This U.S. Geological Survey data release consists multiple datasets used to simulate the extents of flood inundation along the Muddy River, near Moapa, Nevada. Flood-inundation extents equal the maximum area of flood inundation and were estimated using a coupled one-dimensional (1D) and two-dimensional (2D) hydraulic model. The modeled extents represent six annual exceedance probabilities simulated for the current (2019) levee location adjacent to the Muddy River and a new levee location associated with a proposed restoration of a selected reach along the Muddy River. The data release includes: 1) a polygon dataset of the flood-inundation extents (MuddyRiver_Flood_Inundation_p.shp); 2) a zip file containing all relevant files to document and run the PeakFQ flood-frequency analysis used as input into the hydraulic model (0941600_Flood_Frequency_Archive.zip); 3) a zip file containing all relevant files to document and run the coupled 1D and 2D Hydrological Engineering Center-River Analysis System (HEC-RAS) hydraulic model used to generate a polygon dataset of flood-inundation extents (SWmodel_Archive.zip); 4) a polygon dataset of the current and proposed levee locations (MuddyRiver_Levee_p.shp); 5) a point dataset of survey points (RTK-TS_survey_x.shp) collected from April 1 to August 9, 2019, using real-time kinematic global navigation satellite system (GNSS) and total station (TS); and 6) a point dataset of seven static reference locations (Static_GNSS_x.shp) collected from March 29 to August 9, 2019, using a single-baseline online positioning user service – static (OPUS-S) GNSS survey.

We provide county and state-level flood exposure data, precipitation data, and individual flood maps for each SAR frames to understand flood exposure from the 2019 Flood of Iran at the country level utilizing 673 Sentinel-1 Synthetic Aperture Radar intensity images spanning January to February. A complete description of the method used to obtain probabilistic flood maps and exposure can be found in Sherpa and Shirzaei (2020) but is briefly stated below.

We applied a Bayesian framework to SAR intensity images to calculate the probability of a SAR pixel being flooded (Giustarini et al., 2016; Sherpa et al., 2020), for which a likelihood probability density function was estimated, thereby providing a continuous value between 0 and 1 as a probabilistic flood map. To obtain an estimate of likelihood, an image segmentation scheme using the fast marching algorithm (FMA) is implemented (Sethian, 1999). The percent area exposed to flooding is estimated as the pixel area's multiplication with its flooding probability for pixels located within each county or state divided by the county or state area. The population exposure is calculated by multiplying each county or state's percent area exposure values with their population, assuming a uniform population distribution.

Anyone wishing to use this dataset should cite Sherpa and Shrizaei (2022) and this dataset. Please also contact and contact Sonam Futi Sherpa at sfsherpa@vt.edu for any questions with details of their work, so that we may offer guidance in regard to the best usage of our produced dataset.

Sherpa, S. F., & Shirzaei, M. (2021). Country‐wide flood exposure analysis using Sentinel‐1 synthetic aperture radar data: Case study of 2019 Iran flood. Journal of Flood Risk Management, 15(1), e12770. https://doi.org/10.1111/jfr3.12770

Additional references:

Sherpa, S. F., Shirzaei, M., Ojha, C., Werth, S., & Hostache, R. (2020). Probabilistic mapping of august 2018 flood of Kerala, India, using space-borne synthetic aperture radar. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 13, 896-913. 10.1109/JSTARS.2020.2970337 Giustarini, Laura, Renaud Hostache, Dmitri Kavetski, Marco Chini, Giovanni Corato, Stefan Schlaffer, and Patrick Matgen. "Probabilistic flood mapping using synthetic aperture radar data." IEEE Transactions on Geoscience and Remote Sensing 54, no. 12 (2016): 6958-6969. Sethian, J. A. (1999). Level set methods and fast marching methods: evolving interfaces in computational geometry, fluid mechanics, computer vision, and materials science (Vol. 3). Cambridge university press.

Several software codes are provided to support the analysis of flood risk across the LA Metro: Matlab codes (.m files): These codes are to be opened in MathWorks Matlab software and include:

LAexposurecalcs.m (code to compute population and property value exposure to flood hazards) lorenz_socioeconomic.m (code to compute lorenz curves for socio-economic factors and flood hazards) lorenz_race_ethnicity.m (code to compute lorenz curves for race/ethnicity and flood hazards) municipal_hazard_NDI_FER.m (code to compute Flood Exposure Representativeness at the municipal level) cmocean.m & labelpoints.m (dependency codes for figure plotting)

The Matlab codes are designed to be executed using one of the lafloodrisk_2020_p_d_s_XX.csv files at a time. Whereby XX indicates the 100 yr flood hazard percentile (5th, 50th, or 95th). To access the lafloodrisk_2020_p_d_s_XX.csv files first decompress the lafloodrisk_2020.zip file using compression software (e.g. 7-Zip). S...

