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.
Chenghua et al. (Wed,) studied this question.