Natural fluctuations in nearshore turbidity and the relative influences of beach renourishment

NATURAL FLUCTUATIONS IN NEARSHORE TURBIDITY AND THE
RELATIVE INFLUENCES OF BEACH RENOURISHMENT

By

PHILIP E. DOMPE

A THESIS PRESENTED TO THE GRADUATE SCHOOL
OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT
OF THE REQUIREMENTS FOR THE DEGREE OF
MASTER OF ENGINEERING

UNIVERSITY OF FLORIDA

1993

ACKNOWLEDGMENTS

I would like to express my sincere thanks to my advisor and supervisory

committee chairman, Dr. Daniel M. Hanes, for his continuing guidance, trust, and

support throughout my studies at the University of Florida. The opportunity he has

provided and the knowledge I have gained are well beyond my initial expectations. I

would also like to thank Dr. Robert G. Dean and Dr. Robert J. Thieke for their

guidance as members of my advisory committee.

I am extremely indebted to the staff of the Coastal Engineering Laboratory,

especially Sidney Schofield, Vernon E. Sparkman, and Chuck Broward, for their parts

in the construction of the instrument packages. I am most indebted to Mark

Sutherland, Don Mueller, and Vic Adams for their support with the field work and

their humor in the face of adversity. I would also like to thank Sonya Brooks for her

prompt attention in expediting my travel refunds.

I am especially thankful to the Broward County Office of Natural Resources

Protection, particularly, Lou Fisher, Ken Banks, and Joe Ligget, for their support and

assistance in the field work.

Thanks also go to JeffAnton for his help in the initiation of this project, Rusty

Erdman who was instrumental in development of the video systems, and Tarang

Khangaonkor for his help with the analysis software.

My parents deserve a great deal of recognition, for without their support my

involvement in this research would not have been possible. Thanks also go to my

fellow students many of whom helped in some manner with this project and in

maintaining my sanity at such places as Mables, Butler, Tamarindo, Caps, Motel Hell,

St Georges, and last but not least Market Street.

The most important thanks go to my wife for her support and patience through

the numerous late nights at the lab and the many field trips.

I

TABLE OF CONTENTS

Page

ACKNOWLEDEGEMENTS ...................................................................................... ii

L IST O F FIG U R E S ....................... ....................................................... ................ vi

L IST O F T A B L E S ............................................ .............................................. ix

LIST OF SYM BOLS ................................................................... ................. xi

ABSTRACT ................................................................ ............ xiii

CHAPTERS

1 IN TR O D U C T IO N ............................................................... ................ 1
1.1 M motivation .................. ................. .............. .................. ........ ..... .. 1
1.2 Background of Hollywood/Hallandale ...................................................... 3

1.2 .1 Intro du ctio n ........................................... ............ ..... ............... 3
1.2 .2 Site D description .............................. ......................................... 4
1.2.3 Beach N ourishm ent Activities .......................................... ........... 5

1.3 O objectives .......................................................... ............. ... ......... 7

2 M ETH O D OLO G Y ........................................................ ................. 8

2 .1 Introduction .................................................... ................................. ... 8
2.2 Instrum entation ........................................................................ 9
2 .3 F ield Investigation ......................... ...................................................... 14
2 .4 Signal A analysis ..................... ......................... ..................... ......... 18

2.4.1 Introduction ....................................................... ................. 18
2.4.2 Pressure and Current Data ................................. ................ 21
2.4.3 O B S D ata ........................................ ...... ... ................ .. 23
2 .4 .4 Q quality A analysis ................................................ ..... ............ 23

2.5 M eteorological D ata .................................................... .... ............. 26

3 RESULTS AND DISCUSSION ............................................................... 28

3.1 Introduction ........ ............................................................. ................. 28

3.2 D ata Overview ...................... .......................................... .................. 28

3.2.1 Tim e Series Plots .............................................. ................. 29
3.2.2 Deployment Summary Plots ................................................ 33
3.2.3 Monthly Statistics Results .................................................. 35
3.2.4 Overall Statisticsal Results ................................ .... ............. 38
3.2.5 Verification With Video Monitoring ......................................... 42

3.3 Major Influences Upon Turbidity ................................................... 43
3.4 Effects of the Nourishment ......................................... 48

3.4.1 Nourishment and Postnourishment
Relationship Between Turbidity and Waves ................................ 49
3.4.2 The Effect of Turbidity Storm s .................................... .................. 56

4 SUMMARY AND CONCLUSIONS ............................................... 59

4.1 Sum m ary ... ........................................... ........ ........ .. .... ............ 59
4 .2 G general C conclusions ....................................................... .................... 63

APPENDIX: PROGRAM LISTINGS ................................................. 67

REFEREN CES ........................ ............................... ............................ 86

BIO GRAPHICAL SKETCH .................................................................... 88

I

LIST OF FIGURES

Figure Page

1.1 Location of Hollywood and Hallandale with respect to
the B aham as ...... ... ............................ ............... ........ .... .... 4

1.2 Location of the borrow sites for the 1991 project. ................................ 6

2.1 Illustration of Downing and Associates OBS-1P, showing
sensor dimensions and beam pattern................... .. ........... 10

2.2 OBS output (in tap water) versus distance from the
w all of the calibration bucket. .................................. ....... ...... 11

2.3 O B S calibration curve .................................... ............................. ......... 12

2.4 Instrument Package and U/W housing ........... ............... 13

2.5 Sediment analysis results from sites 1 and 2......................................... 15

2.6 Plan view of the sites relative to the fill area and the borrow area.......... 15

2.7 Plan view of site 2 showing the proximity to
hardbottom (reef) communities. .............................................. 16

2.8 Elevation view of the mounting array ............................................... 17

2.9 Example of a calibrated time series illustrating good data for the
entire instrument array. ........................................ .................... 19

2.10 Example of a monthly summary plot ..................................................... 20

2.11 An example of bad turbidity time series due to biofouling ..................... 24

2.12 An example of the effect of biofouling on the deployment
summary plot. Note the apparent exponential increase
in the signal from Julian day 220 till the sensor was
cleaned on Julian day 224. ....................................... ........... 24

2.13 An example of bad pressure and current time series data

typical of instrument failure...................................................... 24

2.14 An example of reduced accuracy OBS data resulting from
a shift in the offset below the data logger's range....................... 25

2.15 An example of reduced accuracy OBS data resulting from
a sm all abnorm ality in the signal ............................... .................. 26

3.1 Site 1 wave data availability................ ........ .. ......... 28

3.2 Site 2 wave data availability..................... ............................ ............... 29

3.3 Turbidity data availability .................................... ......... ................... 29

3.4 Two minute time series, from site 2....................................................... 31

3.5 Thirty minute tim e series, from site 2.................................. ............... ... 32

3.6 Thirty m inute tim e series, from site 2................................ ...... ..... .... 33

3.7 Monthly summary plots from the PUV time series, for site 2 ............... 34

3.8 Monthly summary plots from the turbidity
tim e series, for site 2 ............................................... .. ................... 35

3.9 Portion of the monthly summary plot showing turbidity for
site 2 the upper elevation during the time sediment
discharge activities approached and passed the
beach adjacent to the site............................................................ 42

3.10 Video images showing the dredge discharge location with
respect to site 2, (located below the x) as a function of time....... 43

3.11 Scatter plots of turbidity vs. significant wave height................................. 45

3.12 Prenourishment scatter plots with regression lines
produced using only data corresponding to values
ofHmo greater than 0.6 m for site land 0.5 m for site 2 ............ 47

3.13 Nourishment scatter plots with regression lines produced
using only data corresponding to values ofHmo
greater than 0.6 m for site land 0.5 m for site 2 ........................... 49

3.14 Postnourishment scatter plots with regression
lines produced using only data corresponding
to values of Hmo greater than 0.6 m for site 1
and 0 .5 m for site 2 ................................................ ... ................... 50

3.15 Comparison of the background turbidity relative

I

to the nourishm ent................................................................... 50

3.16 Comparison of the background turbidity including
the error bars ................................ ...................... .... ........... 53

3.17 Plots of the highest intensity storms, prior to, during,
and after the nourishment........................ ..................... 58

4.1 Video image of a turbidity plume eminating
from the hopper dredge located just out of
the field of view .................................... .................................... 64

LIST OF TABLES

Table Page

1.1 Sediment characteristics of the borrow sites ......................................... 6

2.1 Average resolution of each parameter................................................. 14

3.1 Monthly turbidity measurements, site 1......................... ........... 36

3.2 Monthly wave measurements, site 1 ................................................ 37

3.3 Monthly turbidity measurements, site 2..................................... 37

3.4 Monthly wave measurements, site 2 ........................... ................. 38

3.5 O overall statistics, site 1...................................................... ... .................. 39

3.6 Overall statistics, site 2................................................ 39

3.7 Overall statistics before during and after
the nourishm ent, site 1 ........................................ ..... .............. 40

3.8 Overall statistics before during and after
the nourishm ent, site 2 .............................................. .................. 41

3.9 Correlation between parameters site 1
low er sam pling level .................................................................. 44

3.10 Correlation between parameters site 1
upper sam pling level .................................................................... 44

3.11 Correlation between parameters site 2
low er sam pling level ........................................ .................... 45

3.12 Correlation between parameters site 2
upper sam pling level .................................................................... 45

3.13 Prenourishment regression analysis results .......................................... 47

3.14 Nourishment regression analysis results.......................................... 50

3.15 Postnourishment regression analysis results ......................................... 50

3.16 Average of all the storm events for pre-
nourishm ent and nourishm ent ................................. .................. 57

LIST OF SYMBOLS

b offset

c depth of the current sensor

xcx power and cross spectra of the pressure and current time series

g gravity

H wave height

Hmo significant wave height

Hth threshold wave height

h still water depth

hp pressure in meters of water

kc current response factor

kp pressure response factor

k wave number

m slope of the line, or sensitivity of turbidity to wave height

NTUb background turbidity

NTU turbidity, in Nephelometric Turbidity Units

n number of points

P pressure

p depth of the pressure sensor
r correlation coefficient

rs minimum correlation coefficient for 99% confidence
ip density of sea water

o standard deviation

oM standard deviation of the mean
t time

6 compass heading measured clockwise from the north

Uomax maximum excursion velocity at the sea bed

Uo excursion velocity at the seabed

u longshore current

V velocity magnitude

v crossshore current

Co wave frequency

Abstract of Thesis Presented to the Graduate School
of the University of Florida in Partial Fulfillment of the
Requirements for the Degree of Master of Engineering

NATURAL FLUCTUATIONS IN NEARSHORE TURBIDITY AND THE
RELATIVE INFLUENCES OF BEACH RENOURISHMENT

By

Philip E. Dompe

August 1993

Chairman: Daniel M. Hanes
Major Department: Coastal and Oceanographic Engineering

Turbidity is a measure of the clarity of water. Turbidity depends upon the

scattering and absorption of light by suspended particles. The focus of this study was

to obtain quantitative measurements of turbidity in the nearshore zone, along with

measurements of associated wave parameters and currents occurring naturally and

during a beach nourishment project. The objectives were to make quantitative and

qualitative comparisons between natural events and those induced by the dredge and

fill operations, as well as assess the long term effects of the nourishment, upon

turbidity.

In-situ measurements of turbidity and wave climate were obtained at two shore

normal sites off the coast of Hollywood, Florida, from January, 1990 to April, 1992.

The beaches adjacent to the communities of Hallandale and Hollywood were

renourished during the summer of 1991. Thirty minute in-situ observations were

recorded in burst mode every four hours at a frequency of four hertz. Analysis of the

data resulted in descriptions of the wave climate as well as statistics of turbidity for

each observation. Quality of the data was analyzed and each observation was tagged

as either "good data," "data with reduced accuracy," or "bad data." Statistics are

presented describing fluctuations in turbidity and the other parameters on a monthly

time scale, and for the complete data set at each site. Seasonal trends in the data can

be observed from the monthly statistics and the overall variations can be observed

from statistics of the complete data set. Statistics of the data are also tabulated for the

sampling periods before, during, and after the nourishment. Comparisons of the

statistics before, during, and after the nourishment reveal variations in the turbidity and

sea conditions relative to the nourishment. Also included in the data set for

comparison with fluctuations in turbidity are meteorological data collected by other

organizations.

Correlation analysis of the parameters believed to influence turbidity revealed a

significant correlation between wave height and turbidity. Further investigation

indicated the existence of a threshold wave height below which waves do not influence

turbidity. A linear relationship between wave height and turbidity was determined

above the threshold wave height. Although wave height above a threshold correlates

well with turbidity, a significant portion of the fluctuations in turbidity remain

unexplained. For comparison purposes the same analysis was performed on the

nourishment and postnourishment data. Comparisons of the nourishment and

postnourishment results with the prenourishment results revealed some variations in

the parameters that describe the relationships. Possible explanations for the variations

in these parameters are discussed. An analysis of storm events, where storm events

are defined as continuous periods of turbidity greater than the mean turbidity plus 1

standard deviation, revealed natural maximum elevations and exposure to turbidity

experienced by ecosystems under natural conditions, as well as elevations associated

with dredging activities.

CHAPTER 1

INTRODUCTION

1.1 Motivation

Turbidity is the reduction in the clarity of water due to the scattering and

absorption of light by suspended particles. Turbidity in the upper reaches of the water

column is the result of fine particles, typically consisting of silt, clay, finely divided

organic and inorganic matter, plankton and other microscopic organisms. Along with

the fine particles, small sand grains may cause high values of turbidity near the sea bed.

The magnitude of turbidity (or scattering and absorption of light) is dependent on the

concentration of particles, their shape, size, size distribution, refractive index, color,

and absorption spectra. The method of turbidity measurement dictates the units of

measurement. For example, Nephelometric Turbidity Units (NTU) are the units

recorded when a nephelometer is used for measurement of turbidity.

The adverse effects of turbidity in the marine environment are a reduction in

the amount of light penetration through the water column and subsequent

sedimentation of suspended particles onto benthic organisms. Both of these processes

are particularly detrimental to coral communities. Turbidity reduces light penetration

vital to the photosynthetic coral communities, and sedimentation stresses the coral

communities as they divert growth and reproductive energies to the sediment removal

process (Dodge and Fisher, 1988; Goldberg, 1988).

Variations in turbidity are the result of both natural processes and human

activities. During storm events wave action can induce sediment suspension increasing

the level of turbidity. Coral communities are accustomed to and capable of responding

to these natural fluctuations in turbidity which typically occur during their dormant

months in the winter (Dodge and Fisher, 1988). Turbidity can also be greatly elevated

during beach nourishment activities resulting in reduced growth rates as well as

mortality of coral communities (Dodge and Fisher, 1988; Goldberg, 1988).

Unfortunately due to an increase in beach erosion and the need to protect

ocean front development the demand for beach nourishment projects has increased

greatly in recent years. Turbidity is generally elevated during beach nourishment

activities as sediment is removed from offshore borrow areas and redeposited on the

sediment deficient beach. Turbidity may also be elevated after the nourishment as the

new beach adjusts to the perturbation produced by the nourishment. Beach

nourishment projects are typically accomplished using either a hydraulic cutterhead

dredge or a hopper dredge to excavate the offshore borrow area. The hydraulic

cutterhead dredge utilizes a cutterhead to excavate the bottom sediment and a suction

pipe powered by a centrifugal pump to transfer the material. In this manner, assuming

the sediment is transferred directly to the beach, turbidity is elevated in three ways;

first, by the action of the cutterhead (dependent on the cutter speed; see Huston and

Huston, 1976) disturbing bottom sediment not retrieved by the suction pipe, second,

by leakage of the pipeline transporting the slurry, and finally by the discharge of the

material on the beach. The hopper dredge uses a draghead mounted on a self-

propelled ship or barge to excavate sediment which is hydraulically lifted to a hopper

compartment on the vessel using a suction pipe connected to the draghead. The

sediment slurry is then transported to the beach by pipeline. In order to decrease the

water content and increase the amount of heavy sediment retained in the hopper,

turbid water is allowed to overflow the ships gunwales into the ocean. This overflow

process as well as the sediment excavated and not removed by the suction pipe

constitute the source of turbidity (at the borrow site) associated with a hopper dredge.

Other sources include leaks in the pipe system and plumes created near the beach at

I

the dredge discharge. Many aspects of dredge induced turbidity, including techniques

for reduction in turbidity from these sources, have been studied in great detail by

Huston and Huston (1976), Raymond (1984), Goldberg (1988), and LaSalle et al.

(1991).

In an effort to protect coral communities along the coast of Florida, the state

has set guidelines for turbidity created during beach restoration activities. Those

guidelines pertain to a restriction of 29 NTU above the background level within a

turbidity mixing zone for class three waters (class three waters extend approximately

from Jupiter Inlet to the Dry Tortugas). According to Goldberg (1988) there is no

known biological rational for the 29 NTU limit on turbidity. Goldberg (1988)

suggests setting standards similar to the maximum values experienced by coralline

communities in nature. For example, if the frequency of beach renourishment is 2

years, and a 2 year storm elevates turbidity to x NTU for y days then the restrictions

should be based on that criterion. However at this point little data have been collected

within class three waters (or at least published) under storm conditions. The focus of

this study is to make quantitative measurements during storms as well as during and

after beach nourishment activities.

1.2 Background of Hollywood/Hallandale

1.2.1 Introduction

Considerations for the site selection included beach nourishment schedules,

water quality, and the location of the hardbottom communities, which constitute a

major portion of the concern associated with human induced turbidity events. In this

section the study site will be introduced with respect to its location, local benthos

community, and beach restoration activity.

1.2.2 Site Description

The communities of Hollywood and Hallandale are located on the southeast

coast of Florida, at the extreme southern portion of Broward county (Fig. 1.1). Wave

fetch is restricted to the east and southeast by the Bahama Islands; the fetch to the

north is open to the Atlantic Ocean.

OCONA,

Hollywood Beachl

-AM

Figure 1.1 Location of Hollywood and Hallandale
with respect to the Bahamas

There are a series of three hard bottom communities oriented parallel to the

shoreline, in approximately 5, 10, and 20 meters of water. Hermatypic coral are

important organisms in these communities for their ability as reef builders. Over time

their calcium carbonate skeletons supply new material and provide surface relief

utilized as habitat by other species. The optimum environment for hermatypic corals

includes sufficient light penetration to sustain the symbiotic photosynthetic

dinoflagellate living directly within their tissue (Dodge and Fisher, 1988). This

relationship is vital to the growth of these organisms and is dependent on the amount

of light energy available.

1.2.3 Beach Nourishment Activities

The communities of Hollywood and Hallandale, including John U. Lloyd State

Park, are part of an 8.1 mile section of the Broward county erosion prevention plan

identified as segment III. A comprehensive plan to stabilize this area through a series

of beach renourishment projects began in 1976 with the nourishment of John U. Lloyd

State Park. In 1971, prior to the initiation of the Broward county comprehensive plan,

and prior to environmental monitoring, Hallandale beach was nourished with 350,000

cubic yards of material restoring 0.76 miles of beach. That project was followed by

the initial combined nourishment of Hollywood and Hallandale in 1979. The combined

project placed 1,981,000 cubic yards of material on the 5.25 miles of beach fronting

the two communities. Beginning with the John U. Lloyd project, beach nourishment

projects in Broward county have been accompanied by environmental monitoring.

