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.

Led by the Massachusetts Executive Office of Energy and Environmental Affairs (EEA), in partnership with Cornell University, U.S. Geological Survey and Tufts University, the Massachusetts Climate and Hydrologic Risk Project (Phase 1) has developed new climate change projections for the Commonwealth. These new temperature and precipitation projections are downscaled for Massachusetts at the HUC8 watershed scale using Global Climate Models (GCMs) and a Stochastic Weather Generator (SWG) developed by Cornell University.

Stochastic weather generators provide a computationally efficient and complementary alternative to direct use of GCMs for investigating water system performance under climate stress. These models are configured based on existing meteorological records (i.e., historical weather) and are then used to generate large ensembles of simulated daily weather records that are similar to but not bound by variability in past observations. Once fit to historical data, model parameters can be systematically altered to produce new traces of weather that exhibit a wide range of change in their distributional characteristics, including the intensity and frequency of average and extreme precipitation, heatwaves, and cold spells.

The Phase 1 SWG was developed, calibrated, and validated across all HUC8 watersheds that intersect with the state of Massachusetts. A set of climate change scenarios for those watersheds were generated that only reflect mechanisms of thermodynamic climate change deemed to be most credible. These thermodynamic climate changes are based on the range of temperature projections produced by a set of downscaled GCMs for the region. The temperature and precipitation projections presented in this dashboard reflect a warming scenario linked to the Representation Concentration Pathway (RCP) 8.5, a comparatively high greenhouse gas emissions scenario.

The statistics presented in this series of map layers are expressed as either a percent change or absolute change (see list of layers with units and definitions below). These changes are referenced to baseline values that are calculated based on the median value across the 50 model ensemble members associated with the 0°C temperature change scenario derived from observational data (1950-2013) from Livneh et al. (2015). The temperature projections derived from the downscaled GCMs for the region, which are used to drive the SGW, are averaged across 30 years and centered on a target decade (i.e., 2030, 2050, 2070). Projections for 2090 are averaged across 20 years.Definitions of climate projection metrics (with units of change):Total Precipitation (% change): The average total precipitation within a calendar year. Maximum Precipitation (% change): The maximum daily precipitation in the entire record. Precipitation Depth – 90th Percentile Storm (% change): The 90th percentile of non-zero precipitation. Precipitation Depth –99th Percentile Storm (% change): The 99th percentile of non-zero precipitation. Consecutive Wet Days (# days): The average number of days that exist within a run of 2 or more wet days. Consecutive Dry Days (# days): The average number of days that exist within a model run of 2 or more dry days. Days above 1 inch (# days): The number of days with precipitation greater than 1 inch. Days above 2 inches (# days): The number of days with precipitation greater than 2 inches.Days above 4 inches (# days): The number of days with precipitation greater than 4 inches.Maximum Temperature (°F): The maximum daily average temperature value in the entire recordAverage Temperature (°F): Daily average temperature.Days below 0 °F (# days): The number of days with temperature below 0 °F.Days below 32 °F (# days): The number of days with temperature below 32 °F.Maximum Duration of Coldwaves (# days): Longest duration of coldwaves in the record, where coldwaves are defined as ten or more consecutive days below 20 °F.Average Duration of Coldwaves (# days): Average duration of coldwaves in the record, where coldwaves are defined as ten or more consecutive days below 20 °F.Number of Coldwave Events (# events): Number of instances with ten or more consecutive days with temperature below 20 °F.Number of Coldstress Events (# events): Number of instances when a 3-day moving average of temperature is less than 32 °F. Days above 100 °F (# days): The number of days with temperature above 100 °F.Days above 95 °F (# days): The number of days with temperature above 95 °F.Days above 90 °F (# days): The number of days with temperature above 90 °F.Maximum Duration of Heatwaves (# days): Longest duration of heatwaves in the record, where heatwaves are defined as three or more consecutive days over 90 °F.Average Duration of Heatwaves (# days): Average duration of heatwaves in the record, where heatwaves are defined as three or more consecutive days over 90 °F.Number of Heatwave Events (# events): Number of instances with three or more consecutive days with temperature over 90 °F.Number of Heatstress Events (# events): Number of instances when a 3-day moving average of temperature is above 86 °F.Cooling Degree Days (# degree-day): Cooling degree days assume that when the outside temperature is below 65°F, we don't need cooling (air-conditioning) to be comfortable. Cooling degree-days are the difference between the daily temperature mean and 65°F. For example, if the temperature mean is 85°F, we subtract 65 from the mean and the result is 20 cooling degree-days for that day. (Definition adapted from National Weather Service).Heating Degree Days (# degree-day): Heating degree-days assume that when the outside temperature is above 65°F, we don't need heating to be comfortable. Heating degree days are the difference between the daily temperature mean and 65°F. For example, if the mean temperature mean is 25°F, we subtract the mean from 65 and the result is 40 heating degree-days for that day. (Definition adapted from National Weather Service).Growing Degree Days (# degree-day): A growing degree day (GDD) is an index used to express crop maturity. The index is computed by subtracting a base temperature of 50°F from the average of the maximum and minimum temperatures for the day. Minimum temperatures less than 50°F are set to 50, and maximum temperatures greater than 86°F are set to 86. These substitutions indicate that no appreciable growth is detected with temperatures lower than 50° or greater than 86°. (Adapted from National Weather Service).Please see additional information related to this project and dataset in the Climate Change Projection Dashboard on the Resilient MA Maps and Data Center webpage.

