This slope layer was created by Tara Atwood, Intern, Geospatial Centroid, on 3/30/2021. This data is part of an effort to provide base-level spatial data for the Todos Santos region via ArcGIS Hub for CSU researchers and others doing work in this area. The link to the original source of the DEM used is here: http://en.www.inegi.org.mx/app/biblioteca/ficha.html?upc=702825736897
This data was downloaded by Tara Atwood, Intern, Geospatial Centroid, on 3/19/2021 from INEGI. This data is part of an effort to provide base-level spatial data for the Todos Santos region via ArcGIS Hub for CSU researchers and others doing work in this area. The original name for this layer is TS_f12b33_tanque_agua50_p_utm. The link to the original source is here: http://en.www.inegi.org.mx/app/biblioteca/ficha.html?upc=998880003841.
This contour layer was created by Tara Atwood, Intern, Geospatial Centroid, on 3/30/2021. This data is part of an effort to provide base-level spatial data for the Todos Santos region via ArcGIS Hub for CSU researchers and others doing work in this area. The link to the original source of the DEM used is here: http://en.www.inegi.org.mx/app/biblioteca/ficha.html?upc=702825736897
This data was downloaded by Tara Atwood, Intern, Geospatial Centroid, on 3/19/2021 from INEGI. This data is part of an effort to provide base-level spatial data for the Todos Santos region via ArcGIS Hub for CSU researchers and others doing work in this area. The original name for this layer is f12b33_area_cultiv50_a_utm. The link to the original source is here: http://en.www.inegi.org.mx/app/biblioteca/ficha.html?upc=998880003841.
This data was downloaded by Tara Atwood, Intern, Geospatial Centroid, on 3/19/2021 from INEGI. This data is part of an effort to provide base-level spatial data for the Todos Santos region via ArcGIS Hub for CSU researchers and others doing work in this area. The original name for this layer is f12b33_ins_deportiv50_a_utm. The link to the original source is here: http://en.www.inegi.org.mx/app/biblioteca/ficha.html?upc=998880003841.
This data was downloaded by Tara Atwood, Intern, Geospatial Centroid, on 3/19/2021 from INEGI. This data is part of an effort to provide base-level spatial data for the Todos Santos region via ArcGIS Hub for CSU researchers and others doing work in this area. The original name for this layer is f12b33_area_urb50_a_utm. The link to the original source is here: http://en.www.inegi.org.mx/app/biblioteca/ficha.html?upc=998880003841.
This data was downloaded by Tara Atwood, Intern, Geospatial Centroid, on 3/19/2021 from INEGI. This data is part of an effort to provide base-level spatial data for the Todos Santos region via ArcGIS Hub for CSU researchers and others doing work in this area. The original name for this layer is f12b33_camino50_l_utm. The link to the original source is here: http://en.www.inegi.org.mx/app/biblioteca/ficha.html?upc=998880003841.
This data was downloaded by Tara Atwood, Intern, Geospatial Centroid, on 3/19/2021 from Berkeley Library GeoData . This data is part of an effort to provide base-level spatial data for the Todos Santos region via ArcGIS Hub for CSU researchers and others doing work in this area. The original name for this layer is nyu_2451_36786. The link to the original source is here: https://geodata.lib.berkeley.edu/catalog/nyu-2451-36786
This data was downloaded by Tara Atwood, Intern, Geospatial Centroid, on 3/19/2021 from Berkeley Library GeoData . This data is part of an effort to provide base-level spatial data for the Todos Santos region via ArcGIS Hub for CSU researchers and others doing work in this area. The original name for this layer is nyu_2451_36786. The link to the original source is here: https://geodata.lib.berkeley.edu/catalog/nyu-2451-36786
This data was downloaded by Tara Atwood, Intern, Geospatial Centroid, on 3/19/2021 from INEGI. This data is part of an effort to provide base-level spatial data for the Todos Santos region via ArcGIS Hub for CSU researchers and others doing work in this area. The original name for this layer is gm_2020_MX_009004_t. The link to the original source is here: http://en.www.inegi.org.mx/app/biblioteca/ficha.html?upc=998880004124
This hillshade layer was created by Tara Atwood, Intern, Geospatial Centroid, on 3/30/2021. This data is part of an effort to provide base-level spatial data for the Todos Santos region via ArcGIS Hub for CSU researchers and others doing work in this area. The link to the original source of the DEM used is here: http://en.www.inegi.org.mx/app/biblioteca/ficha.html?upc=702825736897
This data was downloaded by Tara Atwood, Intern, Geospatial Centroid, on 3/19/2021 from INEGI. This data is part of an effort to provide base-level spatial data for the Todos Santos region via ArcGIS Hub for CSU researchers and others doing work in this area. The original name for this layer is f12b33_carretera50_l_utm. The link to the original source is here: http://en.www.inegi.org.mx/app/biblioteca/ficha.html?upc=998880003841.
This data was downloaded by Tara Atwood, Intern, Geospatial Centroid, on 3/19/2021 from INEGI. This data is part of an effort to provide base-level spatial data for the Todos Santos region via ArcGIS Hub for CSU researchers and others doing work in this area. The original name for this layer is f12b33_localidad50_p_utm. The link to the original source is here: http://en.www.inegi.org.mx/app/biblioteca/ficha.html?upc=998880003841.
This data was downloaded by Tara Atwood, Intern, Geospatial Centroid, on 3/19/2021 from Berkeley Library GeoData . This data is part of an effort to provide base-level spatial data for the Todos Santos region via ArcGIS Hub for CSU researchers and others doing work in this area. Two INEGI shapefiles were merged to make this layer. The original name for the layers were nyu_2451_36786 and denue_inegi_03_.shp. The link to the original source is here: https://geodata.lib.berkeley.edu/catalog/nyu-2451-36786
This aspect layer was created by Tara Atwood, Intern, Geospatial Centroid, on 3/30/2021. This data is part of an effort to provide base-level spatial data for the Todos Santos region via ArcGIS Hub for CSU researchers and others doing work in this area. The link to the original source of the DEM used is here: http://en.www.inegi.org.mx/app/biblioteca/ficha.html?upc=702825736897
This data was downloaded by Tara Atwood, Intern, Geospatial Centroid, on 3/19/2021 from INEGI. This data is part of an effort to provide base-level spatial data for the Todos Santos region via ArcGIS Hub for CSU researchers and others doing work in this area. The original name for this layer is 702825736897_b. The link to the original source is here: http://en.www.inegi.org.mx/app/biblioteca/ficha.html?upc=702825736897
The purpose of this layer is to inventory and track recreational uses (e.g. hike, bike, OHV... etc.) permitted on designated trails within Colorado Parks & Wildlife properties.
This map layer is a product and property of Colorado Parks & Wildlife, a division of the Colorado Department of Natural Resources. Care should be taken in interpreting these data. Written documents may accompany this map layer and should be referenced. The information portrayed on these map layers should not replace field studies necessary for more localized planning efforts. The data are typically gathered at a scale of 1:24000 or 1:50000; discrepencies may become apparent at larger scales. The areas portrayed here are graphic representations of phenomena that are difficult to reduce to two dimensions. The Colorado Department of Natural Resources is not responsible and shall not be liable to the user for damages of any kind arising out of the use of data or information provided by the Department, including the installation of the data or information, its use, or the results obtained from its use. ANY DATA OR INFORMATION PROVIDED BY THE DEPARTMENT OF NATURAL RESOURCES IS PROVIDED \"AS IS\" WITHOUT WARRANTY OF ANY KIND, EITHER EXPRESS OR IMPLIED, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE. Data or information provided by the Department of Natural Resources shall be used and relied upon only at the user's sole risk, and the user agrees to indemnify and hold harmless the Department of Natural Resources, its officials, officers and employees from any liability arising out of the use of the data or information provided.
The data for the NHDPlus_CRBRivers dataset was compiled from NHDPlus Version 2. Please visit https://www.horizon-systems.com/NHDPlus/NHDPlusV2_data.php for the source data, data disclaimers and metadata if not available in these data layer files. Much of the metadata for the files in the dataset are complete and were imported with the original file. In cases where there are discrepancies, metadata have been added where appropriate.
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Grid with estimated precipitation per month with a spatial resolution of 10 arc minutes. This dataset has been constructed from 27 075 stations with 1961-1990 climatological normals. The dataset consists of 12 ASCII-grids with mean monthly data in mm/day * 10, and one ASCII-grid with yearly data in mm/year. In addition, 12 ASCII-grid with monthly values at 5 arc minutes resolution are made available as input data for a global water balance model (GlobWat).
There are no stated use limitation, the data is publicly available from the Food and Agriculture Organization of the United Nations.
The data for the California dataset was primarily compiled from CalAtlas Geospatial Clearinghouse (https://www.dot.ca.gov/hq/tsip/gis/datalibrary/gisdatalibrary.html) and the Redlands Institute. Please visit their websites and for the source data, data disclaimers and metadata if not available in these data layer files. Much of the metadata for the files in the dataset are complete and were imported with the original file. In cases where there are discrepancies, metadata have been added where appropriate.
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Grid with estimated precipitation per month with a spatial resolution of 10 arc minutes. This dataset has been constructed from 27 075 stations with 1961-1990 climatological normals. The dataset consists of 12 ASCII-grids with mean monthly data in mm/day * 10, and one ASCII-grid with yearly data in mm/year. In addition, 12 ASCII-grid with monthly values at 5 arc minutes resolution are made available as input data for a global water balance model (GlobWat).
There are no stated use limitation, the data is publicly available from the Food and Agriculture Organization of the United Nations.
The data for the California dataset was primarily compiled from CalAtlas Geospatial Clearinghouse (https://www.dot.ca.gov/hq/tsip/gis/datalibrary/gisdatalibrary.html) and the Redlands Institute. Please visit their websites and for the source data, data disclaimers and metadata if not available in these data layer files. Much of the metadata for the files in the dataset are complete and were imported with the original file. In cases where there are discrepancies, metadata have been added where appropriate.
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Grid with estimated precipitation per month with a spatial resolution of 10 arc minutes. This dataset has been constructed from 27 075 stations with 1961-1990 climatological normals. The dataset consists of 12 ASCII-grids with mean monthly data in mm/day * 10, and one ASCII-grid with yearly data in mm/year. In addition, 12 ASCII-grid with monthly values at 5 arc minutes resolution are made available as input data for a global water balance model (GlobWat).
There are no stated use limitation, the data is publicly available from the Food and Agriculture Organization of the United Nations.
The data for the California dataset was primarily compiled from CalAtlas Geospatial Clearinghouse (https://www.dot.ca.gov/hq/tsip/gis/datalibrary/gisdatalibrary.html) and the Redlands Institute. Please visit their websites and for the source data, data disclaimers and metadata if not available in these data layer files. Much of the metadata for the files in the dataset are complete and were imported with the original file. In cases where there are discrepancies, metadata have been added where appropriate.
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http://cloud.insideidaho.org/webApps/util/zip.ashx?fn=http://cloud.insideidaho.org/data/ics/koppen
The Koppen Climate Classification grid coverages were produced using gridded estimates of precipitation, temperature, and elevation from the PRISM (Parameter-elevation Regressions on Independent Slopes Model). The model was developed at Oregon State University and information about the gridded ASCII data sets can be obtained from: http://www.ocs.orst.edu/prism/.
Procedures_Used: 1. Aquired the PRISM ASCII files from Oregon Climate Service (http://www.ocs.orst.edu/prism/). 2. Aquired the United State boundary coverage (1:2,000,000) from the GAP Analysis Project Office at the University of Idaho http://www.wildlife.uidaho.edu/). This coverage was orignally obtained by GAP from the ESRI data site (www.esri.com). 3. Ran the main aml (koeppen.aml) to produce the classification. 4. Merged the individual grids into one grid containing the lowest subclasses.
The data for the California dataset was primarily compiled from CalAtlas Geospatial Clearinghouse (https://www.dot.ca.gov/hq/tsip/gis/datalibrary/gisdatalibrary.html) and the Redlands Institute. Please visit their websites and for the source data, data disclaimers and metadata if not available in these data layer files. Much of the metadata for the files in the dataset are complete and were imported with the original file. In cases where there are discrepancies, metadata have been added where appropriate.
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This is WorldClim 2.0 Beta version 1 (June 2016) downloaded from http://worldclim.org
They represent average monthly climate data for 1970-2000.
Data have been clipped to the Rio Grande Hydrologic Unit
The data for the California dataset was primarily compiled from CalAtlas Geospatial Clearinghouse (https://www.dot.ca.gov/hq/tsip/gis/datalibrary/gisdatalibrary.html) and the Redlands Institute. Please visit their websites and for the source data, data disclaimers and metadata if not available in these data layer files. Much of the metadata for the files in the dataset are complete and were imported with the original file. In cases where there are discrepancies, metadata have been added where appropriate.
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Grid with estimated precipitation per month with a spatial resolution of 10 arc minutes. This dataset has been constructed from 27 075 stations with 1961-1990 climatological normals. The dataset consists of 12 ASCII-grids with mean monthly data in mm/day * 10, and one ASCII-grid with yearly data in mm/year. In addition, 12 ASCII-grid with monthly values at 5 arc minutes resolution are made available as input data for a global water balance model (GlobWat).
There are no stated use limitation, the data is publicly available from the Food and Agriculture Organization of the United Nations.
Mean temperature of the Rio Grande River watershed from 1970 to 2000, during the month of December.
This is WorldClim 2.0 Beta version 1 (June 2016) downloaded from http://worldclim.org
They represent average monthly climate data for 1970-2000.
Data have been clipped to the Rio Grande Hydrologic Unit
No redistribution
Estimated precipitation of the Rio Grande River Basin, during the month of April.
Grid with estimated precipitation per month with a spatial resolution of 10 arc minutes. This dataset has been constructed from 27 075 stations with 1961-1990 climatological normals. The dataset consists of 12 ASCII-grids with mean monthly data in mm/day * 10, and one ASCII-grid with yearly data in mm/year. In addition, 12 ASCII-grid with monthly values at 5 arc minutes resolution are made available as input data for a global water balance model (GlobWat).
There are no stated use limitation, the data is publicly available from the Food and Agriculture Organization of the United Nations.
The Colorado Trail System, now titled the Colorado Trail Explorer (COTREX), endeavors to map every trail in the state of Colorado. Currently there are nearly 40,000 miles of trails mapped. Trails come from a variety of sources (USFS, BLM, local parks & recreation departments, local governments). Responsibility for accuracy of the data rests with the source.
These data were last updated on 9/11/2019
This map layer is a product and property of Colorado Parks & Wildlife, a division of the Colorado Department of Natural Resources. Care should be taken in interpreting these data. Written documents may accompany this map layer and should be referenced. The information portrayed on these map layers should not replace field studies necessary for more localized planning efforts. The Colorado Department of Natural Resources is not responsible and shall not be liable to the user for damages of any kind arising out of the use of data or information provided by the Department, including the installation of the data or information, its use, or the results obtained from its use. ANY DATA OR INFORMATION PROVIDED BY THE DEPARTMENT OF NATURAL RESOURCES IS PROVIDED \"AS IS\" WITHOUT WARRANTY OF ANY KIND, EITHER EXPRESS OR IMPLIED, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE. Data or information provided by the Department of Natural Resources shall be used and relied upon only at the user's sole risk, and the user agrees to indemnify and hold harmless the Department of Natural Resources, its officials, officers and employees from any liability arising out of the use of the data or information provided.
USDA/NRCS - National Geospatial Center of Excellence, USDA Service Center Agencies. Clipped to the Cache la Poudre watershed by Nick Vogel, intern at The Geospatial Centroid, Colorado State University, 04/08/2020
Features in this dataset represent segments of public highways that are maintained by and under the jurisdiction of the Colorado Department of Transportation clipped to the Cache la Poudre watershed. These highways consist of Interstates, US Highways, and State Highways within the watershed. Features are represented by polyline (linear) geographic shapes. Bounding coordinates and feature counts reflect a statewide extent.
U.S. Major Roads represents interstate, U.S., and state highways and other major thoroughfares within the United States clipped to the Cache la Poudre River Watershed.
The National Hydrography Dataset (NHD) is a feature-based database that interconnects and uniquely identifies the stream segments or reaches that make up the nation's surface water drainage system. NHD data was originally developed at 1:100,000-scale and exists at that scale for the whole country. This high-resolution NHD, generally developed at 1:24,000/1:12,000 scale, adds detail to the original 1:100,000-scale NHD. (Data for Alaska, Puerto Rico and the Virgin Islands was developed at high-resolution, not 1:100,000 scale.) Local resolution NHD is being developed where partners and data exist. The NHD contains reach codes for networked features, flow direction, names, and centerline representations for areal water bodies. Reaches are also defined on waterbodies and the approximate shorelines of the Great Lakes, the Atlantic and Pacific Oceans and the Gulf of Mexico. The NHD also incorporates the National Spatial Data Infrastructure framework criteria established by the Federal Geographic Data Committee.
The data for the California dataset was primarily compiled from CalAtlas Geospatial Clearinghouse (https://www.dot.ca.gov/hq/tsip/gis/datalibrary/gisdatalibrary.html) and the Redlands Institute. Please visit their websites and for the source data, data disclaimers and metadata if not available in these data layer files. Much of the metadata for the files in the dataset are complete and were imported with the original file. In cases where there are discrepancies, metadata have been added where appropriate.
", "keyword": [ "California", "CRB_CA", "crb", "watershed", "colorado river basin", "counties", "irrigation districts", "CVWD Management Boundary", "HUC12", "IID Service District", "Salton Sea Authority", "USBR Private Water District", "USBR", "State Water District", "Metropolitan Water District", "Water", "CA", "administrative divisions", "private water districts" ], "issued": "2020-08-13T20:30:19.000Z", "modified": "2020-08-13T20:31:44.946Z", "publisher": { "name": "Colorado State University" }, "contactPoint": { "@type": "vcard:Contact", "fn": "jlc4_CSUrams8", "hasEmail": "mailto: gis@colostate.edu" }, "accessLevel": "public", "spatial": "-83.0295,70.7263,-63.1581,82.2778", "license": "", "distribution": [ { "@type": "dcat:Distribution", "title": "ArcGIS Hub Dataset", "format": "Web Page", "mediaType": "text/html", "accessURL": "https://geospatialcentroid-csurams.hub.arcgis.com/datasets/CSUrams::ca-usbrstatewaterdistricts" }, { "@type": "dcat:Distribution", "title": "ArcGIS GeoService", "format": "ArcGIS GeoServices REST API", "mediaType": "application/json", "accessURL": "https://services1.arcgis.com/KNdRU5cN6ENqCTjk/arcgis/rest/services/CRB_California/FeatureServer/9" }, { "@type": "dcat:Distribution", "title": "CSV", "format": "CSV", "mediaType": "text/csv", "accessURL": "https://geospatialcentroid-csurams.hub.arcgis.com/api/download/v1/items/7949bb91a02741a7961712a7b81b7b9e/csv?layers=9" }, { "@type": "dcat:Distribution", "title": "Shapefile", "format": "ZIP", "mediaType": "application/zip", "accessURL": "https://geospatialcentroid-csurams.hub.arcgis.com/api/download/v1/items/7949bb91a02741a7961712a7b81b7b9e/shapefile?layers=9" }, { "@type": "dcat:Distribution", "title": "GeoJSON", "format": "GeoJSON", "mediaType": "application/vnd.geo+json", "accessURL": "https://geospatialcentroid-csurams.hub.arcgis.com/api/download/v1/items/7949bb91a02741a7961712a7b81b7b9e/geojson?layers=9" }, { "@type": "dcat:Distribution", "title": "KML", "format": "KML", "mediaType": "application/vnd.google-earth.kml+xml", "accessURL": "https://geospatialcentroid-csurams.hub.arcgis.com/api/download/v1/items/7949bb91a02741a7961712a7b81b7b9e/kml?layers=9" } ], "theme": [ "geospatial" ] }, { "@type": "dcat:Dataset", "identifier": "https://www.arcgis.com/home/item.html?id=30fdc5a248254581b173b5e184442856&sublayer=0", "landingPage": "https://geospatialcentroid-csurams.hub.arcgis.com/maps/CSUrams::rg-dem", "title": "RG DEM", "description": "The ASTER Digital Elevation Model (AST14DEM) product is generated using bands 3N (nadir-viewing) and 3B (backward-viewing) of an (ASTER Level 1A) (https://doi.org/10.5067/ASTER/AST_L1A.003) image acquired by the Visible and Near Infrared (VNIR) sensor. The VNIR subsystem includes two independent telescope assemblies that facilitate the generation of stereoscopic data. The band 3 stereo pair is acquired in the spectral range of 0.78 and 0.86 microns with a base-to-height ratio of 0.6 and an intersection angle of 27.7 degrees. There is a time lag of approximately one minute between the acquisition of the nadir and backward images.For a better understanding, refer to this (diagram) (https://lpdaac.usgs.gov/documents/301/ASTER_Along_Track_Imaging_Geometry.png) depicting the along-track imaging geometry of the ASTER VNIR nadir and backward-viewing sensors.
