To Whom It May Concern:

We submit this manuscript entitled “Understanding long-term shoreline change through integration of historical aerial photographic, sedimentological, and computer modeling techniques: Lanikai and Bellows Beaches, Oahu, Hawaii” for publication in your journal, Marine Geology. We feel this work is an original and relevant contribution to our knowledge of coastal sediment transport and a case study in integrating multiple diverse analysis techniques. The results of this study have great applicability to beach renourishment projects, shoreline sediment management, and to our understanding of the strengths and limitations inherent in common coastal analysis techniques. Thank you for your time. Sincerely, Christopher Bochicchio Charles Fletcher

Draft 04/06/09 1

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Title:

Understanding long-term shoreline change through integration of historical aerial

photographic, sedimentological, and computer modeling techniques: Lanikai and Bellows

Beaches, Oahu, Hawaii

Author names and affiliations:

1) Christopher Bochicchio – Dept. of Geology and Geophysics, University of 9

Hawaii at Manoa

2) Charles Fletcher – Dept. of Geology and Geophysics, University of Hawaii at

Manoa

3) Sean Vitousek – Dept. of Geology and Geophysics, University of Hawaii at

Manoa

4) Bradley Romine – Dept. of Geology and Geophysics, University of Hawaii at

Manoa

5) Tomas Smith – United States Army Corp of Engineers, Honolulu Engineering

District

Corresponding author:

Charles Fletcher: fletcher@soest.hawaii.edu

Present/permanent address:

Department of Geology and Geophysics

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University of Hawaii at Manoa

1680 East-West Rd., POST 721

Honolulu, HI 96822, USA

Abstract:

Beach loss is significant to the economy, environment, and quality of life of many coastal

regions, particularly in Hawaii. Understanding multi-decadal trends in shoreline change

is a difficult and complex problem that benefits from the integration of multiple analysis

techniques. Lanikai and Bellows beaches are located on the windward coast of Oahu

Island, Hawaii, and separated by Alala Point. This shoreline has experienced long-term

poorly understood changes in shoreline position ranging 70 meters of width over the last

80 years. In an effort to better understand long-term coastal sediment transport at this site

and provide information for successful management, we integrate three commonly used

techniques for studying shoreline change: 1) grain-size sediment trend analysis (GSTA),

2) hydrodynamic computer modeling (Delph 3D), and 3) historical aerial photoanalysis.

Results of GSTA and hydrodynamic modeling are generally consistent with shoreline

change observed in historical aerial photographs. It is shown that sediment has been

shared between Lanikai and Bellows and that decadal-scale changes in the dominant

trade wind direction are closely tied to shoreline change.

Keywords:

coastal erosion, Hawaii, grain size trend analysis, historical shoreline analysis, Delph 3D

model, trade wind direction

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1. INTRODUCTION

Beach loss poses a serious threat to the economy, ecology, and safety of many coastal

regions. Over the latter part of the 20th century nearly 70% of the world’s beaches have

experienced net erosion (Bird, 1985). Much of this is attributed to the combined

influence on coastal sediment budgets of rising sea level and increasing shoreline

development (NRC, 1995). On the island of Oahu, Hawaii, historical analysis of beach

length shows 24% of all beaches have either narrowed or disappeared over a ~60 year

interval (Fletcher et al, 1997; Coyne et al., 1999). The impact of this beach loss is

particularly profound in Hawaii as sandy beaches drive a multi-billion dollar tourism

industry that accounts for 60% the jobs in the state and represents an important element

of cultural identity.

Beach volume and shoreline position are largely governed by locally unique

trends in longshore and cross-shore sediment transport. These are difficult to observe and

predict over the long time scales needed to develop sustainable coastal management

plans. Hence, where historical observations are available, it is important to investigate

the processes driving shoreline change on poorly understood beaches.

Lanikai Beach on windward (east-facing) Oahu is a developed shoreline

threatened by long-term erosion that is poorly understood. Lanikai has experienced a

series of decadal-scale erosion and accretion events producing >50 m changes in beach

width over a 60-year period. The net trend has been erosional and consequently the total

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beach length has decreased from 2.3 km to 800 m over the period from 1950 to 2007.

Discussion regarding appropriate management of Lanikai Beach has continued for over

30-years without resolution. Central to this debate is the source and fate of beach sand,

and specifically whether sand is exchanged around a rocky headland marking the

southern littoral cell boundary of Lanikai Beach with Bellows Beach (Figure 1).

In this study, we test the hypothesis that littoral sediment transport occurring

between Bellows and Lanikai beaches controls historical shoreline change at Lanikai.

We examine the direction of this exchange and assess factors that have potentially altered

sand transport over time. We integrate grain size trend analysis and hydrodynamic

modeling (Delft 3D) and compare the results with a detailed review of historical

shoreline change (derived from aerial photographs) to evaluate littoral sediment transport

across the Lanikai-Bellows boundary and greater shoreline. We expand our analysis to

include historical shifts in wind direction as a driving factor in shoreline change.

Results indicate that sediment transport does occur around Wailea Point linking

Bellows and Lanikai Beach and hardening of the Bellows shoreline has starved Lanikai

Beach by impounding an apparently important sediment supply. We also find evidence

that historical changes in wind direction have had a significant influence on sediment

transport. These results also indicate that future integration of sediment grain-trend

analysis into shoreline change studies could be beneficial to coastal authorities tasked

with managing poorly understood shorelines. This study is the first major reconstruction

of shoreline dynamics along the Lanikai-Bellows Beach and represents the first

application of sediment grain size trend analysis (GSTA) to studying shoreline change.

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2. REGIONAL SETTING 93

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The Lanikai-Bellows region encompasses roughly 4.3 km of coastline along a broad,

embayed headland marking the boundary between Kailua and Waimanalo Bays. The

study area extends to rocky Alala Point in the north, and the mouth of Waimanalo Stream

in the south. Basaltic Wailea Point, in the center of the study area, marks the boundary

between northeast facing Lanikai Beach and southeast facing Bellows Beach. Northeast

trade winds are dominant with an average speed of 10-20 kts over 90% of the summer

season (April-September) and 50-80% of the winter season (October-March) (Harney et

al., 2000). Trade wind waves dominate during summer months, with average deepwater

significant wave heights of 1-3 m and periods of 6-9 s. During the winter, refracted swell

from the North Pacific reach significant wave heights of 4 m with periods of 10-20 s.

Breaking face heights at the beach are substantially lower (<0.5 m) as a shallow reef crest

and the twin Mokulua Islands dissipate most incoming energy. Typical tidal range in

Hawaii is <1 m.

