Abstract Reservoirs play a crucial role in global water resource management. Monitoring reservoir hydrologic dynamics is critical for assessing climate variability and anthropogenic regulation. However, traditional satellite altimetry faces multiple challenges hindering high frequency and accuracy water level monitoring. This study develops a proof‐of‐concept framework that integrates multi‐source satellite data, with the Surface Water and Ocean Topography (SWOT) mission as the primary data source, to generate high‐resolution reservoir water level time series. The SWOT‐anchored integration framework establishes a unified two‐dimensional height reference by rule‐based virtual station selection and monthly water surface elevation difference fields. On this reference frame, heterogeneous nadir/laser altimetry from multiple missions are cross‐calibrated, while multi‐source area series are converted to dense levels via reservoir‐specific hypsometry model and then fused. The framework's robustness and re‐applicability were confirmed using eight representative Chinese reservoirs. Results demonstrate that the integrated multi‐source water level time series significantly enhanced observation frequency, achieving near‐daily temporal resolution and capturing detailed non‐linear and short‐term water level dynamics. The water level observation frequency for all reservoirs based on SWOT exceeds 20 times per yr, with the highest reaching 38 times. After multi‐sensor synthesis, the water level observation frequency increased by 3.2–8.1 times, yielding an average of 121 observations annually. Validation results showed strong correlations ( R 2 > 0.90) and low errors (0.46 m ≥ MAE ≥ 0.11 m), confirming the method's robustness and effectiveness. Instead of treating SWOT as another input, this framework standardizes water levels across sensors and tracks, enabling a scalable and transferable multi‐mission synthesis for global reservoir monitoring under changing climatic and anthropogenic pressures.
Zhan et al. (Sun,) studied this question.
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