Research has so far focused on optimising the use of satellite observations, supported by both government and commercial initiatives, and numerous datasets from airborne sensors, including aircraft and drones. Recent advancements in Earth observation (EO) and machine learning have further enhanced the monitoring of floods and inland water dynamics, utilising multi-sensor EO data to detect surface water, even in densely vegetated regions. However, despite these advancements, the update frequency and timeliness of most remote sensing data products are still limited for capturing dynamic hydrological processes, which hinders their use in forecasting and data assimilation. This session invites cutting-edge presentations on advancing surface water and flood monitoring and mapping through remotely-sensed data, focusing on:
- Remote sensing for surface water and flood dynamics, flood hazard and risk mapping including commercial satellite missions and airborne sensors
- The use of remotely-sensed data for calibrating or validating hydrological or hydraulic models
- Data assimilation of remotely-sensed data into hydrological and hydraulic models
- Enhancements in river discretization and monitoring through Earth observations
- Surface water and river flow estimation using remote sensing
- Machine learning and deep learning-based water body mapping and flood predictions
- Ideas for developing multi-satellite data products and services to improve the monitoring of surface water dynamics including floods
Early career and underrepresented scientists are particularly encouraged to participate.
Orals: Fri, 8 May, 10:45–18:00 | Room B
Floods are among the most deadly and destructive natural disasters. Improving our understanding of large-scale flood dynamics is crucial to mitigating their dramatic consequences. Unfortunately, systematic observation-based datasets—especially featuring flood depths—have been lacking.
This contribution presents advancements in developing an unprecedented catalogue of satellite-derived flood maps across Europe from 2015 onwards. Results are based on the systematic identification of floods in the entire Sentinel-1 archive at 20 m spatial resolution as provided by the Global Flood Monitoring component of the Copernicus Emergency Management Service. Using a novel algorithm that accounts for terrain topography, flood maps are enhanced and provided with water depth estimates—a critically important information for flood impact assessments.
The resulting dataset represents a significant step towards the creation of a global flood archive. It provides new tools for interpreting flood hazards on large scales, with substantial implications for flood risk reduction, urban development planning, and emergency response.
How to cite: Betterle, A. and Salamon, P.: Ten years of floods across Europe mapped from space with reconstructed water depths , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-7998, https://doi.org/10.5194/egusphere-egu26-7998, 2026.
How to cite: Groth, S., Wieland, M., Geiß, C., and Martinis, S.: Hydrologically-Informed DTM Super-Resolution for Rapid Flood Depth Estimation, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-9758, https://doi.org/10.5194/egusphere-egu26-9758, 2026.
Wetland inundation dynamics are key for understanding flood regulation, ecosystem functioning and greenhouse gas emissions. Synthetic Aperture Radar (SAR) can map water extent independent of cloud cover and can partly penetrate vegetation, particularly at L-band. Many SAR inundation products rely primarily on intensity thresholding and indicators such as specular reflection and double-bounce scattering. However, these approaches can underestimate inundation extent in densely vegetated wetlands where volume scattering can obscure the water signal. Here we demonstrate how L-band interferometric SAR (InSAR) can complement intensity-based inundation mapping under vegetation by exploiting phase differences between repeat SAR acquisitions. Using ALOS PALSAR-1 and PALSAR-2, together providing a nearly two-decade observational archive, we show that L-band InSAR can capture inundation dynamics in tropical floodplain wetlands, such as the Atrato floodplain (Colombia) and Amazon várzea floodplains (e.g., along the Río Pastaza). In the Atrato floodplain, the InSAR-derived flooded vegetation extent shows pronounced seasonal variability, ranging from ~500 to >1500 km² during 2007–2011. Comparison with existing L-band SAR inundation products yields ~70% overall agreement, while InSAR consistently detects broader inundated extents in densely vegetated floodplain areas where intensity-based thresholding underestimates inundation. This complementarity among methodologies is particularly relevant for inundation extent data products from the NASA–ISRO NISAR mission, which are expected to rely largely on SAR backscatter thresholding. Our results highlight the value of integrating InSAR-derived information to strengthen wetland inundation monitoring under vegetated canopies.
How to cite: Hübinger, C., Fluet-Chouinard, E., Escobar, D., and Jaramillo, F.: L-band InSAR to complement SAR inundation mapping under vegetation, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-9354, https://doi.org/10.5194/egusphere-egu26-9354, 2026.
Operational flood intelligence for emergency response and insurance, providing a rapid overview of impacted land, population, and economic damages, requires mapping solutions that remain reliable under adverse observational conditions and across diverse landscapes. Although Sentinel-1 SAR provides consistent global, all-weather and day-and-night coverage, automated flood extraction is challenged by speckle noise, land-cover heterogeneity, and confusion between floodwater and permanent low-backscatter surfaces. These limitations highlight the need for approaches that exploit temporal backscatter changes while maintaining global robustness and computational efficiency.
We present SaferSat, a fully automated Sentinel-1 toolbox for flood-extent mapping, water-depth estimation, and impact assessment. SaferSat is part of SaferPlaces (saferplaces.co), a global Digital Twin platform for flood risk intelligence supporting emergency response and insurance applications. Central to the framework is Pr-RWU-Net (Progressive Residual Wave U-Net), a lightweight deep-learning model with 2.6 million trainable parameters, designed to detect flood-induced backscatter changes using VV-polarized SAR imagery. The model uses a three-channel input; pre-event VV, post-event VV, and their radiometric difference, enhancing inundation sensitivity while mitigating VH instability for global deployment.
SaferSat provides end-to-end processing: automated data retrieval, multi-date flood inference, and Maximum Flood Extent generation. To reduce SAR ambiguities, it generates auxiliary layers: a vegetation mask for SAR "blind spots" and a low-backscatter anomaly mask for permanent dark features. Flood extent layers are integrated with the FLEXTH model and GLO-30 or local high-resolution LiDAR DTMs for water-depth reconstruction. The system also analyzes acquisition patterns to predict short-term revisit opportunities. Impact assessment intersects flood extents with JRC GHS-POP and ESA WorldCover datasets.
The Pr-RWU-Net model was trained on the S1GFloods dataset, containing 5,360 paired pre- and post-event Sentinel-1 GRD images across 42 flood events from 2016–2022. Binary flood masks were generated via semi-automated thresholding and expert quality control. Evaluation on the test split achieved an IoU of 90.0%, F1-score 94.6%, Recall 95.6%, Precision 93.8%, and overall accuracy 96.6%.
Operational applicability was demonstrated on three 2025 flood events: Romania, Pakistan, and France. SaferSat flood extents closely matched SAR manual driven flood references (IoU 89–92%) and CEMS products (IoU 85–88%). Water-depth estimation against a reference hydrodynamic model yielded a MAE of 34–40 cm and correlation R of 0.78–0.82. For a 260 km² flood in Romania, the full processing chain completed in ~3 minutes on a standard CPU, demonstrating suitability for rapid, large-scale deployment.
SaferSat is available globally through SaferPlaces, supporting emergency response and insurance applications. Future developments aim to enhance SaferSat globally via integration of commercial satellite data to reduce revisit time and rapid hydrodynamic modeling to address radar limitations.
How to cite: DaliriSusefi, S., Mazzoli, P., Luzzi, V., Renzi, F., Redaelli, T., Renzi, M., and Bagli, S.: SaferSat: The Saferplaces’s Operational Sentinel-1 Toolbox for Multi-Temporal Flood Extent Mapping, Water-Depth Estimation and Impact Assessment , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-2995, https://doi.org/10.5194/egusphere-egu26-2995, 2026.
Flooding is a major natural hazard across the global tropics. Although flood occurrence is shaped by rainfall characteristics—including duration, frequency, and intensity—accurate prediction remains challenging. A key limitation is the lack of reliable, long-term flood databases that capture events across all spatial scales and durations, hindering a clear understanding of how rainfall variability translates into flood onset. This limitation is particularly critical in the Maritime Continent, where extreme rainfall is common and many small, short-lived, yet severe, floods remain undocumented. To address this limitation, we investigate whether a relatively new approach, global navigation satellite system reflectometry (GNSS-R), can help close this observational gap.
In this work, we assess whether data from the CYGNSS small-satellite constellation can be used to identify small- to regional-scale floods, including short-lived events. Our study focuses on Sumatra, an island within the Maritime Continent that is frequently affected by such hazards. A joint analysis of CYGNSS inundation estimates and two independent flood databases allowed us to evaluate how CYGNSS measurements can be used for flood detection. Three detailed case studies demonstrate that CYGNSS provides an unprecedented ability to monitor day-to-day changes in surface water extent, including floods at the urban scale. Specifically, we show that CYGNSS-derived inundation anomalies can clearly capture evolution of a flooding event, with the largest signature one day after known flood initiation. A systematic analysis of 555 flood events over a 21-month period enabled us to identify characteristic patterns in inundation anomalies that reliably distinguish flood events from non-flooding conditions, through the definition of an inundation-anomaly threshold and a maximum distance between CYGNSS detections and reported flood locations. We established that CYGNSS observations within 15 km not-only significantly differ from base-line conditions, but they allow tracking day-to-day flood dynamics as well.
The proposed methodology is transferable and can be applied to establish flood-inundation thresholds for any region within the global tropics, enabling automated detection of previously unreported flood events or the study of relationships between extreme precipitation and flood evolution. An example of its application is the automatic detection of flooding from CYGNSS data associated with subseasonal variability in tropical circulation: the passage of multiple convectively coupled Kelvin waves embedded within an active Madden–Julian Oscillation in July 2021. These waves propagated eastward across the Maritime Continent, triggering extreme rainfall and widespread flooding in equatorial Indonesia and East Malaysia. The day-to-day evolution of floods could be observed alongside the propagating waves, with the termination of the MJO coinciding with the cessation of the flood events.
Relying on low-cost small satellites, this approach shows strong potential for future scalability with larger constellations, ultimately improving flood monitoring and advancing our understanding of how rainfall patterns shape flood dynamics across global tropics.
How to cite: Bałdysz, Z., Baranowski, D. B., Flatau, P. J., Flatau, M. K., and Chew, C.: Automated Detection of Flood Events from CYGNSS: Observing Flood Evolution Along Propagating Tropical Waves , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-18518, https://doi.org/10.5194/egusphere-egu26-18518, 2026.
The Ganges floodplains are among the most flood-prone regions in India, where recurrent inundations cause significant socio-economic and ecological impacts. Understanding the spatial distribution, frequency, and dynamics of flooding is essential for effective floodplain management and enhancing climate resilience. This study examines the flood frequency and spatial extent across a section of the Ganga River floodplains in Bihar, utilising multi-temporal Sentinel-1 Synthetic Aperture Radar (SAR) data spanning the period from 2016 to 2024. Flooded areas were delineated through an optimal threshold-based classification of VH-polarised backscatter images, with threshold values ranging from -19.5 dB to -22.3 dB. Annual flood extents were mapped, and an inundation frequency composite was generated to identify zones experiencing recurrent flooding. The spatial analysis revealed substantial variability in flood occurrence, with extensive inundation observed in low-lying regions. Several areas were inundated in more than 60% of the study years, indicating chronic flood exposure. The decadal analysis revealed that August and September were the peak months for flooding, with some areas remaining inundated for more than one month, which had an adverse impact on both human settlements and agricultural lands. Validation using optical satellite imagery from Sentinel-2 confirmed a 98% accuracy in the SAR-derived flood extent, reinforcing the reliability of the classification method. The temporal flood frequency analysis provides crucial insights into long-term flood dynamics and helps identify hydrologically sensitive zones. Overall, this study highlights the effectiveness of SAR-based monitoring in understanding floodplain behaviour under changing climatic and hydrological conditions, and supports improved flood hazard mapping, hydrodynamic model calibration, and sustainable flood risk management in the Ganges Basin and other monsoon-affected regions.
Keywords: Flood Inundation, Multi-Temporal, Time-Series, Flood Frequency, Sentinel-1 SAR, Ganges River
How to cite: Sajid, M., Hasan Khan, H., Khan, A., and Ansari, A. A.: Flood Dynamics and Frequency Mapping in the Lower Ganges Floodplain in India Using Multi-Temporal Sentinel-1 SAR Observations (2016–2024), EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-125, https://doi.org/10.5194/egusphere-egu26-125, 2026.
