THE UNIVERSITY OF THE WEST INDIES - C-Change

THE UNIVERSITY OF THE WEST INDIES ST. AUGUSTINE, TRINIDAD AND TOBAGO, WEST INDIES

Faculty of Engineering

Department of Geomatics Engineering and Land Management

GEOM3050 Special Investigative Project

ASSESSMENT AND VALIDATION OF THE SEA LEVEL RISE THREAT

TO GRANDE RIVIERE, TRINIDAD

Farah A Hosein

807004541

Supervisor: Dr. M. Sutherland

April 2011

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ABSTRACT

Grande Riviere is a coastal community that lies at that backshore of the

Grande Riviere beach on the Northern coastline of Trinidad. This beach is famous

for the sight-seeing of leatherback turtles that visit every year to nest on the beach.

It is a very important tourist attraction and as a result has built a thriving economy

for the community of Grande Riviere. Of recent, Sea Level Rise (SLR) as a result

of climate change has been the topic of investigative reports especially along the

coastlines of Small Island Developing States (SIDS).

This report entails a study done on the coastline of Grande Riviere in order

to assess the impact of SLR on the beach and consequently the nesting of

leatherback turtles on the beach. Both primary and secondary data was collected in

terms of beach profiles from previous years and a beach profile was done for the

current year. Datasets were also collected. All data were processed and Arc Map

and Arc Scene were used to illustrate the data in a form of a map. A polygon was

digitized for each map using an elevation of 0.4m which would fall into the

IPCC’s category 1 of their sea level rise scenarios. The polygons that were

digitized were used to analyze the area of the beach that would be impacted by the

0.4m rise in sea level. In addition, line graphs were also created and analyzed in

order to get an assessment of the profile of the beach over time. Once the results

were analyzed and compared, a conclusion in terms of the impact of sea level rise

of 0.4m on the beach was drawn.

The area found common to all polygons that will definitely be impacted

upon by a 0.4m rise in sea level was found to be 1119.7m2 and a conclusion was

also drawn that this rise in sea level will impact the shoreline by accretion and

erosion. It was found that the ideal habitat for the nesting of the leatherback turtles

may be damaged after some time as the form of the beach is likely to change. As a

result, the turtles may find an alternative nesting area which would consequently

devastate the thriving economy of the community of Grande Riviere.

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ACKNOWLEDGEMENTS

This project could not be done without the help of the almighty God that

guides us through life, so gratitude must firstly be paid to God. I would also like to

thank my family, especially my husband and son, for their understanding and

support throughout the completion of this project. Much gratitude must also be

paid to my teacher and supervisor, Dr. Michael Sutherland, who offered words of

encouragement and advice throughout this project. Without his guidance and

support and time this project could not have been successfully completed. I would

also like to thank Amit Seeram for his time and dedication with his assistance to

me in the creations of my maps. Thanks is also directed to the Institute of Marine

Affairs for their cooperation with supplying me with the data from the beach

profiles that they have done. I must also recognize Adam Jehu and Sarah Hosein

as well as Bobby for their assistance and company while surveying the beach at

Grande Riviere. Also, to Akelo Moore and Michael Wilson for their assistance in

doing the beach profiles on March 2011, thank you.

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TABLE OF CONTENTS

ABSTRACT ........................................................................................................................................ i

ACKNOWLEDGEMENTS .............................................................................................................. iii

1 INTRODUCTION ..................................................................................................................... 1

1.1 BACKGROUND ................................................................................................................... 1

1.1.1 SEA LEVEL RISE AND SIDS ........................................................................................... 2

1.1.2 STUDY AREA ................................................................................................................ 2

1.2 PROBLEM STATEMENT ...................................................................................................... 4

1.3 PREVIOUS RESEARCH ........................................................................................................ 4

1.4 RESEARCH QUESTIONS ...................................................................................................... 4

1.5 AIMS AND OBJECTIVES ..................................................................................................... 5

1.6 GENERAL METHODOLOGY ................................................................................................ 5

1.7 ORGANIZATION OF REPORT .............................................................................................. 6

2 LITERATURE REVIEW ........................................................................................................... 7

2.1 INTRODUCTION .................................................................................................................. 7

2.2 SEA LEVEL RISE AND SIDS ............................................................................................... 7

2.3 IMPACT OF SEA LEVEL RISE IN TRINIDAD AND TOBAGO ................................................ 11

2.4 A REVIEW OF MODELLING OF SEA LEVEL RISE USING GIS TECHNOLOGY ..................... 12

2.4.1 BRUUN-GIS MODEL: ................................................................................................... 13

2.4.2 CASE STUDY USING BRUUN-GIS MODEL: ................................................................... 14

2.4.3 FLOOD-TIDE DELTA AGGRADATION MODEL: ............................................................. 15

2.4.4 CASE STUDY USING FLOOD-TIDE DELTA AGGRADATION MODEL: ............................. 15

2.4.5 CASE STUDY 1: ........................................................................................................... 16

2.4.6 CASE STUDY 2: ........................................................................................................... 16

2.4.7 CASE STUDY 3: ........................................................................................................... 18

2.5 A REVIEW OF MODELLING OF SEA LEVEL RISE USING GIS OTHER METHODS ............... 19

2.5.1 GNSS TIDAL GAUGES ................................................................................................ 19

2.5.2 SATELLITE IMAGERY: An advancement in technology for sea level rise modeling

worldwide. ............................................................................................................................... 20

2.5.3 CVI: COASTAL VULNERABILITY INDEX....................................................................... 22

2.5.4 NWLON: NATIONAL WATER LEVEL OBSERVATION NETWORK ................................... 22

2.6 CONCLUSION: .................................................................................................................. 23

3 METHODOLOGY .................................................................................................................. 24

3.1 PRIMARY DATA COLLECTION ......................................................................................... 24

3.2 SECONDARY DATA COLLECTION..................................................................................... 24

3.3 DATA PROCESSING .......................................................................................................... 24

4 RESULTS & ANALYSIS ....................................................................................................... 26

4.1 SPREADSHEETS ................................................................................................................ 26

4.2 RESULTS AND ANALYSIS OF LINE GRAPHS .................................................................... 26

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4.2.1 ANALYSIS OF BEACH PROFILE DATA OBTAINED AT STATION 1 ................................. 27




4.2.5 ANALYSIS OF BEACH PROFILES DONE IN 2011 .......................................................... 32

4.2.6 AN OVERALL ANALYSIS OF THE PROFILE OF THE BEACH FROM THE LINE GRAPHS . 33

4.3 RESULTS AND ANALYSIS OF DIGITISED MAPS FROM ARC MAP AND ARC SCENE .......... 34

4.3.1 AN ANALYSIS OF 0.4M FLOOD POLYGONS: ................................................................ 39

4.4 AN ANALYSIS OF THE EFFICIENCY OF THE METHODOLOGY WITH RESPECT TO THE

RESULTS ....................................................................................................................................... 41

5 CONCLUSION ........................................................................................................................ 42

5.1 AIM: ................................................................................................................................ 42

5.2 CONCLUSION: .................................................................................................................. 42

5.3 RECOMMENDATIONS: ...................................................................................................... 43

6 REFERENCES ........................................................................................................................ 45

7 APPENDICES ......................................................................................................................... 47

7.1 APPENDIX 1: Spreadsheets that were used to derive coordinates and elevation for the

beach profile data of Grande Riviere for 2011. ............................................................................ 47

7.2 APPENDIX 2: Spreadsheets that were used to derive the coordinates and elevation of the

data from the beach profiles taken by IMA in selected years between, 1999-2008 at Station 1 .. 54







7.6 APPENDIX 6: Arc Scene Map of Grande Riviere showing the 0.4m Flood Polygon for the

year 1999. ..................................................................................................................................... 75

7.7 APPENDIX 7: Arc Scene Map of Grande Riviere showing the 0.4m Flood Polygon for

Feb 2002 ...................................................................................................................................... 76


Oct 2002 ....................................................................................................................................... 77

7.9 APPENDIX 9: Arc Scene Map of Grande Riviere showing the 0.4m Flood Polygon for the

year 2006. ..................................................................................................................................... 78


the year 2007 ................................................................................................................................ 79


the year 2008 ................................................................................................................................ 80


the year 2010 ................................................................................................................................ 81


the year 2011 ................................................................................................................................ 82

7.14 APPENDIX 14: MAPS OF GRANDE RIVIERE DONE IN ARC MAP FOR THE

YEARS 1999-2011 ...................................................................................................................... 83

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TABLE OF FIGURES

Figure 1.1: Satellite Image of Trinidad showing Study Area, Grande Riviere ....... 3

Figure 1.2: Picture showing Grande Riviere site ...................................................... 4

Figure 1.3: Diagram showing the layout of the steps of the project ......................... 6

Figure 2.1: Recent Sea Level Rise ............................................................................ 7

Figure 2.2: Most Vulnerable CARICOM Cities to SLR and Storm Surge (top 15

only) ....................................................................................................................... 11

Figure 2.3: A diagram showing the general layout for the calculations of the rate of

shoreline recession ................................................................................................. 14

Figure 2.4: Picture showing Satellite ..................................................................... 20

Figure 4.1: STATION 1 BEACH PROFILES DONE BY IMA IN 5 PAST YEARS .............. 27




Figure 4.5: BEACH PROFILE DONE FROM IMA STATION 1 IN 2011 .......................... 31

Figure 4.6: BEACH PROFILE DONE FROM IMA STATION 2 IN 2011 ........................... 31



Figure 4.9: MAP SHOWING O.4M FLOOD POLYGON AT GRANDE RIVIERE IN 1999 ... 35

Figure 4.10: MAP SHOWING O.4M FLOOD POLYGON AT GRANDE RIVIERE IN FEB

2002 .......................................................................................................................... 35

Figure 4.11: MAP SHOWING O.4M FLOOD POLYGON AT GRANDE RIVIERE IN OCT

2002 .......................................................................................................................... 36

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Figure 4.12: MAP SHOWING O.4M FLOOD POLYGON AT GRANDE RIVIERE IN 2008 . 36





Figure 4.17: A MAP SHOWING THE INTERSECTING PORTION OF THE POLYGONS

CREATED FOR THE PREVIOUS YEARS ..................................................................... 40

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1 INTRODUCTION

1.1 BACKGROUND

Climate change is a critical issue that has been receiving massive

attention globally because its impacts are deemed to have adverse effects

on the earth and human societies. Continuous research on climate change

suggests that the main cause is the effect of global warming which is

initiated by daily human activities. These activities referred to, releases

gases including CO2 and other Green House Gases (GHG), both

contributing to the rise in global temperature. As a result there is an

increase in the average ocean and air temperatures, thermal expansion of

the oceans and an increase in the melting of polar ice sheets (IPCC, 2007)

(UNDP, 2010).

The International Panel on Climate Change (IPCC) Synthesis

Report 2007 states that oceans have been taking up over 80 % of the heat

being added to the climate system. The report also states that since 1993

thermal expansion of the oceans has contributed about 57% of the sum of

the individual contributions to sea level rise, with decreases in glaciers and

ice caps contributing about 28% and losses from polar ice sheets

contributing the remainder (IPCC, 2007).

Polar amplification, as stated by the United Nations Development

Programme (UNDP) 2010 Report on Climate Change, is the increase in

surface air temperatures at the poles as compared with the lower latitudes

as a response to climate change forcing. Therefore polar amplification can

be considered an important factor in the contribution of the melting of ice

caps the result of which is the inevitable rise in sea level. These effects of

global warming are all observable in the affected drastic changes in

weather conditions and contribute to climate changes (UNDP, 2010).

Sea level rise is being monitored by authorities for some time now

and it is now of grave concern because there have been estimated

projections made from examinations of rates of sea level rise over time. It

has been observed that in recent years the rate of sea level rise is higher

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than that of decades before, which suggests that climate change situation is

worsening. This should be expected because the rate of development in

countries has been increasing worldwide and therefore there is an increase

in anthropogenic emissions, which increase the effects of global warming

on climate change (IPCC, 2007).

The most recent (IPCC) 2007 report states that sea level is

estimated to increase by about 26-59cm over the next century. The UNDP

2010 report states that if the 3 million km3 of ice at the Arctic were to melt

there would be an 8m global sea level rise. The US Geological Survey

(2000) however predicts a rise in sea level of 80m if the ice sheets of

Greenland and the Antarctic were to melt. “The effect is damaging

worldwide if this were to happen since [sic] 10 % of the world’s population

live on coastal areas” (McGranahan, 2007).

1.1.1 SEA LEVEL RISE AND SIDS

Sea level rise is predicted to have greater effect on Small Island

States (SIDS) rather than large continents. In the UNDP 2010 Report it

states that, “CARICOM countries contribute less than 1% to GHG

emissions but will be most affected by climate change” They go on to

explain that the effect is severe in CARICOM countries because they are

small land masses surrounded by water and whose populations are highly

economically dependent upon coastal resources. There also exists a high

concentration of population and infrastructure on coastal areas (UNDP,

2010).

1.1.2 STUDY AREA

The study area chosen is Grand Riviere Beach in the small island

state of Trinidad and Tobago. This beach is on the Northern Coast of the

small CARICOM island and is a nesting site for leatherback sea turtles.

According to the website www.seeturtles.org, the beach hosts more than

http://www.seeturtles.org/

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500 nests in a single night during peak season, and the beach is considered

by some to be the most densely nested leatherback beach in the world. A

local newspaper, the Newsday, produced an article on February 28th

2011

by Ralph Banwaire quoting Minister Roodal Moonilal stating that the north

eastern district of the island has attracted a large number of visitors both

foreign and local annually, to witness the nesting spectacle. The article

goes on to say that this interest has increased the socio-economic

development for the region. Additionally, this tourist attraction is of great

importance to the community of Grande Riviere as it is a contributing

factor to its economy. Sea level rise may pose a threat to this economically

boosting nesting of leatherback turtles because the form and extent of the

beach may change as a result of beach erosion or flooding of the beach and

may not be appealing to the turtles to nest. Also, weather extremities due to

climate change and sea level rise may affect the temperature of the beach

sand and it may therefore no longer be ideal for the nesting of the turtles

(Nichols, 2011; Banwaire, 2011).

