TIMESCALES AND MECHANISMS OF SEDIMENT TRANSPORT …€¦ · TIMESCALES AND MECHANISMS OF SEDIMENT TRANSPORT AND SHORELINE MORPHODYNAMICS IN A FRINGING REEF SYSTEM. Michael Vincent

TIMESCALES AND MECHANISMS OF SEDIMENT

TRANSPORT AND SHORELINE MORPHODYNAMICS IN A

FRINGING REEF SYSTEM

Michael Vincent William Cuttler

Bachelor of Science (Geology, Honours), Boston College

This thesis is presented for the degree of Doctor of Philosophy of

The University of Western Australia

The ARC Centre of Excellence for Coral Reef Studies

The UWA Oceans Institute

School of Earth Sciences

2017

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THESIS DECLARATION

I, Michael Vincent William Cuttler, certify that:

This thesis has been substantially accomplished during enrolment in the degree.

This thesis does not contain material which has been accepted for the award of any

other degree or diploma in my name, in any university or other tertiary institution.

No part of this work will, in the future, be used in a submission in my name, for any

other degree or diploma in any university or other tertiary institution without the

prior approval of The University of Western Australia and where applicable, any

partner institution responsible for the joint-award of this degree.

This thesis does not contain any material previously published or written by another

person, except where due reference has been made in the text.

The work(s) are not in any way a violation or infringement of any copyright,

trademark, patent, or other rights whatsoever of any person.

The following approvals were obtained prior to commencing the relevant work

described in this thesis: Regulation 4 Authority CE004515

The work described in this thesis was funded by an Australian Research Council

(ARC) Future Fellowship (FT110100201), ARC Discovery Project grant

(DP140102026), the Gorgon Barrow Island Net Conservation Benefits Fund as

part of the Pilbara Marine Conservation Partnership, the Western Australian

Marine Institute (WAMSI) Dredging Science Note (Theme 2/3), a UWA

Scholarship for International Research Fees (SIRF), a UWA University

International Stipend (UIS), and a UWA Safety Net Top-Up Scholarship.

Technical assistance was kindly provided by: Andrew Pomeroy, Gundula Winter,

Leonardo Ruiz Montoya for fieldwork reported in Chapter 2; Rebecca Green,

Claire Ross, Michael Tropiano, Callum Standing, Anton Kuret, Carlin Bowyer,

Dive Ningaloo, and Oceanwise Expeditions for fieldwork reported in Chapters 3

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and 4; Mark Buckley, Curt Storlazzi, Kurt Rosenberger and Josh Logan from the

USGS Santa Cruz for June 2016 fieldwork reported in Chapter 4.

This thesis contains published work and/or work prepared for publication, some

of which has been co-authored.

Signature:

Date: 30 May 2017

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ABSTRACT Coral reefs create unique coastal sediment dynamics due to the strong link between

ecological processes, the sediment reservoir, and the morphology of the adjacent coastline.

For example, the benthic community provides the skeletal material that erodes to become

the sediment reservoir as well as the physical structure that alters the hydrodynamics and

influences the coastal morphology. Despite the prevalence of reef-fringed coastlines

globally, there is still a lack of understanding of the dynamics governing sediment transport

and shoreline morphology in fringing reef environments. Therefore, predicting future

changes to these coastlines in response to climate change requires a more in-depth, process-

based understanding of these environments. Here we utilize both field-based and numerical

techniques to examine the timescales and mechanisms of sediment transport along a section

of Australia’s largest fringing reef, Ningaloo Reef, under both typical and extreme

(cyclonic) conditions.

The spatial distribution of sediment texture (e.g. grain size, sorting, and skewness)

is the primary geologic evidence for inferring sediment transport pathways in a system.

However, the common techniques for these measurements (e.g. sieve, laser diffraction, and

image analysis) have been developed for regularly shaped (i.e. spherical), uniform density,

clastic (quartz-dominated) sediment. Bioclastic (e.g. reef-derived) sediment, in contrast, has

both irregular shapes and variable densities; thus, classic grain size analysis can lead to

misinterpretation of sediment transport processes in bioclastic environments. To avoid this

misrepresentation, settling velocity (ws) has been suggested to provide better insight into

the sediment transport of bioclastic particles. While models for converting from grain size

to settling velocity exist, they have been developed for clastic sediments and their

applicability to irregular, bioclastic sediment is unknown. Therefore, to determine the best

methodology for converting bioclastic sediment grain size to ws, median grain size (D50)

results from sieve, laser diffraction, and image analysis were converted to ws using three

common models [Gibbs et al., 1973, Dietrich 1982, and Ferguson and Church, 2004] and

compared to measured ws of natural, reef-derived sands. Results indicated that established

models of ws that account for particle shape can be used to estimate settling velocity of

irregularly-shaped, sand-sized bioclastic sediment with a limited amount of error. These

findings will allow for bioclastic grain size data measured with different methods and from

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different sites to be converted to ws and compiled to gain greater understanding of sediment

dynamics at individual localities and in bioclastic environments in general.

Due to the linkages between the benthic community and the sediment reservoir in

reef systems, the spatial distribution of sediment texture (or settling velocity) can be

combined with measurements of the contemporary ecological community and the

composition of the sediment reservoir to gain further insight into sediment transport

pathways. Quantification of sediment transport timescales and mechanisms can then be

gained by direct measurements of sediment ages and sediment fluxes. Habitat mapping

revealed that living coral accounts for less than 5% of the benthic cover at the study site.

However, thin section analysis showed that coral was the dominant sediment constituent,

accounting for up to 40% of deposits regardless of sub-reef environment. Transport

pathways through the system, inferred from the spatial distribution of grain size, agree well

with predicted circulation for wave-dominated fringing reefs (i.e. shore-directed flow over

the reef flat that diverges towards channels in the lagoon). However, due to the low coral

cover on the reef crest, these pathways do not provide evidence of a modern source of

coral-rich sediment to the lagoon. Radiometric dating (uranium-series) of coral fragments

from the outer reef flat, through the lagoon to beach indicates that sediment is ~1400 to

~20,000 years old, respectively. Thus, the sediment reservoir may be temporally

disconnected from the modern calcifying community. Over these time scales, wave-driven

bedload transport (in the form of shoreward migrating ripples) was found to be a key

mechanism of sediment transport across the lagoon towards the shoreline. Observed ripple

migration rates were up to 0.2 m day-1 and directed shoreward. Extrapolation of these rates

suggests that this mechanism could have transported the volume of sediment required to

build the modern shoreline morphology (i.e. large shoreline salient) within the age of the

oldest measured sediment. More generally, our results suggest that the active sediment

reservoir and calcifying community are temporally disconnected (i.e. sediments are derived

from an older reef system), and therefore, despite the recent observations of rapid declines

in net reef accretion, the sediment budget and shoreline morphology in this system may be

more resilient to climate change induced declines in the contemporary reef ecology.

On shorter timescales, tropical cyclones (TCs) have been suggested to be major

drivers of sediment dynamics in reef environments. Ningaloo Reef is located at the western

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boundary of Australia’s most cyclone-prone region (the North West Shelf) making it an

ideal setting to study the short-term impacts of TCs on reef-protected beaches. In situ wave

and water level observations, topographic surveys, and numerical modelling were used to

determine the effects of the direct impact of Tropical Cyclone Olwyn (March 2015).

Despite the extreme forcing (forereef wave heights ~6 m and 140 km hr-1 winds), the beach

showed remarkably minimal morphological change (-3 m3 m-1 on average), especially

compared to prior impacts that have been documented along exposed sandy coasts

worldwide (e.g., -30 m3 m-1for a similar strength TC). The modest morphologic changes are

explained by the spatial variability in pre-cyclone beach slope and the magnitude of

disequilibrium from typical nearshore hydrodynamics, primarily driven by locally-

generated wind waves during TC Olwyn. Within one year, the total sub-aerial beach

volume had increased by 15% beyond pre-cyclone volumes; thus, highlighting the coastal

protection offered by the reef (despite minimal coral cover) and the effectiveness of typical

sediment transport processes in sustaining the beach morphology.

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TABLE OF CONTENTS Thesis Declaration .......................................................................................................................... iii

Abstract ........................................................................................................................................... v

Table of Contents ........................................................................................................................... ix

List of Figures .............................................................................................................................. xiii

List of Tables................................................................................................................................ xix

Acknowledgements ...................................................................................................................... xxi

Authorship Declaration: Co-authored Publications ................................................................... xxiii

Notation ....................................................................................................................................... xxv

1. General Introduction ................................................................................................................ 1

1.1 Importance of reefs ................................................................................................ 1

1.2 Hydrodynamics and reef-associated landforms in fringing reefs .......................... 2

1.3 Reef sediment budgets ........................................................................................... 3

1.4 Research Questions ............................................................................................... 4

1.5 Thesis Outline ........................................................................................................ 5

2. Estimating the settling velocity of bioclastic sediment using common grain-size analysis

techniques .................................................................................................................................... 7

2.1 Abstract ................................................................................................................. 7

2.2 Introduction ........................................................................................................... 7

2.3 Methods ............................................................................................................... 11

2.3.1 Study Area ...................................................................................................... 11

2.3.2 Sediment density measurements .................................................................... 13

2.3.3 Sieve ............................................................................................................... 15

2.3.4 Settling tube ................................................................................................... 15

2.3.5 Laser diffraction ............................................................................................. 16

2.3.6 Image analysis ................................................................................................ 16

2.3.7 Conversion to settling velocity ...................................................................... 18

2.4 Results ................................................................................................................. 19

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2.4.1 Sediment density and compositional analysis ................................................ 19

2.4.2 Selection of ‘Digital Grain Size’ region of interest and conversion to

volume-by-size ............................................................................................... 21

2.4.3 Comparison of settling velocity equations ..................................................... 23

2.5 Discussion ........................................................................................................... 24

2.6 Conclusions ......................................................................................................... 31

A.1 Appendix for Chapter 2 ....................................................................................... 31

3. Temporal disconnect of reef calcifiers and the sediment reservoir: implications for

shoreline morphology of a fringing reef system ....................................................................... 33

3.1 Abstract ............................................................................................................... 33

3.2 Introduction ......................................................................................................... 33

3.3 Data and methods ................................................................................................ 36

3.3.1 Study area and field studies ........................................................................... 36

3.3.2 Habitat data .................................................................................................... 37

3.3.3 Sediment constituents and radiometric dating ............................................... 38

3.3.4 Statistical and spatial analysis ........................................................................ 39

3.3.5 Shoreline sediment supply: bedform migration rates and lagoon

hydrodynamics ............................................................................................... 40

3.4 Results ................................................................................................................. 42

3.4.1 Spatial distribution of reef calcifiers, sediment texture, composition, and

age .................................................................................................................. 42

3.4.2 Lagoon hydrodynamics and ripple migration rates........................................ 46

3.5 Discussion ........................................................................................................... 49

3.5.1 Ecological-sedimentological links ................................................................. 50

3.5.2 Implications for shoreline morphology and maintenance .............................. 52

3.6 Conclusions ......................................................................................................... 54

4. Response of a fringing reef coastline to the direct impact of a tropical cyclone ................... 55

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4.1 Abstract ............................................................................................................... 55

4.2 Introduction ......................................................................................................... 55

4.3 Data and methods ................................................................................................ 57

4.3.1 Study area ....................................................................................................... 57

4.3.2 Meteorological and hydrodynamic data ......................................................... 58

4.3.3 Beach morphology data ................................................................................. 59

4.3.4 Numerical model ............................................................................................ 60

4.4 Results ................................................................................................................. 60

4.4.1 Field observations .......................................................................................... 60

4.4.2 Model simulations .......................................................................................... 63

4.5 Discussion and Conclusions ................................................................................ 64

A.2 Appendix for Chapter 4 ....................................................................................... 66

5. General Discussion and Conclusions ..................................................................................... 75

5.1 Introduction ......................................................................................................... 75

5.2 Estimating settling velocity in bioclastic environments (Chapter 2)................... 75

5.3 Ecological-geomomorphic links in a fringing reef system (Chapter 3) .............. 77

5.4 Tropical cyclone impacts on fringing reef shoreline morphodynamics

(Chapter 4) ............................................................................................................ 78

5.5 Future research .................................................................................................... 79

References ..................................................................................................................................... 81

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LIST OF FIGURES Figure 2.1. Map of (a) Western Australia, (b) the northwest (NW) cape of Western

Australia, and (c) the study site at Tantabiddi, Ningaloo Reef, Western Australia.

Aerial imagery is from the SLIP Portal by the Western Australian Land Information

System (WALIS) and Landgate (https://www2.landgate.wa.gov.au/). White areas in

(c) are due to lack of coverage in the aerial imagery. .............................................. 12

Figure 2.2. Example thin section image used in constituent assemblage analysis. Coral,

coralline algae, quartz, mollusc and foraminifera fragments are highlighted. ......... 13

Figure 2.3. Examples of (a) non-overlapping regions of interest (ROI) and (b) overlapping

ROIs (~30% overlap) used when calculating grain-size distributions with the digital

grain-size graphical user interface (DGS GUI) from Buscombe [2013]. ................. 17

Figure 2.4. Comparison of digital grain-size (DGS) algorithm from Buscombe [2013] with

manual point count results for D50 when region of interest (ROI) is set to (a) the

whole image, (b) multiple, non-overlapping regions, (c) multiple, overlapping

regions, and (d) multiple, overlapping regions with image flattening. .................... 22

Figure 2.5. (a) Comparison of volume-by-size image analysis D50 with sieve D50; squares

and circles denote -0.5 and -1, respectively, for the value of x in Equation 2.4. (b)

Cumulative frequency curves for sieve, x = -0.5, and x = -1 for sample highlighted

in (a). ........................................................................................................................ 23

Figure 2.6. Comparison of equations used to convert (a) sieve, (b), laser diffraction, and (c)

image analysis grain-sizes to settling velocity. In all panels, blue indicates the Gibbs

et al., [1971] equation, red indicates Dietrich [1982], and green indicates Ferguson

and Church [2004]; asterisks indicate aragonite sand standard, triangles indicate

quartz sand standard. ................................................................................................ 24

Figure 2.7. Spatial maps depicting settling velocity boundaries at Tantabiddi, Ningaloo

Reef for (a) measured settling velocities, (b) image analysis-derived settling

velocities, (c) laser diffraction-derived settling velocities, and (d) sieve-derived

settling velocities. For (b), (c), and (d), the Dietrich equation was used with PRI =

3.5, CSF = 0.55 or 0.7, and ρpart = measured values. (e) and (f) depict the settling

velocity boundaries for sieve-derived measurements when ρpart = 1.85 or 1.2 g cm-3,

respectively. ............................................................................................................. 29

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Figure 2.8. Comparison of Rouse number (P) from measured and calculated settling

velocities. Values are shown for results from sieve, laser diffraction, and image

analysis. .....................................................................................................................30

Figure 3.1. (a) Western Australia, with the Ningaloo Peninsula outlined in yellow. (b) The

Ningaloo Peninsula, with the study site at Tantabiddi outlined in yellow. (c) The

study site, Tantabiddi, with locations of sediment samples used for grain size

analysis (black dots), thin section analysis (yellow circles) and uranium-series

dating (red triangles); the location of the quadpod instrument arrays are shown as

blue triangles on the northern and southern side of the salient.. ...............................37

Figure 3.2. (a) Quadpod deployed on the southern side of the salient, with the vertical

(downward-looking) and horizontal (cross-ripple looking) echosounders labelled.

(b) Schematic depicting the ability of the horizontal echosounder to measure and

track multiple ripples crests; data from this instrument was then used to calculate

ripple migration rates. ...............................................................................................41

Figure 3.3. Spatial distribution of unique habitats at Tantabiddi identified via DISPROF

analysis; the colour of the transect line (red, magenta, blue, or green) indicates the

unique habitat associated with that transect and yellow stars indicate approximate

locations of photos in (f) to (i). Breakdown of the benthic composition and example

images are shown for the coral-coralline algae (C-CCA) reef crest ( b and f), the

coralline algae and coral pavement (CCA-C) at the northern channel (c and g), the

macro algae and coralline algae pavement(MA-CCA) of the reef flat (d and h), and

the sandy lagoon (e and i). ........................................................................................43

Figure 3.4. Spatial distribution of (a) mean grain size and (d) sorting; note, for (d), larger

values of sorting correspond to decrease sorting (i.e. more poorly sorted). Spatial

distributions of sediment constituents, including (b) coral, (c) crustose coralline

algae, (e) quartz, and (f) molluscs. ............................................................................44

Figure 3.5. (a) Spatial distribution of biosedimentary facies as determined by DISPROF

analysis. (b) Results from canonical analysis of principle components indicating that

the two significant groups shown in (a) were classified based on percentage of

quartz. ........................................................................................................................45

Figure 3.6. Uranium-series ages of coral sediment dated from surficial sediment samples

collected for this study (red dots) from the fore reef (AWAC), outer reef flat

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(PJ028), back reef flat (PJ033, PJ026, PJ020), lagoon (PJ039 and PJ007), and beach

(Core2). CF or SF denotes that fragments were from the coarse (> 2mm) or sand

(<2mm) fractions, respectively. Uranium-series age of coral fragment dated from

the top of a lagoon sediment core [Collins et al., 2003]. Arrows indicate the

dominant mean wave-driven circulation patterns from model results presented in

Chapter 4.. ................................................................................................................ 47

Figure 3.7. (a) Normalized return amplitude (colour) at each bin (vertical axis) versus time

(horizontal axis) for the duration of the field experiment in May-June 2016 at the

southern instrument site. Each peak in return amplitude indicates the stoss side of an

individual ripple, with the local slope indicating the migration rate. (b)Time-series

of bed elevation at the South quadpod from the vertical (downward-looking)

echosounder. (c) Ripple migration rate at the southern instrument site calculated

from the horizontal-looking echosounder (i.e. data shown in a).............................. 48

Figure 3.8. (a) Directional diagram indicating direction normal to ripple crests (black

arrow), mean current direction (yellow arrow), mean sea-swell wave direction

(magenta arrow), and mean infragravity wave direction (green arrow); note, arrows

are not scaled by magnitude. (b) Offshore (20 m depth) significant wave height. (c)

Local significant wave height; (d) hourly sediment fluxes calculated from the ripple

migration rates; and (e) cumulative total transport due to ripple migration over the

~4 week field experiment at the northern (blue) and southern (red) quadpods. ...... 49

Figure 3.9. Volume calculation of the shoreline salient at Tantabiddi used to determine in-

fill time given average rates of ripple migration. Elevations for onshore locations

are derived from topographic light detecting and ranging (LiDAR) data flown for

the region in October 2015. ...................................................................................... 53

Figure 4.1. (a) Western Australia with the Ningaloo Peninsula in yellow; (b) the Ningaloo

Peninsula with the boundaries of the ‘outer’ Delft3D-FLOW and SWAN domain

indicated in blue; (c) study site and bathymetry with the cross-shore array of

pressure sensors indicated by the white triangles, the ‘inner’ Delft3D-FLOW and

SWAN domain show in grey and the ‘shore’ Delft3D-FLOW grid shown in black.

