This paper provides an in‐depth case study of floods, poverty, and climate change in Vietnam and Ho Chi Minh City, examining the exposure of the total population, and poor people in particular to current and future flood risk. It makes two contributions; the first is that it combines state‐of‐the‐art hazard maps with socioeconomic data to examine distributional impacts of floods at the national‐level in Vietnam. Most previous analyses of floods and climate change in Vietnam at the national‐level have focused on hazard mapping and not its distributional impacts (Institute of Strategy and Policy on Natural Resources and Environment 2009; Ministry of Natural Resources and Environment 2009). The second contribution is the paper’s analysis of flood exposure and poverty at national and local levels: most previous analyses have focused on one or the other (Winsemius et al. 2015; World Bank and Australian AID 2014).

# **3. Data**

To examine population and poverty‐specific exposure to floods, we employ spatial data defining flood hazard and a number of socioeconomic characteristics representing poverty and population density.

# **3.1. Flood hazard data**

# **3.1.1. Flood hazard maps for Vietnam developed for this study**

For this study, we developed flood hazard maps representing riverine, flash‐flood and coastal flood risk for Vietnam. These flood hazard maps estimate the inundation depth at a grid cell level of 3 arc‐seconds, (~ 90m) and provide coastal surge hazard layers, along with pluvial and fluvial layers. The maps provide information on the extent and depth of flood hazard for a specific location. For the coastal component, we explicitly model four return periods – 25, 50, 100, and 200 year events, under current and future climate conditions.

There is a significant amount of uncertainty with regards to how much sea level will rise. For that reason we model three future climate scenarios per return period: a low, medium, and high scenario (Table 1), using estimates from the IPCC (IPCC 2014; IPCC 2007). For the fluvial and pluvial hazards, future climate scenarios were not explicitly simulated owing to the complexity and considerable uncertainties that arise (Smith et al. 2014).<sup>1</sup> Although robust modeling of the magnitude of future extreme rainfall is not yet possible, heavy rainfall is expected to increase in a warmer climate, owing to the increased water holding capacity of the atmosphere. Therefore instead of a direct modeling approach, future climate scenarios were inferred by taking flood hazard maps derived under current climate conditions for different return periods, and using

> 1 These uncertainties largely arise from climate models; global climate models (GCMs) struggle to represent the physical processes that produce extreme rainfall. Indeed even in higher resolution regional climate models (RCMs), heavy rainfall events are poorly represented. As a result the modelled rainfall data must be ‘corrected’, in order to render it realistic. The fact that the underlying models themselves cannot represent flood driving rainfall means that there is little confidence in the projections that they produce. Moreover, at the national scale there is very little river gauge data available in Vietnam. Therefore rainfall‐runoff models, required to transform rainfall projections into river discharge values, would be largely un‐calibrated. This adds an additional source of significant modeling uncertainty to the model cascade. The combination of poorly represented extreme rainfall in climate models, coupled with uncalibrated rainfall‐runoff models, would largely render any projections of future flood risk impractical, owing to the significant uncertainties that arise.

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