Experimental study of the transport of mixed sand and gravel

Wilcock, Peter Richard; Kenworthy, Stephen T.; Crowe, J. C.

doi:10.1029/2001wr000683

Cited by 238 publications

(259 citation statements)

References 13 publications

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“…Wilcock et al [7] performed a series of 38 sediment transport experiments in a flume 0.6 m wide and 7.9 m long supplementing 10 previous experiments for another sediment mixture, thereby totaling 48 flume runs with five sediments in which flow, transport rate, and bed surface grain size were measured over a range of different water discharges. The sediments that comprised these 48 runs varied in bulk sand content (particle sizes smaller than 2 mm), from 6% to 34%.…”

Section: Sediment Transport Governing Equationsmentioning

confidence: 99%

“…The term "model" in this paper is used to describe both the sediment transport relations defined by the Wilcock-Crowe equations as well as the Bayesian statistical model. Readers are referred to [6,7] for further information on the Wilcock-Crowe equations and Schmelter et al [5,12] for further details on Bayesian sediment transport models.…”

Section: Model Formulationmentioning

confidence: 99%

“…The reference shear stress and variance inferred from this model represent the integrated or bulk behavior of the sediment mixture as an ensemble. Each different sediment mixture was analyzed in the model described by Equation (7). The inferred reference stresses and variances are summarized in Table 1.…”

Section: Bulk Transportmentioning

confidence: 99%

“…The Wilcock-Kenworthy-Crowe (WKC) [7] data used in this research were downloaded from the original paper's FTP site hosted by the American Geophysical Union (AGU). Several plots of the data were created for comparison against published figures for verification.…”

Section: Experimental Datamentioning

confidence: 99%

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Multi-Fraction Bayesian Sediment Transport Model

Schmelter

Wilcock

Hooten

et al. 2015

JMSE

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A Bayesian approach to sediment transport modeling can provide a strong basis for evaluating and propagating model uncertainty, which can be useful in transport applications. Previous work in developing and applying Bayesian sediment transport models used a single grain size fraction or characterized the transport of mixed-size sediment with a single characteristic grain size. Although this approach is common in sediment transport modeling, it precludes the possibility of capturing processes that cause mixed-size sediments to sort and, thereby, alter the grain size available for transport and the transport rates themselves. This paper extends development of a Bayesian transport model from one to k fractional dimensions. The model uses an existing transport function as its deterministic core and is applied to the dataset used to originally develop the function. The Bayesian multi-fraction model is able to infer the posterior distributions for essential model parameters and replicates predictive distributions of both bulk and fractional transport. Further, the inferred posterior distributions are used to evaluate parametric and other sources of variability in relations representing mixed-size interactions in the original model. Successful OPEN ACCESSJ. Mar. Sci. Eng. 2015, 3 1067 development of the model demonstrates that Bayesian methods can be used to provide a robust and rigorous basis for quantifying uncertainty in mixed-size sediment transport. Such a method has heretofore been unavailable and allows for the propagation of uncertainty in sediment transport applications.

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Section: Sediment Transport Governing Equationsmentioning

confidence: 99%

Section: Model Formulationmentioning

confidence: 99%

Section: Bulk Transportmentioning

confidence: 99%

Section: Experimental Datamentioning

confidence: 99%

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Multi-Fraction Bayesian Sediment Transport Model

Schmelter

Wilcock

Hooten

et al. 2015

JMSE

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“…Excess coarse bedload also is associated with laterally unstable channels (Herrera 2005). In addition, excess fine sediment may decrease the flow magnitude necessary to mobilize streambed sediments thereby reducing the stability of the streambed and potentially leading to scour of spawning habitat (Wilcock et al 2001). …”

Section: Factors Reducing Spawning Habitat Qualitymentioning

confidence: 99%

Grays River Watershed and Biological Assessment, 2006 Final Report.

