Reference evapotranspiration retrievals from a mesoscale model based weather variables for soil moisture deficit estimation

Abstract

Reference Evapotranspiration (ETo) and soil moisture deficit (SMD) are vital for understanding the hydrological processes, particularly in the context of sustainable water use efficiency in the globe. Precise estimation of ETo and SMD are required for developing appropriate forecasting systems, in hydrological modeling and also in precision agriculture. In this study, the surface temperature downscaled from Weather Research and Forecasting (WRF) model is used to estimate ETo using the boundary conditions that are provided by the European Center for Medium Range Weather Forecast (ECMWF). In order to understand the performance, the Hamon's 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 is utilized to retrieve SMD. The performance statistics such as RMSE, %Bias, and Nash Sutcliffe Efficiency (NSE) indicates that the exponential model (RMSE = 0.226; %Bias = -0.077; NSE = 0.616) is efficient for SMD estimation by using the Observed ETo in comparison to the other linear and non-linear models (RMSE range = 0.019-0.667; %Bias range = 2.821-6.894; NSE = 0.013-0.419) used in this study. On the other hand, in the scenario where SMD is estimated using WRF downscaled meteorological variables based ETo, the linear model is found promising (RMSE = 0.017; %Bias = 5.280; NSE = 0.448) as compared to the non-linear models (RMSE range = 0.022-0.707; %Bias range = -0.207--6.088; NSE range = 0.013-0.149). Our findings also suggest that all the models are performing better during the growing season (RMSE range = 0.024-0.025; %Bias range = -4.982--3.431; r = 0.245-0.281) than the non-growing season (RMSE range = 0.011-0.12; %Bias range = 33.073-32.701; r = 0.161-0.244) for SMD estimation. © 2017 by the authors.