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 cosupervisor 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.
vi
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.
vii
TABLE OF CONTENTS
CHAPTER
1
2
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
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
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
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
ENSO Phenomenon
14
2.7.1 El Nino
15
2.7.2 La Nina
17
Water Quality
18
2.7.1 Temperature Effects
19
Forecasting
19
2.6
2.7
2.8
3
8
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
3.5
The Exponential Smoothing Approach
3.6
Analysis On The Temperature Effect to Dissolved
Oxygen
4
26
26
28
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
4.3
Smoothing and Forecasting
4.4
Correlation Between Mean Sea Level and
Meteorological Parameters
41
45
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
5
54
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
The result of hypothesis test on correlation coefficient
of mean sea level regression lines for selected
37
Result of hypothesis test on correlation coefficient of
mean sea level regression lines (during El Niño year)
for selected stations
40
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
4.4
4.5
4.6
4.10
51
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
Tanjung Gelang mean sea level during 1997 El Niño and
1999 La Niña events
39
Johor Bahru mean sea level during 1997 El Niño and
1999 La Niña events
39
Pulau Pinang mean sea level during 1997 El Niño and
1999 La Niña events
39
Kota Kinabalu mean sea level during northeast monsoon
of El Niño and La Niña years
42
Tanjung Gelang mean sea level during northeast monsoon
of El Niño and La Niña years
42
Johor Bahru mean sea level during northeast monsoon
of El Niño and La Niña years
43
4.6
4.7
4.8
4.9
4.10
4.11
xiii
4.12
Pulau Pinang mean sea level during northeast monsoon
of El Niño and La Niña years
43
Kota Kinabalu mean sea level during southwest monsoon
of El Niño and La Niña years
43
Tanjung Gelang mean sea level during southwest monsoon
of El Niño and La Niña years
44
Johor Bahru mean sea level during southwest monsoon
of El Niño and La Niña years
44
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
The correlation coefficient of Tanjung Gelang sea level
and temperature
50
The correlation coefficient of Pulau Pinang sea level
and temperature
50
The correlation coefficient of Johor Bahru sea level
and temperature
51
The correlation coefficient of Kota Kinabalu sea level
and sea level pressure
52
The correlation coefficient of Tanjung Gelang sea level
and sea level pressure
52
The correlation coefficient of Pulau Pinang sea level
and sea level pressure
53
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
4.13
4.14
4.15
4.16
4.22
4.23
4.24
4.25
4.26
4.27
4.28
xiv
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
fitted value or yc.
ŷ t
xviii
LIST OF APPENDICES
APPENDIX
TITLE
A
Best-Fit and Residual Data
B.1
Monthly Mean Sea Level during Northeast and
Southwest Monsoons during El Niño Year in 1997
B.2
67
68
Monthly Mean Sea Level during Northeast and
Southwest Monsoons During La Niña Year in 1999
C
PAGE
Location of Several Tide Gauge Stations and Tide
Observation Tools
70
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).
Pulau Pinang South China Sea
Kota Kinabalu
Tg Gelang
Straits of Malacca Johor Bahru
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.
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 BoxJenkins 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 offequatorial 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 southwesterly 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).
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
Figure 3.1: Flowchart procedures for current study
23
Table 3.1: Tidal stations geographic coordinates
No.
Station
Latitude (ºN)
Longitude (N)
03º 58’ 30” N
103º 25’ 48” E
05º 25’ 18” N
100º 20’ 48” E
01º 27’ 42” N
103º 47’ 30” E
05º 59’ 00” N
116º 04’ 00” E
Tanjung
1
Gelang
( C 0331)
2
3
Pulau Pinang
(P 0379)
Johor Bahru
(J 0416)
Kota
4
Kinabalu
(No.2018)
Years Of
Record
1984-2007
(24 years)
1985-2007
(23 years)
1984-2007
(24 years)
1988-2007
(20 years)
Table 3.2: Tidal data, meteorological data and historical water quality
for Malaysian waters
Survey date
Agency
1984-2007
DSMM
1997-2007
Marine DOE
1984-2007
MMD
3.3
Water quality
Hydrological data
Tidal data
DO, temperature
mean sea level pressure,
temperature
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,
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
∑ (y
=
t
− yˆ t )
n
yt
× 100
(3.4)
where
3.4
yt
= actual observed value or y
ŷ t
= fitted value or yc.
n
= number of observation
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.
