R. McQueen1, P. Ashmore2, T.
Millard1, and N. Goeller3
1British Columbia Ministry of Forests, Lands, Natural
Resource Operations and Rural Development, Nanaimo, BC, Canada,2Department of Geography, The University of Western
Ontario, London, Ontario, Canada, 3British Columbia
Ministry of Environment and Climate Change Strategy, Victoria, BC,
Canada
Corresponding author: Ryan McQueen
(rmcquee4@uwo.ca)
Key Points:
- Annual bed particle displacements reflect morphologic controls and
differences in the annual flow regime
- Bed particle transport and burial is directly tied to patterns of
bar-scale erosion and deposition
- Tracer deposition focused along bar margins, primarily at or
downstream of the first downstream bar apex
Abstract
Bed particles were tracked using
passive integrated transponder (PIT) tags in a wandering reach of the
San Juan River, British Columbia, Canada, to assess particle movement
around three major bars in the river. In-channel topographic changes
were monitored through repeat LiDAR surveys during this period and used
in concert with the tracer dataset to assess the relationship between
particle displacements and changes in channel morphology, specifically,
the development and re-working of bars. This has direct implications for
virtual velocity and morphologic based estimates of bedload flux, which
rely on accurate estimates of the variability and magnitude of particle
path lengths over time. Tracers were deployed in the river at three
separate locations in the Fall of 2015, 2016, 2017 and 2018, with
recovery surveys conducted during the summer low-flow season the year
after tracer deployment and multiple mobilising events. Tracers
exhibited path length distributions reflective of both morphologic
controls and year to year differences related to the annual flow regime.
Annual tracer transport was restricted primarily to less than one
riffle-pool-bar unit, even during years with a greater number of peak
floods and duration of competent flow . Tracer deposition and burial was
focused along bar margins, particularly at or downstream of the bar
apex, reflecting the downstream migration and lateral bar accretion
observed on Digital Elevation Models (DEMs) of difference. This
highlights the fundamental importance of bar development and re-working
underpinning bedload transport processes in bar-dominated channels.
1 Introduction
In gravel-bed rivers there is a natural feedback between channel
morphology and bedload transport. The morphology of the channel is
developed through the movement and deposition of individual bed
particles and in turn the spatial patterns of bed material transport are
controlled at least in part by the morphology of the channel (Church,
2006; Church and Ferguson, 2015). Therefore, attempts at calculating bed
material transport rates, or more generally in studying bedload
processes, need to consider morphologic controls on bed particle
dynamics. This is particularly relevant when employing the virtual
velocity approach to estimating bedload flux because it relies upon an
accurate measure of the distribution and variability of particle path
lengths (travel distances), which may differ greatly in channels of
different morphology (Ashmore and Church, 1998; Vázquez-Tarrío et al.,
2018). One idea suggested by Neill (1987), is that bed particle path
lengths may be related to, and inferred directly from, the channel
morphology. Depositional features such as bars are self-formed through
individual particle displacements, so it follows that over the long-term
the predominant particle path lengths must be related to the scale and
spacing of the bars. This idea is appealing because with sufficient data
tying particle path length and burial with the morphological development
of bars, it may eventually allow path length to be estimated from
morphology without the need for time-consuming and resource intensive
direct particle tracking. However, evidence from field-based studies to
support the link between bar morphology and particle path length is
currently weak (Hassan and Bradley, 2017).
Throughout the bedload tracking literature, the idea that hydraulic
forcing is the primary control on particle transport is prevalent, and
functional relationships between average particle travel distances and
the combination of flow strength and/or grain size have been developed.
Hassan and Church (1992) demonstrated that mean tracer path length and
excess stream power are positively correlated for single discharge
events. More recently, Phillips and Jerolmack (2014) used an impulse
framework to describe the effects of flow strength on particle
transport. Church and Hassan (1992) showed that there is a non-linear
relationship between mean particle path length and the size of the
particle scaled by the median size of local subsurface bed material,
whereby travel distances of particles smaller than the median are
relatively insensitive to increasing grain size, but that there is a
rapid decline in path length with increasing grain size for particles
larger than the median grain size of local subsurface bed material.
These findings have since been re-affirmed with data from studies across
a range of channel types (Hassan and Bradley, 2017; Vázquez-Tarrío et
al., 2018), though data from larger, bar-dominated channels is lacking.
Milan (2013) demonstrated that this effect is, at least in part, caused
by differences in the duration of competent flow for different grain
sizes. Many tracer studies, however, have noted differences between path
length distributions and theoretical models because of tracers
accumulating at distinct regions related to the river morphology (e.g.
Bradley and Tucker, 2012). One example of this is the tendency of
tracers to be preferentially transported to and stored in gravel bars in
channels with riffle-pool-bar morphologies, especially over longer
time-scales (Ferguson et al, 2002; Haschenburger, 2013).
In a literature review and re-analysis of published tracer data, Pyrce
and Ashmore (2003b) found that the positively skewed path length
distributions consistently reported in the literature occurred during
moderate discharge events or in smaller channels lacking well-developed
sedimentary structures or bar morphology. However, they found that in
bar-dominated channels, high magnitude flows (i.e. those capable of
altering or forming bars) lead to bi- or multi-modal distribution
related to the location of bars. Pyrce and Ashmore’s (2003b) flume
experiments of an alternate bar channel aligned with these findings, as
the authors demonstrated that during bar-forming flows most tracers were
deposited on the first bar downstream from the upstream pool in which
particles were seeded. Only during lower flows at the critical discharge
for gravel entrainment, were positively skewed distributions, with path
lengths shorter than bar spacing, observed. Using the same tracer
dataset, Pyrce and Ashmore (2005) showed that during bar formation and
development bed particle path lengths are commensurate with the spacing
of erosion and depositional sites, and that deposition locations tied to
bar development processes. In another flume experiment, Kasprak et al.
(2015) demonstrated that tracer path lengths were closely related to
erosional and depositional processes associated with bar development in
a braided channel, with average path lengths on the scale of
confluence-diffluence spacing. Similar results have been yielded from
more recent modeling of braided channels (Peirce, 2017; Middleton et
al., 2019). The question remains however, as to whether these
observations are seen in full-scale rivers where conditions are less
controlled.
In a synthesis and re-analysis of previously published field-based
tracer data, Vázquez-Tarrío et al. (2018) explored the influence of both
hydraulic and morphologic controls on particle transport for a range of
channel types. They noted that there was a weak positive correlation
between stream power and average travel distance for the dataset.
However, when travel distance was scaled by a morphological length scale
for each channel type (i.e. the spacing between macroscale bedforms),
the scatter in the relationship was reduced, indicating that tracer
transport has some dependence on channel morphology. Furthermore,
analyses of empirical predictors of path length have pointed toward
channel width as the most significant control on travel distance
(Beechie, 2001; Vázquez-Tarrío and Batalla, 2019). For bar-dominated
channels, this may imply that bar spacing exerts a control on path
length because the longitudinal spacing of bars is proportional to
channel width. These analyses are the starting point to investigating
the relationship between path length, bar development and channel scale,
but currently this lacks tracer-based data collected in larger
bar-dominated channels where morphologic control is expected to be most
significant. Therefore, there remains uncertainty as to whether the
principles of bed particle dynamics and statistics of displacements,
derived from smaller rivers, such as step-pool, plane-bed and low
amplitude pool-riffle channels (Montgomery and Buffington, 1997), are
applicable to bar-dominated channels with more complex morphology and
higher rates of morphological change, and further, if spatial patterns
of tracer deposition and burial are tied to bar development.
The paucity of tracer data collected in larger rivers may be explained
in part by logistical challenges in searching such large areas of
channel, and the potentially deep burial of tracers resulting in low
recovery rates. One solution that is increasingly being used to track
bed particle movement in larger channels, is the use of passive
integrated transponder (PIT) tags (Hassan and Bradley, 2017). PIT tags
are small, glass, cylindrical capsules that operate using radio
frequency identification (RFID) technology. Several factors make PIT
tags an effective technology for bedload tracking including their long
lifespan, resistance to abrasion and breakage (Cassel et al., 2017a),
and the ability to distinguish individual particles from one another
using unique codes. Furthermore, as smaller PIT tags are being
developed, an increasingly wide range of sediment sizes can be tracked
(Hassan and Roy, 2016). Technological improvements in PIT tag
technology, such as the increased read range of antennas (Arnaud et al.,
2015), innovative surveying strategies (Arnaud et al., 2017) and the
development of “wobblestones” (Papangelakis et al., 2019; Cain and
MacVicar, 2020), have made it more possible to track bed particle
movement in larger rivers. Active ultra high frequency (a-UHF) RFID tags
have also been used to explore active layer depths (Brousse et al.,
2018) and particle paths (Misset et al., 2020) in wandering/braided
channels, providing the benefit of larger detection ranges than PIT
tags. Due to their large size however, aUHF tags can only be fit into
natural particles with a b-axis of at least 70 mm or molded into
synthetic pebbles (Cassel et al. 2017b; Cassel et al., 2020).
