This is a ZIP folder containing CMAP's Flood Susceptibility Index data. Within the linked ZIP folder are a collection of datasets and documents describing their contents and how to use them. Contents include the Riverine Flood Susceptibility Index, Urban Flood Susceptibility Index, CMAP Stormwater and Flooding strategy paper, maps, metadata, and appendix.Read more on our agency website.

This national map of flood susceptibility or flood prone areas is based on patterns of historic flood events as predicted by an ensemble machine learning model. The recommended use is national, provincial or regional scale and can be used as a guide for identifying areas for further investigation. The Flood Susceptibility Index (FSI) Dataset, while processed and available at 30m cell size, is not recommended for use at the pixel or street level, given the uncertainty in the modelling process and the variability of results as discussed in https://www.mdpi.com/2673-4931/25/1/18 . For additional details on the methods, tests, models and datasets used to generate this data layer, please see https://geoscan.nrcan.gc.ca/starweb/geoscan/servlet.starweb?path=geoscan/fulle.web&search1=R=329493

This national map of flood susceptibility or flood prone areas is based on patterns of historic flood events as predicted by an ensemble machine learning model. The recommended use is national, provincial or regional scale and can be used as a guide for identifying areas for further investigation. The Flood Susceptibility Index (FSI) Dataset, while processed and available at 30m cell size, is not recommended for use at the pixel or street level, given the uncertainty in the modelling process and the variability of results as discussed in https://www.mdpi.com/2673-4931/25/1/18 . For additional details on the methods, tests, models and datasets used to generate this data layer, please see https://geoscan.nrcan.gc.ca/starweb/geoscan/servlet.starweb?path=geoscan/fulle.web&search1=R=329493

CMAP developed the Stormwater and Flooding Strategy Paper which informs recommendations made in the ON TO 2050 comprehensive plan to address urban and riverine flooding. The paper presents strategies to reduce flooding impact throughout the region by integrating stormwater management into transportation and land use planning, alongside analysis of damages from past flooding events.To help direct these strategies, CMAP developed urban and riverine Flooding Susceptibility Indexes (FSIs) to identify priority areas across the region for flooding mitigation activities. GIS datasets representing flood susceptibility factors were used to construct the indexes by assessing each factor's influence using a GIS-based frequency ratio approach. The approach, inputs, and results are described in greater detail in the technical appendix of the strategy paper. The values in the raster attribute tables represent low (1) to high (10) flood susceptibility.The inputs are listed below, alongside their sources:Flood locations from FEMA, several counties, and the City of ChicagoTopographic Wetness Index (TWI), Urban Only CMAP derived from 5-ft resolution Digital Elevation Model (DEM)Combined sewer service area, received from MWRD and municipalitiesElevation differential between property and nearest FEMA Base Flood Elevation (BFE), Urban onlyPercent Impervious Cover from 2011 National Land Cover DatasetAge of first development from USGS 1974-2012 land use trends dataset, using developed land classesPrecipitation variation, precipitation amounts (inches) for the 10-year, 2- hour storm from NOAA Atlas 14