H 0: 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,
ln o sf = −134.34411 +
1.575701x10 5 6.642308 x10 7
−
Ta
Ta2
1.243800 x1010 8.621949 x1011
+
−
Ta3
Ta4
(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,
⎛
1.0754 x101 2.1407 x10 3 ⎞
⎟⎟
+
ln o ss = ln o sf − S ⎜⎜1.7674 x10 − 2 −
2
T
T
a
a
⎝
⎠
(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
Trend Analysis for MSL
Linear Trend Model
Yt = 248.548 + 0.287170*t
Actual
Mean Sea Level (cm)
258
r = 0.48
Fits
Actual
Fits
253
MAPE:
MAD:
MSD:
248
1987
0.97874
2.47247
8.97976
2007
1997
Year
Figure 4.1 Mean Sea Level of Kota Kinabalu
Trend Analysis for MSL
Linear Trend Model
Yt = 277.628 + 0.190377*t
284
Actual
r = 0.62
Fits
Mean Sea Level (cm)
283
Actual
Fits
282
281
280
279
278
MAPE:
MAD:
MSD:
277
276
1983
1988
1993
1998
2003
2008
Year
Figure 4.2 Mean Sea Level of Tanjung Gelang
0.48872
1.36950
2.81881
33
Trend Analysis for MSL
Linear Trend Model
Yt = 282.756 + 0.189920*t
290
Actual
Mean Sea Level (cm)
r = 0.57
289
Fits
288
Actual
Fits
287
286
285
284
283
0.57949
1.65412
3.64005
MAPE:
MAD:
MSD:
282
1983
1988
1993
1998
2003
2008
Year
Figure 4.3 Mean Sea Level of Johor Bahru
Trend Analysis for MSL
Linear Trend Model
Yt = 266.066 + 0.168126*t
275
Actual
Mean Sea Level (cm)
r = 0.27
Fits
Actual
Fits
270
265
260
MAPE:
MAD:
MSD:
1984
1994
2004
Year
Figure 4.4 Mean Sea Level of Pulau Pinang
1.0369
2.7718
13.3747
34
Table 4.1: Summary of Mean Sea Level Rise at Selected Stations
Stations
Kota Kinabalu
Tg Gelang
Johor Bahru
Pulau Pinang
Range of Years SLR* /100 year
1988-2007
1984-2007
1984-2007
1986-2007
29
19
19
17
* 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
Kota Kinabalu
Year
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
Fits Data
Residual Data
248.84
249.12
249.41
249.70
249.98
250.27
250.56
250.85
251.13
251.42
251.71
251.99
252.28
252.57
252.86
253.14
253.43
253.72
254.00
254.29
2.12
1.76
-1.16
-2.33
-2.43
-2.43
-2.54
0.59
2.88
-3.58
-1.88
5.99
5.94
5.45
-0.07
-0.70
-2.92
-3.28
-1.11
-0.30
Table 4.3: Mean Absolute Percentage Error of Mean Sea Level for Longest Available
Year at Selected Tidal Stations
Station
Kota Kinabalu
Tg Gelang
Johor Bahru
Pulau Pinang
Mean Absolute Percentage Error (MAPE)
9.8%
4.9%
5.8%
10.4%
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,
H 0: ρ = 0
H 1: 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.
= r
n−2
1− r2
= 0.48
20 − 2
1 − 0.48 2
= 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
Stations
Kota Kinabalu
Tg Gelang
Johor Bahru
Pulau Pinang
t-distribution
Statistical Significant
Correlation Test Statistics
(Confidence level at (Test Statistic t > tt
Coefficient , r
distribution?)
