EVALUATION ON THE EFFECTS OF SEA LEVEL DURING EL NIÑO

AND LA NIÑA EVENTS IN MALAYSIAN WATER

ANIE RAFLIKHA BT ABD MALEK

A thesis submitted in fulfillment of the

requirements for the award of the degree of

Master of Engineering (Environmental)

Faculty of Civil Engineering

Universiti Teknologi Malaysia

MARCH 2010

iii

Istimewa Buat:

Suami, Azahar bin Kassim

Abah, En. Abd Malek bin Ibrahim

Mak, Pn. Kalsom bt Ismail

Adik-adik, Ija, Ijam, Ijoi dan Shafiq

iv

ACKNOWLEDGEMENT

Praise be to Allah, the Most Gracious and Most Merciful Who has created the

mankind with knowledge, wisdom and power. First and foremost I would like to

express my thanks to Almighty ALLAH on the successful completion of this

research work and thesis.

I hereby, express my sincere and profound gratitude to my supervisor and co-

supervisor Dr. Noor Baharim bin Hashim and Associate Professor Dr Supiah bt

Shamsuddin for their sincere advice and guidance provided throughout my studies.

Special thanks to Associate Professor Dr Maizah Hura bt Ahmad from Department

of Mathematics, Faculty of Science, UTM, for her guidance in statistical analysis

study. I am also indebted to Miss Maznah Ismail, Puan Ilya Khairanis Othman, Miss

Akmaliza Abdullah and friends who helped me with their fruitful ideas and

comments on this thesis.

My acknowledgement also extends to the following agencies; Department of

Environmental (DOE) for water quality data of Sungai Johor, Department of Survey

and Mapping Malaysia (DSMM) for tidal data, Malaysian Meteorological

Department (MMD) for meteorological data, research funded by MOSTI under VOT

74254 and support from Research Management Center (RMC) and Sultanah

Zanariah Library (PSZ), Universiti Teknologi Malaysia (UTM) Skudai is

appreciated.

A very special gratitude is reserved for my husband, Azahar Kassim for his

understanding, for my parents Abd Malek Ibrahim and Kalsom Ismail for their

kindness and support especially motivate me during completion of my thesis.

v

ABSTRACT

Phenomenon on sea level rise has received a great concern from the

Malaysian government and the community. Due to its location, Malaysia is

vulnerable to sea level rise threat. MINITAB13 software was used to investigate the

sea level rise phenomena using the least square regression method. Mean Absolute

Percentage Error (MAPE) method generated from MINITAB13 was used to measure

the accuracy of fitted time series values, for example the future sea level data. This

was followed by null hypothesis test, test statistics-t, t-distribution and statistical

significant test. Meanwhile, forecasting the future sea level using exponential

smoothing approach as part of time series analysis technique was carried out. These

analyses were performed on sea level data sets ranging from 1984 – 2007 from four

stations across Malaysia. The regression analyses showed that the sea level was

influenced by the 1997 El Niño and 1999 La Niña events as well as the monsoon

season. The impact from the warmth, coolness and the occurrence of monsoon

season were significant with the increased or decreased of dissolved oxygen

saturation in Kuala Sungai Johor. Thus, during the El Niño event in 1997 and 2004,

saturated oxygen value in freshwater was low in Kuala Sungai Johor. However, the

occurrences of pre-monsoon and northeast monsoon (in October and January)

consequently had lowered the temperature, resulting in a higher value of saturated

dissolved oxygen.

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ABSTRAK

Fenomena peningkatan aras laut menerima perhatian yang besar daripada

kerajaan Malaysia and rakyatnya. Disebabkan lokasinya, Malaysia terdedah kepada

ancaman peningkatan aras laut. Perisian MINITAB13 telah digunakan untuk

mengesan fenomena kenaikan aras laut menggunakan teknik regresi kuasadua

terkecil. Peratusan kesilapan bagi purata tetap (MAPE) teknik yang dihasilkan

daripada MINITAB13 telah digunakan untuk mengukur ketepatan nilai tetap siri

masa, sebagai contoh, data aras laut pada masa hadapan. Ini diikuti dengan ujian

hipotesis nol, ujian statistik-t, agihan-t and ujian kepentingan statistik. Sementara

itu, pendekatan menggunakan pendataran eksponen yang mana sebahagian daripada

teknik analisis siri masa telah digunakan untuk meramal aras laut pada masa depan.

Analisis ini telah dijalankan ke atas set data daripada tahun 1984 hingga 2007

meliputi empat stesen dari seluruh Malaysia. Analisis regresi menunjukkan aras laut

dipengaruhi oleh kejadian El Niño pada 1997 dan La Niña pada 1999 serta musim

monsun. Kesan daripada kejadian panas, sejuk dan musim monsun memainkan

peranan penting dengan peningkatan dan pengurangan penepuan oksigen terlarut di

Kuala Sungai Johor. Justeru semasa kejadian El Niño pada 1997 dan 2004, nilai

oksigen terlarut dalam air tawar adalah rendah di Kuala Sungai Johor. Walau

bagaimana pun, kehadiran monsun timur laut dan pra-monsun (pada Oktober dan

Januari) telah menyebabkan penurunan suhu, kesannnya nilai oksigen terlarut adalah

tinggi.

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TABLE OF CONTENTS

CHAPTER TITLE PAGE

AUTHOR’S DECLARATION ii

DEDICATION iii

ACKNOWLEDGEMENTS iv

ABSTRACT v

ABSTRAK vi

TABLE OF CONTENTS vii

LIST OF TABLES x

LIST OF FIGURES xii

LIST OF ABBREVIATIONS xv

LIST OF SYMBOLS xvii

LIST OF APPENDICES xviii

1 INTRODUCTION 1

1.1 Introduction 1

1.2 Problem Statement 2

1.2.1 Description of Study Area 2

1.3 Significance of the Study 4

1.4 Objective of the Study 5

1.5 Scope of Study 5

2 LITERATURE REVIEW 6

2.1 Introduction 6

2.2 Sea Level Change 6

viii

2.2.1 Sea Level Rise, Climate Variability and

Marine Ecosystems 8

2.2.2 Implications of Sea Level Rise 9

2.2.3 The Importance of Coastal Resources 10

2.3 Temperature 11

2.4 Sea Level Pressure 11

2.5 Monsoon Characteristics 12

2.5.1 The northeast monsoon 13

2.5.2 First inter-monsoon period 13

2.5.3 The southwest monsoon 14

2.5.4 Second inter-monsoon period 14

2.6 ENSO Phenomenon 14

2.7.1 El Nino 15

2.7.2 La Nina 17

2.7 Water Quality 18

2.7.1 Temperature Effects 19

2.8 Forecasting 19

3 RESEARCH METHODOLOGY 21

3.1 Introduction 21

3.2 Database 21

3.3 Regression Analysis 23

3.4 The Correlation Coefficient 25

3.4.1 The Significance of The Correlation

Coefficient 26

3.5 The Exponential Smoothing Approach 26

3.6 Analysis On The Temperature Effect to Dissolved

Oxygen 28

4 RESULT AND DISCUSSION 30

4.1 Introduction 30

4.2 Mean Sea Level Analysis 30

4.2.1 Analysis On Mean Sea Level 31

4.2.2 Analysis On Mean Sea Level During

ix

El Nino and La Nina Years 37

4.2.3 Analysis On Monthly Mean Sea Level

During Northeast and Southwest Monsoons

of El Nino and La Nina Years 41

4.3 Smoothing and Forecasting 45

4.4 Correlation Between Mean Sea Level and

Meteorological Parameters 48

4.4.1 Correlation coefficient of sea level and

temperature 48

4.4.2 Correlation coefficient of sea level and sea

level pressure 51

4.4.3 Effect of Temperature to Dissolved Oxygen

Saturation in Sg Johor Estuary 54

5 CONCLUSIONS & RECOMMENDATIONS 57

5.1 Conclusions 57

5.2 Recommendations 58

REFERENCES 59

APPENDICES 67

x

LIST OF TABLES

TABLE NO. TITLE PAGE

2.1 Summary Table of Normal, El Nino and La Nina Years 17

3.1 Tidal stations geographic coordinate 23 3.2 Tidal data, meteorological data and historical water

quality for Malaysian waters 23

4.1 Summary of mean sea level rise at selected stations 34

4.2 Best-fit and residual data of Kota Kinabalu station. 35

4.3 Mean absolute percentage error of mean sea level for longest available year at selected stations 35

4.4 The result of hypothesis test on correlation coefficient of mean sea level regression lines for selected 37

4.5 Result of hypothesis test on correlation coefficient of mean sea level regression lines (during El Niño year) for selected stations 40

4.6 Result of hypothesis test on correlation coefficient of

mean sea level regression lines (during Lal Niña year) for selected stations 40

4.7 Summary of mean sea level rise at selected stations 46 4.8 Mean absolute percentage error of mean sea level after

double exponential smoothing operation at selected stations 46 4.9 Result of hypothesis test on correlation coefficient between

mean sea level and temperature for selected stations 51 4.10 Result of hypothesis test on correlation coefficient between

mean sea level and sea level pressure for selected stations 53

xi

4.11 Kuala Sungai Johor freshwater and saltwater saturated oxygen value 56

xii

LIST OF FIGURES

FIGURES NO. TITLE PAGE

1.1 Selected tidal station in Peninsular Malaysia and Sabah 3

3.1 Flowchart procedures for current study 22

3.2 Dissolved oxygen saturation concentration as a function of temperature and salinityProcedures flowchart of current study 29

4.1 Mean sea level of Kota Kinabalu 32 4.2 Mean sea level of Tanjung Gelang 32 4.3 Mean sea level of Johor Bahru 33 4.4 Mean sea level of Pulau Pinang 33 4.5 Kota Kinabalu mean sea level during 1997 El Niño and

1999 La Niña events 38 4.6 Tanjung Gelang mean sea level during 1997 El Niño and

1999 La Niña events 39 4.7 Johor Bahru mean sea level during 1997 El Niño and

1999 La Niña events 39 4.8 Pulau Pinang mean sea level during 1997 El Niño and

1999 La Niña events 39 4.9 Kota Kinabalu mean sea level during northeast monsoon

of El Niño and La Niña years 42 4.10 Tanjung Gelang mean sea level during northeast monsoon

of El Niño and La Niña years 42 4.11 Johor Bahru mean sea level during northeast monsoon

of El Niño and La Niña years 43

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4.12 Pulau Pinang mean sea level during northeast monsoon of El Niño and La Niña years 43

4.13 Kota Kinabalu mean sea level during southwest monsoon

of El Niño and La Niña years 43 4.14 Tanjung Gelang mean sea level during southwest monsoon

of El Niño and La Niña years 44 4.15 Johor Bahru mean sea level during southwest monsoon

of El Niño and La Niña years 44 4.16 Pulau Pinang mean sea level during southwest monsoon

of El Niño and La Niña years 44 4.17 Kota Kinabalu mean sea level in next 20 years 46 4.18 Tanjung Gelang mean sea level in next 20 years 47 4.19 Johor Bahru mean sea level in next 20 years 47 4.20 Pulau Pinang mean sea level in next 20 years 48 4.21 The correlation coefficient of Kota Kinabalu sea level

and temperature 50 4.22 The correlation coefficient of Tanjung Gelang sea level

and temperature 50

4.23 The correlation coefficient of Pulau Pinang sea level and temperature 50

4.24 The correlation coefficient of Johor Bahru sea level

and temperature 51 4.25 The correlation coefficient of Kota Kinabalu sea level

and sea level pressure 52 4.26 The correlation coefficient of Tanjung Gelang sea level

and sea level pressure 52 4.27 The correlation coefficient of Pulau Pinang sea level

and sea level pressure 53 4.28 The correlation coefficient of Johor Bahru sea level

and sea level pressure 53 4.29 Location of Kuala Sungai Johor Station 55 4.29 Dissolved oxygen level at Kuala Sungai Johor