Landward of the shoreline at both Lanikai and Bellows are unconsolidated

carbonate marine and dune sands (Grossman and Fletcher, 1998; Harney and Fletcher,

2003). A 0.5 to 1.0 km wide reef flat fronts the majority of the site in water generally 1.5

to 3.5 m deep. Three large sand fields extend from the beach to the reef crest, containing

a total of 130 x 103 m3 of sediment with average thicknesses of 0.7-1.3 m (Bochicchio et

al., 2009). A thin veneer of sediment, occasionally observed with ripple marks, is found

over parts of the reef flat. Seaward of the Mokulua Islands the fore-reef slopes to >20 m

depth.

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Beaches at Lanikai and Bellows are narrow with gentle slopes and made up of

poorly sorted medium to fine-grained calcareous sand (Noda, 1989). Changes in beach

volume tend to be related to chronic fluctuations in alongshore sand transport and

sediment deficiencies, rather than event-based erosion because the offshore reef platform

diminishes incoming swell (Fletcher et al., 1997). Currently, the northern and southern

regions of Lanikai as well as northern Bellows Beach are without a beach and protected

by seawalls.

Noda (1989) investigated transport processes at Lanikai and stated that longshore

transport is responsible for substantial historical shoreline change at Lanikai Beach

despite a relatively mild wave climate. Noda found no evidence of sediment transport

occurring around Alala Pt. to the north, indicating that the Kailua-Lanikai cell boundary

is closed. Two sandbars on the southern Lanikai shoreline, located 15 and 30 m offshore

of the sea walled coast, corresponds with the node and anti-node of the mean incoming

wave (Lipp, 1995). Lipp concluded that strong wave reflection off the Lanikai seawalls

is, to some degree, preventing the accretion of a beach.

3. MATERIALS AND METHODS

To test the hypothesis that sand transport between Bellows and Lanikai cells controls

shoreline change, we use grain size trend analysis (GSTA), hydrodynamic modeling, and

historical shoreline change analysis.

3.1 Sediment grain size trend analysis

3.1.1 Sample collection and analysis

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A total of 214 sediment samples were collected on a grid surrounding Wailea Point

(Figure 2). Spacing between sample sites varied from 37.5 m near Wailea Point, to 75 m

within sand fields, and 150 m between sand fields. Samples were recovered from the

ocean bottom using a sediment dredge, which removed between 10 and 30 cm of the

surface sediment. Between 1000 and 2000 g of sediment were recovered in each sample.

The upper 5 cm layer of sample within the dredge was discarded to reduce error caused

by fine sediment potentially billowing out of the dredge mouth as it was pulled to the

surface. Samples were GPS positioned within 4 m. Grain size distributions of each

sample were based on the weight percent of each size fraction from standard sieve

analysis method ASTM C 136 (ASTM, 2006). Sieve openings ranging between -2 and 5

Ø at 0.5 Ø intervals. Mean size, sorting, and skewness were calculated for use as

parameters in the trend analysis.

3.1.2 General background on method

Spatial trends in the grain size of surficial sediments are a direct result of natural

sediment transport processes (Russell, 1939; McCave, 1978; Swift et al., 1972; Harris et

al., 1990). These trends are primarily the effect of transport processes selectively sorting

and abrading sediment in the direction of transport (McLaren and Bowles, 1985; Gao and

Collins, 1992; Le Roux and Rojas, 2007). Using mean size, sorting, and skewness, four

trends have been found to be reliable indicators of transport direction (McLaren and

Bowles, 1985; Gao and Collins, 1992; Gao et al., 1994; Le Roux 1994b).

Trend 1: finer, better sorted, and more negatively skewed

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Trend 2: coarser, better sorted, and more positively skewed

Trend 3: coarser, better sorted, and more negatively skewed

Trend 4: finer, better sorted, and more positively skewed

Accordingly, transport pathways can be identified if a series of sediment samples follows

one of these. Type 2 and 3 trends show distinctive coarsening along the direction of

transport that at first appear counterintuitive. Type 2 and 3 trends are interpreted as

indicators of more energetic transport processes in which a majority of fine-grained

sediment is removed, thus creating a thin and coarse lag deposit that “shields” underlying

deposits that have not been winnowed. This coarse upper layer may be mixed with

underlying fine sediments during sampling, which results in an overall finer-grained

texture upstream of the transport direction (McLaren and Bowles, 1985).

GSTA encompasses a range of techniques for recovering net transport direction

from naturally sorted seafloor sediments by identifying grain size trends in sediment

samples collected around an area of interest. McLaren and Bowles (1985) first proposed

a one-dimensional methodology to accomplish this task. This was followed by a number

of two-dimensional approaches (e.g. Gao and Collins, 1992; Le Roux, 1994b,c;

Asselman, 1999; Rojas et al., 2000; Rojas, 2003). These methods have been used to

characterize sediment transport for a variety of engineering, environmental, and

sedimentological investigations.

In this study we apply two separate methods put forth by Gao and Collins (1992)

and Le Roux (1992) to a dataset collected offshore of Lanikai and Bellows beaches.

These two methodologies use significantly different mathematical approaches for

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locating trends in sediment size data, yet are shown to detect sediment transport at similar

spatial scales (Rios et al., 2002). An overview of each method is provided below to

highlight the differences, provide an instructive reference, and aid in discussion of the

results. Likewise, the respective authors of each method provide full descriptions in Gao

and Collins (1992) and Le Roux (1994b). The Gao-Collins method is described in more

detail using practical examples (Appendix A), because current publications on this

method are limited to theoretical application.

3.1.3 Gao-Collins and Le Roux methodologies

The method put forth in Gao and Collins (1992) determines sediment transport direction

by comparing grain size parameters among a group of sampling sites. Parameters at each

site are compared with those of neighboring sites within a predefined characteristic

distance. The characteristic distance is defined as the spatial scale over which transport is

expected to occur in the study area, generally given as the maximum interval between

any two adjacent sampling sites. This study uses a characteristic distance of 200 m,

which reflects the spatial scale of transport processes anticipated for this region and

maximum distance between potentially related sites. In every case where either Trend 1

or Trend 2 is identified, component vectors with the unit length (i.e. equal to 1) are drawn

in the direction of the neighboring site (Figure 3A). Summing all component vectors at

each site produces a single vector referred to as a transport vector (large arrow in Figure

3A and 3B). Component vectors are relevant only in terms of direction. Their lengths do

not reflect differences in grain size parameters or distance between points. As all

component vector lengths are equal, the number and direction of neighboring sites

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showing a positive transport trend determine both the direction and length of the resulting

transport vector. Determining transport vectors for every point produces a field of

transport vectors (Figure 3B), which can be filtered to reduce noise and reveal the

dominant trends, by averaging the vector at each site with surrounding transport vectors

(Figure 3C). Details of the steps and calculations used in Figure 3 are included in

Appendix A.

