This web map created by the Colorado Governor's Office of Information Technology GIS team, serves as a basemap specific to the state of Colorado. The basemap includes general layers such as counties, municipalities, roads, waterbodies, state parks, national forests, national wilderness areas, and trails.Layers:Layer descriptions and sources can be found below. Layers have been modified to only represent features within Colorado and are not up to date. Layers last updated February 23, 2023. Colorado State Extent: Description: “This layer provides generalized boundaries for the 50 States and the District of Columbia.” Notes: This layer was filtered to only include the State of ColoradoSource: Esri Living Atlas USA States Generalized Boundaries Feature LayerState Wildlife Areas:Description: “This data was created by the CPW GIS Unit. Property boundaries are created by dissolving CDOWParcels by the property name, and property type and appending State Park boundaries designated as having public access. All parcel data correspond to legal transactions made by the CPW Real Estate Unit. The boundaries of the CDOW Parcels were digitized using metes and bounds, BLM's GCDB dataset, the PLSS dataset (where the GCDB dataset was unavailable) and using existing digital data on the boundaries.” Notes: The state wildlife areas layer in this basemap is filtered from the CPW Managed Properties (public access only) layer from this feature layer hosted in ArcGIS Online Source: Colorado Parks and Wildlife CPW Admin Data Feature LayerMunicipal Boundaries:Description: "Boundaries data from the State Demography Office of Colorado Municipalities provided by the Department of Local Affairs (DOLA)"Source: Colorado Information Marketplace Municipal Boundaries in ColoradoCounties:Description: “This layer presents the USA 2020 Census County (or County Equivalent) boundaries of the United States in the 50 states and the District of Columbia. It is updated annually as County (or County Equivalent) boundaries change. The geography is sources from US Census Bureau 2020 TIGER FGDB (National Sub-State) and edited using TIGER Hydrology to add a detailed coastline for cartographic purposes. Geography last updated May 2022.” Notes: This layer was filtered to only include counties in the State of ColoradoSource: Esri USA Census Counties Feature LayerInterstates:Description: Authoritative data from the Colorado Department of Transportation representing Highways Notes: Interstates are filtered by route sign from this CDOT Highways layer Source: Colorado Department of Transportation Highways REST EndpointU.S. Highways:Description: Authoritative data from the Colorado Department of Transportation representing Highways Notes: U.S. Highways are filtered by route sign from this CDOT Highways layer Source: Colorado Department of Transportation Highways REST EndpointState Highways:Description: Authoritative data from the Colorado Department of Transportation representing Highways Notes: State Highways are filtered by route sign from this CDOT Highways layer Source: Colorado Department of Transportation Highways REST EndpointMajor Roads:Description: Authoritative data from the Colorado Department of Transportation representing major roads Source: Colorado Department of Transportation Major Roads REST EndpointLocal Roads:Description: Authoritative data from the Colorado Department of Transportation representing local roads Source: Colorado Department of Transportation Local Roads REST EndpointRail Lines:Description: Authoritative data from the Colorado Department of Transportation representing rail lines Source: Colorado Department of Transportation Rail Lines REST EndpointCOTREX Trails:Description: “The Colorado Trail System, now titled the Colorado Trail Explorer (COTREX), endeavors to map every trail in the state of Colorado. Currently their 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 2/5/2019” Source: Colorado Parks and Wildlife CPW Admin Data Feature LayerNHD Waterbodies:Description: “The National Hydrography Dataset Plus (NHDplus) maps the lakes, ponds, streams, rivers and other surface waters of the United States. Created by the US EPA Office of Water and the US Geological Survey, the NHDPlus provides mean annual and monthly flow estimates for rivers and streams. Additional attributes provide connections between features facilitating complicated analyses.”Notes: This layer was filtered to only include waterbodies in the State of ColoradoSource: National Hydrography Dataset Plus Version 2.1 Feature LayerNHD Flowlines:Description: “The National Hydrography Dataset Plus (NHDplus) maps the lakes, ponds, streams, rivers and other surface waters of the United States. Created by the US EPA Office of Water and the US Geological Survey, the NHDPlus provides mean annual and monthly flow estimates for rivers and streams. Additional attributes provide connections between features facilitating complicated analyses.”Notes: This layer was filtered to only include flowline features in the State of ColoradoSource: National Hydrography Dataset Plus Version 2.1 Feature LayerState Parks:Description: “This data was created by the CPW GIS Unit. Property boundaries are created by dissolving CDOWParcels by the property name, and property type and appending State Park boundaries designated as having public access. All parcel data correspond to legal transactions made by the CPW Real Estate Unit. The boundaries of the CDOW Parcels were digitized using metes and bounds, BLM's GCDB dataset, the PLSS dataset (where the GCDB dataset was unavailable) and using existing digital data on the boundaries.” Notes: The state parks layer in this basemap is filtered from the CPW Managed Properties (public access only) layer from this feature layer Source: Colorado Parks and Wildlife CPW Admin Data Feature LayerDenver Parks:Description: "This dataset should be used as a reference to locate parks, golf courses, and recreation centers managed by the Department of Parks and Recreation in the City and County of Denver. Data is based on parcel ownership and does not include other areas maintained by the department such as medians and parkways. The data should be used for planning and design purposes and cartographic purposes only."Source: City and County of Denver Parks REST EndpointNational Wilderness Areas:Description: “A parcel of Forest Service land congressionally designated as wilderness such as National Wilderness Area.”Notes: This layer was filtered to only include National Wilderness Areas in the State of ColoradoSource: United States Department of Agriculture National Wilderness Areas REST EndpointNational Forests: Description: “A depiction of the boundaries encompassing the National Forest System (NFS) lands within the original proclaimed National Forests, along with subsequent Executive Orders, Proclamations, Public Laws, Public Land Orders, Secretary of Agriculture Orders, and Secretary of Interior Orders creating modifications thereto, along with lands added to the NFS which have taken on the status of 'reserved from the public domain' under the General Exchange Act. The following area types are included: National Forest, Experimental Area, Experimental Forest, Experimental Range, Land Utilization Project, National Grassland, Purchase Unit, and Special Management Area.”Notes: This layer was filtered to only include National Forests in the State of ColoradoSource: United States Department of Agriculture Original Proclaimed National Forests REST Endpoint

