## 3 Results and Discussion
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Parameter Name** & **GLM \%** & **CMA–ES \%** & **GAP\({}_{\bf 16}\) \%** & **GAP\({}_{\bf 13}\) \%** \\ \hline PERC & 0 & \(-\)0.01 & 0.41 & 0.92 \\ UZL & 0 & 0.01 & 0.22 & \(-\)0.59 \\ K0 & 0 & 0.07 & \(-\)2.02 & \(-\)0.38 \\ K1 & 0 & 0 & 0.17 & 0.09 \\ K2 & 0 & \(-\)0.01 & \(-\)0.49 & 2.16 \\ MAXBAS & 0 & 0.03 & \(-\)0.5 & \(-\)0.01 \\ CET & 0 & 0 & \(-\)2.8 & \(-\)0.97 \\ TT & 0 & 0 & 0 & 0 \\ CFMAX & 0 & \(-\)0.68 & \(-\)6.93 & 0.3 \\ SP & 0 & 0.9 & 12.08 & - \\ SFCF & 0 & 0.03 & \(-\)1.72 & 0.3 \\ CFR & \(-\)0.02 & \(-\)2.59 & 10.4 & - \\ CWH & 0 & \(-\)0.07 & 35.15 & - \\ FC & 0 & 0 & 2.07 & 0.25 \\ LP & 0 & 0 & 3.27 & \(-\)0.02 \\ BETA & 0 & \(-\)0.01 & 4.29 & \(-\)0.19 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Parameter relative deviation to “perfect fit” in the calibration procedure. Figure 4: The absolute difference to \(Q_{\rm obs}\) for different calibration procedures in the calibration period from 1 March 1985 to 31 March 1985. On the other hand, parameter values obtained by calibration using the GAP algorithm differ significantly from the true values used for the calculation of the HBV-Land model (Figures 4 and 5, and Table 6). In the case of GAP\({}_{16}\), 10 of 16 parameters deviate by more than 1%; SP, CFR, and CWH parameters deviate even by 12%, 10%, and 35%, respectively. Moreover, the total parameter deviation obtained by the GAP\({}_{16}\) procedure is 82%. CMA-ES performed better than GAP but worse than GLM. The largest deviation (2.6%) in CMA-ES is again observed for the CFR parameter. The second-largest deviation (0.7%) is found for the CFMAX parameter. The total deviation of parameters in case of CMA-ES is 6%. When the number of parameters in a calibration set was reduced from 16 to 13, a more efficient GAP calibration with the total deviation of parameters of 6% resulted. One hundred optimized parameter sets resulted in a maximum, minimum, and median NSE equal to 0.999989, 0.980133, and 0.994431, respectively. For comparing GAP\({}_{16}\) with other calibration methods, the parameter set obtained with maximum NSE was selected. Many researchers found that low-flow calibration is quite problematic [79; 80]. In this study, the only calibration using the GLM procedure gave a perfect fit in low flows (Table 4) without using the additional criteria to calibrate low-flow data series. CMA-ES parameter relative deviation to history matching is close to GLM, as shown in the second column of Table 6. The calibration time can sometimes be significant. The run time depends on computer power, the operating system, and calibration process termination criteria. We need to look at this metric relatively and see how much one method is faster or slower than the other. For the selected case of the hydrological model calibration, the calculation using the parallel computing according to the GLM procedure takes 50 s (Figure 6), the CMA-ES parallel calculation takes one hour, and one GAP calculation takes two minutes with 6000 model runs. To achieve the results shown in Table 6, one hundred repetitions of GAP calculations take about three hours and ten minutes. Figure 5: Graphical presentation of calibrated parameters with different optimizers. The GLM procedure calculated the hydrological model 533 times with 14 iterations, and the CMA-ES ran the model 12,937 times with 925 iterations. For one hundred repetitions with the GAP procedure, 603,150 model runs of the model are required. Better efficiency of the GLM procedure regarding the number of required model calculations in comparison with the methods of evolutionary CMA-ES and GAP is hence more than evident. Moreover, the GLM calibration process in all cases calculated the same NSE value and values of calibrated parameters remained the same, regardless of the number of repetitions of the calibration process.