20201001
09011000
1.0
FALSE
CHWA_Fields
1350615.000000
1785075.000000
1687605.000000
2405235.000000
1
002
Geographic
GCS_WGS_1984
Angular Unit: Degree (0.017453)
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20200830
17070600
20200830
17070600
150000000
5000
dataset
Christopher Wharton
Tetra Tech, Inc.
GIS Analyst
20200830
File Geodatabase Feature Class
1.0
Esri ArcGIS 12.6.0.24783
CHWA_Fields
vector digital data
Chesapeake Healthy Watersheds Assessment for the Chesapeake Bay Watershed
Maintain Healthy Watersheds Goal Implementation Team (GIT 4)
2020-04-01T00:00:00
2020-04-01T00:00:00
2020-04-01T00:00:00
1.0
20200401
1
-80.562890
-73.861751
43.557640
36.515696
<DIV STYLE="text-align:Left;"><DIV><DIV><P><SPAN><SPAN>The Chesapeake Bay Program has a goal of maintaining the long-term health of watersheds identified as healthy by its partner jurisdictions. Quantitative indicators are important to assess current watershed condition, track future condition, and assess the vulnerability of these watersheds to future degradation. Building upon the U.S. Environmental Protection Agency (EPA) Preliminary Healthy Watershed Assessment (PHWA) framework, project analysts assembled and evaluated a set of candidate metrics characterizing multiple aspects of landscape condition, hydrology, geomorphology, habitat, biological condition, and water quality, for integration into an overall watershed health index. Geospatial analyses were structured, where possible, to leverage data from EPA StreamCat, the National Fish Habitat Partnership, the Chesapeake Bay model for nutrient loads, and other regional data sources. A set of vulnerability metrics were derived representing aspects of land use change, water use, wildfire risk, and climate change. Metric values were compiled for the nearly 84,000 NHDPlus catchments Bay-wide, and were used to assess conditions and vulnerability within the catchments associated with the current set of state-identified healthy watersheds. These indicators will be available to federal, state, and local managers as a geospatial tool, providing critical information for maintaining watershed health. The Chesapeake Healthy Watersheds Assessment provides a framework for tracking condition at future intervals, integrating new data that become available.</SPAN></SPAN></P></DIV></DIV></DIV>
Chesapeake
Bay
Healthy
Vulnerable
Watershed
Water
Stream
River
Maryland
Virginia
West Virginia
Delaware
New York
Pennsylvania
Estuary
Landuse
Cover
Development
Forest
Impervious
Loss
Trout
Geology
Index
Hydrology
Assessing the Healthy and Vulnerability of Healthy Watersheds within the Chesapeake Bay Watershed Catchment data at NHD Plus Version 2 scale.
Christopher Wharton
Nancy Roth
Sam Sarkar
Brian Pickard, Ph.D.
Ann Roseberry Lincoln
Tetra Tech, Inc
0
EPSG
6.14(3.0.1)
CHWA_Fields
Feature Class
0
Tetra Tech
Tetra Tech
OBJECTID
OBJECTID
OID
4
0
0
Internal feature number.
Esri
Sequential unique whole numbers that are automatically generated.
Shape
Shape
Geometry
0
0
0
Feature geometry.
Esri
Coordinates defining the features.
COMID
COMID
Integer
4
0
0
NHD Plus Version 2 Catchment Unique Identifier
Horizon Systems
HWUniqID
Healthy Watersheds Unique ID
String
8000
0
0
HWFlag
Healthy Watersheds Flag
Integer
4
0
0
HWGroup
Healthy Watersheds Group
String
8000
0
0
Healthy Watershed flag that identifies a catchment/watershed as within or outside of existing state identified healthy watershed boundary.
Derived using spatial association to determine if a NHD catchment is inside or outside of an existing state identified healthy watershed.
Tetra Tech
State
State
String
8000
0
0
US State Abbreviation Identifier
Derived by spatial join with ESRI state baselayer
Tetra Tech
County
County
String
8000
0
0
US County Name
Derived from spatial join with ESRI counties base layer
Tetra Tech
AgWaterUse
Agricultural Water Use
Double
8
0
0
Daily agricultural water use in the HUC12 (million gallons per day). Agricultural water use includes surface and groundwater that is self-supplied by agricultural producers or supplied by water providers (governments, private companies, or other organizations). Catchments were assigned values from surrounding HUC12.
Water used in a HUC12 may originate from within or outside the HUC12. Calculated by downscaling county water use estimates for 2005 reported by US Geological Survey ("Estimated Use of Water in the United States County-Level Data for 2005") using the 2006 National Land Cover Database (2006 NLCD) Land Cover dataset, the 2010 Cropland Data Layer, and a custom geospatial dataset of irrigated area locations. Counties with zero reported water use were assigned a state-level average value to address issues with water use reporting. This indicator was calculated for EPA EnviroAtlas. Detailed information on source data and calculation methods can be found at: https://edg.epa.gov/metadata/catalog/search/resource/details.page?uuid=%7BD5113083-CFCD-48EC-BC24-0ADA5B9BDDB7%7D
Derived by HUC12 transformation between HUC12 ID and NHDPlus V2 COMID to get Agricultural Water Use for the catchment.
USGS
AvgPctForestLossWs
Avg Pct Forest Loss Ws
Double
8
0
0
Average Forest cover change 2000-2013, Tree canopy cover yrs 2000 - 2013; change (loss) in canopy cover 2000-2013
Derived by aggregating the 2000-2013 Forest Loss produced by StreamCat to obtain an average forest lost for each catchments contributing watershed over those years. See StreamCat methodology for more details https://www.epa.gov/national-aquatic-resource-surveys/streamcat
http://earthenginepartners.appspot.com/science-2013-global-forest/download_v1.1.html
CBPModAGN
CBP Model Nitrogen Load Agricultural Sources
Double
8
0
0
Nitrogen load from agricultural sources
Nitrogen, Phosphorus, and Sediment Load from Chesapeake Bay Model, by Sector (Developed Land, Agriculture, Wastewater, Septic, and CSO), in Watershed (15 separate metrics)
Spatial Analyst tools were used to associate load data with NHDPlusV2 catchments. A python script was then applied to the resulting information to determine total load for the contributing watersheds. Data was then normalized to produce a range of values between 0-1
Chesapeake Bay Program
CBPModAGP
CBP Model Phosphorus Load Agricultural Sources
Double
8
0
0
Phosphorus load from agricultural sources.