A regression model that estimates monthly temperature and precipitation as a function of latitude, longitude, and elevation for the New England area was used to estimate annual growing degree days and precipitation for the state of Massachusetts. For details of the regression model please see the published paper (Ollinger, S.V., Aber, J.D., Federer, C.A., Lovett, G.M., Ellis, J.M., 1995. Modeling Physical and Chemical Climate of the Northeastern United States for a Geographic Information System. US Dept of Agriculture, Forest Service, Radnor, PA, USA).

This excel contains data for Chapter 2 “Precipitation” of the 2017 State of Narragansett Bay & Its Watershed Technical Report (nbep.org). It includes the raw data behind Figure 1, “Annual precipitation at Providence, RI,” (page 64); Figure 2, “Annual precipitation at Worcester, MA,” (page 64); Figure 3, “Annual Palmer Drought Severity Index (PDSI) for Rhode Island,” (page 65); Figure 4, "Annual Palmer Drought Severity Index (PDSI) for Massachusetts," (page 65); Figure 5, "Climate model projection of winter total precipitation in RI or MA to 2100," (page 67); and Figure 6, "Climate model projection of winter annual snowfall in RI or MA to 2100," (page 67). For more information, please reference the Technical Report or contact info@nbep.org. Original figures are available at http://nbep.org/the-state-of-our-watershed/figures/.

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.

Morocco MA: Average Precipitation in Depth data was reported at 346.000 mm/Year in 2014. This stayed constant from the previous number of 346.000 mm/Year for 2012. Morocco MA: Average Precipitation in Depth data is updated yearly, averaging 346.000 mm/Year from Dec 1962 (Median) to 2014, with 12 observations. The data reached an all-time high of 346.000 mm/Year in 2014 and a record low of 346.000 mm/Year in 2014. Morocco MA: Average Precipitation in Depth data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Morocco – Table MA.World Bank.WDI: Land Use, Protected Areas and National Wealth. Average precipitation is the long-term average in depth (over space and time) of annual precipitation in the country. Precipitation is defined as any kind of water that falls from clouds as a liquid or a solid.; ; Food and Agriculture Organization, electronic files and web site.; ;

Year 2013 meteorological measurements at MBL Marshview Farm of air temperature, humidity, precipitation, solar radiation, photosynthetically active radiation (PAR), wind speed and direction and barometric pressure. Sensors conduct measurements every 5 secs and measurements are reported as averages or totals for 15 minute intervals. 15 minute averages are reported for air temperature, humidity, solar radiation, PAR, wind speed and direction and barometric pressure. 15 minute totals are reported for precipitation.

