Abstract. Over the past decade, various methods for bias adjustment of precipitation occurrence or intensity have been proposed. However, the performance of combined methods has not yet been thoroughly evaluated, especially in a hydrological and climate change context. In this study, four occurrence-bias-adjusting methods are combined with one univariate and one multivariate intensity-bias-adjusting method. The occurrence-bias-adjusting methods include thresholding, Stochastic Singularity Removal, Triangular Distribution Adjustment, and are compared with the intensity-bias-adjusting methods without specific adjustment as a baseline. These combined methods are compared with respect to precipitation amount, precipitation occurrence and discharge. This comparison, summarized in terms of the residual bias relative to both the observations and the model bias,shows significant differences in performance. Occurrence-bias-adjusting methods that add stochasticity perform worse, an effect that is reinforced by multivariate intensity-bias-adjusting methods. The use of simpler methods is thus advised until the uncertainty caused by combining methods is better understood.
How to cite. Van de Velde, J., De Baets, B., Demuzere, M., and Verhoest, N. E. C.: Comparison of occurrence-bias-adjusting methods for hydrological impact modelling, Hydrol. Earth Syst. Sci. Discuss. [preprint], https://doi.org/10.5194/hess-2020-83, 2020.
Received: 19 Feb 2020 – Discussion started: 06 Apr 2020
Though climate models have different types of biases in comparison to the observations, most research is focused on adjusting the intensity. Yet, variables like precipitation are also biased in the occurrence: there are too many days with rainfall. We compared four methods for adjusting the occurrence, with the goal of improving flood representation. From this comparison, we concluded that more advanced methods do not necessarily add value, especially in multivariate settings.
Though climate models have different types of biases in comparison to the observations, most...