The geospatial products described and distributed here depict the probability of high-severity fire, if a fire were to occur, for several ecoregions in the contiguous western US.
The ecological effects of wildland fire \u2013 also termed the fire severity \u2013 are often highly heterogeneous in space and time. This heterogeneity is a result of spatial variability in factors such as fuel, topography, and climate (e.g. mean annual temperature). However, temporally variable factors such as daily weather and climatic extremes (e.g. an unusually warm year) also may play a key role.
Scientists from the US Forest Service Rocky Mountain Research Station and the University of Montana conducted a study in which observed data were used to produce statistical models describing the probability of high severity fire as a function of fuel, topography, climate, and fire weather. Observed data from over 2000 fires (from 2002-2015) were used to build individual models for each of 19 ecoregions in the contiguous US (see Parks et al. 2018, Figure 1). High severity fire was measured using a fire severity metric termed the relativized burn ratio, which uses pre- and post-fire Landsat imagery to measure fire-induced ecological change. Fuel included pre-fire metrics of live fuel amount such as NDVI. Topography included factors such as slope and potential solar radiation. Climate summarized 30-year averages of factors such as mean summer temperature that spatially vary across the study area. Lastly, fire weather incorporated temporally variable factors such as daily and annual temperature.
In turn, these statistical models were used to generate \"wall-to-wall\" maps depicting the probability of high severity fire, if a fire were to occur, for 13 of the 19 ecoregions. Maps were not produced for ecoregions in which model quality was deemed inadequate. All maps use fuel data representing the year 2016 and therefore provide a fairly up-to-date assessment of the potential for high severity fire. For those ecoregions in which the relative influence of fire weather was fairly strong (n=6), two additional maps were produced, one depicting the probability of high severity fire under moderate weather and the other under extreme weather. An important consideration is that only pixels defined as forest were used to build the models; consequently maps exclude pixels considered non-forest.
The geospatial products described and distributed here depict the probability of high-severity fire, if a fire were to occur, for several ecoregions in the contiguous western US.
The ecological effects of wildland fire \u2013 also termed the fire severity \u2013 are often highly heterogeneous in space and time. This heterogeneity is a result of spatial variability in factors such as fuel, topography, and climate (e.g. mean annual temperature). However, temporally variable factors such as daily weather and climatic extremes (e.g. an unusually warm year) also may play a key role.
Scientists from the US Forest Service Rocky Mountain Research Station and the University of Montana conducted a study in which observed data were used to produce statistical models describing the probability of high severity fire as a function of fuel, topography, climate, and fire weather. Observed data from over 2000 fires (from 2002-2015) were used to build individual models for each of 19 ecoregions in the contiguous US (see Parks et al. 2018, Figure 1). High severity fire was measured using a fire severity metric termed the relativized burn ratio, which uses pre- and post-fire Landsat imagery to measure fire-induced ecological change. Fuel included pre-fire metrics of live fuel amount such as NDVI. Topography included factors such as slope and potential solar radiation. Climate summarized 30-year averages of factors such as mean summer temperature that spatially vary across the study area. Lastly, fire weather incorporated temporally variable factors such as daily and annual temperature.
In turn, these statistical models were used to generate \"wall-to-wall\" maps depicting the probability of high severity fire, if a fire were to occur, for 13 of the 19 ecoregions. Maps were not produced for ecoregions in which model quality was deemed inadequate. All maps use fuel data representing the year 2016 and therefore provide a fairly up-to-date assessment of the potential for high severity fire. For those ecoregions in which the relative influence of fire weather was fairly strong (n=6), two additional maps were produced, one depicting the probability of high severity fire under moderate weather and the other under extreme weather. An important consideration is that only pixels defined as forest were used to build the models; consequently maps exclude pixels considered non-forest.
Basal Area (BA). 30 meter pixel resolution. Data represents forest conditions circa 2002.
These data are a product of a multi-year effort by the FHTET (Forest Health Technology Enterprise Team) Remote Sensing Program to develop raster datasets of forest parameters for each of the tree species measured in the Forest Service\u2019s Forest Inventory and Analysis (FIA) program. This dataset was created to support the 2013\u20132027 National Insect and Disease Risk Map (NIDRM) assessment. The statistical modeling approach used data-mining software and an archive of geospatial information to find the complex relationships between GIS layers and the presence/abundance of tree species as measured in over 300,000 FIA plot locations. Unique statistical models were developed from predictor layers consisting of climate, terrain, soils, and satellite imagery. Modeled basal area (BA) and stand density index (SDI) datasets for individual tree species were further post-processed to 1) match BA and SDI histograms of FIA data, 2) ensure that the sum of individual species BA and SDI on a pixel did not exceed separately modeled total for all species BA and SDI raster datasets, 3) derive additional tree parameters like quadratic mean diameter and trees per acre. With Landsat image collection dates ranging from 1985 to 2005, and a mean collection date for treed areas of 2002, and FIA plot data generally ranging from 1999 to 2005, the vintage of the base parameter datasets varies based on location, but can be roughly considered as 2002
Basal Area (BA). 30 meter pixel resolution. Data represents forest conditions circa 2002.
These data are a product of a multi-year effort by the FHTET (Forest Health Technology Enterprise Team) Remote Sensing Program to develop raster datasets of forest parameters for each of the tree species measured in the Forest Service\u2019s Forest Inventory and Analysis (FIA) program. This dataset was created to support the 2013\u20132027 National Insect and Disease Risk Map (NIDRM) assessment. The statistical modeling approach used data-mining software and an archive of geospatial information to find the complex relationships between GIS layers and the presence/abundance of tree species as measured in over 300,000 FIA plot locations. Unique statistical models were developed from predictor layers consisting of climate, terrain, soils, and satellite imagery. Modeled basal area (BA) and stand density index (SDI) datasets for individual tree species were further post-processed to 1) match BA and SDI histograms of FIA data, 2) ensure that the sum of individual species BA and SDI on a pixel did not exceed separately modeled total for all species BA and SDI raster datasets, 3) derive additional tree parameters like quadratic mean diameter and trees per acre. With Landsat image collection dates ranging from 1985 to 2005, and a mean collection date for treed areas of 2002, and FIA plot data generally ranging from 1999 to 2005, the vintage of the base parameter datasets varies based on location, but can be roughly considered as 2002
These data are a product of a multi-year effort by the FHTET (Forest Health Technology Enterprise Team) Remote Sensing Program to develop raster datasets of forest parameters for each of the tree species measured in the Forest Service\u2019s Forest Inventory and Analysis (FIA) program. This dataset was created to support the 2013\u20132027 National Insect and Disease Risk Map (NIDRM) assessment. The statistical modeling approach used data-mining software and an archive of geospatial information to find the complex relationships between GIS layers and the presence/abundance of tree species as measured in over 300,000 FIA plot locations. Unique statistical models were developed from predictor layers consisting of climate, terrain, soils, and satellite imagery. Modeled basal area (BA) and stand density index (SDI) datasets for individual tree species were further post-processed to 1) match BA and SDI histograms of FIA data, 2) ensure that the sum of individual species BA and SDI on a pixel did not exceed separately modeled total for all species BA and SDI raster datasets, 3) derive additional tree parameters like quadratic mean diameter and trees per acre. With Landsat image collection dates ranging from 1985 to 2005, and a mean collection date for treed areas of 2002, and FIA plot data generally ranging from 1999 to 2005, the vintage of the base parameter datasets varies based on location, but can be roughly considered as 2002
These data are a product of a multi-year effort by the FHTET (Forest Health Technology Enterprise Team) Remote Sensing Program to develop raster datasets of forest parameters for each of the tree species measured in the Forest Service\u2019s Forest Inventory and Analysis (FIA) program. This dataset was created to support the 2013\u20132027 National Insect and Disease Risk Map (NIDRM) assessment. The statistical modeling approach used data-mining software and an archive of geospatial information to find the complex relationships between GIS layers and the presence/abundance of tree species as measured in over 300,000 FIA plot locations. Unique statistical models were developed from predictor layers consisting of climate, terrain, soils, and satellite imagery. Modeled basal area (BA) and stand density index (SDI) datasets for individual tree species were further post-processed to 1) match BA and SDI histograms of FIA data, 2) ensure that the sum of individual species BA and SDI on a pixel did not exceed separately modeled total for all species BA and SDI raster datasets, 3) derive additional tree parameters like quadratic mean diameter and trees per acre. With Landsat image collection dates ranging from 1985 to 2005, and a mean collection date for treed areas of 2002, and FIA plot data generally ranging from 1999 to 2005, the vintage of the base parameter datasets varies based on location, but can be roughly considered as 2002
Production data were generated using the Normalized Difference Vegetation Index (NDVI) from the Thematic Mapper Suite from 1984 to 2023 at 250 m resolution. The NDVI is converted to production estimates using two regression formulas depending on the level of the NDVI; there is one equation for lower values (and thus lower production values) and one for higher values. This raster dataset yields estimates of annual production of rangeland vegetation and should be useful for understanding trends and variability in forage resources.
This raw lbs/acre data that the Z-scores were derived from as well as the Z-scores dataset can be downloaded from: https://data.fs.usda.gov/geodata/rastergateway/rangelands/index.php.
Production data were generated using the Normalized Difference Vegetation Index (NDVI) from the Thematic Mapper Suite from 1984 to 2023 at 250 m resolution. The NDVI is converted to production estimates using two regression formulas depending on the level of the NDVI; there is one equation for lower values (and thus lower production values) and one for higher values. This raster dataset yields estimates of annual production of rangeland vegetation and should be useful for understanding trends and variability in forage resources.
This raw lbs/acre data that the Z-scores were derived from as well as the Z-scores dataset can be downloaded from: https://data.fs.usda.gov/geodata/rastergateway/rangelands/index.php.
This raster dataset depicts rangelands in the coterminous U.S., including transitional rangelands and small patch-size rangelands. Each 30 meter pixel is assigned a land cover category, including Rangeland, Afforested Rangeland (experiencing encroachment by trees [> 25% tree cover]) and Transitional Rangeland (currently dominated by herbs or shrubs that will likely become forested without management intervention).
This raw lbs/acre data that the Z-scores were derived from as well as the Z-scores dataset can be downloaded from: https://data.fs.usda.gov/geodata/rastergateway/rangelands/index.php.
Production data were generated using the Normalized Difference Vegetation Index (NDVI) from the Thematic Mapper Suite from 1984 to 2023 at 250 m resolution. The NDVI is converted to production estimates using two regression formulas depending on the level of the NDVI; there is one equation for lower values (and thus lower production values) and one for higher values. This raster dataset yields estimates of annual production of rangeland vegetation and should be useful for understanding trends and variability in forage resources. These results were then converted to Z-scores for easier comparison of annual relative productivity in coterminous U.S. rangelands, and for rapid display in online time-enabled applications.
This raster dataset depicts rangelands in the coterminous U.S., including transitional rangelands and small patch-size rangelands. Each 30 meter pixel is assigned a land cover category, including Rangeland, Afforested Rangeland (experiencing encroachment by trees [> 25% tree cover]) and Transitional Rangeland (currently dominated by herbs or shrubs that will likely become forested without management intervention).
This raw lbs/acre data that the Z-scores were derived from as well as the Z-scores dataset can be downloaded from: https://data.fs.usda.gov/geodata/rastergateway/rangelands/index.php.
Production data were generated using the Normalized Difference Vegetation Index (NDVI) from the Thematic Mapper Suite from 1984 to 2023 at 250 m resolution. The NDVI is converted to production estimates using two regression formulas depending on the level of the NDVI; there is one equation for lower values (and thus lower production values) and one for higher values. This raster dataset yields estimates of annual production of rangeland vegetation and should be useful for understanding trends and variability in forage resources. These results were then converted to Z-scores for easier comparison of annual relative productivity in coterminous U.S. rangelands, and for rapid display in online time-enabled applications.
