Environmental Engineering Reference
In-Depth Information
Acknowledgements
systems but it does not address directly the ques-
tion of real-time updating in fully distributed
models, such as detailed hydrodynamic represen-
tation of a catchment, the grid-to-grid models of
Moore and his co-workers (see, e.g., Moore
et al. 2006; Cole and Moore 2008) and other
types of distributed hydrological models. Clearly,
the problems of identifiability severely restrict
attempts at updating the parameters of such
large, over-parameterized models unless some
model-specific procedures are invoked to
constrain the ill-posedness of the problem. In
principle, however, the concepts and methods
for state updating outlined here are applicable to
such large models and can be applied to
them provided the associated state variables are
observable from the available rainfall-flow or
level data. Unfortunately, the complexity of large
hydrodynamic models is such that observability
is difficult to guarantee, so that a truly systematic
approach, such as this, is rarely possible and ad
hoc, partial solutions are normally required. These
are difficult to generalize since they depend so
much on the specific nature of themodel. A recent
example is the use of the EnKF and EnSRFwith the
distributed hydrological model TopNet by Clark
et al. (2008), where the authors report many pro-
blems and the results are not particularly good.
They conclude that: 'New methods are needed to
produce ensemble simulations that both reflect
total model error and adequately simulate the
spatial variability of hydrological states and
fluxes.'
One possible approach to real-time updating in
the case of large models is the idea of emulating a
large dynamic simulation model by a small data-
based mechanistic (DBM) model or some other
form of low-dimensional emulation model. As
Young and Ratto (2008) have suggested, such em-
ulation models can also help to provide a unified
approach to stochastic, dynamic modelling that
combines the hypothetico-deductive virtues of
good scientific intuition, as reflected in the large
hydrological or hydrodynamic simulation model,
with the pragmatism of inductive DBM model-
ling, where more objective inference from data is
the primary driving force.
I amvery grateful tomy colleagues Professor Keith
Beven and Dr David Leedal for checking the chap-
ter and making useful comments. I am also grate-
ful to a reviewer who offered useful suggestions
that I feel have improved the chapter. Of course, I
remain responsible for any remaining errors or
omissions.
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