University, UK Graduated 2003. Morphodynamics of a Ridge-and-Runnel System on an Intertidal Beach. Daniel Buscombe. dbuscombe@usgs.gov Particle Size ‘by Proxy’ 6/39 6/39
2008. Morphodynamics, Sediment Dynamics and Sedimentation of a Gravel Beach. (Advisors: Gerd Masselink, Mark Davidson) Daniel Buscombe. dbuscombe@usgs.gov Particle Size ‘by Proxy’ 8/39 8/39
Sediment Stratification and Altered Resuspension Under Waves. (Daniel Conley, Alex Nimmo-Smith) Daniel Buscombe. dbuscombe@usgs.gov Particle Size ‘by Proxy’ 10/39 10/39
Sediment Stratification and Altered Resuspension Under Waves. (Daniel Conley, Alex Nimmo-Smith) Effective shear stress of graded sediments D. Buscombe1 and D. C. Conley1 Received 17 December 2010; revised 20 March 2012; accepted 20 March 2012; published 3 May 2012. [1] A meta-analysis of fractional mobilization data from 14 sets of experiments, totaling 103 different mixed sand and gravel beds and ﬂow conditions, has been carried out in order to identify an expression for effective shear stress, here deﬁned as the component of bed shear stress that is directly involved in transporting each grain size fraction in graded sediment. In doing so we test the assumption that excess stress should be deﬁned solely in terms of a critical stress rather than effective stress, which exhibits sensitivity to the ﬂow stage. In contrast to the approach which evaluates the size-distribution effects on motion threshold by comparing inferred transport rates, an alternative approach is utilized which is based on the skill of reproducing the measured, mobilized particle size distribution. A simple equation is developed for mobilization of sediment mixtures, based on a well-established transport law, and employing a classical ‘‘hiding function’’ approach to the problem of mitigating the bias toward mobilizing ﬁne material in the mixture. We use inverse methods to ﬁnd the optimal form of the hiding function which provides the best ﬁt with the data. We ﬁnd that the hiding function is indeed sensitive to the ﬂow and bed composition. On this basis, a simple deterministic equation is proposed for fraction-speciﬁc effective stress, which outperforms the next best existing formula based on critical stress by 34% on aggregate. Citation: Buscombe, D., and D. C. Conley (2012), Effective shear stress of graded sediments, Water Resour. Res., 48, W05506, doi:10.1029/2010WR010341. 1. Introduction [2] It is generally accepted that volumetric sediment transport rate per unit width, q, is related to bed shear stress, , such that q ¼ fðnÞ. If bed shear stress is nondi- mensionalized as, ¼ u2 Ã RgD (1) (known as the Shields parameter after Shields [1936]), in which uÃ ¼ ﬃﬃﬃﬃﬃﬃﬃﬃ = p is shear velocity, R ¼ ðs À Þ= is spe- ciﬁc sediment density, and , s are the densities of ﬂuid and sediment, respectively, g is gravitational acceleration, and D is grain diameter, nondimensionalized volumetric sediment transport rate (or sediment ﬂux) is expressed as qÃ ¼ fðn=2Þ, where qÃ ¼ q ﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃ RgD3 p : (2) [3] To highest order, a large proportion of such models can be represented as qÃ ¼ fð3=2Þ [e.g., Meyer-Peter and Müller, 1948; Einstein, 1950; Ashida and Michiue, 1972; Bagnold, 1973; Fernandez-Luque and Van Beek, 1976; Kachel and Sternberg, 1971; Parker and Klingeman, 1982; Wiberg and Smith, 1989; Wong and Parker, 2006] but alternatives exist, and are suggested predominantly for oscillatory ﬂows, such as qÃ ¼ fð4=2Þ [e.g., Sleath, 1978], qÃ ¼ fð5=2Þ [e.g., Hanes and Bowen, 1985], and qÃ ¼ fð6=2Þ [e.g., Madsen and Grant, 1976]. [4] Shields [1936] introduced the concept of a critical normalized shear stress, c , necessary to initiate sediment motion (the ‘‘mobility’’ of the sediment). The existence of such a threshold implies q ¼ 0 when < c , where c rep- resents a critical value of the Shields parameter, which is the value of the Shields parameter at incipient motion. The critical Shields parameter is commonly given as a function of grain