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High-resolution water level and storage variation datasets for 338 reservoirs in China during 2010–2020
Earth System Science Data ( IF 11.4 ) Pub Date : 2022-05-16 , DOI: 10.5194/essd-2021-470
Youjiang Shen , Dedi Liu , Liguang Jiang , Karina Nielsen , Jiabo Yin , Jun Liu , Peter Bauer-Gottwein

Abstract. Reservoirs and dams are essential infrastructures in water management, thus information of their surface water area (SWA), water surface elevation (WSE), and reservoir water storage change (RWSC), is crucial for understanding their properties and interactions on hydrological and biogeochemical cycles. However, knowledge of these reservoir characteristics is scarce or inconsistent at national scale. Here, we introduce comprehensive reservoir datasets of 338 reservoirs in China, with a total of 470.6 km3 storage capacity (50 % Chinese reservoir storage capacity). Given the scarcity of publicly available gauged observations and operational applications of satellites for hydrological cycles, we utilize multiple satellite altimetry missions (SARAL/AltiKa, Sentinel-3 A and B, and CroySat-2) and Landsat satellite data to produce a comprehensive reservoir dataset on the WSE, SWA, and RWSC during 2010–2020. Validation against gauged measurements of 93 reservoirs demonstrates the relatively high accuracy and reliability of our remotely-sensed datasets: (1) Across gauge comparisons of RWSC, the median statistics of CC, NRMSE, and RMSE are 0.76, 15 %, and 0.035 km3, with a total of 75 % validated reservoirs (70 of 93) having good RMSE from 0.002 to 0.35 km3 and NRMSE values smaller than 20 %. (2) Comparisons of WSE retracked by four satellite altimeters and gauges show good agreement. Specifically, percentages of reservoirs having good and moderate RMSE values smaller than 1.0 m for CryoSat-2 (validated in 30 reservoirs), SARAL/AltiKa (8), Sentinel-3A (25), and Sentinel-3B (25) are 90 %, 88 %, 64 %, and 76 % respectively. By taking advantages of four satellite altimetry missions, we are able to densify WSE observations across spatiotemporal scales. Statistically, around 85 % validated reservoirs (53 of 62) have RMSE values below 1.0 m, while 63 % reservoirs (39 of 62) have a good data quality with RMSE values below 0.6 m. Overall, our study fills such a data gap with regard to comprehensive reservoir information in China and provides strong support for many aspects such as hydrological processes, water resources, and other studies. The dataset is publicly available on Zenodo at https://doi.org/10.5281/zenodo.5812012 (Shen et al., 2021).
更新日期:2022-05-16
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