Sediment routing fundamentally influences channel morphology and the propagation of disturbances such as debris flows. The transport and storage of bedload particles across headwater channel confluences, which may be significant nodes of the channel network in terms of sediment routing, morphology, and habitat, are poorly understood, however. We investigated patterns and processes of sediment routing through headwater confluences by comparing them to published results from lower-gradient confluences and by comparing the dispersive behavior of coarse bedload particles between headwater confluence and non-confluence reaches. We addressed these questions with a field tracer experiment using passive-integrated transponder and radio-frequency identification technology in the East Fork Bitterroot River basin, Montana, USA. Within the confluence zone, tracers tended to be deposited towards scour-hole and channel margins, suggesting narrow, efficient transport corridors that mirror those observed in prior studies, many of which are from finer-grained systems. Coarse particles in some confluence reaches experienced reduced depositional probabilities within the confluence relative to upstream and downstream of the confluence. Analysis of particle transport data suggests that variation in the spatial distribution of coarse-sediment particles may be enhanced by passing through confluences, though further study is needed to evaluate confluence effects on dispersive regimes and sediment routing on broader spatial and temporal scales.
The transport and storage of mobile sediment particles through channel networks, i.e., sediment routing (Swanson and Fredriksen, 1982), link sediment supply, flow, and channel morphology and thereby regulate channel evolution (Church, 2002, 2006). In headwater regions, where hillslope–channel connectivity is strong, storage and downstream routing of sediment inputs reflect the influence of spatially and temporally variable forcing by hillslope (e.g., debris flows) and fluvial processes (Montgomery and Buffington, 1997; Brooks and Brierley, 1997; Prosser et al., 2001; Lancaster and Casebeer, 2007). Discrete pulses of coarse sediment delivered to streams can travel downstream as a translating bedload wave, by dispersion, or by a combination of translation and dispersion (Lisle et al., 2001; Sklar et al., 2009).
Flow (top left) and morphology (bottom left) in a gravel-bed
confluence (after Best, 1987). Key variables influencing hydraulics and
morphology include discharge ratio (
Analyses of dispersion based on the premise that particle motion is a random walk have represented downstream transport as a series of intermittent steps and rests (Einstein, 1937). This approach has informed flume and field studies seeking to identify characteristic probability distributions of step length and rest periods (e.g., Hubbell and Sayre, 1964; Yang and Sayre, 1971; Bradley et al., 2010). Various statistical distributions (e.g., exponential and gamma functions) have been found to approximate spatial distributions of bedload-particle displacements in flume and field conditions (e.g., Hassan et al., 1991; Bradley and Tucker, 2012; Martin et al., 2012; Haschenburger, 2013; Phillips et al., 2013) and have been used to approximate dispersive regimes in gravel-bed channels, including plane-bed (Bradley and Tucker, 2012), pool-riffle (Liébault et al., 2012; Milan, 2013), and braided systems (Kasprak et al., 2014). Long-term tracer experiments have noted evolving spatial distributions of bedload particles, suggesting that best-fit statistical distributions may differ depending on the degree of vertical mixing, often a function of time (Haschenburger, 2013). Downward advection of particles into the streambed can reduce the probability of re-entrainment and thus slow streamwise advection (Pelosi et al., 2016). Dispersion models predicting a smooth spatial distribution therefore may not adequately capture the true dispersive behavior of bedload particles across multiple channel morphologies.
The dispersive behavior of coarse-sediment particles has also been considered in terms of changes in the variance of particle displacements with time (e.g., Phillips et al., 2013). Sediment dispersion is thus treated as analogous to one-dimensional diffusion in the downstream direction, with potential diffusion dynamics that include normal diffusion, where the variance of particle displacements increases linearly with time, and anomalous diffusion, which includes both superdiffusion and subdiffusion, when variance increases more quickly or more slowly with time than the linear case, respectively (Metzler and Klafter, 2000; Nikora et al., 2002; Olinde and Johnson, 2015). Improved understanding of variability in dispersive regimes among channel types and other controls on sediment dispersion is needed, however, to facilitate sediment-routing predictions.