The accuracy of the new LP DAAC produced DEMs will meet or exceed accuracy specifications set for the ASTER relative DEMs by the Algorithm Theoretical Basis Document (ATBD) (https://lpdaac.usgs.gov/documents/81/AST14_ATBD.pdf). Users likely will find that the DEMs produced by the new LP DAAC system have accuracies approaching those specified in the ATBD for absolute DEMs. Validation testing has shown that DEMs produced by the new system frequently are more accurate than 25 meters root mean square error (RMSE) in xyz dimensions.
Major roads in Mexico (clipped to WBD) from the UC Davis Rio Grande Basin Dataset
The fundamental flow network, from the National Hydrography Dataset, consisting predominantly of stream/river and artificial path vector features. It represents the spatial geometry, carries the attributes, and contains linear referencing measures for locating features or “events” on the network. Additional NHDFlowline features are canal/ditch, pipeline, connector, underground conduit, and coastline.
The most current version of the National Hydrography Dataset, the NHD High Resolution, is mapped at a scale of 1:24,000 scale or better (1:63,360 or better in Alaska). These data are updated and maintained through Stewardship partnerships with states and other collaborative bodies.
CDSS Flow Stations (Stream Gages), Colorado, Database version 20090701
Aquifer boundaries according to the procedures manual for the delimitation of aquifers from the technical general subdirection of groundwater management of the National Water Commission of Mexico.
This information may be used by any government agency, if it is not part of the government, it will be necessary to send a letter of request to the Underground Water Manager, Mr. Rubén Chávez Guillén at the following address Av. Insurgentes Sur # 2416 piso 9 ala sur, Col. Copilco El Bajo, cp 04340 Mexico City or by e-mail to: ruben.chavez@conagua.gob.mx
The fundamental flow network, from the National Hydrography Dataset, consisting predominantly of stream/river and artificial path vector features. It represents the spatial geometry, carries the attributes, and contains linear referencing measures for locating features or “events” on the network. Additional NHDFlowline features are canal/ditch, pipeline, connector, underground conduit, and coastline.
The most current version of the National Hydrography Dataset, the NHD High Resolution, is mapped at a scale of 1:24,000 scale or better (1:63,360 or better in Alaska). These data are updated and maintained through Stewardship partnerships with states and other collaborative bodies.
Colorado Ownership, Management and Protection is a comprehensive dataset of land ownership and management. This version contains data from Federal agencies, State agencies, The Nature Conservancy, city and county sources and land trust sources. Data was collected from multiple sources and processed into one complete layer
Hydrologic Unit delineation are closed polygons that encompass all area draining toward the lowest point (called outlet or pour point) in the polygon. Because of varying sizes for the different hydrologic unit levels: some polygons do not include all areas up to the drainage divide, but all areas are included up to one or more other upstream hydrologic units. A unique hydrologic unit code is used to identify each hydrologic unit. The hydrologic unit codes start with the 2-digit Region number that contains the 4-, 6-, and 8-digit hydrologic units. Each hydrologic unit has a unique hydrologic code.
Hydrologic Unit delineation are closed polygons that encompass all area draining toward the lowest point (called outlet or pour point) in the polygon. Because of varying sizes for the different hydrologic unit levels: some polygons do not include all areas up to the drainage divide, but all areas are included up to one or more other upstream hydrologic units. A unique hydrologic unit code is used to identify each hydrologic unit. The hydrologic unit codes start with the 2-digit Region number that contains the 4-, 6-, and 8-digit hydrologic units. Each hydrologic unit has a unique hydrologic code.
Hydrologic Unit delineation are closed polygons that encompass all area draining toward the lowest point (called outlet or pour point) in the polygon. Because of varying sizes for the different hydrologic unit levels: some polygons do not include all areas up to the drainage divide, but all areas are included up to one or more other upstream hydrologic units. A unique hydrologic unit code is used to identify each hydrologic unit. The hydrologic unit codes start with the 2-digit Region number that contains the 4-, 6-, and 8-digit hydrologic units. Each hydrologic unit has a unique hydrologic code.
National landcover dataset - 2016. Clipped to Big Thompson watershed.
The U.S. Department of Agriculture, Service Center Agencies should be acknowledged as the data source in products derived from these data.This data set is not designed for use as a primary regulatory tool in permitting or citing decisions, but may be used as a reference source. This information may be interpreted by organizations, agencies, units of government, or others based on needs; however, they are responsible for the appropriate application. Federal, State, or local regulatory bodies are not to reassign to the Service Center Agencies any authority for the decisions that they make. The Service Center Agencies will not perform any evaluations of these data for purposes related solely to State or local regulatory programs.Photographic or digital enlargement of these data to scales greater than at which they were originally mapped can cause misinterpretation of the data. Digital data files are periodically updated, and users are responsible for obtaining the latest version of the data.
The U.S. Department of Agriculture, Service Center Agencies should be acknowledged as the data source in products derived from these data.This data set is not designed for use as a primary regulatory tool in permitting or citing decisions, but may be used as a reference source. This information may be interpreted by organizations, agencies, units of government, or others based on needs; however, they are responsible for the appropriate application. Federal, State, or local regulatory bodies are not to reassign to the Service Center Agencies any authority for the decisions that they make. The Service Center Agencies will not perform any evaluations of these data for purposes related solely to State or local regulatory programs. Photographic or digital enlargement of these data to scales greater than at which they were originally mapped can cause misinterpretation of the data. Digital data files are periodically updated, and users are responsible for obtaining the latest version of the data.
The National Hydrography Dataset (NHD) is a feature-based database that interconnects and uniquely identifies the stream segments or reaches that make up the nation's surface water drainage system. NHD data was originally developed at 1:100,000-scale and exists at that scale for the whole country. This high-resolution NHD, generally developed at 1:24,000/1:12,000 scale, adds detail to the original 1:100,000-scale NHD. (Data for Alaska, Puerto Rico and the Virgin Islands was developed at high-resolution, not 1:100,000 scale.) Local resolution NHD is being developed where partners and data exist. The NHD contains reach codes for networked features, flow direction, names, and centerline representations for areal water bodies. Reaches are also defined on waterbodies and the approximate shorelines of the Great Lakes, the Atlantic and Pacific Oceans and the Gulf of Mexico. The NHD also incorporates the National Spatial Data Infrastructure framework criteria established by the Federal Geographic Data Committee.
None. Acknowledgment of the originating agencies would be appreciated in products derived from these data.
The National Hydrography Dataset (NHD) is a feature-based database that interconnects and uniquely identifies the stream segments or reaches that make up the nation's surface water drainage system. NHD data was originally developed at 1:100,000-scale and exists at that scale for the whole country. This high-resolution NHD, generally developed at 1:24,000/1:12,000 scale, adds detail to the original 1:100,000-scale NHD. (Data for Alaska, Puerto Rico and the Virgin Islands was developed at high-resolution, not 1:100,000 scale.) Local resolution NHD is being developed where partners and data exist. The NHD contains reach codes for networked features, flow direction, names, and centerline representations for areal water bodies. Reaches are also defined on waterbodies and the approximate shorelines of the Great Lakes, the Atlantic and Pacific Oceans and the Gulf of Mexico. The NHD also incorporates the National Spatial Data Infrastructure framework criteria established by the Federal Geographic Data Committee.
None. Acknowledgment of the originating agencies would be appreciated in products derived from these data.
The National Hydrography Dataset (NHD) is a feature-based database that interconnects and uniquely identifies the stream segments or reaches that make up the nation's surface water drainage system. NHD data was originally developed at 1:100,000-scale and exists at that scale for the whole country. This high-resolution NHD, generally developed at 1:24,000/1:12,000 scale, adds detail to the original 1:100,000-scale NHD. (Data for Alaska, Puerto Rico and the Virgin Islands was developed at high-resolution, not 1:100,000 scale.) Local resolution NHD is being developed where partners and data exist. The NHD contains reach codes for networked features, flow direction, names, and centerline representations for areal water bodies. Reaches are also defined on waterbodies and the approximate shorelines of the Great Lakes, the Atlantic and Pacific Oceans and the Gulf of Mexico. The NHD also incorporates the National Spatial Data Infrastructure framework criteria established by the Federal Geographic Data Committee.
None. Acknowledgment of the originating agencies would be appreciated in products derived from these data.
The data for the Colorado dataset was primarily compiled from the Colorado Decision Support Systems (CDSS), GIS Data (https://cdss.state.co.us/GIS/Pages/GISDataHome.aspx). Please visit CDSS website for the source data, data disclaimers and metadata if not available in these data layer files. Much of the metadata for the files in the dataset are complete and were imported with the original file. In cases where there are discrepancies, metadata have been added where appropriate.
", "keyword": [ "Colorado", "CRB", "CRB_CO", "Watershed", "colorado river basin", "Counties", "Districts", "HUC8", "Irrigated Ag", "Lakes", "CDWR", "Agriculture", "Water", "Gunnison", "San Juan", "Yampa", "river", "CO", "lakes", "reservoirs", "administrative divisions", "water conservancy districts" ], "issued": "2020-08-13T20:33:10.000Z", "modified": "2020-08-13T21:31:31.000Z", "publisher": { "name": "Colorado State University" }, "contactPoint": { "@type": "vcard:Contact", "fn": "jlc4_CSUrams8", "hasEmail": "mailto: gis@colostate.edu" }, "accessLevel": "public", "spatial": "-109.8539,36.4937,-101.6936,41.4713", "license": "", "distribution": [ { "@type": "dcat:Distribution", "title": "ArcGIS Hub Dataset", "format": "Web Page", "mediaType": "text/html", "accessURL": "https://geospatialcentroid-csurams.hub.arcgis.com/maps/CSUrams::crb-colorado" }, { "@type": "dcat:Distribution", "title": "ArcGIS GeoService", "format": "ArcGIS GeoServices REST API", "mediaType": "application/json", "accessURL": "https://services1.arcgis.com/KNdRU5cN6ENqCTjk/arcgis/rest/services/CRB_Colorado_gdb/FeatureServer" } ], "theme": [ "geospatial" ] }, { "@type": "dcat:Dataset", "identifier": "https://www.arcgis.com/home/item.html?id=a2df792e3e45441080331b3c7e92b542&sublayer=0", "landingPage": "https://geospatialcentroid-csurams.hub.arcgis.com/datasets/CSUrams::bt-nhdflowlines-1", "title": "BT NHDFlowlines", "description": "The National Hydrography Dataset (NHD) is a feature-based database that interconnects and uniquely identifies the stream segments or reaches that make up the nation's surface water drainage system. NHD data was originally developed at 1:100,000-scale and exists at that scale for the whole country. This high-resolution NHD, generally developed at 1:24,000/1:12,000 scale, adds detail to the original 1:100,000-scale NHD. (Data for Alaska, Puerto Rico and the Virgin Islands was developed at high-resolution, not 1:100,000 scale.) Local resolution NHD is being developed where partners and data exist. The NHD contains reach codes for networked features, flow direction, names, and centerline representations for areal water bodies. Reaches are also defined on waterbodies and the approximate shorelines of the Great Lakes, the Atlantic and Pacific Oceans and the Gulf of Mexico. The NHD also incorporates the National Spatial Data Infrastructure framework criteria established by the Federal Geographic Data Committee.
None. Acknowledgment of the originating agencies would be appreciated in products derived from these data.
CDSS Diversion Structures, Colorado, Database version 20090701
None
The National Hydrography Dataset (NHD) is a feature-based database that interconnects and uniquely identifies the stream segments or reaches that make up the nation's surface water drainage system. NHD data was originally developed at 1:100,000-scale and exists at that scale for the whole country. This high-resolution NHD, generally developed at 1:24,000/1:12,000 scale, adds detail to the original 1:100,000-scale NHD. (Data for Alaska, Puerto Rico and the Virgin Islands was developed at high-resolution, not 1:100,000 scale.) Local resolution NHD is being developed where partners and data exist. The NHD contains reach codes for networked features, flow direction, names, and centerline representations for areal water bodies. Reaches are also defined on waterbodies and the approximate shorelines of the Great Lakes, the Atlantic and Pacific Oceans and the Gulf of Mexico. The NHD also incorporates the National Spatial Data Infrastructure framework criteria established by the Federal Geographic Data Committee.
None. Acknowledgment of the originating agencies would be appreciated in products derived from these data.
CDSS Flow Stations (Stream Gages), Colorado, Database version 20090701
None
CDSS Reservoirs, Colorado, Database version 20090701
None
CDSS Wells with recorded physical characteristics, Colorado, Database version 20070824
At user's own risk.
CDSS Decreed Wells, Colorado, Database version 20090701
None
Downloaded from COGCC.state.co,us and clipped to Big Thompson watershed extent by intern Luke Chamberlain on 2/20/2020.
Downloaded from COGCC.state.co,us and clipped to Big Thompson watershed extent by intern Luke Chamberlain on 2/20/2020.
Downloaded from COGCC.state.co,us and clipped to Big Thompson watershed extent by intern Luke Chamberlain on 2/20/2020.
The U.S. Department of Agriculture, Service Center Agencies should be acknowledged as the data source in products derived from these data.This data set is not designed for use as a primary regulatory tool in permitting or citing decisions, but may be used as a reference source. This information may be interpreted by organizations, agencies, units of government, or others based on needs; however, they are responsible for the appropriate application. Federal, State, or local regulatory bodies are not to reassign to the Service Center Agencies any authority for the decisions that they make. The Service Center Agencies will not perform any evaluations of these data for purposes related solely to State or local regulatory programs. Photographic or digital enlargement of these data to scales greater than at which they were originally mapped can cause misinterpretation of the data. Digital data files are periodically updated, and users are responsible for obtaining the latest version of the data.
Colorado Ownership, Management and Protection is a comprehensive dataset of land ownership and management. This version contains data from Federal agencies, State agencies, The Nature Conservancy, city and county sources and land trust sources. Data was collected from multiple sources and processed into one complete layer
National landcover dataset - 2016. Clipped to Big Thompson watershed.
Features in this dataset represent segments of public highways that are maintained by and under the jurisdiction of the Colorado Department of Transportation clipped to the Big Thompson River watershed. These highways consist of Interstates, US Highways, and State Highways within the watershed. Features are represented by polyline (linear) geographic shapes. Bounding coordinates and feature counts reflect a statewide extent.
National-scale geologic map to support national and regional level projects, including mineral resource and geo-environmental assessments
The U.S. Department of Agriculture, Service Center Agencies should be acknowledged as the data source in products derived from these data.This data set is not designed for use as a primary regulatory tool in permitting or citing decisions, but may be used as a reference source. This information may be interpreted by organizations, agencies, units of government, or others based on needs; however, they are responsible for the appropriate application. Federal, State, or local regulatory bodies are not to reassign to the Service Center Agencies any authority for the decisions that they make. The Service Center Agencies will not perform any evaluations of these data for purposes related solely to State or local regulatory programs.Photographic or digital enlargement of these data to scales greater than at which they were originally mapped can cause misinterpretation of the data. Digital data files are periodically updated, and users are responsible for obtaining the latest version of the data.
40 meter contour map generated using National Elevation Data clipped to the Big Thompson River watershed.
None
These data represent the city limits of Loveland, Colorado (polygonal data).
These data were prepared for use by the City of Loveland for its internal purposes only, and were not designed or intended for general use by members of the public. The City makes no representation of warranty as to its accuracy or completeness, and in particular, its accuracy in labeling, dimensions, contours, property boundaries, or placement or location of any features therein. Further, all users agree to the conditions stated in the Distribution Liability herein.
The data for the Colorado dataset was primarily compiled from the Colorado Decision Support Systems (CDSS), GIS Data (https://cdss.state.co.us/GIS/Pages/GISDataHome.aspx). Please visit CDSS website for the source data, data disclaimers and metadata if not available in these data layer files. Much of the metadata for the files in the dataset are complete and were imported with the original file. In cases where there are discrepancies, metadata have been added where appropriate.
