With a risk index score of 9.9, Vietnam, Egypt, and Bangladesh 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 9.8. 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 30 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 1,700 deaths. In Bangladesh, an estimated 7.2 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.

According to NOAA, "Floods are the most common and widespread of all weather-related natural disasters" and as our earth continues to warm due to climate change, precipitation events are becoming more frequent and more intense, dropping record-setting amounts of water from the sky. Whenever the volume of water on land overcomes the capacity of natural and built drainage systems to carry it away, inland flooding can result. Floods can happen in minutes or over long periods of time, but in either case the effects can be devastating and life-threatening. Building community resilience to inland flooding involves several steps including assessing vulnerability and risk of the community members.© 2024 Adobe Stock. All rights reserved.By utilizing decades of satellite observation data and historical flood maps, models are built that can predict land cover and flood risk in the year 2050. Since we know property and life in flood prone areas is at-risk, we can use the projected maps to plan for flood resilience. This layer displays census tracts that are ranked according to which would benefit most from slowing development. The ranking is based upon a bivariate map built with the following attributes:Percent of Properties with Flooding in 30 Years in the Return Period 100 Scenario (%) - Data no longer available.Average Vulnerability to Landcover ChangeThese attribute links take you to the original data sources. Preprocessing was needed to prepare many of these inputs for inclusion in our index. The links are provided for reference only.This layer is one of six in a series developed to support local climate resilience planning. Intended as planning tools for policy makers, climate resilience planners, and community members, these layers highlight areas of the community that are most likely to benefit from the resilience intervention it supports. Each layer focuses on one specific flood resilience intervention that is intended to help mitigate against the climate hazard. Building flood resilience is something that should be done now, but it's also important to consider the future of our communities, and we are fortunate to have these 2050 projections. With this information, community planners have the opportunity to get ahead of climate change induced hazards like inland flooding. Layers in the inland flooding series include,Where Would Better Flood Awareness Improve Inland Flood Resilience?Where Would Better Evacuation Routes Improve Flood Resilience?Where Would Open Space Preservation Improve Flood Resilience?Where Would Reducing Impervious Surfaces Improve Flood Resilience?Where Would Restoring Built-up Areas Improve Flood Resilience?Where Would Future Flood Prone Areas Benefit From Slowing Development?Did you know you can build your own climate resilience index or use ours and customize it? The Customize a climate resilience index Tutorial provides more information on the index and also walks you through steps for taking our index and customizing it to your needs so you can create intervention maps better suited to your location and sourced from your own higher resolution data. For more information about how Esri enriched the census tracts with exposure, demographic, and environmental data to create composite indices called intervention indices, please read this technical reference.This feature layer was created from the Climate Resilience Planning Census Tracts hosted feature layer view and is one of 18 similar intervention layers, all of which can be found in ArcGIS Living Atlas of the World.

In 2023, there were 170 flood disaster events recorded worldwide. This marks a slight decrease from the 176 disasters in 2022 but remains significantly higher than the average 86 floods per year reported in the 1990s. The peak in the past three decades occurred in 2006, with 226 flood disasters.

Devastating human and economic toll Floods continue to take a heavy toll on human lives and economies worldwide. In 2023, approximately 32 million people were impacted by flooding, including injuries and displacement. Although the number of people affected by floods has decreased since the beginning of the century, due in large part to an improvement in flood protection, better warning systems, and forecasting, the economic burden they cause has increased. Economic loss caused by floods amounted to 453 billion U.S. dollars in the past decade, the highest since the 1970s. Five of the ten costliest floods since 1900 have occurred after 2010, underscoring the increasing financial burden of these events.

Regional disparities in flood impact The impact of floods varies significantly across regions. In 2023, Africa bore the brunt of flood-related fatalities, accounting for over 50 percent of global flood deaths. Asia also suffered severely, with over 2,000 casualties in 2023. Southeast Asian countries, including Bangladesh, Vietnam, and Thailand, are among the most exposed to river flood risk worldwide due to factors such as low elevations, frequent tropical cyclones, and prolonged monsoons.

