Orals: Thu, 7 May, 14:00–18:00 | Room 3.29/30
Riverbank slopes are a fundamental component of river geometry, governing channel–floodplain connectivity, cross-sectional shape, and overbank flow behavior. These slopes constrain ecohydrological processes such as hyporheic exchange, riparian inundation, floodplain residence times, and hydrodynamic models. Despite their importance, riverbank slopes remain poorly quantified at regional to global scales due to the limited ability of remote sensing to resolve near-bank topography and the impracticality of applying high-resolution topobathymetric models across extensive river networks.
The Surface Water and Ocean Topography (SWOT) satellite provides global, simultaneous observations of river width and water surface elevation (WSE), but does not directly measure channel depth, bank geometry, or cross-sectional area. A further limitation is that raw SWOT width measurements exhibit systematic bias as a function of cross-track distance from nadir. As a result, repeated observations of the same river node from different satellite passes can exhibit width differences that exceed expected natural variability, reflecting sensor- and geometry-related bias rather than true hydraulic change. To mitigate this effect, we apply automated width corrections during data processing using the river-spatial-scale repository (available via GitHub at https://github.com/SWOTAlgorithms/river-spatial-scale). This framework implements bias adjustment through spatial comparison of measurements across nodes and applies configurable windowed smoothing to reduce cross-track-dependent errors while preserving natural along-river variations; additional quality filtering ensures consistent and physically meaningful width estimates for hypsometric analysis.
We examine monotonic width–WSE hypsometric curves using these bias-corrected SWOT data, as a proxy for inferring effective riverbank slope. This approach builds on hydraulic geometry theory, which posits that observable hydraulic variables can constrain unobserved channel properties. We construct hypsometric curves using SWOT vector products at node scale, in which individual nodes are spaced at approximately 200 m, across a global set of rivers. We evaluate performance and robustness in comparison with in situ measurements of bathymetry along six rivers in the United States, Colombia, France, and Italy, spanning diverse climatic, geomorphic, hydraulic, and anthropogenic conditions, including systems with significant channel modification and hydraulic infrastructure. We analyze the shape, slope, and regression behavior of the hypsometric curves to estimate effective riverbank slopes and assess spatial variability along river corridors. Results demonstrate that SWOT-derived hypsometric curves provide a practical and scalable proxy for riverbank slope, enabling global-scale characterization of channel geometry using SWOT data. This approach offers new opportunities to investigate spatial patterns in hydraulic geometry, evaluate landscape controls on river form, and support hydrologic, hydraulic, and geomorphic applications in data-limited regions.
How to cite: Vélez, J. D., Pavelsky, T., and Williams, B.: Global Riverbank Slope Patterns Inferred from SWOT-Derived Hypsometry Curves, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-13484, https://doi.org/10.5194/egusphere-egu26-13484, 2026.
Rivers worldwide are increasingly harnessed to meet growing demands for hydropower, irrigation, water supply, and flood control. With over 58,000 large dams, 82,891 smaller hydropower installations, and countless undocumented barriers globally, humans have appropriated more than half of accessible freshwater runoff. Consequently, only an estimated 23% of the world's large rivers (>1,000 km in length) flow uninterrupted to the ocean.
Demand for non-fossil fuel energy and water security will likely increase reliance on river infrastructure throughout the 21st century, with hydropower production projected to grow by 50% by 2030. However, dam construction has modified flow conditions, altered thermal regimes and dissolved gas concentrations, disrupted fish migration routes, and degraded spawning habitats. Freshwater ecosystems are consequently among the most threatened globally, with biodiversity declining faster than in terrestrial or marine systems. In response, dam removals are accelerating in Europe and the United States, and the European Biodiversity Strategy aims to restore at least 25,000 km of free-flowing rivers by 2030 through barrier removal and floodplain restoration. Climate change amplifies these pressures, intensifying droughts and flooding while making river conservation increasingly urgent.
Regularly updating barrier databases is therefore essential for tracking new, existing, and removed structures, as well as those modified for fish passage or sediment transport. Yet significant gaps persist. While Global Dam Watch documents over 41,000 barrier locations and the AMBER database catalogues at least 1.2 million instream barriers across 36 European countries, current detection methods perpetuate systematic biases. Ground surveys are resource-intensive and geographically concentrated in developed regions. Optical satellite imagery cannot reliably identify submerged weirs, low-head structures beneath vegetation canopy, or barriers in cloud-prone areas. Smaller anthropogenic structures—which constitute the majority of barriers globally—remain underrepresented outside well-surveyed regions, and natural obstructions are rarely catalogued despite their ecological and hydraulic significance.
We present a detection framework exploiting multi-temporal Surface Water and Ocean Topography (SWOT) water surface elevation (WSE) observations to identify barriers through diagnostic hydraulic signatures. Any obstruction creating step changes in WSE—whether anthropogenic (dams, weirs, culverts) or natural (waterfalls, logjams, bedrock outcrops)—generates characteristic spatial discontinuities and temporal variations in upstream ponding extent. By analysing WSE patterns across multiple satellite overpasses, the framework identifies anomalous hydraulic behaviour indicative of flow obstruction. Applied globally, the framework successfully detects barriers previously absent from existing databases, proving particularly effective for submerged weirs, recently constructed structures, low-head barriers obscured in optical imagery, and natural obstructions in remote regions. While previous studies report that Europe has the highest density of medium and small river barriers, we found that Asian rivers—especially in China and India—are disproportionately impacted by large dams. This approach represents a paradigm shift from geographically constrained inventories toward continuous, satellite-based global monitoring. The resulting datasets will enhance hydrological modelling, inform ecosystem restoration and flood risk mitigation worldwide.
How to cite: Rong, Y., Bates, P., and Neal, J.: Global River Barrier Detection Using Multi-Temporal SWOT Water Surface Elevation Observations, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-8283, https://doi.org/10.5194/egusphere-egu26-8283, 2026.
Classical satellite altimetry has been successfully used to monitor the water levels of inland waters, such as rivers, lakes and reservoirs, for more than three decades. In December 2022, a new generation of altimeter mission called Surface Water and Ocean Topography (SWOT) was successfully launched. SWOT is equipped with a classical nadir radar altimeter similar to that on Jason-3, as well as a new Ka-band Radar Interferometer (KaRIn). KaRIn uses the principle of SAR interferometry and, thanks to its 120 km wide swath and 21-day repeat science orbit, has the capability to monitor almost every inland water body worldwide.
In this contribution, we present two new methods for deriving hydrological parameters over inland waters. Water level time series are derived for around 2,700 lakes and reservoirs in Germany. For around 250 river reaches, however, we have derived not only water levels, but also time-varying water surface slopes from the high-resolution SWOT pixel cloud dataset. This dataset enables us to monitor the water levels of very small water bodies. We use SWOT data measured during the fast sampling orbit (03/2023 – 07/2023, 1-day repeat cycle) and the science orbit (since 07/2023, 21-day repeat cycle). This contribution also discusses the challenges due to measurement noise, data gaps, and dark water pixels when using SWOT KaRIn data and how the new approach addresses these challenges. In addition, a preliminary quality assessment is performed on the SWOT pixel cloud data of Version C and the new Version D, which has been available since May 2025.
To assess the quality, the resulting time series of water levels and water surface slopes are validated against in-situ data and compared with the official LakeSP and RiverSP SWOT products. The validation of 112 lakes and reservoirs results in a median RMSE of 6.3 cm. The validation of 276 river reaches results in a median RMSE 10.7 cm. Compared with the official LakeSP and RiverSP products, the results of the new approach show higher accuracy and more data points. All time series of water levels and surface water slopes are freely available on the web portal of the „Database of Hydrological Time Series of Inland Waters„ (DAHITI, https://dahiti.dgfi.tum.de).
How to cite: Schwatke, C., Scherer, D., and Dettmering, D.: DAHITI – Monitoring Water Levels and Water Surface Slopes in Small Lakes and Rivers Using SWOT KaRIn Measurements, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-11551, https://doi.org/10.5194/egusphere-egu26-11551, 2026.
River deltas are home to more than 400 million people worldwide and are critical centers for industry and ecosystems. Many low-lying Asian Mega-deltas such as the Mekong Delta and the Ganges-Brahmaputra-Meghna (also known as the Bengal Delta) are increasingly exposed to compound coastal–river flooding, driven by tropical cyclones, storm surge, extreme river discharge, land subsistence, higher tides and sea-level rise. Reliable integrated coastal–river hydraulic models are needed to predict these events and support contingency planning, but model accuracy is often limited by sparse and discontinuous observations across the river–estuary–coastal transition zone. Developing reliable data sets for these environments is challenging because (i) coastal and estuarine water levels vary strongly in space and time, (ii) delta morphology is complex (floodplains, siltation, and human-made structures), and (iii) continuous observations capturing the interaction between river and ocean processes are generally lacking.
While in-situ gauges provide time-series data at a few monitoring locations, they cannot resolve the spatial structure of water levels across the delta. Previous satellite altimetry missions increased the spatial coverage of water surface elevation (WSE) observations but were typically limited to point-based observations (e.g., Sentinel-3) or cross-section tracks (e.g., ICESat-2). SWOT (Surface Water and Ocean Topography) is the first mission to provide two-dimensional WSE observations over both inland waters and the ocean. In this study, we explored the SWOT L2 HR Raster product at 100 m resolution over the Mekong (Vietnam) and Meghna (Bangladesh) rivers to generate continuous river-to-ocean WSE datasets. The raster product is sampled along river centerlines from upstream reaches, through the estuary, and tens of kilometers into the ocean, to generate 1D river-ocean WSE profiles. Assuming along-channel hydraulic continuity in the WSE, we remove outliers and fill gaps in the data to obtain consistent profiles.
