Date of Images:5/5/2024Date of Next Image:N/ASummary:Scientists at NASA's Marshall Space Flight Center created these water extents on May 5, 20224 using PlanetScope imagery. These images can be used to see where open water is visible at the time of the satellite overpass. This product shows all water detected and differentiates between normal water areas and some flooded areas. This product was classified using WorldCover. It's important to note that all flooded areas may not be captured do to the sensors limitations of not being able to "see" through vegetation and buildings. To determine where additional flooding may have occurred, combine this layer with other data sets.Suggested Use:This product shows water that is detected by the sensor with different colors indicating different land cover/land use classifications from WorldCover that appear to have water and are potentially flooded.Blue (1): Known WaterRed (2): Flooded DevelopedGreen (3): Flooded VegetationOrange (4): Flooded Cropland/GrasslandGray (5): Clouds/Cloud Shadow(0): No DataSatellite/Sensor:PlanetScopeResolution:3 metersCredits:NASA Disasters Program, Includes copyrighted material of Planet Labs PBC. All rights reserved.Esri REST Endpoint:See URL section on the right side of page.WMS Endpoint: https://maps.disasters.nasa.gov/ags04/services/texas_flood_202405/planet_waterextents/MapServer/WMSServer

Dates of Images:5/7/2024-4/30/2024; 5/12/2024-5/7/2024Date of Next Image:UnknownSummary:The Alaska Satellite Facility has developed water extent images using the Sentinel-1A/B Synthetic Aperture Radar (SAR) instrument. These images can be used to see where water is located at the time of the satellite overpass. This product shows all water detected and does not differentiate between normal water and flood water. To determine where flooding may have occurred, combine this layer with a reference water layer.The water extent difference maps shown here were created by subtracting the water extent of the first date from the water extent of the second date to show what changes, if any, occurred in the water extent.Suggested Use:Light Blue: Water Present in the Second Date OnlyDark Blue: Water Present in Both DatesOrange: Water Present in the First Date OnlySatellite/Sensor:Synthetic Aperture Radar on European Space Agency's (ESA) Copernicus Sentinel-1A satelliteResolution:30 metersEsri REST Endpoint:See URL section on right side of pageWMS Endpoint:https://maps.disasters.nasa.gov/ags04/services/texas_flood_202405/sentinel1_waterextent_difference/MapServer/WMSServerData Download:https://maps.disasters.nasa.gov/download/gis_products/event_specific/2024/texas_flood_202405/sentinel1/

Dates of Images:4/30/2024, 5/7/2024Date of Next Image:Varies by region, typically 12 days since previous pass. Set time slider to most recent interval and click on area of interest to identify date of last pass.Summary:The Alaska Satellite Facility has developed false color Red, Green, Blue (RGB) and Radiometrically Terrain-Correct (RTC) composites and surface water extent products of the Sentinel-1A/B Synthetic Aperture Radar (SAR) instrument which assigns the co- and cross-polarization information to a channel in the composite. When used to support a flooding event, areas in blue denotes water present at the time of the satellite overpass before or after the start of the flooding event.Sentinel-1 RGB Decomposition of RTC VV and VH imagery over United States coastlines. Blue areas have low returns in VV and VH (smooth surfaces such as calm water, but also frozen/crusted soil or dry sand), Green areas have high returns in VH (volume scatterers such as vegetation or some types of snow/ice), and Red areas have relatively high VV returns and relatively low VH returns (such as urban or sparsely vegetated areas).Suggested Use:In this image, water appears in blue, vegetated areas in shades of green and urban areas in bright orange. It is recommended to use this product with ancillary information to derive flooded areas.Satellite/Sensor:Synthetic Aperture Radar on European Space Agency's (ESA) Copernicus Sentinel-1A satelliteResolution:30 metersEsri REST Endpoint:See URL section on right side of pageWMS Endpoint:https://maps.disasters.nasa.gov/ags04/services/brasil_flood_2024/planet_true/MapServer/WMSServer?request=GetCapabilities&service=WMSData Download:https://maps.disasters.nasa.gov/download/gis_products/event_specific/2024/texas_flood_202405/sentinel1/