ICHARM has developed a concise flood-runoff analysis system as a toolkit for more effective and efficient flood forecasting in developing countries. This system is called Integrated Flood Analysis System (IFAS). IFAS implements interfaces to input not only ground-based but satellite-based rainfall data, GIS functions to create river channel network and to estimate parameters of a default runoff analysis engine and interfaces to display output results. ICHARM also has a plan to hold a training seminar for user to utilize IFAS effectively and to do a co-operative study with local governments, organizations, etc. ICHARM hopes that IFAS will be widely used as a tool as a basis for preparing flood forecasting and warning systems in developing countries.

Uttar Pradesh is home of 31 rivers, out of them Ganga river is the largest one. Uttar Pradesh India is prone to severe floods year after year. Here, is the data of year wise flood events from 1953 to 2017 in Uttar Pradesh. The complete data is publicly available at https://data.gov.in/resources/year-wise-statement-showing-flood-damage-india-uttar-pradesh-madhya-pradesh-and-bihar-1953

This dataset is a merged and unified one from seven individual datasets, making it the longest records ever and wide coverage in the US for flood studies. All individual databases and a unified database are provided to accommodate different user needs. It is anticipated that this database can support a variety of flood-related research, such as a validation resource for hydrologic or hydraulic simulations, climatic studies concerning spatiotemporal patterns of floods given this long-term and U.S.-wide coverage, and flood susceptibility analysis for vulnerable geophysical locations.

Description of filenames:

1. cyberFlood_1104.csv – web-based crowdsourced flood database, developed at the University of Oklahoma (Wan et al., 2014). 203 flood events from 1998 to 2008 are retrieved with the latest version. Data accessed on 11/04/2020.

Data attributes: ID, Year, Month, Day, Duration, fatality, Severity, Cause, Lat, Long, Country Code, Continent Code

2. DFO.xlsx – the Dartmouth Flood Observatory flood database. It is a tabular form of global flood database, collected from news, government agencies, stream gauges, and remote sensing instruments from 1985 to the present. Data accessed on 10/27/2020.

Data attributes: ID, GlodeNumber, Country, OtherCountry, long, lat, Area, Began, Ended, Validation, Dead, Displaced, MainCause, Severity

3. emdat_public_2020_11_01_query_uid-MSWGVQ.xlsx – Emergency Events Database (EM-DAT). This flood report is managed by the Centre for Research on the Epidemiology of Disasters in Belgium, which contains all types of global natural disasters from 1900 to the present. Data accessed on 11/01/2020.

Data attributes: Dis No, Year, Seq, Disaster Group, Disaster Subgroup, Disaster Type, Disaster Subtype, Disaster Subsubtype, Event Nane, Entity Criteria, Country, ISO, Region, Continent, Location, Origin, Associated Disaster, Associated Disaster2, OFDA Response, Appeal, Declaration, Aid Contribution, Disaster Magnitude, Latitude, Longitude, Local Time, River Basin, Start Year, Start Month, Start Day, End Year, End Month, End Day, Total Death, No. Injured, No. Affected, No. Homeless, Total Affected, Reconstruction, Insured Damages, Total Damages, CPI

4. extracted_events_NOAA.csv – The national weather service storm reports. The NOAA NWS team collects weather-related natural hazards from 1950 to the present. Data accessed on 10/27/2020.

