New Approach to Sustainable Stormwater Planning - Visit WordPress

through a complex world. People who take their time do not experience it as an obstacle.
However, time as an obstacle increases when activist groups force the local government
to reduce time intervals. (e.g. “The goals must be met in 2005 instead of 2008!”)
Good timing
Not all moments are suitable for implementing ideas to improve urban water systems.
This is especially true in the planning of open water systems in the urban environment.
It is an art to give planning processes the time they need. Visions about water systems
and society must not be translated into rigid goals too quickly. Pressing the turbo button
in order to achieve the goals must be avoided. Speeding up the planning processes often
results in delay (“More haste, less speed”). Mumford (1947) distinguishes two types of
time: the chronological time and the kairo-logical time (Kairos = the right moment).
The former offers a structure for events and measures, whereas the latter emerges from
knowledge, intuition and experience and consists of “the right moments”. Intervening in
processes requires good timing. To have a feel for the right moment is important, because
sometimes it is better not to act. Although this increases the complexity of interventions,
it also makes them more realistic and effective. Good politicians act in kairological
time.
3.3 Many opportunities lie in the unknown
When we in the planning explore the relationships between goals and measures and
between cause and effect, we meet uncertainty. Sometimes it is possible to predict the
effects of different interventions in the system. However, there are a lot of cases where
the outcome could be a surprise. We can distinguish three degrees of uncertainty in cause
and effect relationships: certain, uncertain and structurally uncertain.
Some processes are easy to predict. For example, when we double the pump-capacity in
a pond system, it is easy to predict the effects on surface water level management. There
are no real uncertainties. However, when we increase the storage capacity within a sewer
system, it is difficult to predict the effects on the pollution load on the surface water. We
know that the larger the storage, the smaller the pollution load. But, predictions of the
effects are often inaccurate – there are uncertainties. Doing more research and applying
better models can reduce these uncertainties.
We talk about structural uncertainty (Walker, 2000) when it is impossible to reduce the
uncertainty. For example, it is very hard to predict how people will react to an integrated
water plan, as it is difficult to model public and political support. On the top of that,
societal systems often show a deterministic chaos. As result it is impossible to predict
long-term future behaviour, even if we have a perfect model.
A strategy often attached to the model of reducing complexity (Model A) is to avoid chaos
and structural uncertainty. Interventions are focused on cause and effect relationships
that can be described very well and exhibit little uncertainty. In practice this means that
only processes in the biophysical water system are taken into account and the interaction
between water and society are taken for granted. Society is in equilibrium. This strategy
of avoiding uncertainty gives rise to concern, because the change will be incremental.
To really change system behaviour, the area of structural uncertainty offers the best prospects.
The reason is that the associated processes have the highest degree of freedom.
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