Ground Penetrating Radar (GPR) is a diagnostic tool that well assessed in a variety of areas, including geophysics, archaeological prospection, civil engineering, and planetary exploration, just to mention few examples.

Notwithstanding the simplicity of the underlying principle, a significant limitation of GPR is concerned with the interpretability of the raw data, mostly presented under the form of radargrams, particularly in scenarios that are complicated and characterised by a multitude of embedded targets. 

To enhance the interoperability of radar images, it is crucial to reliably model the electromagnetic scattering, so that the radar imaging is conceptualised as an inverse scattering problem. For such an inverse problem, the geometric and electromagnetic properties of the targets are retrieved by the field scattered by the target when an incident field impinges on it. Despite the simplicity of the underlying principle, this inverse problem is inherently complex due to its non-linear nature and ill-posedness. These mathematical difficulties have a detrimental effect on the effectiveness of GPR diagnostics in real cases. To facilitate the application of inverse scattering approaches in real-world scenarios, it is necessary to resort to approximate models of electromagnetic scattering. Microwave tomography exploits the linearization of the inverse scattering problem, so that reconstruction approaches can be developed that operate under realistic conditions with the aim of estimating the position and geometry of targets, albeit with limitations on the class of unknowns that can be reconstructed (i.e. resolution limitations) and the impossibility of quantitatively estimating the electromagnetic properties of targets.

In the talk, microwave tomography will presented under a unified mathematical framework based on the solution of an integral equation accounting for arbitrary measurement configurations and background scenarios for both contact and contactless GPR measurements. Furthermore, the investigation of the reconstruction performance of the microwave tomographic approach for different measurement configurations and background scenarios will be presented.

Finally, several cases of exploitation of the microwave tomographic approach in real cases will be shown.

How to cite: Soldovieri, F.: Microwave tomography for subsurface prospecting, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-21957, https://doi.org/10.5194/egusphere-egu25-21957, 2025.

Nuclear Magnetic Resonance (NMR) is a crucial logging technique for the unconventional and complex reservoir evaluation. However, the echo spacing is always an issue of borehole NMR measurement, which limits the performance of NMR tools to acquire the short relaxation components.

In this abstract, we proposed a novel Q-Switch technique aiming at breaking through the limitation of dead-time of borehole NMR logging tool, and to achieve much shorter echo spacing. Instead of using resistors of larger resistance in parallel with the radio-frequency (RF) coil to reduce the active dead-time, an inductive coupling circuit was introduced to decrease the ringing-down time significantly after transmitting the RF pulses with high voltage. The Q-Switch circuit consists of inductive coupling coil, capacitors, resistors and active high-voltage MOSFETs. The ringing-down time of RF system was decreased by at least 10 times compared to the system without using proposed Q-switch scheme, leading to echo spacing lower to 0.3 ms under the condition with resonant frequency lower to 500 kHz.

Both simulations and experiments were in great agreements, validating the feasibility and efficiency of proposed Q-switch scheme, and proved to be promising in the borehole NMR applications.

How to cite: Luo, S., Li, X., Yu, H., Wang, Z., Xing, T., Long, Z., Che, C., Liao, G., and Xiao, L.: A Novel Q-Switch Technique for  Borehole NMR Measurement, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-121, https://doi.org/10.5194/egusphere-egu25-121, 2025.

Soil and groundwater contamination at some sites impacts downstream populations when contaminants migrate from groundwater to rivers. Predictive modeling is challenging since it is required to include detailed subsurface structure and groundwater flow models within the site, as well as watershed-scale models for large-scale transport. Now that climate change impacts are major concerns at many sites, it is important to have the capability to represent the water balance change and its impact on contaminant transport both at the site and watershed scale in a consistent manner. This study introduces a new simulation framework to couple a detailed 2D site/hillslope-scale groundwater model to the 3D watershed-scale model to describe contaminant transport from groundwater to river water within the catchment. Within the site, we estimate the contaminant discharges to the river from contaminant sources based on the Richards equation and advection-dispersion equation. The discharges are then applied as the boundary conditions to the watershed-scale model considering the width of the 2D site/hillslope-scale groundwater model and recharge rates for both models.

We demonstrate and validate our framework based on the tritium concentration datasets in surface water and groundwater collected at the Savannah River Site F-Area. Results show that the method can successfully reproduce the contaminant concentration time series in river water.

How to cite: Sakuma, K., Wainwright, H., Xu, Z., Lawrence, A., and Hazenberg, P.: Multiscale model coupling for watershed-scale contaminant transport modeling from point sources in Savannah River Site, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-4077, https://doi.org/10.5194/egusphere-egu25-4077, 2025.

As one of crucial factor in atmospheric particulate matter, elemental components exhibit distinct distribution features within different particle size ranges. Crustal elements (such as Al, Si, Fe, Ca, Mg) are primarily concentrated in coarse particulate matter, whereas elements originating from anthropogenic pollution sources (such as heavy metal elements including Pb, Zn, Cd, As, Cr) are more frequently distributed in fine particulate matter. Furthermore, some specific elements may also exhibit peak concentrations in particular particle size, which is closely related to their sources and formation processes. In recent years, there are still some challenges and deficiencies. Further research is needed on the particle size distribution characteristics of complex pollution sources (such as industrial emissions and traffic emissions). Additionally, there is a need to enhance the understanding of the transformation mechanisms and health effects of elemental components within particulate matter. This study selected a typical industrial and mining city to investigate particle size distribution characteristics of elemental components in atmospheric particulate matter. Anderson Eight-Stage Particulate Impactor Sampler was used to collect atmospheric particulate matter in the urban area of Huangshi during winter and summer. Nine particle size range samples were obtained spanning from 0 to 0.4 µm, 0.4 to 0.7 µm, 0.7 to 1.1 µm, 1.1 to 2.1 µm, 2.1 to 3.3 µm, 3.3 to 4.7 µm, 4.7 to 5.8 µm, 5.8 to 9.0 µm, and 9.0 to 10 µm. Energy Dispersive X-Ray Fluorescence Spectrometry (ED-XRF) was employed to determine the concentrations of 17 elemental components, including S, Cl, K, Ca, Ti, V, Cr, Mn, Fe, Co, Ni, Cu, Zn, Rb, Sb, Ba, and Pb. Elements Ca, S, Fe, K, Zn, Ba, and Pb were identified as the primary pollutants during the sampling period. All the elemental concentrations exhibited distinct seasonal variations, demonstrating higher levels in winter compared to summer. Each element demonstrated distinct particle size distribution characteristics with peak concentrations for most elements occurring in the 5.8 to 9.0 µm range and peaks for the remaining elements in the 0.4 to 1.1 µm range. The highest elemental concentrations in both summer and winter were mainly distributed in the 5.8 to 9.0 µm and 0.7 to 1.1 µm size ranges. In summer, most elemental concentrations were negatively correlated with relative humidity. However, in winter, there was no significant correlation with relative humidity. Rainfall had a certain scavenging effect on elements but was also influenced by other meteorological factors. Element S had the highest enrichment factor values in both summer and winter. Element Cl was highly enriched in finer particle size fractions in both seasons. Most elements were slightly enriched across all particle size fractions. Principal component analysis further identified the main sources as soil dust and wind-blown sand, coal combustion, vehicle exhaust emissions, biomass burning, mining and construction activities, and other pollution sources. These findings contribute to the formulation of effective pollution control measures and the protection of public health.

How to cite: Liu, H., Zhang, J., Zhan, C., Liu, S., Liu, T., and Xiao, W.: Size distribution of elemental components in atmospheric particulates from a typical industrial and mining city of Central China, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-4641, https://doi.org/10.5194/egusphere-egu25-4641, 2025.

How to cite: Ball, C., Chang, M., Cruise, J., de Valk, C., and Kannan, V.: Industrial High Performance Computing Scalable and FAIR Workflow Opportunities for EO Operations Processing, Operations, and Archiving, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-7004, https://doi.org/10.5194/egusphere-egu25-7004, 2025.

Severe convection, including thunderstorms and related phenomena like flash flooding, hail, and strong winds, can have significant socioeconomic impacts. Nowcasting, which provides real-time, short-term predictions, is vital for issuing timely warnings to mitigate these impacts. Satellite imagery is essential for monitoring convection and offering accurate predictions of storm evolution, thereby enhancing early warning systems. Ensemble forecasting, which generates multiple potential scenarios, helps better quantify uncertainties in nowcasting. However, most ensemble forecasting methods are computationally intensive and typically do not incorporate satellite images directly. The Analog Ensemble (AnEn) method, a lower-cost ensemble approach, identifies similar past weather events based on forecast data. For a given time and location, the AnEn method identifies analogs from past model predictions that are similar to current forecast conditions. Then their associated observations are used as ensemble members. Despite its advantages, AnEn struggles with locality and is sensitive to the choice of similarity metrics. This study presents an improved AnEn system that replaces forecast archives with satellite images to identify analogs of convective conditions. The system utilizes pretrained deep learning algorithms (VGG16, Xception, and Inception-ResNet) to assess image similarity. The training dataset consists of daily convection satellite images from EUMETSAT for the period 2020-2023, and the domain covers 40°N to 20°S and -20°W to 4°E. The year 2024 is used for testing, with ERA5 reanalysis of total precipitation as the verification ground-state. For a present convection satellite image this image is encoded and compared to all past encoded images of the training period using different metrics. The most similar images to the current one are then selected and their associated ERA5 total precipitation reanalysis are considered the members or our ensemble. Preliminary results indicate an average maximum precipitation anomaly of 15 mm between the analog ensemble mean and the current reanalysis, showing that the proposed system offers promising improvements in short-term forecasting.

Key words: Convection; Ensemble Forecasting; Deep Learning; VGG; Xception; ResNet; Analog Ensemble; Morocco; Nowcasting; EUMETSAT; ERA5; Morocco; Satellite Images; Remote Sensing;

How to cite: Alaoui, B., Bounoun, C., and Bari, D.: Leveraging Pretrained Deep Learning Models to Extract Similarities for the Analog Ensemble Method Applied to Convection Satellite Imagery, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-7258, https://doi.org/10.5194/egusphere-egu25-7258, 2025.

Amid global environmental degradation, understanding the spatiotemporal dynamics and trade-offs of ecosystem services (ESs) under varying land-use scenarios is critical for advancing the sustainable development of social–ecological systems. This study analyzed the Chaohu Lake Basin (CLB), focusing on four scenarios: natural development (ND), economic priority (ED), ecological protection (EP), and sustainable development (SD). Using the PLUS model and multi-objective genetic algorithm (MOGA), land-use changes for 2030 were simulated, and their effects on ESs were assessed quantitatively and qualitatively. The ND scenario led to significant declines in cropland (3.73%) and forest areas (0.18%), primarily due to construction land expansion. The EP scenario curbed construction land growth, promoted ecosystem recovery, and slightly increased cropland by 0.05%. The SD scenario achieved a balance between ecological and economic goals, maintaining relative stability in ES provision. Between 2010 and 2020, construction land expansion, mainly concentrated in central Hefei City, led to a marked decline in habitat quality (HQ) and landscape aesthetics (LA), whereas water yield (WY) and soil retention (SR) improved. K-means clustering analysis identified seven ecosystem service bundles (ESBs), revealing significant spatial heterogeneity. Bundles 4 through 7, concentrated in mountainous and water regions, offered high biodiversity maintenance and ecological regulation. In contrast, critical ES areas in the ND and ED scenarios faced significant encroachment, resulting in diminished ecological functions. The SD scenario effectively mitigated these impacts, maintaining stable ES provision and ESB distribution. This study highlights the profound effects of different land-use scenarios on ESs, offering insights into sustainable planning and ecological restoration strategies in the CLB and comparable regions.

How to cite: Jin, A.: Ecosystem Services Trade-Offs in the Chaohu Lake Basin Based on Land-Use Scenario Simulations, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-7785, https://doi.org/10.5194/egusphere-egu25-7785, 2025.

Seasonal ecosystems play a crucial role in environmental regulation and biodiversity by hosting complex ecological dynamics that vary with climatic conditions. The Chaschoc-Sejá wetlands are a key example of such systems in southeastern Mexico. The interaction between the lagoon system and the Usumacinta River is highly dynamic; during the rainy season, the lagoons increase in volume, reaching depths of 8 to 10 meters. However, the lagoons completely dry up during the dry season, leaving vegetation at the surface level. 

This project aims to analyze the dynamics of vegetation cover in this environment by comparing high-resolution satellite images (Planet, 3 m) and ortho-mosaics generated with a DJI Mavic 3 Multispectral drone (10 cm). By combining these datasets, we aim to improve our previous vegetation maps and obtain a more accurate and detailed assessment of the Chaschoc-Sejá Lagoon system. Understanding vegetation patterns at a larger scale during specific periods and the variations in plant life within the lagoon and along its shores is a key focus.

Data processing involved classifying vegetation cover and identifying seasonal changes using indices such as NDVI and NDWI. We also generated 3D models to estimate vegetation height. Results show that integrating both techniques significantly improves spatial resolution and temporal accuracy in monitoring these ecosystems. This study provides essential tools for managing seasonal systems and their conservation in the face of climatic and anthropogenic factors. This monitoring will aid in understanding vegetation status, identifying plant species, and contributing to managing and preserving the lagoon system.

How to cite: Nieto, J., Ramírez Serrato, N. L., Romero Herrera, A., Peralta Carreta, C., Herrera Zamarrón, G., Hernández Hernández, M. A., Hernández García, G. D. J., Olea Olea, S., Morales Casique, E., and Cortez Silva, A.: Integrating high-resolution satellite and multispectral drone Imagery for monitoring vegetation in the Chaschoc-Sejá lagoon system, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-7921, https://doi.org/10.5194/egusphere-egu25-7921, 2025.

Benevento Province, located in the Campania region of Italy, may experience environmental quality impacts from neighboring developed areas such as Naples and Caserta. Previous studies have suggested that some agricultural chemicals from Naples, such as hexachlorobenzene, may be transported through the air to rural areas of Benevento. Additionally, high concentrations of polycyclic aromatic hydrocarbons (PAHs) have been detected in Naples and Caserta, making Benevento Province a potential PAH "sink." This study systematically investigated the occurrence of PAHs in soil from Benevento Province, southern Italy, and their correlations with environmental factors, soil-air exchange processes, and health risks. Over 95% of sampling sites exhibited ∑₁₆PAHs concentrations at non-polluted levels (9.50-1188 ng/g, mean = 55.0 ± 152 ng/g), and four-ring PAHs were the dominant pollutants contributing to 28.3% of ∑₁₆PAHs. The spatial distribution of PAHs presented significant heterogeneity, with hotspots concentrated near landfills. The results of Positive Matrix Factorization (PMF) model showed that the main sources of PAHs were vehicle emissions, coal/biomass combustion, and petroleum products volatilization/leakage, contributing 42.2%, 40.2%, and 17.6%, respectively. Most of PAHs correlated significantly with total organic carbon in the soil and population density, while only Benzo(b)fluoranthene (BbF) showed a significantly negative correlation with pH. The mass inventory of ∑₁₆PAHs ranged from 0.94 to 29.4 tons, averaging 2.45 tons. The synergistic effects of pollution hotspots and the persistent accumulation of PAHs in the soil suggested that the soil might act as a secondary source of PAHs. Toxicity equivalent and probabilistic risk assessments indicated that health risks from PAHs remained within acceptable limits.

How to cite: Qu, C. and Pu, C.: Investigation of polycyclic aromatic hydrocarbons in the soils of Benevento Province, Italy, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10723, https://doi.org/10.5194/egusphere-egu25-10723, 2025.

The adoption of the FAIR data principles has revolutionised data management in Earth System Sciences (ESS), yet challenges persist in achieving true machine-actionability and comprehensive implementation. 

DeSci Labs introduces two innovative tools—Decentralised Persistent Identifiers (DPIDs) and the CODEX protocol—to address the barriers to FAIR data practice implementation in general; whilst fostering widespread uptake of FAIR principles in the ESS community in particular through involvement in the FAIR2Adapt consortium.

DPIDs are globally unique persistent identifiers based on, and linked directly to, the content of the files it refers to. Each version of every file, regardless of type, is automatically assigned a cryptographic fingerprint, ensuring deterministic resolution and transparent versioning. The DPID has been specifically designed to support FAIR digital research objects. The flexibility to alias DPIDs with existing systems (e.g., DOIs) and programmatic publishing capabilities via NodesLib enhances interoperability while preserving data sovereignty.

The CODEX protocol, an open scholarly infrastructure, further complements this by enabling the storage and retrieval of FAIR digital research objects via a decentralised peer-to-peer network (IPFS). This architecture allows multiple copies of the same content to be stored by different network participants using the same PID. By empowering researchers to collaborate within an open-state repository, the protocol minimises reliance on centralised actors, ensuring long-term accessibility, data integrity, and transparency. Its modular design facilitates diverse gateway applications, maximising participation and reducing barriers to entry.

These tools address core challenges in FAIR adoption by providing robust, scalable, and interoperable solutions tailored to the ESS community. By integrating DPIDs and CODEX into data workflows, researchers can enhance data reusability, improve the provenance of research outputs, and safeguard the collective scientific record. This presentation explores how these technologies can catalyse the next decade of FAIR data practices in ESS, fostering trust, reproducibility, and innovation.

How to cite: Raijmakers, L., Hübinette, E., DeSota, E., Iman, S., and Koellinger, P.: Advancing and Supporting FAIR Principle Adoption through Innovative Social Infrastructure Tools, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-13330, https://doi.org/10.5194/egusphere-egu25-13330, 2025.

In mineral exploration and geometallurgical studies, accurately segmenting mineral grains from core scanning datasets may be used to predict metal recovery. This study introduces the application of the Segment Anything Model (SAM), a cutting-edge deep learning tool, to automate the segmentation and extraction of mineral grains from Laser-Induced Breakdown Spectroscopy (LIBS) and hyperspectral core scanning datasets. SAM demonstrates high efficiency and precision in identifying mineral grains, forming the foundation for downstream analyses, including the evaluation of mineral associations, grain size distribution, and other key geometallurgical metrics. Through case studies on pegmatite deposits, this research showcases the potential of SAM to address challenges posed by mineralogically complex ore. By enabling detailed mineralogical characterisation and advancing geometallurgical methods, SAM-based grain extraction presents a transformative tool for supporting sustainable and efficient mining practices.

How to cite: Cai, Y., Manton, R., and Williams, M.: Automated Mineral Grain Extraction for Geometallurgical Studies Using Segment Anything Model (SAM) and Core Scanning Techniques, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-13917, https://doi.org/10.5194/egusphere-egu25-13917, 2025.

Synthetic data has become an indispensable tool in climate science, offering extensive spatio-temporal
coverage to address data limitations in both current and future scenarios. Such synthetic data, derived
from climate simulation models, must exhibit statistical consistency with observational datasets to ensure
their utility. Among global climate simulation initiatives, the Coupled Model Intercomparison Project
Phase 6 (CMIP6) represents the latest and most comprehensive suite of General Circulation Models
(GCMs). However, the substantial High Performance Computing (HPC) resources required for these
physics-based models limit their accessibility to a broader research community. In response, genera-
tive machine learning models have emerged as a promising alternative for simulating climate data with
reduced computational demands.
This study introduces an ensemble model based on the Pix2Pix conditional Generative Adversarial
Network (cGAN) to generate high-resolution spatio-temporal maps of monthly global Sea Surface Tem-
perature (SST) with significantly lower computational cost and time. The proposed model comprises two
components: the GAN, which produces simulated SST climatology data , and the Predictor, which is
trained with the variability of the data that forecasts SST anomaly for the subsequent month using the
output data from the previous month. Both components contain the same architecture, but the training
processes are different. The predictor model can be fine-tuned with observed data for some epochs to
adopt its domain.
The ensemble model was calibrated with monthly SST observations from the COBE dataset as in-
put and output. The Empirical Orthogonal Functions (EOF) shows the model’s ability to simulate the
variabilty of the observed data. The model’s performance was evaluated using the temporal Pearson cor-
relation coefficient and mean squared error (MSE). Results demonstrate that the ensemble cGAN model
generates maps with statistical characteristics closely matching those of CMIP6 simulations and obser-
vations, achieving a mean temporal correlation coefficient around 0.5 and an MSE around 1.13 for both
cases.

How to cite: Chakraborty, D. and Mitra, A.: Simulation of Monthly Global Sea Surface Temperature Data using Ensemble GAN Model, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-14821, https://doi.org/10.5194/egusphere-egu25-14821, 2025.

Deep neural networks have revolutionized various fields due to their remarkable adaptability, enabling them to address related tasks through retraining and transfer learning. These capabilities make them invaluable tools for diverse applications, including climate and hydrological modeling. In an earlier work (Mishra Sharma et al., 2024), we introduced a novel neural network architecture, the Max-Average U-Net (MAUNet), which leverages Max-Average Pooling to downscale gridded precipitation data to higher spatial resolutions. The model demonstrated significant improvements in resolving finer-scale precipitation features, making it well-suited for climate data applications.

In this study, we utilized the MAUNet architecture to tackle the critical task of bias correction in satellite-based precipitation estimates. Bias correction is essential for improving the reliability of precipitation data derived from satellite missions, which often exhibit systematic discrepancies compared to ground-based measurements. Specifically, we focused on correcting biases in precipitation estimates from the Tropical Rainfall Measuring Mission (TRMM) by calibrating them against high-resolution, ground-based gridded datasets from the India Meteorological Department (IMD).

Our experimental results reveal that MAUNet effectively reduces biases in TRMM precipitation estimates, achieving significantly improved agreement with ground truth data. This success is attributed to the model’s robust feature extraction and reconstruction capabilities, which enable it to learn and correct systematic errors in satellite data. The findings also highlight the potential of advanced neural network architectures in addressing bias correction challenges.

This work underscores the utility of deep learning architectures in precipitation modeling, contributing to broader goals of improving the spatial distribution of precipitation estimates. By bridging the gap between satellite observations and ground truth, the MAUNet model offers a comprehensive solution for enhancing precipitation datasets, with significant implications for climate research, hydrological studies, and policy planning.

How to cite: Mishra Sharma, S. C. and Mitra, A.: Leveraging MAUNet for Bias Correction of TRMM Precipitation Estimates, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-14839, https://doi.org/10.5194/egusphere-egu25-14839, 2025.

The rising frequency and severity of landslides, exacerbated by the effects of climate change and human development in unstable areas, call for effective risk management strategies. In this context, a systematic collection of all the available data regarding geotechnical aspects, in particular geomaterial parameters, results plays a crucial role, providing a decisive contribution to define strategies for sustainable landslide risk management.

In this work, we present the translation of a geotechnical database to the aims of the project Tech4You Innovation Ecosystem – Goal 1 - Pilot Project 1, useful to identify the typical landslide scenarios, to identify sufficient knowledge for the definition of the geotechnical model and geomaterials typing in similar geo-environmental contexts. The database contains the results of laboratory tests carried out in the past by researchers at CNR IRPI in Rende, relating to 11 sites in Calabria, of which 10 in the Province of Catanzaro and 1 in the Province of Vibo Valentia.  For each site, geotechnical characterisation data of the geomaterials, which represent a key cognitive element, were grouped by type of laboratory test (grain size, indices, Atterberg limits, oedometric, direct shear and triaxial tests). We uploaded these data to validate a tool, named GeoDataTech vers. 2.0, which is an update of a previous version. In particular, we have tested the correct functioning (display, query, extraction data) with a significant sample of data. GeoDataTech vers. 2.0 can manage 2399 laboratory tests to date: 61 oedometric tests, 636 grain size, 537 indices, 78 Atterberg limits, 454 specific gravity, 512 direct shear tests and 121 triaxial tests.

This tool will be available to a wide range of stakeholders (researchers, professionals, territorial administrations, public bodies and citizens) allowing us to acquire, interrogate, export data and to upload their own files to integrate them into the database of the tool, performing advanced analyses with reference to the typification of geomaterials. By enabling the sharing of such data between researchers, practitioners and public institutions, the geotechnical tool will contribute significantly to improving disaster prevention strategies, in particular with regard to the reduction of landslide risks, thereby responding to the growing demand for accessible and interoperable data networks that increase synergic interdisciplinary research on topics such as landslide hazard.

This work was funded by the Next Generation EU—Italian NRRP, Mission 4, Component 2, Investment 1.5, call for the creation and strengthening of ‘Innovation Ecosystems’, building ‘Territorial R&D Leaders’ (Directorial Decree n. 2021/3277)—project Tech4You—Technologies for climate change adaptation and quality of life improvement, n. ECS0000009. This work reflects only the authors' views and opinions, neither the Ministry for University and Research nor the European Commission can be considered responsible for them.

How to cite: Scarcella, G. E., Aceto, L., and Gullà, G.: A relevant accessible and interoperable geotechnical data tool to support the landslide risk management, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15216, https://doi.org/10.5194/egusphere-egu25-15216, 2025.

Changes in land use and cover (LULC) serve as critical indicators of socioeconomic and environmental shifts induced by both natural and man-made factors. This assessment was carried out in the Imphal valley region to forecast changes in land use and land cover. In order to examine the spatiotemporal distributions of LULC, the LULC Classification was analysed using Landsat images from 2007, 2014, and 2017. The CA-Markov Chain model was used to simulate the future LULC for the year 2030 of Imphal valley region based on these the past LULCs. The model result showed that wetland herbaceous will decline by 3.3% and settlement area will expand by 28.71%. The Imphal city area is where the majority of the expanding settlement area is located. As a vital resource for future planning initiatives, this study suggests planners, environmentalists, and decision-makers to prioritise sustainable practices and make appropriate decisions for the sustainability of the region.

How to cite: Nongthouba, M., Oinam, B., and Sachidananda, K.: Prediction of landuse landcover using CA-Markov model for the valley regions of Manipur, India, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15381, https://doi.org/10.5194/egusphere-egu25-15381, 2025.

GNSS (Global Navigation Satellite System) data play a crucial role in both scientific research and practical applications. GNSS datasets are used to monitor atmospheric conditions, tectonic plate movements, and Earth deformation, providing valuable insights for geodetic and geophysical studies. Although widely accessible, GNSS data often lacks the necessary structure and metadata for effective reuse, particularly for data-driven research based on machine learning. To address these challenges, we applied the FAIR (Findable, Accessible, Interoperable, Reusable) data principles to GNSS RINEX observation files hosted by the EUREF Historical Data Centre (EUREF-HDC).

The EU action plan “Turning FAIR into Reality” introduced the concept of FAIR Digital Objects (FDOs), emphasizing the need for Persistent Identifiers (PIDs) and rich, standardized metadata to ensure data can be reliably found, accessed, utilized, and cited. Building on this foundation, we developed a multi-layered FDO structure centered on GNSS RINEX data. Given the established nature of the EUREF-HDC repository, we adapted the FDO concept by prioritizing structured metadata, followed by persistent identifiers and robust (meta)data access procedures.

To implement this approach, we designed the GNSS-DCAT-AP metadata schema, assigned PIDs to both data and metadata, and developed web services enabling humans and machines alike to seamless search, retrieve, and download (meta)data. The effectiveness of our solution was evaluated using the FAIRsFAIR Data Object Assessment Metrics, demonstrating a significant improvement in FAIR compliance.

This work showcases the feasibility of transforming GNSS RINEX data into FAIR Digital Objects and could provide a practical roadmap for other geospatial data repositories seeking alignment with FAIR principles.

How to cite: Bruyninx, C., Miglio, A., Fabian, A., Legrand, J., Pottiaux, E., and Bamahry, F.: Transforming GNSS Data into FAIR Digital Objects, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-16092, https://doi.org/10.5194/egusphere-egu25-16092, 2025.

Tropical Cyclones (TCs) are among the most impactful weather phenomena, with climate change intensifying their duration and strength, posing significant risks to ecosystems and human life. Accurate TC detection, encompassing localization and tracking of TC centers, has become a critical focus for the climate science community. 

Traditional methods often rely on subjective threshold tuning and might require several input variables, thus making the tracking computationally expensive. We propose a cost-effective hybrid Machine Learning (ML) approach consisting in splitting the TC detection into two separate sub-tasks: localization and tracking. The TC task localization is fully data-driven: multiple Deep Neural Networks (DNNs) architectures have been explored to localize TC centers using a different set of input fields related to the cyclo-genesis, aiming also at reducing the number of input drivers required for detection. A neighborhood matching algorithm is then applied to join previously localized TC center estimates into potential trajectories over time. 

We train the DNNs on 40 years of ERA5 reanalysis data and International Best Track Archive for Climate Stewardship (IBTrACS) records across the East and West North Pacific basins. The hybrid approach is then compared with four state-of-the-art deterministic trackers (namely OWZ, TRACK, CNRM and UZ), reporting comparable or even better results in terms of Probability of Detection and False Alarm Rate, additionally capturing the interannual variability and spatial distribution of TCs in the target domain. 

The resulting hybrid ML model represents the core component of a Digital Twin (DT) application implemented in the context of the EU-funded interTwin project.

How to cite: Donno, D., Accarino, G., Elia, D., Scoccimarro, E., and Gualdi, S.: Hybrid Machine Learning approach for Tropical Cyclones Detection, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-16363, https://doi.org/10.5194/egusphere-egu25-16363, 2025.

The STARS4Water project addresses the critical need to understand the impacts of climate change and anthropogenic activities on freshwater availability and ecosystem resilience at the river basin scale. By developing innovative data services and models tailored to stakeholder needs, the project will improve decision-making processes for sustainable water resource management. A distinctive feature of STARS4Water is its focus on co-creating solutions with local stakeholders using a living lab approach, ensuring that newly developed tools remain relevant and usable beyond the life of the project.

This extension of the original project—funded with a special grant from Unitatea Executivă pentru Finanțarea Învățământului Superior, a Cercetării, Dezvoltării și Inovării (UEFISCDI) from Romania—focuses on a detailed change detection analysis to monitor and quantify land cover transformations in the emblematic Danube Delta region. The objective is to assess how environmental and anthropogenic changes have influenced this ecologically significant wetland over several decades. To achieve this, a comprehensive database of multispectral satellite images from the Landsat archive, spanning from 1985 to 2023, will be constructed. The long-term dataset enables a detailed temporal analysis, important for detecting land cover dynamics over time.

The methodology involves several key phases: (1) data collection and preprocessing of Landsat satellite images to correct errors and align imagery for consistent comparative analysis; (2) sampling and training a deep learning model using convolutional neural network (CNN) architectures, to classify various land cover types; (3) performing land cover classification on the processed images using the trained model, followed by accuracy assessment; and (4) conducting a comprehensive change detection analysis to quantify and interpret the observed transformations in land use and land cover.

The results of this analysis will deliver important knowledge on the long-term dynamics of the Danube Delta landscape, highlighting critical changes with implications for biodiversity, water management and ecosystem services. This approach will support adaptive ecosystem management and contribute to the scientific understanding of climate-related and anthropogenic changes in fragile wetland ecosystems.

Acknowlegments

This work was supported by a grant of the Ministry of Research, Innovation and Digitization, CNCS/CCCDI - UEFISCDI, project number PN-IV-P8-8.1-PRE-HE-ORG-2023-0094, within PNCDI IV.

How to cite: Scrieciu, A. and Toma, A.: Monitoring Long-Term Land Cover Transformations in the Danube Delta using Landsat Satellite Imagery, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-16549, https://doi.org/10.5194/egusphere-egu25-16549, 2025.

Abstract: Cloud cover poses a significant obstacle in harnessing multi-spectral satellite imagery for various earth observation applications including disaster response, land use and land cover mapping. To address this issue, this study investigates the potential of Prithvi WxC foundation model (Johannes Schmude et al., 2024), a deep learning architecture designed for weather and climate applications, to perform cloud gap imputation. By leveraging its ability to capture atmospheric dynamics and predict missing data, Prithvi WxC offers a promising solution.

The primary objective is to assess the accuracy and efficiency of Prithvi WxC in reconstructing cloudy pixels in Moderate Resolution Imaging Spectroradiometer (MODIS) surface reflectance; MOD09 (Eric Vermote, 2015). MOD09 data provides valuable information about earth surface, cloud cover and atmospheric conditions, which are instrumental in informing the Prithvi WxC model during the finetuning and imputation process.

This research evaluates the Prithvi WxC foundation model for cloud gap imputation applications and benchmarks its performance against other foundation models, such as Prithvi EO (Jakubik et al., 2023, 2024). The process begins with preprocessing the MOD09 dataset, filtering out missing and cloudy pixels to create clean visible patches, while real-world cloudy patches are used as masks. The preprocessed data is then resampled to align with the temporal and spatial resolution requirements of both the Prithvi WxC and Prithvi EO foundation models. Through rigorous fine-tuning strategies, these models learn to reconstruct the masked regions, effectively filling the gaps caused by cloud cover. Finally, the fine-tuned foundation models are benchmarked using quantitative metrics, such as the Structural Similarity Index Measure (SSIM) and Mean Absolute Error (MAE), complemented by qualitative visual analysis.

This research explores the potential of Prithvi WxC foundation model, pre-trained on extensive weather and climate data, to improve cloud gap imputation in satellite imagery, and subsequently benchmarks it against earth observation foundation models, such as Prithvi EO. Through this evaluation, we aim to enhance scientific understanding via multi-modality and sensor-independent approaches.

 References

Johannes Schmude, Sujit Roy, Will Trojak, Johannes Jakubik, Daniel Salles Civitarese, Shraddha Singh, Julian Kuehnert, Kumar Ankur, Aman Gupta, Christopher E Phillips, Romeo Kienzler, Daniela Szwarcman, Vishal Gaur, Rajat Shinde, Rohit Lal, Arlindo Da Sil: Prithvi WxC: Foundation Model for Weather and Climate." arXiv preprint arXiv:2409.13598, 2024.

C. Roger, E. F. Vermote, J. P. Ray: https://modis-land.gsfc.nasa.gov/pdf/MOD09_UserGuide_v1.4.pdf. NASA, MODIS Surface Reflectance User’s Guide, Collection 6, 2015.

Daniela Szwarcman, Sujit Roy, Paolo Fraccaro, Þorsteinn Elí Gíslason, Benedikt Blumenstiel, Rinki Ghosal, Pedro Henrique de Oliveira, Joao Lucas de Sousa Almeida, Rocco Sedona, Yanghui Kang, Srija Chakraborty, Sizhe Wang, Ankur Kumar, Myscon Truong, Denys: Prithvi-EO-2.0: A Versatile Multi-Temporal Foundation Model for Earth Observation Applications. https://arxiv.org/abs/2412.02732, 2024.

Johannes Jakubik, Sujit Roy, C. E. Phillips, Paolo Fraccaro, Denys Godwin, Bianca Zadrozny, Daniela Szwarcman, Carlos Gomes, Gabby Nyirjesy, Blair Edwards, Daiki Kimura, Naomi Simumba, Linsong Chu, S. Karthik Mukkavilli, Devyani Lambhate, Kamal Das, Ranji: Foundation Models for Generalist Geospatial Artificial Intelligence, 2023.

Eric Vermote: MOD09 MODIS/Terra L2 Surface Reflectance, 5-Min Swath 250m, 500m, and 1km. NASA LP DAAC., NASA GSFC and MODAPS SIPS, NASA, http://doi.org/10.5067/MODIS/MOD09.061, 2015.

How to cite: Medimem, T. B., Padovani, G., Kurihana, T., Kumar, A., Melgani, F., Anantharaj, V. G., and Fiore, S. L.: Finetuning and Benchmarking an AI Foundation Model for Cloud Gap Imputation , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-16634, https://doi.org/10.5194/egusphere-egu25-16634, 2025.

The urgency of the climate crisis and the need to accelerate learning and climate action requires that we build on previous knowledge, rather than replicating it. There is an abundance of knowledge on climate change adaptation and mitigation dispersed across websites, projects, platforms, and documents. There is either too much information that is not easily discoverable (sitting in silos) or it is too technical or complex, and not ‘usable’ or fit for purpose in terms (e.g. language or format). Both issues cause redundancy and sometimes replication of work, wasting resources. In the worst case, they can also cause unintended consequences such as maladaptation, or increased vulnerability. However, the issue is not a lack of information, but rather how to organise and connect such knowledge to allow people to discover what already exists and put it to effective use. As such, our goal is to make climate action knowledge findable, accessible, interoperable and reusable (FAIR) and reduce climate change knowledge silos. The recently awarded FAIR2Adapt Project aims to establish a comprehensive FAIR and open data framework for CCA and to demonstrate the impact of FAIR data on CCA strategies. By making CCA data FAIR, FAIR2Adapt will accelerate adaptation actions so that they are visible, understandable, and actionable for various purposes and different types of stakeholders. FAIR taxonomies are one approach to help tackle this issue by making climate change knowledge FAIR and by ensuring, that going forward, platforms have a way to make their knowledge FAIR and thus more reusable by the climate change community. 

The Climate Connectivity Hub and Taxonomy seek to visualize and connect online platform data (e.g. Cordis, Climate-ADAPT, weADAPT, PreventionWeb) to increase discoverability, interoperability and a shared understanding of the research results and their potential application in future policy, research and practice. It builds on past knowledge to scale up and accelerate climate action whilst also identifying key knowledge gaps. This presentation will show that: 1) taxonomies are useful supporting the interoperability of online climate knowledge and can usefully emerge from combined expert and machine learning of project results (e.g. Cordis); 2) shared vocabularies and different interpretations of language and terminology add value to project planning and implementation ; and, 3) the visualization of these elements for decision-makers, planners, researchers, policy makers, etc. can help to enable and scale accelerated climate action. 

How to cite: Bharwani, S., Witton, R., Williamson, K., and Butterfield, R.: Visualizing a climate and disaster resilience taxonomy from research evidence: scaling and accelerating knowledge interoperability, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-18822, https://doi.org/10.5194/egusphere-egu25-18822, 2025.

The study explores using advanced deep learning (DL) techniques for spatial-spectral analysis to detect and map peatland degradation at a granular level. Peatlands, vital carbon sinks in global ecosystems, face degradation threats that demand precise and scalable monitoring solutions. Our method combines convolutional neural networks (CNNs), fully convolutional networks (FCNs), and 3D CNNs to examine complex spatial-spectral patterns in SAR, multispectral, and hyperspectral sensor data (e.g., Sentinel-1, Sentinel-2, PRISMA) over the temperate peatland study area of the Monte Bondone region (Latitude: 46°00’48.6” N, Longitude: 11°03'14.6” E), covering an area of 40 hectares as shown in the figures.

CNNs capture spatial relationships between precipitation, temperature, vegetation, soil, and moisture, offering a detailed view of peatland composition. Using multi-dimensional, gridded data from meteorological stations and remote sensing images, CNNs identify patterns affecting peatland health. Fully Convolutional Networks (FCNs) help with spectral unmixing, isolating land cover components at the pixel level, which aids in detecting vegetation degradation and understanding ecosystem changes.

3D CNNs incorporate temporal data to classify Peatland landscapes into different degradation states. The model identifies changes over time, distinguishing between healthy, partially degraded, and fully degraded regions. Deep clustering models also classify peatland areas into degradation states, revealing trends without labeled data.

This deep learning framework supports accurate degradation mapping through spatial-spectral feature extraction, providing precise, pixel-level information to aid ecosystem management and conservation. It helps monitor peatland health and assess environmental changes across diverse landscapes.

How to cite: Kaparthi, H. V. and Vitti, A.: Deep Learning-based Spatial-Spectral Analysis for Peatland Degradation characterization, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-18933, https://doi.org/10.5194/egusphere-egu25-18933, 2025.

Integrating heterogeneous hydrological datasets remains a significant challenge in environmental modelling due to variations in feature spaces, data distributions, and temporal and spatial scales across sources. This study introduces a Model-Agnostic Meta-Learning (MAML) approach to address the challenge of integrating heterogeneous hydrological datasets, leveraging a collection of datasets compiled from diverse sources. These datasets, characterized by varying features, distributions, and temporal and spatial scales, provide an ideal basis for evaluating MAML's ability to handle real-world data heterogeneity.

MAML’s unique capability to learn shared representations across datasets with minimal feature overlap and significant variability allows it to effectively transfer knowledge between subsets, offering a flexible and scalable solution for integrating hydrological data with diverse characteristics.

The proposed approach trains a base model on one subset of the data while utilizing MAML's meta-learning capabilities to adapt and transfer knowledge to other subsets with differing feature distributions. To test the model's adaptability, we simulate scenarios with varying degrees of feature overlap. Model performance is assessed using metrics such as mean squared error (MSE), both before and after fine-tuning on unseen data subsets.

Preliminary results demonstrate that MAML effectively learns shared representations across datasets, achieving significant improvements in prediction accuracy. Fine-tuning further enhances the model's adaptability, particularly for datasets with minimal feature overlap. These findings highlight MAML's potential as a powerful and flexible tool for integrating and predicting across heterogeneous hydrological datasets.

This study bridges the gap between advanced meta-learning techniques and hydrological applications, providing new insights into scalable and adaptable data integration methods for environmental sciences.

Keywords: Model-Agnostic Meta-Learning, hydrological datasets, data integration, heterogeneous data, meta-learning, environmental modelling, machine learning. 

How to cite: Slaimi, A. and Scriney, M.: Model-Agnostic Meta-Learning for Data Integration Across Heterogeneous Hydrological Datasets, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-19489, https://doi.org/10.5194/egusphere-egu25-19489, 2025.

The semi-arid region of Deraa Oued Noun in Morocco faces significant challenges related to water scarcity, which greatly affects the availability of groundwater resources. With recurring droughts and periods of water shortage, it is imperative to address these challenges and implement effective measures for sustainable groundwater resource management. Artificial groundwater recharge has proven to be a viable solution for alleviating water scarcity issues. By capturing and storing excess water during periods of heavy precipitation or surface water availability, artificial recharge can replenish depleted aquifers and provide a reliable water source during drought periods. However, the success of recharge projects depends on identifying suitable sites that meet specific criteria and maximize the efficiency of the recharge process.

The identification of suitable sites for artificial groundwater recharge in Daraa Oued Noun, through the integration of remote sensing, GIS (Geographic Information System), and MCDM (Multi-Criteria Decision Making) techniques, offers a promising solution to address water scarcity challenges in the context of climate change. The proposed research project aims to provide valuable and spatially explicit information for strategic groundwater resource management.

 This study was conducted in the Deraa Oued Noun district, where water shortages have been observed over the years. The research utilized geology, soil, land use, stream data, and Sentinel-2 and DEM images to develop thematic layers, including lithogeology, soil, slope, lineament density, land use, stream density, and water surface. Additionally, data on the vadose zone thickness were incorporated to enhance the analysis.

By integrating GIS and image processing techniques, these thematic layers were utilized to prepare groundwater recharge maps of the area through a weighted overlay method on a GIS platform. The results revealed that artificial recharge potential was high in the northern and western parts of the study area.

By following a systematic and rigorous methodology, including data collection, remote sensing analysis, MCDM evaluation, and site validation, this project aims to contribute to the successful implementation of artificial recharge projects in the region. By maximizing the efficiency of the recharge method, these projects will help ensure sustainable water supply, mitigate the impacts of drought, and promote long-term water security in Derâa Oued Noun and similar semi-arid regions.

How to cite: Fri, R., Scozzari, A., Haida, S., Kili, M., Erraoui, L., Chaou, J., Mridekh, A., Goumghar, L., and El Mansouri, B.: Using Remote sensing and geographic information system for delineating suitable sites for artificial groundwater recharge: A multi-criteria decision-making approach., EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-20324, https://doi.org/10.5194/egusphere-egu25-20324, 2025.

We present LUCIE, a data-driven atmospheric emulator that remains stable during autoregressive inference for a thousand of years with minimal drifting climatology. LUCIE was trained using 9.5 years of coarse-resolution ERA5 data, incorporating 5 prognostic variables, 2 forcing variables, and one diagnostic variable (6-hourly total precipitation), all on a single A100 GPU over a two-hour period. LUCIE autoregressively predicts the prognostic variables and outputs the diagnostic variables similar to AllenAI’s ACE climate emulator. Unlike all the other state-of-the-art AI weather models, LUCIE is neither unstable nor does it produce hallucinations that result in unphysical drift of the emulated climate. The low computational requirements of LUCIE allow for rapid experimentation including the development of novel loss functions to reduce spectral bias and improve tails of the distributions. Furthermore, LUCIE does not impose true sea-surface temperature (SST) from a coupled numerical model to enforce the annual cycle in temperature. We demonstrate the long-term climatology obtained from LUCIE as well as subseasonal-to-seasonal scale prediction skills on the prognostic variables. LUCIE is capable of 6000 years of simulation per day on a single GPU, allowing for O(100)-ensemble members for quantifying model uncertainty for climate and ensemble weather prediction.

How to cite: Guan, H., Arcomano, T., Chattopadhyay, A., and Maulik, R.: LUCIE: A Lightweight Uncoupled ClImate Emulator with long-term stability and physical consistency for O(1000)-member ensembles, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-20616, https://doi.org/10.5194/egusphere-egu25-20616, 2025.

Analysis of pollen events was conducted using Hirst-type volumetric samplers in Athens and Thessaloniki in combination with CALIPSO vertical aerosol profiles. While Hirst-type ‎[1] volumetric samplers are used to confirm and characterize pollen at ground level, the understanding of pollen vertical distribution and transport is still limited. The utilization of Light Detection and Ranging (LIDAR) for identifying different pollen types is increasingly prevalent, as the depolarization ratio is related to the shape of the pollen particles while other non-spherical particle types are absent ‎[2].
Samplers are situated on the buildings’ rooftops of the Physics and Biology Departments, in Athens and Thessaloniki, respectively. Following ‎[2], intense pollen events are considered when the pollen concentration exceeds 400 grains m-3 for a minimum of two hours each day.
CALIPSO provides unique vertical profile measurements of the Earth’s atmosphere on a global scale ‎[3], with the ability to distinguish between feature types (i.e., clouds vs. aerosol) and subtypes (i.e., marine, dust, clean continental). Only case studies where CALIPSO aerosol layers were classified as marine, dusty marine, dust, or polluted dust were analyzed.
Model simulations were used to exclude the presence of other depolarizing aerosol types. HYSPLIT (Hybrid Single-Particle Lagrangian Integrated Trajectory) was used to trace the origin of the air masses. The atmospheric model RAMS/ICLAMS (Regional Atmospheric Modeling System/Integrated Community Limited Area Modeling System) was selected to describe dust and sea-salt emissions and transport.
Mean values of lidar-derived optical properties inside the detected pollen layers are provided from CALIPSO data analysis. Specifically, there are three observed aerosol layers, one over Athens (12-3-2021) and two over Thessaloniki (2-3-2020, 10-4-2020). Particulate color ratios of 0.652 ± 0.194, 0.638 ± 0.362, and 0.456 ± 0.284, and depolarization ratios of 8.70 ± 6.26%, 28.30 ± 14.16%, and 8.96±6.87 % for 12-3-2021, 2-3-2020 and 10-4-2020, respectively, were misclassified by CALIPSO as marine-dusty marine, dust and polluted dust. The pollen analysis conducted on the 12th of March 2021 indicated that the dominant pollen types were 69% Pinaceae and 24% Cupressaceae. On the 2nd of March 2020, Cupressaceae accounted for 97% of the total pollen, while on the 10th of April 2020, Carpinus represented 76% and Platanus 15%. Consequently, during periods of intense pollen presence, CALIPSO vertical profiles and aerobiological monitoring techniques may be used synergistically to better characterize the atmospheric pollen layers.

Acknowledgements
The research work was supported by the Hellenic Foundation for Research and Innovation (H.F.R.I.) under the “Basic Research Financing (Horizontal support for all Sciences), National Recovery and Resilience Plan (Greece 2.0)” (Project Number: 015144).

[1] J. M. Hirst, Annals of Applied Biology 39, 157-293 (1952).
[2] X. Shang et al., Atmos. Chem. Phys. 20, 15323–15339 (2020).
[3] D. M. Winker et al, BAMS 91, 1211–1229 (2010).

How to cite: Karageorgopoulou, A., Christos, S., Thanasis, G., Χiaoxia, S., Ioanna, P., Vassilis, A., and Elina, G.: Assessment of Atmospheric Pollen Presence in Urban Areas of Greece During CALIPSO Overpasses, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-21759, https://doi.org/10.5194/egusphere-egu25-21759, 2025.

The Geostationary Ocean Color Imager-II (GOCI-II), which was launched on February 19, 2020, offers an increased observation times within a day and finer spatial resolution than those of its predecessor, the Geostationary Ocean Color Imager (GOCI), which was launched in 2010. To ensure the reliability of GOCI-II data for practical applications, the accuracy of remote sensing products must be validated. In this study, we employed in situ data from Lake Taihu for validation. We assessed the accuracy of GOCI-II products, including the remote sensing reflectance inverted via two atmospheric correction algorithms (ultraviolet (UV) and near-infrared (NIR) atmospheric correction algorithms), as well as the chlorophyll a (Chl-a) concentration, total suspended matter (TSM) concentration, and phytoplankton absorption coefficient (a_ph). Our results revealed that the UV atmospheric correction algorithm provided a relatively higher accuracy in Lake Taihu, with average absolute percentage deviations (APDs) of the remote sensing reflectance across different bands of 25.17% (412 nm), 29.69% (443 nm), 22.27% (490 nm), 19.38% (555 nm), 36.83% (660 nm), and 33.0% (680 nm). Compared to the products generated using the NIR atmospheric correction algorithm, the derived Chl-a concentration, TSM concentration, and a_ph products from the UV algorithm showed improved accuracy, with APD values reduced by 16.92%, 3.32%, and 10.91%, respectively. When using UV correction, the 412 nm band performed better than the 380 nm band, likely due to the lower signal-to-noise ratio of the 380 nm band and smaller extrapolation errors when assuming a zero signal for the 412 nm band. Considering that the NIR algorithm is suitable for open ocean waters while the UV algorithm demonstrates higher accuracy in highly turbid environments, a combined UV-NIR atmospheric correction algorithm may be more suitable for addressing different types of water environments. Additionally, more suitable retrieval algorithms are needed to improve the accuracy of Chl-a concentration and a_ph in eutrophic waters.

How to cite: Zhao, M., Li, H., Li, H., Zhang, X., Ding, X., and Gong, F.: Assessment of GOCI-II satellite remote sensing products in Lake Taihu, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-2237, https://doi.org/10.5194/egusphere-egu25-2237, 2025.

This study explores studies on lakes and paleolakes originating from natural effects. The main objective is to perform a bibliometric analysis of research on naturally occurring lake environments worldwide, covering the period from 2014 to 2024. Data extracted from 1687 documents in the Scopus database were analyzed using VOSviewer software. The results reveal a strict trend towards a focus on geosciences and the environment, underlined by research. This study particularly highlights the relationships between authors, co-authors, keywords, and publishers of specialized journals in this research field, thus providing essential information to guide future research and to value the role of these geological environments, which are rare in the world, based on essentially multidisciplinary geoscience approaches.

How to cite: Abbach, J., El Moussaoui, S., El Talibi, H., and Bouiss, C. E.: bibliometric analysis of natural lakes and paleolakes origin of natural events, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-4994, https://doi.org/10.5194/egusphere-egu25-4994, 2025.

Rock masses are characterized by the complex hierarchical structures involving various scale levels. The deformation of rock masses is primarily controlled in weak structural layers between rocks, whereas the rock block can be regarded as a non-deformable block and can move as a whole. In consequence, a new dynamic phenomenon, namely the pendulum-type wave, has emerged, which is a kind of nonlinear displacement wave caused by the overall movement of relatively intact large-scale rock blocks. Aiming at the complex hierarchical structures of rock masses and low-frequency characteristics of pendulum-type waves, the blocky rock masses composed of granite blocks and rubber interlayers are simplified into the block-spring model and wave motion model. Based on Bloch theorem and d’Alembert’s principle, the dispersion relation and equations of motion of 1D blocky rock masses are determined. Research shows that with the increase of the rock size and geomechanical invariant, the initial frequency of the first attenuation zone gradually decreases, and only the low-frequency waves lower than that frequency can propagate in blocky rock masses, which reveals the mechanism of low-frequency characteristics of pendulum-type waves theoretically. The equivalent substitution for the two models and their errors are given, and the results show that the equivalent substitution of the two models is not universal and unconditional. Finally, the influence of hierarchical structures on the dispersion relation and dynamic response is further studied. The larger the stiffness ratio, or the higher the order of hierarchical structures, the smaller is the effect of ignoring the high-order hierarchical structures.