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from 1994-1996 55

xv

LIST OF ABBREVIATIONS APHA American Public Health Association atm Atmosphere Chlro Chloride concentration DO Dissolved oxygen DOE Department of Environment DSMM Department of Survey and Mapping Malaysia ENSO El Niño Southern Oscillation IPCC Intergovernmental Panel Climate Change ITCZ Inter-Tropical Convergence Zone MAPE Mean Absolute Percentage Error MINC Malaysia Initial National Communication MMD Malaysian Meteorological Department MSL Mean Sea Level MSLP Mean Sea Level Pressure NE Northeast monsoon NOAA National Ocean and Atmospheric Administration ppt Part per thousand or in percentage (%) SCS South China Sea Sg Sungai or River SST Sea surface temperature

xvi

SW Southwest Monsoon

xvii

LIST OF SYMBOLS α - smoothing constant

τ - a positive number

ρ - population correlation coefficient

a - intercept at y-axis

b - slope of the line

ei - residual or error

H0 - null hypothesis

H1 - alternative hypothesis

n - number of observation or observation

osf - saturation concentration of dissolved oxygen in freshwater at

1 atm (mg/l)

Oss - saturation concentration of dissolved oxygen in saltwater at

1 atm (mg/L)

r - correlation coefficient

S - salinity (g/L = part per thousand (ppt) or in percentage (%))

ST - single smoothed estimate or single smoothed statistic

- double smoothed statistic

T - time/Period

T - temperature in Celsius (0C)

Ta - absolute temperature (K); Ta= T + 273.15

y - actual observed value

yc - computed value

yT - observation at t-th time/period - forecast value ty - fitted value or yc.

xviii

LIST OF APPENDICES

APPENDIX TITLE PAGE A Best-Fit and Residual Data 67

B.1 Monthly Mean Sea Level during Northeast and

Southwest Monsoons during El Niño Year in 1997 68

B.2 Monthly Mean Sea Level during Northeast and

Southwest Monsoons During La Niña Year in 1999 70

C Location of Several Tide Gauge Stations and Tide

Observation Tools

CHAPTER I

INTRODUCTION

1.1 Introduction

The phenomenon on the rise of the sea-level is of great concern to the Malaysian

government and the community. The 1997-98 El Niño event had made the public and

the Malaysian authorities aware, for the first time, of the environmental problems caused

by the ENSO events (Camerlengo, 1999). Peninsular Malaysia, Sabah and Sarawak are

surrounded by the South China Sea (SCS), Celebes Sea, Straits of Malacca, Johor Straits

and Karimata Straits which is located on Sunda Shelf, the shallowest sea compared to

the open seas such as the Pacific Ocean. Strategic locations in the country, such as the

coastal areas, are home to more than 60% of the total population (Malaysia Initial

National Communication (MINC), 2000) and major cities like Pulau Pinang, Johor

Bahru, Kota Bharu, Kuching are located less than 50 kilometres from the coastal region.

Coastal and marine environment are linked to the climate in many ways. The

ocean’s role as the distributor of the planet’s heat could strongly influence the changes

in global climate in the 21st century. The rise of the sea level, the increase of nitrogen

and carbon dioxide in coastal waters are threatening the coral reef populations and these

are among the examples of the impact of the climate change. Study by MINC (2000)

showed that when the temperature increases, it will cause the ocean to expand and the

sea level will rise between 13 to 94 cm or 0.9 cm/year (based on the High Rate of Sea

Level Rise) in the next 100 years. The General Circulation Model, Canadian and

2

Hadley models suggested a mean expected sea-level rise of approximately 45-51cm

above current levels by the end of the century (Boesch et al, 2000). These scenarios are

consistent with the Intergovernmental Panel on Climate Change’s 1995 estimates that

sea-levels would most likely to increase by approximately 37 cm by 2100 (Houghton et

al 1996).

The rise of sea level brings a devastating impact especially to the coastal

community. Human life, places of attractions, infrastructures such as electric power

station and economies will be threatened by the rise of the sea level. According to Klein

et al (1999), even though the sea on the Sunda Shelf is shallower compared to the open

seas, the South China Sea’s surface temperature is closely related to El Niño Southern

Oscillation (ENSO). El Niño represents the warm phase while La Niña represents the

cold phase. It has been observed since 1977 that the El Niño Southern Oscillation

(ENSO) has occurred more frequently.

1.2 Problem Statement

An attempt was made to study the variations of Malaysian sea level during El

Niño and La Niña events. Two meteorological parameters were used in this study to

identify the correlation between the mean sea level and air temperature, mean sea level

and air pressure; and to identify the variations of the sea level in the Straits of Malacca

and the South China Sea during these events. This study is considered an extension of

the studies by Wai (2004) and Camerlango (1999). The effect of El Niño and La Niña

events, particularly the effect of temperature to water quality parameter (dissolved

oxygen) will be also investigated in this study. Therefore, it is hope that this study will

bring benefit to a wide range of professionals who are responsible for policy making,

agriculture, environmental planning, decision making and economies.

3

1.2.1 Description of Study Area

Malaysia lies between the latitude of 10N and 70N and longitude of 990E and

1200E (Figure 1.1). It has an equatorial climate. The mean temperature of the lowland

station is between 260C to 280C with little variation for different month or across

different latitude (MINC, 2000). The ranges of rainfall variations in Malaysia are

highly, regularly and fairly uniform. However, most parts of Malaysia received peak

rainfall during the northeast monsoon season. During this period, the east coast of

Peninsular Malaysia and northeast coast of Borneo island received up to 40% of their

annual rainfall (Andrews and Freestone, 1973).

Figure 1.1: Selected tidal stations in Peninsular Malaysia and Sabah

The South China Sea divides Malaysia into separate sections West Malaysia or

Peninsular Malaysia and East Malaysia, which is also known as Borneo Island. It is the

largest marginal sea (semi-isolated bodies of water) situated in the Southeast Asia. The

sea is surrounded by South China, the Philippines, Borneo Islands, and the Indo China

Peninsula. This sea is shallower than the Pacific Ocean and has different salinity and

temperature from those of typical open ocean seawater. The sea is fed from the north by

the Pacific waters through the Luzon Straits and the Taiwan Straits, while the southern

part of the sea is fed by the Java Sea. Thus, the South China Sea has the most variety of

marine ecosystems which includes the soft-bottom and deep shelves oceanic waters,

mangroves swamps, lagoons, seashores, sea grasses and coral reefs.

 

Tg Gelang

Pulau Pinang 

Kota  Kinabalu

Johor Bahru

South China Sea

Straits of Malacca 

4

The sea has also one of the widest continental shelves and the edges of the sea

are well fed by many rivers. The rivers have supported human activities in the region

and consequently become one of the most populated regions in the world. Because of its

geographical location, the South China Sea surface temperature is closely related to

ENSO (Klein et al, 1999). The Mediterranean and the Caribbean Seas are other

examples of marginal seas which are located in the Atlantic Ocean.

Peninsular Malaysia is hilly and mountainous with few large areas of plains

(Andrews and Freestone, 1973). Human settlements are concentrated along the alluvial

plains towards the coast. Most of the coastal regions are low-lying areas with less than

0.5m above the astronomical tide, or are within 100m inland of the high-water mark and

are vulnerable to sea-level rise.

Southeast Asia is dominated by the monsoon wind system, which produces two

major types of climate in Malaysia, Singapore and Indonesia. First is the monsoon

climate occurs in northern Malaysia, northern Sumatra and eastern Indonesia. Second is

the equatorial rainforest climate which occurs over the southern section of Peninsular

Malaysia, Singapore, southern Indonesia, western Java, Kalimantan and Sulawesi

(Andrews and Freestone, 1973).

1.3 Significant of the study

Basically, this study is considered as an extension or continuation from the study

by Camerlengo (1999) as well as Wai (2004). The variation of sea level during El Niño

and La Niña years is of significant interest in this study. Moreover, further investigation

on the response of sea level during southeast monsoon and southwest monsoon seasons

is investigated.

Additional information on the effect of temperature event to water quality

parameter due to ENSO event is also included since there are few studies on this matter.

5

1.4 Objective of the Study

The main objective of the study is to investigate the behaviour of the sea level

during El Niño and La Niña events. The objectives of the study are listed as follows:

1. To investigate the behaviour of the sea level during El Niño event and La Niña

event in Malaysian waters.

2. To investigate any significant effect of monsoon season to Malaysian sea level

during El Niño and La Niña events.

3. To identify the relationship between temperature and mean sea level pressure

with variety sea levels.

1.5 Scope of the study

In this study, the monthly distribution of mean sea level variability in 4 stations

(Kota Kinabalu, Tanjung Gelang, Johor Bahru and Pulau Pinang) due to the ENSO event

will be analyzed. Furthermore, the effect of monsoon season to the variability of mean

sea level will also been examined and included. For this purpose, the monthly mean sea

level will be divided into the ENSO year (El Niño and La Niña year) for the analyzed

period. Earlier, investigation on Malaysia sea level trend for longest available record is

made. MINITAB13 was used to obtain the residuals, forecast data, fitted data and error

of the fitted data. The relationship between selected meteorological parameters with

mean sea level in the Straits of Malacca and the South China Sea was also included.

Relevant study on the effect of ENSO event on dissolved oxygen parameter in Kuala

Sungai Johor estuaries will be the additional information to support the finding of this

study. According to Chatfield (1989), short time series is not suitable to use Box-

Jenkins method, which required more than 50 data. Therefore double exponential

smoothing approach has been used to forecast the time series.

CHAPTER II

LITERATURE REVIEW

2.1 Introduction

Generally, sea level refers to the average water level over a period of 20 years,

which gives enough time for astronomic and most climatic fluctuations to run through

their complete cycles. Over geological time scales however, sea-levels has fluctuated

greatly. On average, global sea-level has been gradually rising since the last ice age.

However, other factors such as storm surges, winds, currents and rainfall could also

affect water level on short time scales. During the last 100 years, sea-level rise has

occurred at a rate of approximately 1 to 2 millimetres per year, or 10 to 20 centimetres

per century (Gornitz 1995; IPCC 1996) and study by Church et al (2004) on the regional

pattern of sea level rise from 1950 to 2000 has supported this finding. The sea level rise

is 1.8 ± 0.3 mm/year and the maximum sea level rise was observed in the eastern off-

equatorial Pacific and minimum sea level rise was observed in the western Pacific and in

the eastern Indian Ocean.

2.2 Sea Level Change

A change in sea level that is experienced worldwide due to changes in seawater

volume or ocean basin capacity is called eustatic. Changes in sea floor spreading rates

can change the capacity of the ocean basin, resulting in eustatic sea level changes.

Significant changes in sea level due to changes in spreading rate typically take hundreds

7

of thousands to millions of years and may have changed sea level by 1000 meters or

more. For every 10C (1.80F) changes in the average temperature on the ocean surface

waters, sea level changes about 2 meters or 6.6 feet (Thurman and Trujillo, 2004).

Besides eustatic change, the vertical movement of land could cause the apparent local

change in the sea level (Boon, 2004). For instance, the drop of sea level is detected in

Ambon due to the fact that the land is rising at that site (Hadikusumah and Soedibjo,

1991; Safwan, 1991; Ongkosongo, 1993) when sea level increase is observed all across

Indonesian Archipelagos. Furthermore, study by Cheng and Qi (2007) showed that the

mean sea level over the South China Sea has a risen at a rate of 11.3 mm/yr during

1993–2000 and fell at a rate of 11.8 mm/yr during 2001–2005. Experiments by Oguz

(2009) using Archimedes’ Principle, the Law of Conservation of Matter, and the fluidity

of liquids in the laboratory had proved that higher temperature are expected to raise the

sea level by expanding the ocean water, melting the mountain glaciers and small ice caps

and causing portions of the coastal section of the Greenland and Antarctic ice sheets to

melt or slide into the ocean (U.S. Protection Agency, 2008). In other words, sea level

rises with warming sea temperatures and falls with the cooling of the sea. In spite of

this, sea level tends to mirror thermocline depth since water expands when heated

(McPhaden, 2001).

Two major variables have been used to observe sea level change over the last

several hundred thousand years; the thermal expansion or contraction of the sea and the

amount of water that is locked up in glaciers and ice sheets. Current major glaciers and

ice sheets are enough to raise the sea-level by approximately 80 meters if all were

melted and flow to the sea (Boesch et al (2000) and Emery and Aubrey (1991)).

Groundwater mining, deforestation and water released from the combustion of fossil

fuels would have the effect of slightly increasing the global sea level. For example, a

large part of Bangkok is sinking and over 10 million people is below the level of the

river, partly because the drawing up of ground aquifers. Thus, sea level rising will put

many areas at risk. Furthermore, just few kilometres south of Bangkok, land subsided

by 38cm between 1992 and 2000 (Nirmal Ghosh, 2009).

