14 datasets found
  1. H

    Texas Basemap - Lidar Elevation Data (DEM)

    • hydroshare.org
    • beta.hydroshare.org
    • +1more
    zip
    Updated Nov 3, 2023
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    HydroShare (2023). Texas Basemap - Lidar Elevation Data (DEM) [Dataset]. http://doi.org/10.4211/hs.af6ae321e2ad40a1bc6d0b695370fbfc
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    zip(5.5 GB)Available download formats
    Dataset updated
    Nov 3, 2023
    Dataset provided by
    HydroShare
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    Texas
    Description

    This resource contains Lidar-DEM collection status shapefiles from the Texas Natural Resources Information System (TNRIS) [http://tnris.org]. November 2023 updates: this year, TNRIS changed its name to Texas Geographic Information Office (TxGIO). The domain name hasn't changed yet, but the data hub is continually evolving. See [1], [2] for current downloadable data.

    For purposes of Hurricane Harvey studies, the 1-m DEM for Harris County (2008) has also been uploaded here as a set of 4 zipfiles containing the DEM in tiff files. See [1] for a link to the current elevation status map and downloadable DEMs.
    Project name: H-GAC 2008 1m Datasets: 1m Point Cloud, 1M Hydro-Enforced DEM, 3D Breaklines, 1ft and 5ft Contours Points per sq meter: 1 Total area: 3678.56 sq miles Source: Houston-Galveston Area Council (H-GAC) Acquired by: Merrick, QA/QC: Merrick Catalog: houston-galveston-area-council-h-gac-2008-lidar

    References: [1] TNRIS/TxGIO StratMap elevation data [https://tnris.org/stratmap/elevation-lidar/] [2] TNRIS/TxGIO DataHub [https://data.tnris.org/]

  2. U

    1 meter Digital Elevation Models (DEMs) - USGS National Map 3DEP...

    • data.usgs.gov
    • datadiscoverystudio.org
    • +4more
    Updated Jan 27, 2017
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    U.S. Geological Survey (2017). 1 meter Digital Elevation Models (DEMs) - USGS National Map 3DEP Downloadable Data Collection [Dataset]. https://data.usgs.gov/datacatalog/data/USGS:77ae0551-c61e-4979-aedd-d797abdcde0e
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    Dataset updated
    Jan 27, 2017
    Dataset provided by
    United States Geological Surveyhttp://www.usgs.gov/
    Authors
    U.S. Geological Survey
    License

    U.S. Government Workshttps://www.usa.gov/government-works
    License information was derived automatically

    Description

    This is a tiled collection of the 3D Elevation Program (3DEP) and is one meter resolution. The 3DEP data holdings serve as the elevation layer of The National Map, and provide foundational elevation information for earth science studies and mapping applications in the United States. Scientists and resource managers use 3DEP data for hydrologic modeling, resource monitoring, mapping and visualization, and many other applications. The elevations in this DEM represent the topographic bare-earth surface. USGS standard one-meter DEMs are produced exclusively from high resolution light detection and ranging (lidar) source data of one-meter or higher resolution. One-meter DEM surfaces are seamless within collection projects, but, not necessarily seamless across projects. The spatial reference used for tiles of the one-meter DEM within the conterminous United States (CONUS) is Universal Transverse Mercator (UTM) in units of meters, and in conformance with the North American Datum of 1983 ...

  3. d

    ELEVATION_contours_2021

    • catalog.data.gov
    • data.austintexas.gov
    Updated Jul 25, 2025
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    data.austintexas.gov (2025). ELEVATION_contours_2021 [Dataset]. https://catalog.data.gov/dataset/elevation-contours-2021-505bf
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    Dataset updated
    Jul 25, 2025
    Dataset provided by
    data.austintexas.gov
    Description

    ELEVATION.contours_2021 Summary The Texas Natural Resources Information System (TNRIS) contracted Sanborn to fly LiDAR in March of 2021. TNRIS then created the contours in the Spring of 2022 using Global Mapper. Description This layer represents contour elevation lines as of the March 2021. The contours are derived from LiDAR data, collected in the March 2021. Contours were generated using Global Mapper, sample spacing used to create the contours is consistent with the Nominal Point Spacing (NPS), of the source LiDAR dataset from which it was derived. Lines were automatically smoothed while being generated by Global Mapper. Important: The LiDAR data was created using UTM zone 14N and was projected in Central Texas State Plane (NAD 83) FIPS 4203. For contour type: 1 = Minor Contour 2 = Intermediate Contour 3 = Major Contour Credits The Texas Natural Resources Information System (TNRIS) Use limitations This map has been produced by the City of Austin for the cartographic purposes. No warranty is made by the City or TNRIS regarding its accuracy or completeness.

