A COMBINED SENSITIVITY ANALYSIS OF SEVEN POTENTIAL EVAPOTRANSPIRATION MODELS

Abstract

Graphical and partial derivatives approaches were used to analyse the sensitivity of variables for the seven potential evapotranspiration models (PET). The models, which have different data requirements and structures, are Hamon, Hargreaves-Samani, Jensen-Haise, Makkink, Turc, Priestley-Taylor, and Penman. Julian date based mean imputation was used to fill the missing data. Tukey's outlier detection method was employed before estimating the PET. Partial derivative approach was conducted by combining the absolute values of the error term through a root mean square and changing to the finite difference form. According to partial derivatives analysis, Hamon is the most sensitive model followed by Penman, Priestley-Taylor, Hargreaves-Samani, Jensen-Haise, Turc, and Makkink models. Temperature is more sensitive meteorological input in Jensen-Haise and Makkink models while solar radiation is more sensitive ones in Turc and Priestley-Taylor models. Wind speed and relative humidity are the most and less sensitive ones in Penman model. Graphical analysis showed that Hamon was the most sensitive PET model with respect to the temperature while Priestley-Taylor was the one with respect to the solar radiation. Turc is the less sensitive PET model with respect to temperature and solar radiation. Overall, graphical method gives clearly comparison for sensitivity of PET. However, it does not indicate its sensitivity values compared to partial derivative approach.