Groundwater is the water that soaks into the ground from rain and can be stored beneath the ground. Groundwater floods occur when the water stored beneath the ground rises above the land surface. The Historic Groundwater Flood Map shows the observed peak flood extents caused by groundwater in Ireland. This map was made using satellite images (Copernicus Programme Sentinel-1), field data, aerial photos, as well as flood records from the past. Most of the data was collected during the flood events of winter 2015 / 2016, as in most areas this data showed the largest floods on record.This map is to the scale 1:20,000. This means it should be viewed at that scale. When printed at that scale 1cm on the map relates to a distance of 200m.The map is a vector dataset. Vector data portray the world using points, lines, and polygons (area). The floods are shown as polygons. Each polygon has info about the type of flood, the data source, and the area of the flood.The flood extents were calculated using data and techniques with various precision levels, and as such, it may not show the true historic peak flood extents.The Winter 2015/2016 Surface Water Flooding map shows fluvial (rivers) and pluvial (rain) floods, excluding urban areas, during the winter 2015/2016 flood event, and was developed as a by-product of the historic groundwater flood map.This map is to the scale 1:20,000. This means it should be viewed at that scale. When printed at that scale 1cm on the map relates to a distance of 200m.The map is a vector dataset. The floods are shown as polygons. Each polygon has info about the type of flood, the data source, and the area of the flood.The flood extents were made using remote sensing images (Copernicus Programme Sentinel-1), which covered any site in Ireland every 4-6 days. As such, it may not show the true peak flood extents.The Synthetic Aperture Radar (SAR) Seasonal Flood Maps shows observed peak flood extents which took place between Autumn 2015 and Summer 2021. The maps were made using Synthetic Aperture Radar (SAR) images from the Copernicus Programme Sentinel-1 satellites. SAR systems emit radar pulses and record the return signal at the satellite. Flat surfaces such as water return a low signal. Based on this low signal, SAR imagery can be classified into non-flooded and flooded (i.e. flat) pixels.Flood extents were created using Python 2.7 algorithms developed by Geological Survey Ireland. They were refined using a series of post processing filters. Please read the lineage for more information.The flood maps shows flood extents which have been observed to occur. A lack of flooding in any part of the map only implies that a flood was not observed. It does not imply that a flood cannot occur in that location at present or in the future.This flood extent are to the scale 1:20,000. This means they should be viewed at that scale. When printed at that scale 1cm on the maps relates to a distance of 200m.They are vector datasets. Vector data portray the world using points, lines, and polygons (areas). The flood extents are shown as polygons. Each polygon has information on the confidence of the flood extent (high, medium or low), a flood id and a unique id.The Groundwater Flooding High Probability map shows the expected flood extent of groundwater flooding in limestone regions for annual exceedance probabilities (AEP’s) of 10%, which correspond with a return period of every 10 years. The map was created using groundwater levels measured in the field, satellite images and hydrological models.This map is to the scale 1:20,000. This means it should be viewed at that scale. When printed at that scale 1cm on the map relates to a distance of 200m.The map is a vector dataset. The floods are shown as polygons. Each polygon has info on the data source, and the area of the flood.The flood extents were calculated using remote sensing data and hydrological modelling techniques with various precision levels. As such, it should be used with caution.The Groundwater Flooding Medium Probability map shows the expected flood extent of groundwater flooding in limestone regions for annual exceedance probabilities (AEP’s) of 1%, which correspond with a return period of every 100 years. The map was created using groundwater levels measured in the field, satellite images and hydrological models.This map is to the scale 1:20,000. This means it should be viewed at that scale. When printed at that scale 1cm on the map relates to a distance of 200m.The map is a vector dataset. The floods are shown as polygons. Each polygon has info on the data source, and the area of the flood.The flood extents were calculated using remote sensing data and hydrological modelling techniques with various precision levels. As such, it should be used with caution.The Groundwater Flooding Low Probability map shows the expected flood extent of groundwater flooding in limestone regions for annual exceedance probabilities (AEP’s) of 0.1%, which correspond with a return period of every 1000 years.The map was created using groundwater levels measured in the field, satellite images and hydrological models.This map is to the scale 1:20,000. This means it should be viewed at that scale. When printed at that scale 1cm on the map relates to a distance of 200m.The map is a vector dataset. Vector data portray the world using points, lines, and polygons (area). The floods are shown as polygons. Each polygon has info on the data source, and the area of the flood.The flood extents were calculated using remote sensing data and hydrological modelling techniques with various precision levels. As such, it should be used with caution.