NOTE: Due to the higher resolution of this data, it may be slow to load or require the user to zoom to a smaller area of interest.Dates of Images:4/30/2024, 5/5/2024, 5/7/2024Date of Next Image:UnknownSummary:For Planet data:-The Color Infrared composite is created using the near-infrared, red, and green channels, allowing for the ability to see areas impacted by the event. The near-infrared gives the ability to see through thin clouds. Healthy vegetation is shown as red, water is in blue.-The True Color RGB composite provides a product of how the surface would look to the naked eye from space. The RGB is created using the red, green, and blue channels of the respective instrument.For Sentinel-1 Data:-The Alaska Satellite Facility has developed false color Red, Green, Blue (RGB) and Radiometrically Terrain-Correct (RTC) composites and surface water extent products of the Sentinel-1A/B Synthetic Aperture Radar (SAR) instrument which assigns the co- and cross-polarization information to a channel in the composite. When used to support a flooding event, areas in blue denotes water present at the time of the satellite overpass before or after the start of the flooding event.-Sentinel-1 RGB Decomposition of RTC VV and VH imagery over United States coastlines. Blue areas have low returns in VV and VH (smooth surfaces such as calm water, but also frozen/crusted soil or dry sand), Green areas have high returns in VH (volume scatterers such as vegetation or some types of snow/ice), and Red areas have relatively high VV returns and relatively low VH returns (such as urban or sparsely vegetated areas).For Sentinel-2 Data:-The Color Infrared composite is created using the near-infrared, red, and green channels, allowing for the ability to see areas impacted by the event. The near-infrared gives the ability to see through thin clouds. Healthy vegetation is shown as red, water is in blue.-The Short Wave Infrared (SWIR) RGB is a product that is created using the SWIR, NIR, and Red channels of the respective instrument.Suggested Use:A Color Infrared composite depicts healthy vegetation as red, water as blue. Some minor atmospheric corrections have occurred.The Short Wave Infrared (SWIR) RGB is a product that can provides value in flood detection. Areas of water will appear blue, healthy green vegetation will appear as a bright green, urban areas in various shades of magenta, snow will appear as a bright blue/cyan, and bare soils being multicolor dependent on their makeup. Compare pre-event imagery to post-event imagery to identify potential flooding.The True Color RGB provides a product of how the surface would look to the naked eye from space and may show damage caused by severe weather. The True Color RGB is produced using the 3 visible wavelength bands (red, green, and blue) from the respective sensor. Some minor atmospheric corrections have occurred.For the water extent product, water appears in blue, vegetated areas in shades of green and urban areas in bright orange. It is recommended to use this product with ancillary information to derive flooded areas.Satellites/Sensors:PlanetScope, Synthetic Aperture Radar on European Space Agency's (ESA) Copernicus Sentinel-1A satellite, MultiSpectral Instrument (MSI) on European Space Agency's (ESA) Copernicus Sentinel-2A/2B satellitesResolution:PlanetScope: 3 MetersColor Infrared RGB: 10 metersShortwave Infrared RGB: 20 metersTrue Color RGB: 10 metersSentinel-1: 30 metersEsri REST Endpoint and WMS Endpoint:See individual layer content item pages.

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.