The primary objective of this study was to explore the relationship
between channel morphology and particle path lengths in a large,
wandering gravel bed river – the San Juan River, British Columbia,
Canada. Wandering channels are irregularly sinuous and can display
aspects of both meandering and braided channels. These channels are
characterized by complex bar development and some degree of lateral
instability (O’Connor et al., 2003; Beechie et al. 2006). Typically, the
most common bar morphology is a lateral bar and the dominant mode of
deposition is lateral bar accretion (Desloges and Church, 1987; Rice et
al., 2009). Church and Rice (2009) describe a pattern of bar evolution
whereby vertical growth is limited by the height at which sediment can
be elevated, and the longitudinal growth of bars is limited by the
length-scale of the channel, resulting in bars primarily growing
laterally. This pattern of rapid lateral accretion may persist for
decades (Rice et al., 2009) and is accompanied by the erosion of the
opposite bank, producing a laterally unstable channel with less
systematic migration than true meandering channels (McLean et al., 1999;
Fuller et al., 2003). We expect this pattern of bar and channel
evolution to be reflected in spatial patterns of tracer displacements
for the San Juan River, as bars are by definition an expression of the
displacement, transport and deposition of individual bed particles. If
path lengths are tied to morphology in bar-dominated channels, then
particle displacements and burial should be tied to patterns of
morphological change over a defined period. To address this objective,
PIT tags were used to track bed particle movements and repeat LiDAR
surveys were conducted to measure morphologic change and bar development
during the tracer monitoring period. Combining tracers with morphologic
change captured at high resolution is uncommon in the literature and
allows a more comprehensive interpretation of the process-form coupling
of bedload transport and channel morphology than can be achieved via
either method separately (Vericat et al., 2017).
2 Materials and Methods
2.1 Study Site
The San Juan River, also known by its First Nations name, the
Pacheedaht, is located on southern Vancouver Island, British Columbia
and drains an area of about 730 km2 (Figure 1a). The
main channel is over 50 km long with a total relief of 690 m. The San
Juan River valley follows a major east-west fault with distinct
topography and bedrock geology on the north and south sides. Bedrock
north of the river consists of a series of volcanic and intrusive units,
whereas the south side of the valley is underlain almost exclusively by
metamorphic rocks of the Leech River Complex (BCGS, 2019). The river
outlets to the Strait of Juan de Fuca, near the town of Port Renfrew
(Figure 1a).
Forest harvesting in the San Juan River Watershed dates back to the
early 1900s and has been linked to changes in physical habitat and
channel morphology in the mainstem and tributaries (Ltd., 1994). This
study was guided by watershed management objectives, to provide detailed
information on the current sediment dynamics and morphologic changes in
the San Juan River which will help inform future restoration decision
making.
The study focused on the lower alluvial reach of the San Juan River
beginning near Red Creek, downstream of a canyon reach (Figure 1b). The
alluvial channel exhibits a wandering morphology, as defined by Mollard
(1973) and Neill (1973), with an active width varying between 50-150 m
and a reach-averaged slope of 0.0011 m m-1. During
low-flows the river has a single identifiable main channel though it
displays a multi-channel pattern during higher-flows when secondary
channels are active. Riffle-pool-bar sequences are the primary
macroscale bedforms in the alluvial reach, with bars typically on the
order of several hundred metres long and up to 100 m wide. Bars are
composed primarily of gravel, cobble and sand and there is a general
trend of downstream fining of surface sediment calibre both within and
between bars. For referencing purposes, mainstem bars were numbered,
with Bar 1 being the farthest upstream and subsequent bars numbered in
ascending order downstream. Particle tracking focused on Bars 6, 7 and
15, the most accessible sites along the river (Figure 1b). These bars
are representative of the typical length and width, overall appearance,
and grain sizes found in the alluvial reach.
The closest gauging station to the study sites is the Water Survey of
Canada hydrometric station 08HA010, which is installed on the lower San
Juan River, approximately 2.5 km downstream from Bar 15 and 7.5 km from
Bar 6 (Figure 1b). The 2-, 10-, and 100-year floods are approximately
800, 1050 and 1200 m3/s respectively, though the upper
end of the rating curve is uncertain due to the difficulty of obtaining
discharge measurements during peak floods (Figure 2). Mean monthly
discharge varies from a high of 97 m3/s in January to
a low of 4.5 m3/s in August, with a mean annual
discharge of 49 m3/s (WSC, 2019). The discharge regime
closely follows the seasonal trend in rainfall because 99 % of annual
precipitation at low elevations falls as rain (ECCC, 2019), with only
transient snow accumulations (no seasonal snowpack) at higher elevations
within the watershed.
2.2 Tracer stone deployment and tracking
Half duplex low-frequency (134.2 kHz) PIT tags were inserted into
individual gravel bed particles to track annual particle movements
around three major bars in the San Juan River. Low-frequency tags are
ideal for tracking in coastal and fluvial environments because their
signal can pass through water and can penetrate most non-metallic
objects (sediment, wood, etc.) better than high and ultra-high frequency
tags (Chapuis et al., 2014; Schneider et al., 2010). In 2015, 100
tracers were installed along a cross-section at the head of Bar 6, while
between 2016-2018 a further 1199 tracers were installed across the three
study sites (Bars 6, 7, and 15) with between 125-142 tracers per site
annually. A combination of 12, 23, and 32 mm long PIT tags were used in
the original 2015 deployment. However, by 2017 only the 32 mm tags were
used because of their larger read range (Chapuis et al., 2014) and
higher recovery rates during the first recovery survey.
Wolman particle size counts were conducted at each site to characterize
the size distribution of surficial bed material (Table 1) (Wolman,
1954). Native stones were then collected from the San Juan River and
brought back to the lab for preparation, which included drilling a
cavity in each particle, inserting and epoxying an RFID tag in place,
and painting the stone (similar to methods to described in Eaton et al.,
2008). Particles were selectively chosen to reflect the size
distribution of bed surface material in the channel as best as possible
(Figure 3). However, PIT tags did not fit into particles smaller than 22
mm, thus the fine end of the bed material distribution was not well
represented. Differences between the size distribution of bed material
and tracers was greatest for Bar 15, which had the finest material of
the three study bars (Table 1). However, the Bar 15 tracer distribution
does reflect well the size distribution of local surface bed material
greater than 22 mm (Figure 3c). The use of 32 mm tags in 22-32 mm
tracers biased this size class towards particles with an a-axis longer
than 35 mm.
Tracers were deployed annually in the fall, prior to winter flooding,
along launch lines perpendicular to the direction of flow. Each launch
line spanned the bar head, riffle, and tail of the opposite (upstream)
bar, providing the opportunity to observe tracer dispersion around major
bars, and to observe differences in particle mobility and path lengths
across different morphologic units. While particle path lengths are
unlikely to be influenced by seeding position across the channel
cross-section in smaller, plane-bed type streams, it has been shown to
play a significant role in particle movement in riffle-pool channels
with well-developed bar morphology (Liébault et al., 2012). Clusters of
tracers were deployed at one to two metre intervals along the launch
line by replacing particles on the surface of the riverbed with tracer
stones to mimic natural positions and local bed texture. Tracers
starting on the surface of the riverbed are more exposed to flow than
buried or locked particles, and therefore, observed travel distances in
this study are likely to be over-estimates of annual transport distances
for the bed as a whole.
Tracer recovery surveys were conducted annually during the low-flow
seasons at the end of July through August for 2016 to 2019. All
recovered tracers were removed from the channel and redeployed at their
original launch line the following fall so that each year the deployment
strategy was a repeat of the previous one. The winter storm season with
several mobilizing flows is treated as an annual event producing annual
particle displacements in relation to morphology. This allowed us to
assess the morphologic influence on tracer displacements through repeat
tests. This decision was also influenced by the practicalities of
tracking sediment in a large river because tracking after every event
would be onerous and leaving tracers in the channel would likely
necessitate increasing the downstream extent surveyed every year, which
would quickly become untenable in a river of this size. Furthermore,
excavating and removing tracers from the channel allowed us to directly
measure tracer burial depths which provides valuable information on
active layer dimensions and gives context to particle deposition in
relation to morphological development. The maximum downstream extent of
survey differed for each location, though generally the first two bars
downstream of each seeding site were searched. The deepest portions of
pools were omitted from the searching process because they were not
wadable, even at low-flow.
Two antennas were used to search for tracer stones. A small handheld
wand antenna, the ‘BP Plus Portable’, and a larger ‘Cord Antenna
System’, both were purchased from BioMark® (Figure 4). Based on testing
in the lab, the wand antenna had a maximum read range around 40-50 cm
for the 32 mm PIT tags, though the tag signals are anisotropic, and the
read range was as low as 10 cm for certain orientations. The wand
antenna was used as the sole antenna for tracer recovery in 2016,
resulting in a low recovery rate (33 %), and was used only as a
supplementary tool to the larger antenna for subsequent years. Since PIT
tag signals interfere with one another when in close proximity (Lamarre
et al., 2005; Chapuis et al. 2014), the wand antenna was still a useful
tool for distinguishing between PIT tags in areas where tracers were
densely concentrated - typically the launch line, as a fraction of the
tracer population remained immobile. The wand antenna was also effective
for refining the position of tracers after detection with the cord
antenna.
The cord antenna system consisted of the cord antenna cable, secured to
a 15’ x 5’ (5 m x 1.5 m) rectangular PVC pipe frame, mounted to a
backpack frame using a series of ropes, pulleys and cams (Figure 4b).
