An evaluation of regionalization and watershed classification schemes for continuous daily streamflow prediction in ungauged watersheds

Abstract

Regionalization – the process of transferring hydrological information from gauged to ungauged watersheds – has the potential to perform significantly better if these watersheds are classified in advance. In this study, we demonstrate the benefits of classification by a systematic combination of watershed classification techniques, regionalization methods, and rainfall-runoff models. Basins are first classified, then regionalization methods are applied, for continuous daily streamflow estimation at ungauged watersheds in Ontario, Canada. Nonlinear data-driven methods are used as regionalization and watershed classification schemes to transfer the parameters of two conceptual hydrologic models – namely McMaster University Hydrologiska Byråns Vattenbalansavdelning (MAC-HBV) and Sacramento Soil Moisture Accounting (SAC-SMA) – from gauged to ungauged watersheds. Our results suggest that a certain combination of watershed classification technique, regionalization method and hydrologic model can significantly improve the estimation of continuous streamflow at ungauged basins by improving the accuracy of estimated daily mean, low and peak flows. However, some combinations do not provide a clear improvement when compared to the scenario of unclassified basins. For example, the MAC-HBV model coupled with a counter propagation neural network as a regionalization technique provides a clear improvement in estimated daily mean, low and peak flow when the watersheds are first classified using a nonlinear principal component analysis method. Interestingly, a higher improvement is achieved for low flow as well, which is usually difficult to estimate in ungauged basins.