TREND ANALYSIS OF SEA LEVEL RISE FOR WEST COAST OF
PENINSULAR MALAYSIA
AZURA BINTI AHMAD RADZI
A project report submitted in partial fulfillment of the requirements for the award of
the degree of Master of Engineering (Civil – Hydrology and Water Resources)
Faculty of Civil Engineering
Universiti Teknologi Malaysia
NOVEMBER 2009
iii
Praises to Allah S.W.T the Almighty
Dedicated to:
My beloved Husband, Asmawi Abdul Malik,
My lovely Son, Aqiff Nuqman and my unborn little baby,
My dearest Father and Mother, Brothers and Family in-laws,
My supportive and sincere Friends,
Your prayers, support and love always give me
the strength, spirit and patience to accomplish this research.
Thank you…thank you…thank you…
Even a thousand words couldn’t express my gratitude…
iv
ACKNOWLEDGEMENT
Praises to Allah SWT, the Most gracious and Most Merciful, Who has
created the mankind with knowledge, wisdom and power. Being the best creation of
Allah, one still has to depend on others for many aspects directly and indirectly. In
preparing this dissertation, the author was indebted to many personnel, academics
and practitioners. They have contributed towards my understanding and thoughts.
Therefore, I would like to thank all those who gave me the possibility to complete
this dissertation.
First and foremost, I would like to express my sincere gratitude to my
supervisor, Professor Hadibah Binti Ismail who has supported me with her guidance,
encouragement, patience and knowledge throughout the accomplishment of this
thesis. I am also thankful to the panel members for their constructive critics and
comments. MSMD also deserve special thanks for providing the data. My
acknowledgment also extends to Jabatan Pengajian Politeknik dan Kolej Komuniti,
KPTM who sponsored my study.
A special dedication to my beloved husband, Asmawi Abdul Malik and my
lovely son, Aqiff Nuqman, for their love, support, encouragement, understanding
and patience. A warmest gratitude to my father, mother, brothers and family in-laws
for their prayers and supports. And last but not least, special thanks to my truthful
friend, Erina Binti Ismail and all my friends in Polytechnic, UTM and Unikl Mimet.
v
ABSTRACT
Future sea level rise would be expected to have a number of impacts,
particularly on Malaysia coastal systems such as flooding and inundation, coastal
erosion and salt water intrusion. This study analyzes the trend variation of sea level
rise (SLR) for selected locations along the west coast of Peninsular Malaysia. The
rate of future SLR at these stations will then be predicted for the year 2050 and
2100. This study also examines the trend of sea level rise throughout the Straits of
Malacca. The historical mean sea level data at the selected stations were used in the
trend analysis. The non parametric Mann Kendal tests were carried out to determine
trends in sea level rise. From the analysis, the results showed that all the selected
stations along the West Coast of Peninsular Malaysia (i.e: Teluk Ewa Langkawi,
Penang, Lumut, Port Klang, Tanjung Keling Melaka, and Kukup Johor) have an
upward trend of sea level rise. The rate of SLR lies between 0.829 mm/yr to 2.021
mm/yr. The highest rate of SLR is at Teluk Ewa, Langkawi and the lowest is at
Penang. The future projections of the trend line for an estimate SLR in the year 2050
and 2100, for all the selected stations exhibit an increment in sea level rise. In 2050,
the highest incremental SLR is 9.175 cm which is at Teluk Ewa, Langkawi while the
lowest incremental value is 3.994 cm at Penang. Subsequently, in 2100 the highest
increment in SLR is 19.595 cm while the lowest increment is 8.395 cm at Teluk
Ewa, Langkawi and Penang respectively. The trend analysis and the future
projection also prove that the Straits of Malacca will experience a rise in sea level in
2050 and 2100.
vi
ABSTRAK
Kenaikan paras air laut pada masa hadapan dijangka akan memberi impak
terhadap sistem pesisir pantai Malaysia seperti banjir, hakisan pantai dan kemasukan
air masin. Kajian ini menganalisa variasi trend kenaikan paras air laut di lokasilokasi terpilih di sepanjang Pantai Barat Semenanjung Malaysia. Di samping itu,
kadar kenaikan paras air laut pada tahun 2050 dan 2100 untuk setiap lokasi akan
diramal. Kajian ini juga mengkaji trend kenaikan paras air laut di sepanjang Selat
Melaka. Data purata paras air laut daripada rekod terdahulu telah digunapakai di
dalam analisa ini. Ujian Mann-Kendall bukan parametrik dilakukan bagi
menentukan trend dan variasi kenaikan paras air laut. Daripada analisa yang
dijalankan, didapati kesemua stesen di sepanjang Pantai Barat Semenanjung
Malaysia (iaitu: Teluk Ewa Langkawi, Penang, Lumut, Port Klang, Tanjung Keling
Melaka, dan Kukup Johor) menunjukkan trend yang meningkat pada paras air laut.
Kadar kenaikan paras air laut adalah di antara 0.829 mm/tahun sehingga 2.021
mm/tahun. Teluk Ewa Langkawi mencatatkan kadar tertinggi bagi kenaikan paras
air laut dan Pulau Pinang mencatatkan kadar kenaikan paras air laut yang terendah.
Unjuran masa hadapan garisan graf menunjukkan paras air laut akan meningkat pada
tahun 2050 dan 2100 bagi kesemua stesen terpilih. Pada tahun 2050, Teluk Ewa,
Langkawi akan mengalami kenaikan paras air tertinggi iaitu 9.175 cm dan Pulau
Pinang mencatatkan kenaikan paras air laut terendah iaitu 3.994 cm. Selanjutnya,
pada tahun 2100, paras air laut mencatatkan peningkatan tertinggi iaitu sebanyak
19.595 cm dan peningkatan terendah iaitu 8.395 cm di Teluk Ewa, Langkawi dan
Pulau Pinang masing-masing. Hasil analisa dan unjuran masa hadapan yang telah
dibuat juga membuktikan Selat Melaka akan mengalami kenaikan paras air laut pada
tahun 2050 dan 2100.
vii
TABLE OF CONTENTS
TITLE
1
PAGE
TITLE PAGE
i
DECLARATION
ii
DEDICATION
iii
ACKNOWLEDGEMENT
iv
ABSTRACT
v
ABSTRAK
vi
TABLE OF CONTENTS
vii
LIST OF FIGURES
viii
LIST OF TABLES
xiii
LIST OF NOMENCLATURE
xvi
LIST OF SYMBOL
xvii
LIST OF APPENDICES
xviii
INTRODUCTION
1
1.1
Research Background
1
1.2
Problem Statement
3
1.3
Study Area
5
1.4
Objectives of Study
6
viii
2
1.5
Scope of Study
7
1.6
Significance of Study
7
LITERATURE REVIEW
8
2.1
8
Introduction
PART A:
SEA LEVEL RISE (SLR)
10
2.2
The Conceptual Understanding of Sea Level Rise
10
2.3
The Impact of Sea Level Rise
12
2.3.1
Ecological Impacts of Sea Level Rise
14
2.3.2
Social and Economic Impacts of Sea
Level Rise
2.3.2
Impact of Sea Level Rise to Malaysia
Coastal System
2.4
16
18
Global Sea Level Rise Scenario
21
2.4.1
The Evolution of Global Sea Level Rise
21
2.4.2
Global Observation
23
2.5
Sea Level Rise :- South Asia Countries Scenario
29
2.6
Sea Level Rise : Malaysia Scenario
34
PART B:
2.7
TREND ANALYSIS
What is Trend Analysis?
36
2.7.1
Trend Analysis Models
38
2.7.2
Advantages and Disadvantages of Trend
Analysis
2.8
36
Case Examples Using Trend Analysis Method
39
40
ix
2.8.1
Water Quality Evaluation And Trend
Analysis In Buyuk Menderes
Basin, Turkey by Hulya Boyacioglu and
Hayal Boyacioglu (2006)
2.8.2
41
Trend Analysis of Sea levels Along
Turkish Coasts by Burkay SeSeogullar ,
Ebru Eris and Ercan Kahya (2007)
2.8.3
41
A Study Of Hydrological Trends And
Variability Of Upper Mazowe
Catchment-Zimbabwe by W.
Chingombe et al (2006)
3
RESEARCH METHODOLOGY
45
3.1
Introduction
45
3.2
Research Methodology Flowchart
46
3.3
Data Collection
47
3.4
Data Analysis
47
3.4.1
3.4.2
4
43
Statistical Analysis : Non Parametric
(Mann – Kendall Test)
48
Trend Analysis
51
DATA ANALYSIS AND RESULTS
52
4.1
Introduction
52
4.2
Statistical Analysis : Mann – Kendall Test Results
52
4.2.1
Teluk Ewa, Langkawi, Kedah
53
4.2.2
Penang
54
4.2.3
Lumut, Perak
55
x
4.2.4
Port Klang, Selangor
56
4.2.5
Tanjung Keling, Melaka
57
4.2.6
Kukup, Johor
58
4.2.7
Summary of the Mann – Kendall Test
Results
4.3
4.4
4.5
59
Trend Analysis Results
4.3.1
Teluk Ewa, Langkawi, Kedah
60
4.3.2
Penang
61
4.3.3
Lumut, Perak
62
4.3.4
Port Klang, Selangor
63
4.3.5
Tanjung Keling, Melaka
64
4.3.6
Kukup, Johor
65
Sea Level Rise (SLR) Prediction
65
4.4.1
Teluk Ewa, Langkawi, Kedah
66
4.4.2
Penang
68
4.4.3
Lumut, Perak
70
4.4.4
Port Klang, Selangor
72
4.4.5
Tanjung Keling, Melaka
74
4.4.6
Kukup, Johor
76
Summary of the Mean Sea Level Trend Analysis
78
xi
5
CONCLUSION AND RECOMMENDATION
81
5.1
Introduction
82
5.2
Conclusion
80
5.3
Recommendation
83
REFERENCES
APPENDICES A – F
84
88-137
xii
LIST OF FIGURES
FIGURE NO.
TITLE
PAGE
1.1
Map of Malaysia Coastline
3
1.2
Study area locations
6
2.1
UN sea level rise response strategies
9
2.2
Causes of sea level change and the components of sea
level rise
2.3
10
Sources of sea level rise and their contributions in
mm per year for the periods 1961-2003 and 19932003 from the IPCC 2007 assessment
2.4
The ocean beach and bay shore sea level rise
characteristic
2.5
14
Table listed the biophysical and sosio-economic
impacts from sea level
2.7
13
The future prediction of sea level for the next 120
years
2.6
12
20
Time series of global mean sea level (deviation from
the 1980-1999 mean) in the past and as projected for
the future
22
2.8
ASIAN Cities at risk due to sea -level rise
24
2.9
Latin America and Caribbean Cities at risk due to sea
2.10
level rise
25
AFRICAN Cities at risk due to sea -level rise
25
xiii
2.11
Graphs on impact of alternative sea level
26
2.12
Sea-level rise will displace millions across the world
28
2.13
Shoreline changes of the Chao Phraya delta west of
the river mouth in 1952, 1967, 1987, 1995, 2000, and
2004 (modified after Rokugawa et al., 2006). The
shoreline retreated overall more than 1 km
29
2.14
Wat Khun Samutchin
30
2.15
Wat Khun Samutchin where three steps are buried
below the present ground level
31
2.16
An example of a linear trend
37
3.1
Research Methodology Flowchart
46
3.2
MINITAB Release 14 Statistical Software
48
4.1
Mann-Kendall Test for Teluk Ewa, Langkawi, Kedah
53
4.2
Mann-Kendall Test for Penang
54
4.3
Mann-Kendall Test for Lumut, Perak
55
4.4
Mann-Kendall Test for Port Klang, Selangor
56
4.5
Mann-Kendall Test for Tanjung Keling, Melaka
57
4.6
Mann-Kendall Test for Kukup, Johor
58
4.7
Trend analysis of MSL for Teluk Ewa Langkawi
60
4.8
Trend analysis of MSL for Penang
61
4.9
Trend analysis of MSL for Lumut, Perak
62
xiv
4.10
Trend analysis of MSL for Port Klang, Selangor
63
4.11
Trend analysis of MSL for Tanjung Keling, Melaka
64
4.12
Trend analysis of MSL for Kukup, Johor
65
4.13
Graph of predicted MSL for Teluk Ewa, Langkawi in
year 2050 and 2100
66
4.14
Residual pots for Teluk Ewa, Langkawi
67
4.15
Graph of predicted MSL for Penang in year 2050 and
2100
68
4.16
Residual plots for Penang
69
4.17
Graph of predicted MSL for Lumut in year 2050 and
2100
70
4.18
Residual plots for Lumut
71
4.19
Graph of predicted MSL for Port Klang in year 2050
and 2100
72
4.20
Residual plots for Port Klang
73
4.21
Graph of predicted MSL for Tanjung Keling in year
2050 and 2100
74
4.22
Residual plot for Tanjung Keling
75
4.23
Graph of predicted MSL for Kukup in year 2050 and
2100
76
4.24
Residual plots for Kukup
77
4.25
Incremental SLR along Straits of Malacca
80
xv
LIST OF TABLES
TABLE NO.
TITLE
PAGE
2.1
Sea Level Rise Scenario’s for Vietnam (in centimetres)
32
2.2
Summary of changes in the global environment by the
2020s, 2050s and 2080s for the four scenarios
2.3
Observed and predicted sea level rise at Tanjung Piai,
Johor
2.4
35
Observed and predicted sea level rise at Teluk Ewa,
Langkawi
2.5
33
36
Summary of annual sea level trends at 5% significance
level
43
3.1
Information on the selected tidal gauge station
47
4.1
Summary of the Mann – Kendall Test Results on the
MSL data at the selected stations
59
4.2
Summary of the Results
78
4.3
Predicted Sea Level Rise (SLR) in year 2050 and 2100
for all Stations on West Coast of Peninsular Malaysia
79
xvi
LIST OF NOMENCLATURE
Abbreviation
DID
Drainage and Irrigation Department
IPCC
Intergovernmental Panel on Climate Change
IRIN
Integrated Regional Information Networks
LAT
Latitude
LONG
Longitude
MSL
Mean Sea Level
MMD
Malaysian Meteorological Department
MSMD
Malaysia Survey and Mapping Department
NOAA
National Oceanic and Atmospheric Administration
RMN
Royal Malaysian Navy
SLR
Sea Level Rise
SRES
Special Report on Emmission Scenarios
UN
United Nation
USA
United State of America
STN
Station
km
Kilometer
m
Meter
cm
Centimeter
mm
Milimeter
Yr
Year
xvii
LIST OF SYMBOL
n
Sample size
S
Indicator of a trend in a series of data
VAR (S)
A measure of dispersion, or how spread out the data are,
about the mean
z
Normal probability of the data distribution
t
Time
Y
Dependent variable,
Intercept
0
1
,
2
Regression coefficient
t
Regressor variable
a
Exponent variable for common log, a
.
1
xviii
LIST OF APPENDICES
APPENDIX
TITLE
PAGE
A
Monthly MSL Data for Teluk Ewa, Langkawi
88
B
Monthly MSL Data for Penang
95
C
Monthly MSL Data for Lumut
104
D
Monthly MSL Data for Port Klang
112
E
Monthly MSL Data for Tanjung Keling
121
F
Monthly MSL Data for Kukup
129
CHAPTER 1
INTRODUCTION
1.1
Research Background
Sea level can change, both globally and locally, due to (i) changes in the shape
of the ocean basins, (ii) changes in the total mass of water and (iii) changes in water
density. Global mean sea level (MSL) has been rising since the end of the last ice age
almost 18,000 years ago. Factors leading to sea level rise (SLR) under global warming
include both increases in the total mass of water from the melting of land-based snow
and ice, and changes in water density from an increase in ocean water temperatures and
salinity changes.
