scholarly journals Shrubland fire behaviour modelling with microplot data

2000 ◽  
Vol 30 (6) ◽  
pp. 889-899 ◽  
Author(s):  
Paulo M Fernandes ◽  
Wendy R Catchpole ◽  
Francisco C Rego
Keyword(s):  
Wind Speed ◽  
Field Studies ◽  
Fire Spread ◽  
Flame Length ◽  
Fire Behaviour ◽  
Fire Propagation ◽  
Average Value ◽  
Behaviour Modelling ◽  
Rate Of Spread ◽  
Behaviour Models

Fire behaviour modelling has been based primarily on experiments involving the measurement of a certain number of fires, where each variable is represented by an average value per fire. The main objective of this study was to examine if data collected from a microplot sampling design could be used to derive meaningful fire behaviour models. Three burns were conducted in low shrubland of Erica umbellata Loefl., and Chamaespartium tridentatum (L.) P. Gibbs in northeastern Portugal. Wind speed and aerial dead fuel moisture content varied from 5 to 27 km/h and from 14 to 21%, respectively. Rate of spread and flame length ranged from 0.3 to 14.1 m/min and from 0.2 to 3.1 m, respectively. Rate of fire spread could be described effectively in terms of an empirical model with wind speed and fuel height as independent variables. The coefficients that describe the effects of wind speed and fuel height on fire propagation were consistent with published values for similar fuel types. Flame length was strongly related to Byram's fireline intensity. Microplot sampling is not free from methodological problems, which are discussed, but can be effectively used in field studies of fire behaviour.

Got to burn to learn: the effect of fuel load on grassland fire behaviour and its management implications

2018 ◽  
Vol 27 (11) ◽  
pp. 727 ◽  
Author(s):  
Miguel G. Cruz ◽  
Andrew L. Sullivan ◽  
James S. Gould ◽  
Richard J. Hurley ◽  
Matt P. Plucinski
Keyword(s):  
Fire Spread ◽  
Flame Height ◽  
Fuel Load ◽  
Limiting Factor ◽  
Fire Behaviour ◽  
Load Effect ◽  
Fire Propagation ◽  
Rate Of Spread ◽  
Eastern Australia ◽  
Modelling Assumptions

The effect of grass fuel load on fire behaviour and fire danger has been a contentious issue for some time in Australia. Existing operational models have placed different emphases on the effect of fuel load on model outputs, which has created uncertainty in the operational assessment of fire potential and has led to end-user and public distrust of model outcomes. A field-based experimental burning program was conducted to quantify the effect of fuel load on headfire rate of spread and other fire behaviour characteristics in grasslands. A total of 58 experimental fires conducted at six sites across eastern Australia were analysed. We found an inverse relationship between fuel load and the rate of spread in grasslands, which is contrary to current, untested, modelling assumptions. This result is valid for grasslands where fuel load is not a limiting factor for fire propagation. We discuss the reasons for this effect and model it to produce a fuel load effect function that can be applied to operational grassfire spread models used in Australia. We also analyse the effect of fuel load on flame characteristics and develop a model for flame height as a function of rate of fire spread and fuel load.

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.

Modelling the dynamic behaviour of junction fires with a coupled atmosphere–fire model

2017 ◽  
Vol 26 (4) ◽  
pp. 331 ◽  
Author(s):  
C. M. Thomas ◽  
J. J. Sharples ◽  
J. P. Evans
Keyword(s):  
Fire Spread ◽  
Community Safety ◽  
Fire Behaviour ◽  
Ambient Conditions ◽  
Fire Model ◽  
Fire Propagation ◽  
Dynamic Propagation ◽  
Rate Of Spread ◽  
Steady Rate ◽  
In Fire

Dynamic fire behaviour involves rapid changes in fire behaviour without significant changes in ambient conditions, and can compromise firefighter and community safety. Dynamic fire behaviour cannot be captured using spatial implementations of empirical fire-spread models predicated on the assumption of an equilibrium, or quasi-steady, rate of spread. In this study, a coupled atmosphere–fire model is used to model the dynamic propagation of junction fires, i.e. when two firelines merge at an oblique angle. This involves very rapid initial rates of spread, even with no ambient wind. The simulations are in good qualitative agreement with a previous experimental study, and indicate that pyro-convective interaction between the fire and the atmosphere is the key mechanism driving the dynamic fire propagation. An examination of the vertical vorticity in the simulations, and its relationship to the fireline geometry, gives insight into this mechanism. Junction fires have been modelled previously using curvature-dependent rates of spread. In this study, however, although fireline geometry clearly influences rate of spread, no relationship is found between local fireline curvature and the simulated instantaneous local rate of spread. It is possible that such a relationship may be found at larger scales.

