A Frequency–Severity Analysis of Irrigation Demand Deficits Using Optimal Framework Under Uncertainty

Demand for irrigation water varies substantially between upstream and downstream reaches of river basins due to spatial variability in rainfall, agro-climatic situations, and management practices. Upstream areas often experience over-irrigation and waterlogging, while downstream regions are challenged with water scarcity, timing mismatches, and allocation conflicts. This study proposes a novel SWAT–AquaCrop–optimization nexus framework to minimize both the frequency (DDF) and severity (DDS) of irrigation demand deficit under hydro-climatic uncertainty. To enhance numerical stability and a realistic representation of system stress, deficit frequency is formulated using a smooth, differentiable exceedance function instead of conventional binary thresholds. The framework integrates SWAT-based hydrological projections with AquaCrop simulations of crop yield and evapotranspiration-driven water demand, simultaneously evaluating three interlinked objectives: allocation-disparity deficit (equity), yield deficit (productivity), and irrigation-efficiency deficit (operational performance). Hydro-climatic uncertainty is represented through a quantile-based classification, with favorable (S1), normal (S2), and extreme (S3) scenarios defined by the 33rd and 66th percentiles of the time-varying deficit ratio. The results indicate that stage-specific irrigation timing adjustments (advanced by 2–5 days) better align water applications with peak crop water requirements during flowering and grain-filling stages. This enhances downstream reliability, mitigates upstream over-irrigation, and substantially reduces both demand deficit frequency and severity.