Figure 1.1: Satellite Image of Trinidad showing Study Area, Grande Riviere (Google, 2011)

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Figure 1.2: Picture showing Grande Riviere site

1.2 PROBLEM STATEMENT

Sea level rise, as a consequence of climate change due to global

warming, poses a threat to coastlines, especially in SIDS, and therefore an

assessment of its threat to Grande Riviere beach is of high importance as

this beach is the nesting site of the leatherback sea turtles that are of

socioeconomic importance to the community.

1.3 PREVIOUS RESEARCH

Due to the fact that Grande Riviere is of such importance to both

the turtles and the surrounding community, this beach has been chosen as a

study area prior to this research. The IMA (Institute of Marine Affairs)

have done beach profiles over the past number of years in an attempt to

somewhat monitor the change in the form of the beach. As well, research

has been done by Amit Seeram in 2010 where the profile of the beach was

taken and a sea level rise model was generated.

1.4 RESEARCH QUESTIONS

The research questions for this project are as follows:

Is the profile of Grande Riviere beach changing?

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Is sea level rise a threat to the nesting of leatherback turtles at Grande

Riviere beach?

How much of a threat is sea level rise to the nesting of the leatherback

turtles at Grande Riviere beach?

1.5 AIMS AND OBJECTIVES

The general aims of the project are as follows:

To validate previous profile surveys done at Grande Riviere

To create updated sea level rise models based upon a series of prior beach

profile surveys.

To compare and analyze the models in order to assess the sea level rise

threat to Grande Riviere.

1.6 GENERAL METHODOLOGY

The methodology adopted in general can be broken down into the

following steps:

Primary Data Collection

This entails a beach profiling exercise and surveys to collect spot heights

along the beach.

Secondary Data Collection

This entails a collection of previous beach profiles done on Grande Riviere

as well as the collection of contour maps of the beach

A creation of sea level rise models from the previous beach profiles.

An analysis and comparison of the sea level rise models.

A concluding assessment of the threat of sea level rise to the beach of

Grande Riviere.

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1.7 ORGANIZATION OF REPORT

Chapter 5

Conclusion and Recommendations

Chapter 4

Results: Analysis of results, Analysis of efficiency of methodology with respect to results.

Chapter 3

Methodology: Data Collection: Primary and Secondary data collected

Methodology: Prcoessing of Data Collected into sea level rise models.

Chapter 2

Literature Review: Analysis of literature on Climate change, sea level rise and its effects on SIDS

Literature Review: Analysis of different methodologies employed in the analysis of sea level rise activities.

Chapter 1

Background: Climate change and sea level rise, sea level rise and SIDS, description of study area.

Background:Problem Statement, Research Objective, General Methodologies

Figure 1.3: Diagram showing the layout of the steps of the project

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2 LITERATURE REVIEW

2.1 INTRODUCTION

There have been evident changes in climate which are suspected to be as a

result of anthropogenic emissions. Sea level rise as a consequence of climate

change and its repercussions on human societies around the world is being closely

monitored by both the scientific community as well as the general public.

Although a significant amount of the world’s population reside in coastal regions

and may be adversely affected by the rising of sea levels, small island states may

be the most affected by this phenomena as they possess a geological structure such

that they are surrounded by water. As a result, shorelines, barrier islands and

wetlands may adjust by moving in a landward direction and in cases where

landward movement is not possible then the result may be flooding and eventual

collapse of the existing vital ecosystems (NASA, 2008).

Figure 2.1: Recent Sea Level Rise (wildwildweather.com)

2.2 SEA LEVEL RISE AND SIDS

A relevant case, where the effect that sea level rise would have on Small

Island Developing States, is portrayed in the UNDP report of 2010 where the 16

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islands that make up the CARICOM group of islands as well as islands from the

Pacific are used as case studies. The level of the threat of sea level rise to these

islands were determined from measurements acquired and was used to assess the

impacts on each island and hence assess the islands’ vulnerability to sea level rise.

The nations of CARICOM16

in the Caribbean together with Pacific island

countries contribute less than 1% to global greenhouse gas (GHG) emissions

(approx. 0.33%17 and 0.03%18 respectively), yet these countries are expected to

be impacted by climate change the earliest in the decades ahead and they have the

least ability to adapt to these impacts. These nations’ are characterized as being

isolated, small land masses, with concentrated populations and infrastructure in

coastal areas, an economic base that is limited and is highly dependent on natural

resources, combined with limited financial, technical and institutional capacity.

These characteristics enhance their vulnerability to extreme events and impacts of

climate change. Low –lying atolls of these Caribbean nations are highly sensitive

to the increases of sea level rise and will threaten the water and food security,

settlements along the coastline as well as health and infrastructure. (UNDP, 2010).

It is of importance that note is taken of the projected increases in global sea

surface levels of 1.5m to 2m that may even be greater in the Caribbean region due

to the presence of gravitational and geophysical factors. From recent modeling

there is an indication that if perhaps the Greenland Ice Sheet and West Antarctic

Ice Sheet were to melt rapidly (over 100 years) the greatest rises in sea level will

be experienced along the Western and Eastern coasts of North America and the

result will be greater rises (up to 25% more than the global average) in the sea

surface in the Caribbean (Bamber et al. 2009). Partial melting of the ice sheets will

also result in greater rises in sea surface levels in the Caribbean region as

compared to other places around the world (UNDP, 2010).

Hurricanes and storm surges and in cases more prominently before the

1900s, tsunamis, are well known features of Caribbean meteorology, and their

range of inundation as well as their capacity for coastal erosion will increase as the

sea level rises. As a result, these events pose a threat to the Caribbean. The

topography infrastructural developments of each individual country are

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determinant factors in the severity of impact from these events. Topography along

coastlines is a determinant factor for coastal flooding due to climate change. The

steepness of the coastline and the narrowness of low lying areas cause the rise in

sea levels to consume less land and therefore concerns will be focused on the loss

of beaches and damage to developed areas concentrated in relatively flat lands. In

situations where the coastline is low-lying the concern will be focused on the

agricultural land loss, infrastructural damage, and water table salinization. The rate

at which the sea level rises, and the frequency and magnitude of storms, are the

main determinants of the level of impact that will result (UNDP, 2010).

In the UNDP report, the CARICOM countries are broadly categorized into

four groups in terms of their relative vulnerability to coastal flooding. The first

group contains the small islands and cays, which are mainly comprised of coral

reefs: The Bahamas, most of The Grenadines, Barbuda and a few small islands

lying offshore from other countries. These islands that mostly lie below 10m, have

high vulnerability to sea level rise and hurricane storm surge. They are likely to

experience periodic flooding, erosion and retreat of mangroves and seagrass beds,

as well as saltwater intrusion into the small lenses of fresh groundwater upon

which the islands are dependent. Also it is very likely for the islands to experience

additional biophysical impacts to the land masses will from other climate change

drivers such as ocean acidification, increased coastal water temperatures and

changes to currents and wave climates (UNDP, 2010).

The second group is consisted of volcanic islands such as St Christopher

(Kitts) and Nevis, St Lucia, St Vincent, Grenada, Dominica and Montserrat. These

islands are generally vulnerable to beach erosion and local coastal landslides due

to the fact that they have narrow coastal regions. Mangroves and seagrass beds are

also threatened in some of these islands. Coastal roads as well as homes and

infrastructure are also seen as vulnerable especially in the tourism industry.

Saltwater intrusion possesses less vulnerability in this group, but isolated areas of

mangroves and seagrass beds are vulnerable along with coral reefs. Because these

islands are tectonically active, they experience land movement, which could

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mitigate against or exacerbate SLR. In general, these rates of uplift are much less

than the probable rate of SLR (UNDP, 2010).

The third group of countries consists of those that possess large coastal

plains, near the sea level such as in Belize, Guyana and Suriname. They are

considered highly vulnerable to SLR as a result of their topography. Hurricanes

are also of great concern in the case of Belize but less so in the case of Guyana and

Suriname, although other storms may affect all three countries. Because Guyana

and Suriname are part of continental landmasses and because their general source

of fresh water is through land stream flow, saltwater penetration of the

groundwater reservoirs is a main concern. The threat of SLR will cause brackish

water to require further processing than is currently necessary before drinking and

other uses. In these nations, mangroves are more extensive as compared to other

CARICOM countries, and therefore deterioration in the mangroves will lead to

accelerated coastal erosion as the stabilizing root systems will be lost (UNDP,

2010).

Antigua, Barbados, Haiti, Jamaica and Trinidad and Tobago make up the

final group of the CARICOM countries. The coastlines of these countries are

varied and include both steep, sometimes volcanic coastlines and coastal plains,

sometimes with mangroves and seagrass beds along the shore. SLR is of

considerable threat in the form of coastal plain flooding, coastal erosion and

flooding caused by storms (including tropical storms in all areas and hurricanes in

the case of Antigua, Barbados, Haiti and Jamaica). These nations are also

considered tectonically active, and as with the volcanic islands, tectonic activity

may alter SLR projections slightly (UNDP, 2010).

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2.3 IMPACT OF SEA LEVEL RISE IN TRINIDAD AND TOBAGO

The study area of Grande Riviere is located on the Northern Range of

Trinidad which happens to be one of the islands investigated by the UNDP. The

UNDP report portrayed Trinidad and Tobago as a twin island state, where

Trinidad possesses geographical features of rolling grassland between the northern

mountainous ranges, extensive mangroves along the western coastline and

landward the terrain remains near sea level with a large area below 6m (Miller,

2005). Tobago, on the other hand, was described as a volcanic island with a

narrow coastal plain. The country of Trinidad and Tobago was expressed as being

vulnerable to SLR. In Port of Spain, the dockyard area is about 1.8m above mean

sea level and the central shopping area only 1.9m. The main government building

is at 6.6m above sea level rise and to the east the land rises to 8.9m. The city and

the low lying central region of the country are vulnerable to sea level rise given

that the maximum tidal range in the port is 1.5m (UNDP, 2010).

Figure 2.2: Most Vulnerable CARICOM Cities to SLR and Storm Surge (top 15 only)

(UNDP 2010) Figure 2.2: Most Vulnerable CARICOM Cities to SLR and Storm Surge (top 15 only) (UNDP 2010)

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Tectonic movement along the Central Range Fault, which, although

marked by lateral displacement, may also have a vertical component of movement,

could enhance this vulnerability. Modeling of sea level trends between Port of

Spain and Point Fortin indicate that sea level is rising at 4.2mm/year in the coastal

area in south-west Trinidad. The tectonic component is unclear, but given present

and forecast sea level changes from IPCC (+3.1mm/year), there is concern about

future changes. In addition an observation was made in a recent study by Singh et

al. (2006) that on the west coast of Trinidad petroleum installations would be at

severe risk of inundation and erosion derived from SLR and storm surge events. In

Tobago, bleaching of coral has been evident. Although caused by increases in

water temperature rather than sea level rise, this bleaching will inhibit the growth

of reefs and as a result increase their vulnerability to sea level rise. The study done

on the islands unfortunately did not include the important impact of sea level rise

on the nesting of leatherback turtles on the beaches of the twin island state. This,

not only will affect the turtles may also devastate the economy of the coastline

communities as the turtles on the beaches serve as a tourist attraction and therefore

a stabilized source of income for the surrounding communities (UNDP, 2010).

2.4 A REVIEW OF MODELLING OF SEA LEVEL RISE USING GIS TECHNOLOGY

The method with which sea level rise is being monitored is of significant

importance as the results gathered are in most cases used to make projections of

the level of the sea for the future in order to properly prepare for any devastating

impact that may result. Methodologies used for small island states differ from the

methodologies that continental countries have adopted and an analysis of the

different methodologies follows.

Critical to understanding the processes associated with climate change are

Earth process modeling and data visualization tools. One such set of tools is

Geographic Information Systems (GIS). GIS can serve a critical role in geographic

dimension modeling of climate change as well as the impacts of climate change on

the natural environment assessment. GIS may also be used to analyze climate

modeling results in conjunction with datasets of populations in an attempt to make

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an assessment of the impacts of climate change on human society around the globe

(Kostelnick et al, 2008).

GIS has been portrayed as a powerful new platform, in recent decades,

which may integrate digital maps, remote sensing, and other types of geographic

datasets in order to analyze, assess, model, and visualize Earth processes. The

“GIS Revolution” has had far-reaching impacts on both science and society

(Dobson 2004). The GIS model can be used to derive potential consequences of

sea level rise through “what if?” scenario types (e.g., what area of land would be

inundated in a coastal community in a Caribbean island with a 2-meter rise in sea

level and how many buildings would be displaced?). Scientists and educators can

use maps and visualizations, which depict projected sea level rise on the

landscape, as effective tools for portrayal of the potential consequences of sea

level rise to policy makers and the general public. (Kostelnick et al, 2008)

2.4.1 BRUUN-GIS MODEL:

The Bruun-GIS Model adapts the Bruun Model to an aerial GIS approach

following similar methods used originally in New Zealand. In the model shoreline

erosion is defined as a function of sea-level rise and is based on the assumption of

a closed material balance system between the beach and near shore and the

offshore bottom profile. The Model assumes the shoreward translation of an

equilibrium profile and there is re-deposition of eroded material offshore which

allows the original profile to be re-established. The Bruun Model incorporated

within a GIS allows continuous morphological variation alongshore (e.g. change in

dune height), which is an improvement as compared to conventional applications

of the model. The rate of shoreline recession (R) is defined as: R = L / (h + D) * s

Where L = distance between shoreline and depth of closure, h = depth of closure,

D = dune height and s = rate of sea-level rise

(Werner G. Hennecke, 2000)

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Figure 2.3: A diagram showing the general layout for the calculations of the rate of shoreline recession

(Werner G. Hennecke et al, 2000)

2.4.2 CASE STUDY USING BRUUN-GIS MODEL:

To illustrate the application of the Bruun-GIS model, the

Collaroy/Narrabeen Beach was chosen because it has the most intense and highly-

capitalised shoreline development in New South Wales. Various data layers and

studies are available for this site due to its geographical location in the Sydney

Metropolitan Area and its long history of coastal erosion, therefore providing

sufficient information for the modeling experiments. The Bruun-GIS Model was

applied to a publicly available 1:25,000 bathymetric map, to determine the

potential rate of shoreline erosion (R) caused by a rise in sea level on

Collaroy/Narrabeen Beach, which was provided by the New South Wales

Department of Land and Water Conservation (DLWC). From a study on

Collaroy/Narrabeen Beach by Patterson, Britten and Partners (1993) for Warringah

Council, local 'Bruun' parameters were derived. The beach was split into six

sections and based on a function of the area of a section along the beach and its

bounding contour segments, the length between the shoreline and the depth of

closure (L) was determined. Bruun-GIS Model ranges from approximately 7 m to

11 m were used to calculate the rate of recession along the beach, depending on

parameter values for L and D for each of the six sections. (Werner G. Hennecke,

2000)

P a g e | 15

2.4.3 FLOOD-TIDE DELTA AGGRADATION MODEL:

The Flood-tide Delta Aggradation Model is based on research for the

Dutch Wadden Sea. The principal assumption underlying this model is that the

floor of the flood-tide delta in a coastal inlet aggrades upward at the same rate as

sea-level rise, with some lag in time. The rate of shoreline recession along erodible

shorelines along a flood-tide delta inside a coastal inlet is then defined as: R= (A*

s – V_ext) / Les / D

where R = rate of shoreline recession, A = area of the flood-tide delta, s = rate of

sea-level rise, V_ext. = external sediment supply (e.g. littoral sediment transport),

Les = length of erodible shorelines along the flood-tide delta and D = dune height.

