TY - JOUR
T1 - High-resolution water level and storage variation datasets for 338 reservoirs in China during 2010-2021
AU - Shen, Youjiang
AU - Liu, Dedi
AU - Jiang, Liguang
AU - Nielsen, Karina
AU - Yin, Jiabo
AU - Liu, Jun
AU - Bauer-Gottwein, Peter
N1 - Funding Information:
This research has been supported by the National Key Research and Development Project of China (grant no. 2022YFC3202803), the National Natural Science Foundation of China (grant nos. 51879194 and 51579183), and the Danida Fellowship Centre (grant no. 18-M01-DTU).
Publisher Copyright:
© 2022 Youjiang Shen et al.
PY - 2022/12/22
Y1 - 2022/12/22
N2 - 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 470.6km3 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 0.021km3, with a total of 91% validated reservoirs (83 of 91) having good RMSE from 0.002 to 0.31km3 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 1.0m 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 1.0m, while 57% of reservoirs (42 of 74) have good data quality with RMSE values below 0.6m. 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).
AB - 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 470.6km3 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 0.021km3, with a total of 91% validated reservoirs (83 of 91) having good RMSE from 0.002 to 0.31km3 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 1.0m 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 1.0m, while 57% of reservoirs (42 of 74) have good data quality with RMSE values below 0.6m. 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).
UR - http://www.scopus.com/inward/record.url?scp=85145574102&partnerID=8YFLogxK
U2 - 10.5194/essd-14-5671-2022
DO - 10.5194/essd-14-5671-2022
M3 - Article
AN - SCOPUS:85145574102
SN - 1866-3508
VL - 14
SP - 5671
EP - 5694
JO - Earth System Science Data
JF - Earth System Science Data
IS - 12
ER -