20221014 06575300 1.0 FGDC CSDGM Metadata FALSE MDHWA_01_master 002 1394744.998400 1797044.998500 1837784.994600 2040974.994700 1 Projected GCS_NAD_1983_2011 Linear Unit: Meter (1.000000) NAD_1983_2011_Contiguous_USA_Albers <ProjectedCoordinateSystem xsi:type='typens:ProjectedCoordinateSystem' xmlns:xsi='http://www.w3.org/2001/XMLSchema-instance' xmlns:xs='http://www.w3.org/2001/XMLSchema' xmlns:typens='http://www.esri.com/schemas/ArcGIS/10.6'><WKT>PROJCS[&quot;NAD_1983_2011_Contiguous_USA_Albers&quot;,GEOGCS[&quot;GCS_NAD_1983_2011&quot;,DATUM[&quot;D_NAD_1983_2011&quot;,SPHEROID[&quot;GRS_1980&quot;,6378137.0,298.257222101]],PRIMEM[&quot;Greenwich&quot;,0.0],UNIT[&quot;Degree&quot;,0.0174532925199433]],PROJECTION[&quot;Albers&quot;],PARAMETER[&quot;False_Easting&quot;,0.0],PARAMETER[&quot;False_Northing&quot;,0.0],PARAMETER[&quot;Central_Meridian&quot;,-96.0],PARAMETER[&quot;Standard_Parallel_1&quot;,29.5],PARAMETER[&quot;Standard_Parallel_2&quot;,45.5],PARAMETER[&quot;Latitude_Of_Origin&quot;,23.0],UNIT[&quot;Meter&quot;,1.0],AUTHORITY[&quot;EPSG&quot;,6350]]</WKT><XOrigin>-16901100</XOrigin><YOrigin>-6972200</YOrigin><XYScale>10000</XYScale><ZOrigin>-100000</ZOrigin><ZScale>10000</ZScale><MOrigin>-100000</MOrigin><MScale>10000</MScale><XYTolerance>0.001</XYTolerance><ZTolerance>0.001</ZTolerance><MTolerance>0.001</MTolerance><HighPrecision>true</HighPrecision><WKID>102965</WKID><LatestWKID>6350</LatestWKID></ProjectedCoordinateSystem> 20220721 09315100 20220721 09315100 150000000 5000 FGDC Version 6.2 (Build 9200) ; Esri ArcGIS 10.6.0.8321 MDHWA_01_master 2022-06-30T00:00:00 2022-06-30T00:00:00 Nancy Roth Tetra Tech Inc Owings Mills MD Nancy.Roth@TetraTech.com 21117 410-902-3162 Staff 1 -79.777694 -74.712746 40.282380 37.801933 All data were taken to most closely approximate 2017. 2017-01-01T00:00:00 2017-12-31T00:00:00 Assessing the Healthy and Vulnerability of Watersheds within Maryland data at the NHD Plus Version 2 scale. <DIV STYLE="text-align:Left;"><DIV><DIV><P><SPAN>Development of the Maryland Healthy Watersheds Assessment (MDHWA) establishes a framework of watershed health and vulnerability metrics for assessing Maryland waters and watersheds. Development of this statewide assessment was built upon the previously completed Chesapeake Bay Healthy Watersheds Assessment (CHWA, Roth et al. 2020), making use of more recent and refined regional data, and also integrating state-specific data where possible. The assessment is intended to inform watershed management decision-making to sustain the health of state-identified healthy watersheds, which have been defined in Maryland as the watersheds associated with its designated high-quality, Tier II waters. The proposed MDHWA and its suite of health and vulnerability metrics provide information related to specific threats facing Maryland. The MDHWA will increase State capacity to better understand the broad spectrum of health and vulnerability issues affecting Maryland’s streams and healthy watersheds. Data are intended to be useful to the State’s Tier II waters program, especially for assessing vulnerabilities of healthy watersheds to future degradation and helping to target and inform management efforts in these areas. </SPAN></P><P><SPAN>The MDHWA will serve as a model that can be replicated in other jurisdictions and updated in future assessments. Some of the data sets used to build the MDHWA are regional or national in scope and therefore readily available for use in other jurisdictions. Some data sources used in analyses are Maryland-specific, such as data from the Maryland Biological Stream Survey (MBSS), but will serve as examples of the types of data that may be available or could be pursued in other jurisdictions. While the MDHWA will be customized to Maryland’s specific issues, concerns, and data sources, the approach and framework developed for Maryland is intended to be customizable for other locations. </SPAN></P></DIV></DIV></DIV> Nancy Roth, Brian Pickard, Ph.D. Mark Southerland, Ph.D. Paige Hobaugh Chesapeake Bay Healthy Vulnerable Watershed Water Stream River Maryland Estuary Landuse Landcover Development Forest Impervious Hydrology File Geodatabase Feature Class dataset EPSG 8.2.2(10.2.1) 14964 Simple FALSE 14964 TRUE FALSE MDHWA_01_master Feature Class 14964 Contains all data for the MD Healthy Watersheds NHD Plus catchments. Various sources. 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. GRIDCODE GRIDCODE Integer 4 0 0 Unique, compact identifier for a catchment. NHDPlus v2 Unique, compact identifier for a catchment. FEATUREID FEATUREID Integer 4 0 0 Unique identifier of water feature. NHDPlus v2 Unique identifier of water feature. climate_stress magnitude of climate stress in 2080 Single 4 0 0 This data set, developed by the Nature’s Network Project, represents the magnitude of climate stress that may be exerted on habitats in 2080. Areas where 2080 climate conditions depart substantially from conditions where underlying ecosystem type occurs are considered to be stressed. Areas with low or zero climate stress may be candidates to function as climate refugia. Climate Stress was included as a metric characterizing the potential for climate impacts. Attribute information was derived by running zonal statistics for all MD NHD Plus catchments and the climate stress raster. Unit: Index (0-1) https://nalcc.databasin.org/datasets/d207f70858fa403397c631433c2ad57d 0 to 1 Index na Brook_Trout_Occur_current brook trout probability of occurrence Single 4 0 0 Brook Trout probability of occurrence data were developed by the USGS Conte Laboratory (2021) for the Northeast and Mid-Atlantic region from Virginia to Maine. The dataset provides model predictions of brook trout presence using brook trout data and landscape predictors. Predictions represent occurrence 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. Unit: Index (0-1) https://nalcc.databasin.org/datasets/7f3aaf6f9c59423391eb5a1526f28beb/ 0 to 1 Index na Brook_Trout_Occur_2CTempChange brook trout probability of occurrence under 2C temp change Single 4 0 0 Brook Trout probability of occurrence data were developed by the USGS Conte Laboratory (2021) for the Northeast and Mid-Atlantic region from Virginia to Maine. The dataset provides model predictions of brook trout presence using brook trout data and landscape predictors. Predictions represent occurrence 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. Unit: Index (0-1) https://nalcc.databasin.org/datasets/7f3aaf6f9c59423391eb5a1526f28beb/ 0 to 1 Index na Brook_Trout_Occur_4CTempChange brook trout probability of occurrence under 4C temp change Single 4 0 0 Brook Trout probability of occurrence data were developed by the USGS Conte Laboratory (2021) for the Northeast and Mid-Atlantic region from Virginia to Maine. The dataset provides model predictions of brook trout presence using brook trout data and landscape predictors. Predictions represent occurrence 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. Unit: Index (0-1) https://nalcc.databasin.org/datasets/7f3aaf6f9c59423391eb5a1526f28beb/ 0 to 1 Index na Brook_Trout_Occur_6CTempChange brook trout probability of occurrence under 6Ctemp change Single 4 0 0 Brook Trout probability of occurrence data were developed by the USGS Conte Laboratory (2021) for the Northeast and Mid-Atlantic region from Virginia to Maine. The dataset provides model predictions of brook trout presence using brook trout data and landscape predictors. Predictions represent occurrence 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. Unit: Index (0-1) https://nalcc.databasin.org/datasets/7f3aaf6f9c59423391eb5a1526f28beb/ 0 to 1 Index na sb_lat_erosion streambank lateral erosion rate Single 4 0 0 Data represents the streambank lateral erosion rate. FACET (USGS 2019) is a Python tool developed by USGS that uses open source modules to map the floodplain extent and derive reach-scale summaries of stream and floodplain geomorphic measurements from high-resolution digital elevation models (DEMs). Data are summarized to the NHD catchment scale. Unit: cm yr-1 https://www.sciencebase.gov/catalog/item/5f71eb0082cef8d183971684 -4.23 to 0 cm yr-1 na sb_D50 streambed d50 Single 4 0 0 Data represents the streambed d50. FACET (USGS 2019) is a Python tool developed by USGS that uses open source modules to map the floodplain extent and derive reach-scale summaries of stream and floodplain geomorphic measurements from high-resolution digital elevation models (DEMs). Data are summarized to the NHD catchment scale. Unit: mm https://www.sciencebase.gov/catalog/item/5f71eb0082cef8d183971685 0-95 mm na sb_sediment_flux streambank sediment flux Single 4 0 0 Data represents the streambank sediment flux. FACET (USGS 2019) is a Python tool developed by USGS that uses open source modules to map the floodplain extent and derive reach-scale summaries of stream and floodplain geomorphic measurements from high-resolution digital elevation models (DEMs). Data are summarized to the NHD catchment