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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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