Data attributes: BEGIN_YEARMONTH, BEGIN_DAY, BEGIN_TIME, END_YEARMONTH, END_DAY, END_TIME, EPISODE_ID, EVENT_ID, STATE, STATE_FIPS, YEAR, MONTH_NAME, EVENT_TYPE, CZ_TYPE, CZ_FIPS, CZ_NAME, WFO, BEGIN_DATETIME, CZ_TIMEZONE, END_DATE_TIME, INJURIES_DIRECT, INJURIES_INDIRECT, DEATHS_DIRECT, DEATHS_INDIRECT, DAMAGE_PROPERTY, DAMAGE_CROPS, SOURCE, MAGNITUDE, MAGNITUDE_TYPE, FLOOD CAUSE, CATEGORY, TOR_F_SCALE< TOR_LENGTH, TOR_WIDTH, TOR_OTHER_WFO, TOR_OTHER_CZ_STATE, TOR_OTHER_CZ_FIPS, BEGIN_RANGE, BEGIN_AZIMUTH, BEGIN_LOCATION, END_RANGE, END_AZIMUTH, END_LOCATION, BEGIN_LAT, BEGIN_LON, END_LAT, END_LON, EPISODE_NARRATIVE, EVENT_NARRATIVE, DATA_SOURCE

5. FEDB_1118.csv – The University of Connecticut Flood Events Database. Floods retrieved from 6,301 stream gauges in the U.S. after flow separation from 2002 to 2013 (Shen et al., 2017). Data accessed on 11/18/2020.

Data attributes: STCD, StartTimeP, EndTimeP, StartTimeF, EndTimeF, Perc, Peak, RunoffCoef, IBF, Vp, Vb, Vt, Pmean, ETr, ELs, VarTr, VarLs, EQ, Q2, CovTrLs, Category, Geometry

6. GFM_events.csv – Global Flood Monitoring dataset. It is a crowdsourcing flood database derived from Twitter tweets over the globe since 2014. Data accessed on 11/9/2020.

Data attributes: event_id, location_ID, location_ID_url, name, type, country_location_ID, country_ISO3, start, end, time of detection

7. mPing_1030.csv – meteorological Phenomena Identification Near the Ground (mPing). The mPing app is a crowdsourcing, weather-reporting software jointly developed by NOAA National Severe Storms Laboratory (NSSL) and the University of Oklahoma (Elmore et al., 2014). Data accessed on 10/30/2020.

Data attributes: id, obtime, category, description, description_id, lon, lat

8. USFD_v1.0.csv – A merged United States Flood Database from 1900 to the present.

Data attributes: DATE_BEGIN, DATE_END, DURATION, LON, LAT, COUNTRY, STATE, AREA, FATALITY, DAMAGE, SEVERITY, SOURCE, CAUSE, SOURCE_DB, SOURCE_ID, DESCRIPTION, SLOPE, DEM, LULC, DISTANCE_RIVER, CONT_AREA, DEPTH, YEAR.

Details of attributes:

DATE_BEGIN: begin datetime of an event. yyyymmddHHMMSS

DATE_END: end datetime of an event. yyyymmddHHMMSS

DURATION: duration of an event in hours

LON: longitude in degrees

LAT: latitude in degrees

COUNTRY: United States of America

STATE: US state name

AREA: affected areas in km^2

FATALITY: number of fatalities

DAMAGE: economic damages in US dollars

SEVERITY: event severity, (1/1.5/2) according to DFO.

SOURCE: flood information source.

CAUSE: flood cause.

SOURCE_DB: source database from item 1-7.

SOURCE_ID: original ID in the source database.

DESCRIPTION: event description

SLOPE: calculated slope based on SRTM DEM 90m

DEM: Digital Elevation Model

LULC: Land Use Land Cover

DISTANCE_RIVER: distance to major river network in km,

CONT_AREA: contributing area (km^2), from MERIT Hydro

DEPTH: 500-yr flood depth

YEAR: year of the event.

The script to merge all sources and figure plots can be found in https://github.com/chrimerss/USFD.

Current estimates of the magnitude and frequency of floods at gaged and ungaged stream sites are critical for assessing flood risk, delineating flood zones, designing hydraulic structures, and managing flood plains. The Connecticut Department of Transportation collaborated with U.S. Geological Survey (USGS) in a study to improve the flood-frequency estimates in Connecticut and develop regional regression equations for estimating annual exceedance probability discharges at ungaged sites in Connecticut. The results of the study are found in Scientific Investigations Report (add link). This companion data release consists of data compiled and used for the flood-frequency analysis of annual peak flows and development of regional regression equations for Connecticut. The data release is composed of four tables that include: 1) descriptions and locations of streamgages for flood frequency analysis of annual peak flows; 2) updated flood discharges for 152 streamgages for the 50-, 20-, 10-, 4-, 2-, 1-, 0.5-, and 0.2-percent annual exceedance probabilities (AEP) using annual peak data through water year 2015; 3) representation of peak flow data using perception thresholds and interval data in the flood frequency analysis to describe uncertainty and represent missing and historical records; and 4) basin and climatic characteristics that were included as explanatory variables in the regression equations.