The U.S. has been providing national-scale estimates of forest carbon stocks and stock change to meet United Nations Framework Convention on Climate Change reporting requirements for years. Through application of a nearest-neighbor imputation approach, mapped estimates of forest biomass density were developed for the contiguous United States using the annual forest inventory conducted by the USDA Forest Service Forest Inventory and Analysis (FIA) program, MODIS satellite imagery, and ancillary geospatial datasets. This data product would contain the following 7 raster maps: Aboveground Forest Biomass, Belowground Forest Biomass, Forest Tree Bole Biomass, Forest Sapling Biomass, Forest Stump Biomass, Forest Top Biomass, Woodland Specias Biomass. All layers have a 250 meter pixel resolution and values represent biomass pounds per acre. The paper on which these maps are based may be found here: https://dx.doi.org/10.2737/RDS-2013-0004 Access to full metadata and other information can be accessed here: https://dx.doi.org/10.2737/RDS-2013-0004
The U.S. has been providing national-scale estimates of forest carbon stocks and stock change to meet United Nations Framework Convention on Climate Change reporting requirements for years. Through application of a nearest-neighbor imputation approach, mapped estimates of forest biomass density were developed for the contiguous United States using the annual forest inventory conducted by the USDA Forest Service Forest Inventory and Analysis (FIA) program, MODIS satellite imagery, and ancillary geospatial datasets. This data product would contain the following 7 raster maps: Aboveground Forest Biomass, Belowground Forest Biomass, Forest Tree Bole Biomass, Forest Sapling Biomass, Forest Stump Biomass, Forest Top Biomass, Woodland Specias Biomass. All layers have a 250 meter pixel resolution and values represent biomass pounds per acre. The paper on which these maps are based may be found here: https://dx.doi.org/10.2737/RDS-2013-0004 Access to full metadata and other information can be accessed here: https://dx.doi.org/10.2737/RDS-2013-0004
Through application of a nearest-neighbor imputation approach, mapped estimates of forest carbon density were developed for the contiguous United States using the annual forest inventory conducted by the USDA Forest Service Forest Inventory and Analysis (FIA) program, MODIS satellite imagery, and ancillary geospatial datasets. This data product contains the following 8 raster maps: total forest carbon in all stocks, live tree aboveground forest carbon, live tree belowground forest carbon, forest down dead carbon, forest litter carbon, forest standing dead carbon, forest soil organic carbon, and forest understory carbon.
The paper on which these maps are based may be found here: https://dx.doi.org/10.2737/RDS-2013-0004.
Access to full metadata and other information can be accessed here: https://dx.doi.org/10.2737/RDS-2013-0004.
Through application of a nearest-neighbor imputation approach, mapped estimates of forest carbon density were developed for the contiguous United States using the annual forest inventory conducted by the USDA Forest Service Forest Inventory and Analysis (FIA) program, MODIS satellite imagery, and ancillary geospatial datasets. This data product contains the following 8 raster maps: total forest carbon in all stocks, live tree aboveground forest carbon, live tree belowground forest carbon, forest down dead carbon, forest litter carbon, forest standing dead carbon, forest soil organic carbon, and forest understory carbon.
The paper on which these maps are based may be found here: https://dx.doi.org/10.2737/RDS-2013-0004.
Access to full metadata and other information can be accessed here: https://dx.doi.org/10.2737/RDS-2013-0004.
This Biomass and Carbon per Acre dataset was compiled for the 48-state CONUS Area in order to identify local, state and regional trends in forest cover and landscape composition. It can be used as a graphic, as inputs to GIS overlay procedures, or to summarize average or total amounts of forest attributes within analysis areas of interest. Through application of a nearest-neighbor imputation approach, mapped estimates of forest biomass density were developed for the contiguous United States using the annual forest inventory conducted by the USDA Forest Service Forest Inventory and Analysis (FIA) program, MODIS satellite imagery, and ancillary geospatial datasets. This data product contains the following 7 raster maps: Aboveground Forest Biomass, Belowground Forest Biomass, Forest Tree Bole Biomass, Forest Sapling Biomass, Forest Stump Biomass, Forest Top Biomass, Woodland Species Biomass. All layers have a 250 meter pixel resolution and values represent biomass pounds per acre. The paper on which these maps are based may be found here: https://dx.doi.org/10.2737/RDS-2013-0004 Access to full metadata and other information can be accessed here: https://dx.doi.org/10.2737/RDS-2013-0004
This Biomass and Carbon per Acre dataset was compiled for the 48-state CONUS Area in order to identify local, state and regional trends in forest cover and landscape composition. It can be used as a graphic, as inputs to GIS overlay procedures, or to summarize average or total amounts of forest attributes within analysis areas of interest. Through application of a nearest-neighbor imputation approach, mapped estimates of forest biomass density were developed for the contiguous United States using the annual forest inventory conducted by the USDA Forest Service Forest Inventory and Analysis (FIA) program, MODIS satellite imagery, and ancillary geospatial datasets. This data product contains the following 7 raster maps: Aboveground Forest Biomass, Belowground Forest Biomass, Forest Tree Bole Biomass, Forest Sapling Biomass, Forest Stump Biomass, Forest Top Biomass, Woodland Species Biomass. All layers have a 250 meter pixel resolution and values represent biomass pounds per acre. The paper on which these maps are based may be found here: https://dx.doi.org/10.2737/RDS-2013-0004 Access to full metadata and other information can be accessed here: https://dx.doi.org/10.2737/RDS-2013-0004
The National Forest Climate Change Maps project was developed to meet the need 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 and air temperature, including both Alaskan and lower 48 datasets. Data from the lower 48 were downloaded from here: https://www.fs.usda.gov/rm/boise/AWAE/projects/national-forest-climate-change-maps.html, and Alaskan data came from here: https://www.snap.uaf.edu/tools/data-downloads. Historical data are compared with RCP 8.5 projections from the 2080s.
A Raster Function Template is available in this service that will classify the data as originally intended by OSC. The RFT currently works in AGOL but not in ArcGIS Pro.
The National Forest Climate Change Maps project was developed to meet the need 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 and air temperature, including both Alaskan and lower 48 datasets. Data from the lower 48 were downloaded from here: https://www.fs.usda.gov/rm/boise/AWAE/projects/national-forest-climate-change-maps.html, and Alaskan data came from here: https://www.snap.uaf.edu/tools/data-downloads. Historical data are compared with RCP 8.5 projections from the 2080s.
A Raster Function Template is available in this service that will classify the data as originally intended by OSC. The RFT currently works in AGOL but not in ArcGIS Pro.
The National Forest Climate Change Maps project was developed to meet the need 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 and air temperature, including both Alaskan and lower 48 datasets. Data from the lower 48 were downloaded from here: https://www.fs.usda.gov/rm/boise/AWAE/projects/national-forest-climate-change-maps.html, and Alaskan data came from here: https://www.snap.uaf.edu/tools/data-downloads. Historical data are compared with RCP 8.5 projections from the 2080s.
A Raster Function Template is available in this service that will classify the data as originally intended by OSC. The RFT currently works in AGOL but not in ArcGIS Pro.
The National Forest Climate Change Maps project was developed to meet the need 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 and air temperature, including both Alaskan and lower 48 datasets. Data from the lower 48 were downloaded from here: https://www.fs.usda.gov/rm/boise/AWAE/projects/national-forest-climate-change-maps.html, and Alaskan data came from here: https://www.snap.uaf.edu/tools/data-downloads. Historical data are compared with RCP 8.5 projections from the 2080s.
A Raster Function Template is available in this service that will classify the data as originally intended by OSC. The RFT currently works in AGOL but not in ArcGIS Pro.
The National Forest Climate Change Maps project was developed to meet the need 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 and air temperature, including both Alaskan and lower 48 datasets. Data from the lower 48 were downloaded from here: https://www.fs.usda.gov/rm/boise/AWAE/projects/national-forest-climate-change-maps.html, and Alaskan data came from here: https://www.snap.uaf.edu/tools/data-downloads. Historical data are compared with RCP 8.5 projections from the 2080s.
A Raster Function Template is available in this service that will classify the data as originally intended by OSC. The RFT currently works in AGOL but not in ArcGIS Pro.
The National Forest Climate Change Maps project was developed to meet the need 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 and air temperature, including both Alaskan and lower 48 datasets. Data from the lower 48 were downloaded from here: https://www.fs.usda.gov/rm/boise/AWAE/projects/national-forest-climate-change-maps.html, and Alaskan data came from here: https://www.snap.uaf.edu/tools/data-downloads. Historical data are compared with RCP 8.5 projections from the 2080s.
A Raster Function Template is available in this service that will classify the data as originally intended by OSC. The RFT currently works in AGOL but not in ArcGIS Pro.
The National Forest Climate Change Maps project was developed to meet the need 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 and air temperature, including both Alaskan and lower 48 datasets. Data from the lower 48 were downloaded from here: https://www.fs.usda.gov/rm/boise/AWAE/projects/national-forest-climate-change-maps.html, and Alaskan data came from here: https://www.snap.uaf.edu/tools/data-downloads. Historical data are compared with RCP 8.5 projections from the 2080s.
A Raster Function Template is available in this service that will classify the data as originally intended by OSC. The RFT currently works in AGOL but not in ArcGIS Pro.
The National Forest Climate Change Maps project was developed to meet the need 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 and air temperature, including both Alaskan and lower 48 datasets. Data from the lower 48 were downloaded from here: https://www.fs.usda.gov/rm/boise/AWAE/projects/national-forest-climate-change-maps.html, and Alaskan data came from here: https://www.snap.uaf.edu/tools/data-downloads. Historical data are compared with RCP 8.5 projections from the 2080s.
A Raster Function Template is available in this service that will classify the data as originally intended by OSC. The RFT currently works in AGOL but not in ArcGIS Pro.
The National Forest Climate Change Maps project was developed to meet the need 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 and air temperature, including both Alaskan and lower 48 datasets. Data from the lower 48 were downloaded from here: https://www.fs.usda.gov/rm/boise/AWAE/projects/national-forest-climate-change-maps.html, and Alaskan data came from here: https://www.snap.uaf.edu/tools/data-downloads. Historical data are compared with RCP 8.5 projections from the 2080s.
A Raster Function Template is available in this service that will classify the data as originally intended by OSC. The RFT currently works in AGOL but not in ArcGIS Pro.
The National Forest Climate Change Maps project was developed to meet the need 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 and air temperature, including both Alaskan and lower 48 datasets. Data from the lower 48 were downloaded from here: https://www.fs.usda.gov/rm/boise/AWAE/projects/national-forest-climate-change-maps.html, and Alaskan data came from here: https://www.snap.uaf.edu/tools/data-downloads. Historical data are compared with RCP 8.5 projections from the 2080s.
A Raster Function Template is available in this service that will classify the data as originally intended by OSC. The RFT currently works in AGOL but not in ArcGIS Pro.
The National Forest Climate Change Maps project was developed to meet the need 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 and air temperature, including both Alaskan and lower 48 datasets. Data from the lower 48 were downloaded from here: https://www.fs.usda.gov/rm/boise/AWAE/projects/national-forest-climate-change-maps.html, and Alaskan data came from here: https://www.snap.uaf.edu/tools/data-downloads. Historical data are compared with RCP 8.5 projections from the 2080s.
A Raster Function Template is available in this service that will classify the data as originally intended by OSC. The RFT currently works in AGOL but not in ArcGIS Pro.
The National Forest Climate Change Maps project was developed to meet the need 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 and air temperature, including both Alaskan and lower 48 datasets. Data from the lower 48 were downloaded from here: https://www.fs.usda.gov/rm/boise/AWAE/projects/national-forest-climate-change-maps.html, and Alaskan data came from here: https://www.snap.uaf.edu/tools/data-downloads. Historical data are compared with RCP 8.5 projections from the 2080s.
A Raster Function Template is available in this service that will classify the data as originally intended by OSC. The RFT currently works in AGOL but not in ArcGIS Pro.
The National Forest Climate Change Maps project was developed to meet the need 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 and air temperature, including both Alaskan and lower 48 datasets. Data from the lower 48 were downloaded from here: https://www.fs.usda.gov/rm/boise/AWAE/projects/national-forest-climate-change-maps.html, and Alaskan data came from here: https://www.snap.uaf.edu/tools/data-downloads. Historical data are compared with RCP 8.5 projections from the 2080s.
A Raster Function Template is available in this service that will classify the data as originally intended by OSC. The RFT currently works in AGOL but not in ArcGIS Pro.
The National Forest Climate Change Maps project was developed to meet the need 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 and air temperature, including both Alaskan and lower 48 datasets. Data from the lower 48 were downloaded from here: https://www.fs.usda.gov/rm/boise/AWAE/projects/national-forest-climate-change-maps.html, and Alaskan data came from here: https://www.snap.uaf.edu/tools/data-downloads. Historical data are compared with RCP 8.5 projections from the 2080s.
