The regionalization of a parameter of HYMOD, a conceptual hydrological model, using data from across the globe

The approximation of streamflow data in an un-gauged catchment is challenging and is generally addressed through parameter regionalization. Though identifying the relationships between catchment attributes and model parameters is straightforward, the uncertainties in both model parameters and functional relationships lead to poor regionalization. The key to successful regionalization is selecting a parsimonious model structure, proper catchment attributes, a better calibration strategy, and a proper regional model structure. The parameters of the HYMOD model were calibrated from 59 watersheds across the globe using a three-year calibration period and evolutionary algorithms, which are well-suited to account for the multiobjective nature of hydrological model calibration. The optimized parameter set satisfactorily reproduced the observed flow for all catchments used to calibrate the regional models. Over the calibration and validation period, the optimal regional relationship obtained from the multiple polynomial regression (MPR) and the multiobjective regional calibration (MORC) methods were developed and evaluated. For basins used for calibration of regional relationship, the performance loss of the model simulation with parameters estimated from the MPR and MORC methods was 14% and 10% respectively. The performance loss for basins presumed ungauged was 15 and 12.6% for MPR and MORC respectively. MORC was more effective in both basins considered for the derivation and evaluation of regional functions.