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A study on the Physical Characteristics of
Currents and Wave Climate at Ramla Bay
May 2016
Michael Petroni
A dissertation presented to the Institute of Earth Systems of the
University of Malta for the degree of Bachelor of Science (Hons) in
Earth Systems
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Abstract
This baseline study investigates the nature of sea current circulation and wave
climate of the wider Ramla Bay area, specifically the significant wave height
and the wave direction, using numerical models. This study was undertaken
because no existing studies or data on the physical hydrodynamics of the area
are available. This information is unconditionally important for the input into
management decisions. Through reviewed literature, it was expected that a
cellular circulation arising from the formations of rip currents within the bay
prevail. This was thought to be the case at Ramla because of the circular
pattern of Posidonia oceanica seagrass beds in the centre of the bay. The
results showed that rip currents were not prevalent during the studied period.
The mapped sea currents were found to predominantly flow along the contours
of the coastline around the embayment. It was, however, concluded that the
occurrence of strong rip currents, should not be excluded as a possibility during
the winter period when strong wave conditions occur. Data from the SWAN
model for wave direction was found to approach the coast predominantly from
the North East direction. Signifying possible accumulation of sediments on the
west side of the embayed beach. Wave direction was also observed to have a
positive relationship to the ensuing current direction but were found to be
statistically insignificant. The significant wave height wave was also found to
be significantly larger in the winter months compared to the summer months.
Within the bay the temporal variation of SWH exhibited less fluctuation
compared to deeper waters. The SWH was also found to be strongly
associated with alterations in atmospheric pressure and showed that Ramla
Bay is powerfully protected during storm events, by drastically attenuating the
wave energy. This is an essential role when considering the dynamics of the
beach morphology, as well as the dynamics of the backshore dunes.
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Acknowledgements
I would like to express my gratitude for the assistance offered to me by
Professor Anton Micallef and Mr. Anthony Galea and especially Mr. Adam
Gauci for his exceptional guidance as the primary supervisor of this
dissertation.
I would also like to thank my parents for their assistance and support during
the different stages of the study.
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Table of Contents
Declaration of Authenticity…………………………………………………………………………………….i
Abstract ........................................................................................................................ ii
Acknowledgements ................................................................................................... iii
Table of Contents ...................................................................................................... iv
List of Figures ............................................................................................................. vi
List of Tables ............................................................................................................ viii
Chapter 1 Introduction ............................................................................................... 1
1.1 The dynamic nature of physical coastal oceanography ............................. 1
1.2 The Study Area ................................................................................................. 2
1.3 Research Problem and Motivation of the Study .......................................... 6
1.4 Aims and Objectives of the Study ................................................................. 7
1.5 Significance ....................................................................................................... 9
1.6 Review of Dissertation ................................................................................... 10
Chapter 2 Literature Review ................................................................................... 11
2.1 Features of Embayments .............................................................................. 11
2.2 Geological Control ......................................................................................... 13
2.3 Wind ................................................................................................................. 14
2.4 Waves .............................................................................................................. 15
2.4.1 Wave Orbitals .......................................................................................... 16
2.4.2 Wave Shoaling ........................................................................................ 18
2.4.3 Wave Breaking ........................................................................................ 18
2.4.4 Wave Refraction and Diffraction ........................................................... 19
2.5 Currents ........................................................................................................... 22
2.5.1 Turbulence and Radiation Stress ......................................................... 23
2.5.2 Longshore Currents ................................................................................ 24
2.5.3 Undertow .................................................................................................. 25
2.5.4 Rip Currents ............................................................................................. 25
2.5.5 Embayment classification ...................................................................... 27
2.6 Lagrangian Measurements ........................................................................... 28
2.6.1 Drifters....................................................................................................... 29
2.6.2 Rosario-SHYFEM Model ........................................................................ 29
2.6.3 SWAN Model ........................................................................................... 31
v
Chapter 3 Methodology ........................................................................................... 33
3.1 Materials Used ................................................................................................ 33
3.1.1 Bathymetric Depth Equipment .............................................................. 33
3.1.2 Drifters....................................................................................................... 35
3.2 Data Collection ............................................................................................... 36
3.2.1 Bathymetric depth Data .......................................................................... 36
3.2.2 Drifter Data Collection ............................................................................ 37
3.3 Data Processing ............................................................................................. 39
3.3.1 ARCGIS .................................................................................................... 39
3.3.2 Bathymetric map ..................................................................................... 39
3.3.3 Drifter Data ............................................................................................... 40
3.3.3.1 Calculations .......................................................................................... 40
3.3.4 SWAN Data .............................................................................................. 44
3.4 Modelling Data ................................................................................................ 45
3.4.1 SWAN Model ........................................................................................... 45
3.5 Statistical Analysis ......................................................................................... 46
3.6 Limitations ....................................................................................................... 47
Chapter 4 Results and Discussion ........................................................................ 49
4.1 Bathymetric Results ....................................................................................... 49
4.2 Lagrangian Drifter Currents .......................................................................... 52
4.2.1 Drifter Trajectories: Current Velocities ................................................. 53
4.2.2 Drifter Trajectories: Current Direction .................................................. 55
4.3 SWAN Maps ................................................................................................... 62
4.3.1 Significant Wave Height ......................................................................... 62
4.3.2 Wave Direction ........................................................................................ 63
4.4 Time Series Analysis: Significant Wave Height ........................................ 64
Chapter 5 Conclusion .............................................................................................. 69
References ................................................................................................................ 72
Appendices ................................................................................................................ 88
vi
List of Figures Figure 1.1: Study Site boundary for which drifter tracks were recorded at
Ramla l-Hamra Bay, Gozo, Maltese Islands ................................................... 3 1
Figure 1.2: Satellite Image of Ramla Bay with the easily noticeable circular
pattern of Posidonia Oceanica seagrass beds and the presence of a
crescentic alongshore sand bar, as well as the remnants of the seawall,
marked within the red border .......................................................................... 4 1
Figure 1.3: Plan of culturally historical features present at Ramla Bay. .......... 4 1
Figure 1.4: Wind rose diagram for Malta during the period from 1997-2006.
The diagram illustrates how the Maestral winds are predominantly the most
common and relatively strongest. ................................................................... 5 1
Figure 1.5: Bar char representing values of maximum wind speed (Green)
and minimum wind speeds (Yellow) for the year of 2015 over an area within
14.375oN, 36.125oE to 14.375oN, 36.125oE. The (Red) area on the graph
shows the average wind speeds. .................................................................... 6 1
Figure 1.6: Network of Natura2000 sites in Malta, where the MT0000105
marine site encompasses Ramla Bay and much of the north-east coastal
waters of the Maltese Islands........................................................................ 10 1
Figure 2.1 : The physical zonation of the coast………………………………..11 1
Figure 2.2: Profile of zones describing waves processes over a sandy
seabed. ......................................................................................................... 12 1
Figure 2.3: Transposed forward motion of water particles under the forces of
propagating energy. Arrows show orbital motion is off put slightly in the
direction of wave energy. .............................................................................. 17 1
Figure 2.4: Wave refraction occurrence as waves move into shallower water.
Notice how refraction is not complete and waves still break with some angle,
on the beach. ................................................................................................ 20 1
Figure 2.5: The process of wave refraction where incoming waves alter
direction to align their propagation parallel to the beach ............................... 20 1
Figure 2.6: Wave diffraction and refraction as wave orthogonals approach an
embayment at an oblique angle, whereby the headland has significant control
creating a shadow zone behind the headland.. ............................................. 21 1
Figure 2.7: The anatomy of a rip current. ...................................................... 26 1
Figure 2.8: Rosario SHYFEM gridded mesh consisting of triangular elements
and node, which become more refined closer to the coastline for a higher
resolution ...................................................................................................... 30 1
vii
Figure 3.1: Equipment used to measure bathymetric depths. (A) Line with
weight attached at one end, (B) Diver’s analogue pressure depth gauge, (C)
measuring tape with weighted end. ............................................................... 34 1
Figure 3.2: Diagram illustrating the components of the customised drifter and
a photo of the drifter in the field whilst recording during a calm day .............. 35 1
Figure 3.3: Histogram of the number of tracks taken on the days of fieldwork.
...................................................................................................................... 38 1
Figure 3.4: Diagram of the recorded points of a track. The distance and
bearing between each point was calculated using the haversine formulae. .. 41 1
Figure 3.5: Diagram illustrating the quadrants with 0o representing North with
the corresponding ranges of angles and the trigonometric element which
determines how the V and U components would be calculated. ................... 43 1
Figure 3.6: Point grid used in the SWAN model computations. The yellow
border highlights the Transect taken and within in it are the highlighted points
for the Time Series analysis. ......................................................................... 45 1
Figure 4.1: Bathymetric Map produced from in-situ measurements, with the
shallowest depths in red, graduating down to deeper depths in blue. ........... 51 1
Figure 4.2: Bathymetric Map produced data obtained through numerical
modelling performed by the PORG, with the shallowest depths in red,
graduating down to deeper depths in blue. ................................................... 51 1
Figure 4.3: The complete set of drifter tracks recorded throughout the data
collection period superimposed on a map with bathymetric contours
extrapolated from the bathymetric map interpolation. ................................... 52 1
Figure 4.4: First scatter plot of the measured current direction (y-axis)
against the modelled wave direction (x-axis) with outliers included. ............. 56 1
Figure 4.5: Second scatter plot of measured current direction against
modelled wave direction without outliers included. ....................................... 58 1
Figure 4.6: Scatter plot of U (top) and V (bottom) components of current
velocity and wind speed on 04/09/2015 with the U and V components of the
two points from the tracks at 19:01 (green) and 19:06 (yellow) to test the
relationship of these possible outliers. .......................................................... 59 1
Figure 4.7: Scatter plot of U (top) and V (bottom) components of current
velocity and wind speed on 04/09/2015 with the U and V components of the
two points from the tracks at 19:01 (green) and 19:06 (yellow) to test the
relationship of these possible outliers. .......................................................... 60 1
Figure 4.8: Spatial variation graph of the Significant Wave Height across the
transect. ........................................................................................................ 62 1
viii
Figure 4.9: Time series of the SWH from August 2015 to January 2016 over
point 2, with the atmospheric pressure superimposed in dashed black line. . 65
List of Tables
Table 3.1: Model of the table of the procedures used to process drifter track data. .............................................................................................................. 40 1
Table 4. 1: Table of Pearson correlation and level of significance of the relationship between wave direction and current direction with outliers. Produced in SPSS. ....................................................................................... 57 1
Table 4. 2: Table of Pearson correlation and level of significance of the relationship between wave direction and current direction without outliers. Produced in SPSS. ....................................................................................... 58
List of Appendices
Appendix A - Drifter Tracks
List of Figures
Figure A- 1: Drifter tracks on the 5th of July 2015 with wind direction from the
NNW. ............................................................................................................ 89 1
Figure A- 2: Drifter tracks on the 10th of July 2015 with wind direction from the
NW. ............................................................................................................... 89 1
Figure A- 3: Drifter tracks on the 11th of July 2015 with wind direction from the
ENE. ............................................................................................................. 89 1
Figure A- 4: Drifter tracks on the 17th of July 2015 with wind direction from the
NNE. ............................................................................................................. 89 1
Figure A- 5: Drifter tracks on the 18th of July 2015 with wind direction from the
NNW. ............................................................................................................ 89 1
Figure A- 6: Drifter tracks on the 28th of August 2015 with wind direction from
the ENE. ....................................................................................................... 89 1
Figure A- 7: Drifter tracks on the 28th of August 2015 with wind direction from
the ENE. ....................................................................................................... 89 1
Figure A- 8: Drifter tracks on the 30th of August 2015 with wind direction from
the NNE. ....................................................................................................... 89 1
Figure A- 9: Drifter tracks on the 4th of September 2015 with wind direction
from the W. ................................................................................................... 89 1
ix
Figure A- 10: Drifter tracks on the 5th of September with wind direction from
the SW. ......................................................................................................... 89 1
Figure A- 11: Drifter tracks on the 26th of September with wind direction from
the NNW. ...................................................................................................... 89 1
Figure A- 12: Drifter tracks on the 27th of September 2015 with wind direction
from the NNW. .............................................................................................. 89 1
Figure A- 13: Drifter tracks on the 4th of October 2015 with wind direction from
the E. ............................................................................................................ 89 1
Figure A- 14: Velocity time-series of the U (top) and V (bottom) components
of four drifters on the 5th of July 2015. ........................................................... 89 1
Figure A- 15: Velocity time-series of the U (top) and V (bottom) components
of four drifters on the 11th of July 2015. ......................................................... 89 1
Figure A- 16: Velocity time-series of the U (top) and V (bottom) components
of four drifters on the 29th of August 2015. .................................................... 89 1
Figure A- 17: Velocity time-series of the U (top) and V (bottom) components
of four drifters on the 26th of September 2015............................................... 89
List of Tables
Table A- 1: Table of information of drifter tracks. .......................................... 88 1
Table A- 2: Table of the selected drifter track points and SWAN model grid
points that have been selected based on closeness of between the two sets
of points, spatially and temporally. ................................................................ 89
Appendix B – SWAN Significant Wave Height Maps
List of Figures
Figure B- 1: Average Significant Wave Height for August. ................................ 89 1
Figure B- 2: Average Significant Wave Height for September. ......................... 89 1
Figure B- 3: Average Significant Wave Height for October. .............................. 89 1
Figure B- 4: Average Significant Wave Height for November. .......................... 89 1
Figure B- 5: Average Significant Wave Height for December. .......................... 89 1
Figure B- 6: Average Significant Wave Height for January. .............................. 89 1
Figure B- 7: Average Significant Wave Height for the total period of study. ... 89
x
Appendix C – SWAN Mean Wave Direction Maps
List of Figures
Figure C- 1: Average Mean Wave Direction for August. ............................... 89 1
Figure C- 2: Average Mean Wave Direction for September. ......................... 89 1
Figure C- 3: Average Mean Wave Direction for October. .............................. 89 1
Figure C- 4: Average Mean Wave Direction for November. .......................... 89 1
Figure C- 5: Average Mean Wave Direction for December. .......................... 89 1
Figure C- 6: Average Mean Wave Direction for January. .............................. 89
Appendix D – Significant Wave Height Time Series
List of Figures
Figure D- 1: Time-Series for the Significant Wave Height during August
across the five points from the transect. ....................................................... 89 1
Figure D- 2: Time-Series for the Significant Wave Height during September
across the five points from the transect. ....................................................... 89 1
Figure D- 3: Time-Series for the Significant Wave Height during October
across the five points from the transect. ....................................................... 89 1
Figure D- 4: Time-Series for the Significant Wave Height during November
across the five points from the transect. ....................................................... 89 1
Figure D- 5: Time-Series for the Significant Wave Height during December
across the five points from the transect. ....................................................... 89 1
Figure D- 6: Time-Series for the Significant Wave Height during January
across the five points from the transect. ....................................................... 89
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Chapter 1 Introduction
1.1 The dynamic nature of physical coastal oceanography
Wave climate and the hydrodynamics of sub-surface currents vary across time
and space, particularly when migrating from deeper water to shallower waters.
Such changes can be attributed to physical and biological factors, such as
bathymetry, topography, geology and vegetation cover. The properties of wave
fields and the generated longshore currents can cause the sand-bar in an
embayed beach to migrate in the cross-shore and longshore direction over
varying time-scales, ultimately altering the beach shape and sediment balance
(Ojeda, Guillén, & Ribas, 2011). The direction of surface waves is governed
by the wind direction. To an extent, the wave direction controls the direction of
the current. However, this is subject to change due to a temporal response lag
at different depths and the physical influence of the coastal topography and
bathymetry (Röhrs et al., 2012). Currents are also generated within the
nearshore zone due to wave breaking and the loss and transfer of wave energy
(Tang, Lyu, & Shen, 2014). The formation of rip currents may be attributed to
the complex changes in wave properties that occur to the wave field
propagating within an embayment (Xie, 2012).
This dissertation examines and analyses the nature of sub-surface currents
within a small embayment. The study focuses on Ramla Bay, which is situated
on the North coast of Gozo. Observations made using satellite images of
Ramla Bay reveal a circular pattern of Posidonia oceanica in the centre of the
bay that extends some 400m from the beach (Figure 1.2). Posidonia oceanica
rely on currents for dispersing fragments for colonization and supplying
sediments suspended in shallow water to form a consolidated sandy substrata,
usually in embayments where sediments tend to collect (Di Carlo,
Badalamenti, Jensen, Koch, & Riggio, 2005). Understanding the sub-surface
current circulation and wave climate of embayments, such as at Ramla Bay,
may be useful to a set foundation towards future studies pertaining various
fields, such as bather safety, sediment transport, biological interactions and
pollutant diffusivity.
2
In operational oceanography, numerical models are used to simulate scenarios
governed by mathematics and physics to produce outcomes that best
represent what is happening in nature. Numerical models bring together data
from other models to produce these outputs, which in turn aid in better
understanding the seas and ocean from day-to-day situations to extreme
events to long term changes. With this information, predictions can be made
(Stewart, 2008). Numerical modelling was used to obtain data simulating the
wave characteristics within the bay and the surrounding area, which would not
be possible using in-situ techniques. In the analysis of results section, the
acquired in-situ data in the field by drifters is compared to the SWAN coastal
model. In particular, the Significant Wave Height (SWH) and Mean Wave
Direction (MWD), are analysed. Understanding how wave climate changes
spatially at the site and inter-annually has direct application to understanding
the forces shaping beach morphodynamics. This is essential for coastal
engineering and management.
1.2 The Study Area
The area of interest is confined to an open embayment, Ramla l-Hamra Bay,
on the North coast of Gozo (Figure 1.1), located approximately at latitude
36.062671oN and longitude 14.283805oE. Ramla Bay is characterised by an
embayed beach facing northwards. Embayed beaches develop along coasts
of discordant orientations outlined by an alternating hill and valley topography
(Castelle & Coco, 2012; McCarroll, Brander, Turner, Power, & Mortlock, 2014),
where predominant wave climate is directed against the coast. The geological
setting of the headland-bay is the product of the bands of faults that run
perpendicular to the coast as part of the horst and graben system that is typical
of the Maltese archipelago (Devoto et al., 2012; Micallef et al., 2013). Bays are
located within the graben sector (the downthrown block, wedged between two
ridges) where beaches may be formed where the mouth of the valley meets
the sea. Protruding headlands correspond to the horst ridges that have been
uplifted through tectonic faulting (Magri, 2006).
3
Figure 1.1: Study Site boundary for which drifter tracks were recorded at Ramla l-Hamra Bay, Gozo, Maltese Islands (Produced in ArcGIS).
The study area boundary for current measurements is clearly defined to
encompass Ramla Bay (Figure 1.1). However, the area covered by modelled
data may exceed this boundary area, with more extensive coverage,
encompassing the surrounding area. The surface bathymetry at Ramla is
capricious with sandy sediments covering most of the central area of the bay,
rock and boulder fields covering both sides of the bottom of the headlands and
extensive, dense Posidonia oceanica beds dominating the areas beyond the
mouth of the embayment (Figure 1.2). In conjunction with the knowledge of
alongshore variation in bar morphology and bathymetry in general; conditions
specific to Ramla Bay may result in complex hydrodynamics of the circulation
within the nearshore due the presence of the submerged ruins of a historic
defence sea wall (Figure 1.3). The wall runs the entire length of the bay but
has undergone severe degradation from interfering beach users and boating
activity (Figure 1.2).
Esri, HERE, DeLorme, MapmyIndia, © OpenStreetMap contributors, and the GIS user community
Esri, HERE, DeLorme,MapmyIndia, © OpenStreetMapcontributors, and the GIS usercommunity
.
0 250 500 750 1,000125Meters
Ramla l-Hamra Area, Gozo
0 2010Kilometers
4
Figure 1.2: Satellite Image of Ramla Bay with the easily noticeable circular pattern of Posidonia Oceanica seagrass beds and the presence of a crescentic alongshore sand bar, as well as the
remnants of the seawall, marked within the red border (Source: Google Earth).
Figure 1.3: Plan of culturally historical features present at Ramla Bay (Source: Azzopardi, 2015).
5
A study by Farrugia, Fsadni, Yousif and Mallia (2005) reveals that annually,
the Maltese Islands experience winds coming from the general North-West
direction (Figure 1.4). As indicated in Figure 1.5, there exists a pattern in the
season variation in wind speed around the Maltese Islands, whereby the
summer months are characterised by relatively weaker winds than those
during the winter months. Studies have observed rip currents to be more likely
to occur and stronger under high energy wave conditions, particularly with high
swells (Haller, 2002). Acknowledging this fact, drifters were deployed in as
many different sea state conditions possible during the data collection time
frame. Unfortunately, strong waves limited the access to Ramla Bay by boat,
during the winter months.
Figure 1.4: Wind rose diagram for Malta during the period from 1997-2006. The diagram illustrates how the North-westerly winds are predominantly the most common and relatively strongest (Source: Galdies, 2011).
6
Figure 1.5: Bar char representing values of maximum wind speed (Green) and minimum wind speeds (Yellow) for the year of 2015 over an area within 14.375oN, 36.125oE to 14.375oN, 36.125oE. The (Red) area on the graph shows the average wind speeds. (Data adapted from WERMED Interface, 2015).
1.3 Research Problem and Motivation of the Study
Understanding the dynamics of currents in shallow embayments, such as
Ramla, is essential in forecasting sediment budgets and biological processes
(Jones & Monismith, 2008). Most of the previous studies that consider the
same area have been concentrated towards beach morphology, sediment
dynamics, coastal management, tourism, etc. and have excluded a direct focus
on the physical hydrodynamic processes. Presently, there is no existing data
for the dynamics of sea currents and wave climate specific to Ramla Bay. The
research carried out in this project will serve as an addendum to the growing
compilation of such data for the Maltese Islands. Its application could be
effective in the role of management, particularly important to Ramla due to its
status as a Natura2000 site (Figure 1.6).
0
2
4
6
8
10
12
14
16
18
20
Jan Feb March April MAY JUN JUL AUG SEP OCT NOV DEC
Win
d S
peed (
m/s
)
Month
Wind Speeds over the Maltese Islands
Average Wind Speed [m/s] Max Wind Speed [m/s] Min Wind Speed [m/s]
7
1.4 Aims and Objectives of the Study
This study will attempt to answer several questions that can be categorised
into the following fields:
1. Biophysical Field
a. Do coastal currents, particularly with rip currents as the main
mechanism, play a role in shaping the circular pattern of Posidonia
oceanica beds observed in the centre of the bay during the summer
period?
2. Hydrodynamic Field
b. How do sub-surface currents behave throughout the Ramla Bay
embayment?
c. What is the nature of the wave climate of the embayment and how
does it vary over the inter-annual period?
d. What is the nature of the relationship between the incoming wave
direction and current direction within the embayment?
e. What is the relationship between the Significant Wave Height and
the atmospheric pressure?
From the biophysical aspect the hypothesis would suggest that the circulation
of currents adhere to a circular gyre pattern due to the nature of the geography
of the bay and to the meteorological and hydrodynamic characteristics,
whereby rip currents are likely to be the predominant driving force of this
circulation system. If rip currents are present, they are expected to occur as
headland rip currents i.e. on the sides of the headlands rather than in the centre
of the bay.
