Future prediction of Siberian wildfire and aerosol emissions via the improved fire module of the spatially explicit individual-based dynamic global vegetation model
Abstract. Fires are among the most influential disturbances affecting ecosystem structure and biogeochemical cycles in Siberia. Therefore, precise fire modeling via dynamic global vegetation models is important for predicting greenhouse gas emissions and other burning biomass emissions to understand changes in biogeochemical cycles. In this study, we integrated the widely used SPread and InTensity of FIRE (SPITFIRE) fire module into the spatially explicit individual-based dynamic global vegetation model (SEIB-DGVM) to improve the accuracy of fire predictions and then simulated future fire regimes to better understand their impacts. Under the Representative Concentration Pathways 8.5 climate scenario, we estimated that the CO2, CO, PM2.5, total particulate matter (TPM), and total particulate carbon (TPC) emissions in Siberia will continue to increase annually until 2100 by an average of 214.4, 17.16, 2.8, 2.1, and 1.47 Gg species year-1, respectively. Under the same scenario and period, 185 trees ha-1 year-1 are estimated to be killed by wildfires, resulting in a 319.3 g C m-2 year-1 loss of net primary production (NPP). These findings show that Siberia faces an increasing frequency of extreme fire events due to changing climate conditions. Our study offers insights into future fire regimes and provides helpful information for development strategies for enhancing regional resilience and for mitigating the broader environmental consequences of heightened fire activity in Siberia.
Status: open (until 27 Feb 2024)
Model code and software
SEIB-DGVM with SPITFIRE Code https://doi.org/10.5281/zenodo.8299732
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