Articles | Volume 20, issue 5
https://doi.org/10.5194/hess-20-1925-2016
https://doi.org/10.5194/hess-20-1925-2016
Research article
 | 
13 May 2016
Research article |  | 13 May 2016

Testing alternative uses of electromagnetic data to reduce the prediction error of groundwater models

Nikolaj Kruse Christensen, Steen Christensen, and Ty Paul A. Ferre

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Interactive discussion

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: Reconsider after major revisions (28 Nov 2015) by Mauro Giudici
AR by Nikolaj Kruse Christensen on behalf of the Authors (08 Jan 2016)  Author's response   Manuscript 
ED: Referee Nomination & Report Request started (08 Jan 2016) by Mauro Giudici
RR by Anonymous Referee #1 (11 Jan 2016)
RR by Anonymous Referee #3 (03 Feb 2016)
ED: Reconsider after major revisions (10 Feb 2016) by Mauro Giudici
AR by Nikolaj Kruse Christensen on behalf of the Authors (09 Mar 2016)  Author's response   Manuscript 
ED: Referee Nomination & Report Request started (13 Mar 2016) by Mauro Giudici
RR by Anonymous Referee #1 (14 Mar 2016)
RR by Anonymous Referee #3 (17 Mar 2016)
ED: Publish subject to minor revisions (Editor review) (25 Mar 2016) by Mauro Giudici
AR by Nikolaj Kruse Christensen on behalf of the Authors (03 Apr 2016)  Author's response   Manuscript 
ED: Publish subject to minor revisions (Editor review) (12 Apr 2016) by Mauro Giudici
AR by Nikolaj Kruse Christensen on behalf of the Authors (14 Apr 2016)  Author's response   Manuscript 
ED: Publish as is (16 Apr 2016) by Mauro Giudici
AR by Nikolaj Kruse Christensen on behalf of the Authors (25 Apr 2016)
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Short summary
Our primary objective in this study is to provide a virtual environment that allows users to determine the value of geophysical data and, furthermore, to investigate how best to use those data to develop groundwater models and to reduce their prediction errors. When this has been carried through for alternative data sampling, parameterization and inversion approaches, the best alternative can be chosen by comparison of prediction results between the alternatives.