As the world's oceans rise, low-lying coastal areas will disappear. Flooding of
coastal areas will become more common and more severe as storm surges have easier
access to these lower-lying areas. The occurrence of extreme high water events related
to storm surges, high tides, surface waves, and flooding rivers will also increase.
2
Flooding and loss of land will have significant impacts on humans, wildlife, and entire
ecosystems.
Coastal areas are also important economic areas as they are resource-rich.
Tourism, aquaculture, fisheries, agriculture, forestry, recreation, and infrastructure will
all be strongly affected by the effects of rising sea level. Another effect of sea level rise
is contamination of groundwater resources through salt water intrusion. Increasing sea
levels and flooding will have a number of impacts on terrestrial water storage in
addition to seawater intrusion. These include rising water tables from salt water
intrusion, erosion, and impeded drainage.
The UN Intergovernmental Panel on Climate Change (IPCC) Response
Strategies Working Group Scientists, 1990 mentioned that from the officials list of
some seventy (70) nations have expressed their views on the implications of sea level
rise and other coastal impacts of global climate change at Coastal Zone Management
Subgroup workshops in Miami and Perth. They indicated that in several noteworthy
cases, the impacts could be disastrous; that in a few cases impacts would be trivial; but
that for most coastal nations, at least for the foreseeable future, the impacts of sea level
rise would be serious but manageable if appropriate actions are taken.
The UN IPCC 1990 Report also highlighted the needs and the demand of
overcoming the future scenario where research on the impacts of global climate change
on sea level rise should be strengthened, a global ocean observing network should be
developed and implemented, and data and information on sea level change and adaptive
options should be made widely available.
3
1.2
Problem Statement
Malaysia is a coastal nation, rich in biodiversity and natural resources. The
country covers an area of 329,750 km2 with a coastline of 4809 km and is divided into
two landmasses that are separated by the South China Sea. Peninsular Malaysia, in the
west, has an area of 131,590 km2 and a coastline of 2031 km. Peninsular Malaysia
composes of 11 states and the Federal Territory of Kuala Lumpur. Two other states,
Sabah and Sarawak, occupying an area of 73,711 km2 and 124,449 km2 respectively,
and the Federal Territory of Labuan are located in the northwestern coast of Borneo
island. Sabah has a coastline of 1743 km while Sarawak has a coastline of 1035 km. A
map of the Malaysia coastline is shown in Figure 1.1.
Figure 1.1: Map of Malaysia Coastline
Source: DID, 2000
4
The major towns, ports, large agriculture and aquaculture projects of Malaysia’s
coast contribute significantly to the nation’s economic development. It is anticipated
that the physical and economic impact for the whole nation of a greenhouse-induced
sea level rise could be devastating.
According to J.E Ong (2000) as quoted from Geyh et.al (1972), Kamaludin
(1989) and Peltier & Tushingam (1989), there is good geological evidence that showed
over the last 5,000 or so years, sea level around Malaysian coast has been falling at a
mean rate of about 1 mm/yr and the global tidal level is dropping at 2.4 ± 0.9 mm/yr.
Meanwhile, the sedimentation rate which appears to be playing a critical role in relative
sea level change in Malaysia is in the region of a few millimeters per year. In more
recent finding, Malaysia sea level has risen at an average rate of 1.25 mm/yr over 1986
to 2006 (Initial National Communication, 2000 and National Coastal Vulnerability
Index Study, DID, 2007). All of the above findings are signals to show that Malaysia
coastal system might be vulnerable to SLR.
Future SLR would be expected to have a number of impacts, particularly on
Malaysia coastal systems. The stronger waves accompanying a rise in sea level will
destroy the beaches and increase coastal erosion. Beside coastal erosion, the other major
threat of SLR is inundation. About 12 percent of Peninsular Malaysia’s area, where the
western low plains of muddy sediment are home to 2.5 million people, is flood prone
(J.E Ong, 2000). Further more, SLR will also lead to salt water intrusion, changes in
surface water quality and groundwater characteristics, increased loss of life, property
and coastal habitats due to flooding, impacts on agriculture and aquaculture through
decline in soil and water quality, and loss of tourism, recreation, and transportation
functions.
5
Therefore, there is indication of the urgency for Malaysia as one of the coastal
nations to begin the progression of adapting to sea level rise not because there is an
awaiting catastrophe, but because there are opportunities to avoid unpleasant impacts by
acting now, opportunities that may be lost if the process is delayed. Unfortunately, there
is lack of official indication or measurement has been done in Malaysia on SLR.
Hence, how should Malaysians prepare for sea level rise? Thus, this particular study is
required to analyze the trend variation of SLR for selected locations along west coast of
Peninsular Malaysia and to predict SLR in the year 2050 & 2100 so that the
consequences of SLR can be reduced through a proper management and
implementation of adaptation and mitigation measures.
1.3
Study Area
In this study, six locations along the west coast of Peninsular Malaysia are
selected for the purpose of trend analysis. The locations are Langkawi, Penang, Lumut,
Port Klang, Tanjung Keling and Kukup. The locations are selected based on the existing
tidal gauge stations along the west coast of Peninsular Malaysia. Figure 1.2 below
shows the selected locations.
6
Figure 1.2: Study area locations
1.4
Objectives of Study
The objectives of this study are:
i.
To analyze the trend variation of sea level rise for selected locations
along the west coast of Peninsular Malaysia;
ii.
To predict sea level rise for these locations in the year 2050 and 2100.
iii.
To study the trend of sea level rise throughout the Straits of Malacca.
7
1.5
Scope of Study
The scope of this study can best be described as follows:
i.
A review of all literatures related to trend analysis methodologies and to
apply the most suitable technique in the analysis for the six selected
stations along the west Coast of Peninsular Malaysia (i.e. Langkawi,
Penang, Lumut, Port Klang, Tanjung Keling and Kukup).
ii.
Collection of data (tidal records) for all selected stations will be used for
the purpose of trend analysis.
iii.
Conduct of the selected methodology for the sea level rise trend analysis
and prediction for the year 2050 and 2100.
iv.
Determination of the general trend of SLR variation throughout the
Straits of Malacca.
1.6
Significance of Study
The findings from this study are expected to provide:•
The trend/pattern of SLR for each of the selected stations along west coast of
Peninsular Malaysia.
•
The rate of SLR for each of the selected stations along west coast of Peninsular
Malaysia.
•
The prediction rate of future SLR at the selected stations in the year 2050 and
2100.
•
The variation of the SLR rate in the Straits of Malacca.
The results will impose as a strong signal of SLR threat to the west coast of Peninsular
Malaysia and will lead to the recommendations of adaptive measures to mitigate the
SLR in the future.
CHAPTER 2
LITERATURE REVIEW
2.1
Introduction
Naturally, people live in the world's coastal areas, where they are able to
enjoy the richness and beauty of the sea. The sea provides resources, links coastal
cities and provides opportunities for trade. However, the sea often threatens the
inhabitants of the coastal areas, demanding its toll in human and natural resources.
Sea level rise, heavy storms, hurricanes, tsunamis and typhoons can batter the
coastal areas, causing disaster and distress.
The sea level rise is not a recent phenomenon to the world community issue.
Several severe implication of SLR impact can be captured and the mitigation stage
has been taken out all over the world. In November, 1990, the UN
Intergovernmental Panel on Climate Change Response Strategies Working Group
has picked up the scenario as shown in Figure 2.1 with the prediction condition.
9
Figure 2.1: UN sea level rise response strategies
Source: UN Intergovernmental Panel on Climate Change Response
Strategies Working Group, 1990
The sea level rise is certainly able to give impact on the people, industries
and agriculture. The rising sea level caused by melting ice caps in north pole and
south pole would also affect the salinity of the water as well as push salt water into
rivers. Thus, the water quality is expected to change as it makes river water
unsuitable for agriculture and human daily consumption.
10
PART A:
2.2
SEA LEVEL RISE (SLR)
The Conceptual Understanding of Sea Level Rise
Sea level can change, both globally and locally, due to (i) changes in the
shape of the ocean basins, (ii) changes in the total mass of water and (iii) changes in
water density. Factors leading to sea-level rise under global warming include both
increases in the total mass of water from the melting of land-based snow and ice,
and changes in water density from an increase in ocean water temperatures and
salinity changes. Relative sea-level rise occurs where there is a local increase in the
level of the ocean relative to the land, which might be due to ocean rise and/or landlevel subsidence. Figure 2.2 below simplified the causes of sea level change and the
components of sea level rise.
Figure 2.2: Causes of sea level change and the components of sea level rise.
Source: David Criggs, 2001.
11
Sea level rise is due to a number of causes, some of which may exert a more
regional influence than others. These include:
•
Thermal expansion – As seawater becomes warmer it expands. Heat
in the upper layer of the ocean is released quickly into the atmosphere. However,
heat absorbed by the deeper layers of the ocean will take much longer to be released
and therefore, be stored in the ocean much longer and have significant impacts on
future ocean warming.
•
Freshwater inputs – Increase in freshwater inputs from mountain
glaciers, ice sheets, ice caps, and sea ice, as well as other atmospheric and
hydrologic cycles due to rising global surface and ocean temperatures.
•
Physical forces – Subsidence and lifting are associated with tectonic
activity and the extraction of water and resources such as gas and oil. These types of
forces do not actually change the volume of the ocean, only the relative sea level.
However, these changes do affect movement over land, as well as estimates from
satellite altimetry.
•
Ocean current variations – Large, regional ocean currents which
move large quantities of water from one location to another also affect relative sea
level without changing the actual volume of the ocean. For example, el Niño moves
water from one side of the Pacific to the other every three or four years. These largescale variations also affect the relative sea level of certain areas. In normal
conditions, trade winds blow across the Pacific toward the west. According to
NOAA, the trade winds push warm surface water to the west Pacific, so the sea level
is roughly 1/2 meter higher in Indonesia than it is in Ecuador. During el Niño years,
this warm water is pushed over to the eastern Pacific.
•
Atmospheric pressure influences sea level by impacting the surface
itself. This also only affects relative sea level as the water pushed out of one place
will move to another.
12
Figure 2.3: Sources of sea level rise and their contributions in mm per year
for the periods 1961-2003 and 1993-2003 from the IPCC 2007 assessment.
Image credit: modified from Bindoff et al (2007)
2.3
The Impact of Sea Level Rise
The sea level rises due to the global warming might cause certain physical
change and the possible reactions are:
•
The low lying coastal line will be inundated with water, causing
damage to houses, industries and crops.
•
Low level islands could sink and disappear.
•
The quality and salinity will drop when fresh water from the melted
ice caps drain into the ocean.
•
The water levels in rivers will increase and cause flooding in the low
level area.
•
River water temperature and the ocean water temperature will also
13
change accordingly.
•
The river water will mix with salt water from the ocean making it
unsafe for human consumption.
•
The total water density will also change and it changes the freeboard
area on the ship. This is danger especially on the large cargo vessel as
it has lesser distance from the deck to the vessel water line.
•
The marine life, like fish and even coral will have to migrate as to
find waters that are more suitable or perish.
•
Topography of the respective affected country will change and the
country’s size can decrease.
•
Millions of money need to be allocated as to mitigate the global
warming reaction especially on the sea level rise.
James G. Titus, 1998 in his Articles of Rising Seas, Coastal Erosion, and the
Taking Clause: How to save wetlands and beaches without hurting property for
Maryland Review had simplified the sea level rise situation into the following
figure:
Figure 2.4: The ocean beach and bay shore sea level rise characteristic
Source: James G. Titus, 1998
14
Figure 2.5: The future prediction of sea level for the next 120 years
Source: James G. Titus, 1998
2.3.1 Ecological Impacts of Sea Level Rise
The Coastal Zone Management Subgroup (1990), reported that Working
Group II suggests that a rise in sea level could:
(1) increase shoreline erosion;
(2) exacerbate coastal flooding;
(3) inundate coastal wetlands and other lowlands;
15
(4) increase the salinity of estuaries and aquifers;
(5) alter tidal ranges in rivers and bays;
(6) change the locations where rivers deposit sediment;
(7) drown coral reefs.
Estuaries, lagoons, deltas, marshes, mangroves, coral reefs and seagrass beds
are characterized by tidal influence, high turbidity (except coral reefs) and
productivity and a high degree of human activity. Their economic significance
includes their importance for fisheries, agriculture, shipping, recreation, waste
disposal, coastal protection, biological productivity and diversity.
The direct effect of sea level rise in shallow coastal waters is an increase in
water depth. Inter-tidal zones may be modified; mangroves and other coastal
vegetation could be inundated and coral reefs could be drowned. In turn, this may
cause changes in bird life, fish spawning and nursery grounds and fish and shellfish
production. For example, coastal wetlands provide an important contribution to
commercial and recreational fisheries. Equally important is the contribution of
wetlands to commercial and subsistence fisheries in many coastal and island states.
In general, the effects on shallow coastal ecosystems are strongly determined
by local conditions. A good understanding of the physical and biological processes
and topography is required to forecast local impacts. But if the accumulation of
sediments cannot keep pace with rising waters, or if inland expansion of wetlands
and inter-tidal areas is not possible (because of infrastructure or a steeply rising
coast), major impacts could occur.
The estuarine response to rising sea level is likely to be characterized by a
slow but continually adjusting environment. With a change in estuarine vegetation
there could be an adjustment in the animal species living in and around the wetlands.
Climate change may also provoke shifts in the hydrological regimes of coastal rivers
16
and lead to increased discharge and sediment yields and, consequently, to increased
turbidity. These changes, together with a rise in sea level, could modify the shape
and location of banks and channels. If no protective structures are built, wetlands
can migrate inland; however, a net loss of wetlands would still result.
2.3.2 Social and Economic Impacts of Sea Level Rise
Many developing countries have rapid rates of population growth, with large
proportions of their populations inhabiting low lying coastal areas. According to the
report of The Coastal Zone Management Subgroup (1990), a one meter rise in sea
level could inundate 15 percent of Bangladesh, destroy rice fields and mariculture of
the Mekong delta and flood many populated atolls, including the Republic of
Maldives, Cocos Island, Tokelau, Tuvalu, Kiribati, the Marshall Islands and Tomes
Strait Islands. Shanghai and Lagos, the largest cities of China and Nigeria, lie less
than two meters above sea level, as does 20 percent of the population and farmland
of Egypt.
Four highly populated developing countries, India, Bangladesh, Vietnam and
Egypt are especially vulnerable to sea level rise because their low lying coastal
plains are already suffering the effects of flooding and coastal storms. Since 1960,
India and Bangladesh have been struck by at least eight tropical cyclones, each of
which killed more than 10,000 people. In late 1970, storm surges killed
approximately 300,000 people in Bangladesh and reached over 150 kilometers
inland. Eight to ten million people live within one meter of high tide in each of the
unprotected river deltas of Bangladesh, Egypt and Vietnam. Even more people in
these countries would be threatened by increased intensity and frequency of storms.
17
Sea level rise could increase the severity of storm related flooding. The
higher base for storm surges would be an important additional threat in areas where
hurricanes, tropical cyclones and typhoons are frequent, particularly for islands in
the Caribbean Sea, the south eastern United States, the tropical Pacific and the
Indian subcontinent. Had flood defenses not already been constructed, London,
Hamburg and much of the Netherlands would already be threatened by winter
storms.
Many small island states are also particularly vulnerable. This is reflected in
their very high ratios of coastline length to land area. The most seriously threatened
island states would be those consisting solely, or mostly, of atolls with little or no
land more than a few meters above sea level. Tropical storms further increase their
vulnerability and, while less in magnitude than those experienced by some of the
world's densely populated deltas, on a proportional basis such storms can have a
much more devastating impact on island nations.
Disruption could also be severe in industrialized countries as a result of the
high value of buildings and infrastructure. River water levels could rise and affect
related infrastructure, bridges, port structures, quays and embankments. Higher
water levels in the lower reaches of rivers and adjacent coastal waters may reduce
natural drainage of adjacent land areas, which would damage roads, buildings and
agricultural land.