A Raster Function Template is available in this service that will classify the data as originally intended by OSC. The RFT currently works in AGOL but not in ArcGIS Pro.
The National Forest Climate Change Maps project was developed to meet the need 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 and air temperature, including both Alaskan and lower 48 datasets. Data from the lower 48 were downloaded from here: https://www.fs.usda.gov/rm/boise/AWAE/projects/national-forest-climate-change-maps.html, and Alaskan data came from here: https://www.snap.uaf.edu/tools/data-downloads. Historical data are compared with RCP 8.5 projections from the 2080s.
A Raster Function Template is available in this service that will classify the data as originally intended by OSC. The RFT currently works in AGOL but not in ArcGIS Pro.
The National Forest Climate Change Maps project was developed to meet the need 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 and air temperature, including both Alaskan and lower 48 datasets. Data from the lower 48 were downloaded from here: https://www.fs.usda.gov/rm/boise/AWAE/projects/national-forest-climate-change-maps.html, and Alaskan data came from here: https://www.snap.uaf.edu/tools/data-downloads. Historical data are compared with RCP 8.5 projections from the 2080s.
A Raster Function Template is available in this service that will classify the data as originally intended by OSC. The RFT currently works in AGOL but not in ArcGIS Pro.
The National Forest Climate Change Maps project was developed to meet the need 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 and air temperature, including both Alaskan and lower 48 datasets. Data from the lower 48 were downloaded from here: https://www.fs.usda.gov/rm/boise/AWAE/projects/national-forest-climate-change-maps.html, and Alaskan data came from here: https://www.snap.uaf.edu/tools/data-downloads. Historical data are compared with RCP 8.5 projections from the 2080s.
A Raster Function Template is available in this service that will classify the data as originally intended by OSC. The RFT currently works in AGOL but not in ArcGIS Pro.
The National Forest Climate Change Maps project was developed to meet the need 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 and air temperature, including both Alaskan and lower 48 datasets. Data from the lower 48 were downloaded from here: https://www.fs.usda.gov/rm/boise/AWAE/projects/national-forest-climate-change-maps.html, and Alaskan data came from here: https://www.snap.uaf.edu/tools/data-downloads. Historical data are compared with RCP 8.5 projections from the 2080s.
A Raster Function Template is available in this service that will classify the data as originally intended by OSC. The RFT currently works in AGOL but not in ArcGIS Pro.
Average historical temperature change, between 1948-1968 and 1996-2016 averages, in Celsius. Calculated using averages of minimum and maximum monthly values during these time periods. Values are based on TopoWx data, downloaded from here: http://www.scrimhub.org/resources/topowx/
Average historical temperature change, between 1948-1968 and 1996-2016 averages, in Celsius. Calculated using averages of minimum and maximum monthly values during these time periods. Values are based on TopoWx data, downloaded from here: http://www.scrimhub.org/resources/topowx/
Projected change in average number of days of precipitation (>0.1 inch) between 1985-2005 and 2071-2090 (RCP 8.5) time periods
Projected change in average number of days of precipitation (>0.1 inch) between 1985-2005 and 2071-2090 (RCP 8.5) time periods
The maps and tables presented here represent potential variability of projected climate change across the conterminous United States during three 30-year periods in this century and emphasizes the importance of evaluating multiple signals of change across large spatial domains. Maps of growing degree days, plant hardiness zones, heat zones, and cumulative drought severity depict the potential for markedly shifting conditions and highlight regions where changes may be multifaceted across these metrics. In addition to the maps, the potential change in these climate variables are summarized in tables according to the seven regions of the fourth National Climate Assessment to provide additional regional context. Viewing these data collectively further emphasizes the potential for novel climatic space under future projections of climate change and signals the wide disparity in these conditions based on relatively near-term human decisions of curtailing (or not) greenhouse gas emissions. More information available at https://www.fs.usda.gov/nrs/pubs/rmap/rmap_nrs9.pdf. This dataset represents heat zones, or the mean number of days over 30 C, in 4 time periods (1980-2009, 2010-2039, 2040-2069, and 2070-2099), using two emissions scenarios (RCP 4.5 and 8.5, the medium and high scenarios, respectively).
The maps and tables presented here represent potential variability of projected climate change across the conterminous United States during three 30-year periods in this century and emphasizes the importance of evaluating multiple signals of change across large spatial domains. Maps of growing degree days, plant hardiness zones, heat zones, and cumulative drought severity depict the potential for markedly shifting conditions and highlight regions where changes may be multifaceted across these metrics. In addition to the maps, the potential change in these climate variables are summarized in tables according to the seven regions of the fourth National Climate Assessment to provide additional regional context. Viewing these data collectively further emphasizes the potential for novel climatic space under future projections of climate change and signals the wide disparity in these conditions based on relatively near-term human decisions of curtailing (or not) greenhouse gas emissions. More information available at https://www.fs.usda.gov/nrs/pubs/rmap/rmap_nrs9.pdf. This dataset represents heat zones, or the mean number of days over 30 C, in 4 time periods (1980-2009, 2010-2039, 2040-2069, and 2070-2099), using two emissions scenarios (RCP 4.5 and 8.5, the medium and high scenarios, respectively).
The maps and tables presented here represent potential variability of projected climate change across the conterminous United States during three 30-year periods in this century and emphasizes the importance of evaluating multiple signals of change across large spatial domains. Maps of growing degree days, plant hardiness zones, heat zones, and cumulative drought severity depict the potential for markedly shifting conditions and highlight regions where changes may be multifaceted across these metrics. In addition to the maps, the potential change in these climate variables are summarized in tables according to the seven regions of the fourth National Climate Assessment to provide additional regional context. Viewing these data collectively further emphasizes the potential for novel climatic space under future projections of climate change and signals the wide disparity in these conditions based on relatively near-term human decisions of curtailing (or not) greenhouse gas emissions. More information available at https://www.fs.usda.gov/nrs/pubs/rmap/rmap_nrs9.pdf. This map includes plant hardiness zones for 4 time periods (1980-2009, 2010-2039, 2040-2069, and 2070-2099) and 2 RCPs (4.5 and 8.5), representing medium and high emissions scenarios.
The maps and tables presented here represent potential variability of projected climate change across the conterminous United States during three 30-year periods in this century and emphasizes the importance of evaluating multiple signals of change across large spatial domains. Maps of growing degree days, plant hardiness zones, heat zones, and cumulative drought severity depict the potential for markedly shifting conditions and highlight regions where changes may be multifaceted across these metrics. In addition to the maps, the potential change in these climate variables are summarized in tables according to the seven regions of the fourth National Climate Assessment to provide additional regional context. Viewing these data collectively further emphasizes the potential for novel climatic space under future projections of climate change and signals the wide disparity in these conditions based on relatively near-term human decisions of curtailing (or not) greenhouse gas emissions. More information available at https://www.fs.usda.gov/nrs/pubs/rmap/rmap_nrs9.pdf. This map includes plant hardiness zones for 4 time periods (1980-2009, 2010-2039, 2040-2069, and 2070-2099) and 2 RCPs (4.5 and 8.5), representing medium and high emissions scenarios.
Date of freeze for historical (1985-2005) and future (2071-2090, RCP 8.5) time periods, and absolute change between them, based on analysis of MACAv2METDATA.
Date of freeze for historical (1985-2005) and future (2071-2090, RCP 8.5) time periods, and absolute change between them, based on analysis of MACAv2METDATA.
Date of thaw for historical (1985-2005) and future (2071-2090, RCP 8.5) time periods, and absolute change between them, based on analysis of MACAv2METDATA.
Date of thaw for historical (1985-2005) and future (2071-2090, RCP 8.5) time periods, and absolute change between them, based on analysis of MACAv2METDATA.
Greatest number of consecutive summer (May-Sept) dry days (<1/10 inch of rain) was calculated for each year over the historical (1985-2004) and future (RCP 8.5 2071-2090) time periods; absolute and percent change between these was then calculated. This includes three versions: one based on the 20-year average of summer maxima, one based on the overall maximum, and one based on the 90th percentile value of 20-year maxima.
Greatest number of consecutive summer (May-Sept) dry days (<1/10 inch of rain) was calculated for each year over the historical (1985-2004) and future (RCP 8.5 2071-2090) time periods; absolute and percent change between these was then calculated. This includes three versions: one based on the 20-year average of summer maxima, one based on the overall maximum, and one based on the 90th percentile value of 20-year maxima.
Date of freeze for historical (1985-2005) and future (2071-2090, RCP 8.5) time periods, and absolute change between them, based on analysis of MACAv2METDATA.
Date of freeze for historical (1985-2005) and future (2071-2090, RCP 8.5) time periods, and absolute change between them, based on analysis of MACAv2METDATA.
The maps and tables presented here represent potential variability of projected climate change across the conterminous United States during three 30-year periods in this century and emphasizes the importance of evaluating multiple signals of change across large spatial domains. Maps of growing degree days, plant hardiness zones, heat zones, and cumulative drought severity depict the potential for markedly shifting conditions and highlight regions where changes may be multifaceted across these metrics. In addition to the maps, the potential change in these climate variables are summarized in tables according to the seven regions of the fourth National Climate Assessment to provide additional regional context. Viewing these data collectively further emphasizes the potential for novel climatic space under future projections of climate change and signals the wide disparity in these conditions based on relatively near-term human decisions of curtailing (or not) greenhouse gas emissions. More information available at https://www.fs.usda.gov/nrs/pubs/rmap/rmap_nrs9.pdf.
The maps and tables presented here represent potential variability of projected climate change across the conterminous United States during three 30-year periods in this century and emphasizes the importance of evaluating multiple signals of change across large spatial domains. Maps of growing degree days, plant hardiness zones, heat zones, and cumulative drought severity depict the potential for markedly shifting conditions and highlight regions where changes may be multifaceted across these metrics. In addition to the maps, the potential change in these climate variables are summarized in tables according to the seven regions of the fourth National Climate Assessment to provide additional regional context. Viewing these data collectively further emphasizes the potential for novel climatic space under future projections of climate change and signals the wide disparity in these conditions based on relatively near-term human decisions of curtailing (or not) greenhouse gas emissions. More information available at https://www.fs.usda.gov/nrs/pubs/rmap/rmap_nrs9.pdf.
Heat Stress Index, compared between historical and future time periods, including historical and percent change
Heat Stress Index, compared between historical and future time periods, including historical and percent change
Rasters of precipitation including both Alaskan and lower 48 datasets, with historical and projected data, and projected change.
Rasters of precipitation including both Alaskan and lower 48 datasets, with historical and projected data, and projected change.
The National Forest Climate Change Maps project was developed to meet the need 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 April 1 snow water equivalent (SWE), and snow residence time), and stream flow.
The National Forest Climate Change Maps project was developed to meet the need 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 April 1 snow water equivalent (SWE), and snow residence time), and stream flow.
Rasters of air temperature, including both Alaskan and lower 48 datasets, with historical and projected data, and projected change.
Rasters of air temperature, including both Alaskan and lower 48 datasets, with historical and projected data, and projected change.
The U.S. has been providing national-scale estimates of forest carbon stocks and stock change to meet United Nations Framework Convention on Climate Change reporting requirements for years. Through application of a nearest-neighbor imputation approach, mapped estimates of forest biomass density were developed for the contiguous United States using the annual forest inventory conducted by the USDA Forest Service Forest Inventory and Analysis (FIA) program, MODIS satellite imagery, and ancillary geospatial datasets. This data product would contain the following 7 raster maps: Aboveground Forest Biomass, Belowground Forest Biomass, Forest Tree Bole Biomass, Forest Sapling Biomass, Forest Stump Biomass, Forest Top Biomass, Woodland Specias Biomass. All layers have a 250 meter pixel resolution and values represent biomass pounds per acre. The paper on which these maps are based may be found here: https://dx.doi.org/10.2737/RDS-2013-0004 Access to full metadata and other information can be accessed here: https://dx.doi.org/10.2737/RDS-2013-0004
The U.S. has been providing national-scale estimates of forest carbon stocks and stock change to meet United Nations Framework Convention on Climate Change reporting requirements for years. Through application of a nearest-neighbor imputation approach, mapped estimates of forest biomass density were developed for the contiguous United States using the annual forest inventory conducted by the USDA Forest Service Forest Inventory and Analysis (FIA) program, MODIS satellite imagery, and ancillary geospatial datasets. This data product would contain the following 7 raster maps: Aboveground Forest Biomass, Belowground Forest Biomass, Forest Tree Bole Biomass, Forest Sapling Biomass, Forest Stump Biomass, Forest Top Biomass, Woodland Specias Biomass. All layers have a 250 meter pixel resolution and values represent biomass pounds per acre. The paper on which these maps are based may be found here: https://dx.doi.org/10.2737/RDS-2013-0004 Access to full metadata and other information can be accessed here: https://dx.doi.org/10.2737/RDS-2013-0004
This dataset portrays the forest type group for Alaska. Each group is a subset of the National Forest Type dataset which portrays 28 forest type groups across the contiguous United States. These data were derived from MODIS composite images from the 2002 and 2003 growing seasons in combination with nearly 100 other geospatial data layers, including elevation, slope, aspect, ecoregions, and PRISM climate data.
