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

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1 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 Supervisor: Dr. M. Sutherland April 2011

2 P a g e ii 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 m 2 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.

3 P a g e iii 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.

4 P a g e iv TABLE OF CONTENTS ABSTRACT... i ACKNOWLEDGEMENTS... iii 1 INTRODUCTION BACKGROUND SEA LEVEL RISE AND SIDS STUDY AREA PROBLEM STATEMENT PREVIOUS RESEARCH RESEARCH QUESTIONS AIMS AND OBJECTIVES GENERAL METHODOLOGY ORGANIZATION OF REPORT LITERATURE REVIEW INTRODUCTION SEA LEVEL RISE AND SIDS IMPACT OF SEA LEVEL RISE IN TRINIDAD AND TOBAGO A REVIEW OF MODELLING OF SEA LEVEL RISE USING GIS TECHNOLOGY BRUUN-GIS MODEL: CASE STUDY USING BRUUN-GIS MODEL: FLOOD-TIDE DELTA AGGRADATION MODEL: CASE STUDY USING FLOOD-TIDE DELTA AGGRADATION MODEL: CASE STUDY 1: CASE STUDY 2: CASE STUDY 3: A REVIEW OF MODELLING OF SEA LEVEL RISE USING GIS OTHER METHODS GNSS TIDAL GAUGES SATELLITE IMAGERY: An advancement in technology for sea level rise modeling worldwide CVI: COASTAL VULNERABILITY INDEX NWLON: NATIONAL WATER LEVEL OBSERVATION NETWORK CONCLUSION: METHODOLOGY PRIMARY DATA COLLECTION SECONDARY DATA COLLECTION DATA PROCESSING RESULTS & ANALYSIS SPREADSHEETS RESULTS AND ANALYSIS OF LINE GRAPHS... 26

5 P a g e v ANALYSIS OF BEACH PROFILE DATA OBTAINED AT STATION ANALYSIS OF BEACH PROFILE DATA OBTAINED AT STATION ANALYSIS OF BEACH PROFILE DATA OBTAINED AT STATION ANALYSIS OF BEACH PROFILE DATA OBTAINED AT STATION ANALYSIS OF BEACH PROFILES DONE IN AN OVERALL ANALYSIS OF THE PROFILE OF THE BEACH FROM THE LINE GRAPHS RESULTS AND ANALYSIS OF DIGITISED MAPS FROM ARC MAP AND ARC SCENE AN ANALYSIS OF 0.4M FLOOD POLYGONS: AN ANALYSIS OF THE EFFICIENCY OF THE METHODOLOGY WITH RESPECT TO THE RESULTS CONCLUSION AIM: CONCLUSION: RECOMMENDATIONS: REFERENCES APPENDICES APPENDIX 1: Spreadsheets that were used to derive coordinates and elevation for the beach profile data of Grande Riviere for 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, at Station APPENDIX 3: Spreadsheets that were used to derive the coordinates and elevation of the data from the beach profiles taken by IMA in selected years between, at Station APPENDIX 4: Spreadsheets that were used to derive the coordinates and elevation of the data from the beach profiles taken by IMA in selected years between, at Station APPENDIX 5: Spreadsheets that were used to derive the coordinates and elevation of the data from the beach profiles taken by IMA in selected years between, at Station APPENDIX 6: Arc Scene Map of Grande Riviere showing the 0.4m Flood Polygon for the year APPENDIX 7: Arc Scene Map of Grande Riviere showing the 0.4m Flood Polygon for Feb APPENDIX 8: Arc Scene Map of Grande Riviere showing the 0.4m Flood Polygon for Oct APPENDIX 9: Arc Scene Map of Grande Riviere showing the 0.4m Flood Polygon for the year APPENDIX 10: Arc Scene Map of Grande Riviere showing the 0.4m Flood Polygon for the year APPENDIX 11: Arc Scene Map of Grande Riviere showing the 0.4m Flood Polygon for the year APPENDIX 12: Arc Scene Map of Grande Riviere showing the 0.4m Flood Polygon for the year APPENDIX 13: Arc Scene Map of Grande Riviere showing the 0.4m Flood Polygon for the year APPENDIX 14: MAPS OF GRANDE RIVIERE DONE IN ARC MAP FOR THE YEARS

