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A Multi-Objective Hierarchical Model for Irrigation Scheduling in the Complex Canal System

Author

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  • Shanshan Guo

    (Centre for Agricultural Water Research in China, China Agricultural University, Beijing 100083, China)

  • Fan Zhang

    (Centre for Agricultural Water Research in China, China Agricultural University, Beijing 100083, China)

  • Chenglong Zhang

    (Centre for Agricultural Water Research in China, China Agricultural University, Beijing 100083, China)

  • Chunjiang An

    (Department of Building, Civil and Environmental Engineering, Concordia University, Montréal, QC H3G 1M8, Canada)

  • Sufen Wang

    (Centre for Agricultural Water Research in China, China Agricultural University, Beijing 100083, China)

  • Ping Guo

    (Centre for Agricultural Water Research in China, China Agricultural University, Beijing 100083, China)

Abstract

Due to population growth, environmental pollution and climate change, the lack of water resources has become a critical factor which threatens sustainable agricultural development. Reasonable irrigation scheduling strategies can reduce the waste of water and enhance agricultural water-use efficiency. In the present study, the decomposition-coordination theory was adopted to analyze the hierarchical canal system. A novel nonlinear multi-level multi-objective optimization model for complex canal systems was established, taking account of the multiple demands from decision makers and realistic factors of canal operation. An interactive method of the technique for order preference using similarity algorithm and genetic algorithm was proposed to solve the developed model. The developed model was successfully applied for the operational strategy making of a canal system located in the arid area of northwest China. The results indicated that the optimization model could help shorten the operational duration by two days, achieve about 26% reduction of irrigation water consumption, and improve the efficiency of water delivery from 0.566 to 0.687. That will be very favorable for the promotion of the agricultural water productivity, the relief of water shortage crisis and the sustainable development of agriculture. The outcomes can provide a wide range of support for decision making and make irrigation decision-making more scientific and systematic.

Suggested Citation

  • Shanshan Guo & Fan Zhang & Chenglong Zhang & Chunjiang An & Sufen Wang & Ping Guo, 2018. "A Multi-Objective Hierarchical Model for Irrigation Scheduling in the Complex Canal System," Sustainability, MDPI, vol. 11(1), pages 1-15, December.
    • Handle: RePEc:gam:jsusta:v:11:y:2018:i:1:p:24-:d:192148
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    References listed on IDEAS

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    1. Shangguan, Zhouping & Shao, Mingan & Horton, Robert & Lei, Tingwu & Qin, Lin & Ma, Jianqing, 2002. "A model for regional optimal allocation of irrigation water resources under deficit irrigation and its applications," Agricultural Water Management, Elsevier, vol. 52(2), pages 139-154, January.
    2. Dawit Mekonnen & Afreen Siddiqi & Claudia Ringler, 2016. "Drivers of groundwater use and technical efficiency of groundwater, canal water, and conjunctive use in Pakistan’s Indus Basin Irrigation System," International Journal of Water Resources Development, Taylor & Francis Journals, vol. 32(3), pages 459-476, May.
    3. Yu Liu & Xiaohong Hu & Qian Zhang & Mingbo Zheng, 2017. "Improving Agricultural Water Use Efficiency: A Quantitative Study of Zhangye City Using the Static CGE Model with a CES Water−Land Resources Account," Sustainability, MDPI, vol. 9(2), pages 1-15, February.
    4. Suryavanshi, A. R. & Reddy, J. Mohan, 1986. "Optimal operation schedule of irrigation distribution systems," Agricultural Water Management, Elsevier, vol. 11(1), pages 23-30, March.
    5. Shilong Piao & Philippe Ciais & Yao Huang & Zehao Shen & Shushi Peng & Junsheng Li & Liping Zhou & Hongyan Liu & Yuecun Ma & Yihui Ding & Pierre Friedlingstein & Chunzhen Liu & Kun Tan & Yongqiang Yu , 2010. "The impacts of climate change on water resources and agriculture in China," Nature, Nature, vol. 467(7311), pages 43-51, September.
    6. Kuo, Sheng-Feng & Merkley, Gary P. & Liu, Chen-Wuing, 2000. "Decision support for irrigation project planning using a genetic algorithm," Agricultural Water Management, Elsevier, vol. 45(3), pages 243-266, August.
    7. Kang, Shaozhong & Hao, Xinmei & Du, Taisheng & Tong, Ling & Su, Xiaoling & Lu, Hongna & Li, Xiaolin & Huo, Zailin & Li, Sien & Ding, Risheng, 2017. "Improving agricultural water productivity to ensure food security in China under changing environment: From research to practice," Agricultural Water Management, Elsevier, vol. 179(C), pages 5-17.
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