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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17 citations as recorded by crossref.
- Assessment of CMIP6 models and multi-model averaging for temperature and precipitation over Iran N. Azad & A. Ahmadi 10.1038/s41598-024-74789-4
- Assessing temperature and precipitation bias nonstationarity of CMIP6 global climate models over Iran N. Azad & A. Ahmadi 10.1007/s00704-024-05322-w
- Bias‐Adjustment Methods for Future Subdaily Precipitation Extremes Consistent Across Durations H. Van de Vyver et al. 10.1029/2022EA002798
- Future multivariate weather generation by combining Bartlett-Lewis and vine copula models J. Van de Velde et al. 10.1080/02626667.2022.2144322
- Impact of bias correction on climate change signals over central Europe and the Iberian Peninsula A. Ugolotti et al. 10.3389/fenvs.2023.1116429
- Quantification of the surface and groundwater dynamics of Upper Godavari Sub-Basin using SWAT-MODFLOW and CMIP6 climate change scenarios S. Choudhary et al. 10.1080/02626667.2025.2492891
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- Impact of diverse configuration in multivariate bias correction methods on large-scale hydrological modelling under climate change K. Ahn et al. 10.1016/j.jhydrol.2023.130406
- Toward the reliable use of reanalysis data as a reference for bias correction in climate models: A multivariate perspective V. de Padua & K. Ahn 10.1016/j.jhydrol.2024.132102
- Evaluation of bias correction methods for a multivariate drought index: case study of the Upper Jhelum Basin R. Ansari et al. 10.5194/gmd-16-2055-2023
- A non-stationary bias adjustment method for improving the inter-annual variability and persistence of projected precipitation M. Cantalejo et al. 10.1038/s41598-024-76848-2
- ibicus: a new open-source Python package and comprehensive interface for statistical bias adjustment and evaluation in climate modelling (v1.0.1) F. Spuler et al. 10.5194/gmd-17-1249-2024
- Review of bias correction methods for climate model outputs in hydrology A. Menapace et al. 10.1016/j.jhydrol.2025.133213
- On deep learning-based bias correction and downscaling of multiple climate models simulations F. Wang & D. Tian 10.1007/s00382-022-06277-2
16 citations as recorded by crossref.
- Assessment of CMIP6 models and multi-model averaging for temperature and precipitation over Iran N. Azad & A. Ahmadi 10.1038/s41598-024-74789-4
- Assessing temperature and precipitation bias nonstationarity of CMIP6 global climate models over Iran N. Azad & A. Ahmadi 10.1007/s00704-024-05322-w
- Bias‐Adjustment Methods for Future Subdaily Precipitation Extremes Consistent Across Durations H. Van de Vyver et al. 10.1029/2022EA002798
- Future multivariate weather generation by combining Bartlett-Lewis and vine copula models J. Van de Velde et al. 10.1080/02626667.2022.2144322
- Impact of bias correction on climate change signals over central Europe and the Iberian Peninsula A. Ugolotti et al. 10.3389/fenvs.2023.1116429
- Quantification of the surface and groundwater dynamics of Upper Godavari Sub-Basin using SWAT-MODFLOW and CMIP6 climate change scenarios S. Choudhary et al. 10.1080/02626667.2025.2492891
- Compound droughts and hot extremes: Characteristics, drivers, changes, and impacts Z. Hao et al. 10.1016/j.earscirev.2022.104241
- Water Resources Evaluation and Sustainability Considering Climate Change and Future Anthropic Demands in the Arequipa Region of Southern Peru J. Quiroz et al. 10.3390/su152316270
- Assessing the impact of bias correction approaches on climate extremes and the climate change signal H. Zhang et al. 10.1002/met.2204
- Global Downscaled Projections for Climate Impacts Research (GDPCIR): preserving quantile trends for modeling future climate impacts D. Gergel et al. 10.5194/gmd-17-191-2024
- Impact of diverse configuration in multivariate bias correction methods on large-scale hydrological modelling under climate change K. Ahn et al. 10.1016/j.jhydrol.2023.130406
- Toward the reliable use of reanalysis data as a reference for bias correction in climate models: A multivariate perspective V. de Padua & K. Ahn 10.1016/j.jhydrol.2024.132102
- Evaluation of bias correction methods for a multivariate drought index: case study of the Upper Jhelum Basin R. Ansari et al. 10.5194/gmd-16-2055-2023
- A non-stationary bias adjustment method for improving the inter-annual variability and persistence of projected precipitation M. Cantalejo et al. 10.1038/s41598-024-76848-2
- ibicus: a new open-source Python package and comprehensive interface for statistical bias adjustment and evaluation in climate modelling (v1.0.1) F. Spuler et al. 10.5194/gmd-17-1249-2024
- Review of bias correction methods for climate model outputs in hydrology A. Menapace et al. 10.1016/j.jhydrol.2025.133213
1 citations as recorded by crossref.
Latest update: 23 Apr 2025
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...