18
2.3.3 Impact of Sea Level Rise to Malaysia Coastal System
The Director-General of Drainage and Irrigation Department (DID)
Malaysia, Datuk Keizrul Abdullah, 2008, was quoted in the local newspaper The
Star; that “coastal belts – the zone stretching from the coastline inland until it
reaches high ground such as the Main Range – could be inundated, especially during
high tide if the sea level rises.” He was also quoted to say that “While having a
higher sea level would cause erosion and damage mangrove and marine life,
including corals”. The coral would drift to shallow water in order to benefit from the
sun's rays if they are to survive. The significant examples we can see as in
Australia’s Great Barrier Reef and in the Maldives in the early part of the century,
was damaged due to climatic changes. Datuk Keizrul Abdullah, 2008, also
highlighted and embarked any particular study on the sea level rise as it can react as
a basis for recommending measures to mitigate the impact of sea level rise.
If the situation accelerates accordingly, the fear feels becoming very real as
Malaysia grapples with the effects of a rising sea level caused by global warming.
Some of the existing land can disappear under the water.
Future sea level rise would be expected to have a number of impacts,
particularly on Malaysia coastal systems. The stronger waves accompanying a rise
in sea level will destroy the beaches and increase coastal erosion. Besides coastal
erosion, the other major threat of sea level rise is inundation. About 12 percent of
Peninsular Malaysia’s area, where the western low plains of muddy sediment are
home to 2.5 million people, is flood prone (J.E Ong, 2000). Further more, SLR will
also lead to salt water intrusion, changes in surface water quality and groundwater
characteristics, increased loss of life, property and coastal habitats due to flooding,
impacts on agriculture and aquaculture through decline in soil and water quality, and
loss of tourism, recreation, and transportation functions.
19
Sea level rise is also a major concern to electrical power producers because
most of their thermal power plants are located near the sea. Sea level rise and
frequent tropical storms could also ultimately increase the cost of offshore oil
exploration and production. The Paka Power Station in Terengganu, for instance, is
already experiencing the effects of severe coastal erosion and has to be defended by
costly structural works such as concrete sea walls.
According to J.E Ong (2000), there is no official view as to whether rising
sea level is a problem but Malaysia has recently considered a number of scenarios
based on IPCC predictions. The two tables below (Figure 2.6), extracted from the
Malaysia Initial National Communication submitted to the United Nations
Framework Convention on Climate Change (MOSTE, 2000) is the official
vulnerability assessment.
In 2007, the Drainage and Irrigation Department (DID) had undertaken a
major study to identify and index vulnerable areas that could be affected, if the sea
level rises by half a meter to 1m. The National Coastal Vulnerability Index Study
(NCVI) is among the various strategies undertaken by Malaysia to address the
problem of global warming and rising sea level in its Second Communication to the
United Nations Framework Convention on Climate Change. Observed sea levels at
the two tidal stations in the study area recorded a mean sea level rise of about 1.25
mm/yr at Kukup and 0.18 mm/yr at Tg. Pelepas Port. An estimated SLR of 1.3
mm/yr represents a reasonably approximate value on future SLR at the Tg. Piai pilot
site. Meanwhile, an observation for a period of 20 years at Teluk Ewa station
(1986–2006)
indicated
a
long-term
rise
in
sea
level
of
1.00mm/yr.
20
Figure 2.6: Table listed the biophysical and sosio-economic impacts from sea level
rise. Source: MOSTE, 2000.
21
2.4
Global Sea Level Rise Scenario
2.4.1 The Evolution of Global Sea Level Rise
There is strong evidence that global sea level gradually rose in the 20th
century and is currently rising at an increased rate. Sea level is projected to rise at an
even greater rate in this century. The two major causes of global sea level rise are
thermal expansion of the oceans (water expands as it warms) and the loss of landbased ice due to increased melting.
Global mean sea level has been rising and there is high confidence that the
rate of rise has increased between the mid-19th and the mid-20th centuries. The
average rate was 1.7 ± 0.5 mm/ yr for the 20th century, 1.8 ± 0.5 mm/yr for 1961–
2003, and 3.1 ± 0.7 mm/yr for 1993–2003. It is not known whether the higher rate in
1993–2003 is due to decadal variability or to an increase in the longer-term trend.
Spatially, the change is highly non-uniform; e.g., over the period 1993 to 2003, rates
in some regions were up to several times the global mean rise while, in other
regions, sea levels fell (IPCC Technical Paper VI, 2008). Meanwhile, Bruce C.
Douglas (1997), found that the sea level has been rising at a rate of around
1.8 mm/yr for the past century, mainly as a result of human-induced global
warming.
According to Bindoff et. al (2007), global sea level rose by about 120 m
during the several millennia that followed the end of the last ice age (approximately
21,000 years ago), and stabilized between 3,000 and 2,000 years ago. Sea level
indicators suggest that global sea level did not change significantly from then until
the late 19th century. The instrumental record of modern sea level change shows
evidence for onset of sea level rise during the 19th century. Estimates for the 20th
century show that global average sea level rose at a rate of about 1.7 mm yr–1.
22
Global sea level is projected to rise during the 21st century at a greater rate
than during 1961 to 2003. Under the IPCC Special Report on Emission Scenarios
(SRES) A1B scenario by the mid- 2090s, for instance, global sea level reaches 0.22
to 0.44 m above 1990 levels, and is rising at about 4 mm yr–1. As in the past, sea
level change in the future will not be geographically uniform, with regional sea level
change varying within about ±0.15 m of the mean in a typical model projection.
Thermal expansion is projected to contribute more than half of the average rise, but
land ice will lose mass increasingly rapidly as the century progresses (Bindoff et. al,
2007).
Figure 2.7 shows the evolution of global mean sea level in the past and as
projected for the 21st century for the SRES A1B scenario. For the period before
1870, global measurements of sea level are not available. The grey shading shows
the uncertainty in the estimated long-term rate of sea level change. The red line is a
reconstruction of global mean sea level from tide gauges, and the red shading
denotes the range of variations from a smooth curve. The green line shows global
mean sea level observed from satellite altimetry. The blue shading represents the
range of model projections for the SRES A1B scenario for the 21st century, relative
to the 1980 to 1999 mean, and has been calculated independently from the
observations. Beyond 2100, the projections are increasingly dependent on the
emissions scenario. Over many centuries or millennia, sea level could rise by several
meters.
Figure 2.7: Time series of global mean sea level (deviation from the 1980-1999
mean) in the past and as projected for the future. Source: Bindoff et. al, 2007.
23
2.4.2 Global Observation
There are 50 percent of humanity lives in urban areas. UN-HABITAT’s new
State of the World s Cities Report 2008/9: Harmonious Cities sets out to determine
which cities are in danger and which communities might well be drowned out. The
low elevation coastal zone – the continuous area along coastlines that is less than 10
m above sea level – represents 2 per cent of the world’s land area but contains 10
per cent of its total population and 13 per cent of its urban population.
There are 3,351 cities in the low elevation coastal zones around the world.
Of these cities, 64 per cent are in developing regions; Asia alone accounts for more
than half of the most vulnerable cities, followed by Latin America and the Caribbean
(27 per cent) and Africa (15 per cent). Two-thirds of these cities are in Europe;
almost one-fifth of all cities in North America are in low elevation coastal zones.
In the developed world (including Japan), 35 of the 40 largest cities are
either coastal or situated along a river bank. In Europe, rivers have played a more
important role in determining the growth and importance of a city than the sea; more
than half of the 20 largest cities in the region developed along river banks. Quoting a
report by Organization for Economic Cooperation and Development, the authors
note that the populations of cities like Mumbai, Shanghai, Miami, New York City,
Alexandria, and New Orleans will be most exposed to surge-induced flooding in the
event of sea level rise.
In Asia, 18 of the region’s 20 largest cities are either coastal, on a river bank
or in a delta. 17 per cent of the total urban population in Asia lives in the low
elevation coastal zone, while in South-Eastern Asia, more than one-third of the
urban population lives there. Japan, with less than 10 per cent of its cities in low
elevation zones, has an urban population of 27 million inhabitants at risk, more than
24
the urban population at risk in North America, Australia and New Zealand
combined.
The report points out that by 2070, urban populations in cities in river deltas,
which already experience high risk of flooding, such as Dhaka, Kolkata, Rangoon,
and Hai Phong, will join the group of most exposed populations. Also, port cities in
Bangladesh, China, Thailand, Vietnam, and India will have joined the ranks of cities
whose assets are most exposed. Major coastal African cities that could be severely
be affected by the impact of rising sea levels include Abidjan, Accra, Alexandria,
Algiers, Cape Town, Casablanca, Dakar, Dar es Salaam, Djibouti, Durban,
Freetown, Lagos, Libreville, Lome, Luanda, Maputo, Mombasa, Port Louis, and
Tunis. Figure 2.8, 2.9 and 2.10 show the map of Asean, Latin America and
Caribbean and African Cities at risk due to sea level rise according to UN-HABITAT
Global Urban Observatory (2008) respectively.
Figure 2.8: ASIAN Cities at risk due to sea -level rise
Source: UN-HABITAT Global Urban Observatory, 2008.
25
Figure 2.9: Latin America and Caribbean Cities at risk due to sea -level rise
Source: UN-HABITAT Global Urban Observatory, 2008.
Figure 2.10: AFRICAN Cities at risk due to sea -level rise
Source: UN-HABITAT Global Urban Observatory, 2008.
26
The Green Cross Australia in 2008, at the recent Pacific Islands Forum in
August, international aid agency Oxfam released a blueprint1 for Australia’s new
engagement with Pacific nations, which (among other things) recommended urgent
and significant action to mitigate the effects of climate change. It suggested we
reduce greenhouse gas emissions by at least 40 per cent by 2020, and at least 95 per
cent by 2050; provide funding to help Pacific nations adapt to the expected changes
in climate; and commit to assisting communities displaced by the impacts of climate
change, such as sea-level rise.
These are seconded by Susmita Dasgupta et al (2007) the impact of
sea level rise from global warming could be catastrophic for many developing
countries. The World Bank estimates that even a one meter rise would turn at
least 56 million people in the developing world into environmental refugees.
Figure 2.11: Graphs on impact of alternative sea level
Source: Susmita Dasgupta, 2007
27
“Overwhelming evidence and early warning signs of human-induced climate
change confirm the reality of global warming. Our socio-economic research
evaluates the magnitude of the outcome and urgency of formulating preventive and
protective measures in the event of sea level rise. However, the question of when it
will occur can only be determined by scientific studies,” says Senior Economist and
co-author Susmita Dasgupta et al (2007).
“Knowing which countries will be most-affected could allow better targeting
of scarce available resources and could spur vulnerable nations to develop national
adaptation plans now and avoid big losses later,” explains Dasgupta et al (2007).
It is so crucial and important for these countries to know, if the sea level rises
by 1 meter, what will be the impact; what will be the inundation area; population
affected; GDP lost; loss in agricultural area; urban area; and wetlands.
Susmita Dasgupta et al (2007) calculated that, with a one meter sea-level
rise, approximately 0.3 percent, or 194,000 square kilometers and 56 million people
(1.28 percent of the population) in 84 developing countries would be impacted. An
estimated 1.3 percent of GDP would be lost for those countries.
The new projections in sea-level rise, caused by accelerating rates of loss
from ice sheets in Greenland and Antarctica on account of higher global
temperatures, even prompted the UN Environment Programme (UNEP) Year Book
2008 to warn that important tipping points leading to irreversible changes in major
earth systems "may already have been reached or passed".
28
Figure 2.12: Sea-level rise will displace millions across the world
Photo credit: IRIN (UNEP, 2008)
The UNEP Year Book 2008 reported that, “it is difficult to predict future
sea-level rise. It cannot be a smooth curve - we don't know how individual sea
basins will react - but what we know for a fact is: we are on a path towards less ice
and more water in the oceans. The ice-melt is accelerating and the rate is between
'fast and faster' - and the time to take action is now, and for any decision-maker to
ignore this warning would be wrong”.
29
2.5
Sea Level Rise :- South Asia Countries Scenario
Coastal erosion is the implication of sea level rise and it is a crucial ongoing
problem along most Asian coasts especially on the Chao Phraya, Thailand. Along
with a reduction of sediment supply, a relative sea level rise resulting from human
activities can also be an important cause of coastal erosion. Yoshiki Saito et al
(2007) highlighted Chao Phraya delta has pro-graded into the Gulf of Thailand with
an average accretion rate of ~1.5 km2 yr-1 during the past 2000 years, by
experiencing serious sea level rise over the last 40 years and caused coastal erosion.
Figure 2.13: Shoreline changes of the Chao Phraya delta west of the river mouth in
1952, 1967, 1987, 1995, 2000, and 2004 (modified after Rokugawa et al., 2006).
The shoreline retreated overall more than 1 km. Source: Yoshiki Saito et al, 2007.
30
As most of the mangrove ecosystem develops in the upper part of the intertidal zone, between mean sea level and the level of the highest tide, 1 m of
subsidence of sea level rise could cause the mangrove zone to shift landward.
However, such a shift has not been observed, though some landward expansion of
mangrove vegetation has occurred because of saltwater intrusion.
By the expansion of the sea level rise along the one (1) km retreat of the
mangrove zone representing a serious problem, where only 1 km of the 20 km-wide
mangrove zone was submerged, abandoned, and finally eroded in Thailand as
mentioned by Yoshiki Saito et al (2007). Figure 2.14 is part of the proven monument
of the sea level rise effect, where the ‘Wat Khun Samutchin’ has been surrounded by
water.
Figure 2.14: Wat Khun Samutchin. Photograph taken by Ms. Vareerat Unwerawattana
Source: Yoshiki Saito et al (2007)
31
The future sea level rise predicted by the IPCC (2007) is expected to cause
inundation and erosion of some vulnerable muddy coasts. Subsidence resulting from
human activities, as in Thailand, has more serious impacts than natural sea-level rise
because the rate of relative sea-level rise due to subsidence is usually large. The
mangrove forests will be important mechanisms of physical protection and also
sediment trapping for the preservation or restoration of shorelines when relative sea
level rises.
Figure 2.15: Wat Khun Samutchin where three steps are buried below the
present ground level. (Source: Yoshiki Saito et al, 2007)
32
The developments of sea level rise scenarios for Vietnam are summarized in
Table 2.1 below, according to the Adaptation Planning Framework to Climate
Change for the Urban Area of Ho Chi Minh City, Vietnam Fifth Urban Research
Symposium (2009). From the table it is clear to see that within the high emission
scenarios the average presumed sea level rise by 2050 will be 18.5 cm, while the
worst case scenario would be 33.4 cm. By 2050, even the low emission scenarios on
average expect sea level rise of 13 cm. By 2100, the high emission scenarios on
average predict an increase of 52.9 cm, with a worst case scenario of 101.7 cm,
while the low emission scenarios expect on average a 33.6 cm sea level raise
respectively.
Table 2.1: Sea level rise scenario’s for Vietnam (in centimetres)
Source: Prof. Tran Thuc and Associates; Vietnam Institute of Meteorology,
Hydrology and Environment
In Vietnam, Susmita Dasgupta et al (2007) is quoted by saying that an
estimated 10.8 percent of the nation’s population would be displaced with even a 1
meter sea level rise and disproportionately high impacts in the Mekong and Red
River deltas. Looking across regions, East Asia and the Middle East & North Africa
would experience the largest percentage impacts from sea level rise. Within South
33
Asia, Bangladesh would experience the largest percentage of share of land area
impacted. With a 1 meter sea level rise, the populations of Bangladesh and Sri
Lanka experience similar impacts (about 0.8 percent of total population would be
displaced).