scale. Unit: kg-sed m-1 yr-1 https://www.sciencebase.gov/catalog/item/5f71eb0082cef8d183971686 -159 to 0 kg-sed m-1 yr-1 na sb_fine_sed_flux streambank fine sediment flux Single 4 0 0 Data represents the streambank fine sediment flux. FACET (USGS 2019) is a Python tool developed by USGS that uses open source modules to map the floodplain extent and derive reach-scale summaries of stream and floodplain geomorphic measurements from high-resolution digital elevation models (DEMs). Data are summarized to the NHD catchment scale. Unit: kg-finesed m-1 yr-1 https://www.sciencebase.gov/catalog/item/5f71eb0082cef8d183971686 -67 to 0 kg-finesed m-1 yr-1 na sb_fine_sed_sand percent of streambed fines and sands cover Single 4 0 0 Data represents the percent of streambed fines and sands cover. FACET (USGS 2019) is a Python tool developed by USGS that uses open source modules to map the floodplain extent and derive reach-scale summaries of stream and floodplain geomorphic measurements from high-resolution digital elevation models (DEMs). Data are summarized to the NHD catchment scale. Unit: percent https://www.sciencebase.gov/catalog/item/5f71eb0082cef8d183971686 0-75 percent na sb_x_erosion_change predicted streambank cross-sectional lateral erosion area change Single 4 0 0 Data represents the predicted streambank cross-sectional lateral erosion area change. FACET (USGS 2019) is a Python tool developed by USGS that uses open source modules to map the floodplain extent and derive reach-scale summaries of stream and floodplain geomorphic measurements from high-resolution digital elevation models (DEMs). Data are summarized to the NHD catchment scale. Unit: m2 yr-1 https://www.sciencebase.gov/catalog/item/5f71eb0082cef8d183971686 -0.168 - 0 m2 yr-1 na DamDensity_ws Dam density within the watershed Single 4 0 0 Dam density within the watershed for each NHD catchment. Data was taken directly from StreamCat. Unit: dams/km2 https://gaftp.epa.gov/epadatacommons/ORD/NHDPlusLandscapeAttributes/StreamCat/ 0-3.6 dams/km2 na MineDensity_ws mine density within the watershed Single 4 0 0 Mine density within the watershed for each NHD catchment. Data was taken directly from StreamCat. Unit: mines/km2 https://gaftp.epa.gov/epadatacommons/ORD/NHDPlusLandscapeAttributes/StreamCat/ 0-1.19 mines/km2 na RdDens_ws road density in the watershed Single 4 0 0 Road density within the watershed for each NHD catchment. Data was taken directly from StreamCat. Unit: km/km2 https://gaftp.epa.gov/epadatacommons/ORD/NHDPlusLandscapeAttributes/StreamCat/ km/km2 0-19.8 km roads/km2 na RdDensRp100_ws road density within the 100ft riparian buffer for the watershed Single 4 0 0 Road density within the 100 foot riparian buffer for the watershed for each NHD catchment. Data was taken directly from StreamCat. Unit: km/km2 https://gaftp.epa.gov/epadatacommons/ORD/NHDPlusLandscapeAttributes/StreamCat/ 0-26.09 km/km2 na HUDens_ws Housing unit density in the watershed Single 4 0 0 Housing unit density in the watershed for each NHD catchment. Data was taken directly from StreamCat. Unit: housing units/km2 https://gaftp.epa.gov/epadatacommons/ORD/NHDPlusLandscapeAttributes/StreamCat/ 0-2389 housing units/km2 na PopDens_ws population density within the watershed Single 4 0 0 Population density in the watershed for each NHD catchment. Data was taken directly from StreamCat. Unit: people/km2 https://gaftp.epa.gov/epadatacommons/ORD/NHDPlusLandscapeAttributes/StreamCat/ 0-4125 people/km2 na Maloney_FlowAlteration Maloney flow alteration score SmallInteger 2 0 0 Flow alteration score (0-12). USGS has develop a suite of flow alteration metrics for stream reaches throughout the Chesapeake Bay watershed (Maloney et al. 2021) and have demonstrated linkages between flow alteration intensity and degraded biological condition of streams. Using separate random-forest models, they developed predictions of flow status for 12 hydrologic metrics. An overall flow alteration indicator provides combined information from the individual metrics. Unit; Index (0-12) na 0-12 index na RdCrs_ws RdCrs_ws Single 4 0 0 Road crossings in the watershed for each NHD catchment. Data was taken directly from StreamCat. Unit: crossings/km2 https://gaftp.epa.gov/epadatacommons/ORD/NHDPlusLandscapeAttributes/StreamCat/ 0-556 crossings/km2 na TNC_slightly_less_resilient TNC lands identified as slightly less resilient Single 4 0 0 The Nature Conservancy (TNC) has developed extensive spatial data that provide information on climate-resilient sites to assist with conservation planning (https://maps.tnc.org/resilientland/ Anderson et al. 2016). In the face of changing climate, conservation strategies are needed that anticipate changes in conditions and identify areas that will continue to provide valuable habitat. Resilient sites are defined as areas of “land where high microclimatic diversity and low levels of human modification provide species with connected, diverse climatic conditions they will need to persist and adapt to changing regional climates.” Resilience Scores were determined by evaluating and quantifying physical characteristics that foster resilience, particularly the site’s landscape diversity and local connectedness (TNC 2021). Data provides total area (km2) of TNC lands identified as slightly less resilient within the NHD catchment. Data were derived using zonal statistics of TNC resilience layer and NHD catchments. https://maps.tnc.org/resilientland/ 0-8.05 km2 na TNC_avg_med_resilient TNC lands identified as avg/med resilient Single 4 0 0 The Nature Conservancy (TNC) has developed extensive spatial data that provide information on climate-resilient sites to assist with conservation planning (https://maps.tnc.org/resilientland/ Anderson et al. 2016). In the face of changing climate, conservation strategies are needed that anticipate changes in conditions and identify areas that will continue to provide valuable habitat. Resilient sites are defined as areas of “land where high microclimatic diversity and low levels of human modification provide species with connected, diverse climatic conditions they will need to persist and adapt to changing regional climates.” Resilience Scores were determined by evaluating and quantifying physical characteristics that foster resilience, particularly the site’s landscape diversity and local connectedness (TNC 2021). Data provides total area (km2) of TNC lands identified as average/medium resilient within the NHD catchment. Data were derived using zonal statistics of TNC resilience layer and NHD catchments. https://maps.tnc.org/resilientland/ 0-23.65 km2 na TNC_more_resilient TNC land identified as more resilient Single 4 0 0 The Nature Conservancy (TNC) has developed extensive spatial data that provide information on climate-resilient sites to assist with conservation planning (https://maps.tnc.org/resilientland/ Anderson et al. 2016). In the face of changing climate, conservation strategies are needed that anticipate changes in conditions and identify areas that will continue to provide valuable habitat. Resilient sites are defined as areas of “land where high microclimatic diversity and low levels of human modification provide species with connected, diverse climatic conditions they will need to persist and adapt to changing regional climates.” Resilience Scores were determined by evaluating and quantifying physical characteristics that foster resilience, particularly the site’s landscape diversity and local connectedness (TNC 2021). Data provides total area (km2) of TNC lands identified as more resilient within the NHD catchment. Data were derived using zonal statistics of TNC resilience layer and NHD catchments. https://maps.tnc.org/resilientland/ 0-11.05 km2 na TNC_least_resilient TNC lands identified as least resilient Single 4 0 0 The Nature Conservancy (TNC) has developed extensive spatial data that provide information on climate-resilient sites to assist with conservation planning (https://maps.tnc.org/resilientland/ Anderson et al. 2016). In the face of changing climate, conservation strategies are needed that anticipate changes in conditions and identify areas that will continue to provide valuable habitat. Resilient sites are defined as areas of “land where high microclimatic diversity and low levels of human modification provide species with connected, diverse climatic conditions they will need to persist and adapt to changing regional climates.” Resilience Scores were determined by evaluating and quantifying physical characteristics that foster resilience, particularly the site’s landscape diversity and local connectedness (TNC 2021). Data provides total area (km2) of TNC lands identified as least resilient within the NHD catchment. Data were derived using zonal statistics of TNC resilience layer and NHD catchments. https://maps.tnc.org/resilientland/ 0-7.5 km2 na TNC_less_resilient TNC lands identified as less resilient Single 4 0 0 The Nature Conservancy (TNC) has developed extensive spatial data that provide information