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","distribution":[{"@type":"dcat:Distribution","accessURL":"https://data-usfs.hub.arcgis.com/datasets/usfs::us-forest-atlas-fia-forest-type-groups-softwoods-image-service","format":"Web Page","license":"https://creativecommons.org/licenses/by/4.0/","mediaType":"text/html","title":"ArcGIS Hub Dataset"},{"@type":"dcat:Distribution","accessURL":"https://imagery.geoplatform.gov/iipp/rest/services/Vegetation/USFS_EDW_FIA_ForestAtlas_Softwoods_109/ImageServer","format":"ArcGIS GeoServices REST API","license":"https://creativecommons.org/licenses/by/4.0/","mediaType":"application/json","title":"ArcGIS GeoService"},{"@type":"dcat:Distribution","accessURL":"https://www.arcgis.com/sharing/rest/content/items/f999a7b188fe472f913fcf05fda98d6b/info/metadata/metadata.xml?format=iso19139","conformsTo":"[{\"@type\": \"Standard\", \"identifier\": \"https://www.isotc211.org/2005/gmi\"}]","license":"https://creativecommons.org/licenses/by/4.0/","mediaType":"text/xml","title":"ISO-19139 metadata"}],"identifier":"https://www.arcgis.com/home/item.html?id=f999a7b188fe472f913fcf05fda98d6b","issued":"2026-02-19","keyword":["109","2002","2003","CONUS","California Mixed Conifer Group","Contiguous United States","Douglas-fir Group","Exotic Softwoods Group","FIA","Fir","Fir/Spruce/Mountain Hemlock Group","Forest Inventory and Analysis","Hemlock/Sitka Spruce Group","Loblolly/Shortleaf Pine Group","Lodgepole Pine Group","Longleaf/Slash Pine Group","Moderate Resolution Imaging Spectroradiometer","Other Western Softwood Group","Pinyon/Juniper Group","Ponderosa Pine Group","Redwood Group","Softwood","Spruce/Fir Group","Western Larch Group","Western White Pine Group","White/Red/Jack Pine Group"],"landingPage":{"@type":"Document","accessURL":"https://data-usfs.hub.arcgis.com/datasets/usfs::us-forest-atlas-fia-forest-type-groups-softwoods-image-service","title":"US Forest Atlas FIA Forest Type Groups Softwoods (Image Service)"},"license":"https://creativecommons.org/licenses/by/4.0/","modified":"2026-02-26","programCode":["005:059"],"progressCode":"onGoing","publisher":{"name":"U.S. Forest Service","source":"U.S. Forest Service"},"spatial":"[{\"@type\": \"Location\", \"bbox\": \"POLYGON((-127.9584 22.8075, -65.2605 22.8075, -65.2605 51.6509, -127.9584 51.6509, -127.9584 22.8075))\"}]","theme":["geospatial"],"title":"US Forest Atlas FIA Forest Type Groups Softwoods (Image Service)"},"description":"This Softwood type dataset portrays 16 forest type groups across the contiguous United States. These data were derived from MODIS composite images from the 2002 and 2003 growing seasons in combination with nearly 100 other geospatial data layers, including elevation, slope, aspect, ecoregions, and PRISM climate data. The dataset was developed as a collaborative effort between the USFS Forest Inventory and Analysis and Forest Health Monitoring programs and the USFS Remote Sensing.
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Objective: Characterize climate departures for the most recent 5 years as compared with the historical record, identifying locations where recent conditions indicate a significant change from the historical baseline.
Data: Gridded coverages based on DAYMET climate data were used to calculate departure of mean temperature in winter (Dec. \u2013 Feb.), spring (Mar. \u2013 May), summer (Jun. \u2013 Aug.), and fall (Sep. \u2013 Nov.) for the recent 5-year period as compared with previous years in the historical record. Data were summarized at the Subsection scale of the USFS National Hierarchy of Ecological Units and applied to the corresponding landscape (LTA). There is a one-year lag between the most recent available DAYMET data and the TCA Assessment year, for example, the 2024 TCA Assessment used 2019-2023 data for the most recent time period, and 1980-2018 data for the historical baseline.
Data Format: Raster data are continuous;
Units: Fahrenheit
Spatial Resolution: 1000m (1km)
Source data: DAYMET
Additional Resources:
Details on Method Changes and Source Data Versions
Overview of the Terrestrial Condition Assessment: TCA Hubsite or Landfire Office Hour Presentation
Explore the results of the most recent assessment: TCA Interactive Data Viewer
Learn more about the TCA KPI: TCA Dashboard
*if you have trouble viewing the Dashboard, please submit a Tableau Viewer Access Request
Direct Download (Raster Data Gateway)
Objective: Characterize climate departures for the most recent 5 years as compared with the historical record, identifying locations where recent conditions indicate a significant change from the historical baseline.
Data: Gridded coverages based on DAYMET climate data were used to calculate departure of mean temperature in winter (Dec. \u2013 Feb.), spring (Mar. \u2013 May), summer (Jun. \u2013 Aug.), and fall (Sep. \u2013 Nov.) for the recent 5-year period as compared with previous years in the historical record. Data were summarized at the Subsection scale of the USFS National Hierarchy of Ecological Units and applied to the corresponding landscape (LTA). There is a one-year lag between the most recent available DAYMET data and the TCA Assessment year, for example, the 2024 TCA Assessment used 2019-2023 data for the most recent time period, and 1980-2018 data for the historical baseline.
Data Format: Raster data are continuous;
Units: Fahrenheit
Spatial Resolution: 1000m (1km)
Source data: DAYMET
Additional Resources:
Details on Method Changes and Source Data Versions
Overview of the Terrestrial Condition Assessment: TCA Hubsite or Landfire Office Hour Presentation
Explore the results of the most recent assessment: TCA Interactive Data Viewer
Learn more about the TCA KPI: TCA Dashboard
*if you have trouble viewing the Dashboard, please submit a Tableau Viewer Access Request
Direct Download (Raster Data Gateway)
Objective: Characterize climate departures for the most recent 5 years as compared with the historical record, identifying locations where recent conditions indicate a significant change from the historical baseline.
Data: Gridded coverages based on PRISM climate data were used to calculate departure of mean temperature in winter (Dec. \u2013 Feb.), spring (Mar. \u2013 May), summer (Jun. \u2013 Aug.), and fall (Sep. \u2013 Nov.) for the recent 5-year period as compared with previous years in the historical record. Data were summarized at the Subsection scale of the USFS National Hierarchy of Ecological Units and applied to the corresponding landscape (LTA). There is a one-year lag between the most recent available PRISM data and the TCA Assessment year, for example, the 2024 TCA Assessment used 2019-2023 data for the most recent time period, and 1900-2018 data for the historical baseline.
Data Format: Raster data are continuous;
Units: Fahrenheit
Spatial Resolution: 4000m (4km)
Source data: PRISM
Additional Resources:
Details on Method Changes and Source Data Versions
Overview of the Terrestrial Condition Assessment: TCA Hubsite or Landfire Office Hour Presentation
Explore the results of the most recent assessment: TCA Interactive Data Viewer
Learn more about the TCA KPI: Dashboard link when available
Direct Download (Raster Data Gateway)
Objective: Characterize climate departures for the most recent 5 years as compared with the historical record, identifying locations where recent conditions indicate a significant change from the historical baseline.
Data: Gridded coverages based on PRISM climate data were used to calculate departure of mean temperature in winter (Dec. \u2013 Feb.), spring (Mar. \u2013 May), summer (Jun. \u2013 Aug.), and fall (Sep. \u2013 Nov.) for the recent 5-year period as compared with previous years in the historical record. Data were summarized at the Subsection scale of the USFS National Hierarchy of Ecological Units and applied to the corresponding landscape (LTA). There is a one-year lag between the most recent available PRISM data and the TCA Assessment year, for example, the 2024 TCA Assessment used 2019-2023 data for the most recent time period, and 1900-2018 data for the historical baseline.
Data Format: Raster data are continuous;
Units: Fahrenheit
Spatial Resolution: 4000m (4km)
Source data: PRISM
Additional Resources:
Details on Method Changes and Source Data Versions
Overview of the Terrestrial Condition Assessment: TCA Hubsite or Landfire Office Hour Presentation
Explore the results of the most recent assessment: TCA Interactive Data Viewer
Learn more about the TCA KPI: Dashboard link when available
Direct Download (Raster Data Gateway)
Objective: Characterize current moisture deficit and surplus relative to the long-term average.
Data: The indicator represents areas of moisture deficit as a z-score. A z-score is a statistical method for assessing how different a value (i.e., most recent 3 years) is from the mean (historical average). The source climate data is from PRISM. Mean moisture values were derived from historical data on precipitation and Thornthwaite potential evapotranspiration, from 1900 to t-minus 4 (t = current TCA Assessment year). The greater the negative z-value, the larger the departure from average conditions, indicating larger moisture deficits.
These data highlight regions of moisture deficit as a z-score calculated from the reference time period and current conditions. There is a lag between the assessment year and the most recent drought data, for example, the 2024 TCA Assessment used 2021-2023 to represent current conditions and 1900-2020 for the reference time period. Data were summarized at the Subsection scale of the USFS National Hierarchy of Ecological Units and applied to the corresponding landscape (LTA).
Data Format: Raster data are continuous
Units: Mean difference in z-score
Spatial Resolution: 4000m (4km)
Source data: PRISM; z-score calculations provided by the USFS Office of Sustainability and Climate based on the methods found here: https://www.fs.usda.gov/research/treesearch/43361
Additional Resources:
Details on Method Changes and Source Data Versions
Overview of the Terrestrial Condition Assessment: TCA Hubsite or Landfire Office Hour Presentation
Explore the results of the most recent assessment: TCA Interactive Data Viewer
Learn more about the TCA KPI: TCA Dashboard
*if you have trouble viewing the Dashboard, please submit a Tableau Viewer Access Request
Direct Download (Raster Data Gateway)
Objective: Characterize current moisture deficit and surplus relative to the long-term average.
Data: The indicator represents areas of moisture deficit as a z-score. A z-score is a statistical method for assessing how different a value (i.e., most recent 3 years) is from the mean (historical average). The source climate data is from PRISM. Mean moisture values were derived from historical data on precipitation and Thornthwaite potential evapotranspiration, from 1900 to t-minus 4 (t = current TCA Assessment year). The greater the negative z-value, the larger the departure from average conditions, indicating larger moisture deficits.
These data highlight regions of moisture deficit as a z-score calculated from the reference time period and current conditions. There is a lag between the assessment year and the most recent drought data, for example, the 2024 TCA Assessment used 2021-2023 to represent current conditions and 1900-2020 for the reference time period. Data were summarized at the Subsection scale of the USFS National Hierarchy of Ecological Units and applied to the corresponding landscape (LTA).
Data Format: Raster data are continuous
Units: Mean difference in z-score
Spatial Resolution: 4000m (4km)
Source data: PRISM; z-score calculations provided by the USFS Office of Sustainability and Climate based on the methods found here: https://www.fs.usda.gov/research/treesearch/43361
Additional Resources:
Details on Method Changes and Source Data Versions
Overview of the Terrestrial Condition Assessment: TCA Hubsite or Landfire Office Hour Presentation
Explore the results of the most recent assessment: TCA Interactive Data Viewer
Learn more about the TCA KPI: TCA Dashboard
*if you have trouble viewing the Dashboard, please submit a Tableau Viewer Access Request
Direct Download (Raster Data Gateway)
Objective: Characterize climate departures for the most recent 5 years as compared with the historical record, identifying locations where recent conditions indicate a significant change from the historical baseline.