The method of Le Roux (1994b) functions by comparing grain size parameters of

a central site with the closest four neighboring sites in all cardinal directions (i.e. one site

is selected from the North, East, South, and West quadrants) (Figure 4A). The Le Roux

method searches for all four trend types individually, producing a vector field of transport

for each trend.

Trend determination begins with the normalization of all three grain size

parameters between all five sites. These values are combined into a single value (E)

representing the strength of transport along that axis. The process of normalizing and

combining the parameters is modified in a manner depending on the trend type. For

example, Equation 1 is used in the case of Trend 3, where all parameters are expected to

decrease along the direction of transport.

( ) ( )

( )minmin

minminmax

minminmax

sksk sk

33.

varvar varvar33.33mnmn

mn 33.33

−

−−

+−−

maxsk33

mn

−+

=E Equation (1)

In this process, sites with the smallest values receive the highest score (E)

indicating stronger transport potential in the direction of that site. Conversely, to achieve

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the same effect with Trend 1, Equation 1 must be modified so that increasing mean grain

size results in a lower value of E. This is done simply by subtracting 33.33 from the

normalized mean size parameter (Equation 2).

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ter for Trend 2 and the 233

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te (Figure 4B) then the value of the central site 236

is subtr237

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). 243

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earchi ns 245

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( ) ( )

milar adjustments are made to the normalized skewness parameSi

normalized variance parameter for Trend 4 to so that increasing values on these

parameters result in higher values of E.

Values (E) are defined for every si

acted from each adjacent site and the relative difference between sites is used to

define the length of component vectors, which are summed to produce a final transport

vector (Figure 4C). This process is repeated at every site to produce a field of transport

vectors for each trend type. Trend 1 results are shown in Figure 4D. Commonly, the

strongest vectors from each trend type are incorporated into a final vector field. The

Watson (1966) non-parametric test is used to ensure that the final transport vectors are

sufficiently non-random before smoothing the data to reduce noise (Le Roux et al., 2002

The Gao-Collins and Le Roux methods both determine transport direction by

s ng for predefined trends between a single site and adjacent sites, but Gao-Colli

uses only Trends 1 and 2, while Le Roux checks for all four trends. The Gao-Collins

method checks a variable number of sites (all those that fall within the characteristic

distance), while Le Roux only uses a central site and four adjacent sites. With Gao-

( )min

minminmax

minminmax

sksk sk sk

33.33

varvar varvar33.33mnmn

mn mn33.3333.33

−−

+

−−

+⎟⎟⎠

⎞⎜⎜⎝

⎛⎥⎦

⎤⎢⎣

⎡−

−−=E

Equation (2)

minmax

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Collins, direction of transport is determined by relative position of all neighboring si

showing a trend to the central site, with transport occurring in the direction of the most

trend positive sites. In contrast, using Le Roux, transport direction and strength is

determined from the calculated difference between the actual grain size parameters

five sites. Both methods have been shown to give comparable and informative results

(Rios et al, 2002).

tes 249

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of all 252

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.2 Computer hydrodynamic model (DELFT 3D) 256

sed here) solves the unsteady shallow-257

water e258

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The Delft3D-FLOW module (v. 3.24.03 u

quations with the hydrostatic and Boussinesq assumptions. In 2D mode the model

solves two horizontal momentum equations (see Eq. 3-4), a continuity equation (Eq. 5)

and a transport (advection-diffusion) equation (Eq. 6) shown below:

2 2

2 2( )( ) ( )

bx xe

w w

Fu u u u uu v g fvt x y x h h x y

τη νρ η ρ η

∂ ∂ ∂ ∂ ∂ ∂+ + + − + − − + =

∂ ∂ ∂ ∂ + + ∂ ∂0 (3) 262

2 2

2 2( )( ) ( )

by ye

w w

Fv v v v vu v g fut x y y h h x y

0τη ν

ρ η ρ η∂ ∂ ∂ ∂ ∂ ∂

+ + + − + − − + =∂ ∂ ∂ ∂ + + ∂ ∂

(4) 263

[( ) ] [( ) ] 0h u h vu vt x yη η η∂ ∂ + ∂ ++ +

∂ ∂ ∂= (5) 264

[ ] [ ] [ ]H H

hc huc hvc c ch D Dt x y x x y y

⎡ ⎤⎛ ⎞∂ ∂ ∂ ∂ ∂ ∂ ∂⎛ ⎞+ + = +⎢ ⎥⎜ ⎟⎜ ⎟∂ ∂ ∂ ∂ ∂ ∂ ∂⎝ ⎠ ⎝ ⎠⎣ ⎦ (6) 265

266

here u and v = the horizontal velocities in the x and y directions respectively; t = time; g 267

= gravity;

w

η = free surface height; h = water depth; f = coriolis force; wρ = density of 268

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water; bτ = bed friction; F = external forces due to wind and waves, eν = horizontal eddy

viscosity;

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HD = horizontal eddy diffusivity; and c = concentration of suspended

sedimen The equations are solved on a staggered finite difference grid using the

Alternating Direction Implicit (ADI) method after Stelling (1984).

In this study the Delft 3D model is employed to examine the potential for

different transport regimes developing under changing forcing cond

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i s. This element 274

of the ahu’s 275

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wind 285

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th 2005 on the 288

southe289

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taset 291

t.

s

r

tion

ber, 12

tudy focuses on trade winds, as it is the most persistent type of forcing on O

windward shore and most likely to determine equilibrium shoreline conditions. Figure 5

shows a 58-year time series of trade wind direction recorded at Kaneohe Marine Corps,

located on the coastline approximately 9 kilometers north of the study area. These data

show periodic shifts in trade wind direction that persist over decadal-scale time periods

and are in some cases rapid (e.g. 1964, 1974, and 1987). Changes in trade wind direction

were first documented by Wentworth (1949) and implicated as a possible factor in

shoreline change in Lanikai in a report by Noda (1988). This study is the first to extend

the directional dataset presented by Wentworth (1949). The exact cause of these

directional shifts is not currently understood. Using the range of observed wind

directions, this study uses Delft 3D to model the potential influence that changing

direction could have on sediment transport in the Lanikai region.

