Pointers for the Future - Environmental Modelling: Finding Simplicity in Complexity - page 445

The increasing diversity, availability and the move to
free and open source of modelling and Geographical
Information Systems (GIS) software both increases the
reach of environmental modelling to new communities
but also increases the rate of development of modelling
software and systems themselves.
Enhanced visualization tools including geobrowsers
continue to improve and provide an excellent means
for better communication of modelling. More web-based
modelling and the 'model-web' offers the opportunity
for unprecedented integration of models even across the
biophysical and socio-economic divide (see Chapter 18)
as well as near real time connections to ground based
or remotely sensed data (see Chapter 25). Grid distribu-
tion and cloud-based outsourcing of computer hardware
offers the potential for removing some computing barriers
and - once and for all - relieving modellers of the bur-
den of maintaining scientific computing infrastructure.
The increasing diversity of computing platforms and the
advent of the location-aware 'smart phone' both increase
the ubiquity of devices for accessing model output but also
open up the potential for on-site environmental monitor-
ing and modelling and crowdsourced model validation,
especially in agriculture.
All in all, these technical developments are very positive
for the practice and communication of environmental
modelling but many environmental issues and mod-
elling applications remain constrained by lack of - or
poor quality - data, lack of understanding of process and
lack of confidence in unvalidated model outputs. These
may be more challenging to overcome than the technical
problems. One of the dangers of the technology outstrip-
ping the concepts and science is that we will find more
and more glossy and sophisticated ways to model and
communicate fundamentally weak scientific knowledge,
which is not all that helpful in the long run (and perhaps
returns us to Keith Beven's comments on visualization
noted above).

1970s made similar claims as to providing the 'ultimate
answer'. As people became progressively more bogged
down in increasingly complicated (if not complex)
models, disillusion set in as it was realized that increasing
amounts of computer power would only tend to com-
pound problems. As people await sufficient computer
power to run cellular or individual-based models with
10 EXTREMELY LARGE NUMBER of cells/individuals, will we see
history repeating itself? (See Parker and Epstein, 2011, for
one approach where the global population is simulated in
a simple epidemiological model to see how far progress
is being made towards EXTREMELY LARGE NUMBER since the
first edition of this topic was published.) You will be
aware from the introduction that we (still) do not possess
a crystal ball, so this question we will continue to leave
unanswered for the present ...

References

Beven, K.J. (1996) The limits of splitting: hydrology. The Science
of the Total Environment , 183 , 89-97.
Bhaskar, R. (1997) A Realist Theory of Science , 2nd edn, Verso,
London.
Chamberlain, T.C. (1890) The method of multiple working
hypotheses. Science , 15 , 1092. (It may be easier to find the
reprinted versions in Science , 148 , 754-9 (1965) or in R. Hilborn
and M. Mangel (1997) The Ecological Detective , Princeton Uni-
versity Press, Princeton NJ.)
Cooke, R.U. and Reeves, R. (1976) Arroyos and Environmental
Change in the American South-West , Clarendon Press, Oxford.
Cross, M. and Moscardini, A.O. (1985) Learning the Art of Mathe-
matical Modelling , John Wiley & Sons, Chichester.
Favis-Mortlock, D.T. (1998) chapValidation of field-scale soil
erosion models using common datasets, in Modelling Soil Erosion
byWater (eds J. Boardman, and D.T. Favis-Mortlock), Springer-
Verlag, Berlin, pp. 89-128.
Freeman, S. and Pryce, N. (2009) Growing Object-Oriented Soft-
ware, Guided by Tests , Addison Wesley, Upper Saddle River
NJ.
Geller, G.N. and Melton, F. (2008) Looking forward: applying
an ecological model web to assess impacts of climate change.
Biodiversity , 9 (3-4), 79-83.
Grayson, R.B., Moore, I.D. and McMahon, T.A. (1992) Physically
based hydrologic modelling: II. Is the concept realistic ? Water
Resources Research , 28 , 2659-66.
Kirkby, M.J. (1992) Models, in Horizons in Physical Geography
(eds M.J. Clark, K.J. Gregory and A.M Gurnell), Macmillan,
London.
Klemes, V. (1986) Operational testing of hydrologic simulation
models. Hydrological Sciences Journal , 31 , 13-27.
Klemes, V. (1997) Of carts and horses in hydrological modelling.
Journal of Hydrologic Engineering , 1 ,43-9.
Martin, R.C. (ed.) (2008) Clean Code: AHandbook of Agile Software
Craftsmanship , Addison Wesley, Upper Saddle River, NJ.

27.4 Is it possible to find simplicity in
complexity?

The straightforward answer to this question is yes.
Whether we will ever be happy with the particular answer
is another question (probably answered by a resounding
no!). Fundamentally, do the possibilities of complex
system theory offer us progress in terms of finding more
than the sum of the parts of our environmental system?
Much work on general systems theory from the 1960s and