The Localized Flood Map for Climate Vulnerability Screening layer shows potential surface flooding locations in the landscape for the Twin Cities Metro area. These locations, called bluespots, are areas that may be subject to flood during short-term, extreme rain events. The Council's local flood screening tool uses information about the topography of the earth contained in the State of Minnesota's 3-meter digital elevation model (DEM) built from the state's LiDAR effort. Localized flooding locations are determined solely based on depressions in the DEM; no data of existing stormwater infrastructure is considered because this information does not currently exist at a regional scale. This layer should only be used as a screening tool. A low spot shown as a bluespot on this map does not indicate that the area will definitively flood; instead, the area has the potential to flood if a rain event is intense enough and stormwater infrastructure not sufficient.

For more information, visit the Council's Climate Vulnerability Assessment website at: www.metrocouncil.org/cva.

These polygon boundaries, inundation extents, and depth rasters were created to provide an extent of flood inundation along the Neuse River within the community of Kinston, North Carolina. The upstream and downstream reach extent is determined by the location of high-water marks, not extending the boundary far past the outermost high-water marks. In areas of uncertainty of flood extent, the model boundary is lined up with the flood inundation polygon extent. This boundary polygon was used to extract the final flood inundation polygon and depth layer from the flood water surface raster file. The passage of Hurricane Matthew through central and eastern North Carolina during October 7-9, 2016, brought heavy rainfall which resulted in major flooding. More than 15 inches of rain were recorded in some areas. Over 600 roads were closed including Interstates 95 and 40, and nearly 99,000 structures were impacted by floodwaters. Immediately after the flooding, the U.S. Geological Survey (USGS) documented 267 high-water marks (HWM), of which 254 were surveyed. The North Carolina Emergency Management documented and surveyed 353 HWMs. Six communities were mapped using Geographic Information Systems.

Flood tactical maps have currently been developed for the English River, Rainy River, Montreal River, Black River, Trent River, Madawaska, Magnetawan, Muskoka, Mississippi Valley, French, Sturgeon and Nippissing watersheds. The purpose of these maps is to show more succinctly the physiography of the region, the individual river watersheds, ongoing monitoring, location of dams, high risk dams/reservoirs and communities.

Status

Completed: Production of the data has been completed

Maintenance and Update Frequency

Not Stated

Contact

Surface Water Monitoring Centre, Divisional Delivery Branch, Surface.Water@ontario.ca

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

This city's range and depth of possible waterlogging under short-term heavy rainfall conditions (78.8mm/h, 100mm/h, 130mm/h).(*Surface runoff below 15 cm in depth is not displayed on the map) The map data is in KML format, and the layer spatial reference system coordinates are WGS84 coordinates.The map data is based on the design rainfall conditions and specific year topographic data, and is obtained through numerical simulation using an objective hydraulic model. Due to the extremely uneven spatial distribution of actual rainfall due to topographic effects and atmospheric conditions at the time, and the uncertainty of meteorological and hydrological conditions, the map data cannot simulate the actual flooding situation of future individual typhoon flood events. Please pay special attention to this when using it for reference.In addition, according to the "Regulations for the Release of Flood Potential Data" of the Ministry of Economic Affairs and the explanation of the flood potential map on the "Government Open Data Platform" by the Water Resources Agency, the flood potential map is only for use in disaster prevention and relief-related businesses, and is not suitable as a basis for land use control, land use, and other related measures.

Flash flooding is the top weather-related killer, responsible for an average of 140 deaths per year across the United States. Although precipitation forecasting and understanding of flash flood causes have improved in recent years, there are still many unknown factors that play into flash flooding. Despite having accurate and timely rainfall reports, some river basins simply do not respond to rainfall as meteorologists might expect. The Flash Flood Potential Index (FFPI) was developed in order to gain insight into these “problem basins”, giving National Weather Service (NWS) meteorologists insight into the intrinsic properties of a river basin and the potential for swift and copious rainfall runoff.

