## 3 Results  
### Model Calibration  
Calibration of the model was performed independently for each of the 10 rainfall events to best represent the peak discharge and runoff volume. The events included a variety of durations and intensities (Table 3). An NSE value greater than 0.50 indicates that the model has acceptable predictive capability [47], showing that the model effectively replicates the observed runoff and flow patterns for all evaluated events. PBIAS measures the average tendency of the model to overestimate or underestimate the observed values. PBIAS values within the range of \(\pm\)25% to \(\pm\)50% are considered acceptable, according to the criteria of [PERSON] et al. [47]. Negative values indicate that the model tends to systematically overestimate flow or runoff. In all events, the calibrations resulted in an adequately accurate model that also retained realistic parameters for the basin. The statistics, including the root mean square error (RMSE), Nash-Sutcliffe efficiency (NSE) and percent bias (PBIAS), for each event are summarised in Table 4, and examples of observed and modelled hydrographs from the calibration are shown in Figure 4. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Event** & **Impervious** & **Event 1** & **Event 2** & **Event 3** & **Event 4** & **Event 5** & **Event 6** & **Event 7** & **Event 8** & **Event 9** & **Event 10** \\ \hline
**AMC** & **(\%)** & **Average** & **Dry** & **Dry** & **Dry** & **Average** & **Dry** & **Average** & **Dry** & **Average** \\ \hline
**No** & & & & & & & & & & & \\
**solutions** & 8.3 & 74.7 & 74.4 & 36.0 & 77.9 & 57.0 & 63.6 & 61.4 & 65.8 & 65.5 & 88.0 \\ \hline
**Scenario 1** & 7.0 & 74.3 & 74.0 & 35.6 & 77.6 & 56.6 & 63.2 & 61.0 & 65.4 & 65.2 & 87.6 \\ \hline
**Scenario 2** & 6.3 & 74.2 & 73.9 & 35.5 & 77.4 & 56.5 & 63.1 & 60.9 & 65.2 & 65.0 & 87.5 \\ \hline
**Scenario 3** & 6.5 & 74.7 & 74.4 & 36.0 & 77.9 & 57.0 & 63.6 & 61.4 & 65.8 & 65.5 & 88.0 \\ \hline
**Scenario 4** & 3.2 & 73.8 & 73.5 & 35.1 & 77.1 & 56.1 & 62.7 & 60.5 & 64.9 & 64.7 & 87.1 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The curve number (CN) parameter for each event. “No solutions” refers to the CN derived from calibration, which is then changed for each scenario. Rainfall events are also categorised according to the antecedent moisture condition (AMC). The implementation of different scenarios enables the performance of different NbS to be evaluated in terms of efficiency, providing a more complete view on which solutions are most effective in controlling the flow and mitigating the flood risk in each specific context. The graphs for the four events show the discharges for each of the scenarios compared to the value for the scenario without any implementation (Figure 5). For smaller events, such as Event 5 and Event 7, a significant difference in discharges is observed, which can be attributed to the ability of NbS to manage smaller flows more effectively. All events showed a reduction in flow for most scenarios. In particular, Scenario 6, which included the implementation of all NbS, showed the largest decrease in discharge, evidencing its effectiveness in controlling flow. The biggest decrease in peak discharge between scenarios was observed in Scenario 4 in all events, except Event 1 (Table 5; Figure 6). The scenarios for individual solutions had minimal impact on peak discharge, although Scenario 2 (permeable parking) performed \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Event** & **Event 1** & **Event 2** & **Event 3** & **Event 4** & **Event 5** & **Event 6** & **Event 7** & **Event 8** & **Event 9** & **Event 10** \\ \hline
**RMSE SD (m3/s)** & 0.5 & 0.7 & 0.7 & 0.5 & 0.6 & 0.4 & 0.6 & 0.7 & 0.7 & 0.6 \\ \hline
**Nash–Sutcliffe** & 0.8 & 0.5 & 0.5 & 0.8 & 0.6 & 0.8 & 0.6 & 0.5 & 0.5 & 0.6 \\ \hline
**Percent Bias (\%)** & 24.0 & 29.6 & 0.0 & 3.7 & 20.5 & \(-14.7\) & \(-5.8\) & \(-7.4\) & \(-18.5\) & \(-35.1\) \\ \hline \hline \end{tabular}
\end{table}