## 3 Results
### Model Calibration

Table 4: Root mean square error standard deviation (RSME SD), Nash–Sutcliffe efficiency and percent bias results for the calibrated models for each event. Figure 4: Calibration performance of the model for Events 1 and 4.  
slightly better than the permeable streets and green roof. The degree to which the peak discharge was reduced varied greatly between the modelled events and did not appear to be influenced by the magnitude of the original peak discharge of the event. However, the best performance was observed in Event 1, which had the highest original peak discharge. Table 6 shows the percentage decrease in surface runoff compared to the \"No solutions\" scenario for each event. Scenarios 5 and 6 are not included, as surface runoff volume is not affected by the ponds. The ponds' effects are instead a result of water storage, affecting the peak discharge and lag time. Hence, storing water in ponds can significantly slow the runoff process, and the water can potentially be used for other purposes as well. In all events, the percent decrease showed that the applied strategies are effective in reducing runoff, either by increasing water retention or infiltration. This effect is particularly noticeable in Events 2, 5 and 6, where the percent change values are high, suggesting a considerable improvement in water management compared to the \"No solutions\" scenario. Scenario 4 consistently stands out due to its high impact on percent change, showing better values in most events compared to other scenarios. This suggests that the combination of all NbS in this scenario is effective in reducing runoff volume and increasing water retention. Events 10 and 4 exhibit a smaller decrease in percentage change. How  
Figure 5: Selected discharge hydrographs for Events 1, 5, 7 and 9, showing all scenarios. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Event** & **Units** & **Event 1** & **Event 2** & **Event 3** & **Event 4** & **Event 5** & **Event 6** & **Event 7** & **Event 8** & **Event 9** & **Event 10** \\ \hline
**No solutions** & **L/s** & 1040.0 & 10.3 & 242.0 & 372.2 & 27.9 & 36.3 & 36.3 & 21.4 & 197.9 & 316.1 \\ \hline
**Scenario 1** & **\%** & 2.2 & 10.1 & 1.1 & 0.3 & 15.0 & 2.4 & 8.2 & 5.2 & 2.6 & 0.4 \\ \hline
**Scenario 2** & **\%** & 3.0 & 13.6 & 1.5 & 0.4 & 20.2 & 3.2 & 11.0 & 7.0 & 3.5 & 0.6 \\ \hline
**Scenario 3** & **\%** & 1.9 & 12.4 & 1.1 & 0.1 & 22.5 & 2.7 & 10.0 & 5.4 & 2.4 & 0.1 \\ \hline
**Scenario 4** & **\%** & 7.2 & 35.9 & 3.8 & 0.9 & 57.8 & 8.4 & 29.3 & 14.4 & 8.5 & 1.2 \\ \hline
**Scenario 5** & **\%** & 0.7 & 11.1 & 4.5 & 2.5 & 5.9 & 0.0 & 6.0 & 0.0 & 11.3 & 0.3 \\ \hline
**Scenario 6** & **\%** & 7.1 & 46.8 & 7.9 & 3.4 & 64.3 & 11.2 & 34.3 & 14.3 & 18.3 & 2.4 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Percent decrease in peak discharge for each scenario from the “No solutions” model, which is presented with the peak discharge in L/s. ever, this behaviour may be linked to the intensity and magnitude of the event. In Scenario 1, there were relatively small reductions in runoff, with the largest reduction occurring in Event 5. Scenarios 2 and 3 present similar results, although Scenario 3 is less effective in certain events. The scenarios evaluated in Table 7 indicate how implementation of the scenarios can influence the timing of peak discharge. For example, delaying the peak (as in the events in Scenario 3) can help distribute the volume of water in a more manageable way. In contrast, Scenario 4 shows no changes in delay time compared to Scenario 1, in which no interventions were applied. Events 2 and 5 also showed no changes in delay time, while the other events showed variations in a range of between 10 and 40 min.