Data-Model Fusion at AmeriFlux Sites: Towards Predictive Understanding of Seasonal and Interannual Variability in Net Ecosystem Exchange

AmeriFlux 站点的数据模型融合：对网络生态系统交换中的季节性和年际变化的预测性理解

• 批准号: 0444518
• 负责人: Yiqi Luo
• 金额: $ 26万
• 依托单位: University of Oklahoma Norman Campus
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2005
• 资助国家: 美国
• 起止时间: 2005-02-15 至 2009-01-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0444518&HistoricalAwards=false
• 关键词: Data; Model; Fusion; AmeriFlux; Sites

To understand carbon fluxes between the land and the atmosphere, scientists have widely employed the so-called eddy-flux method to quantify land carbon sequestration. The eddy-flux measurements at hundreds of sites across the world all have showed strong year-to-year variation (i.e., interannual variability) in the strength of the terrestrial carbon sink. This project seeks to determine the underlying causes of the seasonal and interannual variation (SIAV) in net ecosystem exchange of carbon dioxide as measured by many long-term micrometeorological sites that make up the AmeriFlux and the Fluxnet eddy-flux networks. While previous efforts have focused on developing a mechanistic understanding of observed seasonal and interannual variability in net ecosystem exchange of carbon dioxide at single sites, far less work has attempted to identify a more general description or model for seasonal and interannual variability based on data from a broad range of ecosystems. This project will use multiple approaches, including data processing, statistical analysis, process-based modeling, and inverse analysis to examine the direct and indirect effects of climatic variation on ecosystem processes, to elucidate general patterns of variability in net ecosystem exchange of carbon dioxide across ecosystem types, and to develop a process-based model for predicting seasonal and interannual variability. The broader impacts of this research lie in the synthetic nature of the work, given the large investment the world is making in eddy flux data, and given the potential for these data sets to substantially improve our knowledge of the global carbon cycle and to inform policy making.

为了了解陆地和大气之间的碳通量，科学家广泛采用所谓的涡通量方法来量化陆地碳封存。 世界各地数百个地点的涡通量测量都显示陆地碳汇强度存在强烈的逐年变化（即年际变化）。 该项目旨在确定二氧化碳生态系统净交换季节性和年际变化 (SIAV) 的根本原因，这是通过构成 AmeriFlux 和 Fluxnet 涡流网络的许多长期微气象站点测量的。 虽然以前的努力主要集中在对单个地点二氧化碳净生态系统交换的观测到的季节和年际变化的机械理解上，但很少有工作试图根据来自广泛生态系统的数据来确定季节性和年际变化的更一般的描述或模型。 该项目将使用多种方法，包括数据处理、统计分析、基于过程的建模和逆分析，以研究气候变化对生态系统过程的直接和间接影响，阐明跨生态系统类型二氧化碳净生态系统交换的一般变异模式，并开发基于过程的模型来预测季节和年际变化。 鉴于世界在涡流数据方面投入了大量资金，并且考虑到这些数据集有可能大幅提高我们对全球碳循环的了解并为政策制定提供信息，这项研究的更广泛影响在于这项工作的综合性质。