This data set is a result of compiling differing source materials of various vintages. Source material examples used to create and maintain dataset include: BLM 100k Surface Map Publications, Master Title Plats, Public Land Survey GIS Data (cadnsdi v.2.0), Field Office GIS Data, Compiled 24k USGS Maps, and Land Records. Ongoing updates are made.

This feature dataset represents the spatial extent and boundaries of inventoried and managed BLM Lands with Wilderness Characteristics. This dataset also also provides BLM and the public with information about naturalness, opportunities for solitude and/or primitive recreation, supplemental values and proximity to Wilderness Areas or Wilderness Study Areas. BLM Inventoried Lands with Wilderness Characteristics is consistent with Section 201 of the Federal Land Policy and Management Act (FLPMA), which requires the BLM Colorado Inventoried Lands with Wilderness Characteristics is consistent with Section 201 of the Federal Land Policy and Management Act (FLPMA), which requires the Secretary of the Interior to “prepare and maintain on a continuing basis an inventory of all public lands and their resource and other values,” and the BLM Land Use Planning Handbook, BLM Colorado has identified and begun an assessment of BLM-managed lands with wilderness characteristics outside of existing WSAs. BLM Manual 6310 - Conducting Wilderness Characteristics Inventory on BLM Lands, provides the guidance from which BLM performs the wilderness characteristic inventory process.

Mobile Map Packages (MMPK’s) can be used in the ESRI Field Maps app (no login required), either by direct download in the Field Maps app or by sideloading from your PC. They can also be used in desktop applications that support MMPK’s such as ArcGIS Pro, and ArcGIS Navigator. MMPK’s will expire quarterly and have a warning for the user at that time but will still function afterwards. They are updated quarterly to ensure you have the most up to date data possible.

