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Distributed AquaCrop simulation-nonlinear multi-objective dependent-chance programming for irrigation water resources management under uncertainty

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  • Wang, Youzhi
  • Guo, Shanshan
  • Yue, Qing
  • Mao, Xiaomin
  • Guo, Ping

Abstract

In the agricultural water resources system, the regional yield is hard to be simulated accurately under the impacts of spatial heterogeneities of soil, weather, and crop types. The optimal water allocation schemes may be not in accordance with actual conditions due to neglecting the actual crop growth process. Meanwhile, the uncertainties in the simulation-optimization models are not easy to be addressed. To deal with the above problems, this paper develops a framework of the distributed AquaCrop simulation nonlinear multi-objective dependent-chance programming (distributed AquaCrop NMOFDCP) for irrigation water resources management under uncertainties. This developed model is applied to a case study of irrigation water resources management in the middle reaches of the Heihe River Basin in China. The 134 homogeneous decision-making units (DMUs) are divided to depict the spatial heterogeneities of the study area, and 472 decision variables (irrigation amount) for 134 DMUs are optimized. Moreover, the model deals with uncertainties expressed as fuzzy goals and tradeoffs relationships between objectives of the yield and water productivity, and measures the satisfactory degrees between objectives and their fuzzy goals. Two groups of Pareto solutions corresponding to the maximum satisfactory degree of the yield, and the maximum satisfactory degree of the water productivity are obtained by the parallel genetic algorithm (PGA) method respectively. In addition, water allocation, satisfactory degree of the yield, and satisfactory degree of water productivity are analyzed at the decision-making unit scale, crop scale, and irrigation-district scale separately. Besides the effects of three weather conditions and four soil types on the system’s outputs are conducted. The results show that weather conditions and soil types have obvious effects on the system’s outputs at three analysis scales, and different water allocation patterns at the growth period affect the yield and water productivity.

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  • Wang, Youzhi & Guo, Shanshan & Yue, Qing & Mao, Xiaomin & Guo, Ping, 2021. "Distributed AquaCrop simulation-nonlinear multi-objective dependent-chance programming for irrigation water resources management under uncertainty," Agricultural Water Management, Elsevier, vol. 247(C).
    • Handle: RePEc:eee:agiwat:v:247:y:2021:i:c:s0378377421000172
    DOI: 10.1016/j.agwat.2021.106752
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    Cited by:

    1. Zhang, Zepeng & Wang, Qingzheng & Guan, Qingyu & Xiao, Xiong & Mi, Jimin & Lv, Songjian, 2023. "Research on the optimal allocation of agricultural water and soil resources in the Heihe River Basin based on SWAT and intelligent optimization," Agricultural Water Management, Elsevier, vol. 279(C).
    2. Liu, Xiao & Yang, Dawen, 2021. "Irrigation schedule analysis and optimization under the different combination of P and ET0 using a spatially distributed crop model," Agricultural Water Management, Elsevier, vol. 256(C).
    3. Zhang, Fan & Cai, Yanpeng & Tan, Qian & Wang, Xuan, 2021. "Spatial water footprint optimization of crop planting: A fuzzy multiobjective optimal approach based on MOD16 evapotranspiration products," Agricultural Water Management, Elsevier, vol. 256(C).

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