Extreme heat was the deadliest weather condition in the United States in 2023, resulting in a total of 207 lives lost that year. This was followed by fire weather, having caused 103 fatalities. On the other side of the spectrum, only one life was lost due to ice in the North American country that year.

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 2024, there were roughly 18,100 reported fatalities caused by natural disaster events worldwide. This was well below the 21st-century average and significantly lower than the fatalities recorded in 2023, which were driven by the earthquakes that hit Turkey and Syria on February and became the deadliest catastrophes in 2023, with nearly ****** reported deaths. Economic losses due to natural disasters The economic losses due to natural disaster events worldwide amounted to about *** billion U.S. dollars in 2024. Although figures in recent years have remained mostly stable, 2011 remains the costliest year to date. Among the different types of natural disaster events, tropical cyclones caused the largest economic losses across the globe in 2024. What does a natural disaster cost? Hurricane Katrina has been one of the costliest disasters in the world, costing the insurance industry some *** billion U.S. dollars. The resilience of societies against catastrophes have been boosted by insurance industry payouts. Nevertheless, insurance payouts are primarily garnered by industrialized countries. In emerging and developing regions, disaster insurance coverage is still limited, despite the need for improved risk management and resilience as a method to mitigate the impact of disasters and to promote sustainable growth.

This statistic shows the share of occurrence and death tolls for weather-related disasters worldwide in the period from 1995 to 2015, by national income level. During the past 20 years, around ** percent of weather-related disasters affected lower-income countries.

Natural disasters and loss – additional information

The years 2014 and 2015 are two of the hottest years recorded since the 1880s. In 2014, there were ** deaths caused by extreme heat in the United States. The increased risk of extreme weather due to climate change has put pressure on countries to develop regulations to better protect infrastructure and human health. Between 1995 and 2015, about a third of the global weather-related disasters occurred in lower-middle income countries, however, almost half of the deaths due to these events affected these countries. The number of deaths caused by the Cyclone Nargis in Myanmar contributed significantly to these statistics. In high-income countries, weather-related deaths are largely due to heat waves. The actual number of casualties in low-income countries is estimated to be much higher and may reflect a lack of reporting.

China and India have been among the most severely impacted countries in the world in terms of weather catastrophes, accounting for some * billion people that have been affected between 1995 and 2015. Economic loss due to these events totaled some ** billion U.S. dollars in the Asia and Oceania regions. Millions of houses as well as public institutions such as schools, clinics, and hospitals have been damaged by weather-related disasters, primarily due to floods and storms. Over the last decades, countries have improved their preparedness as well as their response to natural disasters. Several countries in Asia have begun to follow the Hyogo Framework for Action, a guideline developed to help reduce disaster risk, in efforts to reduce the losses derived from these catastrophes.

There were a total of 1,033 fatalities reported due to heat waves, wildfires, and drought in the United States in 2024. In total, there were about 1,576 fatalities due to severe natural disasters in the United States that year.

Weather, climate, and water-related disasters caused the death of over ******* people in Africa between 1970 and 2019. Most of the fatalities occurred in the *****. Severe droughts hit Ethiopia, Sudan, Mozambique, and Chad in that decade, provoking more than ******* deaths. Overall, Africa registered nearly ***** hazards in the period from 1970 to 2019.

Description of the variables in the dataset.Publication: Rasolofoson, R.A., H.O. Onyango, F.J. Awuor, C.M. Aura and K.J. Fiorella 2024. Climate change: a pointer to increased small-scale fisher drowning deaths. PLOS ONE. doi: 10.1371/journal.pone.0302397.

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This deposit contains three .xlsx files.

The file „01_fatalities_1919-2022“ contains annual numbers of fatalities (males, females and sum) for individual categories of external death causes attributed to weather and natural extremes, excerpted from demographic yearbooks for the Czech Republic for the period 1919–2022.

The file „02_age_categories_1931-2022“ contains eight sheets with annual numbers of weather-related fatalities in the Czech Republic in the period 1931–2022 for eight age categories and for males and females separately. Sheets represent individual categories of death causes – Cold, Heat, Lightning, Natural hazards, Fall on ice or snow, Air pressure. Heat and Natural hazards are divided into two sheets – one with summarized numbers and one with numbers for individual sub-categories.

The file „03_clima_factors“ contains mean temperature of January–February (Brázdil et al., 2012, extended) and mean annual number of days with a thunderstorm in the Czech Republic for the period 1919–2022 and mean temperature of winter season (DJF) in the Czech Republic for the period 1986/1987–2021/2022 (Brázdil et al., 2012, extended).

