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, there were a total of 145 fatalities reported due to floods in the United States, up from 79 fatalities in the previous year. Since 2010, the highest number of lives lost due to floods in a single year was recorded in 2015, with a total of 189.

Natural Disasters Deaths

People killed in natural disasters by country by year

About this dataset

How much do natural disasters cost us? In lives, in dollars, in infrastructure? This dataset attempts to answer those questions, tracking the death toll and damage cost of major natural disasters since 1985. Disasters included are storms ( hurricanes, typhoons, and cyclones ), floods, earthquakes, droughts, wildfires, and extreme temperatures

How to use the dataset

This dataset contains information on natural disasters that have occurred around the world from 1900 to 2017. The data includes the date of the disaster, the location, the type of disaster, the number of people killed, and the estimated cost in US dollars

Research Ideas

• An all-in-one disaster map displaying all recorded natural disasters dating back to 1900.
• Natural disaster hotspots - where do natural disasters most commonly occur and kill the most people?
• A live map tracking current natural disasters around the world

Acknowledgements

License

See the dataset description for more information.

With a risk index score of ***, Bangladesh, Egypt, and Vietnam are the top countries worldwide regarding river flood risk, based on their physical exposure to this type of event. Thailand followed a close second, with a risk index score of ***. Where are flooding events most common? In 2024, nine out of the top 10 countries in terms of exposure to river flood risk were located in Asia, in particular in the south and eastern regions of the continent. Southeast Asia is prone to frequent and intense flooding events due to several factors, which include low average elevations, high incidence of tropical storms and heavy rains, prolonged monsoons, and underdeveloped flood protection infrastructure. In addition, climate change is also contributing to the increase in frequency and severity of these events. It is estimated that the global population exposed to flooding incidents will increase by ** percent in a two-degrees-Celsius warming scenario. Record-breaking floods in Pakistan and Bangladesh Amongst the countries most exposed to floods in Southeast Asia, Bangladesh and Pakistan were particularly affected by floods in 2022. Torrential rain and unceasing downpours struck the countries from early June that year, leading to one of the worst flooding events in their history. In Pakistan, the floods have caused more than ***** deaths. In Bangladesh, an estimated *** million people were affected by widespread damage to homes, infrastructure, croplands, and sanitation facilities. Overall, Pakistan and Bangladesh had some of the largest populations exposed to flood risk worldwide.

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

https://www.acacamps.org/sites/default/files/resource_library_images/naturaldisaster4.jpg" alt="Natural Disasters">

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 homel...

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.

Approximately, 21 million people worldwide could be affected by river floods on average each year, and the 15 countries with the most people exposed, including India, Bangladesh, China, Vietnam, Pakistan, Indonesia, Egypt, Myanmar, Afghanistan, Nigeria, Brazil, Thailand, Democratic Republic of Congo, Iraq, and Cambodia, account for nearly 80 percent of the total population affected in an average year. Summary The Aqueduct Global Flood Risk Country Ranking ranks 163 countries by their current annual average population affected by river floods using the Aqueduct Global Flood Analyzer. Approximately, 21 million people worldwide could be affected by river floods on average each year, and the 15 countries with the most people exposed, including India, Bangladesh, China, Vietnam, Pakistan, Indonesia, Egypt, Myanmar, Afghanistan, Nigeria, Brazil, Thailand, Democratic Republic of Congo, Iraq, and Cambodia, account for nearly 80 percent of the total population affected in an average year. A country-wide estimated average flood protection level was given to each country based on its income level. Cautions Assumption: We assigned a country-wide average flood protection level for each country based on its income level (World Bank). 1) For low-income countries, we assume 10-year flood protection; 2) for lower-middle income countries, we assume 25-year flood protection; 3) for upper-middle income countries, we assume 50-year flood protection; 4) for high-income countries, we assume 100-year flood protection; and 5) for the Netherlands, we assume a 1000-year flood protection. Citation

Introduction

Vietnam’s geographic location and topography make it particularly susceptible to a wide range of disasters, both natural and technological.

