Articles | Volume 28, issue 14
https://doi.org/10.5194/hess-28-3133-2024
https://doi.org/10.5194/hess-28-3133-2024
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

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Cited articles

Abbaszadeh, P., Moradkhani, H., and Daescu, D. N.: The quest for model uncertainty quantification: A hybrid ensemble and variational data assimilation framework, Water Resour. Res., 55, 2407–2431, 2019. a, b, c
Abbaszadeh, P., Gavahi, K., and Moradkhani, H.: Multivariate remotely sensed and in-situ data assimilation for enhancing community WRF-Hydro model forecasting, Adv. Water Resour., 145, 103721, https://doi.org/10.1016/j.advwatres.2020.103721, 2020. a
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Anderson, J. L.: A local least squares framework for ensemble filtering, Mon. Weather Rev., 131, 634–642, 2003. a, b
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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.
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