In 2023, floods caused the death of roughly ***** people across the globe. Nevertheless, this death toll is dwarfed in comparison to the peak recorded in 1999. That year, some ****** people died as a result of floods, mostly the consequence of one of the deadliest flood incidents of the previous century, which hit Venezuela in December. The effects of flooding While the death toll is the most critical impact of flooding incidents, it is not the only one. For example, more than ** million people were impacted by floods worldwide in 2023, including those injured, affected or left homeless. In 2010 alone, almost *** million people were affected. Floods also incur massive economic damage by destroying buildings and infrastructure. Asian countries such as Bangladesh, Vietnam, and Egypt were the most exposed to river flooding as of 2024. Climate change and flooding As climate change causes global temperatures to rise, floods are expected to increase in frequency and severity. A warmer atmosphere is able to retain more moisture, leading to an increase in intense downpours. It also affects snowmelt patterns. According to a 2022 report, the global population exposed to flood was expected to rise by ** percent in the case of an increase in global temperatures of *** degrees Celsius.

In 2024, the number of deaths due to flooding in the United States amounted to *** fatalities. More than half of these deaths occurred in September. Already, ** flood-related fatalities have been reported in the U.S. in 2025, as of April.

In 2023, Africa accounted for over ** percent of flood deaths worldwide, with over ***** fatalities. This stood well over the annual average of the past decade, when the region registered some *** deaths due to floods per year. Asia was also badly hit in 2023, with ***** deaths registered, accounting for ** percent of flood deaths worldwide in that year.

This statistic shows the number of deaths due to flooding disasters in certain countries between 1900 and 2016*. The floods in China in July 1931 led to 3.7 million deaths.FloodsThe 1931 Central China floods caused the most deaths due to a flood in the past century. 28 years later in 1959, the Yellow River flooded into East China killing an estimated 2 million people. The death toll due to this flood has been also associated with the Great sparrow campaign that arose due to the Great Chinese Famine that began in 1958. Citizens were told to kill sparrows and other wild birds that ate crop seeds which lead to an explosive increase in the population of crop-eating insects. This massive ecological shift, starvation, as well as floods and drought lead to the deaths of many Chinese people. More recently, a 1996 flood and 1998 flood in Yangtze, China caused some 30.7 billion U.S. dollars and 24 billion U.S. dollars in damage. In 2014, 38 lives were lost in the United States due to floods or flash floods. Since 1980, the two of the most significant natural disasters have been the earthquake in Haiti in January 2010, which caused 22,570 deaths and the 2004 earthquake and resulting tsunami which caused 220,000 deaths in countries like Thailand and Sri Lanka. Death tolls in Haiti were aggravated by poverty and poor housing conditions that many Haitians experience.

In 2023, there were a total of 79 fatalities reported due to floods in the United States, down from 105 fatalities in the previous year. Since 2010, the highest number of ives lost due to floods in a single year was recorded in 2015, with a total of 189.

EUFF 2.0 (EUropean Flood Fatalities) is a database which contains 2.875 cases of flood fatalities that occurred throughout 41 years (1980–2020) in 12 study areas in Europe (Cyprus; Czech Republic; Germany; Greece; Israel; Italy; Portugal; Turkey; United Kingdom; the Spanish regions of Balearic Islands and Catalonia, and the Mediterranean regions of South France). EUFF 2.0 provides not only the number of fatalities, but also detailed information about the profile of victims and the circumstances of the accidents. Flood fatality cases are georeferenced using NUTS 3 level (Nomenclature of Territorial Units for Statistics), allowing analyses of fatality distribution in respect to geographic and demographic data.

