Chen et al. (2026) Grid-based optimization for irrigation and water quality synergies

Identification

• Journal: Journal of Hydrology
• Year: 2026
• Date: 2026-09-21
• Authors: Yingshan Chen, Qiang Fu, Vijay P. Singh, Yijia Wang, Zhaoqiang Zhou, Yaowen Xu, Luchen Wang, Mo Li
• DOI: 10.1016/j.jhydrol.2026.136460

Research Groups

• School of College of Hydraulic Science and Engineering, Northeast Agricultural University, Harbin, China
• National Key Laboratory of Smart Farm Technologies and Systems, Harbin, China
• International Cooperation Joint Laboratory of Health in Cold Region Black Soil Habitat of the Ministry of Education, Harbin, China
• Research Center for Smart Water Network, Northeast Agricultural University, Harbin, China
• Department of Biological and Agricultural Engineering, Texas A & M University, College Station, USA
• Zachry Department of Civil & Environmental Engineering, Texas A & M University, College Station, USA
• National Water Center, UAE University, Al Ain, United Arab Emirates

Short Summary

This study developed a grid-scale simulation-optimization framework integrating SWAT, DSSAT, and a cellular automata-based irrigation model to assess water-saving and pollution-reduction synergies in the Sanjiang and Songnen Plains under current and future climates, finding that optimized irrigation significantly reduces both pollutant emissions and water use.

Objective

• To develop and apply a grid-scale simulation-optimization framework to assess water-saving and pollution-reduction synergies in agricultural irrigation management under current and future climate scenarios, aiming to alleviate regional water quantity and quality pressures.

Study Configuration

• Spatial Scale: Grid-scale analysis across the Sanjiang and Songnen Plains, China.
• Temporal Scale: Assessment under current and future climate scenarios.

Methodology and Data

• Models used: SWAT (Soil and Water Assessment Tool), DSSAT (Decision Support System for Agrotechnology Transfer), and a cellular automata-based multi-source irrigation model.
• Data sources: Climate data (current and future projections), hydrological data, agricultural data (crop growth, water-use efficiency), and non-point source pollution data.

Main Results

• Optimal irrigation strategies exhibit significant regional variation due to spatial differences in pollutant emissions and water-use efficiency.
• Overall, optimized water-saving irrigation reduces pollutant emissions by approximately 12.96% and irrigation water use by 24.57%.
• In western regions, a 49.25% expansion of water-saving irrigation improves the coordination among economic, environmental, and water-resource objectives by 33.07%.
• Under future climate scenarios, precipitation strongly influences pollutant transport, and pollutant migration is inversely related to water-saving irrigation potential.

Contributions

• Developed a novel grid-scale simulation-optimization framework that integrates hydrological, crop growth, and irrigation models to address climate-driven pollutant transport and spatially heterogeneous irrigation needs, which existing approaches rarely capture.
• Provided a spatially explicit basis for climate-resilient irrigation planning and coordinated agricultural water and pollution management.
• Quantified the synergistic benefits of water-saving irrigation in reducing both water consumption and non-point source pollution under current and future climate conditions.

Funding

• Not specified in the provided text.

Citation

@article{Chen2026Gridbased,
  author = {Chen, Yingshan and Fu, Qiang and Singh, Vijay P. and Wang, Yijia and Zhou, Zhaoqiang and Xu, Yaowen and Wang, Luchen and Li, Mo},
  title = {Grid-based optimization for irrigation and water quality synergies},
  journal = {Journal of Hydrology},
  year = {2026},
  doi = {10.1016/j.jhydrol.2026.136460},
  url = {https://doi.org/10.1016/j.jhydrol.2026.136460}
}