Environmental Engineering Reference
In-Depth Information
(ADCP) technology may provide the most plausi-
ble solution here, but it is likely that such tech-
nology will need to be used in conjunction with
high-resolution flowmodelling in order to extrap-
olate the limited measurements we are likely to
obtain to extreme flow conditions. An alternative,
using airborne remote sensing, may be to image
the flood using airborne SAR with an along-track
interferometric capability, allowingmeasurement
of water surface velocity, from which flow rate
may be inferred (e.g. Costa et al. 2000).
Lastly, networks of low-cost sensors connected
using wireless computing and GSM technology
(e.g. Neal et al. 2007) may also provide an addi-
tional source of model validation data to comple-
ment that available through remote sensing. The
ability to deploy large numbers of sensors may
help overcome the spatial resolution of existing
ground sensor networks and yield new insights
into the hydraulics of flood flows that can be used
to develop a new generation of flood inundation
models.
frommultiplemission ground segments. Thiswill
include a single 'one-stop' mission planning and
programming service to place requests for new
acquisitions on to partner space agencies' ground
segments. The SAR image sequences acquired
may image the rising limb of the hydrograph as
well as the more commonly imaged falling limb.
The availability of image sequences should
make possible more data assimilation studies,
which may provide more rigorous model valida-
tion than using single SAR scenes. If the
SAR images can be made available in geo-
registered form in near real time, they may also
become a powerful tool in operational flood risk
alleviation scenarios.
Full-waveform LIDAR data need to be pro-
cessed to produce more realistic topographic data
in urban areas. Unlike LIDAR systems recording
first and last return, full-waveform systems are
able to record the entire backscattered signal of
each laser pulse (Chauve et al. 2007). Subtle
modelling errors may arise due to the limited
sampling of the LIDARwaveform that is currently
employed (e.g. last return). For example, a wall
may divert flood water, but may not be identified
in LIDAR data because it is obscured by vegeta-
tion, which may subsequently be removed by the
filtering process to leave an estimate of ground
rather than wall height. Further studies are also
required of the fusion of LIDAR data withmap and
other data in urban areas, and the relative trade-
offs between grid and subgrid representation of
urban features.
The improvement of remotely sensed data
sources formodel parameterization and validation
may, in the future, mean that our ability to gauge
river flow accurately may become the limiting
uncertainty in flood riskmodelling. Many gauging
stations are located for low-flow monitoring, and
perform poorly during high-flow periods. More-
over, obtaining accurate flow velocity data at high
flow across a complex floodplain may be difficult
and dangerous. Improved flow gauging is therefore
likely to be a critical research need in the coming
decade, and techniques to achieve this can cur-
rently only be described as experimental at best.
Boat-mounted Acoustic Doppler Current Profiler
References
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Alsdorf, D.E., Melack J.M. and Dunne, T. (2000) Inter-
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on the Amazon flood plain. Nature, 404, 174-177.
Alsdorf, D.E., Smith, L.C. and Melack, J.M. (2001)
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