95%)
0.48
0.62
0.57
0.27
2.321
3.706
3.254
1.285
2.086
2.064
2.064
2.074
Yes
Yes
Yes
No
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).
Mean Sea Level (cm)
Kota Kinabalu
280
y = 1.6503x + 247.26
r = 0.71
270
260
250
y = 0.1989x + 246.55
r = 0.15
240
230
Jan
Feb
Mac
Apr
May
June
July
Aug
Sept
Oct
Nov
Dec
Month
El Nino (1997)
La Nina (1999)
Linear (El Nino (1997))
Linear (La Nina (1999))
Figure 4.5: Kota Kinabalu Mean Sea Level During 1997 El Niño and 1999 La Niña
Event
39
Mean Sea Level (cm)
Tg Gelang
330
y = 0.5099x + 280.64
r = 0.11
310
290
270
y = -0.272x + 280.07
r = 0.19
250
Jan
Feb
Mac
Apr
May
June
July
Aug
Sept
Oct
Nov Dec
Month
El Nino (1997)
La Nina (1999)
Linear (El Nino (1997))
Linear (La Nina (1999))
Figure 4.6: Tanjung Gelang Mean Sea Level During 1997 El Niño and 1999 La Niña
Event
Johor Bahru
Mean Sea Level (cm)
330
y = 0.5036x + 285.52
r = 0.14
310
290
270
y = -0.5448x + 285.97
r = 0.23
250
Jan
Feb
Mac
Apr
May
June
July
Aug
Sept
Oct
Nov Dec
Month
El Nino (1997)
La Nina (1999)
Linear (El Nino (1997))
Linear (La Nina (1999))
Figure 4.7: Johor Bahru Mean Sea Level During 1997 El Niño and 1999 La Niña Event
Pulau Pinang
Mean Sea Level (cm)
300
y = 0.8167x + 267.8
r = 0.49
280
260
240
y = -1.3027x + 266.64
r = 0.47
220
Jan
Feb
Mac
Apr
May
June
July
Aug
Sept
Oct
Nov Dec
Month
El Nino (1997)
La Nina (1999)
Linear (El Nino (1997))
Linear (La Nina (1999))
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
Stations
Correlation
Coefficient , r
Kota Kinabalu
Tg Gelang
Johor Bahru
Pulau Pinang
0.15
0.19
0.23
0.47
t-distribution
Test Statistics
(Confidence level
t
at 95%)
0.644
0.519
0.663
2.514
2.101
2.074
2.074
2.080
Statistical Significant
(Test Statistic t > tdistribution?)
No
No
No
Yes
Table 4.6 Result of hypothesis test on correlation coefficient of mean sea level
regression lines (during La Niña year) for selected stations
Stations
Correlation
Coefficient , r
Kota Kinabalu
Tg Gelang
Johor Bahru
Pulau Pinang
0.71
0.11
0.14
0.49
t-distribution
Test Statistics
(Confidence level
t
at 95%)
4.278
0.519
0.663
2.514
2.101
2.074
2.074
2.080
Statistical Significant
(Test Statistic t > tdistribution?)
Yes
No
No
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.
