Achieving optimal water allocation in an inter-basin water diversion project is a challenge that involves multilevel stakeholders, and the uncertainty and disequilibrium of water allocation further intensify competiti...
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Achieving optimal water allocation in an inter-basin water diversion project is a challenge that involves multilevel stakeholders, and the uncertainty and disequilibrium of water allocation further intensify competition among different stakeholders. This study proposes a bi-level multiobjective programming model for water allocation in inter-basin water diversions that considers equilibrium sustainability. The model integrates interval parameter programming, fuzzy credibility-constrained programming, bi-level multiobjective programming, and the Gini coefficient into a general framework. The model not only balances the stakeholders at different levels to ensure water allocation equity but also comprehensively considers the fuzzy uncertainty of parameters and constraints to obtain the interrelationship among water diversion, water allocation satisfaction, and efficiency objectives. The developed model was applied to allocate water for the Hanjiang-to-Weihe Water Diversion Project in Shaanxi Province, China. The results showed that 1) the model derived a feasible tradeoff between upper basin manager and lower regional managers under different water diversion scenarios;2) water allocations and objectives at each level are sensitive to changes in the credibility of water diversion;3) the new model improves the equity of water allocation and ensures the efficient allocation to various stakeholders at each level based on a comparison of the proposed model with a model that does not consider equilibrium. The proposed model can help decision makers choose the optimal scheme based on their acceptable risk level to achieve a more efficient and sustainable water allocation for inter-basin water diversions.
In this study, integration of analytic hierarchy process method and entropy method (AHP-EW) for quantifying the knowledge and experience accumulated by regional managers as well as the socioeconomic situation, the par...
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In this study, integration of analytic hierarchy process method and entropy method (AHP-EW) for quantifying the knowledge and experience accumulated by regional managers as well as the socioeconomic situation, the partial least squares regression (PLS) for reflecting the relationship between irrigation water use efficiency and agronomic inputs, and the ecosystem service value for measuring environmental impacts of changing crop planting area were considered in one framework simultaneously. With help of these efforts, a bi-levelmultiobjective stochastic approach to improve irrigation water use efficiency and decrease the pollution production of agronomic measures in the process of agricultural production. The proposed framework integrate bi-level multiobjective programming and stochastic expectation programming to not only make tradeoffs among multiple concerns from two-level decision makers, but also deal with the randomness of runoff. Then, the proposed approach was applied to a real-world case in the middle reaches of the Heihe River basin, northwest China. Results show that the developed approach can improve irrigation water use efficiency, reduce CO2 emission, expand ecosystem service values, and provide more profitable and environment-friendly agricultural planting strategies to decision makers, which can further contribute to the sustainable development of agriculture. Furthermore, by comparing the bi-levelmultiobjective stochastic programming (BMSP) model with the other six models originated from developed model, it can be found that 1) the single objective model can obtain the best value of that objective, but cannot readily consider other important aspects;2) the multiobjective models can make tradeoffs among multiple objectives: 3) the BMSP model can reflect the leader-follower relationship in the optimization process. The approach is applicable for arid and semiarid regions that face similar problems to determine agricultural planting strategies. (C) 20
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