Assessing the impacts of

change of coastal Winneba-

Ghana

Ankrah Johnson

Master in Environmental Sciences and Technology

Specialization in Risk: Assessment and Environmental Management

Department of Geosciences, Environment and Spatial Planning

2020

Supervisor

António José Guerner Dias Assistant Professor, Faculty of Sciences

Todas as correções determinadas pelo júri, e só essas, foram efetuadas. O Presidente do Júri,

Porto, ______/______/______

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ACKNOWLEDGEMENTS

My deepest appreciation goes to Professor António José Guerner Dias who supervised and supported me with the needed financial support towards the achievement of this thesis. Professor António José Guerner Dias, I am grateful to you for the important role you have played in this research and in my academic life. I am also grateful to you for the confidence you had in me since the beginning of my programme till the end. All I can say is thank you.

I am again grateful to Professor Caroline dos Santos da Silva at the Department of Physics and Astronomy of the Faculty of Science of the University of Porto who supported my university accommodation expenses for the entire period of my programme. I am very appreciative of your efforts and words of encouragement in my life.

I would also like acknowledge the key role of my parents who are uneducated and yet saw the need to provide for my education and encouraged me to climb high on the academic ladder.

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ABSTRACT

The essential role climate plays in our lives has over the years been threatened due to the increased global contribution of pollutant gases into the atmosphere and the resultant effect on the climate systems. This has resulted in global and its related impacts on all species of on earth. Current and projected changes in climate show an increasing trend accompanied by variations in its variables and related impacts especially in the world‘s coastal areas. This study assesses the problems of climate change on Winneba and intends to contribute to its mitigation and adaptation in the community. Specifically, this study evaluate climate change in the Winneba community (period from 1980-2019); analyse shoreline change along the Winneba coastline under climate change; and examines the physical effects of shoreline change and its implications on the Winneba community. Primary and secondary data were employed in this study. Primary data were sourced through field measurements and observations and secondary data was sourced from relevant climate agencies and institutions such as the Meteorological association, the commission of Ghana and the Winneba municipal hospital. Analyses of these data helped to reveal the climate change problems in the Winneba community. Findings from the evaluation of climate change incidence in the Winneba community indicate that there have been great change and variability in both land and oceanic climatic parameters over the Winneba community. There has been an increasing trend in both land surface maximum and minimum temperature from 1980-2019 and sea surface temperature and sea surface salinity. Findings again revealed an increasing trend in rainfall amidst variations in the community. The annual range of temperature in the community is 2.7°C with a mean of 27.6°C while the average annual rainfall is 761.4 millimetres with a range of 910.9 millimetres. Findings revealed that disease response as a result of variations in rainfall and temperature in the community is immediate and does not take several periods to occur. Findings also show that the variability, seasonality and the array of divergence in rainfall has resulted in the community‘s perception on decreased rainfall in the community even though absolute values revealed an increasing trend. Findings further, revealed that while at night showed an increasing trend, that of the day showed a decreasing trend demonstrating its negative relationship with temperature in the community. Findings moreover revealed a projected increase in both rainfall and temperature in the community with higher rainfall amounts in the month of May, June, July and October and higher temperatures in March, April and December. Findings from analyses of shoreline change along the Winneba coastline revealed erosion rate of -3.2 metres per year with a maximum land displacement of 64 metres iv and an accretion rate of 1.0 metre per year. The study revealed that high rate and magnitude of erosion occur at the eastern portion of the coast and this is due to the increased urbanization of the area which disrupts sediment transport to the coast. Findings revealed that in the next 10 and 20 years, the Winneba shoreline will be displaced by a maximum distance of 115 metres with an average uncertainty of ±7.3. Findings from the analyses of the physical effects of shoreline change and its implication on the community revealed that majority of the infrastructures along the coast have been affected by erosion. Findings therefore, suggest a possible total collapse of these infrastructures and the resultant displacement of people due to the observed rate of erosion and the projected land displacement along the coast. Findings again indicate that erosion has affected the vegetation especially the coconut plantation along the coast and this has reduced their aesthetic value. Findings finally revealed water supply, availability and usage problems in the coastal zone of Winneba due to the extension of sea water into coastal freshwaters and underground aquifers, which has made these inland waters unsafe for domestic and agricultural use. The study concludes that there is climate change incidence in Winneba and the resultant impacts are enormous. There is the need for the strengthening of mitigation and adaptation strategies in order to address this problem. This study therefore, recommends mitigation and adaptation measures such as climate change impact awareness creation and environmental education, buffering of the shoreline zones, implementing ecosystem recovery approaches, decentralization of management policies and enforcement coastal management policies, incorporation of community perception with institutional knowledge and the intensification of coastal zone research.

Keywords: climate change, shoreline change, erosion, accretion, sea water, impacts, coastal community, mitigation, adaptation.

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RESUMO

Ao longo dos anos, o papel essencial que o clima desempenha em nossas vidas foi ameaçado devido ao aumento da contribuição global dos gases poluentes na atmosfera e ao efeito resultante nos sistemas climáticos. Isso resultou em mudanças climáticas globais e seus impactos relacionados em todas as espécies da Terra. As mudanças atuais e projetadas no clima mostram uma tendência crescente acompanhada de variações em suas variáveis e impactos relacionados, especialmente nas áreas costeiras do mundo. Este estudo avalia os problemas das mudanças climáticas em Winneba e pretende contribuir para sua mitigação e adaptação na comunidade. Especificamente, este estudo avalia mudanças climáticas na comunidade de Winneba (período de 1980 a 2019); analisar as mudanças na linha costeira ao longo da costa de Winneba sob mudanças climáticas; e examina os efeitos físicos das mudanças na costa e suas implicações na comunidade de Winneba. Dados primários e secundários foram empregados neste estudo. Os dados primários foram obtidos através de medições e observações de campo e os dados secundários foram obtidos de agências e instituições climáticas relevantes, como a Associação Meteorológica de Gana, a comissão de pesca do Gana e o hospital municipal de Winneba. As análises desses dados ajudaram a revelar os problemas das mudanças climáticas na comunidade de Winneba. Os resultados da avaliação da incidência de mudanças climáticas na comunidade de Winneba indicam que houve grandes mudanças e variabilidade nos parâmetros climáticos terrestres e oceânicos na comunidade de Winneba. Houve uma tendência crescente na temperatura máxima e mínima da superfície terrestre de 1980 a 2019 e na temperatura da superfície do mar e na salinidade da superfície do mar. As descobertas revelaram novamente uma tendência crescente de chuvas em meio a variações na comunidade. A faixa anual de temperatura na comunidade é de 2,7 ° C, com média de 27,6 ° C, enquanto a precipitação média anual é de 761,4 milímetros, com um intervalo de 910,9 milímetros. Os resultados revelaram que a resposta à doença como resultado de variações na precipitação e temperatura na comunidade é imediata e não leva vários períodos para ocorrer. As descobertas também mostram que a variabilidade, a sazonalidade e a variedade de divergências nas chuvas resultaram na percepção da comunidade sobre a diminuição das chuvas na comunidade, mesmo que os valores absolutos revelassem uma tendência crescente. Os resultados revelaram ainda que, enquanto a umidade noturna mostrava uma tendência crescente, a do dia mostrava uma tendência decrescente, demonstrando sua relação negativa com a temperatura na comunidade. Além disso, as descobertas revelaram um aumento projetado nas chuvas e na temperatura na comunidade, vi com maiores quantidades de chuvas nos meses de maio, junho, julho e outubro e temperaturas mais altas nos meses de março, abril e dezembro. Os resultados das análises das mudanças na linha costeira ao longo da costa de Winneba revelaram uma taxa de erosão de -3,2 metros por ano, com um deslocamento máximo de 64 metros e uma taxa de acúmulo de 1,0 metro por ano. O estudo revelou que ocorrem altas taxas e magnitude de erosão na porção leste da costa e isso se deve ao aumento da urbanização da área, que interrompe o transporte de sedimentos para a costa. As descobertas revelaram que nos próximos 10 e 20 anos, a costa de Winneba será deslocada por uma distância máxima de 115 metros, com uma incerteza média de ± 7,3. Os resultados das análises dos efeitos físicos das mudanças na linha de costa e suas implicações na comunidade revelaram que a maioria da infraestruturas ao longo da costa foi afetada pela erosão. Os resultados sugerem, portanto, um possível colapso total dessas infraestruturas e o resultante deslocamento de pessoas devido à taxa de erosão observada e ao deslocamento projetado da terra ao longo da costa. Os resultados indicam novamente que a erosão afetou a vegetação, especialmente a plantação de coco ao longo da costa e isso reduziu seu valor estético. As descobertas finalmente revelaram problemas de abastecimento de água, disponibilidade e uso na zona costeira de Winneba devido à extensão da água do mar em águas doces costeiras e aqüíferos subterrâneos, o que tornou essas águas interiores inseguras para uso doméstico e agrícola. O estudo conclui que há uma incidência de mudanças climáticas em Winneba e os impactos resultantes são enormes. É necessário o fortalecimento de estratégias de mitigação e adaptação para lidar com esse problema. Portanto, este estudo recomenda medidas de mitigação e adaptação, tais como criação de conscientização sobre o impacto das mudanças climáticas e educação ambiental, proteção das zonas costeiras, implementação de abordagens de recuperação de ecossistemas, descentralização de políticas de gestão e aplicação de políticas de gestão costeira, incorporação da percepção da comunidade com o conhecimento institucional e o meio ambiente. intensificação da pesquisa na zona costeira.

Palavras-chave: mudança climática, mudança de linha de costa, erosão, acréscimo, água do mar, impactos, comunidade costeira, mitigação, adaptação vii

DEDICATION

I dedicate this research to my parents, Mr Tawiah Akwasi and Madam Naa Akosua who sacrificed all their resources for the sake of my education.

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TABLE OF CONTENTS

ACKNOWLEDGEMENTS …………………………………………………………………………...... I

ABSTRACT …………………………………………………………………………………………....III

RESUMO ………………………………………………………………………………………………V

DEDICATION …………………………………………………………………………………. ………VII

TABLE OF CONTENTS ……………………………………………………………………………….IX

LIST OF TABLES ……………………………………………………………………………………..XIII

LIST OF FIGURES ……………………………………………………………………………………XV

LIST OF PHOTOS ……………………………………………………………………………...... XVII

LIST OF ABBREVIATIONS ………………………………………………………………………..XIX

CHAPTER ONE ………………………………………………………………………………………….1

1.0 Introduction ……………………………………………………………………………………….1

1.1 Problem Statement ……………………………………………………………………………..3

1.2 Objectives ……………………………………………………………………………………….4

1.3 Research Questions…………………………………………………………………………….4

1.4 Justification ………………………………………………………………………………………4

1.5 Organization of the Thesis …………………………………………………………………...... 5

CHAPTER TWO: LITERATURE REVIEW …………………………………………………………….7

2.0 Introduction ……………………………………………………………………………………….7

2.1 Overview of the Earth‘s Climate System ………………………………………………………7

2.1.1 Climate ………………………………………………………………………………………7

2.1.2 The Climate System ……………………………………………………………………….8

2.1.3 Components of the Earth‘s Climate System ……………………………………………..8

2.1.4 Climate Models ………………………………………………………………………….....11

2.1.4.1 General Circulation Models (GCM) and Regional Circulation Models (RCM) ...11

2.1.4.2 Earth System Models (ESM) ………………………………………………………..11 x

2.2 Climate and Climate Change Scenarios …………………………………………………..12

2.2.1 Climate Change Scenario Construction for Impact Assessment ……………………13

2.3 ………………………………………………………………………………14

2.4 ………………………………………………………………………………16

2.5 Concept of Climate Change ………………………………………………………………….18

2.6 History of Global climate change …………………………………………………………….19

2.6.1 The Icehouse (Ice Age) Era ………………………………………………………………19

2.6.2 The Greenhouse (Industrial Revolution) Era …………………………………………….21

2.6.3 The Intergovernmental Panel on Climate Change (IPCC) ……………………………..22

2.7 Climate variability and change in Africa ……………………………………………………..23

2.8 Overview of ……………………………………………………..25

2.9 Climate variability and change in Ghana ……………………………………………………26

2.10 Overview of Sea Level Rise ………………………………………………………………..27

2.10.1 Causes of Sea Level Rise ………………………………………………………………29

2.10.2 Sea Level Rise Projections ……………………………………………………………..30

2.10.2.1 Physical Models …………………………………………………………………….30

2.10.2.2 Semi-empirical Models …………………………………………………………….31

2.10.3. Local Sea Level Change ……………………………………………………………32

2.11 Climate Change and Shoreline Change in Ghana ………………………………………...33

2.12 Overview of the impacts of Climate Change ………………………………………………35

2.12.1 Impacts on Coastal Areas ……………………………………………………………..35

2.12.2 Impacts on Freshwater Resources …………………………………………………....36

2.12.3 Impacts on Agriculture and Food Security ……………………………………………37

2.12.4 Impacts on Human health, Settlement and Infrastructure …………………………..38

2.13 Impacts Climate change on the Coastal Areas of Africa …………………………………39

2.14 Impacts Climate change on the Coastal Areas of Ghana ………………………………..40 xi

2.15 Summary of Chapter ………………………………………………………………………..41

CHAPTER THREE: STUDY AREA …………………………………………………………………..43

3.0 Introduction ……………………………………………………………………………………...43

3.1 Study area: Location and Population ………………………………………………………...43

3.2 Climate …………………………………………………………………………………………..45

3.3 Geology and geomorphology …………………………………………………………………46

3.3.1 Sediment fluctuations ………………………………………………………………………48

3.4 Biodiversity ………………………………………………………………………………………49

3.5 Soils ………………………………………………………………………………………………51

CHAPTER FOUR: ANALYTICAL FRAMEWORK AND METHODOLOGY ………………………53

4.0 Introduction …………………………………………………………………………………...... 53

4.1 Conceptual Framework …………………………………………………………………….....53

4.2 Analytical framework ……………………………………………………………………….....54

4.3 Methodology …………………………………………………………………………………….55

4.3.1 Data Sources, Type and Validity ………………………………………………………....55

4.3.2 Data Analysis ……………………………………………………………………………….56

4.3.2.1.0 Analysis of Climate Change on the Winneba Community ………………………57

4.3.2.1.1 Analysis of Historical Data ………………………………………………………….57

4.3.2.1.2 Analysis for Future Projected Changes …………………………………………..58

4.3.2.2 Analysis of Shoreline Change along the Winneba Coast …………………………59

4.3.2.2.1 Kalman Filter Model ………………………………………………………………64

4.3.2.3 Analysis of the physical impacts of climate change and its implications on the Winneba community ……………………………………………………………………………………65

CHAPTER FIVE: RESULTS AND DISCUSSIONS …………………………………………………67

5.0 Introduction ….…………………………………………………………………………………..67

5.1 Evaluation of Climate Change on the Winneba community ……………………………….67

5.1.1 Maximum and Minimum Temperature, SST and SSS ………………………………...67 xii

5.1.2 Rainfall ………………………………………………………………………………………72

5.1.3 Relative Humidity (RH) ……………………………………………………………………76

5.1.4 Temperature and Rainfall Projections for Winneba …………………………………....79

5.2 Analysis of Shoreline Change along the Winneba Coastline ……………………………...83

5.2.1 Shoreline Changes between 2000 and 2020 …………………………………………...83

5.3 Analysis of the physical impacts of Climate Change and its implications on the Winneba community ……………………………………………………………………………………………….88

5.3.1 Impacts of shoreline change and erosion on the community ………………………....88

CHAPTER SIX: CONCLUSIONS AND RECOMMENDATIONS ………………………………….95

6.0 Introduction …………………………………………………………………………………….95

6.1 Conclusions …………………………………………………………………………………..95

6.1.1 Objective One ……………………………………………………………………………95

6.1.2 Objective Two ……………………………………………………………………………97

6.1.3 Objective Three ………………………………………………………………………….97

6.2 Recommendations …………………………………………………………………………..97

6.3 Areas for further research …………………………………………………………………..99

6.4 Limitations ……………………………………………………………………………………99

REFERENCES ……………………………………………………………………………………..101

APPENDIX ………………………………………………………………………………………….129

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LIST OF TABLES

Table Page

4.1: Data sources and information collected ………………………………………………………...56

4.2: Estimated uncertainties for Winneba shoreline ………………………………………………..64

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LIST OF FIGURES

Figure Page

2.1: Components of the global climate system and their processes and interactions …………..10

2.2: The main stages required to provide climate change scenarios for assessing the impacts of climate change ………………………………………………………………………………………….14

2.3: Climate zones of Africa …………………………………………………………………………...15

2.4: Inter-tropical Convergence Zone over Africa …………………………………………………..16

2.5: Climate zones of Ghana ………………………………………………………………………….17

2.6: ITCZ position in Ghana in the month of January and July …………………………………....18

2.7: Observed and future possible impacts of climate variability and change over Africa ……...25

2.8: Sea level rise processes from global to local scales ……………………………………….....29

3.1: Municipal map of Effutu (Winneba) ……………………………………………………………..44

3.2: Ecological zones map of Ghana showing Winneba …………………………………………...46

3.3: Elevation map of Winneba …………………………………………………………………….....48

3.4: Map showing the 3 divisions of the Winneba coast …………………………………………...49

4.1: Framework on vulnerability shown in the IPCC AR4 and AR5 reports ……………………..54

4.2: An Analytical framework employed to analyse study results ………………………………...55

4.3: Flow chart showing Statistical downscale method employed in the study ………………….59

4.4: Digital Shoreline Analysis System (DSAS) steps ……………………………………………..62

4.5: Aerial photograph (Satellite imagery) showing the Winneba coast with shoreline and cast transects ………………………………………………………………………………………………..64

5.1: Mean Annual Maximum and Minimum Temperature of Winneba from 1980-2019 ……….68

5.2: Mean Annual Sea Surface Temperature and Sea Surface Salinity of Winneba …………..70 xvi

5.3: Total volumes of fish catch in Winneba from 2000-2018 …………………………………….71

5.4: Total Rainfall and Mean Annual Temperature of Winneba from 1980-2019 ……………….73

5.5: cases and death in Winneba …………………………………………………………..74

5.6: Divergence bar graph showing rainfall for Winneba ………………………………………….76

5.7: Mean annual maximum and Minimum Relative Humidity of Winneba from 1980-2019 …..77

5.8: Annual Temperature and Relative Humidity (from monthly means) of Winneba from 1980- 2019 ……………………………………………………………………………………………………..79

5.9: Projected Monthly Maximum Temperature of Winneba for 2030, 2040 and 2050 ………...80

5.10: Projected Monthly Minimum Temperature of Winneba for 2030, 2040 and 2050 ………..80

5.11: Projected Total Monthly Rainfall of Winneba for 2030, 2040 and 2050 …………………...81

5.12: Mean Annual Change in Projected Temperature and Rainfall of Winneba …………….....81

5.13: End Point Rate and Linear Regression Rate Trends of the Winneba coastline from 2000- 2020 ……………………………………………………………………………………………………...83

5.14: Net Shoreline Movement and Shoreline Change Envelop Trends of the Winneba coastline from 2000-2020 …………………………………………………………………………………………84

5.15: Linear Regression Rate …………………………………………………………………………85

5.16: Linear Regression rates showing erosion and accretion levels along the Winneba shoreline …………………………………………………………………………………………………86

5.17: 10 and 20 year shoreline forecast of the Winneba coastline from the Shoreline years 2000-2020 ……………………………………………………………………………………………….87

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LIST OF PHOTOS

Photo Page

3.1: People waiting to buy fish from fishermen (A) and fishermen arriving from (B)…... 44

3.2: Vegetation found in the seasonally flooded area of the Ayensu River (A) and dependent floodplain wetland (B) ……………………………………………………………………50

3.3: Trees types and grasses found in the grassland habitat (A) and degraded forest and scrubland (B) ……………………………………………………………………………………………51

3.4: Tern (A) and Egret (B) populations found in the Winneba wetland area ……………………51

3.5: Soil type of Winneba ………………………………………………………………………………52

5.1: Fish sellers complaining about the total fish bought at the shore ……………………………72

5.2: Impacts of sea erosion on coastal infrastructure along the Central portion (A1 and A2) and the Eastern portion ……………………………………………………………………………………..89

5.3: Erosion and its impacts coconut plantation along the Western portion (A) and the Eastern portion (B1, B2 and B3) of the Winneba coast ………………………………………………………90

5.4: Sea water extending from the ocean into inland waters; Ayensu River (A) and Muni lagoon (B) along the Winneba coast. …………………………………………………………………………92

5.5: Water problems (A and B) in the coastal areas of Winneba due to climate change incidence………………………………………………………………………………………………....93

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LIST OF ABBREVIATIONS

AGCM - Atmospheric General Circulation Models

Can ESM2 - Canadian Earth System Model

CMIP5 - Climate Model Intercomparison Project

DSAS - Digital Shoreline Analysis System

EPR - End Point Rate

ESM - Earth System Models

GCM - General Circulation Models

GMSL - Global Mean Sea Level

IPCC - Intergovernmental Panel on Climate Change

ITCZ - Inter-tropical Convergence Zone

LRR - Linear Regression Rate

NSM - Net Shoreline Movement

OGCM - Ocean General Circulation Models

RCM - Regional Circulation Models

RCP - Representative Concentration Pathways

SCE - Shoreline Change Envelope

SDSM - Statistical Downscaling Model

SRES - Special Reports on Emission Scenarios

SSS - Sea Surface Salinity

SST - Sea Surface Temperature

UNFCCC - United Nations Framework Convention on Climate Change 1

CHAPTER ONE

1.0 Introduction

The word climate interests many people. This is due to its important role in our day-to-day activities. Our experiences, therefore, form the climate system. The changes in the climate system (climate change) have become one of the topical issues in this century. Climate change is recognized amongst the challenges facing the population of the 21st century. Several definitions have been given to the climate change concept. According to the Intergovernmental Panel on Climate Change (IPCC), climate change is a statistically significant difference in the climate of a given place that continues for a longer period, normally three decades (IPCC, 2007). Thus, changes in any of the climatic factors (temperature, rainfall, humidity, etc.) at a given place over time whether due to natural change or as a result of anthropogenic actions. The United Nations Framework Convention on Climate Change cited in Allen et al, 2009, also defines climate change as any direct or indirect modification of climate at a given place as a result of anthropogenic actions and which modifies the atmospheric structure and the climatic variables over time.

Several pieces of research, shreds of evidence and scientific tests show that the global climate has changed (Russell, 2012). There have been disparities in the climatic variables such as temperature, rainfall, humidity, and the wind among others. Global temperatures have increased and these have serious impacts on all species on earth (Gornitz, 2000). Increased sea surface temperatures have resulted in the melting of glaciers and the resultant opening up of the oceans. This has caused global sea level to rise at an average of about 1 to 2 millimetres in this century (IPCC, 2007). Scientists and researchers in this field have a strong conviction about the continuing rising of the global temperatures for the subsequent decades. This is largely due to anthropogenic actions and the associated greenhouse gas productions. Through anthropogenic activities, unfriendly and harmful gases such as carbon dioxide, nitrous oxide, and methane among others have been accumulated in the atmosphere (IPCC, 2013) and about 30 percent of these gases especially carbon dioxide have been absorbed by the ocean (IPCC, 2013). Higher concentrations of these gases will increase the magnitude of heat waves in many parts of the world. Climate change can alter humidity levels both land and ocean and to intense heat waves. This will, therefore, create unfavourable conditions for all species (Coffel et al, 2018). Coffel et al, in 2018, further indicated this will be worse in Eastern China, India, South America, and Central and Western Africa (Coffel et al, 2018).

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Climate change has also caused rainfall variability. While many regions of the globe receive more, others receive less. For instance, in the of America (USA), rainfall has increased in the northern USA since 1990 and has decreased in the Southwest within the same period (https://climate.nassa.gov/effects). According to the IPCC's 2007 report, Australia and New Zealand will have a water security problem by 2030 as a result of decreased precipitation (IPCC, 2007). This report further stated that summer precipitation is projected to decrease in Central and Eastern Europe and this will result in high water stress. The conditions in Southern Europe are projected to worsen with higher temperatures and drought (IPCC, 2007). The African continent, however, is no exception to these menaces. A study by Nicholson et al, in 2017, on rainfall variability over the African continent reveals a dissimilar trend. Rainfall has decreased more towards the arid regions especially in the Sahel and the western sectors of North Africa (Nicholson et al, 2017). Many productive sectors of the world's economy, therefore, stand the chance of losing their vital services to humans and the ecosystem in general.

The most affected areas amongst the above-mentioned impacts are the coastal areas. The coast offers a lot of benefits. These include recreation, tourism, food, transport and among others (IPCC, 2007). These opportunities and benefits, however, in recent times are being threatened due to climate change. Coastal areas are under threat of sea-level rise, erosion, flooding, and extreme events, etc. The 20th century witnessed a global sea- level rise of about 1.7±0.5millimeter per year with increased mean surface temperatures of about 0.6℃ in the same century (IPCC, 2007). The consequences of sea-level rise are evident. Rising seas invade coastal zones and cause problems such as erosion; flooding of wetlands, saltwater intrusion and several other ecosystems are affected. The impacts of sea-level rise on coastal communities can therefore not be ignored. The rising sea will compel many people to abandon their homes due to flooding. For instance, the Gulf Coast in the USA and coastal Louisiana, in particular, has experienced sea- level rise about 203.3millimeters in the last 50years (USGCRP, 2014). This is expected to increase further due to the effects of land movement. This will, however, increase the risk of flooding thereby harming humans and the ecosystem. Rising sea levels have also been very active on the European continent but with significant regional variations (EEA, n.d). The consequences (both social and economic) due to sea-level rise and the resultant coastal flooding in Europe could reach 1 trillion euros per year by 2100 (Vousdoukas et al, 2018). Asia, especially the coastal zones of East Asia and the Pacific regions again stand a great chance of losing most of its social and economic resources due to the risk of flooding as a result of sea-level rise (IPCC, 2007). The impacts of rising sea-

3 level on the African continent show nothing but a disastrous trend. Most of the coastal zones of Africa are low lying and highly populated. Sea level rise and its associated flooding have destroyed larger areas of the African coasts. This has therefore resulted in many environmental, social and economic problems (Ibe & Awosika, 1999).

The coastal zones in Ghana are faced with a lot of challenges due to the negative impacts of climate change. These include coastal erosion and flooding (Boateng et al, 2016), rising temperatures (Ankrah, 2018) and loss of marine species, etc. Almost all the coastal communities in are already under threat of erosion and flooding. Erosion and flooding are highly evident in the coastal community of Gleefe (Appeaning-Addo & Adeyemi, 2013). Winneba, a coastal community located on the central coast of Ghana is no exception to these threats and is one of the most affected coastal areas concerning climate change impacts. Most of the mitigation and sustainable measures to combat these impacts are highly undertaken in the metropolitan areas of the country. There is, however none of these mitigation and sustainable measures in the Winneba community despite the extreme occurrence of the negative impacts of climate change

1.1 Problem Statement

A lot of benefits are derived from the coast. These benefits make coastal areas one of the most attractive places to dwell. Coastal areas, therefore, serve as a hub for economic activities and again serve as a dwelling place for about 50 percent of the global population (Woodroffe, 2003). Approximately 40 percent of the population in West Africa including Ghana live in coastal areas. This is expected to rise in 2020 (Boko et al, 2007). The inhabitants of coastal Winneba have benefited immensely from the coast. Historically, the Winneba coast served as a port town and is currently having fishing as the second-largest economic activity to services. However, the area is exposed to almost all the negative climate change impacts. The impacts of climate change are highly felt in the community. Inhabitants and fishermen, in particular, are complaining about the disparity in rainfall, increasing temperatures both on land and sea. The incidence of climate change and sea-level rise, in particular, have resulted in the erosion of the coast, the intrusion of seawater into the lagoon, flooding of wetlands and marshes and the alternating shoreline. Underground wells are salty and unsafe for drinking. This harms the inhabitants and other organisms in the ecosystem. The economic and social wellbeing of inhabitants and the security and existence of other organisms are however endangered. Little or no effort has been put in place in addressing this climate change canker. The consequences in

4 the area now outweigh the benefits as used to be previously. The consequences will continue to rise due to the continuous harmful anthropogenic activities in the area. Appropriate mitigations and sustainable measures, therefore, need to be implemented. In response to this problem, the present study assessed several impacts of climate change on coastal Winneba.

This study considered the problems that affect Winneba and intends to point out mitigation and sustainability measures for the coastal area of Winneba, in order to contribute to its adaption to climate change.

1.2 Objectives

This research work aims at assessing the problems of climate change in the coastal area of Winneba and intends to point out mitigation and sustainability measures for the area. It is intended that outcomes from this research will contribute to the development and proper management of the Winneba coast. The above-mentioned aim raises the following specific objectives:

 To evaluate climate change on the coastal community of Winneba (Period from 1980- 2019).  Analyse shoreline change along the Winneba coast under climate change  To examine the physical impacts of climate change and its implications on the community of Winneba.

1.3 Research Questions

Relevant questions asked included:

 How has the climate of the Winneba community changed from the period of 1980-2019?  How has the shoreline of the Winneba coast changed under climate change?  What are the physical impacts of climate change and what implications does it have on the Winneba community?

1.4 Justification

A lot of commitments have been devoted to climate change issues. This is particularly due to the harmful effects it produces. The warming of the atmosphere through the emission of harmful gases has impacted coastal areas negatively. Global coastal areas and coastal Winneba, in particular, have greater exposure to the threats posed by climate change. This research work on

5 assessing the problems of climate change on the Winneba coast, therefore, serves as a coastal management tool for coastal development. With the appropriate evaluation of climate change, several climatic parameters have been explored. Both recent and future effects associated with these factors have been studied on the Winneba coast. Appropriate mitigation and sustainability measures, therefore, can be established. The shoreline change findings is to assist sectors like the real estate, the town and country planning and the coastal inhabitants as a whole in the proper placement of buildings and other infrastructures along the coast. The examination of the physical impacts of climate change and its implications brought to light the current effects that are being faced by both the ecosystem and the inhabitants within the coastal zone. This finding again revealed potential future effects that are likely to perpetuate if appropriate mitigation measures are not employed. The assessed climate change based on the observable impacts will attract several stakeholders that are interested in climate change for further research. This will help in the proper formulation of policies by the government which will enhance coastal zone development and management. More importantly, the outputs of this research has helped in the designation of sustainable mitigation measures and have contributed to the adaptation strategies needed in order to minimize the physical impacts of climate change in the Winneba community.

1.5 Organization of the Thesis

This thesis has been organized in six chapters. Chapter one presents the introduction of the study. It provides the general status of Winneba‘s community with respect to the impacts of climate change. A detailed discussion about the climate change problem and the current studies undertaken on the Winneba community have been stated. A description of the objectives, justification and the need for the study has also been mentioned. Chapter two looks at the review of related scientific literature that is of significance to the study. Chapter three describes the study area of the research such as location and population, climate, geomorphology, vegetation and soil. Chapter four provides the methodology and the approaches that were employed for the research. It gives a detailed description of the data sources and acquisition and other field related surveys. Chapter five deals with results and discussions of the impacts of climate change on the study area and the analyses of the major findings. Chapter six, which is the final chapter, concludes the major findings from this research acknowledging with the policies and frameworks that have been or need to be employed by authorities and stakeholders. This chapter also provides appropriate recommendations to all stakeholders.

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CHAPTER TWO

LITERATURE REVIEW

2.0 Introduction

This chapter reviewed literature that is of significance to the study. This chapter therefore looked at overview of the Earth‘s climate system, the concept of climate change, sea-level change, sea level rise and shorelines changes and the impacts of climate change on the coastal areas.

2.1 Overview of the Earth’s Climate System

2.1.1 Climate

Climate plays a significant role on all forms of life on Earth. Climate forms part of the everyday experiences of humans and is very crucial for human existence. Climate, therefore, is defined as the average weather of an area taking into consideration, its mean and variability over a given time-span (IPCC, 1996). Again, climate is defined as the mean state of the atmosphere, which is averaged over numerous years and all , with emphasis on factors that affect Earth‘s surface temperature considering both local and global means (Vardavas & Taylor, 2007). Goose et al, 2010, further defined climate as the evaluation of the mean and variability of important atmospheric variable such as temperature, precipitation, and wind among others.

Climate differs from one area to the other and is dependent on several geographic factors including; latitude, distance to the sea, presence of vegetation and mountains etc. Besides, climate differs in time; thus, from season to season, decade to decade and even extended time- scales. 30years is the normal period established by the World Meteorological Organization (WMO) for making statistics on climate. Goose et al, in 2010, however, believed a longer time interval than 30years is very effective when analysing climate of the distant past, for instance, the last glacial maximum around 20000 years ago. Definitions of climate that focus on only the atmosphere such as that of Vardavas and Taylor (2007), reveals only a portion of the realism that defines climate. Climate in totality incorporates the atmosphere, the effects of the underlying land, sea and the oceans and several other factors that are not directly experienced by humans (IPCC, 1996). Climate, therefore, is determined by the circulations and interactions of the atmosphere, the ocean and the land, The Earth‘s climate in general, is dependent on factors that influence the radiative balance, such as atmospheric composition, solar radiation or volcanic eruptions. These factors can therefore, not be overlooked if one seeks to understand

8 the Earth‘s climate and its variations. Climate is thus, seen in a broader sense as the statistical description of the climate system (Goose et al, 2010). An intricate system which is composed of various components, including changes and composition of the atmosphere, the ocean, the ice and snow cover, the land surface and its landscapes, the many mutual interactions between them and the huge diversity of physical, chemical and biological processes occurring between and among these components (IPCC, 1996).

2.1.2 The Climate System

The Global Atmospheric Research Program (GARP) of the World Meteorological Organization (WMO) in 1975 defined the climate system as a system consisting of the atmosphere, hydrosphere, cryosphere, the land surface and the biosphere (WMO, 1975). The United Nation Framework Convention on Climate Change in 1992 adapted this definition and defined the climate system as the totality of the atmosphere, hydrosphere, biosphere and geosphere and their interactions (UNFCCC, 1992). The IPCC followed suit in their second assessment report of 1995 and defined the climate system as an interactive system which is composed of five main components (see figure 2.1) namely; the atmosphere, the hydrosphere, the cryosphere, the land surface and the biosphere, which are prejudiced by several external forces with the sun being the most important (IPCC, 1996). Vardavas and Taylor (2007) moreover, see the climate system as a system including the sun, the Earth‘s surface and some features of its interior which causes outgassing and volcanism and also the atmosphere, hydrosphere, biosphere and cryosphere. Even though Gettelman and Rood agree to the above definitions of the climate system, they further added the anthroposphere (the sphere of human effects) in their definition of the climate system looking at the large ―footprint‖ and impacts of humans on the global environment (Gettelman & Rood, 2016).

2.1.3 Components of the Earth’s Climate System

The atmosphere is primarily the first aspect of the climate system we naturally consider.

