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Welcome to the EM-DAT Documentation

Learn to use EM-DAT

This comprehensive documentation will help users by explaining the basics of the EM-DAT international disaster database.

Start by exploring one of the following options:

  • Use the Table of Content in the left sidebar or the menu below to navigate through the documentation.
  • Use the search engine in the top-right corner.
  • Print or export the documentation to PDF or one of its sections using the print option in the right sidebar.

1 - Introduction

Overview of EM-DAT and Latest Updates

Why Document EM-DAT?

Since its creation in 1988, the Emergency Events Database (EM-DAT) has undergone many changes. Tracing these changes over time was a difficult task that required combing through the archives to reconstruct the history of the database structure, concepts, and definitions. This versioned documentation gives you a rigorous tool which you can use to track its evolution and access up-to-date information.

EM-DAT Documentation

EM-DAT documentation 2023.09 was the first release with a Version Control System. As the version number indicates, it was released in September 2023. The current version is 2024.03. The 2023.09 release introduced breaking changes in the public table format, a backup of the legacy public table is available here. It is available under the same conditions.

First version 2023.09 of the EM-DAT Documentation.

  • EM-DAT Documentation: Removed EM-DAT guidelines and replaced them with this new documentation website.
  • EM-DAT Public Data: Made minor changes in the column names and their styling.
  • EM-DAT Public Data: Added new column ‘Historic’.
  • EM-DAT Public Data: Added new column ‘Classification Key’.
  • EM-DAT Public Data: Added new column ‘Entry Date’.
  • EM-DAT Public Data: Added new column ‘Last Update’.
  • EM-DAT Public Data: Removed column ‘Year’. Use ‘Start Year’ instead.
  • EM-DAT Public Data: Removed column ‘Seq’. Sequential number can be retrieved from the ‘Dis No.’ column instead.
  • EM-DAT Public Data: Removed column ‘Disaster Subsubtype’. Use the new 4-level classification tree instead.
  • EM-DAT Public Data: Replaced ‘Glide’ Column with ‘External IDs’.
  • EM-DAT Public Data: Replaced column ‘Continent’ with the ‘Region’ column based on UN M49 standard.
  • EM-DAT Public Data: Replaced column ‘Region’ with the ‘Subregion’ column based on UN M49 standard.
  • EM-DAT Public Data: Replaced the two columns ‘Associated Dis’ and ‘Associated Dis2’ with one column ‘Associated Types’. The new column can store an unlimited number of associated types.
  • EM-DAT Public Data: Replaced columns ‘Adm Level’, ‘Admin1 Code’, ‘Admin2 Code’, and ‘Geo Locations’ with one single column ‘Admin Units’ with JSON entries.
  • EM-DAT Public Data: Columns ‘OFDA Response’, ‘Appeal’, and ‘Declaration’ are now strictly binary (either ‘Yes’ or ‘No’).
  • EM-DAT Public Data: Reviewed and made minor corrections to the data content during the documentation process.

Minor update of the EM-DAT Documentation.

  • EM-DAT Documentation: updated highlighted publications.
  • EM-DAT Documentation: edited ‘Admin Units’ column description.
  • EM-DAT Documentation: edited and added a reference in the ‘Known Issues’ section.
  • EM-DAT Documentation: added a column ‘classif key’ in the table description the main classification system.

Minor update of the EM-DAT Documentation.

  • EM-DAT Documentation: corrected classification for inconsistencies in Mass Movement (dry) and Mass Movement (wet) children subtypes.
  • EM-DAT Documentation: added notice of complex disasters removal in the classification and database.
  • EM-DAT Documentation: added link to the last version of the previous public table in the previous portal layout, including removed complex disasters.
  • EM-DAT Documentation: updated the protocols to reflect the weekly rate of updates of the public table.

Preprint on the EM-DAT Database available, one column name change and minor changes.

  • EM-DAT Documentation: added preprint citation in “How to Cite?”
  • EM-DAT Documentation and Public Table: renamed the column OFDA Response to OFDA/BHA Response, with updates in the related documentation.
  • EM-DAT Documentation: added more references to the legacy public table.

Python Tutorials made available and minor changes in external resources.

  • EM-DAT Documentation: added 2 Python Tutorials in the “External Resources and Tutorials” section.
  • EM-DAT Documentation: removed tidyDisasters from the external resources as the package is not maintained and has been removed from CRAN.
  • EM-DAT Documentation: Removed broken link referring to UN Second-Administrative Level Boundaries (SLAB) program.

Overview of EM-DAT

In 1988, the Centre for Research on the Epidemiology of Disasters (CRED) launched the Emergency Events Database (EM-DAT). EM-DAT was created with the initial support of the World Health Organization (WHO) and the Belgian Government. Since 1999, EM-DAT has been supported by the Bureau for Humanitarian Assistance (BHA, previously the Office of US Foreign Disaster Assistance, or OFDA) within the United States Agency for International Development (USAID).

The initial objective of the database is to serve the purposes of humanitarian action at the national and international levels. Today, EM-DAT is also used to rationalize disaster preparedness and decision-making while providing an objective basis for vulnerability and risk assessment.

The EM-DAT database records mass disasters as well as their health and economic impacts at a country level (see Data Structure). The database contains core data on the occurrence and effects of 26,000 disasters worldwide from 1900 to the present. The database is compiled from various sources of information, including UN agencies, non-governmental organizations, insurance companies, research institutes, and press agencies (see Sources).

How to Cite?

To acknowledge EM-DAT as a data source in publications (e.g., documents, PowerPoint presentations, posters, or image charts), we recommend the following citation syntax:

EM-DAT, CRED / UCLouvain, 2024, Brussels, Belgium – www.emdat.be

Since the Emergency Events Database (EM-DAT) is regularly updated, we advise including the access date to ensure reference accuracy. If you use EM-DAT for a scientific publication, we would appreciate citations to the following database descriptor article (preprint):

Delforge, D. et al.: EM-DAT: The Emergency Events Database, Preprint, https://doi.org/10.21203/rs.3.rs-3807553/v1, 2023.

Highlighted Publications

Check our latest EM-DAT-related research paper:

Jones, R. L., Kharb, A., and Tubeuf, S.: The untold story of missing data in disaster research: a systematic review of the empirical literature utilising the Emergency Events Database (EM-DAT), Environ. Res. Lett., 18, 103006, https://doi.org/10.1088/1748-9326/acfd42, 2023.

All CRED publications relative to EM-DAT are accessible from our online repository.

For a comparison of available disaster loss databases, we recommend reading:

Mazhin, S. A., Farrokhi, M., Noroozi, M., Roudini, J., Hosseini, S. A., Motlagh, M. E., Kolivand, P., and Khankeh, H.: Worldwide disaster loss and damage databases: A systematic review, J Educ Health Promot, 10, 329, https://doi.org/10.4103/jehp.jehp_1525_20, 2021.

For a disaster loss dataset based on EM-DAT with improved geocoding, have a look at:

Rosvold, E. L. and Buhaug, H.: GDIS, a global dataset of geocoded disaster locations, Sci Data, 8, 61, https://doi.org/10.1038/s41597-021-00846-6, 2021.

For a comprehensive global overview of natural hazards and disasters, we recommend reading the following online publication:

Ritchie, H., Rosado, P., and Roser M.(2022) - “Natural Disasters.” Published online at OurWorldInData.org. Retrieved from: ‘https://ourworldindata.org/natural-disasters' [Online Resource]

2 - Data Accessibility

How to Access the Data and Legal Conditions for Using EM-DAT

How to Download the EM-DAT Public Data?

The public EM-DAT records are accessible from our online data portal public.emdat.be. Access for non-commercial use is free after registration and subject to the Terms of Use. Access for Commercial Use is possible on the basis of an annual paid subscription (see Commercial License).

After registration and login, the data can be downloaded using the EM-DAT “Access Data” Tab or Toolbox (see the figure below) as a flat table in Microsoft Excel format (.xlsx). The EM-DAT database is described in the Data Structure Description section. In particular, the Column Description section contains the description of each column of the public table found in the Excel file.

Home Page of the Public Portal

Humanitarian Data Exchange (HDX)

Country profiles

In addition, annual data summaries per country (country profiles) are available for download on the Humanitarian Data eXchange platform (see More on HDX here). These consist of aggregated figures in the following format :

Column name HXL code1 Type Description
Year #date +occurred Classifier See Start Year in Hazard and Disaster Magnitude Units.
Country #country +name Classifier See Country in Column Description.
ISO #country +code Classifier See ISO in Column Description.
Disaster Group #cause +group Classifier See the Disaster Classification System.
Disaster Subgroup #cause +subgroup Classifier See the Disaster Classification System.
Disaster Type #cause +type Classifier See the Disaster Classification System.
Disaster Subtype #cause +subtype Classifier See the Disaster Classification System.
Total Events #frequency Aggregated figure Total count of events.
Total Affected #affected +ind Aggregated figure The sum of injured, affected, and homeless people (see Human Impact Variables).
Total Deaths #affected +ind +killed Aggregated figure The sum of dead and missing people (see Human Impact Variables).
Total Damage (USD, original) Aggregated figure The value of all damage and economic losses directly or indirectly related to the disasters, in the value of the year of occurrence, unadjusted for inflation (see Economic Impact Variables).
Total Damage (USD, adjusted) #value +usd Aggregated figure See Economic Adjustment.
CPI Reference value (annual) Consumer Price Index, see Economic Adjustment.