The sediment volume required for the flood-tide delta aggradation is

defined as a function of the area of the flood-tide delta and the rate of sea-level rise

and is supplied from erosion of shorelines outside and/or inside the inlet. The

Flood-tide Delta Aggradation Model also allows for the continuous morphological

variation alongshore, provided that there is sufficient detail in the resolution of the

terrain data. For this model, the overall assumption is that the larger the external

sediment supply V_ext. the smaller is V_int. and therefore shoreline erosion is

inside the inlet (Werner G. Hennecke, 2000).

2.4.4 CASE STUDY USING FLOOD-TIDE DELTA AGGRADATION MODEL:

Based on the work by Nielsen and Roy (1981), Hennecke (1999) trends of

flood-tide delta aggradation shown for the Wadden Sea was seen to have occurred

between 11,000 years B.P and approximately 6,000 years to 5,500 years B.P. in

estuaries in southeastern Australia. In addition to Bruun effects, it was therefore

assumed that the flood-tide delta of Narrabeen Lagoon will be able to keep pace

with rising sea level in the next 50 years. There is an assumption that a sediment

demand is created which, according to the GIS model, is estimated to be 91,659 m³

for a 0.2 m (mid-range 50-year sea-level rise scenario), given that the surface of

the flood-tide delta of Narrabeen Lagoon is 458,295 m² (or 22.2 % of the total area

P a g e | 16

of the lagoon). A further assumption is made that this demand would be met from

the ocean beach, adjacent to the inlet, recession (Werner G. Hennecke, 2000).

2.4.5 CASE STUDY 1:

In a case study done by students of Haskell University and the University

of Kansas, GIS was used to estimate the effects of hypothetical rise in global sea

level on population. The objective of the project was to use GIS to define

inundation areas that are as a result of sea level rise, and then to compare these

inundated areas to datasets of global population. This objective was in an attempt

to estimate current populations that are at risk both globally and regionally. In a

GIS network based on two parameters a sea level rise model was created. The

parameters used were elevation in relation to mean sea level and connectivity to

the existing ocean. The model inputs a global digital elevation model (DEM) and a

regular grid of elevation values. The model then identifies all grid cells that would

be inundated based on a user-defined increment. For the model results, basic

statistics were computed in order to determine total land area inundated at 1-6

meters intervals. Model results were overlaid with population datasets to estimate

the numbers of people that are currently living in the zones of inundation. The

project illustrated the challenge of developing visually appealing tools, such as

high-resolution animations, such that attraction is drawn to the coastal flooding

risk while still reflecting the considerable uncertainty over the anticipated amounts

of sea level rise. Students were faced with small changes in cartographic

technique, such as including representations of local tidal variation or avoiding

implications of associating a temporal scale to sea level rise, in order to produce

animations and visualizations that would engage the viewer’s attention without

irresponsible exaggeration of the risks of global sea level rise. (Kostelnick et al,

2008)

2.4.6 CASE STUDY 2:

In an investigation by the UNDP, in 2010, on the impact of sea level rise

on CARICOM islands the methodology used for the compilation of data was such

P a g e | 17

that GIS techniques were employed. A study area polygon was created for the

greater Caribbean region and it was used to clip large global datasets such that

there would be an improvement in processing time and a reduction in data

redundancy. All of the vulnerability indicator datasets were collected from public

sources. For all of the geospatial data files there was a careful inspection for data

completeness and after inspection, the World Equal Area projection was used to

project the geospatial data. The World Geodetic System 1984 was used as the

horizontal datum for the study. Tiles from the current (version 4) CIAT SRTM 90

meter grid cell digital elevation model (DEM) was used to derive the coastal

digital terrain model. A continuous sink filled DTM was established by creating a

mosaic of all required tiles in ArcGIS. Six flood scenarios (1 to 6 metres) were

created by conversion of the sink filled DTM into a series of binary raster files.

Within each flood scenario, all inland elevation pixels were manually masked out

to ensure that the analysis only included contiguous coastal pixels. Calculations

were done to estimate vulnerability by overlaying the DTM on the applicable

surface datasets. Four GIS models were built, for each type of surface dataset, in

order to calculate the total effected values. It was assumed that raster cell values

contained an evenly distributed relation (UNDP, 2010).

Polygon area included land area, city areas, airport runways, agriculture

and wetlands was then analyzed by overlaying polygon features with the DTM

using Hawth’s Analysis Tools for ArcGIS (Beyer 2004). The results from the

Hawt/h’s tool analysis were used and affected cells counts were converted into

square kilometers to estimate the total area affected by sea level rise for each

polygon. ArcGIS was then used to summarize the total affected area for each

scenario. Polygon percentage for economic activity and population was then

analyzed by creating a separate GIS model for gridded data with non-spatial pixel

values in terms of millions of dollars and numbers of people. Polygon features

were then created from raster cells which were rounded to the closet value. To

determine the amount of impacted DTM cells within each polygon, an overlay and

Hawth’s analysis was used. Population and economic estimates were then

calculated using the following formula:

P a g e | 18

P / T *100

P = The amount of affected cells in a polygon for a given flood scenario.

T = The total amount of cells within the polygon.

Lines of road networks were then analyzed by creating a GIS model which

identified road segments affected by flooded DTM cells. The lengths of each road

segment were then calculated and ArcGIS was used to summarize each scenario

by country. Points such as major tourism resorts, seaports and airports were then

analyzed by applying a 50 metre buffer to all surface point features. Point features

that intersected with at least one flooded DTM cell were identified as vulnerable

(UNDP, 2010).

2.4.7 CASE STUDY 3:

A new global coastal database called the Dynamic Interactive Vulnerability

Assessment (DIVA) Coastal Database was developed as part of the Dynamic and

Interactive Assessment of National, Regional and Global Vulnerability of Coastal

Zones to Climate Change and Sea-Level Rise (DINAS-COAST) project. The

database was designed as there was a need to model multiple coastal processes and

their interactions simultaneously within a single, well-structured framework. The

database was developed within a GIS because of its spatial nature and the world's

coasts were represented as a series of line segments with reference to data. The

data consisted of more than 80 physical, ecological, and socioeconomic parameters

which included information on factors such as waves, water quality, sediment

fluxes, elevation, population distribution, and gross domestic product density. The

database was intended to be used for impacts and vulnerability analyses on a

global and regional scale such that mitigation and adaptation to sea-level rise could

be assessed. (Vafeidis, 2008)

A fundamental barrier to the improvement of quantification of climate

change and SLR impacts in the Caribbean region and Pacific islands exists due to

the lack of long-term datasets and high-resolution elevation data. Data collection

and investment are urgent requirements for the facilitation of detailed risk

mapping and more accurate evaluations of the impacts of climate change. In

P a g e | 19

addition, thorough cost-benefit analyses of different adaptation options and the

islands’ abilities to cope with different levels of climate change and SLR are

critical (UNDP 2010).

2.5 A REVIEW OF MODELLING OF SEA LEVEL RISE USING GIS OTHER METHODS

2.5.1 GNSS TIDAL GAUGES

Changes in fluctuations of sea level can also be determined by the use of a

GNSS tidal gauge. The basis behind this method is that reflected GNSS signals

from the sea surface are observed which gives measurements of both relative and

absolute sea level change. The use of this application of GNSS in the monitoring

of sea levels has been experimented upon in the west coast of Sweden at the

Onsala Space Observatory in December of 2008 and in China in 2006 in an

experiment called China Ocean Reflection Experiment (CORE) (Lofgren, Haas, &

Johansson, 2010).

The procedure involves the employment of receivers and two antennas; the

RHCP antenna and the LHCP antenna. The RHCP antenna is zenith looking right

hand circular polarized and the LHCP is nadir looking left hand circular polarized.

Both antennas are mounted back to back on a beam over the sea. GNSS signals are

directly received by the RHCP antenna and the reflected signals from the sea

surface are received by the LHCP. Polarisation of the signal changes from RHCP

to LHCP when the signal is reflected and the reflected signal undertakes a path

delay. This suggests that the LHCP antenna is in fact considered virtual below the

surface of the sea and when the sea level changes the variation in the path delay of

the reflected signal will be detected since the position of the antenna in the water

will appear to have changed. The height of the LHCP antenna over the sea surface

is derived from the following equation: h = ½ (a + b)*(1/ (sin E – d))

Where E is the elevation of the transmitting satellite, (a + b) is the additional path

delay of the reflected signal, and d is the vertical separation between the phase

centres of the LHCP and RHCP (Lofgren, Haas, & Johansson, 2010).

Change in the height of the LHCP corresponds to twice the change in sea

level and therefore the antenna installations effectively monitor the change in sea

P a g e | 20

level. Every epoch observations are made from several different satellites which

have different elevation and azimuth angular measurements and which will result

in the derivation of reflected signals of varying incident angles and directions. .

The change in the sea level cannot be considered to originate from one point on

the sea surface, so the changes from an average sea surface formed by different

reflection points are taken. Point distribution is limited by antenna placement

which includes factors such as antenna height, landmass which antenna is on and

obstacles in the sea, and antenna geometry (Lofgren, Haas, & Johansson, 2010).

2.5.2 SATELLITE IMAGERY: An advancement in technology for sea level rise

modeling worldwide.

Figure 2.4: Picture showing Satellite (NASA, 2008)

Since the early part of the 20th

century scientists have directly measured

sea level however it was not known how many of the observed changes in sea

level were real and how many were related to tectonic movements. Satellites have

now changed that by introducing a reference by which change in ocean height can

be determined regardless of land movement. Scientists are now better able to

predict the rate at which sea level is rising and its cause from new satellite

measurements (NASA, 2008).

The Ocean Surface Topography Mission (OSTM), also called Jason 2, is a

joint effort of NASA, the National Oceanic and Atmospheric Administration

(NOAA), the French space agency Centre National d'Etudes Spatiales (CNES) and

the European Organisation for the Exploitation of Meteorological Satellites

(EUMETSAT). Jason 2 is a satellite that will help scientists to better monitor and

P a g e | 21

understand global sea level rise, study ocean circulation and its links to climate

and improve weather and climate forecasts. There would be continuous recording

of sea-surface height measurements which began in 1992 by the NASA-French

space agency TOPEX/Poseidon mission and extended by the NASA-French space

agency Jason 1 mission in 2001 and will extend into the next decade.

"OSTM/Jason 2 will help create the first multidecadal global record for

understanding the vital roles of the ocean in climate change," project scientist Lee-

Lueng Fu of NASA's Jet Propulsion Laboratory (JPL) in California said during a

May 20, 2008 briefing (NASA, 2008).

Measurements from TOPEX/Poseidon and Jason 1 show that mean sea

level has risen by about 3 millimeters a year since 1993, which is twice the rate

estimated from tidal gauges in the past century. However, to determine long term

trends, 15 years of data are not enough. The data collected is expected to advance

the understanding of global climate change."Data from the new mission,” Fu

added, “will allow us to continue monitoring global sea-level change, a field of

study where current predictive models have a large degree of uncertainty.” High-

precision ocean altimetry, developed through NASA and the French space agency,

is a measure of the height of the sea surface relative to Earth's center to within

about 3.3 centimeters. These measurements are called ocean-surface topography

and supply scientists with data concerning the ocean current speed and direction.

Height can also be an indication of where ocean heat is stored because the amount

of heat in the ocean strongly influences sea-surface height. The combination of

heat storage and ocean current data is vital in the understanding of global climate

variation (NASA, 2008).

OSTM/Jason 2 will ride to space aboard a NASA-provided United Launch

Alliance Delta II rocket, entering orbit 10-15 kilometers below the 1,336-

kilometer-high orbit of Jason 1. OSTM/Jason 2 will use thrusters to raise itself into

the same orbital altitude as Jason 1 and move in close behind its predecessor. The

two spacecraft will fly uniformly thereby collecting nearly simultaneous

measurements. It is expected that double the amount of data will be collected, and

P a g e | 22

there would be improvements in tide models in coastal and shallow seas which

would indefinitely help researchers to have a better understanding of ocean

currents and eddies. The OSTM/Jason 2 mission is designed to last at least three

years. CNES will hand over operations and control to NOAA after the spacecraft

has been checked out on orbit. NOAA and EUMETSAT will generate, archive and

distribute data products (NASA, 2008).

2.5.3 CVI: COASTAL VULNERABILITY INDEX

In order to assess the sensitivity of the impact of sea level rise on the

coastline of the United States of America, The United States Geographical Survey

has developed the Coastal Vulnerability Index (CVI). The CVI allows for the

proper assessment such that precautions can be made before the coastline is

exposed to any severe repercussion of the imminent sea level rise situation (NPS,

2011).

2.5.4 NWLON: NATIONAL WATER LEVEL OBSERVATION NETWORK

The NOAA Center for Operational Oceanographic Products and Services

maintains a National Water Level Observation Network of 200 stations throughout

the United States in an attempt to assess local sea-level rise. NOAA analysts have

used over 30 years of data from 117 of these locations to calculate relative sea-

level trends. For approximately half these stations, the relative sea-level trends are

above 2 mm/yr, which is above the IPCC current global sea-level rise estimates.