This dataset provides data as uploaded, on the occurrence and impacts of mass disasters in Vietnam from 1953 to 2024. This includes both natural (biological, climatological, extra-terrestrial, geophysical, hydrological, meteorological), and technological (industrial accident) disasters. Data was extracted from The International Disaster Database (EM-DAT), maintained by the Centre for Research on the Epidemiology of Disasters (CRED), published by Open Development Vietnam.

It documents natural and human-related disasters in Vietnam from 1953 onward, with key fields related to: - Disaster types and sub types (e.g., storm, flood, drought, epidemic). - Start and end dates. - Human impact (deaths, injuries, people affected). - Economic damage (where data is available). - Geo-location and metadata (latitude/longitude, region, event name).

How Vietnam has changed since 1953, with a focus on climate change-driven disasters:

• Storms (including tropical cyclones).
• Floods.
• Droughts.

We explore long-term trends, decadal comparisons, regional distribution, and statistical correlations to understand Vietnam’s evolving climate vulnerability. The aim is to uncover data-driven insights that inform climate adaptation, disaster risk management, and sustainable development planning.

Recommended Reading

Saline Intrusion in the Mekong Delta (2021-2022). Part 1: The devastating effects of climate change; Mekong and Bengal Deltas. link - Kaggle

Rainfall & Temperature: Vietnam from 1901 to 2020. Part 3: The devastating effects of climate change; monsoon pattern changes. link - Kaggle

Trend Overview by Decade: vietnam_climate_disaster_trend_by_decade.csv

A markdown document with the R code for all the below visualisations. link

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F13231939%2F2808eacb7fa88dbb013546f248fe9e27%2FScreenshot%202025-06-15%2010.55.26.png?generation=1749983744158969&alt=media" alt="">

Bar Chart: - Description: - This chart presents normalised values of four metrics; Event Count, Total Affected, Total Damage Adjusted, and Total Deaths. Aggregated by decade from the 1950's to the 2020's. The normalisation allows comparison across different scales.
- Observations: - The 2000's show the highest Event Count, indicating a peak in disaster frequency. - The 1960's had a significant Total Death component. In November 1964, the quick succession of three typhoons (Iris, Joan, and Kate), caused widespread flooding in Vietnam causing 7,000 deaths, as confirmed by dataset analysis. link - Wikipedia: November 1964 Vietnam floods - Total Affected and Total Damage Adjusted peak in the 1990's and 2000's, suggesting increased vulnerability or severity during these periods. - Insights: - This visualisation highlights temporal shifts in disaster impacts, with the 1960's notable for high mortality and the 2000's for frequency, reflecting historical disaster patterns. However, the 1964 Pacific typhoon season was the most active tropical cyclone season recorded globally, with a total of 39 tropical storms forming. It had no official bounds; it ran year-round in 1964, but most tropical cyclones tend to form in the northwestern Pacific Ocean between June and December. The unprecedented and extended tropical storm season of 1964, accounted for the large amount of deaths by disasters, during the 1960's in Vietnam.

*2020s data is partial

Summary

• Rising Disaster Frequency:
  • Climate-related disasters have increased sharply since the 1980's, particularly storms and floods.
  • From just 2 recorded events in the 195...

