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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Cited articles

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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.