A statistical global burned area model for seamless integration into Dynamic Global Vegetation Models

Abstract. Fire-enabled Dynamic Global Vegetation Models (DGVMs) play an essential role in predicting vegetation dynamics and biogeochemical cycles amid climate change. Modeling wildfires has been challenging in process-based biophysics-oriented DGVMs, as human behaviour plays a crucial role. This study aims to reveal a global statistical model for the relationships between biophysical and socioeconomic drivers of wildfire dynamics and monthly burned area (BA) that can be integrated into DGVMs. We developed Generalised Linear Models (GLMs) to capture the relationships between potential predictor variables that are simulated by DGVMs and/or available in future scenarios and the latest global burned area product from GFED5. Predictor variables were chosen to represent aspects of fire weather, vegetation structure and activity, human land use and behaviour and topography. The final model was chosen by minimizing collinearity and by maximizing model performance in terms of reproducing observations. The final model included eight predictor variables encompassing the Fire Weather Index (FWI), a novel Gross Primary Productivity Index (GPPI), Human Development Index (HDI), Population Density (PPN), Percentage Tree Cover (PTC), Percentage Non-Tree Cover (PNTC), Number of Dry Days (NDD), and Topographic Positioning Index (TPI). Given its simplicity, our model demonstrated a remarkable capability, explaining 56.8 % of the burnt area variability, comparable to other state-of-the-art global fire models. FWI, PTC, TPI and PNTC were positively related to BA, while GPPI, HDI, PPN, and NDD were negatively related to wildfire. While the model effectively predicted the spatial distribution of burned areas (NME = 0.72), its standout performance lay in capturing the seasonal variability, especially in regions often characterized by distinct wet and dry seasons, notably southern Africa, Australia and parts of South America (R² > 0.50). Our model reveals the robust predictive power of fire weather and vegetation dynamics emerging as reliable predictors of seasonal global fire patterns. The presented model should be compatible with most, if not all, DGVMs used to develop future scenarios.