The model was calibrated using current and sea-level data collected by two

acoustic doppler velocimeters deployed from August 10th to Septem

n and northern bounds of the study area (Figure 6). The model parameters

included wind driven currents, tidal forcing, open ocean waves, and wave-driven

currents. Ocean swell direction and height was simulated using a representative da

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from a deepwater directional wave buoy located 2 km north-east of the study area

Kailua Bay (National Data Buoy Center number 51001). Tidal forces were modeled

using standard harmonic components. Separate model runs used directional extremes

from the historical dataset to simulate time periods when north-east (51°), east-north-e

(71°), and east (85°) wind conditions dominated.

3.3. Shoreline change analysis

in 292

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ast 295

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nalysis of shoreline change draws from data collected by Romine et al. (in press). 299

ere hand digitized from survey quality aerial photos and 300

988, 301

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. 304

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a 306

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.1. Textual and transport trend analyses 311

re over much of the study area is characterized by distinct, isolated zones 312

shore of Lanikai tend to be coarser (Figure 7), 313

more poorly sorted (Figure 8), and positively skewed (Figure 9) than those at Bellows. 314

A

Historical shoreline positions w

T-sheets acquired in: 1911, 1928, 1949, 1951, 1959, 1963, 1967, 1971, 1975, 1982, 1

1989, 1996, and 2005. Distortion errors from scanning the photos were corrected

(Thieler and Danforth, 1994), and following the methodology of Fletcher et al. (2003), all

photos were orthorectified and mosaicked using software from PCI Geomatics, Inc

Seaward and landward boundaries of the sub-aerial beach were defined as the position of

mean lower low water (MLLW, using the toe of the beach, or base of the foreshore as

proxy) (Bauer and Allen, 1995) and the vegetation line. Horizontal error in shoreline

position was ± 4.49 – 10.78 m.

4. RESULTS

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Sediment textu

of varying size. As a whole, sediments off

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While sediment textures directly adjacent to shore tend to be finer and more negatively

skewed along the entire sample area. Offshore of Wailea Point, in the central portion of

the study area, sediments are generally finer, better sorted, and more negatively skewed

towards the tip of the point. However, closer examination of the entire study area shows

a close juxtaposition of alternating sediment textures indicative of lag and lead deposits.

Results of the Gao-Collins (Figure 10) and Le Roux (Figure 11) methods indicate

the direction and relative probability of sediment transport. A fundamental difference

between the two methodologies is well illustrated by the smooth appearance of the Gao-

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Collins323

nd 324

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” 326

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lts show 328

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ecoming better 332

sorted to the north (box B). Results from the Le Roux method show a majority of 333

transpo t to 334

335

r, 336

better-sorted, and more negatively skewed toward the north, which is the signature of 337

results and the noisier appearance of the Le Roux results. As described in the

methodology, the Le Roux method is more sensitive to small differences in grain size a

to small-scale isolated trends than the Gao-Collins method. Results of the two

methodologies generally agree, with the only major exception being the box labeled “A

in the southern part of the study area, where results differ considerably. In A, the Gao-

Collins results show primarily north-to-northeast trends, while the Le Roux resu

an opposing southeast trend converging with a north-to-northwest trend.

Within 100 m of the Bellows shoreline, sediment textures alternate between

coarse-positively skewed and fine-negatively skewed with all sediments b

rt to the north and Gao-Collins also shows a consistent northern trend adjacen

Bellows Beach.

Offshore sediment immediately south of Wailea Point (box C) becomes fine

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Type 1 transport. Gao-Collins results indicate uniform northern transport of sediment

from Bellows tow

338

ard Wailea Point. Similarly, Le Roux results show northwesterly 339

transpo340

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ux 342

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nt 344

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transport 347

vectors f 348

ent at 349

. 350

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et 357

representing major shifts in 358

position are overlain on a modern (2005) aerial photograph in Figures 12 and 13. Plots 359

rt toward Wailea Point where it meets an opposing transport trend. This is

mirrored to the north in box D, where sediment becomes finer, better-sorted, and more

negatively skewed toward the south. Resulting transport vectors in D from both Le Ro

and Gao-Collins methods are southeasterly and directly oppose transport in B.

Near the northern slightly embayed portion of Wailea Point (box E), sedime

within 250 m of southern Lanikai Beach shows two distinct textures. Nearshore

sediments are finer, better-sorted, and more negatively skewed than sediment farther

offshore. This contrast in sediment texture produces onshore and southeasterly

in the both Gao-Collins and Le Roux methodologies. To the north, both sets o

results show an opposite northwesterly trend in box F along Lanikai Beach. Sedim

F tends to be coarse, poorly sorted, and positively skewed relative to areas to the south

In general, transport trends in F and E show divergence between northerly and southerly

transport. Similar divergence occurs between A and B, while both results show

convergence near Wailea Point between C and D. Le Roux transport vectors seem to

indicate a gyre-like circulation pattern across C and D.

4.2. Shoreline change analysis

Changes in shoreline position are visible in historical aerial photography (e.g. Fletcher

al., 1997, Romine et al., in press). Historical shorelines

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show relative shoreline position over time along transects A through G centered on sites

with the greatest shoreline movement.

Lanikai Beach (Figure 12) shows multi-decadal trends of either accretion or

erosion, indicated on transects B, C, and D. Shoreline position appears relatively stable

between 1912 and 1949, although this c

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ould be due to under sampling (only 3 364

shorelin d 365

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7, 367

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has 370

t 371

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ct 374

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followed by erosion until 1961 resulting in 376

the loss377

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4.3. Hydrodynamic modeling results 382

es). In southern Lanikai (transect D) from 1949 to 1967 the shoreline accrete

significantly, adding approximately 60 m of new land. Beach to the north (transects C,

B, and A) was either stable or lightly accreting over the same period. After 196

accretion turned to erosion until halted by seawalls in 1990. To the north, accretion

intensified and central Lanikai (transect C) added approximately 30 m of new land. This

ended by 1987 and the beach has chronically eroded since. At transect B accretion

persisted and accelerated. At transect A the shoreline was relatively stable throughou

much of the 20th Century until 1975 and has subsequently eroded, culminating with the

construction of seawalls in the early 1980’s.

Figure 13 summarizes chronic erosion at Bellows south of Wailea Point (transe

E) persisting since the beginning of the dataset in 1916. To the south, transect F reveals

an accretionary trend between 1916 and 1928,

of approximately 40 m of land. At transect G, shorelines remain stable until a

general erosional trend developed during the 1961 to 1962 time period. This continued

until seawall construction in 1998. Erosional trends at transects E and F were also halted

by seawalls.

