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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...
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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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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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...
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Fire type predicted for the 2020-2040 period in the Rio Grande area with five classes: 1) shrub vegetation with torching flames; 2) shrub vegetation without torching flames; 3) forest with torching flames; 4) forest without torching flames; 5) grass or non-vegetation. 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...
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Burn probability (BP) raster dataset predicted for the 2020-2040 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 1 (FIL1) with flame lengths in the range of 0-0.6 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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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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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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Fire type predicted for the 2080-2100 period in the Rio Grande area with five classes: 1) shrub vegetation with torching flames; 2) shrub vegetation without torching flames; 3) forest with torching flames; 4) forest without torching flames; 5) grass or non-vegetation. 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...
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This map shows areas that have experienced fire between 1999 and 2010, including fire severity information where available. Determination of "change" due to fire is not possible due to the lack of highly accurate pre- and post-fire maps of vegetation conditions, and the wide range of possible interpretations of what constitutes a change. Instead, the focus was placed on mapping the location of fires and severity; the overall likelihood of significant change in short term vegetation conditions increases with fire severity.
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FY2013The increase in large wildfires at a time when habitat for Greater Sage Grouse and other species dependent on big sagebrush has also increased has led to substantial needs for big sagebrush seeds. Significant decisions on which sagebrush seed to use and on management treatments that affect competing herb layers on the same restoration sites affect the trajectory of habitat.This project will evaluate how seed source, specifically genotype and climate-of-origin, interact with landscape-scale and replicated treatments (fencing, herbicide application, mowing, and seeding).
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This data release includes time-series data from a monitoring site located in a small (0.12 km2) drainage basin in the Las Lomas watershed in Los Angeles County, CA, USA. The site was established after the 2016 Fish Fire and recorded a series debris flows in the first winter after the fire. The station is located along the channel at the outlet of the study area (34 9’18.50”N, 117 56’41.33”W, WGS84). The data were collected between November 15, 2016 and February 23, 2017. The data include two types of time series: (1) continuous 1-minute time series of rainfall and flow stage recorded by a laser distance meter suspended over the channel (LasLomasContinuous.csv), and (2) 50-Hz time series of flow stage and flow-induced...
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This product releases data on soil physical and hydraulic properties in the area affected by the 2011 Las Conchas Fire in New Mexico, USA. Soil samples were collected in the summer of 2015 to assess the state of the watershed following the 2011 wildfire. Data include soil-hydraulic properties of field-saturated hydraulic conductivity and sorptivity from tension infiltrometer measurements on soil cores. Soil physical properties include bulk density, as-sampled volumetric soil-water content, and saturated volumetric soil-water content for 6-cm length soil cores. Soil properties of soil-particle size, bulk density, and soil organic matter content from loss on ignition for soil core splits of 0-1. 1-3, and 3-6 cm depth....
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This data release presents measurements and derived parameters for attributes of bulk density, loss on ignition, soil-water retention, and hydraulic conductivity for a site (Richardson) near Hess Creek in interior Alaska, USA. These measurements are useful for hydrologic modeling and predictions of water availability in this region.
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.


map background search result map search result map Effects of Genotype and Management Treatments of Native and Invasive Herbs on Success of Sagebrush Restoration Colorado Plateau REA MQ E1: Where are the areas that have been changed by wildfire between 1999 and 2009? Burn Probability for Fireline Intensity Class 1, predicted for 2080 to 2100 for Rio Grande study area 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 2020 to 2040 for Rio Grande study area Burn Probability predicted for 2080 to 2100 for Rio Grande study area Conditional Flame Length predicted for 2050 to 2070 for Rio Grande study area Fire type predicted for 2020 to 2040 for Rio Grande study area Fire type predicted for 2080 to 2100 for Rio Grande study area Region 8 FMIS Wildfire and Fuel Treatment Locations 1980-2015 Soil Physical and Hydraulic Properties in the Area Affected by the 2011 Las Conchas Fire in New Mexico Post-wildfire debris-flow monitoring data, Arroyo Seco, 2009 Station Fire, Los Angeles County, California, November 2009 to March 2010. Fire probability for 1900-1929 using GFDL baseline climate values Post-wildfire debris-flow monitoring data, Las Lomas, 2016 Fish Fire, Los Angeles County, California, November 2016 to February 2017 Loss on ignition near Hess Creek in interior Alaska Post-wildfire debris-flow monitoring data, Las Lomas, 2016 Fish Fire, Los Angeles County, California, November 2016 to February 2017 Loss on ignition near Hess Creek in interior Alaska Soil Physical and Hydraulic Properties in the Area Affected by the 2011 Las Conchas Fire in New Mexico Burn Probability for Fireline Intensity Class 1, predicted for 2080 to 2100 for Rio Grande study area 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 Conditional Flame Length predicted for 2050 to 2070 for Rio Grande study area Fire type predicted for 2020 to 2040 for Rio Grande study area Fire type predicted for 2080 to 2100 for Rio Grande study area Burn Probability predicted for 2020 to 2040 for Rio Grande study area Burn Probability predicted for 2080 to 2100 for Rio Grande study area Colorado Plateau REA MQ E1: Where are the areas that have been changed by wildfire between 1999 and 2009? Effects of Genotype and Management Treatments of Native and Invasive Herbs on Success of Sagebrush Restoration Fire probability for 1900-1929 using GFDL baseline climate values Region 8 FMIS Wildfire and Fuel Treatment Locations 1980-2015