A model to reproduce historical wildfire events and to project future burned areas, as well as to assess climate change impacts and adaptation options.

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About FLAM

The wildFire cLimate impacts and Adaptation Model (FLAM) is able to capture impacts of climate, population, and fuel availability on burned areas and associated emissions.

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), a procedure to calibrate spatial fire suppression efficiency, and the ability to include additional variables in fire modeling and prediction.

FAST FACTS

  • FLAM was calibrated and validated using the most recent publicly available GIS and remote sensing datasets, such as European forest Fire Information System (EFFIS), Global Fire Emissions Database (EFED), and MODIS Climate Change Initiative (CCI) burned areas.
  • Burned areas estimated by FLAM are in good agreement with historical observations at various scales.
  • FLAM has been applied to model forest fires in countries such as Sweden, Indonesia, South Korea, Italy, and Austria.
  • FLAM has been used for evaluating adaptation options under different climate scenarios.
Tree and Fire symbols © IIASA

How FLAM works

FLAM operates with a daily time step at various spatial resolutions and uses mechanistic fire modeling algorithms to parameterize the impacts of climate, fuel availability, and human activities on wildfire probabilities, frequencies, and burned areas. Resolution of results is adjustable dependent on the resolution of input data. Past and ongoing projects have operated at various scales: from 100m at a regional level, to 1km at the country level, 10km at a European level and 25km globally.

FLAM uses daily climate data for temperature, precipitation, wind, and relative humidity. Fuel availability is defined as a combination of litter and coarse woody debris (CWD) pools, excluding stem biomass, and is calculated from landcover and biomass maps. Human ignition probability is calculated using gridded population density data. Fire suppression efficiency is implemented in FLAM as the probability of extinguishing a fire on a given day.

FLAM’s modular structure allows for the inclusion of additional fire-related factors into probabilistic algorithms including variables such as distance to roads, cropland, lakes, slope, and elevation. The model is therefore adaptable to unique regional characteristics such as traditional practices of agricultural burning, forest clearing, or peatland draining. Eventually this will allow for the production of integrated regional hot-spot maps for wildland fire risk.

FLAM Scheme © IIASA

FLAM Scheme

 

Case studies implemented with FLAM:

  1. Projections of global burned areas driven by climate change scenarios until 2100;
  2. Modeling of burned areas and adaptation options in Europe;
  3. Modeling of burned areas and their feedback to land-use change in Indonesia, with a particular emphasis on extreme fires due to the impacts of El Nino southern oscillation using historical data and the delta approach for warming climates;
  4. Optimization of the IIASA’s FLAM model to represent forest fires in South Korea.

FLAM projects:

HuT – The  Human-Tech Nexus - Building a Safe Haven to Cope with Climate Extremes

MOSAIC – Managing Protective Forests Facing Climate Change Compound Events

RECEIPT - REmote Climate Effects and their Impact on European sustainability, Policy and Trade

AFF – Austria Fire Futures

ForestNavigator – Navigating European forest and forest bioeconomy sustainability to EU climate neutrality

LAMASUS – Land Management for Sustainability

EU BIOCLIMA – European Union Biodiversity and Climate strategies Assessment

BIOCONSENT – Decision Support for Forest Biodiversity Policy

RESTORE+ – Addressing Landscape Restoration on Degraded Land in Indonesia and Brazil

 

Previous projects:

NORAD OMRRT - Options Market and Risk-Reduction Tools for REDD+

ECONADAPT - Economics of Climate Change Adaptation in Europe

MEDIATION - Methodology for Effective Decision-making on Impacts and Adaptation

 

FLAM Team:

Andrey Krasovskiy profile picture

Andrey Krasovskiy

Senior Research Scholar (AFE)

Johanna San Pedro profile picture

Johanna San Pedro

Researcher (AFE)

Pavel Kiparisov profile picture

Pavel Kiparisov

Researcher (AFE)

Eunbeen Park profile picture

Eunbeen Park

Research Scholar (AFE)

Hyun-Woo Jo profile picture

Hyun-Woo Jo

Research Scholar (AFE)

Related Publications (Selection)

Corning, S. , Krasovskiy, A. , Kiparisov, P. , San Pedro, J. , Viana, C.M., & Kraxner, F. (2024). Anticipating Future Risks of Climate-Driven Wildfires in Boreal Forests. Fire 7 (4) e144. 10.3390/fire7040144.

Krasovskii, A. , 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. 10.3390/f9070437.

Jo, H.-W. , Krasovskiy, A. , Hong, M., Corning, S. , Kim, W., Kraxner, F., & Lee, W.-K. (2023). Modeling Historical and Future Forest Fires in South Korea: The FLAM Optimization Approach. Remote Sensing 15 (5) e1446. 10.3390/rs15051446.

Cimdins, R., Krasovskiy, A. , & Kraxner, F. (2022). Regional Variability and Driving Forces behind Forest Fires in Sweden. Remote Sensing 14 (22) e5826. 10.3390/rs14225826.

Fernandez-Anez, N., Krasovskiy, A. , Müller, M., Vacik, H., Baetens, J., Hukić, E., Kapovic Solomun, M., Atanassova, I., Glushkova, M., Bogunović, I., Fajković, H., Djuma, H., Boustras, G., Adámek, M., Devetter, M., Hrabalikova, M., Huska, D., Martínez Barroso, P., Vaverková, M.D., Zumr, D., Jõgiste, K., Metslaid, M., Koster, K., Köster, E., Pumpanen, J., Ribeiro-Kumara, C., Di Prima, S., Pastor, A., Rumpel, C., Seeger, M., Daliakopoulos, I., Daskalakou, E., Koutroulis, A., Papadopoulou, M.P., Stampoulidis, K., Xanthopoulos, G., Aszalós, R., Balázs, D., Kertész, M., Valkó, O., Finger, D.C., Thorsteinsson, T., Till, J., Bajocco, S., Gelsomino, A., Amodio, A.M., Novara, A., Salvati, L., Telesca, L., Ursino, N., Jansons, A., Kitenberga, M., Stivrins, N., Brazaitis, G., Marozas, V., Cojocaru, O., Gumeniuc, I., Sfecla, V., Imeson, A., Veraverbeke, S., Mikalsen, R.F., Koda, Eu., Osinski, P., Castro, A.C. M., Nunes, J.P., Oom, D., Vieira, D., Rusu, T., Bojović, S., Djordjevic, D., Popovic, Z., Protic, M., Sakan, S., Glasa, J., Kacikova, D., Lichner, L., Majlingova, A., Vido, J., Ferk, M., Tičar, J., Zorn, M., Zupanc, V., Hinojosa, M., Knicker, H., Lucas-Borja, M.E., Pausas, J., Prat-Guitart, N., Ubeda, X., Vilar, L., Destouni, G., Ghajarnia, N., Kalantari, Z., Seifollahi-Aghmiuni, S., Dindaroglu, T., Yakupoglu, T., Smith, T., Doerr, S., & Cerda, A. (2021). Current Wildland Fire Patterns and Challenges in Europe: A Synthesis of National Perspectives. Air, Soil and Water Research 14 e117862212110281. 10.1177/11786221211028185.

Khabarov, N. , Krasovskii, A.A. , Obersteiner, M. , Swart, R., Dosio, A., San-Miguel-Ayanz, J., Durrant, T., Camia, A., & Migliavacca, M. (2016). Forest fires and adaptation options in Europe. Regional Environmental Change 16 (1) 21-30. 10.1007/s10113-014-0621-0.

Krasovskii, A. , 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. 10.1071/WF15012.