Remote sensing-based high-resolution reservoir drought index for identifying the occurrence and propagation of hydrological droughts in a large river basin

Abstract

Reservoir drought is a valuable indicator of regional hydrological drought severity; however, it has received limited attention because of the low quality of reservoir storage data. This study proposes a Remote Sensing-Based High-Resolution Reservoir Drought Index (RS-HRDI) that integrates recent high-resolution satellite observations with historical low-resolution records to construct a long-term reservoir storage dataset. Reservoir droughts are identified by periods of abnormally low reservoir storage using a time-variant threshold. The RS-HRDI was used to detect reservoir droughts in the Yangtze River Basin, one of the most reservoir-regulated and critical river systems globally, from 2018 to 2023, including a record-breaking drought in 2022. The results indicate that the multi-satellite combination significantly improved the reservoir observation frequency from the historical monthly scale to an average of 4.3 d, enabling the detection of rapid reservoir storage reductions within days. The RS-HRDI could effectively identify droughts across various reservoirs and accurately describe their characteristics. Through comprehensive assessments of widespread reservoir networks, the aggregated RS-HRDI effectively characterized basin-scale hydrological droughts, detailing their spatial extent, intensity, and duration. Furthermore, the RS-HRDI highlighted the influence of reservoir operations on the occurrence and propagation of hydrological droughts in a river system. Specifically, upstream reservoir interception advanced downstream droughts by 2–40 d. This study presents a novel reservoir drought assessment method based on remote sensing, highlighting its potential for use in large-scale and timely hydrological drought monitoring and water resource management.