Limitations on the accuracy of model predictions of wildland fire behaviour: A state-of-the-knowledge overview

AlexanderMartin, E; Cruz, Miguel G.

doi:10.5558/tfc2013-067

Cited by 67 publications

(56 citation statements)

References 81 publications

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“…This is due to a wide range of factors such as wind and fuel variability, dynamic interactions between fire and its surrounding environment, long-range spotting and simultaneous ignitions (Alexander and Cruz, 2013b;Cruz and Alexander, 2013;Hilton et al, 2015). Additionally, computational constraints and poorly understood small-scale processes (Beven, 2002) increase the difficulty of accurately predicting fire spread.…”

Section: Introductionmentioning

confidence: 99%

“…Additionally, computational constraints and poorly understood small-scale processes (Beven, 2002) increase the difficulty of accurately predicting fire spread. Although much progress has been made in understanding and modeling the behavior of wildland fires, our ability to produce accurate predictions has evolved very little, mainly due to the spatial and temporal variability of the phenomenon, but also due to the lack of systematic methods for model validation (Alexander and Cruz, 2013a;Alexander and Cruz, 2013b;Salvador et al, 2001).…”

Section: Introductionmentioning

confidence: 99%

“…Modeling fire behavior is intrinsically uncertain due to: i) model applicability, scope and inherent limitations; ii) limitations of current scientific knowledge; iii) inherent accuracy of model structure; iv) parametric uncertainty; v) natural variability; vi) input data reliability; and vii) skill and knowledge of the user (Albini, 1976;Alexander and Cruz, 2013b;Bachmann and Allgöwer, 2002;Beven and Binley, 1992;Cruz, 2010;Liu et al, 2015;Thompson and Calkin, 2011). Under certain conditions, input data reliability can be the dominant source of error in fire spread predictions (Alexander and Cruz, 2013b).…”

Section: Introductionmentioning

confidence: 99%

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Deciphering the impact of uncertainty on the accuracy of large wildfire spread simulations

Benali

Ervilha

Sá

et al. 2016

Science of The Total Environment

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Predicting wildfire spread is a challenging task fraught with uncertainties. 'Perfect' predictions are unfeasible since uncertainties will always be present. Improving fire spread predictions is important to reduce its negative environmental impacts. Here, we propose to understand, characterize, and quantify the impact of uncertainty in the accuracy of fire spread predictions for very large wildfires. We frame this work from the perspective of the major problems commonly faced by fire model users, namely the necessity of accounting for uncertainty in input data to produce reliable and useful fire spread predictions. Uncertainty in input variables was propagated throughout the modeling framework and its impact was evaluated by estimating the spatial discrepancy between simulated and satellite-observed fire progression data, for eight very large wildfires in Portugal. Results showed that uncertainties in wind speed and direction, fuel model assignment and typology, location and timing of ignitions, had a major impact on prediction accuracy. We argue that uncertainties in these variables should be integrated in future fire spread simulation approaches, and provide the necessary data for any fire model user to do so.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Deciphering the impact of uncertainty on the accuracy of large wildfire spread simulations

Benali

Ervilha

Sá

et al. 2016

Science of The Total Environment

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“…The most frequently used wildfire simulation systems in the USA to predict burn severity are based on semi-empirical models of surface fire spread, crown fire initiation, propagation, and spread [89][90][91][92]. As with any application of models, wildfire simulation system outputs are a probabilistic representation of a very complex phenomena which are subject to sources of errors not limited to input data, applicability of use, and model accuracy [93][94][95]. These sources of error can lead to both under-and overprediction of potential fire behavior, and therefore spread, impacting burn probability outputs.…”

