Wetland Prioritization Study Main Page
Single-objective tools
- CNHP Landscape Integrity Model
- CNHP Wetland Profile
- EPA RPS Stressor Exposure Tool
- IDFG Watershed Condition Tool
- IDFG Wetland Condition Tool
- MTNHP Landscape Integrity Model
- TNC Aquatic EA Aquatic System Integrity GIS Model
- TNC Aquatic EA Landscape Context GIS Model
- VIMS Wetland Condition Assessment Tool
- Weller et al. (2007) Landscape (Riverine Wetlands) Tool
Multi-objective tools
- Weller et al. (2007) Biogeochemistry (Flat Wetlands) Tool
- Weller et al. (2007) Biogeochemistry (Riverine Wetlands) Tool
- Weller et al. (2007) Hydrology (Flat Wetlands) Tool
- Weller et al. (2007) Hydrology (Riverine Wetlands) Tool
- Weller et al. (2007) Landscape (Riverine Wetlands) Tool
- Weller et al. (2007) Plant Community (Flat Wetlands) Tool
- Weller et al. (2007) Plant Community (Riverine Wetlands) Tool
CNHP Landscape Integrity Model:1 The Colorado Nautral Heritage Program's LIM ranks wetlands in terms of their "overall landscape integrity," an indicator of the overall stress on each wetland derived by combining four stressor categories comprising 13 total stressors. Each stressor was modeled based on a single landscape feature that served as an indicator of stress on surrounding wetlands and was derived from readily-available GIS data. CNHP assigned a weight to each stressor based on the best professaionl judgment of the LIM team and modeled the decline in the effect of each stressor, or lack thereof, across space using the distance-decay function described by the equation below.
Using the distance-decay curve, the team had the ability to describe the effect of stressors in a variety of ways, ranging from having a high impact (i.e., high weight) but declining rapidly with distance to having a low impact (i.e., low weight) but decaying gradually. Within buffer regions surrounding each wetland, CNHP used these stressor maps to calculate landscape integrity scores.
A distance-decay function was not used, however, to describe the effects of Tamarisk populations and hydrologic modification. Tamarisk effects were described simply in terms of the footprint of Tamarisk distributions. Effects of hydrologic modifications, which accumulate with the water flow downstream rather than propagating equally in all directions, were described using TNC's method for calculating hydrologic disturbance downstream.
CNHP and partners will use the final output map resulting from this process to prioritize wetland protection, restoration, and enhancement efforts. For example, a wetland may be determined to be a good candidate for protection if it is determined to have high landscape integrity (low stress). On the other hand, if a wetland has low landscape integrity (high stress), but is restorable within its natural range of variation, it may represent a good candidate for restoration. A wetland may be a good candidate for enhancement if it has low landscape integrity (high stress) and cannot be restored to within its natural range of variation, but can be enhanced for specific ecological functions.
Factor used in analysis | Data source |
Land use and development | |
Industrial/urban development | LandFire current vegetation: high and medium intensity development |
Suburban/rural development | LandFire current vegetation: low intensity develoment |
Highly modified open space | LandFire current vegetation: developed open space |
Primary roads (interstate highways) | US Census TIGER/Line: primary roads |
Secondary roads (state highways) | US Census TIGER/Line: secondary roads |
Local and primitive roads | US Census TIGER/Line: local/primitive roads |
Agriculture | LandFire current vegetation: pasture/hay and cultivated/irrigated |
Energy development and resource extraction | |
Active oil and gas wells | Colorado Oil and Gas Conservation Commission: active wells |
Inactive oil and gas wells | Colorado Oil and Gas Conservation Commission: plugged/abandoned wells |
Wind turbines | CNHP |
Active sand and gravel mines | Colorado Division of Mine Safety: active sand and gravel mines |
Other active and abandoned mines | Colorado Division of Mine Safety: all other active mines |
Hydrologic modification | |
Reservoir storage as proportion of mean annual flow | TNC Freshwater Measures Database |
Water use as a proportion of mean annual flow | TNC Freshwater Measures Database |
Dams and diversions by stream length | TNC Freshwater Measures Database |
Groundwater wells | Colorado Division of Water Resources: active groundwater wells |
Weed infestations | |
Tamarisk populations | The Tamarisk Coalition and TNC |
CNHP Wetland Profile:1 CNHP has completed wetland profiles for each of Colorado's ten major river basins by summarizing information about the extent and distribution of wetlands within each. Using various classification systems to group wetland types into "bundles" of wetlands sharing similar ecological functions, CNHP is able to obtain information about the abundance and location of wetland functions throughout river basins. For example, profiles obtained for depressional wetlands enable CNHP to make inferences about the status and trends of wetland-dependent wildlife species, such as waterbirds and amphibians, which often depend on habitat provided by depressional wetlands.
