevant environmental data and data on citizens' values, attitudes, behaviors and involvement can be combined for social, political and scientific analysis.

In the project we are combining data from the European Social Survey with air quality data from the European Environmental Agency and climate related data from Copernicus ERA5. Over 50 indicator variables have been produced by social scientists and environmental specialists in collaboration, allowing researchers to study the impact of similar environmental factors on urban citizens attitudes and behaviors.

The project uses the metadata standards DDI-Lifecycle and DDI-Cross domain integration to document data from the project and make them FAIR.

Particular focus has been put on the provenance of the integrated data we provide through the project, showing how the data were computed. A provenance description prototype application has been developed to make the processes used to fully transparent and understandable for people and computers.

This poster presentation will give an overview of the work done in the project and the related deliverables.

How to cite: Orten, H. and Beuster, B.: Data from the European Social Survey in the Context of Climate and Air Quality in Cities , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18735, https://doi.org/10.5194/egusphere-egu24-18735, 2024.

EGU24-19180 | Orals | ITS3.11/CL0.1.13

Participatory modelling and knowledge integration in LTSER platforms 

Veronika Gaube, Claudine Egger, Bastian Bertsch-Hörmann, Andrea Stocker-Kiss, and Barbara Smetschka

Improving sustainability in local socio-ecological systems needs implementation of regionally adapted policies for sustainable development, which are based on place-based knowledge production and engaged stakeholder collaboration. One such approach is the Long-Term Socio-Ecological Research (LTSER) platform. The LTSER network emerged as a bottom-up process where existing local and national initiatives formed a network and were recognised as research infrastructures at European level. Conditions for joining the LTSER network include (usually): support from the local, regional and national authorities of the platform; the existence of long-term data sets (especially biodiversity indicators, but also abiotic variables); and the inclusion and integration of socio-economic data. One of these LTSER platforms is the Eisenwurzen in Austria, which has a long tradition in cooperating in inter- and transdisciplinary social-ecological research.

With the proposed presentation we would like to give an insight into the organisation of the LTSER platform Eisenwurzen and the challenges and successes it faces in promoting inter- and transdisciplinary research. We will present participatory modelling projects carried out in the region. The key challenge for transdisciplinary research, which aims to integrate diverse societal and scientific knowledge systems, is to produce both societal and scientific impacts at the same time. Participatory modelling is a method that uses models in three ways: as a means to generate knowledge, to achieve knowledge integration and to enable societal impact. Agent-based modelling is a computer simulation technique that allows the simulation of different actors as agents, the socio-economic and natural environment in which they are embedded, and the interactions between agents and between agents and their environment. The models with individual farm households as agents simulate how changes in socio-economic and political conditions affect patterns of land use, agricultural production and the socio-economic situation within that region.

We discuss how and why participatory modelling can help to enhance the impact potential of transdisciplinary research, as well as the limitations of different types of models. We show that participatory modelling allows for the integration of relevant societal and environmental knowledge into the models and for the development of scenarios and strategies in collaboration with stakeholders. Participatory modelling shows its strength in structuring communication about future scenarios and recommendations for action to achieve the goals of the different groups involved in transdisciplinary research. Stakeholders can use the model for effective discussion and education processes to find sustainable ways of land use development.

How to cite: Gaube, V., Egger, C., Bertsch-Hörmann, B., Stocker-Kiss, A., and Smetschka, B.: Participatory modelling and knowledge integration in LTSER platforms, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-19180, https://doi.org/10.5194/egusphere-egu24-19180, 2024.

EGU24-19474 | Posters on site | ITS3.11/CL0.1.13

Impact of the 2021 Flood Disasters on human social and mental health, focusing on elderly people in Germany 

Chen Song, Funda Atun, Justine Ianthe Blanford, and Carmen Anthonj

Flooding is one of the most common environmental disasters that cause mental and physical health problems. Flooding can cause loss of life and damage to personal property and critical public health infrastructure. Elderly people are at particular risk of the effects of floods, and their implications on social and mental health. This study is being conducted in the Ahr Valley, Germany which was heavily flooded in July 2021 (Figure 1). This flood destroyed towns and villages in the valley, causing more than 180 casualties and huge material damage (Silvia et al., 2021). The sudden-onset flood disaster caught the Ahr basin residents by surprise and had an impact on the mental and social health of the affected people. This study addresses the mental and social health effects of the 2021 flooding in the Ahr Valley, Germany, on elderly people. Preliminary findings, the research approach to data collection, survey, challenges faced, and their implications on the progress of the project will be introduced. 

How to cite: Song, C., Atun, F., Blanford, J. I., and Anthonj, C.: Impact of the 2021 Flood Disasters on human social and mental health, focusing on elderly people in Germany, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-19474, https://doi.org/10.5194/egusphere-egu24-19474, 2024.

EGU24-19646 | ECS | Posters on site | ITS3.11/CL0.1.13

Fostering Cultural Ecosystem Services: The Impact of Social Media and Online Intermediaries in Promoting Payment for Ecosystem Services  

mona nazari, Nicolas Bilot, Julia Ramsauer, and Harald Vacik

Cultural ecosystem services, encompassing intangible benefits like spiritual enrichment, cognitive development, and aesthetic experiences, play a crucial role in enhancing individual well-being. Despite their profound impact, these services often face limited economic recognition and marketability, highlighting the importance of improved acknowledgment in future ecosystem assessments. The emergence of Payments for Ecosystem Services (PES) as a market-based mechanism offers compensation to landowners for managing their land to deliver various ecosystem services.

While PES provides incentives for conservation, challenges such as the lack of market information, participation avoidance, and mistrust hinder its widespread adoption, especially concerning the physical, emotional, and mental benefits derived from ecosystem services. Bridging this gap requires a focus on education and outreach, emphasizing not only the provisioning and regulating ecosystem services but also the cultural ones. PES programs, being information-intensive, demand a comprehensive understanding of ecosystem services and their management impacts.

To address these challenges, we propose leveraging social media, specifically through local social media influencers (LSMIs), as online intermediaries in PES initiatives. In the modern world, social media has proven to be a potent solution for boosting awareness, trust, and promotion for various businesses, making it a viable avenue for PES. Unlike traditional offline intermediaries, LSMIs on social media platforms can effectively engage with local communities, fostering awareness and trust-building.

Our research focuses on the European context, exploring the role of LSMIs in the preparatory phase of PES programs. Through a literature review, we identified a framework of potential key indicators of social media (SM) and LSMIs. To gain comprehensive perspectives from PES buyers and sellers in online social networks, we conducted a survey involving three PES case studies in Spain, France, and Austria.

The findings underscore YouTube and Instagram's popularity as the preferred social media platforms among both buyers and sellers of ecosystem services within the cultural context. Photos and videos emerged as captivating mediums, with more than 50% expressing the affirmative impact of this contemporary tool in advancing cultural ecosystem services. Geographically, Spain led in leveraging social media for the promotion of cultural ecosystem services, followed by France and Austria.

By understanding the dynamics between LSMIs, social media platforms, and PES initiation, our research contributes to a more comprehensive understanding of social media's role in promoting ecosystem services and sustainable environmental practices.

How to cite: nazari, M., Bilot, N., Ramsauer, J., and Vacik, H.: Fostering Cultural Ecosystem Services: The Impact of Social Media and Online Intermediaries in Promoting Payment for Ecosystem Services , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-19646, https://doi.org/10.5194/egusphere-egu24-19646, 2024.

EGU24-19756 | Posters on site | ITS3.11/CL0.1.13

Towards Sustainable Agriculture: Classifying the Environmental Impact of Italian Wheat Farming 

Gianfranco Giulioni, Concetta Cardillo, Antonella Del Signore, Edmondo Di Giuseppe, Arianna Di Paola, Antonio Gattone, Massimiliano Pasqui, Sara Quaresima, Marco Simonetti, and Piero Toscano

Reducing the environmental impact of food production represents one of the most significant challenges to increase sustainability.

The ECOWHEATALY project - Evaluation of policies for enhancing sustainable wheat production in Italy - aims at tackling the issue of environmental impacts of the wheat production system in a dynamic socio-economic and environmental interaction setting by analyzing the changes in farmers' behavior after the adoption of green policies by the national authorities and in combination with the level of price in the main worldwide markets.

 

In the context of the ECOWHEATALY project, the behavior of farmers operating in Italy is classified into a few macro-typologies according to the farm environmental impact in terms of pesticides, fertilizers, and fossil fuel uses, with their costs and revenue profiled in alignment. To this end, ECOWHEATALY will take advantage of the Farm Accountancy Data Network (FADN), an extensive database of national surveys providing harmonized micro-economic data, including resource uses and costs, for farms in the European Union (EU). Specifically, data on farms' uses of pesticides, fertilizers, and usage time of agricultural machinery (as a proxy of fossil fuel consumption) are fed into the Agglomerative Hierarchical Clustering (HC) algorithm, an unsupervised state-of-the-art machine learning technique widely employed for clustering purposes. The cluster analysis, configured with the cluster number set to 5 based on the corresponding HC dendrogram, yields five distinctive groups, each briefly characterized as follows: G1) Farms exhibiting a pronounced inclination for excessive pesticide use. This group also records the highest quantity of nitrogen per hectare. Notably, these farms utilize few hours of agricultural machinery, suggesting concentrated applications of chemicals; G2) Farms applying a significant amount of nitrogen per hectare but minimal or no phosphorus and potassium, indicating unbalanced fertilizer use tilted towards nitrogen; G3) Farms displaying a high usage of agricultural machinery, accompanied by substantial doses of phosphorus-based fertilizer, moderate quantities of nitrogen, and minimal pesticide use; G4) Farms with a relatively medium to low environmental impact, identified by fertilizer use dominated by phosphorus and followed by potassium; G5) Farms with a relatively low environmental impact, distinguished by lower and balanced use of fertilizers and pesticides.

The resultant groups are characterized using FADN micro-economic variables, including current costs, net farm income, subsidies, and salable gross production. This profiling will enable the ECOWHEATALY project to undertake additional activities to identify green incentives capable of steering farm practices toward greater sustainability. The transformation of the Italian wheat production system, resulting from firms transitioning between different types due to agricultural and environmental policies, will be assessed through the development of an agent-based model at the national level. Moreover, ECOWHEATALY will proceed to gauge the environmental impact of policies by implementing the Life Cycle Assessment (LCA) methodology using the model's outputs, introducing a novelty in the field of green policy evaluations.

 

How to cite: Giulioni, G., Cardillo, C., Del Signore, A., Di Giuseppe, E., Di Paola, A., Gattone, A., Pasqui, M., Quaresima, S., Simonetti, M., and Toscano, P.: Towards Sustainable Agriculture: Classifying the Environmental Impact of Italian Wheat Farming, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-19756, https://doi.org/10.5194/egusphere-egu24-19756, 2024.

EGU24-20080 | ECS | Orals | ITS3.11/CL0.1.13

Assessing the role of stakeholder communication in agricultural adaptation and land-use decision-making 

Bastian Bertsch-Hörmann, Veronika Gaube, Lubos Halada, Ines Rosario, and Karlheinz Erb

In the face of climate change, land users worldwide must adapt their farming practices to increasing abiotic and biotic pressures. This requires acquisition of new knowledge and technologies and farmers have to navigate local-to-global, complex systems with diverse stakeholders. The concept of Agricultural Knowledge and Innovation Systems (AKIS) emerged to better understand and govern knowledge production and innovation uptake in agriculture. Network science principles enable the characterization and assessment of land-use-related communication, its influence on decision-making, and socio-cultural phenomena in natural resource systems.

To contribute to this field, a network survey and analysis was conducted in three Long-Term Socio-Ecological Research (LTSER) Platforms in Austria (Eisenwurzen, EW), Portugal (Montado, MT), and Slovakia (Trnava, TR) to investigate the state of local climate change adaptation and land-users’ communication. Respondents were prompted on socio-demographic, agronomic, and network variables, covering the structure of agricultural/forestry holdings, management intensities, adaptation measures, primary contact persons, and communication characteristics. Local land-users and other stakeholders were surveyed using a snowball approach. Primary data collection occurred between July 2022 and April 2023 via the online open-source application LimeSurvey© (in-person interviews for TR). Datasets were processed and analyzed using Microsoft Excel©, IBM SPSS©, and Gephi© software.

For social network analysis, node-and-edge tables were created, allocating respondents and their contacts to predefined stakeholder groups. Duplicate edges were merged by summing communication frequency values and averaging communication influence values, leading to the creation of farmer-centric and de-centralized land-use networks.

Preliminary results reveal differences and commonalities in the social land-use networks across the study regions. In all three regions, land users communicate most frequently and influentially with fellow land-users, the chambers of agriculture (in EW and TR) and farmers’/foresters’ associations (in MT). EW exhibited more frequent and influential communication with authorities, political representatives, and protected areas than the other regions. The scientific community, however, was prominently rated in MT and TR but not even mentioned in EW. In TR, economic and market actors were among the most frequent/influential contact persons, unlike in MT and EW. MT's land-use network highlights the prominent role of Portuguese land-user associations and private consultants, with a subordinate role for economic and environmental actors.

Calculations of the average degree of influence of the communication on the decision-making varied, with MT having the highest, EW medium, and TR the lowest overall influence. MT also displayed the highest density of actor groups and frequency values, indicating a more coherent network and stronger use of information by Portuguese farmers. Conversely, Slovakian farmers (in TR) appear more reluctant regarding external communication and advice.

In conclusion, network studies prove valuable insights for assessing and analysing AKIS and associated actors, providing a deeper understanding for designing and governing sustainable land-use and climate change adaptation strategies.

How to cite: Bertsch-Hörmann, B., Gaube, V., Halada, L., Rosario, I., and Erb, K.: Assessing the role of stakeholder communication in agricultural adaptation and land-use decision-making, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-20080, https://doi.org/10.5194/egusphere-egu24-20080, 2024.

EGU24-20468 | Orals | ITS3.11/CL0.1.13

Integrating environmental modelling and qualitative social science to evaluate BECCS from abandoned cropland 

Jan Sandstad Næss, Ida Marie Henriksen, and Tomas Moe Skjølsvold

Bioenergy with carbon capture and storage (BECCS) is essential in most climate change mitigation pathways, but the deployment of dedicated bioenergy crops risks enhancing land use competition. Recultivating recently abandoned cropland to produce perennial grasses has been highlighted as an option for near-term bioenergy deployment with reduced sustainability trade-offs. However, the real-world feasibility of utilizing abandoned cropland for bioenergy and BECCS is still unclear.

We used a combination of natural science and qualitative social science methods to assess near-term recultivation opportunities for bioenergy, considering biophysical potentials, future biomass demand, and sociotechnical conditions. Focusing on Norway, we processed high-resolution global gridded land use projections from integrated assessment to unravel how global drivers may affect Norwegian land use with future global climate action. We mapped recently abandoned cropland using satellite data and quantified bioenergy and BECCS resource potentials using a crop yield model. We interviewed local farmers and stakeholders and performed a policy document analysis in the region with the highest resoure potential. Applying the multi-level perspective, we investigated the interplay between technical aspects and social aspects.

Land use projections showed major near-term bioenergy crop deployment in SSP-RCP2.6 scenarios and Trøndelag had the highest Norwegian near-term bioenergy resource potentials from abandoned cropland. While we found a theoretical potential for bioenergy crop expansion, the sociotechnical analysis showed a lack of real-world feasibility of achieving the modelled pace of bioenergy expansion from SSP-RCP2.6 scenarios. Remote sensing insufficiently captured actual local land availability for bioenergy. New policies are needed if BECCS from abandoned cropland is to deliver a meaningful contribution to climate change mitigation. Increased integration of social science perspectives into large-scale modelling exercises is key to better understand the role of BECCS in climate change mitigation.

How to cite: Næss, J. S., Henriksen, I. M., and Skjølsvold, T. M.: Integrating environmental modelling and qualitative social science to evaluate BECCS from abandoned cropland, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-20468, https://doi.org/10.5194/egusphere-egu24-20468, 2024.

The term “ESG” began with UN Global Compact’s (IFC, 2004) initiative “Who Cares Wins-Connecting Financial Markets to a Changing World.” Since then, capital markets have become a key facilitator of the corporate ESG movement. Today, due to the climate change, ESG movement is drawing unprecedented attention from corporations and their stakeholders, among which investors of capital markets also exert unprecedented pressures on corporations’ ESG efforts and performance. However, while every corporation now seems to or claims to strive for corporate ESG, many corporations are performing “greenwashing” instead of true ESG. Some studies showed that greenwashing did enhance corporations' financial performance (Li et al., 2023). Although research results on the relationship between greenwashing and corporate financial performance are inconsistent, it is clear that greenwashing at least helps corporations to escape from the direct pressures from capital markets, in addition to the pressures from other stakeholders. This brings a question: Why should corporations proactively invest in ESG? If we think that stakeholder theory and legitimacy theory have answered this question, we are assuming that corporate greenwashing is not possible, which is just the opposite of the fact. 

To answer the above question, we must come back to a fundamental question: Can true ESG generate competitive advantages? If the answer is no, logically, we may conclude that the corporate ESG movement is not sustainable and vice versa. To answer the second question, we focus on consumers, whose purchasing behavior determines whether true ESG can generate corporations' competitive advantage and the resulting excess profit. Therefore, in the current study, we developed a consumer behavior model of corporate ESG, which models how corporate true ESG may affect consumers’ behavior and hypothesizes a positive relationship between the purchase and the true corporate ESG. Furthermore, we conducted an empirical study to evaluate the hypothesis. The results of the current study have crucial implications on what motivates the consumers' sustainability (or green) purchases and whether corporations should invest in true ESG. Fortunately, the empirical results support our hypothesis on the positive impact of true corporate ESG on the purchase. Based on the consumer behavior model, strategy implications for corporations’ ESG investment were derived. 

How to cite: Ho, S. P. and Hsu, Y.: Is the Corporate ESG Movement Sustainable? A Consumer Behavior View and Evidence, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-22034, https://doi.org/10.5194/egusphere-egu24-22034, 2024.

EGU24-1526 | Orals | ITS3.13/HS12.5 | Highlight

Leveraging Citizen Science for Flood Hazard Management: Harnessing Local Knowledge and Experience 

Peter Fischer-Stabel and Sascha Nau

Floods pose significant challenges to communities worldwide, necessitating effective hazard management strategies. Citizen science here emerges as a pivotal tool in amassing critical knowledge and experiences from local communities, offering an invaluable resource to bolster flood hazard management initiatives. It is able to serve as a conduit for integrating diverse local perspectives and experiences. Harnessing the collective wisdom of community members, who intimately understand the dynamics of their surroundings, becomes instrumental in comprehending flood patterns, vulnerabilities, and impacts and is able to enrich the database for hydrological and hydraulic modelling in the flood context. Nowadays, the advent of citizen science apps represents a paradigm shift in engaging and mobilizing local communities to actively participate in flood hazard management.

Within the framework of the BMBF R&D – Project “Urban Flood Resilience – Smart Tools (FloReST)” one tool developed was a SmartApp engaging local communities in the collection of flash flood related data and experiences. After the definition of the user requirements in collaboration with the local stakeholders, a first prototype was developed, engaging the citizens in the reference municipalities of the FloReST-project to organize App-Journeys collecting data in the field. Beside a description of the problem to be choosen from a predefined list of flood related grievances (e.g. drain blockages, faulty rakes, building activities changing the draining system), the Geolocation of the position, additional textual information, up to three images and a time stamp is collected and send via the smartphone to a Gesoserver at the Backend. There – located ideally at the responsible organizational unit for flood related activities, e.g. the building or the environmental authority- the incoming messages are stored in a database and visualized on a risk-map with different graphical signatures depending on the category of the problem reported. After having received the report, a notice confirming the reception of the message is automatically send back to the client. The SmartApp now is able to facilitate the data collection on flood occurrences, affected areas, and vulnerabilities. Integration of such data with existing models enhances the accuracy and precision of flood risk assessments, enabling authorities to develop targeted mitigation and response plans.

But, the idea behind this SmartApp is not only the collection of flood related local knowledge, moreover, this citizen science initiative intends to promote community engagement and empowerment, fostering a sense of responsibility among residents towards flood resilience.

However, several challenges exist in the implementation of citizen science for flood hazard management: Quality assurance and data reliability remain concerns, necessitating robust protocols for data validation and verification. In addition, the responsible authorities we discussed with were not very happy with that type of citizen science tools for deficiency reporting, because this will force them to action often not possible in a short time because of a lack in resources.

How to cite: Fischer-Stabel, P. and Nau, S.: Leveraging Citizen Science for Flood Hazard Management: Harnessing Local Knowledge and Experience, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1526, https://doi.org/10.5194/egusphere-egu24-1526, 2024.

Climate change poses a significant threat to the well-being of humanity, territories, and resources. In the city of Bologna, Italy, environmental, societal, and digital challenges mirror those experienced globally in urban spaces such as air pollution, and intense urban mobility stemming from escalating urbanization. Addressing these issues, the H2020 I-CHANGE project,  "Individual Change of HAbits Needed for Green European transition," aims to demonstrate the potential for collective behavioral change by actively engaging civil society in innovative citizen science initiatives (Goudeseune et al., 2020; Vohland, 2021). University of Bologna established the Bologna Living Lab, involving a broad network of stakeholders based on the Quintuple Helix of Innovation (Carayannis et al., 2012), to enhance awareness of climate change impacts in urban areas and encourage behavioral shifts toward more socially and environmentally sustainable lifestyles.

Despite ongoing scholarly debates surrounding the definition of citizen science and its capacity to generate accessible and democratic knowledge, the I-CHANGE project embraces a participatory approach. The research methodology incorporates serious game techniques, traditionally applied in educational contexts, to augment citizen science activities. These serious games, blending serious objectives with playful elements, create immersive and engaging experiences, motivating participants to actively contribute to scientific endeavors. This integration marks a paradigm shift in citizen science, fostering increased public involvement in data collection, analysis, and discussion of results, ultimately enhancing the identification of climate change-related phenomena (Wiggins and Crowston, 2011; Irwin et al., 2012). 

The Bologna Living Lab adopts a two-step research approach, utilizing surveys and serious game mapping activities. The focus is on urban mobility, with "Mani in Mappa!" initiative, to investigate how mobility strategies can induce behavioral change toward sustainable and low-emission options. Collaborative serious games are utilized to promote awareness of the need for accessible, equal, and fair public transport. This comprehensive research contributes significantly to understanding the multi-level dynamics of mobility systems. It incorporates social, economic, and technological variables and holds the potential to inform and guide sustainable urban development initiatives. Bologna Living Lab and the I-CHANGE project stand for and promote innovative solutions, leveraging citizen science and serious game methodologies to address critical issues and pave the way for a more sustainable future. 

How to cite: Carlone, T. and Tondini, S.: Game-Changing Cities: Toward Sustainable Transportation with Citizen Science in Bologna’s Living Lab , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1681, https://doi.org/10.5194/egusphere-egu24-1681, 2024.

EGU24-2142 | ECS | Posters on site | ITS3.13/HS12.5

Towards the European Green Deal. Improving awareness through citizen science campaign on extreme temperatures in the I-CHANGE Barcelona Living Lab on Extreme Events  

Paola Barrera Bohórquez, Laura Esbri, Llorenç Puig, Marc Fernàndez, Helena Lasheras, Montserrat Llasat-Botija, and Carmen Llasat

There is a trend of increasing population in urban coastal municipalities in Mediterranean Regions. Particularly, Barcelona, with 160 inhabitants/ha in a surface of 10135,3 ha, can be considered as a Mediterranean coastal megacity.

This huge urban growth in the recent years implies an increase in vulnerability against global warming and climate change. Recent reports had stressed that the annual average temperature of the Mediterranean coast is already 1.5ºC higher than in pre-industrial times. That widespread warming will continue during the 21st century, surpassing the global average by 20% annually and 50% in summer (MedECC 2020). On the other hand, temperatures can vary within cities, influenced by urban morphology, surface cover, materials, structure, and population activity (Aslam & Ahmad Rana, 2022). A better understanding of the effects of those parameters on the city temperature and its thermal comfort is a key to increase the resilience of the citizens against global warming.

The I-CHANGE project seeks to raise awareness and promote changes of habits among citizens to mitigate and better adapt to climate change. It involves the citizens in science activities of collecting and understanding environmental data considering their physical, socioeconomic, and cultural context. The campaign presented here was designed by the Barcelona Living Lab on Extreme Events coordinated by the ICHANGE team of the University of Barcelona. It was scheduled for August 2023 and February 2024. Three citizen volunteers (coauthors in this paper) supervised by the UB team carried out this campaign in Barcelona and two coastal and touristic municipalities (Castelldefels, and Malgrat de Mar) located near the capital.

The volunteers guided by the Barcelona Living Lab technicians worked to design two bicycle routes through their city that they would feel comfortable repeating several times. One route had to be along the coast and the other moving away from it. They used MeteoTrackers on a bicycle to collect temperature, pressure, and humidity data along the transects. Each route was covered on three different days during the summer, one time in the morning and another at night, resulting in a total of 12 transects in each municipality. The routes are expected to be repeated between January and February to collect winter data.

The goal of the campaign is to encourage citizens to reflect on the temperature variation along the Mediterranean coast and the influence of urban characteristics, using urban classifications such as Local Climate Zones. The analysis will also focus on the differences between the three closely located coastal municipalities and the volunteers will have an active role in the data treatment and data visualization process.

The I-CHANGE has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement 101037193.

 

References:

Aslam, A., & Ahmad Rana, I. (2022). The use of local climate zones in the urban environment: A systematic review of data sources, methods, and themes. Elsevier.

MedECC 2020. (2020). Resumen de MedECC 2020 para los responsables de la formulación de políticas. Obtenido de https://www.medecc.org/wp-content/uploads/2021/05/MedECC_MAR1_SPM_SPA.pdf

How to cite: Barrera Bohórquez, P., Esbri, L., Puig, L., Fernàndez, M., Lasheras, H., Llasat-Botija, M., and Llasat, C.: Towards the European Green Deal. Improving awareness through citizen science campaign on extreme temperatures in the I-CHANGE Barcelona Living Lab on Extreme Events , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-2142, https://doi.org/10.5194/egusphere-egu24-2142, 2024.

EGU24-4557 | Posters on site | ITS3.13/HS12.5

Challenges in climate change impact and risks in Jerusalem by the I-CHANGE Jerusalem Living Lab citizens science 

Pinhas Alpert, Yoav Rubin, Gabriel Densy Campos1, Konstantin Romantso, Nitsa Haikin, Amnon Stupp, and Pavel Kishcha

The I-CHANGE (Individual Change of HAbits Needed for Green European transition, 2021-2025) project promotes the active participation of citizens to address climate change. It engages citizens and local stakeholders to take part in science initiatives and support more sustainable behaviour. To this aim, a set of Living Labs located in very different eight cities of socio-economic contexts (Amsterdam, Barcelona, Bologna, Dublin, Genova, Hasselt, Jerusalem and Ouagadougou), were chosen. The I-CHANGE Living Labs address different environmental issues comprising: (i) extreme events, mainly focusing on heavy rainfall, and heat waves, (ii) air pollution & linkages with sustainable transport, (iii) the water cycle and (iv) Waste Management.

Here, the implementation plan for JLL (Jerusalem Living Lab) of the eight Living Lab in the project, is presented. In JLL our main expertise is Atmosphere sciences and Commercial Microwave Links (CML), a new tool for environmental monitoring. The major partner is the Jerusalem municipality interested in mapping urban shadow cover especially over the routes children take to school (summer temperatures reach 40+C). Another partner is the Jerusalem Science Museum which has the joint goal with Tel Aviv University to increase the scope to meteorological parameters and air pollution as well as the Discomfort index for the school routes. In addition, Mapping of Jerusalem LL high-resolution abovementioned variables, particularly humidity from both CMLs (Rubin etal, 2023) and Meteotrackers that measure solar insolation (Alpert, BAMS,1991).

Jerusalem City is unique in its diversity of populations with ~million inhabitants and is located at the border of Mediterranean climate with a significant
variability between the coastal area, including Jerusalem City (annual rainfall~200-700 mm) and the most arid zone of the Dead Sea, 20-30 km to the east (annual rainfall ~50 mm). The spatial-temporal variation of rainfall intensity is the main and not well-known driver that generates the majority of flash floods in the nearby Judean Desert. Hence, its monitoring is crucial in this area as in other remote arid areas worldwide.

Recently, extensive research was performed related to global warming potential risks and their effects on rainfall and temperature over the East Mediterranean. Several major risks were pointed out including extreme temperatures, heat waves, colder nights, and floods. Our first super-high-resolution global climate model projections projected that the ancient “Fertile Crescent” considered as the cradle of civilization, will nearly disappear by the year 2100 (Kitoh et al. 2008). Also, Jerusalem temperatures both maximum and minimum, show that significant increases occurred during 1950-2020 (homogenized dataset, Yosef et al., 2019). A fact that led to definition of the Mediterranean as a “Hot Spot” of global warming.

I-CHANGE is funded by EU Horizon 2020 grant 101037193.

References:

Kitoh, A. Yatagai and P. Alpert, Hydrolo. Res. Lett., 2, 1-4, 2008.

Rubin, Y.; Sohn, S.; Alpert, P. High-Resolution Humidity Observations Based on Commercial Microwave Links (CML) Data—Case of Tel Aviv Metropolitan Area. Remote Sens. 2023, 15, 345. https://doi.org/10.3390/rs15020345.

Y. Yosef, E. Aguilar and P. Alpert, "Changes in Extreme Temperature and Precipitation Indices: Using an Innovative Daily Homogenized Database in Israel". International Journal of Climatology, 1–24.  https://doi.org/10.1002/joc.6125‏, 2019.

How to cite: Alpert, P., Rubin, Y., Densy Campos1, G., Romantso, K., Haikin, N., Stupp, A., and Kishcha, P.: Challenges in climate change impact and risks in Jerusalem by the I-CHANGE Jerusalem Living Lab citizens science, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-4557, https://doi.org/10.5194/egusphere-egu24-4557, 2024.

EGU24-6110 | Posters virtual | ITS3.13/HS12.5 | Highlight

Can climate change training promote pro-environmental behavior in the long term? A pilot study with teenagers 

Francesca Munerol, Antonio Parodi, Lara Polo, Massimo Milelli, Nicola Loglisci, Nadia Rania, Fabrizio Bracco, and Ilaria Coppola

Environmental education (EE) programs are critically important. The EE within the EU Project "I-CHANGE" (https://ichange-project.eu/) aims at global citizenship, in order to generate new knowledge and new, more participatory and conscious ways of acting in the environment. The present study aims to verify the effectiveness of a training intervention based on education on the issues of climate change and on the active participation of the subjects in the small psychological group. 309 students participated in the intervention, equally distributed by gender (52.1% males), 64.4% enrolled in primary school and 35.6% enrolled in lower secondary school. A quantitative protocol was administered to evaluate the effectiveness of the intervention. The study shows an increase in pro-environmental behaviors and their stability even after 15-30 days. The intervention appears to be effective in triggering pro-environmental behaviors and maintaining them over time. The results of this study highlight the need to develop environmental education programs in schools to increase levels of knowledge and awareness on the topic of climate change.

How to cite: Munerol, F., Parodi, A., Polo, L., Milelli, M., Loglisci, N., Rania, N., Bracco, F., and Coppola, I.: Can climate change training promote pro-environmental behavior in the long term? A pilot study with teenagers, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-6110, https://doi.org/10.5194/egusphere-egu24-6110, 2024.

EGU24-7305 * | Orals | ITS3.13/HS12.5 | Highlight

Citizen science in schools to take climate action: uneven air pollution concentrations in Barcelona 

Laura Esbri, Yolanda Sola, Paola Barrera Bohórquez, Raül Marcos, Montserrat Llasat-Botija, Laura Ceraldi, and Maria Carmen Llasat

The city of Barcelona, like many urban centres, deals with the multifaceted challenges posed by air pollution. This abstract enlightens the pivotal role of citizen engagement and citizen science initiatives in catalysing awareness, understanding, and action against air pollution while addressing the broader context of climate change mitigation.

Barcelona's air quality is significantly impacted by anthropogenic activities. In 2022, population exposure to PM2.5 and NO2 tripled the health protection guidelines set by the WHO. Particulate matter concentrations returned to pre-pandemic levels and NO2 exceeded the legal limits in one district, averaging 42 µg/m3 annually (Rico et al., 2023). Long-term exposure to those levels is estimated to cause 1,500 deaths, 900 new childhood asthma cases, and 130 new lung cancer cases annually, with associate social costs over 1 billion and healthcare over 5 million euros. These pollutants not only pose immediate health risks but also contribute to the exacerbation of climate change. Urgent and stronger action is needed to reduce air pollution and safeguard public health.

Citizens, as stakeholders, are pivotal agents in effecting meaningful change. Citizen science initiatives, such as participatory monitoring networks and collaborative research endeavours, empower individuals to actively engage in collecting data, analysing trends, and disseminating information on air quality. This engagement not only fosters a deeper understanding of the intricacies of air pollution but also cultivates a sense of ownership and responsibility among citizens towards their environment. This is the goal of I-CHANGE Living Labs, to encourage behavioural changes and promote eco-friendly practices in everyday life, as individual actions to combat climate change and towards more sustainable patterns.

The Barcelona Living Lab on Extreme Events has partnered with schools of different socioeconomic backgrounds in Barcelona (as stakeholders and citizen scientists) to deploy six low-cost air quality sensors (Smart Citizen Kits) and five meteorological stations. This campaign has consisted of several implementation phases where the sensors were installed, teachers were trained, and workshops were carried out to develop curricular material for different primary and secondary school grades. Students work on projects to understand how the sensors work and the collected data. Within these projects, data is gathered for specific days when variations in pollution levels are observed. Differences between various neighbourhoods and districts (whit sensors) are compared. Students use this information to create hypotheses about potential causes and then try to verify them. Then they are encouraged to understand how air quality affects their daily life and what they and their families can do to improve it and become more resilient to climate change. This contribution shows the methodology followed to develop this collaboration and the different campaigns, the difficulties that had been overcame, and the potential of the co-creative process with schools

The I-CHANGE project has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement 101037193.

 

References:

Rico M, Font L, Arimon J, Gómez A, Realp E. Avaluació de la qualitat de l'aire a la ciutat de Barcelona 2022. Barcelona: Agència de Salut Pública de Barcelona; 2023 (Catalan).

How to cite: Esbri, L., Sola, Y., Barrera Bohórquez, P., Marcos, R., Llasat-Botija, M., Ceraldi, L., and Llasat, M. C.: Citizen science in schools to take climate action: uneven air pollution concentrations in Barcelona, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-7305, https://doi.org/10.5194/egusphere-egu24-7305, 2024.

EGU24-9460 | ECS | Posters on site | ITS3.13/HS12.5 | Highlight

Will extreme precipitation events like July 2021 become more frequent in the future? Insights from Belgium using MAR  

Josip Brajkovic, Sébastien Doutreloup, Nicolas Ghilain, Pierre Archambeau, Michel Pirotton, Kobe Vandelnotte, Fien Serras, and Xavier Fettweis

The July 2021 rainfall event that affected western Germany, the Netherlands and Belgium was of unprecedented intensity, reaching 170 mm of daily totals in some places. To estimate the probability of such events in the near and far future (up to 2100), the regional climate model MAR is used to run simulations at a resolution of 5 km. For this purpose, MAR is coupled with a set of 4 CMIP6 Earth System Models (ESMs) for 4 IPCC SSP scenarios over an area encompassing Belgium and Luxembourg.

An extreme value analysis is applied to the output for the period 1980-2100 for different rainfall durations (1 to 5 days). Our results show that such extreme precipitation events remain extreme throughout the century, but the probability of their occurrence increases by an order of 10 or more in the most pessimistic scenario. However, our analysis suggests that methodological choices can have a major impact on the results. In particular, the Peak Over Threshold approach shows larger changes in frequency than the Annual Maxima  approach, with less uncertainty in the results due to larger sample sizes of extreme events.

 

How to cite: Brajkovic, J., Doutreloup, S., Ghilain, N., Archambeau, P., Pirotton, M., Vandelnotte, K., Serras, F., and Fettweis, X.: Will extreme precipitation events like July 2021 become more frequent in the future? Insights from Belgium using MAR , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-9460, https://doi.org/10.5194/egusphere-egu24-9460, 2024.

The Dutch citizen science project Delft Measures (https://bit.ly/DelftMeasures) focuses on the collaboration between citizens, local institutions, and NGOs to map the weather and changing climate in the city of Delft. It has been running for 4 years, during which citizens of Delft measure long-term changes in rainfall patterns, temperature, and now also soil moisture in their private gardens. Currently, there are over 45 of the Alecto WS5500 citizen-science weather stations spread across neighborhoods in Delft, capturing rainfall variability in different urban microclimates. But in the past years, more than 100 different inhabitants have already been engaged and have helped to collect data.

The data is used by a diverse number of organizations like the National Meteorological Institute, the Delft University of Technology and the Delft Municipality, to answer different scientific, engineering, or policy questions. We collaborate with multiple NGOs in project execution. Considering the diverse interests of all stakeholders, the project addresses a variety of goals from education to improving climate adaptation to implementing open science practices.

All in all, the project grew into a successful co-creation between many different partners. Delft Measures has been growing and changing and it managed to reach a consistent base of enthusiastic citizens that support the goals of the project, engaging them in making changes in the city for climate change adaptation. For Delft, as a city below sea level, this means a better drainage network to deal with the larger showers of summer rain, while retaining water during longer periods of drought. By setting up secure collaborations with the municipality and university, the data citizens collect is used as direct input for the (future) efficiency of the municipality’s city-wide sewer and drainage network. For the university, this is valuable for education and research into how city infrastructure influences local weather patterns and the variability of rainfall, to understand better where high-intensity rainfall events will have the highest effect. Currently, such high spatial resolution on rainfall in cities is scarce. Additionally, the project functions as a case study for the university’s Open Science program, aiming to evaluate the implementation of open science practices in local citizen science projects, while NGOs invested in climate change adaptation in the city roll up their sleeves to help citizens make the practical changes needed for our new climate.

We are currently in the process of writing down the ‘recipe’ of Delft Measures, to help other cities implement similar projects and not to have to reinvent the wheel. We would like to share this recipe during this session, where we will answer questions such as how we manage to collect useful information and increase community involvement and awareness, what kind of participatory approaches we implemented to facilitate community involvement, how we tackle legitimate concerns about potential data biases, inaccuracies and how we ensure the long-term sustainability of the project.

How to cite: Vries, S. and Droste, A.: The Delft Measures Recipe: how to implement a similar citizen science project in other cities, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-11306, https://doi.org/10.5194/egusphere-egu24-11306, 2024.

EGU24-12303 | Posters on site | ITS3.13/HS12.5 | Highlight

The Varsom Regobs System: Enhancing Natural Hazard Forecasting, Community Preparedness and Recreational Outdoor Activities through In-situ Crowd-Sourcing Observations  

Solveig Havstad Winsvold, Jørgen Loe Kvalberg, Aron Widforss, Øystein Myhre, Rune Verpe Engeset, and Tore Humstad

The Varsom platform has been a success in Norway for more than a decade, with users from organizations and the public. It fosters a participatory approach encompassing recreational activities, hazard assessments, emergency preparedness, search and rescue, and forecasting. Here, we will present Varsom Regobs (Registering of Observations), an innovative crowd-sourced system within Varsom enabling registration, sharing, querying, and real-time storage and publication of field observations. Varsom Regobs aids decision-making for the warning services on snow avalanches, landslides, lake ice, and floods at the Norwegian Water Resources and Energy Directorate (NVE). Users utilize the Varsom app, website (www.regobs.no), and API to submit and retrieve diverse in-situ observations on the categories snow, ice, water, and soil, tailoring the level of detail. The app has gained widespread recognition within the community, boasting over 120,000 unique visitors between October 2023 and January 2024. In 2023 alone, a total of 22,000 observations across all categories were submitted. The app and website, available in multiple languages, have gained traction in numerous European countries, recording 500 observations outside Norway in 2023 thanks to the open-access policy.

One successful aspect of enhancing natural hazard and hydrology monitoring has been the reciprocal engagement with users, and specific examples showcasing this will be provided. To address trust issues regarding non-academic observers, a star-based quality system has been implemented, aligning with an observer's training courses. Moreover, all users must possess an NVE-account login to submit their observations. Other challenges, such as managing spam-like entries and delicately targeting and engaging specific user groups for each category, will also be highlighted.

Examples demonstrating the combined usage of the in-situ Varsom Regobs component, NVE's forecasting models, and NVE’s operational products derived from Copernicus satellite data will be showcased. The Regobs API v.5 ensures the utilization of observations in scientific projects by research institutes and universities. Varsom Regobs stands as a sustained citizen science initiative due to its integration into Norway's operational warning services, serving as an exemplary model for long-term engagement and collaboration.

How to cite: Winsvold, S. H., Kvalberg, J. L., Widforss, A., Myhre, Ø., Engeset, R. V., and Humstad, T.: The Varsom Regobs System: Enhancing Natural Hazard Forecasting, Community Preparedness and Recreational Outdoor Activities through In-situ Crowd-Sourcing Observations , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12303, https://doi.org/10.5194/egusphere-egu24-12303, 2024.

EGU24-12308 | Posters on site | ITS3.13/HS12.5

BrantaSae: Sustaining Community-Driven Water Quality Sharing in the Brantas Catchment 

Reza Pramana, Runi Asmaranto, Tri Budi Prayogo, Daru Rini, Schuyler Houser, and Maurits Ertsen

Data scarcity and dispersion are pervasive challenges facing water and environmental managers in many contexts. Such is the case in the Brantas River basin in East Java, Indonesia, where water quality monitoring data and information on pollution sources and attendant management solutions has historically been dispersed and, therefore, challenging to apply in both research and policy analysis. In 2022, a multistakeholder project team launched a citizen science campaign and online data platform, BrantaSae, focusing on water quality monitoring in the Brantas catchment (approximately 12.000 km2). We enabled a local university to host this water quality database. Different communities and students of the local university itself were approached to contribute to this database through training sessions on how to upload, share, and oversee their data. In addition to facilitating the exchange of data, the platform allows communities and researchers to share challenges and solutions related to water quality improvement. BrantaSae serves as a potential clearinghouse for future collaboration and continuous learning amogst universities, communities, and other stakeholders including the local governmental agencies, emphasizing knowledge sharing in fostering a collaborative and informed community. As this research project concludes in 2024, it underscores the ongoing importance of BrantaSae in continuing to map water quality to expand our comprehension of the water quality in the catchment.

How to cite: Pramana, R., Asmaranto, R., Prayogo, T. B., Rini, D., Houser, S., and Ertsen, M.: BrantaSae: Sustaining Community-Driven Water Quality Sharing in the Brantas Catchment, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12308, https://doi.org/10.5194/egusphere-egu24-12308, 2024.

EGU24-15308 | ECS | Orals | ITS3.13/HS12.5

AGORA as the bridge between local actors and communities: objectives and experiences in the Italian Pilot (the city of Rome) 

Alfredo Reder, Marianna Adinolfi, Marta Ellena, Marina Mattera, Paola Mercogliano, and Edoardo Zanchini Di Castiglionchio

Numerous obstacles hinder societal transformation toward a climate-resilient future, often rooted in underlying assumptions prevalent across various domains, including civil society, the public/private sector, and politics. AGORA is a HORIZON Europe project (Grant agreement ID: 101093921) whose aim is to support communities and regions to overcome these obstacles in climate change adaptation. It started in January 2023 and will have a total duration of 3 years.

This initiative supports the EU Mission on Adaptation to Climate Change through four main pillars:

  • Conduction four Pilots in different countries (i.e., Spain – Zaragoza; Italy – Rome; Sweden – Malmö; and Germany –Dresden), focusing on workshops and implementing co-creation strategies;
  • Developing improved strategies by understanding stakeholders' needs and climate change risks; over 50 cross-disciplinary stakeholders, including followers from non-Pilot countries like Portugal, are interested in applying the lessons learned;
  • Empowering local communities through societal transformation; it assesses learning tool needs, hosts workshops on pressing issues, and creates digital tools like a Digital Agora, two digital Academies, and an App – a challenging game for simultaneous entertainment and learning;
  • Evaluating climate change policies in different countries and designing adaptation strategies using participatory democratic.

In the last year, various activities took place in the four AGORA pilot regions, including inception workshops aimed at bringing together different stakeholders on climate adaptation. These workshops specifically focused on identifying vulnerability, risk drivers, and gaps in local adaptive capacity. The goal was to assess vulnerabilities to expected climate hazards, aligning adaptation priorities with the needs of local communities and fostering community strengthening.

Regarding the city of Rome, the inception workshop aligned with the development activities of the city’s Climate Change Adaptation Strategy. The event aimed to foster a fruitful discussion among various local stakeholders regarding adaptation to the anticipated impacts of climate change across multiple sectors (Water - encompassing resource management, drought, and impacts related to precipitation; urban settlements; networks and infrastructure; cultural heritage; health; socioeconomic system; marine and coastal system; agricultural and livestock system; biodiversity and ecosystems). These sectors were identified as key areas most susceptible to risk during the formulation of the Climate Change Adaptation Strategy. The objective was to define the main socioeconomic, structural, and environmental vulnerabilities, as well as the primary needs and critical issues related to the adaptive capacity of the territory and its citizens for each sector under examination.

Through a multidisciplinary, integrated approach, AGORA is a growing, dynamic, pan-European community that creates and shares advanced digital tools to enhance awareness. Informed citizens can actively participate and contribute to ensure safe and sustainable development. Hence the project is the meeting point where citizens share knowledge, practices, expertise and needs, interacting with sciences to design and build a more resilient Europe through a living dialogue between local communities.

How to cite: Reder, A., Adinolfi, M., Ellena, M., Mattera, M., Mercogliano, P., and Zanchini Di Castiglionchio, E.: AGORA as the bridge between local actors and communities: objectives and experiences in the Italian Pilot (the city of Rome), EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15308, https://doi.org/10.5194/egusphere-egu24-15308, 2024.

EGU24-15715 | Posters on site | ITS3.13/HS12.5

The role of citizen science to assess the spatiotemporal pattern of rainfall events in urban areas: a case study in the city of Genoa, Italy 

Giorgio Boni, Arianna Cauteruccio, Francesco Faccini, Nicola Loglisci, Guido Paliaga, and Antonio Parodi

Short-duration and high-intensity rainfall events in the Mediterranean region, enhanced by climate change, produce floodings in cities characterized by a limited extension of the urban catchment area, a high percentage of impervious surfaces and the inefficiency of the urban drainage system. In the present work the historic center of the city of Genoa (Italy) was assumed as a case study. In this area, the spatial variability of intense rainfall events is significant, even across a limited portion of the territory as demonstrated by analysing five rainfall time series (12 years of data) recorded at high-resolution from authoritative rain gauges.

A specific rainfall event that produced floodings on August 27th - 28th, 2023, is analysed with particular focus on the synoptic and mesoscale analysis and assessing the contribution of citizen science rain gauge stations (provided by Acronet network, see e.g., Fedi et al., 2013) to detect the magnitude and spatial distribution of this event.  The comparison between cumulated rain as recorded by the authoritative and citizen science networks shows that the convective nature of the phenomenon results in extremely diverse effects on the territory with very localized intense showers.

The introduction of citizen science observations allowed a better understanding of the spatiotemporal structure of the investigated rainfall event that caused flooding in the study area. In the future, a more structured use of this information, associated to appropriate validation procedures, can provide a fundamental contribution to risk management in densely urbanized areas such as the historic centers of many Mediterranean coastal cities.

Fedi, A., Ferrari, D., Lima, M., Pintus, F., Versace, C., Boni, G., (2013). The “ACRONET paradigm”, an “open hardware” project. Open Water Journal, 2(1), 7.

This study was carried out within the RETURN Extended Partnership and received funding from the European Union Next-GenerationEU (National Recovery and Resilience Plan – NRRP, Mission 4, Component 2, Investment 1.3 – D.D. 1243 2/8/2022, PE0000005) and from the European Union's Horizon 2020 I-CHANGE project ( https://cordis.europa.eu/project/id/101037193).

How to cite: Boni, G., Cauteruccio, A., Faccini, F., Loglisci, N., Paliaga, G., and Parodi, A.: The role of citizen science to assess the spatiotemporal pattern of rainfall events in urban areas: a case study in the city of Genoa, Italy, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15715, https://doi.org/10.5194/egusphere-egu24-15715, 2024.

RiverSnap is a citizen science project as part of the joint project “Zukunftslabor Wasser” that transforms smartphones into measuring instruments for monitoring and analyzing river parameters, responsive to water level changes, natural hazards (e.g., floods), and anthropogenic-induced alterations. A robust stainless-steel smartphone frame is strategically located on or near a bridge for convenient public access to capture river images. This frame facilitates precise image positioning, enabling the capture of river scene images of a predefined and referenced river area that can be uploaded to a centralized database, shared on social media, or sent via email. This collaborative endeavor establishes a community-driven repository documenting river changes over time. Due to water's dynamic nature and structural and sky reflections in close-range images, the RiverSnap project utilizes and develops novel Artificial intelligence (AI) algorithms to extract and predict hydrologic parameters and features.

These advanced algorithms are crucial in detecting water lines, determining positions, and mapping various riverine features with scientific precision. Through this sophisticated technology, RiverSnap transforms community snapshots and additional measurements into a valuable resource for scientifically assessing and understanding alterations in the river environment. As the AI models are data-hungry, RiverSnap is diligently creating benchmark datasets for river water, facilitating the development and training of robust machine learning algorithms. These datasets serve as comprehensive references, allowing the AI models to enhance their understanding of various hydrological patterns, ultimately improving the accuracy and effectiveness of river parameter predictions and feature extractions.

Established in 2023 in Hannover, Germany, the RiverSnap station network has observed significant growth, now covering four monitoring locations around Hannover. Recognizing the pivotal role of detecting the water surface area in approximating riverine parameters, we have developed and implemented various advanced Deep Learning (DL) models for water body segmentation. As part of this initiative, a novel river water dataset named RiverSnap.v1, including 1092 images, has been introduced and is constantly updated. Additionally, various methods have been investigated to geo-reference the analyzed results. In a straightforward approach, artificial or natural markers, such as specific locations of objects around the river or on bridges, were measured with geomatics tools like GNSS receivers and total stations. The DL-extracted water surface was then georeferenced based on these markers to obtain results like the water level. A 3D terrain model derived from LiDAR data or photogrammetric techniques like Structure from Motion (SfM) can be utilized for Geo-referencing parameters and results in more advanced scenarios. This allows for automatically assigning absolute coordinates to each image and subsequent camera pose estimation.

Examples of practical applications of RiverSnap include monitoring high-frequency water level and water line changes and morphological changes in rivers, lakes, wetlands, and urban areas. Additionally, RiverSnap is instrumental in monitoring extended flood areas and observing the time sequence of a flooding event, as demonstrated in data of a German flood of 01/2024.

Funding: This study was performed as part of the joint research project „Zukunftslabor Wasser“ funded by the Lower-Saxon Ministry of Research and Culture (FKZ: 11-76251-1873/2022 (ZN3994))

How to cite: Moghimi, A. and Welzel, M.: RiverSnap: A citizen science project to monitor and Analyse riverine hydrological parameters from close-range remote sensing images, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-16082, https://doi.org/10.5194/egusphere-egu24-16082, 2024.

Scientific research around water security and water quality in the Peruvian Andes often excludes local perspective and knowledge, yet local water users not only directly depend on local water sources, but are also sensitive to changes in water availability, quality, and ecosystems over time. Increasing community involvement and representation is essential for improved understanding and more holistic, sustainable water resource management, however more participatory approaches with local communities often have a myriad of logistical and project constraints. Through a small GCRF funded pilot study, as part of an interdisciplinary and international research team we created and rolled out a smartphone photo elicitation app, “Nuestro Rio”, as a novel tool to gather insights into local perceptions of water quality in the Rio Santa basin, Peruivan Andes (2020-2022). Here we consider the ability of technological approaches such as our Nuestro Rio app to help address some key issues and improve research outcomes to the benefit of the research team and local communities, whilst reflecting on the challenges experienced. Sharing the lessons learnt from pilot projects like Nuestro Rio can help contribute to the growing dialogue on citizen science and participatory approaches, whilst also providing support and guidance for those currently planning or exploring similar research tools and projects.

How to cite: Rangecroft, S. and Clason, C.: Lessons from the Nuestro Rio app: Reflections on exploring local perspectives on water quality in the Peruvian Andes, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17217, https://doi.org/10.5194/egusphere-egu24-17217, 2024.

EGU24-17417 | Posters on site | ITS3.13/HS12.5

Bringing the gap among citizens and ICT tools through storytelling to testify the local impacts of climate change during time 

Paola Salvati, Giuseppe Esposito, Simone Facchinetti, Ivan Marchesini, Umberto Mezzacapo, Simone Sterlacchini, Debora Voltolina, and Antonella Galizia

Citizen science is increasingly used to engage the public in scientific processes to raise awareness and promote actions towards climate change and sustainability. In addition, citizen science initiatives allow the creation of multidisciplinary contexts engaging citizens, and also stakeholders, to foster scientific awareness and active participation to the definition of adaptation actions. With this vision in mind, this abstract describes the citizen science activities set in the municipality of Chiavari (Genoa metropolitan area), where different agreements have been signed with the municipal administration, municipal Civil Protection and two high schools to launch training programs started in May 2023. 
The training activities consider the use of a webapp for landslides and flood reporting to describe past geo-hydrological events. The webapp provides a form aimed at describing the characteristics (speed of the run, height of the water, etc.) a specific phenomenon occurred in a specific date; the output result is a map of the reports. The webapp is based on KoboToolbox, an open source software to create reports and geolocating entities, and students exploit it through their mobiles, and/or the device they prefer. A first field campaign was organized to collect local data and experiences via interviews (and storytelling) with local persons; the campaign was highly impacting for the students since there were also able to reconstruct a local historical memory. In a following meeting, students accessed (via QRcode) videos and/or images of the event with the aim of locate the site observed in the images/videos and compared their map with ARPAL official observation of the event. 
The presentation will outline the entire initiative, from the engagement to the webapp while reporting how the historical local interviews emphasized the actual impact of climate change in our own urban environments. The work is developed within the H2020 projects I-CHANGE (Individual Change of HAbits Needed for Green European transition).

How to cite: Salvati, P., Esposito, G., Facchinetti, S., Marchesini, I., Mezzacapo, U., Sterlacchini, S., Voltolina, D., and Galizia, A.: Bringing the gap among citizens and ICT tools through storytelling to testify the local impacts of climate change during time, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17417, https://doi.org/10.5194/egusphere-egu24-17417, 2024.

EGU24-19094 | Orals | ITS3.13/HS12.5

Building up a Digital Academy in AGORA project to aware citizens, improve access to and use of climate data supporting adaptation 

Marianna Adinolfi, Alfredo Reder, Paola Mercogliano, Andreas Hoy, Massimo Milelli, and Riccardo Biondi

The AGORA (https://adaptationagora.eu/) project aims to support communities and regions exploiting a broad range of approaches, mechanisms and initiatives to meaningfully and effectively engage citizens, civil society organisations, academics, experts, policy-makers, entrepreneurs, marginalities and other relevant actors in all the transformation steps towards a climate-resilient Europe. Beyond the state-of-the-art, AGORA aims to promote societal transformational processes through transdisciplinary tools and approaches in different social, economic and political contexts.  The ambition is to accelerate and enhance the adaptation process by sharing innovative problem-oriented climate adaptation solutions that could be widely adopted across Europe, considering societal transformations and the awareness that there is no one-size-fits-all solution. A set of pilot regions in Italy, Sweden, Germany, and Spain constitutes the co-production arena to co-design, co-develop, and co-implement climate adaptation solutions through specific-context in-person activities (for engagement, capacity building, governance and tackling disinformation). Regions and Communities joining the Mission on Adaptation will also be involved as followers feeding and learning from the AGORA initiative. A roadmap for transformative change and large-scale citizen engagement will be developed to transfer effective policy instruments and ensure a long-term legacy, promoting climate justice, gender equality and equity. AGORA's legacy will be to increase citizens' adaptive capacity and empowerment to proactively support decision-making processes and transformative potential to anticipate innovative behaviour. The pillar of the AGORA project consists in the Digital AGORA, an online space that supports citizens, local government, municipal services and networks, and communities to play a relevant and conscious role in co-developed decision-making processes. It will host two Digital Academies that will aspire to guide and support the targeted audiences to access and use Climate Data and to monitor Climate Risks, and to oppose Climate Change Disinformation. The main goal of the former Digital Academy is to facilitate access and usage of high quality, open source Climate Data as well as Climate Risks Data. The goal is achieved by mapping existing data, sources and platforms that will be gathered in “ad hoc built” inventories on climate data, adaptation and climate risk hubs. The second goal is to empower citizens, stakeholders and policy makers through technical reports and training documents on how to access and use climate data for adaptation. In this perspective, the Digital Academy is based on courses with key scientific information on the usage of climate data at different level of knowledge (entry, base and advanced level). The last goal is to promote information and initiatives fostering climate adaptation supported by citizen science activities. The Digital Academy to access and use Climate Data and to monitor Climate Risks is co-designed and co-developed in different public events, as ECCA (www.ecca2023.eu) and SISC conference 2023 (www.sisclima.it). Such events allowed to connect climate adaptation practitioners with the scientific community, to gather the users’ requirements and provide suggestions and ideas for the advancements in the building up of the Digital Academy.

How to cite: Adinolfi, M., Reder, A., Mercogliano, P., Hoy, A., Milelli, M., and Biondi, R.: Building up a Digital Academy in AGORA project to aware citizens, improve access to and use of climate data supporting adaptation, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-19094, https://doi.org/10.5194/egusphere-egu24-19094, 2024.

EGU24-19298 | Orals | ITS3.13/HS12.5

Eyes on the Water: Leveraging Citizen Photos and AI for River Health Assessment and Management 

Ho Tin Hung, Daniel Pearce, Li-Pen Wang, Susana Ochoa-Rodriguez, and Amy Jones

River pollution is a global challenge recognized as unacceptable by citizens. Despite increasing awareness and investment in river water quality monitoring worldwide, current monitoring strategies fail to well characterise river health. In particular, the spatial and temporal resolution at which river health is currently monitored is insufficient and falls short to identify e.g., pollution spikes and point pollution sources. At the same time, the rise in citizen engagement in river monitoring, driven by increased awareness and widespread availability of smart phones and other monitoring technologies, has generated opportunities to overcome current monitoring barriers. 

 

In this paper, we share our experience collaborating with the community group Friends of Bradford’s Becks (FoBB, UK) to use citizen-collected photos for AI-based detection of health indicators, leading to enhanced river health management. More specifically, FoBB has collected around 100,000 photos of the streams that flow through and under Bradford. These images offer insights into the health of the becks, including specific pollution issues such as discharging overflows, sewage litter, discolouration, amongst other things, as well as how pollution has changed in time and space. The number of photos makes analysis challenging. In this project, we used AI models for automatic image labelling and prototyped several landing solutions for embedding the labelling model into a tool usable by citizens.

 

The project was initially set up in a Hackathon, funded by Natural England, and aimed to develop solutions using AI models. The landing solutions employed classification and object detection deep learning models to assist citizens by offering automatic detection of river health indicators. This not only reduces the cost of reporting but also improves the quality of reporting with comprehensive labels. Through community engagement, high spatio-temporal resolution data can be collected from citizens to fill the data gaps, including pollution levels, natural habitat conditions, and biodiversity. Additionally, while collecting the data, the deep learning models can be further fine-tuned to better assist citizens and managers in river health assessment and management. In summary, the project presents a holistic approach to river health management, combining the strengths of AI with the insights and engagement of local communities. The success of this approach in Bradford offers a template for similar initiatives globally, marking a step towards more informed and responsive river health management strategies.

How to cite: Hung, H. T., Pearce, D., Wang, L.-P., Ochoa-Rodriguez, S., and Jones, A.: Eyes on the Water: Leveraging Citizen Photos and AI for River Health Assessment and Management, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-19298, https://doi.org/10.5194/egusphere-egu24-19298, 2024.

EGU24-20151 | Orals | ITS3.13/HS12.5

Citizen Science in Meteorology: Enhancing weather understanding through innovative instrumentation and community engagement 

Nicola Loglisci, Antonella Galizia, Antonio Parodi, Timoteo Galia, Juri Iurato, and Roberto Monni

Meteorology and climatology have their foundations in the observation of the main meteorological variables. They constitute an essential tool for understanding the meteorological situation in operational forecasting activity, for the construction of the initial conditions for numerical integration in both deterministic and probabilistic models, as well as for the construction of time series for climate analysis.

Moreover, the assimilation of local meteorological observations to the global observational network, plays a crucial role in refining meteorological predictions.

I-CHANGE project offers an in-depth exploration of Citizen Science, emphasizing the use of innovative instrumentation to actively engage citizens in collecting meteorological data. Through the use of advanced sensors, mobile apps, and emerging technologies such as Meteotracker, our project aims to transform individuals into true "citizen scientists," making a significant contribution to the understanding of atmospheric phenomena.

We present a case study illustrating the integration of state-of-the-art instrumentation with community participation in Living Labs, highlighting how Citizen Science can enrich meteorological research. We discuss the challenges and opportunities of this approach, emphasizing the validity of community-collected data and its impact on the accuracy of local weather forecasts.

How to cite: Loglisci, N., Galizia, A., Parodi, A., Galia, T., Iurato, J., and Monni, R.: Citizen Science in Meteorology: Enhancing weather understanding through innovative instrumentation and community engagement, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-20151, https://doi.org/10.5194/egusphere-egu24-20151, 2024.

EGU24-20663 | ECS | Posters on site | ITS3.13/HS12.5

A Freshwater Conservation project: A Joint initiative between academia, environmental associations and companies. 

Alessio Polvani, Amedeo Boldrini, Luisa Galgani, and Steven Arthur Loiselle

Citizen science involves the participation of the public in research projects to enhance scientific knowledge. This kind of activities could bring advantages on the scientific, societal, educational and policy making levels. In the last decade, the breakout of citizen science has raised awareness across all segments of society. Major companies have begun actively participating in public participatory monitoring projects. In this case study, the University of Siena, Legambiente, and the Prada Group joined forces to support a freshwater monitoring project in an industrial area. 

The monitoring is conducted using the FreshWater Watch methodology, a well-established and scientifically validated approach used by citizens worldwide. This approach is based on visual observations and on the analysis of target freshwater parameters like nutrients and turbidity. Additionally, samples are also taken for ICP-MS analysis to provide a spatial coverage of metals presence in freshwaters. 

The project, started in October 2023, has so far proven successful in engaging stakeholders from environmental associations and workers of a renowned fashion brand, thus already providing valuable data from freshwater bodies in an industrial and urbanized area. The surveys, which will last for a year at least, are mostly conducted in the Valdarno region (Tuscany, Italy) on the Arno River and its tributaries and the data collected can be potentially used to support environmental agencies monitoring strategies.

This talk will present analytical methods and results from the surveys up to now (> 100), and will discuss how the data collected are not only scientifically useful, but also demonstrate an important societal impact of the project and an active stewardship of aquatic ecosystems by the participating stakeholders.

How to cite: Polvani, A., Boldrini, A., Galgani, L., and Loiselle, S. A.: A Freshwater Conservation project: A Joint initiative between academia, environmental associations and companies., EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-20663, https://doi.org/10.5194/egusphere-egu24-20663, 2024.

The skills, knowledge, values and rules of common resources management, like surface and groundwater in the M’zab valley (Algeria), are transmitted for centuries from one generation to another orally and on the field through observation and participation in the agricultural and water distribution tasks from a very young age. However, the continuity of intergenerational transfer of traditional knowledge faces challenges. Alterations of the water cycle related to climate change, mainly resulted in water scarcity, and technological transformations like the introduction of mechanised individual pumps, have disrupted the traditional collective organisation and challenged the intergenerational transmission of water management knowledge that prevailed in traditional systems. This has caused a loss of interest among the younger generation in their traditional knowledge around the governance of water resources. The participatory visual approach can facilitate community involvement in different citizen science projects. Our work explored how this approach can be used to address traditional knowledge holders’ concern about how to involve the younger generation in the groundwater management. We propose integrating different forms of knowledge- the research and video made by professional researchers, as well as the videos by four local scouts belonging to the M'zab oasis community.

The experience of participatory video enabled the four scouts to achieve three main things. Firstly, their involvement in concrete and practical projects enabled them to seek out information from knowledge holders from different backgrounds, deepen their own knowledge about the community-managed groundwater recharge and use system groundwater recharge and use system, acquire new skills (i.e. audio-visual and editing), express their perception and vision. Secondly, the four scouts used participatory video combining images and narrative to creatively and engagingly denounce two major environmental problems. Finally, the scouts used the potential of video to launch a call to action, building on the power of images and the emotions that those images can elicit.

Moreover, the interaction between research and artistic methods enables knowledge to be co-produced in a more dynamic and creative way. It also enables to overcome academia's bias against non-academic data. In our case study, the co-production of knowledge is crucial to raise awareness among young people. We believe that connecting different knowledge systems, traditional and scientific expertise, and emotions, can contribute to more sustainable governance of common resources like groundwater, by remembering the past, documenting the present and imagining the future.

How to cite: Hamamouche, M. F., Saidani, M. A., and Fantini, E.: Citizen science project in the M’zab valley oases (Algeria): Making groundwater management visible to young generations through the participatory visual approach, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-21142, https://doi.org/10.5194/egusphere-egu24-21142, 2024.

EGU24-22235 | Posters virtual | ITS3.13/HS12.5

Citizen science and Databases in Agriculture 

ஆனந்தராஜா (Anandaraja) நல்லுசாமி (Nallusamy), Julien Malard-Adam, Ponnusamy Murugan Prithivimangalam, Senthilkumar Manivasagam, and Jaisridhar Palanivelan

Data science and information technologies hold great promise for better decision-making in agriculture, from post-harvest management to value addition, market access and exports. Farmers in India can be reached by different ways, including written and voice messages, pre-recorded videos, and online workshops, each of which must be underpinned by diverse datasets and databases in order to be successful.

In the face of climate and environmental change, national and regional governments are currently encouraging the adoption of micro-irrigation for water conservation and the expansion of irrigated areas in India. At the same time, communities in rain-fed areas must use local water bodies and ponds to store and later use water from heavy rainfall for later irrigation. Meaningful participation of rural communities in development programmes, protection of water resources and agricultural technology adoption is crucial to ensuring societal change. At the same time, data collection and appropriate outreach strategies are necessary in order for this level participation to be possible.

Integrated and diverse database technological stacks can therefore be used to reach farmers and provide appropriate recommendations for field management even in regions without reliable internet connections. The approaches used must be simple for agricultural students, officers, and university researchers to reach farmers and the general population, and should include a variety of computer software, cellphone and virtual communication channels.

How to cite: நல்லுசாமி (Nallusamy), ஆ. (., Malard-Adam, J., Prithivimangalam, P. M., Manivasagam, S., and Palanivelan, J.: Citizen science and Databases in Agriculture, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-22235, https://doi.org/10.5194/egusphere-egu24-22235, 2024.

EGU24-326 | ECS | Posters on site | ITS3.14/BG8.36

Characterization and use of commercial biochar for water purification in real case scenarios 

Lorenzo Animali, Sveva Corrado, Paola Tuccimei, Mattia Bartoli, and Mauro Giorcelli

Biochar has been proven to be a compelling adsorbent for contaminants  in water, however little data are available about real case histories. Moreover, such data are often related to biochar produced solely for the sake of research, this means biochar would not be readily available for actual commercial applications 

The aim of the project is to employ commercial biochar for water purification in a real case study and test its viability as a pollutant adsorber. The chosen study area covers the surroundings of the decommissioned Malagrotta landfill in the Lazio region, Italy. The landfilling site, the largest in Europe, active from 1970 to 2013, has been the subject of numerous social and legal disputes throughout and after its operating period. 

At this stage, a chemical survey of the area’s surface water has been performed to determine its health and to evaluate remediation through biochar. Moreover, nine commercial biochar types produced in Italy and Europe have been characterized before and after experimentation to monitor structural, surface and physical-chemical properties. Post testing analyses are aimed at determining the effects of biochar’s interaction with water. Testing biochar in real case scenarios provides an assessment of its potential in an high added value application such as water purification and provides the constraints to achieve optimal performance.  

Future developments of the project build upon collected data and expertise to identify best practices for the valorisation of biochar as a contaminant adsorber. 

How to cite: Animali, L., Corrado, S., Tuccimei, P., Bartoli, M., and Giorcelli, M.: Characterization and use of commercial biochar for water purification in real case scenarios, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-326, https://doi.org/10.5194/egusphere-egu24-326, 2024.

EGU24-3541 | Posters on site | ITS3.14/BG8.36

Non-invasive monitoring of plant root activities in the framework of the Earth’s Critical Zone and soil-plant-atmosphere interactions. 

Giorgio Cassiani, Luca Peruzzo, Matteo Censini, Benjamin Mary, and Veronika Iván

Bio-geophysics is a very broad discipline, including a variety of physical monitoring techniques applied to biological processes. As such, it is inherently very challenging and, at the same time, very promising. A variety of scales are being investigated, from the cellular scale to the ecosystem scale. More relevant to the latter scale is the investigation of the plants root zone, where the majority of mass and (latent) energy balance takes place between the soil and biota and, from there, to the atmosphere. The response of the soil-water-vegetation system and of the Earth’s critical zone (from the top of the canopy to the bottom of the shallowest aquifer) to climate and land-use change is crucial for the preservation of essential ecosystem services such as carbon storage, primary productivity, food and materials availability, and water and erosion regulation. In addition, the interaction between atmosphere and land surface is one of the most critical points to be resolved to reduce epistemological uncertainties in atmospheric models, both for numerical weather prediction (NWP) and global and regional climate models (GCMs and RCMs). The use of geophysical techniques in this context provides dense high-resolution spatial information as well as, potentially, high temporal resolution monitoring. Two different viewpoints can be taken in this form of “bio-geophysical” monitoring: on one hand, the physical signals of the biological (e.g. root presence and signals) activity can be directly sought; on the other hand, the effects of biological activity (e.g. root water uptake) can be sensed by the resulting changes of the soil/water system state (especially in terms of moisture content, but also temperature, etc.). Examples of both types of approaches, and links to eco-hydrological modelling, will be presented in this contribution, urging towards a more frequent and more accurate applications of these techniques, particularly for their potential contribution towards a better definition of Land Surface Models, i.e. the bottom, critical, and poorly known boundary condition for atmospheric models.

How to cite: Cassiani, G., Peruzzo, L., Censini, M., Mary, B., and Iván, V.: Non-invasive monitoring of plant root activities in the framework of the Earth’s Critical Zone and soil-plant-atmosphere interactions., EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-3541, https://doi.org/10.5194/egusphere-egu24-3541, 2024.

EGU24-3610 | Posters on site | ITS3.14/BG8.36

Identification of long-term irrigation effect on plant water use from geophysical and proximal sensing observations: example of a vineyard 

Luca Peruzzo, Benjamin Mary, Vicente Burchard Levine, Jose Guerra, Miguel Herrezuelo, Raul Lovera, Albert Casas, Giorgio Cassiani, Hector Nieto, and José Pena

This study investigated the influence of long-term irrigation management on plant water use with an emphasis on the development and activity of grapevine root systems in an irrigated vineyard under semi-arid conditions located in the Madrid region (central Spain). The study tested three types of irrigation management, based on the potential evapotranspiration ETp computed with varying crop coefficient Kc (0.2KC, under-irrigated. 0.4KC, control and 0.8KC, over-irrigated). Note that the irrigation water used is considered as highly saline (3890 μS/cm at 20°C).
The interpretation was supported by soil geophysical surveys with electrical resistivity Tomography (ERT), plant physiological traits, and drone-based remote sensing observations. The ERT collected before irrigation showed strong evidence of soil long-term changes, with a gradient of electrical resistivity (ER) increasing with the stress applied, while time lapse ERT before/after the irrigation season showed changes implying deeper root contribution to water uptake in the stressed area. However, uncertainties persisted in interpreting higher ER areas, as it was unclear whether they stemmed from increased soil moisture or were linked to soil salinity caused by soil sodicity.
Insights could be derived from proximal and remote sensing data, revealing patterns consistent with soil responses to the applied irrigation stress. Notably, the higher Normalized Difference Vegetation Index (NDVI), thermal-based actual evapotranspiration rates and stomatal conductance (gs) observed in the over-irrigated area, in contrast to the under-irrigated area, may suggest enhanced plant water accessibility and increased transpiration rates. 
The study paves the way towards the adoption of geophysical methods in combination with remote sensing to control irrigation management particularly in the context of saline water.

How to cite: Peruzzo, L., Mary, B., Burchard Levine, V., Guerra, J., Herrezuelo, M., Lovera, R., Casas, A., Cassiani, G., Nieto, H., and Pena, J.: Identification of long-term irrigation effect on plant water use from geophysical and proximal sensing observations: example of a vineyard, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-3610, https://doi.org/10.5194/egusphere-egu24-3610, 2024.

EGU24-5746 | Posters on site | ITS3.14/BG8.36

EMI surveys under precision irrigation contexts: an orange orchard-case study and methodological challenges 

Giorgio Cassiani, Ulrike Werban, Marco Pohle, Simona Consoli, Giuseppe Longo-Minnolo, Daniela Vanella, and Luca Peruzzo

Electromagnetic induction (EMI) allows time-lapse profiling of electrical conductivity (EC). In recent years, progress has been made in the study of the intra-field variability and soil-plant correlations at the scale of a few meters. Yet, some methodological challenges still hinder the possibility to resolve the spatiotemporal complexity at the smaller scales typically associated with irrigation and evapotranspiration (ET) dynamics, and thus central to the agroecosystems and precision agriculture, particularly in orchard farming.

This study characterizes the 3D EC variability in an orange orchard in eastern Sicily (Italy). To the best of our knowledge, this is the first 3D investigation capturing both irrigation and ET effects at the meter scale. The characterization successfully distinguishes plant rows and interrows dynamics. The EC in the plant rows increases upwards, from the drier root-water-uptake region to the drip irrigation region above. In the interrows, the EC increases downwards from the drier evaporation-dominated layer to the deeper soil where the irrigation water accumulates without significant ET. The intermediate zones, between the plant rows and interrows, show yet another conductivity profile, homogeneous and relatively conductive. Local effects, such as the plant size, further complicate this conceptual model and add both inter- and intra-row heterogeneity.

While the results confirmed the EMI potential, some methodological challenges were equally important. First, a Geophex GEM-2 and a CMD Mini-Explorer were used, the latter in vertical and horizontal configuration. The choice of instruments and surveys appears now suitable for this field site but it is surely not a priori obvious and/or always possible. We highlight how the use of a single instrument would probably lead to misinterpreting the root water uptake or the evaporation contributions.

Second, the quantitative use of the two instruments required alignment and joint inversion. However, a standard GPS system did not provide a reliable alignment of the surveys. Time-consuming GIS corrections were needed for both intra- and inter-dataset shifts. Third, after GPS alignment, the surveys were interpolated over a common grid to allow the joint inversion. Because of the strong anisotropy of the agroecosystems, this required the careful parametrization of a Kriging algorithm.

Fourth, the individual EMI datasets also differ because of their drift and/or calibration. The lack of convenient alternatives initially motivated an ERT-based calibration, but ultimately two of the twelve datasets were dismissed.

Fifth, noise and instrumental errors required the use of a moving-window median. This common practice poses a trade-off between smoothing and resolution that hinders high-resolution surveys.

Sixth, a sub area of the orchard was investigated at finer resolution. This proved fundamental for the identification of the processes acting at the intermediate zones, between the plant rows and interrows, and other meter-scale details.

Overall, this study presents a state-of-the-art EMI application that focuses on small-scale aspects that were less considered in previous studies. The presented challenges explain the lack of similar studies and should be considered when discussing the EMI convenience and adoption for precision irrigation applications.

How to cite: Cassiani, G., Werban, U., Pohle, M., Consoli, S., Longo-Minnolo, G., Vanella, D., and Peruzzo, L.: EMI surveys under precision irrigation contexts: an orange orchard-case study and methodological challenges, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5746, https://doi.org/10.5194/egusphere-egu24-5746, 2024.

Circular economy requires reliable information about the content of old landfills regarding raw materials worth exploiting. Geophysical electrical methods are standard tools for such investigations; however, they require a galvanic contact between electrodes and the ground. Many landfills are covered by a shallow isolating layer consisting of an electrically resistive material (e.g., a PVC layer), which hinders current flow and significantly decreases the resolution and the signal-to-noise ratio (SNR). Low-induction electromagnetic methods have also been suggested for such investigations; yet these methods have a limited depth of investigation. To overcome these limitations, we investigate the applicability of the transient electromagnetic method to characterize an industrial landfill. The investigation is based on a TEM survey to define the positions of two deep boreholes  The combination of borehole and geophysical data aims at identifying the composition and distribution of waste, a prerequisite to evaluate its potential content of raw materials. We conducted TEM measurements in a large industrial landfill (ca. 500 m long, 200 m wide and 20 m deep) sealed by an impermeable PVC layer. The objectives of the TEM survey are: 1) the delineation of the landfill geometry 2) locating possible changes in waste composition and 3) identifying damages in the isolating PVC layer which might result in leachate migration below the landfill. We obtain TEM data with the TEM-FAST 48 instrument in a 12.5 m square single-loop configuration at 81 sounding locations to cover the entire plateau of the landfill. We demonstrate that the TEM signatures are affected by induced polarization effects, which is likely related to the presence of molybdenum and other relevant raw materials. To evaluate this observation, we conducted complementary measurements with the spectral induced polarization method using a large electrode spacing to enhance the SNR. We validated the TEM results using two 40 m deep boreholes that reach from the top of the landfill into the confining clay-rich layer.

How to cite: Aigner, L. and Flores Orozco, A.: Characterization of an urban landfill with the transient electromagnetic and spectral induced polarization methods to quantify raw materials and map leakages, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-6124, https://doi.org/10.5194/egusphere-egu24-6124, 2024.

EGU24-7971 | ECS | Posters on site | ITS3.14/BG8.36

Sensing Cr(VI) retention with spectral induced polarization (SIP) in a magnetite-coated sand pack 

Ali Rahmani, Marc Franz, Frederik Bär, Claudia Backes, James M. Byrne, and Adrian Mellage

Removing Cr from contaminated (ground)water is often attempted via active remediation using easily deployable permeable reactive (barrier) materials, such as iron oxide mineral coatings. In particular, magnetite has been shown to be a highly effective and low-cost option for removing redox-active Cr from solution. Magnetite not only binds Cr, but it also reduces Cr(VI) to the less toxic and immobile Cr(III). Monitoring the extent of Cr retention in remediation schemes, however, relies on down-flow concentration sampling. Consequently, detectable levels of Cr must exit remediation barriers in order to detect the decreasing remediation efficiency of reactive materials with the progression of immobilization. Spectral induced polarization (SIP), a non-invasive geophysical technique sensitive to sorption-induced changes in the surface charging properties of mineral surfaces in porous media, offers a potentially powerful monitoring alternative to detect changes in remediation efficiency in situ without the need for down-flow monitoring and contamination hazard. Here, we apply SIP, as a proxy to monitor the extent of Cr retention in a flow-through column experiment, packed with magnetite-coated sand. We observed a rapid increase in polarization upon Cr(VI) adsorption on magnetite coated sand, followed by a strong continuous decrease. Our joint reactive transport modeling and post-column geochemical measurements highlighted a drop in the remaining sorption capacity of the coated sand, thereby linking the reduced sorption capacity to the drop in SIP signal. The excellent agreement between concentration breakthrough curves, our model and SIP measurements suggests that SIP signals can be used as an early warning tool to detect the approaching saturation of reactive materials deployed in remediation schemes.

How to cite: Rahmani, A., Franz, M., Bär, F., Backes, C., M. Byrne, J., and Mellage, A.: Sensing Cr(VI) retention with spectral induced polarization (SIP) in a magnetite-coated sand pack, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-7971, https://doi.org/10.5194/egusphere-egu24-7971, 2024.

EGU24-11508 | Posters on site | ITS3.14/BG8.36

Bio-Electrochemical Systems to Monitor Biodegradation around Groundwater Plumes 

Rory Doherty, Panagiotis Kirmizakis, Mark Cunningham, and Deepak Kumaresan

This study introduces a straightforward and cost-effective Bio-Electrochemical System (BES) design that can be easily retrofitted into a borehole. The design uses standard bailers and Granular Activated Carbon (GAC) to create electrodes. These electrodes are connected across redox environments in nested boreholes. The  electrodes were installed in pre-existing boreholes surrounding a groundwater plume at a gasworks site. The BES at the plume fringe had the highest electrical response and showed variations in the bacterial and archaeal taxa between the anode and cathode electrodes. The other BES configurations in the plume center and uncontaminated groundwater showed little to no electrical response, suggesting minimal microbial activity. This approach enables rapid decision-making to effectively monitor degradation at groundwater plumes. 

How to cite: Doherty, R., Kirmizakis, P., Cunningham, M., and Kumaresan, D.: Bio-Electrochemical Systems to Monitor Biodegradation around Groundwater Plumes, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-11508, https://doi.org/10.5194/egusphere-egu24-11508, 2024.

EGU24-11986 | ECS | Posters on site | ITS3.14/BG8.36

Geophysical investigation of the Soda Lakes at the Seewinkel National Park (Austria) through electromagnetic and electrical methods 

Anna Hettegger, Adrián Flores Orozco, Nathalie Roser, and Arno Cimadom

The Seewinkel National Park in Burgenland (Austria) encompasses the largest inland soda lakes in central Europe. The shallow aquifer in the soda lakes is confined by an impermeable clay-rich layer, which is nourished with salts through capillary upward transport during the summer periods. Sinking groundwater levels are responsible for a decline in the upward transport and a decrease in salt content within the impermeable unit, threatening the ecological state of the lakes and their rich and unique biosphere. Yet, the extension of the hydraulic barrier, its salt content, and changes within the system accompanying seasonal temperature variations are still open to debate due to the lack of subsurface information with high spatial and temporal coverage. Here, we propose the application of geophysical methods to complement existing drill core data. Our research aims at reconstructing the architecture of the lakes, particularly the geometry and composition of the impermeable layer with a higher spatial and temporal resolution. We applied electromagnetic induction (EMI) for contactless rapid mapping of the lateral extent of the impermeable layer, assumed to have higher electrical conductivity due to its clay and salt content, and solving for the mean features in the shallow aquifer. To resolve vertical variations of the electrical conductivity with high resolution, we applied electrical resistivity tomography (ERT) at selected locations.  The initial independent inversion results from EMI and ERT, inherently ambiguous, showed discrepancies in the thickness of the impermeable layer. To permit an adequate interpretation of the geophysical data and harness the strengths of both methods, we employed numerical simulations, including ERT data to constrain EMI inversion and vice versa, as well as borehole electrode data, which allowed us to resolve for a subsurface electrical conductivity model able to explain both EMI and ERT data. Our results permit us to understand the characteristics of the impermeable layer and to develop a suitable technique to apply EMI and ERT to investigate other lakes in the national park.

How to cite: Hettegger, A., Flores Orozco, A., Roser, N., and Cimadom, A.: Geophysical investigation of the Soda Lakes at the Seewinkel National Park (Austria) through electromagnetic and electrical methods, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-11986, https://doi.org/10.5194/egusphere-egu24-11986, 2024.

EGU24-13784 | ECS | Posters on site | ITS3.14/BG8.36

Multiple benefits of biochar in agrivoltaics including rainfall harvesting and water balance  

Zibo Zhou, Wendy Timms, Sirjana Adhikari, and Stephen Joseph

Biochar as a carbon-negative product from organic waste increases porosity and water availability when deployed and mixed with soil, as one of many benefits for agrivolatics projects. Biochar can mitigate climate change by locking away carbon in concrete and during steel production, supporting food security and a circular economy, producing composites for water treatment and nutrient availability, and restoring soils affected by sodicity or contaminants. However, the utilization of biochar in combined farming and large-scale solar PV projects (i.e., agrivoltaics) provides more opportunities such as credits for carbon dioxide removal (CDR) and increased land-use efficiency. This project at a 7-megawatt solar PV (14-hectare) mixed-use farm at Deakin University in Australia aims to evaluate how biochar could contribute to agrivoltaics, particularly its influence on soil moisture, nutrient availability, and pasture productivity. This presentation focuses on part of the datasets, with initial results for water availability in the soil and pasture for sheep grazing. We applied biochar into a hand-dug trench along the drip line of PV panels, with several reference sampling sites (0.6m deep holes) beyond the pasture that is shaded by the panels. The trench was 0.6 m deep and 0.3 m wide, with 0.1 m of drainage sand at the base, a 0.3 m thick layer of mixed straw-biochar followed by 0.1 m of biochar particles, and 0.1m of soil and grass. Pasture treatments of liquid biochar and fertilisers followed installation. Soil moisture sensors were installed in the trench and sampling sites at 0.1, 0.3, and 0.5 m below ground level, and volumetric soil water content (V-SWC %) was recorded every 15 minutes. The initial results showed that the biochar trench in the mid-depth zone (~0.3m below the ground) can retain ~45% soil moisture after initial rainfall events. The maximum value of V-SWC in the bottom zone of the biochar trench was 47%. Similarly, V-SWC trends at other sites indicated that the middle and bottom zones can hold water for a period of time after rainfall occurs and values were up to 20%. Ongoing analysis will include variations of soil carbon and nitrate and the chemistry of leachate water that is collected from piezometers that were installed in the sand base of the trenches for mini-monitoring. The findings of this project will be useful for wide-scale applications of biochar on agrivoltaics or farming projects in environments sensitive to water balance. Biochar in soils can act as a sponge to store more water, slow down water flows in rivers, and increase groundwater recharge to shallow aquifers. This could ensure local catchments are more resilient to dry periods while benefiting ecosystems, and production of renewable energy and farmland.  

How to cite: Zhou, Z., Timms, W., Adhikari, S., and Joseph, S.: Multiple benefits of biochar in agrivoltaics including rainfall harvesting and water balance , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-13784, https://doi.org/10.5194/egusphere-egu24-13784, 2024.

EGU24-16209 | ECS | Posters on site | ITS3.14/BG8.36

Spectral induced polarization imaging applied to map the extension of root systems in Agroforests 

Sophia Keller, Adrián Flores Orozco, Clemens Moser, Theresa Maierhofer, Jorge Luis Monsalve Martinez, Emilian Tietze, and Theresia Markut

In agroforests, trees are planted on agricultural fields, which helps to reduce the risk of crop failure due to climate change, resulting for example from drought severity, as trees can improve the water supply and increase the amount of organic matter in the soil. Geophysical methods are used to non-invasively characterize the root system, including the root density, architecture, and growth as well as to monitor their activity to better understand the interactions between trees and agricultural fields. Electrical methods have demonstrated their potential for assessing the rhizosphere as the root presents a resistive barrier to current flow, resulting in lower conductivity values in the subsurface when roots are present. The spectral induced polarization (SIP) method provides information about the conductive and capacitive properties of the subsurface and its frequency dependence (commonly below 1 kHz). Laboratory investigations of the SIP method have shown that the conductivity of the roots depends on the root mass density, whereas the polarization effect and its frequency dependence is related to the root activity. The effect is due to the accumulation of charges at the electrical double layer (EDL) formed at the interface between roots and water, as well as within the root cells due to the plasma membrane. Consequently, changes in the electrical conductivity and induced polarization values at lower frequencies (< 100 Hz) can be used to delineate the extension of the root system. Moreover, we hypothesize that changes in the SIP data can be used to discriminate between the roots of trees and those from farming crops. In this study, SIP imaging measurements were conducted at four locations in Austria. The objective of the SIP survey is to delineate the geometry of the tree roots and to investigate changes in soil properties due to root activity based on the frequency dependent nature of the induced polarization. Measurements were conducted in a frequency range of 0.5 to 225 Hz at four sites to evaluate changes in the SIP response due to varying tree age and soil properties. We developed 3D geometries consisting of four lines crossing each other at the centre, where the tree under investigation is located. We used different electrode spacings to reach different resolutions and depths of investigation. Our results reveal that conductivity images can delineate the roots of the different trees, which always revealed the lowest conductivity values. In the area of the roots, the highest IP response is observed at lower frequencies (<5 Hz) and close to the surface (within 30 cm depth), which we interpret as the combined response of the organic carbon and roots. At larger depths, the IP response decreases, likely due to the reduced organic carbon and root activity. A few meters away from the tree, we observe an increase in the conductivity and moderate IP values, with the latter increasing with the frequency, indicating the presence of fine textures (i.e., clay and silts).

How to cite: Keller, S., Flores Orozco, A., Moser, C., Maierhofer, T., Monsalve Martinez, J. L., Tietze, E., and Markut, T.: Spectral induced polarization imaging applied to map the extension of root systems in Agroforests, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-16209, https://doi.org/10.5194/egusphere-egu24-16209, 2024.

EGU24-17693 | ECS | Posters on site | ITS3.14/BG8.36

Enhancing Bioremediation: Insights from a Numerical Modeling Approach 

Noura Eddaoui and Cyprien Soulaine

Bio-remediation of soil contaminated by petroleum hydrocarbon is a highly complex process, requiring coupled interactions and synergistic effects between physical, chemical and biological phenomena. Monitoring and improving the bio-remediation of such system remains a formidable challenge. Our approach involves the development of a comprehensive mathematical and numerical model that couples two-phase flow, bio-reactive transport, and the dynamic of bacterial populations, with the aim of investigating the mechanisms governing pollutant and nutrient transport, bacterial activities and bio-degradation within porous media. Important processes including the effect of biofilm growth on the permeability of the porous media and the interaction between the biofilm matrix and the fluid system, are taken into account. Numerical simulations were carried out to evaluate the effect of biomass accumulation and nutrients availability on the bio-degradation rate, providing new insights into optimizing in-situ bio-remediation processes for effective cleanup. Additionally, key issues such as controlling contaminant mobility and estimating efficiency criteria will be addressed as well.

How to cite: Eddaoui, N. and Soulaine, C.: Enhancing Bioremediation: Insights from a Numerical Modeling Approach, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17693, https://doi.org/10.5194/egusphere-egu24-17693, 2024.

EGU24-363 | Orals | ITS3.15/HS12.3 | Highlight

Water-Energy-Food-Ecosystem Nexus Transition through the Responsible Research and Innovation Roadmap - Lessons learned from four Mediterranean countries. 

Xenia Schneider, Leonor Rodriguez-Sinobas, Daniel Alberto Segovia Cardozo, Mohamed Bahnassy, Basma Hassank, Sendianah Hamdy Khamis Shahin, Rasha Badereldin, Fethi Abdelli, Rudy Rosetto, and Fernando Nardi

Climate change mitigation is becoming increasingly important for curbing severe hydrological events and at the same time for effectively managing natural resources and ensuring food secutiry. The nexus among water, energy, food, and ecosystem has evolved as a resource-management concept to cope with this interlinked set of resources, their complex interactions, and their effect on the natural, innovation, and social ecosystems. The transitioning towards the Water-Energy-Food-Ecosystem (WEFE) Nexus requires awareness among stakeholders, knowledge exchange and mutual learning, before they are able to co-create their WEFE Nexus transition plan and to adopt it for execution. In this respect, the concept of Responsible Research and Innovation (RRI) and its application through the RRI Roadmap is suitable for facilitating the WEFE-Nexus transition through an action plan. This paper will illustrate the RRI Roadmap application for creating WEFE-Nexus awareness among stakeholders enabling them to co-create their common WEFE-Nexus transition plan. The paper exemplifies the lessons learned for creating stakeholders’ awareness in four Mediterranean countries.

How to cite: Schneider, X., Rodriguez-Sinobas, L., Segovia Cardozo, D. A., Bahnassy, M., Hassank, B., Hamdy Khamis Shahin, S., Badereldin, R., Abdelli, F., Rosetto, R., and Nardi, F.: Water-Energy-Food-Ecosystem Nexus Transition through the Responsible Research and Innovation Roadmap - Lessons learned from four Mediterranean countries., EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-363, https://doi.org/10.5194/egusphere-egu24-363, 2024.

EGU24-706 | ECS | Posters on site | ITS3.15/HS12.3

First steps into Water-Energy-Food-Ecosystem Nexus Transition in semiarid depopulated regions: A case of study in the Spanish Duero basin.  

Daniel Alberto Segovia-Cardozo, María Sánchez-Bayo Gonzáles, Mara Vallejos Mihotek, Xenia Schneider, and Leonor Rodriguez-Sinobas

Water scarcity and water stress have become a concern for many countries worldwide, especially to Mediterranean countries like Spain. Which, together with the increase in energy prices, have affected food production and degraded ecosystems. Over the last two decades in Spain, irrigated areas have expanded in the interest of modernizing irrigation systems to cope with increased food consumption and to promote economic development and the maintenance of the rural population. At the same time, managing – rising fertilizer and energy prices and water scarcity have become necessities, but also threatening the natural ecosystem. Until now Water, Energy, Food, and Ecosystems (WEFE) challenges have been traditionally managed independently, contrary to the international community recommendation of treating them together in a WEFE Nexus framework to address their interrelationships and achieve a balance. Considering this framework, two workshops took place in the Duero Basin in Spain with the stakeholders, from the different WEFE entities, aiming at promoting and co-defining WEFE-Nexus transition actions for addressing the WEFE-Nexus challenges and for improving the local WEFE-Nexus conditions. As a first step, the WEFE-Nexus transition requires active engagement and building trust among WEFE stakeholders and as a second step to involve them in a co-creation process with knowledge’s exchange and mutual learning. Thus, the Responsible Research and Innovation (RRI) was applied through the RRI Roadmapãä for co-defining WEFE-Nexus transition actions or plan to improve local WEFE-Nexus conditions and identifying knowledge, technical and scientific gaps and how to bridge them to be successful. The first workshop used storytelling and the participatory method of the World Café to actively engage and motivate the stakeholders. The second workshop was performed from an expert point of view and a NEXUS NESS- Serious Game to foster discussion and create awareness on sustainable management practices; it focused on water-energy resource management, agricultural production, and the impact of climate change.

The methodology and the results from both workshops are presented in this paper.

How to cite: Segovia-Cardozo, D. A., Sánchez-Bayo Gonzáles, M., Vallejos Mihotek, M., Schneider, X., and Rodriguez-Sinobas, L.: First steps into Water-Energy-Food-Ecosystem Nexus Transition in semiarid depopulated regions: A case of study in the Spanish Duero basin. , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-706, https://doi.org/10.5194/egusphere-egu24-706, 2024.

EGU24-2309 | ECS | Orals | ITS3.15/HS12.3

Facilitating the Planning of integrated Water-Energy-Food-Environment Systems through Open Software 

Julian Fleischmann, Werner Platzer, Lars Ribbe, Alexandra Nauditt, and Philipp Blechinger

Addressing climate change, environmental degradation, and resource scarcity while ensuring the 
basic supply of the growing earth population are fundamental global challenges. In this context, 
the integration of water, energy, food, and environment systems for tapping cross-sectoral 
synergies and minimizing trade-offs presents a profound opportunity. However, despite their 
huge potential, integrated water-energy-food-environment systems (iWEFEs) are rarely put 
into practice because of, among others, a lack of site-specific data and open tools to describe, 
model, and plan such integrated infrastructure systems. The project addresses this 
gap through open software developed in a scientific process and applied to respective case studies.
The three main research and software development pillars of the project are the following:
1. Conceptualization of open water, energy, food, and environment modeling framework –
OWEFE enabling the development of an open iWEFEs component database
2. Facilitation and automation of WEFE data collection and analysis - WEFE Site Analyst
3. Development of a software-based configurator for site-tailored iWEFEs – iWEFEs 
Configurator
The open software tools shall support small communities, end-users, and NGOs to improve local
water, energy, and food security while protecting the climate and the environment.

How to cite: Fleischmann, J., Platzer, W., Ribbe, L., Nauditt, A., and Blechinger, P.: Facilitating the Planning of integrated Water-Energy-Food-Environment Systems through Open Software, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-2309, https://doi.org/10.5194/egusphere-egu24-2309, 2024.

EGU24-2345 | Posters on site | ITS3.15/HS12.3

Sustainable Irrigation is Promising for Alleviating Global Water Stress 

Zhipin Ai, Naota Hanasaki, Fadong Li, and Xin Zhao

Irrigation causes serious water stress in a wide range of areas around the world. It remains unknown whether and to what extent global water stress can be alleviated by the sustainable use of water resources for irrigation. Here, we delineated a new distribution of global irrigated croplands via strict conservation of available water resources for crop irrigation using an internally consistent model framework. Then, we compared the differences in global water stress under the conditions of current and re-delineated irrigated croplands, respectively. We demonstrated that irrigation on the re-delineated irrigated croplands can largely alleviate global water stress, particularly for areas currently facing high or very high water stress. The results also indicated that irrigated cropland re-delineation would have a limited negative impact on the production of 4 major crops of maize, wheat, rice, and soybean. Our findings highlight importance of sustainable irrigation water management for food production and its potential benefits for alleviating water stress.

How to cite: Ai, Z., Hanasaki, N., Li, F., and Zhao, X.: Sustainable Irrigation is Promising for Alleviating Global Water Stress, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-2345, https://doi.org/10.5194/egusphere-egu24-2345, 2024.

EGU24-4312 | ECS | Posters on site | ITS3.15/HS12.3

Toward Net Zero in the midst of the Energy and Climate Crises: the Response of Residential Photovoltaic Systems  

Lucia Piazza, Francesco Pietro Colelli, Enrica De Cian, and Wilmer Pasut

This paper aims to provide insights on potential strategies for a sustainable energy transition amidst market fluctuations. We analyze the impact of PV adoption on electricity consumption during a volatile price time span, leveraging high-frequency consumption data of over 10,000 households in Northern Italy during the period of the 2022 energy crisis. Our findings reveal that PV adoption reduces electricity consumption responsiveness during extreme price and temperature events, enhancing energy security and affordability. Also, PV uptake effectively reduces greenhouse gas emissions deriving from electricity consumption in the residential sector. Based on estimated demand, we measure changes in consumer surplus loss, highlighting substantial benefits from PV adoption: the change in the annual consumer welfare due to the 2022 price increase is around minus 300 euros for the median consumer with no PV and minus 133 euros when PV is adopted by a comparable median household. 

This study exploits high-frequency data of households residing in the municipality of Brescia between 2021-2022 to infer the impact of PV adoption and the influence of temperatures on grid electricity consumption, as well as to detect potential differences in price elasticity among different consumption groups and seasons. We find that adopting PV systems significantly reduces grid consumption: by 75% on average and by as much as 100% during sunny hours and warmer seasons. Exploiting the exogenous Russia-Ukraine price shock, we find that households who adopt solar PV are more likely to better manage increased temperatures at higher electricity price levels and price fluctuations. Furthermore, we find that "small" consumers can cope worse with high temperatures and are more sensitive to electricity-prices compared to "medium" and "large" consumers, highlighting electricity as a relevant source of inequality.

How to cite: Piazza, L., Colelli, F. P., De Cian, E., and Pasut, W.: Toward Net Zero in the midst of the Energy and Climate Crises: the Response of Residential Photovoltaic Systems , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-4312, https://doi.org/10.5194/egusphere-egu24-4312, 2024.

EGU24-4559 | ECS | Posters on site | ITS3.15/HS12.3

Dynamics of the water-energy-cropland nexus in China from 2007 to 2017: Implications for the Dual Circulation Strategy 

Ziwen Liu, Deqi Zheng, Xiaoyu Duan, Qingxu Huang, and Shiyu Zhang

Natural resources are fundamental for socioeconomic development and sustainable development. However, our understanding on the dynamic connections of water resources, energy and cropland still remains unclear. This study developed a framework covering multi-sectoral and multi-product water-carbon-cropland nexus, identifying key areas for water, energy and cropland conservation by considering both the resource utilization efficiency and connections between provinces. The new framework revealed that the utilization efficiencies of the three resources improved from 2007 to 2017 in China, with the average values of the direct, indirect, and total coefficients of virtual water consumption, embodied carbon emmisions and virtual cropland use decreasing by 63.3%, 40.6% and 59.2%, respectively. Meanwhile, the inter-provincial connections of water-carbon-cropland nexus have weakened, with a downward trend of pull coefficients. Gansu, Ningxia, and Xinjiang were typical regions with high consumption in water, energy and cropland, with the average value of the total coefficients of the three resources nearly twice the national average. Xinjiang, Ningxia and Inner Mongolia were regions with weak water-energy-cropland connections, and their average pulling coefficient was about 36% of national average. Under the "dual circulation" development pattern of China, it’s necessary to improve resource utilization efficiency in the future by promoting economic cooperation between regions with strong connections and weak connections, and promoting the efficient utilization of resources for regions (e.g., Xinjiang and Gansu) under the help of developed regions (e.g., Beijing and Shanghai). This framework can further capture the food-energy-water nexus (FEW nexus) at urban, provincial and even global scales, and can be used as an important tool to identify the process of multiple sustainable development goals (SDGs 2, 6, 7 and 12).

How to cite: Liu, Z., Zheng, D., Duan, X., Huang, Q., and Zhang, S.: Dynamics of the water-energy-cropland nexus in China from 2007 to 2017: Implications for the Dual Circulation Strategy, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-4559, https://doi.org/10.5194/egusphere-egu24-4559, 2024.

Food, energy and water are essential resources for human survival and development, and they are three elements of the 17 UN Sustainable Development Goals (SDGs). Expansion of human activity and climate warming are exacerbating severe risks of water, energy, and food shortages. How to manage the limited resources in an efficient and synergistic manner is essential to achieving sustainable development. Since there are few studies on the Water-energy-food (WEF) nexus for semi-arid regions in northwest China, we took Xinjiang Uygur Autonomous Region (XUAR) as an example to assess the impacts of climate and policies on the water, energy and food sectors in the context of global warming and identify ways to adapt. Firstly, we developed a non-linear system dynamic model to illustrate the interactions between food, energy and water, then 5 scenarios were set up by mainly change food self-sufficiency rate, clean energy use rate and energy intensity, to figure out the impact of different decisions and strategies on WEF nexus from 2020 to 2060, and provide solutions that are conducive to achieve carbon neutrality goal. Finally, we conducted a multi-objective optimization algorithm to attempt to mitigate the conflict between limited resources, socio-economics and a low-carbon environment. The results showed that: (1) The supply and demand for food and energy resources in XUAR showed an increasing trend between 2000 and 2020, while water resources decrease with greater decline on demand side. (2) For every 10% increase in food self-sufficiency, irrigation water, energy demand and carbon emissions will increase by 3.22%, 0.04% and 0.08%, respectively. And every 10% increase in clean electricity usage will cut down water demand and carbon emissions by 8.21% and 8.84%, respectively. (4) Under future water resources conditions, the feasible scenarios can reduce carbon emissions by 79% and enable a 13% reduction in agricultural water consumption comparing to the baseline scenario. Besides, the water stress will switch from very high to very low, which is a qualitative leap in achieving the Sustainable Development Goals (SDGs), especially SDG6 (Clean water and sanitation). To conclude, by reducing the area of cereals, improving irrigation efficiency and increasing the use of clean energy, we can achieve the goal of carbon peak and carbon neutrality, as well as sustainable development.

How to cite: Huang, Y.: Water-energy-food nexus in Xinjiang Uygur Autonomous Region: Combined Impact of Climate change and Policies, and potential adaptation pathways , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5223, https://doi.org/10.5194/egusphere-egu24-5223, 2024.

EGU24-5913 | ECS | Posters on site | ITS3.15/HS12.3

Predicting  Carbon-13 (δ13C) signatures as a Indicator of Water Use Effciency (WUE) in Cassava using Mid-Infrared Spectroscopy (MIRS) 

Sarata Darboe, Magdeline Vlasimsky, Jonas Van Laere, Gerd Dercon, Maria Heiling, Margit Drapal, Laura Perez, Luis Augusto Becerra, Michael Gomez Selvaraj, and Paul Fraser

The intricate interplay among plant water dynamics, nutritional content, and soil health is pivotal for unravelling the complexities inherent in plant materials, forging a direct link to the intricate web of the water-energy-food nexus. This investigation aims to find more accessible ways of evaluating the interplay between soil characteristics, water use in agriculture, and plant health, contributing crucial insights to sustainable agricultural practices that align with the SDGs 2030 Agenda for zero hunger, better environment, and enhanced human well-being.

Cassava, as a staple crop in many developing countries is the focal point for this study, aiming for proof of a more affordable and accessible way of accessing the impact of water scarcity and nutrient deficiency. This understanding becomes particularly crucial in the development of effective digital technologies tailored to enhance the sustainability of agricultural practices, fostering a balance within the intersection of water, energy, and food systems.

The core objective of this research is to assess the efficacy of Mid-Infrared Spectroscopy (MIRS) in predicting Carbon-13 (δ13C) signatures in cassava, establishing correlations between MIR spectral features and reference C-13 data obtained through Isotope Ratio Mass Spectrometry (IRMS). While Near-Infrared Spectroscopy (NIRS) and IRMS have demonstrated acceptable accuracy in modelling C-13 content in plant material, the underexplored potential of Mid-Infrared Spectroscopy (MIRS) holds promise, given its proven prediction potential with soil parameters as well as the small, required sample size which make it even more affordable, accessible, and sustainable. By grounding this investigation in the larger objective of managing the resource use efficiently, the calibration and validation process aims to contribute to the development of a broadly applicable methodology, across geographic boundaries and mediums and enhancing the collective understanding of the interdependencies within the water-energy-food nexus. 

Carbon-13 (δ13C) signatures in cassava offer invaluable insights into water use and transpiration efficiency and with a data-driven decision-making approach, not only informs farmers about optimal irrigation levels but also contributes to the broader discourse on sustainable resource management. Leveraging a dataset comprised of more than 700 cassava plant samples, this study employs Mid-Infrared Spectroscopy (MIRS) to predict δ13C content primarily in leaf material, utilizing Partial Least-Squares Regression (PLSR) to develop a robust model. Preliminary findings indicate that the indirect estimation is possible. The model's prediction performance, assessed through accepted statistical metrics such as R2 and RMSE, sheds light on the potential of MIRS for plant parameter prediction as an indicator of best soil and water management practices.

How to cite: Darboe, S., Vlasimsky, M., Van Laere, J., Dercon, G., Heiling, M., Drapal, M., Perez, L., Augusto Becerra, L., Gomez Selvaraj, M., and Fraser, P.: Predicting  Carbon-13 (δ13C) signatures as a Indicator of Water Use Effciency (WUE) in Cassava using Mid-Infrared Spectroscopy (MIRS), EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5913, https://doi.org/10.5194/egusphere-egu24-5913, 2024.

EGU24-6351 | ECS | Orals | ITS3.15/HS12.3

Hydropower Dams and the Human Right to Water: an Operational Transdisciplinary Assessment Framework 

Julie Faure, Marc Muller, Leonardo Bertassello, Elizabeth Dolan, Ellis Adams, Rahman Sulaimanov, Diane Desierto, Portia Chigbu, and Jonathan Pabillore

There is growing urgency for actionable and standardized approach to human rights assessments of hydropower dam constructions and operations that incorporates multiple dimensions of the right to water. Yet, the water issues faced by affected communities are determined by local contexts and therefore challenging to map to universal norms like human rights in a way that is both objective and transferrable. Conversely, the human right to water extends beyond the narrow dimensions of water access and availability and also includes cross cutting obligations (e.g, self-determination and non-discrimination) and inter-related rights (e.g., rights to health, healthy environment and livelihood). The nearly universal scope of human rights with respect to water makes them challenging to apply without an operational framework to systematically diagnose challenges to their implementation in practical settings. The framework that we present addresses both challenges with a procedure to systematically diagnose multiple key dimensions of inadequate water access (e.g, green, blue and economic water scarcity or excess) and governance failures (e.g., power asymmetry or threats to hydrosocial relations). The framework then maps the diagnosed issues to specific challenges to implementation of human rights that account for their multi-dimensional nature. This work is a unique transdisciplinary collaboration between water intensive industries and experts from the fields of hydrology, governance, and human rights law. We apply the framework to representative international hydropower cases (e.g., the Lower Sesan 2 Dam in Cambodia, the Muskrat Falls Dam in Canada) to synthesize key insights on the relationship between human rights and the impacts of hydropower projects on water security and governance.

How to cite: Faure, J., Muller, M., Bertassello, L., Dolan, E., Adams, E., Sulaimanov, R., Desierto, D., Chigbu, P., and Pabillore, J.: Hydropower Dams and the Human Right to Water: an Operational Transdisciplinary Assessment Framework, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-6351, https://doi.org/10.5194/egusphere-egu24-6351, 2024.

EGU24-7799 | Posters on site | ITS3.15/HS12.3

Water hyacinths: Use them or lose them? A holistic approach to a multi-faceted problem 

Marloes Penning de Vries, Timothy Dube, Finn Münch, Mgcini Ncube, Carmen Anthonj, Lisette De Senerpont Domis, Piet Lens, Thomas Marambanyika, Ntandokamlimu Nondo, Frank Osei, Cletah Shoko, and Daphne van der Wal

Lakes in tropical regions around the world suffer from the infestation of water hyacinth. Its proliferation is attributable to the influx of nutrient-rich waters, as rivers feeding the lakes are polluted with wastewater and run-off of fertilizer and manure from surrounding agricultural fields and husbandry within the catchment. The weed clogs waterways and intakes and affects aquatic life, water availability, transportation, fishing, irrigation, and tourism. Water hyacinth infestation has implications for human health, as it may facilitate the spread of water-related diseases. While water hyacinth may pose health risks, they have the potential to benefit human livelihoods when exploited for wastewater treatment, as fertilizer, for biofuel production or, when made into handicrafts, as a source of income.

A sustainable solution to these issues tackles both water quality deterioration and water hyacinth infestation, and “uses” water hyacinth instead of only attempting to “lose” them.  We present a research project that identifies such solutions, applicable and appropriate within the local and cultural context of our study region, Lake Chivero, the main source of drinking water to Harare. The project consists of three main pillars: (1) performing systematic studies of causes and effects of water hyacinth spread based on satellite and empirical data; (2) scientifically investigating water hyacinth exploitation methods, and (3) engaging with stakeholders to co-develop strategies to address the challenges of water quality and water hyacinth. The project’s impacts will be a more healthy and resilient lake ecosystem, improved wellbeing of people depending on the lake, and more resilient communities at Lake Chivero and other lakes in Sub-Saharan Africa. It will thereby contribute to the achievement of the United Nations Sustainable Development Goals (SDG) related to health (SDG 3), drinking water (SDG 6), and sustainable communities (SDG 11). Moreover, the project is in line with the South African National Development Plan 2030 and the African Union Agenda 2063.

How to cite: Penning de Vries, M., Dube, T., Münch, F., Ncube, M., Anthonj, C., De Senerpont Domis, L., Lens, P., Marambanyika, T., Nondo, N., Osei, F., Shoko, C., and van der Wal, D.: Water hyacinths: Use them or lose them? A holistic approach to a multi-faceted problem, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-7799, https://doi.org/10.5194/egusphere-egu24-7799, 2024.

EGU24-9934 | Posters on site | ITS3.15/HS12.3 | Highlight

Hybrid Framework for Water-Energy-Food Nexus Digital Twin Data Collection  

Nagham Saeed, Atiyeh Ardakanian, Leo Choe Peng, and Goh Hui Weng

In our research, we investigate the integration of the water-energy-food (WEF) nexus into a unified system and claim it is a critical step in achieving food security, while minimizing environmental degradation. This approach recognizes the interconnectedness of these essential resources and highlights the importance of a holistic and modern strategy in addressing global sustainability challenges. We facilitate this integration through Digital Twins (DTs), offering a virtual representation of this nexus. A critical step in developing a WEF Nexus DT is the collection of relevant data. Our project demonstrates that a hybrid approach is essential to gather comprehensive data for an effective WEF DT model. While traditional methods remain invaluable, they need to be combined with state-of-the-art technology. For instance, water quality, a key parameter in the WEF DT, is currently best assessed through direct sampling rather than IoT sensors or satellite data. Equally, energy parameters can be effectively monitored via satellite, and food production data can be accurately collected using IoT sensors. This hybrid data collection framework underscores the need for a multi-faceted approach, integrating both conventional and advanced technologies, to build a robust and reliable WEF Nexus DT.

How to cite: Saeed, N., Ardakanian, A., Choe Peng, L., and Hui Weng, G.: Hybrid Framework for Water-Energy-Food Nexus Digital Twin Data Collection , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-9934, https://doi.org/10.5194/egusphere-egu24-9934, 2024.

