The Canadian Disaster Database is a publicly accessible web-based repository of historical information about natural and man-made disasters that have taken place since 1900 in Canada or abroad that have directly affected Canadians. The database contains information on over 1000 events and can be used to support research, academic activities and decision-making across a breadth of fields including earth sciences, agriculture, climate change, biology and epidemiology, land use planning, insurance, investment, and the anthropological and sociological aspects of community resilience, among many others. Canada endeavours to provide the best information possible; however, the information contained in the Canadian Disaster Database (CDD) is based on information that is sourced from outside parties and may not be accurate. Canada makes no representations, warranties, or guarantees, express or implied, that the data contained in the CDD may be relied upon for any use whatsoever. Canada accepts no responsibility or liability for inaccuracies, errors or omissions in the data and any loss, damage or costs incurred as a result of using or relying on the data in any way. The CDD may contain material that is subject to licensing requirements or copyright restrictions and may not be reproduced, published, distributed or transferred in whole or in part without the consent of the author. The CDD shares information on events that have fully concluded to ensure that the data reflects the event appropriately (i.e., insurance and disaster recovery payment information is available). For this reason, events for which the costs and/or other impacts have not fully recorded contributes to a delay in making them available through the CDD. If you have technical questions about accessing or using the data in the CDD, please write to us at ps.cdd-bdc.sp@ps-sp.gc.ca.

Source: The Emergency Events Database (EM-DAT) , Centre for Research on the Epidemiology of Disasters (CRED) / Université catholique de Louvain (UCLouvain), Brussels, Belgium – www.emdat.be.Category: Climate and WeatherData series: Climate related disasters frequency, Number of Disasters: TOTAL  Climate related disasters frequency, Number of Disasters: Drought  Climate related disasters frequency, Number of Disasters: Extreme temperature  Climate related disasters frequency, Number of Disasters: Flood  Climate related disasters frequency, Number of Disasters: Landslide  Climate related disasters frequency, Number of Disasters: Storm  Climate related disasters frequency, Number of Disasters: Wildfire Climate related disasters frequency, People Affected: Drought  Climate related disasters frequency, People Affected: Extreme temperature  Climate related disasters frequency, People Affected: Flood  Climate related disasters frequency, People Affected: Landslide  Climate related disasters frequency, People Affected: Storm  Climate related disasters frequency, People Affected: Wildfire Climate related disasters frequency, People Affected: TOTAL  Disaster IntensityMetadata:EM-DAT: The International Disasters Database - Centre for Research on the Epidemiology of Disasters (CRED), part of the University of Louvain (UCLouvain) www.emdat.be, Brussels, Belgium. Only climate related disasters (Wildfire, Storm, Landslide, Flood, Extreme Temperature, and Drought) are covered. See the CID Glossary for the definitions. EM-DAT records country level human and economic losses for disasters with at least one of the following criteria: i.          Killed ten (10) or more people  ii.         Affected hundred (100) or more people  iii.        Led to declaration of a state of emergency iv.        Led to call for international assistance   The reported total number of deaths “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. “People Affected” is the total of injured, affected, and homeless people. Injured includes the number of people with physical injuries, trauma, or illness requiring immediate medical assistance due to the disaster. Affected includes the number of people requiring immediate assistance due to the disaster. Homeless includes the number of people requiring shelter due to their house being destroyed or heavily damaged during the disaster. Disaster intensity is calculated by summing “Total Deaths” and 30% of the “People Affected”, and then dividing the result by the total population. 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 checked by CRED staff members. Nonetheless, it is important to note that these are estimates based on certain assumptions, which have their limitations. For details on the criteria and underlying assumptions, please visit https://doc.emdat.be/docs/data-structure-and-content/impact-variables/human/. Methodology:Global climate related disasters are stacked to show the trends in climate related physical risk factors.