Factor used in analysis | Data source |
Existing wetlands | NWI |
Wetland condition | Landscape integrity model outputs |
EPA RPS Stressor Exposure Tool:2 This tool evaluates ecological condition in terms of stressors and their sources for a set of hydrologic units based on a variety of indicators. The results are comparative within the population of units being assessed. By assessing subwatershed units within HUC-8s, for example, the tool identifies areas that might be targeted for restoration either based on their own restorability alone, or to achieve the largest improvement in condition for the HUC-8 as a whole.
Whereas the simplest form of screening often provides useful insights about general differences in restorability, further analysis of results is possible by evaluating more homogeneous subsets of the full population of waters or watersheds being compared. For example, users of the EPA RPS Stressor Exposure Tool may target pathogen-impaired watersheds, identified as those watersheds for which ecological, stressor, and social indicators that best address pathogen impairment and recovery factors are present. As another example, users of the EPA RPS might target urban watersheds separately from agricultural watersheds. In selecting indicators, EPA RPS eliminates those that are redundant or highly correlated by using statistical analysis to evaluate the similarity between indicator datasets.
Factor used in analysis | Data source(s) |
Watershed-level disturbance | |
Watershed percent agriculture | NLCD 1992(4); NLCD 2001(5); NLCD 2006(6); various state sources for land cover data; USGS cropland data by county(7); NHDplus catchments(4); USDA national GIS crop dataset(8); BLM dataset on range allotments and pastures(9) |
Watershed percent steep slope agriculture | NLCD 1992(3); NLCD 2001(4); NLCD 2006(5); various state source for land cover data; USGS cropland data(7); USGS NED(6); USGS EDNA(7); NHDplus flowline elevation data(4) |
Watershed number of CAFOs | State records mapping CAFO locations and livestock species and numbers |
Watershed number of septic systems (zones of potential septic usage and assumed partial failure rates) | Non-sewered area maps; municipality-level individual septic records; septic failure rate coefficient from watershed studies and TMDLs |
Watershed percent impervious cover | NLCD 2001(4); NLCD 2006(5); NHDplus catchments(4) |
Watershed percent tile-drained cropland | State-level digital soil survey data; NRCS Soil Data Mart(8); NLCD 1992(3); NLCD 2001(4); NLCD 2006(5); various state sources of land cover data; USGS cropland by county(6); USDA national GIS crop dataset(7) |
Watershed percent U index (percent anthropogenic cover types) | NLCD(2); statewide land cover data from state-specific sources |
Watershed percent urban | NLCD(2); statewide land cover data from state-specific sources |
Watershed road density (mean road length per watershed square mile) | Transportation GIS datasets; national road and stream data from the National Atlas(10); ESRI roads dataset(11) |
Other percent watershed stressor (e.g., surface mining for some watersheds) | Dependent upon additional stressors identified |
Corridor and shoreland disturbance | |
Corridor percent impervious cover | NLCD 2001(4) and 2006(5) data for impervious and urban land cover; NHDplus catchments(4) |
Corridor percent tile-drained cropland | State-level digital soil survey data; NRCS Soil Data Mart(8); NLCD 1992(3); NLCD 2001(4); NLCD 2006(5); various state sources of land cover data; USGS cropland by county(6); USDA national GIS crop dataset(7) |
Corridor percent U-index (percent anthropogenic land cover types) | NLCD(2); statewide land cover data from state-specific sources |
Corridor percent urban | NLCD(2); statewide land cover data from state-specific sources |
Corridor percent agriculture | NLCD 1992, NLCD 2001; NLCD 2006; various state sources for land cover data; USGS cropland by county(6); USDA national GIS crop dataset7; BLM data on range allotments and pastures(8) |
Linear percent of channel through agriculture (percent total stream length through agricultural land use or percent agricultural area adjacent to stream) | Stream hydrography data; land cover data |
Corridor road crossings | National road and stream data from the National Atlas(9); Landsat data for roads and stream from USGS Earth Explorer(12); ESRI roads dataset(10); data for unimproved road crossings in remote parts of federal lands from land management agency |
