Monitoring these resources plays a key role in assessing water resources, understanding water dynamics, characterising and mitigating water-related risks and enabling integrated management of water resources and aquatic ecosystems. While in situ measurement networks play a central role in the monitoring effort, remote sensing techniques provide near real-time measurements and long homogeneous time series to study the impact of climate change from local to regional and global scales.
During the past three decades, a large number of satellites and sensors has been developed and launched, allowing to quantify and monitor the extent of open water bodies (passive and active microwave, optical), the water levels (radar and laser altimetry), the global water storage and its changes (variable gravity). River discharge, a key variable of hydrological dynamics, can be estimated by combining space/in situ observations and modelling, although still challenging with available spaceborne techniques. Interferometric Synthetic Aperture Radar (InSAR) is also commonly used to understand wetland connectivity, floodplain dynamics and surface water level changes, with more complex stacking processes to study the relationship between ground deformation and changes in groundwater, permafrost or soil moisture.
Traditional instruments contribute to long-term water level monitoring and provide baseline databases. Scientific applications of more complex technologies like Synthetic Aperture Radar (SAR) altimetry on CryoSat-2, Sentinel-3A/B and Sentinel-6MF missions are maturing, including the Fully-Focused SAR technique offering very-high along-track resolution. The SWOT mission, now opens up many new hydrology-related opportunities. We also welcome submissions of pre-launch studies for CRISTAL, Sentinel-3C/3D/3NG-Topography, Sentinel-6NG, MAGIC/NGGM and and other proposed missions such as Guanlan, HY-2 and SmallSat constellations such as the SMASH concept now called H2R, and covering forecasting.
Dear attendees,
There will be a short discussion time slot at the end of both time blocks. We are looking for feedback to shape the session for next year and we are looking for ECS volunteers to co-convene the session in the future.
Looking forward to meet you at the session.
-The conveners
Orals: Thu, 7 May, 08:30–12:30 | Room 2.44
As one of the most important ecological indicators of wetlands, water level directly reflects hydrological processes and ecological patterns. Therefore, efficient and accurate monitoring of water level is critical for wetland conservation and restoration. Interferometric Synthetic Aperture Radar (InSAR), with its advantages of wide coverage, all-day/all-weather observation, and high measurement precision, has been successfully applied to wetland water level monitoring. However, due to pronounced heterogeneity in internal hydrological connectivity within wetlands, conventional InSAR techniques often suffer from phase discontinuities and error propagation across hydrological boundaries, making it difficult to accurately characterize water level variations over large and complex wetland systems. To address this limitation, we propose an absolute wetland water level monitoring method based on hydrological unit division using the Small Baseline Subset InSAR (SBAS-InSAR) framework, aiming to improve the reliability of InSAR-derived water level estimates under complex hydrological conditions. Taking the floodplain of Louisiana, USA, as a case study, multi-temporal Sentinel-1 SAR imagery combined with global land cover data is used to analyze hydrological connectivity and partition the study area into multiple relatively independent hydrological units. Within each hydrological unit, a small-baseline interferometric network is constructed to retrieve relative water level change time series, which are subsequently calibrated using in situ observations from United States Geological Survey (USGS) hydrological stations. Finally, least-squares estimation is applied to derive the spatiotemporal distribution of absolute water level changes. The experimental results demonstrate that: (1) hydrological unit division significantly improves the reliability of time-series inversion, reducing the overall root mean square error (RMSE) from 13.20 cm to 4.03 cm; (2) hydraulic barriers such as levees and urban infrastructure substantially disrupt the spatial continuity of wetland water level variations; and (3) C-band coherence in wetlands exhibits pronounced seasonal variability, with the highest coherence observed from late winter to early spring and the lowest from late summer to early autumn, mainly influenced by vegetation phenology and inundation conditions. Overall, the proposed method enables centimeter-level, large-scale monitoring of wetland water level changes, providing technical reference and data support for wetland water resource management and ecological protection.
How to cite: Zhao, J., Shen, C., Hou, C., Niu, Y., and Luo, Q.: Wetland Water Level Monitoring Based on Hydrological Unit Division Using SBAS-InSAR: A Case Study in Louisiana, USA, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15716, https://doi.org/10.5194/egusphere-egu26-15716, 2026.
Floods are among the most disastrous natural hazards. Therefore, issuing accurate river water level forecasts is one of the key tasks of the hydrologic community. Such forecasts are usually computed only for gauging stations. Many basins, however, are poorly gauged with only a few monitoring stations available. In contrast, satellite altimetry provides regular water level measurements globally at the so-called virtual stations (VS), i.e. unmonitored river sites observed only by altimetry satellites. The temporal resolution of water level time series at VS is approximately 10-35 days. Due to such a long repeat cycle, altimetry observations have not been used very often for forecasting purposes.
In this study, we present the AltHydro system which represents the first approach to issue forecasts for VS of altimetry satellites. AltHydro computes hourly updated forecasts for VS with a 72-hour lead time. First, vector autoregressive models are employed to calculate water level predictions at gauge stations. Next, linear regressions between gauge and altimetry water levels are established and updated in real time. Finally, the predictions for gauge stations are transferred to the neighbouring VS using the regression coefficients and considering the along-river time lag, driven by the downward water propagation, calculated in real time.
Our approach has been applied to 8 VS of the Sentinel-3A satellite located on the middle Odra/Oder River in southwestern Poland. The Odra/Oder is a transboundary river originating in the Sudetes Mountains. Major floods hit the Odra/Oder river basin regularly, e.g. in 1997, 2010 and 2024. The in situ data were taken from the gauges owned and maintained by the Polish Institute for Meteorology and Water Management — State Research Institute. To properly validate water level predictions at VS, we use both Sentinel-3A (since 2017) and the Surface Water and Ocean Topography (SWOT) measurements (since 2023). The accuracy assessment revealed root mean squared error (RMSE) of 0.17 m (ranging from 0.11 to 0.22 m) and the Nash-Sutcliffe efficiency (NSE) of 0.95 (ranging from 0.92 to 0.98) for the 24-hour predictions. Satisfactory accuracies were also found for the predictions with a lead time of 72 hours, with mean RMSE and NSE of 0.30 m and 0.88, respectively. The system showed robust performance during the major flood of September 2024, especially for the 24-hour lead time. The AltHydro system can lead to increasing the number of stations with water level predictions worldwide, especially when using the unprecedented geometry of the SWOT measurements.
The research presented in this paper has been carried out in frame of the project no. 2020/38/E/ST10/00295 within the Sonata BIS programme of the National Science Centre, Poland. The research has also been supported by the Bekker Programme of the Polish National Agency for Academic Exchange, as well as by the program “Excellence Initiative — Research University”. The experimental version of the system works in an operational fashion, and its real-time predictions are available at: http://althydro.uwr.edu.pl.
How to cite: Niedzielski, T., Halicki, M., and Schwatke, C.: AltHydro: an operational system for real-time water level forecasting at virtual stations on the Odra River, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-18189, https://doi.org/10.5194/egusphere-egu26-18189, 2026.
Accurate representation of river water levels is essential for flood forecasting in Narmada river basin, where complex river networks and limited observations cause significant challenges. In this study, we present data assimilation framework to assimilate surface water elevation observations into the 2D hydrodynamic model Triton. We will use ensemble Kalman filter (EnKF) data assimilation techniques with grid-to-grid along the stream localization by leveraging both upstream and downstream network information to account for hydrodynamic uncertainties. We will assimilate the surface water elevation from the central water commission (CWC) of India and HydroWEB. The proposed approach is expected to improve the simulation of flood propagation, river depth, and inundation dynamics over the Narmada river basin. By integrating observational data directly into Triton, we anticipate enhanced accuracy in peak water levels and flood timing. This study demonstrates the potential of combining hydrodynamic modeling with real-time data assimilation to provide actionable insights for flood risk
How to cite: Prakash, V. and Saharia, M.: Towards High-Resolution River Forecasting over Narmada Using Surface Water Elevation Data Assimilation in Triton, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-1074, https://doi.org/10.5194/egusphere-egu26-1074, 2026.
