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

Vegetation fires control global vegetation distribution, ecosystem functioning, and globalcarbon cycling. Specifically in South America, changes in fire occurrence together with landuse change could accelerate forest fragmentation and increase the vulnerability of tropicalforests and savannas to climate change. Dynamic Global Vegetation Models (DGVMs) arevaluable tools to estimate the effects of fire on ecosystem functioning and carbon cyclingunder future climate changes. However, fire-enabled DGVMs have partly poor performancesin capturing the magnitude, spatial patterns, and temporal dynamics of burned area asobserved by satellites. As fire is controlled by the interplay of weather conditions, vegetationproperties and human activities, fire modules in DGVMs can be improved in variousaspects.As a starting point, we here focus on improving the controls of climate on fire danger andhence fuel moisture content in the LPJmL4-SPITFIRE DGVM in South America andespecially for the Brazilian fire-prone ecoregions Caatinga and Cerrado. We therefore test twoalternative model formulations (Nesterov index and water vapor pressure deficit) for climateeffects on fire danger within a formal model-data integration setup where we estimate modelparameters against satellite datasets of burned area (GFED4) and above ground biomass oftrees.Our results show that an optimized version of the fire danger indices improves the modelperformance significantly especially in the Cerrado/Caatinga region. In addition, the firecontrolled vegetation patterns improve in terms of plant functional types (PFT) distributionand above ground biomass. While both optimized fire danger parameterizationsimproved the model performance, we obtained the best results by using the watervapor pressure deficit (VPD) for the calculation of fire danger. The VPD includes incomparison to the Nesterov index a representation of the air humidity and the vegetationdensity.This work shows the successful application of a model-data integration setup, as well as theintegration of a new fire danger formulation in order to optimize a process based fire model. Itfurther highlights the potential of this approach to be used in a global setting in order toachieve a new level of accuracy in comprehensive fire modeling and prediction. [ABSTRACT FROM AUTHOR]