Date of Images:5/5/2024Date of Next Image:UnknownSummary:The Color Infrared composite is created using the near-infrared, red, and green channels, allowing for the ability to see areas impacted by the event. The near-infrared gives the ability to see through thin clouds. Healthy vegetation is shown as red, water is in blue.The Short Wave Infrared (SWIR) RGB is a product that is created using the SWIR, NIR, and Red channels of the respective instrument.The True Color RGB composite provides a product of how the surface would look to the naked eye from space. The RGB is created using the red, green, and blue channels of the respective instrument.Suggested Use:A Color Infrared composite depicts healthy vegetation as red, water as blue. Some minor atmospheric corrections have occurred.The Short Wave Infrared (SWIR) RGB is a product that can provides value in flood detection. Areas of water will appear blue, healthy green vegetation will appear as a bright green, urban areas in various shades of magenta, snow will appear as a bright blue/cyan, and bare soils being multicolor dependent on their makeup. Compare pre-event imagery to post-event imagery to identify potential flooding.The True Color RGB provides a product of how the surface would look to the naked eye from space and may show damage caused by severe weather. The True Color RGB is produced using the 3 visible wavelength bands (red, green, and blue) from the respective sensor. Some minor atmospheric corrections have occurred.Satellite/Sensor:MultiSpectral Instrument (MSI) on European Space Agency's (ESA) Copernicus Sentinel-2A/2B satellitesResolution:Color Infrared RGB: 10 metersShortwave Infrared RGB: 20 metersTrue Color RGB: 10 metersCredits:NASA/MSFC, USGS, ESA CopernicusEsri REST Endpoint:See URL section on the right side of page.WMS Endpoint:https://maps.disasters.nasa.gov/ags04/services/texas_flood_202405/sentinel2/MapServer/WMSServerData Download:https://maps.disasters.nasa.gov/download/gis_products/event_specific/2024/texas_flood_202405/sentinel2/

Technological accidents which are triggered by natural hazards are known as Natechs. In 2017, Hurricane Harvey caused damage to chemical and process facilities, resulting in massive release of hazardous chemicals (toxic, flammable, explosive). The damage was inflicted due to a combination of flooding, rainfall, and strong wind during the hurricane. We have developed a database of 45 Natechs mainly using official accidents databases and media reports. The database contains: (i) the name and type of affected facilities, (ii) geographical coordinates of the facilities, (iii) type of damaged equipment, and (iv) the hurricane's characteristics such as flood depth, maximum rainfall, and wind speed at the location of affected facilities.

This site provides access to download an ArcGIS geodatabase or shapefiles for the 2017 Texas Address Database, compiled by the Center for Water and the Environment (CWE) at the University of Texas at Austin, with guidance and funding from the Texas Division of Emergency Management (TDEM). These addresses are used by TDEM to help anticipate potential impacts of serious weather and flooding events statewide. This is part of the Texas Water Model (TWM), a project to adapt the NOAA National Water Model [1] for use in Texas public safety. This database was compiled over the period from June 2016 to December 2017. A number of gaps remain (towns and cities missing address points), see Address Database Gaps spreadsheet below [4]. Additional datasets include administrative boundaries for Texas counties (including Federal and State disaster-declarations), Councils of Government, and Texas Dept of Public Safety Regions. An Esri ArcGIS Story Map [5] web app provides an interactive map-based portal to explore and access these data layers for download.

The address points in this database include their "height above nearest drainage" (HAND) as attributes in meters and feet. HAND is an elevation model developed through processing by the TauDEM method [2], built on USGS National Elevation Data (NED) with 10m horizontal resolution. The HAND elevation data and 10m NED for the continental United States are available for download from the Texas Advanced Computational Center (TACC) [3].

The complete statewide dataset contains about 9.28 million address points representing a population of about 28 million. The total file size is about 5GB in shapefile format. For better download performance, the shapefile version of this data is divided into 5 regions, based on groupings of major watersheds identified by their hydrologic unit codes (HUC). These are zipped by region, with no zipfile greater than 120mb: - North Tx: HUC1108-1114 (0.52 million address points) - DFW-East Tx: HUC1201-1203 (3.06 million address points) - Houston-SE Tx: HUC1204 (1.84 million address points) - Central Tx: HUC1205-1210 (2.96 million address points) - Rio Grande-SW Tx: HUC2111-1309 (2.96 million address points)

Additional state and county boundaries are included (Louisiana, Mississippi, Arkansas), as well as disaster-declaration status.