EGU24-10280 | ECS | Posters on site | ITS3.15/HS12.3 | Highlight

The study on Digital Twins in Managing Water-Energy-Food Sectors in South Africa  

Atiyeh Ardakanian, Nagham Saeed, Hloniphani Moyo, and Rabelani Mudzielwana

The study on Digital Twins (DT) in South Africa emphasizes DT's role in enhancing the efficiency of management and governance within the Water-Energy-Food nexus. By integrating data across energy, agriculture, and water sectors, DT provides a more cohesive and informed approach to decision-making where various stakeholders with multiple interests are involved. This integration enables streamlined governance processes and optimal resource utilization. Currently, governance in South Africa's water, energy, and food sectors is characterized by a mix of state and private involvement. The energy sector is overseen by the Department of Mineral Resources and Energy and includes state-owned entities, alongside private independent power producers and regulatory bodies. In agriculture, the Department of Agriculture, Land Reform and Rural Development plays a key role, with additional input from various agricultural bodies and NGOs. Water rights are state-owned, managed by the Department of Water and Sanitation, and regulated through licenses, with local management by water boards and municipalities. The land is a mix of private and state ownership, with a focus on agricultural development and reform. By understanding the priorities and influences of different groups, anticipating conflicts, and fostering cooperation, through DTs, we can ensure that initiatives for environmental conservation are more widely successful. 

How to cite: Ardakanian, A., Saeed, N., Moyo, H., and Mudzielwana, R.: The study on Digital Twins in Managing Water-Energy-Food Sectors in South Africa , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-10280, https://doi.org/10.5194/egusphere-egu24-10280, 2024.

EGU24-12610 | Orals | ITS3.15/HS12.3

Exploring stakeholder priorities regarding decentralized waste-water treatment in the Brantas river basin using Q-methodology 

Maurits Ertsen, Valeria Martinez Rodriguez, Merle De Kreuk, Schuyler Houser, and Mar Palmeros Parada

Next to the challenges of paramount importance represented by water scarcity, food security, energy transition, and environmental protection issues, the obstacles faced on the matter of water, sanitation, and hygiene (WASH) are immense. WASH interventions are essential to support human health, prosperity, and dignity, as they provide the base for an adequate standard of living. In many low- and middle- income countries, especially in rural and low-income areas, decentralized wastewater treatment systems (DEWATS) can offer a solution to convey, treat, and dispose of or reuse wastewater closer to the source and through smaller conveyance networks. In Indonesia, and as such in the Brantas basin on East Java, focus area of this study, the government has recognized DEWATS as their best available option for improving sanitation in dense low-income urban settings. Although the percentage of households with access to proper sanitation in the province of East Java has been increasing steadily, service coverage and the quality of sanitation systems still need to be increased to reach the desired coverage by 2024. Similar to other fields of application, within WASH and concerning DEWATS, stakeholders engagement, ethics and gender dimension are key topics to develop and strengthen integrated approaches. It is challenging to formulate targeted interventions in the watershed since they depend on the willing support of various stakeholders who may have different priorities (even within their own institutions), having diverse (and sometimes conflicting) viewpoints. This may result in stakeholders strongly contesting the appropriateness of various solutions. An exploration of stakeholder priorities is therefore needed to facilitate the application of wastewater treatment technologies. Due to its participatory approach and the type of interpretation that the method allows, Q-methodology was selected to explore this situation. Q-methodology is a set of techniques which allow for the study of ‘subjectivity’, combining statistics with the depth provided by qualitative data. It is composed of the data collection technique (called Q-sorting) and a data analysis step via correlation and factor analysis. In this contribution, we explore the perspectives and priorities of various stakeholders regarding decentralized wastewater treatment solutions to assess the applicability and acceptability of DEWATS in the Brantas river basin. This allows us to identify context-based criteria and challenges to the implementation of DEWATS in the Brantas watershed. As such, we propose the Q-methodology as a strong methodology to further develop the required transdisciplinary scientific efforts to promote relevant insights and solutions through meaningful, pertinent, and effective stakeholder engagement.

How to cite: Ertsen, M., Martinez Rodriguez, V., De Kreuk, M., Houser, S., and Palmeros Parada, M.: Exploring stakeholder priorities regarding decentralized waste-water treatment in the Brantas river basin using Q-methodology, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12610, https://doi.org/10.5194/egusphere-egu24-12610, 2024.

EGU24-12757 | ECS | Posters on site | ITS3.15/HS12.3 | Highlight

Integrated technical and governance analysis of the water-energy-food-ecosystems Nexus in a mountain catchment in Northern Italy 

Enrico Lucca, Luigi Piemontese, Janez Sušnik, Sara Masia, Giulio Castelli, and Elena Bresci

The simultaneous achievement of multiple societal, environmental, and economic goals is challenged by the interconnectedness of global and local resources systems (e.g., water for food and energy production, energy for water extraction and treatment), and by the rules and actors that determine the allocation of such resources. The Water-Energy-Food-Ecosystems (WEFE) Nexus promotes a systemic approach to the management and governance of intertwined systems focusing on the mutual interdependence between sectors and emphasising trade-offs and synergies across sectoral goals. Despite these premises, however, assessments of WEFE Nexus systems often do not address in an integrated manner the multiple dimensions under which the interconnections among water, energy, food, and ecosystems emerge, i.e., from the flows of resources across sectors to their socio-economic implications and their institutional context. In our study, we develop a methodological framework to characterise the interlinkages among water, energy, food, and ecosystems both at the biophysical and at the governance level. Through consultation with local stakeholders, we build casual loop diagrams to show physical relationships between processes and activities in the four sectors, while we apply the network of action situations (NAS) approach to assess interactions between venues of decision making and policy formulation. We apply this integrated approach to the Torrente Orco mountain catchment in Northern Italy, where the interlinkages between cereal production, energy generation and the preservation of natural ecosystems are becoming more evident due to the impact of climate change and sectoral developments. To inform the analysis, we used different data collection methods, including interviews with stakeholders, observation of stakeholder meetings, review of local news and analysis of regional plans and regulations. The results reveal that the water deficits experienced more frequently in recent years has led to key trade-offs between water uses, such as the abrogation of environmental flow requirement to meet irrigation water demands, but it also created important synergies, such as the multi-purpose use of hydropower reservoirs during droughts, the shift towards more water-efficient crops and the modernization of irrigation systems. Furthermore, three venues of decision making are highlighted as key opportunities to reconcile the water balance at the catchment scale: the renewal of hydropower concessions, the definition of the environmental flow requirement, and the renewal of irrigation permits.  The proposed approach was proven useful to reach a comprehensive overview of Nexus interconnections, a first crucial step for any further assessment that aims at understanding how the system might evolve in the future and what technical and non-technical interventions could help increase its resilience.

Acknowledgement

We gratefully acknowledge the ‘PON Ricerca e Innovazione 2014-2020: Istruzione e ricerca per il recupero—REACT-EU’ Programme of the Italian Government, through the PhD scholarship Granted to Enrico Lucca (scholarship n. DOT137M5SZ n. 2, 2022–2024)

How to cite: Lucca, E., Piemontese, L., Sušnik, J., Masia, S., Castelli, G., and Bresci, E.: Integrated technical and governance analysis of the water-energy-food-ecosystems Nexus in a mountain catchment in Northern Italy, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12757, https://doi.org/10.5194/egusphere-egu24-12757, 2024.

EGU24-12900 | Posters on site | ITS3.15/HS12.3

Assessing the capability of SDGs in achieving sustainable development from Nexus and global perspectives concerning water-food security 

Sudeh Dehnavi, Hamideh Nouri, Neda Abbasi, and Marcela Brungnach

While SDGs have become a common ground to address global sustainability systematically, neither the existing synergies and tradeoffs among the different SDGs nor the magnitude of their compound effects at global versus national scales are well understood. Although introducing two indices of Spillover Index and Global Commons Stewardship (GCS) shed light on these issues, the capability of these widely agreed SDGs in fulfilling every nation's needs and dedication to protecting global sustainability is yet questionable. The SDGs' shortcomings are most evident when there are interdependencies and contradictory requirements among SDGs, becoming critical when SDGs at the national level protect one country at the expense of another one. The impact of achieving food security (often in water rich countries) through the import of agricultural products from their trade partner countries (often in water scarce countries) is one of the examples.

Here we aim to understand whether and how lacking a global nexus perspective that takes into account the synergies and tradeoffs among the different SDGs can counteract other nations and SDGs. We investigate the connection between SDG 2 and 6 in the context of water and food security; particularly, the impact of food security strategies of an importing country on the water security of its trade partner countries. The findings present that although neglecting the tight links between SDGs of 2 and 6 may have a positive sectoral effect at a country level, it fails global sustainability as it impacts the involved countries unevenly and often antipodally. it emphasizes the need for a revision of SDG2, as it inadequately captures the perspective of food security from the standpoint of hunger. This study advocates for inclusion of  NEXUS and system thinking in the reformulation of the SDGs, their targets, and the associated indicators.

How to cite: Dehnavi, S., Nouri, H., Abbasi, N., and Brungnach, M.: Assessing the capability of SDGs in achieving sustainable development from Nexus and global perspectives concerning water-food security, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12900, https://doi.org/10.5194/egusphere-egu24-12900, 2024.

EGU24-12977 | Posters on site | ITS3.15/HS12.3

A Fuzzy Cognitive Mapping approach to support WEFE NEXUS policies and decision-making 

Maite Sanchez-Revuelta, Daniel A. Segovia-Cardozo, Xenia Schneider, and Leonor Rodriguez-Sinobas

Water stress, together with rising energy prices, have become a major concern worldwide, especially for food production (the major water consumer) that leads to ecosystem degradation in arid and semiarid countries such as Spain. The Spanish irrigated area ​​ has been continuously expanding and modernizing in response to the food demant increase and to promote economic development and support the rural areas which are aspects generally considered on an individual basis, against the advice of the international community to achieve a balance in  Water, Energy, Food and Ecosystem (WEFE) Nexus. But also, it is crucial to consider stakeholders' perspectives, understand their experiences and opinions to work together and find more realistic and effective solutions. Participatory modeling, such as Fuzzy Cognitive Mapping (FCM), is frequently used, with satisfactory results, within the WEFE Nexus context to understand the perception of stakeholders for decision making.

This work aims to analyze the perceptions of various stakeholders (researchers, policymakers, environmental groups, farmers' associations, food retailers, consumer organizations, water treatment companies, and water reuse experts) regarding interactions in variables related to the four sectors of the WEFE nexus in the Duero River basin. Understanding the perception of the stakeholders about these topics can help to improve policy, decision making and to enhance scientific research, innovation, and knowledge transfer in the fields related to the WEFE Nexus.

To identify the concepts, three workshops were conducted with 14 participants from different sectors related to WEFE NEXUS areas.  The workshop accomplished  different activities with the purpose of highlighting the main ideas about WEFE in a participative way, that were reinforced in the following workshop, to make sure that we gather the real perception of the stakeholders without leaving any of them aside. As a result, 30 concepts have been identified, simplified and used to develop a FCM, in which stakeholders will identify relations between them, assigning weights to these relationships correlations based on their own perception. Then, the final FCM will be analyzed by Metal Modeler program, which will allow to understand interconnections among variables according to the stakeholders´ perception and study future scenarios obtained as a result of performed workshops. Scenario analysis allows to explore the intricate relationships that exist within a system, and to examine how changes in one variable can influence the dynamics of the entire system. Creating narrative pathways from plausible future developments helps decision-makers assess the potential impact of policies from the perspective of multidisciplinary stakeholders.

How to cite: Sanchez-Revuelta, M., A. Segovia-Cardozo, D., Schneider, X., and Rodriguez-Sinobas, L.: A Fuzzy Cognitive Mapping approach to support WEFE NEXUS policies and decision-making, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12977, https://doi.org/10.5194/egusphere-egu24-12977, 2024.

Recent hydropower developments in Brazil were accompanied by promises to enhance the quality of life for the local population residing near the construction site of dams. To convince both locals and the broader national and international audience, the argument often centered around the hydropower project being an unparalleled opportunity to elevate deprived populations to a "developed" status, marked by significantly improved living standards. Our study aims to analyze how the residents of the medium-sized town of Altamira in the Brazilian Amazon perceive the impacts of Belo Monte, the country's second-largest hydropower dam built between 2011 and 2015, on fundamental resources—specifically, water, energy, and food. Additionally, we seek to examine how this perception varies based on sociodemographic characteristics such as age, gender, income, civil status, local or migrant status, ethnicity, and education. We will also explore the spatial distribution of these perceptions within the urban area. Our data consist of a survey based on a probabilistic sample of 500 households conducted across 10 census tracts (50 interviews per tract) in July 2022. Interviews were conducted with the head of the household or another household member aged 18 or over. Eligibility for survey participation required residency in the urban area of Altamira during and after the dam construction. Regarding the impact of Belo Monte on water system provision improvements, our findings suggest that over 59% of respondents indicated a negative impact or no impact. Furthermore, 86.8% of households reported a negative impact on energy prices, indicating that the dam did not contribute to increased energy access; in fact, it had the opposite effect. Lastly, 61% of the sample expressed negative impacts on food, citing high prices during construction that persisted even after completion. Our study also revealed that resettled populations in the urban area of Altamira faced more challenges in accessing water provision, experiencing more shortages compared to the rest of the population (χ² = 25.6401, p-value < 0.05). Additionally, resettled populations perceived energy prices more negatively than the population as a whole (χ² = 9.0392, p-value < 0.05). Our study employs statistical modeling and spatial analysis to investigate the disparities in these perceptions, examining how costs and benefits are unevenly distributed across the socio-spatial landscape, potentially exacerbating existing local inequalities. We advocate for essential interventions aimed at alleviating these disparities, such as subsidizing access to water, energy, and food for the residents of Altamira. Additionally, we provide insights into the unintended consequences of hydropower dam construction, especially in the Global South, where there is a substantial surge in the development of this energy source.

How to cite: Cavallini Johansen, I. and F. Moran, E.: Unfulfilled promises? Investigating the impact of the Belo Monte hydropower dam on water, energy, and food access in the Brazilian Amazon, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-13331, https://doi.org/10.5194/egusphere-egu24-13331, 2024.

EGU24-17304 | Posters on site | ITS3.15/HS12.3

Gender and Climate Change, Analysis in Various Red Cross Intervention Countries 

Javier Sigro, Mercè Cisneros, Jon Olano, Anna Boqué-Ciurana, Caterina Cimolai, Júlia Pastor-Diaz-de-Mera, and Clara Vidal-Bibiloni

Gender inequality and climate change are two major challenges currently confronting the human species. This collaborative project between the Red Cross in Catalonia in collaboration with the Catalan Agency for Development Cooperation and the Center for Climate Change (C3) at the Universitat Rovira i Virgili (URV), Spain, presents a comprehensive summary of the analysis of climate change impacts in diverse intervention countries. The study offers a global perspective on climate change trends, focusing on temperature variations, greenhouse gas concentrations, oceanic changes, cryosphere dynamics, precipitation patterns and, extreme climatic events.

Moving from the global to the regional scale, the report highlights the specific impacts on ecosystems, food systems, hydrological systems, sea levels, and public health. Special attention is given to localized effects in Catalonia, such as wildfires, floods, and water resource challenges.

 The project then explores the nuanced intersection of gender and climate change, emphasizing differentiated impacts and vulnerabilities across demographic groups. An analysis of climate vulnerability evolution with a gender lens includes an examination of international, national, and regional policies and reports.

 Differentiated gender impacts are illustrated through case studies in Guatemala, Colombia, Sahara (Africa), Mozambique (Africa), Afghanistan (Asia), and Iran (Asia). Each case study provides insights into the general context, the intersection of climate change and gender, energy poverty challenges, and the governance and participation of women in climate-related initiatives.

 To ground the analysis in empirical data, the study incorporates an in-depth analysis of the "En Moviment" program's data, covering socio-demographic aspects, climate change perceptions, governance structures, extreme weather events, and access to energy. The abstract concludes with comprehensive insights and recommendations, offering a nuanced understanding of the gendered dimensions of climate change impacts and responses in diverse geographical contexts, suitable for presentation at a congress.

The study delves into the intricate relationship between climate change and gender inequality, underscoring their global significance. The research emphasizes the urgent need for an interdisciplinary approach, exploring how human-induced climate change has escalated atmospheric CO2 levels, altered temperature patterns, and impacted ecosystems. Women, constituting the majority in vulnerable populations, face disproportionate vulnerabilities, exacerbated by gender-based disparities in decision-making, access to resources, and climate-induced poverty. Specific case studies in Catalonia and diverse global regions reveal nuanced gendered impacts, highlighting the crucial role of women in adaptation and mitigation efforts. The study concludes that addressing climate change requires a profound understanding of gender dynamics, advocating for inclusive responses that prioritize gender equality as a cornerstone for building a sustainable and just future.

How to cite: Sigro, J., Cisneros, M., Olano, J., Boqué-Ciurana, A., Cimolai, C., Pastor-Diaz-de-Mera, J., and Vidal-Bibiloni, C.: Gender and Climate Change, Analysis in Various Red Cross Intervention Countries, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17304, https://doi.org/10.5194/egusphere-egu24-17304, 2024.

EGU24-17756 | ECS | Posters on site | ITS3.15/HS12.3

Evaluating the demand for water for agricultural use for adaptation to climate change at the subbasin level (AGUAGRADA) 

Maite Jimenez-Aguirre, Carmen Galea, Sofía Garde-Cabellos, David Ribas-Tabares, Barbara Soriano, Paloma Esteve-Bengoechea, Irene Blanco-Gutierrez, Jon Lisazo, Carlos H Díaz-Ambrona, David Pérez, Leonor Rodriguez-Sinobas, Margarita Ruiz-Ramos, Isabel Bardají, and Ana M Tarquis

Given the decrease in water availability for agriculture caused by climate change (CC) in Mediterranean environments, it is necessary to use water efficiently in food production. As stated in the PNACC (National Plan for Adaptation to Climate Change), knowing the water demand for agricultural use before and after adaptation to CC is essential. In turn, for this, it is necessary to optimize the monitoring of the basins. To this end, AGUAGRADA proposes a monitoring and modeling system at the sub-basin scale and scalable to higher order basins, capable of quantifying the water demand for agricultural use under different climate, management scenarios (compatible with the CAP), and socio-economic and economic conditions of policies. The results of present and future water demands are expressed in PNACC indicators since the project aims to contribute directly to its implementation.
The general objective of this project is to develop and apply a method for evaluating water demand for agricultural use applicable at the sub-basin and basin scale before and after adaptation to climate change (CC). To achieve this, the following specific objectives are defined:

  • Design an optimal methodology for monitoring water demand for agricultural use applicable at the sub-basin and basin scale using PNACC indicators, replicable and scalable to other regions and even at the national level.
  • Co-create with stakeholders/farmers the selection of agricultural practices and CC adaptation measures to optimize water demand for agricultural use at the sub-basin and basin scale and ensure environmental and socio-economic sustainability. Analyze possible incentives for their inclusion in eco-regimes or CAP agri-environmental programs and study the best implementation routes (multidisciplinary approach).
  • Analyze water demand for agricultural use in the future without and with climate change adaptation.
    The actions as the advances achieved in this project will be explained.

Acknowledgements:
Fundación Biodiversidad del Ministerio para la Transición Ecológica y el Reto Demográfico, a través de la Convocatoria de subvenciones para la realización de proyectos que contribuyan a implementar el Plan Nacional de Adaptación al Cambio Climático (2021-2030) (CBIO230220C063)

How to cite: Jimenez-Aguirre, M., Galea, C., Garde-Cabellos, S., Ribas-Tabares, D., Soriano, B., Esteve-Bengoechea, P., Blanco-Gutierrez, I., Lisazo, J., Díaz-Ambrona, C. H., Pérez, D., Rodriguez-Sinobas, L., Ruiz-Ramos, M., Bardají, I., and Tarquis, A. M.: Evaluating the demand for water for agricultural use for adaptation to climate change at the subbasin level (AGUAGRADA), EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17756, https://doi.org/10.5194/egusphere-egu24-17756, 2024.

EGU24-18064 | ECS | Orals | ITS3.15/HS12.3 | Highlight

Towards sustainable water-energy-food-ecosystems governance: an integrating participatory approach and systems modelling for co-exploring the nexus 

Valentina Monico-Gonzalez, Desamparados Martinez-Domingo, and Eulalia Gomez-Martin

Global trends point to a growing challenge to meet the demand for water, energy, and food in the coming years, exacerbated by population growth, economic development, climate change, and environmental degradation. According to reports such as IPCC and EU Environment, this outlook threatens sustainability and equity in using natural resources. Despite the EU's environmental and energy policy efforts, such as the European Green Pact, the Water Framework Directive, and the Common Agricultural Policy (CAP), challenges persist in water management and the its alignment of with food production and energy policies.
The UN 2030 Agenda addresses these challenges, recognizing the interdependence of the Sustainable Development Goals (SDGs). Highlighting the crucial role of water for population and ecosystems, SDG 6 and 15, which intertwines with others. Achieving the 2030 Agenda requires a thorough understanding of the interconnections between the SDGs and coherent water governance policies at different levels and sectors.

The WEFE NEXUS (Water-Energy-Food-Ecosystems) concept has emerged as a promising tool to address these interdependencies and improve policy coordination. However, effectively translating this concept into effective governance practices remains a challenge. The complexity of the NEXUS requires multidisciplinary and holistic approaches, integrating quantitative and qualitative information at various spatio-temporal scales and institutional boundaries. Including stakeholders throughout the process enriches the diversity of perspectives and fosters the conscious and effective adoption of established measures by a significant portion of the population. The proactive participation of stakeholders not only enhances understanding of the interconnections between the SDGs and NEXUS governance but also contributes to creating more effective and sustainable governance practices. This inclusive approach is essential for achieving sustainable and resilient development that reflects the needs and concerns of the community at large.

This work seeks to develop a guide for implementing NEXUS governance practices and policies, co-created with stakeholders and end-users. The objectives include identifying previous challenges the watershed might face, causal relationships among variables, their polarity and weight (importance within the system) with causal loop diagrams, analyzing the influence of different stakeholder perspectives (assuring WEFE representativeness and avoiding power dynamics among them) on the effectiveness of adaptation measures, and assessing the integration of NEXUS into legislative frameworks such as the CAP. A combined literature review methodology, participatory processes, and system dynamics modeling will be used to achieve these objectives.
This study is being carried out in three case studies in Spain: the Júcar, Tagus and Segura River basins. The combination of interviews, group workshops, and participatory modeling activities highlight the active involvement of stakeholders in the co-creation of governance practices. Conceptual and quantitative system dynamics models have been developed, integrating hydrological, climatic, and socio-economic data.
This project will contribute to integrating local knowledge, promoting the co-production of knowledge and fostering more effective and sustainable governance practices. Proactive stakeholder participation will be vital to addressing the complexity of the NEXUS and achieving sustainable and resilient development.

Acknowledgements: This study has received funding from the European Union’s Horizon 2020 research and innovation programme under the GoNEXUS project (GA No 101003722).

How to cite: Monico-Gonzalez, V., Martinez-Domingo, D., and Gomez-Martin, E.: Towards sustainable water-energy-food-ecosystems governance: an integrating participatory approach and systems modelling for co-exploring the nexus, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18064, https://doi.org/10.5194/egusphere-egu24-18064, 2024.

EGU24-18213 | Orals | ITS3.15/HS12.3 | Highlight

Inform to involve: women’s contribution to Water-Energy-Food-Ecosystems (WEFE) Nexus transition in Egypt 

Bianca Maria Rizzo, Tommaso Pacetti, Xenia Theodotou Schneider, Sendianah HamdyKhamis Shahin, Basma Hassank, and Enrica Caporali

Local perspectives provide invaluable insights into the intricate relationships between water, energy, and food systems, ensuring that interventions are aligned with community needs. Empowering local stakeholders fosters ownership, enhances resilience, and promotes equitable resource distribution. Community engagement facilitates the integration of traditional knowledge, optimizing the effectiveness of WEFE Nexus strategies. For this, a structured participatory approach based on the RRI Roadmap©™ is necessary to ensure the  interconnectedness of these vital systems, creating a foundation for holistic, locally adapted WEFE Nexus solutions that address the complex challenges at the intersection of water, energy, food, and ecosystems.

Since women and men often have distinct roles, responsibilities, and knowledge concerning resources use, distribution, and conservation, ignoring these gender dynamics may lead to the marginalization of women and the perpetuation of existing power imbalances, as well as a lack of essential information to support the transition towards a WEFE Nexus approach. Incorporating a gender lens enhances the accuracy and effectiveness of participatory processes, ensuring an effective and lasting WEFE Nexus implementation when these diverse needs and priorities of both genders are considered.

Within the activities of the NEXUS-NESS project, women's contribution to WEFE Nexus transition in Egypt has been investigated, organizing a set of workshops with the community of Wadi Nagamish watershed. The Bedouin community in Wadi Naghamish is characterized by its deep-rooted traditions and resilient way of life. Women play a pivotal role, actively contributing to both the household and community dynamics. Despite the arid surroundings, Bedouin women in Wadi Naghamish are skilled in resourceful practices, such as water conservation and traditional crafts. They are often the guardians of cultural heritage, passing down knowledge through generations. While facing challenges, Bedouin women maintain a strong sense of identity, embodying the community's values. Their roles extend beyond the domestic sphere, influencing decision-making processes and contributing significantly to the social fabric of Wadi Naghamish.

According to Bedouin cultural norms, women cannot share a room with men, who are not strictly related to them, and this prevents them from taking part in the first participatory workshop organized to involve stakeholders in the transition towards the WEFE Nexus.

Hence, for not losing womens knowledge sharing and involvement, the NEXUS-NESS workshops held in Wadi Naghamish were structured to enhance women's engagement. Female experts conducted a tailored capacity-building program in designated spaces, fostering a positive atmosphere for Bedouin women to learn WEFE Nexus concepts and devise solutions for prevailing water challenges.

The workshops’ results provide useful insights on the roles of women concerning resource management and consequently allowing to define a gender sensitive strategy for engaging stakeholders in the transition towards a WEFE Nexus approach.

How to cite: Rizzo, B. M., Pacetti, T., Theodotou Schneider, X., HamdyKhamis Shahin, S., Hassank, B., and Caporali, E.: Inform to involve: women’s contribution to Water-Energy-Food-Ecosystems (WEFE) Nexus transition in Egypt, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18213, https://doi.org/10.5194/egusphere-egu24-18213, 2024.

EGU24-20260 | ECS | Orals | ITS3.15/HS12.3

Data-driven system identification of the WEFE nexus: Challenges and prospects 

Elise Jonsson, Claudia Teutschbein, Malgorzata Blicharska, Andreina Francisco, Andrjiana Todorovic, Janez Sušnik, and Thomas Grabs

The Water-Energy-Food-Ecosystem (WEFE) nexus presents many challenges with regards to modelling. While attempts at conceptual modelling of this nexus have been made, increasing data availability due to electrification, smart infrastructure, and digitization of these sectors encourages a data-driven approach to system identification and control. Data-driven methods have had wide success in disciplines dealing with similar challenges as the WEFE nexus, such as the multiplicity of scales, nonlinearity and chaos, high dimensionality, fuzzy and stochastic social dynamics, as well as rare- or extreme event exposure. Here we provide a brief summary of data-driven methods of system identification that may address these challenges by looking at cross-disciplinary applications and its relevance for the WEFE nexus.

How to cite: Jonsson, E., Teutschbein, C., Blicharska, M., Francisco, A., Todorovic, A., Sušnik, J., and Grabs, T.: Data-driven system identification of the WEFE nexus: Challenges and prospects, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-20260, https://doi.org/10.5194/egusphere-egu24-20260, 2024.

Given current world population, persistence of global diet, and considerable environmental damage related
thereto, food consumption is a major source of concern for environmental sustainability. In relation to these
issues, Greater Geneva agglomeration outlined several legitimate, albeit potentially contrasting set of objectives
for 2050 in its 2022 political commitment for a sustainable transition: preserving and regenerating local
biodiversity, reducing environmental pressures generated by society, while ensuring good health, equity and
inclusion of all its inhabitants, and contributing to the improvement of world population’s well-being. To
arbitrate between these conflicting pledges requires the use of an accounting system able to integrate them
simultaneously. For this purpose, MuSIASEM accounting approach (Multi-Scale Integrated Analysis of Societal
and Ecosystem Metabolism) is applied to the Greater Geneva region and Geneva Canton: it relates information
pertaining to (i) the diet, (ii) the techno-economic performance of the agricultural sector, (iii) environmental
pressures generated by agriculture, (iv) the level of dependence on imports. MuSIASEM allowed to characterize
the region’s food metabolism for a current Swiss diet and a more plant-based diet: with a current Swiss diet,
were Greater Geneva region to internalize all food consumption, it would require considerable increases in the
share of agricultural land and agricultural workers in society. Shifting from an animal to a more plant-based diet
would significantly reduce environmental and social pressures. In addition, viewing Greater Geneva region as
reference political boundary for assessing food security would render the former more environmentally feasible:
thereby making an extension of Geneva Canton’s biodiversity strategy 2030 to Greater Geneva – of protecting
30% of territory for ecological infrastructure – in turn more plausible. This study showed the potential of
MuSIASEM approach in characterizing a region’s food metabolism, yet it could be applied in other domains to
assess a society’s water, energy or human activity metabolism.

How to cite: Folz, A., Lehmann, A., and Giampietro, M.: A tool for governance informed deliberation in Greater Geneva region:impossibility of current circular economy food metabolism, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-21001, https://doi.org/10.5194/egusphere-egu24-21001, 2024.

EGU24-22126 | ECS | Posters virtual | ITS3.15/HS12.3

Integrated Water-Energy Nexus Analysis: Dynamic Simulation of a Combined Hydro-Thermal Power Plant  

maryam siamaki, mohamad Gheibi, Atiyeh Ardakanian, Stanislaw Waclawek, and Kourosh Behzadian

This study presents an integrated water-energy nexus analysis through the dynamic simulation of a combined hydro-thermal power plant, focusing on a case study within the water-scarce region of Iran. The investigation aims to assess the mutual interactions between water resources and energy production, providing valuable insights for sustainable water and energy management practices. The simulation model incorporates system dynamics to capture the complex feedback loops between water availability, energy demand, and the operation of the power plant. The power plant is modeled as a combined hydro-thermal system, where water availability influences both hydroelectric and thermal power generation. The system's response to water availability is further modulated by feedback loops that consider the dynamics of water and energy demand. In the context of the Iranian water plant case study, the simulation is executed over 100-time steps to analyze the dynamic behavior of the system. The water supply response to water availability is characterized by a multiplier, and the energy supply response is modulated by a similar multiplier, reflecting the inherent connection between water and energy in the power generation process. Additionally, the thermal efficiency of the power plant is considered in the simulation to account for the impact of water availability on thermal power generation. The results of the simulation are visually represented through a heat map, providing a comprehensive overview of the temporal evolution of water demand, water supply, and energy supply. The custom colormap enhances visualization, enabling a clear interpretation of the interdependencies within the water-energy nexus [1]. The numerical results derived from the simulation offer valuable insights into the sustainable operation of the combined hydro-thermal power plant. The analysis highlights the importance of considering water availability in energy production decisions, showcasing the impact on both hydroelectric and thermal power generation. Furthermore, the simulation provides quantitative assessments of water shortage and energy shortfall, aiding in the identification of critical time periods and informing strategies for resource allocation and infrastructure planning [2]. By focusing on the Iranian context, where water scarcity is a prevalent concern, this study contributes to the development of region-specific water and energy policies. The findings underscore the need for integrated water and energy management strategies to address the challenges posed by changing water availability patterns and growing energy demands [3]. The presented simulation framework can serve as a valuable tool for policymakers and researchers in optimizing the operation of similar water-energy systems in arid regions, fostering sustainable development in the face of increasing water and energy challenges.

Keywords: Water-Energy Nexus; Power Plants; Programming; Sustainability; Performance assessment

How to cite: siamaki, M., Gheibi, M., Ardakanian, A., Waclawek, S., and Behzadian, K.: Integrated Water-Energy Nexus Analysis: Dynamic Simulation of a Combined Hydro-Thermal Power Plant , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-22126, https://doi.org/10.5194/egusphere-egu24-22126, 2024.

One of the main challenges preventing the sustainable development of the agriculture sector is the lack of a system-thinking approach, which includes economic systems, resource management practices [water and energy], production, and climate change. In Lebanon, the main variables affecting on-farm practices are socio-economic factors and climate change, leading to decreased purchasing power, limiting their access to energy and, thus water for agricultural production. While non-governmental organizations introduced solar power to cut energy costs and enhance water accessibility, they did not account for aquifer depletion resulting from excessive pumping. Additionally, adverse climatic conditions are reducing groundwater recharge, and escalating water demands. Thus, it is crucial to view the agricultural sector as an interconnected system and develop strategic plans for agricultural development where climate, water, energy, and production are collaboratively managed. This paper intertwines the Environmental Nexus and the Sustainable Livelihood Approach (SLA) to study the interlinkages, synergies, and trade-offs between water, energy, food, climate, and livelihood security. To assess on-farm practices and identify farmers' needs, the study employed a bottom-up approach, utilizing surveys, satellite imagery analysis, and interviews. Subsequently, farmers proposed sustainable solutions, which were tested using hydro-climatic models. Analysis of satellite imagery shows a connection between land-use patterns, drought events, and economic shocks. While drought led to economic losses and a subsequent decrease in land cultivation in the following year, the 2021 national economic meltdown in Lebanon had a contrasting effect, leading to an expansion in land cultivation. People sought to secure their food basket or establish a secondary source of income, intensifying competition for natural resources such as water, and increasing market competitiveness. Consequently, there was a substantial decline in farmers’ net revenue by 500-999 USD per dunum, as revealed by survey findings. Many farmers, though receiving aid, remain vulnerable to climate issues, water scarcity, and economic shocks. The modeling exercise, which is based on solutions proposed by farmers and is tested under the SSP3 Climate Change Scenario, indicates transitioning to crops with low water requirements, and high nutritional and economic value—such as 'Triticum turgidum var. durum'—is the most effective approach to reduce vulnerability to climate change and its shocks. While water harvesting and hydropower are considered less effective solutions. Finally, this paper proposes an integration of the Participatory Approach with the Climate-Water-Energy-Food System thinking approach for Socio-economic development.

How to cite: Bou Said, R., Mohtar, R. H., and Moussa, R.: Building Socioeconomic Resilience in a Climate-Pressured Water-Energy-Food System in Underdeveloped Rural Agricultural Farms in Lebanon, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-22390, https://doi.org/10.5194/egusphere-egu24-22390, 2024.

Economic, societal and environmental security are interdependently related to the availability and fair access to natural resources, water, land, food and energy. All four elements (water, food, energy and ecosystems) are 1) highly dependent on each other, 2) crucial for human well-being, and 3) impacting social cohesion and source of geopolitical conflicts. In the Mediterranean region the water scarcity and land degradation do not match sound and sustainable agricultural practices and often the protection of ecosystems is in conflict with economic growth instead of being safeguarded for improving water sources. NEXUS-NESS interlinks consolidated Water-Energy-Food (WEF) Nexus data, knowledge and tools and a three-fold Ecosystem component value (i.e. Environment, Economy and Engagement) to produce a comprehensive WEFE Nexus Service (NNS). State of the art biophysical models, WATNEEDS and FREEWAT, are employed to provide quantitative metrics and geospatial distribution of WEFE nexus parameters and resource-risk scenarios with varying climate, land, crop, energy and socio-environmental variables. The NNS is an analytical geo-service supporting the transferring to Nexus stakeholders and operators of WEFE Nexus models, scenarios and indicators for understanding the benefits of Nexus best practices. The NEXUS-NESS project, funded by Horizon 2020 PRIMA programme, started in 2021 and is ending in 2024, is achieving the following three main objectives:
1) Co-Produce WEFE Nexus management plans for fair and sustainable allocation of resources by applying the NNS into real case conditions through the four Multi-Actor diverse NEXUS Ecosystem Labs (NELs);
2) Operationalize the adoption of the WEFE Nexus by co-defining short to long-term resource management plans and hands-on guidance through application, validation and demonstration actions in the four NELs
3) Enable mindset change for the effective adoption of WEFE Nexus through the implementation of Innovation Ecosystems of private sector, academic, public authorities and citizens in the 4 NELs through the Responsible Research and Innovation (RRI) Roadmap and the six RRI dimensions (public engagement, open science, science education, gender issues, ethics and institutional change through governance).
To achieve these three main objectives, the NEXUS-NESS consortium has specified 4 NELs where multi-actors (stakeholders, private sector, public authorities, academia and citizens) will be engaged in a Living Lab setting by applying the RRI Roadmap. The multi-actors will be identified, motivated and engaged to frame a new WEFE-Nexus vision for their common socio-economic-ecosystem situation, co-design and co-construct WEFE-Nexus management plans and solutions, apply these, measure them, adjust them and intensify them. The NEXUS-NESS 4 NELS are:
1) Coastal Tuscany, Italy: focusing on minimization of groundwater extraction and salinization through non-conventional irrigation via consortia and natural ponds as bioreactors; 2)Rio Daja, Spain: improve agroecosystem and rural life of viticulture and agriculture with improved water-energy use during irrigation. 3) Matrouh Coastal area, Egypt: reduce saline brackish groundwater, increase cultivation through responsible irrigation and apply the WEFE Nexus approach for increasing crop yields.
4) Oued Jir Watershed of Gabes, Tunisia: intensify sustainable agriculture by balancing efficient use of natural resources through novel irrigation and land management by educating the local population of the highly arid area.

How to cite: Nardi, F. and the NEXUS-NESS Consortium: PRIMA NEXUS-NESS: Operationalizing transdisciplinary, stakeholder engagement and biophysical models for co-demonstrating the multiple social, economic and environmental benefits of WEFE Nexus approaches in four Euromed Nexus Ecosystem Labs, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-22567, https://doi.org/10.5194/egusphere-egu24-22567, 2024.

EGU24-768 | ECS | Posters on site | ITS3.18/HS12.4

Comparison of EM38 and Syscal Pro measurements for soil mapping in an agroforestry system 

Marco Carrara, Lorenzo Bonzi, Fatma Hamouda, Mino Sportelli, Angela Puig Sirera, Daniele Antichi, Lorenzo Gabriele Tramacere, Silvia Pampana, and Giovanni Rallo

Abstract: This study aimed to assess and compare the performance of EM38 (Geonics Limited) and Syscal-Pro (Iris instruments) EMI tools in soil spatial heterogeneity mapping. Mainly, the two tools were evaluated for their ability to explain the spatial variability of the soil resistivity, which strongly correlates with the soil’s physical status properties. Moreover, the effect of two surface soil roughness caused by two different tillage modalities has been studied.

The experimental plot (30 m width x 100 m length) consisted of an agroforestry system located in San Piero a Grado (Pisa, Italy, (, 43°41’07” N, 10°20’32” E).  Two 100-meters length deep open drains were located on the edges.  The soil texture is loam, with clay content values from 7.64% to 15.14% and sand content ranging from 22.36% to 49.37%. The intercropping system consisted of wheat (Triticum aestivum L) and pea (Pisum Sativum L) in the inner part of the field, and two rows of poplar (Populus x euramericana Dode Guiner) on the edges experimental plot.

Data were acquired before seed-bed preparation by pulling the two tools over the soil. For the Syscal-pro, 13 cylindrical stainless-steel electrodes were pulled by a tractor, allowing soil resistivity data acquisition according to the reciprocal Wenner-Schlumberger array (Telford, 1976). A total of five transects with 5 m spacing were spanned to the inner field zone, whereas four additional transects allowed to detail the resistivity gradients closed the two deep open drains.

Regarding the EM38 tool, a preliminary laboratory activity allowed the development of a specific data acquisition (DAQ) system for continuous monitoring of the resistivity data recording and spatializing. This DAQ system is based on a CR1000 Data logger (Campbell Scientific, United States), which allows collecting the speed and position of the EM-38 device by carrying it on a specifically designed sled system.

Two Garmin’s GPS (model 79S/SC for Syscal Pro and model GPS16X-HVS for the EM38) enabled georeferencing the collected data.

Preliminary results have shown a range of electrical conductivity values between 30 mS/m and 45 mS/m, spatially distributed according to the pattern obtained by Syscal-Pro. Further investigation is required to better understand the relationship between EM38 and Syscal-Pro measurements, after which the vertical domain explored has been standardised between the two methods.

Keywords: Agroforestry system, EM38, Syscal, soil bulk resistivity, soil bulk conductivity, spatial variability.

How to cite: Carrara, M., Bonzi, L., Hamouda, F., Sportelli, M., Puig Sirera, A., Antichi, D., Tramacere, L. G., Pampana, S., and Rallo, G.: Comparison of EM38 and Syscal Pro measurements for soil mapping in an agroforestry system, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-768, https://doi.org/10.5194/egusphere-egu24-768, 2024.

Accurate estimations of actual crop evapotranspiration are essential to evaluate crop water requirements, to improve water use efficiency in agriculture, and to optimize the use of available freshwater resources. To this aim, several models were developed to allow quantifying crop water requirements based on the knowledge of actual crop evapotranspiration rates, ETa.

The objective of this research was to estimate ETa using a simplified distributed model combining ground and remotely sensed data.

The experiment was carried out in a Mediterranean commercial citrus orchard (C. reticulata cv. Tardivo di Ciaculli) located in the Northwest of Sicily, Italy, during the whole 2019. The experimental layout consisted of: i) a WatchDog 2000 standard weather station (measuring the main climate variables and the precipitation depths, P); ii) a database of irrigation volumes, I, scheduled by the farmer; iii) an Eddy Covariance tower equipped with an open patch gas-analyzer, a three-dimension sonic anemometer, a four-component net radiometer, and a soil heat flux plate iv) a dataset of 75 Sentinel-2 multispectral images, acquired in clear sky condition.

In particular, the daily crop reference evapotranspiration, ETo, was calculated according to the FAO-56 Penman-Montheith equation using the climate variables; the crop coefficient, Kc, the Fractional Vegetation Cover, FVC, and, thus, the potential evapotranspiration, ETp, were computed via the processing of reflectance values in the RED, NIR and SWIR spectral bands. The Available Water, AW, the short-term water stress factor, Cws, and the ETa, were computed by analyzing cumulated ETp and water-supplying values using moving temporal windows characterized by different sizes (from 5 to 400 days).

The validation of the model outputs was carried out by taking into account the ETa of the pixels within the flux tower footprints estimated at each satellite acquisition day (i.e. by selecting the pixels on the basis of the footprint shape and extension). The performance of the model was evaluated for each temporal window size using the following metrics: the Root Mean Square Error, RMSE, the Mean Absolute Error, MAE, the angular coefficient of the regression line forced to the origin, b, and the determination coefficient, R2.

Results suggest that the best temporal window size for this crop is around 85 days allowing to achieve an RMSE of 0.51 mm d-1, a MAE of 0.38 mm d-1, a b value of 0.94 and an R2 of 0.96. The comparison with the model outputs over the whole field (all the pixels within the crop field) revealed that a strong decrease in all the metrics occurs if the validation of the remote sensing products is not properly carried out.

How to cite: De Caro, D., Ippolito, M., Capodici, F., and Ciraolo, G.: Testing the performance of a simplified distributed model to assess actual evapotranspiration in a Mediterranean orchard using ground and remotely sensed data, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-895, https://doi.org/10.5194/egusphere-egu24-895, 2024.

EGU24-1153 | ECS | Posters on site | ITS3.18/HS12.4

Automation of the Atmometer (ETgage) recording by pressure transducers sensors 

Lorenzo Bonzi, Àngela Puig Sirera, Emanuele Dichio, Fatma Hamouda, Andrea Sbrana, Damiano Remorini, and Giovanni Rallo

Abstract. In precision irrigation, it has become imperative to accurately evaluate the crop irrigation needs and return the right amount of water. Concerning to the application of thermodynamic-based models, the crop transpiration  can be determined with the original Penman-Monteith equation (Monteith, 1965) through the so-called "big leaf" approach. According to what was suggested by Jarvis and McNaughton (1986), for its evaluation through sensors, one valid option is the atmometer. This instrument is used to measure the quantity of water evapotranspired in a reference system (ET0), and the actual transpiration (Tc act), is calculated according to the weather-based approach (Allen et al., 1998). The ET0 in the atmometer is evaluated from the variation in the water level of the distilled water source placed inside the instrument tank, hydraulically connected to a porous ceramic capsule  covered with a green fabric (green canvas) which simulates the radiative and resistive behaviour of the reference culture. The most advanced model at present is the model-E with an electronic component for the automatic measurement of ET0 measures. In this model, the evaporated water is based on the emptying of a glass ampoule, with a capacity of 0.25 mm of water, filled automatically through a solenoid valve. Each emptying corresponds to 0.25 mm of evaporated and generates an electrical signal (count) detected by the data logger.

In our study, the atmometer (ETgage) was modified in the device for measuring the relative water level. The modification of the atmometer consists in the insertion of an RS-828-5708 piezoresistive pressure transducer. The pressure transducer returns an analog output in the 4-20 domain (mA) as a function of the hydrostatic head H (cm). The sensor was calibrated on the test bench of the DiSAAA-a AgrHySMo laboratory with paired measurements of hydrostatic head H(cm) and electrical signal read by the datalogger (mV). Therefore, the linear calibration equation between the two measurements was obtained with a slope of 0.3029 m/mV and an intercept of 10.804 m. Finally, the data series were improved thanks to a smoothing process, performed using a 3rd-4th order polynomial function (Savitzky and Golay, 1964) on data clusters equal to 17 points. The improved water level measurement system allows flow measurement at the sub-hourly scale. In open filed, the temporal dynamics of the atmometer were compared with the reference evapotranspiration calculated with the Penman-Monteith. The atmometer measurement showed an improvement compared to the respective estimated with the mathematical analogy, reducing the RMSE from 1.65 to 0.30 mm/day. The first results have demonstrated an accurate performance of the modified atmometer in estimating hourly reference evapotranspiration and its ability for precise irrigation planning based on hourly water consumption.

Keywords. Atmometer, field-instrumentation, sensor and model design, crop water status, precision irrigation.

How to cite: Bonzi, L., Sirera, À. P., Dichio, E., Hamouda, F., Sbrana, A., Remorini, D., and Rallo, G.: Automation of the Atmometer (ETgage) recording by pressure transducers sensors, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1153, https://doi.org/10.5194/egusphere-egu24-1153, 2024.

Data-driven irrigation planning can optimize crop yield and reduce adverse impacts on surface and ground water quality. We evaluated an irrigation scheduling strategy based on soil matric potentials recorded by wireless Watermark (WM) sensors installed in sandy loam and clay loam soils and soil-water characteristic curve data. Five wireless WM nodes (IRROmesh) were installed at each location, where each node consisted of three WM sensors that were installed at 15, 30, and 60 cm depths in the crop rows. Soil moisture contents, at field capacity and permanent wilting points, were determined from soil-water characteristic curves and were approximately 23% and 11% for a sandy loam, and 35% and 17% for a clay loam, respectively. The field capacity level which occurs shortly after an irrigation event was considered the upper point of soil moisture content, and the lower point was the maximum soil water depletion level at 50% of plant available water capacity in the root zone. The lower thresholds of soil moisture content to trigger an irrigation event were 17% and 26% in the sandy loam and clay loam soils, respectively. The corresponding soil water potential readings from the WM sensors to initiate irrigation events were approximately 60 kPa and 105 kPa for sandy loam, and clay loam soils, respectively. Watermark sensors can be successfully used for irrigation scheduling by simply setting two levels of moisture content using soil-water characteristic curve data. Further, the wireless system can help farmers and irrigators monitor real-time moisture content in the soil root zone of their crops and determine irrigation scheduling remotely without time consuming, manual data logging and frequent visits to the field.

How to cite: Jabro, J. and Stevens, W.: Irrigation Scheduling Based on Wireless Sensors Output and Soil-Water Characteristic Curve in Two Soils , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1268, https://doi.org/10.5194/egusphere-egu24-1268, 2024.

EGU24-1840 | ECS | Orals | ITS3.18/HS12.4 | Highlight

Irrigatmo: no-moving parts system for feed-back and feed-forward irrigation scheduling  

Emanuele Dichio, Lorenzo Bonzi, Giovanni Rallo, Angela Puig-Sirera, Damiano Remorini, Roberto Di Biase, Alba Nicoletta Mininni, and Rossano Massai
 

Abstract 

The weather-based approach quantifies the crop water requirements (CWR) using a simplified agrohydrological model coupled with meteorological sensors.  The FAO56 model (Allen et al., 1998) is one of the most used bucket models for CWR. In this model, the daily ET0 is usually estimated by the FAO-Penman-Monteith (PM), which needs as inputs standard atmosphere forcings acquired from weather stations, that often are equipped with ordinary mechatronics sensors that require regular maintenance. An atmometer (ETgage) is an accurate sensor with no moving parts that continuously measures the ET0 based on a physical analogy of the crop reference.  

This study aims to design and validate an expert system, named Irrigatmo, to manage irrigation based on the combined application of the feedforward- (FFc) and feedback- (FBc) control irrigation scheduling protocols. The FFc protocol comprises a Kc-based mass balance model with a modified atmometer and FDR sensors for sub-hourly ET0 and soil water content (SWC) measurements. At the same time, the FBc protocol uses the SWC to quantify the critical condition and the crop stress coefficient to adjust the Kcb value used in the bucket model. The system was implemented in proprietary logic (CR300, Campbell Scientific Inc.) and open-source logic (Arduino Mega 2560, Arduino). The core of the system implements a weather-based water balance model, trained by a modified atmometer and soil moisture sensor for sub-hourly scale ET0 and SWC, as well as an infrared thermometer and a contact thermocouple for quantifying the crop water stress index (CWSI). The ETgage was modified by integrating a pressure transducer sensor, calibrated to measure the water level inside the atmometer tank continuously.  

The results showed that Irrigatmo accurately and rapidly detected the changes in atmospheric and soil water conditions. The system can directly calculate the evapotranspiration reduction factor (Ks), estimating the CWSI based on canopy temperature measurements. This could overcome the uncertainty in the models associated with the water stress function based solely on the soil moisture. The system was built and calibrated within the AgrHySMo laboratory of DiSAAA-a and validated on a commercial kiwifruit orchard of Actinidia chinensis var. chinensis 'Zesy002'. The field testing made it possible to validate the system's ability to model the water stress functions of the crop and the sensitivity to identify the critical water status conditions that mark the transition to a limiting condition. Irrigatmo could manage irrigation autonomously, activating or turning off the solenoid valves, and returning to our field the amount of water lost during the evapotranspiration processes. Future perspectives consider the implementation of the proposed system in a wireless sensor network (WSN) and at the interfacing of the WSN nodes with aerial platforms where the edge-computing systems specialized also in the control of IoT-irrigation actuators will be located.  

How to cite: Dichio, E., Bonzi, L., Rallo, G., Puig-Sirera, A., Remorini, D., Di Biase, R., Mininni, A. N., and Massai, R.: Irrigatmo: no-moving parts system for feed-back and feed-forward irrigation scheduling , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1840, https://doi.org/10.5194/egusphere-egu24-1840, 2024.

Groundwater is a reliable and important source for irrigated agriculture but its use has consequences. In wetter regions, overuse of groundwater can threaten the health of streams that depend on discharge from the groundwater system. In drier regions or when groundwater withdrawals exceed the available groundwater recharge for a long time, groundwater resources will be depleted, and groundwater levels drop. The result is that farmers must extract water from increasingly deeper groundwater wells and incur greater costs for well construction and for the energy required to lift the water to the surface. Ultimately, a farmer can reach the economic limit for groundwater use when the cost of pumping water is larger than the revenue that can be generated with the crop. Farmers should consider this economic limit and adapt their cropping and production methods to safeguard economically sustainable production in the future.

In order to evaluate possible adaptation strategies to avoid or postpone reaching the economic limit we developed a cost-benefit model at the local -farmer’s- level called HELGA (Hydro-Economic Limits as a Global Analysis) balancing the investment costs to deepen the well in the short term against the net present value of added profits from groundwater extraction in the long term. In HELGA, crop water requirements are calculated and satisfied with the available soil water and with irrigation from groundwater to meet the with consideration of the application losses. We include aquifer recharge and other sources of water use (surface water supply to dynamically account for the groundwater requirements of crops. Hence, we place groundwater irrigation within the context of other water resources and consider the groundwater exploitation costs in conjunction with the other costs to produce a crop. To include the impact of groundwater pumping on groundwater depth we couple HELGA to the water resource model PCR-GLOBWB, thus introducing farmer-scale hydro-economic analysis in a global-scale hydrological model with a resolution of 5 arc minutes (~10 x 10 km globally). In this manner, groundwater dynamics and surface hydrology are linked and the competition for groundwater with other sectors included. This coupling allows us to understand globally the implications of groundwater (over) use in the long term and how this defines the solution space from the aggregate farmer’s perspective.

Our results show that farmers eventually reach the economic limit. Energy cost of groundwater pumping is one of the important drivers limiting groundwater use. Additionally, the increasing costs of the water infrastructure (i.e. deeper wells) is an important factor that explains the economic limit. Also, our analysis shows that variations in the irrigation water demand and the groundwater recharge as a result of climate variability strongly influences the profitability of groundwater-fed irrigated agriculture To counteract this, adaptation strategies such as changing the crop mix and increasing irrigation efficiency are effective in increasing the time to reach the economic limit and to extend the lifespan of aquifers. Farmers’ agency towards the management of a depleting resource make a difference in keeping this resource for future generations.

How to cite: Melo Leon, S. F., Van Beek, R., Reinhard, S., and Bierkens, M.: How to avoid or postpone reaching the economic limit of groundwater-fed irrigation? Aggregated analysis for adaptation strategies from the farmer’s perspective., EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-2871, https://doi.org/10.5194/egusphere-egu24-2871, 2024.

EGU24-2920 | ECS | Orals | ITS3.18/HS12.4

Assessing crop growth model accuracy under droughts and heatwaves 

Sneha Chevuru, L.P.H. (Rens) van Beek, Michelle T.H. van Vliet, Gambhir Lamsal, Landon Marston, and Marc F.P. Bierkens

Recent droughts and heatwaves have shown major impacts on the agricultural sector by inhibiting crop growth resulting in reduced crop yield. With an expected increase in the frequency and severity of droughts and heat waves due to climate change, accurate projections of crop yields under these hydroclimatic extremes are required. However, there is only limited knowledge on the accuracy of crop growth models under extreme events such as droughts and heatwaves. Understanding the accuracy of crop models under hydroclimatic extremes is a necessary first step to evaluate the significance of projections of crop yields under climate change.

To this end, our study addresses this gap by quantitatively evaluating three crop growth models— WOFOST, PCRGLOBWB2-WOFOST, and AquaCrop— in terms of their ability to simulate crop yield and hydrological fluxes under drought and heatwave conditions. The evaluation focuses on conditions of hydrological stress induced by droughts and heatwaves in the contiguous United States (CONUS) during the period 1981 to 2019. Our methodological framework utilises harmonised input data in terms of consistent climate forcing, cropping calendars and crop areas, to ensure a standardised comparison. Both rainfed and irrigated crops of three crop growth models are compared for the most abundant crop types (i.e. maize, wheat and soybean). 

The multiple output variables of these models are compared with reported data and satellite observations, most notably crop yield (reported on a county basis), irrigation water withdrawal (reported for a number of states) and leaf area index and evapotranspiration (from satellite observations). Additionally, we compare crop water requirements between the models. These methodological steps aim to discern structural differences among the models and identify key factors influencing performance variations, ensuring a thorough and rigorous evaluation. The findings and insights from this evaluation will advance our understanding of the intricate relationship between hydrological stress, crop growth, and sustainable agricultural practices under droughts and heatwaves.

How to cite: Chevuru, S., van Beek, L. P. H. (., van Vliet, M. T. H., Lamsal, G., Marston, L., and Bierkens, M. F. P.: Assessing crop growth model accuracy under droughts and heatwaves, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-2920, https://doi.org/10.5194/egusphere-egu24-2920, 2024.

In the domain of hydrological modeling, accurately determining initial conditions such as soil moisture content is crucial for enhancing simulation efficiency and applying these models effectively in water resource management, flood prediction, and drought forecasting. Traditional methods often rely on a data-intensive warm-up phase to establish these conditions, which diverts valuable data from calibration and validation. Addressing this challenge, our study introduces an innovative methodology that utilizes an alternative global soil moisture dataset to optimize these initial conditions without the conventional warm-up phase, thereby aiming to improve both the accuracy and efficiency of hydrological simulations. We focused on the Block-wise use of the TOPMODEL (BTOP) and ERA5-Land reanalysis data, specifically analyzing three soil moisture variables within the Fuji and Shinano River Basin, Japan. Through a comprehensive correlation analysis, we examined the dynamics between these variables and employed a range of curve-fitting functions alongside advanced techniques, particularly Long Short-Term Memory (LSTM) networks, to establish a robust relationship between BTOP and ERA5-Land soil moisture variables. The LSTM, known for their effectiveness in handling complex time series data, were instrumental in capturing the intricate spatial and temporal correlations between the variables. To validate the efficacy of our proposed methodology, we conducted four hydrological simulation scenarios, meticulously designed to assess the benefits of incorporating ERA5-Land soil moisture data into the model's initial conditions. The results were compelling: simulations using the enhanced initial conditions significantly outperformed those without the warm-up phase and closely approximated the 'optimal' scenario typically reliant on extensive warm-up data. This study not only underscores the potential of using reanalysis soil moisture data to refine initial conditions, thereby revolutionizing water resource management and forecasting practices, but also presents a scalable solution that can be adapted to various hydrological models and scenarios. Consequently, our research contributes significantly to the ongoing discourse on improving environmental modeling and management practices, advocating for more precise, resource-efficient, and adaptable methodologies in hydrological modeling.

How to cite: Zhou, L. and Liu, L.: Enhancing Hydrological Simulation Efficiency by Improving Initial Soil Moisture Conditions through Reanalysis Data, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5712, https://doi.org/10.5194/egusphere-egu24-5712, 2024.

EGU24-5764 | ECS | Orals | ITS3.18/HS12.4

Harnessing Soil Moisture Data for Enhanced Eco-Hydrological Modeling Precision in Snow-Dominated Catchment in Finland 

Anandharuban Panchanathan, Kedar Ghag, Amir Hossein Ahrari, Björn Klöve, and Mourad Oussalah

Eco-hydrological modeling in water resources management has a pivotal role in the assessment of physical processes at various spatial-temporal scales. However, modeling the hydrological processes intrinsically contains uncertainties. Such uncertainties need to be addressed to develop a reliable hydrological model. In this study, in-situ and remotely sensed soil moisture data are used to enhance the precision of hydrological modeling using the Soil and Water Assessment Tool (SWAT). The objectives of this study are, (i) to assess the uncertainty and their propagation in hydrological modeling using the conventional and multi-source data set, and (ii) to simulate the hydrologic parameters using soil moisture as an indicator to evaluate uncertainties in hydrological forecasting. This study is carried out in the Temmesjoki basin of northern Finland with a basin area of 1190 km2. This region’s land cover is dominated by forest (61%), agricultural lands (18%), and shrubs (13%). The average annual rainfall and annual average temperature in this region are 406.21 mm, and 2.60°C respectively. The mean daily discharge ranges from 0.17 to 34.15 m3/s. The in-situ soil moisture data and Soil Water Index from the Copernicus Global Land Service are used to test the hypotheses. The Sequential Fitting Algorithm (SUFI-2) in R-SWAT was used for sensitivity and uncertainty analysis and calibration of the streamflow and ET. Two conceptual models are built to compare conventional data sources and multi-source data sets for the assessment of uncertainties in the simulation of the hydrological process. Preliminary analysis of hydrologic parameters of the basin reveals higher and non-uniform distribution of rainfall, ET, and discharge during summer months. Furthermore, the application of soil moisture data for the calibration of the SWAT model reveals higher fitness score, and, at the same time, the in-situ soil moisture data are found to reflect more accurately the soil moisture conditions in SWAT model, which results in the reduction of uncertainties. Consequently, the model conceptualized with the multi-source data sets provides a better water budget for the catchment. 

How to cite: Panchanathan, A., Ghag, K., Ahrari, A. H., Klöve, B., and Oussalah, M.: Harnessing Soil Moisture Data for Enhanced Eco-Hydrological Modeling Precision in Snow-Dominated Catchment in Finland, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5764, https://doi.org/10.5194/egusphere-egu24-5764, 2024.

EGU24-6311 | Posters on site | ITS3.18/HS12.4

Hydrological indicators in an irrigated catchment with different crops in Spain: how research can contribute to fulfilling Sustainable Development Goals (SDG) through the basic indices for FabLabs. 

Blanca Cuadrado-Alarcon, Encarnación V. Taguas, Ignacio Domenech, Luciano Mateos, and Helena Gomez-Macpherson

The core of the 2030 Agenda for Sustainable Development, presents the 17 Sustainable Development Goals (SDGs), which constitute of a vital call for action by all world countries. “Clean water and sanitation”, “Industry, innovation and infrastructure”, “Sustainable cities and communities”, “Responsible consumption and production” and “Climate action”, among others, result a challenging field where scientists, farmers and other stakeholders should cowork to create successful tools and management protocols. FabLab approaches pursue to link scientific and technological elements and participatory actions of farmers, administrative institutions, companies and intermediaries for promoting open innovation environments to make technology-enabled products and practices adapted to local needs.

In this study, different types of hydrological signatures evaluated in a catchment of 303 ha with different type of crops, owner profiles and irrigation patterns, are presented as a base to provide the thresholds for alerts and emergency systems related with floods, herbicide peaks and/or sediment loads. Data series of the values of rainfall, runoff, herbicide and sediments collected in the gauge station of the catchment outlet were checked to quantify the impact of rainfall events of different return periods on the catchment responses. The knowledge of these features and procedures is essential to create innovations along the water cycle and improve the alarm protocols and irrigation management in commercial farms.

How to cite: Cuadrado-Alarcon, B., Taguas, E. V., Domenech, I., Mateos, L., and Gomez-Macpherson, H.: Hydrological indicators in an irrigated catchment with different crops in Spain: how research can contribute to fulfilling Sustainable Development Goals (SDG) through the basic indices for FabLabs., EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-6311, https://doi.org/10.5194/egusphere-egu24-6311, 2024.

Root zone soil moisture (RZSM) serves as a crucial metric for assessing water stored in the soil. Modeling approaches are commonly employed in estimating RZSM. However, modelled RZSM often deviate from true RZSM values due to errors from model input data and parameters. Machine learning methods and data fusion techniques can enhance simulation accuracy. In this study, we conducted a comparative analysis of three methods for RZSM data fusion: random forest (RF), extended triple collocation (ETC), and Bayes Three Cornered Hat (BTCH).

Soil moisture observation data from 2018 to 2022 were collected at 2121 sites across China from the China Meteorological Administration (Fig.1). Daily average data were calculated by arithmetically averaging hourly data and used in the analysis. Six RZSM datasets were utilized, including SMAP Level 4, GLDAS-NOAH2.1, GLDAS-Catchment2.2, ERA5, MERRA2, and CRSR. All these data were resampled to 0.25° to maintain the same spatial resolution and were arithmetically averaged as daily averages. Additionally, some parameters related to soil, climate, and vegetation were used to build a machine learning model, specifically a random forest model. 

Fig. 1 Distribution of soil moisture sites and daily soil moisture (m3/m3) at depths ranging from 0–50 cm across China during the period from 2018 to 2019

To investigate the impact of different inputs on the performance of the RF method, three groups of inputs were employed. The specifics of the inputs used for the three methods are outlined in Table 1. The evaluation of the RF method results was carried out using a five-fold cross-validation approach.

Model Inputs
RFmodel1 NOAH, SMAP, ERA5, MERRA2, CFSR, CLSM, LAI, Soil properties, Meteorological data
RFmodel2 NOAH, LAI, Soil properties, Meteorological data
RFmodel3 NOAH, SMAP, ERA5, MERRA2, CFSR, CLSM
BTCH NOAH, SMAP, ERA5, MERRA2, CFSR, CLSM
ETC NOAH, MERRA2, CLSM

 

The boxplots show RFmodel1 performs best, emphasizing the need for comprehensive information in machine learning models. RFmodel2, superior to RFmodel3, highlights the significance of LAI, soil properties, and meteorological data in RZSM estimation. ETC and BTCH outperform individual RZSM datasets, especially in the absence of true data. The superior performance of ETC over BTCH is attributed to ETC's inputs, namely NOAH, MERRA2, and CLSM, which exhibit better accuracy compared to SMAP, ERA5, and CFSR, the inputs used by BTCH.

Fig.2 Boxplots of the Pearson coefficient (R), Root Mean Square Error (RMSE), and bias between in situ root zone soil moisture (RZSM) and its estimates from the three random forest models, Bayes Three Cornered Hat (BTCH), and Extended Triple Collocation (ETC) methods

In summary, the random forest method outperforms BTCH and ETC in the fusion of root zone soil moisture (RZSM) data, highlighting the importance of including leaf area index (LAI), soil properties, and meteorological data in the construction of the random forest model. Both BTCH and ETC demonstrate utility in enhancing RZSM estimates, making them valuable options when true data is unavailable.

How to cite: Tian, J. and Zhang, Y.: Comparison of Root Zone Soil Moisture Data Fusion Using Machine Learning, Triple Collocation, and Three-Cornered Hat Methods, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-7223, https://doi.org/10.5194/egusphere-egu24-7223, 2024.

EGU24-9299 | ECS | Orals | ITS3.18/HS12.4 | Highlight

Combining Remote Sensing and Low-Cost Sensors for LULC and Irrigation Characterization in the South of France  

Christina Anna Orieschnig and Paul Vandôme

In the face of climate change, Mediterranean regions, such as the South of France, are increasingly struggling with drought, water scarcity, and low groundwater levels. For agricultural regions relying on irrigation systems to guarantee summertime crop productivity, this is a central issue. Consequently, optimizing agricultural water uses and understanding the impact of irrigation systems on local and regional hydrological processes is indispensable. At larger scales, another challenge is to identify crop types as well as cropping and irrigation patterns for irrigation water management, reservoir operation, and real-time resource allocation. In this context, remote sensing provides a promising approach.  

This study focuses on combining land use - land cover (LULC) analyses based on Sentinel-1 and -2 data and in-situ measurements realized using innovative low-cost sensors, to characterize irrigation water use in two Southern French case study areas. The first of these, the Crau area in Provence, is specialized in using gravity irrigation to make the production of high-quality hay possible even during the arid summer months. The second area is a viticultural one, centred around the Canal de Gignac approximately 100 km further West, in which the majority of vines are sustained using drip irrigation, provided consistent water access is possible. In both cases, the study aimed first to identify irrigated plots, and then to further characterize the irrigation practices with regard to agricultural water use efficiency. 

The LULC analysis was carried out in Google Earth Engine, using a Gradient Tree Boosting (GTB) algorithm on combined Sentinel-1 and -2 imagery from which several spectral indices as well as Haralick texture features were calculated. The detection of irrigated grassland plots further relied on a temporal characterization of phenological stages. Subsequently, a comparative implementation of different irrigation monitoring approaches was carried out, using soil moisture estimates derived from Sentinel-1 and different optical spectral indices. Data from low-cost sensors and local water user associations was used for calibration and validation. 

Preliminary results indicate that combining these diverse approaches make an operational detection and monitoring of irrigation practices possible. For the detection of irrigated vineyard and grassland plots during the 2023 growing season, overall accuracies of 92% and 95% respectively were achieved. The comparison of different irrigation monitoring approaches showed that the Normalized Difference Moisture Index (NDMI, p=0.002), the Shortwave Infrared Water Stress Index (SIWSI, p=0.001) and the Specific Leaf Area Vegetation Index (SLAVI, p=0.001) showed the highest potential for accurate irrigation detection.

How to cite: Orieschnig, C. A. and Vandôme, P.: Combining Remote Sensing and Low-Cost Sensors for LULC and Irrigation Characterization in the South of France , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-9299, https://doi.org/10.5194/egusphere-egu24-9299, 2024.

EGU24-10617 | ECS | Orals | ITS3.18/HS12.4

Electrical Resistivity Tomography (ERT) to monitor the efficiency of different irrigation systems in horticulture field 

Agnese Innocenti, Veronica Pazzi, Marco Napoli, Rossano Ciampalini, Simone Orlandini, and Riccardo Fanti

Water management in agricultural systems is essential for optimal crop yields without incurring excessive water costs and wastage. The choice of irrigation method is crucial for better water management and distribution. The drip system appears to be among the best methods in the field of precision agriculture. In addition to the irrigation system, mulching with ridge plastic film to drain excess water is widely used to increase crop yields in terms of plant water availability.

In this study, the time-lapse Electrical Resistivity Tomography (ERT), a not-invasive geophysical technique, is proposed as a simple and reliable method to evaluate the effectiveness of the irrigation systems and to monitor the changes in water content over time and over a volume of soil. ERTs data were compared to moisture one retrieved from sensors that record continuously over time, but punctually. The ERT investigations were conducted in melon-growing lands in southern Tuscany (Italy).

The aim of the work was to evaluate, by means of volumetric measures of the soil conductivity, the effectiveness of three different drip systems and of the mulch ridge: a two-wings drip system and a three-wings drip line with the same flow rate and a three-wings drip lines with a higher flow, in two different seasonal periods (spring and summer). In both the monitored fields the ridge was created in a half portion of the field itself, while in the other part the land was left plat.

The data collected showed that the 2-wing system was particularly ineffective, and that the distribution of irrigation water favoured some areas more than others. While they led to satisfactory results for the 3-wing system and same water flow than two wings and the 3-wing system and highest water flow. The first system has shown that the same quantity of water as the classic irrigations system (two wings) distributed over three wings instead of two leads to a greater concentration of water in the root zone over time, slowly draining downwards. On the contrary, the second system distributes the water uniformly like the first system, but the quantity introduced was excessive, leading the soil to always be positioned above the field capacity and draining a lot of water downwards. The excessive accumulation of water below the root zone represents a waste of water, as this cannot be used by the root system. The tests, in addition to considering which system was optimal, also evaluated the effectiveness of the mulch ridge, leading to the deduction that during the spring season a ridge of height equal to or greater than 20 cm is to be considered better than a ridge of less than 20 cm or absent, as it allows excess water, represented by rainfall, to be drained. However, during the summer period, when rainfall is less if not absent, the presence of a much lower ridge (around 10 cm in height) is much more effective as it allows the irrigation water to be retained at the root system avoiding excessive drainage.

How to cite: Innocenti, A., Pazzi, V., Napoli, M., Ciampalini, R., Orlandini, S., and Fanti, R.: Electrical Resistivity Tomography (ERT) to monitor the efficiency of different irrigation systems in horticulture field, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-10617, https://doi.org/10.5194/egusphere-egu24-10617, 2024.

In the Mediterranean region, agricultural water use accounts for a large share of the water demand and is key for food security and socio-economic stability in rural areas. At the same time, both managing irrigation in farms and managing water distribution to farms are not trivial tasks, since the water requirements by crops are site-specific and vary in time because of weather, agronomic management and other factors. In this context, the availability of EO data opens the opportunity to develop tools for the supervision, management and forecast of irrigation, scalable from farms to districts and basins. Time series of observed biophysical parameters of the vegetation and estimates of actual crop evapotranspiration (ETa) are promising resources for these applications. Those data can be assimilated into digital twins that integrate observations from different sources with models of crop development and soil water balance, enabling assessments of irrigation performance and management decision making. Here we describe a decision-making approach for irrigation district managers that assimilates EO data and simulates the water balance parameters of the soil-crop system at each individual plot. The goal is to obtain a dynamic view of irrigation performance scaling from individual plots to the basin, quantifying at real time the progress of crop growth and seasonal water balance, including forecasts of the forthcoming crop water demands under different meteorological scenarios. This approach has been implemented in the Catalan side of the Ebro basin (Spain), on an area of 2600 km2 covering 105 municipalities. A separate digital twin was defined for each of over 130000 agricultural plots listed in the Land Parcel Identification System. For each plot, the agricultural scenario was set according to open data of EU CAP’s Single Farm Payment and a soil map of the area. This included the list of crops declared from 2015 to 2022, the irrigation system and the soil class. From these basic categoric data, more detailed parameters of the crop, soil and irrigation method were assigned according to the description of actual agricultural scenarios on the area. The development of the crop and its soil water balance at each individual plot is simulated at real time, using a customized model based in a rationale similar to FAO’s AquaCrop, but with additional adaptations to permanent crops, localized irrigation and discontinuous canopies. Simulations are updated every day, using online weather data from the Meteorological Service of Catalonia. In parallel, as soon as new Sentinel-2 images are available, fAPAR and LAI are computed through the Biophysical Processor available in the SNAP software and these parameters are assimilated in the model. The output are maps and time series with the estimated ETa, irrigation amounts and available soil water at each plot, accessible at www.irrilleida.cat. Time series cover the whole year, on a week basis, including the forecasts of crop water demands for the remaining part of the year.

How to cite: Casadesús, J., Pàmies, M., and Bellvert, J.: IrriLand, a digital twin assimilating biophysical parameters of vegetation to assess and forecast site-specific crop water requirements at irrigation district scale, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-11546, https://doi.org/10.5194/egusphere-egu24-11546, 2024.

EGU24-11875 | ECS | Orals | ITS3.18/HS12.4 | Highlight

DL-Driven Precipitation Correction for Enhanced Hydrological Simulations over Central Europe 

Kaveh Patakchi Yousefi, Alexandre Belleflamme, Klaus Goergen, and Stefan Kollet

Integrated hydrologic models are useful for assessing the impact of climate change on water resources and associated risks. The performance of these models strongly depends on the quality of precipitation forcing data, where errors can significantly affect the simulation accuracy. Therefore, methods such as data assimilation (DA) bias adjustments, and data-driven (e.g., deep learning, DL) methods are in use to improve precipitation simulation data. However, given the high spatiotemporal variability of hourly precipitation, challenges such as availability of “ground truth” measurements, data imbalance, and evaluation of the methods affect the applicability and assessment of these methods. In this study, we correct precipitation data for the first 24h obtained from the  ECMWF HRES 10-day deterministic forecast using EUMETSAT H-SAF h61 satellite observations, by learning the errors using a U-Net convolutional neural network (CNN) as a DL technique. Our findings show good agreement between the corrected precipitation data (HRES-C) and the reference data (H-SAF) with roughly about 49%, 33%, and 12% improvement in mean error, root mean square error, and Pearson correlation, respectively. Additionally, we investigate the impact of original HRES, H-SAF, and HRES-C corrected products used as forcing data in high-resolution (~0.6km) integrated hydrologic simulations using ParFlow/CLM over central Europe in daily and monthly scales from April 2020 to December 2022. We choose soil moisture (SM) as a diagnostic variable for our evaluation. SM simulations produced with uncorrected HRES 24h show a better agreement with ESA CCI SM satellite data compared to SM produced with HRES-C. Further comparison of the three products with in-situ rain gauge measurements over the same period shows superiority of HRES 24h in representing the “ground truth” precipitation.  Our study highlights the need for better precipitation reference data, challenging reliance only on satellite observations (H-SAF) for DL-based correction of precipitation forcing data in hydrological simulations.

How to cite: Patakchi Yousefi, K., Belleflamme, A., Goergen, K., and Kollet, S.: DL-Driven Precipitation Correction for Enhanced Hydrological Simulations over Central Europe, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-11875, https://doi.org/10.5194/egusphere-egu24-11875, 2024.

EGU24-12660 | Posters on site | ITS3.18/HS12.4

Machine learning approach for prediction of groundwater levels based on ERA5 reanalysis 

Anna J. Żurek, Radosław Szostak, Przemysław Wachniew, and Mirosław Zimnoch

We have examined the feasibility of ECMWF Reanalysis (ERA5) data for groundwater level prediction for 19 groundwater wells from two neighboring Groundwater Bodies (GWB) comprising around 4000 km2. Groundwater level data were retrieved from monitoring wells operated within the framework of the Polish Hydrogeological Survey.  ERA5 reanalysis data  were averaged for all grid points within the modelling area. Predictions were made using various machine learning regression algorithms incorporating autoregression and exogeneous variables derived from ERA5 reanalysis (precipitation amount, evapotranspiration, runoff, snowmelt). Training sets were extracted from time series of data representing period from November 2001 to November 2022. The applied approach allows for predicting groundwater levels based on current meteorological conditions.

This research was funded by National Science Centre, Poland, project WATERLINE (2020/02/Y/ST10/00065), under the CHISTERA IV programme of the EU Horizon 2020 (Grant no 857925).

How to cite: Żurek, A. J., Szostak, R., Wachniew, P., and Zimnoch, M.: Machine learning approach for prediction of groundwater levels based on ERA5 reanalysis, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12660, https://doi.org/10.5194/egusphere-egu24-12660, 2024.

EGU24-12808 | ECS | Orals | ITS3.18/HS12.4

Use of Cosmic-ray Neutron Sensing for soil water management 

Markus Köhli, Jannis Weimar, Patrizia Ney, Felix Nieberding, Patrick Stowell, André Torre Netto, Klaus Goergen, Heye Bogena, and Ulrich Schmidt

Accurate soil moisture (SM) monitoring is key in climate modeling, hydrological observations and irrigation as it can greatly improve water use efficiency, the understanding of energy transfer over the land surface and ground water dynamics. Recently, Cosmic-Ray Neutron Sensors (CRNS) have been recognized as a promising tool in SM monitoring due to their large footprint of several hectares and half a meter in depth. Using this technique one can relate the flux density of albedo neutrons generated in cosmic-ray induced air showers to the amount of water in the environment. CRNS have great potential as to the non-invasive nature of the method and the low-maintenance independently operating sensors. In the last years this type of sensor has been integrated into several national and international monitoring networks like COSMOS, COSMOS-UK, ADAPTER and TERENO sites. Initially, CRNS instruments have relied on the use of a scarce material - helium-3. In order to scale up the method and to reduce costs within the CosmicSense research group recently large-scale instruments have been developed using alternative technologies including readout electronics and data acquisition systems. With a more economical operation the initial focus on hydrological research Cosmic-Ray Neutron Sensors are emerging into applied agricultural contexts, for example irrigation management and soil moisture mapping. Examples are the integration of CRNS into the SWAMP (LoRa) or the Nb-IoT network of the German Chamber of Agriculture. This project, called ADAPTER, involves the development and provision of innovative simulation-based information products for weather- and climate-resilient agriculture. These are daily (”soil”) weather and comprehensive long-term climate change information available to the agricultural community and all interested parties as easy-to-use analyses, data products with forecasts, and information interfaces. Still, challenges for CRNS are posed for scenarios especially for irrigated fields with a size smaller than the CRNS footprint or heterogeneous conditions with respect to the biomass distribution.

How to cite: Köhli, M., Weimar, J., Ney, P., Nieberding, F., Stowell, P., Torre Netto, A., Goergen, K., Bogena, H., and Schmidt, U.: Use of Cosmic-ray Neutron Sensing for soil water management, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12808, https://doi.org/10.5194/egusphere-egu24-12808, 2024.

Across Latin America, floods are one of the major hazards, and their impacts are exacerbated by climate change and poor societal preparedness. The latter is mainly due to the lack of methods that could provide insights about where and when extreme events could happen and what their hydraulic response might be. The data-scarcity and lack of open-source tools are one of the main barriers to improving resilience in the context of flooding. Nonstructural measures such as early warning systems are typically based on empirical approaches relating rainfall thresholds in order to inform about potential floods at country or continental scales. Nevertheless, this ignores the hydraulic behavior and rainfall-runoff mechanics. This research presents the first steps to establish an open-source Early Warning System (EWS) by employing a hydrodynamic model (Hydropol2D) integrated with quasi-global rainfall estimations from PERSIANN PDIR-Now and numerical weather predictions from the Global Forecast System (GFS). The model is capable of running at multiple spatial scales, combining near real-time flood modeling (as a Digital Twin) which shares the current system states as a base scenario for the forecasting system (as an EWS). Additionally, the model features a graphical interface for monitoring current hydraulic conditions and predicting future flooding based on rainfall forecasts. From one year of initial modeling results as a system warm-up, we observed the model's speed viability due to its parallel computing capability. The integration of freely available rainfall data and real-time gauge stations of flow stages and discharge shows the potential of the model as a Digital Twin at a continental scale. However, the model still lacks a recursively parameters updating routine to improve output accuracy, and regular calibration and validation procedures are necessary for each point of interest. Furthermore, the inclusion of evapotranspiration and soil moisture remote sensing data needs to be considered due to their impact on long-term hydrological modeling. These initial steps to combine a Digital Twin and an EWS could strengthen resilience where data is limited, empowering vulnerable communities through participatory adaptation and enhanced capacity. The open-source, customizable platform is accessible for organizations to implement early warning systems within areas with growing risks.

How to cite: Castillo Rápalo, L. M. and Mendiondo, E. M.: Towards establish a continental Early Warning System for flood Preparedness: A study case of South America's data-scarce countries, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-13442, https://doi.org/10.5194/egusphere-egu24-13442, 2024.

EGU24-16164 | ECS | Orals | ITS3.18/HS12.4

Revisiting border irrigation management: benefits of new in-field sensor-based control compared to conventional cutoff times 

Paul Vandôme, Amine Berkaoui, Cedric Guillemin, and Crystele Leauthaud

Surface irrigation is often described as low performing insofar as its practice is labour intensive and involves the use of large water flows that are difficult to quantify and manage. However, this method remains predominant worldwide, and modernisation towards localised irrigation systems is not always feasible or advisable. To support border irrigation management, we previously developed a low-cost sensor for surface irrigation management, which remotely informs the farmer of water arrival downstream of his or her field and therefore of the moment to stop irrigation. The objectives of this study were: i) to determine the optimal position of this sensor lengthwise in the field throughout the season, and ii) to compare the influence of management scenarios (sensor-based or time-based cutoff) on irrigation performance. To this end, an integrated agro-hydraulic model was developed to simulate surface water flow dynamics throughout the season including variations in infiltration and roughness. The model was fed using monitoring data from the border irrigation of a hay field during a whole season. The results showed that the optimal sensor position can change by 10% over the course of the season, depending on inflow rates, initial soil moisture and Manning’s roughness. Sensor-based irrigation control was found to be more efficient than actual practices, and more effective than an optimised cutoff time in limiting performance gaps induced by variability or uncertainty in the initial conditions. The methods and findings should serve as a basis for larger-scale studies integrating the adoption of sensors and real-time data for surface irrigation management.

How to cite: Vandôme, P., Berkaoui, A., Guillemin, C., and Leauthaud, C.: Revisiting border irrigation management: benefits of new in-field sensor-based control compared to conventional cutoff times, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-16164, https://doi.org/10.5194/egusphere-egu24-16164, 2024.

EGU24-16802 | ECS | Orals | ITS3.18/HS12.4

Soil moisture forecast based on gridded historical and forecast datasets 

Mojtaba Saboori, Abolfazl Jalali Shahrood, Kedar Ghag, and Björn Klöve

Continuous monitoring of soil moisture (SM) has become a prevalent approach in precision irrigation control. Fluctuations in SM within the root zone, whether caused by overly wet or dry conditions, can potentially diminish plant transpiration, leading to decreased productivity. Hence, ensuring a timely and appropriate supply of water is essential for effective irrigation management. Though various machine and deep learning models, along with in-situ climate data, have been examined for monitoring SM, the incorporation of gridded historical and forecast climate data into this aspect has not been explored. In this research, we assess forecasting SM by Random Forest (RF) model for the next 7 days using two approaches: A) relying on forecasted data for each day, and B) relying solely on historical data. To this end, the gridded climate data (air temperature, relative humidity, wind speed, precipitation, and reference evapotranspiration-ET0), the soil features (lagged in-situ SM and gridded soil temperature), and vegetation features (Normalized Difference Vegetation Index-NDVI) for different land covers in Oulu, Finland. The findings suggest that using gridded data could be a promising option in places where there is limited data for the SM forecasting. The lagged SM was the most explaining variable, followed by soil temperature, NDVI, and ET0. Furthermore, both scenarios exhibited similar trends, showing a decline in forecasting accuracy as the lead time approached 7 days, and thus scenario B can provide more efficient SM forecasts.

How to cite: Saboori, M., Jalali Shahrood, A., Ghag, K., and Klöve, B.: Soil moisture forecast based on gridded historical and forecast datasets, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-16802, https://doi.org/10.5194/egusphere-egu24-16802, 2024.

EGU24-17122 | Orals | ITS3.18/HS12.4

Spatial and temporal drought analysis in susceptible agroecosystems: the case of Thessaly region, Greece 

Stavros Sakellariou, Marios Spiliotopoulos, Nicolaos Alpanakis, Ioannis Faraslis, Pantelis Sidiropoulos, Georgios Tziatzios, George Karoutsos, Nicolas Dalezios, and Nicholas Dercas

Drought consists one of the most critical environmental hazards for the viability and productive development of crops. This paper is focused on the application of the Standardized Precipitation Index (SPI) for drought analysis and classification. The SPI is a commonly used drought index that calculates the difference between a given time period's precipitation and its long-term average. The objectives of the study are to conduct a spatiotemporal drought analysis, estimate drought severity using the SPI, identify both dry and wet periods, classify drought using the SPI, classify the degree of drought/wetness conditions using a classification scheme for multiple timescales, and calculate and classify SPI12 for each month from 1981-2020. The study area is Thessaly, Greece, which is the country’s largest agricultural productive region facing water availability problems. The innovation of this paper is the spatiotemporal drought analysis through the use of CHIRPS (Climate Hazards Group InfraRed Precipitation with Station data) instead of conventional meteorological data, avoiding the use of a prevailed sparse weather network, and the difficulties arising from that. The study shows that the region has faced two severe years of drought in 1988 and 1989, which led to moderate and extremely drought conditions, respectively. In contrast, extremely wet conditions were observed in 2002-2003, while 2009-2010 experienced moderately wet conditions. In this context, the mapping of spatial and seasonal variability across the study area permits more targeted measures instead of horizontal policies.

Keywords: drought; SPI; CHIRPS; Thessaly; Greece; desertification

How to cite: Sakellariou, S., Spiliotopoulos, M., Alpanakis, N., Faraslis, I., Sidiropoulos, P., Tziatzios, G., Karoutsos, G., Dalezios, N., and Dercas, N.: Spatial and temporal drought analysis in susceptible agroecosystems: the case of Thessaly region, Greece, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17122, https://doi.org/10.5194/egusphere-egu24-17122, 2024.

EGU24-17532 | ECS | Orals | ITS3.18/HS12.4 | Highlight

Novel assessment and development of land surface modelling for irrigation schemes in Mediterranean apple orchards 

Cosimo Brogi, Olga Dombrowski, Heye Reemt Bogena, Harrie-Jan Hendricks-Franssen, Sean Swenson, Vassilios Pisinaras, and Andreas Panagopoulos

Land-surface models (LSM) that simulate agricultural systems can provide key support for decision makers in precision irrigation and in the management of water resources under different climate scenarios. An accurate representation of irrigation in LSM is also crucial to understand how irrigation practices influence land-atmosphere processes from regional to global scale. Irrigation practices are increasingly integrated into LSM. However, challenges such as lack of data for model development and validation undermine the possibility to evolve current LSM into precision irrigation applications as well as into decision-making tools at the catchment scale and beyond.

In this study, we used the Community Land Model version 5 (CLM5) and assessed the representation of irrigation practices and consequent effect on crop yield in the model using a) the existing irrigation scheme of CLM5 and b) a novel irrigation data stream that allows to directly use observed irrigation data. Additionally, we used CLM5 to investigate irrigation requirements as well as the effect of deficit irrigation on crop yield and crop water use efficiency (CWUE) at the catchment scale (~45 km2). Model validation was supported by two highly instrumented apple orchards located in Agia (Greece) within the Pinios Hydrologic Observatory (PHO). From 2020, an ATMOS41 all-in-one climate station for monitoring meteorological data and a SoilNet sensor network for measuring soil moisture and matrix potential at various depths across 12 locations with SMT100 and TEROS21 sensors were used in both orchards. Additionally, a System SP cosmic-ray neutron sensor (CRNS) was installed in the centre of each field to monitor the field-averaged soil moisture, and several water meters were used to monitor irrigation rates in the orchards. Finally, one field was equipped with six SFM-1 sapflow sensors to estimate whole-tree transpiration and with six SnapShot Cloud 4G remote outdoor cameras.

We found that the novel irrigation data stream outperformed the existing scheme in terms of soil moisture simulation, even when the latter was manually adjusted to better mimic actual irrigation practices. However, both methods resulted in similar harvest predictions. Nonetheless, the fact that the existing scheme lacks the necessary flexibility to represent specific irrigation practices can have important implications for the simulation of infiltration, runoff, and sensible and latent heat fluxes. Furthermore, a 25 % irrigation reduction had negligible effect on simulated yield and CWUE at the catchment scale, while a 50 % reduction negatively affected both yield and CWUE depending on climatic conditions, soil properties, and irrigation timing (on average -30 % and -17 %, respectively). Although further process representations, such as the potential impact of deficit irrigation on crop quality, have yet to be implemented in CLM5, our results clearly show how CLM5 could be utilized for irrigation and water resources management at the field and catchment scales.

How to cite: Brogi, C., Dombrowski, O., Bogena, H. R., Hendricks-Franssen, H.-J., Swenson, S., Pisinaras, V., and Panagopoulos, A.: Novel assessment and development of land surface modelling for irrigation schemes in Mediterranean apple orchards, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17532, https://doi.org/10.5194/egusphere-egu24-17532, 2024.

EGU24-19565 | Posters on site | ITS3.18/HS12.4

An open-source tool based on Google Earth Engine for spatially explicit crop yield modelling  

lorenzo crecco, sofia bajocco, mara di giulio, and simone bregaglio

Process-based crop models can predict harvested yield by reproducing the effects of the environment on plant phenology and physiology. Accurate yield forecasts are essential to support strategic and tactical actions in public and private sectors. Applications span from detecting critical areas for food security issues to optimizing selling/buying prices of crop products in main producing regions, to informing farmers on the best agricultural management practices. Most crop models are point-based and must be integrated in a spatially explicit environment to provide the yield information in a target area at the desired spatial resolution. Remote sensing (RS) represents an invaluable resource to inform crop models with actual vegetation dynamics based on consistent and timely views of Earth's surface with time and space continuity. The main advantage of incorporating RS data into crop models is hence the representation of the missing spatial information and the reliable description of the crop’s health condition throughout the growing season. This study presents, an open-source tool developed within the Google Earth Engine environment to monitor crop growth and estimate crop yield. It is based on a generic model (SIMPLE) executed over large areas at run-time and is easily adapted to different crops by adjusting a few physiological parameters. SIMPLE algorithmic implementation uses ERA5-Land as weather source and derives the leaf area index (LAI, unitless) and the actual crop evapotranspiration (ETc, mm day-1) using data from the MODIS Normalized Difference Vegetation Index (NDVI). Results show that integrating RS data into the SIMPLE model allowed currently identifying the limits of the growing season and mapping seasonal crop phenology evolution in the Piedmont region. Abiotic stresses have been correctly spotted, and aboveground and yield of winter wheat and maize have aligned with reference data. Our findings have significant implications for improving yield estimations by identifying spatial patterns of crop growth productivity for summer and winter crops. This tool also shows potential for near-real-time monitoring of crop growth dynamics in response to abiotic stresses in sensitive phenological phases.

How to cite: crecco, L., bajocco, S., di giulio, M., and bregaglio, S.: An open-source tool based on Google Earth Engine for spatially explicit crop yield modelling , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-19565, https://doi.org/10.5194/egusphere-egu24-19565, 2024.

EGU24-19905 | ECS | Posters on site | ITS3.18/HS12.4

Testing a novel microtensiometer sensor in a citrus orchard for feedback control irrigation scheduling 

Vincenzo Alagna, Dario Autovino, Mariachiara Fusco, Girolamo Vaccaro, and Massimo Iovino

Monitoring the plant water status is necessary to identify appropriate irrigation scheduling parameters. Stem water potential (Ψstem) is considered the standard measure of crop water status and its measurements have been conducted by using the Scholander pressure chamber (PC) which do not allow continuous monitoring of crop water status. More recently, microtensiometers have been developed to monitor the water potential of the trunk (Ψtrunk) continuously, potentially overcoming the drawbacks of PC-based measurement.

This study was conducted to test the reliability of the new water status indicator, Ψtrunk, measured by microtensiometer, comparing it with Ψstem values measured with a PC in a 30-year-old mandarin trees.

The research was carried out during the 2022 and 2023 irrigation seasons, on three plots, each with a specific irrigation method. In one of the plots, a sprinkler irrigation system is installed and the irrigation is managed by the farmer (Traditional Irrigation, TI). In the other two plots, a subsurface drip irrigation system is implemented and two irrigation strategies are applied: i) Full Irrigation (FI), in which the entire evapotranspiration is returned, and ii) Deficit Irrigation (DI), consisting in the application of a water saving strategy (1 July - 15 August). In each plot, a representative tree was selected and, starting from July, Ψtrunk was monitored using two microtensiometers (FloraPulse, CA, USA) embedded directly in the trunk. Measurements cycles of Ψstem were taken by the PC on two covered stems, from 6:00 am to 6:00 pm every three hours, on TI tree the day after and three days after the irrigation event in both the 2022 and 2023 irrigation seasons. For DI and FI trees, the same measurements cycles days usually precede and follow the irrigation days. In addition, only in 2022 Ψstem were measured weekly at noon.

The Ψtrunk monitored by the microtensiometer was influenced by the irrigation strategies applied. The greatest variations were observed in the TI thesis, where more negative Ψtrunk values were recorded the day before irrigation. In both the FI and DI thesis, the seasonal variation of Ψtrunk was more limited compared to TI. The water potential values on the stem were generally more negative than on the trunk, as would otherwise be expected, but the cycles of daily measurements, carried out with the PC, showed that the most negative values were usually recorded on the stem at 3:00 pm, whereas on the trunk they were recorded from 1 to 4 hours later. The correlations of the averaged values of Ψstem and Ψtrunk showed value of the coefficient of determination R2= 0.43 when all the dataset was considered. However, when the dataset was split according to irrigation strategy, R2 increased for FI and TI trees, R2 =0.64 and R2 =0.60 respectively, while it decreased for DI trees (R2 =0.28).

In conclusion, the FloraPulse microtensiometer demonstrated the possibility of providing a better understanding of crop water potential variations in the SPA system, but it is necessary to identify Ψtrunk thresholds for feedback control irrigation scheduling different from those already well defined in literature for the Ψstem.

How to cite: Alagna, V., Autovino, D., Fusco, M., Vaccaro, G., and Iovino, M.: Testing a novel microtensiometer sensor in a citrus orchard for feedback control irrigation scheduling, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-19905, https://doi.org/10.5194/egusphere-egu24-19905, 2024.

EGU24-20028 | ECS | Orals | ITS3.18/HS12.4 | Highlight

Assessment of crop water needs and its sustainability based on future climate scenarios: the Aude Department (South-West France) 

Andrea Borgo, Antonio Trabucco, Muhammad Faizan Aslam, Sara Masia, Donatella Spano, and Marta Debolini

Since 1970, South-western European regions (Iberian Peninsula and South France) have been subjected to an air temperature increase of almost 2 °C, while generally southern Europe assisted to a 20% drop in annual precipitation. Agriculture is by far the sector with the greatest freshwater withdrawals, and it is essential to perform an accurate assessment of water consumption for irrigation, in order to develop strategies to reduce water abstractions from the ecosystem. In this context, this work aims at modelling water consumption for agriculture in the Aude river basin (South-West France), in order to assess the amount of water needed during the growing season of each crop in the current conditions, and in the future scenarios of climate change, according to different climate models. This project relies on the application of SIMETAW# model (Simulation of Evapotranspiration of Applied Water), which, from a set of climatic and soil data, computes the daily reference, well-watered crop, and actual evapotranspiration (ET0, ETc, ETa), the evapotranspiration of applied water (ETaw), an irrigation schedule, and crop growth and yield for a specific site. For climate inputs, the work relies on the high-resolution data (0.11-degree resolution) supplied by Copernicus Cordex, which provides historical records and future estimations according to RCPs (Representative Concentration Pathways) 2.6, 4.5 and 8.5. In the calculation of the well-known Penman–Monteith ET0 formulation, SIMETAW# also considers the effect of the increase of atmospheric CO2 concentration on stomatal resistance, which plays as a counterbalance with the increase of temperature due to climate change, by reducing stomatal opening for transpiration in plants, determining lower water loss through stomata. The model calculates ETa in both irrigated and rainfed conditions, distinguishing the irrigation methods according to the most relevant crops of the region, namely wine grapes cultivations, forage crops, wheat, olives, vegetables and fruits. Results show that, in Aude basin, the variation of total irrigation demand between 1990 and 2050 is expected to be very low in scenario RCP 2.6 (< 1%), while in RCP 4.5 a 2.5% increase is foreseen. Differently, RCP 8.5 expects a substantial decrease of irrigation requirements (-23%), due to the large increase of CO2 concentration in the atmosphere. Low water-demanding crops, such as winter wheat and wine grapes, are less sensitive to climate variations, thus their irrigation demand is expected to remain rather stable in the future, however summer crops (fruits and vegetables) will require greater irrigation inputs. The study demonstrates that, in some climate scenarios, crop water requirements may decrease due to the reduction of stomatal conductance. Still SIMETAW#, as most of the crop water models currently applied, does not take into account other climate change effects that can be damaging for the vegetation (e.g., heat waves, floods, spread of pathogens, etc.), together with the reduced availability of water supply in the basin, which can also have a consequence on the irrigation scheduling.

How to cite: Borgo, A., Trabucco, A., Aslam, M. F., Masia, S., Spano, D., and Debolini, M.: Assessment of crop water needs and its sustainability based on future climate scenarios: the Aude Department (South-West France), EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-20028, https://doi.org/10.5194/egusphere-egu24-20028, 2024.

EGU24-20258 | ECS | Posters on site | ITS3.18/HS12.4

Digital twin development for an irrigation machine 

Guillermo Salvador García Lovera, Rafael González, Emilio Camacho, and Pilar Montesinos

Irrigated agriculture, the main user of water resources, is undergoing a change in its management and use. Therefore, tools such as artificial intelligence or digital twins applied to water management can improve it to maximize water use efficiency. Thus, the main objective of this work focuses on the development and implementation of a digital twin in a mobile irrigation system, specifically a universal irrigation machine. The digital twin, DT, is an accurate, real-time virtual representation of a real element (irrigation system), becoming an advanced decision support system for irrigation management, which can incorporate artificial intelligence tools for the implementation of intelligent precision irrigation. This technology allows, in real time, to simulate and analyze multiple operation scenarios before making decisions that affect the actual system. Thus, several interconnected components have been developed to form the DT of a real irrigation machine, located in southern Spain. It reproduces the machine operation in real time using information obtained from sensors (climatic information, soil moisture probes, pressure transducers and flowmeters) located in the study area and in the irrigation machine itself. The DT is made up of different components: i) the hydraulic model of the machine that provides the pressure and flow rate supplied by the emitters of the irrigation machine; ii) the irrigation programming module that manages the machine operation (at what time and for how long) during the irrigation campaign;  iii) The irrigation machine water distribution model that provides water distribution maps, which will allow adjusting the operation of the machine (for example, forward speed) aimed at that each spatial element of ​​the irrigation plot (conditioned by the parameters soil, climate and stage of development of the irrigated crop) receives the required amount of water; and iv) the communication module with sensors. The DT of the irrigation machine provides the amount of water that each spatial unit of the plot receives in each irrigation event throughout the irrigation campaign for different operation conditions of the irrigation machine. This information can be the input of other DTs such as the crop development DT to create more complex DTs that reproduce the operation of an irrigated farm. Finally, the ability to monitor and simulate irrigation in real time by the DT provides farm managers with valuable data to make correct decisions, especially in periods of water scarcity, adjusting irrigation management to the spatial variability of the plot, taking into account the water availability to maximize crop production.

How to cite: García Lovera, G. S., González, R., Camacho, E., and Montesinos, P.: Digital twin development for an irrigation machine, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-20258, https://doi.org/10.5194/egusphere-egu24-20258, 2024.

EGU24-20336 | Orals | ITS3.18/HS12.4

Modeling crop water demand to support adaptation strategies in Mediterranean environment under climate change 

Muhammad Faizan Aslam, Sara Masia, Donatella Spano, Valentina Mereu, Marta Debolini, Richard L. Snyder, Andrea Borgo, and Antonio Trabucco

Water scarcity is arguably a pressing issue for the 21st century in Mediterranean areas, due to limited water resources, expansion of irrigated area to sustain food security and climate change. Water extraction for agriculture sector account about to 70% of global water use, and this demand peaks to 80% of total water withdrawal in several southern Mediterranean countries. In this study, the impact of climate change on evapotranspiration demand, crop water requirements, and crop yield losses due to water shortage, were assessed by using the Simulation of Evapotranspiration of Applied Water (SIMETAW_GIS) model. This crop-soil-water model was implemented over the Sardinia island, a region with a typical Mediterranean climate and agriculture characteristics, assuming impact of climate change for a whole range of relevant Mediterranean crops (Wheat, Barley, Sugar beet, Potato, Lentil, Almond, Maize, Wine Grape, Table Grape, Tomato, Rice, Artichoke, Alfalfa, Olives, Improved Pasture and Orange). Under present analysis, daily climate data from five Earth System Models dynamically downscaled to a spatial resolution of 0.11-degrees (~11 km) from the  EURO-CORDEX project domain and available from the Copernicus Climate Data service (https://climate.copernicus.eu/) were retrieved and ensembled. The impact of climate change on crop water requirements was evaluated under historical (1976-2005) and future (2036-2065) climate conditions following different Representative Concentration Pathways (RCPs: 2.6, 4.5 and 8.5), representing alternative mitigation policies and future emission scenarios.

In the Sardinia region, results show a variegated increase of crop water demand between future (2036-2065) and historical conditions (1976-2005) for different crops, which may pose a challenge for water resource management, especially considering water use conflicts among different sectors. On average wheat and barley will foresee the most significant increase of crop water requirements, ranging on average by 12 to 14% under different RCPs. Other crops (e.g. almond, maize, wine grape, and pasture) are projected to foresee still significant increases of crop water demand, varying between 4-8%.  This work provides information that can support farmers and decision managers to evaluate climate change adaptation strategies linked to different cropping patterns to increase use efficiency of water resources for a more sustainable agriculture production under climate change.

How to cite: Aslam, M. F., Masia, S., Spano, D., Mereu, V., Debolini, M., Snyder, R. L., Borgo, A., and Trabucco, A.: Modeling crop water demand to support adaptation strategies in Mediterranean environment under climate change, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-20336, https://doi.org/10.5194/egusphere-egu24-20336, 2024.

EGU24-20601 | Orals | ITS3.18/HS12.4

Improving satellite estimation of actual evapotranspiration using field monitoring and crop simulation 

Nicholas Dercas, Georgios Tziatzios, Ioannis Faraslis, Nicolas Dalezios, Nicolas Alpanakis, Marios Spiliotopoulos, Stavros Sakellariou, Pantelis Sidiropoulos, and Vagelis Brissimis

Water is a natural resource that is in shortage in many areas of the planet. This fact will be exacerbated in the context of the climate crisis. Agriculture is the major consumer of water in Greece but at the same time an important polluter of the environment (sea intrusion problem, pollution of aquifers with fertilizers, herbicides, pesticides). these conditions, the need to reduce water consumption and use it more efficiently is imperative, aiming at sustainable water management. Today there is technology available that allows the use of satellite images and the application of an energy balance at crop and ground level to estimate actual evapotranspiration. This method, to give values, close to reality, must be calibrated using ground data. For this reason, cotton, and maize fields in Thessaly (Central Greece) were systematically monitored for soil moisture and final yield. These water consuming plants are widely cultivated in the Thessalian plain even though the area has a negative water balance. The data collected from the monitoring together with the simulation with the AquaCrop model led to the estimation of the actual evapotranspiration. The model results are considered to correspond to real evapotranspiration since water balance application conditions were favourable (runoff and deep percolation had small or zero values). As a resiult, using the estimation of ETA in the plot we were led to improve the satellite estimation of evapotranspiration.

Key words: Evapotranspiration, satellite images, monitoring, AquaCrop

How to cite: Dercas, N., Tziatzios, G., Faraslis, I., Dalezios, N., Alpanakis, N., Spiliotopoulos, M., Sakellariou, S., Sidiropoulos, P., and Brissimis, V.: Improving satellite estimation of actual evapotranspiration using field monitoring and crop simulation, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-20601, https://doi.org/10.5194/egusphere-egu24-20601, 2024.

EGU24-20652 | Orals | ITS3.18/HS12.4

Monitoring crop phenology applying biophysical indices from Sentinel-2 data: the case of Thessaly region in Greece 

NIcolas Dalezios, Ioannis Faraslis, Nicolas Alpanakis, Georgios Tziatzios, Marios Spiliotopoulos, Stavros Sakellariou, Pantelis Sidiropoulos, Nicholas Dercas, and Vagelis Brissimis

The newest Earth Observation optical sensors, such as Sentinel-2, provide global biophysical products and vegetation indices at high spatial (decametric or twentimetric resolution) and temporal resolution (about 5 days retrieval). These biophysical parameters are essential for constant crop status monitoring at local scale. Optimizing the water use for irrigation, the weed mapping, quantifying ground above biomass and crop yield production, are some of the benefits of biophysical parameters in agriculture. This research investigates the crop status during the 2021’s growing season in Thessaly agricultural area in Greece. Thus, in maize, biophysical variables, and vegetation indices, that is, Leaf Area Index (LAI), fraction of absorbed photosynthetically active Radiation (FAPAR), Fraction of Vegetation Cover (FVC), Leaf Chlorophyll content (Cab), Canopy Water Content (CWC), Normalized Difference Vegetation Index (NDVI) and Normalized Difference Red Edge Index (NDRedEdge) are retrieved. The PROSAIL radiative transfer model by artificial neural network approach is employed (available of the free SNAP® software) to retrieve the biophysical parameters from Sentinel-2 multispectral imagery. The monitoring of the abovementioned biophysical variables during the growth period of maize crop shows a uniform behavior. Finally, high consistency among vegetation parameters confirms the usefulness of Sentinel-2 products in agriculture.

Keywords: Biophysical indices; phenological stages; monitoring maize crop; Mediterranean agroecosystems

How to cite: Dalezios, N., Faraslis, I., Alpanakis, N., Tziatzios, G., Spiliotopoulos, M., Sakellariou, S., Sidiropoulos, P., Dercas, N., and Brissimis, V.: Monitoring crop phenology applying biophysical indices from Sentinel-2 data: the case of Thessaly region in Greece, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-20652, https://doi.org/10.5194/egusphere-egu24-20652, 2024.

EGU24-20715 | Posters on site | ITS3.18/HS12.4

Agroclimatic zoning methodology for selection of suitable crop in water limited Mediterranean areas 

Ioannis Faraslis, Nicolas Dalezios, Nicolas Alpanakis, Georgios Tziatzios, Marios Spiliotopoulos, Stavros Sakellariou, Pantelis Sidiropoulos, Nicholas Dercas, and Vagelis Brissimis

The agroclimatic classification identifies zones for efficient use of natural resources leading to optimal crop production. In water limited availability regions, such as the Mediterranean region, one problem is the quantification of water use in agriculture in view of the social problems linked to the performance of irrigated systems. The aim of this paper is the development of agricultural sustainable zones, in a typical water limited Mediterranean region, namely Thessaly in Greece. To achieve this, time series analysis with sophisticated geoinformatics techniques is applied. The agroclimatic classification methodology is based on three-stages: first, the microclimate features of the region are considered using aridity and vegetation health indices leading to water limited growth environment (WLGE) zones based on water availability; second, landform features and soil types are associated to WLGE zones to identify non-crop specific agroclimatic zones (NCSAZ); finally, specific restricted crop parameters, are combined with NCSAZ creating the suitability zones for sustainable agriculture. The results are promising as compared with the current crop production systems of the study area under investigation. Due to climate change, the results indicate that arid and semi-arid regions are also faced with insufficient amounts of precipitation for supporting rainfed annual crops. Finally, the proposed methodology reveals that the combination of Remotely Sensed techniques could be a significant tool for creating, shortly, detailed and up to date agroclimatic zones.

Keywords: Agroclimatic zoning; Hydroclimatic zoning; Non-crop specific zoning; Crop-specific zoning; Agricultural suitability zones, Mediterranean agroecosystems

How to cite: Faraslis, I., Dalezios, N., Alpanakis, N., Tziatzios, G., Spiliotopoulos, M., Sakellariou, S., Sidiropoulos, P., Dercas, N., and Brissimis, V.: Agroclimatic zoning methodology for selection of suitable crop in water limited Mediterranean areas, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-20715, https://doi.org/10.5194/egusphere-egu24-20715, 2024.

Plastic debris of size < 5mm are considered as microplastics and are serious concern in the present world due to its persistent nature and ubiquitousness in every spheres of the environment. Waste Water Treatment Plants (WWTP) are one of the main point sources of microplastic to the environment. The primary objective of this study was to identify and characterize microplastics present in wastewater from the dairy industry and to suggest effective management practices for their efficient removal before the effluent is discharged into the environment. The samples were collected from the influents to the WWTP, Aeration-tank, Clarifier, Final-effluent and sludge. The microplastic extraction were done by digestion (30%-H2O2) and density separation (NaCl and NaI) method. Micro-Raman spectroscopy, SEM and SEM-EDS techniques were used for the identification and characterization of microplastics. The findings indicated that the sludge from the WWTP contained a significantly higher particle count (2560 particles/g) compared to the water samples (38 particles/L). Microplastics of different shapes were identified in the study, its abundance is in the following order: fragments>films/sheets>pellets> foam. The size of microplastics ranges from 20µm to 2500 µm and the highest abundance observed in the range between 100-500 µm. Most of the microplastics were transparent (46.87 %), white (31.26%) and blue (15.62%) in color. Seven different varieties of microplastic such as Polyamide, Polyethylene, Poly-vinyl-chloride, Polypropylene, Low-density-polyethylene, Polyurethanes, Nylon were identified. Polyethylene is the predominant microplastic found in all the samples (62.49%) followed by Polypropylene (11.72%) and Poly-vinyl-chloride (9.37%) respectively. Polyurethane (7.81%) and Nylon (3.9%) were found only in sludge samples. SEM images showed cracks, pores (480 nm to 998 nm), fractures on the surface and are prone to breakdown. Some of the microplastics exhibit signs of being colonized by microorganisms or particle-like structures within cracks, signifying the presence of high surface area. It would increase the chance to attach contaminants, resistant microbes and other pollutants to microplastic when discharged/exposed to more complex environment and elevate its toxicity. SEM-EDS analysis shows microplastics association with metals (Mg, Al, Na, Si, Ca, Fe, Pd). The economical and expeditious solution for microplastic removal is to improve, the current treatment process instead of finding a new method. Some recommendations to enhance the removal of microplastics include lengthening the retention time in the sedimentation/skimming processes, altering the materials in the filtration-units, and improving the flocculation/coagulation methods. For example, aluminum-based coagulant is more effective in eliminating microplastic than Fe and polyacrylamide-based coagulant to reduce, comparatively high microplastics content in the influent and aeration-tank. The extraction of microplastic in fat-trap stage using grease and subsequent pyrolysis prevents larger particles to enter the system and helping to curb the elevated concentration of microplastic in sludge. Co-pyrolysis with biomass and hydrothermal reactions can also be adopted. Recommendations for efficient microplastics management practices were also proposed.

How to cite: Vilambukattu Appukuttan Pillai, S. and Udayar Pillai, S.: Identification, characterization and the removal of Microplastic, a persistent neo-contaminant from Dairy Waste Water Treatment Plant (WWTP), EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-3011, https://doi.org/10.5194/egusphere-egu24-3011, 2024.

EGU24-3193 | ECS | PICO | ITS3.24/HS12.9

Studying the Presence and Distribution of Microplastics in a Norfolk Salt Marsh 

Benjamin Grover and Stefanie Nolte

As a rising global pollution issue, microplastics have been discovered in every major environment around the world. Marine and coastal ecosystems in particular are often highlighted for the presence and impacts of plastic pollution; however, salt marshes are quickly gaining interest, and concern, as potential traps and long-term sinks for microplastics.

Fundamental sedimentation processes within salt marshes are hypothesised to be ideal for microplastic accumulation, as well as potential abundant physical trapping from vegetation. Salt marshes also provide ideal natural conditions that promote the breakdown and degradation of plastic, thus leading to several different incoming sources of microplastic. With several possible plastic inputs, there is the potential for high microplastic concentration in salt marshes, however when compared to other coastal ecosystems, there are very few studies within this habitat and so plastic levels are largely unknown.

