Comparing spatially explicit approaches to assess social vulnerability dynamics to flooding

Madagascar is one of the poorest countries in the world, and therefore highly vulnerable to tropical cyclone-induced coastal and inland flooding. This study aims to assess Madagascar's social vulnerability to flooding resulting from tropical storms, in a spatially explicit manner , accounting for dynamics in vulnerability over time. For this, the research applied three different social vulnerability models in a data-scarce context, for the years 2009 and 2018. Around these years, two tropical cyclones hit the island (in 2008 and 2018). The three models are 1) inductive (social vulnerability index: SoVI), 2) deductive (Weighted Median, WM), and 3) social vulnerability profiling (SVP). Our results show that the most vulnerable regions are in the south of Madagascar, which is consistent across all three models. While the calculated vulnerability score (based on hazard and exposure data) indicated a decrease in vulnerability over time, only the SVP predicted a similar vulnerability decrease, but this comparison is surrounded by uncertainty since the 2008 and 2018 events differ in flood hazard characteristics. The application of the SoVI method in such regions has some limitations, for example, introducing subjective modeling decisions. The application of the WM method could be suitable, but only if relations with variables and social vulnerability are known and understood. The SVP model seems a suitable approach for a first scoping study of social vulnerability, but it provides less insight into spatial variation. The main recommendation from this study is to further focus future research on model validation in data-scarce regions and to assess model accuracy by exploring validation data.