# 3.2 DROUGHT

We use Historic Agricultural Drought Frequency data from FAO, which defines drought events based on the Agricultural Stress Index (ASI) (FAO, 2023). Unlike the other hazards used in this paper, this drought data does not follow a probabilistic modeling approach and thus is less amenable to modelling extreme values. The historical frequency of severe droughts is calculated using the entire 39-year time series, spanning from 1984 to 2022. The ASI is based on remote sensing vegetation (NDVI) and land surface temperature (BT4) data, combined with information on agricultural cropping cycles derived from historical data and a global crop mask. Specifically, the vegetation health index (VHI) is defined as a weighted combination of two anomaly indicators of vegetation and temperature, which are based on the deviations of the actual observation from the historical range of NDVI and BT4. The pixels with a VHI value below 35% over a growing season (up to 2 seasons) are considered as experiencing severe drought. The ASI then captures the percentage of crop or grassland pixels within each administrative unit (based on the Global Administrative Unit Layers by FAO) affected by severe drought.<sup>11</sup> FAO provides data with a spatial resolution of 30 arcseconds (approximately 1 km) depicting the frequency of severe drought in areas where 30 or 50 percent of the cropland or grassland is affected by severe drought (as defined by ASI) for two seasons. We restrict drought exposure to rural areas using the GHSL application of the Degree of Urbanisation methodology (stage I) recommended by the UN Statistical Commission for classification (GHS-SMOD – R2023A), available at a resolution of 1km (Schiavina, Melchiorri, and Pesaresi, 2023). We consider return periods ranging from 5 to 40 years (based only on historical frequency) and map rural areas where more than 30 and 50 percent of cropland or grassland were affected in any growing season.

# 3.3 HEAT

We use global extreme heat hazard data from Ridder et al. (2017) prepared for the World Bank.<sup>12</sup> The hazard is categorized using the simplified Wet Bulb Globe Temperature indicator representing temperature, humidity, and radiative impacts. The WBGT is derived from global daily maximum air temperature and dew point temperatures for a 30-year period (1981-2010) from the ERA-Interim Global archive and topographic data from the Global Multi-resolution Terrain Elevation Data 2010. These 10-km resolutions yearly maxima of 3-day mean maximal WBGTs are then used to construct the probabilistic hazard intensity maps for a given return period. We utilize the 5, 20 and 100-year return period maps and define locations exposed to heatwave if the annual maxima of 3-day mean maximal WBGTs exceeds 32, 33, 34, 35 and 36°C.

# 3.4 CYCLONE

We use a global synthetic tropical cyclone dataset from Bloemendaal, Haigh, de Moel et al. (2020), generated using a synthetic resampling algorithm called STORM (Synthetic Tropical cyclOne geneRation Model). STORM is applied to 38 years of historical cyclone track data from the International Best Track Archive for Climate Stewardship (IBTrACS) to statistically extend the dataset to 10,000 years of cyclone activity. The data has been validated against historical observations and previous studies (Bloemendaal,

> 11 The drought hazard maps are available from <u>https://www.fao.org/giews/earthobservation/access.jsp. Refer to Van Hoolst et</u> al. (2016) for more information on the method of constructing ASI. More information on the Global Administrative Unit Layers (GAUL) is available at https://data.apps.fao.org/map/catalog/static/api/records/9c35ba10-5649-41c8-bdfc-eb78e9e65654. At GAUL level 2, there are 63376 districts or counties.

> 12 The extreme heat hazard maps are available from

> <u>https://datacatalog.worldbank.org/search/dataset/0040194/Global%20extreme%20heat%20hazard?version=2.</u>

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