17

A hydrodynamic model is useful for envisioning nearshore currents that develop

under various forcing co

383

nditions. In Figures 14-16 we use a scale along the shoreline to 384

define locations for discussion. Results show substantially different nearshore current 385

th-east (51°), east-north-east (71°), and east (90°) 386

winds a 387

388

389

390

391

392

0 393

394

onable 395

396

397

398

399

ange in the study area is presented in 400

igure 17. The color grid is interpolated from historical shoreline position data with 401

rate of shoreline change (m/yr) during a time period (horizontal axis) 402

al-scale erosion and accretion 403

events 404

405

configurations in the region when nor

re used to force the model. The most noticeable effect of changing wind direction

is the shifting location of longshore current convergence and divergence. Eastern winds

(Figure 14) induce northward transport along the entire study area. East-North-East

winds (Figure 15) create a divergence point in longshore flow near location 40 along the

Bellows shoreline and induce a distinct gyre to the north of Wailea Point. Under

northeast winds (Figure 16) the divergence point shifts to the north along the Bellows

coast to location 10 and divergence develops in southern Lanikai between locations 9

and 100. In general, southern transport becomes more common along both shorelines as

the northern component of wind direction becomes more prominent, which is reas

considering the geometry of the shoreline.

5. DISCUSSION

5.1. Comparing Historical Shoreline Data with Results

A reconstruction of historical shoreline ch

F

color indicating the

over a length of beach (vertical axis). A number of decad

are visible in this data. The most prominent pattern is the aforementioned

expansion and erosion of south Lanikai, followed by central Lanikai. From these

18

changes in shoreline position we can derive an empirical analog for historical littoral

sediment transport direction. Arrows on Figure 17 show the direction of longshore

sediment transport inferred from changes in shoreline position.

It is apparent that there are coinciding system-wide shifts in sediment trans

certain times throughout the dataset. Figure 18 shows major shifts in wind direction

delineated into eight periods (periods II through VIII). These same periods are over

on Figure 17. The good agreement between changes in wind dire

406

407

408

port at 409

410

lain 411

ction and shoreline 412

change413

414

415

416

417

tructure would have a notable effect on shoreline 418

position d in 419

420

atch, 421

422

423

424

425

atterns are overwritten or mixed 426

with ne427

428

suggests that wind direction plays a significant role in driving sediment transport

along the Lanikai-Bellows shoreline.

The relationship between shoreline position and wind direction supports the

results of the hydrodynamic modeling experiments, in which different wind directions

showed the formation of different nearshore currents (Figures 14, 15, and 16).

Presumably, these changes in current s

, however in some cases the currents predicted in the model are not expresse

shoreline positions or are at odds with the observed shoreline history. This pairing of

datasets allows evaluation of the model performance and, where observations m

provides insight to processes driving shoreline change.

Gao (1992) suggests that the time period represented by GSTA is related to the

deposition rate of sediment but the issue is still poorly understood. In coastal settings,

transport patterns can shift on time scales ranging from days to decades, during which

grain size distributions representing previous transport p

w transport. Figure 19 summarizes both Le Roux and Gao-Collins results. The

nearshore vectors show divergence near Lanikai transect 90 and Bellows transect 30.

19

Convergence occurs near Lanikai transect 70 and in the vicinity of Wailea Point, as wel

as an overall northerly transport trend. This pattern is typical of trends observed since

Period IV (Figure 17), suggesting the GSTA results are valid back to the mid-1960’s,

coinciding with the onset of the modern erosive trend on south Lanikai.

5.2. Analysis of Historical Transport Trends

This section takes the form of a timeline detailing observed shoreline changes i

the context of wind direction, model predictions, and GSTA results. A g

l 429

430

431

432

433

434

n 435

eneralized 436

mmary of historical sediment transport trends (Figure 17) and dominant wind direction 437

VIII in Figure 20. The goal for these data 438

is to cre439

440

441

442

443

ave come from erosion in south Lanikai. 444

445

Period 446

447

ng from Bellows was likely transported 448

round Wailea Point into south Lanikai, which shows accretion during this time. 449

ng Lanikai Beach supported the supported the stable or accreting 450

shoreline during this period. 451

su

(Figure 18) is presented for periods II through

ate a predictive, empirical model relating shoreline change to wind direction.

Period I. 1910 – 1928

Southern Lanikai eroded over the first half of this period (1911 to 1928), while

the remainder of Lanikai and Bellows both accreted. Source material for accretion may

h

II. 1928 – 1953

Wind records during period II show that eastern winds dominated (Figures 18 and

20). Modeling indicates that sediment erodi

a

Northward transport alo

20

Modeling under east winds (Figure 14) indicate northward longshore currents

developed along Bellows and Lanikai Beaches and around Wailea Point. Current

intensities are greatest in North Bellows corresponding to with the highest observed

erosion rates during this period. Modeling also shows a slackened current in south

Lanikai thus accounting for sa

452

453

454

455

nd deposition. During this period Lanikai Beach volume 456

was lik et 457

458

459

460

461

462

was interrupted as erosion slowed previously uniform in central 463

Bellows and turned to accretion in south Bellows. However, erosion accelerated in north 464

evelopment of a divergent current. Current divergence also 465

466

e 467

468

469

470

471

ar 472

appear in aerial 473

photos where erosion rates were highest, and expand in later decades. These revetments 474

ely dependant on the delivery of Bellows sediment to balance the sediment budg

in south Lanikai.

Period III. 1953 – 1964

Wind direction during this period shifted to the northeast, which resulted in a

more complex system of littoral transport. The previously uniform transport from

Bellows to Lanikai

Bellows indicating the d

developed in central Lanikai, which eroded substantially during this period. South

Lanikai experienced accretion rates in excess of 3 m/yr over this entire period as larg

volumes of sediment arrived from central Lanikai and north Bellows.

Modeling using 71-degree winds fails to capture converging currents in south

Lanikai or diverging currents in central Lanikai. It does however capture divergence

centered on Bellows transect 35 near the observed erosion hotspot.

Bellows Air Force Base underwent substantial development during and after World W

II (1941-1945) as the shoreline eroded. A series of coastal revetments

21

effectively impounded landward sediment reducing sources to down-drift littoral cells.

South Lanikai especially reflects this event as some part of the source material for the

large accretion event was likely provided by erosion in north Bellows

475

476

. 477

478

479

480

481

482

zone has 483

shifted north. Slight accretion on both sides of Wailea Point suggests the divergence 484

ows has shifted to the south. A convergence zone seems to have also 485

rmed486

ear 487

lso 488

489

as 490

s 491

ere accretion is observed. 492

493

494

495

496

IV. 1964 – 1972

Wind during this period shifts further to the northeast causing a relocation of

convergence and divergence zones. Accreting and eroding areas essentially reversed

during this period marking the start of long erosive trend in south Lanikai. Central

Lanikai experiences accretion during this time, , suggesting the convergence

zone in north Bell

fo in south Bellows where the beach undergoes accretion.

