Hydrological model comparison and assessment: criteria from catchment scales and temporal resolution

This study examines the performance of three hydrological models, namely the artificial neural network (ANN) model, the Hydrologiska Byrans Vattenbalansavdelning-D (HBV-D) model, and the Soil and Water Integrated Model (SWIM) over the upper reaches of the Huai River basin. The assessment is done by using databases of different temporal resolution and by further examining the applicability of SWIM for different catchment sizes. The results show that at monthly scale the performance of the ANN model is better than that of HBV-D and SWIM. The ANN model can be applied at any temporal scale as it establishes an artificial precipitation-runoff relationship for various time scales by only using monthly precipitation, temperature and runoff data. However, at daily scale the performance of both HBV-D and SWIM are similar or even better than the ANN model. In addition, the performance of SWIM at a small catchment size (less than 10000km(2)) is much better than at a larger catchment size. In view of climate change modelling, HBV-D and SWIM might be integrated in a dynamical atmosphere-water-cycle modelling rather than the ANN model due to their use of observed physical links instead of artificial relations within a black box