AIMS This chapter discusses the physical properties of sediments that are found in coastal environments. Sediment properties affect the modes of sediment transport and deposition that take place along coasts. Fluid properties that drive sediment transport are described with respect to shear stress and the development of turbulent fl ow. The development and geometry of the most common bedform types (ripples and dunes) are directly related to fl uid dynamics.

5.1 Introduction

The term sediment refers to both organic and inorganic loose material that can be moved by physical agents including wind, waves, currents and under gravity. The sediments found in coastal environments can be either imported from outside the region (allochthonous) or locally produced (autochthonous). Allochthonous sediments are generally derived from the breakdown and transport of rocks into smaller particles, and along coasts commonly include minerals such as quartz, and clay minerals such as illite and montmorillonite. Autochthonous sediments include materials derived from the mechanical breakdown of rocky shorelines, but more commonly consist of broken-up shells of coastal organisms and/or the chemi- cal precipitates of dissolved minerals within coastal waters and include biogenic carbonate and silica. Globally, allochthonous sediments account for about 92 per cent of sediment in the modern coastal zone. Processes by which this sediment is delivered to the coast are, in decreasing order of importance, river, glacial and wind transport and volcanic eruptions.

Coastal landforms result from patterns of erosion and deposition that take place within larger coastal sediment systems. The wide range of spatial and temporal scales evident in coastal geomorphology (Figure 1.3) indicates that in order to fully understand large-scale morphodynamic processes it is also necessary to understand smaller-scale processes. The sequence of processes that control localised sediment movement are: (1) erosional entrainment of sediment into the flow via fluid-induced stresses and forces acting on the bed; (2) transport of sediment via momentum transfer from the fluid to the sedi-



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ment; and (3) settling or deposition of sediment back on the bed via gravity (Figure 5.1). Depending on the speed at which sediment settles to the bed rela- tive to the speed at which flow conditions change, this sequence can re-initialise in one of two ways, by re-entrainment of sediment that is at rest on the bed, or by remobilisation of sediment that has not yet completed its return to the bed. The detailed mechanics of this cycle vary, depending on the intrinsic properties of the sediment and the fluid.

Both air and water are important agents for coastal sediment transport. While many concepts presented in this chapter apply to both fluid types, they differ markedly in their density and viscosity, and hence their ability to move sediment. This chapter focuses on hydrodynamics and sediment trans- port by water. Aerodynamics and sediment transport by wind are described in Chapter 9. Sediment erosion, transport and deposition processes by water or wind are the mechanisms by which coastal landforms are developed and destroyed. Therefore sediment–fluid interactions are critical to understanding how coastal landforms respond to forcing by tides, waves and climate.

5.2 Sediment properties

5.2.1 Grain size

Grain size is the sediment property most widely measured by coastal geomor- phologists since it is important in a wide range of coastal processes. The simplest measurements of a grain’s size are the lengths of the longest, intermediate and shortest axes which are termed the a, b and c axes, respectively. By conven- tion the b-axis and c-axis are measured at right-angles to the a-axis. The axial dimensions of large grains can be measured directly with callipers, but smaller grains (of sand size or below) are measured indirectly, usually by sieving or laser granulometry. Sieving effectively measures only the b-axis length. In laser gran- ulometric techniques, the sediment sample is mixed with a circulating water source so that the grains are in motion as they move past the laser device. As a result, this technique has equal likelihood of recording any axis of the sample,

Figure 5.1 Schematic representation of the fundamental processes that together constitute sediment dynamics.

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Entrainmenl Deposition



so the averaged b-axis is a good approximation. Goudie (1990) describes various methods for grain-size analysis.

Grains are often classified by their b-axis length using the Udden- Wentworth Scheme (Table 5.1). Individual pebbles, cobbles and boulders are often termed clasts. Note that the class boundaries in Table 5.1 are presented both on a millimetre-scale and a phi-scale ( -scale), which is discussed below. The conversion between grain diameter D on the -scale to diameter on the millimetre-scale is

D = 2– (5.1)

and vice versa

= –log2 D (5.2)

It should be evident from both Table 5.1 and these two equations that a change from one phi class to the next involves a doubling of the axis length.

While it is common to use a single number (often the arithmetic mean) to represent the grain size of a sediment sample, this number is typically obtained from a statistical analysis of the b-axis length of all or a subsample of the indi-

Table 5.1 The Udden-Wentworth Scheme of grain size classifi cation.

mm Class terms

256 –8 128 –7 64 –6 32 –5 16 –4 8 –3 4 –2 2 –1 1 0 0.5 1 0.25 2 0.125 3

0.062 4

0.031 5 0.016 6 0.008 7

0.004 8





very coarse coarse

Sand medium fine

� very fine

coarse mediumSilt


� very fine


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vidual grains in that sample. The procedure typically begins with the grain size measurements from a sediment sample being presented as a frequency histo- gram and cumulative-frequency curve (Figure 5.2). Typically the histogram approximates a log-normal distribution, so when it is plotted on a logarith- mic scale such as the -scale, the histogram then appears normally distrib- uted (Figure 5.2a). It is for this reason that a log transformation of grain size measurements (performed either graphically by plotting on the -scale or by applying Equation 5.2) is performed prior to calculating grain size statistics. It is important to note that large positive -values indicate finer grain sizes and large negative -values indicate coarser grain sizes.