For the field of hydrodynamics, it is hypothesized that the general pattern of
current flow in absence of rip currents would exhibit an alongshore flow parallel
to the coastline and contours. The shallow water processes which alter wave
characteristics, whereby the wave heights increase and the velocity of the
wave orbitals decreases. Therefore, it is hypothesized that weaker currents
measured will be produced within nearshore zone of the bay, where there is
lower energy due to increased dissipation of wave energy due to breaking,
compared to currents in deeper portions of the bay.
8
The wave climate is expected to vary between the calmer summer period and
the high energy winter period. The average direction of the incoming wave field
is suspected to approach predominantly from the north-west direction and
ultimately re-orientate within the bay to be parallel to the beach, due to the
physical control of the embayment geography. The relationship between wave
direction and sub-surface currents should be proportional for poorly stratified
waters. However, because of complex dynamic processes, especially in the
surf-zone, there may be some contradictions to this hypothesis.
It is also hypothesised that the significant wave height (SWH) would be greater
during the winter months and have a larger range of variation between deeper
and shallower water, than would be exhibited in the summer months.
Furthermore, it is suspected that climate has a strong influence on the
behaviour and nature of the SWH. Periods of low atmospheric pressure storm
events would result in accentuating conditions of SWH relative to normal
conditions.
The aim of this study is to develop an understanding of the hydrodynamic
nature concerning the current circulation system and characteristic of the wave
climate present within Ramla Bay, Gozo. The study on wave climate will cover
a six month period between August 2015 and January 2016 (due to the
availability of data) and between July 2015 and October 2015 for current flow
measurements, due to time restraints. Tangentially, it is of additional interest
to conduct a simple bathymetric survey of the bay as a baseline to review the
extent of bathymetric control of mean flows.
The objective is to collect in-situ data on current flow within the defined
boundaries of the study area at Ramla Bay using GPS tracked drifters to
measure sub-surface currents 1m below sea level. The in-situ data is to be
then compared with the modelled output from Rosario-SHYFEM coastal model
for validation. Concurrently, modelled data for wave characteristic produced in
the SWAN model will be statistically analysed to observe the time variance
spatial evolution across the bay. Also, the wave direction will be statistically
correlated to the direction of current flow obtained from drifter measurements
to analyse the relationship between the two parameters. The scope is not to
study the direct biological processes related to the Posidonia oceanica but the
9
physical means by which the biological components may be influenced by local
currents. Furthermore, because of certain limitations and restrictions, the study
could only be focused on a few selected physical parameters, namely, current
direction and speed, wave direction, significant wave height and to a lesser
extent depth and wind. All the raw data collected throughout the study will be
compiled and be made available for future use in other studies.
1.5 Significance
Similar studies on currents, for validating numerical models using empirical
data have been carried out elsewhere in the past, with particular interest on
near-shore circulation and rip currents. However, in Malta, information on the
nature of currents and the influence they exert on the changing environment,
is lacking. Coastal currents, especially those circulating within embayments are
coherent to studies pertaining to sediment transport, the dispersion of
pollutants discharged into the marine environment (Spydell & Feddersen,
2012) and public safety.
Coastal surveyors interested in understanding the evolution the morphological
alterations of the coast, require surveys on hydrodynamic nature and wave
climate of an area and how these also change in time, due to natural or
anthropogenic causes (Inukai, Kuroiwa, Matusbara, & Noda, 2001). More
importantly, at the EU level of management of the marine environment comes
the Marine Strategy Framework Directive (MFSD), accompanied by an
extrinsic link to the NATURA 2000 network (Figure 1.6) and Habitats Directive.
The remnant sand dune systems in the backshore of the embayed beach at
Ramla has recently been observed by the Gaia Foundation, who is responsible
for the management of the area, to migrate towards the sea (A. Micallef,
personal communication, April 20, 2016). This suggests the interplay between
aeolian and wave dynamics altering the beach state, whereby it is possible that
more sediment is accreting to allow for dune migration. This is entirely
speculative as no studies have been undertaken for the area. Nevertheless,
this underlies the importance and relevance of this dissertation as a holistic
source for other studies.
10
Figure 1.6: Network of Natura2000 sites in Malta, where the MT0000105 marine site encompasses Ramla Bay and much of the north-east coastal waters of the Maltese Islands (Source: European Environmental Agency, n.d.). (European Environmental Agency, n.d.).
1.6 Review of Dissertation
In the second Chapter of the dissertation, the compilation of scientific literature
studied on the subject, pertaining to the physical processes of current
formation, wave characteristics and fluid dynamics, will be discussed. The
methods and equipment in oceanographic studies are also discussed. Chapter
three explains the materials used, as well as the methodology undertaken, from
field work to data processing to analysis and the limitations encountered during
the length of the study. The penultimate chapter discusses the results acquired
and the discussion interpreting results. The interpretation makes the link to the
hypotheses and literature reviewed and answers the research questions. The
conclusion outlines the outcome of the project and the significance of the
dissertation to answering the question.
11
Chapter 2 Literature Review
The Literature Review discusses the research conducted in previous studies.
The literature analysed relates to the physical mechanism of the formation and
relationship of waves and currents, particularly within the nearshore zone,
where more complex hydrodynamic processes take place. Also discussed, are
the operations of physical oceanographic research, directly conferring to the
numerical models used in this dissertation.
2.1 Features of Embayments
Embayments are ubiquitous along banded regions of valleys and hills at the
coast; innately identifiable by the curvature of embayed beaches and
accompanying headlands (Castelle & Coco, 2013; Daly, 2013). The general
and simplified profile of any beach will include the swash zone, the surf zone,
the nearshore zone and offshore zone (Figure 2.1).
Figure 2.1 : The physical zonation of the coast (Source: Davidson Arnott, 2009).
The nature of currents and waves changes as they propagate towards and the
coast, whereby the influencing topography and the geology of the coast, are
12
major contributors in deciphering how waves and currents change. Therefore,
it is required to understand not only the wind and wave climate of a study area
but also, the geography of the coast where interactions take place (Conley et
al., 2008).
Figure 2.2: Profile of zones describing waves processes over a sandy seabed (Source: Davidson Arnott, 2009).
Within the surf zone of sandy/shingle beaches, longshore sandbars form
parallel to the shoreline (Figure 2.2). In some cases, crescentic sand bars form.
Sand bars are usually rhythmic and vary seasonally in shape (van Enckevort,
2004). The hydrodynamic influence of the wave climate and subsequent
currents, constantly alter the sand bar and ultimately the features of the sandy,
unconsolidated bathymetric substratum (Price, Ruessink, & Castelle, 2014).
The persistence and changes in hydrodynamic processes are far more variable
within a shorter timeframe compared to morphological processes, which span
longer time scales (Blondeaux, 2001). Storms have a greater capacity to
drastically change a shingle bedform than the usual nearshore hydrodynamic
processes. The morphological profile configuration is reset after major storms
along the coast (Schwartz, 2012). The sand bars are essential for initiating the
wave breaking process, by which the dynamics within the surf zone are
characterized (Van de Lageweg, Bryan, Coco, & Ruessink, 2013). At Ramla
Bay, the sandbar was found to disappear between fluctuating summer and
winter conditions (Azzopardi, 2015). Masselink and Hughes (2003) clarify that
13
the migration of the sand bar infers that the depth of closure and the wave base
also vary accordingly (see Section 2.4.1).
The swash zone refers to the ranging levels of the wave run-up and run-down
along the sloping beach face (foreshore); this is partially where sediment is
uplifted and where wave induced currents are formed (Bakhtyar, Barry, Li,
Jeng, & Yeganeh-Bakhtiary, 2009). Consequently, the hydrodynamic
characteristics of the narrow swash zone alter the morphological shape and
profile of the foreshore seasonally (Larson, Kubota, & Erikson, 2004). These
alterations are largely dependent on the frequency and magnitude of
nearshore waves, the sedimentology of the beach and the general beach
morphology (Vousdoukas, Velegrakis, Dimou, Zervakis, & Conley, 2009).
Subsequent cross-shore currents deliver the suspended material seaward,
where it may be circulated within the bay by alternative currents within the
semi-closed cell of the embayment. This results in erosion of the beach
(Baquerizo, Lozada, Mendoza, & Silva, 2010).
2.2 Geological Control
Geological features are capable of controlling the distribution of wave energy
and therefore the characteristics of the wave spectrum and currents. In the
case of embayments featuring headlands and decreasing depth of the
seafloor, attenuation of the energy of waves propagating towards the coast
occurs (McCarroll et al., 2014). Other than the configuration and shape of the
coast, the surface roughness of the sea bottom will play a major role in the
behaviour and velocity of bottom currents that explicably alter subsequent
current flows above it (Signell, Beardsley, Graber, & Capotondi, 1990). Drag
and surface roughness have greater effects on waves and current dynamics
within the surf zone (i.e. within the wave base zone) rather than outside it.
Bottom roughness is inheritably temporally variable due to modifications in
14
bedform morphology and succession of vegetation growth (Feddersen,
Gallagher, Guza, & Elgar, 2003).
The generalised beach classification parameterisation techniques do not
embody the circumstances undergone with embayed beaches due to the
inherent control and influence that headlands have on nearshore current
circulation (Loureiro, Ferreira, & Cooper, 2013). The topography of headland-
bays coerces the flow of coastal longshore currents, as well as giving rise to
small scale currents localised to an embayment that in some cases give rise to
cellular circulation (Loureiro, Ferreira, & Cooper, 2012).
The Posidonia oceanica seagrass meadows, which are densely abundant
beyond the mouth of the bay, influence wave climate and hydrodynamic
characteristics within the bay by attenuating wave energy. The physiology of
the seagrass meadows, with their flexible and sometimes short aboveground
biomass reduces their capacity to attenuate wave energy, compared to other,
more rigid biomass. However, seagrass meadows compensate by altering the
erodibility of the seabed, making it more consolidated by sediment accretion
(Christianen et al., 2013). Therefore, the morphology of the seabed is more
susceptible to alteration by hydrodynamic processes where it is bare and
unconsolidated. Borg, Rowden, Attrill, Schembri and Jones (2009) studied the
Posidonia oceanica cover at Ramla Bay. They found that the seagrass
coverage is progressively increasing, when contrasted to fragmentation,
despite the high exposure. Evidently, the consolidation of the sandy substrata
is likely to increase, thus the hydrodynamics of the area may be altered due to
this, in the future. Also, they assert the need to understand the localised
hydrodynamic characteristics and wave climate, so as to study the coverage
and fragmentation of Posidonia meadows, as well as other benthic habitats.
2.3 Wind
Numerous forces are responsible for the movement of a body of water. The
oceans are mainly set in motion by surface winds, predominantly by storms in
open waters (Davidson-Arnott, 2009; Klemas, 2012). Wind applies a surface
pressure, which in turn alters the surface elevation of the water, forming waves.
15
For sub-surface currents to be generated, there must be a downward
transmission of momentum from the surface by means of Reynolds Stresses.
Reynolds stress occurs, when the horizontal movement of water at the surface
is out of phase with the surface elevation. Without this process the only method
for momentum transfer down the water column is by turbulent diffusion
(Nielsen, Callaghan, & Baldock, 2011). The veering of surface currents can be
observed with changes in wind direction. However, there tends to be a lag in
veering of sub-surface and deeper currents that are considered to be wind
driven (Drago, 1980).
The wind stress above the sea surface, eventually results in the transfer of
momentum in the form of surface current flow, which can be expressed to be
linear, in essence of a uniform eddy viscosity (Chang, Chen, Tseng, Centurioni,
& Chu, 2012). The height and energy transferred to surface sea waves is
dependent on the wind velocity and duration for which the wind blows in the
same direction (Camilleri, 2012). Concurrently, wind shears have the capability
of dissipating wave energy and therefore current flow (Ardhuin & Jenkins,
2006). Although strong high energy waves in the Mediterranean may occur,
lacks the vast expanses of ocean for a significantly large fetch to develop and
so, is generally lower energy waves compared to coasts surrounding open
ocean systems (Mottershead, Bray, Soar, & Farres, 2014).
2.4 Waves
Waves have considerable effects on the mean flow of water; affecting the
pressure and shear drag (Sullivan, McWilliams, & Moeng, 2000). The
interaction between the atmosphere and sea surface, induces a flux by the
transfer of most of the wind energy and momentum to waves. Wave breaking
results in dissipation of energy, thereby inducing further fluxes in momentum
and turbulent kinetic energy from the wave to surface and sub-surface
boundary layers (Hong & Eng, 2003; Paskyabi & Fer, 2014). Turbulent mixing
is enhanced by the energy flux from wave breaking (Gemmrich & Farmer,
2004), whilst accelerated mean flow is the outcome of stress adhered by the
momentum flux. In saturated sea states, energy and momentum flux that are
16
transferred to wave fields, are balanced by the dissipation of energy from white
capping/wave breaking (Weber, Broström, & Saetra, 2006). If energy is not
dissipated, than momentum and energy is advected by waves and stored
across the wave field (Röhrs et al., 2012).
Haslett (2009) describes the relationship between the surface wind speeds and
the resulting growth in wave height. Wave height is directly proportional to the
square of wind velocity. Sometimes, it is best to obtain a single calculated
measurement for wave height that is representative of the state of the sea at a
point in time. To do this, oceanographers use the Significant Wave Height
(SWH), H1/3, which is the mean of the highest one third of all waves at that point
in time (Brown et al., 1999; Camilleri, 2012). Altunkaynak, ASCE and Özger
(2005) assessed the spatial variation of SWH and found that the climatic
conditions have a powerful influence on the nature of the SWH. They also
explain the importance of SWH in representing and determining the wave
energy. A study on the physical characteristics of the wave climate was
conducted in 2003 for the Maltese Islands. The SWH was the main parameter
measured. However, the scale of the study, using numerical models,
encompassed the entire Maltese Islands and so, the resolution was quite low.
The closest point of model SWH to Ramla was 10km north east of the islands.
The study found that the general 10% of the exceedances of SWH of the year
was below 1.5m (Scott Wilson Kirkpatrick and Co. Ltd, 2003).
The wave height increases with the intensification and duration of onshore
winds. Ultimately waves tend to break further outside the surf zone (Aagaard
& Hughes, 2010). Determining the inter-annual variations in wave climate can
be done by analysing a group of parameters, namely the significant wave
height and the mean wave direction. This understanding can be used to
describe the spatial distribution of wave energy (i.e. wave spectrum) at varying
frequencies and subsequently, describe the wave systems of different sea
states of a given area (Sanil Kumar & Anjali Nair, 2015).
2.4.1 Wave Orbitals
17
Particles at the surface, where waves are present, exhibit a circular orbital
movement; with the highest velocities and forward movement along the crest
and the lowest velocity and backwards movement at the trough (Figure 2.3).
Waves of increasing amplitude, experience an accentuation of this effect. A
net flow is produced , whereby the particles flow in the direction of the travelling
wave (Lentz & Fewings, 2012; Monismith, Cowen, Nepf, Magnaudet, & Thais,
2007; Simpson & Sharples, 2012). This was coined the Lagrangian current,
which is also referred to as the Stokes Drift. Surface drifters are designed to
measure this drift. This orbital effect is transposed down the water column, with
gradually shrinking orbitals succeeding further down. The bottom boundary
layer, present above the seabed, transforms circular orbitals into elliptical orbits
(Simpson & Sharples, 2012). Wave orbitals attenuate down the water column
to a depth equal to half the wavelength – referred to as the wave base. When
the wave base delineation is approached, waves begin to interact with the
seabed. The wave base can be used to define the depth of closure, which
marks the “boundary between the upper and lower shoreface…and can be
used to infer a seaward limit to significant cross-shore sediment transport”
(Masselink & Hughes, 2003, p. 196; Nicholls, Larson, Capobianco, &
Birkemeier, 1998).
Figure 2.3: Transposed forward motion of water particles under the forces of propagating energy. Arrows show orbital motion is off put slightly in the direction of wave energy (Source: “Ocean in Motion: Waves and Tides,” n.d.).
18
2.4.2 Wave Shoaling
Waves propagating towards the coast, i.e. travelling from deep waters to
increasingly shallow water, undergo shoaling (Benetazzo, Carniel, Sclavo, &
Bergamasco, 2013; MacMahan et al., 2011; Newell, Mullarkey, & Clyne, 2005).
Shoaling accounts for the changes of wave properties that eventually lead to
breaking. These changes are characterised by decreasing wavelength, speed
and wave period, which result in increasing wave height and therefore
increased wave steepness (Davidson-Arnott, 2009). The development of
shoals creates alterations in the wave breaking process (Idier, Falqués,
Ruessink, & Garnier, 2011). The interaction of, incoming and outbound, long
waves with shoaling short waves, results in a flux of energy depending on the
phase (Van Dongeren, Svendsen, & Sancho, 1996). Near bottom velocities of
wave orbitals vary greatly and rapidly when waves begin the shoaling process
(Lentz & Fewings, 2012).
2.4.3 Wave Breaking
Wave breaking in the nearshore is the main mechanism for generating physical
processes leading to the set-up and wave-induced currents, including
longshore, rip and undertow currents (Smit, Zijlema, & Stelling, 2013).The
scale of the many different types of motions resulting from breaking waves can
range from large vertical motions to minor turbulence, as a means of releasing
the waves energy (Lubin, Glockner, Kimmoun, & Branger, 2011).
Wave-current interactions have a mutual effect on one another, as waves
contribute to the formation of currents, whilst concurrently currents have an
effect on waves (Benetazzo et al., 2013). This relationship shows that currents
exert the greatest influence when the direction of the current flow opposes that
of the propagating waves. Along shore currents flowing within a shallow
embayments will behave differently depending on the wind stress (Jordi,
Basterretxea, & Wang, 2011), which may give rise to waves or no waves. The
presence of surface waves propagating to shallower water, omitting the area
19
within the surf zone, will drastically reduce the magnitude of the underlying
wind driven sub-surface currents due to frictional drag between the wave-
current interaction (Signell et al., 1990).
2.4.4 Wave Refraction and Diffraction
When the incident wave front travelling from deep water to increasingly
shallower water at an angle to headland-bay coasts, wave refraction occurs
(Kusky, 2008). A physical property of water waves, dictates that wave speed
and wavelength decreases as water depths decrease, whilst the wave height
increases (Pickard & Pond, 1983). Conveyed by Snell’s Law, as one end of
the wave interacts with a shallower depth first, forcing it to decrease in speed,
the other end of the wave continues to travel at the initial speed. As a result,
the wave diverts towards the normal of the beach marked between the deeper
and shallower depths (Figure 2.4). When wave refraction occurs, wave fronts
can be observed to approach shallower water at nearly parallel directions to
the beach (Garrison, 2013; Godin, 2009).
20
Figure 2.5: The process of wave refraction where incoming waves alter direction to align their propagation parallel to the beach (Source: Waugh, 2009). (Waugh, 2009).
Figure 2.4: Wave refraction occurrence as waves move into shallower water. Notice how refraction is not complete and waves still break with some angle, on the beach. (Reproduced from Unit 5: Tsunami Propagation, n.d.).
21
The occurrence of wave orthogonals approaching the headland-bay
perpendicularly, is not a good representation of the general wave climate. For
most of the year, waves approach at an angle. When incident waves approach
an embayment obliquely, the headland controls the direction of the incoming
wave field. By the process of diffraction, coupled with refraction (Yasso, 1965),
wave orthogonals bend around the headland (Davis Jr & Fitzgerald, 2004;
Lutgens & Tarbuck, 2012) and because depth is continuously changing, wave
crests closer to the headland are diverted around it, whilst the further wave
continue moving in their initial direction (Figure 2.5), are subject to refraction.
Diffraction results in variation of wave heights along the incident wave front
(Svendsen, 2006). The outcome gives rise to a shadow zone (Figure 2.6); a
sheltered area where waves are greatly attenuated (Holthuijsen, 2007;
Woodroofe, 2002). It can also occur throughout the water column by means
energy dissipation of bottom waters that interact frictionally when variations in
seabed covers appear, such as seagrass beds on sandy bottom (Dalrymple,
Hwang, & Kirby, 1984).
Figure 2.6: Wave diffraction and refraction as wave orthogonals approach an embayment at an oblique angle, whereby the headland has significant control creating a shadow zone behind the headland. (Source: Woodroofe, 2001).
22
This effectively creates spatial variability in the wave breaking process in the
alongshore direction, such that the shadow experiences low energy waves, as
opposed to the unaffected portion of the shore that receives higher energy
waves. This in turn creates alongshore gradients in radiation stresses
(Aagaard & Vinther, 2008), thus driving longshore currents within the surf zone.
Breaking waves are characterised by two portions, the bore and the roller
region. The bore is the state of the wave after breaking, followed by the roller.
The roller is the surging remnant after the wave has broken whilst approaching
the shore. The vertical turbulence velocities, as well the kinetic energy,
decreases with depth from the trough of the breaking waves. The horizontal
velocities, however, are relatively uniform with depth (Lin & Liu, 1998).
Approaching waves breaking, most commonly at an angle to the shore,
exemplify wave energy dissipation, ensuing a highly turbid region. In this final
stage of the incident waves transformation, momentum is transformed into
currents (Peregrine, 1998).
2.5 Currents
As elaborated in previous sections, currents at and near the surface, are driven
by the wind stress and by Stokes Drift resulting from waves. In deep waters,
the surface current response to wind can be considered to be inertial.
Therefore, there is a delayed lag response between wind and surface currents
(Stewart, 2008). The lag explains why currents can be observed traveling in
different directions to the overlying wind (Capodicasa, Foti, & Scandura, 1991;
Mao & Heron, 2008). The scale at which wave-induced currents flow,
generated in the nearshore, are considered to be unaffected by the planetary
forces, such as the Coriolis force (Jenter & Madsen, 1989; Ozkan-Haller, 2014;
Tang et al., 2014).
In deeper waters, physical layers within the water column can be delineated
from one another by a shifting angle of current direction. This is known as the
Ekman spiral (Garrison, 2013). The transference of stress through the water
column is direct and so water movement follows the direction of the stress
23
applied (Lentz, 1995). Currents in the Maltese Islands are generally weak, as
they are throughout most of the Mediterranean because of the negligible
significance of tides (Basterretxea et al., 2004; Jordi et al., 2011). Drago (1980)
shows that there is a consistency with the dominant large scale current flow
around the Maltese islands in a SE direction and this consistency is disrupted
in cases of large storm events. In shallow waters of Ramla Bay, strong winds
and wind shear at the surface are the main driven of mean flow, with some
negligible influence from tidal pressure gradients, whereby there is little
stratification of currents with depth (Jones & Monismith, 2008).
2.5.1 Turbulence and Radiation Stress
Turbulence occurs due to instability by the breaking process of waves on the
shore (Tang et al., 2014), with maximum turbulence occurring within the
breaker zone and swash zone (Bakhtyar et al., 2009). Swirling vortex of fluid
describes turbulence best and exists on different scales of length and time.
Small morphological variations in the seabed are responsible for the
development of small eddies, by which horizontal convection of eddies is driven
by waves following the bore (Peregrine & Bokhove, 1999). As turbulent eddies
lose mechanical energy, they decrease to smaller scales until the dissipated
energy eventually is transformed into heat by molecular viscosity (Brown et al.,
1999; Mohsin & Tajima, 2014; Svendsen, 2006). This gives rise to augmented
diffusivity, which in turn drive fluxes in momentum and energy that is referred
to as stress (Celik, 1999; Kraichnan, 1976). The eddy viscosity in association
with this turbulence, accounts for the process leading to the production,
advection and dissipation of turbulent flow in the surf zone (Inukai, Kuroiwa,
Matusbara, & Noda, 2001; Mohsin & Tajima, 2014).