How to cite: Jiang, K. and Qi, C.: Research on dispersion relation and dynamic properties of pendulum‑type waves in 1D blocky rock masses with complex hierarchical structures, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-5033, https://doi.org/10.5194/egusphere-egu25-5033, 2025.

Coral reefs in the Mariana Islands serve important roles for the islands’ ecology and economy, contributing to the region’s fisheries, tourism, coastal protection, education, and cultural histories. Despite their immense value, the resilience of these marine ecosystems is threatened by an array of climate-change induced stressors, including ocean acidification and coral bleaching. In response, a team from the University of Guam (UOG) launched a large-scale coral reef mapping campaign to monitor priority reef sites throughout the Mariana Islands using drone technology. The UOG team, consisting of researchers and remote pilots funded by the USGS, Pacific Islands Climate Adaptation Science Center, NASA Guam EPSCoR, and NASA Guam Space Grant, has been conducting drone-based missions to capture high-resolution imagery of priority coral reef sites across Guam and Saipan. Their efforts aim to gather aerial data of coral reefs in Micronesia, providing resource managers with essential information regarding response and recovery. Initially, the campaign used of NASA’s fluid lensing technology developed by Chirayath (2019) for coral reef mapping. This technology combines unmanned aerial systems (UAS), off-the-shelf technology, and machine learning algorithms to create detailed coral reef maps by filtering out distortions caused by light and ocean waves, resulting in clear, high-resolution imagery. In 2024, this process was augmented to employ a new methodology that strategically uses RGB sensors and low tides. This system allows the remote pilots to capture the areas and produce orthomosaic maps at much more efficient rates while maintaining high-resolution quality. By providing these datasets within a shorter turn-around time, local natural resource managers are able to get a timely snapshot of the coral reef sites – providing crucial data of the ecosystem’s health that can help inform conservation decisions. This presentation will outline the collaborative efforts between UOG and regional partners, demonstrating how drones and fluid lensing technology are innovating coral reef monitoring efforts. It will explore how the collected data can help local resource managers make informed decisions regarding coral reef management, showcase coral reefs to the general public, ultimately transforming how local communities can contribute to coral reef resilience.

How to cite: Sayama, J. and Fausto, K.: Innovating Coral Reef Mapping with Drones & NASA Fluid Lensing Technology in the Mariana Islands, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-5359, https://doi.org/10.5194/egusphere-egu25-5359, 2025.

In today's changing climate, there is an urgent need to understand the adverse impacts of climate change on natural environments, infrastructures, and industries.Particularly permafrost regions in the Arctic are highly vulnerable to global warming, impacting both the environment and socioeconomic aspects. Thus, systematic monitoring of such environments, is of paramount significance. Advances in Geoinformation technologies, and in particular in Earth Observation (EO), cloud computing, GIS, web cartography create new opportunities and challenges for Arctic research examining the socioeconomic impact of climate change.The rapid advancements in EOin particular have led to an exponential increase in the volume of geospatial data that come from spaceborne EO sensors. This surge, combined with the fast developments in GIS and web cartography present significant challenges for effective management, access, and utilization by researchers, policymakers, and the public. Consequently, there is a growing need for advanced methodologies to organize, process, and deliver geospatial information that comes from EO satellites in an accessible and user-friendly manner.

Recognizing thepromising potential of geoinformation technologies, the European Union (EU) has funded several research projects that leverage advanced technologies such as geospatial databases and WebGIS platforms to streamline EO data handling and dissemination. One such project is EO-PERSIST (http://www.eo-persist.eu), which aims to create a collaborative research and innovation environment focusing on leveraging existing services, datasets, and emerging technologies to achieve a consistently updated ecosystem of EO-based datasets for permafrost applications. To formulate the socioeconomic indicators, the project exploits state of the art cloud processing resources, innovative Remote Sensing (RS) algorithms, Geographic Information Systems (GIS)-based models formulating, exchanging also multidisciplinary knowledge.EO-PERSIST innovative approach is anticipated to contribute to more informed decision-making and broader data accessibility for researchers, policymakers, and other stakeholders.

The present contribution aim is two-fold: at first, to provide an overview of EO-PERSIST Marie Curie Staff Exchanges EU-funded research project; second, to present some of the key project outputs delivered so far relevant to the selected Use Cases of the project and the geospatial database developed for assessing the socioeconomic impacts of climate change in the permafrost Arctic regions.

This study is supported by EO-PERSIST project which has received funding from the European Union's Horizon 2020 research and innovation program under grant agreement No. 101086386.

KEYWORDS:earth observation, cloud platform, Arctic, socioeconomic impact

How to cite: Tselos, G.-N., Detsikas, S. E., Kroszka, B., Grzybowski, P., and Petropoulos, G. P.: Enhancing the use of Geoinformation technologies to assess the socioeconomic impacts of climate change in the Arctic: Insights from the EO-PERSIST Project, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-6240, https://doi.org/10.5194/egusphere-egu25-6240, 2025.

The U.S. territory of Guam is threatened annually by high-intensity storms and typhoons due to its location in the western Pacific Ocean. The island’s infrastructure – buildings, roads, and utilities – bear the brunt of typhoon damage, which in turn affects public health, the economy, and natural resources. Traditionally, these impacts have been observed via satellite, radar, and official weather stations.  Damages are assessed in the aftermath of the typhoon with a manual, on-the-ground approach led by the National Weather Service (NWS). This is often exhaustive and time-consuming for the assessment team. Observations from the ground can inadvertently create data gaps on damage assessments due to inaccessible areas caused by vegetative and construction debris, and flooded roads and pathways. This may not capture many impacts eligible for local or federal assistance. To address these data gaps and augment damage assessments, the University of Guam (UOG) Drone Corps program aims to assist local and federal government agencies (e.g., utility companies, public health, emergency services, and natural resource management) by collecting high-resolution aerial imagery to help prioritize and allocate limited resources. This presentation highlights the results of this novel collaboration of UOG, NWS, Guam Homeland Security (GHS), and the Office of the Governor of Guam in the creation of the damage assessment of Typhoon Mawar, which ravaged Guam on 24-25 May 2023. Following the typhoon, UOG worked with NWS to identify and capture imagery of vulnerable sites that were heavily impacted. This presentation will also share how UOG Drone Corps’ data was disseminated among other agencies as supplemental data for natural disaster recovery efforts. The presentation will conclude with a summary of the UOG Drone Corps program model as a resource for developing resiliency strategies for vulnerable island communities using advanced and emerging technologies. 

How to cite: Fausto, K. and Sayama, J.: Deploying UAV technology to assess typhoon impacts in vulnerable communities in Guam , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-8063, https://doi.org/10.5194/egusphere-egu25-8063, 2025.

INTRODUCTION. InSAR or Interferometric Synthetic Aperture Radar is a technique for mapping ground deformation using radar images of the Earth's surface collected from orbiting satellites. DInSAR or Differential SAR Interferometry is an active remote sensing technique based on the principle that, due to the very high stability of the satellite orbits, it is possible to exploit the informative contribution carried by the phase difference between two SAR images looking at the same scene from comparable geometries.

AIM. In this setting, the main objective of this study is to evaluate the region near the closed rock salt mine in the south of Albania. Our input for this exercise will be two images of the land near the former rock salt mine in Dhrovjan near the Blue Eye (Saranda, Albania).

RESULTS. By combining the phases of 2 images we produce an interferogram where the phase is correlated to the terrain topography and deformation so if the phase shifts related to the topography are removed from the interferogram, the difference between the resulting products will show surface deformation patterns or cure between the two acquisition dates and this methodology is called differential interferometry Processing, Phase Unwrapping, and at the end creating the displacement map. We use in our study the difference in time with the algorithm which consists of working step by step with these operators: Read the two split products, Applying Orbit files, Back-Geocoding, Enhanced Spectral Diversity, Interferogram, TOPSAR Deburst, and Write. The resulting difference of phases is called an interferogram containing all the information on relative geometry. Removing the topographic and orbital contributions may reveal ground movements along the line of sight between the radar and the target.

The next algorithm we worked with these operators: Read the debursted interferogram, TopoPhaseRemoval, Multilook, Goldstain Filtering, and Write. At the same time from Goldstain Filtering, we add the Snaphu Export operator.

Correct phase unwrapping procedures must be performed to retrieve the absolute phase value by adding multiples of 2π phase values to each pixel to extract accurate information from the signal. In this study, we will use SNAPHU, which is a two-dimensional phase unwrapping algorithm consists of working step by step with these operators: read (the wrapped image) and read (2) the unwrapped image, Snaphu Import, PhaseToDisplacement, and Write. We can display it in Google Earth after saving it as .kmz and also make a profile of the displacements.

DISCUSSION AND CONCLUSIONS

One of the SAR Interferometry applications is deformation mapping and change detection. This work demonstrates the capability of interferometric processing for the observation and analysis of instant relative surface deformations in the radar LOS direction. When two observations are made from the same location in space but at different times, the interferometric phase is proportional to any change in the range of a surface feature directly. All three stages of the work are important and require accurate interpretation knowledge, especially when working with the Snaphu program.

KEY-WORDS

InSAR, DInSAR, Interferogram

How to cite: Belba, P.: The use of InSAR and DInSAR for detecting land subsidence in Albania, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-11808, https://doi.org/10.5194/egusphere-egu25-11808, 2025.

Poincaré established a framework for understanding the dependence of a dynamical system's properties on its topology. Topological properties offer detailed insights into the fundamental mechanisms — stretching, squeezing, tearing, folding, and twisting — that govern the shaping of a dynamical system's flow in state space. These mechanisms serve as a conduit between the system's dynamics and its topology [Ghil & Sciamarella, NPG, 2023]. A topological analysis based on the templex approach [Charó et al., Chaos, 2022] involves finding a topological representation of the underlying structure of the flow by the construction of a cell complex that approximates its branched manifold and a directed graph on this complex. A pivotal feature of the cell complex that facilitates the characterization of the flow dynamics is the joining locus, upon which all the fundamental mechanisms that sculpt the flow leave a pronounced signature.

The local dimension d(x) and the inverse persistence θ(x) of the state x of a dynamical system [Lucarini et al., 2016; Faranda et al., Sci. Rep., 2017] provide information on the rarity and predictability of specific states, respectively. We demonstrate herein that these two measures, d and θ  also provide information about the localization of the joining locus.

The present work proposes a new topological method for fingerprinting a system’s nonlinear behavior using the concept of persistent generatexes. This novel approach integrates the strengths of two topological data analysis methods: the templex and persistent homologies. Rather than employing a single cell complex and a digraph to characterize the flow of the system, our approach emphasizes the localization of the joining locus through the calculation of local dimension and the inverse persistence, leading to the construction of a family of nested digraphs. The dynamical paths, namely the nonequivalent ways of travelling through the flow, are found to be the most persistent cycles; here the concept of persistence is used in the sense of the persistent homology approach [Edelsbrunner & Harer, Contemporary mathematics, 2008]. The dynamical paths give us the ‘topological fingerprinting’ of a system’s dynamics.

How to cite: Charó, G. D., Faranda, D., Ghil, M., and Sciamarella, D.: Topological fingerprinting of dynamical systems, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-12947, https://doi.org/10.5194/egusphere-egu25-12947, 2025.

The Er-Rich region is a focal area for understanding the geological evolution of the central-eastern High Atlas, which it covers almost entirely along a north-south transverse line. It is a hinge region between the two major tectonic structures of the High Atlas (the North and South Atlas faults), which reveal a framework of Meso-Cenozoic carbonate, detrital and magmatic rocks.

Previous studies have highlighted the complexity of mapping in this area. To date, no detailed geological map has been produced for this study area, with the exception of the old provisional 1:200,000 map of the Midelt-Rich High Atlas. Remotely-sensed mapping initiatives have also been carried out in the region, except that they do not provide a final interpretation as a geological map, supported by geological maps covering neighboring regions. A detailed geological map of the Er-Rich region, based on the results of remote sensing and field data, is therefore needed in the area. For this purpose, remote sensing geological mapping techniques have been applied to two types of satellite data: 1) Landsat 8 OLI (Optical Land Imager) multispectral optical data, and the Spot 5 panchromatic band acquired by the HRG-2 (High Geometric Resolution) instrument; 2) Sentinel-1 SAR data with dual polarisation (HV-HH).

All the data underwent several pre-processing or correction stages using appropriate software, in particular radiometric and atmospheric correction for Landsat 8 OLI (Optical Land Imager) images using ENVI software. The corrected product of the three Landsat 8 OLI scenes covering the region were then spatially enhanced using the Spot 5 panchromatic band to produce a multispectral image with a high spatial resolution of 5 m using ENVI software. The Sentinel-1 radar data were pre-processed using SNAP toolbox software by applying a series of corrections.

The results obtained by applying the Optimum Index Factor (OIF) method and Principal Component Analysis (PCA), allowing us to select the most significant colored compositions. Moreover, this combination enabled us to delineate with great precision the large outcrops of carbonate rocks (limestones, marl), siliciclastic rocks (conglomerates, sandstones and silts) and magmatic rocks (igneous intrusions).

The lineaments were extracted manually by visual interpretation of Sentinel-1 radar images, after applying directional filtering folowing four general orientations (N0, N45, N90, N135), enabling us to generate a synthetic structural map of the region.

The results obtained were compared with data from geological maps of adjacent areas and approved by field observations, leading to the production of a high-precision geological map, compiled with pre-existing geological literature.

How to cite: Hdoufane, M., Zafaty, O., and Ettaki, M.: Integrated remote sensing data and field investigations for geological mapping and structural analysis in the Er-Rich area (High Atlas, Morocco), EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-13782, https://doi.org/10.5194/egusphere-egu25-13782, 2025.

How to cite: Mendes, J. and Portier, M.: Uniform Data Access Layer: Advancing Data FAIRness in FAIR-EASE, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15569, https://doi.org/10.5194/egusphere-egu25-15569, 2025.

The impacts of global change, such as extreme heat and water scarcity, are increasingly threatening urban populations. Evapotranspiration (ET) plays a vital role in mitigating urban heat islands and reducing the effects of heat waves. It also serves as a proxy for vegetation water use, making it a critical tool for designing resilient green cities. Despite its importance, high-resolution mapping of urban ET that captures both spatial and temporal dynamics remains limited. This study focuses on the Paris metropolitan area, analyzing ET variability across multiple spatial scales (from 10 m to 10 km) using Sentinel-2 data from the Copernicus system. The Normalized Difference Vegetation Index (NDVI) is calculated with observation scale of 10 m, and then used as a proxy for ET. Universal Multifractal analysis, which have been widely used to characterize and model geophysical fields extremely variable across wide range of space-time scales, are implemented on this new data set. This framework is parsimonious since it basically relies on three parameters only: the mean intermittency codimension C₁, the multifractality index a and the non-conservation parameter H.  Specifically, the multifractality index α (1.3–1.5) and the mean intermittency codimension C₁ (~0.02) were derived to quantify the spatial and temporal heterogeneity of ET. The analysis, spanning 2019–2023, revealed noticeable temporal and spatial variability in ET. The study focuses on a square region of approximately 60 km × 60 km within the area around Paris. This region was further divided into multiple portions of size ranging from 2 to 10 km to assess potential variability over the studied areas. By incorporating both yearly and monthly data, the analysis captured seasonal trends as well as interannual variability, with higher variability observed during the summer months, driven by increased vegetation activity and water demand. Spatially, yearly data was analyzed and ET variability was most pronounced in densely populated areas, such as central Paris, where anthropogenic influences dominate. In contrast, forested areas and urban parks demonstrated significantly more stable ET patterns, underscoring the moderating effect of vegetation cover. These findings highlight the critical role of urban greening in mitigating extreme variability and stress on urban ecosystems.

How to cite: Zhu, S., Gires, A., Schertzer, D., Tchiguirinskaia, I., and Maksimovic, C.: Temporal and Spatial Dynamics of Urban Evapotranspiration in Paris: A Multiscale Perspective, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-16203, https://doi.org/10.5194/egusphere-egu25-16203, 2025.

The ESA Directorate of Earth Observation Programmes has been actively leveraging satellite-based environmental information to address fragility contexts, focusing on areas such as environmental crimes, crimes against humanity, cross-border crimes, and onset of crises. Over the past decade, ESA has explored digital intelligence crime analysis by employing advanced data mining and machine learning tools to uncover hidden patterns and relationships in historical crime datasets, enabling better detection, prediction, and prevention of criminal activities.

Despite these advancements, the integration of Earth Observation (EO) capabilities into investigative practices remains limited. This is due to several challenges, including low awareness of EO's potential, a lack of illustrative use cases showcasing its benefits, inconsistencies in satellite data collection compared to investigative needs, high costs of very high-resolution imagery, and restricted access to national intelligence sources. To overcome these barriers, ESA has been investigating strategies to systematically incorporate EO-derived information into investigative frameworks also as legal evidence, aiming to enhance situational awareness and support stakeholders in developing procedures to exploit EO and OSINT for addressing international crimes and assessing fragility contexts, in cooperation with international organizations including Interpol, UNODC and ICC.

Recent developments in EO technology and methodologies have created significant opportunities for more impactful applications. ESA has focused on tailoring EO-based services and OSINT to meet the case-sensitive requirements of security and development end-users, enabling better integration of EO-derived insights into intelligence models. These efforts include developing advanced EO information products that go beyond routine offerings, testing and evaluating these products in collaboration with end-users, and demonstrating their value in operational settings.

The GDA Fragility, Conflict, and Security initiative has been a cornerstone of ESA’s work, involving partnerships with International Financial Institutions (IFIs) to co-design tools that provide precise and timely information. These tools have supported initiatives aimed at reducing inequalities, promoting economic development, and enhancing environmental safety in fragile and conflict or post conflict-affected areas. By combining geospatial data with diverse data sources, ESA has delivered customized analyses and reports to improve emerging threats analysis and decision-making processes.

Several ESA initiatives have demonstrated the benefits of EO services for assessing fragility risk exposure, characterizing dynamic needs in fragile contexts, planning post-conflict reconstruction, and managing natural resources. ESA constantly engages with stakeholders, including the OECD, security organizations, and humanitarian actors, and its community of industries and research centres to promote the adoption of EO in international development, humanitarian aid, and peacebuilding. Through these efforts, ESA continues to advance the role of EO in supporting justice, accountability, and sustainable recovery in fragile settings.

How to cite: Corvino, M. and the Michela Corvino: Leveraging EO for Security and Resilience, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-17156, https://doi.org/10.5194/egusphere-egu25-17156, 2025.

The Canary Eddy Corridor is a dynamic region of mesoscale eddy activity, playing a critical role in the transport of physical properties (heat and salt) and biogeochemical properties (nutrients, larvae, plankton) in the eastern North Atlantic. This study investigates the Lagrangian evolution of the trapping capacity of mesoscale eddies according to their lifecycle phases and vertical structure (surface vs. subsurface eddies).

We combine OceanParcels (an open-source Python toolbox) and an eddy identification and tracking algorithm with the GLORYS12V1 reanalysis product and altimetry data from AVISO to simulate particle release and track trajectories within eddies. Applying the eddy tracking algorithm at surface and subsurface levels in GLORYS12V1 reveals that subsurface eddies with a surface signal exhibit subsurface rotational velocities at the eddy core that occasionally exceed those of surface eddy cores. This highlights the potential misrepresentation of eddy transport capacity when relying solely on altimetry data, without accounting for the vertical structure, which can be better resolved through a combination of model outputs and observational data, such as non-standard Argo float configurations. Furthermore, a detailed analysis of the eddy lifecycle phases shows that mature eddies exhibit substantially greater trapping depths compared to their growth and decay stages. These findings align with earlier modeling analyses of dipoles originating south of Madagascar, which also highlight enhanced trapping depths in mature eddies.

The results provide a comprehensive view of the trapping capacity of mesoscale eddies throughout their lifecycle and vertical structure, emphasizing their critical role in biophysical coupling, ecological connectivity, and the transport of biogeochemical properties, as well as microplastics and other pollutants.

Acknowledgments: The first author is grateful for the internship grants ERASMUS +, AMI-MESRI, and TIGER. 

How to cite: Vacca, D., Aguiar-González, B., and Morris, T.: Lagrangian Evolution of the Trapping Capacity of Mesoscale Eddies in the Canary Eddy Corridor: A Numerical Modeling Approach, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-17965, https://doi.org/10.5194/egusphere-egu25-17965, 2025.

The abandonment of agricultural land in India, especially paddy fields, has emerged as a significant challenge for food security and ecosystem sustainability in the country. Although rice production is vital for national food security, research on paddy land abandonment in India remains limited. Some Indian states have reported an alarming decline in paddy cultivation area over the past two decades. The study employs the Udupi district of Karnataka, India, a high-rainfall coastal region where paddy has traditionally been the dominant crop and where paddy land abandonment has been observed, as the study area. This study addresses crucial research gaps by framing these objectives for the study: (1) developing a deep learning framework that utilizes both intensity and phase information from polarimetric Synthetic Aperture Radar (SAR) data for abandoned paddy land detection, (2) leveraging recurrent neural networks (RNNs) to capture temporal patterns in abandonment, and (3) demonstrating an automated, all-weather monitoring approach that overcomes the limitations of traditional optical remote sensing in tropical regions.

Conventional monitoring approaches struggle with persistent cloud cover in tropical regions which limits effective assessment of abandonment patterns. SAR data provides unique capabilities for continuous monitoring under all weather conditions, making it particularly well-suited for tropical regions. However, previous studies have primarily underutilized SAR's potential by concentrating solely on backscattering intensity from ground range detected (GRD) products, overlooking the valuable phase information that could offer deeper insights into land use changes.  In this study, we employ Sentinel-1 Single Look Complex (SLC) data, which offers both intensity and phase information. Considering the temporal nature of paddy land abandonment, we developed a deep learning framework utilizing RNNs viz. LSTM, BiLSTM and BiGRU to effectively capture time-series patterns in the data. This framework analyzes backscattering coefficients (VV and VH polarizations) and polarimetric parameters (entropy, anisotropy and alpha angle) derived from SLC data collected during the Kharif seasons from 2017 to 2024. We carried out extensive ground truth data collection of active and abandoned paddy lands to train and validate our models. The backscattering coefficients were processed through orbit correction, radiometric calibration, TOPSAR deburst, multi-looking, speckle filtering and terrain correction. For deriving the polarimetric parameters, after basic preprocessing steps, the covariance matrix was generated followed by the polarimetric decomposition of the phase-preserved data. Results indicate that our RNN models show promising performance in detecting temporal patterns of paddy land abandonment. The method exhibits a robust ability to produce reliable abandoned land maps in regions prone to cloudy and rainy conditions. Future research should explore polarimetric features across various vegetation types in abandoned lands, expand the methodology to other agricultural systems, and examine the impact of socio-economic and topographical factors on abandonment patterns to support evidence-based land management policies.

How to cite: Kasture, S., Hegde A, A., and Umesh, P.: Deep Learning based Paddy Land Abandonment Detection Using Multitemporal Polarimetric SAR Patterns, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-18606, https://doi.org/10.5194/egusphere-egu25-18606, 2025.

The presence of rain in wind farms involves several modeling challenges, as the momentum exchanges between turbulent wakes and the particle phase present subtle phenomena. For instance, rain droplets are typically large enough to exhibit inertia relative to the air carrier phase. Under these conditions, it has been found that the gravitational settling of particles in turbulent flows may be either enhanced or hindered compared to stagnant conditions. While this has significant implications for rainfall transport, ash pollutants, and pollen dispersion, very few studies have been conducted in field conditions. Moreover, the scaling laws and non-dimensional parameters governing this phenomenon have not yet been properly identified, and determining which configurations result in the enhancement or hindrance of settling velocity remains an open question.

We propose a hybrid experimental/numerical approach. Field data from a meteorological mast located at a wind farm in Pays d’Othe, 110 km South-East of Paris, France, were used to characterize the background turbulent flow through a set of sonic anemometers. Additionally, disdrometers were employed to characterize the settling velocity of raindrops, discriminating by particle size. Numerical simulations complement this data analysis. Specifically, 3D space and time vector fields that realistically reproduce the observed spatial and temporal variability of wind fields are generated using multifractal tools. Then, 3D trajectories of non-spherical particles are simulated and their settling velocity derived.

Our findings indicate that the presence of turbulence significantly hinders the settling velocity of raindrops in turbulent environments. Our study covers several distinct rainfall events, allowing us to analyze the influence of turbulent flow properties on this phenomenon.

How to cite: Obligado, M. and Gires, A.: Rainfall Dynamics in Wind Energy Scenarios, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-19070, https://doi.org/10.5194/egusphere-egu25-19070, 2025.

Land degradation is a critical challenge to sustainable development, impacting ecosystems, economies, and communities globally. As part of the FAIR-EASE Earth Critical Zone (ECZ) pilot, this study develops a tailored Land Degradation Assessment tool based on the Trends.Earth approach. The tool aims to enhance data accessibility, integration, and usability across environmental domains, supporting decision-making and policy frameworks aligned with the United Nations Sustainable Development Goals (SDGs).
Building upon the robust Trends.Earth implementation, we can integrate customized workflows and datasets to reflect regional variability in degradation indicators, including vegetation productivity, soil health, and land cover changes. Our approach prioritizes FAIR (Findable, Accessible, Interoperable, and Reusable) principles to ensure broad usability and collaboration across scientific and policy communities.
Preliminary results demonstrate the tool's capacity to enhace the detail of the analysis and to identify degradation hotspots. Furthermore, the integration of open-source geospatial tools and standards supports a scalable framework applicable to diverse environmental contexts.
The tool is designed to be embedded within the LandSupport platform, a geospatial decision support system, further enhancing its accessibility and integration into decision-making processes for land management.
This work contributes to advancing interdomain digital services and illustrates the potential of FAIR principles in addressing complex environmental challenges. We invite feedback from the community to refine, expand and customise the tool's application, fostering collaboration for sustainable land management.

How to cite: Mauriello, I. E., Langella, G., Terribile, F., and Miralto, M.: Customizing Trends.Earth for land degradation assessment in the earth critical zone: a FAIR-EASE approach, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-19225, https://doi.org/10.5194/egusphere-egu25-19225, 2025.

Over thirty years ago, Y. Kagan speculated that seismicity could fruitfully be considered as “the turbulence of solids”.  Indeed, fluid turbulence and seismicity have many common features: they are both highly nonlinear with huge numbers of degrees of freedom.  Beyond that, Kagan recognized that they are both riddled with scaling laws in space and in time as well as displaying power law extreme variability and – we could add – multifractal statistics.

Kagan was referring to seismicity as usually conceived, as a sudden rupture process  occurring over very short time periods.  We argue that even at one million year time scales, that the movement of tectonic plates is “quake-like” and is quantitatively close to seismicity, in spite of being caused by relatively smooth mantle convection. 

To demonstrate this, we develop a multifractal model grounded in convection theory and the analysis of the GPlates data-base of 1000 point trajectories over the last 200 Myrs.  We analyzed the statistics of the dynamically important vector velocity differences where Dr is the great circle distance between two points and Dt is the corresponding time lag.  The longitudinal and transverse velocity components were analysed separately.  The longitudinal scaling of the mean longitudinal difference follows the scaling law <Δv(Δr)> ≈ Δr^H with empirical H close to the mantle convection theory value  H = 1.  This high value implies that  mean fluctuations vary smoothly with distance.  Yet at the same time,  the intermittency exponent C₁ is extremely high (C₁ ≈ 0.55) implying that from time to time there are enormous “jumps” in velocity: “Plate quakes”.  For comparison, laminar (nonturbulent) flow has H = 1 but is not intermittent (C₁ = 0), whereas fully developed isotropic fluid turbulence has the (less smooth) value H = 1/3 (Kolmolgorov) but with non-negligible intermittency C₁ ≈ 0.07 and seismicity has very large C₁ ≈ 1.3.  Our study thus quantitatively shows how smooth fluid-like behaviour for the longitudinal velocity component can co-exist with highly intermittent quake-like behaviour.

Whereas the longitudinal component is well modelled by (highly intermittent) convection, the transverse velocity is well modelled by Brownian motion.  In the temporal domain both components (including their strong correlations) display such diffusion behaviour (i.e. with classical exponent H = ½), but are highly intermittent (C_1time = C_1space/2 ≈ 0.27).  Finally, the extreme velocity differences (that appear as occasional spikes in the velocities) have power law probability tails; the “Guttenberg-Richter” exponents in the seismology literature.

The advection - diffusion model is based on an underlying multifractal space-time cascade process.  Using mantle convection theory, we show how the driving multifractal flux (ψ) is related to vertical heat fluxes, expansion coefficients, densities, viscosities and specific heats. Taking typical values predict driving fluxes very close to the observed mean <ψ> ≈ 1/(400 Myrs).  Trace moment analysis shows that the outer space-time scales of the cascade process are ≈17000 km in space and ≈ 50Myrs in time.   Whereas the former corresponds to half the Earth’s circumference, the latter is the typical time required for a plate to randomly “walk” the same distance.

How to cite: Lovejoy, S., Spiridonov, A., and Balakauskas, L.: The turbulence of solids: a multifractal plate tectonic model with Guttenberg-Richter plate “quakes” , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-20653, https://doi.org/10.5194/egusphere-egu25-20653, 2025.

A wide range of industry and scientific applications require continuous measurement of granular or liquid material levels. For environmental science, these include snow depth, water level, and soil erosion measurements. The modern market offers many different sensors, some of which can be used for these measurements, but many of them are expensive and not autonomous. Current market models also propose difficulties in organizing scalable measurement campaigns and limit coverage of large areas. For example, common snow depth and water level sensors are ultrasonic, which requires substantial power, a datalogger, $1000 cost, special mounting, and provides only a single point measurement. This immediately presents itself as both a problematic and expensive route when trying to organize observations across a large area, such as with snow distribution and redistribution. We present Universal photo-electronic level sensor that can be used to measure changes in levels of a media measuring amount of light received by exposed and covered parts of sensor. The intended use are snow depth, water level, and soil erosion measurements.

How to cite: Krassovski, M., Shaddix, T., Long, G., Clonts, L., and Ericson, N.: Universal photo-electronic level sensor, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-176, https://doi.org/10.5194/egusphere-egu25-176, 2025.

Fibre-optic sensing (FOS) techniques have gained notoriety for geoscientific and industrial applications over the past years. While most research tends to focus on spatially distributed techniques (e.g. Distributed Acoustic/Vibration Sensing), here we investigate the use of State of Polarization (SoP) sensing for ocean observation. SoP is a technique that samples spatially-integrated measurements remotely, i.e. the net change in polarization of light between the ends of an optical fibre link up to hundreds of kilometers long, which can be affected by mechanical strains on one or more sections of the fibre.

We present statistical analyses of an 11-month data set of a continuous SoP recording on a 150 km-long subsea telecommunication cable that crosses the southwestern Norwegian trench between Egersund (Norway) and a shallow (<100 m) FPSO (Floating production storage and offloading) platform on the Eigersunds bank. Our system measures net variations of the S1 Stokes parameter of polarized laser beam injected into the fibre. We observe North sea storms represented in SoP measurements as prominent anomalies with well-defined temporal and spectral features that are consistent with surface gravity wave anomalies. These events are confirmed by comparison with both, hourly ocean wave analysis numerical model time series (0.05° grid resolution) and simultaneous oceanographic measurements at locations adjacent to the cable.

Preliminary results show linear regression coefficients of determination of up to nearly 70% between rms SoP values and modeled significant wave height anomalies above 1 m at frequencies between 0.035-0.56 Hz. Remarkably, variations in the correlation statistics are found as a function of wave propagation direction, which could potentially allow for the discrimination of storms with variable azimuthal properties for a given cable link interrogated with SoP.

We highlight the value of these measurements for in-situ sea state monitoring. Although SoP systems effectively average-out all measurable signals along the entire sensed link, require access to two cable ends (or looped fibres) and are comparatively less sensitive than distributed FOS techniques, they also offer advantages over the latter as well as over established oceanographic sensors due to its relative low cost, sensor simplicity, large coverage, inherent remote data transmission, and relaxed data management requirements.

How to cite: Pelaez, J., Bjørnstad, S., and Thomas, P. J.: Storm detection in the North sea with a subsea telecommunication cable and State of Polarization sensing, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-1913, https://doi.org/10.5194/egusphere-egu25-1913, 2025.

A significant part of paleoclimate/paleoenvironmental-related geoscience research involves high-resolution sedimentological analyses reaching back the Late Pleistocene. High-resolution subaquatic imagery of the first tens of meters is required to understand Pleistocene/Holocene continental sedimentary basin infills. Sub-bottom profiler (SBP) technology is currently the best geophysical equipment to obtain the highest resolution imagery of underwater sediments. Resolution of such systems needs to be in the order of cm/dm to access centennial/millennial timescales. Continental scientific projects, however, suffer from a lack of portable & USV-designed SBP available on the market that can be easily operated in continental environments (lakes, river, lagoon, etc.).

In this context, Exail sonar system division, based in La Ciotat (France), has recently designed a new 10 kHz Chirp sub-bottom profiler for continental & shallow water investigations (5 – 15 kHz). Echoes Compact is a portable sub-bottom profiler for inland & coastal environments reaching in high-resolution Late Pleistocene & Holocene sedimentary archives. We focus our presentation on two case studies: one in maar lake Issarlès with paleosismological reconstructions in Massif central (Ardèche, France), and another in lagoon & coastal areas (Orbetello, Italy) throughout the recent publication from Brocard et al. (2024, Marine Geology), in which Echoes 10 kHz demonstrates outstanding performance in very shallow water environments (1m water depth/12 m penetration without multiple). We also present Echoes 3500 T1 geoscience applications, a 3.5 kHz system (1.8 – 6.2 kHz) with higher penetration & lower resolution than the Echoes Compact, with regards to its performance for exploring deeper coring sites especially in coarse sand environments.

Key words: sub-bottom profiler, acoustic, geophysics, geophysical software, shallow water

Brocard et al. (2024) Double tombolo formation by regressive barrier widening and landside submergence: The case of Orbetello, Italy. Marine Geology 477, 107415.

How to cite: Jouve, G., Chapron, E., and Gilles, B.: New Echoes Compact Sub-Bottom Profiler (SBP): Portable SBP for inland & coastal environments, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-3145, https://doi.org/10.5194/egusphere-egu25-3145, 2025.

Ground Penetrating Radar (GPR) is a cornerstone technology in archaeological research due to its ability to non-invasively detect subsurface structures and artifacts. By emitting electromagnetic waves and analyzing their reflections, GPR enables researchers to map buried features with high precision while preserving the site's integrity. Low-frequency GPR systems, in particular, are well-suited for archaeological contexts, offering the depth penetration required to investigate complex stratigraphic settings and revealing structures that might otherwise remain undetected. The Archaeological Park of Aeclanum, located in Mirabella Eclano (AV), Southern Italy, is a site of great historical importance, hosting remains of a city that flourished under the Samnite and Roman civilizations. Among its most significant areas is the ancient Roman Forum, once the political, religious, and commercial heart of the city. Despite its historical relevance, the Forum had not yet been uncovered, and its exact layout and architectural features remained unknown. Previous investigations using higher-frequency GPR systems were limited in depth penetration, failing to detect deeper buried structures. To overcome these limitations, a low-frequency GPR survey was conducted in the Forum area. The survey employed a monostatic antenna with a center frequency of 80 MHz, enabling a maximum exploration depth of up to 5 meters, far exceeding the capabilities of previous investigations. This deeper penetration facilitated the identification of subsurface anomalies consistent with walls, pavements, and foundations. These anomalies provided the first geophysical evidence of the Forum’s layout and subsurface features, shedding light on a previously unexplored area. The GPR data revealed a series of significant anomalies, particularly at depths ranging from 1 to 3 meters. These features were interpreted as remnants of buried architectural elements associated with the Forum, including masonry walls, paved surfaces, and foundations. The ability to detect these features highlights the critical advantage of using low-frequency equipment in archaeological investigations. To validate the geophysical findings, targeted archaeological excavations were carried out in areas corresponding to the most prominent anomalies. These excavations uncovered well-preserved structural elements, including segments of masonry walls and paved surfaces, precisely matching the GPR-detected anomalies in location, depth, and geometry. Notably, the excavation confirmed the presence of foundational elements at greater depths, which were undetectable in previous surveys. The excellent spatial correlation between the GPR data and the exposed remains demonstrated the reliability and precision of the low-frequency GPR survey in reconstructing the Forum's layout. This study underscores the importance of selecting appropriate GPR configurations for specific archaeological objectives. By combining non-invasive geophysical techniques with targeted excavation, this integrated approach maximized the efficiency of the investigation while minimizing its impact on the site. The findings reinforce the potential of low-frequency GPR as a powerful tool in uncovering and preserving buried archaeological heritage.

How to cite: Famiglietti, N. A., Lo Pilato, S., Massa, B., Memmolo, A., Migliazza, R., Osanna, M., Toniolo, L., Manco, A., and Vicari, A.: Low-Frequency GPR as a Gateway to Archaeological Investigations: The Aeclanum’s Buried Roman-age Forum (Southern Italy), EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-3621, https://doi.org/10.5194/egusphere-egu25-3621, 2025.

The DestinE Platform represents the next generation of cloud-based geospatial services, enabling the integration and management of an ecosystem to facilitate the effective use and sharing of Earth Observation (EO) data. Designed as a distributed and federated digital ecosystem, the platform promotes seamless interaction among interconnected services, building on the concept of federations to support a diverse range of users: including both service providers and consumers such as citizens, researchers, businesses, and policymakers. This collaborative environment enables knowledge-sharing, cross-disciplinary analysis, and co-creation of geospatial solutions.

The platform provides a comprehensive suite of cloud-based services, including user management, Infrastructure-as-a-Service capabilities such as storage, networking, and CPU/GPU processing, data access and retrieval, data traceability and harmonisation, and 2D/3D visualisation tools. It also offers a core software suite for local data analysis, data and software cataloguing, and a flexible framework for service providers to host advanced DestinE applications. Through its use of OVHcloud-based infrastructure and federated services, including access to Copernicus data, the DestinE Platform empowers service providers to expand their offerings, increase visibility and drive innovation.

Cloud-based geospatial platforms are driving the evolution of Earth Observation (EO) services by adopting distributed infrastructures that integrate online data repositories, collaborative computing environments, and advanced data processing tools. The DestinE Platform facilitates the onboarding of external services and resources, fostering flexibility, scalability, and support for community-driven innovation. This approach unlocks new growth opportunities for service providers, offering greater visibility and pathways for and innovation, while emphasizing the importance of maintaining high-quality services to remain competitive.

By establishing clear conditions for participation, the DestinE Platform framework fosters equitable access to resources and strengthens engagement across its user community. This structured approach embodies the shift toward a federated digital ecosystem, capable of meeting the diverse requirements of modern geospatial data consumers and providers. By enabling interoperability and fostering shared development, the platform lays the groundwork for the next generation of Digital Earth ecosystems, shaping the future of EO technology and services.

How to cite: Longuet, A., Cortese, M., Tona, C., Pellegrino, D., Scarda, B., and Giuliani, E.: Shaping tomorrow's geospatial services: The DestinE Platform, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-4149, https://doi.org/10.5194/egusphere-egu25-4149, 2025.

Accurate attitude determination is crucial for orbit determination and time-varying gravity field recovery in low-low Satellite-to-Satellite Tracking (ll-SST) missions, such as GRACE and its successor GRACE-FO. The acquisition of attitude information largely depends on the fusion of data from multiple star cameras (SCs) on board, where the covariance (i.e., noise) along each axis of each SC must be well known. Additionally, the covariance information is also vital for the subsequent reprocessing of the fused attitude. However, previous studies have assumed that the covariance is fixed and diagonal, which may not be realistic. Inspired by noise methods based on relative comparison analysis and combined with GNSS-based attitude determination performance research, considering GNSS-based attitude as an independent reference for spacecraft attitude helps quantify the SC's covariance matrix. Based on this, we propose a GNSS-aided Star Camera Fusion (GSCF) approach based on the quaternion-based generalized least squares principle for statistically optimal attitude determination in ll-SST missions. This method allows for the construction of dynamic noise models for attitude sensors and the acquisition of fused attitude covariance information, while also revealing the strong negative correlation between short-term covariance and solar angle during blind events, along with SC anomaly detection parameter. The study finds that, for daily GRACE-FO operations, compared to traditional methods, GSCF achieves an average improvement of 10 arcseconds in attitude determination, especially in the spectrum of the one Cycle -Per-Revolution (CPR) frequency and its harmonics. In terms of inter-satellite pointing variations, GSCF shows improvements in the pitch angle (maximum improvement of 1.1 arcseconds) and yaw angle (maximum improvement of 0.3 arcseconds). Additionally, GSCF has an impact on along-track measurements of up to 0.02 um/s. While these effects may be negligible for the current GRACE-FO mission, next-generation ll-SST gravity missions with ultra-high-precision payloads will be extremely sensitive to attitude determination, and the proposed GSCF method is expected to provide significant benefits in such missions.

How to cite: Pan, X., Yang, F., and Wu, Y.: A GNSS-aid star camera fusion approach towards statistically optimal attitude determination of ll-SST missions, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-5406, https://doi.org/10.5194/egusphere-egu25-5406, 2025.

How to cite: Padrón, E., Kortman, J., Lyons-Montgomery, S., Reichrath, O., Gironés, A., Taño, D., Trujillo, L., Melián, G. V., Asensio-Ramos, M., Pérez, N. M., and Hernández, P. A.: Soil H2 degassing studies: a useful geochemical tool for monitoring Cumbre Vieja volcano, La Palma, Canary Islands, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9794, https://doi.org/10.5194/egusphere-egu25-9794, 2025.

Colour variations within the same type of gemstone are caused by different abundances of trace elements. But apart from producing pretty colours, the analysis of the trace elements in gemstones is frequently used to distinguish between synthetic and natural specimen, for provenance determination, and treatment detection [1]. Every gemstone element composition is closely tied to its geological origin; thus, it is possible to determine the provenance according to those unique ‘fingerprints’. However, for some gemstones, such as sapphires, provenance determination remains challenging due to geologically similar deposits, consequently often leading to similar trace element abundances [2, 3].

This contribution presents the capabilities of a Laser Ablation and Ionization Mass Spectrometer (LIMS) instrument, called the Laser Mass Spectrometer – Gran Turismo (LMS-GT) [4, 5], in trace element analysis for gemstones. The study examined two samples (provided by the Swiss Gemmological Institute SSEF): a yellow sapphire, treated with beryllium diffusion to create the colour, and a synthetic dark blue spinel, produced via the Verneuil method. A gold coating was applied to mitigate surface charging effects, and peak-blanking was used to enhance the instrument’s limit of detection [6, 7].

The study focused on elements critical for provenance determination, including Mg, Ti, Fe, Ga, and other measured trace elements. The obtained data were compared to measurements performed with other instruments on the same gemstone varieties. This contribution will highlight the current progress of the study and discuss the advantages of the LMS-GT instrument in relation to other methodologies, emphasizing its potential to improve trace element detection and provenance determination in gemological research.

[1] S. Karampelas, et al., 2020, https://doi.org/10.1007/978-3-030-35449-7_3.

[2] Lee A. Groat, et al., 2019, http://dx.doi.org/10.5741/GEMS.55.4.512.

[3] M. Y. Krebs, et al., 2020, https://doi.org/10.3390/min10050447.

[4] M. Tulej, et al., 2021, https://doi.org/10.3390/app11062562.

[5] C. P. de Koning, et al., 2021, https://doi.org/10.1016/j.ijms.2021.116662.

[6] S. Gruchola, et al., 2022, https://doi.org/10.1016/j.ijms.2022.116803.

[7] S. Gruchola, et al., 2023, https://doi.org/10.1039/D3JA00078H.

How to cite: Knecht, L. N., Gruchola, S., Keresztes Schmidt, P., Tulej, M., Riedo, A., and Wurz, P.: Capabilities of LIMS in the Context of Trace Element Analysis and Provenance Determination of Gemstones, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10028, https://doi.org/10.5194/egusphere-egu25-10028, 2025.

Ensuring the structural integrity of concrete, particularly in critical infrastructures like bridges, requires reliable methods for identifying and localizing small-scale defects, such as fractures or damage. This study introduces a non-destructive evaluation technique that leverages the principles of both active ultrasonic testing and passive seismic methods to enhance defect localization. The method, active time-reverse imaging (A-TRI), compares ultrasonic waveforms recorded from concrete specimens in their intact and altered states to pinpoint damage.

Unlike conventional time-reverse imaging approaches that primarily rely on passive signals, A-TRI utilizes an active ultrasound source to generate wavefields. These wavefields propagate through the medium, with multiple receivers capturing the resulting signals. The experimental workflow includes two main steps: (1) Ultrasonic waves are emitted into the concrete specimen from an active transducer while the signals are recorded by an array of receivers; (2) The experiment is repeated under identical conditions, but the concrete specimen includes a predefined defect in the case of one or more arbitrary localized inclusions. The differential signal—representing the changes introduced by the defect—is then reversed in time and used as the input for a subsequent simulation. This backpropagated wavefield converges at the location of the damage, effectively visualizing the defect.

The numerical experiments in this study utilize a heterogeneous concrete model with inclusions that mimic localized changes in material properties. We highlight the current limitations of the method, including scatter size and shape, the number of receivers, and signal length, among others. Imaging conditions are applied to evaluate the precision and success of defect localization.

The results demonstrate the capability of A-TRI to accurately identify and localize multiple defects, even in complex heterogeneous media. By combining active ultrasound methods with time-reverse principles, this approach offers a robust tool for the detailed assessment of concrete structures.

How to cite: Balcewicz, M., Finger, C., Löer, K., and Saenger, E. H.:  The Current State of Active Time-Reverse Imaging: Defect Localization in Digital Concrete Physics, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-11125, https://doi.org/10.5194/egusphere-egu25-11125, 2025.

In situ measurement of the thermal conductivity of soils becomes relevant when laying structures which will dissipate heat. While the difference in thermal conductivity for different solids is relatively small compared to other physical properties, changes in porosity and soil consolidation will significantly change the effective thermal conductivity values. The goal is then not necessarily to perform a measurement with the objective of doing soil identification but as the means to understand the thermal conductivity properties locally, regardless of soil composition. Thermal data, along with industry standard measurements as described in ISO22476-1, can also be used to enhance the observable phase-space and thus improving discrimination capabilities. In this work, we introduce a new apparatus which can perform the in situ thermal conductivity measurement alongside industry-standard cone and piezocone penetration tests (CPTU) with unprecedented level of accuracy, while mitigating the impact of soil disturbances inherent to the cone penetration test.

How to cite: Idarraga Munoz, J. P. and Gregg, J.: Thermal Cone Penetration Test as a direct measurement of thermal conductivity of soils., EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-11155, https://doi.org/10.5194/egusphere-egu25-11155, 2025.

The coupling of laser ablation systems with inductively coupled plasma-mass spectrometers (LA-ICP-MS) was introduced in the 1980s. Since then, this technique has become indispensable for rapid, in-situ trace element and isotopic analysis of both natural and synthetic solid samples. Its applications extend across various fields, including chemistry, material science, geosciences as well as biological and environmental analysis, bio-imaging and forensic investigations. However, analytical advances are still needed to overcome problems in trace element analysis using LA-ICP-MS caused e.g. by interferences that cannot be solved instrumentally.

The novel and non-commercial software tool G.O.Joe is designed to facilitate the calculation of trace element mass fractions in solid samples obtained by LA-ICP-MS analysis. It is written in the Dart programming language using the Flutter framework and operates completely web-based, eliminating the need for installation and allowing access from any location with an internet connection. Since the calculation of the data itself is performed on the user’s computer, no upload of measured data to the G.O.Joe-server is necessary thereby maximizing data safety. In addition to a quick and efficient processing of large datasets (>400 analyses), G.O.Joe includes several types of optional interference corrections.

The intuitive user interface of G.O.Joe simplifies the workflow during data evaluation, including straightforward selections of peak- and background signals, importing instrument settings and reference material compositions as well as mass fractions of the internal standard to convert the measured raw signals into element mass fractions. To ensure a transparency in data processing, the results file (.xlsx) includes the calculated element mass fractions, associated statistical parameters as well as input data alongside instrument settings. In addition, the user can download a more comprehensive file with the results after each step of the calculation. The detailed Major advantages of the software are the implemented correction measures for isobaric interferences and abundance sensitivity.

G.O.Joe’s key features are presented by processing the mineral chemical analyses of two case studies, including trace element analyses of tungstates (e.g., scheelite) and silicates (e.g., garnet). Conclusively, G.O.Joe is a time-efficient, transparent and easy-to-use software, appealing to both experienced LA-ICP-MS users as well as newcomers to LA-ICP-MS data analysis. More details and the latest version of G.O.Joe are available at the following link: https://www.gojoe.software

How to cite: Krause, J., Altenberger, F., Auer, T., Auer, A., and Berndt, J.: G.O.Joe: A novel non-commercial software tool for the processing of LA-ICP-MS data, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-12301, https://doi.org/10.5194/egusphere-egu25-12301, 2025.

The increasing European demand for high-quality, safe agricultural products have led to the development of stringent control measures to certify product authenticity and geographical origin, protecting both producers and consumers from potential fraud. This study focuses on Aloe Vera, a plant containing around 200 potentially active compounds of interest in the health and wellness industry, including vitamins, minerals, anthraquinones, and polysaccharides. The Canary Islands has a unique climate, which, combined with young volcanic soils, produce exceptionally high-quality Aloe Vera. However, fraudulent Aloe Vera products falsely labelled as having Canarian origin currently represent a 21 million euros market. This situation necessitates the development of reliable scientific protocols for geographical tracing of Canarian Aloe Vera and its derivatives (juices, gels, creams, cosmetics). Chemical profiling of Aloe Vera across the Canary Islands and the Iberian Peninsula includes the determination of strontium isotopic ratios (⁸⁷Sr/⁸⁶Sr) by thermal ionization mass spectrometry (TIMS) to trace geographic origin at certified grower plantations, complemented by phytochemical profiling to verify optimal growing conditions and quantitative quality standards. Complete Aloe Vera plants have been analysed, revealing distinct bioactive organic compounds of interest, including phenolic acids, flavonoids, terpenoids, anthraquinones and derivatives, among others. ⁸⁷Sr/⁸⁶Sr ratios in Canarian Aloe Vera plants are higher (0.7065-0.7078) than those expected from their dominantly basaltic volcanic soils (0.7032-0.7068), but lower than soil values observed in mainland Spain (0.7089-0.7124). Therefore, development of a full ´fingerprint´ profile of Canarian Aloe Vera must also quantify ⁸⁷Sr/⁸⁶Sr contributions from irrigation water sources and additives used in the growing and manufacturing process.

How to cite: Cartaya-Arteaga, S., Arencibia, M., Rodríguez-Ramos, R., Claire-Coldwel, B., Asensio-Ramos, M., Melián, G. V., Padrón, E., Socas-Rodríguez, B., Hernández, P. A., Rodríguez-Delgado, M. Á., and Pérez, N. M.: Geographic authentication and quality assessment of Canary Islands Aloe: An isotopic and phytochemical approach, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-17070, https://doi.org/10.5194/egusphere-egu25-17070, 2025.

The OGS PITOP geophysical testing site, located in northeastern Italy, serves as a cutting-edge facility for developing and testing geophysical methods, technologies, and tools under realistic conditions. Covering an area of 22,000 m², PITOP is equipped with instrumented wells, permanent and mobile data acquisition systems, and active seismic and geoelectric sources. This infrastructure provides a unique environment for advancing subsurface characterization techniques.