The U.S. Geological Survey (USGS), in cooperation with the city of Harrisonville, Missouri, assessed flooding of Muddy Creek resulting from varying precipitation magnitudes and durations, antecedent soil moisture conditions, and channel conditions. The precipitation scenarios were used to develop a library of flood-inundation maps that included a 3.8-mile reach of Muddy Creek and tributaries within and adjacent to the city. Hydrologic and hydraulic models of the upper Muddy Creek Basin were used to assess streamflow magnitudes associated with simulated precipitation amounts and the resulting flood-inundation conditions. The U.S. Army Corps of Engineers Hydrologic Engineering Center-Hydrologic Modeling System (HEC–HMS; version 4.4.1) was used to simulate the amount of streamflow produced from a range of rainfall events. The Hydrologic Engineering Center-River Analysis System (HEC–RAS; version 5.0.7) was then used to route streamflows and map resulting areas of flood inundation. The hydrologic and hydraulic models were calibrated to the September 28, 2019; May 27, 2021; and June 25, 2021, runoff events representing a range of antecedent moisture conditions and hydrologic responses. The calibrated HEC–HMS model was used to simulate streamflows from design rainfall events of 30-minute to 24-hour durations and ranging from a 100- to 0.1-percent annual exceedance probability. Flood-inundation maps were produced for USGS streamflow stages of 1.0 feet (ft), or near bankfull, to 4.0 ft, or a stage exceeding the 0.1-percent annual exceedance probability interval precipitation, using the HEC–RAS model. The consequence of each precipitation duration-frequency value was represented by a 0.5-ft increment inundation map based on the generated peak streamflow from that rainfall event and the corresponding stage at the Muddy Creek stage reference _location. Seven scenarios were developed with the HEC–HMS hydrologic model with resulting streamflows routed in a HEC-RAS hydraulic model and these scenarios varied by antecedent soil-moisture and channel conditions. The same precipitation scenarios were used in each of the seven antecedent moisture and channel conditions and the simulation results were assigned to a flood-inundation map condition based on the generated peak flow and corresponding stage at the Muddy Creek reference _location. This data release includes: 1) tables summarizing the model results including the flood-inundation map condition of each model scenario for dry (CNI; Muddy_Creek_summary_table_1_1.csv), normal (CNII; Muddy_Creek_summary_table_1_2.csv), and wet (CNIII; Muddy_Creek_summary_table_1_3.csv) antecedent soil moisture conditions (MuddyCreek_summary_tables.zip); 2) a shapefile dataset of the streamflow inundation extents at Muddy Creek reference _location stages of 1.0 to 4.0 feet (MuddyCreek_inundation_extents.zip containing MudHarMO.shp); 3) a raster dataset of the streamflow depths at Muddy Creek reference _location stages of 1.0 to 4.0 feet (MuddyCreek_inundation_depths.zip containing MudharMO_X.tif where X = 1,2,3,4,5,6,7 corresponding to inundation map stages of 1.0, 1.5 , 2.0, 2.5, 3.0, 3.5, 4.0 feet)); 4) tables of hydrologic and hydraulic model performance and calibration metrics, locations of continuous pressure transducers (PTs; MuddyCreek_PT_locations.zip) and high-water marks (HWMs; MuddCreek_HWM_locations.zip) used in assessment of model calibration and validation, and time series of pressure transducer data (MuddyCreek_PT_time_series.zip) found in MuddyCreek_model_performance_calibration_metrics.zip; 5) hydrologic and hydraulic model run files used in the simulation of dry hydrologic response conditions (CN_I conditions) and effects of proposed detention storage (MuddyCreek_dry_detention.zip); 6) hydrologic and hydraulic model run files used in the simulation and calibration of dry hydrologic response conditions (CN_I conditions) and current (2019) existing channel conditions (MuddyCreek_dry_existing_conditions.zip); 7) hydrologic and hydraulic model run files used in the simulation of normal hydrologic response conditions (CN_II conditions) and effects of cleaned culverts (MuddyCreek_normal_clean_culverts.zip); 8) hydrologic and hydraulic model run files used in the simulation of normal hydrologic response conditions (CN_II conditions) and effects of detention storage (MuddyCreek_normal_detention.zip); 9) hydrologic and hydraulic model run files used in the simulation and calibration of normal hydrologic response conditions (CN_II conditions) and current (2019) existing channel conditions (MuddyCreek_normal_existing_conditions.zip); 10) hydrologic and hydraulic model run files used in the simulation of wet hydrologic response conditions (CN_III conditions) and effects of proposed detention storage (MuddyCreek_wet_detention.zip); 11) hydrologic and hydraulic model run files used in the simulation and calibration of wet hydrologic response conditions (CN_III) and current (2019) existing channel conditions (MuddyCreek_wet_existing_conditions.zip). 12) Service definition files of the Muddy Creek water depths of inundated areas (MuddyCreek_Inundation_depths.sd) and Muddy Creek inundation area polygons (MuddyCreek_inundation_extents.sd) added on September 7, 2022.

