The National Forest Climate Change Maps project was developed by the Rocky Mountain Research Station (RMRS) and the Office of Sustainability and Climate to meet the needs of national forest managers for information on projected climate changes at a scale relevant to decision making processes, including forest plans. The maps use state-of-the-art science and are available for every national forest in the contiguous United States with relevant data coverage. Currently, the map sets include variables related to precipitation, air temperature, snow (including snow residence time and April 1 snow water equivalent), and stream flow.

Historical (1975-2005) and future (2071-2090) precipitation and temperature data for the contiguous United States are ensemble mean values across 20 global climate models from the CMIP5 experiment (https://journals.ametsoc.org/doi/abs/10.1175/BAMS-D-11-00094.1), downscaled to a 4 km grid. For more information on the downscaling method and to access the data, please see Abatzoglou and Brown, 2012 (https://rmets.onlinelibrary.wiley.com/doi/full/10.1002/joc.2312) and the Northwest Knowledge Network (https://climate.northwestknowledge.net/MACA/). We used the MACAv2- Metdata monthly dataset; average temperature values were calculated as the mean of monthly minimum and maximum air temperature values (degrees C), averaged over the season of interest (annual, winter, or summer). Absolute and percent change were then calculated between the historical and future time periods.

Historical (1975-2005) and future (2071-2090) precipitation and temperature data for the state of Alaska were developed by the Scenarios Network for Alaska and Arctic Planning (SNAP) (https://snap.uaf.edu). These datasets have several important differences from the MACAv2-Metdata (https://climate.northwestknowledge.net/MACA/) products, used in the contiguous U.S. They were developed using different global circulation models and different downscaling methods, and were downscaled to a different scale (771 m instead of 4 km). While these cover the same time periods and use broadly similar approaches, caution should be used when directly comparing values between Alaska and the contiguous United States.

Raster data are also available for download from RMRS site (https://www.fs.usda.gov/rm/boise/AWAE/projects/NFS-regional-climate-change-maps/categories/us-raster-layers.html), along with pdf maps and detailed metadata (https://www.fs.usda.gov/rm/boise/AWAE/projects/NFS-regional-climate-change-maps/downloads/NationalForestClimateChangeMapsMetadata.pdf).

Measurements of surface air and ocean temperature are compiled from around the world each month by NOAA’s National Centers for Environmental Information and are analyzed and compared to the 1971-2000 average temperature for each location. The resulting temperature anomaly (or difference from the average) is shown in this feature service, which includes an archive going back to 1880. The mean of the 12 months each year is displayed here. Each annual update is available around the 15th of the following January (e.g., 2020 is available Jan 15th, 2021). The NOAAGlobalTemp dataset is the official U.S. long-term record of global temperature data and is often used to show trends in temperature change around the world. It combines thousands of land-based station measurements from the Global Historical Climatology Network (GHCN) along with surface ocean temperature from the Extended Reconstructed Sea Surface Temperature (ERSST) analysis. These two datasets are merged into a 5-degree resolution product. A report summary report by NOAA NCEI is available here. GHCN monthly mean station averages for temperature and precipitation for the 1981-2010 period are also available in Living Atlas here.What can you do with this layer? Visualization: This layer can be used to plot areas where temperature was higher or lower than the historical average for each year since 1880. Be sure to configure the time settings in your web map to view the timeseries correctly. Analysis: This layer can be used as an input to a variety of geoprocessing tools, such as Space Time Cubes and other trend analyses. For a more detailed temporal analysis, a monthly mean is available here.

The National Forest Climate Change Maps project was developed by the Rocky Mountain Research Station (RMRS) and the Office of Sustainability and Climate to meet the needs of national forest managers for information on projected climate changes at a scale relevant to decision making processes, including forest plans. The maps use state-of-the-art science and are available for every national forest in the contiguous United States with relevant data coverage. Currently, the map sets include variables related to precipitation, air temperature, snow (including snow residence time and April 1 snow water equivalent), and stream flow.Historical (1975-2005) and future (2071-2090) precipitation and temperature data for the contiguous United States are ensemble mean values across 20 global climate models from the CMIP5 experiment (https://journals.ametsoc.org/doi/abs/10.1175/BAMS-D-11-00094.1), downscaled to a 4 km grid. For more information on the downscaling method and to access the data, please see Abatzoglou and Brown, 2012 (https://rmets.onlinelibrary.wiley.com/doi/full/10.1002/joc.2312) and the Northwest Knowledge Network (https://climate.northwestknowledge.net/MACA/). We used the MACAv2- Metdata monthly dataset; average temperature values were calculated as the mean of monthly minimum and maximum air temperature values (degrees C), averaged over the season of interest (annual, winter, or summer). Absolute change was then calculated between the historical and future time periods.Raster data are also available for download from RMRS site (https://www.fs.usda.gov/rm/boise/AWAE/projects/NFS-regional-climate-change-maps/categories/us-raster-layers.html), along with pdf maps and detailed metadata (https://www.fs.usda.gov/rm/boise/AWAE/projects/NFS-regional-climate-change-maps/downloads/NationalForestClimateChangeMapsMetadata.pdf).