  1. Available in the second row, these terms are part of the Humanitarian Exchange Language (HXL) and used in the HDX API to annotate metadata across datasets. See more about the HXL standard↩︎

3 - Data Structure and Content Description

In-depth Presentation of the Structure and Content of the Database

3.1 - General Definitions and Concepts

What is a Disaster in EM-DAT?

EM-DAT was designed in 1988 based on an anthropocentric vision of disasters and emergencies1. It considers disasters to be events involving an unexpected and overwhelming harmful impact on human beings. Formally, EM-DAT’s definition of a disaster is:

EM-DAT inventories only disasters that fit its Inclusion Criteria. EM-DAT records both disasters triggered by natural hazards and technological disasters. The latter are unintentional accidents, and not situations of conflict, violence, or terrorism. For more details, we refer to the Disaster Classification System.


  1. Guha-Sapir, D. and Misson, C.: The Development of a Database on Disasters, Disasters, 16, 74–80, https://doi.org/10.1111/j.1467-7717.1992.tb00378.x, 1992. ↩︎

3.2 - Core Structure of the Database

Basic Relational Structure of the Various Components of a Disaster Event in EM-DAT

EM-DAT data model records disasters and maps their impacts at the country level. Accordingly, the EM-DAT database references disasters with a unique identifier (see Dis No. in Column Description). A disaster may affect more than one country. In EM-DAT, the country-specific information about the disaster is recorded as an “Impact.” The Impact is the level at which EM-DAT reports entries in the EM-DAT Public Table. To document each Impact, we cross-validate and select information from the sources that are available (see EM-DAT sources and Protocols).

The figure below provides a visual representation of the foundational structure of the EM-DAT database’s relational model. On the left is the core structure, and on the right, a hypothetical case serves as an illustrative example. To enhance clarity, the relations have been streamlined, omitting certain tables such as dictionaries, focusing solely on the main connections and the underlying logic of the database.

Disaster
Dis No
Dis No
Disaster Type
Disaster Type
[…]
[…]
Impact
Impact ID
Impact ID
Dis No
Dis No
Country
Country
[…]
[…]
Associated Disaster
Impact ID
Impact ID
Associated Type
Associated Type
[…]
[…]
Sources
Source ID
Source ID
[…]
[…]
N, 0
N, 0
1, N
1, N
1, N
1, N
Disaster Event
e.g. Flood
Disaster Event…
Country A
e.g. Belgium
Country A…
Country B
e.g. Germany
Country B…
Associated Dis
e.g. Slides
Associated Dis…
Source A1
e.g. Munich Re
Source A1…
Source A2
e.g. Press
Source A2…
Source B3
e.g. ECHO
Source B3…
Source B2
e.g. AFP
Source B2…
Source B1
e.g. Munich Re
Source B1…
A. Relational Model
A. Relational Model
B. Example
B. Example
Text is not SVG - cannot display

Cardinality, or the nature of the relationship between entities, is denoted by numbers or the letter “N” on each link. For instance, “1, N” signifies a one-to-many relationship. A clear example of this is the link between the “Disaster” and “Impact” tables. This implies that a single disaster entry might correspond to multiple impacts across various countries. Consider a flood affecting multiple nations, such as Belgium and Germany; the connection between these entries is facilitated by the shared Dis No. attribute.

The primary classification of a disaster, such as its main type, is housed in the “Disaster” table. Consequently, all impacts linked by the same Dis No. will have a consistent primary disaster type. However, EM-DAT introduces a nuance with associated disasters linked to the “Impact” table. Unlike the main type, these associated disasters can vary by country. They represent secondary disasters that either result from or occur simultaneously with the main event. An example would be a landslide triggered by a primary flood event, which is considered to be an associated disaster. It’s important to note that associated disasters are optional, leading to their zero-to-many relationship with the “Impact” table.

Each source can provide data for any given variable within the Impact category. During the validation process (as detailed in Encoding, Quality Control, and Validation Procedure), one value is chosen for each variable, which is independent of other variables. Some figures presented in the EM-DAT Public Table are aggregates of various variables and might originate from multiple distinct sources. For example, figures for deaths and missing persons might be reported at different times after an event. After evaluating and selecting from the available data, the Total Deaths column might consolidate information from various sources.

3.3 - EM-DAT Public Table

Content and Presentation of the Database as Publicly Available

Overview

The EM-DAT Public Table is a flat representation of EM-DAT data in a single downloadable table. Most impact variables are part of the public table (see Impact Variables). The public table provides a flat view of the general structure in which each record (row) corresponds to a disaster impacting a country.

Column Description

Column Name Type Description
Dis No. ID, Mandatory A unique 8-digit identifier including the year (4 digits) and a sequential number (4 digits) for each disaster event (i.e., 2004-0659). In the EM-DAT Public Table, the ISO country code is appended. See column ISO below.
Historic Yes/No, Mandatory Binary field specifying whether or not the disaster happened before 2000, using the Start Year. Data before 2000 should be considered of lesser quality (see Time Bias).
Classification Key ID, Mandatory A unique 15-character string identifying disasters in terms of the Group, Subgroup, Type and Subtype classification hierarchy. See Disaster Classification System.
Disaster Group Name, Mandatory The disaster group, i.e., “Natural” or “Technological.” See Disaster Classification System.
Disaster Subgroup Name, Mandatory The disaster subgroup. See Disaster Classification System.
Disaster Type Name, Mandatory The disaster type. See Disaster Classification System.
Disaster Subtype Name, Mandatory The disaster subtype. See Disaster Classification System.
External IDs IDs List, Optional List of identifiers for external resources (GLIDE, USGS, DFO), in the format “<source>:<identifier>” and separated by the pipe character ("|").
Event Name Optional Short specification for disaster identification, e.g., storm names (e.g., “Mitch”), plane type in air crash (e.g., “Boeing 707”), disease name (e.g., “Cholera”), or volcano name (e.g., “Etna”).
ISO ID, Mandatory The International Organization for Standardization (ISO) 3-letter code referring to the Country. The ISO 3166 norm is used. See Spatial Information and Geocoding.
Country Name, Mandatory Country where the disaster occurred and had an impact, using names from the UN M49 Standard. See Spatial Information and Geocoding. If multiple countries are affected, each will have an entry linked to the same Dis No.
Subregion Name, Mandatory Subregion where the disaster occurred based on UN M49 standard, automatically linked to the Country field. See Spatial Information and Geocoding.
Region Name, Mandatory Region or continent where the disaster occurred based on UN M49 standard, automatically linked to the Country field. See Spatial Information and Geocoding.
Location Text, Optional Geographical location name as specified in the sources, e.g., city, village, department, province, state, or district. Used to identify corresponding GAUL Admin Units (see GAUL Index and Admin Levels).
Origin Text, Optional Additional specifications on the contextual factors that led to the event, e.g., “heavy rains” for floods, or “drought” for a forest fire.
Associated Types Names List, Optional List of secondary disaster types cascading from or co-occurring aside from the main type (optional), e.g., a landslide following a flood or an explosion after an earthquake. Separated by the pipe character ("|").
OFDA/BHA Response Yes/No, Mandatory Binary field specifying whether or not the (former) Office of US Foreign Disaster Assistance (OFDA) or the Bureau of Humanitarian Assistance (BHA) responded to the disaster.
Appeal Yes/No, Mandatory Binary field specifying whether or not there was a request for international assistance from the affected country.
Declaration Yes/No, Mandatory Binary field specifying whether a state of emergency was declared in the country.
Aid Contribution Unadjusted Monetary Amount (‘000 US$), Optional The total amount (in thousands of US$ at the time of the report) of contributions for immediate relief activities to the country in response to the disaster, sourced from the Financial Tracking System of OCHA (1992 to 2015). Not maintained after 2015 due to a lack of availability of information. Some aid contribution information can be found at https://fts.unocha.org/.
Magnitude Disaster-Type-Dependent, Optional The intensity of a specific disaster (see Hazard and Disaster Magnitude Units).
Magnitude Scale Disaster-Type-Dependent, Optional The associated unit for the Magnitude column (see Hazard and Disaster Magnitude Units).
Latitude Degrees [-90;90], Optional North-South coordinates mainly for earthquakes and volcanic activity. Sometimes reported for floods, landslides, and storms (mostly when associated with floods).
Longitude Degrees [-180;180], Optional East-West coordinates mainly for earthquakes and volcanic activity. Sometimes reported for floods, landslides, and storms (mostly when associated with floods).
River Basin Text, Optional Name of affected river basins, typically used for floods.
Start Year Numeric, Mandatory Year of occurrence of the disaster.
Start Month Numeric, Optional Month of occurrence of the disaster. For sudden-impact disasters, this field is well defined. For disasters developing gradually over a longer time period (e.g., drought) with no precise onset date, this field can be left blank.
Start Day Numeric, Optional Day of occurrence of the disaster. For sudden-impact disasters, this field is well defined. For disasters developing gradually over a longer time period (e.g., drought) with no precise onset date, this field can be left blank.
End Year Numeric, Optional Year of disaster conclusion.
End Month Numeric, Optional Month of conclusion of the disaster. For sudden-impact disasters, this field is well defined. For disasters developing gradually over a longer time period (e.g., drought) with no precise end date, this field can be left blank.
End Day Numeric, Optional Day of conclusion of the disaster. For sudden-impact disasters, this field is well defined. For disasters developing gradually over a longer time period (e.g., drought) with no precise end date, this field can be left blank.
Total Deaths Numeric, Optional Total fatalities (deceased and missing combined, see Human Impact Variables).
No. Injured Numeric, Optional Number of people with physical injuries, trauma, or illness requiring immediate medical assistance due to the disaster (see Human Impact Variables).
No. Affected Numeric, Optional Number of people requiring immediate assistance due to the disaster (see Human Impact Variables).
No. Homeless Numeric, Optional Number of people requiring shelter due to their house being destroyed or heavily damaged during the disaster (see Human Impact Variables).
Total Affected Numeric, Optional Total number of affected people (No Injured, No Affected, and No Homeless combined, see Human Impact Variables).
Reconstruction Costs (‘000 US$) Unadjusted Monetary Amount (‘000 US$), Optional Costs for replacement of lost assets in thousands of US dollars (‘000 US$) relative to Start Year, unadjusted for inflation (see Economic Impact Variables).
Reconstruction Costs, Adjusted (‘000 US$) Adjusted Monetary Amount (‘000 US$), Optional Reconstruction Costs (‘000 US$), adjusted for inflation using the Consumer Price Index (CPI column, see Economic Adjustment).
Insured Damage (‘000 US$) Unadjusted Monetary Amount (‘000 US$), Optional Economic damage covered by insurance companies, in thousands of US dollars (‘000 US$), relative to Start Year, unadjusted for inflation (see Economic Impact Variables).
Insured Damage, Adjusted (‘000 US$) Adjusted Monetary Amount (‘000 US$), Optional Insured Damage (‘000 US$) adjusted for inflation using the Consumer Price Index (CPI column, see Economic Adjustment).
Total Damage (‘000 US$) Unadjusted Monetary Amount (‘000 US$), Optional Value of all economic losses directly or indirectly due to the disaster, in thousands of US dollars (‘000 US$), relative to Start Year, unadjusted for inflation (see Economic Impact Variables).
Total Damage, Adjusted (‘000 US$) Adjusted Monetary Amount (‘000 US$), Optional Total Damage (‘000 US$) adjusted for inflation using the Consumer Price Index (CPI column, see Economic Adjustment).
CPI Conversion Ratio, Optional Consumer Price Index from OECD used to adjust US$ values for inflation relative to Start Year (see Economic Adjustment).
Admin Units JSON Array of Objects, Optional Collection of impacted Administrative Units from the FAO GAUL 2015 referential (Global Administrative Unit Layers 2015). Individual objects correspond to Level-1 or Level-2 Administrative Units, with the corresponding fields adm1_code, adm1_name or adm2_code, adm2_name providing the unique identifier to the geometry in the GAUL layer and the name of the unit, respectively. Geocoding is maintained for non-biological natural hazards from 2000 onwards (see Spatial Information and Geocoding).
Entry Date Date, Mandatory The day on which the event record was created in EM-DAT.
Last Update Date, Mandatory The last modification of the event or one of its associated records in EM-DAT. This may not result in a modification of the information in the EM-DAT Public Table as modifications to private fields are recorded as well.

