nuratikahmohammadzulkifli_aa12092_hydrology_fkasa

THE DEVELOPMENT OF RAINFALL INTENSITY-

DURATION-FREQUENCY (IDF) CURVES IN

KLANG VALLEY

NUR ATIKAH BINTI MOHAMMAD ZULKIFLI

B.ENG (HONS.) CIVIL ENGINEERING

UNIVERSITI MALAYSIA PAHANG

THE DEVELOPMENT OF RAINFALL INTENSITY-DURATION-FREQUENCY

(IDF) CURVES IN KLANG VALLEY

NUR ATIKAH BINTI MOHAMMAD ZULKIFLI

Report submitted in partial fulfilment of requirements

for the award of the degree of

B. Eng. (Hons) Civil Engineering

Faculty of Civil Engineering & Earth Resources


JUNE 2016


DECLARATION OF THESIS / UNDERGRADUATE PROJECT PAPER AND COPYRIGHT

Author’s full name : NUR ATIKAH BINTI MOHAMMAD ZULKIFLI

Date of birth : 19 APRIL 1993

Title : THE DEVELOPMENT OF RAINFALL INTENSITY-

DURATION-FREQUENCY (IDF) CURVES IN KLANG

VALLEY

Academic Session : 2015/2016

I declare that this Final Year Project Report is classified as:

CONFIDENTIAL (Contains confidential information under the Official Secret Act

1972)*

RESTRICTED (Contains restricted information as specified by the organization

where research was done)

OPEN ACCESS I agree that my thesis to be published as online open access

(full text)

I acknowledged that Universiti Malaysia Pahang reserves the right as follows:

1. The Final Year Project Report is the property of Universiti Malaysia Pahang.

2. The Library of Universiti Malaysia Pahang has the right to make copies for the

purpose of research only.

3. The Library has the right to make copies of the thesis for academic exchange.

Certified by:

_________________________ ______________________________

SIGNATURE SIGNATURE OF SUPERVISOR

(930419-03-5044) SHAIRUL ROHAZIAWATI BT SAMAT

Date: JUNE 2016 Date: JUNE 2016

NOTES: *If the Final Year Project Report is CONFIDENTIAL or RESTRICTED, please

attach with the letter from the organization with period and reasons for condentially or

restriction.

√

i

SUPERVISOR’S DECLARATION

I hereby declare that I have read this final year project report and in my opinion this

final year project is sufficient in terms of scope and quality for the award of the degree

of Bachelor of Civil Engineering.‖

Signature : ................................................................

Name of Supervisor : SHAIRUL ROHAZIAWATI BT SAMAT

Position : LECTURER

Date : JUNE 2016

Signature : ................................................................

Name of Co-Supervisor : NORASMAN BIN OTHMAN

Position : LECTURER

Date : JUNE 2016

ii

STUDENT’S DECLARATION

―I hereby declare that this final year project report, submitted to Universiti Malaysia

Pahang as a partial fulfillment of the requirements for the degree of Bachelor of Civil

Engineering. I also certify that the work described here is entirely my own expect for

excerpts and summaries whose sources are appropriately cited in the references.‖

Signature :

Name : NUR ATIKAH BINTI MOHAMMAD ZULKIFLI

ID Number : AA12092

Date : JUNE 2016

iii

DEDICATION

Alhamdulillah for all HIS Gift,

To My Family especially:

My Beloved Father and Mother,

MOHAMMAD ZULKFILI BIN MOHAMMAD AND ZURAINI BINTI SULAIMAN,

My Siblings,

MOHAMMAD SYAFIQ and NUR ATILLIA,

To My Teachers and Lectures

Also to All My Friends,

Thanks for Love and Encouragement to Support Me.

"God does not change what is in a people until they change what is in themselves"

(Sura 13, Verse 11)

iv

ACKNOWLEDGEMENT

In the name of Allah S.W.T, most gracious and most merciful, may Allah the

Almighty keep us His blessing and tenders, and praised to Prophet Muhammad s.a.w.

