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Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly available data products, such as lidar, orthophotography, and geomorphic feature sets derived from those, to extract metrics of barrier island characteristics at consistent sampling distances. The metrics are then incorporated...
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Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly available data products, such as lidar, orthophotography, and geomorphic feature sets derived from those, to extract metrics of barrier island characteristics at consistent sampling distances. The metrics are then incorporated...
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Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly available data products, such as lidar, orthophotography, and geomorphic feature sets derived from those, to extract metrics of barrier island characteristics at consistent sampling distances. The metrics are then incorporated...
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State and local county health departments have detected per- and polyfluoroalkyl substances (PFAS) in groundwater downgradient of airports and military and firefighting training areas in Long Island, New York. However, the occurrence and spatial distribution of PFAS throughout the surficial aquifer is not well established. Shallow groundwater samples were collected in 2018 from two observation well networks; the Sentinel network and Wastewater Treatment Plant groundwater (WTPGW) network. The Sentinel network is an island-wide group of wells screened within the shallow upper glacial (surficial, <100 feet deep) aquifer, which were sampled to assess the occurrence of PFAS in different land-use settings. The WTPGW network...
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This dataset has been archived; it has been superseded by version 1.1 (December 2022) which can be found at https://doi.org/10.5066/P9KODN4C. This U.S. Geological Survey data release provides surface-water quality, streamflow, and groundwater-elevation data collected within the Central Pine Barrens (CPB) Region of Suffolk County, New York. The data were collected in cooperation with the Central Pine Barrens Commission and the Town of Brookhaven as part of a five-year comprehensive water-resources monitoring program. Water quality and quality-assurance data from seven sites on two rivers (Carmans River- 5 sites and Peconic River - 2 sites) in the CPB are included. Carmans River sites were sampled four times throughout...
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Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly available data products, such as lidar, orthophotography, and geomorphic feature sets derived from those, to extract metrics of barrier island characteristics at consistent sampling distances. The metrics are then incorporated...
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The U.S. Geological Survey (USGS) is providing online maps of water-table and potentiometric-surface altitude in the upper glacial, Magothy, Jameco, Lloyd, and North Shore aquifers on Long Island, New York, April–May 2016. Also provided is a depth-to-water map for Long Island, New York, April–May 2016. The USGS makes these maps and geospatial data available as REST Open Map Services (as well as HTTP, JSON, KML, and shapefile), so end-users can consume them on mobile and web clients. A companion report, U.S. Geological Survey Scientific Investigations Map 3398 (Como and others, 2018; https://doi.org/10.3133/sim3398) further describes data collection and map preparation and presents 68x22 in. Portable Document...
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The U.S. Geological Survey (USGS) is providing online maps of water-table and potentiometric-surface altitude in the upper glacial, Magothy, Jameco, Lloyd, and North Shore aquifers on Long Island, New York, April–May 2016. Also provided is a depth-to-water map for Long Island, New York, April–May 2016. The USGS makes these maps and geospatial data available as REST Open Map Services (as well as HTTP, JSON, KML, and shapefile), so end-users can consume them on mobile and web clients. A companion report, U.S. Geological Survey Scientific Investigations Map 3398 (Como and others, 2018; https://doi.org/10.3133/sim3398) further describes data collection and map preparation and presents 68x22 in. Portable Document...
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Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly available data products, such as lidar, orthophotography, and geomorphic feature sets derived from those, to extract metrics of barrier island characteristics at consistent sampling distances. The metrics are then incorporated...
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Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly available data products, such as lidar, orthophotography, and geomorphic feature sets derived from those, to extract metrics of barrier island characteristics at consistent sampling distances. The metrics are then incorporated...
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Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly available data products, such as lidar, orthophotography, and geomorphic feature sets derived from those, to extract metrics of barrier island characteristics at consistent sampling distances. The metrics are then incorporated...
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Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly available data products, such as lidar, orthophotography, and geomorphic feature sets derived from those, to extract metrics of barrier island characteristics at consistent sampling distances. The metrics are then incorporated...
