Optimized Allocation of Irrigation Water Resources Based on Uncertainty: Model Construction and Dynamic Regulation Mechanism

Climate change and growing water scarcity necessitate that irrigation districts allocate limited water resources more efficiently, with explicit consideration of multi-source uncertainties. To maximize the effective utilization coefficient of irrigation water, an uncertainty-informed optimization and dynamic regulation framework for agricultural water allocation (UODRA) was developed. The framework quantifies and characterizes uncertainties arising from meteorological forcings, soil heterogeneity, irrigation practices, and water losses during conveyance and field application. The fractional programming model derived therefrom is solved via Dinkelbach’s algorithm, and Monte Carlo simulation is adopted in a reduced scenario space to propagate the dominant uncertainty drivers and assess the distribution characteristics of outcomes and associated risks. A case study was conducted in the Fendong Irrigation District to evaluate three water supply scenarios. The results indicate that with sufficient water supply and diminishing marginal returns, the effective utilization coefficient of irrigation water increases accordingly. Uncertainty mainly exerts an impact on the degree of dispersion and downside risks rather than at the average level. Sensitivity analysis shows that efficiency-related perturbations are the primary drivers of output variability, and their impacts are greater than those of supply-side perturbations and demand-side variation in simulated irrigation demand. Further technical comparison reveals that the adoption of high-efficiency irrigation can significantly improve the performance at the regional level: under drip irrigation conditions, the efficiency reaches 0.614, while that of sprinkler irrigation is 0.499, with a simultaneous improvement in operational stability. Overall, UODRA provides a quantitative decision support method for robust irrigation water resource allocation and adaptive management under uncertain conditions.