Factually, it is the air we breathe. It is however, composed mainly of nitrogen (N2) accounting for

78.1%, Oxygen (O2) 20.9%, argon (Ar) 0.93% (IPCC, 1996). Kasting and Siefert, nevertheless, indicated the oxygen composition to be 16% in their study ―Life and Evolution of Earth‘s Atmosphere (Kasting & Siefert, 2002). These gases play a limited role in the interaction with inbound solar radiation which is emitted by the Earth. This notwithstanding, there exist carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O) and ozone (O3) which serve as trace gases and absorb infrared radiation. These gases are referred to as greenhouse gases with a

9 composition of about 0.1% (Goose et al, 2010; IPCC, 1996; Kasting & Siefert, 2002; Vardavas & Taylor, 2007). These gases, therefore, play an important part in the Earth‘s energy budget. The atmosphere also contains water vapour, which act as a natural greenhouse gas. It is highly mutable and has a composition of about 1% (IPCC, 1996). Ozone definition in the atmosphere is very distinctive. Ozone found in the lower atmosphere, the troposphere and the lower stratosphere; act as a greenhouse gas which absorbs the infrared radiation emitted by the Earth. This, therefore, tend to raise the temperature near the Earth‘s surface. Ozone in the higher stratosphere however, acts as a natural protective layer which absorbs ultra-violet radiations. The atmosphere again contains aerosols- liquid and solid particles and clouds. These interact with the inbound and outbound radiation in a diverse and variable way. Water in the form of vapour, cloud droplets or crystals is the most variable component of the atmosphere. Water vapour is a very powerful greenhouse gas and is able to undergo different phases to absorb and emit energy (Torenbeth et al, 2009). This, therefore, makes water vapour very key to climate, its variability and change (Goose et al, 2010; IPCC, 1996; Vardavas & Taylor, 2007).

The hydrosphere is composed of all liquid surface and subterranean water (IPCC, 1996). These include; rivers, lakes, aquifers, saline waters of the oceans and seas. The world‘s oceans play a very important role in the climate system (Vallis, 2011). The ocean serves as a basin of heat and holds more mass than the atmosphere. The ocean has thin currents. These thin currents are both near the surface and deep down the ocean. The surface currents of the ocean are forced by the Earth‘s rotation and wind. The near ocean surface is generally warmer and less salty than the deep ocean. Density plays a significant role in the circulation of the ocean. Globally, large ocean circulations occur in the deep oceans and are motivated by the sinking of surface water that get saltier and colder until it sinks from the surface to the deep ocean (thermohaline circulation) (IPCC, 1996). The biosphere and the cryosphere in one way or the other are part of the ocean (hydrosphere). The oceans‘ biosphere consists of plants and that dwell in the ocean for instance; phytoplankton, algae and animals that form marine food chain. The ocean again holds some portion of the cryosphere as sea ice (Marshall, 2011).

The cryosphere consists of the ice sheets of Greenland and Antarctica, glaciers and snow fields and sea ice and permafrost. These play a vital role in the climate system due to its high reflectivity (albedo) for solar radiation, its low thermal conductivity, its large thermal inertia and its essential role in driving deep ocean water circulation (IPCC, 1996; Marshall, 2011).

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The land surface includes vegetation and soils. There also exist lakes and rivers that cause precipitation and return to the ocean. Vegetation and soils regulate how the sun‘s energy bounce back long-wave (infrared) radiation which heats the atmosphere as the Earth warms. Some energy evaporates as water either through the soil or the leaves of plants and which brings water back into the atmosphere. Soil moisture has a great influence on surface temperature particularly due to its requirement of energy for evaporation (IPCC, 1996).

The marine and terrestrial biosphere also serves as a major component of the climate system and has a greater impact on the atmosphere. They regulate the actions of greenhouse gases. For instance, forests through photosynthetic process store significant amount of carbon from carbon dioxide. The biosphere, therefore, play a key role in the carbon cycle and in the budget of several other gases such as methane and nitrous oxide (IPCC, 1996).

Although, humans form part of the terrestrial biosphere, our activities are huge enough to be treated as a separate sphere (the anthroposphere) (Gettelman & Rood, 2016). Emissions through human activities currently play a key role in the climate system. Human activities such as deforestation, bush burning, and smoke in the form of carbon monoxide among others add to the greenhouse gas loads of the atmosphere. This, therefore, change the atmospheric chemical processes near the surface and the resultant climate variability and change.

Figure 2.1: Components of the global climate system and their processes and interactions. Source: Adopted from www.ipcc.ch/site/assets/uploads/2018/03/TAR-01.pdf.

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2.1.4 Climate Models Climate models are models that employ quantitative methods to excite the connections of the key drivers of climate such as the oceans, atmosphere, land surface and ice. These models, therefore, help in making predictions on seasonal to decadal time scales and for future projections. Models employed in climate studies ranges from simple energy balance models to complex Earth System Models (ESM) requiring high performance computations. The application of a model however, depends on the scientific problem under study (Collins et al, 2006d). Several climate models have been formulated to help understand climate changes in response to greenhouse gases and aerosol emission (Goose et al, 2010). Climate models, therefore, help to understand the complexities of the Earth‘s climate system and their variations.

2.1.4.1 General Circulation Models (GCM) and Regional Circulation Models (RCM)

General Circulation Models (GCM are very crucial in addressing future climate change (Hudson et al, 2002). GCM were established due to the need for the simulation of the three dimensional structure of winds and currents (Goose et al, 2010). GCM are divided into Atmospheric General Circulation Models (AGCM) and Ocean General Circulation Models (OGCM). In climate studies however, these terms are used together as Atmosphere Ocean General Circulation Models (AOGCM). The AOGCM help to understand the changes in the atmosphere, ocean and land and sea ice and for making predictions based on future greenhouse gas and aerosol forcing (Flato et al, 2013).

Regional Circulation Models (RCM) provides demonstrations of climate developments that are similar to those in the atmospheric and land surface components of AOGCM. RCM are however, limited-area models and are therefore, used to downscale GCM simulations for a specific geographical area and to provide detail information (Laprise, 2008; Rummukainan, 2010). Combination of GCM and RCM simulations provide consistent regional climate change estimates (IPCC, 1996). However, climate change information requires the usage of RCM than a GCM grid-cell. Thus, GCM do not capture details that are needed for national assessment and this is particularly so for Winneba, Ghana where there exist diversities in vegetation, topography, soils and coastline.

2.1.4.2 Earth System Models (ESM)

Earth System Models (ESM) are contemporary models that open up the AOGCM and employ several biogeochemical cycles such as the carbon cycle, the sulphur cycle and or the ozone

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(Flato, 2011). The ESMs offer the most appropriate tools for the developments of past and future response of the climate system to external forcing (Flato et al, 2013).

2.2 Climate and Climate Change Scenarios

Climate scenarios can be defined as plausible future climate that have been constructed for carrying out investigations on the probable effects of anthropogenic climate change (Charron, 2016; IPCC, 2007). These scenarios therefore represent future conditions that are responsible for the human-induced change and natural variability (IPCC, 2007). Climate and climate change scenarios are often used to mean the same in many scientific literatures. However, climate change scenario shows a representation of the difference between the plausible future climate and the current/past climate (IPCC, 2007). A climate change scenario is therefore considered as a useful step toward the construction of a climate scenario. Climate and climate change scenarios are neither predictions nor forecasts of future conditions. They however, describe options of possible futures that follow set of conditions within which they occur (Charron, 2016; Hammond, 1996). The idea behind climate and climate change scenarios is to decrease uncertainty since they are useful in determining possible effects of issues (Fisher, 1996). Climate scenarios have been classified into 3 main types by The International Panel on Climate Change-Task Group on Data and Scenarios Support for Impact and Climate Assessment (IPCC-TGICA 2007). These include; Synthetic scenarios, Analogue scenarios and Climate model-based scenarios. The Synthetic or incremental scenario is employed when climate element is changed by a realistic arbitrary amount from a baseline. It is very easy to create and results can be obtained easily. However, it‘s representation of future climate is unrealistic due to its arbitrary nature. The Analogue scenario also is employed when identifying a climate record that can represent the future climate conditions of a region of interest. This scenario is helpful in both temporal and spatial climate record as analogue of possible future climate. Nonetheless, it is disadvantaged in its temporal and spatial application. This is because, the cause of the past climate change is likely to be different from future climate change and the fact that location is geographically different and which cannot represent future local climate. Climate model-based scenarios employ outputs from GCMs for their construction. They are therefore developed by adjusting a baseline climate (based on a regional observation over a reference period) by the proportional change between the simulated present and future climate. The climate model- based scenarios are highly employed in recent studies. Even with this, however, it still has weakness such as the need for downscaling (IPCC, 2007; Santos et al, 2008).

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2.2.1 Climate Change Scenario Construction for Impact Assessment

Due to the negative and continual impact of climate change and the need for countries to assess future climate change impacts, climate change scenarios are required. To assess the impacts of climate change on Winneba community, temperature, rainfall and humidity data are necessary. The projection of climate change from GCM forms the basis of all climate scenarios for use in adaptation assessment (Jones et al, 2003). This notwithstanding, not all adaptation assessment require climate scenarios. The climate‘s projection is dependent on the future changes in emission and/or concentration of greenhouse gases and other pollutants. These however, are based on the of the IPCC‘s Special Reports on Emission Scenarios (SRES) or the current Representative Concentration Pathways (RCP) published in the Third and Fifth Assessment Report (AR3 and AR5) of the IPCC respectively (Charron, 2016; Jones et al, 2003). The main stages required to provide climate change scenario for assessing the impacts of climate change is shown in figure 2.2.

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EMISSIONS Scenarios from population, energy, economic models

CONCENTRATION

(C02, Methane, Sulphates, etc) Carbon cycles and chemistry models

GLOBAL CLIMATE CHANGE

(Temperature , Rainfall, Sea level etc. ) Coupled global climate models

REGIONAL DETAIL Regional climate models (Mountain effects, extreme weather etc.)

IMPACTS Impacts models (Flooding, coastal erosion, human health etc)

Figure 2.2: The main stages required to provide climate change scenarios for assessing the impacts of climate change.

2.3 Climate of Africa

The climate of Africa is dissimilar and highly variable. It is composed of the arid Sahara desert at one end to the highly humid Congo rainforest at the other end (see figure 2.3). These natural outlines are characterized by the global warming and other human interventions (Collier, 2008). The African climate is characterized by three main processes: tropical convention, the change of the monsoons and the El Niño-Southern Oscillation of the Pacific Ocean. The tropical convention and the change of the monsoons are local processes and are determined by the

15 regional and seasonal forms of temperature and rainfall. The el Niño-Southern Oscillation is more isolated in its origin; nevertheless, it influences the seasonal rainfall and temperature change of Africa (Conway, 2009).

A greater heat is also produced by the Inter-tropical Convergence Zone (ITCZ) (see figure 2.4) and is amongst the major source of the Earth‘s atmospheric warming (Conway, 2009). Dust from the Sahara produces large amount of aerosols and this plays intricate roles on the Earth‘s climate. In some cases, aerosols reflect inbound radiation and cool the planet. Others however, trap heat and add to the greenhouse gas effects. Aerosols in the form of dust can either decrease or increase rainfall. For instance, during low clouds, water attracts dust particles and prevents droplets from becoming heavy enough to fall. However, high clouds attract dust particles and cause heavy rainfall (Brooks, 1999; Nicholson, 1994).

Figure 2.3: Climate zones of Africa. Source: https://geography.name/climate-2/

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Figure 2.4: Inter-tropical Convergence Zone over Africa. Source: Adopted from Van Noordwijk, 1984.

2.4 Climate of Ghana

Ghana, a country in West Africa is located on the Gulf of Guinea, just few degrees north of the equator (UNDP, 2013). Ghana‘s location makes its climate tropical with two main seasons; the wet and the dry season (Igawa & Kato, 2017). Ghana‘s climate is therefore, influenced strongly by the West African monsoon winds (UNDP, 2013). Ghana has four climatic regions namely; the wet-equatorial found in the south-western part of the country, the dry-equatorial in the south- eastern part, the wet semi-equatorial in the middle/transition zone and the dry/tropical continental in the northern part of the country (see figure 2.5). There exist rainfall disparities in these climatic regions. The amount and seasonality of rainfall are controlled by the Inter Tropical Convergence Zone (ITCZ) through the interplays of the tropical maritime winds (West African monsoon) which blow from the Atlantic Coast (Gulf of Guinea) and the dry continental (north east/) winds from the Sahara desert. Thus, the ITCZ oscillates between the north and the south of the Ghana (see figure 2.6). The movement of the ITCZ from the south blows south- westerly moist air from the Atlantic Ocean to the land. The position of the ITCZ in the north of Ghana carries the north-east trade winds (Harmattan) which begins from the Sahara desert. These north-east trade winds bring hot and dusty dry air to the north of Ghana. Continental wind

17 movement in Ghana is therefore, influenced by the position of the ITCZ. The dry/tropical continental and the dry-equatorial climatic regions have single maxima rainfall. Monthly rainfall in the (July to September) for these regions ranges from 150-250 millimetres. The wet and semi wet-equatorial regions have double maxima rainfall (March to July and September to November). Rainfall amount ranges from 250-500 millimetres per month (UNDP, 2013). There also exist disparities in temperature in Ghana. Temperatures are higher in the north and decreases and one descends to the south. In the hottest months of January to April, the dry/tropical continental climatic region records highest temperature of 27-30°C and 25-27°C in the coldest months of July to September. In the dry-equatorial region, the warmest months (January to April) record its highest temperature of 25-27°C and 22-25°C in the coldest months (July to September) (UNDP, 2013).

Figure 2.5: Climate zones of Ghana. Source: Adapted from Google Earth, 2020.

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B A

Figure 2.6: ITCZ position in Ghana in the month of January (A) and July (B) (Black line). Blue arrows show direction of dry continental (North-east trade/harmattan) and tropical maritime (West Africa monsoon) winds on A and B respectively. Source: Adapted from Google Earth, 2020.

2.5 Concept of Climate Change

The definition of climate change is very argumentative. A study by Todorov (1986) on defining climate change reported the concept to be the most complex and controversial in the meteorological science. This notwithstanding, several definitions have been published in the science literature. The United Nations Framework Convention on Climate Change (UNFCCC) eliminates natural climate variability from the definition of climate change. They therefore, define climate change as any direct or indirect modification of climate at a given place as a result of anthropogenic actions and which modifies the atmospheric structure and the climatic variables over time (UNFCCC, 2001). The Intergovernmental Panel on Climate Change (IPCC), a very important institution established by the World Meteorological Organisation (WMO) and the United Nations Environmental Programme (UNEP), however, includes natural variability in its definition. IPCC, therefore, defines climate change as a statistically significant difference in the climate of a given place that continues for a longer period, usually a decade or more thus, changes in any of the climatic factors (temperature, rainfall, humidity, etc.) at a given place over time whether due to natural change or as a result of anthropogenic actions (IPCC, 2007). The

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IPCC, again through its current fifth assessment report, defines climate change as ―a change in the state of the climate that can be identified by changes in the mean and/or the variability of its properties. These changes in the mean and/or the variability persist for an extended period, typically decades or longer‖ (IPCC, 2013). According to the Youth and United Nations Global Alliance (YUNGA, 2016) climate change as a change in the general state of the Earth‘s climate more importantly temperature and rainfall. A report by the Australian Government through the Department of Climate Change and Energy Efficiency (DCCEE) in 2012 attributed climate change to the observed increased in anthropogenic greenhouse gases (DCCEE, 2012).

From the above definitions, it is clear that there exist complexities in the definition of the concept climate change. While many authorities ground their definition on both natural and anthropogenic factors, others base theirs solely on the later. Institutions and authors therefore, ground their definitions on the diverse views expressed by the IPCC and the UNFCCC. In this study however, the IPCC (2007; 2013) definitions of climate change incorporating both natural and anthropogenic variables, has been regarded as the official definition.

2.6 History of Global climate change

A perfect understanding of the history of climate change begins with the transition from the icehouse (ice ages) era to the greenhouse era (which began since the industrial revolution).

2.6.1 The Icehouse (Ice Age) Era

An ice age refers to the extended period of reduction in the temperature of the Earth‘s surface and the atmosphere which results in the expansion of land and polar ice sheets and the alpine glaciers (Ehlers, 2011). In the study of glaciology, ice age simply refers to the presence of extensive ice sheets in both the northern and southern hemispheres (Imbrie et al, 1979). Since the formation of the Earth about 4.6 billion years, there have been at least 5 major ice ages in the Earth‘s history. These include the Huronian, Cryogenian, Andean-Saharan, Late Paleozoic and the recent Quaternary ice age. The Earth however, seems to be ice free outside these ages (Lockwood, 1979; Warren, 2006).

The Huronian ice age extended from 2400 to 2100 million years ago. This period occurred during the Siderian (the first geologic period- 2500 to 2300 million years ago) and the Rhyacian (the second geologic period – 2300 to 2050 million years ago) periods of the Paleoproterozoic (the longest era of the Earth‘s geologic history- 2500 to 1600 million years ago) era (Haoshu and Chen, 2013). This period was characterized by the Great Oxygination Event (GOE). Thus,

20 during this time, atmospheric oxygen increased which decreased atmospheric methane. The combination of oxygen and methane therefore resulted in the formation of carbon dioxide and water which do not retain heat. Earth‘s temperatures then were very low as compared to the current state. The Huronian era of the ice ages is considered the oldest and the longest.

The Cryogenian is a geologic period which lasted from 720 to 635 million years ago (Cohen et al, 2013). It forms the second geologic period of the Neoproterozoic era (1000 to 541 million years ago). During this period, the Sturtian (multiple glaciations- ranged from 717 to 643 million years ago) and the Marinoan (a period of global glaciation which lasted about 650 to 635 million years ago. This is also referred to as Snow ball period) occurred. This ice age era is considered the greatest amongst the 5 ice ages periods. This however, is subjected to several arguments. The key factor to these arguments is whether or not the Snowball (ice covering the entire planet) existed or the sluhball (open seas near the equator) survived.

The Andean-Saharan glaciation occurred during the Paleozoic era (the earliest three geologic eras of the Phanerozoic Eon) and lasted about 450 to 420 million years ago. This happened during the later periods of Ordovician and Silurian (geologic periods and systems which spanned from 41.2 to 485 million years ago). Eyles and Young indicated that there existed polar areas and specifically glaciers in the oldest Ordovician strata in West Africa (Tamadjert- a village in Algeria), Tindouf basin (a major sedimentary basin) in Morocco and in the West- central Saudi Arabia (Eyles & Young, 1994). Eyles and Young, however, reported that these glaciers moved from North-western Africa to South-western America. Thus, glaciers moved from Africa in the Sahara (Ordovician period) to South America in the Amazon of Brazil and Andes of South-western America (Silurian period) (Aber, 2008). Although, this ice age existed, it is considered as a minor ice age period according to Högele (2011).

The Late Paleozoic also referred to as Karoo ice age. This period existed when glaciers were found all over the continent that was part of the Gondwana. These include; African, South America, Antarctica, India, Arabia and Australia (Eyles & Young, 1994). Glaciation in this era lasted about 100 million years and started in the early Carboniferous (360 million years ago), which reached its peak in the late Carboniferous and extended to the early Permian. This period was over by the end of the late Permian (260 million years). There existed many glacial centres in this ice age period and each period experienced continuous glacial advances and retreats. Popular glacial strata include; Dwyka Tillite (Karoo basin) in South Africa, Talchir Boulder Beds in India and Wynyard Formation of Tasmania. In total, two main glacial cycles occurred and

21 there existed gradual expansion of these cycles to about 20 million years before each reaching its maximum in the late Pennsylvanian and early Permian times. Each of these two cycles reached its peak during only 1to10 thousand years (Gastoldo et al, 1996). Glaciations that occurred during the Permo-Carboniferous stages are relevant due to the marked glacio-eustatic changes in sea levels that occurred and which are recorded in non-glacial basins. The glaciation in the late Paleozoic period could therefore be explained by the movement of the super continent across the South Pole (Abate et al, 2015; Eyles & Young, 1994; Soreghan & Giles, 1999).

The Quaternary (Pleistocene) glaciation era started 2.58 million years ago and is on-going (Berger & Lourre, 2000; Ehlers & Gibbard, 2011; Lorens et al, 2004). This glaciation stage is an irregular series of glacial and interglacial period. Geologists see the Quaternary glaciation to be an on-going period since the Earth still has some ice sheets. They however, believe the Earth is presently undergoing interglacial period. Ice sheets appeared during the Quaternary period and expanded in the glacial periods. They however, contracted during the interglacial periods. The Antarctic and Greenland ice sheets have been the main enduring ice sheets since the end of the last glacial period. Other ice sheets such as Laurentide have gone extinct during interglacial. The main consequences of this ice age period have been the incidence of erosion and deposition of materials, changes in river systems and flows; which results in the formation of lakes, changes in sea level, flooding and heavy storms. These consequences, therefore, have negatively affected both lives on land and in the oceans.

2.6.2 The Greenhouse (Industrial Revolution) Era

The need to replaced manual labour with machinery by the Great Britain in the late 18th century and early 19th century, gave rise to the industrial revolution. This was however, evident by the automation of the England‘s textile mills and iron-making. This invention, therefore, led to the usage of more coal than wood. These inventions saw improvements around 1850 and were expanded to other several kinds of industrial machinery including; the usage to power trains, ships etc. Sooner than later, these inventions spread across Europe, United States of America and many other regions. This, therefore, brought about massive changes in people and trade. In the late 19th century, scientists were able to generate electricity and discover oil reserves. This, however, led to the creation of internal combustion engine. This caused a greater change in the lifestyles of humans and their work.

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The world entirely depended on fossil fuels (coal, natural gas, oil, etc.) by the end of the 20th century (Wrigley, 2013). Many scientists believe the results of the intense burning of fossil resources, is increasing concentrations of greenhouse gases that heat the Earth. For instance, carbon dioxide concentrations have increased since the beginning of the industrial revolution (IPCC, 2014). In the 1750‘s, the carbon dioxide content in the atmosphere was 280 part per million for several thousand years and rose to 367 part per million in 1999 (https://www.ipcc.ch). Global usage, however, has increased intensely with the United States accountable for nearly a quarter. This is followed by China and the European Union respectively. Africa on the other hand, has the lowest in terms of carbon dioxide emissions (Roser et al, 2017). Many scientists such as Svante Arrhenius, Al Gore, among others, became concerned about the carbon dioxide emissions. There existed scientific uncertainty as to whether the increasing concentrations of carbon dioxide, were as a result of natural forces or anthropogenic actions. This notwithstanding, carbon dioxide concentrations continued to increase and the Earth heated. This therefore, drew the attention of policy makers, which led to the establishment of the Intergovernmental Panel on Climate Change (IPCC).

2.6.3 The Intergovernmental Panel on Climate Change (IPCC)

The highest global temperature rise recorded in 1988, attracted the attention of several scientists to climate change. This, therefore, incited the world‘s governments to institutionalize the IPCC, an independent panel of global climate scientists. The IPCC was institutionalized as part of the United Nations (UN) and was mandated to examine the existing scientific, technical and socioeconomic proof on human-induced climate change and to give information to the international community about the impacts and the probable solutions. Since inception, the IPCC, have published five (5) assessment reports (AR1-5). The first assessment report in 1990- which was adopted in 1992 by the UN and was, used as a scientific and technical basis for the UN negotiation agreements on global warming (United Nations Framework Convention on Climate Change), the second assessment report in 1995-which led to the 1997 Kyoto Protocol, the third assessment report in 2001- where new and stronger evidence about climate change emerged even though there were still some scientific uncertainties, the fourth assessment report in 2007- this stage revealed the strongest case about climate change and scientists stated that global warming and climate change to be precise is ―unequivocal‖ and that global increases in temperatures are ‗very likely‘ to be as a result of anthropogenic activities such as burning of fossil fuels, the fifth assessment report in 2014 and the sixth assessment report which is expected to be published in 2022( (http:www.ipcc.ch/). The IPCC‘s (AR1-5), indicated

23 anthropogenic activities through greenhouse gas emissions, and is responsible for the global climate change (http:www.ipcc.ch/). Notwithstanding, the apparent conclusions by the IPCC on climate change, a few skeptics exist. Skeptics such as DiMento and Doughman, and Mann and Toles, agree to the fact that the world‘s climate has changed and temperatures increased. However, they believe that the cause to this change is more of natural factors than anthropogenic activities (DiMento & Doughman, 2007; Mann &Toles, 2016).

2.7 Climate variability and change in Africa

Africa‘s contribution to the greenhouse gas emission is very little, less than 3% of the world‘s absolute value (UNDP, 2007). Africa is however, the hardest hit in terms of climate change and variability (Boko et al, 2007; Lobell et al, 2011). The African continent in contemporary times has been warmer than it was 100years ago; the major warming however, occurred since the early part of the 1960‘s (Hulme et al, 2001; James & Washington, 2013). Several pieces of studies over the African continent have revealed that significant changes in both rainfall and temperature and other extreme weather events such as flooding and drought across the continent (Hulme et al, 2001; IPCC, 2007b ; Nicholson, 2001; Nicholson et al, 2000). A study on temperature by James and Washington (2013) again revealed Africa‘s temperatures are projected to increase faster than the global average increase during the 21st century. This notwithstanding, the future warming rate is projected to be between 3°C and 4°C by 2080 to 2099 per the 1980 to 1999 period through the employment of the 20 GCM of the IPCC‘s medium-high emission scenarios (Christensen et al, 2007). The IPCC based on the Regional Climate Projection (RCP) of 2007 reported that Africa‘s average temperatures are expected to rise by 1.5-3℃ (Collier et al, 2008; Gemeda & Sima, 2015).

With a general increase in temperatures across the African continent, rainfall on the other hand has showed significant variability. A study by Williams and Funk on rainfall variability over Eastern Africa revealed a decrease trend over the last 3 decades (Williams & Funk, 2011). A similar study on West Africa by (Hulme et al, 2001; Nicholson et al, 2001) further revealed a decreasing trend in rainfall. Nicholson, however, in his earlier study had attributed the decreasing rainfall trends over West Africa to the El Niño phenomenon, Sea Surface Temperature (SST) and the feedback between land and the atmosphere (Nicholson, 2000). Extreme events such as drought and flooding are dissimilar with some regions receiving higher rainfalls than others (Conway, 2009).

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The coastal areas are at greater threat to the impacts of climate change and variability Aquatic ecosystems are negatively affected due to their sensitivity to the impacts of climate change and variability (Cheung et al, 2010; Ndebele-Murisa et al, 2010). Many aquatic species and even the total coastal ecosystem will be at risk of extinction (Drinkwater et al, 2010). This will therefore have several implications on the various sectors of the African economy. For instance, Lam et al, in 2012, studied the consumption of protein for coastal West Africans inhabitants and found that fish accounts for about 50% of the dietary protein and this, with a 2°C warming higher than the pre-industrial levels however, may reduce (Cheung et al, 2010). Coastal freshwater ecosystem may also be affected by drought and this will lead to nutrient reductions due to reduced river inflows (Ndebele-Murisa et al, 2010). This will result in less ecological production and freshwater fish production will be affected (McDonald et al, 2011). The feedback from this will again be a reduced protein sources and reduced income for the African fishers due to low fish catch (Badjeck et al, 2010). Africa‘s population and West Africa to be precise could be threatened by projected sea level rise and flooding (IPCC, 2007). Besides, coastal population, and infrastructure (Brecht et al, 2012; Hallegatte et al, 2013; Hinkel et al, 2011; Kebede et al, 2012; Neuman et al, 2013) and health (Azongo et al, 2012; Caminade et al, 2014; Lloyd et al, 2011; Smith et al, 2014; WHO, 2009) cannot be ignored due to their high incidence in the coastal areas of Africa. Figure 2.7 shows the observed and possible future impacts of climate variability and change over Africa.

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Figure 2.7: Observed and future possible impacts of climate variability and change over Africa. Source: Adopted from (IPCC, 2007, P.451).

2.8 Overview of Climate Change in Ghana

In Ghana, climate change has added up to the already existing problems of land degradation and deforestation (Badu-Agyei, 2012; Boadi, 2013; Kunateh, 2013; Neville & Mohammed, 2010). Several pieces of studies exist on how the impacts of climate change affect negatively the natural resources such as land, water bodies and forests of Ghana (Ghana Agricultural News Digest, 2012). The climate of Ghana for the past decades has been drier and variable. Resource-dependent sectors including; agriculture, food cultivation, fisheries among others are at greater threats to the impacts of climate change (Ghana Agricultural News Digest, 2012). This notwithstanding, in Ghana, sea-level rise and coastal erosion are the most observed in terms of climate change impacts (Owusu & Waylen, 2012). Besides, the Ghanaian

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Environmental Protection Agency in collaboration with the European Union Delegation in Ghana in 2009 identified ten major areas of potential susceptibility to climate change. These included; land degradation and erosion as already indicated above by Neville and Mohammed (2010), water resources, biodiversity and wildlife, forest reserves, human health, national revenue, tourism, women and the poor, hydropower production and security. These areas, therefore, stands at a greater risk to the impacts of climate change.

From the above, it is evident that climate change incidence in Ghana is a reality. Climate change has a negative influence on all sectors of the Ghanaian economy with the greatest influences felts on the coastal and its associated low lying zones. The country‘s socio-economic and environmental developments therefore, affected. Climate change knowledge and awareness alone, cannot address these negatives influences on the country but a comprehensive national policy framework should be developed through research, technology and innovations in reducing these negative influences.

2.9 Climate variability and change in Ghana

Ghana has become very susceptible to climate change and variability. Most of the Ghanaian lands, about 35% have turned into deserts (EPA, 2008). A study by World Bank on rainfall dynamics in Ghana revealed unusual variability patterns (World Bank, 2010). The World Bank study further revealed declining trends in rainfall over Ghana (World Bank, 2010). In addition, Cameron (2011) used a 20 year rainfall and temperature data and found that, there has been an increasing trend in temperature in all the ecological zones in Ghana while rainfall showed a decreasing trend and becoming progressively unpredictable.

Climate change and variability impacts over Ghana are expected to be high. This notwithstanding, there may be changes in the annual temperatures and rainfall variables. Temperature projections by the World Bank over the period 2010 to 2050 report warming in all zones of Ghana. The greatest warming which is expected to occur in the Northern, Upper East and Upper West with a rise by 2.1-2.4°C by 2050 while the lowest in the Brong Ahafo region with a rise by 1.3-1.6°C. The Central, Ashanti, Western and Eastern on the other hand are expected to have a temperature rise by 1.7-2.0°C by 2050 (World Bank, 2010). Due to the enormous effects associated with climate change and variability in Ghana, several researches on its impacts have been studied. For instance, Codjoe and Owusu in 2011 documented some climate variability incidence in Ghana and the associated experiences. For example: January- July 1976, Ghana experienced very hot weather conditions, 1983-1984; there was drought and

27 a yearlong bushfires, in 1991, Ghana experienced lots of and was throughout the year, very cold winds which occurred in during March to April (Easter) and same from November to January also happened in 2004. Ghana again experienced lots of rains August and September in 2007 (Codjoe & Owusu, 2011). Asante and Amuakwa-Mensah, in 2015, moreover, reported the disastrous floods that occurred in Ghana‘s capital city, Accra in 2012 and 2013 and which recorded several deaths and destroyed properties (Asante & Amuakwa-Mensah, 2015).

The coastal areas and its associated sectors are amongst the greatest hit in terms of climate variability and change, since most of the activities in the coastal areas are dependent on rainfall and temperature variations. Stanturf et al, 2011, in their projections about precipitation in different zones in Ghana and the coastal savannah zone in particular, reported changes in precipitation will range from 52% decrease to 44% increase in the wet season rainfall by the year 2080. Again, sea surface temperature will increase in Ghana‘s waters (Stanturf et al, 2011). This will negatively affect the activities in the coastal areas and others such as coastal expansion and fisheries yield. This notwithstanding, sea level is expected to rise from 0.13m to 0.60m by the late 21st century (Asante & Amuakwa-Mensah, 2015). Stanturf et al, in 201, moreover, employed climate model and three emission scenarios and made temperature projection in the coastal savannah zone from 2050 to 2080 reported that wet season temperature change will be 1.68± 0.38°C by 2050 and 2.54±0.75°C by 2080; dry season changes also will be 1.74± 0.60°C by 2050 and 2.71±0.91°C by 2080. This will continue to obstruct activities in the coastal areas. For instance, marine species and fishes in particular will be affected especially, fish stock and habitat. Increasing and warmer temperature will impact fish stock, migratory pattern and mortality rates and also the cultivation of particular specie at a particular time (Dontwi et al, 2008). All the people whose livelihood is dependent on fisheries therefore suffer the most (WorldFish, 2010).

2.10 Overview of Sea Level Rise

Sea level rise can be defined as the average long-term increase in the global ocean surface which is measured from the ellipsoid (centre of the earth) as obtained from satellite observations (Church et al, 2013). There exist variability in sea level and this variability can range from seconds to decades. Rising sea level therefore, is the averaged increased trend over extended periods and which are observed at several coastal stations. Several pieces of studies reveal that global warming attributable to anthropogenic emissions of greenhouse gases are the major contributing factor that have increased this trend for the last few decades (Church et al, 2013).

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Sea level rise nevertheless, form a fundamental measure of climate change. This is due to its incidence over an extensive range of temporal and spatial scale (Church et al, 2010; Milne et al, 2009).

Several processes influence sea level on the global scales (see figure 2.8). Thermal expansion of the ocean and the transfer of water stored on the land (glaciers and ice sheets) are the main contributing factors of the current sea level change (Church et al, 2011a). Studies have shown that over the recent decades, the ocean has been the largest storage of heat in the climate system. Sea level rise as a result of ocean warming, therefore, forms a vital part of the Earth‘s reaction to increasing greenhouse gas concentrations (Church et al, 2013). Several other climate sensitive factors contributing to sea level rise include; changes in ocean currents, regional atmospheric pressure anomalies, water and ice mass exchange between the land and oceans, vertical earth crust motions and changes in the Earth‘s gravitational field (Church et al, 2013; Dronkers, 2019). Ocean currents, ocean density and sea level variations are interconnected such that a change at one position will affect others that are far from the initial position of change. For instance; changes in sea level at the coast in response to changes in open-ocean temperature (Landerer et al, 2007; Yin et al, 2010). Changes in both temperature and salinity can contribute substantially to regional sea level change (Church et al, 2010), however, changes with respect to temperature contributes substantially to global average ocean capacity change owing to thermal expansion or contraction (Gregory & Lowe, 2000). Regional atmospheric pressure differences again cause sea level to differ through atmospheric loading (Wunsch & Stammer, 1997). These processes influences sea level diversities over a wide range of space and time scales (Miller & Douglas, 2007; White et al, 2005; Zhang & Church, 2012). Also, water and ice mass exchange between the land and oceans contributes to a change in the Global Mean Sea Level (GMSL). This mass however, spread around the globe and all regions experience sea level change at the time of mass addition. In furtherance to this, freshwater inflow changes the temperature and salinity of the ocean and the resultant changes in ocean currents and sea level (Lorbacher et al, 2012; Stammer, 2008; Yin et al, 2009). Besides, water mass exchange between land and the ocean produces sea level configurations (sea level fingerprints) as a result of change in the Earth‘s gravitational field and the vertical motion of the ocean floor and its related crust. This, therefore, affects the Earth‘s inertia and rotation and causing sea- level rise (Conrad & Hager, 1997; Mitrovica et al, 2001; Milne & Mitrovica, 1998). Moreover, human activities and practices that harms underground water or surface water such

29 as lake and other land use changes that influence runoff and/or evapotranspiration rates harm the hydrological cycle and cause sea level change (Wada et al, 2010).