Section: Predictive Vulnerability Assessment Predicting Exposure Thromentioning

confidence: 99%

Assessing Landscape Vulnerability to Wildfire in the USA

2016

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Wildfire is an ever present, natural process shaping landscapes. Having the ability to accurately measure and predict wildfire occurrence and impacts to ecosystem goods and services, both retrospectively and prospectively, is critical for adaptive management of landscapes. Landscape vulnerability is a concept widely utilized in the ecosystem management literature that has not been explicitly defined, particularly with regard to wildfire. Vulnerability more broadly is defined by three primary components: exposure to the stressor, sensitivity to a range of stressor variability, and resilience following exposure. In this synthesis, we define vulnerability in the context of wildfire. We first identify the components of a guiding framework for a vulnerability assessment with respect to wildfire. We then address retrospective assessments of wildfire vulnerability and the data that have been developed and utilized to complete these assessments. Finally, we review the modeling efforts that allow for predictive and probabilistic assessment of future vulnerability. Throughout the synthesis, we highlight gaps in the research, data availability, and models used to complete vulnerability assessments.

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“…Fire behaviour by nature is none-linear making it difficult to make a valid quantitative statement regarding the relationship between input data accuracy and output accuracy (Alexander and Cruz 2013). Further input of fuels in models use specific formats requiring details such as locations and dimensions of individual trees, spatial distribution of understory fuels surface area and moisture content, making data collection difficult and time consuming (Pimont et al 2016).…”

Section: Discussionmentioning

confidence: 99%

Changing Fire Regimes in Tropical and Subtropical Australia

Stewart¹

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The focus of the study is to investigate regional and local past, present and future changes in fire regimes of tropical and subtropical Queensland and shifts in vegetation composition and structure. Fire has been shown to be a significant driver of ecosystem evolution, composition and distribution through its impact on biota. Within Australia fire has long played a role in shaping the landscape, with increased fire frequency, associated with heightened aridity, over the last five million years promoting the expansion of fire adapted sclerophyll vegetation across the continent. Evidence of anthropogenic fires date back to approximately 50 ka (thousand years ago) with the advent of Aboriginal occupation and fire-stick practices, however with the arrival of Europeans there was a decline in fire frequencies, related to fire exclusion that observes an increase in fire intensity and severity.A review of the introduction of tropical African perennial grasses to improve grazing in tropical and semi-arid regions of northern Australia was also undertaken. This introduction has resulted in some exotic grass species such as Gamba grass (Andropogon gayanus), Mission grass (Cenchrus polystachios syn. Pennisetum polystachion) and Guinea grass (Megathyrsus maximus syn. Panicum maximum Jacq. var. trichoglume) becoming invasive pests. Invasion by these exotic grasses has serious implications for ecosystem function, altering fire regime dynamics through increasing the distribution and abundance of fine fuels. With increased fine fuels there is a serious danger that there will be an increase in fire frequency and intensity resulting in higher severity burns and higher vegetation mortality, with possible local species extinctions and habitat modification or change.Macro charcoal and pollen records were used from Fraser Island, subtropical eastern Australia to identify fire and vegetation histories, which show substantial temporal and spatial changes in past fire frequencies and vegetation composition for the last 24,000 years. Pollen records show pyrophobic rainforest taxa dominated and then declined while pyrogenic sclerophyll arboreal taxa increased correlating with an increase in fire frequencies, and a dryer climate. This was followed by a dramatic increase in Restionaceae values at the beginning of the Holocene (~10,000 years ago) that dropped off as a marked peak in mangroves, primarily the Rhizophoraceae and Melaleuca iii occurred, possibly linked with sea level rise approximately 6000 to 5000 years ago, which was also associated with lower fire frequencies. Restionaceae then recovered from around 2 ka to the European settlement period, when a dramatic change in fire frequency occurred linked to fire suppression and was followed by vegetation thickening (i.e. increase in arboreal taxa) in the mid to late 20 th century. Identifying past and present fire regimes and vegetation composition are important for fire modelling as this provides possible scenario based probabilities for changes in fire frequency and intensity....

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Limitations on the accuracy of model predictions of wildland fire behaviour: A state-of-the-knowledge overview

Cited by 67 publications

References 81 publications

Deciphering the impact of uncertainty on the accuracy of large wildfire spread simulations

Deciphering the impact of uncertainty on the accuracy of large wildfire spread simulations

Assessing Landscape Vulnerability to Wildfire in the USA

Changing Fire Regimes in Tropical and Subtropical Australia

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