Description: Forests to Faucets 2.0 builds upon the national Forests to Faucets(2011) by updating base data and adding new threats including wildfire, invasive pests, and future stresses such as climate-induced changes in land use and water quantity. The purpose of this project is to quantify, rank, and illustrate the geographic connection between forests and other natural cover (private and public), surface drinking water supplies, and the populations that depend on them–the ecosystem service of water supply. The project assesses subwatersheds across the US to identify those important to downstream surface drinking water supplies as well as evaluate a subwatersheds natural ability to produce clean water based on its biophysical characteristics: percent natural cover, percent agricultural land, percent impervious, percent riparian natural cover, and mean annual water yield. Using data from a variety of existing sources and maps generated through GIS analyses, the project uses maps and statistics to describe the relative importance of private forests and National Forest System lands to surface drinking water supplies across the United States. The data produced by this assessment provides information needed to identify opportunities for water market approaches or schemes based upon payments for environmental services (PES).September 2023 Update: Water yields (Q_YLD_MM; PER_Q40_45; PER_Q90_45; PER_Q40_85; PER_Q90_85) were updated to tie back to WASSI source data. All Forests to Faucets models and indices were recalculated. HUCs that did not have a corresponding water yield from WASSI were recalculated to the nearest HUC. AttributeDescriptionSourcesAcresHUC 12 AcresCalculated using ArcGISSTATESStatesWBD 2019HUC1212-digit Hydrologic Unit CodeWBD 2019NAME12-digit Hydrologic Unit NameWBD 2019HUTYPEHUC TypeWBD 2019From_HUC 12 From for routingWBD 2019ToHUC 12 To for routingWBD 2019 (edited by USDA FS)LevelHUC levelCalculated HUC level from outlet (1) to headwater (351)NLCDAcres of NLCDNLCDPER_NLCDPercent of HUC with NLCD DataCalculated using ArcGISFOREST_ACAcres of all forestNLCD Forest = 41,42,43, 90NLCDPER_FORPercent ForestCalculated using ArcGISAG_ACAcres of agricultural landNLCD = 81,82NLCDPER_AGPercent agricultural landCalculated using ArcGISIMPV_ACAcres of ImperviousNLCDPER_IMPVPercent ImperviousCalculated using ArcGISNATCOVER_ACAcres of Natural CoverNLCD = 11,12,41,42,43,51,52,71,90,95NLCDPER_NATCOVPercent Natural CoverCalculated using ArcGISRIPNAT_ACAcres of riparian natural coverSinan AboodPER_RIPNATPercent riparian natural coverCalculated using ArcGISQ_YLD_MM Mean Annual Water Yield in mm (Q) based on the historical time period (1961 to 2015). Baseline water yield for 2010.WASSI , Updated September 2023R_NATCOVNatural Cover score for APCWCalculated using ArcGISR_AGAgricultural land score for APCWCalculated using ArcGISR_IMPVImpervious Surface score for APCWCalculated using ArcGISR_RIPRiparian Natural Cover score for APCWCalculated using ArcGISR_QMean Annual Water Yield score for APCWCalculated using ArcGISAPCWAbility to Produce Clean Water (APCW)= (R_NATCOV+R_AG+R_IMPV+R_RIP) * R_QCalculated using ArcGISAPCW_RAPCW Score (0-100 Quantiles)Calculated using RGWNumber of groundwater water intakesSDWISSWNumber of surface water intakes (includes GU, groundwater under the influence of surface water)SDWISGW_POPNumber of groundwater water consumersSDWISSW_POPNumber of surface water consumers (includes GU, groundwater under the influence of surface water)SDWISGL_IntakesNumber of surface water intakes in the Great Lakes*SDWISGL_POPNumber of surface water consumers in the Great Lakes*SDWISSUM_POPP, Total number of Surface water consumers SW_POP+ GL_POPSDWISPRDrinking water protection model PRn = ∑(Wi* Pi)Calculated using RPOP_DSSum of surface drinking water population downstream of HUC 12 ∑(SUM_POPn) ***Cannot be summed across multiple HUC12 due to double counting down stream populations. Only accurate for an individual HUC12.Calculated using RIMPRaw Important Areas for Surface Drinking Water (IMP) Value. As developed in Forests to Faucets (USFS 2011), the Important Areas for Surface Drinking Water (IMP) model can be broken down into two parts: IMPn = (PRn) * (Qn)Calculated using R, Updated September 2023IMP_RIMP, Important Areas for Surface Drinking Water (0-100 Quantiles)Calculated using R, Updated September 2023NON_FORESTAcres of non-forestPADUS and NLCDPRIVATE_FORESTAcres of private forestPADUS and NLCDPROTECTED_FORESTAcres of protected forest (State, Local, NGO, Permanent Easement)PADUS, NCED, and NLCDNFS_FORESTAcres of National Forest System (NFS) forestPADUS and NLCDFEDERAL_FORESTAcres of Other Federal forest (Non-NFS Federal)PADUS and NLCDPER_FORPRIPercent Private ForestCalculated using ArcGISPER_FORNFSPercent NFS ForestCalculated using ArcGISPER_FORPROPercent Protected (Other State, Local, NGO, Permanent Easement, NFS, and Federal) ForestCalculated using ArcGISWFP_HI_ACAcres with High and Very High Wildfire Hazard Potential (WHP)Dillon, 2018PER_WFPPercent of HU 12 with High and Very High Wildfire Hazard Potential (WHP)Dillon, 2018PER_IDRISKPercent of HU 12 that is at risk for mortality - 25% of standing live basal area greater than one inch in diameter will die over a 15- year time frame (2013 to 2027) due to insects and diseases.Krist, et Al,. 2014PERDEV_1040_45% Landuse Change 2010-2040 (low)ICLUSPERDEV_1090_45% Landuse Change 2010-2090 (low)ICLUSPERDEV_1040_85% Landuse Change 2010-2040 (high)ICLUSPERDEV_1090_85% Landuse Change 2010-2090 (high)ICLUSPER_Q40_45% Water Yield Change 2010-2040 (low) WASSI , Updated September 2023PER_Q90_45% Water Yield Change 2010-2090 (low) WASSI , Updated September 2023PER_Q40_85% Water Yield Change 2010-2040 (high) WASSI , Updated September 2023PER_Q90_85% Water Yield Change 2010-2090 (high) WASSI , Updated September 2023WFP(APCW_R * IMP_R * PER_WFP )/ 10,000Wildfire Threat to Important Surface Drinking Water Watersheds Calculated using ArcGIS, Updated September 2023IDRISK(APCW_R * IMP_R * PER_IDRISK )/ 10,000Insect & Disease Threat to Important Surface Drinking Water Watersheds Calculated using ArcGIS, Updated September 2023DEV1040_45(APCW_R * IMP_R * PERDEV_1040_45)/ 10,000 Landuse Change in Important Surface Drinking Water Watersheds 2010-2040 (low emissions) Calculated using ArcGIS, Updated September 2023DEV1090_45(APCW_R * IMP_R * PERDEV_1090_45)/ 10,000 Landuse Change in Important Surface Drinking Water Watersheds 2010-2040 (high emissions) Calculated using ArcGIS, Updated September 2023DEV1040_85(APCW_R * IMP_R * PERDEV_1040_85)/ 10,000 Landuse Change in Important Surface Drinking Water Watersheds 2010-2090 (low emissions) Calculated using ArcGIS, Updated September 2023DEV1090_85(APCW_R * IMP_R * PERDEV_1090_85)/ 10,000 Landuse Change in Important Surface Drinking Water Watersheds 2010-2090 (high emissions) Calculated using ArcGIS, Updated September 2023Q1040_45-1 * (APCW_R * IMP_R * PER_Q40_45)/ 10,000 Water Yield Decrease in Important Surface Drinking Water Watersheds 2010-2040 (low emissions) Calculated using ArcGIS, Updated September 2023Q1090_45-1 * (APCW_R * IMP_R * PER_Q90_45)/ 10,000 Water Yield Decrease in Important Surface Drinking Water Watersheds 2010-2040 (high emissions) Calculated using ArcGIS, Updated September 2023Q1040_85-1 * (APCW_R * IMP_R * PER_Q40_85)/ 10,000 Water Yield Decrease in Important Surface Drinking