Early event detection and response can significantly reduce the societal impact of floods. Currently, early warning systems rely on gauges, radar data, models and informal local sources. However, the scope and reliability of these systems are limited. Recently, the use of social media for detecting disasters has shown promising results, especially for earthquakes. Here, we present a new database for detecting floods in real-time on a global scale using Twitter. The method was developed using 88 million tweets, from which we derived over 10.000 flood events (i.e., flooding occurring in a country or first order administrative subdivision) across 176 countries in 11 languages in just over four years. Using strict parameters, validation shows that approximately 90% of the events were correctly detected. In countries where the first official language is included, our algorithm detected 63% of events in NatCatSERVICE disaster database at admin 1 level. Moreover, a large number of flood events not included in NatCatSERVICE are detected. All results are publicly available on www.globalfloodmonitor.org.

"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

FEMA Disaster Declarations Summary is a summarized dataset describing all federally declared disasters. This dataset lists all official FEMA Disaster Declarations, beginning with the first disaster declaration in 1953 and features all three disaster declaration types: major disaster, emergency, and fire management assistance. The dataset includes declared recovery programs and geographic areas (county not available before 1964; Fire Management records are considered partial due to historical nature of the dataset).rnrnPlease note the unique structure of the disaster sequencing (due to a numbering system that originated in the 1950's-1970's):rn0001-1999 Major Disaster Declarationrn2000-2999 Fire Managementrn3000-3999 Emergency Declaration (Special Emergency)rn4000- Major Disaster DeclarationrnrnFor more information on the disaster declaration process, see https://www.fema.gov/disasters and https://www.fema.gov/disasters/how-declared rnrnThis is raw, unedited data from FEMA's National Emergency Management Information System (NEMIS) and as such is subject to a small percentage of human error. The dataset is primarily composed of historical data that was manually entered into NEMIS after it launched in 1998. The financial information is derived from NEMIS and not FEMA's official financial systems.rnrnDue to differences in reporting periods, status of obligations, and how business rules are applied, this financial information may differ slightly from official publication on public websites such as www.usaspending.gov. This dataset is not intended to be used for any official federal financial reporting.rnrnA newer version of this OpenFEMA data set has been released. This older dataset version will no longer be updated and will be archived by the end of April 2020. The following page details the latest version of this data set: https://www.fema.gov/openfema-dataset-disaster-declarations-summaries-v2. CSV and JSON Files can be downloaded from the 'Full Data' section.rnrnTo access the dataset through an API endpoint, visit the 'API Endpoint' section of the above page. Accessing data in this fashion permits data filtering, sorting, and field selection. The OpenFEMA API Documentation page provides information on API usage. rnrnIf you have media inquiries about this dataset please email the FEMA News Desk FEMA-News-Desk@dhs.gov or call (202) 646-3272. For inquiries about FEMA's data and Open government program please contact the OpenFEMA team via email OpenFEMA@fema.dhs.gov.

This dataset contains a list of FEMA declaration types and the types of assistance authorized. For more information on the disaster declaration process, please visit https://www.fema.gov/disasters/how-declared.rnrnThis is raw, unedited data from FEMA's National Emergency Management Information System (NEMIS) and as such is subject to a small percentage of human error. The dataset is primarily composed of historical data that was manually entered into NEMIS after it launched in 1998. rnrnIf you have media inquiries about this dataset, please email the FEMA News Desk at FEMA-News-Desk@dhs.gov or call (202) 646-3272. For inquiries about FEMA's data and Open Government program, please email the OpenFEMA team at OpenFEMA@fema.dhs.gov.