The Federal Emergency Management Agency (FEMA) produces Flood Insurance Rate maps and identifies Special Flood Hazard Areas as part of the National Flood Insurance Program's floodplain management. Special Flood Hazard Areas have regulations that include the mandatory purchase of flood insurance for holders of federally regulated mortgages. In addition, in the USA, this layer can help planners and firms avoid areas of flood risk and also avoid additional cost to carry insurance for certain planned activities.Dataset SummaryPhenomenon Mapped: Flood Hazard AreasUnits: NoneCell Sizes: 10 meters (default), 30 meters, and 90 metersSource Type: ThematicPixel Type: Unsigned integerData Coordinate System: USA Contiguous Albers Equal Area Conic USGS version (contiguous US, Puerto Rico, US Virgin Islands), WGS 1984 Albers (Alaska), Hawaii Albers Equal Area Conic (Hawaii), Western Pacific Albers Equal Area Conic (Guam, Northern Mariana Islands, and American Samoa)Mosaic Projection: Web Mercator Auxiliary SphereExtents: Contiguous United States, Alaska, Hawaii, Puerto Rico, Guam, US Virgin Islands, Northern Mariana Islands and American Samoa.Source: Federal Emergency Management Agency (FEMA)Publication Date: June 27, 2024ArcGIS Server URL: https://landscape11.arcgis.com/arcgis/This layer is derived from the June 27, 2024 version Flood Insurance Rate Map feature class S_FLD_HAZ_AR. The vector data were then flagged with an index of 88 classes, representing a unique combination of values displayed by three renderers. (In three resolutions the three renderers make nine processing templates.) Repair Geometry was run on the set of features, then the features were rasterized using the 88 class index at a resolutions of 10, 30, and 90 meters, using the Polygon to Raster tool and the "MAXIMUM_COMBINED_AREA" option. Not every part of the United States is covered by flood rate maps. This layer compiles all the flood insurance maps available at the time of publication. To make analysis easier, areas that were NOT mapped by FEMA for flood insurance rates no longer are served as NODATA but are filled in with a value of 250, representing any unmapped areas which appear in the US Census' boundary of the USA states and territories. The attribute table corresponding to value 250 will indicate that the area was not mapped.What can you do with this Layer? This layer is suitable for both visualization and analysis across the ArcGIS system. This layer can be combined with your data and other layers from the ArcGIS Living Atlas of the World in ArcGIS Online and ArcGIS Pro to create powerful web maps that can be used alone or in a story map or other application.Because this layer is part of the ArcGIS Living Atlas of the World it is easy to add to your map:In ArcGIS Online, you can add this layer to a map by selecting Add then Browse Living Atlas Layers. A window will open. Type "flood hazard areas" in the search box and browse to the layer. Select the layer then click Add to Map.In ArcGIS Pro, open a map and select Add Data from the Map Tab. Select Data at the top of the drop down menu. The Add Data dialog box will open on the left side of the box, expand Portal if necessary, then select Living Atlas. Type "flood hazard areas" in the search box, browse to the layer then click OK.In ArcGIS Pro you can use the built-in raster functions to create custom extracts of the data. Imagery layers provide fast, powerful inputs to geoprocessing tools, models, or Python scripts in Pro.The ArcGIS Living Atlas of the World provides an easy way to explore many other beautiful and authoritative maps on hundreds of topics like this one.Processing TemplatesCartographic Renderer - The default. These are meaningful classes grouped by FEMA which group its own Flood Zone Type and Subtype fields. This renderer uses FEMA's own cartographic interpretations of its flood zone and zone subtype fields to help you identify and assess risk. Flood Zone Type Renderer - Specifically renders FEMA FLD_ZONE (flood zone) attribute, which distinguishes the original, broadest categories of flood zones. This renderer displays high level categories of flood zones, and is less nuanced than the Cartographic Renderer. For example, a fld_zone value of X can either have moderate or low risk depending on location. This renderer will simply render fld_zone X as its own color without identifying "500 year" flood zones within that category.Flood Insurance Requirement Renderer - Shows Special Flood Hazard Area (SFHA) true-false status. This may be helpful if you want to show just the places where flood insurance is required. A value of True means flood insurance is mandatory in a majority of the area covered by each 10m pixel.Each of these three renderers have templates at three different raster resolutions depending on your analysis needs. To include the layer in web maps to serve maps and queries, the 10 meter renderers are the preferred option. These are served with overviews and render at all resolutions. However, when doing analysis of larger areas, we now offer two coarser resolutions of 30 and 90 meters in processing templates for added convenience and time savings.