Flood Prediction SaaS Market Outlook

According to our latest research, the global Flood Prediction SaaS market size reached USD 1.37 billion in 2024, reflecting the rapid adoption of advanced cloud-based analytics in disaster risk management. The market is projected to grow at a robust CAGR of 17.2% from 2025 to 2033, reaching an estimated USD 5.12 billion by 2033. This impressive growth is primarily driven by the increasing frequency and severity of flood events worldwide, coupled with the urgent need for real-time, data-driven solutions to mitigate the impact of flooding. As per our 2025 research, the integration of artificial intelligence, machine learning, and IoT technologies into SaaS-based flood prediction platforms is revolutionizing the way governments, insurers, and enterprises prepare for and respond to flood risks.

A major growth factor for the Flood Prediction SaaS market is the escalating impact of climate change, which has led to a surge in unpredictable and extreme weather events globally. The growing incidence of riverine, urban, and coastal floods has underscored the necessity for advanced predictive solutions that can offer timely warnings and actionable insights. Governments and public sector agencies are increasingly investing in sophisticated SaaS platforms that leverage big data, satellite imagery, and real-time sensor networks to enhance flood forecasting accuracy. These platforms not only provide early warnings but also enable dynamic risk assessment, resource allocation, and emergency response planning, making them indispensable tools in modern disaster management strategies.

Another significant driver propelling the growth of the Flood Prediction SaaS market is the rising adoption of cloud computing and the proliferation of IoT-enabled devices. Cloud-based SaaS solutions offer unparalleled scalability, flexibility, and accessibility, allowing end-users to deploy predictive analytics tools without the need for substantial on-premises infrastructure investments. The integration of IoT sensors in river basins, urban drainage systems, and coastal areas provides a continuous stream of real-time data, which, when analyzed through advanced SaaS platforms, enhances the precision of flood forecasting models. Moreover, the subscription-based pricing model of SaaS makes these solutions cost-effective for a wide range of users, from small municipalities to large enterprises, further accelerating market penetration.

The increasing involvement of the private sector, particularly in insurance, agriculture, and utility industries, is also fueling the expansion of the Flood Prediction SaaS market. Insurance companies are leveraging these platforms to refine risk assessment models, optimize premium pricing, and expedite claims processing in the aftermath of flood events. Similarly, agricultural enterprises utilize flood prediction SaaS to safeguard crops and infrastructure, while utilities and transportation sectors rely on these solutions to minimize service disruptions and infrastructure damage. The growing awareness of the tangible economic and operational benefits offered by predictive flood analytics is driving widespread adoption across multiple verticals, creating a robust and diversified customer base for SaaS providers.

Regionally, North America and Europe are leading the adoption of Flood Prediction SaaS solutions, driven by stringent regulatory frameworks, advanced technological infrastructure, and high investments in disaster risk reduction. However, the Asia Pacific region is expected to witness the fastest growth during the forecast period, owing to its vulnerability to frequent and severe flooding events, rapid urbanization, and increasing government initiatives to strengthen climate resilience. Latin America and the Middle East & Africa are also emerging as promising markets, with growing recognition of the need for modern flood management solutions amidst changing climate patterns and urban expansion.