The model, when forced with 51 degree winds (Figure 16), shows divergence n

Lanikai transect 80, which is in agreement with historical observations. The model a

indicates a southerly current moving around Wailea Point that could cause the accretion

observed in north Bellows. The model fails to capture convergence in central Lanikai

there is no opposing current moving south from north Lanikai. Similarly, this model doe

not show convergence at the southern edge of the study area wh

This period is the earliest in which GSTA results match observed shoreline

changes. The dominant littoral transport trends in Figure 19 indicate convergence near

Lanikai transect 70 and on the flanks of Wailea Point. GSTA results also show

divergence near Bellows transect 30. These patterns closely match the observed

22

shoreline trends, suggesting that the GSTA results are incorporating grain size

distributions that were established by transport patterns during this period.

497

498

499

500

501

tes in 502

ted, 503

eloped in 504

orth Bellows while sediment in south Bellows was seemingly dispersed to the north and 505

buildup against the jetties stabilizing Waimanalo Stream suggest 506

souther507

508

509

t to 510

511

512

to expect small changes in wind direction to 513

have a 514

n at 515

516

517

o 518

V. 1972 – 1984

Wind direction during this period shifts back to the east (75 degrees), but

shoreline changes show a unique pattern that was not previously seem. Erosion ra

south and north Lanikai increased during this period while central Lanikai accre

likely gaining source material from the north and south. Mild accretion dev

n

south. Sediment

ly transport along the Bellows coast.

We might expect transport under 75 degree winds to be similar to the 71-degree

winds during Period III, but there is no sign of shoreline accretion in south Lanikai.

Possibly: 1) current patterns between 75 and 71 degree winds are sufficiently differen

shift the convergence zone to central Lanikai and/or 2) hardening of both north Bellows

and south Lanikai prevented sand accumulation along the shoreline. With a nearly

perpendicular onshore wind, it is reasonable

disproportional effect on current patterns. The reduced sediment availability from

north Bellows and wave reflection off the hand shoreline may have prevented accretio

this time.

Model results for 71 degrees (Figure 15) are the closest approximation to wind

direction during this time period and match the observations poorly. GSTA results d

23

show sediment transport from south to central Lanikai, but transport patterns in Bellows

are not well represented.

519

520

521

522

d 523

524

ht accretion in southern Lanikai adjacent to Wailea Point as 525

diment moved to the south. This excursion also marks the start of wide-spread erosion 526

, a northward expansion of the eroding area in south Lanikai, and a 527

northw528

529

530

531

rply 532

rodes. Farther evidence of 533

increased northerly currents is the northern shift and general decrease in accretion rate of 534

in central Lanikai. As the accreting area moves north, central Lanikai 535

536

537

i 538

539

540

VI. 1984 – 1987

Wind direction makes a relatively brief northeastern excursion during this perio

causing subtle changes in the shoreline. As in period IV, in which wind was also more

northeasterly, there is slig

se

on Bellows beach

ard shift in the accreting area in central Lanikai.

VII. 1987 – 1995

Wind direction shifts back to an easterly inducing northerly transport along the

entire study area. The slight accretion on the southern edge of Lanikai disappears sha

and sediment accumulated along the Bellows shoreline e

the accreting zone

develops a strong erosive tendency and portions of south Lanikai experience complete

beach loss.

At this point the north Bellows, south Lanikai, and portions of central Lanika

shorelines are completely hardened by seawalls and revetments. This has reduced the

volume of sediment available to the overall system and increased the reflectivity of the

24

shoreline. Sediment eroding from central and south Bellows is likely bypassing south

and central L

541

anikai to accrete in north Lanikai. 542

543

544

545

546

e southern tip of Bellows reveals the 547

ossibility of southward transport, though the sediment volumes involved are relatively 548

ts indicate southerly transport in south Bellows and diverging 549

550

551

552

553

554

555

nd 556

557

s happens there is the chance that more sediment will be transported from 558

e Bellows shoreline into the Lanikai system. However the presence of revetments in 559

limit the amount of sediment available for transport. 560

imilar561

562

563

VIII. 1995 – 2008

Similar to Period VII, north Bellows and south Lanikai continue to erode to the

point of complete beach loss. The accreting zone in north Lanikai narrows by shifting its

border further to the north. A slight accretion at th

p

small. GSTA resul

transport on either side of Wailea Point. This transport might still be occurring, but with

sediment volumes too small to cause notable beach accretion. In general, the modern

trend of shoreline change appears compatible with the results of the GSTA procedure.

5.3 Future coastal change

Since 1987 wind direction has steadily shifted to the east (Figure 18). If this tre

continues, littoral transport along the Lanikai-Bellows shoreline will become increasingly

northern. As thi

th

north Bellows will severely

S ly, seawalls along most of south and central Lanikai might make beach accretion

problematic. A 1995 study of wave energy and shore-perpendicular bottom profiles at

south Lanikai revealed a sand bar offshore containing approximately 10 000 m3 of

25

sediment (Lipp, 1995). The study also showed that height and period of incoming and

outgoing waves was nearly identical due the highly reflective seawalls. The sand

564

bar is 565

566

567

568

569

570

of 571

n to control the occurrence other aspects 572

f Hawaiian weather (e.g. Kona storms). The influence of a decadal scale cycle, such as 573

ecting the North Pacific High and resulting in 574

575

576

577

578

579

580

581

582

the Lanikai-Bellows 583

oreline, windward Oahu. The results show wind direction to be a major controlling 584

sses. Most major accretion and erosion events can be linked to 585

eriodic shifts in the dominant trade wind direction. Hydrodynamic modeling was 586

located at a distance from shore near the exact anti-node between the incoming and

outgoing waves (1/2 x the mean wave length).

5.4 Directional changes in the wind record

The cause of the decadal directional shift in trade winds is not fully understood. An

explanation might lie in small shifts in the North Pacific High pressure system, north

the Hawaii islands. This system is already know

o

the Pacific Decadal Oscillation, could be aff

the directional shifts seen in the data. It is likely that these long-term changes in trade

wind direction have had a similar impact on sediment availability on other windward

shorelines. The primary obstacle to understanding is the relative scarcity of long-term

continuous directional wind records for islands other than Oahu.

