Improving process understanding using multi-criteria model comparison for different catchments
- 1Christian-Albrechts-University of Kiel, Hydrology and Water Management, Kiel, Germany (bguse@hydrology.uni-kiel.de)
- 2German Research Centre for Geosciences GFZ Potsdam, 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 Hydrology and Meteorology, Dresden, Germany
- 6UFZ – Helmholtz-Centre for Environmental Research GmbH, Computational Hydrosystems, Leipzig, Germany
- 7University of Zurich, Department of Geography, Zurich, Switzerland
- 8IGB Leibniz Institute of Freshwater Ecology and Inland Fisheries, Ecohydrology and Biogeochemistry, Berlin, Germany
- 9Karlsruhe Institute for Technology (KIT), Institute of Water and River Basin Management – Hydrology, Karlsruhe, Germany
- 10Wageningen University and Research, Hydrology and Quantitative Water Management, Wageningen, Netherlands
- 11University of Melbourne, Department of Infrastructure Engineering, Melbourne, Australia
- 12UFZ – Helmholtz-Centre for Environmental Research GmbH, Catchment Hydrology, Halle, Germany
- 13University of Waterloo, Earth and Environmental Science, Waterloo, Canada
- 14Humboldt-University Berlin, Ecohydrology, Berlin, Deutschland
- *A full list of authors appears at the end of the abstract
Hydrological models differ in the way how hydrological processes are implemented. A rigorous comparison of different hydrological model structures is needed to disentangle the link between similarities and differences in process representations and simulated hydrological processes, states and fluxes. A major challenge in model comparison is to identify effects of individual processes. To move a step in this direction, we developed controlled experiments and compared three hydrological models (HBV, mHM, SWAT+) in nine German catchments (400-3000 km²) along an elevation gradient. We aim at presenting a framework for a consistent comparison of process representations in model structures consisting of three steps:
(1) A model comparison protocol was developed for a detailed comparison of process representations in model structures. Consistency was achieved by using the same input data for all models. By grouping the processes in a standardized way, differences and similarities between the models were identified.
(2) To investigate the dominant model components, a daily parameter sensitivity analysis was carried out for the three models with different hydrological variables as target variables (e.g. actual evapotranspiration, soil moisture, snow and discharge). The dominant model parameters and associated processes vary more between the models than between the catchments. This also applies to the temporal variability of the parameter sensitivity.
(3) The model performance was analysed for a set of different performance criteria. The optimal parameter values differ greatly depending on which performance criteria were selected. This is in particular true for soil and evapotranspiration parameters. Typical patterns can be derived between catchments of different landscapes.
The joint analysis of these three methodological steps demonstrates the benefit of a detailed process analysis in model structures for a better understanding of suitable process representations. Therefore, it shows the potentials for improving model structures.
Michael Stölzle (University of Freiburg, Germany), Larissa Scholz (Christian-Albrechts-University of Kiel, Germany), Justine Berg (University of Freiburg, Germany), Tobias Pilz (PIK Potsdam, Germany), Li Han (GFZ Potsdam, Germany), Serena Ceola (University of Bologna, Italy), Jan Seibert (University of Zurich, Switzerland), Frederik Kratzert (Google Research, Austria), Markus Hrachowitz (TU Delft, Netherlands)
How to cite: Guse, B., Herzog, A., Houska, T., Spieler, D., Thober, S., Staudinger, M., Wagner, P., Düthmann, D., Loritz, R., Ehret, U., Kiesel, J., Müller, S., Melsen, L., Pool, S., Tarasova, L., Mai, J., Wagener, T., Tetzlaff, D., and Fohrer, N. and the other members of the DFG scientific network IMPRO: Improving process understanding using multi-criteria model comparison for different catchments, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-16855, https://doi.org/10.5194/egusphere-egu24-16855, 2024.