Corridor road density | National road and stream data from the National Atlas(9); ESRI roads dataset(10) |
Hydrologic alteration | |
Aquatic barriers (count per watershed or relative isolation of specific stream segment of similar Strahler order) | Aquatic barriers to fish passage from the USFWS Decision Support System(13); major dams mapping by the USACE National Inventory of Dams(14); NHD data on dams and divergence structures(6) |
Channelization (percent of total impaired stream length artificially straightened) | USGS NHD(6); local resources; channelization attribute data for 303(d) listed streams included in EPA ATTAINS data system(20) |
Hydrologic alteration (scores waterbody segments downstream of dams or withdrawals based on dam size, active status, role on flow alteration and feasibility of flow management) | Aquatic barriers to fish passage from the USFWS FPDSS(12); major dams mapping by the USACE National Inventory of Dams(13); NHD data on dams and divergence structures(6); State data on water withdrawal locations (e.g. Michigan15) |
Relative net water demand | Gauging station records, which may be used to develop natural flow estimators and calculate water demand relative to natural flow |
Water use intensity | Gauging station records, which may be used to develop natural flow estimators and calculate water demand relative to natural flow |
Biotic or climatic risks | |
Elevation (mean elevation of the watershed or specific stream segment) | USGS NED(6); USGS EDNA(7); NHDplus flowline elevation data(4) |
Invasive species risk | USGS Non-Indigenous Aquatic Species Information Resource(16); Non-Indigenous Species Database Network range maps(17); USDA National Invasive Species Information Center(18) |
Severity of pollutant loading | |
Number of 303(d) listed causes | ATTAINS(20) |
Number of permits | EPA's national geospatial dataset on permits |
CSO or MS4 areas | Spatial data available at state and municipal level |
Age of sewer infrastructure | Spatial data available at municipal level |
Severity of loading (compares current loading with TMDL target loading calculation for percent reduction needed) | ATTAINS data on 303(d)-listed waters(20); loading estimates from TMDLs or watershed models (e.g., from EPA's website)(19) |
Stressor persistence | Project specific |
SPARROW nitrogen loading estimate | Water quality data from EPA's NPDAT website(20); regional modeling data from the USGS decision support system(21) |
SPARROW phosphorus loading estimate | Water quality data from EPA's NPDAT website(19); regional modeling data from the USGS decision support system(20) |
Watershed stream miles impaired | EPA ATTAINS data on 303(d)-listed waters(20) |
Watershed water body acres impaired | EPA ATTAINS data on 303(d)-listed waters(20) |
Modeled watershed aerial N deposition | N/A |
Modeled watershed aerial Hg deposition | REMSAD; CMAQ; range of likely impacts from foreign sources(22) |
Other stressor-specific severity factors | Project-specific stressor data |
Legacy of past, trajectory of future use | |
Land use change trajectory | NLCD 1992(3); NLCD 2001(4); NLCD 2006(5); USGS Land Cover Trends Project(23); USGS Temporal Urban Mapping project(24); USGS Historical Topographic Map Collection(25) |
Legacy land uses | USGS Land Cover Institute(26); NLCD 1992(27); NLCD land use change between 2001 and 2006(3); Historical Topographic Map Collection(24_ |
Watershed percent legacy agriculture | USGS Land Cover Institute(25); NLCD 1992(26); NLCD land use change between 2001 and 2006(3) |
Watershed percent legacy urban | USGS Land Cover Institute(26); NLCD 1992(27); NLCD land use change between 2001 and 2006(3); Historical Topographic Map Collection(24) |
Corridor percent legacy agriculture | USGS Land Cover Institute(25); NLCD 1992(26); NLCD land use change between 2001 and 2006(3) |
Corridor percent legacy urban | USGS Land Cover Institute(26); NLCD 1992(27); NLCD land use change between 2001 and 2006(3); Historical Topographic Map Collection(24) |
IDFG Watershed Condition Tool:28 IDFG ranked individual HUC-12 watersheds using an analysis for which metrics were selected based on the literature review and professional judgment alone. Because watershed reference data were unavailable, no field-based calibration of metrics was completed, as was done for IDFG's wetland condition tool. IDFG summed all metrics and ranked each HUC-12 in terms of six condition classes ranging from "minimally disturbed" (rank = 1) to "completely disturbed" (rank = 6).
