Reformulation of Rothermel’s wildland fire behaviour model for heterogeneous fuelbedsThis article is one of a selection of papers published in the Special Forum on the Fuel Characteristic Classification System.

2007 ◽  
Vol 37 (12) ◽  
pp. 2438-2455 ◽  
Author(s):  
David V. Sandberg ◽  
Cynthia L. Riccardi ◽  
Mark D. Schaaf
Keyword(s):  
Wind Speed ◽  
Environmental Conditions ◽  
Classification System ◽  
Fire Behavior ◽  
Fire Spread ◽  
Fuel Moisture ◽  
Fire Behaviour ◽  
Spread Model ◽  
Fuel Characteristic ◽  
Fire Spread Model

The Fuel Characteristic Classification System (FCCS) includes equations that calculate energy release and one-dimensional spread rate in quasi-steady state fires in heterogeneous but spatially-uniform wildland fuelbeds, using a reformulation of the widely used Rothermel fire spread model. This reformulation provides an automated means to predict fire behavior under any environmental conditions in any natural, modified, or simulated wildland fuelbed. The formulation may be used to compare potential fire behavior between fuelbeds that differ in time, space, or as a result of management, and provides a means to classify and map fuelbeds based on their expected surface fire behavior under any set of defined environmental conditions (i.e., effective wind speed and fuel moisture content). Model reformulation preserves the basic mathematical framework of the Rothermel fire spread model, reinterprets data from two of the original basic equations in his model, and offers a new conceptual formulation that allows the direct use of inventoried fuel properties instead of stylized fuel models. Alternative methods for calculating the effect of wind speed and fuel moisture, based on more recent literature, are also provided. This reformulation provides a framework for the incremental improvement in quantifying fire behaviour parameters in complex fuelbeds and for modeling fire spread.

Sensitivity of a surface fire spread model and associated fire behaviour fuel models to changes in live fuel moisture

2007 ◽  
Vol 16 (4) ◽  
pp. 503 ◽  
Author(s):  
W. Matt Jolly
Keyword(s):  
Fire Spread ◽  
Fuel Moisture ◽  
Fire Behaviour ◽  
Surface Fire ◽  
Fuel Model ◽  
Spread Model ◽  
Fuel Models ◽  
Live Fuel Moisture ◽  
Behaviour Models ◽  
Fire Spread Model

Fire behaviour models are used to assess the potential characteristics of wildland fires such as rates of spread, fireline intensity and flame length. These calculations help support fire management strategies while keeping fireline personnel safe. Live fuel moisture is an important component of fire behaviour models but the sensitivity of existing models to live fuel moisture has not been thoroughly evaluated. The Rothermel surface fire spread model was used to estimate key surface fire behaviour values over a range of live fuel moistures for all 53 standard fuel models. Fire behaviour characteristics are shown to be highly sensitive to live fuel moisture but the response is fuel model dependent. In many cases, small changes in live fuel moisture elicit drastic changes in predicted fire behaviour. These large changes are a result of a combination of the model-calculated live fuel moisture of extinction, the effective wind speed limit and the dynamic load transfer function of some of the fuel models tested. Surface fire spread model sensitivity to live fuel moisture changes is discussed in the context of predicted fire fighter safety zone area because the area of a predicted safety zone may increase by an order of magnitude for a 10% decrease in live fuel moisture depending on the fuel model chosen.

An effective wind speed for models of fire spread

2002 ◽  
Vol 11 (2) ◽  
pp. 153 ◽  
Author(s):  
Ralph M. Nelson, Jr.
Keyword(s):  
Wind Speed ◽  
Slope Angle ◽  
Fire Behavior ◽  
Fire Spread ◽  
Spread Rate ◽  
Ambient Wind ◽  
Rate Of Spread ◽  
Spread Model ◽  
The Difference ◽  
Fire Spread Model

In previous descriptions of wind-slope interaction and the spread rate of wildland fires it is assumed that the separate effects of wind and slope are independent and additive and that corrections for these effects may be applied to spread rates computed from existing rate of spread models. A different approach is explored in the present paper in which the upslope component of the fire's buoyant velocity is used with the speed and direction of the ambient wind to produce effective values of wind speed and direction that determine the rate of spread vector. Thus the effective wind speed can replace the ambient wind speed in any suitable fire spread model and provide a description of the combined effects on the fire behavior. The difference between current and threshold values of the effective wind speed also can be used to determine whether fire will spread in a given fuel type. The model is tested with data from experiments reported by Weise (1993) in which fire spread was in response to variation in both wind speed and slope angle. The Weise spread rate data were satisfactorily correlated using dimensional methods and the observed spread rate was reasonably well predicted with an existing rate of spread model. Directional aspects of the model were not tested because the Weise (1993) study did not include winds with a cross-slope component.