Water Watersheds 2010-2090 (low emissions) Calculated using ArcGIS, Updated September 2023Q1090_85-1 * (APCW_R * IMP_R * PER_Q90_85)/ 10,000 Water Yield Decrease in Important Surface Drinking Water Watersheds 2010-2090 (high emissions) Calculated using ArcGIS, Updated September 2023WFP_IMP_RWildfire Threat to Important Surface Drinking Water Watersheds (0-100 Quantiles)Calculated using R, Updated September 2023IDRISK_RInsect & Disease Threat to Important Surface Drinking Water Watersheds (0-100 Quantiles)Calculated using R, Updated September 2023DEV40_45_RLanduse Change in Important Surface Drinking Water Watersheds 2010-2040 (low emissions) (0-100 Quantiles)Calculated using R, Updated September 2023DEV40_85_RLanduse Change in Important Surface Drinking Water Watersheds 2010-2040 (high emissions) (0-100 Quantiles)Calculated using R, Updated September 2023DEV90_45_RLanduse Change in Important Surface Drinking Water Watersheds 2010-2090 (low emissions) (0-100 Quantiles)Calculated using R, Updated September 2023DEV90_85_RLanduse Change in Important Surface Drinking Water Watersheds 2010-2090 (high emissions) (0-100 Quantiles)Calculated using R, Updated September 2023Q40_45_RWater Yield Decrease in Important Surface Drinking Water Watersheds 2010-2040 (low emissions) (0-100 Quantiles)Calculated using R, Updated September 2023Q40_85_RWater Yield Decrease in Important Surface Drinking Water Watersheds 2010-2040 (high emissions) (0-100 Quantiles)Calculated using R, Updated September 2023Q90_45_RWater Yield Decrease in Important Surface Drinking Water Watersheds 2010-2090 (low emissions) (0-100 Quantiles)Calculated using R, Updated September 2023Q90_85_RWater Yield Decrease in Important Surface Drinking Water Watersheds 2010-2090 (high emissions) (0-100 Quantiles)Calculated using R, Updated September 2023RegionUS Forest Service Region numberUSFSRegionnameUS Forest Service Region nameUSFSHUC_Num_DiffThis field compares the value in column HUC12(circa 2019 wbd) with the value in HUC_12 (circa 2009 wassi)-1 = No equivalent WASSI HUC. Water yield (Q_YLD_MM) was estimating using the nearest HUC.USFS, Updated September 2023HUC_12_WASSIWASSI HUC numberWASSI, Updated September 2023
Description: This database was developed with data from the National Highway Planning Network (NHPN) 2005, produced by the Federal Highway Administration (FHWA): https://www.fhwa.dot.gov/planning/processes/tools/nhpn/.
Description: The Utah Geological Survey Quaternary Fault and Fold Database and Map of Utah is a compilation of existing information on faults and fault-related folds considered to be potential earthquake sources (i.e., “active” faults and folds). The faults and folds contained in the database are those considered to have been sources of large earthquakes (about magnitude 6.5 or greater) during the Quaternary Period (past 2.6 million years); these geologic structures are the most likely sources of large earthquakes in the future. The database is intended to be a comprehensive source of the most current information available for characterizing active faults and folds in Utah for seismic-hazard analysis. This version of the database incorporates fault data from geologic maps and other documents formally published through 2020, along with several additional faults. More information on faults and earthquake hazards in Utah is available at http://geology.utah.gov/hazards/earthquakes-faults/utah-faults.The database was compiled and attributed using ESRI ArcGIS geographic information system software. In general, the database attribute conventions follow those of the Quaternary Fault and Fold Database of the United States maintained by the U.S. Geological Survey (USGS).