Project Vesta: Fire in Dry Eucalypt Forest

2008 ◽  
Author(s):  
JS Gould ◽  
WL McCaw ◽  
NP Cheney ◽  
PF Ellis ◽  
IK Knight ◽  
...  
Keyword(s):  
Wind Speed ◽  
Fire Spread ◽  
Fire Behaviour ◽  
Understorey Vegetation ◽  
Eucalypt Forest ◽  
Age Related ◽  
Vegetation Structures ◽  
Eucalypt Forests ◽  
The Relationship ◽  
New Algorithms

Project Vesta was a comprehensive research project to investigate the behaviour and spread of high-intensity bushfires in dry eucalypt forests with different fuel ages and understorey vegetation structures. The project was designed to quantify age-related changes in fuel attributes and fire behaviour in dry eucalypt forests typical of southern Australia. The four main scientific aims of Project Vesta were: To quantify the changes in the behaviour of fire in dry eucalypt forest as fuel develops with age (i.e. time since fire); To characterise wind speed profiles in forest with different overstorey and understorey vegetation structure in relation to fire behaviour; To develop new algorithms describing the relationship between fire spread and wind speed, and fire spread and fuel characteristics including load, structure and height; and to develop a National Fire Behaviour Prediction System for dry eucalypt forests. These aims have been addressed through a program of experimental burning and associated studies at two sites in the south-west of Western Australia.

A cellular automata model to link surface fires to firebrand lift-off and dispersal

2013 ◽  
Vol 22 (4) ◽  
pp. 428 ◽  
Author(s):  
Holly A. Perryman ◽  
Christopher J. Dugaw ◽  
J. Morgan Varner ◽  
Diane L. Johnson
Keyword(s):  
Wind Speed ◽  
Wildland Fire ◽  
Fire Spread ◽  
Fuel Moisture ◽  
Fuel Loading ◽  
Cellular Automata Model ◽  
Rate Of Spread ◽  
Lift Off ◽  
Fuel Moisture Content ◽  
Spot Fires

In spite of considerable effort to predict wildland fire behaviour, the effects of firebrand lift-off, the ignition of resulting spot fires and their effects on fire spread, remain poorly understood. We developed a cellular automata model integrating key mathematical models governing current fire spread models with a recently developed model that estimates firebrand landing patterns. Using our model we simulated a wildfire in an idealised Pinus ponderosa ecosystem. Varying values of wind speed, surface fuel loading, surface fuel moisture content and canopy base height, we investigated two scenarios: (i) the probability of a spot fire igniting beyond fuelbreaks of various widths and (ii) how spot fires directly affect the overall surface fire’s rate of spread. Results were averages across 2500 stochastic simulations. In both scenarios, canopy base height and surface fuel loading had a greater influence than wind speed and surface fuel moisture content. The expected rate of spread with spot fires occurring approached a constant value over time, which ranged between 6 and 931% higher than the predicted surface fire rate of spread. Incorporation of the role of spot fires in wildland fire spread should be an important thrust of future decision-support technologies.

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.

Modelling the effects of landscape fuel treatments on fire growth and behaviour in a Mediterranean landscape (eastern Spain)

2007 ◽  
Vol 16 (5) ◽  
pp. 619 ◽  
Author(s):  
Beatriz Duguy ◽  
José Antonio Alloza ◽  
Achim Röder ◽  
Ramón Vallejo ◽  
Francisco Pastor
Keyword(s):  
Spatial Distribution ◽  
Fire Spread ◽  
Fire Growth ◽  
Fire Size ◽  
Fire Propagation ◽  
Area Burned ◽  
Rate Of Spread ◽  
Mediterranean Countries ◽  
Fuel Accumulation ◽  
Fire Characteristics

The number of large fires increased in the 1970s in the Valencia region (eastern Spain), as in most northern Mediterranean countries, owing to the fuel accumulation that affected large areas as a consequence of an intensive land abandonment. The Ayora site (Valencia province) was affected by a large fire in July 1979. We parameterised the fire growth model FARSITE for the 1979 fire conditions using remote sensing-derived fuel cartography. We simulated different fuel scenarios to study the interactions between fuel spatial distribution and fire characteristics (area burned, rate of spread and fireline intensity). We then tested the effectiveness of several firebreak networks on fire spread control. Simulations showed that fire propagation and behaviour were greatly influenced by fuel spatial distribution. The fragmentation of large dense shrubland areas through the introduction of wooded patches strongly reduced fire size, generally slowing fire and limiting fireline intensity. Both the introduction of forest corridors connecting woodlands and the promotion of complex shapes for wooded patches decreased the area burned. Firebreak networks were always very effective in reducing fire size and their effect was enhanced in appropriate fuel-altered scenarios. Most firebreak alternatives, however, did not reduce either rate of fire spread or fireline intensity.

