, 2013). In these cases, Xi and/or Q should be replaced by Xi + 1 and/or Q + 1, respectively, in Eqs. (1) and (2). The selection of the explanatory variables Xi, and the calculation of their respective coefficients βi, is performed by weighted least squares regressions applied

to n observations Qj (j = 1, …, n) of Q and their respective m catchment characteristics Xij. A description of the approaches used to obtain the dependent variables Qj and the independent variables Xij is presented in Section 3. Unlike ordinary least square regressions treating the n observations of Qj equally, weighted least square regression ( Tasker, 1980) enables the varying number kj of hydrological years used to calculate each flow statistic Qj and its associated climate characteristics to be taken into account. Values of Qj derived from a greater number of hydrological years are more precise (have lower variance) check details and thus should have a greater weight in the regression. However, this reliability decreases as the variance of Qj increases. Z-VAD-FMK chemical structure To account for these two counteracting

factors, weights (wj) were calculated as follows: equation(3) wj=kjStdev(Qj)where Stdev(Qj) is the standard deviation of Qj. If Qj is the annual flow, wj can be interpreted as the inverse of the standard deviation of a mean Qj estimated from kj years. In this case, wj is the exact weight for the sample mean but is only an approximation of

the weight for Orotic acid all other streamflow metrics presented in Section 3.1. The selection of the best set of explanatory variables X  i in Eq. (2) was guided by the combined use of the selection algorithms knows as “best subsets regression” and “step-wise regression” both of which are widely available in statistical packages. This selection was intended to maximize the prediction R  -squared ( Rpred2) calculated by leave-one-out cross-validations. Unlike the classical R  -squared the maximization of which can lead to model over-fitting and loss of robustness, Rpred2 reflects the ability of the model to predict observations which were not used in the model calibration. Maximizing Rpred2 generally leads to greater parsimony in the number of explanatory variables. An explanatory variable was considered to be statistically significantly different from zero if its p  -value, derived from Student’s t   test, was lower than 0.05. The required homoscedasticity (homogeneity of variance) of the model residuals ɛ   was verified by visual inspection of the residual plots. Possible multi-collinearity among the explanatory variables was controlled with the variance inflation factor (VIF) which should never exceed 8. VIFs for all explanatory variables of our models were found to never and rarely exceed 3 and 2, respectively.