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.

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 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/.

Study ObjectivesThe primary objective of this study was to generate projections of changes in stream temperature and thermal habitat (i.e., cold water fish habitat) due to climate change across the state of Massachusetts. To achieve this, statistical and machine learning models were developed for predicting stream temperatures based on air temperature and various landscape metrics (e.g., land use, elevation, drainage area). The model was then used in conjunction with climate change projections of air temperature increases to estimate the potential changes in stream temperatures and thermal habitat across the state. The results of this study are made available through this web-based tool to inform conservation and management decisions related to the protection of coldwater fish habitat in MassachusettsModeling MethodologyA regional model was developed for predicting stream temperatures in all streams and rivers across the state, excluding the largest rivers such as the Connecticut and Merrimack. The model was comprised of two components: 1) a non-linear regression model representing the functional relationship between air and water temperatures at a single location, and 2) a machine learning model (boosted decision trees) for estimating the parameters of the air-water temperature model spatially based on landscape characteristics. Together, these models demonstrated strong performance in predicting weekly water temperatures with an RMSE of 1.3 degC and Nash Sutcliffe Efficiency (NSE) of 0.97 based on an independent subset of the observed data that was excluded from model development and training.ResultsUnder historical baseline conditions (average air temperatures over 1971-2000), the model results showed more abundant cold water habitat in the western part of the state compared to the eastern and coastal areas. Forest and tree canopy cover were among the most important predictors of the relationship between air and water temperatures. The amount of impounded water due to dams upstream of each reach was also important. The majority of cold water habitat (82% of all river miles) were found in first order streams (i.e., headwaters), which are also the most abundant accounting for 60% of all river miles overall. The Deerfield and Hudson-Hoosic drainage basins had the most cold water habitat, which accounted for 80% or more of the total river miles within each basin. Coastal basins such as Narragansett, Piscataqua-Salmon Falls, Charles River, and Cape Code each had less than 5% cold water habitat.Using a series of projected air temperature increases for the RCP 8.5 emissions scenario, the model predicted a reduction in cold water habitat (mean July temp < 18.45 °C) from 30% to 8.5% (a 72% reduction) statewide by the 2090 averaging period (2080-2100). Furthermore, projections for larger streams (orders 3–5) were projected to shift from predominately cool-water (18.45–22.30 °C) to the majority (> 50%) of river miles being classified as warm-water habitat (> 22.30 °C).ConclusionsThe projected stream temperatures and thermal classifications generated by this project will be a valuable dataset for researchers and resource managers to assess potential climate change impacts on thermal habitats across the state. With this spatially continuous dataset, researchers and managers can identify specific reaches or basins projected to be the most resilient to climate change, and prioritize them for protection or restoration. As more datasets become available, this model can be readily extended and adapted to increase its spatial extent and resolution, and to incorporate flow data for assessing the impacts of not only rising air temperatures but also changing precipitation patterns.AcknowledgementsI would like to thank Jenn Fair (USGS) for her technical review of the model report and assistance in data gathering at the beginning of the project. I would also like to thank Ben Letcher (USGS) for his feedback and long-term collaboration on EcoSHEDS, which led to this project; Matt Fuller (USDA FS), Jenny Rogers (UMass Amherst), Valerie Ouellet (NOAA NMFS), and Aimee Fullerton (NOAA NMFS) for taking the time to discuss their experience, insights, and ideas regarding regional stream temperature modeling; Lisa Kumpf (CRWA) and Ryan O’Donnell (IRWA) for sharing their data directly; and Sean McCanty (NRWA), Julia Blatt (Mass Rivers Alliance), and Sarah Bower (Mass Rivers Alliance) for their assistance in sending out a request to the Mass Rivers Alliance for stream temperature data. Lastly, I am grateful for the countless individuals who collected the temperature data and without whom this project would not have been possible.FundingThis study was performed by Jeffrey D Walker, PhD of Walker Environmental Research LLC in collaboration with MA Division of Fisheries and Wildlife (MassWildlife). Funding was provided by the 2018 State Hazard Mitigation and Climate Adaptation Plan (SHMCAP) for Massachusetts.