Precipitation Hazards Web Service from the Weather Prediction Center(WPC) depicts forecasted Precipitation Hazards where there is a probable threat of Excessive Rainfall Outlook (ERO) for the next five days will exceed flash flood guidance(FFG) within 40 kilometers (25 miles) of a location. The web service’s ERO locations are displayed as polygons. These Hazards are provided by the twelve NWS River Forecast Centers (RFCs) whose service areas cover the lower 48 states. WPC uses national Flash Flood Guidance (FFG), whose 1, 3, and 6-hour values represent the amount of rainfall over those short durations which it is estimated would bring rivers and streams up to bankfull conditions. The primary Precipitation hazard is Flash Flooding and WPC provides guidance with the warnings’ use estimates to build these polygons that contain the likelihood that FFG will be exceeded by assessing environmental conditions (e.g. moisture content and steering winds), recognizing weather patterns commonly associated with heavy rainfall, and using a variety of deterministic and ensemble-based numerical model tools that get at both the meteorological and hydrologic factors associated with flash flooding. These Hazard ERO polygons are produced by a highly collaborative product and benefits from the input of meteorologists and hydrologists among the Weather Forecasted Offices, RFCs, and National Water Center. The EROs polygon are rendered based on Excessive Rainfall Risk Categories.Update Frequency:Every 3 hoursERO Categories are as follows:No Area /Label [Probability Less than 5% Chance of Flash Flood] - Flash floods are generally not expectedMarginal (MRGL) – [At least 5% Chance of Flash Flooding]-Possible Isolated flash flood -Localized and primarily affecting places that can experience rapid runoff with heavy rainfall.Slight (SLGT) [At least 15% Chance of Flash Flooding]- Possible Scattered flash floods that are mainly localized. The most vulnerable are people in urban areas, roads small streams, and washes. Isolates significant flash floods are possible.Moderate (MDT) [At least 40% Chance of Flash Flooding]- Numerous flash floods are likely- Numerous flash flooding events with significant events are possible. Many streams may flood potentially affecting large rivers.High (High) [At least 70% Chance of Flash Flooding] - Widespread flash floods are expected Conditions include severe widespread flash flooding. Areas that do not normally experience flash flooding have conditions where lives and property are in greater danger.Link to graphical web page: https://www.wpc.ncep.noaa.gov/qpf/excessive_rainfall_outlook_ero.phpLink to data download (shp): https://www.wpc.ncep.noaa.gov/qpf/excessive_rainfall_outlook_ero.phpLink to metadataQuestions/Concerns about the service, please contact the DISS GIS teamTime Information:This service is not time enabled.

Louisiana Floods - 2016

Heavy rainfall began Friday, August 12, 2016 in Louisiana, with areas reaching almost 3 feet of rain water, causing local rivers to reach historic flood levels. Thousands were forced to evacuate, there are at least 13 dead, and many reported missing. Areas in South Louisiana in and around Lafayette and Baton Rouge were affected most.

Twenty parishes in Louisiana—Acadia, Ascension, East Baton Rouge, Livingston, Avoyelles, Evangeline, East Feliciana, West Feliciana, Iberia, Iberville, Jefferson Davis, Lafayette, Pointe Coupee, St. Helena, St. Landry, St. Martin, St. Tammany, Washington, Tangipahoa, and Vermillion—were declared major federal disaster areas.

Watson, LA—about 20 miles northeast of Baton Rouge—experienced 31.39 inches of rain, White Bayou, LA saw 26.14 inches. Livingston ended up with 25.52 inches. Baton Rouge received over 19 inches.

Content

Files - train.csv - the training set. - test.csv - the test set, including the labels. - train/ - contains the training satellite images of before/after the flood, and during the flood. - test/ - contains the test satellite images of before/after the flood, and during the flood.

Columns train.csv and test.csv - Image_ID - a unique id for each image. Note: for each before/after the flood image there is a corresponding during the flood image, eg: 3005.png is an image taken before/after the flood and corresponding to that image there is 3005_0.png image which was taken during the flood and the *_0.png implies that the area shown in this image was not flooded. - Normal - Indicate whether the image is before/after the flood or during the flood. 1 -> the image was taken before/after the flood. 0 -> the image was taken during the flood. - Flooded - Indicate whether the image contains flooded regions. 1 -> Flooded 0 -> Not flooded.

Acknowledgement

https://disasterresponse.maps.arcgis.com/apps/StorytellingSwipe/index.html?appid=2e499ec7eb784237bd70fb16ae0f5dcf# http://louisianaview.org/2016/08/historic-louisiana-floods-august-2016/ https://geodesy.noaa.gov/storm_archive/storms/aug2016_lafloods/index.html#

Inspiration

Kerala, a coastal state in India was flooded in 2018 and 2019. More than 500 people died, and almost a million people had to be evacuated from their homes mainly from low lying areas. The people in Kerala and the administration find it difficult to get the geographic areas that were flooded, thus it affected the proper rescue operations. Also people were unaware of the increase in water level around their region, that they didn't leave their homes which eventually lead to more death. So we started developing solutions to help the people/administration to broadcast information, identify flooded regions, alert people and assist them during such events. At some point in time we came across Louisiana flood in 2016 and the websites in the acknowledgement, and we thought of doing some experiments with the data.