These observations reveal key hydrodynamic features that are difficult to resolve with conventional monitoring, including the along-channel spatial variability of tidal propagation and damping, including zones of strong tidal influence, and changes in the tidal signal during high-discharge periods, when river flow alters tidal penetration and water-level gradients. By providing coincidence two-dimensional snapshots of river and coastal water levels, SWOT enables a consistent characterization of how ocean forcing and river discharge interact across the delta. The resulting coastal–river datasets are a foundation for validating integrated coastal–river hydraulic models and improving simulations of WSE along the river–ocean continuum, strengthening compound coastal flood modeling and climate-impact assessments in deltaic environments.
How to cite: Coppo Frias, M., Kittel, C. M. M., Nielsen, K., Shamsudduha, M., Hossain, S., Musaeus, A. F., Toettrup, C., and Bauer-Gottwein, P.: Water surface elevation across the river-ocean interface from SWOT satellite, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-13409, https://doi.org/10.5194/egusphere-egu26-13409, 2026.
Deltas and estuaries represent some of the most dynamic and complex transition zones on Earth, serving as vital ecological hubs that are increasingly vulnerable to the dual pressures of accelerated sea-level rise and anthropogenic modification. The NASA-CNES Surface Water and Ocean Topography (SWOT) mission provides a transformative capability to observe these environments by providing high spatial-resolution, wide-swath measurements of Water Surface Elevation (WSE). Unlike traditional nadir altimetry, SWOT’s interferometric SAR technology allows for the capture of two-dimensional water level gradients across scales of estuarine reaches. This study explores the utility of SWOT time-series measurements in resolving complex tidal components within intricate coastal geometries and assesses how these physical measurements can be translated into critical parameters for evaluating the health and long-term vulnerability of coastal ecosystems. In this presentation, we focus on hydroperiod and salinity.
By performing harmonic analysis on SWOT-derived WSE time-series, we demonstrate the ability to effectively resolve previously unknown but major tidal constituents in estuarine channels. These tidal components allow for the precise derivation of the hydroperiod—the frequency, duration, and depth of tidal inundation. Hydroperiod is the primary environmental driver of vegetation zonation, nutrient cycling, and carbon sequestration potential in mangroves and saltmarshes. Consequently, accurate SWOT-based mapping of inundation patterns offers a new lens through which to view coastal resilience and the potential for "blue carbon" sequestration. Another driver of ecosystem gradation and vulnerability is salinity, which distribution is a non-linear product of the interaction between tidal forcing, freshwater discharge, and complex bathymetry, making it far more difficult to resolve than surface height alone. Indeed, it requires numerical modeling, which presents significant technical hurdles.
We tested the implementation of numerical hydrodynamic models in several distinct geographical settings to evaluate the limits of SWOT-informed simulations. These sites included the high-discharge, tide-dominated Guayas Estuary in Ecuador, the marine-dominated and ecologically sensitive Langebaan Lagoon in South Africa, and the complex, Knysna Estuary, also in South Africa. Our results indicate that while SWOT provides unprecedented boundary conditions for water levels, the ability to simulate salinity and transport remains heavily constrained by a persistent lack of high-resolution bathymetry and other in situ measurements. To improve these simulations, there is an urgent need for better bathymetric data derived from bathymetric lidar for shallow sub-tidal zones and in-situ sonar transects for deeper primary channels. Secondly, the common lack of in situ data along the salinity gradient inhibit robust assessment of the methods. This work highlights the necessity of a synergetic approach, combining SWOT’s wide-swath observations with targeted bathymetric mapping and in situ data assimilation to provide the predictive accuracy required for effective coastal management and ecosystem conservation in a rapidly changing climate.
How to cite: Simard, M., Christensen, A., Payandeh, A. R., and Matte, P.: Evaluating estuarine ecosystem gradation and vulnerability through SWOT observations., EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-8599, https://doi.org/10.5194/egusphere-egu26-8599, 2026.
Bogotá, the capital of Colombia, relies on one of the largest and most complex urban water supply systems in the country. Drinking water is primarily sourced from high-altitude reservoirs located within the Chingaza National Park, situated in the Andean páramo ecosystems surrounding the metropolitan area. Beginning in late 2023 and continuing into 2024, severe drought conditions linked to the El Niño phenomenon led to historically low storage levels in the Chingaza system. As a consequence, in April 2024, Bogotá implemented water rationing for the first time in recent decades.
In this context, this study evaluates the potential of satellite-based observations to support reservoir monitoring and water resources management in Bogotá. We analyze water surface elevation (WSE) and water surface area (WSA) for the Chuza and San Rafael reservoirs—both part of the Chingaza system—by integrating in situ observations with products from the Surface Water and Ocean Topography (SWOT) mission and Sentinel-2 imagery.
First, WSE estimates from SWOT Level-2 products (LakeSP and Raster) were validated against observed WSE between September 2023 and February 2026. Over the same period, WSA derived from SWOT and Sentinel-2 was validated against in situ WSA estimated from reservoir hypsometric curves.
Results show an excellent agreement between SWOT LakeSP WSE and observations for the Chuza reservoir, with a Pearson correlation coefficient of 0.9934. In contrast, performance for the San Rafael was substantially lower, with a correlation of 0.5971 and a systematic negative bias of −8.83 m, indicating reduced accuracy of SWOT-derived WSE in this smaller reservoir. Similar patterns were observed for the SWOT Raster product with moderate correlation for Chuza (0.7138), and large discrepancies for San Rafael. In contrast, WSA time series derived from the Normalized Difference Water Index (NDWI) from Sentinel-2 exhibited acceptable performance for San Rafael, with a correlation of 0.8535 and a bias of −0.37 km², and 0.6207 correlation and -0.61 km² bias for the Chuza reservoir.
Overall, the results demonstrate the strong potential of SWOT and Sentinel-2 products for monitoring strategic water supply reservoirs in the Colombian Andes, while also highlighting pronounced performance differences linked to reservoir size, shape and surrounding topography. The findings emphasize the usefulness of these satellite products for hydrological applications in the Colombian Andes but also underscore challenges such as persistent cloud cover and the need for more robust algorithms that integrate the high precision of SWOT with complementary datasets to improve area estimation.
How to cite: Leal Noy, J. L., Páramo Pérez, E. F., Barrios Montoya, A. E., Morales Marín, L. A., and Rodríguez Sandoval, E. A.: Analysis and Validation of Water Surface Data in Two Reservoirs of Bogotá’s Water Supply System Using SWOT and Sentinel-2 Satellite Products, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-7576, https://doi.org/10.5194/egusphere-egu26-7576, 2026.
The thousands of lakes and reservoirs in West Africa are vital water resources for humans and ecosystems. In addition to marked annual and interannual dynamics, some of these lakes have experienced significant variations over the past sixty years related to climate and anthropogenic changes. Since existing studies have addressed the volume evolution of only a few lakes, large-scale volume trends in West Africa remain poorly documented. Here, we use the unprecedented spatial coverage of Surface Water and Ocean Topography (SWOT) swath altimetry, combined with the extensive temporal coverage of Landsat optical image archives, to derive the annual and 40-year volume dynamics of over 1300 West African lakes. Our results show that a few large reservoirs, mostly located in the sub-humid region, control most of the total annual volume dynamics. These large reservoirs have also driven the total lake volume increase observed across the entire study region over the past 40 years. At the individual scale, over the 1984 and 2024 period, 43% of lakes showed insignificant trends, 49% positive trends, mostly related to increased precipitation and reservoir impoundment during the analyzed period, and 8% negative trends. Furthermore, 17% of lakes have decreased in volume over the past two decades, suggesting a potential recent shift in volume for a number of lakes. This work is a significant step toward comprehensively quantifying annual and long-term changes in West African lake volumes. This information can inform hydrological modeling, global change impact assessments, and water management policies.
How to cite: Girard, F., Kergoat, L., Silva Paiva, J. M., Yonaba, R., and Grippa, M.: 40-Year Volume Changes of West African Lakes Derived from SWOT and Optical Imagery, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-10882, https://doi.org/10.5194/egusphere-egu26-10882, 2026.
The Synthetic Aperture Radar (SAR) frequency on the Surface Water and Ocean Topography (SWOT) Mission is Ka-band with an 8mm wavelength. In contrast, lower frequencies, such as those from NISAR and Sentinel-1, are the preferred method for most land surface studies because they have all-weather capabilities, and they can penetrate vegetation to reveal sub-canopy ground deformations, and they make water detection easy as water surfaces are uniform. Lower frequencies make these types of vegetation and surface water observations easier, while higher frequencies make these observations harder. Because of this, as a high-frequency system, SWOT was never designed to penetrate canopies or examine ground deformations. Rather, the high frequency from SWOT was selected for its potential to produce very high-resolution observations and have strong sensitivities to surface water, with the primary goals of measuring water surface elevations and water surface extents. Despite this, recent studies being published have demonstrated SWOT sensitivities related to 1) wind-driven water surface roughness, 2) vegetation structure, and 3) sub-canopy ponding and soil moisture. This presentation highlights progress in examining SWOT observations for Even More Than Surface Water Topography in support of improving SWOT discharge algorithms and other critical water cycle algorithms, such as for evaporation, transpiration, and canopy interception, for further-reaching improvements to water resources research.