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The Bathymetry surface was created by plotting depths of all data points collected relative to North American Vertical Datum of 1988 (NAVD 88), which was converted using the Vertical Datum Transformation tool created by the National Oceanic and Atmospheric Administration's (NOAA) National Geodetic Survey, Office of Coast Survey, and Center for Operation Oceanographic Products and Services. The elevation of the bathymetric raster surface was interpolated between these points in a GIS using a spline interpolator. A total of 432 points were used for interpolation. The points were used as the input to create a polygon feature class. The Spline tool was applied using the points and polygon to interpolate the bathymetric...
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The U.S. Geological Survey (USGS) is providing online maps of water-table and potentiometric-surface altitude in the upper glacial, Magothy, Jameco, Lloyd, and North Shore aquifers on Long Island, New York, April–May 2016. Also provided is a depth-to-water map for Long Island, New York, April–May 2016. The USGS makes these maps and geospatial data available as REST Open Map Services (as well as HTTP, JSON, KML, and shapefile), so end-users can consume them on mobile and web clients. A companion report, U.S. Geological Survey Scientific Investigations Map 3398 (Como and others, 2018; https://doi.org/10.3133/sim3398) further describes data collection and map preparation and presents 68x22 in. Portable Document...
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This document provides a summary of surface water-quality, streamflow, and groundwater data collected by the U.S. Geological Survey (USGS) within the Central Pine Barrens (CPB) Region of Suffolk County, New York. The data were collected in cooperation with the Central Pine Barrens Commission and the Town of Brookhaven under a five-year comprehensive water resources monitoring program. The surface water-quality data within the CPB for the 2018 water year (October 1, 2017 to September 30, 2018) includes data from the Carmans River and the Peconic River. The streams were sampled several times throughout the year at seven pre-determined locations. The Carmans River was sampled at five locations: 1) CARMANS RIVER AT...
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Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly available data products, such as lidar, orthophotography, and geomorphic feature sets derived from those, to extract metrics of barrier island characteristics at consistent sampling distances. The metrics are then incorporated...
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Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly available data products, such as lidar, orthophotography, and geomorphic feature sets derived from those, to extract metrics of barrier island characteristics at consistent sampling distances. The metrics are then incorporated...
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Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly available data products, such as lidar, orthophotography, and geomorphic feature sets derived from those, to extract metrics of barrier island characteristics at consistent sampling distances. The metrics are then incorporated...
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This data set provides locations and values of water quality parameters from a survey conducted on August 24, 2016 using an Autonomous Underwater Vehicle (AUV) in Nicoll Bay, NY. During the August 24 survey, 17,808 observations of water quality parameters were made. Parameters collected include dissolved oxygen, pH, water temperature, specific conductance, and salinity. Data was collected in approximately east-west transects by the AUV, with the northern-most transects made first. Data was collected between the hours of 1 am and 5 am to obtain minimum DO values in the daily cycle. There are three files available for download in the 'Attached Files' section below. There is a zip file which contains the observation...
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The U.S. Geological Survey (USGS) is providing an online map of water-table altitude contours in the upper glacial and Magothy aquifers on Long Island, New York, April-May 2013. USGS serves this map and geospatial data as a REST Open Map Service (as well as HTTP, JSON, KML, and shapefile), so end-users can use the map and data on mobile and web clients. A companion report, U.S. Geological Survey Scientific Investigations Map 3326 (Como and others, 2015; http://dx.doi.org/10.3133/sim3326) further describes data collection and map preparation and presents 68x22 in. PDF versions, 4 sheets, scale 1:125,000. This polyline shapefile consists of digital contours that represent the water table altitude in the upper...