3.4 - Disaster Classification System

Historical and Current Classification System of Disasters

A Brief History of the EM-DAT Classification System

EM-DAT’s classification system originally started with a simple 20-type list1. However, in 1992, CRED and other international stakeholders proposed a hierarchical classification system2 that distinguishes natural and man-made disasters (described as technological disasters in EM-DAT). A further distinction was based on the timing of disasters: slow vs. rapid onset disasters.

In the 2000s, CRED collaborated with Munich Re and other stakeholders on a common classification system3. Since then, EM-DAT’s main classification system has followed the logic of referring to the hazard or event triggering the disaster. Consequently, some disasters were reclassified, and some types were removed from the primary classification system. This was the case for famines reclassified as drought for the most part4. However, “famine” offers more information than “drought.” Therefore, in order not to lose this added value, EM-DAT has adopted the secondary classification system of Associated Disasters. This describes disasters that coincide with or result from the primary type.

In 2014, the Integrated Research on Disaster Risk (IRDR) working group, which included CRED, established a new reference called the Peril Classification and Hazard Glossary. This document is currently the primary reference for classifying natural hazards in EM-DAT, which divides them into six main groups: Geophysical, Hydrological, Meteorological, Climatological, Biological, and Extra-terrestrial. EM-DAT also includes more detailed subtypes.

Natural Hazards Subgroups and Types in the IRDR Peril Classification and Hazard Glossary

Main Classification Tree

The main classification tree has four levels of depth, so disasters are divided into groups, subgroups, types, and subtypes, as presented in the EM-DAT Public Table columns. The two EM-DAT disaster groups are ‘Natural’ and ‘Technological’. The table below shows the complete tree for the ‘Natural’ and ‘Technological’ groups, with the occurrence for each subtype. Their corresponding definitions are available in the Classification Glossary.

Classification Key Group Subgroup Type Subtype Count5
nat-bio-ani-ani Natural Biological Animal incident Animal incident 1
nat-bio-epi-bac Natural Biological Epidemic Bacterial disease 781
nat-bio-epi-dis Natural Biological Epidemic Infectious disease (General) 142
nat-bio-epi-fun Natural Biological Epidemic Fungal disease 0
nat-bio-epi-par Natural Biological Epidemic Parasitic disease 51
nat-bio-epi-pri Natural Biological Epidemic Prion disease 0
nat-bio-epi-vir Natural Biological Epidemic Viral disease 547
nat-bio-inf-gra Natural Biological Infestation Grasshopper infestation 16
nat-bio-inf-inf Natural Biological Infestation Infestation (General) 9
nat-bio-inf-loc Natural Biological Infestation Locust infestation 67
nat-bio-inf-wor Natural Biological Infestation Worms infestation 3
nat-cli-dro-dro Natural Climatological Drought Drought 804
nat-cli-glo-glo Natural Climatological Glacial lake outburst flood Glacial lake outburst flood 3
nat-cli-wil-for Natural Climatological Wildfire Forest fire 317
nat-cli-wil-lan Natural Climatological Wildfire Land fire (Brush, Bush, Pasture) 92
nat-cli-wil-wil Natural Climatological Wildfire Wildfire (General) 53
nat-ext-imp-air Natural Extra-terrestrial Impact Airburst 0
nat-ext-imp-col Natural Extra-terrestrial Impact Collision 1
nat-ext-spa-ene Natural Extra-terrestrial Space weather Energetic particles 0
nat-ext-spa-geo Natural Extra-terrestrial Space weather Geomagnetic storm 0
nat-ext-spa-rad Natural Extra-terrestrial Space weather Radio disturbance 0
nat-ext-spa-sho Natural Extra-terrestrial Space weather Shockwave 0
nat-geo-ear-gro Natural Geophysical Earthquake Ground movement 1544
nat-geo-ear-tsu Natural Geophysical Earthquake Tsunami 57
nat-geo-mmd-ava Natural Geophysical Mass movement (dry) Avalanche (dry) 5
nat-geo-mmd-lan Natural Geophysical Mass movement (dry) Landslide (dry) 30
nat-geo-mmd-roc Natural Geophysical Mass movement (dry) Rockfall (dry) 9
nat-geo-mmd-sub Natural Geophysical Mass movement (dry) Sudden Subsidence (dry) 1
nat-geo-vol-ash Natural Geophysical Volcanic activity Ash fall 249
nat-geo-vol-lah Natural Geophysical Volcanic activity Lahar 0
nat-geo-vol-lav Natural Geophysical Volcanic activity Lava flow 10
nat-geo-vol-pyr Natural Geophysical Volcanic activity Pyroclastic flow 4
nat-geo-vol-vol Natural Geophysical Volcanic activity Volcanic activity (General) 9
nat-hyd-flo-coa Natural Hydrological Flood Coastal flood 85
nat-hyd-flo-fla Natural Hydrological Flood Flash flood 831
nat-hyd-flo-flo Natural Hydrological Flood Flood (General) 2283
nat-hyd-flo-ice Natural Hydrological Flood Ice jam flood 0
nat-hyd-flo-riv Natural Hydrological Flood Riverine flood 2657
nat-hyd-mmw-ava Natural Hydrological Mass movement (wet) Avalanche (wet) 121
nat-hyd-mmw-lan Natural Hydrological Mass movement (wet) Landslide (wet) 609
nat-hyd-mmw-mud Natural Hydrological Mass movement (wet) Mudslide 79
nat-hyd-mmw-roc Natural Hydrological Mass movement (wet) Rockfall (wet) 3
nat-hyd-mmw-sub Natural Hydrological Mass movement (wet) Sudden Subsidence (wet) 1
nat-hyd-wav-rog Natural Hydrological Wave action Rogue wave 0
nat-hyd-wav-sei Natural Hydrological Wave action Seiche 0
nat-met-ext-col Natural Meteorological Extreme temperature Cold wave 311
nat-met-ext-hea Natural Meteorological Extreme temperature Heat wave 259
nat-met-ext-sev Natural Meteorological Extreme temperature Severe winter conditions 79
nat-met-fog-fog Natural Meteorological Fog Fog 1
nat-met-sto-bli Natural Meteorological Storm Blizzard/Winter storm 226
nat-met-sto-der Natural Meteorological Storm Derecho 6
nat-met-sto-ext Natural Meteorological Storm Extra-tropical storm 148
nat-met-sto-hai Natural Meteorological Storm Hail 111
nat-met-sto-lig Natural Meteorological Storm Lightning/Thunderstorms 189
nat-met-sto-san Natural Meteorological Storm Sand/Dust storm 20
nat-met-sto-sev Natural Meteorological Storm Severe weather 263
nat-met-sto-sto Natural Meteorological Storm Storm (General) 898
nat-met-sto-sur Natural Meteorological Storm Surge 7
nat-met-sto-tor Natural Meteorological Storm Tornado 296
nat-met-sto-tro Natural Meteorological Storm Tropical cyclone 2492
tec-ind-che-che Technological Industrial accident Chemical spill Chemical spill 108
tec-ind-col-col Technological Industrial accident Collapse (Industrial) Collapse (Industrial) 181
tec-ind-exp-exp Technological Industrial accident Explosion (Industrial) Explosion (Industrial) 778
tec-ind-fir-fir Technological Industrial accident Fire (Industrial) Fire (Industrial) 219
tec-ind-gas-gas Technological Industrial accident Gas leak Gas leak 61
tec-ind-ind-ind Technological Industrial accident Industrial accident (General) Industrial accident (General) 124
tec-ind-oil-oil Technological Industrial accident Oil spill Oil spill 8
tec-ind-poi-poi Technological Industrial accident Poisoning Poisoning 76
tec-ind-rad-rad Technological Industrial accident Radiation Radiation 9
tec-mis-col-col Technological Miscellaneous accident Collapse (Miscellaneous) Collapse (Miscellaneous) 305
tec-mis-exp-exp Technological Miscellaneous accident Explosion (Miscellaneous) Explosion (Miscellaneous) 220
tec-mis-fir-fir Technological Miscellaneous accident Fire (Miscellaneous) Fire (Miscellaneous) 788
tec-mis-mis-mis Technological Miscellaneous accident Miscellaneous accident (General) Miscellaneous accident (General) 275
tec-tra-air-air Technological Transport Air Air 1089
tec-tra-rai-rai Technological Transport Rail Rail 645
tec-tra-roa-roa Technological Transport Road Road 2857
tec-tra-wat-wat Technological Transport Water Water 1624