Open Data for Resilience Initiative (OpenDRI) applies the concepts of the global open data movement to the challenges of reducing vulnerability to natural hazards and the impacts of climate change. OpenDRI supports World Bank Regional Disaster Risk Management Teams to build capacity and long-term ownership of open data projects with client countries that are tailored to meet specific needs and goals of stakeholders around three main areas of Sharing Data, Collecting Data, Using Data. All data is published under an open license. Projects include Open Cities Africa, with national projects in: Niger (flood hostpots and mitigation), Uganda (drought risk information and disaster risk financing), Zanzibar (vunlerability to natural disasters), Pacific Islands (Natural Disasters and Climate Change), Sri Lanka (evidence based methods for natural disaster response), Afghanistan (disaster risk decisionmaking), St Vincent and the Grenadines (hydroclimatic disasters), Saint Lucia (post disaster rehabilitation), Jamaica (storm even impact), Serbia (disaster preparedness), Indonesia (disaster management especially flooding), Seychelles (site specific risks of floods, earthquakes, cyclones, storm surge and tsunamis), Muaritius (under development), Madagascar (under development), Vietnam (natural hazards especially flood risks and climate change impacts), Bangladesh (under development), Pakistan (earthquakes and monsoon floods), Nepal (Seismic risk), Haiti (storms, flooding, landslides, environmental degradation), Guyana (under development), Grenada (under development), Dominica (extreme weather events), Colombia (flooding, landslides, increased vulnerability due to insufficient urban planning), Antigua and Barbuda (cyclones, fires and flooding), Belize (storm, flood and tsunami risks), Bolivia (natural hazards and climate change), Kyrgyz Republic (risk data on meteorological, geological, geophyical and boilogical hazards), Philippines (typhoones and monsoon floods recovery data), Tanzania (flood maps), Mozambique (flood, cyclone and windstorms), Comoros (flood, storm, volcanic eruption), Malawi (information to develop schools, healthcare and agriculture against floods and droughts), Armenia (earthquakes, drought, hailstorms, landslides)

The disaster data set from the early 20th century to 2021 was collected by www.emdat.be – Université Catholique de Louvain – Brussels – Belgium and encrypted. The dataset includes 2738 records and associated attributes. They detailed the damage and location as well as the type of disaster.

This database contains summary information on 168 Canadian flood disasters that occurred between 1900 and June 1997. The database is not, by sany means, a complete list of flood events in Canada since the vast majority of the floods did not cause disasters. All mentions of damage costs have not been corrected for inflation. The database also is biased towards the more densely populated areas of Canada where floods are more likely to impact humans.

This entry does not contain data itself, it is for the website, the NASA Disasters Mapping Portal: https://maps.disasters.nasa.gov The Disasters Mapping Portal contains numerous datasets that can be streamed from the Portal into GIS software. The Disasters Applications area promotes the use of Earth observations to improve prediction of, preparation for, response to, and recovery from natural and technological disasters. Disaster applications and applied research on natural hazards support emergency mitigation approaches, such as early warning systems, and providing information and maps to disaster response and recovery teams. NOTE: Removed "2017 - Present" from "Temporal Applicability" since it's not valid NOTE: Removed "Event-Specific and Near-Real Time Products" from "Update Frequency" since it's not valid

Multivariate multilevel negative binomial regression model examining the association between food insecurity (Food Insecurity Experiences Scale) and poor mental health (Daily Experience Index score) among n = 28,292 youth from the 2017 Gallup World Poll survey.

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.

Conflict and disaster population movement (flows) data for India. The data is the most recent available and covers a 180 day time period.

Internally displaced persons are defined according to the 1998 Guiding Principles as people or groups of people who have been forced or obliged to flee or to leave their homes or places of habitual residence, in particular as a result of armed conflict, or to avoid the effects of armed conflict, situations of generalized violence, violations of human rights, or natural or human-made disasters and who have not crossed an international border.

The IDMC's Event data, sourced from the Internal Displacement Updates (IDU), offers initial assessments of internal displacements reported within the last 180 days. This dataset provides provisional information that is continually updated on a daily basis, reflecting the availability of data on new displacements arising from conflicts and disasters. The finalized, carefully curated, and validated estimates are then made accessible through the Global Internal Displacement Database (GIDD). The IDU dataset comprises preliminary estimates aggregated from various publishers or sources.

