Journal cover Journal topic
Geoscientific Model Development An interactive open-access journal of the European Geosciences Union
© Author(s) 2016. This work is distributed
under the Creative Commons Attribution 3.0 License.
Model description paper
14 Dec 2016
Review status
This discussion paper is under review for the journal Geoscientific Model Development (GMD).
Identifying required model structures to predict global fire activity from satellite and climate data
Matthias Forkel1, Wouter Dorigo1, Gitta Lasslop2, Irene Teubner1, Emilio Chuvieco3, and Kirsten Thonicke4 1Remote Sensing Research Group, Department of Geodesy and Geoinformation, Technische Universität Wien, Gusshausstraße 27-29, 1040 Vienna, Austria
2Max Planck Institute for Meteorology, Bundesstr. 53, 20146 Hamburg, Germany
3Department of Geology, Geography and the Environment, University of Alcalá, Colegios 2, 28801 Alcalá de Henares, Spain
4Potsdam Institute for Climate Impact Research, Telegraphenberg A62, 14412 Potsdam, Germany
Abstract. Vegetation fires affect human infrastructures, ecosystems, global vegetation distribution, and atmospheric composition. In particular, extreme fire conditions can cause devastating impacts on ecosystems and human society and dominate the year-to-year variability in global fire emissions. However, the climatic, environmental and socioeconomic factors that control fire activity in vegetation are only poorly understood and consequently it is unclear which components, structures, and complexities are required in global vegetation/fire models to accurately predict fire activity at a global scale. Here we introduce the SOFIA (Satellite Observations for FIre Activity) modelling approach, which integrates several satellite and climate datasets and different empirical model structures to systematically identify required structural components in global vegetation/fire models to predict burned area. Models result in the highest performance in predicting the spatial patterns and temporal variability of burned area if they account for a direct suppression of fire activity at wet conditions and if they include a land cover-dependent suppression or allowance of fire activity by vegetation density and biomass. The use of new vegetation optical depth data from microwave satellite observations, a proxy for vegetation biomass and water content, reaches higher model performance than commonly used vegetation variables from optical sensors. The SOFIA approach implements and confirms conceptual models where fire activity follows a biomass gradient and is modulated by moisture conditions. The use of datasets on population density or socioeconomic development do not improve model performances, which indicates that the complex interactions of human fire usage and management cannot be realistically represented by such datasets. However, the best SOFIA models outperform a highly flexible machine learning approach and the state-of-the art global process-oriented vegetation/fire model JSBACH-SPITFIRE. Our results suggest using multiple observational datasets on climate, hydrological, vegetation, and socioeconomic variables together with model-data integration approaches to guide the future development of global process-oriented vegetation/fire models and to better understand the interactions between fire and hydrological, ecological, and atmospheric Earth system components.

Citation: Forkel, M., Dorigo, W., Lasslop, G., Teubner, I., Chuvieco, E., and Thonicke, K.: Identifying required model structures to predict global fire activity from satellite and climate data, Geosci. Model Dev. Discuss., doi:10.5194/gmd-2016-301, in review, 2016.
Matthias Forkel et al.
Matthias Forkel et al.
Matthias Forkel et al.


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Short summary
Wildfires affect infrastructures, vegetation, and the atmosphere. However, it is unclear how fires should be accurately represented in global vegetation models. By using satellite and climate observations, we introduce here a new fire modelling concept to realistically predict burned area. We found that wet conditions and vegetation biomass are the most important model components. Our results suggest to combine observations and models to better understand the role of fires in the Earth system.
Wildfires affect infrastructures, vegetation, and the atmosphere. However, it is unclear how...