These mobile map packages include the following national datasets along with others.

BLM Administrative Unit Boundaries & Points

BLM Public Land Survey System (PLSS)

TNM Place Names (Geographic Names)

BLM National Grazing Allotments

BLM Trails (GTLF)

BLM Recreation Point Features (RECS)

BLM Recreation Site Boundaries

BLM National Conservation Lands

BLM National Contours (TNM Derived)

BLM Surface Management Agency

ESRI Navigation Basemap Vector Tile Package (Modified)

Last updated 20250929. Contact BLM_Mobile_Apps@blm.gov with any questions, issues or suggestions.

This data set is a result of compiling differing source materials of various vintages.Source material examples used to create and maintain dataset include: BLM 100k Subsurface Maps, Oil and Gas Plats, Coal Plats, Public Land Survey GIS Data (cadnsdi v.2.0), Field Office GIS Data, Compiled 24k USGS Maps, and Land Records.

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/ .

This data release includes GIS datasets supporting the Colorado Legacy Mine Lands Watershed Delineation and Scoring tool (WaDeS), a web mapping application available at https://geonarrative.usgs.gov/colmlwades/. Water chemistry data were compiled from the U.S. Geological Survey (USGS) National Water Information System (NWIS), U.S. Environmental Protection Agency (EPA) STORET database, and the USGS Central Colorado Assessment Project (CCAP) (Church and others, 2009). The CCAP study area was used for this application. Samples were summarized at each monitoring station and hardness-dependent chronic and acute toxicity thresholds for aquatic life protections under Colorado Regulation No. 31 (CDPHE, 5 CCR 1002-31) for cadmium, copper, lead, and/or zinc were calculated. Samples were scored according to how metal concentrations compared with acute and chronic toxicity thresholds. The results were used in combination with remote sensing derived hydrothermal alteration (Rockwell and Bonham, 2017) and mine-related features (Horton and San Juan, 2016) to identify potential mine remediation sites within the headwaters of the central Colorado mineral belt. Headwaters were defined by watersheds delineated from a 10-meter digital elevation dataset (DEM), ranging in 5-35 square kilometers in size. Python and R scripts used to derive these products are included with this data release as documentation of the processing steps and to enable users to adapt the methods for their own applications. References Church, S.E., San Juan, C.A., Fey, D.L., Schmidt, T.S., Klein, T.L. DeWitt, E.H., Wanty, R.B., Verplanck, P.L., Mitchell, K.A., Adams, M.G., Choate, L.M., Todorov, T.I., Rockwell, B.W., McEachron, Luke, and Anthony, M.W., 2012, Geospatial database for regional environmental assessment of central Colorado: U.S. Geological Survey Data Series 614, 76 p., https://doi.org/10.3133/ds614. Colorado Department of Public Health and Environment (CDPHE), Water Quality Control Commission 5 CCR 1002-31. Regulation No. 31 The Basic Standards and Methodologies for Surface Water. Effective 12/31/2021, accessed on July 28, 2023 at https://cdphe.colorado.gov/water-quality-control-commission-regulations. Horton, J.D., and San Juan, C.A., 2022, Prospect- and mine-related features from U.S. Geological Survey 7.5- and 15-minute topographic quadrangle maps of the United States (ver. 8.0, September 2022): U.S. Geological Survey data release, https://doi.org/10.5066/F78W3CHG. Rockwell, B.W. and Bonham, L.C., 2017, Digital maps of hydrothermal alteration type, key mineral groups, and green vegetation of the western United States derived from automated analysis of ASTER satellite data: U.S. Geological Survey data release, https://doi.org/10.5066/F7CR5RK7.