Factor used in analysis | Data source(s) |
ATtILA Landscape metrics | |
Total terrestrial area | NLCD(29) |
Percent cropland | |
Percent pasture | |
Percent all agricultural land use | |
Percent forest | |
Percent man-made barren | |
Percent natural barren | |
Percent natural grassland | |
Percent shrubland | |
Percent urban | |
Percent user-defined class | |
Percent wetland | |
Percent all natural land use | |
Percent all human land use | |
Percent agricultural cropland on slopes › 10% | NLCD(27); NED |
Percent agricultural pasture on slopes › 10% | |
Percent any agricultural on slopes › 10% | |
ATtILA Riparian metrics | |
Percent stream length adjacent to agricultural land use | NLCD(27); NED |
Percent stream length within 30m of agricultural land use | |
Percent stream length wthin 120m of agricultural land use | |
Percent stream length adjacent to cropland | |
Percent stream length within 30m of cropland | |
Percent stream length within 120m of cropland | |
Percent stream length adjacent to pasture | |
Percent stream length within 30m of pasture | |
Percent stream length within 120m of pasture | |
Percent stream length adjacent to urban land use | |
Percent stream length within 30m of urban land use | |
Percent stream length within 120m of urban land use | |
Percent stream length adjacent to human land use | |
Percent stream length within 30m of human land use | |
Percent stream length within 120m of human land use | |
Percent stream length adjacent to natural grassland | |
Percent stream length within 30m of natural grassland | |
Percent stream length within 120m of natural grassland | |
ATtILA Human stressor metrics | |
Density of 4-lane highways | TIGER 2000 (1:100,000)(28) |
Density of 2-lane highways | |
Density of interstate freeways | |
Length of roads within 30m of streams | TIGER 2000 (1:100,000)(30); NHD |
Length of 4-lane highways within 30m of streams | |
Length of 2-lane highways within 30m of streams | |
Length of county, city roads within 30m of streams | |
Number of road/stream crossings | |
Number of 4-lane highway/stream crossings | |
Number of 2-lane highway/stream crossings | |
Number of county, city road/stream crossings | |
Nutrient loading | N/A |
Phosphorus loading | N/A |
Population density (population count/km 2) | N/A |
Percent change in total population | N/A |
Percent impervious cover | NLCD(27) |
ATtILA physical characteristic metrics | |
Area of wetland | NWI |
Stream density | NHD |
Topographic position of wetland | NED |
Desktop GIS-derived metrics | |
Density of canals, ditches (km/km 2) | NHD |
Density of wells (#/km 2) | N/A |
Percent of land likely grazed by livestock | NLCD(27); BLM; ICBEMP |
Pollutant discharge | EPA; ICBEMP |
Railroads | TIGER 2000 (1:100,000)(28) |
Recreation access and navigation improvements | BLM; IDPR |
Recent timber harvest | USGS; Northwest ReGAP project; NatureServe |
Toxic element concentration | EPA; ICBEMP |
Utility corridors | ICBEMP |
Dairies | IDWR |
Dams and reservoirs | IDWR; NHD |
Dredge spoils or other solid waste disposal | EPA |
Effluent discharge (from industrial or energy facility that alters thermal regime) | EPA |
Groundwater pumping: ex-urban development | IDWR |
Mining | IDL; USGS; IDEQ |
IDFG Wetland Condition Tool:28 For each wetland polygon in its north and south study sites, IDFG combined metrics found to be most predictive of wetland condition with an "index of environmental vulnerability" to assign each wetland one of four condition classes ranging from minimally disturbed (rank = 1) to completely disturbed (rank = 4).
