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

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

doi:10.5194/gmd-19-955-2026

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

Data sets

BuRNN: A Data-Driven Fire Model Seppe Lampe https://doi.org/10.5281/zenodo.17778519

The HistLight global lightning stroke density reconstruction (1836–2015) (2022.0.0) J. O. Kaplan and K. H.-K. Lau https://doi.org/10.5281/zenodo.6405396

Global Fire Emissions Database (GFED5) Burned Area (0.1) Yang Chen et al. https://doi.org/10.5281/zenodo.7668424

The World Wide Lightning Location Network (WWLLN) Global Lightning Climatology (WGLC) and time series (v2025.0.0) J. O. Kaplan https://doi.org/10.5281/zenodo.15215319

Model code and software

VUB-HYDR/BuRNN: Version 1.1 Seppe Lampe https://doi.org/10.5281/zenodo.17834206

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.