Component Analysis

The Flood Prediction SaaS market

This map represents Flood Insurance Rate Map (FIRM) data important for floodplain management, mitigation, and insurance activities for the National Flood Insurance Program (NFIP). The National Flood Hazard Layer (NFHL) data present the flood risk information depicted on the FIRM in a digital format suitable for use in electronic mapping applications. The NFHL database is a subset of the information created for the Flood Insurance Studies (FIS) and serves as a means to archive a portion of the information collected during the FIS. The NFHL data incorporates Digital Flood Insurance Rate Map (DFIRM) databases published by Federal Emergency Management Agency (FEMA). The 100-year flood is referred to as the 1% annual exceedance probability flood, since it is a flood that has a 1% chance of being equaled or exceeded in any single year. The primary risk classifications used are the 1-percent-annual-chance flood event, the 0.2-percent-annual-chance flood event, and areas of minimal flood risk. The 1% annual chance (base flood) is the flood that has a 1% chance of being equaled or exceeded in any year. The Special Flood Hazard area is the area subject to flooding by the 1% annual chance flood. Areas of Special Flood Hazard include Zones A, AE, AH, AO, AR, A99, D, V, VE, and X. These flood zones are explained below and reflects the severity or type of flooding in the area. A - Zone A is the flood insurance rate zone that corresponds to the 1-percent annual chance floodplains that are determined in the Flood Insurance Study by approximate methods of analysis. Because detailed hydraulic analyses are not performed for such areas, no Base Flood Elevations or depths are shown within this zone. Mandatory flood insurance purchase requirements apply. AE and A1-A30 - Zones AE and A1-A30 are the flood insurance rate zones that correspond to the 1-percent annual chance floodplains that are determined in the Flood Insurance Study by detailed methods of analysis. In most instances, Base Flood Elevations derived from the detailed hydraulic analyses are shown at selected intervals within this zone. Mandatory flood insurance purchase requirements apply. AH - Zone AH is the flood insurance rate zone that corresponds to the areas of 1-percent annual chance shallow flooding with a constant water-surface elevation (usually areas of ponding) where average depths are between 1 and 3 feet. The Base Flood Elevations derived from the detailed hydraulic analyses are shown at selected intervals within this zone. Mandatory flood insurance purchase requirements apply. AO - Zone AO is the flood insurance rate zone that corresponds to the areas of 1-percent shallow flooding (usually sheet flow on sloping terrain) where average depths are between 1 and 3 feet. Average flood depths derived from the detailed hydraulic analyses are shown within this zone. In addition, alluvial fan flood hazards are shown as Zone AO on the Flood Insurance Rate Map. Mandatory flood insurance purchase requirements apply. AR - Zone AR is the flood insurance rate zone used to depict areas protected from flood hazards by flood control structures, such as a levee, that are being restored. FEMA will consider using the Zone AR designation for a community if the flood protection system has been deemed restorable by a Federal agency in consultation with a local project sponsor; a minimum level of flood protection is still provided to the community by the system; and restoration of the flood protection system is scheduled to begin within a designated time period and in accordance with a progress plan negotiated between the community and FEMA. Mandatory purchase requirements for flood insurance will apply in Zone AR, but the rate will not exceed the rate for an unnumbered Zone A if the structure is built in compliance with Zone AR floodplain management regulations. A99 - Zone A99 is the flood insurance rate zone that corresponds to areas within the 1-percent annual chance floodplain that will be protected by a Federal flood protection system where construction has reached specified statutory milestones. No Base Flood Elevations or depths are shown within this zone. Mandatory flood insurance purchase requirements apply. D - Zone D designation is used for areas where there are possible but undetermined flood hazards. In areas designated as Zone D, no analysis of flood hazards has been conducted. Mandatory flood insurance purchase requirements do not apply, but coverage is available. The flood insurance rates for properties in Zone D are commensurate with the uncertainty of the flood risk. V - Zone V is the flood insurance rate zone that corresponds to areas within the 1-percent annual chance coastal floodplains that have additional hazards associated with storm waves. Because approximate hydraulic analyses are performed for such areas, no Base Flood Elevations are shown within this zone. Mandatory flood insurance purchase requirements apply. VE - Zone VE is the flood insurance rate zone that corresponds to areas within the 1-percent annual chance coastal floodplain that have additional hazards associated with storm waves. Base Flood Elevations derived from the detailed hydraulic analyses are shown at selected intervals within this zone. Mandatory flood insurance purchase requirements apply. X - Zone X is the flood insurance rate zones that correspond to areas outside the 1-percent annual chance floodplain – Areas protected from the 1-percent annual chance flood by levees. No Base Flood Elevations or depths are shown within this zone. Insurance purchase is not required in these zones. More information about the flood zones can be found here. The NFHL data are derived from Flood Insurance Studies (FISs), previously published Flood Insurance Rate Maps, flood hazard analyses performed in support of the FISs and FIRMs, and new mapping data where available. The NFHL data is available at State level. The data is updated on monthly basis and FEMA is in the process of mapping all the flood zones and so some counties do not have complete data. For better visualization, it’s recommended to display the service with 50% transparency. The map service has a county layer that helps differentiate between the counties that have flood data available and those that do not. The flood data is scale dependent and is set to show from 1:3,000,000. This data is as of March 2011.

Analysis of ‘Local Maintenance Areas Flood Protection’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/fb4b3996-ca6d-4135-b493-37c67fdacc90 on 28 January 2022.

