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| Titel |
Empirical orthogonal functions in soil CO2 efflux mapping |
| VerfasserIn |
Alexander Graf, Michael Herbst, Lutz Weihermüller, Johan A. Huisman, Nils Prolingheuer, Ludger Bornemann, Harry Vereecken |
| Konferenz |
EGU General Assembly 2011
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| Medientyp |
Artikel
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| Sprache |
Englisch
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| Digitales Dokument |
PDF |
| Erschienen |
In: GRA - Volume 13 (2011) |
| Datensatznummer |
250050872
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| Zusammenfassung |
| Empirical orthogonal functions (EOF) derived from a principal component analysis (PCA)
have successfully been used to identify recurring spatial patterns in repeated grid sampling
datasets of soil moisture in the past (Perry and Niemann, 2008; Korres et al., 2010). Here, we
test in a case study whether the same applies to field-scale datasets of soil CO2
efflux, which are known to challenge geostatistical analysis and modelling (Rochette
et al., 1991; La Scala et al., 2000; Rodeghiero and Cescatti, 2008; Herbst et al.,
2009).
Our test dataset consists of chamber measurements on 28 dates within a transect
of 18 points spaced 10 m apart on a gently sloping mid-latitude bare soil field.
Transformation of the 28 single-date patterns into few dominant EOFs could indeed improve
the (geo)statistical analysis and empirical modelling of spatiotemporal CO2 efflux
variability. In particular, the first EOF exhibited a much smaller nugget effect in the
semivariogram, and clearer statistical relations to soil properties, than most single
surveys. However, unlike for many other variables, for soil CO2 efflux PCA qualified a
very large portion (about 50%) of the total spatiotemporal variability as "noise",
i.e. as variability associated neither with spatial autocorrelation nor with spatial
correlation to soil properties. This is supported by cross validation, which indicates that
only the first EOF (32% variance) definitely improves the predictive skills of an
EOF-based regression model as well as of a Canonical Correlation based model
(Graf et al., submitted). The non correlated half of spatiotemporal variability is not
necessarily due to random measurement errors, but to a (hypothetically greater)
part to fluctuations on a too small spatial and/or temporal (Graf et al., 2011) scale.
The correlated half indicated that at our site, temperature was the most important
driver of temporal variability of the spatial average of soil CO2 efflux, while soil
moisture was the most important driver of spatial variability. Both the temperature and
the moisture dependence were in agreement with common model assumptions.
Relations to soil biochemical parameters, on the other hand, were weak and sometimes
counter-intuitive.
References
Graf, A., N. Prolingheuer, A. Schickling, M. Schmidt, K. Schneider, D. Schüttemeyer, M. Herbst,
J.A. Huisman, L. Weihermüller, B. Scharnagl, C. Steenpass, R. Harms, and H. Vereecken. 2011.
Temporal downscaling of soil CO2 efflux measurements based on time-stable spatial patterns. Vadose
Zone J., in press.
Graf, A., Herbst, M., Weihermüller, L., Huisman, J.A., Prolingheuer, N., Bornemann, L.,
Vereecken, H. Analyzing spatiotemporal variability of heterotrophic soil respiration at the field scale
using orthogonal functions. Submitted to Geoderma.
Herbst, M., Prolingheuer, N., Graf, A., Huisman, J.A., Weihermüller, L., Vanderborght, J., 2009.
Characterisation and understanding of bare soil respiration spatial variability at plot scale. Vadose Zone
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La Scala, N., Marques, J., Pereira, G.T., Cora, J.E., 2000. Short-term temporal changes in the
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