Retirement Notice: This item is in mature support as of September 2023 and will be retired in December 2025. A new version of this item is available for your use. Esri recommends updating your maps and apps to use the new version. The USGS Protected Areas Database of the United States (PAD-US) is the official inventory of public parks and other protected open space. The spatial data in PAD-US represents public lands held in trust by thousands of national, state and regional/local governments, as well as non-profit conservation organizations. Manager Name describes the land manager or administrative agency name standardized for the Nation (USFS, BLM, State Fish and Wildlife, State Parks and Rec, City, NGO, etc). DOD lands shown with outline.This map provides a general overview of land and water managers including management designations. This map displays locations from the PAD-US version 3.0 symbolized with the manager name field. This map includes 20 layers created from a feature layer and saved in the map that draw at scales of 1:1,000,000 and larger. A vector tile layer is used at scales smaller than 1:1,000,000. The feature layer was saved as individual layers to control drawing order in overlapping features. PAD-US is published by the U.S. Geological Survey (USGS) Science Analytics and Synthesis (SAS), Gap Analysis Project (GAP). GAP produces data and tools that help meet critical national challenges such as biodiversity conservation, recreation, public health, climate change adaptation, and infrastructure investment. See the GAP webpage for more information about GAP and other GAP data including species and land cover.

Infrastructure, such as roads, airports, water and energy transmission and distribution facilities, sewage treatment plants, and many other facilities, is vital to the sustainability and vitality of any populated area. Rehabilitation of existing and development of new infrastructure requires three natural resources: natural aggregate (stone, sand, and gravel), water, and energy http://rockyweb.cr.usgs.gov/frontrange/overview.htm.

The principal goals of the U.S. Geological Survey (USGS) Front Range Infrastructure Resources Project (FRIRP) were to develop information, define tools, and demonstrate ways to: (1) implement a multidisciplinary evaluation of the distribution and quality of a region's infrastructure resources, (2) identify issues that may affect availability of resources, and (3) work with cooperators to provide decision makers with tools to evaluate alternatives to enhance decision-making. Geographic integration of data (geospatial databases) can provide an interactive tool to facilitate decision-making by stakeholders http://rockyweb.cr.usgs.gov/frontrange/overview.htm.

This data set depicts federal lands having restrictions on access or activities -- that is, lands mangaed by the National Park Service, Defense Department, or Energy Department -- in western North America. The data set was created by reformatting and merging state- and province-based ownership data layers originally acquired from diverse sources (including state GAP programs, USBLM state offices and other sources). For each original dataset 3 additional fields, "Pub_Pvt", "CA_OWN", and "SOURCE" were added and populated based on the specific ownership information contained in the source data. The original coverages were then merged based on the "CA_OWN" field. Finally, NPS, DOD, and DOE lands were selected out of the ownership layer. All work was completed in AcMap 8.3. This product and all source data are available online from SAGEMAP: http://sagemap.wr.usgs.gov.

The US Forest Service manages 193 million acres including the nation's 154 National Forests and 20 National Grasslands. These lands provide a wide variety of recreational opportunities, protect sources of clean water, and supply timber and forage.Dataset SummaryPhenomenon Mapped: United States lands managed by the US Forest ServiceGeographic Extent: Contiguous United States, Alaska, and Puerto RicoVisible Scale: The data is visible at all scales.Source: USFS Surface Ownership ParcelsPublication Date: May 2025This layer is a view of the USA Federal Lands layer. A filter has been used on this layer to eliminate non-Forest Service lands. For more information on layers for other agencies see the USA Federal Lands layer.What can you do with this Layer?This layer is suitable for both visualization and analysis across the ArcGIS system. This layer can be combined with your data and other layers from the ArcGIS Living Atlas of the World in ArcGIS Online and ArcGIS Pro to create powerful web maps that can be used alone or in a story map or other application.Because this layer is part of the ArcGIS Living Atlas of the World it is easy to add to your map:In ArcGIS Online you can add this layer to a map by selecting Add then Browse Living Atlas Layers. A window will open. Type "forest service" in the search box and browse to the layer. Select the layer then click Add to Map.In ArcGIS Pro open a map and select Add Data from the Map Tab. Select Data at the top of the drop down menu. The Add Data dialog box will open on the left side of the box expand Portal if necessary then select Living Atlas. Type "forest service" in the search box, browse to the layer then click OK.In both ArcGIS Online and Pro you can change the layer's symbology and view its attribute table. You can filter the layer to show subsets of the data using the filter button in Online or a definition query in ProThe data can be exported to a file geodatabase, a shape file or other format and downloaded using the Export Data button on the top right of this webpage..This layer can be used as an analytic input in both Online and Pro through the Perform Analysis window Online or as an input to a geoprocessing tool, model, or Python script in Pro.The ArcGIS Living Atlas of the World provides an easy way to explore many other beautiful and authoritative maps on hundreds of topics like this one.Questions?Please leave a comment below if you have a question about this layer, and we will get back to you as soon as possible.

