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This dataset is based on U.S. Geological Survey (USGS) resource assessments for “undiscovered” natural gas liquid resources, which are resources that have not yet been extensively proven by drilling (USGS 2014). Individual resource assessments describe the amount of petroleum resources in units with similar geologic features. We quantified the density of natural gas liquid resources by adding together the amounts in spatially overlapping assessment units and dividing these totals by polygon areas. Since assessments for geologic areas used in this analysis were completed at various times, the certainty related to these values is likely to vary according to geologic unit. USGS [U.S. Geological Survey]. 2014. Energy...
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This dataset is based on U.S. Geological Survey (USGS) resource assessments for “undiscovered” oil resources, which are resources that have not yet been extensively proven by drilling (USGS 2014). Individual resource assessments describe the amount of petroleum resources in units with similar geologic features. We focused on the amount of undiscovered continuous oil because technological advances have made exploitation of continuous resources increasingly profitable and large amounts remain undeveloped in comparison with conventional resources. We quantified the density of continuous oil resources by adding together the amounts in spatially overlapping assessment units and dividing these totals by polygon areas....
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Potential for concentrated solar power generation in Wh/sq. m./day determined with the RE Atlas (NREL 2012, Lopez et al. 2012). Lopez, A., B. Roberts, D. Heimiller, N. Blair, and G. Porro. 2012. U.S. Renewable Energy Technical Potentials: A GIS-Based Analysis. U.S. Department of Energy, Office of Energy Efficiency & Renewable Energy, National Renewable Energy Laboratory, Golden, CO. NREL [National Renewable Energy Laboratory]. 2012. Renewable Energy Atlas. U.S. Department of Energy, Office of Energy Efficiency & Renewable Energy.
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This dataset represents the spatial overlap in areas with present (cropland, grazing, and recreation), potential (petroleum resources), or projected future (population) high-intensity land-use. The final areas designated as high-intensity land-use represent high intensity relative to the range of values for that variable in this region, either present or future, and do not represent the rate of change, recent or future, for that particular variable. We defined high-intensity areas as ≥75% quantile for that variable over the entire Colorado Plateau. We combined grazing and cropland high-intensity area into one “high agriculture” variable. Each band represents a unique overlap between the land-use types.
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County cultivated agriculture area from county census data (NASS, 2012). NASS [National Agricultural Statistics Service]. 2014. Quick Stats 2.0. U.S. Department of Agriculture, Washington D.C., USA.
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Annual available energy for photovoltaic solar power generation in Wh/sq. m./day determined with the RE Atlas (NREL 2012, Lopez et al. 2012). Lopez, A., B. Roberts, D. Heimiller, N. Blair, and G. Porro. 2012. U.S. Renewable Energy Technical Potentials: A GIS-Based Analysis. U.S. Department of Energy, Office of Energy Efficiency & Renewable Energy, National Renewable Energy Laboratory, Golden, CO. NREL [National Renewable Energy Laboratory]. 2012. Renewable Energy Atlas. U.S. Department of Energy, Office of Energy Efficiency & Renewable Energy.
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This dataset is based on U.S. Geological Survey (USGS) resource assessments for “undiscovered” gas resources, which are resources that have not yet been extensively proven by drilling (USGS 2014). Individual resource assessments describe the amount of petroleum resources in units with similar geologic features. We focused on the amount of undiscovered continuous gas because technological advances have made exploitation of continuous resources increasingly profitable and large amounts remain undeveloped in comparison with conventional resources. We quantified the density of continuous gas resources by adding together the amounts in spatially overlapping assessment units and dividing these totals by polygon areas....
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This dataset represents the spatial overlap in areas with present (cropland, grazing, and recreation), potential (petroleum resources), or future (population) low-intensity land-use. The final areas designated as low-intensity land-use represent low intensity relative to the range of values for that variable in this region, either present or future, and do not represent the rate of change, recent or future, for that particular variable. We defined low-intensity areas as ≤25% quantile for that variable over the entire Colorado Plateau. We combined grazing and cropland low-intensity area into one “low agriculture” variable. Each band represents a unique overlap between the land-use types.
