Fuel moisture content threshold leading to fire extinction under marginal conditions

Awad, Carmen; Morvan, Dominique; Rossi, Jean-Louis; Marcelli, Thierry; Chatelon, François Joseph; Morandini, Frédéric; Balbi, Jacques-Henri

doi:10.1016/j.firesaf.2020.103226

Cited by 12 publications

(16 citation statements)

References 40 publications

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“…Anderson and Rothermel (1965) and Burrows (1999) found the effect of fuel moisture to vary with wind speed, a detail we were unable to incorporate in our model due to the lack of data describing this behaviour. Similarly, we assume a nominal dead fuel moisture of extinction of 24% in the current model, but it is accepted that this value will vary with fuel structure, namely fuel bed porosity, and wind speed (Wilson 1985;Awad et al 2020). With model fitting based on data in the lower range of the fuel moisture spectrum (i.e.…”

Section: Fuel Moisture Content Effectmentioning

confidence: 99%

An empirical-based model for predicting the forward spread rate of wildfires in eucalypt forests

Cruz

Cheney

Gould

et al. 2021

Int. J. Wildland Fire

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Reliable and accurate models of the speed of a wildfire front as it moves across the landscape are essential for the timely prediction of its propagation, to devise suitable suppression strategies and enable effective public warnings. We used data from outdoor experimental fires and wildfires to derive an empirical model for the rate of fire spread in eucalypt forests applicable to a broad range of wildfire behaviour. The modelling analysis used logistic and non-linear regression analysis coupled with assumed functional forms for the effect of different environmental variables. The developed model incorporates the effect of wind speed, fine dead fuel moisture, understorey fuel structure, long-term landscape dryness and slope steepness. Model evaluation against the data used for its development yield mean absolute percentage errors between 35 and 46%. Evaluation against an independent wildfire dataset found mean percentage errors of 81 and 84% for two landscape dryness conditions. For these wildfires, the mean error was found to decrease with increasing rates of spread, with this error dropping below 30% when observed rates of spread were greater than 2 km h À1 . The modular structure of the modelling analysis enables subsequent improvement of some of its components, such as the dead fuel moisture content or long-term dryness effects, without compromising its consistency or function.

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Section: Fuel Moisture Content Effectmentioning

confidence: 99%

An empirical-based model for predicting the forward spread rate of wildfires in eucalypt forests

Cruz

Cheney

Gould

et al. 2021

Int. J. Wildland Fire

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“…In the process, the study describes the effects of wind speed and the FMC on fire behaviour (fire regime and flame parameters). These effects have already been addressed in the literature using FireStar2D model for calm and moderate wind speeds [18,32]. These studies and many others [13,[33][34][35][36] have highlighted the predictive potential of FireStar2D model in addressing the effects of the wind conditions, the fuel properties (FMC and fuel load), and other parameters (slope, fuel break, wind unsteadiness …) on fire behaviour.…”

Section: Introductionmentioning

confidence: 88%

“…This model can be applied for all type of vegetation, under no slope and weak wind conditions. The effectiveness of Balbi's analytical model in predicting the FMC threshold has been tested [32] using experimental and numerical data. In this study, laboratory fire experiments were carried out in excelsior fuel beds, while the numerical simulations were conducted using the complete physical fire model "FireStar2D" [33][34][35] for a homogeneous grassland and heterogeneous (shrub) fuel bed.…”

Section: Introductionmentioning

confidence: 99%

“…Following on from the previous study [32], the present study is motivated by the limitation of Balbi's model (and the absence of other operational models) in predicting the FMC threshold under active wind conditions. Consequently, the aim of this study is to numerically determine the required fuel properties (fuel load and moisture content) for a successful prescribed burning on leveled ground.…”

Section: Introductionmentioning

confidence: 99%

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Numerical study of the moisture content threshold under prescribed burning conditions

Awad

Frangieh

Marcelli

et al. 2021

Fire Safety Journal

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“…For instance, several studies have showed that the rate of spread decreases with the increase of fuel moisture content (Morvan 2013;Awad et al 2021). Others showed that the rate of spread increases with the fuel bed height, and the fuel load (Morvan and Dupuy 2004;Rossa and Fernandes 2018;Awad et al 2020). However, the effect of the vegetation bulk density and packing ratio on fire behavior has always been an examination subject due to the different point of views of its impact listed in the literature (Morvan and Dupuy 2004;Rossa and Fernandes 2018;Awad et al 2020).…”

Section: Introductionmentioning

confidence: 99%

Impact of the bulk density on fire spread through a homogenous vegetation layer

Awad¹,

Fayad²,

Frangieh³

et al. 2022

Advances in Forest Fire Research 2022

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The bulk density as definition represents the ratio between the packing ratio and the density of the vegetation. Therefore, it is directly related to the fuel load, the height and to the porosity of the vegetation. In fact, the bulk density plays an important role in fire propagation and behavior. Due to its dependence on the fuel porosity, the bulk density influences heat transfers inside the fuel bed, so, it can affect directly the rate of spread. Or, the bulk density influences also the fire intensity and flame characteristics (residence time, height and depth) due to its dependence of the fuel load and fuel bed height. However, despite the important influence of the bulk density on fire propagation, the literature does not clarify its impact on fire behavior, different points of view can be exanimated. So, the aim of this study is to investigate the role played by the bulk density upon both propagation parameters and heat transfer of a surface fire through a homogeneous vegetation layer. Investigations were conducted numerically using â€œFireStar2Dâ€, a complete physical model based on multiphase formulation. Also, experimentally, tests were constructed at the university of Corsica at laboratory scale under no wind and no slope condition. In order to study the elementary effect of the bulk density on fire behavior, three different cases were evaluated: (a) variable fuel load with a constant bulk density, (b) variable fuel load and variable bulk density, (c) variable bulk density with a constant fuel load. Case (a) was only studied numerically, the obtained results are in agreement with the literature: the rate of spread increases with the fuel load until a specific value where the ROS becomes independent of it. Case (b) was evaluated numerically and experimentally using a fix fuel bed height. The numerical and the experimental results showed that the ROS is barely affected by both fuel load and bulk density. Finally, the results of the last case, with a constant fuel load, showed numerically the same tendency proposed by Rothermel: the rate of spread reaches a maximum value at an optimal packing ratio that depends of the surface-volume ratio of the vegetation. Or, experimentally the ROS decreases with the increase of the bulk density. Different variables such as the optical thickness, the fire intensity, the residence time, the radiation and convection heat fluxes have been analyzed.

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Fuel moisture content threshold leading to fire extinction under marginal conditions

Cited by 12 publications

References 40 publications

An empirical-based model for predicting the forward spread rate of wildfires in eucalypt forests

An empirical-based model for predicting the forward spread rate of wildfires in eucalypt forests

Numerical study of the moisture content threshold under prescribed burning conditions

Impact of the bulk density on fire spread through a homogenous vegetation layer

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