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Agricultural Multi-Water Source Allocation Model Based on Interval Two-Stage Stochastic Robust Programming under Uncertainty

Author

Listed:
  • Qiang Fu

    (Northeast Agricultural University
    Northeast Agricultural University)

  • Tianxiao Li

    (Northeast Agricultural University
    Northeast Agricultural University)

  • Song Cui

    (Northeast Agricultural University
    Northeast Agricultural University)

  • Dong Liu

    (Northeast Agricultural University
    Northeast Agricultural University)

  • Xueping Lu

    (Northeast Agricultural University
    Northeast Agricultural University)

Abstract

Numerous uncertainties and complexities exist in the agricultural irrigation water allocation system, that must be considered in the optimization of water resources allocation. In this paper, an agricultural multi-water source allocation model, consisting of stochastic robust programming and two-stage random programming and introducing interval numbers and random variables to represent the uncertainties, was proposed for the optimization of irrigation water allocation in Jiamusi City of Heilongjiang Province, China. The model could optimize the water allocaton to different crops of groundwater and surface water. Then, the optimal target value and the optimal water allocation of different water sources distributed to different crops could be obtained. The model optimized the economic benefits and stability of the agricultural irrigation water allocation system via the introduction of a the penalty cost variable measurement to the objective function. The results revealed that the total water shortage changed from [18.6, 32.3] × 108 m3 to [15.7, 26.2] × 108 m3 at a risk level ω from zero to five, indicating that the water shortage decreased and the reliability improved in the agricultural irrigation water allocation system. Additionally, the net economic benefits of irrigation changed from [287.21, 357.86] × 108 yuan to [253.23, 301.32] × 108 yuan, indicating that the economic benefit difference was reduced. Therefore, the model can be used by decision makers to develop appropriate water distribution schemes based on the rational consideration of the economic benefit, stability and risk of the agricultural irrigation water allocation system.

Suggested Citation

  • Qiang Fu & Tianxiao Li & Song Cui & Dong Liu & Xueping Lu, 2018. "Agricultural Multi-Water Source Allocation Model Based on Interval Two-Stage Stochastic Robust Programming under Uncertainty," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(4), pages 1261-1274, March.
    • Handle: RePEc:spr:waterr:v:32:y:2018:i:4:d:10.1007_s11269-017-1868-2
    DOI: 10.1007/s11269-017-1868-2
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    References listed on IDEAS

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    1. C. Li & L. Zhang, 2015. "An Inexact Two-Stage Allocation Model for Water Resources Management Under Uncertainty," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(6), pages 1823-1841, April.
    2. Pradeep Dogra & V. Sharda & P. Ojasvi & Shiv Prasher & R. Patel, 2014. "Compromise Programming Based Model for Augmenting Food Production with Minimum Water Allocation in a Watershed: a Case Study in the Indian Himalayas," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(15), pages 5247-5265, December.
    3. Manuel Laguna, 1998. "Applying Robust Optimization to Capacity Expansion of One Location in Telecommunications with Demand Uncertainty," Management Science, INFORMS, vol. 44(11-Part-2), pages 101-110, November.
    4. Yong Li & Guo Huang, 2008. "Interval-parameter Two-stage Stochastic Nonlinear Programming for Water Resources Management under Uncertainty," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 22(6), pages 681-698, June.
    5. Yu, Chian-Son & Li, Han-Lin, 2000. "A robust optimization model for stochastic logistic problems," International Journal of Production Economics, Elsevier, vol. 64(1-3), pages 385-397, March.
    6. L. Zhang & C. Li, 2014. "An Inexact Two-Stage Water Resources Allocation Model for Sustainable Development and Management Under Uncertainty," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(10), pages 3161-3178, August.
    7. John M. Mulvey & Robert J. Vanderbei & Stavros A. Zenios, 1995. "Robust Optimization of Large-Scale Systems," Operations Research, INFORMS, vol. 43(2), pages 264-281, April.
    8. Leung, Stephen C.H. & Tsang, Sally O.S. & Ng, W.L. & Wu, Yue, 2007. "A robust optimization model for multi-site production planning problem in an uncertain environment," European Journal of Operational Research, Elsevier, vol. 181(1), pages 224-238, August.
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    Cited by:

    1. Shiang-Jen Wu & Jie-Sen Mai & Yi-Hong Lin & Keh-Chia Yeh, 2022. "Modeling Probabilistic-Based Reliability Analysis for Irrigation Water Supply Due to Uncertainties in Hydrological and Irrigation Factors," Sustainability, MDPI, vol. 14(19), pages 1-25, October.
    2. R. Roozbahani & B. Abbasi & S. Schreider & J. Iversen, 2021. "Dam Location-Allocation under Multiple Hydrological Scenarios," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(3), pages 993-1009, February.
    3. Hassan, Wasim & Manzoor, Talha & Jaleel, Hassan & Muhammad, Abubakr, 2021. "Demand-based water allocation in irrigation systems using mechanism design: A case study from Pakistan," Agricultural Water Management, Elsevier, vol. 256(C).
    4. Gong, Xinghui & Zhang, Hongbo & Ren, Chongfeng & Sun, Dongyong & Yang, Jiantao, 2020. "Optimization allocation of irrigation water resources based on crop water requirement under considering effective precipitation and uncertainty," Agricultural Water Management, Elsevier, vol. 239(C).
    5. Xiaona Li & Xiaosheng Wang & Haiying Guo & Weimin Ma, 2020. "Multi-Water Resources Optimal Allocation Based on Multi-Objective Uncertain Chance-Constrained Programming Model," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(15), pages 4881-4899, December.
    6. Li, Mo & Fu, Qiang & Singh, Vijay P. & Liu, Dong & Gong, Xinglong, 2020. "Risk-based agricultural water allocation under multiple uncertainties," Agricultural Water Management, Elsevier, vol. 233(C).
    7. Zhang, Yu & Ren, Chongfeng & Zhang, Hongbo & Xie, Zhishuai & Wang, Yashi, 2022. "Managing irrigation water resources with economic benefit and energy consumption: an interval linear multi-objective fractional optimization model under multiple uncertainties," Agricultural Water Management, Elsevier, vol. 272(C).

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