Articles | Volume 28, issue 5
https://doi.org/10.5194/hess-28-1215-2024
https://doi.org/10.5194/hess-28-1215-2024
Research article
 | 
14 Mar 2024
Research article |  | 14 Mar 2024

Disentangling coastal groundwater level dynamics in a global dataset

Annika Nolte, Ezra Haaf, Benedikt Heudorfer, Steffen Bender, and Jens Hartmann

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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-2023-180', Anonymous Referee #1, 16 Nov 2023
    • AC1: 'Reply on RC1', Annika Nolte, 15 Dec 2023
  • RC2: 'Comment on hess-2023-180', Anonymous Referee #2, 19 Nov 2023
    • AC2: 'Reply on RC2', Annika Nolte, 15 Dec 2023

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload
ED: Publish subject to minor revisions (further review by editor) (04 Jan 2024) by Nadia Ursino
AR by Annika Nolte on behalf of the Authors (24 Jan 2024)  Author's response   Author's tracked changes   Manuscript 
ED: Publish subject to minor revisions (review by editor) (25 Jan 2024) by Nadia Ursino
ED: Publish subject to technical corrections (25 Jan 2024) by Nadia Ursino
ED: Publish as is (26 Jan 2024) by Nadia Ursino
AR by Annika Nolte on behalf of the Authors (26 Jan 2024)
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Short summary
This study examines about 8000 groundwater level (GWL) time series from five continents to explore similarities in groundwater systems at different scales. Statistical metrics and machine learning techniques are applied to identify common GWL dynamics patterns and analyze their controlling factors. The study also highlights the potential and limitations of this data-driven approach to improve our understanding of groundwater recharge and discharge processes.