The purpose of this dataset is to show BLM Colorado Grazing Allotments, which are areas of land designated and managed for grazing of livestock. It may include private, state, and public lands under the jurisdiction of the Bureau of Land Management and/or other federal agencies. An allotment is derived from its pastures, where the grazing of livestock is occurring. Vacant allotments are shown in a separate layer. Verification of vacancy needs to be made with the respective field office. These vacant allotments are a subset of the Grazing Allotment dataset and were pulled from the Rangeland Administration System (RAS) as of 7/31/19.

Retirement Notice: This item is in mature support as of June 2024 and will be retired in December 2026. A new version of this item is available for your use. Esri recommends updating your maps and apps to use the new version. The Protected Areas Database of the United States provides a comprehensive map of lands protected by government agencies and private land owners. This database combines federal lands with information on state and local government lands and conservation easements on private lands to create a powerful resource for land-use planning. Dataset SummaryPhenomenon Mapped: Areas mapped in the Protected Areas Data base of the United States (GAP Status 1-4) Units: MetersCell Size: 30.92208102 metersSource Type: ThematicPixel Type: 8-bit unsigned integerData Coordinate System: WGS 1984Mosaic Projection: Web Mercator Auxiliary SphereExtent: 50 United States plus Puerto Rico, the US Virgin Islands, Guam, Northern Mariana Islands and American Samoa.Source: USGS National Gap Analysis Program PAD-US version 3.0Publication Date: July 2022 ArcGIS Server URL: https://landscape10.arcgis.com/arcgis/ This layer displays lands mapped in Protected Areas Database of the United States version 3.0 created by the USGS National Gap Analysis Program. This layer displays all four GAP Status classes: GAP Status 1 - Areas managed for biodiversity where natural disturbances are allowed to proceedGAP Status 2 - Areas managed for biodiversity where natural disturbance is suppressedGAP Status 3 - Areas protected from land cover conversion but subject to extractive uses such as logging and miningGAP Status 4 - Areas with no known mandate for protection The source data for this layer are available here. A feature layer published from this dataset is also available. The polygon vector layer was converted to raster layers using the Polygon to Raster Tool using the National Elevation Dataset 1 arc second product as a snap raster. The service behind this layer was published with 8 functions allowing the user to select different views of the service. Other layers created from this service using functions include:USA Protected AreasUSA Protected from Land Cover ConversionUSA Unprotected AreasUSA Protected Areas - Gap Status 1USA Protected Areas - Gap Status 2USA Protected Areas - Gap Status 3USA Protected Areas - Gap Status 4

A digital raster graphic (DRG) is a scanned image of a U.S.Geological Survey (USGS) topographic map. The scanned image includes all map collar information. The image inside the map neatline is georeferenced to the surface of Earth. The DRG can be used to collect, review, and revise other digital data especially digital line graphs (DLG). When the DRG is combined with other digital products, such as digital orthophoto quadrangles (DOQ) or digital elevation models (DEM), the resulting image provides additional visual information for the extraction and revision of base cartographic information. The USGS is producing DRGs of the 1:24,000-, 1:25,000-, 1:63,360-(Alaska), 1:100,000-, and 1:250,000-scale topographic map series. NOTE: This EML metadata file does not contain important geospatial data processing information. Before using any NWT LTER geospatial data read the arcgis metadata XML file in either ISO or FGDC compliant format, using ArcGIS software (ArcCatalog > description), or by viewing the .xml file provided with the geospatial dataset.

