## 5 Discussion and Conclusions  
There have been four types of conventional aggregation frameworks for the IPCC's assessment components in previous studies on the flood vulnerability assessments. The IPCC's three assessment components are independently aggregated with equal weights for \(FVI_{1}\) in Equation (1), or the potential impact as a function of exposure and sensitivity components is compiled with the lack of adaptability for \(FVI_{2}\) in Equation (2), livelihood vulnerability as a sum of exposure and lack of adaptability is compiled with sensitivity for \(FVI_{3}\) in Equation (3), or internal factor as an average of sensitivity and lack of adaptability is compiled with exposure for \(FVI_{4}\) in Equation (4). As for the data characteristics of the IPCC's three primary components for the study site, the data distribution of the exposure component (by heavy rainy days and flooded area ratio) was relatively spread out over the study site while the other two components had skewed data distributions where the high level or low level values were concentrated in most metropolitan areas for sensitivity data (by vulnerable population and major facility densities) or lack of adaptability data (by emergency service density and drainage system capacity), respectively. In the comparison of the four flood vulnerability outcomes ranked corresponding to the composite indicator scores, there were great similarities for the two pairs of flood vulnerability indicators between \(FVI_{1}\) and \(FVI_{3}\), and between \(FVI_{2}\) and \(FVI_{4}\). The similarities of \(FVI_{1}\) and \(FVI_{3}\) were due to a high correlation between heterogeneous parts (\(E\,\times\,A\) and \(E+A\)) of the two different aggregation frameworks. Both \(E\,\times\,A\) and \(E+A\) outcomes were affected by exposure \(E\), which was more sensitive than lack of adaptability \(A\) (values were over 80 in 75% of total districts). The similarities of \(FVI_{2}\) and \(FVI_{4}\) were due to a high correlation between heterogeneous parts (\(S\,\times\,A\) and \(S\,\times\,E\)) of the two different aggregation frameworks. Both \(S\,\times\,A\) and \(S\,\times\,E\) outcomes were affected by sensitivity \(S\) (values were under 30 in 75% of total districts), which was more sensitive than the lack of adaptability \(A\) or exposure \(E\). The sensitivity component typically much higher in the metropolitan areas turned out to be a key factor that had the greatest effect on the flood vulnerability outcomes for the study site. It was interpreted that \(FVI_{1}\) and \(FVI_{3}\) where sensitivity can play a key role as an independent component were most highly correlated with sensitivity, while exposure could have the largest influence on \(FVI_{2}\) and \(FVI_{4}\) where sensitivity was combined with one of the other components as an intermediate component. According to the purpose and function of a flood vulnerability assessment for this study site, the sensitivity component may or may not be treated as the key driver for overall performance by deliberately selecting an aggregation framework. It should be of course explained for the reasons and needs in increasing or decreasing the relative importance of a certain assessment component in the composite indicator. In case this flood vulnerability assessment will be used to prioritize the investment in flood mitigation schemes for the study site, it is preferable to adopt the composite indicator outcomes \(FVI_{2}\) or \(FVI_{4}\) in which the exposure component is more influential than the sensitivity component. Accordingly, higher priority can be given more to the small and medium towns and cities that in general tend to have relatively fewer flood control facilities than metropolitan areas. A strong negative correlation between sensitivity