TC Olwyn track is the red line in (a) and (b). (d) Cross-shore depth profile from the

coastline to the forereef, with the reef crest marked with a red ‘x’ and pressure

sensors denoted by red triangles. ............................................................................. 58

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Figure 4.2. (a) Hsig at the forereef pressure sensor (18 m depth) from December 2014 to

March 2015, with a typical swell event (Hsig,SS ~ 1.8 m) denoted by the blue shading

and TC Olwyn (Hsig,SS = 5.8 m) denoted by the red shading. (b) Cross-shore profiles

of Hsig,SS (solid line) and Hsig,IG (dashed line) within the lagoon (instrument locations

indicated by ‘x’) during the (b) typical and (c) cyclone events. (d) Cross-shore

profiles of setup during a typical (blue) and cyclone (red) swell event (note different

scales). Wave spectra for the (e) forereef and (f) the shoreline sensors from the one

hour interval when waves were largest during the typical (blue) and cyclone (red)

events ........................................................................................................................61

Figure 4.3. (a) Observed beach elevation change (colors). (b) TC-induced beach volume

change and (c) pre-storm beach slope (β), calculated between the 1 m contour and

seaward limit of the beach (the r2 is between the β and beach volume change shown

in (b)). (d) Model-predicted Hsig along the 2 m isobath (yellow dashed line in (a))

for a 1.8 m swell (solid line) and peak TC conditions with (dashed line) and without

wind growth (dashed-x line) enabled. (e) Disequilibrium wave heights

(Hsig,TC/Hsig,Typical) predicted with (dashed) and without(dashed-x) wind growth (i.e.

ratio of black dashed or dashed-x line in (c) to solid line in (c)); the r2 is calculated

between the disequilibrium TC wave heights with wind growth (dashed line) and

post-cyclone beach volume change (data shown in (b)). ..........................................63

Figure 4.4. Comparison of TC impacts on open, sandy coasts (blue symbols) and reef-

fronted coasts (red symbols). For studies that did not report offshore Hsig, wave

heights from WaveWatchIII hindcast model predictions [Tolman, 2009;

ftp://polar.ncep.noaa.gov/pub/history/waves/nww3/] were extracted at the grid cell

nearest to the study location in ~20 m depth (magenta circles). Values for Mahabot

et al. [2016] represent alongshore-averaged values for transects where reef

morphology is constant. ............................................................................................65

Figure A.2.1. (a) Net shoreline movement and (b) linear regression rate of shoreline change

with 95% confidence intervals (red dashed lines) as determined from analysis of

historical aerial imagery (1969 to 2013; 13 total photos) using the Digital Shoreline

Analysis System [Thieler et al., 2009] ......................................................................67

Figure A.2.2. (a-e) Comparison of observed significant wave heights (Hsig,SS), (f-j) water

level (ℎ𝑡𝑡𝑡𝑡𝑡𝑡��), and (k-o) setup (�̅�𝜂) with output from Delft3D-FLOW and SWAN at the

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fore reef (P1), reef flat (P2), outer lagoon (P3), mid-lagoon (P4), and inner lagoon

(P5). .......................................................................................................................... 70

Figure A.2.3. (a) Significant wave height at the fore reef pressure sensor (P1, ~18 m depth)

for the three month deployment, with TC Olwyn indicated by the grey shading. (b)

Water level, (c) wind speed, and (d) wind direction during passage of TC Olwyn

(grey area in (a)). And significant wave height for the sea-swell (SS, blue) and

infragravity (IG, red) frequency bands at the (e) reef flat, (f) back reef flat, (g)

lagoon, and (h) shore. Wind speed shown here (c) is lower than Category 3

(Australian convention) conditions because measurements are taken from the

Milyering Weather Station. ...................................................................................... 71

Figure A.2.4. (a) Aerial image of the Tantabiddi salient with example beach profiles

(shown in b-e) highlighted in yellow. In (b)-(e), blue represents the July 2014 beach

morphology, red represents March 2015 (i.e. post-cyclone), cyan represents October

2015 (6 months post-cyclone), and black represents June 2016 (15 months post-

cyclone). (f) Example of the TC-induced dune erosion on the south side of the

salient (i.e. red line in d)........................................................................................... 72

Figure A.2.5. Model-predicted significant wave heights and circulation patterns from the

onset of TC Olwyn (a,b), the approach of TC Olwyn (c,d), and when peak wave

heights occurred (e,f). Note, for panels (c) and (d), both wind and waves are from a

northerly direction (i.e. approximately alongshore); whereas in (e) and (f) wind and

waves are from a south-westerly direction (i.e. cross-shore). .................................. 73

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LIST OF TABLES Table 2.1. Summary of sediment density values found in the literature (*range includes

measurements on specific sediment constituents) ................................................... 14

Table 2.2. Constituent assemblages for bulk sediment samples from the sub-reef

environments at Tantabiddi, Ningaloo Reef. ........................................................... 20

Table 2.3. Normalized mean absolute error (NMAE) and normalized root-mean-square

error (NRMSE) for (a) sieve, (b) laser diffraction and (c) image analysis due to

varying sediment density for each settling velocity equation. ................................. 21

Table 3.1. Summary of radiometric (Uranium-series) analysis. ......................................... 46

Table A.2.1. Summary of Murphy [1988] skill score (MS) for modelled significant wave

height (Hsig,SS), water level (ℎ𝑡𝑡𝑡𝑡𝑡𝑡��), and setup (�̅�𝜂) at the instrument locations. For MS

a score of 1 indicates perfect agreement between the model results and the

observations. ............................................................................................................ 70

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ACKNOWLEDGEMENTS I would like to thank my supervisors Prof Ryan Lowe and Dr Jeff Hansen for their

support, patience, and encouragement throughout each of the various projects that were part

of this thesis. I would also like to thank my supervisors Prof Malcolm McCulloch and Dr

Jim Falter for helpful feedback and insightful conversations.

A huge thank you to the various people who came to Exmouth throughout the years,

including: Renee Gruber, Callum Standing, Rebecca Green, Claire Ross, Michael Tropiano,

Leonardo Ruiz Montoya, Andrew Pomeroy, Carlin Bowyer, and Anton Kuret. I would also

like to thank Dr Ben Fitzpatrick of Oceanwise Expeditions for helping to coordinate

fieldwork and providing insightful comments on the habitat data analysis; and Dive

Ningaloo for fieldwork assistance.

This research was funded by the ARC Centre of Excellence for Coral Reef Studies

(CE140100020), ARC Future Fellowship (FT110100201), ARC Discovery Project Grant

(DP140102026), the Western Australian Marine Science Institute (WAMSI) Dredging

Science Node (Theme 2/3), the Gorgon Barrow Island Net Conservation Benefits Fund as

part of the Pilbara Marine Conservation Partnership, as well as a UWA Scholarship for

International Research Fees, UWA University International Stipend, and a UWA Safety-

Net Top up scholarships.

I would like to say a special thank you to Prof Gail Kineke who gave me my first

introduction to coastal processes fieldwork and research. I will always be thankful for the

research opportunities you provided me as well as your encouragement and support during

and after my four years at Boston College.

To my friends: Strunky, Choatey, and Dave, you have been my family on this side

of the world; thank you for always helping me to maintain balance in my life. To my

brothers: Bubba and Jeff, thank you for putting in the effort to maintain our brotherly bond

while I’ve been so far away for so long; our relationship is one of the of most special in my

life. To Mom and Enri, thank you for your never-ending support and love throughout my

entire life, without you two, nothing I have achieved would have been possible. Finally, I

would like to thank Claire Ross for her support and love; I have loved every moment of our

time together and I look forward to what the future holds for us.

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AUTHORSHIP DECLARATION: CO-AUTHORED PUBLICATIONS This thesis contains work that has been previously published and is currently in review for

publication.

Details of the work:

Cuttler, M.V.W, Lowe, R.J., Falter, J.L., and Buscombe, D. (2016) Estimating the settling velocities of bioclastic sediment using common grain-size analysis techniques. Sedimentology, doi: 10.1111/sed.12338.

Location in thesis: Forms the entirety of Chapter 2

Student contribution to work: I collected the sediment for analysis, designed and conducted the lab analysis, and wrote the manuscript (80% overall). RJL and JLF helped with experimental design, data analysis, and manuscript editing. DB provided expertise in applying analysis methodology as well as critical review of the manuscript.

Co-author signatures and dates:

Ryan Lowe James Falter Daniel Buscombe

Date: 2 June 2017 Date: 3 June 2017 Date: 7 June 2017


Cuttler, M.V.W, Hansen, J. E., Lowe, R. J., and McCulloch, M. T. (prepared for submission to Marine Geology) Temporal disconnect of reef calcifiers and the sediment reservoir: implications for shoreline resilience in a fringing reef system


Student contribution to work: I conducted fieldwork and data analysis and wrote the manuscript (80% overall). JEH helped with fieldwork and data analysis for ripple migration data and manuscript editing. RJL helped with experimental design, data analysis, and manuscript editing. MTM helped with interpretation of radiometric data and manuscript editing.


Ryan Lowe Jeff Hansen Malcolm McCulloch

Date: 2 June 2017 Date: 31/05/2017 Date: 31/05/2017

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Cuttler, M.V.W, Hansen, J. E., Lowe, R. J., and Drost, E. (in review at Limnology and Oceanography Letters) Impact of a tropical cyclone to a fringing reef coastline.


Student contribution to work: I collected and processed the field data, developed the numerical model and analysed the data, and wrote the manuscript (80% overall). JEH helped with deployment of instruments and numerical model development and analysis. RJL helped write the manuscript. ED helped with developing the numerical model.


Ryan Lowe Jeff Hansen Edwin Drost

Date: 2 June 2017 Date: 31/05/2017 Date: 30/05/2017

Student signature: Date: 30 May 2017

I, Ryan Lowe, certify that the student statements regarding their contribution to each of the works listed above are correct.

Coordinating supervisor signature: Date: 2 June 2017

xxv

NOTATION Symbol Definition Units

β Beachface slope -

C1, C2 Empirical constants from settling velocity equation of

Ferguson and Church (2004) -

CSF Corey (1949) shape factor -

D Grain size mm

D* Non-dimensional grain size -

D50 Median grain size mm

Dn Nominal diameter mm

DGS Digital grain size algorithm developed by Buscombe

(2013) -

ε Surficial sediment porosity -

g Gravitational acceleration m s-2

GSD Grain size distribution -

Hsig Significant wave height (total) m

Hsig,SS Significant wave height in the sea-swell frequency band m

Hsig,IG Significant wave height in the infragravity frequency

band m

IA Image analysis -

k Von Karman’s constant -

LD Laser diffraction -

ζ Instantaneous ripple elevation m s-1

Ms Mass of dry sand g

MFW Mass of volumetric flask with fresh water g

MFSW Mass of volumetric flask with sand and water g

Mripple Cumulative bedload sediment flux due to ripple

migration kg m-1 s-1

η Setup (due to both waves and wind) m

n Sample size -

p(V-W)i Volume-by-weight proportion of the i-th size fraction -

xxvi

p(S)i Image-derived areal proportion of the i-th size fraction -

P Rouse [1937] parameter -

PRI Powers (1953) roundness index -

r Grain radius cm

R1 Empirical equation from Dietrich (1982) relating settling

velocity to particle density and size -


velocity to particle shape -


velocity to particle roundness -

RMSE Normalized root mean square error %

ROI Region of interest used to calculated grain size

distribution with digital grain size algorithm of

Buscombe (2013)

-

S Sieve analysis -

ST Settling tube analysis -

T Correction factor determined by temperature of fresh

water -

t Time s

u* Shear velocity m s-1

Vm Ripple migration rate m s-1

ws Settling velocity cm s-1

W* Non-dimensional settling velocity -

x Empirical constant for conversion (Equation 2.4) -

µ Dynamic viscosity kg m-1 s-1

ν Kinematic viscosity m2 s-1

ρ Fluid density g cm-3

ρs Sediment density kg m-3

ρbulk Bulk density g cm-3

ρmineral Mineral density g cm-3

ρpart Particle density g cm-3

ϕpart Particle porosity -

1

1. GENERAL INTRODUCTION

1.1 Importance of reefs

Coral reefs cover approximately 2x106 km2 of the world’s tropical oceans, creating

the largest biologically constructed features on Earth that in turn support some of the most

productive and diverse ecosystems [Moberg and Folke, 1999; Masselink et al., 2011].

Despite occupying less than 1% of the ocean’s area, reefs provide valuable ecosystem

services to human populations, including supporting fisheries and tourism [Moberg and

Folke, 1999] as well as providing coastal protection from large waves and storm damage

[Ferrario et al., 2014].

Coral reefs are broadly classified based on the relationship between the reef crest

and non-reefal landmass [Darwin, 1842], with fringing reefs occurring where the reef is

close to or attached to the landmass; barrier reefs are separated from land by a large lagoon;

and atolls are annular reefs enclosing a deep, central lagoon [Kennedy and Woodroffe,

2002]. In all reef environments, reef-building organisms (e.g. corals and coralline algae)

create a three-dimensional structure that has a profound impact on the local hydro- and

sediment dynamics of the system. These processes can lead to distinct coastal morphologies

[e.g. shoreline salients in fringing reefs; Sanderson and Eliot, 1996] and/or build the only

habitable land mass [e.g. reef islands in atolls; Perry et al., 2015]. However, the resilience

of these landforms to climate change induced sea level rise and shifts in reef ecology is still

uncertain [Perry et al., 2011; Cheriton et al., 2016a].

The current threats faced by coral reef ecosystems, including mass thermal

bleaching and ocean acidification [Hoegh-Guldberg, 1999; Hughes et al., 2017], are

resulting in declines in net reef accretion rates [Perry and Morgan, 2017]. The persistence

of reef-associated landforms can be innately connected to the health of the reef as the

sediment available for transport and shoreline maintenance is directly derived from the

detrital remains of the reef organisms. Therefore, any declines in reef ecosystem health may

have direct impacts to coastal morphology over some timescale. Coupled to these

2

ecological processes are the changing hydrodynamics over reefs associated with decreasing

coral cover (due to the above threats) and rising sea level, with both factors contributing to

the increase of wave energy reaching reef-protected coastlines [Grady et al., 2013;

Cheriton et al., 2016b]. However, both the timescales (i.e. length of time for key sediment

constituents to appear in the active sediment reservoir from the benthic community) and

mechanisms (i.e. sediment transport processes) of this ecological-geomorphic link remain

poorly understood. Therefore, there is a clear need for an increased understanding of these

complex processes to better predict how these coastlines will respond to future

environmental changes.

1.2 Hydrodynamics and reef-associated landforms in fringing reefs

Incident sea-swell wave energy approaching reef-protected coastlines is primarily

dissipated due to depth-induced breaking at the shallow reef crest [Young, 1989]. The

remaining energy is typically dissipated by bottom friction over the reef flat [Lowe et al.,

2005]. Dissipation at the reef crest surf zone creates wave forces (radiation stress gradients)

that elevate the water level (wave setup) and create pressure gradients that drive flow across

the reef flat towards the lagoon [Monismith, 2007; Lowe and Falter, 2015]. This flow then

returns to the ocean via channels that break the reef structure, with the circulation patterns

determined by the unique geometry of the lagoon and channels [Lowe et al., 2010; Lowe

and Falter, 2015].

These wave-driven flows are then responsible for removing sediment that would

otherwise smother reef organisms [Rogers, 1990; Fabricius, 2005] and redistributing it

through the system to in-fill lagoons or build reef-associated landforms [Morgan and

Kench, 2014]. Fringing reefs are commonly associated with the development of

accretionary landforms such as shoreline salients and tombolos [Gourlay, 1981; Black and

Andrews, 2001a, 2001b]. For example, these features are common along the entire length of

Australia’s largest fringing reef system, Ningaloo Reef [Sanderson and Eliot, 1996]. While

there exists some understanding of the wave-driven circulation patterns that could lead to

salient formation [e.g. Gourlay, 1981; McCormick, 1993], and, more recently, an

understanding of the shoreline response to submerged, artificial structures [Ranasinghe and

Turner, 2006; Ranasinghe et al., 2006], there remains a gap in understanding of the

physical mechanisms (e.g. sediment transport processes) responsible for these formations as

3

well as the timescales over which these processes occur. Furthermore, none of this is

understood in the context of natural, fringing reef environments, which will innately be tied

to the reef ecology as it influences both the hydro- and sediment dynamics of these systems.

Greater understanding of these processes in a contemporary reef system will provide

critical data that can aide interpretation of the stratigraphic record in these environments

[May et al., 2016].

1.3 Reef sediment budgets

The creation of reef framework is the product of a suite of physical, biological, and

chemical processes [i.e. ‘taphonomic processes’; Scoffin, 1992]. A carbonate budget

provides a foundation for quantifying these constructive and destructive processes to assess

net reef accretion or erosion [Perry et al., 2008, 2011]. Constructive processes include

deposition of calcium carbonate material (CaCO3), which can occur through the growth of

primary reef-building organisms (e.g. corals) or secondary encrusting organisms (e.g.

crustose coralline algae), or directly from the death of calcareous reef-dwelling organisms

(e.g. molluscs, foraminifera, etc.). Destructive processes remove CaCO3 through physical

(e.g. wave breaking), biological (e.g. bioerosion), or chemical (e.g. dissolution) erosion of

the reef structure.

Taphonomic processes generate bioclastic sediment directly from the skeletal

remains of the reef-building (e.g. corals and coralline algae) and reef-dwelling (e.g.

molluscs, foraminifera, etc.) organisms. These sediments are then transported through the

system and incorporated into reef-associated landforms [Yamano et al., 2000; Dawson et

al., 2012; Morgan and Kench, 2014]; thus, directly linking the reef ecology to the

geomorphology of the system. Quantification of a detrital sediment budget has been shown

to provide insight into the transport pathways, rates, and ecological-geomorphic links

within a reef system [Harney and Fletcher, 2003; Morgan and Kench, 2014]. A detrital

sediment budget must incorporate the sediment production, the storage time of sediment

within various temporary sinks (e.g. lagoon and/or beach), and the transport rates and

pathways of sediment through the system [Harney and Fletcher, 2003; Morgan and Kench,

2014].

Recently, this budget framework has been applied to understand the sensitivity of

reef islands to changing oceanic forcing [e.g. sea level rise and ocean acidification; Hart

4

and Kench, 2007; Perry et al., 2008, 2011; Kench et al., 2014; Morgan and Kench, 2014,

2016a]. However, despite the abundance of carbonate budget analyses in fringing reef

environments [Land, 1979; Sadd, 1984; Hubbard et al., 1990; Perry et al., 2012, 2013a;

Kennedy et al., 2013], there has been limited inclusion of sediment dynamics [Land, 1979;

Sadd, 1984; Hubbard et al., 1990] or effort to understand the geomorphic stability of these

coastlines [Harney and Fletcher, 2003]. Therefore, there is a need for further

measurements of contemporary sediment dynamics in fringing reefs to better understand

the timescales and mechanisms linking the reef ecology to the adjacent coastline. These

measurements will provide critical, process-based understanding that can be used for

interpretation of the stratigraphic record [May et al., 2016] as well as for predicting the

sensitivity of these coastlines to future changes in ocean forcing.

1.4 Research Questions

The aim of this thesis is to understand the mechanisms and timescales of sediment

transport and shoreline morphodynamics in a fringing reef system. More specifically, this

research addresses the following questions:

i. What is the most representative grain size measure for understanding

transport of bioclastic sediment?

At the most basic level, sediment transport is inferred from the spatial

distributions of sediment texture, especially grain size. However, due to the

biological source of reef-derived sediments, common grain size analysis

techniques can misrepresent the transport and depositional history of

bioclastic deposits; therefore, settling velocity has been suggested as a better

metric for bioclastic particles. Chapter 2 initially assesses the accuracy of

three common settling velocity models for converting results of common

grain size analysis techniques (sieve, laser diffraction, and image analysis) to

settling velocity, and then applies the results to interpret the spatial

distribution of settling velocity at the field site (Tantabiddi, Ningaloo Reef,

Western Australia).

ii. What is the modern calcifying community assemblage and how does

this compare with the spatial distribution of sediment composition?

5

What is the age of the contemporary sediment reservoir and the

dominant transport mechanism linking the reef and the shoreline?

A comparison of the reef community and the composition of the sediment

reservoir provides insight into the ecological-geomorphic linkages within a

reef system. Furthermore, by quantifying the rates of sediment transport and

the age of the sediment (i.e. the timescales of transport), it is possible to

understand sensitivity of this ecological-geomorphic coupling to future

changes in reef ecology. For example, if sediment is young (i.e. 10s to 100s

years old), and transport rates are fast, then the adjacent coastline would be

expected to change rapidly in response to a change in the sediment reservoir.

Chapter 3 analysed field data collected between 2013 and 2016 to establish

the spatial distribution of benthic habitats, biosedimentary facies, and

sediment ages as well as to quantify the sediment flux to the shoreline.

These measurements will provide critical data for the prediction of the future

resilience of reef-protected landforms in response to changing ocean

conditions.

iii. What are the impacts of short-term, extreme events (e.g. tropical

cyclones) on the morphology of a reef-protected coast?

Tropical cyclones have been suggested to have large impacts on sediment

dynamics in reef systems; however, there have been limited studies coupling

direct measurements of hydrodynamics and coastal morphology during the

direct impact of a tropical cyclone. In March 2015, the eye Tropical Cyclone

Olwyn passed within 10 km of the study site. Chapter 4 combines detailed

analysis of field measurements of waves, water levels, and beach

morphology with a numerical model to quantify the forcing mechanisms

responsible for the observed morphological change. This provides an

understanding of the protective capability of reefs in reducing coastal

erosion under extreme storms, particularly compared to exposed, sandy

beaches.

1.5 Thesis Outline

The contents of this these are organised as a series of journal publications (Chapters

2 to 4), each with a separate introductory and discussion section. In order for these chapters

6

to stand as individual, publishable units, some repetition of introductory and

methodological material was necessary. Chapter 2 is an in-depth methods assessment for

determining settling velocity of bioclastic particles from common grain size analysis

techniques; the results are then applied to the study site, Tantabiddi, Ningaloo Reef,

Western Australia. Chapter 3 evaluates the coupling of the reef ecology and sediment

reservoir, provides direct measurements of sediment transport to the shoreline, and assesses

the implications for shoreline development and maintenance. Chapter 4 investigates the

shoreline morphodynamics on short (i.e. single event) timescales through the analysis of

data collected during the passage of a tropical cyclone. Finally, Chapter 5 provides an

overall discussion that draws together the key conclusions of the preceding chapters to

discuss their implications for the future resiliency of reef-protected coastlines.