Alhamdulillah this project has been completed on the time without any uncomfortable

occurrence.

I would like to take this opportunity to express my appreciation to Puan Shairul

Rohaziawati Bt Samat, supervisor in this final year project paper for her valuable

guidance and patience that enable me to making this project a success. My special

thanks to Encik Norasman Bin Othman as a co-supervisor.

My utmost gratitude goes to staffs in Jabatan Pengairan dan Saliran Malaysia

(JPS) cawangan Jalan Ampang, and Faculty of Civil Engineering and Earth Resources

who had giving full co-operation to me to accomplish my study.

I also would like to deliver my million thanks to my roommate, classmate and all

my friends, especially Siti Aisyah. This is the best moment to thank you for all help, and

support, perhaps success always with us. Insya-Allah.

Last but not least, to my beloved family, especially my mother, my father, my

grandmother, my grandfather and my siblings. Thanks for all your loving and

understanding.

v

ABSTRACT

Changing in climate is one of the main parameter that affecting the water resources as it

affects the whole hydrologic cycle thus causes variation in rainfall intensity, duration

and frequency of precipitation. The rainfall Intensity Duration Frequency (IDF) curves

relationship is one of the tools that are commonly used in water resources engineering,

either for planning, designing and operating the water resources project. Department of

Irrigation and Drainage (DID) is a responsible department to produce IDF curve and

published as a guideline for Urban Storm water Management Manual (MSMA2). But

this IDF curves use outdated data from 1990 until 2010 which is not up to date data.

This IDF curves need to be update from time to time in order to ensure the IDF curves

still relevant as reference. This study’s purpose is to develop IDF and also to determine

the appropriate frequency analysis for every district in Klang Valley based on latest

data. There were two methods used in this study such as Gumbel and Log-Normal

distribution. IDF curve requires Annual Maximum Series (AMS) rainfall data from the

period of 5 minutes to 7200 minutes starting from year 1990 to year 2015 for 18 stations

in Klang Valley. To designing the IDF curve, process involved are mean, standard

deviation, frequency factor, and intensity value for 2, 5, 10, 20, 50 and 100 year return

period for both methods. The Kolmogorov-Smirnov (KS) was used in goodness of fit

test to determine the appropriate frequency analysis in Klang Valley. Gumbel

distribution showed to fit the graph than Log-Normal by not rejecting the value above

85% than 54 tests involve for both methods. Thus, Gumbel disribution is an appropriate

method that can be use in developing the IDF curves for districts in Klang Valley than

Log-Normal. Comparison between the constructed IDF curves and the existing IDF

curve provided in MSMA2 had been made with range +96.55% of difference at minutes

15 at duration 100 years ARI and -52.63% of difference at minutes 1440 with duration

2 years ARI.

vi

ABSTRAK

Perubahan iklim merupakan salah satu kriteria utama yang memberi kesan kepada

sumber air kerana ianya boleh menjejaskan kitaran hidrologi keseluruhan dan

menyebabkan perbezaan dalam keamatan hujan, tempoh dan kekerapan hujan. Hujan

Keamatan Tempoh Kekerapan (IDF) adalah salah satu alat yang biasa digunakan di

dalam air kejuruteraan sumber, sama ada untuk merancang, mereka bentuk dan

mengendalikan projek sumber air. Jabatan Pengaliran dan Saliran (JPS) merupakan

jabatan yang bertanggungjawab dalam menghasilkan lengkung keamatan tempoh

frekuensi (IDF) dan taburan hujan yang dihasilkan akan diterbitkan didalam Manual

Saliran Mesra Alam Malaysia (MSMA2). Namun begitu lengkungan keamatan tempoh

frekuensi yang digunakan sekarang mengunakan data dari 1990 hingga 2010 yang tidak

dikemaskini. Lengkungan IDF perlu diperbaharui dari semasa ke semasa untuk

memastikan lengkungan keamatan masih relevan sebagai rujukan. Berdasarkan kajian

ini, lengkungan keamatan hujan bagi Lembah Klang, dan seluruh daerah bagi Lembah

Klang akan dihasilkan berdasarkan data data terkini. Terdapat dua kaedah yang

digunakan dalam kajian ini ialah Gumbel dan Taburan Log-Normal. IDF memerlukan

Siri Maksimum Tahunan data hujan (AMS) data hujan dari tempoh 5 minit hingga 7200

minit bermula pada tahun 1990 hingga tahun 2015 untuk 18 stesen di Lembah Klang.

