Journal of Geographical Sciences ›› 2021, Vol. 31 ›› Issue (11): 1598-1614.doi: 10.1007/s11442-021-1913-1

• Special Issue: Fluvial and Geomorphological Features • Previous Articles     Next Articles

Effect of Three Gorges Dam on Poyang Lake water level at daily scale based on machine learning

HUANG Sheng1,2(), XIA Jun1,2,3,*(), ZENG Sidong4, WANG Yueling3, SHE Dunxian1,2   

  1. 1. State Key Laboratory of Water Resources and Hydropower Engineering Science, Wuhan University, Wuhan 430072, China
    2. Hubei Key Laboratory of Water System Science for Sponge City Construction, Wuhan University, Wuhan 430072, China
    3. Key Laboratory of Water Cycle and Related Land Surface Processes, Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China
    4. Chongqing Institute of Green and Intelligent Technology, CAS, Chongqing 400714, China
  • Received:2021-02-08 Accepted:2021-09-02 Online:2021-11-25 Published:2022-01-25
  • Contact: XIA Jun E-mail:2015huangsheng@whu.edu.cn;xiajun666@whu.edu.cn
  • About author:Huang Sheng (1996-), PhD Candidate, specialized in hydrology and water resources. E-mail: 2015huangsheng@whu.edu.cn
  • Supported by:
    Strategic Priority Research Program of the Chinese Academy of Sciences(XDA23040500);National Natural Science Foundation of China(41890823)

Abstract:

Lake water level is an essential indicator of environmental changes caused by natural and human factors. The water level of Poyang Lake, the largest freshwater lake in China, has exhibited a dramatic variation for the past few years, especially after the completion of the Three Gorges Dam (TGD*However, there is a lack of more accurate assessment of the effect of the TGD on the Poyang Lake water level (PLWL) at finer temporal scales (e.g., the daily scale*Here, we used three machine learning models, namely, an Artificial Neural Network (ANN), a Nonlinear Autoregressive model with eXogenous input (NARX), and a Gated Recurrent Unit (GRU), to simulate the daily lake level during 2003-2016. We found that machine learning models with historical memory (i.e., the GRU model) are more suitable for simulating the PLWL under the influence of the TGD. The GRU-based results show that the lake level is significantly affected by the TGD regulation in the different operation stages and in different periods. Although the TGD has had a slight but not very significant impact on the yearly decline of the PLWL, the blocking or releasing of water at the TGD at certain moments has caused large changes in the lake level. This machine-learning-based study sheds light on the interactions between Poyang Lake and the Yangtze River regulated by the TGD.

Key words: water level, Poyang Lake, machine learning, Three Gorges Dam, Yangtze River