As habitats with important ecosystem services such as biodiversity and carbon storage, it is critical that we improve our understanding of the impacts which microplastics may have upon salt marshes. However, to do this we must first understand what the spread of microplastics in this environment is. This project hopes to measure the amount of microplastics in a natural salt marsh, focussing on their spatial distribution throughout the marsh and neighbouring mudflats. From this initial location data, we will then investigate the impact of physical marsh attributes on any distribution trends and see how much the amount of microplastics across the marsh can be explained by these factors.

How to cite: Grover, B. and Nolte, S.: Studying the Presence and Distribution of Microplastics in a Norfolk Salt Marsh, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-3193, https://doi.org/10.5194/egusphere-egu24-3193, 2024.

EGU24-3999 | PICO | ITS3.24/HS12.9

Plastic pollution monitoring in the wrack line: baseline and seasonality trends along several coastlines from Brittany (Erquy, France) 

Sébastien Rohais, Camille Lacroix, Kevin Tallec, Marine Paul, and Silvère André

Plastic pollution is acknowledged across all environmental compartments, ranging from high mountain ranges to the deepest abyssal plains. It has been identified in the lithosphere (sediment), hydrosphere (water bodies), atmosphere (air), and biosphere (living organisms). In this context of ubiquitous pollution, beaches, and in particular the wrack line, are commonly used as monitoring sites for plastic pollution. There are established monitoring programs to track plastic pollution at different scales along beaches, such as the OSPAR beach litter monitoring program at the North-East Atlantic scale or the French monitoring program for meso- and large microplastics on beaches.

This study aims to build upon the expertise and experience gained from existing monitoring programs to provide a comprehensive approach for understanding the processes of plastic influx, accumulation, and impregnation on beaches. Four types of coasts were selected in Brittany (Erquy, France) to represent various configurations: (i) Accreting sandy beach, (ii) Eroding sandy beach, (iii) Protected cliff (iv) Exposed cliff. The study covers a period from August 2022 to August 2023, where bimonthly statements were conducted, resulting in seven dataset collection points (308 measurements). Each of the four sectors, measuring 100 meters along the wrack line, was studied using eleven 40x40 cm quadrats spaced every 10 meters. The top centimeter of sand was collected using a trowel and filtered through a 1mm mesh sieve. Seawater flotation was employed to separate and recover plastics.

Plastics were then classified into three categories: large microplastic (1-5mm, LMP), mesoplastic (5-25mm) and macroplastics (>25mm). Plastics were counted and weighed within each category. Four indicators were quantified to monitor potential sources of plastic pollution: (i) "Pellet" indicator of chronic or accidental losses along the plastic production chain, (ii) "Port" indicator for port and related recreational activities, (iii) "WWTP" indicator for water network management issues, (iv) "Butt" indicator for activities linked to the improper disposal of cigarette butts.

Results are presented in the form of box plots providing rich information illustrating variability, outliers, and the overall distribution of quadrat measurements. The maximum value by quadrat reaches 706 items/m2 of wrack line. The annual survey provides baseline values for different coast types of 106, 39, 39 and 3 items/m2 of wrack line for accreting sandy beach, eroding sandy beach, protected cliff, and exposed cliff, respectively. Out of the total 308 measurements, 82 of them have the smallest value possible, which is 0. Principal Component Analysis (PCA) was finally carried out to understand the importance of various environmental factors (e.g., wind, wave, tidal range) on the influx, accumulation, and distribution of plastics along the wrack line.

By combining surveys across different coastal types in a specific region, this work enhances the understanding of the dynamics of plastic pollution, especially to implement effective environmental monitoring strategies.

How to cite: Rohais, S., Lacroix, C., Tallec, K., Paul, M., and André, S.: Plastic pollution monitoring in the wrack line: baseline and seasonality trends along several coastlines from Brittany (Erquy, France), EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-3999, https://doi.org/10.5194/egusphere-egu24-3999, 2024.

EGU24-5250 | ECS | PICO | ITS3.24/HS12.9 | Highlight

Defining Plastic Pollution Hotspots 

Paolo Tasseron, Tim van Emmerik, Paul Vriend, Rahel Hauk, Francesca Alberti, Yvette Mellink, and Martine van der Ploeg

Plastic pollution in the natural environment poses a growing threat to ecosystems and human health, prompting urgent needs for monitoring, prevention and clean-up measures, and new policies. To effectively prioritize resource allocation and mitigation strategies, it is key to identify and define plastic hotspots. UNEP's draft global agreement on plastic pollution mandates prioritizing hotspots, suggesting a potential need for a defined term. Yet, the delineation of hotspots varies considerably across plastic pollution studies, and a definition is often lacking or inconsistent without a clear purpose and boundaries of the term. In this paper, we applied four common hotspot definitions to plastic pollution datasets ranging from urban areas to a global scale. For each scale, hotspots were defined according to 1) values above the average of the dataset, 2) values in the highest interval, 3) outliers, and 4) values exceeding the 90th percentile. Our findings reveal that these hotspot definitions encompass between 0.8% to 93.3% of the total plastic pollution, covering <0.1% to 50.3% of the total locations. Given this wide range of results and the possibility of temporal inconsistency in hotspots, we emphasize the need for fit-for-purpose criteria and a unified approach to defining plastic hotspots. Therefore, we designed a step-wise framework to define hotspots by determining the purpose, units, spatial scale, temporal scale, and threshold values. Incorporating these steps in research and policymaking yields a harmonized definition of hotspots, facilitating the development of effective plastic pollution prevention and reduction measures.

How to cite: Tasseron, P., van Emmerik, T., Vriend, P., Hauk, R., Alberti, F., Mellink, Y., and van der Ploeg, M.: Defining Plastic Pollution Hotspots, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5250, https://doi.org/10.5194/egusphere-egu24-5250, 2024.

EGU24-5672 | ECS | PICO | ITS3.24/HS12.9

Analysis of the interactions between coastal morphodynamic processes and Beach Litter distribution.  

Angela Rizzo, Angelo Sozio, Giorgio Anfuso, Marco La Salandra, and Corrado Sasso

Beach litter (BL) poses a constant threat to coastal areas and related ecosystems. Standard monitoring techniques used so far for the identification and classification of BL items consist of in situ visual surveys, which are time-consuming and only allow to cover limited coastal stretches. Recently, innovative and multi-disciplinary approaches have attempted to limit these logistic and practical issues. In this context, a growing number of studies are exploiting the use of aero-photogrammetric surveys, coupled with GIS software post-processing tools, for the monitoring of BL-related pollution. To this purpose, Unmanned Aerial Vehicles (UAVs) are often used to acquire images that can be used to evaluate the BL items' density and the relationships between coastal morphodynamic processes and BL distribution along the beach profile. In this study, carried out in the frame of the RETURN Extended Partnership and RiPARTI Project, the results obtained from a monitoring survey carried out along the Torre Guaceto beach (Apulia region, Italy) are shown. In particular, aero-photogrammetric flights were conducted to obtain RGB georeferenced orthomosaics on which manual image screening and morphodynamic analysis were performed to define the recent shoreline evolution and analyze the potential influence of coastal processes in the dispersion and accumulation of BL along the beach profile. The visual screening process was carried out in QGIS software and 382 BL items were identified and categorized. Artificial polymers/plastic (88%) turned out to be the most frequently represented object, followed by glass and textiles (3.4%). Coastal evolution trends were estimated using a specific GIS tool. Results highlighted a general retreat trend of the shoreline, with erosion rates ranging from 1.4 m/yr to 0.18 m/yr. The limit of the fixed vegetation has also been affected by recent retreat processes, up to 3 m. The zone between the embryo dune and the foredune limit, corresponding to the inner section of the investigated beach, gathered the highest density of BL items (1.24 items/m2). This zone is relatively far from marine or aeolic processes along the shoreline so, objects tend to lay for a longer period of time. These can constitute accretion cores for small embryo dunes that, in turn, will tend to increase the risk of burial for BL items. In conclusion, this study highlights that the exploitation of UAV systems facilitates the monitoring of wide coastal sectors and the analysis of beach morphodynamic trends, supporting the identification of hotspot areas for BL accumulation and the definition and planning of tailored clean-up activities.

How to cite: Rizzo, A., Sozio, A., Anfuso, G., La Salandra, M., and Sasso, C.: Analysis of the interactions between coastal morphodynamic processes and Beach Litter distribution. , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5672, https://doi.org/10.5194/egusphere-egu24-5672, 2024.

EGU24-7368 | ECS | PICO | ITS3.24/HS12.9

AI-driven aerial drones and monitoring app: New developments to facilitate citizen science initiatives on plastic pollution monitoring and clean-ups on beaches 

Javier Delgado, Alae-eddine Barkaoui, Marko Petelin, Andreja Palatinus, and Milica Velimirovic

Due to the geology of the Mediterranean coastline zone and insufficient waste management in many nations, the Mediterranean Sea has become overflowed with plastic litter attributed to its dense population and high level of tourism activity. To mitigate the plastic pollution, protect marine life, and preserve the ecological balance a series of novel approaches for monitoring and detection of marine litter in the Mediterranean sea are needed. The primary objective of this study is to demonstrate the feasibility of using AI-driven aerial drones for the detection of plastic hotspots on beaches, followed by the use of a monitoring app for community-led plastic pollution monitoring and cleanup initiatives that were held at Saidia beach in Morocco in November 2023. For that purpose, artificial intelligence was tested to quantify and identify litter on beaches using drones that flew over the beach being monitored. Specifically, the drone's video stream is processed by an algorithm that first segments (in polygons) the objects in the video stream and then through deep learning (DL) each object is identified to categorise it as plastic or general waste. The acquired images are then used to train the DL algorithm in order to constantly improve the recognition performance of plastic and other generic waste types. This technique will allow the observation in detail of the monitoring area before and after the monitoring/clean up event, and thus, it can serve as a method to validate the grade of execution of the activity and analysis of the monitored/cleaned area. The focus on citizen science is essential to connect the public with the technologies that will allow them to collaborate in the collection of methodical data that can complement the existing data for a more detailed analysis.Together with the drones, another approach is the new app that will include the option to collect data for beach monitoring and for beach clean-ups. Created to function in both IOS and Android operating systems, this smart app for collecting marine litter monitoring data features an intuitive user interface and other advanced tools to enable even non-professional users to properly collect scientific data. The app also is designed to be used simultaneously by multiple users, that is, to collect data from multiple devices and referring to a single monitoring event. At the conclusion of the event, all collected data can be easily reviewed and supplemented with other advanced metadata for subsequent analysis and sharing activities, as well as then shared in the European repository of the EMODnet ML. The compilation of data from these techniques, to be tested on different demo sites, together with the results of future replications in other areas and the input of data from citizens and external organisations, will be the next step to facilitate a more holistic approach to tackle the crucial situation the Mediterranean sea is facing nowadays due the uncontroled discharge of plastics in its waters.

 

Acknowledgements

The authors acknowledge financial support from the European Union’s HORIZON EUROPE innovation program for the project REMEDIES awarded under Grant Agreement No. 101093964.

How to cite: Delgado, J., Barkaoui, A., Petelin, M., Palatinus, A., and Velimirovic, M.: AI-driven aerial drones and monitoring app: New developments to facilitate citizen science initiatives on plastic pollution monitoring and clean-ups on beaches, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-7368, https://doi.org/10.5194/egusphere-egu24-7368, 2024.

EGU24-9448 | ECS | PICO | ITS3.24/HS12.9

Validating monitoring methods for riverine macroplastic pollution 

Paul Vriend, Sylvia Drok, Nadieh Kamp, Frank Collas, Martina Vijver, and Thijs Bosker

Riverine macroplastic pollution (>0.5 cm) is omnipresent and can negatively impact ecosystems and livelihoods. Monitoring data are crucial for understanding the nature and extent of pollution as well as aiding the design of effective intervention strategies. Recent years have marked the development of methods to collect surveillance data, primarily focusing on the monitoring of floating plastics and plastics deposited on riverbanks. Today, these methods need validation. Criteria that are essential in robust monitoring are the accuracy and precision of collected data, and the minimum observable particle size. Addressing these challenges, we have conducted field experiments aimed to review the most widely employed protocols: the RIMMEL protocol for floating macroplastics and the river-OSPAR protocol for macroplastics deposited on riverbanks. We find that the recovery of larger pieces ranges between 80-90% for both methods, with the accuracy decreasing significantly when considering smaller items sizes, item colour, number of observers, and factoring in external variables such as bridge height or riverbank surface type. The precision, however, varied greatly between the different experiments. These results indicate that the limits & usage of data from different protocols are highly context dependent. It further highlights the urgent need to include these uncertainties in their communication and utilization. Our result show the urgency of standardizing the operating protocol to optimize the accuracy and precision for measuring riverine macroplastics, and of the necessity to quantify uncertainty in studies estimating plastic fluxes using the two protocols.

How to cite: Vriend, P., Drok, S., Kamp, N., Collas, F., Vijver, M., and Bosker, T.: Validating monitoring methods for riverine macroplastic pollution, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-9448, https://doi.org/10.5194/egusphere-egu24-9448, 2024.

EGU24-9691 | ECS | PICO | ITS3.24/HS12.9

Detecting Floating Macroplastic Litter with Semi-Supervised Deep Learning 

Tianlong Jia, Rinze de Vries, Zoran Kapelan, and Riccardo Taormina

Researchers are increasingly utilizing Deep Learning methods for computer vision to identify and quantify floating macroplastic litter. While these methods can provide precise assessments of plastic pollution by automatically processing images and videos, they often rely on the availability of large amount of annotated data for supervised learning (SL). Moreover, the manual labeling work is expensive and time-consuming. This hinders obtaining high model generalization capability, which is essential for the development of robust computer vision systems for structural monitoring.

To overcome this challenge, we propose a two-stage semi-supervised learning (SSL) method for detecting floating macroplastic litter based on the SwAV (Swapping Assignments between multiple Views of the same image) approach. SwAV is a self-supervised learning method that extracts the feature representations of data (such as images with macroplastic litter) without manual annotations. In the first stage of the SSL method, we use SwAV to pre-train a ResNet50 (Residual Neural Network with 50 layers) backbone architecture on more than 100K unlabeled images. In the second stage, we add additional layers to ResNet50 to create a Faster R-CNN (Faster Region-based Convolutional Neural Network) architecture, and fine-tune it for object detection using a limited amount of labeled data (<13K images with 2.6K annotations).

We demonstrate the effectiveness and robustness of our methodology for images collected in canals and waterways of the Netherlands and South East Asia. We conduct a thorough comparison with the conventional SL method using the same Faster R-CNN architecture and ImageNet pre-trained weights. The results suggest that our method improves both in-domain and out-of-domain generalization performances over the SL method. Our findings also demonstrate that feature representations learned by the SwAV pre-training on context-related images outperform those learned from much larger, but unrelated, datasets (e.g., ImageNet).

Based on these results, we suggest stakeholders (e.g., researchers, consultants and governmental organizations) to consider SSL methods to develop more robust systems for targeted long-term floating macroplastics monitoring. Future work should focus on scaling up computations by resorting to much larger (e.g., over 1 million images), yet relatively inexpensive, unlabeled datasets to fully exploit SSL.

How to cite: Jia, T., de Vries, R., Kapelan, Z., and Taormina, R.: Detecting Floating Macroplastic Litter with Semi-Supervised Deep Learning, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-9691, https://doi.org/10.5194/egusphere-egu24-9691, 2024.

EGU24-10185 | ECS | PICO | ITS3.24/HS12.9

Quantifying plastic contributions to different components of the river channel and floodplain 

Louise J. Schreyers, Tim H.M. van Emmerik, Fredrik Huthoff, Frank P.L. Collas, Carolien Wegman, Paul Vriend, Anouk Boon, Winnie de Winter, Stephanie B. Oswald, Magriet M. Schoor, Nicholas Wallerstein, Martine van der Ploeg, and Remko Uijlenhoet

Rivers are one of the main conduits that deliver plastic from land into the sea, and also act as reservoirs for plastic retention. Yet, our understanding of the extent of river exposure to plastic pollution remains limited. In particular, there has been no comprehensive quantification of the contributions from different river compartments, such as the surface, water column, riverbank and floodplain, to the overall river plastic transport and storage. Here, we provide an initial quantification of these contributions. First, we identified the main relevant transport processes for each river compartment considered. We then estimated the transport and storage terms, by harmonizing available observations on surface, suspended and floodplain plastic. This approach was applied to two river sections in the Netherlands, with a focus on macroplastics (≥ 2.5 cm). Our analysis revealed that for the studied river sections, suspended plastics account for over 96% of items transported within the river channel, while their relative contribution to mass transport was only 30-37% (depending on the river section considered). Surface plastics predominantly consisted of heavier items (mean mass: 7.1 g/#), whereas suspended plastics were dominated by lighter fragments (mean mass: 0.1 g/#). Additionally, the majority (98%) of plastic mass was stored within the floodplains, with the river channel accounting for only 2% of the total storage. Our study developed, and demonstrates, a harmonised approach for quantifying plastic transport and storage across different river compartments, providing a replicable methodology  which will be applicable to many different river environments. Our findings emphasize the importance of adopting a systematic monitoring approach, across the range of river compartments encountered, in order to achieve a coherent and  comprehensive understanding of riverine plastic pollution dynamics.

How to cite: Schreyers, L. J., van Emmerik, T. H. M., Huthoff, F., Collas, F. P. L., Wegman, C., Vriend, P., Boon, A., de Winter, W., Oswald, S. B., Schoor, M. M., Wallerstein, N., van der Ploeg, M., and Uijlenhoet, R.: Quantifying plastic contributions to different components of the river channel and floodplain, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-10185, https://doi.org/10.5194/egusphere-egu24-10185, 2024.

EGU24-10583 | ECS | PICO | ITS3.24/HS12.9

Building a Comprehensive Dataset for Training Object Detection Algorithms applied on Plastic Transport Monitoring in Riverine Environments 

Khim Cathleen Saddi, Domenico Miglino, Francesco Isgrò, Paolo Tasseron, Matteo Poggi, Tim H. M. van Emmerik, and Salvatore Manfreda

Plastic monitoring is a challenging task worldwide. Currently, limited plastic measurements are available along the river in coastal areas or in the ocean. Such data from traditional manual monitoring can contribute to describing plastic transport dynamics within river networks, but not extensively in both spatial and time scales. Consequently, it is crucial to advance long-term monitoring within the river corridor in order to properly quantify and characterize  plastic transport and fates.

Recent advances in optical sensing, using commercially available camera systems (e.g. fixed cameras, drones, smartphones) provide huge opportunities in scene monitoring, which has been already successfully integrated in environment-controlled plastic recycling facilities. In this context, image processing techniques can represent a valuable tool, since their use in natural environments introduces a number of difficulties related to light conditions, shadows, and environmental changes (e.g., riparian and submerged vegetation). Therefore, there is a need to build robust methods able to handle such disturbances balancing detection performance with computational cost. 

Considering all these factors, this work utilizes four river plastic datasets (taken from Indonesia, Italy, The Netherlands, and Vietnam) and explores the possibility of tier-based plastic detection, characterization based on different levels of plastic type (from generalized “plastic” to more specific types e.g., plastic, plastic bag, plastic bottle etc.). These datasets represent very different water systems, e.g. urban water systems, natural rivers, tidal rivers, tropical rivers with diverse levels of lighting conditions, water spectra, camera angle, and image resolutions. Different data combinations and augmentation were explored which were used to train base models of YOLOv7 and YOLOv8 (You Only Look Once family of single detectors). Resulting models were compared in terms of transfer learning performance, labor and computational cost.

This work is part of a PRIN funded project, RiverWatch: a citizen-science approach to river pollution monitoring. Preliminary results show that with constant training parameters (batch=16, epoch=100), YOLOv8 performs better than YOLOv7 in river plastic detection. In fact, even though YOLOv7 provides a higher plastic count, this often includes false positives, with generally lower inference scores than YOLOv8. In addition, simple brightness adjustments appear to have a varying effect in improving detection performance depending on plastic types. 

We presented data augmentation methods and techniques in order to improve algorithm detection performance without complicating its network architecture, also in this way the dataset will remain workable with future algorithms. Future work includes the exploration of adding pre-detection localization layers in the test data to enhance local features prior to detection. 

Keywords: river plastic detection, optical remote sensing, YOLO, tier-based plastic characterization, data augmentation

How to cite: Saddi, K. C., Miglino, D., Isgrò, F., Tasseron, P., Poggi, M., van Emmerik, T. H. M., and Manfreda, S.: Building a Comprehensive Dataset for Training Object Detection Algorithms applied on Plastic Transport Monitoring in Riverine Environments, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-10583, https://doi.org/10.5194/egusphere-egu24-10583, 2024.

EGU24-11105 | ECS | PICO | ITS3.24/HS12.9

Monitoring of floating macro litter in the Arctic seas and rivers 

Maria Pogojeva, Igor Zhdanov, Anfisa Berezina, Ekaterina Kotova, Maria Mikusheva, Aleksander Kozhevnikov, Eleanora Danilova, and Evgeniy Yakushev

Among other marine environmental problems, the issue of marine litter accumulation in the World Oceans is of increased interest. It is relevant not only in areas with direct intense anthropogenic pressure, but also in remote and presumably pristine areas, such as the Arctic Sea. As the concentration of plastic waste in the marine environment increases, it can have impacts on various components of the marine ecosystem, at sea, on the seafloor, on the coasts and in particular in accumulation areas, while it also can negatively affect human social and economic activities. To reduce the release of plastic debris into the marine environment, litter occurrence and pathways need to be studied in order to identify litter sources, requiring monitoring studies that provide comparable results. Here we present the results of studies of the level of pollution by marine litter floating at sea and flowing with rivers in the Arctic region. Ship-based visual observations at sea were performed in the period 2019-2021 in the White Sea, the Barents Sea, the Kara Sea, the Laptev Sea and the East Siberian Sea. To assess the floating litter input with rivers, regular observations (2 times a month) were carried out by the trained observers on the Northern Dvina and Onega rivers. In all cases a standardized methodology was applied to obtain a unified data and to record the data a Floating Macro Litter mobile application (JRC) was used. The methodology contains a unified list and classification of observed floating sea/riverine litter items, which simplifies the data processing and analysis and allows to compare the data. For the first time a large scale assessment of litter pollution was performed in these remote Arctic regions. The international methodology confirmed the possibility of collecting unified data in the region and at the same time revealed some regional features.

How to cite: Pogojeva, M., Zhdanov, I., Berezina, A., Kotova, E., Mikusheva, M., Kozhevnikov, A., Danilova, E., and Yakushev, E.: Monitoring of floating macro litter in the Arctic seas and rivers, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-11105, https://doi.org/10.5194/egusphere-egu24-11105, 2024.

EGU24-11311 | PICO | ITS3.24/HS12.9

Anthropogenic factors, not hydrometeorology, explains plastic pollution variability in the Odaw river  

Rose Pinto, Tim van Emmerik, Kwame Duah, Martine van der Ploeg, and Remko Uijlenhoet

Variations in macroplastic transport are often linked to hydrometeorological conditions (wind, precipitation, and discharge). However, due to the predominant focus on these hydrometeorological factors as the main driving forces, most research overlooks the impact of anthropogenic factors, such as mismanaged plastic waste (MPW) on plastic transport variability. Here, we investigate the roles of both hydrometeorological and anthropogenic factors on plastic pollution. We collected field data on floating, riverbank, and land litter (macroplastics) between December 2021 to December 2022 at 10 bridge locations along the Odaw river. We tested seasonality in plastic transport/density with the Mann-Whitney U-test. Furthermore, we used multiple regression analysis to evaluate the combined effect of all the hydrometeorological variables (rainfall, discharge, and windspeed) on macroplastic transport. Additionally, we correlated peaks in plastic to peaks in discharge, wind speed, and rainfall, defined with the 90th percentile of a distribution as the threshold. Finally, we correlated the spatial variation in plastic transport/density with MPW and population density. Contrary to previous studies, our results showed no seasonal differences in plastic pollution within the Odaw catchment. Additionally, only weak to no correlations were found between plastic transport and the hydrometeorological variables. Overall, only 14-18% of the plastic peaks corresponded to the hydrometeorological peaks. More of these plastic peaks were associated to windspeed peaks. However, a strong correlation was observed between MPW and plastic transport and riverbank/land plastic density. Therefore, we hypothesize that anthropogenic factors are more important than hydro meteorological factors in plastic pollution variations. Our study emphasizes the need to holistically study the role of both anthropogenic and hydrometeorological factors in explaining plastic transport and retention dynamics at a river basin scale. This insight is vital for developing effective interventions to address plastic pollution in river catchments.

How to cite: Pinto, R., van Emmerik, T., Duah, K., van der Ploeg, M., and Uijlenhoet, R.: Anthropogenic factors, not hydrometeorology, explains plastic pollution variability in the Odaw river , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-11311, https://doi.org/10.5194/egusphere-egu24-11311, 2024.

EGU24-13346 | PICO | ITS3.24/HS12.9

Emission of microplastics by geosynthetics during Snow Farming 

David Gateuille, Emmanuel Naffrechoux, Mathieu Pin, and Frederic Gillet

Geosynthetics are a wide range of materials used in many fields ranging from civil engineering to agriculture, road transport and environmental protection. Made up of synthetic or natural polymers, these materials are characterized by their strip shape of varying width and length. It is estimated that currently 150 million m² of geosynthetics are used in France (data from the French Geosynthetics Committee). Despite this massive and constantly increasing use in recent years, their impact on the environment and in particular in terms of the emission of plastic particles, has been very little studied. It is therefore crucial (1) to quantify the risk of fragmentation and emission of plastic particles by geosynthetics and (2) to investigate how exposure to environmental conditions or implementation methods of these materials are likely to modify the quantities of particles emitted.

In partnership with the Tignes ski slopes authority, the Grande Motte cable car company and the Cimes Conseil design office, a quantification of the fluxes of plastic particles emitted by geosynthetics used for Snow Farming was set up between 2020 and 2023. In a context of climate change, Snow Farming makes it possible to reduce the melting of snow on sensitive parts of the ski area (e.g. ski lifts), during summer periods and thus to optimize the opening periods of the ski stations. The geosynthetics used in this context are exposed to extreme environmental conditions including strong ultraviolet radiation and significant daily temperature variations. These conditions could lead to the fragmentation of plastics and to the subsequent release of microplastics.

The work carried out in this study focused on vertical (through the snow cover) and horizontal (at the surface) particle fluxes. These fluxes were compared to the atmospheric fallout of microplastic at the scale of the glacier on which the ski area is located. In addition, 3 types of geosynthetics were compared: a waterproof PVC tarpaulin, a permeable polypropylene tarpaulin and a tarpaulin made from natural materials. The work showed very contrasting results between the 3 types of tarpaulins.

Permeable polypropylene tarps showed the greatest fluxes of particles (microplastics and mesoplastics) to the snowpack in terms of mass, with transfers exceeding one meter in depth. PVC tarpaulins showed grater fluxes in terms of number of particles and the transfers were limited to snow directly in contact with the tarpaulins. These differences are probably explained by contrasting emissions processes linked either to environmental exposures or to the handling of the tarpaulins. No plastic contamination could be detected in the tarpaulins of natural origin. On the scale of the glacier, the fluxes emitted annually represent approximately 2.3 kg and are of the same order of magnitude as the atmospheric fallout (about 8 kg) while the tarpaulins only cover 0.44% of the glacier surface.

How to cite: Gateuille, D., Naffrechoux, E., Pin, M., and Gillet, F.: Emission of microplastics by geosynthetics during Snow Farming, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-13346, https://doi.org/10.5194/egusphere-egu24-13346, 2024.

Groundwater plays a critical role as a vital and renewable water resource for drinking, domestic, and agricultural purposes. Unfortunately, it is under threat from various emerging contaminants, including antibiotic resistance genes, per- and poly-fluoroalkyl substances (PFAS), and micro- and nano-plastics (MNPs). MNPs act as agents, transporting trace heavy metals, hydrophobic pollutants, and toxic chemicals into groundwater from terrestrial and aquatic environments through physical, chemical, and biological processes. The transported MNPs have an impact on human health and ecological species. The objectives of this study were to: (1) assess the abundance of microplastics based on hydrogeology and well depth; (2) characterize the properties of aquifer; (3) identify possible sources of microplastics. The study aims to establish a baseline for the area, contribute to databases on microplastic pollution, and may lead to new solutions for this type of pollution. Data were collected from 17 wells of the National Groundwater Monitoring Network in South Korea. Sixteen water quality parameters, as well as the abundance and properties of microplastics, were analyzed based on depth and hydrology groups. As a result, the average number of microplastics (MPs) detected in 17 groundwater wells, each with a volume of 1.5 liters, was 4.8 particles per liter. In the groundwater samples, a total of six polymer types were identified, including PP, PE, PVC, PS, PA, and PU, with PP and PE being the predominant polymer types. There is a trend where the concentration of MPs tends to be higher in groundwater wells with shallower depths. The main source of MP contamination in groundwater is expected to be the transport through groundwater flow from adjacent industrial and agricultural areas with higher energy levels.

How to cite: Jeon, C.-H., Kim, H.-J., and Jeong, D.: Occurrence and Sources of Microplastics in groundwater divided by well depth and Hydrogeology in South Korea, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-13594, https://doi.org/10.5194/egusphere-egu24-13594, 2024.

EGU24-14269 | ECS | PICO | ITS3.24/HS12.9

Uncovering Geospatial Patterns Emphasize the Urgency of Tackling Plastic Pollution at its Source 

Jennifer Mathis, Chintan Maniyar, Deepak Mishra, Brajesh Dubey, and Jenna Jambeck

Urban centers worldwide, especially in rapidly developing nations, grapple with significant challenges in solid waste management (SWM). High waste generation, limited finances, and the influx of plastic material into historically plastic-free waste streams resulted in plastic waste accumulation in the environment (or unsustainable open dumping practices). Environmental challenges extend beyond SWM, impacting human life, infrastructure (e.g., waterway, sewage, stormwater network), and diverse ecosystems (e.g., mudflats, beaches, mangroves) crucial for protecting ecological barriers and preserving marine diversity. The ecological and socio-economic concerns spanning from plastic pollution necessitate a nuanced understanding of its abundance and distribution in urban areas to devise effective and targeted interventions. Investigative efforts on plastic pollution accumulation patterns are mainly conducted in industrialized nations, marine settings, and remote locations, creating a knowledge gap that hinders locally influential strategizing. This study assessed geospatial patterns of prominent plastic accumulation sites in Mumbai, India, considering the interplay of geographical and socioeconomic factors. Sampling methods comprised in-situ observations of 249 plastic accumulation sites across the city from April to May 2022, alongside 241 satellite-based remote observations utilizing spectral properties to analyze a broader range of sites. Sites were geospatially analyzed with urban geographical features. Results showed that more than half the sites fall within 100 meters of a residential or commercial building (283) and informal settlement (434), spanning an area of 335,549 and 493,076 m2. Concerning the correlation between the proportion of plastic waste to feature-based land area coverage, we found an accumulation of roughly 2.2 m2 and 2.0 m2 of plastic waste within 100 meters for every 100 m2 of waterway and railway. Although significant, the land area to plastic waste area proportion was less for coastlines (0.1m2), intertidal zones (0.3m2), and coastally-located mangroves (0.2m2), supporting evidence that most plastic accumulates inland and is transported to the ocean via waterways and other mechanisms. Notably, most plastic accumulation sites were closer to waterbodies, green spaces, railways, and buildings, with only a few near roads. Accessing these sites often required a park-and-walk approach. This illustrative study underscores the advantages of identifying specific locations and patterns of plastic pollution accumulation as a crucial first step in achieving integrative material management. The visually compelling narrative equips communities with vital information for targeted strategies, emphasizing early intervention’s significance in curbing environmental impacts and protecting livelihoods. Visual representation fosters transparency, enhancing accountability for policy changes. This study urges a focus on addressing plastic pollution at its source, emphasizing proactive mitigation’s practicality and effectiveness. It underscores the importance of decisive action, advocating for early intervention as a vital strategy against plastic pollution. Mumbai has introduced a range of initiatives to reduce plastic pollution, including implementing legislation to limit the production and usage of single-use plastic products. Like many cities worldwide, it is a reminder of the pressing need to address social inequalities and environmental sustainability in rapidly growing urban areas. 

How to cite: Mathis, J., Maniyar, C., Mishra, D., Dubey, B., and Jambeck, J.: Uncovering Geospatial Patterns Emphasize the Urgency of Tackling Plastic Pollution at its Source, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-14269, https://doi.org/10.5194/egusphere-egu24-14269, 2024.

In the current discourse of marine science, the issue of anthropogenic plastic pollution poses a growing existential threat to marine ecosystems and their inhabitants. The relentless increase in global plastic production further intensifies this ecological challenge, necessitating the adoption of innovative monitoring approaches for marine debris management. This investigation outlines the effectiveness and precision of remote sensing technologies in documenting and monitoring the distribution of macro plastics in marine and coastal environments. It addresses the intricate difficulties in detecting individual plastics due to their diminutive size and demonstrates how remote sensing can surmount these obstacles by identifying accumulations of plastics, with the assistance of natural oceanographic processes like hydrodynamic fronts and eddies. This study is conducted near the fishing harbor in Tharangambadi, Tamil Nadu, India. Experimental methodologies are employed at depths of approximately ten meters to minimize the impact of bottom reflectance and obtain precise spectral signatures of both water and plastics. Utilizing a Fishing Harbor Jetty as a stable platform for drone operations counters challenges related to drone endurance and operational range. A comprehensive setup, employing High-Density Polyethylene (HDPE) nets, buoyancy aids, and anchoring systems, facilitates the deliberate collection of plastic debris for remote detection.
The research methodology incorporates the aggregation of various distinct polymer categories. The experimental setup features two 30 x 30 meter testbeds where waste plastics are secured to HDPE nets using Ziploc ties. These testbeds are strategically placed to enhance the differentiation between water and plastic reflectance. A designated benchmark site near the operational center ensures accurate georectification of images obtained from Unmanned Aerial Vehicles (UAVs), synchronized with the overpass of Sentinel, Landsat, and Planet Scope satellites. Unlike previous studies that used high-resolution aerial RGB imagery from drones to calculate the percentage of plastic coverage in satellite images, this study employs UAVs equipped with push-broom hyperspectral sensors to capture high-resolution (approximately 3nm) spectral signatures ranging from approximately 400nm to 1000nm of aggregated plastics. This approach confirms the feasibility of using satellites to identify macro plastic conglomerations. Concurrent in-situ measurements of the properties of water and plastics provide essential data on the detection of marine macro plastic contaminants.
A comparative analysis between the radiometric measurements of macro plastics' spectral signatures and the hyperspectral data acquired by the drone was conducted. The results demonstrate a strong correlation, suggesting that drone-based hyperspectral data could effectively replace radiometric measurements in future satellite validation or matchup activities. This research represents a significant stride in the remote monitoring and evaluation of plastic pollution, offering a scalable solution with considerable implications for the conservation of marine ecosystems.

Keywords: Macro Plastics, UAV, Hyperspectral Remote Sensing

How to cite: Shanmugam, V. and Palanisamy, S.: Remote Sensing and In-Situ Monitoring of Macro Plastics in Coastal Waters Using Hyperspectral UAV Imaging: A Comprehensive Study near Tharangambadi, Tamil Nadu, India, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-14490, https://doi.org/10.5194/egusphere-egu24-14490, 2024.

Roads are identified by many researchers as important source of waste emissions into the environment [1][2]. Previous works on this topic have analysed spatial distribution of roadside dumping sites as well as composition and amounts of waste they contain [3][4][5]. Recent work has hypothesized that in the case of populated mountain regions, where roads are preferentially located within relatively flat valley bottoms, roads can be an important source of macrolitter to the fluvial system [6]. In this study, we investigate the scale of this phenomenon in the Kamienica Gorczańska catchment in the Polish Carpathians. During fieldwork conducted in 2023, we determined the amount and composition of macrolitter within 103 plots located along various types of roads in the floodplain area of the studied stream. We have distinguished two types roadside macrolitter emission: dispersed and point one. Within plots representing dispersed emission (74) 1759 macrolitter items were reported, including 845 (48.04%) plastic items. Furthermore glass litter had the largest share in the total weight of the colleted waste (56.3%). Moreover, we found that point sources of macrolitter emission (e.g., illegal dumping sites) are most often located along roads surrounded by forests within a distance of up to 100 m from the nearest buildings. Our results highlight the importance of road systems in delivering household waste to the fluvial systems of mountain rivers, suggesting that roadside areas should be more adequately addressed in future waste management strategies.

Keywords: road system, macroplastic, mountain stream, household waste, waste management, Kamienica Gorczańska stream

The study was completed within the scope of the Research Project 2020/39/D/ST10/01935 financed by the National Science Center of Poland

How to cite: Haska, W., Liro, M., and Gorczyca, E.: Road-related macrolitter input to mountain river: the case of the Kamienica Gorczańska stream in Polish Carpathians, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-14992, https://doi.org/10.5194/egusphere-egu24-14992, 2024.

EGU24-16399 | PICO | ITS3.24/HS12.9

Microplastic pollution in marine caves 

Elena Romano, Luisa Bergamin, Letizia Di Bella, and Claudio Provenzani

Marine caves are mostly formed by dissolution processes in carbonate massifs and may be of karst origin, as the last part of a large terrestrial aquifer, or can originate at the sea level through different processes such as chemical dissolution and mechanical action of sea waves. They are affected by wide spatial and temporal environmental variability and/or extreme values of environmental conditions (light, nutrients, oxygen, pH, hydrodynamic conditions, difficulty of larval dispersion etc.). Despite this seemingly hostile environment, marine caves are biodiversity hotspots and refuge habitats, hosting many crevice-dwelling and deep-water species, but also some obligate cave-dwelling organisms.

Studies on anthropogenic pollution of marine caves, generally believed to be pristine environments, are practically missing. Only recently, the microplastic (MP) pollution in sediments, water, and in some benthic, sediment-dwelling organisms (benthic foraminifera, hard-shelled protozoans) of two Mediterranean marine caves has been recorded. The first one was the Bue Marino cave, a huge karst cave of the Gulf of Orosei (Sardinia, Italy) where microplastic was detected at rather low concentrations of 10-27 items kg-1 and 18-22 items l-1, in sediments and water, respectively. Microplastic was also recognised, through Micro Fourier Transform Infrared Spectroscopy (μFTIR), in the shell of the agglutinated foraminifer Eggerelloides advena. Microplastic was also recorded in sediments of the small Argentarola cave (Tuscan coast, Italy) at concentrations of 5.4-11.9 items kg-1, and in the shell of the agglutinated foraminifer Lagenammina difflugiformis. Polyethylene, the most abundant polymer in sediments of both caves, was the one detected in the foraminiferal shells.

These studies have demonstrated that some foraminiferal species, building their shell by collecting sediment particles, also collect tiny MP fragments of the order of magnitude of a few microns due to a scarce selection ability. Consequently, MPs enter the trophic chain because foraminifera are preyed upon by many marine organisms such as gastropods, scaphopods, fishes, decapods, and polychaetes.

The research carried out in marine caves has demonstrated that MP has reached also these remote and enclosed habitats and that MP deposited in sediments is available to the benthic organism and enters the trophic chain at very low phylogenetic levels. Foraminiferal agglutinated species including MP polymers, even if present at low concentrations, may be considered early indicators of MP pollution. A clear indication to consider MP pollution not only in water but also in sediment, to preserve the ecosystem of marine caves, was a relevant result of this research.

How to cite: Romano, E., Bergamin, L., Di Bella, L., and Provenzani, C.: Microplastic pollution in marine caves, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-16399, https://doi.org/10.5194/egusphere-egu24-16399, 2024.

EGU24-16935 | ECS | PICO | ITS3.24/HS12.9

Conceptual framework for exploring riverine macroplastic fragmentation 

Maciej Liro, Anna Zielonka, and Tim H.M. van Emmerik

     Field-based information on the rates of macroplastic fragmentation in rivers is currently mostly unavailable. However, obtaining such data in future research is crucial to understanding the production of secondary micro- and nanoplastics in rivers, the transfer of these harmful particles throughout the natural environment, and assessing the risks they pose to both biota and human health.
     To support future experimental works addressing this gap we developed a conceptual framework which identifies two types of riverine macroplastic fragmentation controls: intrinsic (resulting from plastic item properties) and extrinsic (resulting from river hydromorphology and climate)[1].  First, based on the existing literature, we identify the intrinsic properties of macroplastic items that make them particularly prone to fragmentation (e.g., film shape, low polymer resistance, previous weathering). Then, we conceptualize how extrinsic controls can modulate the intensity of macroplastic fragmentation in perennial and intermittent rivers. Using our conceptual model, we hypothesize that the inundated parts of perennial river channels—as specific zones exposed to the constant transfer of water and sediments—provide particular conditions that accelerate mechanical fragmentation of macroplastic resulting from its interactions with water, sediments, and riverbeds. The unvegetated areas in the non-inundated parts of perennial river channels provide conditions for biochemical fragmentation via photo-oxidation. In the non-inundated sections of perennial river channels, unvegetated areas create conditions favoring biochemical fragmentation through photo-oxidation. In intermittent rivers, the entire channel zone is hypothesized to support both physical and biochemical fragmentation of macroplastics, with mechanical fragmentation prevailing during periods of water flow.
     Our conceptualization can support planning of future experimental and modelling work aimed at the direct quantification of plastic footprint of macroplastic waste in different types of rivers.

The study was completed within the scope of the Research Project 2020/39/D/ST10/01935 financed by the National Science Center of Poland.
References
1. Liro, M., Zielonka, A., van Emmerik, T.H.M., 2023. Macroplastic fragmentation in rivers. Environment International 180, 108186. https://doi.org/10.1016/j.envint.2023.108186

How to cite: Liro, M., Zielonka, A., and van Emmerik, T. H. M.: Conceptual framework for exploring riverine macroplastic fragmentation, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-16935, https://doi.org/10.5194/egusphere-egu24-16935, 2024.

EGU24-17352 | PICO | ITS3.24/HS12.9

The occurrence and sources of microplastics to Arctic and sub-Arctic beaches: human influence on local microplastic hotspots 

Jonathan Dick, Tesni Lloyd-Jones, Stamatia Galata, Timothy Lane, Eoghan Cunningham, and Konstadinos Kiriakoulakis

Plastic pollution, and in particular, microplastic pollution, is a global environmental concern particularly in marine ecosystems. The small size of these particles (< 5 mm) means they are prone to ingestion and accumulation by organisms across all trophic levels. Beaches are situated on the transition between the terrestrial and oceanic ecosystems, an important habitat for many species, and have long been known to be sinks of other environmental pollutants. However, until recently their importance as sinks for microplastics and the sources involved were relatively unknown.

This study investigates the extent and likely sources of microplastic pollution on beaches in Arctic and sub-Arctic regions, focusing on Svalbard and Iceland. Sediments on beaches at four sites in Svalbard and eleven in Iceland were sampled for microplastics. Subsequent laboratory analyses for microplastic particle ID, size, type, and polymer (using micro-FTIR) was then carried out to estimate abundance and potential uses of the microplastics identified. Statistical analyses of these results, in conjunction with environmental and geographical data, were conducted to identify patterns and potential sources.

The results revealed significant variability in microplastic quantity, types, and polymers across all locations. Sites with the lowest microplastic concentrations were situated in the most remote areas, while those with the highest concentrations were in proximity to areas with intense human activities or higher population densities. Statistical analyses showed a clear relationship between observed data and the proximity to human activities/inhabitation, with environmental conditions such as wind direction and currents also playing a significant contributory role. These findings suggest that the lower microplastic concentrations found in remote areas are background contamination from ocean delivered from more distant densely inhabited regions (notably Western Europe), with the high contamination hotspots linked to local activities. These findings underscore the heightened impact of local human factors in driving elevated microplastic pollution in beach sediments over oceanic controls in remote yet inhabited Arctic and subarctic locations.

How to cite: Dick, J., Lloyd-Jones, T., Galata, S., Lane, T., Cunningham, E., and Kiriakoulakis, K.: The occurrence and sources of microplastics to Arctic and sub-Arctic beaches: human influence on local microplastic hotspots, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17352, https://doi.org/10.5194/egusphere-egu24-17352, 2024.

EGU24-18285 | PICO | ITS3.24/HS12.9 | Highlight

Large-scale remote monitoring of riverine litter to evaluate effectiveness of clean-up technologies 

Liesbeth De Keukelaere, Els Knaeps, Robrecht Moelans, Marian-Daniel Iordache, Klaas Pauly, and Ils Reusen

In June 2023 the Horizon Europe project INSPIRE kicked off. INSPIRE will fight against the plastic pollution in rivers by introducing 20 scalable technologies to prevent and eliminate litter. The technologies will be demonstrated in 6 rivers across Europe. Monitoring of the plastic load in the river and the riverbanks is essential to develop a baseline and evaluate effectiveness of the technologies. Here we will introduce a camera and drone-based system to monitor plastic flux in the river and macroplastic densities on the riverbanks. The fixed camera system consists of a series of Commercial Of-The-Shelf (COTS) surveillance cameras with housing and real-time datalink. The cameras work autonomous and will provide a continuous feed of data uploaded to the cloud. The drone system consists of a high resolution RGB and multispectral Micasense camera. Specific attention goes to the conversion from the raw drone data into standardized Analysis Ready Data (ARD) including: (1) image alignment of the multispectral camera. (2) Converting raw drone data into reflectance products (using an irradiance sensor) allows the methodology to be applicable in any circumstance (clear, overcast, cloudy conditions) and transferable to other regions. (3) Sensor fusion, to align high spatial resolution RGB with high spectral resolution MicaSense data.

 

For plastic detection and characterization robust machine learning models are being used including new pre-trained foundation models like Segment Anything. New methods are being tested to transform the camera-based plastic detections into a plastic flux product taking into account the tide effects in the river. This includes feature detection techniques like SIFT (Scale_Invariant Feature Transform), SURF (Speeded-Up Robust Features) or ORB (Oriented FAST and Rotated Binary Robust Independent Elementary Features) in combination with a feature matching algorithm (e.g. FLANN based matcher). Here, we will present the INSPIRE project and its first results demonstrated at the Temse bridge (Belgium) and riverbanks along the Scheldt river (Belgium).

How to cite: De Keukelaere, L., Knaeps, E., Moelans, R., Iordache, M.-D., Pauly, K., and Reusen, I.: Large-scale remote monitoring of riverine litter to evaluate effectiveness of clean-up technologies, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18285, https://doi.org/10.5194/egusphere-egu24-18285, 2024.

Microplastics are detected in the environment, particularly in oceanic waters, and have adverse effects on marine ecosystems, biota, climate dynamics, and human health, primarily through the induction of marine pollution. The microplastics are introduced into marine ecosystems either as primary particles through direct discharge or as secondary particles resulting from the weathering of macroplastics. For this, a new laboratory optical-based measurement technique using the static light scattering (SLS) instrument was proposed for the detection and quantification of the microplastics size distribution and to mitigate marine pollution. The SLS instrument relies on the Lorenz-Mie scattering and Fraunhofer diffraction theories and a single monochromatic laser beam is passed through the sample and measures the light scattered intensity in all the scattering angles and with one or many detectors. SLS analysis yields information about microplastic samples, including the volume fraction of each size class bin and the cumulative log-normal distribution of particles. The volume fraction calculation will give the microplastics mean diameter () and standard deviation (σ). The microplastics considered in the present study, include polyethylene (PE), polypropylene (PP), polystyrene (PS), and polyvinyl chloride (PVC). The mean size and standard deviation for PE samples are 3 µm and 2 µm and similarly, the mean size and standard deviation for PP are 3.5 µm and 2 µm. In the case of PS samples, the mean size and standard deviation are 3.5 µm and 2 µm, whereas PVC samples demonstrate a mean size and standard deviation of 3 µm and 2 µm. The findings of the SLS data show the and σ values are in the range of 3-3.5 µm and 2 µm, respectively, for all the microplastic types. The results of the present study will be helpful for a comprehensive understanding of microplastic behavior, facilitating the development of targeted methodologies for detection using hyperspectral remote sensing data in marine environment.

How to cite: Sandhani, C. G., Shanmugam, P., and Sannasiraj, S. A.: Comprehensive Investigation of Microplastics size distribution in Marine Environment: Detection, Quantification, and Optical Analysis Using Static Light Scattering (SLS), EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18753, https://doi.org/10.5194/egusphere-egu24-18753, 2024.

EGU24-20324 | PICO | ITS3.24/HS12.9

Debris classification based on detailed spectral observations using micro-satellite 

Yukihiro Takahashi, Shaqeer Mohamed, and Shin-ichiro Kako

Remote sensing observations from satellites have the great advantage of surveying large areas in a short time. On the other hand, the pixel size of satellite-borne camera on the ground is larger than that of drones or ground-based measurements, making it difficult to classify types of litter based on their detailed shape. Detailed spectral measurements using hyperspectral cameras are expected to be effective in classifying plastics and wood floating on the ocean, or litter accumulated on the beach, from vegetation, sand and stones, but the typical ground resolution of existing satellite-borne hyperspectral cameras is about 30 m. It is not easy to discriminate between types of litter and other objects. We have established imaging technology with a bandwidth (FWHM) of 10-20 nm, 1 nm steps at the centre wavelength and ground resolution of 5-120 m in the 0.4-1.0 micron wavelength range by developing and operating a 50 kg class micro-satellite equipped with a liquid crystal tunable filter (LCTF). In order to capture plastic features, it is necessary to observe even longer wavelength ranges. Currently, by developing a new spectral camera and satellite attitude control technologies, we plan to achieve a bandwidth of less than 10 nm and a ground resolution of about 10 m at 0.4-1.6 um. It is also important to build up a spectral library of spectra for different types of litter and plastics based on ground-based measurements. In this presentation, we report on the development of our micro-satellites and on-board cameras, as well as the methodology and status of the construction of the spectral library.

How to cite: Takahashi, Y., Mohamed, S., and Kako, S.: Debris classification based on detailed spectral observations using micro-satellite, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-20324, https://doi.org/10.5194/egusphere-egu24-20324, 2024.

EGU24-20411 | ECS | PICO | ITS3.24/HS12.9 | Highlight

Macroplastic pollution hotspots across global mountain river catchments 

Anna Zielonka and Maciej Liro

Mountain rivers in densely populated areas have recently been reported as substantially polluted by macroplastics [1]. Previous works suggest that macroplastic delivered to mountain river channel can be quickly fragmented to microplastic, because of distinct natural characteristics of  mountain river channel (e.g. high energy of flow, steep gradient, coarse bed sediments).  The produced microplastic (and  related risks) can not only affect mountain rivers but can also be transported downstream to lowland rivers and oceans [2]. The information on local, regional, and global patterns of plastic emission within mountain river catchments is crucial for planning effective mitigation strategies.

Here we combine existing databases of river catchments [3] and mismanaged plastic waste (MPW) emission [4] to calculate flux of plastic waste from global mountain river catchments [t yr-1]. Our results demonstrate the highest plastic emissions in Asian mountain river catchments, with the maximum (total MPW 37111630 t yr-1) detected in Himalayas. Similar values were also observed in mountain river catchments in the Chilean Andes; however, the number of hotspots was lower in this region. Mountain river catchments in Europe (especially northern Europe) and Australia are influenced by three times lower emissions of MPW compared to those in Asia and South America. We identified numerous hotspots of MPW emission in mountain river catchments that coincide with areas of extreme rainfall occurrence (particularly in the Southeast Asia region). This spatial correlation may consequently accelerate microplastic production during extreme events and facilitate its downstream transport. The obtained data provide a unique source of information for future detailed research aimed at mitigating the plastic pollution problem in global mountain rivers and highlight areas that require urgent regulations to address the plastic pollution problem.

 

The study was completed within the Research Project 2020/39/D/ST10/01935 financed by the National Science Centre of Poland.

 

References

[1] Liro, M., Mikuś, P., Wyżga, B., 2022. First insight into the macroplastic storage in a mountain river: The role of in-river vegetation cover, wood jams and channel morphology. Sci. Total Environ.838, 156354. https://doi.org/10.1016/j.scitotenv.2022.156354

[2] Liro, M., van Emmerik, T.H.M., Zielonka, A., Gallitelli, L., Mihai, F.C., 2023. The unknown fate of macroplastic in mountain rivers. Sci. Total Environ. 865, 161224. https://doi.org/10.1016/j.scitotenv.2022.161224.

[3] Ouellet, D.C., Lehner, B., Sayre, R., Thieme, M., 2019. A multidisciplinary framework to derive global river reach classifications at high spatial resolution. Environ. Res. Let. 14(2): 024003. https://doi.org/10.1088/1748-9326/aad8e9

[4] Lebreton, L., Andrady, A., 2019. Future scenarios of global plastic waste generation and disposal. Palgrave Commun. 5 (6), 1–11. https://doi.org/10.1057/s41599-018-0212-7

How to cite: Zielonka, A. and Liro, M.: Macroplastic pollution hotspots across global mountain river catchments, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-20411, https://doi.org/10.5194/egusphere-egu24-20411, 2024.

EGU24-20682 | PICO | ITS3.24/HS12.9 | Highlight

Microplastics in the Alpine watercycle – A combination of methods to cover the widest possible size range  

Marcel Liedermann, Sebastian Pessenlehner, Elisabeth Mayerhofer, Wolfgang Schöner, Doris Ribitsch, Georg Gübitz, and Philipp Gmeiner

Plastic waste as a permanent pollutant in the environment is of increasing concern due to its largely unknown long-term effects on biota. The occurrence in rivers, has, compared to research in the oceans, only become the focus of scientific investigations in the last few years. The Austrian Alps in particular are largely unexplored in this respect. Therefore, the Alplast project addresses microplastic transport from the glaciers at the summit over steep mountain torrents to the lowland rivers and aims in conducting a first inventory of the alpine area. Specifically, analyses of microplastic occurrences are being carried out from the Sonnblick glacier via the Rauriser Ache, the Salzach, the Inn and the Danube and are intended to expand the understanding of processes with regard to the behaviour of microplastics in the water cycle from the glacier to the valley. The influence of snowmelt as well as the temporal development, which can be determined from ice cores, are of great interest. In addition, questions regarding the origin and distribution of plastic in flowing waters as well as the possible biological degradation by microorganisms will be clarified.

Since the sampling areas cover entire catchments at different altitudes, different methodologies and devices are used. For the studies on the glaciers, the snow cover as well as ice cores are sampled and analysed. In the rivers a multi-point method is used due to the spatial distribution of plastic particles in the river cross-section. But the net samples at different depths are combined with isokinetic pump sampling in order to detect the widest possible size range. Isokinetically taken pump samples have the great advantage that a weighting process takes place directly during sampling. This means that samples can be taken in different areas (high and low flow velocities) of the cross-section (together with the nets) and then a composite sample can be analysed for the profile. Particle counts, classification and the measurement of concentrations and loads are then used to determine quantities and the most common types of plastics in the alpine environment. The measuring stations were selected in such a way that more and more potential microplastic sources are added in the course of the catchment in order to achieve the best possible process understanding regarding the origin and fate of the plastic waste.

How to cite: Liedermann, M., Pessenlehner, S., Mayerhofer, E., Schöner, W., Ribitsch, D., Gübitz, G., and Gmeiner, P.: Microplastics in the Alpine watercycle – A combination of methods to cover the widest possible size range , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-20682, https://doi.org/10.5194/egusphere-egu24-20682, 2024.

 Highlights

  • We attribute variation across global climate mitigation scenarios to three factors
  • The three factors are climate ambition, scenario background and model choice
  • Many indicators are well-explained by the average effects of one or two factors
  • We also calculate the residual not explained by these average effects
  • This shows which indicators give outliers for some specific input combinations

Abstract

We attribute variations in key energy sector indicators across global climate mitigation scenarios to climate ambition, assumptions in background socioeconomic scenarios, differences between models and an unattributed portion that depends on the interaction between these. The scenarios assessed have been generated by Integrated Assessment Models (IAMs) as part of a model intercomparison project exploring the Shared Socio-economic Pathways (SSPs) used by the climate science community. Climate ambition plays the most significant role in explaining many energy-related indicators, particularly those relevant to overall energy supply, the use of fossil fuels, final energy carriers and emissions. The role of socioeconomic background scenarios is more prominent for indicators influenced by population and GDP growth, such as those relating to final energy demand and nuclear energy. Variations across some indicators, including hydro, solar and wind generation, are largely attributable to inter-model differences. Our Shapley-Owen decomposition gives an unexplained residual not due to the average effects of the other factors, highlighting some (such as the use of carbon capture and storage (CCS) for fossil fuels, or adopting hydrogen as an energy carrier) with outlier results for particular ambition-scenario-model combinations. This suggests guidance to policymakers on these indicators is the least robust.

Graphical Abstract

Keywords

Energy transition, climate change mitigation, Integrated Assessment Models, Shapley-Owen decomposition

How to cite: Al Khourdajie, A., Skea, J., and Green, R.: Climate ambition, background scenario or the model? Attribution of the variance of energy-related indicators in global scenarios, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-656, https://doi.org/10.5194/egusphere-egu24-656, 2024.

This essay navigates the critical juncture of climate change mitigation and sustainable construction practices, employing a comprehensive analytical framework centered on the robust incorporation of a Life Cycle Assessment (LCA). The exploration is rooted in a profound understanding of the construction industry's substantial contribution, amounting to 38% of global emissions, with a specific emphasis on residential buildings and their consequential carbon footprint.

The advocacy for the implementation of a standardized carbon inventory and a meticulously defined system boundary constitutes a foundational aspect of this analysis. This endeavor draws upon well-established ISO standards, simultaneously subjecting the widely acknowledged LEED V4.1 to a comprehensive and rigorous critical examination. The utilization of precise carbon calculations, facilitated by sophisticated regression formulas, emerges as a pivotal tool, enabling the identification of salient life cycle hotspots within the construction sector. In advancing a proactive approach to carbon reduction, this essay delves into historical trends and introduces an institutional management framework, covering GRESB and SDGs. This multifaceted strategy not only addresses immediate challenges but strategically positions organizations within the construction industry to thrive. It adeptly navigates transition risks and seamlessly integrates sustainable practices, thus fostering a transformative paradigm within the sector. Within the specific context of Taiwan, where the majority of green buildings align with the widely accepted LEED system, the mission is unequivocal. The objective is to augment the LEED framework through the judicious incorporation of a tailored life cycle assessment that is attuned to the unique needs of the Taiwanese construction landscape. The overarching goal remains the establishment of an equitable, transparent, and easily comprehensible system, not only to present opportunities but also to effectively mitigate risks for construction companies.

The essay underscores the imperative of ensuring that buildings actively contribute to communities and the environment. This initiative aligns harmoniously with the ambitious target of achieving net-zero emissions by 2050, thereby engendering a positive and enduring impact for both present and future generations. The transformative narrative presented in this exploration emphasizes the pivotal role of sustainable construction practices in reshaping the trajectory of the construction industry towards a resilient and environmentally conscious future.

How to cite: Wang, Y. and Tung, C.: Reducing Transition Risks: A Life Cycle Assessment Approach to Residential Buildings with Integrated Sustainable Framework, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-3063, https://doi.org/10.5194/egusphere-egu24-3063, 2024.

EGU24-4387 | Orals | ITS3.27/ERE6.6

Adaptive Strategies to Reconcile Diverse Equity Preferences in Climate Policies 

Palok Biswas, Jazmin Zatarain Salazar, and Jan Kwakkel

Normative uncertainty, which arises from diverse ethical perspectives and uncertainty about distributional outcomes, poses a significant hurdle in climate policy negotiations. Such uncertainty illustrates the core challenge of achieving agreement on the moral principles or equity considerations that should guide the development of climate policies.

Integrated Assessment Models (IAMs), while influential in shaping decisions, fall short in factoring in this normative uncertainty in climate policies. To address this issue, we developed an IAM framework called JUSTICE. JUSTICE leverages the economic insights of the RICE50+ model to explicitly account for spatiotemporal heterogeneity alongside probabilistic forecasting of the FaIR climate model for a better representation of climate uncertainty. We also reformulate the Social Welfare Function (SWF) in light of four distributive justice principles - Utilitarian, Sufficientarian, Egalitarian, and Prioritarian - to encapsulate the ethical pluralism of different stakeholders. 

We search for adaptive mitigation policies by assimilating two established decision-making frameworks: Multi-Objective Robust Decision-Making (MORDM) and Evolutionary Multi-Objective Direct Policy Search (EMODPS). MORDM rigorously tests potential policies against deep uncertainties to find robust, Pareto-optimal choices. At the same time, EMODPS fine-tunes strategies to reconcile stakeholders' diverse objectives, ensuring policies are adaptive and robust. 

Our findings demonstrate that adaptive policies facilitate deliberation. They identify common ground among policymakers with diverse perspectives by being robust across multiple realizations of deep uncertainties and flexible enough to accommodate conflicting ethical perspectives. Our approach designs climate policies that are both inclusive and adaptive, ensuring they account for immediate necessities while remaining responsive to unfolding future challenges—thereby upholding the tenets of both intra and intergenerational justice. 

 In summary, our study underscores the pivotal role of normative clarity in facilitating stakeholder dialogue and ensuring that climate policies are scientifically sound and socially equitable. Incorporating diverse normative perspectives and acknowledging normative uncertainties, our adaptive strategies limit overconfidence in climate policies, promote inclusivity without subjecting individuals to undue risks, and redefine the application of IAMs in crafting fair and just climate policies.

 

Keywords: Integrated Assessment Models, Climate policy, Distributive justice, Deep Uncertainty, Adaptive strategies, Social Welfare Function, Robust decision-making.

How to cite: Biswas, P., Zatarain Salazar, J., and Kwakkel, J.: Adaptive Strategies to Reconcile Diverse Equity Preferences in Climate Policies, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-4387, https://doi.org/10.5194/egusphere-egu24-4387, 2024.

EGU24-5065 | ECS | Posters on site | ITS3.27/ERE6.6

MEDUSA - Modelling Equity and DistribUtional impacts for Socioeconomic Analysis 

Eva Alonso-Epelde, Clàudia Rodés, and María Moyano-Reina

Addressing the major challenges of the 21st century, such as climate change, will require complex and ambitious policies that promote social justice. To do so, it is necessary to design efficient policies that do not exacerbate existing inequalities, such as gender or income inequality. In this sense, it is essential to carry out impact analyses of policies from a holistic perspective that evaluates the economy, energy, land, and water systems in an integrated manner before implementing them. While Integrated Assessment Models (IAMs) have been a fundamental tool in the past, micro-simulation models for distributional analysis have the advantage of providing more heterogeneous results that help to more robustly identify the socio-economic impacts of the policies to be implemented. These analyses make it possible to identify the people who will be most affected by policies and to implement compensatory measures to make the policy fairer. Thus, the combination of both models (IAMs and microsimulation models) can provide valuable results for decision making.  MEDUSA is an R package that allows the development of distributional analyses in isolation or in connection with other models such as GCAM. Its extensive database allows for highly disaggregated results, taking into account numerous socio-economic and demographic characteristics of households, such as income level, place of residence, type of family or the degree of feminisation of the household. At the moment, the prototype works for Spain, but the idea is to extend it to all EU countries in the short term. However, the package could be extended to all countries that are able to provide the raw data of the model.

How to cite: Alonso-Epelde, E., Rodés, C., and Moyano-Reina, M.: MEDUSA - Modelling Equity and DistribUtional impacts for Socioeconomic Analysis, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5065, https://doi.org/10.5194/egusphere-egu24-5065, 2024.

EGU24-9358 | ECS | Orals | ITS3.27/ERE6.6

Air quality, health and equity impacts of transport electrification in the U.S. Midwest.  

Sara F Camilleri, Anastasia Montgomery, Maxime Visa, Jordan L Schnell, Zac Adelman, Mark Janssen, Emily Grubert, Susan C Anenberg, and Daniel E Horton

Chronic traffic related air pollution (TRAP) exposure is linked to various adverse health outcomes including pediatric and adult asthma incidence, but more importantly can also lead to premature mortality. In the U.S., the majority of people living close to high volume and density roadways are people of color who are exposed to disproportionate levels of associated health harming primary and secondary air pollutants such as NOx (NO + NO2; key precursors for O3 formation) and PM2.5 as well as greenhouse gases (e.g. CO2). Both heavy- and light-duty vehicles (HDVs/LDVs) contribute to on-road TRAP but on a per vehicle basis, the associated air quality and public health impacts are larger for HDVs. One potential climate mitigation strategy is the shift of the transportation sector to battery powered alternatives (EVs). However, the associated air quality, health and equity implications of such a transition are not well understood and lack characterization at fine intra-urban spatial scales.

Given non-linear atmospheric chemistry associated with the formation of secondary pollutants (e.g O3), and the steep spatial gradients exhibited by short lived TRAP (e.g.  NO2), here we use the two-way coupled Weather Research Forecast and Community Multiscale Air Quality (WRF-CMAQ) chemical transport model at 1.3 km to determine changes in simulated NO2, O3 and PM2.5 concentrations from the electrification of 30% of HDVs and LDVs over a central U.S. Midwestern domain. We represent changes in on-road, refueling and idling emissions as well as power plant emissions from the increased electricity demand needed for charging. Altered emissions are then used as inputs to run a month-long simulation for each season. Incorporating high resolution concentration changes with census tract level health data, we estimate changes in health impacts at the census tract level and across different population subgroups.

We find that electrifying 30% of primarily diesel-fueled HDVs reduces NOx emissions by a factor of 10 for each vehicle mile compared to the NOx reductions associated with electrifying 30% of LDVs. We simulate domain-wide annual mean NO2 (~-10%) and PM2.5 (~-2%) reductions that peak along major roadways, however MDA8O3 concentrations increase in urban cores. If 30% HDVs and LDVs are electrified, we estimate that 1,120 and 170 annual premature deaths linked to NO2 and PM2.5 would be avoided, respectively while 80 annual premature deaths associated with MDA8O3 would be added. Additionally, we find that the largest simulated air quality and health benefits are within communities of color. Notably, we find that while the domain as a whole is only 12% Black, communities with the largest NO2-related health benefits are 45% Black.   

Our results demonstrate that incentives aimed at reducing transportation related emissions, especially from HDVs, are beneficial from a climate perspective but also from an air quality, health and economic perspective with the potential to reduce long standing environmental injustices.

How to cite: Camilleri, S. F., Montgomery, A., Visa, M., Schnell, J. L., Adelman, Z., Janssen, M., Grubert, E., Anenberg, S. C., and Horton, D. E.: Air quality, health and equity impacts of transport electrification in the U.S. Midwest. , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-9358, https://doi.org/10.5194/egusphere-egu24-9358, 2024.

EGU24-10785 | Orals | ITS3.27/ERE6.6

Can energy and environmental taxation be progressive in the EU? 

Xaquín García-Muros, Eva Alonso-Epelde, Mikel González-Eguino, and Alejandro Rodríguez-Zúñiga

The success of the targets established in the European Green Deal depends on the correct design of ambitious policies that utilize all available instruments, including energy and environmental taxation. In the “Fit for 55” package, the EC proposed a deep reform of the Energy Taxation Directive (New ETD) to update the current taxation and align it with current environmental goals. However, due to the war in Ukraine, the energy crisis, and the risk of regressive effects the current proposal of the EC is stalled. Therefore, this analysis seeks to provide new evidence from a microsimulation model developed to assess the direct, overnight distributional impacts of the proposed new ETD reform on households. Our aim is to explore whether the proposed EU-level polluter pays instruments can be designed to achieve progressive distributional impacts, to identify policy options that ensure they strengthen social justice without undermining it, and thereby remove social barriers. Moreover, we explore a dimension often underrepresented in distributional analyses, namely gender. Our results indicate that, with the correct design from the outset, environmental tax reforms can be progressive and not increase current inequalities between and within Member States of the EU, including those related to gender.

How to cite: García-Muros, X., Alonso-Epelde, E., González-Eguino, M., and Rodríguez-Zúñiga, A.: Can energy and environmental taxation be progressive in the EU?, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-10785, https://doi.org/10.5194/egusphere-egu24-10785, 2024.

EGU24-12512 | ECS | Orals | ITS3.27/ERE6.6 | Highlight

Why representing gender (in)equality in climate change scenarios matters for the challenges space   

Marina Andrijevic, Caroline Zimm, Jonathan Moyer, Raya Muttarak, and Shonali Pachauri

Socioeconomic challenges to adaptation and mitigation partly hinge on gender (in)equality. A world of equal opportunities for self-realization would be a markedly different place, in ways that are of substantial relevance for adressing climate change. The opposite holds too: in a world of stagnating, or worsening gender inequality, differences in access to resources, education or employment may reduce capacities of societies to both mitigate and adapt.  

Integrated assessment and climate impact models rely heavily on scenarios to understand implications of different socioeconomic futures. In the context of gender equality, these models and scenarios can also serve as tools for broadening our understanding of how societies’ capacities to adapt to and mitigate climate change are enabled or constrained if, broadly speaking, half of their population would gain access to or be further deprived of resources and decision-making power.

In this paper, we propose that the dominant framework of socioeconomic scenarios – the Shared Socioeconomic Pathways (SSPs) – should be extended to explicitly represent indicators of gender equality and their interlinkages with other facets of development. The original narratives underlying the SSP scenarios do feature assumptions about gender equality as part of the demographic elements, with educational attainment and its effect on reducing fertility and therefore population size as the main driver of socio-economic changes (O’Neill et al., 2017). However, only a systematic incorporation into narratives and endogenization of gender (in)equality, can enable the scenarios to reflect ways in which different levels of gender equality could increase or reduce challenges to adaptation and mitigation, and the implications of these challenges for dealing with climate risks. This also applies for other scenario-based work in sustainability and climate change research, for example in devising local energy transition policies whose justice element might be contingent on whether they consider gender aspects. The need for more nuanced accounts of gender has also been highlighted in the context of representation of inequalities in Integrated Assessment Model (IAMs), where gender equality is highlighted as one of the crucial factors when considering climate impacts and policies, their distributional implications and costs (Emmerling and Tavoni, 2021).  

We cover some of the myriad connections between gender and climate from the literature to build the case for why comprehensive assessments of future risks of climate change and of socioeconomic development can benefit from more concrete incorporation of gender aspects in their analyses. We discuss adaptation and mitigation challenges and their interplay with gender. A particular focus is on quantitative models of future societal transformations and assessments of their implications for climate change. We then argue that scenarios can help imagine a world of parity or lack thereof, and show how the SSP framework may change after accounting for gender equality. At the end we discuss how these conceptual and practical advances can feed into more nuanced climate change research and better-informed policy options. 

How to cite: Andrijevic, M., Zimm, C., Moyer, J., Muttarak, R., and Pachauri, S.: Why representing gender (in)equality in climate change scenarios matters for the challenges space  , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12512, https://doi.org/10.5194/egusphere-egu24-12512, 2024.

EGU24-15327 | Orals | ITS3.27/ERE6.6 | Highlight

Modeling Dynamic Systems for Sustainable Development  

Noelle Selin, Amanda Giang, and William Clark

We summarize recent progress in dynamic modeling of nature-society systems to inform efforts towards sustainable development.  Drawing on lessons learned from a series of virtual workshops and a journal Special Feature, we identify and highlight examples of novel methods and advances, focusing on four stages of modeling practice -- defining purpose, selecting components, analyzing interactions, and assessing interventions. We highlight insights for researchers interested in assessing the implementation of system-wide sustainability strategies, with a focus on human well-being as an overarching objective, including methods that incorporate nature-society interactions into sectoral decision-support models, simulating cross-sector connections and differing contexts, and implementing computational and statistical approaches that evaluate decision scenarios under uncertainty. We additionally highlight techniques that can serve to foreground issues of power differentials among actors, including methods that can capture diverse societal actions and their agency, and incorporate different perspectives and normative visions. As a concrete example of the utility of a set of methods and advances from this survey of coupled nature-society systems modeling, we show how advances in computational techniques can be used to assess the degree to which national-scale climate policies in the United States can impact air pollution exposure to different racial/ethnic groups. 

How to cite: Selin, N., Giang, A., and Clark, W.: Modeling Dynamic Systems for Sustainable Development , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15327, https://doi.org/10.5194/egusphere-egu24-15327, 2024.

EGU24-15637 | ECS | Orals | ITS3.27/ERE6.6 | Highlight

Global convergence of incomes in a climate-constrained world 

Yannick Oswald

Sustainable development aims for equal living standards in the Global North and Global South while limiting global warming as much as possible. If measured in monetary terms, it is well known that eradicating extreme poverty (so lifting people beyond $PPP 2.15 consumption expenditure per capita (pc) per day) does not threaten climate goals. However, just beyond extreme poverty is not an acceptable goal for the living standards of people. In Europe, for instance, people live between $PPP 30pc and $PPP 70pc on average depending on the country. Moreover, some research has shown that lifting all people globally to only $PPP 5.5pc per day already implies allocating a much bigger carbon budget share for poverty eradication given current techno-economic parameters. Hence, if global poverty is to be reduced substantially, high-income nations and groups require a more rapid reduction of emissions so that global climate goals remain met. Therefore, in this research, I explore global convergence scenarios of incomes, taking between-country inequality and within-country inequality into account. I do so based on data from the World Inequality Lab and consider the following constraints and parameters: (i) income level towards which countries and groups converge (e.g. $PPP 30pc consumption expenditure a day or $PPP 50pc and so forth) (ii) carbon budgets, (iii) time horizon (for example whether convergence happens until 2050, 2075 or 2100 and so forth) and (iv) technology evolution (the pace of carbon intensity reduction). By studying the trade-offs between these, I elaborate on possible growth pathways for low-income countries and low-income groups world-wide, and corresponding degrowth or steady-state pathways in Global North countries and high-income groups world-wide. I find, for example, that, if all high-income nations continue historic growth trajectories while low- and middle-income countries converge to an acceptably high level of income, strict carbon budgets are difficult to maintain and hence high-income countries might consider steady-state or degrowth trajectories to free up “growth space” in low-income countries. The results may inform country-specific and income-group specific climate-economic scenarios in integrated assessment models and the wider policy debate.   

How to cite: Oswald, Y.: Global convergence of incomes in a climate-constrained world, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15637, https://doi.org/10.5194/egusphere-egu24-15637, 2024.

EGU24-17982 | ECS | Orals | ITS3.27/ERE6.6

Heat-related mortality projections for 335 European NUTs3 regions 

Ane Loroño Leturiondo, Anil Markandya, and Elisa Sainz de Murieta

Under climate change, heat waves are expected to become more frequent, more intense, and longer representing a risk factor in mortality and morbidity and a significant threat to public health [1]. In this study, we have performed a mortality impact assessment due to heat in European regions estimating the number of deaths related to mortality in each European country.

Our dataset includes the relative risk of death related to high-temperature data, as well as baseline mortality (2013) and projections (2030, 2050, and 2070) for adults over 65 years. We have calculated the number of deaths attributed to heat using the World Health Organization (WHO) relative risk model [2]. Adaptation was partly incorporated into the assessment by adjusting the optimum temperature in future periods under 4 combinations of climate and socioeconomic scenarios (RCP2.6-SSP1, RCP4.5-SSP2, RCP7.0-SSP3 and RCP8.5-SSP5) based on the latest CMIP6 data.

Preliminary results show that heat-related risk and the number of deaths increase with time, as expected. In the short term (2030), the increase in mortality measured as the ratio between projected and baseline mortality, does not change much across scenarios. The average rate of daily deaths for the EU27 is 2.054 (1.766,2.354) under SSP1-2.6 (the central estimate is the median, and percentiles 10 and 90 have been used for the interval), and 2.244 (1.853,2.694) in SSP5-8.5. Mortality increases over time, although it varies greatly depending on the scenario considered. By 2070 the number of fatalities reach 3.529 (2.849,4.88) in SSP1-2.6 and 7.658 (5.984,10.07) in SSP5-8.5. We also find significant differences across countries. By 2070, under a middle-row scenario (SSP2-4.5), countries such as Belgium, Bulgaria, Czechia, Denmark, Germany, Estonia, Croatia, Latvia, Lithuania, Finland, and Sweden present an increase in mortality between 2 and 3 fold baseline mortality. Others, mostly in Southern Europe such as Greece, France, Malta, Italy or Cyprus, but also Luxemburg and Slovenia, have a severe increase in mortality, 5 to 9 times baseline mortality.

We also estimated the annual number of deaths in the EU27 due to extreme heat. In the baseline, our results are 72,955 annual deaths, which exceeds previous estimates, such as that obtained for the summer of 2022 [6], but this difference could be linked to the use of more recent CMIP6 data. In 2070, the number of heat-induced deaths in the EU could reach 211.039 (140.686,293.409) in SSP1-2.6 and 435.331 (283.927,671.089) in SSP5-8.5. Some studies [3] show that, under RCP8.5, annual mortality will possibly increase by up to 300,000 excess deaths by the last quarter of the 21st century, accounting for exposures above the minimum mortality temperature, including extremely hot temperatures, so our figures do not differ much of these estimates, even though they are higher than some others [4, 5].

 

 

How to cite: Loroño Leturiondo, A., Markandya, A., and Sainz de Murieta, E.: Heat-related mortality projections for 335 European NUTs3 regions, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17982, https://doi.org/10.5194/egusphere-egu24-17982, 2024.

EGU24-19615 | Posters on site | ITS3.27/ERE6.6

Passenger transport decarbonisation under different equity considerations 

Dirk-Jan Van de Ven and Jon Sampedro

Climate change is often seen as an equity problem, as it is caused primarily by richer countries and households, while its impacts are generally expected to affect poorer countries and households significantly stronger. Climate policy aiming at mitigating these impacts, however, can also have a regressive impact on societies, unless it is designed such that the costs of mitigation are shared progressively depending on wealth differences. At the same time, historical energy transitions have often been driven by wealthy consumers demanding higher quality goods and services, which consequently grew from niche to mainstream technologies. Particularly the transportation sector is a sector difficult to decarbonise, while there are significant differences in contribution between poorer and wealthier users. This study uses a global integrated assessment model (GCAM) with 10 different income groups for each of the 32 regions to compare several decarbonisation scenarios for passenger transportation. On the one hand, implementing a general cap-and-trade policy for transport emissions, while traditionally seen as the economically optimal policy, affects poorer individuals significantly more in terms of access to transport services in a decarbonised world. On the other hand, implementing fixed caps for each country and income group, which cannot be traded with consumers at lower other income groups or countries, and are globally equal for each individual, leads to significantly higher costs for higher income individuals, but does not affect the access to transport services of poorer individuals as strongly. Also, this last alternative leads to a significantly faster take-up of modern clean technologies in transport.

How to cite: Van de Ven, D.-J. and Sampedro, J.: Passenger transport decarbonisation under different equity considerations, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-19615, https://doi.org/10.5194/egusphere-egu24-19615, 2024.

EGU24-21164 | Orals | ITS3.27/ERE6.6

Themes and Recommendations from Pacific Northwest National Laboratory’s Human Well-being Workshop 

Stephanie Waldhoff, Brian O'Neill, James Edmonds, and Bethel Tarekegne

Human well-being has been defined as an inherently multidimensional concept that broadly refers to what constitutes the “good life”. Stiglitz et al. (2009). Well-being cannot be described with a single number. Rather, it requires a wide range of measures of the state of human outcomes. Taken together, these can provide a description of well-being and to better guide decision making.

Here we summarize a set of interdisciplinary conversations that occurred during the course of a two-day, in-person workshop convened by PNNL September 27-28, 2023 in College Park, Maryland, which laid the foundations for a new field of well-being science and application. Here we share a summary of the key themes and recommendations from this workshop.

Themes

The Science of Human Well-being: Understanding well-being requires assembling both quantitative and qualitative data at multiple scales in time, space, and other dimensions, identifying and articulating relationships using tools and techniques drawing from multiple disciplines and applying them to both understand the past and explore the consequences of alternative decisions for the future. Participants identified specific challenges with current model capabilities, data, incorporating qualitative information, metrics, and scenarios.

Applications of Human Well-being Research: The goal of developing a scientific understanding of well-being is to have tools that can inform decisions. Applying the tools of well-being science has two distinct benefits. First, the multi-dimensional, multi-disciplinary tools and data enable better decisions. In addition, the use of well-being science to inform decisions can improve the direction of research and its quality. Participants identified challenges with connecting decision makers and researchers and with policy design and implementation.

Communication of Human Well-being Outcomes: Well-being science needs to communicate across the full spectrum of stakeholders, decision makers, and researchers. The interdisciplinary nature of well-being science results in a language barrier that needs to be overcome within the well-being science community and with stakeholders. Participants discussed challenges with identifying the “correct” stakeholders and communication across all of these groups.

Recommendations

Establish a new field of human well-being science and research: Opportunities for improving communication include (1) developing a Community of Practice on human well-being for researchers and policy makers from different academic and policy domains, (2) holding additional workshops to connect researchers and end users, and (3) writing a commentary piece for an academic journal describing the need for this type of research.

Develop and communicate human well-being applications for decision-making: A primary need identified is for significant model developments and research, as there is currently a mismatch between the types of questions being asked by decision makers and the ability to model those outcomes.

Develop long-term, sustainable funding to support this multi-disciplinary, multi-scale research: The most important recommendation was to increase funding for research and model development. Without this funding, researchers will not be able to provide the analyses and results that decision makers need to account for aspects of equity and justice in their decisions.

 Reference: Stiglitz, J. E., Sen, A., & Fitoussi, J.-P. (2009). Report of the Commission on the Measurement of Economic Performance and Social Progress https://ec.europa.eu/eurostat/documents/8131721/8131772/Stiglitz-Sen-Fitoussi-Commission-report.pdf

How to cite: Waldhoff, S., O'Neill, B., Edmonds, J., and Tarekegne, B.: Themes and Recommendations from Pacific Northwest National Laboratory’s Human Well-being Workshop, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-21164, https://doi.org/10.5194/egusphere-egu24-21164, 2024.

EGU24-21402 | ECS | Orals | ITS3.27/ERE6.6

Analyzing energy security outcomes of decarbonization across income groups in GCAM-USA 

Kelly Casper, Ying Zhang, Stephanie Waldhoff, and Brian O'Neill

The equity implications brought on by climate change and the actions taken in response are a growing area of interest. Such implications are important for the design and implementation of transformative policies but are understudied at subnational levels, especially with considerations to impacts on human well-being. Specifically, integrated assessment models (IAMs), the primary tools for evaluating these policies and their implications, have advanced science and policymaking but lack detailed subnational information. In this study, we developed projections of U.S. state-level income distributions (Casper et al., 2023), represented by income deciles, and incorporated those projections into an IAM (GCAM-USA) to examine the effects of decarbonization policies on residential energy security, a key aspect of human well-being, across ten income groups in each state. Importantly, our projections of residential energy security include several metrics in order to represent the multi-faceted nature of energy security and to explore tradeoffs that consumers at different income levels may need to make in response to changing energy prices. Specifically, we estimate energy service consumption, the satiation gap, and energy burden for each decile. Our study identifies unequal impacts across groups, with the most significant impact observed among mid-to-low income groups. In 2050, the projected energy burden is lower than in 2020 due to the projected increase in income over time relative to changes in energy service prices. However, the lowest income group in most states still experiences ‘high’ energy burden in 2050 under business-as-usual, while the decarbonization policies leads to even higher energy burden for the lowest income group (households spending additional 0.6% out of income for residential energy services).With the lowest income groups experiencing worse outcomes, this work suggests that targeted policy interventions that consider the impacts on different groups will promote more equitable transitions to a net-zero greenhouse gas emissions economy. The results focus solely on the impacts of decarbonization policies on residential energy security metrics, excluding other potential positive effects on human well-being like reductions in air pollution.

References

Casper, K. C., Narayan, K. B., O’Neill, B. C., Waldhoff, S. T., Zhang, Y., & Wejnert-Depue, C. P. (2023). Non-parametric projections of the net-income distribution for all U.S. states for the Shared Socioeconomic Pathways. Environmental Research Letters, 18(11), 114001. https://doi.org/10.1088/1748-9326/acf9b8

How to cite: Casper, K., Zhang, Y., Waldhoff, S., and O'Neill, B.: Analyzing energy security outcomes of decarbonization across income groups in GCAM-USA, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-21402, https://doi.org/10.5194/egusphere-egu24-21402, 2024.

ITS4 – Risk, Resilience and Adaptation

EGU24-947 | ECS | Orals | ITS4.1/CL0.1.7

Identifying the Transitions in the Stable Socio-Environmental System Due to Extreme Events  

Jagriti Jain, Deepak Khare, and Francisco Munoz-Arriola

The critical challenge in a hydrological system is to predict whether the system approaches a critical threshold. The urban centres are grappled by the extreme events especially floods with the shifts from one stable state to another in an urban socio-environmental system. Here, we identified the critical transitions of hydrological processes, including precipitation and runoff, by analyzing their shifting nature. Structural break-regression models, incorporating shifts in both mean and trend, are applied to the series.  The point of change indicates the transition within the system.  These models are then evaluated using two widely employed penalized likelihood criteria for multiple changepoints. These criteria strike a balance between the quality of model fit (measured by likelihood) and the consideration of parsimony. Two models are tested i.e., bisegmentation and penalised maximum likelihood with the white noise detection. The latter was found to be better fit to both precipitation and runoff for the three cities (Guwahati, Mumbai, and Dehradun) in India.

How to cite: Jain, J., Khare, D., and Munoz-Arriola, F.: Identifying the Transitions in the Stable Socio-Environmental System Due to Extreme Events , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-947, https://doi.org/10.5194/egusphere-egu24-947, 2024.

EGU24-2179 | ECS | Posters on site | ITS4.1/CL0.1.7

Analysis of the Fertilizer Footprint of Principal Crops in China: A Spatial Allocation Perspective 

Yifan Wu, Jingyu Liu, and Yong Geng

Utilizing the sophisticated Multi-Regional Input-Output Analysis (MRIO) approach, this investigation meticulously examines the nitrogen (N), phosphorus (P), and potassium (K) fertilizer footprints associated with predominant crops throughout various Chinese provinces. Crucial provinces, namely Heilongjiang, Jiangsu, Shandong, and Henan, manifest a pronounced geographical aggregation in fertilizer footprints. Intriguingly, Heilongjiang, Shandong, and Henan collectively represent 49.2% and 42.7% of the cumulative national footprint.

From a provisioning perspective, the assimilation of N, P, and K fertilizers predominantly gravitates towards Heilongjiang, Shandong, Henan, Jiangsu, and Anhui, cumulatively contributing 32.74%, 35.73%, and 36.48% to the nation's aggregate input. Distinctly, regions such as the Yangtze River Delta, Pearl River Delta, and the Beijing-Tianjin-Hebei conurbation emerge as paramount crop consumption hubs, with aggregate consumptions scaling to 4505.12 Gg, 1741.71 Gg, and 2026.57 Gg, respectively. Notably, the exogenous crop provisions in metropolises like Shanghai and Beijing play a pivotal role in shaping their N, P, and K footprints, quantified at 6.78%, 5.56%, and 5.79%, and 1.26%, 1.37%, and 1.71%, respectively.

Furthermore, three salient regions—the Northeastern Plains, the Huang-Huai-Hai Plains, and the Middle to Lower tracts of the Yangtze River—collectively encompass 57.4%, 66.1%, and 66.26% of the national N, P, and K footprints. Compellingly, the dynamics of crop footprint migration in provinces such as Henan, Heilongjiang, and Shandong appear to be predominantly modulated by wheat and corn.

In summation, this scholarly endeavor elucidates the intricate spatial delineation of the fertilizer footprint, its translocation mechanisms, and its intricate interplay with socio-economic and demographic paradigms, thereby laying a robust theoretical groundwork for augmenting fertilizer efficacy and championing the cause of sustainable agricultural practices.

How to cite: Wu, Y., Liu, J., and Geng, Y.: Analysis of the Fertilizer Footprint of Principal Crops in China: A Spatial Allocation Perspective, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-2179, https://doi.org/10.5194/egusphere-egu24-2179, 2024.

EGU24-2326 | ECS | Posters on site | ITS4.1/CL0.1.7

Shifts in water availability due to environmental flows 

Ye Zhao, Xiang Zhang, Shiyong Tao, Feng Xiong, Zhimin Deng, Jianping Bing, Shaofeng Yan, Jianfeng Liu, and Jun Xia

Human society is grappling with the need to supply reliable and affordable freshwater for growing populations without destroying ecosystems. Environmental flows (EF) have been considered, and implemented, as a promising approach to sustainable water systems since its inception. However, the persistent antagonism between EF and other water demands is questionable, as the loss of hydro-ecological functions due to excessive water withdrawal (WW) could be balanced by the compensatory benefits of EF (i.e., EF improves resilience). Here, we introduce a mathematical push-pull framework to demonstrate how can EF be applied to lead to shifts in water availability explicitly in terms of magnitude and frequency. Our case study in the Yangtze River Basin reveals that EF implementation improves water availability over long time scales. We determine a boundary between EF and WW that leads to an escape from or stabilization within a stable equilibrium attraction. We use this boundary to define reasonable EF tailored to repeated, discrete WWs. Our results support the implementation of EF and its accompanying measures as part of the post-2030 eco-restoration framework.

How to cite: Zhao, Y., Zhang, X., Tao, S., Xiong, F., Deng, Z., Bing, J., Yan, S., Liu, J., and Xia, J.: Shifts in water availability due to environmental flows, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-2326, https://doi.org/10.5194/egusphere-egu24-2326, 2024.

EGU24-2580 | ECS | Orals | ITS4.1/CL0.1.7

The safe operating spaces for grazing in China’s drylands 

Changjia Li, Bojie Fu, Shuai Wang, Lindsay Stringer, Wenxin Zhou, and Zhuobing Ren

Degradation of ecosystems can occur when certain ecological thresholds are passed below which ecosystem responses remain within ‘safe ecological limits’. Ecosystems such as drylands are sensitive to both aridification and grazing, but the combined effects of such factors on the emergence of ecological thresholds beyond which ecosystem degradation occurs has yet to be quantitatively evaluated. This limits our understanding on ‘safe operating spaces’ for grazing, the main land use in drylands worldwide. Here we assessed how 20 structural and functional ecosystem attributes respond to joint changes in aridity and grazing pressure across China´s drylands. Gradual increases in aridity resulted in abrupt decreases in productivity, soil fertility and plant richness. Rising grazing pressures lowered such aridity thresholds for most ecosystem variables, thus showing how ecological thresholds can be amplified by the joint effects of these two factors. We found that 44.4% of China’s drylands are unsuitable for grazing due to climate change-induced aridification, a percentage that may increase to 50.8% by 2100. Of current dryland grazing areas, 8.9% exceeded their maximum allowable grazing pressure. Our findings provide important insights into the relationship between aridity and optimal grazing pressure and identify safe operating spaces for grazing across China’s drylands.

How to cite: Li, C., Fu, B., Wang, S., Stringer, L., Zhou, W., and Ren, Z.: The safe operating spaces for grazing in China’s drylands, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-2580, https://doi.org/10.5194/egusphere-egu24-2580, 2024.

The analysis of global catastrophic events often occurs in isolation, simplifying their study. In reality, risks cascade and interact. Therefore, it is essential to consider the interconnected nature of global risks. This investigation explores the interplay between nuclear winter and planetary boundaries. It may seem reasonable to assume that respecting planetary boundaries, which define a safe operating space for the planet, is preferable before a nuclear war. However, that does not always seem to be the case. For instance, increased nitrogen emissions today could serve as a nutrient buffer during nuclear winter. Contrastingly, mitigating climate change, means an even larger temperature drop in nuclear winter in comparison with pre-industrial times. This exploratory study also highlights planetary boundaries that could enhance human survival if we adhere to their limits, both presently and after a nuclear war. The best example being biosphere integrity, as conserving it has no direct downsides and would make the Earth system more resilient to resist the shock of a nuclear winter.

How to cite: Jehn, F. U.: Anthropocene Under Dark Skies: The Compounding Effects of Nuclear Winter and Overstepped Planetary Boundaries, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-2773, https://doi.org/10.5194/egusphere-egu24-2773, 2024.

EGU24-4134 | ECS | Orals | ITS4.1/CL0.1.7

Resilience of the AMOC 

Valérian Jacques-Dumas, Christian Kühn, and Henk A. Dijkstra

The Atlantic Meridional Overturning Circulation (AMOC) is a crucial part of the climate system that carries warm and saline water towards the northern Atlantic and is an important component in the global meridional heat transport. However, the AMOC is a so-called “tipping element”: there is observational evidence that it is in a bistable regime and may thus collapse under anthropogenic greenhouse gas emissions. Bi-stability has also been found in a hierarchy of models, from a simple two-box model up to a CMIP5 global climate model (CESM1). Considering a possible upcoming tipping, it is critical to assess how likely the AMOC is to undergo a collapse under different greenhouse gas forcing scenarios.  This issue is tightly related to the notion of resilience, which refers to the ability of a system to sustain a certain forcing while remaining in its original state or to return to its original state after being displaced.

Studying the resilience of the AMOC requires to observe its collapse, which is very difficult due to its rarity, especially in very complex models. That is why we use a rare-event algorithm called Transition-Adaptive Multilevel Splitting (TAMS). Given a certain definition of the current-day and collapsed AMOC, TAMS pushes trajectories in the direction of a collapse at a much lower cost than Monte-Carlo simulations. This method outputs typical collapse trajectories starting from a present-day AMOC, under a certain chosen hosing flux. This process is repeated for a wide range of freshwater forcings. From those trajectories, we extract observables (e.g. the AMOC strength), which are scalar functions that are interpreted as resilience observables. By monitoring these observables, we can rank different climate change scenarios depending on the risks they impose on the AMOC. Moreover, we relate these observables to existing mathematical definitions of resilience. Finally, we determine which observables are best suited to describe the resilience of the  AMOC, with a focus on those that can be measured in the field.

How to cite: Jacques-Dumas, V., Kühn, C., and Dijkstra, H. A.: Resilience of the AMOC, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-4134, https://doi.org/10.5194/egusphere-egu24-4134, 2024.

EGU24-6874 | Orals | ITS4.1/CL0.1.7 | Highlight

Assessing impacts of Earth system tipping points on human societies  

Richard Betts, James Dyke, Elizabeth Fuller, Laura Jackson, Laurie Laybourn-Langton, Norman Steinert, and Yangyang Xu

Assessments of climate change effects on humans and ecosystems have previously included only limited information on the consequences of climate tipping points. While some national evaluations have touched on tipping point implications, assessment has been largely qualitative, with minimal quantitative analysis. Understanding and quantification of impacts of tipping points is recognised as a significant knowledge gap, and improving the research base in this area is essential for climate risks to be fully evaluated.

This presentation examines the current knowledge of Earth system tipping point impacts on people, exploring the evidence on impacts from individual tipping points, and assessing specific sectors and their vulnerability to these tipping points. Localised effects arise when climate tipping points, such as permafrost thaw and forest dieback, are crossed. These effects stem from land surface changes and alterations in regional climates and weather extremes. Global impacts manifest through large-scale shifts in atmospheric and oceanic circulations, altering global warming rates and sea level rise. Oceanic dynamics, like collapse of the Atlantic Meridional Overturning Circulation, can reshape regional climates and cause widespread shifts in temperature and precipitation patterns. Similarly, cryospheric tipping points, such as marine ice cliff collapse, have the potential to accelerate sea level rise, affecting flooding hazards like coastal inundation. Biosphere tipping points, such as Amazon dieback, intensify greenhouse gas concentrations, hastening global warming and its associated extreme weather events, regional climate shifts and sea level rise.

All these have the potential to impact the security of water, food and energy, human health, ecosystem services, communities and economies. The body of evidence varies across tipping points and sectors, but the implications for profound impacts across all areas of human society are clear.

How to cite: Betts, R., Dyke, J., Fuller, E., Jackson, L., Laybourn-Langton, L., Steinert, N., and Xu, Y.: Assessing impacts of Earth system tipping points on human societies , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-6874, https://doi.org/10.5194/egusphere-egu24-6874, 2024.

EGU24-7633 | ECS | Orals | ITS4.1/CL0.1.7 | Highlight

Achieving net zero greenhouse gas emissions critical to limit climate tipping risks 

Annika (Ernest) Högner, Tessa Möller, Carl-Friedrich Schleussner, Samuel Bien, Niklas H. Kitzmann, Robin D. Lamboll, Joeri Rogelj, Jonathan F. Donges, Johan Rockström, and Nico Wunderling

Under current emission trajectories, at least temporarily overshooting the Paris global warming limit of 1.5 °C above pre-industrial levels is a distinct possibility. Permanently exceeding this limit would substantially increase the risks of triggering several climate tipping elements with associated high-end impacts on human societies and the Earth system. It is essential to assess this risk under emission pathways that temporarily overshoot 1.5 °C. Here, we investigate the tipping risks associated with a number of policy-relevant future emission scenarios, using a stylised Earth system model that comprises four interconnected core tipping elements. Assessing tipping risks in the year 2300, we find a non-linear increase for overshoots that exceed 1.8 °C peak temperature or persist above 1.5 °C beyond the end of the 21st century. Scenarios following current policies or pledges lead to high tipping risk of 30% (median) and more, with uncertainty from climate sensitivity and carbon-cycle feedbacks translating to large uncertainties in tipping risk (45% and more) for these scenarios. Further, we show that on multi-century timescales achieving and maintaining at least net-zero greenhouse gas emissions is paramount to minimise tipping risks. Our results underscore that stringent emission reductions in the current decade in line with the Paris Agreement 1.5 °C limit are critical for planetary stability.

How to cite: Högner, A. (., Möller, T., Schleussner, C.-F., Bien, S., Kitzmann, N. H., Lamboll, R. D., Rogelj, J., Donges, J. F., Rockström, J., and Wunderling, N.: Achieving net zero greenhouse gas emissions critical to limit climate tipping risks, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-7633, https://doi.org/10.5194/egusphere-egu24-7633, 2024.

The Anthropocene is the current geological epoch characterized by co-evolutionary dynamics between human societies and the Earth system. Linking biogeophysical and social processes is therefore essential to understand current developments in the Earth system. Especially the agricultural sector is a key driver of land system change, biodiversity loss, soil degradation, and a major contributor to global greenhouse gas emissions. To analyse and understand the mechanisms of these interactive systems, we developed the model of Integrated Social-Ecological rEsilient lanD Systems (InSEEDS), which couples the Dynamic Global Vegetation Model LPJmL with the agent-based modeling framework copan:CORE. LPJmL simulates the biogeophysical processes of the Earth system on a global 0.5° grid, in particular the terrestrial carbon, water, and nitrogen cycle, and can model, for example, plant and crop growth or water and fertilizer consumption. Various agricultural management practices can also be modeled, such as tillage, mulching, or cover crop cultivation. copan:CORE, on the other hand, can instantiate agents that reflect the behavior of farmers, management decisions, or interactions of the social world in different regions.
We here describe this novelty of World-Earth modeling and present the first exemplary application of the coupled model system which explores potential pathways for sustainable agricultural practices to spread. In this example we compare the potential social spreading of conservation tillage practices in contrast to conventional tillage practices based on the distribution of two different farmer types in the model, so-called agent functional types.

How to cite: Breier, J.: The InSEEDS Model - coupling LPJmL and copan:CORE towards an integrated human-earth system model of regenerative land-system change, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-7670, https://doi.org/10.5194/egusphere-egu24-7670, 2024.

EGU24-7905 | ECS | Orals | ITS4.1/CL0.1.7

Reviewing climate tipping point interactions and cascades under global warming 

Nico Wunderling and Anna von der Heydt and the GTPR-tipping-interactions-team

Climate tipping elements are large-scale subsystems of the Earth that may transgress critical thresholds (tipping points) under ongoing global warming, with substantial impacts on biosphere and human societies. While recent scientific efforts have improved our knowledge on individual tipping elements, the interactions between them are less well understood. Also, the potential of individual tipping events to induce cascading tipping elsewhere, or stabilize other tipping elements is largely unknown. As a contribution to the Global Tipping Points Report (GTPR) 2023 for COP28, we mapped out the current state of the literature on interactions between climate tipping elements. We find that tipping elements in the climate system are closely interacting, meaning a substantial change in one will have consequences for subsequently connected tipping systems. A majority of interactions between climate tipping systems are destabilising. While confirmation or rejection through future research is necessary, it seems possible that interactions between climate tipping systems destabilise the Earth system in addition to climate change effects on individual tipping systems. Further, we are quickly approaching global warming thresholds where tipping system interactions become relevant, because multiple individual thresholds are being crossed. Concretely, tipping cascades can neither be ruled out on centennial to millennial timescales at global warming levels between 1.5–2.0°C, nor on shorter timescales if global warming would surpass 2.0°C. To address crucial knowledge gaps in tipping system interactions, we propose four strategies forward combining observation-based approaches, Earth system modelling expertise, computational advances, and expert knowledge.

How to cite: Wunderling, N. and von der Heydt, A. and the GTPR-tipping-interactions-team: Reviewing climate tipping point interactions and cascades under global warming, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-7905, https://doi.org/10.5194/egusphere-egu24-7905, 2024.

EGU24-10861 | Posters on site | ITS4.1/CL0.1.7

A tool for objective detection of abrupt transitions in CMIP6 models 

Valerio Lembo, Susanna Corti, Joran Angevaare, and Sybren Drijfhout

We present here a tool for the detection of abrupt transitions in CMIP6 model outputs, that is aimed to update and extend the catalog of tipping points presented in Drijfhout et al. 2015, based on the evaluation of CMIP5 intercomparison.

The tool consists of three fundamental steps: 

  • Data manipulation: model outputs are sampled according to the user’s preferences, aggregated along the integration period and interpolated to a common grid for the whole multi-model ensemble. A 10-years moving average is also applied;
  • Criteria for abrupt transitions: Criteria for the detection of abrupt transitions are computed and combined. These are: exceedance of the preindustrial 99-percentile standard deviation, exceedance of the preindustrial 99-percentile jump over 10 years period, exceedance of the preindustriak 99-percentile yearly anomaly for each year in the last 30 years of the simulation, p-value of a Kolmogorov-Smirnov hypothesis test for normality of the distribution;
  • Masking and clustering: grid points for which the time series of anomalies with respect to preindustrial conditions that satisfy at least 3 out of 4 of the criteria illustrated above are selected. Successively, grid points are clustered in order to exclude sparse points and highlight significant regions affected by widespread abrupt transitions;

We present a preliminary analysis demonstrating the usage of this tool on a set of ocean-sea-ice-related quantities for a number of models participating in CMIP6 project under disparate SSP scenarios. 

How to cite: Lembo, V., Corti, S., Angevaare, J., and Drijfhout, S.: A tool for objective detection of abrupt transitions in CMIP6 models, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-10861, https://doi.org/10.5194/egusphere-egu24-10861, 2024.

EGU24-12076 | ECS | Orals | ITS4.1/CL0.1.7 | Highlight

Interpretable Early Warning Signals in Large Human Groups, using Machine Learning in an Online Game-experiment 

Guillaume Falmagne and Anna B Stephenson

Understanding the emergent dynamics – in particular critical transitions – in complex social-ecological systems is key to foster positive social transformations in the Anthropocene era. Regime shifts in some ecosystems may be preceded by statistical early warning signals, but systems where such signals can be tested systematically are elusive. The r/place game hosted by Reddit is a social experiment that provides data for thousands of subsystems that can undergo critical transitions. It is therefore an excellent testbed for comparing the performance of various warning indicators. In r/place, millions of users collaborated to build many discernible drawings on a canvas of pixels. A drawing undergoes a transition when it is rapidly replaced by another. We build an early warning signal indicator that uses machine learning to combine the predictive power of a number of time-dependent and system-specific variables, and we show that its performance far exceeds that of standard indicators. For example, when training the algorithm and testing its performance on separate parts of the 2022 r/place, we detect half of the transitions coming in less than 20 minutes with only a 0.6% false positive rate. The performance only slightly decreases when training on 2022 data and testing on the 2023 experiment, showing that the predictive power holds across significantly different setups. We use SHAP values to elucidate the drivers of any given warning and highlight generic properties of warnings in online social systems. Some properties, such as a decreasing return time, are at odds with standard statistical indicators. Where sufficient data is available, our tool and resulting insights can contribute to warn of – and possibly trigger or avoid – macroscopic social and ecological change.

How to cite: Falmagne, G. and Stephenson, A. B.: Interpretable Early Warning Signals in Large Human Groups, using Machine Learning in an Online Game-experiment, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12076, https://doi.org/10.5194/egusphere-egu24-12076, 2024.

EGU24-12856 | ECS | Posters on site | ITS4.1/CL0.1.7

Global terrestrial ecosystem resilience: a high-resolution multivariate analysis of patterns and drivers 

Nielja Knecht, Ingo Fetzer, and Juan Rocha

Natural terrestrial ecosystems in different parts of the world have been losing resilience in the past decades. Such losses of resilience can be the precursors for regime shifts on local or regional scales that can have large impacts on ecosystem structure and function as well as nature’s contributions to people. Drivers of resilience loss include mainly changes in the mean and variability of temperature and precipitation, and anthropogenic land modifications of adjacent or remote ecosystems.

Global assessments of ecosystem resilience often exclude areas with direct anthropogenic land use changes and focus instead on remnant natural ecosystems. However, for regional stakeholders it is important to understand how land-use and zoning decisions may affect the resilience of remaining ecosystems and the risk of critical transitions.

In this study, we conduct a high-resolution global assessment of terrestrial ecosystem resilience losses, using time series of multiple remotely-sensed ecosystem indicators, and employing a range of early warning signals. We also evaluate the importance of different climatic and anthropogenic drivers at a local scale of administrative units in causing the detected changes in resilience. This allows us to get a comprehensive and robust understanding of different dimensions of change in global ecosystem resilience and their locally relevant drivers of change.

How to cite: Knecht, N., Fetzer, I., and Rocha, J.: Global terrestrial ecosystem resilience: a high-resolution multivariate analysis of patterns and drivers, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12856, https://doi.org/10.5194/egusphere-egu24-12856, 2024.

EGU24-13183 | ECS | Orals | ITS4.1/CL0.1.7

Climate Change and Social- Ecological Vulnerability Index in the Brazilian Amazon: A study with a cascade model approach 

Moara Almeida Canova, Bianca Rius, João Darela Filho, and David Montenegro Lapola

The Brazilian Amazon is a powerful Ecosystem Service (ES) provider. Simultaneously, many Amazonian local communities still preserve an intrinsic economic and cultural relationship in this Social-Ecological System. Paradoxically, the region concentrates a significant portion of the nation's poorest people, demonstrating the risks and susceptibility to socio-ecological vulnerability that region. Thus, the Amazon Forest dieback hypothesis predicts that the increased CO2 (eCO2), rising temperatures and droughts may push the forest toward a tipping point, which would bring a new composition of ES and would reflect on regional economic - cultural ways of living, as well as, social wellbeing and health. Hence, the research employed a cascade model using the Functional Diversity (FD) approach. The aim was to assess the impact of climate changes on CO2 storage related to Ecosystem Services and their implications for the adaptation capacity of both rural and urban populations in the Brazilian Amazon. The initial analysis, using the CAETE model, evaluated vegetation FD responses in a scenario of 50% precipitation reduction. This revealed a shift in plant composition towards drought-related strategies, leading to a 37-49% reduction in total carbon storage in the basin, resulting in increased carbon release into the atmosphere. This result translates direct impacts to global and local climate regulation and indirect to shifting of water flux and to native provisioning services. The second evaluating was on social dimension ambit, through drafting of Social Ecological Vulnerability Index (SEVI) with secondary data of the municipalities of Manaus, Itacoatiara e Silves in the state of Amazonas and Ilha de Cotijuba in the Belém city in the state of Pará. The SEVI points out that the common factor of the vulnerability among the municipalities was the indicators of the socio-climate exposure for susceptibility to disasters, to rising temperature and FD changes. The SEVI result summed to FD modelling demonstrate that the social well-being of communities is threated due to the impacts on the native ES, even though they are placed in the one of most biodiverse forest from the globe. In addition, the susceptibility to diseases related to climate change increases in the regions greater urbanized (score 2.5, in the range from 0 low to 4 high vulnerability) with in turn can undermine the public health system in the urban centres in expanding in the Amazonia. Thus, the SEVI reveals that the impacts, stemming from the shifting FD of the modelled plant community, do not merely pose a distant threat to social well-being, health, and income; instead, they exacerbate socio-ecological vulnerability. In view that, people recognize and link hazards in infrastructure (ES for erosion control), mobility, and food supply (ES for water flow, fish, and wild food). Therefore, all the results support the challenges for the development of public policies of climate adaptation involving social health, future maintenance of provisioning native ES, above all in the municipalities with inadequate socioeconomic indicators

How to cite: Almeida Canova, M., Rius, B., Darela Filho, J., and Montenegro Lapola, D.: Climate Change and Social- Ecological Vulnerability Index in the Brazilian Amazon: A study with a cascade model approach, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-13183, https://doi.org/10.5194/egusphere-egu24-13183, 2024.

EGU24-13292 | ECS | Orals | ITS4.1/CL0.1.7

Using biosphere metrics to assess the Planetary boundary for functional biosphere integrity 

Fabian Stenzel, Jannes Breier, Johanna Braun, Karlheinz Erb, Dieter Gerten, Sarah Matej, Helmut Haberl, Sebastian Ostberg, Nicolas Roux, Sibyll Schaphoff, and Wolfgang Lucht

In the recent update of the Planetary Boundaries framework, Richardson et al. propose to use human appropriation of net primary productivity (HANPP) as a new indicator for the functional biosphere integrity boundary. They provide a planetary scale analysis and suggest to further complement this by an ecological metric.

To aid with the spatially explicit analysis of both HANPP and an ecological metric in an automated and easy way, we developed the "biospheremetrics" R package. The package combines 2 complementary metrics:

The BioCol metric operationalizes the HANPP framework in order to represent a meaningful Planetary Boundary indicator, and is accompanied by the EcoRisk metric, which quantifies biogeochemical and vegetation structural changes as a proxy for the risk of ecosystem destabilization. Both metrics are computable in a dynamic global vegetation modelling framework.

We spatially explicitly analyse both metrics over the past 500 years with simulations of the dynamic global vegetation model LPJmL and find that presently (period 2007-2016), large regions show modification and extraction of >25% of the preindustrial potential net primary production, leading to drastic alterations in key ecosystem properties and suggesting a high risk for ecosystem destabilization. In consequence of these dynamics, EcoRisk shows particularly high values in regions with intense land use and deforestation, but also in regions prone to impacts of climate change such as the arctic and boreal zone.

We additionally show how both metrics could be combined to inform the Planetary Boundary of functional biosphere integrity, compare our results with other spatially explicit global biosphere integrity metrics and discuss the setting of (provisional) thresholds.

How to cite: Stenzel, F., Breier, J., Braun, J., Erb, K., Gerten, D., Matej, S., Haberl, H., Ostberg, S., Roux, N., Schaphoff, S., and Lucht, W.: Using biosphere metrics to assess the Planetary boundary for functional biosphere integrity, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-13292, https://doi.org/10.5194/egusphere-egu24-13292, 2024.

EGU24-14873 | ECS | Posters on site | ITS4.1/CL0.1.7

Assessing historical and potential future Planetary Boundary transgressions in a consistent modelling framework 

Johanna Braun, Dieter Gerten, Jannes Breier, Fabian Stenzel, Constanze Werner, and Wolfgang Lucht

In an attempt to define a safe operating space for humanity, the Planetary Boundary (PB) framework proposes precautionary limits to human interference with nine critical Earth system processes. However, quantitative assessments of these limits and past, present or potential future statuses and transgressions of PBs are (i) inflicted by differences in definitions, data and models used and (ii) require process-based models of the Earth system in the absence of globally available observational datasets on the PB control variables. To advance such process-based and consistent PB quantifications for terrestrial PBs (land system change, biosphere integrity, freshwater change, biogeochemical flows), we developed an R based software package, “boundaries”, for calculation and visualization of PBs based on outputs from the global terrestrial biosphere model LPJmL. The coupled, spatiotemporally explicit and dynamic simulation of the biogeochemical processes underlying the control variables in LPJmL allows for calculation of the temporal evolution of PB statuses, i.e. if, where and how strongly boundaries are transgressed, at different scales (for both planetary and corresponding subglobal boundaries from regional to grid cell scale).

Next to a short technical overview on boundaries and its structure, the poster shows calculated current spatially-explicit statuses of the four PBs considered as well as their simulated evolution during past decades, based on one consistent modelling framework and applying the latest PB definitions. In addition to contributing to a better understanding of temporal trajectories, spatial patterns and drivers of PB transgressions, boundaries can be applied to evaluate future scenarios in terms of their PB impacts and potentials to return to a safe space within PBs. As one potential critical PB trade-off, the poster focuses on different land-based carbon dioxide removal (CDR) strategies for reducing pressures on the climate change PB. The scenarios’ results show the importance of dietary changes towards less livestock products to release pasture areas for CDR. If forests can be restored on spared land, pressures on multiple PBs could be synergistically alleviated.

