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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.
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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In Alaska, recent research has identified particular areas of the state where both a lack of soil moisture and warming temperatures increase the likelihood of wildfire. While this is an important finding, this previous research did not take into account the important role that melting snow, ice, and frozen ground (permafrost) play in replenshing soil moisture in the spring and summer months. This project will address this gap in the characterization of fire risk using the newly developed monthly water balance model (MWBM). The MWBM takes into account rain, snow, snowmelt, glacier ice melt, and the permafrost layer to better calculate soil moisture replenishment and the amount of moisture that is lost to the atmosphere...
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Globally, changing fire regimes due to climate is one of the greatest threats to ecosystems and society. This dataset presents projections of historic and future fire probability for the southcentral U.S. using downscaled climate projections and the Physical Chemistry Fire Frequency Model (PC2FM, Guyette et al., 2012). Climate data from 1900-1929 and projected climate data for 2040-2069 and 2070-2099 were used as model inputs to the Physical Chemistry Fire Frequency Model (Guyette et al. 2012) to estimate fire probability. Baseline and future time period data are from three global climate models (GCMs): CGCM, GFDL, and HadCM3. The nine associated data sets (tiffs) represent estimated change in mean fire probability...
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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. This dataset is comprised of two different zip files. Zip File 1: The data within this zip file are composed of two wildland fire datasets. (1) A merged dataset consisting of 40 different wildfire and prescribed fire layers. The original 40 layers were all freely obtained from the internet or provided to the authors free of charge with permission to use them. The merged layers were altered to contain a consistent set of attributes including names, IDs, and dates. This raw merged dataset contains all original polygons many of which are duplicates of the same fire. This dataset...
On August 25, 2015 speaker Matt Germino presented on his work restoring sagebrush in the Great Basin. Shrubs are ecosystem foundation species in most of the Great Basin’s landscapes. Most of the species, including sagebrush, are poorly adapted to the changes in fire and invasive pressures that are compounded by climate change. This presentation gives an overview of challenges and opportunities regarding restoration of sagebrush and blackbrush, focusing on climate adaptation, selection of seeds and achieving seeding and planting success. Results from Great Basin LCC supported research on seed selection and planting techniques are presented.
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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...
A warming climate, fire exclusion, and land cover changes are altering the conditions that produced historical fire regimes and facilitating increased recent wildfire activity in the northwestern United States. Understanding the impacts of changing fire regimes on forest recruitment and succession, species distributions, carbon cycling, and ecosystem services is critical, but challenging across broad spatial scales. One important and understudied aspect of fire regimes is the unburned area within fire perimeters; these areas can function as fire refugia across the landscape during and after wildfire by providing habitat and seed sources. With increasing fire activity, there is speculation that fire intensity and...
Wildfire refugia are forest patches that are minimally-impacted by fire and provide critical habitats for fire-sensitive species and seed sources for post-fire forest regeneration. Wildfire refugia are relatively understudied, particularly concerning the impacts of subsequent fires on existing refugia. We opportunistically re-visited 122 sites classified in 1994 for a prior fire refugia study, which were burned by two wildfires in 2012 in the Cascade mountains of central Washington, USA. We evaluated the fire effects for historically persistent fire refugia and compared them to the surrounding non-refugial forest matrix. Of 122 total refugial (43 plots) and non-refugial (79 plots) sites sampled following the 2012...
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Wildfire and fuel treatment locations for the USFWS Pacific Southwest Region (California, Nevada, Klamath Basin OR) extracted from the Fire Management Information System (FMIS) on October 23, 2015, for fiscal years 1980-2015.
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This data release includes time-series data from a monitoring site located in a small drainage basin in the Arroyo Seco watershed in Los Angeles County, CA, USA (N3788964 E389956, UTM Zone 11, NAD83). The site was established after the 2009 Station Fire and recorded a series debris flows in the first winter after the fire. The data include three types of time-series: (1) 1-minute time series of rainfall, soil water content, channel bed pore pressure and temperature, and flow stage recorded by radar and laser distance meters (ArroyoSecoContinuous.csv); (2) 10-Hz time series of flow stage recorded by the laser distance meter during rain storms (ArroyoSecoStormLaser.csv), and (3) 2-second time series of rainfall and...
