Journal cover Journal topic
Hydrology and Earth System Sciences An interactive open-access journal of the European Geosciences Union
Hydrol. Earth Syst. Sci., 21, 1769-1790, 2017
https://doi.org/10.5194/hess-21-1769-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
Research article
27 Mar 2017
A high-resolution dataset of water fluxes and states for Germany accounting for parametric uncertainty
Matthias Zink et al.
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Interactive discussionStatus: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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RC1: 'Review of HESS-2016-443 “A High-Resolution Dataset of Water Fluxes and States for Germany Accounting for Parametric Uncertainty”', Anonymous Referee #1, 11 Nov 2016 Printer-friendly Version 
AC1: 'Interactive comment', Matthias Zink, 19 Dec 2016 Printer-friendly Version Supplement 
 
RC2: 'Review of Zink et al., 2016', Anonymous Referee #2, 16 Nov 2016 Printer-friendly Version 
AC2: 'Interactive comment', Matthias Zink, 19 Dec 2016 Printer-friendly Version Supplement 
 
RC3: 'review of hess-2016-443', Anonymous Referee #3, 21 Nov 2016 Printer-friendly Version Supplement 
AC3: 'Interactive comment', Matthias Zink, 19 Dec 2016 Printer-friendly Version Supplement 
Peer review completion
AR: Author's response | RR: Referee report | ED: Editor decision
ED: Publish subject to revisions (further review by Editor and Referees) (22 Dec 2016) by Erwin Zehe  
AR by Matthias Zink on behalf of the Authors (24 Jan 2017)  Author's response  Manuscript
ED: Referee Nomination & Report Request started (30 Jan 2017) by Erwin Zehe
RR by Anonymous Referee #2 (30 Jan 2017)
RR by Anonymous Referee #3 (05 Mar 2017)  
ED: Publish as is (06 Mar 2017) by Erwin Zehe  
CC BY 4.0
Publications Copernicus
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Short summary
We discuss the estimation of a long-term, high-resolution, continuous and consistent dataset of hydro-meteorological variables for Germany. Here we describe the derivation of national-scale parameter sets and analyze the uncertainty of the estimated hydrologic variables (focusing on the parametric uncertainty). Our study highlights the role of accounting for the parametric uncertainty in model-derived hydrological datasets.
We discuss the estimation of a long-term, high-resolution, continuous and consistent dataset of...
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