The National Forest Climate Change Maps project was developed by the Rocky Mountain Research Station (RMRS) and the Office of Sustainability and Climate to meet the needs of national forest managers for information on projected climate changes at a scale relevant to decision making processes, including forest plans. The maps use state-of-the-art science and are available for every national forest in the contiguous United States with relevant data coverage. Currently, the map sets include variables related to precipitation, air temperature, snow (including snow residence time and April 1 snow water equivalent), and stream flow.

Historical (1975-2005) and future (2071-2090) precipitation and temperature data for the contiguous United States are ensemble mean values across 20 global climate models from the CMIP5 experiment (https://journals.ametsoc.org/doi/abs/10.1175/BAMS-D-11-00094.1), downscaled to a 4 km grid. For more information on the downscaling method and to access the data, please see Abatzoglou and Brown, 2012 (https://rmets.onlinelibrary.wiley.com/doi/full/10.1002/joc.2312) and the Northwest Knowledge Network (https://climate.northwestknowledge.net/MACA/). We used the MACAv2- Metdata monthly dataset; average temperature values were calculated as the mean of monthly minimum and maximum air temperature values (degrees C), averaged over the season of interest (annual, winter, or summer). Absolute change was then calculated between the historical and future time periods.

Raster data are also available for download from RMRS site (https://www.fs.usda.gov/rm/boise/AWAE/projects/NFS-regional-climate-change-maps/categories/us-raster-layers.html), along with pdf maps and detailed metadata (https://www.fs.usda.gov/rm/boise/AWAE/projects/NFS-regional-climate-change-maps/downloads/NationalForestClimateChangeMapsMetadata.pdf).

The National Forest Climate Change Maps project was developed by the Rocky Mountain Research Station (RMRS) and the Office of Sustainability and Climate to meet the needs of national forest managers for information on projected climate changes at a scale relevant to decision making processes, including forest plans. The maps use state-of-the-art science and are available for every national forest in the contiguous United States with relevant data coverage. Currently, the map sets include variables related to precipitation, air temperature, snow (including snow residence time and April 1 snow water equivalent), and stream flow.

Historical (1975-2005) and future (2071-2090) precipitation and temperature data for the contiguous United States are ensemble mean values across 20 global climate models from the CMIP5 experiment (https://journals.ametsoc.org/doi/abs/10.1175/BAMS-D-11-00094.1), downscaled to a 4 km grid. For more information on the downscaling method and to access the data, please see Abatzoglou and Brown, 2012 (https://rmets.onlinelibrary.wiley.com/doi/full/10.1002/joc.2312) and the Northwest Knowledge Network (https://climate.northwestknowledge.net/MACA/). We used the MACAv2- Metdata monthly dataset; monthly precipitation values (mm) were summed over the season of interest (annual, winter, or summer). Absolute and percent change were then calculated between the historical and future time periods.

Raster data are also available for download from RMRS site (https://www.fs.usda.gov/rm/boise/AWAE/projects/NFS-regional-climate-change-maps/categories/us-raster-layers.html), along with pdf maps and detailed metadata (https://www.fs.usda.gov/rm/boise/AWAE/projects/NFS-regional-climate-change-maps/downloads/NationalForestClimateChangeMapsMetadata.pdf).

The National Forest Climate Change Maps project was developed by the Rocky Mountain Research Station (RMRS) and the Office of Sustainability and Climate to meet the needs of national forest managers for information on projected climate changes at a scale relevant to decision making processes, including forest plans. The maps use state-of-the-art science and are available for every national forest in the contiguous United States with relevant data coverage. Currently, the map sets include variables related to precipitation, air temperature, snow (including snow residence time and April 1 snow water equivalent), and stream flow.Historical (1975-2005) and future (2071-2090) precipitation and temperature data for the contiguous United States are ensemble mean values across 20 global climate models from the CMIP5 experiment (https://journals.ametsoc.org/doi/abs/10.1175/BAMS-D-11-00094.1), downscaled to a 4 km grid. For more information on the downscaling method and to access the data, please see Abatzoglou and Brown, 2012 (https://rmets.onlinelibrary.wiley.com/doi/full/10.1002/joc.2312) and the Northwest Knowledge Network (https://climate.northwestknowledge.net/MACA/). We used the MACAv2- Metdata monthly dataset; monthly precipitation values (mm) were summed over the season of interest (annual, winter, or summer). Absolute and percent change were then calculated between the historical and future time periods.Raster data are also available for download from RMRS site (https://www.fs.usda.gov/rm/boise/AWAE/projects/NFS-regional-climate-change-maps/categories/us-raster-layers.html), along with pdf maps and detailed metadata (https://www.fs.usda.gov/rm/boise/AWAE/projects/NFS-regional-climate-change-maps/downloads/NationalForestClimateChangeMapsMetadata.pdf).

