Investigation of the uncertainty of hydrological models and ways to reduce the
uncertainty through integration of additional experimental data
Different parameter sets can lead to equally good model results for a calibration period. This is
problem, often called ‘equifinality problem’, is nowadays widely accepted. Our contribution to this
is to investigate ways to quantify the uncertainty of hydrological models. Therefore a modified
GLUE (generalized likelihood uncertainty estimation) framework, which is based on Monte Carlo
Simulations, was used and different rainfall scenarios were analyzed. Also an efficient parameter
sampling strategy (Latin Hypercube sampling) was tested for a complex hydrological model. In a next
step, the power of additional experimental data to reduce the prediction uncertainty of the model
was analyzed. This allowed also gaining further insights into the spatial and temporal variability of
distributed model predictions.

Key publications:

  • Uhlenbrook S., Seibert J., Leibundgut Ch., Rodhe A., 1999: Prediction uncertainty of conceptual
    rainfall-runoff models caused by problems to identify model parameters and structure.
    Hydrological Sciences Journal, 44, 5, 279-299.
  • Uhlenbrook S., Sieber. A. 2004: On the value of experimental data to reduce the prediction
    uncertainty of a process-oriented catchment model. Environmental Modeling and Software, in press.
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