Which range of streamflow data is most informative in the calibration of an hourly hydrological model?

Abstract

ABSTRACT From which part of the streamflow time series does a hydrological model learn most about the behaviour of a catchment? How does this part change with the objective function used for model calibration? We calibrated the hourly model GR4H (Génie Rural à 4 paramètres au pas de temps Horaire) on data subsets selected based on the flow duration curve, which narrowed the range of streamflow that influenced the identification of model parameters the most. Using 658 catchments in the United States and France, we found that calibrating GR4H on the highest 50% of streamflow values of a given period provided similar parameters to calibration on the whole period, with the top 10% dominating the identification of parameters. The logarithmic transformation of streamflow balanced the contribution from high and low flows, but only the reciprocal transformation emphasized low flows. The proposed methodology identifies the informative ranges of streamflow data and allows anticipating potential model failures in nonstationary hydroclimatic conditions.