The frame held the cable in a (semi-) rigid structure, stabilising the
antenna and allowing it to keep a high current. The operator stood in
the centre of the rectangle wearing the backpack and could manipulate
the height of each corner of the antenna to help navigate obstacles and
changing topography in the field (Figure 4b). The cord antenna covered a
much larger surface area than the wand antenna, making it an ideal tool
for searching large areas efficiently. It also had a much larger range
of detection than the wand antenna, with a maximum read range around
1.75 m, and thus could detect tracer stones buried at greater depths.
Once detected, tracer positions were recorded using one of two methods,
and dug up (where possible) to determine a burial depth. For tracers
that moved only a short distance (less than 20 m or so), a measuring
tape was used to directly measure transport distances from the launch
line. For tracers moving larger distances, a handheld Garmin GPS unit
was used to record tracer locations. GPS waypoint errors were on the
order of two to three metres, which was considered acceptable for the
purpose of determining typical particle path lengths, since average path
lengths were generally around 100 m, resulting in less than five percent
error.
2.3 Geomorphic change detection
2.3.1 Topographic surveying
In addition to particle tracking, repeat aerial LiDAR surveys, flown by
Terra Remote Sensing Inc. in 2015, 2018, and 2019, were contracted for
the study and other projects related to management of the river. Each
LiDAR survey was conducted using the Reigl LMS-1780 sensor from an
airborne platform. Ground accuracy tests, performed by Terra Remote
Sensing Inc., involved both internal and external horizontal and
vertical checks (TRS Inc., 2018a,b, 2019). Internal checks were
conducted via comparison of intra- and inter-flight areas of overlap.
External checks consisted of two components: comparison of the LiDAR
ground surface with control stations not used in the calibration
process, and with a series of check points collected on open surfaces
using Post Processed Kinematic (PPK) GPS. A root mean square error for
vertical precision (RMSEv) of less than ten centimeters
was reported for all surveys (Table 2) (TRS Inc., 208a,b, 2019). Survey
point densities were spatially variable across the study sites. Average
point densities ranged from 12 to 38 points per square meter with
differences based largely on ground cover and topography (Table 2).
LiDAR point clouds were filtered by ground and non-ground classes by
Terra Remote Sensing Inc. (TRS Inc., 2018a,b, 2019). LiDAR point clouds
were used to generate a series of raster-based digital elevation models
(DEMs) of the study sites. Topographic changes between survey dates were
then calculated by processing the LiDAR DEMs using the Geomorphic Change
Detection (GCD) software (Wheaton et al., 2010) to produce DEMs of
difference (DoDs). LiDAR DEMs were produced for each survey at a 10 cm
spatial resolution by converting point clouds to a Trinagulated
Irregular Network (TIN), from which concurrent raster DEMs were
generated using linear interpolation. In-channel areas that were
inundated in both the old and new DEMs, where point cloud returns were
either non-existent or affected by refraction were not used in building
DEMs, restricting change detection analysis to above-water areas. To
capture bank erosion, a minimum level of detection of one metre was used
in change detection analysis for areas that were wet in the new DEM but
were vegetated banks in the old DEM. This threshold allowed us to
observe changes in bank position, as the riverbanks are two metres or
taller throughout the alluvial reach).
The LiDAR-derived DoDs (Difference of DEMs between successive surveys)
were used to interpret patterns of tracer displacement and burial
depths, and to provide information on morphological development of the
bars during the study period. They were not used to calculate complete
reach-scale sediment budgets due to the lack of in-channel topographic
data and stage differences during each LiDAR survey affecting the
relative portion of the river bed that was exposed. Currently,
collecting reach-scale bathymetric data in large channels is challenging
and relies on either boat-based multibeam echo sounding (MBES) systems
or green wavelength LiDAR sensors (Tomsett and Leyland, 2019).
2.3.2 DEM analysis
To account for uncertainty in the DEMs, a spatially variable uncertainty
analysis was conducted using the GCD ArcMap extension. This involves
three main steps: 1) an estimate of uncertainty for each individual DEM;
2) propagation of these errors through the DoD; and 3) an assessment of
the statistical significance of these uncertainties in distinguishing
real geomorphic change from noise (Wheaton et al., 2010). A major appeal
of this method is that it requires little to no additional survey error
information other than the survey data itself. Further, accounting for
spatially-variable error allows for recovery of information in areas
with low elevation uncertainties that would otherwise be lost.
For each DEM, two surfaces were generated for uncertainty analysis using
the built-in tools in the GCD software: a point density raster and a
slope raster. The rationale behind using these surfaces was that steep
areas with low point density have high elevation uncertainty, whereas
flat areas with high survey point density will generally have lower
elevation uncertainty (Wheaton et al., 2010). These surfaces were then
combined on a cell-by-cell basis, using a fuzzy inference system (FIS),
to produce an elevation uncertainty surface. Uncertainty surfaces
associated with individual DEMs were then combined using simple error
propagation (Brasington et al., 2003) to produce a single propagated
error surface for the DoD. The GCD software then uses probabilistic
thresholding to determine the statistical significance of these
uncertainties. The probability that the elevation change associated with
each individual cell of the DoD is then assessed at a user-defined
confidence interval, in this case 80 %. Originally, 95 % was chosen,
however upon examination of output thresholded DoDs, this limit appeared
too restrictive, with real change being removed from areas of eroding
banks and in obvious areas of deposition.
2.4 Hydrological Analysis
Discharge data from the WSC hydrometric station were used to
characterize differences in the hydrologic conditions between study
years. A bankfull discharge (Qbf) of 500
m3 s-1 was estimated from time-lapse
imagery, roughly the one year return interval flood (Figure 5), with
Qbf being defined as the discharge at which the entire
active width of the channel was inundated. This was used as a reference
discharge to approximate the number of mobilising flow events per year.
While previous research indicates that flows less than bankfull may
mobilise coarse bed sediment (Ryan et al., 2002; Pfeiffer et al., 2017;
Phillips and Jerolmack, 2019), a threshold discharge for gravel
entrainment could not be accurately established for this study because
tracers were exposed to multiple potentially mobilising events each
year, rendering it impossible to determine which specific events caused
tracer movement. Further, the complexity of channel morphology and
variability in tracer grain size makes a single threshold discharge
difficult to define for this study. Despite this, Qbfstill provides a relative basis for the number of potential mobilising
flows, and the hydrograph for the period of the tracer study illustrates
that even if the reference discharge were shifted substantially, say 100
m3 s-1, the number of mobilising
events would change very little (Figure 6).
Previous tracer studies have used the total excess flow energy
(ΩT) as a metric to capture the intensity and duration
of competent flow for multiple flood events (Haschenburger, 2013;
Papangelakis and Hassan, 2016). However, this requires knowledge of the
critical discharge, which was unknown for this study. Instead, a
modified ΩT was used in analysis for this study, whereby
the total flow above estimated bankfull was integrated over the period
between tracer deployment (td) and recovery
(tr):
\begin{equation}
\Omega_{T}=\ \rho\text{gS}\int_{t_{d}}^{t_{r}}{\left(Q-Q_{\text{bf}}\right)\text{dt}}\nonumber \\
\end{equation}where ρ is the density of water (1,000 kg m-3), g is
the acceleration due to gravity (9.81 m s-2), and S is
the reach-average slope. Discharge data was integrated at five minute
intervals (as collected at the WSC hydrometric station). The modified
ΩT, along with annual peak instantaneous discharge
(Qmax), and number of potentially mobilising events
(Q>Qbf) were recorded for each study year
to give context to differences in tracer mobility, path lengths, and
burial data that might be the result of different hydrologic conditions
between years. The primary purpose of this analysis is to identify any
differences in annual path lengths that can be attributed to differences
in the annual flow regime, and to then compare any observed differences
with morphologic constraints on path lengths that may occur over the
longer term related to, for example, deposition in bars.
3 Results
Tracer recovery rates were generally high for a river of the size and
scale of the San Juan River (see Table 1 in Chapuis et al., 2015), with
annual recovery rates exceeding 65 % for all but one of the deployments
(Table 3). The low recovery rate (33%) of tracers from the original
2015 deployment was a result of the surveying approach used in searching
for tracers, as only the smaller handheld wand antenna was used in the
initial recovery survey in 2016. The low recovery rate from this
deployment limited interpretation and analysis of the data from this
year, and as such was removed from analyses presented in this section
unless specifically noted.
3.1 Discharge effects on particle dynamics
A total of 21 events with peak greater than 500 m3s-1 occurred from 2015 to 2019, ranging from four to
six events per study year (Table 3). The annual instantaneous
Qmax during the tracer study ranged from 749
m3 s-1 (2016-17) to 1,022
m3 s-1 (2015-16) corresponding to
roughly 2-yr and 6-yr return interval floods (Table 3; Figure 2). Using
the modified ΩT as a metric, the ‘wettest’ year (i.e.
greatest amount of flow exceeding Qbf) was 2017-18, the
‘driest’ year was 2016-17, while 2015-16 and 2018-19 had
ΩT values in between (Table 3). The sensitivity of
ΩT to the defined Qbf was tested, and
shifting Qbf to 400 m3s-1 or 600 m3 s-1made no difference to the relative ranking of ΩT between
study years, and made little difference to proportional differences
between years.
To assess annual differences in tracer movement related to discharge,
ΩT was plotted against the mobility rate, median path
length, and mean burial depth for each site and study year (Figure 7).