NOAA sea-level stations can serve as a reference for coastal planners, building

engineers and the public for information on the local sea level. NOAA is adding an

additional 11 new stations by the end of the year. Communities can use this data to

decide coastal protection measures and policies and plan accordingly. NOAA is

increasing efforts to create a linkage between sea-level measurements to land-

measurement systems. Community planners will then be able to consider projected

sea-level rise estimates when determining the best location for such projects as

highways, hospitals, and other public facilities. NOAA continues to enhance their

products and services in order to provide critical information on local and global

http://tidesandcurrents.noaa.gov/

http://tidesandcurrents.noaa.gov/sltrends/sltrends.html

P a g e | 23

sea-level trends as the monitoring of the effects of climate change on planet Earth

continues. (Lubchenco, 2011).

2.6 CONCLUSION:

Many innovative ways of modeling and monitoring sea level rise are being

developed worldwide however of greater importance is the ability of small island

developing states to also effectively monitor the situation as they are to be affected

the most. Although the continental countries are improving their methodology for

monitoring sea level rise by satellite imagery, small island states continue to use

GIS techniques. As a result, the methodology that was chosen for this research

involved GIS technique because the project study area of Grande Riviere is located

in the small island state of Trinidad and because there are currently no available

more advanced updated technique for the monitoring of sea level rise in the island.

P a g e | 24

3 METHODOLOGY

3.1 PRIMARY DATA COLLECTION

A beach profile was done at Grande Riviere in order to retrieve updated

data on the profile of the beach. A total station with a prism pole was used to take

up profiles along IMA’s 4 profile lines as well as spot heights along the beach. For

each profile line data was obtained in spots where the elevation changed and not at

exact meter intervals. Data retrieved were in the form of bearings and distances,

where the bearing between the start 2 control points were set. Arbitrary control

points were then set down on the beach from which the profiles were taken. Due to

the fact that the coordinates of the control points were known, the bearings and

distances observed could be used to calculate (X, Y, Z) coordinates for each

observed point.

3.2 SECONDARY DATA COLLECTION

Nine beach profiles, to be used in the processing of data for this project, six

of which were used, were obtained from IMA that were taken within the time

period of 1999 – 2008. Contour datasets as well as imagery datasets such as the

datasets for the roads and buildings of Grande Riviere were obtained from the

project completed by Amit Seeram did. The results for the flooding polygon below

0.4m were also obtained. National coordinates for control point A and B as well as

the elevation for control point A were obtained from the same project.

3.3 DATA PROCESSING

The data from the beach profiles that were done in the primary data

collection was inserted in an excel spreadsheet. The bearings and distances were

used along with the appropriate formulae to derive coordinates for all the points,

both spot heights and profiles, which were taken up. Another spreadsheet was

done in order to derive coordinates for the points taken up from the beach profiles

received from IMA. All (X, Y, Z) coordinates derived were in the WGS-84

projection.

P a g e | 25

In Arc Map, contour datasets as well as imagery datasets were uploaded.

Onto this frame the coordinates for each set of profile per year were uploaded one

by one and for each set a TIN file was produced, extracted and saved. From this,

classifications were made using the elevations or Z coordinates to classify a flood

polygon below 0.4 m for each profile. After this was done the polygons were then

digitized and the area was calculated on ArcMap for the digitised polygons. Once

the area was found, the polygons were again digitized to include the Caribbean

Sea. For each of the seven sets of data that this was done for, a polygon for the

Caribbean Sea was done respective to each year so that it was ensured that the

same polygon for the Caribbean Sea was not used for all the years. This was

because the mean sea level mark of the Caribbean Sea along the coastline is

expected to vary according to the sea level rise impact for that particular year and

therefore the polygon representing the Caribbean Sea will differ for each year. For

all polygons generated, shapefiles were created, extracted and saved. The maps for

the seven years were then transferred to a printing format where title, legend and

other text on the maps were edited.

In Arc Scene, the TIN file created was added to the program. It was used as

a base layer unto which shape files of the photograph of Grande Riviere as well as

the digitized polygon that included the Caribbean Sea were overlaid. Other

datasets utilized included roads, buildings and contour lines. Arc Scene was used

to set the base height of the inserted polygon to 0.4m so that the three dimensional

image would give an accurate virtual illustration of the area that would be flooded

by the 0.4m flood polygon. This was done for all 7 years of beach profiles.

The excel spreadsheets that were created for the beach profiles were used

to generate line graphs in excel. The graphs were made per station and each graph

contained data for that particular station over all the years compiled. These were

done for the profiles received from IMA and the spreadsheets for the beach

profiles for 2011 were used separately to generate line graphs for that particular

year.

P a g e | 26

4 RESULTS & ANALYSIS

4.1 SPREADSHEETS

The beach profile that was collected for 2011 was processed using Amit

Seeram’s control points as benchmarks. The WGS-84 coordinates were

determined for all the points of the beach profile as well as for the spot heights that

were taken along the beach, using the excel spreadsheet. The beach profiles that

were collected by IMA were processed in a spreadsheet so that coordinates in

WGS-84 datum were derived. Due to the fact that, in the beach profile done in

2011, the IMA stations were used as the starting point for the beach profiles,

coordinates for the IMA stations were calculated and therefore could be used in

these spreadsheets to process the profiles done by IMA in previous years. The

spreadsheets that were done are shown in the appendix of this report.

4.2 RESULTS AND ANALYSIS OF LINE GRAPHS

The data from the excel spreadsheets, elevation and distance in particular,

for each of the IMA stations for five past years, were used to create line graphs.

Each line graph contained the data from all the years per station, therefore, a total

of four line graphs for the four IMA stations were created. In this way the graphs

could now be analyzed by doing a comparison of all the years per station. The line

graphs produced are as follows:

STATION 1:

This station was located at the western end of the beach at Grande Riviere

and had an elevation in the Mean Sea Level vertical datum of 4.354m. The

coordinates, for this station, were calculated, also in WGS-84 datum, to be

712429.759 E and 1197934.556 N. The foreshore at this station was not ideally

steep but the sand at this part of the beach was relatively soft which is ideal for the

nesting of the prominent leatherback turtles on the beach. As a result, the

sediments are definitely exposed to accretion and erosion by the weather processes

associated with sea level rise. The graph of the profiles done from this station by

IMA between the years 1999-2008 is illustrated below.

P a g e | 27

Figure 4.1: STATION 1 BEACH PROFILES DONE BY IMA IN 5 PAST YEARS

4.2.1 ANALYSIS OF BEACH PROFILE DATA OBTAINED AT STATION 1

The slope change at this part of the beach can be seen from the line graph

to differ by a significant amount between the years 1999-2008. The slope

increased by a large amount between February and October of 2002 after which

the slope decreased for all the following years up until 2008. The drastic change in

slope may have been due to sediment deposition as a result of a storm surge event.

However, in the years that followed there was obvious erosion of the shoreline

which may have been a result of processes brought about by increases in sea level.

For the profiles taken from October 2002, June 2006 and May 2007 there existed a

berm at this part of the beach but for the remainder of the years this berm was

evidently eroded as the slope became steep with no berm. Therefore, a conclusion

can be drawn that the form of the beach at this part varied significantly over the

years as a result of the impacts of sea level changes which include both erosion

and accretion.

STATION 2:

This station was located on the central part of the beach closer to the

western end. The coordinates in WGS-84 datum were 712663.531 E and

-0.500

0.000

0.500

1.000

1.500

2.000

2.500

3.000

3.500

4.000

4.500

5.000

0 4 8 12 16 20 24 28 32 36 40 44 48 52 56 60 64

Oct-02

Apr-08

May-07

Jun-06

Feb-02

Nov-99

P a g e | 28

1197820.334 N with an elevation in the Mean Sea Level vertical datum of 4.662m.

The foreshore at this station was steep with no evidence of a beach berm. The

results from the profiles done from this station by IMA between the years 1999-

2008 are illustrated in the line graph below.



The profiles shown for this portion of the beach are seen to have limited

change but change none the less. Erosion and accretion are evident from the

increases and decreases of the slope line over the years. The profile of the beach

from this station can however be defined as being steeper than the slope formed

from station 1 and in fact the steepest of all four stations. These profiles show no

evidence of a beach berm having ever been present over the years and therefore it

can be suggested that the impact of accretion is least at this part of the beach.

STATION 3:

This station was located in the central part of the beach closer to the eastern

end. The coordinates computed for this station were 712850.148 E and 1197761.8

N in WGS-84 datum and the elevation was 3.999m in the Mean Sea Level datum.

-2.000

-1.000

0.000

1.000

2.000

3.000

4.000

5.000

0 4 8 12 16 20 24 28 32 36 40 44 48 52

Oct-02

Apr-08

May-07

Jun-06

FEB 2002+Sheet4!$F$25:$F$33Nov-99

P a g e | 29

There was evidence of a well defined beach berm followed by a steep foreshore.

The graph below illustrates profiles done by IMA from this station between the

years 1999-2008.



The foreshore at this station was not as steep as that of station 2 but was

steeper than that of station 1. There was an obvious presence of a well defined

beach berm over the years, which would be accounted for by the increase in

accretion at this part of the beach. However, although there is obvious accretion,

erosion can also be said to have been observed over the years which is accounted

for by the changes in elevation of the backshore, berm and foreshore.

STATION 4:

This station was located at the eastern end of the beach and its coordinates

were derived in the WGS-84 datum to be 713122.746 E and 1197713.876 N with

an elevation in the Mean Sea Level datum of 3.224m. There was a well defined

beach berm with a backshore of a virtually lower elevation and a very steep

-0.500

0.000

0.500

1.000

1.500

2.000

2.500

3.000

3.500

4.000

4.500

0 4 8 12 16 20 24 28 32 36 40 44 48 52 56 60

Nov-99

Apr-08

May-07

Jun-06

Feb-02

Oct-02

P a g e | 30

foreshore following the berm. The beach profiles done by IMA from this station

between the years 1999-2008 were plotted and is illustrated in the graph below.



The profile taken in October 2002 shows a very dynamic change in the

profile of the beach which suggests a storm surge event that led to sediment

erosion. However, the berm has been seen to have increased in volume and

elevation over the following years which would suggest an increase in accretion of

the beach. The accretion at this part of the beach can be seen as most prominent

and erosion the least. The berm here is the most defined with the highest elevation

which also adds to the theory that most accretion is occurring in this portion of the

beach. The slope after the berm in a seaward direction is just as steep as that of

station 3 but is at approximately the same angle over the years, except for the

October 2002 profile, which suggests limited erosion taking place.

-1.000

-0.500

0.000

0.500

1.000

1.500

2.000

2.500

3.000

3.500

4.000

0 4 8 121620242832364044485256606468727680848892

Jun-06

Apr-08

May-07

Oct-02

Feb-02

Nov-99

P a g e | 31

The following graphs illustrate the beach profiles taken at the IMA stations for the

year 2011.

Figure 4.5: BEACH PROFILE DONE FROM IMA STATION 1 IN 2011


-2.000

0.000

2.000

4.000

6.000

IMA41IMA42IMA43IMA44IMA45IMA46IMA47IMA48

STATION 1 2011

STATION 1

0.000

1.000

2.000

3.000

4.000

5.000

IMA33 IMA34 IMA35 IMA36 IMA37 IMA38 IMA39 IMA40

STATION 2 2011

STATION 2

P a g e | 32



4.2.5 ANALYSIS OF BEACH PROFILES DONE IN 2011

These profiles were difficult to plot on the same line graphs above and

were therefore analysed separately.

STATION 1: The berm from the profile done at this station was least prominent

and there was evidence of continued erosion of the foreshore before the slope

which would result in a reduction in the steepness of the slope. Therefore a

conclusion can be drawn that there continues to be little accretion on this end of

the beach and more erosion.

0.000

1.000

2.000

3.000

4.000

5.000

STATION 3 2011

STATION 3

0.0000.5001.0001.5002.0002.5003.0003.500

STATION 4 2011

STATION 4 2011

P a g e | 33

STATION 2: The slope from this profile can be described as undulated and

therefore the steepness of the slope has been reduced. This reduction in steepness

may be as a result of accretion but because there is no evidence of a berm then it

can be said that erosion is taking place at the same time preventing a berm from

being formed. At this station the water rolls up the sand towards the backshore

which may be the reason why there is no significant formation of a berm at this

point because when the water retreats to the beach the backwash erodes any

deposited sediments that may have formed a berm.

STATION 3: The profile taken in 2011 shows a definite berm but also shows the

backshore being of a lower elevation than the berm. The foreshore after the berm

continues to be steep as well. This suggests that there is obvious accretion due to

the increase in the size of the berm as compared to profiles illustrated before at this

station. Therefore, it can be said that the berm is protecting the backshore of the

beach from the impacts of sea level rise which is opportune for the nesting of

leatherback turtles on the beach.

STATION 4: The profile of the beach from this station continues to be the same

from the anlalysis done above. However, there was evidence of the berm being

more defined and the slope being steeper which suggests again just as station 3

that there continues to be more accretion taking place at this part of the beach.

4.2.6 AN OVERALL ANALYSIS OF THE PROFILE OF THE BEACH FROM THE LINE

GRAPHS

In general, from the analysis done on all four stations over the years it can

be conclude that the western end of the beach is exposed to more erosion and less

accretion, that is at stations 1 and 2. The eastern end, however, is exposed to more

accretion and less erosion. The berm of the beach is less prominent on the western

end and more prominent in the eastern end. The beach was also seen to be steepest

at station 2 and steep at station 3 and 4 with station 1 being the least steep. The

backshore of stations 3 and 4 were lower than the berm for these stations and

P a g e | 34

therefore the berm can be considered to be acting as a protective barrier for the

backshore from the impacts of sea level rise. This plays a vital role in the idealness

of the beach for the nesting of leatherback turtles. Also, from the analysis of the

profile of the beach there was evidence of a large change along the beach in the

profile between that of February 2002 and October 2002 which suggests a storm

surge event. However, over time because the levels of accretion and erosion are

increasing at the stations where they are most prominent, it can be said that this is

as a result of the impacts of sea level rise on the beach.

