Liu et al. (2025) Innovative synergistic optimization of drip irrigation and subsurface drainage for alleviating salinization and improving crop productivity in arid irrigation district

Identification

• Journal: Agricultural Water Management
• Year: 2025
• Date: 2025-12-19
• Authors: Yi Liu, Chang Ao, Wenzhi Zeng, Zhen Li, Donglin Jiang, Javlonbek Ishchanov
• DOI: 10.1016/j.agwat.2025.110089

Research Groups

• College of Agricultural Science and Engineering, Hohai University, Nanjing, China
• College of Hydraulic Science and Engineering, Yangzhou University, Yangzhou, China
• State Key Laboratory of Water Resources Engineering and Management, Wuhan University, Wuhan, China
• Tashkent Institute of Irrigation and Agricultural Mechanization Engineers, National Research University, Tashkent, Uzbekistan

Short Summary

This study developed a synergistic optimization framework using the SWAT-Salt model and a projection pursuit model with an accelerated genetic algorithm to optimize drip irrigation and subsurface drainage in China's Kaidu River Irrigation District, demonstrating significant improvements in crop yields, water productivity, and soil desalinization.

Objective

• To identify optimal drip irrigation intervals for wheat, maize, tomato, and pepper during their growth stages in the Kaidu River Irrigation District (KRID).
• To assess the ecological and economic outcomes of various drip irrigation and drainage combinations (including winter leaching and drainage design parameters) using an accelerated genetic algorithm and projection pursuit model, recommending optimal coordination strategies under varying groundwater and salinity conditions.
• To implement and validate optimized drip irrigation strategies in KRID, thoroughly evaluating their performance in water conservation, salt mitigation, and yield improvement.

Study Configuration

• Spatial Scale: Kaidu River Irrigation District, China (41°45′–42°26′N, 85°55′–88°10′E), covering approximately 1.4 million hectares, divided into 33 sub-basins and 337 hydrological response units (HRUs).
• Temporal Scale: Simulation period from 2005 to 2020, with 2005–2009 for warm-up, 2010–2015 for calibration, and 2016–2020 for validation.

Methodology and Data

• Models used: SWAT-Salt model, Projection pursuit model coupled with an accelerated genetic algorithm (RAGA).
• Data sources:
  • 30-meter resolution digital elevation model (DEM).
  • Land use maps and soil maps.
  • Meteorological records (temperature, precipitation, wind speed) from Yanqi and Korla stations, supplemented by the Climate Forecast System Reanalysis (CFSR) dataset for solar radiation and missing data.
  • Annual average per-hectare crop yield data (wheat, maize, tomato, pepper) from local agricultural statistical yearbooks and field surveys (2005-2020).
  • Monthly streamflow and salt concentration data for eight major ions (SO₄²⁻, Ca²⁺, Mg²⁺, Na⁺, K⁺, Cl⁻, CO₃²⁻, HCO₃⁻) from Yanqi Hydrological Station (2005-2020).
  • Daily groundwater depth data from 24 monitoring wells (2018-2020), with three selected for calibration/validation.
  • Initial groundwater salt ion levels from 2005 monitoring wells, spatially interpolated using Kriging.
  • Soil layer characteristics (CaCO₃ and CaSO₄ levels) for the 1-meter deep soil layer from the World Soil Database.

Main Results

• Optimal drip irrigation intervals showed significant spatial variability: 9 days for wheat, 7 days for maize, and 7–10 days for tomato and pepper in southern regions with higher percolation rates.
• Optimized drip irrigation, synergized with tailored drainage, enhanced crop yields by 8.7 %-10.1 % for wheat, 12.6 %-16.8 % for maize, 6.7 %-11.6 % for tomato, and 11.4 %-14.5 % for pepper.
• The optimized system reduced deep percolation losses, improving water productivity by 4.0 %-11.7 %.
• Combined with drainage, soil electrical conductivity decreased by 13.3 %-19.3 %.
• Economic benefits increased by 1530–6450 yuan ha⁻¹.
• Key factors influencing optimal strategies were subsurface drainage volume, subsurface salt elimination, crop output, and soil electrical conductivity.
• Regional implementation of optimal plans reduced irrigation and leaching water consumption by 10 % (approximately 149 million cubic meters annually) and mean soil salt levels in farmlands by 15 %.

Contributions

• Developed and validated a synergistic framework for optimizing drip irrigation and subsurface drainage at a regional scale in arid saline environments.
• Applied an integrated modeling approach combining the SWAT-Salt model with a projection pursuit model and an accelerated genetic algorithm for multi-objective optimization of irrigation-drainage scenarios.
• Provided actionable, spatially-variable strategies for optimizing drip irrigation intervals, winter leaching depths, and subsurface drainage parameters tailored to specific crop types, soil textures, groundwater depths, and salinity conditions.
• Demonstrated significant ecological (water conservation, salt mitigation) and economic (increased crop yields and profits) benefits of the optimized system at a regional scale, bridging the gap between field-scale studies and regional application.

Funding

• National Natural Science Foundation of China (NSFC) (52409057, 52479046)
• Natural Science Foundation of Jiangsu Province (Grants No. BK20241530)

Citation

@article{Liu2025Innovative,
  author = {Liu, Yi and Ao, Chang and Zeng, Wenzhi and Li, Zhen and Jiang, Donglin and Ishchanov, Javlonbek},
  title = {Innovative synergistic optimization of drip irrigation and subsurface drainage for alleviating salinization and improving crop productivity in arid irrigation district},
  journal = {Agricultural Water Management},
  year = {2025},
  doi = {10.1016/j.agwat.2025.110089},
  url = {https://doi.org/10.1016/j.agwat.2025.110089}
}