The relative mobility of tracers (rm) appeared
insensitive to differences in ΩT. For each bar, more
than 80 % of tracers were mobilized each year, with the exception of
2016-17 Bar 15 tracers (48 % mobile) (Figure 7a). The low mobility of
these tracers relative to other deployments may be a result of both low
ΩT and also local morphodynamics (see section 3.3).
The median path length of tracers (L50), scaled by the
local bar length, showed a general positive relationship with
ΩT, though this was not statistically significant when
pooling the data between sites (R2 < 0.05)
(Figure 7b). Tracers seeded at each bar tended to show different
responses to increasing ΩT. The influence of
ΩT on Bar 6 and Bar 7 tracers was unclear, as the
wettest year of the study, 2017-18, did not produce the highest
L50 for either site (both had greater
L50’s in 2018-19). However, the path length of Bar 15
tracers sharply increased with ΩT for the three years of
study (Figure 7b). Despite any increases in annual path length
associated with ΩT, most tracers were limited to
transport distances less than one bar-length. This implies that any
longer-term morphologic constraints on path length, such as deposition
within bars, is unlikely to be substantially affected by differences in
the typical annual flow regime. However, this may not hold true for
extreme events capable of major morphologic change in the bars and river
morphology.
Overall, there was an increase in burial depth with increasing
ΩT when pooling the data across all sites
(R2 = 0.4947) (Figure 7c). The largest deviation from
the general linear trend was the 2018-19 deployment of tracers at Bar 6
(Bavg = 20 cm). Bar 15 exhibited the lowest average
burial depths, while Bar 6 and 7 tracers were typically buried deeper
(Figure 7c). Tracer burial is explored in more detail in section 3.3
with respect to morphologic changes observed on DoDs.
Path length frequency
distributions for tracers deployed at Bars 6, 7, and 15 are presented in
Figure 8 and maps illustrating the final position of recovered tracers
are presented in Figures 9, 10, and 11 respectively. The path length
distributions for 2016-17, the ‘driest’ year, were positively skewed for
all three sites (Figure 8) with the lowest median path lengths
(L50) of any of the years of study (Table 3). The
following year, 2017-2018, was the ‘wettest’ of the study period. The
Bar 6 path length distribution was also positively skewed this year,
though the L50 increased from 69 m to 130 m downstream
reflective of the increased hydrologic conditions (Table 3). The 2017-18
Bar 7 and Bar 15 path length distributions followed roughly symmetrical
distributions, with the primary mode of tracer deposition occurring at
the bend apex next to Bar 7 (Figure 10b), and just downstream of the
apex at Bar 15 (Figure 11b). In 2018-19, the year with moderate
ΩT, Bar 6 tracers exhibited a bi-modal distribution with
the primary mode of deposition occurring just upstream of the bend apex
and a secondary mode reflecting short-transport distances (Figure 9b).
Bar 7 and Bar 15 path length distributions for 2018-19 were positively
skewed, though there was a minor peak in the Bar 7 distribution,
reflecting tracers accumulating on the bar tail (Figure 8b, 10c).
Overall, the shape of path length distributions generally reflected
discharge conditions, whereby positively skewed distributions were
observed for the driest year, bi-modal distributions were observed for
two of the three sites for intermediate hydrologic conditions, and
roughly symmetrical distributions, centered around the bend apex at each
bar were observed for two of the three sites during the wettest year.
3.2 Morphologic effects on particle displacements
Tracer path lengths were scaled by the length of the bar at which they
were seeded to better compare particle displacements between the three
bars (Figure 12). The distribution of scaled path lengths were
significantly different (Kruskall-Wallis test, p<0.05) among
all three sites whereby Bar 6, Bar 7 and Bar 15 had median scaled path
lengths (L50) of 0.20, 0.34 and 0.41 ‘bar-lengths’
respectively (Table 4). Bar 6 tracers, while having the highest mobility
rate (89 % mobilised), had the lowest L50, with 90 %
of tracers depositing upstream of the bar apex (which is at
approximately L = 0.50) (Figures 9 and 12). For both 2016 and 2018 Bar 6
deployments, there was a clustering of tracers just upstream of the bend
apex that appears to be related to the growth of a coarse gravel sheet
with a slip face roughly one metre high, which migrated downstream from
the bar head between 2015 and 2019 (Figure 13). While transport past the
apex was more common for Bars 7 and 15, these tracers still tended to be
deposited within the initial bar in which they were seeded, with
deposition focused along bar margins (Figures 10 and 11). This is also
highlighted by the tracer escape rates, that is the fraction of mobile
tracers recovered downstream of the initial bar in which they were
seeded. Less than one percent of tracers escaped Bar 6 annually, five
percent escaped Bar 7, and four percent escaped Bar 15 (Table 4)
indicating that annual particle displacements on the San Juan River tend
to be within one riffle-pool-bar unit.
Tracers recovered in the wetted channel, closer to the thalweg, may be
more likely to be remobilised by future events, and as such represent
potential future frontrunners, while those stored on gravel bars are
less likely to be remobilized, and may become trapped in the bars with
future transport limited by bar development and re-working. For Bar 6,
66 % of tracers were deposited in or on bars, while 34 % were
recovered in the wetted channel. Similarly, 71 % of Bar 7 tracers were
recovered on bars versus 29 % in the wetted channel. Bar 15 appears to
trap less sediment than the other sites, as only 46 % of tracers were
recovered on gravel bars versus 54 % in the wetted channel, as Bar 15
is less-developed laterally than the other study bars. For each of the
sites however, the proportion of tracers recovered on gravel bars
reflects more than just those transported to and deposited on bars, it
also reflects tracers that were originally seeded on bar tails and
remained there. The role of initial seeding morphology was explored by
partitioning the data by the morphologic unit in which tracers were
originally deployed (Table 5). The Bar 6 2016 launch was treated
separately from the rest of the Bar 6 data as the launch line was
located 120 m downstream of the other years, with tracers seeded across
the bar edge and adjacent pool. In general, tracers seeded on bar tails
tended to have lower relative mobility than those seeded on the bar
heads or the in the wetted channel (Table 5). The exception to this was
that 80 % of bar tail tracers were mobilised for Bar 15 versus 73 %
mobility in the wetted channel (Table 5). When breaking this data down
further however, 92 and 93 % of wetted channel tracers were mobilized
at Bar 15 during 2017 and 2018 deployments respectively, and only the
2016 tracers exhibited low mobility in the wetted channel (40 %
mobile). Tracers deployed on bar heads were almost always mobilized (100
% mobile for Bar 6, and 98 % for Bar 15), suggesting that these areas
are active erosional sites. These differences highlight the importance
that deployment strategy and channel morphology can have on tracer
dispersion, as recently shown by McDowell and Hassan (2020).
Box plots of tracer scaled path lengths for each seeding morphologic
unit for each study site is presented in Figure 14. For Bar 6 (2015,
2017, 2018 deployments), tracers seeded on the bar tail had a lower
median scaled path length than those seeded on the bar head or in the
wetted channel (Table 5). However, differences in the distributions
between seeding morphologies was not statistically significant (Kruskall
Wallis test, p> 0.05). Bar 6 tracers tended to be deposited
upstream of the bar apex regardless of where they were initially seeded,
with clustering coincident with the development of the migrating gravel
sheet terminating at the bar apex (Figure 13). For the 2016 Bar 6
deployment, there was a statistically significant difference (Mann
Whitney U test, p<0.001) between the path length distribution
of tracers seeded in the wetted channel (specifically a pool in this
case) and those seeded on the bar edge. Bar edge tracers were restricted
to path lengths less than 0.3 bar lengths downstream, while those seeded
in the pool more frequently travelled farther downstream, with a maximum
observed path length of 0.78 bar lengths (Table 5). Again, bar edge
tracers appear to become incorporated into the migrating gravel sheet at
this location. The scaled path length distributions were significantly
different (Mann Whitney U test, p<0.001) for Bar 7 tracers
seeded in the wetted channel relative to those seeded on the adjacent
bar tail, with median path lengths of 0.39 and 0.21 bar lengths for
wetted channel and bar tail tracers respectively. Similarly, Bar 15
tracers seeded on the bar tail had a significantly different path length
distribution than either those seeded on the bar head
(p<0.001) or bar tail (p<0.05) based on a Kruskall
Wallis test and Dunn’s post hoc comparison. Overall, tracers seeded on
bar tails tended to be both less mobile and were displaced shorter
distances on average than those seeded either in the wetted channel or
on bar heads, underlining the spatially variable nature of bed-material
transport in bar-dominated channels.
3.3 Morphologic change and particle burial
Path length data demonstrated a relation with channel morphology which
can be further explored in relation to the geomorphic change detection
analysis and by incorporating tracer burial depth data. DoDs are
presented for 2015-2018 and 2018-2019 for the Bars 6-7 reach in Figure
15 with tracer burial depths and locations overlaid on top. Bar 15 DoDs
with buried tracers for the same periods are presented in Figure 16.
Tracers that were located using the cord antenna system but were too
deep to be detected by the wand antenna (or physically recovered), were
estimated to be deeper than 30 cm (a conservative estimate of the
maximum detection range of the wand antenna). Tracers recovered in pools
were not physically recovered so burial depth was unknown and they were
not included in burial depth analyses.
