Multi\|objective optimal allocation of generalized water resources in irrigation district under uncertainty
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DOI:10.7606/j.issn.1000-7601.2020.04.21
Key Words: agricultural water resources  optimization allocation  uncertainty  fuzzy credibility\|constrained programming  stochastic multi\|objective programming  Zhanghe Irrigation District
Author NameAffiliation
YUE Qiong Center for Agricultural Water Research in China, China Agricultural University, Beijing 100083, China 
GUO Ping Center for Agricultural Water Research in China, China Agricultural University, Beijing 100083, China 
TANG Yikuan Center for Agricultural Water Research in China, China Agricultural University, Beijing 100083, China 
ZHAO Min Center for Agricultural Water Research in China, China Agricultural University, Beijing 100083, China 
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Abstract:
      To address complexity and uncertainty existed in agricultural water resources allocation system, a fuzzy credibility\|constrained stochastic multi\|objective programming model for optimization of agricultural water resources allocation was developed, with the objective to balance agricultural profit and green water utilization ratio. The model was applied to Zhanghe Irrigation District to optimally allocate irrigation water to rice in different sub\|areas under different time periods in different hydrological years (wet year, normal year, and dry year). Weight scenarios of objectives were set as (0.5, 0.5),(0.75, 0.25), and (0.25, 0.75), and credibility levels were set as 0.5,0.6,0.7,0.8,0.9,and 1.0. According to flexible optimal agricultural water allocation schemes, it can be summarized as: green water utilization ratio of planning schemes were all above 0.835, and system net economic benefit was up to 14.153×108 yuan; multi\|objective programming model performed well in balancing social\|economic development target and water resources conservation goal; optimal results can trade off system benefit and constraint violation risk; the model framework and solving method can apply to other similar regions to guide sustainable agricultural water resources management.