The datasets are streamflow characteristics computed from the 1 million ensembles of the Stochastic Watershed Model for each warming scenario of 0 to 8 degrees Celsius in 0.5-degree intervals for the Squannacook River at West Groton, Massachusetts streamgage location. Each value in the files represents a streamflow characteristic computed from an ensemble that covers a period of 64 years of daily streamflows computed by the Stochastic Watershed Model. The Stochastic Watershed Model was developed at Tufts University (Shabestanipour and others, 2022). The streamflow characteristics include the 2-, 5-, 10-, 25-, 50-, 100-, and 500-year recurrence interval of the annual maximum daily streamflow and the 7-day low flow with a 2- and 10-year recurrence interval. There is one file for each streamflow characteristic. Shabestanipour, G., Broudeur, Z., Farmer, W., Steinschneider, S., Vogel, R., and Lamontagne, J., 2022, Stochastic watershed model ensembles for long-range planning—Verification and validation: Water Resources Research, v. 59, no. 2, 20 p., accessed January 3, 2024 at https://doi.org/10.1029/2022WR032201.

The input datasets are daily precipitation and minimum and maximum temperature for a period of 64 years for warming scenarios of 0 degrees to 8 degrees Celsius, by 0.5-degree increments for the Squannacook River watershed in Massachusetts. The source of the data is the Stochastic Weather Generator (SWG; Steinschneider and Najibi, 2022) and includes 100 ensembles from the SWG. The daily time-series, space-delimited files cover three subwatersheds within the Squannacook River watershed in a format readable by the Precipitation Runoff-Modeling System (PRMS; https://www.usgs.gov/software/precipitation-runoff-modeling-system-prms). The input files were input to PRMS, along with the model control and parameter files, to generate the output files. The output files are daily time-series in comma-delimited format of the resulting discharges for the Squannacook River at the mouth of the river and at the Squannacook River near West Groton, Massachusetts streamgage for each of the ensembles of each of the warming scenarios.

The input datasets are daily precipitation and minimum and maximum temperature for a period of 64 years for warming scenarios of 0 degrees to 8 degrees Celsius, by 0.5-degree increments for the Squannacook River watershed in Massachusetts. The source of the data is the Stochastic Weather Generator (SWG; Steinschneider and Najibi, 2022) and includes 100 ensembles from the SWG. The daily time-series, space-delimited files cover three subwatersheds within the Squannacook River watershed in a format readable by the Precipitation Runoff-Modeling System (PRMS; https://www.usgs.gov/software/precipitation-runoff-modeling-system-prms). The input files were input to PRMS, along with the model control and parameter files, to generate the output files. The output files are daily time-series in comma-delimited format of the resulting discharges for the Squannacook River at the mouth of the river and at the Squannacook River near West Groton, Massachusetts streamgage for each of the ensembles of each of the warming scenarios.

The datasets are gridded daily precipitation and minimum and maximum temperature for a period of 64 years for warming scenarios of 0 to 8 degrees Celsius, by 0.5 degrees for the Nashua River watershed in Massachusetts. The data are output from a Stochastic Weather Generator developed at Cornell University (Steinschneider and Najibi, 2022) and includes 100 ensembles of each warming scenario. The data files are in NetCDF format (https://www.unidata.ucar.edu/software/netcdf/). Steinschneider, S., and Najibi, N., 2022, A weather-regime based stochastic weather generator for climate scenario development across Massachusetts—Technical documentation: Ithaca, N.Y., Cornell University, [Department of] Biological and Environmental Engineering report, 47 p., accessed February 16, 2023, at https://eea-nescaum-dataservices-assets-prd.s3.amazonaws.com/cms/GUIDELINES/FinalTechnicalDocumentation_WGEN_20220405.pdf

The datasets are gridded daily precipitation and minimum and maximum temperature for a period of 64 years for warming scenarios of 0 to 8 degrees Celsius, by 0.5 degrees for the Nashua River watershed in Massachusetts. The data are output from a Stochastic Weather Generator developed at Cornell University (Steinschneider and Najibi, 2022) and includes 100 ensembles of each warming scenario. The data files are in NetCDF format (https://www.unidata.ucar.edu/software/netcdf/). Steinschneider, S., and Najibi, N., 2022, A weather-regime based stochastic weather generator for climate scenario development across Massachusetts—Technical documentation: Ithaca, N.Y., Cornell University, [Department of] Biological and Environmental Engineering report, 47 p., accessed February 16, 2023, at https://eea-nescaum-dataservices-assets-prd.s3.amazonaws.com/cms/GUIDELINES/FinalTechnicalDocumentation_WGEN_20220405.pdf

To provide daily mean streamflow values at ungaged (partial-record) sites within the Yankee Fork Salmon River watershed, the U.S. Geological Survey (USGS), in cooperation with U.S. Bureau of Reclamation, used discharge measurements at three partial-record sites and related those measurements to a nearby USGS real-time streamgage (index site). Daily mean streamflow was estimated by developing a regression relationship between each partial-record site and the index site for water years 2012-2019. These data are intended to provide daily mean streamflow estimates at partial-record sites as part of a larger study (Clark and others, 2021) to estimate sediment loading for each site.

