## Materials and Methods
### Model Calibration and Validation
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Parameter Name** & **Perfect-Fit Value** & **Initial Value** & **Low Boundary** & **High Boundary** \\ \hline PERC & 0.7 & 0.5 & 0.1 & 2 \\ UZL & 20 & 30 & 5 & 50 \\ K0 & 0.2 & 0.3 & 0.1 & 0.9 \\ K1 & 0.08 & 0.1 & 0.01 & 0.2 \\ K2 & 0.03 & 0.05 & 0.00005 & 0.1 \\ MAXBAS & 2.5 & 2 & 1 & 5 \\ CET & 0.1 & 0.2 & 0.01 & 0.3 \\ TT & \(-1\) & 1 & \(-2\) & 1 \\ CFMAX & 5 & 3 & 0.5 & 6 \\ SP & 1 & 0.9 & 0.01 & 1 \\ SFCF & 0.8 & 0.9 & 0.01 & 1 \\ CFR & 0.05 & 0.045 & 0.04 & 0.06 \\ CWH & 0.1 & 0.05 & 0.01 & 0.2 \\ FC & 250 & 200 & 50 & 500 \\ LP & 0.7 & 0.5 & 0.3 & 1 \\ BETA & 3 & 2 & 1 & 5 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The values of initial parameters and boundary conditions. Figure 1: Calibration process workflow using the Parameter Estimation Tool—PEST. it was tested if the parameter values are closer to the initial ones than by using all 16 parameters (GAP\({}_{16}\)), where SP, CFR, and CWH values listed in Table 1, were fixed to the perfect-fit values (Table 2). A similar fixing of some of the parameters to avoid over-parameterization was done with CFR and CWH described in the article [18], where K0 and UZL parameters were not used in the calibration process with GAP. For the initial values of the parameters, the values listed in the third column in Table 2 are taken into account. All calibration procedures, i.e., GLM, CMA-ES, and GAP, are run with the same initial and boundary conditions shown in Table 2.  
During the calibration process, composite sensitivities were calculated and normalized for all observations. Normalized sensitivity shows how the uncertainty in the output of a model can be apportioned to different input parameters. Model validation was performed using measurements of precipitation and temperature for the period from 1 January 2001 to 31 December 2011. Similar to the calibration procedure [54; 55], the first nine months of the calculation are used to warm-up the model. Discharge data are simulated with known parameters from the calibration for the period from 1 October 2001 to 31 December 2011. This validation model is the same as in the calibration process, but with different input of precipitation and temperature data. Evapotranspiration data are the same as in the calibration period. The efficiency of a model is commonly evaluated by the Nash-Sutcliffe efficiency (NSE) coefficient, which has been used as a standard for decades in testing calibration and model validation [29]. The quality of the calibrated model is shown in the validation process. The results of the computations are compared with sample data that were not used in the calibration. A successful calibration model is expected to achieve a similar NSE coefficient to that in the calibration when validating. In addition to the Nash-Sutcliffe efficiency, other measures of the model calibration performance were taken into account in the analysis, namely the coefficient of determination, Kling-Gupta efficiency [77], efficiency for log (Q), flow-weighted efficiency, mean difference, efficiency for peak flows, efficiency at low flows, low-flow difference, and volume error (Table 3). Equations of efficiency for peak flow, model efficiency (R\({}_{\text{eff}}\)), and efficiency for low flow are the same as NSE coefficients, differing just in different data series periods. For a peak flow, a data point with the highest observed discharge (\(Q_{obs}\)) value that is at least three times the average \(Q_{obs}\) within a time window of 15 days is used. Nevertheless, the most fundamental hydrological characteristic is the low flow, which is a seasonal phenomenon and an integral part of every stream [78]. Low flows were identified as challenging to calibrate in several studies [79; 80]. For low flows, the arbitrary upper bound is given by the mean annual runoff, which is a mean value of the available flow time series of the annual flow. Q\({}_{70}\) flows within the range of 70%-time exceedance threshold were used as low flows in this study.