Assessment of evapotranspiration estimation models for use in semi-arid environments

Abstract Limited information exists on reliable estimates of evapotranspiration (ETo), to be used as forecasts, to achieve high irrigation water use efficiency in semi-arid environment, particularly under microirrigation (MI) system. The objectives of this study were to: (i) assess the estimates of ETo obtained using six models against experimentally determined values in a semi-arid environment, Karaj in Iran; (ii) assess the usefulness of short-term weather data in the computation of ETo estimates for forecasting purposes; and (iii) compare ETo computed for a semi-arid environment with that of a humid temperate environment, Tottori in Japan. In-field lysimeter experiments were conducted in 1993 and 1994 in Karaj to compute daily ETo from water-balance data and a similar experiment was conducted in 1972 and 1973 in Tottori. The ETo estimates were obtained using the Penman (PE), Penman–Monteith (PM), Wright–Penman (WP), Blaney–Criddle (BC), Radiation balance (RB), and Hargreaves (HG) models. The ETo forecasts for Karaj and Tottori were obtained using 5- or 8-year weather data and 1- and 2-year return period approach. Compared with the lysimeter values, over- and underestimations of ETo, by all six models, were a norm in Karaj. Nevertheless, the PM model produced the best ETo estimates as assessed by root mean square, mean bias error, and t-test statistics for the semi-arid Karaj, whereas the PE model performed best for the temperate Tottori. In general, the ETo forecasts obtained using 8-year weather data were better than 5-year weather data. The 2-year return period ETo forecasts were better than 1-year return period, regardless of the duration of weather data duration. The results show that: (i) PM model produced best ETo estimates for semi-arid environment whereas the PE model produced the best ETo estimates for humid temperate environment; and (ii) 8-year weather data and 2-year return period approach produced most reliable ETo estimates for forecasting purposes.