The National Forest Climate Change Maps project was developed by the Rocky Mountain Research Station (RMRS) and the Office of Sustainability and Climate to meet the needs of national forest managers for information on projected climate changes at a scale relevant to decision making processes, including forest plans. The maps use state-of-the-art science and are available for every national forest in the contiguous United States with relevant data coverage. Currently, the map sets include variables related to precipitation, air temperature, snow (including snow residence time and April 1 snow water equivalent), and stream flow.Historical (1975-2005) and future (2071-2090) precipitation and temperature data for the contiguous United States are ensemble mean values across 20 global climate models from the CMIP5 experiment (https://journals.ametsoc.org/doi/abs/10.1175/BAMS-D-11-00094.1), downscaled to a 4 km grid. For more information on the downscaling method and to access the data, please see Abatzoglou and Brown, 2012 (https://rmets.onlinelibrary.wiley.com/doi/full/10.1002/joc.2312) and the Northwest Knowledge Network (https://climate.northwestknowledge.net/MACA/). We used the MACAv2- Metdata monthly dataset; average temperature values were calculated as the mean of monthly minimum and maximum air temperature values (degrees C), averaged over the season of interest (annual, winter, or summer). Absolute change was then calculated between the historical and future time periods.Raster data are also available for download from RMRS site (https://www.fs.usda.gov/rm/boise/AWAE/projects/NFS-regional-climate-change-maps/categories/us-raster-layers.html), along with pdf maps and detailed metadata (https://www.fs.usda.gov/rm/boise/AWAE/projects/NFS-regional-climate-change-maps/downloads/NationalForestClimateChangeMapsMetadata.pdf).

The National Forest Climate Change Maps project was developed by the Rocky Mountain Research Station (RMRS) and the Office of Sustainability and Climate to meet the needs of national forest managers for information on projected climate changes at a scale relevant to decision making processes, including forest plans. The maps use state-of-the-art science and are available for every national forest in the contiguous United States with relevant data coverage. Currently, the map sets include variables related to precipitation, air temperature, snow (including snow residence time and April 1 snow water equivalent), and stream flow.Historical (1975-2005) and future (2071-2090) precipitation and temperature data for the contiguous United States are ensemble mean values across 20 global climate models from the CMIP5 experiment (https://journals.ametsoc.org/doi/abs/10.1175/BAMS-D-11-00094.1), downscaled to a 4 km grid. For more information on the downscaling method and to access the data, please see Abatzoglou and Brown, 2012 (https://rmets.onlinelibrary.wiley.com/doi/full/10.1002/joc.2312) and the Northwest Knowledge Network (https://climate.northwestknowledge.net/MACA/). We used the MACAv2- Metdata monthly dataset; average temperature values were calculated as the mean of monthly minimum and maximum air temperature values (degrees C), averaged over the season of interest (annual, winter, or summer). Absolute change was then calculated between the historical and future time periods.Raster data are also available for download from RMRS site (https://www.fs.usda.gov/rm/boise/AWAE/projects/NFS-regional-climate-change-maps/categories/us-raster-layers.html), along with pdf maps and detailed metadata (https://www.fs.usda.gov/rm/boise/AWAE/projects/NFS-regional-climate-change-maps/downloads/NationalForestClimateChangeMapsMetadata.pdf).

🇺🇸 미국 English The National Forest Climate Change Maps project was developed by the Rocky Mountain Research Station (RMRS) and the Office of Sustainability and Climate to meet the needs of national forest managers for information on projected climate changes at a scale relevant to decision making processes, including forest plans. The maps use state-of-the-art science and are available for every national forest in the contiguous United States with relevant data coverage. Currently, the map sets include variables related to precipitation, air temperature, snow (including snow residence time and April 1 snow water equivalent), and stream flow.

Historical (1975-2005) and future (2071-2090) precipitation and temperature data for the contiguous United States are ensemble mean values across 20 global climate models from the CMIP5 experiment (https://journals.ametsoc.org/doi/abs/10.1175/BAMS-D-11-00094.1), downscaled to a 4 km grid. For more information on the downscaling method and to access the data, please see Abatzoglou and Brown, 2012 (https://rmets.onlinelibrary.wiley.com/doi/full/10.1002/joc.2312) and the Northwest Knowledge Network (https://climate.northwestknowledge.net/MACA/). We used the MACAv2- Metdata monthly dataset; average temperature values were calculated as the mean of monthly minimum and maximum air temperature values (degrees C), averaged over the season of interest (annual, winter, or summer). Absolute change was then calculated between the historical and future time periods.