Associated Disasters

In addition to the main classification system, EM-DAT makes it possible to refer to “associated disasters” to describe disaster events in more details (see Associated Dis in EM-DAT Public Table). They represent subsequent or co-occurring hazards that may have contributed to the disaster impact. These associated disasters may not fit into the main classification system and do not have a hierarchical structure. This additional tagging system allows for a better description of disaster events, particularly multi-hazard ones. The figure below shows the main associations found in the database.

Sankey diagram of the associations between main disaster types (MT) and associated disaster types (AT) in EM-DAT. The figure only reports MT-AT associations having an occurrence >= 50 in the database. Gray band sizes between MT and AT are proportional to the occurrence of the association in EM-DAT. Last updated: September 5, 2023.

About 14% of disaster entries in EM-DAT have an associated disaster type, and only 3% mention two associated types. The most common associations are floods with landslides (24% of associations), storms with floods (21%), and storms with landslides (8%). Earthquakes are sometimes associated with landslides (4%) and tsunamis (4%) when their damage is deemed negligible compared to ground movement damage.


  1. Guha-Sapir, D. and Misson, C.: The Development of a Database on Disasters, Disasters, 16, 74–80, https://doi.org/10.1111/j.1467-7717.1992.tb00378.x, 1992. ↩︎

  2. DHA-UNDRO, IDNDR, UNEP, WFP, WHO/PAHO, USAID/FHA, IFRC, and CRED: Proposed principles and guidelines for the collection and dissemination of disaster-related data, Brussels, Belgium, 1992. ↩︎

  3. Below, R., Wirtz, A., and Guha-Sapir, D.: Disaster Category Classification and peril Terminology for Operational Purposes, Centre for Research on the Epidemiology of Disasters, Munich Re, Brussels, Belgium, 2009. ↩︎

  4. Below, R., Grover-Kopec, E., and Dilley, M.: Documenting Drought-Related Disasters: A Global Reassessment, The Journal of Environment & Development, 16, 328–344, https://doi.org/10.1177/1070496507306222, 2007. ↩︎

  5. Number of disasters that occurred at the country level in EM-DAT (1900-present) as of September 5, 2023. ↩︎

3.5 - Classification Glossary

Definitions of Disaster Types

EM-DAT’s definitions related to the group of natural hazards mainly refer to the IRDR Peril Classification and Hazard Glossary (see A Brief History of the EM-DAT Classification System). These are reported in the following sections by disaster subgroups.

EM-DAT definitions related to the groups of complex disasters and technological hazards are listed separately in the Complex and Technological Hazards section. These are legacy definitions from the EM-DAT project and do not refer to a particular glossary serving as an international standard.

3.5.1 - Biological Hazards

Term Level Definition Source
Biological hazard Subgroup A hazard caused by exposure to living organisms and/or their toxic substances (e.g., venom, or mold) or vector-borne diseases that they may carry. Examples are venomous wildlife and insects, poisonous plants, algae blooms, and mosquitoes carrying agents that causes disease such as parasites, bacteria, or viruses (e.g., malaria). IRDR
Animal incident Type Subtype Human encounters with dangerous or exotic animals in both urban and rural environments. IRDR
Epidemic Type Either an unusual, often sudden, increase in the number of cases of an infectious disease that already existed in the region (e.g., flu, or E. coli) or the appearance of an infectious disease previously absent from the region (e.g., plague, or polio). EM-DAT1
Infectious disease Subtype (General) Either an unusual, often sudden, increase in the number of cases of an infectious disease that already existed in the region (e.g., flu, or E. coli) or the appearance of an infectious disease previously absent from the region (e.g., plague, or polio). IRDR1
Bacterial disease Subtype An unusual increase in the number of cases caused by exposure to bacteria either through skin contact, ingestion, or inhalation. Examples include salmonella, Methicillin-Resistant Staphylococcus Aureus (MRSA), and cholera, among others. IRDR2
Parasitic disease Subtype An unusual increase in the number of cases caused by exposure to a parasite, i.e., an organism living on or in a host. Exposure to parasites occurs mostly through contaminated water, food, or contact with insects, animals, etc. Examples are malaria, Chagas disease, giardiasis, and Trichinellosis. IRDR2
Viral disease Subtype
Fungal disease Subtype An unusual increase in the number of cases caused by exposure to fungi either through skin contact, ingestion, or inhalation of spores. Examples are fungal pneumonia, fungal meningitis, etc. IRDR2
Prion disease Subtype A type of biological hazard caused by prion proteins. Prion diseases or transmissible spongiform encephalopathies (TSEs) are a family of rare progressive neurodegenerative disorders that affect both humans and animals. They are characterized by long incubation periods and neural loss. Examples are Bovine Spongiform Encephalopathy (BSE), Creutzfeldt-Jakob Disease (CJD), Kuru, etc. IRDR2
Infestation Type Subtype (General) The pervasive influx, swarming and/or hatching of insects, worms, or other animals affecting humans, animals, crops, and perishable goods. IRDR3
Grasshopper infestation Subtype Infestation of grasshoppers (insects). IRDR3
Locust infestation Subtype Infestation of locusts (insects). IRDR3
Worm Infestation Subtype Infestation of worms. IRDR3

  1. The “Infectious disease” definition corresponds to the definition of “Disease” in the IRDR glossary. The same definition is used to define “Epidemic” in the EM-DAT glossary. ↩︎ ↩︎

  2. With minor modifications. ↩︎ ↩︎ ↩︎ ↩︎

  3. These definitions have been adapted and derived from the IRDR definition of “Insect infestation”. ↩︎ ↩︎ ↩︎ ↩︎

3.5.2 - Climatological Hazards

Term Level Definition Source
Climatological hazard Subgroup A hazard caused by long-lived, meso- to macro-scale atmospheric processes ranging from intra-seasonal to multi-decadal climate variability. IRDR
Drought Type Subtype An extended period of unusually low precipitation that produces a shortage of water for people, animals, and plants. Drought is different from most other hazards in that it develops slowly, sometimes even over the years, and its onset is generally difficult to detect. Drought is not solely a physical phenomenon because its impacts can be exacerbated by human activities and water supply demands. Drought is therefore often defined both conceptually and operationally. Operational definitions of drought, i.e., the degree of precipitation reduction that constitutes a drought, vary by locality, climate, and environmental sector. IRDR
Glacial lake outburst flood Type Subtype These floods occur when water held back by a glacier or moraine is suddenly released. Glacial lakes can be at the front of the glacier (marginal lake) or below the ice sheet (sub-glacial lake). IRDR1
Wildfire Type Subtype (General) Any uncontrolled and non-prescribed combustion or burning of plants in a natural setting such as a forest, grassland, brush land or tundra, which consumes natural fuels and spreads based on environmental conditions (e.g., wind, or topography). Wildfires can be triggered by lightning or human actions. IRDR
Forest fire Subtype A type of wildfire in a wooded area. IRDR
Land fire (Brush, Bush, Pasture) Subtype A type of wildfire in a brush, bush, pasture, grassland, or other treeless natural environment. IRDR2

  1. The definition of “Glacial lake outburst flood” corresponds to the definition of “Glacial lake outburst” in the IRDR glossary. ↩︎

