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@ARTICLE{Graf:20897,
author = {Graf, A. and Herbst, M. and Weihermüller, L. and Huisman,
J.A. and Prolingheuer, N. and Bornemann, L. and Vereecken,
H.},
title = {{A}nalyzing spatiotemporal variability of heterotrophic
soil respiration at the field scale using orthogonal
functions},
journal = {Geoderma},
volume = {181-182},
issn = {0016-7061},
address = {Amsterdam [u.a.]},
publisher = {Elsevier Science},
reportid = {PreJuSER-20897},
pages = {91 - 101},
year = {2012},
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.},
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.},
keywords = {J (WoSType)},
cin = {IBG-3},
ddc = {550},
cid = {I:(DE-Juel1)IBG-3-20101118},
pnm = {Terrestrische Umwelt},
pid = {G:(DE-Juel1)FUEK407},
shelfmark = {Soil Science},
typ = {PUB:(DE-HGF)16},
UT = {WOS:000303958500010},
doi = {10.1016/j.geoderma.2012.02.016},
url = {https://juser.fz-juelich.de/record/20897},
}