18 Sediment storage and transport in coarse bed streams: scale considerations

Hassan, Marwan A.; Smith, Bonnie J.; Hogan, Dan; Luzi, David; Zimmermann, André; Eaton, Brett C.

doi:10.1016/s0928-2025(07)11137-8

Cited by 42 publications

(56 citation statements)

References 26 publications

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“…6 shows the autocorrelation and probability density functions of the WENO data. The numerical data are closely matched by the analytical solution (42) and the gamma distribution Ga(α,β ) with parametersα = b 2 ss /var ss b andβ = var ss b/ b ss . These findings are similar to those previously reported with the explicit version of the upwind scheme [3].…”

Section: The Stochastic Advection-diffusion Langevin Equationsupporting

confidence: 53%

“…One may be surprised that the local variance is not affected by advection whereas the sample variance depends a great deal on it, but this odd behavior can be explained by the development of spatial cross correlations [3]. At leading order, the autocorrelation function is ρ(t) = exp(−(κ +ū s x −1 ) t ) (42) and the probability density function is the gamma distribution Ga(α,β ) with parametersα = b 2 ss /var ss b andβ = var ss b/ b ss . As the dispersal induced by entrainment and deposition looks like diffusion, it is interesting to determine the response system when the process is purely diffusive (ū s = 0 and D u > 0).…”

Section: The Stochastic Advection-diffusion Langevin Equationmentioning

confidence: 99%

“…54]. The partial mobility regime is encountered when Sh < 2 Sh cr [42], with Sh cr the critical Shields number for the onset of sediment motion. It corresponds to situations in which part of the bed sediment is mobilized by the water stream and so, the resulting sediment transport rate is low to moderate.…”

Section: Introductionmentioning

confidence: 99%

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Stochastic-deterministic modeling of bed load transport in shallow water flow over erodible slope: Linear stability analysis and numerical simulation

Bohorquez

Ancey

2015

Advances in Water Resources

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Section: The Stochastic Advection-diffusion Langevin Equationsupporting

confidence: 53%

Section: The Stochastic Advection-diffusion Langevin Equationmentioning

confidence: 99%

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Stochastic-deterministic modeling of bed load transport in shallow water flow over erodible slope: Linear stability analysis and numerical simulation

Bohorquez

Ancey

2015

Advances in Water Resources

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“…Channel change (bed and bank erosion and/or deposition) has been measured annually in the study reach since 1970 as part of the Carnation Creek Fish-Forestry Interaction Program (see Hogan et al, 1998;Haschenburger and Rice, 2004;Tschaplinski et al, 2004;Hassan et al, 2008). Data has been collected for the project using various survey techniques and equipment (e.g.…”

Section: Field Trial 1: Dem Extraction and Error Assessment In Carnatmentioning

confidence: 99%

Photogrammetric monitoring of small streams under a riparian forest canopy

Bird¹,

Hogan²,

Schwab³

2010

Earth Surf Processes Landf

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The recent advent of digital photogrammetry has enabled the modeling and monitoring of river beds at relatively high spatial resolution (0·01 to 1 m) through the extraction of digital elevation models (DEMs). The traditional approach to image capture has been to mount a metric camera to an aircraft, although non-metric cameras have been mounted to a variety of novel aerial platforms to acquire river-based imagery (e.g. helicopters, radio-controlled motorized vehicles, tethered blimps and balloons). However, most of these techniques are designed to acquire imagery at fl ying heights above the riparian tree canopy. In relatively narrow channels (e.g. <20 m bankfull width), streamside trees can obscure the channel and limit continuous photogrammetric data acquisition of both the channel bed and banks, while still providing useful information regarding the riparian canopy and even spot elevations of the channel. This paper presents a technique for the capture and analysis of close-range photogrammetric data acquired from a vertically mounted non-metric camera suspended 10 m above the channel bed by a unipod. The camera is positioned under the riparian forest canopy so that the channel bed can be imaged without obstruction. The system is portable and permits relatively rapid image acquisition over rough terrain and in dense forest. The platform was used to generate DEMs with a nominal ground resolution of 0·03 m. DEMs generated from this platform required post-possessing to either adjust or eliminate erroneous cells introduced by the extraction process, overhanging branches, and by the effects of refraction at the air-water interface for submerged portions of the channel bed. The vertical precision in the post-processed surface generally ranged from ± 0·01 to 0·1 m depending on the quality of triangulation and the characteristics of the surface being imaged.

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“…Dense forest covers most fan surfaces and associated watershed areas. The resulting woody debris often strongly influences channel morphology (Hogan et al, 1998;Hassan et al, 2008). Although broad zones of fresh sediment deposits on the fan surface are found on many fans, channel locations are often discrete, with well-defined channel banks and little indication of recent sedimentation outside of the channel.…”

Section: Regional Settingmentioning

confidence: 99%