How to cite: Braun, J., Gerten, D., Breier, J., Stenzel, F., Werner, C., and Lucht, W.: Assessing historical and potential future Planetary Boundary transgressions in a consistent modelling framework, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-14873, https://doi.org/10.5194/egusphere-egu24-14873, 2024.

EGU24-15269 | ECS | Orals | ITS4.1/CL0.1.7

Advancing Planetary Boundary Science 

Levke Caesar, Niklas Kitzmann, and Johan Rockström

While Planetary Boundary science has advanced tremendously over the past decades, we still lack a deep understanding of the intricate, yet pivotal connections between many biological and physical functions of the Earth system. This is of grave concern, since the stability of the planet and interactions between its components are the foundation of human civilization. Moreover, as it stands, science only has the resources to measure and analyze the planet’s vital signs every 6-8 years (Rockström et al. 2009, Steffen et al. 2015, Richardson et al., 2003), and our imperfect measurement framework has some worrying blind spots.
To address these challenges, the Potsdam Institute for Climate Impact Research and its partners are launching a major scientific effort to close the knowledge gaps, both in terms of our ability to model how the Earth system evolves under the pressure of human activity, as well as our ability to measure the state of the Earth system with high temporal resolution. This will culminate in an annual Planetary Boundary (PB) Health Check, conceived and reviewed by a diverse international scientific and stakeholder community. Employing cutting-edge Earth-system and tipping-point modelling, ambitious whole-Earth monitoring, and exploring artificial-intelligence-based big-data analytics, the Health Check shall offer a comprehensive, timely, and unparalleled assessment of the planet's health. With yearly updates of PB transgressions at its core, the Health Check will further develop the boundary measures themselves and provide important context, e.g. via case studies and policy implications.  It will reveal current risks due to ongoing transgression of PBs and develop transformation pathways to guide global development back to Earth’s safe operating space. Besides peer-reviewed publications, these results will be communicated to the public using state-of-the-art visualizations and communication partnerships.

In this presentation we will give details about this new science initiative, the partners we work with, out short and long-term goals and give an overview of involvement opportunities in this rapidly growing project.

References

Rockström, J., Steffen, W., Noone, K. et al. A safe operating space for humanity. Nature 461, 472–475 (2009). https://doi.org/10.1038/461472a
Steffen, W. et al. ,Planetary boundaries: Guiding human development on a changing planet.Science347,1259855(2015).DOI:10.1126/science.1259855 
Richardson, K. et al., Earth beyond six of nine planetary boundaries.Sci. Adv.9,eadh2458(2023).DOI:10.1126/sciadv.adh2458

How to cite: Caesar, L., Kitzmann, N., and Rockström, J.: Advancing Planetary Boundary Science, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15269, https://doi.org/10.5194/egusphere-egu24-15269, 2024.

EGU24-15331 | Orals | ITS4.1/CL0.1.7 | Highlight

Early opportunity signals of a tipping point in the UK’s second-hand electric vehicle market 

Chris Boulton, Joshua Buxton, and Timothy Lenton

The use of early warning signals to detect the movement of natural systems towards tipping points is well established. Here, we explore whether the same indicators can provide early opportunity signals (EOS) of a tipping point in a social dataset – views of online electric vehicle (EV) adverts from a UK car selling website (2018–2023). The daily share of EV adverts views (versus non-EV adverts) is small but increasing overall and responds to specific external events, including abrupt petrol/diesel price increases, by spiking upwards before returning to a quasi-equilibrium state. An increasing return time observed over time indicates a loss of resilience of the incumbent state dominated by ICEV advert views. View share also exhibits increases in lag-1 autocorrelation and variance consistent with hypothesised movement towards a tipping point to an EV-dominated market. Segregating the viewing data by price range and year, we find a change in viewing habits from 2023. Trends in EOS from EV advert views in low-mid price ranges provide evidence that these sectors of the market may have passed a tipping point, consistent with other evidence that second-hand EVs recently reached price parity with equivalent ICEV models. We provide a case study of how EOS can be used to predict the movement towards tipping in social systems using novel data.

How to cite: Boulton, C., Buxton, J., and Lenton, T.: Early opportunity signals of a tipping point in the UK’s second-hand electric vehicle market, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15331, https://doi.org/10.5194/egusphere-egu24-15331, 2024.

EGU24-15457 | ECS | Posters on site | ITS4.1/CL0.1.7

Assessing the stability of glacial-interglacial cycles: a stochastic model analysis of Earth system resilience 

Jakob S. Harteg, Nico Wunderling, Ann Kristin Klose, and Jonathan F. Donges

Earth system stability commonly denotes the continuation of the Holocene's relatively stable climatic and ecological conditions essential for human civilisation, whereas Earth resilience describes the Earth system’s ability to recover from significant disturbances, such as the transgression of any of the nine planetary boundaries. Given the nature of the Earth system as a non-autonomous, stochastic, non-linear system, it is not clear what exactly constitutes stable states, semi-stable states or mere transients. An alternative approach is to regard the glacial-interglacial cycle as a stable attractor and thus ask, how stable or resilient is this cycle to perturbations? The answer could provide insights relevant for contextualising the embedded transitions of critical tipping points happening on much shorter time scales.

In this study, we explore the stability and resilience of the glacial-interglacial cycle using a conceptual climate model developed by Talento and Ganopolski (2021), based on atmospheric CO2 concentration, global mean temperature, and global ice volume. The model is driven by astronomical forcing and replicates the ice age cycles of the last 800,000 years with a correlation of 0.86. Following the classical idea of Hasselmann, we have extended this model with additive noise to represent unresolved processes. An analysis of an ensemble of trajectories reveals periods of significant divergence and convergence, indicating that the model’s sensitivity to noise varies in response to astronomical forcing. We have further applied a transfer operator approach in an attempt to identify stable and decaying states of the model and to study their evolution with changes in astronomical forcing. Findings shed light on the complexity and sensitivity of the Earth system's dynamics.

References:
Talento, S., & Ganopolski, A. (2021). Reduced-complexity model for the impact of anthropogenic CO2 emissions on future glacial cycles. Earth System Dynamics12(4), 1275-1293.

How to cite: Harteg, J. S., Wunderling, N., Klose, A. K., and Donges, J. F.: Assessing the stability of glacial-interglacial cycles: a stochastic model analysis of Earth system resilience, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15457, https://doi.org/10.5194/egusphere-egu24-15457, 2024.

EGU24-16017 | ECS | Orals | ITS4.1/CL0.1.7

Assessing the relationship between forest structural diversity and resilience in a warming climate 

Mark Pickering, Agata Elia, Marco Girardello, Gonzalo Oton, Samuele Capobianco, Matteo Piccardo, Guido Ceccherini, Giovanni Forzieri, Mirco Migliavacca, and Alessandro Cescatti

Ecosystem resilience represents the capacity of an ecosystem to withstand and recover from external perturbations, an increasingly important property for ecosystem function in an era of escalating climate extremes and anthropogenic pressures. Whilst recent studies have related forest resilience to natural factors such as climate and biomass, the link between forest diversity and resilience is not yet understood.

 

This study quantifies the sensitivity of ecosystem resilience on forest diversity in Europe over the period 2003-2021. Two commonly used resilience indicators are considered based on MODIS kNDVI (kernel Normalized Difference Vegetation Index) data acquired at high spatial and temporal resolution: the 1-lag temporal autocorrelation, relating to the ecosystem memory, and the standard deviation, relating to the ecosystem stability. Forest diversity is expressed in terms of horizontal and vertical structural heterogeneity metrics derived from GEDI (LiDAR) (Light Detection and Ranging) acquisitions. A Random Forest (RF) model is leveraged to isolate the interplay between forest resilience and diversity metrics by disentangling possible confounding environmental variables such as climate. The RF model is then applied to retrieve local sensitivities in terms of Individual Conditional Expectations.

 

The work first finds that European forests with a higher level of vertical and horizontal structural diversity are systematically associated with higher resilience levels. The relationship is coherent across bio-geographical regions in Europe. Importantly, the emerging relation between forest resilience and forest diversity is consistent under increasing temperature patterns. This suggests that forest management targeted to higher levels of forest heterogeneity has the potential to offset the decline in forest resilience associated with the projected climate warming scenarios and the consequent increasing disturbance regimes.

How to cite: Pickering, M., Elia, A., Girardello, M., Oton, G., Capobianco, S., Piccardo, M., Ceccherini, G., Forzieri, G., Migliavacca, M., and Cescatti, A.: Assessing the relationship between forest structural diversity and resilience in a warming climate, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-16017, https://doi.org/10.5194/egusphere-egu24-16017, 2024.

The climate of the Pleistocene is characterized by alternating cold (glacial) and warm (interglacial) periods. This cyclicity is mainly caused by the so-called Milankovitch cycles as a result of periodic changes in Earth’s orbital parameters. Many models have already successfully captured the non-linearities of the climate-cryosphere system responsible for the 100 kyrs cycles and the Mid-Pleistocene transition. However, these models widely differ in the number of explicit physical processes included and in the degree of complexity to solve them (from purely conceptual to Earth-system models). 

In this talk I will present a simple a-dimensional model that sequentially includes ice-sheet dynamics, ice aging and climate-cryosphere feedbacks. This model is able to capture the timing and shape of glacial cycles of the last 2 million years and can also be used to predict future glacial inceptions and thus the duration of the Anthropocene. Following different assumptions of human greenhouse gas emissions, I will show the expected timing of future glacial inceptions as well as the periodicities of the late Anthropocene glacial cycles.

How to cite: Alvarez-Solas, J.: Simulating glacial cycles from the Pleistocene to the end of the Anthropocene, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-16345, https://doi.org/10.5194/egusphere-egu24-16345, 2024.

EGU24-17019 | ECS | Posters on site | ITS4.1/CL0.1.7

Remote sensing-based detection of resilience loss in the terrestrial water cycle 

Romi Lotcheris, Lan Wang-Erlandsson, and Juan Rocha

In the face of Anthropogenic change, ecosystems globally have shown evidence of resilience loss in the past several decades. By governing key processes in terrestrial ecosystems, the hydrological cycle is critical for Earth system stability. A resilient system is able to retain its function and structure in the face of external perturbations. Changes to driving hydrological variables, i.e., precipitation, evaporation, and soil moisture, are thought to be important drivers of terrestrial ecosystem resilience, and vice-versa through land-atmosphere feedbacks. Resilience has been estimated through time series analysis, where an increase in metrics of system recovery time can signal a loss of system resilience. To date, such methods of resilience analysis have not yet been applied to hydrological variables. As a result, there is limited quantification of the role of the water cycle in Earth system resilience.

Here, using remotely sensed time series data, we employ both early warning signals of resilience loss and indicators of rate-based tipping to asses resilience loss in key hydrological variables at the global scale. In doing so, we present a spatially distributed assessment of global water resilience, highlight regions vulnerable to resilience loss, and provide insights into how water resilience affects terrestrial ecosystem resilience. Changes to hydrological variables can have wide-reaching impacts on ecological (e.g., affecting biodiversity, ecosystem structure and function), and social systems (e.g., affecting crop yields in breadbasket regions). Here, we present a new dimension to the characterisation of regions vulnerable to resilience loss.

How to cite: Lotcheris, R., Wang-Erlandsson, L., and Rocha, J.: Remote sensing-based detection of resilience loss in the terrestrial water cycle, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17019, https://doi.org/10.5194/egusphere-egu24-17019, 2024.

EGU24-17216 | Orals | ITS4.1/CL0.1.7

Transgression of the climate change planetary boundary critically affects the status of other boundaries 

Dieter Gerten, Arne Tobian, Johanna Braun, Jannes Breier, and Fabian Stenzel

To date, statues and trajectories of planetary boundaries have mostly been investigated separately, without fully quantifying if and to what extent transgression of one or more boundaries affects the status of respective others. To address this research gap, we have configured the state-of-the-art LPJmL Dynamic Global Vegetation Model so as to represent the terrestrial planetary boundaries (for land-system change, biosphere integrity, freshwater change, and biogeochemical/nitrogen flows) in an internally consistent, process-based framework. As the model simulates these boundaries’ underlying processes and control variables in a spatially explicit and dynamic manner, and as it also accounts for effects of climate change (a fifth planetary boundary considered through external forcing), it enables systematic studies of interactive effects among any of the five boundaries considered.

In a scenario study focused on here, we employed the model to systematically quantify the effects of different transgression levels of the climate change boundary (using gridded climate output from ten CMIP6 models for distinct atmospheric CO2 levels from 350 ppm to 1000 ppm) upon the land-system change boundary (areal extent of temperate, boreal and tropical forest biomes). Changes are analysed both by the end of this century and, to account for long-term legacy effects, by the end of the millennium, respectively. The simulations indicate that staying within the 350 ppm climate change boundary would stabilize the land-system change boundary, not inducing notable expansions or contractions of forest biome extent (on top of the historical shifts that have been brought about by anthropogenic deforestation). However, transgressing the climate change boundary beyond its zone of increasing risk (>450 ppm) is simulated to lead to increasingly substantial forest biome shifts, the higher the ppm level rises and the more time passes. Specifically, this involves a poleward tree-line shift, boreal forest dieback, expansion of temperate forest into today’s boreal zone, and a slight tropical forest expansion.

We furthermore find that these one-way interactions imply changes of the status of other planetary boundaries as well, as shifts in their control variables (e.g. large soil moisture and runoff anomalies) are simulated for the very areas where the forest biome shifts occur. Moreover, the vegetation changes are likely to provide feedback to the climate change boundary itself.

In additional simulations (making use of a planetary boundary simulation package linked to the LPJmL model), we investigate the historical evolution of the terrestrial planetary boundaries’ statuses during the past century. This examination suggests that the timing and spatial location of transgressions differs strongly among boundaries, with multiple boundaries crossed in the late 20th century, and transgression of the climate change boundary gaining increasing impact. Possible cascading and compound effects of these simultaneous transgressions, and particularly their likely aggravation in the future, require comprehensive analyses in further studies.

How to cite: Gerten, D., Tobian, A., Braun, J., Breier, J., and Stenzel, F.: Transgression of the climate change planetary boundary critically affects the status of other boundaries, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17216, https://doi.org/10.5194/egusphere-egu24-17216, 2024.

The capacity for tipping points in the climate system was elucidated decades ago by numerical climate models, which showed that nonlinearities could arise from physical interactions between the ocean, sea ice, and atmospheric components, leading to rapid shifts between qualitatively different states. However, there has been comparatively little work on physical interactions with the human component of the Earth system through numerical modeling due, in part, to the rarity of inclusion of the human system directly in Earth system models. Earth System economics provides a new approach for doing so, by proposing a particular set of physical variables that can be used as a basis for simulating such changes. These variables include spatially resolved population demography, time allocation to activities, a spatially resolved technosphere, and spatial networks that capture transportation fluxes. New global compilations of time use and technosphere data are helping to enable this approach, by quantifying the dependencies of material fluxes on time use and context. This opens the possibility of simulating long-term dynamics through motivated changes to time allocation, with outcomes dependent on the evolution of the technosphere and other coupled features of the Earth system. Examples will be discussed regarding how this approach can provide holistic, physically-grounded ways to identify possible nonlinearities and tipping points, by explicitly resolving aspects of human activities and technosphere changes, constrained by the conservation of mass, energy, and time.

How to cite: Galbraith, E.: Estimating possible nonlinearities in the Human-Earth system with Earth system economics, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17717, https://doi.org/10.5194/egusphere-egu24-17717, 2024.

EGU24-18263 | ECS | Orals | ITS4.1/CL0.1.7

Resilience across the Amazon basin regions under increased drought frequency and severity 

Bianca Rius, Barbara Cardeli, Carolina Blanco, João Paulo Darela Filho, Marina Hirota, and David Lapola

The anticipated rise in the frequency of severe droughts triggered by events such as El Niño and abnormal warming of the Atlantic Ocean is expected to have profound impacts on the Amazon forest. However, whether the Amazon forest can effectively cope with changes in precipitation patterns and maintain its resilience remains to be determined. The impacts can vary across different regions of the Amazon due to the inherent heterogeneity in annual precipitation rate and periodicity in dry and wet periods. Furthermore, it is essential to highlight that resilience assessment frequently revolves around the ecosystem's ability to maintain or restore its carbon stock after a disturbance. Nonetheless, numerous other ecosystem processes and properties, such as evapotranspiration and functional diversity, might signal a shift in resilience before a consistent alteration in carbon stock becomes apparent. To address these concerns, our study will apply the trait-based vegetation model CAETÊ (CArbon and Ecosystem functional Trait Evaluation model). To comprehend the effects of an elevated frequency of decreased precipitation in the Amazon forest, we will apply a 20% precipitation reduction across three different frequencies: 7 years, 3 years, and 1 year. The model will be run across five distinct Amazon regions: northwest, center, south, northeast, and southeast. The assessment of resilience will encompass both resistance and recovery measures and will be evaluated using standard metrics such as carbon stock, while the analysis will extend to include other crucial indicators such as evapotranspiration, net primary productivity, and functional diversity. We anticipate uncovering differences in resilience among the regions, primarily influenced by natural climatic heterogeneity that selects distinct compositions of functional traits, leading to varying levels of functional diversity. Our hypothesis suggests that initially, the northwest region may experience a buffering effect from its naturally high precipitation rate. This could potentially result in more subtle impacts, even in the face of reduced precipitation. However, over time, other regions may demonstrate greater resilience, as their communities might show functional strategies acclimated to prolonged dry conditions and lower precipitation rates. Additionally, we also expect to observe a prior decrease in evapotranspiration and functional diversity before the eventual collapse of carbon stock and net primary productivity. This expectation is rooted in the anticipated intensification of environmental filtering, wherein the ecosystem undergoes a process of selecting more conservative adaptive strategies to deal with drier climatic conditions. By employing this innovative approach to assess resilience, incorporating diverse indicators beyond solely relying on carbon stock, we aim to significantly improve the understanding of Amazonian ecosystem dynamics under changing climatic conditions. Ultimately, our findings may unveil that the Amazon forests are potentially more susceptible to environmental changes than previously envisioned.

How to cite: Rius, B., Cardeli, B., Blanco, C., Darela Filho, J. P., Hirota, M., and Lapola, D.: Resilience across the Amazon basin regions under increased drought frequency and severity, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18263, https://doi.org/10.5194/egusphere-egu24-18263, 2024.

EGU24-18581 | ECS | Posters on site | ITS4.1/CL0.1.7

Assessing the accuracy of GEDI for mapping resilience in the Amazon rainforest along a gradient of disturbance to recovery  

Emily Doyle, Chris Boulton, Hugh Graham, Tim Lenton, Ted Feldpausch, and Andrew Cunliffe

Understanding the resilience of tropical vegetation, its ability to recover from disturbance, is fundamental to assess future responses to environmental and climatic fluctuations. The Amazon rainforest has been identified as a potential tipping element in the Earth’s climate system and there is mounting concern over its persistent degradation. Extreme climate events and continued logging, forest fire and fragmentation threaten the Amazon’s structural integrity and its role as a carbon sink, with remotely sensed data providing observational evidence of resilience loss since the early 2000s. Fragmentation and degradation of tropical forest is suggested to slow recovery from perturbations, ensuing a potential to destabilise the rainforest and cause widespread transition from forest to savanna-like ecosystem state.

Remotely sensed LiDAR data provides a structural blueprint of forest canopy. The Global Ecosystem Dynamics Investigation (GEDI) spaceborne LiDAR characterises a new era of large-scale forest height quantification, with capabilities to further understand forest structure, and therefore forest response to perturbation across the entire Amazon. Although GEDI’s capabilities have been realised in boreal forest early disturbance monitoring, and to assess growth rates of tropical secondary forest, research thus far is yet to assess its ability to identify tropical forest of various degradation and recovery including logged, burned and fragmented over increasing timescales of recovery. Forest degraded by burning is characterised by different structure than selectively logged, or edge forest, and validating the ability of GEDI to represent these states is essential for identifying alternative forest states.  

Here, we investigate the potential of the GEDI LiDAR mission to map tropical forest along a gradient of degradation to recovery. A combination of ground data, MapBiomas secondary forest and burned area products are utilised to classify perturbed forest. We then assess the correspondence of GEDI waveform metrics including relative height and canopy cover, extracted from 2A and 2B products using the newly developed R package ‘chewie’, with airborne LiDAR across the Brazilian Amazon. This research will inform further tropical forest alternative-state study, whilst the assessment of GEDI’s structural capability to represent degraded forest types provides valuable information for forest restoration status to support post-degradation management strategies. 

How to cite: Doyle, E., Boulton, C., Graham, H., Lenton, T., Feldpausch, T., and Cunliffe, A.: Assessing the accuracy of GEDI for mapping resilience in the Amazon rainforest along a gradient of disturbance to recovery , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18581, https://doi.org/10.5194/egusphere-egu24-18581, 2024.

EGU24-18673 | Orals | ITS4.1/CL0.1.7

Impacts and state-dependence of AMOC weakening in a warming climate 

Jost von Hardenberg, Katinka Bellomo, and Oliver Mehling

All climate models project a weakening of the Atlantic Meridional Overturning Circulation (AMOC) strength in response to greenhouse gas forcing. However, the climate impacts of the AMOC decline in relation to other drivers of climate change, cannot be assessed from existing Coupled Model Intercomparison Project (CMIP) simulations. To address this issue, we conduct idealized experiments using the EC-Earth3 climate model. We compare an abrupt 4xCO2 simulation with an identical one, except we artificially fix the AMOC strength at preindustrial levels. With this design, we can formally attribute differences in climate change impacts between these two experiments to the AMOC decline. In addition, we quantify the state-dependence of AMOC impacts by comparing the aforementioned experiments with a preindustrial simulation in which we artificially reduce the AMOC strength. Our findings demonstrate that AMOC decline impacts are state-dependent, thus understanding AMOC impacts on future climate change requires targeted model experiments.

How to cite: von Hardenberg, J., Bellomo, K., and Mehling, O.: Impacts and state-dependence of AMOC weakening in a warming climate, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18673, https://doi.org/10.5194/egusphere-egu24-18673, 2024.

EGU24-18923 | ECS | Posters on site | ITS4.1/CL0.1.7

Rethinking the Intertwined Biosphere 

Chelsea Kaandorp, Juan Rocha, Lan Wang-Erlandsson, Cynthia Flores, Andrew Hattle, Henrik Österblom, and Carl Folke

Transformations towards sustainable futures can only be achieved with an advanced understanding of how human life is intertwined with the whole biosphere. Systems of people and nature are not separate entities but inherently connected across temporal and spatial scales. There is a dynamic interplay between the biosphere and the broader Earth system. Life in the biosphere has evolved with the basic building blocks of planet Earth, like water, carbon, nitrogen, and other biogeochemical cycles. Social conditions, such as health, culture, democracy, power, justice, equity, matters of security, and even survival, are interwoven with the Earth system and its biosphere resulting in a complex interplay of local, regional, and global interactions and dependencies.

In “The Intertwined Biosphere” project at the Anthropocene Laboratory, we explore empirical evidence of biosphere-Earth system dynamics since deep time and synthesise insights that can foster radical changes towards recognising humanity’s embeddedness in the world. By doing so, we aim to contribute to narratives that bridge human-nature dialectics to foster a deeper understanding of the critical interplay of humans as part of the living biosphere. In this presentation, we share our preliminary conceptual model of the biosphere as intertwined. We invite you to discuss human embeddedness in the biosphere and new directions for guiding human actions in the Anthropocene. What are the ontological and epistemological implications of understanding the Anthropocene biosphere as intertwined complex human-nature entanglements? How to study how life shapes its own living conditions?  

How to cite: Kaandorp, C., Rocha, J., Wang-Erlandsson, L., Flores, C., Hattle, A., Österblom, H., and Folke, C.: Rethinking the Intertwined Biosphere, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18923, https://doi.org/10.5194/egusphere-egu24-18923, 2024.

EGU24-19347 | Orals | ITS4.1/CL0.1.7

The Planetary Boundaries Framework: Status (“PB3.0”) 

Katherine Richardson, Will Steffen, and Wolfgang Lucht and the PB3.0-Team

The planetary boundaries framework emerges from Earth system science and was developed to help guide the global community in its efforts to manage Anthroposphere interactions with the Earth’s bio-physical components. In the third iteration of the framework, PB3.0 (September 2023), six of the nine boundaries are found to be transgressed and anthropogenic pressure is increasing on all the boundaries earlier found to be exceeded. Metrics are, for the first time, proposed for all boundaries. Human Appropriation of Net Primary Production is proposed as the control variable for the function of the biosphere as photosynthesis represents the energy input supporting almost all life. The probability of achieving global climate goals is argued to be closely linked to the fate of global forests. Thus, the climate and biodiversity crises must be addressed together. Directions for the framework’s further development are discussed.

How to cite: Richardson, K., Steffen, W., and Lucht, W. and the PB3.0-Team: The Planetary Boundaries Framework: Status (“PB3.0”), EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-19347, https://doi.org/10.5194/egusphere-egu24-19347, 2024.

EGU24-19383 | Orals | ITS4.1/CL0.1.7

The new planetary boundary for freshwater change: key findings and their potential to guide water management and policy 

Miina Porkka, Vili Virkki, Lan Wang-Erlandsson, and Matti Kummu

The recent third planetary boundary (PB) assessment replaced the original PB for ‘freshwater use’ with a new PB for ‘freshwater change’. The new PB is defined by the percentage of global land area experiencing streamflow (blue water component of the PB) and root-zone soil moisture (green water) deviations from pre-industrial baseline conditions. Here, we first present the spatiotemporally explicit results of the comprehensive analysis underlying the new PB, and then discuss possible applications of the approach and the challenges related to providing meaningful guidance for water management and policy across scales.

We find a clear transgression of both the blue and green water components of the freshwater change PB already during the first half of the 20th century. Our spatiotemporally explicit analysis reveals a general pattern of drying across a significant portion of the tropics and subtropics, contrasting with wetting in temperate and subpolar regions as well as numerous highland areas. This overall pattern is likely attributed to alterations in precipitation patterns associated with global warming. Significant increases in streamflow and soil moisture deviations are also found in regions facing the highest direct human pressures, such as irrigation, flow regulation, and land use change. In many cases, both streamflow and soil moisture deviations have increased – underlining the influence of human impacts on the freshwater cycle as a whole.

While our analysis highlights regions undergoing the most substantial freshwater changes and their potential drivers, using the results to guide water policy and management remains challenging. Key knowledge gaps include our limited understanding of the (quantitative) driver–freshwater change–Earth system response relationships, and the mismatches between spatiotemporal scales of 1) human drivers of freshwater change, 2) the Earth system impacts of freshwater change, and 3) water management and governance institutions. We conclude our presentation by proposing a research agenda to bridge these gaps, with a goal to provide policy-relevant information on freshwater change that would enable a stronger adoption of an  Earth system perspective in water management and governance.

How to cite: Porkka, M., Virkki, V., Wang-Erlandsson, L., and Kummu, M.: The new planetary boundary for freshwater change: key findings and their potential to guide water management and policy, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-19383, https://doi.org/10.5194/egusphere-egu24-19383, 2024.

EGU24-19468 | Orals | ITS4.1/CL0.1.7

 Systematic detection of abrupt change and tipping points in TIPMIP  

Sina Loriani, Donovan Dennis, Jonathan Donges, Boris Sakschewski, and Ricarda Winkelmann

With ongoing anthropogenic emissions and ensuing accelerated climate change, the planet is increasingly leaving its long-stable Holocene state. In fact, recent assessments have shown that a range of climate tipping points are at risk of being crossed at warming levels well within temperature projections of the 21st century. However, such assessments have been largely based on expert judgement of scattered literature, with corresponding large uncertainties in critical thresholds and potential tipping dynamics. The Tipping Point Modelling Intercomparison Project (TIPMIP, www.tipmip.org) aims to close this research gap through a standardised framework for numerical experiments exploring tipping across systems and models. Built on precursory experiments, we here introduce the Tipping and Other Abrupt Events Detector (TOAD) method, to automatically identify spatial clusters of dynamically connected regions exhibiting tipping dynamics. This will serve as an evaluation scheme for the suite of experiments generated within the TIPMIP protocol. Overall, this systematic approach to tipping point risks at different levels of human pressures can inform quantification of planetary or Earth system boundaries to map out the safe and just operating space for humanity in the Anthropocene.

How to cite: Loriani, S., Dennis, D., Donges, J., Sakschewski, B., and Winkelmann, R.:  Systematic detection of abrupt change and tipping points in TIPMIP , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-19468, https://doi.org/10.5194/egusphere-egu24-19468, 2024.

EGU24-19685 | ECS | Posters on site | ITS4.1/CL0.1.7

Rapid dietary change can foster desired food system transformations: lessons from past evolutions of dietary patterns. 

Vittorio Giordano, Marta Tuninetti, and Francesco Laio

The global food system is currently at a critical turning point as it is driving the planet’s trajectory towards exceeding 1.5 °C warming and crossing tipping points in the Earth system. It is responsible for one-third of global emissions and the primary cause of freshwater consumption and pollution, biodiversity loss and terrestrial ecosystem destruction. The prevalence of undernourishment is persistent, while unhealthy diets and widespread overnutrition cause diet-related chronic diseases and health damages. To achieve international agreements’ targets on climate and biodiversity its transformation is essential.

Rapid dietary change to more plant-based diets and reduced animal products consumption is a powerful leverage for plummeting the environmental and climate impacts of food habits. It has been referred to as one of the potential positive tipping points that can be harnessed to transform the global food system, profoundly altering its modes of operation. Nevertheless, there is limited empirical evidence regarding whether such non-linear dynamics occur in the food sector, resulting in an important gap in the identification of specific factors that can trigger a desired transition.

We propose a quantitative framework to identify historic and ongoing tipping dynamics in food system transformation. We first implement statistical analyses to explore the past evolution of the dominant dietary patterns within historical data series (1961-2020) of country-scale food supply quantities, across different food categories. We then unravel the drivers behind dietary patterns evolution in time (e.g., per capita GDP, cultural and social factors, supply patterns), also highlighting significant similarities across different countries, possibly suggesting coupled dietary evolutions. The outputs of our statistical framework provide ground for the analysis of past shifts in dietary patterns and the role that potential tipping elements driving dietary shifts - changes of normative consumer beliefs and behaviours, agricultural practices and policies - had in triggering food system transformations, or that may have in accelerating future desired transitions towards a more sustainable food system.

How to cite: Giordano, V., Tuninetti, M., and Laio, F.: Rapid dietary change can foster desired food system transformations: lessons from past evolutions of dietary patterns., EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-19685, https://doi.org/10.5194/egusphere-egu24-19685, 2024.

EGU24-19730 | ECS | Posters on site | ITS4.1/CL0.1.7

Next steps towards the Tipping Point Modelling Intercomparison Project (TIPMIP) 

Donovan Dennis, Jonathan Donges, Sina Loriani, Boris Sakschewski, and Ricarda Winkelmann

Anthropogenic climate change poses considerable risk to the stability of the Earth system. The consequences associated with crossing certain tipping thresholds, wherein relatively small-scale changes in the state of a specific tipping element may induce widespread and potentially irreversible feedbacks, are among the most severe. The Tipping Point Modelling Intercomparison Project (TIPMIP, www.tipmip.org) seeks to systematically investigate tipping risks for the Greenland and Antarctic ice sheets, the Atlantic Meridional Overturning Circulation, tropical and boreal forests as well as high-latitude permafrost  in order to both advance the understanding of the underlying  dynamics as well as to quantify the associated uncertainties around crossing such thresholds. Here, we discuss the initial proposed experimental protocols for TIPMIP for each domain (cryosphere, ocean, biosphere, fully coupled), the next  steps towards their implementation within the modelling community as well as the alignment with other ongoing and planned MIPs. 

How to cite: Dennis, D., Donges, J., Loriani, S., Sakschewski, B., and Winkelmann, R.: Next steps towards the Tipping Point Modelling Intercomparison Project (TIPMIP), EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-19730, https://doi.org/10.5194/egusphere-egu24-19730, 2024.

EGU24-20293 | Orals | ITS4.1/CL0.1.7

Using Ecotron experimentation to quantify planetary boundaries 

Nadia Soudzilovskaia, Francois Rineau, Jonas Schoelynck, Hans De Boek, and Ivan Nijs

As the world’s population grows at unprecedented rates, planetary-scale environmental forcing by humankind continues to push Earth system components out of the equilibrium state. The planetary boundaries framework provides an elegant and comprehensive tool to estimate the extent to which nine key processes of human-induced biosphere alteration affect the stability and resilience of Earth system. Yet quantifying planetary boundaries and especially the interactions between them, based on a process-based understanding of ecosystem functioning, remains a great challenge, as observations and experimentation in natural ecosystems typically provide only a narrow snapshot of a process in question. While conventional controlled environment facilities, such as growth chambers and advanced greenhouses provide a standard tool to simulate environmental change and disentangle processes controlling ecosystem functioning, the capacity of such systems to provide realistic quantifications of ecosystem tipping point is limited, due to (1) a typical focus on a single environmental change process, and (2) a use of simplified, small scale experimental ecosystems. In contrast, novel state-of-the-art terrestrial and aquatic Ecotron research facilities enable both (1) simulation of a wide range of natural environmental conditions, employing  highly realistic scenarios of environmental change, as well as (2) operating with natural ecosystems in their full complexity in replicated design.  An important advantage of ecotrons is a possibility of obtaining long-term (years to decennia scale) and high resolution (minutes-to-days) time series of continues observations of multiple ecosystem functions and their drivers, allowing to infer relations between those in a process-based manner. These advantages are increasingly acknowledged by the scientific community, as having a great potential to help obtaining experimental data to quantify the ecosystem tipping points, accounting for interactions between multiple forces driving planetary boundaries. I will discuss the framework of using a European network of Ecotrons and Ecotorn-like systems within AnaEE ERIC (Analysis and Experimentation on Ecosystems European Research Infrastructure Consortium) in the context of quantification of planetary boundaries, and will present a suit of a case studies illustrating assessments of cascading effects of land use change and climate change on ecosystem integrity, terrestrial above and belowground biodiversity, terrestrial and oceanic biogeochemical cycles, and soil moisture regime. I aim to inspire a discussion about new avenues in assessment of planetary boundary levels based on high throughput experimental and observational data obtained in ecotron-like experimental facilities.

How to cite: Soudzilovskaia, N., Rineau, F., Schoelynck, J., De Boek, H., and Nijs, I.: Using Ecotron experimentation to quantify planetary boundaries, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-20293, https://doi.org/10.5194/egusphere-egu24-20293, 2024.

EGU24-20483 | Orals | ITS4.1/CL0.1.7

Feedbacks and social tipping: A dynamic systems approach to rapid decarbonization 

Sibel Eker, Charlie Wilson, Niklas Hohne, Mark McCaffrey, Irene Monasterolo, Leila Niamir, and Caroline Zimm

Social tipping points are promising levers for accelerating progress towards net-zero greenhouse gas emission targets. They describe how social, political, economic or technological systems can move rapidly into a new state if cascading positive feedback mechanisms are triggered. Analysing the potential for social tipping requires considering the inherent complexity of social systems and their feedbacks. Here, drawing on insights from an expert elicitation workshop, we outline a dynamic systems approach that entails i) a systems outlook involving interconnected feedback mechanisms alongside cross-system and cross-scale interactions, ii) directed data collection efforts to provide empirical evidence and monitoring of social tipping dynamics, and iii) global, integrated, descriptive modelling to project future dynamics and provide ex-ante evidence for interventions aiming to trigger positive feedback mechanisms. We argue how and why this approach will strengthen the climate policy relevance of research on social tipping.

How to cite: Eker, S., Wilson, C., Hohne, N., McCaffrey, M., Monasterolo, I., Niamir, L., and Zimm, C.: Feedbacks and social tipping: A dynamic systems approach to rapid decarbonization, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-20483, https://doi.org/10.5194/egusphere-egu24-20483, 2024.

EGU24-20878 | Orals | ITS4.1/CL0.1.7

Socio-metabolic class conflicts in the Anthropocene 

Ilona M. Otto and Antonia Schuster

The Anthropocene epoch is characterized by an excessive use of natural resources and energy that drives the environmental destruction of the planet. However, large inequalities exist among different social groups that benefit to various degrees from the use of resources and energy, as well as among those suffering from the negative impacts of environmental destruction. In this paper, we systematically analyze these differences and propose a novel social stratification theory based not only on differences in terms of possessions or social status, but also on differences in how these groups can control and benefit from the planetary material cycles and energy flows or suffer the consequences of environmental degradation. Referring to consumption data, we propose six global socio-metabolic classes and show distinctive patterns in the energy use of these classes. More research is needed to reveal differences in the use of natural resources essential for maintaining the biosphere integrity, such as land, water, nitrogen, and phosphorus. Targeted policy measures that address excessive appropriation of energy and natural resources are needed, as are expansions in infrastructure and institutional change that supports the wellbeing of humankind, and especially of the most marginalized classes.

How to cite: Otto, I. M. and Schuster, A.: Socio-metabolic class conflicts in the Anthropocene, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-20878, https://doi.org/10.5194/egusphere-egu24-20878, 2024.

EGU24-20891 | Posters on site | ITS4.1/CL0.1.7

Clams reveal the North Atlantic subpolar gyre has destabilised over recent decades 

Beatriz Arellano Nava, Paul R. Halloran, Chris A. Boulton, and Timothy M. Lenton

Amidst the ongoing climate crisis, there is a pressing need to assess the resilience of different components of the climate system. Two candidate tipping elements involve changes in circulation in the Atlantic Ocean, raising alarms about the potential consequences for the climate system and human societies. An approach to measure changes in resilience consists of assessing signs of critical slowing down by measuring changes in lag-1 autocorrelation and variance. However, this approach requires long-term, regularly spaced time-series, characteristics that are rare among observational records, especially in the ocean. The recent development of annually-resolved proxy records based on information encoded in bivalve shells provides a unique opportunity for assessing resilience in the marine environment. Here, we assess changes in resilience in the northern North Atlantic by measuring changes in lag-1 autocorrelation in a compilation of 29 bivalve-derived records. Our findings indicate that the marine environment has lost stability over the last decades over much of the North Atlantic sea shelves. Records that exhibit significant increasing trends in autocorrelation are highly sensitive to temperature variability over the subpolar gyre region, suggesting that the observed slowing down in variability may be associated with this system. Furthermore, bivalves reveal a basin-scale destabilisation episode preceding a documented regime shift in the northern North Atlantic circulation system around 1920, demonstrating their sensitivity to changes in resilience in circulation elements. Both findings suggest that the subpolar North Atlantic circulation system has lost resilience over recent decades and is potentially approaching a tipping point.

How to cite: Arellano Nava, B., Halloran, P. R., Boulton, C. A., and Lenton, T. M.: Clams reveal the North Atlantic subpolar gyre has destabilised over recent decades, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-20891, https://doi.org/10.5194/egusphere-egu24-20891, 2024.

EGU24-21005 * | Orals | ITS4.1/CL0.1.7 | Highlight

Evolution of the polycrisis: Anthropocene traps that challenge global sustainability 

Peter Søgaard Jørgensen, Raf Jansen, Daniel Avila Ortega, Lan Wang-Erlandsson, Jonathan F. Donges, Henrik Österblom, Per Olsson, Magnus Nyström, Steve Lade, Thomas Hahn, Carl Folke, Garry Peterson, and Anne-Sophie Crepin

The Anthropocene is characterized by accelerating change and global challenges of increasing complexity and most recently by what some have called a polycrisis. Based on an adaptation of the evolutionary traps concept to a global human context, we explore whether the human trajectory of increasing complexity and influence on the Earth system could become a form of Anthropocene trap for humanity. We identify 14 Anthropocene traps and categorize them as either global, technology or structural traps. An assessment reveals that 12 traps (86%) could be in an advanced phase of trapping with high risk of hard-to-reverse lock-ins and growing risks of negative impacts on human well-being. Ten traps (71%) currently see growing trends in their indicators. Revealing the systemic nature of the polycrisis, we assess that Anthropocene traps often interact reinforcingly (45% of pairwise interactions), and rarely in a dampening fashion (3%). We end by discussing capacities that will be important for navigating these systemic challenges in pursuit of global sustainability. Doing so, we introduce evolvability as a unifying concept for such research between the sustainability and evolutionary sciences.

How to cite: Søgaard Jørgensen, P., Jansen, R., Avila Ortega, D., Wang-Erlandsson, L., Donges, J. F., Österblom, H., Olsson, P., Nyström, M., Lade, S., Hahn, T., Folke, C., Peterson, G., and Crepin, A.-S.: Evolution of the polycrisis: Anthropocene traps that challenge global sustainability, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-21005, https://doi.org/10.5194/egusphere-egu24-21005, 2024.

EGU24-21091 | Orals | ITS4.1/CL0.1.7

Safe and just Earth system boundaries 

Steven Lade and the Earth Commission

We present our paper published in Nature last year: https://www.nature.com/articles/s41586-023-06083-8. The work can be viewed as a "deep dive" into a subset of the planetary boundaries on dimensions of justice and operational spatial scales.

Abstract from the paper: The stability and resilience of the Earth system and human well-being are inseparably linked, yet their interdependencies are generally under-recognized; consequently, they are often treated independently. Here, we use modelling and literature assessment to quantify safe and just Earth system boundaries (ESBs) for climate, the biosphere, water and nutrient cycles, and aerosols at global and subglobal scales. We propose ESBs for maintaining the resilience and stability of the Earth system (safe ESBs) and minimizing exposure to significant harm to humans from Earth system change (a necessary but not sufficient condition for justice). The stricter of the safe or just boundaries sets the integrated safe and just ESB. Our findings show that justice considerations constrain the integrated ESBs more than safety considerations for climate and atmospheric aerosol loading. Seven of eight globally quantified safe and just ESBs and at least two regional safe and just ESBs in over half of global land area are already exceeded. We propose that our assessment provides a quantitative foundation for safeguarding the global commons for all people now and into the future.

This work is an output of the Earth Commission, an independent international scientific assessment initiative hosted by Future Earth. The Earth Commission is the scientific cornerstone of the Global Commons Alliance.

How to cite: Lade, S. and the Earth Commission: Safe and just Earth system boundaries, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-21091, https://doi.org/10.5194/egusphere-egu24-21091, 2024.

EGU24-22274 | ECS | Orals | ITS4.1/CL0.1.7

Positive Tipping Points in the Food Systems: the Role of Scales 

Marta Tuninetti, Vittorio Giordano, Sara Constantino, Saverio Perri, Juan Rocha, Luana Schwarz, Jonathan F. Donges, Francesco Laio, and Simon Levin

The global food system is at a critical inflection point with rising awareness of the need for change and progress on several fronts, pertaining both human health and the environment. One of the ten critical transitions envisioned by the Food and Land Use Coalitions states that global diets need to converge towards local variations of the “human and planetary healthy diet” which includes more protective foods a diverse protein supply, and reduced consumption of sugar, salt and highly processed foods. 

Positive tipping points (PTP) offer a new perspective to support and boost the implementation of solutions for sustainable and healthy food systems. A PTP in the food system can be seen as critical points where targeted interventions lead to large and long-term consequences on the evolution of that system, profoundly altering its modes of operation.  While discussions on food PTP dynamics are an intriguing theoretical debate, we still lack empirical evidence if and how such dynamics unfold in practice, especially in the food sector. Literature on inducing positive tipping and feedback dynamics in sustainability transitions almost exclusively focuses on the energy sector, leaving an important gap in the empirical research on the specific enabling factors for triggering these dynamics in respect to food and global diets transformation.  

How do different organizational, geographical, and temporal scales should interact with each other to accelerate a transition to a sustainable food system? In this study we integrate complex network theory tools with systems’ emergent properties to better define multi-scale food systems dynamics. We develop indicators (with country resolution and global coverage) to synthesize the food system’s structure and its weak and strong points where the spread of positive changes can be maximized. This quantitative framework is aimed at supporting the actions of government in repurposed agricultural subsidies, targeted public food procurement, taxes and regulations on unhealthy food; and business in redesigning product portfolio based on the human and planetary health diet. 

How to cite: Tuninetti, M., Giordano, V., Constantino, S., Perri, S., Rocha, J., Schwarz, L., Donges, J. F., Laio, F., and Levin, S.: Positive Tipping Points in the Food Systems: the Role of Scales, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-22274, https://doi.org/10.5194/egusphere-egu24-22274, 2024.

EGU24-1852 | ECS | Posters on site | ITS4.3/NP1.2

Anticipating critical transitions in multi-dimensional systems driven by time- and state-dependent noise 

Andreas Morr, Keno Riechers, Leonardo Rydin Gorjão, and Niklas Boers

When approaching a one-parameter bifurcation, the feedbacks that stabilise the initial state weaken and eventually vanish; a process referred to as critical slowing down (CSD). This motivates the use of variance and lag-1 auto-correlation as indicators of CSD in order to anticipate bifurcation-induced critical transitions. Both indicators require a prior dimension reduction to a one-dimensional time series. The use of variance is further limited to time- and state-independent driving noise, strongly constraining its generality. Here, we propose a data-driven approach based on deriving a multi-dimensional Langevin equation to detect local stability changes and anticipate bifurcation-induced transitions in systems with generally time- and state-dependent noise. Our approach substantially generalizes the conditions underlying existing early warning indicators, which we showcase in the example of a two-dimensional predator-prey model. This reduces the risk of false and missed alarms significantly and allows for a more holistic understanding of the multi-dimensional system at hand.

How to cite: Morr, A., Riechers, K., Rydin Gorjão, L., and Boers, N.: Anticipating critical transitions in multi-dimensional systems driven by time- and state-dependent noise, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1852, https://doi.org/10.5194/egusphere-egu24-1852, 2024.

The Atlantic Meridional Overturning Circulation (AMOC) is projected to weaken due to the increase in buoyancy caused by anthropogenic warming and consequent freshening during the 21st century and beyond. Atmosphere-ocean general circulation models simulate this AMOC weakening due to warming of the surface ocean, changes in the hydrological cycle that shift the North Atlantic salt budget, melting sea-ice, and changes in the atmospheric circulation. However, the freshwater contribution from the melting Greenland ice sheet is often either only considered in idealized scenarios or entirely omitted due to computational constraints. This simplification contributes to the large uncertainty surrounding the possibility of the AMOC crossing a tipping point in the forthcoming centuries. Here we employ the fully coupled Earth system model of intermediate complexity Bern3D v3, which dynamically simulates all ice-ocean-atmosphere interactions. We conduct a set of simulations driven by idealized CO2 concentration paths to investigate the impact of the melting Greenland ice sheet on the stability of the AMOC over the next 3000 years. We find that for a slow CO2 increase of 0.5%/yr up to twice pre-industrial levels, the general trends of the AMOC evolution are independent of whether Greenland meltwater is taken into account, with an initial weakening, but long-term recovery. Yet, the additional meltwater results in a further weakening of about 3 Sv after 100 years, but without leading to a full collapse of the circulation. This effect is due to melt rates remaining relatively low for the initial 100 years and only reaching their peak after 500 years. In the long-term, the curtailed AMOC and hence northward heat transport substantially slows down the disintegration of the Greenland ice sheet. Only in scenarios where the melt rates are kept artificially high, the AMOC does not recover. This highlights that the meltwater-induced AMOC weakening stabilizes the Greenland ice sheet, which in turn limits further AMOC weakening. This suggests that the potential for cascading interactions may be limited.

How to cite: Pöppelmeier, F. and Stocker, T. F.: Impact of future Greenland ice sheet melt on the stability of the Atlantic Meridional Overturning Circulation, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-3874, https://doi.org/10.5194/egusphere-egu24-3874, 2024.

EGU24-4021 | ECS | Orals | ITS4.3/NP1.2

Optimal Transition Paths for AMOC Collapse and Recovery in a Stochastic Box Model 

Jelle Soons, Tobias Grafke, and Henk A. Dijkstra

The present-day Atlantic Meridional Overturning Circulation (AMOC) is considered to be a prominent tipping element and its collapse would have grave consequences on the global climate. Hence, it is important to determine probabilities and pathways for noise-induced tipping events. However, as there is no observational evidence for an AMOC transition over the historical period, a noise-induced transition is expected to be a rare event in models and simple Monte Carlo techniques are not suited for such low-probability events. Here, we use Large Deviation Theory to directly compute the most probable transition pathways for the collapse and recovery of the AMOC in a box model of the World Ocean calibrated to the FAMOUS-model, where we added stochastic freshwater forcing. This allows us to determine the physical mechanisms of noise-induced AMOC transitions. We show that the most likely path of an AMOC collapse starts paradoxically with a strengthening of the AMOC followed by an immediate drop within a couple of years due to a short but relatively strong freshwater pulse. The recovery on the other hand is a slow process, where the North Atlantic Ocean needs to be gradually salinified over a course of decades, and its dynamics are quite close to the recovery in a bifurcation tipping event. The proposed method provides several benefits, including an estimate of probability ratios of collapse between various freshwater noise scenarios, showing that the AMOC is most vulnerable to freshwater forcing into the Atlantic thermocline region.

How to cite: Soons, J., Grafke, T., and Dijkstra, H. A.: Optimal Transition Paths for AMOC Collapse and Recovery in a Stochastic Box Model, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-4021, https://doi.org/10.5194/egusphere-egu24-4021, 2024.

EGU24-5870 | ECS | Orals | ITS4.3/NP1.2

Uncertainties too large to predict tipping times of major Earth system components 

Maya Ben Yami, Andreas Morr, Sebastian Bathiany, and Niklas Boers

Observations are increasingly used to detect critical slowing down (CSD) in potentially multistable components of the Earth system in order to warn of forthcoming critical transitions in these components. In addition, it has been suggested to use the statistical changes in these historical observations to extrapolate into the future and predict the tipping time. We argue that this extrapolation is too sensitive to uncertainties to give robust results. In particular, we raise concerns regarding (1) the modelling assumptions underlying the approaches to extrapolate results obtained from analyzing historical data into the future, (2) the representativeness of individual time series representing the variability of the respective Earth system components, and (3) the effect of uncertainties and preprocessing of the employed observational datasets, with focus on non-stationary observational coverage and the way gaps are filled. We explore these uncertainties both qualitatively and quantitatively for the Atlantic Meridional Overturning Circulation (AMOC). We argue that even under the assumption that these natural systems have a tipping point that they are getting closer to, the different uncertainties are too large to be able to estimate the time of tipping based on extrapolation from historical data.

How to cite: Ben Yami, M., Morr, A., Bathiany, S., and Boers, N.: Uncertainties too large to predict tipping times of major Earth system components, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5870, https://doi.org/10.5194/egusphere-egu24-5870, 2024.

EGU24-5998 | ECS | Orals | ITS4.3/NP1.2

North Atlantic Subpolar Gyre Deep Convection: A Tipping Point Reached Decades Ago? 

Joas Müller, Giuseppe Zappa, and Alessio Bellucci

Recent studies utilizing the CMIP5 and CMIP6 model ensembles reveal that the subpolar North Atlantic (NA) is prone to deep convection collapsing leading to abrupt cooling of sea surface temperatures. Consequently, the latest comprehensive study on tipping points and the first report on global tipping points include the subpolar gyre (SPG) deep convection on the list of core tipping elements of Earth’s climate system.

Here, we investigate the drivers and impacts of a collapse of deep convection in the subpolar NA and the role of internal variability using a coupled climate model large ensemble (namely, the CESM2-LE consisting of 100 ensemble members) under the SSP3-7.0 forcing scenario. We identify that freshening of surface conditions leads to the negative surface density anomaly, eventually resulting in the cessation of deep mixing and the abrupt cooling of sea surface temperatures. The ensemble shows abrupt cooling occurring approximately in 2045 with internal variability leading to a spread of ±11 years. In each ensemble member, the subpolar NA transitions to a new state without deep convection, colder sea surface temperatures, strongly reduced heat loss to the atmosphere, and large circulation changes.

Internal variability does not determine if, but when abrupt cooling occurs, suggesting a forced response to larger-scale changes and a potential tipping point to be reached decades before the prominent abrupt cooling event. We provide evidence for the collapse of deep convection being a component of a positive feedback mechanism resulting in the SPG circulation transitioning to a weaker state. Without deep convection at the center of the circulation, the density gradient-driven part of the gyre circulation vanishes and the circulation strength decreases by approximately 50 %. The tipping point of the subpolar NA is therefore reached decades prior to the abrupt cooling and abrupt cooling is an inevitable consequence of the tipping event.

This points towards a potential misconception concerning drivers of abrupt climate
change in the subpolar NA, connected tipping points, and their thresholds, highlighting
the necessity for clarifying research efforts in the future.

How to cite: Müller, J., Zappa, G., and Bellucci, A.: North Atlantic Subpolar Gyre Deep Convection: A Tipping Point Reached Decades Ago?, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5998, https://doi.org/10.5194/egusphere-egu24-5998, 2024.

EGU24-7515 | Posters on site | ITS4.3/NP1.2

Structural controllability and management of cascading regime shifts 

Juan Rocha and Anne-Sophie Crépin

Abrupt transitions in ecosystems can be interconnected, raising challenges for science and management in identifying sufficient interventions to prevent them or recover from undesirable shifts. Here we use principles of network controllability to explore how difficult it is to manage coupled regime shifts. We find that coupled regime shifts are easier to manage when they share drivers, but can become harder to manage if new feedbacks are formed when coupled. Simulation experiments showed that both network structure and coupling strength matter in our ability to manage interconnected systems. This theoretical insights calls for an empirical assessment of cascading regime shifts in ecosystems and warns about our limited ability to control cascading effects.

How to cite: Rocha, J. and Crépin, A.-S.: Structural controllability and management of cascading regime shifts, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-7515, https://doi.org/10.5194/egusphere-egu24-7515, 2024.

EGU24-8618 | Orals | ITS4.3/NP1.2

Uncertainty quantification for overshoots of tipping thresholds 

Paul Ritchie and Kerstin Lux-Gottschalk

To tip or not to tip? Many subsystems of the Earth are at risk of undergoing abrupt transitions from their current stable state to a drastically different and often less desired state due to anthropogenic climate change. These so-called tipping events often present severe consequences for ecosystems and human livelihood that are difficult to reverse. One common mechanism for tipping to occur is via forcing and driving a nonlinear system beyond a critical threshold that signifies self-amplifying feedbacks inducing tipping. However, previous work has shown that it is possible to briefly overshoot a critical threshold and avoid tipping. Specifically, the peak distance of an overshoot and the time a system can spend beyond a threshold are governed by an inverse square law relationship. In the real world or complex models, critical thresholds and other system features determining the permitted overshoot are highly uncertain. In this presentation, we look at how such uncertainties affect the probability of tipping from the perspective of uncertainty quantification. We show the importance of constraining uncertainty in the location of the critical threshold and the linear restoring rate to the stable state to reduce the uncertainty in the probability of tipping. Using a simple box model for the Atlantic Meridional Overturning Circulation, we highlight the need to constrain the high uncertainty found in wind-driven fluxes represented by a diffusive time scale within the box model to reduce uncertainty in the tipping probability for overshoot scenarios. 

How to cite: Ritchie, P. and Lux-Gottschalk, K.: Uncertainty quantification for overshoots of tipping thresholds, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-8618, https://doi.org/10.5194/egusphere-egu24-8618, 2024.

EGU24-8927 | Posters on site | ITS4.3/NP1.2

Consistency of resilience indicators in terrestrial vegetation models 

Sebastian Bathiany, Lana Blaschke, Andreas Morr, and Niklas Boers

Terrestrial ecosystems are affected by climate change, deforestation and other human influences. There is concern that the resilience of these ecosystems, i.e. their ability to recover from perturbations, is thereby decreased and that their sensitivity to environmental change is increased. In the extreme case, this sensitivity could diverge at a “tipping point”, and propel systems into alternative states. A prominent example is the potential dieback of the Amazon rainforest and the transition to a savanna-like state.

The notion of resilience is a highly complex and multi-faceted concept. Ecological resilience theory and the mathematical properties of dynamical systems suggest that a number of different resilience quantifiers are related to each other, or even equivalent, which would allow improved “resilience monitoring” from space. For instance, indicators based on the phenomenon of “critical slowing down” (CSD) like variance and autocorrelation, and related indicators have been used to detect changes over time. In contrast to empirical recovery rates, these indicators do not require one to directly observe the recovery from rare extreme disturbances. Also, they do not rely on the observation or attribution of the responsible environmental drivers.

Based on the assumption that fluctuations in remotely sensed proxies of vegetation properties (like biomass or vegetation greenness) behave like iconic one-dimensional stochastic models (most importantly, the Ornstein-Uhlenbeck process), CSD-based indicators should be related to empirical recovery rates after perturbations, to the more general Kramers-Moyal coefficients rooted in statistical mechanics, and to the sensitivity of a dynamical equilibrium state to environmental change. It has been shown that in observations, the theoretically expected relationships between some of these measures roughly hold. At the same time, process-based models, as well as observations, can deviate from such simple stochastic models, e.g. when multiple plant types affect the resilience of an ecosystem but not its sensitivity to environmental change.

In our contribution, we show and discuss examples for such deviations in a global vegetation model LPJ. In addition, we compare resilience indicators across a number of state-of-the-art models from CMIP6 and compare the results to an assessment of observations, in order to separate limitations that are related to the practical measurement process (e.g. uncertainties related to retrieval algorithms) from limitations that are associated with unjustified theoretical assumptions. Our results are meant to guide resilience monitoring toward meaningful indicators and to focus on regions and observable properties that can warn of future loss of ecosystem services.

How to cite: Bathiany, S., Blaschke, L., Morr, A., and Boers, N.: Consistency of resilience indicators in terrestrial vegetation models, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-8927, https://doi.org/10.5194/egusphere-egu24-8927, 2024.

EGU24-11804 | ECS | Orals | ITS4.3/NP1.2

Origin in of the AMOC fresh water transport biases in a state-of-the-art climate model 

Elian Vanderborght, Henk Dijkstra, and René Westen

Recent quasi-equilibrium studies performed in the Community Earth System Model (CESM) have revealed a bi-stable regime of the Atlantic Meridional Overturning Circulation (AMOC) in this model. This suggests that the present-day AMOC might exist in a bi-stable regime, emphasizing the need for accurate predictions regarding the probability of an AMOC collapse over the next decades. However, the CESM exhibits notable biases, with a critical freshwater transport bias at 34°S in the Atlantic emerging as a key determinant of AMOC stability. Specifically, this bias enhances the stability of the AMOC, rendering the CESM unable to accurately predict the likelihood of AMOC tipping.

In this study, we establish a direct connection between the freshwater transport bias in the CESM and a corresponding freshwater content bias in the Indian Ocean. By investigating the detailed freshwater balance, we identify specific regions within the Indian Ocean that exert a significant influence on the Atlantic freshwater transport bias at 34°S. This quantitative analysis enables us to construct an optimal surface-flux correction, which reduces the model biases. This physics-based surface-flux correction allows us to adjust the AMOC to its correct stability regime in the CESM without imposing unrealistic flux adjustments

How to cite: Vanderborght, E., Dijkstra, H., and Westen, R.: Origin in of the AMOC fresh water transport biases in a state-of-the-art climate model, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-11804, https://doi.org/10.5194/egusphere-egu24-11804, 2024.

EGU24-12023 | Posters on site | ITS4.3/NP1.2

Fingerprinting the AMOC and predicting a collapse 

Peter Ditlevsen

 In a recent paper [2] we predicted a collapse of the AMOC as soon as mid-century at odds with assessments based on climate model scenarios. The prediction was based on the sub polar gyre fingerprint as a proxy for the AMOC as proposed by Ceasar et al. [2]. Several other fingerprints have been proposed, all showing early warning signals of a forthcoming tipping point [3]. Here we present a statistical analysis, optimally extracting the common signal in the different fingerprints in order to further solidify the assessments. 

[1] Ditlevsen, P., Ditlevsen, S. Warning of a forthcoming collapse of the Atlantic meridional overturning circulation. Nat Commun 14, 4254 (2023)

[2] Caesar, L., Rahmstorf, S., Robinson, A. et al. Observed fingerprint of a weakening Atlantic Ocean overturning circulation. Nature 556, 191–196 (2018)

[3] Boers, N. Observation-based early-warning signals for a collapse of the Atlantic Meridional Overturning Circulation. Nat. Clim. Chang. 11, 680–688 (2021)

How to cite: Ditlevsen, P.: Fingerprinting the AMOC and predicting a collapse, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12023, https://doi.org/10.5194/egusphere-egu24-12023, 2024.

EGU24-12187 | ECS | Orals | ITS4.3/NP1.2

Anticipating rate-induced tipping by a deep learning framework 

Yu Huang, Sebastian Bathiany, Peter Ashwin, and Niklas Boers

Rate-induced tipping (R-tipping) occurs when the forcing rate changes too rapidly for the system to track its quasi-equilibrium state, leading to an unexpected collapse. Currently, there is a lack of valid early warning signals (EWS) for R-tipping, particularly in the presence of noise perturbations. To address this deficiency, we employ a deep learning algorithm to extract the high-order structures hidden within time series data before R-tipping occurs. Then the trained neural networks are taken to provide real-time EWS for R-tipping, demonstrating skillful forecasts with a substantially long lead time, surpassing the performance of conventional critical slowing down indicators. Our progress underscores the predictability of R-tipping, offering the potential to improve the ability to deduce the safe operating space for a wider spectrum of complex systems.

How to cite: Huang, Y., Bathiany, S., Ashwin, P., and Boers, N.: Anticipating rate-induced tipping by a deep learning framework, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12187, https://doi.org/10.5194/egusphere-egu24-12187, 2024.

EGU24-12295 | ECS | Orals | ITS4.3/NP1.2

Analysis of Abrupt Changes in CMIP6 Models Using Edge Detection 

Sjoerd Terpstra, Swinda K.J. Falkena, Robbin Bastiaansen, Sebastian Bathiany, Henk A. Dijkstra, and Anna von der Heydt

Many potential tipping elements have been identified in the climate system over the last decade, although some of them are surrounded by large uncertainties. We perform an updated analysis of abrupt changes in current state-of-the-art climate models to re-evaluate the evidence of these shifts—whether they are tipping points or not. We examine all CMIP6 models (59 in total) under the 1pctCO2 scenario using a Canny edge detection method—adapted for spatiotemporal dimensions—to detect abrupt shifts in climate data. We perform this semi-automatic analysis on 83 two-dimensional variables of the ocean, atmosphere, and land. We aggregate the detected shifts that are connected spatially or temporally. This results in connected regions of abrupt shifts and allows us to map areas that are most at risk of these shifts according to CMIP6 models. We report statistics on number of abrupt changes detected, surface area of abrupt changes, and critical global mean temperature at which these abrupt changes occur. This is done for various climate subsystems and potential tipping elements, such as the Arctic sea ice, Antarctic sea ice and the North Atlantic subpolar gyre. We find evidence for abrupt changes in several systems, but not all models show them equally.

How to cite: Terpstra, S., Falkena, S. K. J., Bastiaansen, R., Bathiany, S., Dijkstra, H. A., and von der Heydt, A.: Analysis of Abrupt Changes in CMIP6 Models Using Edge Detection, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12295, https://doi.org/10.5194/egusphere-egu24-12295, 2024.

EGU24-16007 | Posters on site | ITS4.3/NP1.2

Investigating Early Warning Signals in Climate Simulations using Complex Networks 

Laure Moinat, Jérôme Kasparian, and Maura Brunetti

Early Warning Signals (EWS) are indicators that can be used to anticipate tipping points i.e. abrupt changes in dynamical systems. Detecting EWS is a crucial part of climate science, especially in the context of climate change. Several methods are used to identify tipping points using time series of climate state variables (e.g. temperature, precipitation, etc), but few consider spatial correlations [1]. Spatial detection could identify the starting location of a transition process from a state to another and be directly applied to satellite observations. We consider different state variables on a numerical grid as a complex network, where grid points displaying correlation are connected and the temporal evolution of this network is studied. 
We seek for network properties that can be used as EWS when approaching the state transition. 

The network is generated and analysed using the pyUnicorn package [2], and compared to classical statistical methods. The networks are constructed using two methods: Pearson correlation coefficient and mutual information, allowing us to compare a linear and a causal approach. Multiple network indicators such as the degree of correlation, the average path length, and the area weighted connectivity are compared. To test the method robustness, we look at the network dependencies in terms of the time window, the interval over which the forcing is changed, and the effect of reducing the extent of the network (limited, for example, over polar or equatorial regions). These indicators show tipping points at the global scale, as simulated in a coupled-aquaplanet configuration with the MIT general circulation model, using as forcing parameter the atmospheric CO2 content or the input of solar energy [3] . The application of such indicators as EWS is discussed.

 

[1] van der Mheen et al. Geophysical Research Letters 40, 11  (2013)

[2] Donges et al. Chaos 25, 113101 (2015)

[3] Brunetti \& Ragon, Physical Review E 107, 054214 (2023)

How to cite: Moinat, L., Kasparian, J., and Brunetti, M.: Investigating Early Warning Signals in Climate Simulations using Complex Networks, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-16007, https://doi.org/10.5194/egusphere-egu24-16007, 2024.

EGU24-17709 | Posters on site | ITS4.3/NP1.2

Applicability of CSD-based resilience analyses to remotely sensed Vegetation Indices in the Tropics 

Lana Blaschke, Andreas Morr, Sebastian Bathiany, Fabian Telschow, Taylor Smith, and Niklas Boers

Tropical forests are vital for climate change mitigation as carbon sinks. Yet, research suggests that climate change, deforestation and other human influences threaten these systems, potentially pushing them across a tipping point where the tropical vegetation might collapse into a low-treecover state. Signs for this trend are reductions of resilience defined as the system's capability to recover from perturbations. When resilience decreases, according to dynamic system theory, a critical slowing down (CSD) induces changes in statistical measures such as the variance and the autocorrelation. This allows to indirectly examine resilience changes in the absence of observations of strong perturbations. Yet, deriving estimates of the statistical measures indicating resilience changes based on CSD impose several assumptions on the system under observation. For tropical vegetation, it is not obvious that these assumptions are fulfilled.

Additionally, the conditions of tropical rainforests pose difficulties on the observation of the vegetation. Among other factors, cloud cover, aerosols, and the dense vegetation hinder the reliable retrieval of Vegetation Indices (Vis), especially from data gathered in the optical spectrum. Thus, such data might not be suitable for resilience analyses based on CSD, even if the theory is applicable in principle.

We investigate the different assumptions of CSD and test them on a diverse set of remotely sensed VIs. Hereby, we establish a framework that allows to decide whether a specific dataset is appropriate for resilience analyses based on CSD.

How to cite: Blaschke, L., Morr, A., Bathiany, S., Telschow, F., Smith, T., and Boers, N.: Applicability of CSD-based resilience analyses to remotely sensed Vegetation Indices in the Tropics, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17709, https://doi.org/10.5194/egusphere-egu24-17709, 2024.

EGU24-18039 | Orals | ITS4.3/NP1.2

Methodologies for climate tipping points analysis and risk assessments in TIPMIP 

Jonathan F. Donges, Donovan P. Dennis, Sina Loriani, Boris Sakschewski, Nico Wunderling, and Ricarda Winkelmann

The Tipping Point Modelling Intercomparison Project (TIPMIP) is an international initiative that aims to systematically improve our understanding of potential tipping dynamics in different components of the Earth system and to assess the associated uncertainties (www.tipmip.org). By linking and evaluating different models through a systematic framework, TIPMIP aims to fill critical knowledge gaps in Earth system and climate risks by improving their assessment at different levels of anthropogenic forcing and associated long-term commitments and irreversibilities. The Methods and Risk Assessment Working Group of TIPMIP will further develop the methodological foundations of this systematic approach to the study of tipping dynamics in domain-specific and coupled Earth system  numerical models to support future assessment reports and comprehensive risk analyses. In this contribution, we introduce the Methods and Risk Assessment Working Group within TIPMIP, and highlight relevant lines of methodological development to be pursued, including: (i) systematic and automated detection of tipping points and critical transitions in model output and Earth observation data for TIPMIP (e.g, the TOAD framework), (ii) detection of non-linear regime shifts in time series data for TIPMIP beyond amplitude shifts, e.g. transitions between more regular and more erratic variability (e.g. the pyunicorn toolkit), and (iii) probabilistic analysis and emulator approaches of risks for triggering tipping events and cascading tipping dynamics at different levels of anthropogenic forcing (e.g. the pycascades approach).

How to cite: Donges, J. F., Dennis, D. P., Loriani, S., Sakschewski, B., Wunderling, N., and Winkelmann, R.: Methodologies for climate tipping points analysis and risk assessments in TIPMIP, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18039, https://doi.org/10.5194/egusphere-egu24-18039, 2024.

EGU24-18045 | ECS | Orals | ITS4.3/NP1.2

Tipping cascades, future Earth system trajectories and the prospect of a hothouse: insights from the SURFER model 

Victor Couplet, Marina Martínez Montero, and Michel Crucifix

A tipping cascade is a series of tipping events in the Earth system where transitions in one subsystem can trigger further transitions in other subsystems. A concern for the future is that such a cascade could lock the Earth system in a pathway towards a so-called hothouse state. We investigate this possibility with SURFER, a reduced complexity model with a process-based carbon cycle that can reliably predict CO2 concentrations, global mean temperatures, sea-level rise, and many ocean acidification metrics on timescales from decades to millions of years. We have incorporated in the model a network of interacting tipping elements and their feedback on the climate through albedo changes and additional greenhouse gas emissions. This has allowed for a systematic investigation of the effects of a family of realistic emission scenarios on the future trajectories of the Earth system. Our results show that a permanent shift to a hothouse state within the next few centuries is implausible. On longer time scales, however, tipping cascades can lead to enduring additional warming and particularly sea level rise.

How to cite: Couplet, V., Martínez Montero, M., and Crucifix, M.: Tipping cascades, future Earth system trajectories and the prospect of a hothouse: insights from the SURFER model, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18045, https://doi.org/10.5194/egusphere-egu24-18045, 2024.

EGU24-18126 | ECS | Posters on site | ITS4.3/NP1.2

Cryosphere tipping elements decisive for tipping risks and cascading effects in the Earth system 

Jonathan Rosser, Ricarda Winkelmann, and Nico Wunderling

The Earth's climate system is a complex system that includes key components such as the Arctic Summer Sea Ice or the El Niño Southern Oscillation as well as climate tipping elements like the continental-scale ice sheets or the Amazon rainforest. Crossing warming thresholds of these elements can lead to a qualitatively different climate state, endangering the stability of human societies. Particularly, the cryosphere elements are vulnerable at current levels of global warming (1.2°C) while also having long response times and large structural uncertainties. Investigating a network of interacting Earth system components using an established conceptual model, we systematically assess which uncertainties of key Earth system component have the largest impacts on tipping risks. We find that the cryosphere tipping elements (the Greenland and the West Antarctica ice sheets) are most decisive for tipping risks and cascading effects within our model. At a global warming level of 1.5°C, neglecting the large cryosphere tipping elements can reduce the mean number of disintegrated Earth system components by as much as 56%. This is concerning as overshooting 1.5°C of global warming is fast becoming inevitable, while current state-of-the-art IPCC-type global circulation models do not (yet) include dynamic ice sheets. Our results suggest that urgent integrated Earth system model development and Earth observation efforts including the large polar ice sheets are necessary and a precautionary measure of meeting stringent climate targets is crucial to limit tipping risks.

How to cite: Rosser, J., Winkelmann, R., and Wunderling, N.: Cryosphere tipping elements decisive for tipping risks and cascading effects in the Earth system, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18126, https://doi.org/10.5194/egusphere-egu24-18126, 2024.

Urban Green Spaces (UGS) serve as crucial ecological and social assets in urban areas, significantly contributing to the sustainability and well-being of city life. This research delves into the assessment of UGS quality in Delhi, aligning with the 2030 Agenda for Sustainable Development, specifically Sustainable Development Goal (SDG) 11 - Sustainable Cities and Communities. This study emphasizes the importance of UGS as Nature-Based Solutions. Previous studies have explored diverse attributes to evaluate UGS quality, incorporating elements like percentage green, built-up density, and proximity to green spaces. However, these studies often focused on specific aspects associated with any of the three important elements: impervious areas, vegetation, and population. This approach leaves a gap in comprehensively assessing the overall status of UGS, even if one element is taken out of the picture. To address these limitations, this study adopts a holistic approach by considering nine key attributes, including Proportional Population, Impermeable Surface Area, Proportional Impermeable Surface Area, Per-capita Green Index, Buffer Area around UGS, Normalized Difference Vegetation Index, Soil Adjusted Vegetation Index, Green Space Coverage, and Proportional Green, to offer a quantitative measure of UGS quality in Delhi. The Urban Green Spaces Assessment Index (UGSAI), derived from these attributes, provides a comprehensive understanding of UGS in the city, ranging from 0 to 100. The UGSAI categories, divided into five - Very Low (<20), Low (20-35), Moderate (35-50), High (50-65), and Very High (>65), were carefully determined for effective representation, revealing significant variations among wards. A higher UGSAI value indicates better green space conditions, signifying areas that are more accessible, sufficient to cater to the needs of the population of the particular ward, and have higher-quality green spaces. UGSAI values for the wards ranged from the lowest at 6.10 to the highest at 76.32. The study unveils that over 60% of wards fall into the Very Low to Low category, 33% in Moderate, and only 5% in the High to Very High category of UGSAI. Additionally, the correlation of the nine attributes used was tested with UGSAI, and the results indicated strong correlations between UGSAI and Green Coverage, SAVI, and NDVI (r=0.90), along with a strong negative correlation with Impermeable Surface Area (r = -0.87), revealing the attributes that are crucial for improving the UGSAI of a ward. This underscores the need for local-level improvements in management and an increase in UGS, especially in the identified critical areas. This research, grounded in Nature-Based Solutions, provides valuable insights for decision-makers, promoting informed choices that foster resilient and sustainable urban ecosystems. Moreover, the robust methodology and effectiveness of the Urban Green Spaces Assessment Index (UGSAI) presented in this study underscore its potential as a valuable tool applicable beyond Delhi, offering a comprehensive framework for assessing UGS in diverse urban contexts and guiding sustainable development initiatives.

How to cite: PANWAR, M. and Mina, U.: Nature-Based Solutions for Sustainable Cities using Urban Green Spaces Quality Assessment Index (UGSAI), EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-721, https://doi.org/10.5194/egusphere-egu24-721, 2024.

The integration of Nature-Based Solutions (NBS) in urban stormwater management holds transformative potential, promising enhanced adaptive capacities and numerous benefits for community well-being. Acknowledging the localized nature of NBS projects, primarily managed at the community level, this study delves into the prospects of integrating community governance into the planning, implementation, and success of NBS initiatives, especially in the context of urban flooding.

With the overarching goal of identifying strategic leverage points for enhancing community governance structures in urban NBS implementation, the research employs a System Dynamics modeling approach, to investigate the impact of decentralizing decision-making authority to local communities on the scalability and sustainability of NBS in urban stormwater management. The analysis probes dynamic interactions and causal relationships among decentralized decision-making structures and critical variables such as community participation, institutional frameworks, and resource allocation that define community governance. Emphasizing a comprehensive understanding of feedback mechanisms, the study seeks to unravel processes shaping the adaptive capacities of NBS over time, particularly within the intricate context of adapting to the impacts of climate change.

The study strives to provide insights into the mechanisms governing scalable integration, underscoring the vital role of community involvement and participatory governance in Nature-based flood solutions and in doing so, offering a crucial foundation for fostering sustainable and resilient urban development amid the escalating challenges posed by urban flooding and climate change. 

How to cite: Muwafu, S. P. and Manez Costa, M.: Fostering Community Governance of Nature-Based Solutions for Urban Stormwater Management: A System Dynamics Analysis of Power Decentralization, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1226, https://doi.org/10.5194/egusphere-egu24-1226, 2024.

EGU24-1279 | Posters on site | ITS4.4/ERE6.4

Hybrid measures for flood risk management: combining hydrological investigation and public perception survey 

Nejc Bezak, Pavel Raška, Lenka Slavíková, Jiří Louda, and Jiří Jakubínský

Climate change is expected to affect the frequency, magnitude and seasonality of various precipitation-related hazards, including flooding as one of the costliest hazards in Europe. As natural hazards have a significant impact on infrastructure, human lives, and habitats, it is clear that adaptation measures aimed at both prevention and mitigation need to be considered to address climate change. Green (referred to as Nature-based) measures are currently being promoted by the European Union, but in some planning contexts these measures may not be fully capable of coping with predicted future climate hazards, especially in the case of extreme events. Furthermore, the implementation of such measures is often met with resistance from planning departments and decision makers due to institutional dependencies created by the use of grey infrastructure measures in the past. In addition, scepticism about the effectiveness of green measures goes hand in hand with a preference for grey measures. Hybrid measures do have a prevailing green visual look, they can fulfil some ecosystem services, but they require substantial technical equipment for implementation and may present a feasible complementary measure in planning context with limited space or already existing infrastructure. These solutions therefore combine parts of grey and green measures and present an alternative that can reflect the diversity of environmental conditions.

This study evaluated the effectiveness of selected green (e.g. urban trees, rain gardens), grey (e.g. drywells, permeable sidewalks) and hybrid (e.g. green roofs, stormwater tree tranches) measures on flood risk using hydrological modelling with the HEC-HMS software. This study was carried out in order to define the most effective and suitable flood protection measures for the selected case study, the Glinšcica river basin in the municipality of Ljubljana in Slovenia. Based on the hydrological modelling performed, rain gardens were found to be the most effective measure in terms of reducing peak runoff and runoff volume for the Glinšcica river model. Both green roofs and stormwwater cisterns also showed relatively good results compared to the other measures. The hydrological study was combined with a public perception survey in which we investigated the acceptance, feasibility and effectiveness from the perspective of the Slovenian public. Therefore, we were interested in whether there are differences in the perception of the selected green, grey and hybrid flood risk management measures. We were also interested in which of the contextual (e.g., flood exposure and experience) and compositional (e.g., socio-demographics) factors influence public perception of the acceptability, feasibility, and effectiveness of these measures.

Acknowledgment: The research was conducted within the project [Evaluation of hazard-mitigating hybrid infrastructure under climate change scenarios] co-granted by Slovenian Research Agency (J6-4628) and Czech Science Foundation (22-04520L). 

How to cite: Bezak, N., Raška, P., Slavíková, L., Louda, J., and Jakubínský, J.: Hybrid measures for flood risk management: combining hydrological investigation and public perception survey, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1279, https://doi.org/10.5194/egusphere-egu24-1279, 2024.

EGU24-1829 | ECS | Orals | ITS4.4/ERE6.4 | Highlight

Improving water-related ecosystem services to developing country communities through nature-based solutions 

Kalina Fonseca, Mercy Ilbay, Edgar Espitia-Sarmiento, and Lutz Breuer

The political, economic, social, technological, environmental, and legal (PESTEL) dimensions in a local community shape the adoption of specific nature-based solutions (NbS) to improve water ecosystem services. This study provides crucial insights on integrating NbS tailored to smallholder indigenous and peasant communities in four central Ecuadorian provinces, covering 43.2% of the Andean region. These communities are located in the páramo, a highly valued ecosystem for water-related ecosystem services. However, they face high levels of poverty and malnutrition. Combining a participatory multi-stakeholder approach with a literature review, we gathered insights into PESTEL dimensions impacting páramo ecosystem services. A bibliometric and decision tree analysis was then employed to reveal NbS aligned with PESTEL dimensions in these communities. As a result, limited financial support, urban-centric environmental investment, and insufficient acknowledgment of water-related ecosystem services significantly impact the health of páramo ecosystems from economic and political dimensions, respectively. In the environmental dimension, the overexploitation of this ecosystem, driven by high soil carbon storage combined with superior water quality and the high vulnerability to climate change, contributes to the decline of the páramo remnants. Social, legal, and technological dimensions involve community dissatisfaction and resistance to conservation, lack of sustainable land and water management, and the mismatch between technology, the economy, and data availability. These impacts on páramo ecosystem services occur directly through water purification, regulating soil formation, and maintaining populations and habitats. Indirectly, they affect the provision of water for drinking and non-drinking purposes, fishing and aquaculture, recreation, and spiritual and symbolic appreciation. To enhance water-related ecosystem services, we propose the establishment of artificial floating islands such as NbS. These islands are seen as an innovative restoration method with multiple benefits, such as the need for only limited financial support, the engagement of local communities, the lack of land requirements for implementation, and the use of indigenous community knowledge of appropriate plant species for water treatment, which can even generate additional income. Passive restoration complements this by removing disturbances in the páramo, allowing natural regeneration in basins by state-led land purchase initiatives to ensure the protection by Ecuadorian conservation laws. Our study offers decision-makers a practical approach to secure ecosystem services for vulnerable populations, critically assessing alternatives based on the dimensions and needs of these communities.

How to cite: Fonseca, K., Ilbay, M., Espitia-Sarmiento, E., and Breuer, L.: Improving water-related ecosystem services to developing country communities through nature-based solutions, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1829, https://doi.org/10.5194/egusphere-egu24-1829, 2024.

The integration of Eco-DRR practices in geopark management is a promising method to strengthen community resilience. In the context of the Caota dunes geopark, the level of engagement and support from local residents could significantly contribute to the effectiveness of Eco-DRR approaches through fostering awareness, strengthening the local economy, enhancing ecosystem services, promoting sustainable resource use and facilitating the adoption of resilient livelihood practices. However, it is essential to first understand the needs of the community before implementing these strategies. This study aimed to identify critical factors by exploring community needs and challenges through interviews and focus groups to identify pathways for an inclusive and community-centered approach to the geopark's implementation and conserve the essential functions of the dune landscape.  

Capacity building has contributed to an increased sense of responsibility and commitment from the local community. Nevertheless, insights from local community members highlighted the urgent need for economic benefits and future stability for the geopark through more defined development plans and increased community involvement. This study highlights the importance of incorporating community insights early in the development process  and promote bottom-up approaches, providing greater opportunities for active participation in geopark planning, management, and monitoring.

How to cite: Lin, T.-Y. and van Onselen, V.: Local insights into community participation and Eco-DRR strategies for sustainable geopark management at Caota Sand Dunes Geopark, Taiwan, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-2990, https://doi.org/10.5194/egusphere-egu24-2990, 2024.

EGU24-3038 | Posters on site | ITS4.4/ERE6.4 | Highlight

Alterations of urban greenspace and heat stress risk during Hanoi's Master Plan 2030 implementation 

Yuei-An Liou, Kim-Anh Nguyen, and Duy-Phien Tran

Hanoi City has experienced a remarkable transformation due to implementing Hanoi's Master Plan 2030, which brought forth numerous challenges, notably in preserving urban green space (UGS). The objectives of this work are to (1) explore the changes in UGS distribution, (2) identify areas prone to heat stress by examining abnormal land surface temperature (LST) distributions in conjunction with population vulnerability, and (3) suggest solutions through an advanced UGS management platform. To investigate the UGS changes, we utilized Sentinel-2 satellite images, while the assessment of heat stress risk involved extracting LST data from the thermal infrared band of Landsat 8. Our research was concentrated on the inner region of Hanoi City, tracking UGS alterations from October 2016 to October 2018. The study's evaluation involved utilizing Google Earth images and conducting on-site research.

The results demonstrated a significant decline in woodland and shrubland, decreasing by 1.3% and 4.4%, respectively, while grass cover experienced a growth of 2.4%. Our land cover classification exhibited high accuracy, reaching 96% in 2018 and 88% in 2016. Furthermore, this work unveiled a heightened risk primarily focused in the central inner-city zones, marked by densely populous residential regions and extensive built-up environments. Given that air temperature (Ta) significantly affects human health compared to LST, our forthcoming research will incorporate a spatially continuous Ta dataset to delve deeper into studying heat stress risks. This Ta dataset will be generated through our advanced Ta estimation framework employing Machine Learning algorithms, which have demonstrated exceptional performance. Identifying the heat stress risk patterns is essential, as this draws the attention of city planners, governing bodies, and healthcare institutions.

How to cite: Liou, Y.-A., Nguyen, K.-A., and Tran, D.-P.: Alterations of urban greenspace and heat stress risk during Hanoi's Master Plan 2030 implementation, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-3038, https://doi.org/10.5194/egusphere-egu24-3038, 2024.

EGU24-3413 | ECS | Posters on site | ITS4.4/ERE6.4

Sustainable Management of Golf Courses in Hanoi City: A Remote Sensing Approach for Monitoring Land Distribution and Dynamics 

Kim-Anh Nguyen, Yuei - An Liou, Minh Khanh Luong, and Nguyen Thanh Hoan

In recent years, the role of golf courses in contributing to the economic growth of various Vietnamese cities, including Hanoi, has gained prominence. Nonetheless, debates persist regarding the environmental and societal impacts of golf course development. While golf courses enhance city aesthetics, attract affluent tourists, and align with zero-carbon initiatives, concerns arise over land use, pesticide application, water resources, farmer displacement, and potential environmental degradation.

This study employs remote sensing data to monitor the spatial and temporal distribution of golf courses in the Hanoi City Metropolitan area. Utilizing multi-satellite data and Geographic Information Systems (GIS), the research aims to detect and analyze the spatial dynamics of golf courses, investigating their evolution and impact on the surrounding regions. The outcomes include a remote sensing-based database of golf courses, an examination of dynamic changes in golf course lands over decades, and an assessment of land conversion to golf courses and its consequences. This research is crucial for sustainable golf course management and environmental conservation, providing insights for informed decision-making to achieve environmental sustainability in golf course development.

How to cite: Nguyen, K.-A., Liou, Y.-A., Luong, M. K., and Hoan, N. T.: Sustainable Management of Golf Courses in Hanoi City: A Remote Sensing Approach for Monitoring Land Distribution and Dynamics, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-3413, https://doi.org/10.5194/egusphere-egu24-3413, 2024.

EGU24-5654 | ECS | Orals | ITS4.4/ERE6.4

Exploring community-based adaptive approaches in agriculture and water management to address salinity impacts in coastal Bangladesh 

Khusnur Jahan Shapna, Jianfeng Li, Saifullah Khandker, and Md Lokman Hossain

The coastal region of Bangladesh is greatly impacted by high soil and water salinity levels, worsened by tropical cyclones and rising sea levels. Understanding the extent of salinity and its challenges is vital for sustainable agriculture and safe drinking water. This study employed both quantitative methods, focusing on soil and water parameters, as well as qualitative approaches such as focus group discussions (FGDs) and key informant interviews (KIIs). The objectives of this research were to assess soil and water salinity, and soil nutrient content, and to investigate adaptive practices and challenges in agriculture and drinking water management in six sub-districts in the southwestern coastal region of Bangladesh. Qualitative information obtained from 18 FGDs and 16 KIIs was assessed by thematic and content analysis to evaluate the community-based adaptive techniques and challenges in sustainable agriculture and water management in the salinization-affected region. Using a one-way ANOVA and post hoc Tukey tests, the soil and water parameters of the collected 165 soil samples (croplands), and 132 water samples (ponds and canals) were analyzed to assess the soil nutrients (nitrogen, phosphorus, and potassium) and soil and water salinity in six sub-districts.

The soil nitrogen, phosphorus, and potassium contents exhibited significant variations, whereas there was no notable difference in soil salinity content across the studied sub-districts. Upon examination of pond water salinity levels, significant variations were observed among the six sub-districts. The salinity levels (ds cm-1) in pond water ranged between 13 and 14 ds cm-1 in these sub-districts. Among them, Shyamnagar recorded the highest level of pond water salinity (13.99), followed by Assasuni (13.96), Dacope (13.91), Koyra (13.58), Morrelganj (13.33), and Mongla (13.19) sub-districts. Pairwise comparisons of salinity levels in pond and canal water show that the salinity level in most water samples varied significantly among sub-districts.

Respondents in FGDs and KIIs consistently identified salinity as a major challenge in agriculture and drinking water in the studied sub-districts. Additionally, climate-induced stresses, such as untimely precipitation, and pest outbreaks during droughts were recognized as significant issues impacting sustainable agriculture. In terms of adaptive practices, this research emphasizes the feasibility of rainwater harvesting as an effective technique for managing drinking water and mitigating water and soil salinity. This approach offers a viable solution for addressing water scarcity and salinity issues in the coastal region. One notable finding in agriculture from the research is the positive impact of organic fertilizer (vermicompost) in reducing soil salinity levels. This finding highlights the potential of utilizing organic fertilizer as a nature-based solution to mitigate salinity in the affected regions of Bangladesh and globally. By adopting such adaptive strategies, the region can promote resilient agricultural systems and ensure sustainable water management.

In summary, the study highlights the prevalence of soil and water salinity in the coastal region of Bangladesh and the associated challenges it poses for agriculture and drinking water management. The research emphasizes the significance of adaptive practices, specifically rainwater harvesting and organic fertilizer, as a practical solution to address water scarcity and salinity issues in the region.

How to cite: Shapna, K. J., Li, J., Khandker, S., and Hossain, M. L.: Exploring community-based adaptive approaches in agriculture and water management to address salinity impacts in coastal Bangladesh, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5654, https://doi.org/10.5194/egusphere-egu24-5654, 2024.

EGU24-6142 | ECS | Orals | ITS4.4/ERE6.4 | Highlight

A Community-Led Approach to Environmental Monitoring and Adaptive Capacity Building in the Coastal Bend Region of Texas, USA 

Michelle Hummel, Oswald Jenewein, Karabi Bezboruah, Yonghe Liu, Kathryn Masten, Byeongseong Choi, and Amruta Sakalker

The Coastal Bend Region (CBR) of Texas is vulnerable to acute and chronic environmental stressors stemming from natural and industrial sources, including flooding and erosion from high tides, storm surge events, and ship traffic, as well as higher levels of air and water pollution due to expansion of nearby industrial operations. Communities in the CBR are diverse, spanning a range of sizes, demographics, and geographies, and have varying levels of exposure, vulnerability, and capacity to respond and adapt to the cumulative threats posed by climate change and industrial expansion. Currently, residents of the CBR are engaging in a variety of community organizing and advocacy efforts, including through existing and newly established community-based organizations. These organizations span a range of experience levels and capacities in interfacing with local decision-makers and engaging in collective action to address environmental threats, but all have expressed a need for more comprehensive data about environmental and industrial conditions to advocate for and make informed decisions about risk reduction strategies that mitigate negative impacts on air, water, and land resources.

Here, we discuss an ongoing research program to examine how smart and connected technologies can be integrated into regional communication and advocacy networks to increase awareness of natural and anthropogenic hazards and build community adaptive capacity equitably among the diverse residents in the CBR. We first present the results of a year-long planning study conducted in partnership with one CBR community to (1) evaluate the structure and function of the local communication, information-sharing, and policy-making networks and (2) co-develop a real-time, wireless sensor network and community dashboard to monitor environmental conditions. This study led to the formation of interdisciplinary, academic-civic partnerships that centered community needs in the design and implementation of the research objectives. We then discuss challenges and opportunities in expanding this work to the regional scale to engage a broader diversity of CBR residents using a bottom-up, participatory design approach, with the goal of supporting frontline communities as they advocate for more sustainable and equitable policies for hazard management.

How to cite: Hummel, M., Jenewein, O., Bezboruah, K., Liu, Y., Masten, K., Choi, B., and Sakalker, A.: A Community-Led Approach to Environmental Monitoring and Adaptive Capacity Building in the Coastal Bend Region of Texas, USA, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-6142, https://doi.org/10.5194/egusphere-egu24-6142, 2024.

EGU24-7722 | Posters on site | ITS4.4/ERE6.4

Participatory multi-criteria decision making for optimal siting of dams 

Fabio Castelli, Matteo Masi, and Chiara Arrighi

The impacts of increasing water scarcity as a consequence of climate change determine an urgent demand
for enhanced management of water resources. The construction of new artificial reservoirs to expand
water storage capacity represents a pivotal strategy to address these pressing concerns and fulfil the
community&#39;s needs for drinking water, irrigation, energy generation, and flood risk mitigation. The
selection of sites for new reservoirs can be merely based on topographic and hydrologic assessments.
However, the identification of optimal locations requires a comprehensive evaluation that considers a
multitude of often-competing factors that encompass bio-physical, socio-economic, regulatory, and
environmental aspects. The involvement of communities and citizens in the initial stages of the decision-
making process is crucial. This study introduces a methodology based on multi-criteria decision making
(MCDM), to identify optimal reservoir locations, simultaneously addressing all the aforementioned aspects
through community engagement. This methodology employs an automated algorithm to analyse a large
pool of potential sites, through a Digital Elevation Model (DEM) integrated with hydrologic simulations. For
each site the algorithm optimizes the location and orientation of the dam and calculate the geometrical
characteristics, such as the dam length, dam volume and the water storage volume. In a subsequent step, a
MCDM analysis is conducted to rank the sites based on quantitative selection criteria established through a
comprehensive territorial analysis and hydrological modelling. These criteria include geometric and
morphological aspects (e.g., reservoir volume), hydrological indicators (e.g., water balance, flood
mitigation), anthropization (e.g., population density, infrastructures), landscape, archaeological heritage,
ecology, environmental components, and potential natural hazards. To foster community engagement, we
developed a web-based survey platform that enables the collection of diverse perspectives from various
stakeholders, communities and citizens, allowing them to express their opinions on the relative importance
of each individual criterion. The application of this methodology is demonstrated through a case study in
the Arno river basin, Italy, showcasing its effectiveness in identifying the most suitable reservoir locations
while targeting the highest environmental preservation and community well-being.

How to cite: Castelli, F., Masi, M., and Arrighi, C.: Participatory multi-criteria decision making for optimal siting of dams, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-7722, https://doi.org/10.5194/egusphere-egu24-7722, 2024.

EGU24-12726 | ECS | Posters on site | ITS4.4/ERE6.4 | Highlight

Distributed databases to improve data sovereignty in citizen science 

Julien Malard-Adam, ஷீஜா (Sheeja) குமார் (Kumar), Wietske Medema, நல்லுசாமி (Nallusamy) ஆனந்தராஜா (Anandaraja), Joel Harms, and Johanna Dipple

Citizen science is important for community-led science. However, the knowledge and costs required to configure and manage servers for data management in such community-led projects are major barriers to the adoption of citizen science-based approaches at a larger scale. At the same time, the centralisation of communities’ data onto project servers (whether rented in « the could » or on-premise) also poses questions regarding data sovereignty true community ownership of citizen science projects. (Who owns the data? Who has the power to give or revoke access to it? How will data be accessible once the principal investigators and funding are gone?)

Distributed databases, where data is stored directly on users’ devices and shared in a peer-to-peer network, can address some of these issues by bypassing the need to rely on a centralised server for user authentication and data storage and transmission. While this approach offer solutions to some long-standing challenges of centralised approaches to data collection, distributed databases also bring their own limitations. This presentation will discuss three major questions and paradigm shifts related to the adoption of distributed databases for citizen science, namely authorisation, discovery and accessibility. Approaches for addressing these in the context of community-led participatory projects will be discussed, and examples of using Constellation distributed database software for case studies in citizen science and data sharing will be provided.

How to cite: Malard-Adam, J., குமார் (Kumar), ஷ. (., Medema, W., ஆனந்தராஜா (Anandaraja), ந. (., Harms, J., and Dipple, J.: Distributed databases to improve data sovereignty in citizen science, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12726, https://doi.org/10.5194/egusphere-egu24-12726, 2024.

EGU24-13529 | ECS | Posters on site | ITS4.4/ERE6.4

A serious game approach to promote non-traditional solutions and ecosystem services for water adaptive management 

Gabriel Silva, Marcos Benso, Pedro Silva, and Eduardo Mendiondo

Water problems related to floods, droughts, water quality, and supply demand require methods focused on harnessing the inherent capabilities of ecosystems to cope with water related problems and the ecosystems natural capability to address water-related challenges by leveraging this advantage on maintaining ecological balance. Although it demands more investments than traditional approaches, Nature-based Solutions (NbS) has a significant impact on promoting sustainability and its success relies on careful planning, collaboration with local communities, and adaptive management strategies. This study aims to develop an engaging and educational serious game to demonstrate how NbS can serve as a strategic approach for fostering the development of smart and resilient cities in response to the challenges posed by climate change, water-related risks, and disasters. By engaging individuals in a virtual environment, serious games can effectively communicate the consequences of various water-related decisions and encourage sustainable practices among the public. Thus, designed for a diverse audience, including students, urban planners, decision-makers, and the general public, the game utilizes realistic mathematical models to simulate climate change scenarios and extreme weather events. Yet, players can make challenging decisions in implementing NbS, like creating urban green zones, watershed management, and aquatic ecosystem restoration. The game can also incorporate real-world data for specific geographic areas, emphasizing the effectiveness of NbS in regional contexts. Finally, the game incorporates water modeling techniques, leveraging the robust capabilities of HydroPol2D and/or HyMAP models. These models enable the simulation of water runoff in two dimensions by utilizing Digital Elevation Models (DEMs), land use and rainfall data. Furthermore, collaboration with local communities and adaptive management strategies are crucial components, showcasing the importance of stakeholder engagement.

How to cite: Silva, G., Benso, M., Silva, P., and Mendiondo, E.: A serious game approach to promote non-traditional solutions and ecosystem services for water adaptive management, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-13529, https://doi.org/10.5194/egusphere-egu24-13529, 2024.

EGU24-18315 | Orals | ITS4.4/ERE6.4

Advancing Local Disaster Resilience Strategies: A Transdisciplinary Approach 

Khamarrul Azahari Razak, Liyana Hayatun Syamila Ramlee, Hannani Yusra Sapiee, Yuet Mei Siow, Rahsidi Sabri Muda, Rabieahtul Abu Bakar, Zakaria Mohamed, Zamri Ramli, and Che Siti Noor Koh Poh Lee

This study addresses the urgency to re-strategize our local action to prevent future climatic risk as a result of extreme weather events, urbanization, anthropogenic activities in a changing climate. While progress has been made in implementing the Sendai Framework for Disaster Risk Reduction since its adoption in 2015, no country is on track to achieve the outcome and goal by 2030. Malaysia is not an exception. A holistic approach to multi-scale disaster risk reduction and climate resilience is critically needed to examine new prospective agenda for accelerated action. This study provides a new insight into galvanizing technological advancement, multi-tier partnership and community-led approach to entail more coordinated and programmatic action towards translating resilience thinking approach into risk-informed decision-making. Equipping cities and communities with knowledge and capabilities to manage complexity of risks is a step forward to re-build a resilient society and rejuvenate resilience thinking. UNDRR’s global reports indicated that by providing a 24-hour early warning can reduce the resulting damage by 30%. Therefore, investing in the development of people-centered, end-to-end, multi-hazard early warning system (EWS) is highly regarded to support the 2027’s Early Warning for All agenda. This study highlights smart partnership into co-designing, co-developing, and co-implementing an impact-based EWS for geological risk in Jerai Geopark (Yan, Kedah), towards rejuvenating local resilience strategy through the development of DRR Yan Model and Resilience Living Lab in a national geological heritage area dominated by tourism industry. The key for successful community-led disaster risk reduction (CLDRR) lies in maintaining interest in resilience culture and motivation for local agenda at the grassroot level. We also demonstrate community-led DRR program with a unique localization strategy that addresses dam-related disaster risk. This study acknowledged that 40% of large dams in Malaysia are aging, necessitating new approaches to dam safety. Moreover, regional benchmarking for technological-based sociotechnical systems enabled by collaborative foresight and disaster informatics are a way forward to assess future emerging hazards, systemic risk, and compounding disaster. With good risk governance, evidence-based risk investment, and risk-informed decision making, as supported by all-of-society approach particularly in advancing a new partnership model for the public-private-academia-civil society, this study reports current demands for de-risk strategies that shall be systematically incorporated into decision-making, governance, and investments. The development of new strategies, actions, and initiatives are mutually explored towards inculcating targeted investments related to systemic risk reduction, and mainstreaming urban development planning of unattended risks should be made based on science, coupled by the Local, Traditional and Indigenous Knowledge (LTIK) approach. The de-risk investment efforts often jeopardize by a series of sudden, large-scale geological-induced disaster, resulting into the prolonged economic impacts continues to escalate and underscores the multi-scale investment for DRR agenda at a local level. By adopting a transdisciplinary approach to DRR and forward-looking risk-informed approach, this vulnerable region can further develop its resilience capacity to tackle complex challenges of climate risks. As a conclusion, risk-informed pathway in development planning, and a paradigm shift, can contribute towards promoting equitable and sustainable resilience in geologically risk sensitive regions.

How to cite: Razak, K. A., Ramlee, L. H. S., Sapiee, H. Y., Siow, Y. M., Muda, R. S., Abu Bakar, R., Mohamed, Z., Ramli, Z., and Koh Poh Lee, C. S. N.: Advancing Local Disaster Resilience Strategies: A Transdisciplinary Approach, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18315, https://doi.org/10.5194/egusphere-egu24-18315, 2024.

EGU24-18385 | ECS | Posters on site | ITS4.4/ERE6.4

Are Brazilian schools safe? Incorporating school resilience as response to water related disasters and adaptive management 

Marcos Roberto Benso, Jamil Alexandre Ayach Anache, Denise Taffarello, Suzana Maria Gico Lima Montenegro, Greicelene Jesus da Silva, and Eduardo Mário Mendiondo

In the event of disasters such as droughts, floods, and landslides, social sectors including housing, education, and social protection are the most affected. Here, we present a project that incorporates the school system as a vulnerable sector to water insecurity and a tool to promote resilience. In this sense, we adopt the concept of water security defined by the United Nations (UN), including the availability of water to support socioeconomic development, the preservation of aquatic ecosystems, and the ability to withstand a reasonable amount of risk from floods and droughts. Planning for the supply and use of water at the national level should be based on the four elements of water security. This project is contextualized at the Brazilian National Observatory for Adaptive Water Security and Management (ONSEAdapta) (https://onseadapta.org/en/elementor-642/). Given the importance of schools, the objective of this project is to propose a conceptual framework to incorporate school resilience as a response to water-related disasters and adaptive management. The proposed methodology is divided into two approaches. First, a top-down approach is proposed to collect data from the annual school census of Brazilian schools that is provided at school level by the Anisio Teixeira National Institute of Educational Research and Studies (INEP) and water security data from the National Water and Sanitation Agency of Brazil (ANA). Second, a bottom-up approach is proposed to survey educators and members of the school community to depict how water security is incorporated into schools, what initiatives promote the participation of school and society, and the main implications for reducing disaster risk, building capacity, and increasing disaster resilience. In Brazil, according to the 2022 school census, there were 184,331 schools that accommodated 22% of the Brazilian population (~47 million students). To propose the concept of school resilience as a dimension of water security, we located and diagnosed the number of schools that are in water insecurity by combining the Brazilian water security index (ISH) with the georeferenced map of Brazilian schools. Using the ISH that combines human, ecosystemic, economic, and resilience dimensions, we identified that 11.93, 14.40, 16.04 million students are under minimum to low, medium, and high to maximum water security, respectively. This analysis unveils that almost 28% of Brazilian students are below a low level of water security. These students come from preschool, elementary and secondary education in rural and urban areas. We conceptualize the assessment of school resilience using a comprehensive framework that considers infrastructure, level of water insecurity, impacts on school, emergency preparedness, and community involvement. To foster community involvement and scientific contributions, the next step is the creation of an online platform to promote citizen science, collect data, and engage with educators. By fostering participatory citizenship education in schools, this project aims to create a resilient and well-informed community capable of mitigating the impact of disasters and contributing to general water security and adaptive management.

How to cite: Benso, M. R., Anache, J. A. A., Taffarello, D., Montenegro, S. M. G. L., Silva, G. J. D., and Mendiondo, E. M.: Are Brazilian schools safe? Incorporating school resilience as response to water related disasters and adaptive management, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18385, https://doi.org/10.5194/egusphere-egu24-18385, 2024.

EGU24-19795 | Orals | ITS4.4/ERE6.4 | Highlight

Social and environmental benefits of regenerative design 

Stanislava Boskovic, Jeni Giambona, Ana Mijic, and Doug Baldock

Cities are major contributors to climate change through greenhouse gas emissions, notwithstanding other sources of pollution, conditioning planet health and citizens wellbeing. The increase in urban growth and urbanization results in an expansion of urban hazards - including water scarcity, air pollution and other environmental issues. Therefore, to respond to the need for new urban development, it is necessary to introduce a new systems-based approach able not only to maintain the existing environmental indicators, but to guarantee their improvement. 

To address this complexity, in this work we explore Regenerative Design (RD) definition, scale and proprieties to rethink the ecological challenges we face in a holistic and systematic manner.  Regenerative Design approach, in this study, aspires to demonstrate that order to achieve net-positive outcomes and address social and ecological issues, it is necessary to move beyond the only intention of environmental harm mitigation. The regenerative design process leads to design processes that utilize the insights and relationships of ecological systems of the place as the basis for projects in which human actions positively contribute to the self-healing properties of nature. Therefore, an integration of nature-inspired solutions throughout the design process is required.

This study evidence that a transformative shift towards regenerative design requires not only a change in way of thinking and practice, but also in worldviews and values. It starts with the awareness the way we approach analysis of a design process might not be regenerative. Therefore, there is need for systems change to tackle root causes of degeneration, where the context and the place-based design decisions are of crucial importance.

How to cite: Boskovic, S., Giambona, J., Mijic, A., and Baldock, D.: Social and environmental benefits of regenerative design, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-19795, https://doi.org/10.5194/egusphere-egu24-19795, 2024.

EGU24-19975 | ECS | Posters on site | ITS4.4/ERE6.4

Knowledge Co-Creation for Enhanced Ecosystem Services Management on Islands 

Ina Maren Sieber and Cathleen Cybele

Against the backdrop of aggravating environmental challenges, effective ecosystem services (ES) management is crucial for biodiversity conservation, livelihood support, and economic growth. Including citizens, stakeholders and societies in research is gaining popularity as a suitable tool for both informed decision-making and enhanced community resilience. Our study explores the application of knowledge co-creation methodologies to improve ES management. The French Outermost Region of La Réunion, situated in the Indian Ocean, provides an opportune setting for addressing these challenges. Community members, stakeholders, and experts were engaged in a structured, yet flexible knowledge co-creation process to jointly define the potential for cultural ecosystem services (CES) including landscape aesthetics, recreation and eco-tourism.

The collaborative process empowered the community to identify and prioritize ES through initial interviews and focus groups. This informed a participatory GIS mapping exercise, facilitating community involvement in visually representing ecosystem services and their spatial relationships. Based on this work, additional methods were employed to provide the community with information. Expert elicitation validated the community-generated knowledge, incorporating insights from local and regional professionals. In addition, geotagged photos were analysed to assess actual use of cultural ES. This approach ensured a comprehensive understanding of ecosystem interactions, including ecosystem features, capacity of ecosystems to supply ecosystem services and informed options for sustainable development.

Our results contain qualitative and quantitative assessments of CES within the study area: multiple ES maps that show the distribution of ES on spatial scale, coinciding strongly with landscape features. A high appreciation of the coastline with its scenic cliffs and rocky beaches is visible. The inland provides large potential for recreational activities and tourism, including hiking, mountain biking, horse-riding, bird watching and botany. Geotagged photo analysis added the magnitude of visitors, showing popular trails and locations.

 Yet, the application of co-creation for research proves challenging. The joint definition of research focus and the fuzziness of the approach diverge from current modes of environmental (social) sciences. Further, stakeholder engagement requires time and dedication. We find that co-creation provides aspects of community learning and empowerment. This research contributes to the discourse on knowledge co-creation as a valuable tool for addressing environmental challenges and promoting sustainable development. The insights provide a foundation for applying similar methodologies in diverse socio-ecological contexts, opening up new possibilities for community engagement in ecosystem services research.

How to cite: Sieber, I. M. and Cybele, C.: Knowledge Co-Creation for Enhanced Ecosystem Services Management on Islands, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-19975, https://doi.org/10.5194/egusphere-egu24-19975, 2024.

EGU24-20423 | ECS | Posters on site | ITS4.4/ERE6.4

Evaluating the Environmental Justice of Tree Canopy Connectivity in Pheonix, Arizona, USA 

Zane Havens and Stephen Macko

Phoenix, Arizona, a metropolis located in the Sonoran Desert in southwester USA, is expected to see major disturbances resulting from global warming, including excessive heat events1 and drought2. Urban Green Infrastructure (UGI) has the potential to help mitigate excessive heat3 yet, as water becomes increasingly scarce, efficiency in both the location and configuration of UGI is critical to maximize its positive benefits.  Well-connected UGI provides more ecosystem services and can better mitigate extreme temperatures in urban areas than poorly connected UGI 4. However, owing in part to the current and anticipated scarcity of water, the distribution and connectivity of this lifesaving resource has the potential to be unjust regarding economically vulnerable communities. The purpose of this study is to determine if there are relationships between the coverage/connectivity of Phoenix UGI and sociodemographic variables associated with vulnerability.  Using a 2010 1m landcover classification raster and the landscapemetrics R package, landscape metrics were calculated for sample plots withing areas zoned for single-family residential homes.  Vulnerability statistics were then apportioned for each plot using 2010 ASTER/CDC Social Vulnerability Index data. These variables were then examined to determine relationships between connectivity and vulnerability using Principal Component Analysis.   

 

1.  Stone, B. et al. Climate change and infrastructure risk: Indoor heat exposure during a concurrent heat wave and blackout event in Phoenix, Arizona.Urban Climate 36, 100787 (2021).

2.  Bolin, B., Seetharam, M. & Pompeii, B. Water resources, climate change, and urban vulnerability: a case study of Phoenix, Arizona. Local Environment 15, 261–279 (2010).

3.  Marando, F. et al. Urban heat island mitigation by green infrastructure in European Functional Urban Areas. Sustainable Cities and Society 77, 103564 (2022).

4.  Debbage, N. & Shepherd, J. M. The urban heat island effect and city contiguity. Computers, Environment and Urban Systems 54, 181–194 (2015).

How to cite: Havens, Z. and Macko, S.: Evaluating the Environmental Justice of Tree Canopy Connectivity in Pheonix, Arizona, USA, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-20423, https://doi.org/10.5194/egusphere-egu24-20423, 2024.

EGU24-20438 | Orals | ITS4.4/ERE6.4

Monitoring urban agriculture: a nature-based solution to transform city food systems 

Sebastian Eiter, Wendy Fjellstad, and Loes van Schaik

Urban agriculture is a nature-based solution to increase the economic, social and environmental sustainability of cities and city food systems. However, sustainability is difficult to measure, and there is therefore discussion about whether urban agriculture really contributes positively to sustainability. Monitoring data could provide evidence of the impacts of urban agriculture and help inform decision makers about whether and where to prioritise different forms of urban agriculture above competing interests.

Using case examples from five European cities, we identified the challenges involved in monitoring urban agriculture, from selecting indicators and gathering data, to using the results. We found large differences in approach in terms of what topics to monitor and who was responsible, who gathered the data and when, what data was recorded and how they were stored, and how findings were disseminated or published. Based on these experiences, we recommend stronger involvement of existing interest groups and educational institutions in monitoring urban agriculture, and promotion of convenient tools for data collection by citizen science and for long-term data storage.

How to cite: Eiter, S., Fjellstad, W., and van Schaik, L.: Monitoring urban agriculture: a nature-based solution to transform city food systems, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-20438, https://doi.org/10.5194/egusphere-egu24-20438, 2024.

This study compares and analyzes the impact of land use changes on ecosystem services and ecological security in different ecological backgrounds in Stockholm and Panjin. Using Morphological Spatial Pattern Analysis (MSPA), Analytic Hierarchy Process, and Circuit Theory, the dynamic changes in ecosystem services and ecological security patterns are assessed in these two regions. Based on the characteristics and land use changes from 2000 to 2020, four scenarios for 2050 are simulated using the PLUS model: Business-as-Usual (BAU), Priority Urban Development (PUD), Priority Ecological Protection (PEP), and Balanced Urban-Ecological Development.The results show that in Panjin, the growth rate of construction land was 21.49% from 2000 to 2020, and when this probability was applied to the transfer probability of Priority Urban Development in the Stockholm region, there was a significant change in the ecological security pattern. In contrast, in Stockholm County, the correlation between the change rates of all land use types and other indicators was weak, suggesting limited influence of these factors on land use changes. However, in Panjin, there was a strong positive correlation between the change rates of construction land, unused land, and population and GDP. This implies that in regions with lower economic levels, there is a higher dependence on ecosystem services and ecological security patterns compared to higher economic regions.Observations reveal an increase in forest and grassland area in Panjin City. However, the distribution of high-value ecological source areas is not concentrated enough and exhibits a high rate of change. In contrast, the Stockholm region has maintained a stable pattern of ecological source areas over the past 20 years. The Stockholm region has developed a relatively reasonable ecological security pattern, which is the result of continuous ecological protection and planning efforts over many years.

How to cite: wu, H.: The impact of urbanization on ecosystem services and ecological security patterns, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-20661, https://doi.org/10.5194/egusphere-egu24-20661, 2024.

