Proceedings of IAHS
Proceedings of IAHS
PIAHS
Articles | Volume 387
Articles | Volume 387
https://doi.org/10.5194/piahs-387-17-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/piahs-387-17-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 387
Post-conference publication
 | 
18 Nov 2024
Post-conference publication |  | 18 Nov 2024

A hybrid approach to enhance streamflow simulation in data-constrained Himalayan basins: combining the Glacio-hydrological Degree-day Model and recurrent neural networks

Dinesh Joshi, Rijan Bhakta Kayastha, Kundan Lal Shrestha, and Rakesh Kayastha

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Short summary
This study explores the potential of integrating data science models to enhance the predictive capacity of a theory-guided glacier hydrological model for improved river discharge simulations in the Himalayan basins. By combining data science and physical process models, the study addresses the limitations inherent in each approach.
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