Note: this map service is being replaced by a new set of feature layers, please use these instead:Historical Tsunami EventsTsunami ObservationsSignificant EarthquakesSignificant Volcanic EventsVolcano LocationsCurrent DARTs and Retrospective BPR DeploymentsHistorical MarigramsTsunami-Capable Tide StationsPlate BoundariesNatural hazards such as earthquakes, tsunamis, and volcanoes affect both coastal and inland areas. Long-term data from these events can be used to establish the past record of natural hazard event occurrences, which is important for planning, response, and mitigation of future events. NOAA's National Centers for Environmental Information (NCEI) plays a major role in post-event data collection. The data in this archive is gathered from scientific and scholarly sources, regional and worldwide catalogs, tide gauge reports, individual event reports, and unpublished works. For more information, please see: https://www.ncei.noaa.gov/products/natural-hazardsTo view this service in an interactive mapping application, please see the Global Natural Hazards Data Viewer (NOAA GeoPlatform entry).

OverviewThe multiple hazard index for the United States Counties was designed to map natural hazard relating to exposure to multiple natural disasters. The index was created to provide communities and public health officials with an overview of the risks that are prominent in their county, and to facilitate the comparison of hazard level between counties. Most existing hazard maps focus on a single disaster type. By creating a measure that aggregates the hazard from individual disasters, the increased hazard that results from exposure to multiple natural disasters can be better understood. The multiple hazard index represents the aggregate of hazard from eleven individual disasters. Layers displaying the hazard from each individual disaster are also included.

The hazard index is displayed visually as a choropleth map, with the color blue representing areas with less hazard and red representing areas with higher hazard. Users can click on each county to view its hazard index value, and the level of hazard for each individual disaster. Layers describing the relative level of hazard from each individual disaster are also available as choropleth maps with red areas representing high, orange representing medium, and yellow representing low levels of hazard.Methodology and Data CitationsMultiple Hazard Index

The multiple hazard index was created by coding the individual hazard classifications and summing the coded values for each United States County. Each individual hazard is weighted equally in the multiple hazard index. Alaska and Hawaii were excluded from analysis because one third of individual hazard datasets only describe the coterminous United States.

Avalanche Hazard

University of South Carolina Hazards and Vulnerability Research Institute. “Spatial Hazard Events and Losses Database”. United States Counties. “Avalanches United States 2001-2009”. < http://hvri.geog.sc.edu/SHELDUS/

Downloaded 06/2016.

Classification

Avalanche hazard was classified by dividing counties based upon the number of avalanches they experienced over the nine year period in the dataset. Avalanche hazard was not normalized by total county area because it caused an over-emphasis on small counties, and because avalanches are a highly local hazard.

None = 0 AvalanchesLow = 1 AvalancheMedium = 2-5 AvalanchesHigh = 6-10 Avalanches

Earthquake Hazard

United States Geological Survey. “Earthquake Hazard Maps”. 1:2,000,000. “Peak Ground Acceleration 2% in 50 Years”. < http://earthquake.usgs.gov/hazards/products/conterminous/

. Downloaded 07/2016.

Classification

Peak ground acceleration (% gravity) with a 2% likelihood in 50 years was averaged by United States County, and the earthquake hazard of counties was classified based upon this average.

Low = 0 - 14.25 % gravity peak ground accelerationMedium = 14.26 - 47.5 % gravity peak ground accelerationHigh = 47.5+ % gravity peak ground acceleration

Flood Hazard

United States Federal Emergency Management Administration. “National Flood Hazard Layer”. 1:10,000. “0.2 Percent Annual Flood Area”. < https://data.femadata.com/FIMA/Risk_MAP/NFHL/

. Downloaded 07/2016.

Classification

The National Flood Hazard Layer 0.2 Percent Annual Flood Area was spatially intersected with the United States Counties layer, splitting flood areas by county and adding county information to flood areas. Flood area was aggregated by county, expressed as a fraction of the total county land area, and flood hazard was classified based upon percentage of land that is susceptible to flooding. National Flood Hazard Layer does not cover the entire United States; coverage is focused on populated areas. Areas not included in National Flood Hazard Layer were assigned flood risk of Low in order to include these areas in further analysis.

Low = 0-.001% area susceptibleMedium = .00101 % - .005 % area susceptibleHigh = .00501+ % area susceptible

Heat Wave Hazard

United States Center for Disease Control and Prevention. “National Climate Assessment”. Contiguous United States Counties. “Extreme Heat Events: Heat Wave Days in May - September for years 1981-2010”. Downloaded 06/2016.