  4. 2018 USGS Lidar: South Texas

    • fisheries.noaa.gov
    las/laz - laser
    Updated Oct 11, 2019
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    OCM Partners (2019). 2018 USGS Lidar: South Texas [Dataset]. https://www.fisheries.noaa.gov/inport/item/57941
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    las/laz - laserAvailable download formats
    Dataset updated
    Oct 11, 2019
    Dataset provided by
    OCM Partners
    Time period covered
    Jan 13, 2018 - Feb 23, 2019
    Area covered
    Description

    Product: This lidar data set includes unclassified swath LAS 1.4 files, classified LAS 1.4 files, breaklines, digital elevation models (DEMs), and intensity imagery. Geographic Extent: The South Texas 2018 LiDAR AOI includes 30 counties in Texas, covering approximately 22,229 total square miles. Dataset Description: The South Texas 2018 LiDAR project called for the planning, acquisition, proc...

  5. U

    1/3rd arc-second Digital Elevation Models (DEMs) - USGS National Map 3DEP...

    • data.usgs.gov
    • catalog.data.gov
    Updated Feb 14, 2025
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    U.S. Geological Survey (2025). 1/3rd arc-second Digital Elevation Models (DEMs) - USGS National Map 3DEP Downloadable Data Collection [Dataset]. https://data.usgs.gov/datacatalog/data/USGS:3a81321b-c153-416f-98b7-cc8e5f0e17c3
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    Dataset updated
    Feb 14, 2025
    Dataset provided by
    United States Geological Surveyhttp://www.usgs.gov/
    Authors
    U.S. Geological Survey
    License

    U.S. Government Workshttps://www.usa.gov/government-works
    License information was derived automatically

    Description

    This is a tiled collection of the 3D Elevation Program (3DEP) and is 1/3 arc-second (approximately 10 m) resolution. The 3DEP data holdings serve as the elevation layer of The National Map, and provide foundational elevation information for earth science studies and mapping applications in the United States. Scientists and resource managers use 3DEP data for hydrologic modeling, resource monitoring, mapping and visualization, and many other applications. The elevations in this DEM represent the topographic bare-earth surface. The seamless 1/3 arc-second DEM layers are derived from diverse source data that are processed to a common coordinate system and unit of vertical measure. These data are distributed in geographic coordinates in units of decimal degrees, and in conformance with the North American Datum of 1983 (NAD 83). All elevation values are in meters and, over the continental United States, are referenced to the North American Vertical Datum of 1988 (NAVD88). The seamless ...

  6. 2018 TWDB Lidar DEM: Coastal Texas

    • fisheries.noaa.gov
    geotiff
    Updated Feb 27, 2019
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    OCM Partners (2019). 2018 TWDB Lidar DEM: Coastal Texas [Dataset]. https://www.fisheries.noaa.gov/inport/item/57961
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    geotiffAvailable download formats
    Dataset updated
    Feb 27, 2019
    Dataset provided by
    OCM Partners
    Time period covered
    Jan 13, 2018
    Area covered
    Description

    The Texas Water Development Board (TWDB) in cooperation with their project partners tasked Fugro Geospatial, Inc. (Fugro) under the Department of Information Resources (DIR) Geographic Information Systems (GIS) Hardware, Software and Services contract also known as the Texas Strategic Mapping (StratMap) Contract to acquire high resolution elevation data and associated products from airborne lid...