Average Temperature: West Sumatera Province: Padang Municipality: Teluk Bayur Maritime Meteorology Station data was reported at 29.100 Degrees Celsius in 17 Dec 2024. This records an increase from the previous number of 28.600 Degrees Celsius for 16 Dec 2024. Average Temperature: West Sumatera Province: Padang Municipality: Teluk Bayur Maritime Meteorology Station data is updated daily, averaging 27.600 Degrees Celsius from Jan 2020 (Median) to 17 Dec 2024, with 1810 observations. The data reached an all-time high of 30.800 Degrees Celsius in 23 Feb 2020 and a record low of 23.900 Degrees Celsius in 11 Dec 2022. Average Temperature: West Sumatera Province: Padang Municipality: Teluk Bayur Maritime Meteorology Station data remains active status in CEIC and is reported by Meteorological, Climatological, and Geophysical Agency. The data is categorized under Indonesia Premium Database’s Environment Sector – Table ID.EVB005: Average Temperature: by Regency and Municipality.

Morocco MA: Droughts, Floods, Extreme Temperatures: Average 1990-2009: % of Population data was reported at 0.076 % in 2009. Morocco MA: Droughts, Floods, Extreme Temperatures: Average 1990-2009: % of Population data is updated yearly, averaging 0.076 % from Dec 2009 (Median) to 2009, with 1 observations. Morocco MA: Droughts, Floods, Extreme Temperatures: Average 1990-2009: % of Population 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: Land Use, Protected Areas and National Wealth. Droughts, floods and extreme temperatures is the annual average percentage of the population that is affected by natural disasters classified as either droughts, floods, or extreme temperature events. A drought is an extended period of time characterized by a deficiency in a region's water supply that is the result of constantly below average precipitation. A drought can lead to losses to agriculture, affect inland navigation and hydropower plants, and cause a lack of drinking water and famine. A flood is a significant rise of water level in a stream, lake, reservoir or coastal region. Extreme temperature events are either cold waves or heat waves. A cold wave can be both a prolonged period of excessively cold weather and the sudden invasion of very cold air over a large area. Along with frost it can cause damage to agriculture, infrastructure, and property. A heat wave is a prolonged period of excessively hot and sometimes also humid weather relative to normal climate patterns of a certain region. Population affected is the number of people injured, left homeless or requiring immediate assistance during a period of emergency resulting from a natural disaster; it can also include displaced or evacuated people. Average percentage of population affected is calculated by dividing the sum of total affected for the period stated by the sum of the annual population figures for the period stated.; ; EM-DAT: The OFDA/CRED International Disaster Database: www.emdat.be, Université Catholique de Louvain, Brussels (Belgium), World Bank.; ;

The dataset compiles reconstructed changes in bottom water temperature and global ice volume from 0 to 17 Ma using δ18O in conjunction with Mg/Ca records of the infaunal benthic foraminifer, O. umbonatus from Ocean Drilling Program (ODP) Site 130-806 (equatorial Pacific; ~2500 m). This dataset covers the middle Miocene to present (17-0 Ma) and has an average temporal resolution of ~0.2 Ma. Application of the new equations to the Site 130-806 record leads to the suggestion that global ice volume was greater than today after the Middle Miocene Climate Transition (~14 Ma). ODP Site 130-806 bottom waters cooled and freshened as the Pacific zonal sea surface temperature gradient increased, and climate cooled through the Pliocene, prior to the Plio‐Pleistocene glaciation of the Northern Hemisphere.