The tropical cyclonic strong wind and storm surge model use information from 2594 historical tropical cyclones, topography, terrain roughness, and bathymetry. The historical tropical cyclones used in GAR15 cyclone wind and storm surge model are from five different oceanic basins: Northeast Pacific, Northwest Pacific, South Pacific, North Indian, South Indian and North Atlantic and the tracks were obtained from the IBTrACS database (Knapp et al. 2010). This database represents the repository of information associated with tropical cyclones that is the most up to date. Topography was taken from the Shuttle Radar Topography Mission (SRTM) of NASA, which provides terrain elevation grids at a 90 meters resolution, delivered by quadrants over the world. To account for surface roughness, polygons of urban areas worldwide were obtained from the Socioeconomic Data and Applications Centre, SEDAC (CIESIN et al., 2011). This was considered a good proxy of the spatial variation of surface roughness. A digital bathymetry model is employed with a spatial resolution of 30 arc-seconds, taken from the GEBCO_08 (General Bathymetric Chart of the Oceans) Grid Database of the British Oceanographic Data Centre (2009). Bathymetry is the information about the underwater floor of the ocean having direct influence on the formation of the storm surge. More information about the cyclone wind and storm surge hazard can be found in CIMNE et al., 2015a. Hazard analysis was performed using the software CAPRA Team Tropical Cyclones Hazard Modeler (Bernal, 2014). The vulnerability models used in the risk calculation for GAR correlate loss to the wind speed for 3-seconds gusts. For GAR15, the risk was calculated with the CAPRA-GIS platform which is risk modelling tool of the CAPRA suite (www.ecapra.org). The risk assessment was also conducted by CIMNE and Ingeniar to produced AAL and PML values for cyclone risk.

The National Flood Hazard Layer (NFHL) is a geospatial database that contains current effective flood hazard data. FEMA provides the flood hazard data to support the National Flood Insurance Program. You can use the information to better understand your level of flood risk and type of flooding.The NFHL is made from effective flood maps and Letters of Map Change (LOMC) delivered to communities. NFHL digital data covers over 90 percent of the U.S. population. New and revised data is being added continuously. If you need information for areas not covered by the NFHL data, there may be other FEMA products which provide coverage for those areas.In the NFHL Viewer, you can use the address search or map navigation to locate an area of interest and the NFHL Print Tool to download and print a full Flood Insurance Rate Map (FIRM) or FIRMette (a smaller, printable version of a FIRM) where modernized data exists. Technical GIS users can also utilize a series of dedicated GIS web services that allow the NFHL database to be incorporated into websites and GIS applications. For more information on available services, go to the NFHL GIS Services User Guide.You can also use the address search on the FEMA Flood Map Service Center (MSC) to view the NFHL data or download a FIRMette. Using the “Search All Products” on the MSC, you can download the NFHL data for a County or State in a GIS file format. This data can be used in most GIS applications to perform spatial analyses and for integration into custom maps and reports. To do so, you will need GIS or mapping software that can read data in shapefile format.FEMA also offers a download of a KMZ (keyhole markup file zipped) file, which overlays the data in Google Earth™. For more information on using the data in Google Earth™, please see Using the National Flood Hazard Layer Web Map Service (WMS) in Google Earth™.

Riverine flood hazard: The GAR 15 global flood hazard assessment uses a probabilistic approach for modelling riverine flood major river basins around the globe. The main steps in this methodology consists of: Compiling a global database of stream-flow data, merging different sources gathering more than 8000 stations over the globe. Calculating river discharge quantiles at various river sections. In another word calculating the range of possible discharges from very low to the maximum possible at series of locations along the river. The time span in the global stream-flow dataset is long enough to allow extreme value analysis. Where time series of flow discharges were too short or incomplete, they were improved with proxy data from stations located in the same “homogeneous region.” Homogeneous regions were calculated taking into account information such as climatic zones, hydrological characteristics of the catchments, and statistical parameters of the streamflow data. The calculated discharge quantiles were introduced to river sections, whose geometries were derived from topographic data (SRTM), and used with a simplified approach (based on Manning’s equation) to model water levels downstream. This procedure allowed for the determination of the reference Flood hazard maps for different return periods (6 are shown in the global study: T= 25, 50, 100, 200, 500, 1000 years). The hazard maps are developed at 1kmx1km resolution. Such maps have been validated against satellite flood footprints from different sources (DFO archive, UNOSAT flood portal) and well performed especially for the big events For smaller events (lower return periods), the GAR Flood hazard maps tend to overestimate with respect to similar maps produced locally (hazard maps where available for some countries and were used as benchmark). The main issue being that, due to the resolution, the GAR flood maps do not take into account flood defences that are normally present to preserve the value exposed to floods. This can influence strongly the results of the risk calculations and especially of the economic parameters. In order to tackle this problem some post processing of the maps has been performed, based on the assumption that flood defences tend to be higher where the exposed value is high and then suddenly drop as this value reduces. The flood hazard assessment was conducted by CIMA Foundation and UNEP-GRID. The flood maps with associated probability of occurrence, is then used by CIMNE as input to the computation of the flood risk for GAR15 as Average Annual Loss values in each country. Hazard maps for six main return periods are developed and available, and probable maximum loss calculations are underway which will be available within few months of GAR15 launch. For GAR15, the risk was calculated with the CAPRA-GIS platform which is risk modelling tool of the CAPRA suite (www.ecapra.org). More information about the flood hazard assessment can be found in the background paper (CIMA Foundation, 2015).