Biologists have studied these projects prior to, during, and after dredging activities

(only the latter for the Hallandale project of 1971) to determine their impact on the

coral communities. Although the results vary in the degree of damage sustained, the

initial nourishment in 1971 of Hallandale was the most severe (Goldberg 1988).

Despite the fact there were no environmental monitoring programs at that time, post

nourishment studies were employed, and indicated several hardbottom communities

sustained damage as a direct result of the project. Goldberg (1988) credits the high

silt/clay content, which constitutes a large portion of turbidity plumes, as being

responsible for the damage. Persistent turbidity plumes were noted by Goldberg

(1988) in the nearshore region up to 7 years after completion of the project. The

offshore extent of these plumes is still unknown since there has been no effort to

measure the migratory or spatial variations of these plumes. The nourishment of

interest to this research is the first renourishment of Hollywood and Hallandale. Using

a hopper dredge 1.1 million cubic yards of material was removed from a borrow site

between the second and third reef (Figure 1.2) and placed along 5.3 miles of beach.

Dredging operations began in April of 1991 and continued through August of the

same year. Sediment characteristics from the borrow sites are summarized in Table

1.1 (Broward GDM).

Dania Beach Blvd.

Sheridan St.

Hollywood Blvd.

Hallendale Beach Blvd.

A:

North Project Limit

N
Fill Area
(width not to scale)

---

Borrow [A]
Areas

P [B]

South Project Limit

Figure 1.2 Location of the borrow sites 1991 project.

Table 1.1 Sediment characteristics of the borrow areas and the beach.
VARIABLE BORROW AREA A BORROW AREA B EXISTING BEACH
VOLUME (cu. yd.) 1,629,000 582,000 N/A
MEAN GRAIN SIZE (mm) 0.34 0.51 0.36
MEAN GRAIN SIZE ()) 1.56 0.975 1.47
SORTING 1.30 1.25 1.36

1.3 Objectives

As stated earlier turbidity induced during dredging activities can have an

adverse effect on reef communities by reducing light penetration and increasing the

rate of sedimentation. The extent to which these values exceed (if in fact they do) the

levels naturally occurring during storm events are not known. The overall objectives

of this research were to obtain in-situ measurements of turbidity in an area populated

by hard bottom communities prior to, during, and after a beach restoration project.

These measurements will be used quantitatively to assess the significance of turbidity

resulting from the beach restoration project and compare those human influenced

events with natural events prior to the restoration. Ideally these measurements would

record the magnitude and duration of natural turbidity events such as those created by

large storm wave events. An understanding of the duration and magnitude to which

the natural forces elevate turbidity would give regulatory commissions the ability to set

restrictions on turbidity mixing zones based on natural levels. Analysis of the data

should also assess the significance of the natural forces on turbidity and relate the

natural fluctuations to the natural forces in a quantitative manner. This should result in

a statistical model that could predict from historical wave data (or other significant

perimeters) the maximum expected turbidity events experienced by coastal

ecosystems. However application of such a model would be limited by the amount of

data used in its derivation. More important to this study, the relationship will be used

in the analysis to compare fluctuations in turbidity before, during, and after the beach

nourishment.

CHAPTER 2

METHODOLOGY

2.1 Introduction

The parameters believed to influence turbidity include waves, wave and tidal

induced currents, and to a lesser extent rain and possibly wind. Although the

influence of waves and currents on sediment suspension is well documented, the

influence of wind and rain are not well documented and are included for the following

reasons. Dodge and Fisher (1988) correlated reductions in coral growth rates to canal

discharge variations. Since the canals in south Florida are used to a great extent as

flood control, their discharge is regulated by and highly correlated to rainfall. The

correlation discovered by Dodge and Fisher (1988) was the result of either a

fluctuation in salinity or a reduction in light penetration. The latter would be the

result of turbid water from the canal discharge (high silt/clay content) being

transported to the ocean in the ebb tidal flow. The influence of wind on turbidity

would result from turbidity plumes being transported by wind induced currents. The

actual influence of the parameters will be determined in the analysis section of Chapter

3.

The initial goals were achieved through in-situ measurements at two locations

near the center of the Hollywood and Hallandale renourishment project accompanied

by meteorological data collected by other organizations. The parameters measured at

the in-situ sites were turbidity, wave induced pressure fluctuations, and wave and tidal

induced currents. This chapter describes the in-situ instruments used, methods for

their deployment, sampling, and analysis, as well as origins and descriptions of the

meteorological data.

2.2 Instrumentation

Turbidity is an optical property of water and is measured by instrumentation

that utilizes optical theory. Some environmental considerations involved in measuring

turbidity in the nearshore region include the interference by ambient sunlight, air

bubbles entrained by breaking waves, as well as hydrodynamic interference associated

with the size of the instruments. Turbidity is determined by one of two methods;

either measuring the light transmitted or the light scattered. Instruments measuring the

transmission of light are transmissometers and those measuring light scattered are

nephelometers. Transmissometers are less desirable for in-situ measurements because

they are highly influenced by stray light, particularly broad spectrum light emitted by

the sun. By measuring the transmission of light they are more sensitive in clear water,

however this causes the signal to become nonlinear at higher values of turbidity,

resulting in a narrow range of linearity. Although measurements over a wide range of

turbidity can be accomplished using a multiple path transmissometer, they are bulky

and expensive.

For this study turbidity was measured using two miniature nephelometers,

OBS-1P Optical Backscatterance Sensors (OBS) manufactured by Downing and

Associates (Fig 2.1). These instruments measure infrared light scattered (reflected) at

angles between 140 and 165 degrees. The high angle of scatterance (or

backscatterance) is a result of the close proximity of the infrared emitting diode

(emitter) and the photo diodes (detector). The beam pattern illustrated in Figure 2.1,

irradiates a small (order of 10 cm3) concentration dependent sample volume. Infrared

radiation is advantageous because it is highly attenuated (decreasing in intensity by 63

% for every 5 centimeters traversed ) in clear water. As a result, unlike the

transmissometer, OBS sensors can operate in shallow depths without significant
degradation of the signal from the interference of sunlight.

Figure 2.1 Downing and associates OBS-IP, showing sensor
dimensions and beam pattern.

By utilizing a filter, less than 1 percent of the visible light (below 790 nm)
incident on the sensor is transmitted. The intensity of light back scattered by entrained
air bubbles is low relative to the intensity of light forward scattered (Downing et al.
1981). 'Consequently OBS are less sensitive to entrained air bubbles than
transmissometers. Utilizing a master slave operation, in which one sensor, the master,

I:^

provides timing signals for operating the slave sensor, eliminates the possibility of

interference between sensors. OBS-1P sensors are linear from 0 to 1,500 NTU with a

threshold of 1 NTU. The adjustable gain and offset of each instrument is set to match

the range of turbidity expected in the field, the sensors proximity to the sea bed, and

the input span of the data logger. The lower (0 to 0.5 meters above the sea bed) and

upper sensors (0.5 to 0.85 meters above the sea bed) are typically adjusted to saturate

at 400 NTU and 75 NTU respectively.

Calibration of the OBS is accomplished by comparing OBS output with that of

a portable nephelometer, model DRT-15C manufactured by the Hach Company.

Turbidity during the calibration is created using formazin standard. Formazin, an

aqueous suspension of an insoluble polymer, is accepted by the United States

Environmental Protection Agency (EPA) as the primary turbidity standard. Formazin

is uniform in number size and shape of its particles, consequently it is ideal as a

turbidity standard. Calibration is performed in a 5 gallon bucket with a black matte-

finish. Figure 2.2 illustrates the OBS output is unaffected by the walls of the bucket at

distances greater than 5 centimeters.

2
1.8
1.6
U 1.4
1.2

0.8
o 0.6
0.4
0.2
0 I I I I I I I I I I
24 20 18 15 14 10 8 5 2.5 0.6
Distance to wall (cm)
Figure 2.2 OBS output (in tap water) versus distance from the wall of the
calibration bucket.

The OBS are mounted vertically in the bucket so the beam (see Fig. 2.1) radiates

across the diameter at least 5 centimeters from both the surface and the bottom. The

initial comparisons between OBS output and the nephelometer are made in room

temperature tap water. Comparisons are then repeated over a range of turbidity

(created with the formazin) similar to the typical range expected during the

deployment. The results are analyzed using regression analysis resulting in calibration

curves with regression coefficients near unity (Fig. 2.3). These calibration curves are

used to convert from voltages to physical units.

35

30- OBS #1 OBS#2

25

20

15 OBS#1;
Y = 21.7X 10.8, r = 0.995
10 -
OBS #2;
5- /Y = 108.3X -54.5, r= 0.995

0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2
Volts

Figure 2.3: OBS calibration curve.

Wave amplitude, wave period and tides are recorded using a transmetrics P-21

pressure transducer mounted approximately 0.5 meters above the sea bed. The

pressure transducer measures a steady hydrostatic pressure, in addition to the

oscillating dynamic pressure associated with progressive waves, through changes in

resistance of a strain gauge. The oscillating dynamic pressure at a particular wave

period is proportional to the free surface displacement and hence the wave amplitude

I

(Dean and Dalrymple, 1984). Calibration is achieved in the laboratory using

compressed air, resulting in calibration curves with typical regression coefficients near

unity.

Wave direction and currents are measured using a Marsh McBirney two axis

electromagnetic current meter mounted approximately 1.5 meters above the sea bed.

The current meter operates on the Faraday principle of electromagnetic induction. As

seawater (the conductor) moves in the magnetic field (produced by the current meter)

a voltage is induced that is proportional to the water velocity. Calibration is

performed annually by the manufacturer.

Figure 2.4 Instrument package

Data storage and sampling control are achieved with an Onset Computer

Corporation model Tattletale 6 data logger. The data logger can process eight, 0 to 5

volt analog signals through a 12 bit analog-to-digital converter, and store 20

instrument calibration curves the average resolution of each physical parameter is

presented in Table 2.1. Calibration curves of the instruments varied with deployment

(since calibrations were performed for each deployment), as a result the values listed in

Table 2.1 are referred to as averages and were calculated as such. Power to the

instruments is controlled through the 14 available digital input/output (digital I/O)

lines. Communication with the data logger is achieved through an RS232 port at

19,200 bits per second. The Tattletale operating system TTBASIC, is based on

BASIC programming language.

Table 2.1 Average sensor resolution.
INSTRUMENT AVERAGE RESOLUTION
Pressure Sensor 8.32 millimeters of water/bit
Current Meter 1.86 millimeters/second/bit
OBS (high gain) 0.045 NTU/bit
OBS (low gain) 0.18 NTU/bit

The instrument circuit boards, data logger, and batteries were mounted on an

aluminum frame and housed in an 8 inch schedule 40 PVC housing (Figure 2.4).

Bulkhead connectors mounted to the lid provide communications between the circuit

boards and instruments as well as communication to the data logger.

2.3 Field Investigations

This section describes the site locations, instrument mounting, sampling

scheme, and site visits.

Figure 2.6 illustrates the site locations relative to both the beach renourishment

and the borrow areas. Site 1, located at 26 00.5' north longitude is in 35 feet of water

and is approximately 1/2 mile due east of site 2, which is in 17 feet of water. The

seabed adjacent to site 2 is a sandy region (D50 of 0.56mm Fig. 2.5) populated by

several hard bottom communities (Fig 2.7), while the seabed at site 1 is dominated by

sand (D50 of 0.58mm Fig. 2.5).

0.9

0.8 -
----Site 1
0.7 Site 2
S0.76U--ie-----------------------------------H~--------
. 0.6
I-
z 0.5

S 0.4

0.3

0.2

0.1

0 63 90 106 125 150 180 212 250 300 355 500 710 100
0
GRAIN SIZE (MICRONS)

Figure 2.5 Prenourishment sediment analysis results from sites 1 and 2

Dania Beach Blv

Sheridan St.

Hollywood Blvd.

d.

North Project Limit

A/
Fill Area
(width not to scale)

*j 0

Site 1
a Site 2
0

Borrow [A]
Areas

South Project Limit

Figure 2.6 Plan view of the sites relative to the fill
area and the borrow area.

Figure 2.7 Plan view of site 2 showing the proximity
to hardbottom (reef) communities

Placement of the outer site near hardbottom communities insured the

measurements would reflect the turbidity experienced by those communities.

Turbidity at this location results from waves, currents, and plumes originating at the

borrow sites. Measurements at the inner site represent turbidity induced by, waves,

currents, and plumes migrating from the dredge discharge. Other sources previously

discussed could also influence turbidity at each site. Mounting of the instrumentation

utilized a goal post type system contained within the bottom 2 meters of the water

column (Fig. 2.8). This configuration reduces scour induced sediment suspension

which could introduce errors in the turbidity measurements.

30.o4*

tFw=...x

l -

I
30.0
Ref

Uniaxial

EM

^ --\

I II
I I Jetted 1.5m Jetted 1.5m
I I I I
I I I I

II
Jetted 1.5m I I
I I
II

Figure 2.8 Elevation view of the mounting array.

The sampling strategy was based on burst sampling. Burst sampling allows

records to be obtained simultaneously for each instrument over evenly spaced intervals

(bursts) at high frequency. The main consideration in choosing a sampling strategy is

the maximum rate at which the parameter to be sampled fluctuates. Other

considerations, such as data storage and battery power, can be altered to

accommodate the required sampling scheme. The data recorded should capture high

and low-frequency variations to reflect fluctuations on the required time scales. All of

these factors control, sampling frequency, burst duration, and burst interval. High

frequency variations (wave frequency) determine the sampling frequency, and low

frequency fluctuations determine burst duration (variations in turbidity with wave

groups), and burst interval (to resolve daily variations). For this study the data are

recorded every 4 hours for approximately 30 minutes at a sampling rate of 4 hertz.

The sampling algorithm DATLOG.BAS (see appendix A) controls the sampling

strategy, and data storage to the hard drive. The number of samples or record length

(7166 points), is determined by the burst duration and sampling frequency. There are

184 records for each deployment, totaling more than 20 megabytes of information.

The entire two year data set encompasses more than 600 megabytes of information.

Site visits, achieved using scuba, were scheduled bi-weekly for cleaning, and

monthly for instrument deployment, data recovery, and servicing of the instruments.

Divers were responsible for securing the instruments and package to the mounts and

recording the height and orientation of the instruments. During deployment and

cleaning visits measurements of turbidity were made in the vicinity of the OBS with

the Hach portable nephelometer. These observations were used to resolve

discrepancies in the offset during application of the calibration. The range of turbidity

usually recorded under the relatively calm conditions conducive to field work, varied

from 0.5 NTU to 3 NTU. Servicing the instruments involved cleaning, application of

an anti-biofoulant, calibration when applicable, and data recovery.

Instrument cleaning (particularly for the OBS sensors) was necessitated by

elevated bio-activity associated with class three waters. This subject and its impact

will be discussed in further detail in the quality analysis section.

2.4 Signal Analysis

2.4.1 Introduction

Analysis in this section refers only to the application of the calibration curve,

statistical summaries of each observation, spectral estimates of the wave climate, and a

quality analysis. Relationships between the parameters and impact of the nourishment

will be explored in chapter 3.

All calculations and manipulation of the data including statistics and graphs in

this chapter and the following chapter were achieved using Matlab and the Signal

Toolbox software developed by MathWorks Incorporated.

The files offloaded from the data logger are in ASCII hexadecimal form with

the most significant byte first. Although Matlab is capable of reading hexadecimal files

it reads the first byte as the least significant. Therefore a FORTRAN program was

developed to convert the hexadecimal files to decimal files compatible with Matlab.

Application of the calibration for each instrument and the spectral analysis are

achieved simultaneously in Matlab using m-files (algorithms written in Matlab

programming language).

The time, date, and year, of each observation is converted to Julian days.

Julian days are defined for this study as beginning with zero at midnight on December

31, and incrementing by 1 for each day. For example, January 1 at 12:00 noon, is

equivalent to Julian day 0.5.

tr. urn, m. Mean 6.134 Std Dev -0.2657 7.047

-val, m/ Man -0.03985. Std Dav 0.208 1.000

V-vet. r/1 Mean 0.1937 S 0v -0.1274 0..448

0 10 15 20 250.5
T"me In minutes
T 46trdlty 0 0 I. mlttrl ovt tVZ e he Vd
t r ..r ..an e Std Dev -2.042 3 .57

r.bfd.ty 0 0.1 6 mat 1r a a the I ..d Std -36.3 4"377

Time In minutea
H1B2, Run No. 53, Date 12/20/01, Start Time 4:3, Jd 353.2
Figure 2.9 Example of calibrated time series illustrating good data
for the entire instrument array.

The results of the calibrated time series, statistical analysis, and spectral

analysis, are plots of each observation (Fig 2.9), and plots of the monthly summaries

(Fig 2.10), as well as data files of the observation summaries (mean, maximum,

minimum and standard deviation), and the spectral results (significant wave height,

peak wave period, and peak wave direction). The quality analysis results in data files

consisting of quality rankings of each instrument for each observation, and a record of

the elevation (either 0 to 0.5, or 0.5 to 0.85 meters above the sea bed) of each OBS.

These files are utilized by plotting routines and analysis programs, to either tag the

observations quality or to exclude bad points from the analysis.

WAVE PARAMETERS FOR DEPLOYMENT H162
From: December 11,1991 Julian Day 344.66
To: January 7.1992 Julian Day 6.5
: good data
o : data with reduced accuracy
: bad data

2 Significant Wave Height

E 1

340 345 350 355 360 365 370 375
Julian Day
10 Peak Wave Direction

50
100 -

340 345 350 355 360 365 370 375
Julian Day

15Peak Wove Period
15

8 t E~ .1 nr ni -

Figure 2.10 Example of a monthly summary plot

345 350 355 360 365
Julian Day

375

2.4.2 Pressure and Current Data

The pressure calibration initially in pounds per square inch (p.s.i.) is converted

to meters of sea water by the relationship

144 P
h, '3.28 pg (2.1)

where hp is the pressure in terms of an equivalent water depth (unknown), P is the

pressure in p.s.i., p is the density of sea water, and g is gravity. Units of measurement

for the current meter calibration (meters per second) are in the desired form as

calibrated by the manufacturer. From this point application of the calibration is

straight forward using the mfile DATAIN.M listed in appendix A, resulting in

calibrated time series of pressure fluctuations and two orthogonal components of

water velocity (PUV).

Analysis of these time series results in significant wave height (Hmo), peak

wave period and peak wave direction for each record. Calculations of Hmo utilize the

spectral based definition, which is equal to four times the square root of the spectral

variance. The peak of the directional spectral surface gives the peak frequency, and the

direction of the highest energy waves (Dean and Dalrymple 1984). The mfile

SPEC2.M in appendix A, which is based on the methods ofLonguet-Higgins et al.

(1963), is used to obtain the directional spectrum. The directional spectrum can be

expressed as a fourier sum.

