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

Related authors

EcoPro-LSTM𝑣0: A Memory-based Machine Learning Approach to Predicting Ecosystem Dynamics across Time Scales in Mediterranean Environments
Mitra Cattry, Wenli Zhao, Juan Nathaniel, Jinghao Qiu, Yao Zhang, and Pierre Gentine
EGUsphere, https://doi.org/10.5194/egusphere-2024-3726,https://doi.org/10.5194/egusphere-2024-3726, 2025
This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
Short summary
Learning Evaporative Fraction with Memory
Wenli Zhao, Alexander J. Winkler, Markus Reichstein, Rene Orth, and Pierre Gentine
EGUsphere, https://doi.org/10.5194/egusphere-2025-365,https://doi.org/10.5194/egusphere-2025-365, 2025
Short summary
GTWS-MLrec: global terrestrial water storage reconstruction by machine learning from 1940 to present
Jiabo Yin, Louise J. Slater, Abdou Khouakhi, Le Yu, Pan Liu, Fupeng Li, Yadu Pokhrel, and Pierre Gentine
Earth Syst. Sci. Data, 15, 5597–5615, https://doi.org/10.5194/essd-15-5597-2023,https://doi.org/10.5194/essd-15-5597-2023, 2023
Short summary
The Permafrost and Organic LayEr module for Forest Models (POLE-FM) 1.0
Winslow D. Hansen, Adrianna Foster, Benjamin Gaglioti, Rupert Seidl, and Werner Rammer
Geosci. Model Dev., 16, 2011–2036, https://doi.org/10.5194/gmd-16-2011-2023,https://doi.org/10.5194/gmd-16-2011-2023, 2023
Short summary
Vulnerability of European ecosystems to two compound dry and hot summers in 2018 and 2019
Ana Bastos, René Orth, Markus Reichstein, Philippe Ciais, Nicolas Viovy, Sönke Zaehle, Peter Anthoni, Almut Arneth, Pierre Gentine, Emilie Joetzjer, Sebastian Lienert, Tammas Loughran, Patrick C. McGuire, Sungmin O, Julia Pongratz, and Stephen Sitch
Earth Syst. Dynam., 12, 1015–1035, https://doi.org/10.5194/esd-12-1015-2021,https://doi.org/10.5194/esd-12-1015-2021, 2021
Short summary

Related subject area

Climate and Earth system modeling
A Fortran–Python interface for integrating machine learning parameterization into earth system models
Tao Zhang, Cyril Morcrette, Meng Zhang, Wuyin Lin, Shaocheng Xie, Ye Liu, Kwinten Van Weverberg, and Joana Rodrigues
Geosci. Model Dev., 18, 1917–1928, https://doi.org/10.5194/gmd-18-1917-2025,https://doi.org/10.5194/gmd-18-1917-2025, 2025
Short summary
A rapid-application emissions-to-impacts tool for scenario assessment: Probabilistic Regional Impacts from Model patterns and Emissions (PRIME)
Camilla Mathison, Eleanor J. Burke, Gregory Munday, Chris D. Jones, Chris J. Smith, Norman J. Steinert, Andy J. Wiltshire, Chris Huntingford, Eszter Kovacs, Laila K. Gohar, Rebecca M. Varney, and Douglas McNeall
Geosci. Model Dev., 18, 1785–1808, https://doi.org/10.5194/gmd-18-1785-2025,https://doi.org/10.5194/gmd-18-1785-2025, 2025
Short summary
The DOE E3SM version 2.1: overview and assessment of the impacts of parameterized ocean submesoscales
Katherine M. Smith, Alice M. Barthel, LeAnn M. Conlon, Luke P. Van Roekel, Anthony Bartoletti, Jean-Christophe Golaz, Chengzhu Zhang, Carolyn Branecky Begeman, James J. Benedict, Gautam Bisht, Yan Feng, Walter Hannah, Bryce E. Harrop, Nicole Jeffery, Wuyin Lin, Po-Lun Ma, Mathew E. Maltrud, Mark R. Petersen, Balwinder Singh, Qi Tang, Teklu Tesfa, Jonathan D. Wolfe, Shaocheng Xie, Xue Zheng, Karthik Balaguru, Oluwayemi Garuba, Peter Gleckler, Aixue Hu, Jiwoo Lee, Ben Moore-Maley, and Ana C. Ordoñez
Geosci. Model Dev., 18, 1613–1633, https://doi.org/10.5194/gmd-18-1613-2025,https://doi.org/10.5194/gmd-18-1613-2025, 2025
Short summary
WRF-ELM v1.0: a regional climate model to study land–atmosphere interactions over heterogeneous land use regions
Huilin Huang, Yun Qian, Gautam Bisht, Jiali Wang, Tirthankar Chakraborty, Dalei Hao, Jianfeng Li, Travis Thurber, Balwinder Singh, Zhao Yang, Ye Liu, Pengfei Xue, William J. Sacks, Ethan Coon, and Robert Hetland
Geosci. Model Dev., 18, 1427–1443, https://doi.org/10.5194/gmd-18-1427-2025,https://doi.org/10.5194/gmd-18-1427-2025, 2025
Short summary
Modeling commercial-scale CO2 storage in the gas hydrate stability zone with PFLOTRAN v6.0
Michael Nole, Jonah Bartrand, Fawz Naim, and Glenn Hammond
Geosci. Model Dev., 18, 1413–1425, https://doi.org/10.5194/gmd-18-1413-2025,https://doi.org/10.5194/gmd-18-1413-2025, 2025
Short summary

Cited articles

Abatzoglou, J. T.: Development of gridded surface meteorological data for ecological applications and modelling, Int. J. Climatol., 33, 121–131, https://doi.org/10.1002/joc.3413, 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, https://doi.org/10.1071/WF13019, 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, https://doi.org/10.1073/pnas.1607171113, 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, https://doi.org/10.1071/WF16165, 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, https://doi.org/10.1038/s43247-021-00299-0, 2021a. a
Download
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.
Share