Does bias correction increase reliability of flood projections under climate change? A case study of large rivers in Germany

There is a large uncertainty associated with flood projections driven by different climate scenarios. The bias-corrected regional climate scenarios are widely used to drive hydrological models in climate impact studies, but there are also doubts and questions about the application of bias correction (BC) methods. This study aims to investigate the performance and impacts of BCs on flood projections in Germany. The distribution mapping method was applied to correct the climate data from the regional climate models (RCMs) CCLM (Cosmo-Climate Local Model) and REMO (REgional MOdel) developed in Germany. The results show that BC can effectively reduce bias in the simulated average annual discharge, but the uncertainty of simulated floods remains due to the imperfect correction of extreme precipitations. About 75% of the change directions in the 50-year flood discharge remain the same before and after the BC was used. The relatively short control period of 40 years and the assumption of stationarity of the BC method are two important and problematic issues for flood projections. Hence, it is difficult to prove that BC can increase reliability of flood projections. The direct use of RCM outputs for the control and scenario periods may still be useful for flood impact studies. In addition, a bilateral analysis of RCM and hydrological model performance involving the meteorologists and hydrologists could be helpful for reducing the bias of the RCM outputs in the future.