Floods are among the most devastating natural disasters, affecting both developed and developing regions. However, developing countries often lack sufficient monitoring and early warning systems, making them more vulnerable. The ESA EO4FLOOD project aims to enhance flood forecasting by integrating satellite data with hydrologic and hydraulic models. Within this effort, we introduce a novel joint modelling framework that couples hydrologic and hydraulic models using differentiable programming.
Hydraulic and hydrologic models are constrained by data and traditionally rely on in situ measurements, which are expensive to obtain, may be access-limited, and can be dangerous to collect in remote terrain or during crises. Remotely sensed data from satellites or airborne campaigns offer a potent and low-cost alternative, with satellites providing data irrespective of national or geographic borders. Hydraulic-geometric parameters, water surface elevations (WSE), and slopes (WSS), as well as inputs to the hydrologic model, can be resolved through remote sensing.
With the launch of the Surface Water and Ocean Topography (SWOT) satellite mission, high-accuracy spatially distributed (2D) WSE and WSS observations have become available at a global scale. The primary instrument is a Ka-band radar interferometer that observes two, 50km wide swaths on each side of the ground track of the satellite, with a science requirement to detect rivers larger than 100m in width; however, even smaller rivers can be measured. The ICESat-2 satellite enables accurate global WSS and river topography observations, which can be locally substituted by national topographic LIDAR missions.
We present a differentiable hydraulic-hydrologic framework integrating large-scale Earth observation (EO) data while maintaining physical consistency. Both models are jointly trained using SWOT data, with the output of the hydrologic model serving as input to the hydraulic model. Joint training enables both models to benefit from the information contained in the SWOT data, as well as potentially satellite earth observations of additional state variables (e.g., soil moisture, evapotranspiration, terrestrial water storage). Additionally, the coupled approach allows independence from rating-curve-based discharge, marking a significant leap forward in the global applicability of hydraulic models.
We demonstrate this approach on the Torne River, located between northern Sweden and Finland. With extensive in-situ data, Torne provides an ideal case for validation. Our joint model supports accurate water level and discharge forecasting, aiding flood preparedness, informing local adaptation strategies, and enhancing climate resilience. This proof of concept highlights the method’s global potential under the EO4FLOOD initiative.
How to cite: Köhn, S., Chewning, C., Folkmann Musaeus, A., Aarestrup, P., Löwe, R., Kittel, C., Gustafsson, D., Bauer-Gottwein, P., and Nielsen, K.: Joint training of hydrologic and hydraulic models using Deep Learning and remote sensing data for the Torne River, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-11772, https://doi.org/10.5194/egusphere-egu26-11772, 2026.
Continuous monitoring of river discharge time series is essential for climate applications; however, it remains limited by sparse ground-truth measurements. As a result, there is an increasing demand for river discharge estimation based on satellite-derived observations. Nevertheless, generating daily discharge products remains challenging due to the irregular temporal resolution, which are not complementary for producing temporally continuous time series. Within the framework of the ESA River Discharge Climate Change Initiative (RD-CCI), this study addresses this limitation by producing daily river discharge estimates using Long Short-Term Memory (LSTM) networks that integrate multi-mission optical reflectance data and multi-mission altimetry-derived water level observations.
A major challenge in combining heterogeneous satellite missions is the irregular temporal sampling, which conflicts with the requirement of LSTM models for synchronized and regularly spaced input sequences. To address this issue, Akima interpolation was applied over short consecutive periods to harmonize temporal gaps across input features while preserving natural transitions in the time series. This approach significantly improved data continuity without introducing excessive artificial smoothing.
The LSTM model was implemented using a sliding window scheme of past time inputs to predict the one day ahead discharge value, and compared against other combined river discharge products available from the CEDA catalog (https://catalogue.ceda.ac.uk/uuid/dbba9cfe8d104648b19e39f4c2da1a27/). Input variables include reflectance data from multiple optical missions (Landsat 5, Landsat 7, Landsat 8, Landsat 9, Sentinel 2 Level-1C, Sentinel 3 OLCI, and MODIS on TERRA and AQUA) with orthometric heights obtained from multi-mission altimetry dataset from multiple missions (ERS-1, ERS-2, ENVISAT, Topex/Poseidon, Jason-1, Jason-2, Jason-3, Saral, Sentinel-3A and B, Sentinel-6A).
The LSTM approach was implemented across some diverse river basins, including the Amazon, Colville, Congo, Garonne, Lena, Limpopo, Mackenzie, Maroni, Mississippi, Niger, Ob, and Po rivers to produce daily-based river discharge estimation. Results across representative basins show Nash - Sutcliffe Efficiency values ranging from 0.11 in the Lena (Kyusur station, polar region) to 0.92 in the Amazon (Obidos station, tropical region). Kling–Gupta Efficiency varies between 0.22 for the Limpopo (Beithbrug station, arid region) and 0.95 for the Amazon, while relative Root Mean Square Error ranges from 288 % in arid basins to as low as 9 %in tropical regions. Overall, the results demonstrate that the LSTM model effectively captures the temporal dynamics of river discharge across diverse hydroclimatic regimes.
How to cite: Tural, C. Y., Filippucci, P., and Tarpanelli, A.: 20 Years of Daily River Discharge Estimation by Using Long Short-Term Memory, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-8242, https://doi.org/10.5194/egusphere-egu26-8242, 2026.
Distributed monitoring of river discharge remains a challenging task which require frequent measurements, particularly in data-sparse regions. In this study, we assimilated river discharge estimated using Surface Water and Ocean Topography (SWOT) observations, which were used to derive multi-station discharge distributed over a river basin. The discharge assimilation was performed using a variational method across multiple distributed virtual stations over the Ganga River and its tributaries. In the assimilation the background hydrological model estimates were optimally combined with spatio-temporally distributed SWOT-derived river discharge and associated errors. In this study, we implemented a unique dynamic window algorithm to extract water surface elevations from raw SWOT Level-2 Pixel Cloud (PIXC) data. It was designed such that data gaps and noise due to complex river reaches were excluded and data points with lower elevation uncertainty were selected. Then these elevations were used in Manning's equation with adaptive channel geometries (rectangular and parabolic) for discharge estimation over different reaches of the river. Discharge validation was carried out over multiple virtual stations across the Ganga River basin using gauge observations and GLoFAS discharge. Over stable river reaches exceptional accuracy was found (Gandhighat, Virtual Station (Ganga River): NSE>0.9, R²>0.9, RMSE<3,150 m³/s). However, accuracy degraded over dynamic and shallow river reaches (NSE=0.65–0.90), and performance further degraded in multi-threaded braided sections (NSE=0.28–0.59). In-synchronous field measurements were carried out with satellite overpasses confirm SWOT-derived water surface elevation accuracy within 0.5 m RMSE and discharge estimates aligned with ADCP measurements. The analysis was able to capture basin-scale spatio-temporal discharge variability from monsoon to lean-flow conditions. It established strong performance across study period (July 2023 – December 2025) with discharge range spanning 500–40,000 m³/s. The spatially distributed river discharge from SWOT, assimilated into the WRF-Hydro model demonstrated capabilities from point estimates into basin-scale continuous monitoring. It paves the way for the use of satellite derived river discharge assimilation for improved flood forecasting and water resource management across flood prone rivers of South Asia.
Keywords: SWOT discharge assimilation, Flood, SWOT mission, Ganga River, River discharge, South Asia
How to cite: Dubey, A. K., Kumar, P., Chander, S., Gupta, P. K., and Sharma, R.: Two-Dimensional Variational Data Assimilation of SWOT derived River Discharge over Multiple Virtual Stations into the WRF-Hydro model over the Ganga River Basin, India, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-11283, https://doi.org/10.5194/egusphere-egu26-11283, 2026.
