Abstract: Northeast China, with the largest area of forests in China, holds approximately 40% of the national forest carbon stock. Over the past few decades, extensive afforestation and forest restoration efforts have been implemented in this region, yet their impacts on carbon cycling remain unclear. Accurately predicting the future aboveground carbon storage and carbon sequestration potential of the forests in Northeast China is of significance for devising forest management policies. We utilized publicly available forest inventory data to develop a framework which integrated the ecological process model and forest landscape model to simulate the succession process and carbon storage dynamics of Northeast China’s forests over the next 100 years. We employed multisource data (remote sensing data, forest inventory data, and other model outcomes) to validate the simulation results at multiple scales, to enhance the precision of the model simulations. The study aimed to estimate the carbon sequestration potential of forests in Northeast China and quantify the turning point of carbon sequestration. The results showed that: (1) The spatial distribution of forest aboveground carbon storage simulated by the model aligns closely with spatial distribution data derived from remote sensing in literature (Kappa coefficient = 0.81). Furthermore, we validated the model results from a stand age perspective. The proportions of forests in various age classes and the predominant tree species across different age classes correlated with forest inventory data (R²>0.6). (2) Under the current climate conditions, forest aboveground carbon stock in Northeast China would reach its peak at 6.38 Pg C by the year 2060. Compared to the year 2000, there would be a net increase of 4.57 Pg C in aboveground carbon storage and a net increase of 67.46 Mg·hm^-2 in aboveground carbon density. (3) Without considering climate change and forest management policies, the carbon sequestration rate in Northeast China’s forests exhibited a trend of initially increasing and then decreasing. The peak occurs between 2020-2025, reaching 0.108 Pg C·a^-1. The rate is projected to reach zero between 2070-2075 when forests shift from carbon sink to source.

Key words: forest inventory data, forest landscape model, model validation