Improving flood forecasting capability of physically based distributed  hydrological models by parameter optimization

Chen, Y.; Li, J.; Xu, H.

doi:https://doi.org/10.5194/hess-20-375-2016

Articles | Volume 20, issue 1

https://doi.org/10.5194/hess-20-375-2016

© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.

Special issue:

Modeling hydrological processes and changes

https://doi.org/10.5194/hess-20-375-2016

© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.

Articles | Volume 20, issue 1

Research article

|

21 Jan 2016

Research article |

| 21 Jan 2016

Improving flood forecasting capability of physically based distributed hydrological models by parameter optimization

Y. Chen, J. Li, and H. Xu

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Final revised paper (published on 21 Jan 2016)
Preprint (discussion started on 16 Oct 2015)

Interactive discussion

Status: closed

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

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- Supplement

RC C5077: 'Comments on hess-2015-420', Anonymous Referee #1, 25 Nov 2015
- AC C5809: 'Reply to the comments of Referee # 1', Y. Chen, 23 Dec 2015
RC C5608: 'Hydrological Model', Anonymous Referee #2, 11 Dec 2015
- AC C5810: 'Reply to the comments of Referee # 2', Y. Chen, 23 Dec 2015
- AC C5971: 'revised paper', Y. Chen, 05 Jan 2016

Peer-review completion

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

ED: Publish as is (05 Jan 2016) by Yaning Chen

AR by Yangbo Chen on behalf of the Authors (05 Jan 2016) Manuscript

Short summary

Parameter optimization is necessary to improve the flood forecasting capability of physically based distributed hydrological model. A method for parameter optimization with particle swam optimization (PSO) algorithm has been proposed for physically based distributed hydrological model in catchment flood forecasting and validated in southern China. It has found that the appropriate particle number and maximum evolution number of PSO algorithm are 20 and 30 respectively.