Wildland surface fire spread modelling, 1990 - 2007. 1: Physical and quasi-physical models

Sullivan, Andrew L.

doi:10.1071/wf06143

Cited by 385 publications

(249 citation statements)

References 101 publications

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“…In addition, interactions of fuels and weather with local topography can greatly influence fire activity (Moritz et al 2010;Sharples et al 2012). Fire behaviour models incorporate information on fuels, topography and weather to predict fire spread (Sullivan 2009a(Sullivan , 2009b(Sullivan , 2009c. Comparison of fire behaviour model outcomes against real-world fires has indicated that models typically do not accurately predict fire progression (Papadopoulos and Pavlidou 2011;Finney et al 2013).…”

Section: Discussionmentioning

confidence: 99%

Mapping the daily progression of large wildland fires using MODIS active fire data

Veraverbeke

Sedano

Hook

et al. 2014

Int. J. Wildland Fire

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Abstract. High temporal resolution information on burnt area is needed to improve fire behaviour and emissions models. We used the Moderate Resolution Imaging Spectroradiometer (MODIS) thermal anomaly and active fire product (MO(Y)D14) as input to a kriging interpolation to derive continuous maps of the timing of burnt area for 16 large wildland fires. For each fire, parameters for the kriging model were defined using variogram analysis. The optimal number of observations used to estimate a pixel's time of burning varied between four and six among the fires studied. The median standard error from kriging ranged between 0.80 and 3.56 days and the median standard error from geolocation uncertainty was between 0.34 and 2.72 days. For nine fires in the south-western US, the accuracy of the kriging model was assessed using high spatial resolution daily fire perimeter data available from the US Forest Service. For these nine fires, we also assessed the temporal reporting accuracy of the MODIS burnt area products (MCD45A1 and MCD64A1). Averaged over the nine fires, the kriging method correctly mapped 73% of the pixels within the accuracy of a single day, compared with 33% for MCD45A1 and 53% for MCD64A1. Systematic application of this algorithm to wildland fires in the future may lead to new information about vegetation, climate and topographic controls on fire behaviour.

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Section: Discussionmentioning

confidence: 99%

Mapping the daily progression of large wildland fires using MODIS active fire data

Veraverbeke

Sedano

Hook

et al. 2014

Int. J. Wildland Fire

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“…Here we present a modeling approach that integrates information on the physics of fire spread (19)(20)(21), the effect of humans on different components of fire spread (8,22,23), and the paleoecology of modern humans (24)(25)(26). We use conceptual links between (i) known advances in human manipulation of fire, population growth, and spread and (ii) the parameters of a spatially explicit fire propagation model, to explore the types of impacts humans could have had on fire regimes as they learned to manipulate fire, their environment, and their landscapes.…”

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confidence: 99%

Evolution of human-driven fire regimes in Africa

Archibald

Staver

Levin

2011

Proc. Natl. Acad. Sci. U.S.A.

308

272

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Human ability to manipulate fire and the landscape has increased over evolutionary time, but the impact of this on fire regimes and consequences for biodiversity and biogeochemistry are hotly debated. Reconstructing historical changes in human-derived fire regimes empirically is challenging, but information is available on the timing of key human innovations and on current human impacts on fire; here we incorporate this knowledge into a spatially explicit fire propagation model. We explore how changes in population density, the ability to create fire, and the expansion of agropastoralism altered the extent and seasonal distribution of fire as modern humans arose and spread through Africa. Much emphasis has been placed on the positive effect of population density on ignition frequency, but our model suggests this is less important than changes in fire spread and connectivity that would have occurred as humans learned to light fires in the dry season and to transform the landscape through grazing and cultivation. Different landscapes show different limitations; we show that substantial human impacts on burned area would only have started ∼4,000 B.P. in open landscapes, whereas they could have altered fire regimes in closed/dissected landscapes by ∼40,000 B.P. Dry season fires have been the norm for the past 200-300 ky across all landscapes. The annual area burned in Africa probably peaked between 4 and 40 kya. These results agree with recent paleocarbon studies that suggest that the biomass burned today is less than in the recent past in subtropical countries.human evolution | human ignition | savanna | fire spread model F ire has been a part of the earth system for billions of years (1), but recently-within the past million years at most-humans have provided a new and different source of ignition. Today, fires in all ecosystems are largely started by human ignitions, whether intentionally (for land management or arson) or by accident.From studies in modern systems we know that humans can affect fire regimes via their effects on both ignition (frequency, season, and location) and landscape connectivity. However, we lack an understanding of how these different impacts might have emerged as humans learned to control fire and as they spread throughout the globe. Moreover, the degree to which current human-ignited fire regimes differ from historical, lightning-driven regimes is largely unresolved (2).The question is complicated by the fact that the potential limitations on fire are various and system specific (3), and as one constraint is released, others can come into play. Thus, the number of ignitions can increase without a concomitant increase in area burned (4, 5), and responses of fire to drivers like population density can be nonlinear (6). Most ecosystems in Africa are probably not ignition-limited currently; ignition rates are more than sufficient to burn the available fuel, and climate and landscape connectivity act as the main limitations on fire (5, 7, 8).Interpreting historical human effects on fire regim...

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“…For example, the empirical tree mortality models may someday be replaced by physically based models (Butler and Dickinson 2010). Sullivan (2009aSullivan ( , 2009b) reviewed 39 models for surface fire spread developed from 1990 to 2007, and Alexander and Cruz (2012) list 20 fireline intensity-flame length relationships. Although research users may benefit from access to multiple models, fire managers will appreciate recommended models and methods.…”

Section: Future Needsmentioning

confidence: 99%

Current status and future needs of the BehavePlus Fire Modeling System

Andrews¹

2014

Int. J. Wildland Fire

168

100

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Abstract. The BehavePlus Fire Modeling System is among the most widely used systems for wildland fire prediction. It is designed for use in a range of tasks including wildfire behaviour prediction, prescribed fire planning, fire investigation, fuel hazard assessment, fire model understanding, communication and research. BehavePlus is based on mathematical models for fire behaviour, fire effects and fire environment. It is a point system for which conditions are constant for each calculation, but is designed to encourage examination of the effect of a range of conditions through tables and graphs. BehavePlus is successor to BEHAVE, which was developed in 1977 and became available for field application in 1984. It was updated to BehavePlus in 2002. Updates through version 5 have added features and modelling capabilities. It is becoming increasingly difficult to expand the system. A redesign will address the need for consolidation with other systems and make it easier to incorporate new research results. This paper describes the development history and application of BehavePlus. The design, features and modelling foundation of the current system are described. Considerations for the next generation are presented.

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Wildland surface fire spread modelling, 1990 - 2007. 1: Physical and quasi-physical models

Cited by 385 publications

References 101 publications

Mapping the daily progression of large wildland fires using MODIS active fire data

Mapping the daily progression of large wildland fires using MODIS active fire data

Evolution of human-driven fire regimes in Africa

Current status and future needs of the BehavePlus Fire Modeling System

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