Environmental Engineering Reference
In-Depth Information
choose extreme initial conditions to show the potential difference of the outputs
against 'business as usual' conditions.
If we summarize these limitations, we can list that:
l
Successful models should be based on the awareness that they are abstractions.
Therefore reality can only be reflected in the frame of the abstract input
information.
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Models can provide results only within the limits of the basic assumptions.
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Models cannot mimic the complexity of nature. High model complexity does not
mean high modelling efficiency.
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Models produce uncertainty.
As a consequence of these points, the modeller should try to assess the uncer-
tainty of his predictions and - this may be the most important point - document all
model assumptions and report the uncertainties to the user and the scientific
community. By doing so, modelling as a scientific method does not exclusively
follow the golden scientific rules of comprehensibility and reproducibility. It also
enables the great opportunities that uncertainties provide: when prior conditions are
not fully met and the output allows different interpretations, there is always the
chance to follow sidelines of current scientific knowledge. Then, aside from the
well-trodden trails, some of the most interesting findings and thrilling discoveries
can be made
