Bed Shear Stress in Experimental Flash Flood Bores over Dry Beds and over Flowing Water: A Comparison of Methods

Thappeta, Suresh Kumar; Johnson, J. P.; Halfi, Eran; Peretz, Yael Storz; Laronne, Jonathan B.

doi:10.1061/jhend8.hyeng-13029

Cited by 2 publications

(6 citation statements)

References 52 publications

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“…The significance of turbulence during this initial impingement of the flood wave is corroborated by the degree of covariation of bedload flux and TKE, at least for four of the events reported (Figure 6) and is in agreement with the flume study conducted by Thappeta et al. (2023), where higher TKE values were attained at the arrival of the bore front rather than after bore passage.…”

Section: Discussionsupporting

confidence: 88%

“…Bores that piggyback initial flows generate much lower bedload transport rates than those that first wet the channel, despite their longer duration and greater flow depth (Table 1). We interpret this as indicating that the deeper the quasi‐steady flow preceding the bore, the less the bore causes turbulent forces to impinge on the river bed (Thappeta et al., 2023). This is a reflection of the fact that TKE is generated at, and dissipated over, greater vertical distance in deeper flows.…”

Section: Discussionmentioning

confidence: 93%

“…The second method is calculated from turbulent velocity fluctuations (Biron et al., 2004; Thappeta et al., 2023):

{\tau }_{b}=\frac{1}{2}\rho {C}_{1}\left(\left\langle {{V}_{x}}^{\prime 2}\right\rangle +\left\langle {{V}_{y}}^{\prime 2}\right\rangle +\left\langle {{V}_{z}}^{\prime 2}\right\rangle \right)

where C 1 = 0.19 is a proportionality constant (Stapleton & Huntley, 1995) and ρ is the water density, kg/m 3 .…”

Section: Methodsmentioning

confidence: 99%

“…The second method is calculated from turbulent velocity fluctuations (Biron et al, 2004;Thappeta et al, 2023):…”

Section: Flow Instrumentation and Processingmentioning

confidence: 99%

“…We acknowledge that Equation 2 assumes steady uniform flow conditions, which are not met in the bores under study (e.g., Mrokowska et al, 2015aMrokowska et al, , 2015bThappeta et al, 2023). However, turbulence data during bore arrival in the field were previously unavailable.…”

Section: Flow Instrumentation and Processingmentioning

confidence: 99%

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Transient Bedload Transport During Flashflood Bores in a Desert Gravel‐Bed Channel

Halfi

Thappeta

Johnson

et al. 2023

Water Resources Research

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Many methods have been developed for estimating bedload flux under conditions of quasi-steady flow, in which both turbulent velocity fluctuations and changes in bedload flux are assumed to occur over timescales much shorter than variations in flow velocity. Nonetheless, the prediction of bedload transport rate tends to break down when considering timescales over which turbulent fluctuations matter. For example, Escauriaza and Sotiropoulos (2011) conducted numerical modeling in clear water scour conditions and found that bedload transport may occur even when the average bed shear stress is far below the expected threshold value for entrainment, thereby suggesting the importance of highly transient turbulence forces. Sumer et al. (2003) found that a 20% increase in turbulence intensity in the shear layer above the bed caused a 6-fold increase in bedload flux. Videography has shown that bedload motion is temporally variable (Garcia et al., 2007;Venditti et al., 2010), a behavior largely attributed to bursting events associated with turbulent eddies (Nelson et al., 1995).Unsteady flow is characterized not only by changing discharge through time but also by rapid fluctuations in turbulent velocity which, in turn, cause variable amounts of bed material disturbance (e.g., Dey, 2014;Grass, 1971). Lee et al. (2004) observed that gradually-varying unsteady flows induced bedload fluxes as much as 1.6 times greater than those under similar steady flows. Much less is known about bedload flux in very unsteady flows, such as occur in flash floods. The hydrodynamics of flood bores and the consequences for bedload transport have been examined in flume studies (e.g.,

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Section: Discussionsupporting

confidence: 88%

Section: Discussionmentioning

confidence: 93%

“…The second method is calculated from turbulent velocity fluctuations (Biron et al., 2004; Thappeta et al., 2023):

{\tau }_{b}=\frac{1}{2}\rho {C}_{1}\left(\left\langle {{V}_{x}}^{\prime 2}\right\rangle +\left\langle {{V}_{y}}^{\prime 2}\right\rangle +\left\langle {{V}_{z}}^{\prime 2}\right\rangle \right)

where C 1 = 0.19 is a proportionality constant (Stapleton & Huntley, 1995) and ρ is the water density, kg/m 3 .…”

Section: Methodsmentioning

confidence: 99%

“…The second method is calculated from turbulent velocity fluctuations (Biron et al, 2004;Thappeta et al, 2023):…”

Section: Flow Instrumentation and Processingmentioning

confidence: 99%

Section: Flow Instrumentation and Processingmentioning

confidence: 99%

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Transient Bedload Transport During Flashflood Bores in a Desert Gravel‐Bed Channel

Halfi

Thappeta

Johnson

et al. 2023

Water Resources Research

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Strengths and weaknesses of acoustic pipe microphone systems in ephemeral, sandy, gravel‐bed rivers

Stark,

Cadol,

Varyu

et al. 2024

Earth Surf Processes Landf

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We calibrated an acoustic pipe microphone system to monitor bedload flux in a sandy, gravel‐bed ephemeral channel. Ours is a first attempt to test the limit of an acoustic surrogate bedload system in a channel with a high content of sand. Calibrations varied in quality; significant data subsetting was required to achieve R2 values >0.75. Several data quality issues had to be addressed: (1) apparent pulses, which occur when a sensor records an impulse from sediment impacting the surrounding substrate rather than directly impacting the sensor, were frequent, especially at higher signal amplifications. (2) The impact sensors were frequently covered by gravel sheets. This prompted the development of a cover detection protocol that rejected part of the impact sensor record when at least one sensor was partially or fully covered. (3) Because of the lack of sensor sensitivity to impacts of sand‐sized particles, which was anticipated, and the considerable sand component of bedload in this channel, a grain size‐limited bedload flux was estimated. This was accomplished by sampling the bedload captured by slot samplers and evaluating the variation of grain size with increasing flow strength. This considerably improved the results when compared to attempts at estimating the flux of the entire distribution of grain sizes. This calibration is a successful first attempt, though the impact sensors required several site‐specific calibration steps. A universal set of equations using impact sensors to estimate bedload transport of fine‐gravel with a large content of sand remains elusive. Notwithstanding, our study demonstrates the utility of impact sensor data, producing relatively low root mean square errors that are independent of measurements of flow strength (i.e. discharge). These tools will be particularly useful in settings that would benefit from new methodologies for estimating bedload transport in sand‐rich gravel‐bed rivers, such as the American desert Southwest.

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Bed Shear Stress in Experimental Flash Flood Bores over Dry Beds and over Flowing Water: A Comparison of Methods

Cited by 2 publications

References 52 publications

Transient Bedload Transport During Flashflood Bores in a Desert Gravel‐Bed Channel

Transient Bedload Transport During Flashflood Bores in a Desert Gravel‐Bed Channel

Strengths and weaknesses of acoustic pipe microphone systems in ephemeral, sandy, gravel‐bed rivers

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