May¹,

Geist

2007

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SummaryThe Grays River Watershed and Biological Assessment was funded to address degradation and loss of spawning habitat for chum salmon (Onchorhynchus keta) and fall Chinook salmon (Onchoryhnchus tshawytscha). In 1999, the National Marine Fisheries Service listed lower Columbia River chum salmon as a threatened Evolutionarily Significant Unit (ESU) under the Endangered Species Act of 1973 (ESA). The Grays River watershed is one of two remaining significant chum salmon spawning locations in this ESU. Runs of Grays River chum and Chinook salmon have declined significantly during the past century, largely because of damage to spawning habitat associated with timber harvest and agriculture in the watershed. In addition, approximately 20-25% of the then-remaining chum salmon spawning habitat was lost during a 1999 channel avulsion that destroyed an important artificial spawning channel operated by the Washington Department of Fish and Wildlife (WDFW). Although the lack of stable, high-quality spawning habitat is considered the primary physical limitation on Grays River chum salmon production today, few data are available to guide watershed management and channel restoration activities. The objectives of the Grays River Watershed and Biological Assessment project were to 1) perform a comprehensive watershed and biological analysis, including hydrologic, geomorphic, and ecological assessments; 2) develop a prioritized list of actions that protect and restore critical chum and Chinook salmon spawning habitat in the Grays River based on comprehensive geomorphic, hydrologic, and stream channel assessments; and 3) gain a better understanding of chum and Chinook salmon habitat requirements and survival within the lower Columbia River and the Grays River.The watershed-based approach to river ecosystem restoration relies on a conceptual framework that describes general relationships between natural landscape characteristics, watershed-scale habitat-forming processes, aquatic habitat conditions, and biological integrity. In addition, human land-use impacts are factored into the conceptual model because they can alter habitat quality and can disrupt natural habitatforming processes. In this model (Figure S.1), aquatic habitat-both instream and riparian-is viewed as the link between watershed conditions and biologic responses. Based on this conceptual model, assessment of habitat loss and the resultant declines in salmonid populations can be conducted by relating current and historical (e.g., natural) habitat conditions to salmonid utilization, diversity, and abundance. In addition, assessing disrupted ecosystem functions and processes within the watershed can aid in identifying the causes of habitat change and the associated decline in biological integrity. In this same way, restoration, enhancement, and conservation projects can be identified and prioritized.A watershed assessment is primarily a landscape-scale evaluation of current watershed conditions and the associated hydrogeomorphic riverine processes. The watershed ass...

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The complexity of gravel bed river topography examined with gradual wavelet reconstruction

Keylock

Singh

Foufoula‐Georgiou

2014

J. Geophys. Res. Earth Surf.

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This study analyzes the nonlinear nature of gravel bed elevation data series obtained in a large scale flume facility for four different discharges, including spatial transect series and temporal series at a fixed location. The goals are to infer the degree of complexity, to assess the scales and features in the signals that mostly contribute to this complexity, and to discern the difference in the manner in which this complexity is expressed in time and space. The asymmetry series (magnitude of the first‐order derivative of the signal over a separation distance, Δ, raised to the third power) of the bed elevation data forms the basis of our nonlinearity analysis. An important and novel dimension is the adoption of a recently introduced approach to surrogate data generation, gradual wavelet reconstruction. This produces partially linearized counterparts of the original series that preserve an increasing degree of the underlying nonlinear structure in the original data, while randomizing the rest. This allows us to discern the difference in the manner in which the nonlinear asymmetry is expressed in the temporal and spatial data series. Comparison in the real and phase space of the original and surrogate series is performed, and the analysis reveals that the complexity of both spatial and temporal series increases with discharge and that the nature of nonlinearity is quantitatively and qualitatively different. For the spatial series, asymmetry is expressed at large scales and is shown to result from the organization of intermediate scale features. In contrast, asymmetry in the temporal series is a smaller‐scale phenomenon. This is a consequence of the scale‐dependent propagating velocity of topographic features. Our results have implications for understanding the complexity of geomorphic processes as a function of the space and time scale considered: the complexity over the “bed form Lagrangian time scale” is different in nature to that over the “flow velocity Lagrangian time scale”.

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Experimental study of the transport of mixed sand and gravel

Cited by 238 publications

References 13 publications

Multi-Fraction Bayesian Sediment Transport Model

Multi-Fraction Bayesian Sediment Transport Model

Grays River Watershed and Biological Assessment, 2006 Final Report.

The complexity of gravel bed river topography examined with gradual wavelet reconstruction

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