Articles | Volume 22, issue 8
Research article
03 Aug 2018
Research article |  | 03 Aug 2018

Improvement of model evaluation by incorporating prediction and measurement uncertainty

Lei Chen, Shuang Li, Yucen Zhong, and Zhenyao Shen


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: Publish subject to revisions (further review by editor and referees) (19 Dec 2017) by Tingju Zhu
AR by zhenyao shen on behalf of the Authors (22 Dec 2017)  Author's response    Manuscript
ED: Referee Nomination & Report Request started (13 Jan 2018) by Tingju Zhu
RR by Anonymous Referee #2 (16 Jan 2018)
ED: Publish subject to minor revisions (review by editor) (15 Jun 2018) by Tingju Zhu
AR by zhenyao shen on behalf of the Authors (19 Jun 2018)  Author's response    Manuscript
ED: Publish subject to technical corrections (24 Jul 2018) by Alberto Guadagnini
AR by zhenyao shen on behalf of the Authors (25 Jul 2018)  Author's response    Manuscript
Short summary
In this study, the cumulative distribution function approach (CDFA) and the Monte Carlo approach (MCA) were used to develop two new approaches for model evaluation within an uncertainty framework. These proposed methods could be extended to watershed models to provide a substitution for traditional model evaluations within an uncertainty framework.