6. CONCLUSIONS

This study integrates sedimentological data, hydrodynamic modeling, and historical

shoreline analysis to investigate large-scale enigmatic changes in

sh

factor on littoral proce

p

26

employed to examine the exact mechanism linking wind direction and littoral transpo

Modeled nearshore currents under easterly wind conditions matched observed historica

shoreline change trends, however only portions of the model results matched historical

observations under more northeasterly winds. Two different methods of grain size

sediment trend analysis were applied to samples taken over a portion of the study ar

Roux, 1996 and Gao-Collins 1992). This study represents the first application of GSTA

methods to specifically target coastal change. The results of both methods were in

general agreement. By comparing GSTA results to the observed historical record we

determined that grain size trends reflect the last major shift in sediment transport in

mid-1960’s.

Revealing the relationship between wind direction and coastal change in the

Lanikai-Bellows beach system represents a major step in the creation of a regional

sediment budget and demonstrates the great utility of integrating multiple analysis

techniques. There is strong evidence to support sediment transfer across Wailea Point,

indicating Be

rt. 587

l 588

589

590

ea (le 591

592

593

594

the 595

596

597

598

599

600

llows Beach and Lanikai Beach are dynamically linked. It is also very 601

likely t602

t. 603

hern 604

605

606

ine. 607

608

609

hat coastal hardening in response to erosion in both Lanikai and Bellows has

worsened the over all erosion and further complicated the littoral sediment transpor

Given more recent trends in both wind direction and shoreline position, nort

sediment transport will likely dominate in the immediate future. However, the coastal

armoring in place along both Lanikai and Bellows shorelines will likely negatively

influence sediment availability and the possibility of accretion on the Lanikai shorel

27

7. ACK610

611

612

613

Harold K.L. Castle Foundation. 614

615

616

617

618

619

620

621

622

623

624

625

626

627

628

629

630

631

632

NOWLEDGEMENTS

Support for this project was provided by the United States Army Corps of Engineers:

regional sediment management program, Hawaii Department of Land and Natural

Resources, United States Geological Survey, University of Hawaii Sea Grant, and the

28

633

. APPENDICES 634

igure 3 illustrates the Gao-Collins method. This appendix details the application of the 635

ao-Collins method to a synthetic dataset. Calculations associated with site 9 are 636

cluded. 637

638

able 1 639

in size data for the calculations used in Figure 3. 640

641

642

pe and component vectors calculated in table for site 9 only. 643

644

etermine which sites are within the characteristic distance from the site of 645

al to two, which 646

ncompasses ten sites: 2, 5, 6, 8, 10, 11, 12, 13, 14, and 16. 647

648

site 9) and the 649

roximal sites listed above. Trend 1: sites 2 and 5. Trend 2: sites 6 and 11. 650

651

652

ed to be equal (i.e. value = 653

When a trend is found the vectors are assigned to the site with the highest sorting 654

8

F

G

in

T

Coordinates and gra

* Trend ty

Step 1. D

consideration (site 9). In the example, the characteristic distance is equ

e

Step 2. Check for the existence of trends 1 or 2 between the central site (

p

Step 3. Define component vectors r(x,y)i between the central site and those showing a

transport trend. All component vector magnitudes are assum

1).

29

655

656

657

658

659

660

coefficient. As an example, calculations to determine the component vector from site 9

to site 5 are below:

45.0118.1

)25.1()()( 95 −=

−=

−=

dXX

xr 5661

662

89.0118.1

)23()()( 95

5 =−

=−

=d

YYyr 663

664

d is the distance between site 2 and the central site 0, given as: 665

666

where

118.15.01)()( 22295

295 =+=−+−= YYXXd 667

668

669

Sum all component vectors r(x,y)i to make a sum vector R(x,y): 670

671

[0.26 3.60] 672

673

tep 5. Repeat steps 1 – 4 on every site in the data set to define sum vectors at every site. 674

675

Step 4.

== ∑=

17

19 ),(),(

iiyxryxR

S

Results of this step are presented in Table 2.

30

676

677

678

Result of the average vector. 679

680

vector with the neighboring sum vectors 681

etermined to be within the characteristic distance (i.e. sites identified in Step 1). This 682

ly serves as a low-pass filter with a search radius of 2. For site 9 this process is 683

xpressed as: 684

685

Table 2

*

Step 6. Remove noise by averaging each sum

d

effective

e

[ ] [ ]( )

[ ]77.009.0

50.803.16.326.0)110(

),(),()1(

),( 99

=

++

=⎥⎦

⎢⎣

++

= ∑ qav yxRyxRk

yxR

where q is a lis

11 ⎤⎡

686

687

t of all sites within the characteristic distance of site 0: 688

689

q = 690

691

and k is the total number of such sites: 692

693

k = 10 694

695

696

[ ]1614131211108652

31

Thus, the final averaged transport vector at site 0 has an x-component of 0.09 and a y-697

omponent of 0.77. 698

699

tep 7. Convert average vector into azimuth direction Θ (exact formula will vary) and 700

ector length VL: 701

702

c

S

v

degrees 709.077.0arctan90 ⎜

⎛−=Θ ≈⎟⎠⎞

⎝ 703

704

( ) ( ) ( ) ( ) 78.09.077.0)()( 2+= iav xRVL 222

=+=iav yR 705

706

707

708

709

710

711

712

713

714

715

716

717

718

32

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720

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sylvania (1972), pp. 195–223. 847

848

849

ving 850

al of Coastal Research, v. 10, no. 3, p. 851

49-563. 852

853

Watson (1966) 854

Watson, G.S., 1966. The statistics of orientation data. Journal 855

Russell, 1939

Russell, 1939. R.D. Russell, Effects of transportation of sedimentary particles. In: P.D.

Trask, Editor, Recent Marine Sediments, Society of Economic Pa

M

Stelling (1984)

Stelling, G.S., 1984. On the construction of computational methods for shallow wat

equations. Rijkswaterstaat communication No. 35/19

Swift et al., 1972

Swift et al., 1972. D.J.P. Swift, J.C. Ludwick and W.R. Boehmer, Shelf sediment

transport: a probability model. In: D.J.P. Swift, D.B. Dua

S

Stroudsburg, Penn

Thieler and Danforth, 1994

Thieler, E.R., and Danforth, W.W., 1994. Historical shoreline mapping (I): impro

techniques and reducing positioning errors. Journ

5

38

of Geology 74 (2), 786–797.

Wentworth

856

857

(1949) 858

entworth, C.K. 1949. Directional shift of tradewinds at Honolulu. Pacific Science 3(1): 859

860

861

862

863

864

865

866

867

868

869

870

871

872

873

874

875

876

877

878

W

86-88.

