Monitoring potential of reservoir storage driven by SWOT pixel cloud: a case study of the Gezhouba Reservoir

Reservoirs play a crucial role in mitigating floods, alleviating droughts, regulating runoff, and ensuring water security for human use. Accurate quantification of reservoir storage is essential for water resources management, flood control, and regional water security. However, remote sensing-based methods for reservoir storage estimation often assume a horizontal water surface, thereby neglecting the spatial heterogeneity of actual water surface elevations. This assumption can lead to significant errors in reservoir storage estimation during the flood season, particularly for river-type reservoirs with pronounced longitudinal water surface slopes. To address this issue, this study leverages the unique advantage of the Surface Water and Ocean Topography (SWOT) satellite in capturing spatially continuous two-dimensional water surface elevations (WSE). By integrating SWOT data with multi-source remote sensing imagery and digital elevation model (DEM), a quantitative framework is developed to estimate reservoir storage. Using the Gezhouba Reservoir as a case study, this framework enables the generation of high spatiotemporal resolution water surface elevation rasters (WSER) and, through a pixel cloud raster-based reservoir storage estimation approach, achieves reservoir storage quantification from August 2023 to December 2024 while accounting for dynamic storage capacity. Results demonstrate that: (1) The high spatiotemporal resolution WSER constructed in this research accurately captures both the longitudinal variations and spatial heterogeneity of reservoir water levels. Validation against in-situ measurements and the Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) observations reveals strong agreement (R2>0.837) and low error (RMSE <0.3 m); (2) Traditional static reservoir capacity estimation methods substantially underestimate reservoir storage during the flood season, particularly under conditions of significant upstream inflow. In contrast, the pixel cloud raster-based reservoir storage estimation method proposed in this study reflects the reservoir storage beneath the actual water surface. The maximum difference in estimated storage between the two methods reaches 0.445 km3, with a maximum relative deviation of up to 69.1%. This research demonstrates the significant potential of SWOT satellite data for regional and even global applications in water resource monitoring, management, and flood risk assessment. It provides a novel technical pathway and methodological framework for the refined and remote sensing-driven management and regulation of reservoirs in the future.