features of an event may be important determinants of, for instance, the speed or duration of flooding. This simplification is necessary to define a unique severity threshold per hazard and we use intensity metrics well established in the literature. An intensity threshold helps focus on events that may cause substantial losses or exclude those that are, in general, easy to cope with.

Return periods are a common, but sometimes misunderstood, metric describing the occurrence of hazards. They describe the likelihood that a hazard event occurs at or above a specific intensity: in any given year, there is a 50% probability of experiencing an event as intense or more intense than the 2-year return period event; and a 1% probability of experiencing an event as intense or more intense than the 100-year return period event. It means that it is not impossible to experience two 100-year events in a single year. Table 1 shows the likelihood a person will experience events of different return periods, assuming the hazard distribution does not change over time.

When we count the number of people exposed to the 100-year flood with a threshold at 50 cm, we count the people who have more than a 1% chance of experiencing a 50-cm flood in any given year. This will include people who are much more likely to be flooded (e.g., the people who get flooded every other year in many tropical cities), as well as people who have a 1% chance every year to be affected by much deeper floods (e.g., a 2m deep flood). In sum, we consider locations exposed if events exceed a given intensity with a minimum probability (or maximum return period).

**Table 1: Return periods and likelihood of experiencing events**

|Return period|Likelihood of a person|experiencing eve|nt...|
|---|---|---|---|
|of event|in 10years<br>in 20years|in 50years|in a lifetime|
|10-year|65%<br>88%|99%|99%|
|20-year|40%<br>64%|92%|99%|
|100-year|10%<br>18%|39%|63%|

The hazard datasets used for analysis are summarized in Table 2, including the return periods and intensity thresholds initially considered for each type of event. All datasets are publicly accessible. Different thresholds are initially considered, but for much of the analysis the return period and intensity thresholds are set to specific values discussed further below.

**Table 2: Hazard data and thresholds defining exposed places**

|Hazard|Source|Spatial<br>resolution|Type|Return periods<br>(years)|Intensity thresholds defining<br>extreme events|
|---|---|---|---|---|---|
|Flood|Rentschler, Salhab, &<br>Jafino (2022)|3”<br>(~90 m)|Modelled|100|An inundation depth of at least<br>0.15 m, 0.5m, 1.5m|
|Agricultural<br>drought<br>Heatwave|FAO (2023), and<br>Schiavina, Melchiorri &<br>Pesaresi (2023)<br>Ridder et al. (2017)|~30”<br>(1 km)<br>5’<br>(~10 km)|Historical<br>Modelled|5,10,15,20,40*<br>5, 20, 100|At least 30 percent, and at least<br>50 percent of cropland or<br>grassland affected in any<br>season, confined to rural areas<br>A three-day running mean<br>maximum Wet Bulb Globe<br>Temperature (WBGT) of greater<br>than 32°C, 33°C, 34°C, 35°C, and<br>36°C|
|Cyclone|Bloemendaal, Haigh, de<br>Moel et al. (2020)|6’<br>(~11 km)|Modelled|10, 20, 30, 40,<br>50, 60, 70, 80,<br>90,100|A wind speed of at least the<br>Category 1, Category 2, and<br>Category3 thresholds|

Notes: * If the threshold was reached in any year since records began 39 years ago (1984-2022).

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