IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v11y2018i1p24-d192148.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/11/1/24/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/11/1/24/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Zhang, Fengtai & Xiao, Yuedong & Gao, Lei & Ma, Dalai & Su, Ruiqi & Yang, Qing, 2022. "How agricultural water use efficiency varies in China—A spatial-temporal analysis considering unexpected outputs," Agricultural Water Management, Elsevier, vol. 260(C).
    2. Zhao, Rongqin & Liu, Ying & Tian, Mengmeng & Ding, Minglei & Cao, Lianhai & Zhang, Zhanping & Chuai, Xiaowei & Xiao, Liangang & Yao, Lunguang, 2018. "Impacts of water and land resources exploitation on agricultural carbon emissions: The water-land-energy-carbon nexus," Land Use Policy, Elsevier, vol. 72(C), pages 480-492.
    3. Guangming Yang & Guofang Gong & Qingqing Gui, 2022. "Exploring the Spatial Network Structure of Agricultural Water Use Efficiency in China: A Social Network Perspective," Sustainability, MDPI, vol. 14(5), pages 1-22, February.
    4. Guoqiang Zhang & Bo Ming & Dongping Shen & Ruizhi Xie & Peng Hou & Jun Xue & Keru Wang & Shaokun Li, 2021. "Optimizing Grain Yield and Water Use Efficiency Based on the Relationship between Leaf Area Index and Evapotranspiration," Agriculture, MDPI, vol. 11(4), pages 1-14, April.
    5. Cai, Wenjuan & Jiang, Xiaohui & Sun, Haotian & He, Jiaying & Deng, Chun & Lei, Yuxin, 2022. "Temporal and spatial variation and driving factors of water consumption in the middle Heihe river basin before and after the implementation of the"97 water diversion scheme"," Agricultural Water Management, Elsevier, vol. 269(C).
    6. Zhang, Chenglong & Guo, Ping, 2018. "FLFP: A fuzzy linear fractional programming approach with double-sided fuzziness for optimal irrigation water allocation," Agricultural Water Management, Elsevier, vol. 199(C), pages 105-119.
    7. Zhao, Jiongchao & Han, Tong & Wang, Chong & Shi, Xiaoyu & Wang, Kaicheng & Zhao, Mingyu & Chen, Fu & Chu, Qingquan, 2022. "Assessing variation and driving factors of the county-scale water footprint for soybean production in China," Agricultural Water Management, Elsevier, vol. 263(C).
    8. Lu, Junsheng & Hu, Tiantian & Geng, Chenming & Cui, Xiaolu & Fan, Junliang & Zhang, Fucang, 2021. "Response of yield, yield components and water-nitrogen use efficiency of winter wheat to different drip fertigation regimes in Northwest China," Agricultural Water Management, Elsevier, vol. 255(C).
    9. Jiang, Yao & Xu, Xu & Huang, Quanzhong & Huo, Zailin & Huang, Guanhua, 2016. "Optimizing regional irrigation water use by integrating a two-level optimization model and an agro-hydrological model," Agricultural Water Management, Elsevier, vol. 178(C), pages 76-88.
    10. Xiling Zhang & Yusheng Kong & Xuhui Ding, 2020. "How High-Quality Urbanization Affects Utilization Efficiency of Agricultural Water Resources in the Yellow River Basin under Double Control Action?," Sustainability, MDPI, vol. 12(7), pages 1-16, April.
    11. Zhou, Huiping & Chen, Jinliang & Wang, Feng & Li, Xiaojuan & Génard, Michel & Kang, Shaozhong, 2020. "An integrated irrigation strategy for water-saving and quality-improving of cash crops: Theory and practice in China," Agricultural Water Management, Elsevier, vol. 241(C).
    12. Mateos, Luciano & Lopez-Cortijo, Ignacio & Sagardoy, Juan A., 2002. "SIMIS: the FAO decision support system for irrigation scheme management," Agricultural Water Management, Elsevier, vol. 56(3), pages 193-206, August.
    13. Chen, Shu & Shao, Dongguo & Gu, Wenquan & Xu, Baoli & Li, Haoxin & Fang, Longzhang, 2017. "An interval multistage water allocation model for crop different growth stages under inputs uncertainty," Agricultural Water Management, Elsevier, vol. 186(C), pages 86-97.
    14. Ajay Singh, 2014. "Irrigation Planning and Management Through Optimization Modelling," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(1), pages 1-14, January.
    15. Quan, Hao & Ding, Dianyuan & Wu, Lihong & Qiao, Ruonan & Dong, Qin'ge & Zhang, Tibin & Feng, Hao & Wu, Lianhai & Siddique, Kadambot H.M., 2022. "Future climate change impacts on mulched maize production in an arid irrigation area," Agricultural Water Management, Elsevier, vol. 266(C).
    16. He, Liuyue & Xu, Zhenci & Wang, Sufen & Bao, Jianxia & Fan, Yunfei & Daccache, Andre, 2022. "Optimal crop planting pattern can be harmful to reach carbon neutrality: Evidence from food-energy-water-carbon nexus perspective," Applied Energy, Elsevier, vol. 308(C).
    17. Cao, Zhaodan & Zhu, Tingju & Cai, Ximing, 2023. "Hydro-agro-economic optimization for irrigated farming in an arid region: The Hetao Irrigation District, Inner Mongolia," Agricultural Water Management, Elsevier, vol. 277(C).
    18. Ren, Dongyang & Xu, Xu & Engel, Bernard & Huang, Quanzhong & Xiong, Yunwu & Huo, Zailin & Huang, Guanhua, 2021. "A comprehensive analysis of water productivity in natural vegetation and various crops coexistent agro-ecosystems," Agricultural Water Management, Elsevier, vol. 243(C).
    19. Wu, Zhangsheng & Li, Yue & Wang, Rong & Xu, Xu & Ren, Dongyang & Huang, Quanzhong & Xiong, Yunwu & Huang, Guanhua, 2023. "Evaluation of irrigation water saving and salinity control practices of maize and sunflower in the upper Yellow River basin with an agro-hydrological model based method," Agricultural Water Management, Elsevier, vol. 278(C).
    20. Zhang, Shulin & Su, Xiaoling & Singh, Vijay P & Ayantobo, Olusola Olaitan & Xie, Juan, 2018. "Logarithmic Mean Divisia Index (LMDI) decomposition analysis of changes in agricultural water use: a case study of the middle reaches of the Heihe River basin, China," Agricultural Water Management, Elsevier, vol. 208(C), pages 422-430.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:11:y:2018:i:1:p:24-:d:192148. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.