For the current scenario, we used hydrological data from 1960 through 1999 for generating flood inundations for 9 return periods, from 2-year flood to 1000-year flood, and 2010 GDP, population, and land use data for assessing flood impacts.

For future projections, we used 5 GCMs (Global Climate Models) from CMIP5 (Coupled Model Intercomparison Project Phase 5) projecting future flood inundations under two climate scenarios, RCP4.5 (Representative Concentration Pathway) and RCP8.5, and projected socio-economic changes using SSP2 (Shared Socio-economic Pathway) and SSP3, from the Intergovernmental Panel on Climate Change Assessment Report 5.

For more information on uses, methodology, and limitations, visit wri.org/floods

According to NOAA, "Floods are the most common and widespread of all weather-related natural disasters" and as our earth continues to warm due to climate change, precipitation events are becoming more frequent and more intense, dropping record-setting amounts of water from the sky. Whenever the volume of water on land overcomes the capacity of natural and built drainage systems to carry it away, inland flooding can result. Floods can happen in minutes or over long periods of time, but in either case the effects can be devastating and life-threatening. Building community resilience to inland flooding involves several steps including assessing vulnerability and risk of the community members. © 2024 Adobe Stock. All rights reserved.Riparian areas are lands adjacent to rivers, streams, lakes and other water bodies, and they provide a great deal of value to both wildlife as well as dissipate the energy of storm-generated waves and provide considerable resistance to streambank erosion. The natural sinuosity and complexity of river and stream channels helps to dissipate energy of raging flows during intense precipitation events. Identifying impervious surfaces and other concrete and "built" structures in the riparian zone and removing them allows for natural vegetation to take its place, thus increasing the resilience to flooding at a local level. This layer displays census tracts that are ranked according to which would benefit most from improving evacuation routes. The ranking is based upon a composite index built with the following attributes:Percent of Properties with Flooding Today in the Return Period 100 Scenario (%) - Data source no longer availablePercent Riparian Area (%)Percent of Area to Restore (%) - Expressed as a percentage of the tract with landcover classified as “built-up”.These attribute links take you to the original data sources. Preprocessing was needed to prepare many of these inputs for inclusion in our index. The links are provided for reference only.This layer is one of six in a series developed to support local climate resilience planning. Intended as planning tools for policy makers, climate resilience planners, and community members, these layers highlight areas of the community that are most likely to benefit from the resilience intervention it supports. Each layer focuses on one specific flood resilience intervention that is intended to help mitigate against the climate hazard.Restoring built-up areas to natural habitat has many benefits including mitigating flood damage. For more information, the Commonwealth of Massachusetts has an excellent document that describes the functions of riparian areas for storm damage prevention. Additionally, here is a resource from the EPA.Layers in the inland flooding series include,Where Would Better Flood Awareness Improve Inland Flood Resilience?Where Would Better Evacuation Routes Improve Flood Resilience?Where Would Open Space Preservation Improve Flood Resilience?Where Would Reducing Impervious Surfaces Improve Flood Resilience?Where Would Restoring Built-up Areas Improve Flood Resilience?Where Would Future Flood Prone Areas Benefit From Slowing Development?Did you know you can build your own climate resilience index or use ours and customize it? The Customize a climate resilience index Tutorial provides more information on the index and also walks you through steps for taking our index and customizing it to your needs so you can create intervention maps better suited to your location and sourced from your own higher resolution data. For more information about how Esri enriched the census tracts with exposure, demographic, and environmental data to create composite indices called intervention indices, please read this technical reference.This feature layer was created from the Climate Resilience Planning Census Tracts hosted feature layer view and is one of 18 similar intervention layers, all of which can be found in ArcGIS Living Atlas of the World.

The Global Flood Mortality Risks and Distribution is a 2.5 minute grid of global flood mortality risks. Gridded Population of the World, Version 3 (GPWv3) data provided a baseline population per grid cell from which to estimate potential mortality risks due to flood hazard. Mortality loss estimates per flood event are calculated using regional, hazard-specific mortality records of the Emergency Events Database (EM-DAT) that span the 20 years between 1981 and 2000. Data regarding the frequency and distribution of flood hazard are obtained from the Global Flood Hazard Frequency and Distribution data set. In order to more accurately reflect the confidence associated with the data and the procedures, the potential mortality estimate range is classified into deciles, 10 classes of increasing hazard with an approximately equal number of grid cells per class, producing a relative estimate of flood-based mortality risks. This data set is the result of collaboration among the Columbia University Center for Hazards and Risk Research (CHRR), International Bank for Reconstruction and Development/The World Bank, and Columbia University Center for International Earth Science Information Network (CIESIN).