--- Dataset description provided by original source is as follows ---

In California, there are a variety of political entities that are granted self-taxation powers under various California codes in order to perform the basic goal of flood management within an area. This dataset compiles many of the various datasets together to provide the information in one location. It also includes districts that are no longer active political/management entities for archival or historical purposes. The primary type of flood agency in California are known as reclamation districts, and so represent the majority of the records in this database. The quality of the boundary accuracy is highly variable, due to a variety of reasons, including the fact that the original legal boundaries are frequently tied to Swamp Land Survey boundaries that themselves are poorly located by modern mapping standards. This set of boundary delineations represents the latest in a series of nearly 20 significant revisions primarily by DWR Delta Levees Program between 2000-2017 to a dataset first produced by Office of Emergency Services during the 1997 floods. The accuracy and completeness of the data are therefore higher in the Delta than elsewhere. The Division of Flood Management then stored the boundaries in their levee geodatabase that feeds the web mapping application known as FERIX. To produce this final dataset, in 2018 the Division of Engineering Geodetic Branch merged the data used by FERIX, along with other datasets used by the Delta Levees Program, and normalized the attribute table.

--- Original source retains full ownership of the source dataset ---

The National Flood Hazard Layer (NFHL) data incorporates all Digital Flood Insurance Rate Map(DFIRM) databases published by FEMA, and any Letters Of Map Revision (LOMRs) that have been issued against those databases since their publication date. The DFIRM Database is the digital, geospatial version of the flood hazard information shown on the published paper Flood Insurance Rate Maps(FIRMs). The primary risk classifications used are the 1-percent-annual-chance flood event, the 0.2-percent-annual-chance flood event, and areas of minimal flood risk. The NFHL data are derived from Flood Insurance Studies (FISs), previously published Flood Insurance Rate Maps (FIRMs), flood hazard analyses performed in support of the FISs and FIRMs, and new mapping data where available. The FISs and FIRMs are published by the Federal Emergency Management Agency (FEMA). The specifications for the horizontal control of DFIRM data are consistent with those required for mapping at a scale of 1:12,000. The NFHL data contain layers in the Standard DFIRM datasets except for S_Label_Pt and S_Label_Ld. The NFHL is available as State or US Territory data sets. Each State or Territory data set consists of all DFIRMs and corresponding LOMRs available on the publication date of the data set.

This dataset was generated by the Remote Sensing Group of the TU Wien Department of Geodesy and Geoinformation (https://mrs.geo.tuwien.ac.at/), within a dedicated project by the Join Research Centre (JRC) of the European Commission. Open use is granted under the CC BY 4.0 license.

End of summer 2022 Pakistan was hit by one of the most severe floods in decades. The event was covered by multiple satellite-based emergency services, including the Copernicus Emergency Management Service (CEMS) global flood mapping (GFM) component. As part of the project's consortium, the Technische Universität Wien (TU Wien) developed a dedicated flood mapping algorithm (Bauer-Marschallinger et al. 2022) using the Synthetic Aperture Radar (SAR) satellite Sentinel-1 as an input. The published dataset contains the results of the TU Wien algorithm for the time period August 10 to September 23, 2022 and the covered area is located in the southern part of Pakistan. Besides the binary flood maps, the dataset contains retrieved statistics aiming for presenting the impact of the event as seen from satellite data. With the publication of this dataset, we want to share timely results of our algorithm and support further studies about the event.

It is planned to publish the dataset alongside of a dedicated paper in the journal for "Natural Hazards and Earth System Sciences". Within in this publication, the flood mapping results were evaluated based on the results of the CEMS rapid mapping component.

Dataset Record

Flood mapping results

The flood mapping results (FLOOD-HM-MASKED.zip) are sampled at 20 m pixel spacing, georeferenced to the Equi7Grid and divided into tile of 300km extent ("T3"-tiles). The used folder structure splits up the single file per Equi7Grid tile and the used filenaming can be interpreted as follows:

VAR_TIME_POL_ORBIT_TILE_GRID_VERSION_SENSOR_CREATOR.tif

Where:

• VAR: Variable name ("FLOOD-HM-MASKED")
• TIME: Acquisition time of the used Sentinel-1 scene
• POL: Polarisation of the used Sentinel-1 scene ("VV")
• ORBIT: Orbit direction ("A" for ascending and "D" for descending) and relativ orbit number of the used Sentinel-1 scene
• TILE: Equi7Grid tile code
• GRID: Equi7Grid continent subgrid
• VERSION: Software version and run number
• SENSOR: Used sensor ("S1")
• CREATOR: Creator of the file ("TUWIEN")

The values of each files can be interpreted like this:

• 0: no flood
• 1: flood
• 255: masked

Flood statistics

The dataset consists of two statistical layers: the flood frequency (flood_frequency.tif) and the time of the first flood detection (first_detection.tif). Both layers are available as merged file for the whole study area and georeferences in the WGS84 coordinate system.