Nodes of the channel network that may be especially important with respect
to sediment routing are tributary confluences, where point sources of flow
and sediment connect tributary to trunk streams (Rice et al., 2008; Rice,
2016). The importance of confluences in sediment routing, as well as their
morphologic significance, may depend on factors including drainage densities
(i.e., frequency of confluences; Benda et al., 2004a), the magnitude and
frequency of disturbances such as debris flows (Benda and Dunne, 1997;
Hoffman and Gabet, 2007), and the relative differences in flow, sediment
caliber, and load between tributaries and the trunk streams they enter
(Fig. 1) (Knighton, 1980; Richards, 1980; Ferguson et al., 2006; Swanson
and Meyer, 2014; Rice, 2016). Morphological effects stemming from
disturbance-derived confluence deposits may extend spatially, well beyond
the area of flow convergence, and temporally, persisting for
Study area, including location within the East Fork Bitterroot River's headwaters (upper left) and three study sites: upper and lower confluences and a control reach, outlined in yellow; individual reaches in which PIT-tagged particles were seeded are outlined in red.
Whereas sediment dynamics and the morphology of headwater confluences can be
primarily influenced by disturbances such as debris flows (Benda and Dunne,
1997), what we refer to as “equilibrium” confluence morphology, reflecting
feedbacks between flow hydraulics, sediment transport, and morphology, can
also develop and persist (Fig. 1). Such confluences are well-studied in
sand- and gravel-bed river systems and typically feature a central scour
hole, tributary-mouth bars, and bank-attached bars in areas of flow
recirculation and stagnation (Best, 1987 1988; Rhoads, 1987; Roy and
Bergeron, 1990; Biron et al., 1996; Boyer et al., 2006; Rhoads et al., 2009;
Ribeiro et al., 2012). Physical controls on confluence hydraulics and
associated morphology include junction angle (
In this study we assess how coarse bedload particles are routed through equilibrium confluences in mountain-river headwaters. We address two questions: (i) How do sediment-routing patterns through equilibrium confluences compare to those described in other, primarily lower-gradient gravel-bed river systems? (ii) How do equilibrium confluences affect the dispersive behavior of coarse bedload particles compared to non-confluence reaches? We address these questions with a tracer experiment conducted through two headwater confluences and a non-confluence control reach. We compare spatial distributions of mobilized particles among study sites and apply a dimensionless impulse framework (Phillips et al., 2013) to observed tracer behavior to explore the effects of confluences on sediment routing. We also evaluate our results and their implications in the context of theory regarding confluences and sediment routing through headwater networks. Our study contributes to the growing body of work on particle dispersion and transport dynamics in mountain rivers and is, to our knowledge, the first to investigate these topics with respect to sediment routing through confluences in a field setting.
Here we describe our study area and the preparation, deployment, and measurement of coarse bedload tracer particles. We then describe the analyses we conducted that allow the comparison of particle displacement through the study confluences to that of the control reach and prior transport studies in gravel-bed river systems. This involved the assessment of displacement distributions and a dimensionless impulse, with the goal of evaluating and comparing dispersive regimes. Additional details on these analyses, beyond what is provided below, are in the Supplement and Imhoff (2015).
We selected a study area in the East Fork Bitterroot (EFB) River basin in
western Montana, USA (Fig. 2) that is typical of semiarid,
snowmelt-dominated, montane headwater systems. This location lacks recent
physical disturbances (e.g., post-wildfire debris flows) and contains
confluences exhibiting characteristics of the equilibrium morphology
described above. The field site drains 298 km
Two tributary confluences mark the upstream and downstream extent of the study area. These are herein referred to as the upper confluence, where Moose Creek and Martin Creek combine, and, 1 km downstream, the lower confluence, where Martin Creek enters the EFB. The tributary and main stem stream of each study confluence are considered as separate reaches for the purpose of separately considering incipient motion and transport behavior of tracers starting in each. Between the study confluences is a plane-bed control reach. Combined discharge in the upper confluence is approximately half that of the lower confluence.
