Articles | Volume 22, issue 8
Hydrol. Earth Syst. Sci., 22, 4251–4266, 2018
https://doi.org/10.5194/hess-22-4251-2018
Hydrol. Earth Syst. Sci., 22, 4251–4266, 2018
https://doi.org/10.5194/hess-22-4251-2018

Research article 13 Aug 2018

Research article | 13 Aug 2018

Multi-source data assimilation for physically based hydrological modeling of an experimental hillslope

Anna Botto et al.

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AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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Peer-review completion

AR: Author's response | RR: Referee report | ED: Editor decision
ED: Publish subject to revisions (further review by editor and referees) (14 May 2018) by Harrie-Jan Hendricks Franssen
AR by Anna Botto on behalf of the Authors (25 May 2018)  Author's response    Manuscript
ED: Referee Nomination & Report Request started (01 Jun 2018) by Harrie-Jan Hendricks Franssen
RR by Anonymous Referee #3 (05 Jul 2018)
RR by Anonymous Referee #2 (20 Jul 2018)
ED: Publish as is (24 Jul 2018) by Harrie-Jan Hendricks Franssen
AR by Anna Botto on behalf of the Authors (24 Jul 2018)  Author's response    Manuscript
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Short summary
We present a multivariate application of the ensemble Kalman filter (EnKF) in hydrological modeling of a real-world hillslope test case with dominant unsaturated dynamics and strong nonlinearities. Overall, the EnKF is able to correctly update system state and soil parameters. However, multivariate data assimilation may lead to significant tradeoffs between model predictions of different variables, if the observation data are not high quality or representative.