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Planning seasonal irrigation water allocation based on an interval multiobjective multi-stage stochastic programming approach

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  • Zhang, Fan
  • Guo, Ping
  • Engel, Bernard A.
  • Guo, Shanshan
  • Zhang, Chenglong
  • Tang, Yikuan

Abstract

Water managers in arid and semi-arid areas must allocate limited irrigation water to different water use sectors considering the conflicting objectives, seasonal runoff inflow, and multiple uncertainties. To deal with these problems, an interval multiobjective multi-stage stochastic programming (IMMSP) model was proposed for finding reasonable water-storage scale and optimizing limited irrigation-water resources. Key factors in planning irrigation-water resources, such as random seasonal runoff, interval uncertainty in data collection, economic benefits, water leakage loss, and water deficit, were fully considered in the IMMSP model. Additionally, as an important indicator to describe the seasonal water supply capability of local water supply project, including reservoirs, agricultural ponds and etc., the water-storage scale, defined as the ratio of water storage capacity over total streamflow, is proposed to obtain the quantitative relationship between this indicator and objective of IMMSP. This study attempted to obtain the relationship between water-storage scale and objective of IMMSP as well as provide a reference of determining water-storage scale from the perspective of optimization. In addition, to solve the IMMSP model, a modified minimum deviation (MMD) method was proposed for dealing with uncertainties, making tradeoff among conflicting objectives, and reflecting the different importance of objectives. Both the IMMSP model and MMD method were applied to a real-world water-allocation problem in the middle reaches of the Heihe River basin for verifying its validity. The solutions generate a set of decision alternatives under different seasonal runoff scenarios and further guide local water managers identify the optimal management strategies and project construction scale of water conservancy. Moreover, a sound discussion of contribution to water-storage scale planning is made and the comparisons between IMMSP and each single-objective model (economic benefits, water leakage loss, and water deficit) in this study demonstrate that the results obtained by the proposed approach are more practical than a single objective with the same constraints. These results can not only effectively contribute to local irrigation water management and ecological restoration, but also provide more information to plan regional water-storage scale values.

Suggested Citation

  • Zhang, Fan & Guo, Ping & Engel, Bernard A. & Guo, Shanshan & Zhang, Chenglong & Tang, Yikuan, 2019. "Planning seasonal irrigation water allocation based on an interval multiobjective multi-stage stochastic programming approach," Agricultural Water Management, Elsevier, vol. 223(C), pages 1-1.
    • Handle: RePEc:eee:agiwat:v:223:y:2019:i:c:69
    DOI: 10.1016/j.agwat.2019.105692
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    References listed on IDEAS

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    1. Paul, Manashi & Negahban-Azar, Masoud & Shirmohammadi, Adel & Montas, Hubert, 2020. "Assessment of agricultural land suitability for irrigation with reclaimed water using geospatial multi-criteria decision analysis," Agricultural Water Management, Elsevier, vol. 231(C).
    2. Zhang, Xiaoxing & Guo, Ping & Guo, Wenxian & Gong, Juan & Luo, Biao, 2021. "Optimization towards sustainable development in shallow groundwater area and risk analysis," Agricultural Water Management, Elsevier, vol. 258(C).
    3. 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).
    4. Zhang, Xiaoxing & Guo, Ping & Zhang, Fan & Liu, Xiao & Yue, Qiong & Wang, Youzhi, 2021. "Optimal irrigation water allocation in Hetao Irrigation District considering decision makers’ preference under uncertainties," Agricultural Water Management, Elsevier, vol. 246(C).

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