摘要
内陆水体在全球碳循环中扮演着重要角色,其中二氧化碳(CO_(2))的排放特性直接影响气候变化动态。然而,目前关于内陆水体CO_(2)排放的研究多集中于局部尺度,缺乏跨区域及全球尺度的系统整合。这不仅导致全球CO_(2)通量估算存在显著不确定性,也限制了对内陆水体碳通量时空分布及驱动机制的系统认识。本研究通过系统梳理内陆水体CO_(2)通量的主要监测方法(通量箱法、涡度相关法、边界层模型法)和升尺度转换方法(面积外推法、统计回归法、机理过程模型法),揭示了方法学差异对数据不确定性的影响机制:首先,对比分析了3种监测方法的特征差异及适用场景差异所导致的观测偏差;进而剖析了不同升尺度方法的估算偏差来源,包括面积外推法对环境异质性的忽视、统计回归法中驱动因子代表性的不足,以及机理过程模型的边界条件简化等问题。研究表明,提高数据质量与精度是提升估算准确性的关键。未来研究应在提高监测技术自动化水平、增强数据时空代表性以及综合考量非生物与生物因子的影响和模型优化等方面进一步优化和提升,以提升碳源汇评估精度,支撑全球碳减排决策。
Inland aquatic systems are critical components of the global carbon cycle,as their carbon dioxide(CO_(2))emission characteristics directly influence climate dynamics.However,current research on CO_(2) emissions from these systems is predominantly conducted at local scales,lacking systematic integration across regions and at the global level.This gap introduces substantial uncertainties into global CO_(2) flux estimates and hinders a comprehensive understanding of the spatiotemporal patterns and driving mechanisms of aquatic carbon fluxes.Through a systematic review of primary monitoring methods-including the floating chamber method,eddy covariance technique,and boundary layer method-and upscaling approaches,such as area-based extrapolation,statistical regression,and mechanistic process-based modeling,this study elucidates how methodological differences contribute to data uncertainty.First,the characteristics of the three monitoring methods and the observational biases resulting from their differing applicable scenarios are comparatively analyzed.Next,the sources of estimation bias in the upscaling methods are examined,including the neglect of environmental heterogeneity in area-based extrapolation,insufficient representativeness of driving factors in statistical regression,and oversimplification of boundary conditions in mechanistic process models.The findings underscore that enhanced data quality and precision are essential for improving estimation accuracy.Future studies should prioritize advancing automated monitoring technologies,strengthening the spatiotemporal representativeness of data,integrating interactions between abiotic and biotic factors,and refining model frameworks to improve carbon source/sink assessments and provide robust scientific support for global carbon mitigation strategies.
作者
卢珊
冷佩芳
冉立山
李发东
Lu Shan;Leng Peifang;Ran Lishan;Li Fadong(Institute of Geographic Sciences and Natural Resources Research,Chinese Academy of Sciences,Shandong Yucheng Agro-ecosystem National Observation and Research Station,Beijing 100101,P.R.China;Sino-Danish College,University of Chinese Academy of Sciences,Beijing 100049,P.R.China;College of Resources and Environment,University of Chinese Academy of Sciences,Beijing 100049,P.R.China;Department of Geography,University of Hong Kong,Hong Kong 999077,P.R.China)
出处
《湖泊科学》
北大核心
2026年第1期15-28,共14页
Journal of Lake Sciences
基金
国家自然科学基金项目(42401121,42571126)资助。
关键词
二氧化碳排放通量
通量排放核算
内陆水体
升尺度估算
Carbon dioxide emission flux
flux emission accounting
inland water body
upscaling estimation
