Articles | Volume 26, issue 9
https://doi.org/10.5194/hess-26-2319-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-26-2319-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
03 May 2022
Research article |
| 03 May 2022
| 03 May 2022
Impact of bias nonstationarity on the performance of uni- and multivariate bias-adjusting methods: a case study on data from Uccle, Belgium
Jorn Van de Velde
CORRESPONDING AUTHOR
Department of Environment, Hydro-Climatic Extremes Lab, Ghent University, Ghent, Belgium
KERMIT, Department of Data Analysis and Mathematical Modelling, Ghent University, Ghent, Belgium
Matthias Demuzere
Department of Geography, Ruhr University Bochum, Bochum, Germany
Department of Environment, Hydro-Climatic Extremes Lab, Ghent University, Ghent, Belgium
Bernard De Baets
KERMIT, Department of Data Analysis and Mathematical Modelling, Ghent University, Ghent, Belgium
Niko E. C. Verhoest
Department of Environment, Hydro-Climatic Extremes Lab, Ghent University, Ghent, Belgium
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Short summary
An important step in projecting future climate is the bias adjustment of the climatological and hydrological variables. In this paper, we illustrate how bias adjustment can be impaired by bias nonstationarity. Two univariate and four multivariate methods are compared, and for both types bias nonstationarity can be linked with less robust adjustment.
An important step in projecting future climate is the bias adjustment of the climatological and...
