Articles | Volume 23, issue 3
https://doi.org/10.5194/hess-23-1505-2019
https://doi.org/10.5194/hess-23-1505-2019
Research article
 | 
15 Mar 2019
Research article |  | 15 Mar 2019

Predicting floods in a large karst river basin by coupling PERSIANN-CCS QPEs with a physically based distributed hydrological model

Ji Li, Daoxian Yuan, Jiao Liu, Yongjun Jiang, Yangbo Chen, Kuo Lin Hsu, and Soroosh Sorooshian

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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) (15 Nov 2018) by Frederiek Sperna Weiland
AR by Anna Mirena Feist-Polner on behalf of the Authors (14 Dec 2018)  Author's response
ED: Referee Nomination & Report Request started (09 Jan 2019) by Frederiek Sperna Weiland
RR by Anonymous Referee #1 (07 Feb 2019)
ED: Publish subject to technical corrections (26 Feb 2019) by Frederiek Sperna Weiland
AR by Ji Li on behalf of the Authors (27 Feb 2019)  Author's response    Manuscript
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Short summary
There are no long-term reasonable rainfall data to build a hydrological model in karst river basins to a large extent. In this paper, the PERSIANN-CCS QPEs are employed to estimate the precipitation data as an attempt in the Liujiang karst river basin, 58 270 km2, China. An improved method is proposed to revise the results of the PERSIANN-CCS QPEs. The post-processed PERSIANN-CCS QPE with a distributed hydrological model, the Liuxihe model, has a better performance in karst flood forecasting.