Malaysia, particularly Pahang, experiences devastating floods annually, causing significant damage. The objective of the research was to create a flood susceptibility map for the designated area by employing an Ensemble Machine Learning (EML) algorithm based on geographic information system (GIS). By analyzing nine key factors from a geospatial database, flood susceptibility map was created with the ArcGIS software (ESRI ArcGIS Pro v3.0.1 x64). The Random Forest (RF) model was employed in this study to categorize the study area into distinct flood susceptibility classes. The Feature selection (FS) method was used to ranking the flood influencing factors. To validate the flood susceptibility models, standard statistical measures and the Area Under the Curve (AUC) were employed. The FS ranking demonstrated that the primary attributes to flooding in the study region are rainfall and elevation, with slope, geology, curvature, flow accumulation, flow direction, distance from the river, and land use/land cover (LULC) patterns ranking subsequently. The categories of ’very high’ and ’high’ class collectively made up 37.1% and 26.3% of the total area, respectively. The flood vulnerability assessment of Pahang found that the Eastern, Southern, and central regions were at high risk of flooding due to intense precipitation, low-lying topography with steep inclines, proximity to the shoreline and rivers, and abundant flooded vegetation, crops, urban areas, bare ground, and rangeland. Conversely, areas with dense tree canopies or forests were less susceptible to flooding in this research area. The ROC analysis demonstrated strong performance on the validation datasets, with an AUC value of >0.73 and accuracy scores exceeding 0.71. Research on flood susceptibility mapping can enhance risk reduction strategies and improve flood management in vulnerable areas. Technological advancements and expertise provide opportunities for more sophisticated methods, leading to better prepared and resilient communities.

BackgroundPopulations in coastal cities are exposed to increasing risk of flooding, resulting in rising damages to health and assets. Adaptation measures, such as early warning systems for floods (EWSFs), have the potential to reduce the risk and impact of flood events when tailored to reflect the local social-ecological context and needs. Community perceptions and experiences play a critical role in risk management, since perceptions influence people’s behaviors in response to EWSFs and other interventions.MethodsWe investigated community perceptions and responses in flood-prone periurban areas in the coastal city of Machala, Ecuador. Focus groups (n = 11) were held with community members (n = 65 people) to assess perceptions of flood exposure, sensitivity, adaptive capacity, and current alert systems. Discussions were audio recorded, transcribed, and coded by topic. Participatory maps were field validated, georeferenced, and digitized using GIS software. Qualitative data were triangulated with historical government information on rainfall, flood events, population demographics, and disease outbreaks.ResultsFlooding was associated with seasonal rainfall, El Niño events, high ocean tides, blocked drainage areas, overflowing canals, collapsed sewer systems, and low local elevation. Participatory maps revealed spatial heterogeneity in perceived flood risk across the community. Ten areas of special concern were mapped, including places with strong currents during floods, low elevation areas with schools and homes, and other places that accumulate stagnant water. Sensitive populations included children, the elderly, physically handicapped people, low-income families, and recent migrants. Flood impacts included damages to property and infrastructure, power outages, and the economic cost of rebuilding/repairs. Health impacts included outbreaks of infectious diseases, skin infections, snakebite, and injury/drowning. Adaptive capacity was weakest during the preparation and recovery stages of flooding. Participants perceived that their capacity to take action was limited by a lack of social organization, political engagement, and financial capital. People perceived that flood forecasts were too general, and instead relied on alerts via social media.ConclusionsThis study highlights the challenges and opportunities for climate change adaptation in coastal cities. Areas of special concern provide clear local policy targets. The participatory approach presented here (1) provides important context to shape local policy and interventions in Ecuador, complimenting data gathered through standard flood reports, (2) provides a voice for marginalized communities and a mechanism to raise local awareness, and (3) provides a research framework that can be adapted to other resource-limited coastal communities at risk of flooding.