How to cite: Fayne, J.: More Phenomenology: Updates to Using the Surface Water and Ocean Topography (SWOT) Ka-band Satellite for Novel Inland Retrievals of Hydrological Parameters, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-23252, https://doi.org/10.5194/egusphere-egu26-23252, 2026.
Air-water gas exchange influences aquatic ecosystem processes ranging from photosynthesis and respiration to greenhouse gas emissions. The speed of this exchange, termed the gas exchange rate, is a key parameter for carbon transport through the world’s river basins. Because of this, global maps of the gas exchange rate are often constructed from hydrographic and hydraulic datasets as one step towards constraining global river carbon budgets. However, existing maps rely on DEM-derived slopes that are not necessarily indicative of water surface conditions and are subject to other operational uncertainties. And more mechanistically, existing models assume that only bed-shear-induced near-surface turbulence drives river gas exchange, neglecting other potentially influential physical processes like wind-shear-induced turbulence (especially in wide rivers). Decades of theory and experimental work in lakes, estuaries, and the open ocean show that air-water gas exchange is a variable function of turbulence on both sides of the interface, but similar studies in rivers are limited to a few sites. To address these issues, we integrate (1) previous theoretical work on wide-river gas exchange, (2) global SWOT measurements of water surface slope, and (3) a downscaled wind model to calculate near-surface turbulent dissipation rates for global rivers. We then produce a first-order characterization of global river gas exchange rates and explore the impact of both wind-shear-induced turbulence and direct water surface observations from SWOT. We find that wind can play a significant role in unsheltered, wide, and flat rivers that are often (though not always) located near the mouths of the world’s river systems. Our results suggest that river gas exchange maps should leverage direct water surface measurements and account for air-side processes to better constrain global river carbon emissions, and we provide the first steps towards an empirical framework to do this.
How to cite: Brinkerhoff, C., Raymond, P., Flores, J., Li, M., Zhang, S., Sun, X., and Feng, D.: Using SWOT to revisit global river gas exchange, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-13827, https://doi.org/10.5194/egusphere-egu26-13827, 2026.
Western Queensland’s dryland floodplains are exceptionally low-gradient, with broad anabranching channels and extensive backswamps where the key driver of pastoral impact is not only flood magnitude but the residence time of standing water. Prolonged ponding induces waterlogging and sediment smothering of pastures, creating multi year feed deficits. Leveraging the exceptional rainfall flood sequence of early 2025 as a natural experiment, we investigate the space–time structure of post-flood water persistence across the Cooper Creek floodplain near Windorah and adjacent Channel Country.
We exploit SWOT KaRIn Level-2 “pixel-cloud” hydrology to retrieve day-by-day water surface elevation and inundation and pair these with Bureau of Meteorology rainfall analyses to decouple local rainfall forcing from upstream flood-wave contributions. A conservative, class-aware filtering is applied to stabilize water detection; we then compute persistence diagnostics (e.g., pixel-wise residence time and a backswamp/active-channel persistence contrast index), quantify observational uncertainty via threshold perturbations, and cross-check timing and extent against independent situational reporting. The workflow is designed to be reproducible and extensible, providing assimilation-ready surface water states suitable for integration with hydrodynamic models or multi-sensor frameworks.
By centering persistence rather than peak alone, this study targets a hydrologically meaningful and impact-relevant variable. The contribution is a rigorously specified method for mapping where water lingers after major floods in very low-slope rangelands, with clear pathways to generalization beyond the Cooper system and to fusion with national precipitation reanalyzes and river intelligence products for improved flood-recession understanding, forecasting, and rangeland decision-support.
How to cite: Razeghi, M. and Nursamsi, I.: Mapping Post-Flood Water Persistence with SWOT in Low-Gradient Drylands, Australia , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-16227, https://doi.org/10.5194/egusphere-egu26-16227, 2026.
River discharge is a core element of the global water cycle and an Essential Climate Variable (ECV), yet direct observations remain limited in both space and time. The Surface Water and Ocean Topography (SWOT) mission provides high-resolution (~100 m) measurements of water surface elevation over rivers and lakes worldwide, creating new opportunities to advance the monitoring of surface water dynamics. Here, we present the SWOT-QQ data set, a global SWOT-based river discharge dataset developed by combining SWOT surface water elevation measurements with an extensive collection of historical and contemporary in situ discharge records from over 60,000 gauging stations. Relying on SWOT’s global coverage, SWOT-QQ incorporates substantially more gauges than previous satellite-based discharge products (e.g., SAEM, RSEG), thereby extending both the geographic and hydrological representativeness of the estimates. Discharge time series are derived using the non-parametric quantile mapping (NPQM) approach, enabling the translation of SWOT water surface elevations into discharge across diverse climatic and hydrological regimes. In addition, we develop a near-real-time (NRT) framework in which incoming SWOT observations are converted into discharge using non-parametric rating relationships established during the mission period.
The results show consistent skill across multiple performance metrics in several regions, highlighting the potential of SWOT-QQ to support hydrological studies. We further compare our discharge estimates with outputs from existing SWOT discharge algorithms, including neoBAM, HiVDI, MetroMan, MOMMA, SAD, SIC4DVar, and the consensus product from the latest L4 dataset. Our results show that, after matching gauges to SWOT reaches, SWOT-QQ exhibits a betteragreement with in situ discharge than the reach-based SWOT L4 products. SWOT-QQ is intended as a complementary resource for river discharge algorithm validation, as prior information for inferring flow-law parameters, and as input for hydrological modeling and data assimilation. Through this work, we aim to foster discussion and collaboration within the SWOT community and contribute to improved global river discharge characterization.
How to cite: Saemian, P., Tourian, M. J., Ke, S., Elmi, O., Kitambo, B. M., and Papa, F.: SWOT-QQ: Global River Discharge Estimates at Gauging Stations from SWOT Observations, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-17924, https://doi.org/10.5194/egusphere-egu26-17924, 2026.
Flood inundation mapping is crucial for effective disaster management and well informed land use planning in flood prone regions. Optical sensors are often ineffective during flood events due to persistent cloud cover, limiting the usability of visible and near infrared imagery. While the SAR overcomes these limitations, there are no missions that monitor water from space in two dimensions. In contrast, the SWOT mission, through its Ka-band Radar Interferometer (KaRIn), provides cloud penetrating measurements of water extent and elevation, making it a powerful tool for long term flood inundation mapping despite its 21 day revisit cycle and limited real time availability. However, basin scale application of SWOT data is challenged by large data volumes, variable spatial resolution (10 to 60 m), overlapping swaths, and the absence of a standardized spatial indexing system. As a result, identical geographic locations may not appear consistently across multiple granules, complicating continuous time series extraction. This study uses the L2_HR_PIXC dataset, which is irregularly sampled and more complex than standard raster products, but better preserves critical hydrological information that is often lost during resampling. Although often neglected due to processing complexity, it contains crossover observations that hold valuable information critical for accurate inundation analysis. To address these challenges, a dedicated national scale database was developed for India to efficiently organize and integrate spatiotemporal SWOT pixel data at 30 m resolution. Leveraging this database, a probabilistic clustering based approach was implemented to map seasonal and permanent flood inundation using both water extent and elevation information. The developed framework enables efficient flood inundation mapping over large spatial extents, including river basin scales, overcoming limitations of conventional hydraulic models that are data and resource intensive and often restricted to regional applications. The methodology was applied to major flood prone regions of India namely Bihar, Assam, Manipur, Kerala, and Andhra Pradesh, spanning diverse latitudes and exhibiting substantial variation in SWOT observation frequency. The resulting inundation maps were validated using ground based observations and Sentinel satellite imagery across multiple flood events. These maps effectively differentiate recurrent seasonal flood zones from rarely inundated areas that may be more vulnerable during extreme events, offering valuable insights to support targeted flood risk management and disaster preparedness.
Keywords: SWOT, Cluster, India, Inundation map, Flood, Database
How to cite: Verma, B. K., Thomas, I. R., and Vijay, S.: Long-Term Assessment of Flood Inundation Using SWOT Satellite Observations, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-12954, https://doi.org/10.5194/egusphere-egu26-12954, 2026.
To understand the dynamics of a flood, radar satellite imagery is a valuable tool because it can provide data during the event, even with cloud cover. The SWOT mission has the dual advantage of providing surface elevation readings and mapping flooded areas. While the revisit interval can be a drawback, coupling with a hydrodynamic model allows for real-time interpolation of water levels and velocities across the entire study area. SWOT data integration is achieved through data assimilation methods, which also improve hydrodynamic models, thus being useful for early warning and forecasting future events.
The work presented here demonstrates how to incorporate both water surface elevations and flood front positions from SWOT into a data assimilation framework. Two types of methods are used to assimilate water masks. The first method has already been implemented in other studies; it is an ensemble Kalman filter where the domain is divided into homogeneous zones. Within each zone, the water level is modified at each analysis step to minimize the discrepancy between simulated and measured flooded area. The other method is an ensemble transform Kalman filter where the Chan Vese metric is used to minimize the distance between measured and simulated flood front positions. The novelty of this study therefore stems from the comparison of these two methods, each of which offers advantages for assimilating water masks from SWOT remote sensing data. Furthermore, it is necessary to verify that the two methods are also compatible for assimilating other data, whether in-situ water level measurements or direct water elevations in the river from SWOT products.
