Diagnostic evaluation of conceptual rainfall-runoff models using temporal clustering

Given the structural shortcomings of conceptual rainfall-runoff models and the common use of time-invariant model parameters, these parameters can be expected to represent broader aspects of the rainfall-runoff relationship than merely the static catchment characteristics that they are commonly supposed to quantify. In this article, we relax the common assumption of time-invariance of parameters, and instead seek signature information about the dynamics of model behaviour and performance. We do this by using a temporal clustering approach to identify periods of hydrological similarity, allowing the model parameters to vary over the clusters found in this manner, and calibrating these parameters simultaneously. The diagnostic information inferred from these calibration results, based on the patterns in the parameter sets of the various clusters, is used to enhance the model structure. This approach shows how diagnostic model evaluation can be used to combine information from the data and the functioning of the hydrological model in a useful manner.