Nitrogen, Phosphorus, and Sediment Load from Chesapeake Bay Model, by Sector (Developed Land, Agriculture, Wastewater, Septic, and CSO), in Watershed (15 separate metrics)
Spatial Analyst tools were used to associate load data with NHDPlusV2 catchments. A python script was then applied to the resulting information to determine total load for the contributing watersheds. Data was then normalized to produce a range of values between 0-1
Chesapeake Bay Program
CBPModAGS
CBP Model Sediment Load Agricultural Sources
Double
8
0
0
Sediment load from agricultural sources.
Nitrogen, Phosphorus, and Sediment Load from Chesapeake Bay Model, by Sector (Developed Land, Agriculture, Wastewater, Septic, and CSO), in Watershed (15 separate metrics)
Spatial Analyst tools were used to associate load data with NHDPlusV2 catchments. A python script was then applied to the resulting information to determine total load for the contributing watersheds. Data was then normalized to produce a range of values between 0-1
Chesapeake Bay Program
CBPModCSON
CBP Model Nitrogen Load CSO Sources
Double
8
0
0
Nitrogen load from CSO sources
Nitrogen, Phosphorus, and Sediment Load from Chesapeake Bay Model, by Sector (Developed Land, Agriculture, Wastewater, Septic, and CSO), in Watershed (15 separate metrics)
Spatial Analyst tools were used to associate load data with NHDPlusV2 catchments. A python script was then applied to the resulting information to determine total load for the contributing watersheds. Data was then normalized to produce a range of values between 0-1
Chesapeake Bay Program
CBPModCSOP
CBP Model Phosphorus Load CSO Sources
Double
8
0
0
Phosphorus load from CSO sources
Nitrogen, Phosphorus, and Sediment Load from Chesapeake Bay Model, by Sector (Developed Land, Agriculture, Wastewater, Septic, and CSO), in Watershed (15 separate metrics)
Spatial Analyst tools were used to associate load data with NHDPlusV2 catchments. A python script was then applied to the resulting information to determine total load for the contributing watersheds. Data was then normalized to produce a range of values between 0-1
Chesapeake Bay Program
CBPModCSOS
CBP Model Sediment Load CSO Sources
Double
8
0
0
Sediment load from CSO sources
Nitrogen, Phosphorus, and Sediment Load from Chesapeake Bay Model, by Sector (Developed Land, Agriculture, Wastewater, Septic, and CSO), in Watershed (15 separate metrics)
Spatial Analyst tools were used to associate load data with NHDPlusV2 catchments. A python script was then applied to the resulting information to determine total load for the contributing watersheds. Data was then normalized to produce a range of values between 0-1
Chesapeake Bay Program
CBPModDEVN
CBP Model Nitrogen Load Development Sources
Double
8
0
0
Nitrogen load from development sources
Nitrogen, Phosphorus, and Sediment Load from Chesapeake Bay Model, by Sector (Developed Land, Agriculture, Wastewater, Septic, and CSO), in Watershed (15 separate metrics)
Spatial Analyst tools were used to associate load data with NHDPlusV2 catchments. A python script was then applied to the resulting information to determine total load for the contributing watersheds. Data was then normalized to produce a range of values between 0-1
Chesapeake Bay Program
CBPModDEVP
CBP Model Phosphorus Load Development Sources
Double
8
0
0
Phosphorus load from development sources
Nitrogen, Phosphorus, and Sediment Load from Chesapeake Bay Model, by Sector (Developed Land, Agriculture, Wastewater, Septic, and CSO), in Watershed (15 separate metrics)
Spatial Analyst tools were used to associate load data with NHDPlusV2 catchments. A python script was then applied to the resulting information to determine total load for the contributing watersheds. Data was then normalized to produce a range of values between 0-1
Chesapeake Bay Program
CBPModDEVS
CBP Model Sediment Load Development Sources
Double
8
0
0
Sediment load from development sources
Nitrogen, Phosphorus, and Sediment Load from Chesapeake Bay Model, by Sector (Developed Land, Agriculture, Wastewater, Septic, and CSO), in Watershed (15 separate metrics)
Spatial Analyst tools were used to associate load data with NHDPlusV2 catchments. A python script was then applied to the resulting information to determine total load for the contributing watersheds. Data was then normalized to produce a range of values between 0-1
Chesapeake Bay Program
CBPModSEPN
CBP Model Nitrogen Load Septic Sources
Double
8
0
0
Nitrogen load from septic sources
Nitrogen, Phosphorus, and Sediment Load from Chesapeake Bay Model, by Sector (Developed Land, Agriculture, Wastewater, Septic, and CSO), in Watershed (15 separate metrics)
Spatial Analyst tools were used to associate load data with NHDPlusV2 catchments. A python script was then applied to the resulting information to determine total load for the contributing watersheds. Data was then normalized to produce a range of values between 0-1
Chesapeake Bay Program
CBPModSEPP
CBP Model Phosphorus Load Septic Sources
Integer
4
0
0
Phosphorus load from septic sources
Nitrogen, Phosphorus, and Sediment Load from Chesapeake Bay Model, by Sector (Developed Land, Agriculture, Wastewater, Septic, and CSO), in Watershed (15 separate metrics)
Spatial Analyst tools were used to associate load data with NHDPlusV2 catchments. A python script was then applied to the resulting information to determine total load for the contributing watersheds. Data was then normalized to produce a range of values between 0-1
Chesapeake Bay Program
CBPModSEPS
CBP Model Sediment Load Septic Sources
Integer
4
0
0
Sediment load from septic sources
Nitrogen, Phosphorus, and Sediment Load from Chesapeake Bay Model, by Sector (Developed Land, Agriculture, Wastewater, Septic, and CSO), in Watershed (15 separate metrics)
Spatial Analyst tools were used to associate load data with NHDPlusV2 catchments. A python script was then applied to the resulting information to determine total load for the contributing watersheds. Data was then normalized to produce a range of values between 0-1
Chesapeake Bay Program
CBPModWWN
CBP Model Nitrogen Load Wastewater Sources
Double
8
0
0
Nitrogen load from wastewater sources
