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

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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. The transmission losses are then calculated as the flow gauged at the upstream location divided by the wetted river length. 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 six 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 predict the continuous 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 m3s-1km-1 during most of the 1-year study period. However, shortly after a flood peak the losses could reach up to 1.5 m3s-1km-1. 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.


Original languageEnglish
Pages (from-to)703-722
Number of pages20
JournalHydrology and Earth System Sciences
Issue number3
Publication statusPublished - 9 Feb 2023

External IDs

unpaywall 10.5194/egusphere-2022-833
Scopus 85148694575