Variation in radiation stress within the nearshore is a requirement for
generating currents, whether cross-shore or alongshore. This radiation stress
is the product of the net momentum flux forced by the dissipation of breaking
waves (Newell et al., 2005). The incident shoaling and breaking waves create
a bi-directional variation in the mean sea level, referred to as the set-up and
set-down. On open coasts, when incident waves approach the shore
24
perpendicularly, the radiation stress gradient is balanced by the set-up/set-
down variation (Stive & Wind, 1982) and so no longshore currents are created
(Yu & Slinn, 2003). This does not apply to headland embayments because of
the array of alterations in wave dynamics discussed before. When there is
differential breaking along the shore, then pressure and radiation stress
gradients develop within the surf zone. This is responsible for the momentum
transfer from waves to currents (Peregrine, 1998). Turbulent shear stress is
the mechanism by which the differences between radiation stress gradient and
pressure gradient (uniform with depth) are balanced, in turn driving currents
(Ting & Kirby, 1994). There is a linear decrease in the turbulent stress down
through the water column until reaching the bottom boundary layer (Cox &
Kobayashi, 1996), thusly disrupting the no-slip characteristics of the bottom
boundary layer surface (Garcez Faria, Lippmann, Stanton, & Thornton, 2000).
2.5.2 Longshore Currents
Longshore currents form within the nearshore zone. The mechanism driving
longshore currents, is the variation in radiation stress and wave set-up/set-
down along the shore, which are caused by variance in wave breaking along
the surf zone (Idier et al., 2011). As discussed throughout this Chapter,
differential wave dissipation concurs with (Johnson, 2004):
1. Topographic and bathymetric variations
2. Spatially varying approaching wave fields
3. Approaching high frequency surface waves with low frequency waves,
such as edge waves. This is related to effects of shoaling as described
in the previous section.
4. Wave-current interactions within the surf zone
This forms areas of high energy dissipation that produce a resultant mean flow
towards the zone low energy dissipation (Smith, 1993), brought about by
breaking waves of relatively smaller breaking wave heights. Longshore current
generation occurs exclusively in the surf zone; with maximum speeds occurring
in the middle of the surf zone (Davis Jr & Fitzgerald, 2004). These flows can
25
move beyond the nearshore zone and flow along the periphery of winding coast
(i.e. around headlands). Thus, follow the contours of the coastline. The
generated longshore currents, with speeds ranging from 0.3 – 1 ms-1, exhibit a
proportional relationship with the angle that the propagating wave field makes
with the shoreline and the orbital velocities of waves once within the breaker
zone (Brown et al., 1999).
2.5.3 Undertow
Undertow currents commonly occur under high energy wave conditions and/or
when there is the absence of a longshore bar. They result from the landward
moving water by waves that pile up and elevate the set-up. Momentum balance
is then achieved by means of seaward moving currents below the surface
moving water, i.e. concentrated close to the seabed (Davis Jr & Fitzgerald,
2004; Scandura, 2011). Balance is maintained by returning water by landward
moving waves above (Yu & Slinn, 2003). The set-up is responsible for vertical
imbalances in momentum flux and pressure gradient – the foundation for
driving undertow (Cox & Kobayashi, 1996). Ohlmann, Fewings, & Melton
(2012), used drifters to study how current velocities moving shoreward change
with decreasing depth. They found that under weak winds, drifters decelerated
as they move onshore. This decrease in velocity is brought about by seaward
moving undertow. Their study, however, was conducted on an open system
coast, characterized by long beaches and wide surf zones.
2.5.4 Rip Currents
Rip currents are jets of narrow, relatively fast flowing currents towards the sea
that occur throughout much of the world’s beaches (McCarroll et al., 2014;
Winter, van Dongeren, de Schipper, & van Thiel de Vries, 2014). Rip currents
are considered a danger to beach users, where numerous deaths are
associated with drowning when caught up by these relatively strong currents
(Castelle et al., 2014). The momentum flux generated by incoming breaking
waves within the surf zone creating an imbalance between the inside and
26
outside of the surf zone. The conservation of mass requires there to be balance
and this balance is brought about by the creation of currents that flow seaward
(Johnson, 2004). Topographic and bathymetric variations, particularly with
respect to variations in sand bar morphology can cause this variation in wave
breaking (Miles, Guza, Elgar, Feddersen, & Ruessink, 2001). Where breaks in
sand bars are present, wave breaking will not occur as much (locally) in that
portion, as it would over the rest of the bar. The piling of water behind the bar
will seek to maintain balance by flowing through the break, outwards to sea as
a rip current through the funnelled past of least resistance (i.e. the break)
(Murray & Reydellet, 2001).
Two types of rip currents can be associated with headland-bay embayments;
topographic and mega rips (Macmahan et al., 2009). The intensity of the rip
currents and longshore currents are inversely related, in respect to the factors
governing intensity, which are wave height, wave direction and directional
spread (Dusek & Seim, 2013). Storm events may lead to the formation of mega
rips, which are intensified versions of topographically controlled rip currents
that can cause severe beach erosion (Short, 2010).
Figure 2.7: The anatomy of a rip current (Source: Johnson, 2004).
27
A rip current consists of three integral parts (Figure 2.7). Rip currents develop
by means of feeder current. Feeder current refer to alongshore currents
generated within the surf zone as discussed before. Their existence disrupts
the longshore moving flow of alongshore currents (Johnson, 2004). Obtaining
data sets acquired in the field on rip current measurements is difficult because
of the shallow depths in which they originate and their transient nature, making
it hard to know where and when to take measurements (Haller, 2002). A fully
comprehensive computation of the complex circulation dynamics of rip currents
is plausible with numerical modelling, yet may require bathymetric and wave
data, which may not always be obtainable (Leatherman & Fletemeyer, 2011).
2.5.5 Embayment classification
Masselink & Short (1999) recognised the profound difference in hydrodynamic
circulation between an open and long beach coastal system with that of an
embayment, characterized with headlands or rocky outcrops. They designed a
formula to express this relationship. This design is mainly directed towards
classifying the degree of embaymentisation with the presence of headland rip
currents in relation to the incident wave field (Razak, Dastgheib, Surydai, &
Roelvink, 2014).
As proposed by Castelle & Coco (2012), certain issues arise with the original
design, as it disregards the fact that the wave energy dissipation will be less at
the headlands compared to the attenuating effect the sea bottom has on wave
energy as waves approach shallower depths (Garrison, 2013; Pickard & Pond,
1983) Therefore, for embayments receiving low to moderate energy waves
characterised with prominent headlands, the scaling parameter could be
deceptive.
Three types of embayment circulation may occur. Normal circulation typifies
open beaches, having no headlands or barriers (Razak et al., 2014). The
second type of circulation flow is described as cellular circulation. The
occurrence of cellular circulation is signified by strong headland control, in
which rip currents are formed at either side of the embayment or in less likely
28
instances, in the centre. This type of circulation typically results in the formation
of a small gyre that circulates within the confines of the bay. This has
consequential implications on the migration of the sand bar but also beach
shape (Castelle & Coco, 2012; Daly, 2013; Razak et al., 2014). Normal type
circulation can also be possible in embayments with headlands where the size,
length and shape of the beach is large enough for currents and waves not to
be powerfully affected by interfering topography. This may result in numerous
rips occurring across the beach. This is referred to as transitional circulation,
which may be assumed to be an intermediate of the prior two (Castelle & Coco,
2012).
The significance of the embayment characterisation and the hydrodynamic
processes involved is important when applied to integrated coastal zone
management, by the concept of sediment cells. Sediment cells can be
described as “spatially discrete areas of the coast within which marine and
terrestrial landforms are likely to be connected through processes of sediment
exchange… commonly identified as self-contained where little or no sediment
movement occurs across cell boundaries” (Stul, Gozzard, Eliot, & Eliot, 2015,
p. 5). This coastal cell concept aims to understand the sediment budget of a
closed system, in which studies on the dynamics of currents and wave climate
within a cell need to be understood, not only spatially but over short to long
term periods for future management of the coastal area (Montreuil & Bullard,
2012; Sedrati & Anthony, 2014).
2.6 Lagrangian Measurements
In operational oceanography, currents are measured by either Eularian or
Lagrangian methods. The Eularian approach primarily involves measuring
current vectors from a fixed point on the sea bottom, making it possible to
determine currents distribution and magnitude through the water column
(Sharples & Simpson, 2012). The Lagrangian method does the opposite. The
Lagrangian method tracks the current vectors freely, untethered to the seabed
by means of drifters that flow with the current on the sea surface or sub-surface
(Emery & Thomson, 2004). A drifter deployed in proximity to rip currents should
29
theoretically follow a similar trajectory. This has been recorded to occur in the
field by Schmidt, Woodward, Millikan and Guza (2003), when a drifter was
deployed within the surf zone of a rip channelled bay. Lagrangian
measurements from drifters are subject to the net movement of sub-surface
water movement in the direction of the travelling wave field, which is caused
by the underlying physics of wave orbitals (Capodicasa et al., 1991; Sharples
& Simpson, 2012), known as the Stokes Drift.
2.6.1 Drifters
Drifters can be simple in design and cheap, equipped with a low cost GPS
logger and be just as effective (MacMahan, Brown, & Thornton, 2009). The
accuracy of the equipped GPS can vary significantly depending on whether it
is differential or non-differential. Non-differential GPS trackers are more
accurate than the former, however, they are much larger. The added bulk
would, consequently, impair the drifter’s abilities, particularly when measuring
within and outside the surf zone (Sabet & Barani, 2011). A description of the
custom made drifter used in this project is provided in the Methodology
Chapter.
2.6.2 Rosario-SHYFEM Model
The SHYFEM model (Shallow Water HYdrodynamic Finite Element Model) is
a two-dimensional vertically integrated model, which solves shallow water
hydrodynamic equations of coastal areas, in which time integration is resolved
using a semi-implicit algorithm (Dinu, Bajo, Umgiesser, & Stănică, 2011). The
Rosario-SHYFEM model is a recently developed model specified to the
Maltese Islands. Consequentially, there is a lack of related literature. The
SHYFEM model is governed by the shallow water equation (Equation 2.1).
30
Equation 2.1
𝜕𝑈
𝜕𝑡+ 𝑔𝐻
𝜕𝜁
𝜕𝑥+ 𝑅𝑈 + 𝑋 = 0
𝜕𝑉
𝜕𝑡+ 𝑔𝐻
𝜕𝜁
𝜕𝑦+ 𝑅𝑉 + 𝑌 = 0
𝜕𝜁
𝜕𝑡+
𝜕𝑈
𝜕𝑥+
𝜕𝑉
𝜕𝑦= 0
Where, ζ, is the water level, g is the acceleration due to gravity; t, is time; H is
the depth of water. X and Y are substitutable components for other elements,
such as wind stress or non-linear terms. The U and V components represent
the transports in the horizontal dimension (x,y), which can be extracted from
the U and V velocities in the vertical dimension by means of integration (Desira,
2015; Umgiesser, Canu, Cucco, & Solidoro, 2004). Finite elements of the
model refer to an unstructured numerical grids that are generated by an
automatic mesh generator. The Rosario-SHYFEM model applies a triangular
finite element grid that is defined by nodes (Figure 2.8). The grid becomes
more refined closer to the coastline, resulting in a higher resolution to be
obtained where the variation in hydrodynamics is more complex. The depth is
specified for each element, with the velocities of the currents quantified at the
centre and the water level prescribed at the nodes (Dinu et al., 2010;
Umgiesser et al., 2004).
Figure 2.8: Rosario SHYFEM gridded mesh consisting of triangular elements and node, which become more refined closer to the coastline for a higher resolution (Source: Produced by PORG).
31
SHYFEM is also assembled by a number of models and equations which
compute different physical processes and features. Namely, these are wind
driven waves, wave-driven forces, wave-current interactions, sediment
dynamics, shear and frictional stresses, cohesive and non-cohesive
sediments, advection-diffusion equations, morphodynamics, amongst many
more. Respectively, these models include a hydrodynamic model, spectral
wave model, sediment transport model and bed model. When input into the
hydrodynamic model, the radiation stress gradient of the wave induced forces
produces results for the changes in wave set-up/down and correspondingly,
currents (Ferrarin et al., 2008).
2.6.3 SWAN Model
The SWAN Model is a coastal phase averaged, Eularian based model that
produces predictions of the development of energy within the wave spectrum
of waves approaching the coast (Gorrell, Raubenheimer, Elgar, & Guza, 2011;
Liau, Roland, Hsu, Ou, & Li, 2011). The third generation model, was developed
to produce outcomes for the spectrum of wave conditions under the influence
by indiscriminate wind, current and bathymetric conditions (Daly, 2013; Ris,
Holthuijsen, & Booij, 1999). The general equation (Equation 2.2) run in the
SWAN model is:
Equation 2.2
𝜕
𝜕𝑡𝑁 +
𝜕
𝜕𝑥𝑐𝑥𝑁 +
𝜕
𝜕𝑦𝑐𝑦𝑁 +
𝜕
𝜕𝜎𝑐𝜎𝑁 +
𝜕
𝜕𝜃𝑐𝜃𝑁 =
𝑆
𝜎
Whereby t, is time, x and y are the horizontal coordinates, 𝜃 is the direction
and 𝜎 is the intrinsic frequency. The action density is represented as a function
of all these parameters by means of N(𝜎, 𝜃; 𝑥, 𝑦, 𝑡). Through development of
numerical equations, it has been adapted for coastal shallow waters, so as to
simulate wave processes, such as refraction and shoaling (Ris et al., 1999).
Energy dissipation due to current and/or topographical interactions, as well as
wave breaking and bottom friction are computed. In the initial development of
the model, diffraction may have been omitted but is now possible to compute
32
by using a phase-decoupled refraction-diffraction approximation. Wave-current
interactions may be modelled to produce a more realistic output but requires
input of current information, generated from a current model (Choi & Yoon,
2011). The SWAN coastal model is run on data input from other models, such
as SCMR and SKIRON, which models wind. Therefore, if there is a deficit of
data from the component proxy models, then the data produced from SWAN
will also have data gaps.
The SWAN model in Malta has already been validated by Drago, Azzopardi,
Gauci, Tarasova and Bruschi (2012), using observational wave data collected
by the moored instruments, such as the Datawell Waverider buoy. Ideally,
highly accurate in-situ data is collected for all physical parameters (i.e. wave
climate and currents) but the long term measuring is restricted to a localised
area. Therefore, numerical models are often implemented to extrapolate data
to provide a synoptic interpretation that spans a wider spatial and temporal
range. The SWAN model has a spatial resolution of 0.001o, encompassing a
domain marked by a boundary between 14.040-14.700oE in longitude and
35.665-36.206oN in latitude (Azzopardi, 2012). The outputs of the model can
be generated in three hourly intervals, providing Significant Wave Height, Peak
Wave Period (of the variance density spectrum) and Mean Wave Direction
(Drago et al., 2012).
33
Chapter 3 Methodology
3.1 Materials Used
The field work for this project required the use of a main vessel to travel to the
location, as well as a smaller and easily manoeuvrable vessel, in order to move
around the bay with ease, to deploy and recover the drifters. A logbook was
also kept to take record readings.
3.1.1 Bathymetric Depth Equipment
A bathymetric map was produced for a more comprehensive understanding of
how the local topography and bathymetry may affect sea current flow. This
required the use of GPS application (Satellite Check by DS Apps) and a
pressure depth gauge (used by divers), which was tied to a weighted line.
Unfortunately, because the pressure depth gauge was designed as an
analogue measuring device, the readings were of a low accuracy of
approximately +/- 1m. The accuracy was found to be far higher in deeper water
than in shallower water. This was discovered when comparing the readings of
the pressure gauge with the depth sounder on the vessel (for deeper water)
and with a weighted measuring tape (in shallower water). The presence of the
waves also caused variations in the readings. A measuring tape had to be used
in shallower waters. The gauge consisted of two arms, both of which begin at
the 0m mark. One arm moves across the gauge in response to changes in
pressure. The other arm (orange) served as a marker that moved with the
response arm but remained in place at the deepest reading, even when
brought back to the surface (Figure 3.1).
34
A
B
C
Figure 3.1: Equipment used to measure bathymetric depths. (A) Line with weight attached at one end, (B) Diver’s analogue pressure depth gauge, (C) measuring tape with weighted end.
35
3.1.2 Drifters
The drifters used for this study have been custom designed for the application
of measuring surface currents. A consequence of surface water drifters, is the
inevitable influence of surface winds on the floating device. Therefore, the
design attempt to minimize this frictional interference as much as possible
(Poulain, Gerin, Mauri, & Pennel, 2009). The drifter possesses six components
(Figure 3.2). The buoyancy weight, which kept the structure of the drifter in an
upright position. The current vane is a wooden frame in the shape of a 3D
cross, which resembles the CODE/Davis drogue design. Its function is to
propel the drifter into movement by frictional drag from the surface area of the
vane. The floatation device kept the drifter at the sea surface, while a water
proof compartment was used to hold the GPS tracker. A flag was fixed so that
the drifter would be easily tracked from the vessel, particularly for retrieval. A
Figure 3.2: Diagram illustrating the components of the customised drifter and a photo of the drifter in the field whilst recording during a calm day (Source: Author).
36
1m long rope attached the current vane and the float/pole, as well as one
connecting the vane to the weight. The weights were made from concrete, the
current vane from wood, coated with protective paint and the float was made
from a pool noodle. The container was placed as low as possible on the pole
to reduce the friction forces from surface winds. Unfortunately, this
compromise resulted in the occasional submergence of the container, which
sometimes meant the GPS connection was lost and so, resulted in gaps in the
data for some track recordings.
A miniHomer GPS, funded by the European Union, was used for the drifters.
The miniHomer GPS Position Finder used had some beneficial features, ideal
for the drifter design. Apart from being light-weight and compact in size, it was
also accurate, with low power consumption. The minihomer was capable of
taking a single instantaneous reading or be set to log coordinates continuously,
with a pre-set time interval. Before the drifter was deployed, it was made sure
that the GPS was in sync with the satellites above (NAVIN Corporation, 2013).
The miniHomer is compatible with the Ntrip software, which imports
geographical coordinates, displays the logged points on a map and export files
in a number of formats. Ntrip was used to adjust the data recording setting of
the miniHomer GPS so that recordings were taken every ten seconds.
3.2 Data Collection
3.2.1 Bathymetric depth Data
The pressure gauge was tied to the weighted end of the line and lowered to
the seabed. A reading was marked on its analogue gauge, showing the
deepest point it descended to. Simultaneously, a GPS position was taken using
the smartphone app. The depth gauge was recovered and the reading marked,
was recorded. Since a smaller vessel was used to take measurements, in
cases when the wind was strong enough to cause the vessel to drift, a small
anchor had to be dropped for every reading taken, in order to remain in place
so that the GPS reading would correspond to the same position of the depth
measurement.
37
The diminished accuracy in shallower water mentioned before, required the
use of a measuring tape to be used in water, generally shallower than 5m. At
the end of each session the coordinates and depth readings were input into an
Excel spreadsheet and saved as a Comma Separated Value (CSV) file. A total
of 340 data points were taken.
Bathymetric data encompassing the wider extent of the area across 286 points,
was also obtained from the Physical Oceanography Research Group (PORG),
within the Department of Geosciences of the Faculty of Science. To rasterise
the soundings, Kriging interpolation was conducted on these two sets of data.
The scope of this was to contrast and compare the products of two different
methods of bathymetric surveys.
3.2.2 Drifter Data Collection
Two custom made drifters were supplied by the PORG. The drifters were
deployed from the smaller vessel within the study area. Prior to deployment,
the GPS logger was switched on and some time was allowed for the device to
sync with the satellite in orbit overhead. Data recording was started from the
main vessel and the time of activation was recorded. From the main vessel the
drifters, with the GPS logger already within the waterproof containers, were
taken out and deployed at a random position within the boundaries of the study
area. The time of deployment and retrieval of each drifter for every track was
recorded in a logbook. Whilst still recording, the drifter, was taken to a different
part of the bay for a following deployment. The same procedure was repeated.
During the deployment, the drifters were regularly monitored using a pair of
binoculars.
It was found to be easier not to cease recording of the GPS between
deployments because it would have increased the possibility for human error,
possible water damage from removing the GPS from the container or losing
the connection to satellites when switching the logger off and on. As a
precautionary practice, the GPS logger was taken back on board the main
vessel to download the tracks and view them in NTRIP, to ensure that
38
recordings were being logged. One drifter would have been deployed several
times within a single day and data collection period spanned from between July
till October of 2015 (Figure 3.3).
Figure 3.3: Histogram of the number of tracks taken on the days of fieldwork.
It was planned to record two hour tracks, or longer, if possible. However, this
often could not be done due to numerous interruptions stopping the drifter.
These interruptions were typically due to the presence of anchored boats,
reduced visibility after sunset, as well as other circumstances mentioned in the
Limitations section (Chapter 3). Using the digital wind anemometer on-board
the main vessel a single reading of the local wind direction was recorded in the
logbook that was representative of the general conditions of the day of the
deployments. The wind direction and the starting points (of certain tracks) were
colour coded for organising the final map products. (Table A-1 in Appendix A)
This method of keeping the GPS recording, whilst traveling with the drifter on
board the small vessel and during track recording resulted in excess track data.
Consequently, post data management was required to clean and remove the
excess track data, whereby two different methods were used. One involved
using Ntrip directly, where the entire track (track of current and of small vessel
transportation combined) was split up, using the “Split Track” function. The
4
1
4
1
2 2
7
2
1
4
6
1 1
0
1
2
3
4
5
6
7
8
Nu
mb
er o
f Tr
acks
Date of Fieldwork
Number of Drifter Tracks taken per day
39
necessary tracks of the drifters were kept and saved as CSV file. The second
method option, was to save the entire track as a CSV file and open it in
Textpad. From the documented start and end times recorded in the logbook,
the necessary data was kept and the excess removed. This process had to be
done for every individual track that was recorded in a single session.
3.3 Data Processing
3.3.1 ARCGIS
With the complete set of cleaned data from tracks and bathymetric depth
measurements in CSV format, ARCGIS was used to plot the output. The
WGS_1984 Geographical Coordinate System was used for all map layers.
3.3.2 Bathymetric map
The latitude, longitude and depth, recorded in the logbook, were input into a
Microsoft Excel spreadsheet. The spreadsheet was then saved as a CSV file.
To compute a bathymetric map using ArcGIS, the latitude and longitude
coordinates where placed in the x-axis and y-axis fields, respectively and depth
(in m) in the z field. It was required that the coordinates be in decimal degrees
format for ArcGIS to enable plotting each point. The CSV file was opened into
ArcMaps and converted into a point shapefile.
To interpolate the shapefile and illustrate the depth contours, the Kriging
Interpolation1 method was used because of its tested higher accuracy
compared to other spatial interpolation methods (Jang, Park, & Choi, 2015;
Shanshan, Meijian, Di, & Shaohui, 2015). The interpolated product could not
account for the perimeter of the coastline. The map layer was modified by
clipping the Kriging product using a polygon which outlines the boundaries of
the study area and coastline. The polygon was traced along the coastline in
1 The Kriging Interpolation method is a geostatistical spatial optimal estimator that utilizes a generalized linear regression (Ge & Cheng, 2014).
40
Google Earth and exported as a KML file. The KML file was converted into a
layer file using the conversion tool function. With the polygon boundary layer
on top of the Kriging output, the Image Analysis tool was used to clip out the
area covered by the polygon from the Kriging output.