Flash flooding is the top weather-related killer, responsible for an average of 140 deaths per year across the United States. Although precipitation forecasting and understanding of flash flood causes have improved in recent years, there are still many unknown factors that play into flash flooding. Despite having accurate and timely rainfall reports, some river basins simply do not respond to rainfall as meteorologists might expect. The Flash Flood Potential Index (FFPI) was developed in order to gain insight into these “problem basins”, giving National Weather Service (NWS) meteorologists insight into the intrinsic properties of a river basin and the potential for swift and copious rainfall runoff.The goal of the FFPI is to quantitatively describe a given sub-basin’s risk of flash flooding based on its inherent, static characteristics such as slope, land cover, land use and soil type/texture. It leverages both Geographic Information Systems (GIS) as well as datasets from various sources. By indexing a given sub-basin’s risk of flash flooding, the FFPI allows the user to see which subbasins are more predisposed to flash flooding than others. Thus, the FFPI can be added to the situational awareness tools which can be used to help assess flash flood risk.

In 2023, the Democratic Republic of Congo recorded the highest number of deaths due to floods worldwide, with roughly ***** fatalities. India followed second, with roughly half the number of fatalities, at ***** deaths recorded. Africa accounted for more than half of deaths due to floods worldwide in 2023.

FFEM-DB (Database of Flood Fatalities from the Euro-Mediterranean region) is a database which contains 2.875 cases of flood fatalities that occurred throughout 41 years (1980–2020) in 12 study areas in Euro-Mediterranean area (Cyprus; Czech Republic; Germany; Greece; Israel; Italy; Portugal; Turkey; United Kingdom; the Spanish regions of Balearic Islands and Catalonia, and the Mediterranean regions of South France). FFEM-DB provides not only the number of fatalities, but also detailed information about the profile of victims and the circumstances of the accidents. Flood fatality cases are georeferenced using NUTS 3 level (Nomenclature of Territorial Units for Statistics), allowing analyses of fatality distribution in respect to geographic and demographic data.

Analysis of ‘Natural Disasters Data Explorer’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/mathurinache/natural-disasters-data-explorer on 28 January 2022.

--- Dataset description provided by original source is as follows ---

Context

Disasters include all geophysical, meteorological and climate events including earthquakes, volcanic activity, landslides, drought, wildfires, storms, and flooding. Decadal figures are measured as the annual average over the subsequent ten-year period.

Content

Thanks to Our World in Data, you can explore death from natural disasters by country and by date.

