Articles | Volume 23, issue 1
https://doi.org/10.5194/hess-23-447-2019
https://doi.org/10.5194/hess-23-447-2019
Research article
 | 
25 Jan 2019
Research article |  | 25 Jan 2019

Statistical approaches for identification of low-flow drivers: temporal aspects

Anne Fangmann and Uwe Haberlandt

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Status: closed
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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 revisions (further review by editor and referees) (10 Oct 2018) by Kerstin Stahl
AR by Anne Bartens on behalf of the Authors (10 Oct 2018)  Author's response   Manuscript 
ED: Referee Nomination & Report Request started (11 Oct 2018) by Kerstin Stahl
RR by Martin Hanel (22 Nov 2018)
ED: Publish subject to minor revisions (review by editor) (27 Nov 2018) by Kerstin Stahl
AR by Anne Bartens on behalf of the Authors (17 Dec 2018)  Author's response   Manuscript 
ED: Publish subject to technical corrections (18 Dec 2018) by Kerstin Stahl
AR by Anne Bartens on behalf of the Authors (10 Jan 2019)  Manuscript 
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Short summary
Low-flow events are little dynamic in space and time. Thus, it is hypothesized that models can be found, based on simple statistical relationships between low-flow metrics and meteorological states, that can help identify potential low-flow drivers. In this study we assess whether such relationships exist and whether they can be applied to predict future low flow within regional climate change impact assessment in the northwestern part of Germany.