To carry out this study, a hydrodynamic model was built on an area around the Loire-Vienne confluence that experienced several flood events during 2023 and 2024. Calculations were performed using Telemac 2d software over a three-month period. Dual state-parameter data assimilation method estimates model parameters such as friction coefficients and inflow rates for each analysis cycle, as well as a uniform correction to the model state in subdomains of the flood plain. These parameters can vary depending on the method, and their interpretation provides information on the quality of the data used. Compared to previous studies, the dynamics of the floodplain can be modified by specific friction coefficients, in addition to the potential adjustment of water depth by zone (modification of the system state). These coefficients thus incorporate various phenomena responsible for flood propagation, such as hydraulic structures and land use.
Regardless of the assimilation method, SWOT data significantly reduces the estimation error for water depths and flooded areas with respect to SWOT water depths and flood extents. However, the Chan Vese method improves performance at the expense of computation time. When in-situ data is added, the simulation closely matches measurements in terms of water level at the observation stations. Nevertheless, thanks to the input of elevations measured by SWOT, it is possible to discuss the uncertainties of ground measurements and the best way to integrate data from different sources into the assimilation process.
How to cite: Bonassies, Q., Cassan, L., Nguyen, T. H., Piacentini, A., ricci, S., Rodriquez, R., Peña Luque, S., and Fatras, C.: SWOT data assimilation in 2D hydrodnamic model for flood studies, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-6576, https://doi.org/10.5194/egusphere-egu26-6576, 2026.
The Surface Water and Ocean Topography (SWOT) mission provides the first global measurements of river water surface elevation at reach scale, captured through the vector-based SWORD database. While these observations offer unprecedented spatial detail, most large-scale hydrological models represent rivers on gridded routing networks, creating a structural mismatch that limits the direct use of SWOT data in global analyses. A robust and scalable translation between SWORD reaches and model grid cells is therefore essential for enabling SWOT-based hydrology. Here we present a global, confidence-oriented strategy for aligning SWORD reaches with the 1/12° river network of the CTRIP routing model. The method evaluates candidate associations using several hydrologically meaningful criteria, including geographic proximity, upstream area and basin delineation coherence inherited from MERIT-Hydro, reach morphology, and alignment with D8 flow directions. Each pixel receives a confidence category that distinguishes unambiguous single-reach matches from robust or uncertain multi-reach configurations. This classification provides transparent information on mapping quality and identifies locations where model–observation alignment is intrinsically ambiguous. We demonstrate the performance of the method through a global application at 1/12° resolution. The resulting reach-to-grid associations produce spatially coherent river corridors, consistent basin topology, and near-complete coverage across observable rivers. Diagnostics across continents show that the framework performs reliably in challenging systems such as deltas, braided rivers, and multi-thread channels where simpler geometric approaches commonly fail. The final outputs include confidence-tier maps, reach–pixel match tables, and gridded river masks that translate SWOT’s vector observations into hydrologically meaningful model space. These products provide the community with a ready-to-use, reproducible translation layer that supports a wide range of SWOT-based research activities, including large-scale river characterization, network comparison, uncertainty assessment, and future assimilation experiments. The approach enables consistent use of SWOT observations in global hydrology and opens new avenues for connecting reach-scale satellite measurements with continental-scale hydrological understanding.
How to cite: Verma, K., Munier, S., Boone, A., and LeMoigne, P.: From SWOT Reaches to Model Grids: A Global Solution for Hydrologically Consistent Observation–Model Alignment, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-1531, https://doi.org/10.5194/egusphere-egu26-1531, 2026.
This study presents an evaluation of the Surface Water and Ocean Topography (SWOT) mission for monitoring riverine hydrodynamics, using the Po River (Northern Italy) as a test case. Given the recent launch of SWOT, its application to river hydraulics remains relatively unexplored and requires thorough validation against in-situ observations and specially, physically based models. We assess SWOT Water Surface Elevation (WSE), water surface slope, and recently released discharge products during the science phase, by comparing them with gauge measurements and hydrodynamic simulations over a ~300 km reach of the Po River, including the delta section (a coupled 1D/2D HEC-RAS model is employed to dynamically simulate river hydraulics).
The analysis integrates multiple SWOT products, including the Level-2 High-Rate Pixel Cloud (SWOT_L2_HR_PIXC) and Level-2 River Single-Pass Vector Product (SWOT_L2_HR_RiverSP - RiverSP), explicitly accounting for quality flags and performance under different flow regimes.
Results highlight the critical importance of quality-aware filtering for reliable use of SWOT observations. Good- and degraded-flagged WSE data generally show strong agreement with both in-situ measurements and model simulations, whereas suspect and bad flagged observations exhibit significantly larger discrepancies, with deviations reaching several meters. The analysis also reveals spatial patterns in SWOT performance linked to geophysical and orbital factors.
Comparison of RiverSP WSE node data against 10 in-situ stations shows biases up to ~10 cm for good and degraded data, with mean Kling–Gupta Efficiency (KGE) values of 0.92 and 0.82 for high-flow and low-flow regimes, respectively. Across all 114 analysed SWOT passes (each representing a longitudinal river WSE profile), on average, 68% of node observations per pass, are flagged as good or degraded, while 12 passes contain no usable data. Nevertheless, approximately 82% of the profiles show high agreement with the hydrodynamic model (KGE ≥ 0.8), highlighting the strong performance of SWOT in reproducing river WSE profiles. Profile-based comparisons also reveal orbit-dependent performance variability among different SWOT passes.
To address data limitations and improve spatial coverage, complementary Pixel Cloud products are leveraged for their higher spatial resolution, although these require extensive preprocessing, including spatial filtering and noise/outlier removal.
The study further explores the spatial and temporal performance of SWOT observations in relation to (i) distance from nadir track, (ii) satellite pass orientation, (iii) river planform geometry (e.g. straight vs. meandering reaches), and (iv) flow regime (e.g. rising limb, peak, recession, low flow). Although based on a single case study, the results illustrate both the potential and current limitations of SWOT products for riverine applications. The findings emphasize the importance of integrating quality-controlled satellite observations with physically based hydrodynamic models to support operational hydrology, long-term monitoring, and decision-making for flood and drought risk mitigation in inland-to-coastal environments. The proposed methodology is readily transferable to other river systems for inter-basin comparative analyses under diverse hydraulic conditions.
How to cite: Kurdnezhad, F., Tarpanelli, A., and Domeneghetti, A.: Investigating SWOT observations for river hydrodynamics: Evidences from the Po River, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-17595, https://doi.org/10.5194/egusphere-egu26-17595, 2026.
Evaluating the accuracy of SWOT water surface elevation (WSE) observations, including, slopes, and hydrodynamic signals, is crucial to determine their usefulness for river network monitoring and modeling. The spatially distributed nature of SWOT measurements enables the characterization of local hydraulic signatures, which carry unprecedented information for improving hydraulic-hydrological models, enabling finer inferences of bathymetry and monitoring of river morphological evolution, which could support infrastructure management.
Ledauphin et al. (2025) demonstrated, using a comprehensive in situ dataset over the Franco-German Rhine (130–350 m wide), that SWOT elevation accuracy can exceed expectations for large rivers. Analyses of products from PIXC to reach-averaged scale confirm SWOT’s ability to detect fine-scale hydraulic variations driven by longitudinal hydraulic controls and dynamic phenomena such as flood wave propagation and associated water surface slope.
Building on these results, this study evaluates SWOT’s ability to capture hydraulic signatures over narrower rivers (20–80 m wide) located in France’s Grand Est region, with a focus on the Moselle River (25–80 m) This river, which includes diverse channel morphologies (e.g., step-pool sequences, meanders) as well as hydraulic structures (weirs, dams), benefits from long-term in situ gauge records complemented by field data, such as LiDAR bathymetric surveys and WSE profiles measurements. This rich dataset enabled to build and calibrate a high-resolution 1D HEC-RAS hydraulic model (Piasny G. 2023), providing simulated water surface elevation profiles for a range of discharges used as an independent reference for satellite-based validation. Complementary analysis were also performed on narrower rivers such as the Meurthe (20–40 m) and the Sarre (20–30 m, including a major flood event in May 2024).
Using data from the nominal science orbit, this study investigates SWOT performance close to the limits of its design specifications for narrow rivers. In this context, the use of official SWOT river products becomes challenging, as WSE profiles can be noisy, and multi-pass acquisitions introduce temporal variability in data quality that is difficult to filter with conventional methods, requiring advanced techniques. To overcome these limitations, hydraulic-preserving filtering methods specifically designed for SWOT data are applied to improve local slope estimation (Montazem et al., 2025; Larnier et al., 2025). In the absence of full RiverSP coverage, the analysis here relies on PIXC pixel-cloud classes water-near-land and open-water, spatially filtered using a narrow riverbed polygon and existing flags, then projected onto the river centerline to produce a 1D product.
The impact of these processing and filtering methods is evaluated at fine scale through comparison with in situ measurements taken during the SWOT acquisitions and with WSE profiles from 1D hydraulic models at equivalent discharges. The use of high-resolution hydraulic model profiles enables a robust spatio-temporal validation of swot derived river altimetry and slope profiles at the node scale.
Variations in WSE due to discharge, bathymetry, and exceptional floods are well depicted with filtered SWOT data and validated against independent datasets. SWOT observations therefore demonstrate high accuracy across various hydrological conditions and river morphologies, even for narrow rivers, with a 1‑sigma error below 18 cm and a standard deviation below 30 cm compared to models and in situ measurements.
How to cite: Ledauphin, T., Piasny, G., Garambois, P.-A., Pujol, L., Samine Montazem, A., Larnier, K., Suchet, L., Azzoni, M., Maxant, J., and Yesou, H.: Assessing and Enhancing SWOT Hydraulic Visibility and Slopes in Narrow Rivers Using High-Resolution Models, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-5456, https://doi.org/10.5194/egusphere-egu26-5456, 2026.