8

2.2.1 Sea Level Rise, Climate Variability and Marine Ecosystems Sea-level rise is a significant consequence of climate change on marine

ecosystems. Effects of climate variability and change on coastal areas and marine

resources are made more difficult by the confounding effects on human activities. For

example, naturally the estuarine and coastal wetland environments will migrate inland in

response to the rise of sea-level, but this migration is blocked by coastal development,

diking, filling or hardening of upland areas. For example, the mangroves along the coast

of Southwest Johor had vanished because these mangroves cannot move inland due to

the coastal bunds which were built under World Bank loan at Southwest Johor in 1974

(Zamali and Chong, 1991). Furthermore, the biological communities might be able to

adapt to temperature, salinity or productivity associated with variable climate regimes,

however they were unable to response to the sea level rise phenomena.

The general status and trends in coastal and marines resources have been done by

Boesch et al (2000). Moreover the current and future impacts associated with

population concentration and growth in the coastal zone has been evaluated. The review

on climate forcing such as sea-level rise rates, changes in storm tracks and frequencies,

changes in ocean current patterns have been done. Therefore further detailed

descriptions on these topics were referred to IPCC (1999); Houghton et al (1996); Biggs

(1996); Mann and Lazier (1996). The climate forcing is also intended to provide a brief

foundation for assessing the real and potential impacts of climate and various types of

coastal and marine ecosystems (coastal wetlands, estuaries, shorelines, coral reef and

ocean margins). Case studies on this topic have been inserted by the National Oceanic

and Atmospheric Administration (NOAA) to provide specific examples of the complex

set of interaction between the effects of climate and human activities.

Study by Levitus et al (2000) gave a strong evidence for ocean warming over the

last half-century. Their results indicated that the mean temperature of the oceans

between 0 and 300 meters had increased by 0.310C over the last fifty years. Boesch et al

(2000) concluded that according to climate variability, Levitus’s result was in strong

agreement with those projected by many circulation models.

9

As a consequence of global climate change, the hydrologic cycle has been

intensified with increased precipitation and evaporation, and varying impacts of coastal

runoff. The development of retentive zones defined by stratified waters and associated

fronts are important in maintaining planktonic organisms within regions where the

probability of survival is enhanced. However, strong stratification can impede the

mixing and regeneration of nutrient, resulting in a decrease in primary production of

planktonic organisms in some areas. Nevertheless, an increase in temperature and

enhanced stratification resulted in a decline in production in the California Current

system during the last two decades (McGowan et al, 1998). Stronger pynoclines could

occur and may result in less diffusion of oxygen from the upper part of water column,

meaning, there will be less dissolved oxygen in bottom waters (Rabalais et al, 2009).

Mann and Lazier (1996) also stated that an increase in temperature and river runoff

could lead to an earlier onset of stratification and timing of phytoplankton blooms,

which leads to a shift in phytoplankton species composition.

Increases in temperature will also result in further melting of sea ice in polar and

subpolar regions, with direct effects on the input of fresh water into these systems and

with side effects on buoyancy-driven flow and stratification. The reduction and

potential loss of sea ice has enormous feedback implications for the climate systems

where ice and snow are highly reflective surface, returning 60 to 90% of the sun’s

incoming radiative heat back to outer space, while open oceans reflect only 10 to 20 %

of the sun’s energy.

2.2.2 Implications of Sea Level Rise

Impact from storms may become additional reason to a rising sea level. The

higher waves on the beaches and barrier islands of the nation’s coast, flooding and

erosion damage will be expected to increase in the future (Ruggiero et al, 1996 and

Heinz Center 2000). For example, during both the 1982-83 and the 1997-98 El Niño

events, elevated sea levels of a few tens of centimetres in the Pacific Northwest were

10

sufficient to cause higher wave to impact and erode coastal cliffs, causing widespread

property losses and damages (Komar 1986, Komar 1998).

The worst-case determined by the IPCC (1999) is, if one meter rise in sea level

during the next century, thousands of square of kilometres could be lost, particularly in

low-lying areas such as the Mississippi delta. The effects of sea-level rise are unlikely

to have a catastrophic impact on coastal themselves before the middle of the 21st

century, but when combined with subsidence and other environmental changes, the

consequences may be severe.

In Malaysia, the impact of climate change and sea level rise on coastal resources

can be classified into four categories-- the tidal inundation, shorelines erosion, the

increasing of wave action, which can affect the structural integrity of coastal facility and

installation of thermal power plant, and the salinity intrusion which can pose a potential

threat of water contamination at water abstract points (MINC, 2000). Study by

Jamaluddin (1982) has shown rapid shorelines erosion over the three year period from

1977 to 1980 at Pantai Cahaya Bulan and the adjacent beach, Pantai Kundor. The

average recession rate along this stretch of beach is approximately 5 metres/year. In

addition, wave action have threatened the coastal bund at Southwest Johor in 1974 and

caused a bund revetment which had cost RM2.5 million at that time (Zamali and Chong,

1991). Furthermore, MINC (2000) also predicted that Western Johor Agricultural

Development Project area will be affected if the sea level rise about 0.9cm/year based on

the High Rate of Sea Level Rise. Long-term annual flood will occur and the damage is

estimated at about RM88 millions in Peninsular Malaysia and RM12 millions in Sabah

and Sarawak (based on 1980 price level). Loss of fisheries production and interruption

of port operation also could occur if the sea level rise 0.9cm/year.

2.2.3 The Importance of Coastal Resources

Fisheries for both commercial and recreational are important activities in the

coastal areas. About 4.5 million tons of marine stocks landed in the Unites States coast

11

over the past ten years and it has become the world’s fifth largest fishing nation. Coastal

tourism itself generates multibillion industries in the United States. As a consequence,

clean water, healthy ecosystems and access to coastal areas are critical to maintaining

the tourism industries. While in Malaysia, loss of fisheries production estimated at

RM300 millions will take place if 20% of the mangroves areas are loss (MINC, 2000).

Despite of the increase in coastal populations, the vulnerability of developed

coastal areas to natural hazards is also expanding. In the Unites States, thousands and

millions of the coastal communities are facing disastrous impacts from storm surge,

flood and hurricanes. For example Hurricane Katrina, Hurricane Andrew and George

had caused billion dollars in damages to coastal communities.

2.3 Temperature

Dale (1963) showed evidence that the annual means for temperature on the

western side of the country were slightly higher than those on the east even though the

observed station was in the same latitude. But the difference between the two sides of

the country decreased northwards. The annual variation was less than 2°C except for the

east coast areas of Peninsular Malaysia which were often affected by cold surges

originating from Siberia during the northeast monsoon. This explains why the

temperature variation is below 3°C (Malaysian Meteorological Services, 2009).

2.4 Sea Level Pressure

Station pressure is the actual barometric pressure at the reporting station while

sea-level pressure is the station pressure adjusted for the elevation of the station using a

standard formula. Thus, the difference between them will be a constant percentage for

each station. However, if the station is near sea level, both pressures will always be the

same. Average sea-level pressure is 101.325 kPa (1013.25 mbar) or 29.921 inches of

mercury (in Hg) or 760 millimetres (mmHg). The highest sea-level pressure on earth

12

occurs in Siberia, which is above 1032.0 mbar and the lowest measurable sea-level

pressure is found at the centre of hurricanes (typhoons, baguios).

2.5 Monsoon Characteristics

The oceanic regions most affected by these seasonal changes are in the Indian

Ocean and western Pacific, where the seasonally reversing winds are known as the

monsoons. The word monsoon means ‘winds that change seasonally’ and it is derived

from Arabic word, ‘mausim’. However, the westernmost part of the Pacific Ocean;

Malaysian and Indonesian archipelagos are the most obvious regions affected by the

seasonally reversing winds (Brown et al, 1989).

Changes in the direction and speed of the air-streams across Peninsular Malaysia

lead to the division of the year, in most areas, into four seasons; northeast (NE)

monsoon, southwest (SW) monsoon and two transitional periods. Maximum rainfall

coincides with pre-monsoonal period and minimum rainfall occurs in between the two

monsoons and the time of commencement and duration of these monsoons period vary

slightly with latitude and from year to year (Dale, 1959). Furthermore, Inter-Tropical

Convergence Zone (ITCZ) represents an area of convergence of air at the lower level

and this air tends to rise. This condition means ITCZ represents a highly convective

area and larger value of relative humidity is expected at the passage of the ITCZ. The

passage of the Inter-Tropical Convergence Zone (ITCZ) occurs in April/May and

September/October. These two passages are referred as the inter-monsoon periods.

According to Cheang (1987), the names for winter monsoon or NE monsoon and

summer monsoon or SW monsoon are derived from the low-level prevailing winds of

the two seasons. However in neighbourhood country, like Indonesia, they experience

west and east monsoons seasons, respectively. On the other hand, in the western part of

Peninsular Malaysia and southwest of Thailand, more rains received during two

transitional periods than during the monsoon periods.

13

2.5.1 The northeast monsoon

This monsoon occurred between early November or December to March when

the equatorial low pressure lies to the south of the equator. During this monsoon the sun

is over the Tropic of Capricorn. The wind speeds prevailed during these months are

between 15km/hr to 50km/hr (Andrews and Freestone, 1973). Because of the high

pressure in Asian mainland, the winds blow from Asia toward low pressure area in the

equator. This consequently brings heavy rain to the NE coast of Kudat and Sandakan.

NE monsoon is represented by two air masses from different sources. First air

mass is derived from a high pressures system situated over the northern half of the Asian

continent. In southward motion, this air mass upon reaching South China Sea is shallow

as it rarely exceeds 2000m off the east coast of Peninsular Malaysia. The air masses

become warmer and losses much of their dryness. The second air mass is derived from

the Pacific Ocean. Northeast trade winds will transport the air mass towards the South

China Sea. The air mass is warmer and more humid than the first air mass. During this

season, a maximum rainfall appears in the night or early morning due to landward drift

of showers from the South China Sea. Exposed areas for example in the east coast of

Peninsular Malaysia, Western Sarawak and the northeast coast of Sabah experiences

heavy rain spells (Malaysian Meteorological Services, 2007).

2.5.2 First inter-monsoon period

First inter-monsoon period occurs from April (in the south) to May (in the north).

Rain may occur at almost any hour of the day in contrast to the more regular afternoons

rains commonly found during the monsoon periods. The rainfall varies from one place

to another.

14

2.5.3 The southwest monsoon

Wind blows between the month of June and September or early October. The

country will experience light southerly winds from the southern hemisphere and south-

westerly winds from the Indian Ocean advance across northern Malaysia. These winds

are weaker than north-easterlies. The temperature is high over the mainland Asia and

this resulted in area of low pressure well to the north of Sabah into which winds blow in

a curved path. The wind speeds seldom exceeds 25km/hr.

Camerlengo et al (2002) stated that Southern Sabah is largely affected by the

poleward migration of the area of convergence associated with the SW monsoon.

Larger values of rainfall due to the onset of SW monsoon are observed at the western

coast in May, while lesser rainfall is observed in the eastern coast of Sabah. Relatively,

high percentage of rainfall received during the southwest monsoon may be due to the

diminishing sheltering effects of the mountains of the northern Sumatra compared with

those to the south.

2.5.4 Second inter-monsoon period

This monsoon occurred in October and early November, and followed by the

northeast monsoon season. The southwest and northeast winds gradually become

dominant during this period. This is the reason why the coast of Sabah experienced

heavy rains during this period.

2.6 ENSO Phenomenon

Oceanographic component, El Niño, and its atmospheric component, the

Southern Oscillation was known as ENSO phenomenon. This phenomenon is a

prominent climatic oscillation at the interannual time scale and oscillation between

15

warm and cold events usually referred to as El Niño and La Niña, respectively. The

situation in which a high sea surface pressure occurs in the southeastern and the central

Pacific Ocean and a low sea surface pressure occurs in the western Pacific is referred as

the Southern Oscillation (Philander, 1989). This cycle is discontinued whenever a

warming of the sea surface temperature (SST) at the eastern Pacific takes place.

According to National Ocean Atmospheric Administration or NOAA (2005), typically,

ENSO events occurred irregularly at an interval of 2-7 years and it will last 12-18

months (McPhaden, 2001). This event is usually referred as El Niño (“the child”, in

Spanish), in reference to “Christ Church and El Niño represents the warm phase of the

Southern Oscillation.