Before the widespread availability of computers, grain size statistics were calculated by graphical means. The most widely used formulae are (Folk and Ward, 1957)

Median = 50 (5.3)

16 + 50 + 84Mean = ————–––– 3 (5.4)

84 – 16 95 – 5Sorting = ——–— + ——–— 4 6.6 (5.5)

16 + 84 – 2 50 5 + 95 – 2 50Skewness = ————–––— + ———––––— 2( 84 – 16) 2( 95 – 5) (5.6)

95 + 5Kurtosis = ——–––——– 2.44 ( 75 – 25)


Figure 5.2 Two methods of presenting grain size data: (a) frequency histogram and (b) cumulative-frequency curve. The example shown is for a sample of medium sand that is very well sorted and fine-skewed (cf. Tables 5.1 and 5.2).









-0 .7

5 0 0.

75 1. 5

2. 25 3

3. 75

F re

qu en

cy (%


Grain size (phi)







-0 .7

5 0 0.

75 1. 5

2. 25 3

3. 75

C um

ul at

iv e

fr eq

ue nc

y (%

) Grain size (phi)

50th percentile

Mean = 1.75 Sorting = 0.34 Skewness = 0.30

D50 = 1.63 (a) (b)

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where, for example, 50 is the 50th percentile of the grain-size distribution plotted on the -scale (Figure 5.2b). Note that different percentiles are also used in these formulae. This is because the 16th/84th and 5th/95th percentiles approximate to the ± 2 and ± 3 standard deviations from the mean value of a normal distribution, respectively. These formulae are still widely used when the method of sediment analysis does not yield a complete grain-size distribution, because the calculation does not rely too heavily on the fine and coarse tails of the distribution. Where a significant fraction of the material is very fine (> 4 ), however, a laser granulometer or settling tube method is more appropriate for the sample. When a complete grain-size distribution is available, the statistical descriptors are calculated using the more accurate method of moment meas- ures (Pettijohn et al., 1987)


� fiM i = l

Mean = x– = ———— 100


——————— n

� fi(M – x –)2

i = l Sorting = = ——————–� 100 (5.9)

n fi(M – x –)3

Skewness = �—————— i = l 100 3 (5.10)

n fi(M – x –)4

Kurtosis = �—————— i = l 100


where n is the number of data points, fi is percentage of grains (or percentage of total weight of grains) in each size interval and M is the midpoint of each size interval in phi units.

The mean (1st-moment) is the most common value used to represent sedi- ment grain size, although if the distribution is highly skewed then the median or mode may be better representative. The Udden-Wentworth Scheme is a classification scheme based on mean grain size (Table 5.1), but there are also classification schemes for the other moment measures (Table 5.2). The sort- ing (2nd-moment) is controlled in part by the range of grain sizes present at the sediment source, as well as processes operating during transport and deposition. For example, rapidly-deposited sediment is often poorly sorted, whereas frequently reworked sediment tends to be well sorted. The skew- ness (3rd-moment) is an indicator of the symmetry of the grain size distribu- tion (Figure 5.3a). For a normal distribution, the measure of skewness is zero.

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Negative skewness means that there are more coarse grains than expected in a log-normally distributed sample, and positive skewness means that there are more fine grains than expected. Skewness can arise from the mixing of sedi- ments from different sources, but can also be indicative of sorting during trans- port and deposition. For example, beach sands are typically negatively skewed, because continual agitation by waves can resuspend the finer particles, leaving an apparent excess of coarser sizes in the bed sediment. There is often a signifi- cant shell component in beach sands that also negatively skews the distribution. The kurtosis (4th-moment) is an indicator of the peaked shape of the distri- bution (Figure 5.3b). Distributions with a high peak and low range are termed leptokurtic, whereas distributions with a subdued peak but a wider range are termed platykurtic. These moment measures in combination describe the total- ity of the grain size distribution of a sediment sample, and can often be used to distinguish its genetic origin or depositional environment (Allen, 1985). Other physical properties including grain mineralogy, shape and density also affect how individual grains behave in response to flow conditions.

Table 5.2 Descriptors for sediment sorting, skewness and kurtosis as defi ned by Folk and Ward (1957).

