Changes in the equilibrium river profile due to interventions

Abstract

Human interventions can result in changes in the equilibrium profile of rivers. It is difficult to identify the bed level changes that result from river interventions due to the various causes of bed level changes. Using wavelet filtering, we are able to isolate the effect of river interventions based on the length scale over which they occur. The method presented here can aid in verifying model results. In addition, it can be used to estimate bed level changes that occur over various spatial scales.

Full text

1 INTRODUCTION
River interventions are carried out with various objectives such as increasing the discharge ca-
pacity of the river (e.g., van Denderen et al., 2019), increasing the ecological value of the river
(e.g., Riquier et al., 2015) and to mitigate the impact of human interventions on the river planform
(e.g., Formann et al., 2007). Such river interventions generally reduce the discharge conveyance
in the main channel and this reduces the sediment transport capacity of the channel. The reduced
sediment transport capacity is compensated locally by the river with an increase in the bed level,
i.e. a decrease in the water depth, and an increase of the local bed slope (de Vriend, 2015). These
morphological effects can have a negative impact on functions of the river, such as navigation
(van Vuren et al., 2015), and, at the same time, can have a positive effect on large-scale bed level
changes, such as large-scale bed degradation that results from engineering measures from the
past. The amount of aggradation that occurs due to the construction of the river intervention is a
function of the discharge that occurred. The objective of this paper is to identify the bed level
changes that occurred due to the construction of longitudinal dams in the river and show the var-
iation of this aggradation over time. Longitudinal dams replace groynes and create a secondary
channel within the main channel (Havinga et al., 2009, Collas et al., 2018, de Ruijsscher et al.,
2019). The secondary channel reduces the discharge conveyance of the main channel and thereby
the sediment transport capacity resulting in aggradation. The secondary channel does not attract
discharge during base flow conditions and in combination with a narrower main channel, the
effect on the water depth during base flow conditions should be minimal.
2 METHOD
We focus on the bed level in the river Waal in the Netherlands. The river Waal is the largest of
the Dutch Rhine branches and, because of its important navigational function, the bed level is
measured every two weeks using multi-beam echosounders since 2005. As part for the Room for
the River project the groynes in the river were replaced with longitudinal dams in 2015. This
created a secondary channel and is expected to cause aggradation in the main channel. The bed
level changes in the river occur over various spatial and temporal scales, and can have various
causes. Using a wavelet transform we are able to differentiate between the spatial scales of the
bed level changes (Torrence and Compo, 1998, Gutierrez et al., 2013). We choose the spatial
Changes in the equilibrium river profile due to interventions
R.P. van Denderen
University of Twente, Enschede, The Netherlands
E. Kater & L.H. Jans
Ministry of Infrastructure and Water Management-Rijkswaterstaat, Arnhem, The Netherlands
R.M.J. Schielen
Ministry of Infrastructure and Water Management-Rijkswaterstaat, Lelystad, The Netherlands
Delft University of Technology, Delft, The Netherlands
ABSTRACT: Human interventions can result in changes in the equilibrium profile of rivers. It is
difficult to identify the bed level changes that result from river interventions due to the various
causes of bed level changes. Using wavelet filtering, we are able to isolate the effect of river
interventions based on the length scale over which they occur. The method presented here can aid
in verifying model results. In addition, it can be used to estimate bed level changes that occur over
various spatial scales.

range such that small scale changes such as bed forms and large-scale changes such as the large-
scale bed degradation are filtered out. Using the wavelet transform we can isolate the bed level
changes that are caused by the construction of river interventions and the bed level changes that
are caused by local variations of the river’s geometry.
3 RESULTS
Figure 1 shows the bed level in time in the main channel parallel to the longitudinal dams that
were construction in 2015. The second graph shows the filtered bed level variation around the
time-averaged bed level. Here we can see that large aggradation occurs at the upstream end of the
intervention (yellow areas in Figure 1). This corresponds with the sudden reduction of the sedi-
ment transport capacity in the main channel. At the downstream end, the geometry results in an
acceleration of the flow resulting in large scour (dark blue in Figure 1). The out and inflow of the
other secondary channels cause similar effects, but smaller because the largest gradient in sedi-
ment transport capacity occurs at the upstream and downstream end of the intervention. Figure 2
shows the bed level change averaged over the length of the intervention. The raw data clearly
shows the large-scale bed degradation that occurs in this part of the river. With the wavelet filter-
ing, we can ignore such large scale effects and focus on the bed level changes that are a direct
result of the intervention. It is clear that the average bed level continues to aggrade and that a new
equilibrium has not yet been reached.
Figure 1 Top: The bed level in the main channel as a function of time. Bottom: The bed level variation
around the time-averaged value filtered using the wavelet transform. The red vertical lines denote the
upstream and downstream end of the intervention. The white horizontal and vertical lines are caused by
missing data.

4 DISCUSSION AND CONCLUSIONS
The equilibrium bed profile of a river changes due to river interventions. Such changes can have
secondary negative effects on other functions of the river. Using the wavelet filtering we are able
to identify the bed level changes that occur due to the interventions while ignoring both smaller
scale and larger scale bed level changes. The method presented here can aid in verifying model
results. In addition, it can be used to estimate bed level changes in rivers that occur over various
spatial scales.
ACKNOWLEDGEMENTS
This research is supported by TKI Deltatechnology (UTW01), the Ministry of Infrastructure and
Water Management-Rijkswaterstaat and by the Netherlands Organisation for Scientific Research
(NWO), which is partly funded by the Ministry of Economic affairs, under grant number P12-
P14 (RiverCare Perspective Programme) project number 13516. This research has benefited from
cooperation within the network of the Netherlands Centre for River studies.
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Figure 2 The bed level averaged over the length of the intervention with and without the wavelet filtering.
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