Relative Linkages of Canopy-Level CO2 Fluxes with the Climatic and Environmental Variables for US Deciduous Forests
Relative Linkages of Canopy-Level CO2 Fluxes with the Climatic and Environmental Variables for US Deciduous Forests
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DOI：
10.1007/s00267-014-0437-1
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发表时间：
2015-04-01
影响因子：
3.5
通讯作者：
Abdul-Aziz, Omar I.
中科院分区：
文献类型：
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作者：
Ishtiaq, Khandker S.;Abdul-Aziz, Omar I.
我们使用了一个简单的，系统的数据分析方法，以确定不同的气候和环境变量与冠层水平的相对联系，美国落叶林的半小时CO2通量。利用主成分和因子分析的多变量模式识别技术对气候、环境和生态变量进行分类和分组，根据它们作为驱动因素的相似性，研究它们在不同地点的相互关系模式。开发了解释性偏最小二乘回归模型，以估计CO2通量与气候和环境变量的相对联系。三个生物物理过程组件充分描述了系统数据的差异。“辐射能量”的组成部分有最强的联系与CO2通量，而“空气动力学”和“温度水文”的组成部分是低到中度与碳通量。平均而言，“辐射能量”的组成部分表现出5和8倍强的碳通量的联系比“温度水文”和“空气动力学”的组成部分，分别。在不同的研究地点（代表气候梯度，树冠高度和土壤形成）之间观察到的模式的相似性表明，研究结果是潜在的转移到其他落叶林。这些相似之处也突出了开发简约数据驱动模型的范围，以预测气候和环境变化下生态系统碳的潜在固存。所提出的数据分析提供了一个客观的，经验的基础，以获得关键的机械见解;补充基于过程的模型构建与保证的复杂性。模型的效率和准确性（R（2）= 0.55-0.81;均方根误差与观测标准差的比值，RSR = 0.44-0.67）重申了多变量分析模型对瞬时通量数据填补空白的有用性。
We used a simple, systematic data-analytics approach to determine the relative linkages of different climate and environmental variables with the canopy-level, half-hourly CO2 fluxes of US deciduous forests. Multivariate pattern recognition techniques of principal component and factor analyses were utilized to classify and group climatic, environmental, and ecological variables based on their similarity as drivers, examining their interrelation patterns at different sites. Explanatory partial least squares regression models were developed to estimate the relative linkages of CO2 fluxes with the climatic and environmental variables. Three biophysical process components adequately described the system-data variances. The 'radiation-energy' component had the strongest linkage with CO2 fluxes, whereas the 'aerodynamic' and 'temperature-hydrology' components were low to moderately linked with the carbon fluxes. On average, the 'radiation-energy' component showed 5 and 8 times stronger carbon flux linkages than that of the 'temperature-hydrology' and 'aerodynamic' components, respectively. The similarity of observed patterns among different study sites (representing gradients in climate, canopy heights and soil-formations) indicates that the findings are potentially transferable to other deciduous forests. The similarities also highlight the scope of developing parsimonious data-driven models to predict the potential sequestration of ecosystem carbon under a changing climate and environment. The presented data-analytics provides an objective, empirical foundation to obtain crucial mechanistic insights; complementing process-based model building with a warranted complexity. Model efficiency and accuracy (R (2) = 0.55-0.81; ratio of root-mean-square error to the observed standard deviations, RSR = 0.44-0.67) reiterate the usefulness of multivariate analytics models for gap-filling of instantaneous flux data.