Numerical hydrodynamic models are widely used to simulate and forecast river water surface elevation (WSE) and flow velocity, over lead times ranging from hours to several days. Their predictive skill, however, is limited by multiple sources of uncertainty related to simplified governing equations, numerical solvers, forcing and boundary conditions, and model parameters, e.g. friction coefficients, obtained through calibration. These uncertainties propagate to model outputs and can significantly affect flood forecasts. Data Assimilation (DA) provides a robust framework to reduce such uncertainties by sequentially combining numerical model predictions with observations as they become available, while explicitly accounting for their respective error statistics.
In this work, a joint state-parameter EnKF is implemented to reduce uncertainties in upstream time-varying inflow discharges and spatially distributed friction coefficients through the assimilation of in-situ WSE observations. The performance of the EnKF strongly depends on ensemble size and on the spatial and temporal density of the observing network. However, the limited availability and continued decline of in-situ river gauge stations, particularly in floodplains, motivate the integration of remote-sensing (RS) observations into the DA framework, and with that the uncertainties associated with the flood extent maps.
Recent advances in deep learning (DL) have significantly improved automatic SAR-based flood extent mapping. Nevertheless, most existing approaches provide deterministic flood extent maps without associated uncertainty estimates, which are essential for stochastic DA methods. To address this, we here rely on a unified DL framework, called Density-Aware Conformal Flood Mapping (DACFM), that explicitly quantifies two complementary sources of uncertainty in SAR-derived flood maps: (i) DL model’s knowledge-related uncertainty, caused by finite training data or model misspecification, and (ii) SAR data-related uncertainty arising from image noise and flood/non-flood class ambiguity. DL model’s knowledge uncertainty is characterized using feature density analysis in the latent space of a density-aware neural network, while data-related uncertainty is quantified via softmax entropy. These uncertainty estimates are operationalized through conformal risk control at a user-defined risk level (α, δ), enabling the rejection of out-of-distribution samples and the generation of set-valued predictions for in-distribution inputs. Such a method of uncertainty estimation was evaluated across diverse real-world flooding contexts, including built-up areas, vegetated regions, and bare soil, demonstrating improved uncertainty quantification.
The proposed approach is demonstrated using a high-fidelity TELEMAC-2D hydrodynamic model of the Ohio River reach between the Cannelton and Newburgh locks and dams. RS-derived flood extent products from Sentinel-1 SAR are assimilated in the form of wet surface ratios (WSR) over selected floodplain subdomains, each accompanied by uncertainty estimates derived from the DL-based flood mapping framework. Flood reanalyses for the major flood events of February and April 2025 yield significant WSE error reduction. Independent flood extent maps derived from Sentinel-2, and Landsat-8 optical images were also used to validate the experiments.
How to cite: Nguyen, T. H., Li, Y., Ricci, S., Piacentini, A., Cassan, L., Rodriguez Suquet, R., Peña Luque, S., Bonassies, Q., Fatras, C., Chini, M., and Matgen, P.: Integrating AI-derived SAR Flood Extent Map Uncertainties in Remote Sensing Data Assimilation for Flood Forecasting, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-14113, https://doi.org/10.5194/egusphere-egu26-14113, 2026.
Flooding results in large economic and loss of life, which are further aggravated by the lack of precise forecasts of flood inundation depth and extent. Recent extreme flood events have highlighted the need for reliable operational flood forecasting systems. Conventional physics-based flood models are subject to multiple sources of uncertainty and are computationally demanding, which limits their applicability for real-time operational services. Artificial intelligence (AI)-based flood models, on the other hand, can significantly reduce computational cost and enable near real-time forecasting at high spatial resolutions. Despite recent advances, most AI-based flood models lack mechanisms to correct evolving prediction errors using real-time observations. Flood processes are highly nonlinear, with errors that evolve rapidly in space and time, while Earth Observation (EO) data provide only intermittent and spatially incomplete snapshots of the true system state. Deep data assimilation (DDA) addresses this gap by learning state-dependent error propagation and dynamically integrating multi-source EO information into AI-based flood forecasting models. In the recently funded Indo-German project FLAIR (Flood Forecasting using AI for Regional Sustainability, funded by BMBF), we develop observation operators linking simulated flood states to EO-derived flood extent and water surface elevation within a two-dimensional convolutional long short-term memory framework. DDA is then implemented through a state-parameter augmented approach to update model states in real time, accounting for dynamically evolving flood conditions. The proposed framework is evaluated for two human-altered test catchments with contrasting hydrological characteristics in India and Germany. Forecast performance is benchmarked against an open-loop configuration and a DDA-based CaMa-Flood model across multiple forecast lead times ranging from one to seven days. A specific innovation is the assimilation of reservoir Water Surface Elevations from EO altimeters which help determine their influence on the resulting flood propagation as well as enable reservoir optimization for dampening the flood wave. FLAIR demonstrates the potential of deep data assimilation and multi-source EO data to improve the accuracy and robustness of AI-based flood forecasts as well as builds trust in such forecasts through detailed benchmarking against physics-based models, supporting their application in operational flood risk management.
How to cite: Ramesh, V. and Dasgupta, A.: Towards Operational AI-based Flood Forecasting using Deep Data Assimilation of Multi-source Earth Observations, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-6311, https://doi.org/10.5194/egusphere-egu26-6311, 2026.
Lakes, reservoirs, and small water bodies play a pivotal role in West African drylands. They are widely distributed across the landscape, making them a primary source of water for both people and livestock. However, due to their generally small size and strong temporal variability, their hydrological dynamics remain poorly understood at the regional scale. Moreover, these water bodies are highly sensitive to both climatic and anthropogenic forcing, exhibiting complex and sometimes counter-intuitive dynamics, such as the increase in surface runoff observed in the Sahel despite a decrease in precipitation during and the after the major droughts of the 1970s and 1980s. Understanding the past and present dynamics of these water bodies is therefore crucial to anticipate their future evolution in a context of environmental change and rapid population growth.
Recent satellite missions provide an unprecedented view of small water bodies at large scales by combining high spatial resolution, high temporal frequency, and novel observations of water level and volume. This study relies on recent altimetric sensors coupled with surface water extent derived from optical imagery to investigate the dynamics of water levels and volumes across thousands of lakes within the study area.
Water level dynamics are first estimated using Sentinel-3 SRAL data for lakes intersected by satellite tracks, and then extended spatially by more than one order of magnitude using SWOT observations. We show that SWOT-derived water levels are in excellent agreement with in-situ measurements collected in Niger, Burkina Faso, and Senegal, as well as with water level estimates from other satellite sensors (Girard et al., 2025).
The analysis of dry-season water level dynamics allows to identify distinct hydrological behaviours at the regional scale, and to highlight the influence of anthropogenic water withdrawals in agricultural reservoirs, as well as connections between lakes, the river network, and/or groundwater (de Fleury et al., 2023).
Water volume variations are subsequently obtained by combining water level data with water surface areas. The latter are estimated appliying a U-Net convolutional neural network to optical imagery from Sentinel-2 and the Landsat archive. This approach, specifically developed for the study region, provides accurate estimates of water area for the different types of lakes encountered. These include water bodies covered by vegetation and extremely bright lakes characterized by high suspended sediment loads and very fine particles (de Fleury et al., 2025).
The resulting elevation-area relationships are then used to reconstruct past changes in water volume from Landsat-derived water surface areas (1984 to present) for more than 2,000 lakes and reservoirs (Girard et al., 2026). This analysis reveals long-term changes and trends in hydrological dynamics in relation to environmental drivers (i.e. precipitation, temperature, and land use/land cover) and anthropogenic activities (e.g. reservoir construction and management).
References
- Girard, L. Kergoat, J. S. Paiva, R. Yonaba and M. Grippa (2026) 40-Year Volume Changes of West African Lakes Derived from SWOT and Optical Imagery. Submitted to WRR
- Girard et al. (2025b) https://doi.org/10.1109/JSTARS.2025.3570859
- de Fleury et al (2025) https://doi.org/10.1016/j.rsase.2024.101412
- de Fleury et al. (2023) https://doi.org/10.5194/hess-2022-367
How to cite: Grippa, M., Girard, F., de Fleury, M., Nikiema, D. E., Faye, C., Abdourahamane Touré, A., Yonaba, R., and Kergoat, L.: Monitoring water volume dynamics in West African lakes and reservoirs using altimeters and optical satellite sensors, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-11546, https://doi.org/10.5194/egusphere-egu26-11546, 2026.