Semi-urban vegetation systems play a critical role in ecosystem stability but are increasingly exposed to flood hazards due to climate variability and rapid land-use change. Accurate flood detection in such system remains challenging because radar backscatter is influenced by complex and mixed scattering mechanisms arising from vegetation, built-up structures, and surface water. Conventional intensity-based flood indices struggle to separate flooded vegetation from non-flooded rough surfaces and tend to miss inundated areas under mixed land-cover conditions. To address these limitations, this study presents a physically interpretable flood detection framework that integrates Synthetic Aperture Radar polarimetric descriptors with a machine learning classifier. The proposed approach utilizes dual-polarized Sentinel-1 SAR data to derive polarimetric features from Stokes parameters and the covariance matrix. Specifically, the Degree of Polarization and Linear Polarization Ratio are combined with eigenvalue-based information to capture changes in both amplitude and polarization state between pre-flood and during-flood conditions. These descriptors are integrated into a novel Flood Index (FI) designed to distinguish flooded urban areas dominated by double-bounce scattering from flooded vegetation characterized by depolarized volume scattering. Unlike commonly used indices such as the Normalized Difference Flood Index (NDFI) or VH/VV ratio, the proposed FI exploits polarization behaviour rather than relying solely on backscatter intensity. A Random Forest classifier is trained on the proposed FI using a tile-based sampling strategy to handle class imbalance between flooded and non-flooded pixels. The framework is evaluated across three flood events representing diverse geographic and land-cover conditions: the 2019 Typhoon Hagibis flood in Japan, the 2023 Yamuna River flood in India, and the 2023 Larissa flood in Greece. Model performance is assessed using multiple accuracy metrics, including F1 score, Intersection over Union (IoU), False Positive Rate (FPR), and False Negative Rate (FNR). Results demonstrate that the Random Forest model trained on the proposed Flood Index consistently outperforms threshold-based Otsu methods and NDFI across all study areas. The approach achieves F1 scores ranging from 0.81 to 0.86 and IoU values between 0.70 and 0.76, while maintaining a relatively low False Negative Rate (0.09-0.17), that is critical for minimizing missed flooded areas in disaster response applications. Sensitivity and ablation analyses further confirm the robustness of the Flood Index to speckle noise and highlight the complementary contribution of its individual components. Overall, the proposed framework offers a transferable and computationally efficient solution for flood mapping in semi-urban vegetation systems using widely available dual-polarized SAR data. The results highlight its potential for scalable flood monitoring and rapid damage assessment across regions with heterogeneous land-cover conditions.
How to cite: Adhikari, R. and Bhardwaj, A.: SAR polarimetry-based machine learning method for flood detection in semi-urban vegetation systems, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-21063, https://doi.org/10.5194/egusphere-egu26-21063, 2026.
Flood extent mapping from satellite imagery plays a critical role in disaster response and flood risk management, particularly as flood events become more frequent and severe under a changing climate. At its core, the task involves classifying each pixel in an optical satellite image as flooded or non-flooded. Recent deep learning-based segmentation models have demonstrated strong performance at the global scale. However, despite their accuracy, most existing approaches provide deterministic predictions and offer limited information on the reliability of individual pixel-level outputs. This lack of uncertainty information constrains their operational applicability, especially in high-risk scenarios where models may exhibit overconfident but incorrect predictions.
To address this limitation, we extend a global flood extent segmentation framework by explicitly incorporating uncertainty quantification. Specifically, an Evidential Deep Learning (EDL) approach is integrated into a UNet++ architecture within the ml4floods framework, enabling simultaneous prediction of flood extent and associated pixel-wise uncertainty. Within the EDL formulation, network outputs are interpreted as evidence and parameterised using a Beta distribution, providing a principled estimate of predictive uncertainty. Furthermore, total uncertainty is decomposed into aleatoric and epistemic components, allowing clearer interpretation of whether uncertainty arises from data ambiguity or from limited model knowledge.
The proposed approach is evaluated using the extended WorldFloods global flood dataset. Preliminary results indicate that the EDL-enhanced model maintains promising segmentation performance while producing informative uncertainty maps. Elevated uncertainty is consistently observed in misclassified regions and along land-water boundaries, where optical signals are inherently ambiguous. These results demonstrate that uncertainty estimates offer valuable insight into model reliability and support operational decision-making by highlighting areas that require closer inspection. In practice, uncertainty-guided triage can help prioritise expert review and resource allocation, focusing attention on regions where decision risk is highest.
How to cite: Chen, C. and Wang, L.-P.: Evidential Deep Learning for Uncertainty-Aware Global Flood Extent Segmentation, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-6114, https://doi.org/10.5194/egusphere-egu26-6114, 2026.
Water is essential to life and health of various ecological and social systems. Unfortunately, water is one of the natural resources most impacted by climate change, with increasingly intense hydro-meteorological extremes (floods, droughts, etc.) and growing societal demand. To help manage this vulnerable resource, it is vital to assess and monitor its availability on a regular basis, as well as to track its trajectory over time to better understand the impact of global change on it. Surface water (lakes, rivers, flood plains, etc.) represents an important component of total water resources, and it is of primary importance to monitor it to better understand and manage the consequences of climate change. Surface water resources provide populations around the world with essential ecosystem services such as power generation, irrigation, drinking water for humans and livestock, and space for farming and fishing.
In this context, the SCO-CASCADES project implements end-to-end processing chains for satellite Earth observation data, including Sentinel-1 and 2 (S-1 and S-2), in order to provide surface water products (surface water body and inundation depth maps) that will be made available via an interactive platform co-constructed with identified users.
In the first phase of the project a fully automated Sentinel-1 based processing chain has been implemented. This chain is based on automatic multiscale image histogram parameterization followed by thresholding, region growing and chain detection applied on individual, subsequent pairs, and time series of S1 images. This chain enables us to derive various products: i) an exclusion layer identifying areas where water cannot be detected on Sentinel 1 image (e.g. Urban and forested areas), ii) permanent seasonal water body maps, iii) a water body map for each S1 image, iv) an uncertainty map characterizing the water body classification uncertainty, v) an occurrence map providing the number of times (over the time series) each pixel was covered by open water.
Here, we propose to present and evaluate the robustness of the processing chain and the resulting maps produced using multi-year S1 time series over two large scale sites: the Mekong flood plains between Kratie, the Tonle Sap lake and the Mekong Delta, and the Tsiribihina basin in Madagascar. The kappa score obtained from the comparison between S1 and S2-derived maps shows a good agreement yielding CSI and Kappa Cohen scores most of the time higher than 0.7 and sometimes reaching values higher than 0.9.
How to cite: Hostache, R., Alexandre, C., Heng, C., Catry, T., Herbreteau, V., Ann, V., Révillion, C., and Delenne, C.: A fully automatic processing chain for the systematic monitoring of surface water using Copernicus Sentinel 1 satellite data: first results of the SCO-CASCADES project., EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-22077, https://doi.org/10.5194/egusphere-egu26-22077, 2026.
Monitoring inland surface water in near-real time is a key challenge in cloud-prone tropical regions. Recently, Synthetic Aperture Radar (SAR) products have been widely used to detect surface water. Our area of interest, the Tonle Sap Lake region is a complex environment where very large areas and floodplains are partially or fully submerged seasonally. As the population living around the lake strongly rely on the seasonal flooding dynamics for their socio-economic activities and can at the same time be at risk due to extreme flooding events, it is of main importance to develop tools for the monitoring of flooded areas. In this context, we are adopting and evaluating an algorithm which relies on parametric thresholding, and region growing approaches applied over time series of Sentinel-1 (S1) SAR backscatter images (VV and VH). To evaluate the produced water extent maps based on VV and VH polarizations, we used a cross evaluation using multi-sensor products: high-resolution optical data such as Sentinel-2 (S2) and the coarser resolution Sakamoto flood extend derived from MODIS product. The comparison is made using the Critical Success Index (CSI) and Kappa coefficient performance metrics. During the dry season, the VV polarization demonstrated very good performance using S2-derived maps as a reference, with CSI of 0.84 and a Kappa coefficient of 0.91, indicating highly accurate surface water detection. Performance was similar using the Sakamoto product as a reference (CSI=0.87). However, performance dropped during the rainy season, with the VV polarization's CSI decreasing to 0.76 comparing S2, reflecting challenges in detecting water in the extensive flooded vegetation areas. VH polarization consistently overestimated water extent by misclassifying wet vegetation and rice fields. A merge of VV and VH product yielded an intermediate performance, improving water detection in vegetated areas compared to VV alone. This comprehensive, multi-sensor and multi-season assessment clarifies the specific strengths of each S1 polarization, showing VV's superiority for open water mapping, especially in the dry season. It underscores the importance of selecting the appropriate product (VV for open water, merged for total inundation) and considering seasonal context for operational monitoring, thereby demonstrating the algorithm's robustness while also defining its operational limitations.
How to cite: Heng, C., Ann, V., Catry, T., Herbreteau, V., Alexandre, C., and Hostache, R.: Development of routine flood mapping using SAR satellite observation for long-term monitoring system in the flood-prone regions, Cambodia, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-19963, https://doi.org/10.5194/egusphere-egu26-19963, 2026.
Multi-sensor methodologies are gaining traction within flood monitoring research, grounded in the rationale that data fusion from diverse sources mitigates uncertainty and improves spatiotemporal coverage. However, these assumed benefits are rarely quantified.
This work aims to comprehensively compare the performances of multi-sensor and single-sensor approaches to understand to what extent increasing the number and variegate data source may improve the detection rate and temporal characterisation of flood events. A multi-sensor flood monitoring approach using AMSR2 and VIIRS data is assessed against each sensor individually and against standard benchmarks in EO-based flood detection (e.g., MODIS and Sentinel-1) for major flood events in the Savannakhet Province of Laos.
The comparative analysis evaluates multiple metrics. First, detection comparison classifies events as captured by each considered approach, multi-sensor only, each individual sensor only, or missed by all, to directly quantify the improvement attributable to multi-sensor integration. The spatial agreement is assessed between the multi-sensor and single sensor approaches for jointly detected flood events. Additionally, the temporal component is characterized by an examination of the observation frequency, maximum observation gaps, and peak capture timing. Lastly, the various detection outcomes are related to event characteristics, including cloud cover persistence, flood magnitude, duration, and flood type, quantifying the conditions under which a multi-sensor approach performs optimally.
How to cite: Campo, C., Tamagnone, P., Schumann, G., Duc Tran, T., Choy, S., and Kuleshov, Y.: Comprehensive validation of the benefits of multi-sensor flood monitoring, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-20097, https://doi.org/10.5194/egusphere-egu26-20097, 2026.
Rivers in regions with cold winters can seasonally freeze up. River ice breakup and freeze-up processes can lead to river ice jams, which are a major contributor to flood risk in cold regions (across most of the high latitudes of the northern hemisphere). In Canada, satellite remote sensing is used across the country to provide timely information on the status of river ice. Methods and algorithms to classify various stages of river ice from the Radarsat Constellation Mission (RCM) are available, but the operational implementation of these, especially the integration into larger flood forecasting and early warning systems, requires specific expertise, software and computational resources, and comes with its own set of challenges. In collaboration with various agencies across Canada we have set up operational monitoring systems with the purpose of assisting the daily tasks of forecasters on duty. These have been used in practice over multiple ice breakup and freeze-up seasons, which has highlighted both their usefulness and shortcomings. We will focus on various aspects of such a system and share lessons learned on its design, setup and operational use, as well as a framework to analyse various factors relevant for operational monitoring purposes (e.g. spatiotemporal coverage and latency of the data, critical elements in the support of decision-making relating to floods). In this, we do not shy away from problems and pitfalls, so that others can learn from these. While various challenges remain, this work is a good example of the value in the joint engagement of applied science and end users.
How to cite: Haag, A., Bovenschen, T., Vandebroek, E., Tsiokanos, A., Balk, B., and van der Sanden, J.: Lessons Learned from Remote Sensing of River Ice for Flood Early Warning, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-12249, https://doi.org/10.5194/egusphere-egu26-12249, 2026.
Accurate water cycle representation in data-scarce and flood-prone regions like the Niger River Basin demands stronger integration between remote sensing and hydrological modelling. Spanning ten water-stressed nations, this basin faces critical challenges under climate change, requiring robust water-budget assessments to guide resilience strategies. We employ DHI’s Global Hydrological Model (DHI-GHM) to simulate key hydrological components of the regional water cycle. Model outputs for surface and root-zone soil moisture (SSM and R-ZSM) and terrestrial water storage (TWS) are systematically compared against satellite observations (GRACE/GRACE-FO and multiple soil moisture products) to identify discrepancies and enhance the understanding of regional hydrological behavior. A near real-time SSM data assimilation scheme is implemented to enhance spatiotemporal accuracy of surface and top-soil interactions, particularly beneficial in the flood-sensitive Inner Niger Delta. Post-assimilation hydrological outputs are coupled with the CaMa-Flood surface hydraulic model to simulate inundation dynamics, enabling improved flood prediction and supporting risk management. Finally, we pursue two-way coupling of hydrological and hydrodynamic models by integrating river flow–storage feedbacks to advance flood forecasting and sustainable water-resources planning.
