MODELING OF REFERENCE EVAPOTRANSPIRATION USING REGRESSION TECHNIQUES
Journal Title: International Journal of Agriculture Sciences - Year 2016, Vol 8, Issue 62
Abstract
Prediction of evapotranspiration is important for design and management of irrigation systems, water resources management and climatological studies. The ASCE had recommended Penman-Monteith model (FAO-56) as the sole standard method for determining ETo over the wide variety of climatic situations over the world and it requires all types of data. At many locations, there is either lack of meteorological data or availability of meteorological parameters is limited. It is necessary to find alternative to Penman-Moneith method with limited data availability. In order to carry out study, average weekly meteorological data, viz., maximum temperature, minimum temperature, maximum relative humidity, minimum relative humidity, bright sun shine hours, wind speed and pan evaporation were collected from whether station located at Dhule (Mahaashtra, India) for period of 1980 to 2014. In this study, the potential of Linear Regression is investigated in modeling of reference evapotranspiration (ETo) using the standard FAO-56 Penman–Monteith equation. The four types of linear regression models were developed by varying the independent variables, these are; LR1(pan evaporation); LR2 (maximum temperature and minimum temperature); LR3 (maximum temperature, minimum temperature and bright sun shine hours); LR4 (maximum temperature, minimum temperature, maximum relative humidity, minimum relative humidity and bright sun shine hours). The results of all performance measures for all LR models during development stage varies in the range as R (0.902 to 0.933), d(IA) (0.946 to 0.964), RMSE (0.701 to 0.841), MAE (0.532 to 0.646), MAPE (11.609 to 14.274) and CE (0.813 to 0.870) and showed the performance in sequence of LR4, LR3, LR1 and LR2. It indicates that all LR models performed satisfactorily and showed marginal difference of performance measures among them in development stage. Similar kind of close difference for each performance measure occurred during validation stage of all LR models. It indicates that all LR models were validated satisfactorily and generalized for estimation of ETo. Overall, the performance suggest that all LR models can be an acceptable approach to predict ETo values for Dhule station as per data availability
Authors and Affiliations
D. D. KHEDKAR
Keywords
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