## 5 Conclusions  
The 2015 urban flood events in the Damansara catchment were simulated using the PCSWMM model. The reliability and efficiency of the model were confirmed with the simulations of river flow and drainage overflow. Based on both 1-D and 1-D-2-D flow analysis, the key findings are presented herein:  
1. In comparison with the observations, the 1-D river flow model presents a relatively satisfactory performance in reproducing the water level peaks. The simulated and observed flow almost varies in the same trend over time, with R\({}^{2}\), NSE, and RE values of up to 0.68, 0.52, and 4.79%, respectively.
2. The 1-D-2-D drainage overflow model excellently performs in predicting the flood-prone areas, as in the historic flood event. Good agreement between the simulation and observation can be seen in the reproduction of the flood depth maxima.
3. This work demonstrates the importance of drainage capacity, in which a larger size for the current drainage alleviates floods effectively, leading to a 78% reduction in the flood extent and a change from high to low risk in flood-prone areas.  
Figure 10: Flood risk mapping for scenarios with **(a)** historic drainage in 2015 (scenario 1) and **(b)** current drainage in 2020 (scenario 2); **(c)** Depth-velocity matrix used in generating the flood risk maps.  
Globally, there has been an increase in the precipitation frequency and intensity, as reported in the Fifth Assessment Report (AR5) of IPCC [47]. The rainfall-induced hazards, such as urban floods, are expected to increase in the future. Therefore, a detailed investigation and analysis of the catchment's response to future changes in climate, land use, and urban drainage systems will be included in the next study scopes, using the calibrated and validated PCSWMM model.  
Conceptualization, methodology, L.M.S. and L.H.C.C.; investigation, A.S.M.A. and A.S.J.; supervision, H.B.; writing--original draft preparation, A.S.M.A.; writing--review and editing, W.C.M.; funding acquisition, L.M.S. All authors have read and agreed to the published version of the manuscript.  
This research was funded by BOLD Grant (RJ010594523). The APC was funded by BOLDRefresh Grant (BOLDRefresh2025).  
Not applicable.  
This study is part of Regional Collaborations Programme (Simulation of Urban Floods Using Spatially Distributed Rainfall Derived from Weather Radar) with Deakin University, Australia, Thammasat University, Thailand and Universiti Tenaga Nasional, Malaysia. The authors would like to acknowledge Computational Hydraulics International (CHI) for the technical support given through Assoc. Kim Neil Irvine. Special thanks to [PERSON] and [PERSON] for their assistance in conducting the site visits.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Name** & **Area (ha)** & **Width (m)** & **Flow Length (m)** & **Slope (\%)** & **Imperviousness (\%)** \\ \hline S1 & 3.1546 & 162.978 & 193.56 & 1.550 & 90 \\ S2 & 1.9113 & 180.977 & 105.61 & 1.890 & 50 \\ S3 & 1.6606 & 207.679 & 79.96 & 5.000 & 90 \\ S4 & 5.2218 & 274.168 & 190.46 & 0.525 & 90 \\ S5 & 5.2727 & 270.547 & 194.89 & 2.060 & 90 \\ S6 & 6.8840 & 352.375 & 195.36 & 1.540 & 90 \\ S7 & 7.9970 & 857.128 & 93.30 & 5.460 & 90 \\ S8 & 7.5877 & 1192.472 & 63.63 & 2.980 & 90 \\ S9 & 7.6306 & 296.633 & 257.24 & 0.777 & 90 \\ S10 & 5.9783 & 261.667 & 228.47 & 0.438 & 90 \\ S11 & 4.8595 & 364.444 & 133.34 & 0.750 & 90 \\ S12 & 3.0559 & 183.295 & 166.72 & 0.600 & 90 \\ S13 & 6.3714 & 344.065 & 185.18 & 0.540 & 90 \\ S14 & 3.8718 & 197.098 & 196.44 & 0.509 & 90 \\ S15 & 3.7131 & 366.545 & 101.30 & 1.970 & 90 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Hydrology input parameters for the 1-D river flow model.