Untuk mereka bentuk lengkung IDF, proses yang terlibat adalah purata, standard

penyimpangan, faktor kekerapan dan nilai keamatan bagi tempoh 2, 5, 10, 20, 50 dan

100 tahun kembali kedua-dua kaedah. Kolmogorov-Smirnov (KS) telah digunakan

dalam ketetapan ujian untuk menentukan analisis kekerapan yang sesuai di Lembah

Klang. Taburan Gumbel menunjukkan untuk muat graf daripada Log-Normal dengan

tidak menolak nilai di atas 85% daripada 54 ujian melibatkan untuk kedua-dua kaedah.

Oleh itu Gumbel disribution adalah kaedah yang sesuai digunakan untuk

membangunkan lengkung IDF untuk daerah di Lembah Klang daripada Log-Normal.

Perbandingan antara lengkung IDF dibina dan lengkung IDF yang sedia ada yang

diperuntukkan dalam MSMA2 telah dibuat dengan pelbagai + 96,55 % daripada

perbezaan pada minit 15 pada tempoh 100 tahun kala kembali dan -52,63 % daripada

perbezaan di minit 1440 dengan tempoh 2 tahun kala kembali.

vii

TABLE OF CONTENTS

Page

SUPERVISOR’S DECLARATION i

STUDENT’S DECLARATION ii

DEDICATION iii

ACKNOWLEDGEMENT iv

ABSTRACT v

ABSTRAK vi

TABLE OF CONTENTS vii

LIST OF TABLE xi

LIST OF FIGURES xii

LIST OF SYMBOLS xv

LIST OF ABBREVIATIONS xvi

CHAPTER 1 INTRODUCTION

1.1 Background Study 1

1.2 Problem Statement 2

1.3 Objectives 3

1.4 Scope of Study 3

1.5 Significant of Study

4

CHAPTER 2 LITERATURE REVIEW

2.1 Introduction 5

2.2 Hydrologic Cycle 6

2.3 Intensity-Duration Frequency (IDF) Curve 8

2.4 Depth-Area-Duration Relationship 10

2.5 Mass Curve 12

2.6 Rainfall 15

2.6.1 Rainfall Intensity 15

viii

2.6.2 Storm Duration 16

2.7 Average Recurrence Interval (ARI) 17

2.8 Missing Data Method

2.8.1 Normal Ratio Method

2.8.2 Distance Power Method

2.8.3 Arithmetic Mean Method

18

18

19

20

2.9 Distribution For IDF Curve Development 21

2.9.1 Normal Distribution 21

2.9.2 Log-Normal Distribution 22

2.9.3 Gamma Distribution 24

2.9.4 Gumbel’s Distribution 25

2.9.5 Log-Pearson Type - III Distribution 26

2.9.6 Generalized Extreme Value (GEV) distribution 27

2.9.7 Generalized Poreto 29

2.10 Goodness of Fit

2.10.1 Kolmogorov -Smimov test (KS test)

2.10.2 Chi-square test

2.10.3 Anderson-Darling test

31

31

32

33

CHAPTER 3 METHODOLOGY

3.1 Introduction 35

