Analyzing spatiotemporal variability of heterotrophic soil respiration at the field scale using orthogonal functions
Analyzing spatiotemporal variability of heterotrophic soil respiration at the field scale using orthogonal functions
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DOI：
10.1016/j.geoderma.2012.02.016
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发表时间：
2012-07-01
期刊：
影响因子：
6.1
通讯作者：
Vereecken, Harry
中科院分区：
文献类型：
--
作者：
Graf, Alexander;Herbst, Michael;Vereecken, Harry
在一个坡度平缓、土壤性质有梯度的裸地上，采用封闭室系统沿180 m样带测量了一年中的28天土壤CO2外排。采用主成分分析（PCA）提取CO2外排时空变异的最重要模式（经验正交函数，EOFs）。分析了这些模式的地统计学性质、与室内分析得到的土壤参数的关系，以及它们的加载时间序列与土壤温度和湿度的时间变化的关系。重点分析了描述时空外排变异性的两种统计模型的过拟合行为：i)使用土壤性质的k个首个EOFs来预测外排的n个首个EOFs，然后使用该模型来预测所有日期和所有点的外排；ii)基于EOFs的预期预测能力重新排序的修正多元回归模型。结果表明，主成分分析有助于分离土壤CO2外排测量数据集中有意义的空间相关模式和无法解释的变异。两个PCA分析表明，外排总方差中只有大约一半与土壤性质的场尺度空间变异性有关，而另一半则是归因于分钟时间尺度上的时间波动和分米尺度上的短期空间异质性的“噪声”。CO2外排最重要的空间格局与土壤湿度和驱动土壤物理性质明显相关。另一方面，温度是控制土壤呼吸空间平均时间变异的最重要因素。(C) 2012 Elsevier B.V.版权所有
Soil CO2 efflux was measured with a closed chamber system along a 180 m transect on a bare soil field characterized by a gentle slope and a gradient in soil properties at 28 days within a year. Principal component analysis (PCA) was used to extract the most important patterns (empirical orthogonal functions, EOFs) of the underlying spatiotemporal variability in CO2 efflux. These patterns were analyzed with respect to their geostatistical properties, their relation to soil parameters obtained from laboratory analysis, and the relation of their loading time series to temporal variability of soil temperature and moisture. A particular focus was set on the analysis of the overfitting behaviour of two statistical models describing the spatiotemporal efflux variability: i) a multiple regression model using the k first EOFs of soil properties to predict the n first EOFs of efflux, which were then used to obtain a prediction of efflux on all days and points: and ii) a modified multiple regression model based on re-sorting of the EOFs based on their expected predictive power. It was demonstrated that PCA helped to separate meaningful spatial correlation patterns and unexplained variability in datasets of soil CO2 efflux measurements. The two PCA analyses suggested that only about half of the total variance of efflux could be related to field-scale spatial variability of soil properties, while the other half was "noise" attributed to temporal fluctuations on the minute time scale and short-range spatial heterogeneity on the decimetre scale. The most important spatial pattern in CO2 efflux was clearly related to soil moisture and the driving soil physical properties. Temperature, on the other hand, was the most important factor controlling the temporal variability of the spatial average of soil respiration. (C) 2012 Elsevier B.V. All rights reserved.