Monthly Mean Sea Level
(cm)
Kota Kinabalu
280
270
260
250
240
230
220
Jan Feb Mac Apr May June July Aug Sept Oct Nov Dec
NE (1997)
NE (1999)
Month
Figure 4.9: Kota Kinabalu Sea Level during Northeast Monsoon of El Niño and La Niña
Years
Monthly Mean Sea Level
(cm)
Tg Gelang
340
320
300
280
260
Jan Feb Mac Apr May June July Aug Sept Oct Nov Dec
NE (1997)
NE (1999)
Month
Figure 4.10: Tanjung Gelang Sea Level during Northeast Monsoon of El Niño and La
Niña Years
43
Monthly Mean Sea Level
(cm)
Johor Bahru
340
320
300
280
260
Jan Feb Mac Apr May June July Aug Sept Oct Nov Dec
NE (1997)
NE (1999)
Month
Figure 4.11: Johor Bahru Sea Level during Northeast Monsoon of El Niño and La Niña
Years
Monthly Mean Sea Level
(cm)
Pulau Pinang
300
280
260
240
220
Jan Feb Mac Apr May June July Aug Sept Oct Nov Dec
NE (1997)
NE (1999)
Month
Figure 4.12: Johor Bahru Sea Level during Northeast Monsoon of El Niño and La Niña
Years
Monthly Mean Sea Level
(cm)
Kota Kinabalu
270
260
250
240
230
Jan Feb Mac Apr May June July Aug Sept Oct Nov Dec
SW (1997)
SW (1999)
Month
Figure 4.13: Kota Kinabalu Sea Level during Southwest Monsoon of El Niño and La
Niña Years
44
Monthly Mean Sea Level
(cm)
Tg Gelang
290
280
270
260
250
Jan Feb Mac Apr May June July Aug Sept Oct Nov Dec
SW (1997)
SW (1999)
Month
Figure 4.14: Tanjung Gelang Sea Level during Southwest Monsoon of El Niño and La
Niña Years
Monthly Mean Sea Level
(cm)
Johor Bahru
300
290
280
270
260
250
Jan Feb Mac Apr May June July Aug Sept Oct Nov Dec
SW (1997)
SW (1999)
Month
Figure 4.15: Johor Bahru Sea Level during Southwest Monsoon of El Niño and La Niña
Years
Monthly Mean Sea Level
(cm)
Pulau Pinang
300
280
260
240
220
Jan Feb Mac Apr May June July Aug Sept Oct Nov Dec
SW (1997)
SW (1999)
Month
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
Stations
SLR* /100 year
(cm)
2008-2027
28
2008-2027
12
2008-2027
1
2008-2027
4
*Sea Level Rise
Alpha Value
(α)
1.232
0.551
0.644
0.203
Range of Years
Kota Kinabalu
Tg Gelang
Johor Bahru
Pulau Pinang
Table 4.8: Mean Absolute Percentage Error of Mean Sea Level after Double Exponential
Smoothing Operation at Selected Tidal Stations
Station
Mean Absolute Percentage Error (MAPE)
Kota Kinabalu
Tg Gelang
Johor Bahru
Pulau Pinang
8.2%
5.0%
5.8%
12.5%
Kota Kinabalu
350
Actual
Predicted
Mean Sea Level (cm)
Forecast
Actual
Predicted
300
Forecast
250
y = -317.792 + 0.284933x
200
Smoothing Constants
1.232
Alpha (level):
Gamma (trend): 0.010
MAPE:
MAD:
MSD:
1987
1997
2007
2017
2027
Year
Figure 4.17: Kota Kinabalu mean sea level in next 20 years
0.82454
2.07876
8.93104
47
Tg Gelang
Actual
300
Predicted
Mean Sea Level (cm)
Forecast
Actual
Predicted
290
Forecast
280
y = 24.2146 + 0.128240x
Smoothing Constants
Alpha (level):
0.551
Gamma (trend): 0.068
MAPE:
MAD:
MSD:
270
0.49112
1.37552
3.22187
1983 1988 1993 1998 2003 2008 2013 2018 2023 2028
Year
