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@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"
}


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