FLAM Goes EGU: Exploring Wildfire Research in 2024

In the Biogeosciences Session, Hyun-Woo Jo presented a study titled “Integrating Human Domain Knowledge into Artificial Intelligence for Hybrid Forest Fire Prediction: Case Studies from South Korea and Italy” which highlighted the adaptability and adjustability of FLAM to accurately predict and analyze forest fire dynamics in South Korea and Italy using Artificial Intelligence coupled with process-based algorithms at various scales. Projections of fire frequency under climate change (CMIP6) scenarios and forest management options were presented.

Additionally, Shelby Corning and Andrey Krasovskiy with colleagues from Italy and Portugal convened a two day session titled: “Spatial and temporal patterns of wildfires: models, theory, and reality,” which aimed to share insights from studies on the understanding of wildfire patterns, post-fire recovery and restoration, and wildfire mitigation strategies by bringing together a diverse group of experts. Participants discussed innovative tools and methods for analyzing and modeling wildfires at various scales, focusing on their spatial-temporal patterns and associated risk factors. Presentations covered global studies, as well as regional studies including projects found across Europe, Asia, the Americas, and Australia.