Articles | Volume 26, issue 3
https://doi.org/10.5194/hess-26-795-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-26-795-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
14 Feb 2022
Research article |
| 14 Feb 2022
| 14 Feb 2022
Evaluation and interpretation of convolutional long short-term memory networks for regional hydrological modelling
Department of Earth, Ocean and Atmospheric Sciences, University of British Columbia, Vancouver, BC V6T 1Z4, Canada
Valentina Radić
Department of Earth, Ocean and Atmospheric Sciences, University of British Columbia, Vancouver, BC V6T 1Z4, Canada
Related authors
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Christina Draeger, Valentina Radić, Rachel H. White, and Mekdes Ayalew Tessema
The Cryosphere, 18, 17–42, https://doi.org/10.5194/tc-18-17-2024, https://doi.org/10.5194/tc-18-17-2024, 2024
Short summary
Short summary
Our study increases our confidence in using reanalysis data for reconstructions of past glacier melt and in using dynamical downscaling for long-term simulations from global climate models to project glacier melt. We find that the surface energy balance model, forced with reanalysis and dynamically downscaled reanalysis data, yields <10 % difference in the modeled total melt energy when compared to the same model being forced with observations at our glacier sites in western Canada.
Jonathan P. Conway, Jakob Abermann, Liss M. Andreassen, Mohd Farooq Azam, Nicolas J. Cullen, Noel Fitzpatrick, Rianne H. Giesen, Kirsty Langley, Shelley MacDonell, Thomas Mölg, Valentina Radić, Carleen H. Reijmer, and Jean-Emmanuel Sicart
The Cryosphere, 16, 3331–3356, https://doi.org/10.5194/tc-16-3331-2022, https://doi.org/10.5194/tc-16-3331-2022, 2022
Short summary
Short summary
We used data from automatic weather stations on 16 glaciers to show how clouds influence glacier melt in different climates around the world. We found surface melt was always more frequent when it was cloudy but was not universally faster or slower than under clear-sky conditions. Also, air temperature was related to clouds in opposite ways in different climates – warmer with clouds in cold climates and vice versa. These results will help us improve how we model past and future glacier melt.
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Short summary
We develop and interpret a spatiotemporal deep learning model for regional streamflow prediction at more than 200 stream gauge stations in western Canada. We find the novel modelling style to work very well for daily streamflow prediction. Importantly, we interpret model learning to show that it has learned to focus on physically interpretable and physically relevant information, which is a highly desirable quality of machine-learning-based hydrological models.
We develop and interpret a spatiotemporal deep learning model for regional streamflow prediction...
