Modelling and mapping dynamic variability in large fire probability in the lower Sonoran Desert of south-western Arizona
Miranda E. Gray A C , Brett G. Dickson A B and Luke J. Zachmann A B
+ Author Affiliations
- Author Affiliations
A Laboratory of Landscape Ecology and Conservation Biology, Landscape Conservation Initiative, Box 5694, Northern Arizona University, Flagstaff, AZ 86011, USA.
B Conservation Science Partners, Inc., 11050 Pioneer Trail, Suite 202, Truckee, CA 96161, USA.
C Corresponding author. Email: miranda@csp-inc.org
International Journal of Wildland Fire 23(8) 1108-1118 https://doi.org/10.1071/WF13115
Submitted: 17 July 2013 Accepted: 18 June 2014 Published: 14 October 2014
Abstract
In the lower Sonoran Desert of south-western Arizona, climate change and non-native plant invasions have the potential to increase the frequency and size of uncommon wildfires. An understanding of where and why ignitions are more likely to become large fires will help mitigate the negative consequences of fire to native ecosystems. We use a generalised linear mixed model and fire occurrence data from 1989 to 2010 to estimate the relative contributions of fuel and other landscape variables to large fire probability, given an ignition. For the 22-year period we examined, a high value for the maximum annual Normalised Difference Vegetation Index was among the strongest predictors of large fire probability, as were low values of road density and elevation. Large fire probability varied markedly between years of moderate and high fine fuel accumulation. Our estimates can be applied to future periods with highly heterogeneous precipitation. Our map-based results can be used by managers to monitor variability in large fire probability, and to implement adaptive fire mitigation at a landscape scale. The approaches we present have global applications to other desert regions that face similar threats from changing climate, altered fuels and potential punctuated changes in fire regimes.
Additional keywords: desert fire management, generalised linear mixed model, invasive plant–fire cycle, multi-model inference, NDVI.
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