3.3.3 Drifter Data
The tracks were input into ArcGIS as CSV files and converted into shapefile
files. Each track was illustrated by a series of points (i.e. those point taken
every ten seconds). Each track was formatted to be represented as a line with
an arrow bearing at the heading of travel. The drifter tracks were grouped into
maps according to the day of recording and colour coded according to the
direction of wind (Table A-1).
3.3.3.1 Calculations
Table 3.1 shows the structured model of the table used to process data and
perform the calculations.
Table 3.1: Model of the table of the procedures used to process drifter track data.
Latitude (Radians)
Longitude (Radians)
Distance/second
(m)
Bearing* 1-5
V Component
U Component
Pythagoras Equation
*Split into five separate columns.
The raw data from the GPS was processed to obtain the distance between the
series of recorded points, the bearing of travel between each set of points and
the V and U component of the corresponding current. This enabled the
quantification of the path, direction and velocity of travel for each drifter (i.e.
the current). For every drifter track data file, the latitude and longitude (in
decimal degrees) were inserted into an Excel spreadsheet. The angle in
degrees was converted into radians, which was the required form to be used
in subsequent equations.
41
Figure 3.4: Diagram of the recorded points of a track. The distance and bearing between each point was calculated using the haversine formulae.
The Haversine equation (Equation 3.1 ), which measures the shortest distance
between two points on the curved surface of the Earth (Bullock, 2007; Drago
et al., 2012), was used to calculate the distance between waypoints taken
every ten seconds. The distance was divided by ten in another column to obtain
the distance (in m) travelled in one second. Essentially, this spherical
trigonometric formula is designed for a perfect sphere and computes the plane
intersection of a sphere. Therefore, the Earth is assumed to have a perfect
spherical shape, rather a geoid shape. Between two points with geographical
coordinates (Figure 3.4), the change in latitude and longitude can be found.
The derivation of the Haversine formula requires the radius of the Earth, which
is about 6371km (Javale, Gadgil, Bhargave, Kharwandikar, & Nandedkar,
2014; Kifana, 2012).
42
Equation 3.1
∆𝑙𝑎𝑡 = 𝑙𝑎𝑡2 − 𝑙𝑎𝑡1
∆𝑙𝑜𝑛 = 𝑙𝑜𝑛2 − 𝑙𝑜𝑛1
𝑎 = 𝑠𝑖𝑛2 (∆𝑙𝑎𝑡
2) + cos(𝑙𝑎𝑡1) . cos(𝑙𝑎𝑡2) . 𝑠𝑖𝑛2 (
∆𝑙𝑜𝑛
2)
𝑐 = 2. 𝑎𝑡𝑎𝑛2(√𝑎. √(1 − 𝑎))
𝑑 = 𝑅. 𝑐
Where, d, is the distance between two points and R, is the radius of the Earth.
When input into Excel for computing, the formula has to be altered to be
recognised in Excel (Equation 3.2).
Equation 3.2
= 𝐴𝐶𝑂𝑆( 𝑆𝐼𝑁(𝐿𝐴𝑇1) ∗ 𝑆𝐼𝑁(𝐿𝐴𝑇2) + 𝐶𝑂𝑆(𝐿𝐴𝑇1) ∗ 𝐶𝑂𝑆(𝐿𝐴𝑇2) ∗ 𝐶𝑂𝑆(𝐿𝑂𝑁2
− 𝐿𝑂𝑁1) ) ∗ 6371000
The bearing along the straight path between two points on the Earth’s surface
was calculated using the Forward Azimuth formula (Equation 3.3) so as to
determine the direction of currents. The results were listed under the column
‘Bearing 1’. Equation 3.4 was input into Excel. There is no difference between
Equation 3.3 and 3.4 in terms of result calculated. However, the Excel
programme is designed so as to reverse the argument of the atan function.
Equation 3.3 produces a final output in the range between -180o and 180o,
which would have had to be adjusted to the appropriate compass range (0o-
360o).
Equation 3.3
𝜃 = 𝑎𝑡𝑎𝑛2(𝑠𝑖𝑛(𝑙𝑜𝑛2 − 𝑙𝑜𝑛1) ∗ 𝑐𝑜𝑠(𝑙𝑎𝑡2), 𝑐𝑜𝑠(𝑙𝑎𝑡1) ∗ 𝑠𝑖𝑛(𝑙𝑎𝑡2) − 𝑠𝑖𝑛(𝑙𝑎𝑡1)
∗ 𝑐𝑜𝑠(𝑙𝑎𝑡2) ∗ 𝑐𝑜𝑠(𝑙𝑜𝑛2 − 𝑙𝑜𝑛1))
43
Equation 3.4
𝜃 = 𝐴𝑇𝐴𝑁2(𝐶𝑂𝑆(𝐿𝐴𝑇1) ∗ 𝑆𝐼𝑁(𝐿𝐴𝑇2) − 𝑆𝐼𝑁(𝐿𝐴𝑇1) ∗ 𝐶𝑂𝑆(𝐿𝐴𝑇2) ∗ 𝐶𝑂𝑆(𝐿𝑂𝑁2
− 𝐿𝑂𝑁1), 𝑆𝐼𝑁(𝐿𝑂𝑁2 − 𝐿𝑂𝑁1) ∗ 𝐶𝑂𝑆(𝐿𝐴𝑇2))
The calculated bearing was converted back to degrees, under column labelled
as ‘Bearing 2’, for mathematical simplicity in further calculations. The positive
values were copied to the ‘Bearing 3’ column and the negative values obtained
were added by 360 (into column Bearing 3) to get a positive angle in a
clockwise direction. The resulting list of positive bearings were filtered between
the following ranges;
0 ≥ θ ≥ 90,
90 > θ ≥ 180,
180 > θ ≥ 270
270 > θ ≥ 360.
Figure 3.5: Diagram illustrating the quadrants with 0o representing North with the corresponding ranges of angles and the trigonometric element which determines how the V
and U components would be calculated.
44
It was necessary to filter results before calculating U and V according to the
quadrant in which the bearing lies. The sorted angles from column ‘Bearing 3’
were subtracted by 0, 90, 180 and 360, respective of the corresponding ranges
above and put into column ‘Bearing 4’. This made it possible to find the angle
between the Resultant and the axis. The results were then converted back to
radians under the ‘Bearing 5’ column. Using the appropriate, trigonometric
function, according to the region of the angle (Figure 3.5), the V and U
components were calculated. The sign for both components is an indicator of
the corresponding resultant region. In order to check that the solutions for the
V and U components were calculated correctly, Pythagoras Theorem was
applied to determine the length of the resultant component, which should have
equated to the distance travelled per second, calculated before.
3.3.4 SWAN Data
The SWAN model is run and maintained by PORG. The model produces a
forecast for three and a half days, supplying data with a resolution of 0.001o
and a temporal frequency of three hours. For this study the PRG supplied
monthly extracted data for Ramla Bay. Multi-columnar Point Files were
produced, which contained all the relevant data for a single point. This data
was input in ArcGIS. Each point contains the Latitude, Longitude and a time
series of the Significant Wave Height (SWH) and Mean Wave Direction (MWD)
(J. Azzopardi, 2012). The SWH and MWD, were represented in a grid boundary
(Figure 3.6), comprising of 433 points.
45
Figure 3.6: Point grid used in the SWAN model computations. The yellow border highlights the Transect taken and within in it are the highlighted points for the Time Series analysis.
3.4 Modelling Data
3.4.1 SWAN Model
The modelled data was produced for each point, extrapolated every three
hours for the duration of each corresponding month between August 2015 and
January 2016. For each month and for every grid point, the minimum, the
maximum and the average values of each parameter were calculated. The
geospatial analysis toolbox was used to interpolate the data using a natural
neighbour interpolation method. An interpolated map was produced to illustrate
the spatial distribution that represents the average conditions of the two
parameters of every month. (Zhou, Huang, & Zhang, 2015). Again, the domain
of the interpolated map extended onto land and so it had to be clipped, as was
executed for the bathymetric map.
The averages of all six maps, for the average SWH was computed with the
Raster Calculator function, which utilizes Python syntax to complete map
46
algebra expressions and produce a final raster product. The average had to be
computed for each parameter individually by adding the spectrum of monthly
average raster maps and dividing by six.
A map product was ultimately created, encompassing an area larger than the
intended project boundaries. It was decided to provide a map on a larger scale
because at the smaller scale of the bay area, the interpolated map of the
modelled data would have had a low resolution for proper analysis.
Additionally, it would be beneficial to foresee the external physical conditions
that ultimately influence the conditions within the study area. The map may
also prove advantageous for future studies of the surrounding area.
3.5 Statistical Analysis
The drifter tracks for currents were statistically analysed through a validation
of the SWAN model. The drifter tracks were plotted in ArcGIS across the grid
of the Wave Direction output from SWAN. Only a few tracks were taken, in
which the points along the track, which coincided as close as possible to the
SWAN grid points, spatially and temporally, were used. The selected drifter
tracks had to be individually paired with the 3-hourly Wave Direction output
from SWAN, so as to encompass the time the track was taken within the 3-
hour time range from SWAN. The bearing values of the SWAN grid point of
Wave Direction and the corresponding track points in closest proximity to one
another were recorded in a table for each track. The tabulated data of wave
direction and current direction (Table A-2) and a scatter plot was produced.
The two variables were correlated using Pearson Correlation. The Root Mean
Square Error (RMSE) was also determined to assess the level of association
between the wave and current direction. The process of subsequent to verify
the results is described in Chapter 4.
For current velocity, the U and V velocity components of a selected number of
tracks, grouped according to the day of deployment, were plotted on separately
into a time-series graph. So the individual tracks on the time-series could be
identified and linked to the maps, colour coded marks on the selected tracks,
47
were added at the start of each of the selected track. The colour coded legend
may be found on Table A-1 of Appendix A.
A time-series was drafted for the average SWH of the six months over the
same point in the centre of the bay (Point 2). Additional time-series were
conducted for each month across the five points along a transect of the grid
from the SWAN model (Figure 3.6). The selected grid points ran seawards,
perpendicular to the beach. More points concentrated within the bay were
taken and gradually reduced further out from the beach. Point 1 corresponds
to the point closest to the beach (i.e. shallowest) and every highlighted point
up to the last (Point 5), represents the deeper water. Daily data for the average
atmospheric pressure taken by a meteorological station in Rabat, Gozo, was
acquired (“Underground Weather,” 2016). The average atmospheric pressure
time-series was superimposed to each time-series for analysis.
3.6 Limitations
Ramla Bay is a popular bathing area for people to retreat to during the summer,
both on the coast and at sea. Unfortunately, the majority of deployments could
have only been done during the weekend (due to the availability of the vessel),
when the presence of people and recreational boats were highest. In cases
when interruptions were suspected of occurring, the track would have had to
be stopped and the drifter reset elsewhere. Similarly, a drifter deployment track
would have been ceased when the drifter became entangled in the float line
marking the boundary of the bather area, which was quite a common
occurrence. If the drifter was not removed before colliding with the line, the line
was otherwise raised to allow the drifter to pass and enter the surf zone that
was situated shoreward of the float line. At this point, it would have to be
stopped when the bottom weight of the drifter began caressing the sandy
seabed.
The loss of a drifter on the 18th of July 2015, when it was deployed around the
north-eastern area of the bay and possibly hit by a passing boat, hindered the
capacity of data collection. Consequently, the amount of drifter tracks that
48
could have been recorded, was reduced, in addition to losing the stored GPS
data already stored on the logger of the same day and the day before. The
GPS tracker sometimes failed to take full records, resulting in significant data
gaps for some track recordings. Unfortunately, a few track recordings had to
be discarded. This error was assumed to be the cause of the temporary
submergence of the GPS in rough conditions. A major limitation to the study
was the restricted timeframe in which the area could have been properly
studied. Ideally a more comprehensive study could have been undertaken,
were the annual and seasonal trends would have been represented.
The low accuracy of the pressure gauge used to acquire depth readings for the
bathymetric map entailed that the map product should serve as a reference for
descriptive purposes and analysis. Also, because the embayment’s
substratum is composed predominantly of sandy material, the seabed
morphology is subject to major variations, both spatially and temporally, from
when readings were taken. Therefore, the measurements taken represent the
morphology and bathymetry of the bay at a fixed period (mid-August to mid-
September). The Rosario-SHYFEM hydrodynamical model, which was to be
used for a comparative analysis with the drifter tracks taken and to validate the
model, was not successfully implemented on time and so could not be used for
the analysis. This was a major limitation, as it was significant component of this
dissertation. Accordingly, an alternative methodology was developed to
statistically analysis the data acquired, as discussed above.
49
Chapter 4 Results and Discussion
In this Chapter, the results from the data collected in the field and produced
and processed from models are presented. The study is also critically reviewed
and the link between the results and the material discussed in the literature
review, is made. Quintessentially, the research questions are answered in this
Chapter and the discussion deciphers whether the hypotheses made, are
correct or not. The bathymetric maps and drifter tracks were plotted separately
for descriptive analysis. The same was done for maps produced for the
average SWH and wave direction. The time series analytical method was used
to graphically illustrate the variability of the SWH over time and space. A time-
series was also plot for the U and V velocity components of selected current
tracks. The difference between current velocities in relatively deeper waters
outside the nearshore zone and those generated by wave breaking as
longshore currents, are discussed. To measure the relationship between the
wave and current direction for a selected number of drifter tracks, the Pearson
correlation coefficient was determined and Root Mean Square Error was
performed to check the level of association of the data with the line of best fit.
All maps produced and referred to in this Chapter can be found in the
Appendices.
4.1 Bathymetric Results
Two bathymetric maps of the study area were produced using data collected
in-situ (Figure 4.1) and by the Physical Oceanography Research Group
(PORG) (Figure 4.2). Despite using a low accuracy measuring device for the
in-situ data, the map produced, still proved to have a more detailed, higher
resolution than that of the PORG output. The PORG map does, however,
encompass a wider extent of the area.
The in-situ depth measurement map (Figure 4.1) shows two notable features
of the bathymetry that are not illustrated in the PORG map. The west-side of
the bay appears to have a more gradual slope and broader extent of the
50
shallow depth. On the east side of the bay, the gradient tends to be steeper,
with a narrower extent of sub-marine rock fields. From observations made in
the field at the time of data collection, this can be attributed to the extensive
rock field below sea-level, originating due the erosion of the Upper Coralline
Limestone escarpment on top of the Blue Clay slopes on both headlands. The
more extensive rock field on the west-side is characterised by smaller rocks,
which give the area a highly irregular and variable surface bathymetry. This
irregular surface is not represented properly in the map, which is a limitation of
the method used. The other side of the bay (east-side), was characterised by
fewer and larger boulders close to the shoreline with less variation than the
opposite side of the bay. Generally, along the periphery of the two sides of the
headlands within the bay, a higher rugosity can be observed than in the centre
of the bay, where there is a sandy seabed. The exception to this, are the
remnants of the submerged sea-wall and the patches of Posidonia oceanica
seagrass beds. Secondly, the shape/outline of the sand bar is clearly outlined
on the in-situ measured bathymetric map (Figure 4.1) and corresponds to the
satellite imagery of the bay, if superimposed on one another. This is another
clear advantage the in-situ map has over the PORG map. The sand bar during
the summer period between August and September (when measurements
were taken) has a crescentic shape, as opposed to a linear parallel sand bar
observed by Azzopardi (2015) after the winter period.
51
Figure 4.1: Bathymetric Map produced from in-situ measurements, with the shallowest
depths in red, graduating down to deeper depths in blue.
Figure 4.2: Bathymetric Map produced data obtained through numerical modelling performed by the PO-Unit, with the shallowest depths in red, graduating down to deeper depths in blue.
Source: Esri, DigitalGlobe, GeoEye, Earthstar Geographics, CNES/Airbus DS, USDA, USGS,AEX, Getmapping, Aerogrid, IGN, IGP, swisstopo, and the GIS User Community
¯LegendRamla BayBathymetry
-0.303 - 0
-0.718 - -0.304
-1.12 - -0.719
-1.54 - -1.13
-1.95 - -1.55
-2.37 - -1.96
-2.78 - -2.38
-3.2 - -2.79
-3.61 - -3.21
-4.02 - -3.62
-4.44 - -4.03
-4.85 - -4.45
-5.27 - -4.86
-5.68 - -5.28
-6.1 - -5.69
-6.51 - -6.11
-6.92 - -6.52
-7.34 - -6.93
-7.75 - -7.35
-8.17 - -7.76
-8.58 - -8.18
-9 - -8.59
-9.41 - -9.01
-9.83 - -9.42
-10.1 - -9.84
-10.6 - -10.2
-11.1 - -10.7
0 150 30075 Meters
Source: Esri, DigitalGlobe, GeoEye, Earthstar Geographics, CNES/Airbus DS, USDA, USGS,AEX, Getmapping, Aerogrid, IGN, IGP, swisstopo, and the GIS User Community
0 500250 Meters
¯LegendPORG Bathymetric Map 1
-1.14 - 0.452
0.453 - 2.04
2.05 - 3.63
3.64 - 5.22
5.23 - 6.8
6.81 - 8.39
8.4 - 9.98
9.99 - 11.6
11.7 - 13.2
13.3 - 14.7
14.8 - 16.3
16.4 - 17.9
18 - 19.5
19.6 - 21.1
21.2 - 22.7
22.8 - 24.3
24.4 - 25.9
26 - 27.4
27.5 - 29
29.1 - 30.6
30.7 - 32.2
32.3 - 33.8
33.9 - 35.4
35.5 - 37
37.1 - 38.6
38.7 - 40.2
40.3 - 41.7
41.8 - 43.3
43.4 - 44.9
45 - 46.5
46.6 - 48.1
48.2 - 49.7
52
4.2 Lagrangian Drifter Currents
A map of all the drifters track measurements of recorded currents was compiled
(Figure 4.3). Additionally, the sorted drifter track maps, according to the day of
deployments can be found in Appendix A. Also in Appendix A, is a table of all
the information about the tracks, including the wind direction of the day with
colour coded legend, the times of drifter deployments and retrieval and the
duration of each track. A total of 46 hours and 30 minutes of current recordings
were measured throughout the collection of data, with the average duration of
a single track lasting to approximately 76 minutes.
Figure 4.3: The complete set of drifter tracks recorded throughout the data collection period superimposed on a map with bathymetric contours extrapolated from the bathymetric map interpolation.
The plotted tracks presented, show that under north-westerly winds (red),
currents flowing from the NNW from deeper water and approach the
embayment obliquely, as was observed on the 5th of July 2015 (Figure A-1).
Under NNW winds, longshore currents, as were clearly identified on the 18th of
July 2015 and the 26th of September 2015. The directional flow of these tracks
mirrors the coastline and/or deviates slightly at some point. The same is true
Esri, HERE, DeLorme, MapmyIndia, © OpenStreetMap contributors, and the GIS user community
0 60 120 180 24030Meters
¯
53
for the opposing direction from the NNE (yellow) and ENE (green), in which
longshore currents are clearly identified, flowing along the contours of the
coast. The exception being for currents measured on the 11th of July 2015,
whereby the longest track does not show directional preference to coastal
configuration but to the wind direction, largely because it was deployed in more
open water. Therefore, the results agree with the hypothesis stated, when
currents have direct influence from the shallower bathymetry.
4.2.1 Drifter Trajectories: Current Velocities
The extracted U and V velocity vectors, were initially intended to be used in the
Rosario-SHYFEM model. Instead, they have been presented as time-series
velocity graphs for four selected tracks. Only four tracks were chosen because
of the insurmountable amount of the data available for discussion. The data
not used has been made available with this dissertation.
For the duration of most tracks taken on the 5th of July 2015 (Figure A-14), the
drifter appeared to be stationary for short periods. The highest velocity reached
was during the 16:13-16:41 track, with a peak in the U vector velocity
exceeding 1m/s. The track 12:17-12:37 was the weakest, with velocity
fluctuations not exceeding 0.6m/s. The large peak at the end resulted whilst
retrieving the drifter and not the actual current. This could be observed for
several other tracks. Both tracks occurring between 12:15 – 12:52 and 16:10
– 16:38 were characterised by minor fluctuations in velocities not exceeding
1m/s.
On the 11th of July 2015 (Figure A-15), the strongest currents came from the
tracks 09:28-11:38 and 13:26-14:29, reaching velocities of over 0.8m/s and
1m/s, respectively, both in the westwards direction (signified by the negative
value in of the U component). Thusly, a dominant alongshore flow prevailed.
The remaining two tracks at 13:27-14:37 and 14:46-16:40 were much weaker,
with velocities not reaching more than 0.2m/s. The latter shows a stronger
velocity from the positive V component so the flow is dominantly cross-shore.
This may resemble a rip current but because of its low velocity, it may not
54
necessarily classify as a rip current, since rip currents are typically strong and
fast as explained by Thornton, MacMahan and Sallenger (2007). Interestingly,
track 13:26-14:29, deployed in shallower waters than track 13:27-14:37, was
significantly stronger in comparison. This is significant because both tracks
were taken at relatively the same time but at different depths.
Out of the four tracks selected on the 29th of August 2015 (Figure A-16), the
track between 09:27-11:10 exhibited the highest peak in velocity of 0.58 m/s,
travelling in the southerly, cross-shore direction. Surprisingly, this track
exhibited the highest velocity when it was in the shallowest waters compared
to the other three tracks, which were in much deeper water but all had lower
velocities. The track between 13:39-14:34, was slightly slower at 0.52m/s at its
peak when travelling westwards in the alongshore direction. This track was in
the deepest water of all four tracks.
On the 26th of September 2015 (Figure A-17), the track between 13:41-14:31
shows a highly variable velocity, strongly associated within the U vector rather
than the V vector, indicating a strong alongshore direction of travel. This track
is the strongest amongst all other tracks on this day, with velocities exceeding
0.2m/s. In contrast to previous tracks discussed, it is relatively weak. On this
day, this track, along with the track between 14:50-16:39 were much stronger
than the other two, which fluctuated below 0.1m/s. This is significant because
unlike the prior three cases, the strongest currents on this day (13:41-14:31)
occurred in relatively deeper water compared to other tracks.
The alternating spikes and dips are most likely the cause of strong gusts. The
dips in velocity to 0m/s indicate that the drifter was stationary. This could be an
error caused by the GPS logger, which may have lost connection, as
mentioned in previous chapters. The range of velocities of currents recorded
comply to the range presented in the literature reviewed. The general pattern
observed from drifter measurements submits to a higher degree of alongshore
current flow in shallower waters, flowing from direction of the wind. Some of
these longshore currents (such as between 17:50-19:12 on the 26th of
September 2015) are the products of wave breaking in the surf-zone and
although measurements directly in the surf-zone were not possible to obtain,
these resultant longshore currents are representative of the wave induced
55
current processes. Deployments and therefore, currents, initiated in deeper
water tend to follow the direction of the wind and not always reconfigure to the
coastal perimeter. From the selected current tracks for time-series comparison,
a general trend arose that suggests that the shallow water and nearshore
currents are stronger than those currents in the deeper portions of the bay. The
results presented do not support the hypothesis, which suggested that currents
in shallower water would be weaker than those of deeper waters because of
the significant dissipation in wave energy. The only track that complies with the
hypothesis was between 13:41-14:31 on the 26th of September 2015. This was
not statistically proven, however. From the results present, it may be construed
that with stronger currents in the shallow surf zone, greater transport of beach
sediments is likely to occur, largely in the longshore direction.