Whereas in Indonesia, the Study On Sea Level Rise In The Western
Indonesia by Hadikusumah (1995) showed that mean sea level at Western Indonesia
rise between 3.10 to 9.27 mm per year. Based on the results, the prediction on mean
sea level change in the years of 2000, 2030, 2050 and 2100 for Cirebon location are
17 cm, 39 cm, 55 cm, and 92 cm, respectively. Hulme, M. and Sheard, N. (1999)
suggest a future global-mean sea-level rise of between 2cm and 10cm per decade,
compared to an observed rise over the last century of between 1cm and 2cm per
decade. Future rises in sea-level of this magnitude will pose increased risks for lowlying coastal Indonesian cities such as Jakarta and Surabaya. The largest
contribution to this rise in sea-level comes from the expansion of warmer ocean
water, a slow inexorable process that will ensure that the world's sea-level continues
to rise for centuries to come. Table 2.2 below summarized the changes in the global
environment by the 2020s, 2050s and 2080s for the four scenarios for Indonesia.
Table 2.2: Summary of changes in the global environment by the 2020s, 2050s and
2080s for the four scenarios. Changes are calculated with respect to the 1961-90
average. The effects of sulphate aerosols on climate have not been considered. The
changes in global temperature for the 1980s and 1990s are those observed.
(ppmv = parts per million by volume) Source : Hulme, M. and Sheard, N. (1999)
34
2.6
Sea Level Rise : Malaysia Scenario
The Malaysian coastline varies from scenic bays flanked by rocky headlands
to shallow mud flats lined with mangrove forests and the sandy beaches. On the east
coast of Peninsular Malaysia, the high sediment yield from river discharges and
harsher wave environment create the setting for a coastline of hook-shaped sandy
bays. Whilst on the west coast, the mild wave climate of the Straits of Malacca
make for wide mud shores and coastal forests rich in biodiversity. Similar forms
characterize the beaches of Sarawak and Sabah although certain sandy areas are very
flat. Shore materials include a mix of sand, silts, and even shells with some patches
of gravels and the occasional rock outcrops.
Several close systems to the sea level rise have given a significant sign such
as loss of agriculture production from eroded land situation, loss of fish production
due to mangrove damage and loss, severe interruption of port operation, the
displacement of flood victims with associated disruption of business and economic
activities and also impact on public health.
Increase in sea level could lead to corresponding rise in coastal
vectors with more breeding grounds, as there will be more areas covered with
brackish water and on top of that the increase in rainfall and temperatures will
encourage malaria vectors to survive.
To find out how vulnerable the country is, the Drainage and Irrigation
Department (DID) has undertaken a major study to determine if the rising sea is
going to swallow up low-lying areas along Malaysia’s 4,800 km coastline. The study
is to identify and index vulnerable areas that could be affected, if the sea level rises
by half a meter to 1m. The study is among the various strategies proposed by
Malaysia to address the problem of global warming and rising sea level in its first
communication to the United Nations Framework Convention on Climate Change.
35
The DID embarked on the coastal vulnerability index study as a basis for
recommending measures to mitigate the impact of sea level rise.
Observed sea levels at the two tidal stations in the study area recorded a
mean sea level rise of about 1.25 mm/yr at Kukup (based on 20 years of tidal data)
and 0.18 mm/yr at Tg. Pelepas Port (based on 10 years of tidal data). Therefore,
based on a longer tidal data record, an estimated SLR of 1.3 mm/yr at Kukup was
used to represent a reasonably approximate value of future SLR at the Tg. Piai pilot
site. Meanwhile, an observation for a period of 20 years at Teluk Ewa station (1986
–2006) indicated a long-term rise in sea level of 1.00 mm/yr. Therefore this value is
used to represent local SLR along the west coast of Langkawi. Table 2.3 and 2.4
below show the observed and predicted sea level rise at Tanjung Piai, Johor and
Teluk Ewa, Langkawi respectively, against the global SLR scenarios.
Table 2.3: Observed and predicted sea level rise at Tanjung Piai, Johor.
Source: National Coastal Vulnerability Index Study (Phase 1) - Final Report,
DID (2007)
36
Table 2.4: Observed and predicted sea level rise at Teluk Ewa, Langkawi.
Source: National Coastal Vulnerability Index Study (Phase 1) - Final Report,
DID (2007)
PART B:
2.7
TREND ANALYSIS
What is Trend Analysis?
Trend analysis is a forecasting technique in which:
(1)
a baseline scenario is constructed using trend extrapolation,
(2)
future events that may affect this scenario are identified and
evaluated on the basis of their probability of occurrence and degree
of impact,
(3)
the combined effect of these events is applied to the baseline scenario
to create future scenarios.
37
Trend studies are valuable in describing long-term changes in a population.
They can establish a pattern over time to detect shifts and changes in some event.
Main ideas of the trend analysis method are:
§
Trend analysis uses a technique called least squares to fit a trend line
to a set of time series data and then project the line into the future for
a forecast.
§
Trend analysis is a special case of regression analysis where the
dependent variable is the variable to be forecasted and the
independent variable is time.
§
While moving average model limits the forecast to one period in the
future, trend analysis is a technique for making forecasts further than
one period into the future.
1700
1650
1600
1550
1500
1450
1400
1350
Source: Algirdas Budrevicius (2004)
2003
Figure 2.16: An example of a linear trend
2002
Year
2001
2000
1999
1998
1997
1996
1995
1994
1993
1992
1991
1990
Number of libraries
Municipal public libraries in Lithuania in 1991-2002
38
2.7.1
Trend Analysis Models
Linear
Trend analysis by default uses the linear trend model:
Yt = β0 + β1 t + et
In this model, β represents the average change from one period
1
to the next.
Quadratic
The quadratic trend model which can account for simple
curvature in the data, is:
Yt = β0 + β1∗ t + β2 t2 + et
Exponential
The exponential growth trend model accounts for exponential
growth
growth or decay. For example, a savings account might exhibit
exponential growth. The model is:
Yt = β0 ∗ β1t ∗ et
`
S-curve
The S-curve model fits the Pearl-Reed logistic trend model. This
accounts for the case where the series follows an S-shaped curve.
The model is:
Yt = 10a / (β0 + β1 β2t)
Trend Analysis Characteristics
•
Data is collected from the population at more than one point in time.
•
There is no experimental manipulation of variables, or more
specifically, the investigator has no control over the independent
variable.
39
•
This kind of study involves data collection only. No intervention is
made by the investigator other than his/her method or tool to collect
data.
•
In analyzing the data, the investigator draws conclusions and may
attempt to find correlations between variables. Therefore, trend
studies are uniquely appropriate for assessing change over time and
for situation relating (prediction) questions because variables are
measured at more than one time. However, this method is deficient
for situation producing questions (causal) because there in no
manipulation of the independent variable.
2.7.2 Advantages and Disadvantages of Trend Analysis
Among others there are two important advantages of trend studies:
•
Flexibility
One advantage of trend study is that they can be based on a comparison of
survey data originally constructed for other purposes. Of course in utilizing
such secondary data, the research needs to recognize any differences in
question wording, contexts, sampling, or analysis techniques that might
differ from one survey to the next.
•
Cost effectiveness
Since trend studies allow researchers to use secondary data, it saves time,
money, and personnel.
40
While, the disadvantages of trend studies are:
•
Trend analysis is to provide descriptive t rends of some topic in a
certain period of time. Therefore, there is less concern on internal
validity because it does not aim to provide causal inferences as in the
case of experimental studies or some penal studies.
•
Trend analysis also suffers from similar threats to validity. If data are
unreliable, for example, false trends will show up in the results. If
trend analysis is based on inconsistent measures, the results will be
biased just like instrumentation threat can bias experimental studies.
That is, changes in the way indexes are constructed or the way
questions are asked will produce results that are not comparable over
time. In the worst case, the changes in measures alone can produce a
pseudo trend which might fool both the researchers and readers.
2.8
Case Examples Using Trend Analysis Method
Trend analysis has been widely used among researchers in their field such as
for examples, in water quality evaluation, in hydrological and as well as in sea level
rise observations and forecasting. Below are some examples of previous studies
using trend analysis method.
41
2.8.1 Water Quality Evaluation And Trend Analysis In Buyuk Menderes
Basin, Turkey by Hulya Boyacioglu and Hayal Boyacioglu (2006).
This study attempts to evaluate water quality in Buyuk Menderes River
Basin located in western Turkey, based on observed data. Water quality classes were
determined for the characteristic value that represents each data set. Non parametric
trend analysis including Mann-Kendall test and Sen’s slope method was applied to
detect temporal trends using ‘Minitab 13 Statistical Software’.
Results revealed that surface water quality was strongly affected from
domestic and agricultural uses at the downstream of the river. Water variables which
are indicator of agricultural land uses showed increasing trend over time. Statistical
methods are believed to assist decision makers in assessing water quality and also
determining priorities in management practices.
2.8.2 Trend Analysis of Sea levels Along Turkish Coasts by Burkay
SeSeogullar , Ebru Eris and Ercan Kahya (2007)
This study investigated trend behaviors in sea level data measured along the
Mediterranean, Aegean and Black Sea coasts of Turkey using non parametric MannKendall test. Sea level changes is considered as an indicator of environmental and
climate change. A significant change in sea levels is extremely important to the
coastal communities in Turkey.
In literature, linear secular trends in annual mean sea level data are
calculated as the least squares linear regression to a bivariate distribution of the data
value versus year. The length of the time series is recommended to be 60 years or
42
longer which sometimes is permitted to be as low as 25 years. In this study, the
authors use a non parametric approach to determine trends in sea levels as the
available data comprises of rather short record length. At the same time, the non
parametric methods are more tolerable for the short records, computationally
simpler and distribution-free.
According to the authors, the basic principle of Mann-Kendall test for
detecting a trend in a time series is to examine the sign of all pair wise differences of
observed values. Even though, the Mann-Kendall non parametric test is not
frequently applied to the sea level data; however, it has the following key
advantages (Kahya and Kalayc , 2004; Partal and Kahya, 2006):
(a) It is free from an assumption of underlying probability distribution;
(b) It is robust to the effects of outliers and gross data errors;
(c) It allows the existence of missing data (as only ranks are used);
(d) It also gives the point in time of the beginning of a developed trend
(when its sequential version is used).
The authors used annual sea level records observed from eight typical
stations, for the purpose of trend detection. As a result, five out of eight stations
showed an upward trend as one of them showed a downward trend. No trend was
found for the remaining stations. The authors also fitted a least squared line to
quantify rate of change in sea level. Among the stations showing positive trends, the
highest rate of change was computed in Trabzon (the Black Sea station) whereas the
lowest was computed in Kars yaka (the Aegean Sea station). The results confirmed a
strong signal of sea level rise at global scale.
43
Table 2.5: Summary of annual sea level trends at 5% significance level.
Source: Burkay SeSeogullar et. al (2007)
2.8.3
A Study Of Hydrological Trends And Variability Of Upper Mazowe
Catchment-Zimbabwe by W. Chingombe et al (2006)
This paper describes the development and application of a procedure that
identifies trends in hydrologic variables. The non parametric Mann-Kendall (MK)
statistic test to detect trends was applied to assess the significance of the trends in
the time series. Different pats of the hydrologic cycle were studied through 15
hydrologic variables, which were analyzed for a network of Upper Mazowe
catchment.
A bootstrap test was used since it preserves the cross correlation structure of
the network in assessing the field significance of upward and downward trends over
the network. At the significance level of 0.05, the site significance of trends with
more than 30 years and less than 30 years of trends was assessed by the MK test
with the Trend Free Pre Whitening (TFPW) procedure. The distribution of the
44
significant trends indicate that for the two periods monthly flow significantly
decreased with the exception of the month of September for the less than 30 years
series. The field significance of trends over the two time series was evaluated by the
bootstrap test at the significance level of 0.05 and none of the two flow regimes
expressed field significant changes.
CHAPTER 3
RESEARCH METHODOLOGY
3.1
Introduction
A research methodology defines what the activity involves in the research, how to
proceed, how to measure progress and what constitutes success. The research methodology
process of this study is represented by a sequence of six stages as the following highlighted
item:
Ø Define Objective and Scope of Study
Ø Literature Review Process
Ø Data Collection Process
Ø Data Analysis Process
Ø Result & Discussion
Ø Conclusion & Recommendation
A flow chart on the research methodology involving these six stages is illustrated in Figure
3.1.
46
3.2
Research Methodology Flowchart
In carrying out this study, Figure 3.1 illustrates the steps taken for the research:
START
Define objective and scope of study
•
•
•
Literature Review
Review of related literatures/case examples
Review of trend analysis methodologies
Review of available software
Data Collection
Mean sea level historical data (tidal data) obtained from
MSMD (JUPEM)
•
•
•
•
•
Data Analysis
Trend Analysis using statistical package
Prediction of SLR in year 2050 & 2100 for all selected stations
Overall assessment for West Coast of Peninsular Malaysia
General trend of SLR variation along the Straits of Malacca
Results and Discussion
Conclusion and Recommendations
END
Figure 3.1: Research Methodology Flowchart
47
3.3
Data Collection
The data collection process is an important element in this study. In order to
conduct the trend analysis of sea level rise, the mean sea level historical data (tidal data)
is necessary. The mean sea level data for all the selected locations was obtained from
Malaysia Survey and Mapping Department (MSMD). The detail information of the
selected tidal gauge stations is summarized in Table 3.1 below.
Table 3.1: Information on the selected tidal gauge station
Bil
Station
KOD STN
LAT
LONG
Data Period
1
Langkawi
(Teluk Ewa)
LAN
06 25 51
99 45 51
1986 - 2005
2
Penang
PEN
05 25 18
100 20 48
1985 - 2008
3
Lumut
LUM
04 14 24
100 36 48
1985 - 2008
4
Port Klang
PTK
03 03 00
101 21 30
1984 - 2008
5
Tanjung Keling
TGK
02 12 54
102 09 12
1985 - 2008
6
Kukup
KUK
01 19 31
103 26 34
1986 - 2005
3.4
Duration
20
years
24
years
24
years
25
years
24
years
20
years
Data Analysis
Basically an analysis process was conducted on the collected data. The data
analysis part consists of two types of analysis, i.e. statistical analysis and trend analysis.
Both analyses were conducted using Minitab® Release 14 software, which is a statistics
package. It was developed at the Pennsylvania State University by researchers Barbara
F. Ryan, Thomas A. Ryan, Jr. and Brian L. Joiner in 1972. Minitab® began as a light
version of OMNITAB®, a statistical analysis program by National Institute of Standards
and Technology of USA.
48
Figure 3.2: MINITAB Release 14 Statistical Software
3.4.1 Statistical Analysis : Non Parametric (Mann – Kendall Test)
In this study, the non parametric Mann-Kendall test was applied for identifying
trends in mean sea level data set. The test compared the relative magnitudes of sample
data rather than the data values themselves (Gilbert, 1987). The advantages of MannKendall test are:
•
It is free from an assumption of underlying probability distribution;
•
It is robust to the effect of outliers and gross data errors;
•
It allows the existence of missing data (as only ranks were used); and
49
•
It also gives the point in time of the beginning of a developed trend (where its
sequential version is used).
In Mann-Kendall test, the data values were evaluated as an ordered time series.
Each data value was compared to all subsequent data values. The initial value of the
Mann-Kendall statistic, S, was assumed to be 0 (e.g., no trend). If a data value from a
later time period is higher than a data value from an earlier time period, S is
incremented by 1. On the other hand, if the data value from a later time period is lower
than a data value sampled earlier, S is decremented by 1. The net result of all such
increments and decrements yields the final value of S.
Let X , X ,
1
2
Xn
represent n data points where xj represents the data point at time
j. Then the Mann-Kendall statistic (S) is given by:
(3.1)
where;
A very high positive value of S is an indicator of an increasing trend, and a very
low negative value indicates a decreasing trend. However, it is necessary to compute the
probability associated with S and the sample size, n, to statistically quantify the
significance of the trend.