A comprehensive dataset of extreme hydrological events (EHEs) – floods and droughts, consisting of 2,171 occurrences worldwide, during 1960‐2014 was compiled, and then their economic losses were normalized using a price index in U.S. dollar. The dataset showed a significant increasing trend of EHEs before 2000, while a slight post‐2000 decline. Correspondingly, the EHEs‐caused economic losses increased obviously before 2000 followed by a slight decrease; the post‐2000 decline could be partially attributed to the decreases in drought and flood‐prone area, or climate adaptation practices. Spatially, Asia experienced most EHEs (969), corresponding to the largest share of economic losses (approximately $868 billion for floods and $50 billion for droughts, respectively), while Oceania had the least EHEs (102) and the least economic losses (approximately $19 billion for floods and $45 billion for droughts). The five countries with the highest EHE‐caused economic losses were China, USA, Canada, Australia, and India. Countries that suffered the highest flood‐caused economic losses were China, USA, and Canada. This dataset provides a quantitative linkage between climate science and economic losses at a global scale; and it is beneficial for the regional climatic impact assessments and strategical development for mitigating climate change impacts.

In 2023, the United States experienced 25 natural disasters, which made it the most natural catastrophe-prone country in the world that year. India and China came second on that list with 17 natural disasters occurring in the same year. Floods was the most common type of natural disaster in 2023. Types of natural disasters There are many different types of natural disasters that occur worldwide, including earthquakes, droughts, storms, floods, volcanic activity, extreme temperatures, landslides, and wild fires. Overall, there were 398 natural disasters registered all over the world in 2023. Costs of natural disasters Due to their destructive nature, natural disasters take a severe toll on populations and countries. Storms and floods, which tend to occur most regularly, have the biggest economic impact in the countries that they occur. In 2023, storms caused damages estimated at more than 100 billion U.S. dollars. Meanwhile, the number of deaths due to natural disasters neared 100,000 that year. The earthquake in Turkey in February had the highest death toll, with more than 50,000 fatalities. Scientists predict that some natural disasters such as storms, floods, landslides, and wildfires will be more frequent and more intense in the future, creating both human and financial losses.

This web map is designed to provide an enriched geospatial platform to ascertain the flood potential status of our local place of residence and other land-use activities. Information on the flood risk distribution can be extracted by 5 major magnitudes (very high, high, moderate, low, and very low). The buildings, roads, and rail tracks that are susceptible to flooding based on the identified magnitudes are also included in the web map. In addition, the historical or flood inventory layer, which contains information on the previous flooding disasters that have occurred within the river basin, is included.

This web map is the result of extensive research using available data, open source and custom datasets that are extremely reliable.The collaborative study was done by Dr. Felix Ndidi Nkeki (GIS-Unit, BEDC Electricity PLC, 5, Akpakpava Road, Benin City, Nigeria and Department of Geography and Regional Planning, University of Benin, Nigeria), Dr. Ehiaguina Innocent Bello (National Space Research and Development Agency, Obasanjo Space Centre, FCT-Abuja, Nigeria) and Dr. Ishola Ganiy Agbaje (Centre for Space Science Technology Education, Obafemi Awolowo University, Ile-Ife, Nigeria). The study results are published in a reputable leading world-class journal known as the International Journal of Disaster Risk Reduction. The methodology, datasets, and full results of the study can be found in the paper.

The major sources of data are: ALOS PALSAR DEM; soil data from Harmonised World Soil Database-Food and Agriculture Organisation of the United Nations (FAO); land-use and surface geologic datasets from CSSTE, OAU Campus, Ile-Ife, Nigeria and Ibadan Urban Flood Management Project (IUFMP), Oyo State, Nigeria; transport network data was extracted from Open Street Map; building footprint data was mined from Google open building; and finally, rainfall grid data was downloaded from the Centre for Hydrometeorology and Remote Sensing (CHRS).

