A Multiobjective Optimization Framework for Irrigation Water Allocation

Sustainable irrigation planning requires balancing economic benefits with environmental flow requirements under increasing climatic and resource constraints. Building on the irrigation optimization framework developed by Ullah and Nehring, this study improves the analytical depth of existing approaches by expanding the feasible decision space and systematically characterizing the full economic--environmental trade-off spectrum. Two single-objective formulations, maximizing net agricultural benefit and minimizing environmental flow deficiency (EFD), are solved to identify boundary solutions that define the limits of the feasible space. These are subsequently integrated into a multiobjective optimization framework using scalarization and evolutionary search techniques to generate a high-resolution Pareto frontier. Numerical experiments on the Muhuri Irrigation Project reveal three key outcomes: (i) a complete scenario view with profits ranging from $0.2 \times 10^{9}$ to $1.497 \times 10^{9}$ and EFD values between 0 and 1200~GL, where 1200~GL represents the theoretical annual maximum under a uniform monthly environmental flow target of 100~GL; (ii) explicit trade-offs demonstrating that higher economic returns are consistently associated with greater ecological shortfalls; and (iii) a computationally efficient approach capable of generating nearly 1000 Pareto-optimal solutions within a short runtime ($\sim$10 seconds), substantially improving solution resolution compared to earlier studies. By transforming point-based optimization into comprehensive trade-off mapping, the proposed framework provides a more informative basis for scenario analysis and decision support in irrigation water allocation, offering a practical extension to existing optimization approaches.