Enhancing irrigation water productivity and controlling salinity under uncertainty: A full fuzzy dependent linear fractional programming approach

An integrated simulation-optimization framework is developed under uncertainty to enhance irrigation water productivity and control salinity in an arid area. A full fuzzy dependent linear fractional programming approach is formulated by incorporating fuzzy dependent-chance programming, fuzzy credibility-constrained programming and linear fractional programming within a general framework of irrigation planning. Then, simulation module concerning water, salt balance process and crop water-salt production functions enables to quantify daily physical process of water and salt movement among the soil water, crop root zone and groundwater aquifers. Thus, this study can readily handle fuzzy uncertainty existing concurrently in the ratio objective (i.e., economic water productivity) through the concept of fuzzy dependent chance and double-sided constraints. It can also simultaneously provide the maximum credibility level that the objective is achievable and credibility levels implying that optimal solutions are believable. Besides, daily variations of simulated physical parameters are illustrated corresponding to management strategies. To demonstrate its applicability, it's then applied to a case study of irrigation planning in the Jiefangzha Irrigation Subarea in Hetao Irrigation District, northwest China. Results can clearly analyze tradeoffs among satisfaction degree of fuzzy objective, fuzzy constraints and optimal solutions. Moreover, by examining different management targets and salt accumulation constraints, this study demonstrates the merits and importance of the work to promote irrigation water productivity and control salinity. Dynamic decision making of irrigation planning is possibly made by coupling daily simulation and optimization modules. Therefore, these findings can support decision makers to identify appropriate solutions for irrigation planning.