A model to reproduce historical wildfire events and to project future burned areas, as well as to assess climate change impacts and adaptation options
The wildFire cLimate impacts and Adaptation Model (FLAM) is able to capture impacts of climate, population, and fuel availability on burned areas.
FLAM uses a process-based fire parameterization algorithm that was originally developed to link a fire model with dynamic global vegetation models.
The key features implemented in FLAM include fuel moisture computation based on the Fine Fuel Moisture Code (FFMC) of the Canadian Forest Fire Weather Index (FWI), and a procedure to calibrate spatial fire suppression efficiency.
- FLAM (formerly SFM) was calibrated and validated for Europe and Indonesia using the most recent publicly available datasets, European Forest Fire Information System (EFFIS) and Global Fire Emissions Database (GFED).
- Burned areas estimated by FLAM for European countries and Indonesia are in good agreement with historical observations.
- FLAM has been used for evaluating adaptation options under different climate scenarios in Europe.
How FLAM works
FLAM is based on a state-of-the-art large scale mechanistic fire modeling algorithm.
Currently it operates with a daily time-step at 0.25-arc degree spatial resolution. All inputs in FLAM are adjusted to fit this resolution.
FLAM uses daily climate data for temperature, precipitation, wind, and relative humidity. When calculating the human ignition probability, a gridded population density is used.
Fuel available for burning is defined as a combination of litter and coarse woody debris (CWD) pools, excluding stem biomass.
The fire suppression efficiency is implemented in FLAM as the probability of extinguishing a fire on a given day.