Shi et al. (2025) A sub-seasonal to seasonal climate forecast informed irrigation scheduling tool for the Contiguous United States

Identification

• Journal: Environmental Modelling & Software
• Year: 2025
• Date: 2025-12-05
• Authors: Haiyang Shi, Ximing Cai, Xuefei Hu, Alaa Jamal, Donghui Li, Chao Sun, Xin‐Zhong Liang
• DOI: 10.1016/j.envsoft.2025.106819

Research Groups

• Department of Civil and Environmental Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, USA
• Earth System Science Interdisciplinary Center, University of Maryland, College Park, MD, USA
• Department of Atmospheric and Oceanic Science, University of Maryland, College Park, MD, USA

Short Summary

This study develops a real-time irrigation scheduling tool for cornfields across the Contiguous United States (CONUS) by integrating sub-seasonal to seasonal (S2S) climate forecasts with the Soil Water Atmosphere Plant (SWAP) model. The S2S-informed scheduling improves water use efficiency and net profit compared to default SWAP schedules, offering national applicability.

Objective

• To develop a real-time irrigation scheduling tool for cornfields across the Contiguous United States (CONUS) that integrates sub-seasonal to seasonal (S2S) climate forecasts with the Soil Water Atmosphere Plant (SWAP) model to optimize irrigation scheduling, balancing water cost and crop yield.

Study Configuration

• Spatial Scale: Contiguous United States (CONUS)
• Temporal Scale: Sub-seasonal to seasonal (S2S) climate forecasts, enabling optimization at any day in the season.

Methodology and Data

• Models used: Soil Water Atmosphere Plant (SWAP) model
• Data sources: Sub-seasonal to seasonal (S2S) climate forecasts, various up-to-date CONUS-scale datasets

Main Results

• The S2S-informed irrigation scheduling tool improves water use efficiency and net profit for cornfields compared to default SWAP schedules.

Contributions

• Development of a real-time, nationally applicable irrigation scheduling tool for cornfields across the CONUS, overcoming limitations of site-specific models and reliance on short-term forecasts.
• Integration of sub-seasonal to seasonal (S2S) climate forecasts for long-term irrigation planning and optimization.
• Reduction of dependence on in-situ observations through the use of CONUS-scale datasets, enhancing applicability to diverse field conditions.

Funding

• Not specified in the provided text.

Citation

@article{Shi2025subseasonal,
  author = {Shi, Haiyang and Cai, Ximing and Hu, Xuefei and Jamal, Alaa and Li, Donghui and Sun, Chao and Liang, Xin‐Zhong},
  title = {A sub-seasonal to seasonal climate forecast informed irrigation scheduling tool for the Contiguous United States},
  journal = {Environmental Modelling & Software},
  year = {2025},
  doi = {10.1016/j.envsoft.2025.106819},
  url = {https://doi.org/10.1016/j.envsoft.2025.106819}
}