Period and amplitude of bedload pulses in a macro-rough channel

Ghilardi, Tamara; Franca, Mário J.; Schleiss, Anton

doi:10.1016/j.geomorph.2014.06.006

Cited by 16 publications

(39 citation statements)

References 39 publications

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“…Investigating the data from the experiments with boulders (filled shapes), a close collapse is seen between the present study and that of Ghilardi et al () within the range of < τ gr > * / τ cr * − 1 greater than 0.2. This agreement is present despite the fact that the P L values in this study are more than an order of magnitude less than some of the timescales reported by Ghilardi et al () at similar < τ gr > * / τ cr * . Within the range of < τ gr > * / τ cr * − 1 less than 0.2 and in the presence of boulders, it is not possible to directly compare the two studies since the data from Ghilardi et al () do not extend to these conditions.…”

Section: Resultssupporting

confidence: 87%

“…For the data set in the presence of boulders from Ghilardi et al (), T BE was employed as T hyd similarly to the present experiments (see Appendix B for assumptions concerning

U_{d_{c}}

). For the studies conducted in the absence of boulders, however, T hyd was selected as the timescale associated with large‐scale eddies atop a flat rough bed, T LSE .…”

Section: Resultsmentioning

confidence: 99%

“…Therefore, each q s,e ' was filtered using SWS equal to the previously determined P S timescale in order to reveal the larger timescale of fluctuations, P L . Using similar methods to Ghilardi et al () for quantitative estimation of P L , the normalized autocorrelation function of q s,e ′, A qs ( δ ), was employed. A qs ( δ ) is calculated as

A_{qs} ((), δ) = \int_{0}^{T - δ} \frac{{q_{s, e}}^{'} ((), t) {q_{s, e}}^{'} ((), t + δ)}{σ_{{q^{'}}_{s, e}}^{2}} italicdt,

where δ is the lag time, T is the total test duration, and

σ_{q_{s}^{'}}^{2}

is the time series variance.…”

Section: Methodsmentioning

confidence: 99%

“…A qs ( δ ) is calculated as

A_{qs} ((), δ) = \int_{0}^{T - δ} \frac{{q_{s, e}}^{'} ((), t) {q_{s, e}}^{'} ((), t + δ)}{σ_{{q^{'}}_{s, e}}^{2}} italicdt,

where δ is the lag time, T is the total test duration, and

σ_{q_{s}^{'}}^{2}

is the time series variance. When considering time series smoothed with P S , A qs ( δ ) was expected to exhibit a peak at the δ corresponding to the P L timescale magnitude (Ghilardi et al, ). To ensure that smaller peaks were not erroneously selected, the first statistically significant peak (using 95% confidence limits) in the A qs ( δ ) plot was interpreted as the timescale estimate for P L .…”

Section: Methodsmentioning

confidence: 99%

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Boulder Array Effects on Bedload Pulses and Depositional Patches

et al. 2018

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The influence of boulders in high gradient gravel‐bed rivers can be significant. Yet few studies have isolated their role in regulating bedload. It is hypothesized that boulder relative submergence and the corresponding eddies developing around boulders affect the frequency of bedload pulsations and the location of sediment deposits. Results show the occurrence of two distinct timescales in bedload exiting a boulder array, which were identified via time series analysis. These are the small‐scale periodicity, PS, and the large‐scale periodicity, PL. PS was identified in the range of 4–6 min and is believed to result from congested bedload movement due to reduced conveyance area within the array. PL was identified in the range of 8–46 min. It is suggested that PL corresponds to large bedload releases around boulders, which the authors consider to be caused by feedbacks between boulder eddies and bedload deposits. It is also found that boulder submergence and Froude number, Fr, influence the location of predominant deposition. At High Relative Submergence, deposition occurs in the boulder wake regions. At Low Relative Submergence, deposition instead occurs upstream of boulders in locations that depend on Fr. For Fr < 1, material deposits in the stoss of boulders due to the necklace structure of the horseshoe vortex. However, for Fr > 1, material deposits at the flanks of boulders due to the influence of local wave crests around boulders. The trapping efficiency of boulders reduces the dimensionless mean bedload transport rate by three orders of magnitude compared to conditions without boulders.