Yearly effective energy and mass transfer (EEMT) (MJ m−2 yr−1) was calculated for the Catalina Mountains by summing the 12 monthly values. Effective energy and mass flux varies seasonally, especially in the desert southwestern United States where contemporary climate includes a bimodal precipitation distribution that concentrates in winter (rain or snow depending on elevation) and summer monsoon periods. This seasonality of EEMT flux into the upper soil surface can be estimated by calculating EEMT on a monthly basis as constrained by solar radiation (Rs), temperature (T), precipitation (PPT), and the vapor pressure deficit (VPD): EEMT = f(Rs,T,PPT,VPD). Here we used a multiple linear regression model to calculate the monthly EEMT that accounts for VPD, PPT, and locally modified T across the terrain surface. These EEMT calculations were made using data from the PRISM Climate Group at Oregon State University (www.prismclimate.org). Climate data are provided at an 800-m spatial resolution for input precipitation and minimum and maximum temperature normals and at a 4000-m spatial resolution for dew-point temperature (Daly et al., 2002). The PRISM climate data, however, do not account for localized variation in EEMT that results from smaller spatial scale changes in slope and aspect as occurs within catchments. To address this issue, these data were then combined with 10-m digital elevation maps to compute the effects of local slope and aspect on incoming solar radiation and hence locally modified temperature (Yang et al., 2007). Monthly average dew-point temperatures were computed using 10 yr of monthly data (2000–2009) and converted to vapor pressure. Precipitation, temperature, and dew-point data were resampled on a 10-m grid using spline interpolation. Monthly solar radiation data (direct and diffuse) were computed using ArcGIS Solar Analyst extension (ESRI, Redlands, CA) and 10-m elevation data (USGS National Elevation Dataset [NED] 1/3 Arc-Second downloaded from the National Map Seamless Server at seamless.usgs.gov). Locally modified temperature was used to compute the saturated vapor pressure, and the local VPD was estimated as the difference between the saturated and actual vapor pressures. The regression model was derived using the ISOHYS climate data set comprised of approximately 30-yr average monthly means for more than 300 weather stations spanning all latitudes and longitudes (IAEA).

ukcp09-Gridded datasets based on surface observations have been generated for a range of climatic variables. The primary purpose of this data resource is to encourage and facilitate research into climate change impacts and adaptation. This data set includes monthly ukcp09-Gridded datasets at 5 x 5 km resolution. A grid for each month covering the whole of the UK, downloadable in 10-year blocks.

The format of the grid text files is the same as that used by ESRI GIS software (e.g. ArcView) to import/export gridded data as plain text. Users of such software can import the files without modification.

The datasets have been created with financial support from the Department for Environment, Food and Rural Affairs (Defra) and they are being promoted by the UK Climate Impacts Programme (UKCIP) as part of the UK Climate Projections (UKCP09). http://ukclimateprojections.defra.gov.uk/content/view/12/689/.

To view this data you will have to register on the Met Office website, here: http://www.metoffice.gov.uk/climatechange/science/monitoring/ukcp09/gds_form.html.

The Baltimore radar rainfall dataset was developed from a multi-sensor analysis combining radar rainfall estimates from the Sterling, VA WSR88D radar (KLWX) with measurements from a collection of ground based rain gages. The archived data have a 15-minute time resolution and a grid resolution of 0.01 degree latitude/longitude (approximately 1 km x 1 km); 15-minute rainfall accumulations for each grid are in mm. The dataset spans 22 years, 2000-2021, and covers an area of approximately 4,900 km^2 (70 by 70 grids, each with approximate area of 1 km^2) surrounding the Baltimore, MD metropolitan area (Figure 1). The rainfall data cover the six months from April to September of each year. This is the period with most intense sub-daily rainfall and the period for which radar measurements are most accurate. Figure 1 illustrates the climatological analyses of mean annual frequency of days with at least 1 hour rainfall exceeding 25 mm. The striking spatial variability of convective rainfall is illustrated in Figure 2 by the April-September climatology of annual lightning strikes.

As with many long-term environmental data sets, sensor technology has changed during the time period of the archive. The Sterling, VA WSR88D radar underwent a hardware upgrade from single polarization to dual polarization in 2012. Prior to the upgrade, rainfall was estimated using a conventional radar-reflectivity algorithm (HydroNEXRAD) which converts reflectivity measurements in polar coordinates from the lowest sweep to rainfall estimates on a 0.01 degree latitude-longitude grid at the surface (see Seo et al. 2010 and Smith et al. 2012 for details on the algorithm). The polarimetric upgrade introduced new measurements into the radar-rainfall algorithm. In addition to reflectivity, the operational rainfall product, Digital Precipitation Rate (DPR), directly uses differential reflectivity and specific differential phase shift measurements to estimate rainfall (https://www.ncei.noaa.gov/access/metadata/landing-page/bin/iso?id=gov.noaa.ncdc:C00708; see also Giangrande and Ryzhkov 2008). Details of the algorithm structure and parameterization for the DPR radar-rainfall estimates have been modified during the 10-year period of the data set.

