Vegetation roughness and flood magnitude
Anderson & Rutherford
Vegetation roughness and flood magnitude: A case study of the
relative impact of local and catchment scale effects.
Brett G. Anderson
PhD Candidate, SAGES,
The University of Melbourne,
Melbourne, Australia
E-mail: b.anderson5@pgrad.unimelb.edu.au
Ian D. Rutherfurd
Associate Professor, SAGES,
The University of Melbourne,
Melbourne, Australia
E-mail: idruth@unimelb.edu.au
Andrew W. Western
Senior Researcher,
Department of Civil and Environmental Engineering,
The University of Melbourne,
Melbourne, Australia
E-mail: a.western@civenv.unimelb.edu.au
Abstract
The riparian zone of Australia’s river networks has become a focal point for environmental
rehabilitation efforts in the last decade. Revegetation of the stream corridor is occurring both through
active replanting and natural recovery facilitated by fencing off the stream to exclude stock. These
measures are motivated by the tide of evidence showing that the reinstatement of riparian
communities improves the health of both aquatic and terrestrial ecosystems, and positively contributes
to physical attributes such as water quality and bank stability. However, additional vegetation within
the channel increases the resistance to flow, thereby reducing the volume of water the channel can
carry before flooding commences. The argument levelled against riparian restoration is that it will
increase the incidence of flooding causing increased economic loss primarily to rural landholders, the
same people who are being asked to restore the waterway in the first place. There is an added
complication that is not accounted for in the simple argument above that vegetated streams suffer
increased floods. This is that streams having higher resistance also deliver water more slowly and
tend to attenuate peak flows more effectively. Therefore while the local capacity of a channel may be
reduced, upstream revegetation works tend to reduce the peak discharge delivered for a given flood.
A numerical flood routing model is utilised to investigate the trade-off between these two contrary
effects. The results demonstrate that: 1) broad-scale riparian restoration can actually reduce the
incidence of flooding; 2) that the marginal change in flood stage varies spatially along the network; and
3) that smaller flood events are affected more by riparian vegetation than larger events.
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Vegetation roughness and flood magnitude
1.
Anderson & Rutherford
INTRODUCTION
Revegetating riparian zones and returning large wood to stream channels are widely practiced river
rehabilitation techniques (e.g. Gippel, 1999). These modifications reduce channel conveyance by
acting as additional resistance elements in the main channel and on the flood plain. A major aim of
traditional river management practices, such as stream cleaning and channelisation, was to minimise
flood risk by maximising the discharge capacity of the channel, meaning that over-bank events would
occur less frequently. However, this is true only if the series of inflow hydrographs remains
unchanged. We argue that contemporary rehabilitation programs are sufficiently extensive to alter
inflow hydrographs by modifying the response of the upstream channel network to given rainfall
events. In the past, only the local impact of rehabilitation works has been considered. The purpose of
this paper is to demonstrate that restoration of riparian vegetation has substantial upstream impacts
that change the response of the channel network and thus the local response.
The riparian zone is located at the interface between the hillslopes and stream network, influencing
the rainfall-runoff characteristics by modifying near-stream hydrology (Bendix & Hupp, 2000; Tabacchi
et al., 2000). Once the water is in the stream, the presence of vegetation changes the flow resistance
of a channel (Darby, 1999; Watson, 1987) and of adjacent floodplains (Arcement & Schneider, 1989).
Vegetation also affects sediment transport dynamics and the stability of bank substrate which drives
changes in channel morphology (Zimmerman et al., 1967).
This paper focuses on vegetal impacts on flow resistance, rather than near-stream hydrology or
channel morphology, for two reasons: firstly, the impact of riparian vegetation on flow roughness is
well documented and substantial, with the roughness of well vegetated streams often double that of
cleared streams. Secondly, changes in roughness can be related directly to channel capacity and to
the transmission of hydrographs. By contrast, the impact of riparian vegetation on near-stream
hydrology and channel morphology is not so clear. In regard to hydrology, the presence of vegetation
acts both to accelerate delivery of water to the stream, for instance root networks enhance subsurface flow through macropores, whilst at the same time higher evapotranspiration decreases
antecedent soil moisture levels and the interception store is greater (e.g. McKergow et al. 2003).
Thus, depending on the balance of these contrary impacts, restoration of riparian vegetation may
increase or decrease the runoff coefficient and the rate at which the rainfall excess is delivered to the
stream network. Similarly, there is complex interdependence between climate-soil-vegetation
dynamics and channel form (Rodriguez-Iturbe, 2000; Zimmerman et al., 1967), but as yet no clear
trends have emerged to allow the effect to be generalised to catchment-scale (Abernathy &
Rutherfurd, 1998).
