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.

scholarly journals Coupled atmosphere-wildland fire modeling with WRF-Fire version 3.3

2011 ◽  
Vol 4 (1) ◽  
pp. 497-545 ◽  
Author(s):  
J. Mandel ◽  
J. D. Beezley ◽  
A. K. Kochanski
Keyword(s):  
Level Set Method ◽  
Level Set ◽  
Wildland Fire ◽  
Surface Wind ◽  
Numerical Algorithms ◽  
Fire Spread ◽  
Weather Research And Forecasting ◽  
Time Step ◽  
Spread Model ◽  
Fire Spread Model

Abstract. We describe the physical model, numerical algorithms, and software structure of WRF-Fire. WRF-Fire consists of a fire-spread model, implemented by the level-set method, coupled with the Weather Research and Forecasting model. In every time step, the fire model inputs the surface wind, which drives the fire, and outputs the heat flux from the fire into the atmosphere, which in turn influences the atmosphere. The level-set method allows submesh representation of the burning region and flexible implementation of various kinds of ignition. WRF-Fire is distributed as a part of WRF and it uses the WRF parallel infrastructure for parallel computing.

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.

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.

scholarly journals Fitting a stochastic fire spread model to data

2019 ◽  
Vol 5 (1) ◽  
pp. 57-66
Author(s):  
X. Joey Wang ◽  
John R. J. Thompson ◽  
W. John Braun ◽  
Douglas G. Woolford
Keyword(s):  
Wildland Fire ◽  
Data Extraction ◽  
Fire Risk ◽  
Fire Spread ◽  
Small Scale ◽  
Intermediate Step ◽  
Spread Model ◽  
Recorded Data ◽  
Model Variability ◽  
Fire Spread Model

Abstract. As the climate changes, it is important to understand the effects on the environment. Changes in wildland fire risk are an important example. A stochastic lattice-based wildland fire spread model was proposed by Boychuk et al. (2007), followed by a more realistic variant (Braun and Woolford, 2013). Fitting such a model to data from remotely sensed images could be used to provide accurate fire spread risk maps, but an intermediate step on the path to that goal is to verify the model on data collected under experimentally controlled conditions. This paper presents the analysis of data from small-scale experimental fires that were digitally video-recorded. Data extraction and processing methods and issues are discussed, along with an estimation methodology that uses differential equations for the moments of certain statistics that can be derived from a sequential set of photographs from a fire. The interaction between model variability and raster resolution is discussed and an argument for partial validation of the model is provided. Visual diagnostics show that the model is doing well at capturing the distribution of key statistics recorded during observed fires.

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.

On the need for a theory of wildland fire spread

2013 ◽  
Vol 22 (1) ◽  
pp. 25 ◽  
Author(s):  
Mark A. Finney ◽  
Jack D. Cohen ◽  
Sara S. McAllister ◽  
W. Matt Jolly
Keyword(s):  
Heat Transfer ◽  
Fire Management ◽  
Wildland Fire ◽  
Fire Spread ◽  
Fire Behaviour ◽  
Fire Models ◽  
Model Reliability ◽  
The Difference ◽  
Combustion Processes ◽  
Common Understanding

We explore the basis of understanding wildland fire behaviour with the intention of stimulating curiosity and promoting fundamental investigations of fire spread problems that persist even in the presence of tremendous modelling advances. Internationally, many fire models have been developed based on a variety of assumptions and expressions for the fundamental heat transfer and combustion processes. The diversity of these assumptions raises the question as to whether the absence of a sound and coherent fire spread theory is partly responsible. We explore the thesis that, without a common understanding of what processes occur and how they occur, model reliability cannot be confirmed. A theory is defined as a collection of logically connected hypotheses that provide a coherent explanation of some aspect of reality. Models implement theory for a particular purpose, including hypotheses of phenomena and practical uses, such as prediction. We emphasise the need for theory and demonstrate the difference between theory and modelling. Increasingly sophisticated fire management requires modelling capabilities well beyond the fundamental basis of current models. These capabilities can only be met with fundamental fire behaviour research. Furthermore, possibilities as well as limitations for modelling may not be known or knowable without first having the theory.

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.

Modeling and Estimating Post-Earthquake Fire Spread

2012 ◽  
Vol 28 (2) ◽  
pp. 795-810 ◽  
Author(s):  
Geoff Thomas ◽  
David Heron ◽  
Jim Cousins ◽  
Mairéad de Róiste
Keyword(s):  
Fire Spread ◽  
Spontaneous Ignition ◽  
Broken Windows ◽  
Conditional Probabilities ◽  
Existing Buildings ◽  
Spread Model ◽  
Building Information ◽  
Piloted Ignition ◽  
Further Development ◽  
Fire Spread Model

This paper describes the development of a GIS-based dynamic fire-spread model, with seven distinct modes of fire spread: direct contact, spontaneous ignition of claddings, piloted ignition of claddings, spontaneous ignition through windows, piloted ignition through broken windows, fire spread via non-fire-rated roofs and branding. All except the first two modes include in-built probabilities, but these can be selected individually and given user-defined values. Fire spread modes can be added to the model or altered to suit available building information. Critical details of buildings are obtained from an existing-buildings database, street surveys, or deduced using conditional probabilities from available data. Results show that comparison with actual fires is reasonable. The model could be extended with further development for use as a real time firefighting tool.

Mantras of wildland fire behaviour modelling: facts or fallacies?

2017 ◽  
Vol 26 (11) ◽  
pp. 973 ◽  
Author(s):  
Miguel G. Cruz ◽  
Martin E. Alexander ◽  
Andrew L. Sullivan
Keyword(s):  
Wildland Fire ◽  
Original Data ◽  
Fire Spread ◽  
Empirical Models ◽  
Physical Models ◽  
Fire Behaviour ◽  
Fire Science ◽  
Current State ◽  
Valid Data ◽  
Insight Into

Generalised statements about the state of fire science are often used to provide a simplified context for new work. This paper explores the validity of five frequently repeated statements regarding empirical and physical models for predicting wildland fire behaviour. For empirical models, these include statements that they: (1) work well over the range of their original data; and (2) are not appropriate for and should not be applied to conditions outside the range of the original data. For physical models, common statements include that they: (3) provide insight into the mechanisms that drive wildland fire spread and other aspects of fire behaviour; (4) give a better understanding of how fuel treatments modify fire behaviour; and (5) can be used to derive simplified models to predict fire behaviour operationally. The first statement was judged to be true only under certain conditions, whereas the second was shown not to be necessarily correct if valid data and appropriate modelling forms are used. Statements three through five, although theoretically valid, were considered not to be true given the current state of knowledge regarding fundamental wildland fire processes.

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