  2. Not defined in the IRDR glossary but adapted from the “Wildfire” and “Forest fire” definitions. ↩︎

3.5.3 - Extra-terrestrial Hazards

Term Level Definition Source
Extra-terrestrial hazard Subgroup A hazard caused by asteroids, meteoroids, and comets as they pass near to the Earth, enter the Earth’s atmosphere, and/or strike the Earth, and by changes in interplanetary conditions that effect the Earth’s magnetosphere, ionosphere, and thermosphere. IRDR
Impact Type A type of extra-terrestrial hazard caused by the collision a meteoroid, asteroid, or comet with the Earth. IRDR
Airburst Subtype An explosion of a comet or meteoroid within the Earth’s atmosphere without striking the ground. IRDR
Collision Subtype An impact caused by the collision of a meteoroid, asteroid, or comet with the Earth’s ground. IRDR1
Space weather Type A general term for extra-terrestrial weather conditions driven by solar eruptions such as geomagnetic storms, radio disturbances, and solar energetic particles. IRDR
Energetic particles Subtype Emissions from solar radiation storms consisting of pieces of matter (e.g., protons and other charged particles) moving at very high speed. The magnetosphere and atmosphere block (solar) energetic particles (SEP) from reaching humans on Earth but they are damaging to the electronics of space-borne technology (such as satellites) and pose a radiation hazard to life in space and aircraft traveling at high altitudes. IRDR
Geomagnetic storm Subtype A type of extra-terrestrial hazard caused by solar wind shockwaves that temporarily disturb the Earth’s magnetosphere. Geomagnetic storms can disrupt power grids, spacecraft operations, and satellite communications. IRDR
Shockwave Subtype A shockwave carries energy from a disturbance through a medium (solid, liquid, or gas) similar to the action of a wave, though it travels at much higher speed. It can be a type of extra-terrestrial hazard caused by the explosion (airburst) or impact of meteorites that generate energy shockwaves capable of shattering glass, collapsing walls, etc. IRDR
Radio disturbance Subtype Triggered by x-ray emissions from the Sun hitting the Earth’s atmosphere and causing disturbances in the ionosphere such as jamming of high and/or low frequency radio signals. This affects satellite radio communication and Global Positioning Systems (GPS). IRDR

  1. The “Collision” definition is derived from the IRDR “Impact” and “Airburst” definitions. ↩︎

3.5.4 - Geophysical Hazards

Term Level Definition Source
Geophysical hazard Subgroup A hazard originating from solid earth. This term is used interchangeably with the term geological hazard. IRDR
Earthquake Type Sudden movement of a block of the Earth’s crust along a geological fault and associated ground shaking. IRDR
Ground movement Subtype Surface displacement of earthen materials due to ground shaking triggered by earthquakes or volcanic eruptions. IRDR
Tsunami Subtype A series of waves (with long wavelengths when traveling across the deep ocean) that are generated by a displacement of massive amounts of water through underwater earthquakes, volcanic eruptions, or landslides. Tsunami waves travel at very high speed across the ocean, but as they begin to reach shallow water they slow down, and the wave grows steeper. IRDR
Mass movement (dry) Type Any type of downslope movement of earth materials under hydrological dry conditions. IRDR1
Avalanche (dry) Subtype A large mass of loosened earth material, snow, or ice that slides, flows, or falls rapidly down a mountainside under the force of gravity. Debris Avalanche: The sudden and very rapid downslope movement of a mixed mass of rock and soil. There are two general types of debris avalanches. A cold debris avalanche usually results from an unstable slope suddenly collapsing whereas a hot debris avalanche results from volcanic activity leading to slope instability and collapse. IRDR
Landslide (dry) Subtype Any kind of moderate to rapid soil movement incl. lahars, mudslides, and debris flows (under dry conditions). A landslide is the movement of soil or rock controlled by gravity and the speed of the movement usually ranges between slow and rapid, but it is not very slow. It can be superficial or deep, but the materials must make up a mass that is a portion of the slope or the slope itself. The movement has to be downward and outward with a free face. EM-DAT
Rockfall (dry) Subtype
Sudden subsidence (dry) Subtype Sinking of the ground due to groundwater removal, mining, dissolution of limestone (e.g., karst sinkholes), extraction of natural gas, and earthquakes. In this case, the sinking occurs under dry conditions as a result of a geophysical trigger. IRDR2
Volcanic activity Type Subtype (General) A type of volcanic event near an opening/vent in the Earth’s surface including volcanic eruptions of lava, ash, hot vapor, gas, and pyroclastic material. IRDR
Ash fall Subtype Fine (less than 4 mm in diameter) unconsolidated volcanic debris blown into the atmosphere during an eruption; can remain airborne for long periods of time and travel a considerable distance from the source. IRDR
Lava flow Subtype The ejected magma that moves as a liquid mass downslope from a volcano during an eruption. IRDR
Pyroclastic flow Subtype Extremely hot gases, ash, and other materials with a temperature of more than 1,000 degrees Celsius that rapidly flow down the flank of a volcano (at more than 700 km/h) during an eruption. IRDR
Lahar Subtype Hot or cold mixture of earthen material flowing down the slope of a volcano either during or between volcanic eruptions. IRDR

  1. The definition of “Mass movement (dry)” is adapted from the “Mass movement” IRDR definition. ↩︎

  2. The first definition sentence of “Sudden subsidence (dry)” is the definition of “Subsidence” in the IRDR glossary. The second sentence has been added to distinguish this class from “Sudden subsidence (wet)” in the hydrological group. ↩︎

3.5.5 - Hydrological Hazards

Term Level Definition Source
Hydrological hazard Subgroup A hazard caused by the occurrence, movement, and distribution of surface and subsurface freshwater and saltwater. IRDR
Flood Type Subtype (General) A general term for the overflow of water from a stream channel onto normally dry land in the floodplain (riverine flooding), higher-than-normal levels along the coast (coastal flooding) and in lakes or reservoirs as well as ponding of water at or near the point where the rain fell (flash floods). IRDR
Coastal flood Subtype Higher-than-normal water levels along the coast caused by tidal changes or thunderstorms that result in flooding, which can last from days to weeks. IRDR
Flash flood Subtype Heavy or excessive rainfall in a short period of time that produces immediate runoff, creating flooding conditions within minutes or a few hours during or after the rainfall. IRDR
Riverine flood Subtype A type of flooding resulting from the overflow of water from a stream or river channel onto normally dry land in the floodplain adjacent to the channel. IRDR
Ice jam flood Subtype The accumulation of floating ice restricting or blocking a river’s flow and drainage. Ice jams tend to develop near river bends and obstructions (e.g., bridges). IRDR
Mass movement (wet) Type Types of mass movement that occur when heavy rain or rapid snow/ice melt send large amounts of vegetation, mud, or rock down a slope driven by gravitational forces. IRDR1
Avalanche (wet) Subtype A large mass of loosened earth material, snow, or ice that slides, flows, or falls rapidly down a mountainside under the force of gravity. Snow Avalanche: Rapid downslope movement of a mix of snow and ice. IRDR
Landslide (wet) Subtype Any kind of moderate to rapid soil movement incl. lahars, mudslides, and debris flows (under wet conditions). A landslide is the movement of soil or rock controlled by gravity and the speed of the movement usually ranges between slow and rapid, but it is not very slow. It can be superficial or deep, but the materials must make up a mass that is a portion of the slope or the slope itself. The movement has to be downward and outward with a free face. EM-DAT
Rockfall (wet) Subtype
Sudden subsidence (wet) Sinking of the ground due to groundwater removal, mining, dissolution of limestone (e.g., karst sinkholes), extraction of natural gas, and earthquakes. In this case, the sinking occurs under wet conditions as a result of a hydrological trigger (e.g., rain). IRDR2
Mudslide Subtype
Wave action Type Wind-generated surface waves that can occur on the surface of any open body of water such as oceans, rivers, or lakes. The size of the wave depends on the strength of the wind and the distance traveled (fetch). IRDR
Rogue wave Subtype An unusual single crest of an ocean wave far out at sea that is much higher and/or steeper than other waves in the prevailing swell system. IRDR
Seiche Subtype A standing wave of water in a large semi- or fully-enclosed body of water (lakes or bays) created by strong winds and/or a large barometric pressure gradient. IRDR

  1. The “Mass movement (wet)” definition is adapted from the IRDR definition of “Debris flow, mud flow, rock fall”. ↩︎

  2. The first definition sentence of “Sudden subsidence (wet)” is the definition of “Subsidence” in the IRDR glossary. The second sentence has been added to distinguish this class from “Sudden subsidence (dry)” in the geophysical group. ↩︎