IntroductionClimate Central’s Surging Seas: Risk Zone map shows areas vulnerable to near-term flooding from different combinations of sea level rise, storm surge, tides, and tsunamis, or to permanent submersion by long-term sea level rise. Within the U.S., it incorporates the latest, high-resolution, high-accuracy lidar elevation data supplied by NOAA (exceptions: see Sources), displays points of interest, and contains layers displaying social vulnerability, population density, and property value. Outside the U.S., it utilizes satellite-based elevation data from NASA in some locations, and Climate Central’s more accurate CoastalDEM in others (see Methods and Qualifiers). It provides the ability to search by location name or postal code.The accompanying Risk Finder is an interactive data toolkit available for some countries that provides local projections and assessments of exposure to sea level rise and coastal flooding tabulated for many sub-national districts, down to cities and postal codes in the U.S. Exposure assessments always include land and population, and in the U.S. extend to over 100 demographic, economic, infrastructure and environmental variables using data drawn mainly from federal sources, including NOAA, USGS, FEMA, DOT, DOE, DOI, EPA, FCC and the Census.This web tool was highlighted at the launch of The White House's Climate Data Initiative in March 2014. Climate Central's original Surging Seas was featured on NBC, CBS, and PBS U.S. national news, the cover of The New York Times, in hundreds of other stories, and in testimony for the U.S. Senate. The Atlantic Cities named it the most important map of 2012. Both the Risk Zone map and the Risk Finder are grounded in peer-reviewed science.Back to topMethods and QualifiersThis map is based on analysis of digital elevation models mosaicked together for near-total coverage of the global coast. Details and sources for U.S. and international data are below. Elevations are transformed so they are expressed relative to local high tide lines (Mean Higher High Water, or MHHW). A simple elevation threshold-based “bathtub method” is then applied to determine areas below different water levels, relative to MHHW. Within the U.S., areas below the selected water level but apparently not connected to the ocean at that level are shown in a stippled green (as opposed to solid blue) on the map. Outside the U.S., due to data quality issues and data limitations, all areas below the selected level are shown as solid blue, unless separated from the ocean by a ridge at least 20 meters (66 feet) above MHHW, in which case they are shown as not affected (no blue).Areas using lidar-based elevation data: U.S. coastal states except AlaskaElevation data used for parts of this map within the U.S. come almost entirely from ~5-meter horizontal resolution digital elevation models curated and distributed by NOAA in its Coastal Lidar collection, derived from high-accuracy laser-rangefinding measurements. The same data are used in NOAA’s Sea Level Rise Viewer. (High-resolution elevation data for Louisiana, southeast Virginia, and limited other areas comes from the U.S. Geological Survey (USGS)). Areas using CoastalDEM™ elevation data: Antigua and Barbuda, Barbados, Corn Island (Nicaragua), Dominica, Dominican Republic, Grenada, Guyana, Haiti, Jamaica, Saint Kitts and Nevis, Saint Lucia, Saint Vincent and the Grenadines, San Blas (Panama), Suriname, The Bahamas, Trinidad and Tobago. CoastalDEM™ is a proprietary high-accuracy bare earth elevation dataset developed especially for low-lying coastal areas by Climate Central. Use our contact form to request more information.Warning for areas using other elevation data (all other areas)Areas of this map not listed above use elevation data on a roughly 90-meter horizontal resolution grid derived from NASA’s Shuttle Radar Topography Mission (SRTM). SRTM provides surface elevations, not bare earth elevations, causing it to commonly overestimate elevations, especially in areas with dense and tall buildings or vegetation. Therefore, the map under-portrays areas that could be submerged at each water level, and exposure is greater than shown (Kulp and Strauss, 2016). However, SRTM includes error in both directions, so some areas showing exposure may not be at risk.SRTM data do not cover latitudes farther north than 60 degrees or farther south than 56 degrees, meaning that sparsely populated parts of Arctic Circle nations are not mapped here, and may show visual artifacts.Areas of this map in Alaska use elevation data on a roughly 60-meter horizontal resolution grid supplied by the U.S. Geological Survey (USGS). This data is referenced to a vertical reference frame from 1929, based on historic sea levels, and with no established conversion to modern reference frames. The data also do not take into account subsequent land uplift and subsidence, widespread in the state. As a consequence, low confidence should be placed in Alaska map portions.Flood control structures (U.S.)Levees, walls, dams or other features may protect some areas, especially at lower elevations. Levees and other flood control structures are included in this map within but not outside of the U.S., due to poor and missing data. Within the U.S., data limitations, such as an incomplete inventory of levees, and a lack of levee height data, still make assessing protection difficult. For this map, levees are assumed high and strong enough for flood protection. However, it is important to note that only 8% of monitored levees in the U.S. are rated in “Acceptable” condition (ASCE). Also note that the map implicitly includes unmapped levees and their heights, if broad enough to be effectively captured directly by the elevation data.For more information on how Surging Seas incorporates levees and elevation data in Louisiana, view our Louisiana levees and DEMs methods PDF. For more information on how Surging Seas incorporates dams in Massachusetts, view the Surging Seas column of the web tools comparison matrix for Massachusetts.ErrorErrors or omissions in elevation or levee data may lead to areas being misclassified. Furthermore, this analysis does not account for future erosion, marsh migration, or construction. As is general best practice, local detail should be verified with a site visit. Sites located in zones below a given water level may or may not be subject to flooding at that level, and sites shown as isolated may or may not be be so. Areas may be connected to water via porous bedrock geology, and also may also be connected via channels, holes, or passages for drainage that the elevation data fails to or cannot pick up. In addition, sea level rise may cause problems even in isolated low zones during rainstorms by inhibiting drainage.ConnectivityAt any water height, there will be isolated, low-lying areas whose elevation falls below the water level, but are protected from coastal flooding by either man-made flood control structures (such as levees), or the natural topography of the surrounding land. In areas using lidar-based elevation data or CoastalDEM (see above), elevation data is accurate enough that non-connected areas can be clearly identified and treated separately in analysis (these areas are colored green on the map). In the U.S., levee data are complete enough to factor levees into determining connectivity as well.However, in other areas, elevation data is much less accurate, and noisy error often produces “speckled” artifacts in the flood maps, commonly in areas that should show complete inundation. Removing non-connected areas in these places could greatly underestimate the potential for flood exposure. For this reason, in these regions, the only areas removed from the map and excluded from analysis are separated from the ocean by a ridge of at least 20 meters (66 feet) above the local high tide line, according to the data, so coastal flooding would almost certainly be impossible (e.g., the Caspian Sea region).Back to topData LayersWater Level | Projections | Legend | Social Vulnerability | Population | Ethnicity | Income | Property | LandmarksWater LevelWater level means feet or meters above the local high tide line (“Mean Higher High Water”) instead of standard elevation. Methods described above explain how each map is generated based on a selected water level. Water can reach different levels in different time frames through combinations of sea level rise, tide and storm surge. Tide gauges shown on the map show related projections (see just below).The highest water levels on this map (10, 20 and 30 meters) provide reference points for possible flood risk from tsunamis, in regions prone to them.