To provide daily mean streamflow values at ungaged (partial-record) sites within the Yankee Fork Salmon River watershed, the U.S. Geological Survey (USGS), in cooperation with U.S. Bureau of Reclamation, used discharge measurements at three partial-record sites and related those measurements to a nearby USGS real-time streamgage (index site). Daily mean streamflow was estimated by developing a regression relationship between each partial-record site and the index site for water years 2012-2019. These data are intended to provide daily mean streamflow estimates at partial-record sites as part of a larger study (Clark and others, 2021) to estimate sediment loading for each site.

The datasets herein are the observed instantaneous values of streamflow for the titled U.S. Geological Survey streamgage with precipitation metadata added. Days where streamflow is directly affected by precipitation and the day afterwards is identified with a "B". Two days after a precipitation event is identified with a "C". If the streamflow was affected by snow or ice, the data is identified with a "D". Any data that is not precipitation influenced is identified with an "A". This metadata was applied on the basis of numerous rain gages in the vicinity of the streamgage and radar images obtained from the National Oceanic and Atmospheric Administration website https://www.ncdc.noaa.gov/data-access/radar-data/radar-map-tool (accessed various times throughout the project).

The datasets herein are the observed instantaneous values of streamflow for the titled U.S. Geological Survey streamgage with precipitation metadata added. Days where streamflow is directly affected by precipitation and the day afterwards is identified with a "B". Two days after a precipitation event is identified with a "C". If the streamflow was affected by snow or ice, the data is identified with a "D". Any data that is not precipitation influenced is identified with an "A". This metadata was applied on the basis of numerous rain gages in the vicinity of the streamgage and radar images obtained from the National Oceanic and Atmospheric Administration website https://www.ncdc.noaa.gov/data-access/radar-data/radar-map-tool (accessed various times throughout the project).

Spatial data layers of stream crossing point locations, cross-section polyline, centerline polyline, and bank polyline shapefiles have been developed for selected stream crossings in the Squannacook River basin, Massachusetts. The spatial data and calculated attribute values are model input data for U.S. Army Corps of Engineer’s Hydrologic Engineering Center’s River Analysis System (HEC-RAS) hydraulic models. The stream crossing point locations were derived from the North Atlantic Aquatic Connectivity Collaboration (NAACC) database. The stream channel cross-sections, centerlines, and bank polylines were derived using automated methods in a Geographic Information System (GIS) using ArcGIS Pro and Python programming language. The polyline shapefiles are Z-enabled and have elevation data derived from Light Detection and Ranging (lidar) Digital Elevation Models (DEM) for Z-coordinate vertex values in units of feet. The polyline shapefiles are also M-enabled and have profile stationing values for the M-coordinate vertex values in units of feet. The automated GIS processes delineated a series of stream channel cross-sections along lidar-derived stream centerlines and have stream channel bathymetry estimated from Massachusetts bankfull channel geometry equations (Bent and Waite, 2013). The bankfull equations were also used to derive stream bank polylines. This data release contains the following shapefiles in the Spatial_Data_Layers.zip file: 1. Stream_Crossing_Locations.shp - Esri point shapefile derived from the NAACC stream crossing database. 2. Stream_Crossing_Watersheds.shp - Esri polygon shapefile of lidar-derived watershed boundaries that estimate the upstream drainage area for each stream crossing location. 3. Model_Cross_Sections.shp - Esri Z- and M-enabled polyline shapefile of the cross-section data used for hydraulic model input. 4. Model_Flowpaths.shp - Esri Z- and M-enabled polyline shapefile of the stream centerline and stream bank line data used for hydraulic model input. References: Bent, G.C., and Waite, A.M., 2013, Equations for estimating bankfull channel geometry and discharge for streams in Massachusetts: U.S. Geological Survey Scientific Investigations Report 2013–5155, 62 p., http://dx.doi.org/10.3133/sir20135155