data), we decided to use extent since our flood data assumes no protection. Protection is more likely to impact the depth, rather than the extent, of the flood results.<sup>2</sup> We then overlay this flood layer with the population density data set, to estimate the number of people per population grid cell that are exposed to floods. As the population density data set is at a lower resolution (1km) than the flood data (90m), we estimate the percentage of the population grid cell which is flooded, and multiply this percentage by the population in that grid cell. For instance, if a population grid cell has 500 people, and 10% of that cell is flooded (based on the flood data), then we estimate 50 people to be exposed to floods in that cell. In doing so, we assume that the population is evenly distributed within a grid cell.

We run this analysis for all the scenarios presented in Table 2, and aggregate our results at the district level to estimate the number of people affected. To include the poverty dimension, we use the poverty headcount rate in each district to estimate the percentage of poor people exposed. For instance, if 20,000 people are exposed to floods in District X, and District X has a poverty headcount rate of 20%, 1,000 poor people are exposed to floods in that district. In this analysis, we assume that poverty is evenly distributed within a district.

# **4.2. Slum and urban expansion exposure in Ho Chi Minh City**

For the HCMC analysis, we estimate the general exposure to flooding, for the whole province of HCMC and in each of its 24 districts. The flood maps used here are based on a model of HCMC, and are not the same map as used in the figurative example in Section 4.1.

Exposure to flooding was again evaluated using flood extent (we also evaluate flood depth, for full results, see Appendix 2). We examine the flood extent in three areas: for all urban areas (the whole HCMC province), for those areas defined as potential slums (from the PUMA data set), to examine how exposure to floods is different in slum areas. We do the same for areas defined as urban expansion locations (also from the PUMA data set) to evaluate whether new urban developments within the province of HCMC take place in flood prone areas.

Again we use a number of events, from the case of regular flooding (10‐year event) to more extreme flooding events (1000‐year event). Moreover, we examine how this exposure changes due to climate change (proxied by sea level rise changes), by running the analysis with flood hazard maps taking into account a 30 cm sea level rise. In each district and across the whole city, we examine the percentage of area within each of the three categories (all urban areas, slums, and urban expansion areas) that is exposed to floods (that is, where flood depth > 0cm) and the percentage which is not exposed to floods (that is, where flood depth = 0cm). We then compare these values across the three categories.

# **5. Results**

> 2 There is also  a good reason for examining extent over depth, in terms of the hazard modeling; flood depths within a large scale flood model are very uncertain, and there is much more certainty about extents.

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