Forest‐Fire Models

Preisler, Haiganoush K.; Ager, Alan A.

doi:10.1002/9780470057339.vaf010.pub2

Cited by 12 publications

(11 citation statements)

References 39 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Interestingly, in this regime, a linear front stays unperturbed while it envelops the obstacle, in contrast to a first intuition based on a front of fluid material encountering an obstacle such as lava flow encountering a barrier [ 51 ]. However, a front of forest fire resembles the situation of phage propagating on a lawn of bacteria; indeed, ideas very similar to the model of constant speed are used to predict forest fires [ 52 ]. Our analysis of the front predicted by the constant speed model shows that the width of the obstacle, and not its precise shape, determines the long-term dynamics of the perturbation caused by the obstacle.…”

Section: Discussionmentioning

confidence: 99%

How Obstacles Perturb Population Fronts and Alter Their Genetic Structure

2015

View full text Add to dashboard Cite

As populations spread into new territory, environmental heterogeneities can shape the population front and genetic composition. We focus here on the effects of an important building block of heterogeneous environments, isolated obstacles. With a combination of experiments, theory, and simulation, we show how isolated obstacles both create long-lived distortions of the front shape and amplify the effect of genetic drift. A system of bacteriophage T7 spreading on a spatially heterogeneous Escherichia coli lawn serves as an experimental model system to study population expansions. Using an inkjet printer, we create well-defined replicates of the lawn and quantitatively study the population expansion of phage T7. The transient perturbations of the population front found in the experiments are well described by a model in which the front moves with constant speed. Independent of the precise details of the expansion, we show that obstacles create a kink in the front that persists over large distances and is insensitive to the details of the obstacle’s shape. The small deviations between experimental findings and the predictions of the constant speed model can be understood with a more general reaction-diffusion model, which reduces to the constant speed model when the obstacle size is large compared to the front width. Using this framework, we demonstrate that frontier genotypes just grazing the side of an isolated obstacle increase in abundance, a phenomenon we call ‘geometry-enhanced genetic drift’, complementary to the founder effect associated with spatial bottlenecks. Bacterial range expansions around nutrient-poor barriers and stochastic simulations confirm this prediction. The effect of the obstacle on the genealogy of individuals at the front is characterized by simulations and rationalized using the constant speed model. Lastly, we consider the effect of two obstacles on front shape and genetic composition of the population illuminating the effects expected from complex environments with many obstacles.

show abstract

Section: Discussionmentioning

confidence: 99%

How Obstacles Perturb Population Fronts and Alter Their Genetic Structure

2015

View full text Add to dashboard Cite

show abstract

“…Fire activity varies substantially, and often rapidly, from local to national scales; spatio-temporal variability is one of the main challenges in wildfire management. Because resources are limited, both for mitigating and responding to wildfire risks, predictive models are needed to support planning and decision-making (Andrews, Finney and Fischetti (2007); Preisler and Ager (2013)). Martell (1982) described many of the strategic, tactical and operational decision-making problems faced by fire managers, and of early efforts to bring operations research methods to bear on them.…”

Section: Prediction In Wildfire Managementmentioning

confidence: 99%

“…We paraphrase and expand upon that discourse in what follows; we also summarize a newly developed methodology for monitoring for temporal trends in historical records on fire occurrence, motivated by climate change concerns. For a recent and concise review of fire risk and other forest fire models, see Preisler and Ager (2013).…”

Section: Fire Occurrence Predictionmentioning

confidence: 99%

Wildfire Prediction to Inform Fire Management: Statistical Science Challenges

Taylor¹,

Woolford²,

Dean³

et al. 2013

Statist. Sci.

118

View full text Add to dashboard Cite

Wildfire is an important system process of the earth that occurs across a wide range of spatial and temporal scales. A variety of methods have been used to predict wildfire phenomena during the past century to better our understanding of fire processes and to inform fire and land management decision-making. Statistical methods have an important role in wildfire prediction due to the inherent stochastic nature of fire phenomena at all scales.Predictive models have exploited several sources of data describing fire phenomena. Experimental data are scarce; observational data are dominated by statistics compiled by government fire management agencies, primarily for administrative purposes and increasingly from remote sensing observations. Fires are rare events at many scales. The data describing fire phenomena can be zero-heavy and nonstationary over both space and time. Users of fire modeling methodologies are mainly fire management agencies often working under great time constraints, thus, complex models have to be efficiently estimated.We focus on providing an understanding of some of the information needed for fire management decision-making and of the challenges involved in predicting fire occurrence, growth and frequency at regional, national and global scales.

show abstract

“…Multiple statistical approaches have been proposed in the literature to predict the counts and sizes of wildfires using univariate probability models (Cumming, 2001;Preisler et al, 2004;Preisler and Westerling, 2007;Preisler and Ager, 2013;Pereira and Turkman, 2019). Xi et al (2019) in particular made multiple important contributions to modeling fire risk com-ponents over recent decades, describing some key yet often overlooked fire characteristics, and they highlighted various areas of recent research that may enhance fire risk assesment models.…”

Section: Introductionmentioning

confidence: 99%

A combined statistical and machine learning approach for spatial prediction of extreme wildfire frequencies and sizes

Daniela¹,

Gong²,

Yadav³

et al. 2021

Preprint

View full text Add to dashboard Cite

Motivated by the Extreme Value Analysis 2021 (EVA 2021) data challenge we propose a method based on statistics and machine learning for the spatial prediction of extreme wildfire frequencies and sizes. This method is tailored to handle large datasets, including missing observations. Our approach relies on a four-stage high-dimensional bivariate sparse spatial model for zero-inflated data, which is developed using stochastic partial differential equations (SPDE). In Stage 1, the observations are categorized in zero/nonzero categories and are modeled using a two-layered hierarchical Bayesian sparse spatial model to estimate the probabilities of these two categories. In Stage 2, before modeling the positive observations using a spatiallyvarying coefficients, smoothed parameter surfaces are obtained from empirical estimates using fixed rank kriging. This approximate Bayesian method inference was employed to avoid the high computational burden of large spatial data modeling using spatially-varying coefficients. In Stage 3, the standardized log-transformed positive observations from the second stage are further modeled using a sparse bivariate spatial Gaussian process. The Gaussian distribution assumption for wildfire counts developed in the third stage, is computationally effective but erroneous. Thus in Stage 4, the predicted values are rectified using Random Forests. Posterior inference is drawn for Stages 1 and 3 using Markov chain Monte Carlo (MCMC) sampling. A cross-validation scheme is then created for the artificially generated gaps, and the EVA 2021 prediction scores of the proposed model are compared to those obtained using certain natural competitors.

show abstract

Forest‐Fire Models

Cited by 12 publications

References 39 publications

How Obstacles Perturb Population Fronts and Alter Their Genetic Structure

How Obstacles Perturb Population Fronts and Alter Their Genetic Structure

Wildfire Prediction to Inform Fire Management: Statistical Science Challenges

A combined statistical and machine learning approach for spatial prediction of extreme wildfire frequencies and sizes

Contact Info

Product

Resources

About