How to cite: Azimi, S., Murray, A., Chewning, C., Kittel, C., Madsen, H., Yang, F., Schumacher, M., and Forootan, E.: Satellite-Enhanced Flood Modelling for the Niger River Basin using a Synergy of Hydrological Modelling and Earth Observation Data, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-5752, https://doi.org/10.5194/egusphere-egu26-5752, 2026.
This study presents an integrated and multidisciplinary methodology for investigating coastal flooding and morphodynamic processes in low-lying coastal environments, with a comparative application to two geomorphologically distinct Mediterranean coastal plains: the Volturno Plain and the Fondi Plain. The methodological framework combines high-resolution topographic and bathymetric datasets, aerial remote sensing, sedimentological analyses, statistical wave climate assessment, numerical hydrodynamic modelling, and relative sea-level rise scenarios that incorporate both eustatic trends and local vertical land movements. This approach enables a robust evaluation of how differing coastal configurations influence flooding susceptibility under extreme marine conditions.
For both study areas, the topographic baseline was derived from 2 m resolution LiDAR-based Digital Terrain Models, subsequently refined using site-specific datasets. In the Volturno Plain, extensive GNSS field surveys were conducted along the beach between Volturno and Regi Lagni river mouths. In the Fondi Plain, DTM refinement relied on aerial drone surveys carried out over the beach sector between the Canneto and Sant’Anastasia river mouths. Photogrammetric processing of aerial imagery allowed the generation of high-resolution surface models, which were integrated with the existing LiDAR DTM to enhance the depiction of subtle morphological features critical for flood propagation.
Sedimentological characterization was performed to constrain morphodynamic responses. Granulometric samples were collected along cross-shore transects at elevations ranging from −1.5 m to +2 m. Grain-size distribution analyses supported the calibration and interpretation of sediment transport and wave dissipation processes within numerical models.
Bathymetric modelling was based on high-precision single-beam echo-sounder surveys, with depth data corrected for tidal variations using official tide-gauge records. Emerged and submerged datasets were merged into continuous topo-bathymetric models, ensuring consistency in vertical reference systems and numerical stability.
Marine storms were identified through the analysis of offshore buoy records using a Peak Over Threshold approach. Storm events were classified into five classes using their Storm Power Index calculated by combining significant wave height and event duration. Representative events were selected as boundary conditions for coupled hydrodynamic simulations performed with Delft3D and XBeach. Simulations were run for future scenarios based on high-emission IPCC projections (SSP 5-8.5), integrating local sea-level rise, local subsidence rates, and highest tidal and surge levels.
A comparative analysis of the simulation outcomes highlights marked differences between the two coastal plains. The Volturno Plain results highly prone to inundation, with storm surges overtopping dune systems and propagating inland due to low elevations, local subsidence, and limited effectiveness of existing coastal defenses. Conversely, the Fondi Plain exhibits significantly reduced flood penetration. The presence of a wide bar system, coupled with efficient coastal defense structures, promotes substantial dissipation of incoming wave energy. As a result, even under intense storm conditions, inundation remains confined to a narrow coastal strip immediately landward of the beach.
Overall, the comparative methodological application demonstrates how coastal morphology, sedimentological properties, and defense systems critically control flood dynamics. The proposed framework provides a transferable and decision-oriented tool for assessing coastal vulnerability and supporting adaptation strategies in heterogeneous low-lying coastal settings under climate change pressure.
How to cite: Fasciglione, G., Benassai, G., Mattei, G., and Aucelli, P. P. C.: Mapping and modeling coastal flood dynamics using remote sensing and hydrodynamic models, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-21622, https://doi.org/10.5194/egusphere-egu26-21622, 2026.
The RESCUE_SAT project was launched as part of the “Innovation for Downstream Preparation for Science” (I4DP_SCIENCE) programme (Agreement no. 2025‑2‑HB.0), funded by the Italian Space Agency (ASI), with the goal of enhancing the performance of the RESCUE model through the integration of satellite data. RESCUE is a large‑scale inundation model that enables probabilistic flood‑hazard assessment over large areas by preserving computational efficiency while explicitly representing hydrologic-hydraulic processes along the full drainage network. Primarily based on digital terrain models (DTMs), RESCUE is a hybrid framework that combines a geomorphology-based representation of the river network with simplified hydrological and hydraulic formulations to estimate water levels and inundation extents. The central challenge of the RESCUE_SAT project is to deliver a flood‑modelling tool capable of providing a more reliable and detailed representation of both large‑scale hydrological behavior and local hydraulic processes, including flow interactions with structures such as levees, bridges and dams which are currently not explicitly represented in RESCUE. To this purpose, the Synthetic Aperture Radar (SAR) imagery acquired by the ASI’s COSMO-SkyMed constellation is processed using interferometric techniques to derive high-resolution digital elevation models (DEMs), reaching meter-scale resolution. Starting from high-resolution DEMs derived from COSMO-SkyMed satellite imagery, RESCUE_SAT enables the identification of the locations of structures that interacts with flow propagation, supporting their systematic mapping. Once the infrastructures have been identified and parameterized from the high-resolution DEM, the DEM is resampled and processed to a computationally advantageous coarser resolution, while the detected infrastructure elements are directly integrated into the hydrological–hydraulic model.
How to cite: Volpi, E., Cipollini, S., Pavesi, L., Gagliardi, V., Mwangi, R., Sanvitale, G., Pomarico, I., Fiori, A., Tapete, D., Virelli, M., Ursi, A., and Benedetto, A.: RESCUE_SAT project: Leveraging Satellite Data to Improve Large‑Scale Flood Modeling, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-18308, https://doi.org/10.5194/egusphere-egu26-18308, 2026.
Floods are the costliest and most frequently occurring natural disasters. One of the key factors in preventing and reducing losses is providing a reliable flood map. However, the uncertainty associated with either flood inundation model or data, specifically the Digital Elevation Model (DEM), may have adverse effects on the reliability of flood stage and inundation maps. Therefore, a systematic understanding of the uncertainty is necessary. In this study, an attempt is made to assess whether models are more susceptible to the uncertainties or the data itself. In order to do this, a SCIFRIM (Slope-corrected, Calibration-free, Iterative Flood Routing and Inundation Model) is employed, utilizing a list of DEM datasets to reconstruct the October 2024 Valencia flood event. The modelled flood extents were validated against those derived from multi-sensor remote sensing data. The Critical Success Index (CSI) was calculated to assess the agreement between observed and modelled flood extents, yielding values of 0.49 and 0.59 for October 30th and 31st, respectively, when combining SCIFRIM and Lidar-DEM. Additionally, a multi-model comparison has been performed between SCIFRIM and CaMa-Flood (Catchment-based Macro-scale Floodplain), HEC-RAS (Hydrologic Engineering Center's River Analysis System), and TUFLOW (Two-dimensional Unsteady FLOW), demonstrating its relevance in terms of outputs (flood extent and stage) and model runtime. The findings demonstrate that the proposed modeling framework offers a reliable approach for flood assessment. It has great potential to support rapid assessment and decision-making in data-scarce regions.
How to cite: Tripathi, G., Sarkar, E., and Biswal, B.: Evaluating Slope-corrected, Calibration-free, Iterative Flood Routing and Inundation Model (SCIFRIM)-based Flood Inundation against multi-satellite observation, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-436, https://doi.org/10.5194/egusphere-egu26-436, 2026.
Flash flood disasters have increased by more than 50% in the first 20 years of the 21st century compared to the last 20 years of the 20th century. Monitoring and understanding flood events might lead to better mitigation of this natural hazard. Using SAR and SAR interferometry (InSAR) proved to be a useful tool for mapping flooded areas due to the lower backscatter or decorrelation of the SAR signal in an open-water environment. In Arid regiem, flash flood water is rapidly drained by evaporation or percolation, often before the satellite image is acquired. To overcome this challenge, we propose in this study to use the InSAR coherency loss, created by surface changes during a flash-flood, to map the runoff path and utilize it to quantify peak discharge (Qmax).
We focus on the Ze’elim alluvial fan along the western shore of the Dead Sea, Israel, an arid area affected by seasonal flash floods a few days a year. We use 34 interferograms of X-band (COSMO-SkyMed/TerraSAR-X) SAR data, covering 25 runoff events between 2017 and 2021, and upstream hydrological gauge data. To consider the natural decorrelation processes, we calculate a normalized coherence (ϒn) term, using the average coherence of the study area and the average coherence of a stable reference area, identified by differential LiDAR measurements.
We find a strong correlation between gn and the logarithm of the peak discharge (Qmax). However, the method is limited by a minimal peak discharge—where energy is too low to change the surface—and maximal total water volume—where decorrelation is saturated. The method may provide tools for reconstructing runoff data in arid areas where historical SAR data is available, and for monitoring in difficult access areas or where hydrological stations are sparse or damaged.
How to cite: Nof, R.: Estimating Flash Flood Discharge in Arid Environments Using InSAR Coherence: A Case Study of the Ze’elim Fan, Dead Sea, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-11948, https://doi.org/10.5194/egusphere-egu26-11948, 2026.
In the last decade rapid advancements in remote sensing have opened new frontiers in our ability to monitor freshwater bodies dynamics at the global scale. Most works have taken advantage of the long time series of Landsat constellations (30 m resolution) relying on spectral indices to identify water. Recently, much progress has also been made in the development and use of deep learning models capable of explicit semantic classification of river water, lake water and sediment bars, based on Sentinel-2 (S2) MSI imagery (10 m resolution). In this work, we present an approach that seeks to extend these existing, trained, fluvial landscape classification models to Landsat data in order to observe long-term water and morphological shifts in rivers and lakes. Rather than explicitly re-training the models with Landsat data and labour-intensive manual label data, we apply a domain transfer approach to generate synthetic S2 MSI imagery from Landsat inputs. This approach has the advantage that the training of deep learning domain transfer models only requires synchronous Landsat and Sentinel data and thus obviates the need for manual labels.
The results show that, when using these synthetic images, river water, lake water and sediment bars are classified with an F1 score of 0.8, 0.94, 0.65 respectively, which represents a decrease of ca. 10% for river water and 20% for sediment with respect to real S2 imagery. By adopting this integrated approach, we are therefore able to monitor, for the first time, lake water, river water and sediment bars at 10 m resolution, over a 40-year period, integrating both synthetic S2 and real S2 acquisitions through a single, fluvial landscape segmentation model. Classification obtained from median monthly images can then be aggregated at the yearly or multi-yearly scale to delineate river or lake water fluctuations, and active channels (river water plus sediment bars) trajectories, from specific freshwater bodies to the global scale.
How to cite: Vanzani, F., Carbonneau, P., Bizzi, S., Cecchetto, M., and Bozzolan, E.: Monitoring Freshwater Bodies over the Past 40 Years Using Synthetic Monthly Sentinel-2 MSI Imagery , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-7132, https://doi.org/10.5194/egusphere-egu26-7132, 2026.
Existing global wetland datasets and monitoring approaches emphasizepersistent inundation, while intermittent inundation and waterlogged states—especially where vegetation is present—are underrepresented or of lower accuracy. This leads to inaccurate estimates of greenhouse gas emissions from carbon-rich systems (e.g., peatlands). Meanwhile, the predominance of annual mapping limits the capture of intra-annual variability, further reinforcing these inaccuracies and obscuring sub-seasonal disturbances from human activities (e.g., shifts in rice-cropping intensity). This study presents an unsupervised, wetness-driven framework for improving global wetland monitoring that leverages earth observation data streams. For framework development, the OPtical TRApezoid Model is applied to Harmonized Landsat-Sentinel imagery to retrieve surface wetness, followed by wetland delineation using a scene-adaptive grid-based thresholding algorithm. This framework is applied to 824 globally distributed 0.1° grid cells encompassing 9,781 land-cover-labeled sites and 134 sites with daily wet–dry labels across 28 Ramsar wetlands, and validated for spatial delineation, thematic, and temporal accuracy. Comparative analysis employs Dynamic World, the first global 30 m wetland map with a fine classification system (GWL_FCS30), and the modified Dynamic Surface Water Extent algorithm (DSWE). Our framework achieved moderate spatial delineation accuracy with F1 of 0.64 (recall 0.75, precision 0.56), comparable in F1 to Dynamic World and with higher recall than DSWE and GWL_FCS30. It delivered the highest temporal accuracy (F1 0.72; precision 0.81; recall 0.64) and improved thematic accuracy for vegetated wetland, reducing omission with modest commission. The proposed wetland monitoring framework enables more accurate targeted policy interventions.