Acknowledgements

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Inspiration

List of variables for inspiration: Number of deaths from drought Number of people injured from drought Number of people affected from drought Number of people left homeless from drought Number of total people affected by drought Reconstruction costs from drought Insured damages against drought Total economic damages from drought Death rates from drought Injury rates from drought Number of people affected by drought per 100,000 Homelessness rate from drought Total number of people affected by drought per 100,000 Number of deaths from earthquakes Number of people injured from earthquakes Number of people affected by earthquakes Number of people left homeless from earthquakes Number of total people affected by earthquakes Reconstruction costs from earthquakes Insured damages against earthquakes Total economic damages from earthquakes Death rates from earthquakes Injury rates from earthquakes Number of people affected by earthquakes per 100,000 Homelessness rate from earthquakes Total number of people affected by earthquakes per 100,000 Number of deaths from disasters Number of people injured from disasters Number of people affected by disasters Number of people left homeless from disasters Number of total people affected by disasters Reconstruction costs from disasters Insured damages against disasters Total economic damages from disasters Death rates from disasters Injury rates from disasters Number of people affected by disasters per 100,000 Homelessness rate from disasters Total number of people affected by disasters per 100,000 Number of deaths from volcanic activity Number of people injured from volcanic activity Number of people affected by volcanic activity Number of people left homeless from volcanic activity Number of total people affected by volcanic activity Reconstruction costs from volcanic activity Insured damages against volcanic activity Total economic damages from volcanic activity Death rates from volcanic activity Injury rates from volcanic activity Number of people affected by volcanic activity per 100,000 Homelessness rate from volcanic activity Total number of people affected by volcanic activity per 100,000 Number of deaths from floods Number of people injured from floods Number of people affected by floods Number of people left homeless from floods Number of total people affected by floods Reconstruction costs from floods Insured damages against floods Total economic damages from floods Death rates from floods Injury rates from floods Number of people affected by floods per 100,000 Homelessness rate from floods Total number of people affected by floods per 100,000 Number of deaths from mass movements Number of people injured from mass movements Number of people affected by mass movements Number of people left homeless from mass movements Number of total people affected by mass movements Reconstruction costs from mass movements Insured damages against mass movements Total economic damages from mass movements Death rates from mass movements Injury rates from mass movements Number of people affected by mass movements per 100,000 Homelessness rate from mass movements Total number of people affected by mass movements per 100,000 Number of deaths from storms Number of people injured from storms Number of people affected by storms Number of people left homeless from storms Number of total people affected by storms Reconstruction costs from storms Insured damages against storms Total economic damages from storms Death rates from storms Injury rates from storms Number of people affected by storms per 100,000 Homelessness rate from storms Total number of people affected by storms per 100,000 Number of deaths from landslides Number of people injured from landslides Number of people affected by landslides Number of people left homeless from landslides Number of total people affected by landslides Reconstruction costs from landslides Insured damages against landslides Total economic damages from landslides Death rates from landslides Injury rates from landslides Number of people affected by landslides per 100,000 Homelessness rate from landslides Total number of people affected by landslides per 100,000 Number of deaths from fog Number of people injured from fog Number of people affected by fog Number of people left homeless from fog Number of total people affected by fog Reconstruction costs from fog Insured damages against fog Total economic damages from fog Death rates from fog Injury rates from fog Number of people affected by fog per 100,000 Homelessness rate from fog Total number of people affected by fog per 100,000 Number of deaths from wildfires Number of people injured from wildfires Number of people affected by wildfires Number of people left homeless from wildfires Number of total people affected by wildfires Reconstruction costs from wildfires Insured damages against wildfires Total economic damages from wildfires Death rates from wildfires Injury rates from wildfires Number of people affected by wildfires per 100,000 Homelessness rate from wildfires Total number of people affected by wildfires per 100,000 Number of deaths from extreme temperatures Number of people injured from extreme temperatures Number of people affected by extreme temperatures Number of people left homeless from extreme temperatures Number of total people affected by extreme temperatures Reconstruction costs from extreme temperatures Insured damages against extreme temperatures Total economic damages from extreme temperatures Death rates from extreme temperatures Injury rates from extreme temperatures Number of people affected by extreme temperatures per 100,000 Homelessness rate from extreme temperatures Total number of people affected by extreme temperatures per 100,000 Number of deaths from glacial lake outbursts Number of people injured from glacial lake outbursts Number of people affected by glacial lake outbursts Number of people left homeless from glacial lake outbursts Number of total people affected by glacial lake outbursts Reconstruction costs from glacial lake outbursts Insured damages against glacial lake outbursts Total economic damages from glacial lake outbursts Death rates from glacial lake outbursts Injury rates from glacial lake outbursts Number of people affected by glacial lake outbursts per 100,000 Homelessness rate from glacial lake outbursts Total number of people affected by glacial lake outbursts per 100,000 Total economic damages from disasters as a share of GDP Total economic damages from drought as a share of GDP Total economic damages from earthquakes as a share of GDP Total economic damages from extreme temperatures as a share of GDP Total economic damages from floods as a share of GDP Total economic damages from landslides as a share of GDP Total economic damages from mass movements as a share of GDP Total economic damages from storms as a share of GDP Total economic damages from volcanic activity as a share of GDP Total economic damages from volcanic activity as a share of GDP Entity Year deaths_rate_per_100k_storm injured_rate_per_100k_storm total_affected_rate_per_100k_all_disasters

--- Original source retains full ownership of the source dataset ---

Flood mortality is still a serious concern in both developed and developing countries, requiring a deeper understanding to identify hazardous factors and mitigate the life losses. with this database, we compared the flood fatalities occurred in the period 1990-2022 in two Mediterranean regions characterized by different natural and anthropogenic frameworks and located in western Algeria and southern Italy, respectively. The main goal is to detect, either common features controlling flood mortality or typical factors causing local differences among the two areas, in order to identify the drivers of flood mortality and suggest how alleviate their impact applying mitigation strategies customized to the detected failures. With these purposes we created the database containing information 242 flood fatalities occurred in the two regions in the 33-year study period, including time and place of fatal accidents, age and gender of the victims, death circumstances and victim’s behavior.