CMAP has developed a Stormwater and Flooding strategy paper, which will inform the recommendations made in ON TO 2050 to address urban and riverine flooding. The paper presents strategies to reduce flooding impact throughout the region by integrating stormwater management into transportation and land use planning, alongside analysis of damages from past flooding events. To help direct these strategies, CMAP has developed urban and riverine Flooding Susceptibility Indexes (FSIs) to identify priority areas across the region for flooding mitigation activities. GIS datasets representing flood susceptibility factors were used to construct the indexes, by assessing each factor’s influence using a GIS-based frequency ratio approach. The approach, inputs, and results are described in greater detail in the technical appendix of the strategy paper. The values contained within the raster attribute table represent low (1) to high (10) flood susceptibility. The inputs are listed below, alongside their sources:Reported Flood Locations: FEMA National Flood Insurance Program Claims, FEMA Individual Assistance Grants, FEMA Discovery Data, City of Chicago 311 Standing Water Locations, MWRD Detailed Watershed Plans, DuPage County GIS, Kendall County Department of Planning, Lake County Stormwater Management Commission.Topographic Wetness Index: CMAP analysis of Illinois State Geological Survey (ISGS) Light Detection and Ranging (LiDAR) data.Combined sewer service areas: Metropolitan Water Reclamation District; IL EPA; municipalities with combined sewers outside of Cook County.Elevation differential between property and nearest Base Flood Elevation (BFE): CMAP analysis of ISGS LiDAR data and FEMA BFE data.Impervious cover: 2011 National Land Cover Dataset.Impervious cover of watershed catchment: CMAP analysis of NLCD and National Hydrography Program data.Age of first development: U.S. Geological Survey (USGS) National Water-Quality Assessment (NAWQA) Wall-to-Wall Anthropogenic Land Use Trends (NWALT) 1974-2012 land cover.Precipitation variation: NOAA Atlas 14 10-year, two hour storm event.

The Flood Vulnerability Index (FVI) assesses the distribution of vulnerability to flooding across NYC in order to guide flood resilience policies and programs. Vulnerability contains three components: exposure to a hazard, susceptibility to harm from the exposure, and capacity to recover (Cutter et al., 2009). There are six hazard-specific FVIs, one for each of the six different flood hazard scenarios, which include current and two future storm surge scenarios and current and two future tidal flooding scenarios. Exposures vary for different types of flooding and different scenarios within each flood type. Each FVI consists of two component sub-indices: an exposure index and an index that reflects susceptibility to harm and capacity to recover. The exposure index is different in each FVI in order to capture the different exposures to each of the flood hazard scenarios. The sub-index that reflects susceptibility to harm and capacity to recover -- the Flood Susceptibility to Harm and Recovery Index (FSHRI) -- is the same for each FVI. It aggregates 12 socio-economic indicators correlated with various types of hardships that people may suffer due to flooding and different dimensions of ability to recover. For additional information, please visit this link.

The flash-flood susceptibility index (FFSI) represents the relative potential of Ecuador catchments to generate a flash flood when significant local rainfall occurs. The FFSI is calculated for each HydroSHEDS level 12 catchment units (Lehner et al. 2008, Yamazaki et al. 2014) of Ecuador, using a weighted mean of 7 commonly used flash flood drivers related to the hypsometry, drainage network, and surface properties of catchments.

• Hypsometry characteristics : 1-catchment mean slope and 2- mean curvature. Derived from NASA's Shuttle Radar Topography Mission (SRTM) 90 m Digital Elevation Model (DEM) v4.1 (Jarvis et al. 2008), and cleaned using the Zevenbergen and Thorne (1987) 2nd-degree polynomial adjustment algorithm.

• Drainage network characteristics : 1- Upslope contributing area of a basin, 2- Cumulative drainage density and 3- mean drainage strahler order. Extracted from the World Wildlife Fund (WWF) HydroSHEDS v1 global data, at a resolution of 15 arc-seconds, level-12 hydrological basins (Lehner et al. 2008, Yamazaki et al. 2014) and river routing networks (Lehner and Grill 2013).

• Surface properties: 1- mean Land Use Land Cover (LULC) Index calculated from Copernicus Global Land Operation products (Buchhorn et al., 2020) and 2- the mean ISRIC SoilGrid Sand Fraction to account for the infiltration potential of soils (Hengl et al. 2017).

The weights of each indicators have been estimated from PCA analysis after normalization of the indicators For more details on the methods, see Kruczkiewicz et al. 2021.

The final normalized FFSI results is available, as well as the discretized FFSI into an index (1-10) computed for this case study using a rule-based approach specific to the context of Ecuador and the normalized FFSI spatial distribution.