Factor used in analysis | Data source(s) | |
North region | ||
Percent agricultural land use | NLCD(27) | |
Percent natural grassland | ||
Percent cropland | ||
Percent pasture | ||
Percent urban | ||
Percent stream length within 30m of urban land use | NHD; NLCD(27) | |
Percent stream length within 30m of urban land use | ||
Percent agricultural land use on slopes › 9% | NLCD(27); NED | |
Density of 4-lane highways | TIGER 2000 (1:100,000)(28) | |
Density of 2-lane highways | ||
Length of 4-lane highways within 30m of streams | TIGER 2000 (1:100,000)(28); NHD | |
Length of 2-lane highways within 30m of streams | ||
Number of 4-lane highway/stream crossings | ||
Number of 2-lane highway/stream crossings | ||
Nitrogen loading | N/A | |
Phosphorus loading | N/A | |
Population density | N/A | |
Density of wells (#/km 2) | N/A | |
Percent likely grazed by livestock | NLCD; BLM; ICBEMP | |
Index of environmental vulnerability | Mean elevation | NED (30m) |
Mean precipitation | UM NTSG total precipitation data (1980-1997, 18-year mean, 1 km resolution)(31) | |
Mean slope | NED (30m) | |
Percent forest | 2001 NLCD | |
Percent stream length adjacent to natural land | Streamnet (IDFG 2008, 1:100,000) | |
Percent stream length within 30m of natural land | Streamnet (IDFG 2008, 1:100,000) | |
South region | ||
Percent agricultural land use | NLCD(27) | |
Percent cropland | ||
Percent pasture | ||
Percent urban | ||
Percent human land use | ||
Percent stream length adjacent to agricultural land use | NLCD(27); NHD | |
Percent stream length within 30m of agricultural land use | ||
Percent stream length within 120m of agricultural land use | ||
Percent stream length adjacent to cropland | ||
Percent stream length within 30m of cropland | ||
Percent stream length within 120m of cropland | ||
Percent stream length adjacent to pasture | ||
Percent stream length within 30m of pasture | ||
Percent stream length within 120m of pasture | ||
Percent stream length adjacent to pasture | ||
Percent stream length adjacent to urban land use | ||
Percent stream length within 30m of urban land use | ||
Percent stream length within 120m of urban land use | ||
Percent stream length adjacent to human land use | ||
Percent stream length within 30m of human land use | ||
Percent stream length within 120m of human land use | ||
Percent stream length adjacent to natural grassland | ||
Percent stream length within 30m of natural grassland | ||
Percent stream length within 120m of natural grassland | ||
Density of interstate freeways | TIGER 2000 (1:100,000)(28) | |
Length of roads within 30m streams | TIGER 2000 (1:100,000)(28); NHD | |
Length of county, city roads within 30m of streams | ||
Number of road/stream crossings | ||
Number of county, city road/stream crossings | ||
Nitrogen loading | N/A | |
Phosphorus loading | N/A | |
Area of wetland | NWI | |
Stream density | NHD | |
Density of canals, ditches (km/km 2) | NHD | |
Density of wells (#/km 2) | N/A | |
Percent likely grazed by livestock | BLM; ICBEMP; NLCD(27) | |
Index of environmental vulnerability | Mean elevation | NED (30m) |
Mean precipitation | UM NTSG total precipitation data (1980-1997, 18-year mean, 1 km resolution) | |
Mean slope | NED (30m) | |
Area of wetland | NWI | |
Stream density | Streamnet (IDFG 2008, 1:100,000) |
MTNHP Landscape Integrity Model:32 MTNHP built the LIM using input factors derived from an analysis that determined landscape predictors of wetland condition in addition to metrics for landscape-level stressors shown by the scientific literature to have important impacts on wetland condition. Final metrics comprised four categories - roads, land cover, hydrology, and resource extraction, each of which was made up of individual component layers representing human change to the landscape (e.g., "highways" in the "roads" category). To each component layer, the MTNHP researchers applied an inverse-weighted distance function that assigned a score to each 30m 2 pixel surrounding the wetland that reflected the decrease in magnitude of each impact with distance across the landscape. These individual component layers, scored for impacts, were weighted and summed into overall category rasters, which were in turn weighted and summed to obtain final MTLIM scores.
Factor used in analysis | Data source(s) |
Roads | |
4-wheel drive | 2001 TIGER/US Census Bureau |
Local roads, city streets | |
Highways | |
Land cover | |
Urban | 2001 NLCD |
Crop agriculture | |
Timber harvest | |
Hydrology | |
Artificial flow | 2007 NHD |
Water rights point-of-use | MTDNRC |
Section 404 permits | MTNRIS |
Resource extraction | |
Abandoned mines | MTDEQ |
Oil or gas extraction | MTNRIS |
TNC Aquatic Ecoregional Assessment Aquatic System Integrity GIS model: To develop its portfolio of priority sites, TNC first applied a GIS screening analysis to rank river systems based on landscape variables known to correlate with the biological integrity of aquatic communities. These variables were grouped into three categories: land cover and road impacts (impacts due to roads, urbanization, and agriculture), dam and drinking water supply impacts (impacts caused by altered hydrologic regimes and creation of migration barriers), and point source impacts (potential chemical or nutrient threats due to point sources). By ranking each target river system for each category, TNC was able to identify those river systems that were most intact within each ecological draining unit (EDU).
