Optimizing water resources allocation and soil salinity control for supporting agricultural and environmental sustainable development in Central Asia Academic Article uri icon

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abstract

  • In this study, a stochastic-fuzzy-based fractional programming (SFFP) method is advanced for optimizing water-resources allocation and soil-salinity control under uncertainty. The developed method can address ratio objective optimization problems of complex system in association with stochastic and fuzzy uncertainties, which can help gain in-depth analysis of the interrelationships between marginal effectiveness and system reliability. Then, SFFP is applied to an irrigation region in the lower reach of Amu Darya River basin, where linear crop yield-salinity functions and salt-leaching functions are introduced into the modeling formulation for reflecting the complicated interactions among water resources, soil salinity, arable land, and electricity supply. Solutions under 96 scenarios related to different irrigation efficiencies, water availabilities, and electricity supplies have been obtained. Our findings are: i) increased water availability, electricity supply, and irrigation efficiency result in high marginal benefit; ii) irrigation efficiency is the key factor influencing water allocation patterns for crop irrigation and salt-leaching, promotion of which can facilitate mitigating economic and environmental losses in the water-deficit and soil-salinized region; iii) leaching water allocation patterns for soil-salinity washing is related to salinity characters of crops and regions, and boosting drought- and salt-tolerance crop can be effective in adaption to risks of water scarcity and land salinization. Compared to the conventional approaches, SFFP can generate more flexible alternatives and achieve higher marginal effectiveness. These findings can provide effective decision support to identify desired water management strategies under multiple uncertainties for supporting agricultural sustainability in arid regions.

publication date

  • February 2020