map background search result map search result map Contours of Water Table Altitudes in the Upper Glacial and Magothy Aquifers, April-May 2013 Potentiometric Surface Contours of the Lloyd and North Shore Aquifers, April-May 2016 Water Table Contours in the Upper Glacial and Magothy Aquifers, April-May 2016 Approximate Regional Groundwater Divide on Long Island, New York, April-May 2016 August 24, 2016 AUV Survey - Observation Point Data Bathymetry Raster Surface 2018 Hydrologic Data Summary for the Central Pine Barrens Region, Suffolk County, New York (ver. 2.0, February 2024) Occurrence of per- and polyfluoroalkyl substances, Long Island and New York City, New York DCpts, DTpts, SLpts: Dune crest, dune toe, and mean high water shoreline positions: Fire Island, NY, 2010 Development: Development delineation: Fire Island, NY, 2010–2011 shoreline, inletLines: Shoreline polygons and tidal inlet delineations: Fire Island, NY, 2010 DCpts, DTpts, SLpts: Dune crest, dune toe, and mean high water shoreline positions: Fire Island, NY, 2012 shoreline, inletLines: Shoreline polygons and tidal inlet delineations: Fire Island, NY, 2012 Development: Development delineation: Fire Island, NY, 2014–2015 ElevMHW: Elevation adjusted to local mean high water: Fire Island, NY, 2014 DCpts, DTpts, SLpts: Dune crest, dune toe, and mean high water shoreline positions: Rockaway Peninsula, NY, 2010 Development: Development delineation: Rockaway Peninsula, NY, 2010–2011 SupClas, GeoSet, SubType, VegDen, VegType: Categorical landcover rasters (landcover, geomorphic setting, substrate type, vegetation density, and vegetation type): Rockaway Peninsula, NY, 2010–2011 SupClas, GeoSet, SubType, VegDen, VegType: Categorical landcover rasters (landcover, geomorphic setting, substrate type, vegetation density, and vegetation type): Rockaway Peninsula, NY, 2012 2019 Hydrologic Data Summary for the Central Pine Barrens Region, Suffolk County, New York August 24, 2016 AUV Survey - Observation Point Data DCpts, DTpts, SLpts: Dune crest, dune toe, and mean high water shoreline positions: Rockaway Peninsula, NY, 2010 Development: Development delineation: Rockaway Peninsula, NY, 2010–2011 SupClas, GeoSet, SubType, VegDen, VegType: Categorical landcover rasters (landcover, geomorphic setting, substrate type, vegetation density, and vegetation type): Rockaway Peninsula, NY, 2012 SupClas, GeoSet, SubType, VegDen, VegType: Categorical landcover rasters (landcover, geomorphic setting, substrate type, vegetation density, and vegetation type): Rockaway Peninsula, NY, 2010–2011 Development: Development delineation: Fire Island, NY, 2014–2015 Development: Development delineation: Fire Island, NY, 2010–2011 shoreline, inletLines: Shoreline polygons and tidal inlet delineations: Fire Island, NY, 2012 ElevMHW: Elevation adjusted to local mean high water: Fire Island, NY, 2014 DCpts, DTpts, SLpts: Dune crest, dune toe, and mean high water shoreline positions: Fire Island, NY, 2010 shoreline, inletLines: Shoreline polygons and tidal inlet delineations: Fire Island, NY, 2010 DCpts, DTpts, SLpts: Dune crest, dune toe, and mean high water shoreline positions: Fire Island, NY, 2012 2018 Hydrologic Data Summary for the Central Pine Barrens Region, Suffolk County, New York (ver. 2.0, February 2024) 2019 Hydrologic Data Summary for the Central Pine Barrens Region, Suffolk County, New York Approximate Regional Groundwater Divide on Long Island, New York, April-May 2016 Water Table Contours in the Upper Glacial and Magothy Aquifers, April-May 2016 Contours of Water Table Altitudes in the Upper Glacial and Magothy Aquifers, April-May 2013 Potentiometric Surface Contours of the Lloyd and North Shore Aquifers, April-May 2016 Occurrence of per- and polyfluoroalkyl substances, Long Island and New York City, New York