3.2 Flow Chart 36

3.3 Study Area 37

3.4 Data Collection 38

3.5 Analysis Method 41

3.5.1 Gumbel Distribution 41

3.5.1.1 Frequency Factor

3.5.1.2 Mean and Standard Deviation

3.5.1.3 Flood of Specific Probability

41

41

42

ix

3.5.2 Log-Normal Distribution



3.5.2.3 Flood of Specific Probability

3.6 Goodness of Fit

3.6.1 Kolmogorov-Smirnov

3.6.2 Graphical Method

3.6.3 Confidence Limits

3.8 Percentage of Differences

43

44

44

45

46

46

47

49

51

CHAPTER 4 RESULT AND DISCUSSION

4.1 Introduction 53

4.2 Rainfall data 54

4.3 Mean and Standard Deviation 55

4.3.1 Gumbel Distribution


56

57

4.4 Frequency Factor



56

56

56

4.5 Intensity



58

58

59

4.6 Intensity-Duration-Frequency (IDF) Curve for Gumbel Distribution

4.6.1 Wilayah Persekutuan and Gombak

4.6.2 Hulu Langat

4.6.3 Klang

4.6.4 Petaling

4.6.5 Summary

4.7 Intensity-Duration-Frequency (IDF) Curve for Log-Normal

…………. .Distribution

4.7.1 Wilayah Persekutuan

4.7.2 Hulu Langat

4.7.3 Klang

4.7.4 Petaling

4.7.5 Summary

61

61

72

76

80

83

84

84

95

99

103

106

x

4.8 Comparison of Kolmogorov-Smirnov for Gumbel and Log-Normal

………… ..Distribution

4.8.1 Emperical Calculation for Gumbel

4.8.2 Emperical Calculation for Log-Normal

4.8.3 Comparison of Probability Value between both method

4.9 Comparison between constructed IDF Curve and existing IDF

………… ..Curve..in MSMA

4.10 Summary

107

107

112

116

118

122

CHAPTER 5 CONCLUSION

5.1 Background

5.2 Conclusion

5.3 Recommendation

REFERENCE

APPENDICES

Appendix A Rainfall Data in mm

Appendix B Rainfall Depth in mm

Appendix C Rainfall Intensity in mm/hr

Appendix D Plotting Gumbel Distribution with 95% Confident Intervals

Appendix E Plotting Log-Normal Distribution with 95% Confident Intervals

123

124

126

132

149

158

163

179

xi

LIST OF TABLES

Table No. Title Page

3.1

Selected Rainfall Station 39

3.2

Various Plotting Positions Formula 48

3.3

Value of Kolmogorov-Smirnov 50

4.1

Rainfall data for Kg.Kuala Seleh (Stn. 3217004) 53

4.2

Descriptive Statistic for Kg.Kuala Seleh (Stn. 3217004) for Gumbel

Distribution.

54

4.3

Descriptive Statistic for Kg.Kuala Seleh (Stn. 3217004) for Log-

Normal Distribution.

