Fire Modelling Literacy Program Module 1 - Foundations of Fire Modelling
June 24-25, 2026 | 8 AM PT/11 AM ET - 10 am PT/1 pm ET
The FMLP is designed to build applied fire modelling literacy in vulnerable sectors, including utilities, insurance, agriculture, land management, resilience planning and research. The FMLP will be housed within the GWC Academy, which offers training modules, workshops and webinars on wildfire resilience and recovery through an integrated learning platform.
FMLP Module 1 (Foundations of Fire Modelling), will be launched in multiple formats in 2026, starting with a live-virtual session on 24-25 June (15:00-17:00 UTC). We invite you and your colleagues to register through these links:
The GWC is offering discount codes to make FMLP training more accessible across income levels (by World Bank classification):
Upper-middle income countries: 50% (code 50FMLP)
Lower-middle income countries: 75% (code 75FMLP)
Low-income countries: 90% (code 90FMLP)
Limited fee waivers are available; please contact us if needed
As wildfires grow in scale and complexity, the models we use to understand them must be equally diverse. From predicting fire spread in threatened communities to projecting century-long shifts in forest composition, fire science relies on computational approaches tailored to vastly different questions, scales, and stakeholders.
This June, join leading researchers as they pull back the curtain on their use of modeling frameworks—revealing not just what each approach can accomplish, but where its limits lie and why those boundaries matter. Through comparative case studies, this workshop will illuminate a fundamental truth of fire science: there is no single “best” model, only tools matched—or mismatched—to the problems we’re trying to solve.
During this 2-day, 4-hour workshop, participants will:
Establish a shared vocabulary for fire and fire modeling across sectors
Be introduced to ignition, spread, and simulation modeling frameworks
Build foundational literacy in modeling uncertainty (parameter, structural, scenario)
Strengthen interpretation of probabilistic outputs and ensemble results
Improve decision framing under uncertainty
Whether you’re looking to use science-based modeling to mitigate risks to your organization, a researcher seeking methodological insights, a fire operations professional seeking decision-support tools, or a policymaker navigating risk and resilience strategies, this session offers a rare opportunity to understand how model choice shapes what we can know about fire’s past, present, and future.