  7. Galveston, Texas Coastal Digital Elevation Model

    • ncei.noaa.gov
    • gimi9.com
    • +2more
    html, nc
    Updated May 14, 2007
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    NOAA National Geophysical Data Center (2007). Galveston, Texas Coastal Digital Elevation Model [Dataset]. https://www.ncei.noaa.gov/access/metadata/landing-page/bin/iso?id=gov.noaa.ngdc.mgg.dem:403
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    nc, htmlAvailable download formats
    Dataset updated
    May 14, 2007
    Dataset provided by
    National Centers for Environmental Informationhttps://www.ncei.noaa.gov/
    National Oceanic and Atmospheric Administrationhttp://www.noaa.gov/
    Authors
    NOAA National Geophysical Data Center
    Time period covered
    Jan 1, 1897 - Jan 1, 2006
    Area covered
    Description

    NOAA's National Geophysical Data Center (NGDC) is building high-resolution digital elevation models (DEMs) for select U.S. coastal regions. These integrated bathymetric-topographic DEMs are used to support tsunami forecasting and modeling efforts at the NOAA Center for Tsunami Research, Pacific Marine Environmental Laboratory (PMEL). The DEMs are part of the tsunami forecast system SIFT (Short-term Inundation Forecasting for Tsunamis) currently being developed by PMEL for the NOAA Tsunami Warning Centers, and are used in the MOST (Method of Splitting Tsunami) model developed by PMEL to simulate tsunami generation, propagation, and inundation. Bathymetric, topographic, and shoreline data used in DEM compilation are obtained from various sources, including NGDC, the U.S. National Ocean Service (NOS), the U.S. Geological Survey (USGS), the U.S. Army Corps of Engineers (USACE), the Federal Emergency Management Agency (FEMA), and other federal, state, and local government agencies, academic institutions, and private companies. DEMs are referenced to the vertical tidal datum of Mean High Water (MHW) and horizontal datum of World Geodetic System 1984 (WGS84). Grid spacings for the DEM ranges from 1/3 arc-second (~10 meters) to 3 arc-seconds (~90 meters).

  8. d

    Texas-Harvey Basemap - Addresses and Boundaries

    • search.dataone.org
    • hydroshare.org
    • +1more
    Updated Dec 30, 2023
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    David Arctur; David Maidment (2023). Texas-Harvey Basemap - Addresses and Boundaries [Dataset]. http://doi.org/10.4211/hs.3e251d7d70884abd928d7023e050cbdc
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    Dataset updated
    Dec 30, 2023
    Dataset provided by
    Hydroshare
    Authors
    David Arctur; David Maidment
    Area covered
    Description

    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/]

  9. d

    U.S. Geological Survey Gap Analysis Program- Land Cover Data v2.2

    • search.dataone.org
    • data.globalchange.gov
    • +3more
    Updated Dec 1, 2016
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    U.S. Geological Survey Gap Analysis Program, Anne Davidson, Spatial Ecologist (2016). U.S. Geological Survey Gap Analysis Program- Land Cover Data v2.2 [Dataset]. https://search.dataone.org/view/083f5422-3fb4-407c-b74a-a649e70a4fa9
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    Dataset updated
    Dec 1, 2016
    Dataset provided by
    United States Geological Surveyhttp://www.usgs.gov/
    Authors
    U.S. Geological Survey Gap Analysis Program, Anne Davidson, Spatial Ecologist
    Time period covered
    Jan 1, 1999 - Jan 1, 2001
    Area covered
    Variables measured
    CL, SC, DIV, FRM, OID, RED, BLUE, COUNT, GREEN, VALUE, and 9 more
    Description

