Hydrological modeling to evaluate climate model simulations and their bias correction

We used seven discharge metrics to explore the representation of different hydrological processes under current climate. Specific combinations of biases in GCM-RCM simulations can lead to significant biases in simulated discharge (e.g., excessive precipitation in the winter months combined with a cold temperature bias). Other biases, such as exaggerated snow accumulation, do not necessarily impact temperature over the historical period to the point where discharge is affected.

Our results confirm the importance of bias correction; when all catchments, GCM-RCMs, and discharge metrics were considered, QM improved discharge simulations in the vast majority of all cases. Additionally, we present a ranking of climate models according to their hydrological performance. Ranking GCM-RCMs is most meaningful prior to bias correction since QM reduces differences between GCM-RCM-driven hydrological simulations.

Overall, this work introduces a multivariate assessment method of GCM-RCMs, which enables a more process-oriented evaluation of their simulations.