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May 15, 2026Earth s FutureOpen Access

Accounting for Extremes in Modeling the Size and Likelihood of Large Fires in the United States

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Authors

AAAmirali AsadianMZMasoud ZaerpourSPSimon Papalexiou

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Overview

Framework analyzes large fire characteristics in ecoregions, suggesting tailored predictive models.

Key Points

  • To improve predictions of wildfire size and occurrence by adequately addressing extreme fire events.
  • Analyzed data from 30,331 large fire perimeters from 1984 to 2024 using MTBS data.
  • Fitted Pareto Type II, lognormal, and Weibull distributions to full data and exceedance-based tail samples.
  • Evaluated distribution performance across 105 ecoregions and 10 Geographic Area Coordination Centers.
  • Models focused on the body significantly misestimate exceedance probability by up to three orders of magnitude.
  • Lognormal and Pareto Type II outperform Weibull for body distribution, while Weibull fits tail best at GACC scale.
  • Optimal distribution models vary by region, highlighting the need for tailored approaches in fire management.

Cite This Study

Asadian et al. (2026) studied this question.

synapsesocial.com/papers/6a06b914e7dec685947aba1ehttps://doi.org/10.1029/2025ef007485
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