Sensitivity Analysis of a Fire Spread Model in a Chaparral Landscape

Clark, R.; Hope, Allen; Tarantola, Stefano; Gatelli, Debora; Dennison, Philip E.; Moritz, Max A.

doi:10.4996/fireecology.0401001

Cited by 29 publications

(27 citation statements)

References 24 publications

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“…Through realistic estimation of predictive uncertainty one can improve the accuracy of fire spread simulations and promote a better understanding of model capabilities (Beven, 2002), as well as provide information on the variability and reliability of fire behavior predictions that can be used to improve risk management and decision-making (Bachmann and Allgöwer, 2002; Thompson and Calkin, 2011). However, to the best of our knowledge, this topic has merited little research (Bachmann and Allgöwer, 2002;Clark et al, 2008;Salazar, 1985;Salvador et al, 2001), despite the wide use of fire spread models in recent years.…”

Section: Introductionmentioning

confidence: 96%

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: 96%

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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“…Cary et al 2006). Additionally, a global sensitivity analysis applied to HFire in single-event mode found that wind speed was three times as important as the second-place input (1-h dead fuel moisture) for predicting fire size (Clark et al 2008).…”

Section: Discussionmentioning

confidence: 99%

“…HFire model accuracy and sensitivity have been evaluated in single-event mode by comparing observed and predicted fire spread during historical events (Peterson et al 2009) and for simulated landscapes (Clark et al 2008;Peterson et al 2009). HFire has also been utilised previously in a comparison of empirical fire data, modelled fire regimes, and highly optimised tolerance (HOT) as the mechanism for ecosystem structure in fire-prone areas (Moritz et al 2005).…”

Section: Hfirementioning

confidence: 99%

Modelling long-term fire regimes of southern California shrublands

Peterson

Moritz

Morais

et al. 2011

Int. J. Wildland Fire

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Abstract. This paper explores the environmental factors that drive the southern California chaparral fire regime. Specifically, we examined the response of three fire regime metrics (fire size distributions, fire return interval maps, cumulative total area burned) to variations in the number of ignitions, the spatial pattern of ignitions, the number of Santa Ana wind events, and live fuel moisture, using the HFire fire spread model. HFire is computationally efficient and capable of simulating the spatiotemporal progression of individual fires on a landscape and aggregating results for fully resolved individual fires over hundreds or thousands of years to predict long-term fire regimes. A quantitative understanding of the long-term drivers of a fire regime is of use in fire management and policy.

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“…The capture of such variation necessitates a large number of sampling plots, resulting in trade-offs between the level of detail measured at a sampling unit and the number of sampling units that can be collected. To resolve this requires an understanding of the sensitivities of fire models to the relevant inputs (e.g., [89,90]), although ideally this would be driven by fundamental fire theory [91].…”

Section: Assessing Fuel Attributes In the Fieldmentioning

confidence: 99%

Revisiting Wildland Fire Fuel Quantification Methods: The Challenge of Understanding a Dynamic, Biotic Entity

Duff

Keane

Penman

et al. 2017

Forests

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Abstract:Wildland fires are a function of properties of the fuels that sustain them. These fuels are themselves a function of vegetation, and share the complexity and dynamics of natural systems. Worldwide, the requirement for solutions to the threat of fire to human values has resulted in the development of systems for predicting fire behaviour. To date, regional differences in vegetation and independent fire model development has resulted a variety of approaches being used to describe, measure and map fuels. As a result, widely different systems have been adopted, resulting in incompatibilities that pose challenges to applying research findings and fire models outside their development domains. As combustion is a fundamental process, the same relationships between fuel and fire behaviour occur universally. Consequently, there is potential for developing novel fuel assessment methods that are more broadly applicable and allow fire research to be leveraged worldwide. Such a movement would require broad cooperation between researchers and would most likely necessitate a focus on universal properties of fuel. However, to truly understand fuel dynamics, the complex biotic nature of fuel would also need to remain a consideration-particularly when looking to understand the effects of altered fire regimes or changing climate.

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Sensitivity Analysis of a Fire Spread Model in a Chaparral Landscape

Cited by 29 publications

References 24 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

Modelling long-term fire regimes of southern California shrublands

Revisiting Wildland Fire Fuel Quantification Methods: The Challenge of Understanding a Dynamic, Biotic Entity

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