Modeling of burned areas on a global scale using statistical learning methods

Africa and South America together account for more than 70 % of the global burned area representing nearly 65 % of global fire-related carbon emissions (van der Werf et al., 2017). Beyond carbon release, wildfires emit large amounts of dust and aerosols that influence regional climate through radiative processes. More generally, wildfires strongly modify land surface properties, including vegetation composition, soil carbon stocks, or surface albedo, with far-reaching consequences for regional carbon, water, and energy cycles.

In the ISBA land surface model (Delire et al., 2020), burned area is currently parameterized using grid-cell surface characteristics, a fire-resistance coefficient, soil moisture, and available biomass. While computationally efficient, this simplified formulation may contribute to persistent regional biases in simulated fire activity. To overcome these limitations, we develop a data-driven fire modeling framework based on two artificial neural network architectures: one addressing a regression task and the other a classification task. The models use meteorological conditions, vegetation states, and anthropogenic factors to estimate the daily burned area fraction.

The proposed framework reproduces the spatiotemporal variability of burned areas with some fidelity. It is specially the case in important areas such as Africa, South America, and Australia. These results highlight the potential of deep learning approaches to enhance wildfire representation and prediction in Earth system models. That would be the very future of our research project.