7

2. ESTIMATING THE SETTLING VELOCITY OF BIOCLASTIC

SEDIMENT USING COMMON GRAIN-SIZE ANALYSIS TECHNIQUES

2.1 Abstract

Most techniques for estimating settling velocities of natural particles have been

developed for siliciclastic sediments. Therefore, to understand how these techniques apply

to bioclastic environments, measured settling velocities of bioclastic sedimentary deposits

sampled from a nearshore fringing reef in Western Australia were compared with settling

velocities calculated using results from several common grain-size analysis techniques

(sieve, laser diffraction, and image analysis) and established models. The effects of

sediment density and shape were also examined using a range of density values and three

different models of settling velocity. Sediment density was found to have a significant

effect on calculated setting velocity, causing a range in normalized root-mean-square error

of up to 28%, depending upon settling velocity model and grain-size method. Accounting

for particle shape reduced errors in predicted settling velocity by 3% to 6% and removed

any velocity-dependent bias, which is particularly important for the fastest setting fractions.

When shape was accounted for and measured density was used, normalized root-mean-

square errors were 4%, 10%, and 18% for laser diffraction, sieve, and image analysis,

respectively. The results of this study show that established models of setting velocity that

account for particle shape can be used to estimate setting velocity of irregularly shaped,

sand-sized bioclastic sediments from sieve, laser diffraction, or image analysis-derived

measures of grain size with a limited amount of error. Collectively, these findings will

allow for grain-size data measured with different methods to be accurately converted to

settling velocity for comparison. This will facilitate greater understanding of the hydraulic

properties of bioclastic sediment which can help to increase our general knowledge of

sediment dynamics in these environments.

2.2 Introduction

8

Settling velocity in water is a fundamental physical property of sediments, which

can be used to understand how sediment is entrained, transported and deposited in marine

environments [Miller et al., 1977; Komar and Clemens, 1986; Le Roux et al., 2002;

Paphitis et al., 2002]. Settling velocity is key to understanding both the hydrodynamic

properties of bed sediment, and thus transport mode and/or entrainment thresholds [Shields,

1936; Rouse, 1937], as well as sedimentary plume dynamics, where settling velocity

determines sediment fall-out times and advection length scales [Syvitski et al., 1988].

Furthermore, this can be of particular importance in environments such as estuaries, where

deposition of suspended material can lead to harmful build-ups of heavy metals [Birch and

Taylor, 1999]; or in coral reefs, where increased turbidity can limit light availability

[Storlazzi et al., 2015] and lead to coral stress and/or mortality [Rogers, 1990].

The settling velocity of a particle is determined by its size, shape and density. These

variables have been empirically related for straight-forward conversion from grain

diameter, a commonly measured parameter, to settling velocity [Gibbs et al., 1971;

Dietrich, 1982; Le Roux, 1992; Cheng, 1997; Le Roux et al., 2002; Ferguson and Church,

2004]. Although each model accounts for particle size and density, each has a different

treatment of particle shape. For example, the Gibbs et al. [1971] relationship, makes no

correction for grain shape, accounting only for sediment density and size; Ferguson and

Church [2004] use two constants in their equation that are related to grain shape; and

Dietrich [1982] explicitly accounts for grain shape through the Corey [1949] shape factor

(CSF) and grain roundness through the Powers [1953] roundness index (PRI).

These equations have been developed and tested principally for siliciclastic

sediments; however, it remains unclear how accurate these equations are when applied to

bioclastic sediment which is typically highly irregular in shape and density. Bioclastic

sediment is derived from the physical and chemical breakdown of biogenic structures (e.g.

coral reefs) and the death of organisms (e.g. foraminifera, molluscs, Halimeda, etc.). These

sediment sources have a direct influence on particle morphologies, sizes, and densities

present in the sediment pool as organisms build skeletons with specific porosities, and

therefore densities, and break down into characteristic shapes and sizes [Sorby, 1879; Perry

et al., 2011]. For example, Halimeda and mollusc fragments tend to form plate-like

particles, urchin spines form rod-like particles, foraminifera generally form disc-like

9

particles, and most corals and coralline algae form irregular, block-like particles [Maiklem,

1968; Braithwaite, 1973]. Because these irregularities make it difficult to derive a

characteristic grain diameter for bioclastic deposits using common grain size techniques

(see below), settling velocity has been suggested to be a more meaningful metric for

interpretation of bioclastic sedimentary deposits [Kench and McLean, 1996]. Although,

settling tube is the primary method for analysing settling velocity of bulk samples, this

analysis can be resource intensive and not all labs have this equipment; therefore, other

common grain size methods are frequently applied to study bioclastic environments [Folk

and Robles, 1964; Harney et al., 2000; Larcombe et al., 2001]. Thus, there is a clear need

to understand how accurately results from common grain size techniques can be converted

to settling velocities using established empirical models.

Common methods to measure grain size distributions (GSDs) include: sieves; laser

diffraction; and image analysis. Sieve analysis involves sorting sediment particles through a

series of square-mesh screens, whereby it is assumed that the particles are spherical with a

diameter corresponding to the length of the side of the square sieve openings [Komar and

Cui, 1984]. As particles become more irregular (i.e., less spherical), the length of a grain’s

intermediate (second largest) axis relative to the length of the diagonal of the square sieve

opening determines passage of the grain through the sieve [Rittenhouse, 1943; Komar and

Cui, 1984]. Laser diffraction measures a grain’s ‘nominal’ diameter, or the diameter of a

sphere with the same volume and of the same material as the measured grain, based on the

forward scattering of a parallel beam of monochromatic light, with scatter angle and

intensity related to grain size by a specific optical model [Cheetham et al., 2008]. Thus, like

sieving, grain sizes derived from laser diffraction provide idealised, scalar value

representations of the actual three-dimensional irregularity of natural sediments. Image

analysis estimates GSD from an image of sediment in one of two ways: 1) so-called

‘geometrical’ methods [Buscombe et al., 2010], which measure the apparent grain axes of

each individual grain using image-segmentation techniques [Sime and Ferguson, 2003;

Graham et al., 2005, 2010; Chang and Chung, 2012]; and 2) statistical methods, which

measure GSD based on spatial autocorrelation [Rubin, 2004; Barnard et al., 2007;

Buscombe, 2008; Buscombe and Masselink, 2009; Warrick et al., 2009; Gallagher et al.,

2011; Cheng and Liu, 2015] or spectra of image intensity [Buscombe et al., 2010;

Buscombe and Rubin, 2012]. However, each of these forms of image analysis has

10

limitations. Geometrical methods, based on edge-detection and segmentation of individual

grains, often have difficulty resolving overlapping grains, or touching grains of similar

colour or shade, which can lead to over- and under-segmentation of grains, and thus, has so

far made a universally applicable edge-detection algorithm elusive [see review by

Buscombe, 2013]. Conversely, some statistical methods have relied on the time-consuming

generation of a site-specific reference image catalogue prior to analysis [Barnard et al.,

2007; Buscombe, 2008; Buscombe and Masselink, 2009; Warrick et al., 2009; Gallagher et

al., 2011; Di Maria et al., 2016] or otherwise only provide the mean and/or sorting

coefficient rather than the full GSD [Buscombe et al., 2010; Buscombe and Rubin, 2012].

Buscombe [2013] proposed a statistical method based on wavelet analysis that estimates the

full GSD, without the need to isolate and segment each grain, and without the need for site-

specific calibration. To the authors’ knowledge, this method has been tested only on

siliciclastic sediment [Buscombe et al., 2014; King et al., 2016].

Sieving and laser diffraction methods assume that particles are spherical and of

uniform density [McCave and Syvitski, 1991]. Although either or both of these assumptions

may often be valid in siliciclastic environments, they are both almost always violated in

bioclastic environments (e.g., coral reefs), where sediment is largely derived from

calcifying organisms (corals, coralline algae, foraminifera, molluscs, etc.) with variable

shapes (rods, disks, plates) and densities [Maiklem, 1968; Braithwaite, 1973]. Previous

methodological comparisons using siliciclastic sediment have shown that sieve and settling

tube analysis produce nearly identical GSDs when deposits are well-sorted [Komar and

Cui, 1984]; sieve and laser diffraction analysis yield similar GSDs for sand-sized material

[Cheetham et al., 2008; Di Stefano et al., 2010]; and image analysis can produce GSDs

within 4% of those measured by sieve and settling tube [Barnard et al., 2007; Gallagher et

al., 2011; Buscombe et al., 2014]. However, comparative studies in bioclastic environments

have suggested that sieve can yield significantly larger mean grain size estimates (up to

300%) than settling tube for coarse samples (mean > 1 mm), smaller mean grain size

estimates in fine samples [mean < 1 mm; Kench and McLean, 1997], and that the

relationship between sieve and settling tube can be significantly non-linear [Smith and

Cheung, 2002]. These differences can cause misinterpretations of transport pathways and

deposit-forming processes in bioclastic environments [Kench and McLean, 1996, 1997].

11

There remains a gap in the sedimentological literature concerning how results from

more modern methods (such as laser diffraction and image analysis) relate to more classic

grain size methods (sieve and settling tube) for bioclastic sediments. This is particularly

important given that the more modern methods are usually less time consuming, achieve

greater detail and more accurate analysis of finer material, and, for image analysis, can be

conducted in situ, obviating the need for sample collection and storage; both of which

facilitate higher rates of spatial and temporal sampling [Barnard et al., 2007; Gallagher et

al., 2011; Buscombe et al., 2014] and hence may yield greater insight into sediment

dynamics. Furthermore, it is poorly understood how common settling velocity models,

developed from experiments on siliciclastic particles, perform for more irregular, bioclastic

sediment. Here, we address these gaps by 1) measuring the GSDs of bioclastic sediment

from a nearshore reef environment on the northwest coast of Australia using three common

methods (sieve, laser diffraction and image analysis); 2) calculating settling velocities from

the resulting GSDs according to three different models as developed by Gibbs et al. [1971],

Dietrich [1982], and Ferguson and Church [2004]; and 3) comparing these results against

direct measurements of settling velocities. This study will further our understanding of

sediment dynamics in bioclastic environments by allowing for conversion of previously

existing grain size distributions to settling velocities and thus allowing for the

hydrodynamic properties and transport modes of grains to be established.

2.3 Methods

2.3.1 Study Area

‘Natural’ (n = 73) and ‘standard’ (n = 2) sediment samples were used in this

analysis. The 73 natural samples were collected from Tantabiddi, Ningaloo Reef, Western

Australia (21.8714°S, 113.9903°E; Figure 2.1). Approximately 500 g samples were

collected from the top 5-7 cm of the seabed at each site. The standard samples were well-

rounded, quartz-based sand (D50 ~0.3 mm) and CaribSea AragamaxTM, an oolitic aragonite

sand (D50 ~0.5 mm).

This study focused on the sand fraction (0.063 – 2 mm) only. Therefore, all samples

were washed over a 63 μm sieve and dried to remove silt and clay-sized grains (≤ 1%); a

riffle splitter was then used to generate five statistically equivalent sub-samples. Each of the

four grain size methods (sieve, settling tube, laser diffraction, and image analysis) was used

12

on one of the sub-samples to determine the GSD, and the last sub-sample was used to

determine the composition of each sample.

Figure 2.1. Map of (a) Western Australia, (b) the northwest (NW) cape of Western Australia, and (c) the study site at Tantabiddi, Ningaloo Reef, Western Australia. Aerial imagery is from the SLIP Portal by the Western Australian Land Information System (WALIS) and Landgate (https://www2.landgate.wa.gov.au/). White areas in (c) are due to lack of coverage in the aerial imagery.

Sub-samples for compositional analysis were embedded in epoxy and thin-sectioned

(Figure 2.2); composition was then determined by identifying a minimum of 300 grains

using a petrographic microscope. Grains were divided into 8 categories: coral, coralline

algae, foraminifera, mollusc, echinoderm, quartz, framework, and other.

https://www2.landgate.wa.gov.au/

13

Figure 2.2. Example thin section image used in constituent assemblage analysis. Coral, coralline algae, quartz, mollusc and foraminifera fragments are highlighted.

GSDs were determined using the modified geometric method of Folk and Ward for

sieve, laser diffraction, and image analysis [Folk and Ward, 1957; Blott and Pye, 2001];

median grain size (D50) was then converted to settling velocity for comparison with

measured settling velocities. Normalized root-mean-square error (RMSE) and mean

absolute error (MAE) were used to compare between results for all the various methods.

𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅 (%) = �∑[(𝑅𝑅𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀 − 𝑅𝑅𝑀𝑀𝐸𝐸𝐸𝐸𝐸𝐸𝑀𝑀𝐸𝐸𝑀𝑀𝑀𝑀)2]

𝑛𝑛𝑅𝑅𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑚𝑚𝑚𝑚𝑚𝑚 − 𝑅𝑅𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑚𝑚𝑚𝑚𝑚𝑚

∗ 100 (2.1)

𝑅𝑅𝑀𝑀𝑅𝑅 (%) = 1𝑛𝑛∑(|𝑅𝑅𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀 − 𝑅𝑅𝑀𝑀𝐸𝐸𝐸𝐸𝐸𝐸𝑀𝑀𝐸𝐸𝑀𝑀𝑀𝑀|)

𝑅𝑅𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑚𝑚𝑚𝑚𝑚𝑚 − 𝑅𝑅𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑚𝑚𝑚𝑚𝑚𝑚∗ 100 (2.2)

2.3.2 Sediment density measurements

There exists a wide range of sediment density values in the carbonate sediment

literature; which is primary due to the type of density measured (Table 2.1). Whereas each

type of density reported in Table 2.1 has a specific application (see Discussion, Section

2.4), for sediment transport applications and settling velocity calculations it is appropriate

to use the ‘particle density’ (ρpart), which corresponds to the particle’s specific gravity

[Gibbs et al., 1971; Komar, 1981; Ferguson and Church, 2004] when all intragranular pore

spaces of the particle are filled with water [Smith and Cheung, 2003]. To directly measure

14

ρpart, we employed a method similar to the American Society for Testing and Materials’

method for measuring specific gravity [ASTM International, 2010].

Table 2.1. Summary of sediment density values found in the literature (*range includes measurements on specific sediment constituents)

Approximately 50 g of sediment was combined with fresh water at a known

temperature in a 100 mL volumetric flask and then the solution was sonicated for 1 hr (as

opposed to boiled) to remove entrapped air. Particle density was then calculated as:

15

ρ𝑝𝑝𝑚𝑚𝑝𝑝𝑡𝑡 =𝑅𝑅𝑠𝑠𝑇𝑇

𝑅𝑅𝑠𝑠 + 𝑅𝑅𝐹𝐹𝐹𝐹 −𝑅𝑅𝐹𝐹𝐹𝐹𝐹𝐹∗ ρ𝑤𝑤𝑚𝑚𝑡𝑡𝑤𝑤𝑝𝑝 (2.3)

where Ms is the mass of dry sand, T is a correction factor based on the temperature of water,

MFW is the mass of the volumetric flask filled with water, MFSW is the mass of the

volumetric flask with both sand and water, and ρwater is the standard density of fresh water

at 20 degres Celsius (i.e. 1 g cm-3).

ρpart was measured for 20 natural (bulk) sediment samples spanning the range of

sub-reef sampling sites; these 20 values were then averaged to calculate a representative

ρpart for all natural samples. Similarly, ρpart was measured, and then averaged, for two sub-

samples of each standard (the quartz sand and the aragonite sand). As there exists a range

of sediment density values reported in the literature, even for the same types of measured

densities (Table 2.1), it is necessary to understand the contribution to error in calculated ws

due to specification of sediment density. Therefore, settling velocities were calculated not

only using the measured ρpart values, but also using 1.2 g cm-3 (at the lower-end of densities

reported for carbonate sands) and 1.85 g cm-3 (a mid-range density).

2.3.3 Sieve

Approximately 70 g of sediment was analysed through a sieve stack ranging from 4

phi (0.063 mm) to -1 phi (2 mm) at 0.5 phi intervals (i.e., 11 sieve fractions). Samples were

sieved in a mechanical sieve shaker for 15 min, which was sufficient time to allow

complete separation into size classes [Román-Sierra et al., 2013]. Sediment trapped on

each sieve screen was collected and weighed to calculate the GSD.

2.3.4 Settling tube

Settling tube analysis was conducted in a 24-cm diameter, 212-cm tall water

column. Between 15 and 20 g of sediment were wetted to a sample plate and attached to a

magnetic holder above the water column. An external trigger was used to simultaneously

lower the plate into the water, thus releasing the sediment, and starting the timer. Each run

lasted 10 min, which was sufficient time to measure the fall velocities of the finest material

of interest (~0.063 mm), with the cumulative mass recorded at 1 Hz. The balanced used

16

was accurate to ± 0.05 g (± 1 standard deviation); which corresponds to ±~0.02 cm s-1 for

measured ws.

2.3.5 Laser diffraction

A Malvern Mastersizer 2000 (Malvern Instruments Ltd, Malvern, United Kingdom)

was used for laser diffraction analysis [Cheetham et al., 2008; Di Stefano et al., 2010].

Prior to analysis, samples were sieved through a 0 phi (1 mm) sieve, then added to a

solution of 10 mL of alkaline sodium hexametaphosphate and 800 mL of deionised water,

and then ultrasonically stirred for 30 s [Di Stefano et al., 2010]. Because sample

preparation removed material > 1 mm, only samples that had no fractions coarser than 1

mm from sieve analysis were used for comparison. Laser diffraction converts optical scatter

to GSD using a specified optical model; however, selection of a particular optical model

has been shown to have negligible effects on results for sand-sized particles [Blott and Pye,

2006]. Therefore, GSDs were calculated using Mie theory and the refractive index of either

aragonite (i.e., 1.53 for the natural and aragonite sands) or quartz (i.e., 1.46 for the quartz

sand).

2.3.6 Image analysis

Images of sediment samples were collected using a Canon EOS 5D Mark II, fitted

with a 65-mm macro lens. Sediment samples were arranged on an fixed stage and manually

flattened; this meant that the image plane was parallel to the object plane, and therefore,

spatial resolution was uniform over the entire image and a single conversion factor (0.0062

mm/pixel) could be used to convert GSD results from pixels to mm [Barnard et al., 2007;

Gallagher et al., 2011; Buscombe, 2013]. Previous tests of image analysis have shown that

errors are reduced if multiple images are taken and then averaged; therefore, in this study a

total of 20 images were taken for each sample [Barnard et al., 2007; Gallagher et al., 2011;

Buscombe, 2013].

A MATLAB program implementing the algorithm of Buscombe [2013], ‘Digital

Grain Size’, was used to process the images. The method of Buscombe [2013]

approximates the GSD using the global power spectral density function derived using a

Morlet wavelet. When using Digital Grain Size, there are various options for image

processing; the first option is the region of interest (ROI) used to calculate the GSD. The

user is allowed to specify the entire image as the ROI or to use a subsection, or multiple

17

subsections of the image as ROIs. To determine which was most appropriate, a series of

tests were performed using the whole image as the ROI, multiple non-overlapping equal-

area ROIs, and multiple overlapping equal-area ROIs (Figure 2.3).

Figure 2.3. Examples of (a) non-overlapping regions of interest (ROI) and (b) overlapping ROIs (~30% overlap) used when calculating grain-size distributions with the digital grain-size graphical user interface (DGS GUI) from Buscombe [2013].

Other options in DGS include ‘flattening’ the image, which is a Savitsky-Golay

high-pass filter [Savitzky and Golay, 1964] and can help to eliminate errors that are

introduced by non-uniform lighting conditions, lens curvature, or the sediment surface

being non-parallel to the image plane. These optical artefacts are picked up by spectral

analysis as spuriously high variance at wavelengths greater than any grain length scale,

which can lead to over-estimates of grain size. In order to test the potential effects of this,

the algorithm was run with and without flattening the image. To ensure the algorithm was

producing realistic results, a manual ‘point count’ was conducted on 10 samples following

the method of Barnard et al., [2007] and compared to DGS results, which has become a

18

standard way to evaluate the image-derived grain size estimates with a completely

equivalent metric [Warrick et al., 2009; Buscombe et al., 2010, 2014; Buscombe and Rubin,

2012; Buscombe, 2013].