55

4.4

Frequency Factor base on the Return Period for Gumbel Distribution 56

4.5

Frequency Factor base on the Return Period for Log-Normal Distribution 57

4.6

Rainfall Depth for Kg.Kuala Seleh (Stn. 3217004) in mm 58

4.7

Intensity for Kg.Kuala Seleh (Stn. 3217004) in mm/hr for Gumbel 59

4.8

Rainfall Depth for Kg.Kuala Seleh (Stn. 3217004) in mm before anti-log 60

4.9

Rainfall Depth for Kg.Kuala Seleh (Stn. 3217004) in mm after anti-log 60

4.10

Intensity for Kg.Kuala Seleh (Stn. 3217004) in mm/hr for Log-Normal 60

4.11

Summary Calculation for Plotting Position Formula for Gumbel

Distribution

108

4.12

Summary Calculation for Plotting Position Formula for Log-Normal

Distribution

113

4.13 Maximum Probability Different Between Gumbel and Log-Normal

Distribution

117

4.14

Intensity of Coeffiecient and Percentage of Difference in MSMA

Rainfall Intensity of District in Klang Valley using Gumbel Distribution

119

5.1

Maximum Value of the Intensity 124

5.2 Minimum Value of the Intensity 125

xii

LIST OF FIGURES

Figure No. Title Page

2.1

Hydrologic Cycle 7

2.2 IDF Curve (MSMA 2000) 9

2.3

DAD Curve

11

2.4

Typical IDF Curves

12

2.5

Mass curve

13

2.6

Normal Distribution of Precipitation

21

2.7

Cumulative Log-Normal Distribution of Rainfall

23

2.8

Probability Density Function 29

2.9

Generalized Pareto 30

2.10

Pareto distribution 30

3.1

Flow chart to produce IDF curve 36

3.2

Peninsular Malaysia Mapping 37

3.3

Location of the District in Klang Valley 38

3.4

Selangor Rainfall Station 40

3.5

Cumulative Log-Normal Distribution of Rainfall 43

4.1

IDF Curve for Ldg. Edinburgh Site (Stn. 3116006) 62

4.2

IDF Curve for Kg. Sg. Tua (Stn. 3216001) 63

4.3

IDF Curve for SMJK Kepong (Stn. 3216004) 64

4.4

IDF Curve for Ibu Bekalan Km.16, Gombak (Stn. 3217001) 65

4.5

IDF Curve for Empangan Genting Klang (Stn. 3217002) 66

4.6

IDF Curve for Ibu Bekalan Km. 11, Gombak (Stn. 3217003) 67

4.7

IDF Curve for Kg.Kuala Seleh (Stn.3217004) 68

xiii

4.8

IDF Curve for Kg. Kerdas (Stn. 3217005) 69

4.9

IDF Curve for Air Terjun Sg. Batu2 (Stn. 3317001) 70

4.10

IDF Curve for Genting Sempah (Stn. 3317004) 71

4.11

IDF Curve for S.M. Bandar Tasik Kesuma (Stn. 2818110) 73

4.12

4.13

4.14

4.15

4.16

4.17

4.18

4.19

4.20

4.21

4.22

4.23

4.24

4.25

4.26

4.27

4.28

4.29

4.30

4.31

4.32

IDF Curve for RTM Kajang (Stn. 2917001)

IDF Curve for S.K. Kg. Sg. Lui (Stn. 3118102)

IDF Curve for Pintu Kawalan P/S Telok Gadong (Stn. 2913001)

IDF Curve for JPS Pulau Lumut (Stn. 2913122)

IDF Curve for Ldg. Sg. Kapar (Stn. 3113087)

IDF Curve for Setia Alam (Stn. 3114085)

IDF Curve for Pusat Penyelidikan Getah Sg. Buloh (Stn. 3115079)

IDF Curve for Ldg. Edinburgh Site (Stn. 3116006)

IDF Curve for Kg. Sg. Tua (Stn. 3216001)

IDF Curve for SMJK Kepong (Stn. 3216004)

IDF Curve for Ibu Bekalan Km.16, Gombak (Stn. 3217001)

IDF Curve for Empangan Genting Klang (Stn. 3217002)

IDF Curve for Ibu Bekalan Km. 11, Gombak (Stn. 3217003)

IDF Curve for Kg.Kuala Seleh (Stn.3217004)

IDF Curve for Kg. Kerdas (Stn. 3217005)

IDF Curve for Air Terjun Sg. Batu2 (Stn. 3317001)

IDF Curve for Genting Sempah (Stn. 3317004)

IDF Curve for S.M. Bandar Tasik Kesuma (Stn. 2818110)

IDF Curve for RTM Kajang (Stn. 2917001)

IDF Curve for S.K. Kg. Sg. Lui (Stn. 3118102)

IDF Curve for Pintu Kawalan P/S Telok Gadong (Stn. 2913001)

74

75

77

78

79

81

82

85

86

87

88

89

90

91

92

93

94

96

97

98

100

xiv

4.33

4.34

4.35

4.36

4.37

4.38

IDF Curve for JPS Pulau Lumut (Stn. 2913122)

IDF Curve for Ldg. Sg. Kapar (Stn. 3113087)

IDF Curve for Setia Alam (Stn. 3114085)

IDF Curve for Pusat Penyelidikan Getah Sg. Buloh (Stn. 3115079)

Plotting Gumbel (15 minutes) in station Kg. Kerdas (Stn. 3217005) for

95% Confidence Interval

Plotting Log-Normal (15 minutes) in station Kg. Kerdas (Stn.