The PITOP facility includes five wells (PITOP1-PITOP5) with depths ranging from 150 to 423 m. PITOP2 is equipped with 30 permanently installed three-axial geophones, while PITOP4 houses a Distributed Acoustic Sensing (DAS) fiber optic system. In March 2024, the addition of PITOP5 enabled a series of innovative experiments using the drill bit as a seismic source. These experiments had three primary objectives: (1) enhancing subsurface knowledge of the area, (2) testing new seismic instrumentation, and (3) performing integrated analyses using multidisciplinary methodologies.

During the PITOP5 drilling process, receivers were deployed both at the surface and in wells enabling cross-well tests acquired while drilling. The Seismic While Drilling (SWD) technique utilizes the drill bit as the seismic source. Surface receivers included standard geophones and triaxial nodes, arranged symmetrically around PITOP5.

In addition to SWD, Vibroseis was employed as an active seismic source, complementing the drill bit experiments. This enabled a comparison of results from different seismic energization methods, further enhancing the robustness of the dataset.

Preliminary seismic data analysis revealed the presence of shallow/medium depth reflectors.

Complementary to the seismic investigations, the PITOP5 upgrade included geoelectrical instrumentation. An array of electrodes was installed and cemented within the PITOP5 well to a depth of 260 m, allowing for advanced geoelectrical analyses and interdisciplinary investigations of the subsurface including the seismic methods.

Overall, a key feature of the PITOP upgrade is its capability for integrated geophysical experiments. During the drilling of PITOP5, surface seismic, SWD, and cross-hole experiments were conducted using diverse sources and acquisition systems. Data processing and integration are ongoing, with the aim of combining seismic and electrical information to provide a comprehensive understanding of subsurface structures.

The PITOP testing site represents a significant advancement for the scientific and technical community, offering a versatile platform for multidisciplinary studies. Its capabilities are particularly relevant for projects related to CO2 and energy storage, both of which are critical for climate change mitigation and the transition to sustainable energy systems.

With its state-of-the-art infrastructure and recent upgrades, PITOP is positioned as a leading facility for applied geophysics. The site not only facilitates the development of innovative geophysical techniques but also fosters collaboration among researchers, paving the way for future advancements in geosciences.

References: 

Geophysical exploration case histories at the geophysical test site PITOP - a key facility in the ECCSEL-ERIC consortium: an overview. Bellezza et al., Bulletin of Geophysics and Oceanography, 2025

How to cite: Travan, A., Bellezza, C., Barison, E., Corubolo, P., Meneghini, F., and Schleifer, A.: Integrated geophysical studies at the OGS PITOP testing site: an interdisciplinary approach for subsurface characterization, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-17269, https://doi.org/10.5194/egusphere-egu25-17269, 2025.

How to cite: Arencibia Hernández, M., Hernandez Fung, J., Cartaya, S., Melián, G. V., Asensio-Ramos, M., Padrón, E., Pérez, N. M., Hernández, P. A., and Padilla, G. D.: Soil CO2 efflux and hydrogeochemical monitoring for volcanic surveillance of Tenerife, Canary Islands, Spain, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-17365, https://doi.org/10.5194/egusphere-egu25-17365, 2025.

In recent years, the development of techniques based on integrated machine learning techniques with traditional methods like cross-correlation has dramatically improved the capability of detection, location and characterisation of seismic events.

Machine learning-based techniques have been applied to improve earthquake catalogues, aiding seismicity analysis in different geodynamic contexts. These techniques are especially valuable in volcanic and geothermal contexts, where volcano-tectonic earthquakes and long-period events have low magnitude, often preventing a successful manual analysis. Since 2016, the island of Tenerife has been affected by increased seismicity and gas emissions from the crater of Teide volcano, the most prominent feature of the island of Tenerife. We applied the software Qseek (https://github.com/pyrocko/qseek) to enhance the seismic catalogue based on manual detections in Tenerife.

This reanalysis of Tenerife's seismic dataset revealed intense seismicity related to episodes of magmatic fluid injection into the upper crust. Subsequently, high-resolution relocation techniques based on the software GrowClust (https://github.com/dttrugman/GrowClust) imaged the spatial pattern of the hypocenters, highlighting sources of magmatic fluid injection into the hydrothermal system of Tenerife and the subsequent response of the upper crust to this disturbance.

How to cite: D'Auria, L., Mesa Jiménez, L., Santos Campos, I., Álvarez Hernández, A., García Hernández, R., Martínez van Dorth, D., Ortega Ramos, V., Padilla Hernández, G. D., and Pérez Rodríguez, N. M.: Revealing the hidden seismicity of Tenerife (Canary Islands) through machine learning and cross-correlation, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-18834, https://doi.org/10.5194/egusphere-egu25-18834, 2025.

Geospatial intelligence involves the analysis and interpretation of spatial data to inform decision-making. Understanding and responding to geospatial phenomena, such as seismic events or atmospheric disturbances, is essential for advancing disaster preparedness and environmental monitoring. However, significant challenges arise from the lack of integrated systems capable of securely collecting, processing, and analyzing large-scale geoscience data.
To address this, the "Digital Twin Earth Intelligence for Climate Changes" (DTEClimate) project has developed a platform that integrates real-time data gathered from various sensors, to create visual maps, easily accessible for the general public. In order to process all this data, integration services have been developed, which continuously monitor and process data gathered by sensors. This stored data is then used to create multi-layered maps representing various event types and heatmaps illustrating data concentrations.
On the one hand, in our platform users can visualize the progression and current status of meteorological events, which improves awareness. On the other hand, researchers can use the aggregated data to better monitor the environment or conduct multidisciplinary analyses to explore the interdependencies among different environmental parameters. By bridging the gap between real-time data collection and data visualization, DTEClimate aims to empower both the public and scientific communities to make data-driven decisions in the face of climate change and natural disasters.

Acknowledgements

This work was carried out within PNRR-DTEClimate/REACTIVE project, no. 760008/30.12.2022.

How to cite: Constantin, A., Brezulianu, A.-I., Gavrila, M., Barbuta, D.-E., Ionescu, C., and Toader, V.: DTEClimate: A Digital Platform for Real-Time Geospatial Intelligence and Climate Monitoring, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-20014, https://doi.org/10.5194/egusphere-egu25-20014, 2025.

Responding to the outdoor air pollution being one of the gravest environmental hazards to human health, emissions from mobile sources have been subject to scrutiny and emissions reduction efforts through improved fuels, engine design, combustion control, exhaust aftertreatment, and traffic management. Assessment of the effects of various improvements has gradually extended from basic laboratory measurements to testing under real-world (real-driving) conditions and to additional pollutants.

Fourier-transform infra-red (FTIR) spectrometers have the potential to acquire spectra at high optical and time resolution. The absorption spectra are a convolution of absorption spectra of individual compounds and can be, with varying success, interpreted to obtain the concentrations of the pollutants of interest. To date, several FTIR have been adopted for the use in moving vehicles, which is challenging due to the effects of vibrations on precision multipath low-volume optical cells used to achieve a fast response time. The validation of on-road FTIR instruments typically consists of parallel measurement with reference instruments in the laboratory for all measured pollutants and on the road for those pollutants that can be reliably measured on the road.

In this work, two FTIR analyzers adapted for on-road use, an A&D BOB-1000FT vacuum sampling system operating at 5 Hz and a 35-kg Bruker Matrix extensively modified by Czech University of Life Sciences operating at 2.5 Hz, both with a 5-meter multipath cell and 0.5 cm-1 optical resolution, were used to sample the exhaust from diesel, gasoline and natural gas vehicles with and without exhaust aftertreatment. Concentrations and emissions of all pollutants set to be regulated under Euro 7, health and environment relevant reactive species reactive nitrogen species NO, NO2, NH3, CO and formaldehyde, and greenhouse gases CO2, CH4 and N2O were measured during dynamic driving cycles at -9 C, +23 C and + 35 C ambient temperatures.

The results show a reasonable correlation with reference instruments for all evaluated pollutants, suggesting on-board FTIR, which is not much larger than other instruments used to measure real driving emissions, can be potentially used to measure all gaseous pollutants regulated under Euro VII/7 on the road. FTIR spectra can be, even ex-post, interpreted for additional pollutants of interest.

Funding: This work was supported by the European Union’s Horizon Europe research and innovation programme under grant agreements No 101096133 (PAREMPI: Particle emission prevention and impact: from real world emissions of traffic to secondary PM of urban air) – experiments and No. 101056777 (LENS, L-vehicles Emissions and Noise Mitigation Solutions) – FTIR development.

How to cite: Kuutti, H., Vojtisek, M., Vojtisek, M., Dittrich, L., Dittrich, L., and Pechout, M.: Potential of on-board FTIR as a single instrument for simultaneous measurement of all gaseous pollutants of interest under real-driving conditions., EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-20391, https://doi.org/10.5194/egusphere-egu25-20391, 2025.

  Presently, three fundamental methods are employed for sea cucumber aquaculture: pond culture, dam culture, and submarine seedling culture. However, these methods are susceptible to environmental water quality degradation due to factors such as sea cucumber feces, excessive feed, and climate change, which can impede sea cucumber growth and affect yields.

In order to address the issues outlined above, this study presents the intelligent circulating water system (ICWS), which is composed of a composite low-energy physical liquid-solid separator and a multi-mixed biofilter. A detailed description of these components is provided below.

• Composite Low Energy Physical Liquid-Solid Separator

The liquid-solid separator uses minimal energy because of its innovative composite type. It extracts the contaminant source from the aquatic environment, reducing biofilter bed load and energy demand.

• Multi-mixed Biofilter

The configuration of hybrid arrangement structures increases the specific surface area of the biofilter, leading to a reduction in its volume. The structure controls flow rate, hydraulic residence time, and hydraulic loading, which can be used to regulate temperature, salinity, pH, dissolved oxygen, and ammonia levels. This ensures the provision of high-quality water that meets the needs of sea cucumbers.

The innovative low-energy-consuming water recycling system outlined in this project has the theoretical potential to achieve complete water recycling without the necessity of replenishing the source water. This scenario presents a mutually beneficial opportunity for the sustainable utilization of Earth's water resources and the realm of commercial aquaculture, exhibiting no inherent incompatibility.

How to cite: Chien, K.-H., Huang, W.-S., Chen , J.-C.  .  ., and Ren , X.  .: Development of an intelligent recirculating water system for land-based sea cucumber aquaculture, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-2316, https://doi.org/10.5194/egusphere-egu25-2316, 2025.

The Acoustic Doppler Current Profiler (ADCP) is one of the most commonly used instruments for measuring river discharge by utilizing the Doppler effect of acoustic waves. However, its reliance on a single transducer introduces certain limitations. During the transition between transmission and reception of the acoustic signal, the returning signal cannot be captured, resulting in an inability to measure discharge near the sensor.

Additionally, side-lobe interference generated by acoustic waves reflects off the riverbed and contaminates measurements near the bottom. To mitigate this, discharge data within 5% of the water depth from the bottom are typically excluded from results. Furthermore, in shallow areas where the unmeasured regions near the sensor and near the bottom overlap, discharge cannot be accurately measured.

To address these gaps, discharge in the unmeasured regions of ADCP measurements is typically extrapolated using data from the measured sections or calculated using empirical equations. In this study, a method to improve the measurement accuracy in the unmeasured regions of the ADCP was developed and evaluated.

Acknowledgements 

This work was supported by Korea Environment Industry & Technology Institute(KEITI) through Research and development on the technology for securing the water resouces stability in response to future change Program, funded by Korea Ministry of Environment(MOE)(RS-2024-00336020)

How to cite: Kim, J. and Kim, D.: Improving Discharge Measurement in Unmeasured Zones of ADCPs, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-4974, https://doi.org/10.5194/egusphere-egu25-4974, 2025.

Urban areas need to rethink their policies to strengthen their capacities to prepare for and respond to hazards and become more resilient, intelligent and inclusive. In this context, one of the objectives is to ensure the resilience of their services and systems against multi-hazard scenarios, where the effect of local hazards combines with global challenges such as climate change and pandemics. Moreover, the concept of inclusiveness is becoming crucial, as highlighted during COVID, which showed that the most vulnerable population is the one living in sparsely and densely populated areas, where the level of social and physical services is often inadequate [1].

In this context, one possible response to this need is the development of monitoring and surveillance approaches [2]. The present contribution will focus on three aspects

The first is that resilience must be addressed as a whole, since services and networks are interconnected and interdependent (e.g. health system, transport, energy and water distribution, air quality, protection from extreme weather events, etc.). The main consequence of these interconnections is that the complete collapse of services (blackout) may become a realistic possibility.

The second aspect is that resilience can only be achieved in the presence of continuous and detailed monitoring of both the structures/infrastructure/services and the territory on which they insist, and that without such a monitoring it is impossible to correctly define the interventions to be carried out and their priorization.

The third aspect concerns the development of new monitoring systems based on Earth observation, positioning, navigation, and ICT technologies that exploit the citizen as a sensor and the so-called ‘non-sensors’, i.e. sensors that provide useful information for monitoring even if they are not designed for this purpose. All this ‘sensory’ data must be integrated to obtain a complete and reliable awareness of the scenario; hence the need to process and systematize large amounts of information that can only be processed by AI and HPC.

[1] V. Cuomo F. Soldovieri F. Bourquin, N. -E. El Faouzi, J. Dumoulin. The necessities and the perspectives of the monitoring/surveillance systems for multi-risk scenarios of urban areas including COVID-19 pandemic. Proceedings of the TIEMS Annual Conference, 18-20 November 2020, Paris, France, ISBN: 978-94-90297-19-0, vol. 27

[2] Cuomo V., Soldovieri F., Ponzo F.C., Ditommaso R. (2018). A holistic approach to long-term SHM of transport infrastructures. The International Emergency Management Society (TIEMS) Newsletter 33, pp. 67-84.

How to cite: Soldovieri, F., Cuomo, V., and Dumoulin, J.: Monitoring for  sustainable and inclusive urban areas, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9193, https://doi.org/10.5194/egusphere-egu25-9193, 2025.

Freshwater quality continues to decline despite the adoption of the Water Framework Directive (WFD) almost twenty five years ago with the recovery of water quality in Europe plateauing since the 2010s (Haase et al., 2010).  Pollution from diffuse sources, particularly from agriculture remains a key challenge to restoring water quality to at least ‘good status’ under the WFD (EEA, 2018) compounded by water quality declines due to increased frequency and intensity of hydrodynamic events (van Vliet et al., 2023). 

Assigning accurate WFD classes and detecting changing trends in water quality have been challenging where traditional low frequency monitoring approaches have been implemented (Skeffington et al., 2015).  Higher monitoring frequency and spatial coverage is required to effectively identify improvements in water quality (Westerhoff et al., 2022) particularly when detecting changes over shorter time periods (Mcdowell et al., 2012).  High frequency monitoring is required to identify temporal water quality changes linked to rainfall driven pollutant transfer from land to waters (Métadier and Bertrand-Krajewski, 2012) with monitoring over multiple events required to capture the variability in pollutant concentrations and pollutant loads across events (Kozak et al., 2019).  Furthermore, 50% of surface waterbodies in the EU are impacted by multiple pressures (EEA, 2018), with increased urbanisation requiring a more complex, multi-pollutant approach to assessing impacts on river quality (Strokal et al., 2021).

The aim of this research was to identify if rainfall driven transient pollution events were occurring at two monitoring stations in a river catchment, and if continuous instream monitoring of turbidity and other water quality parameters could be used to capture changes in water quality and potential instances of such events.  One of the objectives was to identify if continuous monitoring could create a site-specific water quality profile that could be used to identify early warning indicators of rainfall driven or other transient pollution events. 

Results from this study indicate that changes in water quality are happening during rainfall events and that turbidity alongside other parameters can be used to track such events, trigger alarms when a probable event is occurring and automatically activate more intense monitoring during these events.  The integrated monitoring approach adopted allows for the tracking of water quality changes across temporal and spatial scales for multiple pollutants and allows for temporal fluctuations, and variation in pollutant loads during hydrodynamic events to be determined. 

The significant advantages of this approach are it’s suitability for remote deployments with no requirement for permanent infrastructure, the use of site specific water quality profiles to identify potential water quality events at individual sites and to activate further monitoring if required, the ability to tailor the monitoring for pollutant screening or more specific pollutants of concern, and the cost effectiveness of moving the integrated monitoring station between different water bodies. 

How to cite: Cronin, L., Taylor, C. M., Briciu-Burghina, C., Regan, F., and Lucy, F. E.: Near real-time, water quality event monitoring in small rivers, in the context of increasing frequency and intensity of hydrodynamic events due to climate change., EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10582, https://doi.org/10.5194/egusphere-egu25-10582, 2025.

The König-Project, funded by the German state, is a long-term project focused primarily on developing a multi-scale wave measurement laboratory to improve flow measurements in an industrial context. Part of this project researches the propagation of ultrasound waves inside a moving fluid. A wide variety of flow scenarios are considered, and new methods for ultrasonic flow measurement can be developed and optimized. One experimental scenario includes the determination of volume fraction and drop size distributions of air dispersed in water using ultrasonic waves.

For this purpose, a modular system is used as an initiative to integrate manufacturer-independent measurement components with open-source software for the acquisition and processing of ultrasound signals. The modular system equipment consists of a multichannel system, which allows the positioning of several transceivers to send and receive ultrasonic waves from different directions along the experimental zone of interest. The concentration of dispersed air in water will be determined by measuring the reduced transit time caused by the added compressibility of the air phase.

Characterizing multiphase flows using other techniques can be time-consuming and the accuracy can fall short as the complexity of the fluid grows. The use of ultrasound to characterize fluid flows has many advantages such: as a non-invasive method that doesn’t alter the fluid path, real-time data acquisition, and high-temporal resolution, it is cost-effective and can be used on opaque fluids. Therefore this technique is gaining more attention in several industrial applications, including oil and gas, hydrogen, and geothermal energy generation. The results of this investigation will be validated and compared with the output of a numerical simulation, in which the boundary conditions and the flow characteristics will be similar to the experimental setup.

How to cite: Calderon, J., Dormann, M., Branß, T., Balcewicz, M., Aberle, J., and Saenger, E.: Advanced Ultrasound Techniques for Investigating Air-Water Two-Phase Flow: An Experimental Approach, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10725, https://doi.org/10.5194/egusphere-egu25-10725, 2025.

The König-project is funded by the German state with the aim to develop a calibrated and virtual measurement laboratory to enhance methods based on ultrasound measurements that find application in the determination of flow velocity or particle movement. By comparing the results of controlled laboratory and real-world experiments with numerical simulations, the understanding of the interaction between ultrasonic waves and fluid flow is intended to be improved. The amount of scatterers within a fractured medium directly affects  the effective velocity of elastic waves. Thus we investigate, if the effects found in solid media can be transferred to fluids. We ran a series of numerical experiments, simulating ultrasound transmission measurements for multiple concentrations of bubbles of varying diameter dissolved in a stationary water layer. For the simulation of elastic wave propagation, we used a rotated staggered finite-difference scheme. We investigate the relation between the effective wave speed and the bubble concentration and compare those to results of laboratory experiments. Future research will then expand to moving fluid-gas mixtures.

How to cite: Dormann, M., Calderon, J., Finger, C., Balcewicz, M., and Saenger, E. H.: Numerical Study to Determine Water-Air Dispersion with Ultrasound Waves, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10832, https://doi.org/10.5194/egusphere-egu25-10832, 2025.

Salar de Uyuni is a salt desert in Bolivia, spanning approximately 10,000 km². During the wet season a thin layer of rainfall water covers the salt flats, making its surface mirror-like and earning it the title of “the largest natural mirror in the world”. The surface reflects the sky like a mirror, and attracts tourists who document this effect only from its outer perimeter. No evidence is documented in the interior, accessible only during the dry season. The only frequent observations of the Salar surface are from satellites, particularly altimetric radars, which are specifically designed to measure topography. Originally developed to measure sea level [1], they have recently been used, with a different metrics, to describe how emitted radar pulses are reflected by the surface, measuring the intensity of the reflected echo, and thus the Radar Cross Section (RCS) of the surface [2]: higher RCSs correspond to smoother surfaces. RCS was initially estimated in [1] with a an approximate method. Later EUMETSAT shared a better estimate by solving the radar equation with satellite parameters that were previously unknown to us [3]. In this study we used Sentinel-3A and 3B RCS measurements over the Salar flats, along six ground tracks, to describe for the first time the evolution of the Salar surface smoothness in space and time. A field campaign (16^th - 20^th of February 2024) was also conducted to validate the interpretation of radar measurements during the Sentinel-3A overpass on the track 167. At the field site, in a water depth of 1.8 cm (horizontal wind 4.5-3.4 ms^-1), we measured a null vertical surface displacement to within ±0.5 mm, which classifies the surface as electromagnetically smooth at the radar frequency. The RCS values near the site were around 120 dBsm, as expected for radar return from a smooth surface. Three peaks are observed on the statistical distribution of the RCS: 87 (dry), 101 (intermediate) and 120 dBsm (wet season).The wet season, characterized by values above 101 dBsm, begins in December, peaking from late January to early March. February thus ensures the highest chance to observe mirror-like effects. Rainfall climatology from Uyuni city meteorological station reflects such statistics. The spatial and temporal evolution of RCS over the Salar, however, do not describe this place like a uniform mirror at the radar frequency, and so it is unlikely to observe such effect at shorter wavelengths, contrary to what is believed in the literature. Finally, satellites can help tourism stakeholders in programming the most enjoyable experience for travellers.

[1] Vignudelli et all. 10.1007/s10712-019-09569-1

[2] Abileah and Vignudelli, 10.1016/J.Rse.2021.112580

[3] Dinardo and Lucas, EUM/RSP/TEN/23/1376566

How to cite: De Biasio, F., Vignudelli, S., Abileah, R., and Pacheco Mollinedo, P.: Radar Altimetry Reveals the Smoothness of the Surface: the Case of Salar de Uyuni, Bolivia, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10867, https://doi.org/10.5194/egusphere-egu25-10867, 2025.

Landslides, alongside earthquakes and floods, are among primary natural phenomena that are responsible for significant social and economic losses. Their impact poses an increasing threat worldwide, particularly in marginal and degraded contexts, affecting urban areas, infrastructures, environmental, historical, and cultural heritage, and, in severe cases, resulting in human casualties. In recent years, the number of infrastructure collapses or severe structural damages due to landslide movements has risen significantly, hindering the functionality of infrastructures, and highlighting the urgent need to deeply understand their interactions. Landslides can endanger roads, bridges, and railways, compromising the accessibility and inclusivity, and exacerbating social and economic exclusion in affected areas. Critical infrastructures are often located in challenging areas, where the susceptibility to landslides and natural hazards is significantly elevated. These sites demand advanced monitoring technologies to ensure infrastructure safety and mitigate the social and economic impacts of landslides. This study explores an innovative approach that integrates Interferometric Synthetic Aperture Radar (InSAR) data with numerical Finite Element Modelling (FEM) to address these challenges. The proposed method was applied to a case study involving a partial interaction between a slow-kinematic landslide, documented in the Inventory of Landslide Phenomena in Italy (IFFI), and a bridge along a highway section in the Liguria Region. Leveraging high-resolution satellite-based data from the Copernicus European Ground Motion Service (EGMS), the InSAR analysis provided spatial and temporal monitoring of ground displacements. Satellite remote sensing offers a wide spatial and temporal coverage over multiple regions, enabling for the detection of extensive or hard-to-access areas with millimetric precision in deformation velocity, ensuring high efficiency at a favourable cost-benefit ratio. However, while InSAR analysis can precisely measure ground motions, it lacks the ability to provide insights into the physical mechanisms under varying loading conditions. To address this limitation, FEM modelling was used to simulate the three-dimensional landslide mechanical behaviour under hydraulic loading, offering a deeper understanding of the slope stability and infrastructure deformations. InSAR data post-processing enabled the estimation of transverse and vertical components of the actual displacement vector, aligning with the observed landslide deformations and facilitating the numerical model validation. Simultaneously, FEM results highlighted significant displacements downstream of the landslide area, indicating a slope stability close to the limit equilibrium condition. Quantitative analysis also revealed relevant deformations at the base of bridge piers located within the landslide, caused by horizontal forces impacting the foundations. The integration of InSAR observations and FEM calculations demonstrated consistency in the identified movement, validating the efficacy of the combined method in identifying critical zones in landslide-prone regions. This study highlights how advanced remote sensing technologies, when coupled with numerical simulations, can enhance the monitoring and maintenance of critical infrastructure, particularly in marginal or extensive contexts. By identifying vulnerable areas and supporting the maintenance strategies, this methodology can contribute to hydrogeological risk management and promote inclusivity in regions where social and economic disparities exacerbate natural hazards impacts.

How to cite: Salciarini, D., Vitaletti, A., Cernuto, E., and Ubertini, F.: Integrating Remote Sensing Technique with 3D Numerical Modelling for Enhanced Maintenance of Critical Infrastructure in Landslide-Prone Areas, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-12996, https://doi.org/10.5194/egusphere-egu25-12996, 2025.

Natural hazards such as floods, storms, and earthquakes present significant threats to urban infrastructures, particularly water supply and distribution networks. These events can severely impact the quality and quantity of water resources, leading to serious consequences for public health and social security. Factors such as unplanned urban development and non-compliance with engineering standards further increase the vulnerability of these systems. Recent advancements in technologies like the Internet of Things (IoT) and Artificial Intelligence (AI) have enabled real-time monitoring and data analysis of these critical infrastructures. IoT-based smart sensors capture essential information, including flow rate, water quality, corrosion, leakage, and pipeline ruptures. These data are processed using machine learning and deep learning algorithms to identify anomalies. Such systems can enhance monitoring capabilities and support effective decision-making in crisis situations. This study explores key criteria for selecting optimal locations for sensor deployment. These criteria include connection points, infrastructure accessibility, water quality, natural hazard risks, and historical incident data. For example, evaluating the location of connection points and their impact on water flow and distribution can help identify optimal routes, reducing costs and response times. Easy access to infrastructure facilitates sensor installation and maintenance, improving system efficiency. Monitoring water quality at various points in the distribution network is also critical to identifying sensitive locations and ensuring water safety. Additionally, identifying areas prone to natural hazards helps prioritize vulnerable regions for monitoring and improve system resilience. Historical data on anomalies and past incidents provide patterns that highlight risk-prone areas and help refine monitoring strategies. Based on these criteria, a multi-criteria decision-making approach is applied to propose the most effective locations for sensor placement. This method suggests prioritizing locations that have the highest impact and accessibility. These recommendations aim to enhance system efficiency and improve response capabilities during emergencies.

Ketwords: Smart Infrastructures, Internet of Things, MCDM, Artificial Intelligence, Natural Hazards

How to cite: Malekmohammadi, B., Rahimi, M., Kerachian, R., Singh, V. P., Falconer, R. A., Noori, R., and Bahmanpouri, F.: An Approach for IoT-Based Smart Sensors Placement in Urban Water Networks Under Natural Hazards, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-13576, https://doi.org/10.5194/egusphere-egu25-13576, 2025.

Rural areas, i.e. areas with a low population density and a small number of anthropogenic environments, represent a significant resource in the pursuit of a green and sustainable development of the European Community [1]. This development involves not only the ecological and balanced use of agriculture and forestry resources, but also policies devoted to environmental protection and monitoring. In this context, drone-based technologies offer valuable opportunities because they facilitate the effective and non-invasive surveillance and monitoring of wide and inaccessible places. These technologies, indeed, allow surface and subsurface explorations, while concomitantly reducing the financial and logistical demands associated with investigation missions.

The present contribution is focused on Unmanned Aerial Vehicle (UAV)-Ground Penetrating Radar (GPR) technology and the potential of UAV-GPR technological solutions in subsurface prospecting [2]. The discussion encompasses the collection and processing of data, emphasising the efficacy and sustainability of the technology. The contribution will address the development of guidelines for the design of the flight grid and the formulation of an effective imaging strategy that can account for deviations in motion relative to the nominal trajectory.

[1] Bizottság, E. (2024). The long-term vision for the EU’s rural areas: key achievements and ways forward. Report from the Commission to the European Parliament, the Council, the European Economic and Social Committee and the Committee of the Regions, Brüsszel, The long-term vision for the EU’s rural areas: key achievements and ways forward, Report from the Commission [Letöltve: 2024.06. 20.].

[2] Noviello, C., Gennarelli, G., Esposito, G., Ludeno, G., Fasano, G., Capozzoli, L., Soldovieri, F., & Catapano, I. (2022). An Overview on Down-Looking UAV-Based GPR Systems. Remote Sensing, 14(14), 3245. https://doi.org/10.3390/rs14143245

Acknowledgements: The communication has been funded by EU - Next Generation EU Mission 4 “Education and Research” - Component 2: “From research to business” - Investment 3.1: “Fund for the realisation of an integrated system of research and innovation infrastructures” - Project IR0000032 – ITINERIS - Italian Integrated Environmental Research Infrastructures System - CUP B53C22002150006.

The authors acknowledge the Research Infrastructures participating in the ITINERIS project with their Italian nodes: ACTRIS, ANAEE, ATLaS, CeTRA, DANUBIUS, DISSCO, e-LTER, ECORD, EMPHASIS, EMSO, EUFAR ,Euro-Argo, EuroFleets, Geoscience, IBISBA, ICOS, JERICO, LIFEWATCH, LNS, N/R Laura Bassi, SIOS, SMINO.

How to cite: Catapano, I., Esposito, G., Gennarelli, G., and Soldovieri, F.: Drone based radar technologies for wide rural areas resources exploration: potentialities and challenges, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-17346, https://doi.org/10.5194/egusphere-egu25-17346, 2025.

Coastal regions are crucial for human settlements and economic development. However, their distinctive environmental characteristics, particularly in deltas, bays, and gulfs, render them highly vulnerable to threats such as erosion phenomena and pollution. The effective management of these areas depends on the accurate predictions of wave dynamics and their interactions with the shoreline and seabed. Reliable forecasts require numerical wave propagation models to be initialized with precise data and detailed bathymetric representations, and their accuracy depends on calibration operations using high-quality sea state observations.

Sea state data are typically collected through in-situ sensors, such as buoys and drifters, or remote sensing devices, including radars and video-monitoring systems [1]. Remote sensing technologies are often preferred due to their ability to provide both spatial and temporal information. Among these, ground-based radar systems like High-Frequency and X-band radars have proven effective in retrieving wave spectra and coastal sea state information. However, these systems face notable limitations, including difficulties in acquiring data near the shoreline. Additionally, they are bulky, heavy, and cumbersome, which complicates the deployment stage.

To address these challenges, the Italian PRIN-PNRR 2022 Project SEAWATCH—Short-Range K-Band Wave Radar System Close to the Coast—was launched on November 30, 2023. SEAWATCH focuses on developing an innovative, portable, short-range K-band radar prototype specifically designed for sea state monitoring in nearshore zones. Thanks to its compact size, lightweight design, and low power requirements, the system enables flexible, on-demand surveys, meeting critical safety and environmental management needs in harbors and coastal zones.

This communication outlines the key activities and initial results achieved during the first year of the SEAWATCH project. This last is organized into six milestones, supported by a robust collaboration between research units to ensure efficient knowledge sharing and steady progress. Preliminary here results shown highlight the radar prototype potential to overcome traditional limitations, offering enhanced spatial resolution and real-time monitoring capabilities near the coastline [2]-[4].

Future efforts will focus on further refining the radar prototype and validating its performance across diverse coastal environments.

References:

• P. Neill, M. Reza Hashemi, Chapter 7 - In Situ and Remote Methods for Resource Characterization, Editor(s): Simon P. Neill, M. Reza Hashemi, In E-Business Solutions, Fundamentals of Ocean Renewable Energy, Academic Press, 2018, Pages 157-191.
• Afolabi, L. A., et al. (2025). Underestimation of Wave Energy from ERA5 Datasets: Back Analysis and Calibration in the Central Tyrrhenian Sea. Energies, 18(1), 3.
• Ludeno, G., Antuono, M., Soldovieri, F., & Gennarelli, G. (2024). A Feasibility Study of Nearshore Bathymetry Estimation via Short-Range K-Band MIMO Radar. Remote Sensing, 16 (2), 261.
• Ludeno, G.; Esposito, G.; Lugni, C.; Soldovieri, F.; Gennarelli, G. A Deep Learning Strategy for the Retrieval of Sea Wave Spectra from Marine Radar Data.  Mar. Sci. Eng.2024, 12, 1609.

Acknowledgment: This work was supported and funded by the European Union—NextGenerationEU PNRR Missione 4 “Istruzione e Ricerca”—Componente C2 Investimento 1.1, “Fondo per il Programma Nazionale di Ricerca e PRIN—SEAWATCH—Short-rangE K-bAnd Wave rAdar sysTem Close to tHe coast CUP B53D23023940001, and partially funded by the research project STRIVE—La scienza per le transizioni industriali, verde, energetica CUP B53C22010110001.

How to cite: Ludeno, G., Contestabile, P., Vicinanza, D., Antuono, M., Lugni, C., Catapano, I., Esposito, G., Noviello, C., Soldovieri, F., and Gennarelli, G.: SEAWATCH Project: A year of advancements in Short-Range K-Band Radar for Coastal Monitoring, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-18196, https://doi.org/10.5194/egusphere-egu25-18196, 2025.

 In recent years, there was a notable technological advancement in geophysical sensors. In the case of magnetometry, several sensors were used having the common feature to be miniaturized and lightweight, thus idoneous to be carried by UAV in drone-borne magnetometric surveys. Moreover, such sensors have the common feature to be very cheap, so that it is in principle very easy to have the resources to combine two or three of them to form gradiometers. Nonetheless, another common feature is that their sensitivity ranges from 0.1 to about 200 nT, thus not comparable to that of alkali vapor, standard flux-gate or even proton magnetometers. However, their low-cost, small volume and weight remain as very interesting features of these sensors. In this communication, we want to explore the range of applications of small tri-axial magnetometers commonly used for attitude determination in several devices. We compare the results of ground-based surveys performed with conventional geophysical instruments with those obtained using these sensors.

How to cite: Accomando, F. and Florio, G.: Applicability of cheap and lightweight magnetic sensors to geophysical exploration, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-18356, https://doi.org/10.5194/egusphere-egu25-18356, 2025.

Volcanic eruptions and earthquakes present significant challenges to developing countries, where limited monitoring infrastructure restricts effective risk mitigation efforts. Satellite remote sensing observations offer essential information, including surface deformation and thermal anomalies, for hazard assessment, early warning, and emergency response. These satellite-based observations enable comprehensive spatial and temporal monitoring, utilising both publicly available medium-resolution and commercial high-resolution datasets. Over the past decade, Sentinel radar and optical observations have been employed in areas with limited in-situ measurement capabilities[1]. Nonetheless, the utilisation of these datasets in developing countries is frequently hampered by insufficient computational and analytical resources.

This study examines the role of remote sensing in strengthening disaster risk management within resource-constrained contexts. We propose a collaborative framework that utilises satellite remote sensing data processing Centres in developed countries to assist developing nations in analysing pre-, during, and post-crisis events. Moreover, we advocate for engaging with space agencies to enhance satellite tasking during crisis observation, thereby improving our understanding of the event’s driving mechanisms. We highlight the critical role of remote sensing through a case study of recent seismic and volcanic activity in the Main Ethiopian Rift, specifically between the Fentale and Dofen volcanoes[2]. While national seismic and geodetic networks provide data on large and medium-magnitude earthquakes and significant deformations, they cannot detect low-magnitude precursory events or local deformations due to their proximity to volcanic centres. Furthermore, the installation of temporary monitoring facilities is often constrained by various limitations. Remote sensing bridges this gap by offering detailed data to support local research, inform timely decision-making, and strengthen crisis management. The crises have impacted under-resourced regions, the primary import-export corridor, and nearby urban centres, including Addis Ababa, where rapid urbanisation has raised safety concerns. This study underscores the necessity of integrated remote sensing solutions and international collaboration to enhance resilience and mitigate risks in disaster-prone areas.

Keywords: Sentinel, Main Ethiopian Rift, Fentale Volcano, Developing Countries, Emergency Management

Acknowledgements

The Authors would like to express their sincere thanks and gratitude to the following trusts, charities, organisations and individuals for their generosity in supporting this project: Lord Faringdon Charitable Trust, The Schroder Foundation, Cazenove Charitable Trust, Ernest Cook Trust, Sir Henry Keswick, Ian Bond, P. F. Charitable Trust, Prospect Investment Management Limited, The Adrian Swire Charitable Trust, The John Swire 1989 Charitable Trust, The Sackler Trust, The Tanlaw Foundation, and The Wyfold Charitable Trust.

References

[1] Tessema, T. T., Biggs, J., Lewi, E., & Ayele, A. (2020). Evidence for active rhyolitic dike intrusion in the northern Main Ethiopian Rift from the 2015 Fentale seismic swarm. Geochemistry, Geophysics, Geosystems, 21, e2019GC008550. https://doi.org/10.1029/2019GC008550

[2] Derek Keir, Alessandro La Rosa, Carolina Pagli, et al. (2024). The 2024 Fentale Diking Episode in a Slow Extending Continental Rift. ESS Open Archive DOI: 10.22541/au.172979388.80164210/v1

How to cite: Tessema, T., Lewi, E., and Tosti, F.: Remote Sensing for Volcanic Eruptions and Earthquake Emergency Management Strategies in Developing Countries, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-18926, https://doi.org/10.5194/egusphere-egu25-18926, 2025.

Critical raw materials (CRMs) are crucial for technological advancements and the global energy transition, especially in sectors such as renewable energy, electronics, and electric mobility. The sustainable and secure management of these materials is increasingly important. Geothermal springs represent a promising source of CRMs, offering valuable materials such as lithium, magnesium, strontium, and boron in addition to clean energy. Depending on where they come from geologically, geothermal springs can have lithium levels that are at least 10 times higher than seawater (0.18 mg/L) and about the same as salt lakes (0.04–3 g/L). The moderate Mg2+/Li+ molar ratio (~35) also shows that the two elements might be better separated, which would allow for more Mg2+ recovery. This study introduces a novel method for the recovery of CRMs from geothermal brines, combining Reverse Osmosis (RO), Nanofiltration (NF), and Membrane Distillation (MD) for efficient separation of water and valuable materials. The experiments are conducted using a synthetic laboratory-reproduced geothermal spring solution, which accurately replicates the pH, temperature, and ionic composition typical of natural geothermal waters. This experimental approach ensures that the results reflect real-world conditions, which is critical for evaluating the feasibility and scalability of the proposed method. The process begins with RO and NF to concentrate the brine and selectively separate multivalent ions (e.g., Mg) from monovalent ions (e.g., Li), leveraging differences in ionic valence. Following this, MD is applied to reduce brine volume and minimize thermal energy consumption, thereby optimizing both water recovery and the concentration of CRMs. A key innovation of this work is the exploitation of the elevated temperature of geothermal brines (> 35°C), which allows the use of MD with minimal external heating. This significantly reduces energy requirements and operational costs. The process minimizes Specific Thermal Energy Consumption (STEC), highlighting its efficiency and sustainability. This method not only enhances the recovery of lithium and magnesium from geothermal springs, but it also offers a cleaner, more sustainable approach to CRM extraction by utilizing renewable geothermal heat.

How to cite: Inzillo, B. M., Santoro, S., Curcio, E., and Straface, S.: Innovative Geothermal Mining through Membrane Technologies, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-19565, https://doi.org/10.5194/egusphere-egu25-19565, 2025.

Distributed hydrological models are crucial for flood prediction, drought analysis, and water resource monitoring. They are typically calibrated using streamflow observations at the watershed outflow to determine the best parameter values within their common ranges. These models are then applied to analyze management and climate scenarios. However, accurately representing hydrological complexities is challenging due to limited knowledge, data availability, and imprecise measurements. Uncertainties in these models arise from parameters, model structure, calibration processes, and data, especially in regions with scarce data. Consequently, hydrological models require extensive hydro-meteorological data for calibration and validation, which can be costly and time-consuming. Recently, remote sensing techniques advanced hydrological modeling by providing regular sampling of essential variables like precipitation, soil moisture, and evapotranspiration. However, thanks to technological advancements, numerous global and regional remote seeing products for the same variable have become freely available. These products vary in their algorithms, approaches, spatial and temporal resolutions, leading to diverse datasets for the same variable. Therefore, different products can perform differently in terms of parameter estimation, model robustness, and water balance predictions within the same area. However, each product may introduce biases or uncertainties, necessitating modelers to assess their performance and carefully choose the most suitable product for their study objectives. This research reviews commonly used remotely sensed products and the techniques and approaches for integrating them into distributed and semi-distributed hydrological models. Additionally, this review examines the uncertainties associated with different existing products and their performance within hydrological models.

How to cite: Taia, S., Ait Brahim, Y., Hssaisoune, M., Scozzari, A., and El Mansouri, B.: Enhancing Hydrological Models with Remote Sensing: A Review of Products, Techniques, and Uncertainties, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-19593, https://doi.org/10.5194/egusphere-egu25-19593, 2025.

To meet European (WFD) and International objectives (SDGs), there is a growing demand for water quality data with elevated spatial and temporal resolution. This has been an ongoing process, achieved by integrating data from governmental agencies with community-based monitoring initiatives (crowdsensing). Community-based monitoring has proven effective in addressing information gaps in managing and monitoring aquatic ecosystems, particularly in small rivers that often lack agency monitoring. However, there are still challenges regarding the reliability of such data. To fill this gap, there is an urgent need to develop affordable, reliable, and open-source instrumentation for water quality monitoring. These instruments should also comply with the recent European guidelines on the use of toxic substances in technology development.

This study presents the development and validation of a RoHS directive-compliant, open-source, low-cost optical sensor for detecting nitrates and phosphates in community-based monitoring initiatives. The sensor setup takes advantage of light-emitting diodes (LED) as light sources and a commercial ambient light detector. A second light sensor positioned at a 90° angle is employed for scattering correction. All components are managed by a Raspberry Pi Zero W microcomputer and housed in a custom 3D-printed poly(lactic acid) case. The device enables data collection, including GPS coordinates, with results stored offline or transmitted in real-time through Wi-Fi. The sensor’s analytical performance was evaluated in both laboratory and field conditions using reference materials and river samples. Results demonstrated accurate and repeatable measurements which were shown to increase resolution and precision compared to standard colorimetric methods. To promote accessibility and replication, the 3D-box CAD model, software, and usage guidelines are freely available online.

How to cite: Cirrone, R., Boldrini, A., Polvani, A., Liu, X., and Loiselle, S.: Advancing Community-Based Water Quality Monitoring through Low-Cost Open-Source Optical Sensors and Data Integration, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-19717, https://doi.org/10.5194/egusphere-egu25-19717, 2025.

Among Non-Destructive Testing (NDT) methods, Ground Penetrating Radar (GPR) and magnetic surveys are among the most widely used techniques for various applications, including geo-environmental, archaeological, geotechnical, and engineering purposes. Their success is attributed to factors such as cost-efficiency, versatility, and data collection capabilities. Additionally, both methods enable the detection of buried targets through their respective magnetic and electromagnetic properties. Integrating the results from these two methodologies can yield excellent outcomes for an in-depth analysis of the investigated environment and significantly enhance the detection capabilities for anomaly sources.

This study presents preliminary results on the integration of simulated GPR and magnetometric data for a representative scenario. Advanced imaging techniques, including the Depth from Extreme Points (DEXP) method for magnetic data and the microwave tomography approach for GPR data, were applied to produce an initial high-resolution visualization of the simulated target.

Building on these results, an arithmetic integration approach was used to merge the two datasets into a single image, enhancing the interpretation of the anomaly source, including its morphology, position, and depth.

These preliminary results demonstrate the potential of this workflow, based on the arithmetic integration of these datasets, to provide more accurate and detailed subsurface models. This approach paves the way for real-world applications, and further developments aim to refine it for broader geophysical purposes.

Acknowledgments: the project ITINERIS "Italian Integrated Environmental Research Infrastructure Systems" (IR0000032), which funded the research

How to cite: Mercogliano, F., Barone, A., Vitale, A., Esposito, G., Tizzani, P., and Catapano, I.: Towards the Integration of GPR and Magnetic Data for the Study of Urban and Rural Areas, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-20136, https://doi.org/10.5194/egusphere-egu25-20136, 2025.

Microwave links from cellular communication networks have been proposed as an opportunistic source of precipitation data more than two decades ago. The first scientific studies demonstrating the potential of this ground-based remote sensing technique, in particular for areas around the world lacking dedicated rainfall observation networks, were published more than 15 years ago. Since then, a small but dedicated community of scientists and engineers working at universities, national meteorological services, engineering firms, mobile network operators and telecommunication equipment manufacturers has been making significant progress in turning this promise into a reality. In the meantime, numerous papers and reports have been published, conference presentations have been given and courses have been delivered. However, real-time access to high-resolution rainfall information from commercial microwave link networks over large continental areas is still a dream. How far have we come after more than 20 years of research and development? What does the future have in stall for the hydrological and meteorological communities? What should be done to turn this dream into a reality? Finally, which other hydrometeorologically relevant variables could potentially be retrieved using received signal levels from commercial microwave links? This sollicited presentation will attempt to provide some preliminary answers to these questions.

How to cite: Uijlenhoet, R.: Hydrometeorological Monitoring using Microwave Links from Cellular Communication Networks: Opportunities and Challenges, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-20151, https://doi.org/10.5194/egusphere-egu25-20151, 2025.

Technological advancements in forest digitization have revolutionized real-time monitoring of tree ecophysiological processes. Direct measurement sensors, such as dendrometers, sap flow sensors, and spectrometers, enable high-resolution insights into tree function and growth. Here, we present a novel dendrometer designed to monitor radial stem increment using a Hall effect-based linear magnetic encoder system integrated into an IoT-enabled platform.

The dendrometer employs a commercially available linear magnetic encoder chip (AMS OSRAM GmbH) that operates without physical contact, ensuring low power consumption and long-term monitoring suitability. Key design components include a linear arm, sensor housing, rail, magnetic tape, and chip braces. Calibration was conducted using a stepper motor for linear movements at 0.1 mm increments, capturing 100 data points per step in four replicates. Regression analysis demonstrated high accuracy, with an R² of 0.99 and an RMSE of 0.05 mm. Temperature sensitivity tests (0–40°C) revealed minimal impact on sensor performance.

Field tests over one growing season involved four dendrometers installed on specimens of spruce (Picea abies (L.) H.Karst)) and silver fir (Abies alba Mill.). Seasonal radial growth patterns captured by the devices aligned closely with established static UMS D1 diameter belt measurements, demonstrating their capacity to detect both long-term trends and short-term diel stem oscillations.

This study highlights the potential of an IoT-driven dendrometer for capturing high-resolution radial growth data, offering insights into tree physiology and forest responses to environmental changes. Future development should focus on enhancing measurement precision through design optimization and improved access to power width modulation components in the AS3511 chip. This dendrometer represents a promising tool for advancing forest monitoring and understanding the impacts of climate change on tree growth dynamics.

How to cite: Belelli Marchesini, L., Yates, J., Renzi, F., and Valentini, R.: Building a Smart Dendrometer: Calibration and Field Deployment of a linear magnetic driven IoT Sensor for Real-Time Radial Growth Assessment, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-20810, https://doi.org/10.5194/egusphere-egu25-20810, 2025.

Wetland ecosystems exhibit large spatial and temporal variability in terms of greenhouse gas (GHG) fluxes, necessitating new technologies to ensure that they are well-monitored. Both manual and automated chamber-based approaches are currently costly and thus limited either in spatial or temporal resolution. Following on from Wang et al. (2022), we propose a new, inexpensive autochamber (TraceCatch) for long-term outdoor installation. Costs for one unit are less than 800€ in total, making it affordable and scalable for long-term ecological research, also in lower income countries such as the global south. The system is based on gathering gas samples over two weeks into four gas bags on a high-frequency sampling schedule. TraceCatch is controlled using an Arduino Uno, connected to a peristaltic pump for sampling of chamber headspace air as well as a number of sensors for air temperature and humidity (SHT-41), air pressure (BMP280), and CO2 concentrations (K30FR; 0–5,000 ppm, 30 ppm resolution). The latter are used to track the sealing condition of the chamber. We validated the system using defined injection amounts of technical gas (100% CO2). In addition, the system was applied to measure GHG fluxes from three wetland cores placed inside three ecotrons (UGT EcoLab flex, manufactured by Umwelt-Geräte-Technik GmbH, Germany). Gas samples were collected 4 times a day for 2 weeks during a 1 hour chamber closure time at t0, t20, t40, t60 and subsequently analyzed using gas chromatography (Nexis GC-2030, manufactured by Shimadzu Corporation, Japan). Average GHG fluxes determined over the two-week period were then compared to single measurements obtained using multi-gas sensors (LI-COR LI-7820 and LI-7810 analyzers, manufactured by LI-COR Biosciences, USA). If adopted, the system’s low cost, scale and robustness for permanent field deployments could help improve wetland GHG monitoring, offering a cost-efficient and practical alternative to traditional methods for global-scale biogeochemical cycle assessments.

How to cite: Kretzschmar, M. S., Dubbert, M., Hoffmann, M., Bielcik, M., Bergmann, J., and Dubbert, D.: A Cost-Effective Automatic Chamber for Permanent CH4 and N2O Assessments inWetland Environments, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-20884, https://doi.org/10.5194/egusphere-egu25-20884, 2025.

The Earth is a complex ecosystem and each element is strictly linked with the others. It is often required to collect data on multiple aspects variously related to the main phenomenon in order to understand its mechanism. Moreover, the increasing use of machine learning algorithms requires the creation of new, reliable and extensive dataset in order to obtain significant results. The increasing demand for accurate, real-time monitoring of tree ecophysiological parameters presents challenges in developing affordable and efficient technologies, in particular in difficult environments such as mountains. TreeTalker Cyber, an innovative IoT platform, addresses these needs by integrating multiple sensors into a single, cost-effective device capable of measuring radial growth, radiation intensity below the canopy across 26 spectral bands, sap-flow, microclimate data, and trunk inclination. This presentation explores its capabilities, practical applications, and potential to transform forest monitoring globally. The use of a single platform to collect all the aforementioned parameter greatly reduces the cost of the equipment per collected parameter providing at the same time all main information required to evaluate the status of a tree, improving the maintenance of the network at the same time. The device is equipped with an NB-IoT or LoRaWAN transmission module to transmit collected data and make them available remotely. A comprehensive description of the platform and real field data are presented along with the technologies used for data transmission and storage with their strength and weaknesses. The OGC SensorThings API is also briefly described along with FROST (FRaunhofer Opensource SensorThings-Server) as an alternative to efficiently store IoT data and make them compliant with the FAIR principles, making them usable by both scientific and public communities. The creation of a dataset of trees ecophysiological parameters will help deepening the knowledge and understanding of forests around the world. TreeTalker Cyber lays the groundwork for advancing forestry research, providing fine-scale data as ground truth for forestry models and a starting point for future scenarios predictions, in particular when based on machine learning algorithms.

How to cite: Renzi, F., Yates, J., Coppola, V., Riggi, S., Chiriacò, M. V., and Valentini, R.: TreeTalker Cyber: A Multi-Sensor, Low-Cost IoT Platform for Real-Time Monitoring of Tree Ecophysiology, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-21862, https://doi.org/10.5194/egusphere-egu25-21862, 2025.

Anatolia is a geography that has experienced major natural disasters from ancient times to decay due to its geological location. In this context, Anatolia has always attracted the attention of researchers. The earthquakes that occurred in 6 provinces of Turkey, namely Anatolia, on February 6, 2022, and caused great loss of life and property, the re-emergence of the earthquake explosion in Anatolia. We, archaeologists, have been exposed to major earthquakes and climates from ancient times to decay, and situations that can be defined as natural have caused great losses. The Hittites, Assyrians, Hellenes, Romans, and societies before and after these civilizations, who continued their existence in Anatolia in ancient times, suffered great material and spiritual losses as a result of natural disasters experienced in the geography of Anatolia, and the ancient geological and climatological documentation of Anatolia has been documented. This is possible, the earthquakes and climatic conditions that occurred in Anatolia from ancient times to decay and are included in historical records due to its service location will be included, and it will be discussed how these earthquakes and climatic events will end archaeological settlements in ancient times and today.