Figure 2.8: Sea level rise processes from global to local scales. Source: https://www.nap.edu/read/13389/chapter/3#14

2.10.1 Causes of Sea Level Rise

Questions on the causes of sea level change is subject to much scientific and public discussion due to its greater impacts and threats posed to countries and coastal inhabitants to be precise (Dasgupta et al, 2008; Micheal, 2007). The causes of sea level change therefore, varies due to the processes that operates on a range of time-scales, from few hours (tides) to millions of years (tectonic movements) (IPCC, 2001). According to Hansen et al (2010), global warming through anthropogenic activities such as burning coal and oil and deforestation increase atmospheric concentration of heat-trapping gases thereby warming ocean and resulting in sea level rise. Siebentritt in 2016, however, indicated that global sea levels rise or fall as a result of thermal expansion and changes in volume of ocean water due to the melting of glaciers and ice sheets and their interactions with land.

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The world‘s oceans play a vital role in the Earth‘s climate system due to their capacity to store and transport large quantities of heat (Church eta al, 2010). Therefore, as the ocean warms, the waters within expands and the resultant sea level rise. The intensity of the expansion however, depends on the amount of heat absorbed and on water temperature, pressure and to some extent salinity (Cazenave, 2010; Church et al, 2010; Lombard, et al, 2005). According to the IPPC, thermal expansion of the ocean was the main cause of sea level rise during the 20th century and which is projected to continue in the 21st century and to the future (IPCC, 2007). Nevertheless, there exist uncertainties about the quantity of contributions by thermal expansion for the future due to the enhanced melting of ice caps and glaciers and ice sheets in the beginning of the 21st century (Cazenave, 2010; Church et al, 2001; Lombard et al, 2005). About two-thirds of global freshwater is stored by land ice (glaciers, ice caps and ice sheets). The land ice however, has lost its storage capacity due to higher temperatures (Trenberth et al, 2007). There has been partial melting of glaciers each summer. This water loss therefore, restores in winter. However, the percentage melt exceeds the restoration. The losing glaciers, ice caps and the ice sheets add up water to the ocean and cause global sea level to rise (Cogley, 2009; Meier et al, 2007). A study by Church et al, in 2011, reveals that lost land ice added about one inch to global sea level from 1993 to 2008 and this accounted for more than half of the increase during that period.

It is evident from the above that sea level rises as a result of both natural and anthropogenic occurrences. However, as indicated by Hansen et al, in 2010, anthropogenic negative activities on the Earth‘s atmosphere particularly for this century have contributed immensely. For instance, the oceans become warm as a result of the harmful emission into the atmosphere by humans. Therefore, proper investigations into the anthropogenic parameters on sea level change should be given equal attention as the physical and natural parameters. This is particularly due to the mutual relationships that exist between these two processes.

2.10.2 Sea Level Rise Projections

There exists strong evidence that the global mean sea level has increased over the past century. This notwithstanding, the increment rate differs from region to region. Sea level rise projections are mostly developed through models of primary processes (physical approaches) and semi-empirical approaches.

2.10.2.1 Physical Models

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The physical approaches/models normally employ processes that contribute to global sea level rise. These processes include; the movement of from melting land ice into the oceans (Chao et al, 2008; Wada et al, 2010), and changes in volume of ocean water mass through warming (thermal expansion) and modifications of the depth of global ocean basin through movement of the Earth‘s crust (Rahmstorf, 2012). Amongst these processes, thermal expansion is the easiest to model. This is due to its compatibility with the General Circulation Models (GCM) of the ocean and the atmosphere. The critical factor to be considered however is the fastness of heat penetration from the surface into the ocean and the exact location. This therefore, results in uncertainty (Rahmstorf, 2012). The melting of land ice is difficult to model due to the large number and variety of glaciers (Radic & Hock, 2010). The semi-empirical models are however, highly employed. There exist uncertainties in the volume and amount of global glacier ice. This notwithstanding, global glacier ice volume is estimated around the range of 24cm to 60cm sea level comparable (Radic & Hock, 2010; Raper & Braithwaite, 2005). Therefore, sea level rise projections by 2100 and the contribution by melting land ice and glaciers may be less than 5cm (Raper & Braithwaite, 2006), around 10cm (IPCC, 2007) or greater than 37cm (Bahr et al, 2009) for reasonable global warming. The fourth assessment report of the IPCC further projected a sea level rise contribution by ice sheets close to zero. This is due to the losing and gaining of ice from the Greenland and Antarctica respectively (IPCC, 2007).

2.10.2.2 Semi-empirical Models

Due to the uncertainties in the physical models, the semi-empirical models were developed. The semi-empirical models explore the associations between observed sea level rise and observed global temperature changes in the past to be able to forecast the future (Rahmstorf, 2012). These models are based on the principle that sea level rises faster as the Earth becomes warmer. These principles are however, based on long term timescales observations. Earlier models by Gornitz and Lebedeff (1987) assumed a linear association between temperature and sea level rise. Rahmstorf (2007) nonetheless, made an improvement and included corrections for the time response features of sea level to temperature forcing as shown below: dH/dt = a (T(t) – T0) (1) Where: - H - is sea level; - T - is mean global temperature;

- T0 - is baseline temperature at which sea level is constant,

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- a - is sea level sensitivity, which measure how much the rate of sea level rises per unit change in global temperature and;

- t - is time. Thus, if temperature increases above T0, sea level will increase indeterminately at the rate determined by the intensity of the temperature rise. Therefore, a linear increase in temperature in time results in a quadratic change in sea level Rahmstorf (2007). There have been several improvements for the semi-empirical models. For instance, Grinsted et al, in 2009, introduced a finite response timescale (r) into the linear equation proposed by Rahmstorf by in 2007 and again introducing a second term (b) for the short term response of sea level by Vermeer and Rahmstorf (2009). A study by Kemp et al, in 2011, reveals that, semi- empirical models provided better data for the past millennium. Rahmstorf in 2010 further argued that semi-empirical models can be used to project future sea level rise from a scenario of future global temperature rise. Rahmstorf in the same study again stated that results from the semi- empirical models are much higher than that of the IPCC by a factor of 2 or more. Thus, semi- empirical models predict more than 1meter rise in sea level by the year 2100 (Rahmstorf, 2010). Even though the semi-empirical models by parameter fitting, provides the observed past sea level rise, it however, limited in that, a simple empirical connection found in the past may not hold for the future. From the above, it is clear that there exist uncertainties in both the physical and semi- empirical models to estimating sea level rise. Numbers on sea level rise from the fourth assessment report of the IPPC are underestimated by many scientists (Horton et al, 2008; Vemeer & Rahmstorf, 2009). Sea level rise numbers still lack accuracy and this affects projections for planning purposes especially in the world‘s coastal areas. Regardless of the inaccuracies in numbers for future sea level rise, global sea level will continue to rise through this century and beyond even if global greenhouse gas emission remains constant. Continuous improvements in both models will help provide accurate number for future sea level rise projection and this will help in the proper planning of the global coastal zones.

2.10.3. Local Sea Level Change

In-depth knowledge about the totality of global, regional and local developments with respect to ocean dynamics and land levels help to understand sea level changes at a specific coast. Coastal planners therefore, are interested about the interaction among global and local sea level rise, regional and local subsidence, as well as disparities in sediment supply in determining the impacts at a particular coast (Rahmstorf, 2012).

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There exist differences in the rate of local sea level change. Local sea level change can therefore, differ from the global mean sea level change for many reasons including: ocean water circulation by wind, gravitational pull by continental ice and vertical land movement (IPCC, 2007). Through ocean by wind, global oceans can maintain their water volume and remain constant. This notwithstanding, there can still be regional sea level changes. This can manifest through both natural changes in the climate system (For example, El Niño/Southern oscillation) and anthropogenic changes (For example, weakening of the Atlantic thermohaline circulation) (IPCC, 2007). Again, land ice reduces its gravitational pull when the ice melts. This poses a great threat to the sea surface. For instance, melting of the Greenland‘s ice contributes greatly to global sea level change. However, a more local effect is felt around the areas of Greenland (Mitrovica et al, 2001). Vertical motion of land also causes sea level to change relative to the land. This can moreover happen through either natural means (such as tectonic processes and the glacial isostatic adjustment (GIA)) or anthropogenic mean (such as groundwater or oil mining that result in coastal subsidence) (IPCC, 2007; Rahmstorf, 2012). Sea level changes can locally differ in few centimetres or more from the global mean sea level changes and even though values and estimates of the global mean sea level changes are important, it is the local sea level change values that are taking into consideration for planning purposes by coastal managers. This nonetheless, is variable depending on the rates of land raise or subsidence. This therefore, makes low lying coastal and deltaic cities more vulnerable.

2.11 Climate Change and Shoreline Change in Ghana

Climate change has become one of the greatest challenges for the sustainable management of the coastal areas of Ghana (Appeaning-Addo & Adeyemi, 2013). Rising sea-surface temperature is expected to increase. Researchers like Stanturf et al, in 200, have projected through models, the rise in sea level by 161mm to 584mm by the end of the 21st century. The Ministry of Environment Science, Technology and Innovation (MESTI) also projected a rise of up to 345 mm by 2080 (MESTI, 2012a). With these projections and the expected increase in land surface temperature, the expected threats abound. Socio-economic activities are expected to be affected by sea level rise in many ways. Fishing boats landing sites and many other archaeological spots may be affected (Appeaning-Addo, 2009; Akyeampong, 2001). Olympio and Amos-Abanyie (2014) also reported the future destruction of several recreational and tourist spots especially in Ghana‘s capital city of Accra by sea level rise. Armah and Amlalo (1998) again revealed several hot spots areas in Ghana where extensive coastal erosion may lead to

34 great changes in the geomorphological features along the coasts and especially in the coastal areas of .

Changes in coastal geomorphological features do not only happen through erosion by sea level rise but also through anthropogenic activities (Dadson et al, 2016; Jonah et al, 2016). Coastal recession has occurred throughout the coastal areas of Ghana. Through the construction of the Akosombo Dam, both the eastern and central coasts of Ghana have been declined (Appeaning- Addo et al, 2018; Codjoe et al, 2020; Jonah et al, 2016; Ly, 1980). The coastal areas of Labadi, Accra after the construction of the dam recorded a retreat with an average of 3 metres per year (m/year). In Keta, before the construction of the Dam, the rate of shoreline retreat was with the average of 3.2m/year. This however, increased to 17m/year after the dam construction and the accompanied sea defence (Angnuureng et al, 2013). A similar study on the same area by Jayson-Quashigal et al, in 2013, revealed an average shoreline retreating rate of 2±0.44m/year. Appeaning-Addo et al, in 2008, employed four different shoreline positions which ranged from 1904 to 2002 in the analysis of the rate shoreline recession of the Accra coast revealed that the Accra coast has been receding by 1.3±0.17m/year. A similar study conducted by Dadson et al, in 2016, on the shorelines of the Central and Western region and and Sekondi to be precise (using three different shoreline positions from 1972 to 2013) indicated the shorelines of these coasts have been retreated. Another study by Jonah et al, in 2016, on shoreline change rates on the Central coast of Ghana and , Cape Coast and Moree to be precise from two shoreline epochs (medium term from 1974-2012) and (short term from 2005-2012) recorded average shoreline change rates of −1.24 m/year and −0.85 m/year in the medium term and short term epochs correspondingly for the area. In addition, Boateng (2012) assessed the physical impacts of sea level rise on Ghana coastlines and indicated the whole eastern coast of Ghana is under threat of shoreline decline.

The benefits we enjoy from the coast are enormous. However, from the above it is clear that a time is coming the coastal areas of Ghana will loss all the benefits it offers its inhabitants. With the projected rise in sea level and the negative impacts it will have on the coasts and its ecosystem, there is an urgent need towards intensive research and development and proper coastal zone management practices. Research and developments should be geared towards the enhancement of the various shoreline change model techniques and application. Shoreline models techniques that will help in better coastal zone management and national policy frameworks should be highly incorporated into coastal zone research.

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2.12 Overview of the impacts of Climate Change

The IPCC‘s fourth assessment report revealed several uncertainties about climate change. The Earth‘s climate system has warmed and is unequivocal (IPCC, 2007). There is strong evidence that global warming is mostly attributed to anthropogenic greenhouse gas emissions and

Carbon dioxide (CO2) in particular (UNFCCC, 2007). Atmospheric concentration of CO2 increased from 278 part per million from the beginning of the pre-industrial era to 379 part per million in 2005. The associated average global temperature for the same period rose by 0.74°C (UNFCCC, 2007). Projections by the IPCC for the 21st century further showed that the Earth will continue to warm by 3°C by 2100. Global average temperature for this same period of projection ranges from a minimum of 1.8°C to a maximum of 4°C (IPCC, 2007).

Climate change will have a continued impact on the environment, and on socio-economic and interrelated sectors such as coastal areas, terrestrial ecosystems and biodiversity, water resources, agriculture and food security and human health (IPCC, 2007). Rising sea levels may pose bigger threat of storm surge, inundation and wave damage to coastal zones and its associated shorelines. Rising temperature will also result in habitat destruction and extinction of coastal terrestrial ecosystem and biodiversity. Rainfall pattern dynamics will again result in severe water shortages, flooding and erosion. Increasing temperature will moreover, alter crop cultivation periods and this may impact negatively on food security. People, especially, inhabitants in the coastal areas will stand the threat of diseases such as malaria and dengue fever and this may be due to the accompanied alterations in the spreading of disease causative agents (vectors) (IPCC, 2007).

2.12.1 Impacts on Coastal Areas

Many vital services are derived from the coast. Despite these important services, global coastal areas have become vulnerable to climate change impacts such as storms, sea level rise, increasing temperatures, erosion, rainfall variability, flooding among others (IPCC, 2007). A study by Bindoff et al, in 2007, revealed a number of essential climate change associated impacts in the coastal areas. Since 1750, CO2 concentrations have dropped ocean surface pH by 0.1 units. Global sea level also increased by 1.7 ± 0.5 millimetres per year throughout the 20th century and global mean sea surface temperature increased about 0.6 ℃ since 1950. These nonetheless, are associated with atmospheric warming in the coastal areas (Bindoff et al, 2007). Many of the world‘s coasts are under threat of erosion and ecosystem losses (Nicholls, 2006). However, there exist uncertainties about these erosion threats as to whether they are

36 being motivated by relative sea level rise due to subsidence or other anthropogenic determiners that causes climate change (Wolters et al, 2005). In addition, aquatic lives also suffer great harm due to the impacts of climate change. For instance, increase in sea surface temperature poses harm to coral reefs, other actions especially anthropogenic activities such as over-fishing speeds up thermal stresses of reef system (Buddemeier et al, 2004). Increased concentrations of CO2 in the coastal zones are being absorbed by the oceans. This leads to ocean acidification and poses harm to the different kinds of marine species found in the ocean (IPCC, 2007).

Coastal landforms and vegetation are again under threat of climate change. Majority of the world‘s beaches, shorelines and cliff coasts have been retreated during the past century and climate change impact and sea level rise is the precise cause (Leatherman, 2001). Mangroves, wetlands and saltmarshes are further at risk. Wetlands are subtle to climate change and long term sea level change. Fluctuations in storm strength can affect wetlands due to its influence on both surface and subsurface soil developments (Cahoon et al, 2003). Saltmarshes are likely to be affected by climate change through alteration of hydrological systems especially the nature and unevenness of hydro period and the number and rigorousness of extreme events (Sun et al, 2002). Sea level rise on the other hand does not certainly lead to loss of saltmarsh areas particularly, where there are major tides, because marshes accrete steeply and maintain their height comparative to the sea level where there is enough supply of sediment (Cohoon et al, 2006). Coastal mangrove reactions to climate change are both positive and negative. Positive reactions result from growth due to CO2 concentrations and temperature. The negative reaction however, is the increased intrusion of saline water and erosion (Saenger, 2002). Groundwater heights play a substantial role in the height of mangrove soils by process affecting soil shrink and swell. The effect of hydrology should therefore be taking into consideration when assessing the impacts of climate change on mangrove systems (Whelan et al, 2005).

2.12.2 Impacts on Freshwater Resources

Water plays essential role in the lives of both humans and other species in the ecosystem. We need freshwater to sustain our health, for agriculture, energy production, navigation among others (USGCRP, 2014). Climate change impacts on freshwater resources (both surface and underground) have been one of the major impacts felt by humans, the ecosystems and other sectors (NECASC, 2016). The fourth assessment report of the IPCC, revealed warming over numerous periods has been associated with dynamics in the large scale hydrological cycle like increasing atmospheric water vapour content, alterations in precipitation patterns, intensity

37 extreme and changes in soil moisture and runoff (IPCC, 2007). The direct impact of climate change on freshwater resources arrives basically from sea water intrusion into surface waters and coastal aquifers. There is also the extension of seawater into lagoons, estuaries and other coastal river systems, widespread coastal inundation and flooding and increased coastal erosion (Hay & Mimura, 2005; IPCC, 2007).The world‘s water challenges attributed to climate change are highly felt in developing countries with a higher percentage of coastal lowland, arid and semi-arid coast and coastal megacities (Ragab & Prudhomme, 2002). Considering the Special Report on Emissions Scenarios (SRES), it is projected that increased water stress would result in a vital impact by 2050‘s when the diverse SRES impacts on population are revealed (Arnell, 2004). Nonetheless, irrespective of the application of the scenarios, dire areas such as the coastal areas will have higher feeling to water stresses perhaps through increased water withdrawal or decrease in water availability (IPCC, 2007).

2.12.3 Impacts on Agriculture and Food Security

The impacts of climate change are highly felt on agriculture and food security (Eastering, 2003). Despite the anthropogenic interferences such as and habitat destruction, climate variability and change also impact negatively on fisheries, coastal areas and other estuarine waters. Global increase in agricultural technologies should contribute greatly to food production in coastal area. However, the impacts of climate change on coastal areas may affect food production regionally or locally (Maracchi et al, 2005). The stability and sustenance of food systems especially in the coastal areas are under threat of climate change (Nelson et al, 2010) and climate change has the potential to raise the food market instability for both production and supply (Wheeler & Braun, 2013). Climate change impacts agriculture and food security in diverse ways. Food production (agriculture) is affected directly through changes in agro-ecological conditions and indirectly through supply and distribution of income. The variability in both global and regional weather and their associated extreme actions such as cyclones, floods and drought their negative impacts on food stability, supply and security are greater in the coastal areas (Schmidhuber & Tubiello, 2007). Increase in temperature can also slow the growth cycle of crops and more recurrent extreme climate actions and higher rainfall intensity and extended dry periods may impact negatively on crop yields (Olesen et al, 2006). In furtherance to this, increase sea levels affect agriculture and food security especially in the coastal areas. Increase sea levels causes inundation and soil salinization and this affect less saline resistant crops such as mangoes and cashew (Wassmann et al, 2004). Future climate change impacts on agriculture and food

38 security will therefore be greater in the coastal areas than many other sectors. However, there may be regional or local variations on these impacts (Brander et al, 2003a; IPCC, 2007).

2.12.4 Impacts on Human health, Settlement and Infrastructure

Health refers to the state of physical, mental, social and psychological well-being of people (Confalonieri et al, 2007). The healthiness of the global population is the ultimate goal of sustainable development. Human experiences to climate change impacts are both direct and indirect. Direct are experiences through dynamics in weather patterns and indirectly through changes in water, air, food value and amount, environments, food production, livelihoods and infrastructure. The outcomes of these direct and indirect experiences are deaths, incapacities and miseries (Confalonieri et al, 2007). Climate change impacts health in many ways; through temperature and rainfall dynamics such as heat waves, floods and drought results in instant mortality effects and mental disorders (Ahern et al, 2005; Haines & Patz, 2004). Temperature and dynamics again can affect the spreading of disease vectors such as malaria and dengue and the resultant diarrhoeal diseases (Langner et al, 2005). Climate change also has the possibility to impact negatively on the biodiversity that humans depend on for health (Haines et al, 2006). There is no doubt that global population health has increased greatly over the last 50years. The increase in global average life expectancy at birth since 1950‘s (WHO, 2004b; WHO, 2003b), nonetheless, the increment has not been on equal basis for every country across the globe. Global inequalities in health have existed within and among countries (Marmot, 2005; McMicheal, et al, 2004). Life expectancy has reduced greatly in the last 20years in some parts of Africa due to the consequences of HIV/AIDS (UNDP, 2005). Even though the impacts of climate change are highly felt on all sectors of the global economies, the most affected are the coastal areas and their inhabitants. Coastal inhabitants, especially, those in the developing and other low income countries have become susceptible to several health impacts as a result of climate change (Haines et al, 2006; IPCC, 2007). The potential impacts of climate change on coastal population will be highly determined by the future health status of the population and their ability to cope with climate hazards and control of infectious disease (Kjuellstrom et al, 2014; McLaughlin et al, 2006). Many coastal areas have in recent time‘s undergone change in land use. There has been greater transition of coastal lands from basically being used for agriculture to being used for residential, commercial and touristic purposes (Cashman & Nagdee, 2017). Low lying coastal areas have been heavily concentrated with infrastructure and these are prone to many natural threats. The impacts of these are however, intensified by climate change and sea level rise

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(Fitzpatrick et al, 2015). Coastal settlements and infrastructure are affected by climate change and sea level rise in various ways. Water levels in coastal areas are being elevated by sea level rise with resultant saltwater intrusion, which affects coastal water supply (IPCC, 2007). The destruction of the coastal protective landforms such as wetlands, beaches, cliffs among others poses greater threat to the natural defence of coastal areas. Increasing concentration of people and urbanization which results in increasing demand for water and other and other coastal development, poses extra effect on these protective natural defence system (Cashman & Nagdee, 2017; IPCC, 2007; Liu et al, 2019). There exist better knowledge on flooding as a natural disaster and the effect of climate change and other factors that are possible to cause flooding in the coastal areas (Hunt et al, 2002). Nonetheless, the difficulties arise from the forecast of a specific location of the flood. Global population especially Asia and Africa may have a continuous problem with coastal flooding due to the large projected population growth in addition to sea level rise (Nicholls, 2006). Continual improvements in flood forecast and prediction mechanisms and education should be highly encouraged especially in the above mentioned regions of the world.

2.13 Impacts Climate change on the Coastal Areas of Africa

Of all the continents in the world, Africa has the longest coastal area with high population density and several livelihood opportunities. Majority of the coastal areas of Africa are low lying and composed of West, Central, East and Mediterranean. Major cities in these areas include; Accra, Abidjan, Lagos, Tripoli, Algiers, Tunis among others (Ibe & Awosika, 1991). African coastal cities are characterized by their increasing population, industries, transportation, tourist and the rich biodiversity that provide various services to human. Despite the great potential of African coastal areas to socio-economic development, they are being challenged with extensive erosion, flooding, ecosystem destruction etc. (IPCC, 2007; Ibe & Awosika, 1991; Serdeczny et al, 2017). All these challenges are exacerbated by climate change and sea level rise and several other anthropogenic actions (Serdeczny et al, 2017). There exists strong evidence on the susceptibility of the African coasts to climate change impacts (IPCC, 2014; Niang et al, 2014). Observed climate change impacts include; increasing temperatures, rising sea levels, dynamics in rainfall patterns (Conway et al, 2008; Nkomo & Nyong, 2006). The anthropogenic actions such as sand mining, destruction of the natural ecosystem through habitat destruction for residential and commercial purposes and other activities that results in greenhouse gases emissions and pollution and their attendant effects on the African coastal areas cannot be ignored (Serdeczny et al, 2017). A study by Russo, et al, in 2016, demonstrated the severity

40 and degree of temperature increment and the associated heat waves and especially in the Eastern African coastal areas (Dosio, 2017). A continual increase in temperature over the African continent and specifically in the coastal areas may create additional threat to the aquatic species and humans (Serdeczny et al, 2017). Though the annual mean changes in sea level rise on the African coastal areas are small, this occurrences may pose greater threat to erosional processes in the coastal low lying areas (Henkel et al, 2012; Brunn, 1962). Coastal flooding which is already a big challenge in some parts of Africa such as Western and Central Africa may get worse with the accelerated sea level rise (Schellnhuber et al, 2014). Increase sea level also poses harm to coastal groundwater resources due to the shallow nature of the water table in the coastal areas. Coastal underground waters in Africa are therefore subjected to contamination and pollution (Malherbe et al, 2018; Yusuf & Abiye, 2019). Dynamics in precipitation over the continent and sub-regions again comes with their own effects. Areas with high precipitation may encounter problems such as flooding, erosion, displacement of people, and disruption of transportation and communication networks. This may however be due to the weak defence and protective mechanisms to flood prevention in Africa and its sub-regions (Sillman et al, 2013). Areas with low precipitation may also face problem with drought (Zomer et al, 2008).

2.14 Impacts Climate change on the Coastal Areas of Ghana

Several benefits are derived from the coastal areas of Ghana and this has led to the increasing concentration of people in the zone. Despite the enormous benefits, the coastal areas of Ghana have become susceptible to the impacts of climate change (Owusu & Waylen, 2012). The observed impacts of climate change on Ghana‘s coastal areas include; increasing temperatures, rainfall changes and sea level rise (Ankrah, 2018). These impacts however, poses greater threats such as erosion, flooding, saltwater intrusion into lagoons and estuaries, salinity of underground water, shoreline changes etc. (Appeaning Addo et al, 2011; MESTI, 2012a; Schneider et al, 2007). Coastal vulnerability studies carried out by the Ministry of Environment, Science, Technology and Innovation (MESTI) of Ghana revealed the current and projected impacts of climate change and their potential threats to inhabitants in drought and flood prone coastal areas (MESTI, 2012a). A 30year temperature and rainfall projection for 2020, 2050 and 2080 on the coastal areas and other agro-ecological zones of Ghana showed a varying trend. Temperatures are expected to change by 0.6°C, 2.0°C and 3.9°C for 2020, 2050 and 2080 respectively while rainfall is expected to decrease and especially in the coastal areas between 1.1% and 3.1% in 2020 and further projected decrease between 13% and 21% in 2050

41 and 2080 respectively. Sea level projections for the same projected periods also showed and increasing trend. Sea levels are likely to increase every decade by an average of 3mm from 36millimeters by 2010 to 345millimeters by 2080 (EPA, 2011). Coastal ecosystems and other resources are at greater risk. Aquatic lives are endangered due to climate change impacts and increasing temperatures in particular. Endangered aquatic lives however, to a greater extent, can be attributed to the harmful activities of inhabitants such as the use of chemicals for fishing in coastal inland waters (Afoakwah, et al, 2018; Eric, 2018; Mutimukuru-Maravanyika, et al, 2013). Increasing temperatures on the Atlantic Ocean and the Gulf of Guinea in particular has led to the movement of marine fisheries into the deep oceans beyond the affordability of the local Ghanaian coastal fishers. The livelihoods of these coastal fishers are greatly affected due to their reliance on fishing for living (Ankrah, 2018; Freduah et al, 2018; Nunoo et al, 2015).

Several changes are evident in the Ghanaian coastal areas due to the impacts of climate change. These impacts and the associated effects are observable and on the rise due to the physic-social processes in the coastal areas. The worry should be about the incidence however, the coping, adaptation and mitigation mechanisms should highly be strengthened.

2.15 Summary of Chapter

This chapter reviewed related scientific literature on climate change and sea level rise impacts on the coastal areas. Review of scientific literature on the above mentioned begun from the global perspective to the continental Africa and to the Ghanaian coastal areas.

Looking at the global increases in greenhouse gases emissions and the effects it poses to the atmosphere, the oceans and the land, there was the need to first look into the Earth‘s climate, its system and what its composition. Many researchers and institutions have come up with the definition of climate. However, the IPCC‘s 1996 definition has been accepted in this study. Several researchers, in their pursuit to describing the climate system and its components, omit the large footprints and impacts of humans on the climate system. Gettelman and Rood in 2016 addition of the anthroposphere is highly utilized and supported. The African climate looks different mutable and is characterized by the tropical convention, change of the monsoons and the El Niño-Southern oscillation of the Pacific Ocean. The ITCZ on the African continent influences the African climate greatly and the contribution to global warming. In Ghana, the position of the ITCZ determines the amount and seasonality of rainfall and other climatic variables.

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Several processes, for instance, increased emissions of greenhouse gases and the significant influence of the ITCZ on the African climate causes variability of the Earth‘s climate resulting in climate change. The definition of climate change even though argumentative, the IPCC‘s (2007; 2013) definitions which incorporates both natural and anthropogenic variables (with the same happening in Ghana and especially the coastal areas) have been accepted as the official definition in this study. On sea level rise, many researchers and institutions (Cazenave, 2010; Cogley, 2009; IPCC, 2007; Siebentritt, 2016) attribute the causes to thermal expansion of oceans, changes in volume of ocean water due to melting of glaciers and ice sheets and their interactions with land. Studies by (Hansen et al, 2010; IPCC, 2001) on anthropogenic and tectonic movements respectively however, cannot be ignored. Even though global sea level change values are of great importance for projections, it is the local sea level change values that are highly utilized by coastal managers for planning purposes. For instance, in Ghana where sea level rise has contributing greatly to shoreline changes and other effects in the coastal areas and retarding sustainable coastal developments, local sea level change values are what is needed to be developed with references to the global values and looking at the impacts for coastal development.

The IPCC‘s fourth assessment report revealed several uncertainties about climate change. The Earth‘s climate system has warmed and is unequivocal. Climate change will have a continued impact on socio-economic, terrestrial ecosystems and biodiversity, water resources, agriculture and food security and human health in the coastal area. The solutions to these impacts however, may be the need for extensive coastal zones researches, lifestyle changes and increased adaptation and mitigation measures.

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CHAPTER THREE

STUDY AREA

3.0 Introduction

This chapter described the physical and socio-economic characteristics of the area that are of significance to the study. Physical characteristics described included: location, climate, geology, geomorphology, vegetation and soils of the area while that of the socio-economic included: population, culture and occupation.

3.1 Study area: Location and Population

Winneba, the capital of the Effutu Municipality is found 56 kilometers (35 Miles) west of Accra, the capital city of Ghana and 140 kilometers (90 Miles) of the ‘s capital Cape Coast. Winneba is located on latitude 5° 20′ N and longitude 0° 37′ W along the Gulf of Guinea (Figure 3.1 is the geographical map of the study area). The total population of Winneba as off 2012 was 58,750 (ghanadistricts.gov.gh). Winneba has majority of its population being economically active 56.2% (GSS, 2014). Fishing (photo 3.1) and farming are the major economic activities in Winneba followed by services. Traditionally, Winneba is known as ―Simpa‖ and this was acquired from their historic leader ―Osimpa‖. Winneba also traces its route from the European sailors who were being frequently assisted by favourable wind to sail along the bay. From the Europeans‘ actions ―Windy bay‖, the name Winneba was coined (GSS, 2014). Winneba has paramouncy with 72 shrines and groves. The popular amongst these shrines is Penkye Otu. The Aboakyir Festival (Deer hunting) is the popular festival in Winneba which is celebrated to pacify Penkye Otu.

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Figure 3.1: Municipal map of Effutu (Winneba). Source: Geographic Information Systems, Remote Sensing and Cartography Section of the University of Education, Winneba, 2019.

A B

Photo 3.1: People waiting to buy fish from fishermen (A) and fishermen arriving from fishing (B). Source: Field Survey, 2020.

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3.2 Climate

The climate of Winneba is tropical and lies within the dry-equatorial climatic zone of Ghana (Dadson, 2012) and the coastal savannah ecological zone division (Klutse et al, 2014). (Figure 3.2 is the map of the ecological zones of Ghana).The area is associated with low rainfall and long dry season of 5 months. It has double maxima rainfall regimes. There exist disparities in the amount and seasonal distribution of rainfall. The highest rainfall is recorded between April and July with June as the month for the highest rainfall and the minor between September and October. The area has relatively low annual mean rainfall. Annual rainfall therefore, averages 800millimeters (Klutse et al, 2014). Mean temperatures ranges from 22°C to 23°C. Rainfall amount in Winneba is highly influenced by the southwest monsoon (tropical maritime) winds and the position of the ITCZ. Winneba falls with the dry-equatorial zone of Ghana and records less rainfall due to the presence of few isolated hills which cannot cause orographic rainfall to the area, the nature of the coast and the presence of cold waters off the cape three points eastwards which cools moist warm air (Acheampong, 1982; www.meteo.gov.gh/). The variation in annual temperature is 3°C. Mean monthly temperatures range from minimum of 24°C in July and August to maximum of 28.9 °C in March. January and June are the driest and the wettest month respectively. The coldest month is August and March as the warmest month (Climate- Data.org). Relative humidity can be as high about 95-100% in the night the month of August.

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Figure 3.2: Ecological zones map of Ghana showing Winneba. Source: Geographic Information Systems, Remote Sensing and Cartography Section of the University of Education, Winneba, 2019.

3.3 Geology and geomorphology

Winneba is a relatively low lying area with granite rocks and isolated hills. Figure 3.3 is the elevation map of Winneba. The Ayensu and the Gyahadze rivers are the biggest rivers that drain the area. There exist diverse features along the coast. The coastal zone of Winneba can be divided into three portions; the eastern, western and central portions (Figure 3.4 is the map

47 showing the three division of the Winneba coastline). The eastern portion is bordered by the Ayensu River with some isolated hills. The rocks found in the river area are quartz schist. The river is surrounded by the Winneba wetlands. Transport of sediments into the coast and the sea is great during rainy season when the river overflow its bank.

The western portion is bordered by the Muni-Pomadze Ramsar site which comprises the Muni Lagoon, the neighbouring flood plains and the nearby sandy beach on the seafront. The catchment area of the Muni-Pomadze Ramsar is gentle undulating plain and is bordered to the north and north-east by the Yenku hills, a maximum height of 290 meters and in the south-west by the Egyasimanku hills, a maximum height of 205 meters. There exists a fairly steep slope up to 20% grades along the face of the lagoon. The slopes of other features of the lagoon are not more than 8% with majority having slopes less than 5%. The western part of the site is characterized by complex rocks from the Upper Brimian, which consists of extrusive and hyperbyssal rocks. The central part of the site is also characterized by lagoonal deposit of sand. The lagoon is situated behind a sandbar and its formation is due to the strong shore littoral drift of sand in west to east direction (Gordon et al, 2000). The surface area of the lagoon is about 3000 ha and is shallow, saline and semi closed in nature.

The central portion consists mostly of a continuous sandy beach with few rocks. This portion is highly utilized by the inhabitants. It is used a landing site for canoes of fishermen, a waiting ground for fish buying, maintenance of fish nets by fishermen and a place for recreation and leisure. However, the erosion rate of this portion is very high due to swash and backwash actions of wave breaks. The swash action is very strong as it carries sea waters through waves and advances towards the coast. The sandy beach along the shoreline of this portion sometimes acts as a buffer and soaks sea waters. However, dependent on the intensity of the wave break and the resultant swash, the sandy beach losses its resistive capacity. The rocks found in this portion have similar characteristics as those in the western portion (The Ramsar site). Hyperbyssal rocks are found on this part. Erosion is less in this zone. This is due to the resistive capacity of the rocks which prevent the swash actions of wave breaks. Nevertheless, this continuous action will result in cracks along the rocks surfaces and increasing temperatures in the area (Ankrah, 2018); will result in the weathering of these rocks through the freeze-thaw process (The Geological Society of London, n.d). These processes will breakdown the rocks (over 100 years), losing its resistive capacity and the incidence of erosion on the part.

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Figure 3.3: Elevation map of Winneba. Source: Geographic Information Systems, Remote Sensing and Cartography Section of the University of Education, Winneba, 2019.