A storm-based (daily) multiplicative mean field bias has been applied to both datasets. The mean field bias is computed as the ratio of daily rain gage rainfall at a point to daily radar rainfall for the bin that contains the gage. The rain gage dataset is compiled from rain gages in the Baltimore metropolitan region and surrounding areas and includes gages acquired from both Baltimore City and Baltimore County, and the Global Historical Climatology Network daily (GHCNd). Mean field bias improves rainfall estimates and diminishes the impacts of changing measurement procedures.

The dataset has been archived in 2 formats: netCDF gridded rainfall, 1 file for each 15-minute time period, and csv or excel format point rainfall (1 point at the center of each grid) in a timeseries format with 1 file per calendar month covering the entire 70x70 domain. The csv files are in folders organized by calendar year. The first five columns in each file represent year, month, day, hour, and minute and can be combined to generate a unique date-time value for each time step. Each additional column is a complete time series for the month and represents data from one of the 1-km2 grid cells in the original data set.

The latitude and longitude coordinates for each pixel in the grid are provided. The latitude and longitude represent the centroid of the cell, which is square when represented in latitude and longitude coordinates and rectangular when represented in other distance-based coordinate systems such as State Plane or Universal Transverse Mercator. There are 4900 pixels in the domain. In order to visualize the data using GIS or other software, the user needs to associate each column in the annual rainfall file with the latitude and longitude values for that grid cell number.

These data may be subject to modest revision or reformatting in future versions. The current version is version 2.0 and is being offered to users who wish to explore the data. We will revise this document as needed.

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 series of datasets has been created by AAFC’s National Agroclimate Information Service (NAIS) of the Agro-Climate, Geomatics and Earth Observations (ACGEO) Division of the Science and Technology Branch. The Canadian Drought Monitor (CDM) is a composite product developed from a wide assortment of information such as the Normalized Difference Vegetation Index (NDVI), streamflow values, Palmer Drought Index, and drought indicators used by the agriculture, forest and water management sectors. Drought prone regions are analyzed based on precipitation, temperature, drought model index maps, and climate data and are interpreted by federal, provincial and academic scientists. Once a consensus is reached, a monthly map showing drought designations for Canada is digitized. AAFC’s National Agroclimate Information Service (NAIS) updates this dataset on a monthly basis, usually by the 10th of every month to correspond to the end of the previous month, and subsequent Canadian input into the larger North American Drought Monitor (NA-DM). The drought areas are classified as follows: D0 (Abnormally Dry) – represents an event that occurs once every 3-5 years; D1 (Moderate Drought) – represents an event that occurs every 5-10 years; D2 (Severe Drought) – represents an event that occurs every 10-20 years; D3 (Extreme Drought) – represents an event that occurs every 20-25 years; and D4 (Exceptional Drought) – represents an event that occurs every 50 years. Impact lines highlight areas that have been physically impacted by drought. Impact labels specify the longitude and magnitude of impacts. The impact labels are classified as follows: S – Short-Term, typically less than 6 months (e.g. agriculture, grasslands). L – Long-Term, typically more than 6 months (e.g. hydrology, ecology).

This historical dataset consists of a series of permanent 1-m^2 quadrats located in mixed-grass prairie in western Kansas, USA. The key aspect of the data is that at the end of each growing season, all individual plants in each quadrat were identified and their basal cover was mapped. The combination of a long time-series with full spatial detail allows analyses of demographic processes and intra- and interspecific interactions among individual plants.

The quadrats were distributed across gradients in soil type that produce distinct plants communities (see Albertson papers below) . Deep soils on the level uplands support a shortgrass community. Shallow limestone soils on hillbrows and slopes support a community dominated by little bluestem. A distinct ecotone separates the shortgrass and little bluestem areas. Most of the quadrats are located inside livestock exclosures.

This distribution contains the following data and data formats: 1) Image files (.tif) of the original, scanned maps; 2) GIS files of the digitized maps in Arc Export (.e00) format; 3) GIS files of the digitized maps in shapefile format; 4) A tabular version of the entire dataset (see "Records of all individual plants" below; this is a table with no spatial information except an x,y coordinate for each individual plant record); 5) A species list, containing information on plant functional types; 6) Quadrat information, such as community type and location in the study site; 7) An inventory of the years each quadrat was sampled; 8) Monthly precipitation; 9) Monthly temperatures.

References: Albertson, F. W. and Weaver, J. E. 1944. Nature and degree of recovery of grassland from the Great Drought of 1933 to 1940. Ecol. Monogr. 14, 393-479. Albertson, F. W. and Tomanek, G. W. 1965. Vegetation changes during a 30-year period in grassland communities near Hays, Kansas. Ecology 46, 714-720.

