The wildFire cLimate impacts and Adaptation Model (FLAM) is able to capture complex interactions among burned areas, climate, population, and fuel availability. 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 Canadian Fine Fuel Moisture Code (FFMC) index, and a procedure to calibrate regional fire suppression efficiency.
FLAM is based on a state-of-the-art large scale mechanistic fire modelling algorithm. Currently FLAM operates on a European scale with a daily time step and a spatial resolution of 25×25 km. All inputs in the FLAM are adjusted to fit this resolution. The 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.
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Last edited: 27 October 2016
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