Derivation of regression models for pan evaporation estimation

Evaporation is an essential component of hydrological cycle. Several meteorological factors play role in the amount of pan evaporation. These factors are often related to each other. In this study, a multiple linear regression (MLR) in conjunction with Principal Component Analysis (PCA) was used for modeling of pan evaporation. After the standardization of the variables, independent components were obtained using the (PCA). The series of principal component scores were used as input in multiple linear regression models. This method was applied to four stations in East Azerbaijan Province in the North West of Iran. Mathematical models of pan evaporation were derived for each station. The results showed that the first three components in all four stations account for more than 90% of the data variance. Performance criteria, namely coefficient of determination (R2) and root mean square error (RMSE), were calculated for models in each station. The results showed that in all the PCA-MLR models, the R2 value was greater than 0.74 (significant at the 5% level) and the RMSE was less than 0.52 mm per day. In general, the results showed an improvement in the results using combination of PCA and MLR models for pan evaporation estimation.