Preprints
https://doi.org/10.5194/hess-2020-639
https://doi.org/10.5194/hess-2020-639

  08 Dec 2020

08 Dec 2020

Review status: a revised version of this preprint is currently under review for the journal HESS.

Impact of bias nonstationarity on the performance of uni- and multivariate bias-adjusting methods

Jorn Van de Velde1,2, Matthias Demuzere3,1, Bernard De Baets2, and Niko E. C. Verhoest1 Jorn Van de Velde et al.
  • 1Hydro-Climatic Extremes Lab, Ghent University, Ghent, Belgium
  • 2KERMIT, Department of Data Analysis and Mathematical Modelling, Ghent University, Ghent, Belgium
  • 3Department of Geography, Ruhr-University Bochum, Bochum, Germany

Abstract. Climate change is one of the biggest challenges currently faced by society, with an impact on many systems, such as the hydrological cycle. To locally assess this impact, Regional Climate Model (RCM) simulations are often used as input for hydrological rainfall-runoff models. However, RCM results are still biased with respect to the observations. Many methods have been developed to adjust these biases, but only during the last few years, methods to adjust biases that account for the correlation between the variables have been proposed. This correlation adjustment is especially important for compound event impact analysis. As a simple example of those compound events, hydrological impact assessment is used here, as hydrological models often need multiple locally unbiased input variables to ensure an unbiased output. However, it has been suggested that multivariate bias-adjusting methods may perform poorly under climate change conditions because of bias nonstationarity. In this study, two univariate and three multivariate bias-adjusting methods are compared with respect to their performance under climate change conditions. To this end, the methods are calibrated in the late 20th century (1970–1989) and validated in the early 21st century (1998–2017), in which the effect of climate change is already visible. The variables adjusted are precipitation, evaporation and temperature, of which the former two are used as input for a rainfall-runoff model, to allow for the validation of the methods on discharge. Although not used for discharge modelling, temperature is a commonly-adjusted variable in both uni- and multivariate settings and therefore important to take into account. The methods are also evaluated using indices based on the adjusted variables, the temporal structure, and the multivariate correlation. For precipitation, all methods decrease the bias in a comparable manner. However, for many other indices the results differ considerably between the bias-adjusting methods. The multivariate methods often perform worse than the univariate methods, a result that is especially notable for temperature and evaporation. As these variables have already changed the most under climate change conditions, this reinforces the opinion that the multivariate bias-adjusting methods are not yet fit to cope with nonstationary climate conditions. Although the effect is slightly dampened by the hydrological model, our analysis still reveals that, to date, the simpler univariate bias-adjusting methods are preferred for assessing climate change impact.

Jorn Van de Velde et al.

 
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Jorn Van de Velde et al.

Model code and software

ImpactofBiasNonstationarity Hydro-Climate Extremes Lab – Ghent University https://doi.org/10.5281/zenodo.4247518

Jorn Van de Velde et al.

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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 three multivariate methods are compared, and for both types the bias nonstationarity can be linked with less robust adjustment. However, the multivariate methods show greater variability and thus univariate methods may currently be preferred.