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Exposure (vulnerability) index for the future time period (2061-2080) representing projected climate conditions from the Meteorological Research Institute's Coupled Atmosphere-Ocean General Circulation Model, version 3, and the rcp85 emissions scenario. The exposure model uses LANDFIRE vegetation data and Worldclim climate data .The raster values represent exposure scores for the corresponding vegetation type. The modeled vegetation types can be spatially associated with the exposure values by overlaying them with the "landfire_veg_sw_300m.tif" raster.Exposure values represent where the location falls in climate space relative to its recent historical distribution:5 (core 5% of historical climate space); 10 (5 -...
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This project had two primary goals: 1) To develop a process for integrating data from multiple sources to improve predictions of climate impacts for wildlife species; and 2) To provide data on climate and related hydrological change, fire behavior under future climates, and species’ distributions for use by researchers and resource managers.We present within this report the process used to integrate species niche models, fire simulations, and vulnerability assessment methods and provide species’ reports that summarize the results of this work. Species niche model analysis provides information on species’ distributions under three climate scenarios and time periods. Niche model analysis allows us to estimate the...
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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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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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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 5 (FIL5) with flame lengths in the range of 2.4-3.7 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...
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Burn probability (BP) for Fireline Intensity Class 4 (FIL4) with flame lengths in the range of 1.8-2.4 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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The purpose of this dataset is to display the physical boundaries of Fire Management Zones within the U.S. Fish & Wildlife Service, Pacific Southwest Region.
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First, we would like to thank the wildland fire advisory group. Their wisdom and guidance helped us build the dataset as it currently exists. Currently, there are multiple, freely available wildland fire datasets that identify wildfire and prescribed fire areas across the United States. However, these datasets are all limited in some way. Time periods, spatial extents, attributes, and maintenance for these datasets are highly variable, and none of the existing datasets provide a comprehensive picture of wildfires that have burned since the 1800s. Utilizing a series of both manual processes and ArcGIS Python (arcpy) scripts, we merged 40 of these disparate datasets into a single dataset that encompasses the known...
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These data show the fire history indicator for Freshwater Non-Forested Wetlands (FNFW) through 2018. Fire Regime is an ecological indicator for the Landscape Conservation Project (LCP) for Florida. The LCP entails a large-scale assessment of and planning for the health of important natural resources, known as Conservation Assets (CAs), in Florida. Conservation planning at the landscape scale provides a framework for safeguarding functional ecosystems, and their interconnected processes required for maintaining healthy resources. Spatially explicit data from the project informs coordination and prioritization for making conservation decisions. Additionally, a suite of ecological indicators was carefully selected...
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This layer represents historic fire perimeters within 50km of the Crown of the Continent Ecosystem (CCE) from 2014 to 2015 within only Alberta and British Columbia. This dataset was developed by the Crown Managers Partnership, as part of a transboundary collaborative management initiative for the Crown of the Continent Ecosystem, based on commonly identified management priorities that are relevant at the landscape scale. The CMP is collaborative group of land managers, scientists, and stakeholder in the CCE. For more information on the CMP and its collaborators, programs, and projects please visit: http://crownmanagers.org/. This dataset was first published in May 2016. Note: There was not any public data available...
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Conditional Flame Length (CFL) is an estimate of the mean flame lengths for each pixel, and was predicted for the 2050-2070 period in the Rio Grande area 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. CFL...