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Burn probability (BP) raster dataset predicted for the 2080-2100 period in the Rio Grande area was generated using: 1) data developed from the 2014 Fire Program Analysis (FPA) system; 2) geospatial Fire Simulation (FSim) system developed by the US Forest Service Missoula Fire Sciences Laboratory to estimate probabilistic components of wildfire risk (Finney et al. 2011); and 3) climate predictions developed using the Multivariate Adaptive Constructed Analogs (MACA) method (Abatzoglou and Brown 2011) which downscaled model output from the GFDL-ESM-2m global climate model of the Coupled Model Inter-Comparison Project 5 for the 8.5 Representative Concentration Pathway.
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The National Forest Management Act (NFMA) of 1976 requires every national forest or grassland managed by the U.S. Forest Service to develop and maintain a Land and Resource Management Plan (often referred to as a forest plan). The forest plan is the principle long-range guidance document for each forest or grassland, providing direction for project and activity decision making. Forest plans articulate goals and objectives, the kinds of uses that are suitable for areas of a national forest, management standards and guidelines that apply to different kinds of activities, and the designation of special areas like Research Natural Areas. Forest plans are strategic in nature and do not compel any action or authorize...
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Burn probability (BP) for Fireline Intensity Class 2 (FIL2) with flame lengths in the range of 0.6-1.2 m predicted for the 2050-2070 period in the Rio Grande area. This raster dataset was generated using: 1) data developed from the 2014 Fire Program Analysis (FPA) system; 2) geospatial Fire Simulation (FSim) system developed by the US Forest Service Missoula Fire Sciences Laboratory to estimate probabilistic components of wildfire risk (Finney et al. 2011); and 3) climate predictions developed using the Multivariate Adaptive Constructed Analogs (MACA) method (Abatzoglou and Brown 2011) which downscaled model output from the GFDL-ESM-2m global climate model of the Coupled Model Inter-Comparison Project 5 for the...
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Burn probability (BP) for Fireline Intensity Class 6 (FIL6) with flame lengths in the range of 3.7-15 m predicted for the 2080-2100 period in the Rio Grande area. This raster dataset was generated using: 1) data developed from the 2014 Fire Program Analysis (FPA) system; 2) geospatial Fire Simulation (FSim) system developed by the US Forest Service Missoula Fire Sciences Laboratory to estimate probabilistic components of wildfire risk (Finney et al. 2011); and 3) climate predictions developed using the Multivariate Adaptive Constructed Analogs (MACA) method (Abatzoglou and Brown 2011) which downscaled model output from the GFDL-ESM-2m global climate model of the Coupled Model Inter-Comparison Project 5 for the 8.5...
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This release contains Active Layer Thickness (ALT) and Organic Layer Thickness (OLT) measurements measured along transects in Alaska, 2015. Site condition information in terms of wildfire burns is also included.
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Background information.—On July 8, 2012, lightning ignited a fire on Bureau of Land Management-managed land on the Miller Homestead in Harney County, Oregon. High winds combined with unusually hot and dry conditions spread the fire through dry grass and sagebrush and 160,801 acres were burned before the fire was contained on July 24, 2012. In the aftermath, it was determined that ecological restoration was necessary since the majority of the fire occurred within prime habitat for sage-grouse, and the fire had burned with such severity that it removed vegetation down to bare soil. Without rehabilitation efforts, desirable vegetation would be unlikely to reestablish and the site would be open to invasion by noxious...
This imagery was collected and produced for a set of large fires sampled from within the Great Northern Landscape Conservation Cooperative study area. This imagery and associated metrics was produced using Landsat 5 and 7. This set of imagery and remote sensing metrics have the following file structure: 1. Each sub-folder in the Fires LC Map folder represents an individual fire. 2. Within the zip folder there are 5 raster tiffs. i. XXX_post_refl.tif The at-sensor-reflectance of the postfire landsat scene, named with the PolyID unique identifier for the fire, stored in 8-bit ii. XXX_pre_refl.tif The at-sensor-reflectance of the prefire landsat scene, named with the PolyID unique identifier for the...
