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

Viewed

Total article views: 2,258 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
1,874 327 57 2,258 35 35
  • HTML: 1,874
  • PDF: 327
  • XML: 57
  • Total: 2,258
  • BibTeX: 35
  • EndNote: 35
Views and downloads (calculated since 07 Sep 2022)
Cumulative views and downloads (calculated since 07 Sep 2022)

Viewed (geographical distribution)

Total article views: 2,258 (including HTML, PDF, and XML) Thereof 2,267 with geography defined and -9 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 18 Nov 2024
Download
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.