# The Platform for Economic Analysis of Climate Hazards
## (PEACH): Gridded Hazards from 2000-2019

Robert Reinhardt

► To cite this version:

Robert Reinhardt. The Platform for Economic Analysis of Climate Hazards (PEACH): Gridded Hazards from 2000-2019. 2025. halshs-05314666

HAL Id: halshs-05314666

https://shs.hal.science/halshs-05314666v1

Submitted on 14 Oct 2025

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_1.png" /></figure>

Distributed under a Creative Commons Attribution 4.0 International License
CES

Centre d'Économie de la Sorbonne
UMR 8174

# The Platform for Economic Analysis of Climate Hazards
## (PEACH): Gridded Hazards from 2000-2019

Robert REINHARDT

2025.22
# The Platform for Economic Analysis of Climate Hazards (PEACH): Gridded Hazards from 2000-2019

Robert Reinhardt<fnref n="1" />

Centre d'Economie de la Sorbonne (CES), Université Paris 1 - Panthéon-Sorbonne

Latest Version: 23 July 2025

**Abstract.** In analyzing the socio-economic impacts of extreme events, it is essential to consider the effects and interactions of climatic variables at the local level. The Platform for Economic Analysis of Climate Hazards (PEACH) combines preexisting datasets to a globally applicable framework to assess socioeconomic effects from 14 continental climate impact drivers (CID), following the IPCC report, and 3 geophysical hazards. The spatial resolution is on a 10 × 10 km grid for each month between 2000 and 2019, with full coverage between 2004 and 2015. Six commonly used socio-economic variables are added including annual population, nighttime-light and normalized-differenced vegetation index. Unlike previous datasets, it avoids imposing thresholds for inclusion whenever possible, enabling the exploration of response and damage functions based on the intensity of shocks.

**Keywords:** Climate Change, Extreme Events, Development

**JEL Codes:** Q54, C80, Q56

## 1 Background & Summary

As the likelihood of extreme events, including those that co-occur, increases, the need for higher-resolution analysis in impact studies becomes more critical. Improved resolution of such studies enables better tailoring of policies for future responses to extreme events, especially in the Global South. In the past, social scientists frequently estimated responses at the country level (Shabnam, 2014; Botzen, Deschenes and Sanders, 2019), which underestimates local effects, at the household level (De and Thamarapani, 2022; Sen, Nayak and Mohanty, 2022), often with gaps in years, or on specific hazards (Hsiang and Jina, 2014; Anderson et al., 2021), potentially underestimating the interaction between different types of hazards (Ridder et al., 2020). Most studies rely on preexisting event-specific datasets such as the widely used Emergency Events Database (EM-DAT)(Rosvold and Buhaug, 2021) or the DesInventar database (United Nations Office for Disaster Risk Reduction (UNDRR), 2024) hosted by the United Nations Office for Disaster Risk Reduction. These datasets are extremely valuable as they provide

<footnote n="1">¹Contact: robert.reinhardt@etu.univ-paris1.fr.</footnote>
detailed information on the economic and societal effects of disasters. Yet, they are
often provided at aggregated administrative levels (country or province level) and do
not always report the intensity of an event; both are needed to better understand the
interaction between society and the environment. Furthermore, EM-DAT has been
criticized for overrepresenting richer countries (Felbermayr and Gröschl, 2014).

In response to upcoming criticism, there has been a shift to down-scale the data to a
more local level in the GDIS project (Rosvold and Buhaug, 2021) or to extend, notably
the EM-DAT database, with intensities for a subset of events in the so-called ifo-GAME
database (Felbermayr and Gröschl, n.d.; Felbermayr and Gröschl, 2014). The last major
improvement in this type of literature has been the introduction of a new methodology
to classify co-occurring, so-called "compound" events (Claassen et al., 2023a) using an
algorithm called "MYRIAD-HES". The MYRIAD-HES project also provides data on
hazard events for eleven hazards on a daily level between 2004 and 2017. The resolution
of the hazard varies depending on the input data used. To determine which events to
include, thresholds are established for each type of hazard. The data presented here is
the closest comparable to the MYRIAD-HES data with three main differences.

First, the Platform for Economic Analysis for Climate Hazards (PEACH), the dataset
described here, attempts to largely avoid imposing thresholds whenever possible. This
is important as for many studies it is necessary to know also if an event is close to
a threshold. For example, strong precipitation might be strong enough to damage
crops, but not strong enough to be classified as an anomaly or an extreme event. The
PEACH data includes, in total, 17 hazard categories on a monthly resolution on a global
unified 10 km grid from 2004 to 2015. For 13 of the 17 hazards, data is available from
2000 to 2019. Thresholds are only applied when the input data is available at a higher
temporal resolution (hourly or daily) and needs to be aggregated to a monthly value.
This affects 6 of the 17 categories. Monthly resolutions come at the cost of not being
able to examine daily co-occurrences as thoroughly as possible with the MYRIAD-HES
event database (Claassen et al., 2023b). However, the approach chosen for PEACH
allows researchers to set their own threshold of what defines an "extreme event," as
this might vary depending on the study context. It also allows for a homogenized panel
of hazard exposure over time.

Second, a key difference between PEACH and previous datasets is that this approach
justifies the inclusion of hazards not by relying on the EM-DAT classifications of
hazards. Instead, the hazards included are guided by the notion of "climate impact
drivers" (CID) (Ruane et al., 2022; Seneviratne et al., 2012), which are specific climate
components that directly affect human systems and ecosystems. These CIDs are
referenced by the Intergovernmental Panel on Climate Change (IPCC) and have been
assessed for their potential to impact socio-economic and environmental outcomes.
As they have already been evaluated, it helps researchers identify which hazards are
most relevant to their specific context and where research gaps exist. Of the 17 hazards
included, 14 are CIDs. The remaining three hazard classes are geophysical hazards
that are not directly driven by climate change, but have substantial impacts on human
health and economic development. Taking into account 17 hazards allows us to further
study the interconnectivity of hazards, which have been found to cluster around global hotspots (Lee et al., 2024; Ridder et al., 2020).

Third and last, the PEACH data adds a selection of socio-economic variables to the dataset. While it is difficult to determine which variables are the most urgent, the chosen variables can be categorized into three classes, all of which are particularly relevant for social science studies: economic activity, hazard resilience/vulnerability, and agriculture.

For economic measures, nightlight is included. The inclusion follows the influential work of (Elvidge et al., 1997), which became widely applied in the economic literature through the work of Henderson, Storeygard and Weil (2011). Economic activity due to electricity consumption appear as brighter spots in satellite images. Since this can originate from either economic activity or population, it's crucial to consider population size and density together with nightlight data. This is important for two reasons: first, rural areas with limited electricity access may have weak or no nightlight, where population estimates help fill data gaps. Second, nightlight can also reflect economic activity unrelated to human settlements, such as gas flaring or petroleum plants. Combining both variables provides a more comprehensive picture of human and economic activity, which is important for impact analysis.

To assess resilience and vulnerability, PEACH incorporates relative wealth and critical infrastructure. Advances in machine learning enable researchers to analyze large datasets —such as daylight images, building footprints, and more—to estimate wealth within a country. When studying the response to hazards, it is essential to consider the income level of a given area. While nightlight reflects economic activity, wealth is a broader measure of economic well-being. For example, a well-lit petroleum plant may not indicate local wealth, as nearby residents might be relatively poor due to lower housing costs. Similarly, the presence of critical infrastructure should be considered, as hazards affecting these areas can amplify negative consequences. Therefore, both relative wealth and critical infrastructure indices are included.

Finally, for agriculture variables, the Normalized Difference Vegetation Index as well as land use is added. The famous work of Reed et al. (1994) or Pettorelli et al. (2005) made the use of NDVI common in the literature. The satellite images used to measure vegetation allow for the study of changes at high temporal and spatial resolutions. In PEACH, due to its aggregation, some spatial detail is lost; however, trends over time remain useful for assessing large-scale changes in farming. However, within each cell, only a portion may actually contain agricultural land. To address this, land use data is incorporated to weight NDVI values based on the fraction of vegetation-prone land. In addition, land use data complement population and nightlight estimates by offering insight into urban land distribution.

To conclude, the PEACH dataset effectively unifies well-established datasets with a globally consistent grid, providing easier access to data for researchers, particularly those in social sciences interested in environmental studies, disaster risk management, and adaptation to climate change. Methodologically, it primarily utilizes weighted statistics and combines data from various sources to create the dataset. Although not all
CIDs are covered, the replication code is written to be easily expandable to additional CIDs. PEACH provides a global grid, yet for one of the 17 hazard classes, there is only quasi-global coverage. This concerns rainfall, as the data is derived from the high-resolution CHIRPS data (Funk et al., 2014; Funk et al., 2015) that reports precipitation only for the 50 °N and 50 °S latitudes in the current version. This is a known limitation, as it excludes snow and ice; nonetheless, CHIRPS is still used due to the high resolution of the data. All other hazard classes have global coverage. A subset of hazard classes is validated with ground truth data from weather stations from the Global Summary of the Day dataset (GSOD) (NOAA National Centers for Environmental Information, 1999) and compared with data from the EM-DAT data set. An overview of the data is provided in Figure 1. The upper panel (a) shows the exposure of individual cells with more than one person per square kilometer to the total number of hazards recorded by quantiles using a potential chosen threshold for extreme events. More information on the thresholds taken is provided in Appendix A. The lower panel (b) shows the covered CIDs and their corresponding impact dimensions, as taken from the IPCC report. In red, it shows the temporal coverage of the individual hazards included.

## 2 Methods

To create a unified framework for the PEACH data, the following steps are taken: First, a global grid is established. Then, the hazard classes are selected and transformed into the grid. Finally, socioeconomic data is added.

### 2.1 Grid Creation

At first, the administrative boundaries of countries are obtained from the geoBoundaries dataset (Runfola et al., 2020). GeoBoundaries provides a consistent and regularly updated version of administrative boundaries. The boundaries for 2023 are used. The boundaries are then divided into 10x10 km cells, which were created using the Pseudo-Mercator projection. While this leads to distortion effects towards the poles, the Pseudo-Mercator projection is chosen because it is widely applied. When grid cells overlap two country boundaries, the cell is assigned to the country with the majority of the cell area. The grid is then reprojected to the standard Mercator projection (EPSG: 4326) for further use. Each cell is assigned a unique cell identifier. To increase computational speed, the global grid is divided into subregions following the classification of the United Nations Statistics Division (UNSD, n.d.). Disputed areas are assigned to the region in which they are located. The 10x10 km grid cell size represents a middle ground between the highest-resolution input data (approximately 90m) and the coarsest input data (up to 75 km). Most of the input data falls within the 5 to 25 km range, making 10 km a practical compromise that preserves spatial heterogeneity while keeping computational efficiency. This resolution is fine enough to capture meaningful subnational variation
<figure><img src="image_2.png" /><figcaption>Figure 1: Overview on the Data</figcaption></figure>

**Note:** The figure shows the global distribution of hazard exposure, the hazard types included, their temporal coverage and their impact channels. Panel (a) shows the total sum of hazard exposure by cell for the years 2004 - 2015 for 15 considered types. For illustrative purposes, in this figure, cells with fewer than 1 person per square km are excluded. More information on the thresholds taken to define events is provided in the Appendix. Colors are defined based on scientific colors (Crameri, Shephard and Heron, 2020). Panel (b) illustrates, on the left, the impact dimension on outcomes, adapted from IPCC Chapter 12, Table 12.2 (IPCC, 2021). The * symbol categories are not included in IPCC and are predicted by us. The right table plots the data coverage in PEACH for each hazard.
(e.g. local hazard exposure) yet coarse enough to ensure that most datasets can be meaningfully aggregated or interpolated without excessive data loss.