For the Bar 6-7 reach, a general pattern of downstream bar migration was
observed over the period of study. Primary areas of erosion include the
heads of Bars 6, 7, the small point bar between Bars 6 and 7, and
erosion of the banks opposite the bars. Scour pools also developed along
the tail of Bar 6 between 2015 and 2018 (Figure 15). Bar surfaces were
net depositional across the Bars 6 and 7 DoDs, with maximum deposition
focused at the bar apexes and bar tails, resulting in lateral accretion
and downstream migration of bars. Areas of indeterminate change were
mostly in-channel areas such as riffles and pools. The Bar 15 DoDs
exhibited a similar pattern of morphologic change, with erosion focused
at the head of Bar 15 and bank retreat opposite the downstream portion
of Bars 15 and 16 (Figure 16), leading to a pattern of downstream bar
and bend migration. The downstream portion of Bars 15 and 16 were net
depositional, with maximum deposition focused downstream of the bar
apexes (Figure 16).
In general, the spatial pattern of tracer deposition was well-reflected
in the DoDs. For Bar 6, 49 % of tracers were recovered in areas
corresponding to known geomorphic change on the DoDs (Table 6). Of these
tracers 88 % resided in depositional cells and 12 % in erosional
cells. The deepest buried tracers (> 30 cm) tended to be
deposited near the apex of Bar 6, which aligns with the downstream
extent of the migrating gravel sheet in this area (Figure 13). For Bar
7, 65 % of recovered tracers were located in areas of known geomorphic
change, with 91 % in depositional cells and 9 % in erosional cells
(Table 6). The deepest buried Bar 7 tracers were recovered either near
the bar apex or on the bar at the launch line (Figure 15). Due to the
lack of topographic data captured in the wetted channel, only 23 % of
tracers from Bar 15 were recovered in areas of known geomorphic change,
in large part due the high proportion of Bar 15 tracers that remained in
the initial riffle in which they were seeded. For those located in areas
of known change, 70 % were located in depositional cells and 30 % in
erosional cells (Table 6). Across all three study sites, tracers tended
to be recovered in areas of deposition on DoDs when there was known
geomorphic change, which supports the idea that particle transport and
deposition is tied to overall channel morphodynamics. For the portion of
tracers recovered in areas of indeterminate change on DoDs the link
between particle deposition and morphologic change cannot be
established. However, these areas were most commonly bar margins and
riffles (Figure 15 and 16), areas that are likely to be depositional
environments.
There was no correlation observed between tracer burial depth and the
corresponding elevation change from DoDs for any of the three sites
(Figure 17). This is perhaps not surprising since the timing between
tracer deployments and recoveries does not match the time between
topographic surveys used to produce the DoDs and because tracers could
only be recovered within approximately 50 cm of the bed surface whereas
scour and fill occurred at depths beyond this. Overall, the mean burial
depth of tracers recovered in depositional cells was greater than those
in recovered in erosional cells for each site, though the low number of
tracers recovered in erosional cells prevents the results from being
statistically significant (Table 6).
The distribution of tracer burial depths for Bar 6, 7 and 15 are
presented in Figure 18. The maximum burial depth for Bar 6 tracers was
52 cm, although 34 % of tracers were not physically recovered, and we
suspect that they are buried deeper than 30 cm (because they were not
detected with the wand antenna) (Figure 18a). Assuming that these
tracers were buried beyond this depth, the median burial depth for Bar 6
tracers was 25 cm. A maximum burial depth of 47 cm was recorded for Bar
7 tracers, with 30 % of tracers not physically recovered, likely due to
deep burial, resulting in an overall median burial depth of 18 cm
(Figure 18b). For both locations, more than 40 % of tracers were
recovered at depths exceeding the commonly quoted assumed active layer
depth of ~2 D90 of the surface (Wilcock
and McArdell, 1997; Hassan and Bradley, 2017). Tracer burial data from
Bars 6 and 7 suggests that maximum active layer thickness may be 50 cm
or greater locally. This indicates that in this type of channel the
particle exchange during bed material transport may operate at depths
beyond a surface layer a few grains thick for at least a portion of the
bed and active layer depth is governed by the distribution of bar-scale
erosion and deposition. The median burial depth of 10 cm for Bar 15
tracers was the lowest of the three study sites (Figure 18c). The lower
tracer burial on Bar 15 relative to the other sites aligns with the
lower magnitudes of morphologic change observed on DoDs relative to the
Bar 6 and Bar 7 reach. Because morphologic change drives burial depth,
and local bar dynamics and morphology differ between sites, this
produces differences in tracer dispersion and burial between sites.
3.4 Grain size effects on tracer mobility and path length
The path length of San Juan River tracers exhibited a similar
relationship with grain size as described previously in the literature
(Church and Hassan, 1992; Hassan and Bradley, 2017), with particles
larger than the local D50 exhibiting a steep decline in
mean path length with increasing grain size, and particles smaller than
the local D50 showing less variation in mean path length
with increasing grain size (Figure 19). However, while the mean travel
distance of each tracer size fraction follow this non-linear trend,
there is a large amount of scatter in the data suggesting that other
factors, such as channel morphology and hydrologic conditions, play an
important role on particle transport. The relative mobility of each
tracer size fraction is presented in Figure 20, where the tracer size
class is scaled by the local surface D50. In general,
relative mobility decreased with increasing relative grain size. This
trend was moderately strong for the 2016-17 tracer displacements
(R2 = 0.6904) and weak for the 2017-18 and 2018-19
displacements (R2 = 0.2893 and 0.3562 respectively).
The 2016-17 season was the ‘driest’ year during the study period, in
terms of both Qmax and the total duration of competent
flow, suggesting that hydrologic conditions limited mobility of larger
sizes compared to the ‘wetter’ years when most of the bed was mobilised
regardless of grain size.
4 Discussion
The results from particle tracking in the San Juan River reveal insights
into bed particle dynamics in a wandering-style gravel-bed river, which
has seldom been a focus in previous studies. The recovery rate of
tracers in this study combined with morphologic change data is unique
for a channel of this size and as such provides important information on
the nature of bedload transport in these systems. This data can also
help to develop tracer displacement statistics and models for this type
of river with respect to morphologic change and bar development.
The intrabedform, or intrabar, transport that was observed on the San
Juan River, aligns with results from the few previous bedload tracking
results on larger, bar-dominated channels. Ona meandering section of the
Ain River, France, Rollet et al. (2008) conducted a PIT tag tracer
study, recovering 36 % of tracers after one year , with an average
travel distance of 50 m, less than one bar length downstream. While
interpretation of particle transport was limited by the low recovery
rate, the authors noted that over the tracer monitoring period, a thick
sedimentary layer had accreted on the edge of the gravel bar immediately
downstream of the tracer injection site and posited that lost tracers
were likely buried at this location, beyond their antenna’s maximum
range of detection. This description of bar growth is similar to changes
observed on Bar 6 of the San Juan River, where tracer clustering and
lateral accretion was focused at the bar apex. In another PIT tag study,
conducted on a wandering riffle-pool reach of the Durance River, France,
Chapuis et al. (2015) recovered 40 % of tracers after four months, with
an average particle path length of 83 m, again indicative of transport
within one riffle-pool unit. As observed on the San Juan River, the
authors noted that particles deployed on bar tails were either immobile
or only displaced short distances, while those seeded closer to the
thalweg were transport farther downstream. Similar variations in
transport conditions between morphologic units were reported from PIT
tag and painted tracer data collect from a wandering reach of the Parma
River, Italy (Brenna et al., 2019). The spatial variability in bedload
transport, intrinsic to the riffle-pool morphology, means that
deployment strategy has a strong influence on tracer mobility and
resultant path length distributions. In the cases of the Ain, Durance,
and San Juan Rivers, tracers tended to remain within one morphological
unit, with minimal transport downstream. This provides direct evidence
that in addition to hydraulic controls, channel morphology influences
particle dynamics in these systems. Particle trapping and burial in
association with bar development appears to be an important
consideration in modeling sediment behaviour in this type of river, and
therefore in the bedload transport process more generally. This
emphasizes the morphologic control on bed particle transport, a point
that was also raised in a re-analysis of bedload tracking data by both
Vázquez-Tarrío et al. (2018) and McDowell and Hassan (2020).
The pattern of particle displacement and deposition on the San Juan
River reflected a combination of morphologic controls and seasonal
variations in the flow regime. During the 2017-18 winter, the wettest
study year, bedload deposition focused at the apex of the first bar
downstream for Bars 7 and 15, and either slightly positive or
symmetrical distributions around the bar apex were observed. However,
during years with more moderate floods and lower competent flow volume,
path length distributions tended to be positively skewed, with lower
tracer mobility. It should be noted here that these results specifically
pertain to particles seeded on the bed surface near the bar head. At
least this is the case for the first movement of tracers, while
subsequent events in the same year could be acting on both surficial and
buried tracers. Differences in the spatial distribution of tracers
between sites also appears related to channel shape and bar morphology.
At Bar 6, the high-amplitude bend, and migration of a coarse bedload
sheet, appear to restrict transport to the bar tail. Whereas tracer
deposition on the bar tail was more common for Bars 7 and 15, bars that
are less well-developed laterally. Overall, the results from this study
provide some validation to the findings from Pyrce and Ashmore’s flume
experiments (2003a; 2005) whereby they observed bi- and multi-modal path
length distributions during bar-forming discharges, with modes
coincident with bar apexes, and spatial patterns of tracer deposition
related to bar-scale patterns of erosion and deposition.
