Synergizing SWOT and virtual-station hypsometry for extreme reservoir monitoring: Characterizing the 2025 Beijing’s Miyun flood

Study region Miyun Reservoir, Beijing, China. Study focus High-frequency monitoring of reservoirs is essential for managing extreme hydrological events but is often constrained by cloud contamination and satellite temporal gaps. This study integrates multi-source data from Sentinel-1, Sentinel-2, and the Surface Water and Ocean Topography (SWOT) satellite mission to monitor the Miyun Reservoir during the extreme "7.25" flood event of July 2025. To mitigate visibility limitations, we propose a Virtual Station (VS) strategy that establishes localized hypsometry relationships within morphologically stable inundation zones. This approach aims to reconstruct high-frequency hydrological dynamics by leveraging localized clear-sky windows rather than requiring whole-reservoir visibility. New hydrological insights for the region The VS strategy yielded a > 30% increase in valid temporal sampling of optical imagery relative to whole-reservoir approaches. Quantitative evaluation showed that the quadratic AVS-H model achieved superior accuracy (R² = 0.90, RMSE = 0.45 m), effectively filling a critical 21-day gap in SWOT overpasses. During the extreme rainfall event, the framework successfully reconstructed the hydrological trajectory, capturing a water level surge from 151.25 m to a peak of 155.14 m (a net rise of 3.89 m) and a cumulative storage increment of 5.84 × 108 m³ . Validated against official gauge records, these findings demonstrate that the proposed method robustly replicates rapid hydrological pulses, offering a scalable solution for continuous flood risk monitoring in cloud-prone and ungauged reservoirs.