Compilation of surficial geology datasets for southern New England

The U.S. Geological Survey, in cooperation with the Federal Highway Administration (FHWA) and the Connecticut, Massachusetts, and Rhode Island Departments of Transportation (DOTs), gathered geospatial data to facilitate the development of a regional Stochastic Empirical Loading and Dilution Model (SELDM) application (Granato and others, 2023). As part of this study, the surficial geology of Connecticut, Massachusetts, Rhode Island, and contributing areas from neighboring states was compiled from disparate datasets and reclassified into two categories that represent presence or absence of sand and gravel deposits (also referred to as stratified drift). This dataset provides a key basin characteristic for the region that may be used to help FHWA and DOTs to address potential environmental impacts of transportation projects in accordance with the National Environmental Policy Act of 1969 (https://www.epa.gov/nepa). Knowledge of local surficial geology also may support the assessment of green infrastructure as methods to reduce the effect of highway and urban receiving waters. Furthermore, this dataset facilitates the estimation of streamflow statistics at ungaged locations in the regions, these statistics were shown to be among the most sensitive input variables for refining SELDM outputs (Granato and others, 2023). This data release provides the compiled raster dataset of sand and gravel deposits as a Georeferenced Tagged Image File Format (GeoTIFF) raster dataset. The spatial extent includes the entirety of Connecticut, Massachusetts, and Rhode Island, as well as portions of contributing area to these states in New Hampshire, New York, and Vermont.

References:
Granato, G.E., Spaetzel, A.B., and Jeznach, L.C., 2023, Approaches for assessing flows, concentrations, and loads of highway and urban runoff and receiving-stream stormwater in southern New England with the Stochastic Empirical Loading and Dilution Model (SELDM): U.S. Geological Survey Scientific Investigations Report 2023–5087, 152 p., https://doi.org/10.3133/sir20235087