Because the site is ungauged, we installed HOBO-U20 water level loggers to
record the stage at 15 min intervals during the 2014 study period. One
transducer was placed along a surveyed cross section of the bed at each
study reach. We also periodically manually measured water surface elevations
and, during wadeable conditions, stream velocities. Above-average flows
during the study period reflected that year's large snowpack. Snow water
equivalent at snow telemetry (SNOTEL) sites within 50 km of the study area registered above
150 % of normal on 1 April 2014 (
Stage hydrograph during spring 2014 runoff period at lower-confluence (East Fork Bitterroot River) study site. Estimated bank-full level, based on cross section topography surveyed at transducer location, is shown as horizontal dotted line.
Channel morphology and bed-material grain-size characteristics at
each study reach. Width and depth values are bank-full dimensions, as
measured along surveyed cross sections;
To characterize study-reach morphology, we completed topographic surveys and grain-size measurements. Topography was surveyed using a Leica TS06 total station during the initial tracer deployment (March 2014), before spring runoff high flows, and the summer (July–September) recovery campaign. Topographic surveys entailed longitudinal profiles, to determine slope, and cross sections at the location of pressure transducers, for use in the incipient motion estimates described below. We also surveyed bedform extents to produce a bedform map. Surface grain-size distributions were measured using Wolman pebble counts across each study reach. Channel slopes, dimensions, grain sizes, and confluence characteristics are shown in Table 1 (also see the Supplement).
Our study employed passive-integrated transponder (PIT) and radio-frequency identification (RFID) technology for tagging and tracing bedload particles. PIT tags are highly recoverable, durable, and cost-effective relative to other particle tracing methods (e.g., Lamarre et al., 2005; Bradley and Tucker, 2012; Chapuis et al., 2015). Moreover, PIT-tagging allows for analyses of transport of both bed-material populations and specific subsets of the grain population (e.g., by size, shape, lithology), displacement distributions and their evolution over time, and other aspects of transport dynamics.
We collected gravel and cobble particles from Moose Creek, upstream of our
study reaches, in January 2014 for tagging. Using a 1 hp drill press, holes
8 mm wide by 30 mm long were drilled using a
The PIT tags used in this study are 12 and 23 mm half-duplex, read-only tags from Oregon RFID. Vertical read range varies based on tag orientation, battery level, noise proximity, and other factors but is generally 0.25 to 0.5 m. Previous work has identified horizontal and vertical detection ranges at 0.5 m (Lamarre et al., 2005) and 0.25 m (Bradley and Tucker, 2012). Chapuis et al. (2014) assessed RFID detection ranges and observed higher uncertainty in radial detection distance than reported in other studies. Uncertainty in tracer position is highest for solitary, buried tracers, which are not visible via snorkel survey and have the largest detection radius; clusters of buried tracers, in contrast, have reduced detection ranges via tag interference. We oriented the antenna parallel to the surface of the bed, at a height of about 0.2 m (after Chapuis et al., 2014). For our analysis, we considered tracer movement below the threshold of detection as immobile and assigned a travel distance of 0 m (after Phillips and Jerolmack, 2014). Particles moving beyond the threshold of detection were labeled the “mobile” fraction. In total, 428 cobble and gravel tracers were prepared for deposition into the three study reaches (Table 2).
We installed the PIT-tagged tracers before the onset of the spring snowmelt, in late March and early April 2014. Our seeding method involved loosely seeding tracer particles on the bed surface near the channel thalweg in a grid (Fig. 5). Mimicking the arrangement of fluvially deposited gravels and minimizing the influence of the initial condition of particle deployment is a challenge in tracer studies, but a regular grid such as ours provides a reproducible initial condition and is consistent with previous work (Ferguson and Wathen, 1998). A sparse grid like the one employed here minimizes disturbance to the bed and flow field (Bradley and Tucker, 2012) while simultaneously avoiding “confusing” the PIT tag detection equipment, which encounters issues when dealing with clusters of particles (Chapuis et al., 2014). The gridded surface ranged from 7 to 13 m wide. We deployed PIT-tagged tracers at equal distances upstream from the confluence in each tributary. Initial tracer positions were recorded using the total station.
Grain-size distribution of tagged tracers (red) and streambed (black) composite over all study sites.
