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This dataset includes spreadsheets with statistical data (mean and median absolute error) used in deciding which interpolation method best fit the corresponding dataset. All statistical data were paired with a visual inspection of the interpolation prior to determining the final raster product. All spreadsheets were generated using an automated python script (Jahn, 2020).
This dataset includes "smoothing points" used in the creation of the Cincinnatus hydrogeologic framework. Smoothing points were manually added by the project team and were used to enhance interpolated layers using geologic assumptions and include: valley edge points, centerline bedrock points (and where applicable L1 and L2 points), and upland bedrock SURGO points.
The Jamestown study area is located within the counties of Chautauqua and Cattaraugus, New York. The predominant population center is the city of Jamestown. Previous USGS reports here include Open-File Report 82-113 (Stelz and others 1982) and Bulletin 58-1960 (Crain, 1966) The five child pages below break the data up into georeferenced and digitized previous report data, interpreted geologic information, well logs, supplemental point data, and interpolation statistics.
This dataset includes spreadsheets with statistical data (mean and median absolute error) used in deciding which interpolation method best fit the corresponding dataset. All statistical data were paired with a visual inspection of the interpolation prior to determining the final raster product. All spreadsheets were generated using an automated python script (Jahn, 2020).
This dataset includes "smoothing points" used in the creation of the Roundout Neversink hydrogeologic framework. Smoothing points were manually added by the project team and were used to enhance interpolated layers using geologic assumptions and include: valley edge points, centerline bedrock points (and where applicable L1 and L2 points), and upland bedrock SURGO points.
This dataset has been archived; it has been superceded by version 2.0 (March 2021), which can be found at https://doi.org/10.5066/P954DLLC. The data contained in this data release support USGS Scientific Investigations Report 2020-5023, "Distribution of selected hydrogeologic characteristics of the upper glacial and Magothy aquifers, Long Island, New York" (Walter and Finkelstein, 2020). This data release contains estimates of aquifer texture describing the Long Island aquifer system. These estimates in total can be considered a model of aquifer texture describing unconsolidated sediments in the following principal units: 1) Upper glacial aquifer, 2) Jameco aquifer, 3) Monmouth Greensand confining unit, and 4)...
This dataset has been archived; it has been superseded by version 2.0 (November 2021) which can be found at https://doi.org/10.5066/P95PT2RV. Static flood inundation boundary extents were created along the entire shoreline of Lake Ontario in Cayuga, Jefferson, Monroe, Niagara, Orleans, Oswego, and Wayne Counties in New York by using recently acquired (2007, 2010, 2014, and 2017) light detection and ranging (lidar) data. The flood inundation maps, accessible through the USGS Flood Inundation Mapping Program website at https://www.usgs.gov/mission-areas/water-resources/science/flood-inundation-mapping-fim-program, depict estimates of the areal extent and water depth of shoreline flooding in 8 segments corresponding...
This dataset includes well logs used in the creation of the Ellicottville hydrogeologic framework. Well logs were used from multiple sources (DEC, DOT, NWIS, ESOGIS, and archived material) and were a crucial component in generating hydrogeologic layer elevations and thicknesses. Well logs are available in their original form on GeoLog Locator (https://webapps.usgs.gov/GeoLogLocator/#!/) and provided here in the digitized form (shapefiles and feature classes), which were used in the generation of the hydrogeologic framework.
This dataset includes spreadsheets with statistical data (mean and median absolute error) used in deciding which interpolation method best fit the corresponding dataset. All statistical data were paired with a visual inspection of the interpolation prior to determining the final raster product. All spreadsheets were generated using an automated python script (Jahn, 2020).
Digital hydrogeologic datasets were developed for the Cortland study area in upstate New York in cooperation with the New York State Department of Environmental Conservation. These datasets define the hydrogeologic framework of the valley-fill aquifer and surrounding till-covered uplands within the study area. Datasets include: bedrock elevation raster, lacustrine silt and clay top and bottom elevation rasters, lidar mean elevation raster, lacustrine extent polygon, valley-fill extent polygon, and surficial geology polygons. Elevation layers were interpolated at 125-foot discretization to match the model grid cell size.
The Fishkill/Wappinger study area is located in the vicinity of the towns of Beacon, Wappingers Falls, Poughkeepsie, and Fishkill. Previous USGS reports here include USGS Scientific Investigations Map 3136 (Reynolds and Calef, 2010) and Open-File Report 80-437 (Snavely, 1980). The five child pages below break the data up into georeferenced and digitized previous report data, interpreted geologic information, well logs, supplemental point data, and interpolation statistics.