The flood frequency is known as the ratio of number of flood detection and number of valid observations of a pixel and is given in percentage in this case. It provides insights about the continuity and duration of a flood classification at a pixel level. For instance, the area which was flooded at least once or during the whole time period can be extracted.

The time of the first flood detection is given as day-of-year (DOY) and indicates the day when the first flood detection was found for a specific pixel. This information can be used to get insights about the progress of the flood.

Related Software

• The flood mapping results are provided in the Equi7Grid, which is accessible by its dedicated open-source package: https://github.com/TUW-GEO/Equi7Grid
• The folder structure and filenaming of the flood mapping results is defined and handled by: https://github.com/TUW-GEO/geopathfinder
• The metadata definition of the flood mapping results is collected in: https://github.com/TUW-GEO/medali
• The flood mapping results follow a dedicated folder and filenaming structure, which can be handled easily by: https://github.com/TUW-GEO/yeoda

Acknowledgements

This study was funded by TU Wien, with co-funding from the project "Provision of an Automated, Global, Satellite-based Flood Monitoring Product for the Copernicus Emergency Management Service" (GFM), Contract No. 939866-IPR-2020 for the European Commission's Joint Research Centre (EC-JRC). The computational results presented have been achieved using i.a. the Vienna Scientific Cluster (VSC).

References

Bauer-Marschallinger, B., Cao, S., Tupas, M. E., Roth, F., Navacchi, C., Melzer, T., Freeman, V., and Wagner, W.: Satellite-Based Flood Mapping through Bayesian Inference from a Sentinel-1 SAR Datacube, Remote Sensing, 14, 3673, 2022.

This is "Flood vectors - ALOS PALSAR (31 July 2015)" of the Flood analysis for Viet Nam which began on 30 July 2015. It includes 22,650 satellite detected water bodies with a spatial extent of 563.87 square kilometers derived from the ALOS PALSAR image ac...

Market Overview: The global Flood Warning System market is projected to reach a value of USD 1.37 billion by 2033, expanding at a CAGR of 6.9%. This growth is driven by factors such as rising concerns over flooding disasters, increasing adoption of advanced technologies like IoT and data analytics for flood monitoring, and government initiatives to enhance disaster preparedness. The market is segmented based on technology, end use, component, deployment type, and region. North America and Europe currently hold significant market shares, while Asia Pacific is expected to witness substantial growth during the forecast period. Market Trends: Emerging trends in the Flood Warning System market include the integration of artificial intelligence (AI) and machine learning (ML) for more accurate flood predictions, the adoption of cloud-based platforms for increased data accessibility and scalability, and the development of mobile apps for real-time flood warnings and evacuation guidance. Moreover, governments and organizations are investing in research and development to enhance the capabilities of flood warning systems, including the incorporation of satellite imagery, social media data analysis, and crowdsourced information. These advancements are expected to improve the accuracy and effectiveness of flood warning systems, leading to better disaster preparedness and reduced socioeconomic impacts. Flood Warning System Market is expected to reach $XX Billion by 2027, from $XX Billion in 2021, at a CAGR of XX% over the forecast period 2022-2027. The flood warning system market is driven by the increasing frequency of natural disasters, growing awareness of flood risks, aging infrastructure, and technological advancements. The market is also expected to benefit from government initiatives and regulations aimed at improving flood preparedness and response. The main factor driving the growth of the market is the increasing frequency of natural disasters. Climate change is leading to more extreme weather events, such as hurricanes, floods, and droughts. These events can cause significant damage to property and infrastructure, and can lead to loss of life. Recent developments include: The Flood Warning System Market is witnessing notable advancements and developments as companies strive to enhance flood prediction and response capabilities. Recent collaborations among key players such as Thales Group, Veolia, and DHI have focused on integrating artificial intelligence and IoT technologies to improve data accuracy and real-time monitoring. Intergraph and Aquascope are advancing their software solutions to facilitate better flood risk assessment, while Sierra Monitor Corporation is exploring innovative sensor technologies to provide precise environmental data., Furthermore, mergers and acquisitions are shaping the market landscape, with reported activities hinting at a strategic consolidation among these companies to enhance their technological offerings. The Weather Company's partnership with other analytics firms aims to leverage big data for enhanced weather forecasting, thereby enabling smarter flood warning systems. Overall, the growth in valuation of companies like RMS, Fathom, and SkyAlert is indicative of increasing investments in flood management solutions, reflecting the rising urgency for effective flood prevention and response driven by climate change and urbanization trends. These developments are influencing market dynamics, pushing for sustainable growth and robust technological evolution within the flood warning sector.. Key drivers for this market are: 1. Advancements in IoT technology, 2. Integration with smart city systems; 3. Increased government funding and regulations; 4. Growing demand for real-time data; 5. Rising climate change awareness and impact. Potential restraints include: 1. technological advancements, 2. increasing climate change impact; 3. government regulations and funding; 4. rising urbanization and population density; 5. public awareness and preparedness initiatives.