3.5.6 - Meteorological Hazards

Term Level Definition Source
Meteorological hazard Subgroup A hazard caused by short-lived, micro- to meso-scale extreme weather and atmospheric conditions that last from minutes to days. IRDR
Extreme temperature Type A general term for temperature variations above (extreme heat) or below (extreme cold) normal conditions. IRDR
Cold wave Subtype A period of abnormally cold weather. Typically, a cold wave lasts for two or more days and may be aggravated by high winds. The exact temperature criteria for what constitutes a cold wave may vary by location. EM-DAT
Heat wave Subtype A period of abnormally hot and/or unusually humid weather. Typically, a heat wave lasts for two or more days. The exact temperature criteria for what constitutes a heat wave may vary by location. EM-DAT
Severe winter conditions Subtype Damage caused by snow and ice. Winter damage refers to damage to buildings, infrastructure, traffic (esp. navigation) inflicted by snow and ice in the form of snow pressure, freezing rain, frozen waterways etc. EM-DAT
Fog Type Subtype Water droplets that are suspended in the air near the Earth’s surface. Fog is, in fact, simply a cloud that is in contact with the ground. IRDR1
Storm Type Subtype (General)
Derecho Subtype Widespread and usually fast-moving windstorms associated with a convection/convective storm. Derechos include downburst and straight-line winds. The damage from derechos is often confused with the damage from tornadoes. IRDR
Hail Subtype Solid precipitation in the form of irregular pellets or balls of ice more than 5 mm in diameter. IRDR
Lightning / Thunderstorms Subtype A high-voltage, visible electrical discharge produced by a thunderstorm and followed by the sound of thunder. IRDR2
Sand/Dust storm Subtype Strong winds carrying particles of sand aloft, but generally confined to less than 50 feet (15 m), especially common in arid and semi-arid environments. A dust storm is also characterized by strong winds but carries smaller particles of dust rather than sand over an extensive area. IRDR
Storm surge Subtype An abnormal rise in sea level generated by a tropical cyclone or other intense types of storm. IRDR
Tornado Subtype A violently rotating column of air that reaches the ground or open water (waterspout). IRDR
Winter storm/Blizzard Subtype A low-pressure system in winter months with significant accumulations of snow, freezing rain, sleet, or ice. A blizzard is a severe snowstorm with winds exceeding 35 mph (56 km/h) for three or more hours, producing reduced visibility (less than 0.25 miles (400 m)). IRDR
Extra-tropical storm Subtype A type of low-pressure cyclonic system in the middle and high latitudes (also called a mid-latitude cyclone) that primarily gets its energy from the horizontal temperature contrasts (fronts) in the atmosphere. When associated with cold fronts, extra-tropical cyclones may be particularly damaging (e.g., European winter/windstorm, or Nor’easter). IRDR
Tropical cyclone Subtype A tropical cyclone originates over tropical or subtropical waters. It is characterized by a warm-core, non-frontal synoptic-scale cyclone with a low-pressure center, spiral rain bands and strong winds. Depending on their location, tropical cyclones are referred to as hurricanes (Atlantic, Northeast Pacific), typhoons (Northwest Pacific), or cyclones (South Pacific and Indian Ocean). IRDR
Severe weather Subtype

  1. Note: the only “Fog” entry in EM-DAT is the Great London SMOG, 1952, which was also accompanied by air pollution. ↩︎

  2. The “Lightning/Thunderstorm” definition corresponds to the definition of “Lightning” in the IRDR glossary. ↩︎

3.5.7 - Complex and Technological Hazards

Term Level Definition Source
Complex disaster Group Subgroup Type Subtype Major famine situation for which drought was not the main causal factor. Removed from EM-DAT in September 2023. EM-DAT
Industrial accident Subgroup Type Subtype (General) Disaster type term used in EM-DAT to describe technological accidents of an industrial nature/involving industrial buildings (e.g. factories). EM-DAT
Miscellaneous accident Subgroup Type Subtype (General) Disaster type term used in EM-DAT to describe technological accidents of a non-industrial or transport nature (e.g., involving houses). EM-DAT
Chemical spill Type Subtype Accident release occurring during the production, transportation, or handling of hazardous chemical substances. EM-DAT
Collapse (Industrial) (Miscellaneous) Type Subtype Accident involving the collapse of a building or structure. Can either involve industrial structures or domestic/non-industrial structures. EM-DAT
Explosion (Industrial) (Miscellaneous) Type Subtype Explosions involving buildings or structures. Can involve industrial structures. EM-DAT
Fire (Industrial) (Miscellaneous) Type Subtype Urban fire involving buildings or structures. Can involve industrial structures. EM-DAT1
Gas leak Type Subtype
Oil spill Type Subtype
Poisoning Type Subtype Poisoning of atmosphere or water courses due to industrial sources of contamination. EM-DAT
Radiation Type Subtype
Transport accident Subgroup Disaster type term used to describe technological transport accidents involving mechanized modes of transport. It comprises four disaster subtypes (i.e., Air, Water, Rail, and Road). EM-DAT
Air Type Subtype Transport accidents involving airplanes, helicopters, airships, and balloons. EM-DAT
Water Type Subtype Transport accidents involving sailing boats, ferries, cruise ships, and other vessels. EM-DAT
Rail Type Subtype Transport accidents involving trains. EM-DAT
Road Type Subtype Transport accidents involving motor vehicles on roads and tracks. EM-DAT

  1. Urban fire is a former class that is now either described as “Fire (industrial)” or “Fire (miscellaneous).” ↩︎

3.6 - Hazard and Disaster Magnitude Units

Additional Information About the Hazard Extent

Some disaster types may have a reported magnitude in the Magnitude and Magnitude Scale columns of the EM-DAT Public Table. EM-DAT disaster magnitude scales vary depending on the disaster type. The table below specifies magnitude property and units for related disaster types.

Disaster Type Magnitude property Magnitude Unit or Scale
Earthquake Amplitude of seismic waves Richter scale1
Flood Flood extent (area) km² (square kilometers)
Drought Drought extent (area) km² (square kilometers)
Extreme temperature The recorded extreme temperature (maximum or minimum depending on whether it is a heat or a cold wave) °C
Epidemic Number of vaccinated people Vaccinated people2
Wildfire Wildfire extent (area) km² (square kilometers)
Storm Recorded wind speed kph (kilometer per hour)
Industrial accident (Chemical spills) Chemical discharged volume m³ (cubic meter)

  1. See warning above. ↩︎

  2. As the magnitude column provides additional hazard-specific information, it is used to report the number of vaccinated people for epidemics. However, it may not be a good indicator of the magnitude of the epidemic, which is also captured by the health impact. ↩︎

3.7 - Impact Variables

What Type of Information Is Collected to Evaluate the Impact of a Disaster? How Is It Aggregated, Reported, or Adjusted?

3.7.1 - Human Impact Variables

People Affected and Death Toll

Five variables describe the human impact of disasters in the EM-DAT Public Table:

The reported total number of deaths (column Total Deaths) includes confirmed fatalities directly imputed to the disaster plus missing people whose whereabouts since the disaster are unknown and so they are presumed dead based on official figures.

Aside from fatalities, the number of injured people (column No. Injured) is entered when the term “injured” is written in the source. Any related word like “hospitalized” is considered as injured. If no precise number is given, such as “hundreds of injured,” 200 injured will be entered (although this figure is probably an underestimate).

The number of affected people (column No. Affected) is often mentioned and is widely used by different actors to convey the extent, impact, or severity of a disaster in non-spatial terms. If only the number of families affected or houses damaged are reported, the figure is multiplied by the average family size for the affected area (×5 for developing countries, ×3 for industrialized countries, according to the UNDP country classification).

Similarly, the indicator No. Homeless is mentioned whenever it is found in reports. If only the number of families that are homeless or houses that are destroyed are reported, the figure is multiplied by the average family size for the affected area (x5 for developing countries, x3 for industrialized countries, according to the UNDP country list).

Finally, the indicator Total Affected is the total of injured, affected, and homeless people. For each disaster and its corresponding sources, the population referred to in these statistics and the apportionment between injured, affected, homeless, and the total is carefully checked by CRED staff members.

Usually, at least the field Total Deaths or Total Affected are found in EM-DAT records as these numbers are involved as entry criteria. However, records often contain incomplete impact statistics (see Accounting Biases).

3.7.2 - Economic Impact Variables

Total Economic Damage, Reconstruction Costs, and Insured Damage

Six variables describe the economic impact of disasters in the EM-DAT Public Table:

These six statistics are the three same statistics (Reconstruction Costs ('000 US$), Insured Damage ('000 US$), and Total Damage ('000 US$)) repeated to also provide an amount corrected for inflation, i.e., “Adjusted” (See Economic Adjustment). Damage and costs are converted and expressed in thousands of US dollars (‘000 US$).

Reconstruction costs are different from total damages as they must consider the current construction or purchase costs of goods, as well as the additional cost of prevention and mitigation measures designed to reduce damage from future disasters. Hence, when reconstruction costs are specified, they are usually greater than the total damage.

Insured damage is usually reported by reinsurance companies that publish figures about disaster losses, e.g., MunichRe, SwissRe, or AON. When insured damage is reported, the total damage is generally reported from the same source for consistency.

3.8 - Spatial Information and Geocoding

Standards for Administrative Regions

Country Codes (ISO-3)

In the EM-DAT Public Table, the ISO column indicates a 3-letter (alpha-3) code representing a specific country, e.g., “BEL” for Belgium. This code is presented according to the international standard ISO-3166 determined by the International Organization for Standardization (ISO). Due to historical changes in the countries’ denomination or boundaries, you may find country codes that are not found in the current ISO 3166 alpha-3 country codes. These extensions are listed in the table below.

Country codes are particularly useful to link the EM-DAT tabular data to a spatial layer using a Geographic Information System (GIS), spatial database, or geoprocessing programming library.

United Nations M49 Standard Country or Area Codes

The UN M49, also known as the Standard Country or Area Codes for Statistical Use (Series M, No. 49), is a set of area codes formulated by the United Nations for data analytics. This standard is curated and upheld by the United Nations Statistics Division. The UN M49 alpha-3 codes largely overlaps with the ISO-3166 alpha-3 norm.

From September 2023 onward, the EM-DAT Public Table refers to the Country, Region, and Subregion names as found in the UN M49 standard. Codes used for the EM-DAT extensions to the ISO alpha-3 codes and UNM49 are listed in the table below.