Whenever the strength of the trade winds (blowing from east to west in tropical

areas) is greater than usual, there will be an enhancement of this warm pool of water, as

the consequent of convective activity. This case is known as ‘La Niña’ (‘the girl’ in

Spanish). It is also referred as ‘anti-El Niño’ or ‘El Viejo’ (‘the old man’, in Spanish).

This case represents the cold phase of the Southern Oscillation (Toole, 1984). Study by

Tangang and Alui (2002) stated that prolonged drought (severe) flooding) associated

with occurrence of an El Niño (La Niña) event is likely to be experienced in the three

areas; Sabah, Sarawak and east coast of Peninsular Malaysia.

2.6.1 El Niño

Every 2-7 years, an abatement of the trade winds usually takes place. Table 2.1

shows the occurrence of the El Niño event since 1950. According to Grim et al. (1998),

there were 12 El Niño events recorded during the last 5 decades. During this event, a

reversal of the winds occurs from 3 to 5 weeks. This situation is known as the “westerly

wind bursts”. The Indonesian Low and its associated area of convective rainfall migrate

eastwards. The warm pool of water also moves towards the eastern boundary. This

situation represents the early stages of the El Niño event. Originally, the term El Niño

denoted a warm southward flowing ocean current that occurred around Christmas time

16

off the west coast of Peru and Ecuador. However the term was later restricted to unusual

strong warming that disrupted local fish and bird populations every years.

During an El Niño event, the thermocline levels across the Pacific Ocean. In

other words, the thermocline deepens at the eastern boundary and surges at the western

boundary. Therefore, the waters off Peru and Ecuador become warmer. Thus, the

warming of the SST off Peru and Ecuador, during an El Niño event may be due to three

different effects: the eastward displacement of the warm pool of water from the western

boundary of the Pacific Ocean, the deepening of the thermocline and the combination of

the two previous phenomena.

In the Atlantic Basin, hurricanes are less prevalent during El Niño years

compared to non-El Niño years. The supposition of storm surges on a sea-level

(3.9mm/year at Atlantic City) has resulted in an increase of storm impact over the length

record, from 200 events of hurricane to 1200 events in past decade (Boesch et al, 2000).

NOAA’s Climate Prediction Center declares the onset of an El Niño episode

when the 3-month average sea-surface temperature exceeds 0.50C in the east-central

equatorial Pacific (between 50N - 50S and 1700W – 1200W). El Niño events may occur

more frequently as a result of global warming (Thurman and Trujillo, 2004).

Presumably, increased ocean temperatures could trigger more frequent and more severe

El Niño.

Rong et al (2006) stated that, the South China Sea lies near the anomalous

descent region where surface wind, air temperature, humidity and cloud cover were

altered there, which in turn influence the ocean circulation and surface heat fluxes and

eventually sea surface temperature (SST). Additionally, their studies indicated every

ENSO event is associated with a change of the SCS SST anomalies.

17

2.6.2 La Niña

La Niña refers to the periodic cooling of ocean surface temperature in the central

and east-central equatorial Pacific that occurs every 3 to 5 years. Table 2.1 shows the

occurrence of the La Niña events since 1950. La Niña represents the cool phase of the El

Niño Southern Oscillation (ENSO) cycle, and is sometimes referred to as Pacific cold

episode. La Niña originally referred to an annual cooling of ocean waters off the west

coast of Peru and Ecuador. Larger pressure difference across Pacific Ocean creates

stronger Walker Circulation and stronger trade winds, which in turn causes more

upwelling, a shallower thermocline in the eastern Pacific and a band of cool than normal

water that stretches across the equatorial South Pacific.

Table 2.1: Summary Table of Normal, El Niño and La Niña Years (from National Ocean and Atmospheric Administration (NOAA, 2007))

Normal Year El Nińo Year La Nińa Year 1950 - 1951

1952 - 1953 1954 - 1956 1957 - 1958

1959 – 1963 1964 1965 – 1966

1966 – 1968 1969 1970 – 1972 1972 1973 – 1976

1977 – 1981 1982 – 1983

1984 1984 1986 – 1987 1988 – 1989

1989 – 1990 1991- 1995

1996 1997 – 1998 1998 – 2001 2002

2003 2004

2005 - 2007

18

2.7 Water Quality

The basic objective of water quality engineering field is the determination of the

environmental controls that must be instituted to achieve a specific environmental

quality objective. Generally, people use water for recreational activities, fisheries, water

supply and agricultures. Thomann and Muller (1987) also clarified the principal

components of water quality problems, which are inputs, the reactions and physical

transport, and the output. Inputs can be divided into two categories, point sources and

non-point sources. While the reactions and physical transports in water quality are the

chemical and biological transformations, the water movement results in different levels

of water quality at different locations in time and in different aquatic ecosystem. The

outputs are the resulting concentration of substance, for example dissolved oxygen or

nutrients at particular location in the water body, during a particular time of the year or

day.

According to Cooter and Cooter (1990), the effect that altered stream shading

due to shifting vegetation regimes (on net radiation) will cause high stream temperature

and this condition was enough to cause critically low dissolved oxygen in certain areas.

Study by Morril et al (2005) shows that an increase in water temperature of about 0.60C

to 0.80C will cause an increase of 10C in air temperature in majority of the streams,

while few streams shows linear 1:1 air/temperature trend. In addition, an increase of

stream temperature at areas with currently low dissolved oxygen will affect the

dissolved oxygen levels to fall into critical range. Therefore this condition would

threaten the aquatic life. Besides that, saturated dissolved oxygen is lowest at higher

streams temperature. Nevertheless, Morril et al (2005) study also showed as air

temperature and stream temperature increase, dissolved oxygen level will decrease.

Moreover the streams under their study showed that the streams temperature is likely to

increase due to global warming. Study by Whipple and Whipple (1911) showed that

oxygen is less soluble in seawater than in freshwater and the solubility is intermediate in

brackish water.

19

2.7.1 Temperature Effects

According to Chapra (1997), as the temperature increase, the rate of most

reactions in natural waters also increased. The rate will double if the temperature rises to

100C. In water quality modelling, many reactions are reported at 200C. This condition is

expressed by

k(T) = k(20)θT-20 (2.1) where k is a temperature-dependent constant, T is temperature and θ is constant values of

water quality parameters. Furthermore, as the temperature increase, density decreases

with salinity. Thomann and Muller (1987) stated reasons why temperature of water body

is significance;

a) the discharges of excess heat from municipal and industrial effluents such as

power stations and industrial cooling water, may positively or negatively affect

the aquatic ecosystem.

b) temperature influences all biological and chemical reactions

c) variations in temperature affect the density of water and hence the transport of

water.

2.8 Forecasting

According to Gilchrist (1976), there are three general methods of forecasting --

intuitive methods which is a classical method of forecasting; causal methods which try

to forecast effects on the basis of knowledge of their causes; and extrapolative methods

which based on the extrapolation into the future of features shown by relevant data in the

past. Several types of forecasting has been given by Gilchrist (1976), for examples,

constant mean model, seasonal model, linear trend model, regression model and

stochastic model.

20

According to Bowerman and O’Connell (1979), the exponential smoothing

approach can be used to forecast such time series and in this study, double exponential

smoothing will be used. Using this approach, values of mean absolute percentage error,

residual values, best fitted values and predicted values can be obtained.

CHAPTER III

METHODOLOGY

3.1 Introduction

In this study, the sea level variation due to El Niño and La Niña events in

Malaysian waters were analyzed. The effect of these events together with the effect of

monsoon seasons was also included in this study. Least square regression with null

hypothesis tests and time series analysis were carried out to view any sea level variation

due to the above mention parameters. The effect of warm and cool events to water

quality parameter (dissolved oxygen) was also included in this study.

The general procedures of this study are illustrated in the following flowchart

(Figure 3.1). These procedures consist of five steps of analysis. Data from several

government agencies were gathered and regression analysis took place. After that, null

hypothesis procedures were conducted on the sea level data. Time series analysis and

forecasting using exponential smoothing technique followed. Result and discussion, and

also the conclusion were discussed in Chapter IV and V.

3.2 Database

Data from four tidal stations were used in this study (Table 3.1). They are Kota

Kinabalu, Tanjung Gelang, and Pulau Pinang. Selection of these stations satisfied two

22

requirements, the geographical locations and historical records. Kota Kinabalu tidal

station was selected for West Malaysia, Tanjung Gelang station represented the east

coast of Peninsular Malaysia while Pulau Pinang station represented the west coast of

Peninsular Malaysia. Meteorological data and historical marine data; dissolved oxygen,

in Sungai Johor was used to support this study and was obtained from government

agencies (Table 3.2).

Figure 3.1: Flowchart procedures for current study

Mean Sea Level, Meteorological

P t W t Q lit P t

Regression Analysis

1. The regression lines of the mean annual values for longest

available year. 2. The regression lines of the monthly mean sea level during the

El Niño and La Niña years. 3. The regression lines of the monthly mean sea level during

northeast and southwest monsoons of El Niño and La Niña years.

4. The correlation of the meteorological parameters and the mean sea level of Malaysian waters. 5. Effect of El Niño and La Niña to water quality.

Null Hypothesis

Forecasting

Result and Discussion

Conclusion

23

Table 3.1: Tidal stations geographic coordinates

Table 3.2: Tidal data, meteorological data and historical water quality

for Malaysian waters

Survey date Agency Water quality Hydrological data

1984-2007 DSMM Tidal data

1997-2007 Marine DOE DO, temperature

1984-2007 MMD mean sea level pressure,

temperature

3.3 Regression Analysis

The analysis that was carried out comprised of:

i) the regression lines of the mean annual sea level for the longest available

year,

No. Station Latitude (ºN) Longitude (N) Years Of

Record

1

Tanjung

Gelang

( C 0331)

03º 58’ 30” N 103º 25’ 48” E 1984-2007

(24 years)

2 Pulau Pinang

(P 0379) 05º 25’ 18” N 100º 20’ 48” E

1985-2007

(23 years)

3 Johor Bahru

(J 0416) 01º 27’ 42” N 103º 47’ 30” E

1984-2007

(24 years)

4

Kota

Kinabalu

(No.2018)

05º 59’ 00” N 116º 04’ 00” E 1988-2007

(20 years)

24

ii) the regression lines of the monthly mean sea level during the El Niño and

La Niña years,

iii) the regression lines of the monthly mean sea level during northeast and

southwest monsoons of El Niño and La Niña years,

iv) the correlation of the meteorological parameters and the mean sea level of

Malaysian waters, and

v) the effect of El Niño and La Niña to water quality.

The criterion of the least squares was used to determine the best fitting regression

line in this study. Principle of least squares was used to ensure the sum of the squared

deviation was minimised as much as possible. yc represents the variable y value,

computed from the relationship for a given value of variable x. Therefore, yc symbol

will differentiate it from y which stands for actual value. The linear regression line and

best-fitting regression line is expressed in following equations.

Equation of straight line

y = a + bx (3.1) Equation of best-fitting regression line

yc = a + bx (3.2) where

y = actual observed value

yc = computed value

b = slope of the line

a = intercept at y-axis

The differences between actual and computed value was referred by deviation, or

residual or error. These differences were measured by:

25

ei = yi - yc (3.3)

Mean Absolute Percentage Error (MAPE) generated from MINITAB13 graph

measured the accuracy of fitted time series values. MAPE expression represented by

=

( )100

ˆ

×

−∑n

yyy

t

tt

(3.4)

where

yt = actual observed value or y

ty = fitted value or yc.

n = number of observation

3.4 The Correlation Coefficient

The correlation coefficient (Equation 3.5) was computed using sample data to

determine relationship between two variables (x and y) and to determine the strength

between the variables. It was also used to identify the direction of a linear relationship

between two variables (Blumann, 2004). The symbol for the sample correlation

coefficient is r and ρ for population correlation coefficient.

(3.5)

where n is the number of data pairs.

26

3.4.1 The Significance of The Correlation Coefficient

According to Bluman (2004), after computing the r value from data obtained

from samples, testing on the r value was needed to investigate either r is high enough to

conclude that there was a significant linear relationship between the variables, or the r

value was due to chance. Thus, procedure of hypothesis testing was conducted on r

value from the graph. The population correlation coefficient, ρ, was computed from

taking all possible (x,y) pairs taken from population.

H0 or null hypothesis means that there is no correlation between the x and y

variables in the population, while H1 or alternative hypothesis means that there is

significant correlation between variables in the population. These hypotheses are

summarized as below.

