Articles | Volume 25, issue 9
Research article
29 Sep 2021
Research article |  | 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, James L. McCreight, Seong Jin Noh, Timothy J. Hoar, Arezoo RafieeiNasab, and Benjamin K. Johnson


Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on hess-2020-642', Anonymous Referee #1, 22 Apr 2021
  • RC2: 'Comment on hess-2020-642', Anonymous Referee #2, 01 Jul 2021

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload
ED: Publish subject to revisions (further review by editor and referees) (22 Jul 2021) by Nadia Ursino
AR by M.E. Gharamti on behalf of the Authors (22 Jul 2021)  Author's response   Author's tracked changes   Manuscript 
ED: Referee Nomination & Report Request started (23 Jul 2021) by Nadia Ursino
RR by Anonymous Referee #1 (11 Aug 2021)
RR by Anonymous Referee #2 (20 Aug 2021)
ED: Publish subject to technical corrections (27 Aug 2021) by Nadia Ursino
AR by M.E. Gharamti on behalf of the Authors (27 Aug 2021)  Author's response   Manuscript 
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.