How to cite: Yüksel Özer, F.: Natural disasters that occurred in ancient times in Anatolia and the damage they caused to ancient settlements, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-1907, https://doi.org/10.5194/egusphere-egu25-1907, 2025.

Architectural heritage in the African context, as a domain of cultural heritage, frequently encounters substantial obstacles for conservationists and custodians due to the lack of fully documented current conditions or as-built blueprints, which serves as the initial obstacle. Most architectural heritage buildings constructed prior to and during the colonial era lack documentation; traditional heritage structures were created through generational knowledge, while colonial buildings were built using imported knowledge, which largely dissipated after independence.
The second primary difficulty is the absence of documented social narratives pertaining to these heritage buildings. Numerous heritage buildings in Africa has profound cultural significance that is gradually being eroded owing to insufficient recording. This essay will introduce a prototype project in Porto-Novo, Benin, wherein the author utilizes local social engagement and digital technologies to chronicle Afro-Brazilian or Aguda architecture, a vanishing architectural heritage in Benin. Afro-Brazilian architecture is a construction style created by formerly enslaved Africans who resettled in the Bight of Benin countries following the abolition of slavery in Brazil. This settlement developed a distinctive architectural style that amalgamated Brazilian and native African influences, particularly Yoruba, significantly affecting the urban morphology of Benin.
The project utilizes LiDAR scanning, photogrammetry, and geolocation technologies to digitize heritage structures and develop interactive immersive interfaces that facilitate engagement with and access to this valuable architectural heritage.

H. Killion Mokwete is Assistant Professor at Northeastern and UK-trained and registered Architect (RIBA-chartered Architect & Urban Designer) and Co-Founder of the community-based design startup Social Impact Collective (SIC). He teaches various design studios both at undergraduate and graduate level and is currently undertaking multidisciplinary research initiative in Benin with local historians at the Ecole du Patrimoine Africain - School of African Heritage (EPA), Benin, Porto-Novo.

How to cite: Mokwete, H.: Digitizing Afro Brazilian Architectural Heritage buildings in Benin through LiDAR technology and social participation, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-2907, https://doi.org/10.5194/egusphere-egu25-2907, 2025.

How to cite: Barco, L., Chiriaco, G., Monopoli, T., Arnaudo, E., and Rossi, C.: From Polygon to Prediction: A Request-Driven Architecture for Disaster Mapping and Impact Assessment , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-4316, https://doi.org/10.5194/egusphere-egu25-4316, 2025.

Extreme weather events driven by climate change, such as floods and droughts, are damaging the structural stability of historic buildings in Central Germany by causing moisture retention and soil desiccation. The alternating wet and dry periods lead to cracks in walls and subsidence from falling groundwater levels. Understanding the impact of these conditions on regional groundwater dynamics and building materials is crucial as droughts and floods are expected to increase in the coming years.
As part of this study, three geoelectric field campaigns with a total of 17 profiles are being carried out between April 2024 and April 2025 at five different field sites of monuments in the federal states of Saxony and Saxony-Anhalt. For investigating seasonal and weather-dependent fluctuations in groundwater conditions, transient trends are observed by repeated electrical resistivity tomography (ERT) measurements. These provide insights into hydrological changes in the subsoil, and thus information on how weather events can affect different layers of the soil as well as foundation structures. In addition to the geoelectrical investigations, 14 groundwater wells are being drilled to a depth of around 10 m to monitor the fluctuations in the groundwater level over time. Furthermore, complementary laboratory tests are being conducted to characterize the soil properties, allowing a reliable interpretation of the ERT inversion results.
Preliminary results indicate that layers down to 25 m depth can be affected by weather-dependent variations in resistivity, depending on the hydraulic properties of the soil material at the respective site. Despite the elevated precipitation during the summer months of June and July, the topsoil underwent significant drying by November 2024, leading to a reduction in the groundwater level and subsequent saturation of the deeper soil layers. Ongoing continuous measurements shall provide further insights.

How to cite: Lehmann, W., Römhild, L., Gossel, W., and Bayer, P.: Historic buildings under the impact of climate change: insights from geoelectric field monitoring, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-4438, https://doi.org/10.5194/egusphere-egu25-4438, 2025.

The Mediterranean Sea has a long record of cultural heritage sites located near its coast, reflecting each nation’s historical continuum and identity. These monuments also attract tourism and provide financial benefits to local communities. However, they are subject to structural damage and decay exacerbated by climate-change-related phenomena including extreme weather events, sea-level rise, etc. Sea Level Rise (SLR) is a major threat to coastal heritage sites as it can  lead to extensive inundation and soil erosion. SLR projections are constructed by studying representative pathway scenarios (RCP), which try to deliver possible alternatives about the future atmospheric composition.  SLR has escalated from an average of 1. 2 mm/year before 1990 to 3 mm/year between 1993 and 2010, with projections indicating a rise of 1–2 meters by 2100 across different scenarios.

The ongoing Triquetra Project, funded by the European Union, aims to design a toolbox for assessing and mitigating climate-related risks and natural hazards, expected to affect Heritage Sites. A methodology has been developed to evaluate future exposure of coastal heritage sites SLR in EU Mediterranean Countries. This workflow uses open-access data to produce SLR projection maps for 2050 and 2100 based on the IPCC (2019) report for RCP 2.6, 4.5, and 8.5. Four main sources of data were used: a hybrid coastline vector file combining national fine-scale datasets with the European Environment’s Agency (EEA) coastline file, FABDEM as the primary source of elevation information,  the European Ground Motion Vertical Service’s (EGMS) L3 product which measures vertical ground movements using Synthetic Aperture Radar Interferometry (InSAR) data from the Sentinel-1 mission, and, finally, NASA’s Sea Level Projection Tool which provides information on all RCP scenarios. Coastal Heritage Sites and Assets were identified using OpenStreetMap and UNESCO’s Word Heritage Site point layer.

The pre-processing stage of the algorithm involves the projection of all datasets into a common coordinate reference system, the clipping of the data into the area of interest (AOI), defined as a 2km buffer zone from the coastline, and the conversion of EGMS and NASA’s SLR data units to meters. The algorithm proceeds with raster calculations to determine the AOI’s elevation for the target years 2050 and 2100 under different RCP scenarios by adding the elevation values to the EGMS data and subtracting NASA’s SLR projected values. Raster calculations and Boolean algebra are performed to identify sub-areas affected by these scenarios. Finally, spatial queries are conducted to find coastal heritage sites at risk from Sea Level Rise, organized by monument type and country for vulnerability assessment.

The results demonstrate that Greece, France, and Italy are expected to be more affected by SLR phenomena due to their extensive coastline and unique geomorphology, with the impact being more severe on Greece and Italy between 2050 and 2100. Finally, more than 240 heritage sites appear to be at risk primarily on the Greek and Italian coast, including UNESCO sites like Delos, the Medieval City of Rhodes, et al.

How to cite: Chalkidou, S., Georgiadis, C., Roustanis, T., and Patias, P.: Assessing the Exposure of Coastal Cultural Heritage Sites to Sea Level Rise Phenomena in the EU Mediterranean Countries using open access data, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-4992, https://doi.org/10.5194/egusphere-egu25-4992, 2025.

Climate change is one of the biggest threats to cultural and natural heritage across marine and coastal ecosystems. Multiple risks interact, cascade, and/or compound broader environmental, socio-economic, and cultural impacts on tangible (places, structures, ecosystems, etc.) and intangible heritage attributes (values, socio-economic activities, etc.). These risks arise from exposure, vulnerability, and responses to such impacts. For example, the physical materiality and integrity of underwater cultural properties are threatened by changes in water temperatures and acidity levels, compounded by extreme weather events causing strong waves and currents, which disrupt livelihoods tied to tourism and fisheries and provide conditions for looting and unregulated diving. This study adopts an empirical, value-based, and stakeholder-driven approach to identify, assess, and map these complex risks and their interactions.  

As part of the THETIDA Horizon Europe project that aims to develop an integrated risk monitoring, preparedness, and management mechanism for underwater and coastal heritage sites, the Living Labs methodology has been employed in the pilot sites. Through public-private-people partnerships, the Living Labs engage relevant national and local stakeholders and community groups to identify values, determine impacts, and assess exposure, vulnerability, and responses. This stakeholder-driven complex risk mapping methodology relies on the framework for complex climate change risk assessment that includes response as the fourth determinant of risks, together with hazard, exposure, and vulnerability [1]. In addition, it builds upon the Climate Vulnerability Index (CVI) for World Heritage, which employs a systematic and value-based approach to assess the climate vulnerability of shared values and attributes of cultural and natural properties [2]. Building upon CVI’s two-stepped procedure targeting to assess impacts on heritage values and communities, this complex risk mapping framework adopts a similar process to determine:

• Risks to heritage values: The heritage values attributed to the sites are identified. Moreover, their vulnerability, exposure to risks, and the impacts of key hazards and climate stressors on the sites are assessed.
• Risks to heritage communities: The heritage communities (stakeholders) and their socio-economic and cultural connections to the sites are identified. At the same time, their vulnerability, exposure to risks, and collective and/or institutional responses to climate-induced impacts are being evaluated.

This paper will present this innovative complex risk mapping framework and its preliminary implementation results in one of the THETIDA underwater sites. These sites include the Ottoman shipwreck in Paralimni, Cyprus, and the Second World War airplane wreck off the coast of Algarve, Portugal.

The complex risks posed to underwater heritage sites and their interactions remain largely underexplored in the existing literature, limiting the adoption of inclusive strategies to address them. This value-based and stakeholder-driven complex risk mapping framework outlined here enables a comprehensive assessment of risks and impacts on heritage values and communities. While initially tested for underwater sites, this framework provides a systematic methodology that can be applied to all heritage types, making it highly relevant for decision- and policy-makers working to safeguard underwater and coastal heritage.

Acknowledgement: This research has been funded by European Union’s Horizon Europe research and innovation funding under Grant Agreement No: 101095253, THETIDA project.

References:

[1] DOI: 10.1016/j.oneear.2021.03.005

[2] DOI: 10.5070/P536146384

How to cite: Ikiz, D., Guzman, P., Veiga-Pires, C., Oliveira, S., Demesticha, S., Demetriou-Patsalidou, A., Giatsiatsou, P., Nicu, I. C., and Michalis, P.: Value-based and stakeholder-driven complex risk mapping for underwater heritage through Living Labs, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-6132, https://doi.org/10.5194/egusphere-egu25-6132, 2025.

Climate change poses increasing challenges to outdoor cultural events, including human towers (castells) festivals, which demand favourable weather conditions. Human towers, recognised by UNESCO in 2010 as an Intangible Cultural Heritage of Humanity, rely on safe and comfortable conditions for participants and audiences alike. Building on a project developed in 2024, this communication wants to present the development of a climate-smart decision-making tool to enhance the management of casteller exhibitions under evolving climatic conditions. The prototype tool named Castells, Llindars i Informació Climàtica- CLICapp (Human Towers, Thresholds and Climate Information) aims to transform climate data into valuable information for decision-makers to manage the human tower exhibitions better, especially in summer (due to extreme temperatures and high humidity values) but not only.

The project’s groundwork is the study of temperature trends from 1951 to 2023 during the central hours of the day (12–15h) at four significant festivals (Sant Joan in Valls, Festa Major of La Bisbal del Penedès, Sant Magí in Tarragona, and Sant Fèlix in Vilafranca del Penedès). Results highlighted rising thermal stress, with Heat Index values underscoring the growing discomfort for Castellers (Olano et al., 2024). Then, participatory workshops based on the co-creation methodology for climate services (Font et al., 2021) were held with 109 castellers from 10 teams (colles castelleres), offering qualitative and quantitative insights into their perceptions of favourable and adverse weather for castells. These workshops also generated adaptation proposals prioritised by feasibility and importance (Saladié et al., 2025).

This communication will outline the two new steps undertaken in this project: the introduction of real-time measurements using temperature and humidity sensors in 11 urban squares during the summer season, which provided empirical data on thermal conditions of the exhibitions, and the initial insights into transforming all this data in useful information (climate raw data and co-creation insights) into an app. This app prototype aims to convert climate data and the information collected from the squares and participant groups into understandable and actionable insights for decision-makers—whether they are the Castellers, organisers (i.e. City Hall), other stakeholders (medical services, businesses, police, civil defence), or the public. The developing tool wants to integrate near real-time weather forecasts to identify potential risks for specific festival dates and times. Combining these insights with adaptive strategies proposed in the co-creation workshops provides a robust framework for pre-event planning. The advanced monitoring capabilities will allow organisers to receive near real-time updates on key parameters such as temperature, humidity, Heat Index, or co-created indices based on the information gathered during the workshops.

This project advances the adaptive management of outdoor cultural events by ensuring casteller festivals remain safe and sustainable amid climate change while preserving their cultural essence, safeguarding heritage, promoting climate innovation, and prioritising the well-being of participants. This initiative provides a replicable model for other cultural manifestations facing similar climate challenges worldwide. Incorporating climate services into intangible cultural event management combines scientific research and innovation with cultural preservation to protect the identity, ensure the sustainability of traditions under climate stress, and safeguard human health.

How to cite: Olano Pozo, J. X., Saladié Borraz, Ò., and Boqué-Ciurana, A.: CLICapp: A co-created tool for climate adaptation and safety in human tower exhibitions , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-8771, https://doi.org/10.5194/egusphere-egu25-8771, 2025.

Underwater cultural heritage (UCH) sites provide insight into past human behavior and history and thus their preservation is crucial. Within the scope of THETIDA, a Horizon Europe project dedicated to developing technologies and methods to protect coastal and underwater cultural heritage, this work aims to predict the physical processes that can put UCH at risk. This risk assessment is applied to a specific site in the Algarve, Portugal where a WWII U.S. B24 bomber plane crashed approximately 3 km offshore Praia de Faro. The plane now sits 21 m deep on the coastal shelf, which consists mainly of sand. The site is exposed to dominant, more energetic waves coming from W-SW and sheltered from less energetic E-SE waves. The mean significant wave height is 0.9 m, but it can rise to above 3 m with the occurrence of storms. As the site is located in the open ocean, a highly energetic environment, the site is subject to risks caused by wave-induced currents and sediment transport. To analyze and predict these risks in real time a numerical framework integrating three operational process-based models was developed. The numerical system is composed of: 1) the wave model SWAN, 2) the hydrodynamic model MOHID, and 3) the sediment transport model MOHID sediment. The operational wave model uses bathymetric data from EMODNET and is forced with wind conditions from the Skiron Atmospheric Modeling and Weather Forecasting Group in Athens and wave conditions at the boundary from the Copernicus Marine Environmental Monitoring Service (CMEMS). The model was calibrated by testing various formulas for the physical parameters attributed to wave propagation. A statistical analysis was completed to determine the best physics formulas to use for the model by comparing the results of each calibration setting with in-situ buoy measurements. SWAN was then two-way coupled to the hydrodynamic modeling system SOMA (Algarve Operational Modeling and Monitoring System), which is powered by MOHID. The coupling mechanism forces the wave model with velocities and water level output from SOMA and forces SOMA with wave results from SWAN. Preliminary results of the coupling revealed that the impact of current velocity and water levels on wave propagation in the study area is negligible in deeper areas, where the observations used for model validation lie. Further investigations are been conducted to analyze the effects of the two-way coupling in nearshore areas such as the location of the B24. The wave-hydrodynamic coupled system is now being used to develop a non-cohesive sediment transport model, which will be used to evaluate in real-time risks on UCH. This forecasting system will be included in the decision support system of the THETIDA platform.

How to cite: Mills, L.: An Operational Numerical Framework for Assessing Risks to Underwater Cultural Heritage, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9465, https://doi.org/10.5194/egusphere-egu25-9465, 2025.

One of the most significant consequences of climate change is the threat it poses to cultural heritage sites. The TRIQUETRA project addresses this critical challenge by applying a comprehensive risk assessment framework. This framework integrates both traditional and advanced technologies, including remote sensing and laser-based spectroscopy, to quantify the severity of risks, monitor their progression, and inform effective mitigation strategies.

Climate risks emerge from the interplay of climate hazards, exposure, and vulnerability. Understanding these risks at the site level is essential to ensure the implementation of appropriate adaptation and mitigation measures. Recent research highlights the compounded impacts of climate-induced geo-hazards, such as landslides and earthquakes, which threaten the physical integrity of monuments and the socio-economic systems they support.
Citizen engagement is a core component of the TRIQUETRA project, which includes a dynamic web and mobile platform where visitors actively participate in monitoring cultural heritage sites. The TRIQUETRA application enables citizens and visitors to contribute valuable datasets by capturing and uploading site photos, complementing and enhancing existing 3D models. A backend system assists cultural site authorities in better monitoring sites by providing up-to-date imagery and reports from visitors. Simultaneously, the TRIQUETRA Citizen Engagement Application creates an interactive and enriched experience for visitors through Virtual Reality (VR) and immersive Augmented Reality (AR) technologies. The application offers additional information through VR and AR experiences, allowing users to learn more about critical features at risk, such as areas affected by climate change or structural vulnerabilities. This fosters awareness and encourages preservation efforts.

The Choirokoitia case study demonstrates the application of the TRIQUETRA methodology in monitoring how the site is affected by climate change while also enhancing the visitor experience. Choirokoitia, a UNESCO World Heritage Site, is one of the best-preserved Neolithic sites in the Mediterranean. It represents the Aceramic Neolithic period of Cyprus at its peak, around the beginning of the 9th millennium BCE. Located in the District of Larnaka, about 6 km from the southern coast of Cyprus, the site leverages crowd-sourced information to provide stakeholders with real-time updates on its condition. By comparing uploaded images to a referenced 3D model, authorities gain valuable insights for preservation.

By integrating advanced technologies and community-driven monitoring, TRIQUETRA ensures a holistic approach to safeguarding cultural heritage. The project establishes a replicable framework that enhances risk assessment and promotes active participation in preservation efforts, offering scalable benefits for cultural heritage sites worldwide.

How to cite: Themistocleous, K., Evripidou, V., and Toumbas, K.: Monitoring Climate Change in Cultural Heritage Sites Through Enhanced Visualisation Experiences and Crowdsourcing , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-12307, https://doi.org/10.5194/egusphere-egu25-12307, 2025.

The Delos archaeological site, inscribed on the UNESCO World Heritage Site List, is situated on a small rocky island in the center of the Aegean Sea. This uninhabited island boasts of monuments with immense significance to human civilization and it is set within a pristine natural landscape. Delos is increasingly vulnerable to risks due to climate change and geodynamic events, which together endanger its cultural and natural heritage. Recently, a multi-hazard environmental monitoring facility has been established in Delos, incorporating climate and numerical prediction modelling, as well as satellite-based and in-situ real-time monitoring of various seismic, atmospheric, and oceanographic parameters. In addition to providing an overview of the overall facility, we discuss the potential long-term changes in atmospheric parameters such as air temperature, and precipitation along with sea level, that could impact the monuments and the landscape in the future, for different socioeconomic scenarios. Furthermore, we discuss how state-of-the-art models have been downscaled and optimized to forecast meteorological conditions, air quality, and wave activity in the Delos area. Local monitoring of earthquake activity and how it is incorporated into the National Seismic Network, as well as measurements of atmospheric and oceanic parameters are also discussed. The project is a groundbreaking initiative aimed at formulating policies and strategies to promote sustainable growth in the economy, tourism, and culture. It also serves as a model for strengthening the resilience of cultural heritage against natural hazards and risks, as well as a pilot program that aims to be applied to other monuments in Greece and abroad with the support of international organizations (e.g., UNESCO, ICOMOS, Europa Nostra, etc.).

Acknowledgments: This work has been performed in the framework of the project: “Development and installation of an integrated system for the monitoring of the impacts of climatic change on the monuments of Delos” that has been funded by benefit foundations of "Protovoulia ‘21“.

How to cite: Fountoulakis, I., Melis, N. S., Solomos, S., Kapsomenakis, J., Poupkou, A., Maris, C., Synolakis, C., and Zerefos, C. S. and the Delos Observatory team: Establishment of a transdisciplinary monitoring facility in Delos, Greece for the protection of Natural Heritage from the impacts of Climate Change, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-13403, https://doi.org/10.5194/egusphere-egu25-13403, 2025.

Andean plateau in Peru and its World Heritage sites are particularly affected by the impacts of climate change. The sacred Valley Archaeological Site around the city of Cuzco, a UNESCO World Heritage Site, is exposed to significant geological risks due to recurrent landslides induced and worsened by climate change effects that threaten its structural integrity, security and exploitation. The Machu Picchu Historic Sanctuary was built on Upper Permian-Lower Triassic (250–300 Ma) igneous rocks, primarily plutonic, which form the Vilcabamba Cordillera's backbone (from 2000m since 6000m a.s.l.) These intrusive formations, oriented ONO–ESE, constitute the elevated regions of the Eastern Cordillera. The area is dominated by a batholith composed mainly of granite and granodiorite, with medium-textured basic granite prominently outcropping within the citadel. The Machu Picchu site and all the sacred valley of Cuzco its surroundings are characterized by instability phenomena driven by complex geomorphological and structural/tectonic conditions worsened by the effects induced at altitude by the climate change (melting of the permafrost, heavy rainfall and increase in temperature). The above mentioned phenomena are exacerbated by the interplay of primary discontinuity families, resulting in recurring processes such as planar slides, rockfalls, topples, debris slides, debris flows, and avalanches. The present work shows the application of Differential Interferometric Synthetic Aperture Radar (DInSAR) technique to measure slow, non-catastrophic morphological changes with millimeter-scale precision. A previous interferometric satellite analysis work carried out in the early 2000s to test the general stability of the Inca Citadel has been resumed and updated. The analysis captures both long-term and seasonal processes triggered by diverse causative factors, enabling informed planning of mitigation strategies. Specifically, DInSAR data processing was conducted for the Machu Picchu archaeological area and for the wider Cusco area, complemented by direct field surveys to validate the results (November 2024). Multi-temporal SAR images from the Sentinel-1 constellation (C-band radar) were processed using advanced DInSAR techniques to generate ground displacement measurement points. The spatial distribution and correlation of these measurements with slope instability and structural damage were analyzed, revealing ground deformation trends from January 2020 to August 2024. Preliminary results indicate that the citadel exhibits average ground and structural displacement of less than 1 mm/year substantially negligible. However, localized analyses highlight distinct patterns of small-scale displacement in the Grupo de las Tres Puertas with slight brick detachment and in the Upper Plaza and Eastern Citadel sector showing relative subsidence compared to adjacent areas, suggesting potential movements of the eastern flank. Monitoring systems (remote and in situ) are recommended. The use of Sentinel-1 DInSAR data provided critical insights into the interaction between ground displacement and archaeological structures. It facilitated the identification of potentially unstable areas, detected anomalies, and traced ground displacement accelerations over time. Displacement anomalies and weather-climate anomalies over time, highlights the effects of the latter on the spatial-temporal increase of instability phenomena. These findings underscore the utility of DInSAR as a powerful tool for addressing preservation of intervention on CH threatened by slope instability, offering data-driven approaches for damage prevention and site management.

How to cite: spizzichino, D., ferrigno, F., leoni, G., and menniti, F.: DInSAR analysis for slope instability monitoring due to Climate Change: CUZCO and Machu Picchu case study., EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15505, https://doi.org/10.5194/egusphere-egu25-15505, 2025.

Kalapodi arcaheological site is located in central Greece, in the region of present-day Fthiotis consisting of a complex of temples and surrounding remains. It comprises a very important sanctuary, being among the most significant of ancient Phokis, providing crucial insights into Greek religious practices and architectural forms from the Mycenaean to the Classical periods. The archaeogical site in Kalapodi has been the focus of extensive cultural heritage management efforts by the German Archaeological Institute (DAI) since 2017.

As a case study in the TRIQUETRA program, funded by the EU Horizon Europe research and innovation program (GA No. 101094818), this site exemplifies the challenges posed by climate change on cultural heritage. TRIQUETRA project aims to develop an integrated methodological model to safeguard archaeological remains, such as those at Kalapodi, from environmental risks and mainly frost. Central to the project is the creation of an evidence-based assessment platform for precise risk stratification, coupled with a comprehensive database of mitigation measures.

In this paper we would like to leverage environmental data and materials analysis from Kalapodi, so as to quantify the impacts of climate change and propose tailored preservation strategies. These include assessing the effects of frost on ancient structures and implementing preventative measures to ensure their long-term stability. To achieve this a pilot site has been designed and constructed on which several monitoring equipment has been attached to understand the influence of environmental conditions on the pilot and hence the ancient monument. The acquired knowledge and the methodology followed highlights the importance of combining scientific research and heritage management to address climate-related challenges and protect cultural heritage for future generations.

How to cite: Oikonomou, A., Sotiropoulos, A., Gourgouleti, P., and Bilis, T.: Safeguarding the Past: Monitoring Climate Change at Kalapodi Sanctuary through the TRIQUETRA Project, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-16404, https://doi.org/10.5194/egusphere-egu25-16404, 2025.

The development of an Agent-Based Model (ABM) has proven highly effective for analyzing how the behavior of different agents leads to aggregated phenomena. Despite the challenges in creating such a model—including conceptualization, agent definition, relationship establishment, behavior design, programming, testing, validation, and reporting—the process allows for valuable testing and rethinking of strategies for enhancing the resilience of cultural landscapes, as the results offer significant insights into phenomena like drought. While not predictive, the observed trends can inform general analysis and highlight key areas for action to achieve specific goals. The RescueMe project developed an ABM that simulates three types of administration and underscores the impact of decision-making on territorial resilience, significantly influenced by timely policies and actions. The primary goal of the model is to simulate how farmers, agricultural plots, and decision-makers interact with each other and their environment, particularly under varying drought conditions. The model tests the hypothesis that decision-makers can intervene to mitigate the effects of drought by creating mechanisms that enhance plot resilience and/or attract new farmers safeguarding the values of the cultural landscapes. In this way, the ABM aims to develop a reflection and awareness-raising tool to allow cultural landscapes to consider the consequences of different climate change adaptation measures and behaviors. The impact chains co-created with the project case studies have been used as a basis for the modeling. The impact chain of drought on agriculture (impact of specific climatic hazards on a given sector) was selected due to its importance for a significant number of cultural landscapes, and the organigraphs created during the early stages of the project were used to help define the agents. The scenarios generated with the ABM simulate the impact of the behavior of agents on landscape resilience and potentially inform the definition of a serious game.

How to cite: Egusquiza, A., Cantergiani, C., and Villanueva, A.: Agent-Based Modeling for analyzing the climate resilience and decision-making impact on drought dynamics in Cultural Landscapes, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-17815, https://doi.org/10.5194/egusphere-egu25-17815, 2025.

Preserving cultural heritage sites demands risk management strategies that capture site-specific vulnerabilities at fine spatial resolutions. The present study introduces a novel framework for flood risk assessments that bridges large-scale hydrological modeling and sub-meter-level hydraulic simulations to provide enhanced insights into potential impacts. Our approach employs state-of-the-art Rain-on-Grid (RoG) hydraulic simulations, targeted field data collection, and high-resolution geometric documentation using UAV imagery and GNSS ground control points to account for detailed terrain characteristics.

Within the scope of the Horizon Europe TRIQUETRA Project, we apply this framework to the Apollo temple in the archaeological site of Kolona on Aegina Island, Greece. A total of 945 vertical and 4900 oblique UAV images were processed following a multi-image photogrammetric workflow, to produce a digital surface model with a resolution of 1 cm. We then use this data to set up the RoG model and to analyze flood scenarios for various return periods to obtain sub-meter-level hydraulic parameters and evaluate how the site’s vulnerability to flood intrusion might change if its existing wall obstructions were to be extended.

The proposed methodology offers a robust means to extract high-resolution boundary conditions for advanced computational fluid dynamics simulations. Using our multi-scale workflow, relevant stakeholders can enhance their data-driven decision-making for cultural heritage protection and preservation purposes.

Acknowledgments: This work is based on procedures and tasks implemented within the project “Toolbox for assessing and mitigating Climate Change risks and natural hazards threatening cultural heritage—TRIQUETRA”, which is a Project funded by the EU HE research and innovation program under GA No. 101094818.

How to cite: Alexopoulos, M. J., Iliopoulou, T., Istrati, D., Soile, S., Verykokou, S., Ioannidis, C., and Koutsoyiannis, D.: A Multi-Scale Framework for Flood Risk Assessment in Cultural Heritage Sites: The Apollo Temple in Aegina, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-18452, https://doi.org/10.5194/egusphere-egu25-18452, 2025.

European Cultural Heritage (CH) is a crucial resource that must be maintained, preserved and made accessible, to counteract degradation enhanced by unfavorable environmental conditions and climate changes. Some of the conservation methodologies nowadays available lack sustainability and cost-effectiveness, and are typically based on energy-consuming processes or non-environmentally friendly materials. This contribution will report on the main results so-far achieved in the EU-funded project GREen ENdeavor in Art ResToration (GREENART), coordinated by the Center for Colloid and Surface Science of the University of Florence (CSGI). Coping with the imperatives of EU Green Deal, the project proposes new solutions based on green and sustainable materials and methods, to preserve, conserve and restore CH. In particular, several innovative materials have been developed and tested:  1) Protective coatings based on green materials from waste and plant proteins, with self-healing and reversibility character, possibly functionalized with organic/inorganic nanoparticles to impart VOC capture, anti-corrosion and barrier behaviors. 2) Foams and packaging materials made by biodegradable/compostable polymers from renewable sources (polyurethanes and natural fibers) to control temperature and relative humidity. 3) Consolidants based on natural polymers from renewable sources, to mechanically strengthen weak artifacts. 4) Gels and cleaning fluids inspired by the most advanced systems currently available to conservators, which will be improved according to green metrics and circular economy requirements. 5) Green tech solutions for monitoring CH assets non-invasively against pollutants and environmental oscillations. Life Cycle Assessment and modeling favor the “safe-by-design” creation of affordable solutions safe to craftspeople, operators and the environment, and minimize energy-consumption in monitoring museum environments. Such holistic approach is granted in GREENART by a multidisciplinary partnership that gathers hard and soft sciences and engineering, including academic centers, innovative industries and SMEs, conservation institutions and professionals, museums whose collections hold absolute masterpieces in need of conservation, public entities and policy makers. Innovative materials and products have been assessed at the lab scale on representative mock-ups of works of art (remedial conservation), or in simulated museum/archive environments (preventive conservation). The project intends to transfer the most promising systems to field assessment on actual artefacts and museums/archives, in cooperation with conservator partners. The best products are also fed into a GREENART open repository and an App to illustrate the new solutions and involve citizens in good preservation practices. Constant feedback from conservators (internal or external to the partnership) can stimulate iterative refinement of the products, triggering a positive loop in this methodological approach. Covering these topics, we provide here an overview of the most advanced green materials for art conservation that can be useful to end-users in this field.

How to cite: Chelazzi, D., Poggi, G., and Baglioni, P.: The GREENART project: "green" and sustainable materials for cultural heritage conservation, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-18757, https://doi.org/10.5194/egusphere-egu25-18757, 2025.

Nowadays Cultural Heritage (CH) monuments face increasing effects of climate change (CC) that vitally impact their sustainability. The TRIQUETRA project, recognising the cruciality of the identification, quantification and mitigation of those CC-driven effects, aims to develop a novel Decision Support System (DSS) that leverages the existing knowledge, to efficiently provide a holistic approach for the conservation of the CH monuments.

More specifically, the TRIQUETRA project focuses on developing a comprehensive, evidence-based DSS for the identification and mitigation of the impacts of climate change on CH sites. TRIQUETRA is based on three key components i.e., Risk Identification, Risk Quantification, and Risk Mitigation. The basis for the DSS is the TRIQUETRA Knowledge Base Platform (KBP), which serves as a dynamic electronic repository equipped with advanced search functionalities and visualisation tools. The KBP concentrates a wide array of validated data regarding a wide variety of CH sites around the world, which climatic, geological and historical records, site-specific attributes, risk assessment, and mitigation strategies are provided through verified research publications.

The DSS features two distinctive modules: the a) Risk Severity Quantification module and b) the Mitigation Measure Selection and Optimisation module. The latter utilises the catalogued information of KBP to provide tailored mitigation measures for each pilot site and verify them based on project outcomes. By incorporating dynamic user stories that consistently reflect the stakeholders' needs, this module facilitates the selection of the most appropriate preservation and mitigation strategies for each site.

Moreover, to enhance functionality, the DSS integrates a search mechanism that allows users to filter results based on a series of criteria such as cost, implementation timeframe, topological effect, etc. The algorithm is adaptable to diverse user inputs and constitutes a scalable solution, leveraging the database of the KBP to identify optimal mitigation solutions, by cross-referencing the characteristics of a given CH site with those of similar sites documented in the relevant literature, providing users with a ranked list of applicable measures.

This adaptive module and the TRIQUETRA DSS as a whole aim to complement research contributing to the protection of cultural heritage against climate change, enabling tailored monitoring and preservation strategies for each pilot CH site.

How to cite: Charalampopoulou, V., Anastasiou, A., Magkoufis, E., Mpotonakis, K., and Kontopoulos, C.: A Smart Decision Support System for the Mitigation of Climate Change Effects on Cultural Heritage, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-19235, https://doi.org/10.5194/egusphere-egu25-19235, 2025.

The combination of future Sea level rise and changes in wave climate in coastal areas represents one of the greatest threats to the preservation of underwater cultural heritage (UCH). This study presents a new methodology to assess climate change’s impacts on UCH preservation in shallow waters, focusing on wave-induced hazards like decontextualization of archaeological object, scouring, and wear erosion caused by sediment transport. The approach uses hybrid downscaling of bias-corrected wave fields to assess the changes on this hazard and associated risk under RCP4.5 and RCP8.5 CMIP5 scenarios. The methodology was applied in the Bay of Cadiz, where an overall reduction in wave energy flux was observed. However, local increases were detected in rocky shoals and in the coastal zone, both areas with high UCH density. As a result, the shallow zones exhibited significant changes in decontextualization and scouring hazards. However, the most relevant risk changes were linked to wear erosion, particularly at sites on rocky outcrops near Cadiz. The developed methodology tested in this study is essential for identifying areas with higher risk and for evaluating UCH preservation under future climate conditions. It offers an effective tool for screening sites at risk and for conducting a long-term assessment of these risks in coastal environments affected by climate change.

How to cite: Ferrero Martín, C., Izquierdo, A., Bethencourt, M., Mentaschi, L., and Fernández Montblanc, T.: Wave Hazards on Underwater cultural Heritage: The Impact of Climate Change on Cadiz Bay , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-19445, https://doi.org/10.5194/egusphere-egu25-19445, 2025.

Wave runup plays a pivotal role in shaping the stability of coastal cliffs, as it generates hydrodynamic pressures that can compromise their structural integrity over time. These cliffs, especially those near cultural heritage (CH) sites, are vital natural structures that indirectly safeguard invaluable assets. Their destabilization, however, poses significant risks, necessitating a comprehensive understanding of the underlying processes that threaten their stability. Despite growing interest in coastal hazard assessments, there remains a paucity of quantitative studies focused on the interplay between wave runup dynamics and the structural characteristics of cliffs. Addressing this gap is essential for improving risk assessment methodologies and developing effective mitigation strategies.

Field measurements conducted in the Horizon Europe project TRIQUETRA revealed that coastal cliffs rarely conform to idealized vertical geometries. Instead, they often exhibit structural irregularities, such as varying inclinations or pre-existing damage like notches, which can exacerbate their exposure to wave-induced pressures. These variations are critical in determining the wave runup and consequently the exposed height of the cliff, which affects its stability. In this study, computational fluid dynamics (CFD) simulations using the Volume of Fluid (VOF) method were employed to model wave-cliff interactions. The analysis focused on the influence of geometric configurations and structural irregularities on the maximum wave runup and the  hydrodynamic pressure distributions, with particular attention to the behavior of steeply inclined cliffs and notched formations. The results demonstrate that wave runup is significantly amplified on near-vertical cliffs, with this effect becoming more pronounced under larger wave conditions. Conversely, notches reduce overall wave runup as their height increases, redistributing hydrodynamic forces along the cliff face and altering the pressure patterns. These findings highlight the intricate relationship between wave dynamics and structural variations, emphasizing the need for site-specific analyses when assessing cliff vulnerabilities.

By advancing the understanding of wave-cliff interactions, this research provides a valuable contribution to coastal hazard studies, offering new insights into the mechanisms driving cliff instability. The outcomes underscore the importance of integrating advanced CFD tools into risk assessments, enabling the design of targeted mitigation strategies to protect coastal regions and preserve the structural integrity of cliffs that play a critical role in safeguarding nearby CH sites.

Acknowledgments: This work is based on procedures and tasks implemented within the project “Toolbox for assessing and mitigating Climate Change risks and natural hazards threatening cultural heritage—TRIQUETRA”, which is a Project funded by the EU HE research and innovation program under GA No. 101094818.

How to cite: Sobhani, R., Istrati, D., Martino, S., Marmoni, G. M., and Feliziani, F.: CFD investigation of wave runup on coastal cliffs for impact assessment on cultural heritage, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-19751, https://doi.org/10.5194/egusphere-egu25-19751, 2025.

Digital twins of cultural heritage are urgently needed for both comprehensive documentation and digitalization of the monuments, and, also, for the efficient planning of restoration activities towards increased resilience to climatic stressors. Here, we present a workflow for geodetic field surveying followed by 3D building information modeling (BIM), to create a digital twin of an example historical building of Western Greece, the ‘Old Hatzikosta Hospital’ in Messolonghi. More specifically, a detailed point cloud was generated, based on data collected with a Terrestrial Laser Scanner. Building features not directly detectable from the ground (e.g., rooftops) were mapped with photogrammetry using an Unmanned Aerial Vehicle (UAV). The collected data were then further analysed to derive a detailed 3D model of the monument. This 3D model could serve as a baseline for future engineering applications, such as planning maintenance and restoration interventions. Moreover, the digitalization of cultural heritage could also assist in raising public’s awareness and making such historical buildings more widely visible and accessible (e.g., virtual tours, interactive geodatabases etc.).

How to cite: Tsikas, P., Kyriou, A., Lyros, E., Nikolakopoulos, K., and Pappas, C.: Aerial and ground-based surveying and 3D modeling of cultural heritage – a case study in Messolonghi, Western Greece, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-21315, https://doi.org/10.5194/egusphere-egu25-21315, 2025.

Integrating High-Performance Computing (HPC) and cloud computing in climate sciences is difficult, due to intricate hardware/software, compatibility, performance and reproducibility issues. Here, we address these challenges in a user-friendly way by leveraging the Conda ecosystem and containers.

Containerization allows to match or exceed native performance on HPC while ensuring bit-for-bit reproducibility for deterministic algorithms and similar processor architectures. This approach simplifies deploying climate models across different platforms; for example, CESM 2.2.2 (Community Earth System Model) provides on various clusters throughputs in simulated years per computational day within +/- 1% of bare-metal performance for simulations spanning thousands of processors.

Exclusively using generic Conda packages for MPI (Message Passing Interface) applications was challenging in HPC. Although OpenMPI included UCX (Universal Communication X) and OFI (Open Fabric Interface), it lacked UCC (Unified Collective Communication) and wasn't optimized by default for high-performance networks like InfiniBand, RoCE (Remote Direct Memory Access over Converged Ethernet) and HPE (Hewlett Packard Enterprise) Slingshot-11, often defaulting to TCP/IP (Transmission Control Protocol/Internet Protocol) or failing. 
 
After updating Conda-Forge’s OpenMPI and MPICH feedstocks, we are adding MVAPICH and ParaStationMPI support to PnetCDF, HDF5, NetCDF-C, NetCDF-Fortran and ESMF (Earth System Modeling Framework) libraries critical for modellers, alongside libFabric and openPMIx (Process Management Interface - Exascale). This incidentally exposed ABI (Application Binary Interface) compatibility issues. Now, MPI toolchains featuring major UCX/OFI/PMIx versions ensure consistent operation across different hosts without affecting numerical results. Using the same Conda environment inside a container, and no hardware-specific optimization, preserves bitwise reproducibility. OMB (Ohio State University Micro-Benchmark) tests for latency, bandwidth and other metrics help confirm if optimal performance can be achieved or not. 

Such developments enable climate scientists to focus on addressing scientific questions rather than sorting out software dependencies and technical problems. One can write code on a laptop then effortlessly scale to cloud or supercomputers, and seamlessly run climate simulations somewhere then continue these wherever compute resources are available without worrying about discontinuities. This also releases expensive HPC resources for production instead of wasting them for training, learning, development or testing which can be performed comfortably elsewhere, without job scheduling constraints, in the very same software environment.

Conda has primarily been developed with a focus on compatibility which limits its suitability in highly performance-sensitive applications where locally optimized builds of specific key components are paramount, typically in climate modeling. Additionally, instead of relying on local engineers to install and maintain host software, Conda users can benefit from the work of thousands of open-source contributors who continuously update and test the entire ecosystem.

This strategy fits the session's theme by providing a framework where cloud resources can be utilized for big data without compromising the performance or rigor of HPC environments. Conda and container technologies ought to change how climate scientists approach software management, focusing on ease of use, scalability and reproducibility, thereby potentially altering practices within the field to improve usage of computational resources and leverage community efforts to remain at the forefront.

How to cite: Iaquinta, J., Fouilloux, A., and Ragan-Kelley, B.: Climate Modeling with Conda and Containers to Improve Computational Resource Usage while Achieving Native Performance and Reproducibility, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-2605, https://doi.org/10.5194/egusphere-egu25-2605, 2025.

A significant challenge in data integration and ML methodologies on cloud infrastructures is accurately determining correlated statistics. Initially, aligning data to a consistent pixel grid is essential, motivating the use of Discrete Global Grid Systems (DGGS). In geophysical studies, data reside on a sphere, and approximating with tangent planes can distort results. Our solution is the HEALPix pixelization as our DGGS framework, standardizing data on a common grid for consistent statistical analysis. HEALPix's unique features, such as its iso-latitude layout and uniform pixel areas, enable the use of spin-weighted spherical harmonics in managing vector fields. This enables the accurate calculation of  correlation statistics, such as between velocity and scalar fields on the sphere, while minimizing biases due to spherical approximations. By utilizing the HEALPix framework, known in cosmology, with TensorFlow or PyTorch as backends, we created the: HEALML library. This library facilitates gradient computations of all derived statistics for AI optimization, and has been validated on the Pangeo-EOSC platform. This library parallelizes the computation of localized spherical harmonics and includes features like scattering covariance calculations, allowing the extraction of more complex nonlinear statistics beyond the power spectrum. We compare these results to traditional 2D planar methods, demonstrating the advantages of sphere-based statistics on platforms like Pangeo-EOSC. Furthermore, we demonstrate: HEALML's ability to emulate using a substantially smaller dataset. This demonstration emphasizes the ways in which incorporating spherical statistical methods into Pangeo-EOSC fosters innovative and efficient statistical analysis within geophysical research.

How to cite: Delouis, J.-M., Allys, E., Mangin, J., Mousset, L., and Odaka, T.: Advancing Geophysical Data Analysis: HEALML for Efficient Sphere-Based Statistics on Pangeo-EOSC, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-6798, https://doi.org/10.5194/egusphere-egu25-6798, 2025.

The eWaterCycle platform provides hydrologists with a platform that allows them to work with each other's models and data without having to become a computer scientist in the process. The eWaterCycle platform supports existing hydrological models and makes them available for scientists using the BMI model interface as a communication layer. Models run in containers for reproducibility and dependency control. Popular hydrological models are readily available (PCRGLobWB, WFLOW, HBV, etc.). Scientists develop their analyses or experiments in the widely known JupyterHub environment. 

While in theory eWaterCycle can be installed and run on any hardware, in practice most users interact with it on the SURF Research Cloud, a cloud computing infrastructure available to the Dutch academic ecosystem. Until recently upscaling from Cloud to HPC infrastructure for larger model runs required extensive knowledge of the HPC system. Here we will present our work on building a seamless workflow that allows scientists to upscale their cloud based work to the Snellius supercomputer and the Spider grid computer without having to worry about technical issues like mounting points for (large) datasets and container engines.

Our workflow opens up the possibility for more scientists to benefit from HPC and Grid resources while focussing on their domain science. We present the workflow in such a format that it should be easily portable to other hybrid cloud - HPC infrastructures, including the DestinE systems.

How to cite: Melotto, M., Hut, R., and Schilperoort, B.: Seamless Upscaling Research from Cloud to HPC using eWaterCycle, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-8127, https://doi.org/10.5194/egusphere-egu25-8127, 2025.

We present recent progress around the EERIE cloud data server (https://eerie.cloud.dkrz.de) and its software stack “cloudify”. The EERIE cloud provides efficient open access to prominent climate datasets stored on disk at the German Climate Computing Center (DKRZ).

A new kerchunk-plugin enables data access to raw model output as-is to enable verifiable data transfer with better latency. STAC (Spatio Temporal Assets Catalog) catalogs are deployed and displayed through the EERIE cloud to make the provided DKRZ datasets findable and accessible. Two in-browser apps can be started, pre-configured for each dataset, by just clicking buttons: (1) the data visualization app “gridlook” as well as a (2) jupyterlite for interactive analysis and monitoring. 

We leverage the python package xpublish, a plugin for Pangeo's central analysis package Xarray. Its main feature is to provide ESM output by mapping any input data to virtual zarr datasets. Users can retrieve these datasets as if they were cloud-native and cloud-optimized.

How to cite: Wachsmann, F.: The EERIE cloud: Apps and Catalogs for Cloudified Earth System Model Output, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-8591, https://doi.org/10.5194/egusphere-egu25-8591, 2025.

Cloud infrastructures play a significant role in delivering secure, scalable and efficient data processing for Earth Observation (EO) and agricultural management applications. As part of the ScaleAgData project, we present a hierarchical Agri-Environmental Monitoring Tool running on a private cloud infrastructure. The system combines data from EO, in-situ sensors and farm management information systems (FMIS), including parcel calendars, to provide farmers and policymakers multi-scale insights.  

The solution is cloud-based and designed with an underlying architecture that ensures both scalability and interoperability, leveraging OGC-compliant data formats where applicable. EO and in-situ data streams can be processed and analyzed efficiently with the help of containerized apps and microservices to facilitate modular development and simplify deployment. By using a web-based dashboard with hierarchical design, stakeholders can navigate from overviews at the municipal level to individual parcels. Aggregated summaries that comply with Common Agricultural Policy (CAP) criteria are useful to policymakers and farmers can get comprehensive parcel-level metrics to optimize irrigation, pesticide use and other agro-related activities.  

Specifically, the tool combines EO data to derive vegetation indices (e.g., NDVI, EVI) and other parameters requiring advanced processing for crop type classification. Furthermore, these datasets are enriched with in-situ sensor measurements (e.g. soil moisture, weather data) and farm logs managed within FMIS (irrigation schedule, pesticide usage). Parcel-level data (L1) is processed to generate statistics, which are then calibrated with nearby parcels data with similar properties and crop type(L2), serving as control level, and finally extrapolated to the municipal level (L3) using spatial averaging techniques  to provide indicators related to irrigation water, pesticide, fertilizer usage, etc.  Farm calendars stored within FMIS provide a reliable source of ground-truth data, enhancing the tool’s ability to validate aggregated metrics. The aggregation at L2 and L3 allows for the identification of regional trends and patterns in agricultural practices, empowering policymakers and stakeholders to implement targeted interventions at both levels, thereby promoting sustainable agriculture.   

This work showcases the potential of private cloud infrastructures to enhance agri-environmental monitoring by processing and integrating heterogeneous data streams (EO, in-situ sensors and farm log data) into a unified system. The system is being applied in diverse agricultural regions of Greece (Crete, Thessaly, Macedonia) with ongoing validation efforts aimed at refining its accuracy and adaptability. Future work includes the integration of cloud-based machine learning models and EO-derived evapotranspiration data to enhance the efficiency of extrapolating parcel-level (L1) and regional (L2) metrics into policy-level indicators (L3). Additionally, alternative aggregation methods, such as model-based approaches, spatial regression, and interpolation techniques like Kriging, will be tested to improve the accuracy and reliability of aggregated insights. 

How to cite: Charvalis, G., Louka, P., Gkoles, V., Manos,  ., Kalatzis, N., Solomos, D., Trypitsidis, A., and Sekkas, O.: Leveraging Cloud, Earth Observation and In-Situ Sensors for Agri-Environmental Monitoring and Policy Decision-Making , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-8754, https://doi.org/10.5194/egusphere-egu25-8754, 2025.

How to cite: Pisa, C., Antonacci, M., Baousis, V., Aspragkathos, S., Sotiropoulos, I., and Rizou, S.: Co-Creating Cloud-Based Tools for Urban Climate-Resilience: The CLIMRES Project, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9432, https://doi.org/10.5194/egusphere-egu25-9432, 2025.

How to cite: Fornari, F., Baousis, V., Albughdadi, M., Antonacci, M., Kaprol, T., Pisa, C., Andreou, C., Panagidi, K., and Hadjiefthymiades, S.: Copernicus data and services uptake with EO4EU platform: an AI-augmented ecosystem for Earth Observation data accessibility and exploitation., EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10683, https://doi.org/10.5194/egusphere-egu25-10683, 2025.

How to cite: Antonacci, M., Baousis, V., Pisa, C., Rizou, S., and Sotiropoulos, I.: Cloud-Powered Earth Observation Tools for Urban Resilience: The BUILDSPACE Project, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10977, https://doi.org/10.5194/egusphere-egu25-10977, 2025.

The DeployAI project [1] designs and delivers a fully operational European AI-on-Demand Platform (AIoDP) to empower the European industry with access to cutting-edge AI technology, and to promote trustworthy, ethical, and transparent European AI solutions, with a focus on SMEs and the public sector. To achieve this, the platform enables the development and deployment of AI solutions through the following core solutions: (i) AI Builder [2], which allows the assembling of reusable AI modules into AI pipelines; (ii) seamless access to Cloud and HPC infrastructures (e.g., MeluXina and LUMI); (iii) a marketplace for the listing and distribution of ready-to-use AI products; (iv) an expansive and growing library of diverse AI-driven use cases.

As part of its domain-driven solutions, AIoDP seeks to empower Environmental Scientists, AI Engineers, Developers, Researchers, and SMEs via the DeployAI Earth Observation Services. These services will accelerate the development of AI-driven environmental applications, by providing pre-trained models that simplify satellite imagery processing, land usage classification, and image segmentation. Key models available as modules within the DeployAI’s AI Builder include:

• Leaf Area Index (LAI) Model: Enables precise monitoring of vegetation health and ecological dynamics by calculating leaf area per unit ground [3]. 
• Object Detection Model: Identifies specific objects in high-resolution satellite images, supporting applications such as  infrastructure monitoring, pollution tracking, and deforestation assessment [4].
• Segment Anything Model (SAM): Simplifies analysis across diverse environmental applications through the capabilities of SAM that allows flexible, prompt-based image segmentation for new datasets, with zero-shot and few-shot learning [5].

These models, along with the broader functionalities of AI Builder, enable users to create custom AI pipelines that address their specific environmental challenges in several environmental areas, including vegetation health monitoring, water balance analysis, climate modeling, urban planning, traffic management, pollution monitoring, and infrastructure maintenance. Users can leverage the visual pipeline editor to easily assemble pipelines from reusable AI modules without needing to write code. Once created, these pipelines can be deployed as AI applications on various execution environments. DeployAI facilitates seamless transitions between these environments by providing connectors to a host of target infrastructures, including Cloud platforms and HPC systems. This empowers users to leverage the most suitable computational resources for their specific needs.

By providing a user-friendly platform with access to cutting-edge AI technology and Cloud/HPC resources, DeployAI empowers users to address critical environmental challenges and unlock new possibilities for sustainable development.