How to cite: Li, Y., Tsendbazar, N.-E., de Beurs, K., Päkkilä, L., and Kooistra, L.: Refining global wetland characterization using an unsupervised, wetness-based dynamic framework, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-5862, https://doi.org/10.5194/egusphere-egu26-5862, 2026.
Accurate long-term monitoring of surface water dynamics in the Niger River and Lake Chad basins is crucial for regional ecological security and sustainable water resource management. However, such monitoring is often hindered by insufficient continuous high-frequency observations—necessary to capture rapid shifts between permanent and seasonal water bodies in semi-arid transition zones—as well as by persistent cloud cover. To address these limitations, we developed a spatio-temporal data fusion framework designed to delineate detailed evolutionary patterns and regime shifts in surface water. Our methodology integrates Sentinel-1 SAR, Sentinel-2 optical imagery, and digital elevation model (DEM) data, adopting a “zoning modeling” strategy to reduce sensor-specific biases and environmental noise, thereby producing annual and seasonal surface water distribution maps. Furthermore, we developed a pixel-level, climate-coupled model based on inundation frequency to quantify changes in the extent, timing, and type of water bodies across a multi-year time series. Integration of these outputs elucidated the spatial heterogeneity of water resources throughout the study region from 2015 to 2024. Validation using randomly distributed reference samples demonstrated strong consistency, with overall accuracy exceeding 90%, confirming the robustness of our framework. Through an ecology-oriented classification scheme, we identified permanent water bodies—largely concentrated in the southern reaches of the Niger River main channel and the central zone of Lake Chad—as serving a “core support” function within the ecosystem. In contrast, seasonal water bodies followed a “dense in the south, sparse in the north” spatial pattern and acted as critical “ecological buffers” for arid northern areas. Notably, seasonal water extent expanded significantly during high-rainfall years such as 2018 and 2022, underscoring its pronounced sensitivity to climatic variability. Compared with current state-of-the-art approaches, the proposed framework enables characterization of high-frequency surface water dynamics and associated ecological interactions as continuous spatio-temporal fields, thereby providing a reliable and scalable tool to inform sustainable watershed management strategies across Africa.
How to cite: Du, L., You, S., Ye, F., and He, Y.: Tracking Dynamic Regimes and Ecological Functions of Surface Water in the Niger-Lake Chad Basins through Multi-Source Fusion (2015–2024), EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-19055, https://doi.org/10.5194/egusphere-egu26-19055, 2026.
Remote sensing technologies provide effective tools for monitoring and assessing the state of inland water bodies, enabling extraction of various hydrological parameters from satellite observation. Central Asian and some African countries are currently implementing practical programs aimed at mitigating water scarcity and improving the management of transboundary water resources. Rivers and their tributaries flowing across national boundaries require continuous monitoring to support early warning of droughts and floods at the basin scale.
Conventional ground-based hydrological stations are traditionally used to measure water level, estimate daily river discharge, and support hydrological forecasting. However, limitations related to accessibility, data-sharing restrictions, and the high cost of installation and maintenance often constrain their spatial coverage and long-term operation. Virtual gauging station (VGS) represents a complementary remote-sensing approach, providing time series derived from the long-term satellite image archives. A VGS is defined as a free-shaped polygon on the map used to analyze data within the borders of this polygon and collect observations based on the requirements. Currently, VGS applications primarily rely on optical satellite imagery from Sentinel-2, Landsat-4, -5, -7, -8, -9 missions to estimate water surface area (WSA) using spectral water index (MNDWI, AWEI or AWEIsh). Variations in WSA serves as a proxy for surface water availability and river dynamics.
In addition, VGS can be used to enrich satellite altimetry-based water level (H) time series. For this purpose, the VGS polygon is calibrated using reference altimetric observations obtained from open-access data source (e.g. SDSS, DAHITI, Hydroweb). Calibration involves estimating the parameters of a regression model describing the functional relationship between water level and water surface area. The resulting values can finally be integrated into hydrological models to support short-term river discharge forecasting. Thus, VGS provides continuous hydrological information independent of ground-based measurements, while optional validation against in-situ observations allows for the assessment of the model uncertainty. Based on the experimental analysis, optimal placement of VGS polygons is recommended dynamically active river sections that account for annual riverbed displacement, as well as in river reaches located near satellite altimeter ground tracks to improve calibration accuracy.
The experiments demonstrated that correlation between ground truth and forecasted water level values is upper 0,85 and mean absolute error is lower than 0,3 m. The following result has been obtained using linear regression which shows that application of more complex forecasting models could significantly improve the results.
How to cite: Mukhamedjanov, I. and Umirzakov, G.: The capabilities of virtual gauging stations in satellite monitoring of water bodies, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-6180, https://doi.org/10.5194/egusphere-egu26-6180, 2026.
Integrated Monitoring of Lake Garda with Radar, Optical Sensors and In Situ Instruments: Insights from the SARLAKES Project
Virginia Zamparelli1, Simona Verde1, Andrea Petrossi1, Gianfranco Fornaro1, Marina Amadori2,3, Mariano Bresciani2, Giacomo De Carolis2, Francesca De Santi4, Matteo De Vincenzi3, Giulio Dolcetti3, Ali Farrokhi3, Raffaella Frank2, Nicola Ghirardi2,5, Claudia Giardino2, Fulvio Gentilin6, Alessandro Oggioni2, Marco Papetti6, Gianluca Pari7 Andrea Pellegrino2, Sebastiano Piccolroaz3, Tazio Strozzi8, Marco Toffolon3, Maria Virelli7, Nestor Yague-Martinez9, and Giulia Valerio6
1Institute for Electromagnetic Sensing of the Environment (IREA), National Research Council, Naples, Italy
2Institute for Electromagnetic Sensing of the Environment (IREA), National Research Council, Milan, Italy
3Department of Civil, Environmental and Mechanical Engineering (DICAM), University of Trento, Trento, Italy
4Institute for Applied Mathematics and Information Technologies (IMATI), National Research Council, Milan, Italy
5 Institute for BioEconomy (IBE), National Research Council, Sesto Fiorentino, Italy
6Department of Civil, Environmental, Architectural Engineering and Mathematics (DICATAM), University of Brescia, Brescia, Italy
7Italian Space Agency (ASI), Rome, Italy
8GAMMA Remote Sensing, Gümligen, Switzerland
9Capella Space Corp., San Francisco, CA, USA
SARLAKES (SpatiAlly Resolved veLocity and wAves from SAR images in laKES) is a PRIN (Projects of National Interest) project funded in 2022 by the Italian Ministry of University and Research. The project is now in its final phase and is scheduled to end at the beginning of 2026. The project developed a novel, advanced and adaptable tool capable of accurately measuring water dynamics in medium- and large-sized lakes.
A key and innovative aspect of the project is the use of spaceborne Synthetic Aperture Radar (SAR) data, which are widely exploited for routine observation of the marine environments but remain relatively underutilized for lake monitoring. SARLAKES investigated the capability of SAR imagery to retrieve the spatial distribution of wind fields, surface currents, and wind-generated waves in lacustrine environments.
The project considers Lake Garda and Lake Geneva as case studies, with Lake Garda—the largest lake in Italy—selected as the primary test site due to the research group’s long-standing experience and the availability of extensive historical data.
This contribution presents the main results obtained over two years of project activity, with particular emphasis on outcomes from a multidisciplinary field campaign conducted on April 2025. The campaign aimed to reconstruct lake surface currents during a strong wind event in the peri-Alpine Lake Garda region.
The field instrumentation included a wave buoy, an acoustic Doppler current profiler (ADCP), Lagrangian drifters, anemometers, a ground-based radar, fixed cameras, a drone, and a conductivity–temperature–depth profiler. Satellite acquisitions from the COSMO-SkyMed Second Generation and Capella Space SAR sensors, as well as from the optical sensor PRISMA were scheduled over the study area during the campaign. Archive data from Sentinel-1, Sentinel-2, Sentinel-3, Landsat, and COSMO-SkyMed missions were also utilized.
The project demonstrates how the integration of in-situ instrumentation, spatially distributed flow measurements from remote sensing, and hydrodynamic modeling provides a comprehensive and scalable approach to next-generation monitoring of complex lake systems.
How to cite: Zamparelli, V. and the SARLAKES project team: Integrated Monitoring of Lake Garda with Radar, Optical Sensors and In Situ Instruments: Insights from the SARLAKES Project, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-21000, https://doi.org/10.5194/egusphere-egu26-21000, 2026.
Rivers play a central role in shaping the Earth's surface and ecosystems through physical, chemical, and biological interactions. The intensity and locations of these interactions change as rivers continuously migrate across the landscape. In recent decades, human activity and climate change have altered river hydrology and sediment fluxes, leading to changes in river position, or migration. However, a comprehensive perspective on and understanding of these recent changes in the rate of river position shifts is lacking. To address this knowledge gap, we created a continuous global dataset of yearly river positions and migration rates over the past four decades and analyzed trends. The global annual river positions were detected using Landsat-derived surface water datasets and processed in Google Earth Engine, a cloud-based parallel computation platform. The resulting river extents and centerlines reflect the yearly permanent position, corresponding to the rivers’ location during base flow. This approach improves the representation of position changes derived from geomorphological rather than hydrological processes. To robustly analyze river position changes across different patterns and complexities and at large scales, we developed and applied a global reach-based quantification method.
Results show that while alluvial rivers maintain stable positions in certain regions, others exhibit trends in the rates of position change. For instance, the Amazon Basin, which has experienced significant deforestation and hydrological modifications, has shown increased rates of river position change in recent decades, directly modifying active floodplains. In this presentation, we will discuss the advantages, limitations, and applications of the global yearly river position dataset, offer insights into the changing rates of river position, and highlight current and future impacts on one of Earth’s most vulnerable hydrologic systems.
How to cite: Dente, E., Gardner, J., Langhorst, T., and Yang, X.: Multidecadal Changes and Trends in Global River Positions, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-16468, https://doi.org/10.5194/egusphere-egu26-16468, 2026.
Wetlands are very sensitive hydrological ecosystems that are essential for groundwater recharge, flood control, and biodiversity. Climate variability, changed river regimes, and unsustainable anthropogenic pressures are all posing new challenges to their stability. The current work evaluates the two-decade hydro-climatic dynamics of the Haiderpur Wetland (Ganga River, India) by merging optical (Landsat), radar (Sentinel-1), and gridded climate (ERA5, CHIRPS) datasets with GRACE-based groundwater anomalies. On the Google Earth Engine (GEE), processing of time-series Landsat (NDVI, NDWI, LST) and Sentinel-1 (SAR) data to monitor all-weather surface inundation and vegetation structure. To disentangle climatic and anthropogenic drivers, these remote sensing products are statistically correlated against ERA5-Land (Evapotranspiration) and CHIRPS (Precipitation) data, alongside GRACE groundwater anomalies. The findings demonstrated a considerable downward trend in pre-monsoon NDWI and wetland water distribution. This was accompanied by a significant increase in LST and an unexpected increase in NDVI. All-weather Sentinel-1 data validated the drying trend. On the other hand, 'greening' (as indicated by NDVI) in a drying environment suggests a structural shift from native wetland vegetation to more drought-tolerant or invasive terrestrial plants. The study assesses the capability of a multifaceted (optical-radar-climate) GEE strategy to quantify the individual contributions of climatic and anthropogenic factors, while also monitoring wetland development. Furthermore, these findings quantify the hydro-ecological vulnerability of major Ramsar wetlands and emphasize the vital need for coordinated water management to sustain ecosystems in the Ganga River Basin, with far-reaching implications for global wetland conservation.
Keywords: Hydrology, GRACE, Climate Change, SAR, NDVI, NDWI, LST
How to cite: Ansari, A. A., Hasan Khan, H., Khan, A., and Sajid, M.: Hydro-Ecological Vulnerability of Ganga River Wetland (India): A Multi-Sensor Remote Sensing and GRACE-based Assessment of the Haiderpur Ramsar Site, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-147, https://doi.org/10.5194/egusphere-egu26-147, 2026.