    This dataset combines the work of several different projects to create a seamless data set for the contiguous United States. Data from four regional Gap Analysis Projects and the LANDFIRE project were combined to make this dataset. In the northwestern United States (Idaho, Oregon, Montana, Washington and Wyoming) data in this map came from the Northwest Gap Analysis Project. In the southwestern United States (Colorado, Arizona, Nevada, New Mexico, and Utah) data used in this map came from the Southwest Gap Analysis Project. The data for Alabama, Florida, Georgia, Kentucky, North Carolina, South Carolina, Mississippi, Tennessee, and Virginia came from the Southeast Gap Analysis Project and the California data was generated by the updated California Gap land cover project. The Hawaii Gap Analysis project provided the data for Hawaii. In areas of the county (central U.S., Northeast, Alaska) that have not yet been covered by a regional Gap Analysis Project, data from the Landfire project was used. Similarities in the methods used by these projects made possible the combining of the data they derived into one seamless coverage. They all used multi-season satellite imagery (Landsat ETM+) from 1999-2001 in conjunction with digital elevation model (DEM) derived datasets (e.g. elevation, landform) to model natural and semi-natural vegetation. Vegetation classes were drawn from NatureServe's Ecological System Classification (Comer et al. 2003) or classes developed by the Hawaii Gap project. Additionally, all of the projects included land use classes that were employed to describe areas where natural vegetation has been altered. In many areas of the country these classes were derived from the National Land Cover Dataset (NLCD). For the majority of classes and, in most areas of the country, a decision tree classifier was used to discriminate ecological system types. In some areas of the country, more manual techniques were used to discriminate small patch systems and systems not distinguishable through topography. The data contains multiple levels of thematic detail. At the most detailed level natural vegetation is represented by NatureServe's Ecological System classification (or in Hawaii the Hawaii GAP classification). These most detailed classifications have been crosswalked to the five highest levels of the National Vegetation Classification (NVC), Class, Subclass, Formation, Division and Macrogroup. This crosswalk allows users to display and analyze the data at different levels of thematic resolution. Developed areas, or areas dominated by introduced species, timber harvest, or water are represented by other classes, collectively refered to as land use classes; these land use classes occur at each of the thematic levels. Raster data in both ArcGIS Grid and ERDAS Imagine format is available for download at http://gis1.usgs.gov/csas/gap/viewer/land_cover/Map.aspx Six layer files are included in the download packages to assist the user in displaying the data at each of the Thematic levels in ArcGIS. In adition to the raster datasets the data is available in Web Mapping Services (WMS) format for each of the six NVC classification levels (Class, Subclass, Formation, Division, Macrogroup, Ecological System) at the following links. http://gis1.usgs.gov/arcgis/rest/services/gap/GAP_Land_Cover_NVC_Class_Landuse/MapServer http://gis1.usgs.gov/arcgis/rest/services/gap/GAP_Land_Cover_NVC_Subclass_Landuse/MapServer http://gis1.usgs.gov/arcgis/rest/services/gap/GAP_Land_Cover_NVC_Formation_Landuse/MapServer http://gis1.usgs.gov/arcgis/rest/services/gap/GAP_Land_Cover_NVC_Division_Landuse/MapServer http://gis1.usgs.gov/arcgis/rest/services/gap/GAP_Land_Cover_NVC_Macrogroup_Landuse/MapServer http://gis1.usgs.gov/arcgis/rest/services/gap/GAP_Land_Cover_Ecological_Systems_Landuse/MapServer

  10. Southwestern Region (Region 3) Geospatial Data

    • agdatacommons.nal.usda.gov
    bin
    Updated Feb 13, 2024
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    USDA Forest Service (2024). Southwestern Region (Region 3) Geospatial Data [Dataset]. https://agdatacommons.nal.usda.gov/articles/dataset/Southwestern_Region_Region_3_Geospatial_Data/24661962
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    binAvailable download formats
    Dataset updated
    Feb 13, 2024
    Dataset provided by
    U.S. Department of Agriculture Forest Servicehttp://fs.fed.us/
    Authors
    USDA Forest Service
    License

    U.S. Government Workshttps://www.usa.gov/government-works
    License information was derived automatically

    Description

    The Southwestern Region is 20.6 million acres. There are six national forests in Arizona, five national forests and a national grassland in New Mexico, and one national grassland each in Oklahoma and the Texas panhandle.The region ranges in elevation from 1,600 feet above sea level and an annual rain fall of 8 inches in Arizona's lower Sonoran Desert to 13,171-foot high Wheeler Peak and over 35 inches of precipitation a year in northern New Mexico. Geographic Information Systems or GIS are computer systems, software and data used to analyze and display spatial or locational data about surface features. One of the strengths of GIS is the capability to overlay or compare multiple feature layers. A user can then analyze the relationship between the layers. Data, reports and maps produced through GIS are used by managers and resource specialists to make decisions about land management activities on National Forests. The National Forests of the Southwestern Region maintain and utilize GIS data for various features on the ground. Some of these datasets are made available for download through this page. Resources in this dataset:Resource Title: GIS Datasets. File Name: Web Page, url: https://www.fs.usda.gov/detail/r3/landmanagement/gis/?cid=STELPRDB5202474 Selected GIS datasets for the Southwestern Region are available for download from this page.Resource Software Recommended: ArcExplorer,url: http://www.esri.com/software/arcexplorer/index.html

  11. a

    OGC Web Map Service (WMS):Petroleum System and Assessment of Oil and Gas,...

