A Method for Evapotranspiration Retrievals From a Mesoscale Model 1 Based on Weather Variables for Soil Moisture Deficit Estimation

Reference Evapotranspiration (ETo) and soil moisture deficit (SMD) are vital for understanding the hydrological processes. Precise estimation of ETo and SMD are required for developing appropriate forecasting system and hydrological modelling. In this study, the surface temperature downscaled from Weather Research and Forecasting (WRF) model is used to estimate ETo using the boundary conditions provided by the European Center for Medium Range Weather Forecast (ECMWF). In order to understand the performance, the Hamon method is employed to estimate the ETo using the temperature from meteorological station and WRF derived variables. After estimating the ETo, a range of linear and non-linear models are utilized to retrieve SMD. The performance statistics such as RMSE, %Bias, and Nash Sutcliffe Efficiency indicates that thesimplistic linear model is efficient for SMD estimation in comparison to other complex models. Findings of this study also showed that the technique is performing better during the growing season than the non-growing season for SMD.