Water security in the Chi River Basin is critical for the agricultural economy and ecosystem stability of Yasothon Province, Thailand. However, effective spatiotemporal monitoring of water surface dynamics is frequently hindered by persistent cloud cover during the monsoon season, limiting the utility of traditional optical remote sensing. This study addresses this challenge by developing a robust Multi-Sensor Deep Learning Fusion system that integrates Synthetic Aperture Radar (SAR) and optical satellite imagery to ensure continuous observation capabilities.
We employ a U-Net convolutional neural network architecture, selected for its high boundary precision and efficiency with limited training datasets. The model is trained on a fused six-channel input configuration, combining Sentinel-1 SAR data (weather-independent) with Sentinel-2 optical bands (RGB), augmented by the Normalized Difference Water Index (NDWI) and Normalized Difference Vegetation Index (NDVI). This multi-modal approach enhances feature extraction, allowing for the accurate differentiation of open water from floating vegetation and flooded agricultural lands in complex transition zones.
The study analyzes the hydrological cycle of 2022, capturing distinct drought, flood, and post-flood conditions. To ensure hydrological validity, the model’s segmentation outputs are not merely visually assessed but are quantitatively validated against ground-truth water level data from the E.20A gauge station in Kham Khuean Kaeo District. By establishing a precise Stage-Area Relationship, this research demonstrates a scalable, cost-effective framework for flood risk assessment and water capital estimation, offering a resilient solution for river basin management in cloud-prone tropical regions.
How to cite: Pruekthikanee, P.: Multi-Sensor Deep Learning Fusion for Spatiotemporal Water Surface Monitoring in the Yasothon Province's Chi River Basin, Thailand, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-4154, https://doi.org/10.5194/egusphere-egu26-4154, 2026.
Satellite-based surface water monitoring is essential for traking the spatiotemporal dynamics of global water bodies. However, most existing systems rely on a single mission or sensor modality, constraining both accuracy and temporal coverage. To overcome these limitations, we propose a multi-mission data fusion framework that integrates SAR Sentinel-1 and optical Sentinel-2 observations. Two U-Net convolutional neural networks were trained independently on the S1S2-Water dataset: one using Sentinel-1 sigma-nought backscatter (VV/VH) and the other using Sentinel-2 RGB and NIR bands, with terrain slope incorporated as ancillary input in both models. Predictive uncertainty is quantified via Monte Carlo dropout embedded within the networks, modeling pixel-wise predictions as Gaussian distributions. These probabilistic outputs are subsequently fused using a Bayesian framework and refined through sensor-specific exclusion masks. Evaluation across 16 geographically diverse test sites demonstrates that the fused probabilistic predictions achieve an overall IoU of 89%, highlighting the synergistic benefits of uncertainty-aware, multi-sensor integration. Furthermore, we show that model evaluation restricted to cloud-free optical imagery introduces substantial bias, limiting applicability for near-real-time monitoring. The proposed framework improves temporal availability, robustness, and reliability, advancing multi-satellite approaches for global surface water monitoring.
How to cite: Hassaan, M., Festa, D., and Wagner, W.: SAR and optical imagery for dynamic global surface water monitoring: addressing sensor-specific uncertainty for data fusion, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-17524, https://doi.org/10.5194/egusphere-egu26-17524, 2026.
In this study, multispectral images were used to detect toxic blooms in Villarrica Lake in Chile, using a time series of water quality data from 1989 to 2024, based on the extraction of spectral information from Landsat 8 and 9 satellite imagery. To explore the predictive capacity of these variables, we constructed 255 multiple linear regression models using different combinations of spectral bands and indices as independent variables, with phycocyanin concentration as the dependent variable. The most effective model, selected through a stepwise regression procedure, incorporated seven statistically significant predictors (p < 0.05) and took the following form: FCA = N/G + NDVI + B + GNDVI + EVI + SABI + CCI. This model achieved a strong fit to the validation data, with an R2 of 0.85 and an RMSE of 0.10 μg/L, indicating high explanatory power and relatively low error in phycocyanin estimation. When applied to the complete weekly time series of satellite observations, the model successfully captured both seasonal dynamics and interannual variability in phycocyanin concentrations (R2 = 0.92; RMSE = 0.05 μg/L). These results demonstrate the robustness and practical utility for long-term monitoring of harmful algal blooms in Lake Villarrica.
How to cite: Rodríguez-López, L., Bustos Usta, D., Bravo Alvarez, L., Duran Llacer, I., Bourrel, L., Frappart, F., and Urrutia, R.: Advanced phycocyanin detection in a South American lake using Landsat imagery and remote sensing, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-4, https://doi.org/10.5194/egusphere-egu26-4, 2026.
Waterlogging in agricultural fields is the condition of temporally inundated areas driven by extreme rainfall, rising groundwater or poor drainage, and has been identified as a major issue by Danish farmers. During the inundation period, plants are deprived of oxygen which negatively affects the root development and leads to decreased yields and grain quality. Additionally, these waterlogged areas are a large source of greenhouse gas (GHG) emissions. The issue is expected to exacerbate under current climate projections through wetter winters and rising groundwater levels in Denmark. Hence, an increased understanding of the spatio-temporal dynamics of waterlogging is required to future-proof the management strategies. The research goals are three-fold: (1) to optimise the detection of waterlogging, (2) to reveal inter- and intra-annual patters across Denmark and (3) to investigate the drivers of waterlogging such as climate, topography and bio-physical conditions. We aim to detect waterlogged areas through a deep learning semantic segmentation approach utilising multi-temporal PlanetScope imagery and nation-wide high resolution elevation data. This approach requires a manually delineated reference dataset to train, validate and test the model which needs to be well-balanced spatially, e.g. covering various soil types, and temporally, e.g. including various illumination conditions. Additionally, we will experiment with various model architectures, backbones and covariate combinations to optimise the segmentation performance. Initial tests using a UNET architecture and building upon a published reference dataset by Elberling et al. (2023), show promising results and lay the foundation for the upcoming model development and extension of the existing reference data.
Elberling, B. B., Kovacs, G. M., Hansen, H. F. E., Fensholt, R., Ambus, P., Tong, X., ... & Oehmcke, S. (2023). High nitrous oxide emissions from temporary flooded depressions within croplands. Communications Earth & Environment, 4(1), 463.
How to cite: Kleinsmann, J., Koch, J., Horion, S., Kovacs, G. M., and Stisen, S.: Detecting Waterlogging in Agricultural Fields in Denmark using High-Resolution PlanetScope Time Series, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-1859, https://doi.org/10.5194/egusphere-egu26-1859, 2026.
Ephemeral pans and seasonal ponds in arid and semi-arid lands supply critical water for pastoral and ecological systems, yet are not routinely monitored due to their small size, highly dynamic and spectral confusion with vegetation and shadows. We present and evaluate a multisensor mapping approach to detect sub-0.5 ha surface water bodies and quantify their linkage to rainfall variability to inform decision making.
Our approach fuses Sentinel-1 SAR, Sentinel-2 optical indices and DEM derived covariates within an ensemble classifier (voting of Random Forest, Gradient Boosting, and Decision Tree models). Predictive uncertainty is mapped using ensemble agreement and class probabilities, and we compare SAR-only, optical-only, terrain-only, and fused configurations. Additionally, rain and ephemeral surface water dynamics are modelled using generalised additive models with CHIRPs and local rain gauge observations to test the lagged relationships in monthly water area anomalies.
Results show the fused model achieves an overall accuracy of 85%, outperforming Sentinel-1, and Sentinel-2 (78% and 72%, respectively). Generalised additive models explain 62% of variance in monthly water area anomalies, with a strong response at 1-3 month lags. These results show multisensor fusion with quantified uncertainty improves detection of ephemeral surface water and enables estimation of rainfall thresholds and lagged dynamics relevant to pastoral water planning and targeted anticipatory action interventions.
How to cite: Muthoka, J., Rowhani, P., Hopling, C., Memarian Sorkhabi, O., and Todd, M.: Multisensor Ensemble Mapping of Sub-hectare Ephemeral Surface Water in Kenyan ASALs, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-6408, https://doi.org/10.5194/egusphere-egu26-6408, 2026.
Flood and surface water mapping from satellite observations remains challenging due to the complementary yet heterogeneous characteristics
of optical and synthetic aperture radar (SAR) data. While deep learning has achieved promising results, existing studies are often evaluated on
isolated datasets or focus on a single modality, limiting their comparability and operational relevance. In this study, we conduct a large-scale and systematic evaluation of optical, SAR, and combined optical–SAR learning strategies for flood and surface water mapping across multiple public satellite benchmarks. Using a common training and evaluation protocol, we compare lightweight convolutional networks and large pretrained vision models under single-modality and multimodal settings. The analysis reveals that attention-based multimodal fusion consistently improves water delineation accuracy on most datasets, while model capacity and preprocessing choices play a critical role in balancing missed detections and false alarms. On global-scale benchmarks, moderately sized backbones coupled with dedicated fusion mechanisms achieve robust performance without relying on extremely large models.These findings provide practical guidance for selecting architectures and fusion strategies in operational flood mapping and establish a reproducible benchmark for future optical and SAR studies.
How to cite: xiao, J., li, Z., and tian, F.: Evaluating multimodal optical and SAR learning strategies for flood and surface water delineation, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-7320, https://doi.org/10.5194/egusphere-egu26-7320, 2026.
Coastal wetlands provide a wide range of ecosystem services, including shoreline protection, attenuation of storm surges and floods, water quality improvement, wildlife habitat and biodiversity conservation. These ecosystems have been observed to sequester atmospheric carbon dioxide at rates significantly higher than many other ecosystems, positioning them as promising nature-based solutions for climate change mitigation. However, projections of coastal wetland conditions under sea-level rise (SLR) remain highly variable, owing to uncertainties in environmental factors as well as the necessary simplifications embedded within the wetland evolution modelling frameworks. Assessing wetland resilience to rising sea levels and the effect of anthropogenic activities is inherently complex, given the uncertain nature of key processes and external influences. To enable long-term simulations that span extensive temporal and spatial scales, models must rely on a range of assumptions and simplifications—some of which may significantly affect the interpretation of wetland resilience.
Here we present a novel eco-hydro-geomorphological modelling framework to predict wetland evolution under SLR. We explore how accretion and lateral migration processes influence the response of coastal wetlands to SLR, using a computational framework that integrates detailed hydrodynamic and sediment transport processes. This framework captures the interactions between physical processes, vegetation, and landscape dynamics, while remaining computationally efficient enough to support simulations over extended timeframes. We examine several common simplifications employed in models of coastal wetland evolution and attempt to quantify their influence on model outputs. We focus on simplifications related to hydrodynamics, sediment transport, and vegetation dynamics, particularly in terms of process representation, interactions between processes, and spatial and temporal discretisation. Special attention is given to identifying modelling approaches that strike a balance between computational efficiency and acceptable levels of accuracy. We will present recent model results to assess the resilience of coastal wetland to SLR on several sites around the world and will discuss new results to assess the effect of human interventions and infrastructure on wetland resilience.
How to cite: Saco, P., Jose, R., Angelo, B., Sandi, E., and Sandi, S.: Modelling wetland resilience to climate change and anthropogenic impacts., EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-8292, https://doi.org/10.5194/egusphere-egu26-8292, 2026.
The global degradation of river ecosystems and the growing impacts of flood hazards have highlighted limitations in current river management approaches. In Europe, the Water Framework and Flood Directives promote integrated, catchment-scale assessments of hydromorphological conditions and flood risk. Such integration is essential for sustainable management. Planform dynamics and river bed aggradation/incision, for example, can modify channel conveyance and compromise flood mitigation measures, whereas granting more space to rivers can both enhance ecological quality and reduce flood peaks.
In this context, the availability of long-term satellite archives and advances in computational and machine-learning methods enable large-scale, high spatiotemporal resolution monitoring of large and medium river systems. However, despite this potential, the operational adoption of satellite-based river monitoring remains limited due to data complexity, interdisciplinary requirements, and the lack of harmonised computational infrastructures.
Thanks to a collaboration between industry, public institutions and the university, we developed a methodology to systematically map monthly water channel, channel width, sediment bars and vegetation dynamics, testing the results on the full archive of Sentinel-2 (10 m resolution) for medium-large Italian rivers (active channel > 30m - i.e. 3 Sentinel-2 pixels). In this talk, I will outline the applied methodology, discuss its applicability at national scale with Sentinel-2 data, and show how the generated products can better inform river habitat mapping, river conservation practices, and flood risk assessments by supporting consistent national scale geomorphic trajectories identification.