[1] https://deployaiproject.eu
[2] https://gitlab.eclipse.org/eclipse/graphene
[3] https://github.com/DeployAI-Environmental-Services/depai-lai
[4] https://github.com/DeployAI-Environmental-Services/depai-yolov8-obb
[5] https://github.com/DeployAI-Environmental-Services/depai-sam-interactive

This work has received funding from the European Union’s Digital Europe Programme (DIGITAL) under grant agreement No 101146490.

How to cite: Troumpoukis, A., Albughdadi, M., Welß, M., Baousis, V., and Klampanos, I.: DeployAI Earth Observation Services: Enabling Environmental Insights on the European AI-on-Demand Platform, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-11810, https://doi.org/10.5194/egusphere-egu25-11810, 2025.

The computational intensity of climate models makes them among the most energy-demanding applications in High-Performance Computing (HPC), resulting in significant computational costs and carbon emissions. Addressing the dual challenge of improving climate predictions —by running higher resolution, more accurate and complex models— and ensuring sustainability requires innovative tools to evaluate both computational efficiency and energy consumption across diverse HPC architectures. To address this, and in the context of the Center of Excellence in Simulation of Weather and Climate in Europe (ESiWACE), we have extended the High-Performance Climate and Weather Benchmark (HPCW) framework to incorporate a standardised set of Climate Performance Metrics for Intercomparison Projects (CPMIPs) and energy consumption monitoring.

HPCW, originally designed to maintain a set of relevant and realistic, near-operational weather forecast workloads to benchmark HPC sites, can provide insights beyond generic benchmarks like High-Performance Linpack (HPL) or High-Performance Conjugate Gradients (HPCG) by focusing on domain-specific workloads.

The inclusion of CPMIPs into HPCW brings a widely accepted set of metrics specifically tailored to the particularities of climate workflows. These metrics, already recognized by the scientific community, are key to better understanding climate model performance and allow us to keep the results from the framework relevant for research and operational runs, as well as improving our capacity for multi-model multi-platform performance comparisons.

By integrating energy monitoring, HPCW enables users to evaluate how critical computational kernels in climate models perform in terms of energy consumption. Our review of energy profiling tools across EuroHPC pre-exascale systems, including MareNostrum 5, LUMI, and Leonardo, highlights a fragmented landscape. Current tools offer varying granularity and portability, but limitations such as system configurations, administrative restrictions, and hardware compatibility often hinder their application. Low-level interfaces like Running Average Power Limit (RAPL) and Performance Application Programming Interface (PAPI) counters offer precise energy measurements but are constrained by accessibility issues.

These advancements aim to improve the allocation of climate experiments, such as those conducted for the Intergovernmental Panel on Climate Change (IPCC) in Coupled Model Intercomparison Projects (CMIPs), to the most suitable HPC resources, while also identifying architectural bottlenecks before running production experiments. Additionally, by enhancing energy consumption quantification, this work contributes to ongoing efforts to measure and reduce the carbon footprint of the climate research community. Furthermore, these analyses are expected to be particularly valuable for climate researchers, especially in the context of upcoming large-scale initiatives like CMIP7, enabling them to make informed resource requests and facilitate robust multi-platform comparisons of climate model performance which were not possible in the past. We anticipate that HPC vendors can also benefit from the outcomes of our work in optimising the systems for climate modelling workloads. By combining performance and energy metrics within a unified framework, we provide critical insights that align computational advancements with sustainability goals, ensuring efficient and environmentally conscious use of HPC resources for climate research.

How to cite: Palomas, S., Acosta, M., Utrera, G., Lennart, O., Beltran, D., Castrillo, M., Schroeter, N., and Mueller, R.: Performance Benchmarking and Energy monitoring for Climate Modelling, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-12070, https://doi.org/10.5194/egusphere-egu25-12070, 2025.

The Pangeo-Fish project processes biologging data to analyze fish movement and migration patterns.  While SciPy’s convolution methods are robust, they are not optimized for handling spherical datasets inherent to Earth system science. To address this limitation, we propose the integration of HEALPix convolution, a method designed for spherical operations, into Pangeo-Fish.

HEALPix convolution offers distinct advantages for geophysical data analysis, particularly when dealing with spherical datasets in Earth system science. It uses the HEALPix pixelization as a core Discrete Global Grid System (DGGS), which ensures equally-sized pixels globally, removing distortions common in flat projections. This consistency is crucial for maintaining the physical relevance of convolutions across locations. Additionally, HEALPix’s dyadic property supports flexible, multiscale resolution adjustments, allowing for downscaling while preserving accuracy. Such scalability is essential for studying oceanic environments where areas of interest, like coastal zones and basins, are often resolution-dependent.

Our approach evaluates the performance of HEALPix convolution in comparison to traditional SciPy methods, focusing on its ability to enhance the accuracy of habitat mapping and migration pathway modeling for fish. 

This integration is particularly relevant within the Global Fish Tracking System (GFTS), which operates under the European Union’s Destination Earth (DestinE) initiative. GFTS utilizes datasets from Copernicus Marine Services and the European Tracking Network (ETN) to model fish habitats, spawning grounds, and migration swimways. HEALPix convolution strengthens the pangeo-fish’s capacity for studying Species such as tuna and eel that exhibit large-scale, transoceanic migrations.    

In conclusion, this work highlights the transformative potential of HEALPix convolution in spherical data processing. By integrating this innovative method, Pangeo-Fish can provide more accurate, scalable, and actionable insights into fish behaviors and habitats, contributing to sustainable management practices and conservation strategies globally.

How to cite: Cap, E., Odaka, T., Delouis, J.-M., Magin, J., and Woillez, M.: Enhancing Pangeo-Fish with HEALPix Convolution: Impact Evaluation and Benefits, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-12918, https://doi.org/10.5194/egusphere-egu25-12918, 2025.

In recent years, technological advances in the use of geospatial data (such as satellite images, anthropogenic and/or environmental raster and vector open data, etc.) for hydrogeological risk assessment, combined with advanced analysis techniques (e.g., machine learning), have become increasingly valuable. These technologies can be utilized by local and national authorities for land planning and emergency management to better understand the dynamics associated with climate change. This understanding can help guide actions aimed at safeguarding not only environmental resources but also socio-economic assets and citizens’ lives.

In pursuit of this goal, a partnership has been established between the Po River Basin District Authority (AdBPo), the Italian Space Agency (ASI), and academic and research institutions such as the University of Bologna (UNIBO), the University of Modena and Reggio Emilia (UNIMORE), the University of Padova (UNIPD), and the Institute of Environmental Geology and Geoengineering of the National Research Council of Italy (CNR-IGAG). The aim is to implement a downstream service for monitoring landscape evolution related to fluvial systems (geomorphological classification), and slope dynamics (including landslides and rock glaciers) and to quantitatively evaluate the exposed assets.

The PARACELSO project (Predictive Analysis, MonitoRing, and mAnagement of Climate change Effects Leveraging Satellite Observations) aims to develop a modular and interoperable cloud-based platform that supports the analysis of natural phenomena (such as fluvial hydrodynamics, landslides, and rock glaciers) using satellite images provided by:

• DIAS platforms deployed by the Copernicus Programme (e.g., Sentinel 1-2);
• ASI missions such as CosmoSkyMed, PRISMA, and SAOCOM.

Furthermore, a methodology integrating Earth Observation and geospatial data analysis has been implemented using open-source libraries.

To facilitate this, the MarghERita supercomputer, named in honor of the scientist Margherita Hack, has been made available by the Emilia-Romagna region. It is used both to store the downloaded satellite images and to run the algorithms developed in the project for studying the temporal evolution of river and slope systems. Finally, it enables the sharing and visualization of processed data.

The project has received funding from ASI through the “I4DP_PA (Innovation for Downstream Preparation for Public Administrations)” Call for Ideas.

How to cite: Zazzeri, M. and the PARACELSO team: Cloud-based platform for the management of hydrogeological risks in the Po Basin , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-13725, https://doi.org/10.5194/egusphere-egu25-13725, 2025.

Over the past decade, weather and climate models have rapidly adopted unstructured meshes to better leverage high-performance computing systems and approach kilometer-scale resolutions. Output from this new generation of models presents many challenges for their subsequent analysis, largely due to a lack of community tools supporting unstructured grid data. Last year, we introduced UXarray, a class extension of Xarray that provides native support for unstructured meshes. UXarray readily runs in a Jupyter Notebook and offers parallelized execution through its compatibility with Dask, demonstrating its flexibility as both a tool for lightweight exploration and communication, and for supporting intensive calculations applied to vast data volumes. Over the past year, UXarray has matured significantly and is now capable of supporting many real-world analysis workflows applied to outputs from a growing number of high-resolution models and dynamical cores, including ICOsahedral Non-hydrostatic (ICON) atmosphere model, the Finite-Element/volumE Sea ice-Ocean Model (FESOM), NSF NCAR’s Model for Prediction Across Scales (MPAS), and the U.S. DOE’s Energy Exascale Earth System Model (E3SM). This presentation will provide an overview of the UXarray’s current capabilities, which include extensive support for plotting and many foundational analysis operators; demonstrate examples in Jupyter Notebooks; present plans for the future;  and discuss ways for Pangeo and the broader earth system science community to help guide new developments. 

How to cite: Clyne, J., Chen, H., Chmielowiec, P., Eroglu, O., Hannay, C., Jacob, R., Jain, R., Medeiros, B., Ullrich, P., and Zarzycki, C.: UXarray: Extending Xarray for Enhanced Support of Unstructured Grids, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-13873, https://doi.org/10.5194/egusphere-egu25-13873, 2025.

Accurate and efficient spatial analysis is crucial for the mapping and sustainable management of marine environments, where large-scale and diverse datasets present significant analytical challenges. Traditional latitude-longitude methods, while widely used, often encounter limitations in data integration and handling distortion caused by Earth’s curvature. Discrete Global Grid Systems (DGGS) have emerged as a promising solution, offering a hierarchical, global, and equal-area framework for geospatial analysis. Despite their potential, the performance in marine spatial analysis remains underexplored.

This study evaluates the impact and suitability of DGGS-based spatial analysis by comparing its performance with the traditional latitude-longitude approaches. Using marine datasets representing point and raster data formats, the workflow begins with quantization, converting the data into DGGS cells.The implementation utilizes open-source Python tools from the Pangeo ecosystem, including xarray-xdggrid, to enable seamless integration and efficient analysis of large geospatial datasets. Three DGGS configurations – ISEA7H, HEALPIX, and ISEA3H are compared alongside traditional latitude-longitude grid for computational efficiency (processing time and memory usage) and their ability to preserve spatial patterns. Spatial analysis methods include density estimation, nearest neighbor evaluation, and clustering for point data, as well as zonal statistics, spatial autocorrelation, and resampling for raster data.

To further illustrate the application of DGGS-based methods, the study includes a case study on estuary characterization. This characterization relies on spatial analysis methods, integrating physical oceanographic parameters from Delft3D-FM, biogeochemical and optical data products, and in-situ point measurements from the Copernicus Marine Environment Monitoring Service (CMEMS). Representing these diverse datasets within the DGGS framework highlights its ability to manage varying data types and scales, offering insights into estuarine environments and demonstrating its scalability for addressing complex marine spatial challenges.

Results indicate that DGGS frameworks deliver comparable computational performance while offering consistent spatial representation. Configuration-specific trade-offs influence their effectiveness, emphasizing the importance of aligning DGGS configurations with specific analytical tasks and applications. Findings suggest that DGGS-based methods offer a promising alternative to traditional analysis techniques, providing greater flexibility in adapting to datasets, scale, and resolution. This contributes to more efficient mapping, sustainable marine environmental management, and advancing geospatial applications through open-source tools from the Pangeo ecosystem.

How to cite: Martinez, K., Kmoch, A., Mészáros, L., Nelson, A., and Uuemaa, E.: Navigating New Grids: Evaluating DGGS Configurations for Marine Spatial Analysis, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-14306, https://doi.org/10.5194/egusphere-egu25-14306, 2025.

To enable wider participation in open science with geospatial data at scale, we need to reduce the effort and custom approaches required for setting up scalable scientific data analysis environments and computing workflows. We have made great strides in this pursuit by evolving and promoting community-developed open source frameworks, tools, and libraries for cloud-native data access and analysis, making them the default for scientists on the public cloud and local systems.

Many of our achievements have been supported by the NASA Visualization, Exploration, and Data Analysis (VEDA) project which seeks to proliferate cloud-native approaches for open science on Earth science data from NASA’s rich archives and many other providers. Our presentation highlights how we have engaged with communities like Pangeo, OpenScapes, Earth Science Information Partners, and the Cloud Native Geospatial Forum to build joint initiatives, target development, and ensure uptake of new solutions. We present key results from working groups, community showcases, and hackdays and hackweeks organized by VEDA team members, as well as specific contributions to the open source ecosystem, including the eoAPI platform for quickly and easily deploying an open-source Earth Observation stack, JupyterHub fancy profiles (with BinderHub) for seamless environment building, and Lonboard for fast, interactive vector visualization.

How to cite: Jones, M., Barciauskas, A., Sølvsteen, J., Freitag, B., Panda, Y., Barron, K., Signell, J., Mandel, A., Daniels, C., Zimmerman, N., Harkins, S., Rodman, H., Deziel, Z., Adhikari, S., Boyd, A., Kirk, A., Bitner, D., and Sarago, V.: A community oriented approach to enabling open science with Earth science data at scale, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-14400, https://doi.org/10.5194/egusphere-egu25-14400, 2025.

How to cite: Kalladath, N., Gudoshava, M., Nath, S., Kinyua, J., Cooper, F., Kimani, H., Koros, D., Maswi, C., Mwai, Z., Teshome, A., Abebe, S., Obai, I., Mason, J., Amdihun, A., and Palmer, T.: Weather Data Streaming with Kerchunk: Strengthening Early Warning Systems , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-14610, https://doi.org/10.5194/egusphere-egu25-14610, 2025.

The growing volume of Earth Observation (EO) and Earth modeling data makes it increasingly impractical to download and analyze it locally. Furthermore, as cloud-native data formats and AI/ML-driven models gain popularity, the community requires powerful computing and storage solutions to efficiently process and analyze EO data. High-performance computing (HPC) and cloud infrastructures can help accomplish this, but both bring significant challenges in maintaining those resources, putting additional workloads on the scientists and developers.

In this paper, we will present our solution, which uses cloud-native technologies and a “Control Plane” approach to seamlessly interact with HPC scheduling endpoints like SLURM and PBS, as well as cloud infrastructure resources, allowing HPC jobs to be submitted and monitored directly from a Kubernetes-based infrastructure. In contrast to traditional IT architecture, Platform Engineering is concerned with lowering operational complexity by introducing control planes to provide self-service capabilities. By abstracting away the complexities of the underlying infrastructure, this method gives teams a customized, scalable, and dependable environment to suit their unique requirements. We will thoroughly analyze existing technologies, including their methodologies, strengths, limits, and potential as universal solutions. Furthermore, we will assess their adaptation to various cloud and HPC infrastructures, providing insights into their suitability for larger applications. 

We will conclude our discussion with practical examples showing how the technical benefits of these two computing paradigms, combined with the Platform Engineering approach, may be effectively used in real-world EO data processing scenarios.

How to cite: Karatosun, A. and Baousis, V.: Platform Engineering for Earth Observation: A Unified Approach to HPC and Cloud Systems, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15406, https://doi.org/10.5194/egusphere-egu25-15406, 2025.

High-resolution regional climate model datasets, such as those produced within the Coordinated Regional Downscaling Experiment (CORDEX) framework, are critical for understanding climate change impacts at local and regional scales. These datasets, with their high spatial and temporal resolution, provide detailed insights into region-specific climate phenomena, including urban heat islands, mountainous climates, and extreme weather events. However, their accessibility and usability are often constrained by technical challenges such as fragmented data storage, inconsistent formats, and limited interoperability.

To address these barriers, we are developing the Climate Service Database (CSD) - a centralized data warehouse designed to streamline the temporal and spatial aggregation of CORDEX datasets for climate service applications. The CSD ingests raw CORDEX datasets and applies automated extraction, transformation, and loading (ETL) workflows to produce analysis-ready datasets tailored to user needs. By leveraging cloud-based infrastructure and adhering to Climate and Forecast (CF) conventions, the CSD ensures consistent, interoperable data products that are optimized for scalable access and analysis.

A core functionality of the CSD is its ability to aggregate datasets at multiple spatial and temporal scales, ranging from daily extremes to decadal averages, and across diverse spatial resolutions (e.g., countries, administrative regions, or watersheds). This capability enables the generation of climate indicators (e.g., hot summer days, heavy precipitation events) that are directly relevant for local decision-making and impact assessments. By providing data in cloud-optimized, analysis-ready formats (ARCO) and offering Software as a Service (SaaS), the CSD significantly lowers the technical barriers for researchers, businesses, and policymakers seeking to access user-tailored climate service datasets.

By centralizing and optimizing the processing of regional climate model datasets, the CSD fosters collaboration across research institutions, public agencies, and climate-tech startups. It enables users to efficiently access consistent and up-to-date data while eliminating the redundancies of localized data storage and processing. This approach also opens new opportunities for applying AI-driven analytics and machine learning models to CORDEX data, paving the way for innovative climate services and applications.

Through its focus on regional climate model datasets, the CSD exemplifies how modern data infrastructures can enhance the usability of high-resolution climate data, empowering stakeholders to develop robust, data-driven adaptation and mitigation strategies in response to the challenges of climate change.

How to cite: Buntemeyer, L.: Advancing Regional Climate Data Accessibility through a Cloud-native Climate Service Database, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-16230, https://doi.org/10.5194/egusphere-egu25-16230, 2025.

Zarr is a key component of the Pangeo ecosystem and instrumental for effectively accessing and processing multi-dimensional Earth data in cloud-based systems. More and more leading satellite data providers are exploring the transition of their data archives to a cloud environment. 

As part of the ESA Copernicus Earth Observation Processor Framework (EOPF), ESA is in the process of providing access to “live” sample data from the Copernicus Sentinel missions -1, -2 and -3 in the new Zarr data format. This set of reprocessed data allows users to try out accessing and processing data in the new format and experiencing the benefits thereof with their own workflows.

To help Sentinel data users to experience and adopt the new data format, a set of resources called the Sentinels EOPF Toolkit is being developed. Development Seed, SparkGeo and thriveGEO, together with a group of champion users (early-adopters), are creating a set of Jupyter Notebooks, plug-ins and libraries that showcase the use of Sentinel data in Zarr for applications across multiple domains for different user communities, including users of Python, Julia, R and QGIS.

This presentation will give a demo of the first set of notebooks and plugins of the Sentinels EOPF toolkit that were developed and that facilitate the adoption of the Zarr data format for Copernicus Sentinel data users. Additionally, we will give an overview of toolkit developments and community activities that are planned throughout the project period.

How to cite: Wagemann, Dr. J., Szeto, S., Mathot, E., and Banting, J.: The Sentinels EOPF Toolkit: Community Notebooks and Plug-ins for using Copernicus Sentinel Data in Zarr format, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-17137, https://doi.org/10.5194/egusphere-egu25-17137, 2025.

Regridding data from diverse sources, such as satellite observations and numerical models, is a critical task in Earth system sciences. Proper interpolation methods are essential to ensure data fidelity when combining or comparing datasets on different grids. This becomes especially relevant in the context of emerging grid systems like Discrete Global Grid Systems (DGGS), specifically HEALPix.

DGGS are spatial reference systems designed to partition the Earth’s surface into a hierarchy of equal-area cells. Unlike traditional latitude-longitude grids, DGGS uses tessellations, such as hexagons, to represent the Earth’s curved surface with minimal distortion. This grid system is particularly suited for handling global-scale geospatial data by providing uniform coverage and resolution, enabling efficient storage, processing, and analysis.

HEALPix (Hierarchical Equal Area isoLatitude Pixelation) is a specific implementation of DGGS widely used in astronomy and Earth sciences. HEALPix divides the sphere into equal-area cells following an iso-latitude structure, making it computationally efficient for operations such as spherical harmonics and multi-resolution analysis. Originally developed for astrophysical applications, it has become increasingly popular in the Earth sciences for representing satellite data, model outputs, and other geospatial datasets in a way that preserves area integrity and facilitates seamless multi-resolution data integration.

By leveraging these grid systems, particularly HEALPix, we can achieve a more accurate and efficient representation of geospatial data.

The Pangeo ecosystem includes an array of powerful regridding tools, each tailored to specific grid types and applications. However, navigating this ecosystem to identify the most suitable tool and workflow can be challenging.

In this presentation, we will show an overview of regridding solutions within Pangeo, highlighting their capabilities and limitations, as well as  their application. We will also demonstrate a practical regridding workflow using model outputs or simulated satellite data such as the Odysea dataset (Aviso+ Altimetry. (n.d.). Simulated Level-2 Odysea Dataset. Retrieved from https://www.aviso.altimetry.fr/en/data/products/value-added-products/simulated-level-2-odysea-dataset.html on January 14, 2025), to the HEALPix grid. This workflow will make use of recent advances in technology to make it reproducible to make it efficient and reproducible, such as virtualizarr for fast metadata access and dask for scalable operations, with the output saved as chunked zarr files for seamless integration with downstream analysis.

How to cite: Magin, J., Delouis, J.-M., Zawadski, L., Petiton, J., Jones, M., and Odaka, T.: Regridding Satellite and Model Data to DGGS (HEALPix) Using the Pangeo Ecosystem, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-18285, https://doi.org/10.5194/egusphere-egu25-18285, 2025.

ECMWF has been providing resources on its operational high-performance computing (HPC) and cloud facilities (European Weather Cloud) to researchers and institutions through the Special Projects framework. This framework has been established almost 50 years ago as part of the creation of ECMWF. ECMWF's HPC facility is specifically designed to support both operational time-critical production of global weather forecasts and typical research workflows, therefore through Special Projects, researchers can get access not only to a top high-performance computing and cloud facility and one of the largest meteorological archives in the world, but also full user support.
Special Projects are defined as experiments or investigations of a scientific or technical nature, undertaken by one or more ECMWF Member States, likely to be of interest to general scientific community. The main aim of this initiative is to facilitate collaboration, enabling the development of innovative methodologies and tools for numerical weather prediction, climate and environmental modelling, and other disciplines within Earth System Sciences. All Special Project applications undergo a review process by ECMWF and its Scientific Advisory Committee (SAC), as well as ECMWF Member State's meteorological services and are ranked primarily by their scientific quality.
This poster will describe the Special Projects framework and showcase three recent Special Projects that illustrate collaborative nature of the initiative using ECMWF's HPC and European Weather Cloud facilities, including validating ICON model on ECMWF systems, the development of next-generation European Earth System Model (EC-EARTH4) and mapping the yet uncharted continuum of cyclone dynamics for the Euro Atlantic domain.
Through these examples, the poster will demonstrate how ECMWF Special Projects foster international collaboration, resource sharing, and innovation, enabling advancement in Earth System Science. 

How to cite: Vuckovic, M. and Hemingway, B.: Advancing Earth System Science through collaboration: An overview of ECMWF Special Projects, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-18336, https://doi.org/10.5194/egusphere-egu25-18336, 2025.

Dhemeter is a weather and environmental data aggregator. It is developed using a microservices architecture to handle a wide variety of data from various providers, such as NOAA, ECMWF, Eumetsat, Météo-France, DWD, and Copernicus. The implementation of aggregation, concatenation, and consistency functionalities has been successfully executed for meteorological data. This versatile tool accommodates numerical model data, in-situ observations, remote sensing data, and reanalyses, allowing for online data retrieval from multiple sources.

Key features of the aggregator include:

• Concatenation of Multiple Data Sources: Users can combine data according to selected categories such as Observations, Forecasts, and Reanalyses.
• Standardization of Physical Data: This involves spatial and temporal interpolation as well as geographical selections to ensure uniformity.
• Storage of Resulting Data Structures: The data is stored in a pivot format that facilitates access and distribution of scientific data, specifically in the NetCDF format.

The microservices architecture of the aggregator allows for the extensibility of the offered data catalog, and an API is available for users to make direct queries to chosen data sources.

In the short to medium term, the goal is to enhance the tool further, evolving it into a comprehensive data distribution and aggregation system that centralizes and simplifies access to various types of data, including meteorological, oceanographic, and air quality data.

Dhemeter focuses on ease of use, extensibility, scalability, and customization, offering users capabilities for data fusion and harmonization.

How to cite: Pénard, C., Amsellem, N., Gratadoux, B., Barthet, B., Pere, J. C., Staufer, J., Chaumat, L., and Mondot, A.: Dhemeter: Data Hub for Environmental and METEorological Resources, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-20590, https://doi.org/10.5194/egusphere-egu25-20590, 2025.

NASA established the Surface Topography and Vegetation (STV) Incubation program to develop and mature the next-generation measurement approaches to precisely map Earth’s changing surface and overlying vegetation structure, and prepare for a dedicated satellite mission within the next decade. Over the past two decades, large archives of 3D surface elevation measurements by airborne and satellite instruments including LiDAR, altimeters, Synthetic Aperture Radar, and stereo optical imagery have been systematically collected, though not always in a coordinated way. Yet, many of these datasets are fortuitously acquired over the same location within a short temporal window (e.g., <1-14 days) and many are now publicly available and hosted on the cloud. In theory, this is a great opportunity to synthesize myriad elevation measurements for STV researchers, but in practice merging these datasets accurately for scientific analysis requires dealing with numerous data formats, complex 4D coordinate reference systems, and securing access to significant computational resources.

We are developing an open-source Python library to identify, curate, and efficiently process coincident elevation measurements spanning the last several decades. This work would not be possible without well-integrated geospatial libraries (e.g. Geopandas, Xarray, Dask), as well as emerging cloud-native data and metadata formats such as Cloud-Optimized Geotiff and STAC-GeoParquet. We will describe our work to-date and reflect on the process of collaborative development across libraries, on our increasing reliance on Cloud resources, and current and future research directions.

How to cite: Henderson, S., Shean, D., Hayes, J., and Bhushan, S.: Integrated geospatial Python libraries for efficient analysis of modern elevation measurements, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-20676, https://doi.org/10.5194/egusphere-egu25-20676, 2025.

Over the past decade, the operational Copernicus Sentinels Data Processors have generated vast amounts of Earth observation data, supporting various scientific and commercial applications. However, the current format used by ESA to provide Copernicus data, known as SAFE (Standard Archive Format for Europe), has become outdated. To address this, ESA has initiated the transition to a new Zarr-based data format. The Earth Observation Processing Framework (EOPF) Sample Service is ESA’s official initiative to support this transition by providing early access to the new format for users. This shift is essential for creating a cloud-native and interoperable solution that enhances data accessibility and integration with modern processing frameworks. The primary goal is to standardize data formats across Sentinel missions, enable scalable processing on cloud platforms, and ensure compatibility with contemporary data science tools. This initiative is crucial for minimizing disruption and ensuring continuity for users, applications, and services built around existing data formats.

The EOPF Sample Service comprises several key components. The EOPF Core Platform re-formats ingested SAFE data products into the new cloud-optimized EOPF Zarr data products and provides data access via STAC API and S3 API. To ensure timely conversion, the platform utilizes Argo Events and the Copernicus Data Space Ecosystem's subscription service. This platform is maintained by experts from EODC and DLR. The EOPF User Platform offers additional user services, including JupyterHub (BinderHub), Dask, and a STAC Browser, which are essential for supporting user adoption by lowering the entrance barrier to cloud applications and data discovery capabilities. The service is designed to make use of advanced technologies such as Kubernetes for container orchestration and Dask for parallel computing. User and identity management is achieved in cooperation with the Copernicus Data Space Ecosystem.

User adoption is further facilitated through Jupyter Notebooks designed by experts within the consortium, including members from the Pangeo community. These notebooks showcase the use of the new format within the community and are continuously improved by incorporating user feedback. In addition, enhancements are made to widely-used software tools like GDAL to support the new format, with practical demonstrations available through Jupyter Notebooks. The consortium selected by ESA to carry out this implementation includes experts from Brockmann Consult, DLR, Ifremer, EURAC, Evenflow, Simula, and EODC, each contributing their specialized knowledge in Earth observation, data management, and user engagement.

This contribution aims to present the EOPF Sample Service initiative and the current status of its implementation. The first Jupyter Notebooks demonstrating the new format will also be showcased, providing users with an intuitive and user-friendly interface for accessing and processing sample data in the new EOPF format.

How to cite: Briese, C., Reimer, C., Briese, C., Reck, C., Papadakis, D., Claus, M., Brandt, G., Fouilloux, A., and Odaka, T.: From SAFE to Zarr: The EOPF Sample Service Initiative, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-21202, https://doi.org/10.5194/egusphere-egu25-21202, 2025.

EarthCODE (Earth Science Collaborative Open Development Environment) is a platform that leverages cloud-native tools to empower Earth system researchers in accessing, analyzing, and sharing data across distributed infrastructures, such as the Copernicus Data Space Ecosystem and Deep Earth System Data Laboratory (DeepESDL). By integrating Pangeo ecosystem tools—including Xarray, Dask, and Jupyter—EarthCODE supports scalable, FAIR-aligned workflows tailored to the challenges of Earth system science.

EarthCODE streamlines cloud-based data analysis and publishing by enabling collaborative research through interoperable workflows for analyzing complex datasets, including satellite observations, climate models, and in-situ measurements. Researchers can publish their analyses and workflows as reusable, executable resources in EarthCODE’s science catalog, fostering alignment with open science principles.

Through its integration of Pangeo tools, EarthCODE offers an intuitive environment for reproducibility, scalability, and collaboration, bridging the gap between data analysis and actionable insights. This presentation will demonstrate EarthCODE’s capabilities, including live, executable Jupyter notebooks that highlight its potential for sharing workflows and engaging diverse user groups. EarthCODE exemplifies the transformative power of cloud-native research, promoting open science and advancing the accessibility of Earth system data.

How to cite: Samardzhiev, D., Fouilloux, A., Odaka, T., and Ragan-Kelley, B.: Advancing Cloud-Native Data Analysis and Publishing with Pangeo Tools in EarthCODE, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-21279, https://doi.org/10.5194/egusphere-egu25-21279, 2025.

The RiOMar (River dominated Ocean Margins) case study, part of the FAIR2Adapt (FAIR to Adapt to Climate Change) project (EU funded project grant agreement No 101188256), focuses on supporting science-based climate change adaptation strategies for coastal water quality and marine ecosystem management. The case study uses large environmental datasets, such as sea temperature, salinity, and other marine parameters, to assess and model the impacts of climate change on coastal ecosystems. As part of the FAIR2Adapt project, which aims to enhance the shareability, accessibility, interoperability, and reusability of environmental data through the FAIR (Findable, Accessible, Interoperable, and Reusable) principles, the RiOMar case study emphasizes the use of cutting-edge data processing and analysis methods to support adaptive strategies for climate resilience.

In this presentation, we present our approach to reading the RiOMar large environmental datasets in netCDF format, creating VirtualZarr archives for efficient data handling, transforming them into a Discrete Global Grid System (DGGS) using the Healpix grid.Leveraging the Pangeo ecosystem, we use tools such as Kerchunk to create simpler access to multiple data sources, parallelize dataset processing using Dask or Cube, enabling scalable analysis of these complex, multi-dimensional data. We will show a comparison of performance between traditional cube-based approaches and Dask, highlighting the advantages of parallelized processing. Furthermore, we will showcase how to interactively visualize these datasets using tools like XDGGs and Lonboard, facilitating seamless exploration and analysis of the underlying environmental patterns. This work underscores the potential of open-source tools, scalable computing techniques, and the Pangeo ecosystem to enhance the accessibility and usability of large geospatial datasets in climate adaptation research.

How to cite: Moa Myklebust, E., Formo Kihle, O., and Magin, J.: The Pangeo Ecosystem Supporting Climate Change Adaptation: The FAIR2Adapt RiOMar Case Study, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-21603, https://doi.org/10.5194/egusphere-egu25-21603, 2025.

Statistical models are a frequently used tool in hydrology, especially when it comes to estimating design floods, i.e. flood events that used to design flood protection systems or reservoirs. The often complex hydrological data, which are affected by e.g. missing values, extremes or time-varying processes, require sophisticated statistical models that take these challenges into account. As a scientist, developing such models can be a lot of fun and provide interesting insights. After months of thinking about the best model under certain statistical assumptions, proving asymptotic theorems and testing the model with synthetic data, you are happy and proud to have developed a new model. This model will hopefully be widely used in future research. The next step is to apply the model to a large real data set. The results look good on average. The results will be shared with practitioners, because of course you want the model to be useful for science and practice. And then: the phone call. You are told that your results are not plausible for a certain catchment area. And in general, the new model is not needed in practice because there is an established model. This example describes such a case and discusses ways of dealing with it. It is intended to illustrate the importance of communication between science and practice and a general understanding between both sides.

How to cite: Fischer, S.: When practical considerations impact your scientific model, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-1620, https://doi.org/10.5194/egusphere-egu25-1620, 2025.

In the early 1990's, fractals and chaos were hot. In 1987, James Gleick had published "Chaos: Making a New Science", popularizing non-linear dynamics. Hydrologists played an important role in the development of fractal theory. Hurst had discovered that sequences of dry and wet years for the Nile showed very long memory effects. Instead of the chance of a dry year following a dry year being 50%, Hurst found that there were surprisingly many long series of dry or wet years. Seven fat years, seven lean years, as it is noted in Genesis. Scott Tyler found fractals in soils ("Fractal processes in soil water retention"). At Cornell, where we were at the time, David Turcotte described "Fractals in geology and geophysics". A few years later, Ignacio Rodríguez-Iturbe and Andrea Rinaldo would publish "Fractal River Basins: Chance and Self-Organization". In short, fractals were exciting scientific gold.

A fractal is not just an obscure mathematical object but something that can actually be found everywhere in nature. Early on, a paper was published in Nature with the title "Fractal viscous fingering in clay slurries" by Van Damme, Obrecht, Levitz, Gatineau, and Laroche. They "only" did an experiment on a fractal embedded in 2D; we should be able to do one better and find the fractal dimension of the surface of cracking clay embedded in 3D. So out we went, collected some clay, mixed it with water in a cement mixer, siliconed together a shallow "aquarium", and poured in the slurry. To observe the cracking of the drying slurry, a video camera was mounted above the experiment, looking down and taking time-lapse images. To access the views from the sides, mirrors were installed at 45 degrees at each of the four sides. Lights made sure the camera captured high quality images. The whole set-up was enclosed in a frame with dark cloth to ensure that lighting was always the same.  We already had some box-counting code ready to calculate the fractal dimension of the surface, called the Minkowski–Bouligand dimension. One variable needed some extra attention, namely the boundary between the clay slurry and the glass sides. If the clay would cling to the sides, it would be difficult to understand the effects that this boundary condition had on the outcome of the experiment. Moreover, the cracks may not have become visible in the mirrors when the sides were covered with mud. So, instead, it was decided to make the sides hydrophobic with some mineral oil. This ensured that when the clay would start to shrink, it would come loose from the sides. Now, all we had to do was wait. It took only a week or so before the consolidated slurry started to shrink and to come loose from the sides. After that, the clay continued shrink for many weeks. This is how we learned that the fractal dimension of a shrinking brick of clay is (very close) to 3.0. 

How to cite: van de Giesen, N. and Selker, J.: The Minkowski–Bouligand dimension of a clay brick, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-1660, https://doi.org/10.5194/egusphere-egu25-1660, 2025.

In 2018, we found exciting new results in landform evolution modeling by coupling the two simplest models of fluvial erosion and hillslope processes. While the stream-power incision model is the simplest model for detachment-limited fluvial erosion, the diffusion equation is the simplest description of hillslope processes at long timescales. Both processes were added at each grid cell without an explicit separation between channels and hillslopes because fluvial erosion automatically becomes dominant at large catchment sizes and negligible at small catchment sizes.

We found that increasing diffusion reduces the relief at small scales (individual hillslopes), but even increases the large-scale relief (entire catchments). As an immediate effect, the hillslopes become less steep. In turn, however, we observed that the network of the clearly incised valleys, which indicates dominance of fluvial erosion over diffusion, became smaller. So a smaller set of fluvially dominated grid cells had to erode the material entering from the hillslopes. To maintain a morphological equilibrium with a given uplift rate, the rivers had to steepen over long time. This steepening even overcompensated the immediate decrease in relief of the hillslopes.

This result was counterintuitive at first, but we were happy to find a reasonable explanation. So we even prepared a short manuscript for a prestigious  journal. We just did not submit it because we wanted to explain the effect quantitatively from the physical parameters of the model. From these theoretical considerations, we found that our numerical results did not only depend on the model parameters, but also on the spatial resolution of the model and noticed that this scaling problem was already discussed in a few published studies. Beyond the scaling problem, we also realized that applying the concept of detachment-limited fluvial erosion to the sediment brought from the hillslopes into the rivers is quite unrealistic. A later study including fluvial sediment transport and a model for hillslope processes that avoids scaling problems did not predict any increase in large-scale relief. So we finally realized that our original findings were mainly the result of a specific combination of models that should not be coupled this way and are not  as relevant for landform evolution as we thought.

This example illustrates many of the pitfalls of numerical modeling beyond purely technical issues. In particular, combining models that are widely used and make sense individually may still cause unexpected problems.

How to cite: Hergarten, S. and Robl, J.: Landslides and hillslope erosion increase relief, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-5035, https://doi.org/10.5194/egusphere-egu25-5035, 2025.

In this presentation, I will discuss my research into the simple climate model Hector, which calculates temperature change based on the impact of various climate scenarios. More specifically, I will discuss how an artistic-led approach through (un)voluntary-caused computational bugs can help document the model's logic and socio-political implications. I will describe methods for collective 'debugging' to produce transdisciplinary knowledge (beyond solely scientific inquiry) to foster conversation about the potential and limits of current climate infrastructure to foster concrete climate actions. This research investigates the field of climate science through artistic practice, software and infrastructure studies, and participatory methods. To expand on the role of bugs in my investigation, I will elaborate on concrete examples of differences in perception of 'error' in the fields of arts and science, looking at case studies where mistakes or glitches have been valorised and mobilised through artistic practice to grapple with, appropriate, and/or repurpose scientific instruments.

How to cite: Legrand, G.: (Re)(De)bugging tragedies with Hector, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-5091, https://doi.org/10.5194/egusphere-egu25-5091, 2025.

"Doesn't this look a bit strange?" 

It began with an innocent question during one of our Master's colloquia. And it could have ended there. "We were just following an approach from the literature". And who could argue against following the literature?

But it bugged me. During a long train ride, I began to think about the issue again. 10 hours and many papers later, I was only more confused: was it really that obvious, and why had no one picked up on it before? But sometimes the most obvious things are the most wicked, and after a few conversations with knowledgeable colleagues, I was sure we were in for an unexpected surprise. 

A commonly used approach to defining heat extremes is as exceedances of percentile-based thresholds that follow the seasonal cycle. Such relative extremes are then expected to be evenly distributed throughout the year. For example, over the 30-year period 1961-1990, we expect three (or 10%) of January 1s to exceed a 90th percentile threshold defined for the same period - and the same for all other days of the year. In a recent study, we show that there are many cases where this does not hold, not even close (Brunner and Voigt 2024).

Here, we tell the story of how this blunder spread in the literature out of the desire to improve extreme thresholds. We show that seemingly innocent changes can sometimes have unintended consequences and that taking the time to check the obvious can help avoid mistakes in science. 

Brunner L. and Voigt A. (2024): Pitfalls in diagnosing temperature extremes, Nature Communications, https://doi.org/10.1038/s41467-024-46349-x

How to cite: Brunner, L., Meindl, M., and Voigt, A.: Improving extreme temperature definitions until they are wrong, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-5951, https://doi.org/10.5194/egusphere-egu25-5951, 2025.

When economists estimate the expected economic damages from current-day CO₂ emissions, they usually calculate the social cost of carbon – that is, the aggregated damage caused by the emission of an additional ton of CO₂. Several cost-benefit integrated assessment models (IAMs) are built to assess this quantity, and among them is the META model. This model is built specifically to assess the effects of tipping points on the social cost of carbon, and it usually operates stochastically. When integrating a deterministic, but small carbon cycle tipping point into the model, however, the social cost of carbon seems to explode: a few gigatons of additional emissions almost double the impact estimates of CO₂ emissions! Well, maybe. In fact, these results are a pure artifact of two things: 1) the way in which social cost of carbon estimates are calculated with IAMs; and 2) the way that tipping points are implemented in the META model. And, of course, 3): a lack of initial thoughtfulness on behalf of myself. A thorough look into this issue shows that, as expected, a marginal change in emissions leads to a marginal change in damage estimates. While that result is rather boring, the previous blunder can actually be instructive about the scarcely-known methods used to obtain economic impact estimates of climate change.

How to cite: Schaumann, F.: Drastic increase in economic damages caused by a marginal increase in CO2 emissions?, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9145, https://doi.org/10.5194/egusphere-egu25-9145, 2025.

Historically, large areas across the globe have been affected by deforestation or irrigation expansion. The replacement of forests with agricultural land and increased water availability in irrigated croplands altered the land’s surface properties, leading to influences of biogeophysical changes on near-surface temperature. From limited observations and mostly idealized simulations, we know that sufficiently large alterations of land surface properties can theoretically lead to systematic temperature and precipitation changes outside and even far from the altered areas. Not only the advection of temperature anomalies, but also changes in circulation and ocean feedbacks have been shown to be potential drivers of such non-local responses in single and multi-model studies.

We tested the robustness of non-local temperature signals to internal variability in the fully coupled Community Earth System Model 2 (CESM2) simulations of the historical period (1850 – 2014) with all forcings vs. all-but-land-use-change forcings. Doing so, we first found seemingly robust non-local temperature effects of land use change on the global and regional scale. But when accounting for the sampling of internal variability in the model using a large initial condition ensemble, the global scale signal was found to be indistinguishable from noise. Only regionally in some hotspots, we found robust and historically important non-local temperature signals. Through increasingly rigorous analysis, we reached a partly negative and unexpected but important finding, which may have implications for future assessments of comparably weak or spatially heterogeneous forcings to the Earth system.

How to cite: Jäger, F., Sieber, P., Simpson, I., Lawrence, D., Lawrence, P., and Seneviratne, S. I.: How robust are modeled non-local temperature effects of historical land use changes really?, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10285, https://doi.org/10.5194/egusphere-egu25-10285, 2025.

Failures are only common in science, and hydrological modelling is no exception. However, we modellers usually do not like to talk about our mistakes or our overly optimistic expectations and, thus, “negative” results usually do not get published. While there are examples where model failures indicated issues with the observational data, in this presentation the focus is on modelling studies, where some more (realistic) thinking could have helped to avoid disappointments. Examples include the unnecessary comparison of numerically identical model variants, naively optimistic expectations about increasing the physical basis of bucket-type models and excessively hopeful assumptions about the value of data.

How to cite: Seibert, J., Clerc-Schwarzenbach, F., van Meerveld, I., and Vis, M.: Think twice – pitfalls in hydrological modelling, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10615, https://doi.org/10.5194/egusphere-egu25-10615, 2025.

The idea of invisible ship tracks for the study of aerosol-cloud interactions sounds promising: We have been studying the effects of aerosols on clouds for many years, among others by investigating the bright lines of clouds left in low marine clouds by ships. However, only a small fraction of ships leaves behind visible tracks. This means we can only study aerosol-cloud interactions under certain meteorological conditions, biasing our understanding. Instead, by studying all clouds polluted by ships ('invisible ship tracks') with a methodology we developed, we should be able to get a full picture of aerosol-cloud interactions. A number of interesting and impactful results have come out of this research, along with several setbacks and corrections to initial results. Here, we examine them in order, showing how correcting for one identified bias can introduce two new ones. Unexpected glitches arise from sources as varied as: choices regarding ship track definition, retrieval geometry, specific weather systems biasing results, and mathematical subtleties. What can we conclude after four years of progress on this methodology? While some results still stand, others had to be significantly corrected. This makes us see invisible ship tracks as an example of research that is closer to a method of 'tinkering' than to a 'magnificent discovery'.

How to cite: Manshausen, P., Tippett, A., Gryspeerdt, E., and Stier, P.: Two steps forward, one step back: four years of progress and setbacks on invisible ship tracks, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-11357, https://doi.org/10.5194/egusphere-egu25-11357, 2025.

Earth system models are important tools used to understand our climate system and project possible changes in our climate due to anthropogenic and natural forcings. Human errors can occur in the development of Earth System models, i.e., bugs, giving an unphysical representation of our climate. A way to identify and solve bugs is to apply physical concepts. Here, we present an experience that occurred in the development of the ICOsahedral Non-hydrostatic model (ICON) as a kilometer-scale Earth System model, in which physically understanding a bug in the surface energy budget fixed land precipitation. 

In a simulation of ICON, referred to as ICON-bug, precipitation over tropical land continuously decreased across the simulation. This led to a ratio of land-ocean precipitation in the tropics of less than 0.7, which, otherwise, should be more than 0.86. As part of the possible explanations, the surface energy budget over land was targeted as a culprit. This idea relies on the influence of the interaction between soil moisture, surface heat fluxes, and winds to generate circulation favoring precipitation over dry land surfaces (Hohenegger and Stevens 2018). Indeed, the surface energy budget over dry surfaces in the ICON-bug showed an error in sensible heat flux. The sensible heat flux transmitted to the atmosphere was 70% of what was calculated for the surface module. Fixing this error closed the surface energy budget and increased land precipitation over the tropics, leading to a ratio of land-ocean precipitation of 0.94, close to observations. 

How to cite: Segura, H., Hohenegger, C., Schnur, R., and Stevens, B.: Physical understanding of bugs to improve the representation of the climate system  , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-12720, https://doi.org/10.5194/egusphere-egu25-12720, 2025.

Whenever you study a phenomenon of mm to a few cm-scale in the laboratory which involves an interface, the question of surface tension arises. Surface tension is due to the fact that molecules prefer to stay with their own kind. Therefore, the creation of an interface between two fluids requires energy, and this influences the dynamics around the interface.

Surface tension can be a blessing: it produces the round shape of rain drops or the nice bubble shapes of colorful liquid in a lava lamp. It allows objects with a higher density to float on a liquid (such as an insect on water, or a silicone plate on sugar syrup). It can generate flow up a capillary.

However, it can also be a curse in the case of thermal convection. Purely thermal convection  develops when a plane layer of fluid is heated from below and cooled from above. The engine of motion is the thermal buoyancy of the fluid. This is what is happening in a planetary mantle on scales of hundreds to thousands kilometers. This is also what is happening in a closed box in the laboratory. But as soon as an interface exists, either between an upper and a lower experimental mantle, or in the case of a free surface at the top of the fluid layer, surface tension effects can become important. For exemple, the variation of surface tension with temperature was responsible for the beautiful honey-comb patterns imaged by Benard (1901) in the first systematic study of thermal convection with a free-surface. Surface tension is also going to act against the initiation of subduction (which acts to break the surface). 

We shall review in this presentation the signatures of surface tension in a convective context, and the different ways to minimize and/or remove the effects of surface tension in convection experiments, such as using miscible liquids, or a layer of experimental « sticky air ».

How to cite: Davaille, A.: Analog studies of mantle convection: the curse of surface tension (or not) ?, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15059, https://doi.org/10.5194/egusphere-egu25-15059, 2025.

In hydrology we measure and follow the water. What if there is too much or too little? It happens a lot. As a field hydrologist, I frequently have to determine the location of a measurement, the time to take the measurement, the location to set up a field experiment, or the amount of a tracer to inject to study a hydrological system. However, this is a very bumpy road, as variability is often not in favor of my decisions because the distribution is wider than expected, bimodal instead of unimodal, or the probability of an event is theoretically small, but still an extreme event occurs during our experiment. I will showcase some examples to demonstrate what I mean and what I experienced, as well as how frequently the PhD students or Postdocs have suffered as a result of my decisions or of the unexpected variability: Climatic variability resulted in a winter without snow, just as new sensors were already deployed. Or the winter snowpack was extremely high, preventing any work at high altitudes in the Alps until mid of July, thereby reducing our field season by half. An ecohydological study to observe the effects of drought in a forest with a rainout shelter was ineffective because it occurred during an extremely dry year, making the control just as dry as our drought treatment. The automatic water sampler was set-up to collect stream water samples, but it was washed away four weeks later by the 50-year flood. The calculated amount of artificial tracer was either way too low, because the transit times of the system were much longer than expected, or it was far too high, resulting in colored streams or samples that had to be diluted by a factor of 100 due to much faster transit times Finally, and most expensively, we installed many trenches along forest roads to measure subsurface stormflow but after three years, we abandoned the measurements because we never measured a drop of water coming out of the trenches, as the bedrock permeability was much higher due to many high permeable fissures that prevented the formation of subsurface stormflow.  These experiments or observations failed because of unexpected variability in input, system properties or a lack of technical variability in the equipment. I will reflect on residual risk of failure in fieldwork related to that crux and discus approaches to reduce this risk.

How to cite: Weiler, M.: The crux with variability: too much or too little, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15457, https://doi.org/10.5194/egusphere-egu25-15457, 2025.

The emergence of global km-scale climate models allows us to study Earth's climate and its changes with unprecedented local detail. However, this step change in spatial resolution to grid spacings of 10 km or less also brings new challenges to the numerical methods used in the models, the storage of model output, and the processing of the output data into actionable climate information. The latest versions of the ICON-Sapphire model developed in the frame of the NextGEMS project address these challenges by running on an icosahedral grid while outputting data on the so-called HEALPix grid. Both grids are unstructured grids, which avoids, for example, the issue of longitude convergence. In addition, HEALPix allows data to be stored in a hierarchy of resolutions at different discrete zoom levels, making it easier for users to handle the data.  

The transition from the native 10 km grid to the output grid is made by a simple but very fast nearest-neighbour remapping. An advantage of this simple remapping approach is that the output fields are not distorted, i.e. the atmospheric states in the output remain self-consistent. As HEALPix only provides discrete zoom levels in the setup of the run, it was decided to remap to the closest available resolution of 12 km rather than to the next finer resolution of 6 km. This decision was made to avoid artificially increasing the number of grid points and to avoid creating duplicates through the nearest neighbour remapping.

As a consequence of this approach, wave-like patterns can emerge due to the Moiré effect that can result from the interaction of two grids. We find these patterns when looking at certain derived precipitation extremes, such as the annual maximum daily precipitation, the 10-year return level of hourly precipitation, or the frequency of dry days. At first, we interpreted these patterns as a plotting issue, as the figures might have too low resolution to cope with the high-resolution global plot (aliasing) leading to a Moiré pattern.

However, zooming in on the affected regions and closer examination of the data revealed that the pattern is in fact in the data. Further investigation with synthetic data confirmed the suspicion that the Moiré pattern was indeed caused by the remapping of the native 10 km icosahedral grid to the slightly coarser 12 km HEALPix grid. We hypothesise that precipitation is particularly affected by this issue, as it typically contains many grid cells with zero precipitation, with local clusters of non-zero values at the 15-minutely output interval. Yet, we cannot exclude the possibility that other variables are also affected.

As a consequence, if remapping is required, it is recommended to first remap from the native resolution to a finer resolution grid. As a next step, the conservative nature of the HEALPix hierarchy can be used to compute the coarser level. In this way it is likely to be possible to avoid aliasing and still keep the amount of output data the same.

How to cite: Poschlod, B., Brunner, L., Blanz, B., and Kluft, L.: Output regridding can lead to Moiré pattern in km-scale global climate model data from ICON, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15826, https://doi.org/10.5194/egusphere-egu25-15826, 2025.