1 2 1
[F(o,0)]= -A + (A cos6+B, sin )+-(A2 cos20+B2 sin20) (2.2)
2 3 6

In this equation, o is the wave frequency, and 6 is the compass heading from which

the waves approach. Power and cross spectra are computed using the mfile

SPECTRUM.M and then filtered to remove noise. In order to avoid amplifying high

frequency noise in the data logging system, wave frequencies corresponding to values

of the pressure response factor (kp) equal to or less than 0.04 are excluded. The

L I

influence ofinfragravity waves are removed by filtering wave periods higher than 20

seconds. The computed power and cross spectrum quantities are converted to values

at the free surface using the pressure and current response functions,

Scosh(k(h+ p)) (2.3)
cosh(kh)

Scosh(k(h + c)) (2.4)
cosh(kh)

where k is the wave number, h is the still water depth, p is the depth of the pressure

sensor, c is the depth of the current meter, and g is gravity. The FORTRAN program

WTOK.FOR (appendix A) iterates the dispersion relationship to obtain the wave

number. The first 5 harmonics of the cross-spectra are determined using the following

expressions.
Ao(m = (2.5)
P
A, () (2.6)

A2 (0)= .. O (2.7)

B, (cP) (2.8)
kok,k n

B2( 0= (2.9)

In these equations 0, is the power and cross spectra of the pressure and current time

series.

The two orthogonal components of water velocity are resolved into a

magnitude and direction using the vector relationships

V = [u2 + 2]2 (2.10)
S= tan- u (2.11)
v

I

where 0 is measure clockwise from the north, Vis the velocity magnitude, u is the

long shore component, and v is the cross shore component of the current signal.

2.4.3 OBS Data

Application of the OBS calibration is straightforward with the exception of an

occasional discrepancy in the offset, and is achieved using the mfile DATAIN.M

(appendix A). Discrepancies in the offset are differences between the OBS data and

the field observations from the portable turbidimeter made during site visits. The

original calibration is applied to the "raw" data and compared with the measurements

recorded in the field during the deployment and cleaning. If there is a discrepancy the

original offset, obtained during the calibration is adjusted to reflect the field

measurement. The result is two calibration curves differing only by their offsets, the

first varies from the deployment to the cleaning, and the second from cleaning to the

recovery. No attempt is made to adjust for offsets caused by biofouling. Although

biofouling is assumed to occur at an exponential rate (based on population growth and

observations of the signal discussed in detail later) the actual rate is unknown. Hence

any attempt to account for biofouling through offset adjustments would produce

meaningless data. Discrepancies in the offset, when they occur, are typically less than

5 NTU.

2.4.4 Quality Analysis

Quality analysis was implemented in an effort to rank each observation as being

"good data" (Fig 2.9), "data of reduced accuracy", or "bad data"(Figures 2.11 and

2.13). Reduction in data quality is the result of both instrument failures and

biofouling. Quality control is achieved through observations of the calibrated time

history plots, which can reveal abnormalities indicative of an instrument failure (Fig

2.11), and the monthly summary plots, which reveal long term interference such as

bio-fouling (Fig. 2.12).

Turbidity 0 0.85 meters above the sea bed
Mean = 67.16. Std Dev = 1.524 67.94
*1 l' ..... rf ,ri, r,, r ,'il67, 4

'' 39.12
I I 10 111 15 20 25
Time in minutes

H162, Run No. 107, Date 12/29/91. Start Time 4:3, jd 362.2
Figure 2.11 An example of bad turbidity time series due to biofouling.

Figure 2.12 An example of the effect of biofouling on the deployment
summary plot. Note the apparent exponential increase in the
signal from Julian day 220 till the sensor was cleaned on Julian
day 224.

.Pressure, m. Mean = 6.227 d ev =0.61 6.553

6.075
U-vel, m/s Mean = 0.06815 Std Dev 0.55691 0.2953

-0.1658
V-vl, m/s Mean = 0.1066 Std Dev =0.04105 0.2302

5 10 1'5 20 25 -0.03194
Time In minutes

Figure 2.13 An example bad pressure and current time series data
typical of instrument failure.

TURBIDITY FOR DEPLOYMENT H131
Sensor Elevation O.lnb
From: July 26. 1991. Julion Day 206.5
To: August 21. 1991. Julion Day 232.3
: good doto
o : daa with reduced accuracy
S: bad dato

20 -Burst Me.-

205 210 215 220 225 230 235
Julian Day

Biofouling is an important aspect of long term turbidity sampling. Turbidity

sensors are extremely sensitive to such interference. This is inherent in the instruments

optical method of measurement. As a result an optical grade anti-biofoulant (Tri-

butalene) was applied to the lens of each instrument prior to deployment and

calibration. This typically inhibited the initiation of growth by 1 to 2 weeks depending

on the time of year. Although biofouling is assumed to occur at an exponential rate

(Fig 2.12) the actual rate is unknown and varies with water temperature and clarity.

Measurements of that rate are beyond the scope of this thesis.

The PUV sensors are much less susceptible to the effects of biofouling. The

scheduled cleaning proved adequate in regulating biofouling such that there were no

effects on the PUV signals.

Turbidity 0 0.85 meters above the sea bed
Intu Mean = 1.951 Std Dev =9.03E 356.7

.1 i .. ... = j i, r | : -L... l .l :L : 0.9999
Turbidity 0 0.16 meters above the sea bed
ntu Mean = 0.693 Std Dev = 1.91 62.46

O -1.173
b 0 *5Z 25 17
Time in minutes

H011, Run No. 93, Date 2/15/90, Start Time 0:3, jd 45

Figure 2.14 An example of reduced accuracy OBS data resulting
from a shift in the offset below the data logger's
range.

Figure 2.15 An example of reduced accuracy OBS data resulting
from a small abnormality in the signal.

The quality of data considered either "good" or "bad" is obvious, as

demonstrated by figures 2.11 through 2.13. However data with "reduced accuracy" is

less obvious and thus requires explanation. Quality of the data is considered to be of

reduced accuracy if the signal exhibits small abnormalities or in cases of partial data

loss. For example, the signal in Figure 2.14 is partially missing due to a shift in the

offset below the threshold of the data loggers input. Figure 2.15 is an example of an

abnormality considered too small to be bad data yet the abnormality reduces the

investigators confidence in the observation to that of reduced accuracy. Both

examples are typical characteristic of observations labeled as data of reduced accuracy.

The data files created by the quality analysis are in matrix form, where rows

represent each observation, and columns represent each instrument. The last row

indicates the elevation (either 0 to 0.5, or 0.5 to 0.85 meters above the sea bed) of the

OBS sensors. These files are utilized by plotting routines and analysis programs, to

either indicate the observations quality on a graph or to exclude bad points from the

analysis.

2.5 Meteorological Data

The meteorological data were collected by other organizations. Wind speed

and direction was obtained through the U.S. Department of Commerce, National

Turbidity @ 0.45 meters above the sea bed 14
ntuM an = 6.408 Std Dev = 1.044 14.24

4.004
1 15 20 25
Time in minutes
H111, Run No. 49, Date 6/7/91, Start Time 12:3, jd 157.5

27

Oceanic and Atmospheric Administration, National Environmental Satellite Data and

Information Service, National Climatic Data Center. Wind data were collected hourly

at Miami International Airport. Rainfall data were collected daily at Fort Lauderdale

International Airport. The only analysis of these data was a conversion from the

standard calendar to the Julian day system used to measure time. Both sets of data are

presented in chapter 3 along with the in-situ data.

I

CHAPTER 3

RESULTS AND DISCUSSION

3.1 Introduction

In chapter three an overview of the data is submitted, from which the

influences on turbidity are quantitatively assessed, and finally the effects of the beach

nourishment are both qualitatively and quantitatively analyzed and discussed.

3.2 Data Overview

This section presents the in-situ data collected and analyzed by the

investigators as well as meteorological data collected by other organizations. Initial

analysis of the data results in calibrated time history plots and statistical summaries of

each deployment. The statistics are presented in plots for each deployment, and in

tables for the monthly statistics and overall statistics. Also included in the data set are

some video images illustrating an agreement between the in-situ instruments and

events recorded on video. Figures 3.1 and 3.2 represent the operational status of the

PUV instruments, and the OBS operational status is presented in Figure 3.3.

Figure 3.1: Site 1 wave data availability.
Figure 3. 1: Site 1 wave data availability.

Site 2

Veloc-ty -

1 990 1 991 1 992
Figure 3.2: Site 2 wave data availability.

SITE 2 UPPER
ELEVATION

SITE 2 LOWER
ELEVATION

SITE 1 UPPER -e --
ELEVATION

SITE 1 LOWER
ELEVATION

J F MAM J J A SONDJFMAM J JA SONDJFMA
1990 1991 1992

Figure 3.3: Turbidity data availability.

Operational status is based on the deployment schedule and the quality rating

of the data from the instrument. Data of "bad" quality are omitted from Figures 3.1

through 3.3. The fraction of total time in which usable data were recorded was 98%

for the PUV data and 51% for the turbidity data.

3.2.1 Time Series Plots

Calibrated time history plots reveal short term variations in turbidity such as

the influence of an individual wave. For example Figure 3.4, a two minute portion of a

30 minute observation, shows an instability in the turbidity over a wave period. This

instability is probably the result of the local resuspension of sediment. Observation of

an entire time series plot is useful in the investigation of the influence of groups of

waves on turbidity as is illustrated in Figure 3.5. Although an analysis on this time

scale is important it is beyond the scope of this study and only mentioned as possible

future work. Consequently the use of each time series will be limited to quality

control discussed previously, and to obtain the statistics used in the following analysis.

The statistics, which include the mean, standard deviation, minimum, and maximum,

describe the conditions during a thirty minute observation. For example, in Figure 3.5

the spikes which are the maximum values indicate the magnitude of the intermittent

sediment suspension, most likely composed of sand grain sized particles (Hanes,

1988). The minimum is the background turbidity which in Figure 3.5 is the baseline

from which the spikes extend. The standard deviation describes fluctuations in

turbidity above background. The background turbidity is composed of fine particles

which both exhibit a high residence time in the water column, and extend to higher

elevations than turbidity created by intermittent events. For example, in Figure 3.5 the

minimum or background turbidity at each elevation is approximately 14 NTU,

however, the maximum, standard deviation and mean indicate the intermittent events

substantially influence turbidity at only the lower elevation. This broad vertical

influence in the water column of the background turbidity results in attenuating more

ambient sun light than the material intermittently suspended. This is a result of the

larger volume associated with the background turbidity. The statistical descriptions

provide an accurate representation of the wave conditions at the sea surface, and the

turbidity within the bottom 2 meters of the water column. This is evident in the

comparison of Figure 3.5 to Figure 3.6, where different conditions prevail.

Figure 3.5 depicts both statistically and graphically an active sea surface, with a

standard deviation equivalent to a significant wave height of almost 2 meters and a

correspondingly active seabed. Figure 3.6 however presents conditions of little

activity both on the sea surface where the significant wave height is less than 1 meter

and at the seabed where both the statistics and the plots show there is no intermittent

sediment suspension. Also the background turbidity of the signal at both elevations in

both figures are within a few NTU of each other. This is particularly evident in Figure

3.6, where the wave activity is low. Here the difference in background turbidity is

within a fraction of an NTU. Figure 3.5 and Figure 3.6 are typical time series of data

recognized as good quality from which the statistics are derived for use in the

following analysis.

Figure 3.4 Two minute time series, from site 2.

Figure 3.5 Thirty minute time series, from site 2.

Time in minutes
Date 12/14/91. Start Time 16:3. j
Figure 3.6 Thirty minute time series, from site 2.

3.2.2 Data Summary Plots

Plots of the statistical summaries produced from the thirty minute time series of

each deployment are described in this section and are presented in Dompe and Hanes

(1992, 1993). From these plots trends in the signal, including periods of significant

elevation in turbidity, can be observed with a resolution of four hours over a month

duration. Figure 3.7 and Figure 3.8 present an example of the statistical summaries

with the quality rating of deployment 16 at the inner site. Each point is derived from

a thirty minute time series, where lines represent high quality data, circles represent

data with reduced accuracy, and stars represent bad data.

34

WAVE PARAMETERS FOR DEPLOYMENT H162
From: December 11,1991 Julian Day 344.66
To: January 7,1992 Julian Day 6.5
: good data
o : data with reduced accuracy
: bad data

2 Significant Wave Height

E 1

0
340 345 350 355 360 365 370 375

Julian Day
150 Peak Wave Direction
150

100-

50

0
340 345 350 355 360 365 370 375

Julian Day

15 Peak Wove Period

10 -

5

0
340 345 350 355 360 365 370 375
Julian Day
Figure 3.7 Monthly summary plots from the PUV time series, for site 2.

I

TURBIDITY FOR DEPLOYMENT H162 TURBIDIY FOR DEPLOYMENT H162
OBS 1261. Height 0.5m OBS #217 Height 0.16m
From: December 11,1991 Jution Dy 344.66 From: December 11.1991 Julion Day 344,66
To: January 7.1992 Juion Ooy 6.5 T: January 7.1992 Julin Day 6.5
-: good doto : good dol
o: data *i0 redued occurcy o : dolo with reduced OCCUrcy
S: bud d0o bd dolo

Burst Ueans Burst Uea~s

350 355 360 365 370 375
Julian Doy
Slondod Oeviotion

340 345 350 355 360 365 370 375
Julion Doy
Standard Deviolion
tO ..r

340 345 350 355 360 365 370 375 340 345 350 355 360 365 370 375
Julian Day Julon Doy
0 iu (--) minimum (--) _________________ mmum --) minimum ----

340 345 350 355 360 365 370 375 340 345 350 355 360 365 370 375
Julion Ooy Julion Doy
Figure 3.8: Monthly summary plots from the turbidity time series, for site 2.

Two important observations should be noted from Figure 3.7 and Figure 3.8:

first, the response of turbidity to the three storm wave events between Julian days 345

and 355, and secondly, all data marked as "bad data" are excluded from the analysis in

the following sections.

3.2.3 Monthly Statistic Results

Statistically reducing the data to monthly observations exposes long term

fluctuations in turbidity at a resolution of thirty days. These monthly observations also

may reveal seasonal trends in turbidity. Table 3.1 and Table 3.3 are the monthly

statistics of the turbidity data, derived from the means of the "good" and "reduced

accuracy" burst samples over the month. Table 3.2 and Table 3.4 are the monthly

statistics of the wave data derived using the respective spectral analysis results of each

thirty minute observation of "good" and "reduced accuracy" data for the month. The

high mean turbidity values in Table 3.3 for the winter and spring of 1991 are a result

of intermittent sediment suspension induced by large wave events, associated with a

30 3
3Ao 345

50 -
0

majority of the observations. For example, in May of 1991 the high mean turbidity is a

direct result of high waves over much of the sampling period. Other instances of high

mean turbidity for the month are a result when a limited number of observations

coincide a high turbidity event. Such is the case for the data from November of 1990

at site 2 where the only data collected was over a three day period during which a

storm wave event occurred. Seasonally turbidity levels appear to be higher in the fall,

winter, and spring months than during the summer months, which corresponds with

the seasonal trends in wave activity. At this resolution a more accurate representation

should include more than 2 years of data.

Table 3.1: Monthy turbidity measurements, site 1
YR. MO TURBIDITY: 0.0 to 0.5 METERS ABOVE THE TURBIDITY: 0.5 to 0.85 METERS ABOVE THE
SEABED (NTU) SEABED (TU)
MEAN STD MAX. MIN. #of MEAN STD MAX. MIN. #of
PTS PTS
90 JAN 1.47 0.45 2.00 1.00 5 N/A N/A N/A N/A N/A
90 FEB 4.71 7.99 46.73 0.00 293 N/A N/A N/A N/A N/A
90 MAR 7.99 11.97 85.53 0.00 164 N/A N/A N/A N/A N/A
90 APR N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A
90 MAY 4.61 1.75 13.81 2.48 52 N/A N/A N/A N/A N/A
90 JUN 1.91 0.81 4.75 0.04 93 2.42 1.01 5.00 0.55 36
90 JUL 2.29 2.02 10.74 0.58 48 2.52 2.13 9.62 0.55 87
90 AUG 2.65 0.77 3.86 0.55 44 8.70 5.89 19.02 0.24 44
90 SEP 1.43 0.78 3.36 0.34 81 1.92 1.31 4.63 000 25
90 OCT N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A
90 NOV 5.14 4.21 20.13 0.33 41 4.00 2.35 12.60 0.93 31
90 DEC 11.95 11.09 78.54 3.68 99 8.63 3.15 16.67 4.70 42
91 JAN N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A
91 FEB N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A
91 MAR N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A
91 APR N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A
91 MAY N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A
91 JUN N/A N/A N/A N/A N/A 2.52 2.19 10.42 1.18 16
91 JUL 1.79 1.18 4.70 0.00 40 2.29 3.74 32.39 0.00 148
91 AUG 2.94 2.28 16.34 0.60 143 3.33 3.45 24.48 0.40 100
91 SEP 6.95 3.76 22.20 1.81 45 6.41 3.66 12.78 2.16 16
91 OCT N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A
91 NOV 2.98 4.80 22.65 0.00 93 3.00 3.81 18.23 0.24 107
91 DEC 4.13 3.68 20.42 1.05 95 4.16 4.94 21.35 0.87 77
92 JAN 2.99 1.86 4.37 0.14 7 3.41 1.17 5.69 0.18 20
92 FEB N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A
92 MAR 9.68 0.20 9.82 9.53 2 4.54 5.00 25.87 2.06 21
92 APR N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A
92 MAY N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A

Table 3.2: M nthly wave measurements, site 1
YR MO SIGNIFICANT WAVE PEAK WAVE PERIOD PEAK WAVE DIRECTION #of
HEIGHT (meters (seconds) (theta) PTS
MEAN STD MAX. MIN. MEAN STI MAX. MIN. MEAN STD MAX. MIN.