Kendall (1975) describes a normal-approximation test that could be used for
datasets with more than 10 values, provided there are not many tied values within the
data set. The test procedure is as follows:
50
• Calculate S using Equation (3.1)
• Calculate the variance of S, VAR(S), by the following equation:
(3.2)
where n is the number of data points, g is the number of tied groups (a tied
group is a set of sample data having the same value), and tp is the number of
data points in the pth group.
• Compute a normalized test statistic Z as follows:
(3.3)
• Compute the probability associated with this normalized test statistic. The
probability density function for a normal distribution with a mean of 0 and a
standard deviation of 1 is given by the following equation:
(3.4)
• Decide on a probability level of significance (confidence level).
51
• The trend is said to be decreasing if Z is negative and the computed
probability is greater than the level of significance. The trend is said to be
increasing if the Z is positive and the computed probability is greater than the
level of significance. If the computed probability is less than the level of
significance, there is no trend.
3.4.2 Trend Analysis
Trend analysis was applied to data set obtained from the six (6) selected stations. In the
trend analysis, raw data were transfered into the Minitab® Software. The linear trend
model was adopted in this trend analysis.
The linear trend model is given by:
Yt = β0 + β1 t + et
In this model, β represents the average change from one period to the next.
1
Based on the trend line plotted, the fitted linear trend model equation was
determined. The equation demonstrates whether the trend has increased or decreased
over time, and if it has, how quickly or slowly the increase or decrease has occurred.
There after, by making future projection using the equation, an estimate of the SLR rate
in the year 2050 and 2100 was obtained.
CHAPTER 4
DATA ANALYSIS AND RESULTS
4.1
Introduction
This chapter reveals the results obtained from the analysis of historical mean sea
level data at six (6) selected tidal stations along West Coast of Peninsular Malaysia. In
this study, the mean sea level data for Teluk Ewa, Langkawi (1986 -2005), Penang
(1985 – 2008), Lumut (1985 - 2008), Port Klang (1984 – 2008), Tanjung Keling (1985
– 2008), and Kukup (1986 -2005) have been processed and analyzed accordingly using
statistical package in order to provide the trend and rate of SLR.
4.2
Statistical Analysis : Mann – Kendall Test Results
The non parametric Mann-Kendall Test was applied to detect trends and to
assess the significance of the trends in the time series. The test was carried out on Teluk
53
Ewa Langkawi, Penang, Lumut, Port Klang, Tanjung Keling and Kukup MSL data.
Results of the test are discussed in detail in the following sections.
4.2.1 Teluk Ewa, Langkawi, Kedah
Figure 4.1 shows the results of Mann-Kendall Test on Teluk Ewa, Langkawi
MSL data for the year 1986 – 2005. From the test, it has indicated enough evidence to
determine that there is an upward trend at confidence level 95% or alpha,
= 0.05. The
p-value of the significant upward trend is 0.0183718 and the calculated z value is
2.08860.
Figure 4.1: Mann-Kendall Test for Teluk Ewa, Langkawi, Kedah
54
4.2.2 Penang
Figure 4.2 shows the results of Mann-Kendall Test on Penang MSL data for the
year 1985 – 2008. From the test, it has indicated enough evidence to determine that
there is an upward trend at confidence level 95% or alpha,
= 0.05. The p-value of the
significant upward trend is 0.0121493 and the calculated z value is 2.25238.
Figure 4.2: Mann-Kendall Test for Penang
55
4.2.3 Lumut, Perak
Figure 4.3 shows the results of Mann-Kendall Test on Lumut MSL data for the
year 1985 – 2008. From the test, it has indicated enough evidence to determine that
there is an upward trend at confidence level 95% or alpha,
= 0.05. The p-value of the
significant upward trend is 0.00373 and the calculated z value is 2.67481.
Figure 4.3: Mann-Kendall Test for Lumut, Perak
56
4.2.4 Port Klang, Selangor
Figure 4.4 shows the results of Mann-Kendall Test on Port Klang MSL data for
the year 1984 – 2008. From the test, it has indicated enough evidence to determine that
there is an upward trend at confidence level 94% or alpha,
= 0.06. The p-value of the
significant upward trend is 0.0535464 and the calculated z value is 1.61140.
Figure 4.4: Mann-Kendall Test for Port Klang, Selangor
57
4.2.5 Tanjung Keling, Melaka
Figure 4.5 shows the results of Mann-Kendall Test on Tanjung Keling MSL data
for the year 1985 – 2008. From the test, it has indicated enough evidence to determine
that there is an upward trend at confidence level 95% or alpha,
= 0.05. The p-value of
the significant upward trend is 0.0031150 and the calculated z value is 2.73542.
Figure 4.5: Mann-Kendall Test for Tanjung Keling, Melaka
58
4.2.6 Kukup, Johor
Figure 4.6 shows the results of Mann-Kendall Test on Kukup MSL data for the
year 1986 – 2005. From the test, it has indicated enough evidence to determine that
there is an upward trend at confidence level 95% or alpha,
= 0.05. The p-value of the
significant upward trend is 0.0109229 and the calculated z value is 2.29304.
Figure 4.6: Mann-Kendall Test for Kukup, Johor
59
4.2.7 Summary of the Mann – Kendall Test Results
Table 4.1 below, summarized the results of Mann-Kendal Test on the MSL data
at the selected stations. From the test, it has indicated that all the selected stations have a
significant upward trend at confidence level 95% or alpha,
Klang which based on 94% confidence level or
= 0.05 except for Port
= 0.06. The p-value of the significant
upward trend is within 0.0031150 to 0.0535464. While, the calculated z value is
between 1.61140 and 2.73254.
Table 4.1: Summary of the Mann – Kendall Test Results on the MSL
data at the selected stations
Station
Teluk Ewa,
Langkawi
Penang
Lumut
Port Klang
Tg. Keling
Kukup
MSL Tidal
Period
1986 - 2005
z - value
2.08860
p-value
0.0183718
Trend
Confidence
Interval
Upward
95%
@
Alpha,
= 0.05
1985 - 2008
2.25238
0.0121493
Upward
1985 - 2008
2.67481
0.03786
Upward
1984 - 2008
1.61140
0.0535464
Upward
1985 - 2008
2.73254
0.0031150
Upward
1986 - 2005
2.29304
0.0109229
Upward
95%
@
Alpha,
= 0.05
95%
@
Alpha,
= 0.05
94%
@
Alpha,
= 0.06
95%
@
Alpha,
= 0.05
95%
@
Alpha,
= 0.05
60
4.3 Trend Analysis Results
4.3.1 Teluk Ewa, Langkawi
Figure 4.7 illustrates the trend analysis plot of MSL for Teluk Ewa, Langkawi
from 1986 to 2005. The linear trend model is given by the equation, Y(t) = 219.861 +
0.0168335*t. From the generated graph equation, it seconded the upward trend of sea
level rise, with 0.0168335 slope and intercept at 219.861 cm on the y-axis of the graph.
Figure 4.7: Trend analysis of MSL for Teluk Ewa, Langkawi
61
4.3.2 Penang
Figure 4.8 illustrates the trend analysis plot of MSL for Penang from 1985 to
2008. The linear trend model is given by the equation, Y(t) = 268.894 + 0.00690944*t.
From the generated graph equation, it seconded the upward trend of sea level rise, with
0.00690944 slope and intercept at 268.894 cm on the y-axis of the graph.
Trend Analysis of Mean Sea Level for Penang (1985 - 2008)
Linear Trend Model
Y(t) = 268.894 + 0.00690944*t
Variable
Actual
Fits
Mean Sea Level, cm
450
400
350
300
250
1985
1989
1993
1997
Year
2001
2005
Figure 4.8: Trend analysis of MSL for Penang
2008
62
4.3.3 Lumut, Perak
Figure 4.9 illustrates the trend analysis plot of MSL for Lumut from 1985 to
2008. The linear trend model is given by the equation, Y(t) = 219.048 + 0.0100656*t.
From the generated graph equation, it seconded the upward trend of sea level rise, with
0.0100656 slope and intercept at 219.048 cm on the y-axis of the graph.
Trend Analysis of Mean Sea Level for Lumut, Perak (1985 - 2008)
Linear Trend Model
Y(t) = 219.048 + 0.0100656*t
Mean Sea Level, cm
400
Variable
Actual
Fits
350
300
250
200
1985
1987
1989
1991
1993
1995
Year
1997
1999
2001
Figure 4.9: Trend analysis of MSL for Lumut, Perak
2003
63
4.3.4 Port Klang
Figure 4.10 illustrates the trend analysis plot of MSL for Port Klang from 1984
to 2008. The linear trend model is given by the equation, Y(t) = 362.265 +
0.00902294*t. From the generated graph equation, it seconded the upward trend of sea
level rise, with 0.00902294 slope and intercept at 362.265 cm on the y-axis of the
graph.
Trend Analysis of Mean Sea Level for Port Klang, Selangor (1984 - 2008)
Linear Trend Model
Yt = 362.265 + 0.00902294*t
430
Variable
Actual
Fits
Mean Sea Level, cm
420
410
400
390
380
370
360
350
340
1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008
Year
Figure 4.10: Trend analysis of MSL for Port Klang, Selangor
64
4.3.5 Tanjung Keling, Melaka
Figure 4.11 illustrates the trend analysis plot of MSL for Tanjung Keling from
1985 to 2008. The linear trend model is given by the equation, Y(t) = 282.965 +
0.0150072*t. From the generated graph equation, it seconded the upward trend of sea
level rise, with 0.0150072 slope and intercept at 282.965 cm on the y-axis of the graph.
Trend Analysis of Mean Sea Level for Tg. Keling (1985 - 2008)
Linear Trend Model
Y(t) = 282.965 + 0.0150072*t
310
Mean Sea Level, cm
300
Variable
Actual
Fits
290
280
270
260
1985 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008
Year
Figure 4.11: Trend analysis of MSL for Tanjung Keling, Melaka
65
4.3.6 Kukup, Johor
Figure 4.12 illustrates the trend analysis plot of MSL for Kukup from 1986 to
2005. The linear trend model is given by the equation, Y(t) = 398.452 + 0.0123033*t.
From the generated graph equation, it seconded the upward trend of sea level rise, with
0.0123033 slope and intercept at 398.452 cm on the y-axis of the graph.
Trend Analysis of Mean Sea Level for Kukup, Johor (1986 - 2005)
Linear Trend Model
Y(t) = 398.452 + 0.0123033*t
Mean Sea Level, cm
420
Variable
A ctual
F its
410
400
390
380
1989
1992
1995
Year
1998
2001
2005
Figure 4.12: Trend analysis of MSL for Kukup, Johor
4.4
Sea Level Rise (SLR) Prediction
Trend lines of mean sea level for all the selected stations are extrapolated for an
estimate SLR in the year 2050 and 2100. The following sub-chapters show the plot of
predicted MSL for all the selected locations.
66
4.4.1 Teluk Ewa, Langkawi
Figure 4.13 shows the actual, fits and predicted/forecasts graph line for Teluk
Ewa MSL data. The trend is seconded by the following linear trend model equation;
Y(t) = 219.861 + 0.0168335*t
The red line indicates the fitted rate of mean sea level from the historical actual
data, while the green line indicates the future projection of mean sea level. The graph
shows that in year 2050, Teluk Ewa’s mean sea level is increase to 232.773 cm and in
year 2100 is increase to 243.193 cm.
Predicted MSL for Teluk Ewa, Langkawi
Linear Trend Model
Y(t) = 219.861 + 0.0168335* t
300
Variable
A ctual
F its
F orecasts
Mean Sea Level, cm
280
260
2100
240
220
2050
200
1985 1995 2005 2015 2025 2035 2045 2055 2065 2075 2085 2095
Year
Figure 4.13: Graph of predicted MSL for Teluk Ewa, Langkawi in year 2050 and 2100
67
Meanwhile, Figure 4.14 indicates the four-in-one residual plot (i.e. normal
probability plot of residuals, histogram of residuals, residuals versus fitted values and
residuals versus order of the data) for Teluk Ewa, Langkawi. Overall, the figure shows
that the data are generally normal distributed, the variance is constant and only one
outlier exist in the data. Even though there was an outlier existing in the data set, the
result may still be considered reliable because the non parametric Mann-Kendall test is
robust to the effects of outliers and gross data errors, and within the 95% confidence
level.
Residual Plots for Teluk Ewa, Langkawi
Normal Probability Plot of the Residuals
Residuals Versus the Fitted Values
99
60
90
40
Residual
Percent
99.9
50
10
-50
0
Residual
50
220
Histogram of t he Residuals
221
222
Fitted Value
223
60
Residual
36
24
12
40
20
0
-20
-30
-15
0
15
30
Residual
45
60
224
Residuals Versus the Order of the Data
48
Frequency
0
-20
1
0.1
0
20
1
20
40
60
80
100 120 140 160 180 200 220
Observation Order
Figure 4.14: Residual plots for Teluk Ewa, Langkawi
68
4.4.2 Penang
Figure 4.15 shows the actual, fits and predicted/forecasts graph line for Penang
MSL data. The trend is seconded by the following linear trend model equation;
Y(t) = 268.894 + 0.00690944*t
The red line indicates the fitted rate of mean sea level from the historical actual
data, while the green line indicates the future projection of mean sea level. The graph
shows that in year 2050, Penang’s mean sea level is increase to 274.760 cm and in year
2100 is increase to 279.161 cm.
Predicted MSL for Penang
Linear Trend Model
Y(t) = 268.894 + 0.00690944* t
Variab le
A ctual
F its
F orec asts
Mean Sea Level, cm
450
400
350
300
2100
250
2050
1985 1995 2005 2015 2025 2035 2045 2055 2065 2075 2085 2095
Year
Figure 4.15: Graph of predicted MSL for Penang in year 2050 and 2100
69
Meanwhile, Figure 4.16 indicates the four-in-one residual plot (i.e. normal
probability plot of residuals, histogram of residuals, residuals versus fitted values and
residuals versus order of the data) for Penang. Overall, the figure shows that the data are
generally normal distributed, the variance is constant and only four outliers exist in the
data. Even though there were outliers existing in the data set, the result may still be
considered reliable because the non parametric Mann-Kendall test is robust to the
effects of outliers and gross data errors, and within the 95% confidence level.
Residual Plots for Penang
Normal Probability Plot of the Residuals
Residuals Versus the Fitted Values
99.9
200
150
90
Residual
Percent
99
50
10
0
100
Residual
200
269.0
Histogram of the Residuals
269.5
270.0
Fitted Value
270.5
271.0
Residuals Versus the Order of t he Dat a
200
100
150
75
Residual
Frequency
50
0
1
0.1
50
25
0
100
100
50
0
-30
0
30
60
90
Residual
120
150
180
1 2 0 4 0 6 0 8 0 0 0 2 0 4 0 6 0 8 0 0 0 2 0 40 60 80
1 1 1 1 1 2 2 2 2 2
Observation Order
Figure 4.16: Residual plots for Penang
70
4.4.3 Lumut, Perak
Figure 4.17 shows the actual, fits and predicted/forecasts graph line for Lumut
MSL data. The trend is seconded by the following linear trend model equation;
Y(t) = 219.048 + 0.0100656*t
The red line indicates the fitted rate of mean sea level from the historical actual data,
while the green line indicates the future projection of mean sea level. The graph shows
that in year 2050, Lumut’s mean sea level is increase to 227.684 cm and in year 2100 is
increase to 233.905 cm.
