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

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Cited articles

Abatzoglou, J. T.: Development of gridded surface meteorological data for ecological applications and modelling, Int. J. Climatol., 33, 121–131,, 2013. a
Abatzoglou, J. T. and Kolden, C. A.: Relationships between climate and macroscale area burned in the western United States, Int. J. Wildland Fire, 22, 1003–1020,, 2013. a
Abatzoglou, J. T. and Williams, A. P.: Impact of anthropogenic climate change on wildfire across western US forests, P. Nl. Acad. Sci. USA, 113, 11770–11775,, 2016. a, b, c
Abatzoglou, J. T., Kolden, C. A., Williams, A. P., Lutz, J. A., and Smith, A. M. S.: Climatic influences on interannual variability in regional burn severity across western US forests, Int. J. Wildland Fire, 26, 269–275,, 2017. a, b
Abatzoglou, J. T., Battisti, D. S., Williams, A. P., Hansen, W. D., Harvey, B. J., and Kolden, C. A.: Projected increases in western US forest fire despite growing fuel constraints, Commun. Earth Environ., 2, 227,, 2021a. a
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.