This layer presents an estimation of the global risk induced by flood hazard. Unit is estimated risk index from 1 (low) to 5 (extreme). For more information, visit the Global Risk Data Platform: http://preview.grid.unep.ch/index.php?preview=data&events=floods&evcat=5&lang=eng

Flood Barrier Market size was valued at USD 1,654.38 Million in 2023 and is projected to reach USD 3,250.23 Million by 2031, growing at a CAGR of 8.75% from 2024 to 2031.

Global Flood Barrier Market Outlook

The need for flood barriers is primarily driven by growing urbanization and infrastructure development, especially in densely populated areas with a higher danger of flood-related damage. There is a growing demand for efficient flood control systems as urban populations grow and cities expand into flood-prone areas. Natural landscapes are frequently altered by urbanization, including the removal of flora and the paving over of porous surfaces. These changes can increase the danger of floods by decreasing the land’s capacity to absorb and release water. Furthermore, the development of roads, houses, and other infrastructure can change how water drains from natural areas, raising the possibility of localized floods after intense precipitation.

Developers and governments understand the significance of implementing flood mitigation measures in response to these difficulties to safeguard people and property. Essential elements of these plans include flood barriers, which act as a physical barrier against rising water levels and aid in keeping floodwaters out of cities. The need for flood barriers is further highlighted by infrastructure development, which includes the erection of residential complexes, commercial structures, transit networks, and industrial facilities. These infrastructure projects are excellent candidates for protection against flood-related damage since they frequently need considerable money and resource commitments.

EcoDRR global classification scheme based on spatial combination of ecosystem coverage and natural hazard physical exposure. The physical exposure data-set shows the product of hazard frequency and people exposed to this hazard in the same 100 square kilometer cell. For a specific natural hazard, a 0.01 degree resolution raster is generated, showing hazard annual frequency weighted with portion of pixel potentially affected. The original flood hazard model includes various return period. The annual frequency is a sum of all frequencies obtained from these return periods layers.

Sources: The dataset uses a probabilistic approach for modelling riverine flood of major river basins around the globe. This has been possible after compiling a global database of stream-flow data, merging different sources and gathering more than 8000 stations over the globe in order to calculate the range of possible discharges from very low to the maximum possible scales at different locations along the rivers. The calculated discharges were introduced in the river sections to model water levels downstream. This procedure allowed for the determination of stochastic event-sets of riverine floods from which hazard maps for several return periods (25, 50, 100, 200, 500, 1000 years) were obtained. The hazard maps are developed at 1kmx1km resolution and have been validated against satellite flood footprints from Dartmouth Flood Observatory archive. This product was designed by UNEP/GRID-Europe and CIMA Research Foundation for the Global Assessment Report on Risk Reduction (GAR). It was modeled using global data. Credit: UNEP/GRID-Europe and CIMA Research Foundation.