Data: Gridded coverages based on PRISM climate data were used to calculate departure of precipitation in winter (Dec. \u2013 Feb.), spring (Mar. \u2013 May), summer (Jun. \u2013 Aug.), and fall (Sep. \u2013 Nov.) for the recent 5-year period as compared with previous years in the historical record. Data were summarized at the Subsection scale of the USFS National Hierarchy of Ecological Units and applied to the corresponding landscape (LTA). There is a one-year lag between the most recent available PRISM data and the TCA Assessment year, for example, the 2024 TCA Assessment used 2019-2023 data for the most recent time period, and 1900-2018 data for the historical baseline.
Data Format: Raster data are continuous;
Units: Percent change
Spatial Resolution: 4000m (4km)
Source data: PRISM
Additional Resources:
Details on Method Changes and Source Data Versions
Overview of the Terrestrial Condition Assessment: TCA Hubsite or Landfire Office Hour Presentation
Explore the results of the most recent assessment: TCA Interactive Data Viewer
Learn more about the TCA KPI: TCA Dashboard
*if you have trouble viewing the Dashboard, please submit a Tableau Viewer Access Request
Direct Download (Raster Data Gateway)
Objective: Characterize climate departures for the most recent 5 years as compared with the historical record, identifying locations where recent conditions indicate a significant change from the historical baseline.
Data: Gridded coverages based on PRISM climate data were used to calculate departure of precipitation in winter (Dec. \u2013 Feb.), spring (Mar. \u2013 May), summer (Jun. \u2013 Aug.), and fall (Sep. \u2013 Nov.) for the recent 5-year period as compared with previous years in the historical record. Data were summarized at the Subsection scale of the USFS National Hierarchy of Ecological Units and applied to the corresponding landscape (LTA). There is a one-year lag between the most recent available PRISM data and the TCA Assessment year, for example, the 2024 TCA Assessment used 2019-2023 data for the most recent time period, and 1900-2018 data for the historical baseline.
Data Format: Raster data are continuous;
Units: Percent change
Spatial Resolution: 4000m (4km)
Source data: PRISM
Additional Resources:
Details on Method Changes and Source Data Versions
Overview of the Terrestrial Condition Assessment: TCA Hubsite or Landfire Office Hour Presentation
Explore the results of the most recent assessment: TCA Interactive Data Viewer
Learn more about the TCA KPI: TCA Dashboard
*if you have trouble viewing the Dashboard, please submit a Tableau Viewer Access Request
Direct Download (Raster Data Gateway)
Objective: Characterize climate departures for the most recent 5 years as compared with the historical record, identifying locations where recent conditions indicate a significant change from the historical baseline.
Data: Gridded coverages based on PRISM climate data were used to calculate departure of precipitation in winter (Dec. \u2013 Feb.), spring (Mar. \u2013 May), summer (Jun. \u2013 Aug.), and fall (Sep. \u2013 Nov.) for the recent 5-year period as compared with previous years in the historical record. Data were summarized at the Subsection scale of the USFS National Hierarchy of Ecological Units and applied to the corresponding landscape (LTA). There is a one-year lag between the most recent available PRISM data and the TCA Assessment year, for example, the 2024 TCA Assessment used 2019-2023 data for the most recent time period, and 1900-2018 data for the historical baseline.
Data Format: Raster data are continuous;
Units: Inches
Spatial Resolution: 4000m (4km)
Source data: PRISM
Additional Resources:
Details on Method Changes and Source Data Versions
Overview of the Terrestrial Condition Assessment: TCA Hubsite or Landfire Office Hour Presentation
Explore the results of the most recent assessment: TCA Interactive Data Viewer
Learn more about the TCA KPI: TCA Dashboard
*if you have trouble viewing the Dashboard, please submit a Tableau Viewer Access Request
Direct Download (Raster Data Gateway)
Objective: Characterize climate departures for the most recent 5 years as compared with the historical record, identifying locations where recent conditions indicate a significant change from the historical baseline.
Data: Gridded coverages based on PRISM climate data were used to calculate departure of precipitation in winter (Dec. \u2013 Feb.), spring (Mar. \u2013 May), summer (Jun. \u2013 Aug.), and fall (Sep. \u2013 Nov.) for the recent 5-year period as compared with previous years in the historical record. Data were summarized at the Subsection scale of the USFS National Hierarchy of Ecological Units and applied to the corresponding landscape (LTA). There is a one-year lag between the most recent available PRISM data and the TCA Assessment year, for example, the 2024 TCA Assessment used 2019-2023 data for the most recent time period, and 1900-2018 data for the historical baseline.
Data Format: Raster data are continuous;
Units: Inches
Spatial Resolution: 4000m (4km)
Source data: PRISM
Additional Resources:
Details on Method Changes and Source Data Versions
Overview of the Terrestrial Condition Assessment: TCA Hubsite or Landfire Office Hour Presentation
Explore the results of the most recent assessment: TCA Interactive Data Viewer
Learn more about the TCA KPI: TCA Dashboard
*if you have trouble viewing the Dashboard, please submit a Tableau Viewer Access Request
Direct Download (Raster Data Gateway)
Objective: Characterize recent drought severity in Alaska.
Data: The indicator represents drought severity as a weighted sum of the number of droughts in each category over the most recent 3-year period using data from the U.S. Drought Monitor. A 30km grid was overlaid on Alaska and the number of droughts in each category we calculated over the time period. The weighted sum of droughts (DwSum) was calculated using the equation:
(D0 [abnormally dry] * 1) + (D1 [moderate drought] *2) + (D2 [severe drought] * 3) + (D3 [extreme drought] * 4) + (D4 [exceptional drought] * 5)
There is a lag between the assessment year and the most recent drought data, for example, the 2024 TCA Assessment used drought data from 2021-2023. Data were summarized at the Subsection scale of the USFS National Hierarchy of Ecological Units and applied to the corresponding landscape (LTA).
Data Format: Raster data are continuous
Units: Number of droughts (weighted sum)
Spatial Resolution: 30000m (30km)
Source data: US Drought Monitor
Additional Resources:
Details on Method Changes and Source Data Versions
Overview of the Terrestrial Condition Assessment: TCA Hubsite or Landfire Office Hour Presentation
Explore the results of the most recent assessment: TCA Interactive Data Viewer
Learn more about the TCA KPI: TCA Dashboard
*if you have trouble viewing the Dashboard, please submit a Tableau Viewer Access Request
Direct Download (Raster Data Gateway)
Objective: Characterize recent drought severity in Alaska.
Data: The indicator represents drought severity as a weighted sum of the number of droughts in each category over the most recent 3-year period using data from the U.S. Drought Monitor. A 30km grid was overlaid on Alaska and the number of droughts in each category we calculated over the time period. The weighted sum of droughts (DwSum) was calculated using the equation:
(D0 [abnormally dry] * 1) + (D1 [moderate drought] *2) + (D2 [severe drought] * 3) + (D3 [extreme drought] * 4) + (D4 [exceptional drought] * 5)
There is a lag between the assessment year and the most recent drought data, for example, the 2024 TCA Assessment used drought data from 2021-2023. Data were summarized at the Subsection scale of the USFS National Hierarchy of Ecological Units and applied to the corresponding landscape (LTA).
Data Format: Raster data are continuous
Units: Number of droughts (weighted sum)
Spatial Resolution: 30000m (30km)
Source data: US Drought Monitor
Additional Resources:
Details on Method Changes and Source Data Versions
Overview of the Terrestrial Condition Assessment: TCA Hubsite or Landfire Office Hour Presentation
Explore the results of the most recent assessment: TCA Interactive Data Viewer
Learn more about the TCA KPI: TCA Dashboard
*if you have trouble viewing the Dashboard, please submit a Tableau Viewer Access Request
Direct Download (Raster Data Gateway)
Objective: An indicator that identifies where tree density has exceeded the levels presumed to be present in pre-Euro-American settlement non-forest vegetation. By our definition, non-forest areas cannot exceed 25% tree canopy cover. Fire exclusion, climate, and human activities affect the progression of trees into non-forested ecosystems. Encroached areas may require restoration to meet ecological objectives. Restoration practices will be ecosystem specific.
Data: The indicator uses areas identified as grasslands using LANDFIRE\u2019s Biophysical Settings (BpS) raster dataset (US_140BPS_20180618) along with a cross-walk based on Reeves MC, Mitchell JE (2011) (https://research.fs.usda.gov/treesearch/41872) to identify BpS units that were likely to be grasslands during pre-European times. Presence of conifer tree species was developed using USGS National Landcover Database (NLCD) legend class \u201c42 \u2013 Evergreen Forest\u201d. The Evergreen Forest class represents \u201careas dominated by trees generally greater than 5 meters tall, and greater than 20% of total vegetation cover. More than 75% of the tree species maintain their leaves all year. Canopy is never without green foliage.\u201d Grassland conifer encroachment is identified where raster pixels are identified as both \u201cgrasslands\u201d and NLCD \u201cEvergreen Forest\u201d. In years when NLCD is not updated, data is reused from the previous year, which is why there is only data for 3 of the 5 assessment years from 2020-2024. NLCD versions used: NLCD 2011 (TCA Assessment 2020), NLCD 2019 (TCA Assessment 2022), NLCD 2021 (TCA Assessment 2023). Encroachment for the 2023 TCA Assessment is 1-bit and therefore only contains values of 1 indicating where encroachment was observed, rather than the binary data from other years.
Format: Raster data are binary: (1) likely encroached or (0) not encroached
Resolution: 250m
Source: Custom grassland-BpS crosswalk, NLCD conifer presence
Additional Resources:
Details on Method Changes and Source Data Versions
Overview of the Terrestrial Condition Assessment: TCA Hubsite or Landfire Office Hour Presentation
Explore the results of the most recent assessment: TCA Interactive Data Viewer
Learn more about the TCA KPI: TCA Dashboard
*if you have trouble viewing the Dashboard, please submit a Tableau Viewer Access Request
Direct Download (Raster Data Gateway)
Objective: An indicator that identifies where tree density has exceeded the levels presumed to be present in pre-Euro-American settlement non-forest vegetation. By our definition, non-forest areas cannot exceed 25% tree canopy cover. Fire exclusion, climate, and human activities affect the progression of trees into non-forested ecosystems. Encroached areas may require restoration to meet ecological objectives. Restoration practices will be ecosystem specific.
Data: The indicator uses areas identified as grasslands using LANDFIRE\u2019s Biophysical Settings (BpS) raster dataset (US_140BPS_20180618) along with a cross-walk based on Reeves MC, Mitchell JE (2011) (https://research.fs.usda.gov/treesearch/41872) to identify BpS units that were likely to be grasslands during pre-European times. Presence of conifer tree species was developed using USGS National Landcover Database (NLCD) legend class \u201c42 \u2013 Evergreen Forest\u201d. The Evergreen Forest class represents \u201careas dominated by trees generally greater than 5 meters tall, and greater than 20% of total vegetation cover. More than 75% of the tree species maintain their leaves all year. Canopy is never without green foliage.\u201d Grassland conifer encroachment is identified where raster pixels are identified as both \u201cgrasslands\u201d and NLCD \u201cEvergreen Forest\u201d. In years when NLCD is not updated, data is reused from the previous year, which is why there is only data for 3 of the 5 assessment years from 2020-2024. NLCD versions used: NLCD 2011 (TCA Assessment 2020), NLCD 2019 (TCA Assessment 2022), NLCD 2021 (TCA Assessment 2023). Encroachment for the 2023 TCA Assessment is 1-bit and therefore only contains values of 1 indicating where encroachment was observed, rather than the binary data from other years.
