Enhanced Satellite Monitoring of Reservoir Water Storage Variations in the Yangtze River Basin from 1990 to 2023

The Yangtze River Basin (YRB) is home to the highest concentration of reservoirs globally, which presents challenges in accurately quantifying their storage dynamics. To address this issue, we integrate multi-source satellite altimetry and optical remote sensing data to develop the YRB Water Reservoir (YWR) dataset, providing monthly observations of area, elevation, and storage from 1990 to 2023. Additionally, we analyze the spatiotemporal storage dynamics of 443 reservoirs in the YRB over the past 3 decades. Key findings include the following: (a) Validation against in situ water level time series from 14 reservoirs shows an average correlation coefficient of 0.76, a root mean square error (RMSE) of 4.19 m, and a normalized RMSE (NRMSE) of 20.64%. (b) Comparisons with elevation–storage relationships derived from in situ measurements for 13 reservoirs demonstrate good consistency, with an average RMSE of 0.65 km 3 and an average NRMSE of 7.21% for reservoir storage estimates. (c) Over the past 30 years, reservoir storage in the YRB has significantly increased at a rate of 1.58 km 3 /year, with a notable shift in growth patterns occurring in 2006. Overall, this study presents the most detailed and comprehensive remotely sensed dataset of reservoir area, elevation, and storage in the YRB. It elucidates the spatiotemporal patterns of reservoir storage over the past 3 decades, providing valuable insights for water resource management across the basin.

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Publication Details

Journal
Journal of Remote Sensing
Published
2025-01-01
DOI
https://doi.org/10.34133/remotesensing.0384
Citations
1
Primary Topic
Flood Risk Assessment and Management
Type
article
Field-Weighted Citation Impact
1.11
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article

Enhanced Satellite Monitoring of Reservoir Water Storage Variations in the Yangtze River Basin from 1990 to 2023

Yinjun Zhao, Yulin Gong, Meiyi Fan, Gang Zhao et al.
1 citations
Journal of Remote Sensing
Flood Risk Assessment and Management
1.11
article

Enhanced Satellite Monitoring of Reservoir Water Storage Variations in the Yangtze River Basin from 1990 to 2023

Yinjun Zhao, Yulin Gong, Meiyi Fan, Gang Zhao, Xiaojun She, Yinghong Jing, Nan Xu, Yong Wang, Yue Ma, Yao Li, Xin Liu
article en
1 citations

Abstract

The Yangtze River Basin (YRB) is home to the highest concentration of reservoirs globally, which presents challenges in accurately quantifying their storage dynamics. To address this issue, we integrate multi-source satellite altimetry and optical remote sensing data to develop the YRB Water Reservoir (YWR) dataset, providing monthly observations of area, elevation, and storage from 1990 to 2023. Additionally, we analyze the spatiotemporal storage dynamics of 443 reservoirs in the YRB over the past 3 decades. Key findings include the following: (a) Validation against in situ water level time series from 14 reservoirs shows an average correlation coefficient of 0.76, a root mean square error (RMSE) of 4.19 m, and a normalized RMSE (NRMSE) of 20.64%. (b) Comparisons with elevation–storage relationships derived from in situ measurements for 13 reservoirs demonstrate good consistency, with an average RMSE of 0.65 km 3 and an average NRMSE of 7.21% for reservoir storage estimates. (c) Over the past 30 years, reservoir storage in the YRB has significantly increased at a rate of 1.58 km 3 /year, with a notable shift in growth patterns occurring in 2006. Overall, this study presents the most detailed and comprehensive remotely sensed dataset of reservoir area, elevation, and storage in the YRB. It elucidates the spatiotemporal patterns of reservoir storage over the past 3 decades, providing valuable insights for water resource management across the basin.

Journal of Remote SensingVol. 5
Southwest University (CN), Hohai University (CN), Shenzhen University (CN), Changjiang Water Resources Commission (CN), Wuhan University (CN), Urban Planning & Design Institute of Shenzhen (China) (CN), Nanning Normal University (CN), Institute of Geographic Sciences and Natural Resources Research (CN)
Clean water and sanitation
Openalex Percentile: Top 21%
Flood Risk Assessment and Management
1.11
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