4.2.2 Drifter Trajectories: Current Direction
Despite attempting to record drifter tracks under as many environmental
conditions as possible in the field, rip currents were unable to be observed and
measured by the drifters during the summer period at Ramla Bay. This does
not conclude that rip currents do not occur indefinitely within the bay but infers
that they may be extremely rare events in the summer period when the wave
climate is drastically dampened in contrast to the higher energy during the
winter months. Therefore, the hypothesis with regards to the biophysical
configuration of Posidona oceanica due to a cellular rip current circulation was
incorrect.
The direction of current flow observed from field measurements show that
currents often flow along the contours of the coastline. The direction of current
flow is dependent on the wave direction to a certain extent. When plot on a
scatter plot (Figure 4.4), the outcome of the correlation reveals itself to distort
the true relationship by the added skewness incurred by three outliers.
56
Figure 4.4: First scatter plot of the measured current direction (y-axis) against the modelled wave direction (x-axis) with outliers included.
The R2 value calculated (0.0176) is closer to 0 than to 1. This suggests that,
although the correlation is positive, the relationship is weak. The Root Mean
Square Error (RMSE) was calculated for the two variables of the seventeen
sets of records listed in Table A-2. This measures the level of association of
the data with the line of best fit. The RMSE was computed to be 107.05o. This
indicates that typically, the points are around 107.05o off from the line of best
fit and because the RMSE value is so high, there is a weak association
between the variables.
The Pearson correlation was used to measure the strength of the relationship
between two variables. Let the null hypothesis (H0) be that there is no
relationship between the direction of current flow and the wave direction if the
p-value exceeds the 0.05 level of significance. The alternative hypothesis (H1)
states that there is a statistical relationship between the wave direction and
current direction if the p-value is less than the 0.05 level of significance.
y = 0.4037x + 100.82R² = 0.0176
0
30
60
90
120
150
180
210
240
270
300
330
360
0 30 60 90 120 150 180 210 240 270 300 330 360
Drift
er
Curr
ent
Direction (
deg)
SWAN Wave Direction (deg)
Wave Direction vs Current Direction
57
Table 4. 1: Table of Pearson correlation and level of significance of the relationship between wave direction and current direction with outliers. Produced in SPSS.
The Pearson Correlation coefficient, r, (Table 4.1) was computed in SPSS to
be 0.132, which indicates that there is a positive correlation between the wave
direction and the current direction. Since, the 0.132 mark is closer to zero than
one, it can be concluded that the proportional relationship between the two
variables is weak. This reaffirms the result of the R2 value. Furthermore, the p-
value of 0.612 is significantly greater than the 0.05 level of significance, hence
the null hypothesis is accepted. This indicates that there is no statistical
significance between wave and current direction.
The three outliers are the likely causes of the statistical insignificance of the
data. The outliers observed occurred on the 4th September 2015 from the 18:03
-19:51 track, with points taken at 19:01:52 and 19:06:33. Again these points
were taken because they were closest to the SWAN grid point (spatially and in
time at 18:00). The second set of outliers occurred on the 5th of September
2015 from the 11:02-12:48 track, with points taken at 12:27:06 and 12:42:26.
These points were temporally closest to the SWAN grid point at 12:00. To test
the alternative relationship that would otherwise be skewed by the outliers, the
outliers were removed and the same statistical analysis was repeated.
58
Figure 4.5: Second scatter plot of measured current direction against modelled wave direction without outliers included.
Table 4. 2: Table of Pearson correlation and level of significance of the relationship between wave direction and current direction without outliers. Produced in SPSS.
With the three outliers removed, the correlation remains positive but the
relationship is much stronger now (Figure 4.5). The null and alternative
hypotheses proclaimed for the first statistical test, still applies to this new test.
The Pearson correlation coefficient of 0.896 (Table 4.2) is now closer to 1
signifying a strong positive relationship between the two variables. Since the
p-value (0) is less than the 0.01 level of significance, the alternative hypothesis
(H1) is supported, whereby it can be established that there is a strong
associated relationship with wave direction and current direction.
y = 2.8497x - 328.5R² = 0.8022
0
30
60
90
120
150
180
210
240
270
300
330
360
0 30 60 90 120 150 180 210 240 270 300 330 360
Drifter
Curr
ent D
irection (
deg)
SWAN Wave Direction (deg)
Wave Direction vs Current Direction
59
To inspect the possible cause of the emergence of the outliers, a further test
was conducted for the data of the corresponding tracks from which the outliers
originated from. This was done to measure the statistical significance of the
wind vector velocities from SKIRON (a component model of SWAN) with the
outlier current vector velocities. The U and V vectors were used because of the
directional and magnitude components. The SKIRON model has a much lower
resolution than the SWAN model so a single grid point (36.05oN, 14.3oE) that
was closest to the bay was taken as a constant for the statistical analysis. By
taking the U velocity of sea currents and wind coinciding at the same time, a
scatter plot was produced, with the line of best fit added. The level of
association was calculated and found to be strong between these points.
Therefore, the line of best fit will serve as a baseline reference to compare the
U velocities of the outliers points in question. The same was repeated for the
V velocity component.
Figure 4.6: Scatter plot of U (top) and V (bottom) components of current velocity and wind speed on 04/09/2015 with the U and V components of the two points from the tracks at 19:01
(green) and 19:06 (yellow) to test the relationship of these possible outliers.
y = 0.0171x + 0.0977R² = 0.0437
-0.02
0
0.02
0.04
0.06
0.08
0.1
0.12
-2.5 -2 -1.5 -1 -0.5 0
Curr
ent
(m/s
)
Wind( m/s)
U vectors of wind against current velocities (04/09/2015)
U component
19:01:52
19:06:33
Line of Best Fit
y = 0.0171x + 0.0448R² = 0.9695
0
0.02
0.04
0.06
0.08
0.1
0 0.5 1 1.5 2 2.5
Curr
ent
(m/s
)
Wind( m/s)
V vectors of wind against current velocities (04/09/2015)
V component
19:01:52
19:06:33
Line of Best Fit
60
For the 4th of September 2015, the R2 values for the U (0.0437) and V (0.9695)
scatter plots show the V vector having stronger relationship between the
current and wind velocities, than the U vector. The RMSE calculated for the U
and V vectors came to be 1.611m/s and 0.831m/s, respectively. The U
component RMSE is much larger than the V component RMSE, which
amalgamates the result of the R2 value. When comparing the outlier
components of 19:01:52 (Figure 4.6), the scatter plot shows a close association
of the outlier V components with the line of best fit. However, the outlier U
component for the same data is far from the line of best fit of the wind U vector
velocity (Figure 4.6). The exact opposite is the case with 19:06:33.
Figure 4.7: Scatter plot of U (top) and V (bottom) components of current velocity and wind speed on 04/09/2015 with the U and V components of the two points from the tracks at 19:01
(green) and 19:06 (yellow) to test the relationship of these possible outliers.
y = 0.1966x + 0.4903R² = 0.9905
-0.05
0
0.05
0.1
0.15
-3 -2.5 -2 -1.5 -1 -0.5 0
Curr
ent
velo
city (
m/s
)
Wind Velocity (m/s)
U vectors of wind against current velocities (05/09/2015)
U component
12:27:06
12:42:26
Line of Best Fit
y = 0.4464x + 0.2026R² = 0.676
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
-0.5 -0.4 -0.3 -0.2 -0.1 0 0.1 0.2 0.3
Curr
ent
velo
city
(m/s
)
Wind Velocity (m/s)
V vectors of wind against current velocities (05/09/2015)
V component
12:27:06
12:42:26
Line of Best Fit
61
The RMSE of the U and V velocities on the 5th September 2015 current and
wind velocities coinciding at the same time was calculated to be 2.3777m/s
and 0.2934m/s, respectively. Both indicate a relatively close association. This
shall be the base which to correlate the outliers with. The scatter plot (Figure
4.7) shows that the two outlier points have a stronger association between the
V vectors than with the U vectors. Unfortunately, since the points skew far from
the line of best fit of the U components, they must be considered as outliers.
This test has shown that since the points remain outliers in relation to the wind
velocity and direction, they are verified outliers on the wave- sea current
correlation and scatter plot.
Both sets of outlier points (4th and 5th September 2015) are themselves outliers
in either the U or V component fields within the SKIRON wind-sea current
velocity test and have no close association with the line of best fit. It can be
concluded that the SKIRON model is not the cause of the outliers through
incorrect modelled data. This was shown by the strong relationship formed by
the line of best fit. The drifter points themselves may have been erroneous due
to a GPS error. Therefore, the outliers observed in the wave-sea current scatter
plot can be taken to be valid. Furthermore, with the first scatter plot of the
wave–sea current direction correlation (i.e. with outliers included) being
accepted, the final determination would suggest that under general conditions,
represented by the selected drifter tracks, there is a positive relationship
between the wave-current direction, albeit weak. As determined from the first
statistical test (Table 4.1), there is no statistical significance between the wave
direction and the current direction. Therefore, the degree of variation in wave
direction is less likely to change current direction at the same extent.
By putting these results into context, one could determine that within the
shallow nearshore zone, the complex hydrodynamic processes statistically
distort the relationship between current and wave direction. However, the
second test, without outliers, may also be accepted for currents in deeper
waters, whereby the influence of the bathymetry (i.e. beyond the influence of
the depth of closure) is irrelevant.
62
4.3 SWAN Maps
4.3.1 Significant Wave Height
Figure 4.8: Spatial variation graph of the Significant Wave Height across the transect.
The average SWH across the transect taken from the SWAN grid runs from
0m at the shoreline to 4225m outside the embayment. A long transect was
taken to observe how the average SWH varied from deep waters to shallow
waters for each month. The results show that SWH decreases as water depth
decreases (Figure 4.8). August 2015 experienced the lowest range variation in
SWH between shallow and deeper points, with a maximum of 0.58m in deep
water and a minimum of 0.25m in shallow water. The extent of variation
between deep and shallow water increased with each subsequent month, in
which January 2016 experienced the highest variance of SWH, with a
maximum SWH of 4.16m and a minimum value of 0.28m. SWHs in December
were the exception of this observation, whereby SWHs were averaged
between August 2015 and September 2015.
On the larger scale, the interpolated maps of SWH (Figures B-1 to B-7) show,
not only a decrease in SWH with water depth but also re-orientation with the
coast. The results also reveal the processes of wave diffraction and refraction
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
0 1000 2000 3000 4000 5000
Ave
rag
e S
ign
ific
an
t W
ave
He
igh
t (m
)
Distance from Shoreline
Average Significant Wave Height Monthly Spatial Variation
August September October
November December January
63
as the wave fields approaching at an oblique angle, converge around the
headland and approach the beach at an angle almost perpendicular to the
beach but still angled to facilitate minor longshore drift of sediment. The spatial
variation in SWH indicates that a directional spread of the wave energy across
the bay results in a gradient of radiation stress, which leads to the formation of
longshore current, as discussed before. Therefore, the directionality changes
in SWH can be linked to the wave direction.
The thickness of the graded bands of the interpolation can be seen to decrease
spatially from deeper to shallower waters. This represents the conversion of
long-crested to short-crested waves. Long-crested waves, as Sundar,
Sannasiiraj and Kaldenhoff (1999) explain, are those waves responding to the
decreasing bathymetric depth contours, resulting in the parallel orientation of
waves to bathymetric contours. Short-crested waves are the product of the
interactions of an irregular bathymetric and collective effects of wave refraction,
diffraction and reflections that the waves endure when approach the
embayment. Acknowledging the shift to short-crested waves is important for
estimating the radiation stresses in the nearshore area.
4.3.2 Wave Direction
During the time scale of the study across six months, the dominant monthly
average direction of wave fields propagating towards the coast was from the
general NE direction throughout August (Figure C-1), September (Figure C-2)
and November (C-4). This does not comply entirely with what was
hypothesized. It was expected that the dominant average monthly mean wave
direction was to approach from the NW, according to the dominant wind
direction from the NW, as mentioned in the Introduction. This may be of
significant importance as a means of identifying a possible shift in the general
trends of wave climate due to climate change. NW wave fields were observed
throughout the limited days of available data in October (Figure C-3). Wave
fields from the North and East were prevalent throughout December (Figure C-
5) and January (Figure C-6), receptively. When approaching from the North,
drastic changes in directionality occur, exhibiting an almost ideal theoretical
64
scenario (Figure C-5) whereby, the high-energy breaking occurring at the
headlands forces currents to flow along the peripheries of the headland into
the bay. During August, September, November and January when the
predominant mean wave direction propagates from the NE, sediments are
likely to be transported to the western side of the beach. Longshore sediment
transport during November and January may be more lateral because of the
more acute angle the waves break to the beach. With the mean wave direction
during October and December showing to propagate in from the NW direction
within the surf zone, longshore drift may be assumed to migrate sediments to
the eastern part of the beach. However, due to the significant data gap of
October and the seemingly erroneous results produced by the model for
December, this may not be representative of reality.
The directionality of the wave fields is observed to change, from a seemingly
unidirectional propagation in open waters to a highly variable nature within
shallower waters. This change is represented by the narrow, vibrant colour
array along the peripheries of the coastline within the bay (Figure C-1 to C-6).
This is because waves adjust due to decrease in water depths once reaching
the depth of closure and due to the boundary configuration of the coast causing
refraction and diffraction of the wave field. This aspect supports the hypothesis.
It can also be noted from the maps that after bending around the headlands,
the waves in actuality propagate within the surf zone at an obtuse angle. This
facilitate the longshore drift of sediments along the beach (Figure C-1 to C-6).
The longshore sediment transport within an embayed beach, which functions
within a semi-closed system, will tend fluctuate to and fro in the alongshore
direction. The average wave direction of each month may provide an estimate
of the direction of longshore transport of sediments. This is, speculative as
other processes may also be playing a role. This field deserves a study in itself.
4.4 Time Series Analysis: Significant Wave Height
A time series of the average SWH for every month was computed across the
five points along the transect running centre cross-shore of the bay (Figure D-
1 to D-6). Typically, the pattern of the temporal variation across each point (i.e.
65
different depths), remains relatively identical. However, there is a change in the
amplitude that distinguishes the variations across the points (depths), with
points in deeper water having a larger amplitude to that of the shallower points.
A high degree of seasonality is evident between the calmer summer period
comparable to the more intense winter SWHs (Figure 4.9). It is noteworthy to
point out the almost overwhelming overlapping similarity, in terms of pattern
and amplitude of SWH characteristics at points two and three, which
corresponds to a point in the centre of the bay and at the outer edge of the bay
mouth, respectively (Figure D-1 to D-6).
Figure 4.9: Time series of the SWH from August 2015 to January 2016 over point 2, with the
atmospheric pressure superimposed in dashed black line.
Since SWAN is the first high resolution coastal model to be applied to Malta,
there was a limited amount of data available for the model. The lack of data
input from proxy models, such as SKIRON (for wind), resulted in data gaps in
SWAN (Table 4.3). Of the six months, the greatest deficiency of output data
was for October (81.85%). An adequate analysis may not be conducted for that
month. Data for August and December were also considerably absent but a
decent representation could be achieved, nonetheless. September, November
and January all had almost complete sets of data to provide a truly meaningful
representation.
995
1000
1005
1010
1015
1020
1025
1030
1035
1040
1045
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
1.8
Atm
ospheric P
ressure
(hP
a)
Sig
nific
ant
Wave H
eig
ht
(m)
Time
Significant Wave Height Time-Series
66
Table 4. 3 Tabulation of the amount of data available from the SWAN model and the percentage of missing data.
Aug Sept Oct Nov Dec Jan
Total 3-hourly Intervals 248 240 248 240 248 248 Missing 3-hourly Intervals 127 26 203 47 89 20
Percentage 51.21% 10.83% 81.85% 19.58% 35.89% 8.06%
The dashed line (black) superimposed on all time-series for every month
represents the daily average atmospheres pressure, in hPa. The plot of
pressure (Figure 4.9) shows variations throughout each month and a seasonal
rising trend in the average pressure from summer to winter. During the summer
months, the frequency of fluctuations is much greater than in the winter
months, whereby from November, variations become less frequent. The
opposite is true for the magnitude, in which summer months exhibit low
amplitudinal variations and range. This natural shift occurs in November when
intensive amplitudinal variations become more violent. These drastic dips in
pressure represent low-pressure storm events. Although some strong low
pressure storms occurred during the summer months, the sheer intensification
of winter storms is unparalleled in comparison. This has significant
ramifications to the nature of SWH.
During August, the average SWH and atmospheric pressure were both
contrastingly lower compared to other months. The offset between the
atmospheric pressure and the SWH time-series may be more significant in
August than for other months (Figure D-1). The inverse relationship can still be
observed however, whereby periods of low atmospheric pressure generally
resulting in slightly higher SWHs as opposed to when the pressure was lower.
The time-series of the SWH in shallower water remains quite well defined, as
opposed to the dampened temporal variation pattern experienced in other
months.
The SWH in September 2015 (Figure D-2) is characterised by a double peak
spectrum, which corresponds to two strong decreases in pressure around the
same time of the month. November (Figure D-4) begins with slightly higher
SWH and gradually levels out for the mid-term period of the month, with values
straggling below 1m, before dramatically increasing closer to the end of the
month, when it reaches it maximum SWH across all points. This pattern is
67
observable for all points (within the bay and outside) with exemption to point 1.
Across the shallowest point, there is minimal variation.
The absence of SWH data available for time-series analysis may alternatively
be assessed by means of the average atmospheric pressure. The average
atmospheric pressure during October 2015 (Figure D-3) is characterised by
two major dips in pressure, occurring around the 10th and 23rd of October.
Other dips in pressure occurred on the 15th and 31st but are not as powerful as
the latter two. The seemingly inverse relationship between pressure and SWH
allows for the presumption that during or around this time of the month SWH
would have peaked. Subsequent peaks in high pressure on the 4th, 13th, 17th
and 25th of October 2015 would have most likely resulted in a compressed sea
surface and reduce the SWH.
December (Figure D-5) is seemingly the exception to the trend on seasonal
increase in SWH. There are dramatic fluctuations between higher and low
SWHs across all points. The SWH never exceeded 0.8m across all points. This
is significant because the maximum SWH is considerably lower than the
minimum fluctuating range of November and January. When viewing Figure
4.9, where the scale of the average atmospheric pressure is not
misrepresented as is on Figure D-5, it is evident that SWH data modelled by
SWAN must be erroneous because of the disproportional amplitudinal
relationship between the average SWH and the average atmospheric
pressure.
The observable relationship from the time-series graphs leads to the actuality
that SWH in the deeper waters than in the shallower waters as was
hypothesized. The direct correspondence between maximum peaks of SWH
and dips in pressure appear to be offset by a few hours, or sometimes days.
This limitation may be caused by the distance of the meteorological station in
Rabat, Gozo and Ramla Bay but could also be due to a response lag between
the conditions of the atmosphere and the sea surface layer. The pressure may
be used as a tool to predict the possible nature and peaks in SWH, where there
are significant data gaps in the SWH data. During times of low pressure and
storm events, there is maximum variance between the SWH in deeper water
(point 5) and shallow water (point 1). When pressure is relatively high the
68
variance between SWH in deep and shallow water in minor. This incurs that
Ramla Bay is strongly protected during storms. This effectively, reduces the
scale of sediment transport and erosion of the dunes that would otherwise
occur if not for the protection of the headlands. This may also explain why the
SWH decreases with depth (Figure 4.8) instead of increasing as was
hypothesised. The severe attenuation of SWH between deep and shallower
water depths within the embayment caused by the level of protection offered
by the headland embayment.
The intensity on wave energy signified by large SWHs during winter periods,
in particular November (Figure D-4) and January (Figure D-6), corresponds to
sharp decreases in the general atmospheric pressure and in the peaks reached
in both November and January. It is during this period, when the beach and
morphology of the bathymetry and features, such as the sand bar are likely to
shift and change shape. Azzopardi (2015) conducted a study on the
morphological variation of the seabed and sandbar at Ramla and found that
there are distinctive shifts in the underwater morphology between the summer
and winter seasons. Storm events are the cause of this reset of the seabed
and sandbar resulting in the attenuation of the extreme curvature of the
sandbar (Figure 4.1), forcing it straighten, becoming more parallel to the beach
and move shorewards (Winter et al., 2014). The supplementary fluctuations in
SWH that are far more minor in terms of amplitude, may be due to the
superposition of two wave groups travelling in the same direction, which will
cause the wave heights to increase and consequently, the SWH to increase.
The study focused on the SWH conditions and not the regular wave conditions
because the SWH represents the average of the largest waves. This is
relevant, especially for coastal engineers and managers because larger waves
are more likely to have a consequential effect on structures and sediment
dynamics (Altunkaynak & Wang, 2012). The process of wave shoaling,
whereby the wave heights increase and the velocity of the wave orbitals
decrease should produce weaker currents measured within the centre or
slightly deeper portions of the bay, where wave shoaling of incoming waves is
likely to occur. Currents generated by wave breaking within the surf-zone of
originating from the surf-zone are expected to be stronger.
69
Chapter 5 Conclusion
A study was conducted to investigate and examine the nature of the
hydrodynamic processes, pertaining to current flow and wave climate at Ramla
Bay. By means of drifter measurements, the flow the circulation of sea-
currents within the bay between July and October were mapped. A time-series
analysis for a selected number of tracks revealed that the velocities of currents
are stronger in the nearshore zone than in deeper portions of the bay, with
velocities ranging from 0 to 1m/s. The general flow pattern of currents followed
the alongshore direction, i.e. along the contours of the embayment. Currents
in deeper waters were recorded to cross the bay in lieu of following the
contours. Attempts to record rip currents using the drifters were unsuccessful,
leading to the conclusion that during the summer months, rip currents are
virtually inexistent. However, the possibility of strong rip currents (mega rips)
during the winter months should not be excluded as a possibility. If this were
to occur, a strongly defined cellular circulation would most likely prevail. For
this reason, similar surveys should be conducted during both summer and
winter months. Laborious statistical tests to determine the relationship between
the current direction and the wave direction within the embayment proved to
be statistically insignificant in shallow waters. However, to fully understand this
relationship, the results would have to be input and compared to modelled
outcomes, as was initially intended for this dissertation.
By means of modelled data of the monthly mean wave direction showed that,
wave directionality is more variable within the shallower nearshore regions of
the bay and that the dominant mean wave direction on average (even in the
surf-zone) originates from the northeast direction. This signifies that, the more
likely accumulation of beach sediments during this six month period, was likely
to occur on the western side of the pocket beach. An inversely proportional
relationship between the atmospheric pressure and the SWH is evident across
the months. The peaks in SWH occurring throughout certain months,
correspond to strong decreases in pressure, i.e. possible storm events. This
shows that climatic variances have a strong influence on the nature of
significant wave height. The spatial variation of the significant wave height
70
appeared to dampen within the shallower depths of the embayment, typical
since wave energy in shallower wave is drastically dissipated due to wave
breaking.
The results acquired, presented and discussed show little support to most of
the hypotheses formulated. Some statistical ambiguity did arise with the further
testing in attempting to correlate wave direction and current direction. The fact
that uncertainties within the modelled data do exist, needs to be acknowledged.
These uncertainties arose, particularly with the SWH for December.