Plastic pollution is a global issue, across all environmental compartments. Rivers connect the terrestrial with the marine environment, and they transport various materials, among these plastic pollution. Rivers not only transport plastic, but also accumulate and store it, especially on riverbanks. In fact, plastic deposition and accumulation on riverbanks is a common occurrence. However, our understanding of why plastic is deposited on a certain riverbank is rather limited. Riverbanks along all major Dutch rivers have been monitored for plastic and other litter twice a year by citizen scientists, in some locations since 2018. This provides an extensive dataset on plastic accumulation, and we used these data with the aim of understanding the factors determining plastic concentration/accumulation variability over time and space. We tested multiple riverbank characteristics, such as vegetation, riverbank slope, population density, etc., hypothesized to be related to plastic litter. After having exhausted a long list of auxiliary data and analysis strategies, we found no significant results. Ultimately, we had a close look at ten consistent hotspots of macroplastic litter, along the Meuse, and Waal river. And once again, they seem to have nothing in common. But, there is a pattern, because some riverbanks have consistently very high densities of plastic litter so it does not seem completely random. We have been looking to explain spatial variability, whereas we might have to look at temporal consistency, and we shall not give up our efforts to bring order to this chaos.

How to cite: Hauk, R., Teuling, A. J., van Emmerik, T. H. M., and van der Ploeg, M.: What river plastic hotspots do not have in common, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-17676, https://doi.org/10.5194/egusphere-egu25-17676, 2025.

Grande Dixence, the tallest gravity dam in the world, is located in the Swiss Alps on the Dixence River with a catchment area of 4 km² at a towering elevation of 2000m. The lake serves as a collecting point of melt water from 35 glaciers and reaches full capacity by late September, subsequently draining during winter and dropping to lowest levels in April. For a reservoir as large as the Grande Dixence, the variation in hydrological load can be expected to induce changes in crustal stress. The goal of this study was to harness the loading effect of the time-varying level of reservoir load as a source of known stress to investigate the variation in seismic velocity of the bedrock due to changes induced in crustal stress and strain rates. 22 seismic nodes were thus deployed along the banks of the reservoir which were operational from mid-August to mid-September, corresponding to the time period when the lake level reaches its maximum. Of the 22 nodes, 18 were deployed in closely spaced patches of six in order to carry out coherent stacking and to increase the signal-to-noise ratio, besides one group of three nodes and one single node. Measurement quality appears satisfactory: small local earthquakes are recorded well, and the probabilistic power spectral densities (PPSDs) computed for data quality validation evidence the ambient noise levels to be well within the global noise limits. However, the recorded noise is unexpectedly complex and, at periods shorter than 1 second, varies strongly by location. The 0.5--5s (0.2--2 Hz) period band at lakes generally records a diurnally varying noise level, often associated with lake generated microseism. Diurnal variations around 1 second of period are observed in our study as well. The amplitude of ambient noise level around 1 second of period is observed to be highest when the lake level changes, along with the prominent diurnal variation. A similar variation is observed in the seismic velocity variation (dv/v) computed from cross-correlated and auto-correlated ambient noise filtered between 0.5--1 Hz, with dv/v exhibiting a drop with rising lake level. These results provide preliminary evidence for possible change in crustal stress state with changing hydrological load. Future direction of this study consists of analytically modeling the results to quantify the influence of thermobarometric parameters on PPSDs and dv/v, and deconvolve it from the lake induced variations.

How to cite: Uthaman, M., Ermert, L., Ling, A., Junker, J., Ghisleni, C., and Obermann, A.: Temporal variation of ambient noise at the Grande Dixence reservoir recorded by a nodal deployment, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-17811, https://doi.org/10.5194/egusphere-egu25-17811, 2025.

Rivers play an important role in the global distribution of plastic pollution throughout the geosphere. Quantifying and understanding river plastic pollution is still an emerging field, which has advanced considerably thanks to broad efforts from science, practice, and society. Much progress in this field has been achieved through learning from failures, negative results, and unexpected outcomes. In this presentation we will provide several examples of serendipity and stupidity that has led to new insights, theories, methods, and completely new research lines. We will share what we learned from rivers flowing in the wrong direction, sensors that disappear, equipment blocked by invasive plants, and dealing with suspicious local authorities. Pushing the science sometimes requires an opportunistic approach, embracing surprises and chaos you may face along the way.

How to cite: van Emmerik, T. and the WUR-HWM River Plastic Team: Advancing river plastic research through serendipity and stupidity, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-18185, https://doi.org/10.5194/egusphere-egu25-18185, 2025.

With the advent of parallel programming in the late 1990s. A port of the than available Max Planck Institutes for Meteorology spectral atmospheric model echam5 to MPI and OpenMP was done. For testing and validation of the hybrid parallelization a coherence algorithm was developed. The implementation has been incorporated into todays NWP and climate model ICON as well. The coherence algoritm consists of several stages: first one MPI rank is running the serial model against an n-task MPI parallelized model. During runtime the state vector is checked for binary-identity. If successfull a m-task MPI version can be compared to an m-task MPI version for high processor counts. The same schema can be used OpenMP parallelization. ONe MPI task runs the model serial using one OpenMP thread and a second MPI task runs k OpenMP threads. Again, the results are compared for binary-identity. As the testing needs to be done automatically, bit-identity is important for testing not necessarily for production.

The tesing revealed plenty of problems during the initial parallelization work of echam5 and showed constant appearing problems in the ICON development phase.

However, far in a couple of century long simulation the bit-identity was just by accident found to be broken: the search of the cause started!

How to cite: Kornblueh, L.: MPI and OpenMP coherence testing and vaildation: the hybris of testing non-deterministic model code, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-18400, https://doi.org/10.5194/egusphere-egu25-18400, 2025.

Addressing positive publication bias and clearing out the file drawer has been at the core of the Journal of Trial and Error since its conception. Publishing the trial-and-error components of science is advantageous in numerous ways, as already pointed out in the description of this panel: errors can lead to unexpected insights and warning others about dead ends can prevent wasted time and other resources. Besides those advantages, publishing negative and null results facilitates conducting robust meta-analyses. In addition, predictive machine learning models benefit from training on data from all types of research rather than just data from studies with positive, exciting results; already researchers are reporting that models trained on published data are overly optimistic.

Besides publishing negative and null results as well as methodological failures, the Journal of Trial and Error couples each published study with a reflection article. The purpose of these reflection articles is to have a philosopher, sociologist or domain expert reflect on what exactly went wrong. This helps contextualize the failure, helping to pinpoint the systematic factors at play as well as helping the authors and other scientists to draw lessons from the reported research struggles which can be applied to improve future research.

Publishing failure brings with it some practical challenges: convincing authors to submit manuscripts detailing their trial-and-error; instructing peer reviewers on how to conduct peer review for the types of articles; differentiating between interesting … and uninformative, sloppy science; and determining the best formats to publish various failure-related outcomes in. Authors are still hesitant to publish their research struggles due to reputational concerns and time constraints. In addition, authors often fear that peer reviewers will be more critical of articles describing research failures compared to articles reporting positive results. To counteract this (perceived) tendency of peer reviewers to be more critical of research without positive results, we provide specific instructions to peer reviewers to only assess the quality of the study without taking into account the outcome. This then also ensures that we only publish research that adheres to the standards of the field rather than sloppy science. Whether submitted research provides informative insights is assed by the editor-in-chief and the handling editor.

Finally, we are constantly evaluating and innovating the types of articles we publish. Various types of errors and failures benefit from differing ways of reporting. For example, recently we introduced serendipity anecdotes, a format where scientists can anecdotally describe instances serendipity which occurred during their research. This format allows researchers to focus on the conditions which allowed for the serendipitous discovery rather than the research itself.    

How to cite: Gaillard, S.: Publishing BUGS: Insights from the Journal of Trial and Error, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-18981, https://doi.org/10.5194/egusphere-egu25-18981, 2025.

It is common to perform two-dimensional simulations of mantle convection in spherical geometry. These have commonly been performed in axisymmetric geometry, i.e. (r, theta) coordinates, but subsequently we (Hernlund and Tackley, PEPI 2008) proposed using (r, phi) spherical annulus geometry and demonstrated its usefulness for low-viscosity-contrast calculations. 

When performing scaling studies in this geometry, however, strange results that did not match what is expected from Cartesian-geometry calculations were obtained when high-viscosity features (such as slabs) were present. It turns out that this is because the geometrical restriction forces deformation that is not present in 3 dimensions. Specifically, in a 2-D spherical approximation, a downwelling is forced to contract in the plane-perpendicular direction, requiring it to extend in the two in-plane directions. In other words, it is "squeezed" in the plane-perpendicular direction.  If the downwelling has a high viscosity, as a cold slab does, then it resists this forced deformation, sinking much more slowly than in three dimensions, in which it could sink with no deformation. This can cause unrealistic behaviour and scaling relationships for high viscosity contrasts. 

This problem can be solved by subtracting the geometrically-forced deformation ("squeezing") from the strain-rate tensor when calculating the stress tensor. Specifically, components of in-plane and plane-normal strain rate that are required by and proportional to the vertical (radial) velocity are subtracted, a procedure that is here termed "anti-squeeze". It is demonstrated here that this "anti-squeeze" correction results in sinking rates and scaling relationships that are similar to those in 3-D geometry whereas without it, abnormal and physically unrealistic results can be obtained for high viscosity contrasts. This correction has been used for 2-D geometries in the code StagYY (Tackley, PEPI 2008; Hernlund and Tackley, PEPI 2008) since 2010.

How to cite: Tackley, P.:  Adventures in Modelling Mantle Convection in a Two-Dimensional Spherical Annulus and Discovering the Need for "Anti-Squeeze”, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-19890, https://doi.org/10.5194/egusphere-egu25-19890, 2025.

The science question: how can we use hydrological process knowledge to understand the timing and magnitude of seasonal streamflow in snow-influenced catchments.

What was known: in general, catchments with colder climates have later and larger seasonal streamflow peaks, because more snow tends to accumulate in colder catchments, and it melts later because the time when melt can occur is later in the year in colder climates. Numerical models with fine space and time resolution were able to resolve these phenomena, but there was no theory which directly linked long term climate to seasonal streamflow.

In 2009 I published a very simple deterministic theory of snow pack evolution. I tested it against snow observations at 6 locations in the western USA and it apparently worked well (although I later discovered that I'd been lucky).

In 2015 I used the snowmelt derived from this deterministic theory to predict timing and magnitude of seasonal streamflow. It did poorly, and revealed untested assumptions in my theory. I tried making the theory slightly more complicated by considering within-catchment variation in climate. This did not help.

In 2016 I created a stochastic version of the theory (a weakness identified in 2015), and then also considered the within-catchment variation in climate. It did better at reproducing measured snow storage, but did not help in understanding seasonal streamflow.

My next step will be to consider all forms of liquid water input, i.e. not just snowmelt but also rainfall.

What survived: I will continue to use the stochastic version of the theory as it is clearly an improvement. I will continue to examine whether within-catchment climate variability is important, but it seems unlikely after two negative results. But whether introducing liquid water input will be sufficient, who can say? I will also try to examine in more detail how it is that the finely-resolved numerical models can do an adequate job, but the theory cannot - it is in this gap that the answer probably lies.  However the models are very complicated, and it is not easy to get a good understanding of exactly what they are doing, even though we know which equations the are implementing.

How to cite: Woods, R.: Some Perfectly Reasonable Ideas that Didn’t Work: Snow Hydrology, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-20057, https://doi.org/10.5194/egusphere-egu25-20057, 2025.

Climate models are not only numerical representations of scientific understanding but also human-written software, inherently subject to coding errors. While these errors may appear minor, they can have significant and unforeseen effects on the outcomes of complex, coupled models. Despite existing robust testing and documentation practices in many modeling centers, bugs’ broader implications are underexplored in the climate science literature.

We investigate a sea ice bug in the coupled atmosphere-ocean-sea ice model ICON, tracing its origin, effects, and implications. The bug stemmed from an incorrectly set logical flag, which caused the ocean to bypass friction from sea ice, leading to unrealistic surface velocities, especially in the presence of ocean eddies. We introduce a concise and visual approach to communicating bugs and conceptualize this case as part of a novel class of resolution-dependent bugs - long-standing bugs that emerge during the transition to high-resolution models, where kilometer-scale features are resolved.

By documenting this case, we highlight the broader relevance of addressing bugs and advocate for universal adoption of transparent bug documentation practices. This documentation complements the robust workflows already employed by many modeling centers and ensures lessons from individual cases benefit the wider climate modeling community.

How to cite: Gärtner, J., Proske, U., Brüggemann, N., Gutjahr, O., Haak, H., Putrasahan, D., and Wieners, K.-H.: A case for open communication of bugs in climate models, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-20866, https://doi.org/10.5194/egusphere-egu25-20866, 2025.

Artificial Intelligence and Machine Learning (AI/ML) are gaining increasing interest due to their capacity to increase precision and productivity in the current big data era. Machine learning has indicated its robustness in geosciences, particularly rock-type classification. Lithological classification in the traditional way has raised critical concerns and the need to curb the limitations it breeds, such as time consumption and subjective results. The gold mineralisation occurrence is structurally controlled in the Obuasi Gold district of Ghana. It exhibits complex patterns and relationships that may not be readily discernible through traditional methods, leading to missing out on discovering new resources or potential exploration targets. Consequently, this work attempts to create a predictive model by exploring the best machine-learning algorithms to predict rock types in the Obuasi Gold District using X-ray fluorescence (XRF) geochemical data. Here we established comparative predictive modelling using four supervised classification algorithms: Gradient Boosting (GBoost), Adaptive Boosting (AdaBoost), Support Vector Machine (SVM) and Random Forest (RF). The acquired XRF data was integrated with the model using the Google Collaboratory cloud-based platform. Results show that the performance evaluation of the models indicated SVM as the best algorithm for deployment with a Classification Accuracy (CA) of 0.902. Therefore, ML algorithms have been a great tool in rock-type classification, whereby SVM emerged as the best in the case of the Obuasi Gold District. However, it is encouraged to understand the geology of a particular area before employing the tool and the datasets must be balanced to yield good results and avoid model overfitting.

Keywords: Artificial intelligence; Machine learning algorithm; Support vector machine; Lithogeochemistry; Rock-type classification; Obuasi Gold District

How to cite: Muhammed, A. B., Sunkari, E. D., and Basit, A. W.: Application of Machine Learning Algorithms to Predict Rock Types Using Geochemical Data: A Case Study from the Obuasi Gold District, Ghana, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-275, https://doi.org/10.5194/egusphere-egu25-275, 2025.

Preserving clean water resources and efficiently treating wastewater is critical for ensuring human survival on Earth and in extraterrestrial environments. Major pollutants, including ammonium, heavy metals, industrial dyes, and chemicals, threaten limited clean water supplies and soil. Among the renowned absorbent materials, natural zeolite minerals have demonstrated their effectiveness for pollutant removal compared to clay minerals and synthetic equivalents like biochar, activated carbon, and MOFs, owing to their relatively extensive reserves and eco-friendly nature. Recently, investigating and optimizing pollutant removal rates from water without conducting laboratory experiments is getting more crucial, considering the time-consuming, expensive, and error-prone nature of laboratory testing due to human factors and potential calibration issues among the chosen analytical techniques.

This study aims to forecast the ammonium removal efficiency (% adsorption) and capacity (mg/g) of natural and modified zeolites from aqueous solutions using the regression ensemble LSBoost (MATLAB R2024b) machine learning (ML) algorithm, which is equivalent to XGBoost open-source library. A total of 527 experiments on 15 different zeolite compositions were gathered from a combination of 14 suitable moderately recent (≥ 2005) and highly referred studies to assess the performance of zeolite minerals on ammonium removal rates from aqueous solutions. The LSBoost algorithm achieved over 0.99 R² fitting for training and overall, 0.95 R² for prediction on the quarterly partitioned testing data for both efficiency and capacity. Throughout the improvement of the ML models using different random forest ML approaches, the number of predictors was successfully reduced to 8 based on importance rates among 31 different features in the initial dataset, with a negligible accuracy loss (<0.1 R²) on both training and testing. This research provides a valuable contribution to optimizing applicable experimental parameters in water treatment processes by effectively identifying the significance of predictors within a comprehensive data set. In addition to this, our model not only provides a robust predictive tool for optimizing zeolite performance in water treatment but also represents the first open-sourced web application in the literature to estimate the water treatment performance of zeolites.

How to cite: Akkaş, E., Ünal, B. C., and Ersoy, O.: AI-Based Prediction and Optimization of Ammonium Removal Efficiency and Capacity of Natural Zeolites Using LSBoost (XGBoost) for Sustainable Ecosystems, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-809, https://doi.org/10.5194/egusphere-egu25-809, 2025.

In the context of CO₂ storage, cost-effective monitor methods are essential to ensure safe and long-term storage. This work explores the use of seismic time-lapse monitoring, combined with deep learning (DL) techniques, to assess potential leakage and migration pathways. The goal is to develop a cost-effective monitoring method while guaranteeing the safety of storage operations. To this end, we propose a Siamese Neural Network (SNN)-based framework to analyse shot gathers, designed to detect and localize changes within the storage complex. We aim to address the challenges of working with large seismic datasets, enabling the identification of significant events with high confidence, while avoiding the need for event-by-event processing. This framework can allow experts to rely on semi-automatic detections while ensuring human evaluation for interpreting and validating the results.

The proposed SNN architecture processes pairs of shot gather from baseline and monitor surveys in a cross-well configuration. It uses two identical neural networks with shared weights to encode the shot gathers into latent feature embeddings, which are then compared to identify similarities and detect changes. By transforming the data into a shared latent space, the model focuses on capturing relevant patterns while filtering out irrelevant variations, ensuring robust and accurate comparisons. When the SNN detects changes between the baseline and the monitor surveys, it highlights the regions where these changes occur. This approach is particularly effective for identifying subtle but important changes in seismic data, such as those caused by CO₂ migration, which alters the velocity and density of the subsurface. Even in noisy data, the SNN can detect these variations, thanks to its ability to learn features that are highly sensitive to small but meaningful changes. The SNN architecture is scalable and can be adaptable to various seismic monitoring tasks, requiring minimal preprocessing. The proposed framework harnesses the power of deep learning to provide insights into the dynamics of the storage complex, with a focus on identifying changes in time-lapse seismic data related to localized variations. The proposed migration detection tool offers a cost-effective and reliable solution to the modern challenges of gas storage monitoring. This study aims to enable operators to identify and address problems promptly, thereby minimising the impact of potential leakages.

How to cite: Pantaleo, G. and Pipan, M.: Assessment of a deep learning framework for time-lapse seismic monitoring, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-882, https://doi.org/10.5194/egusphere-egu25-882, 2025.

Joint inversion is an essential technique in potential field data processing. The current methodology largely relies on the geology model of anomalous bodies, especially for deep, complex structures. Inspired by the excellent nonlinear mapping capability of the image semantic segmentation model and the advantages of supervised learning, a regressive, end-to-end, encoder-decoder structural, convolutional neural network with a double-branch structure called PFInvNet(Potential Field Inversion Neural Network) is proposed for joint 3D  inversion of physical properties from gravity and magnetic data. Its input is a four-channel dataset consisting of gravity and magnetic anomalies and their vertical gradients, and its output is a 3D matrix representing the spatial distribution of the remnant density and the magnetic susceptibility, which are predicted independently through the double-branch structure of the decoders and then concatenated in the final layer. For network training, a large amount of precisely labeled sample is exceedingly demanding; thus, forward modeling becomes a prerequisite approach. Two discretized forward modeling algorithms for gravity and magnetic anomalies of 3D homogeneous arbitrary-shaped bodies based on surface integrals are deduced and verified with analytic solutions of the sphere model. Furthermore, the neural network needs to learn from the anomalies generated by various forms of abnormal bodies with different physical properties. Therefore, different sizes and quantities of cuboids are randomly distributed in the model space to simulate different forms of abnormal bodies. The label represents the combined spatial distribution of remanent density and magnetic susceptibility for the cuboids, encompassing both spatial location information and physical properties information. With the help of the Marching Cubes(MC) algorithm, the surface of the cuboids can be easily extracted and divided into a triangular surface mesh. The surface mesh is then used to calculate the gravity and magnetic anomalies synchronously through the forward modeling algorithms. The anomalies are concatenated in the channel direction as a sample. A set of optimal network parameters has been determined, including the weight initialization method, the gradient calculation methods, the loss function, the training hyperparameters, the regularization method, and the normalization method. The PFInvNet is trained with 500 and 10000 pairs of samples and labels, respectively. The analysis and comparison of training results prove that PFInvNet has two crucial features: one is that the branch structure enables independent prediction of magnetic susceptibility and remanent density; the other is efficient anti-overfitting ability and efficient solution-finding ability .The prediction error of small samples is very close to that of large samples and is also not obviously enhanced by the noise-contaminated data , demonstrating the strong generalization and robustness of the network. Finally, the network is tested with magnetic and gravity anomalies of the Victoria Land Basin in the western Ross Sea through transfer learning and retraining, and definite 3D distributions of apparent remnant density and apparent magnetic susceptibility have been obtained and can be checked with geological evidences.

How to cite: Jiang, W., Zhao, Y., Gao, J., Ge, S., and Xie, Z.: 3D property inversion of gravity and magnetic data based on a double branch regressive CNN trained by synchronous forward modeling ：a case study of the Western Ross Sea, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-1141, https://doi.org/10.5194/egusphere-egu25-1141, 2025.

Shale oil and shale gas are important unconventional resources. Organic matter serves as the primary source of shale oil and gas generation, and high TOC values typically indicate better oil and gas reservoir conditions and production. Therefore, an accurate TOC prediction model is conducive to low-cost evaluation of reservoir hydrocarbon potential and improvement of development efficiency. However, geochemical experimental measurements are costly, and the data obtained is discrete. It is unable to meet the requirements for fine-scale assessment of shale reservoirs. The multiple regression method and ΔlogR method, when directly applied to shale reservoirs, often result in significant errors. In this study, we propose a composite model for accurate TOC prediction in shale reservoirs based on data enhancement and empirically driven. We first address the issue of poorly characterized logging responses and discrete experimental data. The features and quantities of the dataset are enhanced by introducing reconstruction curves and generative adversarial networks (GAN). The validity of the synthesized data is then verified by plotting the data density. In the empirically-driven module, we optimize a density-gamma modified method on traditional ΔlogR method according to the characteristics of shale reservoirs. The modified ΔlogR method will be integrated into the GWO-SVR model as an empirically driven subject in the form of a fitness function. Above, a composite model with both empirical and data-driven components is constructed. We use the Dongying Depression in China as an example for model experiments. The composite model was generalized to wells X and Y. The R² (coefficient of determination) was 0.95 and 0.97, the RMSE (Root Mean Square Error) was 0.31 and 0.29, and the MAE (Mean Absolute Error) was less than 0.3, which indicated a high degree of consistency between the model predictions and the experimental values. Further controlled experiments revealed that the composite model predicted better than the ΔlogR method and the GWO-SVR model alone. Finally, we also performed SHAP interpretability analysis on the model. By revealing the decision-making mechanism inside the model, we verified the rationality of the empirical drive and enhanced the credibility of the model. This provides strong technical support and decision-making basis for the subsequent oil and gas exploration and development work.

How to cite: Hong, Y., Deng, S., Li, Z., and Wei, Z.: TOC Intelligent Prediction Model in Shale Reservoir: Integrating Data Enhancement with Empirically Driven Algorithm, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-2156, https://doi.org/10.5194/egusphere-egu25-2156, 2025.

We present a novel approach to gravity forward modeling using conditional neural operators that establishes a forward generative model from the basin models and hyperparameters (reference basement depth, etc.) to gravity anomaly. Our methodology introduces an innovative adaptive embedding mechanism where scalar hyperparameters are first embedded into a 32-dimensional space and then adaptively expanded to match the dimensions of the basin depth model, enabling effective fusion with basin depth model data. Subsequently, Fourier Convolution Layers are employed to transform the fused data into gravity anomalies. The model demonstrates superior performance compared to existing convolutional neural networks on the test dataset, showcasing improved accuracy in capturing complex geological structures and their gravity responses. A key advantage of our architectural design is that it not only preserves the super-resolution capability of conventional neural operators but also enables controlled generation through different hyperparameters. This dual capability allows for both resolution-flexible modeling and parameter-controlled generation, while training on low-resolution data and producing high-resolution outputs, significantly reducing training data requirements and computational costs. The model's adaptive architecture effectively bridges the resolution gap between training and application scenarios, offering a practical solution for real-world geological surveys. Our results suggest that this approach could substantially improve the accessibility and applicability of gravity forward modeling in various geological settings, particularly in regions with limited high-resolution training data.

How to cite: Kang, R., Geng, M., Yang, Q., and Vega, F.: A Conditional Neural Operator Approach for Resolution-Flexible and Parameter-Controlled Gravity Forward Modelling, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-2242, https://doi.org/10.5194/egusphere-egu25-2242, 2025.

The combination of big data and AI/ML technologies shows tremendous promise within the domain of disaster management. So that benefits of AI/ML can be realized, and risks can be adverted, internationally agreed upon standards are an important mechanism. These can provide guidance on how to apply (and develop policy around) data collection and preprocessing, model training and evaluation, and operational implementation. In conjunction, they can cultivate interoperability and harmonization of AI-based systems. At the United Nations, the Global Initiative on Resilience to Natural Hazards through AI Solutions brings together experts from different disciplines (geosciences, disaster risk management, computer sciences) and sectors (government, research, NGO) to analyze use cases and lay the groundwork for such standards. Through proof-of-concept projects (e.g., HEU-funded MedEWSa), these standards can be further refined. Finally, through education and capacity building activities, the Global Initiative can help to democratize the responsible use of AI for this domain.

How to cite: Kuglitsch, M. and Xoplaki, E.: International standards for responsible AI in disaster management, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-2478, https://doi.org/10.5194/egusphere-egu25-2478, 2025.

Geological discontinuities significantly influence rock mass behaviour. Understanding the origin, setting, and properties of discontinuities is of major relevance, especially in boreholes. Traditionally, manual interpretation of borehole logs is done by geologists, a process that is time-consuming, costly, and subject to variability based on the interpreter's expertise. Recent advancements in artificial intelligence have made it feasible to use machine learning models and automatically detect and differentiate various features in digital images. In this study, we employ a state-of-the-art semantic segmentation model to tackle domain-specific challenges, enabling the identification of discontinuity types (e.g., natural faults, fault zones) and rock mass behaviour features (e.g., breakouts, induced cracks). We applied the SegFormer semantic segmentation model, which integrates a hierarchically structured transformer encoder with a multilayer perceptron (MLP). The borehole data used in this study was collected from the Mont Terri underground rock laboratory. Specifically, we labelled several high-resolution optical logs from one borehole and divided the dataset into training and testing subsets. The borehole considered is an experimental borehole designed to investigate the spatial and temporal evolution of damage around an underground opening in faulted clay shale. Our strategy achieved robust and accurate segmentation results on borehole images. Following segmentation, post-processing techniques were employed to extract critical information such as the total length of induced cracks and the total area of breakouts, as well as their locations and frequencies. The experimental results demonstrate high performance, with the pixel accuracy of 96 % in under three minutes for a 10-meter borehole. Our study lays the groundwork for future research by introducing a powerful tool for extracting geological structures and demonstrating the potential of AI models in geological analysis. By reducing processing time and increasing consistency in the identification, mapping, and classification of geological features, our approach can reveal spatial and temporal patterns associated with the evolution of rock masses.

How to cite: Wang, R., Ziegler, M., Volpi, M., and Manconi, A.: Advances in the identification of geological discontinuities in boreholes with deep learning, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-4298, https://doi.org/10.5194/egusphere-egu25-4298, 2025.

Meteorological data are vast and complex, and their rapid and accurate retrieval is essential for forecasting operations. However, traditional systems have struggled with limited search accuracy and inefficient processing speeds, hindering effective forecast support. To address these challenges, this study developed an AI-based system capable of performing speech recognition, URL search, extreme value detection, and local forecast error analysis. In speech recognition, the Whisper-large model achieved a character error rate (CER) of 3.19%, with GPU memory usage reduced by 15.7% and inference time by 38.18%, enabling real-time processing and scalability in multi-GPU environments. The URL search systems translated natural language inputs into SQL queries and URLs, achieving a Mean Reciprocal Rank (MRR) of 0.92, thereby enhancing data retrieval precision. The extreme value detection systems utilized GPT-4-based template augmentation to expand training data by approximately 111%, significantly improving detection performance and search accuracy. For local forecast error analysis, a prototype chatbot was implemented using prompt engineering and a Text-to-SQL model, allowing for the automated identification of inconsistencies in local forecasts and streamlining the analysis process. These systems have substantially enhanced operational workflows across meteorological tasks, facilitating rapid data retrieval through voice commands, precise responses to complex queries, and real-time analytical support. Future research will focus on further refining these technologies to tackle a wider range of meteorological challenges and integrate them into global forecasting systems for enhanced accuracy and reliability.

How to cite: Kim, B., Shin, H., Cho, A., Park, J., Lee, H., Park, C., Joe, J., Choo, J., and Seo, M.: AI-Enhanced Meteorological Data Retrieval Systems for Improved Forecast Operations, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-4404, https://doi.org/10.5194/egusphere-egu25-4404, 2025.

The shear wave (S-wave) velocity is a key basis for shale reservoir development, particularly for fracability evaluation. Additionally, S-wave velocity also plays a significant role in prestack seismic inversion and amplitude versus offset (AVO) analysis. However, the actual logging data often lack S-wave velocity data, so it is of significant importance for S-wave velocity prediction. We propose a rapid and precise prediction method for the S-wave velocity in shale reservoirs based on class activation maps (CAM) model combined with physically constrained two-dimensional Convolutional Neural Network (2D-CNN). High sensitivity curves related to S-wave velocity are selected as the foundation. Meanwhile, based on the petrophysical theory of pore medium, the petrophysical model of complex multi-mineral components is established. The dispersion effect is reduced to a certain extent and the results are used to constrain the model. The Adam optimization algorithm is used to construct a 2D-CNN model under the constraint of petrophysical model. The CAM is obtained by replacing the global average pooling (GAP) layer with a fully connected layer, which in turn leads to interpretable results. Then, the model is applied to wells A, B1, and B2 in the southern Songliao Basin. Afterwards, comparisons are made with unconstrained model and petrophysical model. The results show that the correlation coefficients and relative errors in the three test wells are 0.96 and 2.14%, 0.97 and 2.35%, and 0.97 and 2.9%, respectively. The higher prediction accuracy and generalization ability of the new method is confirmed. Finally, we present the defined C-factor as a means of evaluating the extent of concern regarding CAMs in regression problems. The C-factor confirms that the focus of 2D-CNN can be significantly enhanced by incorporating the petrophysical model, thereby imposing physical constraints on the 2D-CNN. In addition, we establish the SHAP model to assist in proving the importance of constraints.

Keywords: S-wave velocity prediction; Physically constrained 2D-CNN; Petrophysical model; Class activation mapping technique; Explainable results

How to cite: Li, Z., Deng, S., and Hong, Y.: S-wave velocity prediction of shale reservoirs based on explainable physically-data driven model, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-4699, https://doi.org/10.5194/egusphere-egu25-4699, 2025.

Accurate multivariate parametrization of subsurface properties is essential for subsurface characterization and inversion tasks. Deep generative models, such as variational autoencoders (VAEs) and generative adversarial networks (GANs), are known to efficiently parametrize complex facies patterns. Nonetheless, the inherent complexity of multivariate modeling poses significant limitations to their applicability when considering multiple subsurface properties simultaneously. Presently, diffusion models (DM) offer state-of-the-art performance and outperform GANs and VAEs in several tasks of image generation. In addition, training is much more stable compared to the training of GANs. In this work, we consider score-based DMs in multivariate geological modeling, specifically for the parametrization of categorical (facies) and continuous (acoustic impedance – I­_P) distributions, focusing on a synthetic scenario of sand channel bodies in a shale background. We benchmark modeling performance against results obtained by GAN and VAE networks previously proposed in literature for multivariate modeling. As for the GAN and VAE models, the DM was trained with a training dataset of 3000 samples, consisting of facies realizations and co-located I­_P geostatistical realizations. Overall, the trained DM shows significant improvements in modeling accuracy, for all evaluation metrics considered in this study, except for the sand-to-shale ratio, where the values are comparable to those of the GAN and VAE. In particular, the DM is 26% more accurate at reproducing the average (nonstationary) facies distribution and up to 90% more accurate at reproducing the I_P marginal distributions for both sand and shale classes. Higher accuracy is also found in the reproduction of the facies-to-I­_P joint distribution, whereas the spatial I­_P distributions generated by the DM honour the two-point statistics of the training samples. The iterative generative process in DMs generally makes these networks more computationally demanding than VAEs and GANs. However, we demonstrate that with appropriate network design and training parametrization, the DM can generate realizations with significantly fewer sampling iterations while maintaining accuracy comparable to these benchmarking networks. Finally, since the proposed DM parametrizes the joint prior probability density function with a Gaussian latent space, it is straightforward to perform inversion. In addition to improved modeling accuracy, the mapping between the latent and image representations preserves a better topology than that of GANs, overcoming the well-known limitation of the latter for inference tasks, particularly for gradient-based inversion.

How to cite: Miele, R. and Linde, N.: Multivariate generative modelling of subsurface properties with diffusion models , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-7000, https://doi.org/10.5194/egusphere-egu25-7000, 2025.

Stratigraphic correlation of well log data is a fundamental step in geosciences. It involves correlating stratigraphic units across multiple wells to build a comprehensive understanding of subsurface geology. Currently, stratigraphic correlation is predominantly performed “manually” by geoscientists. The process is labor-intensive and time-consuming, and interpretations may vary among interpreters due to differences in expertise, experience, and perspective.

Recent advancements in the application of the Dynamic Time Warping (DTW) algorithm have demonstrated its potential to automate and enhance the stratigraphic correlation of well logs. DTW can generate multiple correlation scenarios highlighting different interpretations of subsurface continuity. Thus, the aim of this work is to explore the potential of DTW as a supporting tool in the standard workflows of geoscientists and test it on well log data from IODP Expedition 381 from the Gulf of Corinth. We automatically correlate lithostratigraphic subunits within a 700 m thick stratigraphic unit across two wells, using Natural Gamma Ray (NGR) and Magnetic Susceptibility (MAGS) logs. We selected this dataset because it illustrates the evolution of geological interpretations over time. Between the first version of the IODP data interpretations and a second version published a few years later, significant differences in interpretation were proposed. These differences highlight the critical role of geological expertise in refining subsurface data interpretations and correlations.

The automatic correlations interpreted by DTW showed a minimal average absolute difference with the most recent and updated published correlation, making the human and the machine correlation almost identical. By applying DTW to this dataset, we demonstrate it would have been possible to identify discrepancies and challenges in the interpretations of subunits at the initial stages after data acquisition. This approach could have flagged potential issues even before the IODP data were made available on the public site. Such early identification highlights the potential of DTW as a valuable tool for providing immediate feedback and guiding more accurate stratigraphic interpretations faster.

While DTW significantly reduces the time required for the correlation phase, the time investment needed for data formatting upstream should not be underestimated. Future work on larger datasets will be crucial to better quantify and validate the overall time savings provided by DTW, as well as to optimize the preparatory steps to ensure efficiency in broader applications.

In conclusion, we show that DTW can offer innovative approaches to enhance geological investigations and speed up interpretations. More generally, we consider this work illustrates how data science methods can be leveraged to assist geologists in routine tasks, with our Corinth case study highlighting both the promises and current limitations of digital transformation in well correlations.  

How to cite: Christ, A.-B., Hamieh, Z., Armitage, J., Divies, R., Rohais, S., Mattioni, L., and Bouziat, A.: Dynamic Time Warping algorithm: A geoscience aware AI for automatic interpretation in lithostratigraphy? Insights from an application to the Gulf of Corinth (Greece), EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9590, https://doi.org/10.5194/egusphere-egu25-9590, 2025.

In the age of big data, artificial intelligence (AI) is transforming Earth sciences by enabling efficient analysis and visualisation of complex datasets and fostering innovative approaches to solve age-old geoscientific challenges. Kalpa, a Python-based free and cross platform software, represents a pioneering step in this direction. Built with versatility at its core, Kalpa seamlessly integrates AI and machine learning workflows into geoscience applications, offering  customization through its Python plugin architecture. What sets Kalpa apart is its ease of use, even for non-experts. Its intuitive interface lowers the learning curve, enabling a broader audience—including researchers, professionals, and enthusiasts—to leverage advanced geospatial and AI tools without requiring extensive technical expertise.

Kalpa's capabilities span advanced 3D visualization, geospatial data processing, and machine learning model development. It supports global and regional raster and vector dataset visualisation and processing, allowing for interactive analysis in both geographic and cartesian coordinates. With tools to process satellite imagery, geological and geophysical data, uncrewed aerial vehicle (UAV) data, and digital geological maps, Kalpa caters to a wide range of applications, from mineral exploration to natural hazard forecasting. Its machine learning integration supports supervised and unsupervised algorithms for applications such as lithological mapping, mineral prospectivity mapping, land cover and land usage studies, agricultural productivity mapping and natural disaster management. In this study, we demonstrate Kalpa’s transformative potential through three case studies:

• Lithological Mapping in Ladakh, India: Utilizing LANDSAT and SRTM data, we produced accurate lithological maps for this geologically complex region.
• Copper Prospectivity Mapping in Northwest India: Combining remote sensing, geophysical, and geological data, Kalpa predicted copper mineralization zones, with all known deposits falling within areas of predicted probabilities exceeding 0.70.
• Landslide Susceptibility Mapping in Uttarakhand, India: Using remote sensing datasets, Kalpa identified high-risk landslide zones, supporting disaster management efforts.

Kalpa’s user-friendly interface, robust machine learning integration, and publication-ready export capabilities position it as a powerful tool for advancing geoscience research and practical applications. By bridging the gap between domain expertise and cutting-edge AI methodologies, Kalpa empowers Earth scientists, environmental researchers, and GIS professionals to analyze, model, and predict with unprecedented efficiency and precision. This software marks a new frontier in the application of AI to Earth sciences, enabling multidisciplinary research and fostering innovative solutions to pressing geoscientific challenges.

How to cite: Sukhbir, S., Singh, S. P., Singh, U., Kumar, M., and Goyal, T.: Kalpa: Empowering Artificial Intelligence-Driven Geospatial Analysis for Multidisciplinary Applications, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9671, https://doi.org/10.5194/egusphere-egu25-9671, 2025.

Artificial intelligence (AI) is transforming landscape archaeology by enabling the automated analysis of high-resolution datasets, such as airborne laser scanning (ALS). The Automatic Detection of Archaeological Features (ADAF) tool is an example of the potential of AI to streamline the identification of subtle surface features and demonstrate their value in uncovering and understanding archaeological landscapes. By improving the detection of archaeological sites, the ADAF plays a crucial role in the research, management and preservation of cultural heritage.

ADAF uses advanced AI models, including convolutional neural networks (CNNs) for semantic segmentation and object detection, to detect features in ALS datasets. The tool has been trained on a large archive of ALS data from Ireland and processes visualised inputs to detect patterns indicative of archaeological structures. The workflow integrates pre-processing with the Relief Visualisation Toolbox, inference with trained AI models and post-processing to refine the results to ensure reliable outputs with minimal false positives.

Designed with accessibility in mind, ADAF features an intuitive user interface that removes the barriers traditionally associated with AI-driven analyses. Users can process ALS data and export GIS-compatible results without the need for specialised knowledge, making the tool suitable for a wide audience. This approach democratises the use of AI in landscape archaeology and extends its utility to professionals and researchers in the field.

Tests with Irish ALS datasets have shown that ADAF is able to detect both known and previously unrecognised archaeological features in the landscape, while enhancing the spatial accuracy of identified sites. By automating complex data analysis, ADAF underlines the efficiency, precision and scalability of AI in landscape archaeology. In addition, the tool contributes to the preservation of cultural heritage by identifying sites that would otherwise remain undiscovered and enabling their preservation and integration into cultural heritage management strategies.

ADAF represents a significant advance in the application of AI in landscape archaeology, providing a powerful and accessible solution for surface feature recognition. Its development underlines the transformative potential of AI to revolutionise the study and interpretation of archaeological landscapes.

How to cite: Coz, N., Kokalj, Ž., Curran, S., Corns, A., Kocev, D., Kostovska, A., Davis, S., and O'Keeffe, J.: Advancing Landscape Archaeology with AI-driven insights from Airborne Laser Scanning data, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9968, https://doi.org/10.5194/egusphere-egu25-9968, 2025.

Borehole images (BHI) are crucial for resource exploration, providing detailed fracture analysis at millimeter-scale resolution. However, their interpretation is typically carried out manually, a process that is time-consuming, costly, and subject to significant uncertainty due to interpreter bias and variability. Current state-of-the-art AI methods for automated or semi-automated fracture analysis of BHI often rely on field data for training, using manual interpretations as labels. This approach inherently embeds both aleatoric (data-related) and epistemic (manual) uncertainties, which may undermine the reliability and adaptability of these methods. This study proposes an alternative, synthetic data-driven approach to train a set of two deep neural networks (DNNs) connected in sequence. These DNNs are designed to replicate the primary cognitive tasks involved in manual interpretation: the segmentation of the BHI to identify potential edge zones and the tracing of sinusoids over these edges to approximate their best-fitting 2D representation. By utilizing synthetic data, we are able to systematically assess the sensitivity of both networks and explore various training strategies, including curriculum learning (CL) and self-attention mechanisms. Our proposed solution is designed for post-hoc human-machine collaboration, where the model supports but does not replace human expertise. This framework enables the possibility of a multi-level uncertainty assessment—at the human, machine, and human-machine interface levels—opening the door to new ways of understanding and quantifying the sources of uncertainty in BHI analysis. Additionally, the synthetic data-driven approach ensures the generalizability and scalability of the method, as demonstrated by its successful application to low-resolution logging-while-drilling (LWD) and high-resolution fullbore formation microimager (FMI) datasets from multiple global locations. By combining advanced AI techniques with geoscientific knowledge, this study outlines a potential pathway toward more robust, ethical, and sustainable fracture analysis workflows. Beyond the traditional benefits of reduced cost and time, the approach may provide a scientifically grounded framework for exploring the benefits of human-machine collaboration and uncertainty quantification in geoscience practices. If adopted, this framework could significantly advance the field of BHI analysis, offering new tools for resource exploration in hydrocarbon and geothermal applications.

How to cite: Molossi, A., Roncoroni, G., and Pipan, M.: NeuroFit: a robust and scalable synthetic data-driven deep learning solution for automated borehole image analysis at LWD and wireline resolution, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10472, https://doi.org/10.5194/egusphere-egu25-10472, 2025.

Since 2014, ESA Sentinel missions have been producing an ever-growing amount of data for Earth Observation. This creates the opportunity to monitor changes in high temporal and spatial resolution, however, interpreting this huge quantity of data is challenging. In recent years, the rapid advancement and widespread adoption of applied Artificial Intelligence (AI) methods made it feasible to create deep learning models for specific Earth Observation applications. Combining Sentinel datasets with the appropriate amount of ground truth, robust pre-trained models can be created and applied to produce good-quality thematic maps for different years and large areas. Due to responsibilities related to the European Union’s Common Agriculture Policy (CAP) and the motivation for regional yield estimation, crop classification is one of the most frequently studied remote sensing problems these days. Several papers investigate the possible methods to construct robust and generally functioning models to map the spatial distribution of crops as accurately as possible.

In this study, we present the development of a modular, pre-trained deep learning model designed specifically for crop type mapping. The model is tailored to classify the most prevalent crops in Hungary, including winter and autumn cereals, corn, sunflower, alfalfa, rapeseed, grasslands, and other significant types. For pre-training, we leverage country-wide Sentinel-1 Synthetic Aperture Radar (SAR) data such as Sigma Naught or polarimetric descriptors from H-A-alpha decomposition, collected during the 2021–2024 time period. This dataset comprises annual time series of Sentinel-1 pixels at a spatial resolution of 20 meters. Our approach builds upon prior findings that Sentinel-1-based crop type classification performs comparably to methods using Sentinel-2 optical data. However, Sentinel-1 has the added advantage of producing consistent and regular time series, as it is unaffected by atmospheric conditions such as cloud cover.

The proposed model employs a hybrid methodology integrating self-supervised and fully supervised learning paradigms. This modular architecture allows for seamless integration of task-specific classifiers, which can be fine-tuned using supervised learning to address both current and future classification requirements effectively. The fully supervised component is supported by an extensive ground truth dataset covering over 60% of Hungary’s total land area. This proprietary dataset is derived from the national agricultural subsidy database, providing detailed and accurate annotations. The abundance and quality of labeled data enable the construction of robust, highly generalizable models, ensuring reliable performance across diverse classification tasks. This methodology offers great potential to advance national-scale operational tasks, such as early land cover prediction and annual crop type mapping.

How to cite: Richter-Cserey, M., Simon, M., Magyar-Santen, G., Pacskó, V., and Kristóf, D.: Exploring Pretraining Possibilities of Crop Classifiaction Models Using Large-Scale Sentinel-1 Datasets, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-11754, https://doi.org/10.5194/egusphere-egu25-11754, 2025.

Advanced constitutive models, here represented by the hypoplastic clay model, are powerful tools which provide engineers with reliable responses in various practical applications. However, the model calibration is not an easy task. Calibration of these models can be addressed with several approaches, which are generally distinguished as stochastic or deterministic approaches. In general, these approaches extract information from the experimental data and the subsequent optimisation process finds the best combination of parameters to fit the desired constraints. . The deterministic approach was integrated and combined in development of the online automated calibration tool ExCalibre. This paper presents a Machine Learning approach for automated calibration of the Hypoplastic Clay model. By using pairs of input experimental data and calibrated results performed by ExCalibre as training data, a Deep Neural Networks (DNNs) model is constructed to recognise how the experimental data can be used to derive the asymptotic state parameters such as the slope and the interception of the Normal Compression Line (NCL), or the critical friction angle, and the optimised stiffness parameters. The training and testing data comprise of In-house protocols and User-upload data over 3 years of launching the ExCalibre, and synthetic data with small distortion to prevent overfitting. Finally, investigations on how the DNNs model recognises the asymptotic patterns, as well as its calibration results will be presented.

How to cite: Do, P. and Kadlicek, T.: Calibration of the Hypoplastic Clay model with a deep neutral network, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-12065, https://doi.org/10.5194/egusphere-egu25-12065, 2025.

Accurate estimation of Particle Size Distribution (PSD) in rock avalanche deposits is essential for understanding the fragmentation processes and spatial distribution characteristics during mass movement. However, traditional methods, such as physical sieving or visual field estimation, are time-consuming, labor-intensive, and impractical for large-scale field measurements. To address these limitations, this study presents an automated PSD estimation framework that combines UAV imagery and deep learning-based segmentation. A synthetic dataset was used to train the segmentation model, improving its robustness across different scenarios. Image resolution adjustments were applied to improve detection accuracy for small and overlapping particles. Additionally, Fourier analysis was utilized to reconstruct smooth and continuous particle contours, to effectively handle overlapping particles. The reconstructed 2D outlines were further used to estimate 3D particle volumes through the shape-volume model based on laboratory and literature data. Projection correction was applied to mitigate image distortions to ensure precise volume predictions. The proposed approach overcomes the limitations of traditional methods dealing with complex particle distributions in real field environments. The results demonstrate the effectiveness of the proposed method for large-scale particle detection and volume estimation, providing new insights into rock avalanche fragmentation dynamics.

Keywords: Rock avalanche; Particle size distribution (PSD); deep learning; UAV Imagery

How to cite: Lin, R., Jaboyedoff, M., Derron, M.-H., and Lu, T.: Automated Particle Size Distribution Estimation of Rock Avalanches using Deep Learning, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-12242, https://doi.org/10.5194/egusphere-egu25-12242, 2025.

Hazelnuts are a significant crop, with global production exceeding 1.25 million tons by 2023 (INC, 2023). Quality is threatened by biotic agents, including insects causing the cimiciato defect. This defect, from insect bites during fruit growth, results in off-flavor, tissue alterations, and lipid oxidation (De Benedetta et al., 2023). Damage can be external or internal (hidden cimiciato). Industrial quality standards often exceed official regulations, making effective selection crucial. Traditional visual inspection is time-consuming and subjective. Non-destructive methods like NIR and NMR have potential but limited applicability. Deep Learning (DL) has revolutionized image classification, proving effective in agriculture, including disease and pest management (Mohanty et al., 2016; Dhaka et al., 2021; Meena et al., 2023). This study explores Deep Learning (DL) for automated detection of the cimiciato defect in hazelnuts using X-ray radiographs. Cimiciato, caused by insect feeding, degrades hazelnut quality, requiring product selection. Traditional methods are time-consuming and subjective. We propose a Convolutional Neural Network (CNN) model trained on X-ray images to classify hazelnuts as healthy or infected. Results demonstrate the model's effectiveness, offering a non-destructive, automated quality control solution.
Radiographs were acquired using a cone-beam micro-tomograph. Each hazelnut was positioned on a rotating stage. A CNN model was used for classification. CNNs effectively extract features from images. Convolutional layers apply filters to identify features; pooling layers reduce data dimensionality; fully connected layers combine features for classification. The Inception-ResNet-V2 architecture was chosen, combining Inception modules and residual connections (Szegedy et al., 2017). The model was trained (128 image batch size, 0.001 learning rate, 30 epochs), comparing SGD, ADAM, and RMSP optimizers. Images were pre-processed: resizing, pixel normalization, and data augmentation. 
The test dataset evaluated the trained network. SGD, ADAM, and RMSP yielded similar results. Confusion matrices visualize performance. ADAM performed best, but all achieved good results, especially for cimiciato detection. 

Keywords: Cimiciato defect, Hazelnut, Deep Learning, X-ray radiography, CNN

How to cite: Napolitano, A. G., Mele, G., Gargiulo, L., Giaccone, M., and Vitale, A.: Automatic Detection of Cimiciato Defect in Hazelnuts Using Deep Learning and X-ray Radiography, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-12675, https://doi.org/10.5194/egusphere-egu25-12675, 2025.

Artificial Intelligence (AI) is revolutionizing geosciences, aligning perfectly with the themes of session GI2.4 by enabling the analysis of complex, multidimensional datasets and delivering actionable insights at unprecedented scales. This study presents two innovative AI-driven frameworks addressing critical challenges in soil moisture prediction and geomagnetic disturbance forecasting. Both approaches leverage decentralized networks to achieve scalability, foster collaboration, and enhance model performance through continuous refinement by a distributed community of contributors.

For soil moisture prediction, our multi-stream base model integrates data from Sentinel-2 (high-resolution spectral imagery), SMAP L4 (volumetric water content), ERA5 (meteorological variables), and SRTM (elevation data). The model predicts surface and rootzone soil moisture with six-hour lead times, achieving RMSE values of 0.1087 m³/m³ and 0.1183 m³/m³, respectively, across diverse Köppen-Geiger climate zones. By utilizing a decentralized network, contributors perform inference on 100 km² global regions, generating predictions evaluated against SMAP data using Root Mean Square Error (RMSE) and R² metrics. This system ensures robust model performance while addressing the spatial and temporal gaps inherent in traditional observational networks. These advances have significant implications for agriculture, hydrology, and climate modeling, enabling better water resource management, crop planning, and drought mitigation strategies. 

In geomagnetic disturbance forecasting, our GeoMagModel leverages Prophet, a time-series forecasting library, to predict the Disturbance Storm Time (Dst) index, a key indicator of geomagnetic activity. The model achieves an RMSE of 6.37 for December 2024 datasets, effectively capturing both trend shifts and weekly seasonality. The decentralized community enhances predictive accuracy by dynamically integrating historical and real-time Dst data, which is validated by benchmark predictions of the Kyoto World Data Center’s hourly records. This approach provides near-real-time forecasts critical for safeguarding power grids, satellite systems, and other infrastructure vulnerable to space weather events.

By integrating machine learning with decentralized computing and state-of-the-art data sources, these frameworks offer scalable solutions to longstanding challenges in geophysical monitoring. The decentralized network not only improves scalability but also incentivizes the geoscience community to refine baseline models, fostering innovation and enabling systems to outpace state-of-the-art benchmarks. The implications of this work extend beyond immediate applications, paving the way for hybrid models that combine AI-driven predictions with physical process-based simulations. This fusion has the potential to improve understanding and resilience in critical domains such as water resource planning, disaster mitigation, and space weather forecasting. By addressing the limitations of traditional observation systems and delivering actionable insights at scale, these AI-driven frameworks represent a paradigm shift in how we approach and solve complex geoscientific problems.

