Analyzing California Wildfire Data and Modeling Large Fires With a Power Law Distribution
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Authors
Thornberry, Steven Allen
Issue Date
2025
Type
Thesis
Language
en_US
Keywords
Alternative Title
Abstract
California’s fires become larger and more frequent with the change in climate.While the frequency of relatively small fires increased with the advances in the
fire-fighting technology and methods, the large fires became huge. We explore
the problem of modeling these extreme, very large fires. The last two decades
witnessed the largest fires on record: the August Complex fire in 2020 (1,032,700
acres burnt) and the Dixie fire in 2021 (963,405 acres burnt). We use two data sets:
(1) the California Department of Forestry and Fire Protection’s Fire and Resource
Assessment Programs (FRAP) data going back to 1878, and the data set developed
in Kolden and Abatzoglou (2018) (KA) with the purpose of comparing the wind
driven (katabatic) fires to other fires in Southern California. Our focus is on the
fires accelerated by the Santa Ana winds which are common in southern California
from September to May. We show that the distribution of fire size (acres burnt)
is well modeled using power laws, explore two common methods of estimation of
parameters, and present results of fitting power law models to fire data including
estimates of the probabilities of extreme fires.