3.3.1 Sediment fluctuations

The major sources of sediments to the ocean include cliff erosion, river erosion and seabed erosion. There are others that also results from transport through tidal inlets, wind, cross-shore, bio-geneous deposition and biological activity (Appeaning Addo, 2011; Twenhofel, 1932, 1939). Annually, the transport of sediments to the ocean is 109 tonnes per year. The total amount of particulates and dissolved substances from continental sources is 26 x 109 tonnes. Sediments transport through river erosion is about 85% of the total solid materials that enter the ocean. Sediments from these agents are reallocated ocean-wide by wave action and currents from wave breaks dynamism. However, bulk of these sediments is deposited in the coastal areas and on the continental shelf.

The transport of sediments from rivers in Winneba reaches their peak during the rainy season. Most of these rivers including the Ayensu, Gyahadze and Pratu, Ntakofa and Muni are connected to the ocean and the later which is connected to the ocean through Muni lagoon. Ghana‘s lagoons are in two forms: open and the closed lagoons (Gordon, et al, 1998). There is a continuous supply of sediment into the ocean by the open lagoon while the closed lagoons supply sediments into the ocean during rainy season when they overflow the banks (Appeaning-

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Addo, 2011). The Muni lagoon is semi closed in nature and occasionally transports sediment into the ocean especially during the month of June when rainfall in the area becomes intense. There exist an open barrier breach with active transport and sediment accretion in the lagoon. There also exists tidal inlet with small spits on each side that enters into the lagoon (Davies- Vollum et al, 2019). There also exist substantial amount of sediment transport form the Ayensu River into the Winneba floodplains (wetlands) into the ocean during the rainy season.

Eastern Portion

Central Portion

Western Portion

Figure 3.4: Map showing the 3 divisions of the Winneba coast. Source: Adapted from Google Earth, 2018.

3.4 Biodiversity

Winneba lies in the coastal savannah vegetation zone of Ghana. Habitat types found in the area include; the floodplain grassland, the degraded forest and the scrubland (cultivated areas and the sand dune). The floodplain (wetland) grassland is highly diverse and is season dependent (see photo 3.2). Vegetation found in the seasonally flooded areas composes of Sesuvium sp. and Paspalum sp. The drier area is primarily grassland, containing species such as Imperata sp., Cyperus sp., Dactyloctenium sp. and Panicum sp. The degraded forest and the scrubland have a combination of uneven grasses and sedges such as (Vetiveria sp., Fimbristylis sp.,

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Brachiaria sp.,Sporobolus pyramidalis and setaria), trees ( Eucalyptus and cassia) and shrubs (Borrelia,Abutilon and Gymnema). The sand dune area separates the lagoon from the sea and the vegetation in this zone is mostly grasses like Sesuvium portulacastrum, Paspalum virginicum and Sporolobus virginicus and Remirea, Euphorbia and some coconut palms (CBD, 2010; Ntiamoa-Baidu & Gorden, 1991). Photo 3.3 shows the tree types and grasses found in the floodplain grassland and degraded forest and scrubland.

The Winneba coastal zone supports an estimated water birds population of about 23,000. This consists of 27 species of waders, 8 terns and 7 herons/egrets (see photo 3.4). The zone and the Muni lagoon area in particular are very essential for terns. Birds‘ population along the shore are highest in the Winneba coastal area during August to April. This happens when the palaeartic migrant birds reaches the coast of Ghana (Grimes, 1974; Ntiamoa-Baidu & Gorden, 1991; Ntiamoa-Baidu, 1991).

A B

Photo 3.2: Vegetation found in the seasonally flooded area of the Ayensu River (A) and season dependent floodplain wetland (B). Source: Field Survey, 2020.

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A B

Photo 3.3: Trees types and grasses found in the grassland habitat (A) and degraded forest and scrubland (B). Source: Adopted from Gordon et al, 2000.

A B

Photo 3.4: Tern (A) and Egret (B) populations found in the Winneba wetland area. Source: Photo of Tern adopted from CBD, 2010 and photo of Egret was taken through field survey, 2020.

3.5 Soils

Soils differ in landscape and the parent rock from which they were formed. The soils found in the Winneba coastal area are mainly clays and fall under the tropical black earth soil type of Ghana (Ntiamoa-Badu & Gordon, 1991). Majority of the soils in the area were formed from the Dahomean acid schists and gneisses, upper birimian rocks and from the river alluvium (Adu & Asiamah, 1992). Soils derived from schists snd gneisses are pale-coloured sand covering

52 gravelly sandy loam to sandy clay and calcareous clays. This soil type is less cultivated in the area. Soils developed from the upper birimian rocks (composed mainly of volcanic lavas, schists, phyllites and greywackes) are found in the isolated hill area especially the Yenku hills. This soil is shallow and occurs at the peak of the hill. The soil is well drained and consists of about 10cm thick dark brown loam topsoil covering reddish brown fine sandy clay loam with several pieces of decaying rocks. Due to the shallowness of this soil, it is unsuitable for cultivation (CSIR, 2013). Soils derived from the river alluvium are found along the Ayensu river area. Most of these soils are found at the external limits of the river floodplain. These soils vary in texture and are composed of dark brown fine sandy loam topsoil covering reddish brown or yellowish red sandy clay. On the wider flat alluvial floodplain, the soils are poorly drained grey clays. The topsoil is dark greyish brown loam covering a greyish brown clay loam (CSIR, 2013). Soils in this area are under threat of flooding. They have slow interior drainage, surface run-off, permeability and high water holding capacity. Generally, soils in the Winneba coastal area are mainly clays which are impermeable to water and liable to erosion. The soils are also not well suitable for cultivation of cash crops like cocoa and cashew in Ghana. With abundant and intensive irrigation and fertilizer application however, several important crops may survive. Photo 3.5 A and B shows soil of Winneba.

B A

Photo 3.5: Soil type of Winneba. Source: Field Survey, 2020.

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CHAPTER FOUR

ANALYTICAL FRAMEWORK AND METHODOLOGY

4.0 Introduction

This chapter deals with the frameworks and methodology employed in the study. The IPCC methodologies are utilized and their general significance to the study is emphasized. An analytical framework resulting from the IPCC fourth assessment report (AR4) and fifth assessment report (AR5) frameworks are provided to help understand the overall issues in this study. The methodological approaches that ground this study have also been reported. Detailed description of the processes and tools needed to achieve the objectives of the study has again been presented in this section.

4.1 Conceptual Framework

In climate change impact assessment, the most important factor to consider is the reduction of risks to social and natural systems (Sharma & Ravindranath, 2019). The IPCC framework for vulnerability which is assessed in both 2007 (AR4) and 2014 (AR5) reports is highly utilized in this study. This is shown in (figure 4.1). The AR4 framework defines vulnerability as ―the degree, to which a system is susceptible to, an unable to cope with, adverse effects of climate change, including climate variability and extreme‖ (IPCC, 2007). From this definition, vulnerability assessors consider indicators such as exposure, sensitivity and adaptive capacity (old paradigm) in their assessment (Sharma & Ravindranath, 2019). The AR4 framework sees exposure, sensitivity and adaptive capacity as co-factors of vulnerability. Several studies (Kumar et al, 2016; Lung et al., 2013; Simane et al, 2016) have employed the AR4 framework. The AR5 framework on the other hand accepted the IPCC 2012 special report risk mitigation from extreme events and disasters to improve climate change adaptation. The 2012 special report further shows risk resulting from the interfaces of hazard, exposure and vulnerability (Sharma & Ravindranath, 2019). The AR5 adoption of this therefore, defines vulnerability as ―the propensity or predisposition to be adversely affected‖ (IPCC, 2014). The AR5 framework however, does not see any link between vulnerability, exposure and hazard (Sharma et al, 2013). Vulnerability assessors that employ the AR5 framework choose indicators that represent sensitivity and adaptive capacity of the system and with this framework termed as (new paradigm) (Sharma & Ravindranath, 2019). In both frameworks, exposure is keen. It is therefore, seen as hazard in AR4 framework (Jurgilevich, 2017). The AR5 framework on the other hand sees exposure as ―the presence of people, livelihoods, species or ecosystems,

54 environmental functions, services, and resources, infrastructure, or economic, social, or cultural assets in places and settings that could adversely be affected‖ (IPCC, 2014). Under the AR4 framework, exposure, sensitivity and adaptive capacity are functions of vulnerability. Exposure in the AR5 framework however, is seen as an independent element of vulnerability (Sharma & Ravindranath, 2019) and is recognized as a spatial concept (Jurgilevich, 2017).

There exist differences in both the AR4 and AR5 frameworks and both frameworks have been employed differently in several studies; AR4 framework (Hahn et al, 2009; Kumar et al, 2016; Lung et al, 2013; Simane et al, 2016) and AR5 (Sharma & Ravindranath, 2019; Sharma et al, 2017; Shukla et al, 2016). Both frameworks however, have been adopted in this study.

Exposure Sensitivity

Sensitivity Adaptive Capacity

Potential Impact Adaptive Capacity

Vulnerability Vulnerability IPCC AR4 framework IPCC AR5 framework

Figure 4.1: Framework on vulnerability shown in the IPCC AR4 and AR5 reports. Source: Adopted from (Sharma and Ravindranath, 2019).

4.2 Analytical framework

Base on the frameworks adopted in this study, an analytical framework for data analysis and synthesis has been developed. This is shown in (Figure 4.2). Due to the location of the Winneba community, climate assessment was considered on both land (terrestrial) and the ocean. The resultant impacts from this incidence were analysed in both cases. This helped in the proper assessment of the climate change impacts on both inhabitants and on the coastal ecosystem in general. Land (terrestrial) climatic variables employed include; temperature (maximum and minimum), rainfall, and relative humidity (maximum and minimum). Oceanic variable employed were sea surface temperature (SST) and sea surface salinity (SSS).

On the impacts assessment, data from the above mentioned climatic variables were analysed and the consequences on socio-economic systems (human health, economic activities and

55 infrastructure) and the coastal systems (fish catch, coastal erosion, coastline change and salt water intrusion into inland waters) were assessed. To understand properly the climate and climate change incidence on the Winneba community, coping and adaptation strategies resulting from the climate change impacts, even though, not a primary objective in this study was analysed. This is particularly due to the important role that adaptation plays in the coastal risk management.

Climate Systems

Land Climate Change Ocean

Temperature, Sea Surface Rainfall and Temperature, Sea Impacts Relative Humidity surface Salinity

Socio-economic Coastal and Marine (Inhabitants) Shoreline Change Ecosystems

-Human health -Sea erosion -Fish catch (Diseases) -Infrastructure -Salt water intrusion - Economic activities

Figure 4.2: An Analytical framework employed to analyse study results.

4.3 Methodology

4.3.1 Data Sources, Type and Validity

Data important to the study were gathered from relevant agencies and institutions. Primary, secondary and tertiary data sources were employed in this study (Table 4.1). Primary data were sourced through field measurements and field observation. Secondary data were also sourced from agencies and institutions such as Ghana Meteorological Agency (GMA), the Ghana

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Fisheries Commission and the Effutu Municipal hospital. Tertiary data were also sourced from Google Earth and the Canadian Earth System Model (Can ESM2). Validity assessment was carried out specifically on the secondary data utilized in the study. All secondary data were provided in Microsoft Excel format and validity assessment was carried out to see whether there were some missing values and the need to normalize them.

Table 4.1: Data sources and information collected

Data Sources Information Collected

Primary data a. Field measurement (SW map/GPS record 2020 shoreline position of the Winneba track coastline b. Field observation (field photos) Personal observation was undertaken on the coastal area and on coastal infrastructure and photos were taken on the observed impacts as a result of climate change

Secondary data a. GMA Climatic parameters (maximum and minimum temperature, rainfall, and maximum and minimum relative humidity). b. Fisheries Commission of Ghana SST, SSS and total fish catch data c. Winneba Municipal Hospital Disease data

Tertiary data a. Google Earth satellite data Digitized shorelines positions for the years 2000, 2005 and 2019. b. Canadian Earth System Model (Can ESM2) Predicting GCM temperature and rainfall data

4.3.2 Data Analysis

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Data analyses were performed based on the objectives of the study.

4.3.2.1.0 Analysis of Climate Change on the Winneba Community

In analysing data on climate change on the Winneba community, land and oceanic climatic variables such as temperature (maximum and minimum), rainfall, relative humidity (maximum and minimum), SST and SSS respectively were analysed. All data employed in the analysis ranged from 1980-2019 (a 40year climatic data) except the SSS which ranged from 1994-2019. This period were covered due to the study‘s aim of being in consistent with the WMO consideration for climate period in order to assess climate change. Data analysis was done for current observed historical data and the future projected changes.

4.3.2.1.1 Analysis of Historical Data

Since climate and climate change in particular deals with means, Annual means were calculated for maximum and minimum temperatures. Temperature data were provided in daily values and there was the need to make several calculations from daily averages to monthly and annual means. The Microsoft Excel functions made this simpler. Mathematical calculations however, employed is illustrated below.

MTmax = Σd1+d2+………….+ d (4.1)

Ӯ MTmax = (4.2)

Ӯ ATmax = (4.3)

Where:

- M- equals month;

- Tmax- maximum temperature; - y- mean; - D- days - A- annual; and - Nd- number of days in the month

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The same procedure was followed in calculating means for minimum temperature (Tmin). This was done by substituting Tmax for Tmin and the remaining variables unchanged.

In analysing rainfall, the same procedure as used in temperature analysis was used. Since rainfall deals with sum, total rainfall was calculated. Mean annual rainfall was also calculated as shown below.

MR = Σd1+d2+………….+ d (4.4)

TMR = ΣM1+M2+………….+ M (4.5)

ӮMR = (4.6)

Where:

- MR- equals monthly rainfall; - TR- total rainfall; - Ny- number of years in this case 40years

The analysis for Relative Humidity (RH) maximum (RHmax) and minimum (RHmin) followed the same procedure as Tmax and Tmin. SST and SSS data were already in monthly means and so there were no need to calculate monthly mean SST and SSS. Annual means were however, calculated by dividing the sum of the respective months by 12. Mathematical computations for this data variable can be made by substituting SST and SSS distinctively into equation (4.3) above to derive the annual means.

4.3.2.1.2 Analysis for Future Projected Changes

Since climate and climate change scientist believe in future possible changes in climatic variables, this study made projections for temperature (maximum and minimum) and rainfall. GCM‘s data for temperature and rainfall were statistically downscaled to meet the local observed data needs. The Statistical Downscaling Model (SDSM) version 4.2.9 software was employed in this analysis. The statistical method of downscaling was chosen because it is computationally inexpensive and the bias correction which forms part of the computational process (Fowler et al, 2007).

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The observed data used was the historical maximum and minimum temperature already used in this study. The starting year however, in this analysis started from 2005-2019 (15years). The predicted climatic variables were downloaded from the Canadian Earth System Model (Can ESM2) and with the Climate Model Intercomparison Project (CMIP5) experiments. Observed data were entered into the SDSM software. Quality control and screen variables were detected. Data were therefore calibrated and scenarios generated. Scenarios were again generated for Representative Concentration Pathway (RCP) 4.5 and 8.5. Summary statistics were performed on both observed and modelled data. Data were modelled for 2030, 2040 and 2050 in both RCP 4.5 and 8.5 scenarios. Results from this were finally compared with the observed data and the changes reported. The statistical downscale method as applied in this study is shown in Figure 4.3.

Climate Change Projection

SDSM

Quality Screen Calibration Scenario Summary Compare Control Variables Model Generator Statistics Results

Modelled data Observ Analysis Analysis Modelled RCP 4.5 ed data and and Observed data data check Correlation Correlation RCP 8.5 Modelled data Observed years 2030, 2040, data 2050

Figure 4.3: Flow chart showing Statistical downscale method employed in the study.

4.3.2.2 Analysis of Shoreline Change along the Winneba Coast

In analysing shoreline change rate of the Winneba coastline from the period 2000 to 2020, shoreline data for the years mentioned, were acquired. The ArcGIS and the Digital Shoreline Analysis System (DSAS) tools were employed in the shoreline analysis. The ArcGIS is helpful

60 when dealing with geographic information and the DSAS essential for change rates statistics of shorelines.

Shoreline change positions for the years 2000, 2005 and 2019 of the Winneba coast were digitized from Google Earth satellite images under different tidal times; 18 December for the years 2000 and 2005 and 14 November for the year 2019 under high tides. Several studies in contemporary times believe shoreline data can be obtained from other sources apart from topographical sheets (Appeaning- Addo et al, 2008; Hapke et al, 2010). The High Water Line (HWL) was used to define shoreline positions. In order to obtain the current shoreline position of 2020 of the Winneba coast, the SW map (GPS) record track survey was undertaken. The SW maps an android GPS application was downloaded and installed on a mobile phone. The SW map record track analysis was performed in the period 13-14 February, 2020. This analysis was done in the morning (8:00-11:00 am) and in the evening (16:00-19:00pm). The mornings and evenings analysis were done to see wave and tidal differences and to determine consistency in the shoreline data. The starting point of the HWL was identified on the beach and the appropriate traces of track records were followed to acquire the full extent of the shoreline. Shoreline extents were between the tidal inlets of the Muni- Lagoon (Akosua Village) area to the Ayensu River estuary (Sankor) area. Shoreline positions therefore covered the three divisions of the Winneba coast as indicated in Figure 3.4 in this study. Portions of rocks covered by sea water were not considered as part of the coast and the appropriate HWL were included in the track record.

Shorelines were projected into the same coordinate system (Ghana Metre Grid Projection) under the World Geodetic System (WGS) 1984 datum. This helped to make the necessary comparisons. The attribute automator, even though optional helps to add required fields such as date, uncertainty and shoreline types to shorelines or the baseline layers. Default baseline parameter helps to identify baseline orientation to location of relative land either left or right and also baseline placement midshore, onshore or offshore. The shoreline default parameter also helps to select the shoreline layer type, date, uncertainty and intersection parameters. That is either seaward or landward intersections. The metadata settings enable the replication of work and provide automatic record keeping. The maximum search distance allows the user to select a single search distance for all shoreline positions. 600meters was chosen in this study. The transect spacing helped to specify distance in meters between transect along the baseline. 50meters was used in this study. The smoothing distance is user specific and helps to facilitate shoreline intersects and additional baseline. A smoothing value of 2500meters was used in this

61 study. Baseline layer and shorelines can be edited if the required geometry rates are not met. After all shoreline features have been created, edited or updated, the data can be used to compute change statistics such as the end point rates (EPR), net shoreline movement (NSM), shoreline change envelope (SCE) linear regression rate (LRR) etc. The shoreline intersection threshold provides the user an opportunity to select the minimum number of shorelines transect must intersect in order to be included in the change statistics. A threshold value of 1 was selected for the study. Also, a confidence interval of 90% was chosen for the study. The Kalman filter model is used to establish the shoreline position and rate for every 10th of a year and to estimate the positional uncertainty. Thus, it combines observe and model shoreline positions to predict future shoreline positions.

To calculate the rate of change statistics using DSAS, all shorelines were grouped into a single feature class in ArcGIS. Default value of ±1 of uncertainty was set for the shorelines data. Shorelines were assigned a specific date and was incorporated into the feature class attribute table of the shorelines. A baseline was placed at mid shore (combination) towards a seaward intersection of shorelines. Transects were therefore cast from the baseline and vertical to the directions of shorelines. The total length of transects cast by 50meters apart were 176meters from the baseline. This helped in the intersection of shorelines. Null values were recorded at points where transects did not intersect. Transect intersections were helpful in calculating change rates and the computation of relevant statistics. The default uncertainty that is the lowest uncertainty in this study was ±1. Uncertainties were attributed basically to three factors: digitizing, (Appianing-Addo et al, 2008), aerial Photograph (Johah et al, 2016) and the HWL during the field survey (Hapke et al, 2010). Change rate statistics that resulted from this were the end point rate (EPR), linear regression rate (LRR) and the net shoreline movement (NSM). The procedures employed in this study for the DSAS analysis of the Winneba shoreline are shown in Figure 4.4.

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Step 1: Attribute automator INPUTS -Add required field to shoreline and baseline layers

-Baseline

-shorelines (2000-2020) Step 2 : Set default parameters

-Baseline settings -Shorelines uncertainty - Shoreline settings

-Metadata settings

Step 3: Cast transects

-Maximum search distance

-Transect spacing OUTPUTS -Smoothing distance -Transects

Step 4: Edit transects

-Change baseline layer

-Edit distinct shorelines

Step 5: Calculate change statistics OUTPUTS -Select statistics to calculate -Transects rates -Specify confidence interval - Intersects -Shoreline intersection threshold

-Determine rate out display

OUTPUTS

Step 6: Shoreline forecasting (Kalman Filter - Shoreline forecast Model) - Shoreline forecast point - 10 and 20year forecast (polyline and point) - Shoreline forecast uncertainty - Forecast uncertainty

Figure 4.4: Digital Shoreline Analysis System (DSAS) steps. Source: Himmelstoss et al. (2018).

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According to Hapke et al, in 2010, uncertainties for HWL shorelines results from from main activities: digitizing uncertainty (Ud), georeferencing uncertainty (Ug), T-sheet survey uncertainty

(Ut), aerial photograph uncertainty (Ua) and the proxy-datum uncertainty or the HWL (Upd) that occurred at the time of survey. (Ug) and (Ut) uncertainties were not applicable in this study. Since shorelines years (2000, 2005 and 2019) were obtained from same source, uncertainty value of ±1 and ±3 were assigned to (Ud) and (Ua) respectively. The (Ud) value of ±1 is in consistent with past studies (Crowell et al., 1991; Moore, 2000) and that of (Ua) being in consistent with (Hapke et al., 2010). This study accepted the HWL uncertainty value of ±4.5 due to its validity in studies like (Jonah et al., 2016; Hapke et al., 2010). The SW map (GPS) record track uncertainty of ±2 was with the uncertainty estimate by Hapke et al., 2010. Hapke et al. (2010) uncertainty estimate error of ±4.5 was therefore accepted and used in the 2020 shorelines. The total shoreline position at each transect is shown in the equation below.

Upi =√( ) ( ) ( ) (4.7)

Where:

- Upi equals total shoreline position at each transect (2000-2020).

Errors were calculated for all distinctive transects as shown in (table 4.2). The distinctive uncertainty for each shoreline period was therefore integrated and this showed the errors for each transect. Results from these were therefore annualized (UF). This helped to determine the uncertainty estimates for the shoreline change rate at all specified transect. This is shown in the following equation.

U = √( ) ( ) ( ) ( ) (4.8) F

Where:

- Up1, Up2, Up3, Up4, - equals total shoreline position errors for the years and - T - time period of shoreline analysis (20years).

The DSAS software returned a total of 701 transects in the shoreline change analysis. There existed conformity and similarities in values for the changes in shoreline between the connecting transect. Shoreline change averages generated in the shoreline division were

64 greater than the maximum uncertainty computation carried out in this study and therefore, can be accepted.

Table 4.2: Estimated uncertainties for Winneba shoreline (Adapted from Hapke et al, 2010)

Measurement Uncertainty (meters) 2000 2005 2019 2020

Digitizing (Ud) 1 1 1 -

Aerial Photograph (Satellite image) (Ua) 3 3 3 -

High Water Line (HWL) (Upd) 4.5 4.5 4.5 4.5

Total shoreline position uncertainty (Up) 5.5 5.5 5.5 4.5

Total shoreline uncertainty at each transect (2000-2020) (Upi) ±10.5

Annualized transect uncertainty (m/year) (UF) ±2.4

Figure 4.5: Aerial photograph (Satellite imagery) showing the Winneba coast with shoreline and cast transects. Source: Adapted from Google Earth.

4.3.2.2.1 Kalman Filter Model

The Kalman filter model through the Beta shoreline forecasting in DSAS was used to forecast the shoreline position at each successive shoreline time step until another shoreline is reached. This helped to identify changes in the shoreline positions and how they are expected to change.

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The Kalman filter linear equation (equation 4.9) developed by Long et al, in 2020, was adopted in this study.

( ) ( ) (4.9)

Where:

- is the shoreline positions in relation to the baseline; - is time; - is the time step (time from the first observed shoreline until the last forecasted shoreline); and - is the change rates in the shoreline positions in time

The Kalman filter model therefore, performs an analysis to reduce the error between the forecasted shoreline and the shoreline data. This helps to provide better forecast and the associated rate and uncertainty (Long & Plant, 2012). The rate is used in the forecasting of shoreline positions for the continuous shoreline until the last shoreline date is reached. The measurement error in the model is based on uncertainties in the distinct shoreline positions used in the analysis. This helps to reduce the process noise such as the uncertain regular disparities along the coast. The Kalman filter model starts from the confidence interval (CI) and the standard error of estimate (LSE) of the linear regression rates (LRR) calculated by DSAS. Due to the inbuilt nature of the process noise in the Kalman filter model forecast, the uncertainty associated with the forecast will continue to increase each year in the observation until the final year (Himmelstoss et al, 2018).

4.3.2.3 Analysis of the physical impacts of climate change and its implications on the Winneba community.

In analysing the physical impacts of climate change and its implications on the Winneba community, the study focused on the effects of shoreline change and its implications on the community. In this regards, the study through field observations, documented and analysed the impacts of shoreline change and the related coastal erosion through the incidence of climate change in the community. Field observations were therefore, carried out on all the three portions of the coast and the impacts of shoreline change and coastal erosion on human settlement and infrastructure, ecosystem services such as quality water from coastal freshwaters and general coastal environmental degradation were analysed.

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CHAPTER FIVE

RESULTS AND DISCUSSIONS

5.0 Introduction

Results from this study are presented and discussed in this chapter. Results and discussions are presented based on the specific objectives of this study including; evaluation of climate change on the Winneba community (period from 1980-2019), analysis of shoreline change under climate change along the Winneba coastline and the analysis of the impacts of climate change on the Winneba community.

5.1 Evaluation of Climate Change on the Winneba community

In this section, climatic parameters such as maximum and minimum temperature, rainfall, maximum and minimum relative humidity, sea surface temperature (SST) and sea surface salinity (SSS) are presented respectively and reflecting both land and ocean climatic parameters. Future projections for temperature (maximum and minimum) and rainfall are also presented. Both land and ocean climatic parameters have been presented due to the Winneba‘s position in the coastal zones and the attendant climate change impacts resulting from both climatic variables. Changes in both parameters help to understand vividly the climatic and climate change incidence in the Winneba community.

5.1.1 Maximum and Minimum Temperature, SST and SSS

Temperature is an important parameter in climate analysis and its changes over extended period of time provides a basis for climate change assessment. Inferring from this, a 40 year maximum and minimum land surface temperature on Winneba were analyzed. A simple linear regression model has also been introduced to show the long-term annual trends in both temperatures as presented in Figure 5.1.

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35 Mean Annual Maximum and Minimum Temperature (°C) y = 0.0344x + 30.327 R² = 0.3419 30

25 y = 0.0768x + 21.555 R² = 0.7232

20

Mean Annual Min. 15 Temp Mean Annual Max. Temp 10 Linear (Mean Annual Min. Temp) Linear (Mean 5 Annual Max. Temp)

0

1996 2003 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1997 1998 1999 2000 2001 2002 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019

Figure 5.1: Mean Annual Maximum and Minimum Temperature of Winneba from 1980-2019. Source: Field Survey, 2020.

Figure 5.1 shows an increase in both maximum and minimum temperatures on Winneba. Careful analysis indicates that even though there have been increases in both temperatures, the increasing is on a decreasing rate. It can also be seen that both maximum and minimum temperatures increase on a decadal basis with the highest maximum temperature (31.8°C) recorded in the years of 1995 and 2000. This was followed by 1990 (31.7°C) and lowest in 1980 with a value of (29.1°C). Thus, highest maximum temperature values were recorded during the second and third decades and lowest during the start of the first decade as per the available data. Also for minimum temperature, highest temperature was recorded in 2019 a value of (24.7°C), followed by 1998 and 2006 which had the same values (23.9°C) and lowest in 1980 with a value of (19.9°C). On decadal levels, highest minimum temperature was recorded in the fourth decade and was followed by the second and third decades with the lowest occurring in the first decade as per the available data. Linear trends revealed an increasing and stronger co- efficient of determination (R2) in mean annual minimum temperature (R2=0.732) than it was for the mean annual maximum temperature (R2=0.3419).

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Figure 5.1 shows evidence of climate change and supports claims by scientists such as Rusell, who indicated in his work that the global climate have changed (Russell, 2012). The study also supports Gornitz assertion on increasing temperatures over the earth surface (Gornitz, 2000). Increasing temperature on Winneba is in conformity with studies on temperature in Ghana which reports increasing trends in temperature in all the ecological zones of Ghana (Asante & Amuakwa-Mensah, 2015). The underlying cause to the increasing temperature in Winneba is anthropogenic greenhouse gases. Many human activities such as driving, bush burning among others which increases atmospheric carbon dioxide, methane and nitrous oxide have increased in Winneba. That is, the massive changes in population and the changes in land surface activities are gradually turning Winneba into an urban city. The consequences as a result of this, is the increased transformation of economic which intend contributes to the production of harmful atmospheric gases which heat up the atmosphere. The heat in the atmosphere is therefore released and attracted onto the land surface through long wave terrestrial radiation. Increasing temperature negatively affects all species on earth (Gornitz, 2000) and in Winneba, the effects of increasing temperatures are highly felt due to its coastal nature and the associated temperature impacts from both ocean and land. Increasing temperatures have dire impacts on coastal activities and wellbeing starting from agriculture and food security (Olesen et al, 2006), human health and infrastructure (Fitzpatrick et al, 2015; Kjuellstrom et al, 2014) and on coastal freshwater resources (Hay & Mimura, 2005). The interplay between the land and the ocean over Winneba makes the situation worst.

The simple linear regression introduced over the maximum and minimum temperatures showed an increasing trend and stronger co-efficient of determination in mean annual minimum temperature than mean annual maximum temperature. This is particularly due to the urban heat island effect and the impact of the sea (Wu et al, 2019), which is larger at night than at day time. changes in land surfaces and increasing population and urbanization which has increased its mean annual minimum temperature as a result of urban heat island effect is in consistent with a study by Makokha and Shisanya in 2010. This also affects coastal inhabitants negatively. Urban heat island effect is therefore considered important in public health (Wu et al, 2019).

The coastal nature of Winneba prompted the study to again look at some ocean climatic parameters (SST and SSS). The interplay between land and ocean contribute significantly to climate changes and the associated effects on both inhabitants and the aquatic species. SST and SSS are essential climatic variables (ECV) due to their effect on fisheries development and growth and the transferred effects on humans through inland heating as a result of differential

70 heating between the land and the ocean (land and sea breeze). The SST and SSS are shown in Figure 5.2.

27.5 36.5 Mean Annual Sea Surface Temperature (°C) and Sea Surface Salinity (%) 27.0 36

26.5 35.5

C) C) 26.0 35

° SSS (%) SSS

25.5 34.5 SST ( SST 25.0 34 Mean 24.5 SST (℃) 33.5

24.0 Mean 33 SSS (%)

23.5 32.5

2004 2007 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2005 2006 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019

Figure 5.2: Mean Annual Sea Surface Temperature and Sea Surface Salinity of Winneba. Source: Field Survey, 2020.

Figure 5.2 shows a clear connection between SST and SSS. From a critical look at figure 5.2, it can be seen that SST values increased in the mid and late; 1984 (27.1°C) and 1987 (27.2°C) years in the first decade. It then decreased during the early years of the second decade and 1990 in particular with value (25.7°C). Comparing to the first decade, the second decade per the available data reduced greatly but then increased during the third decade. 2013 recorded the lowest SST value of (24.9°C). SST since 2014 from figure 5.2 has been increasing. The SSS on the other hand shows a fluctuating trend with the highest and lowest SSS being 36.1% and 33.9% respectively.

The world‘s oceans have become warmer during the 20th and the 21st century (IPCC, 2007) and are projected to become warmer at the end of this century. Fishes are poikilothermic animals, thus, they change their body temperature as soon as the temperature of the environment changes (Nelson et al, 2016). Changes in these conditions such as surface temperature and chemistry affect their physiology, development and reproduction. Fishes moderate their body temperature through ocean water and an increased SST may reduce their growth rate. Fishes may therefore, encounter natural mortality as a result of their reduced growth and body size. Also, increase SST and SSS may change the spatial distribution of fishes (Pörtner & Knust,

71

2007). SSS with combined with currents may affect the eggs of fishes and this will affect their succession rate. Fishes therefore, as a result of increased SST and SSS, move deep into the ocean since the ocean warms more at the surface. The socio-economic impacts associated with this are enormous. For instance, in Winneba, fishing is among the major economic activities and serves as sources of livelihood to many. Increasing SST and SSS may result fish mortality, extinction or migration into deeper oceans where fishers in Winneba cannot reach with their low harvesting instruments. This may affect incomes of fishers and the quality and quantity of fish catch (see figure 5.3).

SSS that finds its way through sea level rise into coastal lagoons and freshwaters make inland freshwaters unsafe for drinking and also affect human health. In many low coastal areas in Ghana, underground waters are salty and unsafe for domestic use (Appeaning-Addo et al, 2011). In Winneba for instance, saline water finds its way into the coastal freshwater (Ayensu River) during the dry season where the River cannot flow into the sea. The sea water therefore, enters the River estuary and thereby extending saline water into the River. This is also similar at the western portion of the coast (Muni lagoon), where sea water enters the lagoon through its open inlets. In Winneba, up to 1km from the coast to inland, no well can be tunnelled due to the intrusion of saline water into underground aquifers. Winneba as a result of this faces water shortages sometimes.

Total volumes of Fish Catch (tonnes) in Winneba 3500 Fish Catch 3000 (tonnes)

2500

2000

1500

1000

500

0

Figure 5.3: Total volumes of fish catch in Winneba from 2000-2018. Source: Fisheries Commission Ghana.

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Figure 5.3 shows great fluctuations in the total volumes of fish catch in Winneba over the years. Since 2000, the highest volumes of fish catch happened in the 2001 with a total volume of 2874 tonnes. This was followed by the year 2000 with 2773 tonnes and lowest in 2011 with 133 tonnes. The figure shows a decreased trend in the total volumes of fish catch and participants were right in their perspectives that are experiencing low fish catch in the community. Fishing serves as one of the livelihood opportunities for many people including the fishermen and the fish sellers in the community. With reduced fish catch, these people are at greater risks and since they do not have any alternative livelihood strategies, they continue to suffer from low incomes and the associated low socio-economic status. Fish sellers complained bitterly about their reduced fish buying and sales as shown in photo 5.1. They continued to say that due to the low fish catch, fishermen increase the price of the little they bring for sale and sometime the fishermen discriminate amongst them on whom to sell their fish to even though in the last four years there was an increase in fish captures.

Photo 5.1: Fish sellers complaining about the total fish bought at the shore. Source: Field survey, 2020.

5.1.2 Rainfall

Rainfall is another major parameter of climate change assessment. Rainfall dynamics and variability over extended period of times demonstrates the existence of climate change at a particular place or region. A combination of total annual rainfall and mean annual temperature trends reveal a clear analysis of the fluctuations and changes which help to understand clearly the incidence of climate change. In view of this, the study analysed total annual rainfall change

73 and variability and mean annual temperature change as shown in Figure 5.4. A total rainfall divergence in the form of bar graph over Winneba was also analysed to understand clearly the fluctuations that occurred (see figure 5.6).