Sorting ( -scale) Skewness ( -scale) Kurtosis ( -scale)

< 0.35 Very well sorted

> +0.30 Strongly fine- skewed

< 0.67 Very platykurtic

0.35 to 0.50

Well sorted +0.30 to +0.10

Fine- skewed

0.37 to 0.90


0.50 to 0.71

Moderately well sorted

+0.10 to –0.10

Nearly- symmetrical

0.90 to 1.11

Mesokur tic

0.71 to 1.00

Moderately sorted

-0.10 to –0.30

Coarse- skewed

1.11 to 1.50


1.00 to 2.00

Poorly sorted

< –0.30 Strongly coarse- skewed

1.50 to 3.00

Very leptokurtic

> 2.00 Very poorly sorted

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5.2.2 Grain mass and density

The mass of a grain affects its inertia with respect to the forces applied to it by a moving fluid. A grain’s mass is equal to the product of its volume and density. The volume is clearly related to the grain size. The volume of spherical grains, for example, increases as the grain-diameter cubed. The density of a grain is its mass per unit volume, and is largely determined by mineralogy. We can distin- guish between ‘light’ and ‘heavy’ minerals, with a somewhat arbitrary dividing line at a density of 2900 kg m-3, because a similar separation often occurs in the environment. For example, alternating layers of light and dark sand seen on some beaches are usually concentrations of light and heavy mineral types, respectively. Typical ‘light’ minerals common along coasts include quartz,

Figure 5.3 Illustration of the (a) skewness and (b) kurtosis of typical coarse and fi ne sediment samples.

Coarse Fine

Coarse Fine

Positive Negative







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feldspars (plagioclase and orthoclase), clay minerals (montmorillonite, kaoli- nite, illite), and forms of calcium carbonate (aragonite, calcite, dolomite). Typi- cal ‘heavy’ minerals found along coasts include garnet, hornblende, magnetite, tourmaline, zircon and others, typically derived by weathering of igneous and metamorphic rocks.

5.2.3 Grain shape and roundness

Grain shape and roundness are sometimes confused with each other, but they are different measures, and have different interpretations. Grain shape is usually determined using the length ratios of the a, b and c axes. The propor- tionality between these axial lengths allows the grains to be located on a Zingg plot (Figure 5.4), which describes the grain’s outline shape. The ratio of the b and a axes indicates the degree of grain elongation, and the ratio of the c and b axes indicates the degree of grain flattening. Another measure of the grain flat- ness is the Corey Shape Factor CSF (Corey, 1949)

Figure 5.4 Zingg’s (1935) classification of grain shape.

0 c/b ratio

0.66 1




b/ a

ra tio


Oblate (tabular)


Prolate (roller)

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cCSF = —–— �ab


where CSF = 0 represents a flat disc and CSF = 1 represents a perfect sphere. The shape of the original mineral crystal largely determines the shape of a single grain, although dissolution and abrasion can have a modifying effect. Rock type and structure are also important where grains are of pebble size or larger; for example, slate rocks preferentially form blades where the blade thickness (c axis) is determined by the rock’s original cleavage. The shape of a grain influ- ences both its entrainment and settling. Flat grains are more difficult to entrain and settle more slowly than spherical grains. Sorting based on shape takes place on gravel beaches, with prolate and equant clasts predominantly found at the base of the beach face and oblate and bladed clasts found towards the top.

Roundness refers to the three-dimensional shape of the grain that considers in particular the roundness of grain corners and protrusions. Roundness can be distinguished from shape by considering the differences between a cubic box and a sphere or ball-shaped grain of the same size as the box. Both these forms have the same a, b and c axis lengths and therefore the same equant shape, but a box has corners (low roundness) whereas the sphere does not (high round- ness) (Figure 5.5). Therefore grain roundness should be considered alongside grain shape. The degree of roundness usually indicates the susceptibility of the grain to chemical weathering and/or the degree of mechanical abrasion it has experienced. Angular grains often indicate resistant minerals, or a depositional site that is close to source. Well-rounded grains indicate either easily weathered minerals, energetic environments where there is active mechanical abrasion, or a depositional site that is far from source. Grain roundness also infl uences the

Figure 5.5 Powers’ (1953) classifi cation of grain roundness for grains displaying low sphericity and high sphericity (From Tucker, 1995.) (Copyright © 1995 Blackwell Publishers, reproduced with permission.)

hi gh

-s ph

er ic

ity lo

w -s

ph er

ic ity

very-angular angular sub-angular sub-rounded rounded well-rounded

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sediment’s friction angle and packing. Relationships between grain size, shape and roundness are explored in Case Study 5.1.

A classic location for examining the size, shape and roundness relationships of clasts is Chesil Beach, southern England. Here, a linear gravel beach backed by a lagoon (The Fleet) extends for 28 km in a northwest–southeast direction. Beach height, beach width and sediment grain size all decrease progressively towards the northwest in the direction of dominant long- shore transport (Figure 5.6). Clast lithology is mainly quartzite and flint/ chert from the cliffs behind. Although these are relatively hard rock types, the high-energy waves mean that all lithologies are rapidly abraded in the direction of longshore drift. Carr et al. (1970) describe how the quartzite and flint/chert clasts change in size and shape at various points along the

Case Study 5.1 Grain size and shape variations on a gravel beach

b- ax

is le

ng th

(c m










0 0 1

13 10 7


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