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 is based on a state-of-the-art large scale mechanistic fire modeling algorithm. Currently FLAM 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.
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Last edited: 10 April 2019
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Krasovskii A ORCID: https://orcid.org/0000-0003-0940-9366, Khabarov N, Pirker J, Kraxner F, Yowargana P, Schepaschenko D, & Obersteiner M (2018). Modeling Burned Areas in Indonesia: The FLAM Approach. Forests 9 (7): e437. DOI:10.3390/f9070437.
Krasovskii A ORCID: https://orcid.org/0000-0003-0940-9366, Khabarov N, Migliavacca M, Kraxner F, & Obersteiner M (2016). Regional aspects of modelling burned areas in Europe. International Journal of Wildland Fire 25 (8): 811-818. DOI:10.1071/WF15012.
Khabarov N, Krasovskii AA ORCID: https://orcid.org/0000-0003-0940-9366, Obersteiner M, Swart R, Dosio A, San-Miguel-Ayanz J, Durrant T, Camia A, et al. (2016). Forest fires and adaptation options in Europe. Regional Environmental Change 16 (1): 21-30. DOI:10.1007/s10113-014-0621-0.
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