on climate-resilient sites to assist with conservation planning (https://maps.tnc.org/resilientland/ Anderson et al. 2016). In the face of changing climate, conservation strategies are needed that anticipate changes in conditions and identify areas that will continue to provide valuable habitat. Resilient sites are defined as areas of “land where high microclimatic diversity and low levels of human modification provide species with connected, diverse climatic conditions they will need to persist and adapt to changing regional climates.” Resilience Scores were determined by evaluating and quantifying physical characteristics that foster resilience, particularly the site’s landscape diversity and local connectedness (TNC 2021). Data provides total area (km2) of TNC lands identified as less resilient within the NHD catchment. Data were derived using zonal statistics of TNC resilience layer and NHD catchments. https://maps.tnc.org/resilientland/ 0-18.91 km2 na TNC_most_resilient TNC lands identified as most resilient Single 4 0 0 The Nature Conservancy (TNC) has developed extensive spatial data that provide information on climate-resilient sites to assist with conservation planning (https://maps.tnc.org/resilientland/ Anderson et al. 2016). In the face of changing climate, conservation strategies are needed that anticipate changes in conditions and identify areas that will continue to provide valuable habitat. Resilient sites are defined as areas of “land where high microclimatic diversity and low levels of human modification provide species with connected, diverse climatic conditions they will need to persist and adapt to changing regional climates.” Resilience Scores were determined by evaluating and quantifying physical characteristics that foster resilience, particularly the site’s landscape diversity and local connectedness (TNC 2021). Data provides total area (km2) of TNC lands identified as most resilient within the NHD catchment. Data were derived using zonal statistics of TNC resilience layer and NHD catchments. https://maps.tnc.org/resilientland/ 0-4.92 km2 na Ag_H20_gallon Agricultural water use Single 4 0 0 Total H20 gallons used by agriculture based on USGS water use data. Data was provided by US EPA EnviroAtlas at the HUC12 scale. Gallons of water were spatially allocated by huc12 area and then reapportioned to NHD catchments. This methodology is consistent with population and other dasymetric style approaches. Unit: gallons https://www.epa.gov/enviroatlas/forms/enviroatlas-data-download 0-1008573 gallons na Domestic_H20_gallon Domestic water use Single 4 0 0 Total H20 gallons used for domestic consumption based on USGS water use data. Data was provided by US EPA EnviroAtlas at the HUC12 scale. Gallons of water were spatially allocated by huc12 area and then reapportioned to NHD catchments. This methodology is consistent with population and other dasymetric style approaches. Unit: gallons https://www.epa.gov/enviroatlas/forms/enviroatlas-data-download 0-13542105 gallons na Industrial_H20_gallon industrial water use Single 4 0 0 Total H20 gallons used by industrial sources based on USGS water use data. Data was provided by US EPA EnviroAtlas at the HUC12 scale. Gallons of water were spatially allocated by huc12 area and then reapportioned to NHD catchments. This methodology is consistent with population and other dasymetric style approaches. Unit: gallons https://www.epa.gov/enviroatlas/forms/enviroatlas-data-download 0-174 gallons na MD_noncoal_surface_mines Maryland non-coal surface mines SmallInteger 2 0 0 Number of Maryland non-coal surface mines from 2019. Mine locations were summarized for each NHD catchment. Unit: # of non-coal mines https://mde.maryland.gov/programs/LAND/mining/Pages/mapping.aspx 0-5 number of non-coal mines na MD_coal_mines Maryland coal surface mines SmallInteger 2 0 0 Number of Maryland coal surface mines from 2019. Mine locations were summarized for each NHD catchment. Unit: # of coal mines https://mde.maryland.gov/programs/LAND/mining/Pages/mapping.aspx 0-4 number of coal mines na MD_BioNet_Tier1 MD areas critically signficant for biodiversity Single 4 0 0 Maryland DNR’s Natural Heritage Program within the Wildlife and Heritage Service maintains data on the habitats of the state’s most rare plants and animals as well as high quality and rare natural communities and other living resources of conservation concern (DNR 2016). The Biodiversity Conservation Network (BioNet) database incorporates the following types of data: • Only known occurrences of species and habitats • Globally rare species and habitats • State rare species and habitats • Animals of Greatest Conservation Need • Watch List plants and indicators of high-quality habitats • Animal assemblages (e.g., colonial nesting waterbirds, forest interior species) • Hotspots for rare species and habitats • Intact watersheds • Wildlife corridors and concentration areas BioNet provides a ranked prioritization of areas by their significance for biodiversity conservation. Tier 1 represents areas critically significant for biodiversity conservation. This data was derived using zonal statistics of the Tier 1 raster and the NHD catchments. Unit: km2 https://geodata.md.gov/imap/rest/services/Biota/MD_BiodiversityConservationNetwork/MapServer 0-12.33 km2 na MD_BioNet_Tier2 MD areas extremely significant for biodiversity conservation Single 4 0 0 Maryland DNR’s Natural Heritage Program within the Wildlife and Heritage Service maintains data on the habitats of the state’s most rare plants and animals as well as high quality and rare natural communities and other living resources of conservation concern (DNR 2016). The Biodiversity Conservation Network (BioNet) database incorporates the following types of data: • Only known occurrences of species and habitats • Globally rare species and habitats • State rare species and habitats • Animals of Greatest Conservation Need • Watch List plants and indicators of high-quality habitats • Animal assemblages (e.g., colonial nesting waterbirds, forest interior species) • Hotspots for rare species and habitats • Intact watersheds • Wildlife corridors and concentration areas BioNet provides a ranked prioritization of areas by their significance for biodiversity conservation. Tier 2 represents areas extremely significant for biodiversity conservation. This data was derived using zonal statistics of the Tier 2 raster and the NHD catchments. Unit: km2 https://geodata.md.gov/imap/rest/services/Biota/MD_BiodiversityConservationNetwork/MapServer 0-8.09 km2 na MD_BioNet_Tier3 MD areas highly significant for biodiversity conservation Single 4 0 0 Maryland DNR’s Natural Heritage Program within the Wildlife and Heritage Service maintains data on the habitats of the state’s most rare plants and animals as well as high quality and rare natural communities and other living resources of conservation concern (DNR 2016). The Biodiversity Conservation Network (BioNet) database incorporates the following types of data: • Only known occurrences of species and habitats • Globally rare species and habitats • State rare species and habitats • Animals of Greatest Conservation Need • Watch List plants and indicators of high-quality habitats • Animal assemblages (e.g., colonial nesting waterbirds, forest interior species) • Hotspots for rare species and habitats • Intact watersheds • Wildlife corridors and concentration areas BioNet provides a ranked prioritization of areas by their significance for biodiversity conservation. Tier 3 represents areas highly significant for biodiversity conservation. This data was derived using zonal statistics of the Tier 3 raster and the NHD catchments. Unit: km2 https://geodata.md.gov/imap/rest/services/Biota/MD_BiodiversityConservationNetwork/MapServer 0-27.85 km2 na MD_BioNet_Tier4 MD areas moderately significant for biodiversity conservation Single 4 0 0 Maryland DNR’s Natural Heritage Program within the Wildlife and Heritage Service maintains data on the habitats of the state’s most rare plants and animals as well as high quality and rare natural communities and other living resources of conservation concern (DNR 2016). The Biodiversity Conservation Network (BioNet) database incorporates the following types of data: • Only known occurrences of species and habitats • Globally rare species and habitats • State rare species and habitats • Animals of Greatest Conservation Need • Watch List plants and indicators of high-quality habitats • Animal assemblages (e.g., colonial nesting waterbirds, forest interior species) • Hotspots for rare species and habitats • Intact watersheds • Wildlife corridors and concentration areas BioNet provides a ranked prioritization of areas by their significance for biodiversity conservation. Tier 4 represents areas moderately significant for biodiversity conservation. This data was derived using zonal statistics of the Tier 4 raster and the NHD catchments. Unit: km2 https://geodata.md.gov/imap/rest/services/Biota/MD_BiodiversityConservationNetwork/MapServer 