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Related Experiment Video

Updated: Jan 9, 2026

Wind Tunnel Experiments to Study Chaparral Crown Fires

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Published on: November 14, 2017

Statistical dynamics of wildfire burned area from cellular-automata simulators.

Adel Sahila¹, Benedetta Canfora^2,3,4,5, Marzia Canzaniello⁶

¹BCAM - Basque Center for Applied Mathematics, Alameda de Mazarredo 14, 48009, Bilbao, Basque Country, Spain. adelsahila@usal.es.

Scientific Reports

|December 5, 2025

Summary

This study uses a wildfire simulator to analyze forest fire dynamics in the Mediterranean. A four-parameter Beta density function effectively models burned area statistics for both synthetic and historical fires.

Keywords:

Beta distribution Cellular automata Forest fires

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Area of Science:

Environmental Science
Computational Modeling
Statistical Analysis

Background:

Mediterranean forest fires pose significant risks.
Understanding fire dynamics is crucial for effective management.
Previous models often lack detailed statistical analysis of burned areas.

Purpose of the Study:

To investigate the statistical dynamics of synthetic and historical forest fires.
To identify a suitable statistical model for burned area distribution.
To analyze the influence of environmental factors on fire spread.

Main Methods:

Utilized the PROPAGATOR wildfire simulator based on a cellular-automaton scheme.
Analyzed synthetic and historical fire data from Catalonia (Spain) and Liguria (Italy).
Applied Z-score and interquartile range standardization with a customized method of moments.

Main Results:

Burned area statistics showed non-linear growth, influenced by wind and terrain.
A four-parameter Beta density function provided the best fit for all fire scenarios.
Synthetic fire shape parameters (α, β) demonstrated consistent dynamics across conditions.

Conclusions:

The four-parameter Beta distribution is a robust model for forest fire burned area statistics.
This model can serve as a prior in Bayesian approaches for fire analysis.
Real-world fire dynamics are complex, influenced by multiple interacting factors.