The data are downloadable as ESRI Shapefile format. For each of the 1903 HydroSHED (level12) catchment of Ecuador, it contains the HydroSHED unique ID field, the 7 raw indicators, the final normalized FFSI and the reclassified FFSI (1-10).

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

Floods represent the most frequent natural hazard and are the ones that impact the largest number of people worldwide, these events are particularly exacerbated by extreme climatic phenomena, such as the 2017 Coastal El Niño, which was classified as the most intense in the past century, with the Piura region of Peru being the most affected. Flood susceptibility models (FSM) are essential for mitigating the negative impacts of floods through land-use planning, policy and plan formulation, and fostering community resilience for the sustainable occupation and use of floodplains. This study aimed to develop flood susceptibility models (FSM) in northern Peru, particularly in the Piura region, using a hybrid methodology integrating optical and radar remote sensing, GIS, and machine learning (ML) techniques. Sentinel-1 data were used to map flood extent using the Normalized Difference Flood Index (NDFI), while flood susceptibility was modeled using ten topographic variables (derived from a DEM), the Normalized Difference Vegetation Index (NDVI), geology, and geomorphology; issues related to correlation and multicollinearity among topographic variables were addressed through Principal Component Analysis (PCA), selecting four principal components that explained 75.4% of the variance. Six FSMs were generated using Support Vector Machines (SVM) and Random Forest (RF), combined with different methods to estimate the quantitative relationship between variables and flood occurrence: Quantiles (q), Frequency Ratio (FR), and Weights of Evidence (WoE) (SVM-q, SVM-FR, SVM-WoE, RF-q, RF-FR, and RF-WoE). Model validation was performed using metrics such as the Area Under the ROC Curve (AUC), F1-score, and Accuracy, along with a cross-validation analysis. The results revealed that the RF ensemble model with WoE (RF-WoE) exhibited the best performance (AUC = 0.988 in training and >0.907 in validation), outperforming the SVM-based models; the SHAP analysis confirmed the significance of geology, geomorphology, and aspect in flood prediction. The resulting susceptibility maps identified the lower Piura River basin as the most vulnerable area, particularly during the 2017 Coastal El Niño event, due to morphological factors and inadequate land occupation. This study contributes to the field by demonstrating the effectiveness of a hybrid methodology that combines PCA, machine learning, and SHAP analysis, providing a more robust and interpretable approach to flood susceptibility mapping.

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.

Binary raster dataset (.txt format) containing flood susceptibility maps related to 100-year river floods occurring in the continental U.S. These mapping products were derived through terrain analysis and a technique of pattern classification performed on DEMs obtained from HydroSHEDS (http://hydrosheds.cr.usgs.gov/overview.php) with a 3 arc-second resolution (0.00083333 degree, approximatively 90 m at the equator). Specifically, the flood-prone areas were identified by applying a linear binary classifier based upon the Geomorphic Flood Index (Manfreda et al., 2015; Samela et al., 2015; Samela et al., 2016 ). The raster maps have a 90 m resolution and are geo-referenced. The coordinate system of the maps is UTM (Universal Transverse Mercator) Zone 17N, the projection is Transverse Mercator, and the geodetic system is NAD (North American Datum) 1983. To simplify the management and the use of the data, the continental U.S. was divided into eighteen major water resources regions according to the hydrologic units identified by the United States Geological Survey.

Guyana is the country most exposed to the risk of river flooding in Latin America and the Caribbean. According to an index that measures the percentage of population expected to be affected by river floods in an average year, Guyana was graded 4.13 points on a scale from zero to five, where five shows the highest level of riverine flood. Suriname ranked second in the region, with a score of 4.02. Suriname is also one of the countries with the worst physical exposure to floods in Latin America and the Caribbean.

This Ecuador historical flood dataset has been compiled from two different sources : DesInventar (UNISDR) and the Ecuadorian Secretariat for Disaster Management (SNGRE) ranging from 2007 to 2020, and results in 4967 data entries.

