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Data

The New England Water Science Center operates over 500 real-time data collection sites throughout the six New England states. The sites collect surface-water, groundwater, water-quality, and precipitation data. Much of our real-time data is publicly available through NWIS. Additional data releases are also available on the page below.

Filter Total Items: 138

Lakebed Groundwater and Surface-Water Temperatures on September 18, 2020, at Shubael Pond, Marstons Mills, Massachusetts

This data release contains lakebed groundwater and surface-water temperatures collected during a survey on September 18, 2020, by using a handheld thermocouple probe at Shubael Pond, a groundwater flow-through kettle-hole pond, located in Marstons Mills, Massachusetts. The dataset includes a comma separated values (.csv) file, a geospatial dataset (shapefile), and FGDC-compliant metadata.

Baseline Groundwater-Quality Data from a Densely Developed Coastal Neighborhood, Falmouth, Massachusetts (2016 - 2021)(ver. 4.0, February 2022)

This data release provides a comprehensive dataset of sampling-site characteristics and baseline groundwater-quality data collected from a network of multilevel sampling wells installed in a densely developed coastal neighborhood undergoing a conversion from onsite septic systems to municipal sewering. Groundwater samples were collected during multiple events from a total of 227 well screens at 15

Data from Across the USA Used to Assess the Uncertainty of Discharge Estimates Using a Modified Manning's Equation

An expanding effort exists to estimate discharge of rivers utilizing remote sensing measurements. The goal of this investigation is to evaluate the uncertainty associated with estimating streamflow using remote sensing practices. This data release contains in-situ observations of river width, depth, velocity, and discharge from 30 U.S. Geological Survey (USGS) gaged study reaches (USGSGagedTestSit

Predicted Temperature and Precipitation Values Derived from Modeled Localized Weather Regimes and Climate Change in the State of Massachusetts

Predicted temperature and precipitation values were generated throughout the state of Massachusetts using a stochastic weather generator (SWG) model to develop various climate change scenarios (Steinschneider and Najibi, 2022a). This data release contains temperature and precipitation statistics (SWG_outputTable.csv) derived from the SWG model under the surface warming derived from the RCP 8.5 cli

Concentrations of Per- and Polyfluoroalkyl Substances (PFAS) in Lake-Bottom Sediments of Ashumet Pond on Cape Cod, Massachusetts, 2020 (ver. 2.0, February 2024)

Lake-bottom sediment and associated quality-control samples were collected in August 2020 from one coring location (U.S. Geological Survey station 413756070321301, ASHUMET POND, MASHPEE MI-ASHPD-0011) in Ashumet Pond downgradient from a former fire-training area on Cape Cod, Massachusetts. The core was collected to determine if per- and polyfluoroalkyl substances (PFAS) were present in the bottom

MODPATH-NWT and MODPATH6 models used to compare a new general simulation model approach with a conventional inset model approach for groundwater residence time in glacial aquifers

This groundwater-flow model archive/data release contains the model input and output files for 1) edited versions of four of the five NAWQA steady- state, inset MODFLOW-NWT models of regional model of Lake Michigan Basin (https://doi.org/10.3133/sir20185038) and 2) general models simulating the same four basins as the four inset models. Two HUC8 basins in the lower peninsula of Michigan (Kalamazoo

Confirmatory Sampling for Per- and Polyfluoroalkyl Substances (PFAS) in Shallow Soils Across New Hampshire, 2022

Data for per- and polyfluoroalkyl substances (PFAS) and related chemical and physical characteristics are presented from 30 soil sampling locations within the State of New Hampshire. A total of 15 sites were chosen based on the results of sampling efforts published in Santangelo and others(2022). Sites with relatively high concentrations of PFAS observed during the first study were selected for re

Data for Regression Models to Estimate Water Use in Providence, Rhode Island, 2014-2021

This data release contains input data and programs (scripts) used to estimate monthly water demand for retail customers of Providence Water, located in Providence, Rhode Island. Explanatory data and model outputs are from July 2014 through June 2021. Models of per capita (for single-family residential customers) or per connection (for multi-family residential, commercial, and industrial customers)

Concentrations of metals and other water quality data for the Assabet and Concord Rivers, Massachusetts, 2008

This dataset contains water-quality data for stream samples collected by the U.S. Geological Survey at 12 sites on the Assabet and Concord Rivers in eastern Massachusetts in 2008. The samples were collected monthly from June to October, 2008. The water-quality parameters and constituents include field parameters (water temperature, specific conductance, pH, and dissolved oxygen), concentrations of

Field-scale investigation of per- and polyfluoroalkyl substances (PFAS) leaching from shallow soils to groundwater at two sites in New Hampshire, 2021-2022

Per- and polyfluoroalkyl substances (PFAS) and related chemical and physical data are presented from shallow soil and groundwater at two sites in New Hampshire. The two sites, the former Brentwood Fire Training Area and White Farm, were selected because materials known to contain PFAS were used at each site. White Farm is an active farm where biosolids have been applied for several years. At the f

Maine StreamStats Foundation Data Layers

The U.S. Geological Survey (USGS), in cooperation with Maine Department of Transportation, has compiled a series of GIS datasets to be implemented into the USGS StreamStats application for the State of Maine.These data were compiled from the high-resolution National Hydrography Dataset (NHD) and the Maine Lidar-Derived Watersheds (Sturtevant and Schoen, 2022). By using these datasets users will be

Examining the influence of deep learning architecture on generalizability for predicting stream temperature in the Delaware River Basin

This data release and model archive provides all data, code, and modelling results used in Topp et al. (2023) to examine the influence of deep learning architecture on generalizability when predicting stream temperature in the Delaware River Basin (DRB). Briefly, we modeled stream temperature in the DRB using two spatially and temporally aware process guided deep learning models (a recurrent graph