## 2.2 Hazard Classes

The PEACH data includes 14 climate impact drivers (CID) and 3 geophysically related hazards. Not all CIDs mentioned in the IPCC Sixth Assessment Report, Chapter 12 (IPCC, 2021), are included, mainly due to data availability constraints. Most snow and ice categories are missing, as well as all oceanic and coastal hazards. The three non-directly climate-driven hazards are included due to their potential to have substantial impacts on human health and economic development. An overview of all hazards, their sources, and resolutions is provided in Table 1. The merging procedure for combining the source data with the grid of each region is illustrated in Figure 2. For gridded source data, the process depends on whether the resolution of the source data is higher than or lower than the 10km of the grid cells. If the resolution is lower, then zonal statistics are computed based on the majority overlap between cells, relying on the Python rasterstats package. If it is higher, the computed statistics depend on the hazard category. Whenever possible, the weighted mean is computed based on the xagg package (Schwarzwald and Geil, 2024) in Python. For polygon layers, such as earthquakes or floods, again majority overlap conditions are used, and the maximum intensity values are computed. For point geometries, the intersections are computed. In the following, for each hazard class, a discussion on the applied transformation and the final outcomes is included.

*Mean air temperature & Cold Spells* The monthly 2-meter mean temperature data from ERA-5 (Copernicus Climate Change Service, 2022) used here cover each month from 1960 to 2020 and are provided in Kelvin. The data is transformed into degrees Celsius and three standardized z-scores are calculated using the standard formula:

$$x_{z,m} = \frac{x_m - \bar{x}_m}{\sigma_{x_m}}$$

Where x is the monthly temperature in cell x at a time point t. $\bar{x}_m$ is the mean temperature of cell x and $\sigma_x$ the standard deviation for a given month. The scores are always calculated in comparison to the previous months. The differences between the three z-scores are based on differences in the way the mean and standard deviation are calculated. The first one ("temp_z_max") uses all available months since 1960. This score provides the long-term anomalies of the temperature from historical monthly means. The second score ("temp_z_30") computes the mean and standard deviation based on a 30-year moving window, e.g. for January 2002 all January from 1972 to 2001 are used. This definition follows climatological standard normals, also applied by the World Meteorological Organization (WMO) (WMO, n.d.). This provides the medium-run anomalies of temperature. Lastly, the third score ("temp_z_10") is computed in the same way as the second score just with a 10-year moving window. It reflects the short-term anomaly. Using the three different definitions enables us to better investigate
<table><caption>Table 1: Overview on Hazard Data</caption><thead><tr><th>Category</th><th>CID</th><th>Source</th><th>Metric Used</th><th>Aggregation Scheme</th><th>Spatial Resolution</th><th>Temporal Resolution</th><th>Temporal Coverage</th><th>Spatial Coverage</th></tr></thead><tbody><tr><td rowspan="3">Heat & Cold</td><td>Mean air temperature</td><td>ERA-5</td><td>Degree (C°)</td><td>Anomaly</td><td>0.25</td><td>Monthly</td><td>1940-present</td><td>Global</td></tr><tr><td>Extreme Heat</td><td>Mistry (2019)</td><td>WBGT</td><td>Days >Threshold</td><td>0.25</td><td>Daily</td><td>1970-2018</td><td>Global</td></tr><tr><td>Cold spell</td><td>ERA-5</td><td>Degree (C°)</td><td>Anomaly</td><td>0.25</td><td>Monthly</td><td>1940-present</td><td>Global</td></tr><tr><td rowspan="7">Wet & Dry</td><td>Mean precipitation</td><td>CHIRPS</td><td>mm</td><td>Total</td><td>0.05</td><td>Monthly</td><td>1981-present</td><td>50°S-50°N</td></tr><tr><td>Flood (river or pluvial)</td><td>DFO Rentschler, Salhab and Jafino (2022)</td><td>DFO Severity Cell affected by RP100 event</td><td>Maximum & Number Weighted mean of cell</td><td>P 0.01</td><td>Daily</td><td>1985-present</td><td>Global Global</td></tr><tr><td>Heavy Precipitation</td><td>CHIRPS</td><td>mm</td><td>Anomaly</td><td>0.05</td><td>Monthly</td><td>1981-present</td><td>50°S-50°N</td></tr><tr><td>Landslide</td><td>Global Landslide Catalogue + COOLR Global Fatal Landslide Database</td><td>Event count Event count</td><td>Total Total</td><td>P P</td><td>Daily Daily</td><td>2007-present 2004-2016</td><td>Global Global</td></tr><tr><td>Drought (any type)</td><td>HYDRO-Jules</td><td>SPEI 3,6, 12</td><td>Weighted mean of cell</td><td>0.05</td><td>Monthly</td><td>1981-2022</td><td>Global</td></tr><tr><td>Fire Weather</td><td>Fire Weather Index - ERA-Interim Fire Climate Change Initiative</td><td>Fire Weather Index Burned pixel (CI>0.9)</td><td>Days >Threshold Weighted mean of cell</td><td>0.7 0.003</td><td>Daily Monthly</td><td>1980-2018 2001-2022</td><td>Global Global</td></tr><tr><td>Severe Wind Storm</td><td>ERA-5 - CDS Toolbox iBTrACS</td><td>Wind gusts (m/s) Maximum wind speed (m/s)</td><td>Days >Threshold Maximum & Number</td><td>0.25 P</td><td>Hourly Hourly</td><td>1940-present 1850-present</td><td>Global Global</td></tr><tr><td rowspan="2">Wind</td><td>Tropical Cyclone</td><td>Willis Research Network Global Tropical Cyclone Wind Footprint (WRN-TC)</td><td>Maximum wind speed (m/s)</td><td>Maximum & Number</td><td>P</td><td>Daily</td><td>1989-2020</td><td>Global</td></tr><tr><td>Sand and Dust Environments</td><td>Copernicus Atmosphere Monitoring Service (CAMS) - EAC4 ERA-5 - CDS Toolbox</td><td>Dust Aerosol Optical Depth at 550nm Wind gusts (m/s)</td><td>Days >Threshold Days >15 m/s</td><td>0.75 0.25</td><td>Hourly Hourly</td><td>2003-present 1940-present</td><td>Global Global</td></tr><tr><td rowspan="3">Snow & Ice</td><td rowspan="3">Hail</td><td>Prein and Holland (2018)</td><td>Hail Probability CAPE (J/kg) and Convective Precipitation (mm)</td><td>Days >Threshold Days >Threshold</td><td>0.7 0.25</td><td>Daily Hourly</td><td>1979-2015 1940-present</td><td>Global Global</td></tr><tr><td>ERA-5 - CDS Toolbox</td><td>Hail Days</td><td>Days</td><td>P</td><td>Daily</td><td>1973-present</td><td>Quasi-Global</td></tr><tr><td>Global Summary of the Day</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td rowspan="5">Other</td><td rowspan="2">Air Pollution Weather</td><td>Shen et al. (2024)</td><td>PM 2.5</td><td>Weighted mean of cell</td><td>0.1</td><td>Monthly</td><td>1998-2022</td><td>Global</td></tr><tr><td>Copernicus Atmosphere Monitoring Service (CAMS) - EAC4</td><td>PM 2.5 & PM 10</td><td>Weighted mean of cell</td><td>0.75</td><td>Monthly</td><td>2003-present</td><td>Global</td></tr><tr><td>Earthquakes</td><td>Shakemap Catalogue</td><td>Modified Mercalli Intensity (MMI)</td><td>Maximum & Number</td><td>P</td><td>Daily</td><td>1900-present</td><td>Global</td></tr><tr><td>Volcano Eruptions</td><td>Global Volcanism Program</td><td>Volcano Explosivity Index (VEI)</td><td>Maximum & Number</td><td>P</td><td>Daily</td><td>1345 BC - present</td><td>Global</td></tr><tr><td>Tsunami</td><td>Global Historical Tsunami Database Pronk et al. (2024)</td><td>Wave Height at Runup (m) Coastal Elevation (m)</td><td>Maximum & Number Weighted mean of cell</td><td>P 0.0003</td><td>Hourly</td><td>2000BC - present</td><td>Global Global</td></tr></tbody></table>

Note: The table shows the used CID, the measure, associated metric, how the data is aggregated, the spatial resolution of the origin data in degree (P stands for polygon), temporal resolution and coverage as well as spatial coverage. Sources: Mean air temperature: Copernicus Climate Change Service (2022) and Sabater (2019), Extreme Heat: Mistry (2020), Cold spell: Copernicus Climate Change Service (2022), Mean precipitation: Climate Hazards Center (UCSB) (n.d.), Funk et al. (2015) and Funk et al. (2014), Flood: Brakenbridge and Dartmouth Flood Observatory (n.d.) and Rentschler, Salhab and Jafino (2022), Heavy Precipitation: Climate Hazards Center (UCSB) (n.d.), Landslide: Kirschbaum et al. (2010), Kirschbaum, Stanley and Zhou (2015) and Froude and Petley (2018), Drought: Gebrechorkos et al. (2023), Fire Weather: Giuseppe et al. (2019), Vitolo et al. (2019), Copernicus Climate Change Service (2019) and Vermote and Wolfe (2015), Severe Wind Storm: Copernicus Climate Change Service (n.d.), Tropical Cyclone: Gahtan et al. (2018), Knapp et al. (2010), Done (2022) and Done et al. (2020), Sand and Dust Environment: Inness et al. (2019) and Copernicus Climate Change Service (n.d.), Hail: Prein and Holland (2018a), Prein and Holland (2018b), Copernicus Climate Change Service (n.d.) and NOAA National Centers for Environmental Information (1999), Air Pollution: Shen et al. (2024) and Inness et al. (2019), Earthquakes: United States Geological Survey (USGS) (2017), Volcanos: Global Volcanism Program and Venzke (2023), Tsunami: National Geophysical Data Center (2024) and Pronk et al. (2024). Note: During the writing process, the ERA-5 CDS Toolbox that is used has been deprecated and been partially replaced (Copernicus Climate Change Service, 2024). Otherwise, daily statistics can be computed using ERA-5 hourly data (Hersbach et al., 2023).
<figure><img src="image_3.png" /><figcaption>Figure 2: Aggregation and Data Transformation</figcaption></figure>

**Note:** This figure illustrates the spatial merging procedure to convert data from raster files or polygons onto a unified cell. The matching is conducted in Python and replication files are provided.
whether adaptation occurs and to distinguish between long-term climate shifts and short-term temperature variability. PEACH, therefore, includes four variables for the three z-scores and the monthly mean temperature in degrees Celsius. In addition to investigating mean air temperature, z-scores also allow one to compute monthly cold spells by taking negative anomalies on mean temperature (Felbermayr et al., 2022), potentially in addition to a threshold on low mean temperature. This definition of cold spells is suitable for investigating the impact on agricultural productivity rather than on mortality.

