Improving prediction and assessment of global wildfires using neural networks

Fires determine vegetation patterns, impact human societies, and provide complex feedbacks into the global climate system. Empirical and process-based models differ in their scale and mechanistic assumptions, giving divergent predictions of fire drivers and extent. Especially, the role of anthropogenic drivers remains less understood. Taking a data-driven approach, we use an artificial neural network to learn region-specific relationships between fire and its socio-environmental drivers across the globe. As a result, our models achieve higher predictability than previously reported, with global spatial correlation of 0.92, temporal correlation of 0.76, interannual correlation of 0.69, and grid-cell level correlation of 0.6, between predicted and observed burned area. Our analysis reveals universal global patterns in fire-climate interactions, coupled with strong regional differences in fire-human relationships. Given the current socio-anthropogenic conditions, Equatorial Asia, southern Africa, and Australia show a strong sensitivity of fire extent to temperature whereas northern Africa shows a strong negative sensitivity. Overall, forests and shrublands, show a stronger sensitivity of burned area to temperature compared to savannas, potentially weakening their status as carbon sinks under future climate-change scenarios.