1,2,3,4,5,6,7,1,8,9,10,1,11,2,- 1Kiel University, Institute of Natural Resource Conservation, Hydrology and Water Management, Kiel, Germany (bguse@hydrology.uni-kiel.de)
- 2Uni Potsdam, Institute of Environmental Science and Geography, Potsdam, Germany
- 3TUD Dresden University of Technology, Department of Hydrosciences, Institute of Soil Science and Site Ecology, Dresden, Germany
- 4University of Giessen, Department of Landscape Ecology and Resources Management, Giessen, Germany
- 5TUD Dresden University of Technology, Department of Hydrosciences, Institute of Hydrology and Meteorology, Dresden, Germany
- 6University of Calgary, Schulich School of Engineering, Calgary, Canada
- 7University of Zurich, Department of Geography, Zurich, Switzerland;
- 8Freie Universitaet Berlin, Department of Earth Sciences, Berlin, Applied Physical Geography - Environmental Hydrology and Resource Management, Germany;
- 9Eawag - Swiss Federal Institute of Aquatic Science and Technology, Department Water Resources and Drinking Water, Duebendorf, Switzerland;
- 10Karlsruhe Institute of Technology (KIT), Institute of Water and Environment, Karlsruhe, Germany;
- 11Stone Environmental, Montpelier (VT), USA
- 12State Office for the Environment (LfU) of Schleswig-Holstein, Flintbek, Germany
- 13Leibniz Institute of Freshwater Ecology and Inland Fisheries (IGB), Department Ecohydrology and Biogeochemistry, Berlin, Germany
- 14State Office for the Environment Rhineland-Palatinate, Mainz, Germany
- 15The University of Arizona, Hydrology and Atmospheric Sciences, Tucson, Arizona, USA
Analysing parameter sensitivity and identifiability are important steps in hydrological modelling as they help to detect the most relevant parameters and suitable parameter ranges. Within such studies we can categorize parameters is diverse ways – one of them being capacity vs. flux parameters. While capacity parameters regulate the magnitude or storage of hydrological components, flux parameters determine the timing of water flow. We demonstrate in two steps that separating capacity and flux parameters is beneficial for model analyses.
First, we conducted a temporally resolved parameter sensitivity analysis, targeting the rate of change of eight hydrological components in addition to the absolute time series. Using the rate of change as target variable improved the representation of dynamic flux parameters, while capacity parameters were more precisely represented by the absolute time series of the hydrological components.
In a second step, we conducted a parameter identifiability analysis across six contrasting German catchments using sixteen diverse performance metrics and hydrological signatures. Our analysis with four hydrological models (Raven-GR4J, HBV, SWAT+ and mHM) reveals that capacity parameters can be precisely identified using several performance criteria, in particular those related to mid and low flows. In contrast, accurate identification of flux parameters requires specific performance criteria such as hydrological signatures related to the process timing.
Separating parameters into capacity and flux parameters improves the detection of the sensitivity signal and enables a more precise identification of parameter values. The reduced uncertainty in estimating the dominant parameters is a valuable step towards efficient model calibration.
How to cite: Guse, B., Herzog, A., Houska, T., Spieler, D., Staudinger, M., Wagner, P., Pool, S., Loritz, R., Kiesel, J., Pfannerstill, M., Duethmann, D., Wagener, T., Gupta, H., and Fohrer, N.: Capacity and flux model parameters should be addressed separately in parameter sensitivity and identifiability analyses, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-16951, https://doi.org/10.5194/egusphere-egu26-16951, 2026.
