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The Hysteresis Response of Groundwater to Reservoir Water Level Changes in a Plain Reservoir Area

Author

Listed:
  • Yong Huang

    (Hohai University)

  • Kehan Miao

    (Hohai University)

  • Xiaoguang Liu

    (Water Resources Pearl River Planning, Surveying and Designing Co. Ltd (PRPSDC))

  • Yin Jiang

    (Hohai University)

Abstract

Reservoir immersion will lead to some environmental geological problems, such as soil swamping or salinization, reduction of building foundation strength, or even overall instability. Reservoir scope of immersion is closely related to changes in groundwater levels. According to the geological and hydrogeological conditions pertaining in the Jiangxiang reservoir area, the analytical method is employed to calculate the change in groundwater levels in an unconfined aquifer when the reservoir water level rises rapidly to a constant value and changes periodically. Combined with the related functions of MATLAB™ software, the lag and immersion times are determined in different locations around the reservoir. The results show that the change of the groundwater level exhibits hysteresis relative to that of the reservoir water level owing to the low permeability of silty loam and clay. The closer to the reservoir, the faster the groundwater level rises or falls. In the Guo Xiaoxu section, when the reservoir water level rises rapidly to 42.5 m, the groundwater level near the reservoir remains lower than the reservoir water level after 50 years. If the hydraulic conductivity is increased by three orders of magnitude, the groundwater level and the reservoir water level changes are positively correlated, and the hysteresis is not obvious. In the crop areas, the scope of immersion in the Guoxiaowei section is 31 m with the immersion elevation of 43.23 m, and the corresponding immersion time is 15,766 d. In residential areas, the scope of immersion of the Qigang section is 308 m with the immersion elevation of 46.78 m, and the corresponding immersion time is 16,354 d. The calculated scope of immersion and time at different locations provide a scientific basis for the design of the reservoir water level and the range of demolition affecting local residents.

Suggested Citation

  • Yong Huang & Kehan Miao & Xiaoguang Liu & Yin Jiang, 2022. "The Hysteresis Response of Groundwater to Reservoir Water Level Changes in a Plain Reservoir Area," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(12), pages 4739-4763, September.
    • Handle: RePEc:spr:waterr:v:36:y:2022:i:12:d:10.1007_s11269-022-03275-1
    DOI: 10.1007/s11269-022-03275-1
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    References listed on IDEAS

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    1. Sheelabhadra Mohanty & Madan Jha & Ashwani Kumar & K. Sudheer, 2010. "Artificial Neural Network Modeling for Groundwater Level Forecasting in a River Island of Eastern India," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 24(9), pages 1845-1865, July.
    2. Lei Zhang & Zhiqiang Jiang & Shanshan He & Jiefeng Duan & Pengfei Wang & Ting Zhou, 2022. "Study on Water Quality Prediction of Urban Reservoir by Coupled CEEMDAN Decomposition and LSTM Neural Network Model," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(10), pages 3715-3735, August.
    3. Saeed Mozaffari & Saman Javadi & Hamid Kardan Moghaddam & Timothy O. Randhir, 2022. "Forecasting Groundwater Levels using a Hybrid of Support Vector Regression and Particle Swarm Optimization," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(6), pages 1955-1972, April.
    4. Li, Jiang & Mao, Xiaomin & Li, Mo, 2017. "Modeling hydrological processes in oasis of Heihe River Basin by landscape unit-based conceptual models integrated with FEFLOW and GIS," Agricultural Water Management, Elsevier, vol. 179(C), pages 338-351.
    5. Peiman Parisouj & Hamid Mohebzadeh & Taesam Lee, 2020. "Employing Machine Learning Algorithms for Streamflow Prediction: A Case Study of Four River Basins with Different Climatic Zones in the United States," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(13), pages 4113-4131, October.
    6. Mojtaba Poursaeid & Amir Houssain Poursaeid & Saeid Shabanlou, 2022. "A Comparative Study of Artificial Intelligence Models and A Statistical Method for Groundwater Level Prediction," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(5), pages 1499-1519, March.
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