森林碳汇计量监测的不确定性研究OA北大核心

In the context of the"dual carbon"goals,enhancing the accuracy and verifiability of forest carbon sink accounting and monitoring has become an urgent priority.However,limited technology and data gaps have caused major uncertainties in forest carbon sink monitoring results via the mainstream approaches,weakening both carbon accounting reliability and forest management effectiveness.After reviewing the approaches and technologies currently used,uncertainty sources and quantification techniques as well as existing improvement strategies,this study focuses on accounting precision and operability,and proposes a carbon-neutral-oriented framework for improving the accuracy of forest carbon sink accounting and monitoring,and the components include:1)A multi-scale,multi-source data acquisition system that integrates top-down and bottom-up observations;2)A full-process mechanism for identifying,disintegrating and quantifying uncertainties,which locates key error sources through spatiotemporal co-registration,data assimilation,and sensitivity analysis;3)Model optimization and multi-model complementarity centred on parameter localization,deep-learning integration,and cross-validation to reinforce result robustness;and 4)A decision-making-oriented standard system and iterative improvement mechanism that establishes unified parameter libraries,model libraries,and quality-control specifications to support sustainable updates and result comparability.