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 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 U.S. Geological Survey has developed tools for projecting twenty-first century climate and hydrologic risk in Massachusetts in collaboration with Cornell University and Tufts University. These tools included a Stochastic Weather Generator (SWG). Output from the SWG is in this data release. The release includes daily precipitation and minimum and maximum air temperature for a 64-year period in the Nashua River watershed (that includes the Squannacook River) in Massachusetts and New Hampshire. There are 100 ensembles from the SWG for warming scenarios of 0 to 8 degrees Celsius in 0.5-degree increments. The SWG data were converted to a format utilized by the Precipitation-Runoff Modeling System (PRMS; https://www.usgs.gov/software/precipitation-runoff-modeling-system-prms) and input to a PRMS model for the Squannacook River watershed. The PRMS input and output files for the 100 ensembles of each of the 17 warming scenarios are also included in this data release. The 1,700 PRMS output files were utilized by a Stochastic Watershed Modeling tool to correct modeling biases that are inherent with a deterministic model such as PRMS. This data release includes the output from this Stochastic Watershed Model (SWM). For each of the 100 ensembles, the SWM was used to generate 10,000 ensembles, resulting in 1 million ensembles of 64-year periods for each of the warming scenarios. For each ensemble, streamflow characteristics of the annual maximum daily discharge at the 2-, 5-, 10-, 25-, 50-, 100-, and 500-year recurrence interval and of the annual 7-day low flow at the 2- and 10-year recurrence interval were determined.

The U.S. Geological Survey has developed tools for projecting twenty-first century climate and hydrologic risk in Massachusetts in collaboration with Cornell University and Tufts University. These tools included a Stochastic Weather Generator (SWG). Output from the SWG is in this data release. The release includes daily precipitation and minimum and maximum air temperature for a 64-year period in the Nashua River watershed (that includes the Squannacook River) in Massachusetts and New Hampshire. There are 100 ensembles from the SWG for warming scenarios of 0 to 8 degrees Celsius in 0.5-degree increments. The SWG data were converted to a format utilized by the Precipitation-Runoff Modeling System (PRMS; https://www.usgs.gov/software/precipitation-runoff-modeling-system-prms) and input to a PRMS model for the Squannacook River watershed. The PRMS input and output files for the 100 ensembles of each of the 17 warming scenarios are also included in this data release. The 1,700 PRMS output files were utilized by a Stochastic Watershed Modeling tool to correct modeling biases that are inherent with a deterministic model such as PRMS. This data release includes the output from this Stochastic Watershed Model (SWM). For each of the 100 ensembles, the SWM was used to generate 10,000 ensembles, resulting in 1 million ensembles of 64-year periods for each of the warming scenarios. For each ensemble, streamflow characteristics of the annual maximum daily discharge at the 2-, 5-, 10-, 25-, 50-, 100-, and 500-year recurrence interval and of the annual 7-day low flow at the 2- and 10-year recurrence interval were determined.

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 climate change emissions scenario at 30-year moving averages centered around 2030, 2050, 2070, 2090. During the climate modeling process, extreme precipitation values were also generated by scaling previously published intensity-duration-frequency (IDF) values from the NOAA Atlas 14 database (Perica and others, 2015) by a factor per degree expected warming produced from the SWG model generator (Najibi and others, 2022; Steinschneider and Najibi, 2022b, c). These newly generated IDF values (IDF_outputTable.csv) account for expected changes in extreme precipitation driven by variations in weather associated with climate change throughout the state of Massachusetts. The data presented here were developed in collaboration with the Massachusetts Executive Office of Energy and Environmental Affairs and housed on the Massachusetts climate change clearinghouse webpage (Massachusetts Executive Office of Energy and Environmental Affairs, 2022). References: Massachusetts Executive Office of Energy and Environmental Affairs, 2022, Resilient MA Maps and Data Center at URL https://resilientma-mapcenter-mass-eoeea.hub.arcgis.com/ Najibi, N., Mukhopadhyay, S., and Steinschneider, S., 2022, Precipitation scaling with temperature in the Northeast US: Variations by weather regime, season, and precipitation intensity: Geophysical Research Letters, v. 49, no. 8, 14 p., https://doi.org/10.1029/2021GL097100. Perica, S., Pavlovic, S., St. Laurent, M., Trypaluk, C., Unruh, D., Martin, D., and Wilhite, O., 2015, NOAA Atlas 14 Volume 10 Version 3, Precipitation-Frequency Atlas of the United States, Northeastern States (revised 2019): NOAA, National Weather Service, https://doi.org/10.25923/99jt-a543. Steinschneider, S., and Najibi, N., 2022a, A weather-regime based stochastic weather generator for climate scenario development across Massachusetts: Technical Documentation, Cornell University, https://eea-nescaum-dataservices-assets-prd.s3.amazonaws.com/cms/GUIDELINES/FinalTechnicalDocumentation_WGEN_20220405.pdf. Steinschneider, S., and Najibi, N., 2022b, Future projections of extreme precipitation across Massachusetts—a theory-based approach: Technical Documentation, Cornell University, https://eea-nescaum-dataservices-assets-prd.s3.amazonaws.com/cms/GUIDELINES/FinalTechnicalDocumentation_IDF_Curves_Dec2021.pdf. Steinschneider, S., and Najibi, N., 2022c, Observed and projected scaling of daily extreme precipitation with dew point temperature at annual and seasonal scales across the northeast United States: Journal of Hydrometeorology, v. 23, no. 3, p. 403-419, https://doi.org/10.1175/JHM-D-21-0183.1.

