Spatiotemporal Deep Learning for Snow-Water Equivalent Prediction

Colin Fenster; Adrienne Marshall; Soutir Bandyopadhyay; Daniel McKenzie

doi:https://doi.org/10.5194/egusphere-egu26-8409

[Back] [Session HS3.5]

EGU26-8409, updated on 14 Mar 2026

https://doi.org/10.5194/egusphere-egu26-8409

EGU General Assembly 2026

© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.

Spatiotemporal Deep Learning for Snow-Water Equivalent Prediction

Colin Fenster¹, Adrienne Marshall², Soutir Bandyopadhyay¹, and Daniel McKenzie¹

Colin Fenster et al.

¹Colorado School of Mines, Department of Applied Mathematics and Statistics, Golden, United States of America
²Colorado School of Mines, Department of Geology and Geological Engineering, Golden, United States of America

How to cite: Fenster, C., Marshall, A., Bandyopadhyay, S., and McKenzie, D.: Spatiotemporal Deep Learning for Snow-Water Equivalent Prediction, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-8409, https://doi.org/10.5194/egusphere-egu26-8409, 2026.

This abstract has been withdrawn on 11 Aug 2026.