    • catalogue.arctic-sdi.org
    Updated May 23, 2022
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    (2022). OGC Web Map Service (WMS):Petroleum System and Assessment of Oil and Gas, Travis Peak-Hosston Formations, East Texas Basin and Louisiana-Mississippi Salt Basins Provinces, Texas, Louisiana, Mississippi, Alabama, and Florida [Dataset]. https://catalogue.arctic-sdi.org/geonetwork/srv/resources/datasets/c8997b22-359e-4046-a988-f67ee73f034a
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    Dataset updated
    May 23, 2022
    Area covered
    Travis Peak
    Description

    (See USGS Digital Data Series DDS-69-E) A geographic information system focusing on the Cretaceous Travis Peak and Hosston Formations was developed for the U.S. Geological Survey's (USGS) 2002 assessment of undiscovered, technically recoverable oil and natural gas resources of the Gulf Coast Region. The USGS Energy Resources Science Center has developed map and metadata services to deliver the 2002 assessment results GIS data and services online. The Gulf Coast assessment is based on geologic elements of a total petroleum system (TPS) as described in Dyman and Condon (2005). The estimates of undiscovered oil and gas resources are within assessment units (AUs). The hydrocarbon assessment units include the assessment results as attributes within the AU polygon feature class (in geodatabase and shapefile format). Quarter-mile cells of the land surface that include single or multiple wells were created by the USGS to illustrate the degree of exploration and the type and distribution of production for each assessment unit. Other data that are available in the map documents and services include the TPS and USGS province boundaries. To easily distribute the Gulf Coast maps and GIS data, a web mapping application has been developed by the USGS, and customized ArcMap (by ESRI) projects are available for download at the Energy Resources Science Center Gulf Coast website. ArcGIS Publisher (by ESRI) was used to create a published map file (pmf) from each ArcMap document (.mxd). The basemap services being used in the GC map applications are from ArcGIS Online Services (by ESRI), and include the following layers: -- Satellite imagery -- Shaded relief -- Transportation -- States -- Counties -- Cities -- National Forests With the ESRI_StreetMap_World_2D service, detailed data, such as railroads and airports, appear as the user zooms in at larger scales. This map service shows the structural configuration of the top of the Travis Peak or Hosston Formations in feet below sea level. The map was produced by calculating the difference between a datum at the land surface (either the Kelly bushing elevation or the ground surface elevation) and the reported depth of the Travis Peak or Hosston. This map service also shows the thickness of the interval from the top of the Travis Peak or Hosston Formations to the top of the Cotton Valley Group.

  12. d

    FEMA - Harvey Flood Depths Grid

    • dataone.org
    • hydroshare.org
    • +1more
    Updated Dec 5, 2021
    + more versions
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    Federal Emergency Management Administration (FEMA) (2021). FEMA - Harvey Flood Depths Grid [Dataset]. http://doi.org/10.4211/hs.165e2c3e335d40949dbf501c97827837
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    Dataset updated
    Dec 5, 2021
    Dataset provided by
    Hydroshare
    Authors
    Federal Emergency Management Administration (FEMA)
    Description

    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]

  13. z

    GLObal Building heights for Urban Studies (UT-GLOBUS)

    • zenodo.org
    bin, png, txt, zip
    Updated Feb 2, 2025
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    Harsh Kamath; Harsh Kamath; Manmeet Singh; Manmeet Singh; Neetiraj Malviya; Alberto Martilli; Alberto Martilli; Liu He; Daniel Aliaga; Daniel Aliaga; Cenlin He; Fei Chen; Fei Chen; Lori Magruder; Lori Magruder; Zong-Liang Yang; Zong-Liang Yang; Dev Niyogi; Dev Niyogi; Neetiraj Malviya; Liu He; Cenlin He (2025). GLObal Building heights for Urban Studies (UT-GLOBUS) [Dataset]. http://doi.org/10.5281/zenodo.11156602
    Explore at:
    txt, bin, zip, pngAvailable download formats
    Dataset updated
    Feb 2, 2025
    Dataset provided by
    Zenodo
    Authors
    Harsh Kamath; Harsh Kamath; Manmeet Singh; Manmeet Singh; Neetiraj Malviya; Alberto Martilli; Alberto Martilli; Liu He; Daniel Aliaga; Daniel Aliaga; Cenlin He; Fei Chen; Fei Chen; Lori Magruder; Lori Magruder; Zong-Liang Yang; Zong-Liang Yang; Dev Niyogi; Dev Niyogi; Neetiraj Malviya; Liu He; Cenlin He
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Important note: If you get a message that .zip archive is corrupt, please try updating WinRAR or right-click the folder and select Extract All on Windows or use unzip command on Linux terminal. If the issue persists, email: kamath.harsh@utexas.edu

