Environmental Engineering Reference
In-Depth Information
Fig. 2.9 Biomass dynamics of specific Northern-German zooplankton groups from Berlin lakes;
left part
of the figure: real measurements until 2009;
right part
: the projection for a further year on
the basis of an autoregressive moving average model (Jopp, unpublished)
a high quality. But regrettably this is usually the most uninteresting case. Most
models are developed to show potential future developments, and these dynamics
of interest cannot be used for validation, because they are the application cases
(However, later, the model quality can be improved on the basis of wrong predic-
tions of the formerly future dynamics). We can never fully know how forcing
functions and other input variables for our future model projections will develop
through time; we can only know them later, retrospectively . Therefore model
results will remain uncertain to a high extent (see e.g. Fig
2.9
). If the ranges of
the validation data sets are exceeded, the typical nonlinear relations or hysteresis
effects can be responsible for extreme modifications of the system's behaviour (see
Fig.
6.12
). Also, if we apply models to other places than the area or system for
which they were developed, there may be new parameter constellations that could
not be taken into account during the development phase. Summarizing, a model
will never be free of uncertainty and it is essential to respect the range of validity for
each part of the model when discussing its results.
Models Rarely Produce Reliable Prognoses, but Can Be Used
in Scenarios
Taking this point into account, models should not be used for specific prognoses.
But as we still want to benefit from the modelling power, scenarios are a good level
for applied modelling. When defining scenario conditions the user has to be aware
that his model output may never be realized; i.e.; because it is likely that he will