Meteorological data daily averages and daily fluxes for stations located at Governor's Academy and MBL Marshview Farm, Newbury, MA. Data includes air temeprature, precipitation, relative humidity, solar radiation, PAR, wind and air pressure measurements. Years 2000 to 2007 the station was located at Governor's Academy, Newbury, MA and was moved July 30, 2007 to the MBL Marshview Farm field station property where it is currently located.

Link to the ScienceBase Item Summary page for the item described by this metadata record. Service Protocol: Link to the ScienceBase Item Summary page for the item described by this metadata record. Application Profile: Web Browser. Link Function: information

Timeseries data from 'FALL RIVER PRECIPITATION GAGE FALL RIVER, MA (USGS 414204071091700)' (gov_usgs_nwis_414204071091700) _NCProperties=version=2,netcdf=4.8.1,hdf5=1.12.2 cdm_data_type=TimeSeries cdm_timeseries_variables=station,longitude,latitude contributor_email=feedback@axiomdatascience.com contributor_name=Axiom Data Science contributor_role=processor contributor_role_vocabulary=NERC contributor_url=https://www.axiomdatascience.com Conventions=IOOS-1.2, CF-1.6, ACDD-1.3 defaultDataQuery=lwe_thickness_of_precipitation_amount,z,time,lwe_thickness_of_precipitation_amount_qc_agg&time>=max(time)-3days Easternmost_Easting=-71.154722 featureType=TimeSeries geospatial_lat_max=41.701111 geospatial_lat_min=41.701111 geospatial_lat_units=degrees_north geospatial_lon_max=-71.154722 geospatial_lon_min=-71.154722 geospatial_lon_units=degrees_east geospatial_vertical_max=0.0 geospatial_vertical_min=0.0 geospatial_vertical_positive=up geospatial_vertical_units=m history=Downloaded from USGS National Water Information System (NWIS) at id=132793 infoUrl=https://sensors.ioos.us/#metadata/132793/station institution=USGS National Water Information System (NWIS) naming_authority=com.axiomdatascience Northernmost_Northing=41.701111 platform=fixed platform_name=FALL RIVER PRECIPITATION GAGE FALL RIVER, MA (USGS 414204071091700) platform_vocabulary=http://mmisw.org/ont/ioos/platform processing_level=Level 2 references=https://waterdata.usgs.gov/monitoring-location/414204071091700,, sourceUrl=https://waterdata.usgs.gov/monitoring-location/414204071091700 Southernmost_Northing=41.701111 standard_name_vocabulary=CF Standard Name Table v72 station_id=132793 time_coverage_end=2025-08-21T14:30:00Z time_coverage_start=2023-12-15T03:00:00Z Westernmost_Easting=-71.154722

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.

Timeseries data from 'TOWN OF PEABODY PRECIPITATION PEABODY,MA (USGS 423211071004400)' (gov_usgs_nwis_423211071004400) cdm_data_type=TimeSeries cdm_timeseries_variables=station,longitude,latitude contributor_email=feedback@axiomdatascience.com contributor_name=Axiom Data Science contributor_role=processor contributor_role_vocabulary=NERC contributor_url=https://www.axiomdatascience.com Conventions=IOOS-1.2, CF-1.6, ACDD-1.3, NCCSV-1.2 defaultDataQuery=lwe_thickness_of_precipitation_amount,z,time,lwe_thickness_of_precipitation_amount_qc_agg&time>=max(time)-3days Easternmost_Easting=-71.012222 featureType=TimeSeries geospatial_lat_max=42.536389 geospatial_lat_min=42.536389 geospatial_lat_units=degrees_north geospatial_lon_max=-71.012222 geospatial_lon_min=-71.012222 geospatial_lon_units=degrees_east geospatial_vertical_max=0.0 geospatial_vertical_min=0.0 geospatial_vertical_positive=up geospatial_vertical_units=m history=Downloaded from USGS National Water Information System (NWIS) at id=132728 infoUrl=https://sensors.ioos.us/#metadata/132728/station institution=USGS National Water Information System (NWIS) naming_authority=com.axiomdatascience Northernmost_Northing=42.536389 platform=fixed platform_name=TOWN OF PEABODY PRECIPITATION PEABODY,MA (USGS 423211071004400) platform_vocabulary=http://mmisw.org/ont/ioos/platform processing_level=Level 2 references=https://waterdata.usgs.gov/monitoring-location/423211071004400,, sourceUrl=https://waterdata.usgs.gov/monitoring-location/423211071004400 Southernmost_Northing=42.536389 standard_name_vocabulary=CF Standard Name Table v72 station_id=132728 time_coverage_end=2025-08-05T21:30:00Z time_coverage_start=2023-09-01T00:00:00Z Westernmost_Easting=-71.012222