The advent of the Surface Water and Ocean Topography (SWOT) mission has ushered in a new era of global hydrology, providing unprecedented high-resolution 2D observations of water surface elevation and extent. However, standard processing chains for hydrological applications rely heavily on static a priori databases such as the SWOT River Database (SWORD). This reliance introduces significant biases, as static centerlines fail to capture the morphological dynamism of rivers, and divergent flows such as bifurcations, braided reaches, multi-threaded systems, and artificial canals. This leads to reduced accuracy in hydrological and hydraulic modelling, flood forecasting, and water resources management.
In this study, we present a proof-of-concept workflow that uses the SWOT mission’s Pixel Cloud (PIXC) dataset to generate a high-resolution (~20 m), vector-based river network with flow direction. Moving beyond the constraints of the static SWORD, our approach utilizes SWOT-derived surface-water features as inputs for Random Forest and XGBoost classifiers. The resulting classification undergoes a semi-automated post-processing chain including rasterization, skeletonization, cleaning, and vectorization to reconstruct the network topology, with flow direction inferred directly from SWOT water-surface elevation measurements.
We evaluated this methodology in the Indus River Basin (Pakistan), a system distinguished by its dense network of artificial channels and high flood frequency. Results show that the method identifies missing river segments, divergent channels, and small-scale artificial waterways not represented in existing global river datasets, and extends significantly beyond the SWORD database. This work highlights the potential of SWOT Pixel Cloud data to move beyond static river representations and support dynamic river network generation for hydrological applications. Future efforts will focus on full automation and global scalability, as well as integration with operational hydrological and flood forecasting systems. The proposed framework provides a scalable pathway toward next-generation river network products that better exploit SWOT’s unique observational capabilities.
How to cite: Mosaffa, H., Slater, L., Tourian, M. J., Pappenberger, F., Wortmann, M., and Cloke, H.: Beyond Static Priors: Unlocking High-Resolution Dynamic River Networks via a SWOT-Driven Machine Learning Pipeline, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-3842, https://doi.org/10.5194/egusphere-egu26-3842, 2026.
Machine learning-based hydrological forecasting models are conventionally developed as lumped systems that predict watershed outflow using basin-averaged weather forcings. These approaches neglect the spatial information of meteorological inputs and their interactions with river network topology, limiting their ability to produce detailed reach-scale forecasts, i.e., for individual river reaches within the watershed. Here we introduce a new framework that provides reach-scale forecasts trained directly on SWOT water surface elevation (WSE) observations rather than outlet gauges. Incorporating spatially continuous SWOT observations allows the ML model to learn across the river network instead of only at gauged locations. The framework delivers 0- to 9-day forecasts of river and reservoir stages across an entire watershed. Our model integrates a graph neural network (GNN) with a long short-term memory (LSTM) network. The GNN encodes gridded meteorological forcings into the river network in a manner consistent with the runoff generation process. The LSTM component captures temporal dependencies and produces stage forecasts at key reservoirs and river reaches. Additional model inputs include 0- to 9-day Multi-Source Weather (MSWX) forecasts and river attributes from the Global River Topology (GRIT) dataset. The framework is implemented over the Mekong River Basin to generate forecasts for cascading reservoirs. Results demonstrate improved predictive performance relative to baseline Random Forest and LSTM models, highlighting the value of incorporating hydrological connectivity and satellite-based observations to improve forecasting in data-scarce regions.
How to cite: Liu, Y., Moulds, S., Wilby, R., Bui, D., Du, T., Bui, L., Zhang, B., Liu, Y., Wortmann, M., Nguyen, N., Monahan, T., Mosaffa, H., and Slater, L.: SWOT-based spatiotemporal deep learning for reach-scale forecasting of river and reservoir stages in the Mekong, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-6095, https://doi.org/10.5194/egusphere-egu26-6095, 2026.
Posters on site: Fri, 8 May, 08:30–10:15 | Hall A
Rivers in cold-climate regions are fundamental components of coupled hydrological, cryospheric, and ecological systems by governing water availability, ecosystem functioning, and human activities under strong seasonal constraints. Snow accumulation, river ice processes, and freeze-thaw cycles are major factors for tightly controlling river dynamics, making them highly sensitive to climate variability and change. Consequently, to capture changes in flow, ice conditions, and water levels, systematic river monitoring is essential which enables reliable assessment of hydrological extremes, ecosystem responses, and infrastructure risks. However, river monitoring in cold climate regions remains challenging due to prolonged ice cover, limited accessibility during winter, sensor malfunction under freezing temperatures, harsh environmental conditions, and the complex interaction between hydrological and cryospheric processes that are difficult to observe continuously and at high resolution. Therefore, increasing attention have been gained for novel monitoring approaches that minimize direct physical contact, particularly those based on remote sensing techniques, because they enable spatially extensive, non-intrusive, and continuous observation of river dynamics in cold regions and hard to access environments. In this context, by representing a major milestone as the first wide-swath altimetry mission specifically designed to observe surface water dynamics, the Surface Water and Ocean Topography (SWOT) mission was first launched in December 2022. It provides high-resolution, near-global measurements of water surface elevation (WSE), river width, and surface slope using Ka-band Radar Interferometry (KaRIn), and SWOT significantly advances satellite-based monitoring of rivers, lakes, and reservoirs. In this paper, we assess the validity of WSE derived from RiverSP data of the SWOT mission over major Finnish rivers. WSE is extracted for 14 rivers which are available in SWOT mission and representing diverse hydrological settings, including 08 regulated and 06 unregulated, and is evaluated against observed daily water levels from the nearest in-situ gauging stations. This analysis enhances understanding of the influence of river ice cover on SWOT observations and enables evaluation of the associated quality flags. Temporal in-situ satellite derived water temperature observations before and after the ice season are examined to support interpretation of detected ice-cover and open water period with SWOT observation.
How to cite: Abeynayake, H., J. Tourian, M., and Torabi Haghighi, A.: Validation of Surface Water and Ocean Topography (SWOT) river Water Surface Elevation using in-situ gauges in Finnish rivers, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-3368, https://doi.org/10.5194/egusphere-egu26-3368, 2026.
In recent years, advances in radar altimetry, particularly Synthetic Aperture Radar (SAR) techniques, have enabled the observation of fine-scale features over coastal oceans and inland water bodies. Since its launch in late 2022, the Surface Water and Ocean Topography (SWOT) mission has provided unprecedented spatial detail and measurement accuracy for resolving water surface gradients. This capability offers a unique opportunity for the timely and repeated monitoring of ungauged rivers and inland water bodies. In this study, SWOT Level-2 Lake and Pixel Cloud (PIXC) products are employed to monitor lakes, ponds, and reservoirs across Taiwan. Field validation conducted at 14 small ponds and 12 major reservoirs demonstrates that SWOT is capable of capturing water surface elevations and their temporal variations with sub-meter accuracy over mission cycles 3–37. Furthermore, reprocessing the PIXC data through clustering within predefined water masks improves the accuracy to better than 10 cm. These results indicate that SWOT provides a valuable alternative perspective on hydrological parameters and has significant potential to support future water resources monitoring and management.
How to cite: Tseng, K.-H.: Validation of Water Surface Elevation Estimated by SWOT Pixel Cloud Product in Taiwan, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-5144, https://doi.org/10.5194/egusphere-egu26-5144, 2026.
The Surface Water and Ocean Topography (SWOT) mission, equipped with a Ka-band radar interferometer, is designed to provide high-precision measurements of inland water surface elevation (WSE), width, slope, and estimated discharge，, enabling global investigations of hydrological processes, ecosystem dynamics, and climate-driven changes in surface water resources. To fully exploit the advanced global observation capabilities of SWOT for hydrological applications, a robust and systematic validation of its inland water products is essential.
In this study, we conduct a global-scale validation of SWOT-derived water surface elevation (WSE) by comparing SWOT measurements (River and Lake Single-Pass Vector Product) against in situ observations from 4,676 gauges for rivers and 514 stations for Lakes and reservoirs worldwide. To this end, SWOT observations are first coarsely matched to gauge locations using spatial distance thresholds between satellite ground tracks and hydrological stations. This step is followed by station-by-station manual inspection to ensure accurate spatiotemporal matching. The accuracy of SWOT lake and river WSE products is then evaluated at the global scale using least-squares fitting. In addition, we assess the effects of different quality control flags and uncertainty parameters on data accuracy by testing individual and combined filtering strategies under multiple threshold settings. To evaluate the accuracy of SWOT river width estimates, we analyze the relationship between gauge-based WSE and river widths derived from different satellite passes. Gauge WSE is used as an intermediate variable to quantify systematic width offsets among passes and to examine the influence of cross-track distance on river width bias.
In addition, we establish empirical relationships between gauge-based WSE and SWOT-derived river width to assess the accuracy and uncertainty of SWOT width estimates. The analysis focuses on a wide range of inland water bodies, including rivers of varying widths and lakes and reservoirs of different sizes, allowing an assessment of SWOT performance across diverse hydrological conditions. To examine observational consistency across processing stages and to quantify the impact of algorithm updates, we compare SWOT Version C and Version D data, with a focus on changes in WSE and river width accuracy.
This study provides a comprehensive assessment of the accuracy and consistency of SWOT inland water products in global scale and offers practical guidance on data version selection and quality control strategies for long-term hydrological applications of SWOT observations.