6 P a g e vi 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) Figure 2.3: A diagram showing the general layout for the calculations of the rate of shoreline recession Figure 2.4: Picture showing Satellite Figure 4.1: STATION 1 BEACH PROFILES DONE BY IMA IN 5 PAST YEARS Figure 4.2: STATION 2 BEACH PROFILES DONE BY IMA IN 5 PAST YEARS Figure 4.3: STATION 3 BEACH PROFILES DONE BY IMA IN 5 PAST YEARS Figure 4.4: STATION 4 BEACH PROFILES DONE BY IMA IN 5 PAST YEARS Figure 4.5: BEACH PROFILE DONE FROM IMA STATION 1 IN Figure 4.6: BEACH PROFILE DONE FROM IMA STATION 2 IN Figure 4.7: BEACH PROFILE DONE FROM IMA STATION 3 IN Figure 4.8: BEACH PROFILE DONE FROM IMA STATION 4 IN Figure 4.9: MAP SHOWING O.4M FLOOD POLYGON AT GRANDE RIVIERE IN Figure 4.10: MAP SHOWING O.4M FLOOD POLYGON AT GRANDE RIVIERE IN FEB Figure 4.11: MAP SHOWING O.4M FLOOD POLYGON AT GRANDE RIVIERE IN OCT

7 P a g e vii Figure 4.12: MAP SHOWING O.4M FLOOD POLYGON AT GRANDE RIVIERE IN Figure 4.13: MAP SHOWING O.4M FLOOD POLYGON AT GRANDE RIVIERE IN Figure 4.14: MAP SHOWING O.4M FLOOD POLYGON AT GRANDE RIVIERE IN Figure 4.15: MAP SHOWING O.4M FLOOD POLYGON AT GRANDE RIVIERE IN Figure 4.16: MAP SHOWING O.4M FLOOD POLYGON AT GRANDE RIVIERE IN Figure 4.17: A MAP SHOWING THE INTERSECTING PORTION OF THE POLYGONS CREATED FOR THE PREVIOUS YEARS... 40

8 P a g e 1 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 CO 2 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

9 P a g e 2 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 km 3 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) 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) 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 the beach hosts more than

10 P a g e 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 28 th 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)

11 P a g e 4 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?

12 P a g e 5 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.

13 P a g e ORGANIZATION OF REPORT 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 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 3 Methodology: Data Collection: Primary and Secondary data collected Methodology: Prcoessing of Data Collected into sea level rise models. Chapter 4 Results: Analysis of results, Analysis of efficiency of methodology with respect to results. Chapter 5 Conclusion and Recommendations Figure 1.3: Diagram showing the layout of the steps of the project

14 P a g e 7 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

15 P a g e 8 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 CARICOM 16 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

16 P a g e 9 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

17 P a g e 10 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).

18 P a g e 11 Figure 2.2: Most Vulnerable CARICOM Cities to SLR and Storm Surge (top 15 only) Figure 2.2: Most Vulnerable CARICOM Cities to SLR and Storm Surge (top 15 only) (UNDP 2010) (UNDP 2010) 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).

19 P a g e 12 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

20 P a g e 13 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) 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)

21 P a g e 14 Figure 2.3: A diagram showing the general layout for the calculations of the rate of shoreline recession (Werner G. Hennecke et al, 2000) 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 highlycapitalised 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)

22 P a g e 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) 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

23 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) 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) 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

24 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:

25 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) 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

26 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 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

27 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) 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 20 th 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

28 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. Highprecision 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 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

29 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) 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) 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 sealevel 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 landmeasurement 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

30 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.

31 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 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.

32 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.

33 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 E and 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 is illustrated below.

34 P a g e Oct-02 Apr-08 May-07 Jun-06 Feb-02 Nov-99 Figure 4.1: STATION 1 BEACH PROFILES DONE BY IMA IN 5 PAST YEARS 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 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 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 E and

35 P a g e 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 are illustrated in the line graph below Oct Apr-08 May-07 Jun-06 FEB 2002+Sheet4!$F$25:$F$3 3 Nov Figure 4.2: STATION 2 BEACH PROFILES DONE BY IMA IN 5 PAST YEARS ANALYSIS OF BEACH PROFILE DATA OBTAINED AT STATION 2 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 E and N in WGS-84 datum and the elevation was 3.999m in the Mean Sea Level datum.