3217005) for 95% Confidence Interval

101

102

104

105

111

115

xv

LIST OF SYMBOLS

μ

Mean

N

Number of Data

σ

Standard Deviation

P

Probability

T

Return Period

In

Inches

cm

Centimeter

mm

Millimeter

km

Kilometer

hr

Hour

min

K

Pave

P*T

S*

Minutes

Gumbel frequency

The average of the maximum

precipitation in a specific duration

The frequency precipitation

Standard deviation of P* value

xvi

LIST OF ABBREVIATION

MSMA Manual Saliran Mesra Alam

CDF Cumulative Density Function

PDF Probability Density Function

KS Kolmogorov-Smirnov Test

LP3 Log-Pearson Type III

LN Log-Normal

IDF Intensity-Duration-Frequency

DID Department of Irrigation and Drainage

ARI Average Recurrence Interval

VDF Volume Duration Frequency

CHAPTER 1

INTRODUCTION

1.1 BACKGROUND STUDY

Malaysia is one of the countries located at Southeast Asia, close to the equator

which is damp and hot all the year. The area of Malaysia at equator zone gives Malaysia

experience tropical atmosphere with two sort of monsoon season which are the

northeast and southwest through the year. Northeast happen amid November to May

bring moisture and more rainfall. Where southwest give wind blowing monsoon inside

of May to September. These outcomes give average rainfall in Malaysia in 2500 mm

with normal temperature 27oC a year.

Seasonal variety give impact on rainfall pattern rely on upon topography of

Malaysia that encompassed by mountain. This condition give two distinctive

atmosphere which is rely on upon highland and lowland region. Accordingly, both

condition cause temperature seething between 23oC to 32

oC during that time with

humidity somewhere around 75% and 80% and yearly get rainfall between 2000mm to

4000mm with 150 to 200 stormy days.

From this rainfall pattern, the data will be utilized to develop temporal pattern

using rainfall intensity-duration-frequency (IDF) curves. IDF curves can be obtained

based on historical data and are usually employed to evaluate the extreme values of

precipitation in urban drainage systems. For instance, IDF curve estimates are crucial in

urban drainage systems so as to have a consistent estimation of extreme precipitation to

design the conveying and detention infrastructures. Therefore, IDF curves can be

defined as mathematical tools that express the relation between intensity, duration, and

2

average recurrance interval (ARI) of precipitation. Rainfall IDF ought to be up and

coming in accordance with the progressions of rainfall pattern due to worldwide

temperature alteration impact and temperature changes.

That information from rainfall data will be use in frequency investigation system

to create IDF curve. To utilize this method, local history data was expected to get

maximum annual rainfall depth corresponding to various duration. Most recent duration

information will be taken inside of time of 5 minutes to 120 hours with diverse ARI 2,

5, 10, 20, 50 and 100 years. Rainfall intensity-duration-frequency curves describe

rainfall intensity as a function of duration for a given ARI which are important for the

design of storm water drainage systems and hydraulic structures. The IDF curve will

show the infinite number of rainfall event with distinctive average intensity and

duration with same ARI. For a particular ARI, the average intensity will diminish as the

duration increment. As the outcome, for same duration, the average intensity is higher

for longer ARI than the shorter one.

1.2 PROBLEM STATEMENT

The increase in carbon dioxide concentration in the atmosphere due to industrial

activities in the past and recent times has been identified as the major cause of global

warming and climate change. The normal balance of the earth’s hydrological cycle has

been altered due to the changes in the temperature and precipitation patterns. Research

related to the analysis of extreme precipitation indices have projected an increase in the

annual total precipitation during the second half of the past century; the number of days

with precipitation is also expected to increase, with no consistent pattern for extreme

wet events.

