Leveraging a novel hybrid ensemble and optimal interpolation approach for enhanced streamflow and flood prediction

El Gharamti, Mohamad; Rafieeinasab, Arezoo; McCreight, James L.

doi:https://doi.org/10.5194/hess-28-3133-2024

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.

https://doi.org/10.5194/hess-28-3133-2024

© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 28, issue 14

Research article

|

19 Jul 2024

Research article |

| 19 Jul 2024

Leveraging a novel hybrid ensemble and optimal interpolation approach for enhanced streamflow and flood prediction

Mohamad El Gharamti, Arezoo Rafieeinasab, and James L. McCreight

Data sets

Hybrid HydroDART Data M. El Gharamti https://doi.org/10.5281/zenodo.8309815

Model code and software

WRF-Hydro WRF-Hydro team https://ral.ucar.edu/projects/wrf_hydro

DART, Data Assimilation Research Section (DAReS), DAReS/CISL/NCAR DART team https://doi.org/10.5065/D6WQ0202

DART hybrid EnKF-OI code M. El Gharamti https://doi.org/10.5281/zenodo.12707479

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.