Articles | Volume 22, issue 8
https://doi.org/10.5194/hess-22-4401-2018
https://doi.org/10.5194/hess-22-4401-2018
Research article
 | 
21 Aug 2018
Research article |  | 21 Aug 2018

Detecting dominant changes in irregularly sampled multivariate water quality data sets

Christian Lehr, Ralf Dannowski, Thomas Kalettka, Christoph Merz, Boris Schröder, Jörg Steidl, and Gunnar Lischeid

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Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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AR: Author's response | RR: Referee report | ED: Editor decision
ED: Publish subject to minor revisions (further review by editor) (24 May 2018) by Stacey Archfield
AR by Christian Lehr on behalf of the Authors (01 Jun 2018)  Author's response   Manuscript 
ED: Publish subject to minor revisions (review by editor) (24 Jun 2018) by Stacey Archfield
AR by Christian Lehr on behalf of the Authors (29 Jun 2018)  Author's response   Manuscript 
ED: Publish as is (18 Jul 2018) by Stacey Archfield
AR by Christian Lehr on behalf of the Authors (19 Jul 2018)  Manuscript 
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Short summary
We suggested and tested an exploratory approach for the detection of dominant changes in multivariate water quality data sets with irregular sampling in space and time. The approach is especially recommended for the exploratory assessment of existing long-term low-frequency multivariate water quality monitoring data.