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A multi-objective optimization approach to allocate environmental flows to the artificially restored wetlands of China's Yellow River Delta

机译:将环境流量分配到中国黄河三角洲人工修复湿地的多目标优化方法

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In this paper, a multi-objective optimization model has been developed for allocating freshwater to meet the environmental flow requirements of the restored wetlands in the Yellow River Delta, China. The model seeks a rational balance among appropriate water allocation for the wetlands, a healthy ecosystem, and optimum economic returns for the humans that use the wetlands to earn their livelihood, with the degree of ecosystem health represented by the difference between actual and ideal water levels. And then a holistic method has been used for satisfying multiple objectives to avoid the problem of decision-maker subjectivity, and then solved the resulting optimal allocation model for environmental flows of the wetlands using a genetic algorithm. With water from the Yellow River as the source, the model shows how the optimal monthly water release into the restored wetlands can be achieved in dry, average, and wet years. Using the model, 20-56% reductions in water use could be achieved compared with the current pattern of water release, and the optimal water level in each month would coincide better with the ideal value based on an indicator plant community (reeds). The results of the study demonstrate the capability of the multi-objective programming model to optimize ecological water allocation and management for the wetlands of the Yellow River Delta.
机译:本文建立了一个多目标优化模型,用于分配淡水以满足中国黄河三角洲恢复湿地的环境流量需求。该模型寻求在湿地的适当水量分配,健康的生态系统和使用湿地谋生的人类的最佳经济回报之间的合理平衡,生态系统健康的程度由实际水位与理想水位之间的差异表示。然后采用整体方法满足多个目标,避免决策者的主观性问题,然后采用遗传算法求解湿地环境流量的最优分配模型。该模型以黄河的水为源,显示了如何在干旱,平均和湿润年份实现最佳的每月水释放到恢复的湿地中。使用该模型,与当前的水排放模式相比,可以减少20-56%的用水量,并且每个月的最佳水位将与基于指示植物群落(芦苇)的理想值更好地吻合。研究结果表明,多目标规划模型具有优化黄河三角洲湿地生态水分配和管理的能力。

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