This imagery was collected and produced for a set of large fires sampled from within the Great Northern Landscape Conservation Cooperative study area. This imagery and associated metrics was produced using Landsat 5 and 7. This set of imagery and remote sensing metrics have the following file structure: 1. Each sub-folder in the Fires LC Map folder represents an individual fire. 2. Within the zip folder there are 5 raster tiffs. i. XXX_post_refl.tif The at-sensor-reflectance of the postfire landsat scene, named with the PolyID unique identifier for the fire, stored in 8-bit ii. XXX_pre_refl.tif The at-sensor-reflectance of the prefire landsat scene, named with the PolyID unique identifier for the...
Solar radiation grids were produced for a set of large fires sampled from within the Great Northern Landscape Conservation Cooperative study area. This solar radiation grid was produced using the Area Solar Radiation tool in ArcGIS 10.1, using inputs of the associated 30m DEM.
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For his MS thesis, Brendan Rogers used the vegetation model MC1 to simulate vegetation dynamics, associated carbon and nitrogen cycle, water budget and wild fire impacts across the western 2/3 of the states of Oregon and Washington using climate input data from the PRISM group (Chris Daly, OSU) at a 30arc second (800m) spatial grain. The model was run from 1895 to 2100 assuming that nitrogen demand from the plants was always met so that the nitrogen concentrations in various plant parts never dropped below their minimum reported values. A CO2 enhancement effect increased productivity and water use efficiency as the atmospheric CO2 concentration increased. Future climate change scenarios were generated through statistical...
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The integrity of Amazon forests are currently threatened by climate change, deforestation, and fire. However, it is unclear how these agents of change interact over large spatial and temporal domains and reducing this uncertainty is important for projecting changes in carbon stocks and species biogeography, and could better inform continental scale conservation programs. With this in mind, aboveground biomass and tree cover data were produced using the dynamic global vegetation model, LPJmL, with 9 different global climate models (using the SRES A2 emissions storyline) and 2 different deforestation scenarios (from Soares et al.). The existing fire module was modified to include 'escaped fire' associated with deforestation,...
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The integrity of Amazon forests are currently threatened by climate change, deforestation, and fire. However, it is unclear how these agents of change interact over large spatial and temporal domains and reducing this uncertainty is important for projecting changes in carbon stocks and species biogeography, and could better inform continental scale conservation programs. With this in mind, aboveground biomass and tree cover data were produced using the dynamic global vegetation model, LPJmL, with 9 different global climate models (using the SRES A2 emissions storyline) and 2 different deforestation scenarios (from Soares et al.). The existing fire module was modified to include 'escaped fire' associated with deforestation,...
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The Standardized Precipitation Index (SPI) is a probability index that can be calculated for different time periods to indicate periods of abnormal wetness or dryness. SPI is derived solely from monthly precipitation and can be compared across regions with different climates. The SPI is an index based on the probability of recording a given amount of precipitation, and the probabilities are standardized so that an index of zero indicates the median precipitation amount (half of the historical precipitation amounts are below the median, and half are above the median). This dataset shows the average 12-month SPI (in classes ranging from extremely wet to extremely dry) for the three-month forecast period indentified...
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The integrity of Amazon forests are currently threatened by climate change, deforestation, and fire. However, it is unclear how these agents of change interact over large spatial and temporal domains and reducing this uncertainty is important for projecting changes in carbon stocks and species biogeography, and could better inform continental scale conservation programs. With this in mind, above ground biomass and tree cover data were produced using the dynamic global vegetation model, LPJmL, with 9 different global climate models (using the SRES A2 emissions storyline) and 2 different deforestation scenarios (from Soares et al.). The existing fire module was modified to include 'escaped fire' associated with deforestation,...