39

879

0. FIGURES LEGENDS 880

igure 1 881

882

tudy area. Bathymetric contours are in meters. 883

884

igure 2 885

886

of surficial sediment sampling sites for sediment grain size trend analysis. 887

set: samples in the vicinity of Wailea Pt. 888

889

igure 3 890

891

ao-Collins method for determining sediment transport. See Appendix for data and 892

893

le). Circles represent sampling sites; those 894

ontaining “x” show either a trend 1 or trend 2 relationship in grain size parameter with 895

shed arrows indicate component unit vectors (length = 1) drawn in the direction 896

f each trend positive site, while the bold arrow is the summation of the component 897

ich 898

899

900 901

902 903

1

F

S

F

Location

In

F

G

calculations used in figure. A) Illustration of transport determination at site 9 with

characteristic distance equal to 2 (dashed circ

c

site 9. Da

o

vectors. B) The process is repeated at each site producing a transport vector field, wh

is filtered (C) by averaging adjacent vectors.

Figure 4

40

Le Roux method for determining sediment transport. Grain size parameters are identical

to those of Figure 3. This method considers each trend type separately, only trend 1

considered in this example. A) The closest site in the northern, eastern, southern, and

western quadrants is selected for used; dotted

904

is 905

906

lines illustrate quadrants and “x” on a site 907

dicates selection. B) All sites are transformed to lie at an equal distance of the central 908

cardinal radials; site 5 is at the position of site 5A, 10 is moved to 10A, etc. 909

910

911

912

913

914

915

916

917

918

-919

920

921

922

ea level and wave energy calibration for ADVs. 923

924

925

ting, and skewness interpolated from seafloor sediment samples. 926

in

site on the

Grain size parameters are modified to reflect the new positions and summed using the

appropriate form of equation (1) for the trend type being investigated. C) The value of

the central site is subtracted from all sites. The resulting values indicate transport

magnitude in each direction, with negative values indicating transport away from the

central site and positive values towards the central point. Summation of component

vectors determines the final transport vector. D) The process is repeated at every site

with available adjacent sites to produce a vector field for that trend type.

Figure 5

Directional wind data from Kaneohe Marine Corps Air Base. Values range between 1

135 degrees.

Figure 6

S

Figure 7

Mean size, sor

41

927

928

trend analysis. 929

930

931

932

igure 10 933

ns sediment grain-size analysis results. 934

935

igure 11 936

ediment grain-size analysis results. 937

938

939

i Beach. Graphs A, B, C, and D show 940

presentative datasets for each corresponding transect location. Positions are given as 941

an offshore baseline, thus positive shifts indicate accretion and negative 942

ent of a sudden accretion trend. Left map 943

ows a period of accretion in southern Lanikai (1949 – 1971). Right map shows erosion 944

south Lanikai and subsequent accretion at central Lanikai. 945

946

947

948

Figure 8

Results of Gao-Collins method for sediment grain

Figure 9

Results of Roux method for sediment grain size trend analysis.

F

Gao-Colli

F

Le Roux s

Figure 12

Summary of historical shoreline position at Lanika

re

meters from

shifts erosion. Gray boxes track the developm

sh

trend in the

Figure 13

42

Summary of historical shoreline position at north Bellows Beach. Graphs A, B, and C

show representative datasets for each corresponding transect location. Posi

949

tions are 950

s meters from an offshore baseline, thus positive shifts indicate accretion and 951

egative shifts erosion. Map shows persistent erosion across region. Arrows mark 952

of seawall construction in response to erosion. 953

954

955

956

957

958

ydrodynamic model result for 71 degree winds. 959

960

961

ydrodynamic model result for 90 degree winds. 962

963

964

istorical Shoreline record for Lanikai-Bellows beach. Red indicates erosion rate, blue 965

cretion rate. 966

967

igure 18 968

ecord showing divisions used to separate periods of sediment transport. 969

970

971

given a

n

beginning

Figure 14

Hydrodynamic model result for 51 degree winds.

Figure 15

H

Figure 16

H

Figure 17

H

indicates ac

F

The wind r

Figure 19

43

44

ombined interpretation of results from Le Roux and Gao-Collins methods. 972

973

974

ach panel shows the generalized sediment transport pattern inferred from changes in 975

h shown on Figure 17 and discussed in section 5.2. The dotted line represents 976

This dotted 977

ne is designed to help the reader track the movement of littoral sand as it is redistributed 978

-to-period. Wind direction from Figure 18 is also displayed on each panel. 979

C

Figure 20

E

beach widt

an exaggerated view of accreting and eroding areas during each time period.

li

from period

Site mean sorting skewness Trend Type*

Component Vector*

I Xi Yi μ σ Sk r(x)i r(y)i

1 1 4.25 -0.5 1 1.2 - - - 2 2 4 0.3 1 0.8 1 0 1 3 3 4 -1 0.8 1.5 - - - 4 0 3 0.2 0.3 0.7 - - - 5 1.5 3 1 0.8 0.9 1 -0.45 0.89 6 3.25 3.25 -0.7 1.1 1.3 2 0.71 0.71 7 4 3 -1.2 0.9 1.8 - - - 8 0 2 -0.1 0.5 1.5 - - - 9* 2 2 -0.3 1.4 1.1 - - - 10 3.2 2 -0.9 1.6 0.7 - - - 11 4 2 -0.9 1.3 1.4 2 1 0 12 1 1.25 -0.8 1.8 1.3 - - - 13 2 0.75 -0.7 1.3 0.5 - - - 14 3.25 1 -0.4 1.7 0.9 - - - 15 1 0 -0.5 1 1 - - - 16 2 0 1 1.7 0.8 - - - 17 4 0 -0.7 1.4 1 - - -

Site Sum Vector Average Vector Azimuth Direction*

Vector Length*

i R(x)i R(y)i RAv(x)i RAv(y)i Θ VL 1 0.72 -1.95 -0.24 0.83 322 0.392 -0.97 1.24 -0.18 0.48 333 0.393 -0.83 -0.55 -0.58 0.3 316 0.834 0 0 0.18 0.85 143 0.35 0 0 0.05 1.05 6 0.486 -1.22 1.01 -0.35 0.82 340 1.057 -0.71 0.71 -0.53 1.12 322 0.858 0 1 0.57 0.85 44 0.829 0.26 3.6 0.09 0.39 7 0.7810 -0.07 1.06 -0.22 0.98 345 0.8511 -0.51 1.86 -0.19 1.18 349 0.9812 2.59 -0.67 0.15 1.59 7 1.1813 0 0 0.57 0.67 30 1.1514 0.42 -0.62 0.07 1.15 2 1.5915 0 0 0.84 1.23 49 1.1216 0.78 3.62 0.47 0.39 45 0.6717 0 1 0.17 1.21 8 1.23