Articles | Volume 29, issue 22
https://doi.org/10.5194/hess-29-6445-2025
https://doi.org/10.5194/hess-29-6445-2025
Research article
 | 
19 Nov 2025
Research article |  | 19 Nov 2025

Technical note: A low-cost approach to monitoring relative streamflow dynamics in small headwater streams using time lapse imagery and a deep learning model

Phillip J. Goodling, Jennifer H. Fair, Amrita Gupta, Jeffrey D. Walker, Todd Dubreuil, Michael Hayden, and Benjamin H. Letcher

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Bedrock depth influences spatial patterns of summer baseflow, temperature and flow disconnection for mountainous headwater streams
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Cited articles

Bellucci, C. J., Becker, M. E., Czarnowski, M., and Fitting, C.: A novel method to evaluate stream connectivity using trail cameras, River Res. Appl., 36, 1504–1514, https://doi.org/10.1002/rra.3689, 2020. 
Benda, L., Poff, N. L., Miller, D., Dunne, T., Reeves, G., Pess, G., and Pollock, M.: The Network Dynamics Hypothesis: How Channel Networks Structure Riverine Habitats, BioScience, 54, 413, https://doi.org/10.1641/0006-3568(2004)054[0413:TNDHHC]2.0.CO;2, 2004. 
Birgand, F., Chapman, K., Hazra, A., Gilmore, T., Etheridge, R., and Staicu, A.-M.: Field performance of the GaugeCam image-based water level measurement system, PLOS Water, 1, e0000032, https://doi.org/10.1371/journal.pwat.0000032, 2022. 
Briggs, M. A., Lane, J. W., Snyder, C. D., White, E. A., Johnson, Z. C., Nelms, D. L., and Hitt, N. P.: Shallow bedrock limits groundwater seepage-based headwater climate refugia, Limnologica, 68, 142–156, https://doi.org/10.1016/j.limno.2017.02.005, 2018. 
Burges, C., Shaked, T., Renshaw, E., Lazier, A., Deeds, M., Hamilton, N., and Hullender, G.: Learning to rank using gradient descent, in: Proceedings of the 22nd International Conference on Machine Learning, Bonn, Germany, 7–11 August 2005, Association for Computing Machinery, NY, USA, 89–96, https://doi.org/10.1145/1102351.1102363, 2005. 
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Short summary
We describe a stream monitoring method using low-cost cameras and a deep learning model. It produces a relative hydrograph (0–100%). We applied the method to 11 cameras at 8 sites and found model performance sufficient to describe floods and droughts. The models were trained on image pairs annotated by people. We examined how well people performed annotations and how many annotations were needed. We concluded this method can be used to gain new insights into under-monitored small streams.
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