Simulating fire patterns in heterogeneous landscapes

Hargrove, William W.; Gardner, Robert H.; Turner, Monica G.; Romme, William H.; Despain, Don G.

doi:10.1016/s0304-3800(00)00368-9

Cited by 232 publications

(134 citation statements)

References 36 publications

Supporting

Mentioning

122

Contrasting

Unclassified

Order By: Relevance

“…Several dynamical upgrades are also of interest. Spotting can increase the overall spread rate of a fire across a landscape, and this has been observed in fire simulation modelling studies (Hargrove et al 2000). We expect spotting will play an important role in the dynamics of individual fires, including mechanisms for spread of fires into urban areas, but may not have a major impact on long-term statistical metrics.…”

Section: Discussionmentioning

confidence: 78%

“…Historically, mechanistic fire spread models have been considered too complex, computer-intensive and the data requirements too vast for use in long-term fire regime simulations (Hargrove et al 2000;Venevsky et al 2002), though their use would be preferable to empirical or stochastic approaches if they could be implemented (Keane and Finney 2003). Of the eight mechanistic models in the Keane et al (2004) study, only Cary and Banks (1999) and Perera et al (2008) simulated fire spread at hourly time steps with the same rigour as single-event fire spread models (e.g.…”

Section: Landscape Fire Successional Modellingmentioning

confidence: 99%

See 1 more Smart Citation

Modelling long-term fire regimes of southern California shrublands

Peterson

Moritz

Morais

et al. 2011

Int. J. Wildland Fire

View full text Add to dashboard Cite

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.

show abstract

Section: Discussionmentioning

confidence: 78%

Section: Landscape Fire Successional Modellingmentioning

confidence: 99%

Modelling long-term fire regimes of southern California shrublands

Peterson

Moritz

Morais

et al. 2011

Int. J. Wildland Fire

View full text Add to dashboard Cite

show abstract

“…Many authors have employed percolation theoretical methods [58][59][60][61], while many others [55][56][57][115][116][117] have discussed forest fires in the language of self-organized criticality. In models of forest fire development that are based on concepts from self-organized criticality, trees germinate, then age, and become more combustible, while, on occasion, lit matches are thrown in.…”

Section: Forest Fire Dynamicsmentioning

confidence: 99%

“…Some authors have pointed out a close correspondence between turbulence and self-organized criticality [54]. Still other subjects have produced controversies; for example, are observed forest fire statistics and dynamics related more closely to self-organized criticality [55][56][57] or percolation theory [58][59][60][61]. This particular article will neither attempt to resolve that controversy nor attempt to draw general boundaries.…”

mentioning

confidence: 96%

Relevance of percolation theory to power‐law behavior of dynamic processes including transport in disordered media

Hunt

2009

Complexity

View full text Add to dashboard Cite

P ower-law behavior seems ubiquitous in nature as well as in human endeavors. Flood statistics [1][2][3][4][5], water storage [6], drainage basin organization [7], relationships between channel dimensions and flow [8], extreme precipitation events [9], wind and weather fluctuations [10,11], stock and commodity markets fluctuations [12,13] , have all been noted to obey various power laws. Many physicists have searched for unifying concepts to explain such ubiquity. Perhaps, however, powerlaw behavior is not quite as universal as thought. Multifractal distributions originating from cascade processes as a related, but somewhat more complex, organizing principle have often been proposed to be the appropriate universal description [33, 34]. Some [35][36][37] have questioned whether a ''class'' of stretched exponential functions might better represent a larger or smaller fraction of the data. In the ac conductivity of disordered insulators, for example, several other frequencydependences [38-40], universal or not, have been suggested. This small sample of alternate ideas need not imply that alternative scenarios are always correct either. But simply because it is often asserted that nature follows power laws does not require such; in most cases the range and quality of data are probably not sufficient to distinguish the subtle differences between the various proposed distributions. But the recognition of the simplicity of the explanation that self-similar geometrical structures [41] could be the basis of so many geophysical manifestations of apparent power laws has certainly provided impetus to the interpretation that a large number of natural phenomena do follow power-law behavior.In the case that one believes that the observed behavior conforms to true power laws, what kind of underlying mechanism should one seek? At least six such unifying concepts, nonlinear dynamics (chaos) [42], self-organized criticality [43], hierarchical dynamics [44], highly optimized tolerance [45], minimum effort

show abstract

“…Spatial trends in the way fire spreads can result from complex interactions between weather, ignition, vegetation type, fuel moisture, and topography (Hargrove et al 2000), making such patterns difficult to measure empirically. As a result, simulation studies are typically used to model how fires spread and to explain the patterns observed in the field.…”

Section: Introductionmentioning

confidence: 99%

Effects of wind and tree density on forest fire patterns in a mixed-tree species forest

Song

Lee

2016

Forest Science and Technology

View full text Add to dashboard Cite

It is well known that global climate change causes an increase in forest fire frequency and severity. Thus, understanding fire dynamics is necessary to comprehend the mitigation of the negative effects of forest fires. Our objective was to inform how fire spreads in a simulated two-species forest with varying wind strengths. The forest in this study was comprised of two different tree species with varying probabilities of transferring fire that was randomly distributed in space at densities (C tot ) ranging from 0.0 (low) to 1.0 (high). We studied the distribution pattern of burnt trees by using local rules of the two-dimensional model. This model incorporated wind blowing from south to north with strength (P w ) ranging from 0.0 (low) to 1.0 (high). Simulation results showed that when C tot > 0.45 the fire covered the entire forest, but when C tot 0.45 the fire did not spread. The wind effect on the variation of the amount of the burnt tree was maximized at the critical density and dramatically decreased with increasing C tot . Additionally, we found that the term of C tot and P w plays an important role in determining the distribution.

show abstract

Simulating fire patterns in heterogeneous landscapes

Cited by 232 publications

References 36 publications

Modelling long-term fire regimes of southern California shrublands

Modelling long-term fire regimes of southern California shrublands

Relevance of percolation theory to power‐law behavior of dynamic processes including transport in disordered media

Effects of wind and tree density on forest fire patterns in a mixed-tree species forest

Contact Info

Product

Resources

About