High-resolution water level and storage variation datasets for 338 reservoirs in China during 2010–2021

Abstract

Abstract. Reservoirs and dams are essential infrastructure 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 in hydrological and biogeochemical cycles. However, knowledge of these reservoir characteristics is scarce or inconsistent at the 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-3A and Sentinel-3B, CroySat-2, Jason-3, and ICESat-2) and imagery data from Landsat and Sentinel-2 to produce a comprehensive reservoir dataset on the WSE, SWA, and RWSC during 2010–2021. 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 the Pearson correlation coefficient (CC), normalized root mean square error (NRMSE), and root mean square error (RMSE) are 0.89, 11 %, and 0.021 km3, with a total of 91 % validated reservoirs (83 of 91) having good RMSE from 0.002 to 0.31 km3 and NRMSE values smaller than 20 %. (2) Comparisons of WSE retracked by six satellite altimeters and gauges show good agreement. Specifically, the percentages of reservoirs having good and moderate RMSE values smaller than 1.0 m for CryoSat-2 (validated in 30 reservoirs), SARAL/AltiKa (9), Sentinel-3A (34), Sentinel-3B (25), Jason-3 (11), and ICESat-2 (26) are 77 %, 75 %, 79 %, 87 %, 81 %, and 82 %, respectively. By taking advantages of six satellite altimeters, we are able to densify WSE observations across spatiotemporal scales. Statistically, around 96 % of validated reservoirs (71 of 74) have RMSE values below 1.0 m, while 57 % of reservoirs (42 of 74) have 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.7251283 (Shen et al., 2021).

Res-CN: hydrometeorological time series and landscape attributes across 3254 Chinese reservoirs

“…Each altimeter has different repeat cycles, geographical coverages, retracking algorithms, and measurement accuracies (Table S2). Apart from the official algorithms in their source products, we implemented PPCOG (primary peak center of gravity) and NPPTr[0.5/0.8] (narrow primary peak with a 125 0.5 or 0.8 threshold value) algorithms into Sentinel-3, CroySat-2, SARAL/AltiKa to derive range measurements (Shen et al, 2022b). Range measurements are corrected using the atmospheric and geophysical corrections from their source products (Table S2), and then used to determine water level of each sample.…”

“…Reservoir water areas can be extracted from an available global inland water dataset like the SWBD (SRTM Water Body Data, NASA JPL, 2013), the GIEMS (Global Inundation Extent from Multiple Satellites, Papa et al, 2010b), GSW, DAHITI, Hydroweb, Hydrosat, Bluedot Observatory and studies from Tortini et al (2020) and Shen et al (2022b). The derived reservoir water area estimations are limited by the spatial coverage and accuracy restrictions of the initial dataset.…”

“…The errors result from the inaccuracy of the area-storage model developed by DEM as well as the error of water areas at certain reservoirs. To solve this problem, we provide another type of storage variation estimates using satellite water areas and water levels (see section 2.3, Shen et al, 2022b). Thea accuracy of storage variations is improved, with the median statistics of CC, NRMSE, and RMSE of 0.89, 11%, and 0.021 km 3 , respectively.…”

“…Different research projects and studies have produced satellite datasets for reservoirs at regional and global scales. Hydroweb (Crétaux et al, 2011), G-REALM (Global reservoirs and lakes monitor, Birkett et al, 2011), DAHITI (Database for hydrological time series of inland waters, Schwatke et al, 2015), AltEx (Markert et al, 2019), HydroSat (Tourian et al, 2022), Water level On VITO, and several studies (e.g., Gao et al, 2012;Tortini et al, 2020;Shen et al, 2022b) offer time series of altimetry-derived water level for inland waters by incorporating multiple laser or radar altimeters such as Jason-1/2/3, CryoSat-2, Sentinel-3A/B, and ICESat-1/2. Imagery-based water area estimates can be extracted from GSW (Global surface water, Pekel et al, 2016), DAHITI, Hydroweb, HydroSat, Bluedot Observatory, GRSAD (Global reservoir surface area dataset, Zhao & Gao, 2018), RealSAT (Khandelwal et al, 2022), and relevant studies (e.g., Busker et al, 2019;Liu et al, 2021;Donchyts et al, 2022).…”

Toward Improved Parameterizations of Reservoir Operation in Ungauged Basins: A Synergistic Framework Coupling Satellite Remote Sensing, Hydrologic Modeling, and Conceptual Operation Schemes

“…With recent developments in satellite altimetry, it has been possible to derive the water level of reservoirs and lakes remotely, providing a promising solution to overcome the scarcity of reservoir operation data (Avisse et al., 2017; Busker et al., 2019; Crétaux et al., 2015; Fan et al., 2020; Gao et al., 2012; Huang et al., 2020; Li et al., 2019). Since the launch of altimetry satellites, such as Jason, Sentinel, and the Ice, Cloud and land Elevation Satellite (ICESat), many studies have combined satellite‐derived water levels with remotely sensed reservoir surface areas to derive estimates of reservoir water storage (Chen, Song, Luo, et al., 2022; Cooley et al., 2021; Gao, 2015; Shen et al., 2022) and infer reservoir operation policies (Bonnema & Hossain, 2017, 2019). Several studies have also combined the satellite‐derived reservoir storage variation with the conservation of mass principle to determine the reservoir releases for downstream hydrologic simulations (Vu et al., 2022; Yoon & Beighley, 2015; Yoon et al., 2015; R. Zhong et al., 2020).…”