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Hydrology and Earth System Sciences An interactive open-access journal of the European Geosciences Union
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Volume 23, issue 4
Hydrol. Earth Syst. Sci., 23, 2147–2172, 2019
https://doi.org/10.5194/hess-23-2147-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
Hydrol. Earth Syst. Sci., 23, 2147–2172, 2019
https://doi.org/10.5194/hess-23-2147-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 30 Apr 2019

Research article | 30 Apr 2019

A likelihood framework for deterministic hydrological models and the importance of non-stationary autocorrelation

Lorenz Ammann et al.

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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) (09 Nov 2018) by Erwin Zehe
AR by Lorenz Ammann on behalf of the Authors (20 Dec 2018)  Author's response    Manuscript
ED: Referee Nomination & Report Request started (08 Jan 2019) by Erwin Zehe
RR by Anonymous Referee #2 (08 Feb 2019)
RR by Anonymous Referee #3 (04 Mar 2019)
ED: Publish subject to minor revisions (review by editor) (08 Mar 2019) by Erwin Zehe
AR by Lorenz Ammann on behalf of the Authors (25 Mar 2019)  Author's response    Manuscript
ED: Publish as is (26 Mar 2019) by Erwin Zehe
AR by Lorenz Ammann on behalf of the Authors (27 Mar 2019)  Author's response    Manuscript
Publications Copernicus
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Short summary
The uncertainty of hydrological models can be substantial, and its quantification and realistic description are often difficult. We propose a new flexible probabilistic framework to describe and quantify this uncertainty. It is show that the correlation of the errors can be non-stationary, and that accounting for temporal changes in correlation can lead to strongly improved probabilistic predictions. This is a promising avenue for improving uncertainty estimation in hydrological modelling.
The uncertainty of hydrological models can be substantial, and its quantification and realistic...
Citation