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From spark to conflagration: A multiscale thermodynamic-percolation framework for wildland-fire spread
Xiaoping Shi1, Mir Faizal1,2,3,4, Arshid Shabir2
1University of British Columbia Okanagan, Irving K. Barber School of Arts and Sciences, Kelowna, British Columbia V1V 1V7, Canada.
This study links wildland fire ignition physics to landscape-scale spread using a new energy-balance model. It explains fire behavior patterns and why small changes can cause massive wildfires.
Area of Science:
- Wildland fire science
- Combustion physics
- Percolation theory
- Reaction-diffusion systems
Background:
- Wildland fires operate across diverse spatial and temporal scales, connecting local combustion to continental burned area patterns.
- Existing models lack a quantitative link between microscale ignition and macroscale fire spread, using empirical data or simplified combustion states.
Purpose of the Study:
- To develop a unified theoretical framework connecting microscale ignition physics to macroscale wildland fire propagation.
- To derive ignition probabilities from fundamental energy-balance principles.
- To explain scale-free burned-area distributions and abrupt fire regime transitions.
Main Methods:
- Derived ignition probabilities from an energy-balance law.
- Embedded ignition probabilities in a branching-process formulation linked to percolation theory.
- Connected discrete and continuum models using a real-space-renormalization invariant.
Main Results:
- Developed a framework yielding closed-form fuel-load thresholds for fire ignition.
- Explained the observed scale-free distributions of burned areas in wildland fires.
- Clarified the role of meteorological changes in precipitating transitions from smoldering to landscape-spanning fires.
Conclusions:
- The derived framework provides a quantitative bridge between microscale ignition and macroscale fire spread.
- The model successfully explains key characteristics of wildland fire behavior, including burned-area distributions and regime shifts.
- This approach offers new insights into predicting and understanding the dynamics of large-scale wildland fires.
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