The variation in climatic conditions of the regions complicates the process of estimating evapotranspiration using one equation or one way because it needs so much data. The adoption of special method for each region based on the lowest climatic parameters and the historical record can be more useful. Five evapotranspiration (ET_0) models had been analyzed statistically by comparing Penman-FAO-24 (PF) model with: Penman Monteith -FAO-56 model (PM), Penman-Kimberly model (PK), Jensen-Haise model (JH) and Hargreaves model (H). The performances of the simpler models were evaluated using bias, root mean square error and Pearson Correlation Coefficient. Also, Regression analysis for predicting (ET_0) from minimum climatic data (Hargreaves model) has been developed. The results indicated that the models which depends on more climatic data are close from each other and that is very clear in (PF), (PM) and (PK). The differences between models are due to wind function used in each model. The developed linear regression model from minimum climatic data (H) model with slope of 1.254, an interception point of -1.801 and coefficients of determination R^2 of 0.988 matched very closely to (PF) model values.