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Hydrology and Earth System Sciences An interactive open-access journal of the European Geosciences Union
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HESS | Articles | Volume 23, issue 1
Hydrol. Earth Syst. Sci., 23, 447–463, 2019
https://doi.org/10.5194/hess-23-447-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
Hydrol. Earth Syst. Sci., 23, 447–463, 2019
https://doi.org/10.5194/hess-23-447-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

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
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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Peer review completion

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 Fangmann 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 Fangmann on behalf of the Authors (17 Dec 2018)  Author's response    Manuscript
ED: Publish subject to technical corrections (18 Dec 2018) by Kerstin Stahl
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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.
Low-flow events are little dynamic in space and time. Thus, it is hypothesized that models can...
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