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

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Cited articles

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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.
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