H0: ρ = 0

H1:ρ ≠ 0

The t-test (Equation 3.6) with degrees of freedom (n-2) was used to test the

significant of the correlation coefficient in the graphs and the confidence level is 95% or

level of significance is 5%.

(3.6)

3.5 The Exponential Smoothing Approach

The exponential smoothing approach can be used to forecast such time series

(Bowerman and O’Connell, 1979). In this study, double exponential smoothing

approach with optimized alpha value was used. This approach was chosen because

record data of annual mean sea level was less than 50 years and was not suitable to be

used with complicated methods such as Box and Jenkins. Using this approach, values of

27

point forecast and confidence interval forecast were obtained. The double exponential

smoothing operation for certain period involved two types of smoothing operations,

single smooth estimate (Equation 3.7) and double smooth statistic (Equation 3.8). These

operations is expressed by,

(3.7)

(3.8)

where,

ST = Single smoothed estimate or single smoothed statistic

= Double smoothed statistic

yT = Observation at t-th time/period

α = Smoothing constant

Equation 3.7 will update the estimates for each time period t from period 1 to

period T, the present period. Equation 3.8 smoothes the series of ST. After doing the

smoothing operations, forecasting for any period, T + τ , can be made by using the

following expression,

(3.9)

where

= Forecast value

T = Time/Period

τ = A positive number

In this study, the smoothing constant (α) was set at optimized value by the

software. The α values will determine the extent to which past observations influence

28

the forecast. Remote observations dampened out quickly if values of α nearing 1 and

vice versa. Larger value of α will give a weight to the more recent observations of the

time series and will influence rapid response to changes of the time series. Small α value

resulted in the forecasting procedures to react slowly to changes in the parameters of the

time series. From Bowerman and O’Connell (1979), α value from 0.01 to 0.30 will

work quite well. However, the selection of constant value depended on other factors

such as the variance. Small variance needs greater constant, and greater variance needs

small constant.

3.6 Analysis On The Temperature Effect to Dissolved Oxygen

According to Chapra (1997) and Thomann and Mueller (1987), there are several

environmental factors can affect saturation value of dissolved oxygen in equilibrium

with the atmosphere. It is the temperature, salinity and partial pressure variations due to

elevation.

The dependence of oxygen saturation on temperature (APHA 1992) is expressed by,

4

11

3

10

2

75

10621949.810243800.1

10642308.610575701.134411.134ln

aa

aasf

Tx

Tx

Tx

Txo

−+

−+−=

(3.10)

where,

osf = saturation concentration of dissolved oxygen in freshwater at

1 atm (mg/l)

Ta = absolute temperature (K); Ta= T + 273.15

T = temperature in Celsius (0C).

From the equation, the saturation is decrease with increasing temperature.

29

Meanwhile the dependence of saturation on salinity (APHA 1992) is expressed by,

⎟⎟⎠

⎞⎜⎜⎝

⎛+−−= −

2

312 101407.2100754.1107674.1lnln

aasfss T

xT

xxSoo (3.11)

where,

Oss = saturation concentration of dissolved oxygen in saltwater at

1 atm (mg/L)

S = salinity (g/L = part per thousand (ppt) or in percentage (%))

The following approximation can relate the salinity with chloride concentration:

S = 1.80655 x Chlro (3.12)

where Chlro is equal to chloride concentration (ppt) and the values of Chlro will reduce

the saturation value. According to Chapra (1997), the higher the salinity, the less

oxygen can be held by water. Figure 3.2 shows the relationship between water

temperature and dissolved oxygen concentration.

Figure 3.2: Dissolved oxygen saturation concentration as a function of temperature and salinity (Thomann and Mueller, 1987)

CHAPTER IV

RESULT AND DISCUSSION 4.1 Introduction

This chapter discussed the results from the study. Firstly, the results of the

analysis of the sea level will be presented. Analysis on the magnitude of the sea level

during El Niño and La Niña years will also be presented. This discussion is followed by

the analysis of the sea level during monsoon season, and lastly, the analysis of the effect

of meteorological parameters; sea level pressure and temperature to sea level will be

addressed. In addition, effect of temperature to water quality parameter is also included.

4.2 Mean Sea Level Analysis

In this study, analysis on mean sea level was divided into two parts; a study of

the mean sea level for the longest available year and a study of mean sea level during El

Niño and La Niña events. Additional analysis on sea level during northeast and

southwest monsoon will support the second part.

31

4.2.1 Analysis On Mean Sea Level

Analysis on Malaysian sea level by Malaysia Initial National Communication

(2000) showed that Malaysian sea level is expected to increase between 13cm and 94cm

in the next 100 years. From this study, four stations in the East Malaysia and Peninsular

Malaysia; Kota Kinabalu, Tanjung Gelang, Johor Bahru and Pulau Pinang, experienced

a rise in sea level between 0.17cm and 0.29cm per year. Kota Kinabalu’s (Figure 4.1)

sea level experienced the highest rate increment, where the sea level is expected to

increase 29cm in the next 100 years. While Pulau Pinang’s (Figure 4.4) mean sea level is

expected to increase 17cm, the lowest rate compared to other stations. However, mean

sea level in Tanjung Gelang (Figure 4.2) and Johor Bahru (Figure 4.3) will increase

about 19cm during the same period. Analysis on Kota Kinabalu data shows that the

mean sea level in East Malaysia increase more rapidly than mean sea level in Peninsular

Malaysia.

In conjunction with the findings, IPCC (2007) report showed that from 1961 to

2003, the average of sea level rise was 1.8±0.5 mm/year and for the 20th century, the

average rate was 1.7±0.5 mm/year. This rate was consistent with IPCC Third

Assessment Report (TAR) (2001). TAR predicted the mean sea level rise in 2100 would

be between 9cm to 88cm. However, from 1993 to 2003, IPCC (2007) reported the rate

of sea level rise was estimated at 3.1±0.7 mm/year, which was higher than the average

rate. The finding from this study showed that the sea level rise rate in Malaysian waters

was still within the IPCC (2007) results. Even though the projection value of sea level

rise (by IPCC , 2007) is greater than in this study, IPCC (2007) also stated that the sea

level change is non-uniform spatially, and in some regions the values are up to several

times, while other regions experience sea level falling. Table 4.1 shows the summary of

mean sea level rise at selected stations.

32

Figure 4.1 Mean Sea Level of Kota Kinabalu

Figure 4.2 Mean Sea Level of Tanjung Gelang

Actual

Fits Actual Fits

200719971987

258

253

248Mea

n S

ea L

evel

(cm

)

Year

Yt = 248.548 + 0.287170*t

MSD:MAD:MAPE:

8.979762.472470.97874

Trend Analysis for MSLLinear Trend Model

r = 0.48

Actual

Fits Actual Fits

1983 1988 1993 1998 2003 2008

276

277

278

279

280

281

282

283

284

Mea

n S

ea L

evel

(cm

)

Year

Yt = 277.628 + 0.190377*t

MAPE:MAD:MSD:

0.488721.369502.81881

Trend Analysis for MSLLinear Trend Model

r = 0.62

33

Figure 4.3 Mean Sea Level of Johor Bahru

Figure 4.4 Mean Sea Level of Pulau Pinang

Actual

Fits Actual Fits

200820031998199319881983

290

289

288

287

286

285

284

283

282

Mea

n S

ea L

evel

(cm

)

Year

Yt = 282.756 + 0.189920*t

MSD:MAD:MAPE:

3.640051.654120.57949

Trend Analysis for MSLLinear Trend Model

r = 0.57

Actual

Fits Actual Fits

1984 1994 2004

260

265

270

275

Mea

n S

ea L

evel

(cm

)

Year

Yt = 266.066 + 0.168126*t

MAPE:MAD:MSD:

1.0369 2.7718

13.3747

Trend Analysis for MSLLinear Trend Model

r = 0.27

34

Table 4.1: Summary of Mean Sea Level Rise at Selected Stations

Kota KinabaluTg GelangJohor BahruPulau Pinang

1984-20071984-20071986-2007

19

1719

29

Stations Range of Years

1988-2007

SLR* /100 year

* Sea Level Rise

Using MINITAB13 software, best-fit regression line was created. For example,

Kota Kinabalu’s best-fit regression line is presented by yc = a + bx or y = 0.2872x –

322.06. Table 4.2 shows the best-fit data and residual data of Kota Kinabalu stations.

Residual data is the difference between the actual and computed value. It is often

referred by deviation, or residual or error. From Figure 4.1, mean absolute percentage

error (MAPE) of Kota Kinabalu fitted time series is about 9.8% (calculated using

Equation 3.4). While for other stations, the error is between 4% and 10% (Table 4.3).

The effect of El Niño in 1997 and La Niña event in 1999 could be the cause of the wide

range of percentage error in Kota Kinabalu and Pulau Pinang. From Figures 4.1 and 4.4,

the sea level dropped drastically in 1997 before it increased rapidly in 1999.

35

Table 4.2 Best-Fit and Residual Data of Kota Kinabalu Stations

Fits Data Residual Data

1988 248.84 2.121989 249.12 1.761990 249.41 -1.161991 249.70 -2.331992 249.98 -2.431993 250.27 -2.431994 250.56 -2.541995 250.85 0.591996 251.13 2.881997 251.42 -3.581998 251.71 -1.881999 251.99 5.992000 252.28 5.942001 252.57 5.452002 252.86 -0.072003 253.14 -0.702004 253.43 -2.922005 253.72 -3.282006 254.00 -1.112007 254.29 -0.30

YearKota Kinabalu

Table 4.3: Mean Absolute Percentage Error of Mean Sea Level for Longest Available Year at Selected Tidal Stations

Kota Kinabalu 9.8%Tg Gelang 4.9%Johor Bahru 5.8%Pulau Pinang 10.4%

Station Mean Absolute Percentage Error (MAPE)

From Figures 4.1 to 4.4, correlation coefficient, r, of these graphs (calculated

using Equation 3.5) is tested using hypothesis testing, to investigate either r is high

enough to conclude that there is a significant linear relationship between the variables,

or the r value is due to chance. This testing need to be conducted because such

correlations are calculated from the sample data and it might be subjected to sampling

error. Kota Kinabalu’s correlation coefficient, r, is selected to demonstrate this step.

36

Kota Kinabalu correlation coefficient, r = 0.48 Hypothesis Testing,

H0: ρ = 0

H1: ρ ≠ 0

Confidence level for this test is 95% and there are 22 – 2 = 20 degrees of

freedom, and the critical value at a 5% level of significant (using t-distribution) is

+2.086 and -2.086. A t-test will be used to test the hypothesis for a sample less than 30,

with respect to significance of such statistics. Result from t-test (Equation 3.6) will be

compared against t-distribution.

= 212

rnr−−

= 248.0122048.0

−−

= 2.321

Given the fact that the calculated value is larger than 2.086, this means the null

hypothesis is rejected. This in turn, implies that there is significant relationship between

variables. Thus, there is linear relationship between mean sea level and time (year).

From Table 4.4, all stations reject the null hypothesis except for Pulau Pinang. Pulau

Pinang observed small correlation coefficient, r = 0.27 and sea level increase of 0.17cm

per year. As a consequence, the null hypothesis is accepted and this r value is due to

chance.

37

Table 4.4: The result of hypothesis test on correlation coefficient of mean sea level regression lines for selected stations

Kota KinabaluTg GelangJohor BahruPulau Pinang

Stations

0.48

3.254

YesYes3.706

t-distribution (Confidence level at

95%)

2.0862.064

YesNo

Correlation Coefficient , r

Statistical Significant (Test Statistic t > t-

distribution?)

1.285

0.62

0.270.57

Test Statistics t

2.321

2.0742.064

4.2.2 Analysis On Mean Sea Level During El Niño and La Niña Years

Analysis on mean sea level during El Niño and La Niña events in 1997 and 1999

was also included in this study. These events were selected because El Niño event in

1997-1998 is considered the strongest warm event in 50 years by the scientists compared

to El Niño event in 1982-1983. This warm event was followed by La Niña event in

1999.