Reynolds number: Reg ¼ uÃD ; (3) where is the kinematic viscosity of the ﬂuid. A commonly used sediment transport model, which was one of the ﬁrst to attempt to account for the implications of threshold con- ditions, is that of Meyer-Peter and Müller [1948] given by: qÃ ¼ 8ð À 0:047Þ3=2: (4) [5] The fact that their ‘‘threshold’’ is a constant is unsur- prising given that for the majority of their experiments Reg > 102, for which c is a constant %0.047. It has since become common practice in much of deterministic sedi- ment transport modeling to include a critical Shields pa- rameter to provide the mathematical consistency for the 1School of Marine Science and Engineering, University of Plymouth, Plymouth, UK. Copyright 2012 by the American Geophysical Union 0043-1397/12/2010WR010341 W05506 1 of 13 WATER RESOURCES RESEARCH, VOL. 48, W05506, doi:10.1029/2010WR010341, 2012 SCHMIDT NUMBER OF SAND SUSPENSIONS UNDER OSCILLATING GRID TURBULENCE Daniel Buscombe1 and Daniel C. Conley2 In many models of sand suspension under waves, the diffusivity of sediment is related to the diffusivity of momentum by the inverse of the turbulent Schmidt number. The value and parameterization of this number has been the topic of much research, yet a lack of consensus has led to ad hoc adjustments in models of turbulent sediment suspensions, with apparently little physical justification. In order to study sediment diffusivity we conducted laboratory experiments to generate gradient-only sediment diffusion. Concentrations of sand suspended by near-isotropic turbulence generated by an oscillating grid, together with detailed velocity measurements, were used to calculate vertical profiles of the Schmidt number with a range of grain sizes and flow conditions. Initial results suggest that momentum diffusivity is greater than sediment diffusivity, and that the ratio of the two scales with grid Reynolds number. Ongoing work will ascertain whether an apparent grain size dependence could instead be explained by two-way feedbacks between sediment and turbulence. Keywords: oscillating grid turbulence; Schmidt number; sediment diffusivity; suspended sediment; eddy viscosity INTRODUCTION In nearshore (combined wave and current) flows, fluid velocities and sand concentrations vary strongly in time during a wave cycle (Conley and Beach, 2003) which is why so-called (wave) 'phase- resolving' or 'intra-wave' models of suspended sediment transport have gained in popularity in recent years (e.g. Li and Davies, 2001; Holmedal et al., 2004; Henderson et al., 2004; Conley et al., 2008; Ruessink et al., 2009). This type of model predicts the velocity and sand concentration fields in time and space by combining solutions to the basic fluid momentum and continuity equations with an advection-diffusion equation to compute the sediment mass balance. Suspended sand concentrations are obtained by solving a 1DV advection-diffusion equation of the form: ∂C ∂t = ∂ ∂ z (ε s (z) ∂ C ∂ z +w s C ) (1) where C = instantaneous volumetric sand concentration; t = time; z = vertical coordinate; εs = sediment diffusivity; and ws = sand settling velocity. Describing fluid motion requires a corresponding momentum balance equation (e.g. Li and Davies, 2001), the solution of which requires an expression for turbulent eddy viscosity which describes the fluid turbulence. In the approach to modelling turbulent mixing under nearshore waves described above, the simplest treatment of sediment diffusivity is to express it as some fraction of the turbulent momentum diffusivity. The ratio, known as the Schmidt number, in nearshore sediment transport models is the ratio of the turbulent eddy viscosity, νt , to sediment diffusivity, εs : β= ν t ε s (2) Outputs of phase-resolving models of sediment suspension are very sensitive to the Schmidt number (e.g. Davies, 1995; Amoudry et al., 2005; Ruessink et al., 2009). Many models assume β=1 (e.g. Fredsoe et al., 1985, Celik and Rodi, 1988), an assumption which seems safest when the evidence