Format: Raster data are binary: (1) likely encroached or (0) not encroached
Resolution: 250m
Source: Custom grassland-BpS crosswalk, NLCD conifer presence
Additional Resources:
Details on Method Changes and Source Data Versions
Overview of the Terrestrial Condition Assessment: TCA Hubsite or Landfire Office Hour Presentation
Explore the results of the most recent assessment: TCA Interactive Data Viewer
Learn more about the TCA KPI: TCA Dashboard
*if you have trouble viewing the Dashboard, please submit a Tableau Viewer Access Request
National data on burn probability (BP) and conditional flame-length probability (FLP) were generated for the conterminous United States (CONUS), Alaska, and Hawaii using a geospatial Fire Simulation (FSim) system developed by the USDA Forest Service Missoula Fire Sciences Laboratory. The FSim system includes modules for weather generation, wildfire occurrence, fire growth, and fire suppression. FSim is designed to simulate the occurrence and growth of wildfires under tens of thousands of hypothetical contemporary fire seasons in order to estimate the probability of a given area (i.e., pixel) burning under current (end of 2020) landscape conditions and fire management practices. The data presented here represent modeled BP and FLPs for the United States (US) at a 270-meter grid spatial resolution. Flame-length probability is estimated for six standard Fire Intensity Levels. The six FILs correspond to flame-length classes as follows: FLP1 = < 2 feet (ft); FLP2 = 2 < 4 ft.; FLP3 = 4 < 6 ft.; FLP4 = 6 < 8 ft.; FLP5 = 8 < 12 ft.; FLP6 = 12+ ft. Because they indicate conditional probabilities (i.e., representing the likelihood of burning at a certain intensity level, given that a fire occurs), the FLP data must be used in conjunction with the BP data for risk assessment.
These data are a newer edition of the Short et al. (2016, 2020) data publications. This third edition is based on circa 2020 landscape data, which were the most current LANDFIRE products available at the time of production. The methods used to generate these data generally followed the same process used in previous editions, with improvements made at specific steps. The process steps outlined in the Data Quality, Lineage section of this metadata document are expanded from previous editions to more fully explain each step and provide additional details on methods for this edition. Beyond the newer input landscape data from LANDFIRE, we also used updated datasets for other inputs such as fire occurrence, observed gridded daily weather, and wind data from weather stations. To better capture recent climate conditions, we also shortened the time period of historical weather records used to inform the generation of simulated weather streams for simulation runs, using the most recent 15 years this time (2006-2020) rather than full record from 1972-2012 in the second edition. See the process steps described in the Data Quality, Lineage section for more details.
National data on burn probability (BP) and conditional flame-length probability (FLP) were generated for the conterminous United States (CONUS), Alaska, and Hawaii using a geospatial Fire Simulation (FSim) system developed by the USDA Forest Service Missoula Fire Sciences Laboratory. The FSim system includes modules for weather generation, wildfire occurrence, fire growth, and fire suppression. FSim is designed to simulate the occurrence and growth of wildfires under tens of thousands of hypothetical contemporary fire seasons in order to estimate the probability of a given area (i.e., pixel) burning under current (end of 2020) landscape conditions and fire management practices. The data presented here represent modeled BP and FLPs for the United States (US) at a 270-meter grid spatial resolution. Flame-length probability is estimated for six standard Fire Intensity Levels. The six FILs correspond to flame-length classes as follows: FLP1 = < 2 feet (ft); FLP2 = 2 < 4 ft.; FLP3 = 4 < 6 ft.; FLP4 = 6 < 8 ft.; FLP5 = 8 < 12 ft.; FLP6 = 12+ ft. Because they indicate conditional probabilities (i.e., representing the likelihood of burning at a certain intensity level, given that a fire occurs), the FLP data must be used in conjunction with the BP data for risk assessment.
These data are a newer edition of the Short et al. (2016, 2020) data publications. This third edition is based on circa 2020 landscape data, which were the most current LANDFIRE products available at the time of production. The methods used to generate these data generally followed the same process used in previous editions, with improvements made at specific steps. The process steps outlined in the Data Quality, Lineage section of this metadata document are expanded from previous editions to more fully explain each step and provide additional details on methods for this edition. Beyond the newer input landscape data from LANDFIRE, we also used updated datasets for other inputs such as fire occurrence, observed gridded daily weather, and wind data from weather stations. To better capture recent climate conditions, we also shortened the time period of historical weather records used to inform the generation of simulated weather streams for simulation runs, using the most recent 15 years this time (2006-2020) rather than full record from 1972-2012 in the second edition. See the process steps described in the Data Quality, Lineage section for more details.
National data on burn probability (BP) and conditional flame-length probability (FLP) were generated for the conterminous United States (CONUS), Alaska, and Hawaii using a geospatial Fire Simulation (FSim) system developed by the USDA Forest Service Missoula Fire Sciences Laboratory. The FSim system includes modules for weather generation, wildfire occurrence, fire growth, and fire suppression. FSim is designed to simulate the occurrence and growth of wildfires under tens of thousands of hypothetical contemporary fire seasons in order to estimate the probability of a given area (i.e., pixel) burning under current (end of 2020) landscape conditions and fire management practices. The data presented here represent modeled BP and FLPs for the United States (US) at a 270-meter grid spatial resolution. Flame-length probability is estimated for six standard Fire Intensity Levels. The six FILs correspond to flame-length classes as follows: FLP1 = < 2 feet (ft); FLP2 = 2 < 4 ft.; FLP3 = 4 < 6 ft.; FLP4 = 6 < 8 ft.; FLP5 = 8 < 12 ft.; FLP6 = 12+ ft. Because they indicate conditional probabilities (i.e., representing the likelihood of burning at a certain intensity level, given that a fire occurs), the FLP data must be used in conjunction with the BP data for risk assessment.
These data are a newer edition of the Short et al. (2016, 2020) data publications. This third edition is based on circa 2020 landscape data, which were the most current LANDFIRE products available at the time of production. The methods used to generate these data generally followed the same process used in previous editions, with improvements made at specific steps. The process steps outlined in the Data Quality, Lineage section of this metadata document are expanded from previous editions to more fully explain each step and provide additional details on methods for this edition. Beyond the newer input landscape data from LANDFIRE, we also used updated datasets for other inputs such as fire occurrence, observed gridded daily weather, and wind data from weather stations. To better capture recent climate conditions, we also shortened the time period of historical weather records used to inform the generation of simulated weather streams for simulation runs, using the most recent 15 years this time (2006-2020) rather than full record from 1972-2012 in the second edition. See the process steps described in the Data Quality, Lineage section for more details.
National data on burn probability (BP) and conditional flame-length probability (FLP) were generated for the conterminous United States (CONUS), Alaska, and Hawaii using a geospatial Fire Simulation (FSim) system developed by the USDA Forest Service Missoula Fire Sciences Laboratory. The FSim system includes modules for weather generation, wildfire occurrence, fire growth, and fire suppression. FSim is designed to simulate the occurrence and growth of wildfires under tens of thousands of hypothetical contemporary fire seasons in order to estimate the probability of a given area (i.e., pixel) burning under current (end of 2020) landscape conditions and fire management practices. The data presented here represent modeled BP and FLPs for the United States (US) at a 270-meter grid spatial resolution. Flame-length probability is estimated for six standard Fire Intensity Levels. The six FILs correspond to flame-length classes as follows: FLP1 = < 2 feet (ft); FLP2 = 2 < 4 ft.; FLP3 = 4 < 6 ft.; FLP4 = 6 < 8 ft.; FLP5 = 8 < 12 ft.; FLP6 = 12+ ft. Because they indicate conditional probabilities (i.e., representing the likelihood of burning at a certain intensity level, given that a fire occurs), the FLP data must be used in conjunction with the BP data for risk assessment.
These data are a newer edition of the Short et al. (2016, 2020) data publications. This third edition is based on circa 2020 landscape data, which were the most current LANDFIRE products available at the time of production. The methods used to generate these data generally followed the same process used in previous editions, with improvements made at specific steps. The process steps outlined in the Data Quality, Lineage section of this metadata document are expanded from previous editions to more fully explain each step and provide additional details on methods for this edition. Beyond the newer input landscape data from LANDFIRE, we also used updated datasets for other inputs such as fire occurrence, observed gridded daily weather, and wind data from weather stations. To better capture recent climate conditions, we also shortened the time period of historical weather records used to inform the generation of simulated weather streams for simulation runs, using the most recent 15 years this time (2006-2020) rather than full record from 1972-2012 in the second edition. See the process steps described in the Data Quality, Lineage section for more details.
National data on burn probability (BP) and conditional flame-length probability (FLP) were generated for the conterminous United States (CONUS), Alaska, and Hawaii using a geospatial Fire Simulation (FSim) system developed by the USDA Forest Service Missoula Fire Sciences Laboratory. The FSim system includes modules for weather generation, wildfire occurrence, fire growth, and fire suppression. FSim is designed to simulate the occurrence and growth of wildfires under tens of thousands of hypothetical contemporary fire seasons in order to estimate the probability of a given area (i.e., pixel) burning under current (end of 2020) landscape conditions and fire management practices. The data presented here represent modeled BP and FLPs for the United States (US) at a 270-meter grid spatial resolution. Flame-length probability is estimated for six standard Fire Intensity Levels. The six FILs correspond to flame-length classes as follows: FLP1 = < 2 feet (ft); FLP2 = 2 < 4 ft.; FLP3 = 4 < 6 ft.; FLP4 = 6 < 8 ft.; FLP5 = 8 < 12 ft.; FLP6 = 12+ ft. Because they indicate conditional probabilities (i.e., representing the likelihood of burning at a certain intensity level, given that a fire occurs), the FLP data must be used in conjunction with the BP data for risk assessment.
These data are a newer edition of the Short et al. (2016, 2020) data publications. This third edition is based on circa 2020 landscape data, which were the most current LANDFIRE products available at the time of production. The methods used to generate these data generally followed the same process used in previous editions, with improvements made at specific steps. The process steps outlined in the Data Quality, Lineage section of this metadata document are expanded from previous editions to more fully explain each step and provide additional details on methods for this edition. Beyond the newer input landscape data from LANDFIRE, we also used updated datasets for other inputs such as fire occurrence, observed gridded daily weather, and wind data from weather stations. To better capture recent climate conditions, we also shortened the time period of historical weather records used to inform the generation of simulated weather streams for simulation runs, using the most recent 15 years this time (2006-2020) rather than full record from 1972-2012 in the second edition. See the process steps described in the Data Quality, Lineage section for more details.
National data on burn probability (BP) and conditional flame-length probability (FLP) were generated for the conterminous United States (CONUS), Alaska, and Hawaii using a geospatial Fire Simulation (FSim) system developed by the USDA Forest Service Missoula Fire Sciences Laboratory. The FSim system includes modules for weather generation, wildfire occurrence, fire growth, and fire suppression. FSim is designed to simulate the occurrence and growth of wildfires under tens of thousands of hypothetical contemporary fire seasons in order to estimate the probability of a given area (i.e., pixel) burning under current (end of 2020) landscape conditions and fire management practices. The data presented here represent modeled BP and FLPs for the United States (US) at a 270-meter grid spatial resolution. Flame-length probability is estimated for six standard Fire Intensity Levels. The six FILs correspond to flame-length classes as follows: FLP1 = < 2 feet (ft); FLP2 = 2 < 4 ft.; FLP3 = 4 < 6 ft.; FLP4 = 6 < 8 ft.; FLP5 = 8 < 12 ft.; FLP6 = 12+ ft. Because they indicate conditional probabilities (i.e., representing the likelihood of burning at a certain intensity level, given that a fire occurs), the FLP data must be used in conjunction with the BP data for risk assessment.
These data are a newer edition of the Short et al. (2016, 2020) data publications. This third edition is based on circa 2020 landscape data, which were the most current LANDFIRE products available at the time of production. The methods used to generate these data generally followed the same process used in previous editions, with improvements made at specific steps. The process steps outlined in the Data Quality, Lineage section of this metadata document are expanded from previous editions to more fully explain each step and provide additional details on methods for this edition. Beyond the newer input landscape data from LANDFIRE, we also used updated datasets for other inputs such as fire occurrence, observed gridded daily weather, and wind data from weather stations. To better capture recent climate conditions, we also shortened the time period of historical weather records used to inform the generation of simulated weather streams for simulation runs, using the most recent 15 years this time (2006-2020) rather than full record from 1972-2012 in the second edition. See the process steps described in the Data Quality, Lineage section for more details.