Digital flood-inundation maps for a 7.5-mile reach of the White River at Noblesville, Indiana, were created by the U.S. Geological Survey (USGS) in cooperation with the Indiana Department of Transportation. The flood-inundation maps, which can be accessed through the USGS Flood Inundation Mapping Science Web site at http://water.usgs.gov/osw/flood_inundation/, depict estimates of the areal extent and depth of flooding corresponding to selected water levels (stages) at the USGS streamgage 03349000, White River at Noblesville, Ind. Real-time stages at this streamgage may be obtained on the Internet from the USGS National Water Information System at http://waterdata.usgs.gov/nwis or the National Weather Service (NWS) Advanced Hydrologic Prediction Service at http:/water.weather.gov/ahps/, which also forecasts flood hydrographs at this site (NWS site NBLI3). Flood profiles were computed for the stream reach by means of a one-dimensional, step-backwater hydraulic modeling software developed by the U.S. Army Corps of Engineers. The hydraulic model was calibrated using the current stage-discharge rating at the USGS streamgage 03349000, White River at Noblesville, Ind. and documented high-water marks from the floods of September 4, 2003 and May 6, 2017. The hydraulic model was then used to compute 15 water-surface profiles for flood stages at 1-foot (ft) intervals referenced to the streamgage datum ranging from 10.0 ft (the NWS “action stage”) to 24.0 ft, which is the highest stage interval of the current USGS stage-discharge rating curve and 2 ft higher than the NWS “major flood stage.” The simulated water-surface profiles were then combined with a Geographic Information System digital elevation model (derived from light detection and ranging [lidar] data having a 0.98-foot vertical accuracy and 4.9-foott horizontal resolution) to delineate the area flooded at each stage. The availability of these maps, along with Internet information regarding current stage from the USGS streamgage and forecasted high-flow stages from the NWS, will provide emergency management personnel and residents with information that is critical for flood response activities such as evacuations and road closures, as well as for post-flood recovery efforts.

Analysis of ‘Flood Zone B’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from http://data.europa.eu/88u/dataset/b30df1aa-688a-4239-9ac1-b29f20f6eae5 on 12 January 2022.

--- Dataset description provided by original source is as follows ---

Strategic Flood Risk Assessments generated as part of Development Plan / Local Area Plans

--- Original source retains full ownership of the source dataset ---

This is "Flood vectors - MODIS-Aqua (12 March 2008)" of the Flood analysis for Ecuador which began on 10 March 2009. It includes 1 satellite detected water bodies with a spatial extent of 2,318.74 square kilometers derived from the MODIS-Aqua image acquir...

Analysis of ‘European Coastal Flood Risk’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from http://data.europa.eu/88u/dataset/jrc-liscoast-10009 on 07 January 2022.

--- Dataset description provided by original source is as follows ---

In this study we present the results of the coastal flood risk assessment for Europe until the end of the 21st century, by incorporating the impacts of global warming and the different socio-economic development scenarios. The effect of climate change at the extreme total sea level is the main trigger of the increasing coastal inundation and the related losses at the coastal zone. In the absence of further investments on coastal flood protection, the present Expected Annual Damage (EAD) is projected to increase by 2 to 3 orders of magnitude by the end of the century, while the current Expected Annual Number of People Exposed to coastal flooding (EAPE) is projected to reach up to 3.65 million people by that time.

--- Original source retains full ownership of the source dataset ---