36 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 Nov-99 Apr-08 May-07 Jun-06 Feb-02 Oct Figure 4.3: STATION 3 BEACH PROFILES DONE BY IMA IN 5 PAST YEARS ANALYSIS OF BEACH PROFILE DATA OBTAINED AT STATION 3 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 E and 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

37 P a g e 30 foreshore following the berm. The beach profiles done by IMA from this station between the years were plotted and is illustrated in the graph below Jun-06 Apr-08 May-07 Oct-02 Feb-02 Nov Figure 4.4: STATION 4 BEACH PROFILES DONE BY IMA IN 5 PAST YEARS ANALYSIS OF BEACH PROFILE DATA OBTAINED AT STATION 4 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.

38 P a g e 31 The following graphs illustrate the beach profiles taken at the IMA stations for the year STATION STATION IMA41IMA42IMA43IMA44IMA45IMA46IMA47IMA48 Figure 4.5: BEACH PROFILE DONE FROM IMA STATION 1 IN 2011 STATION IMA33 IMA34 IMA35 IMA36 IMA37 IMA38 IMA39 IMA40 STATION 2 Figure 4.6: BEACH PROFILE DONE FROM IMA STATION 2 IN 2011

39 P a g e 32 STATION STATION 3 Figure 4.7: BEACH PROFILE DONE FROM IMA STATION 3 IN STATION STATION Figure 4.8: BEACH PROFILE DONE FROM IMA STATION 4 IN 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.

40 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 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

41 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 , 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:

42 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 1789m 2. 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 1308m 2 along the coastline.

43 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 1622m 2 along the coastline of Grande Rivere. Figure 4.12: MAP SHOWING O.4M FLOOD POLYGON AT GRANDE RIVIERE IN 2008 The flood polygon digitized for the 0.4m sea level rise scenario affected an area of 2365m 2 for the year 2008.

44 P a g e 37 Figure 4.13: MAP SHOWING O.4M FLOOD POLYGON AT GRANDE RIVIERE IN 2007 The 0.4m flood polygon covered an area of 1924m 2 for the year Figure 4.14: MAP SHOWING O.4M FLOOD POLYGON AT GRANDE RIVIERE IN 2006 The 0.4m flood polygon consumed an area of 1409m 2 along the coastline of Grande Riviere.

45 P a g e 38 Figure 4.15: MAP SHOWING O.4M FLOOD POLYGON AT GRANDE RIVIERE IN 2010 Figure 4.16: MAP SHOWING O.4M FLOOD POLYGON AT GRANDE RIVIERE IN 2011 The area that was covered by the o.4m flood polygon in the year 2011 was calculated to be 1068m 2

46 P a g e 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 1000m 2 and 2000m 2 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 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.

47 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 , 2010 and The area of this portion of the beach that is common to all is m 2 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

48 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.

49 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 m2 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

50 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 , 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

51 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.

52 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, 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:: 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: 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): NASA. (2008, 05 22). International Satellite Will monitor Global Sea level Rise. Retrieved 04 12, 2011, from climate.nasa.gov: Nichols, D. W. (2011). Retrieved 2011, from seeturtles: NPS, N. P. (2011). NPS Inventory and Monitoring Programs. Retrieved 04 12, 2011, from

53 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: Werner G. Hennecke, C. A. (2000, ). 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. :

54 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.

55 P a g e 48 POINTS DESCRIPTION DEG MIN SEC HOR.BRG DEG MIN SEC ZENITH SD HD CPB 1 CPA IMA IMA IMA IMA IMA IMA IMA IMA IMA IMA IMA IMA IMA IMA IMA IMA IMA IMA IMA IMA IMA

56 25 SH SH SH SH SH SH SH SH SH SH SH SH SH SH SH SH SH SH SH SH SH SH SH SH SH P a g e 49

57 50 SH SH SH SH SH IMA BP BP BP BP BP BP BP BP BP BP BP BP BP BP BP IMA IMA P a g e 50

58 75 IMA IMA IMA IMA IMA IMA IMA IMA IMA IMA IMA IMA IMA IMA IMA IMA IMA IMA IMA IMA IMA IMA IMA P a g e 51

59 100 IMA SH SH SH SH SH SH SH SH SH SH SH SH SH SH SH SH SH SH SH SH SH SH SH SH P a g e 52

60 P a g e SH SH SH SH SH SH SH SH SH SH SH SH SH SH SH SH SH SH SH SH SH

61 P a g e 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, at Station 1 X Y Z Nov-99 POINTS HD ΔE ΔN E N RED.LEVEL ΔH ELEVATION