Alpha-3 Code Country Name Region Subregion
ANT Netherlands Antilles Americas Latin America and the Caribbean
AZO Azores Islands Europe Southern Europe
CHA Channel Islands Europe Western Europe
CSK Czechoslovakia Czechoslovakia Eastern Europe
DDR German Democratic Republic Europe Western Europe
DFR Germany Federal Republic Europe Western Europe
SCG Serbia Montenegro Europe Southern Europe
SPI Canary Islands Africa Northern Africa
SUN Soviet Union Europe Eastern Europe
YMD People’s Democratic Republic of Yemen Asia Western Asia
YMN Yemen Arab Republic Asia Western Asia
YUG Yugoslavia Europe Southern Europe
TWN Taiwan (Province of China) Asia Eastern Asia

GAUL Index and Admin Levels

Since 2014, EM-DAT has relied on the Global Administrative Unit Layers (GAUL) implemented by the Food and Agriculture Organization (FAO). EM-DAT provides loss statistics at the country level, which corresponds to GAUL Admin-0 level. In addition, the EM-DAT Public Table mentions in the Admin Units column which region is affected by the disaster up to the Admin-2 level (e.g., districts). The mapping of EM-DAT disaster events at this higher level of geographical precision has only been completed for data since 2000. Nevertheless, the Admin-0 human and economic impact variables at the country level are not disaggregated between regions at the Admin-1 or Admin-2 level. Hence, only the occurrence is available at a more precise administrative level, and the impact variables remain representative of the country level.

3.9 - EM-DAT Sources of Information

Where Do the EM-DAT Figures Come From?

The EM-DAT database is compiled from various sources, including United Nations, governmental and non-governmental agencies, insurance companies, research institutes, and the press. As of September 2023, the most common sources are included in the table below. For futher inquiries on the data collection and selection process, see EM-DAT Protocols.

Source name Category Type of disasters covered
Office for the Coordination of Humanitarian Affairs (OCHA) / ReliefWeb United Nations Natural hazards
World Food Programme (WFP)1 United Nations Drought/Famine
World Meteorological Organization (WMO) United Nations Natural hazards
World Health Organization (WHO) United Nations Epidemics
Food and Agriculture Organization (FAO)1 United Nations Drought/Famine
United Nation Environment Programme (UNEP) United Nations Natural hazards
National Governments (Reports) National Gov. Natural and technological hazards
Federal Emergency Management Agency (FEMA) United States Natural hazards (America)
National Oceanic and Atmospheric Administration (NOAA) United States Natural hazards
Office of US Foreign Disaster Assistance (OFDA) United States Natural and technological hazards
US Geological Survey (USGS) United States Earthquakes
Centers for Disease Control and Prevention (CDC) United States Epidemics
National Centers for Environmental Information (NCEI, formerly the National Geophysical Data Center - NGDC)1 United States Natural hazards
European Civil Protection and Humanitarian Aid Operations (ECHO) European Union Natural hazards
Dartmouth Flood Observatory (DFO) Research center Floods, landslides and storms
International Federation of Red Cross and Red Crescent Societies (IFRC) Humanitarian aid organization Natural and technological hazards
World Bank (Reports) Inter-Governmental Organizations Major natural hazards and disasters
SwissRe ReInsurance Companies Natural and technological hazards
MünichRe Reinsurance Companies Natural hazards
AON Benfield Reinsurance Companies Natural hazards
Lloyd’s casualty magazine (paid subscription)1 Reinsurance Companies Natural hazards
Agence France Press (paid subscription) Press/Other Natural and technological hazards
Reuters1 Press/Other Natural and technological hazards
The new humanitarian (former IRIN News)1 Press/Other Natural and technological hazards (mostly Africa)
FloodList Press/Other Floods
Wikipedia Press/Other Natural and technological hazards
Plane Crash Info Press/Other Transport accidents (Air)

  1. Historical source no longer used ↩︎ ↩︎ ↩︎ ↩︎ ↩︎ ↩︎

4 - Protocols

Data Collection Processes

4.1 - Entry Criteria

Minimal Requirements for a Disaster To Be Entered in EM-DAT

The EM-DAT definition of a disaster considers unintended hazards with a substantial impact unforeseen by a community (see General Definitions and Concepts). For management and operational purposes, EM-DAT has a set of entry criteria that specify what substantial impact means. EM-DAT disaster records related to natural and technological hazards meet at least one of the following inclusion criteria:

There are, however, secondary criteria, especially for past events where quantitative data were not available (e.g., “the worst disaster in a country or region” or “an event that resulted in considerable damage”).

4.2 - Encoding, Quality Control, and Validation Procedure

How EM-DAT Data Is Encoded and Controlled?

Source identification and collection can be facilitated and partially automated thanks to online services offered by specific sources (e.g., email alert systems, news feeds, or APIs). However, data collection and encoding are always supervised manually by the database manager. The database manager controls the sources that are selected, the classification of the event, its spatiotemporal delimitation, and the identification of the impact figures. Data encoding and validation in EM-DAT is a three-step process:

In addition, Automated Procedures and Constraints prevent or check for abnormal values or data formats in the database.

Daily Encoding

The database manager checks daily information using the preferred source list (see EM-DAT sources). Whenever a new disaster is identified based on a source, it is added to the database. In the first stage, the event is not made public. It becomes so when an entry criterion is met and confirmed by at least two sources. The figures remain subject to changes. Any publication or modification made on a public disaster entry will be visible to the user after the weekly update routine. This routine is usually executed on mondays but may be triggered by the manager if deemed necessary, e.g. in the case of faulty figures or typos.

The published impact variables could be selected from one or more sources. An event can therefore be validated from several sources of information. For example, the human impact can come from an OCHA report and the economic data from a reinsurance report, depending on specific expertise. If the figures differ between the sources, the database manager decides which ones to attribute to the disaster. The choice depends on several elements: the figure itself and the area and period to which it refers, the sources’ chronology, and its degree of reliability. Because this task is complex and case-dependent, there is no pre-determined rule for selecting figures, and the database manager makes the final choice. Some examples of general, however, not systematic, decision rules are illustrated in the box below.

The rules here are only informal and the database manager may disregard them. For example, suppose a report mentions 200 deaths and a news article says, “regional authorities estimate the death toll now stands at 243 dead and missing”. Although this is a newspaper article, the precision of the statement suggests that this figure is more reliable than the one in the official report.

Quality Control and Annual Validation

Quality control and annual validation are systematic checks of all the entries starting in a specific year. It typically takes place at the beginning of the following year. During this validation, all the disasters that took place in the previous year are reviewed to consolidate the data, identify possible additional sources, and modify the published figures accordingly. In addition, the georeferencing, i.e., the more precise attribution of the disaster to GAUL level 1 or 2 zones, is also finalized during the annual validation period (see GAUL Index and Admin Levels).

Thematic Reviews

The CRED periodically conducts thematic reviews of disasters to mitigate the database’s weaknesses (see Known Issues). This task involves systematically checking entries for a type of disaster over a given period or region. The revision can be a data analysis for further quality control, a systematic review of the scientific literature, or a comparison with other existing databases.

From September 2023 onward, these substantial database content updates are planned to be notified on the EM-DAT website and in the documentation release notes for tracking purposes (see Introduction). The Entry Date and Last Update column have also been introduced in the EM-DAT Public Table.

Automated Procedures and Constraints

In 2023, the constraints of the EM-DAT database have been strengthened. These constraints define the domain of values that can be encoded and, if correctly set, can prevent encoding errors in the value or its format. In addition, there are automated procedure checks for anomalies that are likely to be an error, i.e., those without enough certainty to be a constraint but with sufficient likelihood to be notified to the database manager for verification.

Currently, constraints and automated routines check for the consistency of date and time fields, latitude and longitude values, and hazard magnitude values. These procedures are implemented incrementally each time an error that could be prevented is discovered. Hence, by detecting and reporting issues, users may contribute to developing these routines and improving EM-DAT data quality.

Issue Reporting

The CRED encourages any external initiative that can help us improve our data quality. Any problem with the data content (such as errors, or missing data) can be reported to CRED by email using the contact address (see Send Us an Email). For missing entries, you should be aware that the CRED only publishes disasters for which reliable figures are available and when these are corroborated by at least two sources (see Daily Encoding). The CRED reserves the right to modify the data according to subsequent notifications. For issues concerning specific disaster events, the problem should be explained and notified with a list including the related Dis No. values (see Column Description).

For less specific issues, users who have analyzed the quality of EM-DAT are encouraged to share their reports or scientific studies with the EM-DAT team to improve the database (see Contributing).

4.3 - Economic Adjustment

How are Economic Impact Variables Converted and Adjusted for Inflation?

All economic damage in EM-DAT is expressed in thousands of US$ (see Column Description and Economic Impact Variables). For each disaster, the registered figure corresponds to value of the damage at the moment of the event. If a source reports damage in another currency, it is first converted to US$ before it is entered in EM-DAT using a converter (see this example), at the exchange rate when the damage occurred.

The adjusted economic losses in EM-DAT provide a monetary value in US$ that has been adjusted for inflation (see Column Description). The adjustment is linearly proportional to the OECD Consumer Price Index (CPI) provided by the Organization for Economic Cooperation and Development (OECD). The CPI reflects the change in prices of a basket of goods and services that are typically purchased by specific groups of households. EM-DAT relies on the total CPI, i.e., including both food and energy products in the basket, defined for the USA.

In practice, OECD provides the US CPI with 2015 as the base year (\(CPI_{2015}=100\), see https://data.oecd.org/chart/6RMa ). In EM-DAT, the CPI is rescaled to use the last year as a reference, e.g., \(CPI_{2021}=100\). The rescaling is performed when the OECD CPI value becomes available. The economic losses given for the current year are therefore not adjusted and not reported in the column Total Damages, Adjusted ('000 US$).

5 - Known Issues and Limitations

What Difficulties May Be Encountered While Interpreting EM-DAT Data?

