Articles | Volume 26, issue 21
Hydrol. Earth Syst. Sci., 26, 5431–5447, 2022
Hydrol. Earth Syst. Sci., 26, 5431–5447, 2022
Research article
01 Nov 2022
Research article | 01 Nov 2022

Karst spring recession and classification: efficient, automated methods for both fast- and slow-flow components

Tunde Olarinoye et al.


Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on hess-2021-249', Anonymous Referee #1, 14 Jun 2021
    • AC2: 'Reply on RC1', Tunde Olarinoye, 31 Aug 2021
  • RC2: 'Comment on hess-2021-249', Anonymous Referee #2, 19 Jul 2021
    • AC1: 'Reply on RC2', Tunde Olarinoye, 31 Aug 2021
    • AC3: 'Reply on RC2', Tunde Olarinoye, 31 Aug 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) (08 Sep 2021) by Marnik Vanclooster
AR by Tunde Olarinoye on behalf of the Authors (06 Jun 2022)  Author's response    Author's tracked changes    Manuscript
ED: Referee Nomination & Report Request started (27 Jun 2022) by Marnik Vanclooster
RR by Anonymous Referee #1 (28 Jul 2022)
RR by Benjamin Tobin (09 Aug 2022)
ED: Publish subject to technical corrections (11 Aug 2022) by Marnik Vanclooster
Short summary
Analysis of karst spring recession is essential for management of groundwater. In karst, recession is dominated by slow and fast components; separating these components is by manual and subjective approaches. In our study, we tested the applicability of automated streamflow recession extraction procedures for a karst spring. Results showed that, by simple modification, streamflow extraction methods can identify slow and fast components: derived recession parameters are within reasonable ranges.