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A risk-averse stochastic quadratic model with recourse for supporting irrigation water management in uncertain and nonlinear environments

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  • Zhang, W.J.
  • Tan, Q.
  • Zhang, T.Y.

Abstract

Agricultural water management system are subject to a variety of uncertainties and nonlinearity, which complicate the decision-making process and exaggerate water-shortage risks. Previously, few studies could tackle multiple uncertainties and the associated system risks in nonlinear problems. In this paper, a novel risk-averse optimization model was developed for supporting irrigation water management to mitigate these problems. Based on a risk measurement tool, Conditional Value-at-Risk (CVaR), a CVaR-based interval fuzzy two-stage stochastic quadratic programming (CIFTSQP) was proposed. CIFTSQP could not only provide risk-averse solutions with recourse in response to extreme economic risk quantified as CVaR and water-shortage risk expressed as fuzzy credibility level, but also handle and quantify the nonlinearity in a stochastic program caused by the economy-of-scale effects of facilities. The proposed method has been applied to an irrigation water management case in the northern China. Results suggested that, in response to severe water shortage, sunflowers would be preferred. Meanwhile, groundwater use in Wuyuan County and surface water consumption of Urad Qianqi and Urad Zhongqi should be reduced. Moreover, desired water-allocation strategies with varied risk-aversion levels were generated under different water inflow levels. Results reveal that the highest economic benefits could be achieved when risk aversion degree reaches 30 %, 40 % and 70 % under the low, medium and high inflow levels, respectively. Results reveal that higher risks would bring higher returns under the low and medium inflow levels, and the highest economic benefits could be achieved when risk aversion degree reaches 70 % under the high inflow level.reveal that higher would bring higher returnsunder the low and medium inflow levels, andthe highest economic benefits could be achieved when risk aversion degree reaches 70 % under the high inflow level. The developed CIFTSQP method provided valuable insights into informed irrigation decision-making with respect to economy-of-scale effects, risk aversion and recourse in an agricultural water allocation system. It can also be applied to address other resource allocation problems under uncertainty and risk.

Suggested Citation

  • Zhang, W.J. & Tan, Q. & Zhang, T.Y., 2021. "A risk-averse stochastic quadratic model with recourse for supporting irrigation water management in uncertain and nonlinear environments," Agricultural Water Management, Elsevier, vol. 244(C).
    • Handle: RePEc:eee:agiwat:v:244:y:2021:i:c:s0378377420305588
    DOI: 10.1016/j.agwat.2020.106431
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    1. L. Shao & X. Qin & Y. Xu, 2011. "A Conditional Value-at-Risk Based Inexact Water Allocation Model," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 25(9), pages 2125-2145, July.
    2. 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.
    3. Niu, G. & Li, Y.P. & Huang, G.H. & Liu, J. & Fan, Y.R., 2016. "Crop planning and water resource allocation for sustainable development of an irrigation region in China under multiple uncertainties," Agricultural Water Management, Elsevier, vol. 166(C), pages 53-69.
    4. 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).
    5. Zhang, X.Y. & Huang, G.H. & Zhu, H. & Li, Y.P., 2017. "A fuzzy-stochastic power system planning model: Reflection of dual objectives and dual uncertainties," Energy, Elsevier, vol. 123(C), pages 664-676.
    6. Huang, Y. & Li, Y.P. & Chen, X. & Ma, Y.G., 2012. "Optimization of the irrigation water resources for agricultural sustainability in Tarim River Basin, China," Agricultural Water Management, Elsevier, vol. 107(C), pages 74-85.
    7. Cai, Y.P. & Huang, G.H. & Tan, Q. & Chen, B., 2011. "Identification of optimal strategies for improving eco-resilience to floods in ecologically vulnerable regions of a wetland," Ecological Modelling, Elsevier, vol. 222(2), pages 360-369.
    8. Q. Tan & G. Huang & Y. Cai, 2013. "Multi-Source Multi-Sector Sustainable Water Supply Under Multiple Uncertainties: An Inexact Fuzzy-Stochastic Quadratic Programming Approach," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(2), pages 451-473, January.
    9. 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).
    10. Philippe Artzner & Freddy Delbaen & Jean‐Marc Eber & David Heath, 1999. "Coherent Measures of Risk," Mathematical Finance, Wiley Blackwell, vol. 9(3), pages 203-228, July.
    11. Zhang, Chenglong & Li, Xuemin & Guo, Ping & Huo, Zailin, 2020. "An improved interval-based fuzzy credibility-constrained programming approach for supporting optimal irrigation water management under uncertainty," Agricultural Water Management, Elsevier, vol. 238(C).
    12. Chen, M. J. & Huang, G. H., 2001. "A derivative algorithm for inexact quadratic program - application to environmental decision-making under uncertainty," European Journal of Operational Research, Elsevier, vol. 128(3), pages 570-586, February.
    13. Song, Tangnyu & Huang, Guohe & Zhou, Xiong & Wang, Xiuquan, 2018. "An inexact two-stage fractional energy systems planning model," Energy, Elsevier, vol. 160(C), pages 275-289.
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    1. Zhang, Shuo & Kang, Yan & Gao, Xuan & Chen, Peiru & Cheng, Xiao & Song, Songbai & Li, Lingjie, 2023. "Optimal reservoir operation and risk analysis of agriculture water supply considering encounter uncertainty of precipitation in irrigation area and runoff from upstream," Agricultural Water Management, Elsevier, vol. 277(C).

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