A time-varying parameter estimation approach using split-sample calibration based on dynamic programming

Zhang, Xiaojing; Liu, Pan

doi:https://doi.org/10.5194/hess-25-711-2021

Articles | Volume 25, issue 2

https://doi.org/10.5194/hess-25-711-2021

© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/hess-25-711-2021

© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 25, issue 2

Research article

|

18 Feb 2021

Research article |

| 18 Feb 2021

A time-varying parameter estimation approach using split-sample calibration based on dynamic programming

Xiaojing Zhang and Pan Liu

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Final revised paper (published on 18 Feb 2021)
Preprint (discussion started on 20 Dec 2019)

Interactive discussion

Status: closed

AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment

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- Supplement

RC1: 'Reviewer Comments', Anonymous Referee #1, 14 Feb 2020
- AC1: 'Interactive comment on “A time-varying parameter estimation approach using split-sample calibration based on dynamic programming” by Xiaojing Zhang and Pan Liu', Pan Liu, 26 Sep 2020
RC2: 'Review', Anonymous Referee #2, 30 Aug 2020
- AC2: 'Responses to Reviewer #2', Pan Liu, 26 Sep 2020
EC1: 'Editor's comment', Dimitri Solomatine, 28 Sep 2020
- AC3: 'Responses to Editor', Pan Liu, 07 Nov 2020

Peer-review completion

AR: Author's response | RR: Referee report | ED: Editor decision

ED: Publish subject to revisions (further review by editor and referees) (28 Sep 2020) by Dimitri Solomatine

AR by Pan Liu on behalf of the Authors (02 Oct 2020) Author's response Manuscript

ED: Referee Nomination & Report Request started (14 Oct 2020) by Dimitri Solomatine

RR by Anonymous Referee #1 (08 Nov 2020)

RR by Anonymous Referee #2 (22 Nov 2020)

ED: Publish subject to minor revisions (review by editor) (13 Dec 2020) by Dimitri Solomatine

AR by Pan Liu on behalf of the Authors (21 Dec 2020) Author's response Author's tracked changes Manuscript

ED: Publish as is (22 Dec 2020) by Dimitri Solomatine

Short summary

Rainfall–runoff models are useful tools for streamflow simulation. However, efforts are needed to investigate how their parameters vary in response to climate changes and human activities. Thus, this study proposes a new method for estimating time-varying parameters, by considering both simulation accuracy and parameter continuity. The results show the proposed method is effective for identifying temporal variations of parameters and can simultaneously provide good streamflow simulation.