Factor in the analysis | Data source(s) | |
Land cover and road impacts | Percent developed land | National Land Cover Dataset |
Road density | StreetMap USA (Esri & Tele Atlas) | |
Density of road/stream crossings | StreetMap USA and National Hydrography Dataset | |
Dam and drinking water supply impacts | Dam density | EPA |
Dam storage capacity | EPA | |
Drinking water supply density | EPA | |
Point source impacts | Point source density | EPA |
TNC Aquatic Ecoregional Assessment Landscape context GIS model:34 TNC obtained "landscape context" rankings for each watershed by calculating the percent coverage of each watershed by three spatial variables: percent developed land, percent agricultural land, and total road density per watershed area. TNC applied the criteria listed in the table below to each of these three percentages, using the highest rank obtained among them to represent each watershed's ranking for overall landscape context.
Factor in the analysis | Data source |
Percent developed land | NLCD |
Percent agricultural land | NLCD |
Total road density per watershed area | StreetMap USA |
VIMS Wetland Condition Assessment Tool:34 This tool scored palustrine emergent, scrub/shrub, and forested wetlands within buffer regions surrounding each wetland based on the habitat and water quality stressors associated with surrounding land use types.
Factor used in analysis | Data source |
Watersheds around each wetland | USGS National Elevation Dataset |
Wetlands | NWI |
Density of roads within 200m of each wetland | US Census TIGER/Line roads (2000) |
Land cover data | NLCD |
Weller et al. (2007) Landscape (Riverine Wetlands) Tool:35 This landscape assessment predicted the habitat condition Functional Capacity Index (FCI) score for riverine wetlands, which was calculated based on field data for condition of buffers 0-20m from wetland, condition of buffers 20-100m from wetland, and stream condition outside assessment area. This tool predicted FCI scores by inputting 30m resolution spatial metrics for stream proximity, road proximity, nearest stream condition, and surrounding land cover composition into a GIS-based model.
Factor used in analysis | Data source(s) |
Condition of nearest stream (0 = excavated, 1 = natural) | NWI stream and ditch map |
Percent cropland and grassland within 100m | 2001 NWI |
Percent developed land within 1000m | 2001 NWI |
Percent mixed forest within 1000m | 2001 NWI |
Distance to nearest stream (m) | 1:24,000 NHD |
Percent pixels with zero impervious cover within 100m | 2001 NLCD |
Distance to nearest road (m) | Census TIGER data(36) |
Percent wetland within 100m | Wetlands from NWI and states of MD and DE |
Percent forest within 100m | 2001 NLCD |
Percent wooded wetland within 1000m | 2001 NLCD |
Wetland Prioritization Study Main Page
1Colorado Natural Heritage Program. 2011. Statewide Strategies to Improve Effectiveness in Protecting and Restoring Colorado's Wetland Resource.
2 Webinar: "Recovery Potential Screening: A tool for comparing impaired waters restorability" by Doug Norton and Tatyana DiMascio. Accessed from: http://water.epa.gov/learn/training/wacademy/upload/2012_02_22_slides.pdf.
3US Environmental Protection Agency. Office of Water. Recovery Potential Stressor Indicators. Accessible from: http://owpubauthor.epa.gov/lawsregs/lawsguidance/cwa/tmdl/recovery/indicatorsstressor.cfm.
4 US Environmental Protection Agency. 1992 National Land Cover Data. Accessible from: http://www.epa.gov/mrlc/nlcd.html.
5 US Environmental Protection Agency. 2001 National Land Cover Data. Accessible from: http://www.epa.gov/mrlc/nlcd-2001.html.
6 Multi-resolution Land Characteristics Consortium. National Land Cover Database 2006. Accessible from: http://www.mrlc.gov/nlcd06_data.php.
7 The USGS Land Cover Institute. Cropland by County Since 1850 and Population Since 1790. Accessible from: http://landcover.usgs.gov/cropland/index.php.
8 US Department of Agriculture Natural Resources Conservation Service. Geospatial Data Gateway. http://datagateway.nrcs.usda.gov/GDGHome.aspx.
9 Department of the Interior Bureau of Land Management. GeoCommunicator. Accessible from: http://www.geocommunicator.gov/GeoComm/.
