Simulation of temporal variation for reference evapotranspiration under arid climate

Abstract

Reliable forecasting of evapotranspiration (ET) plays a critical role in the planning and management of water resources. Accordingly, this study aims to investigate the possibility of using autoregressive integrated moving average (ARIMA) models to anticipate monthly reference evapotranspiration (ETo). Thus, a monthly ETo time series of 34 years (1980–2014) is determined according to the FAO Penman-Monteith method. This time series is divided into two sets, which are used for developing and validating the ARIMA models. Subsequently, five tentative ARIMA models are created via the 19-year set (1980–1999). In order to reveal the best ARIMA structure among the developed models, the Akaike information criterion (AIC) and the Hannan-Quinn information criterion (HQC) are computed for comparison. The result of the comparison suggests that the ARIMA (1,0,1) × (0,1,1)12 model is strong enough to justify the goodness-of-fit requirements. Validation of the candidate ARIMA model is then conducted for the 15-year set (2000–2014). The validation result contends that there is a reasonable agreement between forecasted and observed time series with high coefficient of correlation (r = 0.966). Promisingly, it can be concluded that the candidate ARIMA model is capable of anticipating the monthly ETo under arid climate.