Figure 4.18: Tanjung Gelang mean sea level in next 20 years
Johor Bahru
Actual
310
Predicted
Mean Sea Level (cm)
Forecast
300
Actual
Predicted
Forecast
290
y = 266.169 +0.0099321x
280
Smoothing Constants
Alpha (level):
0.644
Gamma (trend): 0.096
270
MAPE:
MAD:
MSD:
260
1983 1988 1993 1998 2003 2008 2013 2018 2023 2028
Year
Figure 4.19: Johor Bahru mean sea level in next 20 years
0.57348
1.63822
4.72726
48
Pulau Pinang
Actual
Predicted
Mean Sea Level (cm)
280
Forecast
Actual
Predicted
Forecast
270
y = 181.476 + 0.0438886x
260
Smoothing Constants
0.203
Alpha (level):
Gamma (trend): 0.129
MAPE:
MAD:
MSD:
1984
1994
2004
2014
1.2471
3.3329
16.7558
2024
Year
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
Mean Sea Level (cm)
Kota Kinabalu
260
r =0.20
255
250
245
26.8
27.0
27.2
27.4
27.6
Temperature
27.8
28.0
28.2
(0C)
Figure 4.21: The correlation coefficient of Kota Kinabalu sea level and temperature
Mean Sea Level (cm)
Tg Gelang
285
r =0.31
280
275
25.5
26.0
26.5
27.0
27.5
28.0
0
Temperature ( C)
Figure 4.22: The correlation coefficient of Tanjung Gelang sea level and temperature
Mean Sea Level (cm)
Pulau Pinang
280
r = 0.14
275
270
265
260
255
27.0
27.5
28.0
28.5
0
Temperature ( C)
Figure 4.23: The correlation coefficient of Pulau Pinang sea level and temperature
51
Mean Sea Level (cm)
Johor Bahru
290
288
r = 0.08
286
284
282
280
25.5
26.0
26.5
Temperature
27.0
27.5
(0C)
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
Stations
Kota Kinabalu
Tg Gelang
Johor Bahru
Pulau Pinang
Correlation
Coefficient , r
Test Statistics t
t-distribution
(Confidence level
at 95%)
0.20
0.31
0.08
0.01
0.858
1.550
0.376
0.065
2.101
2.074
2.074
2.080
Statistical
Significant (Test
Statistic t > tdistribution?)
No
No
No
No
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.
Mean Sea Level (cm)
Kota Kinabalu
260
r =0.50
255
250
245
1008.5
1009.0
1009.5
1010.0
1010.5
1011.0
Mean Sea Level Pressure (hPa)
Figure 4.25: The correlation of Kota Kinabalu sea level and sea level pressure
Mean Sea Level (cm)
Tg Gelang
285
r = 0.58
280
275
1009.0
1009.5
1010.0
1010.5
1011.0
1011.5
Mean Sea Level Pressure (hPa)
Figure 4.26: The correlation coefficient of Tanjung Gelang sea level and sea level
pressure
53
Mean Sea Level (cm)
Pulau Pinang
280
r = 0.52
275
270
265
260
255
1009.0
1009.5
1010.0
1010.5
1011.0
1011.5
Mean Sea Level Pressure (hPa)
Figure 4.27: The correlation coefficient of Pulau Pinang sea level and sea level pressure
Mean Sea Level (cm)
Johor Bahru
290
r = 0.55
288
286
284
282
280
1009.0
1009.5
1010.0
1010.5
1011.0
1011.5
Mean Sea Level Pressure (hPa)
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
Stations
Kota Kinabalu
Tg Gelang
Johor Bahru
Pulau Pinang
Correlation
Coefficient , r
0.50
0.58
0.55
0.52
t-distribution
Test Statistics
(Confidence level
t
at 95%)
2.450
3.382
2.232
2.771
2.101
2.074
2.074
2.080
Statistical
Significant (Test
Statistic t > tdistribution?)