Abstract

The DPHS in Dar es Salaam was conducted in two rounds in November-December 2017 and in September 2018, with the objective to assess the role of poverty and other social factors in urban flooding in the city. The survey data collected in 2017 focused on exposure to frequent flooding, while the follow up survey in 2018, targeting the same households, focused on the impact of a flood event that happened in April 2018. During the follow up survey in 2018, additional households were also added to the sample. The data collected is representative at the city level and overrepresented in areas that are flood prone.

This project was a collaborative effort between Global Facility for Disaster Reduction and Recovery (GFDRR), the Tanzanian Urban Resilience Program (TURP), the Poverty Global Practice and Urban, Disaster Risk Management, Resilience and Land Global Practice (GPURL). Data collection was carried out by UDA Consulting under World Bank supervision.

Geographic coverage

Dar es Salaam, Tanzania.

Analysis unit

• Household
• Individual

Kind of data

Sample survey data [ssd]

Sampling procedure

The selection of households in the survey design had two objectives. First, to select a sample that represents the population of Dar es Salaam and second, to interview enough people who had experienced floods to be able to detect patterns in their socio-economic characteristics.

The sample size was selected to confidently represent the population of Dar es Salaam given the income level and income distribution. Accordingly, a sample size of 105 EAs and 10 households per EA were selected using Probability Proportion to Size (PPS). In 2018, 28 EAs to the original sample as part of an additional round of data collection.

To capture enough households that had experienced floods, a flood risk stratum was designed using the Ramani Huria community flood map. EAs were categorized according to three flood risk strata, i.e., “no risk”, “low to medium risk” and “high risk”, depending on how much of the EA was covered by the flood layer in the map. This categorization of the city was used to oversample in high risk and low-to-medium risk areas by selecting more of those EAs compared to the population living there. Finally, all the selected households were randomly drawn within each EA using satellite imagery.

Sampling weights were calculated to compensate for the oversampling in high-risk areas. When applying the sample weights, the dataset is representative at the city level.

References:

ERMAN, A. E., TARIVERDI, M., OBOLENSKY, M. A. B., CHEN, X., VINCENT, R. C., MALGIOGLIO, S., & YOSHIDA, N. (2019). Wading out the storm: The role of poverty in exposure, vulnerability and resilience to floods in Dar Es Salaam. World Bank Policy Research Working Paper, (8976).

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

The survey questionnaire consists of 13 sections that were used to collect the survey data. See the attached questionnaire.

Cleaning operations

The following data editing was done for anonymization purpose: • Precise location data, such as GPS coordinates, were dropped • Personal information, such as name, citizenship and phone number were dropped • Information on from which region or country the respondent moved from before settling in current dwelling and where respondent was born was categorized into “in Dar es Salaam” and “outside Dar es Salaam” to protect privacy while preserving valuable data. District level information on origin was dropped. • Household size exceeding seven household members was categorized as “above 7 members” • Household member information for 7th member and above was dropped to avoid reconstruction of the household size variable.

For more information on the anonymization process, see the Technical Document.

Response rate

In the 2018 follow up interview, 419 were reached and interviewed out of the 1058 households in the original sample.

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.

The global flood gate market, valued at $440 million in 2025, is projected to experience robust growth, driven by increasing frequency and severity of extreme weather events globally. This necessitates robust flood protection infrastructure in both residential and commercial sectors. The market's 7.6% CAGR from 2025 to 2033 indicates significant expansion, fueled by rising urbanization in coastal and flood-prone areas, stringent government regulations mandating flood mitigation measures, and advancements in flood gate technology leading to more efficient and durable solutions. Key segments within the market include double gate and single gate systems, with commercial applications currently dominating due to large-scale infrastructure projects. However, residential applications are expected to witness substantial growth as awareness of flood risks increases and homeowners seek protective measures. Competitive forces within the market are intensifying, with companies like IBS Technics GmbH, FloodBreak, and others vying for market share through innovation in materials, design, and automation. The geographic distribution of the market reflects the global distribution of flood risk. North America and Europe currently hold significant shares due to established infrastructure and high adoption rates. However, regions like Asia Pacific, particularly China and India, are anticipated to experience rapid growth in demand driven by rapid urbanization and increasing investment in infrastructure development. Market restraints include high initial investment costs for flood gate installations, the need for skilled labor for installation and maintenance, and potential environmental concerns regarding the impact of large-scale flood mitigation projects. Nevertheless, the long-term economic benefits of flood protection, coupled with government incentives and insurance requirements, are expected to mitigate these constraints and propel market growth significantly over the forecast period.