Abstract
Satellite remote sensing of near real-time reservoir storage variations has important implications for flood monitoring and water resources management. However, satellite altimetry data, which are essential for estimating storage variations, are only available for a limited number of reservoirs. This lack of high-density spatial coverage directly hinders the potential use of remotely sensed reservoir information for improving the skills of hydrological modeling over highly regulated river basins. To solve this problem, a reservoir storage dataset with high-density spatial coverage was developed by combining the water surface area estimated from Moderate Resolution Imaging Spectroradiometer (MODIS) imageries with the Digital Elevation Model (DEM) data collected by the Shuttle Radar Topography Mission (SRTM). By including more reservoirs, this reservoir dataset represents 46.6% of the overall storage capacity in South Asia. The results were validated over five reservoirs where gauge observations are accessible. The storage estimates agree well with observations, with coefficients of determination ranging from 0.47 to 0.91 and normalized root mean square errors (NRMSE) ranging from 15.46% to 37.69%. Given the general availability of MODIS and SRTM data, this algorithm can be potentially applied for monitoring global reservoirs at a high density.
Highlights
Human-made reservoirs, which are managed by storing and releasing water under predetermined operation rules, play an important role in mitigating floods and improving the efficiency of the water supply for municipal, industrial, and agricultural demands [1,2,3,4]
The Moderate Resolution Imaging Spectroradiometer (MODIS)-Shuttle Radar Topography Mission (SRTM)-based reservoir storage was validated over 11 reservoirs (Table 2) where gauge observation data were available
An algorithm that leverages the SRTM Digital Elevation Model (DEM) data was developed to improve the spatial coverage of the reservoir monitoring network in South Asia
Summary
Human-made reservoirs, which are managed by storing and releasing water under predetermined operation rules, play an important role in mitigating floods and improving the efficiency of the water supply for municipal, industrial, and agricultural demands [1,2,3,4]. Most (if not all) human operated reservoirs are monitored in real-time, reservoir storage information is not commonly available to the public. This directly limits the effectiveness of reservoir flow regulation with regard to flood control, water supply, and other purposes—especially for those reservoirs located within transboundary river basins. Due to the limited availability of gauge observations—especially with regard to remote locations, restricted locations, and/or observations over large geographical areas—remote sensing technology provides a promising alternative by monitoring reservoirs from space [4,9,10,11,12]. With remotely sensed water surface area and elevation data, reservoir storage information can be inferred. The Geoscience Laser Altimeter System (GLAS) onboard the Ice, Cloud, and Land Elevation Satellite (ICESat) and the Advanced Topographic Laser Altimeter System (ATLAS) onboard ICESat-2 were used to measure the elevation values of relatively small lakes and reservoirs [4,17,18,19,20]
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