Nitrogen, Phosphorus, and Sediment Load from Chesapeake Bay Model, by Sector (Developed Land, Agriculture, Wastewater, Septic, and CSO), in Watershed (15 separate metrics)
Spatial Analyst tools were used to associate load data with NHDPlusV2 catchments. A python script was then applied to the resulting information to determine total load for the contributing watersheds. Data was then normalized to produce a range of values between 0-1
Chesapeake Bay Program
CBPModWWP
CBP Model Phosphorus Load Septic Sources
Double
8
0
0
Phosphorus load from wastewater sources
Nitrogen, Phosphorus, and Sediment Load from Chesapeake Bay Model, by Sector (Developed Land, Agriculture, Wastewater, Septic, and CSO), in Watershed (15 separate metrics)
Spatial Analyst tools were used to associate load data with NHDPlusV2 catchments. A python script was then applied to the resulting information to determine total load for the contributing watersheds. Data was then normalized to produce a range of values between 0-1
Chesapeake Bay Program
CBPModWWS
CBP Model Sediment Load Septic Sources
Double
8
0
0
Sediment load from wastewater sources
Nitrogen, Phosphorus, and Sediment Load from Chesapeake Bay Model, by Sector (Developed Land, Agriculture, Wastewater, Septic, and CSO), in Watershed (15 separate metrics)
Spatial Analyst tools were used to associate load data with NHDPlusV2 catchments. A python script was then applied to the resulting information to determine total load for the contributing watersheds. Data was then normalized to produce a range of values between 0-1
Chesapeake Bay Program
ClimateStress
Climate Stress
Double
8
0
0
The Climate Stress Metric is one of a suite of products from the Nature’s Network project (naturesnetwork.org). Nature’s Network is a collaborative effort to identify shared priorities for conservation in the Northeast, considering the value of fish and wildlife species and the natural areas they inhabit. This dataset represents a measure of the estimated magnitude of climate stress that may be exerted on habitats (ecosystem types) in 2080, on a scale of 30 m2 cells. Cells where 2080 climate conditions depart substantially from conditions where the underlying ecosystem type currently occurs (the ecosystem’s “climate niche”) are considered to be stressed. Cells where the projected 2080 climate conditions are not substantially different from the current climate niche in the Northeast region are considered to be under low climate stress. Areas with low or zero climate stress may be candidates to function as climate refugia; these are places where ecosystems and associated species can persist relatively longer, compared to typical locations where the ecosystems currently occur.
Derived using zonal statistics/tabulate area from the Nature's Network climate stress metric raster to determine the percentage of the catchment (NHD) projected to have climate stress.
https://nalcc.databasin.org/datasets/d207f70858fa403397c631433c2ad57d North Atlantic Landscape Conservation Cooperative (funder), Kevin McGarigal (Principal Investigator), 2017-06-22 (creation), 2017-10-20 (lastUpdate), 2017-03-17 (Publication), Climate Stress Metric, Version 3.0, Northeast U.S.
DamDensityWs
Dam Density Ws
Double
8
0
0
Density of georeferenced dams within watershed (dams/ square km). Shapefile of georeferenced dam locations (points) and associated dam and reservoir characteristics (where available), such as dam height, reservoir volume, and year constructed from the National Inventory of Dams.
Derived by join field with StreamCat Dam Density Ws with this layers COMID. See StreamCat methodology for more details https://www.epa.gov/national-aquatic-resource-surveys/streamcat
STREAMCAT
FutureDev
Future Development
Double
8
0
0
Percent of catchment land projected to undergo development by 2050, according to CBP projections. Year 2050 forecast data were provided by NHD catchment for the Current Zoning (cz2) baseline scenario. Data were provided as simplified table showing just the COMID and mean amount of forecasted development (acres) across 101 simulations for the scenario. Acres of forecasted development were used along with catchment (COMID) area to calculate percent of land projected to undergo future development.
Peter Claggett, USGS Chesapeake Bay Program
HousingUnitDensWs
Housing Unit Density Ws
Double
8
0
0
Mean housing unit density (housing units/square km) within watershed derived from US TIGER Census data
Derived by join field with StreamCat Housing Unit Density Ws with this layers COMID. See StreamCat methodology for more details https://www.epa.gov/national-aquatic-resource-surveys/streamcat
STREAMCAT
MineDensityWs
Mine Density Ws
Double
8
0
0
Density of georeferenced locations (points) of mines and mineral plants in the USA that were considered active in 2003 in the upstream watershed. (sites/km2)
Derived by join field with StreamCat Mine Density Ws with this layers COMID. See StreamCat methodology for more details https://www.epa.gov/national-aquatic-resource-surveys/streamcat
STREAMCAT
OutletAqCondnScore
Outlet Aquatic Condition Score
Double
8
0
0
EPA Office of Research and Development, StreamCat-based model of NRSA biological condition, 2016
Derived by join field with StreamCat Outlet Aquatic Condition Index Score Ws with this layers COMID. See StreamCat methodology for more details https://www.epa.gov/national-aquatic-resource-surveys/streamcat
Chesapeake Bay Program
PctAgOnHydricSoilWs
Pct Agriculture On Hydric Soil Ws
Double
8
0
0
Percentage of land managed for agriculture that has hydric soils within each subwatershed (12-digit HUC) for 2006-2010. This includes all land dedicated to the production of crops, but excludes land managed for pasture.
Data transformed from HUC-12 to NHDPlusV2 COMID using a translation table to attribute catchments within a HUC-12 the percent Agriculture on Hydric Soil. Python scripting was then used to trace upstream catchments to determine the value for the entire contributing watershed.
EPA EnviroAtlas
PctForestRZWs
Pct Forest RZ Ws
Double
8
0
0
Percent Forest in riparian zone within watershed. Cheseapeake Bay Program LULC 10m grids; data provided by Peter Claggett, USGS Chesapeake Bay Program. Applied 100-m riparian buffer. Calculated statistics by catchment and integrated across entire upstream riparian area.
