07 Sep 2022
07 Sep 2022
Status: this preprint is open for discussion.

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

Antoine Di Ciacca1, Scott Wilson1, Jasmine Kang2, and Thomas Wöhling1,3 Antoine Di Ciacca et al.
  • 1Environmental Research, Lincoln Agritech Ltd, Lincoln, New Zealand
  • 2National Institute of Water and Atmospheric Research (NIWA), Christchurch, New Zealand
  • 3Chair of Hydrology, Technische Universität Dresden, Dresden, Germany

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.

Antoine Di Ciacca et al.

Status: open (until 02 Nov 2022)

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 reply
  • RC2: 'Comment on egusphere-2022-833', Anonymous Referee #2, 29 Sep 2022 reply

Antoine Di Ciacca et al.

Antoine Di Ciacca et al.


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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.