Technical Note: The impact of spatial scale in bias correction of climate  model output for hydrologic impact studies

Maurer, E. P.; Ficklin, D. L.; Wang, W.

doi:https://doi.org/10.5194/hess-20-685-2016

Articles | Volume 20, issue 2

https://doi.org/10.5194/hess-20-685-2016

© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.

https://doi.org/10.5194/hess-20-685-2016

© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.

Articles | Volume 20, issue 2

Technical note

|

12 Feb 2016

Technical note |

| 12 Feb 2016

Technical Note: The impact of spatial scale in bias correction of climate model output for hydrologic impact studies

E. P. Maurer, D. L. Ficklin, and W. Wang

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Final revised paper (published on 12 Feb 2016)
Preprint (discussion started on 23 Oct 2015)

Interactive discussion

Status: closed

AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment

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- Supplement

RC C4694: 'The impact of spatial scale in bias correction of climate model output for hydrologic impact studies', Geoff Pegram, 06 Nov 2015
RC C4711: 'Maurer et al', Geoff Pegram, 06 Nov 2015
RC C4830: 'The impact of spatial scale in bias correction of climate model output for hydrologic impact studies', Anonymous Referee #2, 11 Nov 2015
RC C4835: 'Review', Anonymous Referee #3, 12 Nov 2015
RC C5079: 'Referee Comment', Uwe Ehret, 25 Nov 2015
AC C6137: 'Author responses to all reviewer comments', Edwin Maurer, 13 Jan 2016

Peer-review completion

AR: Author's response | RR: Referee report | ED: Editor decision

ED: Publish subject to minor revisions (Editor review) (14 Jan 2016) by András Bárdossy

AR by Edwin Maurer on behalf of the Authors (15 Jan 2016) Author's response Manuscript

ED: Publish as is (26 Jan 2016) by András Bárdossy

AR by Edwin Maurer on behalf of the Authors (26 Jan 2016)

Short summary

To translate climate model output from its native coarse scale to a finer scale more representative of that at which societal impacts are experienced, a common method applied is statistical downscaling. A component of many statistical downscaling techniques is quantile mapping (QM). QM can be applied at different spatial scales, and here we study how skill varies with spatial scale. We find the highest skill is generally obtained when applying QM at approximately a 50 km spatial scale.