Precipitation Hazards Web Service from the Weather Prediction Center(WPC) depicts forecasted Precipitation Hazards where there is a probable threat of Excessive Rainfall Outlook (ERO) for the next five days will exceed flash flood guidance(FFG) within 40 kilometers (25 miles) of a location. The web service’s ERO locations are displayed as polygons. These Hazards are provided by the twelve NWS River Forecast Centers (RFCs) whose service areas cover the lower 48 states. WPC uses national Flash Flood Guidance (FFG), whose 1, 3, and 6-hour values represent the amount of rainfall over those short durations which it is estimated would bring rivers and streams up to bankfull conditions. The primary Precipitation hazard is Flash Flooding and WPC provides guidance with the warnings’ use estimates to build these polygons that contain the likelihood that FFG will be exceeded by assessing environmental conditions (e.g. moisture content and steering winds), recognizing weather patterns commonly associated with heavy rainfall, and using a variety of deterministic and ensemble-based numerical model tools that get at both the meteorological and hydrologic factors associated with flash flooding. These Hazard ERO polygons are produced by a highly collaborative product and benefits from the input of meteorologists and hydrologists among the Weather Forecasted Offices, RFCs, and National Water Center. The EROs polygon are rendered based on Excessive Rainfall Risk Categories.Update Frequency:Every 3 hoursERO Categories are as follows:No Area /Label [Probability Less than 5% Chance of Flash Flood] - Flash floods are generally not expectedMarginal (MRGL) – [At least 5% Chance of Flash Flooding]-Possible Isolated flash flood -Localized and primarily affecting places that can experience rapid runoff with heavy rainfall.Slight (SLGT) [At least 15% Chance of Flash Flooding]- Possible Scattered flash floods that are mainly localized. The most vulnerable are people in urban areas, roads small streams, and washes. Isolates significant flash floods are possible.Moderate (MDT) [At least 40% Chance of Flash Flooding]- Numerous flash floods are likely- Numerous flash flooding events with significant events are possible. Many streams may flood potentially affecting large rivers.High (High) [At least 70% Chance of Flash Flooding] - Widespread flash floods are expected Conditions include severe widespread flash flooding. Areas that do not normally experience flash flooding have conditions where lives and property are in greater danger.Link to graphical web page: https://www.wpc.ncep.noaa.gov/qpf/excessive_rainfall_outlook_ero.phpLink to data download (shp): https://www.wpc.ncep.noaa.gov/qpf/excessive_rainfall_outlook_ero.phpLink to metadataQuestions/Concerns about the service, please contact the DISS GIS teamTime Information:This service is not time enabled.

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.

Precipitation Hazards Web Service from the Weather Prediction Center(WPC) depicts forecasted Precipitation Hazards where there is a probable threat of Excessive Rainfall Outlook (ERO) for the next five days will exceed flash flood guidance(FFG) within 40 kilometers (25 miles) of a location. The web service’s ERO locations are displayed as polygons. These Hazards are provided by the twelve NWS River Forecast Centers (RFCs) whose service areas cover the lower 48 states. WPC uses national Flash Flood Guidance (FFG), whose 1, 3, and 6-hour values represent the amount of rainfall over those short durations which it is estimated would bring rivers and streams up to bankfull conditions. The primary Precipitation hazard is Flash Flooding and WPC provides guidance with the warnings’ use estimates to build these polygons that contain the likelihood that FFG will be exceeded by assessing environmental conditions (e.g. moisture content and steering winds), recognizing weather patterns commonly associated with heavy rainfall, and using a variety of deterministic and ensemble-based numerical model tools that get at both the meteorological and hydrologic factors associated with flash flooding. These Hazard ERO polygons are produced by a highly collaborative product and benefits from the input of meteorologists and hydrologists among the Weather Forecasted Offices, RFCs, and National Water Center. The EROs polygon are rendered based on Excessive Rainfall Risk Categories.Update Frequency:Every 3 hoursERO Categories are as follows:No Area /Label [Probability Less than 5% Chance of Flash Flood] - Flash floods are generally not expectedMarginal (MRGL) – [At least 5% Chance of Flash Flooding]-Possible Isolated flash flood -Localized and primarily affecting places that can experience rapid runoff with heavy rainfall.Slight (SLGT) [At least 15% Chance of Flash Flooding]- Possible Scattered flash floods that are mainly localized. The most vulnerable are people in urban areas, roads small streams, and washes. Isolates significant flash floods are possible.Moderate (MDT) [At least 40% Chance of Flash Flooding]- Numerous flash floods are likely- Numerous flash flooding events with significant events are possible. Many streams may flood potentially affecting large rivers.High (High) [At least 70% Chance of Flash Flooding] - Widespread flash floods are expected Conditions include severe widespread flash flooding. Areas that do not normally experience flash flooding have conditions where lives and property are in greater danger.Link to graphical web page: https://www.wpc.ncep.noaa.gov/qpf/excessive_rainfall_outlook_ero.phpLink to data download (shp): https://www.wpc.ncep.noaa.gov/qpf/excessive_rainfall_outlook_ero.phpLink to metadataQuestions/Concerns about the service, please contact the DISS GIS teamTime Information:This service is not time enabled.

