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

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

doi:https://doi.org/10.5194/gmd-16-3407-2023

Articles | Volume 16, issue 12

https://doi.org/10.5194/gmd-16-3407-2023

© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/gmd-16-3407-2023

© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 16, issue 12

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

Supplement

https://doi.org/10.5194/gmd-16-3407-2023-supplement

Data sets

Western US MTBS-Interagency (WUMI) wildfire dataset Caroline Juang and Park Williams https://doi.org/10.5061/dryad.sf7m0cg72

Model code and software

SMLFire1.0: a stochastic machine learning (SML) model for wildfire activity in the western United States (1.0) Jatan Buch, A. Park Williams, Caroline Juang, Winslow D. Hansen, and Pierre Gentine https://doi.org/10.5281/zenodo.7277980

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.