@Article{DrükeFBSCBKT:2019:ImLPVe,
author = "Dr{\"u}ke, Markus and Forkel, Matthias and von Bloh, Werner and
Sakschewski, Boris and Cardoso, Manoel Ferreira and Bustamante,
Mercedes and Kurths, J{\"u}rgen and Thonicke, Kirsten",
affiliation = "{Potsdam Institute for Climate Impact Research (PIK)} and
{Humboldt Universit{\"a}t zu Berlin} and {Potsdam Institute for
Climate Impact Research (PIK)} and {Potsdam Institute for Climate
Impact Research (PIK)} and {Instituto Nacional de Pesquisas
Espaciais (INPE)} and {Universidade de Bras{\'{\i}}lia (UnB)}
and {Potsdam Institute for Climate Impact Research (PIK)} and
{Potsdam Institute for Climate Impact Research (PIK)}",
title = "Improving the LPJmL4-SPITFIRE vegetation–fire model for South
America using satellite data",
journal = "Geoscientific Model Development",
year = "2019",
volume = "12",
number = "12",
pages = "5029--5054",
month = "Dec.",
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 fireprone 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 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 fireprone 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.",
doi = "10.5194/gmd-12-5029-2019",
url = "http://dx.doi.org/10.5194/gmd-12-5029-2019",
issn = "1991-959X",
language = "en",
targetfile = "druke_improving.pdf",
urlaccessdate = "28 mar. 2024"
}