- 1Christian-Albrechts-University of Kiel, Natural Resource Conservation, Hydrology and Water Management, Kiel, Germany (bguse@hydrology.uni-kiel.de)
- 2GFZ Helmholtz Centre for Geosciences, Section Hydrology, Potsdam, Germany
- 3University Potsdam, Institute for Environmental Sciences and Geography, Potsdam, Germany
- 4Justus-Liebig University Giessen, Institute for Landscape Ecology and Resources Management, Giessen, Germany
- 5TUD Dresden, University of Technology, Institute of Soil Science and Site Ecology, Dresden, Germany
- 6TUD Dresden, University of Technology, Institute of Hydrology and Meteorology, Dresden, Germany
- 7University of Calgary, Department of Civil Engineering, Calgary, Canada
- 8University Zurich, Geography, Zurich, Switzerland
- 9UFZ – Helmholtz-Centre for Environmental Research GmbH, Computational Hydrosystems, Leipzig, Germany
- 10Karlsruhe Institute for Technology (KIT), Institute of Water and River Basin Management – Hydrology, Karlsruhe, Germany
- 11Eawag, Department Water Resources and Drinking Water, Dubendorf, Switzerland
- *A full list of authors appears at the end of the abstract
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.
Uwe Ehret (KIT Karlsruhe, Germany), Doris Duethmann (IGB Berlin, Germany), Larisa Tarasova (UFZ Halle, Germany), Jens Kiesel (CAU Kiel, Germany), Serena Ceola (University of Bologna, Italy), Thorsten Wagener (Uni Potsdam, Germany), Jan Seibert (Uni Zurich, Switzerland), Juliane Mai (Uni Waterloo, Canada), Markus Hrachowitz (TU Delft, Netherlands), Doerthe Tetzlaff (HU Berlin and IGB Berlin, Germany), Nicola Fohrer (CAU Kiel, Germany)
How to cite: Guse, B., Herzog, A., Houska, T., Spieler, D., Staudinger, M., Wagner, P., Thober, S., Loritz, R., and Pool, S. and the DFG Scientific Network IMPRO: Evaluation the suitability of contrasting performance metrics and signature measures with the identifiability quote index, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-14940, https://doi.org/10.5194/egusphere-egu25-14940, 2025.
