Articles | Volume 23, issue 11
https://doi.org/10.5194/hess-23-4783-2019
https://doi.org/10.5194/hess-23-4783-2019
Research article
 | 
25 Nov 2019
Research article |  | 25 Nov 2019

A virtual hydrological framework for evaluation of stochastic rainfall models

Bree Bennett, Mark Thyer, Michael Leonard, Martin Lambert, and Bryson Bates

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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 (further review by editor and referees) (12 Dec 2018) by Nadav Peleg
AR by Bree Bennett on behalf of the Authors (25 Mar 2019)  Author's response    Manuscript
ED: Referee Nomination & Report Request started (28 Mar 2019) by Nadav Peleg
RR by Anonymous Referee #2 (28 Apr 2019)
RR by Anonymous Referee #1 (30 Apr 2019)
ED: Publish subject to revisions (further review by editor and referees) (05 May 2019) by Nadav Peleg
AR by Bree Bennett on behalf of the Authors (27 Jul 2019)  Author's response    Manuscript
ED: Referee Nomination & Report Request started (30 Jul 2019) by Nadav Peleg
RR by Anonymous Referee #1 (20 Aug 2019)
ED: Publish subject to minor revisions (review by editor) (26 Aug 2019) by Nadav Peleg
AR by Bree Bennett on behalf of the Authors (21 Sep 2019)  Author's response
ED: Publish as is (01 Oct 2019) by Nadav Peleg
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Short summary
A new stochastic rainfall model evaluation framework is introduced, with three key features: (1) streamflow-based, to directly evaluate modelled streamflow performance, (2) virtual, to avoid confounding errors in hydrological models or data, and (3) targeted, to isolate errors according to specific sites/months. The framework identified the importance of rainfall in the wetting-up months for providing reliable predictions of streamflow over the entire year despite their low flow volumes.