Environmental Engineering Reference
In-Depth Information
data-based mechanistic (DBM) approach to modelling
discussed in this chapter tries to correct these deficien-
cies. It provides a modelling strategy that not only exploits
powerful statistical techniques but also produces simple
models of apparently complex environmental processes;
models that can be interpreted in physically meaningful
terms and are normally more acceptable to environmen-
tal scientists and engineers, particularly when they are
identified and estimated in terms of continuous-time,
differential equations whose parameters are not depen-
dent on the sampling interval of the data and often have
a direct physical meaning.
Conveniently, the same estimation methods used to
identify and estimate DBM models from real time-
series data can be applied to simulated data generated
from planned experiments on large computer models of
dynamic systems. In this way, it is possible to obtain
reduced order, 'dominant mode' emulation models that
are able to accurately mimic the dynamic behaviour of the
large computer models. The chapter has outlined briefly
the main aspects of the DBM approach to such dynamic
model emulation (DME) and shown how it can help to
build a bridge between hypothetico-deductive and induc-
tive modelling: between modellers who put their primary
trust in their scientific intuition about the nature of an
environmental model and tend to produce quite large and
complex computer simulation models that are not easily
identifiable from data; and those who prefer to rely on
the analysis of observational data to identify the simplest
form of identifiable model that can represent these data.
Beven, K.J., Freer, J., Hankin, B and Schulz, K. (2000) The use of
generalised likelihood measures for uncertainty estimation in
high order models of dynamic systems, in Nonstationary and
Nonlinear Signal Processing (eds W.J. Fitzgerald, A. Walden,
R. Smith and P.C. Young), Cambridge University Press, Cam-
bridge, pp. 115-51.
Beven, K.J., Young, P.C., and Leedal, D. (2008) Computationally
efficient flood water level prediction (with uncertainty), Pro-
ceedings of the European Conference on Flood Risk Management ,
Oxford, UK.
Billings, S.A. and Voon, W.S.F. (1986) Correlation based model
validity tests for nonlinear models, International Journal of
Control , 44 , 235-44.
Box, G.E.P., and Jenkins, G.M. (1970) Time-Series Analysis: Fore-
casting and Control , Holden-Day, San Francisco.
Bryson, A. E. and Ho, Y-C. (1969) Applied Optimal Control,
Blaisdell Publishing, Waltham, MA.
Davis, P.M. and Atkinson, T.C. (2000) Longitudinal dispersion in
natural channels: 3. An aggregated dead zone model applied to
the River Severn, U.K. Hydrology and Earth System Sciences , 4 ,
373-81.
Eickhout B., den Elzen M.G.J. and Kreileman G.J.J. (2004) The
Atmosphere-Ocean System of IMAGE 2.2: a global model
approach for atmospheric concentrations, and climate and sea
level projections. RIVM report no.481508017/2004.
Evensen, G. (2007) Data Assimilation: The Ensemble Kalman Filter ,
Springer-Verlag, Berlin.
Gu, C. (2002) Smoothing Spline ANOVA Models , Springer-Verlag,
Berlin.
Higdon, D., Gattiker, J., Williams, B., and Rightley, M. (2007)
Computer model validation using high-dimensional outputs, in
Bayesian Statistics 8 (eds J. Bernardo, M.J. Bayarri, J.O. Berger
et al .), Oxford University Press, Oxford.
Jakeman, A.J. and Hornberger, G.M. (1993) How much complex-
ity is warranted in a rainfall-runoff model ? Water Resources
Research , 29 , 2637-49.
Jakeman, A.J., Littlewood, I.G and Whitehead, P.G. (1990) Com-
putation of the instantaneous unit hydrograph and identifiable
component flows with application to two small upland catch-
ments. Journal of Hydrology , 117 , 275-300.
Jarvis, A., Leedal, D., Taylor, C.J., and C. Young, P. (2009).
Stabilizing global mean surface temperature: A feedback control
perspective. Environmental Modelling and Software , 24 , 665-74.
Jarvis, A.J., Young, P.C., Leedal, D.T. and Chotai, A. (2008) A
robust sequential emissions strategy based on optimal control
of atmospheric concentrations. Climate Change , 86 , 357-73.
Jarvis, A. J., Young, P. C., Taylor, C. J. and Davies, W. J. (1999) An
analysis of the dynamic response of stomatal conductance to a
reduction in humidity over leaves of cedrella odorata. Plant Cell
and Environment , 22 , 913-24.
Jothityangkoon, C., Sivapalan, M. and Farmer, D.L. (2001) Process
controls of water balance variability in a large semi-arid catch-
ment: downward approach to hydrological model development.
Journal of Hydrology , 254 , 174-98.
Konikow, L.F. and Bredehoeft, J.D. (1992) Ground water models
cannot be validated. Advances in Water Resources , 15 , 75-83.
Kuhn, T. (1962) The Structure of Scientific Revolutions , University
of Chicago Press, Chicago.
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Water Quality (eds M.B. Beck and G. Van Straten), Springer-
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