Volume 18 Issue 2
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Article Navigation >  Water Science and Engineering  > 2025  >  18(2): 187-199
Hai-xin Shang, Jun-qiang Xia, Chun-hong Hu, Mei-rong Zhou, Shan-shan Deng. 2025: Quantification of backwater effect in Jingjiang Reach due to confluence with Dongting Lake using a machine learning model. Water Science and Engineering, 18(2): 187-199. doi: 10.1016/j.wse.2025.02.002
Citation: Hai-xin Shang, Jun-qiang Xia, Chun-hong Hu, Mei-rong Zhou, Shan-shan Deng. 2025: Quantification of backwater effect in Jingjiang Reach due to confluence with Dongting Lake using a machine learning model. Water Science and Engineering, 18(2): 187-199. doi: 10.1016/j.wse.2025.02.002
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Quantification of backwater effect in Jingjiang Reach due to confluence with Dongting Lake using a machine learning model

doi: 10.1016/j.wse.2025.02.002

  • a. State Key Laboratory of Water Resources Engineering and Management, Wuhan University, Wuhan 430072, China;

  • b. State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, China Institute of Water Resources and Hydropower Research, Beijing 100048, China

Funds:

The work was supported by the National Key Research and Development Program of China (Grant No. 2023YFC3209504), the National Natural Science Foundation of China (Grants No. U2040215 and 52479075), and the Natural Science Foundation of Hubei Province (Grant No. 2021CFA029).

  • Received Date: 2024-07-31
  • Accepted Date: 2024-12-30
    • Available Online: 2025-06-24
    Abstract
  • The backwater effect caused by tributary inflow can significantly elevate the water level profile upstream of a confluence point. However, the influence of mainstream and confluence discharges on the backwater effect in a river reach remains unclear. In this study, various hydrological data collected from the Jingjiang Reach of the Yangtze River in China were statistically analyzed to determine the backwater degree and range with three representative mainstream discharges. The results indicated that the backwater degree increased with mainstream discharge, and a positive relationship was observed between the runoff ratio and backwater degree at specific representative mainstream discharges. Following the operation of the Three Gorges Project, the backwater effect in the Jingjiang Reach diminished. For instance, mean backwater degrees for low, moderate, and high mainstream discharges were recorded as 0.83 m, 1.61 m, and 2.41 m during the period from 1990 to 2002, whereas these values decreased to 0.30 m, 0.95 m, and 2.08 m from 2009 to 2020. The backwater range extended upstream as mainstream discharge increased from 7 000 m3/s to 30 000 m3/s. Moreover, a random forest-based machine learning model was used to quantify the backwater effect with varying mainstream and confluence discharges, accounting for the impacts of mainstream discharge, confluence discharge, and channel degradation in the Jingjiang Reach. At the Jianli Hydrological Station, a decrease in mainstream discharge during flood seasons resulted in a 7%–15% increase in monthly mean backwater degree, while an increase in mainstream discharge during dry seasons led to a 1%–15% decrease in monthly mean backwater degree. Furthermore, increasing confluence discharge from Dongting Lake during June to July and September to November resulted in an 11%–42% increase in monthly mean backwater degree. Continuous channel degradation in the Jingjiang Reach contributed to a 6%–19% decrease in monthly mean backwater degree. Under the influence of these factors, the monthly mean backwater degree in 2017 varied from a decrease of 53% to an increase of 37% compared to corresponding values in 1991.

     

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