In order to analyse the causes and circumstances of the fatalities due to hurricane Harvey (2017), a database of reported fatalities was compiled. Information about the victim (age, gender) and the circumstances of death (location, date, cause and circumstances of death) were included. The database is limited to fatalities that occurred within the first two weeks after landfall in Texas (August 25 - September 8, 2017) and that were directly relatable to hurricane Harvey. The dataset was compiled using both official government sources and media sources.

This dataset is a merged and unified one from seven individual datasets, making it the longest records ever and wide coverage in the US for flood studies. All individual databases and a unified database are provided to accommodate different user needs. It is anticipated that this database can support a variety of flood-related research, such as a validation resource for hydrologic or hydraulic simulations, climatic studies concerning spatiotemporal patterns of floods given this long-term and U.S.-wide coverage, and flood susceptibility analysis for vulnerable geophysical locations.

Description of filenames:

1. cyberFlood_1104.csv – web-based crowdsourced flood database, developed at the University of Oklahoma (Wan et al., 2014). 203 flood events from 1998 to 2008 are retrieved with the latest version. Data accessed on 11/04/2020.

Data attributes: ID, Year, Month, Day, Duration, fatality, Severity, Cause, Lat, Long, Country Code, Continent Code

2. DFO.xlsx – the Dartmouth Flood Observatory flood database. It is a tabular form of global flood database, collected from news, government agencies, stream gauges, and remote sensing instruments from 1985 to the present. Data accessed on 10/27/2020.

Data attributes: ID, GlodeNumber, Country, OtherCountry, long, lat, Area, Began, Ended, Validation, Dead, Displaced, MainCause, Severity

3. emdat_public_2020_11_01_query_uid-MSWGVQ.xlsx – Emergency Events Database (EM-DAT). This flood report is managed by the Centre for Research on the Epidemiology of Disasters in Belgium, which contains all types of global natural disasters from 1900 to the present. Data accessed on 11/01/2020.

Data attributes: Dis No, Year, Seq, Disaster Group, Disaster Subgroup, Disaster Type, Disaster Subtype, Disaster Subsubtype, Event Nane, Entity Criteria, Country, ISO, Region, Continent, Location, Origin, Associated Disaster, Associated Disaster2, OFDA Response, Appeal, Declaration, Aid Contribution, Disaster Magnitude, Latitude, Longitude, Local Time, River Basin, Start Year, Start Month, Start Day, End Year, End Month, End Day, Total Death, No. Injured, No. Affected, No. Homeless, Total Affected, Reconstruction, Insured Damages, Total Damages, CPI

4. extracted_events_NOAA.csv – The national weather service storm reports. The NOAA NWS team collects weather-related natural hazards from 1950 to the present. Data accessed on 10/27/2020.