The results of DGS are an area-by-size measure of grain diameter, whereas, sieve,

laser diffraction, and settling tube are a volume- or mass-by-size measure. Although these

two measures of grain diameter are not directly comparable, there exists a common method

to convert from areal- to volume-based measures of grain size [Diplas and Sutherland,

1988; Graham et al., 2005, 2012]

𝑝𝑝(𝑉𝑉 −𝑊𝑊)𝑚𝑚 =𝑝𝑝(𝑅𝑅)𝑚𝑚 ∗ 𝐷𝐷𝑚𝑚𝑚𝑚

∑𝑝𝑝(𝑅𝑅)𝑚𝑚 ∗ 𝐷𝐷𝑚𝑚𝑚𝑚

(2.4)

where p(V-W)i is the volume-by-weight proportion of the i-th size fraction, p(S)i is the

image-derived areal proportion of the i-th size fraction, Di is the grain size of the i-th size

fraction, and x is a conversion constant. Diplas and Sutherland [1988] suggest x should

vary between 0 and -1 depending upon porosity (with x = -1 when porosity = 0%).

According to Diplas and Fripp [1992], it is necessary to use different values for exponent x

depending on grain size, but a pragmatic approach is to use an average value for x, which is

determined empirically [Diplas et al., 2008]. The data from Diplas and Sutherland [1988]

suggests x = -0.47 for natural sediments with ~33% porosity; therefore, when converting to

volume-by-size proportions we tested both x = -0.5 and x = -1.

2.3.7 Conversion to settling velocity

The equations from Gibbs et al., [1971], Dietrich [1982] and Ferguson and Church

[2004] were used to convert grain size results to settling velocities. Gibbs et al., [1971]

(hereafter, ‘Gibbs’) only accounts for particle size and ρpart:

𝑤𝑤𝑠𝑠 = −3𝜇𝜇 + �9𝜇𝜇2 + 𝑔𝑔𝑀𝑀2𝜌𝜌(𝜌𝜌𝑠𝑠𝑠𝑠 − 𝜌𝜌)(0.015476 + 0.19841𝑀𝑀)

𝜌𝜌(0.011607 + 0.14881𝑀𝑀)

(2.5)

where µ is kinematic viscosity of water, g is gravitational acceleration, r is grain radius, and

ρ is fluid density; therefore, measured ρpart and D50 values were used for sieve, laser

diffraction, and image analysis.

19

Dietrich [1982] (hereafter, ‘Dietrich’), requires ρpart, the Corey [1949] shape factor

(CSF) and the Powers [1953] roundness index (PRI) to determine settling velocity:

𝑤𝑤𝑠𝑠 = 𝑅𝑅310𝑅𝑅1+𝑅𝑅2 (2.6)

where R1, R2, and R3 are fitted equations correcting for particle density, shape and

roundness, respectively (see Section A.1 for equations). While the measured ρpart and the

suggested PRI for natural particles (i.e., 3.5) were used for both the standard and natural

samples, 0.7 and 0.55 were used for CSF for the standard and natural samples, respectively.

CSF for the standard samples is the value suggested by Dietrich (i.e., 0.7); whereas 0.55 is

the average CSF determined by Smith and Cheung [2003] for reef-derived material.

Dietrich is valid only when nominal diameters (Dn) are used for grain size. Therefore, the

sieve and image analysis results required conversion to Dn whereas laser diffraction was

directly applicable. Smith and Cheung [2002] proposed an empirical equation to convert

sieve-derived D50 to Dn for carbonate material (Dn = 1.18*D50); given that image analysis is

comparable to sieve diameters after conversion to volume-by-size diameters [Kellerhals

and Bray, 1971; Diplas and Sutherland, 1988] this equation was used to calculate Dn for

both sieve and image analysis results.

Finally, we evaluated the settling velocity equation proposed by Ferguson and

Church [2004] (hereafter, ‘F&C’):

𝑤𝑤𝑠𝑠 = (𝜌𝜌𝑝𝑝𝑚𝑚𝑝𝑝𝑡𝑡 − 𝜌𝜌)𝑔𝑔𝐷𝐷2

𝐶𝐶1𝜈𝜈 + (0.75𝐶𝐶2(𝜌𝜌𝑝𝑝𝑚𝑚𝑝𝑝𝑡𝑡 − 𝜌𝜌)𝑔𝑔𝐷𝐷3)0.5 (2.7)

where D is either sieve or nominal diameter, and C1 and C2 are constants determined by

particle shape. F&C suggest values of C1 and C2 should be 18 and 0.4, respectively, for

smooth spheres; 18 and 1, respectively, for natural sands if sieve diameters are used; 20 and

1.1, respectively, for natural sands if Dn is used; and 24 and 1.2, respectively, for very

angular grains. We set C1 and C2 equal to 20 and 1.1, respectively, and used the same grain

diameters as were input to Dietrich (i.e., Dn).

2.4 Results

2.4.1 Sediment density and compositional analysis

20

There is some variability in constituent assemblages across sub-reef environments

(i.e., reef crest, reef flat, lagoon) at Ningaloo; however, the sands in the study area are

predominantly composed of coral fragments, coralline algae, and molluscs [Table 2.2;

Cuttler et al., 2015].

Table 2.2. Constituent assemblages for bulk sediment samples from the sub-reef environments at Tantabiddi, Ningaloo Reef.

Measured ρpart for the natural, bioclastic sediments was 2.58 ± 0.02 g cm-3 (mean ±

1 standard deviation) and showed no relationship with median settling velocity (slope = -

0.002 ± 0.004, α = 0.05, p = 0.34). Measured ρpart for the standard quartz and aragonite

sands were 2.64 ± 0 g cm-3 and 2.71 ± 0.05 g cm-3, respectively.

Varying ρpart from 1.2 g cm-3 to 2.6 g cm-3 caused a 22% to 28% change in NRMSE

for sieve results; an 11% to 12% change in NRMSE for laser diffraction; and a 20% change

in NRMSE for image analysis (Table 2.3).

21

Table 2.3. Normalized mean absolute error (NMAE) and normalized root-mean-square error (NRMSE) for (a) sieve, (b) laser diffraction and (c) image analysis due to varying sediment density for each settling velocity equation.

2.4.2 Selection of ‘Digital Grain Size’ region of interest and conversion to volume-

by-size

Estimated GSDs from each analysis consisting of different ROIs were tested against

manual point count results to determine the most suitable ROI. RMSE progressively

improved as the number of ROIs and amount of overlap increased (Figure 2.4). Image

flattening, however, caused a slight increased NRMSE; therefore, we used overlapping

ROIs (~30% overlap) without image flattening because this yielded the lowest NRMSE

(13%) when compared to manual point counts.

22

Figure 2.4. Comparison of digital grain-size (DGS) algorithm from Buscombe [2013] with manual point count results for D50 when region of interest (ROI) is set to (a) the whole image, (b) multiple, non-overlapping regions, (c) multiple, overlapping regions, and (d) multiple, overlapping regions with image flattening.

Results of converting image analysis grain sizes from area-by-size to volume-by-

size are presented in Figure 2.5. When compared to sieve analysis, an equivalent measure

of grain size, NRMSE is 28% and 16% when x is set to -0.5 and -1, respectively; therefore,

we used -1 for x in Equation 2.4.

23

Figure 2.5. (a) Comparison of volume-by-size image analysis D50 with sieve D50; squares and circles denote -0.5 and -1, respectively, for the value of x in Equation 2.4. (b) Cumulative frequency curves for sieve, x = -0.5, and x = -1 for sample highlighted in (a).

2.4.3 Comparison of settling velocity equations

The results of calculated ws using the three selected equations versus measured ws

using the settling tube are shown in Figure 2.6. Settling velocities calculated from image

analysis (Figure 2.6c) have the highest NMAE and NRMSE, with a maximum of 22% and

24%, respectively. NMAE and NRMSE from laser diffraction varied from 2% to 6% and

4% to 10%, respectively, for Gibbs, Dietrich, and F&C (Figure 2.6b); whereas NMAE and

NRMSE from sieve diameters varied from 6% to 9% and 8% to 11%, respectively, for

Gibbs, Dietrich, and F&C (Figure 2.6a).

Although Gibbs predicts ws with comparable NRMSE to Dietrich and F&C, there is

a clear trend of increasing over-prediction of ws with increasing observed ws for sieve and

laser diffraction (Figure 2.6a – 2.6b). Furthermore, whereas the relationships between

observed and estimated ws for sieve and laser diffraction are linear (Figure 2.6a – 2.6b), the

same relationships for image analysis (Figure 2.6c) are nonlinear, showing maximum

discrepancy at mid-range ws. These trends are consistent for all tested values of ρpart;

however, the variability in NRMSE between settling velocity models for measured ρpart

24

(3% to 6%) is much smaller than the range in NRMSE caused by varying ρpart for a given

grain size method and settling velocity model (Table 2.3).

Figure 2.6. Comparison of equations used to convert (a) sieve, (b), laser diffraction, and (c) image analysis grain-sizes to settling velocity. In all panels, blue indicates the Gibbs et al., [1971] equation, red indicates Dietrich [1982], and green indicates Ferguson and Church [2004]; asterisks indicate aragonite sand standard, triangles indicate quartz sand standard.

2.5 Discussion

25

The size and shape of the ROI used in the image analyses (Figure 2.3) affected the

calculated GSD. In general, larger ROIs led to the over-estimation of grain size. This

response is consistent with Buscombe and Rubin [2012], who showed that image intensity

in an image of sediment is a non-stationary random field; therefore, by changing the size

and location of the window (i.e. ROI), the 2nd-order (spatial) statistics of image intensity

change. Sayles and Thomas [1978] showed that, in such a situation, the variance in the

power spectral density is directly proportional to the bandwidth (in the frequency domain)

which is related to the window size (in the spatial domain). In this case, the effect was to

coarse-bias the estimates of grain-size for large ROIs. Given that the effects of ROI size on

grain size estimates are likely to be different for each sedimentary population, being related

to numerous factors such as the ratio of grain size to window size, grain sorting, orientation,

and shape, in practice, the size and number of ROIs should ideally be treated as tunable

parameters to be determined empirically, such as here, in the practical implementation of

the method of Buscombe [2013]. It is suggested that averaging results from large numbers

of relatively small ROIs should work best in most cases; however, caution should be used

when applying image analysis to intermediate settling velocity material as this material

yielded the highest errors (Figure 2.6c).

Varying sediment density across the range of reported values (1.15 – 2.8 g cm-3;

Table 1) caused an 11% to 28% range in NRMSE depending upon settling velocity model

and grain size method (Table 2.3). This highlights not only the importance of using an

accurate sediment density in settling velocity calculations but also the need to understand

why there exists such a range in reported values. This range is a function of how different

investigators define the density of their sediments, the nature of their study, the distinct

ways carbonate producers calcify, and the inherent diversity of sediment constituents within

the sediment pool. Studies aimed at the movement of specific facies (e.g., foraminifera)

often report an ‘effective’ density for irregularly shaped particles that implicitly assumes a

particle of an equivalent spherical diameter whose density matches the observed settling

velocity [Berthois and Calvez, 1960; Berger and Piper, 1972; Fok-pun and Komar, 1983].

Studies aimed at constructing reef- or system-scale budgets of carbonate chemistry,

however, often report a ‘bulk’ sediment density (ρbulk), which includes the microporosity of

the particles (ϕpart) and the mineral density (ρmineral; e.g., ρbulk = ρmineral *(1- ϕpart)) so that

26

changes in the mass of calcium carbonate can be computed from changes in sediment

volume [Stearn et al., 1977; Land, 1979; Grigg, 1982; Sadd, 1984; Hubbard et al., 1990].

Previous examinations of the porosity of specific sediment constituents have shown that

corals and coralline algae are ~50-60% porous [Stearn et al., 1977; Grigg, 1982]; therefore,

ρbulk ~1.5 g cm-3 seems to be a reasonable value for budget calculations (see Appendix in

Sadd, 1984 for example calculations).

Finally, studies aimed at understanding the physical suspension, transport, and

deposition of sediments generally report the average density of the particles suspended in

the fluid (i.e. ρpart) which includes the water or seawater that occupies the micro-porosity of

the sediment grains (e.g., ρpart = ρmineral * (1- ϕpart) + ρ * ϕpart) so as to best represent their

hydraulic behaviour in a moving fluid [Kench and McLean, 1997; Paphitis et al., 2002;

Smith and Cheung, 2003]. Both this study and the only other study, to the authors’

knowledge, that measured ρpart with a similar method, reported a value of ~2.6 g cm-3

[Smith and Cheung, 2003]; however, other transport studies have used a value of 1.85 g cm-

3 [Kench and McLean, 1997; Morgan and Kench, 2014], which originates from component-

specific measurements by Jell et al., [1965]. This difference in ρpart can be attributed to a

variety of possible factors including: sediment composition, mean grain size, measurement

technique, and depositional environment. Sediment composition is likely not the cause

because all three studies had similar constituent assemblages. Although the sediment

samples studied by Kench and McLean [1997] generally had a larger mean grain size, both

our results and those of Smith and Cheung [2003] showed that ρpart was independent of

grain size. Unfortunately, Jell et al., [1965] do not explain how they determined specific

gravity; therefore, it is difficult to evaluate their measurement technique against their

derived values. Depositional environment could be an important factor, as this study and

that of Smith and Cheung [2003], both analysed sediments from fringing reefs, whereas

Kench and McLean [1997] analysed sediments from an atoll system. If we assume that 1.85

and 2.6 g cm-3 are representative values for atoll systems and fringing reefs, respectively,

then one explanation for the difference in ρpart could be the age of sediments in the

respective systems. For example, sands in Hawaiian fringing reef systems have been shown

to be derived from older (100s-1000s years old) material [Harney et al., 2000] and material

from the top 1 m of a lagoon core at Tantabiddi has been dated to ~5000 years old [Collins

et al., 2003]. However, work in reef island systems have shown that sediment is derived

27

from modern (10s years old) sources [e.g., modern reef calcifiers; Yamano et al., 2000;

Dawson et al., 2012]. It is therefore possible that due to their age, fringing reef sediments

no longer retain their original internal porosities (e.g., taphonomic processes and/or internal

microboring could alter internal structures), and therefore have a higher ρpart; reef island

sands, on the other hand, likely still retain the microstructure of the biogenic source

material and therefore could have a lower ρpart. However, as there have been so few actual

measurements of ρpart in various bioclastic environments, there is a clear need for more

work like the present contribution to resolve the above discussion and to understand the

range of sediment densities applicable to these different systems.

Shape is also a key factor when determining settling velocities, as any divergence

from spherical can only lead to decreases in settling velocity when compared to spherical

particles of an equivalent weight [Dietrich, 1982]. Although the Dietrich [1982] and

Ferguson and Church [2004] models explicitly account for particle shape, the Gibbs et al.,

[1971] equation does not. Shape effects are visible in the Gibbs data for sieve and laser

diffraction as increasing over-estimation of settling velocity with increasing measured

settling velocity (Figure 2.6a – 2.6b). Similar results have been shown in previous

comparisons between sieve and laser diffraction measurements [Konert and Vandenberghe,

1997; Blott and Pye, 2006], as well as sieve and settling tube measurements [Komar and

Cui, 1984; Kench and McLean, 1997]. Shape effects on settling velocity are primarily due

to the fact that natural particles settle perpendicular to their largest projected area, which

increases drag on the particle and leads to a slower settling velocity [Dietrich, 1982; Komar

and Cui, 1984]. This has been shown to reduce settling velocities by a maximum of ~2x for

particles coarser than ~1.4 mm, or by ~1.5x for those smaller than ~1.4 mm [Dietrich,

1982].

In bioclastic environments, shape is inherently linked to the specific organism from

which the particle is derived [Perry et al., 2011]; therefore, constituent assemblage could

explain some of the increasing discrepancy between measured and estimated settling

velocities for faster settling samples. Past studies of carbonate sediment settling have

shown that typical constituents have specific settling behaviours that enhance the

disagreement between sieve and settling tube results [Maiklem, 1968; Braithwaite, 1973;

Smith and Cheung, 2003]. Mollusc shells, for example, have large physical sizes but tend to

28

settle with a spiral motion; this settling style will lead to slow settling velocities, whereas

their shape will cause them to be trapped on coarser sieves [Maiklem, 1968; Braithwaite,

1973; Smith and Cheung, 2003]. Indeed, the thin section analysis showed an increasing

percentage of mollusc fragments (both broken and intact) in faster settling deposits

collected from Ningaloo which, based on their settling behaviour and irregular shape, could

help explain the over-estimation of calculated ws by Gibbs for these samples.

When shape was accounted for by the settling velocity model (i.e. Dietrich or

F&C), NRMSE was improved by 3% to 6% (Table 2.3) and the velocity-dependent bias

was removed from sieve and laser diffraction results (Figure 2.6a – 2.6b). Furthermore,

although F&C performs better for sieve diameters, Dietrich is more accurate for laser

diffraction and image analysis and is also a more thorough treatment of particle

irregularities. Therefore, Dietrich is suggested here as the preferred equation for settling

velocity calculations.

In practical applications to bioclastic environments, settling velocities can be used

to delineate various sub-environment zones (i.e. different reef zones) and to predict

sediment transport mode; both of these applications are critical to understanding and

predicting sediment transport pathways through these systems. Kench [1997] suggested that

bioclastic sediment can be grouped into distinct settling velocity fractions, ranging from

very slow settling (0.4 cm s-1 to 0.8 cm -1) to very fast settling (25 cm s-1 to 50 cm s-1).

Using this classification and measured settling velocities, there are clearly three settling

velocities zones at Tantabiddi (Figure 2.7a): 1) fast-moderate settling on the fore reef/reef

crest (6.3 cm s-1 to 12.5 cm s-1); 2) Moderate settling on the reef flat and majority of the

lagoon (3.1 cm s-1 to 6.3 cm s-1); and 3) moderate-slow settling in the nearshore area of the

southern channel (1.6 cm s-1 to 3.1 cm s-1).

29

Figure 2.7. Spatial maps depicting settling velocity boundaries at Tantabiddi, Ningaloo Reef for (a) measured settling velocities, (b) image analysis-derived settling velocities, (c) laser diffraction-derived settling velocities, and (d) sieve-derived settling velocities. For (b), (c), and (d), the Dietrich equation was used with PRI = 3.5, CSF = 0.55 or 0.7, and ρpart = measured values. (e) and (f) depict the settling velocity boundaries for sieve-derived measurements when ρpart = 1.85 or 1.2 g cm-3, respectively.

Although no method is able to perfectly match the measured spatial variation, laser

diffraction reproduces the correct number and location zones (Figure 2.7c); sieve

reproduces the correct spatial pattern, but it under-predicts the slowest settling material

(Figure 2.7d); and, finally, when image analysis is used, there is no indication of the

slowest settling zone and the fastest settling zone occupies a much larger area than

measured. Furthermore, similar results are observed when ρpart is varied for sieve results

(i.e. variable number of zones and under-predication of settling velocity; Figure 2.7e –

2.7f).

Settling velocity can also be used to determine entrainment and transport mode

(bedload, suspended load, or wash load) in aquatic environments, most commonly in the

context of the Rouse [1937] number, P = ws/ku*, where k is the von Karman constant (0.4)

and u* is the shear velocity [Rouse, 1937]. Sand ripples have been observed in the Ningaloo

30

lagoon (~10 cm amplitude and ~50 cm wavelength) and wave-induced ripple migration

(i.e., bedload transport) has been suggested to be the primary mode of sediment supply to

the shoreline [Cuttler et al., 2015].

Using Dietrich-derived settling velocities and a typical wave-induced u* value of 7

cm s-1 [Cuttler et al., 2015] to calculate P for the lagoon samples, the use of laser

diffraction and image analysis over-estimate P (i.e., trend towards predicting bedload

transport), whereas the use of S slightly under-estimates P (Figure 2.8); however, the use of

each grain size method would suggest that the majority of lagoon deposits are mobilised as

bedload or partial suspended-load under normal wave conditions (significant wave height

~1-2 m), thus supporting the hypothesis that ripple migration is crucial to shoreline

maintenance at Ningaloo.

Figure 2.8. Comparison of Rouse number (P) from measured and calculated settling velocities. Values are shown for results from sieve, laser diffraction, and image analysis.

31

2.6 Conclusions

Settling velocity is a common characteristic of sedimentary deposits that can be

used to understand sediment dynamics in aquatic environments. Although errors in

predicted settling can be up to 20%, these errors are still small relative to the natural range

of settling velocities of sand-sized material [0.05 to 0.30 cm s-1, or a variation of ~83%;

Smith and Cheung, 2003; Ferguson and Church, 2004]. Furthermore, although image

analysis had the highest errors, we still encourage the use (with caution) and development

of this technique as the greater spatial and temporal sampling it provides will greatly

outweigh a slight increase in error.