Additionally, working numerical models may sometimes be unreliable. For
instance, the absence of data in the SWAN model and the Rosario-SHYFEM
model not operating. Nevertheless, numerical models are sometimes
necessary for studies in physical oceanography and coastal management,
especially because many complex processes and interactions cannot be
measured in the field at all times or at all. Uncertainties in the low accuracy of
the methodology to collect in-situ data may be argued. However, the results
have showed contextual association of the bathymetric map with that of
satellite imagery, as well as previous studies from Azzopardi (2015). For a
higher resolution surface bathymetry map, remote sensing would ideally be
used.
This research project, has been a pioneering study on the physical
characteristics of current and wave climate dynamics at Ramla, in which no
such study or data existed prior. This study may now serve as a baseline of
such information for future works. Even without the complete inclusion of winter
months in this study, the information provided for the summer period, when
human activities are highest at Ramla, may still be successfully applied to
certain aspects of management. From this study, it is suggested that to further
better understand the hydrodynamic processes at Ramla, particularly, on the
nature of rip currents, that annual measurements are taken. This information
should be regarded in the management studies and decisions, especially those
related to sediment dynamics, public safety, project proposals and the
fragmentation of Posidonia oceanica seagrass beds. This newly acquired
knowledge can now be applied to Coastal Management Plans. Such plans,
may be implemented using the sediment cell concept, in which currents and
71
waves are defining boundaries. This approach will help utilise a study such as
this, to implement management strategies to defend the coast in a sustainable
manner, whilst accommodating for the conservation of the area, as Ramla is
part of the NATURA 2000 network.
72
References
Aagaard, T., & Hughes, M. G. (2010). Breaker turbulence and sediment
suspension in the surf zone. Marine Geology, 271(3-4), 250–259.
http://doi.org/10.1016/j.margeo.2010.02.019
Aagaard, T., & Vinther, N. (2008). Cross-Shore Currents in the Surf Zone:
Rips or Undertow? Journal of Coastal Research, 243(243), 561–570.
http://doi.org/10.2112/04-0357.1
Altunkaynak, A., ASCE, M., & Özger, M. (2005). Spatial Significant Wave
Height Variation Assessment and Its Estimation. Journal of Waterway,
Port, Coastal , and Ocean Engineering, 131(6), 277–282.
Altunkaynak, A., & Wang, K. H. (2012). Estimation of significant wave height
in shallow lakes using the expert system techniques. Expert Systems
with Applications, 39(3), 2549–2559. http://doi.org/DOI
10.1016/j.eswa.2011.08.106
Ardhuin, F., & Jenkins, A. D. (2006). On the interaction of surface waves and
upper ocean turbulence. Journal of Physical Oceanography, 36(3), 551–
557. http://doi.org/10.1175/JPO2862.1
Azzopardi, J. (2012). Implementing the SWAN model to run operationally for
the area around the Maltese Islands BlueOceanEnergy Project. Msida:
Physcial Oceangrpahy Unit.
Azzopardi, S. (2015). A baseline study of Ramla Bay’s nearshore zone.
University of Malta.
Bakhtyar, R., Barry, D. a., Li, L., Jeng, D. S., & Yeganeh-Bakhtiary, a.
(2009). Modeling sediment transport in the swash zone: A review. Ocean
Engineering, 36(9-10), 767–783.
http://doi.org/10.1016/j.oceaneng.2009.03.003
73
Basterretxea, G., Orfila, A., Jordi, A., Casas, B., Lynett, P., Liu, P. L. F., …
Tintore, J. (2004). Seasonal Dynamics of a Microtidal Pocket Beach with
Posidonia oceanica Seabeds ( Mallorca , Spain ). Journal of Coastal
Research, 20(4), 1155–1164.
Benetazzo, A., Carniel, S., Sclavo, M., & Bergamasco, A. (2013). Wave –
current interaction : Effect on the wave field in a semi-enclosed basin.
Ocean Modelling, 70, 152–165.
http://doi.org/10.1016/j.ocemod.2012.12.009
Blondeaux, P. (2001). Mechanics of Coastal Forms. Annu. Rev. Fluid Mech.,
33, 339–370.
Borg, J. a., Rowden, a. a., Attrill, M. J., Schembri, P. J., & Jones, M. B.
(2009). Occurrence and distribution of different bed types of seagrass
Posidonia oceanica around the Maltese Islands. Mediterranean Marine
Science, 10(2), 45–61.
Bullock, R. (2007). Great Circle Distances and Bearings Between Two
Locations. Retrieved on November 24, 2015, from
http://www.dtcenter.org/met/users/docs/write_ups/gc_simple.pdf
Camilleri, D. H. (2012). Tsunami and wind-driven wave forces in the
Mediterranean Sea. Proceedings of the ICE - Maritime Engineering,
165(2), 65–79. http://doi.org/10.1680/maen.201024
Capodicasa, E., Foti, E., & Scandura, P. (1991). Steady currents induced by
sea waves propagating over a sloping bottom. Coastal Engineering
2010, 1–10.
Castelle, B., Almar, R., Dorel, M., Lefebvre, J.-P., Senechal, N., Anthony, E.
J., … Penhoat, Y. Du. (2014). Rip currents and circulation on a high-
energy low-tide-terraced beach (Grand Popo, Benin, West Africa).
Journal of Coastal Research, 70(70), 633–638.
http://doi.org/10.2112/SI70-107.1
Castelle, B., & Coco, G. (2012). The morphodynamics of rip channels on
embayed beaches. Continental Shelf Research, 43, 10–23.
http://doi.org/10.1016/j.csr.2012.04.010
74
Castelle, B., & Coco, G. (2013). Surf zone flushing on embayed beaches.
Geophysical Research Letters, 40(10), 2206–2210.
http://doi.org/10.1002/grl.50485
Celik, I. B. (1999). Introductory turbulence modeling. Retrieved on October
20, 2015, from
http://www.fem.unicamp.br/~im450/palestras&artigos/ASME_Tubulence/
cds13workbook.pdf
Chang, Y. C., Chen, G. Y., Tseng, R. S., Centurioni, L. R., & Chu, P. C.
(2012). Observed near-surface currents under high wind speeds. Journal
of Geophysical Research: Oceans, 117(February), 1–6.
http://doi.org/10.1029/2012JC007996
Choi, J., & Yoon, S. B. (2011). Numerical simulation of nearshore circulation
on field topography under random wave environment. Coastal
Engineering, 58(5), 395–408.
http://doi.org/10.1016/j.coastaleng.2010.12.002
Christianen, M. J. A., van Belzen, J., Herman, P. M. J., van Katwijk, M. M.,
Lamers, L. P. M., van Leent, P. J. M., & Bouma, T. J. (2013). Low-
Canopy Seagrass Beds Still Provide Important Coastal Protection
Services. PLOS ONE, 8(5). http://doi.org/10.1371/journal.pone.0062413
Conley, D. C., Trangeled, A., Zappa, G., Gualdesi, L., Guerrini, P., & Holman,
R. a. (2008). Rapid environmental assessment in the nearshore. Journal
of Marine Systems, 69(1-2), 74–85.
http://doi.org/10.1016/j.jmarsys.2007.02.025
Cox, D. T., & Kobayashi, N. (1996). Undertow profiles in the bottom boundary
layer under breaking waves. Coastal Engineering, 1(25), 3194–3206.
Dalrymple, R. A., Hwang, P., & Kirby, T. K. (1984). Wave diffraction due to
areas of energy dissipation. Journal of Waterway Port Coastal and
Ocean Engineering, 110(1), 67–79. http://doi.org/10.1061/(ASCE)0733-
950X(1984)110
Daly, C. (2013). Morphodynamic Equilibria of Embayed Beach Systems.
University of Bremen.
75
Davidson-Arnott, R. (2009). Introduction to Coastal Processes &
Geomorphology (1st ed.). New York: Cambridge University Press.
Retrieved on September 10, 2015, from
https://books.google.com.mt/books?id=UOcgAwAAQBAJ&printsec=front
cover&source=gbs_atb#v=onepage&q&f=false
Davis Jr, R. A., & Fitzgerald, D. M. (2004). Beaches and Coasts. Malden,
USA: Blackwell Science Ltd.
Desira, D. (2015). Coastal Water Mixing around the Maltese Islands using the
Rosario-SHYFEM model. University of Malta.
Devoto, S., Biolchi, S., Bruschi, V. M., Furlani, S., Mantovani, M., Piacentini,
D., … Soldati, M. (2012). Geomorphological map of the NW Coast of the
Island of Malta (Mediterranean Sea). Journal of Maps, 8(1), 33–40.
http://doi.org/10.1080/17445647.2012.668425
Di Carlo, G., Badalamenti, F., Jensen, A. C., Koch, E. W., & Riggio, S.
(2005). Colonisation process of vegetative fragments of Posidonia
oceanica (L.) Delile on rubble mounds. Marine Biology, 147(6), 1261–
1270. http://doi.org/10.1007/s00227-005-0035-0
Dinu, I., Bajo, M., Stănică, A., Dan, S., Maximov, G., & Umgiesser, G. (2010).
Preliminary simulations of coastal currents from the area of Constanţa to
the border between Romania and Bulgaria. Geo-Eco-Marina, 16(1), 67–
74.
Dinu, I., Bajo, M., Umgiesser, G., & Stănică, A. (2011). Influence of wind and
freshwater on the current circulation along the Romanian Black Sea
coast. Geo-Eco-Marina, 17(1), 13–26.
Drago, A., Azzopardi, J., Gauci, A., Tarasova, R., & Bruschi, A. (2012).
Assessing the offshore wave energy potential for the Maltese Islands. In
Sustainable energy 2012 : The ISE Annual Conference (pp. 16–27).
Qawra, Malta: Institute for Sustainable Energy.
Drago, A. F. (1980). A contribution to the study of Sea Water Current and
Stratification around the Maltese Islands. University of Malta.
76
Dusek, G., & Seim, H. (2013). Rip Current Intensity Estimates from Lifeguard
Observations. Journal of Coastal Research, 29(3), 505–518.
http://doi.org/10.2112/JCOASTRES-D-12-00117.1
Emery, W., & Thomson, R. E. (2004). Data Analysis Methods in Physical
Oceanography (2nd ed.). Amsterdam: Elsevier.
European Environmental Agency. (n.d.). Natura 2000 Map Malta. Retrieved
on April 20, 2016, from http://www.eea.europa.eu/data-and-
maps/figures/natura-2000-birds-and-habitat-directives-
3/mtn2k_mid2009.eps
Farrugia, R. N., Fsadni, M., Yousif, C., & Mallia, E. a. (2005). The Renewable
Energy Potential of the Maltese Islands. Xjenza, 10, 32–42.
Feddersen, F., Gallagher, E. L., Guza, R. T., & Elgar, S. (2003). The drag
coefficient , bottom roughness , and wave-breaking in the nearshore.
Coastal Engineering, 48, 189–195. http://doi.org/10.1016/S0378-
3839(03)00026-7
Ferrarin, C., Umgiesser, G., Cucco, A., Hsu, T. W., Roland, A., & Amos, C. L.
(2008). Development and validation of a finite element morphological
model for shallow water basins. Coastal Engineering, 55(9), 716–731.
http://doi.org/10.1016/j.coastaleng.2008.02.016
Galdies, C. (2011). The Climate of Malta : statistics , trends and analysis
1951-2010.
Garcez Faria, A. F., Lippmann, T. C., Stanton, T. P., & Thornton, E. B.
(2000). Undertow over a barred beach. Journal of Geophysical
Research, 105(C7), 16,999–17,010.
http://doi.org/http://dx.doi.org/10.1029/2000JC900084;
doi:10.1029/2000JC900084
Garrison, T. (2013). Oceanography: An Invitation to Marine Science (8th ed.).
Brooks/Cole, VCengage Learning.
Ge, H.-X., & Cheng, R.-J. (2014). A meshless method based on moving
Kriging interpolation for a two-dimensional time-fractional diffusion
77
equation. Chinese Physics B, 23(4), 040203. http://doi.org/10.1088/1674-
1056/23/4/040203
Gemmrich, J. R., & Farmer, D. M. (2004). Near-Surface Turbulence in the
Presence of Breaking Waves. Journal of Physical Oceanography, 34(5),
1067–1086. http://doi.org/10.1175/1520-
0485(2004)034<1067:NTITPO>2.0.CO;2
Godin, O. (2009). Wave refraction at an interface: Snell’s law versus
Chapman’s law. The Journal of the Acoustical Society of America,
125(4), EL117–EL122. http://doi.org/10.1121/1.3082003
Gorrell, L., Raubenheimer, B., Elgar, S., & Guza, R. T. (2011). SWAN
predictions of waves observed in shallow water onshore of complex
bathymetry. Coastal Engineering, 58(6), 510–516.
http://doi.org/10.1016/j.coastaleng.2011.01.013
Haller, M. C. (2002). Experimental study of nearshore dynamics on a barred
beach with rip channels. Journal of Geophysical Research, 107(C6), 1–
21. http://doi.org/10.1029/2001JC000955
Holthuijsen, L. H. (2007). Waves in oceanic and coastal waters. Cambridge:
Cambridge University Press. Retrieved on September 19, 2015, from
http://eur-
lex.europa.eu/LexUriServ/LexUriServ.do?uri=CONSLEG:1979L0409:200
81223:EN:PDF
Hong, Z., & Eng, S. . (2003). Modeling of the Turbulence in the Water
Column under Breaking Wind Waves. Journal of Oceanography, 59,
331–341.
Idier, D., Falqués, a., Ruessink, B. G., & Garnier, R. (2011). Shoreline
instability under low-angle wave incidence. Journal of Geophysical
Research: Earth Surface, 116(September), 1–12.
http://doi.org/10.1029/2010JF001894
Jang, D., Park, H., & Choi, J. (2015). Create aMissing Precipitation Data
78
based on Spatial Interpolation Methods in Not Covered Areas by Region
Climate Change Scenario, 99(Itcs), 109–112.
Javale, P., Gadgil, S., Bhargave, C., Kharwandikar, Y., & Nandedkar, V.
(2014). Accident Detection and Surveillance System using Wireless
Technologies. IOSR Journal of Computer Engineering, 16(2), 38–43.
Retrieved on March 4, 2016, from http://www.iosrjournals.org/iosr-
jce/papers/Vol16-issue2/Version-10/G0162103843.pdf
Jenter, H. L., & Madsen, O. S. (1989). Bottom Stress in Wind-Driven Depth-
Averaged Coastal Flows. Journal of Physical Oceanography, 19, 962–
974. http://doi.org/10.1175/1520-
0485(1989)019<0962:BSIWDD>2.0.CO;2
Johnson, D. (2004). Transient rip currents and nearshore circulation on a
swell-dominated beach. Journal of Geophysical Research, 109(C02026),
1–20. http://doi.org/10.1029/2003JC001798
Jones, N. L., & Monismith, S. G. (2008). The Influence of Whitecapping
Waves on the Vertical Structure of Turbulence in a Shallow Estuarine
Embayment. Journal of Physical Oceanography, 38(7), 1563–1580.
http://doi.org/10.1175/2007JPO3766.1
Jordi, A., Basterretxea, G., & Wang, D. P. (2011). Local versus remote wind
effects on the coastal circulation of a microtidal bay in the Mediterranean
Sea. Journal of Marine Systems, 88(2), 312–322.
http://doi.org/10.1016/j.jmarsys.2011.05.007
Kifana, B. D. (2012). Great Circle Distance Methode for Improving
Operational Control System Based on GPS Tracking System.
Interntional Journal on Computer Science and Engineering, 4(04), 647–
662.
Klemas, V. (2012). Remote Sensing of Coastal and Ocean Currents : An
Overview, (November 2011), 576–587.
http://doi.org/10.2112/JCOASTRES-D-11-00197.1
Kraichnan, R. H. (1976). Eddy Viscosity in Two and Three Dimensions.
Journal of the Atmospheric Sciences. http://doi.org/10.1175/1520-
79
0469(1976)033<1521:EVITAT>2.0.CO;2
Larson, M., Kubota, S., & Erikson, L. (2004). Swash-zone sediment transport
and foreshore evolution: Field experiments and mathematical modeling.
Marine Geology, 212(1-4), 61–79.
http://doi.org/10.1016/j.margeo.2004.08.004
Lentz, S. J. (1995). Sensitivity of the Inner-Shelf Circulation to the Form of the
Eddy Viscosity Profile. Journal of Physical Oceanography.
http://doi.org/10.1175/1520-0485(1995)025<0019:SOTISC>2.0.CO;2
Lentz, S. J., & Fewings, M. R. (2012). The Wind- and Wave-Driven Inner-
Shelf Circulation. Annual Review of Marine Science, 4(1), 317–343.
http://doi.org/10.1146/annurev-marine-120709-142745
Liau, J.-M., Roland, A., Hsu, T.-W., Ou, S.-H., & Li, Y.-T. (2011). Wave
refraction–diffraction effect in the wind wave model WWM. Coastal
Engineering, 58(5), 429–443.
http://doi.org/10.1016/j.coastaleng.2011.01.002
Lin, P., & Liu, P. L. F. (1998). A numerical study of breaking waves in the surf
zone. Journal of Fluid Mechanics, 359(October), 239–264.
http://doi.org/10.1017/S002211209700846X
Loureiro, C., Ferreira, Ó., & Cooper, J. A. G. (2012). Geologically constrained
morphological variability and boundary effects on embayed beaches.
Marine Geology, 329-331, 1–15.
http://doi.org/10.1016/j.margeo.2012.09.010
Loureiro, C., Ferreira, Ó., & Cooper, J. A. G. (2013). Applicability of
parametric beach morphodynamic state classification on embayed
beaches. Marine Geology, 346, 153–164.
http://doi.org/10.1016/j.margeo.2013.09.005
Lubin, P., Glockner, S., Kimmoun, O., & Branger, H. (2011). Numerical study
of the hydrodynamics of regular waves breaking over a sloping beach.
European Journal of Mechanics, B/Fluids, 30(6), 552–564.
http://doi.org/10.1016/j.euromechflu.2011.01.001
80
Lutgens, F. K., & Tarbuck, E. J. (2012). Essentials of Geology (11th ed.).
New Jersey: Pearson.
Macmahan, J., Brown, J., Brown, J., Thornton, E., Reniers, A., Stanton, T., …
Senechal, N. (2009). Mean Lagrangian flow behavior on an open coast
rip-channeled beach : A new perspective. Marine Geology, 268(1-4), 1–
15. http://doi.org/10.1016/j.margeo.2009.09.011
MacMahan, J., Brown, J., & Thornton, E. (2009). Low-Cost Handheld Global
Positioning System for Measuring Surf-Zone Currents. Journal of Coastal
Research, 253, 744–754. http://doi.org/10.2112/08-1000.1
MacMahan, J., Reniers, A., Brown, J., Brander, R., Thornton, E., Stanton, T.,
… Carey, W. (2011). An Introduction to Rip Currents Based on Field
Observations. Journal of Coastal Research, 274, iii–vi.
http://doi.org/10.2112/JCOASTRES-D-11-00024.1
Magri, O. (2006). A geological and geomorphological review of the Maltese
Islands with special reference to the coastal zone. Territoris, 6, 7–26.
Mao, Y., & Heron, M. L. (2008). The Influence of Fetch on the Response of
Surface Currents to Wind Studied by HF Ocean Surface Radar. Journal
of Physical Oceanography, 38(5), 1107–1121.
http://doi.org/10.1175/2007JPO3709.1
Masselink, G., & Hughes, M. G. (2003). Introduction to Coastal Processes
and Geomorphology. London: Hodder Education.
McCarroll, R. J., Brander, R. W., Turner, I. L., Power, H. E., & Mortlock, T. R.
(2014). Lagrangian observations of circulation on an embayed beach
with headland rip currents. Marine Geology, 355, 173–188.
http://doi.org/10.1016/j.margeo.2014.05.020
Micallef, A., Foglini, F., Le Bas, T., Angeletti, L., Maselli, V., Pasuto, A., &
Taviani, M. (2013). The submerged paleolandscape of the maltese
Islands: Morphology, evolution and relation to quaternary environmental
change. Marine Geology, 335, 129–147.
http://doi.org/10.1016/j.margeo.2012.10.017
81
Miles, J. R., Guza, R. T., Elgar, S., Feddersen, F., & Ruessink, B. G. (2001).
Modelling the alongshore current on barred beaches. Journal of
Geophysical Research, 106(10), 22, 451 – 463.
Mohsin, S., & Tajima, Y. (2014). Modeling of Time-Varying Shear Current
Field Under Breaking and Broken Waves With Surface Rollers. Coastal
Engineering Journal, 3(2014), 1450013.
http://doi.org/10.1142/S0578563414500132
Monismith, S. G., Cowen, E. A., Nepf, H. M., Magnaudet, J., & Thais, L.
(2007). Laboratory observations of mean flows under surface gravity
waves. Journal of Fluid Mechanics, 573, 131–147.
http://doi.org/10.1017/S0022112006003594
Montreuil, A. L., & Bullard, J. E. (2012). A 150-year record of coastline
dynamics within a sediment cell: Eastern England. Geomorphology, 179,
168–185. http://doi.org/10.1016/j.geomorph.2012.08.008
Mottershead, D., Bray, M., Soar, P., & Farres, P. J. (2014). Extreme wave
events in the central Mediterranean : Geomorphic evidence of tsunami
on the Maltese Islands. Zeitschrift Fur Geomorphologie, 58(January),
385–411. http://doi.org/10.1127/0372-8854/2014/0129
Murray, A. B., & Reydellet, G. (2001). A rip current model based on a
hypothsized wave/current interaction. Journal of Coastal Research,
17(3), 517–530.
NAVIN Corporation. (2013). miniHomer Key Chain GPS Position Finder.
Retrieved on March 31, 2016, from
http://www.navin.com.tw/miniHomer.htm
Newell, C., Mullarkey, T., & Clyne, M. (2005). Radiation stress due to ocean
waves and the resulting currents and set-up/set-down. Ocean Dynamics,
55(5-6), 499–514. http://doi.org/10.1007/s10236-005-0009-2
82
Nicholls, R. J., Larson, M., Capobianco, M., & Birkemeier, W. A. (1998).
Depth of Closure: Improving Understanding and Prediction. Proceedings
of the Coastal Engineering Conference, 3, 2888–2901. Retrieved on May
2, 2016, from http://www.scopus.com/inward/record.url?eid=2-s2.0-
0032273657&partnerID=40&md5=4ec4b4a0a56ee00bf435809eed912e6
1
Nielsen, P., Callaghan, D. P., & Baldock, T. E. (2011). Downward transfer of
momentum by wind-driven waves. Coastal Engineering, 58(12), 1118–
1124. http://doi.org/10.1016/j.coastaleng.2011.06.008
Ocean in Motion: Waves and Tides. (n.d.). Retrieved on November 10, 2015,
from http://ci.coastal.edu/~sgilman/770Oceansinmotion.htm
Ohlmann, J. C., Fewings, M. R., & Melton, C. (2012). Lagrangian
observations of inner-shelf motions in Southern California: Can Surface
Waves Decelerate Shoreward-Moving Drifters Just outside the Surf
Zone? Journal of Physical Oceanography, 42(8), 1313–1326.
http://doi.org/10.1175/JPO-D-11-0142.1
Ojeda, E., Guillén, J., & Ribas, F. (2011). Dynamics of single-barred
embayed beaches. Marine Geology, 280(1-4), 76–90.
http://doi.org/10.1016/j.margeo.2010.12.002
Ozkan-Haller, H. T. (2014). Vertical Variability of Undertow and Longshore
Currents outside the Surf Zone. Journal of Waterway Port Coastal and
Ocean Engineering, 140(February), 4–13. http://doi.org/Doi
10.1061/(Asce)Ww.1943-5460.0000219
Paskyabi, M. B., & Fer, I. (2014). Turbulence structure in the upper ocean: a
comparative study of observations and modeling. Ocean Dynamics,
64(4), 611–631. http://doi.org/10.1007/s10236-014-0697-6
Peregrine, D. H. (1998). Surf zone currents. Theoretical and Computational
Fluid Dynamics, 10(1-4), 295–309.
http://doi.org/10.1007/s001620050065
83
Peregrine, D. H., & Bokhove, O. (1999). Vorticity and surf zone currents.
