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Geoscientific Model Development An interactive open-access journal of the European Geosciences Union
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Discussion papers
https://doi.org/10.5194/gmd-2019-92
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-2019-92
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Development and technical paper 02 May 2019

Development and technical paper | 02 May 2019

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This discussion paper is a preprint. It is a manuscript under review for the journal Geoscientific Model Development (GMD).

Improving the LPJmL4-SPITFIRE vegetation-fire model for South America using satellite data

Markus Drüke1,2, Matthias Forkel3, Werner von Bloh1, Boris Sakschewski1, Manoel Cardoso4, Mercedes Bustamante5, Jürgen Kurths1,2, and Kirsten Thonicke1 Markus Drüke et al.
  • 1Potsdam Institudte for Climate Impact Research, Telegraphenberg A 31, Potsdam, Germany
  • 2Humboldt Universität zu Berlin, Unter den Linden 6, 10099 Berlin, Deutschland
  • 3TU Wien, Department of Geodesy and Geoinformation, Gusshausstr. 27–29, 1040 Vienna, Austria
  • 4Instituto Nacional de Pesquisas Espaciais, Av. dos Astronautas, 1.758 – Jardim da Granja, São José dos Campos – SP, 12227-010, Brazil
  • 5Instituto de Ciências Biologicas, Universidade de Brasília, Campus Universitário Darcy Ribeiro – Asa Norte, 70910-900 Brasília, Brazil

Abstract. Vegetation fires influence global vegetation distribution, ecosystem functioning, and global carbon cycling. Specifically in South America, changes in fire occurrence together with land use change accelerate ecosystem fragmentation and increase the vulnerability of tropical forests and savannas to climate change. Dynamic Global Vegetation Models (DGVMs) are valuable tools to estimate the effects of fire on ecosystem functioning and carbon cycling under future climate changes. However, fire-enabled DGVMs have partly poor performances in capturing the magnitude, spatial patterns, and temporal dynamics of burnt area as observed by satellites. As fire is controlled by the interplay of weather conditions, vegetation properties and human activities, fire modules in DGVMs can be improved in various aspects. As a starting point, we here focus on improving the controls of climate and hence fuel moisture content on fire danger in the LPJmL4-SPITFIRE DGVM in South America and especially for the Brazilian fire-prone biomes Caatinga and Cerrado. We therefore test two alternative model formulations (standard Nesterov index and a newly implemented water vapor pressure deficit) for climate effects on fire danger within a formal model-data integration setup where we estimate model parameters against satellite data sets of burnt area (GFED4) and above ground biomass of trees. Our results show that the optimized model improves the representation of spatial patterns and the seasonal to inter-annual dynamics of burnt area especially in the Cerrado/Caatinga region. In addition, the model improves the simulation of above-ground biomass and plant functional types (PFTs). We obtained the best results by using the water vapor pressure deficit (VPD) for the calculation of fire danger. The VPD includes, in comparison to the Nesterov index, a representation of the air humidity and the vegetation density. This work shows the successful application of a systematic model-data integration setup, as well as the integration of a new fire danger formulation, in order to optimize a process-based fire-enabled DGVM. It further highlights the potential of this approach to achieve a new level of accuracy in comprehensive global fire modelling and prediction.

Markus Drüke et al.
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Status: open (until 27 Jun 2019)
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Short summary
This work shows the successful application of a systematic model-data integration setup, as well as the implementation of a new fire danger formulation, in order to optimize a process-based fire-enabled dynamic global vegetation model. We have demonstrated a major improvement of the fire representation within LPJmL4-SPITFIRE in terms of the spatial pattern and the inter annual variability of burnt area in South America as well as in the modelling of biomass and the distribution of plant types.
This work shows the successful application of a systematic model-data integration setup, as well...
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