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This data release includes metadata and tabular data that document Acacia koa density, basal area, and grass cover before and after the 2018 Keauhou Ranch Fire in Hawaii Volcanoes National Park. Specifically, we asked three questions: 1) At what level of precision can pre-fire grass cover be accurately estimated from oblique aerial photos? 2) How are post-fire Acacia koa regeneration densities affected by fire severity? 3) How are post-fire A. koa regeneration densities affected by pre-fire grass cover and its interaction with fire severity? We collected burn severity and post-fire regeneration data from 30 transects stratified across mid-elevation woodland, montane woodland, and montane shrubland. We evaluated...
Slope steepness was used to reflect effects on relative fire behavior. It was assumed the steeper the slope, the higher the fire intensity, assuming other variables remain constant (weather; wind; structure, composition, and arrangement of fuels; fuel moisture, etc.). BehavePlus was used to model the relationship of fire intensity and slope. Slope Class Percent Slope Fire Intensity Rating 1 0-10% Low 2 10-30% Low 3 30-60% Moderate 4 >60% High These data were designed to characterize mid-scale patterns across Idaho of the the effects of slope on wildland fire intensity. They were developed specifically for use in characterizing relative wildland fire hazard which was then used to assess the risks of wildland...
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Fire Intensity raster dataset predicted for the 2050-2070 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.
At best, predicting surface and canopy fuel loads from mid-scale data is problematic at best. The structure, composition, and arrangement of fuels are dependent upon the disturbance history of any given stand. Disturbance history includes natural processes (e.g., fire, wind, insects, and pathogens), as well as anthropogenic processes (e.g., silvicultural treatments and grazing practices). The only available proxy to the disturbance history (and consequently fuel loadings) available at a mid-scale level is the current structure and composition of vegetation (e.g., cover type, canopy cover, and size class). Unfortunately, the current structure and composition of vegetation is a very poor predictor of stand history....
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Fire Intensity 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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Fire Intensity 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.
    map background search result map search result map Fire Intensity predicted for 2020 to 2040 for Rio Grande study area Fire Intensity predicted for 2050 to 2070 for Rio Grande study area Fire Intensity predicted for 2080 to 2100 for Rio Grande study area Hawaii Volcanoes National Park plant community and fire severity data, 2018-2020 Hawaii Volcanoes National Park plant community and fire severity data, 2018-2020 Fire Intensity predicted for 2020 to 2040 for Rio Grande study area Fire Intensity predicted for 2050 to 2070 for Rio Grande study area Fire Intensity predicted for 2080 to 2100 for Rio Grande study area