EarthInfo Hard Drive - Database Index Database Title - Summary List of Data Elements (Period of Record) FILE LOCATION Global Climate - Monthly Temperature, Precipitation, Pressure (1697-2010) /gDATA\GC Global Daily - Daily Temperature, Precipitation, Snowfall, Evaporation, Wind (1833-2010) \DATA\GD Global Marine - Monthly Temperature, Pressure, Humidity, Cloudiness, Wind (1800-2002) Region 1 Arctic \DATA\GM\1 Region 2:1 Atlantic \DATA\GM\2.1 Region 2:2 Atlantic \DATA\GM\2.2 Region 2:3 Atlantic \DATA\GM\2.3 Region 2:4 Atlantic \DATA\GM\2.4 Region 3:1 Indian \DATA\GM\3.1 Region 3:2 Indian \DATA\GM\3.2 Region 4:1 Pacific \DATA\GM\4.1 Region 4:2 Pacific \DATA\GM\4.2 Region 4:3 Pacific \DATA\GM\4.3 Region 4:4 Pacific \DATA\GM\4.4 Region 4:5 Pacific \DATA\GM\4.5 NCDC Summary of the Day - Daily Temp, Precipitation, Snowfall, Evaporation (1854-2010) West 1 AZ CA CO NM NV UT WY \DATA\SD\W1 West 2 AK HI ID KS MT ND NE OK OR PI SD TX WA \DATA\SD\W2 Central AL AR IA IL IN KY LA MI MN MO MS OH TN WI WV \DATA\SD\CE East CT DE FL GA MA MD ME NC NH NJ NY PA PR RI SC VA VI VT \DATA\SD\EA NCDC Hourly Precipitation - Hourly Precipitation, Events, Storms (1900-2009) West 1 AZ CA CO NM NV UT WY \DATA\HP\W1 West 2 AK HI ID KS MT ND NE OK OR PI SD TX WA \DATA\HP\W2 Central AL AR IA IL IN KY LA MI MN MO MS OH TN WI WV \DATA\HP\CE East CT DE FL GA MA MD ME NC NH NJ NY PA PR RI SC VA VI VT \DATA\HP\EA NCDC Fifteen Minute Precipitation - Quarter-Hourly Precip, Events, Storms (1970-2006) \DATA\FP NCDC Surface Airways - Hourly Temp, Pressure, Humidity, Sky Cover, Wind (1938-2005) West 1:1 AZ CA \DATA\SA\W1.1 West 1:2 NV UT \DATA\SA\W1.2 West 1:3 CO NM WY \DATA\SA\W1.3 West 2:1 ID MT ND OR SD WA \DATA\SA\W2.1 West 2:2 KS NE OK TX \DATA\SA\W2.2 West 2:3 AK HI PI \DATA\SA\W2.3 Central:1 IA IL MN MO WI \DATA\SA\CE.1 Central:2 IN MI OH WV \DATA\SA\CE.2 Central:3 AL AR KY LA MS TN \DATA\SA\CE.3 East:1 CT MA ME NH NJ NY RI VT \DATA\SA\EA.1 East:2 DC DE MD NC PA VA \DATA\SA\EA.2 East:3 FL GA PR SC VI \DATA\SA\EA.3 NCDC First Order Summary of the Day - Daily Temp, Pressure, Humidity, Wind (1881-2008) West 1:1 AZ CA \DATA\FO\W1.1 West 1:2 CO NM NV UT WY \DATA\FO\W1.2 West 2:1 ID MT OR WA \DATA\FO\W2.1 West 2:2 KS ND NE SD \DATA\FO\W2.2 West 2:3 OK TX \DATA\FO\W2.3 West 2:4 AK HI PI \DATA\FO\W2.4 Central:1 IA IL MN MO WI \DATA\FO\CE.1 Central:2 IN MI OH WV \DATA\FO\CE.2 Central:3 AL AR KY LA MS TN \DATA\FO\CE.3 East:1 CT MA ME NH NJ NY RI VT \DATA\FO\EA.1 East:2 DE MD NC PA VA \DATA\FO\EA.2 East:3 FL GA PR SC VI \DATA\FO\EA.3 USGS Daily Values - Daily Mean Streamflow (1859-2009) West 1 AZ CA CO NM NV UT WY \DATA\DV\W1 West 2 AK HI ID KS MT ND NE OK OR SD TX WA \DATA\DV\W2 Central AL AR IA IL IN KY LA MI MN MO MS OH TN WI WV \DATA\DV\CE East CT DC DE FL GA MA MD ME NC NH NJ NY PA PR RI SC VA VI VT \DATA\DV\EA USGS Peak Values - Maximum Instantaneous Streamflow (1683-2006) \DATA\PV USGS Quality of Water \ Ground - Contaminants, Metals, Organics, Sediment (1875-2006) West 1 AZ CA CO NM NV UT WY \DATA\QWG\W1 West 2 AK HI ID KS MT ND NE OK OR SD TX WA \DATA\QWG\W2 Central AL AR IA IL IN KY LA MI MN MO MS OH TN WI WV \DATA\QWG\CE East CT DC DE FL GA MA MD ME NC NH NJ NY PA PR RI SC VA VI VT \DATA\QWG\EA USGS Quality of Water \ Surface - Contaminants, Metals, Organics, Sediment (1867-2006) West 1 AZ CA CO NM NV UT WY \DATA\QWS\W1 West 2 AK HI ID KS MT ND NE OK OR SD TX WA \DATA\QWS\W2 Central AL AR IA IL IN KY LA MI MN MO MS OH TN WI WV \DATA\QWS\CE East CT DC DE FL GA MA MD ME NC NH NJ NY PA PR RI SC VA VI VT \DATA\QWS\EA

