基于非平衡数据-模型融合方法评估气候与林龄因素对中国森林碳汇的相对贡献

The magnitude and the potential of forest carbon sink (NEP) are not only controlled by its internal factors including the forest types and the forest ages, but also influenced by the external driving factors including climate and environmental factors. Quantitative distinguishing the contribution of the forest ages and the climate factors to forest carbon sink are the foundation to assess the current forest carbon sink and forecast the dynamic of forest carbon sink. There exists great controversy in the aspect of differentiating the relative contributions for forest ages and the climate factors on carbon sink because the limitation of the number of flux observation sites and the spatial distribution. This project will build a nonsteady state data-model fusion system to inverse the site-related NEPs that determined by the site-specific forest ages and the climate condition from the imperfect information, discrete but widely distributed ecological observation data, and establish the NEP-age models for different forest types, and then assess the difference of forest ages to the contribution of vegetation and soil carbon sink. Both the traditional statistical methods and the inverse modeling and random sampling methods help evaluate the extra contributions of climate factors to carbon sink by ruling out the difference of the forests ages. Combined with distribution of the forest ages in China, the spatial distribution pattern of the nonsteady state soil carbon pool is inversed, and regarded these as the initial value of the carbon pool to simulate and predict the dynamic of forest carbon sink under the background of the climate change at the regional scale. The model outputs are helpful to provide the reference for optimizing management of forest carbon sinks.

森林碳汇的大小与潜力不仅受森林类型及林龄等内在的生物因素控制，也受外在的气候因素与环境条件的影响。定量区分林龄与气候因素对碳汇的相对贡献，是评估当前森林碳汇，以及预测未来气候变化情景下碳汇动态的基础。受通量观测站点的数量与空间分布的限制，目前在定量区分林龄与气候因素的碳汇贡献方面还存在很大争议。本项目将构建一个非平衡数据-模型融合系统，使之能从信息不完备、离散但分布广泛的常规生态观测信息中反演出与站点林龄和气候密切相关的碳汇值；从而建立中国不同森林类型的碳汇与林龄的关系模型，评价林龄对植被碳汇、土壤碳汇及总碳汇的贡献；采用传统统计学方法与随机抽样与反演模拟相结合的方法，评估排除林龄差异后的气候因素对碳汇的额外贡献；结合中国森林的林龄分布信息，反演出非平衡条件下土壤碳库的空间分布格局，并以此作为初始值，在区域尺度上模拟气候变化情景下森林碳汇的动态变化，为优化森林碳汇提供管理上的参考。

森林碳汇的大小与潜力不仅受森林类型及林龄等内在的生物因素控制，也受外在的气候因素与环境条件的影响。定量区分林龄与气候因素对碳汇的相对贡献，是评估当前森林碳汇，以及预测未来气候变化情景下碳汇动态的基础。基于项目计划书，研究组开展了森林碳汇与林龄、气候因素之间的关系研究，构建了非平衡条件下的陆地生态系统碳循环数据-模型融合系统，从而优化了碳循环模型的关键参数，进而降低了森林碳汇量及固碳潜力模拟结果的不确定性；建立了中国森林碳汇与林龄之间的定量关系模型，评估了林龄对植被碳汇、土壤碳汇及总碳汇的贡献；建立了森林碳汇与林龄、气候因素之间的参数回归模型与非参数回归模型，评估了排除林龄差异后的气候因素对碳汇的贡献；综合利用遥感与树木年轮等多种资料，揭示了气候因素（尤其是水分）对森林影响的滞后效应和累积效应，发现前期水分条件的变化对森林生长具有重要影响；针对气候变化的趋势性与波动性特征，评估了水分胁迫及其动态特征对植被生长与碳循环的影响，发现树木的径向生长不仅与当前年的气候因素有关，也与之前年的水分状况有关；结合中国森林的林龄分布信息，反演了非平衡条件下凋落物与土壤碳库的空间格局，模拟了其对碳循环的影响，研究发现，包含森林林龄的空间分布信息可以明显改善模型在模拟凋落物空间格局方面的精度，这对于模拟区域尺度上的碳汇潜力的空间格局非常重要。通过该项目的研究，实现了项目既定的研究目标，这些研究成果可为区域尺度上更好地模拟森林碳汇及其动态提供参考。