Water resources are under unprecedented pressure driven by climate change, societal demands, and geopolitical tensions. To address these issues in France, the FR2030 project supported by the Ministry for Ecological Transition, aims to improve water management. In this context, authorities have identified 18,500 water bodies requiring regular monitoring through satellite data. These water bodies of varying nature range from 3ha to several hundred hectares.
Current satellite missions provide data well-suited for regular monitoring thanks to their revisit frequency and spatial resolution, enabling observation of a large number of water bodies. The launch of SWOT in 2023 expanded significantly the number of observable water bodies through its near-global coverage, opening up new possibilities for monitoring water resources.
One of FR2030’s objectives is to provide volume measurements that decision-makers, such as prefectures, regional environmental agencies (DREAL) and other authorities, can rely on to act quickly in crisis situations. Most methods focus on estimating volume variations as this approach is more straightforward. However, end users also need absolute quantitative measurements.
The first developed approach is based on the hypsometric law commonly used for volume estimation (Crétaux et al., 2016). While SWOT provides height and surface data, its surface measurements lack the precision required for quantitative monitoring making a multi-sensor approach preferable. The hypsometric curve is derived by combining Sentinel-2 surface data (Peña-Luque et al, 2021) with water surface elevation data from SWOT_L2_HR_LakeSP_Prior products. Lake bottom information obtained from a DEM and dam base data (e.g. DEM4Water) is needed to compute absolute volume to correct the bias. This 2D approach already provides valuable insights for user but requires prior data.
A second method was developed to overcome this limitation. Water body contours are extracted from multiple clear Sentinel-2 surface images each linked to a water surface elevation from SWOT. Using 3D reconstruction, we derive bathymetry (Khazaei et al., 2022) discretized along a height scale. Water columns at the target elevation are then used to compute lake volume. This innovative 3D approach relying only on surface and height remote sensing data already shows strong potential. Its preliminary results are consistent with established datasets and methods. The method delivers in-situ validated results with an initial error of just 25% on absolute volumes. With several limitations already identified, this approach is on track for significant improvements.
These two approaches illustrate the potential for developing a global framework for dynamic monitoring of reservoir water storage under time constraints. By combining multi-sensor satellite data and advanced reconstruction techniques, they enable direct estimation of absolute water volumes, an innovative breakthrough compared to traditional methods focused on relative variations. While further validation and optimization are required, these methods open promising perspectives for decision-makers with actionable insights at scales relevant for resource management.
References:
- Crétaux et al., 2016, Lake volume monitoring from space: https://doi.org/10.1007/s10712-016-9362-6
- Peña-Luque et al, 2021, Sentinel-1&2 Multitemporal Water Surface Detection Accuracies, Evaluated at Regional and Reservoirs Level: https://doi.org/10.3390/rs13163279
- DEM4Water: https://github.com/CNES/dem4water
- Khazaei et al., 2022, GLOBathy, the global lakes bathymetry dataset: https://doi.org/10.1038/s41597-022-01132-9
How to cite: Tardy, B., Oosterhof, C., De Fleury, M., Aiche, A., and Nicolas, G.: Absolute Water Volume Estimation from Multi-Sensor approach using SWOT and Sentinel-2, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-20510, https://doi.org/10.5194/egusphere-egu26-20510, 2026.
Accurate spatiotemporal monitoring of inland water bodies is crucial, since, apart from numerous ecosystem services they also provide valuable water resources. This is particularly true in water-limited Mediterranean regions where detailed characterization of lake water extent, level and storage could facilitate sustainable water resources management under climate extremes (e.g., droughts). Here, focusing on the Trichonida – Lysimachia lake complex in Western Greece, we synthesized remote sensing observations, in-situ measurements, and auxiliary environmental and geospatial datasets, in order to characterize the spatiotemporal dynamics in their water extent, level, and storage. The Trichonida – Lysimachia lake complex is a sensitive ecosystem, protected as part of the Natura 2000 network; lake Trichonida is the largest natural lake in Greece (surface area of ~93 km2 and maximum depth of ~52 m) and is connected through an open channel with the much smaller and shallower lake Lysimachia (surface area of ~10 km2 and maximum depth of ~8 m). The analysis of optical (Landsat 5, 7, 8, 9, Sentinel 2) and microwave (Sentinel 1) satellite imagery revealed that both lakes displayed significant changes in their areal extent at the seasonal and annual time scale, with these results being more pronounced for the lake with shallower bathymetry (i.e., Lysimachia). The surface area of lake Trichonida (Lysimachia) decreased significantly during the period 1985 – 2024 at a rate equal of 32.3 m2 yr-1 (36.4 m2 yr-1) with hotspots that displayed more than 100 m shift in the shoreline. In-situ water level measurements agreed well with estimates from satellite altimetry (ICESat, SWOT), and, when combined with the detailed bathymetries of the two lakes, detailed water level-area-volume curves were derived. Such curves, synthesize multivariate observations, in-situ measurements, and cross-disciplinary hydrogeodetic techniques and reveal lake-specific 3D patterns. The obtained results offer valuable insights not only towards the sustainable management of the two lakes but can also contribute to the refinement of regional- and global-scale initiatives on satellite-based 3D lake monitoring.
How to cite: Panousis, K., Andreadis, K. M., Langousis, A., Fourniotis, N. Th., and Pappas, C.: Spatiotemporal dynamics of water extent, level, and storage of lakes with contrasting bathymetries: insights from the Trichonida – Lysimachia lake complex in Western Greece, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-18214, https://doi.org/10.5194/egusphere-egu26-18214, 2026.
Traditional in-situ monitoring is often limited to major reservoirs in developed regions. However, rising water scarcity necessitates monitoring smaller, isolated water bodies critical for local agricultural systems. Remote sensing has emerged as an efficient alternative to complement or replace gauge stations. Satellite altimetry missions offer high accuracy, but they can be constrained by coarse spatial resolution and revisit time. Consequently, SAR imagery has been widely exploited. Interferometric techniques use phase to detect level changes but are limited to vegetated wetlands or sub-wavelength changes [1]. On the other hand, amplitude-based methods, which rely on shoreline backscatter differences, are often dependent on accurate DEMs [2]. This research aims to introduce a novel approach for estimating water level changes using SAR amplitude data, without relying on prior morphological information. The approach assumes that the horizontal shift Δ of a shoreline and its water level change Δh are geometrically dependent through the local coastal slope i, under the hypothesis that locally the coastal morphology can be approximated with a plane. From the satellite perspective, the level change on this plane is captured as a variation in the sensor-to-target distanced. By combining d and Δ with other parameters which describe the geometric configuration of the satellite-coast interaction (satellite azimuth, SAR local incidence angle, coastal aspect), a final observation equation is formulated to link the unknown water level change to the measured distance. This scheme can be applied to different coastal zones around the lake, assuming variable slopes, but the same water level change between two epochs, providing redundancy for the implementation. The model is developed first by applying an image-matching technique on coregistered SAR images to detect shoreline displacements in the range direction (d). Then, the displacements are used as input for a least squares approach, which incorporates initial assumptions regarding geometrically known parameters and preliminary estimates of the unknown values, yielding estimates of both the water level changes (Δh) between epochs and the slope of each coastal zone portion (i). A preliminary analysis was focused on Trasimeno Lake in Umbria, Italy, using a stack of 30 Sentinel-1 (S1) SLC images (IW mode, VV polarisation) acquired in 2022 on the same orbit, coregistered using the pyGMSTAR library [3]. When compared to the in-situ data, the differences with the estimated values achieved an accuracy of 4 cm and a NMAD of 9 cm, demonstrating the method's potential using S1 mid-resolution imagery. Other tests are under development to improve the overall performance and support the future integration of the method for enhancing water level monitoring in different basins.
