Parameterising hydrological models – Comparing optimisation and robust parameter estimation

In most conditions, calibration is a prerequisite for successfully applying conceptual and physically based rainfall–runoff models. The goal of this paper is to comparatively analyse the potential of both event-based automatic calibration (PEST) as described in and robust parameter estimation (ROPE) as proposed by . The results of our modelling study in the Rietholzbach catchment (Switzerland) show that ROPE performs better in validation of small to medium sized events. This indicates that ROPE might be better suited to parameterise models when the modellers intention is focussed on a maximising the generalisation capacity of the model, e.g. for evaluating transient process characteristics. We base our study on the hydrological model WaSiM-ETH, using a combined ROPE and automatic parameter estimation approach to investigate optimal parameter sets. The PEST algorithm used in this study outperforms the ROPE application by a factor of roughly 100 in terms of time required for computation.