1400 Temp (y) = 0.0245x + 27.11 Rainfall(y )= 11.84x + 518.73 29

R² = 0.3363 R² = 0.3156

C

28.5 °

1200 28

1000

27.5 Mean Temp

27 800

Total Total Rainfall (mm) Rainfall Total 26.5 Rainfall (mm) 600 26 Mean Temp ℃ 400 25.5

25 Linear 200 (Total 24.5 Rainfall (mm)) 0 24 Linear

(Mean

1996 2013 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2014 2015 2016 2017 2018 2019 1980 Temp ℃)

Figure 5.4: Total Rainfall and Mean Annual Temperature of Winneba from 1980-2019. Source: Field Survey, 2020.

From Figure 5.4, shows significant changes in both total rainfall and mean annual temperature. A careful analysis reveals although there are fluctuations, both rainfall and temperature shows an upward trend. The upward movements in both rainfall and temperature are on a decreasing rate. Although, there is a great variability in rainfall, the highest rainfall was recorded in 1987 with a value of 1144.4mm. That is the highest rainfall in Winneba was recorded in during the later years of the first decade analysed as per the available data with the lowest (233.5mm) occurring in 1980 of the same decade. Mean annual temperature follows a similar increasing trend on a decreasing rate. The highest temperature of 28.4°C occurred in the fourth decade (2019) and the lowest 25.7°C occurring in the first decade (1980). While rainfall shows variability on decadal basis in a decreasing trend towards the last decade per this data, temperature shows an increasing trend towards this same decade. The average annual temperature is 27.6°C with an annual range of temperature is 2.7°C. The average annual rainfall is 761.4mm with an annual range of 910.9mm. The combination of both rainfall and temperature demonstrate the presence of climate change in the Winneba community. The great rainfall

74 variability and temperature change in Winneba is in consistent with studies which indicate that the variability and changes in both rainfall and temperature in Ghana (Asante & Amuakwa- Mensah, 2015; Owusu & Waylen, 2012).

The climate change incidence in Winneba affects the activities of the people. Rainfall and temperature changes disrupt the planting time, growth and harvest of farmers. Agriculture in Winneba and Ghana in general is dependent and farmers rely heavily on it. The variability in rainfall has therefore changed the planting time of many crops and this has put some perception on the minds of the people of Winneba that rainfall levels has been decreasing despite the upward trend shown in Figure 5.4. This is particularly due to the seasonality and variability in rainfall and the associated delays in the start of the farming season.

The rainfall variability and temperature changes also have an adverse effect on human health. Changes in these variables result in morbidity and mortality (Confalonieri et al, 2007). Increasing temperature also increases the urban heat stress thereby causing increase in vector borne diseases such as malaria. Rainfall dynamics again causes diarrhoeal diseases (Asante & Amuakwa-Mensah, 2015; Langner et al, 2005) and mental disorders (Ahern et al, 2005; Haines & Patz, 2004). This study therefore, attributes the variability in rainfall and temperature changes to the occurrence of many malaria caseloads and deaths in Winneba community as shown in Figure 5.5.

4000

3500

3000 Total Hospital Admissions 2500 Total Malaria 2000 Cases

1500 Total Malaria Deaths 1000

500

0

Figure 5.5: Malaria cases and death in Winneba. Source: Winneba Municipal Hospital.

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From Figure 5.5, it can be seen that total malaria cases and deaths were high in the year 2009. That is, of the total 3752 hospital admissions from various diseases, malaria cases were 1552 with deaths of 110. This was followed by the 2013 with total admissions 3247, total malaria cases and deaths being 1539 and 146 respectively making the year 2013 as the year with the highest death with respect to malaria and per this data.

Comparing the respective years in temperature and rainfall in Figure 5.4 and diseases, the year with more temperature was 2019 with a value of 28.4°C that of rainfall is 2015 with amount 1153.4mm and 2009 as the year with more diseases. 2009 is neither the year with more rainfall nor temperature however, it recorded more diseases. The association in variations in rainfall and temperature with diseases is weak and dissimilar. Therefore, the answer in time in diseases occurrence in Winneba is immediate and does not take more times or years to respond.

With the changes in temperature and rainfall, the incidence of many vector borne diseases are expected to increase. The projected increase in both temperature and rainfall on the Winneba community may provide conducive breading grounds for several diseases causative agents especially, that of mosquitoes and dengue (Chowdhury et al, 2018; Langner et al, 2005). Increasing rainfalls amidst poor sanitation in the community may also cause several intestinal infections; diarrhoea and cholera cases (Langner et al, 2005) if the needed good environmental practices and personal hygiene measures are not observed.

The array of divergence in rainfall (Figure 5.6) is very excessive amongst the years and this echoes the inhabitants of Winneba especially the fishermen, fish sellers and the farmers assertion that rainfall in Winneba has decreased greatly. Since improvement in 1987, inhabitants‘ perception on decreasing rainfall continued until 2015 which showed a massive change in rainfall divergence. Since 2015, rainfall amounts have been decreasing and the inhabitants‘ perception of decreased rainfall is still in place.

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Rainfall Divergence from Mean of 846.2mm 400.0 309.8 300.0 261.9 215.6 188.2 200.0 167.7 170.4 166.6 147.8 127.0 106.8 91.4 100.0 74.8 53.0 30.2 22.0 13.9 26.6 0.0 0.0 -18.6 -100.0 -52.8 -64.4 -94.4 -76.8 -137.8 -200.0 -164.7 -158.7 -195.8 -276.3 -300.0 -277.2 -293.3 -261.9 -336.8 -346.7 -317.5 -307.3 Divergence -400.0 -387.4 -379.6 -435.7 -500.0 -451.9 -527.9

-600.0

1999 2001 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 2000 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 1980

Figure 5.6: Divergence bar graph showing rainfall for Winneba. Source: Field Survey, 2020.

5.1.3 Relative Humidity (RH)

Humidity is again another important parameter to determine the state of the Earth‘s climate and the controversial climate change. Humidity tells the moisture content in the atmosphere and the changes in this cause great effect on all species on earth. In view of this, maximum and minimum relative humidity over Winneba was analysed. Maximum and minimum relative humidity was recorded at 06:00 hours and 15:00 hours as presented in Figure 5.7. To clearly show the differences and changes, the study further compared mean annual mean temperature and relative humidity from the monthly means and this is shown in Figure 5.8.

77

99 86

Mean Annual Maximum and Minimum Relative Humidity (%)

85 98

84

97 83

96

82 Maximum RH (AT 06:00 HRS) 81 Minimum(AT RH 15:00 HRS) 95

80 94 RHma 79x

93 78RHmin

92 77

1982 1987 1992 1997 2002 2007 2012 2017 1980 1981 1983 1984 1985 1986 1988 1989 1990 1991 1993 1994 1995 1996 1998 1999 2000 2001 2003 2004 2005 2006 2008 2009 2010 2011 2013 2014 2015 2016 2018 2019

Figure 5.7: Mean annual maximum and Minimum Relative Humidity of Winneba from 1980-2019. Sources: Field Survey, 2020.

From figure 5.7, maximum relative humidity shows an increasing trend while that of minimum relative humidity shows a decreasing trend. The highest and lowest maximum relative humidity values are 98% and 94% respectively while that of minimum relative humidity is 85% and 81% respectively. On decadal basis per this data, highest and lowest maximum relative humidity values occurred during the second decade, continued during the third decade and decreased at the latter years of the same decade. The decrease continued from the latter years of the third decade to the early years of the fourth decade and then showed an upward trend in the mid years while the latter years showing decreasing. The lowest maximum relative humidity occurred during the early years of the first decade. The highest minimum relative humidity occurred in the second decade with the lowest occurring in the same decade. Humidity analysis for the day (15:00 hours) is very important in this study particularly due to the decrease in humidity with increase activities at the day time and the resultant effects on humans.

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Figure 5.7 has shown how close the air in Winneba is close to saturation. There have been fluctuations in both maximum and minimum relative humidity and this is due to the negative relationship between relative humidity and temperature. That is, relative humidity is higher when temperature is low and vice versa. The maximum relative humidity recorded higher values due to the time it was recorded (06:00hours). At 06:00hours, temperature in Winneba becomes low as the sun begins to rise. The atmosphere becomes very humid if not saturated at 06:00hours. After noon and in Winneba at 15:00hours, temperature begins to record higher values due to the position of the sun and this makes the atmosphere very hot and less humid. The changes in humidity in Winneba poses serious threat to humans and plants and this affects human wellbeing and agriculture. When temperature is high and humidity is low, humans can become dehydrated as a result due the sweat that comes out and dry quickly from the human body. People therefore, need to hydrate by drinking more water. On plants and agriculture in general, humidity can affect the water in plants cells. With low humidity, water evaporates very quickly from plants and causes them to wither and reduces more water through transpiration from their cells. Humidity again can cause disease in some plants. For instance, fungi and molds grow very quickly when humidity is high (Velásquez & Castroverde, 2018).

A combination of mean annual temperature and relative humidity (both maximum and minimum) from the monthly means (Figure 5.8) shows a clear negative relationship that exists between temperature and relative humidity. As the temperature of Winneba is increasing, relative humidity is decreasing although, the increasing and decreasing with respect to temperature and humidity is on a decreasing rate. This again confirms the climate change incidence in Winneba community.

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Mean Annual Temperature (°C) and Relative Humidity (%) 92.5 29

92 28.5

C

°

91.5 28

91 27.5

90.5 27 MeanTemp Mean RH (%) RH Mean

90 26.5

89.5 26

89 Mean RH 25.5 (%) 88.5 25 Mean 88 24.5 Temp ℃

87.5 24

1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 1980

Figure 5.8: Annual Temperature and Relative Humidity (from monthly means) of Winneba from 1980-2019. Source: Field Survey, 2020.

5.1.4 Temperature and Rainfall Projections for Winneba

Due to the incidence of climate change at the global and local scenes and the uncertainties associated with it, there is the need for projections into the future. That is, the rate at which the earth‘s climate is changing and the resultant environmental changes, climate scientists and climate change assessors are therefore encouraged to make projections into the future. This helps the relevant stakeholders in the proper development and planning of the areas that are affected. In relation to this, the study saw the need to project the climate of Winneba into the future. The study therefore, made future projections of maximum and minimum temperature and rainfall under the RCP 4.5 and 8.5 as shown in Figure 5.9, Figure 5.10 and Figure 5.11 respectively. The mean annual changes in both maximum and minimum temperature and rainfall under RCP 4.5 and 8.5 were also reported. This was done to see whether there are greater changes in annual means in both temperature and rainfall. This is shown in Figure 5.12.

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34.0 Projected mean Monthly Maximum Temperature (RCP_4.5 and 8.5)

33.0

32.0 Model Mean Max.Temp 31.0 RCP_4.5_2030

30.0 RCP_4.5_2040

29.0 RCP_4.5_2050

28.0 RCP_8.5_2030

RCP_8.5_2040 27.0

RCP_8.5_2050 26.0 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Figure 5.9: Projected Monthly Maximum Temperature of Winneba for 2030, 2040 and 2050. Source: Field Survey, 2020.

26.0 Projected Mean Monthly Minimum Temperature (RCP 4.5 and 8.5) Model Mean 25.0 Min. Temp RCP_4.5_203 0 24.0 RCP_4.5_204 0 23.0 RCP_4.5_205 0 22.0 RCP_8.5_203 0 21.0 RCP_8.5_204 0 20.0 RCP_8.5_205 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 0

Figure 5.10: Projected Monthly Minimum Temperature of Winneba for 2030, 2040 and 2050. Source: Field Survey, 2020.

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250.0 Projected Total Monthly Rainfall (RCP_4.5 and 8.5) Model Total Rainfall RCP_4.5_2030 200.0 RCP_4.5_2040

RCP_4.5_2050 150.0 RCP_8.5_2030

RCP_8.5_2040 100.0 RCP_8.5_2050

50.0

0.0 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Figure 5.11: Projected Total Monthly Rainfall of Winneba for 2030, 2040 and 2050. Source: Field Survey, 2020.

Mean Annual change in Rainfall and Temperature

6 0.12 ) ℃

5 0.1

4 0.08

3 0.06 Temp. Change ( Change Temp.

2 0.04 Rainfall (%)

Rainfall Change (%) Change Rainfall 1 0.02

0 0 Maximum and MinimumTemp (℃)

Figure 5.12: Mean Annual Change in Projected Temperature and Rainfall of Winneba. Source: Field Survey, 2020.

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From Figure 5.9, it can be seen that in the future maximum temperature of Winneba is going to increase under both RCP 4.5 and 8.5. It is clear that apart from the month of January, February and May which lie below the projected mean, the rest of the months lie above and this shows the future increase in maximum temperature of Winneba. The projection on minimum temperature (Figure 5.10) for the years 2030, 2040 and 2050 in Winneba share a similar trend with the maximum temperature amidst fluctuations. All the months except February and August show projected increases and are above their respective projected means. It can also be seen from the rainfall projection (Figure 5.11) that, rainfall is going to increase for the years 2030, 2040 and 2050 since most of the amounts are greater than the projected sum. Rainfall amounts are going to be high in the month of May, June, July and October even though these projections have their own variability. Rainfall amounts are going to decrease in the month of March since the amount to be received in the years 2030, 2040 and 2050 are below the modelled total.

Mean annual change (Figure 5.12) in both projected maximum and minimum temperatures were low (0.1°C) under both RCP 4.5 and 8.5 while that of rainfall showed great fluctuations with increases as the years advance. Even though both RCP 4.5 and 8.5 showed increases in rainfall for 2030, 2040 and 2050 amidst fluctuations, the fluctuations in mean annual change RCP 8.5 for the years 2030, 2040 and 2050 were huge.

The above analyses have shown a continuous increase in both maximum and minimum temperature and rainfall under the RCP 4.5 and 8.5 in Winneba. It is established that even though rainfall is going to increase for the years 2030, 2040 and 2050; these increases are going to be accompanied by greater fluctuations. In the future, both temperature and rainfall are going to increase in Winneba. Maximum and minimum temperatures are going to be high in the months of March, April and December. Changes in these climatic parameters may have both positive and negative consequences. Farmers and agriculture in particular stand a great chance to benefit from this. There will be much water for crops and the resultant increase in yields. The negative consequences have to do with humans and the environment. With increasing temperature and rainfall, several diseases may occur in the community since increasing temperature and rainfall may create suitable conditions for many disease causative agents. This will increase the rate of hospital admissions and put pressure on the health system in the community unless better personal hygiene and preventive measures are ensured. With increasing rainfall, the environment may be subjected to severe erosion.

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5.2 Analysis of Shoreline Change along the Winneba Coastline

This section analyses shoreline change along the Winneba coastline under the presence of climate change in the Winneba community. The study therefore, analysed the shoreline change between 2000 and 2020. The rate of change and the net changes were analysed and reported. The end point rate (EPR) in meter per year was used to analyse the rate of change. Although, the EPR was used in the change rates statistics, the linear regression rates (LRR) was also utilized in this study due to the ignorance in the level of change in erosion or accretion in some cases by the EPR (Crowell et al, 1997). The land loss analyses in meters (m) was also performed for net shoreline movement (NSM) and shoreline change envelop (SCE). The EPR and the LRR analyses show the rates at which the Winneba shoreline has been eroding, accreting or unchanged. The NSM shows highest and the lowest land loss along the shoreline while the SCE shows the greatest displacements in the shoreline positions.

5.2.1 Shoreline Changes between 2000 and 2020

Changes in shoreline positions from the year 2000, 2005, 2019 and 2020 are analysed. The rates of change (EPR and LRR) are shown in Figure 5.13 while that of the maximum and minimum land loss (NSM) and the greatest displacement (SCE) in the shoreline positions are also presented in Figure 5.14.

1.5 1.5

1 1

0.5 0.5

0

0 -0.5 -0.5 -1 -1 -1.5 -1.5

LLR (m/year) LLR -2 EPR (m/year) EPR -2 -2.5 LRR -2.5 -3 (m/year) EPR -3 -3.5 (m/year) 1 21 41 61 81 101 121 141 161 Transects

Figure 5.13: End Point Rate and Linear Regression Rate Trends of the Winneba coastline from 2000-2020. Source: Field Survey, 2020.

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80

60

40

20

0 NSM SCE -20

-40

-60

-80 1 21 41 61 81 101 121 141 161

Figure 5.14: Net Shoreline Movement and Shoreline Change Envelop Trends of the Winneba coastline from 2000-2020. Source: Field Survey, 2019.

It is clear from Figure 5.13 and Figure 5.14 that the Winneba shoreline has experienced changes over the years. From Figure 5.13, the EPR ranged from a high erosion rate of -3.2 m/year and ±0.11 uncertainty to a low erosion of -2.4 m/year and ±0.11 uncertainty while that of the LRR ranged from a high of -2.3m/year and ±0.4 uncertainty to a low of -1.6m/year and ±0.4 uncertainty. The accretion rate from this same analysis also in the EPR ranged from a high accretion rate of 1.0 m/year and ±0.11 uncertainty to a low accretion rate of 0.2 m/year and ±0.11 uncertainty while that of the LRR ranged from a high 1.1m/year and ±0.4 uncertainty and a low of 0.3m/year and ±0.4 uncertainty. The mean EPR is 0.4 m/year and ±0.11 uncertainty and that of the LRR is -0.3m/year and ±0.4 uncertainty. The mean standard error of estimate in the LRR was 2.6 and r2 of 0.19 as shown in Figure 5.15. It can also be seen from Figure 5.14 that, with the change rate in the EPR in Figure 5.13, there has been a maximum land loss and gain of -64m and 20.2m respectively with an SCE of 64m. The average NSM and SCE was - 7.1m and 11.2m respectively. That is, the NSM between 2000 and 2020 has been of erosion other than accretion with the greatest displacement in the shoreline being is 64m.

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1.5

1 y = 0.0041x - 0.653 R² = 0.1946 0.5 LSE = 2.6 LCI90 = 0.4 0

-0.5 LRR (m/year) -1

-1.5

-2

-2.5

-3 0 50 100 150 200

Figure 5.15: Linear Regression Rate

From the above change statistics in the EPR and the LRR, it can be seen that the erosion rates EPR were larger than in the LRR. However, the LRR revealed more erosion rates from the cast transects along the shoreline. This is due to the EPR ignorance of the magnitude in the erosion and accretion rates as argued by Crowell et al. (1997). The study therefore, relied on the LRR to reveal the erosion and accretion rates from the cast transects as shown in Figure 5.16.

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Eastern Portion

Central Portion

Western Portion

Figure 5.16: Linear Regression rates showing erosion and accretion levels along the Winneba shoreline. Source: Field Survey, 2020.

It must be stated over here that, even though the Winneba shoreline has experienced fluctuations over the years, these changes in erosion and accretion are not uniform across the entire coastline. Figure 5.16 shows the portions of the coast that are eroding, accreting or remained neutral. Red lines show high erosion rate of -2.3 to -1.6 m/year, pink lines show low erosion rate of -1.6 to -0.9 m/year, green lines show neutral, deep blue lines show accretion rate of 0.3 to 1.1 m/year and light blue lines show low accretion rate of 0 to 0.3 m/year. From Figure 5.16, it can be seen that, erosion has occurred in all the three divisions evidencing the three different portions of the coast at Winneba. 3 portions of the coast however, the greatest erosion is occurring at the eastern portion (Around the River Ayensu estuary or Sankor area) of the coast. That is, erosion along the Winneba coastline is severe within the eastern portion and this due to urbanization which disrupts fluvial sediment supply along shore. The erosion however, is not occurring throughout this portion as some areas within this portion show high accretion rate. Also, it be seen that on the Western portion (the Muni lagoon or Akosua village area); erosion is occurring at some areas of this portion though at a low rate with majority being neutral. At the

87 central portion (the Fishing harbour or the Prisons area), there are few areas eroding with majority being neutral. The reasons for the majority being neutral is because of the presence of some few patches of rocks alongshore which prevents sea water from eroding or advancing into the land. Also, the thick dry sandy beach amidst the rocks in this portion soaks sea water. That is, dependent on the volume and intensity of sea water to the coast, the rocks and the thick sandy beach serve as resistive mechanisms or buffer to the sea water. Another thing that prevents severe erosion from this portion is the huge refuse dump or waste alongshore that has remained for several years, plied up and compacted into a rock like feature, also serve as resistive mechanism to sea water. Although, this serves as an advantage in terms of erosion prevention nonetheless, it prevents tourists from utilizing the area due to the bad odour it presents to tourists.

It was realized from Figure 5.16, that erosion was occurring in all the 3 portions of the Winneba coastline even though majority of these areas have remained neutral over the years. The study therefore, saw the need to predict the future changes that is likely to occur especially, in the next 10 and 20 years. The Kalman filter model was therefore, employed through the BETA shoreline analysis forecasting in DSAS to predict the shoreline positions base on the current shoreline data as shown in Figure 5.17.

Figure 5.17: 10 and 20 year shoreline forecast of the Winneba coastline from the Shoreline years 2000-2020. Source: Field Survey, 2020.

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Figure 5.17 shows the shoreline forecast for the years 2031 and 2041. That is, 10 and 20 years from the 2020 shoreline. Black and brown colours with red dots denote the predicted shoreline positions and points for the years 2031 and 2041 respectively. The shaded pink with yellow ends and the shaded green and blue ends colours shows the uncertainties in the shorelines positions for the years 2031 and 2041 respectively. The total number of cast transects was 350 with an average distance of 115 m and an average uncertainty of ±7.3 in both years.

The forecasted 2031 and 2041 shoreline positions portray a decadal trend in the movement of the Winneba shoreline. Forecasted shoreline positions do not expect to reveal the precise positions of the shorelines however; it assists and provides directions about the long term changes in the shoreline. Even though process noise such as coastal infrastructure and the rocky coasts were acknowledged, the inadequacy or lack of data posed a challenge since shoreline prediction requires more data across time. The lack of data was evident in the forecast uncertainty at each transect. The uncertainty at each transect kept growing until the needed observation was reached for correction. The forecasted shorelines did not take into consideration the active roles of sea level rise and storms. The Kalman filter model base on the past to predict future changes and so if sea level rise and storm effects has ever happened along the coast, then they are incorporated into the model.

5.3 Analysis of the physical impacts of Climate Change and its implications on the Winneba community

Due to the incidence of climate change (section 5.1 and 5.2) in the Winneba community, the study saw the need to examine the physical impacts climate change and its implications on the community. The study therefore, analysed the impacts of shoreline change through erosion of the coast and its implications on the Winneba community.

5.3.1 Impacts of shoreline change and erosion on the Winneba community

Shoreline change and the associated erosion along the coast and the coastal zone in general has become a major challenge in Ghana and pose several threats to coastal developments. To demonstrate how shoreline change and erosion have affected the coastal zone and its community, field observations were carried out along the three portions of the coast. The impacts on infrastructure, erosion of the coast and vegetation and the extension of saline waters into inland waters were captured through photographs. The impacts on infrastructure are shown in photo 5.2.

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A1 A2 B

Photo 5.2: Impacts of sea erosion on coastal infrastructure along the Central portion (A1 and A2) and the Eastern portion (B). Source: Field Survey, 2020.

Photo 5.2 shows the impacts of sea erosion on infrastructure along the central and eastern portions of the Winneba coast. The numerous advantages provided by the coast attract a lot people and this has led to increased economic activities and the resultant decreases and alterations in the natural landscapes of many coasts (Jonah et al, 2016).

The diverse economic and recreational activities along the central and the eastern portions of the Winneba coast have attracted many people. Findings revealed that, through urbanization, these areas are occupied with infrastructure for residential and commercial purposes. This is in conformity with a study by Cashman and Nagdee in 2017, who indicated the transition of coastal lands into agriculture, commercial and touristic purposes in the Caribbean Island. Coastal infrastructure as in the case of photo A and B in photo 5.1 are located just along the shoreline and this disrupts the natural flow of sea waters and waves. Changing shoreline through sea erosion is however, impacting negatively on these infrastructures as in the case of Photo (A1 and A2) where there is the development of cracks on the building. Observations revealed that, building in photo A has been abandoned due to the cracks. With continued observed erosion rates and the forecasted land displacement (see section 5.2) of the Winneba coast, the implications of coastal infrastructures are enormous. There is the potential

90 displacement of the people as in the case of the households of photo (A1 and A2). There is also the reduction in income and socio-economic status as infrastructure owners may spend more money on mitigations and management of their investment (Jonah et al, 2015) as in the case of photo (B) (Royal Beach Resort) which serves as a major recreational centre along the Winneba coast.

Field observations also revealed that not only coastal infrastructures (buildings) are affected by erosion but coconut plantations along the western and eastern portions of the coast are also affected as shown in photo 5.3.

A B1 B2 B3

Photo 5.3: Erosion and its impacts coconut plantation along the Western portion (A) and the Eastern portion (B1, B2 and B3) of the Winneba coast. Source: Field Survey, 2020.

Photo 5.3 shows erosional impacts on the coconuts plantation along the along the Muni lagoon area (western portion) (A) and the Ayensu River area (Eastern portion) (B 1, B2 and B3) of the Winneba coast. It can be seen from the Photos that through shoreline change under climate change, the erosional actions that result from the dynamics in sea water and waves are very strong and has impacted negatively on the vegetation (coconut plantation).

Coastal vegetation plays an essential role in the beautification of the coast and helps increase its aesthetic purposes. Vegetation also protects the coast against erosion caused by wind and

91 wave actions and again protection the coast from flooding (Anim et al, 2013). The incidence of climate change through sea level rise and the resultant shoreline change and erosion has affected and overcome these essential services provided by coastal vegetation in Ghana (Anim et al, 2013). Vegetation along the Winneba coast and the coconut plantations in particular provide shade and serve as resting place for tourists and other animal species. Shoreline change and related erosion along the coast has destroyed many of these coconut trees thereby reducing the beauty and aesthetic view of the coast. It must be noted that, although, anthropogenic activities and their negative consequences on the coastal vegetation cannot be ignored however, field observations revealed that the vegetation along the Winneba shoreline and the coconut plantations in particular are being destroyed by erosion through sea level rise that advances sea water and erode the land and its vegetation. Coconut trees along the western portion of the coast in the years of 2015 and 2016 as indicated in the study of Davies-Vollum et al, 2019 revealed that although erosion was occurring in the western portion however, coconut trees were still upright and none had fell. This notwithstanding, in the year 2020 and in this study as shown in photo 5.3 (A) reveal the falling and gradual collapse of the coconut plantations in this portion of the coast. The situation is not different from what is happening in the eastern portion photo (B1, B2 and B3) with gradual increase in erosion scrap height and the exposure of tree roots.

The implications of this on the community are enormous. First, the rate at which people troop into the community for touristic purposes may be reduced. With reduced tourism, sectors of the Winneba that depends on tourism may be affected with respect to income generation. Also, the services provided by the coconut plantation in the form of wind break will be reduced and strong winds from the ocean may reach the land and cause destruction of properties. The impacts on other species such as birds cannot also ne ignored as birds species may move deep into the wetlands and the forest out of reach of tourists that may be found on the beach.

Field observation further revealed how climate change through sea level rise extends saline water from the ocean into inland waters along the coast as shown in photo 5.4.

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A B

Ocean Lagoon Ayensu River

Ocean

Photo 5.4: Sea water extending from the ocean into inland waters; Ayensu River (A) and Muni lagoon (B) along the Winneba coast. Black arrow shows direction of flow of sea water. Source: Field Survey, 2020.

Photo 5.4 shows the extension of saline water into the Ayensu River (A) and Muni lagoon (B). The Ayensu River is the major river in the community and provides a lot of benefits to the community especially to the local farmers in the irrigation of their crops and for other domestic purposes. The Muni lagoon also provides food in the form of fish to the community especially the community of Akosua village which is located near the coast and the lagoon.

Over the years, the services provided by the Ayensu River and the Muni lagoon have been threatened by sea level rise and the resultant extension of saline waters into these inland waters. Salt water intrusion as a result of rising sea levels have impacted coastal surface freshwaters and underground water resources and have threatened human and other species dependence on these resources (NECASC, 2016). The direct impact of climate change on coastal freshwater resources arrives basically from rising sea level and related sea water intrusion into surface waters and coastal aquifers. There is also the extension of seawater into lagoons, estuaries and other coastal river systems (Hay & Mimura, 2005; IPCC, 2007). Field observation revealed that residence within the coastal zones of the Winneba especially the community of Akosua village cannot depend on underground water. This is due to the saltiness of the underground water caused by rising sea level and the nearness of the water table to the surface of the earth due to the low lying nature of the area. Residents in this area therefore,

93 depend on tap water connected from inland by the Ghana Water Company Limited (GWCL) as shown in photo 5.5.

Due to the low socio-economic status of these residents, they are not able to connect tap water direct into their homes. Tap waters from the GWCL are constructed at strategic points within the coastal zone and residents have to walk distance to fetch. Field observation further revealed that there exist inconsistencies in the flow and supply of water by the GWCL and there is the problem of water shortages especially during the dry season where major rivers that the GWCL depend on for supply dry out and reduce their volumes. Some residents therefore, connect tap waters into reservoirs during times of high flow from the GWCL. The study also suggests the problem of water quality due to how water is stored as in the case of photo 5.5(B). This finding is in conformity with the IPCC‘s 2007 report which indicated that coastal areas will have higher feeling to water stresses through decrease in water availability (IPCC, 2007). The finding also conforms to the assertion of Ragab and Prudhomme, in 2002, which indicates that global water challenges caused by climate change and related sea level rise and salt water intrusion are highly felt by developing countries where majority of the people live in the coastal zones.

A B

Photo 5.5: Water problems (A and B) in the coastal areas of Winneba due to climate change incidence. Source: Field Survey, 2020.

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CHAPTER SIX

CONCLUSIONS AND RECOMMENDATIONS

6.0 Introduction

The study aims at assessing the problems of climate change in Winneba and intends to point out mitigation and sustainability measures for the coastal area of Winneba, in order to contribute to its adaption.

The study specifically seeks to:

 Evaluate climate change on the Winneba community (Period from 1980-2019)  Analyse shoreline change along the Winneba coastline under climate change  Examine the physical impacts of climate change and its implications on the Winneba community.

6.1 Conclusions

6.1.1 Objective One

Findings from the study suggest that there have been great change and variability in both land and oceanic climatic parameters over the Winneba community. There has been an increasing trend in land surface maximum and minimum temperature from 1980-2019. Findings suggest that although there has been an increasing trend in both maximum and minimum temperature, the trend in minimum temperature is stronger than that of maximum temperature. This trend in minimum temperature is due to the urban heat island effect and the impact of the sea which is larger at night than at day time. Again, findings suggest that there has been an increased trend in both SST and SSS since 1980-2019 and 1994-2019 respectively.

Findings also suggest and increasing trend in rainfall amidst variations in the community. The variability in rainfall and the changes in temperature confirm the presence of climate change in the community. The study revealed an annual range of temperature of 2.7°C with a mean of 27.6°C while the average annual rainfall is 761.4 mm with a range of 910.9 mm. The variability and change in rainfall and temperature affect the planting time of farmers in the community and also affect the general health of the people. The study attributed the increased malaria caseloads and the resultant deaths to the variations and changes in rainfall and temperature and this is affirmed by the study of Asante and Amuakwa-Mensah (2015). Findings further

96 suggest that the variability, seasonality and the array of divergence in rainfall has resulted in the community‘s perception on decreased rainfall in the community even though absolute values revealed an increasing trend. Findings moreover, suggest that while maximum relative humidity showed an increasing trend, minimum relative humidity showed a decreasing trend as measured in different moments in the day. A combination of annual mean temperature and relative humidity revealed a negative relationship. While temperature has been increasing, relative humidity has been decreasing. Besides, findings suggest a projected increase in both maximum and minimum temperature and rainfall under the RCP 4.5 and 8.5 scenarios. These changes are associated with great fluctuations. Findings suggest that rainfall amounts in the future will be high in the month of May, June, July and October while that of maximum and minimum temperature are going to be high in March, April and December.

The above findings clearly indicate the incidence of climate change in Winneba. Attribution and cause to the climate change incidence is due to the increase global emissions of pollutant gases through anthropogenic actions and the related warming of the atmosphere.

6.1.2 Objective Two

Coastal areas are highly inhabited by many people. This is due to the numerous benefits it presents to its dwellers. The incidence of climate change through natural and anthropogenic actions impacts negatively on and threatened the sustenance of the benefits provided by many coasts.

Findings suggest that shoreline erosion rate along the Winneba area based on 2000-2020 data is -3.2 m/year with a maximum land displacement of 64 m and an accretion rate of 1.0 m/year. Erosion rates along the Winneba coastline affirm studies on coastal erosion rates along the Elmina- Cape Coast- Moree and the Accra coastlines conducted by Jonah et al, in 2016 and Appeaning-Addo in 2009 respectively. Findings again revealed that although the end point rates revealed higher erosional values, the linear regression rates revealed more erosional and accretion levels along the three portions of the Winneba coast. This finding is in conformity with Crowell et al, in 1997, arguments on the ignorance in the level of change in erosion or accretion along shorelines in some cases by the end point rate. Findings further suggest that in 10 and 20 years after the year 2020, the Winneba shoreline will be displaced by a maximum distance of 115 m with an average uncertainty of ±7.3. Moreover, findings suggest that the rates and magnitude of erosion along the eastern portion of the coast is highly influenced by urbanization

97 which disrupts sediment transport to the coast. This finding is in consistent with a study by Jonah et al, in 2016, which indicated the influence of urbanization along the Elmina- Cape Coast- Moree coastlines.

6.1.3 Objective Three

The impacts of climate change are felt on all species on earth. Climate change impacts on shoreline change and related erosion of the coast and the coastal ecosystem, affects human and other species that depend on this ecosystem.

Findings suggest that infrastructures (buildings) along the Winneba coast are affected by erosion. Findings again suggest a possible total collapse of these infrastructures and the resultant displacement of people due to the observed rate of erosion along the coast and this may affect the socio-economic status of the residents involved. Findings also revealed that vegetation (coconut plantation) along the eastern and western portions of the coast has been negatively affected by erosion and this has reduced its aesthetic and dwelling purposes for humans and birds species respectively. Findings moreover revealed water supply, availability and usage problems in the coastal zone of the Winneba community due to the extension of sea water into coastal freshwaters and underground aquifers, which has made these inland waters unsafe for domestic and agricultural use.

