Advancing Wildfire Risk Assessment Using Ensemble Fire Weather Predictions

Understanding and predicting wildfire dynamics is critical to mitigating their impacts. This is particularly relevant in regions experiencing increasing wildfire severity and frequency due to climate change. This study addresses the need for improved wildfire prediction by development of a system that uses Ensemble Fire Predictions (EFP), where we use a probabilistic fire model to model wildfire growth. Ensemble-based methodologies are particularly valuable for wildfire modeling as they account for the inherent variability in weather patterns, fuel conditions, and fire behavior that drive wildfire dynamics.

 

Our approach couples fire models with datasets on historical and future climate. Specifically, the system incorporates the Fine Fuel Moisture Code (FFMC) and Duff Moisture Code (DMC), (indicators of surface and deeper layer fuel dryness, respectively) from the Canadian Forest Fire Danger Rating System (CFFDRS) to estimate fuel moisture trends using time series analysis of historical weather station data. It also integrates high-resolution weather and climate datasets, including NASA NEX-GDDP-CMIP6, Ouranos ESPO-G6-R2, and CCRN CanRCM4-WFDEI-GEM-CaPA, to evaluate the impact of alternate climate scenarios. Stochastic time series of daily fuel moisture are probabilistically generated based on historical climatology to reflect seasonal variability and day-to-day fluctuations. Historical and modeled wind speed and direction data are used to construct joint probability distributions, enabling the stochastic generation of realistic wind conditions for simulations. This novel methodology allows us to capture a wide range of possible wildfire scenarios, improving the reliability and robustness of predictions.

 

This research contributes to advancing wildfire spatio-temporal modeling tools by enabling more accurate probabilistic forecasts that can support mitigation strategies and resilience planning. Future work will further develop these methodologies by incorporating the ensemble outputs into Burn-P3, enabling detailed probabilistic modeling of fire spread and burn probabilities, ultimately contributing to better-informed wildfire management and planning, improved resource allocation, and community protection during wildfire events.