Soil organic carbon (SOC) is an important component of the ecosystem carbon pool, and fire is one of the important disturbances in forest ecosystems. With global warming, there has been a gradual increase in boreal forest fires, which has a nonnegligible impact on the SOC dynamics in forests. The CENTURY model was employed in our study to simulate the changes in SOC stocks in boreal forests of the Great Xing’an Mountains, China under different fire severity conditions. Fire severity was represented by the metric of difference normalized burn ratio (dNBR) derived from 30-m Landsat-8 imageries. Changes in forest SOC stocks following fire disturbance were predicted under four future Representative Concentration Pathway (RCP) scenarios (RCP2.6, RCP4.5, RCP6.0, and RCP8.5). We found that the CENTURY model had good results in simulating the SOC stocks in the postfire of China’s boreal forests. Forest SOC dynamics responded differently to fire severities and the larger SOC loss was associated with increasing fire severity. Importantly, a feedback mechanism was found between climate change and SOC stocks, which reduces SOC stocks with increasing temperatures. High-severity forest fires tended to cause serious damage to the SOC pool and delay forest SOC recovery time; after such events, forest SOC stocks cannot be fully recovered to the prefire levels (6.74% loss). In addition, higher CO₂ emissions and warmer temperatures significantly affected the recovery of SOC stocks after fire disturbance, resulting in larger SOC losses. Overall, we projected losses of 10.14%, 12.06%, 12.41%, and 15.70% of SOC stocks after high-severity fires in four RCP scenarios, respectively. Our findings emphasize the importance of fire disturbance and climate change on future dynamics of SOC stocks in China’s boreal forests, providing a scientific basis for future boreal forest management and fire management.

The aim of this paper is to investigate forest litter yield and decomposition rate estimation methods to provide a basic theory for litter production and decomposition studies and a scientific foundation for forest management practices. The Greater Khingan Dahurian larch (Larix gmelinii) forest in China was taken as the study subject. Forest litter was defined as the cumulative product of annual litterfall. The Olson exponential decay model, which is widely recognized in ecological studies, was employed to develop a system of equations representing the dynamic equilibrium among litter production, decomposition, and accumulation. Litter yield and decomposition rate estimation models were formulated based on this system. Model parameters were analyzed using multiple linear regression techniques. The proposed estimation methods were verified through field survey data and one-sample t-tests. The relative error for litter production estimation ranged from 0.01 to 0.25, with an average of 0.13, and the t-test yielded a p-value of 0.108. The relative error of the decomposition rate estimation was 0.00–0.35, with an average of 0.12, and the corresponding t-test yielded a p-value of 0.151. A litter yield and decomposition rate model with easily obtained predictor variables was constructed in this study. The model can rapidly estimate the litter yield and decomposition rate of survey sites and has important application value for litter yield- and decomposition-related studies.