Predicted MSL for Lumut, Perak
Linear Trend Model
Y(t) = 219.048 + 0.0100656*t
Mean Sea Level, cm
400
Variable
A ctual
F its
F o recasts
350
300
250
200
2100
2050
1985 1995 2005 2015 2025 2035 2045 2055 2065 2075 2085 2095
Year
Figure 4.17: Graph of predicted MSL for Lumut in year 2050 and 2100
71
Meanwhile, Figure 4.18 indicates the four-in-one residual plot (i.e. normal
probability plot of residuals, histogram of residuals, residuals versus fitted values and
residuals versus order of the data) for Lumut. Overall, the figure shows that the data are
generally normal distributed, the variance is constant and only three outliers exist in the
data. Even though there were outliers existing in the data set, the result may still be
considered reliable because the non parametric Mann-Kendall test is robust to the
effects of outliers and gross data errors, and within the 95% confidence level.
Residual Plots for Lumut
Normal Probability Plot of the Residuals
Residuals Versus t he Fitted Values
200
99.9
150
90
Residual
Percent
99
50
10
50
0
1
0.1
100
-50
0
50
100
Residual
150
219
Histogram of the Residuals
220
221
Fitted Value
222
Residuals Versus the Order of the Data
200
150
Residual
Frequency
100
75
50
25
0
100
50
0
-30
0
30
60
90
Residual
120
150
180
1 20 4 0 6 0 8 0 00 2 0 4 0 6 0 80 0 0 2 0 4 0 60 8 0
1 1 1 1 1 2 2 2 2 2
Observation Or der
Figure 4.18: Residual plots for Lumut
72
4.4.4 Port Klang, Selangor
Figure 4.19 shows the actual, fits and predicted/forecasts graph line for Port
Klang MSL data. The trend is seconded by the following linear trend model equation;
Y(t) = 362.265 + 0.00902294*t
The red line indicates the fitted rate of mean sea level from the historical actual data,
while the green line indicates the future projection of mean sea level. The graph shows
that in year 2050, Port Klang’s mean sea level is increase to 369.242 cm and in year
2100 is increase to 374.538 cm.
Predicted MSL for Port Klang, Selangor
Linear Trend Model
Y(t) = 362.265 + 0.00902294*t
430
Variable
A ctual
F its
F o recasts
Mean Sea Level, cm
420
410
400
390
380
2100
370
2050
360
350
340
1985 1995 2005 2015 2025 2035 2045 2055 2065 2075 2085 2095
Year
Figure 4.19: Graph of predicted MSL for Port Klang in year 2050 and 2100
73
Meanwhile, Figure 4.20 indicates the four-in-one residual plot (i.e. normal
probability plot of residuals, histogram of residuals, residuals versus fitted values and
residuals versus order of the data) for Port Klang. Overall, the figure shows that the data
are generally normal distributed, the variance is constant and only one outlier exist in
the data. Even though there was an outlier existing in the data set, the result may still be
considered reliable because the non parametric Mann-Kendall test is robust to the
effects of outliers and gross data errors, and within the 94% confidence level.
Residual Plots for Port Klang, Selangor
Normal Probability Plot of the Residuals
Residuals Versus the Fitted Values
99.9
60
40
90
Residual
Percent
99
50
10
1
0.1
-20
0
20
Residual
40
60
362
363
364
Fitted Value
365
Residuals Versus the Order of the Data
60
60
45
Residual
Frequency
0
-20
Histogram of t he Residuals
30
15
0
20
40
20
0
-20
-15
0
15
30
Residual
45
60
1 2 0 4 0 60 80 0 0 2 0 40 60 8 0 0 0 2 0 40 60 8 0 0 0
1 1 1 1 1 2 2 2 2 2 3
Observation Order
Figure 4.20: Residual plots for Port Klang
74
4.4.5 Tanjung Keling, Melaka
Figure 4.21 shows the actual, fits and predicted/forecasts graph line for Tanjung
Keling MSL data. The trend is seconded by the following linear trend model equation;
Y(t) = 282.965 + 0.0150072*t
The red line indicates the fitted rate of mean sea level from the historical actual data,
while the green line indicates the future projection of mean sea level. The graph shows
that in year 2050 and 2100, Tanjung Keling’s mean sea level will increase to 295.836
cm and 305.116 cm respectively.
Predicted MSL for Tanjung Keling, Melaka
Linear Trend Model
Y(t) = 282.965 + 0.0150072*t
310
2100
Mean Sea Level, cm
300
290
Variable
A ctual
F its
F orecasts
2050
280
270
260
1985 1995 2005 2015 2025 2035 2045 2055 2065 2075 2085 2095
Year
Figure 4.21: Graph of predicted MSL for Tanjung Keling in year 2050 and 2100
75
Meanwhile, Figure 4.22 indicates the four-in-one residual plot (i.e. normal
probability plot of residuals, histogram of residuals, residuals versus fitted values and
residuals versus order of the data) for Tanjung Keling. Overall, the figure shows that the
data are normally distributed, the variance is constant and no outlier exist in the data.
Residual Plots for Tanjung Keling, Melaka
Normal Probability Plot of the Residuals
Residuals Versus the Fitted Values
99.9
20
10
90
Residual
Percent
99
50
10
-10
-20
1
0.1
-20
-10
0
Residual
10
20
283
Histogram of t he Residuals
284
285
286
Fitted Value
287
Residuals Versus the Order of the Data
20
40
30
Residual
Frequency
0
20
10
10
0
-10
-20
0
-24
-18
-12
-6
0
Residual
6
12
18
1 2 0 4 0 60 8 0 0 0 20 4 0 60 8 0 0 0 20 4 0 6 0 80
1 1 1 1 1 2 2 2 2 2
Observation Order
Figure 4.22: Residual plot for Tanjung Keling
76
4.4.6 Kukup
Figure 4.23 shows the actual, fits and predicted/forecasts graph line for Kukup
MSL data. The trend is seconded by the following linear trend model equation;
Y(t) = 398.452 + 0.0123033*t
The red line indicates the fitted rate of mean sea level from the historical actual
data, while the green line indicates the future projection of mean sea level. The graph
shows that in year 2050 and 2100, Kukup’s mean sea level will increase to 408.405 cm
and 416.058 cm respectively.
Predicted MSL for Kukup, Johor
Linear Trend Model
Y(t) = 398.452 + 0.0123033*t
420
Mean Sea Level, cm
2100
Variab le
A ctual
F its
F orecasts
410
2050
400
390
380
1985 1995 2005 2015 2025 2035 2045 2055 2065 2075 2085 2095
Year
Figure 4.23: Graph of predicted MSL for Kukup in year 2050 and 2100
77
Meanwhile, Figure 4.24 indicates the four-in-one residual plot (i.e. normal
probability plot of residuals, histogram of residuals, residuals versus fitted values and
residuals versus order of the data) for Kukup. Overall, the figure shows that the data are
normally distributed, the variance is constant and no outlier exist in the data.
Residual Plots for Kukup
Normal Probability Plot of the Residuals
Residuals Versus the Fitted Values
99.9
20
90
Residual
Percent
99
50
10
-20
-10
0
Residual
10
20
399
Histogram of the Residuals
401
20
45
Residual
Frequency
400
Fitted Value
Residuals Versus the Order of the Data
60
30
15
0
0
-10
1
0.1
10
10
0
-10
-15
-10
-5
0
5
Residual
10
15
20
1
20
40
60
80
100 120 140 160 180 200 220
Observation Order
Figure 4.24: Residual plots for Kukup
78
4.5
Summary of the Mean Sea Level Trend Analysis
From the statistical analysis and trend analysis, the result demonstrates that all
the selected stations along the West Coast of Peninsular Malaysia (i.e: Teluk Ewa
Langkawi, Penang, Lumut, Port Klang, Tanjung Keling Melaka, and Kukup Johor) have
an upward trend of sea level rise. The slope ranges are from 0.00690944 to 0.0168335
with Teluk Ewa having the highest slope and Penang with the least slope. The results of
the analysis are summarized as in Table 4.2.
Table 4.2: Summary of the results
Station
Teluk Ewa,
Langkawi
(1986 - 2005)
Penang
(1985 - 2008)
Lumut
(1985 - 2008)
Port Klang
(1984 - 2008)
Tg. Keling
(1985 - 2008)
Kukup
(1986 - 2005)
Linear Trend
Model
Equation
Intercept
Coefficient/Slope
Y(t) = 219.861
+ 0.0168335*t
219.861
0.0168335
Y(t) = 268.894
+ 0.00690944*t
268.894
0.00690944
Y(t) = 219.048
+ 0.0100656*t
Y(t) = 362.265
+ 0.00902294*t
Y(t) = 282.965
+ 0.0150072*t
Y(t) = 398.452
+ 0.0123033*t
219.048
362.265
282.965
398.452
0.0100656
0.00902294
0.0150072
0.0123033
Confidence
Level
95%
@
Alpha,
= 0.05
95%
@
Alpha,
= 0.05
95%
@
Alpha,
= 0.05
94%
@
Alpha,
= 0.06
95%
@
Alpha,
= 0.05
95%
@
Alpha,
= 0.05
Trend
Upward
Upward
Upward
Upward
Upward
Upward
79
According to Table 4.3 below, the rate of SLR lies between 0.829 mm/yr to
2.021 mm/yr. The highest rate of SLR is at Teluk Ewa, Langkawi and the lowest is at
Penang. This value is still within the IPCC global SLR rate for the 20th century which is
1.7 ± 0.5 mm/yr. The future projections of the trend line for an estimate SLR in the year
2050 and 2100, for all the selected stations exhibit an increment in sea level rise. In
2050, the highest increment in SLR is 9.175 cm which is at Teluk Ewa, Langkawi while
the lowest increment is 3.994 cm, which is at Penang. Subsequently, in 2100 the highest
increment in SLR is 19.595 cm while the lowest increment is 8.395 cm at Teluk Ewa,
Langkawi and Penang respectively.
Table 4.3: Predicted sea level rise (SLR) in year 2050 and 2100 for all
Stations on West Coast of Peninsular Malaysia
Station
Rate of
SLR
(mm/yr)
MSL
2005
(cm)
Incremental SLR
(cm)
Predicted SLR
(cm)
2050
2100
2050
2100
Teluk Ewa,
Langkawi
2.021
223.598
9.175
19.595
232.773
243.193
Penang
0.829
270.766
3.994
8.395
274.760
279.161
Lumut
1.207
221.826
5.758
12.079
227.584
233.905
Port Klang
0.996
364.719
4.523
9.817
369.242
374.536
Tg.Keling
1.801
287.302
8.534
17.814
295.836
305.116
Kukup
1.469
401.158
7.247
14.900
408.405
416.058
80
Incremental SLR along Straits of Malacca
in the year 2050 and 2100
25.00
2050
Incremental SLR (cm)
2100
20.00
19.60
17.81
15.00
14.90
12.08
10.00
9.18
9.82
8.40
5.76
5.00
Penang
Lumut
7.25
4.52
3.99
0.00
Teluk Ewa
8.53
Port Klang
Tg. Keling
Kukup
Location
Figure 4.25: Incremental SLR along Straits of Malacca
Graph in Figure 4.25 was produced from the results gained in Table 4.2. The
graph illustrates the incremental SLR along Straits of Malacca in the year 2050 and
2100. The SLR trend formed a sinusoidal pattern along the Straits of Malacca, probably
due to the physical conditions and geological features at different locations along the
West Coast of Peninsular Malaysia. However, these parameters have not been
considered in this study.
CHAPTER 5
CONCLUSION AND RECOMMENDATION
5.1
Introduction
The purpose of this study is to analyze the trend variation of sea level rise for
selected locations along the West Coast of Peninsular Malaysia. Furthermore, rate of
future SLR at those selected stations will be predicted in the year 2050 and 2100.
This study also examines the trend of sea level rise throughout the Straits of
Malacca. In this study, the statistical analysis (Mann-Kendall Test) and trend
analysis were carried out using statistical package in order to determine trends in sea
level rise.
5.2
Conclusion
From the analysis, the result shows that all the selected stations along the
West Coast of Peninsular Malaysia (i.e: Teluk Ewa Langkawi, Penang, Lumut, Port
Klang, Tanjung Keling Melaka, and Kukup Johor) have an upward trend of sea level
rise based on 95% Confidence Level except for Port Klang (94%). The rate of SLR
82
lies between 0.829 mm/yr to 2.021 mm/yr. The highest rate of SLR is at Teluk Ewa,
Langkawi and the lowest is at Penang. This value is still within the IPCC global
SLR rate for the 20th century which is 1.7 ± 0.5 mm/yr.
The future projections of the trend line for an estimate SLR in the year 2050
and 2100, for all the selected stations exhibit an increment in sea level rise. In 2050,
the highest increment in SLR is 9.175 cm which is at Teluk Ewa, Langkawi while
the lowest increment is 3.994 cm, which is at Penang. Subsequently, in 2100 the
highest increment in SLR is 19.595 cm while the lowest increment is 8.395 cm at
Teluk Ewa, Langkawi and Penang respectively.
The trend analysis and the future projection also prove that the Straits of
Malacca will experience a rise in sea level between 4.0 to 9.8 cm in year 2050 and
between 8.4 to 19.6 cm in 2100. The SLR trend formed a sinusoidal pattern along
the Straits of Malacca, probably due to the physical conditions and geological
features at different locations along the West Coast of Peninsular Malaysia.
However, these parameters have not been considered in this study.
As a conclusion, the results of this study impose a signal of SLR threat to the
West Coast of Peninsular Malaysia. It would lead the Government to come out with
a National Plan on adaptive measures to mitigate the SLR impacts especially in
areas with high rate of predicted SLR (i.e at Langkawi, Tanjung Keling and Kukup).
Hence, the consequences of SLR can be reduced.
83
5.3
Recommendation
Due to the constraint of time, costing factor and limited sources, this study still
has a room for improvement. In order to enhance and improve this study in the
future, it is suggested that:
i. A longer data length or time period should be used in the trend analysis of
sea level rise. The longer time period will provide more information and
therefore, the results are more precise. The less number of time periods
available, the smaller the sample size and as usual, increase the potential for
error.
ii. Number of sampling stations can be increased in order to get a more accurate
and convincing results.
iii. Study area should be extended to the East Coast of Peninsular Malaysia,
Sabah and Sarawak. Therefore, the actual picture of SLR threat towards
Malaysian coastal system can be obtained.
iv. Scope of study need to be widened. Future study can consider other
parameters that caused the changes of sea level such as the local ground
conditions such as the rise or fall of land due to plate movements or the
elastic rebound of plates from ice melts (tectonic change) as well as the local
rate of sedimentation in the coastal area.
REFERENCES
Algirdas
Budrevicius
(2004).
Quantitative Forecasting
Methods
in
Library
Management, Faculty of Communication, Vilnius University.
Bates, B.C., Z.W. Kundzewicz, S. Wu and J.P. Palutikof, Eds. (2008). Climate Change
and Water. Technical Paper of the Intergovernmental Panel on Climate Change,
IPCC Secretariat, Geneva.
Bindoff, N.L., J. Willebrand, V. Artale, A, Cazenave, J. Gregory, S. Gulev, K. Hanawa,
C. Le Quéré, S. Levitus, Y. Nojiri, C.K. Shum, L.D.Talley and A. Unnikrishnan
(2007). Observations: Oceanic Climate Change and Sea Level In: Climate
Change 2007: The Physical Science Basis. Contribution of Working Group I to
the Fourth Assessment Report of the Intergovernmental Panel on Climate
Change.
Burkay Seseogullari, Ebru Eris and Ercan Kahya (2007). Trend Analysis of Sea Levels
Along Turkish Coast. Hydraulic Division, Civil Engineering Department,
Istanbul Technical University.
Department of Irrigation and Drainage Malaysia, Ministry of Natural Resources and
Environment (2007). National Coastal Vulnerability Index Study – Phase 1
(Final Report).
85
Douglas C. Montgomery, George C. Runger and Norma Faris Hubele (2004).
Engineering Statistic, Third Edition. Arizona State University. John Wiley and
Sons Inc.
