Effects of fuel spatial distribution on wildland fire behaviour
Adam L. Atchley
A E , Rodman Linn A , Alex Jonko A , Chad Hoffman B , Jeffrey D. Hyman A , Francois Pimont C , Carolyn Sieg D and Richard S. Middleton A
+ Author Affiliations
- Author Affiliations
A Los Alamos National Laboratory, Earth and Environmental Sciences, PO Box 1663 Los Alamos, NM 87545, USA.
B Colorado State University, Warner College of Natural Resources, 1472 Campus Delivery Fort Collins, CO 80523-1472, USA.
C INRAE, URFM, Site Agroparc Domaine Saint Paul F-84914 Avignon, France.
D USDA Forest Service, Rocky Mountain Research Station, 2500 S, Pine Knoll Dr. Flagstaff, AZ 86001, USA.
E Corresponding author. Email: aatchley@lanl.gov
International Journal of Wildland Fire 30(3) 179-189 https://doi.org/10.1071/WF20096
Submitted: 20 June 2020 Accepted: 14 December 2020 Published: 27 January 2021
Abstract
The distribution of fuels is recognised as a key driver of wildland fire behaviour. However, our understanding of how fuel density heterogeneity affects fire behaviour is limited because of the challenges associated with experiments that isolate fuel heterogeneity from other factors. Advances in fire behaviour modelling and computational resources provide a means to explore fire behaviour responses to fuel heterogeneity. Using an ensemble approach to simulate fire behaviour in a coupled fire–atmosphere model, we systematically tested how fuel density fidelity and heterogeneity shape effective wind characteristics that ultimately affect fire behaviour. Results showed that with increased fuel density fidelity and heterogeneity, fire spread and area burned decreased owing to a combination of fuel discontinuities and increased fine-scale turbulent wind structures that blocked forward fire spread. However, at large characteristic length scales of spatial fuel density, the fire spread and area burned increased because local fuel discontinuity decreased, and wind entrainment into the forest canopy maintained near-surface wind speeds that drove forward fire spread. These results demonstrate the importance of incorporating high-resolution fuel fidelity and heterogeneity information to capture effective wind conditions that improve fire behaviour forecasts.
Keywords: fire behaviour modelling, fuel classification, fuel representation, wind response.
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