0-20.7 km2 na MD_BioNet_Tier5 MD areas significant for biodiversity conservation Single 4 0 0 Maryland DNR’s Natural Heritage Program within the Wildlife and Heritage Service maintains data on the habitats of the state’s most rare plants and animals as well as high quality and rare natural communities and other living resources of conservation concern (DNR 2016). The Biodiversity Conservation Network (BioNet) database incorporates the following types of data: • Only known occurrences of species and habitats • Globally rare species and habitats • State rare species and habitats • Animals of Greatest Conservation Need • Watch List plants and indicators of high-quality habitats • Animal assemblages (e.g., colonial nesting waterbirds, forest interior species) • Hotspots for rare species and habitats • Intact watersheds • Wildlife corridors and concentration areas BioNet provides a ranked prioritization of areas by their significance for biodiversity conservation. Tier 5 represents areas significant for biodiversity conservation. This data was derived using zonal statistics of the Tier 5 raster and the NHD catchments. Unit: km2 https://geodata.md.gov/imap/rest/services/Biota/MD_BiodiversityConservationNetwork/MapServer 0-13.2 km2 na MD_priority_fire_sqkm MD fire priority areas Single 4 0 0 DNR has several options for fire risk mapping, including a composite wildfire risk layer used in its Forest Action Plan Assessment (DNR 2020), which represents the best available data for Maryland. The Wildfire Priority Map is designed to highlight areas of the state where wildfire is historically prevalent, has the potential to cause great harm to people and property, and where fuels and other conditions can increase the likelihood and intensity of wildfire. The resulting Fire Priority Areas map depicts areas of high fire priority and was summarized for each NHD catchment. Unit: km2 na 0-39.16 km2 na MBSS_stronghold_watershed_sqkm MBSS stronghold watershe area Single 4 0 0 DNR has also employed MBSS data to identify a suite of watersheds supporting freshwater stream ecosystems where conservation is needed to protect and restore areas of high aquatic biodiversity. Known as Maryland’s “Stronghold Watersheds”, these locations are the places where rare, threatened, or endangered species of fish, amphibians, reptiles, or mussels are found in greatest abundance (DNR 2021a). Data on Stronghold Watersheds is used in conjunction with other data to help DNR identify targeted areas for conservation. The Stronghold Watersheds dataset was provided by MANTA. Target areas were summarized for the total km2 in each NHD catchment. na 0-30.78 km2 na CBC_active_abandoned_mines CBC active and abandoned mines Single 4 0 0 In cooperation with Washington College, Chesapeake Conservancy has developed a data set of active and abandoned mines that covers all of the state of Maryland. USGS mine location point datasets were used to locate mines, and then polygons were hand digitized around them. Unit: km2 na 0-3.84 km2 na PctFor17 Percent of Forest in catchment Double 8 0 0 Percent of Forest in catchment Provided by Sarah McDonald, USGS. 0-100 percent na PctFor17Ws Percent of Forest in upstream watershed of catchment Double 8 0 0 Percent of Forest in upstream watershed of catchment Provided by Sarah McDonald, USGS. 0-100 percent na PctImp17 Percent of impervious in catchment Double 8 0 0 Percent of impervious in catchment Provided by Sarah McDonald, USGS. 0-100 percent na PctImp17Ws Percent of impervious in upstream watershed of catchment Double 8 0 0 Percent of impervious in upstream watershed of catchment Provided by Sarah McDonald, USGS. 0-100 percent na PctNat17 Percent of natural land in catchment Double 8 0 0 Percent of natural land in catchment Provided by Sarah McDonald, USGS. 0-100 percent na PctNat17Ws Percent of natural land in upstream watershed of catchment Double 8 0 0 Percent of natural land in upstream watershed of catchment Provided by Sarah McDonald, USGS. 0-100 percent na PctTC17 Percent of Tree Canopy in catchment Double 8 0 0 Percent of Tree Canopy in catchment Provided by Sarah McDonald, USGS. 0-100 percent na PctTC17Ws Percent of tree canopy in upstream watershed of catchment Double 8 0 0 Percent of tree canopy in upstream watershed of catchment Provided by Sarah McDonald, USGS. 0-100 percent na PctTurf17 Percent of turf grass in catchment Double 8 0 0 Percent of turf grass in catchment Provided by Sarah McDonald, USGS. 0-100 percent na PctTurf17Ws Percent of turf grass in upstream watershed of catchment Double 8 0 0 Percent of turf grass in upstream watershed of catchment Provided by Sarah McDonald, USGS. 0-100 percent na PctWL17 Percent of non-forested wetlands in catchment Double 8 0 0 Percent of non-forested wetlands in catchment Provided by Sarah McDonald, USGS. 0-100 percent na PctWL17Ws Percent of non-forested wetlands in upstream watershed of catchment Double 8 0 0 Percent of non-forested wetlands in upstream watershed of catchment Provided by Sarah McDonald, USGS. 0-100 percent na PctImpCHG Percent of impervious annual rate of change in catchment 2013/14-2017/18 Double 8 0 0 Percent of impervious annual rate of change in catchment 2013/14-2017/18 Provided by Sarah McDonald, USGS. -100-100 percent change na PctForCHG Percent of Forest annual rate of change in catchment 2013/14 - 2017/18 Double 8 0 0 Percent of Forest annual rate of change in catchment 2013/14 - 2017/18 Provided by Sarah McDonald, USGS. -100 - 100 percent na PctImpCHGWs Percent of impervious annual rate of change in upstream watershed of catchment 2013/14 -2017/18 Double 8 0 0 Percent of impervious annual rate of change in upstream watershed of catchment 2013/14 -2017/18 Provided by Sarah McDonald, USGS. -100 - 100 percent na PctForCHGWs Percent of Forest annual rate of change in upstream watershed of catchment 2013/14 - 2017/18 Double 8 0 0 Percent of Forest annual rate of change in upstream watershed of catchment 2013/14 - 2017/18 Provided by Sarah McDonald, USGS. -100 - 100 percent na PctIMP35 Percent of impervious in catchment in 2035, projected by the CBLCM Double 8 0 0 Percent of impervious in catchment in 2035, projected by the CBLCM Provided by Sarah McDonald, USGS. 0-100 percent na PctWsIMP35 Percent of impervious in the upstream watershed of the catchment in 2035, projected by the CBLCM Double 8 0 0 Percent of impervious in the upstream watershed of the catchment in 2035, projected by the CBLCM Provided by Sarah McDonald, USGS. 0-100 percent na PctNAT35 Percent of natural land in catchment in 2035, projected by the CBLCM Double 8 0 0 Percent of natural land in catchment in 2035, projected by the CBLCM Provided by Sarah McDonald, USGS. 0-100 percent na PctWsNAT35 Percent of natural land in the upstream watershed of the catchment in 2035, projected by the CBLCM Double 8 0 0 Percent of natural land in the upstream watershed of the catchment in 2035, projected by the CBLCM Provided by Sarah McDonald, USGS. 0-100 percent na PctAG35 Percent of agriculture in catchment in 2035, projected by the CBLCM Double 8 0 0 Percent of agriculture in catchment in 2035, projected by the CBLCM Provided by Sarah McDonald, USGS. 0-100 percent na PctWsAG35 Percent of agriculture in the upstream watershed of the catchment in 2035, projected by the CBLCM Double 8 0 0 Percent of agriculture in the upstream watershed of the catchment in 2035, projected by the CBLCM Provided by Sarah McDonald, USGS. 0-100 percent na PctPL Percent of protected lands in catchment Double 8 0 0 Percent of protected lands in catchment Provided by Sarah McDonald, USGS. 0-100 percent na PctPLWs Percent of protected lands in upstream watershed of catchment Double 8 0 0 Percent of protected lands in upstream watershed of catchment Provided by Sarah McDonald, USGS. 0-100 percent na PctRpTC Percent tree canopy in the riparian zone in the catchment Double 8 0 0 Percent tree canopy in the riparian zone in the catchment Provided by Sarah McDonald, USGS. 0-100 percent na PctRpNat Percent natural in the riparian zone in the catchment Double 8 0 0 Percent natural in the riparian zone in the catchment Provided by Sarah McDonald, USGS. 0-100 percent na PctRpImp Percent impervious in the riparian zone in the catchment Double 8 0 0 Percent impervious in the riparian zone in the catchment Provided by Sarah McDonald, USGS. 0-100 percent na PctRpTCWs Percent tree canopy in the riparian zone in the upstream watershed of the catchment Double 8 0 0 Percent tree canopy in the riparian zone in the upstream watershed of the catchment Provided by Sarah McDonald, USGS. 0-100 percent na PctRpNatWs Percent natural in the riparian zone in the upstream watershed of the catchment Double 8 0 0 Percent natural in the riparian zone in the upstream watershed of the catchment Provided by Sarah McDonald, USGS. 0-100 percent na PctRpImpWs Percent impervious in the riparian zone in the upstream watershed of the catchment Double 8 0 0 Percent impervious in the riparian zone in the upstream