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Hydrology and Earth System Sciences An interactive open-access journal of the European Geosciences Union
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Volume 21, issue 6
Hydrol. Earth Syst. Sci., 21, 2649–2666, 2017
https://doi.org/10.5194/hess-21-2649-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
Hydrol. Earth Syst. Sci., 21, 2649–2666, 2017
https://doi.org/10.5194/hess-21-2649-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 06 Jun 2017

Research article | 06 Jun 2017

Scaled distribution mapping: a bias correction method that preserves raw climate model projected changes

Matthew B. Switanek1, Peter A. Troch2, Christopher L. Castro2, Armin Leuprecht1, Hsin-I Chang2, Rajarshi Mukherjee2, and Eleonora M. C. Demaria3 Matthew B. Switanek et al.
  • 1Wegener Center for Climate and Global Change, University of Graz, Graz, 8010, Austria
  • 2Department of Hydrology and Atmospheric Sciences, University of Arizona, Tucson, Arizona 85721, USA
  • 3Southwest Watershed Research Center, USDA – Agricultural Research Service, Tucson, Arizona 85719, USA

Abstract. Commonly used bias correction methods such as quantile mapping (QM) assume the function of error correction values between modeled and observed distributions are stationary or time invariant. This article finds that this function of the error correction values cannot be assumed to be stationary. As a result, QM lacks justification to inflate/deflate various moments of the climate change signal. Previous adaptations of QM, most notably quantile delta mapping (QDM), have been developed that do not rely on this assumption of stationarity. Here, we outline a methodology called scaled distribution mapping (SDM), which is conceptually similar to QDM, but more explicitly accounts for the frequency of rain days and the likelihood of individual events. The SDM method is found to outperform QM, QDM, and detrended QM in its ability to better preserve raw climate model projected changes to meteorological variables such as temperature and precipitation.

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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...
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