Between March 2016 and September 2020, the Canadian government supported a Southern African Nutritional Initiative (SANI) project in Malawi, Mozambique and Zambia to enable women of reproductive age and children under the age of 5 to  produce, access, store, preserve and process high nutrient food. We report on Zambia’s agricultural component of the project summarizing the key food production techniques used to encourage sustainable agricultural production through the use of smart agricultural practices. These  practices have the potential to allow small farm holders to adapt to climate change and offer opportunities to reduce and remove Green House Gases from these systems in order to contribute to the  Nationally Determined GHG Contributions under the Paris Agreement and meet national food security and development goals. As part of the study, in person training sessions were conducted with participants on smart agricultural practices such as the promotion of local technologies around seed bed preparation of home gardens and orchards, manuring, and the use of local products to control insects, pests and  diseases instead of chemicals. Apart from receiving training in sustainable practices, participants were also trained in food preservation and value addition to harvested produce and grains in order to increase the shelf life and usability of various food types as a way of promoting food security. Anecdotal evidence through follow up field evaluations and food preparation demonstration sessions showed that project participants were adapting  and moving towards achieving a resilient status. These and scale up issues will be discussed in this contribution.

How to cite: chipanshi, A. and chewe, J.: Capturing the synergies among mitigation, adaptation and food security through smart agriculture practices in Muchinga Province of Zambia, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-20852, https://doi.org/10.5194/egusphere-egu24-20852, 2024.

Professional societies can play important roles in society's climate change response. Sitting at the junction of scientific innovation, policy advocacy, and community engagement, professional societies are uniquely positioned to bridge the gap between principal investigators (PIs), governments, and local communities. As non-governmental actors, these groups streamline the transfer of research into actionable strategies by facilitating knowledge exchange, standardizing methodologies, and fostering multi-stakeholder collaborations.

This presentation will outline how professional societies can amplify the reach and relevance of scientific endeavors and help ensure that community priorities are at the forefront of environmental policies and practices. It will argue for their enhanced involvement in driving interdisciplinary approaches, advocating for inclusive and informed policy-making, and empowering communities through accessible, science-based solutions. The session aims to inspire a cohesive dialogue among community leaders, scientists, and policymakers, highlighting the necessity of a united front to effectively address the pressing environmental challenges of our times. Lastly, the talk will highlight the joint Optica-AGU Global Environmental Measurement and Monitoring Initiative.

How to cite: Lang, D. and Shimamoto, M.: Amplifying Impact: The Role of Professional Societies in Community-Led Environmental Science, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-22285, https://doi.org/10.5194/egusphere-egu24-22285, 2024.

EGU24-1492 | ECS | Posters on site | ITS4.5/GM1.3

Structural shifts in plant functional diversity during biogeomorphic succession: Moving beyond taxonomic investigations in an alpine glacier foreland 

Stefan Haselberger, Robert R. Junker, Lisa-Maria Ohler, Jan-Christoph Otto, and Sabine Kraushaar

The complex interrelation between plants and geomorphic processes is described in the concept of biogeomorphic succession. While ecological research on succession and community assembly has transitioned towards functional approaches, studies on functional diversity in biogeomorphic settings, particularly in glacier forelands, remain limited.

In this study, we investigated abundance of vascular plant species and functional traits in an alpine glacier foreland using data from 199 plots. Our objective was to unravel the development of functional diversity during biogeomorphic succession. Specifically, the study determined whether structural shifts in functional diversity are associated with stability thresholds related to plant cover, geomorphic activity, and examined trait spectra for stages of biogeomorphic succession.

Our findings revealed a non-linear trajectory of functional diversity along the plant cover gradient, marked by two distinct structural shifts at 30% and 74% cover, corresponding to established stability thresholds. Along the gradient of geomorphic influence, we observed an increase in functional diversity until 54% of the plot area was affected, beyond which functional diversity declined below the initial level. The analysis of community-weighted means of traits across four stages of biogeomorphic succession determined by plant cover and absence and presence of geomorphic influence revealed significant differences in trait values.

In the transition to the biogeomorphic stage, associated with the identified initial structural shift, there is a shift from a prevalence of above-ground adaptation and reproductive traits, such as leaf longevity, structure, growth form, and mixed reproductive strategies, to an increased dominance of competitor species and traits related to below-ground structures, including root type and structures, as well as vegetative reproduction.

Our results contribute to understanding the relationship between vegetation succession and geomorphic influence by linking them to plant functional traits. This study advances beyond traditional taxonomic investigations by emphasizing functional approaches to biogeomorphic succession. Moreover, the functional trait data used in this study, easily downloadable from a public repository, can serve as a valuable template for future research in (bio)geomorphology, along with the employed methodologies.

How to cite: Haselberger, S., Junker, R. R., Ohler, L.-M., Otto, J.-C., and Kraushaar, S.: Structural shifts in plant functional diversity during biogeomorphic succession: Moving beyond taxonomic investigations in an alpine glacier foreland, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1492, https://doi.org/10.5194/egusphere-egu24-1492, 2024.

Ecohydrology describes the effects of hydrological processes on ecosystem structure and functioning and the effects of biotic processes on hydrological processes. Recently, there is an increasing emphasis on the role of megafauna, large mammals and birds, on earth system processes, such as nutrient cycling, energy flows and vegetation patterns. Ecohydrology as a field, however, has not yet considered megafauna as central drivers of hydrological processes but focused strongly on the interactions between hydrological processes and plants and soils. Here, we introduce zoohydrology to emphasize the importance of considering the interactions between wild animals and hydrological processes. This includes both the effects of hydrological processes on the occurrence, behavior and life history of animals as well as the effects animals have on hydrology. In this introductory talk, we will outline different pathways through which hydrology affects megafauna and through which megafauna affect hydrological processes using a systems approach. We will illustrate these pathways with concrete examples from different parts of the world and on different species. For example, the importance of hydrological processes and hydromorpho-dynamics for shaping habitats of endangered species, such as the Ganges freshwater dolphin and Bengal tigers in northern India and Nepal, but also for structuring megafauna community dynamics, such as the example of Gorongosa National Park in Mozambique. We will also exemplify how wild animals can affect central hydrological processes in diverse ways; directly (e.g., species such as beaver and hippo as ecosystem engineers of aquatic systems) and indirectly (e.g., elephants that reduce woody cover at large scales, affecting evapotranspiration). Many effects of animals on hydrological processes remain understudied and are often lacking from hydrological models. By introducing the concept of zoohydrology, we stress the potentially pivotal interactions between central hydrological processes, wild animals and their habitats. To unravel the full complexity of these interactions and assess their true importance, zoohydrology must be advocated among scientists, policy makers and practitioners in order to better address biodiversity conservation and restoration, make the concept of environmental flow needs more concrete, and investigate the consequences of biodiversity restoration on hydrological systems.

How to cite: Cromsigt, J., Larsen, A., and Griffioen, J.: An introduction to the concept of Zoohydrology – the interactions between hydrological processes and wild animals, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-2096, https://doi.org/10.5194/egusphere-egu24-2096, 2024.

EGU24-2302 | ECS | Posters on site | ITS4.5/GM1.3

A holistic analysis of Chinese sponge city cases by region: Using PLS-SEM models to understand key factors impacting LID performance 

Zhou Guo, Xiang Zhang, Ryan Winston, Joseph Smith, Yifan Yang, Shiyong Tao, and Haoyuan Liu

Sponge city is an engineering solution proposed by the Chinese government which aims to deal with urban water issues (e.g., flooding, poor water quality) brought on by climate change and urbanization. Various strategies for sponge city construction are required since environmental constraints differ regionally across the country. To identify regional variations, reveal the inner links between externalities and design elements in sponge city construction, and offer practical suggestions, efforts in two directions are made based on the data of 65 sponge city cases around China, 1) discussing design parameters of four Low Impact Development (LID) facilities, including bioretention cell, permeable pavement, grass swale, and sunken green space, under four regionalization maps of hydrologic, climatic, landform and soil texture factors, and 2) building a holistic Partial Least Squares-Structural Equation Modelling (PLS-SEM) model illustrating the relationship between local characteristics, LID system design, and LID system performance in sponge city construction. The results show that: 1) rainfall and landform factor have great impact on LID facilities design, as the depths tend to be higher in water rich areas and coastal areas. 2) LID types and areas are positively influenced (+0.764) by the total area and permeable portion of a project, and the LID system performance (water quantity and quality control) is negatively impacted (-0.417) by the rainfall amount and clay fraction. 3) In the holistic model, there are no significant links between the LID system design and natural characteristics or LID system performance. It is recommended that different design standards and assessment indexing systems be tailored to local environment when constructing sponge city projects.

How to cite: Guo, Z., Zhang, X., Winston, R., Smith, J., Yang, Y., Tao, S., and Liu, H.: A holistic analysis of Chinese sponge city cases by region: Using PLS-SEM models to understand key factors impacting LID performance, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-2302, https://doi.org/10.5194/egusphere-egu24-2302, 2024.

EGU24-3300 | ECS | Posters virtual | ITS4.5/GM1.3

Integration of health indicators and quantification of benefits from BGS urban interventions 

Wenfei Huo, Tasos Temenos, Stanislava Boskovic, and Cedo Maksimovic

    With the growing interest in utilizing Blue-green solutions to mitigate the negative impact of urbanization and climate change, and further enhance human health, it becomes essential to comprehensively understand the extent to which BGS influence human well-being through integrating various indicators. Building upon concepts within the existing framework of the Nature-Based solutions to health theory, this study aims to investigate the changes in heart rate among park users and establish connections between these changes and the benefits brought about by urban green spaces, as well as the potential of integrating wearables to quantify the impact of BGS on human health. The research was conducted at the demo site of the HEART project, the Pedion of Areos Park in Athens. The heart rate data of two participants engaged in walking activities within the park were recorded through wearable devices. By analyzing the associations between factors like the Normalized Difference Vegetation Index (NDVI), air pollutants, temperature with heart rate, as well as the complex interplay of various environmental indicators, this study reveals the positive impact of BGS on human health. The outcomes of quantitative statistical analysis indicate that temperature significantly influences heart rate, while the impact of air pollutants on heart rate is not clearly revealed. The result from spatial analysis further confirms a significant correlation between the increase in NDVI and the reduction in Land Surface Temperature (LST), particularly during the spring season. These research findings demonstrate that heart rate can serve as an effective health indicator to quantify the benefits of BGS. While the generalizability of study results might have limitations, it offers insights into the influence of urban green spaces on human health. In the future, with larger sample sizes, diversified datasets such as GeoHealth data with health status, age, and gender, and long-term observations, we can gain a more comprehensive understanding of these positive impacts, thus providing stronger scientific foundations for urban planning and design. 

How to cite: Huo, W., Temenos, T., Boskovic, S., and Maksimovic, C.: Integration of health indicators and quantification of benefits from BGS urban interventions, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-3300, https://doi.org/10.5194/egusphere-egu24-3300, 2024.

EGU24-3463 | ECS | Posters on site | ITS4.5/GM1.3

Beavers and flood alleviation: Human perspectives from downstream communities 

Roger Auster, Stewart Barr, and Richard Brazier

The activities of an animal – beavers - are increasingly recognised as a nature-based solution to hydrological extremes; dams and wetlands that beavers create attenuate flows downstream whilst delivering multiple benefits for the environment and for people. There can however also be challenges for people living alongside beavers. Q-Methodology is a technique for eliciting an understanding of human perspectives that exist within a context, enabling a rich understanding of human subjectivity within a context. We used Q-Methodology to elicit an understanding of perspectives that exist about beavers and their role in natural flood management among communities living downstream of three beaver sites in England, where Eurasian beavers (Castor fiber) are currently being reintroduced. Diverse perspectives that exhibited a range of value judgements were identified, including favourable viewpoints which valued multiple benefits beaver activities can provide, as well as less favourable viewpoints with some perceiving a reliance on beaver-led natural flood management to be less predictable and of higher risk than relying upon human-led interventions. In response to our findings, we support a catchment-based approach to beaver management so as to incorporate contextual perspectives in decision-making, and to enable dissemination of knowledge about beaver behaviours within communities. Further, we encourage future research into whether Beaver-Dam Analogues (in-stream structures that mimic beaver dams or their function) could be used as ‘starter dams’ to encourage beaver activities in optimal locations, as this may inspire confidence in beaver-led flood defence by working with the animal to develop a 'right dam in the right place' strategy.

How to cite: Auster, R., Barr, S., and Brazier, R.: Beavers and flood alleviation: Human perspectives from downstream communities, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-3463, https://doi.org/10.5194/egusphere-egu24-3463, 2024.

EGU24-3786 | Orals | ITS4.5/GM1.3

Nature-based solutions for improving food security: A systematic globalreview 

Loc Ho, Minh Nguyen, Mukand Singh Babel, and Edward Park

Nature-based solutions (NBSs) have been promoted as a holistic way to solve a variety of societal issues while benefiting biodiversity at the same time. To date, applications of NBS approaches that help ensure food security have yet been systematically reviewed. In this paper, we critically review the specific NBSs for food security, highlighting their limitations, to provide recommendations that promote their applications for improving global food security. Our systematic review of nearly 700 peer-reviewed articles indicated that many NBS approaches can be applied to enhance food security dimensions individually or together. However, there is a strong bias towards food availability and not enough research has been done to link NBSs with
improvements in food access and utilization. Over 80% of the reviewed papers were of short-term studies or without specific timeframes, and 25% offered no information on economic effectiveness of NBSs. Environmental benefits of NBSs were explicitly described in about 60% of these papers, and biodiversity enhancement was measured in only about 10%. We, therefore, recommend future applications of NBSs to safeguard food security be shifted to food access and utilization with careful consultation with local communities to address their specific context, using indicators that are easily measured and managed. Systematic monitoring regime and robust and diversified financial support system are also equally important in efforts to successfully implement NBSs. Moreover, environmental and societal benefits, especially water productivity and biodiversity, must be incorporated into the planning and design of NBSs.

How to cite: Ho, L., Nguyen, M., Babel, M. S., and Park, E.: Nature-based solutions for improving food security: A systematic globalreview, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-3786, https://doi.org/10.5194/egusphere-egu24-3786, 2024.

EGU24-4650 | Orals | ITS4.5/GM1.3

Making Space for Water: Investing in Nature-based Solutions with Beavers 

Alan Puttock, Holly Barclay, Matt Holden, Peter Burgess, and Richard Brazier

Our landscapes and watercourses face intense pressures from climate extremes, land use change, declining biodiversity and increased demand for water resources. It is increasingly proposed that by working with natural processes, Nature-based Solutions (NbS) can increase resilience to these pressures, providing multiple environmental and societal benefits.

Beavers are the archetypal ecosystem engineers and keystone species, which can profoundly alter ecosystem structure and function, creating complex wetland environments (Brazier et al., 2021). Research has shown the return of the Eurasian beaver (Castor fiber) to European landscapes can provide multiple benefits including for biodiversity and water resource management (Puttock et al., 2021). However, beaver activity such as damming and tree-felling within our intensively managed and populated landscapes can also conflict with existing land use (Auster et al., 2019). Therefore, management and policy frameworks are required which mitigate conflicts and maximise the NbS benefits beavers can bring.

The Making Space for Water Programme (Barclay et al., 2023) will be introduced, which aims to support land managers to build a network of nature rich wetlands across South West England. This project led by Devon Wildlife Trust, in partnership with the University of Exeter and local landowners is the first of its kind in the UK, aiming to work with wild beavers to deliver natural solutions to address societal challenges.  Case studies will be presented discussing how geospatial mapping and modelling, stakeholder engagement and green finance approaches are being implemented to make catchments ‘beaver ready’, target financial support and enable NbS to deliver significant and lasting benefits. It is hoped that the approach adopted in this project alongside discussion of challenges and benefits can contribute towards progress in the mainstreaming of nature-led NbS approaches.

References

Auster, R. E., Puttock, A., & Brazier, R. (2019). Unravelling perceptions of Eurasian beaver reintroduction in Great Britain. Area, area.12576. https://doi.org/10.1111/area.12576

Barclay, H., Holden, M., Puttock, A., & Burgess, P. (2023) Making Space for Water: Investing in nature-based solutions with beavers. https://www.flipsnack.com/devonwildlifetrust/dwt-beaver-green-finance-programme/full-view.html

Brazier, R. E., Puttock, A., Graham, H. A., Auster, R. E., Davies, K. H. & Brown, C. M. . (2021). Beaver: Nature’s ecosystem engineers. WIREs Water. DOI:10.1002/wat2.1494

Puttock, A., Graham, H. A., Ashe, J., Luscombe, D. J. & Brazier, R. E. (2021). Beaver dams attenuate flow: A multi‐site study. Hydrological Processes, 35(2), e14017. DOI:10.1002/hyp.14017

How to cite: Puttock, A., Barclay, H., Holden, M., Burgess, P., and Brazier, R.: Making Space for Water: Investing in Nature-based Solutions with Beavers, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-4650, https://doi.org/10.5194/egusphere-egu24-4650, 2024.

EGU24-4763 | ECS | Orals | ITS4.5/GM1.3

The potential of Volcanic Pozzolan from Iceland (VPI) in concrete production to reduce the carbon footprint 

Diego Costa, Jukka Heinonen, David Finger, Sigríður Bjarnadóttir, Ólafur Ögmundarson, Börge Wigum, Björn Þorsteinsson, and Helga Adolfsdóttir

The concentration of atmospheric carbon has overpassed 420ppm calling for urgent action to mitigate climate change and remain below the 1.5-degree warming agreed in the Paris climate agreement. The construction industry, with energy consumption included, is with about 40% a significant contributor to the global carbon emissions. Within concrete production, cement accounts for 90% of the emissions. Notably, cement production alone accounts for 8% of global carbon emissions.

Fly ash is amongst the most used of all Supplementary Cementitious Material (SCM). However, its availability is becoming an issue since many coal power plants are shutting down in Europe. Moreover, its environmental profile is questionable. Fly ash is a side product of a carbon intensive industry. Nonetheless, no environmental load has been allocated to it until now.

This study investigates how Volcanic Pozzolan from Iceland (VPI) in concrete compares to traditional concrete and VPI to fly ash, as well as the potential of reducing carbon emissions in concrete production by using VPI as SCM and substitute for cement.

To assess the environmental impacts of VPI and fly ash in cement production, we conducted a Life Cycle Assessment (LCA). For this purpose, we used the GaBi software and relied on primary data from the developers, Heidelberg Materials, and secondary data from the Ecoinvent database.

Our preliminary results reveal that the utilization of VPI as SCM yields an important reduction in carbon emissions compared to Ordinary Portland Cement (OPC) concrete. This notable decrease in carbon footprint positions VPI as a compelling alternative for sustainable concrete production. Two primary factors support this assertion: i) preliminary tests affirm the comparable properties of VPI concrete to OPC, and ii) the diminishing availability of fly ash in Europe necessitates alternative sources, often located at considerable distances, thereby escalating transportation-related emissions.

In conclusion, the integration of VPI emerges as a viable strategy to combat climate change and curtail the carbon footprint of the concrete and construction industry. This initiative aligns with global environmental objectives outlined in the Paris Agreement, United Nations Climate Change Conference, and the Nordic commitment to carbon neutrality by 2040. Embracing VPI as a sustainable alternative in concrete production reflects a positive stride towards achieving these critical environmental milestones.

How to cite: Costa, D., Heinonen, J., Finger, D., Bjarnadóttir, S., Ögmundarson, Ó., Wigum, B., Þorsteinsson, B., and Adolfsdóttir, H.: The potential of Volcanic Pozzolan from Iceland (VPI) in concrete production to reduce the carbon footprint, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-4763, https://doi.org/10.5194/egusphere-egu24-4763, 2024.

The rapid population growth rate is associated with an increased number of residential buildings worldwide; thereby, the massive consumption of building materials causes negative environmental consequences such as the depletion of natural resources and rising non-renewable energy use. To address the environmental, societal, and economic challenges, using Nature-based Solutions (NbS) in residential building materials became essential and considered a catalyst tool for realizing sustainable development goals (SDGs) of the UN 2030 agenda. Consequently, using green building materials (GBM) based on NbS as a long-term strategy should be considered during the whole building life cycle for applying sustainability. This research aims to investigate the potential role of using NbS in residential building materials to achieve SDG and develop a framework for assessing and identifying the direct and indirect inner relationships that affect resource efficiency, cost-effectiveness, and building occupants' comfort level. The research attempts to answer how using NbS in residential building materials can contribute to achieving SDG. The eco-friendly approach was used based on a comprehensive literature review to identify the sustainability indicators for using the GBM. The system dynamics (SD) is also used for estimating and quantifying the selected materials through the building life span, starting from the early design stage until demolition and disposal to landfill. The causal loop diagram (CLD) was created based on the data collected from the residential building case study in New Capital Administrative in Egypt after applying the sustainability indicators, followed by critical analysis to identify the realization of the SDGs. The results showed the framework promotes the potential benefits of using NbS in residential building materials. GBM has significantly contributed to achieving several SDG goals and their targets. The study recommended that the selection of alternative materials and the occupant's comfort level deserve more attention from the early design stage and need more consideration.

Keywords, Green Building Materials, Nature-based Solutions, System Dynamic, Sustainable Development Goals, Egypt

How to cite: Marey, H., Kozma, G., and Szabó, G.: The Role of Using Natural-Based Solutions in Residential Building Materials for Achieving Sustainable Development Goals, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-6592, https://doi.org/10.5194/egusphere-egu24-6592, 2024.

EGU24-6788 | ECS | Orals | ITS4.5/GM1.3

The role of biochar in a circular economy: from agriculture to water and wastewater treatment applications 

Panagiotis Regkouzas, Ioannis Asimakoulas, Eirini Athanasiadou, Elisavet Koukouraki, and Alexandros Stefanakis

Biochar is a sustainable carbonaceous solid material derived from biomass pyrolysis, which abides to circular economy principles in several ways that concern both its production and its several application fields. Biochar is produced by valorizing different organic waste biomass, such as agricultural waste, municipal solid waste, sewage sludge and industrial biowaste, to create a beneficial and valuable product that can then be used in many fields. Thus, biochar production serves perfectly the circularity paradigm as it renders a previously considered waste material to a valuable input material for a new production process. The grounds for the increasing use of and interest in biochars is their favourable physicochemical characteristics, such as the high carbon, macro- and micro- nutrient content, the high porosity and specific surface area, and the abundance of surface functional groups.

Biochar can be effectively used as soil amendment providing fertilizing properties to the applied soil that leads to higher crop production and increased crop nutrient content and quality. At the same time, it provides a stable source of carbon to the soil for several years after its application, contributing this way to CO2 mitigation. Biochar can also be used as an adsorbent due to its carbonaceous porous structure to remediate polluted soil, water and wastewater from either organic or/and inorganic pollutants, even in low pollutant concentrations.

Τhis abstract will present a comprehensive range of studies on biochar production from different sources and its use in different sectors. One of the latest applications is its use as a substrate in Constructed Wetlands for sustainable wastewater treatment, in order to enhance the various pollutant removal/transformation processes. Three different research studies will be presented where biochar was produced from green waste (e.g., olive tree branches) and used as substrate in various Constructed Wetland pilot units that treat domestic wastewater, landfill leachate and olive mill wastewater as an ecological treatment technology.

Furthermore, an agronomic application of biochar as soil amendment in a pot experiment for the cultivation of lettuce will be shown. Finally, the environmental application of biochar produced from sewage sludge as adsorbent will be presented towards the decontamination of water and wastewater from organic emerging micro-contaminants.

How to cite: Regkouzas, P., Asimakoulas, I., Athanasiadou, E., Koukouraki, E., and Stefanakis, A.: The role of biochar in a circular economy: from agriculture to water and wastewater treatment applications, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-6788, https://doi.org/10.5194/egusphere-egu24-6788, 2024.

EGU24-7509 | Orals | ITS4.5/GM1.3

BirdWatch - a Copernicus-based service for the improvement of habitat suitability of farmland birds in the EU 

Annett Frick, Nastasja Scholz, Sascha Gey, Damaris Zurell, Levin Wiedenroth, Nika Oman Kadunc, Nejc Vesel, Ine Rosier, Rik Hendrix, Annelies de Meyer, Ruth Sonnenschein, Basil Tufail, Bartolomeo Ventura, Tomas Orlickas, and Martynas Rimgaila

BirdWatch, funded by the Horizon Europe Program, focuses on improving the state of biodiversity of the EU's agricultural landscape, in line with major policy targets of the EU Green Deal, the EU Biodiversity Strategy for 2030, and the Farm to Fork Strategy. A healthy agricutural ecosystem forms the necessary basis for the provision of nature-based solutions and, eventually, for the resilience of our society.

Leveraging Copernicus satellite data, the project assesses agricultural areas to identify their suitability for farmland birds and strategises ways to enhance ecological conditions. As indicator species, birds offer insights into overall biodiversity health, contributing to a broader understanding of ecosystem well-being.

The project employs species distribution modeling to link bird occurrence data with habitat requirements, establishing models that gauge habitat suitability and the likelihood of an area being suitable for specific bird species. Utilising remote sensing data, BirdWatch quantifies essential environmental descriptors such as structural variability, land cover type, crop type, mowing intensity and soil moisture. These parameters are then fed into the habitat models to assess landscape suitability.

Knowing the state of habitat suitability and the habitat requirements, BirdWatch identifies which of the agroecological schemes under the EU’s Common Agricultural Policy (CAP), have to be applied to improve the farmland conditions. The agri-environmental schemes are selected in such a way to ensure that they are not in conflict with any spatial or ecological requirements.

Here, BirdWatch uses spatial optimisation, taking into account both the ecological requirements and the economic and operational constraints of the farmers who need to implement the agri-environmental measures as part of their obligations under the CAP.

Benefiting from Copernicus program's high temporal resolution, BirdWatch evaluates the success of agri-environmental measures and makes adjustments as needed.

Upon project completion, the service will be accessible through a web-based GIS application in the project regions of Flanders, Germany, Lithuania, and South Tyrol.

How to cite: Frick, A., Scholz, N., Gey, S., Zurell, D., Wiedenroth, L., Kadunc, N. O., Vesel, N., Rosier, I., Hendrix, R., de Meyer, A., Sonnenschein, R., Tufail, B., Ventura, B., Orlickas, T., and Rimgaila, M.: BirdWatch - a Copernicus-based service for the improvement of habitat suitability of farmland birds in the EU, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-7509, https://doi.org/10.5194/egusphere-egu24-7509, 2024.

EGU24-7697 | ECS | Orals | ITS4.5/GM1.3

Circular management of sewage sludge for the sustainable dewatering and reuse of biosolids: an experimental study 

Ioannis Asimakoulas, Panagiotis Regouzas, Elisavet Koukouraki, and Alexandros Stefanakis

Wastewater treatment generates a by-product material known as sewage sludge. In many countries, most of the produced sludge is disposed in landfills, following a linear management strategy that is based on mechanical and chemical methods for limited dewatering and daily transportation to landfills. This strategy is rather expensive and unsustainable and possesses several environmental risks such as groundwater pollution, insufficient sludge drying and stabilization, high carbon footprint etc. Ecological engineering concepts and technologies can provide a circular sludge management strategy that focuses on the utilization of this valuable by-product with the smallest possible environmental impact.

This work will present an ongoing large research study that investigates different technologies and methods towards transforming this organic by-product to a beneficial material with the minimum environmental impact. Specific tasks of the project are:

  • A setup of 16 pilot-scale units of the sustainable technology of Sludge Treatment Wetlands for sewage sludge treatment. The pilot units have different operation and construction properties, such as planted/unplanted, presence of earthworms, different substrate thickness, different loading rates, in order to eventually result in a highly efficient and optimized design configuration.
  • Composting of sewage sludge along with the reed biomass from the constructed wetlands
  • Production of biochar using sewage sludge and reed biomass as raw materials

 

The experimental results of the first operational year of this project will be presented.

The studied circular model for sludge management will be evaluated regarding the reduction of greenhouse gas emissions due to the non-use of mechanical dewatering methods, avoidance of high energy and chemicals consumption, and cessation of daily transport and disposal in landfills. The various organic materials that are produced will be assessed based on their quality properties and will further be tested by application to tomato crops for the estimation of yield improvement. Ultimately, an assessment of the economic, technical, environmental and social parameters of all methods and material cycles and studied management scenarios, will be carried out in order to determine the optimal circular management strategy.

How to cite: Asimakoulas, I., Regouzas, P., Koukouraki, E., and Stefanakis, A.: Circular management of sewage sludge for the sustainable dewatering and reuse of biosolids: an experimental study, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-7697, https://doi.org/10.5194/egusphere-egu24-7697, 2024.

EGU24-7812 | ECS | Orals | ITS4.5/GM1.3

Algal-bacteria raceway ponds for circular wastewater treatment 

Styliani Biliani and Ioannis Manariotis

Raceway open systems are a highly promising, eco-friendly methodology for wastewater treatment. To assess how effectively nutrients and organic matter were removed from primary and secondary treated wastewater, two laboratory-scale open-raceway algal-bacteria ponds were used. The reactors were operated at organic loading rates (OLR) ranging from 29 to 95 and 9 to 38 g sCOD m3/d, for primary and secondary effluent, respectively. The hydraulic retention time (HRT) of both reactors dropped progressively from 5.5 to 2.2 d, and they thereafter ran at a HRT of 1.1 d. After 130 days, a high biomass concentration of around 2.2 g/ L was maintained with both substrates. Reactors were shown to be functional even at lower HRT levels by the quick removal of organic matter and nutrients. In less than 12 hours, the highly active biomass that was produced with both substrates resulted in the almost complete removal of organic matter and nutrients.
With its exceptional settling capabilities, the algal-bacteria biomass may settle in less than ten minutes. The algal-bacteria content of biomass was 18.3 to 16.5% for lipids, 72.5 to 72.6% for proteins, and 2.6 to 3.2% for carbohydrates. The findings of the present work show that syntrophic algal-bacteria biomass is effective for the high-rate treatment of municipal wastewater. The low operational cost as well as the potential nutrient recovery and biomass valorization make the algal-bacteria process a circular-green model for wastewater management .

How to cite: Biliani, S. and Manariotis, I.: Algal-bacteria raceway ponds for circular wastewater treatment, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-7812, https://doi.org/10.5194/egusphere-egu24-7812, 2024.

Over the past century, the nitrogen pollution problem has grown concomitantly with population growth, intensified agricultural practices, and a warming climate. In Europe, the presence of excess nitrogen in the environment has already exceeded safe planetary boundaries, posing a threat to Earth’s water supply and biodiversity.

While considerable efforts are now focused toward mitigating this problem through increased regulatory measures on wastewater treatment plants and implementation of better agricultural management practices, there is a growing interest in the use of wetlands as nature-based solutions (NBS) to improve water quality and, in particular, to reduce nitrogen loading to downstream water.

Despite these benefits, wetlands are among the most degraded ecosystem in Europe, having experienced significant shrinkage over the past centuries, now constituting only one-third of their 1700 extent. This decline is largely attributed to agricultural expansion on drained productive wetland soils, while also contributing to increased nitrogen pollution from excess use of fertilizers.

To address these issues, starting from nitrogen surplus data and current wetland extent at European scale we estimate with a physically-based model that current removal potential of wetlands is about 1113 ± 101 kt of N per year (~6.5% of total N surplus in European soil). The significance of wetlands in water quality remediation is underscored by the fact that this nitrogen would otherwise enter the river network and, subsequently, the sea. Given that the current riverine loads in EU watersheds amount to about 2730 kt N per year, the loss of current wetlands would increase this figure by over 40%, with detrimental consequences for the status of surface waters and the eutrophication of coastal areas.

We propose a set of restoration scenarios, along with the associated costs, for the restoration of wetlands that have been drained for agricultural purposes. Our analysis aligns with the objective of the Nature Restoration Law, requiring EU member states to implement effective restoration measures to cover at least 20% of the EU’s land and sea areas by 2030. We show that by restoring 2.6% of EU land (equivalent to 20% of historical wetlands), we could nearly double the current nitrogen uptake (2108 ± 187 kt of N per year), and significantly improve riverine water quality by reducing more than 30% of their loads to the sea. In addition, wetland restoration will offer a wide array of ecosystem co-benefits from flood prevention and carbon sequestration to provision of critical habitat for specialized flora and fauna.

How to cite: Bertassello, L., Basu, N., and Feyen, L.: Enhancing Nitrogen Removal in European River Basins: The Crucial Role of Wetland Conservation and Restoration as Nature-Based Solutions, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-11118, https://doi.org/10.5194/egusphere-egu24-11118, 2024.

EGU24-11619 | Orals | ITS4.5/GM1.3

Fostering forest and landscape restoration under the UN Decade on Ecosystem Restoration 

Christophe Besacier, Carolina Gallo Granizo, and Andrea Romero Montoya

The UN Decade on Ecosystem Restoration (2021-2030) was declared by the UN General Assembly driven by the global need to support and scale up efforts to prevent, halt and reverse the degradation of ecosystems worldwide. The Food and Agriculture Organization of the UN (FAO) and the UN Environment Programme (UNEP), as leaders of the Decade, are working together with other relevant stakeholders to achieve the Decade’s mission. To do so, focus is given to the development of capacities to empower professionals and institutions involved in the field of restoration to design, implement, monitor and sustain effective restoration initiatives. In the framework of the Best Practices Task Force (TF) of the Decade, partners have jointly developed a Capacity, Knowledge and Learning Action Plan, based on the findings of a global capacity needs assessment, a stocktake of knowledge products and capacity-development activities, and multiple targeted consultations. The plan identifies the gaps where knowledge products or capacity-development initiatives are required across different stakeholder groups, and provides terms of reference for capacity and knowledge development initiatives tailored for those different stakeholder groups. The TF has published the Standards of Practice to guide ecosystem restoration, providing key recommendations in an effort to facilitate the application of the principles for ecosystem restoration. In addition, the TF has created the Framework for the Dissemination of Good Restoration Practices to help practitioners share and consult information about restoration. This framework is in turn part of the UN Decade’s Framework for Ecosystem Restoration Monitoring (FERM), and allows for the collection and documentation of good practices. It also features a common search engine to connect and facilitate retrieving best practices from different relevant platforms, besides the FERM. These resources provide any restoration actor with an essential base for an effective planning, implementation and monitoring of global and local restoration efforts.

How to cite: Besacier, C., Gallo Granizo, C., and Romero Montoya, A.: Fostering forest and landscape restoration under the UN Decade on Ecosystem Restoration, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-11619, https://doi.org/10.5194/egusphere-egu24-11619, 2024.

EGU24-11821 | Orals | ITS4.5/GM1.3 | Highlight

Forest-landscape dynamics across a climate gradient 

Stuart Grieve, Harry Owen, Paloma Ruiz-Benito, and Emily Lines

Forests and landscapes are fundamentally interconnected, with geomorphic process being modulated by vegetation dynamics, which in turn is influenced by landscape form. Trees play a critical role in shaping landscapes by redistributing sediment across the Earth's surface via gradual processes including tree throw and root growth, and catastrophic processes such as landsliding and debris flows, where spatially variable root cohesion contributes to slope failure likelihood. Conversely, landscape morphology controls the availability of light, water and nutrients for trees and has been observed to dive significant variability in the structure and composition of forests at both local and regional scales. Until recently, our ability to disentangle these processes at broad spatial scales has been limited due to a lack of high resolution data on tree morphology. Advances in Terrestrial Laser Scanning and UAV-LiDAR systems now allow forest plots to be scanned rapidly, capturing the morphology of hundreds of trees alongside the terrain they grow on.

Working across a range of European forest ecosystems, representing a range of climates, we have constructed an unprecedented 3D dataset of European forest-landscape dynamics. From plot-level scans, individual trees are segmented from the digital forest and classified by species. State of the art structural metrics are then computed at an individual, species, and regional level across each distinct climate zone. This pan-European dataset is then coupled with high resolution topographic data, to explore the fundamental linkages between landscapes and vegetation.

How to cite: Grieve, S., Owen, H., Ruiz-Benito, P., and Lines, E.: Forest-landscape dynamics across a climate gradient, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-11821, https://doi.org/10.5194/egusphere-egu24-11821, 2024.

EGU24-12659 | Posters on site | ITS4.5/GM1.3

Valuing Natural Capital in Communities for Health 

Jimmy O'Keeffe, Jolanta Burke, Branislav Kaleta, Stephen Campbell, and Cathal O'Connell

The natural capital and ecosystem services that we rely on have been severely impacted by changes to our ecological, biogeochemical and climate systems. This has been driven by our lifestyle choices, impacting our water, air and soil quality. Left unchecked, environmental degradation threatens to reverse the benefits created, exacerbating the decline of our critical natural capital resources. Among the many benefits we obtain from the natural environment, human health and wellbeing are among the most important, yet least understood. In Ireland, mental health conditions, including depression and anxiety impact up to 42% of the population. The costs of poor mental health to the economy are estimated to be €11 billion each year. Furthermore, the second leading cause of death in Ireland is circulatory disease, such as heart attack, or stroke. This Science Foundation Ireland funded project VNiC-Health (Valuing Natural Capital in Communities for Health) will focus on providing evidence from both a human health and wellbeing, and a quality natural environment point of view, helping to address two of the most critical challenges affecting society - the climate and environmental emergency, and our health crisis.

How to cite: O'Keeffe, J., Burke, J., Kaleta, B., Campbell, S., and O'Connell, C.: Valuing Natural Capital in Communities for Health, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12659, https://doi.org/10.5194/egusphere-egu24-12659, 2024.

EGU24-13080 | ECS | Posters on site | ITS4.5/GM1.3

Long-term monitoring of eco-hydrological effects of Live Pole Drains in large open-air test facility at TUDelft campus 

Linnaea Cahill, Job Augustijn van der Werf, Alejandro Gonzalez-Ollauri, and Thomas Adrianus Bogaard

Live Pole Drains (LPDs) are a plant-based drainage system used to drain natural slopes and prevent shallow gully erosion. LPDs are a Nature-based Solution built by placing a live fascine in a shallow ditch or gully along the slope direction, allowing moderate fluxes of surface runoff or seepage to infiltrate and high water fluxes to be conveyed along the fascine without further eroding the slope. Despite their practical implementation, the transient and long-term eco-hydrological behaviour of LPDs is not well understood. We aim to better understand the LPD’s water balance, the seasonal and life-span changes in hydrological behaviour, as well as the impact of an LPD on surface runoff water quality. To this end, we built and instrumented an artificial slope with full-scale LPDs in an open-air lab (OAL) at TUD. The design of the setup and the monitoring plan of the LPDs were developed in collaboration with Glasgow Caledonia University with insights from the construction and monitoring of three LPDs at different growth stages in their OAL on the east coast of Scotland. Herein, we present the design and possible research experiments that can be performed over the next 5 years, generating a data set to further develop and validate conceptual hydrological modelling of LPDs. We expect this long-term demonstrative setup to generate interest and facilitate a more comprehensive understanding of LPD functions, ultimately leading to the incorporation of LPD design and maintenance standards in engineering toolboxes for slope and gully stabilization.

How to cite: Cahill, L., van der Werf, J. A., Gonzalez-Ollauri, A., and Bogaard, T. A.: Long-term monitoring of eco-hydrological effects of Live Pole Drains in large open-air test facility at TUDelft campus, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-13080, https://doi.org/10.5194/egusphere-egu24-13080, 2024.

Zebra mussels (Dreissena bugensis) and quagga mussels (Dreissena polymorpha) are invasive throughout much of the northern hemisphere. Whilst much attention has been paid to their role in altering aquatic systems via their filter feeding, little attention has been given to their role as geomorphic agents, or the relationships between geomorphology and their ecosystem engineering. We aimed to understand the controls and feedbacks between fluvial geomorphology and Dreissenid mussel invasion, utilising field, laboratory, and numerical modelling approaches. We found important consequences for both geomorphology and ecosystems, with mussel invasion significantly impacting annual sediment transport rates, and positively facilitating the invasion of further priority invasive species.

Quagga mussels attach to benthic sediments using byssal threads, which affects sediment stability and thereby broader river geomorphology. At an invaded gravel bed river, quagga mussels attached >500 g m-2 of mineral sediments together. In ex situ flume experiments, this process increased critical shear stress by 40%. Numerical modelling of flow at the study river was used to upscale these stresses to estimate changes to sediment transport over a recorded five-year flow period, which indicated that typical densities of quagga mussels may reduce the occurrence of a geomorphically active flood event from Q30 to Q2, and reduce sediment transport by 74%. Thus, substantial alterations to bedload sediment transport may occur following quagga mussel invasion.

Dreissenid mussels are also ecosystem engineers, where their shells provide a unique stable habitat in fine-grained rivers. Field surveys found that mussel shells positively facilitate macroinvertebrate communities, but preferentially facilitate co-evolved, high-priority invasive amphipods. The construction of a spatial model of riverbed grainsize across England and Wales, combined with an analysis of Environment Agency nationwide presence/absence records, identified that ecosystem engineering by zebra mussels was particularly powerful in fine-grained river systems to other invasive taxa. Supporting mechanistic aquarium experiments indicated that the ecosystem engineering of zebra mussels may support the invasions of high-profile amphipod species into otherwise unfavourable habitats, which could not be invaded without mussel engineering. Channelisation and dredging, which simplify river channels, may benefit Dreissenid mussel ecosystem engineering and the facilitation of other invasive species. Instead, Nature-based Solutions could be employed to restore the geomorphic functioning of systems, which may improve resilience against high-priority invasive species.

How to cite: Sanders, C.: Geomorphic invaders: Geomorphic potential and landscape controls on the biogeomorphology and ecosystem engineering of Dreissenid mussels, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15884, https://doi.org/10.5194/egusphere-egu24-15884, 2024.

EGU24-16062 | Orals | ITS4.5/GM1.3

Nature-based-Solutions for restoring and developing new mangrove habitats through eco-engineering 

Vicky Stratigaki, Jelle Evenepoel, Mathieu Wille, Emile Lemey, Ignace Stols, Dominic De Prins, Andrea Sofia Reyes Chejin, Julia Peláez Ávila, Marlies Kimpe, Julie Nieto Wigby, Bernd Herremans, Maria Ibanez, Renaat De Sutter, Boris Bohorquez, Stijn Temmerman, and Farid Dahdouh-Guebas

Jan De Nul Group has a long-standing presence in Ecuador, particularly since 2018, when a 25-year concession contract began for performing maintenance dredging for the Access Channel to the port of Guayaquil. This area is part of the Guayas river delta and is covered by mangrove forests that provide important ecosystem services. However, in the last few decades there has been significant loss of mangroves in the area, which intensifies coastal safety problems, as the land around the Guayas river delta becomes more exposed to floods and coastal erosion.

In response to this, the AquaForest innovation project was introduced in 2023. Dredged material from the Access Channel of Guayaquil will be reused for the first time in a circular and sustainable way to create a new mangrove habitat on a new intertidal flat created in the Guayas river delta, located 15km NE of Posorja. AquaForest will become a ‘Nature-based-Solutions’ (NbS) Living lab where important mangrove ecosystem services will be demonstrated and monitored such as protection against floods, biodiversity gain, carbon sequestration and socio-economic benefits for the local communities.

The AquaForest project concept is based on the development of “green-grey infrastructure”. This approach combines conventional engineering techniques for land reclamation with the circular reuse of dredged material to create mangroves through assisted afforestation. At the same time, the initial conditions will be created (e.g. sediment characteristics, hydraulic and hydrodynamic conditions) that are ideal for the growth of mangrove propagules, the proliferation of new accompanying tree seeds and the colonization process of associated biodiversity (micro and macro fauna), though suitable eco-engineering of the project site. Part of the project also focuses on the study of upscaling of this type of Nature-based-Solutions. As such, knowledge obtained from this pilot project regarding the implementation and monitoring of mangrove NbS will be employed in the upscaling of the AquaForest concept in future projects across the region and around the world, particularly in areas where mangrove forests serve as vital components of local ecosystems.

AquaForest demonstrates co-creation between private companies, public institutions, international organisations, local communities and citizens, NGOs, universities and researchers. The project is a collaboration between Jan De Nul Group, Mantis Consulting, HAEDES, Escuela Superior Politécnica del Litoral, Free University of Brussels, University of Antwerp, South Pole, and the Calisur Foundation. The project furthermore has the full support of the Ecuadorian Ministry of Environment, Water and Ecological Transition (MAATE) and all other important local stakeholders.

Acknowledgements: AquaForest is supported by the Government of Flanders (NL: “Departement Omgeving”) through the G-STIC Climate Action Programme 2022, and The International Union for Conservation of Nature (IUCN) through the ‘Blue Natural Capital Financing Facility’.

How to cite: Stratigaki, V., Evenepoel, J., Wille, M., Lemey, E., Stols, I., De Prins, D., Reyes Chejin, A. S., Peláez Ávila, J., Kimpe, M., Nieto Wigby, J., Herremans, B., Ibanez, M., De Sutter, R., Bohorquez, B., Temmerman, S., and Dahdouh-Guebas, F.: Nature-based-Solutions for restoring and developing new mangrove habitats through eco-engineering, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-16062, https://doi.org/10.5194/egusphere-egu24-16062, 2024.

EGU24-16355 | Posters on site | ITS4.5/GM1.3

The effectiveness of oyster reefs as a nature-based erosion control measure under storm events 

Wietse van de Lageweg, Thijs van Steen, Brenda Walles, and Jaco de Smit

Coastal ecosystems such as oyster reefs, salt marshes and mangroves are widely recognised as nature-based solutions reducing coastal erosion. Oyster reefs maintain their own habitat and have the ability to grow at the rate of sea level rise, making them self-sustainable, flexible and cost-effective coastal erosion measures in the face of climate change. By attenuating waves and stabilising sediment as well as facilitating and protecting neighbouring ecosystems, they stimulate coastal resilience. However, effective employment of oyster reefs as a nature-based erosion control measure is not trivial and requires the integration of ecological and engineering parameters. Given the satisfaction of these eco-engineering parameters, recent work demonstrates that oyster reefs lead to a four-fold reduction in erosion in the protected area compared to a non-protected area across a decadal period. Despite this apparent effectiveness across a longer time period, it is still poorly understood how effective oyster reefs are in reducing erosion during individual storm events and how large their morphological footprint during these events is.

We present the findings of a series of detailed morphological field surveys of the Viane oyster reef in the Eastern Scheldt, the Netherlands, during which three storm events (Ciaran, Gerrit and Henk) were captured. These storms led locally to significant wave heights of 1.3-1.5 m, corresponding to the highest percentile of wave events recorded locally. Results show that storm Ciaran resulted in an transect-average erosion of 0.02-0.05 m for the unprotected areas, corresponding to the typical annual erosion for the intertidal flats of this area. In contrast, the reef-protected areas showed a greatly reduced erosion of maximum 0.02 m but typically 0.01 m. It is important to note that the erosion pattern as a result of this storm event is far from homogeneous: erosion is greatest immediately behind the reef (~first 50 m), then reduces up to 150 m behind the reef, followed by a zone of deposition (150-250 m behind the reef) and then transitions into another zone of erosion (250-450 m behind the reef). Complementary numerical modelling with XBeach will be used to obtain additional insights into the role of wave angle, wave period and tidal timing on the flow, sediment transport and morphological changes caused by the Viane reef structure during storm events.

How to cite: van de Lageweg, W., van Steen, T., Walles, B., and de Smit, J.: The effectiveness of oyster reefs as a nature-based erosion control measure under storm events, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-16355, https://doi.org/10.5194/egusphere-egu24-16355, 2024.

Vertical greenery (VG) provides mutliple ecosystem services and diverse forms of implementation. Benefits are linked to maintenance, irrigation, and appropriate planning. The focus of the Fabikli project was to apply these aspects to educational purposes in three highschools in Berlin. This case study delineates the planning, implementation, maintenance, and educational operation of the project.

Complications arose due to planning errors and the still pending building permit of the systems, demonstrating the cumbersome administrative barriers regarding VG. Three energy-efficient rainwater irrigation systems and and a supporting structure that can be harvested from the ground were developed and implemented. Maintenance of these systems is designed to be infrequent and accessible, with low-tech solutions ensuring easy repairs.

At the center of the educational offer were the multidimensional issues addressed by the implemented VG systems. Examples include urban heat stress, land and water use, and CO2 sequestration. The school personal was directly involved in the participation process. Consequently, teachers incorporated the VG-topic creatively into their classes. Here we present the harvest of the VG as an exemplary illustrative showcase.

In addition, the project aimed at a multiplication and propagation of similar systems, which did occur, but with serious design flaws. This demonstrates the importance of appropriate planning, implementation, and continuous attendance, even for low-tech solutions.

In conclusion, schools offer an influential societal overlap between generations and functions such as teachers, parents, or administration. VG can convey several key aspects of environmental education about the ecology with cities.

How to cite: Kluge, B. and Dahm, Y.: Climate adaptation through educational vertical greenery in high schools: key lessons from the city of Berlin, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-16707, https://doi.org/10.5194/egusphere-egu24-16707, 2024.

EGU24-17900 | ECS | Posters on site | ITS4.5/GM1.3

Exploratory analysis of the long(er) term dynamics of Nature-based Solutions: the case of agricultural soil properties 

Neeraj Sah, James Blake, Vicky Bell, Jonathan Evans, Ross Morrison, and Alejandro Dussaillant

Conventional agricultural practices often lead to increased soil compaction, a decline in soil organic matter (SOM) and an associated decrease in structural porosity, compromising the water holding capacity and resilience of agricultural soils to hydrological extremes. Regenerative agriculture practices, with their focus on building healthy soil ecosystems, hold promises for enhancing agricultural resilience to extreme weather events like floods and droughts. These practices, such as reduced tillage, reduced trafficking and stocking density, cover cropping, and afforestation, can improve soil organic matter content, reduce compaction, enhance soil structure, and promote microbial activity, leading to increased soil porosity, water infiltration, and retention. However, due to the slow response of soils to changes in agricultural management, a critical research gap exists in the timely quantification of the potential effectiveness of these practices in mitigating flood and drought risks. Although undoubtedly robust and informative, long-term monitoring of soil properties before and after a management intervention may take decadal timescales to reveal any significant impacts.
We have therefore adopted an exploratory approach to investigate the merits of back-analysing existing long-term soil moisture datasets to reveal any changes in inferred soil porosity due to changes in land use and/or management. The following UKCEH long-term datasets, which include soil moisture information, have been considered: Neutron Probe Soil Moisture Database (~50 years range), UK Greenhouse Gases Flux Network (last 15+ years), and COSMOS-UK TDT probe data (last 10 years). In addition, we have land cover information from UKCEH Land Cover Maps from 1990 onwards. For UK conditions, it is anticipated that an annual maximum soil moisture content, representing saturated conditions, is likely to be attained during most winter seasons (excluding any ‘dry’ winters, excluded based on rainfall data). It is then possible to estimate soil porosity in any particular year by equating it to the maximum soil moisture content, in effect using this as a proxy measurement with due regard for potential air entrapment effects. Any identified long-term changes in soil porosity obtained through trend, wavelet, and before-after-control-impact analysis might then be linked to changes in land use and/or management. Land cover changes may be identified using Land Cover Map data and local site knowledge, the latter of which will also provide insights into changes in land management. COSMOS-UK TDT data is particularly interesting in terms of land management impacts as, when installed, the instrumentation at each site was enclosed by a newly erected fence. The resultant compound therefore excluded stock and vehicle trafficking and initiated a change in land use from generally arable or improved grassland to rough grassland. It will therefore be valuable to understand if the proposed exploratory analysis approach can reveal any significant changes in soil porosity over time due to this intervention. Likely challenges to be discussed include disentangling any long-term changes in maximum soil moisture due to changes in soil porosity from background changes in climate. We will also share lessons learned and provide recommendations for future work on the back-analysis of long-term soil moisture datasets.

 

 

How to cite: Sah, N., Blake, J., Bell, V., Evans, J., Morrison, R., and Dussaillant, A.: Exploratory analysis of the long(er) term dynamics of Nature-based Solutions: the case of agricultural soil properties, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17900, https://doi.org/10.5194/egusphere-egu24-17900, 2024.

EGU24-18207 | Posters virtual | ITS4.5/GM1.3

Freshwater mussels’ valve movement response as early-warning system of river’s ecosystem conditions 

Donatella Termini, Nina Benistati, Ashkan Pilbala Ashkan Pilbala, Vanessa Modesto, Nicoletta Riccardi, Luigi Fraccarollo, Sebastiano Piccolroaz, Dario Manca, and Tommaso Moramarco

Global warming has lead concerns about more frequent high intensity rainfall events and increasing river floods. Changes in the river hydrodynamics affect the biological communities which are controlled by the interplay between physical-chemical and hydraulic processes. Thus, there is increasing interest in identifying the impact of the hydrodynamic stresses, also determined by climate change, on the aquatic environment and, consequently, on the interactions between flow and organisms (Lopez and Vaughn, 2021). To this aim, it is fundamental to use remote sensors to constantly monitor the responses of animals to environmental changes. Among these sensors, bio-indicators have been increasingly used to monitor water quality conditions. Some species, called as “ecosystem engineers”, are especially important in studying the effects of climate changes in rivers (Butler and Sawyer, 2012). The present study considers freshwater mussels which meet the criteria to be considered as typical “ecosystem engineers” and can be considered as sensitive biosensors of environmental disturbance (among others Gerhardt et al. 2006). Monitoring freshwater mussels’ opening and closing valves activities (i.e., valvometric technique) over time has been used to evaluate the behavior of the bivalves in reaction to their environmental exposure. The application of the valvometric technique is not recent and has been mainly applied to analyze the impact of chemical stressors on freshwater mussels. Recent experimental results obtained by the research group of the present work (Modesto et al., 2023; Termini et al., 2023), in sand-bed laboratory flumes with different FMs’ populations, have suggested that the mussels’ behavioural response could be also used as a tool for an early warning system of flow variations in rivers, also in the presence of sediment transport. The present work reports the results both of an experimental investigation conducted in a laboratory flume to analyze the influence of the substrate composition on the freshwater mussels’ response and of an in-situ test conducted in a selected reach of the Paglia river (Italy) to verify the FMs’ response in non-controlled environment. In both cases the FMs’ valvometry data were collected in real-time by using Hall sensors technology. The FMs’ behavioural response was examined in terms of valves’ opening/closure frequency and amplitude. The obtained results have confirmed that FMs’ behavioural response can be used as BEWS for identifying the impacts of hydrodynamic changes in rivers.

References

Butler DR, Sawyer CF, 2012. Introduction to the special issue: zoogeomorphology and ecosystem engineering. Geomorphology 157–158.

Gerhardt A, Ingram MK, Kang IJ, Ulitzur S 2006. In situ on-line toxicity biomonitoring in water: recent developments. Environmental Toxicology and Chemistry 25: 2263–2271.

Lopez J. W., Vaughn C.C., 2021. A review and evaluation of the effects of hydrodynamic variables on freshwater mussel communities. Freshwater Biology 66 (9): 1665-1679.

Modesto, V. et al. 2023. Mussel behaviour as a tool to measure the impact of hydrodynamic stressors, Hydrobiologia, 850, 807–820.

Termini, D. et al. 2023. Identification of hydrodynamic changes in rivers by means of freshwater mussels’ behavioural response: an experimental investigation, Ecohydrology, e2544.

How to cite: Termini, D., Benistati, N., Ashkan Pilbala, A. P., Modesto, V., Riccardi, N., Fraccarollo, L., Piccolroaz, S., Manca, D., and Moramarco, T.: Freshwater mussels’ valve movement response as early-warning system of river’s ecosystem conditions, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18207, https://doi.org/10.5194/egusphere-egu24-18207, 2024.

EGU24-19044 | Orals | ITS4.5/GM1.3

Connectivity as a driver of biodiversity and functioning in riverine landscapes: A dynamic, graph theoretic approach. 

Andrea Funk, Damiano Baldan, Paul Meulenbroek, Didier Pont, Sonia Recinos Brizuela, Elisabeth Bondar-Kunze, and Thomas Hein

Connectivity is a crucial property of the riverine landscape. Reduction of connectivity, i.e. habitat fragmentation and isolation effects, impacting ecological functions and biotic communities, is one of the most critical threats to river-floodplain systems. Using a graph theoretical approach for analyzing possible transport pathways in the system (directed, undirected, overland, seepage), we could show that essential ecological functions related to sediment composition and quality, hydrochemical conditions, and macrophyte coverage can be predicted and importance of waterbodies in the network and their main connectivity deficits can be identified. In a second step we are now integrating biotic communities in the predictive framework. Dependent on dispersal model and habitat preferences the different taxonomic groups show clear pattern i.e. drifting invertebrate organisms are highly driven on directed transport whereas fish as active swimmers are more dependent on connectivity in the waterbody network or organism with terrestrial or flying dispersal (amphibia or flying insects) are dependent on overland connectivity. Further they interact with the ecological functions in the system. Using a temporal dataset based on eDNA (environmental DNA) we can further show that ecosystem conditions and distributions of biotic communities are dependent on different transport/movement pathways changing with hydrological conditions (flood to low flow conditions). The dynamic graph theoretic approach can, therefore, be used as an essential tool for prioritizing water bodies for nature-based solutions.

How to cite: Funk, A., Baldan, D., Meulenbroek, P., Pont, D., Recinos Brizuela, S., Bondar-Kunze, E., and Hein, T.: Connectivity as a driver of biodiversity and functioning in riverine landscapes: A dynamic, graph theoretic approach., EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-19044, https://doi.org/10.5194/egusphere-egu24-19044, 2024.

EGU24-19170 | Posters on site | ITS4.5/GM1.3

Influence of Island Morphology on Micro-Endemic Biodiversity Distribution 

Anaé Lemaire, Jean Braun, and Esteban Acevedo-Trejos

Most islands host an endemic biota, i.e., present nowhere else on Earth like it is the case, for instance, of Madagascar. It has been shown that different populations of lemurs, endemic to the island, are mostly distributed along the watersheds surrounding the central plateau of Madagascar, creating a so-called micro-endemism, while the populations living on the central high-elevated watersheds are not showing this micro-endemism. Here we wish to address the question whether there exists a correlation between the evolution of the landforms (i.e., the morphology of the island) of Madagascar and the hybrid distribution of lemur populations? More broadly, how does the tectono-geomorphic evolution of an island influence the flourishing of micro-biodiversity?

To answer these questions in a quantitative manner, we combined a Landscape Evolution Model based on the Stream Power Law and taking into account Flexural Isostasy, with a Speciation Model. We first developed a morphometric index to differentiate between Π-islands with a central plateau surrounded by smaller basins, like Madagascar, from conical Λ-islands, like Sri Lanka. We then predicted patterns of biodiversity as a function of the index value and its time evolution. We show that the tectono-geomorphic evolution influences patterns of biodiversity and evaluate the influence of varying the values for model parameters, in particular the ones characterising dispersal and mutation. We finally used phylogenetic observations to constrain some of these parameters.

How to cite: Lemaire, A., Braun, J., and Acevedo-Trejos, E.: Influence of Island Morphology on Micro-Endemic Biodiversity Distribution, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-19170, https://doi.org/10.5194/egusphere-egu24-19170, 2024.

EGU24-19856 | ECS | Posters on site | ITS4.5/GM1.3

Quantification of burrowing animals’ impact on landscapes: a review of numerical methods. 

Marta Loreggian, Jantiene Baartman, and Annegret Larsen

The presence of burrowing animals is a recognizable characteristic in almost all types of landscapes and climates. Independent of their size, their activity of mounding and digging plays a significant role in landscape evolution, to the point of being addressed as ecosystem engineers. For example, while tunnels facilitate water infiltration, mounds slow down surface runoff and make soil available for erosion. Several models have included animal activity as a bioturbation process, and many studies have quantified the impact of animals’ presence on soil properties. However, how to best include burrowing animals’ role in other soil hydro-physical processes in hydrological, landscape evolution, or soil erosion models is still unclear. Indeed, the significant heterogeneity of animals’ distribution and their impact at different spatio-temporal scales complicates their inclusion into models. Therefore, this study aims to explore numerical methods (equations, coefficients, ratios) used to quantify the impact of burrowing animals on soil hydro-physical processes. Furthermore, it explores how these methods can be integrated with the most common equations implemented in hydrological, landscape evolution, or soil erosion models to calculate those processes. We focused on surface runoff, soil lateral transport, soil excavation, soil mixing, water infiltration and subsurface preferential flow. Peer-reviewed studies about burrowing animals’ impact on soil hydro-physical processes were collected. Of those articles, we reviewed studies where numerical methods were used to quantify or discriminate the role of the animals. The articles were classified according to the processes measured, the spatio-temporal scale, whether the animal was vertebrate or invertebrate and smaller or bigger than 2.5 cm.

As a first result, the main processes quantified are soil lateral transport, water infiltration and soil mixing. Most of the studies were conducted with field or laboratory experiments on a yearly scale. Because of this, most equations collected were empirical and used to quantify single processes for a specific environment. Rates were the primary means of quantification for runoff or soil lateral transport, and coefficients for soil mixing. Infiltration was quantified as change in soil moisture or as rate. Overall, hydraulic properties were mainly calculated in relation to the presence/absence of earthworms or insects, while mammals and vertebrates were primarily linked to soil physical properties and soil transport. We can argue that, to better incorporate animals’ influence on soil hydro-physical processes, a more comprehensive investigation of their role in soil hydraulic properties is fundamental. However, this might not be sufficient when considering large spatio-temporal scales (centuries, catchments). For this, the development of an ad hoc faunal-hydro-physical module can be used to explore the impact of animal bioturbation on processes at different scales.

How to cite: Loreggian, M., Baartman, J., and Larsen, A.: Quantification of burrowing animals’ impact on landscapes: a review of numerical methods., EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-19856, https://doi.org/10.5194/egusphere-egu24-19856, 2024.

EGU24-20606 | ECS | Orals | ITS4.5/GM1.3

Bioeconomy gaps and opportunities in restored Atlantic forests 

Pedro Krainovic, João Paulo Romanelli, Laura Helena Porcari Simões, Lukas Rodrigues Souza, Rens Brower, Ana Flávia Boeni, Klécia G. Massi, Cássio A. P. Toledo, Ricardo R. Rodrigues, Vinicius C. Souza, Rafael B. Chaves, Sergio de-Miguel8, and Pedro H. S. Brancalion

Forest restoration faces persistent challenges for its financial viability due to high land opportunity costs and insufficient financial returns from restored areas, such as through payments for ecosystem services and timber production. A potential financial pathway is to develop non-timber forest products with bioeconomic potential. Here, we explore the bioeconomic potential of native tree species growing in different types of new wooded lands in Atlantic forests. First, we established 25 30 𝚡 30 m plots in natural regeneration, degraded forest remnants, and actively managed areas (eucalyptus monoculture and active restoration) in the Paraíba Valley, southeastern Brazil, where we sampled all woody individuals with dbh ≥ 5 cm, totaling 284 native tree species. Then, we conducted a literature review and patent survey on the biotechnological potential of the species sampled. Based on this review and survey, we calculated the proportion of sampled species with patents and assessed the species used and general characteristics of patents registered among prominent companies in the market. We found 168 (70%) species with a biotechnological potential based on the presence/absence of articles reporting uses for medicine, cosmetics, food, and other market segments, such as bioinsecticides, bio fertilization, construction, and manufacturing. In the sampled areas, species offer varied potential for use, with higher potential in spontaneous environments. Araucaria angustifolia was the most extensively studied species, with 246 research papers, followed by Euterpe edulis (205), Baccharis dracunculifolia (188), Dodonaea viscosa (170), Cedrela odorata (158), Copaifera langsdorffii (139) and Hymenaea courbaril (132). We found patents worldwide, distributed across more than 20 countries, for the sampled species. The medicinal use of leaf chemicals accounts for the largest use in our survey. Despite these numbers, we found that less than 5% of the investigated articles reported evaluations of final products, while most provided results from in vitro, in vivo, or chemical analytics descriptions. Most patents registered by companies are related to exotic and non-tree species, many associated with existing commodity chains, reinforcing the need to integrate bioeconomy and forest restoration agendas better.

How to cite: Krainovic, P., Paulo Romanelli, J., Helena Porcari Simões, L., Rodrigues Souza, L., Brower, R., Flávia Boeni, A., G. Massi, K., A. P. Toledo, C., R. Rodrigues, R., C. Souza, V., B. Chaves, R., de-Miguel8, S., and H. S. Brancalion, P.: Bioeconomy gaps and opportunities in restored Atlantic forests, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-20606, https://doi.org/10.5194/egusphere-egu24-20606, 2024.

This study presents the preliminary results of the ongoing research project “Re.Nature Cities”, in which  the ability of street trees to act as an effective measure against increased urban air temperatures is evaluated via experimental and simulation means. In the existing literature, numerous studies highlight that the addition of street trees inside the canyons of urban areas may result in a significant reduction of the peak ambient summer Tair, having also a prominent effect on outdoor thermal comfort regulation. Yet, street trees also impact urban ventilation as they act as barriers, disturbing the wind flow and affecting buildings’ energy needs and thermal comfort; the positive effect of wind sheltering during the cold winter period, can be thus significantly counterbalanced during the warmer periods of the year. The existing evidence reveals that the green elements’ implementation in the built environment without holistically accounting for all the vegetation-air-buildings interactions, can even exacerbate human discomfort and deteriorate indoor natural ventilation.

Based on the above, this study evaluates the mitigation potential of a tree type that is commonly encountered in Greek cities – the citrus- since it has low irrigation needs and high drought tolerance. An integrated experimental campaign, employing wind tunnel measurements, albedo and Leaf Area Index/Leaf Area Density (LAI/LAD) measurements is conducted so as to define of the aerodynamic, thermal and foliage characteristics of real trees.  Wind tunnel measurements of total drag are carried out in a wind tunnel section of 3.5m width and 2.5m height, while LAI measurements are conducted using a plant canopy analyzer, with the LAD of each layer (1 m/layer) then calculated from LAI by empirical equations. The obtained values are then used as input parameters in the vegetation model of the ENVI-met microclimate model, which is employed for the evaluation of the thermal environment of typical building blocks in Greece, considering different planting patterns and vegetation coverage scenarios.

The experimental database of foliage, thermal and aerodynamic characteristics of common urban tree species, along with the detailed microclimatic simulations of typical urban districts provide a valuable tool for decision-making regarding the optimal vegetation coverage and the planting pattern for urban areas.

 

How to cite: Tsoka, S., Pappa, V., Markos, N., and Bouris, D.: Assessing the effect of citrus plant on the improvement of the outdoor thermal environment using wind tunnel and ground-based Leaf Area Index measurements, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-20879, https://doi.org/10.5194/egusphere-egu24-20879, 2024.

Cyanobacterial bloom induced by water eutrophication is one of the most serious ecological problems in freshwater lakes. Water diversion, transferring external freshwater into lakes, is proved to be the eco-hydraulic engineering measure rapidly relieving cyanobacterial blooms in eutrophic lakes. To explore the response of phytoplankton community to the changed aquatic habitat influenced by water diversion, we constructed the microcosm experiment modeling water diversion from Yangtze River to Lake Taihu in the laboratory, with one control group and three flow discharges groups of external freshwater from Wangyu River diversion channel during the summer water diversion period. Each modeling microcosm ecosystem had a volume of 5 L and was studied for a period of 20 days (10 days for the water diversion period and 10 days for the stop period). The results showed that the responses of physicochemical parameters in lake microcosms were sensitive, reflecting by the variations in contents of aquatic dissolved oxygen, total nitrogen, total phosphorus and dissolved silicate positively correlated with the flow discharges. During the period of water diversion, the cell abundances of Cyanophyta in all treat groups decreased significantly, while the abundances of Bacillariophyta increased, especially in the group with the highest flow discharge. The diversity and dominant species in phyla of Cyanophyta and Bacillariophyta were changed by water diversion and evidently in the highest flow discharge group. On the 20th day of the stop period, the relative proportion of Microcystis spp. recovered, and Pseudanabaena spp. became one of dominant cyanobacterial species in treat groups, which was related to the dominance of Pseudanabaena spp. in the external river water. The redundancy analysis between aquatic physicochemical parameters and phytoplankton communities revealed that variations in contents of aquatic dissolved oxygen, total nitrogen and dissolved silicate were the dominant environmental factors influencing lacustrine phytoplankton community in addition to the allochthonous inputs from external freshwater. However, the recovery of Microcystis spp. during the stop period of water diversion demonstrated that water diversion from Yangtze River to Lake Taihu has no sustainable effect on changing the dominance of Microcystis spp. in lakes in short time, although the diversity and phytoplankton community composition shifted during water diversion.

How to cite: Dai, J., Wu, X., and Wu, S.: Resilience of lacustrine phytoplankton community to the short-term river-to-lake water diversion, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-21127, https://doi.org/10.5194/egusphere-egu24-21127, 2024.

EGU24-467 | Orals | ITS4.6/SSS0.1.5

Nature-based solutions for leveed river corridors 

Matt Chambers, Dave Crane, Charles van Rees, Matt Shudtz, Craig Landry, Susana Ferreira, Don Nelson, Burton Suedel, Brock Woodson, and Brian Bledsoe

Climate driven changes in hydrologic regimes are increasing riverine flood risks in many parts of the world. Societies that have historically relied on structural flood management infrastructure, e.g., levees and dams, may face significant challenges as these types of infrastructure can be expensive and politically difficult to retrofit for non-stationary and uncertain future flood hazards. Hybridizing conventional infrastructure systems with nature-based solutions (NbS) can help communities adapt to non-stationarity and improve flood resilience. However, despite advances in the academic literature, NbS have failed to become mainstream in many societies. The United States (US) is no exception and has an extensive history of engineering rivers with structural systems to support immediate-term economic growth and with limited consideration for non-stationarity. For example, there are thousands of kilometers of continuously leveed river corridors in the US and many of these levees were built as close to river banks as possible to maximize the commercial prospects of flood protected land use. Such levees are relatively sensitive to non-stationarity and the communities they protect are becoming increasingly vulnerable to climate change-driven flooding. Our research focuses on how to bridge the gap between the scientific development of NbS and implementation in professional practice. We are doing so by example, with levee setbacks on America’s longest river -- the Missouri -- and in collaboration with the US’s primary action agency of flood risk management -- the US Army Corps of Engineers. Setbacks are implicitly an adaptation strategy that buffer a community against uncertainty and non-stationarity by providing additional room for floodwater conveyance. Unfortunately, they are fraught with social and political challenges because -- as a form of managed retreat -- they require some community members to relinquish private property rights so that the broader community can have greater flood protection. Critical to bridging the gap between levee setback research and implementation is understanding the performance of setbacks at scale and the development of simple and repeatable methods for designing setbacks to successfully deliver multiple ecosystem services. The most fundamental of which is how to “size” a setback – in other words – how big of a floodplain reconnection is required to achieve a desired improvement in flood protection services? In this talk, we will discuss sizing methodologies for achieving multiple services, as well as practical engineering, social, ecological, and administrative constraints that have arisen in the process of translating NbS research to practice. The example of levee setbacks on American rivers is particularly useful because it affords experimentation with repeatability (given the thousands of kilometers of continuously leveed river corridors) and the spatial scale of reconnection required to achieve multiple benefits (given the massive size of many levees and floodplains). The results of which may be relatable to many engineered river corridors around the world and will hopefully support mainstreaming NbS in other social and political contexts.

How to cite: Chambers, M., Crane, D., van Rees, C., Shudtz, M., Landry, C., Ferreira, S., Nelson, D., Suedel, B., Woodson, B., and Bledsoe, B.: Nature-based solutions for leveed river corridors, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-467, https://doi.org/10.5194/egusphere-egu24-467, 2024.

As urbanization and climate change continue to pose significant challenges for cities worldwide, green roofs (GRs) has emerged as a viable sustainable solution for supporting traditional infrastructure in managing stormwater runoff. Although their hydrological behavior has been sufficiently documented in literature, conflicting results emerge regarding the potential variations in their retention capacity (RC) over the medium and long-term. Based on preliminary investigations, this research aimed at assessing medium-term changes in the hydrological performance of two experimental GRs (GR1 and GR2), further investigating the potential role played by precipitation severity. The GRs, located in Southern Italy and consisting of three layers (vegetation, substrate and drainage), were set up in 2017 and monitored for two operational periods, 2017-2019 and 2022-2023. The measurements gathered between 2017 and 2019 provide valuable insights into the initial performance of the GRs and their ability to retain water during the early years of operation. Data collected in 2022 and 2023 instead reflect the retention capacity of the GRs after a few years of operation. A total of 29 mild precipitation events were collected during both periods and for both GRs, detecting from the monitoring data their cumulative precipitation (P) and runoff (R) with the objective of assessing the RC (RC = 1 - R/P). Based on the preliminary findings, it appears that there is an overall decline in the RC for both GR1 and GR2, without significant differences between the two. The Aging Indexes (AI) were calculated for GR1 and GR2, representing the average reduction of the runoff coefficient (RC) over time. GR1, which has a drainage layer composed of expanded clay, exhibited an AI of 12%. On the other hand, GR2, characterized by a drainage layer made of MODI' plastic panel filled with expanded clay, exhibited a slightly higher AI of 13%. Further analysis revealed that within each dataset, two groups were identified based on a threshold determined by the growth coefficient g(T) of the precipitation events. For the group of events with g(T) values above 0.12 (sample size of 14), the AI values were 15% and 16% for GR1 and GR2, respectively. On the other hand, the group of events with g(T) values equal to or lower than 0.12 (sample size of 15) experienced AI values of 10% and 11% for GR1 and GR2, respectively. These findings suggest that as the growth coefficient g(T) increases, indicating higher return periods T, the AI and consequently the reduction in hydrological performance of GRs also increase. The highly possible increase in the future of extreme precipitations would pose a considerable limit to the spread of this kind of sustainable drainage infrastructures. However, additional modeling investigations focused at detecting the effects of alternative GRs designs and materials on their long-lasting average hydrological performance would be essential for making informed decisions and investments.

How to cite: D'Ambrosio, R. and Longobardi, A.: Assessing the Medium-Term Changes in Hydrological Performance of Green Roofs: The Influence of Precipitation Severity, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1019, https://doi.org/10.5194/egusphere-egu24-1019, 2024.

EGU24-1035 | ECS | Posters on site | ITS4.6/SSS0.1.5 | Highlight

Multi-scale analysis of green infrastructure morphology for climate change adaptation 

Lou Valide, Pierre-Antoine Versini, and Olivier Bonin

Nature-based Solutions, even if not identified as such, are becoming more and more popular in land planning, especially in cities. Conserving and restoring green infrastructure in urban context is now recognised as being a good practice in the face of climate change adaptation: ecosystem services provided by green spaces can help reduce urban heat island effect and risks of flood, improve resilience of ecosystems to preserve biodiversity and enhance human well-being through access to nature. Simultaneously, cities have to face another challenge: containing land take and urban expansion. The European Commission, in its Roadmap to a Resource Efficient Europe (2011), claimed the “aim to achieve no net land take by 2050”, a goal already transcribed in French law since 2021. Hence, the competition for land use which already existed between housing, industry, roads and recreational purposes will only become fiercer and have to include a new competitor: Nature-based Solutions. In this context, the ability of optimizing the implementation of such solutions – through the different scales at which they provide ecosystem services (building, neighbourhood, city and landscape) – is becoming primordial. Where should we conserve or restore green spaces in priority to ensure the providing of the ecosystem services needed for urban climate change adaptation? This question implies a multi-scale spatial analysis of the impact of green infrastructures on cities. To do so, the question of urban form is tackled by focusing on what is between buildings and streets, where green infrastructure can be deployed and woven into the urban fabric. To establish a multi-scale typology of green infrastructures based on their morphologies, classical approaches are combined with mathematical tools such as fractal analysis for characterizing their dispersion or graph theory for characterizing their connections, essential when studying biodiversity issues. This typology, associated with ecosystem services and biodiversity assessment for different French case studies (including the conurbations of Niort and Dijon), could help understand how to spatially implement Nature-based Solutions within cities, and be integrated into land-planning scenarios.

How to cite: Valide, L., Versini, P.-A., and Bonin, O.: Multi-scale analysis of green infrastructure morphology for climate change adaptation, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1035, https://doi.org/10.5194/egusphere-egu24-1035, 2024.

EGU24-2018 | ECS | Posters on site | ITS4.6/SSS0.1.5

WebGIS for Marine Coastal Ecosystems: A Dynamic Interface for Communicating and Collaborating on Nature-Based Solutions in Climate Change Mitigation and Adaptation 

Jéssica Uchôa, Catarina Fonseca, Rafaela Tiengo, Bruna Almeida, and Artur Gil

As the global community grapples with the complex challenges of climate change, the integration of nature-based solutions (NBS) has emerged as a critical strategy. This work introduces a Web Geographic Information System (WebGIS) designed to showcase and communicate the results of initiatives focused on NBS within the scope of the Marine Coastal Ecosystems Biodiversity and Services in a Changing World (MaCoBioS) project. The platform serves as an interface for decision-makers and stakeholders, providing a spatially contextualized visualization of geospatial data related to marine and coastal ecosystems, climate risks, and adaptation. The MaCoBioS webGIS is based on an open-source platform, using JavaScript and the Leaflet map library to showcase key scenarios developed for case study ecoregions. The platform allows remote access to data irrespective of geographical constraints and is capable of integrating multidisciplinary data, ensuring a comprehensive and up-to-date view of evolving climate-related scenarios. The MaCoBioS webGIS not only facilitates the identification, evaluation, and direction of potential solutions to extant and emergent issues but also affords public access and participation. It serves as a foundational platform for prospective local and regional areas monitoring and management. By integrating qualitative information with scientific data, the aim is to present the results clearly and in a straightforward language, to reach a broader audience, including those who may not have specialized expertise. In so doing, it establishes the groundwork for future initiatives, promoting collaboration and leveraging cutting-edge technology for the betterment of coastal communities and ecosystems. In summary, the webGIS not only serves as a powerful tool for visualizing geospatial data but also acts as an effective means of communication and collaboration. By promoting informed decision-making, and supporting initiatives related to climate change and NBS, the platform contributes to the collective effort in addressing the complexities of our changing climate.

How to cite: Uchôa, J., Fonseca, C., Tiengo, R., Almeida, B., and Gil, A.: WebGIS for Marine Coastal Ecosystems: A Dynamic Interface for Communicating and Collaborating on Nature-Based Solutions in Climate Change Mitigation and Adaptation, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-2018, https://doi.org/10.5194/egusphere-egu24-2018, 2024.

The Great Green Wall (GGW) is a multibillion-dollar African initiative to combat desertification in the Sahel by restoring 100 million hectares of degraded land. The idea of a physical green wall of trees has now been developed into the implementation of scattered green zones throughout arid areas, providing sustainable reforestation, revegetation, and land management. In West Africa, the most important climate feature is the West African Monsoon (WAM), which brings rainfall over the Sahel during the Northern Hemisphere summer. Climate dynamics associated with WAM changes could also play a role on the Atlantic Tropical Cyclones (ATCs) formation and variability. The potential climate impacts of the most recent GGW plan on northern Africa and tropical Atlantic have not yet been adequately evaluated, raising concerns about unforeseen climate ramifications that could affect stability in northern Africa and impact on the ATC variability. Here, we use a high-resolution (~13 km) regional climate model to evaluate the climate impacts of four GGW scenarios with varying vegetation densities under two extreme emission pathways (low and high). Higher vegetation density GGW scenarios under both emission pathways show enhanced rainfall, reduced drought lengths and decreased summer temperatures beyond the GGW region relative to the cases with no GGW. However, all GGW scenarios show more extreme hot days and heat indices in the pre-monsoonal season. Furthermore, in spite of a strong variation in the African Easterly Waves activity, no significant changes are found in terms of ATCs frequency, intensity, meridional motion and translation speed over the North Atlantic area. Small changes in the TC densities are found in front of the cost of West Africa,  in the eastern side of the Main Development Region. These findings highlight the GGW's contrasting climatic effects, emphasizing the need for comprehensive assessments in shaping future policies.

 

How to cite: Ingrosso, R. and Pausata, F. S.: On the climate impacts of four different Great Green Wall scenarios on the northern Africa and the Atlantic Tropical Cyclones variability., EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-2883, https://doi.org/10.5194/egusphere-egu24-2883, 2024.

Given the local pollution near the school in Follonica(Gr)-Italy, specifically at the Gora river’s mouth, students have designed a study (IBSE method) of the chemical and ecological indicators of the river's situation. Analyzing the city's history about climate, the changes of the water regime and the shape of the river during the XX century, they have measured the indicators (physical and chemical parameters of the water, Extended Biotic Index). Creating a website and an interactive map of the river, they have communicated the situation to the local authorities, so the school has become involved in the "Pecora River Agreement", a local project aiming to the redevelopment of the river ecosystem. Students make proposal: plants in the riverbank, activities to sensitize local community and monitoring through ecological index for the future of the city.

How to cite: severi, A.: Requalify our river: from a school project to a city project, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-3616, https://doi.org/10.5194/egusphere-egu24-3616, 2024.

The recently released IPCC Mitigation report placed agroforestry as one of the top three Agriculture, Forestry and Other Land Use (AFOLU) mitigation pathways, noting that it delivers multiple biophysical and socioeconomic co-benefits such as increased land productivity, diversified livelihoods, reduced soil erosion, improved water quality, and more hospitable regional climates, concluding there is ‘high confidence’ in agroforestry’s mitigation potential at field scale. As such, agroforestry is one of the most cited nature-based solutions in development strategies and in reporting of nationally determined contributions (NDC),  both for its potential mitigation benefits, but not least for the adaptation, resilience and livelihood benefits it can provide, across scales from agro-industrial farming to small farmer holdings. Here we present recent global and regional estimates of above- and below-ground biomass on agricultural land based upon IPCC Tier 1 estimates and compare results with an updated carbon density map based on remote sensing, with results indicating the methodology and initial estimations are robust. Two future scenarios are evaluated to estimate carbon sequestration potential of increasing tree cover on agricultural land: 1.) incremental change and 2.) systematic change to agroforestry. Estimates of above- and below ground biomass carbon were combined with a remote sensing-based tree cover analysis to estimate the increase in biomass. Global increases (4-6 Pg C for incremental change; 12-19 Pg C for systematic change) highlight substantial mitigation potential. Increasing global tree cover on agricultural land by 10% would sequester more than 18 Pg C over a decade. South America has the highest potential, followed by Southeast Asia, West and Central Africa, and North America. Brazil, Indonesia, Philippines, India, the United States and China are among the top countries. Additionally, we provide an overview and analysis of the unique and significant contribution agroforestry can provide in mountainous regions and in reducing pressure on irrecoverable carbon.

How to cite: Zomer, R., Xu, J., Spano, D., and Trabucco, A.: Nature-Based Solutions: Evaluating the global carbon sequestration potential of agroforestry and increased tree cover on agricultural land., EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-6018, https://doi.org/10.5194/egusphere-egu24-6018, 2024.

EGU24-6169 | ECS | Orals | ITS4.6/SSS0.1.5

Modelling CO2 flows from extensive green roofs within the TEB (town energy balance) urban canopy model 

Aurélien Mirebeau, Cécile de Munck, Stephan Weber, Aude Lemonsu, and Valéry Masson

To mitigate climate change impacts in cities, nature-based solutions are broadly promoted due to their supposed benefits for biodiversity, rainwater management, evaporative cooling, and sequestration of carbon. Among existing solutions, green roofs show the advantage of tackling the lack of space available for greening in urban areas. But green roofs are still underdeveloped due to their cost and the lack of scientific knowledge around their potential, especially for carbon sequestration. Quantifying the various contributions of green roofs using reliable scientific approaches is a major challenge. Thus, it is essential to build a numerical model capable of simulating green roofs development and functioning at city scale in order to provide information to decision-makers with relevant indicators.

 

Here, the urban canopy model Town Energy Balance (TEB) with the module TEB-GREENROOF is used to model green roofs. The TEB-GREENROOF model, evaluated in previous study for heat and water transfers, is improved by activating the photosynthesis model ISBA-A-gs in order to represent the CO2 exchanges of the vegetation implemented on the green roof. The modelling is informed by 6 years of continuous CO2 flux data on a non-irrigated extensive green roof located in Berlin (Germany) in partnership with the Technische Universität Braunschweig. In order to evaluate and improve the thermal, hydrological and respiration characteristics of the ISBA-A-gs model on a green roof, an initial simulation is carried out by forcing the monthly evolution of the leaf area index (LAI) by LAI data estimated experimentally. The model is then applied with a dynamic calculation of LAI in order to enable it for simulations of roof greening scenarios on a city-wide scale under any climate with no information on the LAI.

 

Results show that the model is able to estimate the annual net ecosystem exchange of the Berlin green roof and to correctly reproduce the CO2 fluxes for both diurnal cycles and annual variation under climate variability, with drier years showing less carbon sequestration.

How to cite: Mirebeau, A., de Munck, C., Weber, S., Lemonsu, A., and Masson, V.: Modelling CO2 flows from extensive green roofs within the TEB (town energy balance) urban canopy model, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-6169, https://doi.org/10.5194/egusphere-egu24-6169, 2024.

EGU24-6608 | Orals | ITS4.6/SSS0.1.5

Nature-based solutions for erosion mitigation : insights from a systematic review for the Andean region 

Veerle Vanacker, Armando Molina, Miluska Rosas, Vivien Bonnesoeur, Francisco Román-Dañobeytia, Boris Ochoa-Tocachi, and Wouter Buytaert

The Andes Mountains stretch over about 8900 km and cross tropical, subtropical, temperate and arid latitudes. More than 85 million people lived in the Andean region by 2020, with the northern Andes being one of the most densely populated mountain regions in the world. The demographic growth and a stagnating agricultural productivity per hectare led to an expansion of the total agricultural land area, either upward to steep hillsides at high elevations covered by native grassland-wetlands ecosystems, or downward to lands east and west of the Andes covered by tropical and subtropical forests. Land use and management have significantly altered the magnitude and frequency of erosion events. 

This study systematically reviews the state of evidence on the effectiveness of interventions to mitigate soil erosion by water and is based on Andean case studies published in gray and peer-reviewed literature. After screening 1798 records, 118 empirical studies were eligible and included in the quantitative analysis on soil quality and soil erosion. Six indicators were pertinent to study the effectiveness of natural infrastructure: soil organic carbon and bulk density of the topsoil, soil loss rate and run-off coefficient at the plot scale, and specific sediment yield and catchment-wide run-off coefficient at the catchment scale. The protection and conservation of natural vegetation has the strongest effect on soil quality, with 3.01 ± 0.893 times higher soil organic carbon content in the topsoil compared to control sites. Soil quality improvements are significant but lower for forestation and soil and water conserva- tion measures. Soil and water conservation measures reduce soil erosion to 62.1 % ± 9.2 %, even though erosion mitigation is highest when natural vegetation is maintained.

Further research is needed to evaluate whether the reported effectiveness holds during extreme events related to, for example, El Niño–Southern Oscillation.

 

 

 

How to cite: Vanacker, V., Molina, A., Rosas, M., Bonnesoeur, V., Román-Dañobeytia, F., Ochoa-Tocachi, B., and Buytaert, W.: Nature-based solutions for erosion mitigation : insights from a systematic review for the Andean region, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-6608, https://doi.org/10.5194/egusphere-egu24-6608, 2024.

EGU24-7898 | ECS | Posters on site | ITS4.6/SSS0.1.5

Nature-based Solutions on privately owned land: Stakeholder engagement matters 

Marion Wallner, Thomas Thaler, Arthur Schindelegger, and Katharina Gugerell

To tackle hydrometeorological extreme events and adapt to climate change, Nature-based Solutions (NbS) are widely considered a promising approach. Yet, their implementation remains challenging. One key reason is that NbS require a lot more land than grey infrastructure – making their implementation dependent on privately owned land and prone to cause or exacerbate conflicts of interest over land use. This request of privately owned land widens the numbers of actors involved in the decision-making process. For this very reason, the realisation of NbS highlights the necessity of meaningful stakeholder engagement. However, in the past, technical mitigation measures were traditionally enforced top down by engineers within the public administration at national or regional level. Stakeholder engagement thus fundamentally changes the way how risk managers and citizens collaborate and is often reported to not live up to its expectations. Therefore, this study will address the role of stakeholder engagement as a decisive factor for the implementation of NbS on privately owned land. More specifically, it aims (i) to analyse what approaches to stakeholder engagement are currently employed on the side of flood risk authorities and (ii) to evaluate how stakeholder engagement processes account for conflicts of interest over land use. For this purpose, a qualitative research design approach will be exerted. This will involve desk research to identify areas in Austria where NbS on privately owned land have already been (and will be) implemented, semi-structured interviews with public water authorities and workshops in our case study site – the Lafnitz catchment in Austria. Lessons learnt will be compared with those of five other regions across Europe, as our study is embedded in the EU Horizon Project “Land4Climate” (Utilization of private land for mainstreaming Nature-based Solutions in the systemic transformation towards a climate-resilient Europe, HORIZON-MISS-2022-CLIMA-01-06). By doing so, our research will provide hands-on knowledge on NbS implementation and foster its mainstreaming across the European Union.

How to cite: Wallner, M., Thaler, T., Schindelegger, A., and Gugerell, K.: Nature-based Solutions on privately owned land: Stakeholder engagement matters, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-7898, https://doi.org/10.5194/egusphere-egu24-7898, 2024.

Idea and Objectives: The health and well-being of urban populations are increasingly under pressure from climate change, for example, due to temperature extremes resulting in heat stress. The demand for heat mitigation is particularly high for urban areas in humid, tropical climates, as they are affected by heat stress already today, and for which a further amplification of heat stress is expected. For the case of Hue, a humid tropical Central-Vietnamese city, based on a typology of selected green-blue infrastructure elements, potential benefits for the regulation of outdoor temperature and outdoor thermal comfort are systematically virtually implemented and modelled. In order to promote acceptance of greening interventions by the public in Hue, citizen demands and preferences towards urban green elements, including potential co-benefits, are considered in this study, and in so-doing, best practices for local action shall be identified.

 

Background: Vietnam is a country that faces multiple challenges. Climate change is anticipated to exacerbate natural hazard risks, i.e., of flooding, storms, and prolonged periods of extreme heat, which are known to increase the risk of mortality, particularly among vulnerable groups. This is compounded by ongoing, rapid urban growth, that urgently necessitates safeguarding urban ecosystem services to facilitate climate change adaptation, and to support human health and well-being. Elements of the urban green-blue infrastructure are typically regarded as efficient nature-based interventions for the delivery of often multiple ecosystem services, including benefits for urban heat mitigation, i.e., the improvement of outdoor thermal comfort. Accordingly, such measures are increasingly being funded, politically recognised and implemented in Southeast Asian countries, including Vietnam. However, specifically for Vietnam, certain knowledge gaps remain with respect to the effectiveness of greening interventions for heat mitigation under local conditions, as well as in regard to ensuring the implementation of locally relevant and thus sustainable and resilient nature-based solutions.

How to cite: Sumfleth, L., Scheuer, S., Nguyen, L., and Haase, D.: Urban green-blue infrastructure as nature-based solutions for urban heat adaptation in Hue city, Central Vietnam – Potential impacts in contrast to citizen demands for urban greenery, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-7963, https://doi.org/10.5194/egusphere-egu24-7963, 2024.

EGU24-8691 | ECS | Posters on site | ITS4.6/SSS0.1.5

Development of wind and fire risk indices for climate-mitigation forestry 

Els Ribbers, Hanna Lee, Priscilla Mooney, Helene Muri, Lei Cai, Jin-Soo Kim, and Lars Nieradzik

Afforestation has long been discussed as a nature-based climate mitigation solution. Although it could be an economic, green, and safe climate mitigation method, several studies suggest the possibility of unforeseen consequences depending on how it is implemented. An important aspect to be taken into account when designing af- and reforestation plans is the risk of damage to the new forest system in the face of climate warming. Recent studies have already shown an increase in both wind and fire damage risks in northern latitudinal forests related to climate warming, with strong winds leading to breakage of individual branches as well as in the knock-over of individual trees or even entire forest areas.

However, the forest system is complex, with a high number of feedback loops between different types of damage and between forest structure and ecological parameters. A few examples: Trees that are weakened by damage from pest outbreaks and snowfall are more susceptible to damage from wind and fire; Gaps in the forest that are created by management or damage both increase wind flow due to an eddy effect and create new forest edges with poorly adapted trees, increasing the risk of wind-throw.

Due to this complexity, the resilience to damage and therefore ability of forests to mitigate climate on a regional scale are still poorly understood. Understanding this complexity requires model work and extensive literature research, as most studies only focus on a few aspects of the forest system, such as the management type or wind effects. The aim of the study is therefore to develop adequate and future-proof wind- and fire risk indices that boreal forest managers can use to improve management strategies to make climate-mitigation forests more effective, resilient and damage resistant.

To do this, output from the Weather Research and Forecasting (WRF) model is used in combination with data on damage, forest management and forest structure to shed some light on possible feedbacks between forest systems and climate on a small-scale basis, in this case 3kmx3km. This information is then used to expand the Canadian Forest Fire Weather Index (FWI) to include ecological, management-related and forest structural parameters. As the structure of the existing FWI is climate-based, the wind risk index will be based on the developed fire risk index.

Our preliminary results show that wind damage was most common and extensive in the south-western coastal area of Norway over the last two decades. In contrast, fire damage was most prevalent in the south, with increased damage extent in the south-west of the country. Furthermore, the FWI shows that under an afforestation scenario in Norway, the mountainous region will have the highest frequency of days with medium to high danger of forest fires under climate warming. In this presentation we will discuss these preliminary results, as well as the methodology we will be using to develop the risk indices. Policymakers and forest owners alike will be able to use the risk indices to make the climate-mitigation forests more resilient against damage in a warming climate.

How to cite: Ribbers, E., Lee, H., Mooney, P., Muri, H., Cai, L., Kim, J.-S., and Nieradzik, L.: Development of wind and fire risk indices for climate-mitigation forestry, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-8691, https://doi.org/10.5194/egusphere-egu24-8691, 2024.

The proliferation of climate-induced stressors has deterred countries' green spaces (GS), which in turn degrade and deplete natural green barriers. Hence, Urban Green Infrastructure (UGI) modelling is grabbing global attention perceiving it as a nature-based mitigation/adaptation strategy to enhance the resilience of urban areas to fight climatic risks. UGI protects and improves the socio-ecological wellness of urban and rural regions. This research intends to investigate thirteen sustainable UGI indicators and their functional linkage with the five vital taxonomies of nature-based green solutions (at the neighborhood level) under a community participatory (CP) approach; out of ten GS elements and twenty-two sustainable UGI indicators developed by the author in his earlier research study [2-5]. It is to develop a sustainable UGI indicator-based framework (tailored to the native-built context) for climate-resilient urbanisation.

The results of the in-depth household survey (192 questionnaires), executed in three KP districts, Charsadda, Peshawar, and Mardan, and results were generated through Relative Importance Index (RII) and Interquartile Range Technique (IQR) show a very good level of coefficient alpha (α) value, (α = 0.7) — an acceptable threshold level [6, 7]. Furthermore, this study acknowledges key GS taxonomies that have achieved RII value ≥ 0.72. This performs a pivotal role in quality improvement and strengthening the resilience (health) of the respective UGI indicators. This scientific research study provides a foundation for an eco-regional paradigm in KP territory that paves the way for an effective implementation of green urbanism to naturally ameliorate the vulnerability to potential climatic stresses (like flooding, drought, the UHI effect) and disastrous impacts on the socio-ecological wellness.

Keywords: sustainable green infrastructure (GI) indicators; participatory planning (PP); nature-based green initiatives; climate change (CC); socio-ecological wellness; KP, Pakistan

References

1. Mell, I. C., Henneberry, J., Hehl-Lange, S., & Keskin, B. (2013). Promoting urban greening: Valuing the development of green infrastructure investments in the urban core of Manchester, UK. Urban Forestry & Urban Greening, 12(3), 296–306. http://dx.doi.org/10.1016/j.ufug.2013.04.006

2. Rayan, M., Gruehn, D., Khayyam, U., (2021b). Green infrastructure planning. A strategy to safeguard urban settlements in Pakistan. In: Jafari, M., Gruehn, D., Sinemillioglu, H., Kaiser, M. (Eds.), Planning in Germany and Iran. Responding Challenges of Climate Change through Intercultural Dialogue. Mensch und Buch Verlag. Berlin, pp. 197–220.

3. Rayan, M., Gruehn, D., & Khayyam, U. (2021a). Green infrastructure indicators to plan resilient urban settlements in Pakistan: Local stakeholder’s perspective. Urban Climate, 38, 100899. https://doi.org/https://doi.org/10.1016/j.uclim.2021.100899

4. Rayan, M.; Gruehn, D.; Khayyam, U (2022a). Frameworks for Urban Green Infrastructure (UGI) Indicators: Expert and Community Outlook toward Green Climate-Resilient Cities in Pakistan. Sustainability 2022,14, 7966. https://doi.org/10.3390/su14137966.

5. Rayan, M.; Gruehn, D.; Khayyam, U (2022b). Planning for Sustainable Green Urbanism: An Empirical Bottom-Up (Community-Led) Perspective on Green Infrastructure (GI) Indicators in Khyber Pakhtunkhwa (KP), Pakistan. Int. J. Environ. Res. Public Health 2022, 19, 11844. https://doi.org/10.3390/ijerph191911844

6. Cortina, J. M. What is coefficient alpha? An examination of theory and applications. J. Appl. Psychol (1993).

7. Peterson, R. A. A Meta-analysis of Cronbach’s Coefficient Alpha. J. Consum. Res (1994).

8. Wu, J., & Wu, T. (2012). Sustainability indicators and indices: an overview. Handbook of Sustainability Management, 65–86. http://dx.doi.org/10.1142/9789814354820_0004

How to cite: Rayan, M., Gruehn, D., and Khayyam, U.: Community-driven sustainable green infrastructure (GI) indicators to plan an eco-friendlier and climate-resilient city-state in Khyber Pakhtunkhwa (KP), Pakistan., EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-8806, https://doi.org/10.5194/egusphere-egu24-8806, 2024.

EGU24-9173 | ECS | Orals | ITS4.6/SSS0.1.5

Opportunities to Restore and Protect Coastal Ecosystems with Enhanced Interdisciplinary Management - The Mediterranean Model. 

Maria Makaronidou, Vito Emanuele Cambria, Evangelia Korakaki, Christos Georgiadis, and Nikos Petrou

Coastal zone ecosystems’ global importance is the primary driver of the wide scientific efforts for their restoration and protection. Over the past three decades, there has been a growing global momentum in the pursuit of initiatives aimed at conserving nature. Regardless of the wide scientific interest, and despite the notable exposure of these ecosystems to degradation and deterioration, numerous habitats, and species, in Europe, have 'vulnerable', or 'near threatened' conservation status. Even in the most favourable circumstances, factors including strong human pressure, urbanization and agriculture, and climate change, exhilarate the current, already, negative trends indicators, related to biodiversity and their associated ecosystem functions and services provision. This project proposes a set of existing and emerging methodologies and solutions for the restoration, conservation, and management practices, which are crucial to improving these profoundly delicate ecosystems in the Mediterranean and similar environmental contexts.

Traditional and innovative ecological restoration solutions have been designed and applied in two such areas along the Greek and Italian coasts, ‘Nestos Delta’ and ‘Bosco di Palo Laziale’, respectively, to improve the conservation status of 'Pannonian-Balkanic turkey oak-sessile oak forests' (habitat 91M0), ‘Alluvial forests with Alnus glutinosa and Fraxinus excelsior’ (habitat 91E0), and 'Mediterranean temporary ponds' (*3170) that have been increasingly exposed to climate change and inappropriate forest and water management.

Analogous, ecological restoration practices include selective trimming of encroaching shrub vegetation (and alien invasive shrubs in the Nestos area), remote-controlled irrigation system, origin-controlled and pathogen-free forestry nursery, ex-situ micro-propagation and in-situ reinforcement of keystone plant populations. An in-depth assessment and quantification of abiotic and biotic factors of the sites' ecosystems were preliminarily conducted to tailor these interventions to the habitats' geo-morphological, climatic, pedological, and physiological conditions.

The EU project LIFE PRIMED (LIFE17 NAT/GR/000511), operates at the Delta of River Nestos in Greece, and the Forest of Palo Laziale in Italy. The results in both areas, thus far, have demonstrated that the collaborative development of innovative water harvesting systems, coupled with adaptation measures, has the potential to enhance water resilience in already degraded forest ecosystems. To date, the project has successfully tackled the effects of escalating irregular rainfall patterns on Mediterranean coastal habitats by implementing a hydraulic system and a wellpoint-based water distribution network in Palo Laziale and Nestos Delta, respectively.

Monospecific approaches for climate and human-related phenomena, such as extreme weather events and agriculture pressure, are disfavoured. Therefore, the LIFE PRIMED project, comprised of an interdisciplinary team of Botanists, Zoologists, Foresters, and Environmental Engineers, has developed and delivered Nature-based transnational, ecosystem-oriented holistic solutions that will have the potential to be replicable and transferable with the greatest aim to recover dysfunctional, poorly managed coastal forest areas, across the Mediterranean region.

How to cite: Makaronidou, M., Emanuele Cambria, V., Korakaki, E., Georgiadis, C., and Petrou, N.: Opportunities to Restore and Protect Coastal Ecosystems with Enhanced Interdisciplinary Management - The Mediterranean Model., EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-9173, https://doi.org/10.5194/egusphere-egu24-9173, 2024.

EGU24-11439 | ECS | Orals | ITS4.6/SSS0.1.5 | Highlight

Assessing the vulnerability to climate change of tree species for urban afforestation 

Cristiano Gala, Gabriele Curci, Loretta Pace, Alessandro Marucci, and Dina Del Tosto

Nature-based solutions are now a key part in climate change adaptation, particularly for urban environments. The integration of natural systems within the urban fabric has the potential to increase cities’ resilience to the predicted changes in climate. Urban forests are one of the most used methods for adding ecosystem services to an urban environment and at the same time address urban-specific climate change challenges such as heat-island effect, intense rainfall and water management. However, the effects of climate change in the long-term on urban forests are not often taken into account when planning interventions such as afforestation. Species selection for urban forests should, among other factors, be based on an assessment of local future climatic conditions, so to ensure the long-term viability of the project. Here we propose a methodology easily applicable to any place in Europe. We use data from interpolated publicly available climate datasets and species distribution data from the European Tree Atlas in order to analyse climatic niches for tree species in Italy. These climatic ranges are then compared to local climatic data, obtained from homogenised time-series measured by a weather station in the city of L’Aquila. The results are summarised in a suitability matrix providing vulnerability scores for each species based on predicted climate changes for the local area. The analysis ranks the species which are less vulnerable to projected future climate conditions. The application to the pilot area of L’Aquila suggests that some species already present will still be suitable also in future climate (e.g. Quercus pubescens) while others will not (e.g. Quercus petraea), and species not traditionally present may become suitable (e.g. Quercus ilex). The importance of obtaining accurate local climate data from observations is a key aspect for municipalities to consider as results of this analysis are greatly dependent on this.

How to cite: Gala, C., Curci, G., Pace, L., Marucci, A., and Del Tosto, D.: Assessing the vulnerability to climate change of tree species for urban afforestation, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-11439, https://doi.org/10.5194/egusphere-egu24-11439, 2024.

EGU24-13018 | ECS | Orals | ITS4.6/SSS0.1.5

Leveraging the co-benefits of large tree protection to inform nature-based management of a forest ecosystem 

Tessa Maurer, Patricia Manley, Christopher Anderson, Nicholas Povak, Philip Saksa, Anu Kramer, and Zachary Peery

In fire-adapted forests around the world, nature-based solutions (NbS) are increasingly used as a tool to promote resilience to catastrophic fire through actions like fuels reduction and prescribed burning. This work also has many potential co-benefits, including climate change mitigation through stable carbon storage and biodiversity through habitat protection. One key mechanism for realizing both of these co-benefits is the protection of large and ancient trees, keystone components that sequester a disproportionate amount of carbon and serve as unique habitat for old forest associated species, many of which are declining or at risk of extinction. However, climate change poses a substantial risk to both tree recruitment and survival, either directly (temperature and drought tolerance) or indirectly (wildfire and insect occurrence). These impacts are not fully understood in the scientific literature nor, as a result, fully accounted for in the design of NbS management projects.

Therefore, to help inform near-term NbS restoration priorities, we investigated how a changing climate will impact the retention of large trees on the landscape and the ecosystem functions they support. Focusing on the Sierra Nevada, California, USA, a biophysically diverse and at-risk mountain ecoregion, we evaluated the intersection of current and future climate with large tree occurrence and two critical functions: carbon storage and habitat for the California spotted owl (Strix occidentalis occidentalis; CSO), an old growth associated species whose core population is limited to the Sierra Nevada and that requires large trees for nesting habitat. We mapped large trees across the Sierra Nevada, evaluated the climatic drivers of large tree biogeography, and forecasted how conditions supportive of large tree populations might shift geographically in the future under two emission levels (RCP 4.5 and 8.5). Using a bivariate fuzzy logic approach, we mapped the joint probability of current CSO occupancy and carbon storage and then evaluated future climate vulnerabilities and associated management strategies. We found that carbon and CSO occupancy corresponded closely with the current distribution of large trees in the Sierra, primarily at mid-elevations in the central Sierra. Similarly, we found that these mid-elevation montane forests are likely to continue to support large trees and CSO habitat and carbon storage through mid-century (e.g., consistent with "monitor" and "protect" climate-informed management strategies). Conversely, climate conditions in the southern Sierra and the upper elevations of the central Sierra are likely to constrain the persistence and recruitment of large trees, affecting the potential to recruit CSO habitat and enhance the carbon storage of higher elevation forests. 

We hope these findings will encourage the design of and investment in climate-informed NbS projects, and we propose that this method could be used in other ecosystems to jointly assess the climate change mitigation and biodiversity impacts of NbS-based management.

How to cite: Maurer, T., Manley, P., Anderson, C., Povak, N., Saksa, P., Kramer, A., and Peery, Z.: Leveraging the co-benefits of large tree protection to inform nature-based management of a forest ecosystem, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-13018, https://doi.org/10.5194/egusphere-egu24-13018, 2024.

Modern cities are highly vulnerable to the adverse effects of climate change, primarily due to the escalating frequency of extreme weather events, including heatwaves. The current state of knowledge leaves no doubt that these effects are exacerbated by ongoing urbanization, leading to the continuous sealing of surfaces and a decrease in green areas in urbanized regions, contributing to the formation of Urban Heat Islands (UHI). These phenomena result in urban space degradation, causing economic, environmental, and demographic losses. Consequently, implementing solutions to enhance cities' resilience to climate threats should be a priority for local governments. Crucial in this context is the development of blue-green infrastructure, with a specific emphasis on micro-retention and the improvement of biologically active surfaces and vegetation habitat conditions. The implementation of such solutions, especially in the face of increasing extreme weather events, is essential for ensuring the sustainable development of smart cities.

This paper will present the results of research on the spatiotemporal distribution of the effectiveness of various components of blue-green infrastructure on a city-wide scale (including: river valleys, forests, urban parks, squares, pocket parks, and larger water bodies) in mitigating the UHI phenomenon in Wrocław, Poland. The study assesses the potential of blue-green infrastructure to mitigate the impact of heatwaves on the population most vulnerable to such threats. As an indicator of urbanized areas' vulnerability to the negative health effects of UHI, we focused on the population aged over 65. The research aims to provide crucial insights into how blue-green infrastructure can be optimized to effectively reduce UHI impacts and minimize health risks, especially within the most vulnerable age groups. This operation constitutes one of the initial stages in creating a prototype of a digital twin of the urban environment of Wrocław. The ultimate goal is to model information about blue-green infrastructure for the purpose of optimizing spatial policy in the context of adapting urbanized areas to climate change. This approach aligns with the Destination Earth initiative developed within the framework of the European Green Deal and EU Digital Strategy.

In the research, data integration was performed using various sources, including multispectral imagery from PlanetScope SuperDove, thermal data from ECOSTRESS LST, point clouds from airborne laser scanning (ALS), Topographic Objects Database (BDOT10k), and demographic data from municipal databases. Importantly, the utilized data are openly accessible and free of charge under the principles of Open Science, enabling the replication of procedures in other cities in Poland and, after identification and adjustment of relevant local data, numerous cities worldwide. In Wrocław, the project aims to provide support in creating and modifying existing and new planning documents, including local spatial development plans, the general plan, and the commune development strategy. This action supports the adaptation of local spatial policy to the growing needs of adaptation to climate change. The research is conducted within the program "Implementation Doctorate – 6th edition" by the Ministry of Education and Science.

How to cite: Budzik, G., Kowalczyk, T., Krajewski, P., Lebiedzińska, M., and Soszyńska, A.: Assessing spatiotemporal distribution of the effectiveness of Blue-Green Infrastructure in mitigating the Urban Heat Island phenomenon in Wroclaw, Poland under the Digital Twin concept for spatial policy optimization, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-13070, https://doi.org/10.5194/egusphere-egu24-13070, 2024.

EGU24-17239 | ECS | Posters on site | ITS4.6/SSS0.1.5

NBS for secondary wastewater effluents infiltration based on soil and woodchips as drainage material: laboratory study    

Pauline Louis, Laurent Lassabatère, Arnold Imig, and Rémi Clément

Wastewater management and treatment are key points in maintaining the quality and the sustainability of water resources. To preserve receiving  water environments, efforts are being conducted to improve the  treatment efficiency . Soil infiltration can therefore be used as a  nature-based solution tertiary treatment, in some areas without surface  water available, or with supplementary water bodies’ protection  regulations. Secondary wastewater effluents (SWE) infiltration surfaces mainly consist of infiltration trenches or flood-meadows. Among the main issues encountered with soil infiltration, two can be highlighted:  the possible low hydraulic conductivity induced by soil clogging, on the  one hand, and the use of non-renewable draining materials such as  pebbles or gravel to ensure the distribution of water in trenches, on  the other hand. In France, in order to overcome those issues,  stakeholders are now considering the replacement of the gravel with  woodchips, a renewable biodegradable material, also prone to  biodiversity in soils. It has been demonstrated through a previous field study that the use of woodchips in infiltration trenches helps maintain infiltration over time, and even improves their performance. However, understanding the underlying mechanisms remains a significant scientific challenge. To better understand the soil and woodchip evolution processes, four columns were set up in a laboratory and fed with secondary treated effluents from a wastewater treatment plant.

 These four columns (with a diameter of 37 cm) are composed as follows:

  • a) Column #1: 80 cm of soil,
  • b) Column #2: 40 cm of wood chips and 40 cm of soil,
  • c) Column #3: 80 cm of soil inoculated with a selection of earthworms ,
  • d) Column #4: 40 cm of wood chips and 40 cm of soil, inoculated with a selection of earthworms .

During the presentation, hydraulic monitoring of the columns will be presented (inlet and outlet flow, column weight monitoring), showing the evolution of the infiltration rate. To analyze the evolution of physical properties within the columns, including parameters like saturated hydraulic conductivity, a modeling study was carried out using Comsol Multiphysics. Specifically, the Richards model (van Genuchten-Mualem) was employed to simulate and understand the changes occurring over time. The models fit the data well. They mainly show that the soil columns (1 and 3) tend to clog early if the hydraulic loads are too excessive. This is reflected by a reduction of hydraulic conductivity at saturation and porosity. In comparison, columns with wood chips seem to maintain their properties, with no major difference between columns with or without earthworms, after two years of monitoring. These results will be compared to the monitoring of physicochemical parameters of the inflow and outflow waters from the columns, allowing for a better understanding of the processes involving woodchips, soil, and macrofauna.

How to cite: Louis, P., Lassabatère, L., Imig, A., and Clément, R.: NBS for secondary wastewater effluents infiltration based on soil and woodchips as drainage material: laboratory study   , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17239, https://doi.org/10.5194/egusphere-egu24-17239, 2024.

EGU24-18619 | ECS | Posters on site | ITS4.6/SSS0.1.5

Nature-Based Solutions for stormwater management: A case study with Multi-Hydro in Parc Molière, France 

Ismael Ávila Vasconcelos, Pierre-Antoine Versini, and Igor da Silva Rocha Paz

Over the last few decades, the urban hydrological cycle has undergone significant changes due to the influence of the built environment, resulting in rapid runoff and increased risk of flooding. Faced with these challenges, nature-based solutions (NBS) are emerging as an appropriate response, especially in densely populated areas, facing the impacts of climate change and biodiversity loss. The application of green infrastructures, as evidenced by Parc Molière in Les Mureaux, France, with its 700 trees, 11,500 m² of flowerbeds, 8,700 m² of grassland and 5,000 m² of gardens, represents a sustainable approach to urban stormwater management. By reintroducing extensive impermeable areas to the open air, Parc Molière strengthens biodiversity, facilitates animal movement, promotes air cooling and reduces urban heat islands, while also modifying hydrological behavior. Carried out in the framework of the LIFE ARTISAN project, this study uses the Multi-Hydro software, developed at the École des Ponts ParisTech, to computationally model the Parc Molière area in two different scenarios: before and after the creation of the green spaces. Based on a fully distributed and physical hydrological model, Multi-Hydro is able to illustrate the influence of NBS by comparing the obtained simulations with instrumented hydrological data. The results should demonstrate that the NBS have a significant impact on peak flow and total runoff volume, mitigating the negative effects in an urban hydrological scenario.

