Preprints
https://doi.org/10.5194/egusphere-2024-105
https://doi.org/10.5194/egusphere-2024-105
16 Jan 2024
 | 16 Jan 2024
Status: this preprint is open for discussion.

Future prediction of Siberian wildfire and aerosol emissions via the improved fire module of the spatially explicit individual-based dynamic global vegetation model

Reza Kusuma Nurrohman, Tomomichi Kato, Hideki Ninomiya, Lea Végh, Nicolas Delbart, Tatsuya Miyauchi, Hisashi Sato, Tomohiro Shiraishi, and Ryuichi Hirata

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.

Reza Kusuma Nurrohman, Tomomichi Kato, Hideki Ninomiya, Lea Végh, Nicolas Delbart, Tatsuya Miyauchi, Hisashi Sato, Tomohiro Shiraishi, and Ryuichi Hirata

Status: open (until 27 Feb 2024)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2024-105', Anonymous Referee #1, 16 Feb 2024 reply
  • RC2: 'Comment on egusphere-2024-105', Anonymous Referee #2, 16 Feb 2024 reply
  • RC3: 'Comment on egusphere-2024-105', Anonymous Referee #3, 17 Feb 2024 reply
Reza Kusuma Nurrohman, Tomomichi Kato, Hideki Ninomiya, Lea Végh, Nicolas Delbart, Tatsuya Miyauchi, Hisashi Sato, Tomohiro Shiraishi, and Ryuichi Hirata

Model code and software

SEIB-DGVM with SPITFIRE Code Reza Kusuma Nurrohman https://doi.org/10.5281/zenodo.8299732

Reza Kusuma Nurrohman, Tomomichi Kato, Hideki Ninomiya, Lea Végh, Nicolas Delbart, Tatsuya Miyauchi, Hisashi Sato, Tomohiro Shiraishi, and Ryuichi Hirata

Viewed

Total article views: 252 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
190 49 13 252 20 2 6
  • HTML: 190
  • PDF: 49
  • XML: 13
  • Total: 252
  • Supplement: 20
  • BibTeX: 2
  • EndNote: 6
Views and downloads (calculated since 16 Jan 2024)
Cumulative views and downloads (calculated since 16 Jan 2024)

Viewed (geographical distribution)

Total article views: 253 (including HTML, PDF, and XML) Thereof 253 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 21 Feb 2024
Download
Short summary
SPITFIRE fire module was integrated into SEIB Dynamic Global Vegetation Model to improve the model's accuracy in depicting forest fire frequency, intensity, and extent in Siberia. Projected fires showed a continuous increase in higher emissions of greenhouse gases and aerosols from 2023 to 2100 under all RCP scenarios. This study contributes to a better understanding of fire dynamics, land ecosystem-climate interactions, and global material cycles under the threat of escalating fires in Siberia.