All stations in East Malaysia and Peninsular Malaysia showed the mean sea level

during La Niña year is higher than mean sea level during El Niño year. The effect of

strong La Niña event to mean sea level is clearly noticed at Kota Kinabalu station

(Figure 4.5) as compared to other stations in Peninsular Malaysia. Kota Kinabalu and

Pulau Pinang’s (Figure 4.8) regression lines show that during this event, the sea level

increased with correlation coefficient, r = 0.71 and r = 0.49 respectively. Tanjung

Gelang and Johor Bahru (Figure 4.6 and Figure 4.7) also showed positive regression

lines. However, the correlation coefficients of Tanjung Gelang and Johor Bahru were

small compared to Kota Kinabalu and Pulau Pinang. The relationship between sea level

and time were considered weak, which might mean the La Niña event did not has a

significant impact to the sea level at Tanjung Gelang and Johor Bahru.

From Figures 4.5 to 4.8, El Niño event in 1997 has caused the lowering of sea

level at all stations. All regression lines except Kota Kinabalu have negative regression

lines. Pulau Pinang regression line is the steepest regression line compared to other

38

stations. Kota Kinabalu station observed a slight increase in mean sea level. The sea

level increased is coincidence with the occurrence of southwest monsoon season, from

May to September, and pre-monsoon season between October and November.

However, beginning February 1997 until end of April, effect of strong El Niño had

caused Kota Kinabalu sea level to decrease for 3 consecutive months. Again, the sea

level decreased at the end of the year as compared to the same period during La Niña

year at Kota Kinabalu.

Additionally, the sea level at Tanjung Gelang and Johor Bahru (Figure 4.6 and

Figure 4.7) did not seem to be affected by the warm event at the end of 1997. The sea

level still tends to increase during El Niño year. The strong effects of northeast monsoon

had caused the sea level to rise (at the end of the year) even though the sea level is lower

than sea level during La Niña year (during the same period).

Kota Kinabalu

y = 0.1989x + 246.55r = 0.15

y = 1.6503x + 247.26r = 0.71

230

240

250

260

270

280

Month

Mea

n Se

a Le

vel (

cm)

El Nino (1997) La Nina (1999) Linear (El Nino (1997)) Linear (La Nina (1999))

Jan Feb Mac Apr May June July Aug Sept Oct Nov Dec

Figure 4.5: Kota Kinabalu Mean Sea Level During 1997 El Niño and 1999 La Niña Event

39

Tg Gelang

y = -0.272x + 280.07r = 0.19

y = 0.5099x + 280.64r = 0.11

250

270

290

310

330

Month

Mea

n Se

a Le

vel (

cm)

El Nino (1997) La Nina (1999) Linear (El Nino (1997)) Linear (La Nina (1999))

Jan Feb Mac Apr May June July Aug Sept Oct Nov Dec

Figure 4.6: Tanjung Gelang Mean Sea Level During 1997 El Niño and 1999 La Niña Event

Johor Bahru

y = -0.5448x + 285.97r = 0.23

y = 0.5036x + 285.52r = 0.14

250

270

290

310

330

Month

Mea

n Se

a Le

vel (

cm)

El Nino (1997) La Nina (1999) Linear (El Nino (1997)) Linear (La Nina (1999))

Jan Feb Mac Apr May June July Aug Sept Oct Nov Dec

Figure 4.7: Johor Bahru Mean Sea Level During 1997 El Niño and 1999 La Niña Event

Pulau Pinang

y = -1.3027x + 266.64r = 0.47

y = 0.8167x + 267.8r = 0.49

220

240

260

280

300

Month

Mea

n Se

a Le

vel (

cm)

El Nino (1997) La Nina (1999) Linear (El Nino (1997)) Linear (La Nina (1999))

Jan Feb Mac Apr May June July Aug Sept Oct Nov Dec

Figure 4.8: Pulau Pinang Mean Sea Level During 1997 El Niño and 1999 La Niña Event

40

Tables 4.5 and 4.6 show the correlation coefficient, r, of Kota Kinabalu, Tanjung

Gelang, Johor Bahru and Pulau Pinang based on regression lines during El Niño and La

Niña year. From Table 4.5, only Pulau Pinang rejects the null hypothesis. This result

implies that there is significant relationship between variables, whereas there are linear

relationship between mean sea level and time. Correlation coefficient of other stations is

so small, therefore null hypothesis is accepted and r value is due to chance. The

occurrence of strong El Niño event after the half year of 1997, might have affected the r

value, even though places like Kota Kinabalu received strong effect of warm event. In

fact, a strong ENSO forcing is observed in East Malaysia (Camerlengo, 1999b)

Meanwhile, Table 4.6 shows that Tanjung Gelang and Johor Bahru (with small r

value, less than 0.2) accept the null hypothesis, which means there is no significant

relationship between sea level and time. Furthermore, the mean sea level during

southwest monsoon for these two places decreased drastically compared to Kota

Kinabalu and Pulau Pinang.

Table 4.5 Result of hypothesis test on correlation coefficient of mean sea level regression lines (during El Niño year) for selected stations

Kota KinabaluTg GelangJohor BahruPulau Pinang

Statistical Significant (Test Statistic t > t-

distribution?)Stations Correlation

Coefficient , rTest Statistics

t

t-distribution (Confidence level

at 95%)

0.19 0.519 2.074 No0.15 0.644 2.101 No

0.47 2.514 2.080 Yes0.23 0.663 2.074 No

Table 4.6 Result of hypothesis test on correlation coefficient of mean sea level regression lines (during La Niña year) for selected stations

Kota KinabaluTg GelangJohor BahruPulau Pinang

Statistical Significant (Test Statistic t > t-

distribution?)

0.71 4.278 2.101 Yes

Stations Correlation Coefficient , r

Test Statistics t

t-distribution (Confidence level

at 95%)

0.11 0.519 2.074 No0.14 0.663 2.074 No0.49 2.514 2.080 Yes

41

4.2.3 Analysis on Monthly Mean Sea Level during Northeast and Southwest

Monsoons of El Niño and La Niña Years

According to Dale (1956), the mean sea level varies with time in apparent

response to the monsoonal changes in the direction of predominant wind stress. Figures

4.9 to 4.12 show the sea level during northeast monsoon of El Niño and La Niña years at

selected stations. A piling up of water during northeast monsoon (from November to

March) strengthened by the La Niña event, occurred at all stations except Pulau Pinang

(Figure 4.12). Generally, southwest monsoon is more pronounced at the west coast of

Peninsular Malaysia, thus, it could be the possible reason why the sea level at Pulau

Pinang decreased during northeast monsoon. Study by Tangang and Alui (2002)

showed that the northeast monsoon appeared to be strengthened during La Niña period

with excess precipitation. Sea level of Kota Kinabalu (Figure 4.9) during northeast

monsoon decreased in coincidence with the 1997 warm event. According to NOAA

(2007), the effect of El Niño was getting stronger by the end of 1997 and according to

McPhaden et al (1999), the 1997–98 El Niño developed so rapidly, thus high sea surface

temperatures in the eastern equatorial Pacific were recorded between June and

December 1997.

Besides that, very heavy rainfalls during northeast monsoon had caused most

rivers and streams to be flooded and as a consequence, these rivers and streams (for

example Sungai Pahang, Sungai Kelantan and Sungai Terengganu) discharged waste

such as nutrients input from agriculture to the open sea, and this affected the water

quality and coastal marine ecosystems through eutrophication (Chua and Charles

(1980); Smith (2003)).

Figures 4.13 to 4.16 show the sea level during southwest monsoon of La Niña

and El Niño years. These figures indicate that the sea level during southwest monsoon

of La Niña year is higher than sea level during southwest monsoon of El Niño year.

Figure 4.13 shows why regression line of El Niño year of Kota Kinabalu (Figure 4.5) is

positive. The high sea level during southwest monsoon had influenced and strengthened

the regression value to become positive even during El Niño event as compared to other

stations (Tanjung Gelang and Johor Bahru) during the same event. In addition, Chua

42

and Charles (1980) stated that during southwest monsoon, the coastal currents in the

northern area (beyond Kuala Terengganu) is unaffected by the inverse direction in May

(caused by the southwest monsoon). As a result, the currents off the coast are still

flowing southeast with reduced velocity. Meanwhile, Halimatun et al (2007) stated that,

during southwest monsoon localized upwelling might be occurring when the isotherms

and isohalines were pushed up.

220

230

240

250

260

270

280

Mon

thly

Mea

n S

ea L

evel

(c

m)

Month

Kota Kinabalu

NE (1997) NE (1999)

Jan Feb Mac Apr May June July Aug Sept Oct Nov Dec

Figure 4.9: Kota Kinabalu Sea Level during Northeast Monsoon of El Niño and La Niña Years

260

280

300

320

340

Mon

thly

Mea

n S

ea L

evel

(c

m)

Month

Tg Gelang

NE (1997) NE (1999)

Jan Feb Mac Apr May June July Aug Sept Oct Nov Dec

Figure 4.10: Tanjung Gelang Sea Level during Northeast Monsoon of El Niño and La Niña Years

43

260

280

300

320

340

Mon

thly

Mea

n S

ea L

evel

(c

m)

Month

Johor Bahru

NE (1997) NE (1999)

Jan Feb Mac Apr May June July Aug Sept Oct Nov Dec

Figure 4.11: Johor Bahru Sea Level during Northeast Monsoon of El Niño and La Niña Years

220

240

260

280

300

Mon

thly

Mea

n S

ea L

evel

(c

m)

Month

Pulau Pinang

NE (1997) NE (1999)

Jan Feb Mac Apr May June July Aug Sept Oct Nov Dec

Figure 4.12: Johor Bahru Sea Level during Northeast Monsoon of El Niño and La Niña Years

230

240

250

260

270

Mon

thly

Mea

n S

ea L

evel

(c

m)

Month

Kota Kinabalu

SW (1997) SW (1999)

Jan Feb Mac Apr May June July Aug Sept Oct Nov Dec

Figure 4.13: Kota Kinabalu Sea Level during Southwest Monsoon of El Niño and La Niña Years

44

250

260

270

280

290

Mon

thly

Mea

n S

ea L

evel

(c

m)

Month

Tg Gelang

SW (1997) SW (1999)

Jan Feb Mac Apr May June July Aug Sept Oct Nov Dec

Figure 4.14: Tanjung Gelang Sea Level during Southwest Monsoon of El Niño and La Niña Years

250

260

270

280

290

300

Mon

thly

Mea

n S

ea L

evel

(c

m)

Month

Johor Bahru

SW (1997) SW (1999)

Jan Feb Mac Apr May June July Aug Sept Oct Nov Dec

Figure 4.15: Johor Bahru Sea Level during Southwest Monsoon of El Niño and La Niña Years

220

240

260

280

300

Mon

thly

Mea

n S

ea L

evel

(c

m)

Month

Pulau Pinang

SW (1997) SW (1999)

Jan Feb Mac Apr May June July Aug Sept Oct Nov Dec

Figure 4.16: Pulau Pinang Sea Level during Southwest Monsoon of El Niño and La Niña Years

45

4.3 Smoothing and Forecasting

The double exponential smoothing method (Equations 3.7 and 3.8) has been used

to smooth and forecast (Equation 3.9) the future value of mean sea level in the next 20

years . Figures 4.17 to 4.20 were referred. Table 4.7 shows the predicted mean sea level

will be increased between 0.1cm to 2.9cm in the next 20 years, or between 10cm and

29cm in the next 100 years. The predicted sea levels at Johor Bahru, Pulau Pinang and

Tanjung Gelang were small compared to the MINC (2000) report. The report revealed

that the sea level is predicted to increase between 13 cm and 94 cm in the next 100

years. The difference might be due to the fact that the data in this study have subjected

to different process (smoothing and forecasting). Hence, the predicted value in this

study differs from the predicted values by MINC (2000). In spite of this, Kota Kinabalu

predicted sea level in the next 100 years as shown in these tables (Tables 4.1 and 4.7) is

almost identical and considered acceptable.

The alpha values (α) obtained from the MINITAB is between 0.2 and 1.2 (Table

4.7). According to Bowerman and O’Connell (1979), the value between 0.01 to 0.30

will work quite well, which means, small α value resulted in the forecasting procedures

to react slowly to changes in the parameters of the time series. Smoothing constant of

Pulau Pinang shows the value is considered good (α = 0.203) while constant value of

Kota Kinabalu is large than 1 (α =1.232). Thus, Kota Kinabalu data might needs another

suitable method to forecast for future values of sea level. However, for the sake of

completeness, Kota Kinabalu result will be taken into account. In addition the α values

of Tanjung Gelang and Johor Bharu are considered acceptable

From Table 4.8, the mean absolute percentage error (MAPE) for Kota Kinabalu

has been reduced after the smoothing operations, as compared to Table 4.3. However,

another two stations, Tanjung Gelang and Pulau Pinang observed an increased

percentage of error between 0.1% and 2.1%, and Johor Bahru did not record any change

to MAPE value.