Pluvial Flood Risk Mapping via Satellite Market Outlook

According to our latest research, the global market size for Pluvial Flood Risk Mapping via Satellite reached USD 2.14 billion in 2024, demonstrating a robust expansion in the adoption of satellite-based flood risk assessment solutions. The market is expected to grow at a CAGR of 12.7% during the forecast period, with projections indicating a market value of USD 6.28 billion by 2033. This growth is primarily driven by the increasing need for accurate and timely flood risk prediction, especially in the face of intensifying climate change impacts and rapid urbanization. As per our latest research, the demand for advanced geospatial intelligence and real-time analytics is significantly shaping the competitive landscape and technological advancements within the sector.

The burgeoning growth of the Pluvial Flood Risk Mapping via Satellite market can be attributed to a confluence of factors, most notably the escalating frequency and severity of extreme weather events globally. As urbanization accelerates and impervious surfaces expand, cities and municipalities are increasingly vulnerable to pluvial flooding, which occurs due to intense rainfall overwhelming drainage systems. This heightened exposure has compelled governments, insurance companies, and private organizations to invest in advanced flood risk mapping solutions that leverage satellite data for enhanced situational awareness and predictive capabilities. The integration of high-resolution satellite imagery with sophisticated data analytics enables stakeholders to proactively identify flood-prone zones, streamline emergency response, and mitigate potential damages, thus amplifying the market’s growth trajectory.

Another critical growth driver is the rapid technological evolution in satellite imaging and geospatial data processing. The advent of next-generation satellites equipped with multispectral and radar imaging capabilities has dramatically improved the precision and frequency of flood risk assessments. These technological advancements allow for near-real-time monitoring of rainfall patterns, surface water accumulation, and land use changes, which are essential for accurate pluvial flood modeling. Additionally, the proliferation of cloud-based Geographic Information Systems (GIS) and the integration of Artificial Intelligence (AI) in data analytics have further enhanced the scalability and accessibility of these solutions. These innovations are not only lowering the barrier to entry for smaller municipalities and emerging markets but also enabling more granular and actionable insights for large-scale infrastructure planning and disaster management.

The increasing emphasis on climate resilience and sustainable urban development has also been instrumental in propelling the Pluvial Flood Risk Mapping via Satellite market forward. Governments and international organizations are mandating stricter compliance with environmental regulations and disaster risk reduction frameworks, which necessitate the adoption of robust flood risk mapping tools. The insurance sector, in particular, is leveraging satellite-based data to refine risk assessment models, optimize premium structures, and expedite claims processing. Furthermore, the growing recognition of the socio-economic costs associated with flood events is prompting cross-sector collaborations and public-private partnerships, thereby fostering a conducive environment for market expansion. These collective efforts underscore the vital role of satellite technology in building resilient communities and safeguarding critical infrastructure.

Regionally, the market exhibits significant heterogeneity in adoption and growth patterns. North America and Europe lead in terms of market share, driven by advanced technological infrastructure, strong regulatory frameworks, and substantial investments in disaster management. Asia Pacific, however, is emerging as the fastest-growing region, propelled by rapid urbanization, frequent flood events, and increasing government initiatives to bolster climate resilience. Latin America and the Middle East & Africa are also witnessing steady growth, albeit from a smaller base, as awareness and access to satellite-based solutions improve. The regional outlook suggests that tailored solutions and localized partnerships will be key to unlocking the full potential of the Pluvial Flood Risk Mapping via Satellite market worldwide.

Solution Analysis

These polygon boundaries, inundation extents, and depth rasters were created to provide an extent of flood inundation along the Rockfish Creek within the community of Hope Mills, North Carolina. The upstream and downstream reach extent is determined by the location of high-water marks, not extending the boundary far past the outermost high-water marks. In areas of uncertainty of flood extent, the model boundary is lined up with the flood inundation polygon extent. This boundary polygon was used to extract the final flood inundation polygon and depth layer from the flood water surface raster file. The passage of Hurricane Matthew through central and eastern North Carolina during October 7-9, 2016, brought heavy rainfall which resulted in major flooding. More than 15 inches of rain were recorded in some areas. Over 600 roads were closed including Interstates 95 and 40, and nearly 99,000 structures were impacted by floodwaters. Immediately after the flooding, the U.S. Geological Survey (USGS) documented 267 high-water marks (HWM), of which 254 were surveyed. The North Carolina Emergency Management documented and surveyed 353 HWMs. Six communities were mapped using Geographic Information Systems.

