Articles | Volume 28, issue 14
https://doi.org/10.5194/hess-28-3133-2024
© Author(s) 2024. 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-28-3133-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
19 Jul 2024
Research article |
| 19 Jul 2024
| 19 Jul 2024
Leveraging a novel hybrid ensemble and optimal interpolation approach for enhanced streamflow and flood prediction
Mohamad El Gharamti
CORRESPONDING AUTHOR
NCAR, Computational and Information Systems Laboratory (CISL), Boulder, CO, USA
Arezoo Rafieeinasab
NCAR, Research Applications Laboratory (RAL), Boulder, CO, USA
James L. McCreight
UCAR, CPAESS Cooperative Programs for the Advancement of Earth System Science, Boulder, CO, USA
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Short summary
This study introduces a hybrid data assimilation scheme for precise streamflow predictions during intense rainfall and hurricanes. Tested in real events, it outperforms traditional methods by up to 50 %, utilizing ensemble and climatological background covariances. The adaptive algorithm ensures reliability with a small ensemble, offering improved forecasts up to 18 h in advance, marking a significant advancement in flood prediction capabilities.
This study introduces a hybrid data assimilation scheme for precise streamflow predictions...
