Enhancement of border irrigation systems: Leveraging simulation–optimization techniques

Surface irrigation systems, while widespread due to their low operational costs, often suffer from significant inefficiencies driven by inappropriate design and management practices. To address this, the current study proposes a new simulation–optimization model aimed to the design of open-end border irrigation systems, seeking to enhance hydraulic performance under field constraints. The model integrates a modified hydro-empirical SCS simulation framework with the Grey Wolf Optimizer (GWO) algorithm, using border length, slope, inflow discharge, and deficit irrigation Factor as decision variables. Performance evaluation is based on five hydraulic indicators, embedded in a weighted single-objective function. The model was applied to three real case studies representing varying soil textures and irrigation requirements. Results demonstrate that the modified SCS model could simulate all four phases of irrigation as well as determine the subsurface infiltration curve across the field. Also optimization consistently reduced the advance time, aligning infiltration opportunity times across the field, and thereby improved distribution uniformity, and requirement efficiency, while substantially reducing total applied water. The findings also highlight the critical influence of decision variables—particularly inflow discharge and field length—on system performance, and emphasize that shortening the advance phase was the most effective strategy for performance enhancement. Ultimately, the proposed model offers a computationally efficient and hydraulically robust approach to designing border irrigation systems with improved resource efficiency and operational resilience. • Combining modified SCS method with GWO algorithm enhanced hydraulic performance. • Modified method was more realistic and robust compared to the traditional SCS. • Optimization showed minimizing the advance time is the most effective strategy. • Inflow discharge had the most influence on objective function minimization.