Do you want to know more about how climate change may impact New South Wales?\r \r The Department of Climate Change, Energy, the Environment and Water has undertaken research to develop climate change information for the NSW public using climate projections from the NSW and Australian Regional Climate Modelling initiative ('NARCliM2.0'). This climate change information is available on the AdaptNSW Interactive climate change projections map (https://www.climatechange.environment.nsw.gov.au/projections-map) as maps and GIS-ready raster data. The Interactive map currently displays data for changes in mean, maximum and minimum temperature, precipitation, hot days, cold nights, and severe fire weather days. This geospatial data available on the Interactive map are accessible below as links to the downloadable data packs. \r These data packs and the Interactive map have been designed to enhance awareness of climate change in NSW and provide climate change data and information for decision making to support climate risk assessment and adaptation planning. \r \r What is included in these data packs?\r \r Data packs provide users with 145 GIS-ready raster datasets in GeoTIFF format and layer files (map symbology). \r The GeoTIFFs display data at 4 km resolution for the entire New South Wales (NSW) region for seven climate variables: \r • minimum, mean and maximum temperature\r • rainfall (precipitation)\r • hot days (35ºC or over)\r • cold nights (below 2ºC)\r • severe fire weather days (FFDI over 50)\r \r The list of GeoTIFFs include historical baseline and future projections under two emission scenarios, providing modelled outputs on a range of plausible climates: Low emissions scenario (SSP1-2.6) and High emissions scenario (SSP3-7.0)\r \r The GeoTIFFs are raster (spatially referenced gridded) data. The data at each 4 km grid cell is calculated as an average from the results of 10 NARCliM2.0 climate models. \r \r Temporally, the GeoTIFFs are 20-year climatologies, defined as the "Historical baseline" (the 1990-2009 period represents a ‘2000’ climatology, serving as a reference period for future projections to be compared with), and "Future projections" (seven future periods or climatologies including 2020-2039, 2030-2049, 2040-2059, 2050-2069, 2060-2089, 2070-2089, and 2080-2099). This is an appropriate temporal resolution for understanding plausible climate change trends in the future. \r \r The GeoTiFFs are also arranged to provide statistics, including:\r • Annual means: calculated from 1 January to 31 December for each 20-year period\r • Seasonal means: calculated for each 20-year period for Summer (December, January, February), Autumn (March, April, May), Winter (Jun, July, August), Spring (September, October, November) \r \r Data provided in two ways or flavours:\r • Absolute values: the projected values for the variable for each period (i.e., degrees Celsius, number of days, mm of rainfall)\r • Percent change: the difference between the future climatology and the historical baseline, presented as a percentage of the historical baseline\r \r Note that a continuous time series of the daily and monthly modelled output can be accessed from the Climate Data Portal https://climatedata.environment.nsw.gov.au/\r \r The data packs also provide, map symbology files (ArcGIS layer files and QGIS layer files) and a “READ ME” file in each data package for more information on the GeoTIFFs.\r \r What is NARCliM?\r \r The New South Wales (NSW) and Australian Regional Climate Modelling ("NARCliM") is a project that was established by the NSW Government to address the need for high-resolution climate change projections for regional decision-making and impact assessments. The NSW Government has released climate projections for over a decade, with latest release (known as NARCliM2.0), being a public commitment under the Climate Change Adaptation Strategy (https://climatechange.environment.nsw.gov.au/about-adaptnsw/nsw-climate-change-adaptation-strategy) and the NSW Climate Change Fund (https://www.energy.nsw.gov.au/nsw-plans-and-progress/government-strategies-and-frameworks/taking-action-climate-change/nsw). See the resource link below to learn more about NARCliM.\r \r What else do I need to know?\r \r For more information, please review the linked resources below. If you have more questions, please contact us at narclim@environment.nsw.gov.au.

Species increasingly face environmental extremes. While responses of morphological traits to changes in average environmental conditions are well-documented, responses to environmental extremes remain poorly understood. Bird bills contribute to thermoregulation, with considerable heat loss possible through the bill surface, and with bill morphology shaped by long-term thermal conditions. We used museum specimens to investigate the relationship of bill surface area (SA) in dark-eyed juncos Junco hyemalis to traditional measures of climate (temperature and precipitation) and to a novel measure of short-term relative temperature extremity, which quantifies the degree to which temperature maxima or minima have diverged from the recent five-year norm. We found that bill SA exhibits different patterns of association with relative extremity depending on the overall temperature regime and on precipitation. While thermoregulatory function predicts larger bill SA at higher relative temperature extremities, we found this to be the case only when the measure of temperature extremity existed in an environmental context that opposed it: relative minimum temperature in a warm climate, or relative maximum temperature in a cool climate. When, instead, environmental context amplified the relative temperature extremity, we found a negative relationship between bill SA and relative temperature extremity. We also found that the strength of associations between bill SA and relative temperature extremity increased as precipitation increased. Our results suggest that trait responses to environmental variation may qualitatively differ depending on the overall environmental context, and that environmental change that extremifies already-extreme environments should be of particular concern. Extreme-on-extreme environmental change may produce responses that cannot be predicted from observations in less extreme contexts, which should make it a priority for research on species' responses to climate change as well as trait evolution generally.Species increasingly face environmental extremes. Morphological responses to changes in average environmental conditions are well-documented, but responses to environmental extremes remain poorly understood. We used museum specimens to investigate relationships between a thermoregulatory morphological trait, bird bill surface area (SA), and a measure of short-term relative temperature extremity (RTE), which quantifies the degree that temperature maxima or minima diverge from the five-year norm. Using a widespread, generalist species, Junco hyemalis, we found that SA exhibited different patterns of association with RTE depending on the overall temperature regime and on precipitation. While thermoregulatory function predicts larger SA at higher RTE, we found this only when the RTE existed in an environmental context that opposed it: atypically cold minimum temperature in a warm climate, or atypically warm maximum temperature in a cool climate. When environmental context amplified the RTE, we found a negative relationship between SA and RTE. We also found that the strength of associations between SA and RTE increased with precipitation. Our results suggest that trait responses to environmental variation may qualitatively differ depending on the overall environmental context, and that environmental change that extremifies already-extreme environments may produce responses that cannot be predicted from observations in less extreme contexts.