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Section: Resultssupporting

confidence: 87%

“…For the data set in the presence of boulders from Ghilardi et al (), T BE was employed as T hyd similarly to the present experiments (see Appendix B for assumptions concerning

U_{d_{c}}

). For the studies conducted in the absence of boulders, however, T hyd was selected as the timescale associated with large‐scale eddies atop a flat rough bed, T LSE .…”

Section: Resultsmentioning

confidence: 99%

A_{qs} ((), δ) = \int_{0}^{T - δ} \frac{{q_{s, e}}^{'} ((), t) {q_{s, e}}^{'} ((), t + δ)}{σ_{{q^{'}}_{s, e}}^{2}} italicdt,

where δ is the lag time, T is the total test duration, and

σ_{q_{s}^{'}}^{2}

is the time series variance.…”

Section: Methodsmentioning

confidence: 99%

“…A qs ( δ ) is calculated as

A_{qs} ((), δ) = \int_{0}^{T - δ} \frac{{q_{s, e}}^{'} ((), t) {q_{s, e}}^{'} ((), t + δ)}{σ_{{q^{'}}_{s, e}}^{2}} italicdt,

where δ is the lag time, T is the total test duration, and

σ_{q_{s}^{'}}^{2}

Section: Methodsmentioning

confidence: 99%

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Boulder Array Effects on Bedload Pulses and Depositional Patches

et al. 2018

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“…Different bed conditions and transport intensities (particularly those higher than described here) may change the active processes and, in turn, the correlation terms. For example, processes such as sediment burial and reappearance in a mobile layer (possibly, in the presence of bedforms) may strongly alter the temporal fluctuations with respect to those for continuously exposed particles (see, e.g., Ghilardi et al, , ; Iwasaki et al, ; Voepel et al, ). Threshold sizes to consider spatial and temporal scales large enough to achieve process uniformity/stationarity are expected to be more demanding than in the present case.…”

Section: Application To Experimental Resultsmentioning

confidence: 99%

Lagrangian and Eulerian Description of Bed Load Transport

et al. 2018

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Sediment particles transported as bed load undergo alternating periods of motion and rest, particularly at weak flow intensity. Bed load transport can be investigated by either following the motion of individual particles (Lagrangian approach) or observing the phenomenon at prescribed locations (Eulerian approach). In this paper, the Lagrangian and Eulerian descriptions are merged into a unifying framework that includes definitions for quantities used to describe the kinematics of particle motion, as well as the relationships among these quantities. The alternation of motion and rest is represented by two complementary descriptions: (i) proportion of motion, indicating either the relative time spent in motion by an individual particle or the relative number of moving particles; (ii) persistence of motion, indicating the extent to which the process consists of relatively few long periods of motion or of many short ones. The framework only involves first moments of the key quantities. The conceptual developments are tested against results from an experiment with weak bed load transport, demonstrating the soundness of the approach. From an operational point of view, a Lagrangian observation is difficult to perform, since the particle motion is usually investigated for finite spatial domains (e.g., a measurement window within a laboratory or natural reach). Strategies to overcome such limitations are described, suggesting the possibility of obtaining unbiased mean values for Lagrangian descriptors. The proposed framework can be used in any study aimed at parameterizing the kinematic properties of bed load particles as functions of the hydrodynamic conditions.

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Bed load fluctuations in a steep channel

Ghilardi

Franca

Schleiss

2014

Water Resources Research

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Bed load transport rate fluctuations have been observed over time in steep rivers and flumes with wide grain size distributions even under constant sediment feeding and water discharge. The observed bed load transport rate pulses are periodic and a consequence of grain sorting. Moreover, the presence of large, relatively immobile boulders, such as erratic stones, which are often present in mountain streams, has an impact on flow conditions. The detailed analysis of a 13 h laboratory experiment is presented in this paper. Boulders were randomly placed in a flume with a steep slope (6.7%), and water and sediment were constantly supplied to the flume. Along with the sediment transport and bulk mean flow velocity, the boulder protrusion, boulder surface, and number of hydraulic jumps, which are indicators of the channel morphology, were measured regularly during the experiment. Periodic bed load transport rate pulses are clearly visible in the data collected during this long-duration experiment, along with correlated fluctuations in the flow velocity and bed morphology. The links among the bulk velocity, the time evolution of the morphology variables, and the bed load transport rate are analyzed via correlational analysis, showing that the fluctuations are strongly related. A phase analysis of all observed variables is performed, and the average shapes of the time cycles of the fluctuations are shown. Observations indicate that the detected periodic fluctuations correspond to different bed states. Furthermore, the grain size distribution through the channel, which varies in time and space, clearly influences these bed load transport rate pulses. Finally, known bed load transport rate formulae are tested, showing that only the application of a drag shear stress allows a correct estimation of the time fluctuations.

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Period and amplitude of bedload pulses in a macro-rough channel

Cited by 16 publications

References 39 publications

Boulder Array Effects on Bedload Pulses and Depositional Patches

Boulder Array Effects on Bedload Pulses and Depositional Patches

Lagrangian and Eulerian Description of Bed Load Transport

Bed load fluctuations in a steep channel

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