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The 2000-2009 national fire perimeter data layer was developed in support of WFDSS for the 2010 fire season. The objective of the layer is to display historic national fire perimeter polygons in WFDSS. This layer will be expanded and grown upon in future years. The 2009 layer was created using the WFDSS 2001-2008 national fire history perimeter data, the interagency California 2009 data, Alaska 2009 data, and the remaining 47 States 2009 fire perimeter data from GeoMAC. The layer contains only those fires 100 acres and larger and from the time period of 2000-2009 from the data sources listed below. Last updated 08-23-2010. Sources: USFS R5 (California) GIS Clearinghouse http://www.fs.fed.us/r5/rsl/clearinghouse/gis-download.shtml/#firehistory...
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Wildland Urban Interface (WUI) Boundaries designated in the Jackson and Josephine County Integrated Fire Plans
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This dataset is a raster of predicted suitable bioclimate using statistical correlations between known habitat and current climate (1950-1999 average) , and then projecting that niche into the future. The future timeslices used are 2020's, which is an average of 2020-2029, and 2050's which is 2050-2059. The Values 1-6 show the degree of model agreement (For example: areas with a value of 1 is where only 1 GCM predicted suitability; pixels with a value of 6 are where 6 GCMs predicted suitability, ect). *see Maxent output pdfs for more details about model inputs and settings.
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The Monitoring Trends in Burn Severity (MTBS) project assesses the frequency, extent, and magnitude (size and severity) of all large fires (includes wildfire, wildland fire use, and prescribed fire) in the conterminous United States (CONUS), Alaska, Hawaii, and Puerto Rico for the period of 1984 through 2011. All fires reported as greater than 1,000 acres in the western U.S. and greater than 500 acres in the eastern U.S. are mapped across all ownerships. MTBS produces a series of geospatial and tabular data for analysis at a range of spatial, temporal, and thematic scales and are intended to meet a variety of information needs that require consistent data about fire effects through space and time. This map layer...
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This dataset is a raster of predicted suitable bioclimate using statistical correlations between known habitat and current climate (1950-1999 average) , and then projecting that niche into the future. The future timeslices used are 2020's, which is an average of 2020-2029, and 2050's which is 2050-2059. The Values 1-6 show the degree of model agreement (For example: areas with a value of 1 is where only 1 GCM predicted suitability; pixels with a value of 6 are where 6 GCMs predicted suitability, ect). *see Maxent output pdfs for more details about model inputs and settings.


map background search result map search result map Wildland Urban Interface (WUI) Boundaries for Jackson and Josephine Counties U.S. National Historical Fire Perimeters (2000-2009) Burn Probability for Fireline Intensity Class 2, predicted for 2050 to 2070 for Rio Grande study area Burn Probability for Fireline Intensity Class 4, predicted for 2050 to 2070 for Rio Grande study area Burn Probability for Fireline Intensity Class 5, predicted for 2080 to 2100 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 Region 8 FMIS Wildfire and Fuel Treatment Locations 1980-2015 Improving Characterizations of Future Wildfire Risk in Alaska Post-wildfire debris-flow monitoring data, Arroyo Seco, 2009 Station Fire, Los Angeles County, California, November 2009 to March 2010. Future changes in southcentral U.S. wildfire probability due to climate change-Data BLM REA MAR 2012 CONUS Thematic Burn Severity Mosaic (2008) BLM REA CBR 2010 Modeled Future Bioclimate 2050 - Northern Rubber Boa BLM REA CBR 2010 Modeled Future Bioclimate 2050 - Sonora Mojave Semi-Desert Chaparral Combined wildland fire datasets for the United States and certain territories, 1800s-Present Wildland Urban Interface (WUI) Boundaries for Jackson and Josephine Counties BLM REA MAR 2012 CONUS Thematic Burn Severity Mosaic (2008) Burn Probability for Fireline Intensity Class 2, predicted for 2050 to 2070 for Rio Grande study area Burn Probability for Fireline Intensity Class 4, predicted for 2050 to 2070 for Rio Grande study area Burn Probability for Fireline Intensity Class 5, predicted for 2080 to 2100 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 BLM REA CBR 2010 Modeled Future Bioclimate 2050 - Northern Rubber Boa BLM REA CBR 2010 Modeled Future Bioclimate 2050 - Sonora Mojave Semi-Desert Chaparral Future changes in southcentral U.S. wildfire probability due to climate change-Data Region 8 FMIS Wildfire and Fuel Treatment Locations 1980-2015 Improving Characterizations of Future Wildfire Risk in Alaska U.S. National Historical Fire Perimeters (2000-2009) Combined wildland fire datasets for the United States and certain territories, 1800s-Present