National data on burn probability (BP) and conditional flame-length probability (FLP) were generated for the conterminous United States (CONUS), Alaska, and Hawaii using a geospatial Fire Simulation (FSim) system developed by the USDA Forest Service Missoula Fire Sciences Laboratory. The FSim system includes modules for weather generation, wildfire occurrence, fire growth, and fire suppression. FSim is designed to simulate the occurrence and growth of wildfires under tens of thousands of hypothetical contemporary fire seasons in order to estimate the probability of a given area (i.e., pixel) burning under current (end of 2020) landscape conditions and fire management practices. The data presented here represent modeled BP and FLPs for the United States (US) at a 270-meter grid spatial resolution. Flame-length probability is estimated for six standard Fire Intensity Levels. The six FILs correspond to flame-length classes as follows: FLP1 = < 2 feet (ft); FLP2 = 2 < 4 ft.; FLP3 = 4 < 6 ft.; FLP4 = 6 < 8 ft.; FLP5 = 8 < 12 ft.; FLP6 = 12+ ft. Because they indicate conditional probabilities (i.e., representing the likelihood of burning at a certain intensity level, given that a fire occurs), the FLP data must be used in conjunction with the BP data for risk assessment.
These data are a newer edition of the Short et al. (2016, 2020) data publications. This third edition is based on circa 2020 landscape data, which were the most current LANDFIRE products available at the time of production. The methods used to generate these data generally followed the same process used in previous editions, with improvements made at specific steps. The process steps outlined in the Data Quality, Lineage section of this metadata document are expanded from previous editions to more fully explain each step and provide additional details on methods for this edition. Beyond the newer input landscape data from LANDFIRE, we also used updated datasets for other inputs such as fire occurrence, observed gridded daily weather, and wind data from weather stations. To better capture recent climate conditions, we also shortened the time period of historical weather records used to inform the generation of simulated weather streams for simulation runs, using the most recent 15 years this time (2006-2020) rather than full record from 1972-2012 in the second edition. See the process steps described in the Data Quality, Lineage section for more details.
National data on burn probability (BP) and conditional flame-length probability (FLP) were generated for the conterminous United States (CONUS), Alaska, and Hawaii using a geospatial Fire Simulation (FSim) system developed by the USDA Forest Service Missoula Fire Sciences Laboratory. The FSim system includes modules for weather generation, wildfire occurrence, fire growth, and fire suppression. FSim is designed to simulate the occurrence and growth of wildfires under tens of thousands of hypothetical contemporary fire seasons in order to estimate the probability of a given area (i.e., pixel) burning under current (end of 2020) landscape conditions and fire management practices. The data presented here represent modeled BP and FLPs for the United States (US) at a 270-meter grid spatial resolution. Flame-length probability is estimated for six standard Fire Intensity Levels. The six FILs correspond to flame-length classes as follows: FLP1 = < 2 feet (ft); FLP2 = 2 < 4 ft.; FLP3 = 4 < 6 ft.; FLP4 = 6 < 8 ft.; FLP5 = 8 < 12 ft.; FLP6 = 12+ ft. Because they indicate conditional probabilities (i.e., representing the likelihood of burning at a certain intensity level, given that a fire occurs), the FLP data must be used in conjunction with the BP data for risk assessment.
These data are a newer edition of the Short et al. (2016, 2020) data publications. This third edition is based on circa 2020 landscape data, which were the most current LANDFIRE products available at the time of production. The methods used to generate these data generally followed the same process used in previous editions, with improvements made at specific steps. The process steps outlined in the Data Quality, Lineage section of this metadata document are expanded from previous editions to more fully explain each step and provide additional details on methods for this edition. Beyond the newer input landscape data from LANDFIRE, we also used updated datasets for other inputs such as fire occurrence, observed gridded daily weather, and wind data from weather stations. To better capture recent climate conditions, we also shortened the time period of historical weather records used to inform the generation of simulated weather streams for simulation runs, using the most recent 15 years this time (2006-2020) rather than full record from 1972-2012 in the second edition. See the process steps described in the Data Quality, Lineage section for more details.
National data on burn probability (BP) and conditional flame-length probability (FLP) were generated for the conterminous United States (CONUS), Alaska, and Hawaii using a geospatial Fire Simulation (FSim) system developed by the USDA Forest Service Missoula Fire Sciences Laboratory. The FSim system includes modules for weather generation, wildfire occurrence, fire growth, and fire suppression. FSim is designed to simulate the occurrence and growth of wildfires under tens of thousands of hypothetical contemporary fire seasons in order to estimate the probability of a given area (i.e., pixel) burning under current (end of 2020) landscape conditions and fire management practices. The data presented here represent modeled BP and FLPs for the United States (US) at a 270-meter grid spatial resolution. Flame-length probability is estimated for six standard Fire Intensity Levels. The six FILs correspond to flame-length classes as follows: FLP1 = < 2 feet (ft); FLP2 = 2 < 4 ft.; FLP3 = 4 < 6 ft.; FLP4 = 6 < 8 ft.; FLP5 = 8 < 12 ft.; FLP6 = 12+ ft. Because they indicate conditional probabilities (i.e., representing the likelihood of burning at a certain intensity level, given that a fire occurs), the FLP data must be used in conjunction with the BP data for risk assessment.
These data are a newer edition of the Short et al. (2016, 2020) data publications. This third edition is based on circa 2020 landscape data, which were the most current LANDFIRE products available at the time of production. The methods used to generate these data generally followed the same process used in previous editions, with improvements made at specific steps. The process steps outlined in the Data Quality, Lineage section of this metadata document are expanded from previous editions to more fully explain each step and provide additional details on methods for this edition. Beyond the newer input landscape data from LANDFIRE, we also used updated datasets for other inputs such as fire occurrence, observed gridded daily weather, and wind data from weather stations. To better capture recent climate conditions, we also shortened the time period of historical weather records used to inform the generation of simulated weather streams for simulation runs, using the most recent 15 years this time (2006-2020) rather than full record from 1972-2012 in the second edition. See the process steps described in the Data Quality, Lineage section for more details.
National data on burn probability (BP) and conditional flame-length probability (FLP) were generated for the conterminous United States (CONUS), Alaska, and Hawaii using a geospatial Fire Simulation (FSim) system developed by the USDA Forest Service Missoula Fire Sciences Laboratory. The FSim system includes modules for weather generation, wildfire occurrence, fire growth, and fire suppression. FSim is designed to simulate the occurrence and growth of wildfires under tens of thousands of hypothetical contemporary fire seasons in order to estimate the probability of a given area (i.e., pixel) burning under current (end of 2020) landscape conditions and fire management practices. The data presented here represent modeled BP and FLPs for the United States (US) at a 270-meter grid spatial resolution. Flame-length probability is estimated for six standard Fire Intensity Levels. The six FILs correspond to flame-length classes as follows: FLP1 = < 2 feet (ft); FLP2 = 2 < 4 ft.; FLP3 = 4 < 6 ft.; FLP4 = 6 < 8 ft.; FLP5 = 8 < 12 ft.; FLP6 = 12+ ft. Because they indicate conditional probabilities (i.e., representing the likelihood of burning at a certain intensity level, given that a fire occurs), the FLP data must be used in conjunction with the BP data for risk assessment.
These data are a newer edition of the Short et al. (2016, 2020) data publications. This third edition is based on circa 2020 landscape data, which were the most current LANDFIRE products available at the time of production. The methods used to generate these data generally followed the same process used in previous editions, with improvements made at specific steps. The process steps outlined in the Data Quality, Lineage section of this metadata document are expanded from previous editions to more fully explain each step and provide additional details on methods for this edition. Beyond the newer input landscape data from LANDFIRE, we also used updated datasets for other inputs such as fire occurrence, observed gridded daily weather, and wind data from weather stations. To better capture recent climate conditions, we also shortened the time period of historical weather records used to inform the generation of simulated weather streams for simulation runs, using the most recent 15 years this time (2006-2020) rather than full record from 1972-2012 in the second edition. See the process steps described in the Data Quality, Lineage section for more details.
National data on burn probability (BP) and conditional flame-length probability (FLP) were generated for the conterminous United States (CONUS), Alaska, and Hawaii using a geospatial Fire Simulation (FSim) system developed by the USDA Forest Service Missoula Fire Sciences Laboratory. The FSim system includes modules for weather generation, wildfire occurrence, fire growth, and fire suppression. FSim is designed to simulate the occurrence and growth of wildfires under tens of thousands of hypothetical contemporary fire seasons in order to estimate the probability of a given area (i.e., pixel) burning under current (end of 2020) landscape conditions and fire management practices. The data presented here represent modeled BP and FLPs for the United States (US) at a 270-meter grid spatial resolution. Flame-length probability is estimated for six standard Fire Intensity Levels. The six FILs correspond to flame-length classes as follows: FLP1 = < 2 feet (ft); FLP2 = 2 < 4 ft.; FLP3 = 4 < 6 ft.; FLP4 = 6 < 8 ft.; FLP5 = 8 < 12 ft.; FLP6 = 12+ ft. Because they indicate conditional probabilities (i.e., representing the likelihood of burning at a certain intensity level, given that a fire occurs), the FLP data must be used in conjunction with the BP data for risk assessment.
These data are a newer edition of the Short et al. (2016, 2020) data publications. This third edition is based on circa 2020 landscape data, which were the most current LANDFIRE products available at the time of production. The methods used to generate these data generally followed the same process used in previous editions, with improvements made at specific steps. The process steps outlined in the Data Quality, Lineage section of this metadata document are expanded from previous editions to more fully explain each step and provide additional details on methods for this edition. Beyond the newer input landscape data from LANDFIRE, we also used updated datasets for other inputs such as fire occurrence, observed gridded daily weather, and wind data from weather stations. To better capture recent climate conditions, we also shortened the time period of historical weather records used to inform the generation of simulated weather streams for simulation runs, using the most recent 15 years this time (2006-2020) rather than full record from 1972-2012 in the second edition. See the process steps described in the Data Quality, Lineage section for more details.
National data on burn probability (BP) and conditional flame-length probability (FLP) were generated for the conterminous United States (CONUS), Alaska, and Hawaii using a geospatial Fire Simulation (FSim) system developed by the USDA Forest Service Missoula Fire Sciences Laboratory. The FSim system includes modules for weather generation, wildfire occurrence, fire growth, and fire suppression. FSim is designed to simulate the occurrence and growth of wildfires under tens of thousands of hypothetical contemporary fire seasons in order to estimate the probability of a given area (i.e., pixel) burning under current (end of 2020) landscape conditions and fire management practices. The data presented here represent modeled BP and FLPs for the United States (US) at a 270-meter grid spatial resolution. Flame-length probability is estimated for six standard Fire Intensity Levels. The six FILs correspond to flame-length classes as follows: FLP1 = < 2 feet (ft); FLP2 = 2 < 4 ft.; FLP3 = 4 < 6 ft.; FLP4 = 6 < 8 ft.; FLP5 = 8 < 12 ft.; FLP6 = 12+ ft. Because they indicate conditional probabilities (i.e., representing the likelihood of burning at a certain intensity level, given that a fire occurs), the FLP data must be used in conjunction with the BP data for risk assessment.
These data are a newer edition of the Short et al. (2016, 2020) data publications. This third edition is based on circa 2020 landscape data, which were the most current LANDFIRE products available at the time of production. The methods used to generate these data generally followed the same process used in previous editions, with improvements made at specific steps. The process steps outlined in the Data Quality, Lineage section of this metadata document are expanded from previous editions to more fully explain each step and provide additional details on methods for this edition. Beyond the newer input landscape data from LANDFIRE, we also used updated datasets for other inputs such as fire occurrence, observed gridded daily weather, and wind data from weather stations. To better capture recent climate conditions, we also shortened the time period of historical weather records used to inform the generation of simulated weather streams for simulation runs, using the most recent 15 years this time (2006-2020) rather than full record from 1972-2012 in the second edition. See the process steps described in the Data Quality, Lineage section for more details.