6.2 Recommendations

The problems identified in this study affect the coastal zone, the community and the general coastal developments. This section of the study therefore, point out mitigation and sustainability measures in the form of recommendations to the stakeholders in climate change and the community members on the necessary measures and policies needed to combat the impacts of climate change in the community. Many studies have been conducted on the impacts of climate change in Ghana. However, little studies on climate change impacts have been conducted on the Winneba community. Findings from this study therefore, serve as an appropriate basis for knowledge contribution and making recommendations on the climate change incidence and the associated impacts in the community. The study therefore, presents the following mitigation and adaptation measures:

 Climate change impact awareness creation and environmental education: Findings suggest great changes in both land and oceanographic climatic parameters in the

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Winneba community. Findings attributed the causes of these parameter changes to the negative anthropogenic activities which changes the earth‘s atmosphere. Considering the educational levels and the attitude of the Winneba community, it will only take the commencement of awareness creation and environmental education on the negative community actions against the environment to reduce the climate change incidence and the associated impacts. Findings suggest increased temperature and rainfall in the future in the community and this will increase the occurrences of vector-borne diseases in the community. This will increase hospital admissions and deaths. The EPA through its mandates should intensify its environmental education on the climate change incidence to the community members on the necessary measures to take and to reduce the climate change incidence and its associated impacts.  Buffering of the shoreline zones: Since the shoreline is influenced by both land and oceanic processes, there is the need to create buffer especially on where to put up coastal infrastructures. Limiting coastal developers through buffering the shoreline zone will allow the free flow of coastal processes like sedimentation.  Implementing ecosystem recovery approaches: ecosystem recovery approaches such as the growing of trees will help protect the shore and the beach area from erosion. Growing of vegetation along the coast will protect especially the sandy beach from wind and wave erosion. Trees that can withstand salt water should be planted in the areas along the coast where sea water extends to.  Decentralization of management policies: Due to the numerous responsibilities undertaken by the EPA on Ghana‘s environment, there is the need to decentralize these responsibilities to the community level. Local coastal community members should be trained and empowered with the needed management tools to monitor the coast and its associated impacts.  Enforcement of coastal management policies: Policies on coastal conservation measures should be enforced. Coastal developers and people who want to undertake projects along the coast should be issued permits. This will allow developers to follow the necessary guidelines and the conservation practices.  Incorporation of community perception with institutional knowledge: Findings suggest that community members especially farmers have negative perception towards rainfall and this affects the planting time and the beginning of their farming season. The Ghana Meteorological Agency should make available the needed weather data to community

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members and the intensive utilization of the agricultural extension officers in helping farmers in choosing seeds at a particular season.  Community health education on hygiene: Findings suggest increased incidence of malaria cases and death in the community. Although the causes of malaria cases are attributed to the climate change incidence in the community, the continued occurrences and deaths are being ignited by the unhygienic attitude of the community members. The government through the health officials in the community should therefore provide the necessary education on personal and environmental hygiene to the community members.  Intensive coastal zone research: Intensive and continued coastal zone research should be encouraged. Several coastal research centres should be established. Established research centres will help provide solutions to the problems facing the coastal zones such as climate change and the related impacts like erosion, sea level change and shoreline change.

6.3 Areas for further research

Due to time and resources, the study was not able to assess the community‘s participation in addressing the erosion problem in the area. Therefore, a further study on local contribution in tackling coastal erosion in the Winneba community is required.

6.4 Limitations

The study encountered limitations during the data collection period. Sea level change data was limited in the study area. Sea level change data was needed in order to know its contribution to shoreline change on the Winneba coast. This limitation occurred due to the breakdown of the tidal gauge in Ghana. The study was again limited by data such wind speed. The wind speed data was need since its one of the essential climatic variable needed in the climate change assessment in the community.

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REFERENCES

Abbate, E., Bruni, P. and Sagri, M. (2015). Geology of Ethiopia: A Review and Geomorphological Perspectives. In: Billi P. (eds) Landscapes and Landforms of Ethiopia. World Geomorphological Landscapes. Springer, Dordrecht DOI: https://doi.org/10.1007/978-94-017-8026-1_2

Aber, J. S. (2008). Glaciation throughout Earth‘s history. Emporia State University. Available: http://academic.emporia.edu/aberjame/ice/labs/lab03.htm

Acheampong, P. (1982). Rainfall Anomaly along the Coast of Ghana. Its Nature and Causes. Geografiska Annaler. Series A, Physical Geography, 64(3/4), 199-211. Doi:10.2307/520646

Adu, S. V. and Asiamah R. D. (1992). Soils of the Ayensu – Densu Basin, Central, Eastern and Greater Accra Regions, Ghana. Memoir No. 9. CSIR – Soil Research Institute, Kwadaso,

Afoakwah, R., Osei, M. B. D. and Effah, E. (2018). A Guide on Illegal Fishing Activities in Ghana. USAID/Ghana Sustainable Project. Narragansett, RI: Coastal Resources Centre, Graduate School of Oceanography, University of Rhode Island. Prepared by the University of Cape Coast, Ghana. GH2014_SCI048_UCC 64 pp.

Ahern, M., Kovats, R.S., Wilkinson, P., Few, R. and Matthies, F. (2005). Global health impacts of floods: epidemiologic evidence. Epidemiol Rev, 27:36–46. DOI:10.1093/epirev/mxi004

Aikins, E.K.W. (2018). Challenges of Sustainable Marine . International Journal of Animal and Veterinary Sciences Vol:12, No:10. Available: https://pdfs.semanticscholar.org/3040/75bf89bca30fda2879837809aa48ebd19132.pdf (Accessed: 13th November, 2019).

Akyeampong, E.K. (2000). Between the Sea and the Lagoon: An Eco-Social History of the Anlo of South-eastern Ghana, c1850 to Recent Times. Athens: Ohio University Press

Allen, R.D., Seaman, S.M. and Delascio, J.E. (2009). Emerging issues: Global Warming Claims and Coverage Issues. Defense Counsel Journal, V.76:1

102

Angnuureng, B. D., Appeaning-Addo, K. and Wiafe, G. (2013) Impact of sea defence structures on downdrift coasts: The case of Keta in Ghana. Academia Journal of Environmental Sciences 1(6):104–121

Anim, O. D., Nti, N. P. and Macperson, D. N. (2013). A Rapid Overview of Coastal Erosion in Ghana. International Journal of Scientific & Engineering Research Volume 4, Issue 2, p.7.

Ankrah, J. (2018). Climate change impacts and coastal livelihoods; an analysis of fishers of coastal Winneba, Ghana. Ocean & Coastal Management, V,161, 141-146 https://doi.org/10.1016/j.ocecoaman.2018.04.029

Appeaning-Addo, K. (2011). Changing morphology of Ghana‘s Accra coast. J Coast Conserv, v, 15: 433. https://doi.org/10.1007/s11852-010-0134-z

Appeaning-Addo, K. (2009). Detection of coastal erosion hotspots in Accra, Ghana. Journal of Sustainable Development in Africa 11(4): 253–265

Appeaning-Addo, K. and Adeyemi, M. (2013). Assessing the impact of sea-level rise on a vulnerable coastal community in Accra, Ghana. Jàmbá: Journal of Disaster Risk Studies, 5(1), 8 pages.doi:https://doi.org/10.4102/jamba.v5i1.60

Appeaning-Addo, K., Larbi, L., Amisigo, B. and Ofori-Danson, P.K. (2011). Impacts of Coastal Inundation Due to Climate Change in a CLUSTER of Urban Coastal Communities in Ghana, West Africa. Remote Sens, 3, 2029-2050. https://doi.org/10.3390/rs3092029

Appeaning-Addo, K., Nicholls, R.J., Codjoe, S.N.A. and Abu, M. (2018). A biophysical and socioeconomic review of the Volta delta, Ghana. Journal of Coastal Research, 34(5), 1216–1226. Coconut Creek (Florida).

Appeaning-Addo, K., Walkden, M., & Mills, J. (2008). Detection, measurement and prediction of shoreline recession in Accra, Ghana. ISPRS Journal of Photogrammetry & Remote Sensing 63, 543–558. DOI: 10.1016/j.isprsjprs.2008.04.001

Armah A. K, Amlalo D. S (1998). Coastal Zone Profile of Ghana. Gulf of Guinea Large Marine Ecosystem Project. Accra, Ghana: Ministry of Environment, Science and Technology, 111

103

Arnell, N.W. (2004). Climate change and global water resources: SRES scenarios and socio- economic scenarios. Glob. Environ. Chang, 14, 31-52. doi:10.1016/j.gloenvcha.2003.10.006

Arnell, N. W., Hudson, D. A. and Jones, R. G. (2003). Climate change scenarios from a regional climate model estimating change in runoff in southern Africa, J. Geophys. Res., 108(D16), 4519, doi: 10.1029/2002JD002782

Asante, F.A. and Amuakwa-Mensah, F. (2015). Climate Change and Variability in Ghana: Stocktaking. Climate 2015, 3, 78-99. Doi: 10.3390/cli3010078

Azongo, D. K., Awine, T., Wak, G., Binka, F. N. and Oduro, A. R. (2012). A timeseries analysis of weather variability and all-cause mortality in the Kasena-Nankana Districts of Northern Ghana, 1995–2010. Glob Health Action. doi:10.3402/gha.v5i0.19073

Badjeck, M. C., Allison, E. H., Halls, A. S. and Dulvy, N. K. (2010). Impacts of climate variability and change on -based livelihoods. Marine Policy 34:375–383. doi:10.1016/j.marpol.2009.08.007

Badu-Agyei, B. (2012). Climate Change impacts on Ghana, are the politicians interested? Available: http://ghananewsagency.org/features/climate-change-impacts-on-ghana-are- the-politicians-interested–52641 (Accessed 16th October, 2019).

Bahr, D. B., Dyurgerov, M., and Meier, M. F. (2009). Sea-level rise from glaciers and ice caps: A lower bound. Geophys Res Let 36, 4 (2009). Doi: L0350110.1029/2008gl036309.

Berger, A. and Loutre, M.F. (2000). "CO2 And Astronomical Forcing of the Late Quaternary". Proceedings of the 1st Solar and Space Weather Euro conference, 25-29 September 2000. The Solar Cycle and Terrestrial Climate. 463. ESA Publications Division. p. 155.

Bindoff, N. J., Willebrand, V., Artale, A., Cazenave, J., Gregory, S., Gulev, K., Hanawa, C., Le, Q. and Co-authors. (2007). Observations: Oceanic climate change and sea level. Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change, S. Solomon, D. Qin, M. Manning, Z. Chen, M. Marquis, K.B. Averyt, M. Tignor and H.L. Miller, Eds., Cambridge University Press, Cambridge 385-432

104

Boadi, S. (2013). Climate change problems to affect Ghana. Daily Guide. Available: http://ghananewsagency.org/features/climate-change-impacts-on-ghana-are-the- politicians-interested–52641 (Accessed 15th October, 2019).

Boateng, I. (2012). An application of GIS and coastal geomorphology for large scale assessment of coastal erosion and management: A case study of Ghana. Journal of Coastal Conservation 16(3): 383–397. Doi: 10.1007/s11852-012-0209-0

Boateng, I., Wiafe, G. and Jayson-Quashigah, P-N. (2016) Mapping Vulnerability and Risk of Ghana's Coastline to Sea Level Rise, Marine Geodesy, 40:1, 23- 39, DOI: 10.1080/01490419.2016.1261745

Boko, M., Niang, I., Nyong, A., Vogel, C., Githeko, A., Medany, M., Osman-elasha, B., Tabo, R. and Yanda, P. (2007). Africa. in: Parry, M.l., Canziani, O.F., Palutikof, J.P., Van Der Linden, P.J. & Hanson, C.E. (eds.). Climate change (2007): Impacts, Adaptation and Vulnerability. Contribution of Working Group II to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge: Cambridge University Press, pp. 433-467.

Boyd, H.K., Westfall, R. and Stasch, S.F. (1981). Marketing Research: text and cases. Richard, D.Inc

Brammer, H. (1962). Soils: In Agriculture and Landuse in Ghana. J.B Wills ed., Oxford University Press London, Accra, New York. Pp. 88-126.

Brander, K., G. Blom, M.F., Borges, K., Erzini, G., Henderson, B.R., MacKenzie, H., Mendes, A.M.P., Santos and Toresen, P. (2003a): Changes in fish distribution in the eastern North Atlantic: are we seeing a coherent response to changing temperature? ICES Marine Science Symposia, 219, 260-273.

Brecht, H., Dasgupta, S., Laplante, B., Murray, S. and Wheeler, D. (2012). Sea level rise and storm surges: high stakes for a small number of developing countries. J Environ Dev 21(1):120–138. Doi: 10.1177/1070496511433601

Brooks, N. (1999). Dust-climate interactions in the Sahel-Sahara zone with particular reference to late twentieth century Sahel drought Unpublished PhD Thesis, University of East Anglia Norwich, 350pp.

105

Brunn, P. (1962). Sea level rise as a cause of shoreline erosion. Journal of Waterways and Harbours, 1, 116-180.

Buddemeier, R.W., Kleypas, J, A. and Aronson, B. (2004). Coral Reefs and Global Climate Change: Potential Contributions of Climate Change to Stresses on Coral Reef Ecosystems. Report prepared for the Pew Centre of Climate Change, Arlington, , 56 pp.

Cahoon, D.R., Hensel, P., Rybczyk, J., McKee, K., Proffitt, C, E. and Perez, B. (2003): Mass tree mortality leads to mangrove peat collapse at Bay Islands, Honduras after Hurricane Mitch. J. Ecol., 91, 1093-1105.

Cahoon, D.R., Hensel, P, F., Spencer, T., Reed, D, J., McKee, K, L. and Saintilan, N. (2006). Coastal wetland vulnerability to relative sea-level rise: wetland elevation trends and process controls. Wetlands as a Natural Resource, Vol. 1: Wetlands and Natural Resource Management, J. Verhoeven, D. Whigham, R. Bobbink and B. Beltman, Eds., Springer Ecological Studies series, Chapter 12. 271-292. Caldeira, K., and M.E. Wickett, 2005: Anthropogenic carbon and ocean pH. Nature, 425, 365.

Cameron, C. (2011). Climate Change Financing and Aid Effectiveness: Ghana Case Study. Available: http://www.agulhas.co.uk (accessed on 3rd January, 2020).

Caminade, C., Ndione, J,A., Kebe, C.M.F., Jones, A.E., Danuor, S, Tay, S., Tourre, Y.M,, Lacaux, J. P., Vignolles, C., Duchemin, J., B, Jeanne, I. and Morse, A. P. (2011) Mapping Rift Valley fever and malaria risk over West Africa using climatic indicators. Atmos Sci Lett 12:96–103. doi:10.1002/asl.296

Cashman, A. and Nagdee, M.R. (2017) Impacts of Climate Change on Settlements and Infrastructure in the Coastal and Marine Environments of Caribbean Small Island Developing States (SIDS), Caribbean Marine Climate Change Report Card: Science Review 2017, pp 155-173.

Cazenave, A. and Llovel, W. (2010). Contemporary sea level rise. Annual Review of Marine Science 2:145–173.

Chao, B. F., Wu, Y. H. and Li, Y. S. (2008). Impact of Artificial Reservoir Water Impoundment on Global Sea Level. Science 320, 212-214 doi: 10.1126/science.1154580.

106

Charron, I. (2016). A Guidebook on Climate Scenarios: Using Climate Information to Guide Adaptation Research and Decisions, 2016 Edition. Ouranos, 94p. Available: https://www.ouranos.ca/publication-scientifique/Guidebook-2016.pdf (Accessed: 12th January, 2020)

Cheung, W.W. L., Lam, V.W.Y., Sarmiento, J.L., Kearney, K., Watson, R., Zeller, D. and Pauly, D. (2010). Large-scale redistribution of maximum fisheries catch potential in the global ocean under climate change. Glob Change Biol 16(1):24–35. doi:10.1111/j.1365- 2486.2009.01995.x

Chowdhury, F. R., Ibrahim, Q. S. U., Bari, M. S., Alam, M. M. J., Dunachie, S. J. and Rodriguez- Morales, A. J. (2018). The association between temperature, rainfall and humidity with common climate-sensitive infectious diseases in Bangladesh. PLoS ONE 13(6): e0199579. https://doi.org/10.1371/journal.pone.0199579

Church, J.A., Clark, P.U., Cazenave, A., Gregory, J.M., Jevrejeva, S., Levermann, A., Merrifield, M.A., Milne, G.A., Nerem, R.S., Nunn, P.D., Payne, A.J., Pfeffer, W.T., Stammer, D. and Unnikrishnan, A.S. (2013). Sea Level Change. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA.

Church, J. A., Gregory, J.M., White, N. J., Platten, S. M. and Mitrovica, J. X. (2011a): Understanding and projecting sea level change. Oceanography, 24, 130–143.

Church, J.A. and White, N. J. (2011). Sea-level rise from the late 19th to early 21st century. Surveys in Geophysics doi: 10.1007/s10712- 011-9119-1.

Church, J. A., Woodworth, P.L., Aarup, T. and Wilson, W.S. (eds.) (2010): Understanding Sea- Level Rise and Variability. Wiley-Blackwell, Hoboken, NJ, USA, 428 pp.

Climate of Winneba. Available: https://en.climate-data.org/africa/ghana/central-region/winneba- 714994/ (Accessed: 19th November, 2019).

Codjoe S. N. A., Appeaning-Addo, K., Tagoe, A. C., Nyarko, K. B., Martey, F., Nelson, A. W., Jayson-Quashigah, P., Atiglo, Y. D., Adjei, O. P., Anderson, K., Mensah, A., Ofori-

107

Danson, K. P., Amisigo, A. B., Ayamga, J., Asmah, E. E., Asenso, K. J., Owusu, G., Quaye, M. R. and Abu, M. (2020). The Volta Delta, Ghana: Challenges in an African Setting. In: Nicholls R., Adger W., Hutton C., Hanson S. (eds) Deltas in the Anthropocene. Palgrave Macmillan, Cham https://doi.org/10.1007/978-3-030-23517- 8_4

Codjoe, S.N.A. and Owusu, G. (2011). Climate change/variability and food systems: Evidence from the Afram Plains, Ghana. Reg. Environ. Change 2011, 11, 753–765. https://doi.org/10.1007/s10113-011-0211-3

Coffel, E. D., Radley, M. H. and Alex de Sherbinin. (2018). Temperature and Humidity Based Projections of a Rapid Rise in Global Heat Stress Exposure During the 21st Century. Environ.Res.Lett.13014001. DOI: https://doi.org/10.1088/1748-9326/aaaooe

Cogley, J.G. (2009). Geodetic and direct mass-balance measurements: Comparison and joint analysis. Annals of Glaciology 50:96–100.

Cohen, K.M., Finney, S.C., Gibbard, P.L. and Fan, J.-X. (2013). The ICS International Chronostratigraphic Chart, Episodes, v. 36, no. 3, pp. 199-204. Available: http://www.stratigraphy.org/ICSchart/Cohen2013_Episodes.pdf

Collier, P., Conway, G. and Venables, T. (2008). Climate change and Africa. Oxford Review of Economic Policy, 24(2), 337–353. https://doi.org/10.1093/oxrep/grn019

Confalonieri, U. B. Menne, R. Akhtar, K.L. Ebi, M., Hauengue, R.S.m Kovats, B.R. and Woodward, A. (2007): Human health. Climate Change 2007: Impacts, Adaptation and Vulnerability. Contribution of Working Group II to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change, M.L. Parry, O.F. Canziani, J.P. Palutikof, P.J. van der Linden and C.E. Hanson, Eds., Cambridge University Press, Cambridge, UK, 391-431.

Conrad, C. P., and Hager, B. H. (1997): Spatial variations in the rate of sea level rise caused by the present-day melting of glaciers and ice sheets. Geophys. Res. Lett., 24, 1503–1506.

Convention on Biological Diversity (CBD), (2010). Clearing House Mechanism of Ghana. Muni- Pomadze Ramsar Site. Available: http://gh.chm-cbd.net/biodiversity/faunal-diversity-

108

ghana/-situ-conservation-2/ramsar-sites/muni-pomadze-ramsar-site (Accessed 15th October, 2019).

Conway, D., Aurelia, P., Sandra, A., Hamisai, H., Claudine, D. and Gil, M. (2008). Rainfall and water resources variability in Sub Saharan Africa during the 20th century. J. Hydrometeorol, 10, 41–59. DOI: https://doi.org/10.1175/2008JHM1004.1

Conway, G. (2009). The science of climate change in Africa: impacts and adaptation. Discussion paper No 1. Grantham Institute for Climate Change. London Imperial College, UK

Council for Scientific and Industrial Research (CSIR), (2013). Report on the Soil Resources of , CentraL Region, Region, Ghana (CSIR / WAAPP NO. 017). Soil Research Institute, Kwadaso, Kumasi. Available:https://waapp.org.gh/waappmedia/reports/33-report-on-the-soil-resources-of- gomoa-east-district/file (Accessed: 27th November, 2019).

Crowell, M., Letherman, S.P. and Buckley, M.K. (1991). Historical shoreline change: Error analysis and mapping accuracy. Journal of Coastal Research 7 (3), 839–852.

Dadson, I.Y. (2012). Integrated Human and Regional Geography. Salt and Light Publication, Accra, Ghana.

Dadson, I. Y., Owusu, B. A. and Osman, A. (2016). ―Analysis of Shoreline Change along Cape Coast-Sekondi Coast, Ghana,‖ Geography Journal, vol. 2016, Article ID 1868936, 9 pages. https://doi.org/10.1155/2016/1868936.

Dasgupta, S., Laplante, B., Meisner, C., Wheeler, D. and Yan, J. (2008). The impact of sea level rise on developing countries: a comparative analysis. Climate Change, 93(3-4), 379-388.

Davies-Vollum, K.S., Zhang, Z. and Agyekumhene. (2019). Impacts of lagoon opening and implications for coastal management: case study from Muni-Pomadze lagoon, GhanaA. J Coast Conserv, v, 23: 293. https://doi.org/10.1007/s11852-018-0658-1

Department of Climate Change and Energy Efficiency (2012). Climate Change in a nutshell. Australian Government, Canberra, Available: http://www.climatechange.gov.au/en/climate-change.aspx, (Accessed 15th October, 2019).

109

DiMento, J. F. C. and Doughman, P. M. (2007). Climate Change: What It Means for Us, Our Children, and Our Grandchildren. The MIT Press. p. 68.

Dontwi, J., Dontwi, I.K., Buabeng, F.N. and Ashong, S. (2008). Vulnerability and Adaptation Assessment for Climate Change Impacts on Fisheries; The Netherlands Climate Assistance Programme (NCAP): Accra, Ghana.

Dosio, A. (2017). Projection of temperature and heat waves for Africa with an ensemble of CORDEX Regional Climate Models. Climate Dynamics, 49, 493–519. https://doi.org/10.1007/s00382-016-335-5

Drinkwater, K., Beaugrand, G., Kaeriyama, M., Kim, S., Ottersen, G., Perry, R.I., Po¨rtner, H-O., Polovina, J.J. and Takasuka, A. (2010). On the processes linking climate to ecosystem changes. J Mar Syst 79:374–388. doi:10.1016/j.jmarsys.2008.12.014

Dronkers, J. (2019). Sea level rise. Available: http://www.coastalwiki.org/wiki/Sea_level_rise (Accessed: 2nd October, 2019).

Easterling, W.E. (2003). Observed impacts of climate change in agriculture and forestry. IPCC Workshop on the Detection and Attribution of the Effects of Climate Change, June 17- 19, New York City, USA.

Ehlers, J. and Gibbard, P. (2011). "Quaternary glaciation". Encyclopaedia of Snow, Ice and Glaciers. Encyclopedia of Earth Sciences Series. pp. 873–882. doi:10.1007/978-90-481- 2642-2_423.

Ekow, J.F., Mensah, A. E., Edziyie, E. R., Agbo, W.N. and Adjei-Boateng, D. (2016). Coastal Erosion in Ghana: Causes, Policies, and Management, Coastal Management, 44:2, 116- 130, DOI: 10.1080/08920753.2016.1135273

Environmental Protection Agency, (EPA), (2008). Ghana Climate Change Impacts, Vulnerability and Adaptation Assessments; Environmental Protection Agency: Accra, Ghana.

Environmental Protection Agency (EPA). (2011). Ghana‘s second national communication to the UNFCCC. Available: UNFCCC website: http://unfccc.int/resource/docs/natc/ghanc2.pdf (Accessed 11th October, 2019).

110

European Environment Agency [DK], (n.d). Global and European Sea Level. Available: https://www.eea.europa.eu/data-and-maps/indicators/sea-level-rise-5/assessment (Accessed 18th October, 2019).

Fisher, R. W. (1996). Future Energy Use. Future Research Quarterly, 31, 43-47.

Fitzpatrick, S, M., Torben, C. R. and Erlandson, J. M. (2015). "Recent Progress, Trends, and Developments in Island and Coastal Archaeology." The Journal of Island and Coastal Archaeology 10 (1):3-27.

Flato, G.J., Marotzke, B., Abiodun, P., Braconnot, S.C., Chou, W., Collins, P., Cox, F., Driouech, S., Emori, V., Eyring, C., Forest, P., Gleckler, E., Guilyardi, C., Jakob, V., Kattsov, C. R. and Rummukainen, M. (2013). Evaluation of Climate Models. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA.

Fowler, H.J., Blenkinsop, S. and Tebaldi, C. (2007). Linking climate change modeling to impacts studies: recent advances in downscaling techniques for hydrological modeling. International Journal of Climatology 27: 1547-15778. DOI: 10.1002/joc.1556

Freduah, G., Fidelman, P. and Smith, T. F. (2019). Adaptive capacity of small‐scale coastal fishers to climate and non‐climate stressors in the Western region of Ghana. Geogr J. 2019;185:96–110.https://doi.org/10.1111/geoj.12282

GARP. (1975). The Physical basis of climate and climate modelling, GARP Publication series No. 6 WMO/ICSU, Geneva. (A landmark description of the climate system).

Gastaldo, R. A., DiMichele, W.A. and Pfefferkorn, H.W. (1996). Out of the icehouse into the greenhouse: A Late Paleozoic analog for modern global vegetational change. GSA Today 6/10, p.1-7.

Gemeda, D. O. and Sima, A. D. (2015). The impacts of climate change on African continent and the way forward. Journal of Ecology and the Natural Environment, 7(10), 256–262.

111

Gettelman A. and Rood, R.B. (2016). Components of the Climate System. In: Demystifying Climate Models. Earth Systems Data and Models, vol 2. Springer, Berlin, Heidelberg DOI 10.1007/978-3-662-48959-8_2

Ghana Agricultural News Digest (2012). Ghana Agricultural News Digest- April 3 (Agricultural Issues). Available: gssp.ifpri.info/2012/04/03/Ghana-agricultural-news-digest (Accessed: 19th November, 2019).

Ghana Meteorological Agency (GMET) (n.d). Climatology. Available: http://www.meteo.gov.gh/website/index.php?option=com_content&view=article&id=62:cli matology&catid=40:feat (Accessed: 19th November, 2019).

Ghana Statistical Service (GSS), (2014). 2010 Population and Housing Census. (District Analytical Report, Effutu Municipality). Available: http://www2.statsghana.gov.gh/docfiles/2010_District_Report/Central/Effutu.pdf (Accessed 20th November, 2019).

Ghana Statistical Service (GSS), (2012). 2010 Population and Housing Census Final Results. Available:http://statsghana.gov.gh/gsscommunity/adm_program/modules/downloads/get _file.php?file_id=27 (Accessed 20th November, 2019).

Goosse H.P.Y., Barriat, W., Lefebvre, M.F. L. and Zunz, V. (2010). Introduction to climate dynamics and climate modelling. Available: http://www.climate.be/textbook

Gordon, C., Ntiamoa-baidu, Y. and Ryan, M.J. (2000). The Muni-Pomadze Ramsar Site. Available: https://www.academia.edu/24845613/The_Muni-Pomadze_Ramsar_site (Accessed 12th November, 2019).

Gordon, C., Yankson K.A., Biney, C.A., Amlalo, D.S., Tumbulto, J and Kpelle, D. (1998): Wetland typology: Contribution to the Ghana National Wetland Strategy. Ghana Wildlife Department, Coastal Wetlands Management Project, Accra.

Gornitz, V. and Lebedeff, S. (1987). Global sea-level changes during the past century, in Sea- Level Fluctuation and Coastal Evolution, D. Nummedal, O.H. Pilkey, and J.D. Howard, eds., SEPM Special Publication 41, Society for Sedimentary Geology, Tulsa, Oklahoma, pp. 3-16.

112

Gregory, J. M. and Lowe, J. A. (2000). Predictions of global and regional sea-level rise using AOGCMs with and without flux adjustment. Geophys. Res. Lett., 27, 3069–3072

Grimes, L. (1974). Radar tracks of Palaearctic waders departing from the coast of Ghana in spring. Ibis, ~,v.116- 165-171. DOI:10.1111/j.1474-919X.1974.tb00236.x

Grinsted, A., Moore, J. C, and Jevrejeva, S. (2009). Reconstructing sea level from paleo and projected temperatures 200 to 2100 AD, Climate Dynamics, 34, 461-472.

Hahn, M. B, Riederer, A. M. and Foster, S. O. (2009). The livelihood vulnerability index: a pragmatic approach to assessing risks from climate variability and change—a case study in Mozambique Glob. Environ. Chang. 19 74–88. https://doi.org/10.1016/j.gloenvcha.2008.11.002

Haines, A., Kovats, R.S., Campbell-Lendrum, D. and Corvalan, C. (2006) Climate Change and Human Health: Impacts, Vulnerability and Public Health. Public Health, 120, 585-596. http://dx.doi.org/10.1016/j.puhe.2006.01.002

Haines, A. and Patz, J. (2004). Health effects of climate change. J Am Med Assoc, 291:99–103. DOI:10.1001/jama.291.1.99

Hallegatte, S., Green, C., Nicholls, R.J. and Corfee-Morlot, J. (2013) Future flood losses in major coastal cities. Nat Clim Change. Doi: 10.1038/NCLIMATE1979

Hammond, A. (1996), Which World? Island Press, Washington, D.C., 306 Pp.

Hansen, J., Ruedy, R., Sato, M. and Lo, K. (2010). Global surface temperature change. Reviews of Geophysics 48:RG4004; doi: 10.1029/ 2010RG000345.

Hapke, C.J., Himmelstoss, E.A., Kratzmann, M., List, J.H. and Thieler, E.R. (2010). National assessment of shoreline change: Historical shoreline change along the New England and Mid-Atlantic coasts: U.S. Geological Survey Open-File Report 2010-1118, 57p.

Hay, J.E. and Mimura, N. (2005). Sea-level rise: Implications for water resources management. Mitigation and Adaptation Strategies for Global Change, 10, 717-737.

Himmelstoss, E.A., Henderson, R.E., Kratzmann, M.G. and Farris, A.S. (2018). Digital Shoreline Analysis System (DSAS) version 5.0 user guide: U.S. Geological Survey Open-File Report 2018–1179, 110 p., https://doi.org/10.3133/ofr20181179.

113

Hinkel, J., Brown, S., Exner, L., Nicholls, R.J., Vafeidis, A.T. and Kebede, A.S. (2012). Sea-level rise impacts on Africa and the effects of mitigation and adaptation: an application of DIVA. Reg Environ Chang 12:207–244. doi.org/10.1007/s10113-011-0249-2

Högele, M. A. (2011). Metastability of the Chafee-Infante equation with small heavy-tailed Lévy Noise. Dissertation; Humboldt-Universität zu Berlin, Mathematisch- Naturwissenschaftliche Fakultät II (https://doi.org/10.18452/16299).

Horton, R., Herweijer, C., Rosenzweig, C., Liu, J., Gornitz, V. and Ruane, A. C. (2008). Sea level rise projections for current generation CGCMs based on the semi-empirical method. Geophysical Research Letters, 35(2), 1-5.

Hulme, M., Doherty, R., Ngara, T., New, M. and Lister, D. (2001). African climate change: 1900- 2100. Climate Research, 17(2): 145-168.

Hunt, J.C.R. (2002). Floods in a changing climate: a review. Philos. T. Roy. Soc. A, 360, 1531- 1543.

Ibe, A. C. and Awosika, L. F. (1991). Sea level rise impact on African coastal zones. In A change in the weather: African perspectives on climate change, ed. S.H. Omide and C. Juma, 105-12. Nairobi, Kenya: African Centre for Technology Studies.

Igawa, M. and Kato, M. (2017). A new species of , Diogenes heteropsammicola (Crustacea, , , ), replaces a mutualistic sipunculan in a walking coral symbiosis. PLoS ONE 12(9): e0184311. https://doi.org/10.1371/journal.pone.0184311

Ilias, M. V. and Taylor, F. (2007). Radiation and Climate: Atmospheric Energy Budget from Satellite Remote Sensing. Oxford University Press Inc., New York. Available:https://books.google.pt/books?id=GnJ0LJFLNbMC&printsec=frontcover&sourc e=gbs_ViewAPI&redir_esc=y#v=onepage&q&f=false

Imbrie, A. E., Imbrie, J., Imbrie, K. P. and Katherine, P. (1979). Ice ages: solving the mystery. Short Hills NJ: Enslow Publishers.

IPCC, (2014). Climate Change 2014 Synthesis Report Summary for Policymakers. Available: https://www.ipcc.ch/site/assets/uploads/2018/02/AR5_SYR_FINAL_SPM.pdf (Accessed: 1st October, 2019).

114

IPCC, (2014). Summary for policymakers. In: Climate change 2014: Impacts, adaptation, and vulnerability. Part A: Global and sectoral aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Field CB, Barros VR, Dokken DJ, Mach KJ, Mastrandrea MD, Bilir TE, Chatterjee M, Ebi YL, Estrada YO, Genova RC, Girma B, Kissel ES, Levy AN, MacCracken S, Mastrandrea PR, White LL (eds)]. Cambridge University Press, Cambridge, UK and New York, USA. pp 1–32

IPCC, (2007). Climate Change 2007: Impacts, Adaptation, and Vulnerability. Contribution of Working Group II to the Third Assessment Report of the Intergovernmental Panel on Climate Change [Parry, Martin L., Canziani, Osvaldo F., Palutikof, Jean P., van der Linden, Paul J., and Hanson, Clair E. (eds.)]. Cambridge University Press, Cambridge, United Kingdom, 1000 pp.

IPCC, (2007). Climate Change (2007): The Physical Science Basis. In: Solomon S, Qin D, Manning M et al. (eds) The Fourth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge Univ. Press, Cambridge UK.

IPCC, (2007). Climate Change 2007 : Synthesis Report. Change 446, (Cambridge University Press, 2007).

IPCC, (2007b). Working Group II 4th Assessment Report. Cambridge University Press, Cambridge.

IPCC, (2001). The Scientific Basis. Contribution of Working Group I to the Third Assessment Report of the Intergovernmental Panel on Climate Change (TAR IPCC)., Cambridge, United Kingdom and New York, NY, USA, p.881: Cambridge University Press, Cambridge.

IPCC, (1996). Climate Change 1995: The Science of Climate Change. Contribution of Working Group I to the Second Assessment Report of the Intergovernmental Panel on Climate Change [Houghton, J.T., L.G. Meira Filho, B.A. Callander, N. Harris, A. Kattenberg, and K. Maskell (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 572 pp

115

IPCC, (n.d). The Carbon Cycle and Atmospheric Carbon Dioxide. Available: https://www.ipcc.ch/site/assets/uploads/2018/02/TAR-03.pdf (Accessed: 1st October, 2019).