Coastal Engineering, 1(3), 745–758. Retrieved on September 3, from
<Go to ISI>://000084505400055
Pickard, S., & Pond, S. (1983). Introductory Dynamical Oceanography (2nd
ed.). Oxford: Pergamon Press.
Poulain, P. M., Gerin, R., Mauri, E., & Pennel, R. (2009). Wind effects of
drogued and undrogued drifters in the Eastern Mediterranean. Journal of
Atmospheric and Oceanic Technology, 26(6), 1144–1152,1154–1156.
Price, T. D., Ruessink, B. G., & Castelle, B. (2014). Morphological coupling in
multiple sandbar systems: A Review. Earth Surface Dynamics, 2(1),
309–321. http://doi.org/10.5194/esurf-2-309-2014
Razak, M. S. ., Dastgheib, A., Surydai, F., & Roelvink, D. (2014). Headland
structural impacts on surf zone current circulations. Journal of Coastal
Research, (70), 65–71.
http://doi.org/http://dx.doi.org/10.1108/17506200710779521
Ris, R. C., Holthuijsen, L. H., & Booij, N. (1999). A third-generation wave
model for coastal regions: Verification. Journal of Geophysical Research,
104, 7667. http://doi.org/10.1029/1998JC900123
Röhrs, J., Christensen, K. H., Hole, L. R., Broström, G., Drivdal, M., &
Sundby, S. (2012). Observation-based evaluation of surface wave
effects on currents and trajectory forecasts. Ocean Dynamics, 62(10-12),
1519–1533. http://doi.org/10.1007/s10236-012-0576-y
Sabet, B. S., & Barani, G. A. (2011). Design of small GPS drifters for current
measurements in the coastal zone. Ocean and Coastal Management,
54(2), 158–163. http://doi.org/10.1016/j.ocecoaman.2010.10.029
Sanil Kumar, V., & Anjali Nair, M. (2015). Inter-annual variations in wave
spectral characteristics at a location off the central west coast of India.
Annales Geophysicae, 33(2), 159–167. http://doi.org/10.5194/angeo-33-
159-2015
84
Scandura, P. (2011). Measurements of wave-induced steady currents outside
the surf zone. In Journal of Hydraulic Research (Vol. 49, pp. 64–71).
http://doi.org/10.1080/00221686.2011.591046
Schmidt, W. E., Woodward, B. T., Millikan, K. S., & Guza, R. T. (2003). A
GPS-tracked surf zone drifter, 14–17.
http://doi.org/http://dx.doi.org/10.1108/17506200710779521
Schwartz, R. K. (2012). Bedform, Texture, and Longshore Bar Development
in Response to Combined Storm Wave and Current Dynamics in a
Nearshore Helical Flow System. Journal of Coastal Research, 28(6),
1512–1535. http://doi.org/10.2112/JCOASTRES-D-11-00102.1
Scott Wilson Kirkpatrick and Co. Ltd. (2003). Malta Significant Wave Height
Study.
Sedrati, M., & Anthony, E. J. (2014). Confronting coastal morphodynamics
with counter-erosion engineering: the emblematic case of Wissant Bay,
Dover Strait. Journal of Coastal Conservation, 18, 483–494.
http://doi.org/10.1007/s11852-013-0300-1
Shanshan, L., Meijian, B., Di, X., & Shaohui, Z. (2015). Improvement of
interpolation methods fo surface micro-topography spatial distribution in
border irrigation simulation. Transactions of the Chinese Society of
Agricultural Engineering, 31(17), 126–132.
Short, A. D. (2010). Role of geological inheritance in Australian beach
morphodynamics. Coastal Engineering, 57(2), 92–97.
http://doi.org/10.1016/j.coastaleng.2009.09.005
Signell, R. P., Beardsley, R. C., Graber, H. C., & Capotondi, a. (1990). Effect
of Wave Current Interaction on Wind Driven Circulation In Narrow
Shallow Embayments. Journal of Geophysical Research, 95(6), 9671–
9678.
Simpson, J. H., & Sharples, J. (2012). Introduction to the Physical and
Biological Oceanography of Shelf Seas. Cambridge: Cambridge
University Press.
85
Smit, P. B., Zijlema, M., & Stelling, G. S. (2013). Depth-induced wave
breaking in a non-hydrostatic, near-shore wave model. Coastal
Engineering, 76, 1–16. http://doi.org/10.1016/j.coastaleng.2013.01.008
Smith, J. M. (1993). Nearshore Wave Breaking and Decay. Vicksburg.
Spydell, M. S., & Feddersen, F. (2012). A Lagrangian stochastic model of surf
zone drifter dispersion. Journal of Geophysical Research: Oceans,
117(February), 1–14. http://doi.org/10.1029/2011JC007701
Stewart, R. H. (2008). Introduction To Physical Oceanography.
Stive, M. J. ., & Wind, H. G. (1982). A study of radiation stress and set up in
the nearshre region. Coastal Engineering, 6, 1–25.
Stul, T., Gozzard, J. R., Eliot, I. G., & Eliot, M. J. (2015). Coastal Sediment
Cells for the Vlamingh Coast Between Cape Naturaliste and Moore
River, Western Australia. Retrieved on May 11, 2016, from
http://www.transport.wa.gov.au/mediaFiles/marine/MAC_R_CoastalSedi
mentCellsReport.pdf
Sullivan, P. P., McWilliams, J. C., & Moeng, C.-H. (2000). Simulation of
turbulent flow over idealized water waves. Journal of Fluid …, 404, 47–
85. http://doi.org/10.1017/S0022112099006965
Sundar, V., Sannasiiraj, S. A., & Kaldenhoff, H. (1999). Directional spreading
of waves in the nearshore zone. Ocean Engineering, 26, 161–188.
http://doi.org/10.1029/1998JC900092
Svendsen, I. A. (2006). Introduction to Nearshore Hydrodynamics: Volume 24
of Advanced series on ocean engineering. Nature (Google Books, Vol.
87). Toh Tuc Link, Singapore: World Scientific. Retrieved on 20 October
2015, from https://books.google.com.mt/books?id=g7-
PHmnrvQcC&pg=PA303&lpg=PA303&dq=E.D.+Christensen,+R.+Deiga
ard,+Large+eddy+simulation+of+breaking+waves,+Coastal+Eng.+42+(2
001)+53–86&source=bl&ots=MJZXR2vZRx&sig=6TuG0DudMTz7-
iIXQ_18vJktY6U&hl=en&sa=X&ved=0ahUKEwiHr
86
Tang, J., Lyu, Y., & Shen, Y. (2014). Numerical study on water waves and
wave-induced longshore currents in Obaköy coastal water. Acta
Oceanologica Sinica, 33(9), 40–46. http://doi.org/10.1007/s13131-014-
0515-5
Thornton, E. B., MacMahan, J., & Sallenger, a. H. (2007). Rip currents,
mega-cusps, and eroding dunes. Marine Geology, 240(1-4), 151–167.
http://doi.org/10.1016/j.margeo.2007.02.018
Ting, F. C. K., & Kirby, J. T. (1994). Observation of undertow and turbulence
in a laboratory surf zone. Coastal Engineering, 24(1-2), 51–80.
http://doi.org/10.1016/0378-3839(94)90026-4
Umgiesser, G., Canu, D. M., Cucco, A., & Solidoro, C. (2004). A finite
element model for the Venice Lagoon. Development, set up, calibration
and validation. Journal of Marine Systems, 51(1-4 SPEC. ISS.), 123–
145. http://doi.org/10.1016/j.jmarsys.2004.05.009
Underground Weather. (2016). Retrieved on April 20, 2016, from
https://www.wunderground.com/personal-weather-
station/dashboard?ID=IGOZOVIC1#history/s20140801/e20140831/mcus
tom
Van de Lageweg, W. I., Bryan, K. R., Coco, G., & Ruessink, B. G. (2013).
Observations of shoreline-sandbar coupling on an embayed beach.
Marine Geology, 344, 101–114.
http://doi.org/10.1016/j.margeo.2013.07.018
Van Dongeren, A. R., Svendsen, I. A., & Sancho, F. E. (1996). Generation of
Infragravity Waves. Proceedings of the 25th International Conference on
Coastal Engineering, Orlando, 1335–1348.
van Enckevort, I. M. J. (2004). Observations of nearshore crescentic
sandbars. Journal of Geophysical Research, 109(C6), C06028.
http://doi.org/10.1029/2003JC002214
Vousdoukas, M. I., Velegrakis, a. F., Dimou, K., Zervakis, V., & Conley, D. C.
(2009). Wave run-up observations in microtidal, sediment-starved pocket
beaches of the Eastern Mediterranean. Journal of Marine Systems.
87
Waugh, D. (2009). Geography: An integrated approach (4th ed.). London,
UK: Nelson Thornes.
Weber, J. E. H., Broström, G., & Saetra, Ø. (2006). Eulerian versus
Lagrangian Approaches to the Wave-Induced Transport in the Upper
Ocean. Journal of Physical Oceanography, 36(11), 2106–2118.
http://doi.org/10.1175/JPO2951.1
WERMED Interface. (2015). Retrieved on November 20, 2015, from
http://www.capemalta.net/maria/pages/interface/
Winter, G., van Dongeren, A. R., de Schipper, M. A., & van Thiel de Vries, J.
S. M. (2014). Rip currents under obliquely incident wind waves and tidal
longshore currents. Coastal Engineering, 89, 106–119.
http://doi.org/10.1016/j.coastaleng.2014.04.001
Woodroofe, C. D. (2002). Coasts: Form, Process and Evolution. Cambridge:
Cambridge University Press. Retrieved on 21 September, 2015, from
https://books.google.com.mt/books?id=jRqW0FVwWh8C&printsec=front
cover&source=gbs_atb#v=onepage&q&f=false
Xie, M. X. (2012). Three-dimensional numerical modelling of the wave-
induced rip currents under irregular bathymetry. Journal of
Hydrodynamics, 24(6), 864–872. http://doi.org/10.1016/S1001-
6058(11)60314-4
Yasso, W. E. (1965). Plan geometry of headland-bay beaches. Journal of
Geology, 73(5), 702–714. http://doi.org/10.1016/0048-9697(92)90273-U
Yu, J., & Slinn, D. N. (2003). Effects of wave-current interaction on rip
currents. Journal of Geology, 108(C3), 1–19.
http://doi.org/10.1029/2001JC001105
Zhou, G., Huang, J., & Zhang, G. (2015). Evaluation of the wave energy
conditions along the coastal waters of Beibu Gulf, China. Energy, 85,
449–457. http://doi.org/10.1016/j.energy.2015.03.094
88
Appendices
Appendix A – Drifter Tracks
Table A- 1: Table of information of drifter tracks.
Date No. of Tracks
Time ** Duration of Tracks
Wind Direction *
Heading Angle
2015/07/05 1 12:15 – 12:52 37 mins NNW 330o
2 12:17 – 12:37 20 mins NNW 330o
3 16:10 – 16:36 26 mins NNW 330o
4 16:13 – 16:41 28 mins NNW 330o
2015/07/10 1 18:09 – 19:18 69 mins NW 300o
2015/07/11 1 09:28 – 11:38 130 mins ENE 60o
2 13:26 – 1429 63 mins ENE 60o
3 13:27 – 14:37 70 mins ENE 60o
4 14:46 – 16:40 114 mins ENE 60o
2015/07/17 1 18:09 – 19:42 93 mins NNW 340o
2015/07/18 1 11:24 – 13:21 117 mins NNW 340o
2 15:56 – 16:16 60 mins NNW 340o
2015/08/28 1 15:51 – 17:44 113 mins ENE 60o
2 17:52 – 18:55 63 mins ENE 60o
2015/08/29 1 08:45 – 09:18 33 mins ENE 65o
2 09:27 – 11:10 103 mins ENE 65o
3 11:22 – 13:17 116 mins ENE 65o
4 13:39 – 14:34 55 mins ENE 65o
5 14:38 – 14:55 17 mins ENE 65o
6 15:11 – 16:20 69 mins ENE 65o
7 16:27 – 17:07 40 mins ENE 65o
2015/08/30 1 11:18 – 13:20 122 mins NNE 35o
2 14:35 – 15:59 84 mins NNE 35o
2015/09/04 1 18:03 – 19:51 108 mins W 270o
2015/09/05 1 09:14 – 10:31 77 mins SW 220o
2 11:02 – 12:48 106 mins SW 220o
3 13:02 – 14:14 72 mins SW 220o
4 15:10 – 16:06 56 mins SW 220o
2015/09/26 1 12:06 – 12:33 27 mins NNW 335o
2 12:54 – 13:31 37 mins NNW 335o
3 13:41 – 14:31 50 mins NNW 335o
4 14:50 – 16:39 109 mins NNW 335o
5 16:50 – 17:40 50 mins NNW 335o
6 17:50 – 19:12 82 mins NNW 335o
2015/09/27 1 09:10 – 11:59 169 mins NNW 340o
2015/10/04 1 12:11 – 13:44 93 mins E 90o
Total 2755 mins
Average 76.52mins
*colour code according to the angle of the wind direction during the day track was taken. **colour code to identify the times of each drifter on the maps, illustrated at the start of each track.
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Esri, HERE, DeLorme, MapmyIndia, © OpenStreetMap contributors, and the GIS user community
0 70 140 210 28035Meters
¯
Figure A- 1: Drifter tracks on the 5th of July 2015 with wind direction from the NNW.
Figure A- 1: Drifter tracks on the 10th of July 2015 with wind direction from the NW.Figure A- 2: Drifter tracks on the 5th of July 2015 with wind direction from the NNW.
89
90
Esri, HERE, DeLorme, MapmyIndia, © OpenStreetMap contributors, and the GIS user community
0 70 140 210 28035Meters
¯
Figure A- 2: Drifter tracks on the 10th of July 2015 with wind direction from the NW.
Figure A- 3: Drifter tracks on the 11th of July 2015 with wind direction from the ENE.Figure A- 4: Drifter tracks on the 10th of July 2015 with wind direction from the NW.
90
91
Esri, HERE, DeLorme, MapmyIndia, © OpenStreetMap contributors, and the GIS user community
0 70 140 210 28035Meters
¯
Figure A- 3: Drifter tracks on the 11th of July 2015 with wind direction from the ENE.
Figure A- 5: Drifter tracks on the 17th of July 2015 with wind direction from the NNE.Figure A- 6: Drifter tracks on the 11th of July 2015 with wind direction from the ENE.
91
92
Esri, HERE, DeLorme, MapmyIndia, © OpenStreetMap contributors, and the GIS user community
0 70 140 210 28035Meters
¯
Figure A- 4: Drifter tracks on the 17th of July 2015 with wind direction from the NNE.
Figure A- 7: Drifter tracks on the 18th of July 2015 with wind direction from the NNW.Figure A- 8: Drifter tracks on the 17th of July 2015 with wind direction from the NNE.
92
93
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0 70 140 210 28035Meters
¯
Figure A- 5: Drifter tracks on the 18th of July 2015 with wind direction from the NNW.
Figure A- 9: Drifter tracks on the 28th of August 2015 with wind direction from the ENE.Figure A- 10: Drifter tracks on the 18th of July 2015 with wind direction from the NNW.
93
94
#
Esri, HERE, DeLorme, MapmyIndia, © OpenStreetMap contributors, and the GIS user community
0 70 140 210 28035Meters
¯
Figure A- 6: Drifter tracks on the 28th of August 2015 with wind direction from the ENE.
Figure A- 11: Drifter tracks on the 28th of August 2015 with wind direction from the ENE.Figure A- 12: Drifter tracks on the 28th of August 2015 with wind direction from the ENE.
94
95
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0 70 140 210 28035Meters
¯
Figure A- 7: Drifter tracks on the 28th of August 2015 with wind direction from the ENE.
Figure A- 13: Drifter tracks on the 30th of August 2015 with wind direction from the NNE.Figure A- 14: Drifter tracks on the 28th of August 2015 with wind direction from the ENE.
95
96
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Figure A- 8: Drifter tracks on the 30th of August 2015 with wind direction from the NNE.
Figure A- 15: Drifter tracks on the 4th of September 2015 with wind direction from the W.Figure A- 16: Drifter tracks on the 30th of August 2015 with wind direction from the NNE.
96
97
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¯
Figure A- 9: Drifter tracks on the 4th of September 2015 with wind direction from the W.
Figure A- 17: Drifter tracks on the 5th of September with wind direction from the SW.Figure A- 18: Drifter tracks on the 4th of September 2015 with wind direction from the W.
97
98
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¯
Figure A- 10: Drifter tracks on the 5th of September with wind direction from the SW.
Figure A- 19: Drifter tracks on the 26th of September with wind direction from the NNW.Figure A- 20: Drifter tracks on the 5th of September with wind direction from the SW.
98
99
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0 70 140 210 28035Meters
¯
Figure A- 11: Drifter tracks on the 26th of September with wind direction from the NNW.
Figure A- 21: Drifter tracks on the 27th of September 2015 with wind direction from the NNW.Figure A- 22: Drifter tracks on the 26th of September with wind direction from the NNW.
99
100
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0 70 140 210 28035Meters
¯
Figure A- 12: Drifter tracks on the 27th of September 2015 with wind direction from the NNW.
Figure A- 23: Drifter tracks on the 4th of October 2015 with wind direction from the E.Figure A- 24: Drifter tracks on the 27th of September 2015 with wind direction from the NNW.
10
0
101
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Figure A- 13: Drifter tracks on the 4th of October 2015 with wind direction from the E.
Table A- 2: Table of the selected drifter track points and SWAN model grid points that have been selected based on closeness of between the two sets of points, spatially and temporally.Figure A- 25: Drifter tracks on the 4th of October 2015 with wind direction from the E.
10
1
102
Time SWAN Drifter
No SWAN Drifter Time Lat Lon Wave θ +/- 180 Lat Lon Drifter θ
1 15:00 20150828_1551_1744 15-56-01 36.063 14.282 44.785 224.785 36.062697 14.281413 288.92
2 18:00 20150828_1551_1744 17-06-31 36.063 14.28 41.991 221.991 36.06323 14.280089 287.19
3 12:00 20150829_1122_1317 11-23-45 36.065 14.282 7.05 187.05 36.065006 14.281881 195.63
4 12:00 20150829_1122_1317 13-06-17 36.065 14.28 12.002 192.002 36.064434 14.279989 290.65
5 15:00 20150829_1511_1620 16-00-42 36.065 14.282 3.425 183.425 36.064466 14.282049 249.12
6 15:00 20150829_1511_1620 15-16-32 36.065 14.284 358.7 178.7 36.064552 14.283901 255.34
7 18:00 20150904_1803_1951 19-01-52 36.065 14.286 68.137 248.137 36.065053 14.285908 348.89
8 18:00 20150904_1803_1952 19-06-33 36.065 14.286 68.137 248.137 36.065065 14.285945 67.59
9 12:00 20150905_1102_1248 11-35-26 36.063 14.284 46.933 226.933 36.063365 14.284479 336.60
10 12:00 20150905_1102_1248 12-27-06 36.065 14.284 71.178 251.178 36.064646 14.284739 43.44
11 12:00 20150905_1102_1248 12-42-26 36.065 14.286 65.736 245.736 36.065023 14.285019 20.33
12 12:00 20150926_1341_1431 13-42-21 36.065 14.284 336.47 156.47 36.064552 14.283973 107.19
13 12:00 20150926_1341_1431 14-08-51 36.065 14.286 333.49 153.49 36.064307 14.28605 90.00
14 12:00 20150926_1450_1639 15-49-56 36.063 14.2824 344 164 36.062604 14.283873 98.32
15 12:00 20150926_1450_1639 15-08-37 36.063 14.282 352.1 172.1 36.06319 14.282091 118.41
16 09:00 20150927_0910_1159 09-48-16. 36.063 14.282 350.98 170.98 36.062999 14.281823 180.00
17 12:00 20150927_0910_1159 11-54-22 36.063 14.282 350.11 170.11 36.062347 14.282307 107.11
10
2
Table A- 2: Table of the selected drifter track points and SWAN model grid points that have been selected based on closeness of between the two sets of points, spatially and temporally.
103
-0.5
0
0.5
1
1.5
2
2.5
3
3.5
0 50 100 150 200 250
Velo
city (
m/s
)
Time (seconds)
U velocity 05/07/2015
12:15 – 12:52
12:17 – 12:37
16:10 – 16:36
16:13 – 16:41
-2
-1.5
-1
-0.5
0
0.5
1
0 50 100 150 200 250
Ve
locity (
m/s
)
Time (seconds)
V velocity 05/07/2015
12:15 – 12:52
12:17 – 12:37
16:10 – 16:36
16:13 – 16:41
Figure A- 14: Velocity time-series of the U (top) and V (bottom) components of four drifters on the 5th of July 2015.
Figure A- 26: Velocity time-series of the U (top) and V (bottom) components of four drifters on the 11th of July 2015.Figure A- 27: Velocity time-series of the U (top) and V (bottom) components of four drifters on the 5th of July 2015.
10
3
104
-1.4
-1.2
-1
-0.8
-0.6
-0.4
-0.2
0
0.2
0 100 200 300 400 500 600 700 800 900
Velo
city (
m/s
)
Time (seconds)
U velocity 11/07/2015
0928-1138
1326-1429
1327-1437
1446-1640
-0.4
-0.2
0
0.2
0.4
0.6
0 100 200 300 400 500 600 700 800 900
Velo
city
(m/s
)
Time (seconds)
V velocity 11/07/2015
0928-1138
1326-1429
1327-1437
1446-1640
Figure A- 15: Velocity time-series of the U (top) and V (bottom) components of four drifters on the 11th of July 2015.
Figure A- 28: Velocity time-series of the U (top) and V (bottom) components of four drifters on the 29th of August 2015.Figure A- 29: Velocity time-series of the U (top) and V (bottom) components of four drifters on the 11th of July 2015.
10
4
105
-0.8
-0.6
-0.4
-0.2
0
0.2
0.4
0 100 200 300 400 500 600 700 800
Velo
city (
m/s
)
Time (seconds)
U velocity 29/08/2015
09:27 – 11:10
11:22 – 13:17
13:39 – 14:34
15:11 – 16:20
-0.8
-0.6
-0.4
-0.2
0
0.2
0.4
0 100 200 300 400 500 600 700 800
Velo
city
(m/s
)
Time (seconds)
V velocity 29/08/2015
09:27 – 11:10
11:22 – 13:17
13:39 – 14:34
15:11 – 16:20
Figure A- 16: Velocity time-series of the U (top) and V (bottom) components of four drifters on the 29th of August 2015.
Figure A- 30: Velocity time-series of the U (top) and V (bottom) components of four drifters on the 26th of September 2015.Figure A- 31: Velocity time-series of the U (top) and V (bottom) components of four drifters on the 29th of August 2015.