Behaviour and effects of prescribed fire in masticated fuelbeds

2011 ◽  
Vol 20 (8) ◽  
pp. 932 ◽  
Author(s):  
Eric E. Knapp ◽  
J. Morgan Varner ◽  
Matt D. Busse ◽  
Carl N. Skinner ◽  
Carol J. Shestak
Keyword(s):  
Pinus Ponderosa ◽  
Ponderosa Pine ◽  
Tree Mortality ◽  
Fire Hazard ◽  
Weather Conditions ◽  
Flame Length ◽  
Fire Behaviour ◽  
Prescribed Burns ◽  
Rate Of Spread ◽  
Fuel Models

Mechanical mastication converts shrub and small tree fuels into surface fuels, and this method is being widely used as a treatment to reduce fire hazard. The compactness of these fuelbeds is thought to moderate fire behaviour, but whether standard fuel models can accurately predict fire behaviour and effects is poorly understood. Prescribed burns were conducted in young ponderosa pine (Pinus ponderosa Laws.) forests at two sites in northern California where the midstorey layer dominated by shrubs had been masticated. Surface fuels were raked from the base of a subset of trees before burning. Rate of spread and flame length were estimated for both backing and heading fires, soil heating measured with thermocouples and tree fire injury recorded. Standard fuel models often over-predicted rate of spread or under-predicted flame length. Custom models generally provided a better balance between the slow rates of spread and moderate flame lengths observed in prescribed burns. Post-fire tree mortality was most strongly associated with crown scorch and tree size; raking fuels from the base of trees did not improve survival. Under severe fire weather conditions, fire behaviour and effect models as well as observations from wildfires suggest that mastication may be more effective for moderating fire behaviour than reducing residual tree mortality. Treating masticated fuels with prescribed burns could potentially improve the resilience of stands to wildfire.

Exploring fire response to high wind speeds: fire rate of spread, energy release and flame residence time from fires burned in pine needle beds under winds up to 27 ms−1

2020 ◽  
Vol 29 (1) ◽  
pp. 81
Author(s):  
Bret Butler ◽  
Steve Quarles ◽  
Christine Standohar-Alfano ◽  
Murray Morrison ◽  
Daniel Jimenez ◽  
...  
Keyword(s):  
Wind Speed ◽  
Energy Release ◽  
Residence Time ◽  
Wildland Fire ◽  
Ground Level ◽  
Fire Spread ◽  
Convective Heating ◽  
Atmospheric Conditions ◽  
Wind Speeds ◽  
Rate Of Spread

The relationship between wildland fire spread rate and wind has been a topic of study for over a century, but few laboratory studies report measurements in controlled winds exceeding 5ms−1. In this study, measurements of fire rate of spread, flame residence time and energy release are reported for fires burning under controlled atmospheric conditions in shallow beds of pine needles subject to winds ranging from 0 to 27ms−1 (measured 5m above ground level). The data suggested that under constant flow conditions when winds are less than 10ms−1, fire rate of spread increases linearly at a rate of ~3% of the wind speed, which generally agrees with other laboratory-based models. When wind speed exceeds 10ms−1, the fire rate of spread response to wind remains linear but with a much stronger dependence, spreading at a rate of ~13% of the wind speed. Radiative and convective heating correlated directly to wind speed, with radiant heating increasing approximately three-fold as much as convective heating over the range of winds explored. The data suggested that residence time is inversely related to wind speed and appeared to approach a lower limit of ~20s as wind exceeded 15ms−1. Average flame residence time over the range of wind speeds was nominally 26s.

Fire spread in chaparral – a comparison of laboratory data and model predictions in burning live fuels

2016 ◽  
Vol 25 (9) ◽  
pp. 980 ◽  
Author(s):  
David R. Weise ◽  
Eunmo Koo ◽  
Xiangyang Zhou ◽  
Shankar Mahalingam ◽  
Frédéric Morandini ◽  
...  
Keyword(s):  
Laboratory Data ◽  
Slope Angle ◽  
Fire Spread ◽  
Flame Length ◽  
Important Variable ◽  
Rate Of Spread ◽  
Model Predictions ◽  
Physically Based ◽  
Live Fuels ◽  
Physically Based Models

Fire behaviour data from 240 laboratory fires in high-density live chaparral fuel beds were compared with model predictions. Logistic regression was used to develop a model to predict fire spread success in the fuel beds and linear regression was used to predict rate of spread. Predictions from the Rothermel equation and three proposed changes as well as two physically based models were compared with observed spread rates of spread. Flame length–fireline intensity relationships were compared with flame length data. Wind was the most important variable related to spread success. Air temperature, live fuel moisture content, slope angle and fuel bed bulk density were significantly related to spread rate. A flame length–fireline intensity model for Galician shrub fuels was similar to the chaparral data. The Rothermel model failed to predict fire spread in nearly all of the fires that spread using default values. Increasing the moisture of extinction marginally improved its performance. Modifications proposed by Cohen, Wilson and Catchpole also improved predictions. The models successfully predicted fire spread 49 to 69% of the time. Only the physical model predictions fell within a factor of two of actual rates. Mean bias of most models was close to zero. Physically based models generally performed better than empirical models and are recommended for further study.

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