Articles | Volume 24, issue 1
https://doi.org/10.5194/hess-24-169-2020
https://doi.org/10.5194/hess-24-169-2020
Research article
 | 
14 Jan 2020
Research article |  | 14 Jan 2020

Temporal rainfall disaggregation using a micro-canonical cascade model: possibilities to improve the autocorrelation

Hannes Müller-Thomy

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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) (08 Jul 2019) by Nadav Peleg
AR by Hannes Müller-Thomy on behalf of the Authors (25 Jul 2019)  Author's response    Manuscript
ED: Referee Nomination & Report Request started (29 Jul 2019) by Nadav Peleg
RR by Elena Volpi (01 Aug 2019)
RR by Anonymous Referee #3 (02 Sep 2019)
ED: Reconsider after major revisions (further review by editor and referees) (02 Sep 2019) by Nadav Peleg
AR by Hannes Müller-Thomy on behalf of the Authors (23 Oct 2019)  Author's response    Manuscript
ED: Referee Nomination & Report Request started (25 Oct 2019) by Nadav Peleg
RR by Elena Volpi (19 Nov 2019)
ED: Publish subject to minor revisions (review by editor) (21 Nov 2019) by Nadav Peleg
AR by Anna Wenzel on behalf of the Authors (29 Nov 2019)  Author's response    Manuscript
ED: Publish as is (30 Nov 2019) by Nadav Peleg

Post-review adjustments

AA: Author's adjustment | EA: Editor approval
AA by Hannes Müller-Thomy on behalf of the Authors (10 Jan 2020)   Author's adjustment   Manuscript
EA: Adjustments approved (10 Jan 2020) by Nadav Peleg
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Short summary
Simulation of highly dynamic floods requires high-resolution rainfall time series. Observed time series of that kind are often too short; rainfall generation is the only solution. The applied rainfall generator tends to underestimate the process memory of the rainfall. By modifications of the rainfall generator and a subsequent optimisation method the process memory is improved significantly. Flood simulations are expected to be more trustable using the rainfall time series generated like this.