How to cite: Moraga, G., Hristopoulos, S., and Pearson Kramer, N.: Harnessing AI and Decentralized Networks for Next-Generation Geophysical Forecasting, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-13315, https://doi.org/10.5194/egusphere-egu25-13315, 2025.

Small satellite platforms are increasingly used for Earth observation due to their cost-effectiveness and flexibility. However, their limited payload size often results in reduced spatial resolution of captured images. In our work, we address this challenge by proposing an advanced multi-image super-resolution (MISR) approach tailored for small satellite applications.

It integrates:

• Sub-pixel image registration and on curvelet transform-based interpolation to preserve high-frequency details while reducing artifacts;
• A novel hybrid method called SP-MISR (Subpixel Multi-Image Super-Resolution), which leverages Convolutional Neural Networks (CNNs) for local detail analysis and Transformers for global spatial relationships.

Our experimental results demonstrate that this combined approach  significantly improves image sharpness, preserves fine details, and reduces artifacts, outperforming traditional super-resolution techniques. Moreover, SP-MISR exhibits robustness in processing noisy and distorted images, making it particularly suitable for the constrained imaging systems of small satellites.

Future developments will focus on improving computational efficiency, reducing interpolation errors, and extending the method to multi-spectral imaging and interplanetary missions, by exploring explore pure deep learning techniques.

This work highlights the potential of integrating traditional and deep learning methodologies to enhance image quality, thus expanding the scientific and operational capabilities of small satellite missions.

How to cite: De Martino, C., Della Corte, V., Inno, L., Cozzolino, F., Ruggiero, G., Da Deppo, V., Zuppella, P., Moualla, L., and Venafra, S.: Advanced Super-Resolution Techniques for Optical Payloads in Earth Observation: Combining Traditional and Deep Learning Methods, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15949, https://doi.org/10.5194/egusphere-egu25-15949, 2025.

Soil carbonates are critical players in the global carbon cycle and have a profound influence on soil health and agricultural productivity. Their quantification is also central to carbon sequestration efforts, where accurate measurement of soil carbonates can inform strategies for reducing atmospheric CO₂. However, conventional methods of carbonate analysis in soil---while effective---are often slow, costly, and labor-intensive ⁽¹⁾.

In this study, we introduce Shifted Excitation Raman Difference Spectroscopy (SERDS) as a rapid, non-destructive alternative, further enhanced by advanced preprocessing techniques and machine learning algorithms. Specifically, we employ Asymmetric Least Squares (ALS) for background correction, Standard Normal Variate (SNV) for normalization, and Savitzky–Golay filtering for smoothing. Unlike conventional Raman spectroscopy, SERDS effectively eliminates background fluorescence and reduces overlapping peaks, resulting in clearer spectral signatures ⁽²⁾. 

We employed Partial Least Squares Regression (PLSR) and eXtreme Gradient Boosting (XGBoost) to predict the inorganic carbon content from the carbonate vibrational modes in conventional Raman and SERDS spectra, benchmarked against total inorganic carbon (TIC) measurements from coulometric titration. Our results show that switching to dual-laser SERDS substantially boosted model performance. For PLSR, the coefficient of determination (R²) improved from 0.8 to 0.88 (an increase of about 10.5%), and the root-mean-square error (RMSE) declined from 0.29 to 0.22 (26% decrease). The XGBoost model exhibited an even greater increase, with R² increasing from 0.63 to 0.93 (approximately 49% improvement) and RMSE dropping from 0.39 to 0.16 (59% reduction).

Figure 1: Left: All SERDS data of soil samples showing the main carbonate peak; Right: XGBoost model prediction of soil inorganic carbon using SERDS data.

These findings underscore the potential of SERDS to replace conventional methods for carbonate quantification, offering reduced cost, faster analysis, and essentially no sample preparation. Furthermore, by providing highly accurate carbonate measurements, this methodology can be pivotal for carbon sequestration assessments and large-scale soil management practices, helping to advance both environmental sustainability and agricultural productivity.

References:

1) Barra I, Haefele SM, Sakrabani R, Kebede F. Soil spectroscopy with the use of chemometrics, machine learning and pre-processing techniques in soil diagnosis: Recent advances–A review. TrAC Trends in Analytical Chemistry. 2021 Feb 1;135:116166.

2) Orlando A, Franceschini F, Muscas C, Pidkova S, Bartoli M, Rovere M, Tagliaferro A. A comprehensive review on Raman spectroscopy applications. Chemosensors. 2021 Sep 13;9(9):262.

How to cite: Poursorkh, Z., Solomatova, N., and Grant, E.: Quantifying Soil Inorganic Matter: Integrating Shifted Excitation Raman Difference Spectroscopy (SERDS) with Machine Learning for Enhanced Analysis of Carbonates, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-16705, https://doi.org/10.5194/egusphere-egu25-16705, 2025.

Seismic exploration heavily relies on the accurate processing of seismic data, as high-quality reconstructed data is essential for reliable imaging and interpretation. In recent years, data-driven approaches have shown great promise in seismic data processing. However, supervised learning methods require large amounts of labeled data, while generative models, such as GANs, often encounter issues like mode collapse and instability. On the other hand, generative diffusion models, leveraging principles from nonequilibrium thermodynamics and Markov processes, have emerged as powerful tools for capturing complex data distributions.

Despite these advantages, Denoising Diffusion Probabilistic Models (DDPM) purely generate data distributions from the latent space with the reliance on random noise, making it inadequate for seismic data reconstruction where the goal is to accurately recover missing traces. Thus, DDPM is  lacks interpretability in seismic data restoration, and may disrupt the structured patterns crucial for interpolating seismic signals. Furthermore, we view the reverse process that starts from noise as unnecessary and inefficient for reconstruction task.

To address these challenges, we propose a novel Conditional Residual Diffusion Model (CRDM) that enhances both certainty and interpretability by incorporating residual diffusion and conditional constraints derived from observed seismic data (Fig.1). This approach better aligns with the inherent structure of seismic signals, enabling more accurate and interpretable reconstruction. The model is grounded in DDPM, with mathematical derivations for loss functions, conditional probability distributions, and reverse inference steps, ensuring both theoretical rigor and practical applicability.

Additionally, Our CRDM utilizes a shallow U-Net architecture featuring one down-sampling and one up-sampling layer integrated with Multi-Head Self-Attention (MHSA), which significantly enhances the model's efficiency and effectiveness. Experimental results (Fig.2) demonstrate that CRDM outperforms DDPM, denoising convolutional neural network (DnCNN), and fast projection onto convex sets (FPOCS), achieving a 15.1% improvement in reconstruction SNR and reducing computation time by 139 times compared to DDPM. Notably, CRDM achieves optimal results in a few diffusion steps, whereas DDPM typically requires thousands of steps.

The innovative approach generates data through residuals for determinism, while guiding the processing with noise for diversity. This not only enhances the interpretability and efficiency of seismic data reconstruction, but also positions the model as a promising tool for advancing data-driven seismic processing through flexible coefficient adjustment. Therefore, we believe this model has great potential for broader applications in geophysical data analysis, offering significant value in accurately depicting complex geological structures and providing more effective guidance for petroleum exploration.

Fig.1 The framework of CRDM. The model consists of two stage: (a) the training stage with forward diffusion process; (b) the sampling stage for seismic data reconstruction.

Fig.2 Reconstruction results and residuals of the 1994 BP seismic data with 50% irregular missing traces. (a) Complete data, reconstruction using (b) FPOCS (SNR=10.5dB), (c) DnCNN (SNR=15.6dB), (d) DDPM(SNR=18.4dB), (e) CRDM(SNR=21.2dB), and (f) observed seismic data with 50% missing traces. (g-j) display the residuals corresponding to reconstructions (b-e), respectively. The red box highlights a zoomed-in region, which is shown in detail in (k-t).

How to cite: Gong, X. and chen, S.: Enhancing Seismic Data Reconstruction with a Conditionally Constrained Residual Diffusion Model, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-16835, https://doi.org/10.5194/egusphere-egu25-16835, 2025.

Recent trends in climate change and global warming have amplified the occurrence of severe convective initiation (CI) and other extreme weather events, underscoring the importance of high-frequency remote sensing. Modern satellite constellations such as EUMETSAT, GOES, Himawari, and GK2A now offer global monitoring intervals of 10 minutes and regional updates as frequent as 1–2 minutes. However, these capabilities are not universally accessible—many developing countries lack in-orbit assets or historical high-frequency data archives.

This study presents a zero-shot video frame interpolation (VFI) approach to generate high-frequency (1–10 minute) satellite imagery from legacy or sparsely sampled observations. Leveraging a flexible Many-to-Many Splatting VFI model, our framework avoids domain-specific retraining while delivering reliable intermediate frames. We validate the method using overlapping data from the KMA GK2A (10-minute full-disk, 2-minute Asia-Pacific) and KMA COMS (3-hour full-disk, 15-minute Asia-Pacific) satellites over the period July 25, 2019, to March 31, 2020.

Our results indicate significant improvements in both PSNR and SSIM metrics, confirming the model’s efficacy in three critical applications:

• Up-sampling Archived Geostationary Data

  • Enhancing the temporal resolution of older satellite imagery (e.g., COMS 30-minute or 3-hour intervals) to match or approximate modern satellite capabilities (e.g., GK2A 10-minute intervals). This harmonization facilitates unified climatology analyses spanning multiple generations of instruments.

• Sensor-Error Correction and Gap Filling

  • Recovering missing or corrupted frames resulting from attitude adjustments, sensor calibrations, or malfunctions on geostationary satellites. Ensuring a continuous record provides more robust inputs to operational forecasting and climate assessments.

• Delayed High-Frequency Observation Services

  • Enabling resource-constrained meteorological agencies to retrospectively produce and disseminate high-frequency satellite products (e.g., near 1-minute intervals) to improve nowcasting, risk assessment, and disaster preparedness.

Our preliminary findings show minimal computational overhead per inference step, making this cost-effective method feasible for near-real-time deployment and post-event analyses alike. By bridging temporal gaps in global satellite datasets, this technique supports advanced level-2 products such as cloud-tracking and convective-initiation alerts, thereby driving broader socioeconomic and scientific benefits in both developed and developing regions.

How to cite: Ryu, H. and Choi, Y.: Toward High-Frequency Satellite Observations in Data-Sparse Regions: A Zero-Shot Interpolation Framework for Missing and Historical Imagery, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-17456, https://doi.org/10.5194/egusphere-egu25-17456, 2025.

Tomographic methods, such as electrical resistivity tomography (ERT), induced polarization (IP) and seismic refraction tomography (SRT) are often effective for detecting geophysical targets in disparate real-world scenarios. However, a final reconstruction expressed only in terms of individual geophysical parameters (resistivity, chargeability, P-wave velocity) leaves room for ambiguity in complex sites exhibiting several transitions between layers or zones having different geophysical properties. In such cases, the sensitivity of the geophysical parameters for the various methods can differ significantly, so that a univocal interpretation based only on a visual comparison of the different models is often ineffective. To overcome these limits, in this work we present a machine learning-based quantitative approach for the detection of geophysical targets associated with both geological and anthropogenic scenarios. We integrate two-dimensional ERT, IP and SRT tomographic data with a soft clustering analysis by the Fuzzy C-Means (FCM) to obtain a final combined section, where each pixel is characterized by a cluster index and an associated membership value. The membership function of the Fuzzy C-Means is a good estimator of the accuracy of the subsurface reconstruction, as it ranges from 0 to 1, with 1 reflecting a high reliability of the clustering analysis. We apply this method to two case studies, related to the detection of leachate accumulation areas in a municipal solid waste landfill and to the bedrock characterization in a site prone to instability. In both cases, we detect the cluster associated with the geophysical targets of interest and our final sections are validated by a good agreement with the available direct information (boreholes and wells). The accuracy of the reconstruction is consistently high across most areas (membership values > 0.75), even though it is reduced in areas where the resolution of geophysical data is lower. Therefore, this approach may be a valuable automatic tool for optimizing the cost-effectiveness of projects where new constructions or remediation interventions have to be planned.

How to cite: De Donno, G., Cercato, M., Melegari, D., Paoletti, V., Penta de Peppo, G., and Piegari, E.: Fuzzy clustering of electrical and seismic data for the detection of geophysical targets, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-17957, https://doi.org/10.5194/egusphere-egu25-17957, 2025.

The Frequency Domain Electromagnetic (FDEM) method is a cost-effective geophysical technique that simultaneously studies the electrical and magnetic properties of a medium, providing data as in-phase and out-of-phase components of the electromagnetic field. Although FDEM yields valuable insights, its results can be complex to interpret, and the two EM field components are normally only visually inspected to support findings from other techniques. This study aims to enhance FDEM data interpretation using an unsupervised learning technique. The proposed approach seeks to automate and expedite the interpretative phase. By applying the K-Means clustering algorithm, we divided the FDEM data into several clusters based on specific intervals of the in-phase and quadrature components, resulting in integrated maps of EM components. Combining these maps with geological and archaeological insights helped identifying areas of potential archaeological interest. This method was applied to the Torre Galli archaeological site in Calabria, Italy, known for its significance in Iron Age studies.

Based on comparisons with the findings of earlier excavations and results from a magnetic survey, the proposed procedure shows promise in improving the efficiency and accuracy of the FDEM method in identifying areas of archaeological interest. This suggests that automating the interpretation process could lead to a better cost management and time optimization in geophysical and archaeological studies.

How to cite: Capozzoli, A., Paoletti, V., Cella, F., La Manna, M., and Piegari, E.: Integrating FDEM data with K-Means clustering for improved archaeological site identification, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-18082, https://doi.org/10.5194/egusphere-egu25-18082, 2025.

Artificial Intelligence (AI) is revolutionizing the field of geomorphology, offering a robust tool for objective and quantitative analyses. This pioneering study proposes an innovative framework based on Machine Learning clustering techniques, capable of classifying drainage patterns into multiple morphological classes. This work follows up on a related study in which an attempt at classifying 156 terrestrial and extraterrestrial (Mars and Titan) river networks was made. Rivers’ outlines are intrinsically noisy, difficult to isolate from the background, and can be ambiguous for the human eye. The previous works have been focused on accurately classifying patterns, using the expertise of morphologists, thus introducing a weak link, the human eye, in the chain. This time, a reliable, automatic, and scalable methodology has been obtained, leveraging computers’ precision, objectivity, and computational power. The HydroRIVERS dataset, a publicly available data bank containing vector data, was utilized in this study. All HydroRIVERS data layers are provided in a geographic projection (latitude/longitude), referenced to the WGS84 datum. Each data layer includes an attribute table with information on the morphometric characteristics of each river reach. The input parameters for the clustering models included morphometric features such as LENGTH_KM, DIS_AV_CMS, ORD_STRA, ORD_CLAS, and ORD_FLOW.
During a preliminary experiment, a local convexity test was conducted to determine the optimal number of clusters (k) to identify the best metric values. This test made sure that the number of clusters with the highest evaluation metric was selected, varying in a closed numeric interval. Each cluster corresponds to a specific river class. Significant results were obtained with k = 6, k = 8, k = 10, and k = 12. Subsequently, the K-Means algorithm was applied, grouping the dataset into distinct clusters based on the morphometric parameters. The results were remarkable, with 10 being the best value for k. The results indicate that the clustering algorithm is able to optimally separate the dataset, producing a high inter-cluster distance and a low intra-cluster distance. The dataset points along the features, as highlighted by the three principal components obtained by performing PCA on the final five-dimensional clustering resulting vector space, are well grouped in relatively small clusters, far away from each other. The next step involves using the centroids obtained from the analysis of the large dataset as a reference for classifying the 155 rivers. In general, the centroids obtained from this kind of Learning could be of great value to the scientific community, establishing a new and innovative way of discerning between different classes of rivers without having to manually analyze and inspect images. This approach promises efficient and accurate classification of both terrestrial and extraterrestrial drainage patterns.

How to cite: D'Aniello, M. and Donadio, C.: A novel approach using Machine Learning to objectively classify terrestrial and extraterrestrial river networks, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-19838, https://doi.org/10.5194/egusphere-egu25-19838, 2025.

The big developments in Artificial Intelligence (AI) generated a mixture of fascination and apprehension, echoing historical responses to unknown phenomena. Like how medieval European societies interpreted certain natural events and instruments as magical, AI is currently perceived by many as a “black box,” blurring the lines between advanced technology and mystical force. This phenomenon fosters misconceptions and uncertainties about AI’s actual mechanisms, benefits, and risks. Consequently, it underscores the urgent need for science communicators and science museums to adopt an active role in enhancing the general public’s AI literacy and in debunking some of its enigmatic traits.

By drawing parallels with the Middle Ages—when objects such as mirrors and magnetite were often attributed supernatural capabilities—modern AI tools LLMs or image and video generators are frequently viewed as possessing a “magical” principle. The public’s limited grasp of how AI processes inputs and produces outputs further intensifies this impression. The lack of transparency (or “black box” effect) in deep learning algorithms, combined with the ambiguity of human language, has been shown to fuel both wonder and anxiety.

Science museums and communicators should have an active role by offering educational programs that demystify AI through different demographic and social activities as well as promoting the public debate. These initiatives could clarify AI’s underlying mathematical and computational principles, highlight practical examples of AI-driven applications, and examine ethical considerations surrounding its deployment. Public understanding of AI’s capabilities and limitations is crucial not only to temper undue fears but also to encourage informed engagement with emerging technologies.

How to cite: Azevedo Rodrigues, L.: Making Sense of AI: The Important Role of Education and Communication, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-20509, https://doi.org/10.5194/egusphere-egu25-20509, 2025.

The environmental and health effects of the transport and diffusion of pollutants released into the atmosphere and ocean due to a nuclear accident must be evaluated rapidly and accurately to ensure the safety of the surrounding population and ecosystem. Since the Fukushima accident in 2011, a web-based nuclear emergency support system named Radiological Accident Preparedness System in Korea (RAPS-K) has been developed to predict the dispersion of radioactive materials released into the environment and estimate dose assessment for humans. The system is composed of atmospheric dispersion, marine dispersion, and dose assessment models, along with a graphic user interface module. It can evaluate the dispersion patterns of radionuclides in the air and ocean, and the short-term and long-term radiological effects of a nuclear accident on humans. The atmospheric dispersion, marine dispersion, and dose assessment models have already been validated by model-to-model comparisons and measurements from the Chernobyl and Fukushima accidents. Especially, atmospheric dispersion model is connected with numerical weather forecast data produced by Korea Meteorological Administration (KMA) in real-time and the air conentrations are rapidly calculated in the system. Atmospheric dispersion model named LADAS(Lagrangian Atmospheric Dose Assessment System) was applied to evaluate the behavior of radioactive material released into the air for the hypothetical nuclear accident. RAPS-K is now operating through a web Graphic User Interface (GUI) on Linux OS. The developed system covers the Northeast Asian and worldwide regions in the event of a nuclear accident.

How to cite: Suh, K.-S., Park, K., Min, B.-I., Kim, S., Choi, Y., Kim, J., Kim, M.-C., KIm, H., and Kim, K.-O.: Atmospheric Dispersion Model for a Nuclear Accident using RAPS-K, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-1280, https://doi.org/10.5194/egusphere-egu25-1280, 2025.

When radiation exposure occurs, evaluating the radiation dose is necessary to assess the risk and implement appropriate protective measures. Typically, radiation workers use personal dosimeters, which conservatively calculate the effective dose based on measured values. However, in scenarios involving potential high radiation exposure, emergency response tasks, or accidental exposure, precise dose evaluation is crucial. Conventional methods estimate human dose from dosimeter readings by applying dosimeter-to-human dose conversion factors under the assumption of a parallel radiation field, but this can introduce significant errors when the radiation field is inhomogeneous.
In this study, Deep Neural Networks (DNN) was applied to rapidly estimate absorbed doses to humans and dose conversion factors in inhomogeneous radiation fields. It was assumed that the radiation field can be described by the location and energy distribution of point sources, and the basic exposure scenario was set to external exposure by a standing adult male from a point source. Through GEANT4-based Monte Carlo simulations, absorbed doses to radiation-sensitive organs and whole-body were calculated, and conversion factors between chest-worn dosimeters and organ doses were determined.
Due to significant skewness in dose data, statistical techniques that transform the data to approximate a normal distribution, such as log transformation and Box-Cox transformation, to facilitate more effective training. The transformed dataset was divided into training, validation, and test sets. Optimization of model hyperparameters was performed using training and validation data and optimized model was trained. The model's predictive performance was verified by evaluating its relative error rate in predicting test data compared to ground truth values. The prediction results demonstrated an acceptable relative error rate, factoring in the uncertainty inherent in simulation-derived data. The results of this study are expected to provide a foundation for easily and quickly assessing the predicted risk to the human body when radiation exposure occurs in an unexpected inhomogeneous source distribution situation. This will help to quickly determine follow-up measures.

How to cite: Choi, Y., Kim, H., Kim, M. C., Kim, S., Min, B.-I., Kim, J., Suh, K.-S., and Lee, J.: Deep Neural Network for risk assessment via organ dose estimation in inhomogeneous radiation fields, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-2406, https://doi.org/10.5194/egusphere-egu25-2406, 2025.

We recently developed a marine environment exposure dose assessment model to evaluate the radiological doses to the public following marine radiological accidents. The following six exposure pathways were considered: external exposure through beach activity, swimming, and boating and internal exposure through inhalation via sea spray, ingestion of seawater during swimming and ingestion of seafood. A model for estimating radionuclide concentration in marine organisms in the present dose assessment model was developed based on the equilibrium model, taking into account realistic aspects related to food intake and data availability related to model input variables. However, when the equilibrium model was applied in the early phase of an accident in which the radionuclide concentration changes significantly, there is a possibility that the evaluation result of nuclide concentration in marine organisms could be overestimated, so a dynamic marine foodchain model was added to the equilibrium model-based marine dose assessment module so that it can be selectively applied according to the purpose of the assessment. When applying the dynamic model, the limitations of the model to the uncertainty about the input variables and assessment results due to the lack of available data must be considered. The dynamic model considered three groups of seafood (fish, invertebrate, seaweed), as in the equilibrium model. The model contains  compartments for water, sediment, and marine organism groups. The radioactivity transfer for compartments is dynamically modeled by a first-order differential equation using rate constant. Radioactive decay is considered in all compartments. To the next step, it is planning to develop a module that can evaluate the public exposure doses by linking the dose assessment model with the marine dispersion model, and apply it to the case study for calculating the public exposure following hypothetical marine radiological accidents.

How to cite: Kim, S., Suh, K.-S., Keum, D.-K., Min, B.-I., Choi, Y., Kim, J., Kim, M., Kim, H.-J., and Park, K.: Improvement of a model for estimating radionuclide concentration in marine organisms in a marine environment exposure dose assessment model, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-2649, https://doi.org/10.5194/egusphere-egu25-2649, 2025.

In this study, the FLEXPART atmospheric transport model was used to simulate the atmospheric transport of Cs-137 following the Fukushima NPP accident in March 2011, using the source term estimation described in [1]. The simulation results were compared with airborne concentration measurements conducted in Japan and globally. It was shown that FLEXPART results as compared to Fukushima are highly sensitive to assumptions regarding size distribution of the radioactivity in the source. Based on our study, 'conventional' assumptions regarding the mean aerodynamic diameter (MAD) of particles, which are typically reported in the range of 0.2 to 0.7 μm in various studies, yielded reasonable agreement between simulated and observed concentrations within Japan. However, at greater distances from the source (e.g., across Eurasia), our results showed that the calculated concentrations were significantly overestimated. As discussed in our study, the size distribution of particles in the plume evolves over time. Therefore, measurements of AMAD conducted in Europe [2], which report a similar range of AMAD values, may not be representative enough to determine the initial AMAD at the source. To address this, we curve-fitted the observed size distribution of emitted particles measured in Japan shortly after the accident, as presented in [3]. This approach resulted in the following estimates for the size distribution: AMAD ≈ 2.5 μm and geometric standard deviation (GSD) ≈ 1.8. Simulations conducted with these updated size distribution parameters showed significant improvement in the agreement between calculated and observed airborne concentrations, as compared to using conventional assumptions.

References

• 1. Terada H, Nagai H., Tsuduki K., et al. (2020) Refinement of source term and atmospheric dispersion simulations of radionuclides during the Fukushima Daiichi Nuclear Power Station accident, Journal of Environmental Radioactivity, Volume 213,106104, https://doi.org/10.1016/j.jenvrad.2019.106104
• 2. Masson O., Ringer W., Malá H., et al. (2013) Size Distributions of Airborne Radionuclides from the Fukushima Nuclear Accident at Several Places in Europe. Environmental Science & Technology 47 (19), 10995-11003 DOI: 10.1021/es401973c
• 3. Miyamoto Y., Yasuda K., Magara M., (2014) Size distribution of radioactive particles collected at Tokai, Japan 6 days after the nuclear accident, Journal of Environmental Radioactivity (132) 1-7, https://doi.org/10.1016/j.jenvrad.2014.01.010

How to cite: Kim, K. O., Kovalets, I., and Park, C.-W.: Evaluation of the parameters of size distribution of emitted aerosols for simulation of radionuclides atmospheric transport following Fukushima accident using FLEXPART model, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-2670, https://doi.org/10.5194/egusphere-egu25-2670, 2025.

According to the World Nuclear Association, the capacity of nuclear power plants around the world is growing steadily. In detail, about 60 reactors are under construction among which 29 reactors are to be launched by 2025 with 19 reactors located on the coast. This increases the risk of radioactive contamination of ocean waters in the case of potential nuclear accidents. Rapid growth of NPPs amount around the world requires the development of the global model that can simulate the release of radioactive materials from any NPP and assess its impact on the adjacent local/regional seas and global ocean. The compartment model POSEIDON with the new global system of boxes could be such a model which simulates transport of a wide range of radionuclides in the marine environment including marine food chains and calculates doses to humans from seafood consumption. Also, the model operates with both atmospheric and direct releases of radionuclides to the marine environment.

A new global system of boxes for the POSEIDON-GM (Global Model) has been developed which consists of 437 boxes of variable sizes. Most ocean boxes have 4 vertical layers in the water column: first surface layer with a depth of 30 m, second layer between 30 and 200 m depth, third layer between 200 and 1000 m depth, and fourth layer below 1000 m. Boxes with an average depth of less than 1000 m have fewer vertical layers depending on their depth. Water fluxes between boxes were calculated from the Global Ocean Physical Multi-Year product by Copernicus Marine Environment Monitoring Service, which contains monthly mean circulation data averaged for 1993-2016.

Annual depositions of ¹³⁷Cs and ⁹⁰Sr in each box of the POSEIDON-GM due to the global fallout have been calculated using data of deposition density from UNSCEAR for the 1945-1987 period with extrapolation for the 1988-2020 period. The transport of ¹³⁷Cs and ⁹⁰Sr entered the World Ocean due to global fallout has been simulated by POSEIDON-GM. Calculated concentrations of both radionuclides in water have been compared with measurement data from the MARIS database for the Southern Hemisphere, where the global fallout was the dominant source of radioactive contamination.

The next stage of the POSEIDON-GM development will include consideration of other sources of radioactive contamination, such as local sources from nuclear weapon tests, releases from nuclear reprocessing plants, sources due to Fukushima and Chornobyl nuclear accidents, etc. The model results will be compared with measurement data for concentrations of radionuclides in water and from different trophic levels of marine organisms from around the World Ocean. 

How to cite: Jung, . T., Bezhenar, R., Maderich, V., Bezhenar, Y., and Kim, H.: Development of the POSEIDON-GM–box-based radioactivity transport model for the global ocean: preliminary results of Cs-137 and Sr-90 distributions due to global fallout, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-2671, https://doi.org/10.5194/egusphere-egu25-2671, 2025.

Since the mid-20th century, exposure to radioactive materials has occurred through various sources, such as nuclear tests, nuclear safety incidents, and releases from reprocessing plants. These materials have undergone global fallout and deposition through ocean-atmosphere circulation. This study aims to calculate the global spatial distribution of radioactive fallout on the Earth's surface based on observational data (fallout database), assuming wet deposition processes, and to evaluate temporal changes. The results will be utilized in a global climate model (GCM), Geophysical Fluid Dynamics Laboratory Earth System Model Version 4 (GFDL-ESM4), to better understand the patterns of deposition. Through this approach, we aim to estimate the extent of radioactive diffusion into the atmosphere and ocean and the subsequent accumulation in biological resources. Furthermore, this study investigates to assess how early 21st-century climate change may influence the global spatial variations of radioactive fallout and deposition.

How to cite: Seung-Hwon, H. and Hyung-Gyu, L.: Study of fallout distribution based on the wet deposition of major radiochemical species and application on the global climate model, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-2690, https://doi.org/10.5194/egusphere-egu25-2690, 2025.

Radioactive cesium-bearing microparticles (CsMPs) were insoluble glassy matrixes derived from Fukushima Daiichi Nuclear Power Plant accident. CsMPs have greater radioactive cesium (Cs) concentration per unit mass than other particles adsorbing Cs such as clay and organic particles (i.e., particulate Cs). In addition, the size of CsMPs was about a few µm. CsMPs may move around the environment as in the case of particulate Cs. Therefore, there is a concern for an internal exposure when CsMPs may enter the living organisms. Furthermore, previous studies have shown that CsMPs may attach to soil and crops, and locally increase Cs concentration of them. On the other hand, the difference of particulate Cs and CsMPs is Cs desorption from those particles. For particulate Cs, some Cs may electrically adsorb onto clay planar site and functional groups, therefore, their Cs may be exchanged with other cations, desorbed, and adsorbed by the crop through the roots. However, Cs in the CsMPs is included in in soluble grassy matrixes. In other words, it is considered that Cs in the CsMPs may be difficult for crops to absorb. Transfer factor (TF) is used to evaluate transfer of Cs from soils to crops. However, the conventional TF is a simple ratio of Cs concentrations between soils and crops. This suggests that conventional TF may not be able to accurately assess Cs transfer from soils to crops, since Cs in the soils contains a mixture of Cs with different crop availability. Since there are many contaminated forest areas in Fukushim, it is necessary to evaluate secondary contamination due to the inflow of Cs and CsMPs from these areas into farmland after decontamination.

In this study, we investigated the changes of Cs and CsMPs deposition in the paddy field in the first year of resumption of farming after the accident. Furthermore, we evaluate the contribution of CsMPs on particulate Cs to grasp the impact of CsMPs on TF of brown rice.

As a result, the accumulation of Cs and CsMPs increased in the entire field of this study in the period from plowing to harvest. The deposition of Cs per unit area increased in the center and the four corners of the paddy field other than inlet and outlet. This may be due to the deposition of suspended matter in areas with relatively litte water movement. On the other hand, there was no clear trend for CsMPs deposition in the paddy filed. This is because CsMPs were only detected in some of the soil samples, and the amounts varied depending on the soil. The Cs concentration in brown rice was less than 100 Bq kg^-1-FW in all samples. This indicated that safe rice could be produced on this study site after decontamination. Furthermore, there was no significant difference between TF values with and without the contribution of CsMPs to Cs concentration in the soil. This suggested that there was no problem to evaluate TF using conventional methods for the paddy filed in Fukushima Prefecture.

How to cite: Tatsuno, T., Nihei, N., and Yoshimura, K.: The impact of radioactive cesium-bearing microparticles on Cs transfer factor of brown rice on the paddy field in the first year of resumption of farming, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-3130, https://doi.org/10.5194/egusphere-egu25-3130, 2025.

The ³H and ³⁶Cl radionuclides, released during the Fukushima nuclear accident in 2011, contaminated forested environments. However, the opportunity to periodically observe these radionuclides was lost due to the catastrophic conditions caused by the earthquake and tsunami associated with the accident. The concentrations of these released nuclides in precipitation and their fates are critical when considering the effects of internal exposure. Given that ³H and ³⁶Cl are reliable hydrological tracers, records of their continuous depositional flux within unsaturated soil layers could provide valuable insights into the contamination levels during the initial years after the accident. Such records are indispensable for understanding the extent of contamination. Using a borehole drilled in 2014 at Koriyama, 60 km from the accident site, we reconstructed the deposition record of atmospheric ³H and ³⁶Cl following the accident. The contributions of ³H and ³⁶Cl from the accident were determined to be 1.4 × 10¹³ and 2.0 × 10¹² atoms m⁻², respectively, at this site. Unlike approaches based on radionuclide migration analysis, the ³H and ³⁶Cl concentrations in precipitation during the approximately six weeks following the accident were accurately recovered (607 Bq/L and 4.74 × 10¹⁰ atoms/L, respectively) by analyzing the depositional flux in the unsaturated soil column from depths of 0 m to 4.25 m. Both the ³H and ³⁶Cl concentration profiles were reassessed at the site in 2016. By that time, both soluble radionuclides had mostly been flushed out of the unsaturated soil zone due to rainfall over the 5.6 years since the accident. Although ³H concentrations in the unsaturated soil water and shallow groundwater had returned to less than 6 tritium units (TU), the ³⁶Cl concentration had not yet returned to the natural cosmogenic background deposition level. In contrast, ¹²⁹I was primarily found in the litter layer and the soil near the ground surface.

How to cite: Ohta, T., Fifield, K., Palcus, L., Tims, S., Pavetich, S., Matsuzaki, H., Tsumune, D., and Mahara, Y.: Record of the Fukushima nuclear accident in unsaturated soil water and the fate of nuclear contamination, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-3386, https://doi.org/10.5194/egusphere-egu25-3386, 2025.

The world has experienced three major nuclear accidents (Chernobyl, Three Mile Island, and Fukushima). We have accumulated a vast amount of data from those accidents as well as from less severe nuclear accidents and incidents tracked in the nuclear industry. It is time for us to develop an open knowledge base that gathers and shares nuclear accident-related data and enables users to utilize those data to prevent future nuclear accidents and implement effective remediation measures when an accident occurs. However, nuclear-related data have peculiar challenges when it comes to understanding and organizing the data because of the behavior of radioisotopes. Having examined the nuclear accident literature, we identified four data axes that are important to organize nuclear accident data systemically. Those axes are: (1) historical time flow vs. nuclear decay (logarithmic radioactive decay), (2) temporal extent affected by radioactive decay vs. spatial extent affected by environmental processes, (3) contamination vs. non-contamination (background) comparison for which a comparison method is decided upon (1) and (2), and (4) radioactivity vs. dose (health risk inflicted on organisms) as a result of exposure to radioactivity. These four axes need to be considered in every step related to nuclear accidents.

For example, when a nuclear accident occurs, the first step is to assess the level of radioactive contamination in the nearby environment. Distinguishing the accident-derived radioactivity from the background radioactivity [axis 3] is crucial to implementing evacuation and remediation procedures and assessing health risks [axis 4]. However, comparing accident-driven radioactivity against the background activity is not straightforward. Identifying a proper background location and collecting a sample involves spatial and temporal considerations [axis 1, 2], such as sampling distance, direction, depth, sampling timing, sampling frequency, etc. So far, a data organization scheme for nuclear accidents has not been established despite the vast amount of accident/incident data accumulated and the possible severe implications of nuclear accidents to society.

We propose ontology as a tool that addresses the peculiar nature of nuclear data and systematically organizes the knowledge we have acquired on nuclear accidents. Ontology originated as a metaphysical study in the field of philosophy to study the properties and relations of all involved entities. With the help of ontology, we provide formal definitions of the entities within the nuclear accident knowledge domain and connect those entities with logical rules. In this study, as the first step of nuclear accident ontology building, we present an ontology-based conceptual model on background comparison for environmental radioactivity utilizing a well-established Basic Formal Ontology (BFO) for upper-level ontology and several ontology models that have been developed in the sub-fields of the nuclear and energy industries. The rule-based entity structure based on ontology will contribute to building a comprehensive knowledge base for nuclear accidents. Our aim is to build a knowledge base adaptable to machine learning that will shorten the time needed for nuclear accident data search and provide insights into the best practices to minimize the adverse effects on humans and the environment from nuclear accidents.

How to cite: Yasumiishi, M. and Bittner, T.: Developing Ontology-Based Nuclear Accident Knowledge Base Part 1: Spatiotemporal Considerations in Background Comparison, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-5199, https://doi.org/10.5194/egusphere-egu25-5199, 2025.

Tritium is a radioactive substance released into the environment through air and water systems during nuclear facility operation, potentially affecting ecosystems. Understanding its concentration variations is essential. This study analyzed tritium concentrations measured in air (unit: Bq/m³) and rainwater (unit: Bq/L) from 2011 to 2022 to evaluate the reliability of sampling methods for atmospheric and precipitation samples. A multi-faceted analysis was conducted using variables such as precipitation, release rates, and seasonal factors. The results showed that increased precipitation led to decreased tritium concentrations in rainwater at all locations, with significant dilution effects observed in northern and western monitoring points (approximately -0.0309 Bq/L and -0.0301 Bq/L per 1mm precipitation increase, respectively). Tritium concentrations in the air also tended to decrease with increasing precipitation, although the magnitude of variation was smaller compared to rainwater. A statistically significant correlation between increased precipitation and decreased air tritium concentrations was observed at the eastern monitoring point (P < 0.01). Comparing tritium concentrations across seasons under similar precipitation conditions revealed no significant differences in most cases; however, slightly higher concentrations were found in winter. This may be related to specific factors such as temperature conditions or emission patterns during winter. This study quantitatively confirmed the environmental behavior of tritium and its dilution effect by precipitation, providing crucial baseline data for the long-term management of radioactive substances around nuclear facilities and contributing to the evaluation of sampling methods' reliability.

How to cite: Yun, J., Lee, J., and Lim, J.-M.: Analysis of the Impact of Precipitation on Tritium Concentrations Based on Nuclear Facility Operation Data, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-7764, https://doi.org/10.5194/egusphere-egu25-7764, 2025.

13 years after the Fukushima nuclear accident, the coastal area in front of the Fukushima Dai-ichi Nuclear Power Plant remains contaminated. First, it relates to the bottom sediments, which were contaminated with Cs-137 during the accidental and post-accidental releases. Additional contamination of bottom sediments could be caused by river runoff of Cs-bearing microparticles, which were formed in the initial phase of the accident, dispersed in the atmosphere, and fell into the watershed of nearby rivers. Cesium may be preserved in such particles for a long time due to its insoluble characteristics. River runoff carries suspended particles along with Cs-bearing microparticles to the coastal areas of the ocean, especially during heavy rains.

In the study, we applied the Lagrangian particle tracking model Parcels to identify places of potential deposition of such Cs-bearing microparticles in the Fukushima coastal area. As input data we used

• 3D circulation data from the detailed ROMS-based ocean circulation model customized to the Fukushima coast for the period 2013-2016;
• Estimates of released Cs-bearing microparticles from rivers during heavy rains in selected period;
• Estimates of microparticles’ sizes define the vertical velocity of their falling in the water column.

Lagrangian particles simulated the transport of Cs-bearing microparticles, which were released from the Abakuma River mouth, by the coastal currents taking into account Stokes settling velocity. Maximums in the distribution of such particles on the bottom identified areas with possible accumulation of Cs-bearing microparticles in the coastal area after heavy rains. Distributions for different particles’ sizes obtained in simulations were analyzed.

How to cite: Bezhenar, R., Takata, H., Tsumune, D., Maderich, V., and Tateda, Y.: Sedimentation areas along the Fukushima coast for Cs-bearing microparticles from the Lagrangian particle tracking, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-8294, https://doi.org/10.5194/egusphere-egu25-8294, 2025.

The Arctic Ocean and the North Pacific are the main reservoirs of anthropogenic radionuclides introduced in the past 80 years. The Lagrangian particle tracking approach was applied to the Arctic oceans to study the pathways of 137Cs contamination from point sources representing the locations of solid radioactive waste in the bays of the Novaya Zemlya archipelago and in the Kara Sea. The model includes radioactive decay and the interaction of dissolved radionuclides with suspended and bottom sediments. Lagrangian model uses accurate approach for simulating the absorption-desorption processes and novel algorithm for the bottom boundary conditions. As input for the model, the 3D hydrodynamic fields from NEMO model simulation with 10 km horizontal resolution and 121 vertical layers were used. 3D velocity fields along with vertical velocity and vertical turbulent mixing coefficient were used from NEMO simulation for 38 years from 1980 to 2018. Suspended sediment concentration fields were reconstructed using the 1D SEDTRANS05 sediment transport model in each grid node.

A set of potential scenarios of radioactive release were considered to analyse the most probable pathways of contamination. In each simulation, 1M of particles was used and individual trajectories were stored. The maps of visitation probability were built to show pathways of radioactivity contamination in the Arctic Ocean from the selected sources.

How to cite: Brovchenko, I., Maderich, V., Lambin, C., and Martazinova, V.: Lagrangian pathways of 137Cs released from multiple sources in the Arctic Ocean, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10014, https://doi.org/10.5194/egusphere-egu25-10014, 2025.

The Radiocaesium (¹³⁷Cs) released as a result of past and potential nuclear accidents is of great concern for agriculture because of the relatively long half-life and easy absorption by plants. A countermeasure is the use of potassium fertilizers, but the relationship between transferability from soil to crop and exchangeable potassium (Ex K) varies depending on the soil. Previous studies have suggested that exchangeable ¹³⁷Cs (Ex ¹³⁷Cs) and the solid/liquid distribution coefficient (Kd) can be important factors explaining the variability of the ¹³⁷Cs dynamic in soil. However, the methods to measure these soil parameters are not suitable for adequate emergency deployment and preparedness because they are expensive and time consuming.

Mid-infrared spectroscopy (MIRS) has been shown to predict soil parameters more quickly and cost-effectively. However, the prediction of Cs parameters (Ex ¹³⁷Cs, Kd) using MIRS has not yet been evaluated. This study aims to evaluate whether MIRS can predict Cs-related parameters such as Kd, Ex ¹³⁷Cs/Total ¹³⁷Cs, and other parameters that may influence on the behavior of ¹³⁷Cs in the soil, such as total carbon (soil total C).

The 1,682 samples were collected in Fukushima Prefecture, Japan from 2013 to 2020, most of them are Andosols. Each soil property (soil total C, Ex ¹³⁷Cs/Total ¹³⁷Cs ) was obtained from the monitoring data of MAFF and NARO. As for Kd for ¹³³Cs, 176 samples were measured at Hokkaido University using ICP-MS. Each sample was dried at 37°C for at least one night and then sieved to less than 0.2 mm before measurement. The spectra data was obtained with four replicates in each soil sample using MIRS. To date, a total of 1,419 samples have been analyzed and their properties predicted using partial least squares regression (PLSR).

The PLSR models provided a relatively high accuracy for the prediction of soil total C, where the R² (coefficient of determination) was 0.9±0.01. However, low accuracy was observed in the prediction of Kd for ¹³³Cs, where the R² was 0.46±0.06, which was attributed to the low concentration of the data and the limited number of samples. On the other hand, the result of Ex ¹³⁷Cs/Total ¹³⁷Cs showed relatively higher value then Kd for ¹³³Cs, where the R² was 0.56±0.04. Variable importance on projection Ex ¹³⁷Cs/ Total ¹³⁷Cs suggests that wavelength range related to clay mineralogy, quartz, and organic matter are influential in the estimation of Ex ¹³⁷Cs/Total ¹³⁷Cs.

 To further evaluate the potential for MIRS for rapid and affordable prediction of Radiocaesium risks, additional samples will be analyzed to improve the representativeness of the model and broaden the dataset, and more modeling techniques (Cubist, Support Vector Machine, etc) will be applied.

How to cite: Iwai, J., Dercon, G., Vlasimsky, M., Albinet, F., Maruyama, H., and Shinano, T.:  Predicting soil Radiocaesium uptake and dynamics using Mid-Infrared Spectroscopy (MIRS), EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10714, https://doi.org/10.5194/egusphere-egu25-10714, 2025.

Thirteen years have passed since the accident at the Fukushima Daiichi Nuclear Power Plant, but the levels of Cs-137 and H-3 near the power plant have not returned to the levels they were at before the accident. In addition, the levels of Cs-137 and H-3 near the power plant continue to be higher than in other areas of the sea. This suggests a persistent leakage from the power plant or the surrounding sea area. However, the mechanism of the leakage is unknown. As the estimated rate of leakage outside the port is higher than the estimated rate of leakage inside the port, it is also possible that the leakage is not via the port.

The ratio of radioactive material concentration does not change in seawater over a short period of time, so it is useful for estimating the source of leakage. Here, we focused on the ratio of H-3 and Cs-137 and analyzed the data.

After 2016, the concentration of Cs-137 near the power plant has hardly decreased.

The concentration of Cs-137 and H-3 is high in the port area and low outside the port area. However, the H-3/Cs-137 ratio is small in the port area and large outside the port area. This suggests that the concentration of Cs-137 and H-3 in the port area does not necessarily affect the concentration outside the port area, and there is a possibility that there is another source.

The H-3/Cs-137 ratio fluctuates greatly over time. This may be due to the presence of multiple sources with different H-3/Cs-137 ratios, or it may be a sampling issue due to large fluctuations in concentration over time and space.

We have now started analyzing the H-3/Cs-137 ratio in nearby river water and groundwater and will discuss the relationship between these at the time of the presentation.

How to cite: Godse, N. S., Tsumune, D., Kato, H., and Onda, Y.: Understanding H-3/Cs-137 Behavior to Track Dispersion Sources in Fukushima’s Marine Environment, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-14589, https://doi.org/10.5194/egusphere-egu25-14589, 2025.

As part of the decommissioning work at the Fukushima Daiichi Nuclear Power Plant, the discharge of ALPS-treated water began in August 2023. ALPS-treated water contains not only H-3, but also other nuclides, albeit at extremely low concentrations. ALPS-treated water is treated so that the total of the concentration ratios of the 30 target nuclides is less than 1, and then diluted so that the H-3 concentration is less than 1500 Bq/L before being discharged. Prior to discharge, a radiation impact assessment was carried out by TEPCO. This assessment used a marine dispersion model that had been verified using observation results for Cs-137 that had leaked out as a result of the Fukushima Daiichi Nuclear Power Plant accident.

The ocean dispersion model was a 1 km x 1 km ROMS model with a variable mesh that was refined to 200 m x 200 m in the vicinity. It was driven by meteorological reanalysis data from the Japan Meteorological Agency, and data assimilation was performed using JCOPE2M ocean reanalysis data to reproduce the Kuroshio and mesoscale eddies.

In this assessment, a H-3 concentration of 0.1 Bq/L was used as the background concentration from atmospheric nuclear testing. However, a concentration of 1 Bq/L of H-3 was observed even before the release, and this is thought to be due to the influence of the supply from the power plant site or surrounding areas, or from rivers. After taking into account the influence of these background concentrations, the results of the model simulations were verified against the results of the monitoring of H-3 concentrations. Because the rate of release of the ALPS processed water was small, the range of influence of the monitoring of H-3 concentrations was limited, but the verification results were consistent. It is necessary to determine the average distribution by continuing to monitor in the future.

How to cite: Tsumune, D., Tsubono, T., Misumi, K., Okamura, T., Abe, H., Kato, H., and Onda, Y.: Simulation of the impact assessment of the discharge of ALPS treated water into the ocean, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-14834, https://doi.org/10.5194/egusphere-egu25-14834, 2025.

The 2011 Fukushima Daiichi Nuclear Power Plant (FDNPP) accident released significant amounts of radioactive Cesium-137 (¹³⁷Cs). Both dissolved and suspended forms of ¹³⁷Cs contribute to river runoff. Although the Ministry of the Environment has monitored ¹³⁷Cs in Fukushima river-bottom sediments since 2011, identifying long-term trends is challenging. Existing adsorption/desorption models focus on ideal lab conditions and lack real-world field data. This study uses long-term monitoring data to examine changes in ¹³⁷Cs concentration in river sediments. Sediment samples were collected every four months, while river water and suspended sediment were sampled quarterly. The model simulates adsorption/desorption reactions between river water and sediments, incorporating fast and slow reaction sites. By adjusting reaction rates, measured and modeled ¹³⁷Cs concentrations were optimized.　Results show ¹³⁷Cs concentrations in particle-size-corrected sediments aligned with suspended forms within a year. An increase in ¹³⁷Cs concentration at 45 sites was observed, a phenomenon not seen in dissolved or suspended forms. The distribution coefficient (Kd) of river bottom sediments fluctuated significantly in the first three years. Slow adsorption dominated ¹³⁷Cs accumulation within six months, while its effect on suspended concentrations was negligible. These findings highlight that slow adsorption/desorption processes are critical for the long-term behavior of 137Cs in river-bottom sediments.

How to cite: Onda, Y., Wada, N., Igarashi, Y., and Smith, J.: Field-Based Modeling of Cesium-137 Adsorption/Desorption Reactions in Fukushima River bottom Sediment, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-14888, https://doi.org/10.5194/egusphere-egu25-14888, 2025.

Large-scale nuclear accidents may contaminate large areas of land with long-lived radionuclides such as ¹³⁷Cs. Rivers are important pathways for transporting these radionuclides from upstream to downstream. The ¹³⁷Cs concentration released during the Chornobyl nuclear accident has been shown to decrease over time. Previous studies also have shown that the concentration of ¹³⁷Cs in rivers is influenced by competing ions, hydrological processes, the amount of adsorption sites on suspended particles, and the land cover of the catchment. However, comparative studies between rivers are limited, and the factors that determine the variation in ¹³⁷Cs concentrations in rivers remain unclear. In this study, we collected time-series measurements of ¹³⁷Cs from nine major rivers across Europe, each from different regions and countries affected by varying environmental conditions. We also characterized the properties of each river’s catchment, including land cover, water chemistry, and hydrological characteristics. By linking the ¹³⁷Cs concentration data with these catchment attributes, we aimed to identify the most significant factors influencing ¹³⁷Cs transport. Our findings reveal a quantitative relationship between the transport of ¹³⁷Cs and the catchment characteristics, highlighting key factors that control radionuclide behavior in rivers. These results can be used for long-term predictions of radionuclide transport in rivers, which is crucial for risk assessment and management in regions affected by nuclear accidents.

How to cite: Igarashi, Y., Onda, Y., and Smith, J.: Long-term trends in 137Cs concentrations in rivers across Europe originating from Chornobyl, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-14889, https://doi.org/10.5194/egusphere-egu25-14889, 2025.

The Fukushima Daiichi Nuclear Power Plant accident, triggered by the Great East Japan Earthquake on March 11, 2011, caused large-scale radioactive contamination across terrestrial areas. Among the released radionuclides, Cesium-137 remains a significant source of radiation due to its long half-life, resulting in persistently high ambient dose rates in contaminated forests. To reduce these dose rates, forest thinning is being implemented as a radiation countermeasure. However, its effectiveness and the specific mechanisms through which thinning influences dose rates remain unclear. This study focuses on the relationship between forest thinning, throughfall, soil moisture, and ambient dose rates. Previous research has shown that rainfall temporarily reduces dose rates by increasing soil moisture, which attenuates gamma radiation from Cesium-137 in the soil. Building on these findings, we investigated the impact of thinning on rainfall reaching the forest floor and its subsequent effect on dose rates. Field studies were conducted at two sites, Iitoi and Fuyuzumi, located approximately 40 km northwest of the FDNPP. Thinning was implemented from October to December 2022, and monitoring devices were installed in April 2024. Results show that thinning increases throughfall and soil moisture, reducing dose rates over time. Soil moisture in thinned plots rose from 33.1% to 35.0%, while control plots decreased from 28.6% to 25.5%. Correspondingly, ambient dose rates dropped from 0.9 μSv/h to 0.75 μSv/h in thinned plots, compared to 0.85 μSv/h in control plots. Based on these observations, we developed a long-term predictive model to estimate ambient dose rates from rainfall and soil moisture data, providing a generalized framework for assessing the long-term impact of forest management on radiation levels in contaminated areas. 