Derived through the use of zonal statistics using the Chesapeake Bay Program LULC and a 100-m stream buffer layer. A python script was then applied to the resulting information to determine percent forest for the contributing watersheds total riparian zone. (total Forest Area RZ Watershed/ total Area RZ Watershed)
CBP
PctForestWs
Pct Forest Ws
Double
8
0
0
Percent Forest in watershed. Used data provided by Peter Claggett, USGS Chesapeake Bay Program. CBP high-resolution land use/land cover data, 2013. Calculated zonal stats.
Derived through the use of zonal statistics using the Chesapeake Bay Program LULC and the NHDPlus v2 catchments layer (zone). A python script was then applied to the resulting information to determine percent forest for the contributing upstream catchments. (total Forest Area Watershed/ total Area Watershed)
CBP
PctImpRZWs
Pct Impervious RZ Ws
Double
8
0
0
Percent impervious in riparian zone within watershed. Used data provided by Peter Claggett, USGS Chesapeake Bay Program. CBP high-resolution land use/land cover data, 2013. Calculated zonal stats.
Derived through the use of zonal statistics using the Chesapeake Bay Program LULC and a 100-m stream buffer layer. A python script was then applied to the resulting information to determine percent Impervious for the contributing watersheds riparian zone. (total Impervious Area RZ Watershed/ total Area RZ Watershed)
CBP
PctImpWs
Pct Impervious Ws
Double
8
0
0
Percent Impervious cover in watershed. Used data provided by Peter Claggett, USGS Chesapeake Bay Program. CBP high-resolution land use/land cover data, 2013. Calculated zonal stats.
Derived through the use of zonal statistics using the Chesapeake Bay Program LULC and a 100-m stream buffer layer. A python script was then applied to the resulting information to determine percent Impervious for the contributing watershed. (total Impervious Area Watershed/ total Area Watershed)
CBP
PctNatlConnectivity
Pct Natural Connectivity
Double
8
0
0
Nature’s Network Conservation Design depicts an interconnected network of lands and waters that, if protected, will support a diversity of fish, wildlife, and natural resources that the people of the Northeast and Mid-Atlantic region depend upon. Includes Core Habitat for Imperiled Species, Terrestrial Core-Connector Network, Grassland Bird Core Areas, Lotic Core Areas, and Lentic Core Areas.
Derived zonal statistics using a raster of interconnected network of lands from Nature's Network and Chesapeake Bay NHDPlus V2 catchments to determine natural connectivity for the catchment. Python scripting was then used to trace upstream catchments to determine the value for the entire contributing watershed.
Natures Network
PctNaturalLandWs
Pct Natural Land Ws
Double
8
0
0
Percent Forest + Percent Wetland = Percent Natural land in watershed. From Chesapeake Bay Program High Resolution Land Use / Land Cover data, 2013. Cheseapeake Bay Program LULC 10m grids (combining WLF, WLO, WLT, and FOR). Data provided by Peter Claggett, USGS Chesapeake Bay Program. Calculated zonal statistics by catchment and integrated across the entire upstream watershed.
Derived by combing the Percent Forest and the Percent Wetland from the CPB hi-res LULC. See PctForestWs or PctWetlandWs to see individual layer development.
CBP
PctProtLandsWs
Pct Protected Lands Ws
Double
8
0
0
Percent of catchment land protected. Protected Lands data provided December 2018 by Renee Thompson, USGS Chesapeake Bay Program. Includes compilatation of protected lands data from: US Geological Survey, Gap Analysis Program (GAP), May 2016, Protected Areas Database of the United States (PADUS), version 1.4 Combined Feature Class (Fee and Easement); Maryland Department of Natural Resources; Maryland Department of Planning; Delaware Department of Natural Resources and Environmental Control (Division of Fish and Wildlife); Freshwater Institute (WV Protected Lands); PA Bureau of Farmland Preservation; PA Department of Conservation & Natural Resources; and VA Department of Conservation and Recreation.
Derived from spatial analyst tools (Tabulate Area) using the CBP provided 10-m raster of protected lands with the NHDPlus V2 catchments to determine percent protected land for each catchment. Python scripting was then used to trace upstream catchments to determine the value for the entire contributing watershed. (Protected Lands Area Watershed / Area Watershed)
CBP
PctVulnGeoWs
Pct Vulnerable Geology Ws
Double
8
0
0
Percent Vulnerable Geology in watershed. Geology makes groundwater (and therefore streams) in some areas especially vulnerable to high nitrogen inputs. These include carbonate and coarse coastal plain geology. Data provided by Emily Trentacoste, EPA Chesapeake Bay Program. Geology shapefile from USGS called “Gen_Lithology”with GENGEOL attribute; values of “carbonate” and “coarse coastal plain” are considered the vulnerable areas. 2018
Derived by converting the Vulnerable Geology layer to a raster. Spatial analyst tools (Tabulate Area) using the resulting raster of vulnerable geology with the NHDPlus V2 catchments to determine percent vulnerable geology for each catchment. Python scripting was then used to trace upstream catchments to determine the value for the entire contributing watershed. (Vulnerable Geology Area Watershed / Area Watershed)
CBP
PctWetlandsWs
Pct Wetlands Ws
Double
8
0
0
Percent wetland in watershed. Used data provided by Peter Claggett, USGS Chesapeake Bay Program. CBP high-resolution land use/land cover data, 2013. Calculated zonal stats.
Derived through the use of zonal statistics using the Chesapeake Bay Program LULC raster and the NHDPlus v2 catchments layer (zone). A python script was then applied to the resulting information to determine percent wetland for the contributing upstream catchments. (total wetland Area Watershed/ total Area Watershed)
CBP
PopDensityWs
Population Density Ws
Double
8
0
0
Mean population density (people/square km) within watershed. Mean of all popden2010 values within the upstream watershed (Ws). Raster of population density derived from an ESRI shapefile of block group-level 2010 US Census data. Density was calculated as block group population / block group area. This shapefile was then converted to 90m x 90m resolution raster. 2014
Derived by join field with StreamCat Population Density Ws with this layers COMID. See StreamCat methodology for more details https://www.epa.gov/national-aquatic-resource-surveys/streamcat
StreamCat
RoadStreamXingDens
Road Stream Xing Density
Double
8
0
0
Density of roads-stream intersections (2010 Census Tiger Lines-NHD stream lines) within watershed (crossings/square km). Sum of all rdstrcrs values within the upstream watershed (Ws) divided by the area of the Ws. A binary raster of road and stream intersections, where 1 = intersection and 0 = no intersection. This raster was provided by James Falcone of the USGS.
