Environmental Engineering Reference
In-Depth Information
suitable for flood modelling. Smith et al. (2006)
have provided an excellent reviewof these, togeth-
er with their advantages and disadvantages for
flood inundation modelling, and this is summa-
rized below. The choice of a suitable model in any
given situation will depend upon a number of
factors, including the vertical accuracy, spatial
resolution and spatial extent required, the model-
ling objectives and any cost limitations. Many air-
and space-borne sensors generate a Digital Surface
Model (DSM), a representation of a surface includ-
ing features above ground level such as vegetation
and buildings. A DTM (also called a Digital Ele-
vation Model (DEM)) is normally created by strip-
ping off above-ground features in the DSM to
produce a 'bald-earth' model.
input flow errors. As a result, it can be difficult to
disentangle the contribution due to friction from
that attributable to compensation. The simplest
method of calibration is to calibrate using two
separate global coefficients, one for the channel
and the other for the floodplain. However, ideally
friction data need to reflect the spatial variability
of friction that is actually present in the channel
and floodplain, and be calculable explicitly from
physical or biological variables.
A final requirement is for suitable data for
model calibration, validation and assimilation. If
a model can be successfully validated using inde-
pendent data, this gives confidence in its predic-
tions for future events of similar magnitude under
similar conditions. Until recently, validation data
for hydraulic models consisted mainly of bulk
flow measurements taken at a small number
of points in the model domain, often including
the catchment outlet. However, the comparison
of spatially distributed model output with only
a small number of observations met with only
mixed success (Lane et al. 1999). The 2-D nature
of modern distributed models requires spatially
distributed observational data at a scale commen-
surate with model predictions for successful val-
idation. The observationsmay be synopticmaps of
inundation extent, water depth or flow velocity. If
sequences of such observations can be acquired
over the course of a flood event, this allows the
possibility of applying data assimilation techni-
ques to further improve model predictions.
Cartography
ADTMcanbe producedbydigitizing contour lines
andspotheights fromacartographicmapof thearea
at a suitable scale, then interpolating the digitized
data to a suitable grid (Kennie andPetrie 1990). The
product generated is a DTM since ground heights
are digitized. While the method is relatively eco-
nomical, contour informationisgenerallysparse in
floodplainsbecauseof their lowslope,whichlimits
the accuracy of the DTM in these areas.
An important exampleof suchaDTMfor theUK
is the Ordnance Survey Landform Profile Plus
DTM,whichisof sufficientlygreatheightaccuracy
and spatial resolution to be useful for flood risk
modelling (www.ordnancesurvey.co.uk/oswebsite/
products/landformprofileplus). This has been devel-
oped from the Landform Profile dataset (which was
generated from 1:10,000 contour maps and covers
the entire UK (Holland 2002)) by combining it with
LIDAR and photogrammetric data. The Profile Plus
DTM has a vertical accuracy and spatial resolution
that depend on land cover type, being
0.5m on a 2-
mgrid in selected urban and floodplain areas,
1.0m
on a 5-m grid in rural areas, and
2.5m on a 10-m
grid in mountain and moorland areas.
Use of Data for Model Parameterization
This section discusses the extent towhich the data
requirements of the previous section can bemet by
existing data sources, including any shortfalls that
exist.
Methods of Digital Terrain Model Generation
The data contained in a DTMof the floodplain and
channel formthe primary data requirement for the
parameterization of a flood inundation model.
Several methods exist for the generation of DTMs
Ground survey
Elevations can be measured directly in the
field using total stations or the Global Positional