Collectively, the results suggest that any of these common grain size analysis

techniques and settling velocity models, which were originally developed for siliciclastic

sediments, can be used to predict settling velocities of irregularly-shaped bioclastic

sediments with reasonable accuracy; however, this agreement is improved when models

account for shape effects. These results highlight the importance of using an accurate

measure of sediment density when calculating settling velocity as an incorrect density can

cause a change in NRMSE of up to 28%. We suggest the use of the Dietrich [1982] model

for estimating settling velocity because it is more accurate for more modern, time-saving

techniques, and in general, is a more thorough treatment of particle irregularities. Finally,

this analysis will allow for the conversion of grain size data from a variety of bioclastic

environments to be converted to settling velocities for comparison; this will greatly enhance

our understanding of sediment dynamics and transport processes in these environments.

A.1 Appendix for Chapter 2

Dietrich [1982] accounts for particle density, shape and roundness when converting

particle size to settling velocity through three fitted equations (see Equation 2.6) based on

the non-dimensional settling velocity (W*) and the non-dimensional grain size (D*):

𝑊𝑊∗ = 𝜌𝜌𝑤𝑤𝑠𝑠3

(𝜌𝜌𝑠𝑠−𝜌𝜌)𝑠𝑠𝑔𝑔 (A.1.1)

𝐷𝐷∗ = (𝜌𝜌𝑠𝑠−𝜌𝜌)𝑠𝑠𝐷𝐷𝑛𝑛3

𝜌𝜌𝑔𝑔2 (A.1.2)

where ρ is fluid density, ρs is sediment density, ν is kinematic viscosity, ws is settling

velocity and Dn is nominal diameter.

32

The three fitted equations in Equation 2.6 account for particle size and density (R1),

shape (R2), and roundness (R3). Particle size and density are related by a fourth-order

polynomial:

𝑅𝑅1 = −3.76715 + 1.92944 log𝐷𝐷∗ − 0.09815 log𝐷𝐷∗2 (A.1.3)

−0.00575 log𝐷𝐷∗3 + 0.00056 log𝐷𝐷∗4

Particle shape is accounted for via the Corey [1949] shape factor (CSF), which

ranges from 0 to 1:

𝑅𝑅2 = �log �1 − 1−𝐶𝐶𝐹𝐹𝐹𝐹0.85

�� − (1 − 𝐶𝐶𝑅𝑅𝐶𝐶)2.3 tanh(log𝐷𝐷∗ − 4.6) (A.1.4)

+ 0.3(0.5 − 𝐶𝐶𝑅𝑅𝐶𝐶)(1 − 𝐶𝐶𝑅𝑅𝐶𝐶)2(log𝐷𝐷∗ − 4.6)

Finally, the effect of particle roundness is incorporated using the Powers [1953]

roundness index (PRI):

𝑅𝑅3 = �0.65 − �𝐶𝐶𝐹𝐹𝐹𝐹2.83

tanh(log𝐷𝐷∗ − 4.6)��1+(3.5−𝑃𝑃𝑅𝑅𝑃𝑃)/2.5

(A.1.5)

These three equations are then combined to give ws (Equation 2.6).

33

3. TEMPORAL DISCONNECT OF REEF CALCIFIERS AND THE

SEDIMENT RESERVOIR: IMPLICATIONS FOR SHORELINE

MORPHOLOGY OF A FRINGING REEF SYSTEM

3.1 Abstract

Coral reefs are unique environments due to the strong link between ecological

processes, the sediment reservoir, and the morphology of the adjacent coastline. Due to the

linkages between the benthic community and the sediment reservoir in reef systems, the

spatial distribution of sediment texture (or settling velocity) can be combined with

measurements of the contemporary ecological community and the composition of the

sediment reservoir to gain further insight into sediment transport pathways. Here, habitat

mapping and thin section analysis revealed that living coral accounts for less than 5% of the

benthic cover; however, coral was the dominant sediment constituent, accounting for up to

40% of deposits regardless of sub-reef environment. Transport pathways through the

system, inferred from the spatial distribution of median grain size, agree well with the

predicted circulation for wave-dominated fringing reefs (i.e. shore-directed flow over the

reef flat that diverges towards channels in the lagoon). However, due to the low coral cover

on the reef crest, these pathways do not provide evidence of a modern source of coral-rich

sediment to the lagoon. Radiometric dating (238U/230Th) of coral fragments from the outer

reef flat through the lagoon to the beach indicate that sediment is ~1000 to ~20,000 years

old, respectively. Thus, suggesting that the active sediment reservoir and calcifying

community may be temporally disconnected (i.e. sediments are derived from an older reef

system). Therefore, despite the recent observations of rapid declines in net reef accretion,

the sediment budget and shoreline morphology in this system may be more resilient to

climate change induced declines in the contemporary reef ecology.

3.2 Introduction

34

The sediments that form tropical beaches along coral reef coastlines are primarily

derived from the calcium carbonate skeletal remains of organisms living within the diverse

sub-reef environments [Harney et al., 2000]. Therefore, landforms in reef environments

(e.g. reef islands or shorelines in the lee of fringing reefs) can be particularly sensitive to

any ecological transitions that modify reef community composition as this will directly

impact the source and supply of sediment to these formations [Perry et al., 2011, 2015a].

Given that reef-dwelling organisms (i.e. coral, crustose coralline algae, molluscs,

Halimeda, etc.) break down into characteristic shapes and sizes [the ‘Sorby principle’;

Sorby, 1879], it is the length of time between the death of these organisms and when they

are broken down to transportable-sized (i.e. sand-sized) material as well as the timescales

of sediment transport processes that will determine the sensitivity of reef-associated

landforms to ecological community shifts.

Comparing the modern benthic community with the spatial distribution of sediment

constituents can give insight into transport pathways through the system and ecological-

sedimentary links between sediment sources and sinks [Yamano et al., 2000; Perry et al.,

2015a; Hamylton et al., 2016]. For example, these data can identify key sediment

producing regions and/or organisms that are key to landform development and maintenance

[Kench, 1997; Yamano et al., 2000; Dawson et al., 2012; Morgan and Kench, 2016].

Investigations into the links between reef ecology and reef-associated landforms (primarily

atoll islands), have relied upon radiometric dating of specific sediment constituents and/or

bulk sediment samples to understand timescales of transport [Harney et al., 2000; Yamano

et al., 2000; Dawson et al., 2012]. For example, large benthic foraminifera have been dated

with carbon-14 (14C) to show transport from source (reef flat) to sink (reef island) takes

place relatively quickly (i.e. ~60 years). In contrast, 14C dating of bulk sediment samples

(i.e. all sediment constituents) from a fringing reef system in Hawaii showed that surface

sediments throughout the system were older (~1000s years), thus suggesting longer

timescales of transport from source (forereef) to sink (beach) [Harney et al., 2000]. Apart

from 14C, uranium-series dating has been used to determine the age of coral pieces from

core material in fringing reefs [Collins et al., 2003] as well as storm-deposited coral

boulders [Lau et al., 2016]. However, radiometric dating provides little understanding of

the sediment transport mechanisms (e.g. bedload or suspended load, storm-induced versus

35

fair-weather wave driven) responsible for the observed age distributions and inferred

transport pathways.

Suspended sediment transport has been studied in reefs for obvious reasons -

increased turbidity negatively impacts corals by decreasing the light availability and/or

smothering them in sediments [Rogers, 1990; Storlazzi et al., 2015]. However, lagoon and

beach sediment tend to be medium-sized sand [Kench, 1997; Morgan and Kench, 2014,

2016] and therefore, unlikely to be suspended by typical mean currents. Bedforms are also

common in these lower energy environments (e.g., lagoon and back-reef; Storlazzi et al.,

2004; Cuttler et al., 2015, 2016), suggesting the importance of bedload transport in these

areas. Direct measurements of sediment transport (both bedload and suspended load) in

reefs has relied upon the use of sediment traps [Kench and Mclean, 2004; Storlazzi et al.,

2004; Morgan and Kench, 2014]. However, where bedforms are present, migration rates

can be used to calculate bedload sediment flux [e.g. Traykovski et al., 1999; Becker et al.,

2007].

Linking reef ecology to sediment transport processes and geomorphic development

is critical to determining the long-term resilience of these landforms [Morgan and Kench,

2014, 2016; Perry et al., 2015b]. Reef associated landforms face significant erosion risk

from sea level rise which has been hypothesized to increase wave transmission across reefs

and therefore increase waves reaching the shoreline [Grady et al., 2013; Quataert et al.,

2015; Cheriton et al., 2016b]. However, sediment supply has been suggested to be a more

dominant control on geomorphic development in some systems [Woodroffe et al., 2007;

Perry et al., 2015b]. Identifying and quantifying the links between reef ecology and coastal

geomorphology can be done through a carbonate sediment budget analysis [Harney and

Fletcher, 2003; Morgan and Kench, 2014]. A carbonate sediment budget requires

measurement of the biological production of carbonate, the residence time of carbonate

within temporary sinks (e.g. lagoon and/or beach), and accurate measurements of the

sediment fluxes between the source and sinks [Harney and Fletcher, 2003]. Previous

carbonate sediment budgets have highlighted that reef islands and platform deposits act as

temporary sinks of sediment, whereas material exported offshore can act as substrate for

reef progradation [Morgan and Kench, 2014]; and that sediment supplied to the beach in a

fringing reef system can be ‘old’ (order 1000s of years) material that has been stored within

36

the lagoon and reef channels [Harney et al., 2000; Harney and Fletcher, 2003]. Further

quantification of these ecological-geomorphic links is key to understanding how future

shifts in reef ecology will affect reef-associated landforms and coastlines [Perry et al.,

2011].

Few studies have linked the ecological timescales (i.e. radiometric dating of the

active sediment reservoir) with direct observations of sediment transport to understand

coastal morphology in reef environments [Harney and Fletcher, 2003]. Here, we assess the

relationship between the modern benthic ecology of a reef with the sediment composition,

radiometric ages of the key sediment constituent (coral) and sediment transport

measurements to understand the timescales and mechanisms of development of a shoreline

salient at Ningaloo Reef, Western Australia. With net reef accretion rates rapidly declining

(i.e. reef erosion greater than reef accretion) [Perry et al., 2013b; Perry and Morgan, 2017],

understanding the timescales of both the ecological processes (i.e. length of time it takes for

primary sediment constituents to appear in the active sediment reservoir) and the physical

processes (sediment transport rates/fluxes) will be vital to predicting the impacts of climate

change on reef-associated landforms.

3.3 Data and methods

3.3.1 Study area and field studies

Ningaloo Reef is Australia’s largest fringing reef, stretching ~270 km south from

Australia’s North West Cape (Figure 3.1a). Ningaloo typically has a steep (~1:20) forereef

slope, a relatively narrow reef flat (100s of m), and a wide (1 to 5 km), but relatively

shallow (less than 5 m deep) lagoon [Collins et al., 2003]. The study area, located at the

northern extent of Ningaloo Reef at Tantabiddi (Figure 3.1), is marked by the presence of a

shoreline salient inland from the reef flat. Salients and other accretionary coastal landforms

(e.g. dunes) are common features along the entire stretch of Ningaloo Reef and the greater

Western Australian coastline [Sanderson and Eliot, 1996; Sanderson, 2000].

37

Figure 3.1. (a) Western Australia, with the Ningaloo Peninsula outlined in yellow. (b) The Ningaloo Peninsula, with the study site at Tantabiddi outlined in yellow. (c) The study site, Tantabiddi, with locations of sediment samples used for grain size analysis (black dots), thin section analysis (yellow circles) and uranium-series dating (red triangles); the location of the quadpod instrument arrays are shown as blue triangles on the northern and southern side of the salient.

The field data presented here were collected over a period between August 2013 and

June 2016, through several field experiments and surveys aimed at understanding the

timescales and mechanisms linking sediment generation on the reef to development of the

shoreline salient. In August 2013 field activities included collection of surficial sediment

samples spanning the range of sub-reef environments at Tantabiddi (e.g. forereef, reef flat,

lagoon, and beach). These samples were used to determine sediment grain size,

composition, and age. Benthic habitat surveys were collected in July 2014 for the reef flat

and lagoon and in June 2016 for the reef crest and upper forereef (max ~11 m depth). These

data were used to map the spatial coverage of reef calcifiers. Lagoon hydrodynamic and

ripple migration rate data were collected between May 2016 and June 2016. These data was

used to quantify the sediment transport mechanism supplying sediment to the salient.

3.3.2 Habitat data

In order to quantify the spatial coverage of modern reef calcifiers (i.e. the source of

bioclastic sediments), diver-recorded video transects were conducted at 15 locations

38

encompassing the range of sub-reef environments (e.g. reef crest, reef flat, and lagoon;

Figure 3.1b, green lines). At each location, five 50 m transects were recorded at ~0.5 m

above the bed. Twenty still images were extracted from each video transect and imported

into Coral Point Count with Excel Extensions (CPCe) to determine percent cover of

individual benthic organisms [Kohler and Gill, 2006]. CPCe allows for randomisation of

percent cover analysis by automatically generating a user-defined number of points within

an image; benthic organisms at the random points are then identified. Ten points were

generated within each photo, yielding a total of 15,000 identification points (10 points per

image x 20 images per transects x 5 transects per location x 15 locations); organisms were

grouped into 11 categories to determine percent cover of the main calcifying organisms:

coral, gorgonian, sponge, mollusc, macroalgae, other live, dead coral with algae, coralline

algae, echinoid, sand/pavement/rubble, and unknown.

3.3.3 Sediment constituents and radiometric dating

To determine the spatial distribution of sediment texture (grain size, sorting, and

skewness) and composition, surficial sediment samples were collected from 57 locations

across the study site (3 forereef, 16 reef flat, 24 lagoon, 10 channel, 4 beach; Figure 3.1).

Approximately 500 g of sediment was collected from the top 5-7 cm of the seabed at each

site [Cuttler et al., 2016]. Standard wet sieve analysis was used to isolate gravel (>2 mm),

sand (0.063 – 2 mm), and fine fractions (<0.063 mm). Hydraulic properties of sand-sized

sediment (0.063 – 2 mm) previously reported in [Cuttler et al., 2016] were converted to

equivalent grain size distribution using Gibbs et al. [1971] and a site-specific sediment

density of 2580 kg m-3 [Cuttler et al., 2016]. These values were then combined with sieve

results for the gravel fraction to determine the entire grain size distribution [Morgan and

Kench, 2016]. Grain size statistics were then calculated using the graphical techniques of

Folk and Ward [1957]. Sub-samples from the sand fraction at 35 sites were embedded in

epoxy and thin-sectioned. Composition was determined by identifying a minimum of 300

grains using a petrographic microscope [Cuttler et al., 2016]. Sediment were categorized

as: coral, coralline algae (CCA), foraminifera, mollusc, echinoderm, quartz, framework,

and other.

Coral sediment from 8 locations (Figure 3.1c, red triangles) throughout the reef

system were dated using uranium-series isotope decay (238U – 234U – 230Th) and multi-

39

collector inductively coupled plasma mass spectrometry (MC-ICPMS). This technique has

been shown to be accurate for dating modern coral (100 to 102 years) as well as ancient (i.e.

Last Interglacial) corals (~105 years) [McCulloch and Mortimer, 2008]. Coral sediment for

radiometric dating was selected from the 0.25 – 0.5 mm sieve fraction by using a binocular

microscope to select individual grains. This size fraction was used because it includes the

median grain size (a common metric used for sediment transport studies) of each sample.

Small sample sizes (≤ 50 mg) were used in order to minimize the number of grains

contributing to each date [Harney et al., 2000]; however, because samples contained 10s-

100s of individual grains, the ages presented here represent average ages of the sand-sized

material at the sample locations. Coral sediment from the most seaward (outer reef flat)

site were also selected from the gravel fraction to gain more insight into the age distribution

at the site theoretically closest to the source of coral (i.e. near the reef crest). At this

location, gravel-sized sediment age was measured on two individual pieces of coral due to

the size and mass of these clasts.

3.3.4 Statistical and spatial analysis

Spatial interpolations of sediment texture (mean size and sorting) and composition

were constructed in ArcGIS v10.3 using ordinary kriging methods. Statistical analysis of

benthic cover and sediment composition and texture was conducted using the Fathom

toolbox for Matlab [Jones, 2015] to determine habitats and biosedimentary facies at

Tantabiddi [Morgan and Kench, 2016].

Agglomerative, hierarchical cluster analysis was conducted using the unweighted,

pair group method with arithmetic mean. For habitat identification, a Bray-Curtis (BC)

dissimilarity matrix of square-root transformed benthic cover data was used for cluster

analysis; whereas, for determining biosedimentary facies, a BC matrix of square-root

transformed sediment texture (mean and sorting) and sediment composition was used

[Kench, 1997; Morgan and Kench, 2016]. Dissimilarity profile analysis (DISPROF) was

used to identify statistically significant, unique habitats and facies from the resulting

dendrograms [Clarke et al., 2008; Legendre and Legendre, 2012]. DISPROF is equivalent

to similarity profile analysis (SIMPROF) proposed by Clarke et al., [2008]; however,

DISPROF is based on dissimilarity measures; both analyses provide an objective method

for identifying members of “real” groups present in the results from clustering analysis

40

[Jones, 2015]. Finally, canonical analysis of principal coordinates (CAP) was used to

assess the driving factors behind the habitat and facies classifications.

3.3.5 Shoreline sediment supply: bedform migration rates and lagoon

hydrodynamics

Sand ripples (~0.1 m height and ~0.5 m wavelength) are a common feature

throughout the lagoon at Tantabiddi [Cuttler et al., 2015]. Previous analysis of bed

sediment at Tantabiddi has suggested that bedload transport is the primary transport mode

through the lagoon [Cuttler et al., 2016] and that a key mechanism for sediment delivery to

the shoreline could be the migration of sand ripples [Cuttler et al., 2015]. To quantify

ripple migration rates, which can be used as a proxy for bedload sediment flux, two

instrument arrays were deployed in the inner lagoon on either side of the salient in ~3.5 m

depth (north side) or ~2.5 m depth (south side) for four weeks in May-June 2016 (Figure

3.1c, blue triangles; Figure 3.2).

Two echosounders (EofE Ultrasonics EchoLogger EA400) were mounted to each

instrument quadpod in the lagoon. One was oriented vertically (downward looking) to

record changes in the sea floor due to ripple migration directly below the quadpod. A

second was mounted ~10 degrees below horizontal and oriented perpendicular to ripple

crests (which maintained a consistent morphology and orientation over the four week

deployment) to measure ripple migration rates (Figure 3.2). Each echosounder recorded

500 measurements at 2 Hz each hour, with the transmit range for each acoustic pulse set to

10 m and 2 m for the near-horizontal and vertical instruments, respectively. Acoustic

returns were averaged over the 500 samples in 7.5 mm bins and then the bed elevation

(vertical sensor) and ripple crest location (near-horizontal sensor) were extracted. For the

vertical sensor, the ‘bottom’ was defined as the bin with the maximum return amplitude of

the hourly average.

41

Figure 3.2. (a) Quadpod deployed on the southern side of the salient, with the vertical (downward-looking) and horizontal (cross-ripple looking) echosounders labelled. (b) Schematic depicting the ability of the horizontal echosounder to measure and track multiple ripples crests; data from this instrument was then used to calculate ripple migration rates.

For the near-horizontal sensor, the hourly averaged returns from each 7.5 mm was

normalized by the time-average return over the entire deployment for the corresponding bin

(to remove the distance dependent variation in the return signal due to the grazing angle of

the transmitted acoustic pulse; Figure 3.2). Next, the locations of peaks (i.e. local maxima

corresponding to the ripple stoss side) in the return signal were determined for each hour.

The locations of peaks from successive hours were then cross-correlated and the lag

distance yielding the highest correlation was identified. This lag distance was converted to

migration rate by first multiplying by bin size to get migration distance and then dividing

by time (i.e. 1 hr). Assuming that the ripple migration equates to bedload transport, the

cumulative bedload sediment flux due to ripple migration (Mripple) was calculated following

Traykovski et al., [1999]:

𝑅𝑅𝑝𝑝𝑚𝑚𝑝𝑝𝑝𝑝𝑝𝑝𝑤𝑤(𝐸𝐸) = � 𝜌𝜌𝑠𝑠(1 − 𝜀𝜀)𝜁𝜁𝑉𝑉𝑚𝑚𝑀𝑀𝐸𝐸𝑡𝑡

0 (3.1)

42

where ρs is sediment density [2580 kg m-3, Cuttler et al., 2016], ε is the porosity (packing)

of bed sediment [assumed 0.35; Sleath, 1984], ζ is the instantaneous ripple elevation (from

the vertical echosounder), Vm is the migration rate, and t is time.

