3-D Flood mapping from SWOT observations during extreme rainfall: a case study of Gangnan Reservoir

Global warming has intensified extreme precipitation events, posing challenges to economic and ecological systems. While the SWOT satellite offers high-resolution water elevation monitoring via Ka-band radar, it faces accuracy issues from noise and cross-orbit error. Therefore, we used SWOT data to evaluate the potential flood risk during the 7/2023 Beijing–Tianjin–Hebei flooding event in China, focusing on the Gangnan Reservoir. High-frequency noise in SWOT water surface elevation was identified using the Fourier transform and denoised with product quality-control parameters. Elevation accuracy was validated against ICESat-2. A monthly flood estimation method was developed based on SWOT features and traditional hydrological models, using probability distribution functions and time series reconstruction to identify flood characteristics such as water level, volume, and peak timing. The denoising process demonstrated centimeter-level accuracy for 100/250 m resolution SWOT products (0.19 ± 0.11 m and 0.09 ± 0.05 m). During the flooding event, the Gangnan Reservoir experienced an 8.46-m rise in water level, leading to a 52.17–125.49% increase in flooded area. The flood peak was observed within 5–15 January 2024. This research holds scientific importance for regional flood disaster monitoring, optimal allocation of water resources, and decision-making for disaster prevention and mitigation.