Derived by join field with StreamCat Road Stream Crossing Density Ws with this layers COMID. See StreamCat methodology for more details https://www.epa.gov/national-aquatic-resource-surveys/streamcat
StreamCat
SPARROWTN
SPARROW Total Nitrogen
Double
8
0
0
Estimated Total Nitrogen Load from SPARROW Model (lbs/acre/yr), in Watershed
Derived by associating SPARROW model dataset with COMID unique identifier with NHDPlusV2 catchment layers clipped to the Chesapeake Bay Watershed. A python script was then applied to the resulting information to determine total load for the contributing watersheds. Data was then normalized to produce a range of values between 0-1
Chesapeake Bay Program
SPARROWTP
SPARROW Total Phosphorus
Double
8
0
0
Estimated Phosphorus Load from SPARROW Model (lbs/acre/yr), in Watershed
Derived by associating SPARROW model dataset with COMID unique identifier with NHDPlusV2 catchment layers clipped to the Chesapeake Bay Watershed. A python script was then applied to the resulting information to determine total load for the contributing watersheds. Data was then normalized to produce a range of values between 0-1
Chesapeake Bay Program
WildfireRiskUrbInterface
Wildfire Risk Urban Interface
Double
8
0
0
The wildland-urban interface (WUI) is the area where houses meet or intermingle with undeveloped wildland vegetation, making the WUI a focal area for human-environment conflicts such as wildland fires, habitat fragmentation, invasive species, and biodiversity decline. WUI 2010 data were used, including interface and intermix categories. Wildland Urban Interface data from Univ. of Wisconsin - Madison SILVIS lab, http://silvis.forest.wisc.edu/data/wui-change/ Data developers integrated U.S. Census and USGS National Land Cover Data to map the Federal Register definition of WUI (Federal Register 66:751, 2001) for the conterminous United States from 1990-2010. Reference: Radeloff, Volker C.; Helmers, David P.; Kramer, H. Anu; Mockrin, Miranda H.; Alexandre, Patricia M.; Bar Massada, Avi; Butsic, Van; Hawbaker, Todd J.; Martinuzzi, Sebastián; Syphard, Alexandra D.; Stewart, Susan I. 2017. The 1990-2010 wildland-urban interface of the conterminous United States - geospatial data. 2nd Edition. Fort Collins, CO: Forest Service Research Data Archive. https://doi.org/10.2737/RDS-2015-0012-2. Credit to the USDA Forest Service Northern Research Station.
Derived through the use of zonal statistics using the wildland-urban interface (WUI) raster and the NHDPlus v2 catchments layer (zone). A python script was then applied to the resulting information to determine percent forest for the contributing upstream catchments.
University of Wisconsin - Madison SILVIS lab. Wildland Urban Interface, 2010 data, published 2017
Health_Index
Health Index
Double
8
0
0
Modified PHWA Health Index.
For the Chesapeake Healthy Watersheds Assessment, candidate metrics in each of the six categories describing ecological attributes of watershed health condition were considered and evaluated as potential indicators of watershed health. Input from CBP partners, HWGIT members, and state data contacts was gathered to inform the process of proposing and selecting candidate metrics. Candidates included the original suite of PHWA metrics, calculated at the catchment rather than HUC-12 scale, along with Chesapeake Bay Watershed-specific renditions of those metrics, based upon regional rather than national data sets, when available. In addition, new metrics were proposed and considered, including those based on additional demographic, geomorphic, habitat, and biological data, as well as nutrient load data from SPARROW and the Chesapeake Bay Watershed Model.
Tetra Tech
MngdTurfHCZWs
Managed Turf HCZ Ws
Double
8
0
0
Percent Managed Vegetation in hydrologically connected zone in watershed,
Cheseapeake Bay Program LULC 10m grids; data provided by Peter Claggett, USGS Chesapeake Bay Program. Applied HCZ mask provided by U.S. EPA; calculated statistics by catchment and integrated across entire upstream riparian area.
Derived through the use of zonal statistics using the Chesapeake Bay Program LULC and a hydrologically active zone buffer layer. A python script was then applied to the resulting information to determine percent Impervious for the contributing watershed. (total Impervious Area Watershed/ total Area Watershed)
CBP
PctForestLoss
Pct Forest Loss
Double
8
0
0
Percent of forest cover loss relative to pre-development forest cover.
Source data were from the Landscape Fire and Resource Management Planning Tools (LANDFIRE) program (http://www.landfire.gov/viewer/). LANDFIRE classifies vegetative cover across the US at 30-meter resolution. Used LANDFIRE Environment Site Potential (ESP) and Existing Vegetation Type (EVT) to get delta (change in forest cover) and then calculated zonal stats for NHDPlus v2.1 catchments. The resulting zonal stats was then put into python code to get the upstream watershed forest change.
LANDFIRE
PctForestRemaining
Pct Forest Remaining
Double
8
0
0
Percent of forest cover remaining relative to pre-development forest cover.
Source data were from the Landscape Fire and Resource Management Planning Tools (LANDFIRE) program (http://www.landfire.gov/viewer/). LANDFIRE classifies vegetative cover across the US at 30-meter resolution.
Used LANDFIRE Environment Site Potential (ESP) and Existing Vegetation Type (EVT) to get delta (change in forest cover) and then calculated zonal stats for NHDPlus v2.1 catchments. The resulting zonal stats was then put into python code to get the upstream watershed forest change.
LANDFIRE
PctWetlandLoss
Pct Wetland Loss
Double
8
0
0
Percent of wetland cover loss relative to pre-development forest cover.
Source data were from the Landscape Fire and Resource Management Planning Tools (LANDFIRE) program (http://www.landfire.gov/viewer/). LANDFIRE classifies vegetative cover across the US at 30-meter resolution. Used LANDFIRE Environment Site Potential (ESP) and Existing Vegetation Type (EVT) to get delta (change in forest cover) and then calculated zonal stats for NHDPlus v2.1 catchments. The resulting zonal stats was then put into python code to get the upstream watershed wetland change.
