Environmental Engineering Reference
In-Depth Information
2.1.2 AModel for environmental research
understanding observations and in developing and testing
theory. Direct observation (as opposed to remote observa-
tion or estimation through spatial or temporal statistical
inference) will always be closer to truth and must remain
the most important component of scientific investigation.
Klemes (1997: 48) describes the forces at work in putting
the modelling 'cart' before the observational 'horse' as is
sometimes apparent in modelling studies:
What do we mean by the term
model
? A model is
an abstraction of reality. This abstraction represents a
complex reality in the simplest way that is adequate for the
purpose of modelling. The best model is always that which
achieves the greatest realism with the least parameter
complexity (parsimony) and the least model complex-
ity. Realism can be measured objectively as agreement
between model outputs and real-world observations, or
less objectively as the process insight or new understand-
ing gained from the model.
Parsimony (using no more complex a model or rep-
resentation of reality than is absolutely necessary) has
been a guiding principle in scientific investigations since
Aristotle who claimed:
It is easier and more fun to play with a computer than
to face the rigors of fieldwork especially hydrologic field-
work, which is usually most intensive during the most
adverse conditions. It is faster to get a result by model-
ing than through acquisition and analysis of more data,
which suits managers and politicians as well as staff
scientists and professors to whom it means more publi-
cations per unit time and thus an easier passage of the
hurdles of annual evaluations and other paper-counting
rituals. And it is more glamorous to polish mathematical
equations (even bad ones) in the office than muddied
boots (even good ones) in the field.
It is the mark of an instructed mind to rest satisfied with
the degree of precision which the nature of the subject
permits and not to seek an exactness where only an
approximation of the truth is possible
Klemes (1997: 48)
though it was particularly strong in Mediaeval times and
was enunciated then by William of Ockham, in his famous
'razor' (Lark, 2001). Newton stated it as the first of his
principles for fruitful scientific research in
Principia
as:
A model is an abstraction of a real system; it is a
simplification in which only those components that are
seen to be significant to the problem at hand are rep-
resented in the model. In this representation, a model
takes influence from aspects of the real system and
aspects from the modeller's perception of the system
and its importance to the problem at hand. Modelling
supports the conceptualization and exploration of the
behaviour of objects or processes and their interaction.
Modelling is a means of better understanding and generat-
ing hypotheses. Modelling also supports the development
of (numerical) experiments in which hypotheses can be
tested and outcomes predicted. In science understanding
is the goal and models serve as one tool in the toolkit used
towards that end (Baker, 1998).
Cross and Moscardini (1985: 22) describe modelling
as 'an art with a rational basis which requires the use of
common sense at least as much as mathematical exper-
tise.' Modelling is described as an art because it involves
experience and intuition as well as the development of
a set of (mathematical) skills (although many mathe-
maticians would argue that mathematics also requires
intuition and experience to be carried out well). Cross
and Moscardini (1985) argue that it is intuition and the
resulting insight that distinguish good modellers from
mediocre ones. Intuition cannot be taught and comes
from the experience of designing, building and using
models. One learns modelling by doing modelling. The
reader should look at the environmental issues presented
We are to admit no more causes of natural things than
such as are both true and sufficient to explain their
appearances.
Parsimony is a prerequisite for effective scientific
explanation, not an indication that nature
necessarily
operates on the basis of parsimonious principles. It is
an important principle in fields as far apart as taxonomy
and biochemistry and is fundamental to likelihood
and Bayesian approaches of statistical inference. In a
modelling context, a parsimonious model is usually the
one with the greatest explanation or predictive power
and the least parameters or process complexity. It is a
particularly important principle in modelling because
our ability to model complexity is much greater than our
ability to provide the data to parameterize, calibrate and
validate those same models. Scientific explanations must
be both relevant
and
testable. Unevaluated models are no
better than untested hypotheses. If the application of the
principle of parsimony facilitates model evaluation then
it also facilitates utility of models.
2.1.3 Thenatureofmodelling
Modelling is not an alternative to observation but,
under certain circumstances, can be a powerful tool in
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