To further compare tracer transport on the San Juan River with results
from the literature, Figure 4 from Vázquez-Tarrío et al. (2018), has
been re-created in Figure 21 with data from Bars 6, 7, and 15 on the San
Juan River. The original figure contains travel distances of tracers
starting from both unconstrained (i.e. initial movement after seeding,
as done in this study) and constrained (movement after tracers have been
incorporated into the bed) positions. In this graph, dimensionless
stream power (ω*) was calculated as in Eaton and Church (2011), to
compare flow strength and particle transport across rivers of different
scales. Mean scaled travel distances were scaled using a morphologic
length scale (i.e. the spacing of macroscale bedforms), which for the
San Juan River meant the riffle-riffle spacing. Data from the 2015-16
deployment on Bar 6 were not plotted on this graph due to the low
recovery rate (33 %) and uncertainty in the statistics of particle
displacement for this year. The San Juan River data appear in line with
results from riffle-pool channels, showing a positive relationship
between dimensionless stream power and mean scaled travel distance,
though travel distances on the San Juan River were relatively low
(Figure 21). What is more interesting though, and as noted by
Vázquez-Tarrío et al. (2018), is that riffle-pool channels share a
common trait in that they rarely exhibit average travel distances beyond
1-2 length-scale units (regardless of constrained or unconstrained
starting position), generally at the lower end of the range for other
channel types. It appears that riffle-pool channels have limited
transport distances compared with other channels, presumably a result of
bars and riffles constraining particle movement. Over the long-term,
path lengths are therefore likely to be limited by the rate of bar
development and re-working, which in turn plays an important role in
overall channel evolution and stability (Rice et al., 2009; Reid et al.,
2019).
The path length distributions presented in this study reflect annual
particle transport over a four year period. The annual displacements
occur from multiple events in the well-defined high-flow season from
October to March. This is consistent each year although exact magnitudes
vary. Particles are ‘slaved’ to the bar development and few move through
to the next bar any given year. The preferential deposition and
incorporation of tracers in bars has been previously demonstrated to
hold true over longer time-scales in gravel-bed rivers in general (e.g.
Ferguson et al., 2002; Haschenburger, 2013). Therefore, we expect that
these annual path lengths are representative of the normal bar
development except in the case of a high magnitude flood sufficient to
disrupt the existing bar and channel configuration, in which case
displacements would be expected to reflect that larger scale
morphological change. For morphological flux calculation this would
produce an annual flux based on net morphological change and tracer
distances scaled to the bars.
The tracer burial data provide context to the path length analysis and
revealed insights into patterns of sediment transfer and storage. Burial
depths up to, and likely exceeding, 50 cm were recorded at each of the
three sites, although the absolute magnitude of maximum particle burial
depth was not obtained in this study due to methodological constraints.
However, the development of “wobblestone” technology looks to provide
a promising solution to estimating particle burial without the need to
physically recover the particles and disturb the bed (Papangelakis et
al., 2019). Overall, tracer burial aligned with the patterns of
bar-scale deposition observed on DoDs, which raises two important
points. First, this suggests that as individual particles are
transported to and buried in local areas of deposition, they become
incorporated into the channel morphology, and future movement will be
limited by the rate of bedform (or bar) migration. This relates back to
Neill’s (1987) original speculation that over the long term, average
particle path lengths may be inferred from the channel morphology.
Secondly, the fact that more than 30 % of buried tracers were recovered
at depths beyond twice the local D90 for each site
suggests that the concept of a shallow active layer less than two
particles deep does not reflect the nature of bedload processes
occurring in this type of channel. It should be noted however, that the
winter storm season was treated as a single event for this study, and
that there were multiple events each year that may have produced the
reported burial depths. As described by Ashmore et al. (2018), it
appears that bed particle deposition and burial in larger, more dynamic
rivers is controlled by bar-scale patterns of deposition, whereby the
active layer is spatially non-uniform and maximum burial depths occur on
the scale of vertical changes in bed level associated with those
dominant morphological processes. This is important because the
dimensions of the active layer, when combined with the virtual velocity
of bed particles, gives the morphological bedload transport rate
(Haschenburger and Church, 1998; Liébault and Laronne, 2008;
Vázquez-Tarrío and Menéndez-Duarte, 2014; Mao et al. 2017; Vericat et
al., 2017).
5 Conclusion
The primary goal of this study was to investigate the interplay between
channel morphology and bed particle displacements in a wandering
gravel-bed river channel, and more specifically, to assess whether
displacement is tied to bar scale and patterns of bar development and
accretion. This has implications for path length when applied to virtual
velocity and morphological estimates of bedload and possible differences
among rivers of different morphology and size.
Tracers exhibited path length distributions related to morphologic
controls (deposition near the bar apex) and differences year to year
related to the annual flow regime, with greater dispersion observed
during years with greater number of peak floods and flow volume above
the threshold discharge for bed mobility. Additionally, tracer
deposition and burial were reflected by areas of deposition on DEMs of
difference (DoDs). Tracers tended to be deposited along bar margins and
to a lesser extent, the surface of the downstream portion of the bars,
reflecting the downstream bar migration and lateral bar accretion
observed on DoDs and active layer depths greater than that typically
assumed in bedload analysis and modeling. This highlights the
fundamental importance of bar development and re-working underpinning
bedload transport processes in bar-dominated channels and supports
recent analyses of morphological effects in tracer dispersion
(Vázquez-Tarrío et al., 2018). Ultimately, short-term particle
displacements in bar-dominated channels are linked with the
morphological style of channel evolution and therefore differs from
bedload processes in small, plane-bed channels, for which much previous
analysis and theory have been developed.
Acknowledgments, Samples, and Data
This work was funded by the British Columbia Ministry of Forests, Lands,
Natural Resource Operations and Rural Development (FLNRORD) and the
Pacheedaht First Nation. The data used in this study can be found in the
Scholars Portal Dataverse (https://doi.org/10.5683/SP2/UQGZCG). We are
thankful to Jesse Schafer for preparation of GIS data, and the following
persons for their field support: Andrew Boxwell, Spencer Ranson, David
Waine, Kyle Fukui, Dave Johnson, Ali Jones, Rachel Dugas, and several
FLNRORD employees. We also thank BioMark for their feedback on
optimising the RFID antenna setup. Finally, we would like to thank three
anonymous reviewers for their thoughtful and constructive comments on
the original manuscript, as it has vastly improved the quality of the
paper.
References
Arnaud, F., Piégay, H., Vaudor,
L., Bultingaire, L., and Fantino, G. (2015). Technical specifications of
low-frequency radio identification bedload tracking from field
experiments: Differences in antennas, tags and operators.Geomorphology, 238 , pp. 37-46.
Arnaud, F., Piégay, H., Béal, D., Collery, P., Vaudor, L., and Rollet,
A. (2017). Monitoring gravel augmentation in a large regulated river and
implications for process-based restoration. Earth Surface
Processes, 42 , pp. 2147-2166.
Ashmore, P.E., and Church, M. (1998). Sediment transport and river
morphology: A paradigm for study. In P.C. Klingeman, R.L. Beschta, P.D.
Komar, and J.B. Bradley (Eds.) Gravel-Bed Rivers in the
Environment (pp. 115-148). Water Resources Publications, LLC, Highland
Ranch, Colorado.
Ashmore, P., Peirce, S., and Leduc, P. (2018). Expanding the “active
layer”: Discussion of Church and Haschenburger (2017) What is the
“active layer?” Water Resources Research 53, 5-10,
Doi:10.1002/2016WR019675. Water Resources Research, 54 , pp. 1-3.
BC Geological Survey (2018). Bedrock geology [Shapefile]. Retrieved
from:
https://catalogue.data.gov.bc.ca/dataset/bedrock-geology/resource/aa3a15f8-02fe-49c6-836c-6866c326203d?inner_span=True
Beechie, T.J. (2001). Empirical predictors of annual bed load travel
distance, and implications for salmonid habitat restoration and
protection. Earth Surface Processes and Landforms, 26 , pp.
1025-1034.
Beechie, T.J., Liermann, M., Pollock, M.M., Baker, S., and Davies, J.
(2006). Channel pattern and river-floodplain dynamics in forested
mountain river systems. Geomorphology, 78 , pp. 124-141.
Bradley, D.N., and Tucker, G.E. (2012). Measuring gravel transport and
dispersion in a mountain river using passive radio tracers. Earth
Surface Processes and Landforms, 37 , pp. 1034-1045.
Brasington, J., Langham, J., Rumsby, B. (2003). Methodological
sensitivity of morphometric estimates of coarse fluvial sediment
transport. Geomorphology, 53, pp. 299-316.
Brenna, A., Surian, N., and Mao, L. (2019). Virtual velocity approach
for estimating bed material transport in gravel-bed rivers: Key factors
and significance. Water Resources Research, 55 , pp. 1651-1674.
Brousse, G., Liébault, F., Arnaud-Fassetta, G., Vázquez-Tarrío, D.
(2018). Experimental bed active-layer survey with active RFID scour
chains: Example of two braided rivers (the Drac and the Vénéon) in the
French Alps. E3S Web of Conferences, 40 , 04016.
Cain, A., and MacVicar, B. (2020). Field tests of an improved sediment
tracer inlucding non-intrusive measurement of burial depth. Earth
Surface Processes and Landforms. DOI :10.1002/esp.4980.
