Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 19, issue 2
Articles | Volume 19, issue 2
https://doi.org/10.5194/gmd-19-955-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-19-955-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 19, issue 2
Model description paper
 | 
29 Jan 2026
Model description paper |  | 29 Jan 2026

BuRNN (v1.0): a data-driven fire model

Seppe Lampe, Lukas Gudmundsson, Basil Kraft, Stijn Hantson, Douglas Kelley, Vincent Humphrey, Bertrand Le Saux, Emilio Chuvieco, and Wim Thiery

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Latest update: 23 May 2026
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Short summary
We introduce BuRNN (BUrned area modelling by Recurrent Neural Networks), a model which estimates monthly burned area based on satellite observations and climate, vegetation, and socio-economic data using machine learning. BuRNN outperforms existing process-based fire models, which improves our capabilities of modelling past and future burned areas.
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