Predicting Potential Evapotranspiration Using Time Series Models(Case Study: Urmia)

Potential evapotranspiration is one of the most important and effective factors for optimizing agricultural water consumption and water resources management. One of methods for prediction of evapotranspiration is to use the time series models. In this research، application of different time series models، such as AR and ARMA، in order to predicting monthly potential evapotranspiration in Urmia synoptic station were evaluated. In this process، monthly potential evapotranspiration since 1971 to 2010 was determined and the first 35 years and last 5 years were used for model calibration and validation respectively. After selecting the best model، the potential evapotranspiration were predicted for the next 5 years. The results showed that AR (11) time series model had the best results in comparing the other models and the trend of AR (11) time series model had least error. The values of R2 and RMSE in AR (11) model were 0. 96 and 1. 85 mm/month، respectively.