Sun et al. (2026) Construction and application of a real-time irrigation decision system based on the multiple sources data assimilation and weather forecasts

Identification

• Journal: Agricultural Water Management
• Year: 2026
• Date: 2026-06-29
• Authors: Boyan Sun, zheng guo, Taiyu He, Yimin Ding, Lei Zhu, Yuanyuan Zha
• DOI: 10.1016/j.agwat.2026.110578

Research Groups

• School of Civil and Hydraulic Engineering, Ningxia University, Yinchuan, Ningxia 750021, China
• Key Laboratory of the Internet of Water and Digital Water Governance of the Yellow River, Ningxia University, Yinchuan 750021, China
• State Key Laboratory of Water Resources Engineering and Management, Wuhan University, Wuhan, Hubei 430072, China

Short Summary

This study proposes a real-time irrigation decision system that couples the SWAP crop simulation model with iterative ensemble smoother (IES) data assimilation to improve irrigation water productivity. The system uses short-term weather forecasts to predict future crop water stress and generates irrigation decisions through two algorithms: SWAP-IES-Iteration (SII) and SWAP-IES-Optimization (SIO).

Objective

• Investigate the effectiveness of real-time data assimilation in improving irrigation decision-making for silage maize crops in arid regions.

Study Configuration

• Spatial Scale: Field-scale, with a focus on a specific experimental site in Ningxia, China.
• Temporal Scale: Real-time, with a 72-hour forecast window and daily irrigation decisions.

Methodology and Data

• Models used: SWAP crop simulation model, iterative ensemble smoother (IES) data assimilation algorithm.
• Data sources: Field observations of soil moisture and leaf area index, three-day weather forecasts from the China Weather Network.

Main Results

• The proposed system improved irrigation water productivity by 21.16-33.88 kg/m³ compared to traditional farming practices.
• Data assimilation reduced the root mean square error (RMSE) of soil moisture predictions to 0.019-0.032 cm³/cm³ and increased the Nash-Sutcliffe efficiency (NSE) to 0.53.

Contributions

• This study demonstrates the potential of real-time data assimilation in improving irrigation decision-making for crops in arid regions.
• The proposed system can be applied to other water-scarce regions with public meteorological data and field observations available.

Funding

• Not specified in the provided text.

Citation

@article{Sun2026Construction,
  author = {Sun, Boyan and guo, zheng and He, Taiyu and Ding, Yimin and Zhu, Lei and Zha, Yuanyuan},
  title = {Construction and application of a real-time irrigation decision system based on the multiple sources data assimilation and weather forecasts},
  journal = {Agricultural Water Management},
  year = {2026},
  doi = {10.1016/j.agwat.2026.110578},
  url = {https://doi.org/10.1016/j.agwat.2026.110578}
}