*Extreme Heat* Extreme heat and extreme temperature are related concepts, but have distinct meanings. In the context of PEACH, extreme heat is primarily concerned with its impact on human health, considering both temperature and humidity factors. Extreme heat is measured by daily maxima of the Wet-Bulb Globe Temperature (WBGT) - Index for the years 2000 to 2018 provided by Mistry (2020). To aggregate the data to a monthly level, two measures are calculated: maxima of rolling-means and the number of days above a fixed threshold. First, maxima of rolling means are computed as the mean WBGT for a one, three and five-day moving window. For example, if the WBGT is 30°C on day 1, 31°C on day 2 and 32°C on day 3, the rolling mean for a three-day window are 31°C. Then the corresponding values per month is the maximum of these one, three, and five day windows recorded. The second measure is the number of days when the WBGT exceeds a threshold. The threshold is set to 32.2°C, following the recommendations of the heat flag system (Patel, Mullen and Santee, 2013; Clark, Konrad and Grundstein, 2024), which is applied, for example, by the US military. Above this temperature, corresponding to a "black flag", physical exercise should be reduced to a minimum. The final data contains four final variables for the three different block maxima, as well as days above the threshold.

*Mean Precipitation and Heavy Precipitation* Information on heavy precipitation such as standardized score and total precipitation in mm per month comes from the CHIRPS database jointly with the Early Warning Explorer (EWX) (Climate Hazards Center (UCSB), n.d.; Funk et al., 2015). The data is available for every month, but unfortunately limited to 50°S-50°N, which needs to be kept in mind when trying to construct models for the utmost northern and southern regions (e.g. Siberia, Scandinavia, Northern Canada, Alaska and Patagonia). As the data is already provided in the correct format, it is merged with the grid using weighted zonal statistics. Two variables are included: the standardized rainfall anomaly score and the total precipitation.

*Flood (River or Pluvial)* The main input to flood is the boundaries of the affected flooded areas of the Dartmouth Flood Observatory (Brakenbridge and Dartmouth Flood Observatory, n.d.). At first, for each event overlaps are computed with the global grid. For each event, the attributed given flood ID ("DFO_ID"), the number of deaths, displaced persons, severity, and duration are added. If multiple flood events are recorded in the same cell and month, the maximum values are taken. To control for the quantity, an
additional dummy variable that counts the number of floods in each cell is added<fnref n="2" />.
However, flooded areas can be quite extensive. While not essential for flood analysis,
recently published data (Rentschler, Salhab and Jafino, 2022) on populations exposed to
flooding helps to pinpoint affected locations more accurately. The data is based on the
Fathom flood model and measures the total number of population exposed to a flood
with a 100-year return period for each 3 arc-second pixel. The exposure data is provided
in tiles (areas of interest) with multiple bands, where each band contains various layers
for different levels of inundation depth. First, it is assessed if all tiles overlap with
the global grid. Following the authors' recommendations, all bands with inundation
depths above 15 cm are combined to identify a population at high risk. Then, if at
least one person is at risk in the pixel, the pixel is considered exposed. Aggregation
with the grid is done by computing the average share of pixels at risk in each cell
("flood_risk"). To obtain a likely location of exposure to a flood, it is recommended to
combine the flood risk variable with the flood observation record. The final dataset
contains a variable for the flood ID, maximum number of deaths, displaced persons,
severity, duration, count and the respective (socio-economic) cell risk.

Landslides Landslides are fairly local events and there are not many global datasets. To get a good coverage of events, the two most common datasets are combined: the Global Landslide Catalog (GLC) (Kirschbaum et al., 2010; Kirschbaum, Stanley and Zhou, 2015; NASA, n.d.) and the Global Fatal Landslide Database (Froude and Petley, 2018). The reason is that the temporal coverage between the GLC and GFLD varies, which is why most events before 2007 are drawn from the GFLD and those after 2016 are exclusively from the GLC. The GLC is a project started in 2007 by NASA and is complemented in this version with the extended user-driven Cooperative Open Online Landslide Repository (COOLR). It provides point coordinates of landslide records, along with accuracy, possible trigger source, fatalities recorded, and date. Although active updating began in 2007, many events from the period prior have been added ex-post. PEACH includes landslides that have a specified location and date. Of the remaining 35 000 events, those are excluded where the accuracy is not known or is less than 25 kilometers accurate. This excludes another 22 000 events. Then to avoid double accounting, the same criteria are applied to the second dataset used. The GFLD accounts only for landslides in which fatalities are recorded. Following a similar approach than a recent study (Gómez, García and Aristizábal, 2023) the two datasets are merged based on date and location. To avoid mismatches based on location precision, locations are rounded to the nearest kilometer. In total, 78 duplicate events are excluded. From the remaining events five variables are generated: first, if the trigger for the event is specified, a dummy is generated indicating the trigger. Then, depending on the accuracy, if it is below 25km, a buffer is drawn around the point coordinate indicating the potentially exposed area of the event. To identify these events, a dummy is generated indicating the precision of the event location. Then, the coordinates of the points are merged with the grid. If multiple events are recorded in a cell for the same

<footnote n="2">²Researchers interested in recovering event-boundaries should refer to the original polygons of Braken-
bridge and Dartmouth Flood Observatory (n.d.)</footnote>
month, a counter variable is added to indicate the number of events in that month. If there are multiple event records, the trigger dummy and precision dummy are adjusted accordingly. If there are no multiple events, then the corresponding event identifier is included. Additionally, a dummy variable is added to indicate whether the event is drawn from the GLC-COOLR or the GFDL. The final data to add to PEACH accounts for the landslide identifier, the trigger cause, the precision of the event and a dummy if the landslide caused any human death and accounts for 16 800 events.

*Drought* The used HYDRO-Jules (Gebrechorkos et al., 2023) already reports monthly Standardized Precipitation Evapotranspiration Index (SPEI) on a five-kilometer resolution. To account for different types of drought, the durations are included: the 3-month SPEI, the 6-month SPEI and the 12-month SPEI. The 3-month SPEI evaluates short-term meteorological drought, the 6-month SPEI extends it rather to a medium-term meteorological drought; both indicate, however, also to some extent agricultural drought. The 12-month SPEI assesses rather long-term drought conditions indicating issues of general aridity. The PEACH dataset therefore contains three included drought measures.

*Fire Weather and Wildfires* Information on fire is divided into fire risk and fire exposure. Data on fire risk is based on the daily Fire Weather Index (FWI) (Giuseppe et al., 2019), which has a daily resolution of 70 km. To compute a monthly estimate, two metrics are computed. First, the monthly 5-day moving average maxima, so the highest value within a 5-day moving window period. Second, using all years from 1980 until 1999 to build the mean and standard deviation, a z-score is computed for each month. To measure exposure to fire, we used information on burned area at 300 m spatial resolution (Copernicus Climate Change Service, 2019; Vermote and Wolfe, 2015), derived from MODIS data and provided by the ESA Climate Change Initiative (Climate Change Initiative Fire Project, Fire CCI). First, to increase computational speed, the resolution is coarsened to one kilometer, so that each aggregated pixel is the share of land burned in this one square kilometer area. Only pixels classified as burnt with at least 90% probability are considered. Then, for each global grid cell, the share of land burned is computed. For very large land masses, the merging part is compartmentalized into smaller subsets. A second variable is created that measures the burned land in areas identified as cropland (irrigated, rainfed, and mosaic land). The final added data contains, therefore, four variables: the FWI 5-day block-maxima, the FWI anomaly, the share of the cell burned, and the share of burned land that is identified as cropland.

*Severe Wind Storm* The computation of severe windstorms is comparable to the MYRIAD-HES approach (Claassen et al., 2023b). The daily maximum of hourly wind gusts at 10m in m*s⁻¹ from the CDS-Toolbox for ERA-5 (Copernicus Climate Change Service, n.d.) is acquired. Aggregating short-term wind gusts on a monthly level requires some decisions. Two statistics are computed: the monthly maximum of hourly wind gusts to see the strongest events and the number of days when wind gusts above 50.4 knots (26 m/s) are recorded, which is defined by the US Federal Emergency Management
Agency (FEMA) and National Oceanic and Atmospheric Administration (NOAA) as minimum criterion for severe thunderstorm environments (Zuzak et al., 2023). Two additional variables are introduced: the maximum wind gusts reported in a month and the number of days with wind speeds exceeding 26 meters per second.

*Tropical Cyclone* Including tropical storms requires the computation of wind speed models around the center of the cyclone. As for the MYRIAD-HES (Claassen et al., 2023b) data, cyclone tracks from iBTraCS (Gahtan et al., 2018) are combined with wind field buffers from the Willis Research Network Global Tropical Cyclone Wind Footprint dataset (WRN-TC) (Done, 2022) based on the storm season and name. If no wind field buffer is available from WRN-TC for a specific storm, a 20-kilometer buffer is drawn around the eye of the tropical cyclone, and for each movement point of the storm track, the maximum wind speed in meters per second is included. Preferably, the NOAA-NHC one-minute averaged wind speed is used; if not available, the next largest estimate from the any agency affiliated to the World Meteorological Organization is included. For each cell and month, the maximum wind speed recorded is computed. Should multiple cyclones strike the same cell in the same month, the maximum recorded wind speed is taken, and a counter variable counts the number of storms within the same month. PEACH, therefore, contains two variables: the maximum wind speed of an event and the number of cyclones recorded in a given month.

*Sand and Dust Environments* To the best of our knowledge, there is no current dataset for dust storm events, yet the presence of high dust paired with stronger wind gusts could indicate at least dusty environments. To include a measure of days with high dust presence, as an indicative proxy of dust storm presence, two sources are combined: first, the 6-hour dimensionless Dust aerosol optical (DAO) depth at 550 nm (Inness et al., 2019) is converted into daily maxima. Then the 90th percentile is computed to capture only extreme dust environments. A dust environment day is defined if the daily maximum is above the 90th percentile in optical depth and at least 0.5. Although this is a low threshold, similar thresholds have been applied in research (Logothetis et al., 2021). In desert-like areas, the percentile will ensure a more extreme event. Second, we add the hourly wind gust data from ERA-5 (Copernicus Climate Change Service, n.d.). A threshold of daily maximums of around 15 m/s is set. As we rely on wind gust and not sustained wind speed a slightly higher threshold than 10 to 14 meters per second as found in some studies (Muhammad Akhlaq, Sheltami and Mouftah, 2012) is used. The two data sets are homogenized using spatial interpolation. A dusty day is defined as a day when both conditions hold: high levels of dust presence and daily wind gusts above 15 m/s. The included variable counts the number of days in a month in which both conditions hold. When used in a study, this variable should be taken with caution. It is indicative of days with high dust presence combined with stronger winds. Sand and dust storms are notoriously hard to model, so these variables could also be indicative of a blowing dust event, but not a full-blown dust storm.