    Abstract

    We introduce GLObal Building heights for Urban Studies (UT-GLOBUS), a dataset providing building heights and urban canopy parameters (UCPs) for major cities worldwide. UT-GLOBUS combines open-source spaceborne altimetry (ICESat-2 and GEDI) and coarse resolution urban canopy elevation data with a random forest model to estimate building-level information. Validation using LiDAR data from six U.S. cities showed UT-GLOBUS-derived building heights had an RMSE of 9.1 meters, and mean building height within 1-km² grid cells had an RMSE of 7.8 meters. Testing the UCPs in the urban Weather Research and Forecasting (WRF-Urban) model resulted in a significant improvement (~55% in RMSE) in intra-urban air temperature representation compared to the existing table-based local climate zone approach in Houston, TX. Additionally, we demonstrated the dataset's utility for simulating heat mitigation strategies and building energy consumption using WRF-Urban, with test cases in Chicago, IL, and Austin, TX. Street-scale mean radiant temperature simulations using the SOlar and LongWave Environmental Irradiance Geometry (SOLWEIG) model, incorporating UT-GLOBUS and LiDAR-derived building heights, confirmed the dataset’s effectiveness in modeling human thermal comfort at Baltimore, MD (daytime RMSE = 2.85°C). Thus, UT-GLOBUS can be used for modeling urban hazards with significant socioeconomic and ecological risks, enabling finer scale urban climate simulations and overcoming previous limitations due to the lack of building information.

    Data

    We are also supplying a vector file to represent the data coverage, and this file will receive updates as data for new city is added. Building-level data is accessible in vector file format (GeoPackage: .gpkg), which can be converted into raster file format (geoTIFF). These formats are compatible with the SUEWS and SOLWEIG models for the simulation of urban energy balance and thermal comfort. The vector files employ the Universal Transverse Mercator (UTM) projection. Both the vector and raster files are compatible with GIS platforms like QGIS and ArcGIS and can be imported for analysis using programming languages such as Python. We are also providing UCPs required by the BEP-BEM urban model in the urban WRF system in binary file format. Additionally, we provide the urban fractions calculated using ESA world cover dataset (https://esa-worldcover.org/en) for WRF model in binary file format. These files can be directly incorporated into the WRF pre-processing system (WPS). The UT-GLOBUS UCPs are determined using a moving kernel with a size of 1 km2 and spacing of 300 meters in both the X and Y directions

    Data coverage

    The 'Coverage_xxxx.gpkg' files provide that geographical extents of cities that are included in our dataset.

    How to find your city in the UT-GLOBUS dataset

    Open the 'coverage' geopackage (.gpkg) files in QGIS or ArcGIS. Click on the city polygons and get the 'Label'/City name. Find a folder with the same 'Label'/City name. All the data for the periticular city will be in the folder.

