2005
DOI: 10.1071/wf04043
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Modeling interactions between fire and atmosphere in discrete element fuel beds

Abstract: In this text we describe an initial attempt to incorporate discrete porous element fuel beds into the coupled atmosphere–wildfire behavior model HIGRAD/FIRETEC. First we develop conceptual models for use in translating measured tree data (in this case a ponderosa pine forest) into discrete fuel elements. Then data collected at experimental sites near Flagstaff, Arizona are used to create a discontinuous canopy fuel representation in HIGRAD/FIRETEC. Four simulations are presented with different canopy and under… Show more

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Cited by 87 publications
(61 citation statements)
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“…Further analysis of the points that fell outside the predictive bounds was limited due to a lack of information regarding many of the independent variables known to influence crown fire behavior. However, four of the six FIRETEC points that showed an over-prediction bias were from a study conducted by Linn et al [22]. In this study, the authors represented the initial atmospheric boundary conditions as a constant no shear wind profile.…”
Section: Discussionmentioning
confidence: 96%
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“…Further analysis of the points that fell outside the predictive bounds was limited due to a lack of information regarding many of the independent variables known to influence crown fire behavior. However, four of the six FIRETEC points that showed an over-prediction bias were from a study conducted by Linn et al [22]. In this study, the authors represented the initial atmospheric boundary conditions as a constant no shear wind profile.…”
Section: Discussionmentioning
confidence: 96%
“…For example, often an investigator will provide a single number when describing the wind speed near the fire or the fuels complex, despite both wind speed magnitude and canopy fuel loading having a strong dependence on the height above ground and significant horizontal variability. Recent numerical simulations have shown that rate of spread predictions from detailed physics-based models are sensitive to small variations in both the spatial pattern of the fuels complex [22,23] and assumptions regarding the atmosphere boundary layer [46]. Clearly, further assessment of detailed physics-based models would benefit from additional data regarding spatial and temporal variability of key fuel and environmental characteristics (i.e.…”
Section: Discussionmentioning
confidence: 98%
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“…Although current physical models (Linn et al, 2005;Mell et al, 2009) represent substantial advances in crown fire modelling, from an operational point of view, two main empirical approaches in predicting crown fire spread continue being the base of the commonly used crown fire prediction systems. The first approach is that used in the models proposed by Rothermel (1991) and Finney (1998), based on the relationship between the predicted rate of fire spread at the surface and the fire rate of spread observed in crown fires in the Rocky Mountains (North America).…”
Section: Introductionmentioning
confidence: 99%