Community-reported severe flood events compared to official reports of rainfall, flood causes, impacts and disease outbreaks.

The objective of StormSense is to enhance the capability of communities to prepare and respond to the disastrous impacts of sea level rise and coastal flooding in ways that are replicable, scalable, measurable, and make a comparable difference worldwide. In pursuit of this, the project's mantra is to advance the field of emergency preparedness by advancing research to help predict flooding resulting from storm surge, rain, and tides.The StormSense Project is an award-winning inundation forecasting research initiative participating in the Global City Teams Challenge (GCTC). The project was initially funded by the National Institute of Standards and Technology via a Replicable Smart City Technologies grant announced by the White House in Sept. 2016. City of Virginia Beach participates regionally. Most of the funding for the sensors within the city are funded through the Capital Improvement Program (CIP).This dataset shows water levels and several other parameters in near real-time as shown in the keywords from different sensor sources in the Hampton Roads region. It does not show any historic data. The data can be plotted on a live map. Current sensor types include ultrasonic, radar and others dependent on the parameter. The data sources are from USGS and StormSense. Each location may have a single or multiple sensors for different purposes. The data collection efforts will provide crucial information about the surface water flow across the region at all times of the year in different weather scenarios. The data will be used to support modeling efforts for research communities, emergency preparedness and planning, decision making processes by city officials during adverse weather conditions such as hurricanes, nor’easter and other rainfall events.

These polygon boundaries, inundation extents, and depth rasters were created to provide an extent of flood inundation along the Neuse River within the community of Smithfield, North Carolina. The upstream and downstream reach extent is determined by the location of high-water marks, not extending the boundary far past the outermost high-water marks. In areas of uncertainty of flood extent, the model boundary is lined up with the flood inundation polygon extent. This boundary polygon was used to extract the final flood inundation polygon and depth layer from the flood water surface raster file. The passage of Hurricane Matthew through central and eastern North Carolina during October 7-9, 2016, brought heavy rainfall which resulted in major flooding. More than 15 inches of rain were recorded in some areas. Over 600 roads were closed including Interstates 95 and 40, and nearly 99,000 structures were impacted by floodwaters. Immediately after the flooding, the U.S. Geological Survey (USGS) documented 267 high-water marks (HWM), of which 254 were surveyed. The North Carolina Emergency Management documented and surveyed 353 HWMs. Six communities were mapped using Geographic Information Systems.

These polygon boundaries, inundation extents, and depth rasters were created to provide an extent of flood inundation along the Tar River within the community of Princeville, North Carolina. The upstream and downstream reach extent is determined by the location of high-water marks, not extending the boundary far past the outermost high-water marks. In areas of uncertainty of flood extent, the model boundary is lined up with the flood inundation polygon extent. This boundary polygon was used to extract the final flood inundation polygon and depth layer from the flood water surface raster file. The passage of Hurricane Matthew through central and eastern North Carolina during October 7-9, 2016, brought heavy rainfall which resulted in major flooding. More than 15 inches of rain were recorded in some areas. Over 600 roads were closed including Interstates 95 and 40, and nearly 99,000 structures were impacted by floodwaters. Immediately after the flooding, the U.S. Geological Survey (USGS) documented 267 high-water marks (HWM), of which 254 were surveyed. The North Carolina Emergency Management documented and surveyed 353 HWMs. The North Carolina Geodetic Survey documented and surveyed 12 HWMs within the town of Princeville. Seven communities were mapped using Geographic Information Systems. This is the model inundated area layer for the community of Princeville.

This dataset is available on Brisbane City Council’s open data website – data.brisbane.qld.gov.au. The site provides additional features for viewing and interacting with the data and for downloading the data in various formats.

This dataset, created in June 2013, provides an indication of the likelihood of a flood occurring from overland flow inside the Brisbane City Council local government area. This layer contributes to the overall Flood Awareness Mapping for Brisbane City Council.

Overland flow is excess rainfall that runs across the land after rain before it enters an underground drainage system or a creek/waterway. Overland flow can also rise to the surface naturally from underground or also as a result of creek/waterway bank failure. Overland flow flooding tends to affect localised areas rather than the whole city at once. Overland flow can probably be considered the most frequent type of flooding in Brisbane.