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 climate change emissions scenario at 30-year moving averages centered around 2030, 2050, 2070, 2090. During the climate modeling process, extreme precipitation values were also generated by scaling previously published intensity-duration-frequency (IDF) values from the NOAA Atlas 14 database (Perica and others, 2015) by a factor per degree expected warming produced from the SWG model generator (Najibi and others, 2022; Steinschneider and Najibi, 2022b, c). These newly generated IDF values (IDF_outputTable.csv) account for expected changes in extreme precipitation driven by variations in weather associated with climate change throughout the state of Massachusetts. The data presented here were developed in collaboration with the Massachusetts Executive Office of Energy and Environmental Affairs and housed on the Massachusetts climate change clearinghouse webpage (Massachusetts Executive Office of Energy and Environmental Affairs, 2022). References: Massachusetts Executive Office of Energy and Environmental Affairs, 2022, Resilient MA Maps and Data Center at URL https://resilientma-mapcenter-mass-eoeea.hub.arcgis.com/ Najibi, N., Mukhopadhyay, S., and Steinschneider, S., 2022, Precipitation scaling with temperature in the Northeast US: Variations by weather regime, season, and precipitation intensity: Geophysical Research Letters, v. 49, no. 8, 14 p., https://doi.org/10.1029/2021GL097100. Perica, S., Pavlovic, S., St. Laurent, M., Trypaluk, C., Unruh, D., Martin, D., and Wilhite, O., 2015, NOAA Atlas 14 Volume 10 Version 3, Precipitation-Frequency Atlas of the United States, Northeastern States (revised 2019): NOAA, National Weather Service, https://doi.org/10.25923/99jt-a543. Steinschneider, S., and Najibi, N., 2022a, A weather-regime based stochastic weather generator for climate scenario development across Massachusetts: Technical Documentation, Cornell University, https://eea-nescaum-dataservices-assets-prd.s3.amazonaws.com/cms/GUIDELINES/FinalTechnicalDocumentation_WGEN_20220405.pdf. Steinschneider, S., and Najibi, N., 2022b, Future projections of extreme precipitation across Massachusetts—a theory-based approach: Technical Documentation, Cornell University, https://eea-nescaum-dataservices-assets-prd.s3.amazonaws.com/cms/GUIDELINES/FinalTechnicalDocumentation_IDF_Curves_Dec2021.pdf. Steinschneider, S., and Najibi, N., 2022c, Observed and projected scaling of daily extreme precipitation with dew point temperature at annual and seasonal scales across the northeast United States: Journal of Hydrometeorology, v. 23, no. 3, p. 403-419, https://doi.org/10.1175/JHM-D-21-0183.1.

This data release provides a set of Hydrological Simulation Program--Fortran (HSPF) model files representing 5 EPA-selected future climate change scenarios for the Upper Charles River Basin in Massachusetts. Output from these models are intended for use as input to EPA Watershed Management Optimization Support Tool (WMOST) modeling. Climate scenarios, based on 2036-2065 changes from 1975-2004 for Representative Concentration Pathways (RCP) 4.5 and 8.5, model the effects of air temperature and precipitation changes (in degrees F for air temperature, in percent for precipitation) made to the input historical meteorological time series for 1975-2004. Meteorological data are from Boston Airport (Boston, MA), T.F. Green Airport (Providence, RI), and Worcester Regional Airport (Worcester, MA). Each set of climate scenario model files are derived from the original calibrated model files created by the Charles River Watershed Association to develop Total Maximum Daily Loads (TMDLs) for nutrients, and modified by USGS to support WMOST modeling (refer to Source Input fields in this metadata file).