", "keyword": [ "Colorado", "CRB", "CRB_CO", "Watershed", "colorado river basin", "Counties", "Districts", "HUC8", "Irrigated Ag", "Lakes", "CDWR", "Agriculture", "Water", "Gunnison", "San Juan", "Yampa", "river", "CO", "lakes", "reservoirs", "administrative divisions", "water conservancy districts" ], "issued": "2020-08-13T20:33:10.000Z", "modified": "2020-08-13T20:33:28.180Z", "publisher": { "name": "Colorado State University" }, "contactPoint": { "@type": "vcard:Contact", "fn": "jlc4_CSUrams8", "hasEmail": "mailto: gis@colostate.edu" }, "accessLevel": "public", "spatial": "-109.2822,38.7864,-105.4716,40.5049", "license": "", "distribution": [ { "@type": "dcat:Distribution", "title": "ArcGIS Hub Dataset", "format": "Web Page", "mediaType": "text/html", "accessURL": "https://geospatialcentroid-csurams.hub.arcgis.com/datasets/CSUrams::co-coloradodiv-counties" }, { "@type": "dcat:Distribution", "title": "ArcGIS GeoService", "format": "ArcGIS GeoServices REST API", "mediaType": "application/json", "accessURL": "https://services1.arcgis.com/KNdRU5cN6ENqCTjk/arcgis/rest/services/CRB_Colorado_gdb/FeatureServer/0" }, { "@type": "dcat:Distribution", "title": "CSV", "format": "CSV", "mediaType": "text/csv", "accessURL": "https://geospatialcentroid-csurams.hub.arcgis.com/api/download/v1/items/0c61955b889a4c069eb40d78b56f0f93/csv?layers=0" }, { "@type": "dcat:Distribution", "title": "Shapefile", "format": "ZIP", "mediaType": "application/zip", "accessURL": "https://geospatialcentroid-csurams.hub.arcgis.com/api/download/v1/items/0c61955b889a4c069eb40d78b56f0f93/shapefile?layers=0" }, { "@type": "dcat:Distribution", "title": "GeoJSON", "format": "GeoJSON", "mediaType": "application/vnd.geo+json", "accessURL": "https://geospatialcentroid-csurams.hub.arcgis.com/api/download/v1/items/0c61955b889a4c069eb40d78b56f0f93/geojson?layers=0" }, { "@type": "dcat:Distribution", "title": "KML", "format": "KML", "mediaType": "application/vnd.google-earth.kml+xml", "accessURL": "https://geospatialcentroid-csurams.hub.arcgis.com/api/download/v1/items/0c61955b889a4c069eb40d78b56f0f93/kml?layers=0" } ], "theme": [ "geospatial" ] }, { "@type": "dcat:Dataset", "identifier": "https://www.arcgis.com/home/item.html?id=3cb880d60668415a8c7ecaec8068c053&sublayer=0", "landingPage": "https://geospatialcentroid-csurams.hub.arcgis.com/datasets/CSUrams::bt-co-census-tract", "title": "BT CO Census Tract", "description": "", "keyword": [ "census", "tracts", "big thompson", "watershed", "bt_boundaries" ], "issued": "2020-04-15T18:36:11.000Z", "modified": "2020-04-15T18:36:21.067Z", "publisher": { "name": "Colorado State University" }, "contactPoint": { "@type": "vcard:Contact", "fn": "jlc4_CSUrams8", "hasEmail": "mailto: gis@colostate.edu" }, "accessLevel": "public", "spatial": "-105.8011,40.2252,-104.7596,40.6292", "license": "", "distribution": [ { "@type": "dcat:Distribution", "title": "ArcGIS Hub Dataset", "format": "Web Page", "mediaType": "text/html", "accessURL": "https://geospatialcentroid-csurams.hub.arcgis.com/datasets/CSUrams::bt-co-census-tract" }, { "@type": "dcat:Distribution", "title": "ArcGIS GeoService", "format": "ArcGIS GeoServices REST API", "mediaType": "application/json", "accessURL": "https://services1.arcgis.com/KNdRU5cN6ENqCTjk/arcgis/rest/services/Census_Tract/FeatureServer/0" }, { "@type": "dcat:Distribution", "title": "CSV", "format": "CSV", "mediaType": "text/csv", "accessURL": "https://geospatialcentroid-csurams.hub.arcgis.com/api/download/v1/items/3cb880d60668415a8c7ecaec8068c053/csv?layers=0" }, { "@type": "dcat:Distribution", "title": "Shapefile", "format": "ZIP", "mediaType": "application/zip", "accessURL": "https://geospatialcentroid-csurams.hub.arcgis.com/api/download/v1/items/3cb880d60668415a8c7ecaec8068c053/shapefile?layers=0" }, { "@type": "dcat:Distribution", "title": "GeoJSON", "format": "GeoJSON", "mediaType": "application/vnd.geo+json", "accessURL": "https://geospatialcentroid-csurams.hub.arcgis.com/api/download/v1/items/3cb880d60668415a8c7ecaec8068c053/geojson?layers=0" }, { "@type": "dcat:Distribution", "title": "KML", "format": "KML", "mediaType": "application/vnd.google-earth.kml+xml", "accessURL": "https://geospatialcentroid-csurams.hub.arcgis.com/api/download/v1/items/3cb880d60668415a8c7ecaec8068c053/kml?layers=0" } ], "theme": [ "geospatial" ] }, { "@type": "dcat:Dataset", "identifier": "https://www.arcgis.com/home/item.html?id=4b9f09ecc6d3495589ab57166632ef21&sublayer=0", "landingPage": "https://geospatialcentroid-csurams.hub.arcgis.com/datasets/CSUrams::bt-co-census-blocks", "title": "BT CO Census Blocks", "description": "", "keyword": [ "census", "blocks", "big thompson", "watershed", "bt_boundaries" ], "issued": "2020-04-15T18:36:48.000Z", "modified": "2020-04-15T18:36:56.685Z", "publisher": { "name": "Colorado State University" }, "contactPoint": { "@type": "vcard:Contact", "fn": "jlc4_CSUrams8", "hasEmail": "mailto: gis@colostate.edu" }, "accessLevel": "public", "spatial": "-105.8011,40.2252,-104.7596,40.6292", "license": "", "distribution": [ { "@type": "dcat:Distribution", "title": "ArcGIS Hub Dataset", "format": "Web Page", "mediaType": "text/html", "accessURL": "https://geospatialcentroid-csurams.hub.arcgis.com/datasets/CSUrams::bt-co-census-blocks" }, { "@type": "dcat:Distribution", "title": "ArcGIS GeoService", "format": "ArcGIS GeoServices REST API", "mediaType": "application/json", "accessURL": "https://services1.arcgis.com/KNdRU5cN6ENqCTjk/arcgis/rest/services/Census_Blocks/FeatureServer/0" }, { "@type": "dcat:Distribution", "title": "CSV", "format": "CSV", "mediaType": "text/csv", "accessURL": "https://geospatialcentroid-csurams.hub.arcgis.com/api/download/v1/items/4b9f09ecc6d3495589ab57166632ef21/csv?layers=0" }, { "@type": "dcat:Distribution", "title": "Shapefile", "format": "ZIP", "mediaType": "application/zip", "accessURL": "https://geospatialcentroid-csurams.hub.arcgis.com/api/download/v1/items/4b9f09ecc6d3495589ab57166632ef21/shapefile?layers=0" }, { "@type": "dcat:Distribution", "title": "GeoJSON", "format": "GeoJSON", "mediaType": "application/vnd.geo+json", "accessURL": "https://geospatialcentroid-csurams.hub.arcgis.com/api/download/v1/items/4b9f09ecc6d3495589ab57166632ef21/geojson?layers=0" }, { "@type": "dcat:Distribution", "title": "KML", "format": "KML", "mediaType": "application/vnd.google-earth.kml+xml", "accessURL": "https://geospatialcentroid-csurams.hub.arcgis.com/api/download/v1/items/4b9f09ecc6d3495589ab57166632ef21/kml?layers=0" } ], "theme": [ "geospatial" ] }, { "@type": "dcat:Dataset", "identifier": "https://www.arcgis.com/home/item.html?id=ee70838b3a4e46ae8b273a73086b01ee&sublayer=0", "landingPage": "https://geospatialcentroid-csurams.hub.arcgis.com/datasets/CSUrams::bt-co-growthmanagementareas", "title": "BT CO GrowthManagementAreas", "description": "", "keyword": [ "Fort Collins", "Greely", "Loveland", "Windsor", "Growth Management Areas", "GMA", "Big Thompson River", "watershed", "Colorado.", "bt_boundaries" ], "issued": "2020-04-15T18:38:19.000Z", "modified": "2020-04-15T18:38:27.592Z", "publisher": { "name": "Colorado State University" }, "contactPoint": { "@type": "vcard:Contact", "fn": "jlc4_CSUrams8", "hasEmail": "mailto: gis@colostate.edu" }, "accessLevel": "public", "spatial": "-105.1598,40.3492,-104.9316,40.4657", "license": "None
This map layer includes cities in the United States, Puerto Rico and the U.S. Virgin Islands. These cities were collected from the 1970 National Atlas of the United States. Where applicable, U.S. Census Bureau codes for named populated places were associated with each name to allow additional information to be attached. The Geographic Names Information System (GNIS) was also used as a source for additional information.This is a revised version of the December 2003 map layer.
None. Acknowledgment of the National Atlas of the United States of America would be appreciated in products derived from these data.
BT_County_boundaries represents counties and their boundaries that are found within the Big Thompson River watershed. This feature class was clipped from a Colorado county map located in the CLP.gdb dataset maintained by the Centroid at Colorado State University.
None.
The Colorado Trail System, now titled the Colorado Trail Explorer (COTREX), endeavors to map every trail in the state of Colorado. Currently there are nearly 40,000 miles of trails mapped. Trails come from a variety of sources (USFS, BLM, local parks & recreation departments, local governments). Responsibility for accuracy of the data rests with the source.
These data were last updated on 9/11/2019
This map layer is a product and property of Colorado Parks & Wildlife, a division of the Colorado Department of Natural Resources. Care should be taken in interpreting these data. Written documents may accompany this map layer and should be referenced. The information portrayed on these map layers should not replace field studies necessary for more localized planning efforts. The Colorado Department of Natural Resources is not responsible and shall not be liable to the user for damages of any kind arising out of the use of data or information provided by the Department, including the installation of the data or information, its use, or the results obtained from its use. ANY DATA OR INFORMATION PROVIDED BY THE DEPARTMENT OF NATURAL RESOURCES IS PROVIDED \"AS IS\" WITHOUT WARRANTY OF ANY KIND, EITHER EXPRESS OR IMPLIED, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE. Data or information provided by the Department of Natural Resources shall be used and relied upon only at the user's sole risk, and the user agrees to indemnify and hold harmless the Department of Natural Resources, its officials, officers and employees from any liability arising out of the use of the data or information provided.
Mexico cities with populations greater than 5,000 within the Rio Grande River Basin.
The map contains the geographical identification of the 192 245 inhabited localities, as a result of the 2010 Population and Housing Census. Each registry includes the values of length, latitude, altitude and total population.
This shapefile was created by selecting all the localities with populations greater than 5,000 within the boundary of the RGB watershed, as delinated in the UC-Davis geodatabase (located here: L:\\Interns_stuff\\Kristin\\GIS\\From_Pablo\\170311_RGB_geodatabase\\Hydrology_and_Climate\\RGB_watershed). This shapefile was created by Kristin Davis, CSU Geospatial Centroid intern, on November 27, 2018 for the Rio Grande Basin Environmental Justice project.
It is not allowed to use this data for profit and the source of the map and CONABIO must be mentioned. View full license at http://creativecommons.org/licenses/by-nc/2.5/mx/
Mexico cities with populations greater than 10,000 within the Rio Grande River Basin.
The map contains the geographical identification of the 192 245 inhabited localities, as a result of the 2010 Population and Housing Census. Each registry includes the values of length, latitude, altitude and total population.
This shapefile was created by selecting all the localities with populations greater than 10,000 within the boundary of the RGB watershed, as delinated in the UC-Davis geodatabase (located hereL:\\Interns_stuff\\Kristin\\GIS\\UC-Davis_geodatabases\\Complete_database\\170311_RGB_geodatabase\\Hydrology_and_Climate\\RGB_watershed), from the UC-Davis's MX_cities shapefile. This shapefile was created by Kristin Davis (Geospatial Centroid Intern) on November 27, 2018 for the Rio Grande Basin Environmental Justice project.
It is not allowed to use this data for profit and the source of the map and CONABIO must be mentioned. View full license at http://creativecommons.org/licenses/by-nc/2.5/mx/
Nuevo Leon state boundary.
The map represents the boundaries of the States and the outline of the Mexican Republic. These limits are the union of the cartographic material indicated in the quotation of the information, the result is a map with the datalle of the scale 1: 250000 handled by the INEGI. This map was prepared in CONABIO with cartographic information provided by INEGI. The map contains information on names of States and Capitals, according to INEGI.
It is not allowed to use this data for profit. The source of the map and CONABIO must be mentioned. The Subdirección of Geographic Information Systems of CONABIO is only responsible for the information presented on the map and is out of the use that is given to it.
Durango state boundary.
The map represents the boundaries of the States and the outline of the Mexican Republic. These limits are the union of the cartographic material indicated in the quotation of the information, the result is a map with the datalle of the scale 1: 250000 handled by the INEGI. This map was prepared in CONABIO with cartographic information provided by INEGI. The map contains information on names of States and Capitals, according to INEGI.
It is not allowed to use this data for profit. The source of the map and CONABIO must be mentioned. The Subdirección of Geographic Information Systems of CONABIO is only responsible for the information presented on the map and is out of the use that is given to it.
Tamaulipas state boundary.
The map represents the boundaries of the States and the outline of the Mexican Republic. These limits are the union of the cartographic material indicated in the quotation of the information, the result is a map with the datalle of the scale 1: 250000 handled by the INEGI. This map was prepared in CONABIO with cartographic information provided by INEGI. The map contains information on names of States and Capitals, according to INEGI.
It is not allowed to use this data for profit. The source of the map and CONABIO must be mentioned. The Subdirección of Geographic Information Systems of CONABIO is only responsible for the information presented on the map and is out of the use that is given to it.
The data for the Colorado dataset was primarily compiled from the Colorado Decision Support Systems (CDSS), GIS Data (https://cdss.state.co.us/GIS/Pages/GISDataHome.aspx). Please visit CDSS website for the source data, data disclaimers and metadata if not available in these data layer files. Much of the metadata for the files in the dataset are complete and were imported with the original file. In cases where there are discrepancies, metadata have been added where appropriate.
", "keyword": [ "Colorado", "CRB", "CRB_CO", "Watershed", "colorado river basin", "Counties", "Districts", "HUC8", "Irrigated Ag", "Lakes", "CDWR", "Agriculture", "Water", "Gunnison", "San Juan", "Yampa", "river", "CO", "lakes", "reservoirs", "administrative divisions", "water conservancy districts" ], "issued": "2020-08-13T20:33:10.000Z", "modified": "2020-08-13T20:33:28.555Z", "publisher": { "name": "Colorado State University" }, "contactPoint": { "@type": "vcard:Contact", "fn": "jlc4_CSUrams8", "hasEmail": "mailto: gis@colostate.edu" }, "accessLevel": "public", "spatial": "-109.2827,38.7864,-105.4716,40.5072", "license": "", "distribution": [ { "@type": "dcat:Distribution", "title": "ArcGIS Hub Dataset", "format": "Web Page", "mediaType": "text/html", "accessURL": "https://geospatialcentroid-csurams.hub.arcgis.com/datasets/CSUrams::co-coloradodiv-districts" }, { "@type": "dcat:Distribution", "title": "ArcGIS GeoService", "format": "ArcGIS GeoServices REST API", "mediaType": "application/json", "accessURL": "https://services1.arcgis.com/KNdRU5cN6ENqCTjk/arcgis/rest/services/CRB_Colorado_gdb/FeatureServer/1" }, { "@type": "dcat:Distribution", "title": "CSV", "format": "CSV", "mediaType": "text/csv", "accessURL": "https://geospatialcentroid-csurams.hub.arcgis.com/api/download/v1/items/0c61955b889a4c069eb40d78b56f0f93/csv?layers=1" }, { "@type": "dcat:Distribution", "title": "Shapefile", "format": "ZIP", "mediaType": "application/zip", "accessURL": "https://geospatialcentroid-csurams.hub.arcgis.com/api/download/v1/items/0c61955b889a4c069eb40d78b56f0f93/shapefile?layers=1" }, { "@type": "dcat:Distribution", "title": "GeoJSON", "format": "GeoJSON", "mediaType": "application/vnd.geo+json", "accessURL": "https://geospatialcentroid-csurams.hub.arcgis.com/api/download/v1/items/0c61955b889a4c069eb40d78b56f0f93/geojson?layers=1" }, { "@type": "dcat:Distribution", "title": "KML", "format": "KML", "mediaType": "application/vnd.google-earth.kml+xml", "accessURL": "https://geospatialcentroid-csurams.hub.arcgis.com/api/download/v1/items/0c61955b889a4c069eb40d78b56f0f93/kml?layers=1" } ], "theme": [ "geospatial" ] }, { "@type": "dcat:Dataset", "identifier": "https://www.arcgis.com/home/item.html?id=21e8de63ad5147f3bc14ffe70b3984b9&sublayer=0", "landingPage": "https://geospatialcentroid-csurams.hub.arcgis.com/datasets/CSUrams::nm-state-1", "title": "NM state", "description": "New Mexico state boundary.
The 2015 cartographic boundary shapefiles are simplified representations of selected geographic areas from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). These boundary files are specifically designed for small-scale thematic mapping. When possible, generalization is performed with the intent to maintain the hierarchical relationships among geographies and to maintain the alignment of geographies within a file set for a given year. Geographic areas may not align with the same areas from another year. Some geographies are available as nation-based files while others are available only as state-based files. States and equivalent entities are the primary governmental divisions of the United States. In addition to the fifty states, the Census Bureau treats the District of Columbia, Puerto Rico, and each of the Island Areas (American Samoa, the Commonwealth of the Northern Mariana Islands, Guam, and the U.S. Virgin Islands) as the statistical equivalents of states for the purpose of data presentation.
The intended display scale for this file is 1:500,000. This file should not be displayed at scales larger than 1:500,000. These products are free to use in a product or publication, however acknowledgement must be given to the U.S. Census Bureau as the source. The boundary information is for visual display at appropriate small scales only. Cartographic boundary files should not be used for geographic analysis including area or perimeter calculation. Files should not be used for geocoding addresses. Files should not be used for determining precise geographic area relationships.
Texas state boundary.
The 2015 cartographic boundary shapefiles are simplified representations of selected geographic areas from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). These boundary files are specifically designed for small-scale thematic mapping. When possible, generalization is performed with the intent to maintain the hierarchical relationships among geographies and to maintain the alignment of geographies within a file set for a given year. Geographic areas may not align with the same areas from another year. Some geographies are available as nation-based files while others are available only as state-based files. States and equivalent entities are the primary governmental divisions of the United States. In addition to the fifty states, the Census Bureau treats the District of Columbia, Puerto Rico, and each of the Island Areas (American Samoa, the Commonwealth of the Northern Mariana Islands, Guam, and the U.S. Virgin Islands) as the statistical equivalents of states for the purpose of data presentation.
The intended display scale for this file is 1:500,000. This file should not be displayed at scales larger than 1:500,000. These products are free to use in a product or publication, however acknowledgement must be given to the U.S. Census Bureau as the source. The boundary information is for visual display at appropriate small scales only. Cartographic boundary files should not be used for geographic analysis including area or perimeter calculation. Files should not be used for geocoding addresses. Files should not be used for determining precise geographic area relationships.
Coahuila De Zaragoza state boundary.
The map represents the boundaries of the States and the outline of the Mexican Republic. These limits are the union of the cartographic material indicated in the quotation of the information, the result is a map with the datalle of the scale 1: 250000 handled by the INEGI. This map was prepared in CONABIO with cartographic information provided by INEGI. The map contains information on names of States and Capitals, according to INEGI.
It is not allowed to use this data for profit. The source of the map and CONABIO must be mentioned. The Subdirección of Geographic Information Systems of CONABIO is only responsible for the information presented on the map and is out of the use that is given to it.
Nuevo Leon municipalities within the Rio Grande River Basin.
The set of localities is 49379 of which 4546 are urban and 44833 rural. These digital representations correspond to the closing of the Intercensal Survey 2015, and locality contains boundaries and keys of the Basic Geostatistics Area (AGEB), block polygons and surrounding polygon(s) of the corresponding locality; Also contains street names, cultural and natural features (water bodies), main access roads to the locality and the location of the main services (church, school, medical assistance, municipal palace or help desk, market, cemetery and plaza or garden ). All elements contain attributes (geostatistical keys, update date, name of the geographical element, among others). The identification of the localities is conformed by a key of 9 digits of which the 2 first correspond to the state, the 3 following to the municipality and the 4 remaining to the locality.
INEGI is not responsible for the use you give the data
Durango municipality within the Rio Grande River Basin.
The set of localities is 49379 of which 4546 are urban and 44833 rural. These digital representations correspond to the closing of the Intercensal Survey 2015, and locality contains boundaries and keys of the Basic Geostatistics Area (AGEB), block polygons and surrounding polygon(s) of the corresponding locality; Also contains street names, cultural and natural features (water bodies), main access roads to the locality and the location of the main services (church, school, medical assistance, municipal palace or help desk, market, cemetery and plaza or garden ). All elements contain attributes (geostatistical keys, update date, name of the geographical element, among others). The identification of the localities is conformed by a key of 9 digits of which the 2 first correspond to the state, the 3 following to the municipality and the 4 remaining to the locality.
INEGI is not responsible for the use you give the data
Coahuila municipalities within the Rio Grande River Basin.
