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In-Depth Information
an existing model structure with a more complete or more satisfactory representation of the local processes
(perhaps including next generation components such as those discussed in Chapter 9). Some recent work
on flexible modelling systems has already been reported, notably the FLEX and FUSE systems cited in
Sections 2.4 and 4.6.4. Branger
et al.
(2010) discuss past work in providing distributed modelling systems
and introduce the LIQUID modelling system. There has also been work on standards for linking different
model components, such as the OpenMI initiative (see Section 3.11) or the Python-based catchment
modelling framework (CMF) (Kraft
et al.
, 2011). Some sources of software for linking model components
are given in Appendix A.
12.5 Guidelines for Good Practice
One way forward in this situation is to agree upon assumptions by consensus of the parties involved,
both those setting up the model and those who will use the model results. The advantage in such an
approach comes from the use of a simple, transparent decision process as a communication tool with
users and stakeholders. If decisions about different sources of uncertainty have to be agreed upon (or at
least be open to scrutiny and discussion) then a greater understanding will develop on both sides about
the uncertainties essential to making a particular decision. The resulting assumptions might well be quite
wrong but this might only become apparent in hindsight when reviewing the process. Because of the
nature of epistemic uncertainties, some sources might also be left out of the analysis but, again, this might
only be evident in hindsight. The essence of such a consensus would be not to knowingly underestimate
the potential uncertainties in making a decision.
Clearly, however, we can use experience to reach consensus, experience that might be encapsulated
in sets of rules or guidelines for good practice (Beven and Alcock, 2011). Such guidelines can set out
the decisions needed in considering sources of uncertainty for different types of application and provide
advice on how they have been handled previously. Those decisions can provide a useful structure for in-
teraction with stakeholders and users, serving to structure the translationary discourse for communication
of uncertainty advocated by Faulkner
et al.
(2007).
Agreeing on assumptions about different sources of uncertainty is a heuristic approach to allow for
uncertainty in model predictions. Any resulting assessment of uncertainties in model predictions that
might be used in decision making will necessarily be approximate since we cannot be sure that all sources
of uncertainty have been considered, nor if those that have been considered are properly represented.
In fact, just like the model structures themselves, we will be pretty certain that we do not know how
to properly represent different types of uncertainty. However, the very process of defining and debating
the assumptions within some guidelines for good practice produces an agreed-upon working tool. As a
heuristic process, it is implicit that the assumptions should be evaluated and refined in the future as more
information about system responses becomes available. This is all part of the learning process.
Applying the guidelines will produce a range, possibly a wide range, of potential outcomes (or else,
where the model predictions can be evaluated, possibly a conclusion that all the models tried can be
rejected and decisions have to be made in some other way). Consideration of these outcomes in decision
making should reveal the range of conditions under which a potential future decision might not satisfy the
decision criteria. This is already a more robust heuristic than relying on some “best estimate” prediction.
Ideally, a decision would be taken that satisfies the decision criteria over all potential outcomes, at
reasonable cost, without compromising future decisions. By considering the uncertainty in the predictions
more explicitly, the possibility for failure of a decision, conditional on the potential outcomes, can be
assessed directly and judgements made as to whether the resulting risk is acceptable or not (see also
Beven, 2011). Such a judgement is likely to be highly dependent on the context, particularly where
extremes in the potential outcomes might involve catastrophic failures.