How to cite: Ávila Vasconcelos, I., Versini, P.-A., and da Silva Rocha Paz, I.: Nature-Based Solutions for stormwater management: A case study with Multi-Hydro in Parc Molière, France, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18619, https://doi.org/10.5194/egusphere-egu24-18619, 2024.

EGU24-18701 | ECS | Orals | ITS4.6/SSS0.1.5 | Highlight

A multi-ecosystem service assessment for urban climate adaptation in Singapore  

Emma Ramsay, Leanne Tan, Yuan Wang, and Perrine Hamel

Nature-based solutions are an important tool to adapt to climate change in cities. Green spaces including nature reserves, parks and green streetscapes are essential to mitigate urban heat and also provide important recreation opportunities that benefit peoples physical and mental health. Effectively planning climate resilient and liveable cites thus requires quantitative, spatially explicit information about these ecosystem services. Such data are especially important in dense cities where vacant land is limited and trade-offs must be made to prioritise certain services. Here we present a multi-ecosystem service assessment for Singapore using the urban InVest models to evaluate urban cooling and urban nature access. We generate future greening scenarios based on policy targets to plant one million trees and increase the land area of parks by 50% by 2030 and compare ecosystem service provision for each scenario when either cooling or nature access is maximised in the spatial configuration of scenarios. We compare the benefits and trade-offs achieved by each scenario and explore the potential to quantify these through health indicators. Finally, we discuss how multi-ecosystem service assessment cans be integrated into urban planning and the implications for cities in an uncertain climate future.

How to cite: Ramsay, E., Tan, L., Wang, Y., and Hamel, P.: A multi-ecosystem service assessment for urban climate adaptation in Singapore , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18701, https://doi.org/10.5194/egusphere-egu24-18701, 2024.

EGU24-18875 | Orals | ITS4.6/SSS0.1.5

Suitability assessment of the location for the Natural Based Solution application on drainage systems 

Milica Vranešević, Milica Knežević, Radoš Zemunac, and Maja Meseldžija

The introduction of Natural Based Solution (NBS) into sustainable agricultural practices is a key issue on which the balancing of intensive agricultural activities with environmental protection depends. In lowland areas with intensive agricultural production, occurrences of extreme amounts of excess water, caused by climate change, increase the need for efficient drainage systems. Within the comprehensive framework of drainage system improvement, NBS are emerging as key and versatile interventions. The principal challenge lies in reconciling these solutions with the prevalent technical paradigms in both land reclamation and agriculture. The most important change is the strategic integration of the use of riparian buffers as supplementary melioration measures in delineated areas, especially aimed at reducing the inflow of excess water into the canal network. Deciding where to implement NBS for better drainage systems comes down to assessing the risks that may occur as a consequence to natural resources such as water and soil. When the implementation of NBS determines the crucial factors and evaluates them effectively, then it can categorize and map the optimal places where improvement of the drainage system is possible and efficient. In this study the aim was to delineate suitable zones for implementing nature-based solutions along watercourses through the application of Geographic Information System (GIS) methodology. By overlaying different layers, including pedological and geomorphological maps, digital terrain models indicating land slope, land use classifications, and drainage classes, it is intended to analyze and identify optimal locations. Some of the characteristic drainage systems in Vojvodina have been selected to provide a relevant case study illustrating how GIS can be applied to demonstrate the potential of nature-based solutions in improving drainage systems. This approach not only enhances the efficiency of the existing drainage systems. It also provides insights for strategic afforestation and the increase of biodiversity in agricultural areas.

How to cite: Vranešević, M., Knežević, M., Zemunac, R., and Meseldžija, M.: Suitability assessment of the location for the Natural Based Solution application on drainage systems, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18875, https://doi.org/10.5194/egusphere-egu24-18875, 2024.

The main concern with public policies and strategies for integrating nature-based solutions is to facilitate access to innovative interventions to reach cities and communities that are more sustainable and climate resilient. However, there is an impediment to linking information on the results of projects and the expected impact of the European Commission in the framework programmes for research funding. Here we show how projects targeting nature-based solutions help to implement and review public policies under the EU Strategy for Adaptation to Climate Change 2013 – 2020 and European Green Deal. These policies have a positive impact in various areas, especially in green transition, with the potential to analyse the link between the scientific results of nature-based projects and the strategic orientations of research and innovation. We focused on the evaluation of 150 projects funded at the Horizon 2020 and Horizon Europe level, within three main programmes that provide funding for projects based on nature, resilience and adaptation to climate change: (1) Climate action, Environment, Resource Efficiency  and Raw Materials, (2) Climate, Energy and Mobility and (3) Food, Bioeconomy, Natural Resources, Agriculture and Environment. The main analyzed elements are the number and type of partners, the level of funding, the main objectives of the projects, types of nature-based solutions and their distribution by geographical regions in Europe. This analysis leads to the filling in the existing knowledge of the results that produce science, so that it can be exploited throughout the community. Our results consist in (1) overview of climate challenges in EU R&I framework programmes Horizon 2020 and Horizon Europe, (2) Main NBS designed by European R&I organizations, (3) NBS for climate resilience implemented through EU R&I funding in Horizon 2020 and Horizon Europe, (4) NBS for climate resilience – key pathways of knowledge valorization for ecosystem restoration, preservation and management. Overall, they show that the aspects analyzed in the selected funded projects support the development of nature-based solutions and what are the main actions that lead to long-term impact.

How to cite: Barbu, G.-R. and Niță, M.-R.: Nature-based solutions for climate resilience in EU R&I framework programmes Horizon 2020 and Horizon Europe, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-19213, https://doi.org/10.5194/egusphere-egu24-19213, 2024.

Rural European landscapes are increasingly faced with the interlinked and cascading hazards of flooding and drought, exacerbated by both unsustainable land use practices and climate change. Sponge measures are particularly promising for addressing such multi-hazard risk from a participatory and social-ecological perspective. Sponge measures are nature-based solutions (NbS) that preserve, restore, enhance or create ecosystems to increase landscape and soil water retention while providing co-benefits for people and nature through biodiversity and ecosystem services. As NbS, they interact in complex ways with the socio-ecological systems (e.g. watershed boundaries) in which they are implemented. Thus, participatory processes are needed to ensure a systemic and interdisciplinary understanding of impacts while capturing diverse stakeholder values and interests. NbS design and planning often lacks 1) a shared understanding of the spatially-explicit impacts of NbS on the social-ecological system among stakeholders; 2) consideration of a broad spectrum of impacts as (co-)benefits and trade-offs; and 3) consideration of scales beyond the immediate measure and within diverging future scenarios.

As a promising approach to address these shortcomings, we propose the use of geodesign - an iterative framework for multidisciplinary, stakeholder-driven, and context-sensitive spatial decisions based on the integration of stakeholder inputs, geospatial data, and technology to generate real-time feedbacks and inform smart decision-making. This process also can support participation through fostering shared understandings and reconciling stakeholder conflicts. Despite promising applications in urban and landscape planning, knowledge is lacking on how and with what impacts geodesign can be applied to facilitate the planning of sponge measures at landscape scale. The aim of our research is to assess the utility of geodesign in the context of adaptive sponge measures by combining a systematic literature review with practical application of geodesign in two European catchments faced with increasing risk of hydrometeriological extremes. The review will quantify the adoption and past effectiveness of geodesign practices in similar landscape planning contexts. Based on these insights, a geodesign approach will be developed and implemented within the EU SpongeScapes project (spongescapes.eu) in selected case studies to generate future scenarios to increase landscape resilience against climate change. We present the research plan, including initial hypotheses and preliminary findings as conducted within the context of ongoing PhD research. With the increasing implementation of NbS in Europe in response to unfolding climate change and its consequences, our research will provide insights into the potential benefits and limitations of geodesign to improve their co-design, support policy creation, and inform decision-making.

How to cite: Jajeh, S., Anderson, C. C., and Albert, C.: Collaborative planning of nature-based solutions for climate resilience at landscape scale: exploring the potential of geodesign, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-19504, https://doi.org/10.5194/egusphere-egu24-19504, 2024.

Nature-Based Solutions as a tool to reduce coastal risks have gained in popularity in the last 10 years. However, in France, their development still faces some limits and oppositions from local populations and stakeholders. The main reasons for this are the lack of knowledge and feedback, and the fear of being less protected with against floods with Nature-Based Solutions than with sea walls. This work will present the example of Criel-sur-Mer, in the North of France, where a project of restoration of intertidal habitats to reduce coastal risks is currently discussed and capitalize on feedbacks from three finalized projects from the Netherlands and England.

This study is part of a PhD work on the mobilization of Nature-Based Solutions in coastal protection projects. This presentation is based: on field trips conducted between March and April in the Netherlands and England, on the sites of Hedwige & Prosperpolder (Netherlands, Belgian border), Freiston Shore and Abbotts Hall (England), and in September 2022 and March 2024 in Criel-Sur-Mer (France); on semi-structured interviews conducted with stakeholders on those sites; on semi-structured interviews conducted with 39 coastal engineers and environmentalists between June and August 2023 in Artelia, the engineering firm in charge of the project of intertidal habitats restoration in Criel-sur-Mer; and on observative participation to a public consultation workshop with local actors and stakeholders for the project of Criel-sur-Mer.

The cross-study of the three Dutch and English projects gives us useful examples of the effectiveness of Nature-Based Solutions used as a tool to reduce coastal risks, that can be reused to enrich the project of Criel-sur-Mer. As the two English projects have been finalized in 2002, they are a source of extensive feedback on the evolution of intertidal ecosystems with managed realignment and their efficiency facing storms. The Dutch example started in 2005, but was finalized only in 2023, as it faced numerous social and political oppositions. These projects can thus be used as feedback on governance, project structuration and finding the right balance between different interests for the Criel-sur-Mer example.

How to cite: d'Avdeew, M.: Nature-Based Solutions for coastal risks protection: lessons learned from Dutch and English examples, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-20731, https://doi.org/10.5194/egusphere-egu24-20731, 2024.

EGU24-20974 | Orals | ITS4.6/SSS0.1.5

Role of blue and green spaces in mitigating heat stress and providing biodiversity co-benefits in India’s cities  

Jagdish Krishnaswamy, Kiran Chandrasekharan, Dhananjayan Mayavel, and Ravi Jambhekar

Cities and urbanizing spaces combine heat stress from both heat island effect due to the built environment as well as global warming.  India with its high rate of urbanization is no exception. However, many Indian cities have blue and green spaces with various levels of protection from land-use and land-cover change. 

Blue and green spaces (BGS) are potentially nature-based solutions for mitigating heat stress through evaporation and transpiration besides sequestering carbon and as a habitat for urban biodiversity.  The effectiveness of BGS in mitigating heat stress depends on size, shape, weather, and climate variables, especially humidity.  

We use satellite derived land surface temperature (LST) to quantify and map negative temperature anomalies (cooling) with respect to spatial average across the city in years with different levels of summer temperature, especially due to El Nino.   We analyse the diverse types of blue and green spaces in three metropolitan cities in India and classify them in terms of biodiversity value (using e-bird data and other published sources). 

Cooling more than few degrees Celsius with respect to city wide averages from blue and green infrastructure has been observed and is much higher if compared to nearby built areas.  The geometry and landscape ecology of existing urban blue and green infrastructure can help inform future planning for blue and green spaces as adaptation in a warming urban environment. 

How to cite: Krishnaswamy, J., Chandrasekharan, K., Mayavel, D., and Jambhekar, R.: Role of blue and green spaces in mitigating heat stress and providing biodiversity co-benefits in India’s cities , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-20974, https://doi.org/10.5194/egusphere-egu24-20974, 2024.

EGU24-1556 | ECS | Posters on site | ITS4.8/CL0.1.16

Vulnerability functions based on insurance data for wind, precipitation, hail and flood damages for residential and commercial buildings in the Netherlands 

Daan van Ederen, Wouter Botzen, Jeroen Aerts, Veronica Lupi, Paolo Scussolini, Hans de Moel, and Koos Gubbels

Global warming is changing the climate and causing more frequent extreme weather events, such as floods and precipitation extremes. Future financial losses are expected to rise further due to a continued increase in economic exposure to intensifying extremes. As a result, climate change is recognized as an important source of risk for financial institutions. Insurance companies use natural catastrophe models to estimate the expected climate-related risk (in terms of losses) of their non-life insurance portfolios. Within these models, the vulnerability function describes the susceptibility of objects to  damages from natural hazards, which is of fundamental importance to the sound estimation of natural catastrophe losses. This paper constructs empirically based vulnerability functions for natural catastrophe models that estimate wind, precipitation, hail and flood damages for distinct object classes (i.e., residential and multiple commercial building types). For this, we leverage a unique insurance dataset from Achmea with high quality damage claims for different perils. This dataset contains the claim amount, building reconstruction value, location and multiple building characteristics (e.g., building use and material) at the object level for more than half a million claims over the past 40 years in the Netherlands. The vulnerability functions describe multivariate relationships between the damage ratio of objects and one or multiple natural hazard intensity measures (e.g. wind speeds and direction), primary and secondary modifiers (i.e., building characteristics). In addition, both confidence and prediction intervals are constructed. This study innovates upon the literature by using large samples of high quality damage claims data to estimate vulnerability functions for multiple natural catastrophes and object classes in the Netherlands. Our analysis pays special attention to model assumptions, the goodness-of-fit and uncertainty intervals. The results can serve as inputs for public, academic and open-source natural catastrophe models to facilitate the estimation of accurate natural catastrophe damages now and in the future.

 

How to cite: van Ederen, D., Botzen, W., Aerts, J., Lupi, V., Scussolini, P., de Moel, H., and Gubbels, K.: Vulnerability functions based on insurance data for wind, precipitation, hail and flood damages for residential and commercial buildings in the Netherlands, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1556, https://doi.org/10.5194/egusphere-egu24-1556, 2024.

EGU24-1637 | ECS | Posters on site | ITS4.8/CL0.1.16

Climate Risk Assessment Framework for Real Estate Investments 

Thijs Endendijk, Daan van Ederen, Wouter Botzen, Hans de Moel, and Jeroen Aerts

Climate change is forming an increasingly larger risk for the financial sector, although climate-related financial risks may be underestimated by financial institutions and markets. Financial institutions, such as banks, pension funds, and insurers are mainly exposed to physical climate risks through their investments in real estate. In the absence of any adaptation actions, physical climate risks for these real estate investments are expected to increase because of the higher frequency and intensity of natural disasters in a changing climate. In response to the increasing financial risks associated with climate change, regulatory bodies have been actively shaping new legislation over the past years (e.g. TCFD, CSRD, EU Green Taxonomy).

One of the main channels through which the financial sector is affected by flood risk is through physical damage to real estate. After this physical damage, housing prices decrease, and houses located in flood-prone regions sell with a discount compared to similar houses in other areas. Additionally, the credit standing of households diminishes, making mortgages more likely to default, increasing mortgage credit risks for lenders. The 2008 global financial crisis has shown that real estate and its underlying values are a pivotal part of the modern financial system. For this reason, it is imperative to monitor and assess how flood risk affects real estate markets and investors through both direct and indirect channels.

These impacts from flooding are currently not yet fully integrated within the risk assessment framework of institutional investors. Dynamic integrated models for insurance markets do exist in the literature, where standard catastrophe flood risk models are matched with insurance sector outcomes. There is currently no clear overview of how physical climate risks affect the balance sheets and profitability of (institutional) real estate investors. This study provides a structured integrated framework for evaluating both the direct and indirect flood-related risks associated with investments in both residential and commercial real estate. Although our bottom-up Dynamic Integrated Flood Real Estate Impacts (DIFREI) model can be applied to other international contexts, we use a real estate portfolio from one of the largest financial service providers in the Netherlands to illustrate the framework’s use and outputs. The DIFREI models can be used to draw lessons for applications on real estate investment portfolios.

How to cite: Endendijk, T., van Ederen, D., Botzen, W., de Moel, H., and Aerts, J.: Climate Risk Assessment Framework for Real Estate Investments, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1637, https://doi.org/10.5194/egusphere-egu24-1637, 2024.

EGU24-2056 | Orals | ITS4.8/CL0.1.16 | Highlight

Assessing the risk of climate change to a business 

Andy Pitman, Ed Saribatir, Catherine Greenhill, Sam Green, and Samuel Pitman

The realisation that climate change threatens economic systems has led investors, standard-setters and regulators to call on businesses to assess their exposure to climate-related risks, and to disclose the financial impact of these in their annual reports and financial statements where material. Indeed, mandatory disclosure requirements have already been implemented in some jurisdictions and are being proposed elsewhere. Mandatory disclosure of physical climate risk by a single business predisposes that the business can reasonably assess this risk. Here, we use the analogy of a spider’s web to examine how changes in the frequency and magnitude of extremes, that break parts of the web, combine to affect the efficiency of a hypothetical business. We demonstrate that the precise location of an extreme event, the precise characteristics of the event, and whether a subsequent event occurs close to or distant from an earlier event strongly influences vulnerability. In short, to estimate the impact of climate change induced extremes on a business requires not merely the general frequency of events, but the precise geolocation of the event mapped on the vulnerabilities of the business. We conclude that mandatory disclosure of future climate risk by a business cannot be other than deeply uncertain and this is not resolvable via foreseeable advances in global or regional climate modelling.

How to cite: Pitman, A., Saribatir, E., Greenhill, C., Green, S., and Pitman, S.: Assessing the risk of climate change to a business, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-2056, https://doi.org/10.5194/egusphere-egu24-2056, 2024.

EGU24-2381 | ECS | Orals | ITS4.8/CL0.1.16 | Highlight

Asset-level assessment of climate physical risk matters for adaptation finance 

Giacomo Bressan, Anja Duranovic, Irene Monasterolo, and Stefano Battiston

Climate physical risk assessment is crucial to inform adaptation policies and finance. However, science-based and transparent solutions to assess climate physical risks are still missing. This is a main limitation to fill the adaptation gap. We provide a methodology that quantifies physical risks on geolocalized productive assets, considering their exposure to both chronic and acute impacts (hurricanes) across the scenarios of the Intergovernmental Panel on Climate Change. Then, we translate asset-level shocks into economic and financial losses. We illustrate the methodology in an application to Mexico, a country that is highly exposed to physical risks, and attracts adaptation finance and foreign investments. We find that investor losses are underestimated up to 70% when neglecting asset-level information, and up to 82% when neglecting acute risks. Therefore, neglecting the asset-level and acute dimensions of physical risks can lead to large errors in the identification of the relevant adaptation policy response, investments and finance tools aimed to build resilience to climate change.

How to cite: Bressan, G., Duranovic, A., Monasterolo, I., and Battiston, S.: Asset-level assessment of climate physical risk matters for adaptation finance, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-2381, https://doi.org/10.5194/egusphere-egu24-2381, 2024.

EGU24-3055 | ECS | Orals | ITS4.8/CL0.1.16

Choose Your Model Wisely: Navigating Uncertainties in Future Global Tropical Cyclone Risks 

Simona Meiler, Chahan Kropf, Kerry Emanuel, and David N. Bresch

Future tropical cyclone risks will evolve depending on climate change and socio-economic development, entailing significant uncertainties. A comprehensive uncertainty and sensitivity analysis of future tropical cyclone risk changes is thus vital for robust decision-making not least in the context of physical climate risk disclosure. However, the outcomes of such uncertainty and sensitivity analyses are closely tied to the chosen model setup, warranting caution in interpretation and extrapolation. Our study investigates how four distinct tropical cyclone hazard models as well as alternate representations of socio-economic development influence future tropical cyclone risks. We find that average tropical cyclone risk increases 1-5% by 2050 across all models and global study region. But the estimated maximum risk increases by the end of the century range from 10-400% depending on the hazard model choice. Such diverging results are critically relevant for climate risk assessment in the financial and insurance sectors where usually model choices are made a priori and uncertainties are not quantified systematically. Additionally, socio-economic factors drive risk increase more strongly across all models, while the uncertainty in these risk drivers is hazard model-specific. For instance, the MIT model-based results are sensitive to the choice of global climate model, while estimates from CHAZ, STORM, and climate-conditioned IBTrACS are mainly influenced by exposure scaling based on Shared Socio-economic Pathways. Finally, we assert that quantitative estimates of uncertainty and sensitivity to model parameters greatly enhance the value and depth of climate risk assessments, which are essential for robust decision-making in the financial and insurance sector.

How to cite: Meiler, S., Kropf, C., Emanuel, K., and Bresch, D. N.: Choose Your Model Wisely: Navigating Uncertainties in Future Global Tropical Cyclone Risks, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-3055, https://doi.org/10.5194/egusphere-egu24-3055, 2024.

In the ECB top-down, economy-wide climate stress test, we developed novel damage functions to measure damages to physical capital from different natural hazards at granular firm-level. Combining address-level, forward-looking physical risk scores from Moody’ Four Twenty Seven with projected damages from acute and chronic physical risk from NGFS, we translated firms’ exposure towards floods, wildfire and sea level rise risk to future losses on their physical capital. Using loan-level information from the euro area credit registry, we assessed the deterioration in firms' profitability and indebtness due to physical damages and subsequently the change in default probabilities and expected losses on banks' corporate loan portfolios.  The dataset is unprecedented in terms of coverage, integrating both regulatory and private data sources and comprising financial and climate risk data for a total of 2.6 million European firms and 1,600 euro area banks, covering around 80% of total loan exposures of the euro area regulatory credit registry.  

Losses from physical risk were calculated as the product of firms’ future exposure towards the frequency and intensity of wildfire risk, flood risk and sea level rise and combining this with the expected physical damages as a share of GDP from the NGFS scenarios. Annual firm-level losses from physical risk were calculated between 2020 and 2050 and for three different scenarios, i.e. the NGFS Net Zero 2050, Delayed Transition and Current Policies scenarios. The results show that acute physical risk will lead to moderate to high damages on firms’ physical capital in the long term, depending on the expected temperature increase of the scenario in question. By 2050, damages will be disproportionately higher in a Current Policy scenario relative to the other scenarios, leading to a maximum deterioration of 3% of firms’ assets compared to a maximum deterioration of 1% in a Net Zero 2050 scenario. The results show that until 2050, the credit risk of borrowers most vulnerable to physical risk is around 25% higher in a Current Policy scenario relative to a Net Zero 2050 scenario.

How to cite: Emambakhsh, T.: Measuring physical damages from natural hazards in the ECB top-down, economy-wide climate stress test , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-4075, https://doi.org/10.5194/egusphere-egu24-4075, 2024.

EGU24-5217 | ECS | Orals | ITS4.8/CL0.1.16 | Highlight

The risks of climate tipping points for financial investors 

Paul Waidelich, Lena Klaaßen, Stefano Battiston, and Bjarne Steffen

While financial investors are increasingly alert to the economic threats of climate change, most academic and regulatory assessments of financial risk have not accounted for climate tipping points. Here, we combine recent advances in the integrated assessment modeling of tipping points with return projections for major stock indices to assess index-specific risk exposures to climate change damages. We find that for the MSCI World, a globally diversified stock index, tipping points increase the expected loss due to climate change damages under SSP2-4.5 by 62% (USD 0.2 trillion)—a magnitude comparable to moving from meeting the Paris targets to the "hothouse world" scenario RCP8.5. The reason is that investment horizons are more affected by near-term risks of tipping points than by long-term differences in mitigation outcomes. Risk increases are driven by methane-related tipping points (permafrost thaw and ocean methane hydrates) and ice sheet disintegration, with the highest increases for investments in emerging markets with extensive coastal areas, such as India or Indonesia. The absolute magnitude of financial risks varies substantially across damage functions and assumptions regarding damage persistence. However, the relative importance of tipping points is robust across different damage specifications and investor discount rates. Therefore, our results call for integrating tipping points into climate scenario analyses in the financial sector and climate risk stress tests by regulators.

How to cite: Waidelich, P., Klaaßen, L., Battiston, S., and Steffen, B.: The risks of climate tipping points for financial investors, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5217, https://doi.org/10.5194/egusphere-egu24-5217, 2024.

EGU24-6194 | Posters on site | ITS4.8/CL0.1.16

Estimating Flood Risk under Global Warming: An Approach from the Insurance Industry 

Sumeet Kulkarni, Shubham Choudhary, Francesco Zuccarello, Marie Ekström, and Giulia Giani

The (re)insurance sector has established methods and tools to assess historical and current risk for several weather driven hazards in many geographical regions. Using those same methods to estimate risk under global warming is fraught with challenges as one may expect complex changes to all four risk components (hazard, exposure, vulnerability, and disaster response capability).

Nevertheless, despite much uncertainty about how weather hazards may change under climate change, the insurance sector is increasingly expected to include risk estimates for future-looking business strategies. Supervisors (across different regulatory domains) are currently working with the insurance sector to better understand the transmission channels for climate risk and provide guidance on how to meaningfully estimate future risk due to weather driven hazards.

To encourage discussion and transparency on methodology used to assess risk for insurance purposes (such as developing underwriting layers, or portfolio management) we demonstrate a recent approach developed by the global (re)insurance broker Gallagher Re to estimate risk scores of future floods aligned, and therefore comparable, with current flood risk estimates. We demonstrate the approach for both pluvial and fluvial flood and discuss how challenges (such as those detailed above) were addressed to derive a methodology that can be deployed globally, given access to robust and credible projections of extreme precipitation and streamflow.

How to cite: Kulkarni, S., Choudhary, S., Zuccarello, F., Ekström, M., and Giani, G.: Estimating Flood Risk under Global Warming: An Approach from the Insurance Industry, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-6194, https://doi.org/10.5194/egusphere-egu24-6194, 2024.

Climate change induced extreme precipitation poses a significant threat to agricultural production. Such impacts extend beyond local agricultural production regions, generating remote and cross-sector impacts that disrupt global supply chains (GSCs). While the direct impacts of extreme precipitation on agricultural production have been widely studied, how such local impacts cascade through supply chain networks to remote places remains elusive, partly because of the complex interdependencies within the global trade systems. To address this, we propose a Resilience Enhancement in Supply Chains Under Environmental Shocks (RESCUES) framework. RESCUES couples an agricultural production loss model with a dynamic recursive economic network model. It allows us to identify channels through which the impacts of climate change on agricultural production propagate along GSCs to interconnected sectors and regions. We design nine climate shock scenarios (i.e., dry, wet, and compound precipitation anomalies with extreme, severe, and moderate levels of severity) using the latest Coupled Model Intercomparison Project Phase 6 (CMIP6) under two Shared Socioeconomic Pathways (SSPs) scenarios (SSP126 and SSP585). We then use RESCUES to simulate the GSCs dynamics over 2016-2050 under these nine scenarios. We find that direct agricultural losses driven by local precipitation anomalies can spread through GSCs to a wider range of countries and regions across the globe, creating large spatial spillover effects with direct and indirect economic losses. We estimate that the averaged per event total value-added (VA) losses caused by compound extremes is around $20.4/22.6 billion under SSP126/585, followed by dry extremes ($16.4/15.0 billion) and wet extremes ($8.7/11.6 billion). Moreover, the global distribution of direct and indirect losses exhibits high spatial heterogeneity. Countries with large agricultural outputs tend to have both high direct and indirect VA losses, especially in China, India, the United States, Russia, and Brazil. In contrast, poorer countries, such as Tanzania, Sudan, Myanmar, Yemen, Afghanistan, and Nepal, experience relatively larger direct losses, while rich regions heavily dependent on agricultural imports, including Hong Kong, Qatar, and Singapore, suffer relatively larger indirect losses. Considering that nations frequently implement export restrictions to ensure food self-sufficiency, we further design a hypothetical scenario to assess the global trade and economic impacts of near-term (2025-2030) agricultural export restrictions in four key food production regions (China, India, the United States, and Indonesia) under extreme precipitation anomalies. Our study highlights the importance of an integrated and comprehensive assessment of the risk footprint of climate change-related shocks, encompassing both direct and indirect impacts on GSCs. 

How to cite: Zhang, S. and He, X.: Vulnerabilities of Global Supply Chains to Agricultural Production Disruptions Caused by Individual and Compound Climate Shocks, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-6973, https://doi.org/10.5194/egusphere-egu24-6973, 2024.

In today's global economy, the importance of transparent and quantitative sustainability reporting is escalating, reshaping corporate disclosure standards. This evolving landscape presents challenges to traditional business models and management tools, necessitating innovative approaches for effective adaptation. The development of standards such as IFRS S2, a set of global standards for climate-related disclosures that mandates companies to report on their environmental impact and climate risks, further complicates the reporting and compliance environment. This study explores the utility of the Sustainable Balanced Scorecard (SBSC) as a strategic instrument to enhance environmental, social, and governance (ESG) performance in light of heightened compliance demands. Utilizing the SBSC framework, the research begins with the development of a sustainability strategy map for a Taiwanese port logistics company, outlining its sustainability objectives and providing a foundation for analyzing the impact of IFRS S2. The research also employ the Decision-Making Trial and Evaluation Laboratory (DEMATEL) method to analyze the causations among strategy goals, enriching the understanding of their interconnections and influence. The study then delves into the specifics of IFRS S2, assessing how these standards affect the company's financial disclosures, strategic planning, and governance framework. This dual approach highlights the intricate relationship between corporate strategy, sustainability integration, and IFRS S2 requirements. It identifies key areas where these elements intersect, offering insights into potential improvement areas and gaps. This research is particularly relevant for entities in the port logistics sector and related industries, emphasizing the critical role of innovative management tools like the SBSC in aligning business strategies with global sustainability goals and managing climate risks effectively.

How to cite: Wu, H.: Advancing Corporate Governance through SBSC: Navigating Compliance with IFRS S2 in Port Logistics, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-6989, https://doi.org/10.5194/egusphere-egu24-6989, 2024.

EGU24-7412 | ECS | Orals | ITS4.8/CL0.1.16

The Impact of Compound Hot-and-Dry Events on Household Well-being 

Jessie Ruth Schleypen

The socioeconomic impacts of compound extremes are sudden, severe, and multidimensional. Without precautionary measures, social and economic safety nets including community support and insurance, the negative effects of a single, short-run shock on households can extend to the long-run and persist over many years. Studies on the impacts of compound extremes have focused on objective measurements of well-being, including income, health, education; with much fewer studies on subjective well-being. Looking into subjective well-being takes an evaluative perspective on the quality of life, wherein the recovery from a disaster takes more than just the return to employment, for instance. Previous studies have shown that subjective well-being is also a good predictor of life expectancy, productivity, educational performance, and voting behaviour. Using econometric methods on sub-national, household panel data from the EU Survey of Income and Living Conditions (EU-SILC) and a composite index for the simultaneous occurrence of droughts and heatwaves, I quantify and compare the impacts of compound dry-and-hot events (CDHE) in Europe on objective and subjective measurements of well-being. The results of this study provide new information on the magnitude, as well as, the persistence of effects from CDHE, based on both the traditional income-based measurements versus the self-reported measurements of well-being.

How to cite: Schleypen, J. R.: The Impact of Compound Hot-and-Dry Events on Household Well-being, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-7412, https://doi.org/10.5194/egusphere-egu24-7412, 2024.

EGU24-10342 | Orals | ITS4.8/CL0.1.16

Navigating Nature and Climate Risks: An Integrated Framework for Economic Assessment 

Miodrag Stevanovic, Patrick José von Jeetze, Justin Andrew Johnson, Andrej Ceglar, and Alexander Popp

Biodiversity loss and ecosystem degradation could pose a substantial threat to financial stability and the wider economy. Despite scientific evidence of the ongoing ecosystem degradation, methodological and data challenges have so far prevented a detailed assessment of the economic and financial risks.  While progress has been made in assessing climate change related risks, our understanding of the linkages between the economy and ecosystem service degradation is still limited. Here we pioneer a nuanced approach to understanding the emerging financial risks of ecosystem change.  Using the LPLmL-MAgPIE-SEALS modeling framework, we assess physical, transition and financial risks considering feedbacks from climate change, land use, and degrading ecosystem services. Focusing mainly on the EU, we also assess interconnectedness with other global regions where loss of ecosystem services is more pervasive. Our framework includes climate-sensitive spatially explicitly biophysical data within a partial equilibrium land-system model. Modelled land-use patterns are downscaled to derive fine-scale changes in ecosystem service supply and associated economic feedbacks. We assess various scenarios that build on the existing NGFS (Network for Greening the Financial System) framework. These scenarios range from a degraded world without policy interventions, to an integrated climate-nature scenario, with ambitious policies to mitigate both climate and ecosystem service change. The results indicate diverging biodiversity response based on varying climate and nature policy ambition, emphasizing the need to extend biodiversity safeguarding beyond exclusive reliance on climate mitigation policies. Financial risks are assessed through an analysis of sectoral dependencies on various ecosystem services, laying out the basis for a comprehensive framework that supports informed decision-making facing emerging climate and nature-related risks.

How to cite: Stevanovic, M., von Jeetze, P. J., Johnson, J. A., Ceglar, A., and Popp, A.: Navigating Nature and Climate Risks: An Integrated Framework for Economic Assessment, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-10342, https://doi.org/10.5194/egusphere-egu24-10342, 2024.

EGU24-11080 | Posters on site | ITS4.8/CL0.1.16

Beyond Single Company Risk Disclosure – Exploring the Efficient Frontier in Physical Risk Reporting 

Victor Wattin Håkansson, Sarah Hülsen, Simona Meiler, Leonie Villiger, Chahan M. Kropf, Jamie W. McCaughey, and David N. Bresch

Climate change is intensifying natural hazards, significantly increasing financial risks for businesses and stakeholders. This shift in physical risk is transforming companies' risk-return profiles and driving the need for transparent risk disclosure, in line with the guidelines from the Task Force on Climate-Related Financial Disclosure (TCFD; now further developed as IFRS S2). Despite many companies beginning to disclose risks, standardization efforts by regulatory bodies are still evolving. The varied and proprietary nature of climate risk information from commercial providers has hindered transparency and accessibility in risk scoring. This complicates the comparison and evaluation of risks, as well as the aggregation of risks at the portfolio level. Additionally, the scarcity of natural catastrophe models in non-OECD countries and the need for a globally consistent framework incorporating future climate scenarios pose further challenges.

Our study introduces an event-based reporting approach to address these challenges in climate risk disclosure. Companies are required to report modeled financial impacts of standardized hazard sets, including both gross and net risks due to their insurance protection. This method offers a solid foundation for risk metrics, risk-return profiling, and inter-comparison of risks at both individual company and portfolio levels. Leveraging CLIMADA (CLIMate ADAptation), an open-source climate risk assessment platform, we create a globally consistent, interoperable framework with reference hazard event sets for main perils under current and future climate conditions, accessible through a data API. 

By applying this method to the balance sheets of hypothetical multinational companies, we effectively assess financial risks and perform risk-return analyses, demonstrating the approach's practicality and potential in climate risk management and disclosure. We show, for instance, the potential for evaluating sectoral and cross-sectoral risk, which is only visible in the cross-company risk profile, and how portfolio risks due to spatial correlations can be captured.

How to cite: Wattin Håkansson, V., Hülsen, S., Meiler, S., Villiger, L., Kropf, C. M., McCaughey, J. W., and Bresch, D. N.: Beyond Single Company Risk Disclosure – Exploring the Efficient Frontier in Physical Risk Reporting, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-11080, https://doi.org/10.5194/egusphere-egu24-11080, 2024.

EGU24-11284 | Orals | ITS4.8/CL0.1.16 | Highlight

Exploring the efficient frontier in physical risk reporting 

David N. Bresch

An increasing number of countries request large companies to disclose their physical climate-related risks based on regulations inspired by work of the Task Force for Climate-related Financial Disclosure (TCFD). Current reports do not lend themselves to direct comparison of physical risks across companies and by no means allow investors to build a portfolio optimised with respect to physical risks. Methods such as event-based probabilistic natural catastrophe risk assessment exist and would allow for aggregation of pertinent information, taking into account global diversification of risk. Convergence of TCFD-reporting towards such methods would enable investors and financial intermediaries to construct portfolios with respect to an efficient frontier in terms of physical risks. In the true spirit of TCFD, this would allocate capital towards companies best positioned to cope with the impacts of climate change and hence incentivise economic actors to strategically embrace climate adaptation. We present a fully transparent and easily replicable open-source and -access approach to construct such an efficient frontier and will discuss resulting risk-reward profiles and implications for corporate strategy development in the context of climate change.

How to cite: Bresch, D. N.: Exploring the efficient frontier in physical risk reporting, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-11284, https://doi.org/10.5194/egusphere-egu24-11284, 2024.

EGU24-11412 | Orals | ITS4.8/CL0.1.16

Indirect impacts of region-specific heat extremes along the global supply network  

Xudong Wu, Lennart Quante, and Anders Levermann

The last decade has witnessed a surging occurrence of extreme heat worldwide. This can directly dampen local production capacity and also induce indirect repercussions through the global supply network. Yet, the cascading effect of region-specific extreme heat may differ greatly, which is by far poorly understood. By combining temperature observations with Acclimate—a dynamic agent-based model, we identify the region-specific temperature threshold for dampening local production and investigate the response of the global supply network to extreme heat in a region-by-region manner. Economic agents with significant repercussions on the globe are identified and indirect benefits along the global supply network from local heat adaptation are revealed. The outcome of this study supports common but differentiated adaptation strategies towards extreme heat.

How to cite: Wu, X., Quante, L., and Levermann, A.: Indirect impacts of region-specific heat extremes along the global supply network , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-11412, https://doi.org/10.5194/egusphere-egu24-11412, 2024.

EGU24-11534 | Posters on site | ITS4.8/CL0.1.16

Financial risk management needs to integrate compound events in physical climate risk assessment 

Andrej Ceglar, Nicola Ranger, Kai Kornhuber, Michaela Dolk, and Olivier Mahul

The world has recently witnessed many unprecedented climate disasters, often coinciding with other crises such as pandemics, socio-economic instabilities and ecosystem degradation (closely linked to biodiversity loss). These compound shocks exert profound effects on human, environmental, and economic dimensions, presenting substantial implications also from a financial risk standpoint. Consequently, it becomes imperative to transcend the isolated assessment of individual events and associated risks and progress towards an integrated evaluation of interconnected crises. Compound shocks exhibit characteristics marked by non-linear, intricate, and often unpredictable effects on both society and the economy. Consequently, discerning their impacts cannot be simplified to a mere summation of the effects of their individual shocks. The intricate nonlinearities have the potential to amplify the repercussions of climate-related shocks, presenting considerable challenges to financial stability. Recent advancements in the fields of climate impact modelling, catastrophe risk modeling, machine learning, and macroeconomic modeling hold promise in addressing the existing gaps in modeling compound risks. Our study builds on a survey we conducted among twenty-six central banks and supervisory bodies, revealing a consensus on the crucial importance of considering compound shocks in climate change scenario analyses, specifically pertaining to physical. Leveraging the insights garnered from this survey, we set up a research direction towards integration of compound risks into the development of scenario narratives, storylines and (macro-)economic models capable of effectively capturing compound shocks.

How to cite: Ceglar, A., Ranger, N., Kornhuber, K., Dolk, M., and Mahul, O.: Financial risk management needs to integrate compound events in physical climate risk assessment, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-11534, https://doi.org/10.5194/egusphere-egu24-11534, 2024.

EGU24-12089 | ECS | Orals | ITS4.8/CL0.1.16

The Green-Scorpion: a preliminary study on the potential amplification of physical climate financial risks by nature-related risks and feedbacks 

Jimena Alvarez, Nicola Ranger, Anna Freeman, Thomas Harwood, Michael Obersteiner, Estelle Paulus, and Juan Sabuco

Climate change and biodiversity loss are not happening in isolation. The erosion of natural capital by human activities will compound and amplify physical climate risks, and vice versa. We present new analyses that demonstrates that ignoring nature in physical climate financial risk assessment will lead to significant underestimates of the scale of the risks. This has implications for financial institutions and for the prudential policies of Central Banks and supervisors. We develop the first set of integrated climate-nature scenarios to explore the potential scale of physical risks, building upon the NGFS conceptual framework, alongside a global risk assessment approach that combines the ENCORE tool with global natural capital datasets and a multi-regional input-output modelling approach. We produce estimates of risks for five ecosystem services - surface water, ground water, pollination, air quality and water quality - across 7 sectors and 44 countries and 5 rest of world regions. Our analysis suggests that nature-related risks are material in scale, exceeding $7 trillion value at risk. Based on analyses of historical analogues and risk transmission channels we show that nature and climate risks are strongly interconnected and share characteristics in their potential for non-linear, cascading impacts. We propose a set of principles for scenario analysis and a framework for developing decision-relevant scenarios, including an inventory of almost eighty potential nature-related physical risk shocks (hazard-primary economic receptor pairs) that can form the basis to scenario development.

How to cite: Alvarez, J., Ranger, N., Freeman, A., Harwood, T., Obersteiner, M., Paulus, E., and Sabuco, J.: The Green-Scorpion: a preliminary study on the potential amplification of physical climate financial risks by nature-related risks and feedbacks, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12089, https://doi.org/10.5194/egusphere-egu24-12089, 2024.

EGU24-12339 | ECS | Orals | ITS4.8/CL0.1.16 | Highlight

Physical climate risk, sovereign credit ratings, and the benefits of adaptation 

Mark Bernhofen, Matt Burke, Nicola Ranger, and Gireesh Shrimali

Climate change is a risk to the financial stability of countries. The economic impacts of rising temperatures, increasingly frequent and intense extreme events, as well as the costs of adapting to these risks have the potential to significantly strain government (sovereign) finances. Perceived national climate risk hotspots may also discourage investment, reduce economic growth, and increase global inequality. To gauge sovereign financial risk, investors rely on sovereign credit ratings that assess a nation’s ability to repay its debt. A country’s credit rating determines its borrowing costs, influences investor confidence, and has impacts on economic stability and growth.

Recent estimates show that climate-induced sovereign credit downgrades could materialize for nearly 60 countries by 2030 (Klusak et al, 2023) because of the labour productivity impacts of increasing temperatures (Kahn et al, 2021). These sovereign climate risk estimates are severe, yet likely still an underestimate, as they do not consider the materialization of extreme events (acute climate risk) (Stern, 2016).

In this study, we provide new estimates of climate-induced sovereign credit downgrades by combining the sovereign climate risk model developed by Klusak et al. (2023) with models of acute climate risk. We focus on countries in south-east Asia and calculate the extreme losses from river floods and tropical cyclones under different future warming scenarios and the implications for sovereign credit risk. We also explore different options to adapt to these risks nationally, their associated costs, and model the risk reduction benefits of their implementation.

There is a failure to integrate extreme climate risk into economic and financial assessments (Stern et al, 2022). Many of these risks are underestimated in the current financial assessment of climate change (Trust et al, 2023) and may support more credible assessments of short-term risk. Our findings add to the growing body of work highlighting the importance of considering acute climate risk in estimates of climate financial risk (Pittman et al, 2022). We also show that adaptation can significantly reduce future losses and resultant sovereign credit risk, which serves as evidence against divestment from risk-prone countries and for investment in adaptation. We conclude by exploring the fiscal policy implications of our analysis for Thailand.

 

Kahn, M. E., Mohaddes, K., Ng, R. N., Pesaran, M. H., Raissi, M., & Yang, J. C. (2021). Long-term macroeconomic effects of climate change: A cross-country analysis. Energy Economics

Klusak, P., Agarwala, M., Burke, M., Kraemer, M., Mohaddes, K. (2023). Rising Temperatures, Falling Ratings: The Effect of Climate Change on Sovereign Creditworthiness. Management Science

Pitman, AJ., Fiedler, T., Ranger, N., Jakob, C., Ridder, N., Perkins-Kirpatrick, S., Wood, N., Abramowitz G. (2022). Acute climate risks in the financial system: examining the utility of climate model projections. Environmental Research: Climate

Stern, N. (2016). Economics: Current climate models are grossly misleading. Nature 

Stern, N., Stiglitz, J., & Taylor, C. (2022). The economics of immense risk, urgent action and radical change: towards new approaches to the economics of climate change. Journal of Economic Methodology

Trust, S., Joshi, S., Lenton, T., Oliver, J. (2023). The Emperor's New Climate Scenarios. Institute and Faculty of Actuaries and University of Exeter.

How to cite: Bernhofen, M., Burke, M., Ranger, N., and Shrimali, G.: Physical climate risk, sovereign credit ratings, and the benefits of adaptation, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12339, https://doi.org/10.5194/egusphere-egu24-12339, 2024.

EGU24-13865 | Orals | ITS4.8/CL0.1.16

NGFS scenarios: Scope, design limitations and gaps 

Sebastian Werner, Alex Pui, and Motoshi Tomita

There has been increasing focus on climate risk disclosure within the industry, evidenced by a shift from guidance (TCFD) to standards (ISSB) based approach.  However, surveys show that climate scenario modelling remains challenging, with high complexity and lack of expertise cited as key reasons.

While there are global scenarios such as NGFS to support practitioners by providing key analytical foundations and parameters, concerns have been raised regarding the robustness of physical and transition risk assessment methodologies, and hence the fitness for such scenarios.  Given that the primary aim of climate scenario analysis at an entity level is to inform prudent risk management and business strategy, it is instructive to explore fundamental questions and context around the design of these scenarios, leading to an improved interpretation of end results.

To this end, we aim to critically review the fourth iteration of NGFS scenarios that have recently been released, with a particular focus on 3 areas: First, the evolution of scenarios since the first vintage in 2020. Secondly, the design limitations of IAMs which do not feature frictions that could allow for misprinting and price bubbles. Thirdly, we discuss how the scenario design could benefit from incorporating uncertainty into its variable projections.

How to cite: Werner, S., Pui, A., and Tomita, M.: NGFS scenarios: Scope, design limitations and gaps, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-13865, https://doi.org/10.5194/egusphere-egu24-13865, 2024.

EGU24-15905 | ECS | Orals | ITS4.8/CL0.1.16 | Highlight

Climate services for finance, lessons learned and feedback for the public sector 

Graham Reveley, James Brennan, Sally Woodhouse, Laura Ramsamy, Nicholas Leach, Patricia Sullivan, Jonathan Davies, and Joe Stables

Driven by regulations to understand and attempt to mitigate risk from climate change there is an increase in demand for climate risk data from the financial sector. This has led to the generation of 3rd party data providers, such as Climate X, who aim to bridge the gap between academic research and the requirements of the financial sector. This requires a multi-disciplinary team bringing together hazard, remote sensing, and climate scientists which allows us to combine open-source earth observations and climate model data with in-house hazard modelling to generate metrics and losses that are useful and useable for our clients.

In this talk we will cover the key requirements of our clients: asset-level and global intelligence, multi-hazard and loss information and multiple scenarios. We will outline how we address these, and how academic researchers can engage with the private sector to make their work as relevant as possible.

How to cite: Reveley, G., Brennan, J., Woodhouse, S., Ramsamy, L., Leach, N., Sullivan, P., Davies, J., and Stables, J.: Climate services for finance, lessons learned and feedback for the public sector, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15905, https://doi.org/10.5194/egusphere-egu24-15905, 2024.

EGU24-17148 | ECS | Posters on site | ITS4.8/CL0.1.16

How do interest rates affect decarbonisation pathways? A stakeholder-driven multi-model analysis. 

Natasha Frilingou, Dirk-Jan van de Ven, Shivika Mittal, Karamaneas Anastasios, Thomas Nikolakakis, Francesco Gardumi, Konstantinos Koasidis, and Alexandros Nikas

Decarbonisation of the energy sector is a critical task in the efforts to mitigate climate change. As sectoral emissions cuts in modelled pathways aligned with the Paris Agreement are projected to come from at-scale diffusion of emerging or new technologies as well as further development of existing solutions, energy-sector decarbonisation entails major investments in low-carbon technologies. At the same time, a significant chunk of these investments must be made in emerging and developing economies, which currently receive just one-fifth of global energy investments. This underinvestment is, at least partly, due to the large disparities in financing conditions and higher-risk profiles in said countries. Models used to assess decarbonisation pathways typically assume a uniform cost of capital; such assumption, however, does not do justice to real-world conditions and may therefore lead to inaccurate policy recommendations. Moreover, there is considerable uncertainty over how these costs may evolve in the future. In this study, we apply an empirical dataset of estimated cost of capital differentiated by technology and country and explore stakeholder-driven pathways of (de-)risking investments in clean energy vs. fossil-fuel technologies, using an ensemble of two global integrated assessment models and one electricity-system model. Furthermore, we attempt to incorporate a corrective justice dimension in our narratives by assessing the impacts of risk underwriting for low-carbon investments through taxing corporate windfall profits for 2022 and distributing the revenue as subsidies towards high-risk regions.

How to cite: Frilingou, N., van de Ven, D.-J., Mittal, S., Anastasios, K., Nikolakakis, T., Gardumi, F., Koasidis, K., and Nikas, A.: How do interest rates affect decarbonisation pathways? A stakeholder-driven multi-model analysis., EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17148, https://doi.org/10.5194/egusphere-egu24-17148, 2024.

EGU24-17265 | ECS | Posters on site | ITS4.8/CL0.1.16

Modelled Multidecadal Trends in (Very) Large Hail in Europe, the United States and Globally 

Francesco Battaglioli, Pieter Groenemeijer, Mateusz Taszarek, Tomas Pucik, and Anja Rädler

Large hail events worldwide result in extensive damage, with individual events occasionally exceeding USD 1 billion in losses. Addressing the lack of comprehensive global observational networks, we developed Additive Logistic Regression Models for mapping the frequency of large and very large hail. These models were trained with data from lightning observations, hail reports, and convective parameters from the ERA5 reanalysis. Applying these models to ERA5 data spanning from 1950 to 2021, we reconstructed the probability of large and very large hail events across Europe and the United States. In the United States, hail trends during this period were generally weak and statistically non-significant. In Europe, trends were predominantly positive and significant with northern Italy standing out as a hotspot. Here, the convective activity has seen an abrupt increase with very large hail being 3 times more likely in recent years (2012-2021) than it was in the 1950s. This trend was corroborated by recent observations in the region, including the establishment of a new European hail record with hailstones measuring 19 cm in north-eastern Italy in July 2023. To create a globally applicable hail model, we used a training dataset of hail reports from Europe, the United States, and Australia combined. This effort resulted in the development of a comprehensive global climatology for very large hail. Additionally, we compared the modelled changes in hail frequency to observed changes in insured losses to better understand the complex relationship between hail frequency and hail risk across different regions worldwide.

How to cite: Battaglioli, F., Groenemeijer, P., Taszarek, M., Pucik, T., and Rädler, A.: Modelled Multidecadal Trends in (Very) Large Hail in Europe, the United States and Globally, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17265, https://doi.org/10.5194/egusphere-egu24-17265, 2024.

EGU24-17399 | Orals | ITS4.8/CL0.1.16 | Highlight

The Tipping Point Modelling Intercomparison Project (TIPMIP) 

Ricarda Winkelmann, Donovan Dennis, Jonathan Donges, Sina Loriani, Boris Sakschewski, and Johan Rockström

While tipping points in the Earth system are recognized in the public and policy debate as one of the major risks of anthropogenic climate change, our current knowledge of their dynamics involves a broad range of uncertainties, and so far there is no systematic risk assessment quantifying the likelihood as well as the impacts of exceeding tipping points in the Earth system. 

Here we introduce the Tipping Point Modelling Intercomparison Project (TIPMIP, www.tipmip.org), a major international initiative setting out to fill this gap in a multi-model approach: Based on ensembles of simulations with Earth system models as well as offline models combined with current observations, the experiments will serve to assess (1) the risk of crossing critical thresholds in the Greenland and Antarctic ice sheets, the Atlantic Meridional Overturning Circulation, tropical and boreal forests as well as high-latitude permafrost; (2) the short- and long-term (committed) impacts of crossing individual tipping points; (3) the (ir)reversibility of impacts on different timescales; and (4) the role of the forcing rate. TIPMIP also sheds light on potential model shortcomings when it comes to such highly-nonlinear dynamics in the Earth system which may significantly change projections for the 21st century and beyond. 

The TIPMIP outcome will serve to generate a risk map, highlighting regions in the world which are most vulnerable to tipping transitions, which will be an important basis for forward-looking policy decisions. 

How to cite: Winkelmann, R., Dennis, D., Donges, J., Loriani, S., Sakschewski, B., and Rockström, J.: The Tipping Point Modelling Intercomparison Project (TIPMIP), EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17399, https://doi.org/10.5194/egusphere-egu24-17399, 2024.

EGU24-18055 | Orals | ITS4.8/CL0.1.16

Countries with future highest exposure to unprecedented climate extremes  

Jonathan Spinoni, Leonardo Chiani, Alessandro Dosio, Johannes Emmerling, Jacopo Ghirri, Marta Mastropietro, and Massimo Tavoni

In the last decades, the World experienced an increasing frequency and severity of weather-related extremes. Such events can remarkably affect multiple sectors as food, energy, and biosphere. In the framework of the activities of the ERC project EUNICE, and in order to understand the possible future impacts caused by climate extremes on population and socio-economic indicators, we firstly constructed a global database of climate indicators including eleven hazards (e.g., heatwaves, droughts, rainfall extremes, and windstorms), ranging from 1881 to 2100. For each grid point (0.5°), we provided different metrics as frequency, intensity, and number of unprecedented events at annual scale, dividing the future into five SSPs (plus two including temperature overshoot), and using the bias-adjusted CMIP6-based ISIMIP3b dataset as input. We therefore aggregated the parameters at country-scale - for each hazard - and we focused on the exposure of population and GDP to unprecedented future climate extremes, i.e. events never recorded in the past. We performed the analyses for two 30-year periods (2041-2070 and 2071-2100) and four Global Warming Levels (GWLs from 1.5 °C to 4 °C). Depending on the selected SSP and period, we present a structured ranking of countries that show the highest socioeconomic exposure to single or combined climate impact drivers. In this presentation, we also discuss the cost, in terms of cumulated events, of temperature overshoot above the 1.5 °C level to comply with Paris Agreement's goals. At a later stage, this new set of climate indicators will be also used to quantify the added value of including climate extremes in dedicated damage functions.

How to cite: Spinoni, J., Chiani, L., Dosio, A., Emmerling, J., Ghirri, J., Mastropietro, M., and Tavoni, M.: Countries with future highest exposure to unprecedented climate extremes , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18055, https://doi.org/10.5194/egusphere-egu24-18055, 2024.

Overall uncertainty in climate model projections is composed of scenario, model, and internal variability components. While scenario uncertainty is expressed by considering different climate scenarios, model uncertainty and internal variability components are largely ignored by climate information service providers. Instead, model projections are often expressed through the ensemble mean, which may lead to both overly optimistic assessments of risk, or on the other hand misinformed maladaptation strategies.

Here, we propose a new uncertainty quantification approach that better informs end users of climate projections, showing that the multi-model internal variability, owing to its chaotic nature, is in fact virtually irreducible, and that model uncertainty grows moderately throughout the 21st century. For three future scenarios, we quantified the global internal variability of two metrics: annual precipitation (PRCP) and boreal summer average maximum daily temperature (TXJJA), by employing a single realization of each CMIP6 climate model. Our results showed that observed internal variability of the 1981-2010 period for the TXJJA metric has a negligible variation throughout the 21st century for all three scenarios. For the PRCP metric, small changes of internal variability were detected towards the end of the 21st century in the most adverse scenario (SSP3-7.0). Importantly, we observed that characterizing uncertainty in such manner produced a nuanced, and non-misleading results compared to that of the ensemble mean approach. Furthermore, the proposed uncertainty quantification approach can be expanded to similarly evaluate the uncertainty in indices of extreme weather.

How to cite: Gomez-Garcia, M. and Pui, A.: Demystifying model uncertainty and internal variability in climate change projections over the 21st century, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18103, https://doi.org/10.5194/egusphere-egu24-18103, 2024.

EGU24-18139 | ECS | Orals | ITS4.8/CL0.1.16

Climate damage projections beyond annual temperature 

Jarmo Kikstra, Paul Waidelich, Fulden Batibeniz, James Rising, and Sonia Seneviratne

Projections of economic damages from climate change are key for evaluating the benefits of climate mitigation and informing discussions around adaptation needs. So far, global and country-level top-down assessments of GDP damages have focused on annual mean temperature changes and annual precipitation. Recent backward-looking studies have identified further impacts of variability and extremes in precipitation and temperatures on income growth.

Here, we examine GDP impacts and uncertainties under different global warming levels by combining empirical dose-response functions for temperature variability, rainfall deviations, and extreme precipitation with climate projections of 33 CMIP6 models. The main contribution of this work is to understand the projected relative contributions of multiple climate variables under many possible future climates.

We find that at a +3°C global warming level, global average losses reach 10% of GDP, with worst effects (up to 17%) in poorer, low-latitude countries. Relative to annual temperature damages, which find to seemingly capture heat wave impacts, the additional GDP impacts of projecting variability and extremes are relatively small and dominated by inter-annual variability, especially in lower latitudes. However, accounting for variability and extremes when estimating the temperature dose-response function still raises global GDP losses by nearly 2%-pts and exacerbates tail risks for economic growth.

Our results call for region-specific risk assessments and complementary research into climatic extremes not considered here, including their indirect effects on temperature dose-response functions. Additionally, it will be very important to further the work on understanding historical and future persistence and adaptive capacities for these different impact channels.

How to cite: Kikstra, J., Waidelich, P., Batibeniz, F., Rising, J., and Seneviratne, S.: Climate damage projections beyond annual temperature, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18139, https://doi.org/10.5194/egusphere-egu24-18139, 2024.

EGU24-18364 | Orals | ITS4.8/CL0.1.16 | Highlight

Multi-hazards risk indicators for climate risk reporting 

Benoît Guillod, Alessio Ciullo, Quentin Bourgeois, Lukas Bodenmann, Jere Lehtomaa, and Sebastian Glink

In recent years, it has become more and more clear that climate change and its impacts do severely affect companies’ business. For example, acute climate risks driven by e.g. floods and tropical cyclones can impact physical assets and halt productions, whereas chronic climate risks such as droughts and temperature increases can have severe impacts on e.g. crop production, labour productivity and water availability. This increased understanding of climate risk on companies’ performances led to the establishment of the Task Force on Climate-related Financial Disclosure (TCFD) which provides a framework for disclosing and reporting climate-related risks and opportunities.

As TCFD requires businesses to quantify, rate and manage climate risks across various perils and regions, there is the need to develop climate risk indicators which comply with its recommendations. In this talk, we will introduce the indicators developed by CLIMADA Technologies - an open-core ETH spin-off company - for multiple hazards, incl. tropical cyclones, floods, winter storms, wildfires, droughts, heat waves, and cold spells. The indicators allow assessing and coherently summarising climate risk information in line with TCFD recommendations and thus support companies in taking resilient actions.

How to cite: Guillod, B., Ciullo, A., Bourgeois, Q., Bodenmann, L., Lehtomaa, J., and Glink, S.: Multi-hazards risk indicators for climate risk reporting, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18364, https://doi.org/10.5194/egusphere-egu24-18364, 2024.

As our knowledge of the physical impacts associated with climate change develops, translating those insights into accurate estimates of societal and financial repercussions has become a key concern for a variety of decision-makers, including policymakers, insurance specialists, investors and regulators. Although this task is daunting, it can leverage the deep knowledge of the financial impacts of extreme natural events amassed over the past decades in the (re)insurance industry, where detailed assessments of location-level and portfolio-level risk are now commonly used.

In particular, Moody’s RMS has been at the forefront of catastrophe modelling for over 30 years, developing and supporting models for the US$2.5 trillion global (re)insurance market. These granular, bottom-up models bring together carefully calibrated stochastic simulations of extreme events, together with detailed regional assessments of the vulnerability of a wide range of building and infrastructure types, which are then converted into loss distributions that incorporate local market considerations, such as repair/replacement costs and business interruption costs. Those models have been validated not only against extensive geophysical observations, but also against hundreds of billions of dollars of granular damage and building-specific claims data.

In this context, Moody’s RMS has developed a novel bottom-up approach to assess the financial impacts of climate change, which leverages the respective strengths of catastrophe models and general circulation models. The ‘Climate on Demand Pro’ platform provides damage estimates at both location- and portfolio-levels, and incorporates an aggregation methodology that reflects the impacts of portfolio concentration or diversification. Those metrics are provided globally across the 21st century for various climate scenarios, across a suite of six acute and chronic climate perils (tropical cyclones, wildfires, inland floods, coastal floods, heat stress and water stress), as well as earthquake risk.

This presentation will include an overview of the models, showcase some key results and discuss various use cases across the financial sector. The importance of such detailed loss-based climate risk metrics for present and future regulatory requirements will be emphasized, together with the need for increased collaboration between academia, industry and regulators in addressing the challenges ahead.

How to cite: Roy, K. and Khare, S.: Leveraging Catastrophe Modelling Insights for Bottom-Up Assessments of Climate Change Physical Risk: The ‘Climate on Demand Pro’ Platform, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-19069, https://doi.org/10.5194/egusphere-egu24-19069, 2024.

In the realm of finance for loss and damage, new funding mechanisms are emerging, yet the task of identifying and quantifying the losses and damages from extreme weather events attributable to climate change remains a complex challenge. Impact attribution, which extends beyond traditional attribution analyses of extreme weather events, is gaining more attention and methods are improving. However, their systematic integration into the loss and damage finance architecture will not be possible any time soon. With the rapidly escalating impacts of climate change, financial solutions designed to support affected communities and countries must align with the real-world necessity for predictability and swiftness of disaster risk finance.

Insurance, while not a panacea, has traditionally been envisioned as an important player in the domain of loss and damage finance. Nonetheless, insurance premia become prohibitively expensive in many regions and specific risks inch towards becoming uninsurable. Increasing the uptake of insurance and making it more affordable, e.g. through subsidies, can relieve some of the impacts and support affected communities with reliable financial flows. Here, parametric insurance is posited as a generally suitable solution with advantages over traditional indemnity insurance. It provides transparent and quick financial responses after extreme weather events, is less exposed to moral hazard and adverse selection.  

This research develops a scalable, objective, transparent, and pragmatic framework for the quantification and attribution of payout and premium increases of parametric insurance due to climate change. Apt for incorporation into new solutions such as the loss and damage fund and the Global Shield initiative, the framework would allow to mobilise substantial funding by blending public and private funds and leveraging the infrastructure of insurance companies. Employing this framework within a loss and damage finance architecture not only capitalizes on the inherent benefits of parametric insurance but also ensures that the allocation of resources is more closely aligned with changes in weather patterns, and therefore impacts, that are attributable to climate change.

The framework is applied to the context of tropical cyclone parametric insurance in various locations, as well as to heatwave parametric insurance in India. The results illustrate the alterations in payouts and premia attributable to climate change and quantify the loss and damage finance required to compensate for the climate-change related risk increases, whether as direct payments to policyholders/insurance companies affected by the insured events or as subsidies for insurance premia.

How to cite: Fabian, F.: Quantifying and attributing pay-out and premia increases of parametric insurance to climate change – A framework for scalable, objective, transparent and pragmatic integration into a loss and damage finance architecture, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-19327, https://doi.org/10.5194/egusphere-egu24-19327, 2024.

EGU24-21474 * | Posters on site | ITS4.8/CL0.1.16 | Highlight

Greening the financial system: How can national meteorological services drive the transition? 

Elizabeth Wright and Niall Robinson

Financial markets are key catalysts for a net-zero future by 2050, with trillions of capital and resources ready to be unlocked. So what is holding them back? Green finance is pushing to be at the forefront of any structured financial activity, however the lack of clear definitions, standards, and regulations, misaligned incentives and interests, scarce data and information, and a gap between the demand and supply of green finance is slowing down its impact and implementation. This talk examines the role of government agencies, such as the UK Met Office, in helping markets to address climate risk. By aligning the financial system with the Paris Agreement and Sustainable Development Goals, green finance can reduce the exposure of financial institutions to climate-related risks, such as stranded assets, physical damages, and transition costs and help to address some of the key challenges they are facing. 

How to cite: Wright, E. and Robinson, N.: Greening the financial system: How can national meteorological services drive the transition?, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-21474, https://doi.org/10.5194/egusphere-egu24-21474, 2024.

Against the backdrop of the global heightened geopolitical tensions and climate change, the issue of food security is a major topic currently faced by countries around the globe. As the most populous country in the world, China's food security issues will impact the sustainability of global food production and supply stability. Despite the emphasis on food security and cultivated land protection, China is facing the latent threat to food production caused by the non-grain use of cultivated land, where land previously used for cultivating food crops is being extensively planted with cash crops or used for forestry development. Not only will this phenomenon increase the pressure on China's self-sufficiency in food, but it will also damage the stability of the agricultural ecosystem and weaken the sustainability of food production in the long term. As the main grain-producing area in Sichuan Province and even in western China, the Sichuan Basin has a solid agricultural foundation. In recent years, the phenomenon of non-grain use has become increasingly prominent, necessitating an exploration of its driving mechanisms and the implementation of governance measures. Set in the Sichuan Basin, this paper employed the sliding window method to continuously monitor and extract the non-grain patches between 1991-2018 in the study area based on the annual China Land Cover Dataset (CLCD). We used advanced data-driven approaches, including geographically weighted regression models and geographical detector models, to explore the direction and strength of the impact of driving factors on the non-grain phenomenon. Finally, using process tracing based on policy texts, non-grain evolution is interpreted. In conclusion, increased economic activity exacerbates non-grain use, and objective spatial positions constrain the impact of locational factors on non-grain use. Natural factors fundamentally and decisively explain the level of non-grain use. Decreased temperature and increased slope will intensify this phenomenon, and the impact of precipitation on non-grain exhibits a threshold effect. China's three agricultural structural adjustments have potentially influenced the overall trend of the non-grain phenomenon. The Wenchuan earthquake and subsequent reconstruction had a short-term impact, while the central and local government's attention to the issue of non-grain and a series of arable land protection measures are the main reasons for the sharp decrease in the non-grain phenomenon after 2015. Differentiated policy measures are recommended for mountainous and plain regions to address these socio-ecosystem changes, balancing the goals of food production and ecological protection. This approach will ensure grain production is more adaptable to climate change and aligned with the intensity of economic activities.

How to cite: Chen, S., Xiao, W., Xu, S., Niu, L., and Zhang, Z.: Unveiling the Spatio-Temporal Characteristics and Driving Mechanisms of Non-Grain Land Use Dynamics in Agricultural Socio-Ecosystems: A Case Study of the Sichuan Basin, China, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-644, https://doi.org/10.5194/egusphere-egu24-644, 2024.

EGU24-905 | ECS | Orals | ITS4.10/NH13.1

Navigating climate risk in humanitarian action: The potential of storyline approaches 

Martha Marie Vogel and Christopher David Jack

The humanitarian community has a long history of attempting to reduce the human impact of extreme weather and climate events. Over the past decade there has been an increasing shift in the humanitarian community towards using climate science to better anticipate climate impacts on vulnerable communities and hence guide humanitarian planning and responses. However, large uncertainties, climate and non-climate, and complex compounding risks pose significant challenges to integrating climate information into humanitarian planning.

In the glossary of the IPCC Working Group I contribution to the Sixth Assessment Report storylines are defined as “A way of making sense of a situation or a series of events through the construction of a set of explanatory elements” and “can be used to describe plural, conditional possible futures or explanations of a current situation, in contrast to single, definitive futures or explanations”.

With this they are valuable for the humanitarian sector as storylines related approaches including impact pathways and complex risk frameworks offer the potential to provide robust and valuable understanding of risk, as well as supporting the development of effective interventions. They do not remove the underlying uncertainty, however, they do help to shift the questions asked from “What is going to happen”, to “What would unfold if this storyline occurred".  This shift has the potential to connect with decision making options and processes far more effectively than presentations of aggregate uncertainty ranges.

We explore the potential value  of storylines for climate risk management within the humanitarian sector, we present practical examples of effectively applying them to estimate and describe  systemic climate-related risks, especially in vulnerable regions.

How to cite: Vogel, M. M. and Jack, C. D.: Navigating climate risk in humanitarian action: The potential of storyline approaches, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-905, https://doi.org/10.5194/egusphere-egu24-905, 2024.

EGU24-1496 | ECS | Orals | ITS4.10/NH13.1

Analyzing impact cascades: an integrative approach for assessing the interconnected effects of extreme events across sectors and systems 

Mariana Madruga de Brito, Jan Sodoge, Zora Reckhaus, Miguel Mahecha, and Christian Kuhlicke

In today’s interconnected world, assessing the risks of extreme events has become increasingly complex. These events often trigger far-reaching consequences that spread throughout various sectors and systems due to complex interactions, resulting in compound and cascading impacts. While qualitative and quantitative approaches are commonly used separately in systemic impact research, we argue that methodological pluralism is necessary to address the complexity of these social-ecological systems. In this talk, we propose an integrative methodological approach for studying impact cascades and exemplify it via two case-study applications. The first focuses on using dimensionality reduction and pattern-mining techniques to assess spatiotemporal patterns in the occurrence of drought impacts in Germany. We explore how these patterns differ during multi-year drought events in contrast to short-lived droughts. Second, we leverage qualitative cognitive maps derived from 25 stakeholder interviews to investigate how drought impacts propagate in a case study in Thuringia, Germany. By using graph theory, we identify influential variables and show how pooling the knowledge of diverse stakeholder crowds can create new, emergent knowledge. We find that combining different methods helps revealing various facets of impact cascades and helps compensating for the limitations of individual methods. This can strengthen the research confidence as results that agree across different methods are less likely to be artefacts.

How to cite: Madruga de Brito, M., Sodoge, J., Reckhaus, Z., Mahecha, M., and Kuhlicke, C.: Analyzing impact cascades: an integrative approach for assessing the interconnected effects of extreme events across sectors and systems, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1496, https://doi.org/10.5194/egusphere-egu24-1496, 2024.

EGU24-1616 | Orals | ITS4.10/NH13.1 | Highlight

Extreme Event Impact Attribution: The state of the art 

Ilan Noy, Daithi Stone, and Tomas Uher

Extreme weather events lead to many adverse societal, economic, and environmental consequences. Anthropogenic climate change has been identified as a factor that, in many cases, increases the frequency and intensity of these weather extremes. In the last two decades, the methods of Extreme Event Attribution (EEA) have been used to quantify the extent to which climate change affected the nature of specific recent extreme weather events. More recently, these methods are being combined with socioeconomic impact data to quantify extreme weather’s impacts attributable to climate change in what we term Extreme Event Impacts Attribution (EEIA). EEIA is a quickly developing field that considers which kinds of questions about the impacts of climate change on extreme weather events we should ask, what methods are best suited to answer them, how to interpret the results these methods provide, and what purpose these results can serve. In this survey, we discuss the basic structure and methods of EEIA, review the results of the existing EEIA studies, and discuss the implications and outlook for this strand of research including its relevance for quantification of climate change costs, the Loss and Damage Fund, climate litigation, or adaptation planning.

How to cite: Noy, I., Stone, D., and Uher, T.: Extreme Event Impact Attribution: The state of the art, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1616, https://doi.org/10.5194/egusphere-egu24-1616, 2024.

Cities are facing increasing challenges of flood risk due to combined effects of climate change and socioeconomic development. At the same time understanding of the complexity of urban flood risk is still limited, hampering decision-making and effective urban adaptation planning. A socio-ecological system (SES) perspective offers a promising approach to analyze risk as a non-isolated entity by recognizing human and natural systems as complex and coupled structures and considering their interactive dynamics (e.g., delays, feedbacks, and non-linearity). Qualitative system dynamics modeling tools, such as causal loop diagrams, are particularly useful for this, as they allow the inclusion of different kinds of system variables.

This study applies a qualitative system dynamics modeling framework to holistically investigate urban flood risk under climate change and barriers to adaptation in a coupled SES using the city of Hamburg as a case study. The study deals with urban flood risk in the context of ‘water from 4 sides’ addressing questions in the growing research field of climate hazard interactions and compound risks. In a stepwise approach, a qualitative system dynamics model was developed based on an integrated interdisciplinary knowledge of researchers. Disciplinary mental maps were created by the researchers in various group interviews, followed by the development of an overall group causal loop diagram based on the disciplinary mental maps to form a holistic qualitative model. For the model analysis, causal chains of sub-processes and feedback loops were visually isolated and highlighted. Particular emphasis is placed on identifying and analyzing the reinforcing feedback loops underlying the complex urban system in order to understand the vicious circles of barriers that perpetuate and thus hinder the adaptation process. The findings on the system’s feedback loops help to understand why and how system behavior evolves in a specific direction. The integrated model shows that the main drivers of urban flood risk growth in the system are linked to socio-economic and institutional processes. Climate change mainly affects the city externally by increasing flood hazards, while the city itself contributes to flood risk through processes of exposure and social vulnerability. The results show that increasing flood risk and barriers to adaptation in the city are linked to the amplifying feedback loops of path dependency, river engineering measures, urban development, car dependency, the ‘levee effect’, poverty, urban health and silo-thinking.

The case study demonstrates the usefulness of the qualitative system dynamics modelling approach in developing a shared understanding of the complex social, economic, environmental and political and institutional interactions among multiple drivers of flood risk. Causal loop diagrams can be successfully used to articulate the viscous circles of barriers and lock-in effects of unsustainable development in urban adaptation. However, it should be noted that the model reflects the state of knowledge of the researchers involved in the model-building process and therefore only represents a ‘dynamic hypothesis’ of the structure and dynamics of the system under consideration. Further work is in progress to place this qualitative system dynamics model in the broader context of decisions support and policy through stakeholder involvement.

How to cite: Hanf, F. S. and the 'water from 4 sides' project team: A socio-ecological system perspective on urban flood risk and barriers to adaptation under climate change using causal loop diagrams – Case study of the city of Hamburg, Germany, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-2008, https://doi.org/10.5194/egusphere-egu24-2008, 2024.

EGU24-2158 | ECS | Orals | ITS4.10/NH13.1

Personalized warnings – Swiss public’s preferences and needs  

Lorena Daphna Kuratle, Irina Dallo, and Michèle Marti

There are numerous efforts globally to enhance societies’ ability to prepare for and cope with disasters triggered by natural and human-made hazards such as heatwaves, flash floods, terrorist attacks, or earthquakes. Some of these efforts aim to enhance the effect of warnings by personalizing them. By addressing individual factors such as health issues and caregiving responsibilities and including tailored behavioral recommendations, they can become more inclusive. However, the compilation of these personalized warnings requires data, which can either be generated by a (one-time) query or extracted from individuals’ digital footprints. Thereby, the following key questions arise: Is there a desire for personalized warnings? Do these warnings improve safety culture, enhancing preparedness and responses in the face of disasters? Moreover, is the public aware of the type of data required to receive such warnings?

We will answer these questions by the means of a representative online survey in Switzerland with a between-subjects experiment by assigning participants to personalized heat warnings. It allows us to assess if people would like to receive personalized warnings and whether those warnings influence their intention to take protective measures and enhance inclusiveness. Further, we will analyze people’s data sharing preferences, their trust in warnings, and the influence of their online behavior (e.g., online-shopping, use of smart watches) on their preference for those warnings. Moreover, we will assess participants’ demographics to find patterns in what type of data different social groups are willing to share.

In our talk, we will present the first results of this survey and discuss implications for the further development of personalized warning messages.

This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 101021746

How to cite: Kuratle, L. D., Dallo, I., and Marti, M.: Personalized warnings – Swiss public’s preferences and needs , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-2158, https://doi.org/10.5194/egusphere-egu24-2158, 2024.

EGU24-2376 | ECS | Posters on site | ITS4.10/NH13.1

Understanding the multiple linkages between climate risks and water supply: a case study of Southern Sweden 

Jeanne Fernandez, Giuliano Di Baldassarre, Claudia Teutschbein, and Johanna Mård

There are numerous studies of the impacts of climate-related natural hazards, such as droughts, heatwaves and wildfires, to water supply. These range from the global mapping of water scarcity to local-level evaluations of damages to production and distribution infrastructure. However, comprehensive and dynamic assessments of climate impacts to water supply that consider both fast (e.g., floods, landslides) and slow onset risks (e.g., drought) as well as changes in water consumption are still lacking, especially in regions perceived as “water-rich”.

This study reviewed climate change impacts to water supply in northern temperate climates which, in recent years, have been exposed not only to multiple floods but also to seasonal droughts despite predicted increases in average precipitation. By adopting an extended risk framework, we developed a conceptual overview and visualization of the linkages between climate, water, and society in the context of Southern Sweden.

The results highlight the multiple knowledge gaps in the Swedish water sector related to climate change uncertainties at local scales, compound and cascading risks, and the challenge of implementing adaptation measures in practice. When acknowledging intersectoral connections, the conceptualization becomes increasingly complex, emphasizing broader implications for a functional society as a whole.

This research contributes to a sparse literature on the impacts of climate change to water supply in northern regions. We argue that conceptual and systemic approaches can benefit water utilities and municipalities where drought risk tends to be overlooked and discuss possible venues for moving adaptation forward.

How to cite: Fernandez, J., Di Baldassarre, G., Teutschbein, C., and Mård, J.: Understanding the multiple linkages between climate risks and water supply: a case study of Southern Sweden, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-2376, https://doi.org/10.5194/egusphere-egu24-2376, 2024.

Climate risk is systemic risk. Despite this, extreme risk cascades from climate change are underexplored. This is a mistake since such cascades are likely to occur even at relatively low temperature rises of 1.5-2°C. Such heating risks triggering six or more tipping elements in the Earth system.  Here we use a novel form of expert elicitation and systems mapping to trace out potential paths from climate impacts to societal collapse at 2°C of warming. We contacted 8 experts from a range of different fields, including climatology, earth systems science, and existential risk studies, and had them compose systems diagrams of the most likely scenarios in which expected climate impacts cascade into widespread systems failures. We then compared and synthesised these to identify key, common feedbacks and pathways. These include food crises and extreme weather events undermining state legitimacy and triggering socio-political violence. Climate resilience efforts need to account for such extreme cascades.  

How to cite: Stephenson, S. and Kemp, L.: Mapping the end of the world: Understanding plausible routes to collapse from 2°C of warming, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-3917, https://doi.org/10.5194/egusphere-egu24-3917, 2024.

EGU24-4128 | ECS | Posters on site | ITS4.10/NH13.1

Oil palm yield in Southeast Asia impacted by management and climate change 

Luri Nurlaila Syahid, Xiangzhong Luo, and Janice Ser Huay Lee

Oil palm is a primary commodity in the Southeast Asian region and has replaced a substantial portion of natural forests in this area, resulting in a shift in regional ecosystem and ecosystem-climate interactions. Previous site-level evidence suggested that oil palm activities (i.e., production yield) are influenced by social, biological, and climatic factors, however, it remains unclear how oil palm yield has changed across Southeast Asia and the dominant driver for the changes. In this study, we used ground survey of oil palm yield, in combination of remote sensing of near-infrared reflectance (NIRv), to examine the dynamics of oil palm yield from 2001 to 2017 in Southeast Asia, particularly in Malaysia and Indonesia. Utilizing multiple sources of open datasets, we investigated the roles of management (i.e., smallholder and industrial), biotic (i.e., stand age) and climate in influencing oil palm yield in Southeast Asia, and provide a quantification of their respective contributions to yield changes. The study advances our understanding of the historical changes in oil palm yield and their dominant factors, providing guidance to the future management of oil palm for sustainable production and ecosystem services.

How to cite: Syahid, L. N., Luo, X., and Lee, J. S. H.: Oil palm yield in Southeast Asia impacted by management and climate change, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-4128, https://doi.org/10.5194/egusphere-egu24-4128, 2024.

Amidst the growing urgency to sustain forest ecosystems, this study presents a crucial analysis of forest fragmentation susceptibility in Poland's Tuchola Forest—a region recurrently devastated by windstorm events. Our research aims to innovatively harness remote sensing technologies for a comprehensive assessment of forest fragmentation from 2015 to 2020. The study primarily revolves around three objectives: selecting the most suitable remote sensing dataset for monitoring fragmentation, identifying key contributing factors to forest fragmentation, and developing a susceptibility map to illustrate the forest’s fragmentation dynamics.

Employing a comparative analysis with the GTB tool, we scrutinized the capabilities of PALSAR (25m resolution) and Dynamic World (10m resolution) datasets. Our findings highlighted PALSAR's superior proficiency in detecting rare-patchy fragments, despite its marginally higher resolution. To construct a forest fragmentation susceptibility map, we used fragmented patches observed over the last six years as indicators of regions prone to intense fragmentation. These patches were further analyzed through the Weight-of-Evidence (WOE) method, where causative factors were normalized and scrutinized using a Correlation matrix.

The results indicate a heightened vulnerability of forest areas proximal to agricultural lands (<200 m) and barelands (<50 m), especially those with younger trees (5-15 years) and shorter tree heights (<18m). Such areas are more susceptible to fragmentation, exacerbated by high wind speeds (25-27 m/sec) and moderate vegetation water content. In contrast, regions distant from agricultural lands, particularly those on steeper slopes, demonstrate lower fragmentation susceptibility.

Our methodology, validated with an 82% accuracy, calls for immediate conservation measures in Tuchola Forest's fragile areas. It offers a scalable approach, underscoring the critical role of forest conservation in maintaining biodiversity and resilience against climate adversities. This study marks a pivotal contribution to Polish forestry research, providing actionable insights for decision-makers in forest reforestation, restoration, and afforestation strategies.

How to cite: Dutt, S.: Forest Ecosystem on the Edge: Mapping Forest Fragmentation Susceptibility in Tuchola Forest, Poland, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5038, https://doi.org/10.5194/egusphere-egu24-5038, 2024.

EGU24-5296 | ECS | Posters on site | ITS4.10/NH13.1

Application of climate risk assessment framework for selected Italian airports: a focus on extreme temperature and extreme precipitation events 

Carmela De Vivo, Giuliana Barbato, Marta Ellena, Vincenzo Capozzi, Giorgio Budillon, and Paola Mercogliano

Due to increased extreme weather events as a consequence of climate change, climate risk analysis has become an essential issue for all critical infrastructures, including airports. The aim of this study is to apply a climate risk assessment framework to evaluate the impacts of extreme temperatures and extreme precipitation events on several Italian airports: Malpensa, Linate, Bergamo, Ciampino, Fiumicino, Napoli, Catania, Palermo, and Cagliari. According to the risk definition recommended in the Sixth Assessment Report of IPCC (2022), specific hazard, exposure and vulnerability indicators were identified. The hazard indicators were calculated using the UERRA regional reanalysis for the observed period (1981-2010). The climate variations were evaluated by an ensemble mean of high-resolution climate projections from the EURO-CORDEX initiative for the short (2021-2050), medium (2041-2070), and long-term future period (2071-2100), under RCP 2.6, RCP 4.5, and RCP 8.5 scenarios. Exposure and vulnerability data were collected from multiple sources, such as official airports documents or websites. The final risk index obtained from the combination of these three factors allowed us to identify which of the selected airports are probable to face the major impacts due to extreme temperature and precipitation events.

In addition, starting from this study, a further innovative methodology is currently being evaluated to be adopted for climate change risk assessment by an individual airport. Main steps of this procedures involve the identification of hazard indicators, exposure and vulnerability factors. The success of the analysis performed is closely linked to the ability to actively involve the airport managers/operators through the participation to the workshops as well as the compilation of specific questionnaires in order to establish a participatory process with the aim to provide a comprehensive and detailed analysis.

All the methods and analysis (planned and ongoing) have the main goal of supporting the risk assessment airports and providing key information to enable the definition, selection and implementation of appropriate adaptation strategies in relation to characteristics of the airports and then to improve their resilience to climate change.

How to cite: De Vivo, C., Barbato, G., Ellena, M., Capozzi, V., Budillon, G., and Mercogliano, P.: Application of climate risk assessment framework for selected Italian airports: a focus on extreme temperature and extreme precipitation events, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5296, https://doi.org/10.5194/egusphere-egu24-5296, 2024.

EGU24-5548 | ECS | Orals | ITS4.10/NH13.1

Examining the Spatial-Temporal Evolution of the Chongzhen Drought (1627-1644) in China and Its Impact on Famine 

Siying Chen, Yun Su, Xudong Chen, and Liang Emlyn Yang

Climate change is a critical context for the development of human civilization. Case studies on the societal impacts of climate change contribute to the understanding of the mechanisms of interactions between natural forces, ecosystems, and human societies. This study investigates the Chongzhen Drought occurring in 1627-1644 in China, which was very likely the worst drought in eastern China in the past 1500 years. Its duration, scope, and number of people affected are rare in history. At the same time, a large-scale famine broke out, which is argued as a main trigger of the peasant uprisings that led to the fall of the Ming Dynasty. This paper extracted 1,802 drought records and 1,977 famine records from Chinese historical documents (mainly local chronicles and history books) and reconstructed the spatio-temporal evolution of drought from 1627 to 1644 in eastern China as well as its impact on famine. First, we classified the drought events into four levels according to the duration and severity, based on the semantic differences. Then the kernel density estimation was used to reconstruct the spatial pattern of drought at annual resolution, as well as a series of Drought Kernel Density Index (DKDI) in different regions. The main drought area in 1627-1644 was located north of 29°N and shifted from Northwest China to North China and then expanded to the south. The development of drought in different regions was not synchronized. The DKDI series of North China approximated a single-peaked curve, with the drought gradually worsening from 1633-1640; the peak of DKDI in Northwest China also appeared in 1640. However, the DKDI series of the Yangtze-Huai Region showed a multi-peaked curve, constantly experiencing a cycle of drought aggravation-reduction in the early period and reaching its peak in 1641. Second, the spatio-temporal evolution of famine was also reconstructed and compared with drought. It showed that the range of drought and famine largely overlapped and their developing trends were generally similar.  However, the movement of the Famine Kernel Density Index (FKDI) series tended to be 1-2 years later than that of DKDI, suggesting a lag and continuation of transmission of drought impacts to the human system. Finally, the regression analysis showed that drought was the most critical factor triggering famine in this case with a contribution weight of 67.3%. The weight is higher at 73.4% in North China in comparison with other subnational regions. The study identified the transmission pathway from climate change to social consequences through “persistent drought → declining agricultural harvests → food shortage → famine”. While socioeconomic factors and human behaviours also played various roles in regulating the transmission process.

How to cite: Chen, S., Su, Y., Chen, X., and Yang, L. E.: Examining the Spatial-Temporal Evolution of the Chongzhen Drought (1627-1644) in China and Its Impact on Famine, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5548, https://doi.org/10.5194/egusphere-egu24-5548, 2024.

The losses caused by flood emerges from the intricate interplay between natural and human systems. Particularly noteworthy is the flood disaster induced by exceptionally intense precipitation within densely populated mega-citys. A poignant illustration of this dynamic unfolded on July 20th, 2021, in the city of Zhengzhou, China. This event precipitated 380 fatalities, accompanied by a direct economic loss  exceeding  5.6 billion U.S. dollars, thereby exerting profound repercussions on national economic and social development. To unravel the intricate interactions between human systems and the extreme natural setting, our investigation delves into specific disaster events, such as fatalities occurring in metro line 5 and the tunnel of Jing-guang urban expressway, the overtopping peril of Guojiazui reservoir, and the explosion at an aluminum alloy factory. Employing a systemic perspective, we analyze human activities during the phases of early warning, response and disposal. Our findings underscore pivotal factors contributing to the substantial loss of life, including inefficient organization and preparedness by local government entities, inadequate emergency response measures from various departments, and a lack of readiness among the local populace. In response to these identified shortcomings, we proffer concrete recommendations for disaster prevention. These suggestions serve as valuable references for mega-cities, advocating measures such as fortifying the linkage mechanism among governmental departments and enhancing public awareness regarding flood hazards.

How to cite: Qi, S.: Analysis and research on interactions between human systems and the extreme rainstorm setting, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5590, https://doi.org/10.5194/egusphere-egu24-5590, 2024.

EGU24-5855 | Orals | ITS4.10/NH13.1

IRISCC: supporting society’s capacity to address and strengthen resilience to climate change risks  

Janne Rinne, Magdalena Brus, Nikolaos Nikolaidis, Jaana Bäck, Paolo Laj, Werner Kutsch, Dick Schaap, Klaus Steenberg Larsen, Sabine Phillippin, Rosa Petracca Altieri, Cathrine Lund, Katrine Vendelboe, Päivi Haapanala, Säde Virkki, Niku Kivekäs, and Sanna Sorvari Sundet

Anthropogenic climate change, driven by elevated levels of greenhouse gases, is accelerating at an unprecedented rate, causing significant changes in climatic and biogeochemical conditions. The adverse effects of climate change include detrimental impacts on natural and managed ecosystems, as well as on socio-economic systems, human health, and welfare. Recognising the urgent need to address these challenges, the IRISCC (Integrated Research Infrastructure Services for Climate Change Risks) project aims to provide scientific and knowledge-based services to support societal adaptation to climate change. The project is funded by the European Union under grant agreement No 101131261 (HORIZON Research and Innovation Actions in Research Infrastructure Programme topic HORIZON- INFRA-2023-SERV-01-01) and has over 70 partners providing research services.

For the researchers focusing on climate change risks, IRISCC will offer services, such as open data and access to research platforms via transnational access and virtual access programs. Here IRISCC employs an integrated approach to understanding climate change risks, encompassing hazards, exposure, and vulnerability. By fostering interdisciplinary collaboration, the project strives to support science enabling all users of IRISCC services to better predict, mitigate, and adapt to climate-related risks affecting human and natural systems. The project's overarching mission is to facilitate in-depth knowledge production on climate change risks and accelerate the translation of scientific knowledge into innovative solutions.

The IRISCC consortium comprises expertise from several research infrastructures, covering domains such as Earth systems, health and environment, and social sciences, each bringing in their research service portfolios. Through inter- and transdisciplinary approaches, the project aims to provide transnational and virtual access to cutting-edge research, innovation, training, and digital services. 

In summary, the IRISCC project aligns with the session's focus on systems thinking approaches, presenting a comprehensive strategy supporting users to tackle the interconnected issues of climate-related hazards, risks, and impacts. The commitment of the project to provide integrated research infrastructure services positions it as a key player in advancing our ability to predict, mitigate, and adapt to the multifaceted challenges posed by climate change in European regions and cities.

How to cite: Rinne, J., Brus, M., Nikolaidis, N., Bäck, J., Laj, P., Kutsch, W., Schaap, D., Steenberg Larsen, K., Phillippin, S., Petracca Altieri, R., Lund, C., Vendelboe, K., Haapanala, P., Virkki, S., Kivekäs, N., and Sorvari Sundet, S.: IRISCC: supporting society’s capacity to address and strengthen resilience to climate change risks , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5855, https://doi.org/10.5194/egusphere-egu24-5855, 2024.

During Hurricane Katrine in New Orleans in 2005, the failure of telecommunication systems was a disaster by itself, creating chaos and seriously hampering mitigation measures during and directly after the event. Half of the telecom towers was destroyed by the heavy wind, the electrical grid was destroyed and an area as large as The Netherlands and Belgium combined was flooded. The rest of the telecom towers ceased operation 48 hours later, when their backup power was depleted. In some parts of New Orleans the water stood 4.5 meters high, and debris was making roads impassable, blocking emergency repairs. This created a disaster in a disaster, leaving local authorities and first responders without intercommunication and status updates, rendering well-informed and coordinated actions impossible.

Similarly, during Hurricane Irma in 2017, on the island of St. Maarten, 50% of the telecom towers were blown over, seriously hampering communications in large sections of the island. Fortunately, the sea cables connecting the island to the rest of the world remained unharmed, although even that was a close shave. Therefore, while the mobile phone network failed in large areas, the Emergency Support Functions of the government could still communicate with the outside world via the internet, to ask for support and specific equipment for emergency repairs (such as new telecom towers).

Similarly, after the Nepal earthquake in 2015, roads were rendered impassable by debris and all telecommunication networks were silenced, and the electrical grid destroyed. The first messages to the outside world were conveyed by radio amateurs, via ionospheric radio. Several inland villages remained isolated for several days, with no means to issue a call for assistance or medical help.

Despite these and other examples, most of the models and impact chains drawn by scientist to investigate disaster events ignore the role of telecommunication failure that aggravates the situation in the field. Also, scientific tools to predict risks and support decisions when the disaster unfolds and directly after it, are often provided via internet links, ignoring the likelihood of them being inaccessible when they are needed most, due to a telecom blackout.

It is therefore of the utmost importance to draw more attention of researcher to the role of telecommunications in impact chains, even when that is not their direct competence, and to interact with telecommunication experts and emergency organizations in the field to better prepare for telecommunication failure during and after disasters. A good example of such an initiative was shown in PARATUS, a scientific project on societal resilience, where information gathering on St. Maarten specifically included telecommunication during disasters. Crossing these boundaries between sectors will greatly amplify the practical impact of the scientific work.

How to cite: Witvliet, B.: Telecommunication – a blind spot in disaster resilience science, yet essential for disaster mitigation and recovery, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-6634, https://doi.org/10.5194/egusphere-egu24-6634, 2024.

Loss exceedance curves have fat tails arising from cascading losses.  Even though such losses are rare, insight can be gained by considering alternative downward counterfactual realizations of historical events.  The use of downward counterfactuals provides a methodology for constructing climate change storylines (e.g. Climate Risk Management, 2021).

The downward counterfactual search for cascading losses can identify potential tipping points for disasters.  Such tipping points can arise from the perturbation of a historical system state through additional climate forcing, combined with human factors, such as human error, negligence or malicious action.   

Examples are given of how lessons learned from historical compound events, e.g. wind and heatwave, might have averted disaster.  The Californian utility, Pacific Gas & Electric (PG&E), narrowly missed liability for the 2017 Tubbs Fire in Northern California.  Increased inspection of their electricity power lines would have mitigated the risk of liability from future wildfires.  The following year, the Camp fire occurred, the deadliest and most destructive in Californian history.  PG&E were indicted for repeatedly ignoring warnings about its aging power lines and faulty maintenance, and in early 2019, PG&E were forced to file for Chapter 11 bankruptcy.

On 9 September 2023, Storm Daniel transitioned into a Mediterranean tropical cyclone, and made landfall near Benghazi in Libya, the following day.  The intense rainfall caused the collapse of the two Wadi Derna dams on 11 September, and the devastation of Derna.  Counterfactual analysis would have given prior warning. A Libyan hydrologist had pointed out in 2022 that the 1959 storm would have caused the failure of the dams, had they existed then.

Exploration of downward counterfactuals would augment societal resilience against climate extremes and compound events.

 

 

 

How to cite: Woo, G.: Downward counterfactual search for cascading losses, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-7449, https://doi.org/10.5194/egusphere-egu24-7449, 2024.

EGU24-7487 | ECS | Orals | ITS4.10/NH13.1

River Regulation Reshaped Human-water Interaction in the Lower Yellow River Floodplain 

Chentai Jiao, Xutong Wu, Shuang Song, Shuai Wang, Bei Xiang, and Bojie Fu

Floodplains have been crucial agricultural and populated areas throughout history and in present. Rivers typically shape the human activities within floodplains through water supply and flood risk, forming unique human-water interaction patterns. Here, we focus on the Lower Yellow River Floodplain, where continuous levees divide homogenous cultivated plain with different flood risk, creating a quasi-natural experiment, while the river's hydrology has undergone dramatic transformation since the 1990s. We utilize Landsat-based data including open-surface water bodies, cropland and NDVI to analyze the mechanism of river-agriculture interaction and whether this mechanism has changed. The results reveal that agriculture activities were less developed inside the floodplain than outside, and were even worse in regions closest to the river. This was attributed to frequent channel diversions, heightened flood threat, and actual inundation within the floodplain. However, the Lower Yellow River experienced a silt-load reduction, trenching, and channel stabilization after the late 1990s, while submerged cropland area in the floodplain also decreased. The declining flood threat has encouraged cultivation and agriculture investment in the floodplain, consequently reducing the productivity difference across the levees. This study illustrates a prototypical human-water interaction pattern in floodplains, underscoring the significance of effective river management for sustainable development in these regions, and provides a reference on understanding regional human-environment relationship in other floodplain areas.

How to cite: Jiao, C., Wu, X., Song, S., Wang, S., Xiang, B., and Fu, B.: River Regulation Reshaped Human-water Interaction in the Lower Yellow River Floodplain, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-7487, https://doi.org/10.5194/egusphere-egu24-7487, 2024.

EGU24-7653 | Orals | ITS4.10/NH13.1

Identifying financial actors exposed to tipping point risks 

Juan Rocha and Victor Galaz

Ecosystems around the world are showing symptoms of resilience loss. With them there is an increasing risk of critical transitions or regime shifts: large, abrupt and difficult to reverse changes in the function and structure of ecosystems. When regime shifts occur they often impact the flow of benefits that people get from nature, and with them the ability of companies, cities or nations to satisfy human needs. Here we ask who is exposed to ecological regime shift risks, and by being exposed, who has the agency or power to intervene and perhaps avoid tipping points?

To answer this question we match companies whose activities imply the use or extraction of natural resources in places vulnerable to regime shifts. First, we use Earth observations to quantify resilience loss in marine systems. We also used temeprature records to quantify the probability of extreme and severe heat wave events in the oceans. Both are conditions that can reduce primary productivity and impact fisheries. Then, we identify vessels that fish in these areas of the world and match their owners and shareholders when available in public databases.

For publicly listed companies we reconstruct social networks of companies ownership and investments. The networks serve to identify financial actors exposed to ecological tipping points through several investments or regions of the world. The multilayer network can be centred around companies, shareholders, investors, or countries. Clustering at different levels of aggregation allow us to identify actors with disproportional risk exposure, but also companies, investors or countries who could make a difference in mitigating risk.

How to cite: Rocha, J. and Galaz, V.: Identifying financial actors exposed to tipping point risks, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-7653, https://doi.org/10.5194/egusphere-egu24-7653, 2024.

EGU24-7721 | Orals | ITS4.10/NH13.1

Assessing the deforestation embodied in the European Union consumption and trade of forest risk commodities.  

Mirco Migliavacca, Teresa Bras, Paul Rougieux, Selene Patani, Giovanni Bausano, Frederic Achard, Valerio Avitabile, Rene Beuchle, Clement Bourgoin, Alessandro Cescatti, Guido Ceccherini, Rene Colditz, Valeria De Laurentiis, Vasco Orza, Christelle Vancutsem, and Sarah Mubareka

Deforestation and forest degradation, particularly in the tropics, are recognised as important drivers of global warming and biodiversity loss. Forest loss can be driven by several factors, including the expansion of agriculture and pastureland to produce commodities and agroforestry. 

On 29 June 2023, the European Union (EU) Regulation on deforestation-free products came into force, promoting the consumption of 'deforestation-free' products with the aim of reducing the EU's impact on global deforestation and forest degradation, as well as greenhouse gas emissions and biodiversity loss. 

The Regulation states that products related to cattle, timber, cocoa, soy, palm oil, coffee and rubber must be produced on land that is free from deforestation, after 31 December 2020.  

In this contribution, we combine the use of statistics on agricultural and wood production, trade flow data, earth observation on land use change and deforestation, with physically based land footprint and a land use balance models to calculate the impacts embodied in EU bilateral trade and consumption of the selected forest risk commodities. Specifically, we evaluated the impact in terms of land area of forest biomass stocks displaced for the production and consumption of the commodities listed in the Deforestation Free Product Regulation.  

Our evaluation reveals that, in relative terms, the EU significantly contributes to the impacts linked to the production of cocoa and coffee. Soy, cattle, and palm oil emerge as the overall major contributors to deforestation embodied in the EU consumption and are globally responsible for most forest biomass loss. 

How to cite: Migliavacca, M., Bras, T., Rougieux, P., Patani, S., Bausano, G., Achard, F., Avitabile, V., Beuchle, R., Bourgoin, C., Cescatti, A., Ceccherini, G., Colditz, R., De Laurentiis, V., Orza, V., Vancutsem, C., and Mubareka, S.: Assessing the deforestation embodied in the European Union consumption and trade of forest risk commodities. , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-7721, https://doi.org/10.5194/egusphere-egu24-7721, 2024.

EGU24-7883 | ECS | Orals | ITS4.10/NH13.1 | Highlight

Increased temperature-related newspaper coverage and more negative sentiment during hot and cold extremes 

Ekaterina Bogdanovich, Mike S. Schäfer, Alexander Brenning, Markus Reichstein, Kelley De Polt, Lars Guenther, Dorothea Frank, and René Orth

More frequent, intense, and prolonged temperature extremes due to climate change increase the risk of human morbidity and mortality. However, public perception of these risks is often low, while people's awareness is crucial to reducing the health impact of temperature extremes. News media plays a key role in raising awareness by providing essential information on heat waves and cold spells such as releasing warnings, sharing weather forecasts, and discussing impacts. Any sentiment conveyed within newspaper articles about temperature extremes, either through positive, negative, or neutral phrasing, can influence the audience's perception of risk and, potentially, their actions. Newspaper coverage of temperature extremes and the related sentiment may be influenced by multiple factors other than the actual temperature anomalies, such as political alignment or editorial decisions, but also potentially the countries’ vulnerability to climate change. 

In this study, we analyze and compare the sentiment of temperature-related newspaper articles in eight countries (Israel, Malaysia, New Zealand, Philippines, Singapour, Pakistan, South Africa, and the United Kingdom) with different climates and societal vulnerabilities to climate change (food, water, health, ecosystem services, human habitat, and infrastructure). We consider leading English language, national newspapers and use daily maximum temperature data from the day of each article’s publication from the ERA5 reanalysis. The sentiment is determined in an automated way based on the fraction of positive and negative words in text. In addition to the sentiment, we determine whether or not each article mentions climate change. 

We find clear differences during times of extreme temperatures versus times with near-normal temperatures in all countries. During days with comparatively cold or warm temperatures (i) more temperature-related articles are published, (ii) their sentiments are more negative, and (iii) climate change is mentioned less frequently. While the latter finding is surprising, it suggests that there are unobserved confounding factors that require further research, which might relate to other events and anomalies occurring simultaneously. A comparison of the results across countries shows more negative sentiment and fewer mentions of climate change in countries with higher climate change vulnerability. Being aware of these media reporting patterns of extreme temperatures may help media outlets reassess their role in aiding public health responses.

How to cite: Bogdanovich, E., Schäfer, M. S., Brenning, A., Reichstein, M., De Polt, K., Guenther, L., Frank, D., and Orth, R.: Increased temperature-related newspaper coverage and more negative sentiment during hot and cold extremes, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-7883, https://doi.org/10.5194/egusphere-egu24-7883, 2024.

EGU24-8646 | ECS | Orals | ITS4.10/NH13.1

A Global Climate-Driven Stochastic Drought Model for Risk Assessment  

Marie Shaylor, Nicolas Bruneau, Mathis Joffrain, Frederic Azemar, and Thomas Loridan

Drought affects people, agriculture, and businesses across all sectors in every populated continent on Earth, and with climate change, both drought frequency and duration are increasing globally. Losses of $124 Billion to the global economy over the last two decades (1998 - 2017) have been directly attributed to drought. Hence, it is vital to gain an accurate understanding of drought risk in the present and how it may change in the future. 

Here, we describe the development of a climate-driven drought model which provides a global view of drought risk for (re)insurers. 

First, a historical catalogue (1950-present) consisting of yearly aggregated drought severity and duration footprints is derived by combining a selection of state-of-the-art drought indexes over varied time scales. Second, leveraging this historical catalogue, a large stochastic set of drought footprints is generated via the use of Principle Component Analysis, in which the drought risk is conditioned to the climate state. The model is then deployed on historical climate conditions (ERA5) or alternative and future climate conditions (indicated by the CEMS-LENS multi-member reanalysis model (present-2100)). 

These products are critical to inform damage models in the (re)insurance sector, with the model thus far proving useful in predicting subsidence risk in a France-based use case. Showcased results will provide an evaluation of drought risk both in the historical and changing future climate, as well as a newly developed risk score metric based on merged severity and duration information.

How to cite: Shaylor, M., Bruneau, N., Joffrain, M., Azemar, F., and Loridan, T.: A Global Climate-Driven Stochastic Drought Model for Risk Assessment , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-8646, https://doi.org/10.5194/egusphere-egu24-8646, 2024.

EGU24-8746 | ECS | Posters on site | ITS4.10/NH13.1

The feedback of greening on local hydrothermal conditions in Northern China 

Yu Zhang, Xiaoming Feng, Chaowei Zhou, Ruibo Zhao, Xuejing Leng, Yunqiang Wang, and Chuanlian Sun

Northern China has experienced a significant increase in vegetation cover over the past few decades. It lacks a comprehensive understanding of how greening impacts local hydrothermal conditions. To address this issue, in our study, the RegCM-CLM45 model was used to conduct a thorough assessment of the impacts of greening on temperature, vapor pressure deficit (VPD), precipitation, and soil moisture. The findings revealed that the local climatic effects of greening varied across different drought gradients based on the aridity index (AI). In drier regions with AI<0.3, the increased energy induced by greening tended to dissipate as sensible heat, exacerbating both warming and drought conditions. Conversely, in wetter regions with AI>0.3, a greater proportion of energy was lost through evapotranspiration, attenuating warming. Additionally, greening enhanced precipitation and soil moisture in drier regions and moderated their decline in wetter regions. Significantly, our research emphasized the effectiveness of grassland expansion and conservation as prime strategies for ecological restoration, particularly in drylands, where they could effectively alleviate soil drought. Given the diverse responses of different land cover transformations to local hydrothermal conditions in drylands, there is an urgent need to address potential adverse effects arising from inappropriate ecological restoration strategies and to develop an optimal restoration framework for the future.

How to cite: Zhang, Y., Feng, X., Zhou, C., Zhao, R., Leng, X., Wang, Y., and Sun, C.: The feedback of greening on local hydrothermal conditions in Northern China, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-8746, https://doi.org/10.5194/egusphere-egu24-8746, 2024.

EGU24-9524 | ECS | Orals | ITS4.10/NH13.1

Global economic impact of weather variability on the rich and the poor 

Lennart Quante, Sven Willner, Christian Otto, and Anders Levermann

The distribution of temperature and precipitation has been shown to impact economic productivity all around the world.
These heterogeneous patterns change under future warming and impact consumers not only locally but also remotely through supply chains. Due to the possibility of a non-linear economic response, these effects are difficult to quantify and have been subject to limited empirical assessment focusing on direct impacts of weather extremes.
Here we show in numerical simulations of weather-induced production disruptions (of more than 7000 profit-maximising producers and utility-optimising consumers with more than 1,8 million supply linkages) that, under present-day climatic conditions, consumption loss risks resulting from production disruptions propagating through the economic network are larger for lower than for higher income groups within countries. Comparison between countries shows that risks are larger for medium-income countries than for low and high-income countries, which emerges from differing trade dependencies as well as heterogeneous exposure and response.
Projecting observed econometric relations of weather variability and economic productivity until 2040, we find an amplification of loss risks due to near-term climate change in most regions. This amplification increases with income for middle and high-income countries, while it is homogeneous across income groups in low-income countries. 
Global warming thus poses an increasing challenge to consumers through supply chains around the globe which needs to be addressed by fostering resilience. To avoid further harm to productivity and consumer welfare the climate has to be stabilised. 

How to cite: Quante, L., Willner, S., Otto, C., and Levermann, A.: Global economic impact of weather variability on the rich and the poor, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-9524, https://doi.org/10.5194/egusphere-egu24-9524, 2024.

EGU24-9721 | ECS | Posters on site | ITS4.10/NH13.1

Uncertainties in flood damage assessment under projected future extreme rainfall conditions: a case study in Northeastern Sicily 

Jeewanthi Sirisena, Armelle Remedio, Cecilia Nievas, Giuseppe Aronica, and Laurens Bouwer

Floods are among the world’s most frequently occurring natural hazards, affecting more people than other natural disasters while causing enormous damage to the socio-economy, developments, and environment. Because of the increasing frequency of heavy precipitation events and storm surges, large areas are at increasing risk of inundation. Many countries, thus, are forced to spend millions of dollars every year to recover from the floods’ aftermath as well as on disaster prevention, mitigation, and adaptation. Over the last decades, these kinds of extreme events have presented a significant challenge in Europe, particularly in the Mediterranean region which experienced intense rainfall and flash floods. Many coastal urban areas in France, Italy and Spain have undergone severe damages and losses due to extreme rainfall events causing flash floods. This situation may further exacerbate due to the climate-change-driven impacts and intense human activities in the region.

As key components of risk assessment, modelling of hazard, vulnerability, and exposure are required to categorize the potential future damages and events. However, uncertainties in damage and risk estimation can be introduced from different sources such as model input data and model structure and parameters. Especially short-duration extreme events are often under-researched. Here, we focus on addressing uncertainties in the chain of multi-hazard risk assessment, particularly floods in the Mili and Santo Stefano di Briga Basins in the Northeastern Sicily. This study is a part of the “risk workflow for CAScading and COmpounding hazards in COastal urban areas” (CASCO) project, which aims to develop a framework to evaluate the damage as well as economic and human losses due to a series of several important natural hazards acting in a quick temporal succession: floods, earthquakes, tsunamis, heat waves, and landslides.

In this study, we use daily and sub-daily in-situ observations (2001 - 2022) and projected hourly rainfall from 8 ensemble runs of the EURO-CORDEX regional climate change projections under the RCP 8.5 scenario (2031-2060) to establish the intensity-depth-frequency (IDF) curves and drive a hydrological model for short-duration rainstorm events between 6 and 12 hours. The resulting flood depths, area, and velocities were obtained from 1D/2D hydrodynamic modelling. To model subsequent flood damages, we investigate different fragility curves in the literature relevant for Italian building classes. The exposure data are obtained from the newly developed European High-Resolution Exposure (EHRE) model (Nievas et al. 2023).

Our results show that in general, future rainfall extremes are projected to be more frequent and severe in the study area, leading to increasing flood hazard levels. As a consequence, damages in several areas are projected to increase as well. Overall damage estimation depends on the inputs at different stages of the modelling chain, which cause uncertainties and variability in the model estimations and resulting risk evaluations.  

Keywords: Extreme rainfall, Flood hazard and damage, Sicily, Uncertainty

Reference: 

Nievas, C. I., Kriegerowski, M., Delattre, F., Garcia Ospina, N., Prehn, K., Cotton, F. (2023): The European High-Resolution Exposure (EHRE) Model, (Scientific Technical Report STR ; 23/05), Potsdam : GFZ German Research Centre for Geosciences, 64 p. https://doi.org/10.48440/gfz.b103-23055

How to cite: Sirisena, J., Remedio, A., Nievas, C., Aronica, G., and Bouwer, L.: Uncertainties in flood damage assessment under projected future extreme rainfall conditions: a case study in Northeastern Sicily, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-9721, https://doi.org/10.5194/egusphere-egu24-9721, 2024.

EGU24-10132 | ECS | Orals | ITS4.10/NH13.1

An assessment of global-scale drivers of climate disaster impacts and risk 

Khalil Teber, David Montero, and Miguel D. Mahecha

The complex interplay between environmental and anthropogenic factors across the globe results in differing impacts of climate and weather related disasters. Communities living in deprived socioeconomic conditions are usually subjected to severe impacts, as they are more vulnerable to disasters. However, in a world of rapid natural and anthropogenic change, this is not the only cause behind amplified risks and impacts. The aim of this study is to identify the pivotal global-scale factors that make a significant contribution to the impact severity of climate disasters.

Using an expansive dataset with disaster impact records, complemented with socioeconomic indicators and climate variables from >6000 events in >150 countries, our work is a global-scale investigation of the roles of hazard severity, exposure, and vulnerability influencing the impacts and risk of climate disasters. We use a spatio-temporal stratified approach to define hazard severity, exposure, and vulnerability with relevant variables at the local, regional and global levels. Then, we determine the main factors contributing to impact severity across regions and disaster types using a robust machine learning pipeline.

This study illustrates the importance of considering comprehensive aspects of risk in order to build resilient societies to climate extremes. Our research contributes to advancing scientific understanding of the drivers of climate disaster impacts, with the aim of developing more effective policies. It highlights the importance of integrating diverse data sources and advanced analytical methods to better anticipate, prepare for, and respond to the multifaceted challenges posed by climate disasters in a changing climate.

How to cite: Teber, K., Montero, D., and Mahecha, M. D.: An assessment of global-scale drivers of climate disaster impacts and risk, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-10132, https://doi.org/10.5194/egusphere-egu24-10132, 2024.

Keywords: risk communication, protection-motivation, flood type, household, survey

Whether and how flood-affected people prepare for flooding is commonly assumed to depend on their perception of risk, options to cope and responsibilities. However, the influence of different flood types, i.e., fluvial, flash and urban pluvial floods, is unclear, but might be relevant for effective risk communications. We use survey data from more than 3000 households affected by different types of flooding in Germany to investigate their influence on adaptive behaviour and influencing factors. We use descriptive statistics, Kruskal-Wallis Tests and single factor ANOVA to identify differences and similarities between respondents. We use linear regressions to identify factors that influence adaptive behaviour of households in the context of fluvial, pluvial and flash flooding.