*Hail* Data on global hail occurrence is scarce. Here, we primarily rely on a compiled daily database of hail probabilities (Prein and Holland, 2018a) from 1970 to 2015, which
has been utilized in similar studies (Ridder et al., 2020). The resolution is with 0.75 degrees (or around 80km) quite large, but to our knowledge, it is the only available dataset applicable worldwide. The hail probabilities are estimated based on calibrated ground truth data from the US, Australia and Europe given the availability of the data. To create meaningful hazard variables, we compute two indicators: the number of days in a month that exceed a 90% and 99% hail probability. Both serve as lower-bound estimates of the events. For some parts of the world, this data set lacks ground truth data. Meteorological conditions might make hail probable, but the hail might melt in high-temperature areas on the way to Earth. This should be taken into account when using the variable. To further add information on likely hail locations, a metric of thunderstorm environments that could cause hail is created. To achieve this, based on reported hail events from weather stations, global thresholds are defined for two indicators used to assess the stability of thunderstorm environments. If then both indicators exceed the fitted thresholds defined by weather station data, an indicator variable is assigned the value 1, 0 otherwise. Finally, the number of days when the indicator equals one is counted. Therefore, from a global sample of weather stations (NOAA National Centers for Environmental Information, 1999), all reported hail events between 2000 and 2020 are extracted. Then, for each location, the corresponding daily maximum hourly values for the two chosen indicators, Convective Available Potential Energy (CAPE) and Convective Precipitation (CP) (Copernicus Climate Change Service, n.d.), are added. CAPE and CP are amongst other factors indicators for unstable thunderstorm conditions (Taszarek et al., 2021). The 93 687 hail records of 7 417 stations are binned into latitude bins of 1 degree for CAPE and 5 degrees for CP. Next, within each bin the 95th percentile of CAPE within that bin is computed as well as the median value of CP. The median is chosen as excessive rainfall might hinder hail formation. Using the computed percentile or median in each bin, a polynomial function is fitted (4th order for CAPE and cubic for CP) to get a continuous function of thresholds by latitude for CAPE and CP. Mathematically this corresponds to:

$$ \widehat{\text{CAPE}}(\phi) = \alpha_4\phi^4 + \alpha_3\phi^3 + \alpha_2\phi^2 + \alpha\phi + \alpha_0 \quad (1) $$

where $\phi$ is the observed CAPE$_{95}$ in a latitude bin. A similar approach is used for CP:

$$ \widehat{\text{CP}}(\phi) = \beta_3\phi^3 + \beta_2\phi^2 + \beta\phi + \beta_0 \quad (2) $$

where $\phi$ corresponds to the CAPE$_{50}$ in a latitude bin. The fitting is shown in Figure 3. To avoid inclusion of too weak convection events CAPE is limited to 150 J*kg$^{-1}$ and CP to a minimum of 0.25 mm*h$^{-1}$ as defined for thunderstorm environments in literature (Taszarek et al., 2021). Most thresholds for latitude bins are beyond 1000 J/kg CAPE, which has also been associated with hail formation for a study applied to Europe (Kaltenböck, Diendorfer and Dotzek, 2009). Using hourly data aggregated to daily maxima (Copernicus Climate Change Service, n.d.), days in which observed values exceed the CAPE and CP latitude-specific thresholds are considered to be days with thunderstorm environments potentially favorable for hail formation. For PEACH,
the number of days each month is then counted. In the future, this indicative measure should be improved by using alternative hail indices, such as the Significant Hail Parameter (SHIP), or at least by adding information on the 0-6 km vector wind shear. The final dataset will have three variables: the number of days when hail has been likely and the number of days with thunderstorm environments potentially favorable for hail formation.

<figure><img src="image_4.png" /><figcaption>Figure 3: Smoothing and Fitting the Hail Thresholds</figcaption></figure>

**Note:** The figure shows the smoothing of the thresholds for Convective Available Potential Energy (CAPE) and convective precipitation (CP) by latitude. The thresholds are based on the 95th percentile of CAPE and the median of CP in each latitude bin. The thresholds are smoothed using a polynomial function. The gray dots represent reported hail events at weather stations. For illustrative purposes, 79 hail events with a CAPE>5000 are excluded from plotting.

*Air pollution weather* To account for ambient air pollution two sources are utilized: monthly mean fine particulate matter at 2.5 µm (PM<sub>2.5</sub>) for the full period (Shen et al., 2024) and, at a lower resolution, data from an alternative data source (Inness et al., 2019) on monthly mean particulate matter at 10 µm (PM<sub>10</sub>) from 2003 to 2019. The data comes in different units. The PM<sub>2.5</sub> is provided in µg &middot; m<sup>-3</sup>, while PM<sub>10</sub> originally is provided in kg&middot;m<sup>-3</sup>. Thus, PM<sub>10</sub> is converted into µg &middot; m<sup>-3</sup>. PEACH includes three variables: two PM<sub>2.5</sub> estimates from both data sources, and one for PM<sub>10</sub> from the second.

*Earthquakes* The intensities of earthquakes are taken from the USGS ShakeMaps product (United States Geological Survey (USGS), 2023), which provides an interpolated intensity on an event basis for most recorded earthquakes. Two datasets are obtained from USGS using the Command Line Interface: All events with a minimum moment magnitude of 4.5 recorded and all that include polygons of intensities (so-called "shake maps"). First, for all epicenters reported, a 400 km buffer is drawn to see if they intersect with any cell or are uniquely located in the ocean. Then, for every reported ShakeMap, the average and highest Modified Mercalli Intensity (MMI) in every cell is computed. For all events where no ShakeMap is available, the grid is merged with the remaining epicenters, and the maximum reported magnitude is included. Magnitude here can
refer to various types of magnitude (e.g., moment, regional, or body-wave), but is taken preferentially from the official USGS-reported magnitude of the event. The number of earthquakes in a cell is counted, and if only a single event occurred, the USGS identifier is included. PEACH therefore contains five variables: whenever possible the USGS identifier of the event, the total number of events, if ShakeMaps are available, the mean and maximum reported MMI, and for non-ShakeMap events the maximum magnitude.

*Volcano Eruption* Having obtained information on all reported volcano eruptions (Global Volcanism Program and Venzke, 2023), the magnitude of the eruption is included based on the *Volcanic explosivity index* (VEI). If no end date is reported, the eruption is excluded. That concerns 33 eruptions. Following the methodology used in MYRIAD-HES (Claassen et al., 2023a), originally mentioned in Shi and Kasperson (2015), the radius of influence of a volcano is calculated based on the formula:

$$L = 3.0408e^{0.6956VEI}$$

The final variables included are the VEI of the eruption in the area of influence, as well as the volcano number.

*Tsunami* From the Global Historical Tsunami Database (National Geophysical Data Center, 2024) the subset of Tsunami run-ups for the period covered is obtained. The analysis is predominantly based on the arrival time and the maximum water heights in meters. Although the run-up data also reports the horizontal inundation depth for some events, the issue is that proximate areas with similar elevation might also be affected but without having the location recorded. To also include such grid cells, for analysis a digital elevation model is included for all coastal cells (Pronk et al., 2024). The resolution is on a 30m. To combine it with the 10km grid, the median elevation of each cell is calculated by first scaling down the 30m resolution to 1km and then computing the median elevation. Around each reported run-up location, a 25 km buffer is created, which serves as lower bound estimation. The idea is to account for at least one neighboring cell of a treated cell. Cells with reported tsunamis are always considered treated, while nearby cells are considered treated if the observed tsunami wave height is greater than the median elevation of the cell. For PEACH, the included information is the maximum wave height in a month recorded, as well as, if possible, the corresponding NOAA Tsunami identifier.

## 2.3 Socioeconomic Data

PEACH encompasses a range of commonly used variables to assess the economic and social performance of an area. The list of included variables is based on the availability of higher-resolution socioeconomic outcomes.

*Nighttime Light* In many developing countries local measures of economic activity is scarce. Nighttime light has commonly been applied to measure the effects on economic activity (see for instance recent discussions by Felbermayr et al., 2022; Pérez-Sindín, Chen and Prishchepov, 2021; Hu and Yao, 2022). Data on annual nighttime light is
based on a homogenized time series between the two standard sources: the Defense Meteorological Satellite Program (DMSP) and the Visible Infrared Imaging Radiometer Suite (VIIRS) (Li et al., 2023c). The data is on a 1km resolution and for each cell globally the mean, median and maximum nighttime light intensity are computed. Following the authors' advice, cells with a light intensity below seven are considered to be 0 to avoid the inclusion of stray light.

*Population* Population is an essential factor to measure the potential impact of a hazard event. The data is taken from the annual GlobPop dataset (Liu et al., 2024a) and is available with a resolution of 1km. The data is provided in two variables: the total number of people and the population density per square kilometer. Both are obtained and merged with the grid: the population is aggregated at a 10km resolution as the total sum of population counts within a cell that lies within its boundaries. The population density is calculated as the weighted mean within the cell.

*Relative Wealth* To measure the relative wealth of an area, the microestimates of wealth from Chi et al. (2022) are included. The relative wealth of a 5km cell is computed by combining household surveys, satellite images, cellular network data, and other anonymized data from Facebook. Using all these data sources, different types of classification and deep learning algorithms are used. The final relative wealth index provides information on whether an area is, in relative terms, wealthier or less wealthy compared to other parts of the country. The variable is time-invariant and aggregated to the 10km grid by using the minimum, mean and maximum recorded in each cell as well as the aggregated mean error. The data is available for 135 middle and low-income countries.

*Critical Infrastructure* To measure the resilience of an area, information on critical infrastructure is included. The data is taken from Nirandjan et al. (2022) and is time-invariant. The data is derived from OpenStreetMaps of November 2020 and is provided on a 0.1-degree resolution. The source data provides an index from 0-1 on the global scale. To compute the index subindices for telecommunication, health, education, waste, energy transportation, and water (so-called *"systems"*) are calculated based on initial OpenStreetMap assets. For studies investigating the effects of climate impact drivers or hazards in general, the type of infrastructure might matter, the mean critical infrastructure spatial index (CISI) as well as the score for every system is included. To homogenize with PEACH the weighted sum within each cell of the index is computed using the overlapping area share between the CISI index and the 10km cells.