The set of localities is 49379 of which 4546 are urban and 44833 rural. These digital representations correspond to the closing of the Intercensal Survey 2015, and locality contains boundaries and keys of the Basic Geostatistics Area (AGEB), block polygons and surrounding polygon(s) of the corresponding locality; Also contains street names, cultural and natural features (water bodies), main access roads to the locality and the location of the main services (church, school, medical assistance, municipal palace or help desk, market, cemetery and plaza or garden ). All elements contain attributes (geostatistical keys, update date, name of the geographical element, among others). The identification of the localities is conformed by a key of 9 digits of which the 2 first correspond to the state, the 3 following to the municipality and the 4 remaining to the locality.
INEGI is not responsible for the use you give the data
Chihuahua municipalities within the Rio Grande River Basin.
The set of localities is 49379 of which 4546 are urban and 44833 rural. These digital representations correspond to the closing of the Intercensal Survey 2015, and locality contains boundaries and keys of the Basic Geostatistics Area (AGEB), block polygons and surrounding polygon(s) of the corresponding locality; Also contains street names, cultural and natural features (water bodies), main access roads to the locality and the location of the main services (church, school, medical assistance, municipal palace or help desk, market, cemetery and plaza or garden ). All elements contain attributes (geostatistical keys, update date, name of the geographical element, among others). The identification of the localities is conformed by a key of 9 digits of which the 2 first correspond to the state, the 3 following to the municipality and the 4 remaining to the locality.
INEGI is not responsible for the use you give the data
Tamaulipas municipalities within the Rio Grande River Basin.
The set of localities is 49379 of which 4546 are urban and 44833 rural. These digital representations correspond to the closing of the Intercensal Survey 2015, and locality contains boundaries and keys of the Basic Geostatistics Area (AGEB), block polygons and surrounding polygon(s) of the corresponding locality; Also contains street names, cultural and natural features (water bodies), main access roads to the locality and the location of the main services (church, school, medical assistance, municipal palace or help desk, market, cemetery and plaza or garden ). All elements contain attributes (geostatistical keys, update date, name of the geographical element, among others). The identification of the localities is conformed by a key of 9 digits of which the 2 first correspond to the state, the 3 following to the municipality and the 4 remaining to the locality.
INEGI is not responsible for the use you give the data
Colorado counties within the Rio Grande River Basin.
The 2015 cartographic boundary shapefiles are simplified representations of selected geographic areas from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). These boundary files are specifically designed for small-scale thematic mapping. When possible, generalization is performed with the intent to maintain the hierarchical relationships among geographies and to maintain the alignment of geographies within a file set for a given year. Geographic areas may not align with the same areas from another year. Some geographies are available as nation-based files while others are available only as state-based files. The primary legal divisions of most states are termed counties. In Louisiana, these divisions are known as parishes. In Alaska, which has no counties, the equivalent entities are the organized boroughs, city and boroughs, municipalities, and for the unorganized area, census areas. The latter are delineated cooperatively for statistical purposes by the State of Alaska and the Census Bureau. In four states (Maryland, Missouri, Nevada, and Virginia), there are one or more incorporated places that are independent of any county organization and thus constitute primary divisions of their states. These incorporated places are known as independent cities and are treated as equivalent entities for purposes of data presentation. The District of Columbia and Guam have no primary divisions, and each area is considered an equivalent entity for purposes of data presentation. The Census Bureau treats the following entities as equivalents of counties for purposes of data presentation: Municipios in Puerto Rico, Districts and Islands in American Samoa, Municipalities in the Commonwealth of the Northern Mariana Islands, and Islands in the U.S. Virgin Islands. The entire area of the United States, Puerto Rico, and the Island Areas is covered by counties or equivalent entities.
The intended display scale for this file is 1:500,000. This file should not be displayed at scales larger than 1:500,000. These products are free to use in a product or publication, however acknowledgement must be given to the U.S. Census Bureau as the source. The boundary information is for visual display at appropriate small scales only. Cartographic boundary files should not be used for geographic analysis including area or perimeter calculation. Files should not be used for geocoding addresses. Files should not be used for determining precise geographic area relationships.
Colorado state boundary.
The map represents the boundaries of the States and the outline of the Mexican Republic. These limits are the union of the cartographic material indicated in the quotation of the information, the result is a map with the datalle of the scale 1: 250000 handled by the INEGI. This map was prepared in CONABIO with cartographic information provided by INEGI. The map contains information on names of States and Capitals, according to INEGI.
It is not allowed to use this data for profit. The source of the map and CONABIO must be mentioned. The Subdirección of Geographic Information Systems of CONABIO is only responsible for the information presented on the map and is out of the use that is given to it.
Colorado state boundary.
The 2015 cartographic boundary shapefiles are simplified representations of selected geographic areas from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). These boundary files are specifically designed for small-scale thematic mapping. When possible, generalization is performed with the intent to maintain the hierarchical relationships among geographies and to maintain the alignment of geographies within a file set for a given year. Geographic areas may not align with the same areas from another year. Some geographies are available as nation-based files while others are available only as state-based files. States and equivalent entities are the primary governmental divisions of the United States. In addition to the fifty states, the Census Bureau treats the District of Columbia, Puerto Rico, and each of the Island Areas (American Samoa, the Commonwealth of the Northern Mariana Islands, Guam, and the U.S. Virgin Islands) as the statistical equivalents of states for the purpose of data presentation.
The intended display scale for this file is 1:500,000. This file should not be displayed at scales larger than 1:500,000. These products are free to use in a product or publication, however acknowledgement must be given to the U.S. Census Bureau as the source. The boundary information is for visual display at appropriate small scales only. Cartographic boundary files should not be used for geographic analysis including area or perimeter calculation. Files should not be used for geocoding addresses. Files should not be used for determining precise geographic area relationships.
United States Forest Service lands within the Rio Grande River Basin.
The USGS Protected Areas Database of the United States (PAD-US) is the nation's inventory of protected areas, including public open space and voluntarily provided, private protected areas, identified as an A-16 National Geospatial Data Asset in the Cadastral Theme (http://www.fgdc.gov/ngda-reports/NGDA_Datasets.html). PAD-US is an ongoing project with several published versions of a spatial database of areas dedicated to the preservation of biological diversity, and other natural, recreational or cultural uses, managed for these purposes through legal or other effective means. The geodatabase maps and describes public open space and other protected areas. Most areas are public lands owned in fee; however, long-term easements, leases, and agreements or administrative designations documented in agency management plans may be included. The PAD-US database strives to be a complete “best available” inventory of protected areas (lands and waters) including data provided by managing agencies and organizations. The dataset is built in collaboration with several partners and data providers (http://gapanalysis.usgs.gov/padus/stewards/). See Supplemental Information Section of this metadata record for more information on partnerships and links to major partner organizations. As this dataset is a compilation of many data sets; data completeness, accuracy, and scale may vary. Federal and state data are generally complete, while local government and private protected area coverage is about 50% complete, and depends on data management capacity in the state. For completeness estimates by state: http://www.protectedlands.net/partners. As the federal and state data are reasonably complete; focus is shifting to completing the inventory of local gov and voluntarily provided, private protected areas. The PAD-US geodatabase contains over twenty-five attributes and four feature classes to support data management, queries, web mapping services and analyses: Marine Protected Areas (MPA), Fee, Easements and Combined. The data contained in the MPA Feature class are provided directly by the National Oceanic and Atmospheric Administration (NOAA) Marine Protected Areas Center (MPA, http://marineprotectedareas.noaa.gov ) tracking the National Marine Protected Areas System. The Easements feature class contains data provided directly from the National Conservation Easement Database (NCED, http://conservationeasement.us ) The MPA and Easement feature classes contain some attributes unique to the sole source databases tracking them (e.g. Easement Holder Name from NCED, Protection Level from NOAA MPA Inventory). The "Combined" feature class integrates all fee, easement and MPA features as the best available national inventory of protected areas in the standard PAD-US framework. In addition to geographic boundaries, PAD-US describes the protection mechanism category (e.g. fee, easement, designation, other), owner and managing agency, designation type, unit name, area, public access and state name in a suite of standardized fields. An informative set of references (i.e. Aggregator Source, GIS Source, GIS Source Date) and "local" or source data fields provide a transparent link between standardized PAD-US fields and information from authoritative data sources. The areas in PAD-US are also assigned conservation measures that assess management intent to permanently protect biological diversity: the nationally relevant "GAP Status Code" and global "IUCN Category" standard. A wealth of attributes facilitates a wide variety of data analyses and creates a context for data to be used at local, regional, state, national and international scales. More information about specific updates and changes to this PAD-US version can be found in the Data Quality Information section of this metadata record as well as on the PAD-US website, http://gapanalysis.usgs.gov/padus/data/history/.) Due to the completeness and complexity of these data, it is highly recommended to review the Supplemental Information Section of the metadata record as well as the Data Use Constraints, to better understand data partnerships as well as see tips and ideas of appropriate uses of the data and how to parse out the data that you are looking for. For more information regarding the PAD-US dataset please visit, http://gapanalysis.usgs.gov/padus/. To find more data resources as well as view example analysis performed using PAD-US data visit, http://gapanalysis.usgs.gov/padus/resources/. The PAD-US dataset and data standard are compiled and maintained by the USGS Gap Analysis Program, http://gapanalysis.usgs.gov/ . For more information about data standards and how the data are aggregated please review the “Standards and Methods Manual for PAD-US,” http://gapanalysis.usgs.gov/padus/data/standards/ .
It is strongly recommended that these data are directly acquired from the U.S. Geological Survey Gap Analysis Program website and server (http://gapanalysis.usgs.gov/padus/data/ ), and not indirectly through other sources which may have changed the data in some way. It is also strongly recommended that careful attention be paid to the contents of the metadata file associated with these data (http://gapanalysis.usgs.gov/padus/data/metadata/ ). The U.S. Geological Survey and all contributing data partners and aggregators shall not be held liable for improper or incorrect use of the data described and/or contained herein. All information is created with a specific end use or uses in mind. This is especially true for GIS data, which is expensive to produce and must be directed to meet the immediate program needs. However, these data were created with the expectation that they would be used for other applications; therefore, we list below both appropriate and inappropriate uses. This list is in no way exhaustive but should serve as a guide to assess whether a proposed use can or cannot be supported by these data. For many uses, it is unlikely that PAD-US will provide the only data needed, and for uses with a regulatory outcome, field surveys should verify the result. In the end, it will be the responsibility of each data user to determine if these data can answer the question being asked, and if they are the best tool to answer that question. Contact the PAD-US Coordinator for guidance. While it is impossible to predict all the uses of these data we have listed several possible appropriate and inappropriate uses from GAP's perspective. Use constraints: Appropriate uses of the data: primarily as a coarse map for a large area such as the nation, a region or state (completeness in counties varies) or to provide context for finer-level maps. A general list of possible applications includes: -National, regional or statewide biodiversity planning, research, management, or policy making -National, Regional or state habitat conservation planning, research, management, or policy making -Outdoor recreation planning, research, management or policy making -Public health planning, research, management or policy making -County comprehensive planning -Large-area resource management planning -Coarse-filter evaluation of potential impacts or benefits of major projects or plan initiatives on biodiversity, such as utility or transportation corridors, wilderness proposals, habitat connectivity proposals, climate change adaption proposals, regional open space and recreation proposals, etc. -Determining relative amounts of management responsibility for specific biological resources among land stewards to facilitate cooperative management and planning. -Basic research on regional distributions of plants and animals and to help target both specific species and geographic areas for needed research. -Environmental impact assessment for large projects or military activities. -Estimation of potential economic impacts from loss of biological resource-based activities. -Education at all levels and for both students and citizens. Inappropriate Uses: It is far easier to identify appropriate uses than inappropriate ones, however, there is a \"fuzzy line\" that is eventually crossed when the differences in resolution of the data, size of geographic area being analyzed, and precision of the answer required for the question are no longer compatible. Examples include: -Using the data to map small areas (less than thousands of hectares), typically requiring mapping resolution at 1:24,000 scale and using aerial photographs or ground surveys in areas where data are incomplete. -Combining these data with other data finer than 1:100,000 scale to produce new hybrid maps or answer queries. -Generating specific areal measurements from the data finer than the nearest thousand hectares -Establishing exact boundaries for regulation or acquisition. -Establishing definite occurrence or non-occurrence of any feature for an exact geographic area -Determining abundance, health, or condition of any feature. -Establishing a measure of accuracy of any other data by comparison with GAP data. -Altering the data in any way and redistributing them as a GAP data product. -Using the data without acquiring and reviewing the metadata.
United States Bureau of Reclamation lands within the Rio Grande River Basin.
The USGS Protected Areas Database of the United States (PAD-US) is the nation's inventory of protected areas, including public open space and voluntarily provided, private protected areas, identified as an A-16 National Geospatial Data Asset in the Cadastral Theme (http://www.fgdc.gov/ngda-reports/NGDA_Datasets.html). PAD-US is an ongoing project with several published versions of a spatial database of areas dedicated to the preservation of biological diversity, and other natural, recreational or cultural uses, managed for these purposes through legal or other effective means. The geodatabase maps and describes public open space and other protected areas. Most areas are public lands owned in fee; however, long-term easements, leases, and agreements or administrative designations documented in agency management plans may be included. The PAD-US database strives to be a complete “best available” inventory of protected areas (lands and waters) including data provided by managing agencies and organizations. The dataset is built in collaboration with several partners and data providers (http://gapanalysis.usgs.gov/padus/stewards/). See Supplemental Information Section of this metadata record for more information on partnerships and links to major partner organizations. As this dataset is a compilation of many data sets; data completeness, accuracy, and scale may vary. Federal and state data are generally complete, while local government and private protected area coverage is about 50% complete, and depends on data management capacity in the state. For completeness estimates by state: http://www.protectedlands.net/partners. As the federal and state data are reasonably complete; focus is shifting to completing the inventory of local gov and voluntarily provided, private protected areas. The PAD-US geodatabase contains over twenty-five attributes and four feature classes to support data management, queries, web mapping services and analyses: Marine Protected Areas (MPA), Fee, Easements and Combined. The data contained in the MPA Feature class are provided directly by the National Oceanic and Atmospheric Administration (NOAA) Marine Protected Areas Center (MPA, http://marineprotectedareas.noaa.gov ) tracking the National Marine Protected Areas System. The Easements feature class contains data provided directly from the National Conservation Easement Database (NCED, http://conservationeasement.us ) The MPA and Easement feature classes contain some attributes unique to the sole source databases tracking them (e.g. Easement Holder Name from NCED, Protection Level from NOAA MPA Inventory). The "Combined" feature class integrates all fee, easement and MPA features as the best available national inventory of protected areas in the standard PAD-US framework. In addition to geographic boundaries, PAD-US describes the protection mechanism category (e.g. fee, easement, designation, other), owner and managing agency, designation type, unit name, area, public access and state name in a suite of standardized fields. An informative set of references (i.e. Aggregator Source, GIS Source, GIS Source Date) and "local" or source data fields provide a transparent link between standardized PAD-US fields and information from authoritative data sources. The areas in PAD-US are also assigned conservation measures that assess management intent to permanently protect biological diversity: the nationally relevant "GAP Status Code" and global "IUCN Category" standard. A wealth of attributes facilitates a wide variety of data analyses and creates a context for data to be used at local, regional, state, national and international scales. More information about specific updates and changes to this PAD-US version can be found in the Data Quality Information section of this metadata record as well as on the PAD-US website, http://gapanalysis.usgs.gov/padus/data/history/.) Due to the completeness and complexity of these data, it is highly recommended to review the Supplemental Information Section of the metadata record as well as the Data Use Constraints, to better understand data partnerships as well as see tips and ideas of appropriate uses of the data and how to parse out the data that you are looking for. For more information regarding the PAD-US dataset please visit, http://gapanalysis.usgs.gov/padus/. To find more data resources as well as view example analysis performed using PAD-US data visit, http://gapanalysis.usgs.gov/padus/resources/. The PAD-US dataset and data standard are compiled and maintained by the USGS Gap Analysis Program, http://gapanalysis.usgs.gov/ . For more information about data standards and how the data are aggregated please review the “Standards and Methods Manual for PAD-US,” http://gapanalysis.usgs.gov/padus/data/standards/ .
It is strongly recommended that these data are directly acquired from the U.S. Geological Survey Gap Analysis Program website and server (http://gapanalysis.usgs.gov/padus/data/ ), and not indirectly through other sources which may have changed the data in some way. It is also strongly recommended that careful attention be paid to the contents of the metadata file associated with these data (http://gapanalysis.usgs.gov/padus/data/metadata/ ). The U.S. Geological Survey and all contributing data partners and aggregators shall not be held liable for improper or incorrect use of the data described and/or contained herein. All information is created with a specific end use or uses in mind. This is especially true for GIS data, which is expensive to produce and must be directed to meet the immediate program needs. However, these data were created with the expectation that they would be used for other applications; therefore, we list below both appropriate and inappropriate uses. This list is in no way exhaustive but should serve as a guide to assess whether a proposed use can or cannot be supported by these data. For many uses, it is unlikely that PAD-US will provide the only data needed, and for uses with a regulatory outcome, field surveys should verify the result. In the end, it will be the responsibility of each data user to determine if these data can answer the question being asked, and if they are the best tool to answer that question. Contact the PAD-US Coordinator for guidance. While it is impossible to predict all the uses of these data we have listed several possible appropriate and inappropriate uses from GAP's perspective. Use constraints: Appropriate uses of the data: primarily as a coarse map for a large area such as the nation, a region or state (completeness in counties varies) or to provide context for finer-level maps. A general list of possible applications includes: -National, regional or statewide biodiversity planning, research, management, or policy making -National, Regional or state habitat conservation planning, research, management, or policy making -Outdoor recreation planning, research, management or policy making -Public health planning, research, management or policy making -County comprehensive planning -Large-area resource management planning -Coarse-filter evaluation of potential impacts or benefits of major projects or plan initiatives on biodiversity, such as utility or transportation corridors, wilderness proposals, habitat connectivity proposals, climate change adaption proposals, regional open space and recreation proposals, etc. -Determining relative amounts of management responsibility for specific biological resources among land stewards to facilitate cooperative management and planning. -Basic research on regional distributions of plants and animals and to help target both specific species and geographic areas for needed research. -Environmental impact assessment for large projects or military activities. -Estimation of potential economic impacts from loss of biological resource-based activities. -Education at all levels and for both students and citizens. Inappropriate Uses: It is far easier to identify appropriate uses than inappropriate ones, however, there is a \"fuzzy line\" that is eventually crossed when the differences in resolution of the data, size of geographic area being analyzed, and precision of the answer required for the question are no longer compatible. Examples include: -Using the data to map small areas (less than thousands of hectares), typically requiring mapping resolution at 1:24,000 scale and using aerial photographs or ground surveys in areas where data are incomplete. -Combining these data with other data finer than 1:100,000 scale to produce new hybrid maps or answer queries. -Generating specific areal measurements from the data finer than the nearest thousand hectares -Establishing exact boundaries for regulation or acquisition. -Establishing definite occurrence or non-occurrence of any feature for an exact geographic area -Determining abundance, health, or condition of any feature. -Establishing a measure of accuracy of any other data by comparison with GAP data. -Altering the data in any way and redistributing them as a GAP data product. -Using the data without acquiring and reviewing the metadata.
United States National Park Service lands within the Rio Grande River Basin.