62 P a g e Feb-02 POINTS HD ΔE ΔN E N RED.LEVEL ΔH ELEVATION Jun-06 POINTS HD ΔE ΔN E N RED.LEVEL ΔH ELEVATION

63 P a g e May-07 POINTS HD ΔE ΔN E N RED.LEVEL ΔH ELEVATION Apr-08 POINTS HD ΔE ΔN E N RED.LEVEL ΔH ELEVATION

64 P a g e Oct-02 POINTS HD ΔE ΔN E N RED.LEVEL ΔH ELEVATION

65 P a g e APPENDIX 3: Spreadsheets that were used to derive the coordinates and elevation of the data from the beach profiles taken by IMA in selected years between, at Station 2 X Y Z Nov-99 POINTS HD ΔE ΔN E N RED.LEVEL ΔH ELEVATION

66 P a g e Feb-02 POINTS HD ΔE ΔN E N RED.LEVEL ΔH ELEVATION

67 P a g e 60 Jun-06 POINTS HD ΔE ΔN E N RED.LEVEL ΔH ELEVATION May-07 POINTS HD ΔE ΔN E N RED.LEVEL ΔH ELEVATION

68 P a g e Apr-08 POINTS HD ΔE ΔN E N RED.LEVEL ΔH ELEVATION Oct-02 POINTS HD ΔE ΔN E N RED.LEVEL ΔH ELEVATION

69 P a g e APPENDIX 4: Spreadsheets that were used to derive the coordinates and elevation of the data from the beach profiles taken by IMA in selected years between, at Station 3 X Y Z Nov-99 POINTS HD ΔE ΔN E N RED.LEVEL ΔH ELEVATION

70 P a g e Feb-02 POINTS HD ΔE ΔN E N RED.LEVEL ΔH ELEVATION

71 P a g e Jun-06 POINTS HD ΔE ΔN E N RED.LEVEL ΔH ELEVATION

72 P a g e Apr-08 POINTS HD ΔE ΔN E N RED.LEVEL ΔH ELEVATION Oct-02 POINTS HD ΔE ΔN E N RED.LEVEL ΔH ELEVATION

73 P a g e 66

74 P a g e APPENDIX 5: Spreadsheets that were used to derive the coordinates and elevation of the data from the beach profiles taken by IMA in selected years between, at Station 4 X Y Z Nov-99 POINTS HD ΔE ΔN E N RED.LEVEL ΔH ELEVATION

75 P a g e Feb-02 POINTS HD ΔE ΔN E N RED.LEVEL ΔH ELEVATION

76 P a g e Jun-06 POINTS HD ΔE ΔN E N RED.LEVEL ΔH ELEVATION

77 P a g e 70

78 P a g e May-07 POINTS HD ΔE ΔN E N RED.LEVEL ΔH ELEVATION

79 Apr-08 POINTS HD ΔE ΔN E N RED.LEVEL ΔH ELEVATION P a g e 72

80 P a g e Oct-02 POINTS HD ΔE ΔN E N RED.LEVEL ΔH ELEVATION

81 P a g e 74

82 7.6 APPENDIX 6: Arc Scene Map of Grande Riviere showing the 0.4m Flood Polygon for the year P a g e 75

83 7.7 APPENDIX 7: Arc Scene Map of Grande Riviere showing the 0.4m Flood Polygon for Feb 2002 P a g e 76

84 7.8 APPENDIX 8: Arc Scene Map of Grande Riviere showing the 0.4m Flood Polygon for Oct 2002 P a g e 77

85 7.9 APPENDIX 9: Arc Scene Map of Grande Riviere showing the 0.4m Flood Polygon for the year P a g e 78

86 7.10 APPENDIX 10: Arc Scene Map of Grande Riviere showing the 0.4m Flood Polygon for the year 2007 P a g e 79

87 7.11 APPENDIX 11: Arc Scene Map of Grande Riviere showing the 0.4m Flood Polygon for the year 2008 P a g e 80

88 7.12 APPENDIX 12: Arc Scene Map of Grande Riviere showing the 0.4m Flood Polygon for the year 2010 P a g e 81

89 7.13 APPENDIX 13: Arc Scene Map of Grande Riviere showing the 0.4m Flood Polygon for the year 2011 P a g e 82

90 7.14 APPENDIX 14: MAPS OF GRANDE RIVIERE DONE IN ARC MAP FOR THE YEARS P a g e 83

91 P a g e 84

92 P a g e 85

93 P a g e 86

94 P a g e 87

95 P a g e 88