Wildfire danger assessment and forecast through fire spread simulations

Background Traditional empirical fire danger rating systems are largely based on generalized historical weather-fire relationships. By contrast, fire behavior models incorporate interactions among weather, fuels and topography to simulate fire spread across landscapes, offering opportunities for wildfire danger evaluation. Aims This study is to develop a novel Wildfire Danger Assessment and Forecast (WDAF) framework that estimates daily fire spread potential using spatially explicit fire simulations. Methods The framework integrates weather, fuel and topographic data into a fire behavior model to simulate daily fire sizes. To determine fire danger level for a specific day, the framework compares the simulated fire size for that day against a statistical distribution of fire sizes generated from a multi-year reference period. The simulated daily fire size and its corresponding historical percentile serve as the quantitative metrics to assess fire potential. Key Results Fire potential metrics showed excellent performance in predicting occurrences of large wildfires. WDAF offers several advantages: intuitive fire spread potential quantification, normalization against historical baselines and improved physical realism. Conclusions The framework has strong potential as a complementary tool to existing fire danger rating systems. Implications This study highlights the potential of simulation-based approaches for wildfire danger assessment and forecasting.