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The integrity of Amazon forests are currently threatened by climate change, deforestation, and fire. However, it is unclear how these agents of change interact over large spatial and temporal domains and reducing this uncertainty is important for projecting changes in carbon stocks and species biogeography, and could better inform continental scale conservation programs. With this in mind, above ground biomass and tree cover data were produced using the dynamic global vegetation model, LPJmL, with 9 different global climate models (using the SRES A2 emissions storyline) and 2 different deforestation scenarios (from Soares et al.). The existing fire module was modified to include 'escaped fire' associated with deforestation,...
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The integrity of Amazon forests are currently threatened by climate change, deforestation, and fire. However, it is unclear how these agents of change interact over large spatial and temporal domains and reducing this uncertainty is important for projecting changes in carbon stocks and species biogeography, and could better inform continental scale conservation programs. With this in mind, above ground biomass and tree cover data were produced using the dynamic global vegetation model, LPJmL, with 9 different global climate models (using the SRES A2 emissions storyline) and 2 different deforestation scenarios (from Soares et al.). The existing fire module was modified to include 'escaped fire' associated with deforestation,...


map background search result map search result map Simulated PNW biomass consumed (g C/m2) under MIROC 3.2 medres A2 (2070-2099 average) Standardized precipitation index forecast June - December 2011 (based on ECHAM 7-mo weather forecast) Percent change in above ground tree cover for the Amazon Basin under UKMO HADCM3 climate and GOVernance deforestation scenarios with no fire (2020s) Percent change in above ground tree cover for the Amazon Basin under UKMO HADCM3 climate scenario and current deforestation with no fire (2080s) Percent change in above ground tree cover for the Amazon Basin under MPI ECHAM 5 climate and GOVernance deforestation scenarios with fire (2020s) Aboveground biomass (Mg C/ha) for the Amazon Basin under GISS climate, no deforestation, and no fire scenarios (2020s) Aboveground biomass (Mg C/ha) for the Amazon Basin under GFDL CM2 climate, no deforestation, and fire scenarios (2080s) Northern Leopard Frog: 2030 Habitat Suitability Consensus of All Models Burn Probability for Fireline Intensity Class 2, predicted for 2050 to 2070 for Rio Grande study area Burn Probability for Fireline Intensity Class 6, predicted for 2080 to 2100 for Rio Grande study area Burn Probability predicted for 2080 to 2100 for Rio Grande study area Post-Wildfire Restoration in Southeast Oregon - Miller Homestead Fire United States Department of Agriculture Forest Service Southwestern Region Plan Revision Permafrost Soil Measurements; Alaska, 2015 Permafrost Soil Measurements; Alaska, 2015 Northern Leopard Frog: 2030 Habitat Suitability Consensus of All Models Burn Probability for Fireline Intensity Class 2, predicted for 2050 to 2070 for Rio Grande study area Burn Probability for Fireline Intensity Class 6, predicted for 2080 to 2100 for Rio Grande study area Burn Probability predicted for 2080 to 2100 for Rio Grande study area Post-Wildfire Restoration in Southeast Oregon - Miller Homestead Fire Simulated PNW biomass consumed (g C/m2) under MIROC 3.2 medres A2 (2070-2099 average) United States Department of Agriculture Forest Service Southwestern Region Plan Revision Percent change in above ground tree cover for the Amazon Basin under UKMO HADCM3 climate and GOVernance deforestation scenarios with no fire (2020s) Percent change in above ground tree cover for the Amazon Basin under UKMO HADCM3 climate scenario and current deforestation with no fire (2080s) Percent change in above ground tree cover for the Amazon Basin under MPI ECHAM 5 climate and GOVernance deforestation scenarios with fire (2020s) Aboveground biomass (Mg C/ha) for the Amazon Basin under GISS climate, no deforestation, and no fire scenarios (2020s) Aboveground biomass (Mg C/ha) for the Amazon Basin under GFDL CM2 climate, no deforestation, and fire scenarios (2080s) Standardized precipitation index forecast June - December 2011 (based on ECHAM 7-mo weather forecast)