Study ObjectivesThe primary objective of this study was to generate projections of changes in stream temperature and thermal habitat (i.e., cold water fish habitat) due to climate change across the state of Massachusetts. To achieve this, statistical and machine learning models were developed for predicting stream temperatures based on air temperature and various landscape metrics (e.g., land use, elevation, drainage area). The model was then used in conjunction with climate change projections of air temperature increases to estimate the potential changes in stream temperatures and thermal habitat across the state. The results of this study are made available through this web-based tool to inform conservation and management decisions related to the protection of coldwater fish habitat in MassachusettsModeling MethodologyA regional model was developed for predicting stream temperatures in all streams and rivers across the state, excluding the largest rivers such as the Connecticut and Merrimack. The model was comprised of two components: 1) a non-linear regression model representing the functional relationship between air and water temperatures at a single location, and 2) a machine learning model (boosted decision trees) for estimating the parameters of the air-water temperature model spatially based on landscape characteristics. Together, these models demonstrated strong performance in predicting weekly water temperatures with an RMSE of 1.3 degC and Nash Sutcliffe Efficiency (NSE) of 0.97 based on an independent subset of the observed data that was excluded from model development and training.ResultsUnder historical baseline conditions (average air temperatures over 1971-2000), the model results showed more abundant cold water habitat in the western part of the state compared to the eastern and coastal areas. Forest and tree canopy cover were among the most important predictors of the relationship between air and water temperatures. The amount of impounded water due to dams upstream of each reach was also important. The majority of cold water habitat (82% of all river miles) were found in first order streams (i.e., headwaters), which are also the most abundant accounting for 60% of all river miles overall. The Deerfield and Hudson-Hoosic drainage basins had the most cold water habitat, which accounted for 80% or more of the total river miles within each basin. Coastal basins such as Narragansett, Piscataqua-Salmon Falls, Charles River, and Cape Code each had less than 5% cold water habitat.Using a series of projected air temperature increases for the RCP 8.5 emissions scenario, the model predicted a reduction in cold water habitat (mean July temp < 18.45 °C) from 30% to 8.5% (a 72% reduction) statewide by the 2090 averaging period (2080-2100). Furthermore, projections for larger streams (orders 3–5) were projected to shift from predominately cool-water (18.45–22.30 °C) to the majority (> 50%) of river miles being classified as warm-water habitat (> 22.30 °C).ConclusionsThe projected stream temperatures and thermal classifications generated by this project will be a valuable dataset for researchers and resource managers to assess potential climate change impacts on thermal habitats across the state. With this spatially continuous dataset, researchers and managers can identify specific reaches or basins projected to be the most resilient to climate change, and prioritize them for protection or restoration. As more datasets become available, this model can be readily extended and adapted to increase its spatial extent and resolution, and to incorporate flow data for assessing the impacts of not only rising air temperatures but also changing precipitation patterns.AcknowledgementsI would like to thank Jenn Fair (USGS) for her technical review of the model report and assistance in data gathering at the beginning of the project. I would also like to thank Ben Letcher (USGS) for his feedback and long-term collaboration on EcoSHEDS, which led to this project; Matt Fuller (USDA FS), Jenny Rogers (UMass Amherst), Valerie Ouellet (NOAA NMFS), and Aimee Fullerton (NOAA NMFS) for taking the time to discuss their experience, insights, and ideas regarding regional stream temperature modeling; Lisa Kumpf (CRWA) and Ryan O’Donnell (IRWA) for sharing their data directly; and Sean McCanty (NRWA), Julia Blatt (Mass Rivers Alliance), and Sarah Bower (Mass Rivers Alliance) for their assistance in sending out a request to the Mass Rivers Alliance for stream temperature data. Lastly, I am grateful for the countless individuals who collected the temperature data and without whom this project would not have been possible.FundingThis study was performed by Jeffrey D Walker, PhD of Walker Environmental Research LLC in collaboration with MA Division of Fisheries and Wildlife (MassWildlife). Funding was provided by the 2018 State Hazard Mitigation and Climate Adaptation Plan (SHMCAP) for Massachusetts.

