Time-varying sensitivity analysis across different hydrological model structures, variables and time scales
- 1GFZ German Research Centre for Geosciences, Section Hydrology, Potsdam, Germany (bjoern.guse@gfz-potsdam.de)
- 2UFZ – Helmholtz-Centre for Environmental Research GmbH, Computational Hydrosystems, Leipzig, Germany
- 3Technische Universität Dresden, Institute for Hydrology and Meteorology, Dresden, Germany
- 4Wageningen University and Research, Hydrology and Quantitative Water Management, Wageningen, Netherlands
- 5Christian-Albrechts-University Kiel, Hydrology and Water management, Kiel, Germany
- 6University Zürich, Geography, Zürich, Switzerland
- 7Karlsruhe Institute for Technology (KIT), Institute of Water and River Basin Management – Hydrology, Karlsruhe, Germany
- 8University Freiburg, Environmental Hydrological Systems, Freiburg, Germany
- 9Potsdam-Institute for Climate Impact Research (PIK), Climate Resilience – Hydroclimatic Risks, Potsdam, Germany
- 10IGB Leibniz Institute of Freshwater Ecology and Inland Fisheries, Ecohydrology and Biogeochemistry, Berlin, Germany
- 11Technical University Dresden, Institute for Soil science and Site ecology, Dresden, Germany
- 12University of Melbourne, Department of Infrastructure Engineering, Melbourne, Australia
- 13UFZ – Helmholtz-Centre for Environmental Research GmbH, Catchment Hydrology, Halle, Germany
- *A full list of authors appears at the end of the abstract
Temporal sensitivity analyses can be used to detect dominant model parameters at different time steps (e.g. daily or monthly) providing insights on their temporal patterns and reflecting the temporal variability in dominant hydrological processes. However, hydrological processes do not only vary in time under different hydrometeorological conditions, but also the time scales of implemented processes are different. Here, the impact of different time scales (e.g. daily vs. monthly) on sensitivity patterns is investigated.
A temporal parameter sensitivity analysis is applied to three hydrological models (HBV, mHM and SWAT) for nine catchments in Germany. These catchments represent the variability of landscapes in Germany and are dominated by different runoff generation processes. In addition to discharge, further model fluxes and states such as evapotranspiration or soil moisture are used as target variables for the sensitivity analysis.
To analyse the impact of different time scales, two approaches are compared. In a first approach, daily simulated time series are used for the sensitivity analysis and aggregated then to monthly averaged sensitivities (Post-Agg). In a second approach, the simulated time series is first aggregated to a monthly time series and than used as input for the sensitivity analysis (Pre-Agg).
Our analysis shows that monthly averaged sensitivity patterns of different model outputs vary between Post- and Pre-Aggregation approach. Model parameters that are related to fast-reacting runoff processes, e.g. surface runoff or fast subsurface flow, are more sensitive when using daily time series for the sensitivity analysis (Post-Agg). In contrast, model parameters related processes with longer time scales such as snowmelt or evapotranspiration are more emphasized in monthly time series (Pre-Agg). These differences in the sensitivity results between Post-Agg and Pre-Agg are in particularly pronounced when using the integrated value of discharge as the target variable. Instead, the differences are smaller when applying the sensitivity analysis directly to represent model fluxes.
Moreover, our analysis shows changes in dominant parameters along a north-south gradient which can be explained by the physiographic characteristics of the catchments. The differences in the sensitivity results between the models can be related to the different model structures.
Based on our analysis, we recommend to either using model outputs of the major hydrological variables or different time scales for the sensitivity analysis to derive the maximum information from the diagnostic model analysis and to understand how model parameters describe hydrological systems.
Dung Nguyen (GFZ Potsdam, Hydrology, Germany), Serena Ceola (Uni Bologna, Civil, Chemical, Environmental and Materials Engineering, Italy) , Jan Seibert (University Zürich, Geography, Switzerland), Frederik Kratzert (Google Research, Vienna, Austria), Markus Hrachowitz (TU Delft, Water Management, Netherlands), Dörthe Tetzlaff (IGB Berlin, Ecohydrology and Biogeochemistry, Germany), Nicola Fohrer (CAU Kiel, Hydrology and Water management, Germany), Thorsten Wagener (University Potsdam, Environmental Sciences and Geography, Germany)
How to cite: Guse, B., Herzog, A., Thober, S., Spieler, D., Melsen, L., Kiesel, J., Staudinger, M., Wagner, P., Loritz, R., Müller, S., Stölzle, M., Scholz, L., Berg, J., Pilz, T., Ehret, U., Düthmann, D., Houska, T., Pool, S., and Tarasova, L. and the other members of the DFG Scientific network IMPRO: Time-varying sensitivity analysis across different hydrological model structures, variables and time scales, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8423, https://doi.org/10.5194/egusphere-egu23-8423, 2023.