Yes
Yes
Yes
Yes
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)
Dissolved Oxygen (mg/L)
Kuala Sg Johor
10
9
8
7
6
5
4
18.0
Freshwater
20.0
Saltwater
22.0
24.0
26.0
28.0
30.0
32.0
34.0
0
Temperature ( C)
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
22-Feb-95
27-Apr-95
12-Sep-95
24-Oct-95
27-Aug-96
24-Oct-96
10-Apr-97
10-Jun-97
6-Aug-97
6-Oct-97
19-Feb-98
21-Apr-98
22-Oct-98
16-Dec-98
23-Feb-99
30-Apr-99
15-Jun-99
19-Aug-99
22-Sep-99
8-Dec-99
27-Jan-00
24-Feb-00
26-Apr-00
30-May-00
27-Jun-00
30-Aug-00
31-Oct-00
12-Dec-00
12-Feb-01
28-May-01
26-Jun-01
27-Aug-01
29-Jan-02
21-Feb-02
29-Apr-02
20-Jun-02
20-Aug-02
25-Sep-02
31-Dec-02
14-Jan-03
16-Apr-03
24-Jun-03
29-Sep-03
30-Jan-04
29-Apr-04
15-Jul-04
7-Jan-05
28-Apr-05
10-Aug-05
13-Oct-05
28-Apr-06
16-Jun-06
Freshwater
Temperature
Saturation
Value
0
( C)
(mg/L)
7.559
30.0
7.559
30.0
7.559
30.0
7.625
29.5
7.691
29.0
7.828
28.0
7.430
31.0
7.430
31.0
7.718
28.8
9.092
20.0
7.305
32.0
7.305
32.0
7.559
30.0
7.559
30.0
7.968
27.0
7.559
30.0
7.625
29.5
7.559
30.0
7.625
29.5
7.705
28.9
7.870
27.7
7.718
28.8
7.572
29.9
7.430
31.0
7.559
30.0
7.651
29.3
7.625
29.5
7.651
29.3
7.678
29.1
7.268
32.3
7.598
29.7
7.546
30.1
7.800
28.2
7.520
30.3
7.232
32.6
7.494
30.5
7.664
29.2
7.559
30.0
7.625
29.5
7.773
28.4
7.507
30.4
7.559
30.0
7.656
29.3
8.093
26.1
7.475
30.7
7.735
28.7
31.1
7.412
30.9
7.442
29.7
7.596
29.9
7.571
31.6
7.358
29.5
7.619
Salinity (ppt)
24.0
24.0
33.0
33.0
24.0
18.0
19.0
7.8
57.9
18.1
18.0
15.0
28.0
23.0
21.0
18.0
21.5
17.0
17.0
26.7
25.0
26.3
24.1
26.0
25.9
26.8
28.2
28.0
28.7
23.3
26.8
31.3
21.3
23.9
25.9
18.6
16.5
22.6
16.8
15.8
21.7
10.4
22.4
21.5
30.1
30.7
27.8
25.4
29.7
24.7
Saltwater
Dissolved Oxygen
Saturation Value
Saturation (%)
(mg/L)
6.625
87.65
6.625
87.65
6.305
83.42
6.356
83.37
6.735
87.57
7.081
90.46
6.698
90.15
7.120
95.83
5.601
72.57
8.172
89.87
6.626
90.70
6.734
92.19
6.481
85.74
6.662
88.13
7.083
88.89
6.847
90.58
6.772
88.82
6.885
91.08
6.943
91.06
6.646
86.26
6.845
86.98
6.672
86.45
6.632
87.59
6.447
86.77
6.556
86.74
6.599
86.25
6.527
85.60
6.556
85.68
6.552
85.33
6.407
88.15
6.556
86.28
6.354
84.21
6.929
88.83
6.596
87.72
6.288
86.95
6.768
90.32
6.997
91.29
6.676
88.32
6.950
91.16
7.120
91.60
6.791
7.631
6.614
6.864
6.290
6.294
6.517
6.583
6.262
6.651
88.70
94.30
88.49
88.75
84.86
84.58
85.80
86.95
85.10
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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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
Kota Kinabalu
Year
Fits Data
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
248.84
249.12
249.41
249.70
249.98
250.27
250.56
250.85
251.13
251.42
251.71
251.99
252.28
252.57
252.86
253.14
2004
2005
2006
2007
253.43
253.72
254.00
254.29
Tg Gelang
Johor Bahru
Pulau Pinang
Residual
Residual
Residual
Residual
Fits Data
Fits Data
Fits Data
Data
Data
Data
Data
2.12
1.76
-1.16
-2.33
-2.43
-2.43
-2.54
0.59
2.88
-3.58
-1.88
5.99
5.94