Classification

Heat wave was classified by dividing counties based upon the number of heat wave days they experienced over the 30 year time period described in the dataset.

Low = 126 - 171 Heat wave DaysMedium = 172 – 187 Heat wave DaysHigh = 188 – 255 Heat wave Days

Hurricane Hazard

National Oceanic and Atmospheric Administration. Coastal Services Center. “Historical North Atlantic Tropical Cyclone Tracks, 1851-2004”. 1: 2,000,000. < https://catalog.data.gov/dataset/historical-north-atlantic-tropical-cyclone-tracks-1851-2004-direct-download

. Downloaded 06/2016.

National Oceanic and Atmospheric Administration. Coastal Services Center. “Historical North Pacific Tropical Cyclone Tracks, 1851-2004”. 1: 2,000,000. < https://catalog.data.gov/dataset/historical-north-atlantic-tropical-cyclone-tracks-1851-2004-direct-download

. Downloaded 06/2016.

Classification

Atlantic and Pacific datasets were merged. Tropical storm and disturbance tracks were filtered out leaving hurricane tracks. Each hurricane track was assigned the value of the category number that describes that event. Weighting each event by intensity ensures that areas with higher intensity events are characterized as being more hazardous. Values describing each hurricane event were aggregated by United States County, normalized by total county area, and the hurricane hazard of counties was classified based upon the normalized value.

Landslide Hazard

United States Geological Survey. “Landslide Overview Map of the United States”. 1:4,000,000. “Landslide Incidence and Susceptibility in the Conterminous United States”. < https://catalog.data.gov/dataset/landslide-incidence-and-susceptibility-in-the-conterminous-united-states-direct-download

. Downloaded 07/2016.

Classification

The classifications of High, Moderate, and Low landslide susceptibility and incidence from the study were numerically coded, the average value was computed for each county, and the landslide hazard was classified based upon the average value.

Long-Term Drought Hazard

United States Drought Monitor, Drought Mitigation Center, United States Department of Agriculture, National Oceanic and Atmospheric Administration. “Drought Monitor Summary Map”. “Long-Term Drought Impact”. < http://droughtmonitor.unl.edu/MapsAndData/GISData.aspx >. Downloaded 06/2016.

Classification

Short-term drought areas were filtered from the data; leaving only long-term drought areas. United States Counties were assigned the average U.S. Drought Monitor Classification Scheme Drought Severity Classification value that characterizes the county area. County long-term drought hazard was classified based upon average Drought Severity Classification value.

Low = 1 – 1.75 average Drought Severity Classification valueMedium = 1.76 -3.0 average Drought Severity Classification valueHigh = 3.0+ average Drought Severity Classification value

Snowfall Hazard

United States National Oceanic and Atmospheric Administration. “1981-2010 U.S. Climate Normals”. 1: 2,000,000. “Annual Snow Normal”. < http://www1.ncdc.noaa.gov/pub/data/normals/1981-2010/products/precipitation/

. Downloaded 08/2016.

Classification

Average yearly snowfall was joined with point location of weather measurement stations, and stations without valid snowfall measurements were filtered out (leaving 6233 stations). Snowfall was interpolated using least squared distance interpolation to create a .05 degree raster describing an estimate of yearly snowfall for the United States. The average yearly snowfall raster was aggregated by county to yield the average yearly snowfall per United States County. The snowfall risk of counties was classified by average snowfall.

None = 0 inchesLow = .01- 10 inchesMedium = 10.01- 50 inchesHigh = 50.01+ inches

Tornado Hazard

United States National Oceanic and Atmospheric Administration Storm Prediction Center. “Severe Thunderstorm Database and Storm Data Publication”. 1: 2,000,000. “United States Tornado Touchdown Points 1950-2004”. < https://catalog.data.gov/dataset/united-states-tornado-touchdown-points-1950-2004-direct-download

. Downloaded 07/2016.