Cassel, M., Piégay, H., and Lavé, J. (2017a). Effects of transport and
insertion of radio frequency identification (RFID) transponders on
resistance and shape of natural and synthetic pebbles: applications for
riverine and coastal bedload tracking. Earth Surface Processes and
Landforms, 42 , pp. 399-413.
Cassel, M., Dépret, T., and Piégay H. (2017b). Assessment of a new
solution for tracking pebbles in rivers based on active RFID.Earth Surface Processes and Landforms, DOI:10.1002/esp.4152.
Cassel, M., Piégay, H., Fantino, G., Lejot, J., Bultingaire, L., Michel,
K., and Perret, F. (2020). Comparison of ground-based and UAV a-UAF
artificial tracer mobility monitoring methods on a braided river.Earth Surface Processes and Landforms, 45 , pp. 1123-1140.
Chapuis, M., Bright, C.J., Hufnagel, J., and MacVicar, B. (2014).
Detection ranges and uncertainty of passive Radio Frequency
Identification (RFID) transponders for sediment tracking in gravel
rivers and coastal environments. Earth Surface Processes and
Landforms, 39 (15), pp. 2109-2120.
Chapuis, M., Dufour, S., Provansal, M., Couvert, B., and de Linares, M.
(2015). Coupling channel evolution monitoring and RFID tracking in a
large, wandering, gravel-bed river: Insights into sediment routing on
geomorphic continuity through a riffle-pool sequence.Geomorphology, 231, pp. 258-269.
Church, M. (2006). Bed material transport and the morphology of alluvial
river channels. The Annual Review of Earth and Planetary Science,
34 , pp. 325-354.
Church, M., and Ferguson, R.I. (2015). Morphodynamics: Rivers beyond
steady state. Water Resources Research, 51 , pp. 1883-1897.
Church, M., and Hassan, M.A. (1992). Size and distance of travel of
unconstrained clasts on a streambed. Water Resources Research,
28 , pp. 299-303.
Church, M., and Rice, S.P. (2009). Form and growth of bars in a
wandering gravel-bed river. Earth Surface Processes and Landforms,
34 , pp. 1422-1432.
Desloges, J.R., and Church, M. (1987). Channel and floodplain facies in
a wandering gravel-bed river. In Recent development in fluvial
sedimentology, 39 (pp. 99-109). Society of Economic Paleontologists and
Mineralogists.
Eaton, B., Hassan, M., and Phillips, J. (2008). A Method for Using
Magnetic Tracer Stones to Monitor Changes in Stream Channel Dynamics.Streamline: Watershed Management Bulletin, 12 (1), pp. 22-28.
Eaton, B., and Church, M. (2011). A rational sediment transport scaling
relation based on dimensionless stream power. Earth Surface
Processes and Landforms, 37 , pp. 901-910.
Environment and Climate Change Canada (2019). Canadian Climate Normals
1981-2010 Station Data. Retrieved from:
http://climate.weather.gc.ca/climate_normals/results_1981_2010_e.html?stnID=82&autofwd=1
Ferguson, R.I., Bloomer, D.J., Hoey, T.B., and Werritty, A. (2002).
Mobility of river tracer pebbles over different timescales. Water
Resources Research, 38 (5), pp. 3-1 – 3-8.
Fuller, I.C., Large, A.R.G., and Milan, D.J. (2003). Quantifying channel
development and sediment transfer following chute cutoff in a wandering
gravel-bed river. Geomorphology, 54 , pp. 307-323.
Haschenburger, J.K. (2013). Tracing river gravels: Insights into
dispersion from a long-term field experiment. Geomorphology, 200 ,
pp. 121-131.
Haschenburger, J.K., and Church, M. (1998). Bed material transport
estimated from the virtual velocity of sediment. Earth Surface
Processes and Landforms, 23 , pp. 791-808.
Hassan, M.A., and Bradley, D.N. (2017). Geomorphic controls on tracer
particle dispersion in gravel bed rivers. In D. Tsutsumi, and J.B.
Laronne (Eds.) Gravel Bed Rivers: Processes and Disasters, 1 (pp.
159-184). John Wiley & Sons Ltd.
Hassan, M.A., and Roy, A.G. (2016). Coarse particle tracking in fluvial
geomorphology. In G.M. Kondolf, and H. Piégay (Eds.) Tools in
Fluvial Geomorphology. Second Edition (pp. 306-323). Chichester, West
Sussex England: Wiley Blackwell.
Hassan, M.A., Church, M., and Ashworth, P.J. (1992). Virtual rate and
mean distance of travel of individual clasts in gravel-bed channels.Earth Surface Processes, 17 , pp. 617 - 627.
Kasprak, A., Wheaton, J.M., Ashmore, P.E., Hensleigh, J.W., and Peirce,
S. (2015). The relationship between particle travel distance and channel
morphology: Results from physical models of braided rivers.Journal of Geophysical Research: Earth Surface, 120 , pp. 55-74.
Lamarre, H., MacVicar, B., and Roy, A.G. (2005). Using passive
integrated transponders (PIT) tags to investigate sediment transport in
gravel-bed rivers. Journal of Sedimentary Research, 75 , pp.
736-741.
Liébault, F., and Laronne, J.B. (2008). Evaluation of bedload yield in
gravel-bed rivers using scour chains and painted tracers: the case of
the Esconavette Torrent (Southern French Prealps). Geodinamica
Acta, 21 , pp. 23-34.
Liébault, F., Bellot, H., Chapuis, M., Klotz, S., and Deschâtres, M.
(2012). Bedload tracing in a high-sediment-load mountain stream.Earth Surface Processes and Landforms, 37 , pp. 385-399.
Mao, L., Picco, L., Lenzi, M.A., and Surian, N. (2017). Bed material
transport estimate in large gravel-bed rivers using the virtual velocity
approach. Earth Surface Processes and Landforms, 42, pp. 595-611.
McDowell, C., and Hassan, M.A. (2020). The influence of channel
morphology on bedload path lengths: Insights from a survival process
model. Earth Surface Processes and Landforms . DOI:
10.1002/esp/4946.
McLean, D.G., Church, M., and Tassone, B. (1999). Sediment transport
along lower Fraser River 1. Measurements and hydraulic computations.Water Resources Research, 35 (8), pp. 2533-2548.
Middleton, L., Ashmore, P., Leduc, P., and Sjogren, D. (2019). Rates of
planimetric change in a proglacial gravel-bed braided river: Field
measurement and physical modelling. Earth Surface Processes and
Landforms, 44 , pp. 752-765.
Milan, D.J. (2013). Virtual velocity of tracers in a gravel-bed river
using size-based competence duration. Geomorphology, 198 , pp.
107-114.
Misset, C., Recking, A., Legout, C., Bakker, M., Bodereau, N., Borgniet,
L., Cassel, M., Geay, T., Gimbert, F., Navratil, O., Piegay, H.,
Valsangkar, N., Cazilhac, M., Poirel, A., and Zanker, S. (2020).
Combining multi-physical measurements to quantify bedload transport and
morphodynamics interactions in an alpine braiding river reach.Geomorphology, 351 .
Mollard, J.D. (1973). Airphoto interpretation of fluvial features. InFluvial processes and sedimentation. National Research Council of
Canada, Ottawa, ON (pp. 341-380). Inland Waters Directorate, Department
of the Environment.
Montgomery, D.R., and Buffington, J.M. (1997). Channel-reach morphology
in mountain drainage basins. Geological Society of America
Bulletin , pp. 596 – 611.
Neill, C.R. (1973). Hydraulic and morphologic characteristics of
Athabasca River near Fort Assiniboine (pp. 1-23). Alberta Research
Council, Edmonton, Highway and River Engineering Division Report
REH/73/7.
Neill, C.R. (1987). Sediment balance considerations linking long-term
transport and channel processes. In C.R. Thorne, J.C. Bathurst, and R.D.
Hey (Eds.) Sediment Transport in Gravel-Bed Rivers (pp. 225-239).
John Wiley & Sons Ltd.
Northwest Hydraulic Consultants Ltd. (1994). Impact of forest harvesting
in terrain stability, stream channel morphology and fisheries resources
of the San Juan River Watershed, Vancouver Island.
http://a100.gov.bc.ca/pub/acat/public/viewReport.do?reportId=23277
O’Connor, J.E., Jones, M.A., and Haluska, T.L. (2003). Flood plain and
channel dynamics of the Quinault and Queets Rivers, Washington, USA.Geomorphology, 51 , pp. 31-59.
Papangelakis, E., and Hassan, M., (2016). The role of channel morphology
on the mobility and dispersion of bed sediment in a small gravel-bed
stream. Earth Surface Processes and Landforms, 41 , pp. 2191-2206.
Papangelakis, E., Muirhead, C., Schneider, A., and MacVicar, B. (2019).
Synthetic Radio Frequency Identification tracer stones with weighted
inner ball for burial depth estimation. Journal of Hydraulic
Engineerin g, 145(12), 06019014.
https://doi.org/10.1061/(ASCE)HY.1943-7900.0001650
Pfeiffer, A.M., Finnegan, N.J., and Willebring, J.K. (2017). Sediment
supply controls equilibrium channel geometry in gravel rivers.Proceedings of the National Academy of Sciences, 114 (13), pp.
3346-3351.