Compilation notes: The Texas Commission for State Emergency Communications (CSEC) provided the first 3 million address points received, in a single batch representing 213 of Texas' 254 counties. The remaining 41 counties were primarily urban areas comprising about 6.28 million addresses (totaling about 9.28 million addresses statewide). We reached the GIS data providers for these areas (see Contributors list below) through these emergency communications networks: Texas 9-1-1 Alliance, the Texas Emergency GIS Response Team (EGRT), and the Texas GIS 9-1-1 User Group. The address data was typically organized in groupings of counties called Councils of Governments (COG) or Regional Planning Commissions (RPC) or Development Councils (DC). Every county in Texas belongs to a COG, RPC or DC. We reconciled all counties' addresses to a common, very simple schema, and merged into a single geodatabase.

November 2023 updates: In 2019, TNRIS took over maintenance of the Texas Address Database, which is now a StratMap program updated annually [6]. In 2023, TNRIS also changed its name to the Texas Geographic Information Office (TxGIO). The datasets available for download below are not being updated, but are current as of the time of Hurricane Harvey.

References: [1] NOAA National Water Model [https://water.noaa.gov/map] [2] TauDEM Downloads [https://hydrology.usu.edu/taudem/taudem5/downloads.html] [3] NFIE Continental Flood Inundation Mapping - Data Repository [https://web.corral.tacc.utexas.edu/nfiedata/] [4] Address Database Gaps, Dec 2017 (download spreadsheet below) [5] Texas Address and Base Layers Story Map [https://www.hydroshare.org/resource/6d5c7dbe0762413fbe6d7a39e4ba1986/] [6] TNRIS/TxGIO StratMap Address Points data downloads [https://tnris.org/stratmap/address-points/]

Description: Floods are common natural disasters worldwide and pose substantial risks to life, property, food production, and natural resources. Effective measures for flood mitigation and warning are important. Southeast Texas is still at substantial risk of flooding and Lamar University is assisting the region with asset management of a flood sensor network for flooding events. This network provides real-time water stage information. To make these data more useful for flood monitoring and mapping, Lamar University developed a program to measure elevation and coordinates for the various sensor locations. This paper overviews the measurement of the elevation and coordinates of 74 networked flood sensors and various thresholds at critical points used by flood decision-makers for reference at each site. These sensors, in the first phase of this program, were deployed throughout a 7-county region spanning nearly 6000 square miles in Southeast Texas. The latitude and longitude of the sensors, along with their elevations, were determined using survey-grade Global Navigation Satellite System (GNSS) technology. This is an accurate, rapid, and relatively low-cost surveying method. Various Continually Operating Reference Stations (CORS) were examined during post-processing to achieve the most accurate horizontal and vertical results. After differential corrections were applied, accuracies of 0.4 in. (or better) were achieved. Each site's critical points and thresholds were also established using this method. The thresholds, elevations, and positions of these sensors and their surrounding critical points are transmitted to various dashboards on websites. These data are used to aid with decisions related to road closures or modeling efforts by mitigation decision-makers, emergency managers, and the public, including the Texas Department of Transportation, Houston Transtar, the National Weather Service, and the Sabine River Authority of Texas (SRA). This data may also be used in the development of flood hydrological models in Southeast Texas watersheds and sub-basins. This program currently involves the Flood Coordination Study team which is part of the Center for Resiliency at Lamar University in collaboration with various entities such as the U.S. Department of Homeland Security Science and Technology Directorate, the Southeast Texas Flood Control District, and various other regional agencies, municipalities, and industries.