Gilbert, R.O. (1987). Statistical Methods for Environmental Pollution Monitoring. Van
Nostrand Reinhold, New York.
Hadikusumah (1995). Study on Sea Level Rise in The Western Indonesia. International
Journal, Research and Development Center for Oceanology, Indonesian Institute
of Science, Jakarta, Indonesia.
Harry Storch, Nigel Downes, Nguyen Xuan Thinh, Hans-Peter Thamm, Ho Long Phi,
Tran Thuc, Nguyen Thi Hien Thuan, Guenter Emberger, Manfred Goedecke,
Joern Welsch, and Michael Schmidt (2009). Adaptation Planning Framework to
Climate Change for Urban Area of Ho Chi Minh, Vietnam, Fifth Urban
Research Symposium 2009.
Hulme, M. and Sheard, N. (1999) Climate Change Scenarios for Indonesia. Climatic
Research Unit, Norwich, UK, 6pp.
Hülya Boyacioglu and Hayal Boyacioglu (2006). Water Quality Evaluation And Trend
Analysis In Buyuk Menderes Basin, Turkey. Faculty of Engineering, Department
of Environmental Engineering, Dokuz Eylul University.
IPCC WGI Fourth Assessment Report (2007). Climate Change 2007: The Physical
Science Basis Summary for Policymakers.
James G. Titus et.al (1996). The Risk of Sea Level Rise. U.S Environmental Protection
Agency.
86
James G. Titus (1998) Rising Seas, Coastal Erosion, And The Takings Clause: How To
Save Wetlands And Beaches Without Hurting Property Owners. Volume 57,
Number 4, Maryland Law Review.
J. E. Ong (2000). Vulnerability of Malaysia To Sea-Level Change. Centre for Marine
and Coastal Studies, Universiti Sains Malaysia, Penang, Malaysia.
Kendall, M.G. (1975). Rank Correlation Methods, Fourth Edition, Charles Griffin,
London.
Meehl, G.A., T.F. Stocker, W.D. Collins, P. Friedlingstein, A.T. Gaye, J.M. Gregory,
A. Kitoh, R. Knutti, J.M. Murphy, A. Noda, S.C.B. Raper, I.G. Watterson, A.J.
Weaver and Z.-C. Zhao (2007): Global Climate Projections. In: Climate Change
2007: The Physical Science Basis. Contribution of Working Group I to the
Fourth Assessment Report of the Intergovernmental Panel on Climate Change.
Report of the Coastal Zone Management Subgroup (1990). Strategies For Adaption To
Sea Level Rise. Intergovernmental Panel on Climate Change Response
Strategies Working Group.
Susmita Dasgupta, Benoit Laplante, Craig Meisner, David Wheeler and Jian Yan
(2007). The impact of Sea Level Rise on Developing Countries; a Comparative
Analysis. New World Bank Working Paper.
UN Environment Programme (UNEP) Year Book, 2008.
UN-HABITAT’s new State of the World’s Cities Report 2008/9: Harmonious Cities.
UN-HABITAT Global Urban Observatory, 2008.
87
W. Chingombe, J.E.Gutierrez, E. Pedzisai and E. Siziba (2006). A Study Of
Hydrological Trends And Variability Of Upper Mazowe Catchment-Zimbabwe.
William Mendenhall et.al (2003). Probability and Statistics. Thomson Learning, USA.
Yoshiki Saito, Niran Chaimanee, Thanawat, Jarupongsakul and James P.M. Syvitski
(2007). Shrinking Megadeltas in Asia: Sea-level Rise and Sediment Reduction
Impacts from Case Study of the Chao Phraya Delta. Geological Survey of
Japan, AIST.
Zainal Akamar Bin Harun (2008). Paper Presenter for Coastal Vulnerability Ministry of
Natural Resources and Environment (Nre), Second National Conference on
Extreme Weather and Climate Change: Understanding Science and Risk
Reduction.
APPENDIX A
Monthly MSL Data for Teluk Ewa, Langkawi (1986 – 2005)
Year
1986
1987
1988
Month
January
February
March
April
May
June
July
August
September
October
November
December
January
February
March
April
May
June
July
August
September
October
November
December
January
February
March
April
May
June
July
August
September
October
November
December
MSL (cm)
204.7
200.5
204.8
218.8
231.3
226.4
229.8
229.3
221.2
223.7
226.7
201.3
196.1
206.6
220.8
228.8
290.8
221.7
213.9
220.7
237.5
226
212.
212.4
210.7
221.2
234
233.6
223.8
224.2
222.6
234.1
236.5
220.3
211.6
206.7
89
Year
1989
1990
1991
Month
January
February
March
April
May
June
July
August
September
October
November
December
January
February
March
April
May
June
July
August
September
October
November
December
January
February
March
April
May
June
July
August
September
October
November
December
MSL (cm)
212.5
219.7
234.2
233.2
233.2
226.1
222.6
225.5
228.7
206.6
198.6
198.6
202.6
222.1
229
234.2
220.7
231.9
223.3
224.7
226.9
211.3
204.1
206.4
206.4
219.1
223.7
227.7
223.9
222.1
223.7
226.6
223.7
211.5
201.4
198.5
90
Year
1992
1993
1994
Month
January
February
March
April
May
June
July
August
September
October
November
December
January
February
March
April
May
June
July
August
September
October
November
December
January
February
March
April
May
June
July
August
September
October
November
December
MSL (cm)
204.4
213.6
229.2
237.7
230.1
231.3
225.9
236.6
224.9
217.2
205.3
201.1
207.2
218.7
224.8
229.6
227.8
231.3
223.6
228.4
229.4
225.4
215
212
213.5
218.9
221
224.7
225.7
219.6
218.7
215.9
207.2
210.1
198.8
203.8
91
Year
1995
1996
1997
Month
January
February
March
April
May
June
July
August
September
October
November
December
January
February
March
April
May
June
July
August
September
October
November
December
January
February
March
April
May
June
July
August
September
October
November
December
MSL (cm)
213.5
224.3
230.4
232.1
233.7
230
224.9
230.4
238.2
218.5
206.6
205.8
169.409
166.135
170.538
180.361
188.387
190.625
188.145
185.941
186.264
187.863
185.611
178.454
169.597
166.012
170.793
180.389
188.468
190.833
188.199
185.78
186.403
187.782
185.694
178.199
92
Year
1998
1999
2001
Month
January
February
March
April
May
June
July
August
September
October
November
December
January
February
March
April
May
June
July
August
September
October
November
December
January
February
March
April
May
June
July
August
September
October
November
December
MSL (cm)
169.798
166.146
171.062
180.486
188.454
190.597
188.347
185.591
186.319
188.038
185.917
178.616
169.503
166.205
170.444
180.056
188.239
190.458
188.105
185.968
186.625
188.132
185.861
178.79
216.386
216.111
223.794
230.954
232.675
230.944
233.042
233.651
223.661
230.109
230.165
226.116
93
Year
2001
2002
2003
Month
January
February
March
April
May
June
July
August
September
October
November
December
January
February
March
April
May
June
July
August
September
October
November
December
January
February
March
April
May
June
July
August
September
October
November
December
MSL (cm)
169.785
166.28
170.954
180.472
188.669
190.847
188.145
185.497
186.333
187.769
185.847
178.038
169.53
166.369
170.86
180.403
188.374
190.583
188.28
185.632
186.389
187.957
185.889
178.495
169.435
166.399
170.417
180.208
188.266
190.375
188.065
186.116
186.431
187.997
185.917
178.763
94
Year
2004
2005
2006
Month
January
February
March
April
May
June
July
August
September
October
November
December
January
February
March
April
May
June
July
August
September
October
November
December
January
February
March
April
May
June
July
August
September
October
November
December
MSL (cm)
169.718
166.034
170.659
180.556
188.616
190.625
187.984
185.82
186.681
187.997
185.194
178.239
169.785
166.458
170.995
180.278
188.562
190.792
188.212
185.376
186.097
187.903
185.792
177.93
169.53
166.28
170.874
180.431
188.253
190.514
188.293
185.887
186.167
187.944
186.236
178.495
95
APPENDIX B
Monthly MSL Data for Penang (1985 – 2008)
Year
1985
1986
1987
Month
January
February
March
April
May
June
July
August
September
October
November
December
January
February
March
April
May
June
July
August
September
October
November
December
January
February
March
April
May
June
July
August
September
October
November
December
MSL (cm)
254.971
258.352
259.296
264.081
273.851
287.013
301.088
268.902
267.125
274.683
275.01
264.461
258.75
247.348
252.492
265.04
276.25
271.444
275.483
274.087
267.715
270.285
273.472
250.075
245.064
247.899
245.034
252.413
266.747
273.496
276.069
266.773
259.842
266.468
284.162
274.347
96
Year
1988
1989
1990
Month
January
February
March
April
May
June
July
August
September
October
November
December
January
February
March
April
May
June
July
August
September
October
November
December
January
February
March
April
May
June
July
August
September
October
November
December
MSL (cm)
260.733
260.069
257.421
266.985
279.55
279.187
267.12
268.99
267.735
281.401
284.272
269.297
260.461
254.71
259.211
266.256
280.393
279.619
278.046
272.215
267.757
271.797
276.275
255.88
252.128
246.293
250.258
270.314
274.64
280.037
270.802
277.767
269.81
269.747
273.054
259.558
97
Year
1991
1992
1993
Month
January
February
March
April
May
June
July
August
September
October
November
December
January
February
March
April
May
June
July
August
September
October
November
December
January
February
March
April
May
June
July
August
September
October
November
December
MSL (cm)
252.359
253.391
254.027
265.845
269.329
275.811
268.245
265.973
269.444
462.388
270.39
451.289
249.629
247.223
251.513
261.358
276.636
283.174
274.496
277.48
272.05
337.079
269.917
264.148
248.746
249.031
254.75
266.646
270.681
275.039
274.394
276.64
269.367
273.616
277.71
264.989
98
Year
1994
1995
1996
Month
January
February
March
April
May
June
July
August
September
October
November
December
January
February
March
April
May
June
July
August
September
October
November
December
January
February
March
April
May
June
July
August
September
October
November
December
MSL (cm)
261.788
258.452
260.229
266.149
266.872
270.6
269.797
263.336
264.03
260.294
254.415
257.093
246.251
253.24
260.32
271.125
276.843
278.115
276.921
275.171
270.614
276.597
286.224
264.933
254.948
254.116
253.017
270.843
278.948
269.975
277.281
272.835
277.282
283.539
283.092
272.168
99
Year
1997
1998
1999
Month
January
February
March
April
May
June
July
August
September
October
November
December
January
February
March
April
May
June
July
August
September
October
November
December
January
February
March
April
May
June
July
August
September
October
November
December
MSL (cm)
256.621
260.058
258.813
253.851
277.956
266.56
258.738
267.22
260.004
251.282
245.317
239.446
245.241
246.627
252.627
261.348
275.18
283.396
276.035
277.105
276.253
281.413
288.015
274.806
271.351
263.402
265.147
281.721
275.833
270.489
273.102
269.729
268.247
280.89
283.744
273.638
100
Year
2000
2001
2002
Month
January
February
March
April
May
June
July
August
September
October
November
December
January
February
March
April
May
June
July
August
September
October
November
December
January
February
March
April
May
June
July
August
September
October
November
December
MSL (cm)
263.113
265.126
271.075
278.26
279.308
278.128
277.761
280.157
268.751
278.565
278.574
272.704
270.46
264.885
266.208
270.14
281.027
278.511
278.091
278.964
271.613
281.214
280.376
264.864
241.991
253.665
253.231
262.974
281.47
277.882
280.333
272.523
265.254
269.75
267.114
267.259
101
Year
2002
2003
2004
Month
January
February
March
April
May
June
July
August
September
October
November
December
January
February
March
April
May
June
July
August
September
October
November
December
January
February
March
April
May
June
July
August
September
October
November
December
MSL (cm)
241.991
253.665
253.231
262.974
281.47
277.882
280.333
272.523
265.254
269.75
267.114
267.259
257.493
255.766
258.474
263.999
283.278
267.061
265.376
277.094
273.678
280.066
279.306
268.801
251.005
250.504
263.383
266.989
287.235
283.819
274.542
271.344
268.932
267.581
267.322
261.031
102
Year
2005
2006
2007
Month
January
February
March
April
May
June
July
August
September
October
November
December
January
February
March
April
May
June
July
August
September
October
November
December
January
February
March
April
May
June
July
August
September
October
November
December
MSL (cm)
257.727
250.414
248.017
267.446
276.426
278.646
274.832
268.589
274.728
274.414
282.8
273.692
262.788
253.824
260.344
270.315
274.997
270.81
277.177
266.136
264.582
265.586
256.593
251.86
257
253.173
254.2
267.857
276.917
271.322
272.519
274.61
269.982
273.293
283.319
272.919
103
Year
2008
Month
January
February
March
April
May
June
July
August
September
October
November
December
MSL (cm)
266.461
264.261
275.491
279.507
278.176
268.096
337.387
276.984
279.295
280.699
277.017
277.558
104
APPENDIX C
Monthly MSL Data for Lumut (1985 – 2008)
Year
1985
1986
1987
Month
January
February
March
April
May
June
July
August
September
October
November
December
January
February
March
April
May
June
July
August
September
October
November
December
January
February
March
April
May
June
July
August
September
October
November
December
MSL (cm)
208.519
210.888
211.871
216.307
224.152
231.525
227.259
219.663
221.903
227.992
228.269
218.172
207.997
201.707
204.542
215.663
224.495
220.971
225.007
226.266
217.981
221.130
225.579
204.409
198.677
200.493
197.917
202.988
216.343
222.149
217.685
216.243
212.151
216.503
234.496
228.140
105
Year
1988
1989
1990
Month
January
February
March
April
May
June
July
August
September
October
November
December
January
February
March
April
May
June
July
August
September
October
November
December
January
February
March
April
May
June
July
August
September
October
November
December
MSL (cm)
214.238
211.635
209.622
218.015
230.111
228.862
218.453
219.082
217.974
231.769
236.140
223.105
213.284
207.680
211.001
217.815
242.515
230.565
227.722
222.401
218.450
222.848
399.101
209.559
205.094
198.360
199.892
218.067
225.367
229.835
217.638
227.679
219.950
221.516
225.811
214.539
106
Year
1991
1992
1993
Month
January
February
March
April
May
June
July
August
September
October
November
December
January
February
March
April
May
June
July
August
September
October
November
December
January
February
March
April
May
June
July
August
September
October
November
December
MSL (cm)
205.687
206.307
205.617
216.684
219.239
224.935
217.518
269.114
219.753
222.590
223.310
214.025
203.426
198.392
202.501
210.744
225.765
231.963
224.470
225.828
223.019
233.829
224.593
219.293
207.922
201.749
204.706
216.025
220.764
225.042
224.160
227.114
219.351
225.139
228.197
226.768
107
Year
1994
1995
1996
Month
January
February
March
April
May
June
July
August
September
October
November
December
January
February
March
April
May
June
July
August
September
October
November
December
January
February
March
April
May
June
July
August
September
October
November
December
MSL (cm)
217.128
212.367
210.925
216.719
217.555
220.885
218.845
213.407
215.075
213.400
207.171
220.474
201.102
204.943
212.461
222.058
226.590
228.692
227.565
226.871
222.106
228.058
238.258
222.700
209.304
206.845
204.454
218.956
230.664
220.664
225.832
224.371
228.299
233.175
236.235
228.345
108
Year
1997
1998
1999
Month
January
February
March
April
May
June
July
August
September
October
November
December
January
February
March
April
May
June
July
August
September
October
November
December
January
February
March
April
May
June
July
August
September
October
November
December
MSL (cm)
211.577
211.716
210.832
204.490
226.023
218.645
207.027
216.878
211.374
202.942
198.629
190.992
197.667
199.378
204.156
211.008
222.222
291.923
225.879
226.890
224.545
233.262
239.088
227.690
223.935
217.865
216.881
231.207
226.979
221.393
