Environmental Engineering Reference
In-Depth Information
condition for successful modelling. It is essential to know the purpose and the
modelling objective before one can decide how far the complexity of reality should
be reduced in the modelling approach. The underlying theory is crucial for the
interpretation and validation of the results.
Models Cannot Function Without a Data Base for Model
Development and Testing
The correctness of a model has to be re-evaluated again and again during the model
developmental procedure. The outputs have to be compared with the target system,
usually the ecological reality. Thus, the modeller needs data from the reference
system during several steps of model development.
The improvement of model quality proceeds in different steps. For these proce-
dures, a deep understanding of the modelled subject is necessary, as well data for
different types of model testing. The procedural steps generally are the following
(Nielsen 2009):
1.
Consistency check and sensitivity analysis
. The model is checked versus logical
predictions of what is likely to be the result of any change in the parameters and
forcing functions of the model. Furthermore the question of parameter sensitiv-
ity should be assessed.
2.
Calibration
. One major issue to be addressed here is that the chosen parameter
values need to be justified. This means that several aspects (e.g. uncertainty of
the parameters, their accuracy, their significance for the model) have to be
considered. Also, the results of the calibration can be observed by comparing
the outputs with data sets.
3.
Validation
. This phase is the highest level of model quality assessment. It is a
test of how well, model predictions (prognoses) are matched with actual obser-
vations. The higher the potential effect of the modelling results is, the higher
should be the emphasis in testing model results against independent data. For
validation, one or more datasets are required that describe the modelled situation
independent
of the data used during model development.
Different possibilities to apply data sets for model validation, for quality assess-
ment of models and to secure correctness of model results are described in Chap. 23.
Models Have to Be Treated Skeptically When They Are Applied
Outside the Validation Regimes
A consequence of the validation strategy is information on the range of validity of
the model. If it is applied within the validation range, the results usually are of
