This document explores bias correction of temperature, precipitation, and streamflow predictions for the Rhine River. It finds that bias correcting temperature and precipitation predictions improves their accuracy by 20-60% and 20-30% respectively, but does not consistently translate to improved streamflow prediction accuracy. Preserving the spatial and cross-variable dependencies between predictions is important for streamflow skill. Future work will focus on determining whether the benefits of bias correcting forcing inputs are maintained after also bias correcting streamflow predictions.

1. Streamflow prediction in River Rhine: Exploring combinations of bias-correcting forcing and bias-correcting flow Jan Verkade (Deltares and Delft University of Technology) James Brown (NOAA-NWS-OHD and UCAR)

3. Research design Raw forcing (T,P) Hydrologic model Raw flow Ensemble verification B-C forcing (T,P) Hydrologic model Raw forcing (T,P) Hydrologic model Scenario 1 Scenario 2 Baseline B-C streamflow B-C streamflow

5. Data kindly provided by Florian Pappenberger @ ECMWF

6. Observed forcing data: E-OBS dataset Downloadable from KNMI @ http://eca.knmi.nl/