The spatial polygon data is aggregated by the State of Colorado Governor's Office of Information Technology Geospatial Information Systems Team. This dataset represents an on-going relationship between the counties, municipalities and the State of Colorado to provide accurate updated parcel information. Updates are collected annually. This data layer reflects an evolving data model of the Colorado State Parcel Dataset.To download this data as a file geodatabase, please visit the link below:File Geodatabase Download

The data here were originally posted to facilitate timely and transparent peer review. The final public data release with formal metadata is now available from at the following location:

Nauman, T.W., and Duniway, M.C., 2020, Predictive soil property maps with prediction uncertainty at 30 meter resolution for the Colorado River Basin above Lake Mead: U.S. Geological Survey data release, https://doi.org/10.5066/P9SK0DO2.

Associated publication:

Nauman, T. W., and Duniway, M. C., 2020, A hybrid approach for predictive soil property mapping using conventional soil survey data: Soil Science Society of America Journal, v. 84, no. 4, p. 1170-1194. https://doi.org/10.1002/saj2.20080.

UPDATE: WE FOUND A RENDERING ERROR IN MANY AREAS OF THE 5 CM MAP. WE HAVE RECREATED THE MAP AND INCLUDED IN THIS VERSION OF THE REPOSITORY.

Repository includes maps of organic matter content (% wt) as defined by United States soil survey program.

These data are preliminary or provisional and are subject to revision. They are being provided to meet the need for timely best science. The data have not received final approval by the U.S. Geological Survey (USGS) and are provided on the condition that neither the USGS nor the U.S. Government shall be held liable for any damages resulting from the authorized or unauthorized use of the data.

This data should be used in combination with a soil depth or depth to restriction layer map (both layers that will be released soon as part of this project) to eliminate areas mapped at deeper depths than the soil actually goes. This is a limitation of this data which will hopefully be updated in future updates.

The creation and interpretation of this data is documented in the following article. Please note this article has not been reviewed yet and this citation will be updated as the peer review process proceeds.

Nauman, T. W., Duniway, M. C., In Preparation. Predictive reconstruction of soil survey property maps for field scale adaptive land management. Soil Science Society of America Journal.

File Name Details:

ACCURACY!! Please see manuscript and Github repository (https://github.com/naumi421/SoilReconProps) for full details on accuracy. We do provide cross validation (CV) accuracy plots in this repository for both the overall sample (_CV_plots.tif). These plots compare CV predictions with observed values relative to a 1:1 line. Values plotted near the 1:1 line are more accurate. Note that values are plotted in hex-bin density scatter plots because of the large number of observations (most are >3000). Predictions are also evaluated with the U.S. soil survey laboratory database soil organic carbon (SOC) data. The SOC measurements were coverted to OM matter values using the common 1.724 conversion factor. The converted OM values are compared to predicted OM values using an accuracy plot (OM_SOC_plots.tif).

Elements are separated by underscore (_) in the following sequence:

property_r_depth_cm_geometry_model_additional_elements.extension

Example: om_r_0_cm_2D_QRF_bt.tif

Indicates soil organic matter content (om) at 0 cm depth using a 2D model (separate model for each depth) employing a quantile regression forest. This file is the raster prediction map for this model. There may be additional GIS files associated with this file (e.g. pyramids) that have the same file name, but different extensions. The _bt indicates that the map has been back transformed from ln or sqrt transformation used in modeling.

The following elements may also exist on the end of filenames indicating other spatial files that characterize a given model's uncertainty (see below).

_95PI_h: Indicates the layer is the upper 95% prediction interval value.

_95PI_l: Indicates the layer is the lower 95% prediction interval value.

_95PI_relwidth: Indicates the layer is the 95% relative prediction interval (RPI). The RPI is a standardization of the prediction interval that indicates that model is constraining uncertainty relative to the original sample. RPI values less than one represent uncertainty is being improved by the model relative to the original sample, and values less than 0.5 indicate low uncertainty in predictions. See paper listed above and also Nauman and Duniway (In revision) for more details on RPI.

