Articles | Volume 25, issue 9
https://doi.org/10.5194/hess-25-5315-2021
© Author(s) 2021. 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-25-5315-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
29 Sep 2021
Research article |
| 29 Sep 2021
| 29 Sep 2021
Ensemble streamflow data assimilation using WRF-Hydro and DART: novel localization and inflation techniques applied to Hurricane Florence flooding
Mohamad El Gharamti
CORRESPONDING AUTHOR
NCAR, Computational and Information Systems Laboratory (CISL), Boulder CO, USA
James L. McCreight
NCAR, Research Application Laboratory (RAL), Boulder CO, USA
Seong Jin Noh
Civil Engineering, Kumoh National Institute of Technology, Gumi, South Korea
Timothy J. Hoar
NCAR, Computational and Information Systems Laboratory (CISL), Boulder CO, USA
Arezoo RafieeiNasab
NCAR, Research Application Laboratory (RAL), Boulder CO, USA
Benjamin K. Johnson
NCAR, Computational and Information Systems Laboratory (CISL), Boulder CO, USA
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Short summary
The article introduces novel ensemble data assimilation (DA) techniques for streamflow forecasting using WRF-Hydro and DART. Model-related biases are tackled through spatially and temporally varying adaptive prior and posterior inflation. Spurious and physically incorrect correlations, on the other hand, are mitigated using a topologically based along-the-stream localization. Hurricane Florence (2018) in the Carolinas, USA, is used as a test case to investigate the performance of DA techniques.
The article introduces novel ensemble data assimilation (DA) techniques for streamflow...
