Geography Reference
In-Depth Information
We calculated the sediment budget at the bend-scale,
rather than using standardised length DGO, to provide
geomorphically meaningful information and to compare
for each bend what it is reintroduced by bank erosion
and transferred downstream with what is stored from
upstream. This estimate is possible on the Ain River over
three decades only because of the unidirectional chan-
nel shifting. If the channel had rejuvenated several times
the surface over the period, the estimate would be low.
Over the study reach from the bend 2 to the bend 32
(Figure 11.8), 2.9 million m
3
of sediment were eroded as
opposed to 2.01 million m
3
stored, the difference being
then very low 86,000 m
3
(5% of the storage). Annually
56,000 and 55,000 m
3
of sediment are locally introduced
and stored, respectively, which is a very important contri-
bution to transport processes within a fluvial corridor for
which the mean annual bedload transport is estimated to
12 000 m
3
/yr (Rollet, 2007). This analysis also shows that
the budget is not uniform over the study area, with some
of the bends trapping much sediment (bends 19, 21, or 24)
whereas others are mainly affected by sediment introduc-
tion (bends 17, 22, 25). Very few bends have a balanced
sediment budget. Interestingly, we do not observe a clear
bend-to-bend bedload transfer over multiple decades as
expected, the upstream bend erosion accumulating on
the one immediately downstream following the theo-
retical schemes (e.g. Friedkin, 1945). Therefore, these
results open interesting discussion on the integration of
floodplain/channel sediment exchanges in the sediment
transport modelling conducted on such shifting rivers.
(Wiederkehr et al., 2010a). The entire network was
divided by 10 m (for water polygons) or 100 m (for
active channel polygons) long DGO, and AGO units
are then created based on valley width (provided from
geomatic procedure applied to a DEM) and active chan-
nel width detected from orthophotography using an
object-oriented remote sensing procedure which is more
powerful than a pixel by pixel approach when using low
spectral resolution images. The procedure resulted in the
identification of 53 reaches along the main stem. In each of
these reaches, geomorphic metrics were calculated (con-
finement index, mean active channel width divided by the
catchment size, the sinuosity ratio of the active channel,
the proportion of aquatic area within the active chan-
nel). A cluster analysis was then performed to identify
AGO with similar geomorphic characteristics, ultimately
classifying them into five unique geomorphic types.
This preliminary step was then used to characterise
the aquatic meso-habitats within each homogeneous
geomorphic reach and answer to the first question. Meso-
habitat characterization is illustrated on the downstream
part of the Dr ome main stem where the aquatic zone
is wide enough to be studied with a 50 cm pixel reso-
lution. Aquatic in-channel features were detected by an
object-oriented procedure based on radiometric param-
eters. 4100 polygons were then detected over 20 km.
410 polygons were then randomly selected and classified
(e.g., pool, riffle, gravel bench, lentic and lotic channels).
They were then used to perform a discriminant analysis
and calculate a discriminant function allowing predic-
tion of habitat type for each of the polygons. A map
was then produced showing the position of in-channel
habitats. Moreover, longitudinal patterns of meso-habitat
occurrence were plotted, as were in-stream habitat statis-
tics from each homogeneous reach type (e.g., braided,
embanked or wandering). Results show that there are
no obvious differences in habitat structure between the
embanked, braided or wandering reaches (Figure 11.9).
The second approach illustrates an upscaling approach
using the homogeneous reaches (Bertrand & Piegay,
unpublished data). The previous procedure was applied
to the entire network (597 km) and detected types cor-
responded well in characteristics and spatial distribution
with the theoretical ones identified by Pont et al. (2009).
The aim of the present analysis was to test sensitivity of
geomorphic reaches to change following riparian forest
harvesting intended to promote sediment reintroduction.
Following Pont et al. (2009), we assume that the homo-
geneous geomorphic reaches represent good integrative
units of biological potential, so called functional reach
11.4.2 The Drome network: example of up- and
downscaling approach using
homogeneous geomorphic reaches
In this section, the geographical approach is illustrated in
a multi-scaled perspective. All the development was based
on geomorphic reaches identified using the DGO/AGO
procedure. Practical questions are twofold: (i) Are the
mesohabitat characteristics (e.g. fish habitats) different
between natural (braided or wandering) and embanked
reaches, or does step-pool frequency and spacing vary
from one reach to another? (ii) Can we expect sediment
reintroduced to prevent channel incision to reduce the
availability of aquatic habitat at network scale?
The methodology is based on the identification of
geomorphic reaches considering they are integrating fea-
tures of biological communities following the nested-scale
framework concept (Frissell et al. 1986). The geomor-
phic units were detected using the DGO/AGO procedure
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