Multisite evaluation of an improved SWAT irrigation scheduling algorithm for corn (Zea mays L.) production in the U.S. Southern Great Plains. (August 2019) - Chen, Y.; Marek, G.W.; Marek, T.H.; Gowda, P.H.; Xue, Q.; Moorhead, J.E.; Brauer, D.K.; Srinivasan, R.; Heflin, K.R.
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 corn ( Zea mays L.) grown at six research sites across the Southern Great Plains. Simulated monthly 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 monthly field-based irrigation amounts well (Nash-Sutcliffe efficiency; NSE  > 0.56). Comparisons revealed the MAD algorithm outperformed the plant water demand and soil water content approaches in SWAT, which tended to underestimate and overestimate irrigations, respectively. Highlights: FAO-56 based corn irrigation scheduling data were accessed from six study sites. Newly developed MAD algorithm simulated irrigation well compared to FAO-56 method. SWAT existing soil water content algorithm tended to overestimate actual irrigation. SWAT existing plant water demand algorithm underestimated actual irrigation. SWAT MAD algorithm should provide benefit for scheduling of field-based irrigation.