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.

Flooding is among the most common and costly natural disasters in the United States. Flood impacts have been on the rise as flood mitigating habitats are lost, development places more people and infrastructure potentially at risk, and changing rainfall results in altered flood frequency. Across the nation, communities are recognizing the value of flood mitigating habitats and employing green infrastructure alternatives, including restoring some of those ecosystems, as a way to increase resilience. However, communities may under value green infrastructure, because they do not recognize the current benefits of risk reduction they receive from existing ecosystems or the potential benefits they could receive through restoration. Freshwater wetlands have long been recognized as one of the ecosystems that can reduce flood damages by attenuating surface water. Small-scale community studies can capture the flood-reduction benefits from existing or potentially restored wetlands. However, scalability and transferability are limits for these high resolution and data intensive studies. This paper details the development of a nationally consistent dataset and a set of high-resolution indicators characterizing where people benefit from reduced flood risk through existing wetlands. We demonstrate how this dataset can be used at different scales (regional or local) to rapidly assess flood-reduction benefits. At a local scale we use other national scale indicators (CRSI, SoVI) to gauge community resilience and recoverability to choose Harris County, Texas as our focus. Analysis of the Gulf Coast region and Harris County, Texas identifies communities with both wetland restoration potential and the greatest flood-prone population that could benefit from that restoration. We show how maps of these indicators can be used to set wetland protection and restoration priorities.

Precipitation Hazards Web Service from the Weather Prediction Center(WPC) depicts forecasted Precipitation Hazards where there is a probable threat of Excessive Rainfall Outlook (ERO) for the next five days will exceed flash flood guidance(FFG) within 40 kilometers (25 miles) of a location. The web service’s ERO locations are displayed as polygons. These Hazards are provided by the twelve NWS River Forecast Centers (RFCs) whose service areas cover the lower 48 states. WPC uses national Flash Flood Guidance (FFG), whose 1, 3, and 6-hour values represent the amount of rainfall over those short durations which it is estimated would bring rivers and streams up to bankfull conditions. The primary Precipitation hazard is Flash Flooding and WPC provides guidance with the warnings’ use estimates to build these polygons that contain the likelihood that FFG will be exceeded by assessing environmental conditions (e.g. moisture content and steering winds), recognizing weather patterns commonly associated with heavy rainfall, and using a variety of deterministic and ensemble-based numerical model tools that get at both the meteorological and hydrologic factors associated with flash flooding. These Hazard ERO polygons are produced by a highly collaborative product and benefits from the input of meteorologists and hydrologists among the Weather Forecasted Offices, RFCs, and National Water Center. The EROs polygon are rendered based on Excessive Rainfall Risk Categories.Update Frequency:Every 3 hoursERO Categories are as follows:No Area /Label [Probability Less than 5% Chance of Flash Flood] - Flash floods are generally not expectedMarginal (MRGL) – [At least 5% Chance of Flash Flooding]-Possible Isolated flash flood -Localized and primarily affecting places that can experience rapid runoff with heavy rainfall.Slight (SLGT) [At least 15% Chance of Flash Flooding]- Possible Scattered flash floods that are mainly localized. The most vulnerable are people in urban areas, roads small streams, and washes. Isolates significant flash floods are possible.Moderate (MDT) [At least 40% Chance of Flash Flooding]- Numerous flash floods are likely- Numerous flash flooding events with significant events are possible. Many streams may flood potentially affecting large rivers.High (High) [At least 70% Chance of Flash Flooding] - Widespread flash floods are expected Conditions include severe widespread flash flooding. Areas that do not normally experience flash flooding have conditions where lives and property are in greater danger.Link to graphical web page: https://www.wpc.ncep.noaa.gov/qpf/excessive_rainfall_outlook_ero.phpLink to data download (shp): https://www.wpc.ncep.noaa.gov/qpf/excessive_rainfall_outlook_ero.phpLink to metadataQuestions/Concerns about the service, please contact the DISS GIS teamTime Information:This service is not time enabled.