How to cite: Yu, S., Saemian, P., and Tourian, M. J.: Global Validation of SWOT Water Surface Elevation with Gauge Data and Analysis of River Width, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15154, https://doi.org/10.5194/egusphere-egu26-15154, 2026.
In flat sedimentary landscapes, shallow lakes and ponds (hereafter, SLPs) are a highly dynamic component that provides habitat for local and migratory fauna, water resources for surrounding vegetation and livestock, as well as regulation of local climate and biogeochemical fluxes. In the Pampas of central Argentina, SLPs are highly sensitive to precipitation and evapotranspiration fluctuations, displaying strong multi-year (rather than seasonal) expansion-recession cycles. Here, with the aid of SWOT data, we report for the first time the volume stored by these SLPs, leveraging recent historical records of their recession minimum (early 2023) and subsequent expansion rate (2023-2025) and maximum (late 2025). This hydroclimatic turnover in concurrence with the deployment of the satellite mission provides a unique opportunity to study local to regional changes in water surface elevation, area, and inferred volumetric changes in small and temporary water bodies.
We focus our study on the “Pampa Interior Plana” subregion, with 85,000 km2 and regional slopes < 0.01%, hosting > 50,000 SLPs of a median area of 1.3 ha that cover 10% of the region on the long-term average. We tracked the five highest rainfall events between October 2023 and December 2025 (40-130 mm), and corresponding Lake-SP-derived water surface elevation and area changes at regional (> 7,000 water bodies) and local (ten SLPs ranging 0.5-2000 ha) scales.
Preliminary results indicate directional increases in elevation over the whole region of up to 3 meters between the driest state and the maxima observed in 2025, which translates into more than 5 billion cubic meters of stored water. When comparing individual SLP responses to a gradient of rainfall intensity, a marked heterogeneity can be observed, with some systems exhibiting rapid surface elevation and area increases, others showing less-sensitive variations in elevation. This variability suggests either (a) the strong modulation by antecedent conditions in soil moisture and water table level, (b) morphometric differences between these shallow water bodies dictating e.g., predominantly lateral vs. combined lateral and vertical expansion, or (c) difficult to isolate, evaporative forcings between successive SWOT observations. Importantly, this work illustrates the insights that SWOT enables into the hydrological functioning of extensive lowland freshwater systems.
How to cite: Torre Zaffaroni, P., Niborski, M., and Jobbágy, E.: SWOT enables the regional quantification of a record water stock fluctuation in the shallow lakes of the Pampas following a multi-year drought, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-20675, https://doi.org/10.5194/egusphere-egu26-20675, 2026.
Monitoring changes in lake water elevation and volume in mountainous regions is crucial for hydro-electricity management, natural hazard mitigation and quantifying water resources. The SWOT mission is revolutionising lake monitoring from space since its swath altimetry sensor provides data with a global spatial coverage and a better spatial resolution than previous nadir altimeters. This opens the door to the study of smaller lakes. However some challenges remain, especially in mountainous regions. It includes the impact of layover in steep terrain and the poor quality of the SWOT water area products. These limitations hinder the derivation of hypsometric curves (i.e. the area/height relationship) that are required to calculate changes in lake volume. One way to overcome this is to measure lake areas using the segmentation of SAR and optical images. However, mountain lake areas from SAR images are affected by layover and shadow effects while those derived from optical images are distorted by cloud cover.
In this context we first evaluate the quality of the SWOT Pixel Cloud (L2_HR_PIXC) products over several mountain lakes. We developed a processing chain to filter PIXC data based on quality flags, backscatter signals and spatio-temporal statistics. The filtered water surface elevations are validated using in-situ data from the Lacs Sentinelles network in the French Alps and from the FOEN network in Switzerland.
Next, we present a methodology for combining Sentinel-1 and Sentinel-2 areas with SWOT surface elevation using the hypsometric function, and a spatio-temporal interpolation of the lake water levels. Lake surface areas are derived from the Sentinel-1 OASIS index, a highly sensitive measure for water body detection, while Sentinel-2 based lake surface areas are extracted from the CNES Surfwater products, available on the hydroweb.next open access platform. We illustrate our strategy on three different mountain lakes of various sizes and in different environments: Joux Lake in the Jura Mountains, Switzerland (8-9 km²) ; the Lauvitel Lake in the Ecrins Massifs, French Alps (1-3 km²) and the Rosolin Lake, a supra-glacial lake in the Vanoise Massif, French Alps (0.01-0.03 km²). This work is a step towards a better quantification of changes in lake elevation and volume in complex terrains.
How to cite: Charrier, L., Karbou, F., James, G., Gasnier, N., Guerou, A., and Peña-Luque, S.: Estimation of water elevation and volume changes over mountain lakes using Sentinel-1/2 and SWOT data, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-16531, https://doi.org/10.5194/egusphere-egu26-16531, 2026.
The Surface Water and Ocean Topography (SWOT) mission recently overcame the spatial limitation of satellite altimetry by using the wide-swath interferometry to simultaneously observe Water Surface Elevations (WSE) of inland waters within a 120 km wide swath. Thanks to this unique measurement principle, many river reaches are observed multiple times (typically two to four times) during SWOT’s 21-day repeat cycle. Nevertheless, even with these multiple observations, the resulting temporal resolution remains insufficient to capture the rapid sub-weekly dynamics of flood events. In this study, we present a novel approach to bridge this gap by exploiting the unique SWOT data geometry to densify multi-mission observations at Virtual Stations (VS) into a daily product.
Our method, named Reach-Reg, introduces a first approach to generate daily WSE time series from multi-mission satellite altimetry. Reach-Reg employs WSE data from Sentinel-3A/B, Sentinel-6A, and SWOT (provided by DAHITI, https://dahiti.dgfi.tum.de/). Our method introduces a chained regression approach that utilizes WSE from concurrent SWOT overflights to establish linear relationships between neighbouring river reaches. These regressions enable the precise transfer of the WSE measurements from various VS to a central Reference Station (RS). To ensure physical consistency, Reach-Reg employs a Manning-based time-lag correction using at-a-station hydraulic geometry simplification and calibration based on cross-validation. The daily product is achieved through the automated outlier rejection, smoothing, aggregation, and interpolation. Crucially, Reach-Reg also provides a rigorous uncertainty estimate.
We evaluated Reach-Reg across 95 RS on eight rivers (Elbe, Ganges, Mississippi, Missouri, Oder, Rhine, Po, Solimões) spanning four continents. These rivers represent diverse hydrological regimes and bed morphologies. Despite this variability, the method achieved consistent high performance, with mean RMSE of 0.30 m, normalised RMSE of 4.2%, and Nash-Sutcliffe Efficiency of 0.94. Notably, Reach-Reg significantly outperforms existing multi-mission densification techniques by improving both temporal resolution (daily vs. 2–5 days) and vertical accuracy (0.30 m vs. ≈1 m mean RMSE). Furthermore, this approach is computationally efficient (processing one station in ≈1 minute), open-source, and based solely on altimetry, ensuring global transferability. The introduction of daily WSE from multi-mission satellite altimetry will enable better understanding and modelling of river dynamics. It will also make it possible to issue WSE forecasts even in ungauged basins.
The research has been carried out in frame of the project no. BPN/BEK/2024/1/00047 within the Bekker Programme of the Polish National Agency for Academic Exchange. The Python implementation of the Reach-Reg method is available on GitHub (https://github.com/MichalHalicki4/Reach-Reg) and the daily WSE time series can be obtained from the Zenodo repository (https://doi.org/10.5281/zenodo.17928117).
How to cite: Halicki, M., Niedzielski, T., Schwatke, C., Scherer, D., and Dettmering, D.: Towards daily river monitoring from space: Reach-Reg, a first approach for spatiotemporal water level reconstruction using the SWOT geometry, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-11677, https://doi.org/10.5194/egusphere-egu26-11677, 2026.
The Surface Water and Ocean Topography (SWOT) mission provides unprecedented spatial detail for observing inland surface waters. Yet, the behavior and reliability of surface-type classification in the Level-2 KaRIn High-Rate Pixel Cloud (L2_HR_PIXC) product remain insufficiently characterized, due to the complexity of inland waterbodies, particularly across diverse hydromorphological and climatic settings. Because PIXC classification forms the foundation of all higher-level SWOT inland water products, a systematic evaluation of its performance is essential.
In this study, we systematically evaluate the SWOT PIXC surface classification and associated water fraction estimates using multiple river case studies spanning different climate regimes defined by the Köppen–Geiger classification. For each case study, PIXC surface classes and water fraction values are grouped by classification type and analyzed against independent water occurrence information derived from DSWx-HLS (Harmonized Landsat& Sentinel-2), DSWx-S1 (Sentinel-1), and complementary long-term water occurrence datasets from Global Surface Water (Landsat).
Beyond inter-product comparison, we investigate how discrepancies between PIXC classification and imagery-based water occurrence depend on key KaRIn observables and geometric variables, including interferometric coherence, radar backscatter (σ⁰), phase noise standard deviation, incidence angle, etc. This analysis enables a process-oriented interpretation of classification behavior across surface classes, environments, and viewing conditions. The results provide a structured assessment of the strengths and limitations of SWOT PIXC classification, supporting informed use of SWOT inland water products and contributing to ongoing processing algorithm evaluation and future refinement efforts.
How to cite: Tourian, M. J., Ettehadieh, S., Elmi, O., Oubanas, H., and Pavelsky, T.: Assessing SWOT PIXC surface classification using imagery-based products, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-9542, https://doi.org/10.5194/egusphere-egu26-9542, 2026.