Overland flow tends to occur during high rainfall events. It travels across the land following low-lying, natural drainage paths. Such flooding may occur when underground drainage system exceeds capacity. Overland flow flooding can be unpredictable and occur without warning.

You can identify overland flow by looking at how water may flow across the land around your property. Consider these natural flows when you are looking to renovate, build a fence or put in a shed.

There are three different overland flow flooding impact areas in Flood Awareness Map, namely High, Medium and Low.

The overland flow High impact layer consists of H5 and H6* hazard zones during a 5% Annual Exceedance Probability (AEP) (20 year Average Recurrence Interval (ARI)) flood event. The flood data was sourced from the Citywide Creek and Overland Flow Path mapping study (GHD, 2017).

The overland flow Medium impact layer consists of H3, H4, H5 and H6* hazard zones during a 2% AEP (50 year ARI) flood event (outside high impact area). The flood data was sourced from the Citywide Creek and Overland Flow Path mapping study (GHD, 2017).

The overland flow Low impact layer consists of H2, H3, H4, H5 and H6* hazard zones during a 1% AEP (100 year ARI) flood event (outside high and medium impact areas). The flood data was sourced from the Citywide Creek and Overland Flow Path mapping study (GHD, 2017).

* Hazard ranges from H1 to H6 and is based on the flood hazard, depth and velocity vulnerability thresholds. For more information, refer to Australian Disaster Resilience Guideline 7-3 Flood Hazard (AIDR 2017).

Due to a system issue, this data is not displayed here. To access the data, please use the ArcGIS Hub Datasets link in the Data and resources section on this page.

Hurricane Harvey made landfall near Rockport, Texas on August 25 as a category 4 hurricane with wind gusts exceeding 150 miles per hour. As Harvey moved inland the forward motion of the storm slowed down and produced tremendous rainfall amounts to southeastern Texas and southwestern Louisiana. Historic flooding occurred in Texas and Louisiana as a result of the widespread, heavy rainfall over an 8-day period in Louisiana in August and September 2017. Following the storm event, U.S. Geological Survey (USGS) hydrographers recovered and documented 2,123 high-water marks in Texas, noting location and height of the water above land surface. Many of these high-water marks were used to create flood-inundation maps for selected communities of Texas that experienced flooding in August and September, 2017.
Nineteen flood-inundation maps in 11 river and coastal basins were created by using GIS for areas near rivers that flooded as a result of Harvey in southeastern Texas and southwestern ...

OC Hydstra Flood Station Data Feed DescriptionThis feature layer contains the cumulative data (multiple temporal observations per station) processed by Orange County's Hydstra Flood Monitoring ALERT System Station Data Feed on latest rainfall amounts and rates for every 15-minutes interval.The following list provides the temporal levels of rainfall data reporting summary:Hourly Levels:Current Hourly Rainfall Intensity (inches)1-Hour Rainfall (inches)3-Hours Rainfall (inches)6-Hours Rainfall (inches)12-Hours Rainfall (inches)Daily Levels:1-Day Rainfall (inches)2-Day Rainfall (inches)3-Day Rainfall (inches)Historical SummaryALERT stands for Automated Local Evaluation in Real Time, a method of using remote sensors to transmit data to a central computer in real time. This standard was developed in the 1970s by the National Weather Service and has been used by organizations of all levels such as the National Weather Service, Army Corps of Engineers and Bureau of Reclamation.Orange County initiated its ALERT System in 1983 to provide additional quantitative weather information to support storm operations personnel. Sensors were initially installed along the Santa Ana River and in four South County Channels: San Juan Creek, Arroyo Trabuco Creek, Oso Creek, and Aliso Creek. The system has subsequently been expanded to monitor other flood control channels and retarding basins.The Orange County ALERT System consists of three computer base stations and three radio repeaters. The radio repeater located on Santiago Peak receives and re-transmits telemetry from field sensors located in Los Angeles, San Bernardino, and Orange Counties. One of the two receiver base stations located at the OC Public Works yard on Glassell Street in Orange is a server connected to the OC Public Works Department Intranet, providing access to Department personnel from their workstations. In response to extreme weather conditions the OC Public Works - Department Operations Center (DOC) opens to coordinate monitoring and response to threats of flooding, mudslides, and debris flows. During these periods, the ALERT System provides crucial continuous information to the DOC.

Louisiana Floods - 2016

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

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

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

Content

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

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

Acknowledgement

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

Inspiration

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