90 JAN 0.38 0.20 0.61 0.26 3.6 0.5 4.2 3.2 106 33 131 68 3
90 FEB 0.74 0.42 1.80 0.11 5.0 1.6 10.2 3.2 95 38 167 6 168
90 MAR 0.76 0.41 2.31 0.16 5.4 1.7 11.1 3.2 89 33 161 11 157
90 APR 0.23 0.06 0.37 0.13 5.3 2.2 8.8 3.2 49 0 49 49 26
90 MAY 0.42 0.34 1.55 0.11 4.0 0.8 6.6 3.1 123 22.2 152 68 51
90 JUN 0.26 0.14 0.71 0.12 3.9 1.5 13.4 3.2 118 40 146 49 93
90 JUL 0.34 0.21 0.83 0.12 3.8 0.8 8.8 3.2 105 32 161 38 87
90 AUG 0.26 0.18 0.75 0.10 3.6 0.6 5.4 3.2 138 19 161 114 96
90 SEP 0.27 0.12 0.74 0.11 3.4 0.7 11.1 3.2 N/A N/A N/A N/A 4
90 OCT 1.2 0.56 3.01 0.55 3.3 0.0 3.4 3.2 N/A N/A N/A N/A 27
90 NOV 0.65 0.41 1.76 0.15 5.6 2.5 12.2 3.2 72 31 131 8 47
90 DEC 0.64 0.38 1.43 0.13 5.1 2.1 11.1 3.2 70 31 133 6 124
91 JAN 0.63 0.14 0.83 0.38 4.2 0.6 5.0 3.2 94 36 135 49 12
91 FEB N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A
91 MAR N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A
91 APR 0.44 0.20 0.97 0.18 3.9 0.5 5.0 3.2 115 27 148 62 28
91 MAY 0.48 0.27 1.11 0.11 4.1 0.7 5.9 3.2 96 27 133 41 122
91 JUN 0.27 0.19 1.06 0.11 4. 6 2.6 13.4 3.2 58 24 156 36 173
91 JUL 0.24 0.13 0.72 0.10 3.7 0.9 8.2 3.2 134 36 176 81 175
91 AUG 0.24 0.14 0.85 0.09 4.3 1.7 10.2 3.2 80 47 161 8 143
91 SEP 0.29 0.17 0.87 0.10 5.4 2.6 12.2 3.2 59 33 176 28 126
91 OCT N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A
91 NOV 0.71 0.40 1.76 0.12 6.0 2.7 13.4 3.2 73 30 148 13 147
91 DEC 0.60 0.49 2.21 0.10 5.1 1.8 9.5 3.2 70 31 133 6 134
92 JAN 0.51 0.29 1.40 0.12 6.6 2.6 12.2 3.2 68 33 139 30 132
92 FEB 0.71 0.21 1.15 0.36 7.2 2.6 10.2 3.2 70 45 150 30 27
92 MAR 0.41 0.31 1.69 0.10 5.7 2.2 10.2 3.2 72 42 144 28 55
92 APR N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A
92 MAY 0.50 0.27 0.82 0.11 5.1 2.0 10.2 3.2 54 29 133 26 21

Table 3.3: Monthly turbidity measurements, site 2
YR MNTH TURBIDITY: 0.0 to 0.5 METERS ABOVE THE TURBIDITY: 0.5 to 0.85 METERS ABOVE THE
SEABED (NTU) SEABED (NTU)
MEAN STD MAX. MIN. #of MEAN STD MAX. MIN. #of
rIS PTS
90 JAN N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A
90 FEB N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A
90 MAR 14.30 17.74 114.73 2.33 250 N/A N/A N/A N/A N/A
90 APR N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A
90 MAY 15.89 18.06 127.54 2.71 131 N/A N/A N/A N /A A N/A
90 JUN 4.07 4.24 33.96 0.00 267 N/A N/A N/A N/A N/A
90 JUL 1.94 1.98 13.74 0.00 180 N/A N/A N/A N/A N/A
90 AUG 5.50 2.42 9.45 1.00 26 3.04 2.34 12.10 0.00 39
90 SEP 6.91 9.18 49.84 0.81 25 2.44 2.99 22.91 0.67 59
90 OCT 30.26 45.93 222.35 0.21 52 25.24 29.34 97.76 0.80 23
90 NOV 34.15 27.23 102.93 3.97 13 N/A N/A N/A N/A N/A
90 DEC N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A
91 JAN 9.05 7.32 43.39 0.73 63 9.45 7.27 36.86 0.67 68
91 FEB 45.51 15.36 95.45 31.28 18 N/A N/A N/A N/A N/A
91 MAR 19.33 14.17 50.28 8.46 25 10.19 5.84 23.05 2.18 24
91 APR 25.22 26.42 165.82 0.81 96 24.52 12.64 55.86 8.72 40
91 MAY 43.22 58.48 259.38 0.60 116 2.58 0.62 3.33 1.40 11
91 JUN 4.99 2.51 9.68 1.00 16 13.80 8.80 52.61 0.00 127
91 JUL 30.10 30.18 213.40 0.52 134 3.57 2.69 17.01 0.00 66
91 AUG 11.29 13.39 89.10 0.61 48 6.56 7.81 53.57 0.28 118
91 SEP N/A N/A N/A N/A N/A 9.73 3.80 25.73 5.06 39
91 OCT N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A
91 NOV 21.78 20.00 106.61 0.00 160 13.76 9.96 53.08 0.00 169
91 DEC 16.34 19.78 82.24 0.00 183 15.26 11.94 42.52 0.00 164
92 JAN 7.84 3.84 19.22 0.00 24 N/A N/A N/A N/A N/A
92 FEB 3.11 2.74 16.15 0.82 66 1.76 1.78 9.53 0.19 47
92 MAR 1.77 0.40 2.02 1.31 3 5.24 7.74 39.99 0.83 71
92 APR N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A
92 MAY N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A

Table 3.4: Monthly wave measurements, site 2
YR MNTH SIGNIFICANT WAVE PEAK WAVE PERIOD PEAK WAVE DIRECTION # of
HEIGHT (meters (seconds) theta) PTS
MEAN STI MAX. MIN. MEAN STD MAX. MIN. MEAN ST MAX. MIN.
90 JAN N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A
90 FEB N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A
90 MAR 0.67 0.31 1.79 0.21 5.0 1.7 11.1 2.3 82 34 174 4 159
90 APR 0.27 0.05 0.38 0.18 3.1 1.5 7.3 2.3 114 78 178 2 24
90 MAY 0.44 0.30 1.30 0.10 3.9 1.1 6.6 2.3 120 15 148 84 87
90 JUN 0.32 0.16 0.79 0.09 3.9 2.0 12.2 2.3 96 32 139 36 179
90 JUL 0.36 0.20 0.78 0.11 3.7 1.0 8.2 2.4 105 29 170 38 90
90 AUG 0.25 0.14 0.69 0.10 3.1 0.8 5.4 2.4 N/A N/A N/A N/A 74
90 SEP 0.30 0.12 0.69 0.11 3.7 2.0 12.2 24 N/A N/A N/A N/A 106
90 OCT 0.63 0.40 1.86 0.10 5.2 2.4 15.0 2.5 N/A N/A N/A N/A 108
90 NOV 0.73 0.44 1.46 0.20 5.5 1.3 7.3 2.6 N/A N/A N/A N/A 28
90 DEC N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A
91 JAN N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A
91 FEB N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A
91 MAR 0.74 0.30 1.34 0.17 4.2 0.9 5.9 2.5 124 16 150 79 33
91 APR 0.64 0.37 1.64 0.12 4.4 1.3 8.2 2.4 98 30 159 15 174
91 MAY 0.58 0.35 1.80 0.13 4.1 1.1 7.3 2.3 98 24 140 32 161
91 JUN 0.34 0.22 1.08 0.11 3.8 2.1 15.0 2.5 80 36 167 30 162
91 JUL 0.28 0.13 0.70 0.12 3.3 1.1 8.8 2.3 97 26 141 45 174
91 AUG 0.27 0.12 0.73 0.13 3.6 1.6 9.5 2.3 87 38 141 32 171
91 SEP 0.30 0.14 0.73 0.13 4.8 2.7 11.1 2.4 57 36 161 11 126
91 OCT N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A
91 NOV 0.68 0.32 1.56 0.13 6.0 2.7 17.0 2.4 66 30 144 13 146
91 DEC 0.56 0.34 1.79 0.13 4.9 1.9 12.2 2.4 73 35 148 6 167
92 JAN 0.38 0.17 0.80 0.18 8.2 3.5 13.4 2.4 37 25 103 0 38
92 FEB 0.38 0.17 0.90 0.12 5.6 3.2 13.4 2.0 83 44 154 15 123
92 MAR 0.90 0.35 1.99 0.47 2.9 1.1 9.5 2.5 N/A N/A N/A N/A 81
92 APR 0.90 0.51 1.63 0.23 4.6 3.5 13.4 2.4 N/A N/A N/A N/A 20
92 MAY 0.40 0.21 0.85 0.13 4.6 2.5 12.2 2.3 N/A N/A N/A N/A 125

3.2.4 Overall Statistical Results

The overall statistics in Table 3.5 and Table 3.6 are presented to show

statistically the variations in the parameters during the entire sampling period, and to

relate the turbidity statistics to the field measurements. Field observations of turbidity

using the portable turbidimeter, discussed in chapter 2, where typically less than 2

NTU and generally around 1 NTU. Agreement between turbidity from the site visits,

and the mode, defined as the most frequently occurring value, of the in-situ turbidity

observations indicate the reliability of the in-situ measurements. Table 3.7 and Table

3.8 are the overall statistics broken into sections with respect to the nourishment.

Table 3.5: Overall statistics, site 1
PARAMETER MEAN MEDIAN MODE STANDARD MAXIMUM MINIMUM #OF
DEVIATION PTS
TURBIDITY SITE 1 LOWER 4.72 2.41 0.95 7.24 85.53 0.00 1345
ELEVATION (NTU)
TURBIDITY SITE 1 UPPER 3.70 2.36 1.04 4.03 32.39 0.00 782
ELEVATION (NTU)
SIGNIFICANT WAVE 0.5 0.4 0.1 0.4 3.0 0.1 2271
HEIGHT (METERS)
PEAK WAVE PERIOD 4.8 4.0 3.3 2.1 13.4 3.1 2271
(SECONDS)
PEAK WAVE DIRECTION 81 66 45 36 176 6 2123
(DEGREES)
CURRENT MAGNITUDE 0.08 0.07 0.05 0.04 0.31 0.00 2123
(METERS/SECOND)
CURRENT DIRECTION 189 188 177 93 360 0 2123
(DEGREES)
WIND MAGNITUDE 8 7 3 4 28 0 20423
(KNOTS)
WIND DIRECTION 164 130 130 104 360 0 20423
(DEGREES) ___ ___ __
DAILY RAINFALL 0.18 N/A 0 0.54 7.09 0.00 761
(INCHES)___ __ __

Table 3.6: Overall statistics, site 2
PARAMETER MEAN MEDIAN MODE STANDARD MAXIMUM MINIMUM #OF
DEVIATION PTS
TURBIDITY SITE 2 LOWER 16.03 6.97 2.05 25.67 259.38 0.00 1896
ELEVATION
TURBIDITY SITE 2 UPPER 10.48 6.59 1.38 11.03 97.76 0.00 1065
ELEVATION
SIGNIFICANT WAVE 0.5 0.4 0.2 0.3 2.0 0.0 2662
HEIGHT (METERS)
PEAK WAVE PERIOD 4.4 4.0 2.6 2.2 17.0 2.0 2662
(SECONDS)
PEAK WAVE DIRECTION 86 88 126 37 180 2 2009
(DEGREES)
CURRENT MAGNITUDE 0.08 0.07 0.05 0.05 0.33 0.00 2009
(METERS/SECOND)
CURRENT DIRECTION 182 184 177 99 360 0 2009
(DEGREES)
WIND MAGNITUDE 8 7 3 4 28 0 20423
(KNOTS)
WIND DIRECTION 164 130 130 104 360 0 20423
(DEGREES) __ __ _
DAILY RAINFALL 0.18 N/A 0 0.54 7.09 0.00 761
(INCHES)_ __ __

Table 3.7: Overall statistics before during and after the nourishment, site 1
PRENOURISHMENT NOURISHMENT POSTNOURISHMENT
PARAMETER MEAN MED. MODE STD MAX # OF MEAN MED. MODE STD MAX # OF MEAN MED. MODE STD MAX # OF
PTS PTS PTS
TURBIDITY
LOWER 5.27 2.26 1.02 8.35 85.52 920 3.06 2.76 2.50 2.42 16.34 124 3.77 2.46 2.18 4.04 22.65 301
ELEV.(NTU)
TURBIDITY
UPPER 4.62 2.75 1.51 4.22 19.02 265 2.48 1.51 1.29 3.34 32.39 226 3.80 2.42 1.02 4.29 25.87 278
ELEV. (NTU)
SIGNIFICANT
WAVE HT. 0.5 0.4 0.1 0.4 3.0 953 0.3 0.2 0.1 0.2 1.1 582 0.5 0.4 0.2 0.4 2.2 736
(METERS)
PEAK WAVE
PERIOD 4.5 3.9 3.3 1.7 13.4 953 4.0 3.4 3.3 1.6 13.4 582 5.7 5.0 3.3 2.4 13.4 736
(SECONDS)
PEAK WAVE
DIRECTION 90 85 64 35 167 953 91 101 50 35 176 582 68 58 45 33 176 736
(DEGREES)
CURRENT
MAGNITUDE 0.08 0.07 0.05 0.04 0.31 841 0.07 0.07 0.06 0.04 0.26 582 0.07 0.06 0.08 0.04 0.24 700
(MIS)
CURRENT
DIRECTION 172 189 181 99 360 841 234 232 195 75 360 582 172 168 170 87 360 700
(DEGREES)
WIND
MAGNITUDE 7.9 7.0 4.0 4.1 28 11472 7.8 7.0 5.0 4.4 24 2712 7.0 7.0 3.0 3.8 25 6239
(KNOTS)
WIND
DIRECTION 162 130 100 101 360 11472 150 130 130 82 360 2712 176 140 330 116 360 6239
(DEGREES) ______ _____ ______ ______ _____ _____ _____ _____ ________ ___ ______ _____ _____
RAINFALL
(INCHES) 0.16 N/A 0.01 0.48 4.45 478 0.25 N/A 0.04 0.61 4.20 114 0.20 N/A 0.04 0.64 7.1 169

41

Table 3.8: Overall statistics before during and after the nourishment, site 2
PRENOURISHMENT NOURISHMENT POSTNOURISHMENT
PARAMETER MEAN MED. MODE STD MAX # OF MEAN MED. MODE STD MAX # OF MEAN MED. MODE STD MAX # OF
PTS PTS PTS
TURBIDITY
LOWER 12.16 5.81 2.19 19.47 222.4 1118 29.70 14.03 1.30 42.28 259.4 332 15.54 6.09 3.11 18.68 106.6 446
ELEV.(NTU)
TURBIDITY
UPPER 10.72 5.85 1.16 13.96 97.76 253 9.40 6.70 3.88 9.22 53.57 262 10.84 6.65 2.03 10.26 53.08 550
ELEV. (NTU)
SIGNIFICANT
WAVEHT. 0.5 04 0.2 0.3 1.8 1026 0.4 0.3 0.2 0.3 1.8 618 0.5 0.4 0.2 0.3 2.0 1018
(METERS)
PEAK WAVE
PERIOD 4.2 4.0 2.6 1.8 15.0 1026 3.6 3.2 2.6 1.4 15.0 618 5.0 4.3 5.7 2.7 17.0 1018
(SECONDS)
PEAK WAVE
DIRECTION 97 105 119 36 180 1026 95 101 100 29 167 618 70 58 45 36 161 1018
(DEGREES)
CURRENT
MAGNITUDE 0.09 0.08 0.04 0.06 0.33 709 0.07 0.06 0.05 0.05 0.30 618 0.08 0.08 0.05 0.05 0.30 682
(MIS)
CURRENT
DIRECTION 147 163 20 104 360 709 222 203 183 99 360 618 185 180 177 78 358 682
(DEGREES)
WIND
MAGNITUDE 7.9 7.0 4.0 4.1 28 11472 7.8 7.0 5.0 4.4 24 2712 7.0 7.0 3.0 3.8 25 6239
(KNOTS)
WIND
DIRECTION 162 130 100 101 360 11472 150 130 130 82 360 2712 176 140 330 116 360 6239
(DEGREES)
RAINFALL
(INCHES) 0.16 N/A 0.01 0.48 4.45 478 0.25 N/A 0.04 0.61 4.20 114 0.20 N/A 0.04 0.64 7.1 169

3.2.5 Verification With Video Monitoring

Video images are mentioned for comparison with the in-situ measurements as a

verification tool in a qualitative manner. For example, none of the natural forces such

as wave height is consistent with the high turbidity at site 2 in Figure 3.9. However,

the video images of Figure 3.10 indicate a correspondence between turbidity

fluctuations and the proximity of the dredge discharge relative to the site.

TURBIDITY FOR DEPLOYMENT H112
OBS #182 Height = 0.77m
From: June 14,1991
To: June 17,1991
: good data
o : data with reduced accuracy
: bad data

40Burst Means

20

14 14.5 15 15.5 16 16.5 17 17.5 18 18.5
June
Figure 3.9: Portion of the monthly summary plot showing turbidity for
site 2 the upper elevation during the time sediment discharge activities
approached and passed the beach adjacent to the site.

Figure 3.7: Video images showing the dredge discharge location with
respect to site 2, (located below the x) as a function of time.

Such comparisons are only useful in a qualitative manner therefore their inclusion

will be limited to the images in Figure 3.7. Quantitative comparisons, require calibration

of the video images with the assumption turbidity is homogeneous with depth, and are

therefore beyond the scope of this study.

3.3 Major Influences Upon Turbidity

Major influences on turbidity in this section will encompass only natural forces,

such as described by the statistics in the previous sections. Man induced turbidity events,

specifically beach nourishment and dredging activities, will be addressed in section 3.4.

This section will analyze quantitatively the relationship between the natural

fluctuations in turbidity and natural forces, such as wave height. To reduce any bias

that may arise from the nourishment, data used for the development of this relationship

will be limited to observations recorded prior to the nourishment. Initially the

parameters must be compared to one another to establish the significance of their

relationship to turbidity as well as their dependency on one another. This is achieved

using the correlation analysis in Matlab that returns the correlation coefficient matrices

of Tables 3.9 to 3.12. These results are referenced to the minimum correlation

coefficient rs required to describe a relationship greater than a random correlation for n

number of points (where each point represents a thirty minute observation) with a 99%

confidence (Snedecor and Cochran 1980). The correlation coefficients for rainfall are

much less than rs and therefore are not included in the following figures. The

correlation coefficients of Tables 3.9 to 3.12 indicate a significant correlation exists

between wave height and turbidity.