Classification

Each tornado touchdown point was assigned the value of the Fujita Scale that describes that event. Weighting each event by intensity ensures that areas with higher intensity events are characterized as more hazardous. Values describing each tornado event were aggregated by United States County, normalized by total county area, and the tornado hazard of counties was classified based upon the normalized value.

Volcano Hazard

Smithsonian Institution National Volcanism Program. “Volcanoes of the World”. “Holocene Volcanoes”. < http://volcano.si.edu/search_volcano.cfm

. Downloaded 07/2016.

Classification

Volcano coordinate locations from spreadsheet were mapped and aggregated by United States County. Volcano count was normalized by county area, and the volcano hazard of counties was classified based upon the number of volcanoes present per unit area.

None = 0 volcanoes/100 kilometersLow = 0.000915 - 0.007611 volcanoes / 100 kilometersMedium = 0.007612 - 0.018376 volcanoes / 100 kilometersHigh = 0.018377- 0.150538 volcanoes / 100 kilometers

Wildfire Hazard

United States Department of Agriculture, Forest Service, Fire, Fuel, and Smoke Science Program. “Classified 2014 Wildfire Hazard Potential”. 270 meters. < http://www.firelab.org/document/classified-2014-whp-gis-data-and-maps

. Downloaded 06/2016.

Classification

The classifications of Very High, High, Moderate, Low, Very Low, and Non-Burnable/Water wildfire hazard from the study were numerically coded, the average value was computed for each county, and the wildfire hazard was classified based upon the average value.

The Natural Disaster Relief and Recovery Arrangements (NDRRA) are a joint funding initiative of the Commonwealth and State Governments to provide disaster relief and recovery payments and …Show full descriptionThe Natural Disaster Relief and Recovery Arrangements (NDRRA) are a joint funding initiative of the Commonwealth and State Governments to provide disaster relief and recovery payments and infrastructure restoration to help communities recover from the effects of natural disasters. This dataset shows which Local Government Authorities have been activated for each event and for which category. Further information can be found at [the Queensland Reconstruction Authority website](https://www.qra.qld.gov.au/activations

This Map shows natural climatological drought disasters occurrence from 1900 to 2015. The data source is from the Centre for Research on the Epidemiology of Disasters, EM-DAT database.

EM-DAT is a global database on natural and technological disasters that contains essential core data on the occurrence and effects climatological disasters in the world from 1900 to present. EM-DAT is maintained by the Centre for Research on the Epidemiology of Disasters (CRED) at the School of Public Health of the Université catholique de Louvain located in Brussels, Belgium. The database is compiled from various sources, including UN agencies, non-governmental organisations, insurance companies, research institutes and press agencies. The main objective of the database is to serve the purposes of humanitarian action at national and international levels in order to rationalise decision making for disaster preparedness, as well as providing an objective base for vulnerability assessment and priority setting. In EM-DAT data are considered at the country level for two reasons: first, it is at this level that they are usually reported; and second, it allows the aggregation and disaggregation of data. In order to facilitate the comparison over time, the event start date has been used as the disaster reference date.

Affected people are the number of people requiring immediate assistance during a period of emergency; this may include displaced or evacuated people. Total affected are the sum of injured, homeless and affected. Total Deaths are the number of people who lost their life because the event happened (it includes also the missing people based on official figures). Homeless are the number of people whose house is destroyed or heavily damaged and therefore need shelter after an event.

The important role of information management in improving baseline data for natural hazards has been demonstrated through a collaborative pilot project between Geoscience Australia, Mineral Resources Tasmania and the University of Wollongong. The result is a 'virtual' landslide database that makes full use of diverse data across three levels of government and has enabled landslide data to be collated and accessed from a single source.

Such a system establishes the foundation for a very powerful and coordinated information resource in Australia and provides a suitable basis for greater investment in data collection. This paper highlights the capacity to extend the methodology across all hazards and describes one solution in facilitating a sound knowledge base on natural disasters and disaster risk reduction.