All rainwater design in Malaysia must refer to the Urban Storm Water

Management Manual Second edition (MSMA2) to take follow standard. Taking into

account perception in MSMA2, the data of IDF curve for Klang Valley was overhauled

until 2009. Heavy rainfall and under design drainage system can occur at Klang Valley.

In Malaysia, flash flood event occur frequently in urban areas such as Klang Valley.

3

The environmental change in Malaysia in storm rainfall intensity may influence the

information by change of most recent expansion data (MSMA2, 2012).

Based on the Urban Storm water Management Manual (MSMA), the data period

for Klang Valley IDF curve mostly, between 1970 until 1990. This data not suitable as

a reference to design a drainage and stormwater management because in lately the

climate change increase in storm rainfall intensity (MSMA, 2000).

Besides that, there is not all stations in Klang Valley stated in MSMA2 because in

MSMA2 the data only represent for major towns. This means that there is a large

potential error in extrapolating to long ARI such as 100 years. The lower limit of the

duration analyzed was 15 minutes and the limits of rainfall ARI between two years and

100 years (MSMA, 2000). The existing IDF curve in MSMA not reliable and need to

reviewed using the additional data and latest method.

1.3 OBJECTIVES

The objectives of this study are;

i. To develop IDF curves using frequency analysis such as Gumbel

distribution and Log-Normal distribution in Klang Valley.

ii. To analyse the appropriate frequency analysis for developing IDF curves

in Klang Valley.

iii. To compare the rainfall intensity values between MSMA2 and

appropriate frequency analysis.

1.4 SCOPE OF STUDY

This study was conducted in Klang Valley area using Annual Maximum Series

(AMS) rainfall data to develop IDF curves. The duration of IDF curves from 5, 10, 15,

30, 60, 180, 360, 720, 1440, 2880, 4320 and 7200 minutes and the ARI including 2, 5,

10, 20, 50 and 100 years. The data collections are from Department of Irrigation and

Drainage (DID).

4

There were two frequency analysis used intensity were obtained used such as

Gumbel Distribution and Log-Normal Distribution. Based on this methods values of the

intensity were obtained. 18 stations were selected to represent each district. To ensure

the data in confidence intervals, the Kolmogorov-Smirnov (KS) test was done. The

Kolmogorov-Smirnov goodness of fit test is used to evaluate the accuracy of the fitting

of a distribution. Besides, this study also did comparison on percentage of error between

constructed IDF curve and existing IDF curve in MSMA2.

1.5 SIGNIFICANT OF STUDY

All the rainwater design in Malaysia use MSMA2 as reference for engineer to

design. To test the reliable of this curve to be used as reference, it needs to be compare

with other method to see the reliability of the IDF curve in MSMA2. By developing

new IDF curve can plan awareness to MSMA2 user about the changes in MSMA2 due

to the climate change in Malaysia. Based on the analysis and observation the extreme

prediction can determine and it can be as a latest to design hydrology project and it can

reduce error the hydrology design project from damage while using MSMA2 for

drainage system. The element involves designing IDF curve are intensity, duration and

frequency relationship.

By developing new IDF curves, new location for new IDF curve was developed

and it can be used as designing material based on value of intensity for that location

area. Limited location for IDF curves use in MSMA2 can be covered with new location.

Thus nearest design location can refer to new IDF curves in new location to predict

more reliable rainfall intensity value compared to MSMA2.

Besides that, the information in this study can may use for future study and the

intensity can be used by other researches from different agencies.

CHAPTER 2

LITERATURE REVIEW

2.1 INTRODUCTION

The scope of this chapter focuses on how researchers have identified, develop,

and assessed uncertainty on any aspect of hydrology using IDF curves in previous

study. Inadequate hydrologic data and the need for proper planning of water resources

development have forced engineer to analyze available data more critically. This is

particularly so in developing countries. The Intensity-Duration-Frequency relationship

is one of the most commonly use basis for water resources planning and development.