Table 3.9 Correlation between parameters site 1 lower sam ling level.
n= 681 TURBIDITY SIGNIFICANT PEAKWAVE CURRENT CURRENT WIND WIND
r, = 0.115 WAVE HT. PERIOD MAGNITUDE DIRECTION DIRECTION MAGNITUDE
SIGNIFICANT 0.523
WAVE HT.
PEAKWAVE 0.358 0.510
PERIOD
CURRENT -0.015 0.045 0.100
MAGNITUDE
CURRENT 0.003 -0.329 -0.102 -0.077
DIRECTION
WIND -0.187 -0.403 -0.114 0.033 0.175
DIRECTION
WIND 0.236 0.584 0.258 0.029 -0.391 -0.265
MAGNITUDE
MAXVEL. @ 0.353 0.918 0.223 0.027 -0.401 -0.411 0.569
THE SEABED__ _

Table 3.10 Correlation between parameters site 1 upper sam ling level
n= 265 TURBIDITY SIGNIFICANT PEAKWAVE CURRENT CURRENT WIND WIND
r = 0.1596 WAVE HT. PERIOD MAGNITUDE DIRECTION DIRECTION MAGNITUDE
SIGNIFICANT 0.284
WAVE HT.
PEAKWAVE 0.224 0.525
PERIOD
CURRENT -0.055 0.017 -0.004
MAGNITUDE
CURRENT 0.098 -0.088 -0.041 -0.473
DIRECTION
WIND 0.018 -0.297 -0.074 0.063 0.046
DIRECTION
WIND 0.005 0.361 0.229 -0.026 -0.108 -0.250
MAGNITUDE
MAX VEL. @ 0.215 0.896 0.190 0.023 -0.100 -0.337 0.263
THE SEABED

Table 3.11 Correlation between parameters site 2 lower sam ling level.
n= 829 TURBIDITY SIGNIFICANT PEAK WAVE CURRENT CURRENT WIND WIND
r = 0.115 WAVE HI. PERIOD MAGNITUDE DIRECTION DIRECTION MAGNITUDE
SIGNIFICANT 0.619
WAVE HT.
PEAK WAVE 0.272 0.282
PERIOD
CURRENT 0.203 0.488 -0.017
MAGNITUDE
CURRENT 0.079 0.135 0.051 0.241
DIRECTION
WIND -0.152 -0.286 -0.178 0.001 0.100
DIRECTION
WIND 0.398 0.386 0.102 0.228 0.180 -0.187
MAGNITUDE
MAX VEL. @ 0.353 0.844 -0.100 0.534 0.173 -0.148 0.243
THE SEABED

Table 3.12 Correlation between parameters site 2 upper sam ling level.
n= 232 TURBIDITY SIGNIFICANT PEAK WAVE CURRENT CURRENT WIND WIND
r = 0.1704 WAVE HT. PERIOD MAGNITUDE DIRECTION DIRECTION MAGNITUDE
SIGNIFICANT 0.589
WAVE HT.
PEAKWAVE 0.302 0.368
PERIOD
CURRENT 0.142 0.467 -0.060
MAGNITUDE
CURRENT 0.242 0.439 -0.065 0.786
DIRECTION
WIND -0.082 -0.141 -0.369 0.164 0.189
DIRECTION
WIND 0.557 0.549 0.283 0.326 0.485 -0.112
MAGNITUDE
MAX VEL. @ 0.291 0.787 -0.144 0.551 0.496 0.082 0.261
THE SEABED

1 UU I ,

0 a

50 o o

S 1 2

Sig. Wave Ht. (m)

3 Site 2 Lower Elevation
300 .

2001-

o a o

S 0.5 1 1.5

Sia. Wave Ht. (m)

z
. 10
.0
I-

0

0 00
o:aor

0 0

0o

OI I -
0 0.5 1 1.5

Sig. Wave Ht. (m)

S Site 2 Upper Elevation
0 0

50 -

I-

0 00
S o 0o
0 0
o o o B
a^*_ _. ;

0 0.5 1 1.5

Sia. Wave Ht. (m)

Figure 3.11 Scatter plots of turbidity vs. significant wave height

Further investigation using the scatter plots in Figure 3.11 to compare

significant wave height to turbidity indicates a general trend toward increasing

turbidity with increasing wave height, but considerable variability is also indicated. It

Site 1 Lower Elevation _- Site 1 Upper Elevation

also appears that turbidity correlates to wave height only above a threshold wave

height. Further observations reveal a variation in the threshold between the two sites.

Hmo appears to begin to influence site 2 at approximately 0.5 meters and site 1 at 0.6
meters. This variation between sites is a result of the attenuation of the wave induced

velocity with depth and can be quantified using linear wave theory. From linear wave

theory the maximum wave induced velocity is expressed as a function of depth,

frequency, and wave height as follows.

H cosh(k(h + z))
Uo = -- c a+ z cos(kx ot) cos(kx at) = 1 at Um (3.3)
2 sinh(kh)

H cosh(k(h + z))
oma sinh(kh)

Solving Equation 3.4 for the two cases results in an equivalent velocity at the seabed

of =0.43 meters per second confirming the assumption that the different thresholds

exert an equivalent force on the seabed at their respective sites.

The relationship between wave height and turbidity in Figure 3.12 is a constant

up to the threshold wave height (Hth) at which point it appears to increase linearly.

The constant line, referred to as the background turbidity (NTUb), is the turbidity

resulting from some other environmental influence and is calculated from the mean of

the turbidity observations below Hth. Above Hb the relationship can be expressed as;

NTU NTUb = m(Hm Hh) (3.5)

where m is the slope, Hmo is significant wave height, and NTUis the resulting

turbidity. Solving for NTU, and combing the threshold terms results in;

NTU = mH,, +(NTUb -mHh) (3.6)

where the offset is expressed in terms of the thresholds as follows.

1 I

b = NTUb mHt, (3.7)

This results in the familiar equation of a straight line.
NTU =mH,, +b (3.8)

Applying linear regression to the data limited by the threshold results in Table 3.13.

Table 3.13 Prenourishment regression analysis results.
LOCATION m b r # ofpts r, Ht NTUh
SITE 1 Lower Elevation 21.27 -9.97 0.639 380 0.132 0.6 2.8
SITE 1 Upper Elevation 7.18 -0.11 0.505 58 0.330 0.6 4.2
SITE 2 Lower Elevation 75.84 -32.87 0.804 362 0.135 0.5 5.0
SITE 2 Upper Elevation 36.71 -15.10 0.618 89 0.269 0.5 3.2

Prenourishment scatter plots with regression lines produced using
only data corresponding to values of Ho greater than 0.6 m
for site 1 and 0.5 m for site 2.

The correlation coefficients from the linear regression analysis in Table 3.13 affirm the

assumption that above a threshold the relationship is approximately linear. The

correlation coefficient for site 1 upper elevation indicates only a poor relationship

exists. It appears that the wave climate observed during this study was nearly always

less than the wave energy required to raise the turbidity level to the upper elevation in

that depth of water. The difference in slope (m) between the sites is a result of the

sensitivity of turbidity to, sediment size, elevation, and wave energy at the seabed.

Although the regression coefficients when applying a background threshold for

the two sites have increased considerably, they are significantly less than unity and

therefore some portion of turbidity remains unaccounted for. The results in Table

3.13, specifically the slope m, indicate the sensitivity of turbidity to elevation above the

seabed. Dividing the data into only two elevation dependent groups assumes that

turbidity does not vary greatly with distance above the seabed. This is however not

the case and although breaking the data into a larger number of elevation dependent

groups may increase the regression coefficients it would decrease the amount of data

and hence the statistical significance of the results.

The use of the same threshold for the near bed orbital velocity at both sites

assumes that the sediment size distributions were similar and as a result may not be

valid after the beach nourishment. Also application of such a model would be limited

by the amount of data used in its development, consequently, use of this model to

predict turbidity during a hundred year storm may not be valid. A nonlinear

relationship may improve the fit but it is not justified by the present data.

3.4 Effects of Nourishment

To quantitatively asses the impact of the beach nourishment, a comparison of

the statistics describing turbidity fluctuations before during and after the beach

nourishment must be made. The measurements must take into account variations in

the other parameters or compare periods of similar conditions. For example, to what

magnitude do h size waves affect turbidity during or after the nourishment as opposed

to before the nourishment? Therefore two methods will be employed, the first

compares the prenourishment relationships between wave height and turbidity to those

during and after the nourishment and also compares the statistics of those data points

not influenced by waves, and the second compares turbidity storm events prior to,

during, and after the nourishment.

3.4.1 Nourishment and Postnourishment Relationship Between Turbidity and Waves

The original relationship between turbidity and wave height in section 3.3.1

was established using prenourishment data. The same analysis (including the same

Hth) was used to determine the relationship between nourishment and postnourishment

turbidity and wave height (Fig. 3.13, Fig. 3.14, Table 3.14, and Table 3.15). By

establishing this type of relationship qualitative comparisons of any short term or long

term changes resulting from the nourishment can be made.

0.5
Sig. Wave Ht. (m)

Site 2 lower turbidity
I u I
03

0
So o

o0 o
ut~p"j
I..-.....f o

1
i. Wave Ht. (m)

I--
, 20
oo
-
I-
00

0.5
Sig. Wave Ht.

Site 2 upper tui

40 0
0Da

5 20 o
I a^ a

0.5
Sig. Wave Ht.

Scatter plots with regression lines produced using only data
corresponding to values of Hogreater than 0.6 m for site 1 and
0.5 m for site 2, measurementsobtained during thenourishment.

0

Table 3.14 Nourishment regression analysis results.
LOCATION m b r # ofpts r Hth NTUh
SITE 1 Lower Elevation -1.46 3.86 -0.342 3 0.959 0.6 3.0
SITE 1 Upper Elevation 39.82 -21.46 0.779 5 0.874 0.6 2.4
SITE 2 Lower Elevation 171.90 -65.66 0.869 90 0.267 0.5 20.3
SITE 2 Upper Elevation 23.33 -2.66 0.530 46 0.368 0.5 9.0

Figure 3.14 Postnourishment scatter plots with regression lines produced
using only data corresponding to values of HIo greater than 0.6 m
for site 1 and 0.5 m for site 2.

Table 3.15 Postnourishment regression analysis results.
LOCATION m b r #ofpts r Hth NTUh
SITE 1 Lower Elevation 8.69 -2.22 0.644 90 0.267 0.6 3.0
SITE 1 Upper Elevation 7.83 -1.29 0.735 102 0.255 0.6 3.4
SITE 2 Lower Elevation 45.51 -17.66 0.609 225 0.173 0.5 5.1
SITE 2 Upper Elevation 15.72 -0.92 0.424 295 0.150 0.5 6.9

Tables 3.14 and 3.15 list the results of the relationship between turbidity and

wave height during and after the nourishment. Comparing these results to the results

in Table 3.13 leads to several observations; first, there is a small increase in NTUb

during the beach nourishment at the inner site at both elevations and following the

beach nourishment at the inner site upper elevation (Fig. 3.15); however, this should

be verified by considering the uncertainty, second, the correlation coefficients

decreased considerably during the nourishment at all but the lower elevation at site 2

and increased subsequently after the nourishment at the outer site while decreasing at

the inner site, and last, the slope of the lines (m) varies greatly before during and after

the nourishment.

Site 1 Upper Elevation

Site 1 Lower Elevation

1 NTU

POSTNOURISH

POSTNOURISH

Site 2 Upper Elevation

Site 2 Lower Elevation

-49111"W ~NTUb
POSTNOURISH

Figure 3.15: Comparison of the background turbidity relative to the nourishment

Variations in turbidity associated with the nourishment are most likely

attributed to an increase in the amount of fine material (percent fines) available for

resuspension. Fisher et al. (1992) found the nourishment and postnourishment amount

NOURISH

4.5
4
3.

Nt 2.5

0
PRENOURISH

NOURISH

--- 4-_"QR 11TU1
POSTNOURISH

of percent fines (< 0.063mm) within the project area increased over the

prenourishment amount. The increase in the percent fines (silt/clay) results in turbidity

plumes with a high residence time and a larger amount of easily suspended sediment.

The beach renourishment project was conducted during the summer months,

which as Tables 3.2 and 3.4 indicate are periods of relatively low wave energy.

However the prenourishment and postnourishment data, as indicated by Tables 3.2 and

3.4 as well as Tables 3.7 and 3.8, were collected during periods of both high and low

wave energies. Therefore comparisons between the prenourishment, nourishment, and

postnourishment means (Figure 3.15) are achieved using the background turbidity

NTUb, which is calculated from the turbidity observations perceived as unaffected by
waves (or the observations below Hth). This reduces bias caused by variations in wave

energy between prenourishment, nourishment, and postnourishment. Neither the small

increase in turbidity after the nourishment nor the larger increases during the

nourishment, observed in Figure 3.15 could be significant if the uncertainty of the

measurements is included. The uncertainty in these values (Table 3.15) is determined

by;

(3.9)

where a, is the standard deviation of the mean, a is the standard deviation of the set

of measurements, and n is the number of measurements in the set (Holman 1984).

Figure 3.16 re-examines the effects of the renourishment accounting for the

uncertainty in the NTUb (mean background turbidity).

Although the outer site appears unaffected during the nourishment, statistics at

the inner site indicate an increase in NTUb. Depending on the direction of error, NTUb

at the lower elevation may have increased by as much as 30 NTU or decreased by 1

53

NTU. Assuming the largest error in NTUb for the upper elevation at the inner site

results in an increase of as little as 2 NTU, or more than 9.4 NTU, however in either

case there is a small increase. This increase is most likely the result of the inner sites'

proximity to the dredge discharge.

Site 1 Lower Elevation
PRENOURISH NOURISH POSTNOURISH

Z r

S MAXIMUM HIGH ORIGINAL MAXIMUM LOW
S ERROR CALCULATION ERROR

Site 2 Lower Elevation
PRENOURISH NOURISH POSTNOURISH
"T

I *MAXIMUMHIGH
ERROR
ORIGINAL
CALCULATION
MAXIMUM LOW ERROR

t

Site 1 Upper Elevation
PRENOURISH NOURISH POSTNOURISH

i I

*MAXIMUM HIGH
ERROR
ORIGINAL
CALCULATION
MAXIMUM LOW ERROR

Site 2 Upper Elevation
PRENOURISH NOURISH POSTNOURISH

12 -

4 !
2
*MAXIMUM HIGH ORIGINAL MAXIMUM LOW
ERROR CALCULATION RROR

Figure 3.16 Comparison of the threshold turbidity including the error bars

The postnourishment concentrations at the upper elevation at site 2 are

significant even after consideration of the uncertainty is included. Using the

uncertainty analysis and calculating the minimum increase in turbidity at site 2 after the

nourishment the upper elevation increased by 2.9 NTU and the lower elevation did not

change. These results are consistent with the variations reported by Fisher et al.

(1992). However, accounting for uncertainty in the measurements at the outer site

indicates virtually no change at the lower sensor and a possible a decrease at the upper

elevation. The postnourishment turbidity elevations in the nearshore region (site 2)

are a result of the natural adjustment to the perturbation in the coastline caused by the

beach nourishment in both cross shore and long shore directions, and the greater

portion of fines present.

It was shown previously that turbidity induced by waves is depth dependent.

For example, small waves (less than Hth ) may only influence turbidity in the swash

zone. After the nourishment an increase in silt/clay in this area would indirectly

increase the spatial influence of the smaller waves on turbidity. Again this results

when small waves in the swash zone create turbidity plumes with long residence times,

which are transported offshore by currents or by diffusion. The transport mechanisms

are more likely to extend to the inner site (site 2) than the outer site, hence the lack of

influence at site 1. The increase at the site 2 the upper elevation is no greater than the

prenourishment conditions at the lower elevation. There may be a layer of turbidity

which following the nourishment (due to the increase in the fines and the imbalance in

the beach profile) was increased to encompass a thicker portion of the water column

(up to the upper elevation).

The variations in correlation coefficients (r) between the sites and the

deployment period may be attributed to one, or a combination of, changes in the

amount of data, changes in the sediment distributions, imbalances in the beach profile

and planform, and variations in the wave climate. The decrease in r (or the lack of any

relationship) during the nourishment at all but the lower elevation at the inner site, is

most likely the result of insufficient wave energy during the beach renourishment

project as indicate by Tables 3.2 and 3.4. The increase in r for the lower elevation at

the inner site during the nourishment is due to a May storm at the beginning of the

project (discussed in further detail below and in section 3.4.3) which produced large

waves (and possibly to fewer points) unfortunately during this event no instruments

where deployed (or operating) at the other locations. The increase in r after the

nourishment at the lower elevation the outer site may be a result of fewer points, while

the increase at the upper elevation may be attributed to a higher percentage of fines.

The postnourishment decrease in r for the lower elevation at site 2 is likely due to both

the increase in the silt/clay content, and an imbalance in the beach profile and

planform. Both the increase in fine sediments and the imbalance in the beach increase

the sensitivity of sediment resuspension in the swash zone. The fine sediments have a

high residence time in the water column and are therefore more likely to have a broad

spatial influence, which would vary the magnitude of turbidity at site 2 only when a

transport mechanism is available. The decrease at the upper elevation may be the

result of these factors as well as an increase in the number of points.

The slope of the line describing the relationship between turbidity and wave

height can be thought of as a measure of the sensitivity of turbidity to wave climate.

The most obvious influence on the sensitivity is the sediment size distribution, which

may vary during and after the nourishment from the initial conditions. Variations in

the sensitivity can be used to determine the relative effects of the nourishment on

turbidity. Comparing Tables 3.13 and Table 3.15 indicate the general trend was a

decrease in the postnourishment sensitivities. Comparison of Tables 3.13 and 3.14

during the nourishment reveals an increase in sensitivity at the site 2 lower sensor.

The general decrease in sensitivity following the nourishment may be the result of an

increase in the mean grain diameter (Table 1.1, borrow area B versus the existing

beach) or in choosing the wave threshold. For example the threshold may vary

between the pre, during, and post stages of the nourishment. This variation may

control the slope, however without more information choosing a slope is limited to

visual inspection (from which consistent values were used). The increase in sensitivity

during the nourishment is due to a large wave event coinciding with and following

dredging activities. This may be the result of an elevation in the percentage of fines

during dredging activities (Fisher et al., 1992) providing a larger portion of easily

resuspended materials, which subsequently resulted in an increase in the sensitivity.

Instruments were not deployed at either site 1 or the upper elevation at site 2 during

this event.

3.4.3 The Effect of turbidity Storms

In this section the magnitude and duration of natural and man induced turbidity

events will be described statistically leading to a comparison that will establish the

relative impact of the renourishment. The definition of storm events in this context is

based on elevations in turbidity above a site specific criterion equal to the sum of the

mean plus one standard deviation of the overall data (from Tables 3.5 and 3.6). The

storm criteria for each site as well as statistics describing the magnitude and duration

for the worst case storm event, where the worst case storm is defined as the turbidity

event with the highest magnitude and the longest duration (turbidity storm), are listed

in Table 3.16 and plotted along with wave height in Figure 3.17.

Exposure is defined as the area under the storm curve (NTU versus time) and is

calculated using the trapeziodal approximation to the integral (the mfile TRAPZ.M),

with units of NTU-Days.

For the prenourishment and postnourishment cases, storms exhibited a higher

mean (and exposure) at the lower elevations with approximately equal duration at both

elevations. Although the storm intensity decreased from pre- to postnourishment for

site 1, it increased considerably for site 2. The intensity of the maximum storm during

the nourishment was much higher than the maximum prior to the nourishment at site

two, particularly for the lower sensor. This extreme event was due in part to an

extreme wave event at the beginning of the dredging activities. These high waves

transported and resuspended the freshly dredged sediment (most likely containing a

I

higher percentage of fines). Instruments were not deployed at either site 1 or the

upper elevation at site 2 during this event. The intensity of the maximum storm at the

outer site decreased from the prenourishment maximum. Again this is a result of

variations in the wave height, and the proximity of the inner site to the dredge

discharge.

Table 3.16: Highest of all the storm events for pre-nourishment and nourishment.
PRENOURISHMENT
SITE AND CRIERIA MEAN TIME MAX. MIN. EXPOSURE
LOCATION (NrU) (NIU) (DAYS) (NTU) (NTU) (NTUdays)
TURBIDITY SITE1 13.60 36.10 1.83 78.50 27.40 89.90
LOWER ELEVATION
TURBIDITY SITE 1 8.84 15.98 2.17 19.02 14.40 47.30
UPPER ELEVATION
TURBIDIY SITE 2 31.63 127.32 1.17 222.35 65.68 201.38
LOWER ELEVATION
TURBIDITY SITE 2 24.68 42.80 1.17 55.86 30.27 42.18
UPPER ELEVATION
NOURISHMENT
SITE AND CRrTERIA MEAN TIME MAX. MIN. EXPOSURE
LOCATION (NTU) (NIU) (DAYS) (NTU) (NTU) (NTU day)
TURBIDITY SITE 13.60 16.34 0.02 107.20 2.52 0.34
LOWER ELEVATION
TURBIDITY SITE 1 8.84 10.80 0.33 11.82 9.79 1.84
UPPER ELEVATION
TURBIDITY SITE 2 31.63 151.88 3.33 259.38 42.19 498.89
LOWER ELEVATION
TURBIDITY SITE 2 24.68 36.13 1.33 52.61 26.04 47.56
UPPER ELEVATION
POSTNOURISHMENT
SITE AND CRITERIA MEAN TIME MAX. MIN. EXPOSURE(N
LOCATION (NTU) f(N ) (DAYS) (NfTU) (NI) TU day)
TURBIDITY SITE 1 13.60 18.47 0.83 22.65 15.21 12.24
LOWER ELEVATION
TURBIDITY SITE 1 8.84 15.49 1.00 21.34 10.61 12.68
UPPER ELEVATION
TURBIDITY SITE 31.63 63.04 2.83 82.24 52.25 252.30
LOWER ELEVATION
TURBIDITY SITE 2 24.68 35.05 2.83 42.52 29.96 140.49
UPPER ELEVATION

The importance of this section is the storm exposure data prior to the

nourishment, considered to be the natural exposure to turbidity. Although the

duration of these storms is less than 3 days, their magnitudes in most cases are much

greater than the State of Florida's limits of 29 NTU.

