基于中国陆地生态系统通量观测研究网络(ChinaFLUX)4个站点(2个森林站和2个草地站)的涡度相关通量观测资料,分析了CO2通量数据处理过程中异常值剔除参数设置、夜间摩擦风速(u*)临界值(u*c)确定及数据插补模型选择对CO2通量组分估算的影响.结果表明:3种数据处理方法均对净生态系统碳交换量(NEE)年总量估算有显著影响,其中u*c确定是影响NEE估算的重要因子;异常值剔除、u*c确定及数据插补模型选择导致NEE年总量估算偏差分别为0.62～21.31 g C.m-2.a-1(0.84%～65.31%)、4.06～30.28 g C.m-2.a-1(3.76%～21.58%)和0.69～27.73 g C.m-2.a-1(0.23%～55.62%),草地生态系统NEE估算对数据处理方法参数设置更敏感;数据处理方法不确定性引起的总生态系统碳交换量和生态系统呼吸年总量估算相对偏差分别为3.88%～11.41%和6.45%～24.91%.
Vegetation phenology is an important parameter in models of global vegetation and land surfaces, as the accuracy of these simulations depends strongly on the description of the canopy status. Temperate forests represent one of the major types of vegetation at mid-high latitudes in the Northern Hemisphere and act as a globally important carbon sink. Thus, a better understanding of the phenological variables of temperate forests will improve the accuracy of vegetation models and estimates of regional carbon fluxes. In this work, we explored the possibility of using digital camera images to monitor phenology at species and community scales of a temperate forest in northeastern China, and used the greenness index derived from these digital images to optimize phenological model parameters. The results show that at the species scale, the onset dates of phenological phases （Korean pine, Mongolian oak） derived from the images are close to those from field observations （error 〈 3d）. At the community scale the time series data accurately reflected the observed canopy status （A^2=0.9） simulated using the phenological model, especially in autumn. The phenological model was derived from simple meteorological data and environmental factors optimized using the greenness index. These simulations provide a powerful means of analyzing environmental factors that control the phenology of temperate forests. The results indicate that digital images can be used to obtain accurate phenologicai data and provide reference data to validate remote-sensing phenological data. In addition, we propose a new method to accurately track phenological phases in land-surface models and reduce uncertainty in spatial carbon flux simulations.
生态系统长期观测(Long-term Observation for Ecosystem,EcoLTO)是开展生态系统演变规律及生态系统对全球变化响应和适应等生态学研究的重要支撑手段,开展高质量监测、获取高质量数据、进行高效数据管理,提供优质数据共享服务是EcoLTO的使命和目标。从“统一监测”“统一数据管理”到“统一数据产品管理”是实现EcoLTO数据管理目标所经历的3个循环上升阶段。本文提出EcoLTO产品化思想和EcoLTO数据产品概念,介绍具有自主知识产权的EcoLTO数据产品标准规范的概要组成,以及标准规范在中国生态系统研究网络(CERN)数据中心和国家生态科学数据中心(NESDC)“生态网络云”系统的产品存储库中的应用效果。实践证明标准规范具有可行性,可为我国相关行业、机构开展数据全生命周期中数据质量控制、数据产品开发及体系建立、数据管理及共享提供重要依据和参考。