Year 2020 meteorological measurements at MBL Marshview Farm of air temperature, humidity, precipitation, solar radiation, photosynthetically active radiation (PAR), wind speed and direction and barometric pressure. Sensors conduct measurements every 5 secs and measurements are reported as averages or totals for 15 minute intervals. 15 minute averages are reported for air temperature, humidity, solar radiation, PAR, wind speed and direction and barometric pressure. 15 minute totals are reported for precipitation.

Land cover (%) and climatic data (mean annual temperature, mean annual rainfall and xerothermic index) of the three areas (TC: Tyrrhenian Coast, MA: Maremma, CP: Central plains).

AERA5-Asia: A long-term Asian precipitation dataset (0.1°, 1 hourly, 1951–2015, Asia) is developed by organically combining the ERA5-Land dataset with high spatiotemporal resolutions and continuity and the APHRODITE dataset with high quality.

How to cite: Ma, Z., Xu, J., Ma, Y., Zhu, S., He, K., Zhang, S., Ma, W., Xu, X., 2022. AERA5-Asia: A long-term Asian precipitation dataset (0.1°, 1 hourly, 1951–2015, Asia) anchoring the ERA5-Land under the total volume control by APHRODITE. Bulletin of American Meteorological Society, 103 (4)., DOI: https://doi.org/10.1175/BAMS-D-20-0328.1.