This paper is organised into three parts. The first section briefly outlines the outcomes of a
comprehensive literature review to quantify the impact of vegetation on channel roughness. The
second section introduces the theory used to examine these vegetal impacts on the capacity of the
local channel, and on catchment-scale network response. Finally, this model is applied to the
Murrumbidgee catchment to demonstrate the importance of taking into account changes in network
response, giving some insight into the possible ramifications of broad-scale revegetation works.
1.1.
Defining characteristics of the riparian zone
The riparian zone consists of the land and vegetation immediately adjacent to a stream, a river or a
body of water such as lakes and dams. Riparian land is usually more fertile than the adjoining
hillslopes, having better access to water and other nutrients (e.g. Werren & Arthington, 2002). As a
result, riparian zones have the capacity to support a higher density and diversity of plant species
(Tabacchi et al., 1998). Six distinct vegetation types can be defined according to plant size,
branch/leaf structure, and flexibility (Figure 1).
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Vegetation roughness and flood magnitude
Anderson & Rutherford
Figure 1. Structural elements of a fully-featured riparian zone
1.2.
Dimensions of vegetal resistance
Field and laboratory studies show that the hydraulic resistance of flow through a particular plant or
plant community, varies with the density of foliage and the branch structure (Jarvela, 2002; Wu et al.
1999). Many plants respond dynamically to increased flow velocity, with the flexure of stems and
branches, and streamlining of leaves, dramatically reducing the net drag (Kouwen & Fathi-Moghadam,
2000). The evidence suggests that where vegetation cover is complete, channel roughness values
(Manning’s n) of between 0.15–0.20 are reasonable (Anderson et al., 2001), compared with
recommended ranges of between 0.01–0.04 for sand-bed streams and 0.02–0.07 for gravel or
cobbled streams (Bathurst, 1993). However, flow roughness declines rapidly as plants are
submerged, with a layer of unobstructed flow able to develop above the vegetation canopy. Thus
plant height represents a critical dimension, with vegetal roughness characteristics varying principally
with flow depth. Figure 2a depicts a simplified roughness profile for a riparian assemblage that
captures these essential features.
For riparian vegetation on a river, the depth of flow is determined by the stage height, by the shape of
the cross-section, and by the location of the vegetation around the cross-section. Thus, the impact of
vegetation on channel roughness depends on how vegetation is distributed laterally around the
cross-section, and on the cross-section geometry itself. A numerical model, described in detail
elsewhere (Anderson et al., 2003), was developed to compute channel roughness profiles for different
vegetation scenarios.
The magnitude of vegetal roughness is dependent both on the density, height and lateral distribution
of vegetation and on the shape and size of the cross-section. Vegetation properties and channel form
both vary down the stream network. For example, vegetation is less able to encroach on the channel
further downstream as flow is more persistent and energetic in rivers than small streams (Prosser et
al., 1999). However, the height of a tree, or a stand of reeds remains essentially constant regardless
of it’s position along the stream network. In contrast, the expansion in the dimensions of the channel
is dramatic. Headwater streams may have a bankfull width of less than one metre, and a depth of
ten’s of centimetres. At the other end of the catchment, lowland rivers can be hundreds of meters
wide and many metres deep. Thus, cross-section expansion spans two orders of magnitude, while
the dimensions of riparian stands is, by comparison, constant, and the relationship between the
vegetation and the channel is scale-dependent.
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Anderson & Rutherford
Figure 2. a) Model for the variation of vegetal roughness with flow depth. b) Channel roughness
profiles computed for different sized streams of simple cross-section.
An example of the impact of downstream cross-sectional variation on channel roughness is reported in
Figure 2b. The three roughness profiles shown are for channels of a simple cross-section having
dimensions that represent headwater, midland and lowland streams (bankfull areas of 1.0, 10 and
100m2 respectively) with the properties of the riparian stand held constant. This example is not
entirely realistic, for instance downstream channels are likely to have floodplains rather than being
confined, however it demonstrates that riparian vegetation is likely to increase roughness more in
small streams than large rivers.
2.
THEORY FOR NUMERICAL MODELLING
The principal change associated with riparian restoration is an increase in channel roughness.