*Normalized Vegetation Index* For many agricultural settings, it is crucial to know the plant health of a region. The Normalized Difference Vegetation Index (NDVI) is a standard measure for assessing plant productivity. Information on monthly NDVI is taken from a combined homogenized time series of the Advanced Very High Resolution Radiometer (AVHRR) and Moderate Resolution Imaging Spectroradiometer (MODIS) (Li et al., 2023a) since 1982 on 8km resolution. Following the author's recommendation, values with scarce vegetation below the value 0.1 are excluded. To obtain a valuable measure for PEACH, three variables are included and calculated: first, the weighted monthly
mean NDVI in the cell, which serves as a general measure of plant health. Then based on the last 10 years and 18 years a moving standardized z-score is computed for each month. The moving z-score shall ensure that changing climate conditions are taken into account. PEACH thus contains three variables: mean NDVI, NDVI anomaly based on monthly expected values in the last 10 years and the previous 18 years.

*Land Use* Lastly, to calculate the annual changes in land use over time, which are essential for adaptation studies, information on 1km land use is taken from the Historic Land Dynamics Assessment (HILDA +) project (Winkler et al., 2020). As land classes for each cell the share of urban land, cropland, pasture or range land, forest, unmanaged grass or shrub land and sparse land with no vegetation is computed.

## 3 Validation

The data used is compiled from already peer-reviewed published articles, meaning the validity can be assured. Errors would occur only during spatial merging with the grid. To assess accuracy data, temperature, precipitation, hail, and wind speed is validated against ground-truth data from the Global Surface Summary of the Day (GSOD) (NOAA National Centers for Environmental Information, 1999). At first, data from 8 000 to 12 000 weather stations, depending on the year, for daily reported meteorological statistics is taken. The locations of each weather station overlap with the PEACH grid and aggregate on a monthly level, similar to how PEACH is generated. The same metrics for wind are calculated as they are computed in PEACH. Three different biases are evaluated: monthly deviations, yearly deviations and geographic deviations. It is assessed if values vary trend-wise by month, year and region. The results are shown in Figure 4.

The temperature is, on average, underestimated by half a degree (0.25 degrees median) in comparison to weather station data on a monthly basis. The standard deviation is around 1.78 degrees. The largest bias is around April. Regionally, most parts of the world do not see large deviations; only some weather stations in western China, Turkiye, the west coast of the United States, and the Pacific coast of Latin America see larger deviations. On an annual level it appears that the difference is slowly decreasing over time.

For precipitation, it becomes visually clear that PEACH is limited to latitudes of 50 °. The dataset sees on average 3.8 mm (1.2 mm median) in total precipitation, less rain than the individual dataset from the weather station, with a standard deviation of around 13 mm. The largest bias occurs in December and January, particularly in the Northern latitudes, where the bias becomes more pronounced. This might be due to precipitation taking place especially in Northern hemisphere winter months more often in the form of snow, as well as lower light availability in Winter, which might be affecting satellite-based infra-red measures. The bias does not decrease over time.

For monthly maximum wind gusts, PEACH overestimates the wind speed by 0.62 m/s (0.5 m/s median) with a standard deviation of 3.5m/s. There is no clear annual trend,
<table><caption>Table 2: Overview on Socioeconomic and Environmental Data</caption><thead><tr><th>Category</th><th>Name</th><th>Source</th><th>Metric Used</th><th>Aggregation Scheme</th><th>Spatial Resolution</th><th>Temporal Resolution</th><th>Temporal Coverage</th><th>Spatial Coverage</th></tr></thead><tbody><tr><td rowspan="3">Socioeconomic Factors</td><td>Nighttime Light</td><td>Li et al. (2020)</td><td>Light Intensity</td><td>Weighted mean of cell</td><td>0.01</td><td>Yearly</td><td>1992-2021</td><td>Global</td></tr><tr><td>Population</td><td>Liu et al. (2024)</td><td>Population Count & Density</td><td>Weighted mean of cell</td><td>0.01</td><td>Yearly</td><td>1990-2020</td><td>Global</td></tr><tr><td>Relative Wealth</td><td>Chi et al. (2022)</td><td>Relative Wealth Index</td><td>Weighted mean of cell</td><td>0.05</td><td>-</td><td>2022</td><td>Quasi-Global</td></tr><tr><td>Infrastructure</td><td>Critical Infrastructure</td><td>Nirandjan et al. (2022)</td><td>Critical Infrastructure Index</td><td>Weighted mean of cell</td><td>0.1</td><td>-</td><td>-</td><td>Global</td></tr><tr><td rowspan="2">Environmental</td><td>Normalized Vegetation Index</td><td>Li et al. (2023)</td><td>NDVI Anomaly</td><td>Mean and Anomaly</td><td>0.08</td><td>Monthly</td><td>1981-present</td><td>Global</td></tr><tr><td>Land-Use</td><td>Winkler et al. (2020)</td><td>Land Use Share</td><td>Mean</td><td>0.1</td><td>Yearly</td><td>1960-2019</td><td>Global</td></tr></tbody></table>

**Note:** The table shows the included socio-economic measures, the measure, associated metric, how the data is aggregated, the spatial resolution of the origin data in degree, temporal resolution and coverage as well as spatial coverage. Sources: Nightlight: Li et al. (2023c) and Li et al. (2020), Population: Liu et al. (2024a) and Liu et al. (2024b), Relative Wealth: Chi et al. (2022), Critical Infrastructure: Nirandjan et al. (2021) and Nirandjan et al. (2022), NDVI: Li et al. (2023a) and Li et al. (2023b), Land Use: Winkler et al. (2020) and Winkler et al. (2021).
<figure><img src="image_5.png" /><figcaption>Figure 4: Validation of PEACH data with Global Summary of the Day</figcaption></figure>

**Note:** Panel (a) Shows the mean difference between PEACH and GSOD by month. In dark blue is the mean difference and in lighter blue the standard deviation. Panel (b) shows the mean difference by variable and station. Blue color indicates an underestimation of the value by PEACH and red color an overestimation. Panel (c) shows the mean difference by variable and year. In dark blue is the mean difference and in lighter blue the standard deviation.
but the summer months in the Northern Hemisphere see rather underestimation than overestimation. The days of wind gusts above 26 m/s are broadly comparable with weather station data and see on average less than 0.001 days of difference. The largest bias for wind geographically arises from Northern Europe. whereas parts of western China, Brazil, and the Midwest of the United States rather see an underestimate of wind gusts.

Finally, hail days for days with a 99% likelihood of hail are on average 0.01 (0 days median) day overestimated to station records. The low overestimation is due to the training of the hail probability data also drawn from weather station data. The standard deviation is around 0.05 days. No clear trend monthly or annual is observable.

# 4 Data Records

The Platform for Economic Analysis of Climate Hazards (PEACH) is a derived dataset for 14 continental climate impact drivers (CID) and 3 other hazards with data on socioeconomic, infrastructure and environmental data. It aims to provide easier accessible access to disaster and economic data. The data is on a global grid on 10km resolution projected in EPSG:4326. An exception is rainfall that is only quasi-global up to 50° latitude. It has full coverage for the years 2004 to 2015 with more than 80% of all variables being available between 2000 and 2019. Data is provided in the Apache Arrow Feather format that is usable in most software applications. Sample code is provided to help you get started in both R and Python should you not be familiar with either. This study uses data provided by the European Space Agency's Climate Change Initiative (Climate Change Initiative Fire Project, Fire CCI).

The PEACH dataset is a compilation of publicly available climate hazard and socioeconomic data sources, harmonized under a unified spatial framework. Due to varying license terms among the source datasets, the compilation is released under a CC-BY-NC-SA 4.0 license and is available on-demand basis (Reinhardt, 2025). Access for non-commercial academic research can be requested via the Zenodo repository under 10.5281/zenodo.17271530. The data is provided in sub-regions of the World based on the United Nations Statistics Division (M49) classification.

# 5 Usage Notes

The PEACH data can be used as a global, regional or national dataset, and the files are provided by the ISO-3 classifier stored in subregions of the world. The definition of ISO-3 and the subregions follows the classification of the United Nations Statistics Division (M49). The dataset is provided in the Apache Arrow Feather format, which can be read in Python and R with the feather package. In the download folder there is also an easy-to-use Python and R script to convert the data into a CSV format and to plot the data. Although it is possible to convert the files into a Microsoft Excel format
and combine them with the Shapefiles in QGIS, users are advised to, if possible, use R or Python for further analysis to reduce storage space. Information on the scaling of values and missing values can be found in the README file.

The usage is mostly intended for social scientist who want to obtain information on hazard intensities to validate their results. As the data is compiled from peer-reviewed publications from experts in the respective domains, the aim of PEACH is not to serve as a new source of climatological data, but rather to make climatological data more accessible for social scientists. For social scientists many more variables might be of interest. If the data of interest is provided on the country level it can be easily linked to cells using the ISO-3 identifier. This would be the case especially for country-level data from international organisations such as the World Bank, the World Health Organization or the International Monetary Fund. The temporal dimension also allows for the study of structural trends and temporal persistence of effects, which might be relevant for studies on adaptive capacity or climate risk insurance.

Another notable comment refers to the selection process of the included variables. As with any data compilation, decisions were made on why to use this specific data, which might lead to some uncertainty in direct comparison with other sources. This concerns socioeconomic variables, such as population and land use, which are often derived from different sources or employ different methodologies to interpolate the data to a higher resolution. There are ongoing academic debates on the best performing model, which if this is the outcome of interest, it might be noteworthy to see if the used data here is outperformed by more recent models.

Although PEACH has global coverage, it is recommended that it be applied to middle- and low-income countries, where national hazard reporting is more scarce. In western Europe, China, Australia, and the United States more accurate national data is available. In addition, as mentioned in the text, for the two variables: days with thunderstorm environments favorable for hail and days with high dust presence, should be taken with caution, as these two variables are self-calibrated and not derived from peer-reviewed articles. If the primary focus of the study to which PEACH is applied is studying distance-relevant outcomes, one needs to keep in mind that the projection used to create cells is based on the Pseudo-Mercator projection, which leads to distortions in area size as a function of latitude. This means that studies with a primary focus on land area (e.g. through the computation of nightlight per square kilometer) may distort results if the region of interest spans a wide range of latitudes (e.g., in the case of Chile). If equal area and distance within each cell are of key importance to the study, one might want to re-run the replication code provided on alternative projections such as the Icosahedral Snyder Equal Area Aperture 3 Hexagon (ISEA3H) projection.

The PEACH database is only possible due to amazing research done by the climate, earth and social science community. A list of references to the datasets used is provided within this article and also in the README file of the associated data repository. Users are requested to cite both the PEACH dataset and also the primary input data to
give credit to these researchers. The data is provided under the Creative Commons Attribution-NonCommercial Share-Alike (CC-BY-NC-SA) license.

# 6 Code availability

The goal of the PEACH platform is to be replicable and accessible to researchers less familiar with Python. The replication files will be uploaded on Zenodo upon acceptance of the article. The code is written in Python 3.9 and run on a server with substantial computational power. Replication requires 600 GB of storage space and at least 16Gb of RAM for most code. While most code is written to be replicable, including the download of the data, some data sets require manual download, which is specified in the README. The code is written in a modular manner and can be easily adapted to other datasets.
References

Anderson, W., C. Taylor et al. (2021). 'Violent Conflict Exacerbated Drought-Related Food Insecurity between 2009 and 2019 in Sub-Saharan Africa'. *Nature Food* 2.8, pp. 603–615. DOI: 10.1038/s43016-021-00327-4.

