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| Titel |
Can the heterogeneity in stream dissolved organic carbon be explained by contributing landscape elements? |
| VerfasserIn |
A. M. Ågren, I. Buffam, D. M. Cooper, T. Tiwari, C. D. Evans, H. Laudon |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1726-4170
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| Digitales Dokument |
URL |
| Erschienen |
In: Biogeosciences ; 11, no. 4 ; Nr. 11, no. 4 (2014-02-27), S.1199-1213 |
| Datensatznummer |
250117250
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| Publikation (Nr.) |
 copernicus.org/bg-11-1199-2014.pdf |
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| Zusammenfassung |
| The controls on stream dissolved organic carbon (DOC) concentrations were
investigated in a 68 km2 catchment by applying a landscape-mixing model
to test if downstream concentrations could be predicted from contributing
landscape elements. The landscape-mixing model reproduced the DOC
concentration well throughout the stream network during times of high and
intermediate discharge. The landscape-mixing model approach is conceptually
simple and easy to apply, requiring relatively few field measurements and
minimal parameterisation. Our interpretation is that the higher degree of
hydrological connectivity during high flows, combined with shorter stream
residence times, increased the predictive power of this whole watershed-based
mixing model. The model was also useful for providing a baseline for residual
analysis, which highlighted areas for further conceptual model development.
The residual analysis indicated areas of the stream network that were not
well represented by simple mixing of headwaters, as well as flow conditions
during which simple mixing based on headwater watershed characteristics did
not apply. Specifically, we found that during periods of baseflow the larger
valley streams had much lower DOC concentrations than would be predicted by
simple mixing. Longer stream residence times during baseflow and changing
hydrological flow paths were suggested as potential reasons for this pattern.
This study highlights how a simple landscape-mixing model can be used for
predictions as well as providing a baseline for residual analysis, which
suggest potential mechanisms to be further explored using more focused field
and process-based modelling studies. |
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