The use of airborne scanning laser altimetry for improved river flood prediction
Airborne scanning laser altimetry (LiDAR) is an important new data source for many environmental
applications, mapping topographic and surface object height to high vertical
(±15-20cm) and horizontal (±30-100cm) accuracy over large areas, both time and cost effectively.
These data offer improvements in 2D hydraulic flood models by providing floodplain
bathymetry and vegetation height for the parameterisation of friction. Current parameterisations
use one (temporally constant) value of friction for the floodplain, and one for the
channel, with these values determined through a calibration procedure which limits the physical
basis of the model and, hence, its applicability to different catchments and flood events.
Primarily for this reason, a LiDAR data processing system is developed that segments a rural
scene into water and three vegetation height classes. The vegetation and topographic heights
in each class are calculated, and are accurate to ±14cm and ±17cm (respectively) for the
class 'crops and grasses'. The vegetation heights are subsequently converted. using existing
empirical equations, into friction coefficients that vary with the local flow depth and velocity.
This friction parameterisation is implemented in the TELEMAC-2D model and a Hood that
occurred on the river Severn in 1998 is simulated. When compared with a Synthetic Aperture
Radar (SAR) image of the event, the model accurately predicts the inundation extent.
The LiDAR vegetation segmentation is also used to drive a new mesh generator which decomposes
the mesh around automatically identified vegetation features so t.hat. regions of abruptly
changing boundary friction (and, as it. transpires, topographic gradient) are represented more
explicitly in t.he model. Further model simulations demonstrate (i) an improvement in inundation
extent prediction using the spatiotemporally variable friction parameterisation on the
decomposed mesh, and (ii) variations in predicted velocities which may be observable using
remote sensing. Indeed. velocity patterns are identified as important in model validation as
the SAR data exhibit inadequacies.