The data contained in this data release support USGS Scientific Investigations Report 2020-5023, "Distribution of selected hydrogeologic characteristics of the upper glacial and Magothy aquifers, Long Island, New York" (Walter and Finkelstein, 2020). This data release contains estimates of aquifer texture describing the Long Island aquifer system. These estimates in total can be considered a model of aquifer texture describing unconsolidated sediments in the following principal units: 1) Upper glacial aquifer, 2) Jameco aquifer, 3) Monmouth Greensand confining unit, and 4) Magothy aquifer. The Lloyd aquifer, a major aquifer on Long Island, is not included in the model due to a lack of available data. Aquifer texture...
Digital hydrogeologic datasets were developed for the Olean study area in upstate New York in cooperation with the New York State Department of Environmental Conservation. These datasets define the hydrogeologic framework of the valley-fill aquifer and surrounding till-covered uplands within the study area. Datasets include: bedrock elevation raster, lacustrine silt and clay top and bottom elevation rasters (where present) for the main lacustrine unit, lacustrine silt and clay top and bottom elevation rasters (where present) for an upper lacustrine unit, LIDAR minimum elevation raster, lacustrine extent polygons, valley-fill extent polygon, and surficial geology polygons. Elevation layers were interpolated at 125-foot...
This dataset includes georeferenced TIFF files from three separate reports for the Olean study area that have been digitized into feature classes within ArcGIS. Not all digitized and georeferenced data was necessarily used in the final interpolations, however they may have contributed to understanding the local hydrogeology.
The town of Cincinnatus is located in Cortland County, New York. No previous USGS publications are available for the Cincinnatus study area. Subsequently, all subsurface hydrogelogic data was derived from driller well logs. The four child pages below break the data up into interpreted geologic information, well logs, supplemental point data, and interpolation statistics.
This dataset has been archived; it has been superseded by version 3.0 (November 2021) which can be found at https://doi.org/10.5066/P954DLLC . The data contained in this data release support USGS Scientific Investigations Report 2020-5023, "Distribution of selected hydrogeologic characteristics of the upper glacial and Magothy aquifers, Long Island, New York" (Walter and Finkelstein, 2020). This data release contains estimates of aquifer texture describing the Long Island aquifer system. These estimates in total can be considered a model of aquifer texture describing unconsolidated sediments in the following principal units: 1) Upper glacial aquifer, 2) Jameco aquifer, 3) Monmouth Greensand confining unit, and...
This data release contains the output from a Soil-Water-Balance (SWB) model (Westenbroek and others, 2010), used to estimate potential recharge to the Long Island regional aquifer system from 1900-2019. Output data for two SWB simulations are included. The first simulation uses available land-use/land-cover datasets to estimate recharge with changing land use from 1900-2019 (referred to as the post-development simulation). The second simulation assumed a forested, undeveloped (predevelopment simulation) condition across Long Island for the same period. The same soil coverages and time-series climate data were used throughout both simulations. Potential recharge was spatially distributed as gridded output across...
This dataset includes well logs used in the creation of the Cincinnatus hydrogeologic framework. Well logs were used from multiple sources (DEC, DOT, NWIS) and were a crucial component in generating hydrogeologic layer elevations and thicknesses. Well logs are available in their original form on GeoLog Locator (https://webapps.usgs.gov/GeoLogLocator/#!/) and provided here in the digitized form (shapefiles and feature classes), which were used in the generation of the hydrogeologic framework.
Digital hydrogeologic datasets were developed for the Fishkill and Wappinger Falls study area in upstate New York in cooperation with the New York State Department of Environmental Conservation. These datasets define the hydrogeologic framework of the valley-fill aquifer and surrounding till-covered uplands within the study area. Datasets include: bedrock elevation raster, lacustrine silt and clay top and bottom elevation rasters (where present), LIDAR minimum elevation raster, lacustrine extent polygon, valley extent polygon, and surficial geology polygons. Elevation layers were interpolated at 125-foot discretization to match what was done in previous work.
This dataset includes spreadsheets with statistical data (mean and median absolute error) used in deciding which interpolation method best fit the corresponding dataset. All statistical data were paired with a visual inspection of the interpolation prior to determining the final raster product. All spreadsheets were generated using an automated python script (Jahn, 2020).
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