Articles | Volume 19, issue 4
Hydrol. Earth Syst. Sci., 19, 1659–1676, 2015
https://doi.org/10.5194/hess-19-1659-2015
Hydrol. Earth Syst. Sci., 19, 1659–1676, 2015
https://doi.org/10.5194/hess-19-1659-2015

Research article 09 Apr 2015

Research article | 09 Apr 2015

Improving operational flood ensemble prediction by the assimilation of satellite soil moisture: comparison between lumped and semi-distributed schemes

C. Alvarez-Garreton 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: Reconsider after major revisions (07 Jan 2015) by Erwin Zehe
AR by Camila Alvarez-Garreton on behalf of the Authors (10 Feb 2015)  Author's response    Manuscript
ED: Referee Nomination & Report Request started (13 Feb 2015) by Erwin Zehe
RR by Anonymous Referee #1 (14 Feb 2015)
RR by Uwe Ehret (24 Feb 2015)
ED: Publish subject to minor revisions (Editor review) (02 Mar 2015) by Erwin Zehe
AR by Camila Alvarez-Garreton on behalf of the Authors (06 Mar 2015)  Author's response    Manuscript
ED: Publish as is (09 Mar 2015) by Erwin Zehe
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Short summary
We assimilate satellite soil moisture into a rainfall-runoff model for improving flood prediction within a data-scarce region. We argue that the spatially distributed satellite data can alleviate the model prediction limitations. We show that satellite soil moisture DA reduces the uncertainty of the streamflow ensembles. We propose new techniques for the DA scheme, including seasonal error characterisation, bias correction of the satellite retrievals, and model error representation.