Steps to reproduce: A Trimble GEOX7 Global Navigation Satellite System (GNSS) handheld device, which employs Trimble H-StarTM technology, and a ZIPLEVEL PRO-2000 High Precision Altimeter was used to determine the coordinates and elevations of the sensors and surrounding critical points. Post-processing of the GNSS data used the Trimble GPS Pathfinder Office software. The closest CORS base stations were used for differential corrections and the NAD 1983 (2011) (epoch 2010.00) horizontal datum was used as the geographic coordinate system. Furthermore, orthometric heights were calculated using GEOID 18 which is referenced to the North American Vertical Datum of 1988 (NAVD 88). ArcGIS Pro 3 was used to create a map of the sensors and critical points, as well as a watershed delineation relative to Southeast Texas landmarks. Data were gathered in Southeast Texas watersheds and sub-watersheds in order to monitor and map the elevation and movement of water in the drainages. Vertical and horizontal positions of the 74 flood sensors installed in the first phase of the project and their surrounding critical points, including the node (solar panels, battery, and transmission device), the bottom of the posts that nodes attached (bottom of the node from now on), top of the bank, the bottom of the ditch, the bottom of the bridge's deck, and the center of the road and edges, have been gathered accordingly. Also, the relative elevations between these points are important and were collected.

This resource describes a dataset of gridded depth at horizontal resolution of 3 meters, published November 15, 2017, downloaded from FEMA [1] and hosted in this archive at the University of Texas Advanced Computing Center (TACC) [2].. The raster dataset is contained within an Esri ArcGIS geodatabase. This product utilized Triangulated Irregular Network (TIN) interpolation, four quality assurance measures (identifying dips, spikes, duplication, and inaccurate/unrealistic measurements). High Water Marks were obtained from the Harris County Flood Control District (HCFCD), US Geological Survey (USGS), and other inspection data. Elevation data comprised a mosaic of 3 meter resampled elevations from 1M & 3M LiDAR, and IFSAR data. One section of the IfSAR data was found to be erroneous, and replaced with a blended 10 meter section. [This description was in correspondence January 22, 2018, from Mark English, GeoSpatial Risk Analyst, FEMA Region VIII, Mitigation Division.]

A preliminary version of these depths dated September 10, 2017 can be viewed in a FEMA web map [3]. This web map shows a forecasted depth grid, based on National Weather Service (NWS) Advanced Hydrologic Prediction Service (AHPS) forecasted water levels.

See FEMA's Natural Hazard Risk Assessment Program (NHRAP) ftp site [4] for additional HWM-based depth grids and inundation polygons: - Harris County AOIs and Inundation Boundaries [5] - Harris County Depth Grids [6] - Aransas, Nueces, and San Patricio Coastal Depth Grids and Boundaries [7] FEMA notes on these Modeled Preliminary Observations: o Based on observed Water Levels at stream gauges interpolated along rivers, downsampled to 5m resolution DEM o Depth grids updated with new observed peak crest as they become available o Will include High Water Mark information as it becomes available o Extents validated with remote sensing o Use for determining damage levels on specific structures

See also FEMA's journal of mitigation planning and actions related to Harvey [8].

References and related links: [1] FEMA_Depths_3m_v3.zip (39 gb ftp download) [https://data.femadata.com/Region8/Mitigation/Data_Share/] [2] TACC 39gb wget or ftp download [https://web.corral.tacc.utexas.edu/nfiedata/Harvey/flood_data/FEMA_Harvey_Depths_3m.gdb.zip] [3] FEMA map viewer for Hurricane Harvey resources (flood depths is bottom selection in layers list) [https://fema.maps.arcgis.com/apps/webappviewer/index.html?id=50f21538c7bf4e08b9faab430bc237c9] [4] FEMA NHRAP ftp [https://data.femadata.com/FIMA/NHRAP/Harvey/] [5] [https://data.femadata.com/FIMA/NHRAP/Harvey/Harris_AOIandBoundaries.zip] [6] [https://data.femadata.com/FIMA/NHRAP/Harvey/Harris_Mosaic_dgft.zip] [7] [https://data.femadata.com/FIMA/NHRAP/Harvey/Rockport_DG_unclipped.zip] [8] Hurricane Harvey Mitigation Portfolio - FEMA map journal [https://fema.maps.arcgis.com/apps/MapJournal/index.html?appid=70204cf2762d45409553fd9642700b7f]

This layer is sourced from maps.cor.gov.

