# Estimating Flood Inundation in Urban Areas Using a Scenario Generation Method and Inundation Graphs
## 4 Discussion
The advantage of the method developed in this study is that by using the stored database, the user can secure speed and convenience in calculating the estimated water level without using a time-costly two-dimensional flow model in case of flash floods. The inundation graphs for various flow conditions according to flood frequency are prepared and displayed so they can be easily utilized by composing the result of water level calculation in the form of several graphs according to the flow rate. In order to use an arbitrary return period or starting point water level that has not been pre-calculated and stored in the inundation graph database, an additional application process using interpolation or an index matrix would be helpful. For consideration, some limitations exist in the fact that the modeling performance to the target area for application should have been evaluated with actual observed data at the site. Due to the recent typhoon in 2020, nearby areas that were 1 km away from the site experienced flood damage from the sudden and large typhoon. Nevertheless, the target area did not have an official water gauge site to measure the increased water elevation or have areas where the inundation was measured for the incident. With actual measured stage values, a stage-discharge equation would have been applied to the boundary condition, which is the typical method for river modeling. However, since this area was an ungauged site, values in which ranges were expected had to be applied to the model. Other methods using discharge estimation and rating curve development at ungauged river sites could require additional routing methods such as the Muskingum stage hydrograph routing method [38]. Using the conventional power function equation \(Q=C\left(h-I_{0}\right)^{m}\) where \(C\), \(h_{0}\), and \(m\) are constants would be another option if some data were available [39]. The constant \(m\) in some cases is known to be near 2 [39], but estimating the \(C\) without actual data proved to be difficult. Stage-discharge equations developed in a nearby city had \(C\) values of near 1100, which was used in the model. Using nearby streams with similar characteristics would originally require regionalization methods which involve utilizing information from gauged sites to ungauged sites based on similarities in physical and climate characteristics [40]. The nearby city watershed had similar climate and channel materials, and the channel geometric characteristics had similarities in bottom width. Figure 10: Flood hazard rating index matrix to specific buildings. Although some aspects of regionalization were conducted, there still remains the fact that actual measured data directly from the site were not used, which leads to uncertainty in flood simulation results and a limitation for this study. Another point for discussion is the uncertainty of using theoretical statistical distributions for extreme floods. Figure 3 shows an underestimation of rainfall depth compared to the largest recorded rainfall, which is the return year of 58, but this type of discrepancy is also shown in previous research [41]. This could be reasonable, but the fact remains that high return periods have uncertainty in estimation and must be considered in using theoretical statistical distributions. Since extreme events are by definition rare, it is difficult to reliably estimate extreme quantiles from the site data, which frequently comes from limited record lengths. Regional frequency analysis could be conducted to deal with this problem, but this was out of the scope of this research. Finally, instead of using an empirical equation for the flow rate estimation based on rainfall, an additional rainfall-runoff model would produce more accurate results. The utilization of a runoff model and the routing method was out of scope for this study, but it could be considered for future research.