4.3 RESULTS AND ANALYSIS OF DIGITISED MAPS FROM ARC MAP AND ARC

SCENE

Now that the profile of the beach has been analyzed for a number of years,

a safe assumption can be made with respect to the findings that there is evidence

of sea level rise and its impacts of gradual erosion and accretion are also evident

on the beach. The potential impact of coastal flooding along the beach can now be

analyzed. The profiles received from IMA and the profiles done in 2011 were used

to create a TIN file in Arc Map from which a 0.4m polygon was classified and

digitized. This 0.4m polygon represents coastal flooding from the Caribbean Sea

unto the Grande Riviere beach at a height of 0.4m and illustrates the level of

impact upon the coastline. The elevation of 0.4m was chosen because it is the first

category in IPCC’s 2007 projections for sea level rise scenarios. As polygons were

made for a total of seven years, within the period of 1999-2011, with 2 polygons

being made for the year 2002, a complete analysis can therefore be made for

coastal flooding at this level. The digitized maps created from these polygons can

be found in the appendix of this report and the layouts created in Arc Scene to be

analyzed are as follows:

P a g e | 35

Figure 4.9: MAP SHOWING O.4M FLOOD POLYGON AT GRANDE RIVIERE IN 1999

The 0.4m flood polygon created for the year 1999 showed an impact on the

coastline that covered an area of 1789m2.

Figure 4.10: MAP SHOWING O.4M FLOOD POLYGON AT GRANDE RIVIERE IN FEB 2002

In February 2002, the 0.4m flood polygon consumed an area of 1308m2

along the coastline.

P a g e | 36

Figure 4.11: MAP SHOWING O.4M FLOOD POLYGON AT GRANDE RIVIERE IN OCT 2002

In October of the same year 2002, the 0.4m flood polygon covered an area

of 1622m2 along the coastline of Grande Rivere.


The flood polygon digitized for the 0.4m sea level rise scenario affected an

area of 2365m2 for the year 2008.

P a g e | 37


The 0.4m flood polygon covered an area of 1924m2 for the year 2007.


The 0.4m flood polygon consumed an area of 1409m2 along the coastline

of Grande Riviere.

P a g e | 38



The area that was covered by the o.4m flood polygon in the year 2011 was

calculated to be 1068m2

P a g e | 39

4.3.1 AN ANALYSIS OF 0.4M FLOOD POLYGONS:

The flood polygons created all show that the 0.4m rise in sea level will

have an impact mainly on the eastern end of the beach where the sea will join with

the adjacent river of Grande Riviere. It is also observed from the flood polygons

created that there will be an impact on the coastline. The coastline will definitely

be impacted by accretion or erosion where the volume of sand sediments on the

beach will be affected. This may have a resultant impact on the idealness of the

berm and slope of the beach as well as the idealness of the texture and temperature

of the sand to the nesting of the leatherback turtles. When comparing the areas

calculated along the coastline that were affected by the flood polygon, it was also

observed that the areas lie between 1000m2

and 2000m2 which can have a definite

impact on the form of the beach. The areas over the years have both increased and

decreased but there was more evidence of increases which would suggest that over

the years investigated the sea level has definitely been rising. Although the rise

may not have been drastic there is still evidence of rise especially between the

years 2006 and 2008. The use of these polygons over a number of years have

proven to be vital in a more accurate analysis of coastal flooding at 0.4m and

therefore a better understanding of the level of impact of sea level rise on the

beach was developed.

P a g e | 40

Figure 4.17: A MAP SHOWING THE INTERSECTING PORTION OF THE POLYGONS CREATED

FOR THE PREVIOUS YEARS

The portion of the coastline that is outlined in the purple region and is

shaded a darker blue than the blue of the sea, is that which is common to all the 0.4

flood polygons created for the years 1999-2008, 2010 and 2011. The area of this

portion of the beach that is common to all is 1119.7m2 and therefore as this portion

is common to all then it can be safely concluded that this portion of the beach can

be projected to be flooded if the sea level were to rise by 0.4m.

From the flood polygons created and from the intersecting polygon that

showed the flooded are common to all for a 0.4m rise in sea level, it was seen that

no buildings, roads or vegetation of Grande Riviere would have been flooded but

only the sediments of the beach may have been affected. Therefore, for a 0.4m rise

in sea level, the buildings and infrastructure of the community of Grande Riviere

will not be subjected to impact, but the beach of Grande Riviere will be subjected

P a g e | 41

to impacts of sea level rise such as accretion and erosion. This, however, is

important as the nesting of the leatherback turtles may be disturbed by the change

in the form of the beach. If this were to be affected there will be a consequent

impact on the community in terms of its economy as it thrives on the tourism that

is brought about by the sight-seeing of the nesting of the leatherback turtles.

4.4 AN ANALYSIS OF THE EFFICIENCY OF THE METHODOLOGY WITH RESPECT

TO THE RESULTS

The spreadsheets used were sufficient for the derivation of coordinates for

the beach profiles so that they could be inputted into Arc Map effectively. Excel

was used to produce line graphs from the spreadsheets of the beach profiles and

these line graphs proved effective and sufficient for the analysis of the profile of

the beach over the years. The use of one graph per station for all the years was

very efficient as change in slope over the selected time period was both virtual and

quantitative and was therefore easily analyzed. The use of Arc Map and Arc Scene

was a high-quality approach to the mapping and portrayal of a 0.4m flood polygon

along the coastline. It proved to be visually effective in analyzing the level impact

of the flood to the Grande Riviere beach by the comparison of the different areas

of flooding for each year of all the investigated years. In general, the use of GIS

for this project was sufficiently effective for the level of study carried out for the

investigation of the impacts of sea level rise and sea level rise modeling.

P a g e | 42

5 CONCLUSION

5.1 AIM:

The aim of this project was to validate previous survey profiles, create

updated sea level rise models based upon the beach profile survey data collected

and to compare and analyze these models in order to assess the level of threat on

Grande Riviere as a result of Sea Level Rise.

5.2 CONCLUSION:

The previous surveys collected from IMA proved to be valid and useful

data as they were all collected from the same stations at Grande Riviere over the

time period and they were all done at useful intervals that expressed the form of

the profile. This could be concluded from the analysis of the line graphs that were

created from the spreadsheets used to compute the data. The data collected both

primarily and secondarily were used to derive coordinates so that they could be

inputted into Arc Map along with existing datasets to create Sea Level Rise

Models. These models were then classified and digitized so that a0.4m flood

polygon was created. The elevation of 0.4m was used as it is the first category of

IPCC’s 2007 sea level rise scenarios projections. The area of these polygons was

calculated so that a comparison of the areas that would be impacted by the flood

polygon could be easily done. An intersecting polygon was created by overlaying

all the polygons in one image and using the intersecting tool, the area common to

all was found. This area of 1119.7m2 can be said to surely flood if the sea level

were to rise by 0.4m.

Also, from the analysis done a conclusion can be drawn that the affected

area was more concentrated on the eastern end of the beach and that no buildings

or infrastructure of the community of Grande Riveire would be affected by this

rise in sea level. Only the beach would be affected by this rise analyzed, however,

the impacts of the sea level rise of accretion and erosion could change in the form

of the beach drastically over time. This change can be seen as evident as there

were obvious changes in the profile of the beach from the line graphs created. The

P a g e | 43

change may have an effect on the idealness of the beach to the nesting of the

leatherback turtles. There would be further impacts on the community Grande

Riviere if the beach, over time, is no longer ideal for the nesting of the turtles.

Because the community’s economy depends greatly on the tourism drawn by the

nesting of the turtles, there would be a devastating dent on the economy of the

community. Most of the community consists of businesses that thrive from the

visitors to the community such as hotels and tour guide companies, as well as

vendors which would all be affected if the turtles were stop nesting at the beach.

Therefore it can be concluded that the aims and objectives of this project

were achieved as the impact of sea level on the beach over a number of years was

successfully analyzed from the sea level rise models created and the level of

impact on the beach was determined.

5.3 RECOMMENDATIONS:

The spot height data used for this project was generally not sufficient. For

the year 2011 more spot heights taken up along the beach not including the beach

profiles done, would have increased the accuracy of the TIN generated. Although a

contour dataset was utilized this dataset was not from the year 2011 and so

discrepancies in terms of accuracy are introduced. Also, for the beach profiles that

were received from IMA between the years 1999-2008, there was no spot height

data along the beach. If there was spot height data along the beach, a more

accurate TIN file for each of these years would have been generated and so a more

accurate 0.4m flood polygon would have been generated. Therefore if more spot

height data was available for each respective year then the assessment of the area

of impact at Grande Riviere would have been much more precise.

With respect to the data that was taken up in 2011, the beach profiles from

each station were done by taking up points at each change in elevation. Now, this

should have been done along with taking elevation at defined intervals such as that

of the intervals done for IMA’s beach profiles. This would have made it easier to

plot a more accurate line graph for the year 2011 and include the line graph with

P a g e | 44

those from IMA on the same graph per station so a more accurate comparison

between 2011 and the previous years would have been made.

A further analysis should be done using the data acquired for the rest of the

sea level rise scenarios of IPCC so that a complete assessment on the impact of the

sea level rise on buildings and infrastructure can also be made.

P a g e | 45

6 REFERENCES

Seeram, A. (2010). Developing A Prdeictive GIS model of Sea Level Rise.

Banwaire. (2011, 02 28). Turtle watchers get $653,000. Newsday , p.

Section A: 22.

IMA. (2011) Beach profiles of Stations 1-4 for the years 1999, 2002, 2006,

2007, 2008. Institute of Marine Affairs, Trinidad and Tobago.

IPCC, I. P. (2007). Climate Change 2007: Synthesis Report. IPCC.

Kostelnick, J., Rowley, R., McDermott, D., & Bowen, C. (2008, 04 06).

Earth Zine. Retrieved 04 12, 2011, from Earth Zine:: www.earthzine.org

Lofgren, J. S., Haas, R., & Johansson, J. M. (2010). Sea level monitoring

using a gnss-based tide gauge.

Lubchenco, D. J. (2011). How sea level changes affect coastal planning.

Retrieved 04 12, 2011, from NOAA: http://www.noaa.gov/about-noaa.html

McGranahan, G. D. (2007). Environment and Urbanization. The rising

tide: Assessing the risks of climate change and human settlements in low

elevation coastal zones , 19(1): 17-37.

NASA. (2008, 05 22). International Satellite Will monitor Global Sea level

Rise. Retrieved 04 12, 2011, from climate.nasa.gov: www.climate.nasa.gov

Nichols, D. W. (2011). www.seeturtles.org. Retrieved 2011, from

seeturtles: www.seeturtles.org/1380/global-warming.html

NPS, N. P. (2011). NPS Inventory and Monitoring Programs. Retrieved 04

12, 2011, from http://science.nature.nps.gov:

www.nature.nps.gov/geology/coastal/monitoring.cfm

P a g e | 46

UNDP, U. N. (2010). An Overview of Modeling Climate Change: Impacts

in the Caribbean Region with contribution from the Pacific Islands.

Caribbean: United Nations Development Programme.

Vafeidis, A. R. (2008). A new global database for impact and vulnerability

analysis to sea level rise. Journal of Coastal Research , 24: 917-924.

Werner G. Hennecke, C. A. (2000, 09 2-8). GIS-based modeling of sea -

level rise impacts for coastal management in southeastern Australia.

Retrieved 04 15, 2011, from 4th International Conference on Integrating

GIS and Environmental Modeling (GIS/EM4): Problems, Prospects and

Research Needs. :

http://www.colorado.edu/research/cires/banff/pubpapers/242/

P a g e | 47

7 APPENDICES

7.1 APPENDIX 1: Spreadsheets that were used to derive coordinates and elevation

for the beach profile data of Grande Riviere for 2011.

P a g e | 48

POINTS DESCRIPTION DEG MIN SEC HOR.BRG DEG MIN SEC ZENITH SD HD

CPB

1 CPA 6 9 18.7 6.155 87 37 35 87.626 151.173 151.043

2 1 10 36 9 10.603 89 25 35 89.426 43.570 43.568

4 IMA1 161 3 28 161.058 89 11 52 89.198 29.250 29.247

5 IMA2 158 27 25 158.457 90 2 36 90.043 27.205 27.205

6 IMA3 147 18 59 147.316 90 30 35 90.510 20.220 20.219

7 IMA4 140 12 17 140.205 90 45 1 90.750 17.465 17.464

8 IMA5 137 34 17 137.571 91 2 52 91.048 16.720 16.717

9 IMA6 135 5 54 135.098 91 8 31 91.142 15.965 15.962

10 IMA7 131 54 8 131.902 90 48 52 90.814 15.400 15.398

11 IMA8 102 54 7 102.902 90 7 3 90.118 12.010 12.010

12 IMA9 99 14 36 99.243 90 53 55 90.899 11.905 11.904

13 IMA10 95 7 9 95.119 92 12 15 92.204 11.960 11.951

14 IMA11 90 14 4 90.234 92 51 53 92.865 12.150 12.135

15 IMA12 78 34 2 78.567 92 45 28 92.758 12.800 12.785

16 IMA13 74 31 32 74.526 92 33 24 92.557 13.200 13.187

17 IMA14 54 18 51 54.314 91 11 3 91.184 16.070 16.067

18 IMA15 38 39 35 38.660 90 9 38 90.161 20.170 20.170

19 IMA16 35 12 6 35.202 89 49 20 89.822 22.030 22.030

20 IMA17 31 11 53 31.198 90 57 15 90.954 25.175 25.172

21 IMA18 29 9 58 29.166 91 44 20 91.739 27.230 27.217

22 IMA19 26 4 46 26.079 92 46 1 92.767 29.375 29.341

23 IMA20 25 25 57 25.433 91 11 15 91.188 30.895 30.888

24 IMA21 22 46 57 22.783 92 2 9 92.036 35.580 35.558

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25 SH1 193 40 13 193.670 89 48 0 89.800 26.645 26.645

