The method to estimate exposure proceeds as follows. First, we generate categorical hazard maps for several return periods indicating the areas exposed to events exceeding each intensity threshold. Second, since the hazard and population data come from different sources with varying resolutions, we resample the hazard maps to align grid cells with the GHS population grid using nearest-neighbor matching. For flood, we resample the 3-arcsecond flood map to align with the GHS population grid of the same 3- arcsecond resolution.<sup>8</sup> We count the population exposed to different levels of flooding at this high resolution and then aggregate to a 30-arcsecond grid (approximately 1 km) for overlaying with other hazards.<sup>9</sup> Hazard data for drought, heatwaves and cyclones uses a coarser spatial resolution than the flood maps (1 km, 10 km, and 11 km, respectively), also reflecting that these events are less localized. We resample each of these three hazard maps to the 30-arcsecond GHS population grid. Finally, we calculate the number of people exposed at grid level by overlaying the population grid. Figure 5 illustrates the approach to estimating the number of people exposed to each type of extreme event and to any.

In the following subsections we describe the hazard data used in more detail, and how the four hazards are overlaid.

# 3.1 FLOOD

We use flood data from Rentschler, Salhab, and Jafino (2022) which indicates the maximum inundation depth for a 100 year return period considering the three most common flood types: (1) Fluvial flooding, occurring when intense precipitation or snow melt causes rivers to overflow; (2) Pluvial flooding, occurring when rainwater builds up beyond the absorptive capacity of soil; and (3) Coastal flooding, caused by storm surges and high tides in coastal areas. Country-level pluvial and fluvial flood data are from the 2019 version of the Fathom Global 2.0 flood hazard dataset (Sampson et al., 2015). These were combined with a separate coastal flood hazard map generated using the LISFLOOD-FP hydrological model (Rentschler et al. 2023). While Rentschler, Salhab, and Jafino (2022) provide gridded exposure headcounts to flood risk by admin area, we ignore the existing population count in their dataset and use only the information on inundation depths for 100-year return period flooding. We merge the data for all 187 countries to create a global dataset at a 3-arcsecond resolution and consider the following inundation depths as thresholds for being exposed to floods: 15cm, 50cm and 150cm.<sup>10</sup> It is important to note that the dataset used does not account for artificial flood protection structures. Although the use of the undefended flood maps may lead to an overestimation of exposure in areas with flood protection systems, there is evidence that many low-income lower-middle income countries do not have any flood protection system against even light flooding (Hallegatte et al., 2017; Rozenberg and Fay, 2019; Rentschler, Salhab, and Jafino, 2022).

> 8 The maximum misalignment using nearest neighbor resampling at this resolution is approximately 45 meters. 9 Smith et al. (2019) use the High-Resolution Settlement Layer (HRSL) population density map (at 1 arcsecond) and observe that current estimates of flood exposure using GHSL may overstate flood risks in rural regions while potentially underestimating these risks in urban areas. Other alternatives such as WorldPop an LandScan also overestimate the number of people exposed to floods. However, the HRSL is only available for the year 2015.

> 10 The flood hazard maps are available from <u>https://datacatalog.worldbank.org/search/dataset/0062763/Global-FloodExposure--Gridded-exposure-headcounts-by-country. The inundation depth is the maximum from the three types of flooding</u> considered. Inundation depths are assigned one of five categories: no risk, low risk (0 – 15 cm), moderate risk (15– 50 cm), high risk (50 – 150cm), and very high risk (> 150 cm).

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