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The coupled model of water delivery and distribution regulation for single-canal pool systems

Author

Listed:
  • Zhou, Ke
  • Fan, Yu
  • Gao, Zhanyi
  • Chen, Haorui
  • Zheng, Xinrong
  • Yang, Yun
  • Liu, Jie

Abstract

The coordinated operation of open canal water delivery and distribution systems is fundamental for achieving safe, reliable, efficient, timely, and economical water supply in irrigation districts. In the past, the processes of water delivery and distribution were usually loosely connected, making it difficult to achieve synergistic regulation of both. To solve this problem, this paper establishes the coupled model of canal optimal water distribution and canal control, which realizes the synergistic coupled regulation of water delivery and distribution scheduling under different water supply flow rates. The model initially allocates a water distribution scheme, and then adjusts the water distribution scheme through the hydraulic calculation for the uniform flow area and the backwater area of the canal pool. It subsequently determines the target water level and realizes the coordination and cooperation between the revised and optimized water distribution scheme and the check gate scheduling. Additionally, it scientifically formulates the regulation strategy of the delivery and distribution scheme under different scenarios. The coupled model is applied to the Bojili irrigation district. The results show that the coupled model provides the target water levels, water distribution schemes, and scheduling schemes under three kinds of water supply flow rates of large, medium, and small. It realizes the comprehensive objectives of reducing the frequency and amplitude of gate regulation, minimizing water shortage or oversupply, and the safe operation of the canal. The research results can provide diversified water distribution and regulation schemes for irrigation districts, which have strong practicability and guiding value.

Suggested Citation

  • Zhou, Ke & Fan, Yu & Gao, Zhanyi & Chen, Haorui & Zheng, Xinrong & Yang, Yun & Liu, Jie, 2025. "The coupled model of water delivery and distribution regulation for single-canal pool systems," Agricultural Water Management, Elsevier, vol. 313(C).
    • Handle: RePEc:eee:agiwat:v:313:y:2025:i:c:s0378377425001891
    DOI: 10.1016/j.agwat.2025.109475
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    References listed on IDEAS

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    1. Jingjing Wu & Jian Chen & Yu Han & Tongshu Li, 2020. "Study on Unsteady Flow Based on Optimized Water Distribution Model in Irrigation District," Sustainability, MDPI, vol. 12(4), pages 1-13, February.
    2. Yue, Qiong & Zhang, Fan & Zhang, Chenglong & Zhu, Hua & Tang, Yikuan & Guo, Ping, 2020. "A full fuzzy-interval credibility-constrained nonlinear programming approach for irrigation water allocation under uncertainty," Agricultural Water Management, Elsevier, vol. 230(C).
    3. S. M. Hashemy Shahdany & A. R. Firoozfar, 2017. "Providing a Reliable Water Level Control in Main Canals under Significant Inflow Fluctuations at Drought Periods within Canal Automation," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(11), pages 3343-3354, September.
    4. Yu, Kunjie & Liang, J.J. & Qu, B.Y. & Cheng, Zhiping & Wang, Heshan, 2018. "Multiple learning backtracking search algorithm for estimating parameters of photovoltaic models," Applied Energy, Elsevier, vol. 226(C), pages 408-422.
    5. Jingxiu Qin & Weili Duan & Shan Zou & Yaning Chen & Wenjing Huang & Lorenzo Rosa, 2024. "Global energy use and carbon emissions from irrigated agriculture," Nature Communications, Nature, vol. 15(1), pages 1-12, December.
    6. Gaiqiang Yang & Mo Li & Lijuan Huo, 2019. "Decision Support System Based on Queuing Theory to Optimize Canal Management," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(12), pages 4367-4384, September.
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