Comparison of conceptual model performance using different representations of spatial variability

The objective in this study is to explore a solution to the question whether model input data having higher spatial resolution and higher model resolution, as most people assume, lead to better model performance within a given modelling objective. An attempt was made to modify the conceptual rainfall–runoff model HBV to incorporate a spatially distributed structure. Additionally, three more model structures based on the HBV model concept were designed: lumped, semi-lumped and semi-distributed. An automatic calibration procedure based on simulated annealing optimization algorithm was followed for maximizing an objective function composed of Nash–Sutcliffe coefficients of several temporal aggregation steps. The predictive performance from each model was then assessed and compared with other model structures with respect to stream flow prediction at the catchment outlet. The spatial variation of the meteorological input was produced using external drift kriging method from available limited point measurements. The models were applied to a mesoscale catchment located in central Europe. The simulated hydrographs obtained using different model structures were analyzed through comparison of their Nash–Sutcliffe coefficients and other goodness-of-fit indices. For the present study, semi-distributed and semi-lumped model structures outperformed the distributed and fully-lumped model structures. A possible explanation why the distributed model did not perform better than the simpler model structures is the use of limited available spatial information. The models use interpolated precipitation and temperature as input, which probably cannot reflect the true spatial variability. Another possible explanation is that only discharge at the catchment outlet was predicted; which is the purpose for which lumped and semi-distributed models were actually designed.