1,2,3,4,1- 1Vrije Universiteit Brussel, Department of Water and Climate, Brussels, Belgium (seppe.lampe@vub.be)
- 2Institute for Atmospheric and Climate Science, ETH Zürich, Zürich, Switzerland
- 3School of Sciences and Engineering, Universidad del Rosario, Bogotá, Colombia
- 4Environmental Remote Sensing Research Group, Universidad de Alcalá, Alcalá de Henares, Spain
Wildfires play a key role in the Earth system by shaping ecosystem dynamics and influencing the carbon cycle and atmospheric composition. Data-driven models have recently emerged as powerful tools for reproducing observed fire activity, particularly burned area, across a range of spatial and temporal scales. The first version of BuRNN (Burned area and emissions modelling through Recurrent Neural Networks) focused solely on burned area and outperformed all process-based fire-coupled DGVMs from ISIMIP over a wide range of spatial, temporal and spatio-temporal skill metrics. Here we present the 2nd version of BuRNN, a data-driven model that now jointly represents burned area and fire-related emissions.
How to cite: Lampe, S., Gudmundsson, L., Kraft, B., Hantson, S., Chuvieco, E., and Thiery, W.: Modelling burned area and emissions with deep learning, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-19404, https://doi.org/10.5194/egusphere-egu26-19404, 2026.
