Research papersImpacts of combining reanalyses and weather station data on the accuracy of discharge modelling

•We combined reanalyses and observational data over 830 North America watersheds.•We assessed the impacts of that combination on hydrological modeling.•The combination significantly improved the accuracy of discharge simulations.•Reanalyses can successfully compensate for deficiencies in surface observation data.

Reanalyses are important sources of meteorological data. Recent studies have shown that precipitation and temperature data from reanalysis present a strong potential for hydrological modelling, especially in regions with a sparse observational network. The objective of this study is to evaluate the impacts of the combination of three global atmospheric reanalyses - ERA-Interim, CFSR and MERRA - and one gridded observation dataset on the accuracy of hydrological model discharge simulations. Two combination approaches were used. The first one combined reanalyses and the observational database using a weighted average of the precipitation and temperature inputs. The second one consisted in using all meteorological inputs separately and combining the simulated hydrographs. The combinations were performed over 460 Canadian watersheds (representing regions with a low density of weather stations) and 370 US watersheds (representing regions with a higher density of weather stations). Results showed significant improvements in the simulated discharges for 68% and 92% of the Canadian watersheds for the input combinations and output combinations, respectively. Moreover, both approaches led to significant improvements in the simulated discharges for 72% of the US watersheds studied. For all watersheds where simulated discharges using observational data had a Nash Sutcliffe efficiency (NSE) lower than 0.5, the combination with reanalyses resulted in a median NSE increase of 0.3. This indicates that reanalysis can successfully compensate for deficiencies in the surface observation record and provide significantly better hydrological modelling performance.