46

Table 4.7: Summary of Mean Sea Level Rise at Selected Stations

Kota Kinabalu 1.232Tg Gelang 0.551Johor Bahru 0.644Pulau Pinang 0.203

2008-2027 122008-2027 1

Alpha Value (α)

Stations Range of Years SLR* /100 year (cm)

2008-2027 28

2008-2027 4 *Sea Level Rise

Table 4.8: Mean Absolute Percentage Error of Mean Sea Level after Double Exponential

Smoothing Operation at Selected Tidal Stations

Kota Kinabalu 8.2%Tg Gelang 5.0%Johor Bahru 5.8%Pulau Pinang 12.5%

Station Mean Absolute Percentage Error (MAPE)

Actual

Predicted

Forecast Actual Predicted Forecast

1987 1997 2007 2017 2027

200

250

300

350

Mea

n S

ea L

evel

(cm

)

Smoothing ConstantsAlpha (level):Gamma (trend):

MAPE:MAD:MSD:

1.2320.010

0.824542.078768.93104

Kota Kinabalu

Year

y = -317.792 + 0.284933x

Figure 4.17: Kota Kinabalu mean sea level in next 20 years

47

Actual

Predicted

Forecast Actual Predicted Forecast

1983 1988 1993 1998 2003 2008 2013 2018 2023 2028

270

280

290

300

Mea

n S

ea L

evel

(cm

)

Year

Smoothing ConstantsAlpha (level):Gamma (trend):

MAPE:MAD:MSD:

0.5510.068

0.491121.375523.22187

Tg Gelang

y = 24.2146 + 0.128240x

Figure 4.18: Tanjung Gelang mean sea level in next 20 years

Actual

Predicted

Forecast Actual Predicted Forecast

2028202320182013200820031998199319881983

310

300

290

280

270

260

Mea

n S

ea L

evel

(cm

)

Year

MSD:MAD:MAPE:

Gamma (trend):Alpha (level):Smoothing Constants

4.727261.638220.57348

0.0960.644

Johor Bahru

y = 266.169 +0.0099321x

Figure 4.19: Johor Bahru mean sea level in next 20 years

48

Actual

Predicted

Forecast Actual Predicted Forecast

1984 1994 2004 2014 2024

260

270

280

Mea

n S

ea L

evel

(cm

)

Year

Smoothing ConstantsAlpha (level):Gamma (trend):

MAPE:MAD:MSD:

0.2030.129

1.2471 3.3329

16.7558

Pulau Pinang

y = 181.476 + 0.0438886x

Figure 4.20: Pulau Pinang mean sea level in next 20 years 4.4 Correlation Between Mean Sea Level and Meteorological Parameters

A study to investigate the correlation coefficient between sea level and

meteorological parameters has been separated into two parts; to find the correlation

coefficient of sea level and temperature, and to find the correlation coefficient of sea

level and sea level pressure.

4.4.1 Correlation coefficient of sea level and temperature

Figures 4.21 to 4.24 show the correlation between sea level and temperature.

Relationship between mean sea level and temperature at all stations is considered weak

(not strongly correlated), with correlation coefficient between 0.01 and 0.31. Study by

Camerlengo (1999b) showed that the correlation coefficients of Johor Bahru and

Tanjung Gelang are also small, where r value is less than 0.30, while Kota Kinabalu’s r

value is 0.49, higher than r value in this study (r = 0.31). Different value of r may be

49

due to the additional sets of data that have been added in this study, in contrast to

previous study.

Table 4.9 summarized the hypothesis test on correlation coefficients. This table

shows that there is no significant linear relationship between sea level and temperature at

all stations, which means the r value is due to chance. In the mean time, from these

figures, higher sea level was observed at 260C to 280C. However, these figures illustrate

that lower temperature does not cause higher sea level and vice versa. For example,

lower temperature (below 260C) at Tanjung Gelang and Johor Bahru (Figures 4.22 and

4.24) does not cause the sea level to rise. This condition verifies result of the hypothesis

test. On the other hand, study by Wai (2004) showed the ENSO forcing is more

significant in East Malaysia than in Peninsular Malaysia. Thus, the effect of ENSO and

monsoons which cause higher or lower sea level cannot be neglected.

Besides that, surface air temperature increases (particularly during the northeast

monsoon) as high as 0.50C higher than the monthly mean. In contrast during the La

Nina event, the temperature was lower as much as 0.50C than the monthly averages

(Tangang and Alui, 2002). Moreover, simulation model made by Halimatun et al (2007)

showed that temperature during northeast monsoon was cooler than during southwest

monsoon due to the advection of cooler water from the northern part of South China Sea

associated with strengthening prevailing winds from December to March.

Figure 4.22 also illustrates that the variation of the temperature in the east coast

of Peninsular Malaysia is more than 20C as compared to other stations, which ascertain

the report of climate by Malaysian Meteorological Department (MMD). MMD (2009)

stated that the annual variation of temperature at the east coast of Peninsular Malaysia is

more than 20C (below 30C) even though the annual means for temperature on the

western side of the country are slightly higher than those on the east, although the

observed station is in the same latitude (Dale, 1963). In addition, the variation of

temperature in the east coast of Peninsular Malaysia is affected by cold surges

originating from Siberia during the northeast monsoon.

50

245

250

255

260

26.8 27.0 27.2 27.4 27.6 27.8 28.0 28.2

Mea

n Se

a Le

vel (

cm)

Temperature (0C)

Kota Kinabalu

r =0.20

Figure 4.21: The correlation coefficient of Kota Kinabalu sea level and temperature

275

280

285

25.5 26.0 26.5 27.0 27.5 28.0

Mea

n Se

a Le

vel (

cm)

Temperature (0C)

Tg Gelang

r =0.31

Figure 4.22: The correlation coefficient of Tanjung Gelang sea level and temperature

255

260

265

270

275

280

27.0 27.5 28.0 28.5

Mea

n Se

a Le

vel (

cm)

Temperature (0C)

Pulau Pinang

r = 0.14

Figure 4.23: The correlation coefficient of Pulau Pinang sea level and temperature

51

280

282

284

286

288

290

25.5 26.0 26.5 27.0 27.5

Mea

n S

ea L

evel

(cm

)

Temperature (0C)

Johor Bahru

r = 0.08

Figure 4.24: The correlation coefficient of Johor Bahru sea level and temperature Table 4.9: Result of hypothesis test on correlation coefficient between mean sea level and temperature for selected stations

Kota KinabaluTg GelangJohor BahruPulau Pinang No

2.1012.0742.0742.080

Stations

0.200.310.08

Correlation Coefficient , r Test Statistics t

t-distribution (Confidence level

at 95%)

Statistical Significant (Test

Statistic t > t-distribution?)

0.01

0.8581.5500.3760.065

NoNoNo

4.4.2 Correlation coefficient of sea level and sea level pressure

Figures 4.25 to 4.28 exhibit the correlation of sea level pressure and mean sea

level at Kota Kinabalu, Tanjung Gelang, Johor Bahru and Pulau Pinang. All stations

observed moderate strong relationship with r value is above 0.50. The hypothesis test

had rejected the null hypothesis. This result shows that there is a significant linear

relationship between sea level and sea level pressure at all stations (Table 4.10).

Study by Camerlengo (1999b) showed the correlation coefficient of sea level and

sea level pressure is small, where r is below 0.40 at Kota Kinabalu, Tanjung Gelang and

Pulau Pinang. Hence, result from this study has improved the result (r value) from

52

previous study with additional set of data. Figures 4.25 to 4.28 also illustrate when the

sea level pressure is low, the sea level will rise, and if the sea level pressure is high, sea

level will fall.

Kota Kinabalu

245

250

255

260

1008.5 1009.0 1009.5 1010.0 1010.5 1011.0

Mean Sea Level Pressure (hPa)

Mea

n Se

a Le

vel (

cm)

r =0.50

Figure 4.25: The correlation of Kota Kinabalu sea level and sea level pressure

275

280

285

1009.0 1009.5 1010.0 1010.5 1011.0 1011.5

Mea

n S

ea L

evel

(cm

)

Mean Sea Level Pressure (hPa)

Tg Gelang

r = 0.58

Figure 4.26: The correlation coefficient of Tanjung Gelang sea level and sea level pressure

53

255

260

265

270

275

280

1009.0 1009.5 1010.0 1010.5 1011.0 1011.5

Mea

n Se

a Le

vel (

cm)

Mean Sea Level Pressure (hPa)

Pulau Pinang

r = 0.52

Figure 4.27: The correlation coefficient of Pulau Pinang sea level and sea level pressure

280

282

284

286

288

290

1009.0 1009.5 1010.0 1010.5 1011.0 1011.5

Mea

n Se

a Le

vel (

cm)

Mean Sea Level Pressure (hPa)

Johor Bahru

r = 0.55

Figure 4.28: The correlation coefficient of Johor Bahru sea level and sea level pressure Table 4.10: Result of hypothesis test on correlation coefficient between mean sea level and sea level pressure for selected stations

Kota KinabaluTg GelangJohor BahruPulau Pinang

Yes

2.074 Yes

Stations Correlation Coefficient , r

Test Statistics t

t-distribution (Confidence level

at 95%)

Statistical Significant (Test

Statistic t > t-distribution?)

0.50 2.450 2.101

0.52 2.771 2.080 Yes

0.58 3.382 2.074 Yes0.55 2.232

54

4.4.3 Effect of Temperature to Dissolved Oxygen Saturation in Sungai Johor Estuary

Kuala Sungai Johor stations have been selected to be observed for any significant

effect of temperature on dissolved oxygen saturation value. Figure 4.29 shows the

location of Kuala Sungai Johor station (no. 1440916). Meanwhile Figure 4.30 illustrates

saturation concentration of oxygen in freshwater and saltwater at Kuala Sungai Johor.

According to the study conducted by Morril et al (2005), an increase in water

temperature of about 0.6 to 0.80C for every 10C increase in air temperature with few

streams shows a linear relationship 1:1 air/temperature. Their study also showed that as

air temperature and stream temperature increase, dissolved oxygen level will decrease.

On the other hand, saturated oxygen is lowest at higher streams temperature.

In conjunction with their findings, this study shows that when high temperature

takes place, saturated oxygen in freshwater value is low in Kuala Sungai Johor except in

October 1997 and January 2004. During October 1997 and January 2004, the low

temperature could be due to the occurrence of pre-monsoon and northeast monsoon

which brought more rain than usual and consequently lowered the temperature. Thus,

the saturated dissolved oxygen is high. Meanwhile, effect of low/high salinity had

caused the low/high percentage of saturated dissolved oxygen in saltwater in Kuala

Sungai Johor. The range of saturated oxygen in freshwater is between 7.232 and 9.092

mg/L and for saturated oxygen in saltwater, it is between 5.60 and 8.172. Figure 4.30

and Table 4.11 are referred for this purpose. Hence, study by Whipple and Whipple

(1911) supported this finding which shows that oxygen is less soluble in seawater than

in freshwater. However, the observed dissolved oxygen levels will depend on the

amount of oxygen being used by chemical and biological processes. In addition, Diaz

(2001) stated that dissolved oxygen conditions of many major coastal ecosystems around

the world have been badly affected through the eutrophication process. Rabalais et al

(2009) also mentioned that a decline in salinity because of an increase of 10C

temperature will strengthen pynoclines and stratify the water column. The condition of

less dissolved oxygen may occur, resulted from less diffusion of oxygen from the upper

part of the water column to the lower part of water column.

