Articles | Volume 16, issue 12
https://doi.org/10.5194/gmd-16-3407-2023
https://doi.org/10.5194/gmd-16-3407-2023
Development and technical paper
 | 
19 Jun 2023
Development and technical paper |  | 19 Jun 2023

SMLFire1.0: a stochastic machine learning (SML) model for wildfire activity in the western United States

Jatan Buch, A. Park Williams, Caroline S. Juang, Winslow D. Hansen, and Pierre Gentine

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Latest update: 24 Feb 2024
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Short summary
We leverage machine learning techniques to construct a statistical model of grid-scale fire frequencies and sizes using climate, vegetation, and human predictors. Our model reproduces the observed trends in fire activity across multiple regions and timescales. We provide uncertainty estimates to inform resource allocation plans for fuel treatment and fire management. Altogether the accuracy and efficiency of our model make it ideal for coupled use with large-scale dynamical vegetation models.