26 SH2 189 29 22 189.489 89 16 41 89.278 57.290 57.285

27 SH3 188 24 44 188.412 89 11 53 89.198 88.065 88.056

28 SH4 188 0 16 188.004 90 5 44 90.096 18.820 18.820

29 SH5 172 36 54 172.615 90 2 44 90.046 17.400 17.400

30 SH6 145 49 49 145.830 90 25 21 90.423 28.850 28.849

31 SH7 137 41 42 137.695 90 32 39 90.544 39.975 39.973

32 SH8 126 52 41 126.878 90 47 13 90.787 45.490 45.486

33 SH9 112 38 32 112.642 90 47 20 90.789 48.455 48.450

34 SH10 104 47 27 104.791 90 56 36 90.943 57.555 57.547

35 SH11 104 20 4 104.334 90 59 48 90.997 53.865 53.857

36 SH12 102 22 39 102.378 90 56 19 90.939 53.155 53.148

37 SH13 95 28 53 95.481 90 44 47 90.746 57.810 57.805

38 SH14 94 29 25 94.490 91 1 58 91.033 58.140 58.131

39 SH15 92 47 36 92.793 90 28 42 90.478 43.730 43.728

40 SH16 91 57 29 91.958 90 47 56 90.799 43.680 43.676

41 SH17 90 22 23 90.373 91 14 16 91.238 43.905 43.895

42 SH18 87 26 29 87.441 91 22 24 91.373 39.430 39.419

43 SH19 82 29 27 82.491 91 26 14 91.437 38.745 38.733

44 SH20 80 34 54 80.582 91 21 0 91.350 43.435 43.423

45 SH21 81 36 51 81.614 90 51 36 90.860 62.460 62.453

46 SH22 79 38 22 79.639 90 40 19 90.672 83.745 83.739

47 SH23 78 17 4 78.284 90 32 57 90.549 104.840 104.835

48 SH24 76 49 4 76.818 90 21 52 90.364 94.960 94.958

49 SH25 76 10 57 76.183 90 16 53 90.281 80.480 80.479

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50 SH26 74 55 44 74.929 90 20 42 90.345 63.020 63.019

51 SH27 74 54 57 74.916 90 20 40 90.344 62.995 62.994

52 SH28 73 45 1 73.750 90 34 54 90.582 48.145 48.143

53 SH29 74 22 34 74.376 91 13 51 91.231 31.580 31.573

54 SH30 74 44 45 74.746 90 43 24 90.723 28.360 28.358

55 IMA22 18 6 53 18.115 92 23 7 92.385 36.350 36.319

56 BP1 130 55 50 130.931 90 36 41 90.611 45.285 45.282

57 BP2 128 1 22 128.023 90 44 0 90.733 43.835 43.831

58 BP3 126 46 36 126.777 90 29 14 90.487 43.185 43.183

59 BP4 125 18 23 125.306 90 43 32 90.726 42.615 42.612

60 BP5 112 18 17 112.305 90 44 1 90.734 37.620 37.617

61 BP6 97 10 43 97.179 90 25 52 90.431 34.865 34.864

62 BP7 94 44 39 94.744 90 57 17 90.955 34.670 34.665

63 BP8 93 2 22 93.039 91 15 10 91.253 34.530 34.522

64 BP9 81 43 24 81.723 91 2 26 91.041 34.175 34.169

65 BP10 65 0 37 65.010 90 33 3 90.551 36.715 36.713

66 BP11 57 56 26 57.941 90 6 4 90.101 38.420 38.420

67 BP12 55 58 16 55.971 90 27 45 90.463 39.455 39.454

68 BP13 51 39 36 51.660 90 5 58 90.099 41.295 41.295

69 BP14 43 26 52 43.448 91 16 23 91.273 46.380 46.369

70 BP15 38 34 52 38.581 91 56 27 91.941 48.895 48.867

71 3 278 42 21 278.706 89 47 58 89.799 171.200 171.199

72 4 273 51 53 273.865 90 22 56 90.382 91.275 91.273

73 IMA23 193 23 58 193.399 84 4 18 84.072 12.200 12.135

74 IMA24 192 35 18 192.588 86 41 54 86.698 12.175 12.155

P a g e | 51

75 IMA25 193 18 47 193.313 90 55 44 90.929 11.730 11.728

76 IMA26 194 36 44 194.612 92 55 5 92.918 11.075 11.061

77 IMA27 195 57 1 195.950 93 29 36 93.493 9.285 9.268

78 IMA28 197 46 27 197.774 93 0 55 93.015 6.535 6.526

79 IMA29 359 42 45 359.713 88 48 19 88.805 6.965 6.963

80 IMA30 1 37 1 1.617 88 39 11 88.653 8.100 8.098

81 IMA31 2 26 58 2.449 90 32 44 90.546 9.160 9.160

82 IMA32 11 2 29 11.041 92 30 58 92.516 22.045 22.024

83 5 285 26 39 285.444 89 48 29 89.808 193.060 193.059

84 IMA33 215 31 13 215.520 80 20 4 80.334 5.835 5.752

85 IMA34 215 58 56 215.982 82 29 26 82.491 5.660 5.611

86 IMA35 219 53 1 219.884 83 32 5 83.535 3.365 3.344

87 IMA36 281 23 53 281.398 89 43 55 89.732 1.375 1.375

88 IMA37 341 1 30 341.025 94 0 39 94.011 2.665 2.658

89 IMA38 9 18 46 9.313 97 57 21 97.956 5.675 5.620

90 IMA39 16 44 7 16.735 98 43 27 98.724 11.550 11.416

91 IMA40 11 26 4 11.434 98 2 16 98.038 15.940 15.783

92 6 296 11 52 296.198 90 1 46 90.029 255.700 255.700

93 IMA41 246 28 32 246.476 94 15 1 94.250 8.400 8.377

94 IMA42 247 8 44 247.146 89 1 52 89.031 8.280 8.279

95 IMA43 269 40 37 269.677 89 23 55 89.399 6.765 6.765

96 IMA44 301 49 39 301.828 89 59 16 89.988 6.480 6.480

97 IMA45 308 35 7 308.585 92 3 59 92.066 6.755 6.751

98 IMA46 323 44 51 323.748 96 16 31 96.275 7.765 7.718

99 IMA47 348 53 7 348.885 98 12 59 98.216 13.860 13.718

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100 IMA48 358 6 19 358.105 97 51 11 97.853 17.625 17.460

101 SH30 271 2 50 271.047 89 41 52 89.698 18.520 18.520

102 SH31 293 59 18 293.988 90 17 8 90.286 17.615 17.615

103 SH32 319 15 55 319.265 95 3 53 95.065 19.965 19.887

104 SH33 359 47 51 359.798 99 13 8 99.219 10.805 10.665

105 SH34 314 29 9 314.486 90 47 20 90.789 3.990 3.990

106 SH35 222 51 12 222.853 89 33 50 89.564 6.040 6.040

107 SH36 113 3 32 113.059 93 17 1 93.284 3.380 3.374

108 SH37 115 31 10 115.519 92 24 31 92.409 5.855 5.850

109 SH38 112 56 1 112.934 94 49 26 94.824 5.945 5.924

110 SH39 54 6 53 54.115 97 56 11 97.936 13.050 12.925

111 SH40 123 0 27 123.008 91 50 42 91.845 9.040 9.035

112 SH41 122 7 32 122.126 93 45 59 93.766 9.495 9.474

113 SH42 140 19 47 140.330 90 34 25 90.574 12.520 12.519

114 SH43 138 57 17 138.955 91 57 22 91.956 12.885 12.877

115 SH44 130 20 13 130.337 92 17 3 92.284 15.240 15.228

116 SH45 71 13 52 71.231 95 38 10 95.636 18.720 18.630

117 SH46 126 45 50 126.764 90 30 24 90.507 19.220 19.219

118 SH47 108 45 39 108.761 90 24 35 90.410 22.490 22.489

119 SH48 106 40 42 106.678 91 52 14 91.871 22.300 22.288

120 SH49 83 22 10 83.369 93 38 39 93.644 29.575 29.515

121 SH50 111 41 29 111.691 89 58 31 89.975 41.495 41.495

122 SH51 105 29 13 105.487 90 22 20 90.372 43.005 43.004

123 SH52 91 48 37 91.810 92 15 47 92.263 46.765 46.729

124 SH53 110 21 37 110.360 90 7 39 90.128 62.375 62.375

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125 SH54 103 7 31 103.125 90 7 58 90.133 61.415 61.415

126 SH55 95 8 23 95.140 91 37 16 91.621 73.995 73.965

127 SH56 104 11 14 104.187 90 5 28 90.091 89.715 89.715

128 SH57 98 52 25 98.874 91 11 53 91.198 96.655 96.634

5 116 11 56 116.199 90.029 255.700 255.700

129 SH58 298 54 35 298.910 90 14 21 90.239 129.155 129.154

130 SH59 303 7 8 303.119 90 57 40 90.961 127.430 127.412

131 SH60 297 37 16 297.621 90 8 33 90.143 113.180 113.180

132 SH61 303 58 21 303.973 91 10 6 91.168 108.615 108.592

133 SH62 295 58 10 295.969 90 18 50 90.314 95.860 95.859

134 SH63 305 59 53 305.998 91 27 47 91.463 94.505 94.474

1 10 36 9 10.603 89 25 35 89.426 43.570 43.568

135 SH64 315 27 17 183.055 89 42 5 89.701 22.215 22.215

136 SH65 35 30 4 263.101 89 14 29 89.241 46.120 46.116

137 SH66 43 44 8 271.336 89 20 11 89.336 80.625 80.620

138 SH67 52 39 42 280.262 89 32 34 89.543 102.155 102.152

139 SH68 56 20 11 283.936 89 32 52 89.548 79.715 79.713

140 SH69 63 9 5 290.751 89 10 36 89.177 49.895 49.890

141 SH70 89 23 6 316.985 89 25 46 89.429 25.535 25.534

142 SH71 112 16 11 339.870 91 36 0 91.600 32.165 32.152

143 SH72 80 48 32 308.409 90 54 59 90.916 50.235 50.229

144 SH73 63 50 25 291.440 90 29 31 90.492 83.215 83.212

145 SH74 59 49 29 287.425 90 25 21 90.423 106.900 106.897

P a g e | 54

7.2 APPENDIX 2: Spreadsheets that were used to derive the coordinates and elevation of the data from the beach profiles taken by

IMA in selected years between, 1999-2008 at Station 1

X Y Z

712429.7588 1197934.556 4.35436

Nov-99

POINTS HD ΔE ΔN E N RED.LEVEL ΔH ELEVATION

1 0 712429.76 1197934.56 8.800 4.354

2 0 0 0 712429.759 1197934.556 8.030 0.77 3.584

3 0.6 0 0.6 712429.759 1197935.156 7.960 0.84 3.514

4 0.8 0 0.8 712429.759 1197935.356 8.130 0.67 3.684

5 1.15 0 1.15 712429.759 1197935.706 7.960 0.84 3.514

6 1.4 0 1.4 712429.759 1197935.956 8.290 0.51 3.844

7 2.05 0 2.05 712429.759 1197936.606 8.260 0.54 3.814

8 2.55 0 2.55 712429.759 1197937.106 8.160 0.64 3.714

9 2.55 0 2.55 712429.759 1197937.106 7.310 1.49 2.864

10 4 0 4 712429.759 1197938.556 7.110 1.69 2.664

11 8 0 8 712429.759 1197942.556 6.600 2.2 2.154

12 12 0 12 712429.759 1197946.556 6.070 2.73 1.624

13 16 0 16 712429.759 1197950.556 5.610 3.19 1.164

14 20 0 20 712429.759 1197954.556 5.120 3.68 0.674

15 24 0 24 712429.759 1197958.556 4.770 4.03 0.324

16 28 0 28 712429.759 1197962.556 4.770 4.03 0.324

17 32 0 32 712429.759 1197966.556 4.680 4.12 0.234

18 36 0 36 712429.759 1197970.556 4.490 4.31 0.044

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19 40 0 40 712429.759 1197974.556 4.370 4.43 -0.076