National data on burn probability (BP) were generated for the conterminous United States (CONUS), Alaska, and Hawaii using a geospatial Fire Simulation (FSim) system developed by the USDA Forest Service Missoula Fire Sciences Laboratory. The FSim system includes modules for weather generation, wildfire occurrence, fire growth, and fire suppression. FSim is designed to simulate the occurrence and growth of wildfires under tens of thousands of hypothetical contemporary fire seasons in order to estimate the probability of a given area (i.e., pixel) burning under current (end of 2020) landscape conditions and fire management practices. The data presented here represent modeled BP and FLPs for the United States (US) at a 270-meter grid spatial resolution. Flame-length probability is estimated for six standard Fire Intensity Levels. The six FILs correspond to flame-length classes as follows: FLP1 = < 2 feet (ft); FLP2 = 2 < 4 ft.; FLP3 = 4 < 6 ft.; FLP4 = 6 < 8 ft.; FLP5 = 8 < 12 ft.; FLP6 = 12+ ft. Because they indicate conditional probabilities (i.e., representing the likelihood of burning at a certain intensity level, given that a fire occurs), the FLP data must be used in conjunction with the BP data for risk assessment.These data are a newer edition of the Short et al. (2016, 2020) data publications. This third edition is based on circa 2020 landscape data, which were the most current LANDFIRE products available at the time of production. The methods used to generate these data generally followed the same process used in previous editions, with improvements made at specific steps. The process steps outlined in the Data Quality, Lineage section of this metadata document are expanded from previous editions to more fully explain each step and provide additional details on methods for this edition. Beyond the newer input landscape data from LANDFIRE, we also used updated datasets for other inputs such as fire occurrence, observed gridded daily weather, and wind data from weather stations. To better capture recent climate conditions, we also shortened the time period of historical weather records used to inform the generation of simulated weather streams for simulation runs, using the most recent 15 years this time (2006-2020) rather than full record from 1972-2012 in the second edition. See the process steps described in the Data Quality, Lineage section for more details.
National data on burn probability (BP) were generated for the conterminous United States (CONUS), Alaska, and Hawaii using a geospatial Fire Simulation (FSim) system developed by the USDA Forest Service Missoula Fire Sciences Laboratory. The FSim system includes modules for weather generation, wildfire occurrence, fire growth, and fire suppression. FSim is designed to simulate the occurrence and growth of wildfires under tens of thousands of hypothetical contemporary fire seasons in order to estimate the probability of a given area (i.e., pixel) burning under current (end of 2020) landscape conditions and fire management practices. The data presented here represent modeled BP and FLPs for the United States (US) at a 270-meter grid spatial resolution. Flame-length probability is estimated for six standard Fire Intensity Levels. The six FILs correspond to flame-length classes as follows: FLP1 = < 2 feet (ft); FLP2 = 2 < 4 ft.; FLP3 = 4 < 6 ft.; FLP4 = 6 < 8 ft.; FLP5 = 8 < 12 ft.; FLP6 = 12+ ft. Because they indicate conditional probabilities (i.e., representing the likelihood of burning at a certain intensity level, given that a fire occurs), the FLP data must be used in conjunction with the BP data for risk assessment.These data are a newer edition of the Short et al. (2016, 2020) data publications. This third edition is based on circa 2020 landscape data, which were the most current LANDFIRE products available at the time of production. The methods used to generate these data generally followed the same process used in previous editions, with improvements made at specific steps. The process steps outlined in the Data Quality, Lineage section of this metadata document are expanded from previous editions to more fully explain each step and provide additional details on methods for this edition. Beyond the newer input landscape data from LANDFIRE, we also used updated datasets for other inputs such as fire occurrence, observed gridded daily weather, and wind data from weather stations. To better capture recent climate conditions, we also shortened the time period of historical weather records used to inform the generation of simulated weather streams for simulation runs, using the most recent 15 years this time (2006-2020) rather than full record from 1972-2012 in the second edition. See the process steps described in the Data Quality, Lineage section for more details.
Heat Zones map the distribution of potential heat stress for plants and animals, including humans. Heat zones, defined as the number of days per year with maximum daily temperature >= 30 \u00b0C (86 \u00b0F). Daily maximum temperature values >= 30 \u00b0C during the period 1980 - 2009 were tallied annually and are reported as the average annual number of days during a 30-year period.
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Heat Zones map the distribution of potential heat stress for plants and animals, including humans. Heat zones, defined as the number of days per year with maximum daily temperature >= 30 \u00b0C (86 \u00b0F). Daily maximum temperature values >= 30 \u00b0C during the period 1980 - 2009 were tallied annually and are reported as the average annual number of days during a 30-year period.
Heat Zones map the distribution of potential heat stress for plants and animals, including humans. Heat zones, defined as the number of days per year with maximum daily temperature >= 30 \u00b0C (86 \u00b0F). Daily maximum temperature values >= 30 \u00b0C during the period 2070 - 2099 were tallied annually and are reported as the average annual number of days during a 30-year period.
Heat Zones map the distribution of potential heat stress for plants and animals, including humans. Heat zones, defined as the number of days per year with maximum daily temperature >= 30 \u00b0C (86 \u00b0F). Daily maximum temperature values >= 30 \u00b0C during the period 2070 - 2099 were tallied annually and are reported as the average annual number of days during a 30-year period.
Heat Zones map the distribution of potential heat stress for plants and animals, including humans. Heat zones, defined as the number of days per year with maximum daily temperature >= 30 \u00b0C (86 \u00b0F). Daily maximum temperature values >= 30 \u00b0C during the period 2040 - 2069 were tallied annually and are reported as the average annual number of days during a 30-year period.
Heat Zones map the distribution of potential heat stress for plants and animals, including humans. Heat zones, defined as the number of days per year with maximum daily temperature >= 30 \u00b0C (86 \u00b0F). Daily maximum temperature values >= 30 \u00b0C during the period 2040 - 2069 were tallied annually and are reported as the average annual number of days during a 30-year period.
The Snow Residence Time 2080 data contains modeled future snow residence time values calculated using a spatial analog model. The model ingests mean winter average temperature and precipitation. For more information about the model, please see Luce et al., [2014] and Lute and Luce [2017]. Absolute changes in snow residence time are in units of days. The temperature and precipitation data are ensemble mean values across 20 global climate models from the CMIP5 experiment [Taylor et al., 2012], downscaled to a 4km grid. For more information on the downscaling method and to access the raw data used to create this dataset, please see Abatzoglou and Brown, [2012] and the Northwest Climate Science Center. We used the MACAv2-metdata monthly precipitation and minimum and maximum temperature datasets for the period 2071-2090 (RCP8.5). Average temperature was calculated as the arithmetic mean of minimum and maximum temperature datasets. Average temperature was averaged over the winter months (November through March) and precipitation was summed over the winter months.
The Snow Residence Time 2080 data contains modeled future snow residence time values calculated using a spatial analog model. The model ingests mean winter average temperature and precipitation. For more information about the model, please see Luce et al., [2014] and Lute and Luce [2017]. Absolute changes in snow residence time are in units of days. The temperature and precipitation data are ensemble mean values across 20 global climate models from the CMIP5 experiment [Taylor et al., 2012], downscaled to a 4km grid. For more information on the downscaling method and to access the raw data used to create this dataset, please see Abatzoglou and Brown, [2012] and the Northwest Climate Science Center. We used the MACAv2-metdata monthly precipitation and minimum and maximum temperature datasets for the period 2071-2090 (RCP8.5). Average temperature was calculated as the arithmetic mean of minimum and maximum temperature datasets. Average temperature was averaged over the winter months (November through March) and precipitation was summed over the winter months.
The Snow Residence Time; Absolute Changes contains modeled absolute changes in snow residence time (SRT) calculated using a spatial analog model. The model ingests mean winter average temperature and precipitation. For more information about the model, please see Luce et al., [2014] and Lute and Luce [2017]. Absolute changes in snow residence time are in units of days. The temperature and precipitation data are ensemble mean values across 20 global climate models from the CMIP5 experiment [Taylor et al., 2012], downscaled to a 4km grid. For more information on the downscaling method and to access the raw data used to create this dataset, please see Abatzoglou and Brown, [2012] and the Northwest Climate Science Center. We used the MACAv2-metdata monthly precipitation and minimum and maximum temperature datasets for the period 1975-2005 (historical) and 2071-2090 (RCP8.5, future). Average temperature was calculated as the arithmetic mean of minimum and maximum temperature datasets. Average temperature was averaged over the winter months (November through March) and precipitation was summed over the winter months. More information on the project associated with this dataset is available from the U.S. Forest Service Rocky Mountain Research Station.
The Snow Residence Time; Absolute Changes contains modeled absolute changes in snow residence time (SRT) calculated using a spatial analog model. The model ingests mean winter average temperature and precipitation. For more information about the model, please see Luce et al., [2014] and Lute and Luce [2017]. Absolute changes in snow residence time are in units of days. The temperature and precipitation data are ensemble mean values across 20 global climate models from the CMIP5 experiment [Taylor et al., 2012], downscaled to a 4km grid. For more information on the downscaling method and to access the raw data used to create this dataset, please see Abatzoglou and Brown, [2012] and the Northwest Climate Science Center. We used the MACAv2-metdata monthly precipitation and minimum and maximum temperature datasets for the period 1975-2005 (historical) and 2071-2090 (RCP8.5, future). Average temperature was calculated as the arithmetic mean of minimum and maximum temperature datasets. Average temperature was averaged over the winter months (November through March) and precipitation was summed over the winter months. More information on the project associated with this dataset is available from the U.S. Forest Service Rocky Mountain Research Station.
This raster contains historical snow residence time values calculated using a spatial analog model. The model ingests mean winter average temperature and precipitation. For more information about the model, please see Luce et al., [2014] and Lute and Luce [2017]. Absolute changes in snow residence time are in units of days. The temperature and precipitation data are ensemble mean values across 20 global climate models from the CMIP5 experiment [Taylor et al., 2012], downscaled to a 4km grid. For more information on the downscaling method and to access the raw data used to create this dataset, please see Abatzoglou and Brown, [2012] and the Northwest Climate Science Center. We used the MACAv2-metdata monthly precipitation and minimum and maximum temperature datasets for the period 1975-2005. Average temperature was calculated as the arithmetic mean of minimum and maximum temperature datasets. Average temperature was averaged over the winter months (November through March) and precipitation was summed over the winter months. More information on the project associated with this dataset is available from the U.S. Forest Service Rocky Mountain Research Station.
This raster contains historical snow residence time values calculated using a spatial analog model. The model ingests mean winter average temperature and precipitation. For more information about the model, please see Luce et al., [2014] and Lute and Luce [2017]. Absolute changes in snow residence time are in units of days. The temperature and precipitation data are ensemble mean values across 20 global climate models from the CMIP5 experiment [Taylor et al., 2012], downscaled to a 4km grid. For more information on the downscaling method and to access the raw data used to create this dataset, please see Abatzoglou and Brown, [2012] and the Northwest Climate Science Center. We used the MACAv2-metdata monthly precipitation and minimum and maximum temperature datasets for the period 1975-2005. Average temperature was calculated as the arithmetic mean of minimum and maximum temperature datasets. Average temperature was averaged over the winter months (November through March) and precipitation was summed over the winter months. More information on the project associated with this dataset is available from the U.S. Forest Service Rocky Mountain Research Station.
The SnowResidenceTime_PctChange contains modeled percent changes in snow residence time (SRT) calculated using a spatial analog model. The model ingests mean winter average temperature and precipitation. For more information about the model, please see Luce et al., [2014] and Lute and Luce [2017]. Absolute changes in snow residence time are in units of days. The temperature and precipitation data are ensemble mean values across 20 global climate models from the CMIP5 experiment [Taylor et al., 2012], downscaled to a 4km grid. For more information on the downscaling method and to access the raw data used to create this dataset, please see Abatzoglou and Brown, [2012] and the Northwest Climate Science Center. We used the MACAv2-metdata monthly precipitation and minimum and maximum temperature datasets for the period 1975-2005 (historical) and 2071-2090 (RCP8.5, future). Average temperature was calculated as the arithmetic mean of minimum and maximum temperature datasets. Average temperature was averaged over the winter months (November through March) and precipitation was summed over the winter months. More information on the project associated with this dataset is available from the U.S. Forest Service Rocky Mountain Research Station.
The SnowResidenceTime_PctChange contains modeled percent changes in snow residence time (SRT) calculated using a spatial analog model. The model ingests mean winter average temperature and precipitation. For more information about the model, please see Luce et al., [2014] and Lute and Luce [2017]. Absolute changes in snow residence time are in units of days. The temperature and precipitation data are ensemble mean values across 20 global climate models from the CMIP5 experiment [Taylor et al., 2012], downscaled to a 4km grid. For more information on the downscaling method and to access the raw data used to create this dataset, please see Abatzoglou and Brown, [2012] and the Northwest Climate Science Center. We used the MACAv2-metdata monthly precipitation and minimum and maximum temperature datasets for the period 1975-2005 (historical) and 2071-2090 (RCP8.5, future). Average temperature was calculated as the arithmetic mean of minimum and maximum temperature datasets. Average temperature was averaged over the winter months (November through March) and precipitation was summed over the winter months. More information on the project associated with this dataset is available from the U.S. Forest Service Rocky Mountain Research Station.