Contains features related to the 2015 Bond Program which includes streets, traffic signals, traffic improvements, playground and building enhancements

© COR

Geospatial data about Williamson County, Texas FEMA Flood Hazard Area. Export to CAD, GIS, PDF, CSV and access via API.

This layer is sourced from maps.ci.sherman.tx.us.

Quick Start This is a collection of flood datasets to support hydrologic research for Hurricane Harvey, August-September 2017. The best way to start exploring this collection is by opening the Hurricane Harvey 2017 Story Map [2]. It has separate sections for the different content categories, and links to the relevant HydroShare resources within this collection.

More Details This is the root collection resource for management of hydrologic and related data collected during Hurricane Harvey on the Texas-Louisiana Gulf coast. This collection holds numerous composite resources comprising streamflow forecasts, inundation polygons and depth grids, flooding impacts, elevation grids, high water marks, and numerous other related information sources. Texas address points are included to support estimating storm and flood impacts in terms of structures within an affected area.

The data providers for this collection are the Texas Division of Emergency Management, NOAA National Weather Service, NOAA National Hurricane Center, NOAA National Water Center, FEMA, 9-1-1 emergency communications agencies, and many others. Esri and Kisters also provided invaluable tools, data and geoprocessing services to support the initial data production, and these are included or referenced.

User-contributed resources from 2017 US Hurricanes may also be shared with The CUAHSI 2017 Hurricane Data Community group [1] to make them accessible to interested researchers, Anyone may join this group.

An ArcGIS Story Map [2] has been created which provides example data views and interactive access to this collection.

This collection has been produced by work on a US National Science Foundation RAPID Award "Archiving and Enabling Community Access to Data from Recent US Hurricanes" [3].

References [1] CUAHSI 2017 Hurricane Data Community group [https://www.hydroshare.org/group/41] [2] Hurricane Harvey 2017 Archive Story Map [https://arcg.is/1rWLzL0] [3] NSF RAPID Grant [https://nsf.gov/awardsearch/showAward?AWD_ID=1761673]

This dataset consists of long-term (~150 years) shoreline change rates for the Texas coastal region from Sabine Pass at the Louisiana border to the Rio Grande at the Mexico border. Rate calculations were computed within a GIS using the Digital Shoreline Analysis System (DSAS) version 4.3, an ArcGIS extension developed by the U.S. Geological Survey. Long-term rates of shoreline change were calculated using a linear regression rate based on available shoreline data for a minimum 50-year period. A reference baseline was used as the originating point for the orthogonal transects cast by the DSAS software. The transects intersect each shoreline establishing measurement points, which are then used to calculate long-term rates. Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline data along open-ocean sandy shores of the conterminous United States and parts of Alaska and Hawaii under the National Assessment of Shoreline Change project.There is no widely accepted standard for analyzing shoreline change. Existing shoreline data measurements and rate calculation methods vary from study to study and prevent combining results into state-wide or regional assessments. The impetus behind the National Assessment project was to develop a standardized method of measuring changes in shoreline position that is consistent from coast to coast. The goal was to facilitate the process of periodically and systematically updating the results in an internally consistent manner.

Geospatial data about Montgomery County, Texas Flood Plain Area. Export to CAD, GIS, PDF, CSV and access via API.

This layer is sourced from gis.dentoncounty.com.

Add to Favorites SETx Building Footprints along with SVI data and estimated population per building for day and night. Summary from data source (Texas Water Development Board; https://twdb-flood-planning-resources-twdb.hub.arcgis.com/pages/property): To provide an hybrid blend of Open Street Map and Microsoft's Artificial Intelligence buildings to cover the State of Texas.

