Impact of Climatological Parameters on Reference Crop Evapotranspiration Using Multiple Linear Regression Analysis

International Journal of Civil Engineering

© 2015 by SSRG - IJCE Journal

Volume 2 Issue 1

Year of Publication : 2015

Authors : H R Mahida, V N Patel

10.14445/23488352/IJCE-V2I1P103

How to Cite?

H R Mahida, V N Patel, "Impact of Climatological Parameters on Reference Crop Evapotranspiration Using Multiple Linear Regression Analysis," SSRG International Journal of Civil Engineering, vol. 2,  no. 1, pp. ):21-24, 2015. Crossref, https://doi.org/10.14445/23488352/IJCE-V2I1P103

Abstract:

The reference crop evapotranspiration (ETo) of Bhaniyara station have been estimated using multiple linear regression (MLR) technique in XLSTAT tool. The meteorological data such as maximum temperatures, sunshine hours, relative humidity and wind speed were collected for the Bhaniyara station of Vadodara district, Gujarat state, India for the period of nine years and the missing value of that data series was also determine using SPSS20 software. The observed ETo values have been estimated using the equation of evapotranspiration (FAO-56).MLR is carried out using ETo as predictor variable and maximum temperatures, sunshine hours, relative humidity and wind speed as independent variable to find out predominant factor on ETo. This whole procedure is done for five different Models. In model 1, Maximum temperature, relative humidity, sunshine hours and wind speed are correlated with ETo. In model 2, Maximum temperature, relative humidity and sunshine hours are correlated with ETo. In model 3, Maximum temperature, sunshine hours and wind speed are correlated with ETo. In model 4, Maximum temperature, relative humidity and wind speed are correlated with ETo. In model 5, wind speed, sunshine hours and relative humidity are correlated with ETo. In case of model 1 the value of R, R2 and RMSE for 70% dataset is 0.911, 0.830 and 0.341 respectively and for 30% dataset it is 0.954, 0.910 and 0.325 respectively. As the value of R and R2 are nearer to 1 and the value of RMSE is low, which is good. As the model 1 gives the best correlation values as compared to model 2 model 3, model 4 and model 5 it can be accepted as the best fit model for prediction of ETo. Considering maximum temperature the model gives good correlation values hence maximum temperature is accepted as predominant factor and the presence of relative humidity does not play an important role in prediction of ETo for this study area.

Keywords:

Climate change, multiple linear regression, performance evaluation, reference crop evapotranspiration

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