The importance of this section is the storm exposure data prior to the

nourishment, considered to be the natural exposure to turbidity. Although the

duration of these storms is less than 3 days, their magnitudes in most cases are much

greater than the State of Florida's limits of 29 NTU.

58

PRENOURISHMENT
50 site 1 tloer eleviono 20 site 1 upper elevolon
300 ,site 2 lower evtion 60 site 2 upper elevolion

200 0 40 60 0 30

30 16

0 20 40 60 0 50 00 10 0 10 2 30
0 10 20 30 0 10 20 30
hours hours
hours hours

2 04 2 1.5-

1E' 0 3 l I
0.2

0.5 0.1 01 .5
0 20 40 60 0 50 0 10 20 30 0 10 20 30
hour hours hours hours

NOURISHMENT
300 site 2 lower elevoon 60 site 2 upper elevolon
site lower elev lion 12 site I upper elev io n 60 2

I1
2000

0010
0 --- --- --- --- 20 --- --- --
16- 9 0 20 40 60 80 0 10 20 30 40
0 1 0 1 2 3 4
hours hours
hours hours

E 0 E
-005 0.15

0.05 0.168 0.5 0.1
0 1 0 1 2 3 4 0 20 40 60 80 0 10 20 30 40
hours hours hours hours

POSTNOURISHMENT
25 site 1 lower elevotion 25 site upper elvotion 0 sae 2 loer elevo;on 0 site 2 upper elevolon

20
z 20 t 40
15 15
15 10 50 30
0 5 10 15 20 0 5 10 15 20 0 20 40 60 80 0 20 40 60 80
hours hours hours hours

1.5 0.6 2 2

0.5 1.5

0.5 0.2 0.5 0.5
0 5 10 15 20 0 5 10 15 20 0 20 40 60 80 0 20 40 60 80
hours hours hours hours

Figure 3.17 Plots of the highest intensity storms, prior to, during, and after the nourishment.

Further discussions regarding the implications of the results of this chapter and

suggestions for further investigation and for changes in some procedures for future

investigators follow in the succeeding chapter.

CHAPTER IV

SUMMARY AND CONCLUSIONS

4.1 Summary

Turbidity was described as a measure of the clarity of water due to the

scattering and absorption of light by suspended particles. The particles suspended in

the water column were described as being either composed of fine materials (such as

silt/clay) with high residence times, or intermittently suspended sand grain sized

materials near the seabed. The combination of fines and sand grain sized materials

near the seabed were noted as having the ability to greatly elevate the magnitude of

turbidity. Turbidity was believed to adversely affect the marine environment by

reducing light penetration through the water column which is vital to photosynthetic

organisms and then subsequently suffocating benthic organisms by the sedimentation

of the suspended particles. Elevations in turbidity were attributed to both natural

processes and human activities. Wave action during storm events was cited as an

example of a naturally induced increase in the magnitude of turbidity, while human

elevations in turbidity are attributed to beach restoration activities. In an effort to

protect the environment the State of Florida has set restrictions of 29 NTU above the

background level which can not be exceeded during dredging activities. It was noted

that marine biologists cite a lack of biological rational for this limit and suggest setting

limits based on natural fluctuations in turbidity occurring during storm conditions.

Unfortunately, there has been little or no data collected during natural storm

conditions.

The objective of this study was to measure natural fluctuations in turbidity

under a variety of wave and weather conditions near the seabed and compare those

measurements to fluctuations during a beach nourishment project. The objective was

achieved through in-situ turbidity measurements as well as wave and current

measurements at two nearshore sites prior to, during, and after a beach nourishment.

Turbidity was collected using optical backscatterance sensors (OBS), which

measure infrared light scattered at angles between 140 and 165 degrees. Wave

amplitude and tidal pressure fluctuations were recorded using a strain gauge type

pressure transducer, while wave direction and currents were recorded using a dual axis

electromagnetic current meter. The data storage and sampling scheme was controlled

by the data logger. The two shore normal sites were located in 5 meters (inner) and

10 meters (outer) of water off Hollywood Beach. The instrument mounting was

configured similar to a goal post to reduce turbidity induced by scour. The OBS were

mounted at elevations either between 0 and 0.5 meters above the sea bed or 0.5 and

0.85 meters above the seabed. Sampling was based on burst measurements, recorded

every 4 hours for 30 minutes at a frequency of 4 hertz. Site visits were scheduled bi-

weekly for cleaning (necessitated by biofouling of the OBS), and monthly for

instrument deployment, data recovery, and servicing of the instruments. During site

visits measurements of turbidity were made in the vicinity of the OBS using a portable

turbidimeter. These observations were used to resolve discrepancies in the offset

during application of the calibration and typically varied from 0.5 NTU to 3 NTU.

Data analysis consisted of application of calibration curves, statistical

summaries of the observations, spectral estimates of the wave climate, and a quality

analysis. Although calibrating the OBS data included a comparison with field

observations and adjustment to compensate for any variations in the offset, calibrating

data from the other instruments was straight forward. Estimates of the wave climate

were achieved through spectral analysis. Quality analysis was implemented in an effort

to remove bad data by ranking data as "good data," "data of reduced accuracy," or

"bad data." Reduction in data quality was the result of either instrument failures or

I

biofouling. Quality analysis was achieved through observations of the calibrated time

history plots and the monthly summary plots. OBS sensors are extremely sensitive to

biofouling and although they were cleaned regularly and an optical grade anti-foulant

was applied to the lens, a large amount of data was tagged as "bad data" as a result of

biofouling. Only data tagged as "good data" or "data of reduced accuracy" are used in

the analysis.

The results of the in-situ measurements are more than 2 years of both turbidity

and wave climate observations collected under a variety of wave and weather

conditions at each location. Included in the data set are observations of

meteorological parameters collected by other organizations covering the same time

period. Limited time series plots and monthly summary plots were presented to show

variations in turbidity with the other parameters on a 4 hertz and 4 hour resolution

respectively. The complete listings of the statistical summary plots are presented in

Dompe and Hanes (1992, 1993). Tables of the statistical summaries of the entire

month of observations are presented to show seasonal trends in turbidity and the other

parameters. Tables of the statistics divided into sections relative to the nourishment

were presented. Dividing the data in this manner revealed variations in the parameters

such as wave height, indicating any variations in the natural conditions relative to the

nourishment.

Although correlation analysis of the prenourishment data (perceived to be the

natural conditions) revealed a significant correlation between wave height and

turbidity, there was little (or no) correlation between turbidity and the other

parameters. Scatter plots of wave height versus turbidity indicated a wave height

threshold exists below which turbidity is unaffected by wave height. The mean of the

data below the threshold was referred to as the background turbidity, while the

turbidity fluctuations above the threshold were considered to be wave induced. A

relationship was developed where the turbidity is a constant equal to the background

I

turbidity below the threshold and then increases linearly above the threshold. The

correlation coefficients for the linear relationships above the threshold were as high as

0.804 and as low as 0.505. These correlation coefficients indicate some portion of the

variability in turbidity above the threshold remains unaccounted for. The slope of the

line describing the relationship between wave height and turbidity was considered to

be the sensitivity of turbidity to wave height and varied between elevations and sites.

The previously discussed analysis was applied to the data collected during and

after the nourishment. A significant relationship between wave height and turbidity

during the nourishment could only be established at the 5 meter site the lower OBS

elevation. Significant relationships between wave height and turbidity after the

nourishment were established at both elevations and sites. Comparisons of the

nourishment and postnourishment results with the prenourishment results revealed

some variations in the parameters that describe the relationships. During the

nourishment the sensitivity of turbidity to wave height increased at the 5 meter (inner)

site. This was believed to be the result of a larger amount of easily erodable material

being present during dredging activities. Comparisons of the background turbidity

revealed an increase in the background turbidity at the inner site during the

nourishment of approximately 6 and 15 NTU at the upper and lower elevations

respectively. Postnourishment background turbidity increased only at the upper

elevation the inner site and only by less 2 NTU.

The magnitude and duration of the highest turbidity storm for each site and

OBS elevation were statistically described, where turbidity storms were defined as an

elevation in the magnitude of turbidity above a criterion equal to the mean plus 1

standard deviation. The area under the turbidity versus time curve was calculated

using the trapezoidal approximation and defined as the exposure. The prenourishment

storms indicate the natural turbidity conditions the marine environment may be

exposed to. Comparisons of prenourishment turbidity storms to those occurring

during and after the nourishment were made. These comparisons revealed increases in

turbidity occurred only at the inner site during the nourishment and no increases at the

outer site.

4.2 General Conclusions

This study has just begun to quantify the influence on turbidity imposed by

both human and natural events. We found that turbidity is naturally a highly variable

quantity, with maximum values which sometimes exceed 29 NTU during storm events.

Large waves correlated with high turbidity near the seabed, but this correlation only

explained a portion of the variability in turbidity. Comparisons of the prenourishment

measurements to measurements during and after the project were used to asses the

relative impact of the beach nourishment. The impact of the nourishment is difficult to

asses because the conditions such as waves, differed before during and after the

nourishment. The relationship between wave height and turbidity developed during

this study explained only a part of the forces responsible for the natural fluctuations in

turbidity. Therefore continued in-situ measurements are needed to determine the

unknown factors responsible for the natural fluctuations in turbidity and to establish a

relationship between turbidity and those factors.

The results of this study may not describe the conditions at all locations along

the southeast coast of Florida. For example, depending on swell direction wave

height can vary in this area as a result of the shadowing effect of the Bahamas Islands.

Sediment distribution specifically, the fine fraction constituent naturally occurring in a

particular area can have a great influence on the natural variations in turbidity.

Finally, based upon the experiences and results of this study, recommendations

can be made for future studies.

Video monitoring should be included in future projects for estimating the

spatial patterns of turbidity plumes at the water surface. Intensive sampling of the

water surface of selected turbidity plumes could result in calibrations of such images.

Strategic placement of several video systems along the length of the nourishment and

adjacent to the project could resolve the extent of the nourishment's influence. For

example, in Figure 4.1 produced from the video images recorded during this project,

plumes can be observed migrating from the hopper dredge (most likely a byproduct of

the overflow discussed in Chapter 1). Although these plumes were recorded using a

single camera, successful documentation of the extent of their reach may require

multiple units to gain the necessary field of view.

size and concentration measurements. Measurements of the size distribution of the
:' r

sediments responsible for the turbidity would reveal the mechanics involved in their

resuspension. These measurements combined with periodic measurements of the

ripple geometry could be used to relate turbidity observations with theoretical

suspension models. This could lead to a numerical model for predicting turbidity

based on wave climate, sediment size distribution and seabed roughness.

Biofouling in future experiments may be better managed using an antifoulant

hood manufactured by Oceanographic Industries. The hood is impregnated with

tributal and attaches directly to the OBS. According to Oceanographic Industries the

hood retards biofouling for up to three months.

Control sites are important in monitoring fluctuations outside the influence of

the nourishment for comparative purposes. This however would increase the number

of packages, resulting in increased cost.

Time and money could be saved in the future by recording PUV data at only 1

location. This would reduce equipment costs specifically by reducing the number of

PUV instruments, using less expensive smaller data loggers for the OBS only

packages, all of which result in lower battery requirements. The investigator's time

could be saved through reductions in the amount of data analysis, equipment

maintenance and construction, calibrations, and field work.

However more time should be invested by the investigator at the study site

particularly during the nourishment. Emphasis should be placed on instrument

cleaning (depending on the successes of the antifoulant hood), obtaining periodic

independent turbidity samples near the in-situ instruments, and samples of turbidity

associated with the turbidity plumes visible to the video monitoring system. Regular

cleaning (particularly during the summer) and periodic turbidity samples would

decrease the amount of data lost to biofouling. Samples at the water surface, mid

depth, and, near the bottom of the turbidity plumes along with the video images would

reveal the structure of such plumes. This would be useful in understanding the extent

of their influence both horizontally and vertically in the water column.

I

66

In general more attention should be placed on combining the technology

developed during this study along with more intensive field work to understand the

mechanics behind turbidity with respect to migration patterns, variations with other

parameters and details of the influence of waves.

APPENDIX
PROGRAM LISTINGS

DATLOG.BAS
' Written Oct. 3 1989 by S.L. Schofield'
t #
' Modification history'
'Oct. 30 1989 by S.L.S. added code for auxilary power supply'
control on Pin(5) '
SMoved time/date write of data run to just before run'
SI
' Nov. 08 1989 by S.L.S. put auxiliary 1.8432 Mhz clock on pin 11'
' Nov. 11 1989 by S.L.S. moved analog off to pin 2'
'Dec. 08 1989 by S.L.S. modified disk offload routine.'
'Mar. 27 1989 by S.L.S. changed offload algorithm'

' Dan Hanes data logging program for the'
' Tattletale Model VI. Sampling from 8'
' channels at a 4 Hz. rate every four hours.'
' three minute instrument warm up at beginning'
'of data burst. Then approx 30 minutes of data.'
'control structure modeled after Fernla program'
'communication has ultimate control of system'
Sdio pin (0) indicates presence of terminal'
'dio pin (1) indicates warm up in progress / and analog power on'
'dio pin (2) turn off analog power needs to be strobed'
'dio pin (3) turn on analog power needs to be strobed'
'dio pin (5) turn on/off auxilary power supply'

'initially no warmup and no data taking'
100 PCLR 1,2,3,5:SLEEPO:PSET 2:SLEEP 50:PCLR 2
' initialize variables to known state'
110 I=0:X=32:D=0:N=7166:Y=1:C=8:F=25:L=1
' date/time defaults to 1/1/90 00:00:03
115 ?(5)=90:?(4)=1:?(3)=1:?(2)=0:?(1)=0:?(0)=3
118 STIME
120 SLEEP 0
'wait for terminal or data taking'
130 SLEEP 100
140 IF PIN(0)=1 GOTO 300
145 IF L=0 GOTO 130
150 IF I=1 GOTO 1000
' CHECK FOR WARM UP /DATA TAKING TIME'
160 GOTO 700
' TERMINAL HANDLING ROUTINES'
300 PRINT "DATLOG VI.10 >";:V=?:IF PIN(0)=0 GOTO 120
' ALPHA TEXT INTO FIRST 20 LOCATIONS OF DATA BUFFER'

310 W=0:ITEXT W,1000:IF W<>0 GOTO 330
' IF NO TERMINAL INPUT FOR 2 MINS. THEN BACK TO DATA MODE'
315 IF ?-V>12000 GOTO 120
'WAIT FOR MORE'
320 GOTO 310
'ALPHA TEXT RECEIVED SO GRAB IT AND MAKE ALL UPPERCASE'
330 W=0:A=GET(W,#1):IF A>91 A=A-32
331 W=0:STORE W,#1,A:Z=13:STORE W,#1,Z:W=0:OTEXT W
' THEN CLEAR OUT BUFFER'
335 W=0:ITEXT W,0:IF W<>0 GOTO 335
' COMMAND PARSER'
it
'T TIME'
340 IF A=84 GOTO 400
'D DATE'
341 IF A=68 GOTO 400
'O OFFLOAD DISK FILES TO HOST'
345 IF A=79 GOTO 450
'R RESET DISK FILE POINTER'
350 IF A=82 GOTO 500
'I INITIALIZE DATA FILE POINTER'
360 IF A=73 GOTO 550
'E EXIT MONITOR TO DATA WAIT LOOP'
370 IF A=69 GOTO 600
'S SCAN CHANNELS FOR TESTING'
380 IF A=83 GOTO 650
'P STORE/OFFLOAD PRESENT DATA FILE'
385 IF A=80 GOTO 670
'X EXIT TO MONITOR DEBUG USE ONLY'
390 IF A=88 STOP
I I
' display/set time/date'
391 PRINT:GOTO 300
400 RTIME:PRINT ?(2),":",#02,?(1),":",#02,?(0)," ";
405 PRINT ?(4),"-",?(3),"-",?(5)
406 H=?(5):IF A=84 GOTO 430
410 INPUT "YEAR "H:H=H%100:IF H=0 GOTO 300
420 INPUT "MONTH "?(4):INPUT "DAY "?(3)
430 INPUT "HOUR "?(2):INPUT "MINUTE "?(1):INPUT "SECONDS "?(0)
440 ?(5)=H:STIME:GOTO 300
' offload disk files'
450 PRINT OFFLOAD DISK FILES ":INPUT "STARTING DISK FILE "A
' 0 FOR BEGINNING FILE INDICATES TO OFFLOAD ENTIRE DISK'
455 T=I:U=Y-l:IF A=0 GOTO 470
' GET ENDING DISK FILE. CAN NOT BE HIGHER THAN LAST FILE ON DISK'
460 T=A:INPUT "ENDING DISK FILE "A:IF A ' ENDING FILE MUST BE >= BEGINNING FILE'
465 IF T>U U=T
' GO AHEAD AND START THE OFFLOAD'
470 FOR E=T TO U
471 PRINT "FILE",#02,E,".DAT"
472 INPUT A:IF A<>I GOTO 471
473 DFREAD E,Z:OFFLD 32,229375
477 NEXT E
I

