Environmental Engineering Reference
In-Depth Information
minimum uncertainty. We therefore now address
more explicitlyhowuncertainty is accommodated
in flood risk management decisions.
analysis of flood risks and costs. This leads to a
requirement for national scale risk assessment
methodologies, which need to be based upon da-
tasets that can realistically be assembled at a
national scale (Hall et al. 2003a). Topographical,
land use and occupancy data are typically available
at quite high resolutions on a national basis.
The logical scale for strategic planning is at
the scale of river basins and hydrographically
self-contained stretches of coast (the latter from
a sedimentary point of view). At this scale (Evans
et al. 2002), there is need and opportunity to
examine flood risk management options in a lo-
cation-specific way and to explore spatial combi-
nations and sequences of intervention. Decisions
to be informed include land use planning, flood
defence strategy planning, prioritization of main-
tenance and the planning of flood warnings. The
datasets available at river basin scale are more
manageable than at a national scale and permit
the possibility of more sophisticated treatment of
the statistics of boundary conditions, the process
of runoff and flow, the behaviour of flood defence
systems and the likely human response.
At a local scale, the primary decisions to be
informed are associated with scheme appraisal
and optimization, taking a broad definition of
'scheme' to includewarning systems, spatial plan-
ning and perhaps temporary flood defences. This
therefore requires a capacity to resolve in appro-
priate detail the components that are to be ad-
dressed in the design and optimization or
engineering structures, or in the development and
deployment of non-structural alternatives or com-
plementary measures.
Implicit in this hierarchy of risk analysis meth-
ods is a recognition that different levels of analysis
will carry different degrees of associated uncer-
tainty. Similarly, different decisions have very
different degrees of tolerance of uncertainty. Pol-
icy analysis requires evidence to provide a ranking
of policy options by their efficiency or effective-
ness, which can be based on approximations,
whilst engineering optimization yields design
variables that are to be constructed to within a
given tolerance: if loss of life is threatened in
that context, we need maximum precision and
Responding to Change
It is increasingly recognized that flooding systems
are subject to change on a very wide range of
timescales. Whilst global climate change is most
often cited as the driving force behind these pro-
cesses of change (Milly et al. 2008), the UK Fore-
sight Future Flooding Project (Evans et al. 2004)
identified a host of drivers of future change.
A driver of change is any phenomenon that may
change the time-averaged state of the flooding
system(Hall et al. 2003b; Evans et al. 2004; Thorne
et al. 2007). Some of these drivers will be under the
control of flood managers, for example construc-
tion and operation of flood defence systems, or
introduction of flood warning systems to reduce
the consequences of flooding (i.e. reduce the num-
ber of human receptors). Many other drivers, such
as rainfall severity, or increasing values of house
contents, are outside the control of floodmanagers
and even government in general. The distinction
between these two types of driver is not crisp and
in terms of policy relates to the extent to which
government has power to influence change and
the level of government at which power is exer-
cised. For example, decisions regarding local flood
management and spatial planning are devolved to
local decision-makers, whereas decisions to limit
emissions of greenhouse gases are taken at nation-
al and international levels.
The range of drivers thatmay influenceflooding
systems was surveyed in the UK Foresight Future
Flooding project. The drivers identified in that
project as being of relevance to fluvial flooding are
reproduced in Table 1.2. The Foresight study
(Evans et al. 2004) went on to rank drivers of
change in terms of their potential for increasing
flood risk in the future, in the context of four
different socioeconomic and climate change sce-
narios. Whilst the ranking was based largely upon
expert judgement and a broad scale of quantified
risk analysis, it did provide some indications of the
