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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.

EGU24-1245 | Posters on site | ITS1.1/CL0.1.17

Seasonal prediction of typhoon track density using deep learning based on the CMIP datasets 

Yuan Sun, Zhihao Feng, Wei Zhong, Hongrang He, Shilin Wang, Yao Yao, Yalan Zhang, and Zhongbao Bai

Tropical cyclones (TCs) seriously threaten the safety of human life and property especially when approaching coast or making landfall. Robust, long-lead predictions are valuable for managing policy responses. However, despite decades of efforts, seasonal prediction of TCs remains a challenge. Here, we introduce a deep-learning prediction model to make skillful seasonal prediction of TC track density in the Western North Pacific (WNP) during the typhoon season, with a lead time up to four months. To overcome the limited availability of observational data, we use TC tracks from CMIP5 and CMIP6 climate models as the training data, followed by a transfer-learning method to train a fully convolutional neural network named SeaUnet. Through the deep-learning process (i.e., heat map analysis), SeaUnet identifies physically based precursors. We show that SeaUnet has a good performance for typhoon distribution, outperforming state-of-the-art dynamic systems. The success of SeaUnet indicates its potential for operational use.

How to cite: Sun, Y., Feng, Z., Zhong, W., He, H., Wang, S., Yao, Y., Zhang, Y., and Bai, Z.: Seasonal prediction of typhoon track density using deep learning based on the CMIP datasets, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1245, https://doi.org/10.5194/egusphere-egu24-1245, 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.

A Catboost-based intelligent tropical cyclone (TC) intensity-detecting model is built to quantify the intensity of TCs over the Western North Pacific (WNP) with the cloud-top brightness temperature (CTBT) data of Fengyun-2F (FY-2F) and Fengyun-2G (FY-2G) and the best-track data of the China Meteorological Administration (CMA-BST) in recent years (2015-2018). Catboost-based model is featured with the greedy strategy of combination, the ordering principle in optimizing the possible gradient bias and prediction shift problems, and the oblivious tree in fast scoring. Compared with the previous studies based on the pure convolutional neural network (CNN) models, the Catboost-based model exhibits better skills in detecting TC intensity with the root mean square error (RMSE) of 3.74 m s-1. Besides of the three mentioned model features, there are also two reasons on model design. On one hand, the Catboost-based model uses the method of introducing prior physical factors (e.g., the structure and shape of the cloud, deep convections and background fields) into its training process, on the other hand, the Catboost-based model expands the dataset size from 2342 to 13471 samples by hourly interpolation of the original dataset. Furthermore, this paper investigates the errors of the model in detecting different categories of TC intensity. The results show that the deep learning-based TC intensity-detecting model proposed in this paper has systematic biases, namely, overestimation (underestimation) of intensities in TC which are weaker (stronger) than typhoon level, and the errors of the model in detecting weaker (stronger) TCs are smaller (larger). This implies that more factors than the CTBT should be included to further reduce the errors in detecting strong TCs.

How to cite: Zhong, W., He, H., Wang, S., Sun, Y., and Yao, Y.: A Catboost-based Model for Intensity Detection of Tropical Cyclones over the Western North Pacific Based on Satellite Cloud Images, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1277, https://doi.org/10.5194/egusphere-egu24-1277, 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.

Urban growth and infrastructure development, especially road network growth, are two interactive, coevolving processes, and to understand long-term urban growth dynamics, it is crucial to model these two processes codependently. Hence, in this study, we present a modeling framework that is capable of capturing the feedback between urban land and road network in forecasting the amount and spatial patterns at large regional scales. While this proposed model with road length as a model parameter forecasts up to 1.2 times new urban areas globally under different scenarios, traditional models with no road length consideration forecasted 1.5–3.7 times more urban areas in 2050. We also forecasted the growth in road network length and pattern considering urban areas as the attraction point. Our model forecasted a substantial amount of new roads to be added to existing global road inventory by 2050– ranging between 1.67 million km and 3.37 million km under five Shared Socio-economic Pathways (SSPs) scenarios. We present Nigeria, Brazil and Bangladesh as case studies where significant new road development is forecasted in currently underdeveloped areas. The overall output from this codependent modeling process will inform the updated connectivity pattern along with an urban growth forecast. This approach enables us to capture the influence of transportation development and the ongoing large-scale transportation infrastructure development projects on urban growth at large, regional- and global- levels for more realistic assessments of the impacts of these projects on the environment.

How to cite: Ahasan, R. and Güneralp, B.: An integrated, scale-invariant model to forecast global urban growth and transportation network development, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-165, https://doi.org/10.5194/egusphere-egu24-165, 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

D.S. Jenkinson, P.B.S. Hart, J.H. Rayner and L.C. Parry, "Modelling the turnover of organic matter in long-term experiments at Rothamsted". INTECOL Bulletin 15 (1987): 1–8

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.