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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 well logs used in the creation of the Cortland 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 Rondout-Neversink 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 minimum 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.
This dataset includes well logs used in the creation of the Olean hydrogeologic framework. Well logs were used from multiple sources (DEC, DOT, NWIS, ESOGIS, and recently digitized 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 "smoothing points" used in the creation of the Jamestown hydrogeologic framework. Smoothing points were manually added and were used to enhance interpolated layers using geologic assumptions and include: valley edge points, centerline bedrock points, and upland bedrock SSURGO points.
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 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 georeferenced TIFF files from two separate reports for the Jamestown 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.
Digital hydrogeologic datasets were developed for the Greene 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 minimum 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.
Digital hydrogeologic datasets were developed for the Jamestown 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-fill 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 georeferenced TIFF files from two separate reports for the Fishkill and Wappinger Falls 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.
This dataset includes georeferenced tiff files from two separate reports for the Greene study area, where appropriate data 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
This dataset includes georeferenced tiff files from two separate reports for the Cortland 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
This dataset includes georeferenced tiff files from three separate reports for the Rondout Neversink 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.
Supplementary Points for the Fishkill and Wappinger Falls sourcewater study area in upstate New York
This dataset includes "smoothing points" used in the creation of the Fishkill and Wappinger Falls hydrogeologic framework. Smoothing points were manually added and were used to enhance interpolated layers using geologic assumptions and include: valley edge points, report points, and upland bedrock SSURGO points.
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.
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.
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 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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