Using GRanD Database and Surface Water Data to Constrain Area–Storage Curve of Reservoirs

Abstract

Basic information on global reservoirs is well documented in databases such as GRanD (Global Reservoir and Dam) and ICOLD (International Commission on Large Dams). However, though playing a critical role in estimating reservoir storage variations from remote sensing or hydrological models, area–storage curves of reservoirs are not conveniently obtained nor publicly shared. In this paper, we combine the GRanD database and Landsat-based global surface water extent (GSW) data to derive area–storage curves of reservoirs. The reported storage capacity in the GRanD database and water surface area from GSW data were used to constrain the area–storage curve. The proposed method has the potential to derive area–storage curves of reservoirs larger than 1 km2 archived in the GRanD database. The derived curves are validated with in situ reservoir data collected in US and China, and the results show that in situ records are well captured by the derived curves both in large and small reservoirs with various shapes. The derived area–storage curves could be employed to advance global monitoring or modeling of reservoir storage dynamics.

The albedo–climate penalty of hydropower reservoirs

“…Reservoir surface areas in the GRanD 44 database are calculated from polygons encompassing the impounded reservoir derived on the basis of some kind of remote sensing data at one point in time 57 . Since reservoir water levels are typically subject to significant seasonal variations, these surface areas tend to be biased towards low-fill states, as demonstrated by Mu et al 58 by merging the GRanD 44 database with a global high-resolution map of surface waters 59 . To account for this uncertainty, the GRanD 44 database includes a most representative and multiple alternative surface area estimates, which was exploited to estimate the uncertainty of the reservoir surface areas as the standard deviation of the alternative area estimates with respect to the most representative estimate for 1227 reservoirs.…”

Remote Sensing of Surface Water Dynamics in the Context of Global Change—A Review

“…In the case of Ref. [204], GSW observations and topographic information from the ETOPO1 Global Relief Model are used to estimate water height and volume. Using GSW and GRSAD for lake area, and including lake water level based on ICESat and ICESat-2 data, [205] creates a monthly time series of global lake volume for 2003-2020.…”