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

Viewed

Total article views: 131 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
93 33 5 131 12 6 5
  • HTML: 93
  • PDF: 33
  • XML: 5
  • Total: 131
  • Supplement: 12
  • BibTeX: 6
  • EndNote: 5
Views and downloads (calculated since 18 Nov 2024)
Cumulative views and downloads (calculated since 18 Nov 2024)

Viewed (geographical distribution)

Total article views: 124 (including HTML, PDF, and XML) Thereof 124 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 04 Mar 2025
Download
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.
Read more
Share