Objective Forest fallen leaves play a crucial role in the occurrence and development of forest fires as ignition and flammable materials. This article takes the Betula platyphylla forest in Daxing’an Mountains of northeastern China as an example to construct a mathematical model from the perspective of forest leaf litter generation and decomposition, predict the future dynamics of forest leaf litter load, and provide a theoretical basis for forest fire prevention scientific research.

Method The forest floor litter was regarded as accumulation of residual amounts from decomposition of litter over years. Using the Olson single-exponential decomposition equation, a set of litter decomposition equations was constructed. Mathematical methods such as series summation, substitution, and stepwise search were applied to simplify the equations, and the litter decomposition rate coefficient and decomposition turnover period were solved. Subsequently, the litter decomposition rate coefficient and decomposition turnover period were used to derive a litter load prediction model. Model variable data were obtained through the establishment of survey sample site to verify the operability and accuracy of prediction model.

Result A forest litter load prediction model based on litter decomposition rate was established, which predicted the litter load in survey sample plots for the next two years. The relative error between the predicted and measured litter loads ranged from 0.05 to 0.26, with an average error of 0.14. Overall, the model’s predicted values were relatively consistent with the measured values.

Conclusion Model predictions reveal that the litter load in the Daxing’an Mountains forest region follows periodic growth-decay cycles over time. Stands with higher decomposition rates exhibit shorter cycles and maintain more stable litter loads, whereas those with slower decomposition rates show prolonged cycles with persistent load fluctuations. The developed model effectively predicts litter load dynamics, providing critical support for quantifying forest fuel accumulation and assessing regional fire risk levels.