Phillips, C.B., and Jerolmack, D.J. (2014). Dynamics and mechanics of
bed-load tracer particles. Earth Surface Dynamics, 2 , pp.
513-530.
Phillips, C.B., and Jerolmack, D.J. (2019). Bankfull transport capacity
and the threshold of motion in coarse-grained rivers. Water
Resources Research, 55 , pp. 11316-11330.
Peirce, S.E.K. (2017). Morphological bedload transport in gravel-bed
braided rivers. Electronic Thesis and Dissertation Repository .
4595. https://ir.lib.uwo.ca/etd/4595
Pyrce, R.S., and Ashmore, P.E. (2003a). Particle path length
distributions in meandering gravel-bed streams: Results from physical
models. Earth Surface Processes and Landforms, 28 , pp. 951-966.
Pyrce, R.S., and Ashmore, P.E. (2003b). The relation between particle
path length distributions and channel morphology in gravel-bed streams:
a synthesis. Geomorphology, 56 , pp. 167-187.
Pyrce, R.S., and Ashmore, P.E. (2005). Bedload path length and point bar
development in gravel-bed river models. Sedimentology, 52 , pp.
839-857.
Reid, H.E., Williams, R.D., Brierley, G.J., Coleman, S.E., Lamb, R.,
Rennie, C.D., and Tancock, M.J. (2019). Geomorphological effectiveness
of floods to rework gravel bars: Insight from hyperscale topography and
hydraulic monitoring. Earth Surface Processes and Landforms, 44 ,
pp. 595-613.
Rice, S.P., Church, M., Woolridge, C.L., and Hickin, E.J. (2009).
Morphology and evolution of bars in a wandering gravel-bed river; Lower
Fraser River, British Columbia, Canada. Sedimentology, 56 , pp.
709-736.
Rollet, A.J., MacVicar, B., Piégay, H., Roy, A.G. (2008). A comparative
study on the use of passive integrated transponders to estimate sediment
transport: first results (in French). La Houille Blanche, 4, pp.
110-116.
Ryan, S.E., Porth, L.S., and Troendle, C.A. (2002). Defining phases of
bedload transport using piecewise regression. Earth Surface
Processes and Landforms, 27 , pp. 971-990.
Schneider J., Hegglin, R., Meier, S., Turowski, J.M., Nitsche, M., and
Rickenmann, D. (2010) Studying sediment transport in mountain rivers by
mobile and stationary RFID antennas. In A. Dittrich, K. Koll, J. Aberle,
and P. Geisenhainer (Eds.) River Flow (pp. 1723-1730). Karlsruhe:
Bundesanstalt für Wasserbau. S.
Terra Remote Sensing Inc. (2018a). Project report: Renfrew. September 4,
2018. (pp. 1-29). Sidney, BC.
Terra Remote Sensing Inc. (2018b). Project report: Aerial LiDAR and
imagery survey at San Juan River, Vancouver Island, BC. April 24, 2018.
(pp. 1-51). Sidney, BC.
Terra Remote Sensing Inc. (2019). Project report: Juan River change
detection survey. Port Renfrew, BC. November 12, 2019. (pp. 1-45).
Sidney, BC.
Tomsett, C., and Leyland, J. (2019). Remote sensing of river corridors:
A review of current trends and future directions. River Research
and Applications, pp. 1-25.
Vázquez-Tarrío, D., and Batalla, R.J. (2019). Assessing controls on the
displacement of tracers in gravel-bed rivers. Water, 11 , pp.
1-21.
Vázquez-Tarrío, D., and Menéndez-Duarte, R. (2014). Bedload transport
rates for coarse-bed streams in an Atlantic region (Narcea River, NW
Iberian Peninsula). Geomorphology, 217 , pp. 1-14.
Vázquez-Tarrío, D., Recking, A., Liébault, F., Tal, M., and
Menéndez-Duarte, R. (2018). Particle transport in gravel-bed rivers:
Revisiting passive tracer data. Earth Surface Processes and Landforms,
44, pp. 112-128.
Vericat, D., Wheaton, J.M., and Brasington, J. (2017). Revisiting the
morphological approach. In D. Tsutsumi and J.B. Laronne (Eds.)Gravel Bed Rivers: Processes and Disasters, 1 (pp. 121-158). John
Wiley & Sons Ltd.
Water Survey of Canada (2019). Monthly Discharge Data for SAN JUAN RIVER
NEAR PORT RENFREW (O8HA010) [BC]. Retrieved from:
https://wateroffice.ec.gc.ca/report/historical_e.html?stn=08HA010&mode=Table&type=h2oArc&results_type=historical&dataType=Monthly¶meterType=Flow&year=2017&y1Max=1&y1Min=1
Wheaton, J.M., Brasington, J., Darby, S.E., and Sear, D.A. (2010).
Accounting for uncertainty in DEMs from repeat topographic surveys:
improved sediment budgets. Earth Surface Processes and Landforms,
35 , pp. 135-156.
Wilcock, P.R., and McArdell, B.W., (1997). Partial transport of a
sand/gravel sediment. Water Resources Research, 33 , pp. 235-245.
Wolman, M.G. (1954). A method of sampling coarse river-bed material.Transactions, American Geophysical Union, 35 (6), pp. 951-956.
Figure 1 . Location of (a) the San Juan River watershed
and (b) study sites for tracer monitoring.
Figure 2 . San Juan River annual flood frequency (data sourced
from WSC, 2019).
Figure 3 . Grain size distributions for surface bed-material and
tracers for (a) Bar 6, (b) Bar 7, and (c) Bar
15.
Figure 4 . Antennas used during tracer recovery surveys. Panel(a) shows the BP Plus Portable wand antenna, and panel(b) shows the Cord Antenna System.
Figure 5 . Time-lapse imagery of the apex of Bar 6 on(a) September 18th, 2017 at 1:45 pm. The
hydrometric station recorded a discharge of 1.2 m3/s
at the time of this image. The apex of Bar 6 on (b) November
19th, 2017 at 3:45 pm. The hydrometric station
recorded a discharge of 560.4 m3/s at the time of this
image.
Figure 6 . San Juan River hydrograph from September 2015 to
September 2019. Data from WSC, 2019.
Figure 7 . (a) Relative tracer mobility, (b)median scaled path length, and (c) mean burial depth, plotted
against total excess flow energy.
Figure 8 . Path length frequency distribution for (a)Bar 6, (b) Bar 7, and (c) Bar 15 mobile tracers (i.e.
transported more than 10 m downstream).
Figure 9 . Tracer recovery locations for Bar 6 deployed in(a) 2015, (b) 2016, (c) 2017, and(d) 2018. Note that these maps include tracers recovered in
surveys two or more years after deployment.
Figure 10 . Tracer recovery locations for Bar 7 deployed in(a) 2016, (b) 2017, and (c) 2018. Note that
these maps include tracers recovered in surveys two or more years after
deployment.
Figure 11 . Tracer recovery locations for Bar 15 deployed in(a) 2016, (b) 2017, and (c) 2018. Note that
these maps include tracers recovered in surveys two or more years after
deployment.
Figure 12 . Box plots of scaled tracer path lengths for each
study site. Note: nm is the number of mobile tracers;
the box represents the 25th and 75thpercentiles; the line inside the box is the median; vertical lines
represent the 10th and 90thpercentiles; black dots represent the maximum observed path length.
Figure 13 . Planform view of the migrating bedload sheet at the
apex of Bar 6 (from 2017). Inset shows the one-metre tall slip face at
the downstream extent of the sheet
Figure 14 . Box plots of scaled tracer path lengths as a
function of initial morphologic unit. Note: the box represents the
25th and 75th percentiles; the line
inside the box is the median; vertical lines represent the
10th and 90th percentiles.
Figure 15 . Tracer burial for Bar 6 and Bar 7. The position of
buried tracers that were recovered between 2015 and 2018 are overlaid on
the corresponding DoD in the upper panel. Those recovered in 2019 are
shown in the lower panel overtop of the 2018-2019 DoD.
Figure 16 . Tracer burial for Bar 15. The position of buried
tracers that were recovered between 2015 and 2018 are overlaid on the
corresponding DoD in the upper panel. Those recovered in 2019 are shown
in the lower panel overtop of the 2018-2019 DoD.
Figure 17. Tracer burial depth plotted against net elevation
change in the DoD for the cell in which the tracer was buried.
Figure 18 . Tracer burial depths for (a) Bar 6,(b) Bar 7, and (c) Bar 15.
Figure 19 . Path length (L) of individual tracers as a function
of scaled grain size (D) for each size fraction of tracers. Path length
is scaled by the mean path length of the size fraction containing the
local D50. Grain size is scaled by the
D50 of local bed surface material. Yellow dots represent
the mean path length for each tracer size fraction across the three
study sites. Note that previous studies have scaled particle size by the
local subsurface D50, whereas the San Juan data was
scaled by the local surface D50 as no bulk grain size
sampling was carried out.
Figure 20 . Relative tracer mobility plotted as a function of
grain size for each tracer size fraction across all study sites. Grain
size is scaled by the D50 of local surface bed material.
Figure 21 . Mean scaled travel distance as a function of
dimensionless stream power for various channel types and the San Juan
River. This figure is re-created from Figure 4 in Vázquez-Tarrío et al.
(2018).
Table 1. Grain size data for tracers and surficial bed
material.