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Wildfires are among the most influential disturbances affecting ecosystem structure and biogeochemical cycles in Siberia. Therefore, accurate fire modeling via dynamic global vegetation models is important for predicting greenhouse gas emissions and other biomass-burning emissions to understand changes in biogeochemical cycles. 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. The model can reproduce the spatiotemporal variation in biomass, fire intensity, and fire-related emissions well compared to the recent satellite-based estimations: aboveground biomass (R2=0.847 , RMSE = 18.3 Mg ha -1), burned fraction (R2=0.75 , RMSE =0.01), burned area (R2=0.609 , RMSE =690 ha), dry-matter emissions (R2=0.624 , RMSE =0.01 kg DM m -2 ; dry matter), and CO 2 emissions (R2=0.705 , RMSE =6.79 Tg). We then predicted that all of the 33 fire-related gas and aerosol emissions would increase in the future due to the enhanced amount of litter as fuel load from increasing forest biomass production under climate forcing of four Representative Concentration Pathways: RCP8.5, RCP6.0, RCP4.5, and RCP2.6. The simulation under RCP8.5 showed that the CO 2 , CO, PM 2.5 , total particulate matter (TPM), and total particulate carbon (TPC) emissions in Siberia in the present period (2000–2020) will increase relatively by 189.66±6.55 , 15.18±0.52 , 2.47±0.09 , 1.87±0.06 , and 1.30±0.04 Tg species yr -1 , respectively, in the future period (2081–2100) and the number of burned trees will increase by 100 %, resulting in a 385.19±40.4 g C m -2 yr -1 loss of net primary production (NPP). Another key finding is that the higher litter moisture by higher precipitation would relatively suppress the increment of fire-related emissions; thus the simulation under RCP8.5 showed the lowest emissions among RCPs. Our study offers insights into future fire regimes and development strategies for enhancing regional resilience and for mitigating the broader environmental consequences of fire activity in Siberia. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Rainfall distribution pattern is very important for sustainable production in the agricultural sector in Tropical region. The near future rainfall amount under changing climate was predicted from 2009 to 2028 by Adaptive Neuro Fuzzy Inference System (ANFIS), which was trained with the rainfall observation data from 1979 to 2013, spatiotemporally in the Central Java Province, Indonesia. Our analysis showed that the predicted rainfall data using ANFIS could represent actual rainfall conditions. Rainfall predicted from 2009 – 2028 in Central Java will experience a decrease in high rainfall in an area of 1,615,125.2 hectare, which can cause drought. The area that predicted to experience drought in the future are Kebumen, Jepara, Pati, Rembang, Kudus, Grobogan, Blora, Sragen, and parts of the regions of Purworejo, Cilacap, Banyumas, Brebes, Wonogiri, Karanganyar, and Demak. [ABSTRACT FROM AUTHOR]