485 PRINT "FILEOO.DAT":GOTO 300
' initialize disk file pointer'
500 PRINT' ARE YOU SURE YOU WANT TO INITIALIZE THE DISK POINTER [1/0]'
501 INPUT E:IF E=0 GOTO 300
502 INPUT' VALUE TO INIT TO'Y
503 IF Y=0 Y=1
504 PRINT' DISK FILE POINTER BEING INITIALIZED TO ',Y,' ARE YOU SURE?'
505 INPUT E:IF E<>1 GOTO 502
507 PRINT'DISK POINTER INITIALIZED ':PRINT:GOTO 300
' initialize data file pointer'
550 X=32
570 PRINT'DATA FILE INITIALIZED ':PRINT:GOTO 300
' return to data wait mode'
600 SLEEP 0:PRINT'RETURNING TO DATA MODE:'
610 PRINT'DISCONNECT DATA CONNECTION PLEASE:'
620 SLEEP 6000:PRINT:GOTO 120
' sample from A/D and output to terminal'
' q= number of samples, s=starting channel, rending channel'
650 INPUT NUMBER OF SAMPLES "Q:INPUT" BEGINNING CHANNEL "S
' MUST TAKE AT LEAST 1 SAMPLE'
651 IF Q<1 Q=I
'TURN ON ANALOG POWER. LOWEST CHANNEL IS O'
652 SLEEP 0:PCLR 2:PSET 1,3,5:SLEEP 50:PCLR 3:IF S<0 S=0
'HIGHEST CHANNEL IS 8'
653 IF S>8 S=8
'HIGHEST ENDING CHANNEL IS 8'
655 INPUT" ENDING CHANNEL "R:IF R>8 R=8
'ENDING CHANNEL MUST BE >= STARTING CHANNEL'
656 IF R ' START THE TESTING'
657 FOR E=l TO Q
'voltage on each channel is (5000*/(16*4096))*Conversion'
'OUTPUT THE RESULTS'
658 FOR G=S TO R
659 A=CHAN(G)+CHAN(G)+CHAN(G)+CHAN(G)+CHAN(G)
660 A=(1000*A)/(16*4096):PRINT A/1000,'.',#03,A%1000,'';
661 NEXT G
' DELAY A LITTLE AND RESAMPLE'
665 SLEEP 0:SLEEP 75:PRINT:NEXT E
' TURN OFF THE ANALOG POWER AND RETURN'
667 SLEEP 0:PSET 2:SLEEP 50:PCLR 1,2,5:GOTO 300
' store or offload the present data file'
670 PRINT TO OFFLOAD DATA FILE [1] ":PRINT" TO PUT DATA FILE TO DISK [2] "
675 INPUT A:IF A=l GOTO 695
OFFLOADINGG DISK'
680 IF L<>0 GOTO 690
' IF DISK FULL CAN NOT OFFLOAD TO DISK'
685 PRINT "DISK FILE IS FULL":GOTO 300
'DO THE OFFLOAD'
690 A=X:GOSUB 1500
' RETURN TO TERMINAL HANDLER'
692 X=A:GOTO 300
' OFFLOADING FROM DATA FILE'
695 OFFLD 32,X:GOTO 300

' check for data taking time'
700 IF D>0 GOTO 750
705 RTIME:IF ?(0)%10=0 PRINT ?(2),':',#02,?(1),':',?(0)
706 IF ?(0)>=2 GOTO 130
707 E=3:IF PIN(7)=1 GOTO 710
708 PRINT' External Power not connected can not start data run!':GOTO 130
' if not at top of hour not time for data'
710 IF ?(1)<>0 GOTO 130
' if not divisible by four not time for data'
715 IF ?(2)%4<>0 GOTO 130
' turn on analog power, no data taking yet, three minute warm up'
720 PCLR 2,5:PSET 1,3:SLEEP 50:PCLR 3:D=180
' Send out sync bytes'
721 E=E-1:IF E=0 GOTO 724
722 IF PIN(6)<>1 GOTO 720
724 STORE X,#2,43605:STORE X,#2,43605
' then send out number of samples,sampling rate,number of channels'
725 STORE X,#4,N*256+C:STORE X,#2,F
' and wait for data time'
745 GOTO 130
' count the seconds for wait'
750 D=D-1
'TURN ON AUX POWER 30 SECONDS BEFORE DATA TIME'
' if time up turn on data in progress bit'
760 IF D>0 GOTO 130
765 I=1:GOTO 130
' SET UP THE PACER FOR 250 MILLISECOND WAITS'
' WRITE TIME/DATE OF DATA RUN'
' then store the present time and date'
1000 RTIME
1010 FOR E=5 TO 0 STEP -1
1020 STORE X,#1,?(E)
1025 NEXT E
1027 PRINT' Starting data run at ',?(2),':',#02,?(1),':',?(0);
1028 PRINT' on ',?(4),'/',7(3),'/',?(5)
' CHECK FOR TERMINAL CONNECTED'
1030 SLEEP 0
1031 FOR E=N TO 1 STEP -1
1032 IF PIN(0)=1 GOTO 300
' WAIT FOR THE PACE'
1035 SLEEP F:BURST X,C,2
' TURN OFF AUX POWER 10 SECONDS AFTER BURST STARTS'
' TURN ON AUX POWER 40 SECONDS BEFORE END'
1048 NEXT E
'DONE SAMPLING TURN OFF POWER AND DATA FLAG'
1050 PCLR 3:PSET 2:SLEEP 0:SLEEP 50:PCLR 1,2,3,5:1=0
1051 PRINT' Data run finished'
1060 IF X+2*N*C+16<=229376 GOTO 120
1070 GOSUB 1500
1080 GOTO 120
' CLEAR UNUSED DATAFILE LOCATIONS'
1500 IF X>229375 GOTO 1550
' CLEAR IN GROUPS OF 4 BYTES AS LONG AS POSSIBLE'
1510 IF 229375-X<8 GOTO 1540

71

1520 FOR B=X TO 229375-8 STEP 4
1530 STORE X,#4,0:NEXT B
' CLEAR THE REMAINING BYTES ONE AT A TIME'
1540 FOR B=X TO 229375
1545 STORE X,#1,0:NEXT B
' CLEAR THE ALPHA STRING LOCATIONS BEFORE STORING'
1550 X=0
1555 FOR B=1 TO 8
1560 STORE X,#4,0:NEXT B
' SAVE THE DATA FILE'
1570 DFSAVE Y,Z
'UPDATE THE DISK FILE POINTER IF OVER 93 THEN OUT OF DISK SPACE'
'RESET THE DATA FILE POINTER'
1580 X=32:Y=Y+1:IF Y<94 RETURN
' Disable logging if no more disk storage left'
1590 L=0:RETURN

/00/o%%%%%%/o%%%%%%% SPEC2.M /%%%%%%%%%%%%//O/0 /O/O/
%% A PROGRAM TO COMPUTE THE DIRECTIONAL SPECTRUM FROM THE
%% PRESSURE prees, VELOCITIES uvel and vel. (PUV ANALYSIS)
/%% THIS PROGRAM USES A FORTRAN PROGRAM (wtok.for)
%%/ TO CALCULATE THE WAVE NUMBER TO WAVE FREQUENCY RELATION BASED
%% ON LINEAR DISPERSION. AVERAGE VALUE OF WATER DEPTH IS TAKEN
%% FROM THE VALUES COMPUTED EARLIER AND STORED IN AVE.DAT.
%% THIS PROGRAM ALSO CALLS fit.m which fits a JONSWOP SPECTRAL
%% SHAPE TO THE UNIDIRECTIONAL WAVE SPECTRUM BASED ON PRESSURE
%%/ TIME HISTORY. AREA UNDER THE UNIDIRECTIONAL SPECTRUM GIVES
%% VARIENCE AND THEREFORE THE SIGNIFICANT WAVE HEIGHT.
%% DIRECTIONAL SPECTRUM IS COMPUTED BASED ON LONGUET HIGGINS
%% METHOD. PEAK DIRECTION AND WAVE PERIOD ARE OBTAINED FROM THE
%% PEAK OF THE F(W, THETA), THE DIRECTIONAL SPERTRUM SURFACE.

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%THE
EXECULABLE EXTERNAL FORTRAN PROGRAM WHICH COMPUTES WAVE
%% NUMBERS CORRESPONDING TO EVENLY SPACED WAVE FREQUENCIES IS
%%/EXECUTED.
%% AND THE RESULTING FILE WK.DAT IS LOADED.
%% h = WATER DEPTH M.
%% zp = PRESSURE SENSOR HEIGHT FROM THE BED
%% zc = CURRENTMETER HEIGHT FROM THE BED
%%nn= n= 1024
%%
!wtok
load wk.dat;
h=mp+hp
zp=hp-h
zc=hc-h
n=nn
%%
%% w = WAVE ANGULAR FREQUENCY
%% k = WAVE NUMBER
%%
w=wk(l:n/2,1);
k=wk(l:n/2,2);
g=9.81;
pi=3.14159
dw-2*pi/((n-l)*dt)
t=linspace(0,(n-l)*dt,n);
t-t';
%%
%% kp = PRESSURE RESPONSE FUNCTION
%% kk = CURRENT RESPONSE FUNCTION
kp=cosh(k*(h+zp))./cosh(k*h);
kk=cosh(k*(h+zc))./cosh(k*h);
kk=(kk.*k*g)./w;

%%%%%%%%%%%%%%%%%%%
%% SPECTRAL ANALYSIS BEGINS. MATLAB FILE SPECTRUM.M IS MADE USE
%% (REFER TO MANUAL FOR DETAILS). SPECTRUM OF THE ENTIRE TIME SERIES OF 7166
%% POINTS IS COMPUTED 1024 POINTS AT A TIME AND AVERAGED WITH AN OVERLAP
%%/ OF 512 POINTS.

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%
nlap=512
pp=spectrum(press,uvel, 1024,nlap);
phipp=pp(:,1);
%%
%% FIX THE UPPER CUT OFF. **** EDIT THIS LINE *****
%% (*) LOOK AT kp ARRAY after a test run with hcut=arbit value = 85
%% FOR THAT VALUE OF I AT WHICH 1/kp(I) EXEEDS 25, I = hcut
%%
%****************************************************
inkp= ones(n/2,1)./kp;
hcut=1;
diff=25-inkp(hcut);
while diff >= 0;
hcut=hcut+1;
diff= 25-inkp(hcut);
end
hcut
%
%% BASED ON DR.HANES SUGGESTION WAVES WITH PERIOD HIGHER THAN 20 S ARE
%% FILTERED OUT ALSO.
%% LOWER CUTOFF lcut=dt*n/(cutoff period)
lcut=round(n*dt/20)+l
%%
%% FILTER WHICH FORCES AMPLITUDE TO ZERO BEYOND THE high
%% CUTOFF FREQUENCY below the lower cutoff frequency TO 0.
/0%%
filt=[ones(lcut, 1)*1.e-8
ones(hcut-lcut, 1)
zeros(n-hcut-hcut+l,1)
ones(hcut-lcut, 1)
ones(lcut-1,1)*l.e-8
];
%%
%% IF NECESSARY FOLLOWING LINE COULD BE UNCOMMENTED FOR GENERATING
%% FREE SURFACE ELEVATION ETA
%%
%pressw=fft(press);
%pressw=pressw. *filt;
%eta=ifft(pressw./kp);
%pressw((n/2+l):n)=[ ];
filt((n/2+l):n)=[ 1;
%%
phippl=phipp.*filt;
sigp=sqrt(sum(phippl));
%%
%% sigp = PRESSURE STANDARD DEVIATION.
%% SIG = FREE SURFACE ELEVATION STANDARD DEVIATION.

phippl=phipp 1./(kp.*kp);
%%
%% fit.m FILE IS CALLED TO FIT JONSWOP SPECTRAL SHAPE TO PHIPPI
%%

fit
%%%
sig=sqrt(sum(phippl));
%%
%% USING THE M.FILE SPECTRUM.M POWER AND CROSS SPECTRA OF PUV COMPONENTS
%% ARE CALCULATED AND ARE NAMED phi**
0%%0
phiuu=pp(:,2);
phiup=pp(:,3);
conf=[pp(4) pp(5) pp(6)]
pp=spectrum(press,vvel,1024,nlap);
phivp=pp(:,3);
phivw=pp(:,2);
pp=spectrum(uvel,vel, 1024,nlap);
phiuv=pp(:,3);
% 0 00 00 0000 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 00000 000000000
%%%%%%%%%%%%%%%%%%%%%%%
%% HIGH FREQUENCY NOISE IS FILTERD OUT BY MULTIPLICATION WITH filt
p%%
phipp=phipp.*filt;
phiuu=phiuu.*filt;
phivw=phiv. *filt;
phiup=real(phiup).*filt;
phivp=real(phivp). *filt;
phiuv=real(phiuv).*filt;
%%
%% THE FOLLOWING SUBPLOTS WILL ALLOW A LOOK AT THE SPECTRA IF NECESSARY
%%
%subplot(221),plot(w,phipp)
%title('pressure-spec')
%xlabel('w /s')
%ylabel('phipp m.m.sec')
%meta ggl
%pause
%clg
%subplot(221),plot(w,phiuu)
%title('Velocity U -spec')
%xlabel('w /s')
%ylabel('phiuu m.m./s')
%%
%subplot(222),plot(w,phivv)
%title('Velocity V spec')
%xlabel('w /s')
%ylabel('phivv m.m./s')
%%
%subplot(223),plot(w,phiuv)
%title('U-V cross-spec')
%xlabel('w /s')
%ylabel('phiuv m.m./s')
%% i
%subplot(224),plot(w,phiup)
%title('U-P cross-spec')
%xlabel('w /s')
%ylabel('phiup m.m')

%meta gg2
%pause
clg
/%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%
%%%%%%%%%%%%%
%%
%% THE FOURIER COEFFICIENTS (ONLY THE 1ST 5 TERMS OF THE SERIES)
%% ARE COMPUTED (LONGUET HIGGINS)
%%
a0=phipp./(2*pi*kp.*kp);
al=phiup./(pi*kk.*kp);
a2=2*phiuu./(pi*kk.*kk)-phipp./(pi*kp.*kp);
% BOTH ARE SAME a2=phipp./(pi*kp.*kp)-2*phiw./(pi*kp.*kp);
bl=phivp./(kk.*kp.*pi);
b2=2.*phiuv./(kk.*kk*pi);
/%%
%% PLOTS OF THE COEFFICIANTS PUT ON SCREEN
%%
subplot(221),plot(w,a0)
title('AO')
xlabel('w /s')
%meta gl
%pause
clg
subplot(221),plot(w,al)
title('Al')
xlabel('w /s')
subplot(222),plot(w,a2)
title('A2')
xlabel('w /s')
subplot(223),plot(w,bl)
title('bl')
xlabel('w /s')
subplot(224),plot(w,b2)
title('b2')
xlabel('w /s')
%meta g2
%pause
clg
%%
%% THE SIZE OF THE ARRAY IS REDUCED BY USING THE DECIMATE
%% COMMAND. OPPOSITE OF INTERPOLATION, ARRAY SIZE FROM 512
%% IS REDUCED TO 86 FOR AO,A1,A2,B2,B1,W.
%%
ml=2
a0=decimate(a0,ml);
al=decimate(al,ml);
a2=decimate(a2,ml);
bl=decimate(bl,ml);
b2=decimate(b2,ml);
w=decimate(w,ml);
phippl=decimate(phippl,ml);
a0(86:n/ml)=[ ];
al(86:n/ml)=[ ];

a2(86:n/ml)=[ ];
bl(86:n/ml)=[ ];
b2(86:n/ml)=[ ];
w(86:n/ml)=[ ];
save w.dat w
phippl(86:n/ml)=[ ];
theta=linspace(-pi/2,pi/2,85);
%%%L.HIGGINS original computation
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%f=al*cos(theta)+bl*sin(theta)+a2*cos(2*theta)+b2*sin(2*theta);
%f=f+aO*ones(theta);
%f=f/dw;
%%
%%/o%%%%%%% weighted average
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%% THE FORMULA FOR COMPUTING DIRECTIONAL SPECTRUM BELOW ALSO GIVEN
%% BY L.HIGGINS IS PREFFERED. IT GIVES A WEIGHTED AVERAGE WHICH
%% AVOIDS NEGATIVE VALUES IN THE POWER SPECTRUM.
f=2/3*(al*cos(theta)+bl*sin(theta))+l/6*(a2*cos(2*theta)+b2*sin(2*theta));
f=f+a0*ones(theta);
%%
/%% DIVISION BY DW BRIGS THE SPECTRUM IN THE STANDARD FORM SUCH THAT
%% INTEGRAL WITH RESPECT TO THETA AND W WHICH GIVES THE AREA UNDER THE
CURVE
%% IS THE VARIENCE OF THE FREE SURFACE ELEVATION.
%% THIS F IS THE REQUIRED DIRECTIONAL SPECTRAL SURFACE.
%%
f-f/dw;
%%%%%%%%%%%%%%%%%o%%%%%%%%%%%%%%%%%%%%%%%%%%%%/%%%%%%
%%%%%%%%%%%
%mesh(f)
theta=theta';
%%
%% PEAK OF THE SPECTRAL SURFACE GIVES THE LOCATION OF PEAK WAVE DIRECTION
AND
%% PEAK WAVE FREQUENCY AND THEREFORE THE PERIOD.
[y,i]=max(f);
[mj]=max(y);
v=[0.95*m 0.8*m 0.7*m 0.6*m 0.5*m 0.4*m 0.3*m 0.2*m 0.1*m]';
duml=i(j)
dum2=j
spectrum_peak=f(duml,dum2)
peakfrequency-w(dum1)
Tp=2*pi/peak_frequency
peak_direction=theta(dum2)
%%
%% THE FOLLOWING CHANGE IN PEAK DIRECTION, AND theta IS DONE TO INDICATE THE
%% DIRECTION THAT WAVES ARE COMING IN.
%%
peak_direction=(theta(dum2))*360./(2*pi);
peak_direction=90.-peakdirection
theta=(theta)*360./(2*pi);
theta=90.-theta;
%%

Hmo=4*sig
Hrms=2.*(sqrt(2))*sig
%%pause
f=rot90(f);
%%
%% CONTOUR PLOT OF THE DIRECTIONAL SPECTRUM
%%
axis([0 3. 0. 180]);
contour(f,v,w,theta)
axis;
title('Directional Wave Power Spectrum')
xlabel('Frequency w /s.')
ylabel('Angle (theta deg.)')
%meta g3
%pause
%/clg
0%%%%%%%%%%%%%%%%%%%%%%%%/%%%%%%/%%%%% %%%%
%% spec2.m IS THE LAST PROGRAM THAT IS CALLED BY THE MAIN PROGRAM
%% datain.m. THEREFORE AT THIS STAGE ALL THE COMPUTED VALUES SO FAR
%% THAT ARE PRESENT IN MATLAB MEMORY ARE PUT INTO A COMMON ARRAY
%% CALLED mom (for momentss.

mom(nrun, l)=Hmo;
mom(nrun,2)=Hrms;
mom(nrun,3)=Tp;
mom(nrun,4)=peakdirection;
mom(nrun,5)=CC(1);
mom(nrun,6)=CC(2);
mom(nrun,7)=mp;
mom(nrun,8)=mpp;
mom(nrun,9)=mppp;
mom(nrun, 10)=mpppp;
mom(nrun, 1)=mu;
mom(nrun, 12)=muu;
mom(nrun, 13)=muuu;
mom(nrun, 14)=muuuu;
mom(nrun, 15)=muuuuu;
mom(nrun, 16)=mv;
mom(nrun, 17)=mw;
mom(nrun, 18)=mvvv;
mom(nrun, 19)=mvvvv;
mom(nrun,20)=mvvvvv;
%
mom(nrun,21)=mnl;
mom(nrun,22)=mnlnl;
mom(nrun,23)=mn2;
mom(nrun,24)=mn2n2;
%
mom(nrun,25)=mtem;
mom(nrun,26)=mtemtem;
mom(nrun,27)=mds;
mom(nrun,28)=mdsds;

mom(nrun,29)=munl;