How to cite: Bozzolan, E., Micotti, M., Matteligh, E., Piovesan, A., Vanzani, F., Carbonneau, P., and Bizzi, S.: Operational, national-scale monitoring of river trajectories using satellite imagery , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-13343, https://doi.org/10.5194/egusphere-egu26-13343, 2026.
Flood inundation mapping has become increasingly critical as climate change intensifies the frequency and severity of flooding worldwide, amplifying risks to populations, infrastructure, and ecosystems. Recent advances in Earth Observation (EO) have shown unprecedented opportunities to monitor flood dynamics across large spatial scales.. However, significant challenges remain due to the limitations of single-sensor approaches. While multispectral imagery provides rich semantic information, it is frequently constrained by cloud cover during flood events. Conversely, Synthetic Aperture Radar (SAR) offers all-weather capability but suffers from signal ambiguity in complex terrains and urban environments. Effectively integrating these heterogeneous modalities therefore remains a challenge, particularly with limited labelled flood event data.
In this study, we propose a deep learning-based cross-modal fusion framework that leverages the representational capacity of Remote Sensing Foundation Models (RSFMs). High-level feature embeddings are extracted from Sentinel-1 and Sentinel-2 multispectral imagery by initializing modality-specific encoders with pretrained weights from state-of-the art multi-modal foundation models, providing a robust and semantically aligned feature space despite limited task-specific training data
To integrate the multi-modal representations, we adopt a Gated Cross-Modal Attention mechanism, which adaptively modulates the information flow from each modality based on their observation reliability. Specifically, the model is trained to prioritise SAR features to ensure spatial continuity under cloud-obscured conditions, while simultaneously leveraging richer optical semantics to disambiguate SAR signals, correcting for example false detections caused by radar shadowing or smooth impervious surfaces.
To assess the generalisation of the proposed framework across diverse regions and sensor conditions, we trained and evaluated our model using a comprehensive dataset compiled from publicly available benchmarks, including Kuro Siwo and WorldFloods. Our framework not only establishes a new benchmark for all-weather flood monitoring but also demonstrates the critical role of remote sensing foundation models in overcoming the limitations of traditional, data-hungry fusion approaches.
How to cite: Chen, Y. C. and Wang, L. P.: Integrating SAR and Multispectral Satellite Observations for Flood Inundation Mapping: A Cross-Modal Fusion Framework Leveraging Foundation Models and Gated Attention Mechanism, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-13502, https://doi.org/10.5194/egusphere-egu26-13502, 2026.
Flood mapping plays a key role in understanding hazard impacts, supporting emergency response, and guiding long-term risk planning. Remote sensing is now widely used in flood studies because it offers low-cost data, avoids the need for dangerous field surveys, and provides rapid observations over large areas. Despite these advantages, comparative research remains limited, particularly with respect to differences among flood-mapping algorithms, such as machine-learning versus threshold-based approaches, and the performance of optical versus radar sensors. This research addresses these gaps by applying multiple flood-mapping methods to the same flood event in Pakistan, and then comparing their performance with respect to a validation benchmark to provide a clearer insight into how data selection and methodological design influence flood detection outcomes
This study evaluates four distinct methods for mapping floods using multi-sensor satellite data. To ensure a fair comparison, three unsupervised machine-learning approaches including a synergetic Sentinel-1 and Sentinel-2 workflow, a method integrating harmonized Landsat–Sentinel data with radar, and a daily MODIS imagery technique were tested alongside a traditional Otsu thresholding baseline. All four were tested on the same 2025 Pakistan flood event, characterized by intense monsoon rains and flash flooding across regions such as Sindh and Punjab in mid- to late-2025. The flood maps were then validated against UNOSAT flood reports for this event, where UNOSAT’s flood extent closely matches the results produced by the Sentinel-1/Sentinel-2 workflow, which yields the most conservative flood extent among the tested methods.
Larger flood extents from some methods, especially the Sentinel-1 Otsu thresholding approach, include areas not clearly flooded in optical images. This happens because SAR backscatter also responds to wet soil and saturated vegetation, which a simple threshold can misclassify as water, leading to flood overestimation.
Overall, the results show that flood maps are not just different versions of the same answer, they reflect different satellite data and the utilized algorithms detect flooding. Approaches that combine multiple data sources with machine-learning strike a better balance, producing flood extents that are both spatially consistent and physically realistic. This indicates that multi-sensor, machine-learning–based methods are better suited for operational flood monitoring than simple thresholding, which is too sensitive to surface noise and often overestimates flooding.
How to cite: Mones, J., Mhanna, S., Halloran, L., and Brunner, P.: A Comparative Assessment of Threshold-Based and Machine Learning Methods for Flood Detection, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-13888, https://doi.org/10.5194/egusphere-egu26-13888, 2026.
The research focused on developing the framework for assessing marine, nearshore and transitional waters across Ireland and validated for generalization of the framework across at any geospatial scale using remote sensing (RS) products. To the best of authors knowledge, existing most of the studies only have demonstrated for retrieving particular water quality (WQ) indicators like turbidity, salinity or chlorophyll a without in depth validation results. Recently the authors comprehensively reviewed several studies focusing on the RS applications for assessing WQ using computational intelligence techniques (CIT) like machine learning, artificial intelligence, statistical approaches etc. Unfortunately, the reviewed findings reveals that most of the research are questionable in terms of using data transparency, and validation with independent or other geospatial domains applications of the existing developed tools. Therefore, the research aim was to develop a novel framework and validated with independent datasets including new domain(s) adaptation or validation. For developing the framework, to achieve the goal of the research, the study utilized Sentinel-3 (S3) OLCI RS reflectance data. For obtaining RS data, the study utilized S3-OLCI level 3(L3) and level 4 (L4) reflectance data Rhow_1 to Rhow_11 form the Copernicus Marine Services (CMS) repository datasets for 2016 to 2024. To obtain the overall WQ, the research considered 49 (in-situ) EPA, Ireland monitoring sites across various transitional and coastal waterbodies for computing the overall WQ (IEWQI scores) scores using recently developed and widely validated the IEWQI model. After than the RS data prepared and match-up with 49 considering monitoring sites. For predicting IEWQI scores, the research utilized the multi-scale signal processing framework (MSSPF) by following configurations: data augmentations: 2x to 20x, noise level from 0.0001 to 0.05, and data spilled ratios 60-20-20 and 70-20-10, respectively for train, test and validation of 43 CIT models using RS data from 2016 to 2023 both L3 and L4, whereas the 2024 dataset using for testing independent dataset to generalize the model prediction capabilities. Utilizing four identical model performance evaluation metrics, the results reveals that the PyTorchMLP could be effective (train performance : R2 = 0.86, RMSE =0.09, MSE = 0.008, and MAE = 0.067; test performance : R2 = 0.84, RMSE =0.094, MSE = 0.008, and MAE = 0.071; and validation performance : R2 = 0.81, RMSE =0.095, MSE = 0.009, and MAE = 0.074, respectively at 7x augmentation with 0.0001 of noise level for 60-20-20) compared to the 43 CIT models in terms of predicting and validating independent dataset (independent dataset validation performance for 2024 : R2 = 0.62, RMSE =0.164, MSE = 0.026, and MAE = 0.12). Based on the predicted IEWQI scores, the WQ ranked “marginal”, “fair” and “good” categories for Irish waterbodies. The findings of the framework align with the traditional EPA, Ireland monitoring approaches. However, findings of the research reveals that the proposed framework could be effective to monitoring WQ general purposes using RS data across any geospatial resolution.
Keywords: remote sensing; Copernicus database; MSSPF, IEWQI, Ireland.
How to cite: Uddin, M. G., Diganta, M. T. M., Sajib, A. M., Rahman, A., and Indiana, O.: A comprehensive framework for assessing marine, nearshore and transitional waters quality integrating Irish Water quality Index (IEWQI) model from remote sensing products using computational intelligence techniques, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-20016, https://doi.org/10.5194/egusphere-egu26-20016, 2026.
Floods are among the most damaging natural hazards, motivating the development of rapid and scalable tools for floodplain mapping across multiple return periods and for post-event assessment. The Geomorphic Flood Index (GFI) is widely used to identify flood-prone areas using topographic information, but it can exhibit reduced reliability under complex hydraulic conditions—particularly near confluences where backwater controls water levels—and it may systematically overestimate inundation extents when used as a binary classifier.
This study advances the GFI framework by explicitly accounting for backwater effects at river confluences and along tributary junctions. In parallel, to reduce the intrinsic overestimation of GFI-derived floodplains, we test a suite of Artificial Intelligence (AI) classifiers—Random Forest, XGBoost, and Neural Networks—trained through a multi-parametric formulation that combines GFI with auxiliary predictors, including precipitation, lithology, land use, and slope. The approach is evaluated across multiple Italian catchments, using satellite-derived inundation and hydrodynamic simulations as independent benchmarks. Model performance is quantified against the baseline GFI approach using a standard threshold-based binary classification using an optimal cutoff.
The proposed framework aims to improve post-event flood delineation under observational constraints (e.g., satellite data gaps due to cloud cover, vegetation, or imaging limitations) and to provide a computationally efficient surrogate for extending hydrodynamic information to additional return periods or large basins where full numerical modelling is impractical. Preliminary results indicate that Random Forest provides the most robust performance across study sites. Incorporating backwater effects yields clear gains at confluences, primarily by reducing omission errors and improving the representation of hydraulically controlled inundation patterns. Moreover, the AI-based correction substantially mitigates the overestimation typically associated with standard GFI mapping, resulting in floodplain delineations that are more consistent with complex hydrodynamic processes and suitable for scalable flood hazard applications.
How to cite: Saavedra Navarro, J., Zhuang, R., Samela, C., and Manfreda, S.: Flood Susceptibility Mapping with GFI 2.0 and Artificial Intelligence Models, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-21507, https://doi.org/10.5194/egusphere-egu26-21507, 2026.
Precise monitoring of water surface elevation (WSE) in data-deficient areas such as the Ganga River stretch is essential for hydrological modelling, flood prediction, and comprehensive water resource management. This study introduces a comprehensive evaluation framework for Level-2 Geophysical Data Records (GDR L2) derived from various satellite altimetry missions, including Sentinel-3A/B, Sentinel-6A, Jason-3, and SWOT Nadir, validated against in-situ gauge stations from the Central Water Commission (CWC) across a range of hydrological conditions. The process includes advanced geographical analysis. Gaussian-process Kriging interpolation generates continuous longitudinal WSE profiles across strategically placed virtual stations; rigorous outlier detection employs interquartile range (IQR) and Hampel filters; bias correction employs dry-season median alignment to a common orthometric datum; and Kalman filter smoothing effectively reduces measurement noise while preserving critical hydrological signal dynamics.
Comprehensive performance evaluations employ co-located time series analysis, scatter plots, and flow duration curves (FDCs), with seasonal stratification distinguishing monsoon high-flow variability from stable non-monsoon baseflow conditions. The evaluation stresses physically significant parameters based on Kling-Gupta Efficiency (KGE) and RMSE. Sentinel-6A is the strongest performer in all situations with high non-monsoon accuracy (KGE 0.894, RMSE 0.089 m) and monsoon performance (KGE 0.57, RMSE 3.08 m) despite turbulent flow issues, but SWOT Nadir's processing potential is limited by specific hooking artifacts. During non-monsoon periods, measurement reliability is consistently 2-4 times higher. This proven multi-mission system demonstrates satellite altimetry as an operationally viable method for WSE retrieval in major braided rivers, allowing for accurate rating curve generation and discharge computation. In future machine learning data fusion and hydrodynamic modelling can be incorporated to increase basin-scale forecast capabilities.
How to cite: Kumar, B., Dwivedi, S. B., and Gaur, S.: Assessment of Multi-Mission Satellite Altimetry GDR L2 Products for River Water Surface Elevation in the Ganga Basin, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-21631, https://doi.org/10.5194/egusphere-egu26-21631, 2026.
