FLAM is a wildfire model that reproduces historical fires, projects future burned areas, and assesses climate change impacts and adaptation options.

Explore the documentation Browse projects FLAM at IIASA

Open source

FLAM code is open source.

Based on Public Data

FLAM is calibrated and validated using public data, such as EFFIS, EFED, and MODIS CCI.

Globetrotter

FLAM has been used to study fires all around the world—at the country, regional, and global levels.

High accuracy

FLAM has proven its accuracy by matching up with past observations of burned areas.

Latest publications

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Projecting Forest Fire Probability in South Korea under Climate Change, Population, and Forest Management Scenarios Using AI & Process-Based Hybrid Model (FLAM-Net) (2025)

This study aimed to integrate human domain knowledge into a machine learning framework.

Flammable futures—storylines of climatic impacts on wildfire events and palm oil plantations in Indonesia (2024)

To better understand how climate-driven drought events and adaptation efforts affect burned area, agricultural production losses, and land use decisions, we developed a storyline approach centered on Indonesia's 2015 fire events, which saw significant production losses of palm oil.