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

Abstract

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.

CM2Mc-LPJmL v1.0: Biophysical coupling of a process-based dynamic vegetation model with managed land to a general circulation model

“…Since its original implementation by Sitch et al (2003), LPJmL has been improved by a better representation of the water balance (Gerten et al, 2004), the introduction of agriculture (Bondeau et al, 2007), and new modules for fire (Thonicke et al, 2010), permafrost (Schaphoff et al, 2013) and phenology (Forkel et al, 2014). In this study, we use the updated version of the fire model SPITFIRE as described in Drüke et al (2019). Since LPJmL5, all LPJmL versions include the nitrogen and nutrient cycle (Von Bloh et al, 2018), which are however deactivated in this study (further adaptions would be necessary to include the nitrogen cycle in the coupled model which is beyond of scope here).…”

“…The fire module SPITFIRE (Thonicke et al, 2010) requires human population density as input, which is taken from Goldewijk et al (2011), as well as lightning flashes which are taken from the OTD/LIS satellite product (Christian et al, 2003). In the coupled LPJmL5 version, we activated permafrost, the new phenology and SPITFIRE using the vapor pressure deficit as the fire danger index (Drüke et al, 2019). The nitrogen-cycle, which is part of LPJmL5 (Von Bloh et al, 2018), was deactivated in this study.…”

Climate-induced hysteresis of the tropical forest in a fire-enabled Earth system model

“…We used the coupled Earth system model CM2Mc-LPJmL v.1.0 (see Fig. 1), which combines the fast but coarse-grained atmosphere and ocean model CM2Mc [23] with the state-of-the-art DGVM LPJmL5.0-FMS [24,25], using the process-based fire model SPITFIRE [26] which was recently optimized for South America [27]. The technical details of the biophysical coupling between CM2Mc and LPJmL are published in a separate article [28].…”

“…LPJmL simulates water balance [33], impacts of agriculture [34], wildfires in natural vegetation (SPITFIRE) [26], permafrost [35] and specified multiple climate drivers on phenology [36]. Recently, using an optimization approach, several important parameters in LPJmL have been newly estimated [37] and the fire model has been improved by developing a new fire danger index, to obtain a more realistic fire representation [27]. We applied the optimized and improved SPITFIRE in this study.…”

A social-ecological approach to identify and quantify biodiversity tipping points in South America's seasonal dry ecosystems

“…Combining respective disturbance modules, e.g. process-based fire models optimized for the Cerrado (Drüke et al, 2019) or herbivory effects from grazer population dynamics (Pachzelt et al, 2015;Pfeiffer et al, 2019), in flexible-trait DGVMs allows quantification of the impact of changes in the disturbance regimes and their interaction on functional EI metrics 370 which could have secondary effects on BD-TPs (Table 1).…”