References

Nauman, T. W., and Duniway, M. C., In Revision, Relative prediction intervals reveal larger uncertainty in 3D approaches to predictive digital soil mapping of soil properties with legacy data: Geoderma

The data here were originally posted to facilitate timely and transparent peer review. The final public data release with formal metadata is now available from at the following location:

Nauman, T.W., and Duniway, M.C., 2020, Predictive soil property maps with prediction uncertainty at 30 meter resolution for the Colorado River Basin above Lake Mead: U.S. Geological Survey data release, https://doi.org/10.5066/P9SK0DO2.

Associated publication:

Nauman, T. W., and Duniway, M. C., 2020, A hybrid approach for predictive soil property mapping using conventional soil survey data: Soil Science Society of America Journal, v. 84, no. 4, p. 1170-1194. https://doi.org/10.1002/saj2.20080.

Repository includes maps of oven dry bulk density (dbovendry, g/cm3) as defined by United States soil survey program. Content is calculated on the fine earth fraction (<2mm).

These data are preliminary or provisional and are subject to revision. They are being provided to meet the need for timely best science. The data have not received final approval by the U.S. Geological Survey (USGS) and are provided on the condition that neither the USGS nor the U.S. Government shall be held liable for any damages resulting from the authorized or unauthorized use of the data.

The creation and interpretation of this data is documented in the following article. Please note this article has not been reviewed yet and this citation will be updated as the peer review process proceeds.

Nauman, T. W., Duniway, M. C., In Preparation. Predictive reconstruction of soil survey property maps for field scale adaptive land management. Soil Science Society of America Journal.

File Name Details:

ACCURACY!! Please see manuscript and Github repository (https://github.com/naumi421/SoilReconProps) for full details on accuracy. We do provide cross validation (CV) accuracy plots in this repository for both the overall sample (NRCS field pedons plus NRCS laboratory pedons; file ending _CV_plots.tif) and for just the CV results at laboratory pedons (file ending _CV_SCD_plots.tif). These plots compare CV predictions with observed values relative to a 1:1 line. Values plotted near the 1:1 line are more accurate. Note that values are plotted in hex-bin density scatter plots because of the large number of observations (most are >3000).

Elements are separated by underscore (_) in the following sequence:

property_r_depth_cm_geometry_model_additional_elements.extension

Example: dbovendry_r_0_cm_2D_QRF_bt.tif

Indicates oven dry bulk density (dbovendry) at 0 cm depth using a 2D model (separate model for each depth) employing a quantile regression forest that is has gone through transfomation and backtransformation (_bt) in the modeling process. This file is the raster prediction map for this model. There may be additional GIS files associated with this file (e.g. pyramids) that have the same file name, but different extensions.

The following elements may also exist on the end of filenames indicating other spatial files that characterize a given model's uncertainty (see below).

_95PI_h: Indicates the layer is the upper 95% prediction interval value.

_95PI_l: Indicates the layer is the lower 95% prediction interval value.

_95PI_relwidth: Indicates the layer is the 95% relative prediction interval (RPI). The RPI is a standardization of the prediction interval that indicates that model is constraining uncertainty relative to the original sample. RPI values less than one represent uncertainty is being improved by the model relative to the original sample, and values less than 0.5 indicate low uncertainty in predictions. See paper listed above and also Nauman and Duniway (In revision) for more details on RPI.

References

Nauman, T. W., and Duniway, M. C., In Revision, Relative prediction intervals reveal larger uncertainty in 3D approaches to predictive digital soil mapping of soil properties with legacy data: Geoderma.

Publics lands of Gilpin County based on Gilpin County Assessor records. James Peak Wilderness boundary is based on "The Exterior Boundary of James Peak Wilderness, Arapaho and Roosevelt National Forests, Rocky Mountain Region, August 21, 2002" survey by Carl Sumpter (PLS 35577), Regional Land Surveyor, US Forest Service dated February 9, 2009.