Field recovery campaigns to detect tracer locations and measure particle
displacement took place after recession of high flows, once the streams were
wadeable. The bed was scanned with a 0.5 m diameter loop antenna in
conjunction with a backpack reader. Once a tracer was located, the loop
antenna was brought towards its detection field from all directions. This
helped to identify other tracers in a cluster by reading different tags
first, depending on the direction the cluster is approached. Each tracer's
position was recorded using the total station. The uncertainty associated
with individual total station measurements of tracer position and travel
distance is
Tracer recovery and transport statistics by study reach.
To investigate how tracers routed through confluences compared to those in
our plane-bed control reach, we compared the spatial distribution of tracers
at initial deployment and after the 2014 flood among sites by plotting the
distribution of tracers versus streamwise distance. Differences in the pre-
and post-flood distributions are indicative of transport distances and of
changes in depositional probability; e.g., a reduction in the slope of the
distribution from pre-flood to post-flood conditions indicates reduced
depositional probability and enhanced transport (after Haschenburger, 2013).
We nondimensionalized transport distances by scaling each tracer's
transport distance (
Tracer positions at initial installation (left) and following the
2014 flood (right) at
We also analyzed tracer displacement data with respect to a cumulative
dimensionless impulse
Because our tracer equipment could not directly detect initial motion
conditions, we estimated
These two estimates for
We recovered 68–86 % of the seeded tracers, depending on the reach (Table 2).
Recovery was greatest within study reaches with low
Similar percentages of recovered tracers (41, 39, and 50 %) left each seed reach. At the upper confluence, tracer configurations within the seed reach retained the signature of their streamwise spatial pattern in Moose Creek after movement but not in Martin Creek, which contained more boulders to facilitate trapping and clustering of particle tracers (Fig. 5). Particles seeded in Moose Creek also constituted the majority of tracers exported into the confluence itself. Within the confluence particles tended to be deposited towards channel margins and were less frequently deposited within the scour hole (Fig. 6). Particles deposited within the scour hole were segregated by the contributing stream. Tracers from the upper-confluence seed reaches had short travel distances and, even after being mobilized, remained within the confluence zone (Fig. 6).
Digitized patch map of bedforms and tracer recovery positions at
the
Spatial distribution of tracer positions at the time of initial
deployment (pre) and after the 2014 flood (post) for
Critical shear velocity (
Normalized transport distances (X
Particles recovered in the lower confluence largely retained the signature of the gridded arrangement of their initial positioning at both seed reaches, even after mobilization. The relative contribution of tracers into the confluence was more evenly distributed than in the upper confluence: 55 % of deposited tracers came from the East Fork, with the remaining 45 % from Martin Creek. Similar to the upper confluence, tracer particles remained segregated as they progressed through the confluence, stranding preferentially on bank-attached depositional bars. Deposition within the scour hole was limited and segregated, further agreeing with the upper confluence. An additional group of tracers, seeded at the upstream junction corner, was immobile. Similar to the upper confluence, large boulders were effective in trapping mobile tracer particles. Of the recovered tracers in the entire lower confluence, 23 % left the confluence zone completely, with 58 % of post-confluence tracers originating in the East Fork and 42 % in Martin Creek. Recovered particles downstream of the lower confluence ceased to be segregated after about 30 m and were recovered approximately in the channel center.
Tracers from the upper confluence, upon entering the confluence, exhibited reduced depositional probabilities and enhanced particle transport (Fig. 7a).This is demonstrated by changes in the shape of the overall distribution of tracers that correlates to entering the confluence. Slope reduction upon entering the confluence zone indicates a reduced depositional probability within the confluence (Haschenburger, 2013), whereas similar slopes among the pre- and post-flood distributions would indicate a consistent depositional probability in space. Although most of the particles seeded in Martin Creek did not enter the upper confluence, those that did experienced a similar reduction in depositional probability as the Moose Creek tracers. The stepped pre-flood distributions of upper-confluence particles (Fig. 7a) reflected prevailing ice conditions and likely translated into the post-flood distributions. Regardless, additional particles lie within the zone of reduced depositional probability post-flood, indicating enhanced transport within the confluence.
