Impact of bias nonstationarity on the performance of uni- and multivariate bias-adjusting methods : a case study on data from Uccle, Belgium

Climate change is one of the biggest challenges currently faced by society, with an impact on many systems, such as the hydrological cycle. To assess this impact in a local context, regional climate model (RCM) simulations are often used as input for 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 an illustration, a hydrological impact assessment exercise 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 four multivariate bias-adjusting methods are compared with respect to their performance under climate change conditions. To this end, a case study is performed using data from the Royal Meteorological Institute of Belgium, located in Uccle. The methods are calibrated in the late 20th century (19701989) 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 modeling, temperature is a commonly adjusted variable in both uni- and multivariate settings and we therefore also included this variable. The methods are evaluated using indices based on the adjusted variables, the temporal structure, and the multivariate correlation. The Perkins skill score is used to evaluate the full probability density function (PDF). The results show a clear impact of nonstationarity on the bias adjustment. However, the impact varies depending on season and variable: the impact is most visible for precipitation in winter and summer. All methods respond similarly to the bias nonstationarity, with increased biases after adjustment in the validation period in comparison with the calibration period. This should be accounted for in impact models: incorrectly adjusted inputs or forcings will lead to predicted discharges that are biased as well