Botzen, W. J. W., O. Deschenes and M. Sanders (2019). 'The Economic Impacts of Natural Disasters: A Review of Models and Empirical Studies'. *Review of Environmental Economics and Policy* 13.2, pp. 167–188. DOI: 10.1093/reep/rez004.

Brakenbridge, G. and Dartmouth Flood Observatory (n.d.). *Global Active Archive of Large Flood Events*. URL: https://floodobservatory.colorado.edu/Archives/index.html (visited on 27/03/2023).

Burke, M., S. Hsiang and E. Miguel (2015). 'Global Non-Linear Effect of Temperature on Economic Production'. *Nature* 527.7577, pp. 235–239. DOI: 10.1038/nature15725.

Burke, M., M. Zahid et al. (2024). *Are We Adapting to Climate Change?* w32985. Cambridge, MA: National Bureau of Economic Research, w32985. DOI: 10.3386/w32985.

Chi, G., H. Fang et al. (2022). 'Microestimates of Wealth for All Low- and Middle-Income Countries'. *Proceedings of the National Academy of Sciences* 119.3, e2113658119. DOI: 10.1073/pnas.2113658119.

Claassen, J. N., P. J. Ward et al. (2023a). 'A New Method to Compile Global Multi-Hazard Event Sets'. *Scientific Reports* 13.1, p. 13808. DOI: 10.1038/s41598-023-40400-5.

- (2023b). *MYRIAD – Hazard Event Sets (MYRIAD-HES)*. DOI: 10.5281/ZENODO.8269679.

Clark, J., C. E. Konrad and A. Grundstein (2024). 'The Development and Accuracy Assessment of Wet Bulb Globe Temperature Forecasts'. *Weather and Forecasting* 39.2, pp. 403–419. DOI: 10.1175/WAF-D-23-0076.1.

Climate Hazards Center (UCSB) (n.d.). *CHIRPS: Rainfall Estimates from Rain Gauge and Satellite Observations*. URL: https://chc.ucsb.edu/data/chirps/ (visited on 29/06/2023).

Copernicus Climate Change Service (n.d.). *Daily Statistics Calculated from ERA5 Data*. URL: https://cds.copernicus.eu/cdsapp#!/software/app-c3s-daily-era5-statistics?tab=overview (visited on 11/07/2024).

- (2019). *Fire Burned Area from 2001 to Present Derived from Satellite Observations*. DOI: 10.24381/CDS.F333CF85.

- (2022). *ERA5-Land Monthly Averaged Data from 1950 to Present*. DOI: 10.24381/cds.68d2bb30.

- (2024). *ERA5-Land Post-Processed Daily Statistics from 1950 to Present*. DOI: 10.24381/cds.e9c9c792.

Crameri, F., G. E. Shephard and P. J. Heron (2020). 'The Misuse of Colour in Science Communication'. *Nature Communications* 11.1, p. 5444. DOI: 10.1038/s41467-020-19160-7.

De, P. K. and D. Thamarapani (2022). 'Impacts of Negative Shocks on Wellbeing and Aspirations – Evidence from an Earthquake'. *World Development* 154, p. 105876. DOI: 10.1016/j.worlddev.2022.105876.

Done, J. (2022). *Willis Research Network Global Tropical Cyclone Footprint Dataset*. DOI: 10.6084/M9.FIGSHARE.19178426.V1.

Done, J. M., M. Ge et al. (2020). 'Modelling Global Tropical Cyclone Wind Footprints'. *Natural Hazards and Earth System Sciences* 20.2, pp. 567–580. DOI: 10.5194/nhess-20-567-2020.

Eberenz, S., D. Stocker et al. (2020). 'Asset Exposure Data for Global Physical Risk Assessment'. *Earth System Science Data* 12.2, pp. 817–833. DOI: 10.5194/essd-12-817-2020.

Elvidge, C. D., K. E. Baugh et al. (1997). 'Relation between Satellite Observed Visible-near Infrared Emissions, Population, Economic Activity and Electric Power Consumption'. *International Journal of Remote Sensing* 18.6, pp. 1373–1379. DOI: 10.1080/014311697218485.

Felbermayr, G. and J. Gröschl (n.d.). *Ifo GAME – the Geological and Meteorological Events Database*. URL: https://www.ifo.de/en/ebdc-dataset/ifo-game-geological-and-meteorological-events-database (visited on 18/10/2024).

- (2014). 'Naturally Negative: The Growth Effects of Natural Disasters'. *Journal of Development Economics* 111, pp. 92–106. DOI: 10.1016/j.jdeveco.2014.07.004.
Felbermayr, G., J. Gröschl et al. (2022). 'The Economic Impact of Weather Anomalies'. World Development 151. DOI: 10.1016/j.worlddev.2021.105745.

Froude, M. J. and D. N. Petley (2018). 'Global Fatal Landslide Occurrence from 2004 to 2016'. *Natural Hazards and Earth System Sciences* 18.8, pp. 2161–2181. DOI: 10.5194/nhess-18-2161-2018.

Funk, C., P. Peterson et al. (2014). 'A Quasi-Global Precipitation Time Series for Drought Monitoring'. *U.S. Geological Survey Data Series* 832, p. 4. DOI: 10.3133/ds832.

Funk, C., P. Peterson et al. (2015). 'The Climate Hazards Infrared Precipitation with Stations—a New Environmental Record for Monitoring Extremes'. *Scientific Data* 2.1, p. 150066. DOI: 10.1038/sdata.2015.66.

Gahtan, J, K. Knapp et al. (2018). *International Best Track Archive for Climate Stewardship (IBTrACS) Project, Version 4*. DOI: 10.25921/82ty-9e16.

Gebrechorkos, S., J. Peng et al. (2023). *Hydro-JULES: Global High-Resolution Drought Datasets from 1981-2022*. application/xml. DOI: 10.5285/AC43DA11867243A1BB414E1637802DEC.

Giuseppe, F. D., C. Vitolo et al. (2019). *Fire Weather Index - ERA-Interim*. Version 3.0. DOI: 10.5281/zenodo.3251000.

Global Volcanism Program and E. Venzke (2023). *Volcanoes of the World, v.5.1.0*. DOI: 10.5479/si.GVP.VOTW5-2023.5.1.

Gómez, D., E. F. García and E. Aristizábal (2023). 'Spatial and Temporal Landslide Distributions Using Global and Open Landslide Databases'. *Natural Hazards* 117.1, pp. 25–55. DOI: 10.1007/s11069-023-05848-8.

Henderson, V., A. Storeygard and D. N. Weil (2011). 'A Bright Idea for Measuring Economic Growth'. *American Economic Review* 101.3, pp. 194–199. DOI: 10.1257/aer.101.3.194.

Hersbach, H., B Bell et al. (2023). *ERA5 Hourly Data on Single Levels from 1940 to Present*. DOI: 10.24381/CDS.ADBB2D47.

Hsiang, S. (2016). 'Climate Econometrics'. *Annual Review of Resource Economics* 8.1, pp. 43–75. DOI: 10.1146/annurev-resource-100815-095343.

Hsiang, S. and A. Jina (2014). *The Causal Effect of Environmental Catastrophe on Long-Run Economic Growth: Evidence From 6,700 Cyclones*. w20352. Cambridge, MA: National Bureau of Economic Research, w20352. DOI: 10.3386/w20352.

Hu, Y. and J. Yao (2022). 'Illuminating Economic Growth'. *Journal of Econometrics* 228.2, pp. 359–378. DOI: 10.1016/j.jeconom.2021.05.007.

Inness, A., M. Ades et al. (2019). 'The CAMS Reanalysis of Atmospheric Composition'. *Atmospheric Chemistry and Physics* 19.6, pp. 3515–3556. DOI: 10.5194/acp-19-3515-2019.

IPCC (2021). *Climate Change 2021 – The Physical Science Basis: Working Group I Contribution to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change*. Ed. by V. Masson-Delmotte, P. Zhai et al. 1st ed. Cambridge, United Kingdom and New York, NY: Cambridge University Press. DOI: 10.1017/9781009157896.

Kaltenböck, R., G. Diendorfer and N. Dotzek (2009). 'Evaluation of Thunderstorm Indices from ECMWF Analyses, Lightning Data and Severe Storm Reports'. *Atmospheric Research* 93.1–3, pp. 381–396. DOI: 10.1016/j.atmosres.2008.11.005.

Kirschbaum, D., T. Stanley and Y. Zhou (2015). 'Spatial and Temporal Analysis of a Global Landslide Catalog'. *Geomorphology* 249, pp. 4–15. DOI: 10.1016/j.geomorph.2015.03.016.

Kirschbaum, D. B., R. Adler et al. (2010). 'A Global Landslide Catalog for Hazard Applications: Method, Results, and Limitations'. *Natural Hazards* 52.3, pp. 561–575. DOI: 10.1007/s11069-009-9401-4.

Knapp, K. R., M. C. Kruk et al. (2010). 'The International Best Track Archive for Climate Stewardship (IBTrACS): Unifying Tropical Cyclone Data'. *Bulletin of the American Meteorological Society* 91.3, pp. 363–376. DOI: 10.1175/2009BAMS2755.1.

Lee, R., C. J. White et al. (2024). 'Reclassifying Historical Disasters: From Single to Multi-Hazards'. *Science of The Total Environment* 912, p. 169120. DOI: 10.1016/j.scitotenv.2023.169120.

Li, M., S. Cao et al. (2023a). 'Spatiotemporally Consistent Global Dataset of the GIMMS Normalized Difference Vegetation Index (PKU GIMMS NDVI) from 1982 to 2022'. *Earth System Science Data* 15.9, pp. 4181–4203. DOI: 10.5194/essd-15-4181-2023.
Li, M., S. Cao et al. (2023b). *Spatiotemporally Consistent Global Dataset of the GIMMS Normalized Difference Vegetation Index (PKU GIMMS NDVI) from 1982 to 2022 (V1.2)*. Version V1.2. DOI: 10.5281/ZENODO.8253971.

Li, X., Y. Zhou et al. (2020). 'A Harmonized Global Nighttime Light Dataset 1992-2018'. *Scientific Data* 7.1, p. 168. DOI: 10.1038/s41597-020-0510-y.

- (2023c). *Harmonization of DMSP and VIIRS Nighttime Light Data from 1992-2021 at the Global Scale*. DOI: 10.6084/M9.FIGSHARE.9828827.V8.

Liu, L., X. Cao et al. (2024a). 'A 31-Year (1990-2020) Global Gridded Population Dataset Generated by Cluster Analysis and Statistical Learning'. *Scientific Data* 11.1, p. 124. DOI: 10.1038/s41597-024-02913-0.

- (2024b). *GlobPOP: A 33-Year (1990-2022) Global Gridded Population Dataset Generated by Cluster Analysis and Statistical Learning*. Version 2.0. DOI: 10.5281/zenodo.7813301.

