EconPapers    
Economics at your fingertips  
 

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

Haoteng Zhao, Liping Di (), Liying Guo, Chen Zhang and Li Lin
Additional contact information
Haoteng Zhao: Center for Spatial Information Science and Systems, George Mason University, Fairfax, VA 22030, USA
Liping Di: Center for Spatial Information Science and Systems, 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

Sustainability, 2023, vol. 15, issue 17, 1-17

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.

Keywords: irrigation management; HRLDAS; water conservation; threshold optimization; yield estimation (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2071-1050/15/17/12908/pdf (application/pdf)
https://www.mdpi.com/2071-1050/15/17/12908/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:15:y:2023:i:17:p:12908-:d:1225836

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().

 
Page updated 2025-03-19
Handle: RePEc:gam:jsusta:v:15:y:2023:i:17:p:12908-:d:1225836