Feb-02


1 0 712429.76 1197934.56 8.810 4.354

2 0 0 0 712429.759 1197934.556 8.490 0.32 4.034

3 4 0 4 712429.759 1197938.556 8.000 0.81 3.544

4 8 0 8 712429.759 1197942.556 7.710 1.1 3.254

5 9 0 9 712429.759 1197943.556 7.550 1.26 3.094

6 12 0 12 712429.759 1197946.556 7.170 1.64 2.714

7 16 0 16 712429.759 1197950.556 6.590 2.22 2.134

8 20 0 20 712429.759 1197954.556 6.180 2.63 1.724

9 24 0 24 712429.759 1197958.556 5.820 2.99 1.364

10 28 0 28 712429.759 1197962.556 5.510 3.3 1.054

11 32 0 32 712429.759 1197966.556 5.120 3.69 0.664

12 36 0 36 712429.759 1197970.556 4.790 4.02 0.334

13 40 0 40 712429.759 1197974.556 4.280 4.53 -0.176

Jun-06


1 0 712429.76 1197934.56 8.360 4.354

2 4 0 4 712429.759 1197938.556 7.920 0.44 3.914

3 8 0 8 712429.759 1197942.556 7.760 0.6 3.754

4 12 0 12 712429.759 1197946.556 7.870 0.49 3.864

5 16 0 16 712429.759 1197950.556 7.910 0.45 3.904

P a g e | 56

6 17 0 17 712429.759 1197951.556 7.730 0.63 3.724

7 20 0 20 712429.759 1197954.556 6.720 1.64 2.714

8 24 0 24 712429.759 1197958.556 6.130 2.23 2.124

9 28 0 28 712429.759 1197962.556 5.720 2.64 1.714

10 32 0 32 712429.759 1197966.556 5.070 3.29 1.064

May-07


1 0 712429.76 1197934.56 8.360 4.354

2 4 0 4 712429.759 1197938.556 8.110 0.250 4.104

3 8 0 8 712429.759 1197942.556 7.740 0.620 3.734

4 12 0 12 712429.759 1197946.556 7.770 0.590 3.764

5 16 0 16 712429.759 1197950.556 7.530 0.830 3.524

6 20 0 20 712429.759 1197954.556 7.420 0.940 3.414

7 24 0 24 712429.759 1197958.556 7.030 1.330 3.024

8 24.7 0 24.7 712429.759 1197959.256 6.860 1.500 2.854

9 28 0 28 712429.759 1197962.556 6.640 1.720 2.634

10 32 0 32 712429.759 1197966.556 6.170 2.190 2.164

11 36 0 36 712429.759 1197970.556 5.770 2.590 1.764

Apr-08


1 0 712429.76 1197934.56 8.650 4.354

2 2 0 2 712429.759 1197936.556 8.600 0.05 4.304

3 3.1 0 3.1 712429.759 1197937.656 8.370 0.28 4.074

P a g e | 57

4 4 0 4 712429.759 1197938.556 7.920 0.73 3.624

5 4.4 0 4.4 712429.759 1197938.956 7.690 0.96 3.394

6 8 0 8 712429.759 1197942.556 7.110 1.54 2.814

7 12 0 12 712429.759 1197946.556 6.550 2.1 2.254

8 16 0 16 712429.759 1197950.556 6.100 2.55 1.804

9 20 0 20 712429.759 1197954.556 5.500 3.15 1.204

10 24 0 24 712429.759 1197958.556 5.230 3.42 0.934

11 28 0 28 712429.759 1197962.556 4.890 3.76 0.594

Oct-02


1 0 712429.76 1197934.56 8.400 4.354

2 1.5 0 1.5 712429.759 1197936.056 8.180 0.220 4.134

3 4 0 4 712429.759 1197938.556 7.920 0.480 3.874

4 8 0 8 712429.759 1197942.556 7.670 0.730 3.624

5 12 0 12 712429.759 1197946.556 7.630 0.770 3.584

6 16 0 16 712429.759 1197950.556 7.750 0.650 3.704

7 20 0 20 712429.759 1197954.556 7.860 0.540 3.814

8 24 0 24 712429.759 1197958.556 7.980 0.420 3.934

9 27 0 27 712429.759 1197961.556 8.000 0.400 3.954

10 28 0 28 712429.759 1197962.556 7.900 0.500 3.854

11 32 0 32 712429.759 1197966.556 7.200 1.200 3.154

12 36 0 36 712429.759 1197970.556 6.530 1.870 2.484

13 40 0 40 712429.759 1197974.556 5.840 2.560 1.794

14 44 0 44 712429.759 1197978.556 5.210 3.190 1.164

P a g e | 58

15 48 0 48 712429.759 1197982.556 4.760 3.640 0.714

16 52 0 52 712429.759 1197986.556 4.890 3.510 0.844

17 56 0 56 712429.759 1197990.556 4.830 3.570 0.784

18 60 0 60 712429.759 1197994.556 4.810 3.590 0.764

19 64 0 64 712429.759 1197998.556 4.720 3.680 0.674



X Y Z

712663.5309 1197820.334 4.661994

Nov-99


1 0 712663.53 1197820.33 9.100 4.662

2 0 0 0 712663.531 1197820.334 8.920 0.180 4.482

3 2 0 2 712663.531 1197822.334 8.620 0.480 4.182

4 4 0 4 712663.531 1197824.334 8.310 0.790 3.872

5 6 0 6 712663.531 1197826.334 8.060 1.040 3.622

6 8 0 8 712663.531 1197828.334 7.970 1.130 3.532

7 9 0 9 712663.531 1197829.334 7.980 1.120 3.542

8 10 0 10 712663.531 1197830.334 7.880 1.220 3.442

9 11 0 11 712663.531 1197831.334 7.600 1.500 3.162

10 12 0 12 712663.531 1197832.334 7.380 1.720 2.942

11 12 0 12 712663.531 1197832.334 7.380 1.720 2.942

P a g e | 59

12 14 0 14 712663.531 1197834.334 7.070 2.030 2.632

13 16 0 16 712663.531 1197836.334 6.690 2.410 2.252

14 20 0 20 712663.531 1197840.334 6.080 3.020 1.642

15 24 0 24 712663.531 1197844.334 5.450 3.650 1.012

16 28 0 28 712663.531 1197848.334 4.870 4.230 0.432

17 32 0 32 712663.531 1197852.334 3.880 5.220 -0.558

18 36 0 36 712663.531 1197856.334 4.190 4.910 -0.248

19 40 0 40 712663.531 1197860.334 4.200 4.900 -0.238

20 44 0 44 712663.531 1197864.334 3.900 5.200 -0.538

21 48 0 48 712663.531 1197868.334 3.690 5.410 -0.748

22 52 0 52 712663.531 1197872.334 3.570 5.530 -0.868

Feb-02


1 0 712663.53 1197820.33 9.100 4.662

2 0 0 0 712663.531 1197820.334 8.510 0.590 4.072

3 4 0 4 712663.531 1197824.334 7.750 1.350 3.312

4 7 0 7 712663.531 1197827.334 7.370 1.730 2.932

5 8 0 8 712663.531 1197828.334 7.230 1.870 2.792

6 12 0 12 712663.531 1197832.334 6.620 2.480 2.182

7 16 0 16 712663.531 1197836.334 5.990 3.110 1.552

8 20 0 20 712663.531 1197840.334 5.400 3.700 0.962

9 24 0 24 712663.531 1197844.334 4.890 4.210 0.452

10 28 0 28 712663.531 1197848.334 4.460 4.640 0.022

11 32 0 32 712663.531 1197852.334 3.620 5.480 -0.818

P a g e | 60

Jun-06


1 0 712663.53 1197820.33 8.350 4.662

2 1.3 0 1.3 712663.531 1197821.634 8.200 0.150 4.512

3 2.4 0 2.4 712663.531 1197822.734 7.760 0.590 4.072

4 4 0 4 712663.531 1197824.334 7.610 0.740 3.922

5 8 0 8 712663.531 1197828.334 7.300 1.050 3.612

6 12 0 12 712663.531 1197832.334 6.520 1.830 2.832

7 16 0 16 712663.531 1197836.334 5.200 3.150 1.512

8 20 0 20 712663.531 1197840.334 4.980 3.370 1.292

9 24 0 24 712663.531 1197844.334 4.240 4.110 0.552

May-07


1 0 712663.53 1197820.33 8.720 4.662

2 0 0 0 712663.531 1197820.334 8.430 0.290 4.372

3 1.5 0 1.5 712663.531 1197821.834 8.280 0.440 4.222

4 2 0 2 712663.531 1197822.334 7.960 0.760 3.902

5 4 0 4 712663.531 1197824.334 7.810 0.910 3.752

6 8 0 8 712663.531 1197828.334 7.570 1.150 3.512

7 12 0 12 712663.531 1197832.334 7.340 1.380 3.282

8 14.4 0 14.4 712663.531 1197834.734 6.490 2.230 2.432

9 16 0 16 712663.531 1197836.334 6.160 2.560 2.102

P a g e | 61

10 20 0 20 712663.531 1197840.334 5.420 3.300 1.362

11 24 0 24 712663.531 1197844.334 4.760 3.960 0.702

12 28 0 28 712663.531 1197848.334 4.510 4.210 0.452

Apr-08


1 0 712663.53 1197820.33 8.500 4.662

2 4 0 4 712663.531 1197824.334 7.810 0.690 3.972

3 7 0 7 712663.531 1197827.334 7.460 1.040 3.622

4 8 0 8 712663.531 1197828.334 7.410 1.090 3.572

5 12 0 12 712663.531 1197832.334 6.650 1.850 2.812

6 16 0 16 712663.531 1197836.334 5.910 2.590 2.072

7 20 0 20 712663.531 1197840.334 5.220 3.280 1.382

8 24 0 24 712663.531 1197844.334 4.830 3.670 0.992

9 28 0 28 712663.531 1197848.334 4.440 4.060 0.602

10 32 0 32 712663.531 1197852.334 3.630 4.870 -0.208

Oct-02


1 0 712663.53 1197820.33 9.100 4.662

2 0 0 0 712663.531 1197820.334 8.560 0.540 4.122

3 1.2 0 1.2 712663.531 1197821.534 8.250 0.850 3.812

4 2.7 0 2.7 712663.531 1197823.034 7.800 1.300 3.362

5 4 0 4 712663.531 1197824.334 7.710 1.390 3.272

6 8 0 8 712663.531 1197828.334 7.710 1.390 3.272

P a g e | 62

7 12 0 12 712663.531 1197832.334 7.430 1.670 2.992

8 14 0 14 712663.531 1197834.334 7.370 1.730 2.932

9 16 0 16 712663.531 1197836.334 7.210 1.890 2.772

10 20 0 20 712663.531 1197840.334 6.330 2.770 1.892

11 24 0 24 712663.531 1197844.334 5.550 3.550 1.112

12 28 0 28 712663.531 1197848.334 4.850 4.250 0.412

13 32 0 32 712663.531 1197852.334 4.420 4.680 -0.018

14 36 0 36 712663.531 1197856.334 3.690 5.410 -0.748

15 40 0 40 712663.531 1197860.334 4.130 4.970 -0.308

16 44 0 44 712663.531 1197864.334 4.150 4.950 -0.288

17 48 0 48 712663.531 1197868.334 4.150 4.950 -0.288

18 52 0 52 712663.531 1197872.334 4.090 5.010 -0.348



X Y Z

712850.1484 1197761.8 3.999

Nov-99


1 0 712850.148 1197761.80 9.2 3.999

2 0 0 0 712850.148 1197761.80 9.12 0.08 3.919

3 2 0 2 712850.148 1197763.80 9.01 0.19 3.809

4 3.55 0 3.55 712850.148 1197765.35 9.08 0.12 3.879

P a g e | 63

5 3.8 0 3.8 712850.148 1197765.60 8.82 0.38 3.619

6 6 0 6 712850.148 1197767.80 8.77 0.43 3.569

7 8 0 8 712850.148 1197769.80 8.72 0.48 3.519

8 10 0 10 712850.148 1197771.80 8.61 0.59 3.409

9 12 0 12 712850.148 1197773.80 8.5 0.7 3.299

10 14 0 14 712850.148 1197775.80 8.45 0.75 3.249

11 16 0 16 712850.148 1197777.80 8.43 0.77 3.229

12 18 0 18 712850.148 1197779.80 8.38 0.82 3.179

13 20 0 20 712850.148 1197781.80 8.2 1 2.999

14 24 0 24 712850.148 1197785.80 7.55 1.65 2.349

15 28 0 28 712850.148 1197789.80 6.87 2.33 1.669

16 32 0 32 712850.148 1197793.80 6.33 2.87 1.129

17 36 0 36 712850.148 1197797.80 5.86 3.34 0.659

18 40 0 40 712850.148 1197801.80 5.07 4.13 -0.131

19 44 0 44 712850.148 1197805.80 4.98 4.22 -0.221

20 48 0 48 712850.148 1197809.80 5.35 3.85 0.149

21 52 0 52 712850.148 1197813.80 5.32 3.88 0.119

22 56 0 56 712850.148 1197817.80 5.14 4.06 -0.061

23 60 0 60 712850.148 1197821.80 4.99 4.21 -0.211

Feb-02


1 0 712850.148 1197761.80 9.2 3.999

2 0 0 0 712850.148 1197761.80 8.97 0.23 3.769

3 1.8 0 1.8 712850.148 1197763.60 8.61 0.59 3.409

P a g e | 64

4 2.2 0 2.2 712850.148 1197764.00 8.43 0.77 3.229

5 4 0 4 712850.148 1197765.80 8.44 0.76 3.239

6 8 0 8 712850.148 1197769.80 8.48 0.72 3.279

7 12 0 12 712850.148 1197773.80 8.57 0.63 3.369

8 16 0 16 712850.148 1197777.80 8.57 0.63 3.369

9 17 0 17 712850.148 1197778.80 8.52 0.68 3.319

10 18 0 18 712850.148 1197779.80 8.33 0.87 3.129

11 20 0 20 712850.148 1197781.80 7.92 1.28 2.719

12 24 0 24 712850.148 1197785.80 7.22 1.98 2.019

13 28 0 28 712850.148 1197789.80 6.55 2.65 1.349

14 32 0 32 712850.148 1197793.80 6 3.2 0.799

15 36 0 36 712850.148 1197797.80 5.69 3.51 0.489

16 40 0 40 712850.148 1197801.80 5.09 4.11 -0.111

Jun-06


1 0 712850.148 1197761.80 9.27 3.999

2 2.7 0 2.7 712850.148 1197764.50 8.75 0.52 3.479

3 4 0 4 712850.148 1197765.80 8.62 0.65 3.349

4 8 0 8 712850.148 1197769.80 8.38 0.89 3.109

5 12 0 12 712850.148 1197773.80 8.04 1.23 2.769

6 14 0 14 712850.148 1197775.80 8.02 1.25 2.749

7 16 0 16 712850.148 1197777.80 8.25 1.02 2.979

8 18.8 0 18.8 712850.148 1197780.60 8.09 1.18 2.819

9 20 0 20 712850.148 1197781.80 8.01 1.26 2.739

P a g e | 65

10 24 0 24 712850.148 1197785.80 7.22 2.05 1.949

11 28 0 28 712850.148 1197789.80 6.42 2.85 1.149

12 32 0 32 712850.148 1197793.80 5.87 3.4 0.599

13 36 0 36 712850.148 1197797.80 5.43 3.84 0.159

14 40 0 40 712850.148 1197801.80 5.16 4.11 -0.111

15 44 0 44 712850.148 1197805.80 5.02 4.25 -0.251

Apr-08


1 0 712850.148 1197761.80 9.14 3.999

2 4 0 4 712850.148 1197765.80 8.77 0.37 3.629

3 8 0 8 712850.148 1197769.80 8.6 0.54 3.459

4 12 0 12 712850.148 1197773.80 8.37 0.77 3.229

5 16 0 16 712850.148 1197777.80 8.3 0.84 3.159