LANDFIRE
PctWetlandRemaining
Pct Wetland Remaining
Double
8
0
0
Percent of wetland cover remaining relative to pre-development forest cover.
Source data were from the Landscape Fire and Resource Management Planning Tools (LANDFIRE) program (http://www.landfire.gov/viewer/). LANDFIRE classifies vegetative cover across the US at 30-meter resolution. Used LANDFIRE Environment Site Potential (ESP) and Existing Vegetation Type (EVT) to get delta (change in wetland cover) and then calculated zonal stats for NHDPlus v2.1 catchments. The resulting zonal stats was then put into python code to get the upstream watershed wetland change.
LANDFIRE
DomesticWaterUse
Domestic Water Use
Double
8
0
0
Daily domestic water use in the HUC12 (million gallons per day). Domestic water use includes indoor and outdoor household uses, such as drinking, bathing, cleaning, landscaping, and pools. Domestic water can include surface or groundwater that is self-supplied by households or publicly-supplied. EPA EnviroAtlas "Domestic Water Demand by 12-Digit HUC for the Conterminous United States" dataset. December 15, 2015 version.Water used in a HUC12 may originate from within or outside the HUC12. Calculated by downscaling county water use estimates for 2005 reported by US Geological Survey ("Estimated Use of Water in the United States County-Level Data for 2005") using the 2006 National Land Cover Database (2006 NLCD) Land Cover dataset and 2010 US Census population estimates from the US Census Bureau. This indicator was calculated for EPA EnviroAtlas. Additional information on source data and calculation methods can be found at: https://edg.epa.gov/metadata/catalog/search/resource/details.page?uuid=%7BC6DBEBAB-03EF-43C8-8DCA-8D2845E06A96%7D
Derived by associating water use data by HUC12 ID. Crosswalk needed to be done from HUC12ID to NHDv1 to NHDPlusV2. This final result could then be associated with the catchment layer based on COMID.
USGS
IndustrialWaterUse
Industrial Water Use
Double
8
0
0
Daily industrial water use in the HUC12 (million gallons per day). Industrial water use includes water used for chemical, food, paper, wood, and metal production. Only includes self-supplied surface water or groundwater by private wells or reservoirs. Industrial water supplied by public water utilities is not counted. EPA EnviroAtlas "Industrial Water Use by 12-Digit HUC for the Conterminous United States" dataset. May 7, 2015 version.Water used in a HUC12 may originate from within or outside the HUC12. Calculated by downscaling county water use estimates for 2005 reported by US Geological Survey ("Estimated Use of Water in the United States County-Level Data for 2005") using a geospatial dataset on the location of industrial facilities as of 2009/10. Water use by industrial facilities in counties that were reported to have zero industrial water use in the USGS dataset was estimated from values for nearby facilities. This indicator was calculated for EPA EnviroAtlas. Additional information on source data and calculation methods can be found at: https://edg.epa.gov/metadata/catalog/search/resource/details.page?uuid=%7B4E58C04B-8A17-4B07-9EE4-1D9365D5B0D9%7D
Derived by associating water use data by HUC12 ID. Crosswalk needed to be done from HUC12ID to NHDv1 to NHDPlusV2. This final result could then be associated with the catchment layer based on COMID.
USGS
HUC12_ID
HUC12 ID
String
12
0
0
Hydrologic Unit Code(HUC), 12 digit unique identifier
Derived using HUC12 to COMID (NHDPlusV2) crosswalk.
USGS
HUC12_Acres
HUC12 Acres
Double
8
0
0
Hydrologic Unit Code(HUC), 12 digit area in acres
Derived using HUC12 to COMID (NHDPlusV2) crosswalk.
USGS
HUC12_DS
HUC12 Downstream
String
12
0
0
Hydrologic Unit Code(HUC), 12 digit downstream HUC12
Derived using HUC12 to COMID (NHDPlusV2) crosswalk.
USGS
HUC12_Name
HUC12 Name
String
80
0
0
Hydrologic Unit Code(HUC), 12 digit name
Derived using HUC12 to COMID (NHDPlusV2) crosswalk.
USGS
HUC12_Type
HUC12 Type
String
1
0
0
Hydrologic Unit Code(HUC), 12 digit type
Derived using HUC12 to COMID (NHDPlusV2) crosswalk.
USGS
Headwater
Headwater
String
255
0
0
Identifies the unique catchment as a headwater of a stream system.
Derived using NHD attributes layer for headwater streams/catchments association.
Tetra Tech
RoadDensityRZWs
Road Density RZ Ws
Double
8
0
0
Density of roads (2010 Census Tiger Lines) within watershed and within a 100-m buffer of NHD stream lines (km/square km)
Derived by associating NHDPlusV2 catchment layer clipped to the Chesapeake Bay watershed with the StreamCat dataset using COMID as the unique identifier. See StreamCat methodology for more details https://www.epa.gov/national-aquatic-resource-surveys/streamcat
StreamCat
HabConditionIndexLC
Habitat Condition Index Local Catchment
Double
8
0
0
Local catchment Habitat Condition Index (HCI) score. From National Fish Habitat Partnership, national assessment. Mean Habitat Condition Index (HCI) score for the catchment from the National Fish Habitat Partnership (NFHP) 2015 National Assessment. Scores range from 1 (high likelihood of aquatic habitat degradation) to 5 (low likelihood of aquatic habitat degradation) based on land use, population density, roads, dams, mines, and point-source pollution sites. Source data were NFHP 2015 National Assessment Local Catchment HCI scores. See http://ecosystems.usgs.gov/fishhabitat/nfhap_download.jsp and http://assessment.fishhabitat.org/ for more information on the NFHP National Assessment.
Derived by associating the shapefile of habitat condition with the NHDPlus V2 catchment layer by COMID.
USGS
HabConditionIndexNC
Habitat Condition Index Near Catchment
Double
8
0
0
Network catchment Habitat Condition Index (HCI) score. From National Fish Habitat Partnership, national assessment. Mean Habitat Condition Index (HCI) score for the network catchment from the National Fish Habitat Partnership (NFHP) 2015 National Assessment. Scores range from 1 (high likelihood of aquatic habitat degradation) to 5 (low likelihood of aquatic habitat degradation) based on land use, population density, roads, dams, mines, and point-source pollution sites. Source data were NFHP 2015 National Assessment Local Catchment HCI scores. See http://ecosystems.usgs.gov/fishhabitat/nfhap_download.jsp and http://assessment.fishhabitat.org/ for more information on the NFHP National Assessment.
