Abstract's details
An Enhanced Framework for Improving SWOT-based Surface Water Level and River Slope Estimations
Event: 2025 SWOT Science Team Meeting
Session: Hydrology: HR SWOT Data (Data Validation & Enhancement)
Presentation type: Oral
The Surface Water and Ocean Topography (SWOT) satellite, launched in 2022, provides unprecedented observations of water surface elevation (WSE), river width, and slope for global rivers.
The project DETECT-REDS (DETECT-REfecct-Discharge-Storage) continues the REFECCT project of the SWOT Science Team 2020-2023. The central hypothesis is that SWOT and SAR altimetry outperform conventional altimetry with new products to monitor surface water and study hydrodynamic processes. The main objective is to evaluate the scientific usability of the SWOT data in inland waters, with a specific focus on applications related to river discharge and storage change.
In the DFG-founded DETECT project, we evaluate the applicability of SWOT in medium and small size rivers and lakes to determine spatial and temporal detection limits of the space techniques. Standard SWOT products (RiverSP and LakeSP) exhibit constrained accuracy and spatial definitions. Building upon SWOT Pixel Cloud (PIXC) and Raster products, we develop an enhanced framework to overcome these limitations for estimating surface water level, incorporating quality flags and gaussian mixture distribution clustering.
For lake and reservoir from Raster product, our approach achieves 4 cm median STDD with 1 cm bias against Swiss in situ gauges, effectively extending SWOT's observational capacity to sub-1 km² water bodies. For river applications, pixel cloud processing enables 6 cm height accuracy and 0.65 cm/km slope uncertainty along the Rhine River, also successfully detecting 50m-wide rivers – half the width threshold of SWOT's operational RiverSP specifications.
Additionally, we extend our framework to whole-European scale to analyze spatiotemporal variations in surface water level, assessing the combined impacts of climate-driven hydrological changes and anthropogenic disturbances (e.g., dam operations, irrigation). To ensure robustness, the derived dataset is undergoing multi-source validation against Hydroweb-next and DAHITI reference databases, enabling cross-comparison of SWOT-based retrievals with in situ gauges, nadir-altimetry.
Back to the list of abstractThe project DETECT-REDS (DETECT-REfecct-Discharge-Storage) continues the REFECCT project of the SWOT Science Team 2020-2023. The central hypothesis is that SWOT and SAR altimetry outperform conventional altimetry with new products to monitor surface water and study hydrodynamic processes. The main objective is to evaluate the scientific usability of the SWOT data in inland waters, with a specific focus on applications related to river discharge and storage change.
In the DFG-founded DETECT project, we evaluate the applicability of SWOT in medium and small size rivers and lakes to determine spatial and temporal detection limits of the space techniques. Standard SWOT products (RiverSP and LakeSP) exhibit constrained accuracy and spatial definitions. Building upon SWOT Pixel Cloud (PIXC) and Raster products, we develop an enhanced framework to overcome these limitations for estimating surface water level, incorporating quality flags and gaussian mixture distribution clustering.
For lake and reservoir from Raster product, our approach achieves 4 cm median STDD with 1 cm bias against Swiss in situ gauges, effectively extending SWOT's observational capacity to sub-1 km² water bodies. For river applications, pixel cloud processing enables 6 cm height accuracy and 0.65 cm/km slope uncertainty along the Rhine River, also successfully detecting 50m-wide rivers – half the width threshold of SWOT's operational RiverSP specifications.
Additionally, we extend our framework to whole-European scale to analyze spatiotemporal variations in surface water level, assessing the combined impacts of climate-driven hydrological changes and anthropogenic disturbances (e.g., dam operations, irrigation). To ensure robustness, the derived dataset is undergoing multi-source validation against Hydroweb-next and DAHITI reference databases, enabling cross-comparison of SWOT-based retrievals with in situ gauges, nadir-altimetry.