Environmental Engineering Reference
In-Depth Information
precision of a few centimetres (Frappart
et al. 2006), suitable for detecting river surface
slopes along long river reaches or between multi-
ple crossings of a channel. Also the potential to
measure water stage beneath vegetation (Harding
and Jasinski 2004) could prove interesting for flood
monitoring and modelling.
with both amplitude and phase coherence stronger
than surrounding non-flooded terrain, permitting
determination of the interferometric phase
(Alsdorf et al. 2001). Alsdorf (2003) also used these
characteristics and found that decreases in water
levels were correlated with increased flow-path
distances between main channel and floodplain
water bodies that could be modelled in a GIS. This
correlation function allowed changes in water
storage to be mapped over time.
Altimeters (onboard ERS, ENVISAT or JASON
mission satellites) emit a radar wave and analyse
the return signal. Surface or water height is the
difference between the satellite's position in orbit
with respect to an arbitrary reference surface and
the satellite-to-surface range. Although range
accuracies usually lie within 5 to 20 cm for oceans
and sea ice (Rees 2001) but typically
50 cm for
rivers (Alsdorf et al. 2007), the altimeter footprint
is only in the range of 1 to 5 km and seems thus
only suitable for rivers or inundated floodplains of
large width (Birkett et al. 2002; Fig. 11.5). For large
lakes accuracies may improve to
<
5 cm root mean
squar (RMS) error (Birkett et al. 2002; Alsdorf
et al. 2007). Another disadvantage of altimetry
for water stage retrieval over land is that its
success relies primarily on adequate re-tracking
of complex contaminated waveforms (Garlick
et al. 2004).
However, the launch of ICESat in 2003 has
made space-borne LIDAR altimetry available
for terrestrial water bodies with an elevation
Indirect measurements
Some interesting developments in extracting
water levels from remote sensing are those that
integrate topographic data (Raclot 2006). Topo-
graphic maps with small interval contours and
level data may provide an excellent ground truth
check for water levels on flood shorelines from
aerial photography (Currey 1977) or satellite im-
agery (Oberstadler et al. 1997; Brakenridge
et al. 1998). LIDAR or photogrammetric DEMs
can be intersected with lines from flood deposits
on aerial photographs (Lane et al. 2003) or high-
resolution space-borne imagery from visible
bands. Even heights from flooded vegetation may
be used (e.g. Horritt et al. 2003).
The floodplain can also be segmented into
polygons in which water levels are supposed to
be horizontal, similar to possible approaches with
most 1-D hydrodynamic models. In order to en-
sure a decreasing water trend with flow direction,
extracted water stages are adjusted using an auto-
mated algorithm based on hydraulic constraints
[see Puech and Raclot (2002) for application to
Fig. 11.5
River Danube water
level fluctuations from 1993 to
2002. Bars indicate the standard
deviation. Data provided by the
LEGOS hydroweb (http://www.
legos.obs-mip.fr/en/equipes/
gohs/resultats/i_hydroweb).