Derived by associating the shapefile of habitat condition with the NHDPlus V2 catchment layer by COMID.
USGS
CatchmentArea_sqkm
Area sqkm
Double
8
0
0
NHD Plus version 2 derived watershed catchments area (sqkm) from original dataset.
Horizon Systems
WaterUseSubindex
Water Use Subindex
Double
8
0
0
Water Use sub-index scores for catchments in state-identified healthy watersheds
Derived by following the prescribed PHWA framework for the Water Use subindex through R-scripting then an association of the result based on COMID
PHWA
LandUseChangeSubindex
Land Use Change Subindex
Double
8
0
0
Land Use Change sub-index scores for catchments in state-identified healthy watersheds
Derived by following the prescribed PHWA framework for the LandUse Change subindex through R-scripting then an association of the result based on COMID
PHWA
ClimateChangeSubindex
Climate Change Subindex
Double
8
0
0
Climate Change sub-index score for catchments in state-identified healthy watersheds. Ongoing CBP work to develop indicators related to climate change trends and impacts may provide new information at a scale applicable to assessing the vulnerability of healthy watersheds.
Derived by following the prescribed PHWA framework for the Climate Change subindex through R-scripting then an association of the result based on COMID
PHWA
WaterQualitySubindex
Water Quality Subindex
Double
8
0
0
Aquatic ecosystems are substantially affected by the quality of their water, but also by the chemical and physical characteristics of the air, surrounding watershed soils and sediment transported through the aquatic system. EPA and states have established water quality criteria for freshwater ecosystems that address important ecological constituents. Chemical and physical constituents include:
concentrations of organic and inorganic constituents, such as nutrients, trace metals and dissolved organic matter;
additional chemical parameters indicative of habitat suitability, such as pH and dissolved oxygen; and
physical parameters, including water temperature and turbidity.
Many of these parameters are dynamic and related to natural watershed processes. For example, dissolved oxygen fluctuations in streams are related to nutrient cycling, biotic activity, stream flow and temperature.
Derived by following the prescribed PHWA framework for the Water Quality subindex through R-scripting then an association of the result based on COMID
USEPA
GeomorphologySubindex
Geomorphology Subindex
Double
8
0
0
Watershed inputs (water, sediment and organic matter) and valley characteristics (valley slope and width, bedrock and surficial geology, soils and vegetation) determine a river channel’s form (pattern, profile and dimension). Although watershed inputs and channel form vary over time, they are balanced in natural systems. This natural balance is termed “dynamic equilibrium” and refers to sediment size and volume being in balance with stream slope and discharge.
Any time one of these variables changes, the other variables will respond to bring the stream back to a dynamic equilibrium. Disturbances such as floods or forest fires are natural, episodic events that cause a stream to become unbalanced. After such disturbances, the stream will “seek” equilibrium conditions through adjustment of the other components until the stream is once again in a form that allows it to efficiently perform its functions of water and sediment discharge.
These periodic disturbances, of natural intensity and frequency, can increase aquatic biodiversity by creating opportunities for some species and scaling back the prevalence of others. When disturbances are of extreme intensity or frequency, as many human disturbances are, a stream channel will undergo adjustment to a new form. This can result in habitat degradation and threats to public safety and infrastructure.
Derived by following the prescribed PHWA framework for the Geomorphology subindex through R-scripting then an association of the result based on COMID
USEPA
HabitatSubindex
Habitat Subindex
Double
8
0
0
Freshwater habitats are comprised of flowing (i.e., streams and rivers) and standing (i.e., lakes, ponds, and wetlands) waters. Habitat extent and quality are directly related to landscape condition and hydrologic and geomorphic processes. Habitat quality is also affected by the physical and chemical characteristics of the water (e.g., water temperature). The number and distribution of different habitat types and their connectivity influence species population health.
Derived by following the prescribed PHWA framework for the Habitat subindex through R-scripting then an association of the result based on COMID
USEPA
LandCondSubindex
Landscape Condition Subindex
Double
8
0
0
Landscape condition assessments examine the condition and configuration of natural land cover in the landscape. Natural vegetative cover stabilizes soil, regulates watershed hydrology and provides habitat to terrestrial and riparian species. The type, quantity, and structure of the natural vegetation within a watershed have important influences on aquatic habitats. Natural land cover provides connectivity among riparian habitats and between terrestrial and aquatic ecosystems.
Many aquatic organisms depend on being able to move through connected systems to habitats in response to variable environmental conditions. Forested riparian zones are often some of the best remaining corridors for connecting habitat patches on the landscape. Vegetated landscapes cycle nutrients, retain sediments, and regulate surface and ground water hydrology. Natural disturbances on the landscape, such as fire, help to regulate nutrient and organic matter input to aquatic ecosystems. Derived by following the prescribed PHWA framework for the Landscape Condition subindex through R-scripting then an association of the result based on COMID
USEPA
BioCondSubindex
Biological Condition Subindex
Double
8
0
0
Freshwater aquatic biodiversity refers to the richness of native species (e.g., fish, invertebrates and plants), genetic variety, and multiple habitats and ecosystems types (e.g., lakes, ponds, and reservoirs, rivers and streams, groundwater and wetlands). The biological condition of an aquatic ecosystem is often thought of as the ultimate indicator of watershed health, as aquatic organisms and communities reflect the cumulative conditions of all other watershed components.
Biological condition is measured in a variety of ways. For example, multimetric indices measure the presence, numbers and condition of aquatic organisms and communities in an aquatic ecosystem. They are intended to represent the biological condition of an aquatic ecosystem relative to some regionally-defined reference condition. RIVPACS (River Invertebrate Prediction and Classification System) models quantify biological condition by comparing the observed (O) taxa at a site to expected (E) taxa in the absence of human-caused stress. The O/E ratio is the index of biological integrity and measures loss of native taxa or biodiversity. Biodiversity is also measured by presence of rare, threatened and endangered (RTE) species. State natural heritage programs have inventories of aquatic RTE.
