Time stability of model parameters in hydrological models of headwater catchments of the river Rhine (C. Fleischer)


Usually, hydrological models are calibrated and validated to relative short-term datasets of years to several decades. For these relative short durations, model parameters appear to be stable for calibration and validation periods as long as watershed properties (e.g. landuse) and climatic conditions do not change. A few studies have shown that the assumption of temporal stability of watershed model parameters is violated in long-term modeling experiments with strong implications for climate impact analysis (e.g. Merz et al., 2011). However, rarely has the time stability been tested in long-term model applications of more than 100 years. The question then arises if the resulting model uncertainty still allows the detection and attribution of observed long-term changes.


The project should test the assumption of temporal parameter stability of a hydrological model using long-term climate and discharge data (+100 years) in several headwater catchments in Switzerland. The main objective will be to assess the effect (direction and magnitude of the impact) of potential parameter variability on long-term simulations and observed changes in discharge processes.

Daten und Methoden

Within the KHR (International Commission on the Hydrology of the River Rhine) Project “Snow and glacier melt contribution to the discharge of the river Rhine and its tributaries with regards to the changing climate” a new dataset of gridded long-term daily climate data starting in 1901 has been derived. In addition streamflow observations form a number of headwater catchments in the Rhine Basin covering long time-periods have been collected and analysed. Furthermore, the Uni Zürich version of the HBV model has been applied to some of these catchemnts and has been equipped with a number of features for parameter uncertainty analyses. This model will be used and an approach will be developed to automatically parameterize the model to different time slices. The model will be benchmarked against discharge from other time slices, but more importantly, against observed hydrological changes due to climate change. In this context, also data uncertainty and land cover changes can be considered. The selected catchments should cover a range of climatic conditions in the Rhine river from alpine to lowland.


Hydrological model setup and modelling, coding and performing analyses of long time series and parameter stability


Kerstin Stahl, Jan Seibert (Uni Zürich)


Deutsch oder Englisch


Merz, R., J. Parajka, and G. Blo ̈schl (2011), Time stability of catchment model parameters: Implications for climate impact analyses, Water Resour. Res., 47, W02531, doi:10.1029/2010WR009505.

Coron, L., Andréassian, V., Bourqui, M., Perrin, C. and Hendrickx, F., 2011. Pathologies of hydrological models used in changing climatic conditions: a review, Hydro-Climatology: Variability and Change. IAHS Publication. 344, pp. 39-44.

Coron, L., V. Andréassian, C. Perrin, J. Lerat, J. Vaze, M. Bourqui, and F. Hendrickx, 2012. Crash testing hydrological models in contrasted climate conditions: An experiment on 216 Australian catchments, Water Resources Research, 48, W05552, doi:10.1029/2011WR011721

Seibert, J. and Vis, M. J. P., 2012. Teaching hydrological modeling with a user-friendly catchment-runoff-model software package, Hydrol. Earth Syst. Sci., 16, 3315-3325, doi:10.5194/hess-16-3315-2012

thesis/parastability.txt · Zuletzt geändert: 2015/06/04 07:52 von mweiler