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Configurations of fuel break networks influence landscape-level fire-risk in Southern California

Year of Publication
2026
Publication Type

Linear fuel treatments, Fuel Break Networks, are widely implemented in California, USA fuel types to improve firefighter safety and facilitate fire containment. Despite frequent construction, landscape scale evaluations of their effectiveness with fire modeling remain limited in this region. This study presents a framework to assess how fuel break configuration, arrangement, and firefighter tactics influence fire control opportunities using a customized spatial metric for Uncontrollable Wildfire Risk (UWR). UWR combines outputs from fire modeling software widely available to fire and land management practitioners with suppression difficulty weights derived from previous literature. Fire spread simulations were conducted across four case study fuel break configurations in Southern California: Single Segment, Branching Network, Enclosed Network, and Multiple Segment Network. Three leverage scenarios (unstaffed, firebreak, and firing operations) were applied to each landscape. Linear mixed effects models and spatial analysis quantified the effects of distance from treatment, wind alignment, topography, treatment width, length, sinuosity, and proximity to other treatments on UWR. Results showed that increased leverage intensity consistently reduced UWR, while treatment geometry and spatial arrangement influenced risk reduction in some models. Notably, in some instances unstaffed fuel breaks increased burned area due to changes in fuel characteristics and subsequent fire behavior. This research highlights the importance of selecting appropriate outcomes for wildfire modeling evaluations of fuel break placement and operational utilization.

Authors
Andrew S. Johnson, Brandon M. Collins, Philip N. Omi, John J. Battles, Matthew P. Thompson, Scott L. Stephens
Citation

A.S. Johnson, B.M. Collins, P.N. Omi, et al., Configurations of fuel break networks influence landscape-level fire-risk in Southern California, Ecological Informatics (2024), https://doi.org/10.1016/j.ecoinf.2026.103691.

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