The Social Flood Risk Index (SFRI) is a measure of where social vulnerability and exposure to flooding coincide. It is a relative index and has no defined units. SFRI incorporates the chance of flooding occurring in the floodplain (accounting for defences), the number of people living within the floodplain and the overall social vulnerability of the neighbourhood. High positive scores identify neighbourhoods where large numbers of the most vulnerable people are exposed to flooding. High negative values are a result of high numbers of people living in the floodplain in a neighbourhood with low social vulnerability (below the UK mean). Neighbourhoods where no-one lives in the floodplain have a value of zero. This layer shows the SFRI for Coastal & Fluvial flooding for a 2 degree rise in Global Mean Temperature (from the 1961-90 baseline as used in the UKCP09 climate change projections) by 2050 scenario.Data downloaded from the Climate Just website. More information is available HERE.

Flood Risk Mapping Studies / Public Information Maps Flooding is a natural process that is a necessary component to the survival and health of many types of ecosystems. The processes and conditions that result in floods are often predictable and usually occur in the same areas, known as floodplains. Unfortunately people are often drawn to inhabit and develop in floodplains as this is usually flat, fertile land. Because of this, floods are one of the most common natural hazards in Newfoundland and Labrador and are often the most costly. Floods can cause considerable damage to property and infrastructure, threaten human lives and cost millions in emergency assistance, clean-up and remediation.Flooding and erosion processes are quite difficult to control and avoid. As such, the best and most cost effective method of minimizing their impact is proper management and planning of known floodplains. Floodplain management usually involves the adoption of land use regulations that limit human exposure to areas prone to flooding events. For more information on the Government’s approach to management of identified flood zones see the Policy for Flood Plain Management.

Precipitation Hazards Web Service from the Weather Prediction Center(WPC) depicts forecasted Precipitation Hazards where there is a probable threat of Excessive Rainfall Outlook (ERO) for the next five days will exceed flash flood guidance(FFG) within 40 kilometers (25 miles) of a location. The web service’s ERO locations are displayed as polygons. These Hazards are provided by the twelve NWS River Forecast Centers (RFCs) whose service areas cover the lower 48 states. WPC uses national Flash Flood Guidance (FFG), whose 1, 3, and 6-hour values represent the amount of rainfall over those short durations which it is estimated would bring rivers and streams up to bankfull conditions. The primary Precipitation hazard is Flash Flooding and WPC provides guidance with the warnings’ use estimates to build these polygons that contain the likelihood that FFG will be exceeded by assessing environmental conditions (e.g. moisture content and steering winds), recognizing weather patterns commonly associated with heavy rainfall, and using a variety of deterministic and ensemble-based numerical model tools that get at both the meteorological and hydrologic factors associated with flash flooding. These Hazard ERO polygons are produced by a highly collaborative product and benefits from the input of meteorologists and hydrologists among the Weather Forecasted Offices, RFCs, and National Water Center. The EROs polygon are rendered based on Excessive Rainfall Risk Categories.Update Frequency:Every 3 hoursERO Categories are as follows:No Area /Label [Probability Less than 5% Chance of Flash Flood] - Flash floods are generally not expectedMarginal (MRGL) – [At least 5% Chance of Flash Flooding]-Possible Isolated flash flood -Localized and primarily affecting places that can experience rapid runoff with heavy rainfall.Slight (SLGT) [At least 15% Chance of Flash Flooding]- Possible Scattered flash floods that are mainly localized. The most vulnerable are people in urban areas, roads small streams, and washes. Isolates significant flash floods are possible.Moderate (MDT) [At least 40% Chance of Flash Flooding]- Numerous flash floods are likely- Numerous flash flooding events with significant events are possible. Many streams may flood potentially affecting large rivers.High (High) [At least 70% Chance of Flash Flooding] - Widespread flash floods are expected Conditions include severe widespread flash flooding. Areas that do not normally experience flash flooding have conditions where lives and property are in greater danger.Link to graphical web page: https://www.wpc.ncep.noaa.gov/qpf/excessive_rainfall_outlook_ero.phpLink to data download (shp): https://www.wpc.ncep.noaa.gov/qpf/excessive_rainfall_outlook_ero.phpLink to metadataQuestions/Concerns about the service, please contact the DISS GIS teamTime Information:This service is not time enabled.