Channel roughness modifies the at-a-station stage-discharge relationship (local scale), as well as the
celerity (speed of the flood wave) and hydraulic diffusivity of a hydrograph as it travels down the
network. The dependence on roughness is quantified by standard hydraulic equations, equations that
underpin the local and network scale impact of vegetal roughness.
2.1.
Stage-Discharge curves
Manning’s equation (1) is a one-dimensional flow resistance relationship that relates discharge (Q) to
the slope of the hydraulic grade-line (S) and measurable flow properties, the cross-sectional area (A)
and hydraulic radius (R), through a roughness parameter (n) known as “Manning’s n”. In the absence
of flow gauging data Manning’s equation can be used to estimate the stage-discharge relationship.
Q
1
AR 2 / 3S 1/ 2
n
(1)
Flow area and hydraulic radius vary with stage according to the shape of the cross-section, and
channel slope is fixed for a particular reach and is usually a reasonable approximation to S. Assuming
that these geometric properties remain essentially unaffected by riparian restoration, the vegetal
roughness model described earlier can be applied in conjunction with Manning’s equation to predict
changes in the stage-discharge relationship.
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Vegetation roughness and flood magnitude
2.2.
Anderson & Rutherford
Celerity and diffusivity
Floods are driven by rainfall events, controlled by the delivery of water from hillslopes and the
transmission of flow through the channel network. Assuming these processes are independent and
linear, an output hydrograph (the catchment response) can be obtained by convolving excess rainfall
input with unit hydrographs representing the hillslope and the network responses (Figure 1a). These
assumptions have been previously shown to be justified for catchment-scale modelling, as long as the
catchment area exceeds tens of square kilometres (Naden, 1992; Robinson et al., 1995). The reader
is referred to either Naden (1992) or Rodriguez-Iturbe and Rinaldo (1997) for a full discussion of the
unit hydrograph approach.
Variable channel roughness modifies the network response, which is constructed as the convolution of
the network width function and a channel response function. The width function represents the
number of channel links at discrete distances from the outlet (Figure 1b), describing the distribution of
path lengths in the network. Flood waves are routed along these paths using the convective-diffusion
solution (2) to the one-dimensional Saint-Venant equations (e.g. Robinson et al., 1995; Sturm, 2001).
q
q
2q
+c =D 2
t
x
x
where: c =
5R 2 / 3S 1/ 2
3 An
; D=
(2)
AR 2 / 3
2nBS 1/ 2
The wave represented by the convective-diffusion model moves toward the outlet with speed c (m/s),
and with a shape that flattens out over time (and distance) according to the hydraulic diffusivity
parameter, D (m2/s). Both wave speed and diffusivity are related to channel roughness. In similar unit
hydrograph applications the characteristic wave speed and diffusion coefficient are taken as the
values at the flow peak. However, for vegetated channels where roughness varies substantially along
the wave, previous work has indicated that depth-average values characterise the hydrograph
response more closely (Anderson et al., 2003). To see how this theory works in the context of a real
catchment, a sample analysis was performed on the Murrumbidgee catchment.
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Vegetation roughness and flood magnitude
Anderson & Rutherford
Figure 1. Semi-distributed unit hydrograph method of computing catchment response.
a) catchment response is driven by rainfall and shaped by the hillslope and network unit
hydrographs. b) network response is the convolution of the channel width function with an
advective-dispersion routing function.
3.
CASE STUDY: THE UPPER MURRUMBIDGEE CATCHMENT
A river network was extracted from a digital elevation map of the Murrumbidgee Catchment (upstream
of Wagga Wagga). The study area is the network of channels with drainage areas greater than
50km2, and the channel inputs are supplied by an accordingly mature sub-catchment hydrograph (right
skewed gamma distribution). The resulting node-linked network has over 350 links with an average
length of 6.6km (Figure 2). Three sub-catchments were identified (A, B, C), introducing catchment
size as a variable. The properties of each of the sub-catchments are described at the right of Figure
2. These statistics reveal that first and second order channels, where vegetal roughness has the
largest impact, account for over 70% of the total stream length. Stream width and depth were
estimated from upstream catchment area using hydraulic geometry relationships. In this, we followed
the procedures of Prosser et al. (2001) to establish representative cross-sections for each channel
order. For each cross-section, roughness profiles were established for a low roughness “normal”
condition, and a high roughness “restored” condition. Thus, normal and restored stage-discharge
curves were computed at each outlet (A, B, C). Similarly, channel routing parameters (c, D) were
computed for each channel order, for a range of flood event magnitudes (remembering that vegetal
roughness is sensitive to flow depth), and for normal and restored channel conditions. The net wave
speed and diffusion coefficients for the network were calculated as a weighted average of stream
length (e.g. Robinson et al., 1995).
