Assessing the Efficacy of the SWAT Auto-Irrigation Function to Simulate Irrigation, Evapotranspiration, and Crop Response to Management Strategies of the Texas High Plains Academic Article uri icon

abstract

  • © 2017 by the authors. In the semi-arid Texas High Plains, the underlying Ogallala Aquifer is experiencing continuing decline due to long-term pumping for irrigation with limited recharge. Accurate simulation of irrigation and other associated water balance components are critical for meaningful evaluation of the effects of irrigation management strategies. Modelers often employ auto-irrigation functions within models such as the Soil and Water Assessment Tool (SWAT). However, some studies have raised concerns as to whether the function is able to adequately simulate representative irrigation practices. In this study, observations of climate, irrigation, evapotranspiration (ET), leaf area index (LAI), and crop yield derived from an irrigated lysimeter field at the USDA-ARS Conservation and Production Research Laboratory at Bushland, Texas were used to evaluate the efficacy of the SWAT auto-irrigation functions. Results indicated good agreement between simulated and observed daily ET during both model calibration (2001-2005) and validation (2006-2010) periods for the baseline scenario (Nash-Sutcliffe efficiency; NSE ≥ 0.80). The auto-irrigation scenarios resulted in reasonable ET simulations under all the thresholds of soil water deficit (SWD) triggers as indicated by NSE values > 0.5. However, the auto-irrigation function did not adequately represent field practices, due to the continuation of irrigation after crop maturity and excessive irrigation when SWD triggers were less than the static irrigation amount.

published proceedings

  • WATER

altmetric score

  • 1

author list (cited authors)

  • Chen, Y., Marek, G. W., Marek, T. H., Brauer, D. K., & Srinivasan, R.

citation count

  • 34

complete list of authors

  • Chen, Yong||Marek, Gary W||Marek, Thomas H||Brauer, David K||Srinivasan, Raghavan

publication date

  • July 2017

publisher