Canadian Forest Service Publications

Wildfire Prediction to Inform Fire Management: Statistical Science Challenges. 2013. Taylor, S.W.; Woolford, D.G.; Dean, C.B.; Martell, D.L. Statistical Science. 28(4) 586–615.

Year: 2013

Available from: Pacific Forestry Centre

Catalog ID: 35440

Language: English

CFS Availability: PDF (request by e-mail)

Available from the Journal's Web site.
DOI: 10.1214/13-STS451

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Abstract

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.

Plain Language Summary

Wildfires can have damaging impacts on human health, property and forest resources. Wildfire behavior is very challenging to predict because it is so variable and random; it will become even more difficult with changing climates. Wildfire managers need effectual tools to predict fire events so they can reduce the risk, respond to fires quicker, and make informed decisions on how to respond to them. Physical models are used to predict wildfire spread, but they aren’t as effective in wildfire prediction. Models that use statistical science may be the key to better predictability - resulting in better decision-making. The goal is to integrate statistical models into operational tools. This paper reviews some of the North American models that predict fire occurrence, growth and frequency. It also discusses several types of data that have been used to run wildfire predictive models (e.g. historic reports, field experiments, and tree ring data). This research is a step towards the collaboration of several disciplines (statisticians, fire managers, ecologists) in the field of wildfire management.

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