Methods Morphological data collection

We measured museum specimens of two subspecies of dark-eyed junco belonging to the "Oregon junco" group, Junco hyemalis pinosus and J. h. thurberi, collected between 15 March and 30 September from 1900 to 1950 within the state of California (Figure 1). We chose this 50-year timespan because it includes >90% of California junco specimens in the collections we accessed; after 1950, specimens are temporally and geographically sparse. We focused on the time period between 15 March and 30 September to ensure that the individuals studied were present to breed; juncos present in the winter may be long-distance migrants wintering in California. Specimens are held in the collections of the Museum of Vertebrate Zoology or the California Academy of Sciences. Metadata associated with the specimens were downloaded from VertNet (http://vertnet.org).

Bill length, width, and depth, as well as tarsus length and wing chord, were measured by KL with digital calipers, and bill surface area was calculated from the three linear bill measurements following Greenberg et al. "Heat loss may explain bill size differences between birds occupying different habitats.," PLoS ONE, vol. 7, no. 7, p. e40933, 2012. Further details can be found in LaBarbera et al. "Complex relationships among environmental conditions and bill morphology in a generalist songbird.," Evolutionary Ecology, vol. 31, pp. 707-724, 2017.

Obtaining environmental measures for each specimen

We obtained records for four monthly climate variables from the PRISM historical climate dataset (PRISM climate group 2015): precipitation and mean, minimum, and maximum temperature. Temperature and precipitation are standard measures of abiotic climate (Danner and Greenberg, "A critical season approach to Allen’s rule: bill size declines with winter temperature in a cold temperate environment," Journal of Biogeography, vol. 42, no. 1, pp. 114-120, 2015). The PRISM historical dataset provides GIS raster files containing the monthly means of the four climate variables as measured over 4 km-by-4 km grid cells across California. To determine which environmental conditions were associated with a given specimen, we used the latitude and longitude at which each specimen was collected to assign a raster cell. A buffer code was used to convert environmental values for each cell to those for a circle with a radius of 15 km centered on the collection locality for each specimen. For each calendar month, values for cells entirely within this circle were averaged to generate a single mean value per variable; raster cells that were only partially located within the 15 km radius were not included in these analyses. This procedure was performed for each of the five years prior to the collection date of a specimen.

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.

Click anywhere on earth to see how the water balance is changing over time. This app is based on data from GLDAS version 2.1, which uses weather observations like temperature, humidity, and rainfall to run the Noah land surface model. This model estimates how much of the rain becomes runoff, how much evaporates, and how much infiltrates into the soil. These output variables, calculated every three hours, are aggregated into monthly averages, giving us a record of the hydrologic cycle going all the way back to January 2000. Because the model is run with 0.25 degree spatial resolution (~30 km), these data should only be used for regional analysis. A specific farm or other small area might experience very different conditions than the region around it, especially because human influences like irrigation are not included.This app can also be seen as a useful template for sharing other climate datasets. If you would like to customize it for your own organization, or use it as a starting point for your own scientific application, the source code is available on github for anyone to use.

This dataset consist of inputs and intermediate results from the coastal scenario modelling. It is an analysis of the bio-physical factors that best explain the changes in QLUMP land use change between 1999 and 2009 along the Queensland coastal region for the classifications used in the future coastal modelling.

Methods:

The input layers (variables etc) were produced using a range of sources as shown in Table 1. Source datasets were edited to produce raster dataset at 50m resolution and reclassified to suit the needs for the analysis.

The analysis was made using the IDRISI Land Use Change Modeler using multi-layer perceptron neural network with explanatory power of bio-physical variables. In this process a range of bio-physical layers such as slope, rainfall, distance to roads etc (see full list in Table 1) are used as potential explanatory variables for the changes in the land use. The neutral network is trained on a subset of the data then tested against the remaining data, thereby giving an estimate of the accuracy of the prediction. This analysis produces suitability maps for each of the transitions between different land use classifications, along with a ranking of the important bio-physical factors for explaining the changes.