    How to run BEP-BEM model in WRF using UT-GLOBUS urban canopy parameters

    Step 0: Before compiling WRF, go to 'dyn_em' folder and open 'module_initialize_real.F'.
    Change line 3121 (in version 4.5.2):
    From
    grid%HI_URB2D(i,k,j) = grid%URB_PARAM(i,k+117,j)
    To
    grid%HI_URB2D(i,k,j) = grid%URB_PARAM(i,k+117,j)*100.
    1. Change the name of the binary files 'ufrac' and 'urb_param' inside 'urb_fra' and 'GLOBUS_morph' folders, respectively to 00001-tile_x.00001-tile_y.
    Values for tile_x and tile_y can be found in the index file inside the 'urb_fra' and 'GLOBUS_morph' folders. Make sure to append zeros before tile_x and tile_y values to make 5 digits.
    Ex: tile_x = 260 and tile_y = 219; Then the binary files should be renamed as 00001-00260.00001-00209
    2. Copy the 'urb_fra' and 'GLOBUS_morph' folders to WRF static data directory.
    3. Change the paths to 'URB_PARAM' and 'FRC_URB2D' variables inside GEOGRID.TBL file as follows:
    ===============================
    name=URB_PARAM
    priority=1
    optional=yes
    dest_type=continuous
    fill_missing = 0.
    z_dim_name=num_urb_params
    interp_option=default:nearest_neighbor
    abs_path= Your_WPS_static_data_folder/GLOBUS_morph/
    flag_in_output=FLAG_URB_PARAM
    ===============================
    name=FRC_URB2D
    priority=1
    optional=yes
    dest_type=continuous
    fill_missing = 0.
    interp_option=default:nearest_neighbor
    abs_path= Your_WPS_static_data_folder/urb_fra/
    flag_in_output=FLAG_FRC_URB2D
    ===============================
    4. Run geogrid.exe. If the domain covers the chosen city:
    -- 'FRC_URB2D' variable will show the urban fraction.
    -- 'URB_PARAM[91,:,:]' will show the plan area fraction.
    -- 'URB_PARAM[94,:,:]' will show the area averaged building heights.
    -- 'URB_PARAM[95,:,:]' will show the building surface to total area fraction.
    -- 'URB_PARAM[118-132,:,:]' will show the building height histograms with 5-meter bin size.
    5. If you see the data in 'FRC_URB2D' and 'URB_PARAM' variables after running the geogrid.exe, GLOBUS data is ingested in WPS and you can continue with ungrib and metgrid as usual.
    6. For running the model over the domain area which covers more that one city, UT-GLOBUS UCPs can be stitched together. For instance, if two cities are covered in the domain, step number 3 should be modified as follows:
    ===============================
    name=URB_PARAM
    priority=1
    dest_type=continuous
    fill_missing = 0.
    z_dim_name=num_urb_params
    interp_option=default:nearest_neighbor
    abs_path=Your_WPS_static_data_folder/GLOBUS_morph_for_city-1/
    flag_in_output=FLAG_URB_PARAM
    ===============================
    name=FRC_URB2D
    priority=1
    dest_type=continuous
    fill_missing = 0.
    interp_option=default:nearest_neighbor
    abs_path= Your_WPS_static_data_folder/urb_fra_for_city-1/
    flag_in_output=FLAG_FRC_URB2D
    ===============================
    name=URB_PARAM
    priority=2
    dest_type=continuous
    fill_missing = 0.
    z_dim_name=num_urb_params
    interp_option=default:nearest_neighbor
    abs_path= Your_WPS_static_data_folder/GLOBUS_morph_for_city-2/
    ===============================
    name=FRC_URB2D
    priority=2
    dest_type=continuous
    fill_missing = 0.
    interp_option=default:nearest_neighbor
    abs_path= Your_WPS_static_data_folder/urb_fra_for_city-2/
    ===============================
    References
    1. Skamarock, W., Klemp, J., Dudhia, J., Gill, D., Liu, Z., Berner, J., Wang, W., Powers, J., Duda, M., Barker, D., Huang, X., 2021. A Description of the advanced research WRF model.
    2. Martilli, A., Clappier, A., Rotach, M.W., 2002. An urban surface exchange parameterisation for mesoscale models. Boundary Layer Meteorol 104, 261–304. https://doi.org/10.1023/A:1016099921195
    3. Sun, T., Grimmond, S., 2019. A Python-enhanced urban land surface model SuPy (SUEWS in Python, v2019.2): Development, deployment and demonstration. Geosci Model Dev 12, 2781–2795. https://doi.org/10.5194/gmd-12-2781-2019
    4. Lindberg, F., Holmer, B., Thorsson, S., 2008. SOLWEIG 1.0 - Modelling spatial variations of 3D radiant fluxes and mean radiant temperature in complex urban settings. Int J Biometeorol 52, 697–713. https://doi.org/10.1007/s00484-008-0162-7
    5. Software: QGIS (https://www.qgis.org/en/site/)
  14. H