In the control reach, observed transport distances were comparable to those in the upper-confluence reaches (Fig. 7a, b). This was in spite of considerably larger Recking-estimate impulse values, reflecting the fact that the upper-confluence reaches together provide the control reach's component discharge (Table 3). Within the post-flood spatial position of tracers in the control reach, small steps were present (e.g., at about the 40th percentile), which appear to correspond to steps present in the pre-flood distribution (e.g., near the 60th percentile), reflecting downstream translation of the curve across a portion of its distribution (Fig. 7b). The post-flood distribution decays exponentially, suggesting a relatively constant depositional probability throughout the reach (Fig. 7b), in contrast to the depositional probabilities at the upper confluence.
At the lower confluence, transport distances are greater than in the upstream reaches (Fig. 7c). Evidence of enhanced transport within the confluence is strong for Martin Creek: depositional rates upstream and downstream of the confluence exceed those within the confluence, and there is no relic pattern carried over from the pre-flood spatial distribution of tracers (Fig. 7c). Confluence effects are less clear among the East Fork tracers, largely because tracers seeded at the upstream junction corner in the East Fork did not enter the confluence and are visible as a near-vertical line in the pre- (> 80th percentile) and post- (20–40th percentile) flood spatial distributions (Fig. 7c). Other than these tracers the East Fork tracers show a similar pattern as in Martin Creek. Overall, the dispersive growth of the lower-confluence tracers assumes a thin-tailed decay similar to that of the control, though the altered depositional probability within the confluence, especially among Martin Creek tracers, differentiates the control and lower-confluence distributions.
Dimensionless displacement distributions for both confluence and
non-confluence reaches are reasonably characterized by an exponential
distribution (Fig. 8). This further suggests that particle dispersion at
the site is thin-tailed during the 2014 flood. Front-running particles at
the upper-confluence reaches travel relatively shorter distances beyond the
population average compared to the lower confluence. This, along with far
shorter transport distances, suggests that larger cumulative excess shear
stresses (i.e., larger
Our estimates of the critical Shields number ranged from 0.06 to 0.11 (Table 3),
slightly larger than often assumed values of
Linear and power-law relations between dimensionless impulse and
We found <
Our study used PIT/RFID technology to investigate coarse-sediment
transport across tributary confluences of mountain streams. Maximum
transport distances along scour-hole flanks and segregation are similar to
the findings of Mosley (1976) and Best (1988). Because we detect no tracers
beyond the extent of the upper confluence, we take the depositional pattern
in Fig. 6 to reflect a tendency of our tracers to route along, rather than
through, the scour hole. We see similar depositional patterns for tracers
that were detected within the lower confluence and posit that similar
transport corridors apply. We consider these transport patterns to reflect
the controlling influences of
Our data also agree with the assertions of Best (1988) and others as to how
the position and orientation of the scour hole is influenced by
Our comparison of the pre- and post-flood spatial distributions of bedload tracers provides evidence of reduced depositional probability and enhanced transport within confluences (Fig. 7). This is most evident in Moose Creek, at the upper confluence, and Martin Creek, at the lower confluence. Where the pre-flood spatial distribution of particles is not continuous, such as the upper Martin Creek reach and the East Fork, these patterns are less evident. The spatial distribution of tracers in the control reach does not substantially differ from the lower-confluence reaches, even when the confluence zone is clearly transport-efficient. This may be because the confluence zone in the lower-confluence reaches represents a small portion of the total tracer transport distances, which are greater here than in upstream reaches, muting confluence effects on transport when the entire distribution of tracers is considered and as tracer transport becomes more influenced by plane-bed morphology than confluence effects. At the upper confluence, in contrast, the confluence zone occupied a much larger portion of the transport reach, and consequently the post-flood distribution differs more from the control.
Exceedance probabilities of normalized transport distances show a steeper form in the upper-confluence reaches than the control reach and lower confluences (Fig. 8). The steeper trend ensures that front-running tracers travel a relatively shorter distance beyond the population average, though a greater proportion of the tracer population travels near or beyond the average distance. The control reach, despite similar average and maximum tracer transport distances to the upper-confluence reaches, shows more similar distributions to the lower-confluence reaches. This difference is suggestive of enhanced transport within the equilibrium confluence; when tracers entering the confluence are able to continue transporting downstream, a greater proportion of tracers will reside in the front of the plume, past the average transport distance, as we see with the upper confluence. Because the trend is absent for the lower confluence, we postulate that the confluence effect (enhanced transport, reduced deposition) is muted once particles have transported a sufficient distance beyond the confluence zone. Despite evidence suggesting confluence effects on transport, our study lacks sufficient spatial and temporal resolution to differentiate in a statistically rigorous manner between confluence and non-confluence reaches – all study reaches may be considered as exhibiting a thin-tailed dispersive growth pattern, given their linear form in semi-log space. Recent works suggest that thin-tailed dispersion is dominant for coarse bedload particles (e.g., Hassan et al., 2013), and our work is no exception.
