Articles | Volume 21, issue 2
https://doi.org/10.5194/hess-21-839-2017
https://doi.org/10.5194/hess-21-839-2017
Research article
 | 
14 Feb 2017
Research article |  | 14 Feb 2017

Can assimilation of crowdsourced data in hydrological modelling improve flood prediction?

Maurizio Mazzoleni, Martin Verlaan, Leonardo Alfonso, Martina Monego, Daniele Norbiato, Miche Ferri, and Dimitri P. Solomatine

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Status: closed
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 (31 Mar 2016) by Stacey Archfield
AR by Maurizio Mazzoleni on behalf of the Authors (04 Apr 2016)  Author's response    Manuscript
ED: Referee Nomination & Report Request started (10 May 2016) by Stacey Archfield
RR by Anonymous Referee #1 (01 Jun 2016)
RR by Anonymous Referee #3 (06 Jun 2016)
ED: Reconsider after major revisions (24 Jun 2016) by Stacey Archfield
AR by Maurizio Mazzoleni on behalf of the Authors (01 Aug 2016)  Author's response    Manuscript
ED: Referee Nomination & Report Request started (23 Aug 2016) by Stacey Archfield
RR by Anonymous Referee #1 (30 Sep 2016)
ED: Reconsider after major revisions (18 Oct 2016) by Stacey Archfield
AR by Maurizio Mazzoleni on behalf of the Authors (29 Nov 2016)  Author's response    Manuscript
ED: Publish subject to minor revisions (further review by Editor) (23 Dec 2016) by Stacey Archfield
AR by Anna Mirena Feist-Polner on behalf of the Authors (03 Jan 2017)  Author's response    Manuscript
ED: Publish subject to technical corrections (21 Jan 2017) by Stacey Archfield
Short summary
This study assesses the potential use of crowdsourced data in hydrological modeling, which are characterized by irregular availability and variable accuracy. We show that even data with these characteristics can improve flood prediction if properly integrated into hydrological models. This study provides technological support to citizen observatories of water, in which citizens can play an active role in capturing information, leading to improved model forecasts and better flood management.