Co-located observations of wave height, water level, and velocity were measured at

each site for 40 min each hour at 1 Hz for ~4 weeks by a downward-looking Aquadopp HR

(~1 m above bed; 60 mm bin). Incident (i.e. offshore) waves were simultaneously measured

by an acoustic wave and current profiler (Nortek AWAC) deployed in 20 m depth offshore

of the reef crest. The tidal component of the water level for each burst was removed using a

linear trend. Spectral properties of the sea surface elevation were estimated from Fourier

transforms of forty minute, de-tided pressure data segments collected each hour and block

averaged using a Hamming window (2048 samples) with 75% overlap, yielding 9 degrees

of freedom [Thomson and Emery, 2014]. Hourly significant wave heights were estimated

from the variance of sea surface elevation in the sea-swell (SS; 0.04<f<0.2 Hz) and

infragravity (IG; 0.003<f<0.04 Hz) energy bands (Hsig,SS and Hsig,IG, respectively). Velocity

measurements were rotated into the local cross- (u) and alongshore (v) directions, with the

cross-shore direction oriented perpendicular to the ripple crests and positive directed

towards the shoreline (i.e. onshore); profiles were depth-averaged and then hourly-mean

velocities were calculated.

3.4 Results

3.4.1 Spatial distribution of reef calcifiers, sediment texture, composition, and age

Four unique habitat types were identified at Tantabiddi via DISPROF analysis (Pi =

0.07, p<0.05): a coral-coralline algal dominated area found on the reef crest (C-CCA), a

macroalgal-coralline algal pavement found on the reef flat (MA-CCA), a sand dominated

lagoon, and a small, isolated coralline algae-coral (CCA-C) area found at the northern

extent of the study area (Figure 3.3). Within the two habitats containing live coral, coral

cover was ~20%; nevertheless, when this was averaged over all transects, coral contributed

less than 5% of the total benthic cover (mean ± standard error; 4 ± 2%). Apart from the reef

crest, benthic cover was dominated by a combination of crustose coralline algae (CCA; 20

± 5%), macroalgae (primarily Sargassum sp., 46 ± 5%), and sand (28 ± 6%). Sand

dominated the lagoon, whereas CCA was a significant benthos along the lateral channels

and on the outer reef flat (i.e. areas of higher energy).

43

Figure 3.3. Spatial distribution of unique habitats at Tantabiddi identified via DISPROF analysis; the colour of the transect line (red, magenta, blue, or green) indicates the unique habitat associated with that transect and yellow stars indicate approximate locations of photos in (f) to (i). Breakdown of the benthic composition and example images are shown for the coral-coralline algae (C-CCA) reef crest ( b and f), the coralline algae and coral pavement (CCA-C) at the northern channel (c and g), the macro algae and coralline algae pavement(MA-CCA) of the reef flat (d and h), and the sandy lagoon (e and i).

Sediment at Ningaloo was predominantly sand-sized, with silt-sized or smaller

material (<0.063 mm) accounting for less than 3% at all sites. Percentage of gravel sized

material (>2 mm) decreased with increasing distance from the reef crest. Mean grain size

was observed to decrease from the reef crest towards lagoon, and then again from the

lagoon towards the lateral channels (Figure 3.4a). Although sediment on the southern side

of the lagoon was slightly coarser than the northern side, grain size variation was limited

44

throughout the lagoon, with the majority of deposits classified as medium sands (Figure

3.4a). Sorting exhibited similar patterns to grain size, with sorting values decreasing with

increase distance from the reef crest (note, smaller values of sorting equate to deposits

being more ‘well’ sorted). Deposits varied from moderately to poorly sorted, with the

highest degree of sorting observed near the southern lateral channel (Figure 3.4b).

Figure 3.4. Spatial distribution of (a) mean grain size and (d) sorting; note, for (d), larger values of sorting correspond to decrease sorting (i.e. more poorly sorted). Spatial distributions of sediment constituents, including (b) coral, (c) crustose coralline algae, (e) quartz, and (f) molluscs.

Although coral accounted for less than 5% of the benthic cover, coral was the

dominant sediment constituent (34 ± 2%). Significant sediment contributors also included

CCA (20 ± 1%) and molluscs (18 ± 1%; Figure 3). Quartz (15 ± 3%) was only significant

in small regions near the shoreline at the southern and northern channels; however,

percentage of quartz decreased with increasing distance from the shoreline (Figure 3.4e).

DISPROF analysis showed only two significant groupings (Pi = 7.19, p<0.05) for bed

sediment, primarily differentiated by the percentage of quartz (Figure 3.5). Thus, there are

limited compositional differences between sediment from the reef crest, reef flat, lagoon,

and beach.

45

Figure 3.5. (a) Spatial distribution of biosedimentary facies as determined by DISPROF analysis. (b) Results from canonical analysis of principle components indicating that the two significant groups shown in (a) were classified based on percentage of quartz.

Uranium-series ages of coral sediment (i.e. the dominant sediment constituent)

revealed that sediments are predominantly ‘old’ (order 1,000s to 10,000s of years),

however, the youngest sediment was ~70 years old (Table 3.1). Sediment age was shown

to be related to both distance from source and grain size. The youngest coral material was

dated from the forereef (i.e. in the vicinity of living coral; Figure 3.3) and derived from a

gravel-sized clast. However, sand-sized material from the same location was dated to

~3,000 years before present (ka BP; Figure 3.6). Sediment age increased along a typical

flow pathway from the outer reef flat to the channel on both sides of the salient. Outer reef

flat sands were dated to ~1.5 ka BP whereas southern channel sands were ~5 ka BP Collins

et al., [2003] and northern channel sands were ~15 ka BP (Figure 3.6). The oldest dated

sediment was from the beach at the tip of the salient; this sediment was ~19 ka BP (Figure

3.6).

46

Table 3.1. Summary of radiometric (Uranium-series) analysis.

a Square brackets denote activity ratios b Errors in parentheses are ±2 standard errors c Subscript m or i denotes measured and corrected initial values, respectively. d Uncorrected age (ka) using the decay constants of Jaffey et al. [1971] (238U) and Cheng et al. [2013] (234U and 230Th). e Correct age (ka) calculated assuming initial, non-radiogenic [230Th/232Th] = 1 ± 1 [McCulloch and Mortimer, 2008]

3.4.2 Lagoon hydrodynamics and ripple migration rates

Ripple migration rates ranged from -0.72 to 2.52 m day-1 (mean = 0.2 m day-1,

positive is in the local onshore direction) and from -0.72 to 1.08 m day-1 (mean = 0.08 m

day-1) at the south and north quadpods, respectively. Despite the variable migration rates,

the geometry of the ripples showed insignificant changes (small decrease in amplitude to

~0.07 m; Figure 3.7b) throughout the experiment.

47

Figure 3.6. Uranium-series ages of coral sediment dated from surficial sediment samples collected for this study (red dots) from the fore reef (AWAC), outer reef flat (PJ028), back reef flat (PJ033, PJ026, PJ020), lagoon (PJ039 and PJ007), and beach (Core2). CF or SF denotes that fragments were from the coarse (> 2mm) or sand (<2mm) fractions, respectively. Uranium-series age of coral fragment dated from the top of a lagoon sediment core [Collins et al., 2003]. Arrows indicate the dominant mean wave-driven circulation patterns from model results presented in Chapter 4.

48

Figure 3.7. (a) Normalized return amplitude (colour) at each bin (vertical axis) versus time (horizontal axis) for the duration of the field experiment in May-June 2016 at the southern instrument site. Each peak in return amplitude indicates the stoss side of an individual ripple, with the local slope indicating the migration rate. (b)Time-series of bed elevation at the South quadpod from the vertical (downward-looking) echosounder. (c) Ripple migration rate at the southern instrument site calculated from the horizontal-looking echosounder (i.e. data shown in a).

Mean currents near the salient were weak (mean magnitude 0.04 m s-1) and directed

towards the channels (Figure 3.6; Figure 3.8a, yellow arrows), which is consistent with

existing mean flow dynamics in fringing reef environments (e.g. Lowe et al., 2009b).

Ripple crests, however, were oriented so that migration was approximately shore-normal

(i.e. approximately perpendicular to mean currents) and aligned with the mean sea-swell

wave direction (Figure 3.8a, black and magenta arrows). Incident wave heights (Hsig) varied

from ~1 m to 3 m, with four swell events (Hsig > 2m) occurring during the ~4 week

deployment (Figure 3.8b). Local wave heights at the nearshore instrument arrays were

significantly smaller than the incident waves at the forereef (~0.5 m compared to 2 to 3 m)

and were tidally modulated, with larger waves consistent with higher water levels on the

49

reef crest (Figure 3.8c). Ripple migration rates were larger at the shallower (~2.5 m)

southern site than at the deeper (~3.5 m) norther site. The ripple migration rates resulted in

sediment fluxes ranging from -84 to 204 kg m-1 day-1 at the southern site and from -73 to 85

kg m-1 day-1 at the northern site (positive indicates onshore transport; Figure 3.8d).

Integrated over the entire experiment, ripple migration resulted in onshore transport of

~325 kg m-1 and 116 kg m-1 at the southern and northern sites, respectively (Figure 3.8e).

Figure 3.8. (a) Directional diagram indicating direction normal to ripple crests (black arrow), mean current direction (yellow arrow), mean sea-swell wave direction (magenta arrow), and mean infragravity wave direction (green arrow); note, arrows are not scaled by magnitude. (b) Offshore (20 m depth) significant wave height. (c) Local significant wave height; (d) hourly sediment fluxes calculated from the ripple migration rates; and (e) cumulative total transport due to ripple migration over the ~4 week field experiment at the northern (blue) and southern (red) quadpods.

3.5 Discussion

The data presented here has highlighted three key findings: (1) the contemporary

sediment reservoir is comprised of ‘old’ (≥ 1.4 ka) sediment and is dominated by coral

(~34% on average); (2) despite coral being the most prevalent constituent of the sediment

reservoir, live coral cover is low (<5%, averaged over all surveys); and (3) that shoreward

migrating ripples are likely a key mechanism of sediment delivery to the shoreline.

50

3.5.1 Ecological-sedimentological links

The results suggest that there is a disconnect between the reef calcifying community

and the sediment reservoir. While previous studies have demonstrated examples of reef

environments where the sediment reservoir contains disproportionately less of a given

organism than benthic surveys would suggest [i.e. low coral percent in sand while it is a

dominant benthic constituent; Chevillon, 1996; Harney et al., 2000], here, we show the

opposite: coral is the primary sediment constituent (34% on average) but is nearly absent in

the benthic community (less than 5%). This disconnect could be due to a variety of reasons

including a spatial disconnect from the source, an ecological modification of the sediment

reservoir, a shift in the calcifiers contributing to the sediment source, or a temporal

disconnect from the source. A spatial disconnect from the source material would suggest

that coral sediment is derived from outside the surveyed area. However, it is unlikely that

the sediment at Tantabiddi was imported from adjacent reef sections with higher coral

cover due to the dominant circulation patterns (i.e. limited alongshore transport; Figure

3.6). Furthermore, regional hyperspectral mapping suggests that our results agree well with

spatially-averaged coral cover for the northern Ningaloo Reef [270 km2 mapped; Kobryn et

al., 2013]. Data used for calibrating the hyperspectral maps showed that the sanctuary zone

immediately south of the study area has similar coral cover to that found here, and that it is

not until Osprey Bay (~40 km to the south) that there is high (~40%) coral cover [Langdon,

2012].

The eco-sedimentary disconnect could be due to the preferential breakdown of coral

material to sand-sized particles by bioerosion (e.g. fish or urchins). For example, parrot fish

have been shown to be key producers of coral sediment in a range of reef environments

[Perry et al., 2015b] and parrot fish are a prominent species at shallow depths around

Ningaloo [Fitzpatrick et al., 2012]. Similarly, urchin bioerosion has also been shown to be

significant at Ningaloo [Langdon, 2012]. Over-abundance of these coral predators, could

lead to a decrease in benthic coral cover while increasing the percentage of coral sediment.

Further, given the minimal coverage of living coral, it could be possible that these

organisms are feeding on intact dead coral (e.g. the reef framework), thus producing a

sediment reservoir reflecting the age of this source (potentially 1000s of years old).

51

Another possibility is that the Tantabiddi sediment reservoir is undergoing a shift in

sediment supply from coral-dominated to some other calcifiers [Yamano et al., 2005; Perry

et al., 2011]. Although coral is the dominant sediment constituent (~35% of the sediment),

65% of lagoon sediment is derived from other material (primarily CCA and mollsucs).

CCA was shown to be a primary benthic organism, however, the sampling method

provided limited information on the cover of molluscs (and other direct sediment

producers). A more detailed benthic survey as well as more radiometric dating [e.g. 14C;

Harney et al., 2000] is needed to understand the coupling between the benthic coverage of

these calcifiers and the sediment reservoir However, if molluscs and CCA prove to be

modern (i.e. 50 to 100 years old), then the disconnect shown here suggests that the

Tantabiddi sediment budget could be in a transition phase from coral being the primary

sediment source to CCA and molluscs dominating the sediment source [Perry et al., 2011].

Furthermore, because CCA and molluscs account for ~40% of the sediment reservoir, any

decline in the supply of these sediments (e.g. due to ocean acidification) on modern

timescales could drive the sediment budget into a negative state and lead to coastal erosion.

However, if we assume that the age of coral sediment is representative of the entire

sediment reservoir, the eco-sedimentary disconnect could represent a temporal decoupling

from the source material (i.e. sediments are reflective of a relic reef community). Given

that most of the coral sediment were older than 1 ka BP, the results presented here suggest

that sediments at Tantabiddi are derived from a reef community that was living ~1 to ~5 ka

BP; and that these ‘old’ sediment are stored in surficial sand bodies (e.g. offshore areas

and/or channels) and then transported into the lagoon by waves, similar to Hawaiian

fringing reefs [Harney et al., 2000; Harney and Fletcher, 2003]. This hypothesis assumes

that living coral is effectively eroded in place (likely due to mechanical breakdown) and

then broken down by either mechanical, biological, or chemical processes until it is sand-

sized and transportable. This assumption appears to be supported by the radiometric dates

presented here with gravel-sized clasts on the forereef (i.e. located near living coral) dated

to 70 years BP, and sand-sized material at the same location dated to ~3 ka BP.

Furthermore, the ages of coral sediment fit well with the local sea level curve and previous

benthic community composition [Collins et al., 2003; Twiggs and Collins, 2010]. The most

recent (local) sea level high stand occurred 5.8 ka BP, when sea level was approximately 1

to 2 m higher than present [Collins et al., 2003]. Prior to this time (~8 ka BP to 5.8 ka BP),

52

reef growth had been in a rapid, ‘catch up’ phase [Twiggs and Collins, 2010]; and core data

indicates that benthic cover during that time was dominated by corals [Collins et al., 2003;

Twiggs and Collins, 2010]. Over the last ~5.8 ka, sea level has fallen to present levels and

Ningaloo has been marked by a ‘detrital build up and aggradational’ phase [Twiggs and

Collins, 2010]. It was during this time period that the modern sediment reservoir was likely

created, as a decrease in local sea level would have exposed the coral-dominated reef to

increasing amounts of wave energy that would have eroded these communities and

generated the sediment that is now slowly cycling through the system, with some of the

oldest material still present near the channels and on the beach (Figure 3.6). Although more

data is needed to verify to complete decoupling of the sediment reservoir from the benthic

ecology, if the ages of coral sediment are representative of the entire sediment reservoir, the

temporal disconnect shown here suggests that this system could be slower to respond to

future shifts in the benthic ecology because sediment is stored in the active sediment

reservoir (and therefore available for shoreline maintenance) for 1000s of years.

3.5.2 Implications for shoreline morphology and maintenance

Rouse [1937] numbers (P) are commonly used for predicting sediment transport mode

(i.e. suspended load versus bedload); where P is the ratio of the sediment’s settling velocity

to the shear velocity of the overlying flow [Rouse, 1937]. Spatial distributions of grain size

agreed well with observed mean circulation patterns, suggesting that these currents are

responsible for the larger scale distribution of sediment around the reef. However, in

agreement with P calculated from the settling velocities of bed sediment [Cuttler et al.,

2016], bedload transport through the lagoon, via ripple migration, is shown here to be a

significant mechanism for delivery of sediment to the shoreline. Interestingly, ripples were

oriented so that ripple crests were perpendicular to the coastline and in line with the mean

sea-swell wave direction. Ripples have been shown to orient themselves so as to maximize

transport rate [Gallagher et al., 1998], suggesting that the wave orbital motions are

primarily responsible for the bedload transport with the local direction of transport

determined by wave refraction around the reef edges and into the lagoon (Figure 3.8a).

In order for bedload transport via wave orbital motions to be responsible for building

the shoreline salient at the study site, it must be operating at similar timescales to the supply

of sediment to the lagoon (i.e. order 1000s of years). Assuming the coastline immediately

53

behind the reef would be approximately straight if not for the shoreline salient, then the

salient volume is approximately 6,400,000 m3 (Figure 3.9). Using a mean sediment flux

derived from ripple migration rates (averaged over both north and south sites) of 9 kg m-1

day-1, and a bulk porosity of 0.35, ripple migration could have filled the salient volume in

~1.5 ka. However, this is likely a lower-bound (i.e. most rapid in-fill time) estimate, given

that our migration rates are from a winter period where waves are more energetic and that

we assume that all sediment that reaches the shoreline is mobilized by aeolian processes

and stored in the dune system. If migration rates are halved and sediment volume is

doubled (e.g. storage efficiency is 0.5), then the estimated fill time increases to ~15 ka,

which is still within the range of the dated material.

Figure 3.9. Volume calculation of the shoreline salient at Tantabiddi used to determine in-fill time given average rates of ripple migration. Elevations for onshore locations are derived from topographic light detecting and ranging (LiDAR) data flown for the region in October 2015.

Given that both the active sediment reservoir and the dominant mechanism of sediment

delivery to the shoreline are operating on long (order 1000s of years) timescales, the results

collectively imply that shoreline morphology in this environment will likely be resilient to

future shifts in benthic community structure. However, changing environmental conditions

may still pose a threat to these coastlines as the changing benthic community (i.e. decrease

in coral cover) and decline in net reef accretion as well as sea level rise, could allow more

54

wave energy to reach the coastline and directly impact the shoreline [Grady et al., 2013;

Cheriton et al., 2016b].

3.6 Conclusions

Coral reef environments are unique in that there is a strong link between the reef

ecology and the sediment reservoir; however, these connections may increase the

sensitivity of reef-associated landforms (e.g. reef islands or fringing reef-fronted coastlines)

to changes in reef ecology. Data presented here highlights that for a section of Australia’s

largest fringing reef, Ningaloo Reef, the benthic ecology (e.g. coral cover) and the active

sediment reservoir may be temporally disconnected. Assuming the age of coral sediment

(the primary sediment constituent) is representative of the entire sediment reservoir, then

sediments are derived from a benthic community that occupied the reef ~1,400 to 5,000

years ago. Over this same timescale, bedload transport (in the form of migrating sand

ripples) has been acting to deliver sediment to the large shoreline salient that is present in

the lee of the reef. Together, these results indicate that reef systems similar to Ningaloo

may be more resilient to future shifts in benthic ecology, with the supply of sediment

decoupled from the modern benthic community; however, declines in net reef accretion still

pose a threat, as any increase in wave transmission over the reef flat may result in direct

wave attack of these coastlines.

55

4. RESPONSE OF A FRINGING REEF COASTLINE TO THE DIRECT

IMPACT OF A TROPICAL CYCLONE

4.1 Abstract

Tropical cyclones (TCs) generate extreme hazards along coastlines, often leading to

losses of life and property. Although coral reefs exist in cyclone-prone regions around the

world, few studies have measured the hydrodynamic conditions and morphological

responses of reef-fronted coastlines to TCs. Here we examine the impact of TC Olwyn on a

section of Australia’s largest fringing reef (Ningaloo Reef) using in situ wave and water

level observations, topographic surveys, and numerical modeling. Despite forereef

significant wave heights reaching 6 m and local winds of 140 km hr-1, average beach

volume change was only -3 m3 m-1. Numerical simulations indicate that this erosion was

due to locally-generated wind waves within the lagoon rather than the offshore waves that

were dissipated on the reef crest. A comparison of these volume changes to observations of

TC impacts along exposed sandy beaches quantitatively demonstrates the substantial

coastal protection reefs can provide against extreme storms.