watershed of the catchment Provided by Sarah McDonald, USGS. 0-100 percent na 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. HUDEN_1990 housing unit density 1990 Single 4 0 0 The SILVIS lab provides block level data on population density, housing density, and change in housing density from the decennial U.S. Census Data. The most recent data available are from the 2010 census. Housing unit density for 1990 was calculated using zonal statistics from the SILVIS provided density raster and the NHD catchments. Unit: housing units/km2 http://silvis.forest.wisc.edu/data/housing-block-change/ 0-3695 housing units/km2 na HUDEN_2000 housing unit density 2000 Single 4 0 0 The SILVIS lab provides block level data on population density, housing density, and change in housing density from the decennial U.S. Census Data. The most recent data available are from the 2010 census. Housing unit density for 2000 was calculated using zonal statistics from the SILVIS provided density raster and the NHD catchments. Unit: housing units/km2 http://silvis.forest.wisc.edu/data/housing-block-change/ 0-4393.96 housing units/km2 na HUDEN_2010 housing unit density 2010 Single 4 0 0 The SILVIS lab provides block level data on population density, housing density, and change in housing density from the decennial U.S. Census Data. The most recent data available are from the 2010 census. Housing unit density for 2010 was calculated using zonal statistics from the SILVIS provided density raster and the NHD catchments. Unit: housing units/km2 http://silvis.forest.wisc.edu/data/housing-block-change/ 0-4771.78 housing units/km2 na NatNetwork_01 terrestrial, aquatic and imperiled species cores overlap Single 4 0 0 Nature’s Network Conservation Design data depict 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. Conservation Design areas include Core Habitat for Imperiled Species, Terrestrial Core-Connector Network, Grassland Core Areas, Lotic Core Areas, and Lentic Core Areas. Development of Nature’s Network was led by the North Atlantic Landscape Conservation Cooperative (LCC) and the Northeast Association of Fish and Wildlife Agencies (NEAFWA), who coordinated a team of partners from 13 states, the U.S. Fish and Wildlife Service, nongovernmental organizations, and universities, to develop a regional conservation design for the Northeastern United States (Nature’s Network 2021). The Conservation Design data were used to construct a series of metrics characterizing high quality habitat. This attribute represents the km2 of terrestrial, aquatic and imperiled species cores overlap within each NHD catchment. 0-0.729 km2 na www.naturesnetwork.org/data-tools/landscape-conservation-design/ NatNetwork_02 terrestrial and imperiled species cores overlap Single 4 0 0 Nature’s Network Conservation Design data depict 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. Conservation Design areas include Core Habitat for Imperiled Species, Terrestrial Core-Connector Network, Grassland Core Areas, Lotic Core Areas, and Lentic Core Areas. Development of Nature’s Network was led by the North Atlantic Landscape Conservation Cooperative (LCC) and the Northeast Association of Fish and Wildlife Agencies (NEAFWA), who coordinated a team of partners from 13 states, the U.S. Fish and Wildlife Service, nongovernmental organizations, and universities, to develop a regional conservation design for the Northeastern United States (Nature’s Network 2021). The Conservation Design data were used to construct a series of metrics characterizing high quality habitat. This attribute represents the km2 of terrestrial and imperiled species cores overlap within each NHD catchment. 0-22.77 km2 na www.naturesnetwork.org/data-tools/landscape-conservation-design/ NatNetwork_03 terrestrial cores overlap Single 4 0 0 Nature’s Network Conservation Design data depict 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. Conservation Design areas include Core Habitat for Imperiled Species, Terrestrial Core-Connector Network, Grassland Core Areas, Lotic Core Areas, and Lentic Core Areas. Development of Nature’s Network was led by the North Atlantic Landscape Conservation Cooperative (LCC) and the Northeast Association of Fish and Wildlife Agencies (NEAFWA), who coordinated a team of partners from 13 states, the U.S. Fish and Wildlife Service, nongovernmental organizations, and universities, to develop a regional conservation design for the Northeastern United States (Nature’s Network 2021). The Conservation Design data were used to construct a series of metrics characterizing high quality habitat. This attribute represents the km2 of terrestrial cores overlap within each NHD catchment. 0-1.87 km2 na www.naturesnetwork.org/data-tools/landscape-conservation-design/ NatNetwork_04 aquatic and imperiled species cores overlap Single 4 0 0 Nature’s Network Conservation Design data depict 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. Conservation Design areas include Core Habitat for Imperiled Species, Terrestrial Core-Connector Network, Grassland Core Areas, Lotic Core Areas, and Lentic Core Areas. Development of Nature’s Network was led by the North Atlantic Landscape Conservation Cooperative (LCC) and the Northeast Association of Fish and Wildlife Agencies (NEAFWA), who coordinated a team of partners from 13 states, the U.S. Fish and Wildlife Service, nongovernmental organizations, and universities, to develop a regional conservation design for the Northeastern United States (Nature’s Network 2021). The Conservation Design data were used to construct a series of metrics characterizing high quality habitat. This attribute represents the km2 of aquatic and imperiled species cores overlap within each NHD catchment. 0-2.95 km2 na www.naturesnetwork.org/data-tools/landscape-conservation-design/ NatNetwork_05 terrestrial cores overlap Single 4 0 0 Nature’s Network Conservation Design data depict 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. Conservation Design areas include Core Habitat for Imperiled Species, Terrestrial Core-Connector Network, Grassland Core Areas, Lotic Core Areas, and Lentic Core Areas. Development of Nature’s Network was led by the North Atlantic Landscape Conservation Cooperative (LCC) and the Northeast Association of Fish and Wildlife Agencies (NEAFWA), who coordinated a team of partners from 13 states, the U.S. Fish and Wildlife Service, nongovernmental organizations, and universities, to develop a regional conservation design for the Northeastern United States (Nature’s Network 2021). The Conservation Design data were used to construct a series of metrics characterizing high quality habitat. This attribute represents the km2 of terrestrial cores overlap within each NHD catchment. 0-35.94 km2 na www.naturesnetwork.org/data-tools/landscape-conservation-design/ NatNetwork_06 aquatic cores overlap Single 4 0 0 Nature’s Network Conservation Design data depict 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. Conservation Design areas include Core Habitat for Imperiled Species, Terrestrial Core-Connector Network, Grassland Core Areas, Lotic Core Areas, and Lentic Core Areas. Development of Nature’s Network was led by the North Atlantic Landscape Conservation Cooperative (LCC) and the Northeast Association of Fish and Wildlife Agencies (NEAFWA), who coordinated a team of partners from 13 states, the U.S. Fish and Wildlife Service, nongovernmental organizations, and universities, to develop a regional conservation design for the Northeastern United States (Nature’s Network 2021). The Conservation Design data were used to construct a series of metrics characterizing high quality habitat. This attribute represents the km2 of aquatic cores overlap within each NHD catchment. 0-5.37 km2 na www.naturesnetwork.org/data-tools/landscape-conservation-design/ NatNetwork_07 core habitat and imperiled species Single 4 0 0 Nature’s Network Conservation Design data depict 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. Conservation Design areas include Core Habitat for Imperiled Species, Terrestrial Core-Connector Network, Grassland Core Areas, Lotic Core Areas, and Lentic Core Areas. Development of Nature’s Network was led by the North Atlantic Landscape Conservation Cooperative (LCC) and the Northeast Association of Fish and Wildlife Agencies (NEAFWA), who coordinated a team of partners from 13 states, the U.S. Fish and Wildlife Service, nongovernmental organizations, and universities, to develop a regional conservation design for the Northeastern United States (Nature’s Network 2021). The Conservation Design data were used to construct a series of metrics characterizing high quality habitat. This attribute represents the km2 of core habitat and imperiled species within each NHD catchment. 