The Surface Water and Ocean Topography (SWOT) mission, launched at the end of 2022, marked a new area within satellite altimetry. Based on the data collected so far, many new and innovative results have already been obtained, e.g., for inland water, coastal areas, and the ocean. In addition to reflections from these classical, permanent water sources, SWOT also provides potentially valid surface water elevations from temporary wet areas, such as fields and similar surfaces. In this initial study, we intend to investigate the quality of SWOT surface elevation from these surfaces. Denmark is continuously (in 5-year periods) mapped by airborne lidar campaigns to reconstruct an accurate (5 cm error) high-resolution (40 cm) digital elevation model. This high-quality data set enables investigation of the quality of SWOT observations over wetland areas. In this study, we will apply pixel cloud observations from several scenes to generate a SWOT-based DEM, assuming the surface is stationary, so that variations and noise in the SWOT observations will average out over time. Here, we will investigate the potential for selected areas in Denmark.
How to cite: Nielsen, K. and Köhn, S. J.: Can surface elevation from SWOT over wet land areas be used to generate a DEM?, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-10458, https://doi.org/10.5194/egusphere-egu26-10458, 2026.
Early analysis conducted on a reach of the Upper Rhine during the daily revisit Calibration/Validation phase of the SWOT mission (Cal/Val 1-day) have demonstrated the very high quality of SWOT observations, both in terms of absolute water levels and the retrieval of fine hydraulic signatures such as riffles, pools, gravel bars, and slope breaks (Ledauphin et al., 2025). SWOT data have notably shown their ability to accurately reflect channel morphology, as well as temporal variations in the longitudinal water surface profile as a function of discharge. However, the strong hydrological and hydro-sedimentary dynamics of the Rhine cannot be fully captured during the SWOT Science phase, characterized by a 21-day revisit period and one or two overpasses, which is likely to miss rapid processes such as floods and related morphological adjustments.
In this context, this study focuses on the long Old Rhine, a 50-km long bypassed reach with a width ranging from 80 to 150 m, resulting of the successive of the Rhine’s course over the past two centuries, including the construction of hydroelectric plants. This reach has a minimum flow of 52 m3/s through it, most of which is diverted into the Grand Canal d’Alsace to supply power plants. The site is subject to pronounced hydro-sedimentary dynamics and benefits from a particularly dense observational framework, including in situ gauging stations, bathymetric surveys, topo-bathymetric LiDAR data, and drone acquisitions. It is also a calibration site for several satellite altimetry missions, including SWOT (Tier-1 site during the Cal/Val phase) and Sentinel-3 (ESA St3TART projects (Da Sylva et al., 2023)), making it a well-known reference site for multi-mission altimetric studies.
The primary objective of this study is to exploit the SWOT Cal/Val 1-day phase to consistently compare and validate observations from different satellite altimetry missions at equivalent dates. This multi-mission approach allows the assessment of data quality, consistency, and complementarity in a dynamic fluvial environment. Hydraulic-preserving filtering methods are applied to improve and homogenize water surface longitudinal profiles and derived slopes, particularly in morphologically complex areas, with their performance evaluated using in situ measurements (Montazem et al. 2025; Larnier et al., 2025). As a result, WSE profiles where longitudinal signatures and non linearities due to morphological variability are well preserved/depicted
In a second step, the complementarity of multi-mission observations is analyzed during the SWOT Science phase to determine to what extent their combination enables a denser temporal sampling and a more detailed monitoring of the long Old Rhine dynamics compared to individual missions. Finally, the combined datasets are used to assess the potential for calibrating and cross-validating hydraulic models, integrating recent topo-bathymetric data. Preliminary results highlight the strong potential of SWOT data and multi-mission altimetry for the dynamic monitoring of large rivers and for improving hydraulic modeling at reach to regional scales.
How to cite: Pujol, L., Ledauphin, T., Azzoni, M., Garambois, P.-A., Schmitt, L., Chardon, V., Fouqueau, V., and Yesou, H.: From SWOT Cal/Val to Science Phase : Assessing and Enhancing Multi-Mission Satellite Altimeter Data (SWOT, Sentinel-3) for Hydraulic Visibility of the Natural Upper Rhine, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-7501, https://doi.org/10.5194/egusphere-egu26-7501, 2026.
Canada possesses one of the world's most extensive and diverse river networks, characterized by seasonal and regional variation in nival, glacial, and pluvial hydrological regimes and landscapes from lowland agricultural, to lakes and forests, to tundra and high mountains. Long snow-covered winters and a large spring freshet characterize the hydrological regime of most of the country. Monitoring river floods in this region faces significant challenges because of the sparse hydrometric gauging network, especially in northern Canada, and limited traditional remote sensing capabilities. The Surface Water and Ocean Topography (SWOT) satellite mission represents a transformative advance by providing all-weather, wide-swath measurements of water surface elevation (WSE) and water surface slope. This study presents a comprehensive framework to extract and classify river water wave dynamics across Canada using the SWOT_L2_RiverSP products. First, a quality-control protocol was established based on intrinsic orbital and geometric attributes. Evaluation of river width, cross-track distance, and WSE against observations from Water Survey of Canada hydrometric stations identified the river reaches where SWOT signals were most reliable. A novel Slope-WSE Phase Space method to classify river floods distinguished ice jam floods, confluence backwater effects, and freely propagating floods due to nival and pluvial mechanisms. The flood classification was then diagnosed using multi-source datasets, including ERA5 reanalysis precipitation/temperature and Landsat/Sentinel remote sensing imagery. SWOT successfully monitored floods, and the distinct hydraulic gradients of ice-jam induced backwater where water surface dynamics were previously unobservable by in-situ hydrometry. Even in the narrow rivers (< 100 m), SWOT had good capability to measure the temporal propagation and hydraulic gradients of flood waves when compared with downstream hydrometric records. This research demonstrates the SWOT’s ability to monitor not just river water level dynamics, but also the underlying hydraulic processes of river systems under a wide range of cold regions processes on a continental scale, providing critical insights for observing and managing diverse flooding hazards.
How to cite: Zhang, J., He, Z., and Pomeroy, J.: Flood Monitoring and Classification in Canadian Rivers Using SWOT Observations, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-6157, https://doi.org/10.5194/egusphere-egu26-6157, 2026.
River discharge is traditionally estimated using stage–discharge rating curves, yet their calibration relies on sparse and costly in situ measurements and remains highly uncertain, particularly under extreme flow conditions. The Surface Water and Ocean Topography (SWOT) mission provides unprecedented observations of river surface elevation, slope, and width; however, inferring discharge from these variables alone is fundamentally ill-posed and susceptible to structural biases. Overcoming these limitations requires additional physical constraints at the river-network scale, motivating the use of hydrological–hydraulic closure and data assimilation to derive robust, observation-conditioned rating curves from SWOT data. In this study, we propose a multi-scale Markov Chain Monte Carlo (MCMC) framework to infer three stage–discharge rating curve formulations by jointly exploiting SWOT observations, hydrological model outputs, and in situ discharge measurements. Our results demonstrate the feasibility of using SWOT data to infer reliable parametric stage-discharge relationships including a conceptual river hydraulic geometry, while revealing spatially coherent patterns in model discharge quality consistent with previous studies. The analysis also highlights current limitations in SWOT data processing quality and establishes a foundation for deriving prior hydraulic knowledge to support future end-to-end hydrology–hydraulic learning frameworks.
How to cite: Nguyen, N. B., Larnier, K., Renard, B., Le Coz, J., and Garambois, P.-A.: A Bayesian framework for Stage–Fall–Discharge Laws estimation from SWOT altimetry and slopes, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-13034, https://doi.org/10.5194/egusphere-egu26-13034, 2026.
Monitoring global river discharge is fundamentally limited by the sparse distribution and ongoing decline of ground-based gauging stations. This observational gap is most critical in "ungauged basins," where lack of data hinders water resource management and flood forecasting. The Surface Water and Ocean Topography (SWOT) satellite mission serves as a transformative solution, providing the first global inventory of Earth’s surface water by measuring river height, width, and slope from space.
Estimating discharge from satellite observations is a complex inverse problem because key hydraulic parameters, such as river bathymetry (bottom elevation) and bed roughness, are unknown in ungauged regions. This research explores various advanced methodologies to bridge this gap by integrating satellite-derived measurements of water surface elevation, width, and slope into hydraulic models. These approaches allow for the simultaneous estimation of unknown parameters and river flow, providing a globally consistent, observation-based record of discharge even in basins where ground-based statistics are entirely unavailable
The studies demonstrate that the unique spatial coverage of SWOT allows for a transition from traditional single-point calibration to a "multi-point" parameter selection approach. This strategy uses observations from numerous points across a river network, which significantly improves the model's ability to identify the correct hydrologic parameters compared to relying on a single virtual gauge.
While the satellite data contains inherent measurement noise and systematic biases, the spatially distributed nature of the observations helps compensate for these errors. The research indicates that SWOT is particularly effective at resolving temporal variations in discharge, providing a reliable record of hydrologic events even when absolute discharge values carry uncertainty. However, the effectiveness of the mission can be influenced by river "flashiness"—basins with very rapid changes in flow may be harder to characterize due to the satellite's specific overpass timing.
SWOT observations provide a vital resource for constraining hydrological processes globally. By enabling the calibration of hydrologic models in previously unmonitored regions, the mission allows for a better understanding of how streamflow responds to rainfall. This capability is expected to lead to transformative science in global hydrology, offering a consistent and observation-based measure of discharge that far exceeds the accuracy of existing uncalibrated global models.
