Articles | Volume 27, issue 20
https://doi.org/10.5194/hess-27-3803-2023
https://doi.org/10.5194/hess-27-3803-2023
Research article
 | 
27 Oct 2023
Research article |  | 27 Oct 2023

A Bayesian updating framework for calibrating the hydrological parameters of road networks using taxi GPS data

Xiangfu Kong, Jiawen Yang, Ke Xu, Bo Dong, and Shan Jiang

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Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on hess-2023-7', Jeffrey M Sadler, 05 Apr 2023
    • AC1: 'Reply on RC1', Xiangfu Kong, 06 May 2023
  • RC2: 'Comment on hess-2023-7', Anonymous Referee #2, 07 Apr 2023
    • AC2: 'Reply on RC2', Xiangfu Kong, 06 May 2023
    • AC1: 'Reply on RC1', Xiangfu Kong, 06 May 2023

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload
ED: Reconsider after major revisions (further review by editor and referees) (19 May 2023) by Yue-Ping Xu
AR by Xiangfu Kong on behalf of the Authors (20 Jun 2023)  Author's response   Author's tracked changes   Manuscript 
ED: Referee Nomination & Report Request started (14 Jul 2023) by Yue-Ping Xu
RR by Anonymous Referee #2 (19 Aug 2023)
RR by Anonymous Referee #3 (04 Sep 2023)
RR by Jeffrey M Sadler (07 Sep 2023)
ED: Publish subject to technical corrections (11 Sep 2023) by Yue-Ping Xu
AR by Xiangfu Kong on behalf of the Authors (14 Sep 2023)  Author's response   Manuscript 
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Short summary
To solve the issue of sparsity of field-observed runoff data, we propose a methodology that leverages taxi GPS data to support hydrological parameter calibration for road networks. Novel to this study is that a new kind of data source, namely floating car data, is introduced to tackle the ungauged catchment problem, providing alternative flooding early warning supports for cities that have little runoff data but rich taxi data.