BEHAVE: fire behavior prediction and fuel modeling system - BURN subsystem, Part 2

Andrews, Patricia L.; Chase, Carolyn H.

doi:10.2737/int-gtr-260

Cited by 78 publications

(60 citation statements)

References 7 publications

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“…The moisture content of 1-hour fuels from 8 a.m. to 6 p.m. was obtained with the MOISTURE module of the FIRE2 program within BEHAVE software [42]. MOISTURE implements a model [43] that improves on the Fine Fuel Moisture Code of the Canadian FWI system [44], namely by accounting for the drying effect of solar radiation, as determined by latitude, time of year, time of day, topography, and shade (from stand characteristics).…”

Section: Potential Fire Behaviormentioning

confidence: 99%

Microclimate and Modeled Fire Behavior Differ Between Adjacent Forest Types in Northern Portugal

Pinto

Fernandes

2014

Forests

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Fire severity varies with forest composition and structure, reflecting micrometeorology and the fuel complex, but their respective influences are difficult to untangle from observation alone. We quantify the differences in fire weather between different forest types and the resulting differences in modeled fire behavior. Collection of in-stand weather data proceeded during two summer periods in three adjacent stands in northern Portugal, respectively Pinus pinaster (PP), Betula alba (BA), and Chamaecyparis lawsoniana (CL). Air temperature, relative humidity and wind speed varied respectively as CL < PP < BA, PP < CL < BA, and CL < BA < PP. Differences between PP and the other types were greatest during the warmest and driest hours of the day in a sequence of 10 days with high fire danger. Estimates of daytime moisture content of fine dead fuels and fire behavior characteristics for this period, respectively, from Behave and BehavePlus, indicate a CL < BA < PP gradient in fire potential. High stand density in CL and BA ensured lower wind speed and higher fuel moisture content than in PP, limiting the likelihood of an extreme fire environment. However, regression tree analysis revealed that the fire behavior distinction between the three forest types was primarily a function of the surface fuel complex, and more so during extreme fire weather conditions. OPEN ACCESSForests 2014, 5 2491

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Section: Potential Fire Behaviormentioning

confidence: 99%

Microclimate and Modeled Fire Behavior Differ Between Adjacent Forest Types in Northern Portugal

Pinto

Fernandes

2014

Forests

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“…HIGRAD has been previously linked to a BEHAVElike fire model (Reisner et al 1998;Andrews 1986;Andrews and Chase 1989). The fire model uses empirical functions (Rothermel 1972(Rothermel , 1991 to determine the rate of fire spread.…”

Section: Introductionmentioning

confidence: 99%

Comparison of a diagnostic wildfire modeling system (HIGRAD/BEHAVE) with a self-determining wildfire modeling system (HIGRAD/FIRETEC)

Reisner¹,

Linn²,

Bossert³

1998

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SUMMARYIn this paper details of our wildfire modeling system are illustrated. First our dynamical model, HIGRAD, capable of accurately resolving regions of strong gradients is described. Next, our two wildfire models FIRETEC and BEHAVE are introduced. Unlike the traditional point-functional approach used in our BEHAVElike fire model, FIRETEC is a self-determining fire model. The benefits of the HIGRAD/FIRETEC approach with respect to HIGRAD/BEHAVE are shown in the results section of this paper. Two major conclusions are drawn in this section. The first conclusion is that the rate of spread of a fire to a first approximation is independent of the local wind velocity. The second conclusion is that HIGRAD/FIRETEC can be used to develop point-functions for use in HIGRAD/BEHAVE. INTRODUCTIONThe ability to accurately forecast the spread of a wildfire would signiScantly reduce human suffering and loss of life, the destruction of property, and expenditures for assessment and recovery. To help achieve this goal we have developed a model which accurately simulates the interactions between winds and the heat source associated with a wildfire. We have termed our new model HIGRAD or a HIgh resolution model for strong GRADient applications. HIGRAD employs sophisticated numerical techniques to prevent numerical oscillations from occurring in the vicinity of the fire. As well, HIGRAD uses a numerical technique which enables the compressible equation set to be solved to an accuracy of second-order, but using a time step which is not governed by the speed of sound.HIGRAD has been previously linked to a BEHAVElike fire model (Reisner et al. 1998; Andrews 1986; Andrews and Chase 1989). The fire model uses empirical functions (Rothermel 1972(Rothermel , 1991 to determine the rate of fire spread. By design the BEHAVE model is computationally efficient; however, whether a simple empirical model can accurately forecast fire spread is somewhat debatable. For example, the rates of spread

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“…WAF was not calculated in the fire behavior prediction portion of BEHAVE (Andrews 1986;Andrews and Chase 1989). The fuel modeling program TSTMDL calculated an unsheltered WAF for custom fuel models (Burgan and Rothermel 1984).…”

Section: Behavementioning

confidence: 99%

Modeling wind adjustment factor and midflame wind speed for Rothermel's surface fire spread model

Andrews¹

2012

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Rothermel's surface fire spread model was developed to use a value for the wind speed that affects surface fire, called midflame wind speed. Models have been developed to adjust 20-ft wind speed to midflame wind speed for sheltered and unsheltered surface fuel. In this report, Wind Adjustment Factor (WAF) model equations are given, and the BehavePlus fire modeling system is used to demonstrate WAF calculation and effect on modeled fire behavior. There are differences in implementation of the same basic wind adjustment models in various fire behavior applications, including the Fireline Handbook and FARSITE. Differences are due to assumptions such as tree shape and rules for transition from sheltered to unsheltered conditions. Specifics are given for differences among WAF tables and calculation applications. This technical documentation is useful to analysts, system developers, fire weather meteorologists, and those who are interested in model background and foundation.

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BEHAVE: fire behavior prediction and fuel modeling system - BURN subsystem, Part 2

Cited by 78 publications

References 7 publications

Microclimate and Modeled Fire Behavior Differ Between Adjacent Forest Types in Northern Portugal

Microclimate and Modeled Fire Behavior Differ Between Adjacent Forest Types in Northern Portugal

Comparison of a diagnostic wildfire modeling system (HIGRAD/BEHAVE) with a self-determining wildfire modeling system (HIGRAD/FIRETEC)

Modeling wind adjustment factor and midflame wind speed for Rothermel's surface fire spread model

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