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.
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.
the Creative Commons Attribution 3.0 License.
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
Santa Clara University, Civil Engineering Department, Santa Clara,
CA 95053-0563, USA
D. L. Ficklin
Indiana University, Department of Geography, Bloomington, IN 47405,
USA
W. Wang
California State University at Monterey Bay, Department of Science and
Environmental Policy and NASA Ames Research Center, Moffett Field, CA 94035,
USA
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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.
To translate climate model output from its native coarse scale to a finer scale more...