0-6.0156 km2 na www.naturesnetwork.org/data-tools/landscape-conservation-design/ NatNetwork_08 terrestrial core to core connectors Single 4 0 0 Nature’s Network Conservation Design data depict 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. Conservation Design areas include Core Habitat for Imperiled Species, Terrestrial Core-Connector Network, Grassland Core Areas, Lotic Core Areas, and Lentic Core Areas. Development of Nature’s Network was led by the North Atlantic Landscape Conservation Cooperative (LCC) and the Northeast Association of Fish and Wildlife Agencies (NEAFWA), who coordinated a team of partners from 13 states, the U.S. Fish and Wildlife Service, nongovernmental organizations, and universities, to develop a regional conservation design for the Northeastern United States (Nature’s Network 2021). The Conservation Design data were used to construct a series of metrics characterizing high quality habitat. This attribute represents the km2 of terrestrial core to core connectors within each NHD catchment. 0-11.6127 km2 na www.naturesnetwork.org/data-tools/landscape-conservation-design/ mean_sum_temp mean summer temperature Single 4 0 0 USGS and its partners has developed a suite of temperature indicators for streams in the northeast. The SHEDS stream temperature model was developed to predict daily stream temperatures at both gaged and un-gaged catchments across the northeast U.S. based on geospatial characteristics and weather conditions. The model is based on a linear mixed effects framework that accounts for spatial and temporal correlations using a hierarchical Bayesian structure. Letcher et al. (2016) describe the initial development of this model framework. Mean summer steam temperature was downloaded as SHEDS catchments and then rasterized. Using this raster zonal summaries for NHD catchments were derived. Unit: Celsius http://db.ecosheds.org/ 13.6-26.6 deg C na mean_sum_temp_air2 mean summer temp w air temp +2 degC Single 4 0 0 USGS and its partners has developed a suite of temperature indicators for streams in the northeast. The SHEDS stream temperature model was developed to predict daily stream temperatures at both gaged and un-gaged catchments across the northeast U.S. based on geospatial characteristics and weather conditions. The model is based on a linear mixed effects framework that accounts for spatial and temporal correlations using a hierarchical Bayesian structure. Letcher et al. (2016) describe the initial development of this model framework. Mean summer steam temperature with air temp +2 degC was downloaded as SHEDS catchments and then rasterized. Using this raster zonal summaries for NHD catchments were derived. Unit: Celsius http://db.ecosheds.org/ 14.2-27.16 deg C na mean_sum_temp_air4 mean summer temp w air temp +4 degC Single 4 0 0 USGS and its partners has developed a suite of temperature indicators for streams in the northeast. The SHEDS stream temperature model was developed to predict daily stream temperatures at both gaged and un-gaged catchments across the northeast U.S. based on geospatial characteristics and weather conditions. The model is based on a linear mixed effects framework that accounts for spatial and temporal correlations using a hierarchical Bayesian structure. Letcher et al. (2016) describe the initial development of this model framework. Mean summer steam temperature with air temp +4 degC was downloaded as SHEDS catchments and then rasterized. Using this raster zonal summaries for NHD catchments were derived. Unit: Celsius http://db.ecosheds.org/ 14.5-28.1 deg C na mean_sum_temp_air6 mean summer temp w air temp +6 degC Single 4 0 0 USGS and its partners has developed a suite of temperature indicators for streams in the northeast. The SHEDS stream temperature model was developed to predict daily stream temperatures at both gaged and un-gaged catchments across the northeast U.S. based on geospatial characteristics and weather conditions. The model is based on a linear mixed effects framework that accounts for spatial and temporal correlations using a hierarchical Bayesian structure. Letcher et al. (2016) describe the initial development of this model framework. Mean summer steam temperature with air temp +6 degC was downloaded as SHEDS catchments and then rasterized. Using this raster zonal summaries for NHD catchments were derived. Unit: Celsius http://db.ecosheds.org/ 14.8-29.06 deg C na n_day_temp_gt_18 mean number of days per yr temp greater than 18C Single 4 0 0 USGS and its partners has developed a suite of temperature indicators for streams in the northeast. The SHEDS stream temperature model was developed to predict daily stream temperatures at both gaged and un-gaged catchments across the northeast U.S. based on geospatial characteristics and weather conditions. The model is based on a linear mixed effects framework that accounts for spatial and temporal correlations using a hierarchical Bayesian structure. Letcher et al. (2016) describe the initial development of this model framework. Mean # days per year temp greater than 18 C was downloaded as SHEDS catchments and then rasterized. Using this raster zonal summaries for NHD catchments were derived. Unit: days http://db.ecosheds.org/ 0.07-246.95 days na n_day_temp_gt_22 mean number of days per yr temp greater than 22C Single 4 0 0 USGS and its partners has developed a suite of temperature indicators for streams in the northeast. The SHEDS stream temperature model was developed to predict daily stream temperatures at both gaged and un-gaged catchments across the northeast U.S. based on geospatial characteristics and weather conditions. The model is based on a linear mixed effects framework that accounts for spatial and temporal correlations using a hierarchical Bayesian structure. Letcher et al. (2016) describe the initial development of this model framework. Mean # days per year temp greater than 22 C was downloaded as SHEDS catchments and then rasterized. Using this raster zonal summaries for NHD catchments were derived. Unit: days http://db.ecosheds.org/ 0-170.51 days na stream_imp_miles Miles of stream with 303d impairments Single 4 0 0 EPA has compiled information on the impairment status of waterbodies, i.e., waters listed as impaired through state 303(d) listings through its ATTAINS database. The data represents the stream miles of 303d impairments within each NHD catchment. Data was downloaded from EPA attains website and stream miles were summarized for each NHD catchment. Unit: miles https://www.epa.gov/waterdata/waters-geospatial-data-downloads 0-17.73 miles na tp_ip incremental total phosphorus load Single 4 0 0 Developed by USGS (Ator 2019a, Ator 2019b), the Spatially Referenced Regression on Watershed Attributes (SPARROW) Model provides estimates of nitrogen, phosphorus, and sediment loads to Chesapeake Bay. SPARROW total and source-specific incremental loads data are available for specific sectors (e.g., manure, inorganic fertilizer, atmospheric deposition, point sources, septic systems, urban) and are useful to characterize the various types of nutrient and sediment loads, in terms of kg/year. Data are available by NHDPlus V2 catchment. “Incremental” refers to values for each local catchment, rather than upstream aggregated values. This attribute represents the Incremental total phosphorus load, kg/yr https://www.sciencebase.gov/catalog/file/get/5d4192aee4b01d82ce8da477?f=__disk__81%2F5d%2F3d%2F815d3deb08f82c1662ff94eb941074ff99c75088&transform=1&allowOpen=true 0-71468 kg/yr na tp_ip_poin Incremental total phosphorus load from point-source wastewater treatment facilities Single 4 0 0 Developed by USGS (Ator 2019a, Ator 2019b), the Spatially Referenced Regression on Watershed Attributes (SPARROW) Model provides estimates of nitrogen, phosphorus, and sediment loads to Chesapeake Bay. SPARROW total and source-specific incremental loads data are available for specific sectors (e.g., manure, inorganic fertilizer, atmospheric deposition, point sources, septic systems, urban) and are useful to characterize the various types of nutrient and sediment loads, in terms of kg/year. Data are available by NHDPlus V2 catchment. “Incremental” refers to values for each local catchment, rather than upstream aggregated values. This attribute represents the Incremental total phosphorus load from point-source wastewater treatment facilities, kg/yr https://www.sciencebase.gov/catalog/file/get/5d4192aee4b01d82ce8da477?f=__disk__81%2F5d%2F3d%2F815d3deb08f82c1662ff94eb941074ff99c75088&transform=1&allowOpen=true 0-71374 kg/yr na tp_ip_fert Incremental total phosphorus load from fertilizer applications Single 4 0 0 Developed by USGS (Ator 2019a, Ator 2019b), the Spatially Referenced Regression on Watershed Attributes (SPARROW) Model provides estimates of nitrogen, phosphorus, and sediment loads to Chesapeake Bay. SPARROW total and source-specific incremental loads data are available for specific sectors (e.g., manure, inorganic fertilizer, atmospheric deposition, point sources, septic systems, urban) and are useful to characterize the various types of nutrient and sediment loads, in terms of kg/year. Data are available by NHDPlus V2 catchment. “Incremental” refers to values for each local catchment, rather than