Fire behaviour in wheat crops – effect of fuel structure on rate of fire spread

2020 ◽  
Vol 29 (3) ◽  
pp. 258 ◽  
Author(s):  
Miguel G. Cruz ◽  
Richard J. Hurley ◽  
Rachel Bessell ◽  
Andrew L. Sullivan
Keyword(s):  
Experimental Study ◽  
Prediction Models ◽  
Fire Spread ◽  
Propagation Rate ◽  
Forward Rate ◽  
Fire Behaviour ◽  
Natural Grass ◽  
Spread Model ◽  
Crop Condition ◽  
Fire Spread Model

A field-based experimental study was conducted in 50×50m square plots to investigate the behaviour of free-spreading fires in wheat to quantify the effect of crop condition (i.e. harvested, unharvested and harvested and baled) on the propagation rate of fires and their associated flame characteristics, and to evaluate the adequacy of existing operational prediction models used in these fuel types. The dataset of 45 fires ranged from 2.4 to 10.2kmh−1 in their forward rate of fire spread and 3860 and 28000 kWm−1 in fireline intensity. Rate of fire spread and flame heights differed significantly between crop conditions, with the unharvested condition yielding the fastest spreading fires and tallest flames and the baled condition having the slowest moving fires and lowest flames. Rate of fire spread in the three crop conditions corresponded directly with the outputs from the models of Cheney et al. (1998) for grass fires: unharvested wheat → natural grass; harvested wheat (~0.3m tall stubble) → grazed or cut grass; and baled wheat (<0.1m tall stubble) → eaten-out grass. These models produced mean absolute percent errors between 21% and 25% with reduced bias, a result on par with the most accurate published fire spread model evaluations.

scholarly journals Coupled Fire–Atmosphere Simulations of the Rocky River Fire Using WRF-SFIRE

2016 ◽  
Vol 55 (5) ◽  
pp. 1151-1168 ◽  
Author(s):  
Mika Peace ◽  
Trent Mattner ◽  
Graham Mills ◽  
Jeffrey Kepert ◽  
Lachlan McCaw
Keyword(s):  
Fire Behavior ◽  
South Australia ◽  
Simulation Models ◽  
Fire Spread ◽  
Fire Plume ◽  
Spread Model ◽  
Mesoscale Convective ◽  
Rayleigh Benard ◽  
Fire Spread Model ◽  
Extreme Fire

AbstractThe coupled atmosphere–fire spread model “WRF-SFIRE” has been used to simulate a fire where extreme fire behavior was observed. Tall flames and a dense convective smoke column were features of the fire as it burned rapidly up the Rocky River gully on Kangaroo Island, South Australia. WRF-SFIRE simulations of the event show a number of interesting dynamical processes resulting from fire–atmosphere feedback, including the following: fire spread was sensitive to small changes in mean wind direction; fire perimeter was affected by wind convergence resulting from interactions between the fire, atmosphere, and local topography; and the fire plume mixed high-momentum air from above a strong subsidence inversion. At 1-min intervals, output from the simulations showed fire spread exhibiting fast and slow pulses. These pulses occurred coincident with the passage of mesoscale convective (Rayleigh–Bénard) cells in the planetary boundary layer. Simulations show that feedback between the fire and atmosphere may have contributed to the observed extreme fire behavior. The findings raise questions as to the appropriate information to include in meteorological forecasts for fires as well as future use of coupled and uncoupled fire simulation models in both operational and research settings.

Empirical modelling of surface fire behaviour in maritime pine stands

2009 ◽  
Vol 18 (6) ◽  
pp. 698 ◽  
Author(s):  
Paulo M. Fernandes ◽  
Hermínio S. Botelho ◽  
Francisco C. Rego ◽  
Carlos Loureiro
Keyword(s):  
Wind Speed ◽  
Moisture Content ◽  
Fire Spread ◽  
Fire Intensity ◽  
Fuel Moisture ◽  
Fire Behaviour ◽  
Spread Rate ◽  
Surface Fire ◽  
Rate Of Spread ◽  
Fuel Moisture Content

An experimental burning program took place in maritime pine (Pinus pinaster Ait.) stands in Portugal to increase the understanding of surface fire behaviour under mild weather. The spread rate and flame geometry of the forward and backward sections of a line-ignited fire front were measured in 94 plots 10–15 m wide. Measured head fire rate of spread, flame length and Byram’s fire intensity varied respectively in the intervals of 0.3–13.9 m min–1, 0.1–4.2 m and 30–3527 kW m–1. Fire behaviour was modelled through an empirical approach. Rate of forward fire spread was described as a function of surface wind speed, terrain slope, moisture content of fine dead surface fuel, and fuel height, while back fire spread rate was correlated with fuel moisture content and cover of understorey vegetation. Flame dimensions were related to Byram’s fire intensity but relationships with rate of spread and fine dead surface fuel load and moisture are preferred, particularly for the head fire. The equations are expected to be more reliable when wind speed and slope are less than 8 km h–1 and 15°, and when fuel moisture content is higher than 12%. The results offer a quantitative basis for prescribed fire management.

