Derivation of regression models for pan evaporation estimation

Abstract

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