3.1.
Results and Discussion
Analysis of the channel network yielded discharge hydrographs at each of the sub-catchment outlets,
and for a range of rainfall events. Two sets of these hydrographs at outlet C and outlet A for a one-
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Anderson & Rutherford
hour event, yielding 20mm of runoff are pictured in Figure 3a. These curves indicate that riparian
restoration substantially attenuates and delays the arrival of the hydrograph.
These discharge hydrographs were converted to stage hydrographs by reference to the local
stage-discharge relationship. There are two stage-discharge relationships, representing the clear and
restored condition. Thus, for each rainfall event, there are two input hydrographs which give two
different local responses based on the condition of the output section. A sample of these four different
stage hydrographs is depicted in Figure 3b. The impact of increased local roughness is shown as
dashed lines. For the particular case shown, the local increase in stage is smaller than the decrease
in the peak caused by attenuation through the network.
The impact of local and network roughness is indicated by the change in peak stage. The changes
are quantified by the percentage change in stage from the baseline condition – that is the stage
hydrograph where both the network and local section are clear of vegetation. With reference to Figure
3b, three ratios are pertinent:
Local impact:
“XS with veg.”
(network clear)
P1  P0
P0
Network impact:
“Network with veg.”
(XS clear)
P2  P0
P0
Combined impact:
P3  P0
P0
These ratios were evaluated for a range of rainfall event sizes (Figure 3c) and at each of the
catchment outlets (Figure 3d). The relative magnitude of the local and network impact ratio indicates
whether the local or network impact is dominant, and if the impact of restoration increases or
decreases the peak flood stage.
The effect of increasing event size on the response at the catchment outlet (A) is shown in Figure 3c.
The top curve shows that roughening the cross-section increases hydrograph stage by around 10%,
with the impact declining as event size increases to less than 5% for 80mm of rainfall over the
catchment. Attenuation of the flood peak caused by roughening the network is shown by the lower
curve. The peak stage of small floods is attenuated by over 20%. However, larger floods are
attenuated by a diminishing percentage, in this case less than 10% for an 80mm event. However,
network attenuation remains the dominant factor overall, as shown by the central curve. This curve
represents the overall change in stage, and demonstrates that riparian restoration tends to reduce
flood stage at the catchment outlet, with the reduction most substantial for smaller events.
A similar plot is constructed at different points in the network for a common rainfall event. Sample
results for a 20mm event are depicted in Figure 3d. These results show that network attenuation is
dominant at the larger catchment sizes (outlets A and B), but that local roughness causes a net
increase in flood stage at outlet C.
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Vegetation roughness and flood magnitude
Anderson & Rutherford
Figure 2. Murrumbidgee channel network, divided into three sub-catchments of increasing size
(Table 1).
Figure 3. Sample simulation results: a) discharge hydrographs at outlets A and C for network
with vegetation or clear of vegetation; b) stage hydrographs at outlet A, comparing the relative
impact of revegetation at the cross-section and through the network; c) impact on peak flood
stage with event size; and, d) impact on peak flood stage with upstream catchment area.
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Vegetation roughness and flood magnitude
4.
Anderson & Rutherford
CONCLUSION
The case study results demonstrate that the response of the channel network can be substantially
modified by changes in channel roughness. Increased roughness delays and attenuates the flood
peak, in some circumstances causing a net reduction in flood stage. The impact of riparian restoration
on channel roughness is complex. Roughness declines with increasing flood magnitude, as
vegetation becomes submerged, and has a smaller impact down the catchment as channel size
increases. Consequently, the case study results demonstrate that the marginal change in flood stage
varies spatially along the network, and that smaller flood events are affected more by riparian
vegetation than larger events.
The results presented herein represent the case of complete catchment restoration. While restoration
at such a scale may be impossible to achieve in the short term, this work demonstrates that
rehabilitation is not all down-side as far as flooding is concerned. At a broader level, given that human
intervention causes changes at catchment-scale, to understand the ramifications of such change
requires an understanding of catchment-scale dynamics.
5.
ACKNOWLEDGEMENTS
This work was part of the PhD research of the first author who is supported by an APA scholarship at
the University of Melbourne, by the Riparian Lands Program of Land and Water Australia, and by the
Cooperative Research Centre for Catchment Hydrology.
6.
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