annual and inter‐annual variability (Johnson et al., 2011), we use the difference in annual exposed people between GCM forced model runs in the future and the past to establish changes in the exposure.

# **3.2. Poverty data sets**

A comprehensive spatial database to examine the distribution of poverty within and across countries is not yet available at the required spatial resolution.<sup>2</sup> However, household surveys do contain some spatial information to approximate the location of a household, which we employ in this analysis. Our main analysis is undertaken using spatial wealth data sets from USAID’s DHS surveys, and is supplemented by small‐area estimates of poverty developed by the World Bank and partner countries.<sup>3</sup> Fifty‐two country‐wide DHS surveys contain household‐level data on wealth, as well as geo‐referenced data providing the coordinate location of the survey cluster (see Appendix B). While the sampling frame differs in each survey, there are typically 500‐1,000 survey clusters for each survey, with each cluster containing approximately 25 households. The specific indicator that we use in this study is the DHS “wealth index”, an indicator that was used previously to represent poverty (e.g. Barros et al., 2012; Fox, 2012; Ward and Kaczan, 2014). The wealth index is country‐specific and assigns an overall score to a household based on weighted scores of the assets they own.<sup>4</sup> All households in each country are classified in five quintiles (with quintile 1 having the lowest wealth, and quintile 5 the highest). We furthermore classified urban and rural households into quintiles, which enabled us to investigate the exposure across urban and rural populations separately. Importantly, as the wealth index used by DHS is generated relative to other households in the same country, it is comparable only within a country, and not between countries. Wealth index scores can therefore not be directly compared internationally.

To guarantee anonymity of the interviewed households, the geographical locations of the clusters have been randomly allocated by DHS within a radius of maximum 2 km from the real location for urban areas, and 5 km for rural areas. Since floods (opposed to droughts) have a high spatial variability, this uncertainty may affect the flood exposure estimates in particular. To investigate the propagation of the uncertainty of the geographical locations into our flood exposure estimates, we perform 100 random displacements of all geographical locations with a radius of 2 km for urban and 5 km for rural areas, and compute the exposure for each of the 100 displacements. This uncertainty analysis provides an idea of the robustness of results with respect to the random displacement of household locations, but also to errors in the flood maps, for instance linked to topography.

One issue with DHS surveys – and almost all other household surveys – is that they have not been designed to be representative at small spatial scales. Our randomization technique does not explore this error. As a result, they cannot be used to analyze exposure at the very small scale, e.g. in one

> 2 Although recent initiatives try and estimate global poverty at high‐resolution gridded scales, see for example WorldPop (2015).

> 3 <u>https://openknowledge.worldbank.org/handle/10986/6800</u>

> 4 ‐ For more information, see the DHS wealth index page: http://dhsprogram.com/topics/wealth <u>index/</u>

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