222.027
219.862
218.349
229.936
236.124
228.970
109
Year
2000
2001
2002
Month
January
February
March
April
May
June
July
August
September
October
November
December
January
February
March
April
May
June
July
August
September
October
November
December
January
February
March
April
May
June
July
August
September
October
November
December
MSL (cm)
217.496
215.904
221.989
228.215
229.560
226.378
228.638
228.336
218.810
228.083
229.875
225.734
221.528
217.649
217.574
219.365
229.728
226.647
226.391
228.999
221.626
232.038
229.800
222.866
202.048
204.602
205.207
212.629
229.333
227.011
229.511
223.918
216.718
219.637
217.828
218.945
110
Year
2003
2004
2005
Month
January
February
March
April
May
June
July
August
September
October
November
December
January
February
March
April
May
June
July
August
September
October
November
December
January
February
March
April
May
June
July
August
September
October
November
December
MSL (cm)
212.309
208.021
208.165
213.679
221.861
212.566
213.625
226.698
223.914
231.156
231.311
224.148
204.467
195.254
214.530
217.044
238.694
234.525
218.199
221.500
219.070
219.114
221.082
215.751
211.176
203.519
199.468
217.469
226.216
229.072
225.672
220.130
219.728
226.351
236.337
229.827
111
Year
2006
2007
2008
Month
January
February
March
April
May
June
July
August
September
October
November
December
January
February
March
April
May
June
July
August
September
October
November
December
January
February
March
April
May
June
July
August
September
October
November
December
MSL (cm)
219.970
226.891
206.891
213.214
221.717
228.826
220.026
226.806
218.367
213.490
217.992
207.583
205.529
211.388
224.698
234.607
222.447
228.315
222.070
224.633
219.419
223.805
237.143
225.796
219.409
215.734
223.315
228.690
229.691
216.986
219.426
226.730
227.982
228.837
235.581
230.346
112
APPENDIX D
Monthly MSL Data for Port Klang (1984 – 2008)
Year
1984
1985
1986
Month
January
February
March
April
May
June
July
August
September
October
November
December
January
February
March
April
May
June
July
August
September
October
November
December
January
February
March
April
May
June
July
August
September
October
November
December
MSL (cm)
360.363
355.868
362.831
366.594
373.624
371.149
367.970
371.051
372.293
378.664
363.969
358.641
352.843
356.217
357.503
360.522
367.027
380.942
370.235
361.289
364.521
373.317
373.474
364.017
352.753
347.223
349.035
359.539
365.668
364.149
368.589
371.043
363.461
367.753
372.287
349.911
113
Year
1987
1988
1989
Month
January
February
March
April
May
June
July
August
September
October
November
December
January
February
March
April
May
June
July
August
September
October
November
December
January
February
March
April
May
June
July
August
September
October
November
December
MSL (cm)
343.692
345.586
341.394
346.574
359.035
362.790
367.903
358.860
354.840
360.004
380.260
373.575
358.827
355.974
354.223
361.253
372.774
371.415
360.734
361.911
360.985
377.577
379.018
367.732
357.359
352.260
355.441
363.242
373.812
374.574
370.149
365.938
362.368
368.461
374.238
354.218
114
Year
1990
1991
1992
Month
January
February
March
April
May
June
July
August
September
October
November
December
January
February
March
April
May
June
July
August
September
October
November
December
January
February
March
April
May
June
July
August
September
October
November
December
MSL (cm)
350.242
342.881
344.270
361.396
368.926
372.442
361.425
370.153
368.698
362.114
365.915
423.685
359.716
348.421
351.266
338.765
360.800
362.293
366.629
359.344
356.941
362.532
363.285
365.722
359.149
348.238
343.411
347.601
355.644
370.285
373.869
367.325
368.863
367.547
378.619
369.602
115
Year
1993
1994
1995
Month
January
February
March
April
May
June
July
August
September
October
November
December
January
February
March
April
May
June
July
August
September
October
November
December
January
February
March
April
May
June
July
August
September
October
November
December
MSL (cm)
363.747
350.737
346.003
348.282
359.935
364.130
368.215
366.481
369.394
362.550
367.854
372.770
372.693
361.359
355.784
353.493
357.565
360.921
359.536
359.329
355.184
358.042
358.363
351.031
358.401
347.040
349.423
357.055
364.766
368.825
371.524
364.147
367.347
365.725
373.800
379.430
116
Year
1996
1997
1998
Month
January
February
March
April
May
June
July
August
September
October
November
December
January
February
March
April
May
June
July
August
September
October
November
December
January
February
March
April
May
June
July
August
September
October
November
December
MSL (cm)
369.853
344.443
351.601
349.099
363.678
374.164
363.783
368.719
358.095
372.639
378.858
382.235
375.743
357.491
358.027
355.638
349.357
369.867
362.319
350.390
359.618
358.522
348.359
342.038
339.899
344.333
345.615
349.933
355.501
364.573
378.196
368.657
370.016
368.446
378.919
385.458
117
Year
1999
2000
2001
Month
January
February
March
April
May
June
July
August
September
October
November
December
January
February
March
April
May
June
July
August
September
October
November
December
January
February
March
April
May
June
July
August
September
October
November
December
MSL (cm)
374.891
369.360
362.595
362.667
376.568
370.508
364.475
364.358
363.266
363.099
375.844
383.415
376.681
364.415
367.560
367.818
373.291
374.544
369.721
372.063
369.455
362.183
375.332
377.096
374.231
367.468
363.930
363.854
367.074
372.398
370.657
370.337
373.573
366.147
379.000
375.085
118
Year
2002
2003
2004
Month
January
February
March
April
May
June
July
August
September
October
November
December
January
February
March
April
May
June
July
August
September
October
November
December
January
February
March
April
May
June
July
August
September
October
November
December
MSL (cm)
369.324
346.481
348.793
349.722
356.534
371.074
369.042
371.310
367.156
361.169
363.672
363.264
363.671
357.435
352.339
352.792
357.567
375.094
359.981
356.370
369.884
367.703
377.900
379.079
368.918
349.313
346.915
359.156
360.139
379.555
377.208
368.675
364.418
363.001
366.231
366.226
119
Year
2005
2006
2007
Month
January
February
March
April
May
June
July
August
September
October
November
December
January
February
March
April
May
June
July
August
September
October
November
December
January
February
March
April
May
June
July
August
September
October
November
December
MSL (cm)
360.264
355.710
349.281
344.206
360.844
369.032
371.511
368.519
363.543
370.551
372.034
382.504
376.251
366.701
362.000
349.150
370.161
363.107
371.171
362.358
358.104
361.753
352.618
350.653
354.750
349.643
351.514
367.000
370.445
364.085
364.622
367.327
363.150
369.883
376.884
357.125
120
Year
2008
Month
January
February
March
April
May
June
July
August
September
October
November
December
MSL (cm)
361.870
361.747
365.407
369.668
369.589
356.168
357.496
363.724
369.640
362.418
371.986
378.204
121
APPENDIX E
Monthly MSL Data for Tanjung Keling (1985 – 2008)
Year
1985
1986
1987
Month
January
February
March
April
May
June
July
August
September
October
November
December
January
February
March
April
May
June
July
August
September
October
November
December
January
February
March
April
May
June
July
August
September
October
November
December
MSL (cm)
278.359
280.748
282.832
282.368
287.582
294.463
288.306
283.270
285.679
293.695
293.294
288.155
280.777
273.530
275.439
281.250
285.461
284.486
286.735
288.871
285.022
290.175
295.967
278.339
273.511
271.802
267.061
271.124
279.915
282.917
286.832
281.220
278.801
278.801
297.360
298.513
122
Year
1988
1989
1990
Month
January
February
March
April
May
June
July
August
September
October
November
December
January
February
March
April
May
June
July
August
September
October
November
December
January
February
March
April
May
June
July
August
September
October
November
December
MSL (cm)
283.395
283.395
280.394
278.548
282.897
289.663
289.853
283.044
284.328
283.537
296.110
303.381
290.821
282.781
270.744
279.108
280.982
291.492
289.849
288.279
285.257
285.278
283.127
298.015
281.915
278.336
271.975
270.950
282.378
288.620
288.853
281.038
286.370
285.185
288.226
293.443
123
Year
1991
1992
1993
Month
January
February
March
April
May
June
July
August
September
October
November
December
January
February
March
April
May
June
July
August
September
October
November
December
January
February
March
April
May
June
July
August
September
October
November
December
MSL (cm)
284.358
277.457
276.583
273.530
281.217
283.309
285.038
279.661
277.429
282.649
287.362
289.426
283.449
275.940
261.250
271.320
276.824
287.159
288.958
286.012
285.661
296.676
293.606
287.546
277.925
271.615
272.610
281.542
284.629
285.399
285.289
287.106
284.818
292.961
294.456
296.801
124
Year
1994
1995
1996
Month
January
February
March
April
May
June
July
August
September
October
November
December
January
February
March
April
May
June
July
August
September
October
November
December
January
February
March
April
May
June
July
August
September
October
November
December
MSL (cm)
286.284
278.487
278.215
281.097
281.424
280.767
279.933
278.602
279.960
282.468
279.788
283.280
276.266
276.814
279.286
284.697
287.160
288.509
288.136
287.003
287.076
293.699
302.907
294.218
279.241
279.188
271.817
284.000
291.136
283.747
286.896
286.613
289.266
296.624
299.713
295.593
125
Year
1997
1998
1999
Month
January
February
March
April
May
June
July
August
September
October
November
December
January
February
March
April
May
June
July
August
September
October
November
December
January
February
March
April
May
June
July
August
September
October
November
December
MSL (cm)
274.687
280.918
276.848
272.569
286.520
296.966
274.418
277.933
278.164
272.405
268.822
269.687
271.969
270.393
287.347
276.289
282.063
292.065
285.957
288.161
288.382
296.461
301.046
297.476
292.903
285.775
283.293
292.643
289.115
283.772
283.016
284.841
285.607
294.875
300.925
300.090
126
Year
2001
2002
2003
Month
January
February
March
April
May
June
July
August
September
October
November
December
January
February
March
April
May
June
July
August
September
October
November
December
January
February
March
April
May
June
July
August
September
October
November
December
MSL (cm)
290.392
287.753
284.066
284.558
289.833
287.493
288.910
290.710
287.960
296.640
297.318
292.133
274.953
274.577
274.239
278.768
288.341
287.581
288.750
287.144
282.719
285.820
286.051
286.397
283.760
278.286
276.819
277.757
289.589
281.499
278.958
287.993
287.146
297.304
296.742
295.358
127
Year
2004
2005
2006
Month
January
February
March
April
May
June
July
August
September
October
November
December
January
February
March
April
May
June
July
August
September
October
November
December
January
February
March
April
May
June
July
August
September
October
November
December
MSL (cm)
275.106
271.677
281.246
280.729
294.999
292.800
287.198
283.491
285.015
287.977
287.022
284.985
279.649
273.077
269.054
284.857
291.167
288.064
286.454
282.692
293.121
295.001
295.105
299.321
288.750
280.370
279.606
284.226
288.242
282.543
286.151
282.766
280.817
284.499
277.000
280.575
128
Year
2007
2008
Month
January
February
March
April
May
June
July
August
September
October
November
December
January
February
March
April
May
June
July
August
September
October
November
December
MSL (cm)
283.497
272.827
274.735
287.076
285.275
283.789
283.966
286.098
284.747
290.940
304.751
293.069
288.625
284.983
287.521
292.517
293.086
282.306
282.895
288.278
290.299
294.749
302.506
304.750
129
APPENDIX F
Monthly MSL Data for Kukup (1986 – 2005)
Year
1986
1987
1988
Month
January
February
March
April
May
June
July
August
September
October
November
December
January
February
March
April
May
June
July
August
September
October
November
December
January
February
March
April
May
June
July
August
September
October
November
December
MSL (cm)
400.9
391.2
393.9
395.0
396.9
396.7
398.1
400.3
398.6
403.6
411.1
397.8
395.0
390.1
384.8
387.6
393.8
396.1
398.3
394.2
392.2
397.2
409.6
416.0
410.1
397.1
395.6
397.5
400.1
400.8
396.3
397.0
396.7
409.2
418.6
408.5
130
Year
1989
1990
1991
Month
January
February
March
April
May
June
July
August
September
October
November
December
January
February
March
April
May
June
July
August
September
October
November
December
January
February
March
April
May
June
July
August
September
October
November
December
MSL (cm)
401.8
396.4
396.7
395.2
403.5
401.5
400.2
398.2
398.2
406.1
412.4
401.2
398.1
391.2
388.4
396.2
402.4
400.3
394.1
397.0
398.0
402.4
408.1
403.0
397.1
396.0
390.4
396.1
0.0
0.0
392.9
0.0
400.2
406.2
409.8
405.7
131
Year
1992
1993
1994
Month
January
February
March
April
May
June
July
August
September
October
November
December
January
February
March
April
May
June
July
August
September
October
November
December
January
February
March
April
May
June
July
August
September
October
November
December
MSL (cm)
398.0
390.1
391.9
0.0
399.4
397.0
397.6
397.1
397.8
411.6
410.2
404.8
397.2
389.7
389.8
394.6
396.6
396.2
396.1
397.2
397.0
405.0
407.3
412.8
403.8
394.5
396.0
396.0
394.8
393.1
391.2
390.2
391.3
397.3
398.4
401.1
132
Year
1995
1996
1997
Month
January
February
March
April
May
June
July
August
September
October
November
December
January
February
March
April
May
June
July
August
September
October
November
December
January
February
March
April
May
June
July
August
September
October
November
December
MSL (cm)
397.3
396.9
394.9
398.5
399.3
400.6
401.8
399.7
400.4
407.4
415.8
411.9
398.0
400.1
388.9
398.5
402.5
397.1
398.2
399.0
402.1
410.9
414.5
411.8
400.4
399.5
394.3
389.1
398.7
396.6
388.9
388.5
392.4
389.3
388.9
390.5
133
Year
1998
1999
2000
Month
January
February
March
April
May
June
July
August
September
October
November
December
January
February
March
April
May
June
July
August
September
October
November
December
January
February
March
April
May
June
July
August
September
October
November
December
MSL (cm)
392.5
389.2
391.6
390.8
395.9
403.2
399.0
401.1
402.1
411.2
416.0
414.5
410.5
403.5
399.0
406.0
403.3
397.3
395.2
398.5
400.1
409.9
415.0
419.5
405.4
404.6
405
401.9
403.0
400.3
403.1
400.9
401.3
406.7
414.1
413.1
134
Year
2001
2002
2003
Month
January
February
March
April
May
June
July
August
September
October
November
December
January
February
March
April
May
June
July
August
September
October
November
December
January
February
March
April
May
June
July
August
September
October
November
December
MSL (cm)
408.5
405.6
400.4
399.8
402.7
400.1
401.8
403.2
403.3
410.6
412.7
410.9
396.8
394.5
392.4
395.0
401.6
401.0
400.6
400.5
396.6
399.9
402.4
403.8
403.6
397.9
394.1
392.6
402.0
395.2
392.6
399.4
399.6
411.3
0.0
0.0
135
Year
2004
2005
2006
Month
January
February
March
April
May
June
July
August
September
October
November
December
January
February
March
April
May
June
July
August
September
October
November
December
January
February
March
April
May
June
July
August
September
October
November
December
MSL (cm)
394.8
390.8
397.6
395.3
405.9
403.7
399.9
395.0
399.1
402.9
0.0
403.8
398.3
390.1
386.8
392.7
397.8
399.3
398.0
394.0
402.1
404.9
413.5
413.6
396.8
394.5
392.4
395.0
401.6
396.6
388.9
388.5
392.4
389.3
388.9
390.5