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林业科学 ›› 2026, Vol. 62 ›› Issue (7): 230-239.doi: 10.11707/j.1001-7488.LYKX20250708

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基于林木生长过程的森林生态系统碳汇长期预测方法——理论基础与构架分析

韩学正1,2,何念鹏1,3,*(),蔡伟祥4,李维刚1   

  1. 1. 寒温带森林生态系统保护修复国家林业与草原局重点实验室 东北林业大学 哈尔滨150040
    2. 东北林业大学生态学院, 碳中和技术创新研究院 哈尔滨 150040
    3. 中国科学院兴安岭地球关键带与地表通量观测研究站 塔河165200
    4. 水土保持与荒漠化整治全国重点实验室 西北农林科技大学水土保持科学与工程学院 杨凌 712100
  • 收稿日期:2025-11-24 出版日期:2026-07-10 发布日期:2026-07-16
  • 通讯作者: 何念鹏 E-mail:henp@igsnrr.ac.cn
  • 基金资助:
    国家自然科学基金项目(32430067, 42301544);黑龙江省林草局科技专项计划项目,黑龙江省造林碳汇项目评估方法学(2024–2025)。

Methods of the Long-Term Dynamics Prediction for Forest Ecosystem Carbon Sink based on Forest Growth Processes: Theoretical Basis and Framework Analysis

Xuezheng Han1,2,Nianpeng He1,3,*(),Weixiang Cai4,Weigang Li1   

  1. 1. Key Laboratory of Boreal Forest Ecosystem Conservation and Restoration National Forestry and Grassland Administration Northeast Forestry University Harbin 150040
    2. Institute of Carbon Neutrality, School of Ecology, Northeast Forestry University Harbin 150040
    3. Earth Critical Zone and Flux Research Station of Xing’an Mountains, Chinese Academy of Sciences Tahe 165200
    4. State Key Laboratory of Soil and Water Conservation and Desertification Control College of Soil and Water Conservation Science and Engineering, Northwest A & F University Yangling 712100
  • Received:2025-11-24 Online:2026-07-10 Published:2026-07-16
  • Contact: Nianpeng He E-mail:henp@igsnrr.ac.cn

摘要:

森林生态系统是陆地生态系统碳汇功能的主体部分,科学、准确评估森林碳汇是提升陆地生态系统碳汇能力和应对气候变化的前提,尤其是预测其长期动态变化,对优化森林经营策略和实现我国“双碳”战略目标具有重要价值。目前应用较多的过程模型主要基于森林生态系统生产力的预测思路,依托“光量子传递?大叶模型”解析森林碳汇形成机理,借助 GPP、NPP 等生态系统生产力指标研判森林碳汇短期动态,弥补了传统实地观测法难以预判森林生态系统碳汇变化的短板。理论上,森林群落属性在很大程度上决定了森林生态系统碳汇功能的时空变化特征。在气候作用与物种相互竞争的双向调节下,森林碳汇动态主要受群落林龄和植被生长状况调控。因此,在开展森林碳汇动态评估时,需充分考量该调控机制。然而,目前有关森林生态系统碳汇形成机制的研究难以科学地解释森林群落的动态增长模式,导致基于生产力的森林碳汇预测思路无法支持过程模型去评估未来生态系统碳汇的长期增长规律及其时空变异动态,进而使得相关评估结果难以为政府制定森林生态系统碳汇提升策略提供有效的科学依据。针对上述不足,本文系统阐述了基于林木生长过程预测森林生态系统长期碳汇动态的研究框架。该框架基于生长方程,刻画了林龄驱动的森林生物质碳库长期增长模式,并在传统森林植被生长规律的科学假设中融入了森林碳汇对气候、土壤因素的响应机制,从而实现了基于林木生长过程的森林植被碳汇动态预测。基于该框架,本研究发展了森林碳固持(forest carbon sequestration, FCS )模型。该模型采用 Logistic 生长方程与关键参数刻画森林植被碳汇的长期动态规律,并在此基础上借鉴过程模型的碳周转研究思路,构建了由森林植被动态驱动其他主要碳库(死有机质碳库、土壤有机碳库)储量增长的森林生态系统碳汇预测模型。利用中国典型森林生态系统的系统调查数据对模型进行参数化,研究表明 FCS 模型能够很好地量化森林生态系统碳汇在时空协变过程中的变异规律。基于林木生长过程的森林生态系统碳汇预测框架为森林碳汇预测提供了新的理论范式,有效提升了森林生态系统碳汇长期动态变化的模拟能力。

关键词: 森林碳汇, 生态系统模型, 林龄, 演替理论, 碳中和

Abstract:

Forest ecosystems constitute the foundation of terrestrial carbon (C) sinks. Precise quantification of their sequestration capacity is an essential prerequisite for enhancing C storage in terrestrial ecosystems and mitigating climate change. Especially, it is of great value in predicting long-term C dynamics for optimizing forest management strategies and achieving China’s dual C strategic objectives. The currently widely used process models are mainly based on the prediction frameworks of forest ecosystem productivity, utilizing “light quantum transfer-large leaf model” to characterize C sink formation mechanisms. These models evaluate short-term ecological dynamics through key biometric indicators including gross primary production (GPP) and net primary production (NPP), thereby overcoming inherent limitations in traditional methods for predicting ecosystem C sequestration potential. Theoretically, community attributes significantly influence the variation in forest carbon sink functions. Under the dual regulation of climatic effects and interspecific competition, the dynamics of forest carbon sinks are primarily dictated by stand age and vegetation growth processes. Therefore, assessments of forest carbon sink dynamics should incorporate a full consideration of these regulatory mechanisms. However, current research on the formation mechanism of forest ecosystem carbon sinks is difficult to scientifically explain the forest community growth trajectories. Productivity-based predictive approaches demonstrate limited capacity to support process models in evaluating long-term growth trends and spatiotemporal heterogeneity of C sequestration, consequently constraining the effectiveness of scientific evidence available for policy formulation. To address these methodological constraints, this study systematically elaborates on a research framework for predicting the long-term forest C sequestration based on tree growth processes. This framework, based on growth equations, characterizes the long-term expansion of biomass C pools driven by stand age dynamics, integrates the response mechanisms of C sink to climatic and soil variables into the traditional vegetation growth hypotheses, and thus, achieves a process-based prediction model for forest C dynamics that incorporates both ecological and environmental determinants. The forest C sequestration model (FCS model) was developed using the logistic growth equation and key parameters to characterize long-term dynamic patterns of forest vegetation C sinks. This model builds upon a process-based C turnover approach to create a predictive framework for forest ecosystem C sequestration, wherein the dynamics of forest vegetation drive the stock growth of other major C pools, such as the dead organic matter C pool and soil organic C pool. The model was parameterized using systematic survey data from typical forest ecosystems in China. Studies have shown that the FCS model effectively quantifies the spatiotemporal covariation patterns of forest ecosystem C sinks. This predictive framework, grounded in tree growth processes, offers a novel theoretical paradigm for C sink projections. It significantly improves the simulation capability of long-term dynamic changes in forest ecosystem C sinks, thereby providing stronger support for China’s efforts to achieve its “C neutrality” strategic objectives.

Key words: forest carbon sequestration, ecosystem model, stand age, succession theory, carbon neutrality

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