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From 1950 to 2024, the cyclone Bhola that hit Bangladesh in 1970 was the deadliest natural disaster in the world. The exact death toll is impossible to calculate, but it is estimated that over 300,000 lives were lost as a result of the cyclone. The Tangshan earthquake in China in 1976 is estimated to have caused the second-highest number of fatalities. The Haiti earthquake The fifth-deadliest natural disaster during this period was the earthquake in Haiti in 2010. However, death tolls vary between 100,000 and 316,000, meaning that some estimates make it the deadliest natural disaster in the world since 1950, and the deadliest earthquake since 1900. Sixty percent of the country’s hospitals and eighty percent of the country’s schools were destroyed. It was the worst earthquake to hit the Caribbean in 200 years, with a magnitude of 7.0 at its epicenter only 25 kilometers away from Haiti’s capital, Port-au-Prince. Poor construction practices were to blame for many of the deaths; Haiti’s buildings were not earthquake resistant and were not built according to building code due to a lack of licensed building professionals. High population density was also to blame for the high number of fatalities. One fourth of the country’s inhabitants lived in the Port-au-Prince area, meaning half of the country’s population was directly affected by the earthquake. Increasing extreme weather As global warming continues to accelerate climate change, it is estimated that natural catastrophes such as cyclones, rainfalls, landslides, and heat waves will intensify in the coming years and decades. For instance, the economic losses caused by natural disasters worldwide increased since 2015. Moreover, it is expected that countries in the Global South will be affected the most by climate change in the coming years, and many of these are already feeling the impact of climate change.

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 notebook that generates this dataset is here: https://www.kaggle.com/johnjdavisiv/us-counties-weather-sociohealth-location-data

The 3,142 counties of the United States span a diverse range of social, economic, health, and weather conditions. Because of the COVID19 pandemic, over 2,400 of these counties have already experienced some COVID19 cases.

Combining county-level data on health, socioeconomics, and weather can help us address identify which populations are at risk for COVID19 and help prepare high-risk communities.

Temperature and humidity may affect the transmissibility of COVID19, but in the United States, warmer regions also tend to have markedly different socioeconomic and health demographics. As such, it's important to be able to control for factors like obesity, diabetes, access to healthcare, and poverty rates, since these factors themselves likely play a role in COVID19 transmission and fatality rates.

This dataset provides all of this information, formatted, cleaned, and ready for analysis. Most columns have little or no missing data. A small number have larger amounts of missing data; see the kernel that generated this dataset for details.

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Mozambique reported ** natural disasters, such as floods and storms, between 2013 and 2020. The country is regularly affected by weather hazards, which lead to destruction, deaths, and thousands of people being forced to leave their homes. In recent years, the most devastating natural disasters occurred in 2019, when Mozambique was hit by two cyclones in only two months. Cyclones Idai and Kenneth caused over *** deaths and destroyed some *** thousand houses.

Stakeholder analysis matrix around the development of a comprehensive policy on fisher drowning.

This metadata considers the data on total and insured economic losses and the number of fatalities from weather- and climate-related events in EU Member States and EEA member countries since 1980. Weather- and climate-related hazards considered are those types classified as meteorological hazards (e.g. storms), hydrological hazards (e.g. floods) and climatological hazards (e.g. heatwaves) based on the classification by the International Council for Science (ICSU). The geophysical hazards (e.g. earthquakes and volcanoes) are included for comparison purposes. An event can occur in several countries, but the information is split per country.

The data is based on the RiskLayer CATDAT and the MunichRe NatCatSERVICE datasets (both received under institutional agreement), and on the Eurostat collection of economic indicators, whereas data from earlier years not covered by Eurostat have been completed using data from the Annual Macro-Economic Database of the European Commission (AMECO), the International Monetary Fund’s (IMF) World Economic Outlook (WEO), the Total Economy Database (TED) and the World Bank database. The average population of a country over the period of the time series is used.

The data contains details related to EEA’s indicator “Economic losses from climate-related events in Europe” (https://www.eea.europa.eu/ims/economic-losses-from-climate-related), updated annually. Additional detail on the data and the indicator can be found in the EEA briefing "Economic losses and fatalities from weather- and climate-related events in Europe", 2022 (https://www.eea.europa.eu/publications/economic-losses-and-fatalities-from/economic-losses-and-fatalities-from).

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