Spatio-temporal calibration of Hargreaves-Samani model to estimate reference evapotranspiration across U.S. High Plains

Temperature-based grass-reference evapotranspiration (ET_o) estimation methods (e.g., Hargreaves−Samani [HS] model) present advantages over combination-based methods that require full-suite weather data. The U.S. High Plains region has scarce and short-term full-suite weather sites. This data scarcity presents challenges for combination-based ET_o estimation. The performance of HS model against the American Society of Civil Engineers (ASCE) standardized Penman−Monteith (PM) model was assessed using long-term data at 124 full-suite weather sites across nine states in the U.S. High Plains. The HS model underestimated ET_o at arid (mean bias error [MBE] = −1.68 mm d⁻¹), semi-arid (MBE = −0.34 mm d⁻¹), and dry subhumid sites (MBE = −0.16 mm d⁻¹) and overestimated ET_o at humid sites (MBE = 0.14 mm d⁻¹). There was a significant relationship (p <.01) between HS model performance and aridity index. The HS model performed better (27% lower root mean squared difference [RMSD]) in summer months than the rest of the year at semi-arid and dry subhumid sites. The model performance was non-ideal during the summer months in subhumid climates. Spatio-temporal annual zonal (climate division), monthly zonal, annual site-specific, and monthly site-specific calibration resulted in 12, 16, 20, and 26% reduction in RMSD and 11, 16, 17, and 23% reduction in relative error, respectively. Monthly site-specific calibration performed the best and was used to quantify annual and growing season ET_o across the region. The research characterized performance patterns of the HS model over an important agroecosystem-dominated region. Practical data-driven strategies were proposed to better estimate PM ET_o using limited weather data at any given site (with similar aridity) and time of the year.