Akbari et al. (2025) Enhancement of border irrigation systems: Leveraging simulation–optimization techniques

Identification

• Journal: Agricultural Water Management
• Year: 2025
• Date: 2025-10-15
• Authors: Mahmood Akbari, Saeed Farahani
• DOI: 10.1016/j.agwat.2025.109891

Research Groups

• Department of Water Science and Engineering, Faculty of Agriculture and Environment, Arak University, Arak, Iran
• Department of Irrigation & Reclamation Engineering, College of Agriculture & Natural Resources, University of Tehran, Karaj, Iran

Short Summary

This study proposes a novel simulation–optimization model for designing open-end border irrigation systems to enhance hydraulic performance under field constraints. Integrating a modified hydro-empirical SCS simulation framework with the Grey Wolf Optimizer, the model significantly improves distribution uniformity and requirement efficiency, substantially reduces total applied water, and identifies shortening the advance phase as the most effective strategy for performance enhancement.

Objective

• To modify the hydraulic–empirical SCS design method to fully simulate border irrigation with lower computational cost compared to numerical models.
• To achieve optimal design of border irrigation by integrating the modified SCS model with meta-heuristic optimization techniques (Grey Wolf Optimizer), considering border length, slope, inflow discharge, and deficit irrigation factor as decision variables, along with design constraints.
• To determine which decision variable (length, slope, or inflow rate) has the greatest influence on the optimal hydraulic performance of border irrigation.

Study Configuration

• Spatial Scale: Field scale, applied to three real case studies representing varying soil textures and irrigation requirements (Alfalfa, Barley, and Beans fields in Arak County, Iran).
• Temporal Scale: Single irrigation event, analyzing advance, storage, depletion, and recession phases.

Methodology and Data

• Models used:
  • Modified hydro-empirical SCS simulation framework (combining original SCS with Volume Balance approach and Strelkoff (1977) design method).
  • Grey Wolf Optimizer (GWO) algorithm for optimization.
  • Newton-Raphson method for solving equations.
  • Trapezoidal method for numerical integration.
  • CropWat software (for determining maximum evapotranspiration and net irrigation depth).
• Data sources:
  • Field data from United States Department of Agriculture (1974) for Field 1 (Alfalfa).
  • Field and laboratory operations conducted on two farms in Arak County, Iran, for Field 2 (Barley) and Field 3 (Beans).
  • Soil samples analyzed using the Hydrometer method (soil texture), Pressure Plate (Field Capacity and Permanent Wilting Point), and double-ring infiltrometer (final soil infiltration rate).
  • Crop-specific parameters (rooting depth, Management Allowed Depletion (MAD)).

Main Results

• The modified SCS model successfully simulated all four phases of irrigation and accurately determined the subsurface infiltration curve across the field.
• Optimization consistently reduced the advance time, leading to better alignment of infiltration opportunity times across the field.
• The optimized designs significantly improved distribution uniformity (DU) and requirement efficiency (Er), while substantially reducing total applied water (e.g., 83.04% reduction in total applied water volume for Field 1).
• For Field 1, DU increased by 1.66% and application efficiency (Ea) by 17.42%, with tail water ratio (TWR) reduced by 20.7%.
• For Fields 2 and 3, DU increased by 11.67% and 28.57% respectively, and Ea improved from 42.12% to 51.74% (Field 2) and 28.88% to 53.95% (Field 3).
• Inflow discharge (Qu) was identified as having the most significant impact on improving border design, followed by border length (L) and slope (s).
• Shortening the advance phase was determined to be the most effective strategy for enhancing irrigation performance.
• Optimal values for L and Qu consistently showed a decreasing trend, often approaching their lower bounds, while optimal s values tended to increase, approaching their upper bounds.
• The objective function value, representing overall poor performance, decreased significantly after optimization across all three fields (e.g., from 1.28 to 0.91 for Field 1).

Contributions

• Development of a novel simulation–optimization model that enhances the design of open-end border irrigation systems.
• Introduction of a modified hydro-empirical SCS method capable of fully simulating all four phases of border irrigation and determining subsurface infiltration profiles, offering a computationally efficient alternative to complex numerical models.
• Successful integration of the modified SCS model with the meta-heuristic Grey Wolf Optimizer algorithm for multi-objective optimization of border irrigation design.
• Comprehensive evaluation of five key hydraulic performance indicators (application efficiency, deep percolation ratio, tail water ratio, requirement efficiency, and distribution uniformity) within a single-objective optimization framework.
• Identification of the critical influence of decision variables (inflow discharge, border length, and slope) on system performance and pinpointing shortening the advance phase as the most effective strategy for improvement.
• Provides a robust and computationally efficient tool for irrigation system designers to optimize resource efficiency and operational resilience without requiring extensive computational resources.

Funding

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Citation

@article{Akbari2025Enhancement,
  author = {Akbari, Mahmood and Farahani, Saeed},
  title = {Enhancement of border irrigation systems: Leveraging simulation–optimization techniques},
  journal = {Agricultural Water Management},
  year = {2025},
  doi = {10.1016/j.agwat.2025.109891},
  url = {https://doi.org/10.1016/j.agwat.2025.109891}
}