How to cite: Hazhir, S. and Gohari, A.: Estimation of river discharge in areas without statistics using SWOT satellite , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-21648, https://doi.org/10.5194/egusphere-egu26-21648, 2026.
In regions with low density of ground observational network, hydrodynamic models and satellite observations are able to reproduce the river water level regime and inundated areas with an accuracy sufficient for monitoring climate change. However, very limited number of studies demonstrated the capacity of synergy of satellite altimetry and hydrodynamic modelling in complex Arctic environment due to lack of certainty in interpretation of altimetric measurements over river ice and high errors in calculation of winter water levels. However, it is the winter regime of arctic and boreal rivers that experiences the most significant climate change. These changes are seen as a decrease in ice duration and thickness, ice jam occurrence and an increase in winter water levels. We hypothesized that the river ice properties varying in space and time may affect the performance of water level retrievals from satellite altimetry and investigated the validity of our hypothesis using the synergy of satellite measurements and numerical experiment with hydrodynamic models. Two models MIKE 11DHI and STREAM 2D were adapted for 90-300 km-long river reaches located on the Severnaya Dvina, Lena and Kolyma Rivers. The models were run for winter periods with ice modules switched on/off. Comparison of simulation results showed that in case of smooth ice of the Kolyma River the altimetry retrievals rather showed the elevation of the ice bottom, while in case of rough S.Dvina and Lena ice the satellite measurements were close to the elevation of the ice top. The latter allowed to reproduce with the altimetric measurements the mid-winter ice jam conditions occurred in 2019 on the S.Dvina River and to validate the model in ungauged sections.
The verification of the altimetry return signal (waveform) over the test sites demonstrated that over the Kolyma's smooth ice, the waveforms expressed two distinct peaks. The second peak dominated in power and was used by the SAMOSA 2 retracker algorithm for the range and elevation estimation. In cases of rough ice, the return signals had one peak, which, according to comparison with the modeled levels, was produced by reflection of the signal from the ice top. Our finding can have an important implication for future adaptation of satellite altimetry for high latitude river hydrology and can explain the variable in space and time performance of the satellite altimetry over frozen rivers.
The new interferometric altimetric instrument installed on the SWOT satellite (on orbit since 2023) globally maps the river water surface topography and potentially may indicate the locations and severity of the ice jams. However, the low instrument incidence angle makes surface elevation retrievals unrealistic in many locations characterized by low water/ice roughness. We investigated the performance of the SWOT surface elevation retrievals in winter period in our test sites and demonstrated that its measurements may have precision compared with the model simulations. This makes the SWOT measurements extremely valuable for hydrodynamic model runs in winter.
The study was supported by CNES TOSCA SWIRL project; Water Problems Institute, RAS Governmental Order FMWZ-2025-0003 and RSF project №24-17-00084.
How to cite: Zakharova, E., Krylenko, I., Golovlyev, P., Lisina, A., Sazonov, A., Semenova, N., and Kouraev, A. V.: Use of satellite altimetry for monitoring river ice state and ice jams, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-12506, https://doi.org/10.5194/egusphere-egu26-12506, 2026.
This study investigates the potential of the Surface Water and Ocean Topography (SWOT) mission to contribute to the study of snow cover and its hydrological impacts. Preliminary results indicate promising capabilities for detecting and potentially characterizing snow covers. This additional information can help interpret water‑level variations that are directly influenced by snow quantity and snowmelt processes in northern regions during the spring melt season. To better understand the behavior of the KaRIn backscatter signal over snow, in situ observations, reanalysis products, and modelled backscatter data generated with the Snow Microwave Radiative Transfer (SMRT) model are compared with SWOT measurements. This integrated analysis allows us to examine both the sensitivity of KaRIn to snow cover and the associated SWOT‑derived water‑elevation changes. Overall, this work contributes to ongoing efforts aimed at advancing the remote sensing of snow properties—such as snow water equivalent—and improving our ability to link snowpack evolution with hydrological responses.
How to cite: Bourgault-Brunelle, C., Browser, C., and Guun, G.: Using SWOT KaRIn Backscatter and SMRT Modeling to Link Snow Properties with Melt‑Driven Water Level, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-23237, https://doi.org/10.5194/egusphere-egu26-23237, 2026.
Precipitation is a fundamental component of the Earths hydrological cycle, with profound implications for water resource management, marine traffic, and disaster risk mitigation. Accurate rainfall estimation is critical for understanding weather patterns, forecasting flood events, and modeling climate change scenarios. While ground-based systems, such as the NEXRAD WSR-88D network, provide high-resolution data, they are geographically limited and leave vast oceanic regions unmonitored. Alternatively, satellite missions offer global coverage but often lack the necessary spatial resolution for fine-scale analysis (e.g., IMERG provides rates at ~8 km/pixel) or have limited acquisition in open oceans (e.g., Sentinel-1's constellation default observation mode in open ocean is composed of scattered imagettes).
In this context, the Surface Water and Ocean Topography (SWOT) mission presents a novel opportunity to bridge the gap between global coverage and high spatial resolution. Although primarily designed for altimetry, SWOT’s Ka-band Radar Interferometer (KaRIn) is sensitive to atmospheric hydrometeors. KaRIn offers sub-kilometer resolution (250 m/pixel), matching NEXRAD resolution in range, and provides continuous data over both coastal zones and the open ocean.
We present a machine learning framework to estimate precipitation rates using SWOT observations. We build a NEXRAD/SWOT dataset between August 2023 and February 2025, composed of 7009 patches (512×512 pixels) out of which 1090 contains precipitation (more than 1 mm/h on more than 1\% of the observation). A U-Net architecture was trained to retrieve Digital Precipitation Rates (DPR) from the WSR-88D. The input features include the backscattering coefficient (normalized to mitigate the incidence angle variability), total coherence, incidence angle, and a wind speed prior from atmospherical models. To ensure robust performance, the training loss is spatially weighted: pixels closer to NEXRAD stations are prioritized to minimize ground-truth uncertainty related to radar beam broadening and elevation in altitude, while null-DPR pixels are down-weighted to address class imbalance. Furthermore, quantile mapping was applied to align the model’s output distribution with NEXRAD's statistics, ensuring the accurate replication of heavy rainfall tails. An ensemble of independently trained models allow to compute a consensus score, providing a metric for estimating confidence.
Evaluation against NEXRAD data shows the model achieves 67\% accuracy in categorical classification (rainless, low, high intensity), a performance comparable to dual-station consistency checks. In the open ocean, validation against collocated IMERG tracks reveals strong correlations of their respective time series, reaching 95\% in the Pacific Inter-Tropical Convergence Zone (ITCZ) and 75\% in the Atlantic ITCZ. However, correlation degrades at higher latitudes, suggesting a sensitivity to convective precipitation regimes. This behavior is consistent with observations from C-Band SAR rainfall retrieval such as the future rainfall product of the Sentinel-1 constellation.
These results demonstrate the feasibility of using SWOT KaRIn high-resolution products for robust rainfall estimation, particularly in tropical and equatorial regions. By unlocking precipitation data in data-sparse regions, this approach offers a significant contribution to global precipitation monitoring and hydrological modeling.
How to cite: Picard, B., Colin, A., and Husson, R.: Estimating precipation in open ocean with SWOT at sub-kilometer resolution, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-20219, https://doi.org/10.5194/egusphere-egu26-20219, 2026.
Posters virtual: Thu, 7 May, 14:00–18:00 | vPoster spot A
EGU26-10090 | Posters virtual | VPS10
Characterizing Baseflow in Indian River Basins Using SWOT Discharge ObservationsThu, 07 May, 14:39–14:42 (CEST) vPoster spot A
Baseflow is a crucial component of streamflow, essentially driven by changes in groundwater storage, and is vital for sustaining flows during dry periods. Traditional techniques for baseflow quantification using graphical analysis or digital filters require long-term river discharge observations, which are often limited in their spatial extent. However, with the launch of the Surface Water and Ocean Topography (SWOT) mission, global estimates of river discharge are now available over a period of two years, offering a high-resolution dataset with at least one observation every 21 days. Despite its relatively coarse temporal resolution, prior studies have demonstrated SWOT’s ability to accurately estimate average baseflow even at one observation per cycle, based on synthetic SWOT discharge estimates. The high spatial resolution provided by ‘SWOT discharge’ can be utilized to estimate baseflow at a reach-scale and gain new insights into groundwater-surface water interactions in water-stressed river basins.
In this study, we will utilize SWOT’s discharge products over Indian river basins to characterize baseflow dynamics at reach-scale resolution and examine the effects of climate variability and land-use changes on baseflow. By accurately estimating the baseflow recession parameter (k), this study will be able to identify the gaining-to-losing transition in a basin. Furthermore, the research will explore SWOT’s ability to detect temporal shifts in the baseflow recession parameter (k) during the pre-monsoon period and evaluate the effects of anthropogenic extractions on the groundwater table. Finally, these estimates will be integrated into a mass-balance model, baseflow will be converted into upstream groundwater storage (GWS) changes and validated against independent GWS anomalies derived from the Gravity Recovery and Climate Experiment (GRACE) satellites. This study will demonstrate the capability of SWOT to bridge the gap between reach-scale hydraulics and basin-scale storage, providing a vital tool for sustainable water resource management in water-stressed regions.
How to cite: Deshpande, R. S., Vidushi, V., and Syed, T. H.: Characterizing Baseflow in Indian River Basins Using SWOT Discharge Observations, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-10090, https://doi.org/10.5194/egusphere-egu26-10090, 2026.