Floods are highly dynamic hazards whose spatial extent can change rapidly within hours. Timely and accurate monitoring is essential for early warning, emergency response, and post-disaster assessment. A major challenge in current Earth Observation (EO) based approaches is the difficulty of capturing the complete evolution of a flood event, including its maximum flood extent. This information is often missing due to temporal gaps in Synthetic Aperture Radar (SAR) acquisitions and cloud cover in optical imagery. Missing the peak extent limits the accuracy of impact assessments and poses challenges for applications such as parametric insurance, which depend on reliable measurements of flood magnitude. Although daily flood products exist, they are often based on large-scale multi-spectral sensors and struggle during persistent cloud cover as well as with resolution for smaller events, creating an urgent need for a more reliable method for daily flood estimation from higher-resolution SAR datasets. To address these challenges, we propose a novel deep learning framework that fuses EO-based coarse dynamic hydrometeorological data with static geospatial datasets to produce high-resolution daily flood extent maps. Our approach integrates static flood conditioning inputs, including elevation, Height Above Nearest Drainage, Urban Development Area, flow direction, Normalized Difference Vegetation Index, Normalized Difference Built-up Index, soil clay and sand content, and pre-flood SAR and multispectral imagery with dynamic hydrometeorological variables such as daily precipitation and soil moisture. The model adopts a multi-stage vision transformer architecture: encoders extract multi-level latent representations from all inputs, which are then fused using cosine similarity, normalization, and temporal attention mechanisms. A decoder reconstructs high-resolution flood extent, followed by a Gaussian filter to reduce high-frequency noise. The framework is fully supervised using the globally available KuroSiwo flood mask dataset, ensuring transferability across diverse geographic regions and climate zones. In addition, this research provides a complete data preparation workflow that converts flood mask shapefiles into standardized image patch datasets, including a modular input selection interface that removes dependence on inputs included in specific datasets, directly suitable for deep learning training, enabling straightforward implementation and practical applicability. The model is trained and evaluated across three distinct climate zones on multiple continents, demonstrating a robust capability to overcome the temporal limitations of SAR data and cloud-induced gaps in optical observations. Held-out region tests with strict geographic separation to minimize spatial autocorrelation induced data leakage, further ensure unbiased evaluation and true transferability. Preliminary tests across multiple continents yield stable performance, with cross-site metric variations remaining within approximately 5-7 percent. This study introduces the first deep learning framework for daily fine-scale flood extent mapping using purely EO data which are globally accessible, providing a scalable and transferable solution for real-time flood monitoring, disaster management, and potential applications in parametric insurance by improving flood mapping cadence and reliably estimating maximum flood extents.
Keywords: spatio-temporal fusion, vision transformer, high-resolution flood mapping
How to cite: Surojaya, A., Kumar, R., and Dasgupta, A.: DeepFuse2.0: Novel Deep Learning-based Fusion of Satellite-based Hydroclimatic Data and Flood Conditioning Factors for Daily Flood Extent Mapping, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-1047, https://doi.org/10.5194/egusphere-egu26-1047, 2026.
Fully automated, globally applicable flood-mapping systems must earn user trust, which in turn requires systematic testing across diverse environmental conditions to understand performance stability and a clear understanding of model transferability. While some recent studies have evaluated cross-site performance of flood mapping algorithms, the cross-biome transferability of Random Forest (RF) models for SAR-based flood delineation has not yet been thoroughly evaluated. In this study, we assess how well RF classifiers trained for binary flood detection generalize across biomes using primarily Synthetic Aperture Radar (SAR) data. Our feature stack comprises 14 variables, including 9 SAR-derived features (Sentinel-1 VV and VH backscatter and associated temporal-change metrics) which provide information on the flood-induced land surface changes and 4 contextual predictors such as land cover and topographic indices which influence radar backscatter and help to reduce as well as mitigate uncertainties. Experiments were conducted across 18 flood events distributed equally amongst 6 distinct biomes: (1) Deserts and Xeric Shrublands, (2) Tropical and Subtropical Moist Broadleaf Forests, (3) Temperate Broadleaf and Mixed Forests, (4) Temperate Coniferous Forests, (5) Mediterranean Forests, Woodlands and Scrub, (6) Temperate Grasslands, Savannas and Shrublands. Model transferability is evaluated using a two-level nested cross-validation approach. First, intra-biome performance is established through an inner 3-fold Leave-One-Group-Out Cross-Validation (LOGO-CV), in which models are trained on all but one site within a biome and evaluated on the held-out site iteratively. Second, inter-biome transferability is quantified using an outer 6-fold LOGO-CV, treating each biome as a distinct group. In this setup, models are trained on all biomes except one and evaluated on all sites of the held-out biome. Classification performance is assessed using Overall Accuracy (OA), F1-score, Precision, Recall, and Intersection over Union (IoU), with all experiments repeated across 10 independent iterations to capture model structural and sampling variability.
Preliminary results on select biomes show substantial variation in inter-biome transferability. Notably, in some cases, models transferred between biomes outperform those trained within the same biome. These findings highlight the need for comprehensive biome-level transferability assessments to better understand the capabilities and limitations of RF-based flood mapping under globally diverse conditions, ultimately supporting more transparent and trustworthy flood-mapping products for end users.
How to cite: Hosch, P. C. and Dasgupta, A.: Cross-Biome Transferability of SAR-based Flood Mapping with Random Forests, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-1092, https://doi.org/10.5194/egusphere-egu26-1092, 2026.
Flood mapping using machine learning methods such as Random Forests (RF) requires informed feature engineering and selection. Despite feature-importance rankings across different biomes and land covers varying substantially, the stability of these feature rankings has not been evaluated specifically for RF-based flood delineation. In this study, we investigate the consistency of RF feature-importance rankings in a binary flood-classification task primarily based on Synthetic Aperture Radar (SAR) imagery. The feature stack comprises 14 variables, including 9 SAR-based features, Sentinel-1 VV and VH polarizations and their temporal-change metrics which inform the flood extent identification, and 4 contextual features such as land cover and topographic indices which provide information on backscatter uncertainties. The classification task was conducted across 18 flood events spanning six distinct biomes: (1) Deserts and Xeric Shrublands, (2) Tropical and Subtropical Moist Broadleaf Forests, (3) Temperate Broadleaf and Mixed Forests, (4) Temperate Coniferous Forests, (5) Mediterranean Forests, Woodlands and Scrub, and (6) Temperate Grasslands, Savannas and Shrublands. Three feature-attribution methods were evaluated: (1) Shapley Additive exPlanations (SHAP) provides a game-theoretic framework for feature attribution and is widely recognized for its consistency and interpretability; (2) Mean Decrease in Impurity (MDI), computed during tree growth, is the most commonly used importance metric for RF models; (3) Permutation feature importance (MDA) offers a model-agnostic approach that assesses importance by measuring the reduction in model accuracy when feature values are randomly shuffled. Both feature cardinality and feature correlation, which bias the feature rankings for these algorithms in different ways, were considered during interpretation. All experiments were repeated across 10 independent iterations to account for random variability. We first examined feature-importance rankings independently across the three sub-sample studies within each biome to establish baseline intra-biome variability, followed by quantification of inter-biome variability to assess whether feature-importance patterns transfer across different environmental conditions. Preliminary results across select biomes indicate stable rankings for SAR-based features, with VV and VH event polarizations dominating the decision boundary, while contextual descriptors, particularly terrain indices such as Height Above the Nearest Drainage, exhibit greater variability both within and between biomes. Understanding the transferability of feature-importance patterns and feature stacks across biomes is critical for developing an RF-based flood-mapping pipeline that operates reliably under diverse environmental conditions worldwide and ultimately builds user trust in the resulting products.
How to cite: Havakhor, P., Hosch, P., and Dasgupta, A.: Cross-Biome Feature Importance Stability Analysis for SAR-based Flood Mapping with Random Forests, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-1266, https://doi.org/10.5194/egusphere-egu26-1266, 2026.
Floods are among the most destructive natural hazards worldwide, causing severe impacts on human health, ecosystems, cultural heritage and economies. Over the past decades, both developed and developing regions have experienced increasing flood-related losses, a trend that is expected to intensify under climate change due to shifts in precipitation patterns and the frequency of extreme events. In many large river basins, particularly in data-scarce regions, flood forecasting remains highly uncertain because of limited in situ observations and complex hydrological and hydraulic dynamics.
EO4FLOOD is an ESA-funded project aimed at demonstrating the added value of advanced Earth Observation (EO) data for improving flood forecasting at regional to continental scales. The project focuses on the integration of multi-mission satellite observations with hydrological and hydrodynamic modelling frameworks to support flood prediction up to seven days in advance, with an explicit treatment of uncertainty.
A key outcome of EO4FLOOD is the development of a comprehensive and openly available EO-based dataset designed to support flood modelling and forecasting studies. The dataset covers nine large and hydrologically complex river basins worldwide, selected to represent a wide range of climatic, physiographic and anthropogenic conditions, and characterized by limited or heterogeneous availability of ground-based observations. It integrates high-resolution satellite products from ESA and non-ESA missions, including precipitation, soil moisture, snow variables, flood extent, water levels and satellite-derived river discharge.
Within EO4FLOOD, these EO datasets are combined with hydrological and hydraulic models, enhanced by machine learning techniques, to improve flood prediction skill and to better quantify predictive uncertainty in data-scarce environments. The project also investigates the role of human interventions, such as reservoirs and land-use changes, in modulating flood dynamics across the selected basins.By making this multi-variable EO dataset publicly available, EO4FLOOD aims to support the broader hydrological community in testing, benchmarking and developing flood modelling and forecasting approaches in challenging large-basin settings. The project provides a unique opportunity to explore the potential and limitations of EO-driven flood forecasting and contributes to advancing the use of satellite observations for global flood risk assessment and management.
How to cite: Tarpanelli, A. and the EO4FLOOD Team: Advancing Flood Forecasting in Large River Basins Using Multi-Mission Satellite Data: the EO4FLOOD project, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-3018, https://doi.org/10.5194/egusphere-egu26-3018, 2026.
Accurate and timely flood delineation is a cornerstone of disaster response and hydrological risk management. Synthetic Aperture Radar (SAR) is uniquely suited to this task because it operates independently of cloud cover and illumination, yet its interpretation remains challenging due to speckle, terrain effects, vegetation scattering, and ambiguities between flooded and permanent water as well as shadows and smooth surfaces such as tarmac. While deep learning has substantially advanced SAR-based flood segmentation, most existing models are trained from scratch and often struggle to generalize across regions and flood regimes. Recently, geospatial foundation models (GFMs) pretrained on massive satellite archives have shown promise, but their benefits for SAR-based flood mapping remain insufficiently quantified. This paper presents a controlled, large-scale global scale evaluation and benchmarking of a vision-transformer based GFM (NASA IBM Prithvi) against two task-specific segmentation architectures, the SegFormer (hierarchical transformer) and the commonly used U-Net (convolutional neural network), including lightweight variants, for post-event SAR-based flood mapping. All models were trained and evaluated under a standardized pipeline that explicitly addresses extreme class imbalance via stratified negative sampling and weighted loss functions. Training and validation used the expert-annotated Kuro Siwo dataset (43 flood events, 67,490 Sentinel-1 VV/VH tiles), while generalization is assessed on both the in-distribution Kuro Siwo test set and the out-of-distribution Sen1Floods11 hand labelled benchmark dataset. Results show that stratified negative sampling (controlling how many background-only tiles are shown to the model in each training epoch) increases precision by approximately 6% and mean Intersection-over-Union (mIoU) by about 7% relative to no sampling, while stabilizing training loss dynamics. On the in-distribution data, all architectures reach similar performance (mIoU ≈ 0.82), indicating that well-designed task-specific models remain competitive with GFMs. However, under out-of-distribution conditions, the foundation model Prithvi (mIoU 0.768) closely matches the performance of the SegFormer (mIoU 0.772) and clearly outperforms the U-Net (mIoU 0.712), highlighting the robustness of transformer-based representations when transferring across datasets. Pretraining on optical imagery yields only modest gains for SAR (+3.4% mIoU), suggesting that architectural inductive biases and data handling matter more than cross-modal pretraining. Notably, lightweight GFM variants achieve comparable accuracy with up to 94% fewer parameters, demonstrating strong potential for operational deployment. Scene-level analysis reveals that CNNs suppress scattered false alarms due to the neighborhood contextualization but miss large, continuous floods, while transformers preserve spatial coherence yet overpredict along complex boundaries and scattered surface water ponding, especially near permanent water bodies. Findings demonstrate that while SAR-based flood mapping accuracy requires a combination of appropriate model architectures and class imbalance-aware training, rather than foundation-scale pretraining alone. However, for spatial and statistical transfer to out of distribution datasets, GFMs offer substantial advantages and provide above-average performance for unseen cases, even without localized fine-tuning.
