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Updated: Sep 7, 2025

Wind Tunnel Experiments to Study Chaparral Crown Fires
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Quantifying the environmental limits to fire spread in grassy ecosystems.

Anabelle W Cardoso1,2, Sally Archibald2, William J Bond3

  • 1Ecology and Evolutionary Biology Department, Yale University, New Haven, CT 06511.

Proceedings of the National Academy of Sciences of the United States of America
|June 23, 2022
PubMed
Summary

Fire spread models benefit from infection process analogies, revealing nonlinear thresholds crucial for management. These thresholds, validated with field data, improve predictions under changing environmental conditions.

Keywords:
fire modelfire thresholdsfuel moistureinfection modelpercolation

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Area of Science:

  • Ecology
  • Fire Science
  • Mathematical Modeling

Background:

  • Fire spread is often modeled using infection process analogies, which predict nonlinear thresholds for spread based on fuel connectivity and infection probability.
  • Applied fire models frequently use quasi-empirical approaches, creating a tension with theoretical infection models.
  • Understanding these thresholds is vital for effective fire management and theoretical fire spread research.

Purpose of the Study:

  • To quantify local fire spread thresholds using field data in grassy ecosystems.
  • To evaluate how these thresholds scale regionally and across time.
  • To resolve the tension between infection models and quasi-empirical approaches in fire modeling.

Main Methods:

  • Utilized field data from 1,131 individual fires across a precipitation gradient (496–1,442 mm).
  • Evaluated fire spread patterns using an infection model and compared it with competing models.
  • Assessed landscape burn proportions using grass biomass, fuel moisture, and vapor pressure deficit.
  • Analyzed data regionally across 533 sites and temporally from 1989–2012 and 2016–2018 in Kruger National Park, South Africa.

Main Results:

  • An infection model provided a better fit to observed individual fire spread patterns than alternative models.
  • Grass biomass, fuel moisture, and vapor pressure deficit effectively described the proportion of the landscape burned.
  • Regional analysis, averaging across variability, revealed quasi-linear fire spread patterns.

Conclusions:

  • Nonlinear fire spread thresholds, as captured by infection models, are essential for predicting fire responses to global change.
  • Linear approximations may suffice for medium-term fire trend predictions in stable climates.
  • Integrating infection models with empirical data enhances fire spread modeling accuracy.