Hydrology and Earth System Sciences
Hydrology and Earth System Sciences
HESS
Articles | Volume 27, issue 3
Articles | Volume 27, issue 3
https://doi.org/10.5194/hess-27-703-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-27-703-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 27, issue 3
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: 2,126 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
1,768 307 51 2,126 32 32
  • HTML: 1,768
  • PDF: 307
  • XML: 51
  • Total: 2,126
  • BibTeX: 32
  • EndNote: 32
Views and downloads (calculated since 07 Sep 2022)
Cumulative views and downloads (calculated since 07 Sep 2022)

Viewed (geographical distribution)

Total article views: 2,126 (including HTML, PDF, and XML) Thereof 2,137 with geography defined and -11 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 26 Jul 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.
Read more