Knowledge-guided machine learning for predicting the carbon sequestration potential of natural forests in Heilongjiang Province

Abstract Key message Integrating ecological process knowledge into machine learning substantially improves the robustness and extrapolation of forest carbon stock predictions. The knowledge-guided model maintained strong predictive performance over 10–15-year horizons, when conventional machine-learning performance declined markedly, providing a promising framework for long-term forest carbon assessment under climate change. Context Accurate quantification of forest carbon stocks and their dynamics is essential for understanding ecosystem carbon sequestration potential. However, traditional machine learning models often exhibit limited extrapolation capability in long-term forest carbon predictions because they rely primarily on statistical relationships learned from historical observations and lack explicit representation of ecological processes. Aims This study aimed to develop a knowledge-guided machine learning (KGML) framework that integrates ecological process knowledge with data-driven algorithms to improve the accuracy, stability, and long-term generalization of forest carbon stock predictions. Methods The study focused on natural forests in Heilongjiang Province, China, defined as stands classified as having a natural origin in forest inventory records. Forest fixed plot observations from 1976 to 2015 were combined with historical and projected climate data and atmospheric CO 2 concentration data. The CO 2 fertilization effect was incorporated into an optimized forest carbon sequestration (FCS) process model, and its outputs and process-based constraints were subsequently integrated with machine learning algorithms to construct the KGML framework. Model performance was compared with that of standalone machine learning and process-based models across forest types and prediction horizons. Results Incorporating the CO 2 fertilization effect improved the predictive performance of the FCS model, increasing the coefficient of determination ( R 2 ) from approximately 0.85 to above 0.90. The KGML model consistently outperformed the standalone machine learning and process-based models across the evaluated forest types. Its advantage was particularly evident for prediction horizons of 10 years or longer, over which the performance of conventional machine learning models declined substantially. Scenario simulations indicated that natural forests in Heilongjiang Province had the greatest projected carbon sequestration potential under SSP126. Depending on model configuration and forest type, projected carbon stocks in 2060 ranged from 1371.37 ± 422.04 to 1442.38 ± 316.77 Tg. Conclusion Integrating ecological process knowledge with machine learning can improve the accuracy and long-term extrapolation capability of forest carbon stock predictions. The proposed KGML framework provides a potentially transferable approach for regional forest carbon assessment. Nevertheless, the projections remain subject to uncertainty arising from climate scenarios, process-model assumptions, forest inventory coverage, and the representation of disturbances and management effects.