[1] Aminjafari, S., Brown, I., Mayamey, F. V., & Jaramillo, F. (2024). Tracking centimeter-scale water level changes in Swedish lakes using D-InSAR. Water Resources Research, 60, e2022WR034290
[2] Lee, S., Kim, D.-j., Li, C., Yoon, D., Song, J., Kim, J., & Kang, K. (2024). A new model for high-accuracy monitoring of water level changes via enhanced water boundary detection and reliability-based weighting averaging. Remote Sensing of Environment, 313, 114360
[3] Pechnikov, A. (2024). PyGMTSAR (Python InSAR) (Version 2024.2.8)
How to cite: Ranaldi, L., Belloni, V., Nascetti, A., and Crespi, M.: A novel approach for water level changes with SAR amplitude data: first results using Sentinel-1 imagery on Trasimeno Lake, Italy, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-21316, https://doi.org/10.5194/egusphere-egu26-21316, 2026.
Accurate monitoring of inland water levels is essential for quantifying surface water storage, understanding hydrological extremes, and constraining hydrodynamic models through data assimilation. Satellite radar altimetry provides a unique long-term and global perspective on water surface height (WSH), yet its application over rivers and lakes remains challenging due to complex geometries, heterogeneous surface conditions, and limited characterisation of observation uncertainty. In the Cryo-TEMPO project, we present the Inland Water dataset delivering enhanced CryoSat-2 derived WSH products over lakes and rivers for the period 2011–2025. The processing relies on the CLS Data Handling and Processing System and integrates four retracking algorithms (OCOG, TFMRA, SAMOSA+, and MwAPP). Major advances rely on the improved spatial extraction of river observations using the global SWOT River Database (SWORD), combined with adaptive buffering. This increases the number of valid river measurements by up to a factor of five compared to previous baselines, while preserving physically consistent longitudinal water surface profiles over large river systems. A key innovation of the dataset is a new data-driven uncertainty estimation framework designed to support downstream applications, including hydrodynamic modelling and data assimilation. This approach at 20 Hz yields more representative and internally consistent uncertainty estimates, significantly reducing the occurrence of high-uncertainty outliers relative to earlier processing phases. Internal evaluation and external validation against in situ gauge records, ICESat-2 observations, and Hydroweb time series confirm good agreement for both lakes and rivers. Over rivers, OCOG and TFMRA retrackers provide the most robust results, while residual outliers are mainly associated with SARin measurements in complex or ice-affected regions.
CryoSat-2, Satellite Altimetry, Inland Waters, Water Level, Hydrology
How to cite: Guilhen, J., Tapanelli, A., Nielsen, K., and Di Bella, A.: Improved Inland Water Level Retrievals from CryoSat-2: Enhanced Spatial Coverage and Uncertainty Characterisation for Hydrological Applications, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-9013, https://doi.org/10.5194/egusphere-egu26-9013, 2026.
Global water storage faces a crisis driven by climate warming, with significant declines observed in 53% of large water bodies (Yao et al., 2023). Crucially, recent analyses reveal that surface water dynamics are predominantly driven by seasonal variability (Li et al., 2025). However, as current assessments are biased toward large lakes, the high-frequency storage dynamics of small systems remain unquantified due to the spatiotemporal limitations of current satellite observations (Cooley et al., 2021). Scotland offers an ideal case study to address this observational gap: it hosts ~25,500 water bodies, of which > 90% are small (<0.1 km²) and poorly monitored (Taylor, 2021).
Currently, Scotland is undergoing a fundamental hydro-climatic transition, indicated by a pronounced intensification of seasonality, with substantially wetter winters but markedly drier summers (Lowe et al., 2018), challenging the reliability of these water resources. Recent extremes, such as Loch Ness recording its lowest levels since 1990 in May 2023, highlight the vulnerability of existing storage capacity (SEPA Water Scarcity Report, 2023). To effectively manage these emerging risks, a comprehensive understanding of storage dynamics is essential. Yet, a multi-decadal, daily-resolution dataset of water storage changes remains absent. Consequently, this study aims to bridge this gap by reconstructing continuous storage dynamics from 1980 to the present.
To account for heterogeneous basin morphology and anthropogenic regulation, we develop a scalable, typology-based framework that categorizes water bodies into three representative classes: (1) shallow/responsive basins (e.g., Loch Leven), where surface area is highly sensitive to water level changes; (2) deep, morphologically constrained basins (e.g., Loch Ness), where storage variability is primarily volumetric; and (3) regulated reservoirs (e.g., Loch Katrine), which exhibit non-natural level fluctuations due to abstraction. Targeting these calibration sites, we integrate Sentinel-1 (SAR) and Sentinel-2 (optical) imagery (2017-2024) with daily in-situ water level observations from SEPA to derive class-specific area-level relationships and validate model performance across contrasting hydrological regimes.
To extend storage reconstructions beyond the satellite era, we employ a machine learning approach driven by long-term meteorological reanalysis data. Models trained on the high-resolution dynamics of the Sentinel era are applied retrospectively to reconstruct daily water storage changes dating back to 1980. By including a dedicated class for regulated systems (Loch Katrine), this framework incorporates features to distinguish human-driven storage patterns from natural climatic responses. The resulting dataset provides the first multi-decadal quantification of Scottish water storage, enabling the identification of historical low water extremes and attribution of their climatic and anthropogenic drivers. This work provides a critical baseline for assessing hydrological resilience and water security in temperate regions under increasing climate variability.
How to cite: Zhu, Z., Bass, A., and Zhang, W.: Reconstructing Multi-Decadal Daily Water Storage Changes in Scottish Standing Waters: A Classification-Based Remote Sensing Framework, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-5932, https://doi.org/10.5194/egusphere-egu26-5932, 2026.
Inland lakes increasingly face multiple stresses driven by climate change, anthropogenic pressures, hydrological modifications, and long-term ecosystem alterations. In this context, hydrological resilience refers to a lake’s ability to maintain stable water-level behaviour under disturbance. Whether inland lakes are losing resilience or approaching critical state transitions remains unclear, in part due to fragmented monitoring networks and limited availability of long-term lake-level observations.
This study introduces a resilience assessment framework that integrates multi-mission satellite altimetry to evaluate stability patterns in lake-level dynamics. The approach relies on radar and laser satellite altimetry to construct harmonized lake-level time series, using data from missions such as Sentinel-3, ICESat-2, and SWOT where available. In-situ measurements are incorporated as an independent validation benchmark to assess signal reliability. The resulting dataset is analysed within a resilience-based diagnostic framework. The aim is to determine whether observed fluctuations reflect stable hydrological functioning or signal increasing variability and reduced resilience.
Preliminary analysis indicates that satellite-derived lake water-level observations can provide meaningful signals for resilience-oriented assessment. These signals can reveal emerging hydrological instability earlier, particularly in lakes where field measurements are limited or challenging to maintain. These findings highlight the value of satellite-based lake-level monitoring for early-warning applications and adaptive management planning. The proposed framework is scalable and transferable, enabling resilience assessment across lakes with diverse monitoring and data conditions.
How to cite: Kılıç Germeç, H. and Germeç, E.: Assessing Hydrological Resilience in Inland Lakes Using Multi-Mission Satellite Altimetry, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-850, https://doi.org/10.5194/egusphere-egu26-850, 2026.