The USGS Protected Areas Database of the United States (PAD-US) is the nation's inventory of protected areas, including public open space and voluntarily provided, private protected areas, identified as an A-16 National Geospatial Data Asset in the Cadastral Theme (http://www.fgdc.gov/ngda-reports/NGDA_Datasets.html). PAD-US is an ongoing project with several published versions of a spatial database of areas dedicated to the preservation of biological diversity, and other natural, recreational or cultural uses, managed for these purposes through legal or other effective means. The geodatabase maps and describes public open space and other protected areas. Most areas are public lands owned in fee; however, long-term easements, leases, and agreements or administrative designations documented in agency management plans may be included. The PAD-US database strives to be a complete “best available” inventory of protected areas (lands and waters) including data provided by managing agencies and organizations. The dataset is built in collaboration with several partners and data providers (http://gapanalysis.usgs.gov/padus/stewards/). See Supplemental Information Section of this metadata record for more information on partnerships and links to major partner organizations. As this dataset is a compilation of many data sets; data completeness, accuracy, and scale may vary. Federal and state data are generally complete, while local government and private protected area coverage is about 50% complete, and depends on data management capacity in the state. For completeness estimates by state: http://www.protectedlands.net/partners. As the federal and state data are reasonably complete; focus is shifting to completing the inventory of local gov and voluntarily provided, private protected areas. The PAD-US geodatabase contains over twenty-five attributes and four feature classes to support data management, queries, web mapping services and analyses: Marine Protected Areas (MPA), Fee, Easements and Combined. The data contained in the MPA Feature class are provided directly by the National Oceanic and Atmospheric Administration (NOAA) Marine Protected Areas Center (MPA, http://marineprotectedareas.noaa.gov ) tracking the National Marine Protected Areas System. The Easements feature class contains data provided directly from the National Conservation Easement Database (NCED, http://conservationeasement.us ) The MPA and Easement feature classes contain some attributes unique to the sole source databases tracking them (e.g. Easement Holder Name from NCED, Protection Level from NOAA MPA Inventory). The "Combined" feature class integrates all fee, easement and MPA features as the best available national inventory of protected areas in the standard PAD-US framework. In addition to geographic boundaries, PAD-US describes the protection mechanism category (e.g. fee, easement, designation, other), owner and managing agency, designation type, unit name, area, public access and state name in a suite of standardized fields. An informative set of references (i.e. Aggregator Source, GIS Source, GIS Source Date) and "local" or source data fields provide a transparent link between standardized PAD-US fields and information from authoritative data sources. The areas in PAD-US are also assigned conservation measures that assess management intent to permanently protect biological diversity: the nationally relevant "GAP Status Code" and global "IUCN Category" standard. A wealth of attributes facilitates a wide variety of data analyses and creates a context for data to be used at local, regional, state, national and international scales. More information about specific updates and changes to this PAD-US version can be found in the Data Quality Information section of this metadata record as well as on the PAD-US website, http://gapanalysis.usgs.gov/padus/data/history/.) Due to the completeness and complexity of these data, it is highly recommended to review the Supplemental Information Section of the metadata record as well as the Data Use Constraints, to better understand data partnerships as well as see tips and ideas of appropriate uses of the data and how to parse out the data that you are looking for. For more information regarding the PAD-US dataset please visit, http://gapanalysis.usgs.gov/padus/. To find more data resources as well as view example analysis performed using PAD-US data visit, http://gapanalysis.usgs.gov/padus/resources/. The PAD-US dataset and data standard are compiled and maintained by the USGS Gap Analysis Program, http://gapanalysis.usgs.gov/ . For more information about data standards and how the data are aggregated please review the “Standards and Methods Manual for PAD-US,” http://gapanalysis.usgs.gov/padus/data/standards/ .
It is strongly recommended that these data are directly acquired from the U.S. Geological Survey Gap Analysis Program website and server (http://gapanalysis.usgs.gov/padus/data/ ), and not indirectly through other sources which may have changed the data in some way. It is also strongly recommended that careful attention be paid to the contents of the metadata file associated with these data (http://gapanalysis.usgs.gov/padus/data/metadata/ ). The U.S. Geological Survey and all contributing data partners and aggregators shall not be held liable for improper or incorrect use of the data described and/or contained herein. All information is created with a specific end use or uses in mind. This is especially true for GIS data, which is expensive to produce and must be directed to meet the immediate program needs. However, these data were created with the expectation that they would be used for other applications; therefore, we list below both appropriate and inappropriate uses. This list is in no way exhaustive but should serve as a guide to assess whether a proposed use can or cannot be supported by these data. For many uses, it is unlikely that PAD-US will provide the only data needed, and for uses with a regulatory outcome, field surveys should verify the result. In the end, it will be the responsibility of each data user to determine if these data can answer the question being asked, and if they are the best tool to answer that question. Contact the PAD-US Coordinator for guidance. While it is impossible to predict all the uses of these data we have listed several possible appropriate and inappropriate uses from GAP's perspective. Use constraints: Appropriate uses of the data: primarily as a coarse map for a large area such as the nation, a region or state (completeness in counties varies) or to provide context for finer-level maps. A general list of possible applications includes: -National, regional or statewide biodiversity planning, research, management, or policy making -National, Regional or state habitat conservation planning, research, management, or policy making -Outdoor recreation planning, research, management or policy making -Public health planning, research, management or policy making -County comprehensive planning -Large-area resource management planning -Coarse-filter evaluation of potential impacts or benefits of major projects or plan initiatives on biodiversity, such as utility or transportation corridors, wilderness proposals, habitat connectivity proposals, climate change adaption proposals, regional open space and recreation proposals, etc. -Determining relative amounts of management responsibility for specific biological resources among land stewards to facilitate cooperative management and planning. -Basic research on regional distributions of plants and animals and to help target both specific species and geographic areas for needed research. -Environmental impact assessment for large projects or military activities. -Estimation of potential economic impacts from loss of biological resource-based activities. -Education at all levels and for both students and citizens. Inappropriate Uses: It is far easier to identify appropriate uses than inappropriate ones, however, there is a \"fuzzy line\" that is eventually crossed when the differences in resolution of the data, size of geographic area being analyzed, and precision of the answer required for the question are no longer compatible. Examples include: -Using the data to map small areas (less than thousands of hectares), typically requiring mapping resolution at 1:24,000 scale and using aerial photographs or ground surveys in areas where data are incomplete. -Combining these data with other data finer than 1:100,000 scale to produce new hybrid maps or answer queries. -Generating specific areal measurements from the data finer than the nearest thousand hectares -Establishing exact boundaries for regulation or acquisition. -Establishing definite occurrence or non-occurrence of any feature for an exact geographic area -Determining abundance, health, or condition of any feature. -Establishing a measure of accuracy of any other data by comparison with GAP data. -Altering the data in any way and redistributing them as a GAP data product. -Using the data without acquiring and reviewing the metadata.
United States Protected Areas within the Rio Grande River Basin.
The USGS Protected Areas Database of the United States (PAD-US) is the nation's inventory of protected areas, including public open space and voluntarily provided, private protected areas, identified as an A-16 National Geospatial Data Asset in the Cadastral Theme (http://www.fgdc.gov/ngda-reports/NGDA_Datasets.html). PAD-US is an ongoing project with several published versions of a spatial database of areas dedicated to the preservation of biological diversity, and other natural, recreational or cultural uses, managed for these purposes through legal or other effective means. The geodatabase maps and describes public open space and other protected areas. Most areas are public lands owned in fee; however, long-term easements, leases, and agreements or administrative designations documented in agency management plans may be included. The PAD-US database strives to be a complete “best available” inventory of protected areas (lands and waters) including data provided by managing agencies and organizations. The dataset is built in collaboration with several partners and data providers (http://gapanalysis.usgs.gov/padus/stewards/). See Supplemental Information Section of this metadata record for more information on partnerships and links to major partner organizations. As this dataset is a compilation of many data sets; data completeness, accuracy, and scale may vary. Federal and state data are generally complete, while local government and private protected area coverage is about 50% complete, and depends on data management capacity in the state. For completeness estimates by state: http://www.protectedlands.net/partners. As the federal and state data are reasonably complete; focus is shifting to completing the inventory of local gov and voluntarily provided, private protected areas. The PAD-US geodatabase contains over twenty-five attributes and four feature classes to support data management, queries, web mapping services and analyses: Marine Protected Areas (MPA), Fee, Easements and Combined. The data contained in the MPA Feature class are provided directly by the National Oceanic and Atmospheric Administration (NOAA) Marine Protected Areas Center (MPA, http://marineprotectedareas.noaa.gov ) tracking the National Marine Protected Areas System. The Easements feature class contains data provided directly from the National Conservation Easement Database (NCED, http://conservationeasement.us ) The MPA and Easement feature classes contain some attributes unique to the sole source databases tracking them (e.g. Easement Holder Name from NCED, Protection Level from NOAA MPA Inventory). The "Combined" feature class integrates all fee, easement and MPA features as the best available national inventory of protected areas in the standard PAD-US framework. In addition to geographic boundaries, PAD-US describes the protection mechanism category (e.g. fee, easement, designation, other), owner and managing agency, designation type, unit name, area, public access and state name in a suite of standardized fields. An informative set of references (i.e. Aggregator Source, GIS Source, GIS Source Date) and "local" or source data fields provide a transparent link between standardized PAD-US fields and information from authoritative data sources. The areas in PAD-US are also assigned conservation measures that assess management intent to permanently protect biological diversity: the nationally relevant "GAP Status Code" and global "IUCN Category" standard. A wealth of attributes facilitates a wide variety of data analyses and creates a context for data to be used at local, regional, state, national and international scales. More information about specific updates and changes to this PAD-US version can be found in the Data Quality Information section of this metadata record as well as on the PAD-US website, http://gapanalysis.usgs.gov/padus/data/history/.) Due to the completeness and complexity of these data, it is highly recommended to review the Supplemental Information Section of the metadata record as well as the Data Use Constraints, to better understand data partnerships as well as see tips and ideas of appropriate uses of the data and how to parse out the data that you are looking for. For more information regarding the PAD-US dataset please visit, http://gapanalysis.usgs.gov/padus/. To find more data resources as well as view example analysis performed using PAD-US data visit, http://gapanalysis.usgs.gov/padus/resources/. The PAD-US dataset and data standard are compiled and maintained by the USGS Gap Analysis Program, http://gapanalysis.usgs.gov/ . For more information about data standards and how the data are aggregated please review the “Standards and Methods Manual for PAD-US,” http://gapanalysis.usgs.gov/padus/data/standards/ .
It is strongly recommended that these data are directly acquired from the U.S. Geological Survey Gap Analysis Program website and server (http://gapanalysis.usgs.gov/padus/data/ ), and not indirectly through other sources which may have changed the data in some way. It is also strongly recommended that careful attention be paid to the contents of the metadata file associated with these data (http://gapanalysis.usgs.gov/padus/data/metadata/ ). The U.S. Geological Survey and all contributing data partners and aggregators shall not be held liable for improper or incorrect use of the data described and/or contained herein. All information is created with a specific end use or uses in mind. This is especially true for GIS data, which is expensive to produce and must be directed to meet the immediate program needs. However, these data were created with the expectation that they would be used for other applications; therefore, we list below both appropriate and inappropriate uses. This list is in no way exhaustive but should serve as a guide to assess whether a proposed use can or cannot be supported by these data. For many uses, it is unlikely that PAD-US will provide the only data needed, and for uses with a regulatory outcome, field surveys should verify the result. In the end, it will be the responsibility of each data user to determine if these data can answer the question being asked, and if they are the best tool to answer that question. Contact the PAD-US Coordinator for guidance. While it is impossible to predict all the uses of these data we have listed several possible appropriate and inappropriate uses from GAP's perspective. Use constraints: Appropriate uses of the data: primarily as a coarse map for a large area such as the nation, a region or state (completeness in counties varies) or to provide context for finer-level maps. A general list of possible applications includes: -National, regional or statewide biodiversity planning, research, management, or policy making -National, Regional or state habitat conservation planning, research, management, or policy making -Outdoor recreation planning, research, management or policy making -Public health planning, research, management or policy making -County comprehensive planning -Large-area resource management planning -Coarse-filter evaluation of potential impacts or benefits of major projects or plan initiatives on biodiversity, such as utility or transportation corridors, wilderness proposals, habitat connectivity proposals, climate change adaption proposals, regional open space and recreation proposals, etc. -Determining relative amounts of management responsibility for specific biological resources among land stewards to facilitate cooperative management and planning. -Basic research on regional distributions of plants and animals and to help target both specific species and geographic areas for needed research. -Environmental impact assessment for large projects or military activities. -Estimation of potential economic impacts from loss of biological resource-based activities. -Education at all levels and for both students and citizens. Inappropriate Uses: It is far easier to identify appropriate uses than inappropriate ones, however, there is a \"fuzzy line\" that is eventually crossed when the differences in resolution of the data, size of geographic area being analyzed, and precision of the answer required for the question are no longer compatible. Examples include: -Using the data to map small areas (less than thousands of hectares), typically requiring mapping resolution at 1:24,000 scale and using aerial photographs or ground surveys in areas where data are incomplete. -Combining these data with other data finer than 1:100,000 scale to produce new hybrid maps or answer queries. -Generating specific areal measurements from the data finer than the nearest thousand hectares -Establishing exact boundaries for regulation or acquisition. -Establishing definite occurrence or non-occurrence of any feature for an exact geographic area -Determining abundance, health, or condition of any feature. -Establishing a measure of accuracy of any other data by comparison with GAP data. -Altering the data in any way and redistributing them as a GAP data product. -Using the data without acquiring and reviewing the metadata.
United States Bureau of Land Management lands within the Rio Grande River Basin.
The USGS Protected Areas Database of the United States (PAD-US) is the nation's inventory of protected areas, including public open space and voluntarily provided, private protected areas, identified as an A-16 National Geospatial Data Asset in the Cadastral Theme (http://www.fgdc.gov/ngda-reports/NGDA_Datasets.html). PAD-US is an ongoing project with several published versions of a spatial database of areas dedicated to the preservation of biological diversity, and other natural, recreational or cultural uses, managed for these purposes through legal or other effective means. The geodatabase maps and describes public open space and other protected areas. Most areas are public lands owned in fee; however, long-term easements, leases, and agreements or administrative designations documented in agency management plans may be included. The PAD-US database strives to be a complete “best available” inventory of protected areas (lands and waters) including data provided by managing agencies and organizations. The dataset is built in collaboration with several partners and data providers (http://gapanalysis.usgs.gov/padus/stewards/). See Supplemental Information Section of this metadata record for more information on partnerships and links to major partner organizations. As this dataset is a compilation of many data sets; data completeness, accuracy, and scale may vary. Federal and state data are generally complete, while local government and private protected area coverage is about 50% complete, and depends on data management capacity in the state. For completeness estimates by state: http://www.protectedlands.net/partners. As the federal and state data are reasonably complete; focus is shifting to completing the inventory of local gov and voluntarily provided, private protected areas. The PAD-US geodatabase contains over twenty-five attributes and four feature classes to support data management, queries, web mapping services and analyses: Marine Protected Areas (MPA), Fee, Easements and Combined. The data contained in the MPA Feature class are provided directly by the National Oceanic and Atmospheric Administration (NOAA) Marine Protected Areas Center (MPA, http://marineprotectedareas.noaa.gov ) tracking the National Marine Protected Areas System. The Easements feature class contains data provided directly from the National Conservation Easement Database (NCED, http://conservationeasement.us ) The MPA and Easement feature classes contain some attributes unique to the sole source databases tracking them (e.g. Easement Holder Name from NCED, Protection Level from NOAA MPA Inventory). The "Combined" feature class integrates all fee, easement and MPA features as the best available national inventory of protected areas in the standard PAD-US framework. In addition to geographic boundaries, PAD-US describes the protection mechanism category (e.g. fee, easement, designation, other), owner and managing agency, designation type, unit name, area, public access and state name in a suite of standardized fields. An informative set of references (i.e. Aggregator Source, GIS Source, GIS Source Date) and "local" or source data fields provide a transparent link between standardized PAD-US fields and information from authoritative data sources. The areas in PAD-US are also assigned conservation measures that assess management intent to permanently protect biological diversity: the nationally relevant "GAP Status Code" and global "IUCN Category" standard. A wealth of attributes facilitates a wide variety of data analyses and creates a context for data to be used at local, regional, state, national and international scales. More information about specific updates and changes to this PAD-US version can be found in the Data Quality Information section of this metadata record as well as on the PAD-US website, http://gapanalysis.usgs.gov/padus/data/history/.) Due to the completeness and complexity of these data, it is highly recommended to review the Supplemental Information Section of the metadata record as well as the Data Use Constraints, to better understand data partnerships as well as see tips and ideas of appropriate uses of the data and how to parse out the data that you are looking for. For more information regarding the PAD-US dataset please visit, http://gapanalysis.usgs.gov/padus/. To find more data resources as well as view example analysis performed using PAD-US data visit, http://gapanalysis.usgs.gov/padus/resources/. The PAD-US dataset and data standard are compiled and maintained by the USGS Gap Analysis Program, http://gapanalysis.usgs.gov/ . For more information about data standards and how the data are aggregated please review the “Standards and Methods Manual for PAD-US,” http://gapanalysis.usgs.gov/padus/data/standards/ .
It is strongly recommended that these data are directly acquired from the U.S. Geological Survey Gap Analysis Program website and server (http://gapanalysis.usgs.gov/padus/data/ ), and not indirectly through other sources which may have changed the data in some way. It is also strongly recommended that careful attention be paid to the contents of the metadata file associated with these data (http://gapanalysis.usgs.gov/padus/data/metadata/ ). The U.S. Geological Survey and all contributing data partners and aggregators shall not be held liable for improper or incorrect use of the data described and/or contained herein. All information is created with a specific end use or uses in mind. This is especially true for GIS data, which is expensive to produce and must be directed to meet the immediate program needs. However, these data were created with the expectation that they would be used for other applications; therefore, we list below both appropriate and inappropriate uses. This list is in no way exhaustive but should serve as a guide to assess whether a proposed use can or cannot be supported by these data. For many uses, it is unlikely that PAD-US will provide the only data needed, and for uses with a regulatory outcome, field surveys should verify the result. In the end, it will be the responsibility of each data user to determine if these data can answer the question being asked, and if they are the best tool to answer that question. Contact the PAD-US Coordinator for guidance. While it is impossible to predict all the uses of these data we have listed several possible appropriate and inappropriate uses from GAP's perspective. Use constraints: Appropriate uses of the data: primarily as a coarse map for a large area such as the nation, a region or state (completeness in counties varies) or to provide context for finer-level maps. A general list of possible applications includes: -National, regional or statewide biodiversity planning, research, management, or policy making -National, Regional or state habitat conservation planning, research, management, or policy making -Outdoor recreation planning, research, management or policy making -Public health planning, research, management or policy making -County comprehensive planning -Large-area resource management planning -Coarse-filter evaluation of potential impacts or benefits of major projects or plan initiatives on biodiversity, such as utility or transportation corridors, wilderness proposals, habitat connectivity proposals, climate change adaption proposals, regional open space and recreation proposals, etc. -Determining relative amounts of management responsibility for specific biological resources among land stewards to facilitate cooperative management and planning. -Basic research on regional distributions of plants and animals and to help target both specific species and geographic areas for needed research. -Environmental impact assessment for large projects or military activities. -Estimation of potential economic impacts from loss of biological resource-based activities. -Education at all levels and for both students and citizens. Inappropriate Uses: It is far easier to identify appropriate uses than inappropriate ones, however, there is a \"fuzzy line\" that is eventually crossed when the differences in resolution of the data, size of geographic area being analyzed, and precision of the answer required for the question are no longer compatible. Examples include: -Using the data to map small areas (less than thousands of hectares), typically requiring mapping resolution at 1:24,000 scale and using aerial photographs or ground surveys in areas where data are incomplete. -Combining these data with other data finer than 1:100,000 scale to produce new hybrid maps or answer queries. -Generating specific areal measurements from the data finer than the nearest thousand hectares -Establishing exact boundaries for regulation or acquisition. -Establishing definite occurrence or non-occurrence of any feature for an exact geographic area -Determining abundance, health, or condition of any feature. -Establishing a measure of accuracy of any other data by comparison with GAP data. -Altering the data in any way and redistributing them as a GAP data product. -Using the data without acquiring and reviewing the metadata.
