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

Drüke, M., Forkel, M., von Bloh, W., Sakschewski, B., Cardoso, M., Bustamante, M., Kurths, J., Thonicke, K. (2019): Improving the LPJmL4-SPITFIRE vegetation-fire model for South America using satellite data. - Geoscientific Model Development, 12, 12, 5029-5054.
https://doi.org/10.5194/gmd-12-5029-2019

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 Creators:
Drüke, Markus1, Author              
Forkel, M.2, Author
von Bloh, Werner1, Author              
Sakschewski, Boris1, Author              
Cardoso, M.2, Author
Bustamante, M.2, Author
Kurths, Jürgen1, Author              
Thonicke, Kirsten1, Author              
Affiliations:
1Potsdam Institute for Climate Impact Research, ou_persistent13              
2External Organizations, ou_persistent22              

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 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, most fire-enabled DGVMs have problems in capturing the magnitude, spatial patterns, and temporal dynamics of burned 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. In this study we focus on improving the controls of climate and hence fuel moisture content on fire danger in the LPJmL4-SPITFIRE DGVM in South America, especially for the Brazilian fire-prone biomes of 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 datasets of burned area (GFED4) and aboveground biomass of trees. Our results show that the optimized model improves the representation of spatial patterns and the seasonal to interannual dynamics of burned area especially in the Cerrado and Caatinga regions. In addition, the model improves the simulation of aboveground biomass and the spatial distribution of 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 modeling and prediction.

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 Dates: 2019
 Publication Status: Finally published
 Pages: -
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 Table of Contents: -
 Rev. Type: -
 Identifiers: DOI: 10.5194/gmd-12-5029-2019
PIKDOMAIN: RD1 - Earth System Analysis
PIKDOMAIN: RD4 - Complexity Science
eDoc: 8525
Research topic keyword: Climate impacts
Research topic keyword: Tipping Elements
Research topic keyword: Ecosystems
Model / method: LPJmL
Model / method: Open Source Software
Regional keyword: South America
Organisational keyword: RD1 - Earth System Analysis
Organisational keyword: RD4 - Complexity Science
Working Group: Ecosystems in Transition
Working Group: Development of advanced time series analysis techniques
Working Group: Network- and machine-learning-based prediction of extreme events
 Degree: -

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Title: Geoscientific Model Development
Source Genre: Journal, SCI, Scopus, p3, oa
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Pages: - Volume / Issue: 12 (12) Sequence Number: - Start / End Page: 5029 - 5054 Identifier: CoNE: https://publications.pik-potsdam.de/cone/journals/resource/journals185