James, R. and Washington, R. (2013). Changes in African temperature and precipitation associated with degrees of global warming. Clim. Change 117(4):859-872

Jayson-Quashigah, P-N., Appeaning-Addo, K. and Kufogbe, S.K. (2013). Medium resolution satellite imagery as a tool for monitoring shoreline change. Case study of the Eastern coast of Ghana. Journal of Coastal Research, Special Issue No. 65: 511–516. doi:10.2112/SI65-087.1

Jonah, F. E. (2015). Managing coastal erosion hotspots along the Elmina, Cape Coast and Moree area of Ghana. Ocean and Coastal Management, doi:10.1016/j.ocecoaman.2015.02.007

Jonah, F. E., Boateng, I., Mensah, A. E., Adu-Boahen, K. and Adams, O. (2016). Shoreline Change Analysis Using End Point Rate and Net Shoreline Movement Statistics: an application to Elmina, Cape Coast and Moree Section of Ghana‘s Coast. Submitted to Journal of Coastal Conservation: Planning and Management. DOI: 10.1016/j.rsma.2016.05.003

Jones, R., Hassell, D., Hudson, D., Wilson, S., Jenkins. G. and Mitchell, J. (2003). Workbook on generating high resolution climate change scenarios using PRECIS. Hadley Centre for Climate Prediction and Research, Met Office, Bracknell, UK. Available: https://www.uncclearn.org/sites/default/files/inventory/undp17.pdf (Accessed: 14th January, 2020)

Jurgilevich, A., Räsänen, A. and Groundstroem, F. J. S. (2017). A systematic review of dynamics in climate risk and vulnerability assessments Environ. Res. Lett. 12 013002. DOI: 10.1088/1748-9326/aa5508

Kasting, J. F. and Siefert, J. L. (2002). ―Life and the Evolution of Earth‘s Atmosphere.‖ Science, 296(5570): 1066–1068.

116

Kebede, A.S, Nicholls, R.J, Hanson, S, Mokrech, M. (2012). Impacts of climate change and sea- level rise: a preliminary case study of Mombasa, Kenya. J Coastal Res 278:8–19. doi:10.2112/JCOASTRES-D-10-00069.1

Kemp, A., Horton, B.P., Donnelly, J.P., Mann, M.E., Vermeer, M. and Rahmstorf, S. (2011). Climate related sea-level variations over the past two millennia, Proceedings of the National Academy of Sciences, 108, 11,017-11,022.

Kjellstrom, T., Lemke, B., Hyatt, O. and Otto, M. (2014). Climate change and occupational health: A South African perspective. South African Medical Journal, 104, 586. https://doi.org/10.7196/SAMJ.8646

Kumar, P., Geneletti, D. and Nagendra, H. (2016). Spatial assessment of climate change vulnerability at city scale: a study in Bangalore, India Land Use Pol. 58 514–32. http://dx.doi.org/10.1016%2Fj.landusepol.2016.08.018

Kunateh, M.A. (2013). Climate change threatens Ghana‘s food security. The Chronicle. Available: http://thechronicle.com.gh/climate-change-threatens-ghana%E2%80%99s- food-security (Accessed: 2nd October, 2019).

Lam, V.W.Y., Cheung, W.W.L., Swartz, W. and Sumaila, U.R. (2012). Climate change impacts on fisheries in West Africa: implications for economic, food and nutritional security. Afr J Mar Sci. doi:10.2989/1814232X.2012.673294

Landerer, F. W., Jungclaus, J.H. and Marotzke, J. (2007). Regional dynamic and steric sea level change in response to the IPCC-A1B scenario. J. Phys. Oceanogr., 37, 296–312.

Langner, J., Bergstom, R. and Foltescu, V. (2005). Impact of climate change on surface ozone and deposition of sulphur and nitrogen in Europe. Atmos Environ, 39:1129–41.

Laprise, R. (2008). Regional climate modelling. J. Comput. Phys., 227, 3641–3666.

Leatherman, S.P. (2001). Social and economic costs of sea-level rise. Sea-level rise, History and Consequences, B.C. Douglas, M.S. Kearney, and S.P. Leatherman, Eds., Academic Press, 181-223.

117

Lloyd, S.J, Kovats, R.S. and Chalabi, Z. (2011). Climate change, crop yields, and under nutrition: development of a model to quantify the impact of climate scenarios on child under nutrition. Environ Health Perspect. doi:10.1289/ehp.1003311

Lobell, D.B., Bänziger, M., Magorokosho, C. and Vivek, B. (2011) Nonlinear heat effects on African maize as evidenced by historical yield trials. Nature Climate Change, 1(1): 42-45.

Lockwood, J. (1979). The Antarctic Ice-Sheet: Regulator of Global ?: Review. The Geographical Journal, 145(3), 469-471. Doi: 10.2307/633219

Lombard, A. A., Cazenave, P.Y., Le, T, and Ishii, M. (2005). Contribution of thermal expansion to present-day sea level rise revisited. Global and Planetary Change 47:1–16.

Long, J.W. Henderson, R.E. and Thompson, D.M. (2020). Forecasting future beach width—A case study along the Florida Atlantic coast: U.S. Geological Survey Open-File Report 2019–1150, 13 p., https://doi.org/10.3133/ofr20191150.

Long, J.W, and Plant, N.G. (2012). Extended Kalman filter framework for forecasting shoreline evolution: Geophysical Research Letters, v. 39, no. 13, L13603, 6 p., accessed July 7, 2012, at https:// doi.org/10.1029/2012GL052180.

Lorbacher, K., Marsland, S.J., Church, J.A., Griffies, S, M. and Stammer, D. (2012). Rapid barotrophic sea-level rise from ice-sheet melting scenarios. J. Geophys. Res., 117, C06003.

Lorens, L., Hilgen, F., Shackelton, N.J., Laskar, J. and Wilson, D. (2004). "Part III Geological Periods: 21 The Neogene Period". In Gradstein, Felix M.; Ogg, James G.; Smith, Alan G. (eds.). A Geologic Time Scale 2004. Cambridge University Press. p. 412.

Lung, T., Lavalle, C., Hiederer, R., Dosio, A. and Bouwer, L. M. (2013). A multi-hazard regional level impact assessment for Europe combining indicators of climatic and non-climatic change Glob. Environ. Chang. 23 522–36. https://doi.org/10.1016/j.gloenvcha.2012.11.009

Ly, C.K. (1980). The role of the Akosombo Dam on the Volta River in causing coastal erosion in central and eastern Ghana (West Africa). Marine Geology 37(3-4):323–332. doi: 10.1016/0025-3227(80)90108-5

118

Makokha, G. L. and Shisanya, C. A. (2010). Trends in Mean Annual Minimum and Maximum Near Surface Temperature in Nairobi City, Kenya. Advances in Meteorology V, 2010: 6. doi:10.1155/2010/676041

Malherbe, H., Gebel, M. and Pauleit, S. (2018). Land Use Pollution Potential of Water Sources Along the Southern Coast of South Africa. Change and Adaptation in Socio-Ecological Systems, 4(1), pp. 7-20. Retrieved 12 Nov. 2019, from doi:10.1515/cass-2018-0002

Mann, M. E. and Toles, T. (2016). The Madhouse Effect. New York Chichester, West Sussex: Columbia University Press. Doi: 10.7312/mann17786.

Maracchi, G., Sirotenko, O. and Bindi, M. (2005). Impacts of present and future climate variability on agriculture and forestry in the temperate regions: Europe. Climatic Change, 70, 117-135.

Marmot, M. (2005). Social determinants of health inequalities. Lancet, 365, 1099- 1104.

Marshall, S. J. (2011). The Cryosphere. Princeton, NJ: Princeton University Press.

McDonald, R.I., Green, P., Balk, D., Fekete, B.M., Revenga, C., Todd, M. and Montgomery, M. (2011) Urban growth, climate change, and freshwater availability. Proc Natl Acad Sci USA 108(15):6312–6317. doi:10.1073/pnas.1011615108

McLaughlin, J. A., DePaola, C.A., Bopp, K.A., Martinek, N.P., Napolilli, C.G., Allison, S.L., Murray, E.C., Thompson, M.M.B. and Middaugh, M.D. (2006): Outbreak of Vibrio parahaemolyticus gastroenteritis associated with Alaskan oysters. N. Engl. J. Med., 353, 1463-1470.

McLeman, R. (2010). Impacts of population change on vulnerability and the capacity to adapt to climate change and variability: a typology based on lessons from ―a hard country‖. Popul Environ, V, 31: 286-316. https://doi.org/10.1007/s11111-009-0087-z

McMichael, A.J., McKee, M., Shkolnikov, V. and Valkonen, T. (2004). Mortality trends and setbacks: global convergence or divergence? Lancet, 363, 1155-1159. McMichael, A.J., R.E. Woodruff and S. Hales, 2006: Climate change and human health: present and future risks. Lancet, 367, 859-869

119

Meier, M.F., Dyurgerov, M.B., Rick, U.K., O‘Neel, S., Pfeffer, W.T., Anderson, R.S., Anderson, S.P. and Glazovsky, A. F. (2007). Glaciers dominate eustatic sea-level rise in the 21st century. Science 317:1064–1067; doi:10.1126/science.1143906.

Micheal, J. (2007). Episodic flooding and the cost of sea level rise. Ecological Economics, 63(1), 149-159.

Miller, L. and Douglas, B. C. (2007). Gyre-scale atmospheric pressure variations and their relation to 19th and 20th century sea level rise. Geophys. Res. Lett., 34, L16602

Milne, G. A., Gehrels, W. R., Hughes, C. W. and Tamisiea, M. E. (2009). Identifying the causes of sea-level change. Nature Geosci., 2, 471–478.

Milne, G. A. and Mitrovica, J. X. (1998). Postglacial sea-level change on a rotating Earth. Geophys. J. Int., 133, 1–19.

Ministry of Environment, Science, Technology and Innovation (MESTI). (2012a). National Climate Change Adaptation Strategy. Available: http://www.undpalm.org/sites/default/files/downloads/ghana_national_climate_change_a daptation_strateg y_nccas.pdf (Accessed 11th October, 2019).

Mitrovica, J. X., Tamisiea, M. E, Davis, J. L. and Milne, G. A. (2001). Recent mass balance of polar ice sheets inferred from patterns of global sea-level change. Nature, 409, 1026– 1029.

Moore, L. J. (2000). Shoreline mapping techniques. Journal of Coastal Research, 16(1), 111– 124.

Mutimukuru-Maravanyika, T., Asare, C., Ameyaw, G., Mills, D. and Agbogah, K. (2013). Ghana Coastal Fisheries Governance Dialogue: Developing Options for a Legal Framework for Fisheries Comanagement in Ghana. USAID, Coastal Resources Centre of University of Rhode Island and WorldFish Center, 59 pp.

Ndebele-Murisa, M.R., Musil, C.F. and Raitt, L. (2010). A review of phytoplankton dynamics in tropical African lakes. S Afr J Sci 106(1/2):13–18. doi:10.4102/sajs.v106i1/2.64

120

Nelson, G. C., Rosegrant, M. W., Palazzo, A., Gray, I., Ingersoll, C., Robertson, R., Tokgoz, S., Tingju, Z., Sulser, T. B., Ringler, C. and Msangi, S. (2010). Food Security, Farming, and Climate Change to 2050: Scenarios, Results, Policy Options (IFPRI, Washington, DC).

Nelson, J.S., Grande, T.C. and Wilson, M.V.H. (2016). Fishes of the World. 5th Edition, John Wiley and Sons, Hoboken. https://doi.org/10.1002/9781119174844

Neuman, J., Emanuel, K.A., Ravela, S., Ludwig, L.C. and Verly, C. (2013). Assessing the risk of cyclone-induced storm surge and sea level rise in Mozambique. WIDER Working Paper 2013/03. UNUWorld Institute for Development Economics Research, Helsinki

Niang I., Ruppel, O.C., Abdrabo, M.A., Essel, A., Lennard, C., Padgham, J. and Urquhart, P. (2014) Africa. In: Climate change 2014: impacts, adaptation and vulnerability. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge.

Nicholls, R.J. (2006). Storm surges in coastal areas. NaturalDisasterHotspots: Case Studies. Disaster Risk Management 6,M.Arnold, R.S. Chen, U. Deichmann,M. Dilley, A.L. Lerner- Lam, R.E. Pullen and Z. Trohanis, Eds., The World Bank, Washington, District of Columbia, 79-108.

Nicholson, S.E. (2001). Climatic and environmental change in Africa during the last two centuries. Climate Research, 17(2): 123-144.

Nicholson, S. E. (1994). Recent rainfall fluctuations in Africa and their relationship to past conditions over the continent The Holocene, 4, 121-131

Nicholson, S.E., Some, B. and Kone, B. (2000). An analysis of recent rainfall conditions in West Africa, including the rainy seasons of the 1997 El Niño and the 1998 La Niña years. Journal of Climate, 13(14): 2628-2640.

Nkomo, J.C. and Nyong, A.O. (2006). The impacts of climate change in Africa. Available: http://www.xn--deutscher-fluglrmdienst-97b.de/Presse/PMitt/2006/061030c8.pdf. (Accessed: 12th November, 2019).

Northeast Climate Adaptation Science Centre (NECASC), (2016). Climate change impacts on freshwater resources and ecosystems. Available:

121

https://necsc.umass.edu/content/climate-impacts-freshwater-resources-and-ecosystems (Accessed: 9th November, 2019).

Ntiamoa-Baidu, Y. (1991). Seasonal changes in the importance of coastal wetlands in Ghana for wading birds, Biological Conservation, V, 57, 2, (139-158),doi: 10.1016/0006- 3207(91)90135-.

Ntiamoa-Baidu, Y. and Gordon, C. (1991). Coastal Wetlands Management Plans: Ghana. Available:http://documents.worldbank.org/curated/en/396621468251981053/pdf/511660 ESW0Whit10Box342025B01PUBLIC1.pdf (Accessed: 19th November, 2019).

Nunoo, F.K.E., Asiedu, B., Olauson, J. and Intsiful, G. (2015). ―Achieving sustainable fisheries management: A critical look at traditional fisheries management in the marine artisanal fisheries of Ghana, West Africa‖, JENRM vol. 2, pp.15-23.

Olesen, J.E., Carter, T.R., Díaz-Ambrona, C.H., Fronzek, S., Heidmann, T., Hickler, T., Holt, T., Minguez, M.I. and Co-authors, (2006). Uncertainties in projected impacts of climate change on European agriculture and terrestrial ecosystems based on scenarios from regional climate models. Climatic Change, doi: 10.1007/s10584-006-9216-1.

Olympio, G.F. and Amos-Abanyie, S. (2014). Effects of shoreline erosion on infrastructure development along the coastal belt of Ghana: Case of Nkontompo community. Journal of Science and Technology (Ghana) 33(3): 39. doi:10.4314/just.v33i3.5

Owusu, K. and Waylen, P. R. (2012). The Changing Rainy Season Climatology of mid Ghana. Theoretical and Applied Climatology. http://dx.doi.org/10.1007/s00704- 012-0736-5

Pörtner, H. O. and Knust, R. (2007). Climate change affects marine fishes through the oxygen limitation of thermal tolerance. Science 315, 95–97. DOI: 10.1126/science.1135471

Radic, V. and Hock, R. (2010). Regional and global volumes of glaciers derived from statistical upscaling of glacier inventory data. Journal of Geophysical Research-Earth Surface 115, F01010. Doi: 10.1029/2009jf001373

Ragab, R. and C. Prudhomme, (2002): Climate change and water resources management in arid and semi-arid regions: prospective and challenges for the 21st century. Biosystems Engineering, 81, 3-34.

122

Rahmstorf, S. (2012). Modeling sea level rise. Nature Education Knowledge 3(10):4

Rahmstorf, S. (2010). A new view on sea level rise, Nature Climate Change, 4, 44-45.

Rahmstorf, S. (2007). A semi-empirical approach to projecting future sea-level rise. Science 315, 368-370.

Raper S. C. B. and Braithwaite R. J. (2006). Low sea level rise projections from mountain glaciers and icecaps under global warming. Nature 439, 311-313 (2006).

Raper, S. C. B. and Braithwaite R. J. (2005). The potential for sea level rise: New estimates from glacier and ice cap area and volume distributions. Geophys Res Let 32, 4. Doi: L0550210.1029/2004gl021981

Roser, M. and Ritchie, H. (2017). ―CO2 and other Greenhouse Gas Emissions‖. Our World in Data. Available: https://ourworldindata.org/co2-and-other-greenhouse-gas- emissions#per-capita-co2-emissions (Accessed: 1st October, 2019).

Rummukainen, M. (2010). State-of-the-art with regional climate models. Clim. Change, 1, 82– 96.

Russo, S., Marchese, A. F., Sillmann, J. and Immé, G. (2016). When will unusual heat waves become normal in a warming Africa? Environmental Research Letters, 11, 54016. https://doi.org/10.1088/1748-9326/11/5/054016

Saenger, P. (2002). Mangrove Ecology, Silviculture and Conservation. Kluwer, 360 pp

Santos, H., Idinoba, M. and Imbach, P. (2008). Climate Scenarios: What we need to know and how to generate them. Working Paper No. 45. Available: https://www.cifor.org/publications/pdf_files/WPapers/WP45Santoso.pdf (Accessed: 12th January, 2020)

Schellnhuber, H.J., Reyer, C., Hare, B., Waha, K., Otto, I.M., Serdeczny, O., Schaeffer, M., Schleußner, C-F., Reckien, D., Marcus, R., Kit, O., Eden, A., Adams, S., Aich, V., Albrecht, T., Baarsch, F., Boit, A. C., Trujillo, N., Cartsburg, M., Coumou, D., Fader, M., Hoff, H., Jobbins, G., Jones, L., Krummenauer, L., Langerwisch, F., Le Masson, V., Ludi, E., Mengel, M., Mo¨hring, J., Mosello, B., Norton, A., Perette, M., Pereznieto, P., Rammig, A., Reinhardt, J., Robinson, A., Rocha, M.; Climate change impacts in Sub-

123

Saharan Africa: from physical changes to their social… 123 Author's personal copy Sakschewski, B., Schaphoff, S., Schewe, J., Stagl, J. and Thonicke, K. (2014). Turn down the heat: confronting the new climate normal. The World Bank, Washington, DC

Schmidhuber, J. and Tubiello, F. N. (2007). Global food security under climate change. Proceedings of the National Academy of Sciences of the United States of America, 104(50), 19703–19708. doi:10.1073/pnas.0701976104

Schneider, S. H., S. Semenov, S., Patwardhan, I., Burton, C. H. D., Magadza, M. and Oppenheimer, A. B. (2007). ―Assessing key vulnerabilities and the risk from climate change.‖ In M. L. Parry, O. F. Canziani, J. P. Palutikof, P. J. v. d. Linden, and C. E. Hanson, eds. Climate Change 2007: Impacts, Adaptation and Vulnerability. Contribution of Working Group II to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge, UK: Cambridge University Press.

Serdeczny, O., Adams, S., Baarsch, F., Coumou, D., Robinson, A., Hare, W., Schaeffer, M., Perrette, M., and Reinhardt, J. (2017). Climate change impacts in Sub-Saharan Africa: from physical changes to their social repercussions. Reg Environ Change, V, 17: 1585- 1600. https://doi.org/10.1007/s10113-015-0910-2

Sharma, J., Chaturvedi, R. K., Bala, G. and Ravindranath, N. H. (2013). Challenges in vulnerability assessment of forests under climate change Carb. Mgmt. 4 403–11. DOI: 10.4155/cmt.13.35

Sharma, J. and Ravindranath, N.H. (2019). Applying IPCC 2014 framework for hazard-specific vulnerability assessment under climate change. Environ. Res. Commun. 1 051004 DOI: 10.1088/2515-7620/ab24ed

Sharma J, Upgupta, S., Kumar, R., Chaturvedi, R. K, Bala, G. and Ravindranath, N. H. (2017). Assessment of inherent vulnerability of forests at landscape level: a case study from Western Ghats in India Mitigation and Adaptation Strategies Global Change 22 29–44. https://doi.org/10.1007/s11027-015-9659-7

Sharon, E. N., Chris, F. and Andreas, H. F. (2017). Rainfall over the African continent from the 19th through the 21st Century. Global and Planetary Change V,165.2018.114-127. DOI: https://doi.org/10.1016/j.gloplacha.2017.12.014

124

Shukla, R., Sachdeva, K. and Joshi, P. K. (2016). Inherent vulnerability of agricultural communities in Himalaya: a village-level hotspot analysis in the Uttarakhand state of India Appl. Geo. 74 182–98. DOI: 10.1016/j.apgeog.2016.07.013

Siebentritt, M. (2016). Understanding sea-level rise and climate change, and associated impacts on the coastal zone. CoastAdapt Information Manual 2, National Climate Change Adaptation Research Facility, Gold Coast.

Sillmann, J., Kharin, V.V., Zweiers, F.W., Zhang, X. and Bronaugh, D. (2013). Climate extreme indices in the CMIP5 multi-model ensemble. Part 2: future climate projections. J Geophys Res Atmos 118(6):1–55. doi:10.1002/jgrd.50188

Simane, B., Zaitchik, B. F. and Foltz, J. D. (2016). Agro ecosystem specific climate vulnerability analysis: application of the livelihood vulnerability index to a tropical highland region Mitig. Adapt. Strateg. Glob. Chang. 21 39–65. Doi: 10.1007/s11027-014-9568-1

Smith, K.R., Woodward, A., Campbell-Lendrum, D., Chadee, D. D., Honda, Y., Liu, Q., Olwoch, J.M, Revich, B. and Sauerborn, R. (2014). Human health: impacts, adaptation and co- benefits. In: Climate change 2014: Impacts, adaptation and vulnerability. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge, U.K., and New York, US

Soreghan, G.S. and GILES, K.A. (1999). Ampltudes of Late Pennsylvanian glacioeustasy. Geology 27/3:255-258.

Stammer, D. (2008). Response of the global ocean to Greenland and Antarctic ice melting. J. Geophys. Res. Oceans, 113, C06022.

Stanturf, J.A., Warren, M.L., Charnley, S., Polasky, S.C., Goodrick, S.L., Armah, F. and Nyako, Y.A. (2011). Ghana Climate Change Vulnerability and Adaptation Assessment; United States Agency for International Development: Accra, Ghana.

Sun, G., McNulty, S.G, Amatya, D.M., Skaggs, R.W., Swift, L.W., Shepard, P. and Riekerk, H. (2002). A comparison of watershed hydrology of coastal forested wetlands and the mountainous uplands in the Southern US. J. Hydrol., 263, 92-104. Syvitski, J.P.M., C.J. Vörösmarty, A.J. Kettner and P. Green, 2005: Impact of humans on the flux of terrestrial sediment to the global coastal ocean. Science, 308, 376-380.

125

Tang, H. and Chen, Y. (2013). "Global glaciations and atmospheric change at ca. 2.3 Ga". Geoscience Frontiers. 4 (5): 583–596. Doi: 10.1016/j.gsf.2013.02.003

The Delegation of the European Commission to Ghana, (2009). News Letter, The European Union in Ghana, Accra.

The Geological Society of London. (n.d). Physical Weathering. Available: https://www.geolsoc.org.uk/ks3/gsl/education/resources/rockcycle/page3561.html (Accessed 12 th November, 2019).

Trenberth, K. E., Fasullo, J. T. and Kiehl, J. (2009). ―Earth‘s Global Energy Budget.‖ Bulletin of the American Meteorological Society, 90(3):311–323.

Trenberth, K.E., Smith, L., Qian, T., Dai, A. and Fasullo, J. (2007). Estimates of the global water budget and its annual cycle using observational and model data. Journal of Hydrometeorology 8:758–769.

Twenhofel, W., H. (1939). Principles of Sedimentation. University of California, McGraw-Hill Book Company, Incorporated, 66pp.

Twenhofel, W. H. (1932). Treatise on sedimentation. 2nd cd., completely revised, Baltimore, WilliamsandWWins Co., 926pp.

UNDP, (2013). Climate Change Country Profile: Ghana. Available: http://www.ncsp.undp.org/document/undp-climate-change-country-profile-11 Accessed: 24th September, 2018.

UNDP, (2007). Human Development Report 2007/2008: fighting climate change: human solidarity in a divided world. New York: United Nations Development Programme.

UNDP, (2005). World Population Prospects: The 2004 Revision. III. UNDP, New York, 105 pp

UNFCCC, (2011). Fact sheet: Climate Change science – the status of Climate Change science today. UNFCC Available: http://unfccc.int/files/press/backgrounders/application/pdf/press_factsh_science.pdf, (Accessed 15th October, 2019).

UNFCCC, (1992). Article 1, definitions. Available: https://unfccc.int/resource/docs/convkp/conveng.pdf

126

USGCRP, (2014). Georgakakos, A., Fleming, P., Dettinger, M., Peters-Lidard, C., Terese T.C. Richmond, K., Reckhow, K. W. and Yates, D. Ch. 3: Water Resources. Climate Change Impacts in the United States: The Third National Climate Assessment, J. M. Melillo, Terese (T.C.) Richmond, and G. W. Yohe, Eds., U.S. Global Change Research Program, 69-112.

USGCRP (2014). Moser, S. C., Davidson, M.A., Kirshen, P., Mulvaney, P., Murley, J.F., Neumann, J.E., Petes, L. and Reed, D. (2014): Ch. 25: Coastal Zone Development and Ecosystems. Climate Change Impacts in the United States: The Third National Climate Assessment, J. M. Melillo, Terese (T.C.) Richmond, and G. W. Yohe, Eds., U.S. Global Change Research Program, , 579-618.

Velásquez, A. C., Castroverde, C. and He, S. Y. (2018). Plant-Pathogen Warfare under Changing Climate Conditions. Current biology: CB, 28(10), R619–R634. https://doi.org/10.1016/j.cub.2018.03.054

Vermeer, M. and Rahmstorf, S. (2009). Global sea level linked to global temperature, Proceedings of the National Academy of Sciences, 106, 21,527-21,532.

Vousdoukas, I. M., Mentaschi, L., Voukouvalas, E., Bianchi, A., Dottori, F. and Feyen, L. (2018). Climatic and Socio economic controls of future coastal floods risk in Europe. Nature Climate Change Vol8, 2018, 776-780. DOI: 10.1038/s4/558-018-0260-4

Wada, Y., van Beek, L. P. H., van Kempen, C. M., Reckman, J. W. T. M., Vasak, S. and Bierkens M. F. P. (2010). Global depletion of groundwater resources, Geophys. Res. Lett., 37, L20402, doi: 10.1029/2010GL044571.

Warren, J. K. (2006). Evaporites: sediments, resources and hydrocarbons. Birkhäuser. p. 289.

Wassmann, R., Nguyen, X.H., Chu, T, H. and To, P.T. (2004). Sea-level rise affecting the VietnameseMekong Delta: water elevation in the flood season and implications for rice production. Climatic Change, 66, 89-107.

Whelan, K.T., Smith, T.J., Cahoon, D.R., Lynch, J.C. and Anderson, G.H. (2005). Groundwater control of mangrove surface elevation: Shrink and swell varies with soil depth. Estuaries, 28, 833-843.

127

Wheeler, T. and Joachim Von Braun, (2013). Climate Change Impacts on Global Food Security. Science 341, 508 (2013); DOI: 10.1126/science.1239402.

White, N. J., Church, J.A. and Gregory, J.M. (2005). Coastal and global averaged sea level rise for 1950 to 2000. Geophys. Res. Lett., 32, L01601.

Williams, A. P. and Funk, C. (2011). A westward extension of the warm pool leads to a westward extensions of the Weaker circulation, drying eastern Africa, Climate Dynamics, 37 (11-12), 2417-2435.

William, D. C., Maurice, L. B., Gordon, B. B., Christopher, S.B., Carton, J. A., Chang, P., Doney, S. C., Hack, J. J., Henderson, T. B., Kiehl, J. T., Large, G. W., Mckenna, D. S., Santer, D. B. and Smith, D. R. (2006d): The Community Climate System Model version 3 (CCSM3). J. Clim., 19, 2122–2143.

Wolters, M., Bakker, J.P., Bertness, M.D., Jefferies R.L. and Möller, I. (2005). Saltmarsh erosion and restoration in south-east England: squeezing the evidence requires realignment. J. Appl. Ecol., 42, 844-851.

Woodroffe, C.D. (2003). Coasts: Form, process and evolution, Cambridge University Press, Cambridge.

WorldFish, (2010). Gleaner, fisher, trader, processor: Understanding gendered employment in the fisheries and sector. Doi:10.1111/j.1467-2979.2010.00368.x

World Bank, (2010). Economics of Adaptation to Climate Change. Ghana Country Study; World Bank: Washington, DC, USA, 2010.

World Health Organization, (2009). Protecting health from climate change: connecting science, policy and people. World Health Organization, Geneva, pp 1–36

World Health Organization, (2004b). Malaria epidemics: forecasting, prevention, early warning and control – from policy to practice. Report of an informal consultation, Leysin, Switzerland, 8–10 December 2003. World Health Organization, Geneva, 52 pp

World Health Organization, (2003b). The World Health Report 2003: Shaping the Future. World Health Organization, Geneva, 210 pp.

128

Wrigley, E. A. (2013). Energy and the English Industrial Revolution371Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences http://doi.org/10.1098/rsta.2011.0568

Wu, X., Zhang, L. and Zang, S. (2019). Examining seasonal effect of urban heat island in a coastal city. PloS one, 14(6), e0217850. https://doi.org/10.1371/journal.pone.0217850

Wunsch, C. and Stammer, D. (1997). Atmospheric loading and the oceanic ―inverted barometer‖ effect. Rev. Geophys., 35, 79–107.

Yaro, J. A. (2013). The perception of and adaptation to climate variability/change in Ghana by small-scale and commercial farmers. RegEnviron Chang 13:1259–1272. DOI: 10.1007/s10113-013-0443-5

Yin, J. J., Griffies, S.M. and Stouffer, R.J. (2010). Spatial variability of sea level rise in twenty- first century projections. J. Clim., 23, 4585–4607.

Yin, J. J., Schlesinger, M.E. and Stouffer, R.J. (2009). Model projections of rapid sea level rise on the northeast coast of the United States. Nature Geosci., 2, 262–266.

Yusuf, M. and Abiye, T. (2019). Risks of groundwater pollution in the coastal areas of Lagos, southwestern Nigeria. Groundwater for sustainable development, 9, doi: 10.1016/j.gsd.2019.100222

Zhang, X. B. and Church, J.A. (2012). Sea level trends, interannual and decadal variability in the Pacific Ocean. Geophys. Res. Lett., 39, L21701.

Zhifeng, L., Yanjie, Y., Chunyang, H. and Mengzhao, T. (2019). Climate change will constrain the rapid urban expansion in drylands: A scenario analysis with the zoned Land Use Scenario Dynamics-urban model. Science of The Total Environment, V, 651, 2772-2786. doi.org/10.1016/j.scitotenv.2018.10.177.

