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Self-organization, the cascade model, and natural hazards
Donald L Turcotte1, Bruce D Malamud, Fausto Guzzetti
1Department of Earth and Atmospheric Sciences, Cornell University, Ithaca, NY 14853, USA. turcotte@geology.cornell.edu
Summary
Natural hazards like forest fires and landslides exhibit power-law distributions. Cellular automata models, such as the forest fire and sand pile models, effectively simulate this fractal behavior through self-similar inverse cascades.
Area of Science:
- Geophysics
- Ecology
- Complex Systems
Background:
- Natural hazards, including forest fires and landslides, often display predictable statistical patterns.
- Previous research suggests these patterns may follow fractal distributions.
Purpose of the Study:
- To analyze the frequency-size statistics of forest fires and landslides.
- To investigate the applicability of cellular automata models in simulating these natural hazard phenomena.
Main Methods:
- Statistical analysis of frequency-size data for forest fires and landslides.
- Modeling hazard dynamics using two cellular automata: the forest fire model and the sand pile model.
Main Results:
- Both forest fires and landslides demonstrate approximate power-law (fractal) distributions across various conditions.
- Cellular automata models successfully replicate the observed fractal statistics.
- The underlying mechanism in both models is a self-similar inverse cascade.
Conclusions:
- Power-law distributions are a key characteristic of forest fire and landslide frequency-size statistics.
- Cellular automata provide valuable frameworks for understanding the complex dynamics of these natural hazards.
- The concept of self-similar inverse cascades unifies the behavior of disparate natural systems.