Multisite evaluation of an improved SWAT irrigation scheduling alogrithm for corn (Zea mays L.) production in the U.S. Southern Great Plains

Authors: CHEN, YONG - Texas A&M University; Marek, Gary; MAREK, THOMAS - Texas A&M Agrilife; Gowda, Prasanna; XUE, QINGWU - Texas A&M Agrilife; Moorhead, Jerry; Brauer, David; SRINIVASAN, R - Texas A&M University; HEFLIN, KEVIN - Texas A&M Agrilife

Submitted to: Journal of Environmental Modeling and Software
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 4/5/2019
Publication Date: 4/15/2019
Citation: Chen, Y., Marek, G.W., Marek, T.H., Gowda, P.H., Xue, Q., Moorhead, J.E., Brauer, D.K., Srinivasan, R., Heflin, K.R. 2019. Multisite evaluation of an improved SWAT irrigation scheduling algorithm for corn (Zea mays L.) production in the U.S. Southern Great Plains. Journal of Environmental Modeling and Software. 118:23-24. https://doi.org/10.1016/j.envsoft.2019.04.001.
DOI: https://doi.org/10.1016/j.envsoft.2019.04.001

Interpretive Summary: Water scarcity due to drought and groundwater depletion has led to an increased emphasis on irrigation strategies for extending limited water resources. The Soil and Water Assessment Tool (SWAT), a widely used hydrologic model, is increasingly being used to evaluate the impacts of irrigation strategies. However, deficiencies in default auto-irrigate functions in SWAT resulted in the development and localized testing of an alternative algorithm based on the management allowed depletion (MAD) of soil water. Therefore, scientists from ARS (Bushland, TX) and Texas A&M AgriLife further evaluated the MAD algorithm by comparing simulated irrigation, crop water use (ET), plant growth, and yield to measured values for corn grown at multiple research sites across the Southern Great Plains. Results indicated that the alternative MAD function outperformed the default auto-irrigation algorithms in SWAT.

Technical Abstract: Modeling alternative irrigation strategies can be a cost-effective and time-saving approach to field-based experiments. However, the efficacy of irrigation scheduling algorithms should be verified using field data from multiple locations. In this study, an auto-irrigation algorithm recently developed for Soil and Water Assessment Tool (SWAT) was further evaluated using irrigation data for maize (Zea mays L.) grown at six research sites across the Southern Great Plains. Simulated irrigation, based on the management allowed depletion (MAD) of plant available soil water, was compared to measured data for irrigation applied in accordance with crop water requirement guidelines outlined by the Food and Agriculture Organization Irrigation and Drainage Paper 56. Overall, results indicated the MAD algorithm simulated field-based irrigation events well (Nash-Sutcliffe efficiency; NSE > 0.56). Comparisons revealed the MAD algorithm outperformed both the plant water demand and soil water content approaches in SWAT, which tended to underestimate and overestimate actual irrigations, respectively.