The Civil Air Patrol is routinely tasked by FEMA and local public safety officials with taking aerial photographs. This collection comprises nearly 30,000 photos taken over the Hurricane Harvey study area, between August 19, 2017 and June 2, 2018. The majority of this collection were taken over southeast Texas from August 10 to September 2, 2017. These were originally uploaded to the web using the GeoPlatform.gov imageUploader capability, and hosted as a web map layer [1]. For this Harvey collection, I exported the dataset of photo location points to a local computer, subset it to the Harvey event, and created a shapefile, which is downloadable below. The photos and thumbnails were not included in this archive, but are attribute-linked to the FEMA-Civil Air Patrol image library on Amazon cloud [2].

The primary resource for these photos is the University of Texas at Austin Center for Space Research (UT CSR), hosted at the Texas Advanced Computational Center (TACC) [3]. These photos are organized by collection date, and each date folder has photo metadata in Javascript (js) and json format files. UT CSR has published a separate web app for browsing these photos [4], as well as several other flood imagery sources.

Note: The cameras used by the Civil Air Patrol do not have an electronic compass with their GPS to record the viewing direction. The easiest way to determine the general angle is to look at consecutive frame counterpoints to establish the flightpath direction at nadir and adjust for the photographer's position behind the pilot looking out the window hatch on the port (left) side of the aircraft. The altitude above ground level is typically between 1000-1500 feet, so it's easy to locate features in reference orthoimages.

Another source of aerial imagery is from the NOAA National Geodetic Survey (NGS) [5]. This imagery was acquired by the NOAA Remote Sensing Division to support NOAA homeland security and emergency response requirements.

References [1] US federal GeoPlatform.gov Image Uploader map service (ArcGIS Server) [https://imageryuploader.geoplatform.gov/arcgis/rest/services/ImageEvents/MapServer] [2] FEMA-Civil Air Patrol image library on Amazon cloud [https://fema-cap-imagery.s3.amazonaws.com] [3] UT CSR primary archive for Harvey photos on TACC [https://web.corral.tacc.utexas.edu/CSR/Public/17harvey/TxCAP/] [4] UT CSR web app for browsing CAP photos [http://magic.csr.utexas.edu/hurricaneharvey/public/] [5] NOAA NGS Hurricane Harvey Imagery [https://storms.ngs.noaa.gov/storms/harvey/index.html#7/28.400/-96.690]

This dataset comes from the FEMA S_Fld_Haz_Ar table. The S_Fld_Haz_Ar table contains information about the flood hazards within the flood risk project area. A spatial file with location information also corresponds with this data table. These zones are used by FEMA to designate the SFHA and for insurance rating purposes. These data are the regulatory flood zones designated by FEMA. A spatial file with location information also corresponds with this data table.This information is needed for the following tables in the FIS report: Flooding Sources Included in this FIS report, and Summary of Hydrologic and Hydraulic Analyses.The spatial elements representing the flood zones are polygons. The entire area of the jurisdiction(s) mapped by the FIRM should have a corresponding flood zone polygon. There is one polygon for each contiguous flood zone designated.FEMA Regulatory Floodway are flood zone polygons marked as a regulatory floodway.FEMA 100 year are flood zone polygons where there is a 1% Annual Chance, also known as the 100 year.FEMA 500 year are flood zone polygons where there is a 0.2% Annual Chance, also known as the 500 year.This map is not intended for insurance rating purposes and is for information only. This map is a representation and approximation of the relative location of geographic information, land marks and physical addresses. The map may not be 100% accurate in locating your address. The floodplains shown on this mapping tool are those delineated on the Federal Emergency Management Agency’s (FEMA) Digital Flood Insurance Rate Map (DFIRM or floodplain map) for Montgomery County. This map is not an official FEMA Digital Flood Insurance Rate Map. The effective DFIRMs are produced, maintained, and published by FEMA and not by Montgomery County. Official determinations are provided by FEMA.