Logothetis, S.-A., V. Salamalikis et al. (2021). '15-Year Variability of Desert Dust Optical Depth on Global and Regional Scales'. *Atmospheric Chemistry and Physics* 21.21, pp. 16499-16529. DOI: 10.5194/acp-21-16499-2021.

Mistry, M. N. (2020). 'A High Spatiotemporal Resolution Global Gridded Dataset of Historical Human Discomfort Indices'. *Atmosphere* 11.8, p. 835. DOI: 10.3390/atmos11080835.

Muhammad Akhlaq, T. R. Sheltami and H. T. Mouftah (2012). 'A Review of Techniques and Technologies for Sand and Dust Storm Detection'. *Reviews in Environmental Science and Bio/Technology* 11.3, pp. 305-322. DOI: 10.1007/s11157-012-9282-y.

NASA (n.d.). *Global Landslide Catalog Downloadable Products Gallery*. URL: https://maps.nccs.nasa.gov/arcgis/apps/MapAndAppGallery/index.html?APPID=574f26408683485799d02e857e5d9521 (visited on 27/03/2023).

National Geophysical Data Center (2024). *Global Historical Tsunami Database*. DOI: 10.7289/V5PN93H7.

Nirandjan, S., E. E. Koks et al. (2021). *A Spatially-Explicit Harmonized Global Dataset of Critical Infrastructure*. Version v1.0.0. DOI: 10.5281/ZENODO.4957647.

- (2022). 'A Spatially-Explicit Harmonized Global Dataset of Critical Infrastructure'. *Scientific Data* 9.1, p. 150. DOI: 10.1038/s41597-022-01218-4.

NOAA National Centers for Environmental Information (1999). *Global Surface Summary of the Day - GSOD*. 1.0.

Patel, T., S. P. Mullen and W. R. Santee (2013). 'Comparison of Methods for Estimating Wet-Bulb Globe Temperature Index From Standard Meteorological Measurements'. *Military Medicine* 178.8, pp. 926-933. DOI: 10.7205/MILMED-D-13-00117.

Pérez-Sindín, X. S., T.-H. K. Chen and A. V. Prishchepov (2021). 'Are Night-Time Lights a Good Proxy of Economic Activity in Rural Areas in Middle and Low-Income Countries? Examining the Empirical Evidence from Colombia'. *Remote Sensing Applications: Society and Environment* 24, p. 100647. DOI: 10.1016/j.rsase.2021.100647.

Pettorelli, N., J. O. Vik et al. (2005). 'Using the Satellite-Derived NDVI to Assess Ecological Responses to Environmental Change'. *Trends in Ecology & Evolution* 20.9, pp. 503-510. DOI: 10.1016/j.tree.2005.05.011.

Prein, A. F. and G. Holland (2018a). *Daily Large Hail Probability on a Global Scale (1979 to 2015), Version 2, Link to netCDF Files*. text/tab-separated-values. DOI: 10.1594/PANGAEA.893160.

- (2018b). 'Global Estimates of Damaging Hail Hazard'. *Weather and Climate Extremes* 22, pp. 10-23. DOI: 10.1016/j.wace.2018.10.004.

Pronk, M., A. Hooijer et al. (2024). 'DeltaDTM: A Global Coastal Digital Terrain Model'. *Scientific Data* 11.1, p. 273. DOI: 10.1038/s41597-024-03091-9.

Reed, B. C., J. F. Brown et al. (1994). 'Measuring Phenological Variability from Satellite Imagery'. *Journal of Vegetation Science* 5.5, pp. 703-714. DOI: 10.2307/3235884.

Reinhardt, R. (2025). *Platform for Economic Analysis of Climate Hazards (PEACH): Gridded Hazards from 2000-2019*. Version V2.0. DOI: 10.5281/ZENODO.14220524.

Rentschler, J., M. Salhab and B. A. Jafino (2022). 'Flood Exposure and Poverty in 188 Countries'. *Nature Communications* 13.1, p. 3527. DOI: 10.1038/s41467-022-30727-4.
Ridder, N. N., A. J. Pitman et al. (2020). 'Global Hotspots for the Occurrence of Compound Events'. *Nature Communications* 11.1, p. 5956. DOI: 10.1038/s41467-020-19639-3.

Rosvold, E. and H. Buhaug (2021). *Geocoded Disasters (GDIS) Dataset*. DOI: 10.7927/ZZ3B-8Y61.

Ruane, A. C., R. Vautard et al. (2022). 'The Climatic Impact-Driver Framework for Assessment of Risk-Relevant Climate Information'. *Earth's Future* 10.11, e2022EF002803. DOI: 10.1029/2022EF002803.

Runfola, D., A. Anderson et al. (2020). 'geoBoundaries: A Global Database of Political Administrative Boundaries'. *PLOS ONE* 15.4. Ed. by W. Tang, e0231866. DOI: 10.1371/journal.pone.0231866.

Sabater, J Muñoz (2019). *ERA5-Land Monthly Averaged Data from 1950 to Present*. DOI: 10.24381/CDS.68D2BB30.

Schwarzwald, K. and K. Geil (2024). 'Xagg: A Python Package to Aggregate Gridded Data onto Polygons'. *Journal of Open Source Software* 9.104, p. 7239. DOI: 10.21105/joss.07239.

Sen, S., N. C. Nayak and W. K. Mohanty (2022). 'Estimating Household Vulnerability to Tropical Cyclones: An Investigation of Tropical Cyclone Shocks in Coastal Villages of Eastern India'. *International Journal of Disaster Risk Reduction* 83, p. 103404. DOI: 10.1016/j.ijdrr.2022.103404.

Seneviratne, S., N. Nicholls et al. (2012). 'Changes in Climate Extremes and Their Impacts on the Natural Physical Environmen'. *Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation*. Ed. by C. Field, V. Barros et al. Vol. Special Report of Working Groups I and II of the Intergovernmental Panel on Climate Change. Cambridge, UK and New York, USA: Cambridge University Press, pp. 109-230.

Shabnam, N. (2014). 'Natural Disasters and Economic Growth: A Review'. *International Journal of Disaster Risk Science* 5.2, pp. 157-163. DOI: 10.1007/s13753-014-0022-5.

Shen, S., C. Li et al. (2024). 'Enhancing Global Estimation of Fine Particulate Matter Concentrations by Including Geophysical a Priori Information in Deep Learning'. *ACS ES&T Air* 1.5, pp. 332-345. DOI: 10.1021/acsestair.3c00054.

Shi, P. and R. Kasperson, eds. (2015). *World Atlas of Natural Disaster Risk*. IHDP/Future Earth-Integrated Risk Governance Project Series. Berlin, Heidelberg: Springer Berlin Heidelberg. DOI: 10.1007/978-3-662-45430-5.

Taszarek, M., J. T. Allen et al. (2021). 'Global Climatology and Trends in Convective Environments from ERA5 and Rawinsonde Data'. *npj Climate and Atmospheric Science* 4.1, p. 35. DOI: 10.1038/s41612-021-00190-x.

The World Bank (2024). *GDP (Current USD)*.

United Nations Office for Disaster Risk Reduction (UNDRR) (2024). *Desinventar Sendai - Framework for Disaster Risk Reduction*.

United States Geological Survey (USGS) (2017). *ShakeMap - Earthquake Ground Motion and Shaking Intensity Maps*. DOI: 10.5066/F7W957B2.

- (2023). *ANSS Comprehensive Earthquake Catalog (ComCat)*.

UNSD (n.d.). *Standard Country or Area Codes for Statistical Use (M49)*.

U.S. Environmental Protection Agency (2024). *Final Reconsideration of the National Ambient Air Quality Standards for Particulate Matter (PM)*. URL: https://www.epa.gov/system/files/documents/2024-02/pm-naaqs-air-quality-index-fact-sheet.pdf (visited on 13/11/2024).

Vermote, E. and R. Wolfe (2015). *MOD09GQ MODIS/Terra Surface Reflectance Daily L2G Global 250m SIN Grid V006*. DOI: 10.5067/MODIS/MOD09GQ.006.

Vitolo, C., F. Di Giuseppe et al. (2019). 'A 1980-2018 Global Fire Danger Re-Analysis Dataset for the Canadian Fire Weather Indices'. *Scientific Data* 6.1, p. 190032. DOI: 10.1038/sdata.2019.32.

Winkler, K., R. Fuchs et al. (2020). *HILDA+ Global Land Use Change between 1960 and 2019*. text/tab-separated-values. DOI: 10.1594/PANGAEA.921846.

- (2021). 'Global Land Use Changes Are Four Times Greater than Previously Estimated'. *Nature Communications* 12.1, p. 2501. DOI: 10.1038/s41467-021-22702-2.

WMO (n.d.). WMO Climatological Normals | World Meteorological Organization. URL: https://community.wmo.int/en/wmo-climatological-normals (visited on 25/03/2024).

Wu, J., H. Yao et al. (2022). 'A Framework for Assessing Compound Drought Events from a Drought Propagation Perspective'. *Journal of Hydrology* 604, p. 127228. DOI: 10.1016/j.jhydrol.2021.127228.
Zuzak, C., E. Goodenough et al. (2023). *National Risk Index Technical Documentation*. Washington, DC, p. 272.
# A Definition of Cut-Offs

Figure 1 of the article shows in Panel (a) the total exposure of each cell to hazards included in PEACH for the period 2004-2015. To build the total sum of events the intensities of each considered hazard need to be transformed into binary dummy variables. The following paragraph will describe the cut-offs used and provide a rationale for each one.