To develop a more complete understanding of how dispersive patterns observed
on the scale of individual confluences influence sediment connectivity and
routing on the larger basin scale in mountain watersheds, longer-term
studies across a larger number of confluence sites and channel morphologies
are needed. Such work could test how confluence effects on sediment routing
are dependent on both confluence (e.g.,
Network structure, in terms of both geometry and variations in sediment
transport capacity, has been found to influence how sediment inputs in
headwaters propagate downstream through basins (e.g., Czuba and
Foufoula-Georgiou, 2014; Gran and Czuba, 2016) and relates to the questions
raised in the NDH about basin shape and associated confluence effects (Benda
et al., 2004a, b). In our study area and in semiarid mountain watersheds in
general, for example, post-fire erosion is an important sediment source with
implications for humans and aquatic ecosystems; downstream propagation of
post-fire sediment inputs may vary depending on basin shape and confluence
effects. For example, propagation of sediment routing may differ between
unglaciated, compact basins with dendritic channel networks (as are present
in our study area and the surrounding Sapphire Mountains) compared to
formerly glaciated, elongate basins with trellis drainage networks (as are
present in the Bitterroot Range
In gravel-bed headwater systems, equilibrium confluences are unique locations that may affect local patterns of sediment transport and deposition. Our study is the first to date of tracer-based coarse-sediment routing through mountain-river confluences. We observed that, on the reach scale, coarse sediment is routed through confluences along the flanks of a well-defined scour hole, in agreement with observations and flume studies from other gravel-bed systems. Certain confluence reaches showed evidence for enhanced transport during a single snowmelt flood, although understanding whether confluences influence bedload dispersion in a geomorphically significant manner on larger spatial and temporal scales would require further study. The dimensionless impulse metric (Phillips et al., 2013) was shown to correlate to tracer transport and dispersion over a single flood event, further supporting its use in future sediment transport studies. Our study also illustrates the utility of tracer studies using PIT/RFID technology for providing field-based insights into sediment transport dynamics. Longer-term sediment transport studies across confluence and non-confluence reaches, combined with an analysis of changes in bed elevation and texture in intervening reaches to place the work in a mass conservation framework, would further clarify sediment-routing patterns in mountain channel networks and thus inform a range of problems. These include the understanding of how confluences influence sediment cascades and connectivity (Fryirs, 2013); links among confluences, sediment routing, and basin morphology (Benda et al., 2004a, b; Rice, 2016); and applied problems including solid-phase contaminant transport (Bradley et al., 2010), cosmogenic radionuclide accumulation (Gayer et al., 2008), sediment budgeting (Malmon et al., 2005), and the duration and topographic impact of pulses on aquatic habitat (Lisle et al., 2001).
Pre- and post-flood tracer data are provided by the authors in a publically accessible
online data repository (Imhoff and Wilcox, 2015) at
We thank P. A. Duvillard, A. Maphis, D. Davis, M. Jahnke, and A. Sawyer for field assistance; M. Hassan, L. Eby, M. Maneta, S. Bywater-Reyes, and R. Manners for insight into the planning and implementation of this work; N. Bradley and C. Legleiter for aid in model use and coordinate transformation; and C. Phillips for assistance in performing impulse analyses. Comments from two anonymous reviewers and from Associate Editor Dimitri Lague greatly improved the manuscript. This work was supported by the Montana Institute on Ecosystems' award from the National Science Foundation EPSCoR Track-1 program under Grant no. EPS-1101342 and by the Montana Geological Society, the Geological Society of America, and the Northwest Scientific Association. Edited by: D. Lague Reviewed by: two anonymous referees