Quantifying the Impact of Compounding Influencing Factors to the Water Level Decline of China’s Largest Freshwater Lake
Publication: Journal of Water Resources Planning and Management
Volume 146, Issue 6
Abstract
As a large open lake that connects to the lower Yangtze River, the Poyang Lake, China, has persisted dramatic water level decline since 2003. For better management of lake water resources and ecosystem, this study quantitatively examined the contributions and spatiotemporal differences of compounding influencing factors to this hydrological change. Attempts were achieved through the reconstruction of lake water level series by two combined neural network models. The results indicate that with reference to the period 1980–1999, the average contributions to lake decline during 2003–2014 of lake-bottom topography change, the operation of the Three Gorges Dam (TGD), and climate change and other human activities over the Yangtze River basin were 50%, 18%, and 32%, respectively. The response of the lake water level to the three factors shows obvious spatiotemporal differences due to different influencing mechanisms. It is worth noting that the effect of lake-bottom topography change is still increasing across the lake. In addition, the reduction of precipitation in the Yangtze River basin should be highly correlated to the water level decline of the lake. From the perspective of maintaining a certain water level of the lake and preventing continuous decline and shrinkage, constructing the proposed water control structure would be an effective solution.
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Data Availability Statement
Some or all data, models, or code generated or used during the study are available from the corresponding author by request.
1.
Monthly discharge and water level data at Yichang and Hankou stations during 1980–2014;
2.
Monthly inflow and outflow series of the Three Gorges Reservoir during 2003–2014;
3.
Monthly water level data of the four gauging stations in the lake during 1960–2014;
4.
Monthly discharge of the six gauging stations from the lake catchment during 1960–2014;
5.
Monthly precipitation data of 62 weather stations across the YRB during1960–2014;
6.
Reconstructed the monthly discharge of Yichang station during 2003–2014;
7.
Reconstructed the monthly discharge and water level of Hankou station during 2003–2014;
8.
Reconstructed monthly lake water level of the four gauging stations during 2003–2014 () that represents the scenario under the impacts of TGD regulation as well as climate change and other human activities but excludes the impact of lake-bottom topography change.
9.
Reconstructed monthly lake water level of the four gauging stations during 2003–2014 () that represents the scenario under the impacts of climate change and other human activities but excludes the impact of TGD regulation as well as lake-bottom topography change.
10.
The proposed two combined neural network models.
Acknowledgments
This work was financially supported by the Fundamental Research Funds for the Central Universities (XDJK2019B074). Cordial thanks are extended to Prof. Qi Zhang, Dr. Xianghu Li, Yunliang Li, and Jing Yao from the Key Laboratory of Watershed Geographic Sciences, Nanjing Institute of Geography and Limnology, China, for their constructive comments and data support.
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Published In
Journal of Water Resources Planning and Management
Volume 146 • Issue 6 • June 2020
Copyright
©2020 American Society of Civil Engineers.
History
Received: Apr 26, 2019
Accepted: Dec 6, 2019
Published online: Mar 25, 2020
Published in print: Jun 1, 2020
Discussion open until: Aug 25, 2020
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