UK Postcode-level Flood Risk Data (Rivers and Sea)

Identifying Risk Areas for Flood Protection and Planning

By IBM Watson AI XPRIZE - Environment [source]

About this dataset

Welcome to the UK Postcode-level Flood Risk Dataset. This open source dataset contains detailed information on flood risk levels by postcode in the UK, allowing you to map out potential problems and plan accordingly. With this dataset, you can assess each postcode's growing risk of floods due to human land use change and climate change-related weather patterns, as well as historical occurrences specific to each area.

We pull data from organizations including Risk of Flooding from Rivers & Sea, Open Postcode Geo, Royal Mail copyright & database right (2017), National Statistics data Crown copyright & database right (2017), and Environment Agency data licensed under the Open Government Licence v3.0. The associated columns in this dataset are detailed below:

• Postcode - unique identifier for the postal code district where flood risk area is located
• FID - Unique ID for each location point
• PROB 4BAND - Flood risk level for a given postcode determined according to a four tier grade system (High, Medium, Low or Very Low)
• SUITABILITY - Suitability of location based on environment factors assessed according to OFRA criteria
• PUB_DATE - Date when data was published or last updated
• RISK FOR INSURANCE SOP - Standard Operating Procedure assigned according the Probability 4 band Risk rating
• Easting/Northing/Latitude/Longitude – Coordinates associated with a given postcode location

This data can be used by local authorities and agencies conducting flood mapping projects; insurers assessing assets at specified locations using an agreed set of methodology; advisors assessing locations for development purposes; forecasters aiding contingency planning; homeowners/commercial businesses seeking insurance cover for claims arising from flooding events etc. Ultimately we hope citizens around the world use this dataset as an important tool to predict areas exposedto potential flooding risks so that preventive measures may be taken beforehand!

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How to use the dataset

This Kaggle dataset provides postcode-level flood risk data for the UK, including the flood risk level, coordinates, and other related information. This dataset is derived from Risk of Flooding from Rivers and Sea (provided by the British government) and Open Postcode Geo. It is licensed under the OGL 3.0 open government license.

In this data set you will find columns for each postcode as well as unique identifiers for a particular region (FID), an overall four band flood risk level (PROB_4BAND), whether a specific location or building is suitable or not (SUITABILITY), when it was published so you can be sure you are getting reliable up to date information (PUB_DATE), Easting/Northing which roughly measure distance eastwards/northwards of locations in meters(EASTING / NORTHING), LATITUDE & LONGITUDE that point to a precise location on google map & finally RISK_FOR_INSURANCE SOP which clearly distinguishes between sites which should generate warnings with regard to various kinds of insurance policies. This allows companies applying digital transformation solutions like hazard mapping solutions to show what risks certain locations present in relation to possible flood damage using digital technologies such as GIS systems or location intelligence tools etc., allowing organizations apply data science models or techniques like predictive analytics that may be used in decision making processes such as those taken by municipalities when signing off disaster management plans etc..

You can use this dataset for research purposes, share your findings on websites through charts & graphs to develop an educational understanding about possible hazards associated with areas that people inhabit around UK particularly at times when storm systems are localized heavily over specific regions making it most likely due causing major catastrophic event across British Isles . People living there can always access their respective postcodes very easily via our Flood Map by Postcode page here Flood Map.

When writing reports acknowledging source material properly , kindly take into account our acknowledgements including; Contains OS data © Crown copyright and database right 2017, Contains Royal Mail data © Royal Mail copyright and Database right 2017 , Contains National Statistics ...