For each event it contains information about:

• Location (administrative level, 3 regions, exact location name and XY coordinates)
• Date of the event (Day, Hour, Minutes)
• Flash Flood Identifiers detected from the event description
• Impacts (12 different fields)

In addition, several indexes have been computed:

• FFCI (1-10) : Flash Flood confidence index from events description FF identifiers tagging
• FFSI (1-10) : the Flash Flood Susceptibility Index (from geomorphology characteristics) of the catchment the point event is falling in
• EFFCI (1-10) : Enhanced FFCI combining information about FFSI and FFCI
• Impact_Severity (1-6) : computed from the estimated number of people affected.

As of 2025, the Netherlands was the European country most exposed to coastal flood risk, with an index score of **. The physical risk index is based on the estimated number of people exposed to this type of event per year. Germany ranked second, with a risk index score of ***** points. The Netherlands ranked high in the list of countries most exposed to coastal floods worldwide.

National Risk Index Version: March 2023 (1.19.0)Riverine Flooding is when streams and rivers exceed the capacity of their natural or constructed channels to accommodate water flow and water overflows the banks, spilling out into adjacent low-lying, dry land. Annualized frequency values for Riverine Flooding are in units of event-days per year.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.

Flood is the most devastating and prevalent disaster among all-natural disasters. Every year, flood claims hundreds of human lives and causes damage to the worldwide economy and environment. Consequently, the identification of flood-vulnerable areas is important for comprehensive flood risk management. The main objective of this study is to delineate flood-prone areas in the Panjkora River Basin (PRB), eastern Hindu Kush, Pakistan. An initial extensive field survey and interpretation of Landsat-7 and Google Earth images identified 154 flood locations that were inundated in 2010 floods. Of the total, 70% of flood locations were randomly used for building a model and 30% were used for validation of the model. Eight flood parameters including slope, elevation, land use, Normalized Difference Vegetation Index (NDVI), topographic wetness index (TWI), drainage density, and rainfall were used to map the flood-prone areas in the study region. The relative frequency ratio was used to determine the correlation between each class of flood parameter and flood occurrences. All of the factors were resampled into a pixel size of 30×30 m and were reclassified through the natural break method. Finally, a final hazard map was prepared and reclassified into five classes, i.e., very low, low, moderate, high, very high susceptibility. The results of the model were found reliable with area under curve values for success and prediction rate of 82.04% and 84.74%, respectively. The findings of this study can play a key role in flood hazard management in the target region; they can be used by the local disaster management authority, researchers, planners, local government, and line agencies dealing with flood risk management.

With a risk index score of 9.9, 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 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.

Activation date: 2019-08-02
Event type: Flood

Activation reason:
The specific activation focuses on deepening the analysis concerning the occurrence of floods within a transboundary AOI that corresponds to the Drin catchment area and more specifically to the part that extends into four countries, namely Albania, Kosovo, the Former Yugoslavian Republic of Macedonia and Montenegro.The service and the products aim to serve the scope of the Drin River Cooperation agreement on sustainable management of water resources in the Drin catchment area through providing information relevant for risk preparedness (hazard, historical floods and economic risk assessment) and sustainable development (economic assets mapping, economic risk assessment).Historical Analysis of Floods Events of 12/2004 - Bar, Cetinje, Danilovgrad, Nicksic, Podgorica, ShkodraComprehensive knowledge of a number of disasters that occurred in the past is provided through the spatial extent of the floods, mainly linked with rainfall, and assessment of their impact on existing assets, as well as information on Land Use/Cover and existing assets and infrastructures.To efficiently support "informed" decisions on flood risk management activities and planning of prevention, protection and preparedness measures, through accounting for the area characteristics, the flood hazard was assessed.Flood Risk Mapping – Hazard Assessment Towards reducing the adverse consequences of flooding for both population and economic activity, flood Hazard assessment was implemented within the Drin catchment area.Due to the characteristics of the Drin river basin, for the estimation of the maximum flood extent a morphological approach was deployed for the catchment area upstream of Skadar lake (based upon the Flood Susceptibility Index/FSI), while a hydraulic modelling was implemented for the Buna river, within a mainly flat area of the AOI, from the Skadar lake to the Adriatic Sea (TELEMAC-2D model through using a very high-resolution DEM).Flood Risk Mapping - Economic Risk Assessment The overall economic risk assessment was implemented through the use of the RASOR platform. Hazard assessment data, economic assets and vulnerability information were accounted to generate a percentage damage of the single asset types. The economic risk was then evaluated on the basis of the economic value information of the asset type/category per square metre, and the effective area occupied by each category.

