Derivation of regression models for pan evaporation estimation

Evaporation is an essential component of hydrological cycle. Several meteorologicalfactors play role in the amount of pan evaporation. These factors are often related to eachother. In this study, a multiple linear regression (MLR) in conjunction with PrincipalComponent Analysis (PCA) was used for modeling of pan evaporation. After thestandardization of the variables, independent components were obtained using the (PCA).The series of principal component scores were used as input in multiple linear regressionmodels. This method was applied to four stations in East Azerbaijan Province in the NorthWest of Iran. Mathematical models of pan evaporation were derived for each station. Theresults showed that the first three components in all four stations account for more than90% 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 resultsshowed that in all the PCA-MLR models, the R2 value was greater than 0.74 (significant atthe 5% level) and the RMSE was less than 0.52 mm per day. In general, the results showedan improvement in the results using combination of PCA and MLR models for panevaporation estimation.