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.

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 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.

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.

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.

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.

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.

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.

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.

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.

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.

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 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.

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.

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.

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.

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.

Environmental digital twins face significant interdisciplinary challenges in their development and operation, particularly in managing complex environmental data and facilitating effective communication among diverse stakeholders. While these virtual representations of environmental systems offer powerful capabilities for monitoring and decision-making, they often struggle to bridge the communication gap between technical experts, decision-makers, and end-users.

Data storytelling is the practice of narrating messages derived from data to address specific needs and visually communicating these messages to an audience in an ordered manner that is easily understandable. Interestingly, digital twins share a similar objective: both aim to simplify and communicate complex data through intuitive and meaningful narratives.

Building on this shared characteristic, we propose an approach that adapts data storytelling techniques to the creation of digital twins. This abstract focuses on how data storytelling can enhance the creation and communication of digital twin data through visual formats tailored to specific audiences, addressing their unique needs to support monitoring, decision-making, and actionable insights.

This innovative integration of data storytelling and environmental digital twins establishes a comprehensive approach to address three key challenges:

• Documenting and structuring the development process  from data to communication to incorporate stakeholder needs and communication requirements from the outset.
• Facilitating collaboration among interdisciplinary teams through shared narrative frameworks.
• Ensuring environmental insights are effectively translated into actionable knowledge.

We present a methodology that leverages data storytelling techniques to enhance the accessibility and impact of environmental digital twins, ultimately improving their effectiveness in environmental monitoring, decision-making, and stakeholder engagement.

How to cite: EL outa, F. and Dechambenoit, G.: What if Data story telling was the corner stone for environmental digital twins?, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-3702, https://doi.org/10.5194/egusphere-egu25-3702, 2025.