Uncertainty associated with model predictions of surface and crown fire rates of spread

Cruz, Miguel G.; Alexander, Martin E.

doi:10.1016/j.envsoft.2013.04.004

Cited by 159 publications

(136 citation statements)

References 67 publications

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“…Unfortunately, there is no generally agree upon standard that currently exists within the wildland fire community in regard to what constitutes as an acceptable error in a prediction from a fire behavior model. However, Cruz and Alexander (2013) established that a ±35% mean absolute percent error should constitute a reasonable and conservative standard for fire spread rate model performance. Since all the simulations in this study presented MAPE values above 35%, there is a need to evaluate other models or build new ones in order to better predict fire behavior in Brazilian commercial eucalypt plantations.…”

Section: Discussionmentioning

confidence: 99%

“…Under-prediction bias by the Rothermel (1972) surface fi re spread model was already reported by several other authors such as McCaw (1995) in Australian shrubland; Grabner et al (1999) in oak savannas in the USA; Stephens et al (2008) and Weise et al (2016) in chaparral fuel beds of California; and others. Cruz and Alexander (2013) in a review over 49 different publications that evaluated the effi ciency of several different rate of spread predicting models (including the Rothermel model), determined that only 3% of the predictions (35 out of 1278) were considered to be exact. According the same authors, under-prediction bias was prevalent in 75% of the models and more than half of them had mean absolute percent error between 51 and 75%.…”

Section: Discussionmentioning

confidence: 99%

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Fire Behavior Predicting Models Efficiency in Brazilian Commercial Eucalypt Plantations

White

Ribeiro

et al. 2016

CERNE

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Knowing how a wildfire will behave is extremely important in order to assist in fire suppression and prevention operations. Since the 1940's mathematical models to estimate how the fire will behave have been developed worldwide, however, none of them, until now, had their efficiency tested in Brazilian commercial eucalypt plantations nor in other vegetation types in the country. This study aims to verify the accuracy of the Rothermel (1972) fire spread model, the Byram (1959) flame length model, and the fire spread and length equations derived from the McArthur (1962) control burn meters. To meet these objectives, 105 experimental laboratory fires were done and their results compared with the predicted values from the models tested. The Rothermel and Byram models predicted better than McArthur's, nevertheless, all of them underestimated the fire behavior aspects evaluated and were statistically different from the experimental data. EFICIÊNCIA DE MODELOS DE PREVISÃO DO COMPORTAMENTO DO FOGO EM PLANTAÇÕES COMERCIAIS DE EUCALIPTO NO BRASILRESUMO: Saber como o fogo irá se comportar durante um incêndio florestal é de fundamental importância a fim de aprimorar e desenvolver atividades de prevenção e combate. Desde a década de 1940, modelos matemáticos para estimar o comportamento do fogo têm sido desenvolvidos no mundo inteiro, entretanto, nenhum deles, até hoje, teve sua eficiência avaliada em plantações comerciais de eucalipto no Brasil nem, tampouco, em outras vegetações encontradas no país. Este estudo tem por objetivo verificar a eficiência do modelo de propagação do fogo superficial de Rothermel (1972), do modelo de comprimento das chamas de Byram (1959) e das equações de velocidade de propagação e comprimento das chamas baseadas nas tabelas de McArthur (1962

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Fire Behavior Predicting Models Efficiency in Brazilian Commercial Eucalypt Plantations

White

Ribeiro

et al. 2016

CERNE

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“…Models belonging to the first class are typically 1D models that are then extended to 2D landscape propagation through proper approaches. These models currently suffer from some limitations due to low accuracy while forecasting fire propagation [26]. In contrast, physical models are accurate, but they entail large computational costs and cannot be used as emergency management tools [27].…”

Section: Introductionmentioning

confidence: 99%

Entropy Generation Analysis of Wildfire Propagation

Guelpa

Verda

2017

Entropy

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Entropy generation is commonly applied to describe the evolution of irreversible processes, such as heat transfer and turbulence. These are both dominating phenomena in fire propagation. In this paper, entropy generation analysis is applied to a grassland fire event, with the aim of finding possible links between entropy generation and propagation directions. The ultimate goal of such analysis consists in helping one to overcome possible limitations of the models usually applied to the prediction of wildfire propagation. These models are based on the application of the superimposition of the effects due to wind and slope, which has proven to fail in various cases. The analysis presented here shows that entropy generation allows a detailed analysis of the landscape propagation of a fire and can be thus applied to its quantitative description.

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“…However, simulation methods necessarily use a large number of assumptions, the most important of which is that the underlying fire behaviour model developed under a narrow range of controlled conditions is applicable to wildfires. Studies which compare predictions of fire shapes, sizes and rates of spread produced by simulators against actual fires generally find only moderate accuracy (Fujioka 2002;Duff et al 2012;Duff et al 2013;Metcalf and Price 2013;Filippi et al 2014), partly because the fire-spread models used for simulation tend to under-predict rate of spread under severe weather conditions (McCaw 2008;Cruz and Alexander 2013).…”

Section: Introductionmentioning

confidence: 99%

An empirical wildfire risk analysis: the probability of a fire spreading to the urban interface in Sydney, Australia

Price¹,

Borah²,

Bradstock³

et al. 2015

Int. J. Wildland Fire

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We present a method and case study to predict and map the likelihood of wildfires spreading to the urban interface through statistical analysis of past fire patterns using 15 000 lines from 677 fires with known ignition points and date and random potential end points on the urban interface of Sydney, Australia. A binomial regression approach was used to model whether the fire burnt to the end point of the lines as a function of measures of distance, fuel, weather and barriers to spread. Fire weather had the strongest influence on burning likelihood followed by the percentage of the line that was forested, distance and time since last fire. Fuel treatments would substantially reduce risk from fires starting 1-4 km away from the interface. The model captured 90% of variation in burning with 98% predictive accuracy on test data and was not affected by spatial autocorrelation. We apply the method to map fire risk in Sydney and discuss how the method could be expanded to estimate total risk (from ignition to impact on assets). The method has considerable promise for predicting risk, especially as a complement to simulation methods. AbstractWe present a method and case study to predict and map the likelihood of wildfires spreading to the urban interface through statistical analysis of past fire patterns using 15,000 lines from 677 fires with known ignition points and date and random potential end points on the urban interface of Sydney, Australia. A binomial regression approach was used to model whether the fire burnt to the end point of the lines as a function of measures of distance, fuel, weather and barriers to spread. Fire weather had the strongest influence on burning likelihood followed by the percentage of the line that was forested, distance and time since last fire. Fuel treatments would substantially reduce risk from fires starting 1 -4 km away from the interface. The model captured 90% of variation in burning with 98% predictive accuracy on test data and was not affected by spatial autocorrelation. We apply the method to map fire risk in Sydney and discuss how the method could be expanded to estimate total risk (from ignition to impact on assets). The method has considerable promise for predicting risk, especially as a compliment to simulation methods.

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Uncertainty associated with model predictions of surface and crown fire rates of spread

Cited by 159 publications

References 67 publications

Fire Behavior Predicting Models Efficiency in Brazilian Commercial Eucalypt Plantations

Fire Behavior Predicting Models Efficiency in Brazilian Commercial Eucalypt Plantations

Entropy Generation Analysis of Wildfire Propagation

An empirical wildfire risk analysis: the probability of a fire spreading to the urban interface in Sydney, Australia

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