Articles | Volume 30, issue 3
https://doi.org/10.5194/hess-30-709-2026
https://doi.org/10.5194/hess-30-709-2026
Research article
 | 
06 Feb 2026
Research article |  | 06 Feb 2026

Image-based classification of stream stage to support ephemeral stream monitoring

Sarah E. Ogle, Garrett McGurk, Anahita Jensen, Fred Martin Ralph, and Morgan C. Levy

Viewed

Total article views: 2,850 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
2,404 398 48 2,850 59 72
  • HTML: 2,404
  • PDF: 398
  • XML: 48
  • Total: 2,850
  • BibTeX: 59
  • EndNote: 72
Views and downloads (calculated since 20 Aug 2025)
Cumulative views and downloads (calculated since 20 Aug 2025)

Viewed (geographical distribution)

Total article views: 2,850 (including HTML, PDF, and XML) Thereof 2,770 with geography defined and 80 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 21 Mar 2026
Download
Short summary
Intermittent streams are vital to ecosystems and water supply, but are hard to monitor and increasingly affected by climate change. To address this, we used field camera images from 2017 to 2023 at a stream in northern California to train a machine learning model that classifies streamflow as dry, low, or high. This low-cost method enables monitoring of changing intermittent stream conditions and supports water management in data-scarce regions.
Share