10 US Department of the Interior. National Atlas. Accessible from: http://nationalatlas.gov/.
11 ArcGIS. Updated May 2013. World Street Map. Accessible from: http://www.arcgis.com/home/item.html?id=3b93337983e9436f8db950e38a8629af.
12 US Geological Survey. Earth Explorer. Accessible from: http://earthexplorer.usgs.gov/ .
13 US Fish and Wildlife Service. GeoFin Geospatial Fisheries Information Network. Accessible from: http://edcsns17.cr.usgs.gov .
14 US Army Corps of Engineers. National Inventory of Dams. Accessible from: http://geo.usace.army.mil/pgis/f?p=397:12
15 State of Michigan Department of Environmental Quality. Water Withdrawal Reports, Data and Graphics. Accessible from: http://michigan.gov/deq/0,1607,7-135-3313_3677_3704-72931--,00.html .
16 US Geological Survey. Nonindigenous Aquatic Species. Accessible from http://nas.er.usgs.gov/default.aspx .
17 Steves, B. P. 2003. An International Nonindigenous Species Database Network. Smithsonian Environmental Research Center. Accessible from: http://www.nisbase.org/nisbase/index.jsp .
18 US Department of Agriculture National Agricultural Library. Resource Library Databases. Accessible from: http://www.invasivespeciesinfo.gov/resources/databases.shtml .
19 US Environmental Protection Agency. AskWaters. Accessible from: http://iaspub.epa.gov/pls/waters/f?p=ASKWATERS:EXPERT:0
20 US Environmental Protection Agency. Nitrogen and Phosphorus Pollution Data Access Tool . Accessible from: http://www2.epa.gov/nutrient-policy-data/nitrogen-and-phosphorus-pollution-data-access-tool .
21 US Geological Survey. SPARROW Decision Support System. Accessible from: http://cida.usgs.gov/sparrow/ .
22 US Environmental Protection Agency. Total Maximum Daily Loads Technical Support Documents. Accessible from: http://water.epa.gov/lawsregs/lawsguidance/cwa/tmdl/techsupp.cfm .
23 US Geological Survey. Land Cover Trends Project Download Overview. Accessible from: http://landcovertrends.usgs.gov/download/overview.html.
24 US Geological Survey Land Cover Institute. Urban Dynamics: Temporal Urban Mapping. Accessible from: http://landcover.usgs.gov/urban/umap/.
25 US Geological Survey. The National Map: Historical Topographic Map Collection. http://nationalmap.gov/historical/.
26 The USGS Land Cover Institute. Cropland by County Since 1850 and Population Since 1790. Accessible from: http://landcover.usgs.gov/cropland/index.php.
27 US Geological Survey Land Cover Institute. National Land Cover Dataset 1992. Accessible from: http://landcover.usgs.gov/natllandcover.php.
28 Idaho Department of Fish and Game. 2010. Development of a landscape-scale wetland condition assessment tool for Idaho.
29 NLCD data are available from: http://www.mrlc.gov/nlcd2001.php
30 TIGER roads/railroads data available from: http://www.census.gov/geo/maps-data/data/tiger.html
31 UM NTSG precipitation data available from: http://www.ntsg.umt.edu/project/daymet
32 Vance LK. 2009. Assessing Wetland Condition with GIS: A Landscape Integrity Model for Montana. A Report to The Montana Department of Environmental Quality and The Environmental Protection Agency. Montana Natural Heritage Program, Helena, MT. 23 pp. plus appendices.
33 The Nature Conservancy. 2009. The Nature Conservancy's watershed approach to compensation planning for the Virginia Aquatic Resource Trust Fund.
34 Center for Coastal Resources Management, Virginia Institute of Marine Science, College of William and Mary. 2007. Development of a nontidal inventory and monitoring strategy for Virginia - completion of phase II (coastal plain and piedmont physiographic provinces): Final report to the Environmental Protection Agency Region III.
35 Center for Coastal Resources Management, Virginia Institute of Marine Science, College of William and Mary. 2007. Development of a nontidal inventory and monitoring strategy for Virginia - completion of phase II (coastal plain and piedmont physiographic provinces): Final report to the Environmental Protection Agency Region III.
36 US Department of Commerce US Census Bureau. Maps and Data TIGER Products. Accessible from: http://www.census.gov/geo/maps-data/data/tiger.html.