A lack of independent, quality-assured data prevents scientists from effectively evaluating predictions and uncertainties in fire models used by land managers. This paper presents a summary of pre-fire and post-fire fuel, fuel moisture and surface cover fraction data that can be used for fire model evaluation and development. The data were collected in the south-eastern United States on 14 forest and 14 non-forest sample units associated with 6 small replicate and 10 large operational prescribed fires conducted during 2008, 2011, and 2012 as part of the Prescribed Fire Combustion and Atmospheric Dynamics Research Experiment (RxCADRE). Fuel loading and fuel consumption averaged 6.8 and 4.1 Mg ha–1 respectively in the forest units and 3.0 and...
Surface fuels data are of critical importance for supporting fire inci-dent management, risk assessm...
Significant quantities of forest land burn in the south-eastern United States as a result of wildfir...
The First Order Fire Effects Model (FOFEM) is a predictive tool that allows estimation of fire effec...
Characterizing pre-fire fuel load and fuel consumption are critical for assessing fire behavior, fir...
Reliable predictions of fuel consumption are critical in the eastern United States (US), where presc...
In estimating total carbon emissions from wildland fire, three inputs are needed: area burned, carbo...
Fuel consumption predictions are necessary to accurately estimate or model fire effects, including p...
Abstract. Small-scale experiments have demonstrated that fire radiative energy is linearly related t...
An empirical model is presented which relates fractional reduction in loading to fuel element diamet...
Means, standard deviations, and quartiles of fuel loadings wre determined for litter, for downed woo...
Fire behavior was measured and modeled from eight 1 ha experimental plots located in the Francis Mar...
Landscape fires show large variability in the amount of biomass or fuel consumed per unit area burne...
Atmospheric composition is strongly influenced by wildfire emissions, which have a strong variabilit...
The moisture content of vegetation and litter (fuel moisture) is an important determinant of fire ri...
Effective wildfire management and prevention strategies depend on accurate forecasts of fire occurre...
Surface fuels data are of critical importance for supporting fire inci-dent management, risk assessm...
Significant quantities of forest land burn in the south-eastern United States as a result of wildfir...
The First Order Fire Effects Model (FOFEM) is a predictive tool that allows estimation of fire effec...
Characterizing pre-fire fuel load and fuel consumption are critical for assessing fire behavior, fir...
Reliable predictions of fuel consumption are critical in the eastern United States (US), where presc...
In estimating total carbon emissions from wildland fire, three inputs are needed: area burned, carbo...
Fuel consumption predictions are necessary to accurately estimate or model fire effects, including p...
Abstract. Small-scale experiments have demonstrated that fire radiative energy is linearly related t...
An empirical model is presented which relates fractional reduction in loading to fuel element diamet...
Means, standard deviations, and quartiles of fuel loadings wre determined for litter, for downed woo...
Fire behavior was measured and modeled from eight 1 ha experimental plots located in the Francis Mar...
Landscape fires show large variability in the amount of biomass or fuel consumed per unit area burne...
Atmospheric composition is strongly influenced by wildfire emissions, which have a strong variabilit...
The moisture content of vegetation and litter (fuel moisture) is an important determinant of fire ri...
Effective wildfire management and prevention strategies depend on accurate forecasts of fire occurre...
Surface fuels data are of critical importance for supporting fire inci-dent management, risk assessm...
Significant quantities of forest land burn in the south-eastern United States as a result of wildfir...
The First Order Fire Effects Model (FOFEM) is a predictive tool that allows estimation of fire effec...