This data release provides a set of Hydrological Simulation Program--Fortran (HSPF) model files representing 5 EPA-selected future climate change scenarios for the Upper Charles River Basin in Massachusetts. Output from these models are intended for use as input to EPA Watershed Management Optimization Support Tool (WMOST) modeling. Climate scenarios, based on 2036-2065 changes from 1975-2004 for Representative Concentration Pathways (RCP) 4.5 and 8.5, model the effects of air temperature and precipitation changes (in degrees F for air temperature, in percent for precipitation) made to the input historical meteorological time series for 1975-2004. Meteorological data are from Boston Airport (Boston, MA), T.F. Green Airport (Providence, RI), and Worcester Regional Airport (Worcester, MA). Each set of climate scenario model files are derived from the original calibrated model files created by the Charles River Watershed Association to develop Total Maximum Daily Loads (TMDLs) for nutrients, and modified by USGS to support WMOST modeling (refer to Source Input fields in this metadata file).

.\Stochastic Meteorology\ I. .\genpet.csv: This file contains stochastic monthly PET values for each weather station used to develop stochastic climate data in Kolars and others (2016). Column A is the weather station number, column B is the year in a stochastic simulation, column’s C through N are January through December PET values in millimeters, respectively. Simulations are stacked in order from 1 to 100 for each weather station. II. .\genprec.csv: This file contains stochastic monthly precipitation values for each weather station used to develop stochastic climate data in Kolars and others (2016). Column A is the weather station number, column B is the year in a stochastic simulation, column’s C through N are January through December precipitation values in millimeters, respectively. Simulations are stacked in order from 1 to 100 for each weather station. III. .\ grid_pet_stoch.csv: This file provides monthly stochastic PET values for each grid point used in the WBM presented in Kolars and others (2016). Column A is an unnamed index, column B, “lat”, is the latitude for a grid point, column C, “lon”, is the longitude for a grid point, column D is the year in a stochastic simulation, and columns E through P are the values of PET for January through December in millimeters, respectively. Simulations are ordered and placed on top of one another. IV. .\ grid_prec_stoch.csv: This file provides monthly stochastic precipitation values for each grid point used in the WBM presented in Kolars and others (2016). Column A is an unnamed index, column B, “lat”, is the latitude for a grid point, column C, “lon”, is the longitude for a grid point, column D is the year in a stochastic simulation, and columns E through P are the values of precipitation for January through December in millimeters, respectively. Simulations are ordered and placed on top of one another. V. .\PETMM_basinAv_stoch.csv: This file contains the Souris River Basin average PET for each month of the stochastic PET time series. Column A is an unnamed index, column B, “simnum” is the simulation number, column C, “yr”, is the year in a stochastic simulation, and columns D through O are the PET values in millimeters for January through December, respectively. VI. .\PrecipMM_basinAv_stoch.csv: This file contains the Souris River Basin average precipitation for each month of the stochastic precipitation time series. Column A is an unnamed index, column B, “simnum” is the simulation number, column C, “yr”, is the year in a stochastic simulation, and columns D through O are the precipitation values in millimeters for January through December, respectively.

This data release provides a set of Hydrological Simulation Program--Fortran (HSPF) model files representing 5 EPA-selected future climate change scenarios for each of two river basins: Taunton and Sudbury, in Massachusetts. Output from these models are intended for use as input to EPA Watershed Management Optimization Support Tool (WMOST) modeling. Climate scenarios, based on 2036-2065 change from 1975-2004 Representative Concentration Pathways (RCP) 4.5 and 8.5, model effects of air temperature and precipitation changes (in degrees F for air temperature, in percent for precipitation) made to the input historical meteorological time series 1975-2004. Taunton meteorological data is from T.F. Green Airport and the Sudbury meteorological data is from Worcester Regional Airport. Each set of climate scenario model files are derived from the original calibrated model files developed to support WMOST modeling (refer to Source Input fields in this metadata file).