## 3 One-Dimensional River Flow Modeling  
For river flow modeling, 35 sub-catchments were created with 205 conduits and 206 junctions, while the river downstream was treated as an outfall, as shown in Figure 3. The rivers were simulated as irregularly shaped conduits, with an initial flow of 7.95 m\({}^{3}\)/s, computed from Equation (3). An extreme precipitation event in December 2015 was reproduced using the rainfall data (at 5-min intervals) from six rainfall stations within the Damansara catchment, as listed in Table 2. A relatively simple, nearest neighbor-based method [42] was adopted, in which each sub-catchment was assigned rainfall input data from the nearest rainfall station to the sub-catchment's centroid (Figure A1).  
Model calibration and validation should always be undertaken prior to any model application. Among the inferred parameters, the Manning roughness coefficient was selected as the calibration parameter in this study due to its sensitivity to the peak runoff. The Manning roughness of conduits were tested from the initial values of 0.035 to 0.050 (dependent on the river's surface cover) and adjusted until the optimum values were achieved. The historical December 2015 flood events: 1 st-7 th and 9 th-15 th, were chosen for model calibration and validation, respectively. The daily rainfall time series are given in  
Figure 2: Input parameters used in PCSWMM for flood simulation.  
In this study, the simulated peak flows and total flow volumes were satisfactorily predicted with relative errors of less than 10%. The comparison of the observed and modeled water level time series is shown in Figure 4. The previous results from another proprietary software, InfoWorks Integrated Catchment Modeling (ICM) [43], are also presented for comparison purposes in the same plot. In Figure 4, the overall water profiles match well with the observation for both the calibration and validation periods. Variations are expected due to the cross-sectional changes in rivers over the years. It is also noteworthy that both models produce different results. As [PERSON] and [PERSON] [44] pointed out, it is likely to be caused by various solution schemes used in solving the governing equations (as in Equations (1) and (2)). In addition to visual observations, some goodness-of-fit measures were adopted to quantify discrepancies between the models and observation, including the coefficient of determination (R\({}^{2}\)), Nash-Sutcliffe model efficiency coefficient (NSE), and relative error (RE), expressed as:  
\[NSE=1-\frac{\sum_{i=1}^{N}(h_{obs,i}-h_{sim,i})^{2}}{\sum_{i=1}^{N}(h_{obs,i} -h_{m,obs})^{2}} \tag{4}\]  
\[RE(\%)=\frac{\sum_{i=1}^{N}(h_{obs,i}-h_{sim,i})}{\sum_{i=1}^{N}h_{obs,i}} \times 100 \tag{5}\]  
where \(h_{obs,i}\) and \(h_{sim,i}\) are the \(i\)th water level values obtained from the observation and simulation, respectively; and \(h_{m,obs}\) is the mean of the observed water levels. Table 3 present the model performance in terms of R\({}^{2}\), NSE, and RE. The calibration performance is confirmed, with R\({}^{2}\), NSE, and RE values of 0.68, 0.52, and 4.79%. During validation, the results show that the PCSWWM model yields NSE and RE values of 0.10 and 10.58%, respectively. Compared to InfoWorks ICM, higher discrepancies (in terms of NSE and RE) are observed for PCSWMM, which can be explained by the water-level fluctuation observed between 9 and 12 December 2015 (Figure 4b). However, the PCSWMM model still performs satisfactorily in terms of the R\({}^{2}\) value (0.62) and the reproduction of water level peaks, though some of the peaks are somewhat underestimated.  
Figure 3: Catchment discretization for 1-D river flow modeling.