Balancing irrigation planning and risk preference for sustainable irrigated agriculture: A fuzzy credibility-based optimization model with the Hurwicz criterion under uncertainty

Abstract

The challenge of irrigation planning is that it involves various system components, which is further exacerbated by uncertainties and risk preference in making decisions. This study presents a simulation-optimization framework under uncertainty by incorporating fuzzy credibility-based optimization model with the Hurwicz criterion and daily water balance simulation for irrigation planning. The developed methodology can effectively handle fuzzy uncertainty in the objective function and constraints and readily analyze tradeoffs between optimal solutions of irrigation planning and risks associated with the decision-makers’ preferences. It can also derive several physical parameters expressed as water depth from the water balance of root zone for determining optimal irrigation water amount. The Hurwicz criterion, fuzzy credibility constraints and other irrigation-related constraints are imposed on the study system through the developed framework. Then, its applicability is illustrated with a real case study in the Jiefangzha irrigation subarea in the Hetao Irrigation District, northwest China, which is an arid area. Optimal results using three risk-related parameters, i.e., nine preference weights (λ = 0.1, 0.2, … and 0.9) of objective function, and three credibility levels (α = 0.7, 0.8 and 0.9, β = 0.7, 0.8 and 0.9) of fuzzy constraints, have been generated for supporting irrigation planning. Based on these outcomes, system benefits resulting from agricultural production have a slight growth with preference weights increase from 0.1 to 0.9, showing that optimistic criterion plays a decisive role in optimal irrigation planning solutions, especially when preference weights are greater than 0.7. Daily groundwater depth is presented in the given 81 scenarios for comparison, showing the usefulness of the developed framework in practical applications. Therefore, these findings are helpful for supporting irrigation planning and balancing tradeoffs between irrigation planning and risk preference for sustainable irrigated agriculture.