Investigation of the accuracy of linear and nonlinear time series models in modeling and forecasting of pan evaporation in IRAN

Abstract

Water evaporation process is one of the main components of the hydrological cycle, according to which the correct estimation of this phenomenon plays an important role in irrigation management and river flow forecasting. Statistical and random models in the form of time series analysis are commonly used methods of estimation and forecasting. In this study, ARMA (autoregressive moving average), ARIMA (autoregressive integrated moving average), PARMA (periodic autoregressive moving average), and BL (bilinear) time series models (TSMs) are used to predict the annual and monthly pan evaporation values from the evaporation stations in several provinces of Iran in a 20-year statistical period. Results showed that PARMA model’s accuracy in term of correlation coefficient and Nash–Sutcliffe efficiency for almost all 31 stations had a precise forecasting among other proposed TSMs. For PARMA model, the forecasting accuracy in term of NSE indicated that PARMA model almost was the promising model among other linear and nonlinear TSMs in prediction of Epan for all stations, except Arak (NSE = 0.94) and Qom (NSE = 0.93). The ARIMA model for Khorramabad and Bandar Abbas with NSE = 0.52 had the unreliable prediction for Epan compared with other stations. In addition, Arak station in term of RMSE had the least error, 23.79 mm/day and 10.90 mm/day, respectively, for training and testing stages among the other stations.