United States Bureau of Indian Affairs lands within the Rio Grande River Basin.
The USGS Protected Areas Database of the United States (PAD-US) is the nation's inventory of protected areas, including public open space and voluntarily provided, private protected areas, identified as an A-16 National Geospatial Data Asset in the Cadastral Theme (http://www.fgdc.gov/ngda-reports/NGDA_Datasets.html). PAD-US is an ongoing project with several published versions of a spatial database of areas dedicated to the preservation of biological diversity, and other natural, recreational or cultural uses, managed for these purposes through legal or other effective means. The geodatabase maps and describes public open space and other protected areas. Most areas are public lands owned in fee; however, long-term easements, leases, and agreements or administrative designations documented in agency management plans may be included. The PAD-US database strives to be a complete “best available” inventory of protected areas (lands and waters) including data provided by managing agencies and organizations. The dataset is built in collaboration with several partners and data providers (http://gapanalysis.usgs.gov/padus/stewards/). See Supplemental Information Section of this metadata record for more information on partnerships and links to major partner organizations. As this dataset is a compilation of many data sets; data completeness, accuracy, and scale may vary. Federal and state data are generally complete, while local government and private protected area coverage is about 50% complete, and depends on data management capacity in the state. For completeness estimates by state: http://www.protectedlands.net/partners. As the federal and state data are reasonably complete; focus is shifting to completing the inventory of local gov and voluntarily provided, private protected areas. The PAD-US geodatabase contains over twenty-five attributes and four feature classes to support data management, queries, web mapping services and analyses: Marine Protected Areas (MPA), Fee, Easements and Combined. The data contained in the MPA Feature class are provided directly by the National Oceanic and Atmospheric Administration (NOAA) Marine Protected Areas Center (MPA, http://marineprotectedareas.noaa.gov ) tracking the National Marine Protected Areas System. The Easements feature class contains data provided directly from the National Conservation Easement Database (NCED, http://conservationeasement.us ) The MPA and Easement feature classes contain some attributes unique to the sole source databases tracking them (e.g. Easement Holder Name from NCED, Protection Level from NOAA MPA Inventory). The "Combined" feature class integrates all fee, easement and MPA features as the best available national inventory of protected areas in the standard PAD-US framework. In addition to geographic boundaries, PAD-US describes the protection mechanism category (e.g. fee, easement, designation, other), owner and managing agency, designation type, unit name, area, public access and state name in a suite of standardized fields. An informative set of references (i.e. Aggregator Source, GIS Source, GIS Source Date) and "local" or source data fields provide a transparent link between standardized PAD-US fields and information from authoritative data sources. The areas in PAD-US are also assigned conservation measures that assess management intent to permanently protect biological diversity: the nationally relevant "GAP Status Code" and global "IUCN Category" standard. A wealth of attributes facilitates a wide variety of data analyses and creates a context for data to be used at local, regional, state, national and international scales. More information about specific updates and changes to this PAD-US version can be found in the Data Quality Information section of this metadata record as well as on the PAD-US website, http://gapanalysis.usgs.gov/padus/data/history/.) Due to the completeness and complexity of these data, it is highly recommended to review the Supplemental Information Section of the metadata record as well as the Data Use Constraints, to better understand data partnerships as well as see tips and ideas of appropriate uses of the data and how to parse out the data that you are looking for. For more information regarding the PAD-US dataset please visit, http://gapanalysis.usgs.gov/padus/. To find more data resources as well as view example analysis performed using PAD-US data visit, http://gapanalysis.usgs.gov/padus/resources/. The PAD-US dataset and data standard are compiled and maintained by the USGS Gap Analysis Program, http://gapanalysis.usgs.gov/ . For more information about data standards and how the data are aggregated please review the “Standards and Methods Manual for PAD-US,” http://gapanalysis.usgs.gov/padus/data/standards/ .
It is strongly recommended that these data are directly acquired from the U.S. Geological Survey Gap Analysis Program website and server (http://gapanalysis.usgs.gov/padus/data/ ), and not indirectly through other sources which may have changed the data in some way. It is also strongly recommended that careful attention be paid to the contents of the metadata file associated with these data (http://gapanalysis.usgs.gov/padus/data/metadata/ ). The U.S. Geological Survey and all contributing data partners and aggregators shall not be held liable for improper or incorrect use of the data described and/or contained herein. All information is created with a specific end use or uses in mind. This is especially true for GIS data, which is expensive to produce and must be directed to meet the immediate program needs. However, these data were created with the expectation that they would be used for other applications; therefore, we list below both appropriate and inappropriate uses. This list is in no way exhaustive but should serve as a guide to assess whether a proposed use can or cannot be supported by these data. For many uses, it is unlikely that PAD-US will provide the only data needed, and for uses with a regulatory outcome, field surveys should verify the result. In the end, it will be the responsibility of each data user to determine if these data can answer the question being asked, and if they are the best tool to answer that question. Contact the PAD-US Coordinator for guidance. While it is impossible to predict all the uses of these data we have listed several possible appropriate and inappropriate uses from GAP's perspective. Use constraints: Appropriate uses of the data: primarily as a coarse map for a large area such as the nation, a region or state (completeness in counties varies) or to provide context for finer-level maps. A general list of possible applications includes: -National, regional or statewide biodiversity planning, research, management, or policy making -National, Regional or state habitat conservation planning, research, management, or policy making -Outdoor recreation planning, research, management or policy making -Public health planning, research, management or policy making -County comprehensive planning -Large-area resource management planning -Coarse-filter evaluation of potential impacts or benefits of major projects or plan initiatives on biodiversity, such as utility or transportation corridors, wilderness proposals, habitat connectivity proposals, climate change adaption proposals, regional open space and recreation proposals, etc. -Determining relative amounts of management responsibility for specific biological resources among land stewards to facilitate cooperative management and planning. -Basic research on regional distributions of plants and animals and to help target both specific species and geographic areas for needed research. -Environmental impact assessment for large projects or military activities. -Estimation of potential economic impacts from loss of biological resource-based activities. -Education at all levels and for both students and citizens. Inappropriate Uses: It is far easier to identify appropriate uses than inappropriate ones, however, there is a \"fuzzy line\" that is eventually crossed when the differences in resolution of the data, size of geographic area being analyzed, and precision of the answer required for the question are no longer compatible. Examples include: -Using the data to map small areas (less than thousands of hectares), typically requiring mapping resolution at 1:24,000 scale and using aerial photographs or ground surveys in areas where data are incomplete. -Combining these data with other data finer than 1:100,000 scale to produce new hybrid maps or answer queries. -Generating specific areal measurements from the data finer than the nearest thousand hectares -Establishing exact boundaries for regulation or acquisition. -Establishing definite occurrence or non-occurrence of any feature for an exact geographic area -Determining abundance, health, or condition of any feature. -Establishing a measure of accuracy of any other data by comparison with GAP data. -Altering the data in any way and redistributing them as a GAP data product. -Using the data without acquiring and reviewing the metadata.
The data for the Colorado dataset was primarily compiled from the Colorado Decision Support Systems (CDSS), GIS Data (https://cdss.state.co.us/GIS/Pages/GISDataHome.aspx). Please visit CDSS website for the source data, data disclaimers and metadata if not available in these data layer files. Much of the metadata for the files in the dataset are complete and were imported with the original file. In cases where there are discrepancies, metadata have been added where appropriate.
", "keyword": [ "Colorado", "CRB", "CRB_CO", "Watershed", "colorado river basin", "Counties", "Districts", "HUC8", "Irrigated Ag", "Lakes", "CDWR", "Agriculture", "Water", "Gunnison", "San Juan", "Yampa", "river", "CO", "lakes", "reservoirs", "administrative divisions", "water conservancy districts" ], "issued": "2020-08-13T20:33:10.000Z", "modified": "2020-08-13T20:34:04.320Z", "publisher": { "name": "Colorado State University" }, "contactPoint": { "@type": "vcard:Contact", "fn": "jlc4_CSUrams8", "hasEmail": "mailto: gis@colostate.edu" }, "accessLevel": "public", "spatial": "-109.2307,37.6708,-106.1654,39.3404", "license": "", "distribution": [ { "@type": "dcat:Distribution", "title": "ArcGIS Hub Dataset", "format": "Web Page", "mediaType": "text/html", "accessURL": "https://geospatialcentroid-csurams.hub.arcgis.com/datasets/CSUrams::co-gunnisondiv-districts" }, { "@type": "dcat:Distribution", "title": "ArcGIS GeoService", "format": "ArcGIS GeoServices REST API", "mediaType": "application/json", "accessURL": "https://services1.arcgis.com/KNdRU5cN6ENqCTjk/arcgis/rest/services/CRB_Colorado_gdb/FeatureServer/10" }, { "@type": "dcat:Distribution", "title": "CSV", "format": "CSV", "mediaType": "text/csv", "accessURL": "https://geospatialcentroid-csurams.hub.arcgis.com/api/download/v1/items/0c61955b889a4c069eb40d78b56f0f93/csv?layers=10" }, { "@type": "dcat:Distribution", "title": "Shapefile", "format": "ZIP", "mediaType": "application/zip", "accessURL": "https://geospatialcentroid-csurams.hub.arcgis.com/api/download/v1/items/0c61955b889a4c069eb40d78b56f0f93/shapefile?layers=10" }, { "@type": "dcat:Distribution", "title": "GeoJSON", "format": "GeoJSON", "mediaType": "application/vnd.geo+json", "accessURL": "https://geospatialcentroid-csurams.hub.arcgis.com/api/download/v1/items/0c61955b889a4c069eb40d78b56f0f93/geojson?layers=10" }, { "@type": "dcat:Distribution", "title": "KML", "format": "KML", "mediaType": "application/vnd.google-earth.kml+xml", "accessURL": "https://geospatialcentroid-csurams.hub.arcgis.com/api/download/v1/items/0c61955b889a4c069eb40d78b56f0f93/kml?layers=10" } ], "theme": [ "geospatial" ] }, { "@type": "dcat:Dataset", "identifier": "https://www.arcgis.com/home/item.html?id=6ec6812f94794c5383686de67fe9d604&sublayer=0", "landingPage": "https://geospatialcentroid-csurams.hub.arcgis.com/datasets/CSUrams::mx-protected-areas-rg-1", "title": "MX Protected Areas RG", "description": "Mexico Protected Areas within the Rio Grande River Basin. Proporcionar datos espaciales de las Áreas Naturales Protegidas Federales de la República Mexicana.
Datos espaciales de las Áreas Naturales Protegidas Federales de la República Mexicana construidos en apego a los decretos de creación publicados en el Diario Oficial de la Federación. Manejo de datos espaciales con herramientas de los sistemas de información geográfica de la Comisión Nacional de las Áreas Naturales Protegidas.
No existe restricción alguna para el uso de la información, sin embargo su uso es responsabilidad de quien la utilice y deberá citarse la fuente.
United States Non Government Organization lands within the Rio Grande River Basin.
The USGS Protected Areas Database of the United States (PAD-US) is the nation's inventory of protected areas, including public open space and voluntarily provided, private protected areas, identified as an A-16 National Geospatial Data Asset in the Cadastral Theme (http://www.fgdc.gov/ngda-reports/NGDA_Datasets.html). PAD-US is an ongoing project with several published versions of a spatial database of areas dedicated to the preservation of biological diversity, and other natural, recreational or cultural uses, managed for these purposes through legal or other effective means. The geodatabase maps and describes public open space and other protected areas. Most areas are public lands owned in fee; however, long-term easements, leases, and agreements or administrative designations documented in agency management plans may be included. The PAD-US database strives to be a complete “best available” inventory of protected areas (lands and waters) including data provided by managing agencies and organizations. The dataset is built in collaboration with several partners and data providers (http://gapanalysis.usgs.gov/padus/stewards/). See Supplemental Information Section of this metadata record for more information on partnerships and links to major partner organizations. As this dataset is a compilation of many data sets; data completeness, accuracy, and scale may vary. Federal and state data are generally complete, while local government and private protected area coverage is about 50% complete, and depends on data management capacity in the state. For completeness estimates by state: http://www.protectedlands.net/partners. As the federal and state data are reasonably complete; focus is shifting to completing the inventory of local gov and voluntarily provided, private protected areas. The PAD-US geodatabase contains over twenty-five attributes and four feature classes to support data management, queries, web mapping services and analyses: Marine Protected Areas (MPA), Fee, Easements and Combined. The data contained in the MPA Feature class are provided directly by the National Oceanic and Atmospheric Administration (NOAA) Marine Protected Areas Center (MPA, http://marineprotectedareas.noaa.gov ) tracking the National Marine Protected Areas System. The Easements feature class contains data provided directly from the National Conservation Easement Database (NCED, http://conservationeasement.us ) The MPA and Easement feature classes contain some attributes unique to the sole source databases tracking them (e.g. Easement Holder Name from NCED, Protection Level from NOAA MPA Inventory). The "Combined" feature class integrates all fee, easement and MPA features as the best available national inventory of protected areas in the standard PAD-US framework. In addition to geographic boundaries, PAD-US describes the protection mechanism category (e.g. fee, easement, designation, other), owner and managing agency, designation type, unit name, area, public access and state name in a suite of standardized fields. An informative set of references (i.e. Aggregator Source, GIS Source, GIS Source Date) and "local" or source data fields provide a transparent link between standardized PAD-US fields and information from authoritative data sources. The areas in PAD-US are also assigned conservation measures that assess management intent to permanently protect biological diversity: the nationally relevant "GAP Status Code" and global "IUCN Category" standard. A wealth of attributes facilitates a wide variety of data analyses and creates a context for data to be used at local, regional, state, national and international scales. More information about specific updates and changes to this PAD-US version can be found in the Data Quality Information section of this metadata record as well as on the PAD-US website, http://gapanalysis.usgs.gov/padus/data/history/.) Due to the completeness and complexity of these data, it is highly recommended to review the Supplemental Information Section of the metadata record as well as the Data Use Constraints, to better understand data partnerships as well as see tips and ideas of appropriate uses of the data and how to parse out the data that you are looking for. For more information regarding the PAD-US dataset please visit, http://gapanalysis.usgs.gov/padus/. To find more data resources as well as view example analysis performed using PAD-US data visit, http://gapanalysis.usgs.gov/padus/resources/. The PAD-US dataset and data standard are compiled and maintained by the USGS Gap Analysis Program, http://gapanalysis.usgs.gov/ . For more information about data standards and how the data are aggregated please review the “Standards and Methods Manual for PAD-US,” http://gapanalysis.usgs.gov/padus/data/standards/ .
It is strongly recommended that these data are directly acquired from the U.S. Geological Survey Gap Analysis Program website and server (http://gapanalysis.usgs.gov/padus/data/ ), and not indirectly through other sources which may have changed the data in some way. It is also strongly recommended that careful attention be paid to the contents of the metadata file associated with these data (http://gapanalysis.usgs.gov/padus/data/metadata/ ). The U.S. Geological Survey and all contributing data partners and aggregators shall not be held liable for improper or incorrect use of the data described and/or contained herein. All information is created with a specific end use or uses in mind. This is especially true for GIS data, which is expensive to produce and must be directed to meet the immediate program needs. However, these data were created with the expectation that they would be used for other applications; therefore, we list below both appropriate and inappropriate uses. This list is in no way exhaustive but should serve as a guide to assess whether a proposed use can or cannot be supported by these data. For many uses, it is unlikely that PAD-US will provide the only data needed, and for uses with a regulatory outcome, field surveys should verify the result. In the end, it will be the responsibility of each data user to determine if these data can answer the question being asked, and if they are the best tool to answer that question. Contact the PAD-US Coordinator for guidance. While it is impossible to predict all the uses of these data we have listed several possible appropriate and inappropriate uses from GAP's perspective. Use constraints: Appropriate uses of the data: primarily as a coarse map for a large area such as the nation, a region or state (completeness in counties varies) or to provide context for finer-level maps. A general list of possible applications includes: -National, regional or statewide biodiversity planning, research, management, or policy making -National, Regional or state habitat conservation planning, research, management, or policy making -Outdoor recreation planning, research, management or policy making -Public health planning, research, management or policy making -County comprehensive planning -Large-area resource management planning -Coarse-filter evaluation of potential impacts or benefits of major projects or plan initiatives on biodiversity, such as utility or transportation corridors, wilderness proposals, habitat connectivity proposals, climate change adaption proposals, regional open space and recreation proposals, etc. -Determining relative amounts of management responsibility for specific biological resources among land stewards to facilitate cooperative management and planning. -Basic research on regional distributions of plants and animals and to help target both specific species and geographic areas for needed research. -Environmental impact assessment for large projects or military activities. -Estimation of potential economic impacts from loss of biological resource-based activities. -Education at all levels and for both students and citizens. Inappropriate Uses: It is far easier to identify appropriate uses than inappropriate ones, however, there is a \"fuzzy line\" that is eventually crossed when the differences in resolution of the data, size of geographic area being analyzed, and precision of the answer required for the question are no longer compatible. Examples include: -Using the data to map small areas (less than thousands of hectares), typically requiring mapping resolution at 1:24,000 scale and using aerial photographs or ground surveys in areas where data are incomplete. -Combining these data with other data finer than 1:100,000 scale to produce new hybrid maps or answer queries. -Generating specific areal measurements from the data finer than the nearest thousand hectares -Establishing exact boundaries for regulation or acquisition. -Establishing definite occurrence or non-occurrence of any feature for an exact geographic area -Determining abundance, health, or condition of any feature. -Establishing a measure of accuracy of any other data by comparison with GAP data. -Altering the data in any way and redistributing them as a GAP data product. -Using the data without acquiring and reviewing the metadata.
A geo-referenced, crop-specific land cover data layer within the Rio Grande River Basin.
The USDA, NASS Cropland Data Layer (CDL) is a raster, geo-referenced, crop-specific land cover data layer. The 2017 CDL has a ground resolution of 30 meters. The CDL is produced using satellite imagery from the Landsat 8 OLI/TIRS sensor, the Disaster Monitoring Constellation (DMC) DEIMOS-1 and UK2, the ISRO ResourceSat-2 LISS-3, and the ESA SENTINEL-2 sensors collected during the current growing season.
Some CDL states used additional satellite imagery and ancillary inputs to supplement and improve the classification. These additional sources can include the United States Geological Survey (USGS) National Elevation Dataset (NED) and the imperviousness and canopy data layers from the USGS National Land Cover Database 2011 (NLCD 2011).
Agricultural training and validation data are derived from the Farm Service Agency (FSA) Common Land Unit (CLU) Program. The most current version of the NLCD is used as non-agricultural training and validation data.
Please refer to the 'Supplemental_Information' Section of this metadata file for a complete list of all imagery, ancillary data, and training/validation data used to generate this state's CDL.
The strength and emphasis of the CDL is agricultural land cover. Please note that no farmer reported data are derivable from the Cropland Data Layer.
A crop-specific land cover data layer of the United States within the Rio Grande River Basin.
