Regional aspects of modelling burned areas in Europe

Andrey Krasovskii1*, Nikolay Khabarov1, Mirco Migliavacca2, Florian Kraxner1, Michael Obersteiner1 1International Institute for Applied Systems Analysis, Schlossplatz 1, 2361 Laxenburg, Austria 2Max Planck Institute for Biogeochemistry, Hans-Knöll-Str. 10, 07745 Jena, Germany Abstract: This paper presents a series of improvements to the quantitative modelling of burned areas in Europe under historical climate. The Standalone Fire Model (SFM) based on a state-of-the-art large scale mechanistic fire modelling algorithm is used to reproduce historical burned areas reported in the two publicly available datasets – European Forest Fire Information System (EFFIS) and Global Fire Emissions Database (GFED). The most recent versions of these sources allow a broader validation of SFM’s modelled burned areas at a country level. Our analysis is carried out for the years 2000-2008 for 17 European countries utilizing both EFFIS and GFED datasets for model benchmarking. We suggest improving the original model by modifying the fire probability function reflecting fuel moisture. This modification allows for a dramatic improvement of accuracy in modelled burned areas for a range of European countries. We also explore in detail a pixel-level parametrization of firefighting efficiency in SFM along with modifications of the biomass map. In comparison with the aggregated country-level approach, the advantages of the finer calibration are quite minor for the most recent version of the GFED dataset. Overall, the annual burned areas modelled by this improved SFM version are in good agreement with historical observations. Brief summary: This paper presents a series of improvements to the quantitative modelling of burned areas in Europe under historical climate. Our analysis is carried out for 2000-2008 for 17 European countries. The annual burned areas modelled by an improved version of the Standalone Fire Model (SFM) are in good agreement with historical observations. Additional keywords: fire model, fuel moisture, probability of fire, validation.