**Cold spell** events are defined as temperature anomalies that are more than 2.3 standard deviations, so approximately a 99th percentile, below the historic 30-year monthly average. For **heatwaves**, a monthly block maximum from above 33 degrees on the Wet Bulb Global Temperature is taken. This would be equivalent to a black flag within the US Military's Flag system (Patel, Mullen and Santee, 2013), so a heat level where physical exercise is supposed to be kept to a minimum. **Droughts** are characterized as extreme "compound" droughts, where the definition follows the idea of the intersection of meteorological and hydrological droughts (Wu et al., 2022). A drought month event is defined as one in which the Standardized Precipitation Evapotranspiration Index (SPEI) for both 3-month (meteorological) and 12-month (hydrological) periods is simultaneously below -1.5. **Flood** events are defined as events, where a cell falls within a recorded Dartmouth Flood Observatory flood event (Brakenbridge and Dartmouth Flood Observatory, n.d.) with a recorded intensity and at more than 5% of the cell are at high risk of being flooded using the population at risk data mentioned in the paper (Rentschler, Salhab and Jafino, 2022). A **wildfire** event in a cell is recorded if at least 20% of the cell is burned with a probability of more than 90%. As wildfires are seasonal events that may occur in some regions annually, the deviation of the burned fraction from the expected median burned fraction for the corresponding month is calculated. The median burned fraction is the median observed value for the corresponding month across the entire time period covered. A wildfire event, thus, is finally defined if at least 20% are burned and on average 10% more is burned than the median fraction over the 17-year period (wildfire coverage starts in 2003). A **tropical storm** event is defined if the recorded cell is exposed to winds stronger than 33 meters per second. This corresponds broadly to a Category 1 Hurricane on the Saffir-Simpson Hurricane scale. A **severe wind** event is defined if the wind gusts exceed 30 meters per second on at least one day in the month. Such a threshold, if wind speeds are sustained, would define a violent storm on the Beaufort Wind Scale. However, wind gusts are substantially shorter events. Nonetheless, even if sustained for a short time, winds exceeding this threshold become damaging to infrastructure and pose a risk to human lives by causing trees to fall. A **hail** event is defined if at least a single day in a month is observed where the probability for hail exceeds 90%. **Dust storm** events are solely based on having at least a single day of what is described in the paper as a "dust environment day". **Landslides** are considered based on the count of events within a cell. There needs to be at least a single event counted. **Earthquakes** are included if the cells have experienced a ground motion with at least scale five on the Modified Mercalli Intensity scale. **Volcano eruptions** are considered as an event if, for the cell, an eruption with at least 1 on the
volcano explosivity scale (VEI) is reported. **Tsunami events** are defined as cells having a reported wave height from a tsunami greater than zero. **Extreme air pollution events** are considered if the monthly mean air pollution level exceeds 75 µg/m3 for PM2.5. Based on a recent update by the U.S. Environmental Protection Agency (EPA) this would correspond to an unhealthy air quality index (U.S. Environmental Protection Agency, 2024). As this index is commonly computed on a 24-hour interval, if monthly averages exceed it, one can be certain that these levels are unhealthy for human life.

In this figure, temperature and rainfall anomalies are excluded because of their correlation with flood, heatwave and wildfire occurrences. Using data on population density (Liu et al., 2024a) cells below a density of one person per square km in 2019 are excluded.

# A Examples for Potential Use Cases

The primary purpose of this article is to explain the methodology and how PEACH was created. The use case of PEACH is versatile and not limited to a specific application. Here, for illustrative purposes, we present two potential use cases, among many, that researchers might be interested in.

## A.1 National Hotspot Identification

First, the correlation between hazard exposure from 2003 to 2015 and estimated Gross Domestic Product (GDP) at the cell level is illustrated in a descriptive manner. This might help researchers to identify locations where fluctuations in country-level aggregates stem from. Most of the descriptive analysis is based on the data found in PEACH. Yet, PEACH does not include any gridded GDP dataset. To get a proxy of estimated GDP, we rely on the *LitPop* methodology described by Eberenz et al. (2020). This follows the idea that one can distribute country-level GDP based on a function of population and nighttime light, both of which are variables that we have. The weights by cell are computed here in the following way:

$$Lit^nPop_{gc,2015} = (NTL_{gc,2015} + 1)^n \times Pop_{gc,2015}^m \quad (3)$$

Where *NTL* is the average nighttime light (NTL) value in cell g and country c for the chosen year 2015 and *Pop* the total population in that cell in 2015. The factors *n* and *m* are exponents giving the dynamics of the interaction between the two values. The plus one term for nightlight is added to prevent cells with recorded population from being dropped in the event of no available nightlight. Unlike the original version of the paper, we use cell-level aggregates of PEACH here, rather than the pixel-level resolution mentioned in Eberenz et al. (2020). In their article, they test for several combinations of *n* and *m* but show that *n*=1 and *m*=1 show the highest overall correlation, which is why we also use *n*=1 and *m*=1. To create a weight, we compute the GDP in cell g using
the following equation:

$$GDP_{gc,2015} = GDP_{c,2015} \times \frac{LitPop_{gc,2015}}{\sum_{g \in c} LitPop_{gc,2015}} \quad (4)$$

The relative value of the GDP in 2015 of cell g in country c is the product of the total GDP of that country times the relative weight of LitPop over the total sum of LitPop in that country. The GDP data is the value of GDP in current USD from the World Development Indicators of the The World Bank (2024)<fnref n="3" />.

Figure A.1 shows the total number of hazard shocks for the most balanced sample from 2003 to 2015 against the relative GDP within each country for the year 2015. All hazards are converted from intensities into binary values using the thresholds explained in section A. We use the year 2015 for GDP and LitPop, as it is the last year for which PEACH has 100% data coverage. To compute the relative GDP and hazard exposure within each country, we create quantile-based ranks (for three bins) and map the observed values to the tertiles. Cells with null cell GDP are excluded from the map, as this figure focuses on economically relevant areas. Cells with null GDP are areas no population recorded. For an integrated analysis, these areas might still be relevant (e.g. through biodiversity loss), but for this figure, they are excluded. Panel (b) and (c) plot the within-country GDP and hazard exposure ranks, respectively. The figure aims to illustrate national hazard exposure hotspots. Policy makers might be interested in identifying national areas that see a high number of climate impact drivers, but either high or low GDP. The same analysis can also be performed hazard by hazard or for other outcomes such as critical infrastructure, relative wealth, or, for instance, relative share of agricultural or pasture land.

## A.2 Effect of Hazards on Economic Growth

Another application of the data could be to study the interaction of economic growth or nightlight growth, as is often done in economic studies, and cumulative hazard exposure. Quantifying the economic damages of extreme events requires a solid methodological background, as for instance demonstrated in (Burke, Hsiang and Miguel, 2015; Burke et al., 2024; Hsiang, 2016). Here, for illustrative purposes, we use a simple methodology broadly based on the baseline methodology of Felbermayr et al. (2022):

$$\Delta \text{NTL}_{gt} = \text{NTL}_{gt-1}\alpha + D_{gt}\beta + X_{gt}\delta + \delta_t + \delta_g + \epsilon_{gt} \quad (5)$$

Where Δ is the first difference of logged nightlight, which measures economic growth using solely nightlight. Note that this differs from the measure used in Figure A.1, where GDP is estimated as the relative weight of country-level GDP by population and nightlight, jointly. Like in most growth equations, the initial pre-shock level of nightlight is included. D is either the total number of shocks of the 14 hazards described

<footnote n="3">³For Venezuela, the 2014 value is taken given that GDP is not reported for 2015.</footnote>
<figure><img src="image_6.png" /><figcaption>Figure A.1: National Hazard Hotspots (2003-2015)</figcaption></figure>

**Note:** Panel (a) shows in a bivariate map centers with relatively high levels of gridded GDP with respect to the rest of the country and relatively high levels of hazard exposure with respect to the rest of the country. Gridded GDP follows the LitPop definition of (Eberenz et al., 2020) and based on values of 2015. Hazard exposure is based on 15 hazard classes between 2004 and 2015. Both are converted into tertile ranks within the country. Gray areas indicate either low GDP and low-risk areas or areas with no population and therefore no recorded GDP. Panel (b) and (c) plot the relative GDP tertile and hazard exposure tertile, respectively.
above<fnref n="4" /> per year or the number of shocks by each individual class, so disaggregated, by year. D additionally includes the lag of each variable. As control, similar to Felbermayr et al. (2022), we include logged population. Cell-fixed effects absorb region-specific time-invariant factors, while time-fixed effects control for global common shocks. Standard errors are clustered at the cell-year level. This is only a very simplified version, where in contrast to Felbermayr et al. (2022) neither spatial autocorrelation nor the distinct intensities for hazards is taken into account. The regression is run at the cell level and by geographic regions. The regressions are divided by geographic regions, as the composition of the hazards and economic growth varies widely between these regions. Thus, a comparison pooled over the whole world might overlook these differences. Geographic regions follow the United Nations' classification. The excluded territories in the analysis are Greenland and Russia due to their large uninhabited surface areas.

First, the results when D is replaced by the total number of the 14 hazards considered are shown in Figure A.2. One can see that for South East Asia, Central Asia, North America and Oceania, any additional shock always reduces nightlight growth significantly. While for Central America and the Caribbean and East Asia, the effect is positive in the year of treatment, the year after sees larger negative growth rates. For all remaining regions the effect is positive. The results broadly indicate that additional exposure to shocks tends to reduce nightlight growth, except for Europe, the MENA region and South America. This is a descriptive analysis, thus, the effects shown are not causal. Hazard might have strong non-linear effects as found in literature for temperature or rainfall (e.g. see Burke, Hsiang and Miguel, 2015). Here, the model neither accounts for non-linearity, spatial and temporal autocorrelation nor for the respective intensity of the hazards. However, despite all this, more hazards appear to be harmful to nightlight growth in many parts of the world.

Finally, the same regression is used to estimate the effect of including all hazard classes individually. Therefore, on a cell level, we maintain the other hazard shocks at their respective regional means. The results are shown in Figure A.3. It becomes directly and visually clear that most hazards, regardless of whether they involve lagged or direct responses, tend to be harmful to nightlight growth. Some hazards always show negative effects on growth independent of the region. This is especially the case for floods, droughts, heat waves, and hail. It also becomes clear that for some hazard-region pairs there are substantially larger effects (for instance, for cyclones and central Asia). This is likely driven by either a small sample size of treated cells or other factors that might drive the growth response. Some regions, such as Europe, seem especially an increase in nightlight in years of additional shocks. Yet, for policy-relevant conclusions, a more in-depth approach should be considered that takes also the interconnections between individual hazards into account and proxies human activity in a more holistic way, accounting for country-specific time-varying factors such as the adaptation capacity,

<footnote n="4">⁴Mean and extreme precipitation are excluded as they only have quasi-global coverage. Air pollution is excluded as it is likely endogenous to economic activity and population.</footnote>
<figure><img src="image_7.png" /><figcaption>Figure A.2: Effects of Any Additional Hazard on NTL Growth (2004-2015)</figcaption></figure>

**Note:** The coefficients show the effects of a single shock more reported in year t (left) and t+1 (right) onto nightlight growth. The model controls for logged population and includes a lagged nightlight control. The individual regressions include time and cell fixed effects, and standard errors are clustered at the cell level. The total shock metric is computed based on a conversion from continuous to binary dummies using the thresholds discussed in section A.

public policies, fiscal stability or social welfare programs.

This descriptive discussion on the economic effects aims to highlight potential use cases of PEACH. One might use it to identify national hotspots that need more attention (relatively low GDP but relatively high hazard exposure). Another potential use case could be to revisit the literature on the connection between economic growth and hazard exposure. The descriptive correlation analysis hinted that the response to shocks varies across geographical regions and across the type of shock considered. Most hazards tend to exhibit a negative correlation with GDP growth; however, a more nuanced and detailed analysis is needed to validate these descriptive findings.
<figure><img src="image_8.png" /><figcaption>Figure A.3: Effects of Any Additional Shock by Hazard on NTL Growth (2004-2015)</figcaption></figure>

Note: The coefficients show the impact of an additional hazard shock (x-axis) on nightlight growth in year t (top) and t+1 (bottom). Regressions control for logged population, lagged nightlights, time and cell fixed effects, with standard errors clustered at the cell level. Shocks are converted from continuous to binary dummies using thresholds from section A. Only coefficients significant at the 5% level are shown; blanks indicate insignificance or lack of data.