The USDA, NASS Cropland Data Layer (CDL) is a raster, geo-referenced, crop-specific land cover data layer. The 2019 CDL has a ground resolution of 30 meters. The CDL is produced using satellite imagery from the Landsat 8 OLI/TIRS sensor, the Disaster Monitoring Constellation (DMC) DEIMOS-1 and UK2, the ISRO ResourceSat-2 LISS-3, and the ESA SENTINEL-2 sensors collected during the current growing season.
Some CDL states used additional satellite imagery and ancillary inputs to supplement and improve the classification. These additional sources can include the United States Geological Survey (USGS) National Elevation Dataset (NED) and the imperviousness and canopy data layers from the USGS National Land Cover Database 2011 (NLCD 2011).
Agricultural training and validation data are derived from the Farm Service Agency (FSA) Common Land Unit (CLU) Program. The most current version of the NLCD is used as non-agricultural training and validation data.
Please refer to the 'Supplemental_Information' Section of this metadata file for a complete list of all imagery, ancillary data, and training/validation data used to generate this state's CDL.
The strength and emphasis of the CDL is agricultural land cover. Please note that no farmer reported data are derivable from the Cropland Data Layer.
The USDA, NASS Cropland Data Layer and the data offered on the CropScape website is provided to the public as is and is considered public domain and free to redistribute. The USDA, NASS does not warrant any conclusions drawn from these data.
Colorado ditches within the Rio Grande River Basin.
Ditch headgates associated with the 2015 RGDSS Irrigated Lands data.
CWCB and CDWR provide no warranty to the users of this information as to the accuracy, reliability or completeness of the data. Information is subject to change according to additional input during subsequent phases of the RGDSS project. The user assumes all responsibility for the use of this data.
Colorado irrigation within the Rio Grande River Basin.
Spatial and informational database of irrigated parcels in Division 3 of the Rio Grande Basin for the 2017 growing season in support of the RGDSS.
CWCB and CDWR provide no warranty to the users of this information as to the accuracy, reliability or completeness of the data. Information is subject to change according to additional input during subsequent phases of the RGDSS project. The user assumes all responsibility for the use of this data.
Texas water wells within the Rio Grande River Basin.
These layers contain Public Water Supply sites in the State of Texas. The locations were obtained by the
Water Supply Division as recorded from various sources and built using the best existing location data
available from these sources. Although some location errors were found and corrected in the process,
some errors still remain. As resources allow, TCEQ intends to improve the accuracy of these locations to
meet the standards set forth in the agency's Positional Data Policy. This layer was developed to support
the TCEQ's Source Water Assessment and Protection Program (SWAP).
Acknowledgment of the Texas Commission on Environmental Quality on any products using or deriving data from this digital dataset.
New Mexico water wells within the Rio Grande River Basin.
THIS OSE PODS DATASET IS ACCOMPANIED BY A SEPARATELY DOWNLOADED DATA DICTIONARY (METADATA). (http://www.ose.state.nm.us/GIS/PODS/nmose_WATERS_PODs_data_dictionary_v8.xlsx) The NM Office of the State Engineer (OSE) "Point of Diversions" (POD) layer includes well locations, surface declarations, or surface permits updated on a monthly basis. These data were extracted from the OSE W.A.T.E.R.S. (Water Administration Technical Engineering Resource System) database and geo-located (mapped). These data have varying degrees of accuracy and have not been validated. Data included in this dataset only includes PODs that have coordinates located within the State of New Mexico. This message is to alert users of this data to various changes regarding how this POD data is generated and maintained by the NM Office of the State Engineer. In addition, all attribute fields are fully described in the metadata, including descriptions of field codes. Please read the metadata accompanying this GIS data layer for further information. Any questions regarding this GIS data should be directed NM OSE Information Technology Systems Bureau GIS at the contact information given below.
The New Mexico Office of the State Engineer (OSE) provides this geographic data and any associated metadata “as is” without warranty of any kind, including but not limited to its completeness, fitness for a particular use, or accuracy of its content, positional or otherwise. It is the sole responsibility of the user to determine the usability of the data for their purposes. In no event shall OSE have any liability whatsoever for payment of any consequential, incidental, indirect, special, or tort damages of any kind arising out of the use of or reliance on the geographic data or metadata. In providing this data or access to it, OSE assumes no obligation to assist the user in the use of such data or in the development, use, or maintenance of any applications applied to or associated with the data or metadata.
Colorado water wells within the Rio Grande River Basin.
Wells associated with the 2017 RGDSS Irrigated Lands data.
CWCB and CDWR provide no warranty to the users of this information as to the accuracy, reliability or completeness of the data. Information is subject to change according to additional input during subsequent phases of the RGDSS project. The user assumes all responsibility for the use of this data.
Raster layer of irrigated land within the Rio Grande River Basin.
This product uses three primary data inputs, (a) USDA county-level irrigation area statistics for 2002, (b) annual peak eMODIS Normalized Difference Vegetation Index (NDVI), and (c) a land cover mask for agricultural lands derived from NLCD to map the spatial distribution of irrigated lands across the conterminous United States. The validation of MIrAD-US is a challenge because no other single-source current datasets are available at a national scale for comparison. Thus, this dataset should be considered provisional until a formal accuracy assessment can be completed. The product update is planned for every 5 years, synchronized with the update of the Census of Agriculture by the U.S Department of Agriculture (USDA) but contingent upon availability of Collection 6 (C6) Aqua eMODIS data and funding.
Dams within the Rio Grande River Basin.
US: This map layer portrays major dams on United States. The map layer was created by extracting dams 50 feet or more in height, or with a normal storage capacity of 5,000 acre-feet or more, or with a maximum storage capacity of 25,000 acre-feet or more, from the 79,777 dams in the U.S. Army Corps of Engineers National Inventory of Dams. This is a replacement for the April 1994 map layer.
MX: Represent the geographic location of the dams registered by the National Water Commission of Mexico (CONAGUA)
Note: storag units listed in gigaliters
Ortiz-Partida, J. Pablo and Sandoval-Solis, Samuel with data from:
US: None. Acknowledgment of the National Atlas of the United States of America would be appreciated in products derived from these data.
MX: As established in the Federal Law on Transparency and Access to Governmental Public Information. All governmental information referred to in the Federal Law on Transparency and Access to Governmental Public Information is public and individuals will have access to it in the terms it indicates.
Texas roads within the Rio Grande River Basin.
The TIGER/Line shapefiles and related database files (.dbf) are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). The MTDB represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line shapefile is designed to stand alone as an independent data set, or they can be combined to cover the entire nation.
Primary roads are generally divided, limited-access highways within the interstate highway system or under State management, and are distinguished by the presence of interchanges. These highways are accessible by ramps and may include some toll highways. The MAF/TIGER Feature Classification Code (MTFCC) is S1100 for primary roads. Secondary roads are main arteries, usually in the U.S. Highway, State Highway, and/or County Highway system. These roads have one or more lanes of traffic in each direction, may or may not be divided, and usually have at-grade intersections with many other roads and driveways. They usually have both a local name and a route number. The MAF/TIGER Feature Classification Code (MTFCC) is S1200 for secondary roads.
New Mexico roads within the Rio Grande River Basin.
The TIGER/Line shapefiles and related database files (.dbf) are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). The MTDB represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line shapefile is designed to stand alone as an independent data set, or they can be combined to cover the entire nation.
Primary roads are generally divided, limited-access highways within the interstate highway system or under State management, and are distinguished by the presence of interchanges. These highways are accessible by ramps and may include some toll highways. The MAF/TIGER Feature Classification Code (MTFCC) is S1100 for primary roads. Secondary roads are main arteries, usually in the U.S. Highway, State Highway, and/or County Highway system. These roads have one or more lanes of traffic in each direction, may or may not be divided, and usually have at-grade intersections with many other roads and driveways. They usually have both a local name and a route number. The MAF/TIGER Feature Classification Code (MTFCC) is S1200 for secondary roads.
Colorado roads within the Rio Grande River Basin.
The TIGER/Line shapefiles and related database files (.dbf) are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). The MTDB represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line shapefile is designed to stand alone as an independent data set, or they can be combined to cover the entire nation.
Primary roads are generally divided, limited-access highways within the interstate highway system or under State management, and are distinguished by the presence of interchanges. These highways are accessible by ramps and may include some toll highways. The MAF/TIGER Feature Classification Code (MTFCC) is S1100 for primary roads. Secondary roads are main arteries, usually in the U.S. Highway, State Highway, and/or County Highway system. These roads have one or more lanes of traffic in each direction, may or may not be divided, and usually have at-grade intersections with many other roads and driveways. They usually have both a local name and a route number. The MAF/TIGER Feature Classification Code (MTFCC) is S1200 for secondary roads.
The New Mexico administrative management areas for the Water Rights Division - updated August 2008. District boundaries based on declared groundwater basins, with the exception of the northern Tularosa Basin - which is part of District 1- ABQ office.
None. Acknowledgment of the originating agency would be appreciated in products derived from these data.
Rio Grande River Basin Groundwater Data from the World-wide Hydrogeological Mapping and Assessment Programme (WHYMAP).
The World-wide Hydrogeological Mapping and Assessment Programme (WHYMAP) provides data and information about the Earth´s major groundwater resources. The programme compiles hydrogeological information from regional, national and global sources, and visualizes the groundwater data as printed maps, web map application and web map service. These products provide information on quantity, quality and vulnerability of the groundwater resources on Earth and help communicating groundwater related issues to water experts, decision makers and the general public.
The 8 major rivers that make up the Rio Grande River Basin.
The fundamental flow network, from the National Hydrography Dataset, consisting predominantly of stream/river and artificial path vector features. It represents the spatial geometry, carries the attributes, and contains linear referencing measures for locating features or “events” on the network. Additional NHDFlowline features are canal/ditch, pipeline, connector, underground conduit, and coastline.
The most current version of the National Hydrography Dataset, the NHD High Resolution, is mapped at a scale of 1:24,000 scale or better (1:63,360 or better in Alaska). These data are updated and maintained through Stewardship partnerships with states and other collaborative bodies.
The data for the Colorado dataset was primarily compiled from the Colorado Decision Support Systems (CDSS), GIS Data (https://cdss.state.co.us/GIS/Pages/GISDataHome.aspx). Please visit CDSS website for the source data, data disclaimers and metadata if not available in these data layer files. Much of the metadata for the files in the dataset are complete and were imported with the original file. In cases where there are discrepancies, metadata have been added where appropriate.
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The fundamental flow network, from the National Hydrography Dataset, consisting predominantly of stream/river and artificial path vector features. It represents the spatial geometry, carries the attributes, and contains linear referencing measures for locating features or “events” on the network. Additional NHDFlowline features are canal/ditch, pipeline, connector, underground conduit, and coastline.
The most current version of the National Hydrography Dataset, the NHD High Resolution, is mapped at a scale of 1:24,000 scale or better (1:63,360 or better in Alaska). These data are updated and maintained through Stewardship partnerships with states and other collaborative bodies.
Water planning areas in Colorado, New Mexico and Texas.
This file is a combination of the the Texas water planning areas (http://www.twdb.texas.gov/waterplanning/rwp/index.asp) and the water districts of Colorado and New Mexico.
The main rivers of the Rio Grande River Basin.
The fundamental flow network, from the National Hydrography Dataset, consisting predominantly of stream/river and artificial path vector features. It represents the spatial geometry, carries the attributes, and contains linear referencing measures for locating features or “events” on the network. Additional NHDFlowline features are canal/ditch, pipeline, connector, underground conduit, and coastline.
The most current version of the National Hydrography Dataset, the NHD High Resolution, is mapped at a scale of 1:24,000 scale or better (1:63,360 or better in Alaska). These data are updated and maintained through Stewardship partnerships with states and other collaborative bodies.
The dataset show the climate units in the Mexican side of the Rio Grande/Bravo.
The dataset was clipped for the Mexican side of the Rio Grande/Bravo from the climatic units of Mexico.
The Climas information scale 1: 1 000 000 was obtained through the conversion of analog to digital information, with methodology and processes designed by INEGI, which comply with the technical norms required for its integration and processing in Geographical Information Systems (GIS). The expectations of the use of this type of information are extended, offering, thus, new horizons of possibilities to satisfy the diverse needs and objectives of the users.
This information represents the distribution of the different types of climate that exist in the Mexican Republic, according to the Climate Classification System of Koppen, modified by E. García, with INEGI contributions, for the particular conditions of Mexico. For the construction of this dataset, average temperature and total precipitation of approximately 4000 meteorological stations existing in the country during the period of cartographic elaboration were used.
According to the above-mentioned classification system, climate information is presented in its traditional form; By groups, subgroups, types and subtypes of climate, which are represented by climatic formulas that also include the characteristics of the temperature and precipitation regime of each type.
The importance of this information is that it is indispensable for all types of studies and can be correlated with many or all the characteristics of the landscape, since the classification and delimitation of climatic zones assumes the existence of regions with climate type Similar, with discernible limits that share common characteristics, especially as regards vegetation and soil.
The U.S. Department of Agriculture, Service Center Agencies should be acknowledged as the data source in products derived from these data.This data set is not designed for use as a primary regulatory tool in permitting or citing decisions, but may be used as a reference source. This information may be interpreted by organizations, agencies, units of government, or others based on needs; however, they are responsible for the appropriate application. Federal, State, or local regulatory bodies are not to reassign to the Service Center Agencies any authority for the decisions that they make. The Service Center Agencies will not perform any evaluations of these data for purposes related solely to State or local regulatory programs. Photographic or digital enlargement of these data to scales greater than at which they were originally mapped can cause misinterpretation of the data. Digital data files are periodically updated, and users are responsible for obtaining the latest version of the data.
These data and any derivatives may not be used for commercial or any revenue generating activities without prior written permission from IUCN. All forms of reposting, sub-licensing, reselling or other forms of redistribution of these data in their original format are also prohibited without prior written permission from IUCN. Please refer to the Terms and Conditions of Use at: http://www.iucnredlist.org/info/terms-of-use.
CDSS Reservoirs, Division 1, Database version 20090701
South Platte River basin soil data.
Data are in WGS84, but have not been projected.
Irrigation canals and ditches in the South Platte River Basin clipped to the Cache la Poudre River watershed boundary. Canals and ditches are assigned to a HydroBase Diversion structure with a WDID.
Downloaded from COGCC.state.co,us and clipped to Cache la Poudre watershed extent by intern Nick Vogel on 4/20/2020.
Publisher: Colorado Department of Transportation, Series issue: 10/06/2010
Downloaded from COGCC.state.co,us and clipped to Big Thompson watershed extent by intern Nick Vogel on 05/20/2020.
The data for the Colorado dataset was primarily compiled from the Colorado Decision Support Systems (CDSS), GIS Data (https://cdss.state.co.us/GIS/Pages/GISDataHome.aspx). Please visit CDSS website for the source data, data disclaimers and metadata if not available in these data layer files. Much of the metadata for the files in the dataset are complete and were imported with the original file. In cases where there are discrepancies, metadata have been added where appropriate.
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The USGS Protected Areas Database of the United States (PAD-US) is the nation's inventory of protected areas, including public open space and voluntarily provided, private protected areas, identified as an A-16 National Geospatial Data Asset in the Cadastral Theme (http://www.fgdc.gov/ngda-reports/NGDA_Datasets.html). PAD-US is an ongoing project with several published versions of a spatial database of areas dedicated to the preservation of biological diversity, and other natural, recreational or cultural uses, managed for these purposes through legal or other effective means. The geodatabase maps and describes public open space and other protected areas. Most areas are public lands owned in fee; however, long-term easements, leases, and agreements or administrative designations documented in agency management plans may be included. The PAD-US database strives to be a complete “best available” inventory of protected areas (lands and waters) including data provided by managing agencies and organizations. The dataset is built in collaboration with several partners and data providers (http://gapanalysis.usgs.gov/padus/stewards/). See Supplemental Information Section of this metadata record for more information on partnerships and links to major partner organizations. As this dataset is a compilation of many data sets; data completeness, accuracy, and scale may vary. Federal and state data are generally complete, while local government and private protected area coverage is about 50% complete, and depends on data management capacity in the state. For completeness estimates by state: http://www.protectedlands.net/partners. As the federal and state data are reasonably complete; focus is shifting to completing the inventory of local gov and voluntarily provided, private protected areas. The PAD-US geodatabase contains over twenty-five attributes and four feature classes to support data management, queries, web mapping services and analyses: Marine Protected Areas (MPA), Fee, Easements and Combined. The data contained in the MPA Feature class are provided directly by the National Oceanic and Atmospheric Administration (NOAA) Marine Protected Areas Center (MPA, http://marineprotectedareas.noaa.gov ) tracking the National Marine Protected Areas System. The Easements feature class contains data provided directly from the National Conservation Easement Database (NCED, http://conservationeasement.us ) The MPA and Easement feature classes contain some attributes unique to the sole source databases tracking them (e.g. Easement Holder Name from NCED, Protection Level from NOAA MPA Inventory). The "Combined" feature class integrates all fee, easement and MPA features as the best available national inventory of protected areas in the standard PAD-US framework. In addition to geographic boundaries, PAD-US describes the protection mechanism category (e.g. fee, easement, designation, other), owner and managing agency, designation type, unit name, area, public access and state name in a suite of standardized fields. An informative set of references (i.e. Aggregator Source, GIS Source, GIS Source Date) and "local" or source data fields provide a transparent link between standardized PAD-US fields and information from authoritative data sources. The areas in PAD-US are also assigned conservation measures that assess management intent to permanently protect biological diversity: the nationally relevant "GAP Status Code" and global "IUCN Category" standard. A wealth of attributes facilitates a wide variety of data analyses and creates a context for data to be used at local, regional, state, national and international scales. More information about specific updates and changes to this PAD-US version can be found in the Data Quality Information section of this metadata record as well as on the PAD-US website, http://gapanalysis.usgs.gov/padus/data/history/.) Due to the completeness and complexity of these data, it is highly recommended to review the Supplemental Information Section of the metadata record as well as the Data Use Constraints, to better understand data partnerships as well as see tips and ideas of appropriate uses of the data and how to parse out the data that you are looking for. For more information regarding the PAD-US dataset please visit, http://gapanalysis.usgs.gov/padus/. To find more data resources as well as view example analysis performed using PAD-US data visit, http://gapanalysis.usgs.gov/padus/resources/. The PAD-US dataset and data standard are compiled and maintained by the USGS Gap Analysis Program, http://gapanalysis.usgs.gov/ . For more information about data standards and how the data are aggregated please review the “Standards and Methods Manual for PAD-US,” http://gapanalysis.usgs.gov/padus/data/standards/ .