Study ObjectivesThe primary objective of this study was to generate projections of changes in stream temperature and thermal habitat (i.e., cold water fish habitat) due to climate change across the state of Massachusetts. To achieve this, statistical and machine learning models were developed for predicting stream temperatures based on air temperature and various landscape metrics (e.g., land use, elevation, drainage area). The model was then used in conjunction with climate change projections of air temperature increases to estimate the potential changes in stream temperatures and thermal habitat across the state. The results of this study are made available through this web-based tool to inform conservation and management decisions related to the protection of coldwater fish habitat in MassachusettsModeling MethodologyA regional model was developed for predicting stream temperatures in all streams and rivers across the state, excluding the largest rivers such as the Connecticut and Merrimack. The model was comprised of two components: 1) a non-linear regression model representing the functional relationship between air and water temperatures at a single location, and 2) a machine learning model (boosted decision trees) for estimating the parameters of the air-water temperature model spatially based on landscape characteristics. Together, these models demonstrated strong performance in predicting weekly water temperatures with an RMSE of 1.3 degC and Nash Sutcliffe Efficiency (NSE) of 0.97 based on an independent subset of the observed data that was excluded from model development and training.ResultsUnder historical baseline conditions (average air temperatures over 1971-2000), the model results showed more abundant cold water habitat in the western part of the state compared to the eastern and coastal areas. Forest and tree canopy cover were among the most important predictors of the relationship between air and water temperatures. The amount of impounded water due to dams upstream of each reach was also important. The majority of cold water habitat (82% of all river miles) were found in first order streams (i.e., headwaters), which are also the most abundant accounting for 60% of all river miles overall. The Deerfield and Hudson-Hoosic drainage basins had the most cold water habitat, which accounted for 80% or more of the total river miles within each basin. Coastal basins such as Narragansett, Piscataqua-Salmon Falls, Charles River, and Cape Code each had less than 5% cold water habitat.Using a series of projected air temperature increases for the RCP 8.5 emissions scenario, the model predicted a reduction in cold water habitat (mean July temp < 18.45 °C) from 30% to 8.5% (a 72% reduction) statewide by the 2090 averaging period (2080-2100). Furthermore, projections for larger streams (orders 3–5) were projected to shift from predominately cool-water (18.45–22.30 °C) to the majority (> 50%) of river miles being classified as warm-water habitat (> 22.30 °C).ConclusionsThe projected stream temperatures and thermal classifications generated by this project will be a valuable dataset for researchers and resource managers to assess potential climate change impacts on thermal habitats across the state. With this spatially continuous dataset, researchers and managers can identify specific reaches or basins projected to be the most resilient to climate change, and prioritize them for protection or restoration. As more datasets become available, this model can be readily extended and adapted to increase its spatial extent and resolution, and to incorporate flow data for assessing the impacts of not only rising air temperatures but also changing precipitation patterns.AcknowledgementsI would like to thank Jenn Fair (USGS) for her technical review of the model report and assistance in data gathering at the beginning of the project. I would also like to thank Ben Letcher (USGS) for his feedback and long-term collaboration on EcoSHEDS, which led to this project; Matt Fuller (USDA FS), Jenny Rogers (UMass Amherst), Valerie Ouellet (NOAA NMFS), and Aimee Fullerton (NOAA NMFS) for taking the time to discuss their experience, insights, and ideas regarding regional stream temperature modeling; Lisa Kumpf (CRWA) and Ryan O’Donnell (IRWA) for sharing their data directly; and Sean McCanty (NRWA), Julia Blatt (Mass Rivers Alliance), and Sarah Bower (Mass Rivers Alliance) for their assistance in sending out a request to the Mass Rivers Alliance for stream temperature data. Lastly, I am grateful for the countless individuals who collected the temperature data and without whom this project would not have been possible.FundingThis study was performed by Jeffrey D Walker, PhD of Walker Environmental Research LLC in collaboration with MA Division of Fisheries and Wildlife (MassWildlife). Funding was provided by the 2018 State Hazard Mitigation and Climate Adaptation Plan (SHMCAP) for Massachusetts.

This excel contains data for Chapter 1 “Temperature” of the 2017 State of Narragansett Bay & Its Watershed Technical Report (nbep.org). It includes the raw data behind Figure 1, “Annual average water temperature at Woods Hole, MA 1880-2015,” (page 51); Figure 2, “Annual mean air temperatures at Worcester, MA 1949-2015,” (page 54); Figure 3, “Annual mean air temperature at Warwick, RI 1895-2015,” (page 54); Figure 4, "Annual mean surface water temperatures in Narragansett Bay 1960-2010," (page 55); Figure 5, "Annual mean river/stream water temperatures in Scituate Reservoir," (page 55); Figure 6, "Annual mean river/stream water temperatures at Millville, MA," (page 56); Figure 7, "Annual mean river/stream water temperatures from 2007-2014," (page 56); and Figure 8 "Seasonal air temperature projections for RI from 1950-2100," (page 58). 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/.