Articles | Volume 26, issue 9
Hydrol. Earth Syst. Sci., 26, 2405–2430, 2022
https://doi.org/10.5194/hess-26-2405-2022
Hydrol. Earth Syst. Sci., 26, 2405–2430, 2022
https://doi.org/10.5194/hess-26-2405-2022
Research article
09 May 2022
Research article | 09 May 2022

Karst spring discharge modeling based on deep learning using spatially distributed input data

Andreas Wunsch et al.

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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-2021-403', Kuo-Chin Hsu, 03 Sep 2021
  • RC2: 'Comment on hess-2021-403', Anonymous Referee #2, 20 Oct 2021

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision
ED: Reconsider after major revisions (further review by editor and referees) (10 Nov 2021) by Yue-Ping Xu
AR by Andreas Wunsch on behalf of the Authors (19 Nov 2021)  Author's response    Author's tracked changes    Manuscript
ED: Referee Nomination & Report Request started (29 Nov 2021) by Yue-Ping Xu
RR by Anonymous Referee #1 (21 Dec 2021)
RR by Anonymous Referee #2 (20 Jan 2022)
ED: Publish subject to revisions (further review by editor and referees) (24 Jan 2022) by Yue-Ping Xu
AR by Andreas Wunsch on behalf of the Authors (07 Feb 2022)  Author's response    Author's tracked changes    Manuscript
ED: Referee Nomination & Report Request started (16 Mar 2022) by Yue-Ping Xu
RR by Anonymous Referee #2 (04 Apr 2022)
ED: Publish as is (17 Apr 2022) by Yue-Ping Xu
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Short summary
Modeling complex karst water resources is difficult enough, but often there are no or too few climate stations available within or close to the catchment to deliver input data for modeling purposes. We apply image recognition algorithms to time-distributed, spatially gridded meteorological data to simulate karst spring discharge. Our models can also learn the approximate catchment location of a spring independently.