Data attributes: BEGIN_YEARMONTH, BEGIN_DAY, BEGIN_TIME, END_YEARMONTH, END_DAY, END_TIME, EPISODE_ID, EVENT_ID, STATE, STATE_FIPS, YEAR, MONTH_NAME, EVENT_TYPE, CZ_TYPE, CZ_FIPS, CZ_NAME, WFO, BEGIN_DATETIME, CZ_TIMEZONE, END_DATE_TIME, INJURIES_DIRECT, INJURIES_INDIRECT, DEATHS_DIRECT, DEATHS_INDIRECT, DAMAGE_PROPERTY, DAMAGE_CROPS, SOURCE, MAGNITUDE, MAGNITUDE_TYPE, FLOOD CAUSE, CATEGORY, TOR_F_SCALE< TOR_LENGTH, TOR_WIDTH, TOR_OTHER_WFO, TOR_OTHER_CZ_STATE, TOR_OTHER_CZ_FIPS, BEGIN_RANGE, BEGIN_AZIMUTH, BEGIN_LOCATION, END_RANGE, END_AZIMUTH, END_LOCATION, BEGIN_LAT, BEGIN_LON, END_LAT, END_LON, EPISODE_NARRATIVE, EVENT_NARRATIVE, DATA_SOURCE

5. FEDB_1118.csv – The University of Connecticut Flood Events Database. Floods retrieved from 6,301 stream gauges in the U.S. after flow separation from 2002 to 2013 (Shen et al., 2017). Data accessed on 11/18/2020.

Data attributes: STCD, StartTimeP, EndTimeP, StartTimeF, EndTimeF, Perc, Peak, RunoffCoef, IBF, Vp, Vb, Vt, Pmean, ETr, ELs, VarTr, VarLs, EQ, Q2, CovTrLs, Category, Geometry

6. GFM_events.csv – Global Flood Monitoring dataset. It is a crowdsourcing flood database derived from Twitter tweets over the globe since 2014. Data accessed on 11/9/2020.

Data attributes: event_id, location_ID, location_ID_url, name, type, country_location_ID, country_ISO3, start, end, time of detection

7. mPing_1030.csv – meteorological Phenomena Identification Near the Ground (mPing). The mPing app is a crowdsourcing, weather-reporting software jointly developed by NOAA National Severe Storms Laboratory (NSSL) and the University of Oklahoma (Elmore et al., 2014). Data accessed on 10/30/2020.

Data attributes: id, obtime, category, description, description_id, lon, lat

8. USFD_v1.0.csv – A merged United States Flood Database from 1900 to the present.

Data attributes: DATE_BEGIN, DATE_END, DURATION, LON, LAT, COUNTRY, STATE, AREA, FATALITY, DAMAGE, SEVERITY, SOURCE, CAUSE, SOURCE_DB, SOURCE_ID, DESCRIPTION, SLOPE, DEM, LULC, DISTANCE_RIVER, CONT_AREA, DEPTH, YEAR.

Details of attributes:

DATE_BEGIN: begin datetime of an event. yyyymmddHHMMSS

DATE_END: end datetime of an event. yyyymmddHHMMSS

DURATION: duration of an event in hours

LON: longitude in degrees

LAT: latitude in degrees

COUNTRY: United States of America

STATE: US state name

AREA: affected areas in km^2

FATALITY: number of fatalities

DAMAGE: economic damages in US dollars

SEVERITY: event severity, (1/1.5/2) according to DFO.

SOURCE: flood information source.

CAUSE: flood cause.

SOURCE_DB: source database from item 1-7.

SOURCE_ID: original ID in the source database.

DESCRIPTION: event description

SLOPE: calculated slope based on SRTM DEM 90m

DEM: Digital Elevation Model

LULC: Land Use Land Cover

DISTANCE_RIVER: distance to major river network in km,

CONT_AREA: contributing area (km^2), from MERIT Hydro

DEPTH: 500-yr flood depth

YEAR: year of the event.

The script to merge all sources and figure plots can be found in https://github.com/chrimerss/USFD.

The data used to generate the figures in the article: Flood-related deaths and displacement influence human migration in floodplains of developing countries

In 2023, California and Pennsylvania saw the highest number of flood fatalities across the United States, each with 10 deaths as a result of flooding events. Arizona and Colorado followed, with seven deaths registered due to floods. Overall, the number of flood fatalities across the whole North American country amounted to 79 that year.