National Risk Index Version: March 2023 (1.19.0)The National Risk Index Counties feature layer contains county-level data for the Risk Index, Expected Annual Loss, Social Vulnerability, and Community Resilience.The National Risk Index is a dataset and online tool that helps to illustrate the communities most at risk for 18 natural hazards across the United States and territories: Avalanche, Coastal Flooding, Cold Wave, Drought, Earthquake, Hail, Heat Wave, Hurricane, Ice Storm, Landslide, Lightning, Riverine Flooding, Strong Wind, Tornado, Tsunami, Volcanic Activity, Wildfire, and Winter Weather. The National Risk Index provides Risk Index values, scores and ratings based on data for Expected Annual Loss due to natural hazards, Social Vulnerability, and Community Resilience. Separate values, scores and ratings are also provided for Expected Annual Loss, Social Vulnerability, and Community Resilience. For the Risk Index and Expected Annual Loss, values, scores and ratings can be viewed as a composite score for all hazards or individually for each of the 18 hazard types.Sources for Expected Annual Loss data include: Alaska Department of Natural Resources, Arizona State University’s (ASU) Center for Emergency Management and Homeland Security (CEMHS), California Department of Conservation, California Office of Emergency Services California Geological Survey, Colorado Avalanche Information Center, CoreLogic’s Flood Services, Federal Emergency Management Agency (FEMA) National Flood Insurance Program, Humanitarian Data Exchange (HDX), Iowa State University's Iowa Environmental Mesonet, Multi-Resolution Land Characteristics (MLRC) Consortium, National Aeronautics and Space Administration’s (NASA) Cooperative Open Online Landslide Repository (COOLR), National Earthquake Hazards Reduction Program (NEHRP), National Oceanic and Atmospheric Administration’s National Centers for Environmental Information (NCEI), National Oceanic and Atmospheric Administration's National Hurricane Center, National Oceanic and Atmospheric Administration's National Weather Service (NWS), National Oceanic and Atmospheric Administration's Office for Coastal Management, National Oceanic and Atmospheric Administration's National Geophysical Data Center, National Oceanic and Atmospheric Administration's Storm Prediction Center, Oregon Department of Geology and Mineral Industries, Pacific Islands Ocean Observing System, Puerto Rico Seismic Network, Smithsonian Institution's Global Volcanism Program, State of Hawaii’s Office of Planning’s Statewide GIS Program, U.S. Army Corps of Engineers’ Cold Regions Research and Engineering Laboratory (CRREL), U.S. Census Bureau, U.S. Department of Agriculture's (USDA) National Agricultural Statistics Service (NASS), U.S. Forest Service's Fire Modeling Institute's Missoula Fire Sciences Lab, U.S. Forest Service's National Avalanche Center (NAC), U.S. Geological Survey (USGS), U.S. Geological Survey's Landslide Hazards Program, United Nations Office for Disaster Risk Reduction (UNDRR), University of Alaska – Fairbanks' Alaska Earthquake Center, University of Nebraska-Lincoln's National Drought Mitigation Center (NDMC), University of Southern California's Tsunami Research Center, and Washington State Department of Natural Resources.Data for Social Vulnerability are provided by the Centers for Disease Control (CDC) Agency for Toxic Substances and Disease Registry (ATSDR) Social Vulnerability Index, and data for Community Resilience are provided by University of South Carolina's Hazards and Vulnerability Research Institute’s (HVRI) 2020 Baseline Resilience Indicators for Communities.The source of the boundaries for counties and Census tracts are based on the U.S. Census Bureau’s 2021 TIGER/Line shapefiles. Building value and population exposures for communities are based on FEMA’s Hazus 6.0. Agriculture values are based on the USDA 2017 Census of Agriculture.

A natural disaster event can stretch emergency personnel to breaking point. However, at the very time that the community is most pre-occupied with response, important but perishable information on the event is available. The information helps us to understand how and why the event impacted on the community, and systematic efforts are needed to collect the data.

Risk managers need to base their decisions on accurate and reliable forecasting of the future. Organisations such as Geoscience Australia provide risk assessments to assist this decision making. Post-disaster data collection is essential to test risk assessment models against what has happened in real events.

Data collection technologies can also assist response teams by transmitting near real-time spatial information between field personnel and coordinating centres.