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Understanding and simulating cropland and non-cropland burning in Europe using the BASE (Burnt Area Simulator for Europe) model

Authors

Forrest,  Matthew
External Organizations;

Hetzer,  Jessica
External Organizations;

/persons/resource/maik.billing

Billing,  Maik
Potsdam Institute for Climate Impact Research;

Bowring,  Simon P. K.
External Organizations;

Kosczor,  Eric
External Organizations;

/persons/resource/luke.oberhagemann

Oberhagemann,  Luke
Potsdam Institute for Climate Impact Research;

Perkins,  Oliver
External Organizations;

Warren,  Dan
External Organizations;

Arrogante-Funes ,  Fátima
Potsdam Institute for Climate Impact Research;

/persons/resource/Kirsten.Thonicke

Thonicke,  Kirsten
Potsdam Institute for Climate Impact Research;

Hickler,  Thomas
External Organizations;

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Citation

Forrest, M., Hetzer, J., Billing, M., Bowring, S. P. K., Kosczor, E., Oberhagemann, L., Perkins, O., Warren, D., Arrogante-Funes, F., Thonicke, K., Hickler, T. (in press): Understanding and simulating cropland and non-cropland burning in Europe using the BASE (Burnt Area Simulator for Europe) model. - Biogeosciences.


Cite as: https://publications.pik-potsdam.de/pubman/item/item_30456
Abstract
Fire interacts with many parts of the Earth system. However, its drivers are myriad and complex, interacting differently in different regions depending on prevailing climate regimes, vegetation types, socioeconomic development, and land use and management. Europe is facing strong increases in projected meteorological fire danger as a consequence of climate change, and has experienced extreme fire seasons and events in recent years. Here, we focus on understanding and simulating burnt area across a European study domain using remote sensing data and Generalised Linear Models (GLMs). We first examined fire occurrence across land cover types and found that all non-cropland vegetation types (NCV, comprising 26 % of burnt area) burned with similar spatial and temporal patterns, which were very distinct from those in croplands (74 % of burned area). We then used GLMs to predict cropland and NCV burnt area at ~9x9 km and monthly spatial and temporal resolution, respectively, which together we termed BASE (Burnt Area Simulator for Europe). Compared to satellite burned area products, BASE effectively captured the general spatial and temporal patterns of burning, explaining 32 % (NCV) and 36 % (cropland) of the deviance, and gave similar performance of state-of-the-art global fire models. The most important drivers were fire weather and monthly indices derived from gross primary productivity, followed by coarse socioeconomic indicators and vegetation properties. Crucially, we found that the drivers of cropland and NCV burning were very different, highlighting the importance of simulating burning in different land cover types separately. Through the choice of predictor variables, BASE was designed for coupling with dynamic vegetation and Earth System models, and thus enabling future projections. In particular, the strong model skill of BASE when reproducing seasonal and interannual dynamics of NCV burning (i.e. temporally evolving wildfire risk), and the novel inclusion of cropland burning, recommend it for this purpose. In addition to this, the BASE framework may serve as a basis for further studies using additional predictors to further elucidate drivers of fire in Europe. Through these applications, we suggest BASE may be a useful tool for understanding, and therefore adapting to, the increasing fire risk in Europe.