Zomer, R.J., Trabucco, A., Bossio, D. and Verchot, L.V. (2008). Climate change mitigation: a spatial analysis of global land suitability for clean development mechanism afforestation and reforestation. Agric Ecosyst Environ 126(1–2):67–80. doi:10.1016/j.agee.2008.01.014

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APPENDIX

A: MEAN MONTHLY TEMPERATURE YEAR JAN FEB MAR APRL MAY JUN JUL AUG SEPT OCT NOV DEC MEAN 1980 25.8 26.1 26.3 25.4 25.8 25.1 25.5 25 24.3 26.3 26.5 26.5 25.7 1981 26.9 26.4 27.6 26.4 26.9 25.9 26.4 25 25.4 27.6 26 27.1 26.5 1982 28.2 27.2 29 27.7 28.2 26.3 26.7 26.7 25.8 29 26.8 28.5 27.5 1983 28.5 28 28.5 28 28.5 27.5 28 26.8 26 28.5 27.8 28.1 27.9 1984 27.9 28.1 28.2 27.9 27.9 26.1 25.9 26.1 25.1 28.5 27.6 26.3 27.1 1985 26.1 28.5 27 26.4 25.3 25.3 25.8 26.1 26.1 27.2 27.3 26.8 26.5 1986 27.1 27.1 29.1 27.5 26.2 27.3 26.4 26.5 25.9 27.6 27.8 27.9 27.2 1987 28.4 28.6 28.4 28.1 28.4 26.7 26.8 26.5 27.1 28.1 27.8 28.1 27.8 1988 28.3 28.5 29.8 29.0 29.8 27.7 28.4 25.9 28.2 27.6 28.1 26.5 28.2 1989 26.9 27.6 28.3 27.4 27.5 26.2 26.6 26.2 25.9 26.8 27.9 28.3 27.1 1990 28.5 28.0 29.4 28.6 28.2 27.7 26.2 26.8 26.6 27.6 27.8 28.1 27.8 1991 28.1 28.2 28.8 27.4 27.8 27.3 26.9 26.0 26.4 26.8 27.4 27.8 27.4 1992 27.2 29.1 29.6 29.1 27.9 26.4 25.6 25.7 26.2 27.5 27.4 28.3 27.5 1993 27.2 28.1 28.8 28.3 29.1 26.9 26.0 26.0 26.5 28.1 27.9 28.0 27.6 1994 27.6 28.5 29.1 29.1 28.6 26.7 26.0 25.9 26.0 27.3 27.5 27.8 27.5 1995 28.1 29.1 29.3 29.2 28.7 27.1 26.8 27.1 26.7 28.0 27.6 28.0 28 1996 28.3 28.8 29.2 28.6 28.1 26.7 26.8 26.3 25.9 27.6 27.8 27.9 27.7 1997 28.3 28.5 28.4 27.8 28.1 26.6 26.1 25.9 26.9 28.1 27.9 28.1 27.5 1998 27.8 29.6 30.7 30.4 28.7 27.3 26.8 26.8 26.8 27.6 28.2 28.7 28.3 1999 28.6 28.4 29.1 28.4 28.5 27.5 26.9 26.3 26.0 26.9 27.0 28.5 27.7 2000 28.4 27.9 29.2 28.3 28.7 27.5 25.7 26.3 26.8 27.7 27.9 28.3 27.7 2001 28.4 28.6 28.9 28.6 28.5 26.7 26.3 25.1 25.6 27.6 27.7 28.4 27.5 2002 28.1 28.6 29.0 28.4 28.8 26.8 26.5 25.9 26.2 27.9 28.0 28.6 27.7 2003 28.8 28.9 29.6 28.3 28.5 26.7 26.7 26.1 26.6 27.6 27.8 27.9 27.8 2004 28.6 28.4 29.3 28.7 28.3 26.0 26.1 25.8 27.1 27.2 27.5 28.7 27.6 2005 27.4 29.1 29.2 29.1 28.2 26.6 26.2 25.7 27.0 28.1 28.5 28.6 27.8 2006 28.6 28.9 29.1 29.2 28.1 27.1 26.8 26.3 26.3 27.5 27.8 28.7 27.9 2007 27.4 29.0 29.3 28.9 28.9 26.9 26.6 26.5 26.2 27.4 27.6 28.4 27.8 2008 26.9 28.7 29.3 28.3 28.2 27.1 26.2 26.0 26.3 28.0 28.3 28.4 27.6 2009 28.0 28.5 29.0 28.3 28.6 26.3 26.2 26.1 26.7 28.5 28.0 28.7 27.7 2010 29.1 29.0 29.7 28.6 27.9 26.3 26.3 26.2 26.5 27.3 28.2 28.7 27.8 2011 27.9 28.0 28.9 28.4 28.6 26.5 26.8 26.5 26.4 28.0 27.8 28.1 27.7 2012 28.3 28.4 29.3 28.7 28.6 26.6 26.3 25.9 26.7 28.4 28.3 28.6 27.8 2013 28.6 29.1 29.7 28.8 29.0 26.9 26.1 25.5 26.2 28.6 28.6 28.5 28 2014 28.7 28.1 28.8 28.3 28.6 26.8 26.5 26.2 27.2 28.0 27.8 28.3 27.8 2015 27.8 28.5 29.2 28.6 28.8 26.4 26.3 26.3 26.7 27.7 27.4 27.8 27.6 2016 28.3 29.1 29.0 28.8 28.9 26.7 26.5 25.9 26.6 27.9 28.3 28.5 27.9 2017 28.7 28.5 29.3 28.5 28.7 27.1 26.5 26.8 27.3 28.7 28.7 28.0 28.1 2018 28.1 28.5 29.0 28.7 28.0 26.8 26.7 26.4 26.7 27.8 28.1 28.8 27.8 2019 29.0 29.1 29.2 29.1 28.6 26.9 26.7 26.7 29.1 28.6 28.9 29.1 28.4

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B: MONTHLY MEAN MAXIMUM TEMPERATURE YEAR JAN FEB MAR APR MAY JUN JUL AUG SEPT OCT NOV DEC MEAN 1980 30.2 29.8 28.6 29.9 29.6 27.9 27.6 27.5 28.7 29.8 29.9 30.0 29.1 1981 30.5 30.0 29.9 30.1 28.2 27.9 27.9 27.8 29.8 29.6 30.0 30.3 29.3 1982 30.6 30.5 30.0 30.2 29.0 28.8 28.1 28.0 27.9 29.9 30.1 30.4 29.5 1983 32.0 30.0 29.9 31.0 29.9 29.0 28.3 28.1 28.5 28.9 29.9 31.2 29.7 1984 31.1 29.9 29.8 30.3 29.5 29.4 28.9 28.8 29.9 29.7 30.4 31.0 29.9 1985 30.7 29.9 29.8 30.4 29.8 29.4 29.3 29.1 29.9 29.9 30.1 30.5 29.9 1986 31.6 30.5 30.0 31.1 30.0 29.9 28.6 28.7 29.3 31.2 32.1 31.4 30.4 1987 32.4 31.4 31.0 32.4 30.2 29.7 29.1 29.3 30.5 29.9 32.3 32.1 30.9 1988 31.7 32.1 31.0 32.1 32.8 29.9 29.7 28.2 30.5 30.9 31.1 31.5 31 1989 31.9 32.3 32.7 32.5 31.6 30.1 29.7 29.2 29.3 30.2 32.6 32.5 31.2 1990 32.5 32.5 33.8 33.0 31.8 31.8 28.8 30.0 30.3 31.2 32.4 32.3 31.7 1991 32.5 32.8 33.2 32.2 31.7 30.9 29.4 28.4 29.7 30.2 31.8 31.8 31.2 1992 32.2 33.3 33.6 33.5 31.4 29.9 27.9 28.6 29.7 31.1 31.8 32.4 31.3 1993 31.8 32.8 33.4 33.1 32.8 30.2 28.5 28.8 30.2 32.1 32.5 31.7 31.5 1994 31.7 32.6 33.0 33.6 32.6 30.0 28.6 28.6 29.0 30.8 32.0 32.5 31.3 1995 32.7 33.5 33.4 33.6 32.3 30.5 29.5 29.8 30.4 31.9 32.0 32.1 31.8 1996 32.2 33.1 33.1 33.0 31.7 30.3 29.8 28.8 29.3 31.2 32.1 31.4 31.3 1997 32.0 33.0 32.1 31.8 31.6 29.7 29.1 29.1 30.5 31.6 32.3 32.1 31.2 1998 32.6 34.2 30.0 31.3 32.4 31.2 29.9 29.4 30.5 30.9 32.6 32.5 31.5 1999 32.5 33.2 33.5 32.6 32.6 31.4 30.1 29.7 29.2 30.3 31.3 32.9 31.6 2000 32.6 33.3 32.9 32.5 32.9 31.8 28.8 29.3 30.8 31.9 32.4 32.5 31.8 2001 32.3 33.2 33.2 32.6 32.2 30.1 28.7 27.8 28.6 31.3 31.8 32.1 31.2 2002 32.1 32.8 32.5 32.4 32.2 30.3 28.8 28.7 30.0 31.7 32.4 32.5 31.4 2003 32.9 33.4 33.2 32.0 32.1 30.4 30.1 29.3 30.0 31.2 32.1 32.3 31.6 2004 32.5 32.9 33.3 32.6 31.7 28.8 28.4 28.2 31.0 30.5 31.6 32.7 31.2 2005 32.2 33.4 32.7 32.9 30.9 29.2 28.7 28.4 30.3 31.6 33.0 32.4 31.3 2006 32.2 33.0 32.7 33.3 31.2 30.8 29.3 28.7 29.3 30.8 32.3 32.9 31.4 2007 32.4 33.2 33.0 33.1 32.5 30.0 29.3 29.3 29.4 30.8 31.9 32.4 31.4 2008 32.5 33.0 33.2 32.3 31.6 30.4 28.2 28.2 29.3 31.6 33.0 32.3 31.3 2009 32.4 32.5 32.4 32.4 32.3 29.1 28.4 28.4 30.3 32.3 32.4 32.5 31.3 2010 33.0 32.9 33.0 32.9 30.1 28.5 28.7 28.7 29.7 30.1 32.9 33.0 31.1 2011 32.2 32.2 32.2 32.2 31.8 28.7 29.3 29.3 29.7 31.8 32.2 32.1 31.1 2012 32.7 32.7 32.7 32.7 32.3 29.5 28.9 28.9 30.2 32.3 32.7 32.7 31.5 2013 33.0 33.0 33.0 33.0 32.8 29.8 28.1 28.1 29.0 32.8 33.0 33.1 31.6 2014 32.2 32.1 32.2 32.1 31.6 29.5 28.9 28.9 31.0 31.6 32.1 32.2 31.2 2015 32.6 32.3 32.6 32.3 31.8 28.9 28.5 28.9 29.8 30.4 31.0 32.1 30.9 2016 32.7 32.9 32.1 32.2 31.8 29.5 28.8 28.1 29.6 30.7 32.4 32.3 31.1 2017 32.2 32.0 32.6 32.0 31.7 29.8 28.4 28.4 29.7 31.8 32.1 31.6 31 2018 31.6 32.0 32.4 32.3 30.8 29.9 28.8 28.9 29.8 31.0 32.1 32.7 31 2019 32.4 32.7 32.6 32.8 31.6 29.5 28.6 28.6 32.9 31.6 32.4 32.6 31.6

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C: MONTHLY MEAN MINIMUM TEMPERATURE YEAR JAN FEB MAR APR MAY JUN JUL AUG SEPT OCT NOV DEC MEAN 1980 19.8 20.4 19.8 19.8 19.8 19.8 19.8 19.8 19.8 19.8 20.5 19.8 19.9 1981 21.5 19.9 20.2 20.7 20.1 19.6 19.5 19.0 20.0 19.5 19.6 20.9 20 1982 23.7 21.0 21.4 23.3 21.2 20.2 20.0 19.9 21.2 20.5 21.3 22.0 21.3 1983 23.5 23.5 22.1 23.0 21.1 21.0 20.6 20.2 22.2 21.1 23.0 23.5 22.1 1984 22.6 23.6 22.2 23.3 22.6 21.9 21.4 21.5 20.3 22.6 23.6 19.7 22.1 1985 19.8 23.7 20.2 20.3 19.8 21.2 21.0 21.1 22.3 21.9 22.7 20.9 21.3 1986 21.0 22.0 24.4 22.5 19.7 22.7 21.5 23.4 22.5 23.1 23.4 23.4 22.5 1987 23.6 22.9 22.4 23.7 21.9 21.7 21.2 21.0 22.9 22.3 23.3 23.2 22.5 1988 23.6 23.0 22.9 23.8 21.9 21.5 21.2 21.0 23.1 22.4 23.0 20.8 22.3 1989 21.0 22.8 22.9 22.3 22.5 22.4 22.7 22.5 22.5 22.5 23.2 23.1 22.6 1990 24.5 23.4 24.0 23.4 22.9 21.2 21.0 20.9 23.0 22.7 23.0 23.9 22.8 1991 22.7 23.6 23.4 22.5 22.9 23.7 23.4 22.7 23.1 22.4 23.0 22.9 23 1992 21.3 24.8 24.7 24.8 23.5 23.0 22.5 21.9 22.8 23.1 23.0 23.2 23.2 1993 21.6 23.4 23.3 23.6 24.4 23.5 22.6 22.4 22.8 23.2 23.3 23.4 23.1 1994 22.6 24.3 24.1 24.6 23.7 23.4 22.5 22.3 22.9 22.9 23.0 22.2 23.2 1995 22.6 24.7 24.2 24.7 24.1 23.6 23.2 23.4 23.1 23.3 23.2 23.1 23.6 1996 23.6 24.5 24.4 24.3 23.5 23.1 23.0 22.9 22.5 23.1 23.4 23.4 23.5 1997 23.6 23.9 23.7 23.8 23.8 23.5 22.3 21.8 23.2 23.6 23.4 23.2 23.3 1998 22.5 25.0 25.9 26.0 24.2 23.4 22.8 22.7 23.2 23.4 23.8 23.5 23.9 1999 23.7 23.6 23.8 24.2 23.4 23.6 22.9 22.0 22.7 22.6 22.7 23.1 23.2 2000 23.3 22.5 24.4 24.1 23.5 23.2 21.8 22.5 22.7 22.6 23.3 23.1 23.1 2001 23.6 23.9 23.7 24.5 23.9 23.3 23.1 21.6 22.5 22.9 23.5 23.8 23.4 2002 23.2 24.4 24.5 24.4 24.5 23.3 23.4 22.2 22.4 23.3 23.5 23.8 23.6 2003 23.8 24.3 25.0 24.5 24.1 22.9 22.3 22.1 23.2 23.2 23.5 22.5 23.4 2004 23.7 24.0 24.4 24.7 24.0 23.2 22.9 22.6 23.1 23.0 23.4 23.8 23.6 2005 21.7 24.8 24.7 25.3 24.6 24.1 23.0 22.2 23.7 23.6 23.9 23.8 23.8 2006 24.0 24.8 24.6 25.1 24.1 23.5 23.4 23.2 23.4 23.4 23.4 23.6 23.9 2007 21.4 24.7 24.7 24.7 24.5 23.8 23.0 22.9 23.0 23.1 23.3 23.5 23.6 2008 20.5 24.3 24.5 24.3 23.9 23.9 23.3 22.9 23.2 23.4 23.7 23.6 23.5 2009 22.7 24.5 24.7 24.2 24.0 23.4 23.0 22.9 23.0 23.7 23.6 23.9 23.6 2010 24.3 25.1 25.5 24.2 24.8 24.1 23.2 23.0 23.4 23.6 23.4 23.4 24 2011 22.7 23.8 24.8 24.7 24.5 24.3 23.4 22.8 23.2 23.3 23.3 23.2 23.7 2012 23.0 24.0 24.9 24.7 24.1 23.6 22.8 22.2 23.2 23.6 23.8 23.5 23.6 2013 23.3 25.2 25.5 24.7 24.3 23.9 23.2 22.0 23.4 23.5 24.3 23.0 23.8 2014 24.3 24.0 24.5 24.5 24.6 24.0 23.2 22.7 23.3 23.5 23.5 23.6 23.8 2015 22.0 24.7 24.8 25.0 24.8 23.8 23.2 22.9 23.5 24.2 23.8 22.5 23.8 2016 23.1 25.4 25.0 25.4 25.1 23.8 23.5 22.9 23.6 24.1 24.3 23.9 24.2 2017 24.3 24.9 25.2 25.0 24.7 24.4 23.6 24.3 24.9 24.7 25.2 23.4 24.6 2018 23.6 25.1 24.8 25.1 24.4 23.8 23.7 23.0 23.7 23.7 24.2 24.1 24.1 2019 24.7 25.4 24.8 25.4 24.6 24.2 23.8 23.8 25.3 24.6 25.4 24.7 24.7

132

D: MONTHLY RAINFALL Total YEAR JAN FEB MAR APR MAY JUN JUL AUG SEPT OCT NOV DEC Rainfall 1980 3.6 0 3.6 0 0 194 31 1.3 0 0 0 0 233.5 1981 4.1 5.2 13.5 9.6 29.5 196.1 297.3 21.5 29.5 29.5 1.4 0 637.2 1982 9.1 0 0 0 0 400.9 23.9 7.7 0 0 0.2 0 441.8 1983 0 1.2 0 17.8 82.9 127.7 85 12.1 1.2 5.3 2.6 0 335.8 1984 0 0 1.3 3.2 78.7 139.6 31.4 9.7 4.7 37 59 0 364.6 1985 8.2 0 47 66.9 48.9 170.2 10.4 7.6 35.2 48.1 23.1 0.4 466 1986 0 0.4 28.3 39.8 71.7 99.6 12.1 36 15.1 20.5 74.8 37.2 435.5 1987 0 0 172 104.2 148.2 340.3 59.4 127 5.3 149.7 0 38.3 1144.4 1988 0 3.3 52.3 44.7 89.9 83.9 22 15.8 1 194.2 0.4 0 507.5 1989 0 5.1 50 146.8 219.3 284.4 14.3 17.7 52.6 73.7 1.5 0 865.4 1990 3.6 29.7 0 114.8 57.9 33.5 31 1.3 40.5 49.4 40.5 52.4 454.6 1991 6.2 5.7 28.6 203.8 255.6 83.6 297.3 21.5 37.5 51.9 23 0.3 1015 1992 0 0 11.4 75.5 225.7 180.9 23.9 7.7 21.9 91.7 20.6 17.3 676.6 1993 17.8 16 9.1 109.5 51.9 148.7 12.8 25.8 84.9 8.5 80.6 4.6 570.2 1994 39.4 27.2 84.1 15.9 165.5 235.8 13.7 45.3 38.2 94 46.2 0 805.3 1995 0 0.5 92.5 148.8 86.3 261.9 130.8 13.5 6.7 37.3 76.8 27.1 882.2 1996 0 12.5 44.8 170.6 266.3 240.5 60.1 36 2.8 20.5 74.8 37.2 966.1 1997 0 0 172 104.2 148.2 340.3 59.4 8.4 23.6 44 40.3 84.6 1025 1998 26.2 5.1 0 11.4 178 83.9 22 15.8 1 194.2 0.4 1.6 539.6 1999 12.3 27.2 14.2 94.3 75.2 247.5 13 26.9 30.4 33.9 9.9 0.4 585.2 2000 11.8 0 100.2 60.8 106.3 194 10.1 8.1 10.1 54 5.5 21.1 582 2001 0 4.3 58.7 103.1 260.4 196.1 51.5 5.8 100.6 6 5.7 4.2 796.4 2002 12.6 73.5 33.1 131 217.8 400.9 63.7 7.1 16.7 32.6 54.8 0 1043.8 2003 1.7 50.8 113.9 187.1 74.4 264.9 30.5 6.8 33.1 91.4 29 17.6 901.2 2004 0 18.9 39.7 21.3 123 76.7 58.4 14.2 75.5 205.2 39.6 18.1 690.6 2005 10.4 29.5 132.7 76.8 245.5 188.5 7.5 9.4 15.5 64 20.1 22.8 822.7 2006 0 13.2 41 35.2 291.4 131.5 31.6 59.9 48.6 221.1 12.1 0.8 886.4 2007 0 4 74.7 88 80 123 89.6 38 84.2 133.9 21.5 10.8 747.7 2008 6.6 0 19.9 182.8 331.4 123.9 111.8 18.9 12.9 33 42.3 100.1 994.4 2009 3.1 45.6 19.5 82 62 444.1 396.9 8.5 7.9 6.4 3 0.4 1079.4 2010 65.8 139.9 17.1 43.4 113.2 230.5 25.4 41.1 167.5 102.4 29.3 12 987.6 2011 0 102.8 14.1 207.7 280 205.4 53.2 30.6 33.7 109.3 0.3 72.5 1101.6 2012 4 17.7 31.1 113.6 186.3 189.7 34.8 10.5 57.2 188.1 41.2 11.5 885.7 2013 27 0.4 57.7 52 100.5 156.8 56.9 4.8 19.6 6.7 63.8 47.2 593.4 2014 25.9 47.2 94.8 78.3 119.7 252.2 151 107 44.8 56.4 40.6 8 1025.9 2015 8.7 51.2 53.1 107.1 228 421.4 19.8 0.5 10.8 81.9 137.3 15.6 1135.4 2016 8.1 1.1 250.7 84.6 123.5 138.2 37.8 36.2 22.2 126.6 12.8 19.2 861 2017 65 20.2 47 108.4 142.8 193.8 36.5 27.1 134.2 117.8 65.3 20.5 978.6 2018 0 40.8 13.9 55.6 190.2 158 4.5 19.9 39.5 71.3 36.9 1.3 631.9 2019 5.8 72.1 29.1 54.7 104.7 107 68.2 22.5 128.8 142.6 24.8 0.3 760.6

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E: MEAN MONTHLY MAXIMUM RELATIVE HUMIDITY YEAR JAN FEB MAR APR MAY JUN JUL AUG SEPT OCT NOV DEC MEAN 1980 96 92 96 92 94 95 96 96 96 92 96 97 95 1981 80 94 96 95 95 98 97 97 97 95 96 98 95 1982 94 94 97 94 94 98 98 98 98 94 97 99 96 1983 88 91 94 94 96 97 98 98 98 85 94 93 94 1984 94 94 94 95 94 88 95 95 94 95 94 94 94 1985 93 97 93 94 95 93 95 94 95 94 95 92 94 1986 92 93 98 93 95 93 93 99 98 98 97 98 96 1987 93 96 98 98 98 99 98 95 95 98 96 98 97 1988 93 96 98 98 98 98 98 97 97 99 97 95 97 1989 89 95 96 96 96 96 96 96 96 97 96 97 96 1990 95 96 96 96 97 95 96 96 95 96 96 97 96 1991 94 95 96 97 97 96 97 98 98 99 98 98 97 1992 93 98 97 97 97 98 98 99 98 98 98 99 97 1993 96 97 97 98 98 98 98 98 98 97 98 98 98 1994 98 97 97 97 97 97 98 98 98 98 98 97 98 1995 97 97 98 97 97 98 98 98 98 98 98 98 98 1996 98 98 98 98 98 98 98 98 99 98 98 98 98 1997 98 98 98 98 98 99 98 98 98 98 98 98 98 1998 98 99 98 98 98 98 98 99 99 99 99 99 98 1999 99 99 98 96 96 96 98 98 98 99 99 98 98 2000 98 98 98 95 98 98 97 97 98 98 98 98 98 2001 98 98 98 98 98 98 98 99 98 98 98 98 98 2002 98 98 99 98 98 98 98 98 98 98 98 98 98 2003 96 95 96 97 97 97 98 97 97 98 97 97 97 2004 98 97 96 97 97 97 97 97 98 98 98 97 97 2005 97 97 95 96 97 99 98 98 98 99 98 98 97 2006 99 99 99 99 99 99 99 99 98 97 97 96 98 2007 93 96 95 95 93 94 95 95 95 96 96 96 95 2008 93 96 95 96 98 98 97 97 97 97 97 97 97 2009 96 97 97 96 97 97 97 97 97 97 96 97 97 2010 97 97 98 98 97 97 97 98 95 98 98 98 97 2011 95 97 98 97 97 97 97 97 97 96 96 96 97 2012 97 94 96 97 97 97 97 97 97 97 97 97 97 2013 97 96 97 97 97 98 98 98 98 97 97 97 97 2014 98 97 97 98 98 98 98 98 98 98 97 98 98 2015 97 97 98 97 97 98 98 98 97 98 97 98 98 2016 96 98 98 98 97 97 98 98 97 97 97 96 97 2017 97 97 97 98 98 98 98 98 98 98 98 98 98 2018 98 97 97 97 98 98 98 98 98 97 97 97 97 2019 97 97 97 97 97 98 98 98 97 97 97 97 97

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F: MEAN MONTHLY MINIMUM RELATIVE HUMIDITY YEAR JAN FEB MAR APR MAY JUN JUL AUG SEPT OCT NOV DEC MEAN 1980 80 76 80 83 83 80 85 86 84 81 80 78 81 1981 79 79 77 82 83 85 90 91 90 84 83 82 84 1982 80 80 81 81 84 89 90 88 85 82 81 80 83 1983 77 80 80 83 83 86 85 87 85 86 84 80 83 1984 79 79 80 80 82 87 91 88 83 87 85 81 84 1985 82 77 78 81 85 91 86 85 83 81 79 78 82 1986 82 81 83 79 84 87 87 88 86 84 80 78 83 1987 83 83 81 81 81 85 86 85 87 85 82 80 83 1988 82 79 80 81 84 87 87 87 84 80 80 83 83 1989 80 86 88 86 80 86 91 90 81 81 79 79 84 1990 81 80 81 80 85 85 87 86 82 81 79 82 82 1991 78 83 84 83 82 90 88 88 79 81 81 81 83 1992 82 81 82 81 84 86 86 85 80 78 81 71 81 1993 78 84 86 84 80 88 84 87 82 79 81 67 82 1994 83 85 87 85 84 86 89 88 81 79 78 75 83 1995 84 85 85 82 83 86 87 88 85 79 82 82 84 1996 82 79 81 80 85 90 89 90 88 84 81 83 84 1997 84 82 83 85 85 91 89 89 87 83 81 82 85 1998 77 78 77 77 81 81 84 82 80 81 80 80 80 1999 78 77 78 78 78 80 83 83 88 88 86 81 82 2000 80 76 80 83 83 85 85 86 84 81 80 82 82 2001 79 80 81 82 83 89 90 91 90 84 83 82 84 2002 80 80 80 81 84 86 90 88 85 82 81 80 83 2003 77 79 80 84 83 87 85 87 85 86 84 80 83 2004 79 77 78 80 82 91 91 88 83 87 85 81 83 2005 82 81 83 81 85 87 86 85 83 81 79 78 83 2006 81 83 81 79 84 85 87 88 86 84 80 78 83 2007 71 79 80 81 81 87 86 85 87 84 82 80 82 2008 67 79 78 81 84 86 85 87 84 80 80 83 81 2009 75 81 81 82 80 86 90 91 84 80 78 78 82 2010 80 81 81 79 82 88 86 87 88 85 79 77 83 2011 71 81 80 80 84 86 88 88 86 82 80 79 82 2012 76 78 79 82 86 88 85 86 84 84 83 79 83 2013 75 78 79 81 80 86 87 84 85 80 80 81 81 2014 82 79 81 83 82 87 88 89 88 84 82 78 83 2015 74 81 82 79 81 86 86 87 82 83 83 72 81 2016 72 78 82 83 83 85 87 86 83 84 79 76 81 2017 78 81 80 82 82 87 86 86 86 85 81 81 83 2018 78 81 80 79 84 85 87 85 84 83 81 77 82 2019 78 80 80 78 83 85 87 87 80 80 78 79 81

135

G: MEAN MONTHLY RELATIVE HUMIDITY YEAR JAN FEB MAR APR MAY JUN JUL AUG SEPT OCT NOV DEC MEAN 1980 90 84 90 88 90 88 92 93 90 88 88 89 89 1981 81 87 88 88 90 92 95 96 93 91 89 91 90 1982 89 87 90 87 90 94 96 95 92 89 89 91 91 1983 84 85 89 88 91 92 93 94 92 87 89 88 89 1984 88 86 89 87 90 87 94 93 88 92 90 89 89 1985 89 87 87 87 92 92 92 91 89 89 87 87 89 1986 89 87 92 86 91 90 92 95 92 93 89 90 90 1987 90 89 91 90 91 92 94 91 91 93 89 91 91 1988 89 87 91 90 92 93 94 94 91 91 89 90 91 1989 86 90 93 91 90 91 95 95 88 90 88 90 91 1990 90 88 90 88 92 90 93 92 88 90 88 91 90 1991 88 89 91 90 91 93 94 94 89 91 90 91 91 1992 89 89 91 89 92 92 93 94 89 90 90 86 90 1993 88 91 93 91 90 93 92 94 90 89 89 84 90 1994 92 91 93 91 92 91 95 95 89 90 88 87 91 1995 92 91 93 90 92 92 94 94 91 90 90 92 92 1996 91 88 91 89 93 94 95 95 93 92 90 92 92 1997 92 90 92 92 93 95 95 95 93 92 90 92 92 1998 89 88 89 87 91 89 93 92 89 91 89 91 90 1999 90 88 90 87 88 88 92 92 93 95 92 91 91 2000 91 87 91 89 92 92 93 93 91 91 89 91 91 2001 90 89 91 90 92 94 96 96 94 93 90 92 92 2002 91 89 91 90 92 92 96 95 92 92 89 91 92 2003 88 87 90 90 91 92 93 94 91 93 91 90 91 2004 90 87 88 88 91 94 96 94 90 94 91 91 91 2005 91 89 90 89 93 93 94 93 90 91 89 90 91 2006 91 91 91 89 93 92 94 95 92 92 89 89 92 2007 83 87 89 88 89 91 92 91 91 91 89 90 89 2008 82 87 88 88 92 92 93 94 91 90 89 91 90 2009 87 89 90 89 90 91 95 96 90 90 87 89 90 2010 90 89 91 88 91 92 93 94 91 93 89 89 91 2011 84 89 90 88 92 92 94 94 91 90 88 89 90 2012 88 86 89 89 93 93 93 93 91 92 90 90 90 2013 88 87 89 89 90 92 94 93 91 90 89 91 90 2014 91 88 91 90 91 92 95 95 93 92 90 89 91 2015 87 89 91 88 91 92 94 94 90 92 90 86 90 2016 85 88 91 90 92 91 94 93 90 92 88 88 90 2017 89 89 90 90 92 92 93 93 92 93 89 91 91 2018 89 89 90 88 92 91 94 93 91 92 89 89 91 2019 89 89 90 88 92 91 94 94 89 90 88 90 90

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H: MEAN MONTHLY SEA SURFACE TEMPERATURE YEAR JAN FEB MAR APR MAY JUN JUL AUG SEPT OCT NOV DEC MEAN 1980 26.1 27.7 27.2 28.1 27.6 26.2 22.7 23.5 24.6 27.1 27.8 27.1 26.3 1981 26.3 28.0 27.6 27.6 27.7 26.4 22.9 23.7 24.9 27.6 28.6 28.4 26.6 1982 26.3 27.9 28.8 28.1 27.4 26.7 22.9 21.1 22.6 26.5 27.5 27.1 26.1 1983 25.8 27.3 27.2 27.0 26.9 26.1 21.7 22.1 24.2 27.2 27.8 28.5 26.0 1984 28.9 29.2 29.6 29.4 29.0 26.6 23.3 22.8 24.5 27.3 27.6 27.1 27.1 1985 25.9 27.5 26.8 28.9 28.5 26.7 22.8 22.8 24.3 27.2 28.2 26.6 26.4 1986 25.9 27.3 29.1 28.5 28.9 26.7 21.9 21.8 23.1 26.6 27.5 27.0 26.2 1987 26.8 28.0 28.6 29.2 28.6 25.0 24.6 25.8 26.5 27.7 29.0 26.8 27.2 1988 27.5 29.1 28.0 29.3 26.6 26.5 25.1 22.3 24.9 27.4 28.6 27.1 26.9 1989 27.3 28.9 27.9 28.4 27.6 26.3 25.2 21.9 24.7 26.8 28.1 27.5 26.7 1990 25.2 25.4 28.4 25.4 25.0 26.4 23.7 22.1 24.8 26.7 28.0 27.0 25.7 1991 27.6 27.4 27.6 26.2 27.5 27.0 25.2 23.0 25.7 26.3 27.9 26.7 26.5 1992 26.8 27.4 27.0 28.2 27.3 24.9 23.0 22.7 24.8 26.6 27.7 27.2 26.1 1993 25.9 27.4 27.1 26.9 28.1 25.7 23.3 23.2 24.5 27.8 27.8 27.5 26.3 1994 25.0 27.1 27.1 26.8 27.2 25.1 22.9 23.1 23.7 26.2 27.1 26.6 25.7 1995 25.5 27.7 27.5 28.2 27.8 25.8 24.0 23.9 23.2 26.2 28.0 27.3 26.2 1996 26.1 27.5 27.9 28.1 28.9 26.3 24.8 23.5 21.6 25.2 27.0 25.6 26.0 1997 24.3 27.3 27.0 27.5 27.7 27.3 24.2 21.7 26.7 27.7 28.2 27.7 26.4 1998 27.1 27.3 28.0 27.3 27.0 26.2 22.5 22.5 23.6 26.7 28.4 27.3 26.2 1999 25.6 27.5 27.7 28.5 27.8 27.1 23.7 21.6 23.0 25.5 28.2 27.7 26.1 2000 25.7 27.2 28.2 27.4 27.7 25.5 22.6 21.8 23.4 25.6 28.1 26.4 25.8 2001 26.4 27.9 28.1 28.0 28.6 25.8 27.6 21.8 24.4 26.5 26.9 27.8 26.6 2002 26.9 28.2 28.1 28.4 29.3 27.2 24.1 22.2 22.3 27.4 28.3 27.0 26.6 2003 26.5 27.1 27.7 27.3 27.3 25.4 21.0 21.2 24.6 27.2 27.8 27.0 25.8 2004 27.1 27.3 26.9 26.4 27.2 25.6 21.8 21.7 25.8 27.4 27.9 27.2 26.0 2005 26.1 27.9 27.4 27.4 27.1 25.6 23.7 21.1 24.9 27.5 28.4 26.9 26.2 2006 25.8 25.9 28.0 28.4 26.9 26.1 23.7 23.5 25.5 27.5 28.4 26.6 26.4 2007 25.9 27.8 27.8 27.8 27.7 25.6 23.9 24.7 24.2 27.3 27.9 27.3 26.5 2008 25.1 27.3 27.2 28.5 28.2 26.5 24.5 21.6 23.6 26.8 27.8 27.2 26.2 2009 24.5 26.8 27.0 25.5 26.7 26.7 25.7 22.5 26.6 26.0 28.0 28.4 26.2 2010 27.1 28.4 28.2 28.4 28.6 26.8 24.0 22.7 25.4 26.7 27.6 28.3 26.9 2011 27.3 26.8 28.5 27.6 27.8 26.4 26.2 22.0 27.5 27.8 27.8 27.6 26.9 2012 26.7 26.0 27.2 28.3 27.7 26.1 26.3 21.7 26.2 27.7 28.0 25.1 26.4 2013 27.2 27.2 27.3 27.3 25.8 27.2 21.8 20.5 24.6 25.1 23.3 21.6 24.9 2014 22.6 22.8 28.5 29.0 28.9 25.9 25.0 21.8 27.0 27.4 28.3 28.0 26.3 2015 26.6 28.0 28.4 28.6 28.5 27.4 24.4 22.1 21.3 26.4 28.4 28.0 26.5 2016 27.0 27.4 28.5 28.0 27.0 26.5 23.4 22.2 25.1 27.4 29.1 28.1 26.6 2017 27.0 28.7 28.0 28.7 28.4 26.1 24.2 22.0 27.2 25.5 26.2 22.8 26.2 2018 25.8 28.4 28.7 29.1 28.2 27.2 24.6 22.1 26.9 25.9 28.3 28.2 27.0 2019 28.2 28.4 28.3 28.5 25.9 25.0 24.7 22.3 25.5 27.5 28.4 28.0 26.7

137

I: MEAN MONTHLY SEA SURFACE SALINITY YEAR JAN FEB MAR APR MAY JUN JUL AUG SEPT OCT NOV DEC MEAN 1994 34.3 35.3 35.2 34.8 34.8 34.7 34.7 34.8 34.7 35.1 33.7 34.8 34.7 1995 34.3 33.8 35.0 35.8 35.3 35.8 34.8 35.0 34.9 33.6 34.2 34.4 34.7 1996 35.6 35.4 35.7 35.0 34.7 34.2 35.5 35.5 35.6 35.4 34.9 35.7 35.3 1997 35.3 35.0 34.8 34.9 35.0 34.3 35.7 35.3 35.3 34.7 34.3 35.0 35.0 1998 34.3 35.3 35.2 34.8 34.8 34.8 35.5 35.5 34.6 35.3 33.9 35.0 34.9 1999 35.1 35.1 35.2 34.9 35.1 34.7 37.2 38.0 37.7 35.4 35.3 35.0 35.7 2000 35.3 34.5 34.2 35.1 34.9 34.4 34.4 35.2 35.6 35.5 35.0 34.9 34.9 2001 35.5 34.7 34.6 35.4 34.6 34.4 34.6 35.5 35.4 35.3 34.1 34.9 34.9 2002 34.7 34.2 34.6 34.5 34.9 34.4 36.5 35.3 35.5 34.6 34.0 34.8 34.8 2003 34.4 34.9 35.0 34.7 34.5 34.9 35.4 35.6 35.6 35.0 35.0 34.5 34.9 2004 34.5 33.7 34.1 35.8 35.3 35.8 34.8 35.0 34.9 33.6 34.2 34.4 34.7 2005 34.1 34.3 35.1 35.4 35.4 35.1 34.4 35.4 34.5 34.2 33.7 33.9 34.6 2006 34.1 34.2 34.1 34.0 33.9 34.4 35.5 35.5 35.6 35.3 35.0 35.8 34.8 2007 33.5 36.1 35.0 35.0 34.9 34.7 33.9 34.1 33.4 31.6 32.3 36.5 34.3 2008 36.6 35.7 36.6 36.1 35.4 35.2 36.3 36.2 36.2 35.9 35.3 35.4 35.9 2009 36.2 35.8 35.6 35.4 34.4 35.5 35.9 35.9 35.9 35.8 35.3 35.3 35.6 2010 35.1 35.0 35.2 34.9 34.6 34.2 34.8 35.2 34.8 34.3 35.6 33.5 34.8 2011 33.9 34.6 34.6 34.3 32.6 32.4 32.5 33.7 34.7 34.6 34.5 34.7 33.9 2012 35.5 36.6 35.5 35.5 35.8 35.2 35.6 35.3 35.7 35.7 35.6 36.6 35.7 2013 35.6 34.5 34.6 35.1 35.3 34.2 34.2 34.4 35.0 35.5 33.7 35.7 34.8 2014 35.7 35.1 35.0 34.6 35.6 34.7 35.0 35.3 35.7 35.7 35.2 34.8 35.2 2015 34.1 35.2 35.3 35.1 35.1 35.0 35.6 35.7 35.8 35.9 38.2 35.0 35.5 2016 34.3 35.6 35.6 34.4 35.7 35.9 35.8 35.9 36.1 35.5 34.4 35.3 35.4 2017 35.5 35.2 35.3 34.9 35.6 35.6 35.8 35.8 35.8 35.6 36.2 36.0 35.6 2018 36.5 36.4 36.3 36.6 35.5 35.0 35.4 35.5 36.0 36.5 36.3 36.7 36.1 2019 35.3 35.8 35.9 35.8 35.3 35.3 35.0 35.4 35.7 35.7 36.2 36.6 35.7

138

J: TOTAL FISH CATCH Year Tonnes 2000 2773 2001 2874 2002 893 2003 981 2004 507 2005 422 2006 583 2007 333 2008 173 2009 151 2010 277 2011 133 2012 322 2013 325 2014 284 2015 647 2016 1136 2017 941 2018 2423

K: Disease Year Total Hospital Admissions Total Malaria Cases Total Malaria Deaths 2007 3066 941 84 2008 3066 941 84 2009 3752 1552 110 2010 3434 1394 104 2011 2366 1121 70 2012 2616 1257 84 2013 3247 1539 146 2014 3234 1436 47 2015 2980 853 24 2016 2547 715 33 2017 2102 633 14 2018 2101 629 6 2019 2633 616 7