5.45
-0.07
-0.70
277.82
278.01
278.20
278.39
278.58
278.77
278.96
279.15
279.34
279.53
279.72
279.91
280.10
280.29
280.48
280.67
280.86
281.06
281.25
281.44
3.60
-0.86
-0.40
-1.68
1.77
0.91
-1.32
-0.54
-1.94
-1.98
-2.37
1.35
0.08
-2.15
-0.21
3.28
2.39
2.48
-0.50
0.59
1.99
-0.58
-0.34
-1.48
1.86
1.14
-1.77
-0.92
-1.40
-1.41
-2.27
2.57
0.21
-2.98
-0.70
3.00
2.42
3.40
1.63
1.62
282.95
283.14
283.33
283.52
283.71
283.90
284.09
284.28
284.47
284.66
284.85
285.04
285.23
285.42
285.61
285.80
285.99
286.18
286.37
286.55
266.43
266.59
266.76
266.93
267.09
267.26
267.42
267.59
267.76
267.92
268.09
268.26
268.42
268.59
268.76
268.92
269.09
269.26
-0.59
-3.39
3.60
1.61
-1.29
-2.69
0.12
3.22
-4.81
1.70
2.47
-10.08
2.66
4.52
5.51
6.05
-2.59
0.03
-2.92
-3.28
-1.11
-0.30
281.63
281.82
282.01
282.20
-1.08
-0.35
-0.22
-0.82
-0.94
-3.59
-0.35
-1.12
286.74
286.93
287.12
287.31
269.42
269.59
269.75
269.92
0.31
-0.70
-4.99
-0.67
68
APPENDIX B
Figure B1: Monthly Mean Sea Level during Northeast and Southwest Monsoons
During El Niño Year in 1997
Monthly Mean Sea Leve
(cm)
Kota Kinabalu
260
250
240
230
Jan
Feb Mac Apr May June July Aug Sept Oct
Nov Dec
Month
Northeast Monsoon
Southwest Monsoon
Figure B.1(a): Kota Kinabalu Mean Sea Level during Northeast and Southwest
Monsoon in 1997
Tg Gelang
Monthly Mean Sea
Level (cm)
320
300
280
260
240
Jan
Northeast Monsoon
Feb Mac Apr May June July Aug Sept Oct Nov Dec
Southwest Monsoon
Month
Figure B.1(b): Tg Gelang Mean Sea Level during Northeast and Southwest
Monsoon in 1997
69
Johor Bahru
Monthly Mean Sea
Level (cm)
310
300
290
280
270
260
Jan
Northeast Monsoon
Feb Mac Apr May June July Aug Sept Oct Nov Dec
Southwest Monsoon
Month
Figure B.1(c): Johor Bahru Mean Sea Level during Northeast and Southwest
Monsoon in 1997
Pulau Pinang
Monthly Mean Sea
Level (cm)
280
270
260
250
240
230
Jan
Feb Mac Apr May June July Aug Sept Oct Nov Dec
Month
Northeast Monsoon
Southwest Monsoon
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
Monthly Mean Sea
Level (cm)
280
270
260
250
240
Jan Feb Mac Apr May June July Aug Sept Oct Nov Dec
Month
Northeast Monsoon
Southwest Monsoon
Figure B.2(a): Kota Kinabalu Mean Sea Level during Northeast and Southwest
Monsoon in 1999
Tg Gelang
Monthly Mean Sea
Level (cm)
340
320
300
280
260
240
Jan Feb Mac Apr May June J uly Aug Sept Oct Nov Dec
Northeast Monsoon
Southwest Monsoon
Month
Figure B.2(b): Kota Kinabalu Mean Sea Level during Northeast and Southwest
Monsoon in 1999
71
Johor Bahru
Monthly Mean Sea
Level (cm)
320
310
300
290
280
270
260
Jan
Feb Mac Apr May June July Aug Sept Oct Nov Dec
Month
Northeast Monsoon
Southwest Monsoon
Figure B.2(c): Kota Kinabalu Mean Sea Level during Northeast and Southwest
Monsoon in 1999
Pulau Pinang
Monthly Mean Sea
Level (cm)
290
285
280
275
270
265
260
Jan Feb Mac Apr May June July Aug Sept Oct Nov Dec
Northeast Monsoon
Southwest Monsoon
Month
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