Akbari et al. (2025) Redesigning the SCS method structure within a simulation–optimization framework to improve performance indicators of basin irrigation

Identification

• Journal: Applied Water Science
• Year: 2025
• Date: 2025-12-27
• Authors: Mahmood Akbari, Saeed Farahani
• DOI: 10.1007/s13201-025-02690-0

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 developed a simulation-optimization model by modifying the Soil Conservation Service (SCS) method and integrating it with the Grey Wolf Optimizer (GWO) algorithm to improve hydraulic performance indicators of basin irrigation. The model successfully optimized basin length and inflow discharge, leading to significant improvements in application efficiency and distribution uniformity while substantially reducing deep percolation and total water consumption.

Objective

• To develop and evaluate a modified Soil Conservation Service (SCS) method integrated into a simulation-optimization framework to enhance hydraulic performance indicators of basin irrigation systems.
• To determine if the SCS method can be modified for comprehensive basin irrigation simulation with reduced computational cost compared to hydrodynamic methods.
• To assess the effect of integrating optimization with the simulation model on improving the hydraulic condition of basin irrigation.

Study Configuration

• Spatial Scale: Single basin irrigation system in an experimental field.
• Temporal Scale: Single irrigation event; inflow discharge assumed constant and steady.

Methodology and Data

• Models used:
  • Simulation: Modified hydro-empirical Soil Conservation Service (SCS) method.
  • Optimization: Grey Wolf Optimizer (GWO) meta-heuristic algorithm.
  • Numerical methods: Newton–Raphson and Fixed-Point methods for solving implicit equations.
  • Auxiliary: Volume Balance method for cut-off time calculations.
• Data sources: Field data from a case study (Cuenca, 1989) for initial simulation and subsequent optimization.

Main Results

• Initial Design Performance:
  • Application Efficiency (Ea): 61%.
  • Distribution Uniformity (DU): 84%.
  • Deep Percolation Ratio (DPR): 39%.
  • Objective function value (z): 0.93.
  • Basin length (L): 360 m.
  • Inflow discharge per unit width (Qu): 0.005 m²/s.
  • Advance time (Tt): 11700 s (195 min).
  • Total water volume applied (Vt): 58 m³/m.
  • Deep percolation volume (Vdpr): 22 m³/m.
• Optimized Design Performance:
  • Application Efficiency (Ea): Improved by 27% to 88%.
  • Distribution Uniformity (DU): Increased by 13% to 97%.
  • Deep Percolation Ratio (DPR): Decreased by 27% to 12%.
  • Objective function value (z): Decreased to 0.22.
  • Optimal basin length (L): 50 m (reduced from 360 m).
  • Optimal inflow discharge per unit width (Qu): 0.01 m²/s (increased from 0.005 m²/s).
  • Optimal advance time (Tt): 546 s (9.1 min).
  • Total water volume applied (Vt): Reduced to 5.4 m³/m.
  • Deep percolation volume (Vdpr): Reduced to 0.4 m³/m.
• Key Strategy: The primary effective strategy was reducing basin length and increasing discharge to minimize advance time, which significantly reduced deep percolation at the upstream end.
• Sensitivity Analysis: Variations in basin length had a greater impact on achieving the optimal state than changes in inflow discharge.
• Computational Efficiency: The simulation was completed within a fraction of a second, and the simulation-optimization process took only a few seconds, demonstrating significant computational cost reduction compared to Full Hydrodynamic models.

Contributions

• Developed a significantly enhanced SCS-based simulation model for basin irrigation that overcomes the limitations of the original SCS method by providing comprehensive calculations for critical irrigation times (advance, cut-off, depletion, recession), infiltrated water depths at various points, and detailed volumetric components (total applied, infiltrated, net infiltrated, deep percolated, tail water volumes).
• Integrated the modified SCS simulation model with the Grey Wolf Optimizer (GWO) meta-heuristic algorithm to create a robust simulation-optimization framework for automatically determining optimal design parameters (basin length, inflow discharge, net irrigation depth).
• Demonstrated substantial improvements in key hydraulic performance indicators (Application Efficiency, Distribution Uniformity, Requirement Efficiency) and significant reductions in water consumption and deep percolation losses in basin irrigation systems through optimization.
• Provided a computationally efficient alternative to complex hydrodynamic models for surface irrigation design and optimization, making advanced design tools more accessible.
• Highlighted the critical role of basin length as a decision variable in optimizing basin irrigation performance, showing its greater impact compared to inflow discharge under the studied conditions.

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

Citation

@article{Akbari2025Redesigning,
  author = {Akbari, Mahmood and Farahani, Saeed},
  title = {Redesigning the SCS method structure within a simulation–optimization framework to improve performance indicators of basin irrigation},
  journal = {Applied Water Science},
  year = {2025},
  doi = {10.1007/s13201-025-02690-0},
  url = {https://doi.org/10.1007/s13201-025-02690-0}
}