Preprints
https://doi.org/10.5194/egusphere-2022-833
https://doi.org/10.5194/egusphere-2022-833
07 Sep 2022
 | 07 Sep 2022

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

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

Abstract. Transmission losses are the loss in the flow volume of a river as water moves downstream. These losses provide crucial ecosystem services, particularly in ephemeral and intermittent river systems. Transmission losses can be quantified at many scales using different measurement techniques. One of the most common methods is differential gauging of river flow at two locations. An alternative method for non-perennial rivers is to replace the downstream gauging location by visual assessments of the wetted river length on satellite images. We used this approach to estimate the transmission losses in the Selwyn River (Canterbury, New Zealand) using 147 satellite images collected between March 2020 and May 2021. The location of the river drying front was verified in the field on five occasions and seven differential gauging campaigns were conducted to ground-truth the losses estimated from the satellite images. The transmission loss point data obtained using the wetted river lengths and differential gauging campaigns were used to train an ensemble of random forest models to reconstruct the hourly time series of transmission losses and their uncertainties. Our results show that the Selwyn river transmission losses ranged between 0.25 and 0.65 m3/s/km during most of the 1-year study period. However, shortly after a flood peak the losses could reach up to 1.5 m3/s/km. These results enabled us to improve our understanding of the Selwyn River groundwater – surface water interactions and provide valuable data to support water management. We argue that our framework can easily be adapted to other ephemeral rivers and to longer time series.

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Journal article(s) based on this preprint

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
Hydrol. Earth Syst. Sci., 27, 703–722, https://doi.org/10.5194/hess-27-703-2023,https://doi.org/10.5194/hess-27-703-2023, 2023
Short summary
Antoine Di Ciacca, Scott Wilson, Jasmine Kang, and Thomas Wöhling

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

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 

Journal article(s) based on this preprint

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
Hydrol. Earth Syst. Sci., 27, 703–722, https://doi.org/10.5194/hess-27-703-2023,https://doi.org/10.5194/hess-27-703-2023, 2023
Short summary
Antoine Di Ciacca, Scott Wilson, Jasmine Kang, and Thomas Wöhling
Antoine Di Ciacca, Scott Wilson, Jasmine Kang, and Thomas Wöhling

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The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.

Short summary
We present a novel framework to estimate how much water is lost by partly dry 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 partly dry rivers and could be applied to long time series.