Derived by following the prescribed PHWA framework for the Biological Condition subindex through R-scripting then an association of the result based on COMID
USEPA
HydrologySubindex
Hydrology Subindex
Double
8
0
0
Watershed hydrology is driven by climatic processes; surface and subsurface characteristics such as topography, vegetation, and geology and human activities such as water and land use. Aquatic ecosystems are dependent on surface and/or ground water hydrology. For example, groundwater-dependent ecosystems rely on water that infiltrates to the subsurface discharging to nearby streams or recharging to an aquifer and then discharging to springs, seeps, wetlands, streams, and lakes.
Hydrologic regimes (flows in rivers and water levels in lakes and wetlands) create habitat and are important to aquatic species life histories (e.g., providing cues for spawning and migration during discrete times of the year). Natural flow regimes are composed of seasonally varying environmental flow components, including high flows, base flows, pulses and floods that can be characterized in terms of their magnitude, frequency, duration, timing and rate of change. Natural lake levels will vary depending on precipitation, evaporation and/or ground and surface water hydrology. Derived by following the prescribed PHWA framework for the Hydrology subindex through R-scripting then an association of the result based on COMID
USEPA
WildfireSubindex
Wildfire Subindex
Double
8
0
0
Wildfire Risk sub-index scores for catchments in state-identified healthy watersheds
Derived by following the prescribed PHWA framework for the Wildfire subindex through R-scripting then an association of the result based on COMID
PHWA
HabConditionIndexCUM
Habitat Condition Index Cumulative
Double
8
0
0
Freshwater habitats are comprised of flowing (i.e., streams and rivers) and standing (i.e., lakes, ponds, and wetlands) waters. Habitat extent and quality are directly related to landscape condition and hydrologic and geomorphic processes. Habitat quality is also affected by the physical and chemical characteristics of the water (e.g., water temperature). The number and distribution of different habitat types and their connectivity influence species population health. Derived by associating the shapefile of habitat condition with the NHDPlus V2 catchment layer by COMID.
USEPA
Pct303dImpairedCat
Pct 303d Impaired Cat
Double
8
0
0
Percent of stream within the local catchment that are identified as 303d Impaired
Derived by clipping the 303d impaired stream lines layer with the NHDPlusV2 catchments. NHDPlusV2 flowline information was also obtained to determine the total number streams miles present in each catchment. These two pieces of information were used to determine the percentage of stream within a catchment designated at 303d impaired.
Tetra Tech
StateFIPS
StateFIPS
Integer
4
0
0
The Federal Information Processing Standard is a code which uniquely identified states and territories of the United States, certain U.S. possessions, and certain freely associated states.
Derived from spatial association with existing FIPS shapefile.
National Institute of Standards and Technology
CountyFIPS
CountyFIPS
Integer
4
0
0
The Federal Information Processing Standard is a code which uniquely identified counties and county equivalents in the United States, certain U.S. possessions, and certain freely associated states.
Derived from spatial association with existing FIPS shapefile.
National Institute of Standards and Technology
FIPS
FIPS
Integer
4
0
0
The Federal Information Processing Standard is a code which uniquely identified states, counties and county equivalents in the United States, certain U.S. possessions, and certain freely associated states.
Derived from spatial association with existing FIPS shapefile.
National Institute of Standards and Technology
Brook_Trout_Occur_6CTempChang
Brook Trout Occurance @ 6C
Double
8
0
0
Brook Trout probability of occurrence was developed by the Conte Lab for the Northeast and Mid-Atlantic region from Virginia to Maine. The dataset provides predictions under current environmental conditions and for future increases in stream temperature. Data are available for four scenarios: current condition, plus 2 degrees C, plus 4 degrees C, and plus 6 degrees C. Data and information are available through the North Atlantic Landscape Conservation Cooperative at:
https://nalcc.databasin.org/datasets/7f3aaf6f9c59423391eb5a1526f28beb For further information see http://conte-ecology.github.io/Northeast_Bkt_Occupancy/ Reference: Benjamin Letcher (Principal Investigator), North Atlantic Landscape Conservation Cooperative (administrator), 2017-06-22 (creation), 2017-10-20 (lastUpdate), 2017-05 (Publication), Brook Trout Probability of Occurrence, Northeast U.S. https://www.sciencebase.gov/arcgis/rest/services/Catalog/594be372e4b062508e385070/MapServer/ Derived by using ArcGIS tools to associate the Brook trout occurrence data to the NHDPlus V2 catchments by COMID. Model data was only used on the catchment scale. The resulting values were then normalized for a range of 0-1.
Conte Lab for the Northeast and Mid-Atlantic region from Virginia to Maine
Brook_Trout_Occur_Current
Brook Trout Occurance Current
Double
8
0
0
Brook Trout probability of occurrence was developed by the Conte Lab for the Northeast and Mid-Atlantic region from Virginia to Maine. The dataset provides predictions under current environmental conditions and for future increases in stream temperature. Data are available for four scenarios: current condition, plus 2 degrees C, plus 4 degrees C, and plus 6 degrees C. Data and information are available through the North Atlantic Landscape Conservation Cooperative at:
https://nalcc.databasin.org/datasets/7f3aaf6f9c59423391eb5a1526f28beb For further information see http://conte-ecology.github.io/Northeast_Bkt_Occupancy/ Reference: Benjamin Letcher (Principal Investigator), North Atlantic Landscape Conservation Cooperative (administrator), 2017-06-22 (creation), 2017-10-20 (lastUpdate), 2017-05 (Publication), Brook Trout Probability of Occurrence, Northeast U.S. https://www.sciencebase.gov/arcgis/rest/services/Catalog/594be372e4b062508e385070/MapServer/
Derived by using ArcGIS tools to associate the Brook trout occurrence data to the NHDPlus V2 catchments by COMID. Model data was only used on the catchment scale. The resulting values were then normalized for a range of 0-1.
Conte Lab for the Northeast and Mid-Atlantic region from Virginia to Maine
Shape_Length
Shape_Length
Double
8
0
0
Length of feature in internal units.
Esri
Positive real numbers that are automatically generated.
Shape_Area
Shape_Area
Double
8
0
0
Area of feature in internal units squared.
Esri
Positive real numbers that are automatically generated.
Simple
FALSE
0
TRUE
FALSE