Flood is the most frequent and destructive natural disaster, causing significant negative impacts on humans and built and natural ecosystems. While it is extremely challenging to prevent floods, their associated hazards can be mitigated through well-planned and appropriate measures. The present study combined the analytical hierarchy process (AHP) analysis and an ArcGIS-based multi-criteria decision-making (MCDM) approach to assess, categorize, quantify, and map the flood-prone areas in Khyber Pakhtunkhwa, Pakistan, a region particularly vulnerable to recurrent flooding. Eight key factors including precipitation, rivers/streams, slope, elevation, soil, normalized difference vegetation index, and land use were used for flood susceptibility modeling. The weighted sum overlay tool in global positioning system ArcGIS was utilized to give weightage to each raster layer, based on the AHP ranking to produce a flood susceptibility map for the study area. According to the AHP analysis, the most impactful factors defining the flood susceptibility in our study area were streams (0.29%), precipitation (0.23%), slope of the area (14%), and LST (10%). Our flood model achieved excellent accuracy, with Area Under the Curve (AUC) value of 0.911. The model predicted that 9% of the total area is classified as very high risk, while 14% is identified as high risk, covering approximately 923,257 hectares and 1,419,480 hectares, respectively. These high-risk zones are predominantly concentrated in the central and lower northern, densely populated districts of the province. Our flood susceptibility results would assist policymakers, concerned departments, and local communities in assessing flood risk in a timely manner and designing effective mitigation and response strategies.

Activation date: 2018-07-27
Event type: Flood

Activation reason:
The activation focuses on two flood disaster management levels.The first refers to post‐disaster situation analysis concerning the impact of intense rain (16 December 2017) over the Villa Santa Lucia, in the commune of Chaitén, Chile (CL).Mudflow Delineation - Damage AssessmentComprehensive knowledge of the disaster is provided through a number of information layers, as is the spatial extent of the flood/ landslide (mudslide) caused by the rainfall, the impact on population and assets, as well as information on Land Use/Cover and existing assets and infrastructures. The second refers to the efficient support of flood risk management activities and planning of prevention, protection and preparedness measures through accounting for the areas characteristics.Flood Risk Mapping - Population at RiskTowards reducing the adverse consequences of flooding for both population and economic activity (pre-disaster) flood risk assessment was implemented within a large area: the valleys connecting Futaleufú, Palena, Villa Vanguardia, Villa Santa Lucia and Puerto Cárdenas. The Hazard layer is based on the Flood Susceptibility Index, calculated from the DEM according to Samela, C., Troy, T. J., & Manfreda, S. (2017). Geomorphic classifiers for flood-prone area delineation for data-scarce environments. Advances in Water Resources, 102, 13-28.Exposure is defined on the basis of Asset Category in the areas located inside a flood hazard area. To each category a specific level of vulnerability is assigned.The vulnerability assessment of the exposed elements was calculated by considering specific criteria that characterize flood vulnerability and distinguish urban from non-urban areas.The risk level is mapped combining hazard and vulnerability through specific lookup tables.Mitigation measures are defined in terms of level of risk for bridges, urban structural and non-structural measures and river structural measures.Flood Risk Mapping - Assets and Transportation Network at Risk