Effect of Climate on Wildfire Size: A Cross-Scale Analysis
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Theory predicts that wildfires will encounter spatial thresholds where different drivers may become the dominant influence on continued fire spread. Studying these thresholds, however, is limited by a lack of sufficiently detailed data sets. To address this problem, we searched for scale thresholds in data describing wildfire size at the Avon Park Air Force Range, south-central Florida. We used power-law statistics to describe the “heavy-tail” of the fire size distribution, and quantile regression to determine how the edges of data distributions of fire size were related to climate. Power-law statistics revealed a heavy-tail, a pattern consistent with scale threshold theory, which predicts that large fires will be rare because only fires that cross all thresholds will become large. Results from quantile regression suggested that different climate conditions served as critical thresholds, influencing wildfire size at different spatial scales. Modeling at higher quantiles (≥75th) implicated drought as driving the spread of larger fires, whereas modeling at lower quantiles (≤25th) implicated that wind governed the spread of smaller fires. Fires of intermediate size were negatively associated with relative humidity. Our results are consistent with the idea that fire spread involves scale thresholds, with the small-scale drivers allowing fires to spread after ignition, but with further spread only being possible when large-scale drivers are favorable. These results suggest that other data sets that have heavy-tailed distributions may contain patterns generated by scale thresholds, and that these patterns may be revealed using quantile regression.
Keywordswildfire size cross-scale interactions scale thresholds quantile regression power-law statistics climate
We thank the Avon Park Air Force Range (Department of Defense, USA) for funding, and Samuel Van Hook and Brent Bonner for help collecting data. We thank Paul Ebersbach (Chief of the Environmental Flight at the range) for his continued interest and support of fire research. For help with understanding patterns in climate and fire we thank Andrew Wood (University of Washington, Experimental Surface Water Monitor), Paul Trimble (South Florida Water Management District) and Michael Crimmins (Department of Soil, Water and Environmental Science, University of Arizona). We thank Brian Cade (U. S. Geological Survey, Fort Collins Science Center) for help with quantile regression, and Mark Newman (Department of Physics and Center for the Study of Complex Systems, University of Michigan) for help with power-law statistics. Finally, we thank Edwin Bridges and Mindy McCallum for helpful comments and suggestions on the writing of the manuscript.
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