for its value seems so contradictory. Indeed there are approximately as many studies in the literature which have used a value less than 1 as those which have a value greater than 1. An argument commonly stated for β >1 is that particles lose correlation with fluid motion as they settle through turbulent eddies (e.g. Fredsoe and Diegaard, 1992). A counter argument (which leads to β <1) is that centrifugal forces have a larger effect on particles than they do on the surrounding fluid, due to particle inertia, thought to be the case above a rippled bed (e.g. van Rijn, 1984; Davies and Thorne, 2005). Nearshore sediment transport literature reports values between 0.1 and 10. This large variation inadequate parameterization of β, which is therefore allowed to vary with model equations and boundary conditions used. The Schmidt number is often used as a tunable parameter (e.g. Ruessink et al., 2009), which isn't a satisfactory situation. 1 School of Marine Science and Engineering, Plymouth University, Drake Circus, Plymouth, Devon, PL4 8AA, UK 2 School of Marine Science and Engineering, Plymouth University, Drake Circus, Plymouth, Devon, PL4 8AA, UK 1 Evaluating Unsupervised Methods to Size and Classify Suspended Particles Using Digital In-Line Holography EMLYN J. DAVIES,* DANIEL BUSCOMBE,1 GEORGE W. GRAHAM,# AND W. ALEX M. NIMMO-SMITH School of Marine Science and Engineering, Plymouth University, Plymouth, United Kingdom (Manuscript received 13 August 2014, in ﬁnal form 3 December 2014) ABSTRACT Substantial information can be gained from digital in-line holography of marine particles, eliminating depth- of-ﬁeld and focusing errors associated with standard lens-based imaging methods. However, for the technique to reach its full potential in oceanographic research, fully unsupervised (automated) methods are required for focusing, segmentation, sizing, and classiﬁcation of particles. These computational challenges are the subject of this paper, in which the authors draw upon data collected using a variety of holographic systems developed at Plymouth University, United Kingdom, from a signiﬁcant range of particle types, sizes, and shapes. A new method for noise reduction in reconstructed planes is found to be successful in aiding particle segmentation and sizing. The performance of an automated routine for deriving particle characteristics (and subsequent size distributions) is evaluated against equivalent size metrics obtained by a trained operative measuring grain axes on screen. The unsupervised method is found to be reliable, despite some errors resulting from over- segmentation of particles. A simple unsupervised particle classiﬁcation system is developed and is capable of successfully differentiating sand grains, bubbles, and diatoms from within the surfzone. Avoiding miscounting bubbles and biological particles as sand grains enables more accurate estimates of sand concentrations and is especially important in deployments of particle monitoring instrumentation in aerated water. Perhaps the greatest potential for further development in the computational aspects of particle holography is in the area of unsupervised particle classiﬁcation. The simple method proposed here provides a foundation upon which fur- ther development could lead to reliable identiﬁcation of more complex particle populations, such as those containing phytoplankton, zooplankton, ﬂocculated cohesive sediments, and oil droplets. 