10
5
106
-0.2
-0.1
0
0.1
0.2
0.3
0 100 200 300 400 500 600 700
Velo
city (
m/s
)
Time (seconds)
U Velocity 26/09/2015
13:41 – 14:31
14:50 – 16:39
16:50 – 17:40
17:50 – 19:12
-0.2-0.15
-0.1-0.05
00.050.1
0.150.2
0.250.3
0.35
0 100 200 300 400 500 600 700
Velo
city
(m/s
)
Time (seconds)
V Velocity 26/09/2015
13:41 – 14:31
14:50 – 16:39
16:50 – 17:40
17:50 – 19:12
Figure A- 17: Velocity time-series of the U (top) and V (bottom) components of four drifters on the 26th of September 2015.
10
6
107
Source: Esri, DigitalGlobe, GeoEye, Earthstar Geographics, CNES/Airbus DS, USDA,USGS, AEX, Getmapping, Aerogrid, IGN, IGP, swisstopo, and the GIS UserCommunity
¯
0 1,000 2,000500 Meters
LegendSWH August Average
0.14 - 0.14
0.15 - 0.15
0.16 - 0.15
0.16 - 0.16
0.17 - 0.17
0.18 - 0.17
0.18 - 0.18
0.19 - 0.18
0.19 - 0.19
0.2 - 0.19
0.2 - 0.2
0.21 - 0.2
0.21 - 0.21
0.22 - 0.21
0.22 - 0.22
0.23 - 0.22
0.23 - 0.23
0.24 - 0.23
0.24 - 0.24
0.25 - 0.25
0.26 - 0.25
0.26 - 0.26
0.27 - 0.26
0.27 - 0.27
0.28 - 0.27
0.28 - 0.28
0.29 - 0.28
0.29 - 0.29
0.3 - 0.29
0.3 - 0.3
0.31 - 0.3
0.31 - 0.31
Figure B- 1: Average Significant Wave Height for August.
Ap
pe
nd
ix B
– S
WA
N S
ign
ifica
nt W
ave
He
igh
t Ma
ps
10
7
108
Source: Esri, DigitalGlobe, GeoEye, Earthstar Geographics, CNES/Airbus DS, USDA,USGS, AEX, Getmapping, Aerogrid, IGN, IGP, swisstopo, and the GIS User Community
¯
0 1,000 2,000500 Meters
LegendSWH Sept Average
0.15 - 0.16
0.17 - 0.17
0.18 - 0.18
0.19 - 0.19
0.2 - 0.2
0.21 - 0.21
0.22 - 0.22
0.23 - 0.23
0.24 - 0.24
0.25 - 0.25
0.26 - 0.25
0.26 - 0.26
0.27 - 0.27
0.28 - 0.28
0.29 - 0.29
0.3 - 0.3
0.31 - 0.31
0.32 - 0.32
0.33 - 0.33
0.34 - 0.34
0.35 - 0.35
0.36 - 0.36
0.37 - 0.36
0.37 - 0.37
0.38 - 0.38
0.39 - 0.39
0.4 - 0.4
0.41 - 0.41
0.42 - 0.42
0.43 - 0.43
0.44 - 0.44
0.45 - 0.45
Figure B- 2: Average Significant Wave Height for September.
10
8
109
Source: Esri, DigitalGlobe, GeoEye, Earthstar Geographics, CNES/Airbus DS, USDA,USGS, AEX, Getmapping, Aerogrid, IGN, IGP, swisstopo, and the GIS User Community
¯
0 1,000 2,000500 Meters
LegendSWH Oct Average
0.25 - 0.26
0.27 - 0.27
0.28 - 0.29
0.3 - 0.3
0.31 - 0.31
0.32 - 0.33
0.34 - 0.34
0.35 - 0.35
0.36 - 0.37
0.38 - 0.38
0.39 - 0.39
0.4 - 0.41
0.42 - 0.42
0.43 - 0.43
0.44 - 0.45
0.46 - 0.46
0.47 - 0.47
0.48 - 0.49
0.5 - 0.5
0.51 - 0.51
0.52 - 0.53
0.54 - 0.54
0.55 - 0.55
0.56 - 0.57
0.58 - 0.58
0.59 - 0.59
0.6 - 0.61
0.62 - 0.62
0.63 - 0.63
0.64 - 0.64
0.65 - 0.66
0.67 - 0.67
Figure B- 3: Average Significant Wave Height for October.
10
9
110
Source: Esri, DigitalGlobe, GeoEye, Earthstar Geographics, CNES/Airbus DS, USDA,USGS, AEX, Getmapping, Aerogrid, IGN, IGP, swisstopo, and the GIS User Community
¯
0 1,000 2,000500 Meters
LegendSWH Nov Average
0.22 - 0.24
0.25 - 0.26
0.27 - 0.28
0.29 - 0.3
0.31 - 0.31
0.32 - 0.33
0.34 - 0.35
0.36 - 0.37
0.38 - 0.39
0.4 - 0.4
0.41 - 0.42
0.43 - 0.44
0.45 - 0.46
0.47 - 0.48
0.49 - 0.5
0.51 - 0.51
0.52 - 0.53
0.54 - 0.55
0.56 - 0.57
0.58 - 0.59
0.6 - 0.6
0.61 - 0.62
0.63 - 0.64
0.65 - 0.66
0.67 - 0.68
0.69 - 0.69
0.7 - 0.71
0.72 - 0.73
0.74 - 0.75
0.76 - 0.77
0.78 - 0.78
0.79 - 0.8
Figure B- 4: Average Significant Wave Height for November.
11
0
111
Source: Esri, DigitalGlobe, GeoEye, Earthstar Geographics, CNES/Airbus DS, USDA,USGS, AEX, Getmapping, Aerogrid, IGN, IGP, swisstopo, and the GIS User Community
¯
0 1,000 2,000500 Meters
LegendSWH Dec Average
0.17 - 0.17
0.18 - 0.18
0.19 - 0.19
0.2 - 0.19
0.2 - 0.2
0.21 - 0.21
0.22 - 0.21
0.22 - 0.22
0.23 - 0.23
0.24 - 0.23
0.24 - 0.24
0.25 - 0.25
0.26 - 0.25
0.26 - 0.26
0.27 - 0.26
0.27 - 0.27
0.28 - 0.28
0.29 - 0.28
0.29 - 0.29
0.3 - 0.3
0.31 - 0.3
0.31 - 0.31
0.32 - 0.32
0.33 - 0.32
0.33 - 0.33
0.34 - 0.34
0.35 - 0.34
0.35 - 0.35
0.36 - 0.36
0.37 - 0.36
0.37 - 0.37
0.38 - 0.38
Figure B- 5: Average Significant Wave Height for December.
11
1
112
Source: Esri, DigitalGlobe, GeoEye, Earthstar Geographics, CNES/Airbus DS, USDA,USGS, AEX, Getmapping, Aerogrid, IGN, IGP, swisstopo, and the GIS User Community
¯
0 1,000 2,000500 Meters
LegendSWG Jan Average
0.23 - 0.25
0.26 - 0.27
0.28 - 0.29
0.3 - 0.31
0.32 - 0.33
0.34 - 0.35
0.36 - 0.37
0.38 - 0.39
0.4 - 0.41
0.42 - 0.43
0.44 - 0.45
0.46 - 0.47
0.48 - 0.49
0.5 - 0.51
0.52 - 0.53
0.54 - 0.55
0.56 - 0.57
0.58 - 0.59
0.6 - 0.61
0.62 - 0.63
0.64 - 0.65
0.66 - 0.67
0.68 - 0.7
0.71 - 0.72
0.73 - 0.74
0.75 - 0.76
0.77 - 0.78
0.79 - 0.8
0.81 - 0.82
0.83 - 0.84
0.85 - 0.86
0.87 - 0.88
Figure B- 6: Average Significant Wave Height for January.
11
2
113
Source: Esri, DigitalGlobe, GeoEye, Earthstar Geographics, CNES/Airbus DS, USDA,USGS, AEX, Getmapping, Aerogrid, IGN, IGP, swisstopo, and the GIS User Community
¯LegendSWH Total Average
0.216 - 0.231
0.232 - 0.241
0.242 - 0.251
0.252 - 0.262
0.263 - 0.274
0.275 - 0.285
0.286 - 0.296
0.297 - 0.308
0.309 - 0.319
0.32 - 0.33
0.331 - 0.341
0.342 - 0.353
0.354 - 0.363
0.364 - 0.373
0.374 - 0.384
0.385 - 0.395
0.396 - 0.406
0.407 - 0.418
0.419 - 0.429
0.43 - 0.44
0.441 - 0.452
0.453 - 0.463
0.464 - 0.474
0.475 - 0.485
0.486 - 0.497
0.498 - 0.508
0.509 - 0.519
0.52 - 0.532
0.533 - 0.545
0.546 - 0.555
0.556 - 0.565
0.566 - 0.576
0 2,0001,000 Meters
Figure B- 7: Average Significant Wave Height for the total period of study.
11
3
114
F
F F
F F F F F F F
F
F F F F F F F
F
F F F F F F F F F F F F F
F
F F F F F F F F F F F F F F
F F F F F F F F F F F F F F F
F
F
F F F F F F F F F F F F F F F
F
F F F F F F F F F F F F F F F F F F F F F
F F F F F F F F F F F F F F F F F F F F F F
F
F F F F F F F F F F F F F F F F F F F F F F F F
F F F F F F F F F F F F F F F F F F F F F F F F F
F F F F F F F F F F F F F F F F F F F F F F F F F F
F F F F F F F F F F F F F F F F F F F F F F F F F F
F F F F F F F F F F F F F F F F F F F F F F F F F F
F F F F F F F F F F F F F F F F F F F F F F F F F F
F F F F F F F F F F F F F F F F F F F F F F F F F F
F F F F F F F F F F F F F F F F F F F F F F F F F F
F F F F F F F F F F F F F F F F F F F F F F F F F F
F F F F F F F F F F F F F F F F F F F F F F F F F F
F F F F F F F F F F F F F F F F F F F F F F F F F F
F F F F F F F F F F F F F F F F F F F F F F F F F F
Source: Esri, DigitalGlobe, GeoEye, Earthstar Geographics, CNES/Airbus DS, USDA,USGS, AEX, Getmapping, Aerogrid, IGN, IGP, swisstopo, and the GIS User Community
¯
0 1,000 2,000500 Meters
LegendWD August Average
F29 - 35
36 - 42
43 - 48
49 - 55
56 - 61
62 - 68
69 - 74
75 - 81
82 - 87
88 - 93
94 - 100
110 - 110
120 - 110
120 - 120
130 - 130
140 - 130
140 - 140
150 - 150
160 - 150
160 - 160
170 - 160
170 - 170
180 - 180
190 - 180
190 - 190
200 - 200
210 - 200
210 - 210
220 - 220
230 - 220
230 - 230
240 - 240
Figure C- 1: Average Mean Wave Direction for August.
Appendix
C –
SW
AN
Me
an
Wa
ve
Dire
ctio
n
Ma
ps
11
4
115
F
F F
F F F F F F F
F
F F F F F F F
F
F F F F F F F F F F F F F
F
F F F F F F F F F F F F F F
F F F F F F F F F F F F F F F
F F F F F F F F F F F F F F F F F
F F F F F F F F F F F F F F F F F F F F F F
F F F F F F F F F F F F F F F F F F F F F FF
F F F F F F F F F F F F F F F F F F F F F F F F
F F F F F F F F F F F F F F F F F F F F F F F F F
F F F F F F F F F F F F F F F F F F F F F F F F F F
F F F F F F F F F F F F F F F F F F F F F F F F F F
F F F F F F F F F F F F F F F F F F F F F F F F F F
F F F F F F F F F F F F F F F F F F F F F F F F F F
F F F F F F F F F F F F F F F F F F F F F F F F F F
F F F F F F F F F F F F F F F F F F F F F F F F F F
F F F F F F F F F F F F F F F F F F F F F F F F F F
F F F F F F F F F F F F F F F F F F F F F F F F F F
F F F F F F F F F F F F F F F F F F F F F F F F F F
F F F F F F F F F F F F F F F F F F F F F F F F F F
Source: Esri, DigitalGlobe, GeoEye, Earthstar Geographics, CNES/Airbus DS, USDA,USGS, AEX, Getmapping, Aerogrid, IGN, IGP, swisstopo, and the GIS User Community
¯
0 1,000 2,000500 Meters
LegendWD Sept Average
F230 - 230
230 - 220
220 - 220
220 - 210
210 - 210
200 - 200
200 - 190
190 - 190
190 - 180
180 - 180
170 - 170
170 - 160
160 - 160
160 - 150
150 - 150
150 - 140
140 - 140
130 - 130
130 - 120
120 - 120
120 - 110
110 - 110
95 - 100
89 - 94
83 - 88
77 - 82
71 - 76
65 - 70
59 - 64
53 - 58
47 - 52
41 - 46
Figure C- 2: Average Mean Wave Direction for September.
11
5
116
FF F
F F F F F F FF
F F F F F F FF F F F F F F F F F F F F FF F F F F F F F F F F F F F F
F F F F F F F F F F F F F F F
F F F F F F F F F F F F F F F F FF F F F F F F F F F F F F F F F F F F F F F
F F F F F F F F F F F F F F F F F F F F F FF F F F F F F F F F F F F F F F F F F F F F F F F
F F F F F F F F F F F F F F F F F F F F F F F F F
F F F F F F F F F F F F F F F F F F F F F F F F F F
F F F F F F F F F F F F F F F F F F F F F F F F F F
F F F F F F F F F F F F F F F F F F F F F F F F F F
F F F F F F F F F F F F F F F F F F F F F F F F F F
F F F F F F F F F F F F F F F F F F F F F F F F F FF F F F F F F F F F F F F F F F F F F F F F F F F FF F F F F F F F F F F F F F F F F F F F F F F F F FF F F F F F F F F F F F F F F F F F F F F F F F F FF F F F F F F F F F F F F F F F F F F F F F F F F FF F F F F F F F F F F F F F F F F F F F F F F F F F
Source: Esri, DigitalGlobe, GeoEye, Earthstar Geographics, CNES/Airbus DS, USDA,USGS, AEX, Getmapping, Aerogrid, IGN, IGP, swisstopo, and the GIS User Community
¯
0 1,000 2,000500 Meters
LegendWD Oct Average
F32 - 36
37 - 39
40 - 43
44 - 46
47 - 50
51 - 53
54 - 57
58 - 60
61 - 64
65 - 67
68 - 71
72 - 74
75 - 78
79 - 82
83 - 85
86 - 89
90 - 92
93 - 96
97 - 99
100 - 100
110 - 110
120 - 110
120 - 110
120 - 120
130 - 120
130 - 120
130 - 130
140 - 130
140 - 130
140 - 140
150 - 140
150 - 150
Figure C- 3: Average Mean Wave Direction for October.
11
6
117
Figure C- 4: Average Mean Wave Direction for November.
F
F FF F F F F F FF
F F F
F F F F
F
F F F F F F F F F F F F F
F
F F F F F F F F F F F F F F
F F F F F F F F F F F F F F F
F F
F F F F F F F F F F F F F F F
F F F F FF F F F F F F F F F F F F F F F F
F F F F F F F F F F F F F F F F F F F F F F
FF F F F F F F F F F F F F F F F F F F F F F F F
F F F F F F F F F F F F F F F F F F F F F F F F F
F F F F F F F F F F F F F F F F F F F F F F F F F F
F F F F F F F F F F F F F F F F F F F F F F F F F F
F F F F F F F F F F F F F F F F F F F F F F F F F F
F F F F F F F F F F F F F F F F F F F F F F F F F F
F F F F F F F F F F F F F F F F F F F F F F F F F F
F F F F F F F F F F F F F F F F F F F F F F F F F F
F F F F F F F F F F F F F F F F F F F F F F F F F F
F F F F F F F F F F F F F F F F F F F F F F F F F F
F F F F F F F F F F F F F F F F F F F F F F F F F F
F F F F F F F F F F F F F F F F F F F F F F F F F F
Source: Esri, DigitalGlobe, GeoEye, Earthstar Geographics, CNES/Airbus DS, USDA,USGS, AEX, Getmapping, Aerogrid, IGN, IGP, swisstopo, and the GIS User Community
¯
0 1,000 2,000500 Meters
LegendWD Nov Average
F22.5 - 29.8
29.9 - 37.2
37.3 - 44.6
44.7 - 51.9
52 - 59.3
59.4 - 66.7
66.8 - 74
74.1 - 81.4
81.5 - 88.8
88.9 - 96.1
96.2 - 103
104 - 111
112 - 118
119 - 126
127 - 133
134 - 140
141 - 148
149 - 155
156 - 162
163 - 170
171 - 177
178 - 185
186 - 192
193 - 199
200 - 207
208 - 214
215 - 221
222 - 229
230 - 236
237 - 243
244 - 251
252 - 258
11
7
118
F
F
FF F F
F F F FF
F F F F F F FF
F F F F F F F F F F F F FFF F F F F F F F F F F F F F
F F F F F F F F F F F F F F FF
F
F F F F F F F F F F F F F F FF
F F FF F F F F F F F F F F F F F F F F F
F F F F F F F F F F F F F F F F F F F F F FF
F F F F F F F F F F F F F F F F F F F F F F F F
F F F F F F F F F F F F F F F F F F F F F F F F FF F F F F F F F F F F F F F F F F F F F F F F F F FF F F F F F F F F F F F F F F F F F F F F F F F F FF F F F F F F F F F F F F F F F F F F F F F F F F FF F F F F F F F F F F F F F F F F F F F F F F F F FF F F F F F F F F F F F F F F F F F F F F F F F F FF F F F F F F F F F F F F F F F F F F F F F F F F FF F F F F F F F F F F F F F F F F F F F F F F F F FF F F F F F F F F F F F F F F F F F F F F F F F F FF F F F F F F F F F F F F F F F F F F F F F F F F FF F F F F F F F F F F F F F F F F F F F F F F F F F
Source: Esri, DigitalGlobe, GeoEye, Earthstar Geographics, CNES/Airbus DS, USDA,USGS, AEX, Getmapping, Aerogrid, IGN, IGP, swisstopo, and the GIS User Community
¯
0 1,000 2,000500 Meters
LegendWD Dec Average
F29 - 35
36 - 41
42 - 47
48 - 52
53 - 58
59 - 64
65 - 70
71 - 75
76 - 81
82 - 87
88 - 93
94 - 98
99 - 100
110 - 110
120 - 120
130 - 120
130 - 130
140 - 130
140 - 140
150 - 140
150 - 150
160 - 160
170 - 160
170 - 170
180 - 170
180 - 180
190 - 180
190 - 190
200 - 200
210 - 200
210 - 210
220 - 210
Figure C- 5: Average Mean Wave Direction for December.
11
8
119
F
F FF F F F F F F
F
F F F
F
F F F
F
F F F F F F F F F F F F F
F
F F F F F F F F F F F F F F
F F F F F F F F F F F F F F F
F F F F F F F F F F F F F F F F F
F F F F F F F F F F F F F F F F F F F F F F
F F F F F F F F F F F F F F F F F F F F F F
F
F F F F F F F F F F F F F F F F F F F F F F F F
F F F F F F F F F F F F F F F F F F F F F F F F F
F F F F F F F F F F F F F F F F F F F F F F F F F F
F F F F F F F F F F F F F F F F F F F F F F F F F F
F F F F F F F F F F F F F F F F F F F F F F F F F F
F F F F F F F F F F F F F F F F F F F F F F F F F F
F F F F F F F F F F F F F F F F F F F F F F F F F F
F F F F F F F F F F F F F F F F F F F F F F F F F F
F F F F F F F F F F F F F F F F F F F F F F F F F F
F F F F F F F F F F F F F F F F F F F F F F F F F F
F F F F F F F F F F F F F F F F F F F F F F F F F F
F F F F F F F F F F F F F F F F F F F F F F F F F F
Source: Esri, DigitalGlobe, GeoEye, Earthstar Geographics, CNES/Airbus DS, USDA,USGS, AEX, Getmapping, Aerogrid, IGN, IGP, swisstopo, and the GIS User Community
¯
0 1,000 2,000500 Meters
LegendWD Jan Average
F23 - 31
32 - 39
40 - 47
48 - 54
55 - 62
63 - 70
71 - 77
78 - 85
86 - 93
94 - 100
110 - 110
120 - 120
130 - 120
130 - 130
140 - 140
150 - 150
160 - 150
160 - 160
170 - 170
180 - 180
190 - 190
200 - 190
200 - 200
210 - 210
220 - 220
230 - 220
230 - 230
240 - 240
250 - 250
260 - 250
260 - 260
270 - 270
Figure C- 6: Average Mean Wave Direction for January.
11
9
120
1010
1011
1012
1013
1014
1015
1016
1017
1018
1019
1020
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
Atm
osph
eric P
ressure
(h
Pa
)
Sig
nific
ant W
ave
He
igh
t (m
)
Time
Average Significant Wave Height Time-Series of August
August pt 2 August pt 1 August pt 3
August pt 4 August pt 5 Pressure (hPa)
August pt 2
Figure D- 1: Time-Series for the Significant Wave Height during August across the five points from the transect.
Ap
pe
nd
ix D
– S
ign
ifica
nt W
ave
He
igh
t Tim
e S
erie
s
August pt 1
12
0
121
Figure D- 2: Time-Series for the Significant Wave Height during September across the five points from the transect.
1008
1010
1012
1014
1016
1018
1020
1022
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
1.8
Atm
osph
eric P
ressure
(h
Pa
)
Sig
nific
ant W
ave
He
igh
t (m
)
Time
Average Significant Wave Height Time-Series of September
September pt 1 September pt 2 September pt 3
September pt 4 September pt 5 Pressure (hPa)
12
1
122
1004
1006
1008
1010
1012
1014
1016
1018
1020
1022
1024
0
0.2
0.4
0.6
0.8
1
1.2
Atm
osph
eric P
ressure
(h
Pa
)
Sig
nific
ant W
ave
He
igh
t (m
)
Time
Average Significant Wave Height Time-Series of Ocotober
October pt 1 October pt 2 October pt 3October pt 4 October pt 5 Pressure (hPa)
Figure D- 3: Time-Series for the Significant Wave Height during October across the five points from the transect.
12
2
123
1000
1005
1010
1015
1020
1025
1030
0
0.5
1
1.5
2
2.5
3
3.5
4
Atm
ospheric P
ressure
(hP
a)
Sig
nific
ant
Wave H
eig
ht
(m)
Time
Average Significant Wave Height Time-Series of November
November pt 1 November pt 2 November pt 3November pt 4 November pt 5 Pressure (hPa)
Figure D- 4: Time-Series for the Significant Wave Height during November across the five points from the transect.
12
3
124
1020
1025
1030
1035
1040
1045
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
Atm
osph
eric P
ressure
(h
Pa
)
Sig
nific
ant W
ave
He
igh
t (m
)
Time
Average Significant Wave Height Time-Series of December
December pt 2 December pt 1 December pt 3December pt 4 December pt 5 Pressure (hPa)
December pt 2December pt 1
Figure D- 5: Time-Series for the Significant Wave Height during December across the five points from the transect.
12
4
125
1000
1005
1010
1015
1020
1025
1030
1035
1040
0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
Atm
osph
eric P
ressure
(h
Pa
)
Sig
nific
ant W
ave
He
igh
t (m
)
Time
Average Significant Wave Height Time-Series of January
January pt 1 January pt 2 January pt 3January pt 4 January pt 5 Pressure (hPa)
Figure D- 6: Time-Series for the Significant Wave Height during January across the five points from the transect.
12
5
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