GNSS Interferometric Reflectometry (GNSS-IR) has evolved from an opportunistic use of geodetic reference stations towards purpose-built, low-cost sensors optimized for water-surface monitoring. Affordable GNSS-IR instruments are now specifically designed and positioned to observe water surfaces with optimized antenna geometry and controlled viewing conditions. This means we are no longer just picking up reflections when and where they happen to occur but instead purposefully measuring them for hydrological and environmental applications. This also makes GNSS-IR attractive for current and future satellite altimetry validation, particularly in regions where geoid uncertainty, sparse in-situ gauges or complex hydrodynamics limit traditional approaches. With this rapid development and increasing community interest, we present GNSS4SurfaceWater, an open data hub for sharing water-level time series from affordable GNSS-IR sensors following open-science hardware and software principles. The platform provides interactive visualization tools for exploring time series, station metadata, and site characteristics. It works as an independent, ground-based service for monitoring both current and historical surface water levels. GNSS4SurfaceWater highlights ongoing projects using low-cost GNSS instrumentation, promotes reproducible processing workflows and supports community contributions through standardized data upload formats. GNSS-IR sea-level products are also distributed through the Permanent Service for Mean Sea Level (PSMSL) GNSS-IR portal. This portal also aggregates contributions from multiple providers and ensures long-term data continuity. PSMSL focuses on long-term archiving, whereas GNSS4SurfaceWater is designed to provide community-driven near-real-time data availability with low latency to support rapid monitoring and event detection. The two platforms complement each other by supporting open and scalable GNSS-IR surface water monitoring and helping to broaden the adoption of GNSS-IR for hydrological observations.
How to cite: Karegar, M., Martín Furones, Á., Reyes, R., Rietbroek, R., Santamaría, A., Tourian, M. J., and Williams, S.: GNSS4SurfaceWater: an open data hub for rapid GNSS-IR surface water monitoring, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-13284, https://doi.org/10.5194/egusphere-egu26-13284, 2026.
Monitoring variations in groundwater storage (GWS) is essential for sustainable groundwater resource management, particularly in regions where groundwater constitutes the primary source of potable water. Effective management and planning of groundwater use further require a thorough understanding of the factors controlling GWS variability, including meteorological conditions, regional hydrogeological characteristics, and anthropogenic influences.
In this study, we investigate temporal changes in GWS in the Bug River basin, located along the border of Poland, Ukraine, and Belarus. GWS estimates are derived from in-situ point measurements as well as satellite- and model-based data. Satellite-based GWS is obtained from downscaled terrestrial water storage (TWS) anomalies derived from Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow-On (GRACE-FO) observations, in combination with data from the Global Land Data Assimilation System (GLDAS) model. We analyse long-term trends, seasonal components, and non-seasonal variability in both in-situ and satellite-derived GWS. Furthermore, we examine the relationships between GWS variations and potential driving factors, including precipitation, evapotranspiration, land surface temperature, and climate indices such as the Standardized Precipitation Index (SPI) and the Standardized Precipitation-Evapotranspiration Index (SPEI). We further analyse GWS variability in relation to groundwater table depth and lithology. Additionally, the correspondence between in-situ observations and GRACE-derived GWS is investigated.
The study demonstrates a high level of agreement between in-situ and satellite-based GWS, with correlation coefficients ranging from 0.69 to 0.95. The strength of this relationship depends on groundwater table depth, with the highest correlations observed for shallow aquifers. Seasonal variations in GWS, which are mainly controlled by precipitation and evapotranspiration, exhibit the strongest agreement between in-situ and satellite data. Overall, the study area exhibited negligible long-term GWS trends (0.0 to +1.0 mm/year) despite rising evapotranspiration over the past decade. Nevertheless, the period 2013–2023 was characterized by episodic positive and negative anomalies, which were more typical of deeper groundwater layers and more clearly captured by in-situ measurements. These findings highlight the value of integrating in-situ observations with satellite gravimetry for improving the understanding of groundwater dynamics and supporting sustainable groundwater management in transboundary river basins.
How to cite: Śliwińska-Bronowicz, J., Solovey, T., Stradczuk, A., Janica, R., and Brzezińska, A.: Driving factors of groundwater storage variability in the transboundary Bug River Basin, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-4930, https://doi.org/10.5194/egusphere-egu26-4930, 2026.
Posters on site: Fri, 8 May, 08:30–10:15 | Hall A
Sentinel-3 is an Earth Observation satellite series developed by the European Space Agency (ESA) as part of the European Copernicus Programme, currently composed of the two Sentinel-3A and Sentinel-3B satellites. Both satellites carry on-board SAR Radar Altimeter (SRAL), which aims at supplying operational topography measurements of the Earth’s surface. Over inland waters, the main objective is to provide accurate Water Surface Height (WSH) measurements to support the monitoring of freshwater stocks through dedicated Level-2 Hydrology Thematic Products. As part of the ESA Sentinel-3 Altimetry Mission Performance Cluster (MPC) project, the Hydrology Expert Support Laboratories (HY-ESL) evaluates the product performance using dedicated validation methodologies and proposes potential enhancements to the Hydrology Thematic Products.
In this study, the performance of the Hydrology Thematic Products over rivers and lakes is assessed using complementary validation methodologies to better estimate WSH uncertainties over inland waters. First, Sentinel-3 WSH timeseries are compared with in-situ WSH timeseries over rivers using nadir validation method combined with river slope estimates derived from SWOT products. These results are complemented with the innovative off-nadir validation technique that redefines the notion of virtual station, reducing WSH uncertainties induced by the river slope bias. Cross-validation is then performed between Sentinel-3 and SWOT products to leverage the large spatial coverage of the SWOT mission, resulting in a distribution of WSH differences from thousands of lakes. Statistical metrics from this distribution are analyzed with respect to lake size and specularity. Finally, mean lake surfaces inferred from SWOT products are used over large lakes to quantify WSH uncertainties due to errors in global geoid models, which is currently the main contributor to the error budget over large lakes.
How to cite: Renou, J., Chapellier, M., Nielsen, K., Taburet, N., Aublanc, J., Di Bella, A., Catapano, F., and Restano, M.: Insight into validation methods of Sentinel-3 Hydrology Thematic Products , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-17279, https://doi.org/10.5194/egusphere-egu26-17279, 2026.
Rivers play a central role in the Earth’s hydrological system, acting as pathways for freshwater transport and supporting ecosystems, human societies, and economic activities. Accurate monitoring of river discharge is essential for understanding the global water cycle, managing water resources, and addressing the increasing pressures associated with climate change. Despite its importance, discharge monitoring based on in-situ measurements remains limited, with sparse and uneven coverage, particularly in remote and ungauged regions. In this context, satellite observations offer a unique opportunity to overcome these limitations by enabling large-scale and consistent estimation of river discharge across diverse environments.
This study presents an advanced framework that combines satellite observations from optical and altimetry sensors to generate a global river discharge product tailored for hydrological applications. Building on the capabilities of EUMETSAT satellite systems and Copernicus contributing missions, the framework integrates data from multiple satellite platforms to enhance information content and improve accuracy relative to single-sensor approaches. A key innovation lies in the fusion of complementary datasets (optical and altimetry), which improves both spatial and temporal resolution, especially in areas where ground-based observations are scarce or absent.
The analysis focuses on more than 300 sites distributed worldwide, covering a wide range of climatic conditions and hydrological regimes. This dataset enables an assessment of the long-term potential of satellite-derived discharge estimates for water resource management and climate impact studies. Particular emphasis is placed on evaluating the added value of the global product in ungauged basins, as well as identifying its limitations in monitoring smaller rivers, where higher spatial resolution is often required.
To ensure the robustness and transferability of the proposed framework, multiple river discharge estimation methods are systematically tested on a representative subset comprising approximately 30% of the analyzed sites. This intercomparison aims to identify the most reliable and scalable approach, which is then adopted to generate river discharge estimates for the full dataset. The outcomes of this evaluation are presented here and subsequently extended to a global-scale application.
The results highlight the strong potential of satellite-based technologies for river discharge monitoring, enabling more robust, consistent and timely information to support decision-making in the context of global environmental change.
How to cite: Ciabatta, L., Tural, C. Y., Filippucci, P., Nielsen, K., Burini, A., and Tarpanelli, A.: Optical and altimetry data integration for river discharge estimation on a global scale, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-17780, https://doi.org/10.5194/egusphere-egu26-17780, 2026.
Hydrodynamic models are vital for water resource management and flood forecasting, but their application is limited by available data sources. In addition to observations of water surface elevation (WSE) and discharge, river channel and floodplain geometry must be estimated to calibrate, validate and operate hydrodynamic models.