The 1999 - 2009 Land use change was analysed with of which 4 were found to be the strongest predictors of the change for various transitions between one land use and another. This dataset includes the rasters of the 4 best predictors along with a sample of the highest accuracy transition probability maps.

Format:

Table 1 (Table 1 NERP 9_4 e-atlas dataset) This table contains the list of names, short descriptions, data source and data manipulation for the input rasters for the land use change model

All GIS files are in GDA 94 Albers Australia coordinate system.

1999.tif This layer shows a rasterised form of the QLUMP land use (clipped to the GBR coastal zone as defined in 9.4) for 1999 used for analysis of bio-physical predictors of land use change. The original QLUMP data was re-classified into 18 classes then rasterised at 50m resolution. This raster was then resampled to a 500m resolution.

2009.tif This layer shows a rasterised form of the QLUMP land use (clipped to the GBR coastal zone as defined in 9.4) for 2009 used for analysis of bio-physical predictors of land use change. The original QLUMP data was re-classified into 18 classes (with addition of tourism land use) then rasterised at 50m resolution. This raster was then resampled to a 500m resolution.

Rainfall.rst This layer shows the average annual rainfall (in mm) sourced from the Average Yearly Rainfall Isohyets Queensland dataset (clipped to the GBR coastal zone as defined in 9.4) used for analysis of bio-physical predictors of land use change. The data was re-classified and resampled at 50m resolution.

Slope.rst This layer shows the slope (in degrees) value at 50m pixel resolution (clipped to the GBR coastal zone as defined in 9.4) used for analysis of bio-physical predictors of land use change. The slope was derived from the Australian Digital Elevation Model in ArcGIS (using the Slope tool of the 3D analyst Tools) at a 200m resolution. The data was resampled at 50m resolution.

SeaDist.rst This layer shows the distance (in m) to the nearest coastline (including estuaries) at 50m pixel resolution used for analysis of bio-physical predictors of land use change. It was created by applying an Euclidean distance function (in ArcGIS in the Spatial Analyst toolbox) to the “Mainland coastline” feature in the GBR features dataset available from GBRMPA.

UrbanDist.rst This layer shows the distance (in m) to the nearest pixel of urban land use at 50m pixel resolution used for analysis of bio-physical predictors of land use change. It was created by applying an Euclidean distance function (in ArcGIS in the Spatial Analyst toolbox) to the QLUMP 2009 dataset on the selected urban polygons.

Transition_potential_Other_to_DryHorticulture.rst This layer shows the probability for each pixel (50m resolution) of the coastal to transition from the land use class Other to Rain-fed Horticulture. Areas originally of a different land use class are given no values. This was produced by analysing the patterns of land use change between 1999 and 2009 in IRDISI as part of the Land Use Change Modeler where the main bio-physical variables affecting the pattern of change were identified. See details in the model results file. A high accuracy rate of 92% was calculated during testing.

Land Change Modeler MLP Model Results_Rain-fed_horticulture.docx This shows the results of the analysis of change from land use Others to rain-fed horticulture between 1999 and 2009 using four variables: Distance to existing horticulture, Rainfall, Soil type and Slope.

Transition_potential_Other_to_Drysugar.rst This layer shows the probability for each pixel (50m resolution) of the coastal to transition from the land use class Other to Rain-fed Sugar cane. Areas originally of a different land use class are given no values. This was produced by analysing the patterns of land use change between 1999 and 2009 in IRDISI as part of the Land Use Change Modeler where the main bio-physical variables affecting the pattern of change were identified. See details in the model results file. A high accuracy rate of 84% was calculated during testing.

Land Change Modeler MLP Model Results_Rain-fed_sugar.docx This shows the results of the analysis of change from land use Others to rain-fed sugar between 1999 and 2009 using three variables: Rainfall, Soil type and Slope.

Transition_potential_Other_to_Forestry.rst This layer shows the probability for each pixel (50m resolution) of the coastal to transition from the land use class Other to Forestry. Areas originally of a different land use class are given no values. This was produced by analysing the patterns of land use change between 1999 and 2009 in IRDISI as part of the Land Use Change Modeler where the main bio-physical variables affecting the pattern of change were identified. See details in the model results file. A good accuracy rate of 73% was calculated during testing.

Land Change Modeler MLP Model Results_Forestry.docx This shows the results of the analysis of change from land use Others to Forestry between 1999 and 2009 using three variables: Rainfall, Soil type and Proximity to existing forestry.

Transition_potential_Other_to_Urban.rst This layer shows the probability for each pixel (50m resolution) of the coastal to transition from the land use class Other to Urban. Areas originally of a different land use class are given no values. This was produced by analysing the patterns of land use change between 1999 and 2009 in IRDISI as part of the Land Use Change Modeler where the main bio-physical variables affecting the pattern of change were identified. See details in the model results file. A good accuracy rate of 75% was calculated during testing.

Land Change Modeler MLP Model Results_Urban.docx This shows the results of the analysis of change from land use Others to Urban between 1999 and 2009 using two variables: Slope and Proximity to existing urban areas.