Data Format: GeoTIFF

Spatial Coverage: 60°E–150°E, 15°S–55°N, land.

AERA5-Asia (0.1°/ hourly, 1951–1966, Asia) is available at https://doi.org/10.5281/zenodo.6367463

AERA5-Asia (0.1°/ hourly, 1962–1981, Asia) is available at https://doi.org/10.5281/zenodo.6369796

AERA5-Asia (0.1°/ hourly, 1982–1998, Asia) is available at https://doi.org/10.5281/zenodo.4266081

description: 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).; abstract: 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).

The Island Mass Effect has been primarily attributed to nutrient enhancement of waters surrounding oceanic islands due to physical processes, whereas the role of land runoff has seldom been considered. Land runoff can be particularly relevant in mountainous islands, highly susceptible to torrential rainfall that rapidly leads to flash floods. Madeira Island, located in the Northeast Atlantic Ocean, is historically known for its flash flood events, when steep streams transport high volumes of water and terrigenous material downstream. A 22-year analysis of satellite data revealed that a recent catastrophic flash flood (20 February 2010) was responsible for the most significant concentration of non-algal Suspended Particulate Matter (SPM) and Chlorophyll-a at the coast. In this context, our study aims to understand the impact of the February 2010 flash flood events on coastal waters, by assessing the impact of spatial and temporal variability of wind, precipitation, and river discharges. Two specific flash floods events are investigated in detail (2 and 20 February 2010), which coincided with northeasterly and southwesterly winds, respectively. Given the lack of in situ data documenting these events, a coupled air-sea-land numerical framework was used, including hydrological modeling. The dynamics of the modeled river plumes induced by flash floods were strongly influenced by the wind regimes subsequently affecting coastal circulation, which may help to explain the differences between observed SPM and Chlorophyll-a distributions. Model simulations showed that during northeasterly winds, coastal confinement of the buoyant river plume persisted on the island’s north coast, preventing offshore transport of SPM. This mechanism may have contributed to favorable conditions for phytoplankton growth, as captured by satellite-derived Chlorophyll-a in the northeastern coastal waters. On the island’s south coast, strong ocean currents generated in the eastern island flank promoted strong vertical shear, contributing to vertical mixing. During southwesterly winds, coastal confinement of the plume with strong vertical density gradient was observed on the south side. The switch to eastward winds spread the south river plume offshore, forming a filament of high Chlorophyll-a extending 70 km offshore. Our framework demonstrates a novel methodology to investigate ocean productivity around remote islands with sparse or absent field observations.

Contours_1.gif: 0 mA/g - pristine state

Contours_2.gif: 30 mA/g

Contours_3.gif: 80 mA/g

Contours_4.gif: 130 mA/g

Contours_5.gif: 180 mA/g

Contours_6.gif: 230 mA/g

Contours_7.gif: 330 mA/g - no remaining solid sulphur

Described here is a mass spectrometry-based proteomics approach for the large-scale analysis of protein–drug interactions. The approach involves the evaluation of ligand-induced protein folding free energy changes (ΔΔGf) using chemical denaturation and protein precipitation (CPP) to identify the protein targets of drugs and to quantify protein–drug binding affinities. This is accomplished in a chemical denaturant-induced unfolding experiment where the folded and unfolded protein fractions in each denaturant containing buffer are quantified by the amount of soluble or precipitated protein (respectively) that forms upon abrupt dilution of the chemical denaturant and subsequent centrifugation of the sample. In the proof-of-principle studies performed here, the CPP technique was able to identify the well-known protein targets of cyclosporin A and geldanamycin in a yeast. The technique was also used to identify protein targets of sinefungin, a broad-based methyltransferase inhibitor, in a human MCF-7 cell lysate. The CPP technique also yielded dissociation constant (Kd) measurements for these well-studied drugs that were in general agreement with previously reported Kd or IC50 values. In comparison to a similar energetics-based technique, termed stability of proteins from rates of oxidation (SPROX), the CPP technique yielded significantly better (∼50% higher) proteomic coverage and a largely reduced false discovery rate.