The first version of EUFF (EUropean Flood Fatalities) contained 2466 FFs which occurred during a 39-year period (1980–2018) in 8 Euro-Mediterranean countries that are further divided into 9 study areas (Czech Republic, Israel, Italy, Turkey, Greece, Portugal, South France, Catalonia and Balearic Islands). This database is an updated version of EUFF, improved throughout the introduction of new data and details of flood fatalities emerged from ongoing historical research in the Czech Republic and South France study areas.
EUFF contains 2483 flood fatalities, which occurred during the same period (1980–2018) in the above-mentioned study areas. The methodological approach is based on the systematic collection of data about floods that killed any people, which are named here as flood events. All cases of flood events triggered by rainfall were included, without severity thresholds: EUFF contains all the cases of flood events, independently of the number of fatalities per flood event. The methodological approach was based on the systematic collection of fatal events descriptions from documentary sources as newspapers, websites, and technical reports. Narratives of flood events were disaggregated in database fields describing victim’s profile and the circumstances of the deaths. Each record contains data related to a single flood fatality, organized in fields with information about event (place, date, and hour), fatality (age, gender, conditions, residency, and activity), and victim-event interactions (accident place, accident dynamic, death causes, protective and/or hazardous behaviours). EUFF database represents a source of data contributing to a better understanding of the population exposure to floods. The EUFF database, with its great potential to be extended spatially and temporally, represents a unique European database with high scientific and practical potential. One aspect of the database is its vitality, as we strive to improve it by extending the study period, and enlarging the domain. The EUFF database and its potential will hopefully motivate and encourage further researchers to join this database with data on flood fatalities available in their countries.

Association between personal and behavioral factors and flood death in Iran floods in 2019, (n = 387).

"Disaster data for countries along the belt and road, mainly from the global disaster database.The records information of disaster database are from the United Nations, government and non-governmental organizations, research institutions and the media. It's documented in detail such as the country where the disaster occurred, the type of disaster, the date of the disaster, the number of deaths and the estimated economic losses. This study extracts the natural disaster records of the countries along the One Belt And One Road line one by one from the database, and finally forms the disaster database of 9 major disasters of the 65 countries. The natural disaster records collected can be roughly divided into nine categories, including: floods, landslides, extreme temperatures, storms, droughts, forest fires, earthquakes, mass movements and volcanic activities. From 1900 to 2018, a total of 5,479 disaster records were recorded in countries along the One Belt And One Road. From 2000 to 2015, there were 2,673 disaster records. On this basis, the natural disasters of the countries along the belt and road are investigated from four aspects, including disaster frequency, death toll, disaster-affected population and economic loss assessment. Overall, since 1900, a total of 5479 natural disasters have occurred in countries along the One Belt And One Road, resulting in about 19 million deaths and economic losses of about 950 billion us dollars. Among them, the most frequent occurrence is flood and storm; the biggest economic losses are floods and earthquakes; the most affected people are flood and drought; drought and flooding are the leading causes of death

Association between demographic factors and flood death in Iran floods in 2019, (n = 387).

Flash flooding is the top weather-related killer, responsible for an average of 140 deaths per year across the United States. Although precipitation forecasting and understanding of flash flood causes have improved in recent years, there are still many unknown factors that play into flash flooding. Despite having accurate and timely rainfall reports, some river basins simply do not respond to rainfall as meteorologists might expect. The Flash Flood Potential Index (FFPI) was developed in order to gain insight into these “problem basins”, giving National Weather Service (NWS) meteorologists insight into the intrinsic properties of a river basin and the potential for swift and copious rainfall runoff.The goal of the FFPI is to quantitatively describe a given sub-basin’s risk of flash flooding based on its inherent, static characteristics such as slope, land cover, land use and soil type/texture. It leverages both Geographic Information Systems (GIS) as well as datasets from various sources. By indexing a given sub-basin’s risk of flash flooding, the FFPI allows the user to see which subbasins are more predisposed to flash flooding than others. Thus, the FFPI can be added to the situational awareness tools which can be used to help assess flash flood risk.