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Geoscientific Model Development An interactive open-access journal of the European Geosciences Union
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Volume 8, issue 8
Geosci. Model Dev., 8, 2597–2609, 2015
https://doi.org/10.5194/gmd-8-2597-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
Geosci. Model Dev., 8, 2597–2609, 2015
https://doi.org/10.5194/gmd-8-2597-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.

Development and technical paper 21 Aug 2015

Development and technical paper | 21 Aug 2015

Improving the representation of fire disturbance in dynamic vegetation models by assimilating satellite data: a case study over the Arctic

E. P. Kantzas1,2, S. Quegan1, and M. Lomas1 E. P. Kantzas et al.
  • 1School of Mathematics and Statistics, University of Sheffield, Hicks Building, Hounsfield Rd, Sheffield S37RH, UK
  • 2Nansen International Environmental and Remote Sensing Centre, Vasilievsky Island, 199034, St. Petersburg, Russia

Abstract. Fire provides an impulsive and stochastic pathway for carbon from the terrestrial biosphere to enter the atmosphere. Despite fire emissions being of similar magnitude to net ecosystem exchange in many biomes, even the most complex dynamic vegetation models (DVMs) embedded in general circulation models contain poor representations of fire behaviour and dynamics, such as propagation and distribution of fire sizes. A model-independent methodology is developed which addresses this issue. Its focus is on the Arctic where fire is linked to permafrost dynamics and on occasion can release great amounts of carbon from carbon-rich organic soils. Connected-component labelling is used to identify individual fire events across Canada and Russia from daily, low-resolution burned area satellite products, and the obtained fire size probability distributions are validated against historical data. This allows the creation of a fire database holding information on area burned and temporal evolution of fires in space and time. A method of assimilating the statistical distribution of fire area into a DVM whilst maintaining its fire return interval is then described. The algorithm imposes a regional scale spatially dependent fire regime on a sub-scale spatially independent model; the fire regime is described by large-scale statistical distributions of fire intensity and spatial extent, and the temporal dynamics (fire return intervals) are determined locally. This permits DVMs to estimate many aspects of post-fire dynamics that cannot occur under their current representations of fire, as is illustrated by considering the modelled evolution of land cover, biomass and net ecosystem exchange after a fire.

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Short summary
Despite its importance, land surface models poorly simulate fire disturbance and its dynamic effects. Here we present a novel and model-independent methodology of implementing a realistic fire size distribution in a dynamic vegetation model by assimilating satellite data and employing blob detection. While focusing on the Arctic, we verify our results against field data and showcase the improved fire representation in the model.
Despite its importance, land surface models poorly simulate fire disturbance and its dynamic...
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