Geography Reference
In-Depth Information
Water surface elevation (m)
Water surface elevation (m)
(a)
(b)
Figure 7.7b
Map of inundation and water-surface elevation in the study reach predicted by FASTMECH model using in-channel
topography collected with a) a ground survey b) in-channel topography collected with EAARL sampled at the same locations as the
ground survey.
model FaSTMECH (Flow and Sediment Transport with
Morphologic Evolution of Channels) was applied to
predict depth, velocity, and water-surface elevation. FaST-
MECH solves the vertically and Reynolds averaged Navier
Stokes equations on curvilinear, orthogonal coordinate
system. A 5-m by 5-m curvilinear orthogonal numeri-
cal grid was created using the grid generation tools in
MD_SWMS. The topographic points were mapped to the
grid using a template method which searches in a bin
of specified dimensions around each grid node for the
topographic point that comes closest to it. If a single point
is located in the search bin, the node is given that eleva-
tion value. If multiple points are found, the node is given
an elevation value that is the inverse distance weighted
average of those points. If no point is located, the search
bin is expanded in size until a point or points are found.
The boundary conditions for the model were specified:
a discharge (19 m
3
/s) and a downstream water-surface
elevation. The downstream water-surface elevation was
obtained from the ground survey as were longitudi-
nal measurements of water-surface elevation collected
throughout the reach. A single-valued drag coefficient was
used to parameterise hydraulic roughness in the reach.
The drag coefficient was adjusted to provide the best
fit between the predicted and measured water-surface
elevations through the reach (root mean square error
0.045 m). Aerial thermography was used to identify the
locations of emergent sandbars at the modeled streamflow
(Figure 7.7a-D). The predicted water-surface elevation in
the reach using the ground survey derived measurements
of in-channel topography and the FaSTMECH model is
shown in Figure 7.7b-A. There was good correspondence
between the prediction of unwetted area in the channel
(Figure 7.7b-A) and the locations of emergent sandbars
(Figure 7.7a-D).
To ensure any vertical bias in the LiDAR survey was
identified and corrected for, the ground-based GPS mea-
surements made in 2002 on the emergent sand targets, less
complex with regard to laser backscatter than submerged
targets, were compared with EAARL points processed
with the first-return algorithm and located less than
0.50 m away from a surveyed ground-truth point. The
GPS ellipsoid heights were subtracted from the corre-
sponding EAARL ellipsoid heights and a mean error
of 0.18 m was identified between the data sets. Because
the measured EAARL heights were less than the corre-
sponding GPS measurements, 0.18 m was added to each
EAARL point to remove the bias associated with the mean
error. The shallow-water bathymetry algorithm was then
applied to the data set to identify those waveforms, and
the same bias adjustment was applied.
A second topographic dataset was created for mod-
elling that included EAARL-derived floodplain and island
points and EAARL in-channel points. The EAARL in-
channel points were selected from a dataset that included
a combination of EAARL points processed with the
bathymetry algorithm and EAARL points processed with
the first-return algorithm. Points processed with both
algorithms were required to map the emergent sandbars
and submerged areas within the channel. We removed
the first-return points that fell inside a 2-m radius of each
EAARL bathymetric point. This was done to minimise the
influence of the more spatially dense first-return points
on the neighbouring sparser bathymetric points but also
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