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Future county population was based on projections for 2100 from the Spatially Explicit Regional Growth Model (SERGoM; Theobald 2005). SERGoM simulates population based on existing patterns of growth by census block, groundwater well and road density, and transportation distance to urban areas, while constraining the pattern of development to areas outside of protected areas and urban areas (Theobald 2005). The dataset here is a projection for a “baseline” growth scenario that assumes a similar trajectory to that of current urban growth (Bierwagen et al. 2010). SERGoM accuracy is estimated as 79–99% when compared to 1990 and 2000 census data, with the accuracy varying by urban/exurban/rural categories and increasing...
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These geospatial data characterize the potential for geographic overlap among areas likely to experience climate drying (aridification) and high intensity land-use with population growth, recreation tourism, agriculture, energy development, and mining on the Colorado Plateau. Spatial overlap between areas of high land-use intensity and aridification were used to create scenarios and corresponding consensus maps for areas with high potential to experience detrimental effects to ecosystem attributes (recreation economy, water availability, vegetation and wildlife habitat, and spiritual and cultural resources). This analytical framework for assessing the potential impacts of overlapping land-use and climate change...
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Annual visitor records were downloaded for each NPS unit within the study area (Visitor Use Statistics, NPS 2015). NPS [National Park Service]. 2015. NPS Stats: National Park Service visitor use statistics. National Park Service, Department of Agriculture, Washington D.C., USA.
Areas with high potential for utility-grade wind power were based on wind energy adjusted to 80 m and excluding protected areas and land-cover types unsuitable for wind installations (Elliot et. al. 1986, NREL 2010). Data were originally in classes, 3-7, but were assigned 1/0 values for this dataset, and then averaged to create a 10 km2 grid. See citations for details on raw data. Elliot, D. L., C. G. Holladay, W. R. Barchet, H. P. Foote, and W. F. Sandusky. 1986. Wind Energy Resource Atlas of the United States. Pacific Northwest Laboratory, prepared for the U.S. Department of Energy, Richland, Washington. NREL [National Renewable Energy Laboratory]. 2010 wind data: wind power class (exclusions applied). U.S. Department...
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We calculated the intensity of recreation visitation in 2014 based on Forest Service (FS) visits reported in the National Visitor Use Monitoring System at the level of a National Forest (English et. al., 2002). Recreation use on FS lands is tracked at five-year intervals (2010–2014), therefore these data represent a rough estimate of potential 2014 visits. English, D. B. K., S. M. Kocis, S. J. Zarnoch, and J. R. Arnold. 2002. Forest service national visitor use monitoring process: research method documentation. Gen. Tech. Rep. SRS-57. U.S. Department of Agriculture, Forest Service, Southern Research Station, Asheville, North Carolina, USA.
We assessed the spatial pattern and extent of potential impacts of spatially overlapping high intensity land-use and aridification trends on landscape attributes: crop productivity, soil productivity, vegetation and wildlife habitat, and recreation tourism economy, as well as two ecosystem services: water availability (provisioning service) and spiritual and cultural values (cultural service). The intensity indices (0–1) for the land-use and climate change variables were combined using different weights for four scenarios (see publication) to estimate the magnitude of potential impact of spatial overlap, with higher values indicating greater impact. For each scenario, we defined areas as high- or low-intensity impact...
