Articles | Volume 22, issue 10
https://doi.org/10.5194/hess-22-5259-2018
https://doi.org/10.5194/hess-22-5259-2018
Research article
 | 
15 Oct 2018
Research article |  | 15 Oct 2018

Rainfall disaggregation for hydrological modeling: is there a need for spatial consistence?

Hannes Müller-Thomy, Markus Wallner, and Kristian Förster

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Interactive discussion

Status: closed
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 (further review by editor and referees) (05 Mar 2018) by Florian Pappenberger
AR by Hannes Müller-Thomy on behalf of the Authors (06 Mar 2018)  Author's response   Manuscript 
ED: Referee Nomination & Report Request started (18 Mar 2018) by Florian Pappenberger
RR by Nadav Peleg (23 Mar 2018)
RR by Anna Sikorska-Senoner (25 Apr 2018)
ED: Publish subject to revisions (further review by editor and referees) (07 May 2018) by Florian Pappenberger
AR by Hannes Müller-Thomy on behalf of the Authors (15 Jun 2018)
ED: Reconsider after major revisions (further review by editor and referees) (20 Jun 2018) by Florian Pappenberger
ED: Referee Nomination & Report Request started (24 Jul 2018) by Florian Pappenberger
ED: Publish subject to minor revisions (review by editor) (16 Aug 2018) by Florian Pappenberger
AR by Hannes Müller-Thomy on behalf of the Authors (31 Aug 2018)
ED: Publish as is (03 Sep 2018) by Florian Pappenberger
AR by Hannes Müller-Thomy on behalf of the Authors (04 Sep 2018)  Author's response   Manuscript 
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Short summary
Rainfall time series are disaggregated from daily to hourly values to be used for rainfall–runoff modeling of mesoscale catchments. Spatial rainfall consistency is implemented afterwards using simulated annealing. With the calibration process applied, observed runoff statistics (e.g., summer and winter peak flows) are represented well. However, rainfall datasets with under- or over-estimation of spatial consistency lead to similar results, so the need for a good representation can be questioned.