A two-dimensional model of fire spread across a fuel bed including wind combined with slope conditions

2002 ◽  
Vol 11 (1) ◽  
pp. 53 ◽  
Author(s):  
Frédéric Morandini ◽  
Paul A. Santoni ◽  
Jacques H. Balbi ◽  
João M. Ventura ◽  
José M. Mendes-Lopes
Keyword(s):  
Fire Management ◽  
Wildland Fire ◽  
Threshold Value ◽  
Fire Spread ◽  
Management Tool ◽  
Fire Behaviour ◽  
Dimensional Model ◽  
Proposed Model ◽  
Spread Model ◽  
Fire Spread Model

In a previous work (Santoni et al., Int. J. Wildland Fire, 2000, 9(4), 285–292), we proposed a twodimensional fire spread model including slope effects as another step towards our aim to elaborate a fire management tool. In the present study, we improve the model to include both wind conditions and wind combined with slope conditions. For this purpose the effect of wind and slope are considered similar, in the sense that they both force the flames to lean forward. However, this analogy remains acceptable only when flame tilt is below a threshold value. Simulation results are compared to experimental data under wind and no-slope conditions. The proposed model is able to describe the fire behaviour. Predictions of the model for wind and slope conditions are then considered and comparisons with observations are also provided.

Assessing the probability of sustained flaming in masticated fuel beds

2015 ◽  
Vol 45 (1) ◽  
pp. 68-77 ◽  
Author(s):  
T.J. Schiks ◽  
B.M. Wotton
Keyword(s):  
Wind Speed ◽  
Moisture Content ◽  
Fire Behavior ◽  
Fire Risk ◽  
Field Testing ◽  
Behavior Modeling ◽  
Fuel Moisture ◽  
Fire Behaviour ◽  
Fire Weather Index ◽  
Fuel Moisture Content

Mechanical mastication is increasingly used as a fuel management treatment to reduce fire risk at the wildland–urban interface, although ignition and fire behaviour in these novel fuel beds are poorly understood. We investigated the influence of observed fuel moisture content, wind speed, and firebrand size on the probability of sustained flaming of masticated fuel beds under both laboratory and field settings. Logistic regression techniques were applied to assess the probability of sustained flaming in both datasets. Models for the field were also developed using estimated moisture from three sets of weather-based models: (i) the hourly Fine Fuel Moisture Code (FFMC) from the Canadian Forest Fire Weather Index System, (ii) the National Fire Danger Rating System (NFDRS) moisture estimates for 1 h and 10 h fuels, and (iii) a masticated surface fuel moisture model (MAST). In both laboratory and field testing, the likelihood of a successful ignition increased with decreasing moisture content and increasing wind speed; the effect of firebrand size was only apparent in laboratory testing. The FFMC, NFDRS, and MAST predictions had somewhat reduced discriminative power relative to direct moisture in predicting the probability of sustained flaming based on our field observations. Our results speak to the disparity between the fire behaviour modeling that occurs in the laboratory and the fire behavior modeling that occurs in the field, as the methodology permitted comparison of predictions from sustained flaming models that were developed for one experimental setting and applied to the other.

Examination of the wind speed limit function in the Rothermel surface fire spread model

2013 ◽  
Vol 22 (7) ◽  
pp. 959 ◽  
Author(s):  
Patricia L. Andrews ◽  
Miguel G. Cruz ◽  
Richard C. Rothermel
Keyword(s):  
Wind Speed ◽  
Wildland Fire ◽  
Fire Spread ◽  
Speed Limit ◽  
Limit Function ◽  
Surface Fire ◽  
Rate Of Spread ◽  
Fire Modelling ◽  
Spread Model ◽  
Fire Spread Model

The Rothermel surface fire spread model includes a wind speed limit, above which predicted rate of spread is constant. Complete derivation of the wind limit as a function of reaction intensity is given, along with an alternate result based on a changed assumption. Evidence indicates that both the original and the revised wind limits are too restrictive. Wind limit is based in part on data collected on the 7 February 1967 Tasmanian grassland fires. A reanalysis of the data indicates that these fires might not have been spreading in fully cured continuous grasslands, as assumed. In addition, more recent grassfire data do not support the wind speed limit. The authors recommend that, in place of the current wind limit, rate of spread be limited to effective midflame wind speed. The Rothermel model is the foundation of many wildland fire modelling systems. Imposition of the wind limit can significantly affect results and potentially influence fire and fuel management decisions.

Sign in / Sign up

Export Citation Format

Share Document