6 17.3 0 17.3 712850.148 1197779.10 8.18 0.96 3.039

7 20 0 20 712850.148 1197781.80 7.9 1.24 2.759

8 24 0 24 712850.148 1197785.80 7.45 1.69 2.309

9 28 0 28 712850.148 1197789.80 6.96 2.18 1.819

10 32 0 32 712850.148 1197793.80 6.39 2.75 1.249

11 36 0 36 712850.148 1197797.80 5.97 3.17 0.829

12 40 0 40 712850.148 1197801.80 5.66 3.48 0.519

Oct-02


1 0 712850.148 1197761.80 8.47 3.999

P a g e | 66

2 0 0 0 712850.148 1197761.80 8.24 0.23 3.769

3 2 0 2 712850.148 1197763.80 7.85 0.62 3.379

4 2 0 2 712850.148 1197763.80 7.57 0.9 3.099

5 3.8 0 3.8 712850.148 1197765.60 7.47 1 2.999

6 5.4 0 5.4 712850.148 1197767.20 6.95 1.52 2.479

7 8.2 0 8.2 712850.148 1197770.00 6.95 1.52 2.479

8 10.2 0 10.2 712850.148 1197772.00 7.13 1.34 2.659

9 11 0 11 712850.148 1197772.80 6.46 2.01 1.989

10 13 0 13 712850.148 1197774.80 6.47 2 1.999

11 13.4 0 13.4 712850.148 1197775.20 6.61 1.86 2.139

12 16 0 16 712850.148 1197777.80 6.88 1.59 2.409

13 20 0 20 712850.148 1197781.80 7.07 1.4 2.599

14 20.5 0 20.5 712850.148 1197782.30 7.04 1.43 2.569

15 24 0 24 712850.148 1197785.80 6.25 2.22 1.779

16 28 0 28 712850.148 1197789.80 5.56 2.91 1.089

17 32 0 32 712850.148 1197793.80 5.05 3.42 0.579

18 36 0 36 712850.148 1197797.80 4.8 3.67 0.329

19 40 0 40 712850.148 1197801.80 4.78 3.69 0.309

20 44 0 44 712850.148 1197805.80 4.74 3.73 0.269

21 48 0 48 712850.148 1197809.80 4.64 3.83 0.169

22 52 0 52 712850.148 1197813.80 4.5 3.97 0.029

P a g e | 67



X Y Z

713122.7 1197713.876 3.224

Nov-99


1 0 713122.746 1197713.876 9 3.224

2 0 713122.746 1197713.876 8.89 0.11 3.114

3 4 0 4 713122.746 1197717.876 8.51 0.49 2.734

4 5.15 0 5.15 713122.746 1197719.026 8.42 0.58 2.644

5 5.35 0 5.35 713122.746 1197719.226 9.1 -0.1 3.324

6 5.5 0 5.5 713122.746 1197719.376 9.06 -0.06 3.284

7 5.55 0 5.55 713122.746 1197719.426 8.64 0.36 2.864

8 5.65 0 5.65 713122.746 1197719.526 8.64 0.36 2.864

9 5.65 0 5.65 713122.746 1197719.526 8.27 0.73 2.494

10 6.5 0 6.5 713122.746 1197720.376 8.29 0.71 2.514

11 6.6 0 6.6 713122.746 1197720.476 8.03 0.97 2.254

12 8 0 8 713122.746 1197721.876 7.99 1.01 2.214

13 12 0 12 713122.746 1197725.876 7.86 1.14 2.084

14 16 0 16 713122.746 1197729.876 7.8 1.2 2.024

15 20 0 20 713122.746 1197733.876 7.8 1.2 2.024

16 24 0 24 713122.746 1197737.876 8.03 0.97 2.254

P a g e | 68

17 28 0 28 713122.746 1197741.876 8.26 0.74 2.484

18 32 0 32 713122.746 1197745.876 8.55 0.45 2.774

19 36 0 36 713122.746 1197749.876 8.84 0.16 3.064

20 40 0 40 713122.746 1197753.876 9.05 -0.05 3.274

21 42.2 0 42.2 713122.746 1197756.076 9.07 -0.07 3.294

22 43 0 43 713122.746 1197756.876 8.93 0.07 3.154

23 44 0 44 713122.746 1197757.876 8.7 0.3 2.924

24 48 0 48 713122.746 1197761.876 7.9 1.1 2.124

25 52 0 52 713122.746 1197765.876 7.14 1.86 1.364

26 56 0 56 713122.746 1197769.876 6.55 2.45 0.774

27 60 0 60 713122.746 1197773.876 5.69 3.31 -0.086

28 64 0 64 713122.746 1197777.876 5.47 3.53 -0.306

29 68 0 68 713122.746 1197781.876 6.05 2.95 0.274

30 72 0 72 713122.746 1197785.876 5.97 3.03 0.194

31 76 0 76 713122.746 1197789.876 5.94 3.06 0.164

32 80 0 80 713122.746 1197793.876 5.8 3.2 0.024

33 84 0 84 713122.746 1197797.876 5.7 3.3 -0.076

34 88 0 88 713122.746 1197801.876 5.68 3.32 -0.096

Feb-02


1 0 713122.746 1197713.876 9.00 3.224

2 0 713122.746 1197713.876 8.90 0.10 3.124

3 4 0 4 713122.746 1197717.876 8.44 0.56 2.664

4 6.55 0 6.55 713122.746 1197720.426 8.28 0.72 2.504

P a g e | 69

5 6.6 0 6.6 713122.746 1197720.476 8.14 0.86 2.364

6 8 0 8 713122.746 1197721.876 8.16 0.84 2.384

7 16 0 16 713122.746 1197729.876 8.15 0.85 2.374

8 20 0 20 713122.746 1197733.876 8.19 0.81 2.414

9 24 0 24 713122.746 1197737.876 8.32 0.68 2.544

10 28 0 28 713122.746 1197741.876 8.47 0.53 2.694

11 32 0 32 713122.746 1197745.876 8.66 0.34 2.884

12 36 0 36 713122.746 1197749.876 8.73 0.27 2.954

13 40 0 40 713122.746 1197753.876 9.04 -0.04 3.264

14 41.5 0 41.5 713122.746 1197755.376 9.17 -0.17 3.394

15 44 0 44 713122.746 1197757.876 9.20 -0.20 3.424

16 48 0 48 713122.746 1197761.876 8.91 0.09 3.134

17 51 0 51 713122.746 1197764.876 8.50 0.50 2.724

18 52 0 52 713122.746 1197765.876 8.34 0.66 2.564

19 56 0 56 713122.746 1197769.876 7.57 1.43 1.794

20 60 0 60 713122.746 1197773.876 6.97 2.03 1.194

21 64 0 64 713122.746 1197777.876 6.47 2.53 0.694

22 68 0 68 713122.746 1197781.876 6.10 2.90 0.324

23 70 0 70 713122.746 1197783.876 5.83 3.17 0.054

24 71 0 71 713122.746 1197784.876 5.44 3.56 -0.336

25 72 0 72 713122.746 1197785.876 5.40 3.60 -0.376

26 76 0 76 713122.746 1197789.876 5.13 3.87 -0.646

Jun-06


P a g e | 70

1 0 713122.746 1197713.876 8.92 3.224

2 4 0 4 713122.746 1197717.876 8.69 0.23 2.994

3 8 0 8 713122.746 1197721.876 8.58 0.34 2.884

4 12 0 12 713122.746 1197725.876 8.54 0.38 2.844

5 16 0 16 713122.746 1197729.876 8.57 0.35 2.874

6 20 0 20 713122.746 1197733.876 8.60 0.32 2.904

7 24 0 24 713122.746 1197737.876 8.62 0.30 2.924

8 28 0 28 713122.746 1197741.876 8.61 0.31 2.914

9 32 0 32 713122.746 1197745.876 8.56 0.36 2.864

10 36 0 36 713122.746 1197749.876 8.58 0.34 2.884

11 40 0 40 713122.746 1197753.876 8.81 0.11 3.114

12 44 0 44 713122.746 1197757.876 8.74 0.18 3.044

13 48 0 48 713122.746 1197761.876 8.65 0.27 2.954

14 49.4 0 49.4 713122.746 1197763.276 8.54 0.38 2.844

15 50.7 0 50.7 713122.746 1197764.576 8.23 0.69 2.534

16 52 0 52 713122.746 1197765.876 7.90 1.02 2.204

17 56 0 56 713122.746 1197769.876 7.80 1.12 2.104

18 60 0 60 713122.746 1197773.876 6.63 2.29 0.934

19 64 0 64 713122.746 1197777.876 6.04 2.88 0.344

20 68 0 68 713122.746 1197781.876 5.74 3.18 0.044

21 72 0 72 713122.746 1197785.876 5.77 3.15 0.074

22 76 0 76 713122.746 1197789.876 5.89 3.03 0.194

23 80 0 80 713122.746 1197793.876 5.66 3.26 -0.036

24 84 0 84 713122.746 1197797.876 5.63 3.29 -0.066

25 88 0 88 713122.746 1197801.876 5.59 3.33 -0.106

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26 92 0 92 713122.746 1197805.876 5.34 3.58 -0.356

May-07


1 0 713122.746 1197713.876 8.92 3.224

2 4 0 4 713122.746 1197717.876 8.62 0.30 2.924

3 8 0 8 713122.746 1197721.876 8.55 0.37 2.854

4 12 0 12 713122.746 1197725.876 8.55 0.37 2.854

5 16 0 16 713122.746 1197729.876 8.56 0.36 2.864

6 20 0 20 713122.746 1197733.876 8.61 0.31 2.914

7 24 0 24 713122.746 1197737.876 8.63 0.29 2.934

8 28 0 28 713122.746 1197741.876 8.44 0.48 2.744

9 32 0 32 713122.746 1197745.876 8.42 0.50 2.724

10 36 0 36 713122.746 1197749.876 8.78 0.14 3.084

11 40 0 40 713122.746 1197753.876 8.82 0.10 3.124

12 44 0 44 713122.746 1197757.876 8.96 -0.04 3.264

13 48 0 48 713122.746 1197761.876 8.62 0.30 2.924

14 50.8 0 50.8 713122.746 1197764.676 7.97 0.95 2.274

15 52 0 52 713122.746 1197765.876 7.75 1.17 2.054

16 56 0 56 713122.746 1197769.876 6.99 1.93 1.294

17 60 0 60 713122.746 1197773.876 6.52 2.40 0.824

18 64 0 64 713122.746 1197777.876 6.03 2.89 0.334

19 68 0 68 713122.746 1197781.876 5.73 3.19 0.034

20 72 0 72 713122.746 1197785.876 5.79 3.13 0.094

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Apr-08


1 0 713122.746 1197713.876 8.90 3.224

2 1 0 1 713122.746 1197714.876 8.67 0.23 2.994

3 4 0 4 713122.746 1197717.876 8.59 0.31 2.914

4 5 0 5 713122.746 1197718.876 8.57 0.33 2.894

5 5.3 0 5.3 713122.746 1197719.176 9.05 -0.15 3.374

6 5.5 0 5.5 713122.746 1197719.376 8.58 0.32 2.904

7 8 0 8 713122.746 1197721.876 8.57 0.33 2.894

8 12 0 12 713122.746 1197725.876 8.57 0.33 2.894

9 16 0 16 713122.746 1197729.876 8.62 0.28 2.944

10 20 0 20 713122.746 1197733.876 8.70 0.20 3.024

11 24 0 24 713122.746 1197737.876 8.57 0.33 2.894

12 28 0 28 713122.746 1197741.876 8.74 0.16 3.064

13 32 0 32 713122.746 1197745.876 8.93 -0.03 3.254

14 36 0 36 713122.746 1197749.876 8.97 -0.07 3.294

15 38.2 0 38.2 713122.746 1197752.076 8.76 0.14 3.084

16 40 0 40 713122.746 1197753.876 9.02 -0.12 3.344

17 41.2 0 41.2 713122.746 1197755.076 8.76 0.14 3.084

18 44 0 44 713122.746 1197757.876 8.65 0.25 2.974

19 47.3 0 47.3 713122.746 1197761.176 8.50 0.40 2.824

20 48 0 48 713122.746 1197761.876 8.38 0.52 2.704

21 52 0 52 713122.746 1197765.876 7.58 1.32 1.904

22 56 0 56 713122.746 1197769.876 6.96 1.94 1.284

23 60 0 60 713122.746 1197773.876 6.51 2.39 0.834

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24 64 0 64 713122.746 1197777.876 6.12 2.78 0.444

25 68 0 68 713122.746 1197781.876 5 3.90 -0.676

Oct-02


1 0 713122.746 1197713.876 9 3.224

2 0 713122.746 1197713.876 8.91 0.09 3.134

3 4 0 4 713122.746 1197717.876 8.38 0.62 2.604

4 6.55 0 6.55 713122.746 1197720.426 8.3 0.7 2.524

5 6.6 0 6.6 713122.746 1197720.476 8.18 0.82 2.404

6 8 0 8 713122.746 1197721.876 8.16 0.84 2.384

7 11.8 0 11.8 713122.746 1197725.676 8.12 0.88 2.344

8 13 0 13 713122.746 1197726.876 7.28 1.72 1.504

9 25 0 25 713122.746 1197738.876 7.41 1.59 1.634

10 28 0 28 713122.746 1197741.876 7.19 1.81 1.414

11 32 0 32 713122.746 1197745.876 7.52 1.48 1.744

12 36 0 36 713122.746 1197749.876 7.64 1.36 1.864

13 40 0 40 713122.746 1197753.876 7.72 1.28 1.944

14 41 0 41 713122.746 1197754.876 7.69 1.31 1.914

15 44 0 44 713122.746 1197757.876 7.25 1.75 1.474

16 48 0 48 713122.746 1197761.876 6.65 2.35 0.874

17 52 0 52 713122.746 1197765.876 6.26 2.74 0.484

18 56 0 56 713122.746 1197769.876 6.06 2.94 0.284

19 60 0 60 713122.746 1197773.876 6.02 2.98 0.244

20 64 0 64 713122.746 1197777.876 5.93 3.07 0.154

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21 68 0 68 713122.746 1197781.876 5.82 3.18 0.044

22 72 0 72 713122.746 1197785.876 5.53 3.47 -0.246

23 76 0 76 713122.746 1197789.876 5.54 3.46 -0.236

24 80 0 80 713122.746 1197793.876 5.48 3.52 -0.296

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7.6 APPENDIX 6: Arc Scene Map of Grande Riviere showing the 0.4m Flood Polygon for the year 1999.

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7.7 APPENDIX 7: Arc Scene Map of Grande Riviere showing the 0.4m Flood Polygon for Feb 2002

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7.8 APPENDIX 8: Arc Scene Map of Grande Riviere showing the 0.4m Flood Polygon for Oct 2002

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7.9 APPENDIX 9: Arc Scene Map of Grande Riviere showing the 0.4m Flood Polygon for the year 2006.

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7.10 APPENDIX 10: Arc Scene Map of Grande Riviere showing the 0.4m Flood Polygon for the year 2007

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7.14 APPENDIX 14: MAPS OF GRANDE RIVIERE DONE IN ARC MAP FOR THE YEARS 1999-2011

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THE UNIVERSITY OF THE WEST INDIES - C-Change

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