Articles | Volume 27, issue 3
https://doi.org/10.5194/hess-27-703-2023
https://doi.org/10.5194/hess-27-703-2023
Research article
 | 
09 Feb 2023
Research article |  | 09 Feb 2023

Deriving transmission losses in ephemeral rivers using satellite imagery and machine learning

Antoine Di Ciacca, Scott Wilson, Jasmine Kang, and Thomas Wöhling

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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 egusphere-2022-833', Howard Wheater, 20 Sep 2022
    • AC1: 'Reply on RC1', Antoine Di Ciacca, 21 Oct 2022
  • RC2: 'Comment on egusphere-2022-833', Anonymous Referee #2, 29 Sep 2022
    • AC2: 'Reply on RC2', Antoine Di Ciacca, 21 Oct 2022

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) (12 Nov 2022) by Efrat Morin
AR by Antoine Di Ciacca on behalf of the Authors (22 Dec 2022)  Author's response   Author's tracked changes   Manuscript 
ED: Referee Nomination & Report Request started (26 Dec 2022) by Efrat Morin
RR by Anonymous Referee #2 (16 Jan 2023)
RR by Anonymous Referee #1 (17 Jan 2023)
ED: Publish as is (24 Jan 2023) by Efrat Morin
AR by Antoine Di Ciacca on behalf of the Authors (25 Jan 2023)  Author's response   Manuscript 
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Short summary
We present a novel framework to estimate how much water is lost by ephemeral rivers using satellite imagery and machine learning. This framework proved to be an efficient approach, requiring less fieldwork and generating more data than traditional methods, at a similar accuracy. Furthermore, applying this framework improved our understanding of the water transfer at our study site. Our framework is easily transferable to other ephemeral rivers and could be applied to long time series.