EM-DAT is the only comprehensive, free-access disaster loss database with effective global coverage1. However, it has limitations due to the limited number of sources and limitations related to how effectively disasters are reported worldwide. This can lead to biases in the data over which CRED may have limited control, and that could be overlooked in the literature2. Nevertheless, EM-DAT remains a key resource for understanding disaster events and impacts. No current impact database is completely accurate. The United Nations emphasizes the importance of global improvements in documenting disasters in global agendas such as the SENDAI Framework for Disaster Risk Reduction (SFDRR).

Understanding the limitation of a dataset such as EM-DAT is of paramount importance for those who wish to adequately use the data and mitigate its weaknesses for the following purposes: disaster risk management, emergency planning, scientific research, and raising public awareness.


  1. Mazhin, S. A., Farrokhi, M., Noroozi, M., Roudini, J., Hosseini, S. A., Motlagh, M. E., Kolivand, P., and Khankeh, H.: Worldwide disaster loss and damage databases: A systematic review, J Educ Health Promot, 10, 329, https://doi.org/10.4103/jehp.jehp_1525_20, 2021. ↩︎

  2. Jones, R. L., Kharb, A., and Tubeuf, S.: The untold story of missing data in disaster research: a systematic review of the empirical literature utilising the Emergency Events Database (EM-DAT), Environ. Res. Lett., 18, 103006, https://doi.org/10.1088/1748-9326/acfd42, 2023. ↩︎

5.1 - General Issues

Understanding Broad Data Quality Concerns in EM-DAT

Three types of data quality issues can be considered:

A cross-comparison of EM-DAT with a local database and/or a disaster-specific database can help identifiy Issue 1 (e.g., Koç & Thieken, 20181; Lin et al., 20212). For an account of missing values for existing events, we refer to Jones et al. 20213 and the section on Accounting Biases. Issue 3 is partially related to the data collection sources, protocols, or reporting systems generally used by different databases.

Data quality issues within EM-DAT are related to the data collection protocols from dedicated sources. EM-DAT’s completeness reflects the coverage of its sources. Since source reporting has improved over the years, EM-DAT data coverage has improved significantly over the last 30 to 40 years. Nevertheless, gaps and quality issues remain. EM-DAT protocols are meant to guide the way information is monitored and collected from sources. However, no universally applied protocol ensures that different sources report disaster impact and losses using the same guidelines to define, for instance:

  • the beginning and end of disaster events.
  • the geographical footprint of a disaster.
  • impact variables such as deaths (in particular, when computed based on excess mortality), affected people, or economic costs.
  • the disaster type selected by the sources.

Some references illustrate the issues and challenges related to collecting and maintaining a disaster database, e.g., Guha-Sapir & Misson 19924, Kron et al. 20125, and Wirtz et al. 20146.

To some extent, EM-DAT owes its popularity to its simplicity. It reports disaster events as rows in an Excel table. However, this simplicity comes at the cost of conceptual limitations in dealing with complex and compound events and situations. In such cases, as exemplified in the box below, EM-DAT will probably report the disaster in the same way as the source which presented it. The EM-DAT database manager can only choose to select some numbers over some others (see Daily Encoding). However, no model is involved in correcting differences in reporting protocols because this task goes beyond the information monitoring conducted at the CRED by the EM-DAT team.

Such biases that result from differences in the impact reporting systems were generally referred to by Gall et al. 20097 as systemic biases. Some studies point to systemic biases by highlighting that EM-DAT does not correlate well with other databases (e.g., Moriyama et al., 20188; Panwar & Sen, 20209). In their article, Gall et al. 20097 cover four other types of biases: time, hazard-related, spatial, and accounting biases. These are illustrated in the next sections.


  1. Koç, G., and Annegret H. T. “The Relevance of Flood Hazards and Impacts in Turkey: What Can Be Learned from Different Disaster Loss Databases?” Natural Hazards 91, No. 1 (2018): 375408. https://doi.org/10.1007/s11069-017-3134-6↩︎

  2. Lin, Y. C., Khan, F., Jenkins, S. F. and Lallemant, D. “Filling the Disaster Data Gap: Lessons from Cataloging Singapore’s Past Disasters.” Int. J. Disaster Risk Sci. 12, 188–204 (2021). https://doi.org/10.1007%2Fs13753-021-00331-z↩︎

  3. Jones, R. L., Guha-Sapir, D., and Tubeuf, S.: “Human and economic impacts of natural disasters: can we trust the global data?”, Sci Data, 9, 572 (2022). https://doi.org/10.1038/s41597-022-01667-x↩︎

  4. Guha-Sapir, D. and Misson, C.: “The Development of a Database on Disasters.”, Disasters, 16, 74–80 (1992), https://doi.org/10.1111/j.1467-7717.1992.tb00378.x↩︎

  5. Kron, W., Steuer, M., Löw, P., and Wirtz, A. “How to Deal Properly with a Natural Catastrophe Database – Analysis of Flood Losses.” Natural Hazards and Earth System Sciences 12, No. 3,53550 (2012). https://doi.org/10.5194/nhess-12-535-2012↩︎

  6. Wirtz, A., Kron, W., Löw, P., and Steuer, M. “The Need for Data: Natural Disasters and the Challenges of Database Management”. Natural Hazards 70, No. 1, 13557 (2014). https://doi.org/10.1007/s11069-012-0312-4↩︎

  7. Gall, M., Kevin A. B., and Susan L. C. “When Do Losses Count?: Six Fallacies of Natural Hazards Loss Data.” Bulletin of the American Meteorological Society 90, No. 6,799810 (2009). https://doi.org/10.1175/2008BAMS2721.1↩︎ ↩︎

  8. Moriyama, K., Daisuke S., and Yuichi O. “Comparison of Global Databases for Disaster Loss and Damage Data.” Journal of Disaster Research 13, No. 6, 100714 (2018). https://doi.org/10.20965/jdr.2018.p1007↩︎

  9. Panwar, V. and Subir S. “Disaster Damage Records of EM-DAT and DesInventar: A Systematic Comparison.” Economics of Disasters and Climate Change 4, No. 2, 295317 (2020). https://doi.org/10.1007/s41885-019-00052-0↩︎

5.2 - Specific Biases

Understanding Particular Data Quality Concerns in EM-DAT

Time Bias

Time biases result from unequal reporting quality and coverage over time1. The figure below shows the occurrence of disasters in EM-DAT. The figure shows a significant increase that starts in the 1960s. This increase coincides with the creation of OFDA. In 1973, OFDA started compiling disaster data, and the CRED was created2. In 1988, the CRED took over the disaster database and created EM-DAT. In the meantime, communication technologies have improved, with the first personal computers and satellites appearing in the 1970s and the advent of the World Wide Web in the 1990s (see also History of EM-DAT)

Technologies and initiatives can be considered responsible for the dominant trend observed. Therefore, it is challenging to infer insight into the actual drivers of disasters such as climate change, population growth, or disaster risk management. Accordingly, excluding pre-2000 data from trend analyses based on EM-DAT is strongly recommended. From September 2023 onward, the CRED refers to pre-2000 data as Historic data in the EM-DAT Public Table.

Hazard-related biases result from unequal reporting quality and coverage for different hazard types1. For example, in EM-DAT, data related to biological hazards (e.g., epidemics) and extreme temperature hazards (e.g., heat waves) are less covered and the cover of lower quality. Some hazard-related biases are illustrated in the Accounting Biases and Geographic Biases sections.

Threshold Biases

Threshold biases result from unequal reporting quality and coverage for disasters of different magnitudes1. High-impact disasters attract more attention, resulting in better media coverage and reporting. This could lead to threshold biases in EM-DAT. The EM-DAT entry criteria introduce a kind of threshold bias, as shown in the figure below regarding disaster mortality, while some studies have shown locally that small disasters may have a high cumulative impact, e.g.3. Regarding disasters that fit EM-DAT’s entry criteria, it is fair to assume that disasters close to the entry criteria are more likely to be missing than high-impact disasters. However, as shown in the figure below, the cumulative mortality associated with low-mortality events exceeds the cumulative impact of higher-mortality events.

Accounting Biases

Accounting biases result from unequal reporting quality and coverage for different impact variables1. For instance, in EM-DAT, the economic losses are, on average, less frequently reported than the human impact variables, which may also depend on the hazard type (see the figure below). Furthermore, insured damages are naturally more reported than uninsured damages, which creates a geographic bias where there is a lack of insurance coverage, as in Africa. For droughts, EM-DAT fails to capture the associated mortality because it is overlooked as an indirect impact, as evidenced by the UNDRR 2021 Global Assessment Report on Droughts.

Besides, it cannot be assumed that because an impact is reported in EM-DAT, there is no accounting bias. In general, direct impacts are often reported by EM-DAT sources, while indirect impact estimates are less available. For instance, indirect deaths for a flood event correspond to the number of fatalities occurring during the event, while indirect deaths result from disease outbreaks due to deteriorated sanitary conditions. Yet, indirect mortality is sometimes more important than direct mortality, for examples.4

Geographic Biases

Geographic biases result from unequal reporting quality and coverage across space1. In general, EM-DAT has a relatively worse coverage for Sub-Saharan Africa regarding the occurrence and the accounting of impact variables5. Any disaster type may be subject to geographic biases in EM-DAT as there may be discrepancies between reporting systems from one country to another (see General Issues).

This issue is particularly pronounced regarding heat waves6, as shown in the figure below. Heat waves are often overlooked in Sub-Saharan Africa7. About 52% of heatwave events in EM-DAT occurred in nine countries: Japan, India, Pakistan, the USA, followed by Western European countries (France, Belgium, United Kingdom, Spain, and Germany).

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