upstream aggregated values. This attribute represents the Incremental total phosphorus load from fertilizer applications, kg/yr https://www.sciencebase.gov/catalog/file/get/5d4192aee4b01d82ce8da477?f=__disk__81%2F5d%2F3d%2F815d3deb08f82c1662ff94eb941074ff99c75088&transform=1&allowOpen=true 0-1657 kg/yr na tp_ip_manu Incremental total phosphorus load from manure applications Single 4 0 0 Developed by USGS (Ator 2019a, Ator 2019b), the Spatially Referenced Regression on Watershed Attributes (SPARROW) Model provides estimates of nitrogen, phosphorus, and sediment loads to Chesapeake Bay. SPARROW total and source-specific incremental loads data are available for specific sectors (e.g., manure, inorganic fertilizer, atmospheric deposition, point sources, septic systems, urban) and are useful to characterize the various types of nutrient and sediment loads, in terms of kg/year. Data are available by NHDPlus V2 catchment. “Incremental” refers to values for each local catchment, rather than upstream aggregated values. This attribute represents the Incremental total phosphorus load from manure applications, kg/yr https://www.sciencebase.gov/catalog/file/get/5d4192aee4b01d82ce8da477?f=__disk__81%2F5d%2F3d%2F815d3deb08f82c1662ff94eb941074ff99c75088&transform=1&allowOpen=true 0-3777 kg/yr na tp_ip_urb Incremental total phosphorus load from urban non-point sources Single 4 0 0 Developed by USGS (Ator 2019a, Ator 2019b), the Spatially Referenced Regression on Watershed Attributes (SPARROW) Model provides estimates of nitrogen, phosphorus, and sediment loads to Chesapeake Bay. SPARROW total and source-specific incremental loads data are available for specific sectors (e.g., manure, inorganic fertilizer, atmospheric deposition, point sources, septic systems, urban) and are useful to characterize the various types of nutrient and sediment loads, in terms of kg/year. Data are available by NHDPlus V2 catchment. “Incremental” refers to values for each local catchment, rather than upstream aggregated values. This attribute represents the Incremental total phosphorus load from urban non-point sources, kg/yr https://www.sciencebase.gov/catalog/file/get/5d4192aee4b01d82ce8da477?f=__disk__81%2F5d%2F3d%2F815d3deb08f82c1662ff94eb941074ff99c75088&transform=1&allowOpen=true 0-2045 kg/yr na tn_in Incremental total nitrogen load Single 4 0 0 Developed by USGS (Ator 2019a, Ator 2019b), the Spatially Referenced Regression on Watershed Attributes (SPARROW) Model provides estimates of nitrogen, phosphorus, and sediment loads to Chesapeake Bay. SPARROW total and source-specific incremental loads data are available for specific sectors (e.g., manure, inorganic fertilizer, atmospheric deposition, point sources, septic systems, urban) and are useful to characterize the various types of nutrient and sediment loads, in terms of kg/year. Data are available by NHDPlus V2 catchment. “Incremental” refers to values for each local catchment, rather than upstream aggregated values. This attribute represents the Incremental total nitrogen load, kg/yr https://www.sciencebase.gov/catalog/file/get/5d4192aee4b01d82ce8da477?f=__disk__81%2F5d%2F3d%2F815d3deb08f82c1662ff94eb941074ff99c75088&transform=1&allowOpen=true 0-970109 kg/yr na tn_in_poin Incremental total nitrogen load from wastewater treatment facility point sources Single 4 0 0 Developed by USGS (Ator 2019a, Ator 2019b), the Spatially Referenced Regression on Watershed Attributes (SPARROW) Model provides estimates of nitrogen, phosphorus, and sediment loads to Chesapeake Bay. SPARROW total and source-specific incremental loads data are available for specific sectors (e.g., manure, inorganic fertilizer, atmospheric deposition, point sources, septic systems, urban) and are useful to characterize the various types of nutrient and sediment loads, in terms of kg/year. Data are available by NHDPlus V2 catchment. “Incremental” refers to values for each local catchment, rather than upstream aggregated values. This attribute represents the Incremental total nitrogen load from wastewater treatment facility point sources, kg/yr https://www.sciencebase.gov/catalog/file/get/5d4192aee4b01d82ce8da477?f=__disk__81%2F5d%2F3d%2F815d3deb08f82c1662ff94eb941074ff99c75088&transform=1&allowOpen=true 0-969925 kg/yr na tn_in_fert Incremental total nitrogen load from fertilizer applications Single 4 0 0 Developed by USGS (Ator 2019a, Ator 2019b), the Spatially Referenced Regression on Watershed Attributes (SPARROW) Model provides estimates of nitrogen, phosphorus, and sediment loads to Chesapeake Bay. SPARROW total and source-specific incremental loads data are available for specific sectors (e.g., manure, inorganic fertilizer, atmospheric deposition, point sources, septic systems, urban) and are useful to characterize the various types of nutrient and sediment loads, in terms of kg/year. Data are available by NHDPlus V2 catchment. “Incremental” refers to values for each local catchment, rather than upstream aggregated values. This attribute represents the Incremental total nitrogen load from fertilizer applications, kg/yr https://www.sciencebase.gov/catalog/file/get/5d4192aee4b01d82ce8da477?f=__disk__81%2F5d%2F3d%2F815d3deb08f82c1662ff94eb941074ff99c75088&transform=1&allowOpen=true 0-31697 kg/yr na tn_in_sept Incremental total nitrogen load from septic system effluent Single 4 0 0 Developed by USGS (Ator 2019a, Ator 2019b), the Spatially Referenced Regression on Watershed Attributes (SPARROW) Model provides estimates of nitrogen, phosphorus, and sediment loads to Chesapeake Bay. SPARROW total and source-specific incremental loads data are available for specific sectors (e.g., manure, inorganic fertilizer, atmospheric deposition, point sources, septic systems, urban) and are useful to characterize the various types of nutrient and sediment loads, in terms of kg/year. Data are available by NHDPlus V2 catchment. “Incremental” refers to values for each local catchment, rather than upstream aggregated values. This attribute represents the Incremental total nitrogen load from septic system effluent, kg/yr https://www.sciencebase.gov/catalog/file/get/5d4192aee4b01d82ce8da477?f=__disk__81%2F5d%2F3d%2F815d3deb08f82c1662ff94eb941074ff99c75088&transform=1&allowOpen=true 0-11780 kg/yr na tn_in_manu Incremental total nitrogen load from manure applications Single 4 0 0 Developed by USGS (Ator 2019a, Ator 2019b), the Spatially Referenced Regression on Watershed Attributes (SPARROW) Model provides estimates of nitrogen, phosphorus, and sediment loads to Chesapeake Bay. SPARROW total and source-specific incremental loads data are available for specific sectors (e.g., manure, inorganic fertilizer, atmospheric deposition, point sources, septic systems, urban) and are useful to characterize the various types of nutrient and sediment loads, in terms of kg/year. Data are available by NHDPlus V2 catchment. “Incremental” refers to values for each local catchment, rather than upstream aggregated values. This attribute represents the Incremental total nitrogen load from manure applications, kg/yr https://www.sciencebase.gov/catalog/file/get/5d4192aee4b01d82ce8da477?f=__disk__81%2F5d%2F3d%2F815d3deb08f82c1662ff94eb941074ff99c75088&transform=1&allowOpen=true 0-17119 kg/yr na tn_in_urb Incremental total nitrogen load from other urban non-point sources Single 4 0 0 Developed by USGS (Ator 2019a, Ator 2019b), the Spatially Referenced Regression on Watershed Attributes (SPARROW) Model provides estimates of nitrogen, phosphorus, and sediment loads to Chesapeake Bay. SPARROW total and source-specific incremental loads data are available for specific sectors (e.g., manure, inorganic fertilizer, atmospheric deposition, point sources, septic systems, urban) and are useful to characterize the various types of nutrient and sediment loads, in terms of kg/year. Data are available by NHDPlus V2 catchment. “Incremental” refers to values for each local catchment, rather than upstream aggregated values. This attribute represents the Incremental total nitrogen load from other urban non-point sources, kg/yr https://www.sciencebase.gov/catalog/file/get/5d4192aee4b01d82ce8da477?f=__disk__81%2F5d%2F3d%2F815d3deb08f82c1662ff94eb941074ff99c75088&transform=1&allowOpen=true 0-25362 kg/yr na ss_is_strm Incremental suspended-sediment load from streambank erosion Single 4 0 0 Developed by USGS (Ator 2019a, Ator 2019b), the Spatially Referenced Regression on Watershed Attributes (SPARROW) Model provides estimates of nitrogen, phosphorus, and sediment loads to Chesapeake Bay. SPARROW total and source-specific incremental loads data are available for specific sectors (e.g., manure, inorganic fertilizer, atmospheric deposition, point sources, septic systems, urban) and are useful to characterize the various types of nutrient and sediment loads, in terms of kg/year. Data are available by NHDPlus V2 catchment. “Incremental” refers to values for each local catchment, rather than upstream aggregated values. This attribute represents the Incremental suspended-sediment