    Height Above Nearest Drainage (HAND) for CONUS

    • beta.hydroshare.org
    • hydroshare.org
    • +2more
    zip
    Updated Nov 28, 2023
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    Yan Liu (2023). Height Above Nearest Drainage (HAND) for CONUS [Dataset]. http://doi.org/10.4211/hs.73aaa3efcda2465ba6227f535400f36b
    Explore at:
    zip(774.3 KB)Available download formats
    Dataset updated
    Nov 28, 2023
    Dataset provided by
    HydroShare
    Authors
    Yan Liu
    Description

    Height Above Nearest Drainage (HAND) is an approach for estimating the vertical height of any point on the landscape from the nearest stream surface or bed. The first version 0.1 of this dataset is based on the U.S. Geological Survey's National Elevation Dataset (NED) with 10-meter horizontal resolution, comprising raster data for the 331 HUC-6 units in conterminous U.S. (CONUS), excluding the five units of the great lakes. This was developed at the UIUC CyberGIS supercomputing facility, and is now archived at the UT Austin TACC (Texas Advanced Computing Center) for download [1]. As of summer 2020, it has been updated to version 0.2, now hosted at Oak Ridge National Lab's HPC server [2]. The 2017 Harvey subset of CONUS HAND is at [3].

    In 2023, Oak Ridge National Laboratory (ORNL) computed the 3-meter HAND for Texas, see [4].

    To interactively select HAND data by HUC6 basin in either the Harvey or Irma hydrologic study area, use the Harvey Archive Story Map [https://arcg.is/1rWLzL0] or the Irma Archive Story Map [http://arcg.is/PSOKH] and click on the HAND tab. To directly browse this data for anywhere in CONUS, visit [1] or [2].

    References: For a bibliography of technical papers leading to the development of HAND, see the PrimaryRefs_NWM-HAND_Jan2018.pdf file in the contents list below. For an explanation of the contents of the nfiedata folder at TACC, see the README-Nfiedata_HAND.pdf file in the contents list below.

    [1] University of Texas Advanced Computing Center (TACC) repository for version 0.1 of 10m CONUS HAND [https://web.corral.tacc.utexas.edu/nfiedata/] [2] Most recent HAND products at ORNL [https://cfim.ornl.gov/data/] [3] Harvey subset of national HAND [https://cfim.ornl.gov/data/nfiedata/Harvey/] [4] 3m HAND for state of Texas computed by ORNL [https://web.corral.tacc.utexas.edu/nfiedata/pin2flood/texas/]

  15. Not seeing a result you expected?
    Learn how you can add new datasets to our index.

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HydroShare (2023). Texas Basemap - Lidar Elevation Data (DEM) [Dataset]. http://doi.org/10.4211/hs.af6ae321e2ad40a1bc6d0b695370fbfc

Texas Basemap - Lidar Elevation Data (DEM)

Related Article
Explore at:
zip(5.5 GB)Available download formats
Dataset updated
Nov 3, 2023
Dataset provided by
HydroShare
License

Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically

Area covered
Texas
Description

This resource contains Lidar-DEM collection status shapefiles from the Texas Natural Resources Information System (TNRIS) [http://tnris.org]. November 2023 updates: this year, TNRIS changed its name to Texas Geographic Information Office (TxGIO). The domain name hasn't changed yet, but the data hub is continually evolving. See [1], [2] for current downloadable data.

For purposes of Hurricane Harvey studies, the 1-m DEM for Harris County (2008) has also been uploaded here as a set of 4 zipfiles containing the DEM in tiff files. See [1] for a link to the current elevation status map and downloadable DEMs.
Project name: H-GAC 2008 1m Datasets: 1m Point Cloud, 1M Hydro-Enforced DEM, 3D Breaklines, 1ft and 5ft Contours Points per sq meter: 1 Total area: 3678.56 sq miles Source: Houston-Galveston Area Council (H-GAC) Acquired by: Merrick, QA/QC: Merrick Catalog: houston-galveston-area-council-h-gac-2008-lidar

References: [1] TNRIS/TxGIO StratMap elevation data [https://tnris.org/stratmap/elevation-lidar/] [2] TNRIS/TxGIO DataHub [https://data.tnris.org/]

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