Copyright Text: Emily J. Kleber, Michael D. Hylland, Bill D. Black, Christopher B. Duross, Greg N. McDonald, Adam I. Hiscock, Suzanne Hecker,
Gordon E. Douglass, Corey D. Unger, Steve D. Bowman, Sophia Agopian, and Tara Hansen.
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Description: An area encompassing all the National Forest System lands administered by an administrative unit. The area encompasses private lands, other governmental agency lands, and may contain National Forest System lands within the proclaimed boundaries of another administrative unit. All National Forest System lands fall within one and only one Administrative Forest Area.
Description: This polygon feature class represents the spatial extent and boundaries for completed BLM Vegetation Treatment Area polygons. A vegetation treatment area is in "completed" status once it has been established by official process.
Description: HazFuelTrt_PL (Hazardous Fuel Treatments - Polygon) represents activities of hazardous fuel treatment reduction that are polygons. All accomplishments toward the unified hazardous fuels reduction target must meet the following definition: "Vegetative manipulation designed to create and maintain resilient and sustainable landscapes, including burning, mechanical treatments, and/or other methods that reduce the quantity or change the arrangement of living or dead fuel so that the intensity, severity, or effects of wildland fire are reduced within acceptable ecological parameters and consistent with land management plan objectives, or activities that maintain desired fuel conditions. These conditions should be measurable or predictable using fire behavior prediction models or fire effects models." The data came from the Forest Service's Natural Resource Manager (NRM) Forest Activity Tracking System (FACTS), which is the agency standard for managing information about activities related to fire/fuels, silviculture, and invasive species. FACTS is an activity tracking application for all levels of the Forest Service.
Copyright Text: United States Forest Service Natural Resource Manager (NRM) Forest Activity Tracking System (FACTS)
Description: U.S. Counties represents the counties (or county equivalents) of the United States in the 50 states and the District of Columbia. U.S. Counties are polygons containing population totals from the 2020 Census.
Description: These data were created for planimetric display and tax area analysis.Procedures_Used:The principal method of data entry used coordinate geometry software.Digitizing from paper maps and use of digital planimetric data were supplemental. Conversions, filling of gaps, georeferencing, reconciliations, and reformatting were often necessary to create a coherent database. Boundary updates are occasionally accepted from local GIS departments when the USTC has not received all relevant boundary change information through required channels. Updates have been made in this manner to Sandy, some Cache, Washington, Utah, Wasatch, and Carbon County cities.Revisions: Municipal boundaries are revised as documents are filed with the Lt. Governor's Office.Reviews_Applied_to_Data:Digital sources were visually compared with planimetric data. Digitized data were overlaid with source material and visually compared. Technical errors were also identified and corrected with ArcGIS Software.Notes: This metadata document contains a composite of information for alltiles in the library.Current thru Dec. 31, 2015
Description: These data were created for planimetric display and tax area analysis.Procedures_Used:The principal method of data entry used coordinate geometry software.Digitizing from paper maps and use of digital planimetric data were supplemental. Conversions, filling of gaps, georeferencing, reconciliations, and reformatting were often necessary to create a coherent database. Boundary updates are occasionally accepted from local GIS departments when the USTC has not received all relevant boundary change information through required channels. Updates have been made in this manner to Sandy, some Cache, Washington, Utah, Wasatch, and Carbon County cities.Revisions: Municipal boundaries are revised as documents are filed with the Lt. Governor's Office.Reviews_Applied_to_Data:Digital sources were visually compared with planimetric data. Digitized data were overlaid with source material and visually compared. Technical errors were also identified and corrected with ArcGIS Software.Notes: This metadata document contains a composite of information for alltiles in the library.Current thru Dec. 31, 2015
Description: This dataset reflects the most current GCDB version of Utah county boundaries plus modifications made to correct known issues along the Davis-Weber and Duchesne-Uintah county boundaries. (20111108) and boundary agreement (certified 20120612) between Juab and Millard Counties. And an adjustment between Morgan and Summit Counties (4/3/2013). Minor adjustments were made to align with the Newest PLSS-GCDB layers from BLM (CadNSDIv2), any where from 10 to 50 feet all in non-populated areas (7/18/2014).