Good representation of the hydrological system in models is required to provide reliable predictions. The selection of a suitable set of performance criteria is a core decision in identifying the optimal parameter set(s) during model calibration. As each performance criterion focuses on different parts of the hydrograph, their selection often determines which parameter values are selected as optimal for representing the rainfall-runoff behaviour in a catchment. Knowning which performance criteria are most suitable for which purpose, model or catchment is difficult to determine.

We therefore selected a set of 16 classical performance metrics and signature measures which together cover all phases of the hydrograph to test their suitability for identifying different types of parameters. We used four hydrological models (HBV, SWAT+, mHM and RAVEN-GR4J) in six catchments belonging to diverse landscapes in Germany. All model parameters were grouped into five process groups (snow, evapotranspiration, soil, surface and subsurface processes) to make the parameters comparable between the models. We then developed a metric called “identifiability quote index” which shows the degree of identifiability for each combination of parameter and performance criterion.

Our results show that the classical performance criteria (e.g. NSE, KGE) are not sufficient to identify suitable values for all parameters. Signature measures (e.g. flashiness index, baseflow index) often have a higher “identifiability quote index” for specific cases and are suitable for either capacity or flux parameters. The degree of identifiability tends to vary between processes and models, but evapotranspiration parameters are generally highly identifiable with water-balance related metrics. The more complex a model is (e.g. mHM, SWAT+), the more difficult it is to determine parameter identifiabilities.

In conclusion our study shows that a set of contrasting performance metrics and signature measures are needed to represent the whole hydrological system and to accurately identify the parameters.