While traditional terrain surveying is limited by physical access, political boundaries, safety, or cost, remotely sensed terrain data provides an alternative in data-scarce areas. Digital Elevation Models (DEMs) based on regional or global products have been used for river and floodplain geometry, but their accuracy is limited by low resolution and the inability to estimate geometry in the submerged section of the river channel. Airborne LIDAR missions, where available, provide high resolution point clouds of terrain and water surface elevation. In addition, novel satellite missions provide new opportunities for sensing hydraulic parameters remotely, when airborne LIDAR is not accessible. Estimating hydraulic parameters from these LIDAR datasets allows for the development of hydrodynamic models in flood-prone areas where it was previously not possible to reach sufficient accuracy for effective operation.
With the launch of ICESat-2, water surface slope (WSS) observations became available on a global scale. The LIDAR instrument on ICESat-2 records both terrain and water surface elevation, and the six LIDAR tracks provide 6 simultaneous measurements of WSE, allowing for a WSS estimate. The spatial resolution of just 0.7 m allows for cross-section delineation. But, both ICESat-2 and airborne LIDAR observations reflect strongly on water, hindering observations of submerged channel geometry.
We present a method of combining airborne or ICESat-2 LIDAR observations of the exposed cross-section and WSS with discharge to estimate the conveyance curve for the submerged part of the cross-section. The 1D de Saint-Venant equations are solved while assuming diffusive wave conditions, where acceleration terms are neglected. Under these conditions, water surface slope is equal to the friction slope. Manning’s equation can then be solved for conveyance in the submerged section using observed discharge and water surface slope. With an assumed shape and Manning’s resistance number, a full cross-section is delineated.
The method was initially developed for use with ICESat-2 altimetry measurements but has been extended to work with Airborne LIDAR point clouds when available. The method is shared in an open-source python package, containing functions for processing ICESat-2 or airborne LIDAR data, calculating WSS and producing cross-sections in a preferred data format, using a discharge input from observations or from a hydrological model, provided by the user. The package will allow users to estimate cross-sections in data-scarce areas, ready to be implemented in hydrodynamic models.
How to cite: Musaeus, A. F., Köhn, S. J., Kittel, C. M. M., Nielsen, K., Luchner, J., and Bauer-Gottwein, P.: Estimating river cross-sections for hydrodynamic models from space-borne and airborne LIDAR altimetry, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-11822, https://doi.org/10.5194/egusphere-egu26-11822, 2026.
The monitoring of global surface water is of critical scientific and societal importance, as these resources are essential for human activities and pose significant risks during extreme flood events. Accurately measuring river hydrodynamics, particularly water surface elevation (WSE), is fundamental for improving flood forecasting, validating hydraulic models, and understanding the global water cycle.
The launch of the Surface Water and Ocean Topography (SWOT) satellite in December 2022 represents a paradigm shift in remote sensing of hydrology. Equipped with the novel Ka-band Radar Interferometer (KaRIn), SWOT provides wide-swath, high-resolution measurements of water elevation and extent across two 50-km-wide swaths. Unlike traditional nadir altimeters, SWOT's 2D imaging capabilities allow for the characterization of complex hydrological processes at unprecedented scales. Despite these advancements, a major challenge remains in accurately observing "narrow" rivers—those below the mission's formal science requirement of 100 meters (with a goal of 50 meters). At the spatial resolution of current SAR sensors, extracting these narrow features is extremely difficult due to strong multiplicative speckle noise, low water-land contrast, and interference from surrounding land structures like roads or terrain artifacts. Furthermore, standard operational algorithms often rely on fixed prior databases (e.g., SWORD or GRWL) that may not account for real-time changes in river morphology, such as meandering or seasonal variations, or may suffer from positional shifts in radar geometry.
In this work, we assess SWOT’s capability to detect water surface elevation and slope of a narrower (~40 m wide) man-made canal. Instead of RiverSP and Raster products, PIXC offers the opportunity to detect such a narrower channel. Preliminary results show that SWOT can detect the canal although the data quality is not very high. In general, the longitudinal profile obtained from SWOT generally agrees with existing documentation, showcasing the potential to monitor narrower canals by analyzing PIXC product.
How to cite: Jiang, L. and Xia, T.: Detecting water surface dynamics of a narrower man-made canal using SWOT, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15544, https://doi.org/10.5194/egusphere-egu26-15544, 2026.
Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow-On (GRACE-FO) observations provide unique large-scale information on terrestrial water storage (TWS), yet their coarse spatial and temporal resolutions limit their applicability for regional and event-scale hydrological analyses. In this study, we investigate the performance of different hydrological forcing datasets in a flexible three-step downscaling framework to derive daily, 1 km terrestrial water storage change (TWSC) estimates over the Naryn–Kara Darya basins and the Fergana Valley in Central Asia. The framework integrates monthly GRACE-derived TWSCs with high-resolution precipitation, evapotranspiration, and runoff information from multiple sources, including GLDAS, FLDAS-CA, ERA5-Land, and a mixed forcing combination based on MSWEP, GLEAM, and GloFAS. Temporal downscaling is achieved by constraining daily water-balance-derived storage changes with GRACE observations, while spatial downscaling maps coarse GRACE signals onto fine-scale hydrological predictors. Model performance is assessed using multiple validation strategies, including comparison with the ITSG-Grace2018 daily solution, consistency tests, and event-based analyses, accounting for the scarcity of in situ observations in the region. Our results demonstrate that the choice of hydrological forcing dataset strongly influences the quality of downscaled TWSCs. While all forcing scenarios capture the dominant seasonal and interannual variability, substantial differences emerge in their representation of trends, variability, and short-term events. In particular, the mixed forcing dataset shows the most consistent performance across validation metrics and better reproduces both long-term TWS changes and hydrologically relevant extreme events. These findings highlight the critical role of forcing data selection in GRACE downscaling applications and demonstrate the transferability of the proposed framework to other data-sparse regions.
How to cite: Liu, S., Schaffhauser, T., and Pail, R.: Evaluating hydrological forcing datasets for GRACE-based terrestrial water storage downscaling in Central Asia, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-2572, https://doi.org/10.5194/egusphere-egu26-2572, 2026.
On the Korean Peninsula, where precipitation is highly concentrated during the summer monsoon season, reservoirs play a critical role as primary sources of available water. Effective monitoring of reservoir storage is therefore essential for water resource management and drought–flood mitigation. However, a large proportion of reservoirs in Korea are ungauged, making continuous in situ monitoring of water storage difficult. In this context, satellite remote sensing, which enables systematic observation over large and inaccessible areas, provides an effective alternative for reservoir monitoring. Conventional satellite-based water body monitoring has primarily relied on optical and SAR imagery, such as Sentinel-1 and Sentinel-2 data. While these datasets are effective for estimating reservoir surface area, they have inherent limitations in retrieving water surface elevation. To overcome these limitations, recent studies have increasingly utilized Surface Water and Ocean Topography (SWOT) data, which enable direct observation of water surface elevation and water surface slope. These capabilities provide essential hydrodynamic information that cannot be derived from conventional optical or SAR imagery. In this study aims to estimate reservoir water storage volume by integrating reservoir surface area derived from SAR and optical satellite imagery with water surface elevation and slope information obtained from SWOT observations. The SWOT-based reservoir storage volume estimates are validated by comparison with in situ water storage measurements. This approach has the potential to support reservoir storage estimation in ungauged regions and contribute to regional-scale water resource monitoring.
Keywords: Water storage volume, Reservoir, SWOT
Acknowledgment
This research was supported by the BK21 FOUR (Fostering Outstanding Universities for Research) funded by the Ministry of Education (MOE, Korea) and National Research Foundation of Korea (NRF). This work is financially supported by Korea Ministry of Land, Infrastructure and Transport (MOLIT) as 「Innovative Talent Education Program for Smart City」. This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (RS-2022-NR070339). This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (RS-2024-00416443). This work was supported by Korea Environment Industry & Technology Institute(KEITI) through Technology development project to optimize planning, operation, and maintenance of urban flood control facilities, funded by Korea Ministry of Climate, Energy and Environment(MCEE)(RS-2024-00398012).
