Global Diagnosis of Reservoir Filling-Up Problems Using Satellite-Derived Surface Area Time Series (2001–2023)

Reservoir storage is critical for climate resilience and water security, yet many reservoirs are failing to reach their normal capacity due to intensified droughts—an underexplored global challenge. This study provides the first comprehensive global assessment of reservoir-filling dynamics, leveraging a gap-free monthly surface area time series (2001–2023) generated by a U-Net deep learning framework that fuses MODIS and Landsat data. After analyzing 6754 reservoirs worldwide, in this study, we introduce the reservoir area index (RAI) to characterize the anomalies and severity of filling problems, validated against 131 in situ storage records. The results reveal a clear wetting trend from 2001 to 2011, followed by increasing levels of underfilling after 2012, peaking during the 2021–2023 droughts. Both small and large reservoirs, especially in arid regions, show heightened vulnerability. Compared to previous altimetry-based studies limited to around 500 large reservoirs and in shorter periods after 2011, our findings uncover decadal trends and size-dependent disparities in reservoir filling. Despite some uncertainties, this dataset offers valuable insights to inform adaptive water management and supports its future refinement through improved area–volume relationships.