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Bureau of Land Management (BLM) field office recreation data for 2014 was downloaded from the Recreation Management Information System (Yuan et al. 1995, Nickels 2003, Leuders 2015). The unit is number of participants in 2014, which was estimated at the scale of an individual BLM Field Office. Leuders, A. 2015. Fiscal year 2015 end-of-year recreation management information system data call. Instruction Memorandum No. 2015-125, Bureau of Land Management, Department of the Interior, Washington, D.C., USA. Nickels, M. 2003. Guidelines for Reporting Recreation Visitation. Bureau of Land Management. Department of the Interior, Washington, DC. Yuan, S., B. Maiorano, M. Yuan, S. M. Kocis, and G. T. Hoshide. 1995. Techniques...
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Mine density at 10 km2 scale calculated with records from the Mineral Resources Data System (USGS 2011). Records are from pre-Hispanic conquest through 2014. USGS [U.S. Geological Survey]. 2011. Mineral Resources On-Line Spatial Data: Mineral Resources Data System (MRDS). https://mrdata.usgs.gov/mrds/. Department of the Interior, Reston, Virginia, USA.
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Future annual aridity index (AI, precipitation divided by potential evapotranspiration [PET]) was calculated from monthly temperature and precipitation data extracted from 10 General Circulation Models from the Coupled-Model Intercomparison Project, Phase 5 (CMIP5) downscaled to 1/8° spatial resolution (Maurer et al. 2007, Bureau of Reclamation 2013). The models were selected for their independence (Knutti et al. 2013) and performance, assessed by comparing model hindcast results and observed climate values in the Pacific Northwest (Rupp et al. 2013) and southwest United States (David E. Rupp, personal communication). We selected projections based on the highest representative concentration pathway, or emissions...
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County livestock data (sheep and cattle totals) from county census data (NASS, 2012). NASS [National Agricultural Statistics Service]. 2012. Census of agriculture. U.S. Department of Agriculture, Washington D.C., USA.


    map background search result map search result map Potential Land-use Intensity, Aridification Trends, Overlap, and Impact Scenarios, Geospatial Data, Colorado Plateau, USA Projected Change in Aridity Index from 2016-2075 for the Colorado Plateau BLM Recreation Visitors, 2014, Colorado Plateau Concentrated Solar Power Potential, 2012, Colorado Plateau County Livestock 2012 Colorado Plateau County Population 2100 Baseline Scenario Colorado Plateau County Proportion of Cultivated Agriculture 2012 Colorado Plateau Forest Service Visits 2014 Colorado Plateau High Intensity Land-use Overlap Colorado Plateau Low Intensity Land-use Overlap Colorado Plateau Mine density 2014, Colorado Plateau National Park Service Visitors 2014 Colorado Plateau Photovoltaic Solar Power Potential 2012 Colorado Plateau Scenario for Impacts to Ecosystem Attributes Colorado Plateau Undiscovered Continuous Gas Colorado Plateau Undiscovered Continuous Oil, Colorado Plateau Undiscovered Natural Gas Liquids Colorado Plateau Wind Power Potential 2010 Colorado Plateau Concentrated Solar Power Potential, 2012, Colorado Plateau Photovoltaic Solar Power Potential 2012 Colorado Plateau Wind Power Potential 2010 Colorado Plateau BLM Recreation Visitors, 2014, Colorado Plateau County Livestock 2012 Colorado Plateau County Population 2100 Baseline Scenario Colorado Plateau Forest Service Visits 2014 Colorado Plateau High Intensity Land-use Overlap Colorado Plateau Low Intensity Land-use Overlap Colorado Plateau Mine density 2014, Colorado Plateau National Park Service Visitors 2014 Colorado Plateau Scenario for Impacts to Ecosystem Attributes Colorado Plateau Undiscovered Continuous Gas Colorado Plateau Undiscovered Continuous Oil, Colorado Plateau Undiscovered Natural Gas Liquids Colorado Plateau Projected Change in Aridity Index from 2016-2075 for the Colorado Plateau County Proportion of Cultivated Agriculture 2012 Colorado Plateau Potential Land-use Intensity, Aridification Trends, Overlap, and Impact Scenarios, Geospatial Data, Colorado Plateau, USA