The effects of spatial discretization and model parameterization on the prediction of extreme runoff characteristics

Water resources management in mesoscale river basins requires, among other things, reliable predictions on extreme runoff characteristics such as magnitude and frequency of floods and droughts. Hydrologic models are increasingly used for these prediction purposes. Outputs of these models, however, are sensitive to various factors like the spatial representation of hydrologic processes, the parameterization method, and the type of estimator used for calibration. This study aimed to investigate the possible effects of these factors on extreme runoff characteristics derived from simulated streamflow. For this purpose, lumped and distributed versions of the conceptual mesoscale hydrologic model (mHM) were implemented in 22 German basins ranging in size from 58 to 4000 km2. The distributed mHM version was, in turn, parameterized with hydrological response units (HRU) and multiscale parameter regionalization (MPR) methods. Free parameters of both model versions were calibrated with three objective functions emphasizing high flows, low flows, and a combination of both. Six extreme runoff characteristics were derived from daily streamflow simulations for winter and summer. Results indicated that the model performance evaluated with both daily streamflow and seasonal runoff characteristics was sensitive to the type of estimator, the spatial discretization, and the parameterization method employed. The lumped version exhibited the highest sensitivity to previous factors and the least performance, whereas the opposite behavior was noticed for the distributed version parameterized with the MPR technique. Furthermore, the efficiency of the model parameterized with MPR were higher than that obtained with the HRU parameterization, in particular, when the model was evaluated in locations not used for calibration.