TY - JOUR
T1 - Scaled distribution mapping
T2 - A bias correction method that preserves raw climate model projected changes
AU - Switanek, B. Matthew
AU - Troch, A. Peter
AU - Castro, L. Christopher
AU - Leuprecht, Armin
AU - Chang, Hsin I.
AU - Mukherjee, Rajarshi
AU - Demaria, M. C.Eleonora
N1 - Publisher Copyright:
© Author(s) 2017.
PY - 2017/6/6
Y1 - 2017/6/6
N2 - 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.
AB - 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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U2 - 10.5194/hess-21-2649-2017
DO - 10.5194/hess-21-2649-2017
M3 - Article
AN - SCOPUS:85020436712
SN - 1027-5606
VL - 21
SP - 2649
EP - 2666
JO - Hydrology and Earth System Sciences
JF - Hydrology and Earth System Sciences
IS - 6
ER -