Analyzing spatiotemporal variability of heterotrophic soil respiration at the field scale using orthogonal functions

Graf, A.; Prolingheuer, N.; Bornemann, L.; Vereecken, H.; Huisman, J.A.; Weihermüller, L.; Herbst, M.

doi:10.1016/j.geoderma.2012.02.016

Journal Article

PreJuSER-20897

Analyzing spatiotemporal variability of heterotrophic soil respiration at the field scale using orthogonal functions

Graf, A.FZJ* ; Herbst, M.FZJ* ; Weihermüller, L.FZJ* ; Huisman, J. A.FZJ* ; Prolingheuer, N.FZJ* ; Bornemann, L. ; Vereecken, H.FZJ*

2012
Elsevier Science Amsterdam [u.a.]

Geoderma 181-182, 91 - 101 (2012) [10.1016/j.geoderma.2012.02.016]

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Please use a persistent id in citations: http://hdl.handle.net/2128/7453 doi:10.1016/j.geoderma.2012.02.016

Abstract: 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.

Keyword(s): J ; Closed chamber (auto) ; Empirical orthogonal functions (auto) ; Principal component analysis (auto) ; Semivariogram (auto) ; Soil CO2 efflux (auto)

Classification:

Soil Science

Note: A. Graf gratefully acknowledges financial support by the DFG (Deutsche Forschungsgemeinschaft) project "Links between local scale and catchment scale measurements and modelling of gas exchange processes over land surfaces" (GR2687/3-1). Instrument funding was provided by the Helmholtz project FLOWatch. M. Herbst, L Bornemann, W. Amelung and H. Vereecken would like to thank the DFG for funding in the framework of the Transregional Collaborative Research Centre SFB/TR32. We would like to thank Rainer Harms, Christina Ganz, and Martin Hank for additional help with the manual chamber measurements; Axel Knaps for providing climate information, the ZCH personnel for a part of the chemical analysis and Budiman Minasny (University of Sydney) for providing helpful code for semivariogram analysis. We would also like to thank two anonymous reviewers for suggestions that improved the clarity of the manuscript.

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