We found that most respondents were motivated to protect themselves, but that there were flood type specific differences in the factors influencing an adaptive response. For example, those affected by fluvial events have had implemented most often measures before the last flooding, showed signs of emotional coping frequently and were less likely to implement (more) measures, while those affected by flash flooding showed less confidence in the effectiveness of measures, but were less likely to rate their costs as too high and were most likely to implement measures after the event. We argue that inter alia the severity and experience of flooding, as well as the management of flooding, shapes adaptive behaviour. We further found that regardless of the type of flooding, the perception of the effectiveness of adaptive measures and a positive perception of personal responsibility are critical to promote the protection motivation of those affected. We found, that these two key elements can be strengthened by offering financial support for adaptive measures. We also found that communication on a municipality level enhances the sense of self-responsibility. We conclude that communication and management strategies need to involve municipalities and should be tailored to the type of flooding. Up to now, risk communication mainly addresses fluvial flooding situations.

How to cite: Dillenardt, L. and Thieken, A.: Individual Flood Risk Adaptation in Germany: Exploring the Role of Different Types of Flooding, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-10652, https://doi.org/10.5194/egusphere-egu24-10652, 2024.

EGU24-10949 | ECS | Orals | ITS4.10/NH13.1

Projected changes in compound dry-hot events in South Asia 

Farhan Saleem, Torsten Weber, and Armelle Remedio

South Asia is one of the hotspot regions for extreme weather and climate events such as heatwaves, droughts, and extreme precipitation. This work aims to present a comprehensive analysis of the future changes (2071-2100) in the frequency and duration of compound dry-hot extremes in South Asia. Given the current gap in specific data for such compound events in this region, our approach involves utilizing state-of-the-art regional climate models from the Coordinated Output for Regional Evaluations (CORE) project embedded in the Coordinated Regional Climate Downscaling Experiment (CORDEX) framework. The focus will be on understanding the interplay between dry and hot days conditions, and how their concurrent occurrence may exacerbate environmental and socio-economic challenges. We will analyze an ensemble of regional climate projections to identify potential trends in these compound events by the end of the 21st century. The outcomes of this study are expected to provide valuable insights into the evolving nature of compound climate extremes in South Asia, thereby informing policy and adaptation strategies for enhanced regional resilience.

How to cite: Saleem, F., Weber, T., and Remedio, A.: Projected changes in compound dry-hot events in South Asia, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-10949, https://doi.org/10.5194/egusphere-egu24-10949, 2024.

EGU24-11122 | ECS | Posters on site | ITS4.10/NH13.1

Impacts of climate-modified disturbance regimes on coastal ecosystems and their services 

Sarah Hülsen, Laura Dee, Chahan Kropf, Nicolas Colombi, and David Bresch

Coastal ecosystems, such as mangroves, coral reefs, salt marsh, seagrass, and kelp forests, provide crucial regulating, provisioning, and cultural services to human societies. Previous research has demonstrated the various ways in which these ecosystems can reduce disaster risk and contribute to climate change adaptation. Simultaneously, the potential effects of climate extremes and extreme weather events on ecosystem composition and functioning are increasingly gaining attention.

While it is apparent that these ecosystems are subject to changing disturbance regimes under climate change, assessments of what these future disturbance regimes are likely to look like in the future have rarely been attempted and are often limited to single ecosystem and hazard pairs.

Therefore, we propose a global multi-layer hazard assessment for coastal ecosystems to assess i) the changing disturbance regimes coastal ecosystems are exposed to with regards to tropical cyclones, storm surge, sea level rise, and marine heatwaves, ii) potential ecological responses to these changes, and iii) implications for ecosystem service provision. We will present preliminary results as a starting point for further discussion.

How to cite: Hülsen, S., Dee, L., Kropf, C., Colombi, N., and Bresch, D.: Impacts of climate-modified disturbance regimes on coastal ecosystems and their services, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-11122, https://doi.org/10.5194/egusphere-egu24-11122, 2024.

EGU24-11552 | ECS | Posters on site | ITS4.10/NH13.1

Impact-based forecast for critical infrastructure during Tropical Cyclones 

Gabriela Espejo Gutierrez, Zélie Stalhandske, Evelyn Mühlhofer, David Bresch, and Stefan Brönnimann

Critical infrastructures, such as healthcare facilities or roads, play a vital role in society as they provide essential services for the functioning of communities. Disruptions to these infrastructures have far-reaching consequences, affecting public health, safety, security, well-being, and economic activities. Weather extremes, such as tropical cyclones or floods, can lead to widespread failures in lifeline services such as power, communication, transportation, and healthcare. Forecasting the potential impact of weather extremes in the weeks to days before they happen can help increase the preparedness in the areas that might be affected. The emerging field of research on impact-based forecast models is instrumental in this regard, aiding international organizations and governments in making informed decisions, taking early actions, and allocating resources efficiently. This study aims to build upon the pioneering research by developing an impact forecast tool of tropical cyclones on critical infrastructure. While earlier efforts concentrated on estimating the potential affected population, our focus shifts to understanding the impact on critical infrastructure, starting with healthcare facilities, schools and road networks. We present a case study of Tropical Cyclone (TC) Freddy, which hit Mozambique and Madagascar in 2023. We calculate direct impacts using two sets of vulnerability curves for structural damage and another based on the Saffir-Simpson scale to ensure global applicability when needed. To better understand the significance of these impacts, we further assess their indirect impacts on the population. Additionally, to ensure the utility of this tool for international organizations, we exchange with stakeholders from these entities.

How to cite: Espejo Gutierrez, G., Stalhandske, Z., Mühlhofer, E., Bresch, D., and Brönnimann, S.: Impact-based forecast for critical infrastructure during Tropical Cyclones, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-11552, https://doi.org/10.5194/egusphere-egu24-11552, 2024.

EGU24-11668 | Orals | ITS4.10/NH13.1

Water resilience to policy implementation and engineered measures in the Yellow River Basin 

Lu Yu, Shuang Song, Xutong Wu, Shuai Wang, and Bojie Fu

River basins couple the natural ecosystem with the socio-economic system. Regime shifts due to climate change and social-economic factors highlight the importance of quantifying and strengthening the water resilience in the river basin. Water resilience in river basin systems requires more specific quantification. The Yellow River Basin (YRB) was well-known for its historically severe water supply pressure in recent decades, profoundly adjusted by the social-economic system. A system dynamic model named the Water-Sediment-Social Economic-Ecological Model (WSSEEM) was proposed around the interactions and feedback among water supply, sediment discharge, vegetation changes, food production and social-economic development in the YRB. Using WSSEEM, we simulate water supply and demand resilience to changes and distributions, including policy implementation and engineered measures during the historical time (1981-2020) and future scenarios. Our result indicates that technology enhancement and engineered measures are instructive in water management and sediment discharge. The WSSEEM offers a comprehensive approach to representing the river basin system, providing valuable insights into using model simulation to achieve sustainable goals and resilient water management.

How to cite: Yu, L., Song, S., Wu, X., Wang, S., and Fu, B.: Water resilience to policy implementation and engineered measures in the Yellow River Basin, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-11668, https://doi.org/10.5194/egusphere-egu24-11668, 2024.

EGU24-11895 | ECS | Posters on site | ITS4.10/NH13.1

Flooding risk assessment of power grids by modelling and simulation 

Panagiotis Asaridis, Daniela Molinari, Francesco Di Maio, Francesco Ballio, and Enrico Zio

Power grids can be significantly affected by floods that can cause power outages with widespread impact on social and economic activities. In this work, we propose a “What-if” scenario analysis by modelling and simulation, which makes use of a hydraulic model to simulate hazard scenarios, fragility curves to describe the process of failure of the power grid components, and a power flow model to assess power outages. A synthetic case study is worked out with reference to the IEEE 14 bus system benchmark serving different categories of electricity customers. The proposed modelling and simulation-based analysis can be used to identify the most critical components to protect for the security of power supply.

How to cite: Asaridis, P., Molinari, D., Di Maio, F., Ballio, F., and Zio, E.: Flooding risk assessment of power grids by modelling and simulation, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-11895, https://doi.org/10.5194/egusphere-egu24-11895, 2024.

EGU24-12087 | ECS | Orals | ITS4.10/NH13.1

Preferences on funding humanitarian aid and disaster management under climatic losses and damages: A multinational Delphi panel 

Juha-Pekka Jäpölä, Sophie Van Schoubroeck, and Steven Van Passel

Losses and damages from climate change and the frequency of extreme events will burden our global budgetary constraints and adaptive capacities. Scientific and analytical support for allocating public funding in humanitarian aid and disaster management to counter them involves determining the most pertinent criteria to use or where to design forecasting. Their priorities are often assumed, and assumptions can be ill-fitting. Thus, we asked the key users of such information for their preferences.

A two-round anonymous Delphi method utilising global frameworks for a funding allocation simulation was employed to survey the stated preferences of a stratified panel of losses and damages experts (N=36). They were experts from 19 countries of origin representing international organisations (e.g., United Nations, European Union, World Bank), the research sector, the public sector, and civil society (e.g., Save the Children, World Vision). The consensus was analysed with parametric measures.

We find that the near-future preference for magnitude-indicating criteria, such as people-centric and disaster risk-based, outweighs the importance of indicators related to governance, the rule of law, or a socio-economic aspect. Likewise, financing adaptation options to climate change-related risks to food security, human health, and water security are a high near-future priority for minimising losses and damages compared to, for example, risks to living standards or risks to terrestrial and ocean ecosystems. The covariance suggests that these priorities are an emergent preference in the losses and damages sector. Thus, it raises further questions on what we can and should prioritise with scarce resources.

How to cite: Jäpölä, J.-P., Van Schoubroeck, S., and Van Passel, S.: Preferences on funding humanitarian aid and disaster management under climatic losses and damages: A multinational Delphi panel, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12087, https://doi.org/10.5194/egusphere-egu24-12087, 2024.

Floods impact a series of interconnected urban systems (referred to as the Urban Multiplex) that include the power grid and transportation networks, surface water and groundwater, sewerage and drinking water systems, inland navigation and dams – all intertwined with the natural environment and socioeconomic and public health sectors. While the Urban Multiplex is physically and functionally connected, the data produced within its individual sectors are not. This is the core reason why we still do not fully understand the total impact of floods on cities.

The Urban Flooding Open Knowledge Network (UFOKN) is an information infrastructure that (i) integrates Urban Multiplex data, (ii) produces real-time and long-term flood forecasts across the continental U.S., and (iii) serves as the foundation to evaluate the total impact of floods on cities. The latter includes assessment of cascading economic impacts of floods across multiple sectors, as well as cascading failures across infrastructure, ecosystems, and communities.

UFOKN aims to provide actionable answers to questions such as:

  • Real-time flood mitigation and response: Will my house or place of business flood? Will I have access to water and power? Which district to evacuate first? When? Which traffic routes are safe? Will this storm disrupt the power grid, drinking water treatment plant, or a bridge?
  • Long-term design, planning and research: Which critical urban infrastructure will likely fail in a future flood? Which failures will affect the most people or the most vulnerable people? Which areas will experience repeated flooding? Which houses should the city buy out? Should a hospital be built at location X? What are the common triggers of Urban Multiplex failures?

The interdisciplinary team behind this project has brought together academic researchers, industry, federal government, U.S. National labs and local stakeholders. UFOKN is funded by the U.S. National Science Foundation’s Convergence Accelerator Program that is structured to enable rapid advancement in highly complex problems of critical societal importance.

How to cite: Yeghiazarian, L. and the Lilit Yeghiazarian: The Urban Flooding Open Knowledge Network: From Real-Time Flood Forecasts to Cascading Failures Through Infrastructure, Ecosystems, Communities and Economy, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-13258, https://doi.org/10.5194/egusphere-egu24-13258, 2024.

EGU24-13297 | ECS | Posters on site | ITS4.10/NH13.1

Atmospheric Rivers as a Component of Multi-hazards and their Influence on Western U.S. Water Management 

Eric Shearer, Emily Wells, Sharmin Siddiqui, Amir AghaKouchak, Christine Albano, Jeremy Giovando, Ian Floyd, and Cary Talbot

The Forecast Informed Reservoir Operations (FIRO) initiative, led by the United States Army Corps of Engineers (USACE) in collaboration with multiple agencies and university partners, marks a significant shift in integrating advanced weather and hydrologic forecasting into reservoir management, particularly on the US West Coast. At the heart of FIRO is the utilization of the predictability of atmospheric river (AR) landfall over multi-day timescales. However, challenges remain in fully understanding and predicting the interactions of ARs with critical environmental conditions, such as post-burn fire scars, saturated watersheds, and heavy snowpack, as well as other phenomena that contribute to hazards, including in the context of water management. Given the evolving patterns of rain, snow, and wildfires in the region, these factors underscore the urgency for proactive insights into multi-hazards and their effect on water management.

In addressing these challenges, our project conducts a thorough analysis of literature regarding ARs as drivers of hazards and their contributions to multi-hazard events, noting that their interactions vary geographically and temporally in response to climate change, necessitating spatially distributed climate change adaptation strategies for USACE water management for hazard conditions. This project is complimented by the creation of a comprehensive multi-hazard inventory for California, encompassing various hazards across different timescales. This inventory is supported by diverse data sources, including the NOAA NCEI Storm Event Data, the Rutz AR Catalog, USGS/USDA Monitoring Trends in Burn Severity Fire Data, and USACE's annual state-level flood damage reports. A key aspect of our study is the inclusion of the location of USACE infrastructure, particularly dams and reservoirs, to identify those most vulnerable to multi-hazard events historically.

The outcomes of this project are anticipated to offer critical insights and practical tools for decision-makers within the USACE water management community and beyond. These tools and insights are aimed at equipping them to better navigate the complex and evolving challenges presented by climate change. Through this initiative, we aim to contribute significantly to the development of more resilient and adaptive water management strategies in the face of a dynamic and changing environment.

How to cite: Shearer, E., Wells, E., Siddiqui, S., AghaKouchak, A., Albano, C., Giovando, J., Floyd, I., and Talbot, C.: Atmospheric Rivers as a Component of Multi-hazards and their Influence on Western U.S. Water Management, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-13297, https://doi.org/10.5194/egusphere-egu24-13297, 2024.

EGU24-13615 | ECS | Posters on site | ITS4.10/NH13.1

Analysis of the potential direct and indirect impacts of dam failures on storage services for multiple purposes 

Maria Castro, Luis Rapalo, Pedro Silva, and Eduardo Mendiondo

Unprecedented natural disasters due to the climate crisis are a global issue, threatening sustainable development, mostly because they are associated with life, assets, and infrastructure losses and damages to the environment. Recent disasters, such as floods, were responsible for the death and displacement of thousands of people. A storm could subsequently trigger dam failures, reinforcing the disastrous consequences, as these could be part of an infrastructure network that would irrigate and supply surrounding cities and communities with much-needed water. This shows that natural disasters can induce an interruption in water services due to a cascade effect on existent measures. Since dams are usually employed to tackle multiple water management problems such as water supply and flood control, the collapse is an event that causes direct, intense and rapid impacts, and indirect ones in the medium and long term. According to the National Dam Safety Information System (SNISB), the dam risk degree depends on the Risk Category (CRI) and their Associated Potential Damage (DPA), these consider variables such as technical characteristics, dam safety plan, potential for human losses, among others. However, this classification is mostly associated with direct impacts,, which does not prioritize hydrodynamic modeling associated with the socioeconomic impacts resulting from the interruption of service to user sectors, this will be considered an indirect impact. This research aims to evaluate the direct and indirect effects of the impacts caused by the failure of the 10 largest volume and high-risk dams in the state of Pernambuco, a region with semi-arid characteristics in northeastern Brazil. Here, we employed a simplified model of the explicit Saint-Venant equations, the HydroHP-1D. Based on the preliminary dam break simulations, parameters such as maximum flow, depth, speed, extravasated volume, area flood wave and flood wave arrival time were estimated and used to identify the region’s vulnerability degree, which direct impacts have a significant correlation. The proposal of assessing the indirect impacts involves quantifying the storage service loss from the dam considered through a water balance of the studied area. This service interruption covered the supply of water to human activities, energy generation or even combating drought in certain regions. The expected results of the modeling associated with quantification of the interruption of the storage service of the respective dam made through a water balance of the study area, can reinforce the need to consider, in the preparation of contingency and water security plans, studies that explore the consequences of the interruption of supply to dams, in case of rupture. In general, this research promotes relevant discussions about disaster risk water resources management through the phases of prevention, reduction, preparation, response and recovery, which is enhanced when society is aware of the conditions of vulnerability, in order to prevent losses of life and property.

How to cite: Castro, M., Rapalo, L., Silva, P., and Mendiondo, E.: Analysis of the potential direct and indirect impacts of dam failures on storage services for multiple purposes, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-13615, https://doi.org/10.5194/egusphere-egu24-13615, 2024.

EGU24-13940 | ECS | Posters on site | ITS4.10/NH13.1

A “Parallel Evolution” of Flood Risk Management along the Rhine and the Sacramento Rivers 

Ben Daniels, Indumanti Roychowdhury, Andrew Calderwood, Lidia Mezei, Bethany Rader, Kathy Schaefer, Erin Tracy, Mariana Webb, Nicholas Pinter, Jay Lund, Helen Dahlke, and Sarah Yarnell

The Sacramento River in California, USA, and the Rhine River in Europe both have histories of major flooding events and great efforts to manage flood risk. We compare these two watersheds with an interdisciplinary lens to explore the goals, approaches, outcomes, and “parallel evolution” of differing flood risk management paradigms.

The two basins share hydrologic similarities, but each approach to managing floods reflects the basin’s unique historical, environmental, and governance context. The Sacramento basin is entirely within the state of California, whereas the Rhine is a transnational river that drains nine European countries. The Rhine basin is larger and has a much larger population compared with the Sacramento basin.  The Sacramento basin has high interannual precipitation variability and receives most of its precipitation in the winter with significant mountain snowfall. The hydrology of the Rhine is also strongly influenced by mountain snowpack, but has precipitation that is more evenly distributed throughout the year. Flood-risk management on both the Sacramento and Rhine Rivers has evolved from ad hoc and local approaches, towards more systematic planning, culminating in significant state-level control in California, and state, federal, and transnational management on the Rhine. This transition was driven in recent years by the Central Valley Flood Protection Act and the European Floods Directive.

Management of each basin has been shaped by an event-based evolution, in which disasters have driven management responses, tools, and approaches. Flood-risk paradigms in both basins include significant investment in engineering protection and, increasingly, soft-policy adaptations. Over time, flood management methods and objectives in each basin have become more diverse. For example, single-objective approaches have evolved towards multi-benefit projects. Both basins are expanding consideration of floodplain ecosystem importance and both now consider climate change to some in flood risk management.  Flood-protection levels are higher on the Rhine than on the Sacramento. Some areas of the Rhine have 1000-year or better protection whereas a  200-year-level protection for urban areas is now required in the Sacramento basin.

The Sacramento River and the Rhine River are geographically and hydrologically similar in surprisingly many ways, including in the flood risk they pose.  But the flood-risk management paradigms in the two basins have evolved differently.  We argue that the differences are a form of “parallel evolution,” reflecting historical and political contrasts between the two systems.  Such contrasts present opportunities for alternative tools and lessons that can be explored and perhaps imported in both directions.

How to cite: Daniels, B., Roychowdhury, I., Calderwood, A., Mezei, L., Rader, B., Schaefer, K., Tracy, E., Webb, M., Pinter, N., Lund, J., Dahlke, H., and Yarnell, S.: A “Parallel Evolution” of Flood Risk Management along the Rhine and the Sacramento Rivers, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-13940, https://doi.org/10.5194/egusphere-egu24-13940, 2024.

EGU24-14111 | ECS | Orals | ITS4.10/NH13.1

Cascading impacts of extreme events across an interconnected and warming world 

Laurie Huning and Manuela Brunner

Extreme events (e.g., heatwaves, wildfires, droughts, floods, etc.) are anticipated to become more severe, persistent, and frequent throughout many parts of the world due to warming. Such extreme events occur across a diverse set of ecosystems and climatic regions and their multifaceted impacts cascade in space, time, and across sectors (e.g., water, energy, agriculture, economic, human health). To better understand the cascading impacts of extreme events and their feedbacks, we draw on recent examples such as the 2023 heatwaves and wildfires in Canada. In addition, we also examine other extreme events (e.g., droughts, floods) around the world and their feedbacks and interactions that pose challenges for modeling, monitoring, and managing associated risks. For example, we quantify how snowpack changes and drought across agricultural regions have wide-reaching impacts that affect remote areas. Our study highlights that the impacts of extreme events have important feedbacks that should be considered in resource and risk models and management as well as remote impacts that are not yet fully understood or well-tracked. Furthermore, we identify other challenges, existing knowledge gaps, and future directions to guide global monitoring and modeling of impact cascades for improved mitigation, adaptation, and climate change resilient policy advancements.

How to cite: Huning, L. and Brunner, M.: Cascading impacts of extreme events across an interconnected and warming world, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-14111, https://doi.org/10.5194/egusphere-egu24-14111, 2024.

EGU24-15454 | Posters on site | ITS4.10/NH13.1

Metryc and DeepCyc: Pioneering Tools from Reask in Disaster Risk Financing and Humanitarian Impact Mitigation 

David Schmid, Thomas Loridan, and Marie Shaylor

The escalation of climate-related disasters presents an urgent need for innovative risk financing mechanisms. Metryc, a groundbreaking product by Reask, emerges as a pivotal tool in this domain, especially in the context of tropical cyclones. It represents a significant advance in parametric insurance, facilitating swift, post-disaster financial recovery. Metryc’s intensity-based approach, distinct from traditional distance-based models, minimizes basis risk and offers cost-effective risk transfer solutions. This method’s superior accuracy in modeling wind speeds post-cyclone landfall enables rapid insurance payouts, crucial for immediate disaster response and recovery.

Complementing Metryc, DeepCyc, another Reask product, stands as a probabilistic hazard model integrating current climate data and future climate scenarios. This climate-connected model transcends the limitations of conventional models reliant on historical data, thus offering a more robust and future-oriented risk assessment. DeepCyc’s high-resolution (1x1 km²) probabilistic hazard modeling is instrumental in precise insurance structuring and premium determination, reflecting modern-day climatic realities.

The humanitarian impact of these tools is profound. By ensuring expedited financial relief, Metryc significantly enhances the capacity of affected communities to recover from catastrophic events. This rapid response mitigates the long-term socio-economic impacts of disasters, facilitating quicker restoration of livelihoods and infrastructure. Moreover, DeepCyc’s forward-looking approach in risk modeling acknowledges the evolving nature of climate risks, ensuring that risk assessments remain relevant and effective in a changing world.

In summary, Metryc and DeepCyc represent a synergistic approach in disaster risk financing. Metryc’s immediate post-disaster financial support and DeepCyc’s comprehensive, climate-informed risk assessment model together provide a robust framework for mitigating the humanitarian and economic impacts of climatic disasters. This dual approach underscores the potential of advanced technology in transforming disaster risk management and resilience-building in the face of climate variability and change.

How to cite: Schmid, D., Loridan, T., and Shaylor, M.: Metryc and DeepCyc: Pioneering Tools from Reask in Disaster Risk Financing and Humanitarian Impact Mitigation, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15454, https://doi.org/10.5194/egusphere-egu24-15454, 2024.

EGU24-15783 | ECS | Orals | ITS4.10/NH13.1

AutoVal: A framework for scientific validation of flood catastrophe models 

Ashleigh Massam, Douglas Burns, Owen Jordan, Barbara Nix, Niamh O'Malley, Philip Oldham, Brijkishore Sahu, and Ksenija Vasiljeva

Catastrophe models are complex numerical models that simulate extreme events to estimate the economic cost of natural disasters, usually developed by model providers and adopted by clients in the (re)insurance, finance, and other sectors. Before adopting a model, the model user typically engages in an evaluation process that can be a challenging and resource-intensive task, and challenging for non-experts. This process is not standardised across the industry, so model users need to establish their approach from scratch. Yet effective evaluation is repetitive and takes time and resources – an effort that is being duplicated across organisations and teams, which would be better placed on an exploratory side of testing that progresses knowledge and understanding of models. By automating the repetitive part of model evaluation, data visualisations and results can be reproduced quickly. This would allow for more frequent assessment, leading to an improved understanding of the limitations of catastrophe models and increased confidence in the insights gained from using a catastrophe model.

We present AutoVal, a catastrophe model evaluation tool that automates standard loss validation tests. The premise of AutoVal is very simple: third party data is transformed at the point of use into benchmark expectations, for assessment against catastrophe model outputs. In our work to date, third party insurance claims data or published estimates of event losses are used to calculate average annual losses, loss exceedance curves, and map spatial and temporal distributions of loss for comparison with modelled estimates. Further work is ongoing to expand the capability of AutoVal to evaluate components within the natural catastrophe model, including vulnerability functions and hazard maps.

AutoVal can aid model users from both industry and academic backgrounds through the application of standard, repeatable tests. It assists with the effective review of model configurations across a range of perils or scenarios, allowing decision makers to better understand model sensitivity and behaviour. AutoVal also has the potential to remove the repetition in model evaluation, allowing for more frequent model evaluation and faster feedback loops between developers and users. In this presentation, we will share our progress so far with designing and automating the evaluation of our catastrophe models, including – but not limited to – standardised schemas for benchmark data, validation exercises to assess modelled estimates of loss, and new approaches to interpreting model sensitivity.

To realise the potential of AutoVal, we invite colleagues from the risk management sector to discuss our ongoing work towards establishing a set of benchmark tests that can complement industry-wide progress towards consistent and common standards.

How to cite: Massam, A., Burns, D., Jordan, O., Nix, B., O'Malley, N., Oldham, P., Sahu, B., and Vasiljeva, K.: AutoVal: A framework for scientific validation of flood catastrophe models, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15783, https://doi.org/10.5194/egusphere-egu24-15783, 2024.

EGU24-15846 | ECS | Posters on site | ITS4.10/NH13.1

Measuring Resilience: A Systematic Meta-Review 

Tony Wei-Tse Hung, Tina Comes, and Giulia Piccillo

Resilience to natural hazards and climate change is a rather wicked and complex subject, such that it requires the knowledge of several disciplines such as economics, governance, engineering, sociology, and environmental science. The intertwining of these different disciplines results in a symphony of clashes and harmony, but all of this relies heavily on the foundations of research methods.

With a PRISMA-guided systematic meta-review on the measurement of social, community, disaster, organizational, urban, and infrastructural resilience angles, 708 records of systematic reviews were filtered down to 29 reviews after snowballing through articles. Through social-computational analysis, our meta-review focuses on three key questions:

  • How are the different resilience reviews related?
  • What are the most apparent resilience characteristics considered?
  • What are some general characteristics of quantitative metrics from different resilience angles?

 

Through our analysis, several insights could be deduced:

  • Through bibliographic coupling, infrastructural resilience reviews are tightly coupled together, and are distinct from the other resilience angles.
  • Using text analysis, such as word clouds and hierarchical clustering, definitions of resilience are diverse and vary within and between resilience angles.
  • By surveying the indicators used in quantifying resilience, there is a clear disconnect between infrastructural resilience quantification and other angles of resilience. In particular, non-infrastructural resilience measurements tend to focus on the inherent capacity approach of resilience, whereas infrastructure resilience tends to capture both inherent resiliency of systems and the performance approach of resilience.

 

Lastly, we have found that infrastructural resilience measurements tend to fixate on the technical domain it is in, whereas urban resilience measurements tend to take a more comprehensive approach encompassing several disciplines. This distinction highlights the need for a comprehensive and integrated approach to measuring resilience. We urge that infrastructural resilience should not solely focus on the functionalities of its systems, but also include the actors, users, and societal dynamics which critical infrastructure systems are embedded in. With the emergence of the System of Systems approach, it is a ripe opportunity to transform beyond disciplinary boundaries and focus on the interdependencies between humans and the built environment.

How to cite: Hung, T. W.-T., Comes, T., and Piccillo, G.: Measuring Resilience: A Systematic Meta-Review, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15846, https://doi.org/10.5194/egusphere-egu24-15846, 2024.

EGU24-16716 | Orals | ITS4.10/NH13.1

Novel instruments for flood risk mitigation in small, fast reacting watersheds by means of sensor integration, advanced computing and socio-technical translation 

Paolo Reggiani, Svenja Fischer, Andreas Kolb, Kristof Van Laerhoven, and Cornelius Schubert

Increasingly frequent extreme hydro-meteorological events, attributable among others to non-stationary climate, can lead to devastating flooding and cause large costs to affected societies. A recent and prominently featured example includes the July 2021 flood in Germany with more than 140 casualties and billions of Euros in material damages. Such types of events could strike other parts of Europe at any time. Especially small-scale systems are likely to be affected more frequently by high intensity events. To ready society against such occurrences, preparedness needs to be increased form different viewpoints: 1) extreme value statistics and forecasting, 2) real-time data acquisition and high performance computing, 3) socio-technical translation into domains.

First, fast and accurate forecasting with reduced uncertainties requires integration of predictors at different spatial and temporal scales. Such predictors must be conditioned by local, real-time information retrieved from multi-sensor systems, whereby special attention is devoted to extreme event statistics. The uncertainty of prediction resulting from sources like forecast model deficiencies, measurement errors, or various critical system states, need to be adequately represented, as well as de-biased and sharpened in real-time through statistical approaches.

Second, real-time acquisition of local data plays an essential role in risk detection. To this end, novel sensor system integration must provide robust real-time information, either on the basis of flexibly positioned or body-mounted devices. The extended complexity of the methods involved make the efficiency of the computational methods and the integration of model-driven physical processes with data driven approaches and ubiquitous computing as key factor. This also concerns the reduction of computational complexity without compromising efficacy as well as the efficient interoperability between different system components or scales.

Third, such novel instruments need to be socio-technically translated for decision-makers as well as emergency services and citizens. User involvement needs to be bidirectional, that is, stakeholders and their concerns must be understood and taken seriously for them to gain confidence and adopt innovative, multi-sensor integrated forecasting technologies for risk mitigation. Moreover, proper visual mapping of hazardous situations including uncertainties and potential options for decision-support need to be provided.

 

How to cite: Reggiani, P., Fischer, S., Kolb, A., Van Laerhoven, K., and Schubert, C.: Novel instruments for flood risk mitigation in small, fast reacting watersheds by means of sensor integration, advanced computing and socio-technical translation, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-16716, https://doi.org/10.5194/egusphere-egu24-16716, 2024.

EGU24-17384 | Posters on site | ITS4.10/NH13.1

Building databases of multi-hazards and compound events 

Carlo De Michele and Fabiola Banfi

Multi-hazards and compound (climate-related or weather-related) events are characterized by complex dynamics with interactions between various physical processes across multiple spatial and temporal scales. Examples of these include the joint/successive occurrence of landslides and floods, or heatwaves, droughts, and wildfires.

In literature, databases of natural hazards are in general single hazard, like databases of floods (European Flood Database, AVI database), landslides (Global Fatal Landslide Database, AVI database), droughts (European Drought Observatory).

The assessment and understanding of multiple hazards and compound events require an integrated perspective, with the integration of data from multiple variables, combining multiple databases.

Here, we try to address this emerging need, illustrating a possibility of building a database of multi-hazards/compound events, and presenting some examples.

How to cite: De Michele, C. and Banfi, F.: Building databases of multi-hazards and compound events, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17384, https://doi.org/10.5194/egusphere-egu24-17384, 2024.

EGU24-17789 | ECS | Orals | ITS4.10/NH13.1

Comprehensive assessment of hazard, exposure, and vulnerability using a new database of climate impact indicators to identify hotspots for adaptation needs 

Michaela Werning, Edward Byers, Daniel Hooke, Marina Andrijevic, Volker Krey, and Keywan Riahi

In the 21st century, an increasing global population will be exposed to various risks caused by climate change. The impact depends not only on the geophysical climate change hazards, but also on the population’s vulnerability, its spatial distribution, and its capacity to adapt. Here we present a new database of climate impact indicators at various global warming levels (1.2 - 3.5°C) using global climate and hydrological model data from the latest Coupled Model Intercomparison Project (CMIP6) and Inter-Sectoral Impact Model Intercomparison Project (ISIMIP3b) simulation rounds.

Indicators include a variety of temperature and precipitation extremes, heatwaves, drought intensity, hydrological variability, and water stress. Building on previous work (Byers et al. 2018),  the first novel aspect of this work is the development of a bi-variate hazard index that includes statistics on the absolute hazard level (e.g. low or high precipitation) and the relative change under global warming compared to the historical baseline (e.g. a large change from low to high precipitation).

We combine this new index with gridded projections of population from the Shared Socioeconomic Pathways (SSPs) and land area to calculate temporal and spatial exposure.  Finally, to allow for risk assessment, we introduce the layer of vulnerability measured through various socio-economic indicators, such as income, inequality, or the Notre-Dame Global Adaptation Index (ND-GAIN).

In aggregate, we find that impacts manifest substantially even in the near-term at lower global warming levels. For example, even at 1.5°C 93% of the population of South Asia will face a medium exposure to heatwave events. Countries predominantly in the low latitudes and global south are comparatively more severely affected by multiple climate impacts. The window for reducing the risk burden is rapidly closing while there is substantial unavoidable risk even at 1.5C, thus adaptation actions will be key. By analysing impact and vulnerability hotspots, our work can help identify these adaptation needs, i.e. for financial assessments or loss and damage, down to high spatial resolution but also at the country level. With further categorisation, we can assess populations at the highest risk, such as those with high impacts, high vulnerability and lowest institutional governance capacities.

How to cite: Werning, M., Byers, E., Hooke, D., Andrijevic, M., Krey, V., and Riahi, K.: Comprehensive assessment of hazard, exposure, and vulnerability using a new database of climate impact indicators to identify hotspots for adaptation needs, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17789, https://doi.org/10.5194/egusphere-egu24-17789, 2024.

When evaluating a view of risk for the purposes of pricing insurance business or mitigating potential large losses, one salient question that arises is whether the view is representative of the present-day. What time-period the ‘present-day’ represents is not a trivial decision, as it very much depends on the timeframe of the business you insure. An insurer that specialises in high-frequency transactions may choose to adopt a transient short-term view of risk, whereas insurers involved with real estate (e.g. mortgages) would require a much longer, stable view of the present-day to encapsulate the longevity of their liabilities. This study presents a framework and example of reconditioning a long-term historical modelled baseline, as one might determine from any catastrophe model, for North Atlantic Hurricane towards a 5-year medium-term present-day. This study takes a data-driven compartmentalised approach to reconditioning hurricane risk, by separately adjusting storm frequency, intensity, regionality and the temporal distribution of storms (i.e. storm clustering), such that each component is explicitly accounted for. This study aims to elucidate on the most pertinent sources of uncertainty present when reconditioning a view of risk, with application beyond hurricane risk.

The results of this study suggest a coherent poleward shift in hurricane risk along the contiguous US coastline, alongside a general increase in hurricane risk. The explicit representation of clustering supports non-local inter-hurricane dependency and subsequently a change in the relationship between two key insurance metrics, the occurrence loss (max in a given year) and the aggregate loss (sum in a given year).

How to cite: Webber, C.: Reconditioning a North Atlantic Hurricane View of Risk to a Chosen Present-Day, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17991, https://doi.org/10.5194/egusphere-egu24-17991, 2024.

EGU24-18130 | ECS | Orals | ITS4.10/NH13.1

Towards hurricane impact forecasting for the Dutch Caribbean   

Nadia Bloemendaal, Rob Sluijter, Jos Diepeveen, and Elco Koks

The Royal Netherlands Meteorological Institute (KNMI) has been responsible for weather forecasting in the Dutch Caribbean (Bonaire, St. Eustatius and Saba – the BES islands) since 2016. And while weather patterns in the Caribbean often exhibit homogeneous characteristics, this region is also prone to some of the most violent storms on earth in the form of hurricanes. The most infamous example of this is Hurricane Irma (2017), which passed close to Saba and St. Eustatius but made a direct landfall on and severely impacted several other Caribbean islands, including Sint Maarten. Over 90% of the buildings on St. Maarten were damaged, including most of the infrastructure on the island. The estimated damage totaled to be around 2.7 billion USD (approximately 200% of the country's GDP).  

With its extensive weather forecasting expertise as a solid foundation, KNMI is now moving towards impact-based forecasting through the development of the Early Warning Centre (EWC). For the BES islands, this means that we will design a hurricane impact model, combining KNMI's forecasting experience with impact modeling expertise nested within academia. With respect to the latter, we follow the traditional risk modeling approach and set up a hazard – exposure – vulnerability type of model chain. In such model chain, it is predominantly the choice of hazard data that determines the nature and applicability of the output data. For instance, (ensemble) forecast tracks provide insights into possible impacts of an imminent hurricane. Similarly, using synthetic hurricane tracks from a statistical model like STORM will result in a full spectrum of risk and associated probabilities. We will also incorporate local knowledge to develop and improve exposure and vulnerability input data. 

In this presentation, we discuss the different input datasets needed to build an impact model, and how the different output products can assist weather forecasters in better understanding the impact of imminent hurricanes in the Dutch Caribbean.

How to cite: Bloemendaal, N., Sluijter, R., Diepeveen, J., and Koks, E.: Towards hurricane impact forecasting for the Dutch Caribbean  , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18130, https://doi.org/10.5194/egusphere-egu24-18130, 2024.

EGU24-19407 | Posters on site | ITS4.10/NH13.1

Realistic storm surge scenarios for UK (re)insurers 

Carlotta Scudeler and Iain Willis

The UK has experienced several coastal floods over the last century, which have threatened the society and the (re)insurance industry. The Winter of 2013/2014 was especially notable, in that several events have largely affected many different areas in England as 50 defence breaches occurred. Thus, simulating credible scenarios, capable of capturing the likelihood of coastal flood events arising at different locations during the same storm and modelling the impact of breaching of defence, is crucial to both disaster management planning as well as the insurance industry. In this study, which was carried out jointly by the Gallagher Research Centre and the research partner HR Wallingford (HRW), two extreme but realistic UK storm surge scenarios were developed separately for the East and West coasts of UK. The scenarios explore simultaneous flooding along extended coastline and the impact of realistic defence breaching, both in the present day and in 2050. A high-resolution footprint for each scenario run (present day non-breach, present day breach, future non-breach, and future breach) was generated by means of a 2D hydrodynamic model run on a 5m LiDAR Digital Elevation Model. Flood breaching was based on a national set of fragility curves created by HRW’s defence model. To account for climate change, the UKCP6 (UK climate projections) were used to assign the projected RCP 4.5 surge estimates for 2050. Finally, the loss potential for each simulated footprint was estimated for Gallagher Re’s insured market portfolio. Among the major findings of the analysis, it is shown how defence breaching has a significant impact on the potential loss, particularly for the East coast scenario, for which it results in a 10x increase in the number of properties affected. Climate change has also two impacts, on the number of properties flooded, but also on the depth of flooding experienced by properties already at risk, further exacerbating the potential loss. Finally, it is shown how these Realistic Disaster Scenarios are supporting UK (re)insurers in helping stress test their exposure to storm surge, while helping build a robust view of risk.

How to cite: Scudeler, C. and Willis, I.: Realistic storm surge scenarios for UK (re)insurers, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-19407, https://doi.org/10.5194/egusphere-egu24-19407, 2024.

EGU24-19535 | ECS | Posters on site | ITS4.10/NH13.1

Bridge Failure and Consequences: the Existing Infrastructures Need of Mitigation Techniques 

Pietro Giaretta and Paolo Salandin

Bridges represent a critical issue in case of hazardous events, like extreme floods and debris flows, being their proper operation of fundamental relevance to avoid cascading effects on population.

The reduction of the risk of failure of river crossings is fundamental to ensure the service given by the infrastructural network for the safety of the populations. The serviceability of the road and railway network must be guaranteed during hazardous events, when the efficiency of the infrastructure becomes fundamental to ensure the mobility of the rescue and a possible evacuation of the inhabitants during critical situations. Moreover, the safety of the bridge affects the surrounding environment with impacts on social and economic activities, representing a connection between different populations living along the sides of the river.

Hydraulic phenomena are responsible of more than 50% of bridge failure (e.g. Montalvo et al., 2020; Wardhana & Hadipriono, 2003), and scouring around piers and abutments always causes serious damages if proper deepening of foundation is not provided in the design. These aspects are exacerbated due to the climate change that in the last decades increases the frequency of extreme events, occurring flood events more often than in the past (e.g. Seneviratne et al., 2021). Lacks in scientific and technical knowledges have led in the past to the realization of inadequate foundations, and this fact joined with the occurrence of hazardous events in the climate change context, amplifies the risk of failure of bridges. However, many bridges realized in the past are actually still working probably thanks to the ancient custom of filling the riverbed around bridge piers and abutments with stones and boulders after relevant flood events as an empirical maintenance technique.

Therefore, the effectiveness of the described technique to reduce the risk of failure needs to be investigated to establish its effectiveness. Here this is done by physical modelling of the sediment-flow-structures interaction, technique that leads the possibility to check the performances of countermeasures like riprap mattresses, investigating the size of the boulders, the lined area, and their durability over time.

The experiments have been developed in a rectangular flume 1 m wide and 15 m long, using quite uniform sands (median grain size d50=0.4mm) to simulate the riverbed. Different pier geometries and water depths are considered in the experiments developed in steady state clear water conditions. According to the Froude and Shields similitudes, different arrangements and boulders size have been tested, evaluating the scour evolution in long time experiments.

The effectiveness of the aforementioned maintenance techniques is analysed to understand the reduction of the risk of failure of bridges to limit the resulting cascading effects.

How to cite: Giaretta, P. and Salandin, P.: Bridge Failure and Consequences: the Existing Infrastructures Need of Mitigation Techniques, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-19535, https://doi.org/10.5194/egusphere-egu24-19535, 2024.

EGU24-20037 | ECS | Orals | ITS4.10/NH13.1

From systemic risks to systemic resilience: A pathways approach for disaster and climate risk management in Malawi and South Africa 

Edward Sparkes, Davide Cotti, Albert Manyuchi, Stern Kita, Nkemakonam Naomi Ukatu, Samira Pfeiffer, Saskia E. Werners, and Michael Hagenlocher

To comprehensively manage the impacts from hazards and disasters, a nuanced understanding of the systemic nature of risks is needed. The effects of natural hazards, climate change and other human-generated shocks transcend borders, sectors and systems, highlighting the interconnected nature of risks. The lack of resilience in one sector can propagate risks across multiple other sectors, and interventions in response can generate trade-offs and unintended negative consequences leading to maladaptation. This emphasises that not only do we need to analyse risk from a systemic perspective, we must also approach risk management and adaptation to consider interconnected positive and negative cascading effects. 

Despite recent progress in complex risk assessment, translating information into actionable inputs for risk management remains a challenge. These challenges are especially pressing in countries burdened by increasing exposure to natural hazards and extreme climate effects. To support addressing this challenge, we integrated a novel  systemic risk analysis method named Impact Webs (Sparkes et al., 2023) with a pathways approach, to co-create disaster and climate risk management pathways with stakeholders, using the Republics of Malawi and South Africa as case studies. 

Impact Webs are system-oriented conceptual risk models that identify interconnections between hazards, risks, impacts, interventions, drivers of risks and root causes, mapping their interaction  across different sectors and at various scales. We co-developed Impact Webs with stakeholders, building on them to identify lessons for risk management adopting a pathways approach. Pathways are a flexible planning approach that incorporates stakeholders’ perspectives into decision making, reducing path dependencies and managing trade-offs. Decisions are taken based on how future conditions unfold. Our pathways development was also driven by stakeholders’ inputs, first using Impact Webs to identify entry points for risk management options. Barriers to implementing options were then identified, as well as enabling conditions to overcome them. We then engaged with potential trade-offs and positive cascading effects, identifying pathways for Malawi and South Africa that could strengthen resilience across multiple sectors. We took a transformational pathways approach, developing pathways for wide-ranging system changes needed to reach high resilience futures. The work was done over four workshops with a range of expert stakeholders, and was complemented by desk study and interviews.

Reflecting on the approach, a challenge arose in sequence actions, i.e., justifying the selection of one risk management option before another. This was due to developing pathways at the national scale across many sectors, therefore they were not targeted towards a specific decision or group of decision-makers. Despite this, the integration of the Impact Webs and pathways provides a useful methodology to move from systemic risk analysis to systemic risk management. Collecting feedback from stakeholders during the workshops, the co-creation process, and engaging with the visual output of an Impact Web, helped them think about risks and risk management in an interconnected manner, by considering cascading effects and response risks of interventions. This can foster understanding among decision makers about the interdependencies between sectors, thus supporting disaster and climate risk management that strengthens system-wide resilience across multiple sectors.

How to cite: Sparkes, E., Cotti, D., Manyuchi, A., Kita, S., Naomi Ukatu, N., Pfeiffer, S., Werners, S. E., and Hagenlocher, M.: From systemic risks to systemic resilience: A pathways approach for disaster and climate risk management in Malawi and South Africa, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-20037, https://doi.org/10.5194/egusphere-egu24-20037, 2024.

EGU24-21292 | ECS | Posters on site | ITS4.10/NH13.1

Analysis of extreme hydrological events over the Great Hungarian Plain based on Earth Observation data 

Edina Birinyi, Anikó Kern, Dániel Kristóf, Roland Hollós, and Zoltán Barcza

In Hungary, especially in the Great Hungarian Plain, hydrological cycle related extreme events – such as floods, inland excess water and droughts – are recurrent problems of increasing economic importance. These extremes often occur in the same area and sometimes within the same growing season, largely affecting agricultural production and raising questions related to water conservation and potential land use adjustments. In addition to climate change, the regulation of large rivers and poor water management are also likely to influence the phenomenon. The last major extreme events occurred in 2022 (drought) and 2023 (inland excess water). Relevant studies are mostly based on meteorological data, with one of the most comprehensive describing the frequency of extremes for the period 1931–2010. However, based on more than two decades of MODIS time series, it is possible to analyze variables such as vegetation conditions and water-covered areas, and hence, to investigate the relationship between the vegetation state and the environmental factors. Our study attempts to provide objective, time-series based statistical evidence specifically on the vulnerability of arable lands of the Great Plain and the relationship between environmental and EO-based variables for the period 2000-2023. In addition to spectral indices and land surface temperatures and their anomalies derived from MODIS measurements, land cover (CORINE), meteorology (FORESEE), soil moisture (ERA5-Land), soil properties (DoSoReMi), optical-based relative inland excess water incidence map (1998–2023), radar-based relative inland excess water incidence maps (2020– 2023), as well as aggregated yield loss compensation claims submitted to the Agricultural Risk Management System are included in the analysis. All the variables are aggregated to a spatial grid of 1-km resolution, and their relationship is analysed with mathematical methods (e.g. BORUTA, linear regression). Project no. 993788 has been implemented with the support provided by the Ministry of Culture and Innovation of Hungary from the National Research, Development and Innovation Fund, financed under the KDP-2020 funding scheme and by the TKP2021-NVA-29 project of the Hungarian National Research, Development and Innovation Fund and by the OTKA FK-146600 and by National Multidisciplinary Laboratory for Climate Change, RRF-2.3.1-21-2022-00014 project.

How to cite: Birinyi, E., Kern, A., Kristóf, D., Hollós, R., and Barcza, Z.: Analysis of extreme hydrological events over the Great Hungarian Plain based on Earth Observation data, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-21292, https://doi.org/10.5194/egusphere-egu24-21292, 2024.

Climate change remains one of the greatest challenges of the 21st century. Humans are not only the key driver of climate change, but are also affected by its consequences, with profound impacts on human life arising through changes in environmental and social circumstances. Climate change impacts on human behaviour are observed via two principle data types which both reflect daily human activity: social media and mobility data. Specifically, social media data are employed to analyse temperature impacts on hate speech online. Even though links between temperature and physical aggression are known, it remains unclear how these patterns extend to online environments, where hate speech can have detrimental consequences for the mental health of the affected persons. Using machine learning classifiers to identify hate speech in four billion geolocated tweets, we show that temperature has strong non-linear impacts on the occurrence of online hate speech across the USA with hate-tweet levels remaining low at moderate temperatures but sharply rising during both hot and cold extremes. This pattern persists across income groups, religious beliefs, and election outcomes and even various climate zones, including those where heat is common which suggests adaptation limits to hot temperatures. A complementary analysis for six European countries finds quasi-quadratic, nonlinear temperature impacts on digital racism and xenophobia across Europe. To assess not only the impacts of heat but also the ability to adapt we employ mobility data from New York City. An analysis of daily passenger data of more than 400 subway stations over six years shows that there is not only a strong, non-linear temperature impact on subway usage but also disparities between neighborhoods with respect to the capacity for heat mitigation. Correlations between neighbourhood-level mobility reductions and socioeconomic indicators suggest that the ability to reduce mobility on hot days is afforded by those that also hold other privileges, hence leading to unequal, compounding health impacts in disadvantaged neighbourhoods. Finally, we harness a combination of mobility data, Google search trends and Covid-19 data to explore how behavioural responses may develop under a prolonged or repeated risk exposure. The econometric approach explores changes in the response to Covid-19 risk through two channels: risk perception and the resulting behavioural response, i.e. mobility reduction. Across both channels, the risk response diminishes over time even before the availability of vaccines. This highlights the attenuation of behavioural responses to prolonged risks, with implications for managing long-term crises such as increasingly repeated exposure to weather extremes under ongoing climate change.

How to cite: Stechemesser, A.: Living in a warming world – climate impacts on society , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-21492, https://doi.org/10.5194/egusphere-egu24-21492, 2024.

Nighttime Light (NTL) data, offering insights into cross-regional human activities and infrastructure changes, has gained widespread use in disaster monitoring. This study explores the application of NASA's Black Marble daily data in monitoring post-typhoon responses. In a landscape characterised by a high mix of urban built-up areas and peri-urban villages, we investigate differences in nighttime light recovery across various land-use types after disasters. Combining interpretable machine learning, we explore the reasons behind these disparities by comparing Shapley values and specific Accumulated Local Effects (ALE) between regions, evaluating high importance of individual predictive factors and identifying potential non-linear patterns and threshold effects.
  Our findings reveal more instances of sustained nighttime light decline in rural areas (residential and agricultural land), while urban areas exhibit increased nighttime light during disasters. These differences primarily relate to infrastructure features, especially roads. Meteorological factors, such as precipitation probability and wind speed, impact NTL predictions in urban and rural areas. Post-disaster relief activities significantly influence NTL changes in rural settlements. Additionally, the occurrence of extreme weather increases the likelihood of cascading disasters. Our study finds that disaster impact zones in coastal areas extend deeper into the mainland, posing threats to adjacent mountainous regions and elevating the risk of secondary disasters like landslides.
  In conclusion, this study provides a regional assessment of resilience differences and influencing mechanisms using nighttime light data. It offers valuable information for policymakers to identify key factors influencing typhoon disaster resilience, enabling them to mitigate systemic risks and enhance overall system resilience. The significance of this research extends to serving as a valuable reference for data-driven recovery quantification from typhoon hazards and other crises.

How to cite: Ma, Y.: Urban-Rural Disparity in Disaster Resilience: Harnessing Nighttime Light Data and Interpretable Machine Learning, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-21837, https://doi.org/10.5194/egusphere-egu24-21837, 2024.

EGU24-5814 | PICO | ITS4.18/CL0.1.12

Climatic Zones Classification and Building Energy Efficiency in Spain. 

Blanca Arellano, Qianhui Zheng, and Josep Roca

In Spain, the adaptation of the European Directive on energy performance of buildings (2010) has been implemented through the Technical Building Code (TBC), which divides the territory into climate zones and evaluates the energy performance of buildings based on them (2013). The TBC segments Spain according to seasons, differentiating winter months (from October to May), which correspond to those where heating is necessary, and summer months (from June to September), those where air conditioning is necessary. Resulting in a characterization according to the climate severity of summer (SCS) or winter (WCS) to evaluate their energy efficiency. However, this classification methodology could be improved if taking into account the warming process of recent decades.

Between 1971-2022 in Spain, the maximum temperatures increased on average, 3.54°C, as well as the minimum temperatures, 2.73°C; as well as an exponential increase in heat waves over the last decades (Roca et al., 2023). "Summer" has increased by almost two more months, with a corresponding reduction in the "winter months”. For this reason, the research proposes a modification of the SCS and WCS, considering that “summer” runs from May to October and “winter” from November to April. Therefore, the research aims to study the limitations of the BTC climate zones classification, and propose a new climatic classification that allows a more accurate energy performance certification of buildings.

The study uses the E-OBS dataset, with a spatial resolution of 0.1°x 0.1°. Its continuity over time helps to track and analyze long-term climate change trends. For this purpose, the paper obtained daily data of average (tg), maximum (tx) and minimum (tn) temperature, and solar radiation (qq) from 1991 to 2020. At the same time, the study incorporates a series of climatic indices into the analysis to differentiate more precisely the different climates. For warm season, we introduce thermal indices such as CD25 and CN20 through 'Summer Days' (tx>25) and 'Tropical Nights' (tn>20). These outdoor temperatures, tx>25 and tn>20, indicate the thresholds above which the indoor environment of homes should be cooled. On the other hand, for the cold season, were calculated the cold indices HD15 and HD0 through 'Winter Days' (tg<15) and the 'Frost Days' (tn<0).

Through Principal Component Analysis (PCA), the determining factors of the climatic severities of "summer" and "winter" are extracted. These factors allow, through K-means classification, the delimitation of the different climatic zones that, require cooling (SCS) or heating (WCS). To obtain higher resolution climate data, the climate classification obtained by E-OBS has been downscaled to 1000 meters using multiple regression analysis (OLS), considering longitude, latitude, altitude and sea distance as independent variables, and SCS and WCS as dependent variables.

Finally, the research proposes an improved climatic zones classification, and, therefore, establish a more accurate energy efficiency valuation of buildings. This improved methodology not only reflects regional climate variations more accurately, but can also serve as a key tool for urban planners and building designers, allowing them to implement more effective strategies based on local climate.

How to cite: Arellano, B., Zheng, Q., and Roca, J.: Climatic Zones Classification and Building Energy Efficiency in Spain., EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5814, https://doi.org/10.5194/egusphere-egu24-5814, 2024.

EGU24-6000 | ECS | PICO | ITS4.18/CL0.1.12

Enhancing the Design of Climate Service Training Programs: Identifying Targeted Audiences for the User Learning Services for the Copernicus Program 

Maria del Pozo, Bregje van der Bolt, Judith Gulikers, Perry den Brok, and Fulco Ludwig

This research tackles the multifaceted challenges inherent in the design of climate service training programs, with a specific focus on the context of C3S User Learning Services. The heterogeneity of actors involved, including producers, providers, intermediaries, and users, often leads to misalignments attributable to overlooked nuances in learning needs. The primary objective is to establish consensus among trainers involved in the C3S User Learning Services regarding the identification of targeted audiences, their associated knowledge and skills, and the interests pivotal for the success of capacity-building initiatives. Utilizing the Delphi method, trainers participate in iterative rounds of questionnaires, wherein statistical measures and qualitative assessments guide the refinement process. The study introduces specific levels of agreement, distinguishing between poor, average, and strong agreement based on percentage evaluations. The structured yet flexible approach incorporates a pre-testing stage involving external experts to ensure survey clarity. With the potential inclusion of a fourth round in cases of low consensus, the research aspires to comprehensively address the diverse learning needs within the climate service domain, ultimately enhancing the efficacy of training programs, exemplified by C3S User Learning Services

How to cite: del Pozo, M., van der Bolt, B., Gulikers, J., den Brok, P., and Ludwig, F.: Enhancing the Design of Climate Service Training Programs: Identifying Targeted Audiences for the User Learning Services for the Copernicus Program, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-6000, https://doi.org/10.5194/egusphere-egu24-6000, 2024.

EGU24-7472 | ECS | PICO | ITS4.18/CL0.1.12

Climate and Weather Information Services for better governance and risk reduction of wildfires in North Western Europe 

Hugo Lambrechts, Cathelijne Stoof, Carolien Kroeze, Fulco Ludwig, and Spyros Papa

Wildfires are an emerging risk in NW Europe, primarily due to increasingly conducive weather conditions resulting from climate change. This work examines the vital role of climate adaptation services to contribute to knowledge sharing and network building among professional stakeholders on a national level and within the region. Based on online survey responses from land managers/owners, forest managers/owners, fire services, and governments, we explore the intricacies of wildfire risk perception and the necessity of tailored climate and weather information for effective wildfire governance.

Our research investigates how climate information services can bolster wildfire risk reduction, emphasizing the development of these services as a knowledge-sharing and network-building approach. We explore how tailored, locally relevant solutions and a thorough process of knowledge exchange and learning can build networks, ultimately delivering actionable knowledge that fosters an awareness culture among stakeholders.

The work delves into the current perception and awareness of wildfire risks among professional stakeholders. We examined their risk awareness, preparedness, and responsibility perceptions, questioning whether experience with wildfires correlates with higher awareness or if stakeholders outside civil protection have lower preparedness perceptions. Additionally, we investigated the specific information stakeholders utilize for wildfire risk reduction, discerning whether weather, climate, or risk reduction information is more beneficial. This exploration includes an analysis of how this information correlates with preparedness, awareness, and responsibility perceptions and whether discrepancies exist between the use and needs of stakeholders.

Preliminary result indicate that the development of a wildfire weather annd climate infomration service may contribute to wildfire governance and risk reduction in North Western Europe. Currently there is high awareness among most wildfire professionals, but that stakeholders do not feel prepared for future wildfire conditions. More than half of the respondents didn't know about the Copernicus EFFIS wildfire services, indicating that marketing and usibility of these products need to be increased. Stakeholders prioritised short-term weather forecasts and risk reduction information above other information.

In conclusion, we argue for the strategic use of climate information services as a means of enhancing the governance of wildfires in NW Europe. By identifying the climate and weather information needs of professionals and examining their perceptions and awareness of wildfire risks, we aim to contribute to the development of more effective, informed strategies for wildfire prevention and management in the face of changing climatic conditions.

How to cite: Lambrechts, H., Stoof, C., Kroeze, C., Ludwig, F., and Papa, S.: Climate and Weather Information Services for better governance and risk reduction of wildfires in North Western Europe, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-7472, https://doi.org/10.5194/egusphere-egu24-7472, 2024.

EGU24-12624 | ECS | PICO | ITS4.18/CL0.1.12

Data2Resilience: Data-driven Urban Climate Adaption – A Biometeorological Sensor Network for Dortmund, Germany 

Charlotte Hüser, Luise Weickhmann, Panagiotis Sismanidis, Jonas Kittner, and Benjamin Bechtel

Extreme heat endangers human health and well-being and impairs the use of public spaces. Dortmund’s Integrated Climate Adaption Master Plan prioritizes actions and measures to improve heat resilience. This project supports the city of Dortmund (Germany) in attaining this goal, by deploying a state-of-the-art biometeorological sensor network and developing a nowcasting service for monitoring thermal comfort across the city. The project aims to pioneer the integration of thermal comfort data in smart-city ecosystems and provide actionable insights for the development of Dortmund’s Heat Action Plan. Modeled, remotely sensed, and in-situ data will be used to provide near-real-time information regarding the outdoor thermal conditions. City-officials of Dortmund are involved in the design of the dashboard, and the weather station network, ensuring they meet their needs. The collected data will be used in a series of on-ground actions, supporting the evaluation of existing climate adaption measures, and the design of new ones. These actions include the mapping of areas with high potential for planting trees , the investigation of changes in human behavior during hot days, and the assessment of backyard greening strategies. To engage with the local stakeholders, promote the role of citizen scientists, and disseminate the project, a series of workshops and on-site events are planned, such as climate comfort labs, mobile measurement campaigns, or climate walks with citizens. The overall goal of the project is for the city of Dortmund to adopt and integrate the developed network and nowcasting service into its smart-city ecosystem.

How to cite: Hüser, C., Weickhmann, L., Sismanidis, P., Kittner, J., and Bechtel, B.: Data2Resilience: Data-driven Urban Climate Adaption – A Biometeorological Sensor Network for Dortmund, Germany, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12624, https://doi.org/10.5194/egusphere-egu24-12624, 2024.

EGU24-17230 | PICO | ITS4.18/CL0.1.12

Snow load climatology for design working lives of the greenhouse structures in Croatia 

Ksenija Cindric Kalin, Ivan Lukacevic, irena Nimac, and Melita Percec Tadic

The climatic loads should be considered in the structural design and construction of the greenhouses to ensure their overall stability and durability. The snow load (SL), defined as the weight of snow on a surface area per square meter, is particularly important because it can cause structure collapse and consequently significant economic damages. Characteristic snow load for different constructions is usually 50 years, however, greenhouse structures are usually designed for shorter periods. The classification and design of greenhouses are based on the European standard EN 13031–1 which also provides the procedure for snow load adjustments to appropriate return values. In this study, characteristic snow loads are analysed for Croatia. First, the general climatology of maximum snow load is prepared according to snow depth data from 117 stations across the country covering the period from 1968 to 2020. The results revealed four main climate snow regions in Croatia: mainland, mountainous, coastal hinterland, and Adriatic. The trend analysis showed a decreasing trend in maximum snow load data for the highest elevation stations, while a slight increase was detected for central continental and middle Adriatic areas, however, the trend is statistically significant only at two stations in the highlands. For calculating the characteristic snow load, the value associated with a 50-year return period, the Gumbel distribution was used. Non-stationarity of snow load data was tested by the likelihood ratio method which revealed no significant changes in the Gumbel distribution parameters. This led to the conclusion that a stationary model is sufficient to describe data at most stations. Besides the characteristic SL, the return values of maximum SL associated with the return periods of 5, 10, 15 and 50 years were estimated. Moving to the engineering perspective, the adjustment factors for the design of greenhouse structures given in the standard are also discussed.

How to cite: Cindric Kalin, K., Lukacevic, I., Nimac, I., and Percec Tadic, M.: Snow load climatology for design working lives of the greenhouse structures in Croatia, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17230, https://doi.org/10.5194/egusphere-egu24-17230, 2024.

EGU24-17708 | PICO | ITS4.18/CL0.1.12

Modelling and forecasting of water resources availability in mountainous Mediterranean springs 

Raquel Gómez-Beas, Eva Contreras, María José Polo, and Cristina Aguilar

The amount of water available for the production of natural mineral water is affected by the variability of the flow regime in the springs from which the water is extracted. This variability occurs at different time scales (seasonal and inter-annual), being more pronounced in mountainous Mediterranean areas. Since water quality remains constant in the aquifers throughout the hydrological year, the main uncertainty in the plant's production lies in the springs flow regime. In snow dominated areas it is necessary to analyse both the influence of snow dynamics on the springs flow regime, and to establish the response time between the rainfall events and the increase in the subsurface flow regime.

A forecasting model has been developed for several springs within the Guadalfeo river basin (southern Spain), where a bottling plant is operated by an international company. The model combines two approaches: a conceptual model (MCAL); and a seasonal forecast model (MPEL).

MCAL is based on linear adjustments between measured monthly mean flow data at the different locations of the springs, and measured series of rainfall and snowfall from two meteorological stations in the area, as well as adjustments with the mean monthly flow in the antecedent months. The best results were obtained between mean monthly flow and the mean monthly flow of antecedent months, with low relative errors (0,2%-10%) in all the locations for twelve months ahead.

MPEL allows to forecast groundwater supplies six months ahead in the different locations, from two products generated by the European Centre for Medium-Range Weather Forecasts (ECMWF): Multi-model seasonal reforecasts of river discharge for Europe and Multi-model seasonal forecasts of river discharge for Europe from January 2021 to present. The hydrological model WiMMed (Watershed Integrated Model in Mediterranean Areas) has been implemented and calibrated, to generate historical simulations in periods when there are no flow measurements at the springs. Using the ECMWF products and performing a bias-adjustment, the forecasts of the groundwater supplies are obtained for several possible future scenarios.

The results obtained showed the lowest mean relative error values with the MCAL forecasts from May to October (0.8%-8%), whereas the mean monthly flow from November to January was better predicted with the MPEL forecasts (1.3%-12%). The relative errors were similar with both models between February and April (3%-20%).

How to cite: Gómez-Beas, R., Contreras, E., Polo, M. J., and Aguilar, C.: Modelling and forecasting of water resources availability in mountainous Mediterranean springs, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17708, https://doi.org/10.5194/egusphere-egu24-17708, 2024.

Successful adaptation to climate change worldwide will require many local climate change risk assessments. To this end, societies need access to usable climate change information to better prepare and adapt to future risks as well as opportunities. Co-produced, user-driven climate services are a recognized means of improving the effective generation and utilization of climate information to inform decision-making and support adaptation to climate change. However, there is a structural lack of appropriate, tailored climate services and tools, particularly in developing countries. In addition, there has been limited evaluation of the process of co-developing climate service products.

This study describes and evaluates the steps and methods used to co-develop a global hydrological climate service (in the frame of the CO-MICC project), specifically, a knowledge portal on global freshwater-related hazards of climate change, in a transdisciplinary, participative process jointly with providers, local to regional users, and water experts. This comprised the co-production of (i) the relevant hydrological indicators (to be both user-relevant and scientifically sound concerning the global multi-model information basis), (ii) the integration of uncertainty in the provided visual representations of these indicators, and (iii) the necessary supporting information that guides and enables utilization of the provided hazard information. Participants from seven workshops with stakeholders from focus regions in Europe and Northern Africa included local researchers, experts from meteorological services and decision-makers from regional and national hydrological agencies. Together, we co-produced relevant model output variables and appropriate end-user products encompassing static and dynamically generated information in a web portal. The global-scale information products include interactive maps, diagrams, time series graphs, and suitably co-developed statistics, with appropriate visualization of uncertainty.

In addition, the integration of local needs into new co-developed indicators was necessary where standard indicators are not scientifically suitable with respect to the information basis. Specifically based on understanding the underlying need of the stakeholder and the capabilities of the global hydrological model output, an alternative indicator “consecutive dry years” was co-developed to integrate freshwater deficit information for water managers. Lessons learned will be discussed with a particular focus on the challenges of the participatory process in the context of the climate service co-development.

The CO-MICC knowledge portal (www.co-micc.eu) enables access to this information to a broad range of stakeholders from around the world (policy makers, NGOs, the private sector, the research community, the public in general) for their region of interest, enabling them to account for climate change in their risk portfolios. In addition, it provides information on the optimal design and methods of co-development processes.

How to cite: Kneier, F., Woltersdorf, L., and Döll, P.: Co-developing a global hydrological service to support climate change risk assessment and adaptation: providing stakeholder-elicited hazard information processed from uncertain multi-model ensemble output, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-19228, https://doi.org/10.5194/egusphere-egu24-19228, 2024.

The escalating challenge of climate change, notably the changes in rainfall patterns, poses a significant threat to agricultural practices in Brazil, particularly in regions like Novo Progresso, Para, notorious for extensive deforestation and the annual "Day of Fire". We introduce an innovative mobile Augmented Reality (AR) application designed to aid farmers and local communities in adapting to these shifting rainfall patterns.

 

Our AR climate service application, developed for smartphones running the iOS and Android platforms using Unity 3D and ARKit/ARCore libraries, offers an interactive visualization of the study area. Users can view a detailed map, including administrative boundaries, protected zones, and geographical features, to explore various land uses and simulate potential changes in rainfall and crop yield. By selecting a specific plot of land within the app, users gain the capability to tailor the land's usage parameters, including the type of crops cultivated (if any) and the agricultural management strategies employed. Combining their input of local knowledge with climatic and agricultural models, the tool is able to provide them with projections of the rainfall change for the selected plot as well as the anticipated effect on crop yields. Stakeholders can experiment with different crops and management strategies and observe simulated outcomes on crop yields under different climate scenarios. Additionally, the tool supports multi-user simulations to enable effective community planning. This interactive approach is aimed at improving local decision-making regarding land use, highlighting the potential consequences of various agricultural strategies.

 

The content and features of the AR tool are grounded in interviews conducted in Para, Brazil, with a focus on incorporating local insights regarding crops, soil types, and existing management strategies. The initial phase of this project included pre-interviews which revealed a general lack of urgency among farmers regarding climate change. Our application aims to visually demonstrate the significance of climate change, linking the farmers’ perceived changes in rainfall with larger environmental trends.

 

The first iteration of the application was presented to a diverse group of stakeholders in the town of Santa Julia, including farmers, local government officials, and agricultural experts. Their engagement with the tool was followed by semi-structured interviews to gather feedback on usability and effectiveness. The response was highly encouraging, with stakeholders unanimously supporting further development and recognizing the application's potential in visualizing and combating the impacts of climate change.

 

Our presentation will discuss the iterative development process of the AR application, insights from stakeholder pre-surveys and testing sessions, and plans for further development. Emphasis will be placed on the tool's role in facilitating community-scale decision-making in a region marked by complex power dynamics and environmental challenges. Through this climate service tool, we aim to bridge the gap between scientific research and practical, community-led climate adaptation strategies.

How to cite: Metelitsa, V. and Máñez Costa, M.: Visualizing change, cultivating resilience: An augmented reality driven approach to climate adaptation planning in Brazilian agriculture, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-19822, https://doi.org/10.5194/egusphere-egu24-19822, 2024.

EGU24-19874 | PICO | ITS4.18/CL0.1.12

From weather forcing to economic losses: an integrated climate service for long term projections on water availability. 

Elisa Delpiazzo, Guido Rianna, Roberta Padulano, Alfredo Reder, and Francesco Bosello

Long term projections suggest the Mediterranean area as a hotspot for increasing drought and extreme heat events with remarkable cascading effects on several economic sectors, such as agriculture, energy production, urban uses and on ecosystem services. The adoption of climate services designed to aid near real time operational choices, including seasonal forecasts, could help in better planning the use of a scarce resource, as water, both at the local (e.g., farm) and the basin level.

These short-term tools may have positive feedback also in a longer run. The PRIMA Project ACQUAOUNT (https://www.acquaount.eu/) aims to produce climate services to support robust decision making for water resource allocation at an operational time scale, and an off-line tool to evaluate how the adoption of such tools together with innovative management policies will affect water availability in a longer perspective. It will integrate the hydrological, climatological, and economic dimensions to provide information on long term sustainability of water availability to decision makers and water users in four pilot sites, namely the Tirso basin (Sardinia), Zarqa river basin (Jordan), Jeffara basin (Tunisia), and Upper Litani River basin (Lebanon). They are characterized by remarkable differences in terms of water availability, water sources, users, and management options; thus, the off-line tool will combine users’ needs and a simplified framework to be applied both in information rich and scarce contexts.

 

The tool is forced by weather observations available in-situ (where available) and complemented/replaced by authoritative data sources freely available (e.g., Copernicus Regional Reanalysis, CERRA); over the future time horizons up to 2100, an ensemble of global climate projections is adopted, which included in 6th Coupled Model Intercomparison Project (CMIP6) informing the most update cycle of IPCC Assessment Reports. The main weather outputs regulating soil water budget are statistically downscaled by exploiting a non-parametric quantile mapping approach calibrated by using CERRA reanalysis under two concentration scenarios (Shared Socio-Economic Pathway, SSP): SSP2_4.5 and SSP5_8.5, a “mid-way” and “pessimistic” scenario, respectively.

Finally, the physical effects, (i.e., water anomalies), are translated into economic terms using a simplified avoided losses approach, evaluating changes in co-designed indicators for water uses according to different scenarios. Future water availability is compared with a management rule for water provisioning, such that there will be a connection between physical water availability and restrictions that affect the amount of water available for different uses in the pilot site. Finally, water restrictions impact the economic, social, and environmental performance of selected sectors. Primarily, the socio-economic part will assess changes in economic, social, and environmental indicators to evaluate and compare costs in each scenario.

 

The final aim of the integrated service is to compare alternative future pathways of water availability. These pathways are co-developed with local stakeholders and include a status quo scenario, where current management rules for water distribution are supposed, an ACQUAOUNT integrated scenario, where the water resource is supposed to be deployed using the AQUAOUNT short term tools, and site-specific scenarios e.g. inclusion of new management rules or new water sources.

How to cite: Delpiazzo, E., Rianna, G., Padulano, R., Reder, A., and Bosello, F.: From weather forcing to economic losses: an integrated climate service for long term projections on water availability., EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-19874, https://doi.org/10.5194/egusphere-egu24-19874, 2024.

EGU24-22171 | ECS | PICO | ITS4.18/CL0.1.12

Hydro-climate information services for smallholder farmers: DROP app design, implementation, and evaluation 

Lisanne Nauta, Samuel Sutanto, Iwan Supit, Gordana Kranjac-Berisavljevic, Richard Dogbey, Baba Jamaldeen, and Spyridon Paparrizos

Rainfed agriculture constitutes the backbone of the economy in many regions of the Global South. Historically, smallholder farmers used their local knowledge to forecast the weather. However, with the increase in climatic variability, they can no longer solely rely on their experience to accurately forecast the weather. DROP App is a hydro-climate information service developed through a co-production approach to address the weather and climate information needs of farmers. The app gathers weather forecast from both local farmers and scientific sources, and presents this information to users to enable them to make informed decisions regarding agriculture. To test its proof-of-concept, the DROP app was implemented in five rice communities in northern Ghana. The app was introduced to farmers, who received training on it use, as well as built their capacity on weather and climate-related phenomena and the use of Information and Communication Technologies (ICT). Following the end of the cropping season, farmers evaluated the app and the results revealed that co-production of information played a crucial role to its adoption in relation to other similar platforms. Farmers consider the app as a relatively accurate and reliable source of information for planning agricultural activities. Using forecasts obtained from the app, farmers adjusted their farming activities, such as time of sowing, planting and weeding dates, fertilizer and herbicide application, and harvesting. They additionally demonstrated a significant level of knowledge about weather phenomena as a result to their engagement and capacity building. Although some limitations exist, the DROP app has potential to deliver actionable knowledge for climate-smart farm decision-making and thus, facilitate effective agriculture management.

How to cite: Nauta, L., Sutanto, S., Supit, I., Kranjac-Berisavljevic, G., Dogbey, R., Jamaldeen, B., and Paparrizos, S.: Hydro-climate information services for smallholder farmers: DROP app design, implementation, and evaluation, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-22171, https://doi.org/10.5194/egusphere-egu24-22171, 2024.

ITS5 – General ITS sessions

EGU24-1949 | Posters on site | ITS5.2/SSP1.13

Plio-Pleistocene Southern Ocean Paleoceanography: Latitudinal drilling in the Southwestern Indian sector of the Southern Ocean 

Minoru Ikehara, Xavier Crosta, Samuel Jaccard, Tim Naish, and Yasuyuki Nakamura

Proposal 918-pre in the Southern Ocean exists in the current science evaluation system of IODP. Site surveys were completed in 2019. We would like to develop an international research project based on this proposal by conducting new drilling/coring in the IODP3. First, we would like to drill by riserless drilling vessel, but if drilling is not feasible, we plan to consider giant piston coring as an option.

 The Southern Ocean (SO) is a key region that profoundly influences climate variability throughout the Cenozoic. Because the SO redistributes heat, fresh water, carbon, and nutrients around the global ocean it plays a key role in the climate system. The growth of ice sheets in the Antarctic continent and changes in sea ice in the surrounding ocean are important variables in Earth’s climate system. Upwelling of deep waters in the Antarctic Circumpolar Current (ACC), in particular, is a key process of the meridional overturning circulation (MOC) as it constitutes the return path for deeply-sequestered carbon and nutrients towards the surface and hence important in the partitioning of carbon between the ocean and the atmosphere. Furthermore, physical and biogeochemical processes modulate nutrient export through SO-sourced intermediate waters that ventilate 75% of the world’s thermocline, thus playing a vital role in influencing low-latitude productivity and ecosystems.

The western Indian sector of the SO, located at the confluence of the SO overturning cells and the MOC return surface flow, is a key region to document the links/teleconnections between the SO, global ocean/atmospheric circulations and hence climate (Fig. 1). It provides a unique opportunity to obtain exceptionally high-resolution sediment records to document and unravel the interaction and feedbacks between atmosphere, ocean and cryosphere from millennial to orbital-timescales during the late Neogene and Quaternary, focus on past 6 Ma (Fig. 2).

Specifically, the proposal aims to constrain further – (A) past changes in the upwelling and latitudinal position of the ACC; (B) the dynamic controls of circum-Antarctic deep ocean ventilation/overturning circulation; (C) their link to the global ocean circulation; (D) past changes in the sea ice coverage and dust inputs; and (E) their implications for the marine biogeochemical cycles of carbon and nutrients. The anticipated results will elucidate the evolution of the SO carbon cycle, identify potentially dominant physical and biogeochemical mechanisms of change, document past oceanic bipolar teleconnections with global MOC dynamics, and provide constraints on its future evolution in response to anthropogenic warming.

Our scientific objectives relate to Strategic Objective 3 (Earth’s climate system), Strategic Objective 4 (Feedbacks in the Earth system), and Flagship Initiative 1 (Ground truthing future climate change) in the 2050 Science Framework.

How to cite: Ikehara, M., Crosta, X., Jaccard, S., Naish, T., and Nakamura, Y.: Plio-Pleistocene Southern Ocean Paleoceanography: Latitudinal drilling in the Southwestern Indian sector of the Southern Ocean, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1949, https://doi.org/10.5194/egusphere-egu24-1949, 2024.

International Ocean Discovery Program (IODP) Expedition 366 recovered cores from three serpentinite mud volcanoes that also contain clasts that originate from the subduction-channel along the Philippine Sea Plate – Pacific Plate boundary. The drilled and sampled mud volcanoes (Yinazao, Fantangisña, and Asùt Tesoru) are located at distances of 55 to 72 km from the Mariana Trench.

In general the recovered cores comprise serpentinite mud with lithic clasts from the underlying forearc lithosphere and from the subducting Pacific plate. This aloows the reconstruction of mass transport processes and geochemical cycling within the forearc, the spatial variability of slab-related fluids within the forearc, and water-rock-reactions in subduction and supra-subduction zone settings, the metamorphic and tectonic history of the subduction channel, and the timing and rates of these processes.

Mafic rock clasts, embedded within a serpentine mud matrix, from the flanks and summits of both Asùt Tesoru and Fantangisña Seamounts were analyzed for reconstruction of their metamorphic and deformational overprint in order to reveal the tectono-metamorphic conditions at the metamorphic peak within the subduction channel and the subsequent low-grade overprint during exhumation.

Several seamounts comprise clasts of lower plate metabasites with different metamorphic overprint (from low-grade greenschist facies to lower blueschist facies). The metabasites are also associated with clasts of fossiliferous carbonates and cherts with different degrees of metamorphic and deformational overprint, that also originated from the Pacific lower Plate. This implies that these rocks were exhumed from different depths within the subduction channel before being regurgated within a serpentinite mud matrix. The blueschist facies metamorphic rocks, being affected by metamorphic pressures in the range of 11 to 13.8 kbar at minimum, were very likely exhumed from greater depth within the subduction channel before being captured by uprising, localized serpentine mud flows, indicating evidence that corner flow is actually taking place along the Mariana convergent margin, and, to our knowledge, this is the first direct evidence of exhumation of high-pressure rocks by corner flow in an active subduction zone. Final exhumation, however, is related to the embedding of the rocks within a serpentinite mud matrix and the buoyant ascent of serpentinite mudflows along forearc fracture zones extending from the plate boundary to the upper plate sea floor.

Biostratigraphic analyses of calcareous nannofossils and planktonic foraminifera from serpentinite mud flows, and intercalated pelagic sediments immediately above the metabasites analysed in this study give an age record of ~ 6.10 Ma (late Miocene, Messinian) to 4.20 Ma (early Pliocene, Zanclean), indicating that the final exhumation of the metabasites occurred during late Miocene times, slightly before 6.10 Ma.

How to cite: Kurz, W., Miladinova, I., Auer, G., and Del Gaudio, A. V.: Exhumation of high-pressure rocks by corner flow and serpentinite mud volcanism – implications from serpentinite mud seamounts along the Mariana convergent margin (IODP Expedition 366), EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-3044, https://doi.org/10.5194/egusphere-egu24-3044, 2024.

EGU24-3074 | Posters on site | ITS5.2/SSP1.13

A new deep sea reference record for the Paleocene-Eocene Thermal Maximum: IODP Expedition 392 Site U1580 (Agulhas Plateau, Southwest Indian Ocean) 

Thomas Westerhold, Steve Bohaty, Donald Penman, Ashely Burkett, and Edoardo Dallanave and the Expedition 392 Scientists

The abrupt onset of the Paleocene-Eocene Thermal Maximum (PETM) 56 million years ago represents one of the largest transient greenhouse gas-driven global warming event in the last 100 million years. Caused by a geologically rapid injection of exogenic carbon into the ocean and atmosphere system, the PETM is associated with large-scale ocean acidification. Related widespread dissolution of marine pelagic carbonate deposits, particularly in the early stages of the event, complicates marine paleoclimatic reconstructions and the establishment of robust age models at many sites. Recently, a new deep-sea sediment record spanning the PETM was recovered from the southern Agulhas Plateau in the Southwest Indian Ocean during International Ocean Discovery Program Expedition 392. The uppermost Paleocene/lowermost Eocene interval at Site U1580 was drilled in two parallel holes at 2560 m water depth, and consists of 75‒95% carbonate across the event, with a reduction to 75‒65% at the PETM onset. X-ray fluorescence-derived core scanning elemental data at 5mm and 10mm resolution and an unprecedented high-resolution bulk carbonate stable carbon and oxygen isotope record define a new marine composite reference record for the PETM at this site. The record is comparable to Ocean Drilling Program Site 690 (2914 m water depth) in the Atlantic sector of the Southern Ocean, where the event was first described and is still a primary reference sequence for paleoclimate reconstructions. Unlike Site 690, however, Site U1580 elemental data shows a clear cyclicity throughout the event that can be utilized for cyclostratigraphy. Additionally, the highly resolved bulk carbonate stable carbon isotope record provides a new reference for global correlation, which establishes a new benchmark for the different phases of the PETM. Here we present this new record and discuss the implications for timing and duration of the event, and set the stage for multi-proxy paleoclimate reconstructions spanning the PETM at IODP Site U1580.

How to cite: Westerhold, T., Bohaty, S., Penman, D., Burkett, A., and Dallanave, E. and the Expedition 392 Scientists: A new deep sea reference record for the Paleocene-Eocene Thermal Maximum: IODP Expedition 392 Site U1580 (Agulhas Plateau, Southwest Indian Ocean), EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-3074, https://doi.org/10.5194/egusphere-egu24-3074, 2024.

EGU24-3380 | Orals | ITS5.2/SSP1.13

Legacy IODP Cores Data in the Era of Big Data 

Cédric M. John

Core data is pivotal for understanding our planet’s past, present, and future. Despite this richness, extracting meaningful insights from core description poses significant challenges due to the inherent complexity and variability of the data, the amount of existing material, and the subjectivity of the interpreter. IODP (and the preceding programs) offers a rich, well labelled source of core images that can be used in machine learning and deep learning.

Focusing largely (but not exclusively) on carbonate rocks, characterized by their heterogeneity at all observational scales, I will discuss how my research group and I have pioneered the application of deep-learning computer vision to geological core interpretation. This technology transcends the traditional, tedious manual interpretations of cores, offering a rapid, and often more accurate, alternative for delineating depositional environments and sequence stratigraphy. Convolutional neural networks (CNNs) form the backbone of our approach, enabling us to process core data with unprecedented efficiency. I will show that these sophisticated models, when correctly trained and fed with substantial datasets, serve as invaluable tools for geologists, outpacing conventional methods in speed without compromising on precision.

Our early work was centred on transfer learning, an AI approach that adapts pre-existing models to new data. I will show that this remains one of the best way to train classification algorithms for geological dataset. But we also worked on generative algorithms that fill gaps in our sampling of core imagery: for instance, we use Generative Adversarial Networks (GANs) to transform the resistivity images from formation micro scanners into representations mirroring actual core photographs, thus enhancing the interpretability for geologists irrespective of their background in downhole tools.

We tackle the often-limiting factor of dataset size in two ways. First, we recourse to generative AI to oversample our training set. Second, we also explore semi-supervised learning techniques.  I will demonstrate that we successfully train models on core deformation images from IODP with minimal labelled data, achieving accuracy on par with, if not exceeding, that of transfer learning models.

None of our achievements would have been possible without the recourse to IODP data. Hence, this presentation serves as a clear illustration of the value of legacy IODP data for future geoscientists.

How to cite: John, C. M.: Legacy IODP Cores Data in the Era of Big Data, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-3380, https://doi.org/10.5194/egusphere-egu24-3380, 2024.

 The ICDP-DSeis (Drilling into Seismogenic zones of M2.0 – M5.5 earthquakes in deep South African gold mines) project recovered rock samples from a fracture zone that hosted the aftershocks of the 2014 Orkney earthquake (M5.5). The fracture zone was formed in an altered lamprophyre dike (Lamprophyre, hereafter) intruded into the Crown Formation (local name of altered basaltic andesite). One of the DSeis holes intersected another dike (named the Onstott dike) ~300 m east of the Lamprophyre. A fissure containing ancient (1.2 Ga) hypersaline brine rich in DOC (dissolved organic carbon) was found in the Onstott dike. The formation and metamorphism of these dikes are discussed in Ogasawara et al. (EGU24). This study describes the frictional properties of Lamprophyre that motivated us to propose a new drilling project PROTEA.

The southern shallowest section of the fault intersected by the DSeis hole did not slip significantly during the Orkney earthquake mainshock, but hosted high aftershock activity. This implies that the fault intersected by the DSeis hole was stable frictionally and decelerated coseismic slip to halt the dynamic propagation of the mainshock rupture. Thereafter, the fault transitioned to an unstable state and produced aftershocks. The recovered rocks revealed the Lamprophyre did not contain any quartz and was rich in talc, biotite, amphibole, and calcite. Loss of drilling water into the fault zone suggested that the pore pressure in the fault zone was low (<6 MPa, water head pressure in the borehole). Consequently, sliding-rate step tests were conducted under wet (saturated, but without pore pressure) condition using the powdered samples to investigate evolution of the frictional property with increasing sliding distance.

 The friction coefficient of Lamprophyre was ~0.3, much lower than that of the Crown Formation (~0.7). The friction coefficients of both lithologies were almost independent of sliding distances. Lamprophyre showed rate-strengthening behavior irrespective of sliding distance. Acoustic emission (AE) activity, which mimics aftershock activity, in the Lamprophyre gouge became higher with increasing sliding distance. This implies a hierarchical evolution of frictional property of Lamprophyre. These experimental results explain the spatial coincidence of the mainshock rupture termination and high aftershock activity.

The weakness of Lamprophyre may enable the formation of a fault in Lamprophyre. However, its rate-strengthening behavior would prevent the nucleation and spontaneous propagation of the rupture on a fault in Lamprophyre. Frictional properties, the stress state, the pore pressure, and/or lithology around the hypocenter should differ from those in the aftershock zone. Therefore, we propose a new drilling project PROTEA (Probing the heart of an earthquake and life in the deep subsurface) to drill a hole targeting the hypocenter and the strong motion source of the Orkney earthquake. It will explore the frictional properties, stress state, pore pressure, and lithology that enabled the nucleation and the initiation of the dynamic rupture, as well as the radiation mechanism of strong motions. PROTEA will drill multiple holes intersecting the Onstott dike, not only to elucidate locality and universality of ecosystems that exist in the fissure brine, but also to investigate the interaction between seismicity and microbial activity.

How to cite: Yabe, Y., Ogasawara, H., and Durrheim, R.: Frictional properties of the fault hosting aftershocks of the 2014 Orkney earthquake (M5.5), South Africa, and proposal of a new drilling project PROTEA to probe the heart of the earthquake, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-3723, https://doi.org/10.5194/egusphere-egu24-3723, 2024.

EGU24-4956 | ECS | Posters on site | ITS5.2/SSP1.13

Pretraining Foundation Models: Unleashing the Power of Forgotten Spectra for Advanced Geological Applications 

An-Sheng Lee, Hsuan-Tien Lin, and Sofia Ya Hsuan Liou

X-ray fluorescence (XRF) core scanning, renowned for its high-resolution, non-destructive, and user-friendly operation, is pivotal in geological research for analyzing chemical, physical, and biological signals. Despite the extensive applications of XRF data for various research purposes, the quantification of this data into specific geological proxies remains challenging due to the inherent non-linearity caused by simple sample pretreatment during core scanning. Leveraging advancements in deep learning, computing power and large-scale scientific drilling programs, our study aims to address this non-linearity by harnessing the often-overlooked raw XRF spectra stored in laboratory databases. We introduce an approach involving self-supervised pretraining on 54,643 spectra from marine sediments in the high-latitude sectors of the Pacific Ocean (cruises SO178, SO264, PS97, PS75, LV29). Our model, underpinned by a deep bidirectional image transformer (ViT-base), is trained to reconstruct heavily masked spectra (75%) with an R2 accuracy of 0.996, demonstrating its proficiency in feature extraction from limited data portions. This foundational model is anticipated to serve as a versatile tool for various downstream geological applications after finetuning with specific labeled data, such as quantifying high-resolution calcium carbonate (CaCO3) and detecting machinery anomalies. Future work includes expanding the spectrum database with diverse materials and machine settings to enhance the model's generalizability, ultimately broadening its applicability beyond core scanning for geological applications to encompass all XRF measurement techniques.

How to cite: Lee, A.-S., Lin, H.-T., and Liou, S. Y. H.: Pretraining Foundation Models: Unleashing the Power of Forgotten Spectra for Advanced Geological Applications, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-4956, https://doi.org/10.5194/egusphere-egu24-4956, 2024.

EGU24-5974 | Posters on site | ITS5.2/SSP1.13 | Highlight

Scientific core drilling of the Lower Palaeozoic succession in the Swedish sector of the Baltic Sea – investigation of the CO2 storage potential 

Mikael Erlström, Jan-Erik Rosberg, Peter Dahlqvist, Carl-Erik Hjerne, and Henning Lorenz

With an aggregated thickness of c.100 m, a porosity of up to 15 % and a permeability above hundred millidarcy, previous studies have assessed the three widely distributed Cambrian sandstone members in the Swedish sector of the Baltic Sea as the most potential CO2-storage candidates in Sweden. Existing models indicate an effective storage capacity between 450–1500 Mt CO2. However, these rough numbers are uncertain as they are related to vintage and partly inadequate data sets, especially regarding physical property values needed for a more certain evaluation of the storage capacity. Hence, as part of a larger screening and evaluation programme, launched by the Swedish government to identify and quantify potential storage sites in Sweden, two scientific core drillings were completed in 2023 on the southernmost part of the island of Gotland in the Baltic Sea. The primary aim was to collect complementary and missing data on the Lower Palaeozoic succession including both caprocks and reservoirs. The scientific evaluations and results of the core drillings on south Gotland will together with geophysical logging of the boreholes, new seismic data and 3D models constitute an essential part in improving the models of the effective storage capacity of the Cambrian reservoirs in the Swedish sector of the Baltic Sea. The coring, monitoring and investigations were managed by the Geological Survey of Sweden and the Swedish national research infrastructure for scientific drilling, “Riksriggen”, operated by the department of Engineering Geology at Lund university. H-dimension triple tube coring (96/61 mm hole/core diameter) was successfully performed with an Atlas Copco CT20C rig. The two wells, Nore-1 and Nore-2, penetrate 470 m of Silurian marlstone and claystone, 85 m of Ordovician argillaceous limestone and 225 m of Cambrian sandstone, siltstone and shale before finishing in the Precambrian crystalline basement of potassium porphyritic granite at 791.5 m and 787.7 m, respectively. The operation managed to reach the set goals despite challenges of over pressured formations and up to metre-thick bentonites. Initial results show a thick, tight, and homogenous caprock and that the Faludden Sandstone, one of the three Cambrian sandstone members, have hydraulic properties that qualifies it as a possible storage reservoir. The preliminary results from the two wells are here presented together with evaluations of drilling performance, monitoring programme, logging and test operations. 

How to cite: Erlström, M., Rosberg, J.-E., Dahlqvist, P., Hjerne, C.-E., and Lorenz, H.: Scientific core drilling of the Lower Palaeozoic succession in the Swedish sector of the Baltic Sea – investigation of the CO2 storage potential, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5974, https://doi.org/10.5194/egusphere-egu24-5974, 2024.

We have detected an event of pore pressure changes (hereafter, we refer it to “pore pressure event”) from borehole stations in real time in March 2020 and March 2023, owing to the network developed by connecting three borehole stations to the Dense Oceanfloor Network System for Earthquakes and Tsunamis (DONET) observatories near the Nankai Trough. Slow earthquake is thought to have longer duration time with smaller stress drop than regular earthquake under the same magnitude. This means that the slow earthquake is more sensitive to external stress perturbation and useful to monitor the processes of stress accumulation and release. However, the pore pressure is also affected by tidal and oceanic fluctuations. To overcome this problem, we use the seafloor pressure gauges of DONET stations nearby boreholes instead of the reference by introducing time lag between them. The obtained results demonstrate the detectability of volumetric strain change for nano-scale. We also investigate the impact of seafloor pressure due to ocean fluctuation on the basis of ocean modelling, which suggests that the decrease of effective normal stress from the onset to the termination of the SSE is explained by Kuroshio meander and may promote updip slip migration, and that the increase of effective normal stress for the short-term ocean fluctuation may terminate the SSE as observed in the Hikurangi subduction zone. The evaluation of the ocean impact is to be applied to a fiber-optic submarine cable, and 50 km-long distributed acoustic sensing (DAS) recordings, where the DAS measurement can be sensitive for hydroacoustic signals in a frequency range from 0.1 to a few tens of Hz.

How to cite: Ariyoshi, K. and Matsumoto, H.: Precise monitoring of subduction plate coupling status on the basis of DONET and borehole data analyses, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-7109, https://doi.org/10.5194/egusphere-egu24-7109, 2024.

EGU24-7120 | Posters on site | ITS5.2/SSP1.13

Chemical-Based Event-Stratigraphic Correlation along the Japan Trench by XRF-CS Chemical Fingerprint and Multivariate Statistics 

Jun-Ting Lin, Jyh-Jaan Huang, Ken Ikehara, Michael Strasser, Ta-Wei Hsu, Chih-Chieh Su, Yu-Hsun Shao, Yen-Hsi Wu, and Astuko Amano

Megathrust earthquakes in subduction zones, such as the AD 2011 Tohoku-oki earthquake, are known to generate turbidity currents that transport sediment into deep marine trenches, creating distinctive event deposits. These deposits are pivotal in submarine paleoseismology, which seeks to extend earthquake records and assess disaster potentials by meticulously analyzing the spatiotemporal distribution of these deposits through precise distinction and correlation. In our research, over 800 meters of sediment cores from 15 sites along the Japan Trench were collected during the International Ocean Discovery Program (IODP) Expedition 386, focusing on these event deposits to trace earthquake history. Utilizing X-ray Fluorescence Core Scanning (XRF-CS), we performed efficient and non-destructive high-resolution analysis of the chemical characteristics of these deposits. Our methodology integrates XRF-CS data with multivariate statistical techniques, such as Principal Component Analysis (PCA) and Cluster Analysis (CA). This integration enables us to objectively differentiate and correlate event deposits based on their unique chemical properties. Significant discoveries were made at Site M0083, where we detected variations in background sediments and event deposits associated with major historical earthquakes, including the AD 1454 Kyotoku and AD 869 Jogan events. These unique chemical fingerprints were also traced to adjacent trench-fill basins at sites M0089 and M0090, revealing consistent event-stratigraphic sequences across the basins and affirming the efficacy of our chemical-based correlation technique. In our ongoing analysis, we aim to explore the diverse chemical fingerprints of background sediments, turbidite bases, and turbidite tails. This investigation seeks to uncover potential spatial and stratigraphic provenance changes, and includes examining mineral and biogenic compositions, such as clay minerals and smear slides, to elucidate the reasons behind variations in chemical signals. Our findings underscore the effectiveness of combining chemical analysis with statistical methods for event-stratigraphic correlation. This novel approach not only sheds light on provenance changes but also helps establish a detailed spatiotemporal distribution framework for event deposits along the Japan Trench. Moreover, this integrated methodology could inform subsequent sampling strategies by objectively selecting sampling locations based on the chemostratigraphy framework. It also has the potential to be adapted to other research areas that require event-stratigraphic correlation and comprehensive spatiotemporal analysis.

How to cite: Lin, J.-T., Huang, J.-J., Ikehara, K., Strasser, M., Hsu, T.-W., Su, C.-C., Shao, Y.-H., Wu, Y.-H., and Amano, A.: Chemical-Based Event-Stratigraphic Correlation along the Japan Trench by XRF-CS Chemical Fingerprint and Multivariate Statistics, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-7120, https://doi.org/10.5194/egusphere-egu24-7120, 2024.

EGU24-8374 | ECS | Posters on site | ITS5.2/SSP1.13

Advantages of DTW windowing function in automated correlation of stratigraphic time series 

Rohit Samant, Toni Giorgino, Emilia Jarochowska, and David De Vleeschouwer

The Dynamical Time Warping (DTW) technique has been originally developed for speech recognition in the late 1960s and early 1970s and has more recently been applied in geoscientific studies. One of the key objectives of dtw is to stretch or compress two complementary series locally in order for one series to resemble the other as much as possible. In our project, we aim to correlate industrial and scientific downhole wireline logs from offshore Australia, with the ultimate goal to obtain a regional paleoclimate reconstruction at high spatial resolution (“All around Australia”). Here, we propose a novel way to constrain the alignment of two sedimentary sequences based on a-priori stratigraphic information. Therewith, we provide a means to make dtw calculations less computationally expensive, while still evaluating all possible stratigraphic correlations, even for long time-series.

The “All around Australia” project focuses on the automated correlation of thousands of scientific and industrial time-series. Hence, it is important to speed up the calculations and reduce the computational costs. The global constraint on dtw, also known as the window function, speeds up the calculations by limiting the 2-dimensional space of possible alignments between two time-series. As a case study, IODP Site U1463 (Northwest Shelf of Australia) serves as the reference to which two industrial sites (Finucane-1 and Angel-2) are correlated. Biostratigraphic datums of Site U1463 have few meters of depth uncertainty. Their corresponding depths at the industrial sites are manually determined, albeit with a depth uncertainty that is one order of magnitude higher (so-called “slack”).  These manually determined correlation points are then utilized to create custom-made windows, reflecting a priori knowledge of the large-scale stratigraphy of the studied basin. In this case, the comparison of the computational time and the goodness-of-fit for ‘no-window’ and ‘windowed’ dtw calculations reveals that the quality of the correlation improves and computational time is reduced by 15-20%. Hence, the novel window function is primarily useful for stratigraphers to guide the dtw algorithm in creating warping paths that are stratigraphically more plausible. 

How to cite: Samant, R., Giorgino, T., Jarochowska, E., and De Vleeschouwer, D.: Advantages of DTW windowing function in automated correlation of stratigraphic time series, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-8374, https://doi.org/10.5194/egusphere-egu24-8374, 2024.

EGU24-11720 | ECS | Orals | ITS5.2/SSP1.13

IODP Expedition 398 Reveals a Major Normal Fault along the Kolumbo Volcanic Chain 

Jonas Preine, Christian Hübscher, Abigail Metcalfe, Katharina Pank, Adam Woodhouse, Olga Koukousioura, Shun Chiyonobu, Timothy Druitt, Steffen Kutterolf, Paraskevi Nomikou, Thomas Ronge, Sarah Beehte, Miachel Manga, Iona McInsoth, Masako Tominga, Gareth Crutchley, and Jens Karstens and the IODP Expedition 398 Scientists

 

Many hazardous volcanic systems worldwide are located in extensional back-arc systems, where the crust is influenced by pervasive faulting. However, our knowledge about the spatial and temporal relationship between crustal faults and the emplacement of volcanic edifices is immature. Located on the South Aegean Volcanic Arc, the Christiana-Santorini-Kolumbo volcanic field formed in a continental rift zone and represents an ideal natural laboratory to study the structural interaction between volcanism and tectonism. From December 2022 to February 2023, IODP Expedition 398 drilled 12 sites across the volcanic rift system. We will present the results of core-seismic integration of several sites from the rift basins. Two of these drill sites lie on the hanging wall and footwall of the Kolumbo Fault, respectively. This fault strikes parallel to the Kolumbo Volcanic Chain and was previously considered a fault with little vertical offset. However, tephra and biostratigraphic markers identified in recovered cores from IODP Expedition 398 indicate a major vertical offset of >200 m (~260 ms TWT) along this fault. Seismic data reveal that this fault is a major NE-SW-directed normal fault and represents an important structural element of the rift system but subsequent rapid sedimentation of volcanoclastic material buried this fault. The volcanic edifices of the Kolumbo Volcanic Chain formed on the hanging wall of this fault at a distance of approx. 6 km from the surface trace. Adjacent, non-volcanic rift basins show pervasive internal fault zones at a similar distance from the respective basin-bounding faults, indicating that these faults may be the preferred pathway for magma to reach the surface. Our study implies a fundamental tectonic control of the emplacement of volcanoes at the Christiana-Santorini-Kolumbo volcanic field, a process that might be present at other back-arc systems.

How to cite: Preine, J., Hübscher, C., Metcalfe, A., Pank, K., Woodhouse, A., Koukousioura, O., Chiyonobu, S., Druitt, T., Kutterolf, S., Nomikou, P., Ronge, T., Beehte, S., Manga, M., McInsoth, I., Tominga, M., Crutchley, G., and Karstens, J. and the IODP Expedition 398 Scientists: IODP Expedition 398 Reveals a Major Normal Fault along the Kolumbo Volcanic Chain, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-11720, https://doi.org/10.5194/egusphere-egu24-11720, 2024.

EGU24-12110 | Posters on site | ITS5.2/SSP1.13

A legacy plan and an innovative access framework for the next decades of Italian geoscientists involved in scientific drilling: the role of ECORD/IODP-Italy in the ITINERIS project 

Annalisa Iadanza, Andrea Argnani, Chiara Boschi, Angelo Camerlenghi, Giulia Casalena, Elisabetta Erba, Fabio Florindo, Biagio Giaccio, Hanno Kinkel, Marco Sacchi, Andrea Schleifer, Riccardo Tribuzio, and Paola Vannucchi

In the framework of the Research Infrastructures (RIs), scientific drilling represents a globally ranging, distributed RI that generates a wide variety of subsurface data. The ongoing project “Italian Integrated Environmental Research Infrastructures System (ITINERIS)” aims at building the Italian Hub of RIs in the environmental scientific domain by coordinating a network of national nodes from 22 RIs, including the Italian participation in the European Consortium for Ocean Research Drilling (ECORD) and in the International Continental Scientific Drilling Project (ICDP). The main goal of ITINERIS is to promote cross-disciplinary research in environmental sciences through the use and re-use of existing (or pre-operational) data and services and new observations, and to address scientifically and societally relevant issues.

The impact of ITINERIS on the Italian geoscientists involved in scientific drilling is twofold. First, it will improve the access to both the ECORD and the ICDP infrastructures. This will result in increasing the national participation in terms of proposal writing, drilling expeditions/projects, initiatives to use legacy samples/data, and training activities. Secondly, it will allow to collect and systematize the great amount of data produced by Italian scientists in the past scientific drilling programs (DSDP-ODP-IODP). This will facilitate the data handling and interoperability approach. A thematic digital archive of ECORD/ICDP-related data will be provided within the following thematic areas: micropaleontology, petrology, elemental and isotope geochemistry, paleomagnetism, stratigraphy/lithology, structural geology, borehole geophysics and site survey. This structured and accessible scientific dataset will represent a milestone for further implementation following FAIR data principles and best practices for ongoing and future drilling projects. Further developments of this digital archive might also serve as an additional tool to be integrated within the SPARCs initiative of IODP3.

How to cite: Iadanza, A., Argnani, A., Boschi, C., Camerlenghi, A., Casalena, G., Erba, E., Florindo, F., Giaccio, B., Kinkel, H., Sacchi, M., Schleifer, A., Tribuzio, R., and Vannucchi, P.: A legacy plan and an innovative access framework for the next decades of Italian geoscientists involved in scientific drilling: the role of ECORD/IODP-Italy in the ITINERIS project, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12110, https://doi.org/10.5194/egusphere-egu24-12110, 2024.

EGU24-13796 | Orals | ITS5.2/SSP1.13

Using the LIMS with Lithology (LILY) Database to Probe IODP Density, Porosity, and P-Wave Velocity Data 

Gary Acton, Laurel Childress, and Vincent Percuoco

We use the LIMS With LithologY (LILY) database compiled by Childress et al. (https://zenodo.org/records/8408297) to examine relationships between physical properties and the lithology of marine drill cores collected by the International Ocean Discovery Program (IODP) and its precursor program between 2009 and 2019. Within LILY, lithologic information such as the principal lithologic name and the major and minor lithologic modifiers, along with other metadata, have been added to each of the more than 34 million observations from the standard data available in IODP’s LIMS Database, which is accessible through the LIMS Online Reports (LORE) portal (web.iodp.tamu.edu/LORE/). The ability to compare and combine descriptive lithologic information across expeditions and to integrate these descriptions with multisensor track and discrete sample measurements allows for a wealth of scientific investigation not possible under the original data structure. One of the obvious values of LILY is the ability to characterize the basic physical, chemical, and magnetic properties of different lithologies from a very large number of observations. As an example of this, we compute grain densities for all available lithologies using the Moisture and Density (MAD) density data from over 24,000 measurements. Once the grain densities are known, then the bulk densities can be used to determine porosity. This is important because besides the over 24,000 MAD bulk densities, there are 3.7 million gamma ray attenuation (GRA) bulk densities measured by the Whole Round Multi-Sensor Logger (WRMSL). Comparison of MAD and GRA bulk densities permits biases in the GRA density dataset to be corrected. These corrected GRA bulk densities are then used to compute a new high-resolution porosity dataset (https://zenodo.org/records/10001855). We further merge this large bulk density and porosity dataset with the P-wave velocity data from a P-wave Logger that is part of WRMSL, a P-wave Caliper, and P-wave Bayonets to characterize lithologic-dependent relationships between density, porosity, and P-wave velocity.

How to cite: Acton, G., Childress, L., and Percuoco, V.: Using the LIMS with Lithology (LILY) Database to Probe IODP Density, Porosity, and P-Wave Velocity Data, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-13796, https://doi.org/10.5194/egusphere-egu24-13796, 2024.

EGU24-14233 | Posters on site | ITS5.2/SSP1.13

From DSeis to PROTEA - Probing the heart of an earthquake, especially the interaction between metasedimentary rocks and mantle-derived intrusions.  

Hiroshi Ogasawara, Yasuo Yabe, Raymond Durrheim, Musa Manzi, Thomas Kieft, Devan Nisson, Julio Castillo, Alba Gómez-Arias, Bennie Liebenberg, and Team DSeis and PROTEA

The ICDP DSeis project accomplished full-core drilling and borehole-logging of the seismogenic zone of the 2014 M5.5 Orkney earthquake, South Africa. Three NQ-holes (total 1.6 km in length), drilled from 2.9 km depth at the Moab Khotsong gold mine, penetrated mostly intact hard rock, including 2.9 Ga meta-sedimentary and altered andesite (Crown) formations dipping ~20°SE. Subparallel altered gabbroic diorite sills intrude the formations.

After the borehole penetrated the Crown Formation and approached the steeply-dipping planar cluster of earthquake aftershocks, it encountered a potassic lamprophyre dyke several meters thick. The lamprophyre was intact close to the dyke contact, with mineral assemblages of augite, actinolite, and biotite. The talc and calcite content and magnetic susceptibility increased towards the centre of the dyke, while the augite and actinolite content decreased. The lamprophyre rock mass then became brecciated, with a substantial fraction of gouge. The Crown Formation adjacent to the dyke contact was also brecciated. Friction tests made on lamprophyre gouge (which contains about 20 wt% talc) yielded very low friction coefficients, similar to the results of previous wet friction experiments (Yabe et al. EGU 2024).

The DSeis drilling also intersected a non-potassic dyke rich in actinolite and chamosite about 300 m east of the potassic lamprophyre dyke. Whilst this dyke hosted no indications for aftershocks, the extracted brine was more hypersaline and older than any brine previously sampled from deep South African gold mines (Nisson et al., 2023). The hypersaline brine was non-meteoric in composition, with dissolved organic carbon concentrations sufficient to support deep life.

Both dykes show significant spatial variation in composition which we attributed to contamination/assimilation and metamorphism, depending on which formations (~20°SE dip) the dykes cut. We postulate that the localization of aftershocks in ‘streaks’ subparallel to the strata is a result of this compositional heterogeneity.

DSeis has successfully penetrated, sampled, and studied the aftershock sequence on the upper edge of the Orkney earthquake rupture. However, important questions regarding the nucleation and rupture of the earthquake that will only be solved by studying the strong motion source of the mainshock. The proposed PROTEA scientific drilling project aims to probe the Orkney earthquake's strong motion sources (the heart).

The existing DSeis hole, the new PROTEA hole, and the connecting horizontal tunnels at 2.9 km depth will allow us to deploy a 3D distributed acoustic sensing (DAS) network with a vertical span of several hundreds of meters, and a horizontal span of about 1 km. Using both active and passive seismic sources, we expect to image the 3D structure of the reflectors precisely.

Moab Khotsong has offered the team access to borehole cores that have sampled numerous dykes and sills; as well as access to the database of lithology and geological structure mapped on the mining horizons at 2-3 km depth. These data cover a much broader volume than the DSeis and PROTEA projects, and will significantly extend and enhance the interpretation.

Acknowledgements: South African gold mines, related firms, SATREPS, Kakenhi (21224012), ICDP, JSPS Core-to-Core Program, MEXT Kochi Core Center, NSF, DFG, NRF, Ritsumeikan and Kyoto Univs.

How to cite: Ogasawara, H., Yabe, Y., Durrheim, R., Manzi, M., Kieft, T., Nisson, D., Castillo, J., Gómez-Arias, A., Liebenberg, B., and DSeis and PROTEA, T.: From DSeis to PROTEA - Probing the heart of an earthquake, especially the interaction between metasedimentary rocks and mantle-derived intrusions. , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-14233, https://doi.org/10.5194/egusphere-egu24-14233, 2024.

EGU24-14690 | Posters on site | ITS5.2/SSP1.13 | Highlight

SVALCLIME – Targeting deep-time Arctic climate archives of Svalbard 

Denise K. Kulhanek, Valentin Zuchuat, Morgan Jones, Jiri Barta, William J. Foster, Wolfram H. Geissler, Sten-Andreas Grundvåg, Henning Lorenz, Sverre Planke, Kim Senger, Grace Shepherd, Kasia K. Sliwinska, Aleksandra Smyrak-Sikora, Lidya G. Tarhan, Madeleine Vickers, Maximilian Weber, Weimu Xu, and Daniel Kramer

The Svalbard archipelago, located in the Norwegian High Arctic, preserves more than 650 million years of near-continuous sedimentary rock records spanning from the Neoproterozoic to the Cenozoic. The polar paleogeographic location of Svalbard in the late Mesozoic and the Cenozoic makes sites in Svalbard unique amongst well-studied temporally equivalent successions from lower paleolatitudes, allowing investigation of the polar amplification climatic effect over geological time. The sedimentary record of Svalbard has been largely controlled by northward drift of constituent geological provinces throughout much of the Phanerozoic and evolving tectono-stratigraphic environments including the influence of several Large Igneous Provinces (LIPs) and global climate fluctuations. 

The SVALCLIME initiative aims to systematically drill and core the sedimentary successions in Svalbard. Two sub-projects currently being evaluated by the ICDP materialized from an international workshop held in Longyearbyen in October 2022. The first is a full ICDP proposal focused on hyperthermals from the Permian to Paleogene (SVALCLIME P2P) and an ICDP-IODP Land to Sea preproposal on hothouse to coldhouse transitions in the late Paleozoic and across the Eocene–Oligocene transition (SVALCLIME Hot2Cold).

The SVALCLIME P2P project aims to investigate the high-resolution Arctic paleoclimate record from 255 to 45 Ma onshore Svalbard that encompasses several Mesozoic and Cenozoic hyperthermal events and the near-field impacts of three LIPs (the Siberian Traps, the High Arctic LIP and the North Atlantic Igneous Province). Our focus will also be on the deep biosphere to uncover the relationship between mineral substrates and taxonomic and metabolic diversity of intraterrestrial microbiomes. We propose to core seven boreholes at three locations (Nordenskiöldfjellet, Botneheia and Kropotkinfjellet), with a cumulative total cored length of ~3.4 km. 

