20201001
09011000
1.0
FALSE
CHWA_BaltCarroll_FutureDevelopment
1350615.000000
1785075.000000
1687605.000000
2405235.000000
1
002
0.000
Projected
GCS_WGS_1984
Linear Unit: Meter (1.000000)
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WGS_1984_Web_Mercator_Auxiliary_Sphere
20210916
16365000
20210916
16365000
150000000
5000
dataset
Christopher Wharton
Tetra Tech, Inc.
GIS Analyst
20210916
Shapefile
1.0
0.000
Esri ArcGIS 12.7.0.26828
CHWA_BaltCarroll_FutureDevelopment
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>
ChesapeakeBayHealthyVulnerableWatershedWaterStreamRiverMarylandVirginiaWest VirginiaDelawareNew YorkPennsylvaniaEstuaryLanduseCoverDevelopmentForestImperviousLossTroutGeologyIndexHydrology
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
6.18.3(9.3.1.2)
CHWA_BaltCarroll_FutureDevelopment
Feature Class
0
Tetra Tech
Tetra Tech
FID
FID
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
10
10
0
NHD Plus Version 2 Catchment Unique Identifier
Horizon Systems
HWUniqID
HWUniqID
String
254
0
0
HWFlag
HWFlag
Integer
10
10
0
HWGroup
HWGroup
String
254
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
254
0
0
US State Abbreviation Identifier
Derived by spatial join with ESRI state baselayer
Tetra Tech
County
County
String
254
0
0
US County Name
Derived from spatial join with ESRI counties base layer
Tetra Tech
PctNatural
PctNatural
Double
19
0
0
PctForestR
PctForestR
Double
19
0
0
PopDensity
PopDensity
Double
19
0
0
HousingUni
HousingUni
Double
19
0
0
MineDensit
MineDensit
Double
19
0
0
AvgPctFore
AvgPctFore
Double
19
0
0
MngdTurfHC
MngdTurfHC
Double
19
0
0
LandCondSu
LandCondSu
Double
19
0
0
DamDensity
DamDensity
Double
19
0
0
RoadDensit
RoadDensit
Double
19
0
0
AgWaterUse
AgWaterUse
Double
19
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
PctVulnGeo
PctVulnGeo
Double
19
0
0
Geomorphol
Geomorphol
Double
19
0
0
PctAgOnHyd
PctAgOnHyd
Double
19
0
0
PctForestW
PctForestW
Double
19
0
0
PctFores_1
PctFores_1
Double
19
0
0
PctWetland
PctWetland
Double
19
0
0
CBPModAGN
CBPModAGN
Double
19
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
RoadStream
RoadStream
Double
19
0
0
PctWetla_1
PctWetla_1
Double
19
0
0
HydrologyS
HydrologyS
Double
19
0
0
PctNatlCon
PctNatlCon
Double
19
0
0
HabConditi
HabConditi
Double
19
0
0
HabCondi_1
HabCondi_1
Double
19
0
0
HabCondi_2
HabCondi_2
Double
19
0
0
HabitatSub
HabitatSub
Double
19
0
0
OutletAqCo
OutletAqCo
Double
19
0
0
BioCondSub
BioCondSub
Double
19
0
0
Pct303dImp
Pct303dImp
Double
19
0
0
CBPModAGP
CBPModAGP
Double
19
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
CBPModAGS
Double
19
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
CBPModCSON
Double
19
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
CBPModCSOP
Double
19
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
CBPModCSOS
Double
19
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
CBPModDEVN
Double
19
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
CBPModDEVP
Double
19
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
CBPModDEVS
Double
19
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
CBPModSEPN
Double
19
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
CBPModSEPP
Integer
10
10
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
CBPModSEPS
Integer
10
10
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
CBPModWWN
Double
19
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
CBPModWWP
Double
19
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
CBPModWWS
Double
19
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
FutureDev
FutureDev
Double
19
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
PctImpRZWs
PctImpRZWs
Double
19
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
PctImpWs
Double
19
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
WaterQuali
WaterQuali
Double
19
0
0
Health_Ind
Health_Ind
Double
19
0
0
SPARROWTN
SPARROWTN
Double
19
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
PctForestL
PctForestL
Double
19
0
0
PctProtLan
PctProtLan
Double
19
0
0
LandUseCha
LandUseCha
Double
19
0
0
ClimateStr
ClimateStr
Double
19
0
0
Brook_Trou
Brook_Trou
Double
19
0
0
Brook_Tr_1
Brook_Tr_1
Double
19
0
0
ClimateCha
ClimateCha
Double
19
0
0
SPARROWTP
SPARROWTP
Double
19
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
DomesticWa
DomesticWa
Double
19
0
0
Industrial
Industrial
Double
19
0
0
WaterUseSu
WaterUseSu
Double
19
0
0
WildfireRi
WildfireRi
Double
19
0
0
WildfireSu
WildfireSu
Double
19
0
0
PctWetla_2
PctWetla_2
Double
19
0
0
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_Acre
HUC12_Acre
Double
19
0
0
HUC12_DS
HUC12_DS
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
254
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
CatchmentA
CatchmentA
Double
19
0
0
StateFIPS
StateFIPS
Integer
10
10
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
10
10
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
10
10
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
Shape_Leng
Shape_Leng
Double
19
0
0
Shape_Area
Shape_Area
Double
19
0
0
Area of feature in internal units squared.
Esri
Positive real numbers that are automatically generated.
Simple
FALSE
0
FALSE
FALSE