78

mom(nrun,30)=mun2;
mom(nrun,31)=mvnl;
mom(nrun,32)=mvn2;
mom(nrun,33)=mumnl;
mom(nrun,34)=mvmnl;
mom(nrun,35)=mumn2;
mom(nrun,36)=mvmn2;
%
mom(nrun,37)=cur;
mom(nrun,38)=jd;
%
mom(nrun,39)=mcl;
mom(nrun,40)=mc2;
mom(nrun,41)=mclcl;
mom(nrun,42)=mc2c2;
%
mom(nrun,43)=mucl;
mom(nrun,44)=muc2;
mom(nrun,45)=mvcl;
mom(nrun,46)=mvc2;
mom(nrun,47)=mumcl;
mom(nrun,48)=mvmcl;
mom(nrun,49)=mumc2;
mom(nrun,50)=mvmc2;
%%%%%%%%%%%%%%%%%%%
mom(nrun,60)=mxp;
mom(nrun,61)=mnp;
mom(nrun,62)=mxu;
mom(nrun,63)=mnu;
mom(nrun,64)=mxv;
mom(nrun,65)=mnv;
mom(nrun,66)=mxnl;
mom(nrun,67)=mnnl;
mom(nrun,68)=mxn2;
mom(nrun,69)=mnn2;
mom(nrun,70)=mxcl;
mom(nrun,71)=mncl;
mom(nrun,72)=mxc2;
mom(nrun,73)=mnc2;
mom(nrun,74)=mxds;
mom(nrun,75)=mnds;
mom(nrun,76)=mxtem;
mom(nrun,77)=mntem;
mom(nrun,78)=2*pi/((lcut-l)*dw);
mom(nrun,79)=2*pi/((hcut-l)*dw);
%%%%%%%%%%%%%%%%%%%%%%%
%thO(nrun,:)=f(1,:);
%th30(nrun,:)=f(14,:);
%th60(nrun,:)=f(28,:);
%th90(nrun,:)=f(42,:);
%thl20(nrun,:)=f(56,:);
%thl50(nrun,:)-f(70,:);
phippl=phippl/dw;
phielev(nrun :)=phippl(:)';

79

aa0(nrun,:)=a0';
aal(nrun,:)=al';
aa2(nrun,:)=a2';
bbl(nrun,:)=bl';
bb2(nrun,:)=b2';
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
/%%%0%//o% DATAIN.M %%%%/o////%%%
%% THIS IS THE MAIN PROGRAM. IT RUNS ALL THE OTHER .M PROGRAMS
%% DOES THE DATA CONVERSION TO VOLTS AND THEN TO PHYSICAL
%% UNITS. STATISTICAL ANALYSIS IS DOEN (MOMENTS.M). PLOT OF
%% TIME SERIES IS PREPARED (PLSER2.M). SPECTRAL ANALYSIS IS
%% DONE (SPEC2.M). GENERATED OUTPUT DATA ARE SAVED (TSAVE.M).
%% check save statements at the end of the program before executing.
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% PRINTING OF THE TIME SERIES LOCKS UP A LOT OF MEMORY. QUITING
%% FROM MATLAB WOULD BE REQUIRED FOR PRINTING THE NEXT PLOT. THIS
%% PROGRAM CONSTRUCTED IN AN UNCONVENTIONAL MANNER OVERCOMES THIS
%% PROBLEM BY USING MATLAB-EDIT COMMAND (at the end) WHICH QUITS
%% AND RETURNS TO MATLAB AUTOMATICALLY. PLOTS FROM PREVIOUS RUNS
%% ARE PLOTTED AT THE BEGINNING OF THE CURRENT RUN.

!gpp386 tser /djet
!copy tser.jet pm /b
!del tser.*
%%
%% AFTER PLOTTING THE PREVIOUS RUN VARIABLES ARE RESTORED
%%
load
%%
%% MAXRUN = RUN NUMBER OF THE LAST RUN IN A DEPLOYMENT, USUALLY 184
%% THIS NUMBER IS REQUIRED TO DO AN INTERPOLATION BETWEEN PRE AND POST
%% CALIBRATIONS OF OBS SENSORS.
%%!!!!!! EDIT BEFORE RUNNING !!!!!!!!!!!!!! !!!!!!!!!!!! !!!!!!!!!!!!!!
%%
maxrun=162
if maxrun < none;
tsave
quit
end
%%
%% THE VARIABLE idep IS THE DEPLOYMENT IDENTIFICATION. THIS LINE SHOULD
%%!!!!!! EDIT BEFORE RUNNING !!!!!!!!!!!!!!!!! i!!!! !!!!!!!!!!!!!!!
%%
idep='H162';
%%
%% none = CURRENT RUN NUMBER, ADVANCED BY ONE AUTOMATICALLY.
%%
/%%%%%%%%%%%%%%%%%%%%%

%% BEGINNING OF THE GLOBAL LOOP
for ii=none:none
%%
%% THE ACQUIRED DATA FILES FILEO1.DAT TO FILE92.DAT ARE IN BINARY FORMAT.
%%/ THEY HAVE TO BE READ REWRITTEN IN A FORM THAT IS MATLAB READABLE.
%% THIS IS DONE FOR EACH RUN, USING AN EXTERNAL FORTRAN PROGRAM rmat.for
%%%
nrun=ii
!erase run.dat
save run.dat ii /ascii

!rmat
%%
%% RMAT TAKES A FILE EG. FILEO1.DAT READS THE SPECIFIED RUN AND WRITES IT
%% INTO data, A FILE WHICH CAN BE NOW LOADED IN TO MATLAB ENVIRONMENT.
%%
load data
%%
%%TO FIX ERROR IN DATE
%head(8)=head(8)+1
%% THIS JULDAY.M FILE COMPUTES THE TIME IN JULIAN DAYS FROM HEADERS IN DATA
%%
julday;
jd-fix(100*jd)/100;
jdr=jd
%%
%% INFORMATION REGARDING THE DEPLOYMENT, INSTRUMENT HEIGHTS hp,hc, Time step
dt,
%% number of FFT points, nn, BITS to VOLTS conversions ical(l:8,1:2), VOLTS to
%% PHYSICAL UNITS vcal(l:8,1:2) etc... ARE READ IN FROM AN ASCII EXTERNAL
%% INPUT DATA FILE CALLED INFOIDEP.DAT, EG. INFOH071.DAT
/%%
eval(['load info' idep '.dat'])
infol=eval(['info'idep]);
%%%% create ical and vocal %%%%
ical(:,l)=infol(3,l:8)';
ical(:,2)=infol(4,1:8)';
vcal(:,l)=infol(5,1:8)';
vcal(:,2)=infol(6,1:8)';
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
hp=infol(l,1);
hc=infol(1,2);
dt=infol(l,3);
nn=infol(1,4);
dtheta=infol(1,5);
cux=infol(1,6);
cuy=infol(l,7);
mux=infol(l,8);
muy=infol(1,9);
%%
%% CALIBRATION GAINS AND OFFSET ARE MODIFIED FOR EACH RUN WITH
%% A LINEAR INTERPOLATION BETWEEN THE PRE AND POST CALIBRATION GAINS
%% AND OFFSETS.
%%
%ical(5,1)=ical(5,1)+(infol(1,10)-ical(5,1))*(nrun-l)/(maxrun-1);
%ical(5,2)=ical(5,2)+(infol (1,1 l)-ical(5,2))*(nrun-l)/(maxrun-1);
%ical(6,1)=ical(6,1)+(infol(1,12)-ical(6, ))*(nrun-l)/(maxrun-1);
%ical(6,2)=ical(6,2)+(infol(1,13)-ical(6,2))*(nrun-1)/(maxrun-1);
%ical(5,:)
%ical(6,:)
%%
%% FURTHER, THE OBS OFFSETS ARE SHIFTED UP OR DOWN TO MATCH THE FIELD
%% MEASUREMENTS. USUALLY THERE ARE THREE FIELD POINTS AND SHIFTS FOR
%%/ EACH RUN ARE COMPUTED BY LINEAR INTERPOLATION BETWEEN EACH FIELD
POINT.

%%
%% UNCOMMENT FOLLOWING LINES FOR DEPLOYMENT H071 (GOES ACROSS AN YEAR
BOUNDARY
%% SCREWS UP INTERPOLATION BASED ON JULIANDYS AS TIME AXIS.
%U if nrun > 170
%U jd =jd+365;
%U end
%%THE FOLLOWING LINES CREATE OBS OFFSETS SUCH THAT THE DATA CORRESPONDS
%%TO FIELD DATA
ifjd < infol(2,8)
nloff=infol(2,1)
n2off=infol(2,4)
else
nloff=infol(2,2)
n2off=infol(2,5)
end
%%
%% CONVENTION OF DATA -- (1) BITS TO VOLTS
%% (2) VOLTS TO PHYSICAL QUANTITIES
%%
[n,p]=size(data)
e=ones(n,l);
volt=data.*(e*vcal(l:8,1)') + e*vcal(l:8,2)';
data=volt.*(e*ical(1:8,1)') + e*ical(1:8,2)';
clear volt
%%
%% THE FOLLOWING SIGN CHANGES FOR THE CURRENTS AND THE ROTATION OVER
%% ANGLE THETA ARE REQUIRED TO ORIENT FLOWW IN +X DIRECTION DUE EAST
%% AND + Y FLOW DUE NORTH OFF HALLANDALE BEACH.
%%/ mux and muy are 1 and 1 for current meter up.
%% uvel TIME SERIES, x direction fluid velocity (east) in m/s.
%% vvel TIME SERIES, y direction fluid velocity (north) in m/s.
%% press TIME SERIES, pressure in m of water.
ds=data(7035:7166,8);
data=data(l:7166,1:8);
ux=mux*data(:,cux);
uy=muy*data(:,cuy);
ux=-ux;
%% 2 %%% transform to N-S coordinates
%%%o/co%%I%%%%%%%%%%%%%%ioO%%%%%%%
uxstar=ux*cos(dtheta)+uy*sin(dtheta);
uystar--ux*sin(dtheta)+uy*cos(dtheta);
%% 3 %% change to uvel crossshore vel log shore %%%%%%%%%%%%%%%%%%%%%%
uvel=uystar;
vel=-uxstar;
press=data(:,2);
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%0%%%%
%%%%%%%%%%%o%%%%%%%%%

%%
%% OBS channel 4 time series nl ntu.
%% OBS channel 5 time series n2 ntu.
d0%%
nl=data(:,5)+nloff;
n2=data(:,6)+n2off;

II

%%
%% NTU READINGS ARE CONVERTED TO SAND CONCENTRATION BASED ON LAB
CALIBRATION.
/%% CALIBRATION ALSO ADJUSTED LINEARLY FOR PASSAGE OF TIME
%% cl concentration corresponding to nl in g/l.
%% c2 concentration corresponding to n2 in g/l.
o%%o
convl=infol(7,1)
conv2=infol(7,3)
offl=infol(7,2)
off2=info1(7,4)
cl=nl*convl+offl;
c2=n2*conv2+off2;
%% DUE TO CALIBRATION UNCERTAINTIES, IF CONCENTRATION COMES OUT
%% LESS THAN ZERO, IT IS FIXED TO ZERO.
for kkk=1:7166
%
if cl(kkk)< 0,
cl(kkk)=0;
end
if c2(kkk) < 0,
c2(kkk)=0;
end
%
end
%%
%% tem = TEMPERATURE TIME SERIES, DEG.C.
%% ds = DATASONICS TIME HISTORY, CM.
%% pow = BATTERY VOLTAGE TIME HISTORY, V.
%%
tem=data(:,7);
pow=data(:,l);
%%%%%%%%%%%%%%%%%%%%%%%%%% END OF DATA PROCESSING
%%%%%%%%%%%%%%%%%%%%%%%%
/0%%
%% MAXIMUM AND MINIMUM VALUES OF ALL TIME HISTORIES ARE COMPUTED FOR
%% STATISTICAL PURPOSES AND SCALING OF THE PLOTS
%%/0
mxpow=max(pow);
mnpow=min(pow);
%
mxp=max(press);
mnp=min(press);
%mdp=median(press);
mxu=max(uvel);
mnu=min(uvel);
%mdu=median(uvel);
mxv=max(vel);
mnv=min(vwel);
%mdv=median(vel);
mxnl=max(nl);
mnnl=min(nl);
%mdnl=median(nl);
mxn2=max(n2);

mnn2=min(n2);
%mdn2=median(n2);
mxcl=max(cl);
mncl=min(cl);
%mdcl=median(cl);
mxc2=max(c2);
mnc2=min(c2);
%mdc2=median(c2);
mxds=max(ds);
mnds=min(ds);
%mdds=median(ds);
mxtem=max(tem);
mntem=min(tem);
%mdtem=median(tem);
%%
%% SCALED TIME SERIES ARE COMPUTED FOR PLOTTING PURPOSES. THEY CARRY AN
%% EXTRA S. THE FINAL PLOT ALSO CARRY OTHER STATISTICAL INFORMATION.
diff=mxp-mnp;
spress=(press-mnp)/diff;
diff=mxu-mnu;
suvel=(uvel-mnu)/diff;
diff=mxv-mnv;
svvel=(vel-mnv)/diff;
diff=mxnl-mnnl;
snl=(nl-mnnl)/diff;
diff=mxn2-mnn2;
sn2=(n2-mnn2)/diff;
diff=mxds-mnds;
sds=(ds-mnds)/diff;
%%
%% STATISTICAL ANALYSIS FILE moments.m IS CALLED. THIS COMPUTES MOMENTS
%% UP TO 5TH ORDER FOR MOST OF THE QUANTITIES.
%%
moments
%%%%%%%%%
clear uxstar uystar ux vx
%%
%% PLOTTING FILE plserl.m IS CALLED. THIS CREATED PLOTFILE (tser.met)
%% WHICH ALSO CONTAINS SOME INFORMATION FROM mom.mat COMPUTED BY
MOMENTS.
%%
plserl
%%
clear spress suvel svel sni sn2 sds
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
meancur=sqrt(muA2+mvA2)
[n,p]=size(data)
O/%%o
%% AVERAGE VALUES mp mu my, COMPUTED BY MOMENTS, ARE REQUIRED FOR
%% SPECTRAL ANALYSIS.
ave=[mu my mp n];
save ave.dat ave /ascii
%%%%%%%%%/ %%%%%%%%%%%%%%% END STATISTICAL ANALYSIS
%%%%%%%%%%%%%%%%%%%%%%%%%%

%%
%% SPECTRAL ANALYSIS OF P-U-V DATA
%% spec2.m FILE PERFORMS PUV ANALYSIS. COMPUTES UNIDIRECTIONAL AND
DIRECTIONAL
%% SPECTRUM. GIVES SIGNIFICANT WAVE HEIGHTS, PEAK DIRECTION, PEAK PERIOD,
%% AND THE DIRECTIONAL SPECTRUM COEFFICIANTS.
%%/
spec2
%mommat
%%%%%%%%%%%%%%%%%%%%%%%%%%
clear ave meancur spectrum_peak bl mep t b2 meu theta chek mev tt
clear conf mi cux mphipp ux cuy mux uxstar data muy uy diff n uystar
clear dt nl v dtheta nl val duml nlap veal dum2 nn dw np e nrun wk
clear p wm filt peakdirection ww g peak_frequency y h perc zc he phipp
clear zp head phippl hp phiup ii phiuu
clear A alpha mw sig AA ans m sigp C bet me phiuv CC ical phivp Hmo
clear mp phiv Hrms infol mpp pi Tp infoh041 inf0h031 mu pp
clear aO j muu al muv seta a2 kk my si alfa
clear press tem uvel vel cl c2 ds f
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%
%% THESE FOLLOWING STATEMENTS ARE PART OF PROGRAMMING THAT ALLOWS
%% CONTINUOUS RUNNING WITHOUT GETTING INTO MEMORY PROBLEMS AND
SHOULD
%% BE LEFT ALONE
!del matlab.mat
'matlab.mat deleted'
none=none+l
save
/%%%%%%%%%%%%%
clear
edit
0

REFERENCES

Baker, Edward T., and J. William Lavelle (1984). "The Effect of Particle Size on the Light
Attenuation Coefficient of Natural Suspensions," Journal of Geophysical Research,
Vol. 89, No.C5, 8197-8203, Sept. 20, 1984.

Dean, Robert G., and Robert A. Dalrymple (1984). "Water Wave Mechanics for
Engineers and Scientists." Prentice-Hall, Englewood Cliffs, NJ 353, pp. 79-202

Dodge, Richard E., and Louis Fisher (1988). "Reef Coral Growth Rate: Effects From
Past Beach Renourishment," Problems and Advancements in Beach Nourishment,
Beach Preservation Technology 1988, Proceedings, FSBPA, Tallahassee, FL, 255-
260.

Dompe, Philip, and D. M. Hanes (1992) "Wave Data Summary: Hollywood Beach,
Florida, January 1990 To May 1992," UFL/COEL-92/016, Coast. and Ocean Eng.
Dept., University of Florida, Gainesville, FL.

Dompe, Philip, and D. M. Hanes (1993) "Turbidity Data: Hollywood Beach,
Florida, January 1990 To April 1992," UFL/COEL-93/002, Coast. and Ocean
Eng. Dept., University of Florida, Gainesville, FL.

Downing, John P., R. W. Sternberg, and C. R. B. Lester (1981)."New Instrumentation
for the Investigation of Sediment Suspension Process in the Shallow Marine
Environment." Marine Geology. 42, 19-34.

Fisher, Louis E., Richard E. Goldberg, Charles G. Messing, Walter M. Goldberg, and
Steven Hess (1992). "The First Renourishment at Hollywood and Hallendale
(Florida) Beaches: Monitoring of Sediment Fallout, Coral Communities, and
Macroinfauna: Preliminary Results." Proceedings of the 1992 National Conference
on Beach Technology, FSBPA, Tallahassee, FL, 209-226.

Goldberg, Walter M. (1988). "Biological Effects of Beach Restoration in South Florida:
The Good, the Bad and the Ugly." Problems and Advancements in Beach
Nourishment, Beach Preservation Technology 1988, Proceedings, FSBPA,
Tallahassee, FL, 19-26.

Hanes, Daniel M., (1988). "Intermittent Sediment Suspension and Its Implications to Sand
Tracer in Wave-Dominated Environments", Marine Geology. 81, 175-183,

I

MILLISECOND	CLASS.METHOD	MESSAGE
0	sobekcm_page_globals.constructor
0	sobekcm_page_globals.constructor	Application State validated or built
0	sobekcm_database.verify_item_lookup_object
0	sobekcm_page_globals.constructor	Navigation Object created from URI query string
0	sobekcm_database.verify_item_lookup_object
0	sobekcm_page_globals.display_item	Retrieving item or group information
0	sobekcm_page_globals.get_entire_collection_hierarchy	Retrieving hierarchy information
0	sobekcm_assistant.get_entire_collection_hierarchy
0	cached_data_manager.retrieve_item_aggregation
0	cached_data_manager.retrieve_item_aggregation	Found item aggregation on local cache
0	item_aggregation_builder.get_item_aggregation	Found 'all' item aggregation in cache
0	system.web.ui.page.page_load (ufdc.page_load)
0	sobekcm_page_globals.constructor.on_page_load
0	html_echo_mainwriter.add_style_references	Adding style references to HTML
0	html_echo_mainwriter.add_text_to_page	Reading the text from the file and echoing back to the output stream
2	html_echo_mainwriter.add_text_to_page	Finished reading and writing the file