The SVALCLIME Hot2Cold project aims to address global transitions from hothouse to icehouse conditions during the late Paleozoic and the Eocene to Oligocene. In the preproposal we identify suitable drill sites both onshore and offshore to characterize these periods. The Forlandsundet Graben in western Spitsbergen offers an opportunity to decipher the evolution of the Fram Strait and its impact on global oceanographic circulation during the Eocene–Oligocene transition. The Upper Carboniferous to Early Permian syn and post-rift deposits of the Billefjorden Trough will be targeted to investigate >130 cyclothems originating from glacioeustatic sea level fluctuations.

In this contribution, we outline the background and motivation of the SVALCLIME initiative and present the scientific objectives and the proposed drill sites.

How to cite: Kulhanek, D. K., Zuchuat, V., Jones, M., Barta, J., Foster, W. J., Geissler, W. H., Grundvåg, S.-A., Lorenz, H., Planke, S., Senger, K., Shepherd, G., Sliwinska, K. K., Smyrak-Sikora, A., Tarhan, L. G., Vickers, M., Weber, M., Xu, W., and Kramer, D.: SVALCLIME – Targeting deep-time Arctic climate archives of Svalbard, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-14690, https://doi.org/10.5194/egusphere-egu24-14690, 2024.

EGU24-15118 | ECS | Orals | ITS5.2/SSP1.13

Integrated interpretation of downhole geophysical measurements of the Lower Continental Crust in the Ivrea-Verbano Zone (Western Alps, Italy) at the DIVE DT-1B borehole 

Junjian Li, Eva Caspari, Andrew Greenwood, Simona Pierdominici, Marco Venier, Mattia Pistone, Kim Lemke, György Hetényi, and Luca Ziberna

The Drilling the Ivrea-Verbano zonE (DIVE) project completed its first borehole DT-1B in December 2022, recovering a continuous drill core to 578.5 m depth. The objective of DT-1B is to explore the upper part of the Lower Continental Crust. The 100% core recovery provides an excellent opportunity to integrate downhole geophysical measurements with core observations in rarely drilled lithologies. The primary goal of this integrated study is to characterize the rock mass and constrain the factors that influence seismic velocity variations and the origin of reflectivity in lower crustal rocks. For this purpose, we have collected a comprehensive suite of downhole measurements, comprising of natural gamma ray, magnetic susceptibility, dual laterolog resistivity, single point resistance, mud parameter, full waveform sonic, acoustic and optical televiewer, and vertical seismic profiling data. Complementary bulk density measurements have been performed on 96 core sections with a multi-sensor core logger as well as laboratory ultrasonic velocity and bulk density measurements on 15 selected core samples at ambient conditions. To characterize the rock mass mechanically and structurally, a detailed analysis of the acoustic televiewer data was carried out, which identified several natural and drilling-induced fractures. Natural fractures have two predominant azimuthal orientations: NW to NE and SSE to SE. Their dips range from 10° to 85°, with a higher average dip in the upper section that decreases in the lower section of the borehole. Fractures correlate with an abundance of anomalies in the electrical logs and affect sonic velocities. Due to the impact of fractures on these logs, only natural gamma ray and magnetic susceptibility logs are used for lithological classification of the rock masses, into three distinct clusters by fuzzy c-means clustering. Two of the clusters, 1 and 3, are attributed mainly to felsic metasediments, while cluster 2 is attributed to metamafics identified in the cores. Cluster 1 is characterized by high magnetic susceptibility and natural radiation, while cluster 3 is characterized by low magnetic susceptibility and natural radiation, indicating two distinct groups of metasediments. Concerning the elastic properties, it is expected that the velocities of the metamafics are higher than those of the metasediments. However, a systematic correlation between velocities and lithologies (or clusters) is not observed. To investigate the factors contributing to seismic velocity variations, velocities from core measurements, sonic logging, and vertical seismic profiling are compared. The velocities are consistent across the three scales, with P-wave velocities ranging from 5 to 6 km/s and S-wave velocities around 3 km/s, however, the values are much lower than expected. One reason might be the presence of microcracks, as indicated by the P-wave velocity difference between saturated and dry core samples. Together with the observed impact of fractures on the sonic log data, this suggests that the velocities are governed by brittle deformation at various scales, which explains their low values and overprints the lithological response. Consequently, reflections are expected to be caused by large scale fractures, but lithological reflections may still be observed due to the density contrast between metasediments and metamafics.

How to cite: Li, J., Caspari, E., Greenwood, A., Pierdominici, S., Venier, M., Pistone, M., Lemke, K., Hetényi, G., and Ziberna, L.: Integrated interpretation of downhole geophysical measurements of the Lower Continental Crust in the Ivrea-Verbano Zone (Western Alps, Italy) at the DIVE DT-1B borehole, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15118, https://doi.org/10.5194/egusphere-egu24-15118, 2024.

EGU24-16278 | Orals | ITS5.2/SSP1.13

A million years of regional hydroclimate oscillations in West Africa reconstructed from Lake Bosumtwi 

Mathias Vinnepand, Christian Zeeden, Thomas Wonik, William Gosling, Anders Noren, Jochem Kück, Simona Pierdominici, Silke Voigt, Mehrdad Sadar-Abadi, Arne Ulfers, Sylvester Danour, Kweku Afrifa, and Stefanie Kaboth-Bahr

Situated within a 1.07 million-year-old meteorite crater, Lake Bosumtwi in Ghana stands as a pivotal location for comprehending fluctuations in the hydro-climatic situation in sub-Sahara West Africa. The region is highly sensitive to climate oscillations due to the movements of the tropical rain belt driven by atmospheric circulation leading to pronounced dry or wet conditions on seasonal to orbital scales. Considering that climatic changes may trigger severe socio-economic crises in this area due to negative impacts on the agricultural sector- especially the cacao farming, a better understanding on the responses of the regional hydro-climatic situation to global warming tendencies is crucial. Recently a robust age-depth model was developed for the lacustrine sequence of Lake Bosumtwi, the only continental record spanning the last million years in West Africa. This provides the unique opportunity to gain detailed insights into the hydroclimatic situation. Yet, the natural gamma radiation (NGR) signal that we interpret as a proxy for terrestrial sediment input throughout the 300 m thick record, triggered by fluvial in wash from the crater rims, shows quasi-cyclic patterns. Based on this along with evidence from additional proxies, we discuss these patterns at Lake Bosumtwi and their relation to orbital forcing including fluctuations in the hydroclimate.

How to cite: Vinnepand, M., Zeeden, C., Wonik, T., Gosling, W., Noren, A., Kück, J., Pierdominici, S., Voigt, S., Sadar-Abadi, M., Ulfers, A., Danour, S., Afrifa, K., and Kaboth-Bahr, S.: A million years of regional hydroclimate oscillations in West Africa reconstructed from Lake Bosumtwi, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-16278, https://doi.org/10.5194/egusphere-egu24-16278, 2024.

EGU24-16452 | Orals | ITS5.2/SSP1.13

ReC23-01 – Initial Results of the first KCC-J-DESC Repository Core Re-Discovery Program (ReCoRD) 

Gerald Auer, Junichiro Kuroda, Yusuke Kubo, Or Mordecai Bialik, Anna Joy Drury, Beth Christensen, Arisa Seki, Theresa Nohl, Jumpei Yoshioka, Xabier Puentes Jorge, Tamara Hechemer, Jing Lyu, An-Sheng Lee, Natsumi Okutsu, David De Vleeschouwer, Werner E Piller, and Minoru Ikehara

In 2023, the ReCoRD program was initiated by a joint venture of the Kochi Core Center (KCC), Kochi University and the Japan Drilling Earth Science Consortium (J-DESC) as a new workshop type, providing access to IODP cores archived at the KCC in Kochi, Japan. The first ReCoRD workshop, ReC23-01, ”Tracing Intermediate Water Current Changes and Sea Ice Expansion in the Indian Ocean”, was held between the 27th of August and the 5th of September 2023 at the KCC in Kochi. The goals of ReC23-01 were to gather new data to test the hypothesis that the expansion of sea ice around Antarctica impacted water circulation in the Indian Ocean through changes in intermediate water formation and the northward expansion of the Antarctic polar front through the Middle to Late Miocene following the Middle Miocene Climatic Transition (< 13.8 Ma).

During ReC23-01, we targeted a latitudinal transect from the high southern latitudes to the tropical Indian Ocean consisting of 1 DSDP and 2 ODP sites. DSDP Site 266 represents the high-latitude target site located just south of the present-day location of the polar front. Data gathered for Site 266 during ReC23-01 is a new tracer location for ice-rafted debris (IRD) accumulation and changes in the Southern Hemisphere frontal system for the Neogene in the Indian Ocean. ODP Site 752 on the Broken Ridge provides a unique record of mid-latitude intermediate water paths, including SAMW and AAIW originating from the high latitudes and the Tasman Leakage. ODP Site 707 represents a critical end member of the south equatorial current and related Indonesian Intermediate Waters in the tropical Indian Ocean.

The ReC23-01 workshop within the ReCoRD program allowed international research collaborators to fully benefit from the legacy of over 50 years of International Ocean Drilling Research from the Deep Sea Drilling Program (DSDP), Ocean Drilling Program (ODP), and International Ocean Discovery Program (IODP). Combining in-tandem sedimentological core descriptions with existing and new core data provides a unique opportunity to re-investigate and evaluate archived (legacy) core material. In particular, the availability of computer tomography (CT) core images provided critical information in assessing sedimentology and drilling disturbance in older DSDP and ODP core material to gather new data from over 50-year-old cores.

ReC23-01 illustrates how ReCoRD-style workshops can offer a new way to explore research questions that could not be easily addressed by single sea-going expeditions. These workshops provide additional and powerful research opportunities based on legacy core material beyond individual sample and data requests, with large-scale community benefits. For instance, ReC23-01 provided an excellent training opportunity for early career researchers in a shipboard-like setting.

How to cite: Auer, G., Kuroda, J., Kubo, Y., Bialik, O. M., Drury, A. J., Christensen, B., Seki, A., Nohl, T., Yoshioka, J., Puentes Jorge, X., Hechemer, T., Lyu, J., Lee, A.-S., Okutsu, N., De Vleeschouwer, D., Piller, W. E., and Ikehara, M.: ReC23-01 – Initial Results of the first KCC-J-DESC Repository Core Re-Discovery Program (ReCoRD), EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-16452, https://doi.org/10.5194/egusphere-egu24-16452, 2024.

EGU24-16500 | Orals | ITS5.2/SSP1.13

IODP Expeditions 384/395C/395: Reykjanes Mantle Convection and Climate. Preliminary results 

Anne Briais, Ross Parnell-Turner, and Leah LeVay and the Expedition 395 Science Party

International Ocean Discovery Program Expeditions 384, 395C and 395 investigated the interactions between variations in the Iceland hotspot activity, ocean crust formation at the Reykjanes Ridge, ocean circulation, and climate in the North Atlantic, and sediment drift deposition on the flanks of the mid-ocean ridge. Variations in crustal production along the Reykjanes Ridge produced V-shaped ridges and troughs located on the flanks of the mid-ocean ridge, and the role of the Iceland hotspot in their generation is debated. Changes in hotspot activity, and therefore in the associated dynamic topography, likely influenced the depth of the oceanic gateways formed by the Greenland-Scotland Ridge between the North Atlantic and the Norwegian and Arctic Seas. Such variations might thus have controlled the strength of cold, deep water currents, and the accumulation rate of sediment drifts on the flanks of the ridge: Björn and Gardar drifts on the eastern flank and Eirik drift to the west. Expeditions 384 in 2020, 395C in 2021, and 395 in 2023 collected cores from a transect of five drill sites along a plate-spreading flowline spanning seafloor ages from 2.8 to 32 Ma and crossing Björn and Gardar drifts on the eastern ridge flank, as well as a sixth site along the eastern Greenland margin crossing Eirik drift. Combined, over 400 m of oceanic basalt and over 5.8 km of sediment core was recovered, including continuous records through key Pleistocene and Pliocene sequences, and a unique record of progressive basalt alteration. Here we present preliminary results of the expedition, featuring new insights into crustal accretion variations through time, constraints on the onset of sedimentation at Björn, Gardar and Eirik contourite drifts, and new records of climatic cycles on thousand-year timescales. These sites also provide a unique view on how crust interacts with fluids and sediment over millions of years, while in-situ samples obtained from the cores yield insights into chemical exchanges and microbial systems in the ocean, sediment, and crust. The vast amount of sediments, basalts and measurements collected during Expeditions 384, 395C and 395 will provide a major advance in our understanding of mantle dynamics and the linked nature of Earth’s interior, oceans, and climate.

How to cite: Briais, A., Parnell-Turner, R., and LeVay, L. and the Expedition 395 Science Party: IODP Expeditions 384/395C/395: Reykjanes Mantle Convection and Climate. Preliminary results, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-16500, https://doi.org/10.5194/egusphere-egu24-16500, 2024.

EGU24-17124 | ECS | Posters on site | ITS5.2/SSP1.13

Seismic while drilling with a diamond drill bit in project DIVE DT-1B borehole in the Ivrea-Verbano Zone (Western Alps, Italy) 

Bernd Trabi, Andrew Greenwood, and Florian Bleibinhaus

A unique Seismic While Drilling (SWD) experiment, whereby a diamond coring drill rig as the seismic source has been conducted in the Val d’Ossola, Western Alps, Italy. For the SWD experiment 64 3C-sensors are employed in an array at the surface and the vibrational action of coring the rock acts as an active seismic source within the borehole. The maximum offset of the sensor array is 480 m with non-uniform spacing that increases with distance. The drilling operation took place from early October until mid-December 2022 and reached a depth of approximately 580 m. The seismic sensors recorded at a sampling rate of 1 ms, which is more than sufficient for an expected frequency of up to 200 Hz. The proposed SWD experiment is to evaluate the potential and limitations of the SWD method for diamond core drilling commonly utilized in scientific drilling projects with a focus on fundamental developments of the methodology and data processing techniques. Ideally the drill-bit seismic record should produce a seismic image around the bore hole and ahead of the drill bit. First it is important to determine if a signal can be detected, and to what depth, from a diamond core drill bit. In contrast to percussion or reverse circulation drilling, the diamond core drilling method produces a very weak signal. The seismic data is also heavily contaminated by coherent and random noises generated at the drill site, including rig engines, generators and mud-pumps, vehicles, and the movement of equipment. Separation of theses coherent noises using radon transform has thus far failed and other wavefield separation methods are investigated. Using seismic interferometric methods for unknown source positions, we aim to detect the weak signal at known drill bit positions. This is promising especially at drilling depths where the drill-rig and drill-bit wave-fields are spatial or temporal separated from each other, due to their different origins and velocities. Interferograms are obtained using the cross-coherence method, which is applied to the recorded passive seismic data. These are computed from 30sec time windows of the continuous recordings and then stacked into the final interferogram to increase the signal-to-noise ratio. Instead of migration summation, semblance is measured for the interferometric migration process. For the migration process, a constant velocity model is sufficient in this hard-rock environment. The major noise sources that we image are the vibrations of the drill rig and power generator, which appear to mask the weaker signal from the drill bit. In an ongoing second experiment, we utilize grid power, reducing the noise sources to the mud-pumps, rotating string, and rig.

How to cite: Trabi, B., Greenwood, A., and Bleibinhaus, F.: Seismic while drilling with a diamond drill bit in project DIVE DT-1B borehole in the Ivrea-Verbano Zone (Western Alps, Italy), EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17124, https://doi.org/10.5194/egusphere-egu24-17124, 2024.

Core-drilling and groundwater well operation in topographic recharge areas have been rarely applied for groundwater quality monitoring or ecosystem exploration, simply due to lacking of productive water bodies in up to >100 m thick aeration zones. The scarcity of observers and data, and neglection in modelling opposes our efforts to unravel the role of thick hillslope aeration zones for services like water provision, water purification and biogeochemical cycling (Lehmann & Totsche, 2020). To fundamentally understand how groundwater quality comes about as a function of inputs and how climate and land use change will affect this quality, we concentrate our investigations on the soil-aeration zone-phreatic zone continuum in topographic highs. From our Hainich Critical Zone Exploratory in central Germany, we present methods and workflows for low-impact scientific drilling and construction of monitoring wells optimized for representative sampling of the total mobile inventory (Lehmann et al. 2021), and results from the analysis of drill cores, borehole geophysical data and multi-year environmental monitoring data. We found that transient (fluid) flow patterns contribute to groundwater quality dynamics, whereby overall aeration zone-phreatic zone-interactions cause quality fluctuations even in deep and isolated habitats. Results from recent drilling campaigns (2023) comprise the detection of narrow oxic zones (fractures, flow paths) also within mudstone-dominated strata (anoxic aquifer-storeys) and weathering-induced hydrofacies differences that indicate further complexity of habitat structures and ecosystem functioning across recharge(-discharge) zones.

 

 

References:

Lehmann, R., Totsche, K. U. (2020). Multi-directional flow dynamics shape groundwater quality in sloping bedrock strata. Journal of Hydrology 580, 124291. https://doi.org/10.1016/j.jhydrol.2019.124291

 

Lehmann, K., Lehmann, R., Totsche, K. U. (2021) Event-driven dynamics of the total mobile inventory in undisturbed soil account for significant fluxes of particulate organic carbon. Sci. Total Environ. 756, 143774. https://doi.org/10.1016/j.scitotenv.2020.143774

How to cite: Lehmann, R., Aehnelt, M., Grelle, T., Lehne, C., and Totsche, K. U.: “Looking deep” into shallow ground: scientific drilling and continuous monitoring in recharge areas as a key to the understanding of groundwater quality dynamics and subsurface ecosystem functioning, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18764, https://doi.org/10.5194/egusphere-egu24-18764, 2024.

EGU24-19299 | ECS | Posters on site | ITS5.2/SSP1.13

TriGgeR mechanisms of Antarctic ice sheet INStability across the Plio-pLeistocene trAnsitIoN - GRAINSPLAIN project 

Giulia Matilde Ferrante, Laura De Santis, Sergio Andò, Robert McKay, Denise Kulhanek, Jenny Gales, Matteo Perrotti, Luca Zurli, Satish Singh, Michele Rebesco, Renata Giulia Lucchi, Tina Van Der Flierdt, Tim Van Peer, and Caterina Morigi

Growing evidence suggests that portions of the Antarctic Ice Sheet (AIS) could cross a tipping point over the next decades due to global warming. The Mid-Pliocene Warm Period (mPWP, 3.3-3 Ma, +2°C) is regarded as one possible geologic analog to the climate of the near future, and paleo-sea level during mPWP interglacials indicates that portions of the AIS were lost at that time. However, due to a lack of ice-proximal data, the timing, magnitude and trigger mechanisms of AIS retreats remain unconstrained. Here, we focus on the Ross Sea, where the IODP Exp. 374 Site U1523 recovered the first Antarctic Plio-Pleistocene record from a current-controlled sediment drift in an environment evolving from ice-proximal to open marine over time. U1523 is located where intrusions of warm deep water and outflows of cold water occur today, controlled mainly by the strength and route of the Antarctic Slope Current. To constrain the relative influence of oceanic currents and AIS dynamics on sediment erosion, transport and deposition across the Plio-Pleistocene transition (3.3-2.6 Ma), we integrate grain size, morpho-mineralogical, magnetic fabric analysis and geophysical logs from site U1523 with the multi-channel seismic line IT94-127A. We complement our dataset with a closeby box core (PNRA ODYSSEA exp., box core 08), that can be regarded as a present day analogue. Here, we present our morpho-mineralogical results on the box core and some specific intervals of the mPWP from site U1523. In particular, we perform single mineral Raman spectroscopy which, together with the entire suite of minerals and their relative abundance, highlight the different depositional environments and the source of the detritus, identifying local vs distant and magmatic vs metamorphic sources. Furthermore, we use the geophysical logs to perform rock physics correlation and we tie them to the seismic line, allowing the analysis to be extrapolated along the shelf.

How to cite: Ferrante, G. M., De Santis, L., Andò, S., McKay, R., Kulhanek, D., Gales, J., Perrotti, M., Zurli, L., Singh, S., Rebesco, M., Lucchi, R. G., Van Der Flierdt, T., Van Peer, T., and Morigi, C.: TriGgeR mechanisms of Antarctic ice sheet INStability across the Plio-pLeistocene trAnsitIoN - GRAINSPLAIN project, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-19299, https://doi.org/10.5194/egusphere-egu24-19299, 2024.

EGU24-22323 | Posters on site | ITS5.2/SSP1.13 | Highlight

Sensivity of the West Antarctic Ice Sheet to 2° Celsius of Warming. The SWAIS2C project. 

Arne Ulfers, Tina van de Flierdt, Richard Levy, Gavin Dunbar, Huw Horgan, Denise Kulhanek, and Molly Patterson and the SWAIS2C Science Team

The West Antarctic Ice Sheet (WAIS) is currently experiencing accelerated mass loss and contains enough ice to raise global sea levels by up to five meters if it were to melt completely. The objective of the international and interdisciplinary SWAIS2C project (Sensivity of the West Antarctic Ice Sheet to 2 Degrees Celsius of Warming) is to understand past and present factors influencing WAIS dynamics and to reconstruct WAIS response to warmer temperatures, including those exceeding the +2°C target outlined in the Paris Climate Agreement. The project will drill two deep boreholes beneath the Ross Ice Shelf to obtain sediment sequences from a site close to the grounding line of the Kamb Ice Stream site (KIS-3) and the Crary Ice Rise (CIR). The geological data will be used to improve model-based projections of future sea level contributions from Antarctica and to answer  the overarching question under what climatic conditions the WAIS collapsed in the past.

Here we present an overview of the SWAIS2C project, its’a aims and current progress. In the first season 2023/24, hot water drilling was successfully completed at KIS-3 to penetrate the ~580 m thick Ross Ice Shelf. Oceanographic measurements were taken in the ~55 m ocean cavity beneath the ice shelf, together with videos of the seafloor and ice shelf, and installation of permanent moorings. Gravity and hammer coring yielded 7.6 m of sediment, which have been subsampled for microbiology and geochemistry, and described using field-based x-ray images. The sediments recovered include the longest sediment core from the Siple Cost, measuring 1.92 m.

The sedimentological and drilling experience gained will be of great value for the 2024/25 season, when a team of drillers and scientists will return to KIS-3 for deep drilling with the Antarctic Intermediate Depth Drill (AIDD). A combination of hydraulic piston coring and rotary coring will be used to retrieve a sediment core of up to 200 m below sea floor. Drilling operations will be complemented by geophysical downhole logging with wireline tools from the Leibniz Institute for Applied Geophysics (LIAG) and a logging while tripping system provided by the German Research Centre for Geosciences (GFZ). The inclusion of different methods will allow downhole logging of several parameters over the entire sediment sequence and minimizes the influence of unstable borehole walls on the measurements.

How to cite: Ulfers, A., van de Flierdt, T., Levy, R., Dunbar, G., Horgan, H., Kulhanek, D., and Patterson, M. and the SWAIS2C Science Team: Sensivity of the West Antarctic Ice Sheet to 2° Celsius of Warming. The SWAIS2C project., EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-22323, https://doi.org/10.5194/egusphere-egu24-22323, 2024.

EGU24-493 | ECS | Orals | ITS5.12/CL0.1.11

Nitrogen removal and carbon mineralization under coastal salinity intrusion 

Ziyan Wang and Benoit Thibodeau

Rapid population growth and intensification of human activities have led to a massive increase in the release of nitrogen (N) to the environment, often ending up in aquatic ecosystems. Coastal wetlands, a transition ecosystem in the freshwater-to-marine continuum, play a vital role in reducing nitrogen through natural processes, including denitrification and anaerobic ammonium oxidation (anammox). Considering denitrification's risk of producing nitrous oxide—a potent greenhouse gas—and anammox's efficient co-removal of ammonium and nitrite, it's crucial to identify what controls the balance between these two key processes. However, the identity of the drivers controlling the relative abundance of these two N-removal processes and their respective interactions with carbon (C) and sulfur cycles are not well-documented, especially in coastal wetlands.

This study investigated salinity's role in N reduction with carbon remineralization in coastal wetlands facing salinity intrusion. Using air-dried mangrove sediments mixed with anoxic artificial seawater of contrasting salinities (0, 10, 20, and 30 ppt) over a 28-day period, we monitored N and C transformation by the concentration of NH4+, NO2-, NO3-, dissolved inorganic carbon (DIC) and total alkalinity in the supernatant, and microbial community adaptation in sediment by molecular analysis. We applied the revised 15N-paring isotope technique in slurry incubation to quantify the potential of N loss pathways.

Preliminary results indicate that significant N removal starts after a week of internal cycling between organic and inorganic N, with the maximum removal potential at 30 ppt salinity. Depletion of NO3- in the last week of incubation makes anammox stand out by utilizing NH4+ and NO2-. The rate of DIC release decreased with increasing salinity, displaying an inverse pattern to that of N species. This decoupling points to the co-existence of autotrophic anammox, heterotrophic denitrification, and sulfate reduction processes. The stoichiometric ratio of total alkalinity to DIC suggests a shift of the predominant carbon decomposition process as salinity increased, from denitrification to sulfate reduction. This shift could enhance the total nitrogen removal potential while slowing carbon remineralization, indicating a positive feedback loop for both nitrogen removal and blue carbon storage in response to salinity intrusion. We will further focus on 15N2 samples and microbial evidence to elucidate the interplay among nitrogen removal processes.

How to cite: Wang, Z. and Thibodeau, B.: Nitrogen removal and carbon mineralization under coastal salinity intrusion, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-493, https://doi.org/10.5194/egusphere-egu24-493, 2024.

EGU24-516 | ECS | Posters on site | ITS5.12/CL0.1.11

The use of nature-based solutions (NbS) for coastal restoration actions and biodiversity protection: the A-AAgora project for Ireland. 

Melanie Biausque, Darragh O'Suilleabháin, Lee Wah-Pay, and Emma Verling

Nature-based solutions (NbS) at the coast are, by definition, methods developed to work with nature to sustainably protect, restore and/or manage the shore. They can be classified into 4 main categories such as fully natural solutions, managed natural solutions, hybrid solutions and ‘green’ engineering solutions. As part of the EU Mission: ‘Restore our ocean and waters by 2030’, the Horizon Europe-funded Atlantic-Arctic Agora (A-AAgora) project identifies innovative solutions, including NbS, to co-develop coastal restoration actions in association with nature and people, throughout 3 demonstration areas. In this context, Demo Ireland locally adapted the ‘living lab’ approach via community-led actions undertaken at Harper’s Island, Co. Cork. Managed and hybrid NbS, for instance livestock grazing, control of invasive species (Spartina), development of pollinator areas, etc…, were successfully tested, supporting coastal wetland restoration and significantly enhancing local biodiversity. NbS deployed by communities at Harper’s Island, with the support of Cork County Council, were then described and reported, allowing their replication to the whole island of Ireland, and overseas. Moreover, additional sites facing coastal erosion and tidal flooding issues were selected and monitored along the Co. Cork coastline. Preliminary results allowed us to identify the main coastal challenges for each site in association with local geomorphological patterns and hydrodynamics, in a context of climate change. The next step for the A-AAgora project in Ireland is to identify suitable NbS as sustainable solutions and long-term management actions, to tackle coastal challenges in those areas. Moreover, this ongoing work is carried-out with the collaboration of multiple stakeholders, such as scientists, decision makers and communities. While these methods have been developed at local scales in the south of Ireland, they can be reproduced and upscaled in other areas, further raising global awareness about coastal adaptation and coastal sustainable solutions/managements.

How to cite: Biausque, M., O'Suilleabháin, D., Wah-Pay, L., and Verling, E.: The use of nature-based solutions (NbS) for coastal restoration actions and biodiversity protection: the A-AAgora project for Ireland., EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-516, https://doi.org/10.5194/egusphere-egu24-516, 2024.

EGU24-1790 | Posters on site | ITS5.12/CL0.1.11

Surface sediment permeability and reactivity in a shallow coastal environment 

Stefan Forster, Hanna Schade, and Werna Werna

Coastal sediments are frequently permeable due to their relatively large grains size. Knowledge on exchange processes across the sediment-water interface and metabolism in these sediments is limited however. We characterize permeable sediments at about 5 m water depth in a coastal stretch of ~3.5 km² at the southern Baltic coast off Germany. Permeability ranged from 1.4 .10-12 m² to 11.3 .10-11 m² (organic content: 0.1% - 0.2% dry mass). We determined total oxygen uptake, TOU, of 10 – 28 mmol O2 m² d-1 from in situ measurements in the dark. Benthic net primary production determined in situ varied between 1 - 14 mmol O2 m² d-1.
We observed an increase in volumetric oxygen uptake rates in flow-through experiments when highly reactive glucose was supplied as substrate, pointing to the pivotal role of reactive organic substrate availability. However, we could detect only marginally enhanced TOU (uptake doubled at one out of three locations) when applying stirring rates inducing pore water flow in benthic chambers under natural conditions. We conclude that stimulating effects of permeability associated with pore water flow are not detectable in benthic exchange rates below a threshold of 7 .10-11 m² under field conditions. This threshold is higher than previously reported.
Ex situ experiments demonstrated that the distribution of oxygen in the sediment was affected by photosynthetic activity of microphytobenthos and by pore water flow. Benthic primary production determined by the dark-light shift method exceeded the summed fluxes of oxygen into the water and into the sediment driven by concertation gradients, and increased with light intensity as well as with organic substrate availability. These findings indicate that calculated net ecosystem metabolism can shift from autotrophy to heterotrophy owed to an increased consumption within the sediment during advection. We argue that under advective conditions the export flux of photosynthetically produced oxygen may differ from the flux under diffusive conditions. This may seriously impair photosynthesis rate determinations obtained from incubation experiments.

How to cite: Forster, S., Schade, H., and Werna, W.: Surface sediment permeability and reactivity in a shallow coastal environment, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1790, https://doi.org/10.5194/egusphere-egu24-1790, 2024.

Construction of coastal infrastructure, e.g. seaward port facilities, frequently calls for sediment removal (dredging). Deposition of the dredging spoil at designated offshore sites (dumping grounds) disturbs the dumping ground sedimentary system, including the biota. Assessment of environmental effects of dumping requires monitoring of the system’s responses to the disturbance severity and persistence. In 2011-2017, we followed changes in sediment characteristics and descriptors of benthic (meio- and macrofaunal) assemblages (abundance, biomass, composition) in a shallow southern Baltic coastal area serving as a dumping site for dredging waste from a new harbour under construction at the coast. At the initial phase of the disturbance, the benthos responded rapidly (abundance and biomass reduction, altered composition), and equally rapidly recovered when dumping was temporarily suspended. After the dumping operations were resumed, the responses intensified, although apparent colonizers (benthic copepods in the meiobenthos and juvenile molluscs in the macrobenthos) tended to appear intermittently in the disturbed areas. The benthos remained impoverished in the altered habitat after dumping was terminated, reflecting the severity of habitat change.

How to cite: Radziejewska, T., Wawrzyniak-Wydrowska, B., and Bieniek, B.: A human driver of change in the southern Baltic coastal sedimentary system: monitoring effects of dredging spoil dumping on benthic communities , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-3213, https://doi.org/10.5194/egusphere-egu24-3213, 2024.

EGU24-3820 | Posters on site | ITS5.12/CL0.1.11

Collaborative Citizen Science to Support Coastal Management 

Joseph Earl, Suzana Ilic, Alexandra Gormally-Sutton, and Michael R. James

Coastal communities in North West England face numerous anthropogenic challenges, including high vulnerability to the impacts of climate change, namely enhanced coastal erosion and flooding from sea level rise (Sayers et al., 2022), and marine litter. To manage heightening climate impacts, Flood and Coastal Management has transitioned from a defence to risk-based management, including a focus on building coastal system resilience through Nature-based Solutions (NbS) rather than physical defences. Building the resilience of people, including coastal communities, is critical to this transition, whereby their voices are heard and they can better prepare for these risks (EA, 2020). However, despite the strategic intent to engage and involve people, public participation in practice has been restricted by numerous challenges, perpetuating a continued lack of public involvement in decision making or resilience building.

This interdisciplinary project investigates whether such a deficit in public engagement in decision making can be overcome through a case study citizen science project called Coast Watchers at Rossall on the North West coast, which aims to collaboratively engage people in monitoring and responding to coastal challenges. The research embarked on several study phases to iteratively design, test and evolve the citizen science project collaboratively, involving various coastal monitoring activities and social science investigations. Results suggest that it is important to account for people’s local coastal values, motivations and concerns (Earl et al., 2022) when designing a collaborative approach to public engagement.

Crucially, the work explores the extent to which coastal communities can be engaged beyond citizen science monitoring and become active participants in a resilient and collaborative coastal management. The talk will present outcomes from a series of interviews with coastal practitioners and community members in the North West, exploring the challenges and opportunities for communities to be more involved in a collaborative coastal management. Findings will be discussed within a wider context, whereby they are contributing towards a Flood and Coastal Resilience Innovation Project, Our Future Coast (EA, 2022), which seeks to engage people in adaptation planning and co-designing NbS to better protect coastal communities around the North West coast from current and future challenges.

 

References

Earl, J., Gormally-Sutton, A., Ilic, S. and James, M.R. (2022). ‘Best day since the bad germs came’: Exploring changing experiences in and the value of coastal blue space during the COVID-19 pandemic, a Fylde Coast case study. Coastal Studies & Society, 1(1), pp.97-119.

Environment Agency (2020) National Flood and Coastal Erosion Risk Management Strategy for England. https://www.gov.uk/government/publications/national-flood-and-coastal-erosion-risk-management-strategy-for-england--2. [14/9/23]

Environment Agency (2022) Flood and Coastal Resilience Innovation Programme. https://engageenvironmentagency.uk.engagementhq.com/innovation-programme. [7/2/23]

Sayers, P., Moss, C., Carr, S. and Payo Garcia, A. (2022) Responding to climate change around England's coast: the scale of the transformational challenge. Ocean & Coastal Management, 225.

How to cite: Earl, J., Ilic, S., Gormally-Sutton, A., and James, M. R.: Collaborative Citizen Science to Support Coastal Management, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-3820, https://doi.org/10.5194/egusphere-egu24-3820, 2024.

EGU24-4081 | Orals | ITS5.12/CL0.1.11

Variable effects of ecosystem restoration in a eutrophic coastal lagoon: reoxygenation by increasing water exchange 

Niels A.G.M. van Helmond, Olga M. Zygadlowska, Robin Klomp, Wytze K. Lenstra, Mike S.M. Jetten, and Caroline P. Slomp

Increased anthropogenic activities are affecting water quality, e.g. leading to eutrophication and deoxygenation, culminating in biodiversity loss in coastal ecosystems globally. In the Southwest Delta in the Netherlands, large scale engineering to protect coastal areas against storm surges has turned several tidal inlets and estuaries into coastal lagoons and (marine) lakes. The water quality in these ecosystems has strongly deteriorated as a result of stagnation of bottom waters in combination with eutrophication. One such ecosystem, Lake Veere, showed signs of recovery after restoration of water exchange with the adjacent tidal marine Eastern Scheldt in 2004. In recent years, regular water monitoring has revealed the return of low-oxygen conditions, however, along with other signs of worsening water quality such as fish kills and jellyfish blooms. Here, we assess the role of the sediments in the (re)occurrence of low-oxygen conditions in Lake Veere. During two sampling campaigns in 2022, water column and sediment samples were collected. Geochemical analysis, including direct in-situ flux measurements with a benthic lander, revealed an increasing sedimentary oxygen demand (SOD) from the western (sea-side) part of the lake to the east, from ~10 to >100 mmol O2 m-2 d-1. This gradient in SOD opposes the observed trend in water column deoxygenation, with low-oxygen conditions predominantly prevailing in the central and western part of the lake and not in the east. This indicates that, despite restoration efforts, large parts of the lake are still highly sensitive to deoxygenation. Sediment analyses show the near-absence of iron-oxides, hence little capacity to buffer toxic hydrogen sulfide, which indeed accumulated in pore waters, reaching concentrations of up to 10 mmol L-1. In the central part of the lake, hydrogen sulfide even accumulated in the bottom waters, pointing towards its potential involvement in the observed fish kills in the region. Our results illustrate the difficulty of improving water quality through changes in water exchange alone because of strong legacy effects of eutrophication and deoxygenation in the sediment.  

How to cite: van Helmond, N. A. G. M., Zygadlowska, O. M., Klomp, R., Lenstra, W. K., Jetten, M. S. M., and Slomp, C. P.: Variable effects of ecosystem restoration in a eutrophic coastal lagoon: reoxygenation by increasing water exchange, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-4081, https://doi.org/10.5194/egusphere-egu24-4081, 2024.

EGU24-7418 | ECS | Posters on site | ITS5.12/CL0.1.11

Iron mediated organic matter cycling in permeable surface sediments 

Zhe Zhou and Shouye Yang

Coastal permeable sediments cover 50-60% of the continental shelves and are important filters and bioreactors that sitting between the land and ocean. In permeable surface sediments, the dynamic porewater advection can lead to frequent redox oscillation, which significantly affects the coupled cycling of organic matter (OM) and iron. In our study, we focused on the most redox active iron fraction (extractable by 0.5 M HCl), and investigated their effects on OM degradation and retention. During the transition of redox conditions, Fe(III) oxyhydroxides were quantitatively found as the dominant electron acceptors for anaerobic OM remineralization. However, the release of reduced Fe was significantly delayed, with most Fe(II) (~96%) remaining in the solid phase either through adsorption or formation of authigenic Fe(II)-bearing minerals. Under frequent redox oscillation as typically observed in natural coastal permeable sediments, Fe(II) in the solid phase can be re-oxidized and repetitively used as electron acceptor for anaerobic OM remineralization (Iron “redox battery”). In addition, based on our field study along near- to offshore transect in the North Sea, we found that the most redox active iron trapped abundant of dissolved OM (54±20 times than DOM in porewater) that enriched in aromatic and oxygen-rich compounds. It indicates that iron may preferentially promote the retention of terrigenous and aromatic DOM in permeable sediments, thus serving as an important temporal storage for terrigenous OM in the coastal ocean. Further investigations of the dynamic Fe-OM interactions in coastal sediments are warranted to better understand carbon cycling in the coastal area. 

How to cite: Zhou, Z. and Yang, S.: Iron mediated organic matter cycling in permeable surface sediments, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-7418, https://doi.org/10.5194/egusphere-egu24-7418, 2024.

EGU24-8832 | Posters on site | ITS5.12/CL0.1.11

Dynamics of carbon pools and fluxes in the Don River Delta, Southern (European) Russia, and the estuary under the conditions of increasing marine factors 

Sergey Venevsky, Sergey Berdnikov, Victoria Gerasjuk, Vera Sorokina, Aleksey Kleshchenkov, Igor Sheverdyaev, Valerii Kulygin, and Natalia Lichtanskaia

The Don River Delta, bordering the Taganrog Bay in the Sea of Azov, is one of the major deltas of Europe, providing important ecological and economic services. The Sea of Azov is an enclosed sea, which is also the shallowest sea on the globe (the mean depth is 7 meters) with rich biological productivity.

It was indicated recently that both the Sea of Azov (Berdnikov et al, 2023) and the Don Delta (Venevsky et al, 2023), as well as the estuary area have undergone significant environmental transformations in the last four decades. The water temperature and salinity in the sea and the estuary increased to the never observed values mostly due to climate change (Berdnikov et al, 2023) and the prevailing wind directions changed to the westerlies bringing strong upward surges to the delta. Meanwhile, the Don River runoff significantly dropped started from 2007, while fluvial sediments delivery to the Don Delta were steadily diminishing already during 70 years due to the constructions of dams, human land use and runoff regulation (Venevsky  et al, 2023). Significant amount of suspended sediments from the Taganrog Bay enters the delta and salty waters intrusions to the delta are frequent during surges driven by the westerlies. Thus, the role of marine factors in the delta and estuary area of the Don increased in the last few decades in comparison with fluvial factors. Carbon sequestration in coastal areas considered to be the so-named natural solution for climate change mitigation. Thus, it is important to estimate the past, present and future carbon balance of the Don Delta and the estuary, especially accounting that the delta undergoes changes from being fluvial dominated to marine (wave and surge) dominated one.

We are currently involved in the study focusing on the quantification of carbon pools and fluxes in the Don Delta and the estuary. The study combines modelling approach with field observations and remote sensing data. Our field data included seasonal observations for 2006-2020 of total suspended solids, salinity, concentration of dissolved and suspended organic matter, and chlorophyll-a concentration in the river-delta-estuary continuum (the Lower Don River -Delta-Taganrog Bay). Remote sensing included Landsat and Sentinel images for upward surges episodes for the same period.  We use three combined models:  a hydrological model of the Don estuary area (DonDeltaHECRAS) for simulation of the river flow and water levels during surges; model of suspended matter dynamics (DonDeltaBalanceModel), which allows us to calculate the suspended matter dynamics in the Don estuary area; and model of vegetation and soil dynamics (DonDeltaEcoModel), which is aimed at estimating the carbon accumulation in vegetation and soil in the delta. We found out that with the recent frequency of surges on average 20% of organic chemicals transported with the river runoff is deposited in the delta. Thus, marine factors affect accretion of soil within the delta and change both the carbon pools and fluxes in the delta and the estuary.

How to cite: Venevsky, S., Berdnikov, S., Gerasjuk, V., Sorokina, V., Kleshchenkov, A., Sheverdyaev, I., Kulygin, V., and Lichtanskaia, N.: Dynamics of carbon pools and fluxes in the Don River Delta, Southern (European) Russia, and the estuary under the conditions of increasing marine factors, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-8832, https://doi.org/10.5194/egusphere-egu24-8832, 2024.

EGU24-9443 | ECS | Posters on site | ITS5.12/CL0.1.11

Salinity influence on plant traits and photosynthesis in selected peatland macrophytes 

Amabelle Go, Hendrik Schubert, and Gerald Jurasinski

Coastal peatlands, despite their ecological importance are at risk from a range of disturbances that render this habitat vulnerable, affecting their productivity and could potentially trigger ecosystem shift. Salinity is one of the factors affecting the structural and functional aspects of macrophytes in peatland environments. This study aims to assess the impacts of different salinity levels on the growth, biomass, and photosynthetic performance of peatland plants using a mesocosm approach. Four treatments of varying salinity were implemented: Saline (C+) with salinity of 20 ppt, Freshwater (C-) with salinity of 0 ppt, 22 and 55 pulses where the plants were exposed alternately to water with salinities of 20 ppt and 0 ppt every 2 and 5 days, respectively.  Two macrophyte species, Phragmites australis and Typha latifolia, were planted in mesocosm tanks. Over a 16-week period, various parameters including leaf length, leaf area, plant height, growth, biomass, and photosynthetic responses were monitored to evaluate the extent of salinity-induced stress. Results indicate that P. australis exhibited no significant difference in growth rates and biomass across treatments. Growth monitoring showed peak observed at the 8th week post-transplanting. Leaf area and leaf production also showed no significant variations. While shoot production increased initially, peaked at the 8th week, and declined thereafter. T. latifolia on the other hand, displayed growth rate variations favoring the freshwater (C-) and less frequent water change (55) treatments. The 55 pulses exhibited the highest absolute growth rate, but growth regressed after 8th week in treatments exposed to salinity changes. Leaf production in saline (C+) and higher frequency of water changes (22) showed a steep decline from 10th week onward. Saline treatment resulted in the lowest leaf production, leaf area, and biomass. This study contributes insights on the varying responses of macrophytes to salinity stress, demonstrating acclimation kinetics, and identifying salinity limits. 

How to cite: Go, A., Schubert, H., and Jurasinski, G.: Salinity influence on plant traits and photosynthesis in selected peatland macrophytes, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-9443, https://doi.org/10.5194/egusphere-egu24-9443, 2024.

EGU24-10491 | ECS | Posters on site | ITS5.12/CL0.1.11

Stratification in non-tidal shallow coastal lagoons during extreme summer heatwaves  

Lloyd Reese, Ulf Gräwe, Xaver Lange, and Hans Burchard

Due to their shallow depth, coastal lagoons are often considered to be vertically well-mixed. However, past studies have shown that, depending on forcing conditions, vertical density stratification may in fact occur even in lagoons of only a few meters depth. Further, many coastal lagoons are faced with a multitude of anthropogenically caused pressures, including eutrophication as well as a likely increasing occurrence of summer heatwaves and other extreme atmospheric conditions due to climate change. While eutrophication leads to increased biological productivity and a subsequently increased oxygen demand, high water temperatures during heatwaves lead to reduced oxygen solubility, thus aggravating the risk of anoxic conditions within the water body. As vertical stratification acts to suppress vertical mixing, it may facilitate the occurrence of bottom oxygen depletion in such waters. Since coastal lagoons are of great ecological and economical interest due to their multiple ecosystem services, e.g., as spawning grounds for fish, it is of utmost importance to assess the conditions under which vertical stratification may occur. Only with such knowledge it will be possible to estimate the future development of coastal lagoon ecosystems. While many past studies have covered stratification of freshwater lakes during heatwaves, there is a significant gap of research covering coastal lagoons under the same conditions, where an additional forcing is added via the connection to the open sea. In particular, non-tidal, non-choked lagoons are currently understudied with respect to summer heatwaves. In our study, we therefore aim to assess the conditions under which stratification may occur in such lagoons during a mid-latitude summer heatwave. To this end, we have applied a non-dimensional parameter space analysis to a numerical, one-dimensional water column simulation of such a lagoon. Here, we present first results from this analysis.

How to cite: Reese, L., Gräwe, U., Lange, X., and Burchard, H.: Stratification in non-tidal shallow coastal lagoons during extreme summer heatwaves , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-10491, https://doi.org/10.5194/egusphere-egu24-10491, 2024.

EGU24-11191 | ECS | Posters on site | ITS5.12/CL0.1.11

Seagrass meadows provide essential coastal protection against future marine storms 

Julia Jaca Estepa and Gabriel Jordà Sánchez

Climate change is already modifying the marine environment, and these alterations will presumably increase in the coming decades. Some of the most significant changes expected during this period include ocean warming, rise of sea level, and modifications to circulation and wind wave patterns. For instance, in the Mediterranean, ocean surface temperatures are projected to increase by 1-4 ºC by the end of the century, triggering a chain of impacts on marine ecosystems, such as species migration, significant mortality in some species, and an increase in harmful algal blooms.
Furthermore, sea levels are expected to rise, reaching values ranging from 30 cm to over 1 m by the end of the century. The consequences include the increased permanent flooding of low-lying areas, the salinization of coastal water reservoirs, and damage caused by marine storms.

In this context, despite ongoing efforts to reduce greenhouse gas emissions, it is crucial to develop realistic and effective plans for adapting to climate change. Nature-based solutions (NBS) present a particularly interesting approach to addressing climate change impacts. One NBS option suitable for reducing the impacts of climate change in coastal areas is to increase seagrass meadows through restoration interventions. The interaction of seagrasses with water flow leads to a reduction in flow energy, thereby limiting the impact of waves reaching the coast.
However, ocean warming poses a threat to seagrass meadows, as some species are particularly vulnerable to marine heatwaves. Therefore, the primary goal of the SEAFRONT project is to quantify the potential benefits of seagrass meadows in protecting the coast from future marine storms under different scenarios of global warming and seagrass evolution. SEAFRONT focuses on Spanish coastal areas, which exhibit a variety of hydrodynamical situations and seagrass coverages.
Specifically, SEAFRONT aims to 1) assess the impacts of marine storms over the last decades, evaluating the role of seagrasses; and 2) generate future scenarios of physical and economic impacts.

In this presentation, we share the results of numerical simulations focused on measuring the total water level at the shore under various scenarios. These simulations account for sea level changes, wave patterns, coastal shapes, and seagrass coverage. Additionally, we discuss the economic impacts of marine storms based on information from insurance companies.
Our initial analyses suggest that restoring seagrass meadows is a highly effective way to adapt to marine storms, countering the effects of rising sea levels. However, in areas where seagrasses already exist, losing them could lead to severe consequences, increasing the impact of marine storms.

How to cite: Jaca Estepa, J. and Jordà Sánchez, G.: Seagrass meadows provide essential coastal protection against future marine storms, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-11191, https://doi.org/10.5194/egusphere-egu24-11191, 2024.

EGU24-11676 | Posters on site | ITS5.12/CL0.1.11

Estuaries under pressure – surveying the extreme shallow water environments  

Aarno T. Kotilainen, Mia M. Kotilainen, Sami Jokinen, Meri Sahiluoto, Joonas J. Virtasalo, and Anu M. Kaskela

River estuaries are diverse coastal ecosystems that have significant ecological, social, cultural and economic value. Estuaries worldwide are stressed by increasingly intensive human activities, also in the Baltic Sea, a European inland sea. Human pressures include e.g., dredging, port constructions, river water acidification and pollutants. In the latest assessment of threatened habitat types in Finland, coastal estuaries were assessed as an Endangered (EN) habitat complex due to historical abiotic and biotic quality changes.

As estuaries are often very shallow environments with turbid water column, it is not easy to acquire detailed seabed information from those areas. In the ongoing SeaMoreEco project we use remote sensing methods such as shipborne acoustic surveys, floating drones, flying drones and satellites, as well as seabed sampling and underwater video observations to map and monitor shallow water areas of the Gulf of Bothnia (GoB), northern Baltic Sea. We provide information e.g., on seabed geology and underwater vegetation. Here, we focus on seabed sediment data produced in the SeaMoreEco and in some other projects.

Anthropogenic radionuclides and heavy metal pollution are typical pressures widely affecting river estuaries and other marine ecosystems. For example, the fallout from the April 1986 Chernobyl nuclear power plant accident has rendered the Baltic Sea as the most polluted marine body in the world with respect to 137Caesium (137Cs). In the present study we determined the levels of 137Cs activity and heavy metal content in the bottom sediments, and their spatial and vertical distribution in the subsurface sediments of the GoB.

Activity contents of 137Cs and heavy metal contents in seabed surface sediments of the GoB have generally declined over the last decades. In some estuaries however, 137Cs values in subsurface sediments remain at elevated levels relative to values measured from other areas of the Baltic Sea. In some areas, also the contents of heavy metals (e.g., cadmium, lead, zinc) in the subsurface sediments are quite high. This is typical for areas close to e.g., the metal industry and the areas affected by the loading from acid sulfate soils.

Data on harmful substances (e.g., radionuclides) in seabed sediments is important for coastal management and marine spatial planning while assessing risks associated with dredging and other operations. Dredging in areas where bottom sediments contain a lot of harmful substances can cause the re-mobilization and transport of these contaminants. Increasing anthropogenic pressures in coastal and marine areas will likely increase risk associated with polluted bottom sediments. Climate change might also shift many of the parameters (precipitation,  river discharge) that affect sediment distribution and pollution in the coastal and marine areas, also in the GoB.

This study is part of the Interreg Aurora funded SeaMoreEco project, the EMODnet Geology project funded by The European Climate, Environment, and Infrastructure Executive Agency (CINEA) through contract EASME/EMFF/2020/3.1.11/Lot 2/SI2.853812 - EMODnet Geology, the EMODnet Ingestion 3 project funded by the CINEA through contract CINEA/EMFAF/2021/3.4.10/02/SI2.868290, and the MAAMERI project funded by the Ministry of Environment, Finland. The study utilized research infrastructure facilities provided by FINMARI (Finnish Marine Research Infrastructure network).

How to cite: Kotilainen, A. T., Kotilainen, M. M., Jokinen, S., Sahiluoto, M., Virtasalo, J. J., and Kaskela, A. M.: Estuaries under pressure – surveying the extreme shallow water environments , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-11676, https://doi.org/10.5194/egusphere-egu24-11676, 2024.

Coastal waters worldwide are increasingly affected by oxygen loss due to human-induced eutrophication and global warming. This coastal deoxygenation has dramatically altered biogeochemical processes with major consequences for marine life. Prominent examples of large anthropogenic coastal “dead zones” include the Gulf of Mexico, Baltic Sea and Chesapeake Bay but numerous small coastal systems are also strongly affected. Many efforts are currently underway to restore the water quality of these coastal waters, but these are not always effective. In this presentation, I will discuss how the interplay of biogeochemical processes and hydrodynamics may affect present-day restoration efforts in coastal systems. Using examples from a range of field and modelling studies performed by my group, I will specifically discuss legacy effects resulting from accumulation of organic-rich sediments, the potential for reoxygenation of coastal waters through increased water column mixing and/or lateral water exchange and the expected short-term and long-term effects of nutrient load reductions. Taken together, our results highlight that there is no one-size-fits-all approach to rapidly improve water quality in coastal waters suffering from eutrophication and deoxygenation.

 

How to cite: Slomp, C. P.: Eutrophication and deoxygenation of coastal waters: how to improve water quality? , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12502, https://doi.org/10.5194/egusphere-egu24-12502, 2024.

EGU24-12654 | ECS | Orals | ITS5.12/CL0.1.11

Brackish water rewetting of a temperate coastal peatland: Effects on biogeochemistry, microorganisms and greenhouse gas emissions 

Cordula Gutekunst, Susanne Liebner, Anna-Kathrina Jenner, Erwin Don Racasa, Klaus-Holger Knorr, Sara E. Anthony, Daniel Lars Pönisch, Michael Ernst Böttcher, Manon Janssen, Jens Kallmeyer, Franziska Koebsch, Gregor Rehder, and Gerald Jurasinski

Around 4 % of global greenhouse gas (GHG) emissions originate from drained peatlands. Unlike rewetting drained peatlands with freshwater, brackish water rewetting of coastal peatlands might not only reduce CO2 emissions, but also keep methane (CH4) emissions low. The re-establishment of the natural brackish water regime of coastal peatlands with high sulfate levels should favor sulfate reducing bacteria as well as sulfate-driven anaerobic methane oxidizers and therefore limit CH4 production and/or lead to increased CH4 consumption. Here, we compared CO2 and CH4 fluxes, pore water geochemistry, and associated microbial communities of a coastal fen along a moisture gradient before, and a water level gradient after rewetting.

Brackish water rewetting increased the abundances of CH4 producing archaea (methanogens) as well as the abundances of sulfate reducing bacteria (SRB) in most of the study site, except at former ditch areas, where methanogenic and SRB abundances had been high before. At the same time, the aerobic methanotroph community was less present, indicating lower aerobic CH4 oxidation potentials after rewetting. Pore water CH4 and CO2 concentrations along with δ13C records suggest that both, methanogenesis and CH4 oxidation, increased after rewetting. Brackish water rewetting raised average CH4 emissions from 2 to 25 mg CH4 m-2 d-1 at locations that were previously drained, which is lower than CH4 emissions reported from most freshwater peatlands. Net CO2 emissions remained high after rewetting with values around 4 g CO2 m-2 d-1. However, since ecosystem respiration strongly decreased from on average 19 to 6 g CO2 m-2 d-1, the remaining net CO2 emissions were mostly associated with low CO2 uptake due to extensive die-back of the vegetation. Hence, brackish water rewetting can keep CH4 emissions relatively low, but, as in freshwater peatlands, hydrological management must allow for the re-establishment of site-specific vegetation to sustain net CO2 uptake.

How to cite: Gutekunst, C., Liebner, S., Jenner, A.-K., Racasa, E. D., Knorr, K.-H., Anthony, S. E., Pönisch, D. L., Böttcher, M. E., Janssen, M., Kallmeyer, J., Koebsch, F., Rehder, G., and Jurasinski, G.: Brackish water rewetting of a temperate coastal peatland: Effects on biogeochemistry, microorganisms and greenhouse gas emissions, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12654, https://doi.org/10.5194/egusphere-egu24-12654, 2024.

EGU24-12994 | ECS | Posters on site | ITS5.12/CL0.1.11

Oxygen dynamics in the Baltic Sea under reduced nutrient input 

Lev Naumov, H.E. Markus Meier, and Thomas Neumann

The Baltic Sea is a semi-enclosed sea located in the Northern Europe. Due to the limited exchange with the Global Ocean, which leads to the long residence time (approx. 30 years), and permanent halocline, the Baltic Sea is naturally prone to hypoxic conditions, especially in the deep basins. However, the hypoxic area in the deep Baltic Sea has been rapidly increasing since the second half of the 20th century following the elevated nutrient input caused by human activity. To mitigate the eutrophication of the Baltic Sea, countries surrounding it started to reduce their nutrient loads following the Baltic Sea Action Plan. Despite the substantial nutrient input reduction, no significant decrease in the hypoxic area has yet been observed. In addition, climate change might promote deoxygenation of the Baltic Sea, further hampering nutrient load reduction efforts. The non-linear response to changes in nutrient input raises the question of when to expect the robust reduction of the hypoxic area, whether it is possible for the Baltic Sea to return to its natural state with a limited hypoxic area, and how the composition of the oxygen budget will change following the reduction of hypoxia.

To answer those questions, we conducted two sensitivity simulations utilizing a 3-dimensional coupled physical-biogeochemical model. The simulations followed the two nutrient reduction pathways – Baltic Sea Action Plan Maximum Allowable Input (BSAP) and the more radical half of the BSAP MAI (0.5 BSAP). Both simulations spanned 71 years and were compared to the reference scenario (Ref.) employing observed nutrient loads from 1948 to 2018. The lowering of the hypoxic area was observed in both scenarios. Most rapid re-oxidation was observed in the remote northern and western Gotland Basins, especially in the 0.5 BSAP scenario. The redistribution of the biggest oxygen consumption from the water column to the sediments followed it. Changes in nutrient loads explain more than 60% of the oxygen sources and sinks variability, making it the dominant driver of changes in the oxygen budget of the Baltic Sea, at least in the near future. The Baltic Sea could return to its initial state (1948) within the simulation period, but only following the radical 0.5 BSAP scenario.

How to cite: Naumov, L., Meier, H. E. M., and Neumann, T.: Oxygen dynamics in the Baltic Sea under reduced nutrient input, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12994, https://doi.org/10.5194/egusphere-egu24-12994, 2024.

Nearshore strategic placement—in addition to direct placement—has been proposed as a nature-based solution to reuse dredged sediment in support of mitigating the effects of sea level rise in the San Francisco Bay Area. The success of nearshore strategic placement relies on hydrodynamic forces moving sediment from the placement site to mudflats and marshes over time. Sediment transport and pathway models can be used to evaluate and prioritize potential placement sites, placement methods, transport rates (informing amount and frequency of sediment placement), sediment fate, and longevity. Models can also be used to predict the evolution of sites after initial placement and as sea level and sediment supply conditions evolve. This model-based information is needed to design wetland restoration and maintenance operations, inform the permitting approval process, and evaluate the costs and benefits of using strategic placement techniques to restore and maintain Bayland habitats in San Francisco Bay. This talk will focus on the estuarine process modeling as well as in-situ observation efforts that are being undertaken to assess sediment fate, sediment transport rates and sediment transport dynamics associated with nearshore strategic placement.

How to cite: Savant, G.: Modeling the San Francisco Bay Estuary to Inform Nature-Based Sediment and Baylands Management , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-13084, https://doi.org/10.5194/egusphere-egu24-13084, 2024.

EGU24-15787 | Posters on site | ITS5.12/CL0.1.11

Assessing the spatial and temporal trophic properties of the Marano and Grado Lagoon, Italy with the coupled physical-biogeochemical model SHYFEM-BFM  

Isabella Scroccaro, Celia Laurent, Leslie Aveytua, Cosimo Solidoro, and Donata Canu

Coastal and transitional areas worldwide are affected by a range of human pressures and are subjected to high natural variability. In the Marano and Grado lagoon, located in the densely anthropized north-eastern coastal area of Italy, the conservation of biodiversity and the presence of important socio-economic activities require planning and management tools and measures. Coupled physical and biogeochemical models are useful tools to support trophic studies in complex systems such as the Marano and Grado lagoon by integrating field information with relevant hydrodynamic and biogeochemical processes shaping the system. The coupled SHYFEM–BFM model was applied to the Marano-Grado lagoon, adding new features to account for the contribution of macrophytes (such as seagrasses). Results were validated against available in situ observations, and trophic properties were investigated using trophic state indices that allow to reproduce spatial and temporal variability under different scenarios.

How to cite: Scroccaro, I., Laurent, C., Aveytua, L., Solidoro, C., and Canu, D.: Assessing the spatial and temporal trophic properties of the Marano and Grado Lagoon, Italy with the coupled physical-biogeochemical model SHYFEM-BFM , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15787, https://doi.org/10.5194/egusphere-egu24-15787, 2024.

EGU24-15795 | ECS | Orals | ITS5.12/CL0.1.11

Step-by-Step Strategies to Tackle Coastal Erosion: Insights from Calabaia Beach (Calabria, Italy) 

Guglielmo Federico Antonio Brunetti, Manuela Carini, Maria Antonietta Scarcella, Francisco Xavier Pilier, and Mario Maiolo

Coastal areas globally are invaluable assets and strategic resources from both environmental and social perspectives, as well as for the sustainable development of the marine economy, often referred to as “Blue Growth”. This understanding highlights the crucial need to protect coastal areas from climate change phenomena such as sea-level rise, flooding, and erosion. Previous research has shown the high vulnerability of the Mediterranean Sea's coasts to these phenomena, with ecosystems and biodiversity increasingly under threat. Despite past efforts to address these issues, many aspects still require further investigation, and solutions necessitate a holistic approach and a step-by-step strategy. Our research contributes to this context by providing valuable insights from Calabaia Beach (Calabria, Italy), where specific step-by-step strategies were implemented to mitigate erosion processes and restore the coastal and marine environment. The research site, located within the Marine Experimental Station of Capo Tirone (Belvedere Marittimo, Calabria, Italy), is of significant relevance as it has experienced various sea-defense interventions over the years, ranging from hard defenses to soft defenses, to the adoption of nature-based solutions. This study highlights that investigating the efficacy of these interventions over time can offer essential insights into the potential of each to sustainably curb erosion processes. From this standpoint, practitioners can establish a solid foundation to predict how future interventions for tackling erosion could effectively impact the entire coastal ecosystem of the area. Moreover, our research suggests that a step-by-step approach could be implemented also for aspects related to local hydrodynamics, pollutant dispersion, seawater intrusion, and marine biology. The case study of Calabaia Beach clearly illustrates that a time-dependent strategy could be successfully applied when there is a need to balance coastal environmental protection with social interests and the development of “Blue Growth”. This approach could be further explored in other case studies, keeping in mind that the specific characteristics of the area represent a determining factor.

Acknowledgements. This research was supported by ”NAUTILOS” project (GA 101000825) and by the Next Generation EU - Italian NRRP, Mission 4, Component 2, Investment 1.5, call for the creation and strengthening of ’Innovation Ecosystems’, building ’Territorial R&D Leaders’ (D. D. 2021/3277) - project Tech4You, n. ECS0000009. This work reflects only the authors’ views and opinions, neither the Ministry for University and Research nor the European Commission can be considered responsible for them.

How to cite: Brunetti, G. F. A., Carini, M., Scarcella, M. A., Xavier Pilier, F., and Maiolo, M.: Step-by-Step Strategies to Tackle Coastal Erosion: Insights from Calabaia Beach (Calabria, Italy), EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15795, https://doi.org/10.5194/egusphere-egu24-15795, 2024.

EGU24-15930 | Posters on site | ITS5.12/CL0.1.11

Pore-Size-Class Dependent Carbon Turnover in Peat Soils 

Bernd Lennartz, Rosa Cambinda, Haojie Liu, and Fereidoun Rezanezhad

Carbon loss from peatlands involves both gaseous emissions and a significant contribution from the water-bound fraction, specifically dissolved organic carbon (DOC), during mineralization and degradation processes. Our hypothesis proposes that DOC production is dependent on pore size, with elevated concentrations occurring in finer pores. To test this hypothesis, we extracted pore water at well-defined pressure heads (-60 and -600 hPa), representing macro- and mid-size pore domains, in degraded peat samples. Topsoil and subsoil samples exhibited soil organic matter contents of 34wt% and 57wt%, respectively. Remarkably, the more degraded topsoil consistently displayed significantly higher average DOC concentrations than the subsoil, with 1.5 times greater levels at -60 hPa and 2.4 times higher at -600 hPa. This trend suggests that more degraded peat soils are prone to releasing higher amounts of DOC. Furthermore, in topsoil samples, DOC concentrations were consistently higher at the -600 hPa pressure head compared to -60 hPa. To enhance our understanding, we computed hydraulic conductivities at -60 and -600 hPa using Van Genuchten parameter values, subsequently estimating the DOC load under unit gradient conditions. This calculation is particularly relevant for real-field situations, especially in partially saturated (degraded) peat soils. The hydraulic conductivity at -600 hPa was nearly a hundred times lower than at -60 hPa, leading to the conclusion that macro-pores serve as the primary pathways for DOC release in peat soils, irrespective of higher DOC concentrations in the fine pore domain.

How to cite: Lennartz, B., Cambinda, R., Liu, H., and Rezanezhad, F.: Pore-Size-Class Dependent Carbon Turnover in Peat Soils, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15930, https://doi.org/10.5194/egusphere-egu24-15930, 2024.

EGU24-16829 | Orals | ITS5.12/CL0.1.11

Climate change and islands’ ecosystem services: a global meta-analysis  

George Zittis, Shiri Zemah-Shamir, Mirela Tase, Savvas Zotos, Nazli Demirel, Christos Zoumides, Tamer Albayrak, Cigdem Kaptan Ayhan, Irene Christoforidi, Turgay Dindaroglu, Mauro Fois, Paraskevi Manolaki, Attila Sandor, Ina Sieber, Stamatiadou Valentini, Elli Tzirkalli, Ioannis Vogiatzakis, Ziv Zemah-Shamir, and Aristides Moustakas

Islands are hotspots of biological and cultural diversity, which, compared to mainlands, are more vulnerable to environmental degradation, climate change, uncontrolled land use changes and financial or societal crises. Particularly when combined, these factors can increasingly impact the environmental and socioeconomic services in many of such isolated ecosystems and communities. Atmospheric warming, ocean acidification or other abrupt climate changes can directly impact the biodiversity of islands and surrounding water bodies, the associated Ecosystem Services and, in turn, the well-being of islanders. Although existing techniques can adequately predict climate-induced ecological changes over the continents or in the larger islands, this is not the case for smaller islands, where refined climate information is typically not available. The primary objective of the present review is to better understand the linkages between Ecosystem Services and climate change on islands from the global to regional and local scales. This is not limited to the direct positive or negative impacts of changes in environmental and climate conditions but also includes the potential of ecosystem services to provide nature-based solutions for climate change mitigation and adaptation. Non-climatic drivers, e.g., land use changes, that may augment or alleviate the effects of climate change on islands’ Ecosystem Services are also explored.

How to cite: Zittis, G., Zemah-Shamir, S., Tase, M., Zotos, S., Demirel, N., Zoumides, C., Albayrak, T., Kaptan Ayhan, C., Christoforidi, I., Dindaroglu, T., Fois, M., Manolaki, P., Sandor, A., Sieber, I., Valentini, S., Tzirkalli, E., Vogiatzakis, I., Zemah-Shamir, Z., and Moustakas, A.: Climate change and islands’ ecosystem services: a global meta-analysis , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-16829, https://doi.org/10.5194/egusphere-egu24-16829, 2024.

EGU24-18394 | ECS | Posters on site | ITS5.12/CL0.1.11

Groundwater quality in two coastal fens and the influence of storm surge flooding and rewetting with seawater 

Erwin Don Racasa, Haojie Liu, Miriam Toro, and Manon Janssen

Coastal peatlands are unique ecosystems situated at the interface of land and sea. Past human activities, specifically drainage, have turned these carbon sink coastal regions into carbon sources. To mitigate climate change, recent management strategies focus on rewetting drained coastal peatlands. In this study, we aimed at characterizing surface and groundwaters in two coastal fens and examine the impacts of seawater input events caused by a storm surge (freshwater-rewetted) and rewetting with seawater (seawater-rewetted). Prior to the events, our findings reveal variable marine influence on surface and groundwater in the past which depends on distance from the coast, peat thickness, and possibly, drainage networks. After the storm surge, increases in specific conductivity (SC), chloride, and sulfate concentrations in surface waters persisted for up to a year. Increases in surface water dissolved inorganic carbon (DIC) and dissolved organic carbon (DOC) concentrations were also observed. In peat groundwater, a sustained increase in DOC concentrations that reached 526 mg DOC L-1 was observed at a shallower depth (max: -0.59 masl) while a delayed increase was observed at a deeper depth (max: -1.41 masl). High dissolved carbon concentrations were still observed in peat groundwater until the end of the observation period, three years after the storm surge. For the seawater-rewetted fen, significant changes in surface water properties were observed, which included SC, chloride, pH, DOC, DIC. The initial DOC concentrations in peat groundwater decreased, but later, showed the same high concentrations similar to the storm surge flooded fen. No apparent impacts to deeper sandy aquifers from both sites were observed. Overall, storm surge flooding impact on surface water properties lasted for a limited time while rewetting with seawater significantly and drastically changed the surface waters as the peatland was transformed into a lagoon-like environment. Peat groundwater properties in both sites did not change significantly, however, depth-dependent variable increases in DOC concentrations could be expected. The increases in DOC concentrations in peat groundwater were accompanied by increased SC and decreased pH conditions. Lastly, the ongoing salinization of seawater-rewetted fens may lead to brackish-rewetted environments with higher concentrations of seawater salts and potentially create new biogeochemical reactive mixing zones of ground- and seawater.

How to cite: Racasa, E. D., Liu, H., Toro, M., and Janssen, M.: Groundwater quality in two coastal fens and the influence of storm surge flooding and rewetting with seawater, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18394, https://doi.org/10.5194/egusphere-egu24-18394, 2024.

EGU24-18426 | ECS | Posters virtual | ITS5.12/CL0.1.11

The influence of microtopography on soil carbon accumulation and nutrient release from a rewetted coastal peatland 

Miaorun Wang, Haojie Liu, Fereidoun Rezanezhad, Dominik Zak, and Bernd Lennartz

Coastal peatlands have been frequently blocked from the sea and artificially drained for agriculture. With an increasing awareness of ecosystem functions, several of these coastal peatlands have been rewetted through dike removal, allowing seawater flooding. In this study, we investigated a recently rewetted peatland on the Baltic Sea coast to characterize the prevailing soils/sediments with respect to organic matter accumulation and the potential release of nutrients upon seawater flooding. Eighty disturbed soil samples were collected from two depths at different elevations (–0.90 to 0.97 m compared to sea level) and analyzed for soil organic matter (SOM) content and carbon:nitrogen (C:N) ratio. Additionally, nine undisturbed soil cores were collected from three distinct elevation groups and used in leaching experiments with alternating freshwater and Baltic Sea water. The results demonstrated a moderate to strong spatial dependence of surface elevation, SOM content, and C:N ratio. SOM content and C:N ratio were strongly negatively correlated with elevation, indicating that organic matter mineralization was restricted in low-lying areas. The results also showed that the soils at low elevations release more dissolved organic carbon (DOC) and ammonium (NH4+) than soils at high elevations. For soils at low elevations, higher DOC concentrations were observed when flushing with freshwater, whereas higher NH4+ concentrations were found when flushing with brackish water. Recorded NH4+ concentrations in organic-rich peat reached 14.82 ± 9.25 mg L–1, exceeding Baltic seawater (e.g., 0.03 mg L–1) by two orders of magnitude. A potential sea level rise may increase the export of NH4+ from low-lying and rewetted peat soils to the sea, impacting adjacent marine ecosystems. Overall, in coastal peatlands, geochemical processes (e.g., C and N cycling and release) are closely linked to microtopography and related patterns of organic matter content of the soil and sediments.

(The original article has been published in Geoderma, Volume 438, 116637; DOI: 10.1016/j.geoderma.2023.116637)

How to cite: Wang, M., Liu, H., Rezanezhad, F., Zak, D., and Lennartz, B.: The influence of microtopography on soil carbon accumulation and nutrient release from a rewetted coastal peatland, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18426, https://doi.org/10.5194/egusphere-egu24-18426, 2024.

EGU24-18677 | ECS | Orals | ITS5.12/CL0.1.11

Geochemistry of tropical coastal lagoon sediments from Sungai Kilim, Langkawi, Malaysia: Implications for provenance and weathering 

Nur Sakinah Abdul Razak, Yang Shouye, Hasrizal Shaari, Vasquez Ana Cristina, Guo Junjie, and Wu Xuechao

The interaction between land and sea in the coastal zone is dynamic and highly sensitive. It not only records past transgression history, coastal environmental evolution, and sea level changes, but also provides information on climate fluctuations, ocean and river changes, ecological environmental evolution, and human-induced environmental impacts. Coastal zone deposition plays a crucial role in preserving records of paleoenvironment changes and is therefore a key component larger ‘source to sink’ systems at continental margin. Therefore, it has attracted great academic interest in the field of geoscience in recent years. In this study, we measured trace elements and rare earth elements (REEs) in 20 surface sediment samples and a core (LKC 2) collected from the coastal lagoon of Sungai Kilim, Langkawi, Malaysia, to determine the possible sources and to reveal the variations in response to climate change and human activities. The distribution of trace elements (e.g., Li, Ti, Cr, Co, Ni, Cu, Zn, and Mn) was enriched in surface sediments, indicating those elements are affected by human activities. Besides, the concentrations of trace element in LKC 2, combined with AMS dating further confirmed the anthropogenic provenance in the uppermost core layers as a result of deforestation and urbanization in recent decades. However, the low Rb/Sr ratios in surface sediments and LKC 2 corresponds to higher intensity chemical weathering, resulting in higher concentrations of dissolved Sr in the sediments. The enrichment of REEs in surface sediments and LKC 2 indicates typical minerals present in the study area. Overall, the elemental flux patterns observed in this study are responses to complex interactions between intensified human activities and natural climate variability.

How to cite: Abdul Razak, N. S., Shouye, Y., Shaari, H., Ana Cristina, V., Junjie, G., and Xuechao, W.: Geochemistry of tropical coastal lagoon sediments from Sungai Kilim, Langkawi, Malaysia: Implications for provenance and weathering, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18677, https://doi.org/10.5194/egusphere-egu24-18677, 2024.

EGU24-18697 | Posters on site | ITS5.12/CL0.1.11

Nitrogen fixation in the shallow waters off a coastal wetland with outcropping peat 

Angelina Klett, Iris Liskow, and Maren Voss

Biological nitrogen (N) fixation is the microbial transformation of atmospheric N2 to ammonia, which is carried out by various groups of microorganisms and in all environments. The organisms, called diazotrophs, do not rely on bioavailable combined N such as nitrate or ammonium, which are often limiting ecosystem productivity. On the other hand, their activity provides nutrients to the otherwise N-limited ocean. In the central Baltic Sea high nitrogen fixation occurs each summer in surface waters introducing up to 792 000 t N per year, but was also identified in the deep and anoxic waters. In coastal waters the heterotrophic and autotrophic N2 fixation is not well studied and even less is known about the annual cycle and its regulation by the environment. Since coastal environments are considered to act as a filter for nutrients and organic matter, knowledge on an additional N source through N2 fixation is of great importance.

Here, we present N2 fixation rates for bulk water and sediment slurries (upper 5 cm), incubated for 24 hours in the dark and during a daily light cycle. We selected three stations near a peatland with outcropping peat layers and sandy sediments. Monthly sampling over the course of one year was done together with in-situ measurements of temperature, salinity, pH, nutrient concentrations and dissolved organic substances. Incubations were spiked with 15N2 gas and incubated in the lab. The fixation rates ranged from our detection limit up to 285 nmol N L-1 d-1 in water and 2 nmol N gdw-1 d-1 in sediments with a mean fixation rate of 11.2 nmol N L-1 d-1 and 0.1 nmol N gdw-1 d-1 for water and sediment, respectively. We could not find significant difference between stations and overall, the rates were much lower than in the surface waters of the central Baltic Sea. Though the rates in the water observed in June 2022 agree well with the rates of a cyanobacterial bloom in late summer (4.3 – 7.8 µmol N m-3 h-1). The rates for the water as for the sediment showed significant positive correlation (Spearman, sig. level 0.05) with variables affected by the seasonal change as temperature, daylength, pH and oxygen saturation. during winter and spring, the rates in the water were low to non-detectable and highest in summer. Also, in the sediment the lowest rates were found during winter and highest rates in spring. In general, the light cycle treatment showed higher rates than the dark incubation, with the exception of spring where the dark incubated sediments had higher rates than the ones in a daily light cycle. The outcropping peat layer seemed to induce some variability in N2 fixation rates, reflecting the heterogeneity of substrate which was sometimes covered with sand layers of different thickness.

Even though the rates in this study are comparably low for both water and sediment, a seasonal pattern became visible. Sediments and shallow waters clearly deserve more attention to better understand the process and the potential role as food and nitrogen source.

How to cite: Klett, A., Liskow, I., and Voss, M.: Nitrogen fixation in the shallow waters off a coastal wetland with outcropping peat, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18697, https://doi.org/10.5194/egusphere-egu24-18697, 2024.

EGU24-19197 | ECS | Orals | ITS5.12/CL0.1.11

The importance of Seasonality for Seagrass as Coastal Protection 

Veronika Mohr, Wenyan Zhang, Corinna Schrum, and Tobias Dolch

Seagrass is regarded with great expectations when it comes to nature-based coastal protection measures. Seagrass meadows dampen waves, reduce currents, and stabilize sediments in the coastal environment. However, most modeling studies estimating the magnitude of the coastal protection effect by seagrass assume a constant seagrass cover throughout the year. In temperate climates such as Northern and Central Europe the seagrass cover has considerable annual and interannual variations. The seagrass cover is highest in late summer and autumn and lowest in winter and early spring. At the same time, the physical forcing of waves and currents is at its maximum in winter, indicating a discrepancy between the seasons with the highest benefits of seagrass to coastal protection and the seasons with the most threat to the stability of the coast. In this study, we use a 3D baroclinic circulation model (SCHISM) coupled with a sediment model and a model of seagrass growth dynamics for estimating the significance of seasonality for coastal protection. A case study of a tidal basin in the northern Wadden Sea indicates that disregarding the seasonality can lead to substantial overestimations of the effectivity of seagrass for coastal protection.

How to cite: Mohr, V., Zhang, W., Schrum, C., and Dolch, T.: The importance of Seasonality for Seagrass as Coastal Protection, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-19197, https://doi.org/10.5194/egusphere-egu24-19197, 2024.

EGU24-19604 | Posters on site | ITS5.12/CL0.1.11

AI classification of marine birds and mammals based on aerial imagery of the German North and Baltic Seas 

Christian Sommer, Mathias Seuret, Nora Gourmelon, Vincent Christlein, and Matthias Braun

Following the current expansion of offshore constructions for the production of renewable energy as well as shipping traffic, assessments of impacts on marine ecosystems are becoming increasingly important. Thus, accurate knowledge of the spatial and temporal distribution of animal species is mandatory regarding the preservation of biodiversity and management of offshore wind farms and further economic activities. High-resolution optical imagery of airborne remote sensing sensors enables the observation of marine birds and mammals within large ocean areas. However, the identification of features at the ocean surface as well as the separation of animals and further objects, such as wave structures, ships or buoys, requires time-consuming visual inspection of the acquired image sequences by trained personnel. Here, we apply an AI-based approach to automatically detect and classify various features above the sea surface based on aerial imagery of the German North Sea and Baltic Sea. A large number of optical images at a spatial resolution of 2 cm have been acquired by the German Federal Agency for Nature Conservation (BfN) during repeated monitoring flights since 2018. These images are preprocessed and geolocated by assigning respective auxillary informations to create an extensive database on marine animal observations. The AI method which we are developing has to be responsible both for detecting birds in images, and for tracking instances of a same element present on multiple frames in order to avoid counting an individual multiple times. Some of the main challenges which will have to be dealt with are the following. First, luminosity conditions cannot be controled and might be suboptimal in a large fraction of the images, rendering animals completely white or black, or difficult to distinguish from the background. Second, smaller animals might consist only of little pixel blobs, and thus be difficult to distinguish. Third, flying birds might have shadows, which, while bird-shaped, must not be classified as birds. Fourth, in bird flocks overlapping tricks the AI into detecting one bird instead of several ones, which renders tracking significantly more challenging. We aim at tackling the third and fourth issues by incorporating cinematic estimation of the plane‘s and animal‘s movements, and estimating the direction of the sun in each frame, into the tracking system. In the future, our system will be used by the German Federal Agency for Nature Conservation (BfN) to monitor bird and mammal populations, and evaluate the effectiveness of preservation measures. 

How to cite: Sommer, C., Seuret, M., Gourmelon, N., Christlein, V., and Braun, M.: AI classification of marine birds and mammals based on aerial imagery of the German North and Baltic Seas, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-19604, https://doi.org/10.5194/egusphere-egu24-19604, 2024.

EGU24-425 | ECS | Posters on site | ITS5.14/GD7.3

Dynamic recrystallization of olivine during simple shear: evolution of microstructure and crystallographic preferred orientation from full-field numerical simulations 

Yuanchao Yu, Maria-Gema Llorens, Albert Griera, Enrique Gomez-Rivas, Paul D. Bons, Daniel Garcia-Castellanos, Baoqin Hao, and Ricardo A. Lebensohn

The deformation of the upper mantle is predominantly governed by the mechanical behavior of olivine (Karato et al., 1989). During mantle flow, olivine undergoes crystal-plastic deformation, leading to the development of crystallographic preferred orientations (CPOs). In this process, the a-axes of olivine polycrystalline aggregates align with the flow direction (Hansen et al., 2012). Consequently, the observed CPOs in olivine-rich rocks serves as an indicator of the mantle flow direction. While the influence of plastic deformation is well understood, the role of dynamic recrystallization during deformation remains not fully comprehended, hindering our ability to interpret the deformation history of naturally-deformed rocks.

This contribution employs microdynamic numerical simulations of olivine polycrystalline aggregates with varying iron content (fayalite content) to explore the CPO and grain size response to dynamic recrystallization. Utilizing a full-field approach with explicit simulation of viscoplastic deformation (http://www.elle.ws; Bons et al., 2008; Piazolo et al., 2019) and dynamic recrystallization processes under simple shear boundary conditions up to high strain, this study indicates that simulations with only dislocation glide and also those including recrystallization successfully reproduce such steady state conditions, without requiring other potential mechanisms. The model establishes a framework for understanding the development of olivine CPOs in mantle rocks, highlighting the interplay between plastic deformation and dynamic recrystallization processes, including grain boundary migration, intracrystalline recovery, and new grain nucleation.

Acknowledgements: Yuanchao Yu acknowledges funding by the China Scholarship Council for a PhD scholarship (CSC-202008130104). This work has been developed using the facilities of the Laboratory of Geodynamic Modelling of GEO3BCN-CSIC.

How to cite: Yu, Y., Llorens, M.-G., Griera, A., Gomez-Rivas, E., Bons, P. D., Garcia-Castellanos, D., Hao, B., and Lebensohn, R. A.: Dynamic recrystallization of olivine during simple shear: evolution of microstructure and crystallographic preferred orientation from full-field numerical simulations, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-425, https://doi.org/10.5194/egusphere-egu24-425, 2024.

EGU24-2189 | ECS | Orals | ITS5.14/GD7.3

Modeling ice and olivine CPO evolution and its affect on large-scale flow in two-way coupled simulations 

Nicholas Rathmann, David Lilien, Christine Hvidberg, Aslak Grinsted, Dorthe Dahl-Jensen, Klaus Mosegaard, Ivanka Bekkevold, and David Prior

We present a spectral-space CPO model that allows for efficient and seamless simulation of anisotropic polycrystalline flows at large scale, relevant for ice sheets and Earth’s upper mantle. The CPO model is two-way coupled with a bulk orthotropic power-law rheology using a linear grain homogenization scheme, making analytical and frame-independent calculations of CPO-induced viscous anisotropy possible and computationally cheap. The effect of two-way coupling flow and CPO evolution is explored in idealized finite element simulations of ice stream flow and mantle thermal convection. In both cases, we find that strain-rate fields are non-trivially affected, and we briefly discuss the consequences for ice-stream self-reinforcement and the coupling between plate motions and the sublithospheric mantle.

This contribution is mainly focused on introducing our modeling framework “specfab” to the wider community.

How to cite: Rathmann, N., Lilien, D., Hvidberg, C., Grinsted, A., Dahl-Jensen, D., Mosegaard, K., Bekkevold, I., and Prior, D.: Modeling ice and olivine CPO evolution and its affect on large-scale flow in two-way coupled simulations, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-2189, https://doi.org/10.5194/egusphere-egu24-2189, 2024.

EGU24-4569 | Orals | ITS5.14/GD7.3 | Highlight

A structural geologist's view on the Northeast Greenland Ice Stream 

Paul D. Bons, Steven Franke, Daniela Jansen, Yu Zhang, and Ilka Weikusat

The Northeast Greenland Ice Stream (NEGIS) is a fascinating, over 500 km long structure in the Greenland Ice Sheet. The ice stream shows many features, such as folds and shear zones, that are also common in other ductile rocks. Geological methods and expertise may contribute to a better understanding of NEGIS and similar deformation structures in ice sheets. It is standard practice in oil and gas exploration to create 3D-structural models from parallel seismic lines. This approach, applied to radar profiles, is relatively new in glaciology (Bons et al., Nat. Comm. 2016, DOI: 10.1038/ncomms11427) but provides far more insight into the structural architecture and evolution of ice sheets than single radar sections. A 3D-structural model of upstream NEGIS reveals how pre-existing folds are offset within the ice stream. With that, classical strain analysis methods can be applied to quantify the deformation of these folds in the shear margins. This reveals that the total offset at the level of the EGRIP drilling project is in the order of up to 75 km and that the finite shear strain in the shear margins is around 18. With present-day shear-strain rates in the shear margins, such a finite offset and shear strain are achieved in ≤2000 yrs. This strain analysis also proves that ice does not flow through shear margins, but that the shear margins instead advect with the ice. This means that 'flow lines' (which should better be called 'streamlines') are not the same as 'path lines', as is now often assumed. The two are only the same in a time-invariant velocity field, which does not apply to NEGIS. Shear zones in other ductile rocks show that rocks never flow through shear zones, but shear zones can shift or 'jump' to new locations, as is actually observed in NEGIS. Geological principles to analyse and date the formation and activity of salt diapirs and syn-sedimentary faults can also be applied to folds observed in and around NEGIS. This reveals that fold amplification inside the shear margins ceased about 2000 yrs ago, which can be explained by the formation of the shear margins and concomitant reorientation of the CPO. A combination of several structural geological methods thus enables constraining the age of NEGIS as we now know it to about 2000 yrs, which is much less than previously assumed. The surprisingly late appearance of NEGIS, as well as the demise of ice streams in the Holocene (based on 3D-analyses of folded stratigraphy; Franke et al., Nature Geosci. 2022, Doi: 10.1038/s41561-022-01082-2) indicates that ice sheets are very dynamic, mostly due to the highly non-linear (n=4) and anisotropic rheology of ice.

How to cite: Bons, P. D., Franke, S., Jansen, D., Zhang, Y., and Weikusat, I.: A structural geologist's view on the Northeast Greenland Ice Stream, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-4569, https://doi.org/10.5194/egusphere-egu24-4569, 2024.

EGU24-5872 | ECS | Orals | ITS5.14/GD7.3

How ice anisotropy contributes to fold and ice stream in large-scale ice-sheet models 

Yu Zhang, Paul D. Bons, Till Sachau, and Steven Franke

Satellite and airborne sensors have provided detailed data on ice surface flow velocities, englacial structures of ice sheets and bedrock elevations. These data give insight into the flow behaviour of ice sheets and glaciers. One significant phenomenon observed is large-scale folds (over 100 m in amplitude) in the englacial stratigraphy in the Greenland ice sheet. A large population of folds is located at ice streams, where the flow is distinctly faster than in the surroundings, such as the North-East Greenland Ice Stream (NEGIS). While there is no consensus regarding the formation of large-scale folds, unraveling the underlying mechanisms presents significant potential for enhancing our understanding of the formation and dynamics of ice streams.

Ice in ice sheets is a ductile material, i.e., it can flow as a thick viscous fluid with a power-law rheology. Furthermore, ice is significantly anisotropic in its flow properties due to its crystallographic preferred orientation (CPO). Here, we use the Full-Stokes code Underworld2 (Mansour et al.,2022) for 3D modelling of the power-law and transversely isotropic ice flow, also in comparison with the isotropic ice models.

Our simulated folds with anisotropic ice show complex patterns on a bumpy bedrock, and are classified into three types: large-scale folds (fold amplitudes >100 m), small-scale folds (fold amplitudes <<100 m, wavelength <<km) and recumbent basal-shear folds. Our results indicate that bedrock topography contributes to perturbations in ice layers, and that ice anisotropy due to the CPO amplifies these into large-scale folds in convergent flow by horizontal shortening. As for our ice stream model, we simulate convergent flow as initial condition, which subsequently initiates the development of shear margins due to the rotation of the ice crystal basal planes. As soon as the shear margins develop, the ice stream starts to propagate upstream in a short time and narrows in the upstream part. Our modeling shows that the anisotropic rheology of ice and CPO change play a significant role for large-scale folding and for the initiation of ice streams with distinct shear margins. Hence, we promote the implementation of ice anisotropy in large-scale ice-sheet evolution models as it holds the potential to introduce novel perspectives to the glaciological community on the dynamics of ice flow.

 

References

John Mansour, Julian Giordani, Louis Moresi, Romain Beucher, Owen Kaluza, Mirko Velic, Rebecca Farrington, Steve Quenette, & Adam Beall. (2022). Underworld2: Python Geodynamics Modelling for Desktop, HPC and Cloud (v2.12.0b). Zenodo. https://doi.org/10.5281/zenodo.5935717

How to cite: Zhang, Y., Bons, P. D., Sachau, T., and Franke, S.: How ice anisotropy contributes to fold and ice stream in large-scale ice-sheet models, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5872, https://doi.org/10.5194/egusphere-egu24-5872, 2024.

EGU24-7333 | Orals | ITS5.14/GD7.3

A physically-based formulation for texture evolution during dynamic recrystallization. A case study for ice 

Maurine Montagnat, Thomas Chauve, Véronique Dansereau, Pierre Saramito, Kevin Fourteau, and Andréa Tommasi

Dynamic recrystallization can have a strong impact on texture development during the deformation of polycrystalline materials at high temperature, in particular for those with strong viscoplastic anisotropy such as ice. Owing to this anisotropy, recrystallization is essential for ensuring strain compatibility. The development of recrystallization textures leads to significant mechanical softening, both in laboratory or natural conditions (glaciers, ice sheets). Accurately predicting ice texture evolution due to recrystallization during tertiary creep remains a challenge, yet is crucial to account adequately for texture-induced anisotropy in large-scale models of glacial ice flow. We propose a new formulation for texture evolution due to dynamic recrystallization. This formulation is physically-based on an orientation attractor which maximizes the Resolved Shear Stress (RSS) on the easiest slip system in the crystal (basal slip for ice). The attractor is implemented in an equation of evolution of the crystal orientation with deformation, which is coupled to an anisotropic viscoplastic law (Continuous Transverse Isotropic - CTI) that provides the mechanical response of the ice crystal. The set of equations, which is the core of the R3iCe open source model is solved using finite elements method with a semi implicit scheme coded using the Rheolef library. R3iCe is validated by comparison with laboratory creep data for ice polycrystals under simple shear, uniaxial compression and tension. It correctly reproduces the texture evolution and the mechanical softening observed during tertiary creep. R3iCe therefore allows predicting enhancement factors that may be implemented in large-scale flow models. Although the validation was performed for ice, the R3iCe implementation is generic and applies for any material adequately described using a CTI law.

How to cite: Montagnat, M., Chauve, T., Dansereau, V., Saramito, P., Fourteau, K., and Tommasi, A.: A physically-based formulation for texture evolution during dynamic recrystallization. A case study for ice, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-7333, https://doi.org/10.5194/egusphere-egu24-7333, 2024.

EGU24-8169 | ECS | Posters on site | ITS5.14/GD7.3

The coupled evolution of crystal orientation fabric and ice flow in ice streams 

Laura Rysager, Nicholas Rathmann, Christine Hvidberg, and Aslak Grinsted

The evolution of grain orientations as a function of flow in polycrystalline glacier ice can greatly affect the bulk viscous anisotropy of ice, and hence mass loss from Earth’s large ice sheets through fast-flowing ice streams where such effects are thought to be important. In this study, we model the strain-induced evolution of grain orientation (fabric) of Lagrangian parcels of ice propagating into, and through, the North-East Greenland Ice Stream (NEGIS) given the local deformation as observed from satellite-derived surface strain rate fields. This allows us to estimate the local flow enhancement factors to be better at understanding the relevance of viscous anisotropy of ice in the ice streams. As the parcels move into and through the ice stream, very different strain-rate regimes are encountered (outside, in the shear margin, and inside the ice stream) which change the fabric over short spatial/temporal scales. To test the model predictions, we compare the modeled fabric eigenvalues with horizontal eigenvalue differences inferred from radar measurements made near the EGRIP drill site.

How to cite: Rysager, L., Rathmann, N., Hvidberg, C., and Grinsted, A.: The coupled evolution of crystal orientation fabric and ice flow in ice streams, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-8169, https://doi.org/10.5194/egusphere-egu24-8169, 2024.

Rocks from the Earth mantle and polar ices have in common a nonlinear rheology and low crystal symmetries leading often to a limited number of independent slip systems for the glide or climb of dislocations. Both deform at elevated homologous temperatures, mostly under creep. Very large plastic deformation occurs during large scale geophysical flows, leading to pronounced crystallographic texture and an associated anisotropic rheology. Polar ice is a pure material, whereas several mineral phases are present simultaneously the mantle. The mantle deforms at extremely slow strain-rates, 10 orders of magnitude smaller than standard laboratory strain-rates, and thus the estimation of the mantle behaviour requires a drastic extrapolation from lab data. A consequence of the features outlined above is that deformation of mantle rocks or polar ices leads to a strong heterogeneity of the stress and strain-rate fields inside the polycrystalline aggregates, at the intragranular (micron) scale. This field heterogeneity has strong implication in terms of texture evolution, recrystallization, but also on the effective flow stress. Another consequence is that simple or ad-hoc micromechanical models are often inaccurate when the goal is to estimate the in situ nonlinear and anisotropic rheology, and the microstructure evolution at large strain, as the activation of slip systems is highly sensitive to stress fluctuations. In this presentation, we will review existing mean-field models for polycrystalline aggregates, show their capabilities / limitations with respect to reference full-field solutions, and show the benefit of the fully-optimized second order self-consistent scheme recently proposed by Song and Ponte Castañeda [2018]. Examples for ice and few mantle minerals will be given for illustrative purpose.

 

D. Song and P. Ponte Castañeda, Fully optimized second-order homogenization estimates for the macroscopic response and texture evolution of low-symmetry viscoplastic polycrystals, Int. J. plasticity 110 (2018), 272–293

How to cite: Castelnau, O. and Ponte Castañeda, P.: Accurate mean-field micromechanical modelling of the nonlinear anisotropic response of polycrystalline aggregates, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-8435, https://doi.org/10.5194/egusphere-egu24-8435, 2024.

EGU24-10371 | ECS | Posters on site | ITS5.14/GD7.3

Eggs and sausages: wireless instrumentation for measuring ice anisotropy and kinematics 

Lisa Craw, Michael Prior-Jones, Nicolas Rathmann, Jonathan Hawkins, Christine Dow, and Elizabeth Bagshaw

Field observations of ice flow properties on large temporal and spatial scales are vital to improve our understanding of ice sheet and glacier dynamics. However, we are currently limited in what we can observe, and on what timescales, with wired instrumentation and remote sensing. We present preliminary tests of wireless instrumentation to measure the kinematics and anisotropy of flowing ice.

We used a spherical probe ("cryoegg") emitting VHF radio waves to measure birefringence in 19 azimuthal directions around a borehole in the Northeast Greenland Ice Stream (NEGIS). From these data we are able to infer information about crystal anisotropy in the ice in three dimensions, and compare with a transfer matrix radio propagation model. This is a significant improvement on previous monostatic radar methods, which are limited to observations of crystal orientations in the horizontal plane.

Additionally, we present initial observations of borehole tilt, temperature, pressure and conductivity from Donjek Glacier, Canada, collected using wireless borehole instruments ("cryowursts''). These data were transmitted through up to 170m of ice, and received at a solar-powered and satellite-enabled receiving station on the glacier surface. There is potential for these instruments to transmit data continuously from surging glaciers over multiple years.

These preliminary studies demonstrate new possibilities for collecting exciting long-term datasets for glaciology.

How to cite: Craw, L., Prior-Jones, M., Rathmann, N., Hawkins, J., Dow, C., and Bagshaw, E.: Eggs and sausages: wireless instrumentation for measuring ice anisotropy and kinematics, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-10371, https://doi.org/10.5194/egusphere-egu24-10371, 2024.

EGU24-11119 | ECS | Posters on site | ITS5.14/GD7.3

Direct estimation of anisotropic viscosity parameters using texture scores of olivine polycrystals 

Ágnes Király, Clinton P. Conrad, Lars N. Hansen, Yijun Wang, and Ben Mather

Earth’s various layers – from the inner core to the cryosphere – exhibit mechanical anisotropy, meaning their properties depend on the direction in which forces are applied. In the upper mantle, the primary source of anisotropy is the crystallographic preferred orientation (CPO) of olivine that is a result of sub-grain rotation during plastic deformation. The alignment of olivine grains allows the anisotropic behavior of single olivine crystals to add up leading to a macroscopic scale anisotropic viscosity (AV) linked to the CPO.

The role of anisotropic viscosity has been examined in various geodynamic scenarios. However, due to the computational complexity of the problem, there has not been a comprehensive integration of olivine CPO development with the linked anisotropic viscous behavior into geodynamic models. Here, we present an approach that directly derives anisotropic viscosity parameters from the orientation distribution (texture) of olivine grains.

Olivine polycrystals exhibit an orthotropic symmetry within the CPO’s reference frame, i.e., when the models' reference frame is aligned with the mean orientation of the olivine symmetry axes. In this case, AV can be characterized by six independent parameters, which are related to the Hill plastic yield criteria (Hill, 1948; Signorelli et al., 2021).  To determine these independent parameters, existing micromechanical models are employed, enabling the calculation of the stress required to achieve a specific strain rate on an aggregate. By applying the micromechanical model to a given texture, we can evaluate different strain rates and use the anisotropic constitutive equation (e.g. Signorelli et al., 2021) to fit the calculated strain rates with those employed in the micromechanical model, thereby identifying the best-fitting anisotropic parameters. However, simply applying this method inside a geodynamic model is too computationally costly. Thus, we built a large database (>10 000 entries) of textures occurring in geodynamic simulations, describing each texture with a set of scores derived from the orientation matrices of the three olivine symmetry axes. For each texture we applied the micromechanical model by Hansen et al., (2016), and used a minimum search function to find the best fitting AV parameters. Finally, linear regression models were utilized to establish a straightforward mapping of anisotropic parameters directly from a combination of textures scores. To determine which combination of texture scores provides the best outcome, we tested the results against both laboratory data and on a simple shear (numerical) experiment.

The approach presented here is advantageous for integrating anisotropic viscosity into 4D geodynamic models because it allows for a direct determination of the viscosity tensor from the evolving rock texture, saving a large amount of computational time.

 

Hansen, L.N., Conrad, C.P., Boneh, Y., Skemer, P., Warren, J.M., and Kohlstedt, D.L., 2016a, Viscous anisotropy of textured olivine aggregates: 2. Micromechanical model: Journal of Geophysical Research: Solid Earth

Hill, R., 1948, A theory of the yielding and plastic flow of anisotropic metals: Proceedings of the Royal Society of London. Series A. Mathematical and Physical Sciences

Signorelli, J., Hassani, R., Tommasi, A., and Mameri, L., 2021, An effective parameterization of texture-induced viscous anisotropy in orthotropic materials with application for modeling geodynamical flows

How to cite: Király, Á., Conrad, C. P., Hansen, L. N., Wang, Y., and Mather, B.: Direct estimation of anisotropic viscosity parameters using texture scores of olivine polycrystals, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-11119, https://doi.org/10.5194/egusphere-egu24-11119, 2024.

EGU24-11580 | ECS | Orals | ITS5.14/GD7.3

Deformation and recrystallization inside the Northeast Greenland Ice Stream – findings from microstructural analysis of the EastGRIP ice core 

Kyra Streng, Johanna Kerch, Paul Bons, Nicolas Stoll, Daniela Jansen, and Ilka Weikusat

Solid ice discharge from land-based ice masses into the ocean raises the global sea level and accelerates due to anthropogenic climate change. Modelling ice flow dynamics aims to provide better projections of future sea level rise. The Antarctic and Greenland ice sheets are predominantly drained through ice streams, which are regions of higher ice flow velocity than their surroundings, and thus play an important role in ice sheet dynamics. However, little is known about their rheology. Therefore, they may introduce large uncertainties in ice sheet models.

In order to study the main deformation and recrystallization mechanisms dominant in an ice stream, we conducted microstructural analyses on samples from the EastGRIP ice core that was drilled in the largest Greenlandic ice stream, the Northeast Greenland Ice Stream (NEGIS).

The data set contains 1064 samples, oriented vertically and horizontally to the ice core axis, from depths between 111 and 2121 m. Analyses of the deepest 550 m of the ice core are pending. All samples were scanned with 5 µm resolution under bright-field illumination with a Large Area Scanning Macroscope (LASM). The obtained microstructure, i.e. grain shape, size, and elongation, was extracted using digitalised grain boundary networks by means of a machine-learning based image analysis software. We determined six different rheological regimes through the ice column. Most microstructural changes were interpreted as changes in recrystallization mechanisms, whereas the dominant deformation mode, horizontal extension, appears to remain fairly constant below 500 m of depth. Previous numerical high-strain ice deformation simulations showed strain localisation with the development of visible shear bands. A similar setting was expected inside ice streams, but at the investigated depths of the EastGRIP ice core, no clear shear bands could be discerned so far for the applied sampling resolution.

These results indicate that NEGIS has no strong high-strain localisation down to 2121 m depth but probably deforms as a block with extension along flow. The high ice flow velocities, therefore, might have to be compensated either in the lowest 500 m or below the ice.

How to cite: Streng, K., Kerch, J., Bons, P., Stoll, N., Jansen, D., and Weikusat, I.: Deformation and recrystallization inside the Northeast Greenland Ice Stream – findings from microstructural analysis of the EastGRIP ice core, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-11580, https://doi.org/10.5194/egusphere-egu24-11580, 2024.

EGU24-12319 | ECS | Posters on site | ITS5.14/GD7.3

Refractive Index Matched (RIM) PIV in Free Surface Flows of Particle-Laden Yield Stress Fluids 

Kasra Amini, Yanan Chen, Christophe Ancey, Outi Tammisola, and Fredrik Lundell

Flow of lava, avalanches, mudslides, and many geophysical and planetary flow systems are examples of free-surface flows of Yield Stress Fluids (YSFs). This category of fluids is known for its dual behavior below- and above a yielding threshold for the applied shear stress on each fluid element. The material behaves as an amorphous elastic solid below the yielding threshold and fluidizes above it. This will lead to the presence of unyielded plug regions translating and rotating as solid-like segments within the yielded surrounding fluids. The existence of macroscopic particles in the fluid adds to the complexity of the flow setting. Transport of debris in the riverbeds and avalanches, dispersion of the cooled-down agglomerates of lava in the molten medium, and migration of solid material such as icy rocks in high pressure YSF-like, sub-terranean oceans of Europa (Jupiter’s moon) are among numerous natural examples of particle-laden flows of YSFs. To replicate the conditions experimentally, aqueous solutions of Carbopol with yield stress  are used in combination with hydrogel particles. The elastic hydrogel particles have been used in volume fractions φ = 0, 10, 20, and 30 % as mono- and duodispersed suspensions. The excellent refractive index matching of these elastic particles with Carbopol permits accurate recording of the illuminated flow field seeded with tracer particles for PIV measurements, without optical blockage of the macro particles in the optical path. Measurements are performed with channel inclinations ranging from zero to 18°, with controlled deployment of gate opening ranging from 3 cm (i.e. 50 % of the channel width) to a full open dam-break situation. Stream-wise PIV recordings of the transient and semi-steady field are complemented with span-wise recordings targeting statistical results on the particle migration and sedimentation. The results are put in context with the experiments on Newtonian and YSFs in free surface flumes containing rigid particles [1,2], as well as duct flow experiments on Carbopol with the same elastic particles [3].      

References

 [1] Christophe Ancey, Nicolas Andreini, Gaël Epely-Chauvin, The dam-break problem for concentrated suspensions of neutrally buoyant particles, J. Fluid Mech. (2013), vol. 724, pp. 95–122.

[2] G Rousseau, C Ancey, An experimental investigation of turbulent free-surface flows over a steep permeable bed, J. Fluid Mech. (2022), vol. 941, A51.

[3] Sagar Zade, Tafadzwa John Shamu, Fredrik Lundell, Luca Brandt, Finite-size spherical particles in a square duct flow of an elastoviscoplastic fluid: an experimental study, J. Fluid Mech. (2020), vol. 883, A6

How to cite: Amini, K., Chen, Y., Ancey, C., Tammisola, O., and Lundell, F.: Refractive Index Matched (RIM) PIV in Free Surface Flows of Particle-Laden Yield Stress Fluids, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12319, https://doi.org/10.5194/egusphere-egu24-12319, 2024.

EGU24-14098 | ECS | Orals | ITS5.14/GD7.3

Nonlinear Viscoelastic Model for Ice and Olivine, Constrained by Experimental Data using MCMC 

Ron Maor, Lars Hansen, and David Goldsby

Mechanisms of energy dissipation in ice and olivine have been studied experimentally in the past, with an observed strain-amplitude dependence that indicates nonlinear viscoelastic behavior resulting from the presence and motion of dislocations. In a range of low to moderate stress amplitudes, dislocations can ”bow out” between pinning points. If the resolved shear stress is sufficiently large, dislocations may escape their pinning points and elastically interact with each other. The transition from pinned to unpinned motion, along with the subsequent interactions and recovery processes, are associated with the shift from anelastic to steady-state viscous behavior. This transition forms the basis of a viscoelastic model. Despite the experimental evidence of nonlinear mechanisms, the availability of comprehensive nonlinear viscoelastic models for geological materials is limited. In this work, we propose a nonlinear viscoelastic model that captures the effect of dislocation dynamics on energy dissipation. The model is based on the well-known linear Burgers model, modified to incorporate non-linear steady-state viscous flow, and enhanced by the integration of fabric and grain-size evolution dynamics. The proposed model is tested against data from constant strain-rate and forced oscillation experiments, and the parameters are constrained using Markov Chain Monte Carlo (MCMC) methods. The model successfully reproduces data from deformation experiments in the dislocation creep regime and can be extended to experiments involving other deformation mechanisms as well.

How to cite: Maor, R., Hansen, L., and Goldsby, D.: Nonlinear Viscoelastic Model for Ice and Olivine, Constrained by Experimental Data using MCMC, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-14098, https://doi.org/10.5194/egusphere-egu24-14098, 2024.

The rheology and deformation mechanisms of mafic blueschists play a key role in the mechanical behavior of subducting oceanic crust in subduction zones. While mafic blueschists are often ubiquitous along the plate interface from the base of the seismogenic zone (~35 km) to the sub-arc depths (~100 km), the strength of this lithology still remains poorly constrained. Observations of blueschists from exhumed subduction terranes suggests that blueschist can accommodate significant strain, largely partitioned into the sodic amphibole glaucophane. However, it remains an open question whether the observed deformation is accommodated by dislocation or diffusion deformation processes.

We present microstructural and textural analyses to investigate the glaucophane fabric and deformation mechanisms in three naturally deformed blueschists exhumed from variable P-T conditions: (1) a lawsonite blueschist from the Catalina Schist (Santa Catalina Island, CA, USA), (2) higher-grade epidote blueschist from the Bandon blueschist (Bandon, OR, USA) and (3) an epidote-blueschist from the Cycladic Blueschist Unit (Tinos, GR). We used electron backscatter diffraction (EBSD), scanning electron microscopy (SEM), and energy-dispersive X-ray spectroscopy (EDS) to interpret the textural and geochemical record of deformation mechanisms that were active during the subduction history of these exhumed blueschists. All three blueschists display well-developed foliations and lineations which are defined by interconnected layers of glaucophane. EBSD microstructural analysis of glaucophane in the samples reveals evidence of dislocation accommodated deformation including: (1) strong crystallographic preferred orientation (CPO) development, (2) intragranular orientation gradients, (3) activity of dislocation motion on multiple slip systems, and (4) subgrain boundary formation. Core-mantle structures in which the daughter grains display evidence of a weakened CPO inherited from the mother (core) grains imply the activity of subgrain boundary recrystallization in the samples. Taken together, this microstructural evidence implies that dislocation creep accommodated deformation was active in all three blueschists during their deformation history. SEM images and EDS maps of glaucophane reveal evidence of chemical zoning in grains with higher Ca and Al concentrations in the rims and along the walls of  (micro)fractures within the grains (Bandon, OR Sample). The Catalina lawsonite blueschist displays interspersed evidence of microfractures with higher concentrations of Fe and lower Al and Mg concentrations. This chemical zoning and microfractures suggest micro-boudinage and/or coupled dissolution-precipitation occurred in these samples, and that potential fluid-mediated diffusion accommodated deformation processes may be preserved in these two mafic blueschists. We leverage the relationships between the textural and chemical evidence in concert with P-T estimates for their host terranes to interpret the deformation histories of these samples during subduction and exhumation. Crosscutting relationships between the chemical zoning and intragranular orientation gradients in the samples suggests that dislocation-related deformation was prograde and predates diffusion-related processes which became active in the Catalina and Bandon samples at or near peak conditions and during retrogression. Together, these results suggest that glaucophane can readily deform by dislocation creep, and also record fluid-mediated processes during deformation.

How to cite: Ott, J., Condit, C., Pec, M., and Journaux, B.: Microstructural evidence of dislocation creep and diffusion accommodated deformation of glaucophane in naturally deformed lawsonite and epidote blueschists, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-14104, https://doi.org/10.5194/egusphere-egu24-14104, 2024.

EGU24-19147 | ECS | Orals | ITS5.14/GD7.3

Multi-scale anisotropy development in viscous flow due to fabric evolution: Numerical modelling, upscaling, and application for strain localization 

William R. Halter, Roman Kulakov, Thibault Duretz, and Stefan M. Schmalholz

Viscous flow controls large parts of tectonic deformation. Viscous strain localization and associated softening mechanisms are important for subduction initiation and the generation of tectonic nappes. However, viscous flow of geologic materials can have a complex behaviour due to their evolving microstructure, such as an evolving anisotropy due to fabric development or a crystallographic preferred orientation, or due to other evolving microstructure, like, e.g., grain size or dynamic recrystallization.

In this contribution, we focus on strain localization in viscous rock due to the generation of anisotropy resulting from fabric evolution. Particularly, we focus on multi-scale anisotropy evolution in shear zones with many strong or weak inclusions, representing for example porphyroclasts. The shape change and relative alignment of the inclusions during shearing generates an anisotropy on the scale of the inclusions, termed here macroscale. We spatially resolve this macroscale anisotropy in the numerical simulations. Additionally, we consider the evolution of a microscale anisotropy in the shear zone matrix, representing the formation of a mylonitic foliation. We do not spatially resolve this microscale anisotropy but model it with an anisotropic flow law that involves different normal and tangential viscosities. We calculate the finite strain ellipse during shearing and use its aspect ratio as proxy for the anisotropy that governs the ratio of normal to tangential viscosity. To track the orientation of the anisotropy during deformation we apply a director method.

We perform numerical simulations with the two-dimensional state-of-the-art thermo-mechanical code MDoodz (Duretz et al. 2021). We evaluate the impact of micro- and macroscale anisotropy on strain softening and localization in shear zone up to shear strains in the order of ten. We further discuss the quantification of effective anisotropies that can be used for upscaling, for example for lithospheric scale numerical models. Moreover, we compare the numerical results to the analytical solution and the numerical results of Dabrowski et al. (2012). A particular feature of some simulations is the formation of buckle folds in regions with highly stretched weak inclusions.

 

Bibliography

Duretz T., R. de Borst and P. Yamato (2021), Modeling Lithospheric Deformation Using a Compressible Visco-Elasto-Viscoplastic Rheology and the Effective Viscosity Approach, Geochemistry, Geophysics, Geosystems, Vol. 22 (8), e2021GC009675

Dabrowski, M., D. W. Schmid, and Y. Y. Podladchikov (2012), A two-phase composite in simple shear: Effective mechanical anisotropy development and localization potential, J. Geophys. Res., 117, B08406, doi:10.1029/2012JB009183

How to cite: Halter, W. R., Kulakov, R., Duretz, T., and Schmalholz, S. M.: Multi-scale anisotropy development in viscous flow due to fabric evolution: Numerical modelling, upscaling, and application for strain localization, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-19147, https://doi.org/10.5194/egusphere-egu24-19147, 2024.

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