Articles | Volume 17, issue 4
https://doi.org/10.5194/gmd-17-1525-2024
https://doi.org/10.5194/gmd-17-1525-2024
Development and technical paper
 | 
21 Feb 2024
Development and technical paper |  | 21 Feb 2024

Quantifying wildfire drivers and predictability in boreal peatlands using a two-step error-correcting machine learning framework in TeFire v1.0

Rongyun Tang, Mingzhou Jin, Jiafu Mao, Daniel M. Ricciuto, Anping Chen, and Yulong Zhang

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Cited articles

Arief, A. T., Nukman, and Elwita, E.: Self-Ignition Temperature of Peat, J. Phys. Conf. Ser., 1198, 042021, https://doi.org/10.1088/1742-6596/1198/4/042021, 2019. 
Bali, S., Zheng, S., Gupta, A., Wu, Y., Chen, B., Chowdhury, A., and Khim, J.: Prediction of Boreal Peatland Fires in Canada using Spatio-Temporal Methods, Climate Change AI. ICML 2021 Workshop on Tackling Climate Change with Machine Learning. Climate Change AI, https://www.climatechange.ai/papers/icml2021/12 (last access: 19 January 2023), 2021. 
Bedia, J., Herrera, S., and Gutiérrez, J. M.: Assessing the predictability of fire occurrence and area burned across phytoclimatic regions in Spain, Nat. Hazards Earth Syst. Sci., 14, 53–66, https://doi.org/10.5194/nhess-14-53-2014, 2014. 
Behrangi, A., Christensen, M., Richardson, M., Lebsock, M., Stephens, G., Huffman, G. J., Bolvin, D., Adler, R. F., Gardner, A., Lambrigtsen, B., and Fetzer, E.: Status of high-latitude precipitation estimates from observations and reanalyses, J. Geophys. Res.-Atmos., 121, 4468–4486, https://doi.org/10.1002/2015JD024546, 2016. 
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
Carbon-rich boreal peatlands are at risk of burning. The reproducibility and predictability of rare peatland fire events are investigated by constructing a two-step error-correcting machine learning framework to tackle such complex systems. Fire occurrence and impacts are highly predictable with our approach. Factor-controlling simulations revealed that temperature, moisture, and freeze–thaw cycles control boreal peatland fires, indicating thermal impacts on causing peat fires.
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