4.2 Introduction

The coastal impacts of tropical cyclones (TCs), namely erosion, flooding, and

habitat destruction, have been well documented globally [Castillo et al., 2012; Woodruff et

al., 2013]. Existing studies of TC impacts on coral reef-fringed coastlines have primarily

focused on the impact to the coral communities [Harmelin-Vivien, 1994], the

hydrodynamic conditions over the reefs [Péquignet et al., 2011], or the fate of storm

deposits [Scoffin, 1993]. However, few studies have focused on the hydrodynamic and

morphodynamic processes driving changes in reef-fringed beach morphology during a TC.

In this study we quantify the morphological change due to a direct cyclone impact along a

56

reef-fringed coast, identify the physical mechanisms responsible for the observed beach

erosion patterns, and assess the coastal protection provided by the reef relative to exposed

sandy coasts experiencing similar extreme met-ocean conditions.

Fringing reefs, which can range from shore-attached to several kilometers offshore,

are the most prevalent class of coral reefs along tropical coasts [Hopley, 2004]. Reefs

provide natural coastal protection by dissipating offshore wave energy through both wave

breaking and bottom friction [Lowe et al., 2005]. Despite the abundance of reef

hydrodynamic studies, few studies have quantified the processes governing coastal

morphological changes in reefs, particularly during TCs. Previous studies conducted under

more typical (non-TC) conditions have shown that shorelines fringed by reefs can

experience morphological changes on short (weekly) to intermediate (seasonal) timescales

due to changes in wind and wave direction [Eversole and Fletcher, 2003; Beetham and

Kench, 2014]; and that reef-fronted beaches are generally more stable than exposed beaches

[Ruiz de Alegria-Arzaburu et al., 2013]. The limited studies that have directly measured

TC-induced shoreline changes have suggested that shoreline response is inversely related to

reef flat width, with the magnitude of beach erosion decreasing with increasing reef flat

width [Mahabot et al., 2016]. These same studies have also suggested that the relative

coastline orientation (i.e. in relation to local wind and wave direction) can influence the

magnitude of coastal erosion. However, due to the episodic nature of TCs and the rarity of

capturing a direct impact with in situ instrumentation, there still remains a gap in

understanding of how much coastal protection is offered by fringing reefs during TCs, and

moreover, which factors specifically govern coastal responses.

Rigorously assessing the coastal protection afforded by reefs is particularly

important given that a number of recent studies have suggested that the coastal protection

offered by reefs is under threat due to both coral degradation (i.e. declining rates of net reef

accretion and decreasing bottom roughness) and sea level rise, which will increase

submergence depths and therefore expose reef-protected coastlines to larger waves [Grady

et al., 2013]. Collectively, these studies emphasize that it is critical to understand the

physical drivers of morphologic change in reef environments to better predict future

changes to these coastlines. In this study we combine in situ measurements (wave heights,

water levels), repetitive topographic beach surveys, and numerical simulations from a

57

coupled wave-current model (Delft3D/SWAN) to quantify the coastal response from the

direct impact of TC Olwyn (2015) at Ningaloo Reef, Western Australia.

4.3 Data and methods

4.3.1 Study area

Ningaloo Reef is Australia’s largest fringing reef, stretching ~270 km south from

Australia’s North West Cape (Figure 4.1a). The morphology of Ningaloo is characterized

by a steep (1:20) forereef slope, a relatively narrow reef flat (100s of m), and a wide (1 to 5

km), but shallow (< 5 m deep) lagoon [Collins et al., 2003]. The study area is located at the

northern end of Ningaloo Reef at Tantabiddi (Figure 4.1c) and features a shoreline salient

inland from the reef flat; a common feature along the reef-fringed coastline of Western

Australia, including at Ningaloo [Sanderson, 2000]. The beach has a variable slope (β),

ranging from ~ 0.03 north of the salient to ~ 0.14 south of the salient, is relatively narrow

(≤ 100 m), and is backed by a dune system (heights from 3 to 10 m).

Ningaloo Reef is located along Australia’s Northwest Shelf, which is the most

cyclone prone region of Australia, being influenced, on average, by 1 to 2 TCs per year

[Bureau of Meteorology, 2016]. In order to opportunistically capture the impact of a TC on

this reef-fronted coastline, five pressure sensors (Figure 4.1c and 4.1d) were deployed from

December 2014 to May 2015. On 11 March, TC Olwyn developed off the northwest coast

and tracked southward until crossing land on 13 March (Figure 4.1a and 4.1b). On 12

March its eye passed ~10 km to the west (offshore) of the study area (Figure 4.1b) as a

Category 3 (Australian tropical cyclone intensity scale) cyclone with an estimated central

pressure of 965 hpa, sustained winds of 140 km hr-1, and gusts up to 200 km hr-1 [Bureau of

Meteorology, 2017].

58

Figure 4.1. (a) Western Australia with the Ningaloo Peninsula in yellow; (b) the Ningaloo Peninsula with the boundaries of the ‘outer’ Delft3D-FLOW and SWAN domain indicated in blue; (c) study site and bathymetry with the cross-shore array of pressure sensors indicated by the white triangles, the ‘inner’ Delft3D-FLOW and SWAN domain show in grey and the ‘shore’ Delft3D-FLOW grid shown in black. TC Olwyn track is the red line in (a) and (b). (d) Cross-shore depth profile from the coastline to the forereef, with the reef crest marked with a red ‘x’ and pressure sensors denoted by red triangles.

4.3.2 Meteorological and hydrodynamic data

Meteorological data (atmospheric pressure, wind speed, and wind direction) were

measured ~18 km south of the study site at the Milyering weather station, maintained by

the Australian Institute of Marine Science. A cross-shore array of pressure sensors

(RBRvirtuoso), extending from the forereef (~18 m depth) to the shoreline recorded

observations of waves and water levels (Figure 4.1c and 4.1d). All sensors logged

continuously at 1 Hz until 19 March when they were recovered following TC Olwyn.

Atmospheric pressure was removed from the pressure observations and the tidal component

of the water level was removed using the time series predicted by T_TIDE [Pawlowicz et

al., 2002] with constituents derived from the forereef pressure sensor. Hourly estimates of

the sea surface energy-frequency spectrum were obtained from Fourier transforms of one-

hour de-meaned and de-tided data segments and block averaged using a Hamming window

(1024 samples) with 50% overlap, yielding 17 degrees of freedom [Thomson and Emery,

2014]. Hourly significant wave heights were estimated from the variance of sea surface

59

elevation in the sea-swell (SS; 0.04<f<0.4 Hz, where f is frequency) and infragravity (IG;

0.002<f<0.04 Hz) energy bands (Hsig,SS and Hsig,IG, respectively) using linear wave theory.

The pressure observations were also used to estimate setup following:

�̅�𝜂 = ℎ𝑡𝑡𝑡𝑡𝑡𝑡�� − �̅�𝜂𝑡𝑡 − ℎ𝑡𝑡 (1)

where �̅�𝜂 denotes setup (due to both waves and wind), ℎ𝑡𝑡𝑡𝑡𝑡𝑡�� is total measured depth

(assuming hydrostatic pressure), 𝜂𝜂𝑡𝑡� denotes tidal elevation, ho is still water level, and

overbars denote hourly averaging. Still water level (ho) is a constant at each site and was

calculated as the depth at each site when forereef waves were low (Hsig,SS < 0.8 m), wind

speed was low (<10 km hr-1), and tidal elevation was highest (𝜂𝜂𝑡𝑡� > 0.6 m; i.e. assuming no

wave setup) [Raubenheimer et al., 2001]. All measurements were then referenced to the

forereef sensor to remove any water level fluctuations due to shelf-scale water level

variations [Lowe et al., 2009b].

4.3.3 Beach morphology data

Annual beach surveys have been conducted at Tantabiddi since August 2013 using a

backpack-mounted differential GPS receiver (DGPS). The pre-cyclone morphology was

recorded in July 2014, approximately eight months prior to the cyclone, and a ‘post-

cyclone’ survey was conducted 10 days after TC Olwyn. Two subsequent ‘cyclone

recovery’ surveys were conducted in October 2015 and June 2016. Although the pre-

cyclone survey was conducted eight months prior to TC Olwyn, there were no significant

storms (i.e. TCs) in this period and analysis of historical aerial imagery (from 1969 to

2014) of the Tantabiddi salient using the “Digital Shoreline Analysis System” [Thieler et

al., 2009] as well as previous work along the greater Ningaloo coastline [Sanderson, 2000]

provides no evidence to suggest significant seasonal variability in shoreline position

(Figure A.2.1).

Positions from the DGPS surveys (uncertainty of 0.05 m in the vertical and

horizontal directions) were organized into a triangulated irregular network and converted to

a grid to produce a 1 m cell-size topographic surface of the surveyed beach. Successive

topographic surfaces were differenced to determine the location and extent of erosion or

60

accretion. Cross-shore transects were extracted at 50 m alongshore intervals from the

survey grids to assess beach elevation and volume change.

4.3.4 Numerical model

The numerical model Delft3D-FLOW [Lesser et al., 2004] coupled with the wave

model SWAN [Booij et al., 1999] was used to simulate the hydrodynamic conditions

during the cyclone. Delft3D solves the unsteady shallow-water equations in two- or three-

dimensions [Lesser et al., 2004] and has been used effectively in coral reef environments

under both non-storm and storm conditions [Grady et al., 2013; Hoeke et al., 2015].

Numerical details, governing equations, and underlying assumptions of Delft3D are

described in detail in Lesser et al. (2004); brief details of the model resolution and settings

used for this study are described below. Further details of model settings and validation are

provided in the Supporting Information.

Delft3D-FLOW (version 6.02.02.5562M) was run in depth-averaged mode and

consisted of three domains (‘outer’, ‘inner’, ‘shore’) of varying resolution that were two-

way coupled using “domain decomposition” [Hummel and de Goede, 2000]. The ‘outer’

domain had a 50x50 m resolution, the ‘inner’ domain had a 17x17 m resolution, and the

‘shore’ domain had a 5x5 m resolution (Figure 4.1b and 4.1c).

Delft3D-FLOW was two-way coupled with SWAN (version 40.72) over two nested

domains that had the same resolutions as the ‘outer’ and ‘inner’ circulation domains.

Although SWAN does not model IG energy, which has been shown to be important to

lagoonal processes under non-TC conditions [Pomeroy et al., 2012], we show below that

SS energy was the dominant forcing mechanism adjacent to the shoreline during these TC

conditions.

4.4 Results

4.4.1 Field observations

The eye of TC Olwyn passed ~10 km offshore of the study site on 12 March at

~17:00 UTC (Figure 1b) resulting in incident waves on the forereef reaching ~6 m (Hsig,SS),

which is ~3 times larger than the average swell (Hsig,SS ~1-2 m) observed at Ningaloo

[Pomeroy et al., 2012]. To place the TC Olwyn conditions in the context of typical wave

61

conditions, the observed waves and water levels during the cyclone were compared to a

‘typical’ swell event (Hsig,SS =1.8 m) that occurred prior to TC Olwyn (Figure 4.2).

Figure 4.2. (a) Hsig at the forereef pressure sensor (18 m depth) from December 2014 to March 2015, with a typical swell event (Hsig,SS ~ 1.8 m) denoted by the blue shading and TC Olwyn (Hsig,SS = 5.8 m) denoted by the red shading. (b) Cross-shore profiles of Hsig,SS (solid line) and Hsig,IG (dashed line) within the lagoon (instrument locations indicated by ‘x’) during the (b) typical and (c) cyclone events. (d) Cross-shore profiles of setup during a typical (blue) and cyclone (red) swell event (note different scales). Wave spectra for the (e) forereef and (f) the shoreline sensors from the one hour interval when waves were largest during the typical (blue) and cyclone (red) events.

The cross-shore distribution of wave heights and setup (due to both wind and

waves) were considerably different during the TC compared to typical swell conditions.

Under typical swell conditions, after dissipation of incident wave energy commences near

the reef crest, Hsig,SS continues to decrease across the reef flat and lagoon towards shore;

and Hsig,SS and Hsig,IG become of comparable magnitude (Figure 4.2b). However, at the peak

of TC Olwyn, although Hsig,SS decreases near the reef crest by 89% due to incident wave

breaking, Hsig,SS increases across the lagoon towards the shoreline, with Hsig,SS reaching 1.5

62

m (~1.5 times reef flat Hsig,SS) and thus, substantially larger (~6 times) than Hsig,IG (Figure

4.2c).

The shoreward growth in Hsig,SS corresponded to a change in the wind direction that

increased the shoreward-directed component of the wind vector (Figure A.2.3d). The

observed wave spectra show that energy was primarily enhanced at high frequencies (0.1-

0.3 Hz) indicating local growth of wind waves within the lagoon (Figure 4.2f). A similar

pattern was observed in the cross-shore setup profile; under typical conditions setup

decreased across the lagoon towards the shoreline whereas during the cyclone setup

increased towards shore, coincident with the shoreward increasing wave heights and

onshore winds (Figure 4.2d).

The observed changes in beach morphology were remarkably modest in response to

the TC, with alongshore-averaged, sub-aerial profile volume changes of only -3 m3 m-1 and

net volume change (integrated over the entire sub-aerial beach) of -14,457 m3 (-8%);

however, this change was variable alongshore (Figure 4.3a and 4.3b). The north side of the

salient showed slight accretion (+3 m3 m-1 on average), whereas the south side was

predominantly eroded (-8 m3 m-1; Figure 4.3a and 4.3b). In regions where erosion occurred,

it corresponded to a flattening of the cross-shore beach profile, primarily due to erosion of

the dune (removing up to 1.5 m elevation; Figure A.2.4).

The initial post-cyclone survey (October 2015) showed limited beach recovery, with

a total sub-aerial beach volume change of only +3,885 m3 (+2%) from to March 2015;

which is still -6% compared to the pre-cyclone beach volume. However, this is likely due to

another TC of similar magnitude and storm track (TC Quang) impacting the study area ~6

weeks after TC Olwyn. Although the October 2015 profiles showed alongshore-averaged,

net erosion compared to July 2014 (-2 m3 m-1), profiles between 0 and -600 m (i.e.

locations of largest cyclone-induced erosion, Figure 4.3b) actually returned to pre-cyclone

volumes (+11 m3 m-1 recovery from March 2015; Figure A.2.4d). The final recovery survey

(June 2016) showed complete beach volume recovery, with a total sub-aerial beach volume

change of +26,677 m3 (15%) compared to the pre-cyclone (July 2014) beach volume.

63

Figure 4.3. (a) Observed beach elevation change (colors). (b) TC-induced beach volume change and (c) pre-storm beach slope (β), calculated between the 1 m contour and seaward limit of the beach (the r2 is between the β and beach volume change shown in (b)). (d) Model-predicted Hsig along the 2 m isobath (yellow dashed line in (a)) for a 1.8 m swell (solid line) and peak TC conditions with (dashed line) and without wind growth (dashed-x line) enabled. (e) Disequilibrium wave heights (Hsig,TC/Hsig,Typical) predicted with (dashed) and without(dashed-x) wind growth (i.e. ratio of black dashed or dashed-x line in (c) to solid line in (c)); the r2 is calculated between the disequilibrium TC wave heights with wind growth (dashed line) and post-cyclone beach volume change (data shown in (b)).

4.4.2 Model simulations

To further investigate how the nearshore wave fields likely drove the observed

spatial variability in beach changes, we used the numerical model to extend the in situ

results over the entire study area. Modeled wave heights were extracted along the 2 m

isobaths (as an indicator of the wave energy along the beach), which is further offshore on

the south side of the salient due to the southern portion of the lagoon generally being

shallower than the north (Figure 4.3a).

Modeled wave heights during TC Olwyn were slightly larger on the northern side of

the salient, despite the observations indicating accretion in this area (Figure 4.3d; Figure

64

A.2.5). Examination of the alongshore beach morphology showed that TC-induced beach

volume change was significantly correlated with the pre-cyclone beach slope (r2 = 0.62,

p<<0.01); with northern (accreted) beaches having shallower slopes and the southern

(eroded) beaches having steeper slopes (Figure 4.3b and 4.3c). Under typical wave

conditions, the shoreline on the southern side of the salient (y = -1000 m to 0 m) is exposed

to smaller waves than the northern side (y > 0 m; Figure 3d), likely explaining the typical

alongshore variability in beach slope [Wright and Short, 1984]. Note, there are no

significant alongshore differences in beach sediment characteristics [Cuttler et al., 2016].

These results suggest that this reef-fronted beach is in equilibrium with the prevailing

conditions, and that the morphological response to TC Olwyn was determined by how

much the cyclone waves locally deviated from typical conditions, i.e. the alongshore beach

morphological response was related to the spatial pattern in the disequilibrium of TC wave

heights (Hsig,TC/Hsig,Typical) from a typical swell event (r2 = 0.53; Figure 4.3e).

To confirm the critical role of wind-wave growth within the lagoon, SWAN was run

with and without wind-wave growth. Without wind growth included in the model, the

nearshore wave heights from the cyclone swell (Hsig,SS = 5.8 m) are predicted to be ~2 times

larger than during a typical incident swell (Hsig,SS = 1.8 m). However, with wind growth

enabled, nearshore TC wave heights are up to 5 times larger than those from a typical swell,

and the magnitude of disequilibrium from typical conditions is larger on the southern side

of the salient, consistent with the observed beach erosion (Figure 4.3e). Thus, despite the

lagoon being relatively shallow (<5 m), locally-generated wind waves within the lagoon

were a dominant mechanism driving the observed beach changes.

4.5 Discussion and Conclusions

This study highlights the effectiveness of coral reefs at providing natural coastal

protection. Despite the extreme offshore wave conditions, beach morphology changes were

relatively modest; particularly when compared to the responses of open, sandy coastlines

exposed to comparable met-ocean conditions (Figure 4.4). For example, average beach

volume loss at Tantabiddi was 3 m3 m-1, whereas, areas in Florida, USA that were impacted

by similar offshore wave heights from Hurricane Ivan experienced ~5 to 10 times greater

beach erosion [Wang et al., 2006], with comparable erosion rates also observed during

other Atlantic Ocean TCs (blue symbols, Figure 4.4). Although the magnitude of beach

65

response in reef environments is much less than sandy environments, the underlying

forcing mechanism appears to be similar, with the magnitude of divergence (or

disequilibrium) of nearshore wave heights from typical nearshore wave heights a key factor

determining storm beach response [Yates et al., 2009].

Figure 4.4. Comparison of TC impacts on open, sandy coasts (blue symbols) and reef-fronted coasts (red symbols). For studies that did not report offshore Hsig, wave heights from WaveWatchIII hindcast model predictions [Tolman, 2009; ftp://polar.ncep.noaa.gov/pub/history/waves/nww3/] were extracted at the grid cell nearest to the study location in ~20 m depth (magenta circles). Values for Mahabot et al. [2016] represent alongshore-averaged values for transects where reef morphology is constant.

The magnitude of beach response observed here agrees well with the few previous

observations of TC-induced beach morphology changes in fringing reefs (Figure 4.4, red

x’s) [Mahabot et al., 2016]. These previous observations were conducted in shore-attached

(i.e. no significant lagoon) reef systems and highlighted the importance of wave direction

and reef flat width in determining morphological response; however here, in a system with

a significant lagoon, we suggest that wind-driven processes can also be very significant (or

dominant) in driving beach morphodynamics during a TC. We note that the influence of

these wind-driven processes will likely scale with distance from the TC and lagoon width.

For example, if TC Olwyn had passed further offshore of the study site, incident wave

ftp://polar.ncep.noaa.gov/pub/history/waves/nww3/

66

heights may have been similar (i.e. ~6 m) but local winds would have been much less.

Therefore, nearshore wave heights would have been significantly smaller and beach volume

changes likely would have been even less than those observed here.

These results show that the offshore TC-generated waves were largely dissipated by

the reef and played a minimal role in driving the beach changes. Instead, locally-generated

wind waves played a critical role in determining the morphological change, with the wind

waves reaching the shoreline ultimately being larger than the residual offshore waves that

were transmitted across the reef. Although future climate changes are likely to increase

wave transmission over coral reefs, primarily due to sea level rise but also due to

degradation of reef habitats [Grady et al., 2013], the results presented here show that while

these structures still exist, they can provide natural coastal protection from offshore waves,

even under extreme conditions. However, our results also reveal that even over the small

scales (order 1 km) of a nearshore reef-lagoon system, local wind wave generation can play

a dominant role in sediment transport and erosion responses during TC conditions, a

process traditionally neglected in nearshore model applications to predict beach response to

extreme events (e.g. models that can only consider remotely generated waves, such as

Xbeach). Furthermore, because the reef geomorphology at Ningaloo (e.g. shallow reef crest

that gradually deepens to a shallow lagoon) represents a ‘standard’ reef [Falter et al.,

2013], accounting for the local wind growth of waves is expected to be critical to

determining beach response to TCs in reef systems worldwide.

