International Journal of Wildland Fire International Journal of Wildland Fire Society
Journal of the International Association of Wildland Fire
RESEARCH ARTICLE

Spatial variability in wildfire probability across the western United States

Marc-André Parisien A B G , Susan Snetsinger C , Jonathan A. Greenberg D , Cara R. Nelson C , Tania Schoennagel E , Solomon Z. Dobrowski F and Max A. Moritz B

A Northern Forestry Centre, Canadian Forest Service, Natural Resources Canada, 5320 122nd Street, Edmonton, AB, T5H 3S5, Canada.

B Department of Environmental Science, Policy and Management, University of California – Berkeley, 137 Mulford Hall 3114, Berkeley, CA 94720, USA.

C Department of Ecosystem and Conservation Sciences, College of Forestry and Conservation, University of Montana, 32 Campus Drive, Missoula, MT 59812, USA.

D Department of Geography, University of Illinois at Urbana-Champaign, 607 South Mathews Avenue, MC 150, Urbana, IL 61801, USA.

E Department of Geography, University of Colorado, Boulder, Boulder, CO 80309, USA.

F Department of Forest Management, College of Forestry and Conservation, University of Montana, Missoula, MT 59812,USA.

G Corresponding author. Email: marc-andre.parisien@nrcan-rncan.gc.ca

International Journal of Wildland Fire 21(4) 313-327 https://doi.org/10.1071/WF11044
Submitted: 23 March 2011  Accepted: 18 August 2011   Published: 8 February 2012

Abstract

Despite growing knowledge of fire–environment linkages in the western USA, obtaining reliable estimates of relative wildfire likelihood remains a work in progress. The purpose of this study is to use updated fire observations during a 25-year period and a wide array of environmental variables in a statistical framework to produce high-resolution estimates of wildfire probability. Using the MaxEnt modelling technique, point-source fire observations that were sampled from area burned during the 1984–2008 time period were related to explanatory variables representing ignitions, flammable vegetation (i.e. fuels), climate and topography. Model results were used to produce spatially explicit predictions of wildfire probability. To assess the effect of humans on the spatial patterns of wildfire likelihood, we built an alternative model that excluded all variables having a strong anthropogenic imprint. Results showed that wildfire probability in the western USA is far from uniform, with different areas responding to different environmental drivers. The effect of anthropogenic factors on wildfire probability varied by region but, on the whole, humans appear to inhibit fire activity in the western USA. Our results not only provide what appear to be robust predictions of wildfire likelihood, but also enhance understanding of long-term controls on wildfire activity. In addition, our wildfire probability maps provide better information for strategic planning of land-management activities, especially where fire regime knowledge is sparse.

Additional keywords: climate, fuels, ignitions, MaxEnt algorithm, spatial modelling, topography.


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