How to cite: Dasgupta, A. and Zouaidi, M.: Do Geospatial Foundation Models Improve SAR-Based Flood Mapping? , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-6586, https://doi.org/10.5194/egusphere-egu26-6586, 2026.
Synthetic Aperture Radar (SAR) imagery offers weather-independent observation capabilities critical for monitoring flood events. However, SAR-based flood detection workflows typically require specialized software, local computational resources, and expert knowledge in remote sensing. This work presents SARFlood, a web-accessible application that automates the complete SAR flood detection pipeline using the OpenEO platform. SARFlood is built on a Flask backend architecture designed for accessibility and reproducibility. Users interact with the system through a web interface that guides them through case study creation, including Area of Interest (AOI) definition via shapefile upload, event date specification, and optional ground truth data integration. The application implements OpenEO OAuth 2.0 authentication using the device code flow, enabling secure access to the Copernicus Data Space Ecosystem (CDSE) backend without requiring users to manage API credentials locally. Session-based project management allows users to track processing progress in real-time through a status reporting system that monitors each pipeline stage. Data acquisition is performed server-side via OpenEO, while feature engineering processors execute locally. The data acquisition module fetches multiple data sources through a unified OpenEO interface: pre-event and post-event Sentinel-1 VV and VH imagery, Digital Elevation Models (DEM) with automatic source fallback (FABDEM, Copernicus 30m/90m), and ESA WorldCover land cover classification. The OpenStreetMap water body features and the FathomDEM are acquired via their own APIs/websites. A caching system prevents redundant API calls for previously acquired datasets, significantly reducing processing time for iterative analyses, while keeping licensing in mind so only users who are logged in and have the according license will be able to access the cached files. The processing pipeline computes a comprehensive feature stack for flood detection. SAR derivatives include intensity bands, VV/VH polarization ratios, and change detection metrics computed in decibel space to enhance flood signal discrimination. Topographic features encompass slope and Height Above Nearest Drainage (HAND) derived from the DEM, as key indicators of flood susceptibility. Flow direction calculations use an expanded bounding box to determine the extended HAND computation domain to address edge artifacts, finally cropped to the original AOI during band compilation, ensuring computationally efficient and accurate flow routing. Additionally, stream burning is implemented to improve drainage network delineation. Further, contextual features include Euclidean Distance to Water and rasterized land cover classification. Users can currently upload ground truth shapefiles (e.g., Copernicus EMS), which are automatically rasterized and compiled into the output stack, enabling supervised classification workflows.
SARFlood includes integrated sampling and training modules. Multiple strategies such as Simple Random, Stratified, Generalized Random Tessellation Stratified, and Systematic Grid sampling are supported. The training module implements Random Forest classification with Leave-One-Group-Out Cross-Validation across multiple case studies, hyperparameter optimization via Bayesian search, and feature importance assessment through Mean Decrease Impurity, permutation importance, and SHAP values. The platform-, data- and model-agnostic design principles used in developing SARFlood, support open science and FAIR practices in the geoscience community. By combining web accessibility with robust feature engineering and machine learning integration, SARFlood provides researchers with a reproducible platform for generating uncertainty-aware flood labels lowering barriers to use.
How to cite: Wilhelm, P., Hosch, P., and Dasgupta, A.: SARFlood: A Web-Based, Cloud-Native Platform for Automated and Optimized ML-based SAR Flood Mapping , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-6617, https://doi.org/10.5194/egusphere-egu26-6617, 2026.
Near-real-time satellite-based flood maps support disaster risk management and emergency response. One widely used service is the Global Flood Monitoring (GFM) product of the Copernicus Emergency Management Service, launched in 2021 and based on Sentinel-1 Synthetic Aperture Radar (SAR) data. The GFM service combines three flood-mapping algorithms: pixel-based thresholding, region-based approaches, and change-detection techniques, merged using a majority-voting scheme to generate the final flood extent product. Another key strength of the GFM service is its rapid analysis, providing flood maps within approximately five hours of satellite image acquisition through a fully automated processing chain. As the product is increasingly relied upon by practitioners and decision-makers, there is a growing need to assess its accuracy and robustness. Understanding false alarms and missed detections is critical for improving the reliability and usability of the service.
In this study, we systematically compare GFM flood maps across twenty real-world flood events using high-resolution reference datasets. To ensure temporal consistency, the GFM-derived flood maps are generated using Sentinel-1 acquisitions from the same day as the reference observations. Spatial agreement between datasets is quantified using the Intersection-over-Union metric.
Our results suggest that the GFM service performs well for large, extensive flood events but degrades for smaller, localized ones. Many of the observed errors come not from flood detection itself, but from inaccuracies in the reference water layer - while surface water is correctly identified, misclassification of permanent or seasonal water bodies leads to false alarms and missed floods. We evaluate the three-underlying flood-mapping algorithms individually for consistent patterns of misdetection or false alarms. In addition, we develop an automated framework to rapidly compare any external flood map with the GFM outputs, enabling near-instant evaluation of agreement and error patterns.
This framework provides practical insights into where and why the GFM services achieve successes and failures and offers continuous validation and iterative improvement of global flood mapping services.
How to cite: Garg, S., He, N., Selvakumaran, S., and Borgomeo, E.: Evaluating Copernicus Global Flood Monitoring (GFM) Service trade-offs in near-real-time flood mapping, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-21734, https://doi.org/10.5194/egusphere-egu26-21734, 2026.
Posters virtual: Thu, 7 May, 14:00–18:00 | vPoster spot A
EGU26-22246 | ECS | Posters virtual | VPS10
Man-Made or Natural: Deciphering the Complex Factors Behind the 2023 Derna FloodDisasterThu, 07 May, 14:30–14:33 (CEST) vPoster spot A
On September 10, 2023, the city of Derna in northeastern Libya experienced one of the deadliest flood disasters in Mediterranean and African history. Storm Daniel delivered unprecedented rainfall, with Al-Bayda recording 414 mm and Derna receiving over 100 mm within 24 hours, approximately 270 times the region's typical September average of 1.5 mm. This study employs Synthetic Aperture Radar (SAR) from Sentinel and high-resolution Planet imagery to provide a comprehensive analysis of the flood's spatial extent, infrastructure damage, and the interplay between natural and anthropogenic factors that amplified this disaster.
Our flood extent mapping reveals catastrophic impacts on urban infrastructure. The river channel expanded dramatically from 50 meters to approximately 500 meters in width, while the maximum inundated area extended 1.2 km² from the collapsed dams to the Mediterranean Sea over a distance of 2.5 km. The analysis identifies critical damage to infrastructure including the collapse of two upstream dams, destruction of five road flyovers, and significant damage to ports, bridges, and residential areas.
The disaster's severity was substantially amplified by anthropogenic factors. Historical urban development had rerouted the river through artificial canals, with roads and settlements subsequently constructed on the natural riverbed. The two dams, built in the 1970s and unmaintained since 2002, catastrophically failed, releasing an estimated 30 million cubic meters of water. Mann-Kendall trend analysis of 122-year climatic records reveals a statistically significant warming trend (p ≈ 0, Sen's slope = 0.00798) alongside decreasing overall precipitation (p = 0.027, Sen's slope = -0.0389), suggesting a paradoxical pattern where less frequent but more intense rainfall events are becoming more likely.
The socio-economic impacts were devastating, with nearly 4,000 confirmed fatalities in Derna alone, over 10,000 missing, and economic losses estimated at $80 million. Our findings underscore the critical vulnerability created when urban expansion encroaches upon natural floodplains without adequate infrastructure resilience.
This study demonstrates the power of multi-source satellite remote sensing for rapid disaster assessment and highlights the urgent need for integrated flood risk management that considers both climatic extremes and anthropogenic modifications to natural water systems. The lessons from Derna have profound implications for urban planning, dam safety protocols, and climate adaptation strategies in vulnerable Mediterranean regions facing increasingly extreme weather events.
How to cite: Agarwal, V. and Kumar, M.: Man-Made or Natural: Deciphering the Complex Factors Behind the 2023 Derna FloodDisaster, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-22246, https://doi.org/10.5194/egusphere-egu26-22246, 2026.
EGU26-10679 | Posters virtual | VPS10
Contribution of Remote Sensing to the Analysis of the Drying Process of Lake Tanma under Strong Climate VariabilityThu, 07 May, 14:33–14:36 (CEST) vPoster spot A
Climate variability represents one of the most severe threats to lacustrine ecosystems worldwide, leading to water loss in nearly half of the world’s lakes and reservoirs. On the one hand, this variability is associated with drought conditions, manifested by a decline in precipitation. On the other hand, it is linked to rising temperatures, which enhance evaporation rates at lake surfaces.
In Senegal, a Sahelian country, the prolonged drought period of the 1970s led to the desiccation of several water bodies, including Lake Tanma. In this context, the present study contributes to a better understanding of the drying process of Lake Tanma under climate variability conditions, using remote sensing techniques. Lake Tanma is located in Thiès region, approximately 70 km from Dakar.
To achieve this objective, Landsat Earth observation products were used at the beginning and end of each decade between 1984 and 2024. The time series consists of multispectral images acquired in October, corresponding to the end of the rainy season in Senegal. This choice ensures the capture of the lake’s maximum water extent, thereby minimizing seasonal fluctuations. All data were acquired and processed using the Google Earth Engine platform. The Modified Normalized Difference Water Index (MNDWI) was computed for the entire time series to accurately delineate and characterize water-covered surfaces.
The results reveal a highly variable evolution of the inundated surface area of Lake Tanma, with a variation coefficient of 57.8%. The largest flooded area was observed in 1984, covering 969.33 ha, while the smallest extent was recorded in 2024, with only 76.18 ha. Analysis of intra-decadal variations shows a slight decrease (7%) in the flooded surface, between 1984 and 1989. In contrast, subsequent decades exhibit a marked and progressive regression of the lake’s water surface, reaching 21% during the 2000–2009 decade, 61% during 2010–2019, and up to 89% over the 2020–2024 period.
These decrease trends highlight the influence of hydro-climatic parameters, particularly precipitation and evaporation, which constitute the primary drivers of lake recharge and desiccation. Consequently, further investigation, of hydro-climatic factors, namely rainfall and temperature, is required, to better understand the drying process of Lake Tanma and to assess the impacts of hydro-climatic variability on its long-term dynamics.
How to cite: Ndeye, M. D. B., Diene, S. M., Ndao, S., Sarr, S., Guèye, A., and Tamba, S.: Contribution of Remote Sensing to the Analysis of the Drying Process of Lake Tanma under Strong Climate Variability, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-10679, https://doi.org/10.5194/egusphere-egu26-10679, 2026.
EGU26-16435 | Posters virtual | VPS10
Ecohydrological and multitemporal analysis of Andean wetlands under climate variabilitiyThu, 07 May, 14:36–14:39 (CEST) vPoster spot A
Bofedales are high-altitude wetlands whose functioning is closely linked to surface water availability, playing a key role in hydrological regulation and ecosystem resilience in the Andes. Despite their importance, spatially explicit information on their surface water dynamics remains limited, particularly in data-scarce mountain regions. This study investigates the ecohydrological dynamics of bofedales in the Alto Pampas sub-basin (Huancavelica, Peru) over the 2015–2024 period using a multitemporal remote sensing approach combined with climatic information. Seasonal patterns of bofedal extent and surface water presence were mapped from Landsat 8 imagery using vegetation and moisture indices (NDVI and NDII), together with topographic variables. Bofedales were identified through a Random Forest classification framework and subsequently categorized as permanent or seasonal based on the temporal persistence of hydric signals. Changes in surface water extent and bofedal productivity were quantified, and temporal trends were assessed using the Mann–Kendall test. In addition, generalized additive models were applied to examine potentially nonlinear relationships between climatic drivers and key ecohydrological indicators. The results reveal contrasting surface water trajectories among bofedales, reflecting heterogeneous sensitivity to climate variability within the sub-basin. These findings demonstrate the value of satellite-based monitoring for assessing surface water dynamics in high-Andean wetlands and provide relevant insights for water resources management and climate change adaptation in data-poor mountainous regions.
How to cite: Sansoni-Koga, S., Laurente-Torres, J., Ccaico-Atoccsa, M., Flores-Quispe, S., Meza-Fajardo, G., and Cárdenas-Gaudry, M.: Ecohydrological and multitemporal analysis of Andean wetlands under climate variabilitiy, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-16435, https://doi.org/10.5194/egusphere-egu26-16435, 2026.