A.2 Appendix for Chapter 4

Note, this section is arranged to coincide with the order it is referenced in the above

text (Chapter 4); this is different from the format in which it was submitted to Limnology

and Oceanography Letters.

67

Figure A.2.1. (a) Net shoreline movement and (b) linear regression rate of shoreline change with 95% confidence intervals (red dashed lines) as determined from analysis of historical aerial imagery (1969 to 2013; 13 total photos) using the Digital Shoreline Analysis System [Thieler et al., 2009]. Numerical model

For the circulation model, a water level boundary was prescribed along the offshore

boundary (~80 m depth) based on the pressure record from the forereef sensor (P1 ~18 m

depth) and Neumann boundaries were used along the two lateral boundaries (Figure 1b). A

spatially-varying bottom roughness (CD) was calculated using the White-Colebrook friction

formulation and a Nikuradse roughness length (kn) of 0.5 m [Lowe et al., 2009b]. For the

depths on the reef flat and in the lagoon (i.e. 0.5 m to 5 m), this kn corresponds to a CD of

0.007 to 0.026, which is well within the range of CD values reported for numerical and field

investigations of reef environments [Lowe et al., 2009a; Péquignet et al., 2011; Taebi et al.,

2012; Van Dongeren et al., 2013; Quataert et al., 2015]. Enhancement of bed shear stresses

due to waves was modelled as a function of the ratio of the near-bed current to wave

velocities using the formulation of Fredsoe (1984).

68

Stationary SWAN simulations were run at 60 min intervals using updated currents,

water levels, and wind from Delft3D-FLOW. TC boundary conditions were derived from a

larger, regional scale SWAN model (Drost et al., 2017). Simulations were also run with

boundary conditions derived from in situ frequency-directional spectra measurements

recorded by a Nortek AWAC (~10 m depth) during a ‘typical’ winter swell event (Hsig,SS ~

1.8 m) in August 2013 [Pomeroy, 2016]. The boundary conditions were applied uniformly

along the offshore (~80 m depth) and lateral boundaries of the SWAN domain (Figure 1b).

Spectral properties of waves were modelled over the frequency range from 0.04 – 1 Hz;

however, when comparing model results with field observations, wave spectra were

compared over the same frequency range as was used to analyze the pressure data (0.04 –

0.4 Hz). Wave dissipation from depth limited breaking was modelled using the default

Battjes and Janssen (1978) formulation with the breaking coefficient set to 0.73.

Dissipation due to bottom roughness was modelled following Madsen et al. (1988), with a

roughness coefficient of 0.1. Wind growth and dissipation from whitecapping were also

modelled following Van der Westhuysen (2007).

Local wind speed and direction (spatially uniform over the model domains) were

derived from the regional scale cyclone model developed for northwestern Australia and

validated with the Australian Bureau of Meteorology Tropical Cyclone Database (Drost et

al., 2017; Bureau of Meteorology, 2017); these conditions were then applied uniformly

across all domains and were incorporated into both the flow and wave model. Bathymetry,

for both the flow and wave models, was derived from hyperspectral photogrammetry for

depths less than 7 m [Taebi et al., 2011]; for all other depths, bathymetry was interpolated

from a 50 m by 50 m resolution grid produced by Geoscience Australia (2012).

Model validation

Model-data comparisons were made for 31 hours using hourly averages calculated

from 10 minute model outputs for water level/setup, one hourly simulation of wave height

(from SWAN), and hourly in situ observations (Figure S2). The root mean square error

normalized by the variance of the observations (NRMSE), bias, and squared correlation

coefficient (r2) between the hour-averaged modelled and observed significant wave height,

water level, and setup were calculated at each instrument site (Table A.2.1). The Murphy

Skill [Murphy, 1988] was also evaluated; Murphy Skill (MS) is calculated as:

69

𝑅𝑅𝑅𝑅 = 1 − ∑ (𝑋𝑋𝑚𝑚𝑚𝑚𝑚𝑚−𝑋𝑋𝑚𝑚𝑜𝑜𝑠𝑠)2𝑁𝑁𝑖𝑖=1∑ (𝑋𝑋𝑚𝑚𝑜𝑜𝑠𝑠−𝑋𝑋�𝑚𝑚𝑜𝑜𝑠𝑠)2𝑁𝑁𝑖𝑖=1

(S1)

or

𝑅𝑅𝑅𝑅 = 𝑅𝑅2 − �𝑅𝑅 − 𝜎𝜎𝑚𝑚𝑚𝑚𝑚𝑚𝜎𝜎𝑚𝑚𝑜𝑜𝑠𝑠

�2− �𝑋𝑋

�𝑚𝑚𝑚𝑚𝑚𝑚−𝑋𝑋�𝑚𝑚𝑜𝑜𝑠𝑠𝜎𝜎𝑚𝑚𝑜𝑜𝑠𝑠

�2 (S2)

where Xmod and Xobs are the modelled and observed variables of interest,

respectively, σ is the standard deviation of the modelled (mod) or observed (obs) variable,

and overbars represent time averaging over the length of the time series with N samples. A

skill of one indicates perfect agreement, zero indicates the model predictive ability is

equivalent to using a mean of the observations, and an MS less than zero indicates the

predictive ability is worse than using a mean of the observations. Furthermore, in Equation

S2, the first term is the squared correlation coefficient, the second term quantifies the

ability of the model to reproduce the variance in the observations, and the third term

represents the disagreement of the model and observational means (bias) and corresponds

to the linear regression intercept [Murphy, 1988; Ralston et al., 2010; Hansen et al., 2015].

Here, we consider 0.0<MS<0.5 to represent “moderate” skill scores, with “high” and

“poor” skills for higher and lower scores, respectively [Hansen et al., 2015].

The model was able to reproduce the TC conditions with a high skill for

nearly all variables and instrument locations; average MS was 0.77, 0.95, and 0.62 for

Hsig,SS, ℎ𝑡𝑡𝑡𝑡𝑡𝑡��, and �̅�𝜂, respectively (Table A.2.1). The lowest MS was for the reef flat sensor

(P2) where Hsig,SS was under-predicted (Figure S2b) but �̅�𝜂 was slightly over-predicted

(except during the maximum setup; Figure S2k); these discrepancies are likely due to the

highly variable bathymetry associated with coral reef flats and parameterization of wave

dissipation.

70

Table A.2.1. Summary of Murphy [1988] skill score (MS) for modelled significant wave height (Hsig,SS), water level (𝜼𝜼𝒕𝒕𝒕𝒕𝒕𝒕��), and setup (𝜼𝜼�) at the instrument locations. For MS a score of 1 indicates perfect agreement between the model results and the observations.

Figure A.2.2. (a-e) Comparison of observed significant wave heights (Hsig,SS), (f-j) water level (𝒉𝒉𝒕𝒕𝒕𝒕𝒕𝒕��), and (k-o) setup (𝜼𝜼�) with output from Delft3D-FLOW and SWAN at the fore reef (P1), reef flat (P2), outer lagoon (P3), mid-lagoon (P4), and inner lagoon (P5).

71

Figure A.2.3 (a) Significant wave height at the fore reef pressure sensor (P1, ~18 m depth) for the three month deployment, with TC Olwyn indicated by the grey shading. (b) Water level, (c) wind speed, and (d) wind direction during passage of TC Olwyn (grey area in (a)). And significant wave height for the sea-swell (SS, blue) and infragravity (IG, red) frequency bands at the (e) reef flat, (f) back reef flat, (g) lagoon, and (h) shore. Wind speed shown here (c) is lower than Category 3 (Australian convention) conditions because measurements are taken from the Milyering Weather Station.

72

Figure A.2.4. (a) Aerial image of the Tantabiddi salient with example beach profiles (shown in b-e) highlighted in yellow. In (b)-(e), blue represents the July 2014 beach morphology, red represents March 2015 (i.e. post-cyclone), cyan represents October 2015 (6 months post-cyclone), and black represents June 2016 (15 months post-cyclone). (f) Example of the TC-induced dune erosion on the south side of the salient (i.e. red line in d).

73

Figure A.2.5. Model-predicted significant wave heights and circulation patterns from the onset of TC Olwyn (a,b), the approach of TC Olwyn (c,d), and when peak wave heights occurred (e,f). Note, for panels (c) and (d), both wind and waves are from a northerly direction (i.e. approximately alongshore); whereas in (e) and (f) wind and waves are from a south-westerly direction (i.e. cross-shore). Contour labels in (b), (d), and (f), indicate depth.

74

75

5. GENERAL DISCUSSION AND CONCLUSIONS

5.1 Introduction

Fringing coral reefs establish unique coastal sediment dynamics, given that the

sediment cycling through the system and accumulating to build landforms is directly

derived from the calcium carbonate skeletons of the reef biota. The future resilience of reef-

associated landforms is currently under threat due to ocean warming and acidification,

which, through substantial modifications to reef ecosystems, has the potential to decrease

the sediment supply to these landforms. In addition, sea level rise is predicted to increase

the wave energy reaching these shorelines. Therefore, the overarching aim of this thesis

was to increase our understanding of the timescales and mechanisms of sediment transport

processes in reef environments; this knowledge will allow us to better predict the future

changes these coastlines will undergo in response to changing oceanic conditions (e.g. sea

level rise, ocean warming, and ocean acidification). Furthermore, enhancing our

understanding of contemporary processes will allow for more accurate interpretation of

evidence from the stratigraphic record in these environments. In Sections 5.2 to 5.4, the

main conclusions of this thesis are summarized; in Section 5.5, future research based on the

findings in this thesis is proposed.

5.2 Estimating settling velocity in bioclastic environments (Chapter 2)

Sediment transport is typically inferred from the spatial distribution of grain size

parameters; however, measurement of these properties in bioclastic environments is not

straightforward because the irregular shapes and densities of carbonate grains violate the

assumptions of common grain size analysis techniques. Therefore, application of these

techniques can lead to misinterpretations of sediment transport processes in these

environments. Settling velocity has been proposed as a more accurate method for

understanding the hydraulic behavior of carbonate grains; however, common grain size

analyses are still used throughout the carbonate sediment literature, potentially leading to

misunderstanding of carbonate sediment dynamics. Although models exist for converting

grain size to settling velocity, these tools have been developed for nearly spherical,

76

siliciclastic particles (i.e. quartz-dominated), with little understanding of their accuracy for

bioclastic sediment. Therefore in order to determine the most accurate method for

converting grain size to settling velocity for bioclastic particles (and thus provide data for a

more accurate interpretation of sediment dynamics), a comprehensive method analysis was

undertaken (Chapter 2), which was then integrated throughout the thesis.

Grain size data, derived from three common methods (sieve, laser diffraction, and

image analysis), for 73 natural, bioclastic sediment samples were converted to settling

velocity using three common models: Gibbs et al., [1971], Dietrich [1982], and Ferguson

and Church [2004]. While each of these models accounts for particle density, they treat

particle morphology (shape and roundness) differently; for example, Dietrich [1982]

accounts for grain roundness and shape, whereas Gibbs et al., [1971] has no parameter for

particle morphology. Particle density was shown to have a greater effect on estimated

settling velocities than the settling velocity model (i.e. particle morphology); with variation

in density causing an average range in normalized-root-mean-square-error of (NRMSE)

~20%, and settling velocity model only causing a range in NRMSE of ~5% (Table 2.3).

However, when the measured particle density was used, and particle morphology accounted

for, results from all grain size methods tested could be accurately used to estimate settling

velocity (NRMSE < 20%).

The results clearly highlighted the importance of using an appropriate sediment

density; however, this was shown to be highly variable in the literature. Although a method

for measuring particle density is presented in Chapter 2, more in situ densities are needed to

better capture the range of densities for bioclastic environments and to determine if

characteristic densities exist for specific types of reefs (i.e. fringing reefs were found to

have densities ~2.6 g cm-3). However, Chapter 2 provides a relatively easy method for

compiling grain size results from various studies and converting them to the same measure

for comparison; this will ultimately lead to greater understanding of the sediment dynamics

operating in bioclastic environments.

Interestingly, and more specific to Ningaloo Reef, Chapter 2 shows that Ningaloo

sediment density is larger than other values reported in the literature, and may indicate this

is due to the age of the sediment (i.e. that it 1000s of years old). Use of the settling

velocities from Chapter 2 to predict Rouse numbers, also suggested that bedload transport

77

would dominate the lagoon at Ningaloo. Both of these suggestions were explored, and

ultimately supported by the work presented in Chapter 3.

5.3 Ecological-geomomorphic links in a fringing reef system (Chapter 3)

An understanding of the reef benthic ecology and sediment composition can provide

further insight into sediment dynamics in reef environments due to the range of reef biota

directly contributing to the sediment reservoir. Understanding the timescales of these

connections provides quantitative data for assessing the resiliency of reef sediment budgets

and reef-associated landforms to future changes in ocean conditions and reef ecology.

While measures of these ecological-geomorphic links have been conducted in atoll/reef

island systems, there have been comparatively fewer in fringing reef systems. Shoreline

salient and other accretionary landforms are common features along fringing reef

coastlines; however, there is little understanding of mechanisms of their development.

Therefore, targeted field studies were conducted at Ningaloo Reef to determine the

timescales and mechanisms of sediment delivery to the shoreline (Chapter 3).

A disconnect was found to exist between the benthic ecology (dominated by

marcoalgae) and the sediment reservoir (dominated by coral), with most of the coral-

dominated deposits dated between 1000 and 5000 years old. This disconnect could

represent a spatial disconnect, an ecological effect, a shift in sediment supply, or a temporal

disconnect. Typical circulation patterns at Ningaloo suggest that sediment import is highly

unlikely; however, the other hypotheses require further investigation. An ecological effect

would be caused by the preferential feeding of organisms such as parrot fish and urchins on

‘old’ coral material (e.g. the reef framework). However, there is limited data available to

fully assess the contribution of either of these organisms to the sediment reservoir. It is also

possible that the source of sediment is shifting from coral-dominated to other calcifiers (e.g.

crustose coralline algae or molluscs). Unfortunately, the sampling method employed here

provided limited data on their spatial coverage. Further assessment of their spatial

coverage and their relative ages (e.g. using radiocarbon) would verify whether the entire

sediment reservoir is decoupled from the reef ecology, or if the disconnect applies only to

coral. Finally, if the age of coral represents the age of the entire sediment reservoir, then the

data presented here suggests that the modern sediment reservoir is derived from a much

older reef community. This hypothesis seems to fit well with previous data on the local sea

78

level history and benthic composition, however, more consideration needs to be given to

other sediment constituents.

Interestingly, should the temporal disconnect prove to be true, it suggests that if

there is a mechanism for delivering sediment to the shoreline on these timescales (i.e. 1000s

of years), the shoreline salient will likely respond to shifts in benthic community on these

long-period timescales. A key mechanism for sediment transport through the lagoon to the

shoreline was found to be bedload transport in the form of slowly migrating sand ripples (as

suggested in Chapter 2), driven by the asymmetric waves generated in the lee of the reef.

Ripples were found to migrate towards the shoreline at ~0.2 m day-1 on the south side of the

salient and ~0.08 m day-1 on the north side. This migration contributed ~325 kg m-1 and

116 kg m-1 of sediment towards the shoreline on the south and north sides, respectively.

When the average rate of ripple migration (9 kg day-1 m-1) was extrapolated to estimate the

time it would take to build the shoreline salient (~6,400,000 m3), it was found that this

mechanisms could have built the salient within 1500 to 15,000 years (i.e. similar to the age

range of coral sediment). These results indicate that on geologic timescales, the sediment

budget and shoreline morphology at Ningaloo will likely be resilient to declines in reef

ecology due to changing ocean conditions. However, it remains unclear as to whether or not

these long-period sediment transport processes can maintain the shoreline in response to

short-term (e.g. storm-induced) shoreline erosion.

5.4 Tropical cyclone impacts on fringing reef shoreline morphodynamics

(Chapter 4)

Coral reefs exist in cyclone-prone regions of the world, and cyclones have been

suggested to be major drivers of sediment dynamics in reef environments. However, there

have been limited studies combining in situ hydrodynamic and beach morphology data to

quantify the morphologic response of reef-fringed beaches to tropical cyclones (TC). Field

and numerical techniques were used here to quantify the morphologic response and

determine the forcing mechanisms responsible for the observed changes of a reef-fringed

shoreline to the direct impact from TC Olwyn (category 3, Australian tropical cyclone

system).

Despite offshore waves reaching ~6 m during TC Olwyn, the beach experienced

remarkably minimal morphological change (-3 m3 m-1 alongshore-averaged over the total

79

sub-aerial beach). Interestingly, the two sides of the salient responded differently to the TC

forcing, with the north side experiencing slight accretion, and the south side experiencing

erosion. The coastal protection of the reef was further highlighted by comparing the

observed volume changes to those documented at exposed, sandy coasts for similar met-

ocean conditions; reef environments were shown to experience an order of magnitude less

erosion for similar strength cyclones. The minimal beach changes were found to be driven

by the disequilibrium nearshore wave heights produced during the TC; with the south side

experiencing ~5 times larger waves than normal, whereas the northern side only

experienced ~2-3 times larger waves than normal. Interestingly, numerical results suggest

that the nearshore TC wave heights were primarily driven by local wind growth within the

lagoon, which is a process typically ignored in common nearshore models for assessing

storm impacts in reef environments. Finally, the beach volume was increased 15% beyond

pre-storm values within one year of direct impact, thus, highlighting the ability of normal

sediment transport processes (i.e. Chapter 3) to supply sediment to the coastline. These data

further suggest that intermittent, high energy events are potentially responsible for delivery

of large pulses of sediment to these systems that are then slowly re-worked and

incorporated into the coastal morphology by fair-weather sediment transport processes.

In conclusion, this thesis has advanced our understanding of sediment dynamics in

reef environments in the following ways:

1) Caution should be used when interpreting sediment dynamics from classic grain

size analysis techniques. However, there is a clear method for converting grain

size results to settling velocity, which is a more accurate metric for

understanding sediment transport processes in bioclastic environments.

2) A temporal disconnect exists (and is potentially common to fringing reefs)

between the reef ecology and the sediment reservoir; thus, suggesting these

coastlines may be resilient to future, rapid shifts in benthic ecology.

3) The fringing reef provides substantial coastal protection from extreme (tropical

cyclone) events, with the shoreline response governed by the local growth of

wind waves.

5.5 Future research

Regional Ningaloo comparison

80

Chapter 3 raised questions pertaining to the coupling of the reef ecology and the

sediment reservoir. Comparison of these results with Hawaiian fringing reefs (e.g. Harney

and Fletcher, 2003), suggests that ‘old’ sediment is a common characteristic of fringing

reefs; even those with higher living coral cover than the section of Ningaloo investigated

here. The coral cover is extremely varied (from ~5% to near 100%) along the full length of

Ningaloo Reef; thus, providing a unique possibility for a regional scale study that would

increase local understanding of Ningaloo as well as have broader implications for fringing

reefs in general. To investigate this hypothesis, a regional-scale study along the length of

Ningaloo could be conducted using similar methods to those used in Chapter 3. However,

the methodology should be amended to include more radiometric dating (particularly using 14C) and a more detailed benthic survey that accounts for the cover of direct sediment

producers. Given the diversity of reef geomorphology and ecology along the entirety of

Ningaloo, results from such a study would provide insight into whether or not the temporal

disconnect observed at Tantabiddi is a regional phenomenon or a common feature to

fringing reefs worldwide.

Coastal risk assessment for fringing reef

The results of Chapter 4 demonstrated that local wind wave growth can be a

significant driver of beach erosion in fringing reefs. Given that the amount of wind growth

possible will be governed by the lagoon width and depth of a particular fringing reef, there

should be a spectrum of beach responses that are predictable and dependent upon the

lagoon morphology. A simple, one-dimensional wave model could be set up to investigate

the amount of wind growth for variable lagoon morphology parameters, beginning with the

data presented in Chapter 4 as a test case. This framework could then be extended to

encompass the full range of lagoon widths and depths (i.e. from shore-attached to ~20 km

offshore, and from ~1m to 10 m depth, respectively) and therefore provide a quantitative

framework for determining TC-induced beach erosion risk for fringing reefs worldwide.

81

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TIMESCALES AND MECHANISMS OF SEDIMENT TRANSPORT …€¦ · TIMESCALES AND MECHANISMS OF SEDIMENT TRANSPORT AND SHORELINE MORPHODYNAMICS IN A FRINGING REEF SYSTEM. Michael Vincent