rj
\(\overline{f(r)}\) is mean of all observed values.
Different interpretations are drawn from the above formulated performance measures. Coefficient of determination is adopted very commonly as a performance measuring criterion. Li and Heap (2008) has discussed that R2 solely cannot represent the performance of any model; hence other measures are also used to compare the performances of models used in this study. As per Hallak and Pereira (2011), MBE does not provide a clear picture about the individual errors; this is because the positive and negative error gets cancelled out. To overcome this MAE is calculated, Fox (1981) introduced MSE is another performance measure which is used to check the accuracy of models to predict, but according to Ponce-Hernandez (2006) MAE shows the error on large scale because the errors are squared and errors get amplified. RMSE is another parameter which is commonly used for measuring the performance of models. Willmot (1982) found that RMSE is one of the best tools to measure the error of a model as it gives us a summary about the average difference of values between observed ones and predicted ones. Model efficiency (ME) is also taken into consideration for evaluation of the models. According to Nash and Sutcliffe (1970) ME can take any value in the range of (-∞, 1] and best model is the one which is closer to unity. For the model having ME as zero, it can be inferred that the predicted values are just the mean of the observed values in search radius. For ME less than zero it should be inferred that mean of observed values are better estimates than the predicted one (Krause et al., 2005).