load from streambank erosion (megagrams per year) https://www.sciencebase.gov/catalog/file/get/5d4192aee4b01d82ce8da477?f=__disk__81%2F5d%2F3d%2F815d3deb08f82c1662ff94eb941074ff99c75088&transform=1&allowOpen=true 0-120712 megagrams per year na ss_is_othr Incremental suspended-sediment load from non-agricultural and non-urban uplands Single 4 0 0 Developed by USGS (Ator 2019a, Ator 2019b), the Spatially Referenced Regression on Watershed Attributes (SPARROW) Model provides estimates of nitrogen, phosphorus, and sediment loads to Chesapeake Bay. SPARROW total and source-specific incremental loads data are available for specific sectors (e.g., manure, inorganic fertilizer, atmospheric deposition, point sources, septic systems, urban) and are useful to characterize the various types of nutrient and sediment loads, in terms of kg/year. Data are available by NHDPlus V2 catchment. “Incremental” refers to values for each local catchment, rather than upstream aggregated values. This attribute represents the Incremental suspended-sediment load from non-agricultural and non-urban uplands (megagrams per year) https://www.sciencebase.gov/catalog/file/get/5d4192aee4b01d82ce8da477?f=__disk__81%2F5d%2F3d%2F815d3deb08f82c1662ff94eb941074ff99c75088&transform=1&allowOpen=true 0-195 megagrams per year na ss_is_ufin Incremental suspended-sediment load from urban uplands with fine sediment Single 4 0 0 Developed by USGS (Ator 2019a, Ator 2019b), the Spatially Referenced Regression on Watershed Attributes (SPARROW) Model provides estimates of nitrogen, phosphorus, and sediment loads to Chesapeake Bay. SPARROW total and source-specific incremental loads data are available for specific sectors (e.g., manure, inorganic fertilizer, atmospheric deposition, point sources, septic systems, urban) and are useful to characterize the various types of nutrient and sediment loads, in terms of kg/year. Data are available by NHDPlus V2 catchment. “Incremental” refers to values for each local catchment, rather than upstream aggregated values. This attribute represents the Incremental suspended-sediment load from urban uplands with fine sediment (megagrams per year) https://www.sciencebase.gov/catalog/file/get/5d4192aee4b01d82ce8da477?f=__disk__81%2F5d%2F3d%2F815d3deb08f82c1662ff94eb941074ff99c75088&transform=1&allowOpen=true 0-1212 megagrams per year na ss_is_umed Incremental suspended-sediment load from urban uplands with medium or coarse sediment Single 4 0 0 Developed by USGS (Ator 2019a, Ator 2019b), the Spatially Referenced Regression on Watershed Attributes (SPARROW) Model provides estimates of nitrogen, phosphorus, and sediment loads to Chesapeake Bay. SPARROW total and source-specific incremental loads data are available for specific sectors (e.g., manure, inorganic fertilizer, atmospheric deposition, point sources, septic systems, urban) and are useful to characterize the various types of nutrient and sediment loads, in terms of kg/year. Data are available by NHDPlus V2 catchment. “Incremental” refers to values for each local catchment, rather than upstream aggregated values. This attribute represents the Incremental suspended-sediment load from urban uplands with medium or coarse sediment (megagrams per year) https://www.sciencebase.gov/catalog/file/get/5d4192aee4b01d82ce8da477?f=__disk__81%2F5d%2F3d%2F815d3deb08f82c1662ff94eb941074ff99c75088&transform=1&allowOpen=true 0-168 megagrams per year na ss_is_ures Incremental suspended-sediment load from urban uplands with residuum Single 4 0 0 Developed by USGS (Ator 2019a, Ator 2019b), the Spatially Referenced Regression on Watershed Attributes (SPARROW) Model provides estimates of nitrogen, phosphorus, and sediment loads to Chesapeake Bay. SPARROW total and source-specific incremental loads data are available for specific sectors (e.g., manure, inorganic fertilizer, atmospheric deposition, point sources, septic systems, urban) and are useful to characterize the various types of nutrient and sediment loads, in terms of kg/year. Data are available by NHDPlus V2 catchment. “Incremental” refers to values for each local catchment, rather than upstream aggregated values. This attribute represents the Incremental suspended-sediment load from urban uplands with residuum (megagrams per year) https://www.sciencebase.gov/catalog/file/get/5d4192aee4b01d82ce8da477?f=__disk__81%2F5d%2F3d%2F815d3deb08f82c1662ff94eb941074ff99c75088&transform=1&allowOpen=true 0-2003 megagrams per year na ss_is_afin Incremental suspended-sediment load from agricultural uplands with fine sediment Single 4 0 0 Developed by USGS (Ator 2019a, Ator 2019b), the Spatially Referenced Regression on Watershed Attributes (SPARROW) Model provides estimates of nitrogen, phosphorus, and sediment loads to Chesapeake Bay. SPARROW total and source-specific incremental loads data are available for specific sectors (e.g., manure, inorganic fertilizer, atmospheric deposition, point sources, septic systems, urban) and are useful to characterize the various types of nutrient and sediment loads, in terms of kg/year. Data are available by NHDPlus V2 catchment. “Incremental” refers to values for each local catchment, rather than upstream aggregated values. This attribute represents the Incremental suspended-sediment load from agricultural uplands with fine sediment (megagrams per year) https://www.sciencebase.gov/catalog/file/get/5d4192aee4b01d82ce8da477?f=__disk__81%2F5d%2F3d%2F815d3deb08f82c1662ff94eb941074ff99c75088&transform=1&allowOpen=true 0-1977 megagrams per year na ss_is_ares Incremental suspended-sediment load from agricultural uplands with medium or coarse sediment or residuum Single 4 0 0 Developed by USGS (Ator 2019a, Ator 2019b), the Spatially Referenced Regression on Watershed Attributes (SPARROW) Model provides estimates of nitrogen, phosphorus, and sediment loads to Chesapeake Bay. SPARROW total and source-specific incremental loads data are available for specific sectors (e.g., manure, inorganic fertilizer, atmospheric deposition, point sources, septic systems, urban) and are useful to characterize the various types of nutrient and sediment loads, in terms of kg/year. Data are available by NHDPlus V2 catchment. “Incremental” refers to values for each local catchment, rather than upstream aggregated values. This attribute represents the Incremental suspended-sediment load from agricultural uplands with medium or coarse sediment or residuum (megagrams per year) https://www.sciencebase.gov/catalog/file/get/5d4192aee4b01d82ce8da477?f=__disk__81%2F5d%2F3d%2F815d3deb08f82c1662ff94eb941074ff99c75088&transform=1&allowOpen=true 0-1285 megagrams per year na ss_is Incremental suspended-sediment load Single 4 0 0 Developed by USGS (Ator 2019a, Ator 2019b), the Spatially Referenced Regression on Watershed Attributes (SPARROW) Model provides estimates of nitrogen, phosphorus, and sediment loads to Chesapeake Bay. SPARROW total and source-specific incremental loads data are available for specific sectors (e.g., manure, inorganic fertilizer, atmospheric deposition, point sources, septic systems, urban) and are useful to characterize the various types of nutrient and sediment loads, in terms of kg/year. Data are available by NHDPlus V2 catchment. “Incremental” refers to values for each local catchment, rather than upstream aggregated values. This attribute represents the Incremental suspended-sediment load (megagrams per year) https://www.sciencebase.gov/catalog/file/get/5d4192aee4b01d82ce8da477?f=__disk__81%2F5d%2F3d%2F815d3deb08f82c1662ff94eb941074ff99c75088&transform=1&allowOpen=true 0-120730 megagrams per year na FIBI_predict Predicted FIBI score (good, fair, poor) String 50 0 0 Predicted FIBI score (good, fair, poor) based on random forest modeling efforts. Derived. good, fair, poor good, fair, poor na BIBI_predict Predicted BIBI score (good, fair, poor) String 50 0 0 Predicted BIBI score (good, fair, poor) based on random forest modeling efforts. Derived good, fair, poor good, fair, poor na pct_imp_change Change in percent impervious from 2017 to 2025 Single 4 0 0 Change in percent impervious from 2017 to 2025 0-100 percent na CBT to provide HUDEN_2010_1990 Change in housing unit density from 1990 to 2010 Single 4 0 0 Change in housing unit density from 1990 to 2010. Data is provided by the SILVIS lab at the block level on population density, housing density, and change in housing density from the decennial U.S. Census Data. The most recent data available are from the 2010 census. -788 - 2556 housing units/km2 na SILVIS lab BrTrout_change change in brook trout occurance from current to 6CC temperature Single 4 0 0 Brook Trout probability of occurrence data were developed by the USGS Conte Laboratory (2021) for the Northeast and Mid-Atlantic region from Virginia to Maine. The dataset provides model predictions of brook trout presence using brook trout data and landscape predictors. Predictions represent occurrence 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. 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