It is strongly recommended that these data are directly acquired from the U.S. Geological Survey Gap Analysis Program website and server (http://gapanalysis.usgs.gov/padus/data/ ), and not indirectly through other sources which may have changed the data in some way. It is also strongly recommended that careful attention be paid to the contents of the metadata file associated with these data (http://gapanalysis.usgs.gov/padus/data/metadata/ ). The U.S. Geological Survey and all contributing data partners and aggregators shall not be held liable for improper or incorrect use of the data described and/or contained herein. All information is created with a specific end use or uses in mind. This is especially true for GIS data, which is expensive to produce and must be directed to meet the immediate program needs. However, these data were created with the expectation that they would be used for other applications; therefore, we list below both appropriate and inappropriate uses. This list is in no way exhaustive but should serve as a guide to assess whether a proposed use can or cannot be supported by these data. For many uses, it is unlikely that PAD-US will provide the only data needed, and for uses with a regulatory outcome, field surveys should verify the result. In the end, it will be the responsibility of each data user to determine if these data can answer the question being asked, and if they are the best tool to answer that question. Contact the PAD-US Coordinator for guidance. While it is impossible to predict all the uses of these data we have listed several possible appropriate and inappropriate uses from GAP's perspective. Use constraints: Appropriate uses of the data: primarily as a coarse map for a large area such as the nation, a region or state (completeness in counties varies) or to provide context for finer-level maps. A general list of possible applications includes: -National, regional or statewide biodiversity planning, research, management, or policy making -National, Regional or state habitat conservation planning, research, management, or policy making -Outdoor recreation planning, research, management or policy making -Public health planning, research, management or policy making -County comprehensive planning -Large-area resource management planning -Coarse-filter evaluation of potential impacts or benefits of major projects or plan initiatives on biodiversity, such as utility or transportation corridors, wilderness proposals, habitat connectivity proposals, climate change adaption proposals, regional open space and recreation proposals, etc. -Determining relative amounts of management responsibility for specific biological resources among land stewards to facilitate cooperative management and planning. -Basic research on regional distributions of plants and animals and to help target both specific species and geographic areas for needed research. -Environmental impact assessment for large projects or military activities. -Estimation of potential economic impacts from loss of biological resource-based activities. -Education at all levels and for both students and citizens. Inappropriate Uses: It is far easier to identify appropriate uses than inappropriate ones, however, there is a \"fuzzy line\" that is eventually crossed when the differences in resolution of the data, size of geographic area being analyzed, and precision of the answer required for the question are no longer compatible. Examples include: -Using the data to map small areas (less than thousands of hectares), typically requiring mapping resolution at 1:24,000 scale and using aerial photographs or ground surveys in areas where data are incomplete. -Combining these data with other data finer than 1:100,000 scale to produce new hybrid maps or answer queries. -Generating specific areal measurements from the data finer than the nearest thousand hectares -Establishing exact boundaries for regulation or acquisition. -Establishing definite occurrence or non-occurrence of any feature for an exact geographic area -Determining abundance, health, or condition of any feature. -Establishing a measure of accuracy of any other data by comparison with GAP data. -Altering the data in any way and redistributing them as a GAP data product. -Using the data without acquiring and reviewing the metadata.
Verified private counts of Arundo Donax and tamarisk in the Rio Grande basin in Texas.
These data show the number of Giant Reed (A. Donax) anf Tamarisk as sighted and reported by private citizens; compiled and verified by EDDSmaps, a community driven, real time mapping system for tracking invasive species in the US. These data are representative of populations of invasive species, but are not - and should not be used as - actual population numbers.
EDDMapS is a web-based mapping system for documenting invasive species distribution. It is fast, easy to use and doesn't require Geographic Information Systems experience. Launched in 2005 by the Center for Invasive Species and Ecosystem Health at the University of Georgia, it was originally designed as a tool for state Exotic Pest Plant Councils to develop more complete distribution data of invasive species.
EDDMapS goal is to maximize the effectiveness and accessibility of the immense numbers of invasive species observations recorded each year. As of May 2019, EDDMapS has over 4.8 million records.
EDDMapS combines data from other databases and organizations as well as volunteer observations to create a national network of invasive species distribution data that is shared with educators, land managers, conservation biologists, and beyond. This data will become the foundation for a better understanding of invasive species distribution around the world.
Mean Temperature of the Rio Grande River watershed from 1970 to 2000, during the month of August
This is WorldClim 2.0 Beta version 1 (June 2016) downloaded from http://worldclim.org
They represent average monthly climate data for 1970-2000.
Data have been clipped to the Rio Grande Hydrologic Unit
No redistribution
Mean Temperature of the Rio Grande River watershed from 1970 to 2000, during the month of October
This is WorldClim 2.0 Beta version 1 (June 2016) downloaded from http://worldclim.org
They represent average monthly climate data for 1970-2000.
Data have been clipped to the Rio Grande Hydrologic Unit
Mean temperature of the Rio Grande River watershed from 1970 to 2000, during the month of September.
This is WorldClim 2.0 Beta version 1 (June 2016) downloaded from http://worldclim.org
They represent average monthly climate data for 1970-2000.
Data have been clipped to the Rio Grande Hydrologic Unit
Mean temperature of the Rio Grande River watershed from 1970 to 2000, during the month of July.
This is WorldClim 2.0 Beta version 1 (June 2016) downloaded from http://worldclim.org
They represent average monthly climate data for 1970-2000.
Data have been clipped to the Rio Grande Hydrologic Unit
Mean temperature of the Rio Grande River watershed from 1970 to 2000, during the month of June.
This is WorldClim 2.0 Beta version 1 (June 2016) downloaded from http://worldclim.org
They represent average monthly climate data for 1970-2000.
Data have been clipped to the Rio Grande Hydrologic Unit
Mean temperature of the Rio Grande River watershed from 1970 to 2000, during the month of May.
This is WorldClim 2.0 Beta version 1 (June 2016) downloaded from http://worldclim.org
They represent average monthly climate data for 1970-2000.
Data have been clipped to the Rio Grande Hydrologic Unit
Mean temperature of the Rio Grande River watershed from 1970 to 2000, during the month of April.
This is WorldClim 2.0 Beta version 1 (June 2016) downloaded from http://worldclim.org
They represent average monthly climate data for 1970-2000.
Data have been clipped to the Rio grande Basin Hydrologic unit
No redistribution
Estimated precipitation of the Rio Grande River Basin, during the month of January.
Grid with estimated precipitation per month with a spatial resolution of 10 arc minutes. This dataset has been constructed from 27 075 stations with 1961-1990 climatological normals. The dataset consists of 12 ASCII-grids with mean monthly data in mm/day * 10, and one ASCII-grid with yearly data in mm/year. In addition, 12 ASCII-grid with monthly values at 5 arc minutes resolution are made available as input data for a global water balance model (GlobWat).
There are no stated use limitation, the data is publicly available from the Food and Agriculture Organization of the United Nations.
Estimated precipitation of the Rio Grande River Basin, during the month of May.
Grid with estimated precipitation per month with a spatial resolution of 10 arc minutes. This dataset has been constructed from 27 075 stations with 1961-1990 climatological normals. The dataset consists of 12 ASCII-grids with mean monthly data in mm/day * 10, and one ASCII-grid with yearly data in mm/year. In addition, 12 ASCII-grid with monthly values at 5 arc minutes resolution are made available as input data for a global water balance model (GlobWat).
There are no stated use limitation, the data is publicly available from the Food and Agriculture Organization of the United Nations.
Estimated precipitation of the Rio Grande River Basin, during the month of June.
Grid with estimated precipitation per month with a spatial resolution of 10 arc minutes. This dataset has been constructed from 27 075 stations with 1961-1990 climatological normals. The dataset consists of 12 ASCII-grids with mean monthly data in mm/day * 10, and one ASCII-grid with yearly data in mm/year. In addition, 12 ASCII-grid with monthly values at 5 arc minutes resolution are made available as input data for a global water balance model (GlobWat).
There are no stated use limitation, the data is publicly available from the Food and Agriculture Organization of the United Nations.
Estimated precipitation of the Rio Grande River Basin, during the month of February.
Grid with estimated precipitation per month with a spatial resolution of 10 arc minutes. This dataset has been constructed from 27 075 stations with 1961-1990 climatological normals. The dataset consists of 12 ASCII-grids with mean monthly data in mm/day * 10, and one ASCII-grid with yearly data in mm/year. In addition, 12 ASCII-grid with monthly values at 5 arc minutes resolution are made available as input data for a global water balance model (GlobWat).
There are no stated use limitation, the data is publicly available from the Food and Agriculture Organization of the United Nations.
Estimated precipitation of the Rio Grande River Basin, during the month of July.
Grid with estimated precipitation per month with a spatial resolution of 10 arc minutes. This dataset has been constructed from 27 075 stations with 1961-1990 climatological normals. The dataset consists of 12 ASCII-grids with mean monthly data in mm/day * 10, and one ASCII-grid with yearly data in mm/year. In addition, 12 ASCII-grid with monthly values at 5 arc minutes resolution are made available as input data for a global water balance model (GlobWat).
There are no stated use limitation, the data is publicly available from the Food and Agriculture Organization of the United Nations.
Estimated precipitation of the Rio Grande River Basin, during the month of August.
Grid with estimated precipitation per month with a spatial resolution of 10 arc minutes. This dataset has been constructed from 27 075 stations with 1961-1990 climatological normals. The dataset consists of 12 ASCII-grids with mean monthly data in mm/day * 10, and one ASCII-grid with yearly data in mm/year. In addition, 12 ASCII-grid with monthly values at 5 arc minutes resolution are made available as input data for a global water balance model (GlobWat).
There are no stated use limitation, the data is publicly available from the Food and Agriculture Organization of the United Nations.
The data for the Colorado dataset was primarily compiled from the Colorado Decision Support Systems (CDSS), GIS Data (https://cdss.state.co.us/GIS/Pages/GISDataHome.aspx). Please visit CDSS website for the source data, data disclaimers and metadata if not available in these data layer files. Much of the metadata for the files in the dataset are complete and were imported with the original file. In cases where there are discrepancies, metadata have been added where appropriate.
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Overall Range is defined as the area which encompasses all known activity areas within the observed range of a population. This information was derived from field personnel. A variety of data capture techniques were used including drawing on mylar overlays at 1:50,000 scale USGS county mapsheets and implementation of the SmartBoard Interactive Whiteboard using stand-up, real-time digitizing at various scales (Cowardin, M., M. Flenner. March 2003. Maximizing Mapping Resources. GeoWorld 16(3):32-35).</
TurkeyOverallRange is an ESRI SDE Feature Class showing the overall range for Wild Turkey (Melagris gallopavo) in Colorado. Overall Range is defined as that area which encompasses all known seasonal activity within the observed range of a population of Wild Turkeys. This information was derived from field personnel. A variety of data capture techniques were used including drawing on mylar overlays at 1:50,000 scale USGS county mapsheets and implementation of the SmartBoard Interactive Whiteboard using stand-up, real-time digitizing at various scales (Cowardin, M., M. Flenner. March 2003. Maximizing Mapping Resources. GeoWorld 16(3):32-35).
The Colorado Trail System, now titled the Colorado Trail Explorer (COTREX), endeavors to map every trail in the state of Colorado. Currently there are nearly 40,000 miles of trails mapped. Trails come from a variety of sources (USFS, BLM, local parks & recreation departments, local governments). Responsibility for accuracy of the data rests with the source.
These data were last updated on 9/11/2019
This map layer is a product and property of Colorado Parks & Wildlife, a division of the Colorado Department of Natural Resources. Care should be taken in interpreting these data. Written documents may accompany this map layer and should be referenced. The information portrayed on these map layers should not replace field studies necessary for more localized planning efforts. The Colorado Department of Natural Resources is not responsible and shall not be liable to the user for damages of any kind arising out of the use of data or information provided by the Department, including the installation of the data or information, its use, or the results obtained from its use. ANY DATA OR INFORMATION PROVIDED BY THE DEPARTMENT OF NATURAL RESOURCES IS PROVIDED \"AS IS\" WITHOUT WARRANTY OF ANY KIND, EITHER EXPRESS OR IMPLIED, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE. Data or information provided by the Department of Natural Resources shall be used and relied upon only at the user's sole risk, and the user agrees to indemnify and hold harmless the Department of Natural Resources, its officials, officers and employees from any liability arising out of the use of the data or information provided.
The Colorado Trail System, now titled the Colorado Trail Explorer (COTREX), endeavors to map every trail in the state of Colorado. Currently there are nearly 40,000 miles of trails mapped. Trails come from a variety of sources (USFS, BLM, local parks & recreation departments, local governments). Responsibility for accuracy of the data rests with the source.
These data were last updated on 9/11/2019
This map layer is a product and property of Colorado Parks & Wildlife, a division of the Colorado Department of Natural Resources. Care should be taken in interpreting these data. Written documents may accompany this map layer and should be referenced. The information portrayed on these map layers should not replace field studies necessary for more localized planning efforts. The Colorado Department of Natural Resources is not responsible and shall not be liable to the user for damages of any kind arising out of the use of data or information provided by the Department, including the installation of the data or information, its use, or the results obtained from its use. ANY DATA OR INFORMATION PROVIDED BY THE DEPARTMENT OF NATURAL RESOURCES IS PROVIDED \"AS IS\" WITHOUT WARRANTY OF ANY KIND, EITHER EXPRESS OR IMPLIED, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE. Data or information provided by the Department of Natural Resources shall be used and relied upon only at the user's sole risk, and the user agrees to indemnify and hold harmless the Department of Natural Resources, its officials, officers and employees from any liability arising out of the use of the data or information provided.
Coordinates for the Pingree Park USGS topographic quadrangle and the 8 surrounding quadrangles was used to create this polygon.\t
Over the past few years, Bing Maps has generated high-quality building footprints leveraging AI and harnessing the power of computer vision to identify map features at scale. Applying Deep Neural Networks and ResNet34 to detect building footprints from Bing imagery. Ensuring the best outputs, noise and suspicious data are removed from the predictions.
U.S. Major Roads represents interstate, U.S., and state highways and other major thoroughfares within the United States clipped to the quadrangle surrounding Pingree Park, CO.
Local Roads are functionally identified as local and excludes major roads or highways. Each road is segmented with a to and from description. The direction, length, type (paved/dirt), width (in feet), local route name and id, number of lanes, one/two-way bridges, average daily traffic counts, locations of count stations, if it is congested, if maintenance is under shared jurisdiction and the year it was built or last updated from the Colorado Department of Transportation (CDOT).
Dataset created: Jan 13, 2015
Updated: May 7, 2020
Downloaded: June 3, 2020
Features in this dataset represent segments of public highways that are maintained by and under the jurisdiction of Colorado Department of Transportation clipped to a quadrangle surrounding Pingree Park, CSU mountain campus. These highways consist of Interstates, US Highways, and State Highways within the watershed. Features are represented by polyline (linear) geographic shapes.
The National Hydrography Dataset (NHD) is a feature-based database that interconnects and uniquely identifies the stream segments or reaches that make up the nation's surface water drainage system. NHD data was originally developed at 1:100,000-scale and exists at that scale for the whole country. This high-resolution NHD, generally developed at 1:24,000/1:12,000 scale, adds detail to the original 1:100,000-scale NHD. (Data for Alaska, Puerto Rico and the Virgin Islands was developed at high-resolution, not 1:100,000 scale.) Local resolution NHD is being developed where partners and data exist. The NHD contains reach codes for networked features, flow direction, names, and centerline representations for areal water bodies. Reaches are also defined on waterbodies and the approximate shorelines of the Great Lakes, the Atlantic and Pacific Oceans and the Gulf of Mexico. The NHD also incorporates the National Spatial Data Infrastructure framework criteria established by the Federal Geographic Data Committee.
The National Hydrography Dataset (NHD) is a feature-based database that interconnects and uniquely identifies the stream segments or reaches that make up the nation's surface water drainage system. NHD data was originally developed at 1:100,000-scale and exists at that scale for the whole country. This high-resolution NHD, generally developed at 1:24,000/1:12,000 scale, adds detail to the original 1:100,000-scale NHD. (Data for Alaska, Puerto Rico and the Virgin Islands was developed at high-resolution, not 1:100,000 scale.) Local resolution NHD is being developed where partners and data exist. The NHD contains reach codes for networked features, flow direction, names, and centerline representations for areal water bodies. Reaches are also defined on waterbodies and the approximate shorelines of the Great Lakes, the Atlantic and Pacific Oceans and the Gulf of Mexico. The NHD also incorporates the National Spatial Data Infrastructure framework criteria established by the Federal Geographic Data Committee.
AbertsSquirrelOverallRange is an ESRI SDE Feature Class encompassing the observed range of a population of Aberts squirrel in Colorado. This information was derived from field personnel. A variety of data capture techniques were used including drawing on mylar overlays at 1:50,000 scale USGS county mapsheets and implementation of the SmartBoard Interactive Whiteboard using stand-up, real-time digitizing at various scales (Cowardin, M., M. Flenner. March 2003. Maximizing Mapping Resources. GeoWorld 16(3):32-35).</
ZONE\t DESCRIPTION
A\t Areas with a 1% annual chance of flooding and a 26% chance of flooding over the life of a 30-year mortgage. Because detailed analyses are not performed for such areas; no depths or base flood elevations are shown within these zones.
AE\t The base floodplain where base flood elevations are provided. AE Zones are now used on new format FIRMs instead of A1-A30 Zones.
A1-30\t These are known as numbered A Zones (e.g., A7 or A14). This is the base floodplain where the FIRM shows a BFE (old format).
AH\t Areas with a 1% annual chance of shallow flooding, usually in the form of a pond, with an average depth ranging from 1 to 3 feet. These areas have a 26% chance of flooding over the life of a 30-year mortgage. Base flood elevations derived from detailed analyses are shown at selected intervals within these zones.
AO\t River or stream flood hazard areas, and areas with a 1% or greater chance of shallow flooding each year, usually in the form of sheet flow, with an average depth ranging from 1 to 3 feet. These areas have a 26% chance of flooding over the life of a 30-year mortgage. Average flood depths derived from detailed analyses are shown within these zones.
AR\t Areas with a temporarily increased flood risk due to the building or restoration of a flood control system (such as a levee or a dam). Mandatory flood insurance purchase requirements will apply, but rates will not exceed the rates for unnumbered A zones if the structure is built or restored in compliance with Zone AR floodplain management regulations.
A99\t Areas with a 1% annual chance of flooding that will be protected by a Federal flood control system where construction has reached specified legal requirements. No depths or base flood elevations are shown within these zones.
A \"Regulatory Floodway\" means the channel of a river or other watercourse and the adjacent land areas that must be reserved in order to discharge the base flood without cumulatively increasing the water surface elevation more than a designated height. Communities must regulate development in these floodways to ensure that there are no increases in upstream flood elevations. For streams and other watercourses where FEMA has provided Base Flood Elevations (BFEs), but no floodway has been designated, the community must review floodplain development on a case-by-case basis to ensure that increases in water surface elevations do not occur, or identify the need to adopt a floodway if adequate information is available. http://www.fema.gov/national-flood-insurance-program-2/floodway
This data set represents the extent, approximate location and type of riparian habitats in the western United States. These data delineate the areal extent of riparian habitats as defined by A System For Mapping Riparian Areas in the United States (USFWS 2009). Certain riparian habitats may be excluded from the National mapping program because of the limitations of aerial imagery as the primary data source used to detect riparian habitats.
Data are in WGS84.
None. Acknowledgement of the U.S. Fish and Wildlife Service and (or) the National Wetlands Inventory would be appreciated in products derived from these data
A layer containing the water districts and their boundaries that are within the Cache la Poudre watershed boundary.
The U.S. General Soil Map was developed by the National Cooperative Soil Survey and supersedes the State Soil Geographic (STATSGO) dataset. More information can be found at http://soils.usda.gov/survey/geography/ssurgo/description_statsgo2.html. The data are in WGS84, not projected, the resolution is 2 km, and the extent has been clipped to the Cache La Poudre watershed
The SSURGO database contains information about soil as collected by the National Cooperative Soil Survey over the course of a century. For more information see: http://soils.usda.gov/survey/geography/ssurgo/description.html. The data are in WGS84, not projected, the resolution is 30m, and the extent has been clipped to the Cache La Poudre watershed
DEM of the Cache la Poudre Watershed
CDSS Decreed Wells, Colorado, Database version 20090701
Division of Water Resources, ArcCDSS export. Clipped to the CLP div1 boundary by Nick Vogel, intern at The Geospatial Centroid, Colorado State University, 3/25/2020.