What does this research mean for the field?

The uncertainty-informed optimization and dynamic regulation framework (UODRA) significantly improves the effective utilization coefficient of irrigation water resources under varying supply scenarios. Novelty: ClaimNovelty.NOVEL_FINDING. Consensus alignment: ConsensusAlignment.SUPPORTS_CONSENSUS.

What question did this study set out to answer?

The aim is to develop a framework for optimal irrigation water allocation that considers uncertainties and enhances resource efficiency.

March 6, 2026Open Access

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

Key Points

The aim is to develop a framework for optimal irrigation water allocation that considers uncertainties and enhances resource efficiency.
Developed a dynamic regulation framework for agricultural water allocation (UODRA).
Quantified uncertainties from meteorological conditions, soil variability, irrigation practices, and water loss.
Applied Dinkelbach’s algorithm for fractional programming to solve the optimization model.
Conducted Monte Carlo simulations to assess uncertainty impacts and evaluate various water supply scenarios.
Increased irrigation water efficiency observed with sufficient supply and diminishing returns.
Uncertainty affected dispersion and downside risks more than average outcomes.
High-efficiency irrigation improved performance significantly: 0.614 under drip vs. 0.499 under sprinkler irrigation.

Abstract

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.

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