How to cite: Uehara, Y., Onda, Y., Takahashi, J., Nakanishi, M., Zhang, Y., and Takamura, S.: Elucidation and modeling of the effects of forest management on air dose rates, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-14919, https://doi.org/10.5194/egusphere-egu25-14919, 2025.

Anthropogenic radionuclides were released into the world's oceans as a result of large-scale weapons testing in the late 1950s and early 1960s. These anthropogenic radionuclides have long lifetimes and are still present in the oceans today. The behavior of long-lived radionuclides is useful for seawater circulation in the global ocean.

In this study, we focus on the spatiotemporal variations in the ¹³⁷Cs and ⁹⁰Sr activity concentrations in the surface seawater and vertical distributions. Most of data were included into the database, Historical Artificial radioactivity database in Marine environment, Global integrated version 2021 (Inomata and Aoyama, 2023). The ratio of ¹³⁷Cs to ⁹⁰Sr from large-scale nuclear tests was reported by Harley et al. (1965) to be 1.45. These ratios were in agreement with the average value of the ratio found in open ocean seawater (1.43±0.70) (Bowen et al., 1974). However, the ratios analysed in the present study did not always consistent with the reported values. This may be due to the different behaviour of ⁹⁰Sr and ¹³⁷Cs, e.g. ¹³⁷Cs is mainly present in the dissolved form, whereas ⁹⁰Sr is incorporated into particles in seawater. Furthermore, the ⁹⁰Sr/¹³⁷Cs ratio may be closely related to seawater circulation. However, as data on ⁹⁰Sr are very scarce compared to ¹³⁷Cs, detailed analysis of long-term variations in the ¹³⁷Cs/⁹⁰Sr ratio is limited to the North Pacific Ocean and its marginal sea.

The results showed that the ¹³⁷Cs/⁹⁰Sr ratio varied in the northern North Pacific and adjacent waters (northern North Pacific Ocean (NNPO), western North Pacific Ocean (WNPO), Sea of Japan (SOJ), Sea of Okhotsk (OKH) and East China Sea (ECS)). The apparent half-life (Tap) of ⁹⁰Sr in ECS were longer than those in other area. Considering that a longer Tap indicates a larger inflow into the area and a shorter Tap indicates a lower inflow, it can be assumed that the OKH has sources of ¹³⁷Cs and ⁹⁰Sr. At some SOJ stations, the ratio of ⁹⁰Sr/¹³⁷Cs was large at depths greater than 2000 m. Furthermore, inventories of ¹³⁷Cs and ⁹⁰Sr have increased since 1990 at the SOJ stations. The increase in inventories may be attributed to the dumping of radioactive material.

How to cite: Inomata, Y., Tsumune, D., and Hirose, K.: Long term variations of 137Cs and 90Sr distribution in the Sea of Japan , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-16740, https://doi.org/10.5194/egusphere-egu25-16740, 2025.

Understanding the transport of ¹³⁷Cs released during the Fukushima accident remains challenging, as existing source terms fail to adequately capture the critical emissions leading to the high-deposition zone. For the problem, this study presents an objective inverse reconstruction method that uses total deposition and atmospheric concentration data. The deposition data is used to extract the a-priori emissions by novel identifying the critical temporal formation process of these depositions in high-deposition areas, with the help of the WRF-Chem model, and deriving the corresponding emissions. This deposition-based prior was then fused with the concentration data within an inversion framework, compensating the spatiotemporal information of incomplete data and ensuring the continuity feature of the emissions.

The reconstructed source term reveals two prominent emission peaks on March 15, 2011, occurring between 10:00-11:00 and 14:00-15:00. These peaks align with in-situ pressure measurements and accident analysis, suggesting that they were caused by pressure drops in the primary containment vessels of Units 3 and 2, respectively. This finding provides an explanation for the observation of spherical ¹³⁷Cs particles, likely formed through the condensation of vaporized or liquefied substances. The reconstructed source term also independently validates the widely adopted reverse estimation results by JAEA.

Simulations based on the reconstructed source term show significantly better agreement with various observational data than simulations using other source terms. The two-peak emission pattern accounts for the high-deposition areas, supporting the accuracy of the reconstruction. Furthermore, the proposed method outperforms traditional direct fusion approaches that combine deposition and atmospheric concentration data, which often fail to provide satisfactory results due to insufficient temporal information in deposition observations.

This new method offers a powerful tool for multi-observation fusion, providing a more accurate extraction of temporal information from total depositions. It represents a significant advancement in source term reconstruction, especially for complex nuclear accidents. The approach has broader implications for understanding the transport of short-lived radionuclides, with potential applications in iodine emission reconstruction, thyroid dose evaluation, and improving future environmental assessments in nuclear accident scenarios.

How to cite: Fang, S., Dong, X., Xu, Y., and Hu, H.: Uncovering the Key 137Cs Emission Sources Contributing to High-Deposition Zones After the Fukushima Accident, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-20921, https://doi.org/10.5194/egusphere-egu25-20921, 2025.

This study examines the dynamics of limnological parameters of a South American lake located in southern Chile with the objective of predicting chlorophyll-a levels, which are a key indicator of algal biomass and water quality, by integrating combined remote sensing and machine learning techniques. Employing four advanced machine learning models, the research focuses on the estimation of chlorophyll-a concentrations at three sampling stations within Lake Ranco. The data span from 1987 to 2020 and are used in three different cases: using only in situ data (Case 1), using in situ and meteorological data (Case 2), using in situ, and meteorological and satellite data from Landsat and Sentinel missions (Case 3). In all cases, each machine learning model shows robust performance, with promising results in predicting chlorophyll-a concentrations. Among these models, LSTM stands out as the most effective, with the best metrics in the estimation, the best performance was Case 1, with R2 = 0.89, an RSME of 0.32 μg/L, an MAE 1.25 μg/L and an MSE 0.25 (μg/L)2, consistently outperforming the others according to the static metrics used for validation. This finding underscores the effectiveness of LSTM in capturing the complex temporal relationships inherent in the dataset. However, increasing the dataset in Case 3 shows a better performance of TCNs (R2 = 0.96; MSE = 0.33 (μg/L)2; RMSE = 0.13 μg/L; and MAE = 0.06 μg/L). The successful application of machine learning algorithms emphasizes their potential to elucidate the dynamics of algal biomass in Lake Ranco, located in the southern region of Chile. These results not only contribute to a deeper understanding of the lake ecosystem but also highlight the utility of advanced computational techniques in environmental research and management.

How to cite: Rodríguez-López, L., Bravo Alvarez, L., Duran Llacer, I., Ruíz-Guirola, D., Montejo-Sánchez, S., Martínez-Retureta, R., Bourel, L., Frappart, F., and Urrutia, R.: Leveraging Machine Learning and Remote Sensing for Water Quality Analysis in Lake Ranco, Southern Chile, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-240, https://doi.org/10.5194/egusphere-egu25-240, 2025.

How to cite: D'Anastasio, E., Hanson, J. B., Sherburn, S., Groom, J., and Rattenbury, M.: Integrated and Open geohazard monitoring data in Aotearoa New Zealand: developing an interoperable data service for GeoNet’s time series datasets.  , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-2206, https://doi.org/10.5194/egusphere-egu25-2206, 2025.

Land take, a significant driver of land degradation, poses challenges for sustainable land management, particularly in regions under high anthropogenic pressure. Addressing these challenges necessitates robust, data-driven approaches to monitor, quantify, and mitigate land take. This contribution explores the integration of datacube technology within the LandSupport Regions platform, leveraging advances from the European LandSupport project and its extension under the Italian GeoSciences-IR project.

Raster datacubes, structured as multidimensional arrays, enable efficient management and analysis of large-scale spatio-temporal datasets, overcoming traditional file-based limitations. The LandSupport Regions platform utilizes a datacube-based Spatial Decision Support System (S-DSS) to enhance the monitoring of land consumption, land cover, and land use at multiple scales—from municipal to national levels. The system integrates heterogeneous datasets, including satellite imagery (e.g., Copernicus), regional land use maps, and environmental indicators (such as high resolution and multi-temporal imperviousness maps), within a common infrastructure, adhering to the FAIR principles.

A key focus is on land take quantification, supported by high-resolution datacubes capable of tracking land-use changes over time. The platform offers decision-makers a suite of tools for generating actionable indicators, assessing compliance with land-use policies, and proposing mitigation strategies aligned with zero net land take objectives. Moreover, the system’s interoperability and open-access characteristics allow integration of user-defined data and models, fostering innovation and scalability.

The platform’s capabilities are demonstrated through use cases in Italy, where local administrations leverage datacube analytics to refine urban and regional planning. These use cases underscore the role of datacubes in delivering accurate, timely insights for sustainable land management. By aligning regional initiatives with European Green Deal objectives, the LandSupport Regions platform – produced under the GeoSciences-IR project – exemplifies the potential of datacube-enabled S-DSSs to advance environmental governance.

How to cite: Langella, G., Manna, P., and Mileti, F. A.:  Datacubes as enablers for land take quantification in LandSupport Regions, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-2826, https://doi.org/10.5194/egusphere-egu25-2826, 2025.

Over the past three years, we have successfully launched an open data service for Mindat, one of the largest databases focused on mineral species and their global distributions. Our achievements include: 1) a comprehensive review of existing data records, covering the list of data subjects, their characteristics, and inherent biases, 2) the establishment of an open data API (application programming interface) alongside Python and R packages to integrate the API into workflow platforms, and 3) fostering community collaboration on data standards and best practices for open data, such as mineral nomenclature, rock classification, and technical frameworks for applying the FAIR (findable, accessible, interoperable, and reusable) principles. Mindat is both crowd-sourced and expert-curated, and for the past decades it has been proven to be an effective approach to engage both data contributors and users. Mindat has been popular amongst geoscience professionals and the public alike. Through our open data initiatives and community engagement, we have also gathered valuable insights to guide future developments of Mindat open data. In this presentation, we will highlight the current open data capabilities, provide an overview of the review of Mindat's data records, and share our vision for leveraging advanced artificial intelligence technologies to expand and enhance Mindat in the future.

How to cite: Ma, X., Zhang, J., and Ralph, J.: Mindat: A crowd-sourced and expert-curated open data ecosystem for mineralogy, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-3362, https://doi.org/10.5194/egusphere-egu25-3362, 2025.

Achieving fast access to analysis-ready data from a large number of multidisciplinary data resources is key for contributing to many of the nowadays societal and scientific challenges via Digital Twins of the Oceans or virtual research environments. However, achieving this kind of performance is a major challenge as original data is often organised in millions of (observation) files which makes it hard to achieve fast responses. Next to this, data from different domains are stored in a large variety of data infrastructures, each with their own data-access mechanisms, which causes researchers to spend much time on trying to access relevant data. In a perfect world, users should be able to retrieve analysis-ready data in a uniform way from different data infrastructures following their selection criteria, including for example spatial or temporal boundaries, parameter types, depth ranges and other filters. 

Therefore, as part of several European projects, MARIS has developed a software system called BEACON with a unique indexing and dynamic chunking system that can, on the fly with high performance, extract specific data based on the user’s request from millions of (observational) data files, containing multiple parameters in diverse units. The system returns one single harmonised file as output, regardless of whether the input contains many different data types or dimensions. In January 2025, BEACON 1.0.0 was made publicly available as an open-source software, allowing everyone to set-up their own BEACON node to enhance the access to their data or use existing BEACON nodes from well-known data infrastructures such as Euro-Argo or the World Ocean Database for fast and easy access to harmonized data subsets. More technical details, example applications and general information on BEACON can be found on the website https://beacon.maris.nl/.

The presentation would focus on one of the core features of BEACON called “Relative Optimized Chunking (ROC)”, which is a unique dynamic chunking technology that has been developed specifically to make the data retrieval as fast as possible. This optimized way of chunking reduces the number of chunks BEACON has to search through when a data request has been made. This is done by applying variable sized chunking on multiple levels at the same time such as geo-location, depth and time, which means that data that is relatively close to each other is chunked accordingly. This enhances the speed because it allows BEACON to traverse the millions of datasets using its index with much more precision by not only finding the relevant datasets, but also the exact data blocks containing the relevant data.

The demonstration will involve the use of an existing BEACON node in the field of marine science to access data subsets via its REST API and demonstrate its performance. This will be done in a Jupyter Notebook by querying data via a JSON request to the BEACON system. By going through the Notebook, it will be explained how the BEACON system can be accessed and used by developers including the most recent developments.

How to cite: Kooyman, R., Thijsse, P., Schaap, D., and Krijger, T.: BEACON - Accelerating access to multidisciplinary data with Relative Optimized Chunking technology, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-4337, https://doi.org/10.5194/egusphere-egu25-4337, 2025.

Air traffic today is immense, with large numbers of humans and goods transported routinely, but also in search and rescue missions, military contexts, and hobby piloting, etc. Still, aviation today is safer than it ever has been, thanks to advanced technology and procedures which are continuously revisited and, where necessary, improved. Of central importance for planning and conducting flights is the atmospheric condition the aircraft is flying in, represented by various relevant weather parameters. Hence, these are continuously monitored.

In the Cube4EnvSec project, a federated datacube demonstrator has been established which illustrates ad-hoc assessment of atmospheric conditions relevant for aircraft. Data stem from two sources, DWD and WWLLN. 

From German Weather Service (Deutscher Wetterdienst, DWD), WAWFOR (World Aviation Weather Forecast) data are obtained, a digital aviation meteorological dataset based on the ICON6_Nest model in support of air traffic management based on geostationary weather satellites. Components currently used are wind speed, icing parameters, Cumulonimbus tops, temperature, tropopause, turbulence, lightning, precipitation radar, volcanic ash, and dust. Updates are provided every 6 hours, temporal resolution is 1 hour with a forecast window of currently 78 hours. The update batches are harvested from DWD and merged into the respective datacubes, extending it by 6 hours further into the future. The 6 hours not overwritten by the new forecast are retained and create a growing "long tail" of historical weather data, currently about 17,000 timeslices. Some datacubes are 3D x/y/t, most however are 4D x/y/h/t with a spatial resolution of 0.0625° x 0.0625° (approximately 6.5km x 6.5km), altitude between ground and 18,000 feet (FL180).

Lightning data are obtained from the World Wide Lightning Location Network (WWLLN) by the Colin Price research group at Tel Aviv University and aggregated into a 3D x/y/t timeseries of lightning strikes observed. Spatial resolution is 0.1°, temporal resolution is 1 hour.

Altogether, the datacubes have a footprint of currently about 20 TB. APIs offered by the Aviation Safety service include the adopted standards WMS, WMTS, WCS, and WCPS, and additionally the OGC drafts OAPI-Coverages and GeoDataCube. Any client conforming to these APIs can be utilized; in the demonstration the rasdaman dashboard will be used which is configurable for manifold datacube interaction techniques (see Figure at bottom).

The demonstration presented includes the following steps:

• general overview of the datacubes offered by the service;
• visualization of the combined forecast/history weather datacubes;
• information relevant for pilot flight planning: weather hazards overview; severe weather conditions along historic routes;
• same for ad-hoc chosen flight paths, with 4D corridor cutout;
• various analytical queries related to flight weather conditions.

Most parts of this demo are publicly accessible under https://cube4envsec.org/aviation-dashboard . Any standard Web browser can access it without any plugin etc. to be installed.

Acknowledgement
Cube4EnvSec has received funding by the NATO Science for Peace and Security (SPS) program.

Fig.: Aviation Safety datacube dashboard

How to cite: Baumann, P., Price, C., Merticariu, V., Pham Huu, B., and Misev, D.: Aviation Safety Datacubes, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-4843, https://doi.org/10.5194/egusphere-egu25-4843, 2025.

Datacubes are an accepted cornerstone for Analysis-Ready Data (ARD). One analysis technique of skyrocketing importance today is AI, and this begs the question: how to generalize evaluation of pre-trained models on datacubes?

From a theoretial viewpoint, the connection is immediate: datacubes mathematically resemble tensors, and EO models evaluate tensors. In practice, though, the situation is less straightforward as our experiments with different models have shown. A main issue is the variety and the lack of standardized interfaces of ML models: different input data are processed, data need model-specific preprocessing, and several more. In our research towards offering ML-on-datacubes as a commodity in a federated datacube infrastructure we have collected challenges and methods for presentation.

In our demo, we present AI-Cubes as an enabling concept uniting AI and datacubes. The demos will approach the theme from two sides:

- AI support for datacube query writing: We have trained a chatbot to explain and assist with datacube queries in the OGC/ISO/INSPIRE WCPS standard. This can act as a productiity-enhancing tool for both expert and non-expert users. We demonstrate live how specific questions get answered, such as phrasing NDVI on Sentinel-2 data.

- AI model evaluation on datacubes: particularly attractive is that datacubes allow simple navigation to any area, any time, and even from federated services. This we demonstrate live.

We also highlight challenges coming with this simple data access: models do not convey the same performance anywhere, anytime. This has led to new research on "model fencing", ie: attempting to restrict model application to situations where they exhibit sufficient accuracy. We present first ideas of this research.

Altogether, we cast light on the combination of datacubes and AI from a service and infrastructure perspective. 

How to cite: Mishev, D. and Baumann, P.: AI and Datacubes - a Happy Marriage?, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-6020, https://doi.org/10.5194/egusphere-egu25-6020, 2025.

At the Helmholtz Association, we strive to establish a well-structured, harmonized data space that seamlessly connects information across distributed data infrastructures. Achieving this goal requires the standardization of dataset descriptions through consistent metadata practices, such as leveraging persistent identifier (PID) metadata, to ensure interoperability and machine actionability.

While developing concepts to harmonize PID metadata is a crucial first step in creating a unified data space, it is not sufficient on its own. The practical application of PIDs to facilitate the compilation of rich, relevant metadata for datasets necessitates knowledge, training, support, and cooperation among diverse stakeholder groups, each responsible for different aspects of the information lifecycle.

For example, ORCID is a PID system designed to identify individuals contributing to research. Traditionally, this has primarily applied to scientists publishing journal articles. However, in the context of research data, other stakeholders also play vital roles. These include technicians operating instrumentation, data management personnel curating research data and repositories, and administrative staff maintaining institutional data relevant to research. Currently, these stakeholders are often unaware of their potential roles in data management, and the information they collect is typically not harmonized. To address this, workflows must be implemented to manage, structure, and connect the information they produce to research data where appropriate. In the case of ORCID, these workflows should begin at the earliest stages of the research process, such as during employee onboarding.

PIDINST, a PID system introduced by an RDA working group, provides a simple metadata schema to collect essential information about instruments and registers them with unique IDs. These IDs are invaluable for identifying measurements conducted with the same or similar devices. Therefore, we strongly recommend the adoption of PIDInst within the Helmholtz Association. For PIDINST, successful implementation would involve integrating the workflow into existing processes, starting with the acquisition of an instrument or sensor at the research center. Relevant information would then be passed to technicians responsible for maintaining up-to-date records. For researchers, PIDINST provides reliable identification of devices used in scientific processes.

In this presentation, we highlight critical positions within the centers where minor adjustments to established workflows can significantly support the registration of specific PIDs and the engagement of stakeholder groups. We also explore strategies for implementing these changes across the Helmholtz Association. Furthermore, we assign clear responsibilities for metadata maintenance to appropriate stakeholders. The conclusions drawn from this process aim to redefine roles and responsibilities within our organization, fostering a more integrated and effective approach to data management.

How to cite: Soeding, E., Kottmeier, D., Poersch, A., Malinovschii, S., and Lorenz, S.: Establishing Institutional Workflows to Engage Stakeholder Groups in PID Metadata Maintenance, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-7038, https://doi.org/10.5194/egusphere-egu25-7038, 2025.

The World Data System (WDS), a member of the International Science Council, serves a membership of over 150 trusted data repositories and related organizations. It builds on the data sharing legacy of World Data Centers that were initiated seven decades ago. Governed by a Scientific Committee, the WDS consists of an International Program Office (WDS-IPO) based in Oak Ridge, Tennessee, USA, and an International Technology Office (WDS-ITO) based in Victoria, BC, Canada. The WDS mission is to enhance the capabilities, impact, and sustainability of our member data repositories and data services. In this presentation, we outline the 2025 to 2027 Action Plan objectives, highlighting activities and collaborations that are underway or planned to progress open science, integrated data infrastructures and FAIR/CARE/TRUST Principles. 

How to cite: Jenkyns, R.: Advancing Open Science through Trusted Data Repository Intersections at the World Data System, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-7551, https://doi.org/10.5194/egusphere-egu25-7551, 2025.

Developing climate models often requires the ability to access and share extremely large datasets (spanning tens to hundreds of terabytes) that are discoverable and optimised for high-performance computing (HPC) applications. This is a major challenge, as researchers frequently lack the storage resources and specialised support needed to ensure efficient data management and sharing practices across the full data life-cycle. The challenges of sharing data are evident even when dealing with curated datasets that are prepared for broad access, citation, and reuse. However, these challenges are amplified during the rapid and iterative stages of model development and prototyping. At this point, multiple versions of datasets must be shared and evaluated by a wide range of experts before the data is finalised and curated for public use. This iterative process requires robust infrastructure and coordination to avoid bottlenecks that can hinder progress.

To help overcome these barriers, Australia’s Climate Simulator (ACCESS-NRI) provides a dedicated merit allocation scheme for compute and storage resources. This includes 3PB of storage for datasets that are for use by community members to undertake scientific research, support model development and/or will be shared for reuse. Experience has shown, that if the usage of these storage resources is not managed then the data can quickly go from being an asset to a liability.  Therefore, to maximise the value of both the data and investment in storage, ACCESS-NRI has developed an approach for sharing datasets that is designed to support science and innovation while enhancing the current practices for making data more accessible and usable in accordance with the FAIR and CARE principles.

We will present the motivating use cases and show how this approach supports the model development cycle while making data and the science it underpins more transparent, open and accessible. This approach encourages data generators to transition their datasets from unmanaged, undocumented spaces into managed environments where curation and oversight are aligned with the data’s intended purpose and use. It acknowledges that supporting FAIR principles does not always require full curation to the standards of a long-term publication. Instead, it focuses on reducing barriers to data sharing by promoting active data management practices. These practices enhance discoverability, trust, and reliability, ensuring that shared data is fit for purpose without imposing unnecessary burdens.

ACCESS-NRI is a national research infrastructure (NRI) established to support the Australian Community Climate and Earth System Simulator, or ACCESS. The ACCESS suite of software and data outputs are essential tools used to simulate past and future climate, weather and Earth systems and to support research and decision making within Australia.

ACCESS-NRI's mission is to build an open collaborative infrastructure that will accelerate research in Earth system, climate and weather modelling as well as enable new research not currently possible. The facility brings together skills in software development, high-performance computing, data management and analysis to enhance the ACCESS modelling framework, making it easier to use and lowering the barrier for scientific innovation.

How to cite: Richards, C., Druken, K., Beucher, R., and Chun, F.: Breaking down data sharing barriers and uplifting FAIR for climate data at scale, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-7959, https://doi.org/10.5194/egusphere-egu25-7959, 2025.

Lightning is a hazard for many sectors and industries, including the power utility sector, wind turbines, forest management, and civil aviation.  Commercial aircraft are struck by lightning approximately once every year, but most airlines try to avoid thunderstorms if possible by rerouting around these turbulent and electrified storms.  However, such diversions can delay flights, add costs to fuel demands, while increasing greenhouse gas emissions for the aircraft company.  In this study using data cubes we have combined lightning data from the World Wide Lightning Location Network (WWLLN) together with civil aviation flight data from FlightRadar24 to better understand the risks of lightning to civil aviation.  Combining historic lightning and aviation data we can address questions about risks to aircraft from thunderstorms, the frequency of close encounters with thunderstorms, and the frequency of rerouted flights due to thunderstorm activities.  The emerging concept of Analysis-Ready Data (ARD) attempts to find concepts and methods towards services operating on homogenized data. For spatio-temporal data, datacubes are an accepted cornerstone for ARD providing Big Geo Data easier for users and applications, ready for analysis, visualization, fusion, etc. As part of the Cube4EnvSec NATO Science for Peace and Security (SPS) project we will present live demos of our data cube tools and services related to lightning risks for civil aviation over Europe.  Derived analytics from the datacube will also be presented.

How to cite: Price, C., Shay, A., and Baumann, P.: Using Data Cubes to Investigate Links Between Lightning and Civil Aviation, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9104, https://doi.org/10.5194/egusphere-egu25-9104, 2025.

The DynAWI Extreme Weather Toolbox represents an innovative approach to addressing climate-related challenges in agriculture. This publicly accessible web application offers three primary functions: a historical agricultural weather indicator atlas, a dynamic configurator for calculating user-specified weather indexes, and a forecast model for predicting reduced yields or complete crop failure due to weather extremes. The web application can perform real-time analyses based on multi-dimensional spatio-temporal data.

The technical implementation is based on a client-server architecture, utilizing a scalable geodata infrastructure and an array database management system rasdaman, enabling efficient processing of multidimensional geodata. The system allows real-time analysis of extreme weather events, such as droughts, heatwaves, and heavy rainfall, dating back to 1995. The toolbox aims to provide stakeholders—from farmers to policymakers—with a comprehensive platform for weather-related risk assessment and decision support in agriculture.

In a live demonstration, we will showcase the platform's key features, emphasizing its interactive capabilities and extensive parameter customization options.

How to cite: de Kock, A., Waldau, T., Batista, P., Baumann, P., Behrens, T., Fiener, P., Foeller, J., Moeller, M., Noehles, I., Schmidt, K., and Golla, B.: DynAWI Extreme Weather Toolbox: an online platform for agricultural risk assessment and decision support, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10578, https://doi.org/10.5194/egusphere-egu25-10578, 2025.

The ‘Exploitation Platform’ concept derives from the need to access and process an ever-growing volume of data. Many web-based platforms have emerged - offering access to a wealth of satellite Earth Observation (EO) data, increasingly collocated with cloud computing resources and applications for exploiting the data. Rather than downloads, the exploitation platform offers a cloud environment with EO data access and associated compute and tools facilitating the analysis and processing of large data volumes. Users benefit from the scalability & performance of the cloud infrastructure, the added-value services offered by the platform – and avoid the need to maintain their own hardware. Data hosted in the cloud infrastructure reaches a wider audience and Infrastructure Providers gain an increased cloud user base.

Users are beginning to appreciate the advantages of exploitation platforms. However, the market now offers a plethora of platforms with various added value services and data access capabilities. This ever-increasing offer is intimidating and confusing for most users, often facing challenges such as inconsistent interfaces, proprietary software and limited interoperability. To fully exploit the potential of these complementary platform resources, interoperation amongst the platforms is needed, such that users of one platform may consume the services of another directly platform-to-platform.

EOEPCA (EO Exploitation Platform Common Architecture) is a European Space Agency (ESA) funded project with the goal to define and agree a re-usable exploitation platform architecture using standard interfaces to encourage interoperation and federation between operational exploitation platforms - facilitating easier access and more efficient exploitation of the rapidly growing body of EO and other data. Interoperability through open standards is a key guiding force for the Common Architecture. EOEPCA adheres to standards from organisations such as Open Geospatial Consortium (OGC) and follows best practices in data management, including implementation of OGC Web Services and emerging OGC API specifications for features, coverages and processes. Platform developers are more likely to invest their efforts in standard implementations that have wide usage; off-the-shelf clients and software are more likely to be found for standards-based solutions.

The EOEPCA system architecture is designed to meet defined use cases for various user levels(expert application developers to data analysts and end users). The architecture is defined as a set of Building Blocks (BBs), exposing well-defined open-standard interfaces. These include Identity and Access Management, Resource Discovery, Data Access, Processing Workflows, Data Cube Access, Machine Learning Operations, and more. Each of these BBs are containerized for Kubernetes deployment, providing an infrastructure-agnostic deployment target.

The exploitation platform is conceived as a ‘virtual work environment’,  withusers accessing data, developing algorithms, conducting analysis and sharing value-adding outcomes. The EOEPCA architecture facilitates this through a Workspace BB, providing collaboration environments for groups of users, including dedicated storage and services for analysis, processing and publishing of added-value data and applications. This is supported by an Application Hub BB, providing interactive web-tooling for analysis, algorithm development, data exploitation and providing a web dashboard capability, whereadded-value outcomes are showcased.

Our presentation will highlight the generalised architecture, standards, best practice and open source software components available.

How to cite: Conway, R., Hinton, J., Taposeea, C., Iacopino, C., Pinto, S., and Hunter, S.: EOEPCA+: a method for an open-sourced EO Exploitation Platform Common Architecture, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10583, https://doi.org/10.5194/egusphere-egu25-10583, 2025.

In recent years, the concept of data spaces has gained prominence, particularly in industry, as a framework for organizing and sharing data across business ecosystems and institutional and disciplinary boundaries. While the term itself is not yet widely adopted in the scientific community , it can be directly applied to research. Data spaces provide a  structured environment for integrating data sets from diversedisciplines, methods or fieldsand making themaccessible for collaboration and analysis. Climate and climate impact research, which relies on data from different fields such as meteorology, hydrology or socio-economics, is in a unique position to benefit from the application from this approach.

In line with the principles of open science, researchers are increasingly adopting frameworks that promote transparency, accessibility and reproducibility. FAIR Digital Objects (FDOs) offer effective means of achieving these goals while also enabling interactions between different data spaces. As standardized, interoperable, and machine-readable entities, FDOs link data, metadata and software, simplifying integration and promoting reuse across disciplines.

Using an example from climate research, we demonstrate how climate model data from an institutional data space, observational data from field campaigns, and satellite data (e.g., from the Destination Earth Data Lake) can be combined. By employing STAC (Spatio Temporal Asset Catalog) catalogs defined as FAIR Digital Objects facilitating the European Open Science Cloud (EOSC) Data Type Registry, we address a specific interdisciplinary research question. This approach not only illustrates the practical application of FDOs but also highlights how they can provide a robust framework for tackling larger and more complex scientific challenges by streamlining workflows and enabling collaboration across disciplinary and institutional boundaries.

How to cite: Anders, I., Krüss, B., Kulüke, M., Peters-von Gehlen, K., Thiemann, H., and Widmann, H.: Climate Science Meets Data Spaces: FAIR Digital Objects as a Gateway to Interdisciplinary Science, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10638, https://doi.org/10.5194/egusphere-egu25-10638, 2025.

Research to address global environmental and societal challenges increasingly depends on the availability of large-scale, multidisciplinary datasets, making the need for robust systems that ensure data discoverability, accessibility, and interoperability evermore critical. However, having the data is not enough, one also needs to know about—and understand the connections among—related outputs and entities that support the veracity and reproducibility of the research.

The International Generic Sample Number (IGSN) is a persistent identifier (PID) for material samples arising from any research discipline. Originally developed in the Earth Sciences, the IGSN provides a vital component in solving the abovementioned challenges, enabling seamless integration of sample data across diverse platforms, disciplines, and organizational and geographic boundaries. By uniquely and permanently linking samples to their descriptions (provided as structured metadata), analytical results, and associated publications, IGSNs facilitate transparency, traceability, and reusability of material samples in line with the FAIR and CARE Principles. This is underpinned by the proven interoperability of the IGSN with the scientific communication infrastructure, which also enables citations of samples in the literature to be automatically captured.

This presentation will showcase the collaborative efforts of the IGSN Organization (IGSN e.V.) and DataCite to establish a resilient, cross-disciplinary, globally harmonized PID system for material samples. Use cases will illustrate how IGSNs enhance research workflows, enabling researchers to be more effective and attributed. We will also discuss governance, technical standards, and best practices that promote trust in the IGSN-DataCite partnership and scalability of sample PID adoption, aligning with UNESCO’s Open Science recommendations.

How to cite: Edmunds, R., Klump, J., Elger, K., Wyborn, L., Lehnert, K., Powers, L., and Kohlmann, F.: Advancing Open, FAIR, and Responsible Science through the International Generic Sample Number, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15095, https://doi.org/10.5194/egusphere-egu25-15095, 2025.

The Drill Core Library (DCL) of the Natural Science Institute of Iceland is Iceland’s national repository for drill cores and cuttings. As such, the DCL is responsible for preserving these geological samples and making them available to the scientific community. The library comprises an estimated 100 km of core and a 470 km equivalent of cuttings from over 4,000 boreholes, as well as a growing database of analytical results. The collection spans Iceland’s range of diverse geological environments and houses core from significant research projects including the SUSTAIN drilling project in Surtsey, sponsored in part by the International Continental Scientific Drilling Program, and the Iceland Research Drilling Project. The DCL’s drill cores and cuttings are available for study and sampling for research purposes, and DCL staff are available for consultation and assistance in identifying and collecting suitable samples. The DCL’s on-site facilities are maintained in collaboration with the University of Iceland’s Research Centre in Breiðdalsvík, East Iceland.

An emphasis has been placed on developing digital infrastructure to improve access to the collections for the scientific community. To facilitate sample identification, an online map-based interface and WFS service have been created where the collection can be examined and contextualized with geological data. The database of the DCL has also been partly integrated into the European Plate Observing System (EPOS), a collaborative initiative enabling FAIR (Findable, Accessible, Interoperable, and Reusable) and open access to geoscientific data from across Europe.

The latest advance in digital access is the ongoing population of the DCL database with core photographs. These are linked directly to the WFS and map viewer, forming a “visual library” that enables direct examination of the library collections, thereby facilitating identification of sampling targets by researchers around the world. At present, 16% of the drill core collection has already been photographed, with 50% set as a target for the end of 2025. Further development of the interface will be carried out in consultation with users of the DCL collections, and cores of interest to researchers are prioritized for photography.

How to cite: Guðmundsdóttir, M. H., Birgisson, K., Hannesson, H., Jónasson, K., Meier, A. Th., Óskarsson, B. V., and Sigurðsson, B. D.: The Visual Drill Core Library: A Tool for Improving Access to Samples from the Natural Science Institute of Iceland, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-16668, https://doi.org/10.5194/egusphere-egu25-16668, 2025.

This study introduces a scalable and integrated datacube framework for efficient geospatial data processing and analysis. Leveraging the advanced cloud infrastructure of the National Center for High-Performance Computing (NCHC), the framework combines the openEO API and OGC services to address challenges in managing multidimensional datasets. By ensuring interoperability, security, and high-performance computing, the framework provides a reliable solution for researchers and practitioners to tackle complex geospatial challenges.

Framework Architecture

The framework architecture integrates advanced tools and services, focusing primarily on the openEO API and OGC standard services (e.g., Web Coverage Service and Web Coverage Processing Service). The openEO API provides a unified interface supporting multiple programming languages, allowing users to design and execute customized workflows and enabling batch processing.

openEO integration
The openEO API plays a central role in the framework, performing the following functions:

• Unified Data Access and Processing Interface: openEO offers a standardized access and processing layer for Earth observation data, abstracting underlying complexities and enabling users to uniformly access multidimensional data from various sources, such as satellite imagery and terrain datasets.
• Process Graphs and User-Defined Processes: openEO supports User-Defined Processes and Process Graphs, enabling users to create tailored data processing pipelines based on specific analytical requirements. This is particularly valuable for advanced analyses like temporal change detection or spatial statistics.
• Seamless Integration with OGC Services: openEO works seamlessly with OGC services (e.g., WCS and WCPS) in the framework, enhancing its ability to handle multi-source data. While openEO provides high-level data access and analytical capabilities, OGC services ensure interoperability and standardization at the data layer.

API Proxy Architecture Design

The API proxy is a critical component of the framework, bridging the openEO API and the backend infrastructure to ensure efficient, secure, and stable interactions between users and the system. Its main functions include authentication, authorization management, traffic control, and caching. With the API proxy, openEO can provide a simplified user experience while ensuring optimal utilization of backend data and resources.

Application Scenarios

1. Terrain Analysis
By transforming digital terrain models (DTMs) into multidimensional structures, the framework significantly improves the processing speed and accuracy of large-scale datasets. openEO’s role includes providing a unified interface for data access, enabling users to quickly retrieve and process data for custom slope calculations, visibility analyses, and more. Simultaneously, API proxy security layers ensure strict management of data access and usage.

2. Temporal Analysis Using Landsat Imagery
Temporal analysis of Landsat imagery involves handling large volumes of time-series data. Here, openEO acts as the analytical hub, allowing users to submit analysis requests (e.g., calculating the Normalized Difference Water Index (NDWI)) via the API. The framework then automatically invokes OGC services for data processing and result generation.

Conclusion

The proposed datacube framework successfully integrates openEO API and OGC services, offering a scalable, interoperable, and high-performance solution. As a unified data access and analytical interface, openEO provides flexible and robust tools that significantly simplify complex data processing workflows. By lowering technical barriers and enhancing analytical accessibility, the framework delivers unprecedented convenience for geospatial data analysis, making it a key tool in research and decision-making processes.

How to cite: Hao, C.-Y., Chang, J.-Y., Shih, I.-L., and Change, Y.-C.: Scalable and Interoperable Datacube Framework for Advanced Geospatial Data Analysis, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-16708, https://doi.org/10.5194/egusphere-egu25-16708, 2025.

In environmental sciences, observational data remains indispensable for monitoring and understanding of natural processes, validating earth system models and remote sensing products, and training of data driven methods. However, unified standards and interfaces for ensuring that such data is consistently available, usable, and compliant with FAIR and Open Science principles are still lacking.

The so-called DataHub initiative of the Helmholtz Research Field Earth & Environment, involving seven large environmental research Centers across Germany, addresses this gap by collaboratively developing a cohesive and unified research data space, including consistent data formats, metadata standards, tools, interfaces and services.

Since the beginning of the DataHub, we have been particularly focusing on unifying time-series data from environmental sensor systems, which are operated across all participating Centers. In this context, we have developed a digital ecosystem, that enhances and links existing and established research data infrastructures with well-defined interfaces and metadata standards. This ecosystem now covers the full processing chain from the integration of new sensor systems and their metadata over automatic and manual quality checks and flagging schemes to the visualization via dashboards and data portals or the usage in data analysis frameworks. In particular, our framework consists of multiple independent tools and services like the Sensor Management System (Brinckmann et al., 2024) as dedicated system for managing sensor metadata, the System for Automated Quality Control (SaQC, Schäfer et al. 2024) as common framework for QA/QC, a tailored metadata profile which adapts the SensorThings API (STA) from the Open Geospatial Consortium to common requirements from environmental sciences (Lorenz et al. 2024), the Earth Data Portal (https://earth-data.de) as overarching data portal and visualization suite as well as tools and services that link all these different building blocks.

While the first concepts of this ecosystem were based on temporary tools and interfaces, we have now reached a level of maturity, that allows us to confidently scale our solutions to new communities and user groups. In this presentation, we will hence give a brief overview of our ecosystem as well as the integrated tools and services. The main focus will be on a hands-on demonstration of the full workflow from deploying a new sensor system, the integration into the contributing services, the (meta)data provision via STA as well as the integration in different downstream systems like the Earth Data Portal for data visualization.

By this, we want to promote the potential of a decentralized research data infrastructure, that has been developed and adopted across multiple research Centers and reach out for new communities and user groups for ultimately creating a FAIR and inter-institutional open data space for our environmental sciences.

How to cite: Louisot, B., Koppe, R., Heß, R., Loup, U., Sorg, J., Adolf, M., Faber, C., Lehmann, A., Brickmann, N., Hanisch, M., Schäfer, D., Baldewein, L., Kleeberg, U., Ryan, M., Barthlott, S., Lorenz, C., Obersteiner, F., and van der Schaaf, H.: FAIRification of sensor-based time-series data – a demonstration of the Helmholtz DataHub digital ecosystem , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-18594, https://doi.org/10.5194/egusphere-egu25-18594, 2025.

The main objective of the OceanUCA project is the modernization of the technological infrastructure of the University of Cadiz in relation to atmospheric and hydrodynamic numerical modeling specifically configured to simulate physical processes on the coast of Andalusia (Spain).
The initiative focuses on the improvement of modeling systems (oceanographic and atmospheric) and the modernization of servers, mainly THREDDS and ERDDAP. THREDDS facilitates connectivity between scientific data providers and end users, while ERDDAP simplifies the sharing and visualization of time series data through common formats, graphics and maps. The project aims to optimize access, organization and storage of data, create a complete data bank and standardize protocols.
For the storage of data from numerical models, a file server is acquired that allows the custody of large volumes of information related to simulated physical processes, especially focused on the Andalusian coasts. In the future, this server will also facilitate the storage of data from other sources for further calculation, processing and sampling. This acquisition contributes to centralizing the files, currently distributed across different storage sources, and to improving communication across the THREDDS/ERDDAP servers.
The project includes a web application that presents the models in a user-friendly and interpretable format, especially for the scientific community, through the visualization of images.
The technological infrastructure will allow significant advances by facilitating the download of numerical data and taking advantage of graphical processing and high-performance computing to process large data sets. This approach improves the scalability and resolution of forecasts, making them more accessible to the public. By adopting an open-source framework, the project promotes collaboration and knowledge sharing at national and international scales, empowering both the scientific community and the public to use coastal and atmospheric data for informed decision-making and sustainable resource management.

How to cite: Vasquez Rojas, J., Carbone, J., Izquierdo González, A., Benavente González, J., Gómez Enri, J., Fernández -Montblanc, T., Martins, F., Cabos Narvaez, W., Yagüe, C., Román-Cascón, C., Álvarez, O., Fonteles, C., Marques, B., and Campuzano, F.: OceanUCA: Technological innovation for the management and communication of coastal data in Andalusia through numerical modelling and open source technology., EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-18601, https://doi.org/10.5194/egusphere-egu25-18601, 2025.

The Specialized Information Service for Geosciences (FID GEO) is a German Research Foundation (DFG)-funded initiative that has been serving the geoscience community in Germany for almost a decade. FID GEO provides essential publication services through its partner domain repositories GFZ Data Services (for research data and software) and GEO-LEOe-docs (for text publications). Beyond these repositories, FID GEO actively supports the digital transformation and supports researchers in adopting Open Science practices mainly through workshops, publications, conference contributions and active participation in topic-specific meetings. 

Collaboration is a cornerstone of FID GEO’s work. It engages closely with geoscientific societies, national infrastructures and initiatives such as the German National Research Data Infrastructure (NFDI), while also contributing to policy-making processes such as the planned German Research Data Act. Recognizing the inherently global nature of geosciences, FID GEO further aligns its activities with international developments, striving to synchronize national progress with global standards and best practices for data management and distribution. FID GEO acts as an interface between scientists, libraries, repositories and the world of digital data management and thus support the transformation of the publication culture in the geosciences at national and international level.

For many years, FID GEO has received feedback from researchers expressing a strong desire for a ‘single source’ platform to manage and share their increasingly large datasets, publications, and projects. At the same time, researchers often feel overwhelmed by the complexity and number of existing infrastructures. However, not only does a one-size-fits-all solution appear technically out of reach, it also faces issues in scalability and sustainable maintenance. A viable way forward is the widespread implementation of machine-readable (meta)data standards that also enable the connection between distributed data systems. Additional metadata properties enable persistent digital links between different research outputs and the unique identification of authors and institutions through persistent identifiers. Another significant challenge within the research landscape is the often competing nature of infrastructures, driven by limited funding opportunities and overlapping goals. Through its extensive network and active collaborations, FID GEO addresses these challenges by guiding researchers through this complex landscape and demonstrates practical ways to make their scientific outputs visible, reusable, and aligned with the FAIR and Open Science principles.

This presentation will share best practices, lessons learned, and future directions for fostering a collaborative and open research environment. FID GEO envisions a geoscience community empowered by shared data and cooperative infrastructures, better equipped to address pressing global challenges.

How to cite: Lorenz, M., Elger, K., Achterberg, I., and Semmler, M.: One platform will not solve everything: How FID GEO strengthens Germany’s Open Science Landscape for the geosciences., EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-19278, https://doi.org/10.5194/egusphere-egu25-19278, 2025.

The Earth System Grid Federation (ESGF) is the international partnership responsible for the distribution, cataloging and archiving of both the Coupled Model Intercomparison Project (CMIP) and the Coordinated Regional Climate Downscaling Experiment (CORDEX). In operation since 2009, it was the first decentralised climate data repository of its kind, storing and serving many petabytes of data across tens of global and region data centre partners.

Over the last five years, the system has been fully rearchitected, introducing a cloud-ready deployment architecture and a new system for distributed search, fundamental to ESGF’s federated model for data access. This has involved innovations, translating successful experience with the STAC (Spatio-Temporal Asset Catalogue) specification from the EO world and developing a profile for its use with global climate projections data. Providing a STAC interface to ESGF archives has allowed us to explore alternate access methods for cloud-accessible analysis-ready data ready formats through the use of tools such as Kerchunk, a lightweight non-conversion approach for referencing existing data, which works with open-source python packages like fsspec and Xarray. Use of STAC also provides the potential for greater integration between EO and climate modelling domains essential for the validation of model outputs.

ESGF has traditionally used a distributed model for search services which though powerful has led to challenges around consistency of search content. Over the last twelve months, in preparation for CMIP7, a further fundamental innovation has been made in the architecture to address these issues. The new system adopts a centralised model, with two search nodes, one in the US and one in Europe each hosted on public cloud. These two nodes are synchronised together using a new event-driven architecture. This approach, driven by a shared messaging framework between the nodes, ensures eventual-consistency across the nodes, to reduce or eliminate errors caused by individual node down time and simplify processes such as the replication and retraction of data from the archives distributed at sites across the federation.

The move to a message based, event driven architecture has been integrated with STAC records and services. In ESGF-NG data is shared between nodes as messages in the form of STAC Item records, ensuring a consistent, publicly documented archive distributed across many nodes. The ESGF team have contributed several changes to the STAC project to facilitate this change. Looking forward, we see potential in this new event driven architecture for search systems as a means to integrate across federations - in the European context this could include the ESA Climate Change Initiative open data portal, work with the Copernicus Climate Data Store and DestinE.

How to cite: Evans, R., Poulter, D., Kershaw, P., Foster, I., Ananthakrishnan, R., Hoffman, F., Radhakrishnan, A., Kinderman, S., Ames, S., and Westwood, D.: ESGF Next Generation and preparations for CMIP7, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-19453, https://doi.org/10.5194/egusphere-egu25-19453, 2025.

We describe a simple interface for accessing time series numeric data: the Heliophysics Application Programmer's Interface (HAPI). Although it started in NASA's Heliophysics domain, no Heliophysics idioms are present in the standard, and HAPI can be used to serve any tabular, numeric data that is indexed by time.  HAPI was the result of a community push to standardize similar access methods at multiple data centers, and it is now in use at 12 data centers around the world, with over 12,000 datasets available in a standard way. HAPI offers a more conceptual view of the data, independent of the storage arrangements at a server. It also is not intended to replace an existing server's API, but to sit alongside that API.  The project is mature, with a reference server available, as well as clients in multiple programming languages.  We will present an overview of the API and compliance with FAIR principles. We also will describe some of the visualization and analysis tools being developed now that standardized access is becoming a reality. We invite discussion with other time series data providers in other domains.

How to cite: Vandegriff, J., Weigel, R., Faden, J., and Antunes, A.: A Grass-roots Standard for Time Series Data in any Domain: HAPI, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-20533, https://doi.org/10.5194/egusphere-egu25-20533, 2025.

As founders and former chief editors of Earth System Science Data (ESSD), the authors are concerned about the reproducibility and availability of important scientific sources and findings, and about timely access to scientific data and data-related services. We are discussing (1) incidents with the availability of DOIed datasets and their metadata and (2) a recent outage of an important data infrastructure.

Both observations are considered sufficiently serious that the authors wonder why the underlying facts and realities are not discussed widely in this community.

1) The most cited dataset published through ESSD is the series of yearly reports on the Global Carbon Budget, e.g. the latest, https://doi.org/10.5194/essd-2024-519. These articles are cited in scientific publications by the hundreds of times and routinely inform the United Nations climate change conferences (COPs). The first datasets of the series were held and provided DOIs by the Carbon Dioxide Information Analysis Center (CDIAC), which was hosted by the Oak Ridge National Laboratory. When CDIAC was shut down in 2017, the datasets were transferred to a repository at another US National Lab, loosing most of the metadata in the process, most notably authorship. Thankfully, hosting of post-2017 additions to the dataset series has been taken over by the Integrated Carbon Observing System (ICOS) and DOIs to all elements of the series still resolve (albeit, in a sloppy manner for pre-2018 data). One could argue that the most reliable holder of metainformation about this – not just scientifically – important data are not the repositories but ESSD, operated by a commercial publisher, Copernicus. 

2) When tropical storm Helene hit North Carolina, in September 2024, power and internet connectivity went out from the Asheville headquarter site of NOAA’s NCEI, an aggregator, archive and service provider for environmental data. Although NCEI is hosted at four geographically dispersed sites, NCEI data ingest and services came to a halt for several weeks. It appears that most data from the period during and after Helene have been collected retroactively, and services are fully available again. While NOAA’s real-time weather services, important to deal with the emergency, seem to have been available during Helene, one is tempted to ask if they could become interrupted under similar circumstances.

Both these and some other observations – which will be discussed at EGU2025 - create the uncomfortable impression that the huge efforts of this community wrt. the FAIRness of data and in the creation of a multitude of publicly funded infrastructure elements do not achieve to meet today’s needs, and possibly may not meet them tomorrow. If government labs and agencies of a rich nation cannot achieve this – who can?

(Part of this work has been presented before, at a pre-conference workshop to RDA20, Gothenburg, 2023)

How to cite: Pfeiffenberger, H. and Carlson, D.: Are Publicly Funded Data-Infrastructures Reliable?, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-20550, https://doi.org/10.5194/egusphere-egu25-20550, 2025.

The Planetary Data System (PDS) provides key structural support for Open Science by meeting the tenants of "Free, unrestricted access¹.” Here we will discuss the need to expand our offerings by improving support for the OS tenants of "Ease of use.”

Analysis-ready data (ARD) provides data in formats that, while different than what was provided by the mission team, are orders of magnitude more useful to scientific researchers. 

For NASA Planetary Science Missions, the data is provided to us in stable and long-term formats that are well documented.  However, the data formats for each mission are typically different.  Additionally, many processing steps are not done by the science team for the archived products, such as ortho-rectification, geospatial positioning, or co-alignment with digital terrain models.  Additionally, there is little consensus within Planetary Science for a standard format for almost any data type, for example images that can be in FITS, VICAR, custom IMG formats, or sometimes JPEG.

PDS nodes have begun to host such ARD as either part of the official archive or outside of the archive using the new PDS annexes².  We have several initiatives to support ARD.  These include the Small Bodies Image Browser and digital terrain models in both ISIS and GeoTiff formats. While generated data in these formats initially requires additional effort, once created they continuously provide value to the data user community.

Analysis-ready data can significantly increase "ease of use" in many different ways.  They typically will be preprocessed, saving data users significant effort that they would have spent learning how to process the data themselves. This preprocessing also lowers the technical barriers and eases the use of complex data sets. In addition to the preprocessing, datasets can be provided in standardized, commonly used data formats that are more useable and accessible than many of the current formats. Streamlining the ARD would greatly ease both researchers' and the public’s ability to use data spanning many different missions in ways that is not currently possible. Focusing on providing the most interoperable and usable data to the community also enables more interdisciplinary collaboration and increases reproducibility — all key goals of Open Science.  

Analysis-ready data in the PDS will be essential to create more open and usable data. As the complexity of planetary mission data increases, ARD can allow the PDS to maximize the scientific return of these valuable datasets.

References:
[1] NASA Science Mission Directorate. (2023). Open-Source Science Guidance, Version 2.1.
[2] Mouginis-Mark, P., Williams, D., Bleacher, J., et al. (2023). Analysis Ready Data (ARD) within the Planetary Data Ecosystem: Benefits for the Science Community. 54th Lunar and Planetary Science Conference.

How to cite: Palmer, E. E., Lopez, K., and Drum, M.: Efforts of the Small Bodies Node in Providing “Analysis Ready Data” to Support Open Science, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-21042, https://doi.org/10.5194/egusphere-egu25-21042, 2025.