Articles | Volume 21, issue 6
https://doi.org/10.5194/hess-21-2649-2017
© Author(s) 2017. 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-21-2649-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Scaled distribution mapping: a bias correction method that preserves raw climate model projected changes
Matthew B. Switanek
CORRESPONDING AUTHOR
Wegener Center for Climate and Global Change, University of Graz,
Graz, 8010, Austria
Peter A. Troch
Department of Hydrology and Atmospheric Sciences, University of
Arizona, Tucson, Arizona 85721, USA
Christopher L. Castro
Department of Hydrology and Atmospheric Sciences, University of
Arizona, Tucson, Arizona 85721, USA
Armin Leuprecht
Wegener Center for Climate and Global Change, University of Graz,
Graz, 8010, Austria
Hsin-I Chang
Department of Hydrology and Atmospheric Sciences, University of
Arizona, Tucson, Arizona 85721, USA
Rajarshi Mukherjee
Department of Hydrology and Atmospheric Sciences, University of
Arizona, Tucson, Arizona 85721, USA
Eleonora M. C. Demaria
Southwest Watershed Research Center, USDA – Agricultural Research
Service, Tucson, Arizona 85719, USA
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Latest update: 14 Dec 2024
Short summary
The commonly used bias correction method called quantile mapping assumes a constant function of error correction values between modeled and observed distributions. Our article finds that this function cannot be assumed to be constant. We propose a new bias correction method, called scaled distribution mapping, that does not rely on this assumption. Furthermore, the proposed method more explicitly accounts for the frequency of rain days and the likelihood of individual events.
The commonly used bias correction method called quantile mapping assumes a constant function of...