Articles | Volume 18, issue 13
https://doi.org/10.5194/gmd-18-4103-2025
https://doi.org/10.5194/gmd-18-4103-2025
Model description paper
 | 
04 Jul 2025
Model description paper |  | 04 Jul 2025

ELM2.1-XGBfire1.0: improving wildfire prediction by integrating a machine learning fire model in a land surface model

Ye Liu, Huilin Huang, Sing-Chun Wang, Tao Zhang, Donghui Xu, and Yang Chen

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Cited articles

Arora, V. K. and Boer, G. J.: A parameterization of leaf phenology for the terrestrial ecosystem component of climate models, Glob. Change Biol., 11, 39–59, 2005. 
Balch, J. K., Bradley, B. A., Abatzoglou, J. T., Nagy, R. C., Fusco, E. J., and Mahood, A. L.: Human-started wildfires expand the fire niche across the United States, P. Natl. Acad. Sci. USA, 114, 2946–2951, 2017. 
Beguería, S., Vicente-Serrano, S. M., Reig, F., and Latorre, B.: Standardized precipitation evapotranspiration index (SPEI) revisited: parameter fitting, evapotranspiration models, tools, datasets and drought monitoring, Int. J. Climatol., 34, 3001–3023, 2014. 
Buch, J., Williams, A. P., Juang, C. S., Hansen, W. D., and Gentine, P.: SMLFire1.0: a stochastic machine learning (SML) model for wildfire activity in the western United States, Geosci. Model Dev., 16, 3407–3433, https://doi.org/10.5194/gmd-16-3407-2023, 2023. 
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Short summary
This study integrates machine learning with a land surface model to improve wildfire predictions in North America. Traditional models struggle with accurately simulating burned areas due to simplified processes. By combining the predictive power of machine learning with a land model, our hybrid framework better captures fire dynamics. This approach enhances our understanding of wildfire behavior and aids in developing more effective climate and fire management strategies.
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