1. Introduction Characterizing particles suspended in seawater has become a critical component in understanding the or- ganic carbon cycle, ocean acidiﬁcation, oceanic circula- tion, and future climate predictions. Possessing a method to accurately and automatically characterize these parti- cles has therefore become important for many areas of marine science and monitoring. For example, suspended particles serve as passive tracers that aid the un- derstanding of turbulent mixing of plankton, heat, and salinity. The measurement and understanding of sus- pended sediment ﬂux is crucial for the prediction of coastal and estuarine change, the operation of ports and harbors, and the safe passage of shipping. Suspended particles also play a key role in controlling radiative transfer (therefore, the interpretation of satellite ocean color imagery) and primary productivity. Particles also scatter sound—a principle that enables acoustic mea- surements of ﬂow velocities, suspended mineral sedi- ments, and bathymetric mapping. Information on the type (organic, inorganic, photosynthesizing, non- photosynthesizing), size, shape, and concentration of particles in seawater provides the necessary insight re- quired to advance understanding of these fundamental processes within the marine environment. Denotes Open Access content. * Current afﬁliation: Department of Environmental Technology, SINTEF Materials and Chemistry, Trondheim, Norway. 1 Current afﬁliation: Grand Canyon Monitoring and Research Center, and Southwest Biological Science Center, U.S. Geological Survey, Flagstaff, Arizona. # Current afﬁliation: Sir Alister Hardy Foundation for Ocean Science, Plymouth, United Kingdom. Corresponding author address: Emlyn J. Davies, Department of Environmental Technology, SINTEF Materials and Chemistry, P.O. Box 4760 Sluppen, NO-7465 Trondheim, Norway. E-mail: emlyn.john.davies@sintef.no JUNE 2015 D A V I E S E T A L . 1241 DOI: 10.1175/JTECH-D-14-00157.1 Ó 2015 American Meteorological Society Daniel Buscombe. dbuscombe@usgs.gov Particle Size ‘by Proxy’ 10/39 10/39
geomorphology of large rivers, Acoustic Remote Sensing. (Paul Grams, Matt Kaplinski) Daniel Buscombe. dbuscombe@usgs.gov Particle Size ‘by Proxy’ 11/39 11/39
geomorphology of large rivers, Acoustic Remote Sensing. (Paul Grams, Matt Kaplinski) Daniel Buscombe. dbuscombe@usgs.gov Particle Size ‘by Proxy’ 11/39 11/39
means (from physical samples) at relatively few discrete locations. • Accurate, high resolution Daniel Buscombe. dbuscombe@usgs.gov Particle Size ‘by Proxy’ 13/39 13/39
means (from physical samples) at relatively few discrete locations. • Accurate, high resolution • Costly, slow Daniel Buscombe. dbuscombe@usgs.gov Particle Size ‘by Proxy’ 13/39 13/39
• Monitoring continuously over space and/or time with lower accuracy. • Inferring particle size from remotely sensed signals • Track changes in particle size as landforms evolve (dependent vs.independent variable) Daniel Buscombe. dbuscombe@usgs.gov Particle Size ‘by Proxy’ 17/39 17/39
by indirect means (usually remotely sensed). • Continuous in space/time • Less costly, fast Daniel Buscombe. dbuscombe@usgs.gov Particle Size ‘by Proxy’ 18/39 18/39
by indirect means (usually remotely sensed). • Continuous in space/time • Less costly, fast • Non-intrusive/destructive Daniel Buscombe. dbuscombe@usgs.gov Particle Size ‘by Proxy’ 18/39 18/39
by indirect means (usually remotely sensed). • Continuous in space/time • Less costly, fast • Non-intrusive/destructive • Develop proxies Daniel Buscombe. dbuscombe@usgs.gov Particle Size ‘by Proxy’ 18/39 18/39
using high-frequency sound to classify riverbed substrates continuously in space Daniel Buscombe. dbuscombe@usgs.gov Particle Size ‘by Proxy’ 19/39 19/39
high-resolution topography to classify terrestrial landcover and substrates continuously in space Daniel Buscombe. dbuscombe@usgs.gov Particle Size ‘by Proxy’ 19/39 19/39
using in-line digital particle holography to size and classify surf zone suspensions continuously in time Daniel Buscombe. dbuscombe@usgs.gov Particle Size ‘by Proxy’ 19/39 19/39
Surf zone at Perranporth Beach, North Cornwall Microscopic plankton. Image: FAU Daniel Buscombe. dbuscombe@usgs.gov Particle Size ‘by Proxy’ 27/39 27/39
measuring particle size by studying the interactions of particles with sound, light and EM radiation Daniel Buscombe. dbuscombe@usgs.gov Particle Size ‘by Proxy’ 30/39 30/39
coverage and resolution, we can track changes in particle size as landforms evolve Daniel Buscombe. dbuscombe@usgs.gov Particle Size ‘by Proxy’ 30/39 30/39