The data here were originally posted to facilitate timely and transparent peer review. The final public data release with formal metadata is now available from at the following location:

Nauman, T.W., and Duniway, M.C., 2020, Predictive soil property maps with prediction uncertainty at 30 meter resolution for the Colorado River Basin above Lake Mead: U.S. Geological Survey data release, https://doi.org/10.5066/P9SK0DO2.

Associated publication:

Nauman, T. W., and Duniway, M. C., 2020, A hybrid approach for predictive soil property mapping using conventional soil survey data: Soil Science Society of America Journal, v. 84, no. 4, p. 1170-1194. https://doi.org/10.1002/saj2.20080.

Repository includes maps of 1:1 soil pH as defined by United States soil survey program.

These data are preliminary or provisional and are subject to revision. They are being provided to meet the need for timely best science. The data have not received final approval by the U.S. Geological Survey (USGS) and are provided on the condition that neither the USGS nor the U.S. Government shall be held liable for any damages resulting from the authorized or unauthorized use of the data.

The creation and interpretation of this data is documented in the following article. Please note this article has not been reviewed yet and this citation will be updated as the peer review process proceeds.

Nauman, T. W., Duniway, M. C., In Preparation. Predictive reconstruction of soil survey property maps for field scale adaptive land management. Soil Science Society of America Journal.

File Name Details:

ACCURACY!! Please see manuscript and Github repository (https://github.com/naumi421/SoilReconProps) for full details on accuracy. We do provide cross validation (CV) accuracy plots in this repository for both the overall sample (NRCS field pedons plus NRCS laboratory pedons; file ending _CV_plots.tif) and for just the CV results at laboratory pedons (file ending _CV_SCD_plots.tif). These plots compare CV predictions with observed values relative to a 1:1 line. Values plotted near the 1:1 line are more accurate. Note that values are plotted in hex-bin density scatter plots because of the large number of observations (most are >3000).

Elements are separated by underscore (_) in the following sequence:

property_r_depth_cm_geometry_model_additional_elements.extension

Example: ph1to1h2o_r_0_cm_2D_QRF.tif

Indicates soil pH in 1:1 water mixture (ph1to1h2o) at 0 cm depth using a 2D model (separate model for each depth) employing a quantile regression forest. This file is the raster prediction map for this model. There may be additional GIS files associated with this file (e.g. pyramids) that have the same file name, but different extensions.

The following elements may also exist on the end of filenames indicating other spatial files that characterize a given model's uncertainty (see below).

_95PI_h: Indicates the layer is the upper 95% prediction interval value.

_95PI_l: Indicates the layer is the lower 95% prediction interval value.

_95PI_relwidth: Indicates the layer is the 95% relative prediction interval (RPI). The RPI is a standardization of the prediction interval that indicates that model is constraining uncertainty relative to the original sample. RPI values less than one represent uncertainty is being improved by the model relative to the original sample, and values less than 0.5 indicate low uncertainty in predictions. See paper listed above and also Nauman and Duniway (In revision) for more details on RPI.

References

Nauman, T. W., and Duniway, M. C., In Revision, Relative prediction intervals reveal larger uncertainty in 3D approaches to predictive digital soil mapping of soil properties with legacy data: Geoderma

These data are created by the CPW GIS Unit. CPW Property point locations are created by dissolving CPWAllProperties by the property name (after the removal of property unit names), property type and public access designation. One location point is generated for the center of each unique Property Name/Property Type set of parcel boundaries with the point forced inside one of the boundaries for properties with non-contiguous parcels. Public Access for each parcel is evaluated and a list of all the Public Access designations occurring for the property can be found in the attribute field 'PublicAccess'. All parcel data correspond to legal transactions made by the CPW Real Estate Unit. The boundaries of the CPW Parcels were digitized using metes and bounds, BLM's GCDB dataset, the PLSS dataset (where the GCDB dataset was unavailable) and using existing digital data on the boundaries.