55

Figure 4.29: Location of Kuala Sungai Johor Station (No. 1440916, marked by red square)

Kuala Sg Johor

4

5

6

7

8

9

10

18.0 20.0 22.0 24.0 26.0 28.0 30.0 32.0 34.0

Temperature (0C)

Dis

solv

ed O

xyge

n (m

g/L

)

Freshwater Saltwater

Figure 4.30: Dissolved oxygen (DO) level at Kuala Sungai Johor from 1994-2006

56

Table 4.11: Kuala Sungai Johor Freshwater and Saltwater Saturated Oxygen Value

Sampling Date

Temperature (0C)

Freshwater Saturation Value

(mg/L)Salinity (ppt)

Saltwater Saturation Value

(mg/L)

Dissolved Oxygen Saturation (%)

22-Feb-95 30.0 7.559 24.0 6.625 87.6527-Apr-95 30.0 7.559 24.0 6.625 87.6512-Sep-95 30.0 7.559 33.0 6.305 83.4224-Oct-95 29.5 7.625 33.0 6.356 83.3727-Aug-96 29.0 7.691 24.0 6.735 87.5724-Oct-96 28.0 7.828 18.0 7.081 90.4610-Apr-97 31.0 7.430 19.0 6.698 90.1510-Jun-97 31.0 7.430 7.8 7.120 95.836-Aug-97 28.8 7.718 57.9 5.601 72.576-Oct-97 20.0 9.092 18.1 8.172 89.87

19-Feb-98 32.0 7.305 18.0 6.626 90.7021-Apr-98 32.0 7.305 15.0 6.734 92.1922-Oct-98 30.0 7.559 28.0 6.481 85.7416-Dec-98 30.0 7.559 23.0 6.662 88.1323-Feb-99 27.0 7.968 21.0 7.083 88.8930-Apr-99 30.0 7.559 18.0 6.847 90.5815-Jun-99 29.5 7.625 21.5 6.772 88.8219-Aug-99 30.0 7.559 17.0 6.885 91.0822-Sep-99 29.5 7.625 17.0 6.943 91.068-Dec-99 28.9 7.705 26.7 6.646 86.2627-Jan-00 27.7 7.870 25.0 6.845 86.9824-Feb-00 28.8 7.718 26.3 6.672 86.4526-Apr-00 29.9 7.572 24.1 6.632 87.5930-May-00 31.0 7.430 26.0 6.447 86.7727-Jun-00 30.0 7.559 25.9 6.556 86.7430-Aug-00 29.3 7.651 26.8 6.599 86.2531-Oct-00 29.5 7.625 28.2 6.527 85.6012-Dec-00 29.3 7.651 28.0 6.556 85.6812-Feb-01 29.1 7.678 28.7 6.552 85.3328-May-01 32.3 7.268 23.3 6.407 88.1526-Jun-01 29.7 7.598 26.8 6.556 86.2827-Aug-01 30.1 7.546 31.3 6.354 84.2129-Jan-02 28.2 7.800 21.3 6.929 88.8321-Feb-02 30.3 7.520 23.9 6.596 87.7229-Apr-02 32.6 7.232 25.9 6.288 86.9520-Jun-02 30.5 7.494 18.6 6.768 90.3220-Aug-02 29.2 7.664 16.5 6.997 91.2925-Sep-02 30.0 7.559 22.6 6.676 88.3231-Dec-02 29.5 7.625 16.8 6.950 91.1614-Jan-03 28.4 7.773 15.8 7.120 91.6016-Apr-03 30.4 7.50724-Jun-03 30.0 7.55929-Sep-03 29.3 7.656 21.7 6.791 88.7030-Jan-04 26.1 8.093 10.4 7.631 94.3029-Apr-04 30.7 7.475 22.4 6.614 88.4915-Jul-04 28.7 7.735 21.5 6.864 88.757-Jan-05 31.1 7.412 30.1 6.290 84.86

28-Apr-05 30.9 7.442 30.7 6.294 84.5810-Aug-05 29.7 7.596 27.8 6.517 85.8013-Oct-05 29.9 7.571 25.4 6.583 86.9528-Apr-06 31.6 7.358 29.7 6.262 85.1016-Jun-06 29.5 7.619 24.7 6.651 87.29

CHAPTER V

CONCLUSION AND RECOMMENDATIONS

In this study, least squares regression line, null hypothesis and time series

analysis were used to study the trend, and variability such as the residual and forecast

values. The variability of the mean sea level induced by the El Niño and La Niña events

is addressed in this study. The relationship between the mean sea level with mean sea

level pressure and temperature is also investigated. Additional information on the effect

of temperature to water quality parameter (dissolved oxygen) is also included.

5.1 Conclusions

From this study, the sea level of the observed Kota Kinabalu station will rise by

29cm in the next 100 years, which is higher compared to the other 3 stations. During the

La Niña years, Malaysia’s mean sea level was always at the maximum level except for

Pulau Pinang. Meanwhile, during El Niño years, all stations observed an inverse

regression line whereas the sea level has decreased, except for Kota Kinabalu’s sea

level. The increase in Kota Kinabalu’s sea level might be influenced by the southwest

monsoon period. Furthermore, maximum mean sea level during the northeast monsoon

is found more significant in the East Coast of Peninsular Malaysia and Kota Kinabalu. In

addition, the west coast of Peninsular Malaysia is influenced more by the southwest

monsoon. Besides that, the occurrence of El Niño and La Niña event during the

northeast and southwest monsoons has influenced the Malaysia’s mean sea level.

Generally, correlation coefficient with meteorological parameters shows that the

58

correlation between mean sea level and temperature is weakly correlated. Moreover,

mean sea level pressure is moderately correlated with sea level. The impact of high

temperatures especially during warm years on the flow of the river is significant with the

increase/decrease of dissolved oxygen saturation in Kuala Sungai Johor.

5.2 Recommendations

Throughout this study, several questions came up and these questions need to be

further investigated in the future. The effects from global warming such as the rise of

the sea level have been enhanced by the ENSO phenomena and this situation is

threatening the life of billions of people. Therefore, further research on the effect of the

temperature to Malaysian waters should be conducted in the future because of its effects

to the marine life and marine ecosystems, and also its effect to the human population in

the coastal areas. For example, the new developing city in Nusajaya, Johor, Malaysia,

will be home to thousands or perhaps millions of people. Since its location is at the

coastal area, Nusajaya is vulnerable to the threat of sea level rise. The combination of

development and global warming could be the cause to why the aquatic and human lives

are in danger. A modeling on this subject should be sufficient to give a better view and

understanding on this subject matter. Continuous study on this particular issue should

be carried out after a certain period, or after the development is completed. Forecasting

value of future mean sea level in this study would be useful as a benchmark for the

researcher to investigate the effect of mean sea level rise at the area of investigation.

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67

APPENDIX A Best–Fits and Residual Data

Table A.1: Best-Fit and Residual Data of Kota Kinabalu, Tg Gelang, Johor Bahru and Pulau Pinang Stations

Fits Data Residual Data Fits Data Residual

Data Fits Data Residual Data Fits Data Residual

Data1984 277.82 3.60 1.99 282.951985 278.01 -0.86 -0.58 283.141986 278.20 -0.40 -0.34 283.33 266.43 -0.591987 278.39 -1.68 -1.48 283.52 266.59 -3.391988 248.84 2.12 278.58 1.77 1.86 283.71 266.76 3.601989 249.12 1.76 278.77 0.91 1.14 283.90 266.93 1.611990 249.41 -1.16 278.96 -1.32 -1.77 284.09 267.09 -1.291991 249.70 -2.33 279.15 -0.54 -0.92 284.28 267.26 -2.691992 249.98 -2.43 279.34 -1.94 -1.40 284.47 267.42 0.121993 250.27 -2.43 279.53 -1.98 -1.41 284.66 267.59 3.221994 250.56 -2.54 279.72 -2.37 -2.27 284.85 267.76 -4.811995 250.85 0.59 279.91 1.35 2.57 285.04 267.92 1.701996 251.13 2.88 280.10 0.08 0.21 285.23 268.09 2.471997 251.42 -3.58 280.29 -2.15 -2.98 285.42 268.26 -10.081998 251.71 -1.88 280.48 -0.21 -0.70 285.61 268.42 2.661999 251.99 5.99 280.67 3.28 3.00 285.80 268.59 4.522000 252.28 5.94 280.86 2.39 2.42 285.99 268.76 5.512001 252.57 5.45 281.06 2.48 3.40 286.18 268.92 6.052002 252.86 -0.07 281.25 -0.50 1.63 286.37 269.09 -2.592003 253.14 -0.70 281.44 0.59 1.62 286.55 269.26 0.032004 253.43 -2.92 281.63 -1.08 -0.94 286.74 269.42 0.312005 253.72 -3.28 281.82 -0.35 -3.59 286.93 269.59 -0.702006 254.00 -1.11 282.01 -0.22 -0.35 287.12 269.75 -4.992007 254.29 -0.30 282.20 -0.82 -1.12 287.31 269.92 -0.67

YearPulau PinangJohor BahruTg GelangKota Kinabalu

68

APPENDIX B

Figure B1: Monthly Mean Sea Level during Northeast and Southwest Monsoons During El Niño Year in 1997

Kota Kinabalu

230

240

250

260

Month

Mon

thly

Mea

n Se

a L

eve

(cm

)

Northeast Monsoon Southwest Monsoon

Jan Feb Mac Apr May June July Aug Sept Oct Nov Dec

Figure B.1(a): Kota Kinabalu Mean Sea Level during Northeast and Southwest Monsoon in 1997

Tg Gelang

240

260

280

300

320

Month

Mon

thly

Mea

n Se

aL

evel

(cm

)

Northeast Monsoon Southwest Monsoon

Jan Feb Mac Apr May June July Aug Sept Oct Nov Dec

Figure B.1(b): Tg Gelang Mean Sea Level during Northeast and Southwest Monsoon in 1997

69

Johor Bahru

260

270

280

290

300

310

Month

Mon

thly

Mea

n Se

aL

evel

(cm

)

Northeast Monsoon Southwest Monsoon

Jan Feb Mac Apr May June July Aug Sept Oct Nov Dec

Figure B.1(c): Johor Bahru Mean Sea Level during Northeast and Southwest Monsoon in 1997

Pulau Pinang

230

240

250

260

270

280

Month

Mon

thly

Mea

n Se

aL

evel

(cm

)

Northeast Monsoon Southwest Monsoon

Jan Feb Mac Apr May June July Aug Sept Oct Nov Dec

Figure B.1(d): Pulau Pinang Mean Sea Level during Northeast and Southwest Monsoon in 1997

70

Figure B.2: Monthly Mean Sea Level during Northeast and Southwest Monsoons During La Niña Year in 1999

Kota Kinabalu

240

250

260

270

280

Month

Mon

thly

Mea

n Se

aL

evel

(cm

)

Northeast Monsoon Southwest Monsoon

Jan Feb Mac Apr May June July Aug Sept Oct Nov Dec

Figure B.2(a): Kota Kinabalu Mean Sea Level during Northeast and Southwest Monsoon in 1999

Tg Gelang

240

260

280

300

320

340

Month

Mon

thly

Mea

n Se

aL

evel

(cm

)

Northeast Monsoon Southwest Monsoon

Jan Feb Mac Apr May June J uly Aug Sept Oct Nov Dec

Figure B.2(b): Kota Kinabalu Mean Sea Level during Northeast and Southwest Monsoon in 1999

71

Johor Bahru

260270280290300310320

Month

Mon

thly

Mea

n Se

aL

evel

(cm

)

Northeast Monsoon Southwest Monsoon

Jan Feb Mac Apr May June July Aug Sept Oct Nov Dec

Figure B.2(c): Kota Kinabalu Mean Sea Level during Northeast and Southwest Monsoon in 1999

Pulau Pinang

260265270275280285290

Month

Mon

thly

Mea

n Se

aL

evel

(cm

)

Northeast Monsoon Southwest Monsoon

Jan Feb Mac Apr May June July Aug Sept Oct Nov Dec

Figure B.2(d): Kota Kinabalu Mean Sea Level during Northeast and Southwest Monsoon in 1999

72

APPENDIX C

Figure C.1: Location of Several Tide Gauge Stations and Tide Observation Tools

Figure C.1(a): Tide staff at Johor Bahru tide gauge station

73

Figure C.1(b): Sea level observation tool at Johor Bahru tide gauge station

Figure C.1(c): Johor Bahru tide gauge station

74

Figure C.1(d): Location of Johor Bahru tide gauge station

Figure C.1(e): Tg Sedili tide gauge station at Lembaga Kemajuan Ikan Malaysia (LKIM) jetty

75

Figure C.1(f): Tg Sedili tide gauge station at Lembaga Kemajuan Ikan Malaysia (LKIM) jetty

Figure C.1(g): Kukup tide gauge station

76

Figure C.1(h): Sea level observation tool at Kota Kinabalu tide gauge station

Figure C.1(i): General information at Kota Kinabalu tide gauge station

77

Figure C.1(j): Kota Kinabalu tide gauge station