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Hydrology and Earth System Sciences An interactive open-access journal of the European Geosciences Union
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Volume 20, issue 4
Hydrol. Earth Syst. Sci., 20, 1387–1403, 2016
https://doi.org/10.5194/hess-20-1387-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
Hydrol. Earth Syst. Sci., 20, 1387–1403, 2016
https://doi.org/10.5194/hess-20-1387-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 08 Apr 2016

Research article | 08 Apr 2016

Downscaling future precipitation extremes to urban hydrology scales using a spatio-temporal Neyman–Scott weather generator

Hjalte Jomo Danielsen Sørup et al.

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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 (19 Jul 2015) by Axel Bronstert
AR by Hjalte Jomo Danielsen Sørup on behalf of the Authors (28 Aug 2015)  Author's response    Manuscript
ED: Referee Nomination & Report Request started (04 Sep 2015) by Axel Bronstert
RR by Anonymous Referee #1 (16 Sep 2015)
RR by Anonymous Referee #3 (28 Nov 2015)
ED: Reconsider after major revisions (03 Dec 2015) by Axel Bronstert
AR by Hjalte Jomo Danielsen Sørup on behalf of the Authors (20 Jan 2016)  Author's response    Manuscript
ED: Referee Nomination & Report Request started (29 Jan 2016) by Axel Bronstert
RR by Anonymous Referee #1 (29 Jan 2016)
RR by Anonymous Referee #3 (06 Mar 2016)
ED: Publish subject to technical corrections (24 Mar 2016) by Axel Bronstert
AR by Hjalte Jomo Danielsen Sørup on behalf of the Authors (29 Mar 2016)  Author's response    Manuscript
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Short summary
Fine-resolution spatio-temporal precipitation data are important as input to urban hydrological models to assess performance issues under all possible conditions. In the present study synthetic data at very fine spatial and temporal resolution are generated using a stochastic model. Data are generated for both present and future climate conditions. The results show that it is possible to generate spatially distributed data at resolutions relevant for urban hydrology.
Fine-resolution spatio-temporal precipitation data are important as input to urban hydrological...
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