How to cite: Kim, W., Cho, S., Cho, S., Chun, Y., and Choi, M.: Water Storage Volume Monitoring Using SWOT-Derived Water Level and Surface Slope Data, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-17389, https://doi.org/10.5194/egusphere-egu26-17389, 2026.
Tropical wetland hydrology is often poorly monitored by in situ gauges, yet it is critical for understanding the global carbon budget and water resource management. Earth observation (EO) data has long been used to calibrate and update hydrological and hydraulic models, providing valuable insights in poorly instrumented catchments. The recently launched Surface Water and Ocean Topography (SWOT) mission provides simultaneous observations of surface water extent and height at near global coverage. SWOT has been extensively used for informing river hydraulic and hydrodynamic models, but much less for integrated wetland hydrological models that represent surface water – groundwater interaction. Here we focus on modeling the hydrology of the Usangu wetlands in Tanzania. Usangu is representative of a wide range of tropical wetlands featuring a variety of land cover types (grasslands, forests, marshes, crops, permanent and seasonal flooding) and presents strong changes in hydrology both seasonally and interannually due to human impact.
We developed a MIKE SHE integrated hydrological model for the Usangu wetlands and surrounding alluvial fans. The model is coupled with a 1D river routing model and forced by a lumped-conceptual rainfall runoff model at all major river inlets to the wetland. Model forcing data includes daily CHIRPS v2.0 precipitation data and FAO reference evapotranspiration data. From Sentinel-2 multispectral imagery we extract river widths used to inform cross section shape. Vegetation maps are built from a combination of MODIS leaf area index (LAI), maps from aerial surveys, and global land cover maps. For calibrating the base model, we use three river discharge stations located along three separate rivers feeding the Usangu wetlands. SWOT pixel cloud data is processed to make dynamic flood extent maps over the wetland area. To improve flood extent estimation under dense vegetation, additional radar satellites (PALSAR-2, Sentinel-1) are used in combination with SWOT. SWOT pixel cloud data is also used to estimate river heights and establish an updated rating curve at the main outlet of Usangu, along the Great Ruaha River.
We present the first results characterizing the Usangu wetland hydrology as seen from multiple earth observation satellites (SWOT, Sentinel-2, other radar satellites) and compare with predictions from the integrated MIKE SHE model. SWOT-derived flood extent maps are compared with the modeled flood extent over the wetland domain using overlap-based metrics such as CSI and F-score. River heights from SWOT are compared with modeled river water levels.
How to cite: Franze, S., Senty, P., Cremer, C., Toettrup, C., and Bauer-Gottwein, P.: Informing an integrated hydrological model with SWOT in the tropical Usangu wetland, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-16373, https://doi.org/10.5194/egusphere-egu26-16373, 2026.
Posters virtual: Thu, 7 May, 14:00–18:00 | vPoster spot A
EGU26-8732 | Posters virtual | VPS10
Tracking The GER Dam Impoundment Stages Using SWOT and Other Radar Altimetry ProductsThu, 07 May, 14:27–14:30 (CEST) vPoster spot A
Monitoring of inland water bodies is considered crucial for effective water resource management. In this study, a combination of satellite imagery and altimetry products was utilized to monitor changes in water level and surface extent during the different operational filling phases of the Grand Ethiopian Renaissance (GER) Dam. The primary objective was to utilize diverse remote sensing products to provide an accurate estimation of water volume changes over time. Sentinel-1 data were processed using an unsupervised edge Otsu algorithm to map reservoir extents. These output maps were validated against Planet and Sentinel-2 water masks, and a high level of agreement was observed, with overall accuracy values ranging from 0.97 to 0.99. Furthermore, various Surface Water and Ocean Topography (SWOT) satellite products were evaluated for the estimation of reservoir extents. It was found that the SWOT Lake Single Product performed poorly, with an Intersection over Union (IOU) value of approximately 0.33 being recorded. In contrast, moderate agreement with validation sets was demonstrated by the SWOT water mask raster and pixel cloud products, with overall accuracy values ranging from 0.78 to 0.89 being observed. Volume variation across different dam operational phases was estimated through the application of satellite-based observations and a DEM contouring method. Although a high correlation (R2 value of 0.98) was exhibited by both methods, significant differences in absolute values were identified (RMSE value of 2736.35 km3). These discrepancies are attributed to a potential scaling error and the inherent water slope present within the GER Dam reservoir.
How to cite: Tazwar, M., Rietbroek, R., Maathuis, B. H. P., and Shakya, A.: Tracking The GER Dam Impoundment Stages Using SWOT and Other Radar Altimetry Products, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-8732, https://doi.org/10.5194/egusphere-egu26-8732, 2026.
EGU26-20408 | Posters virtual | VPS10
Centralizing in-situ Hydrological measurements for satellite altimetry validation: the INSIGHT platformThu, 07 May, 14:54–14:57 (CEST) vPoster spot A
Reliable validation of satellite altimetry over inland waters relies on long-term, high-quality in-situ water height measurements over different types of waterbodies. The strategy implemented in the St3TART Follow-On (FO) project relies on controlled super sites to produce high quality Fiducial Reference Measurements (FRMs) and on a high number of data provided by public national hydrological networks considered as opportunity sites.
However, these measurements from national hydrological networks remain highly heterogeneous in terms of formats, units, and metadata description, limiting their direct large-scale use for Cal/Val activities. The first step of data uniformization has been performed by vorteX-io team during St3TART-FO project. As an adaptation of the validation strategy for Sentinel-3 is considered for CRISTAL inland waters products, this uniformization work should be extended to cover more virtual stations for other altimetry missions.
This contribution presents the hydrological component of the Hydro-Cryo in-situ platform, INSIGHT, an ESA-funded project, extension of CRISTAL IN-PROVA project, aiming at the centralization and harmonization of publicly available in-situ water surface height data across Europe. This work participates in the preparation for the Cal/Val phase of the future CRISTAL mission and in support of ongoing Sentinel-3 validation activities, with support from the European Environment Agency (EEA) as coordinator of the Copernicus In-Situ component.
In this first phase, the platform will integrate data from twelve national hydrological networks covering France, Switzerland, Belgium (Wallonia), Ireland, Portugal, Norway, Poland, Italy, Slovenia, Croatia, the Netherlands and Germany. The data from fixed in-situ sensors deployed on Cal/Val super sites for Sentinel-3 will also be integrated in the platform. The back-end architecture is designed to easily integrate additional networks in Europe and all over the world. Native temporal resolutions provided by in situ sensors are preserved without aggregation or resampling, and up to ten years of historical observations are considered when available.
The harmonized hydrological datasets will be disseminated on a dedicated Data Hub developed by NOVELTIS together with reference Cryosphere data for satellite altimetry validation. This open-access platform is designed to serve the Cal/Val community by providing a unified entry point for inland water and cryosphere reference measurements relevant to multiple altimetry missions.
The core objective of the hydrological processing chain is the harmonization of in-situ water height measurements by standardizing measurement units and metadata across heterogeneous national public datasets. Attention is given to the consistency of the altimetric reference of the in-situ sensors. This harmonization is essential for the use of in situ stations as FRMs for the validation of both Sentinel-3 and CRISTAL, as well as for others satellite altimetry missions.
Beyond the altimetry community, this platform addresses the broader hydrological community by providing access to a standardized water height dataset from public national networks. By lowering technical barriers to data use, the infrastructure supports cross-border hydrological studies and contributes to the reuse of public hydrological observations.
This project, currently under development, establishes the data infrastructure for the needs of inland water altimetry validation, while simultaneously enabling wider scientific exploitation of harmonized in-situ water level observations at the European scale.
How to cite: Dechamp-Guillaume, M., Fouqueau, V., Hahn, J., Gil, P., Grenier, E., Poisson, J.-C., Le Merle, E., El Hajj, M., Restano, M., and Catapano, F.: Centralizing in-situ Hydrological measurements for satellite altimetry validation: the INSIGHT platform , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-20408, https://doi.org/10.5194/egusphere-egu26-20408, 2026.
