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An Automated Data-Driven Irrigation Scheduling Approach Using Model Simulated Soil Moisture and Evapotranspiration

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  • Haoteng Zhao

    (Center for Spatial Information Science and Systems, George Mason University, Fairfax, VA 22030, USA
    Department of Geography and Geoinformation Science, George Mason University, Fairfax, VA 22030, USA)

  • Liping Di

    (Center for Spatial Information Science and Systems, George Mason University, Fairfax, VA 22030, USA
    Department of Geography and Geoinformation Science, George Mason University, Fairfax, VA 22030, USA)

  • Liying Guo

    (Center for Spatial Information Science and Systems, George Mason University, Fairfax, VA 22030, USA)

  • Chen Zhang

    (Center for Spatial Information Science and Systems, George Mason University, Fairfax, VA 22030, USA)

  • Li Lin

    (Center for Spatial Information Science and Systems, George Mason University, Fairfax, VA 22030, USA)

Abstract

Given the increasing prevalence of droughts, unpredictable rainfall patterns, and limited access to dependable water sources in the United States and worldwide, it has become crucial to implement effective irrigation scheduling strategies. Irrigation is triggered when some variables, such as soil moisture or accumulated water deficit, exceed a given threshold in the most common approaches applied in irrigation scheduling. A High-Resolution Land Data Assimilation System (HRLDAS) was used in this study to generate timely and accurate soil moisture and evapotranspiration (ET) data for irrigation management. By integrating HRLDAS products and the crop growth model (AquaCrop), an automated data-driven irrigation scheduling approach was developed and evaluated. For HRLDAS ET and soil moisture, the ET-water balance (ET-WB)-based method and soil-moisture-based method were applied accordingly. The ET-WB-based method showed a 10.6~33.5% water-saving result in dry and set seasons, whereas the soil moisture-based method saved 7.2~37.4% of irrigation water in different weather conditions. Both of these methods demonstrated good results in saving water (with a varying range of 10~40%) without harming crop yield. The optimized thresholds in the two approaches were partially consistent with the default values from the Food and Agriculture Organization and showed a similar trend in the growing season. Furthermore, the forecasted rainfall was integrated into this model to see its water-saving effect. The results showed that an additional 10% of irrigation water, which is 20~50%, can be saved without harming the crop yield. This study automated the data-driven approach for irrigation scheduling by taking advantage of HRLDAS products, which can be generated in a near-real-time manner. The results indicated the great potential of this automated approach for saving water and irrigation decision making.

Suggested Citation

  • Haoteng Zhao & Liping Di & Liying Guo & Chen Zhang & Li Lin, 2023. "An Automated Data-Driven Irrigation Scheduling Approach Using Model Simulated Soil Moisture and Evapotranspiration," Sustainability, MDPI, vol. 15(17), pages 1-17, August.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:17:p:12908-:d:1225836
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    References listed on IDEAS

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    1. Marjan Aziz & Sultan Ahmad Rizvi & Muhammad Sultan & Muhammad Sultan Ali Bazmi & Redmond R. Shamshiri & Sobhy M. Ibrahim & Muhammad A. Imran, 2022. "Simulating Cotton Growth and Productivity Using AquaCrop Model under Deficit Irrigation in a Semi-Arid Climate," Agriculture, MDPI, vol. 12(2), pages 1-18, February.
    2. Anzhen Qin & Dongfeng Ning & Zhandong Liu & Sen Li & Ben Zhao & Aiwang Duan, 2021. "Determining Threshold Values for a Crop Water Stress Index-Based Center Pivot Irrigation with Optimum Grain Yield," Agriculture, MDPI, vol. 11(10), pages 1-16, October.
    3. Sandhu, Rupinder & Irmak, Suat, 2019. "Performance of AquaCrop model in simulating maize growth, yield, and evapotranspiration under rainfed, limited and full irrigation," Agricultural Water Management, Elsevier, vol. 223(C), pages 1-1.
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