Speaker
Description
Wildfires disrupt ecosystems, with much evidence to show that climate change is exacerbating vulnerability in regions poorly adapted to such disturbances. These events are driven by complex, multi-scale interactions where small perturbations in environmental factors can trigger large-scale shifts, complicating prediction efforts. We propose a coupled convection-reaction-diffusion system for modelling wildfire spread dynamics. This system integrates spatial and temporal variability to identify thresholds for spread and identify impacts of abrupt environmental changes on burnt areas and rates of propagation.
We incorporate environmental, meteorological, and historical fire data from the Global Wildfire Information System and project partner, the Department for Environment, Food and Rural Affairs (UK), with a specific focus on UK moorland fires. In applying Bayesian inference techniques and Monte Carlo methods for parameter estimation and uncertainty quantification, we aim to produce robust model validation against unseen data. Recent wildfire events around the globe highlight the need for insights into environmental vulnerability, property loss, and infrastructure risk. By enabling near-real-time simulations, this work aims to provide a computational tool for emergency response, long-term management strategies, and assessments of climate change-induced outlier weather patterns influencing fire behaviour.