Proposed a methodology for the plan and development of IDF bends using so as

to utilize data from recording station exact formulae/mathematical statement and the

correlation between the given formulae and picking the best comparison that could be

illustrative for Malaysia (Naht etc., 2006). The method proposed in the study was

sensibly relevant for ungauged rainfall areas, which was confirmed by checking with

additional rain gauge station.

Rainfall IDF is one of the most important tools in hydrology and hydraulic design

use by engineer in planning, designing, and operate rainwater infrastructure like

drainage structure and flood elevation in urban and rural area (Le Minh Nhat et al.,

2007). So they are important in order to prevent flooding, thereby reducing the loss of

life and property, insurance of water damage, and evaluation of hazardous weather.

Failing in implant the IDF estimation in design can cause public safety or fund at risk.

6

Break-point, short duration, rainfall data are not generally available in the

historical records at the locations. Generalized accumulated rainfall patterns developed

by DID were matched with rainfall data for the locations of study, and the advanced

pattern had the best fit with the observed characteristics was used to break down

recorded daily totals into shorter duration rainfall data. The method of annual maxima

series was used to select data sets for the rainfall analysis.

In the statistical method, the Type I extreme-value distribution (Gumbel) was

applied to the annual maximum series for each of stations to estimate the relevant

parameters of the IDF model. The non-parametric Kolmogorov-Smimov test and the

test were used to confirm the appropriateness of the fitted distributions for the locations.

2.2 HYDROLOGIC CYCLE

The hydrological cycle is a water changing phase, from liquid to solid to gas and

back to liquid as it moves through the earth system (Trenberth et al., 2007). The starting

point of the cycle is in the oceans. Due to the heat energy, water in the oceans evaporate

and moves upwards to form clouds. As the clouds condense, it falls back to oceans as

rain. Rain falling on earth may enter a water body directly, travel over the land surface

from the point of impact to a watercourse, or infiltrate into the ground. Some rain is

intercepted by vegetation which means the intercepted water is temporarily stored on

the vegetation until it evaporates back to the atmosphere. Some rain is stored in surface

depressions with almost all of the depression storage infiltrating into the ground.

Water stored in depressions, water intercepted by vegetation, and water that

infiltration into the soil during the early part of the storm represent initial losses. The

loss is water that does not appear as runoff during or immediately following a rainfall

event. Water entering the upload streams travels to increasing larger rivers and then to

seas and oceans. The water that infiltrates into the ground may percolate to the water

table or travel in the unsaturated zone until it reappears as surface flow. The amount of

water stored in the soil determines, in part, the amount of rain that will infiltrate during

the next storm event. Water stored in lakes, seas, and ocean evaporates back to the

atmosphere, where it completes the cycle and is available for interception is filled, the

7

water will immediately fall from the plant surfaces to the ground and infiltrate into the

soil in the same way that water falling on bare ground infiltrates. Some of the water

stored in the near plants is taken up by the roots of the vegetation and subsequently is

passed back to the atmosphere from the leaves of the plants; this process is called

transpiration (Richard, 2004).

In an attempt to compensate for lost natural storage, many localities require the

replacement of lost natural storage with human-made storage. While the storm water

detention basin is the most frequently used method of storm water management, other

methods are used, such as infiltration pits, rooftop and parking lot storage, and porous

pavement. These engineering works do not always return the runoff characteristics to

those that existed in the natural environment. In fact, poorly conceived of control have,

in some cases, made flood-runoff conditions worse (Richard, 2004). Figure 2.1 is a

schematic representation of the hydrological cycle for a natural environment.

Figure 2.1: Hydrologic Cycle

Source: (http://www.lifewater.ca/Appendix_C.htm)

8

2.3 INTENSITY-DURATION FREQUENCY (IDF) CURVE

IDF relationship of rainfall amounts is one of the most commonly used tools in

water resources engineering for planning, design, and operation of water resources

projects (Elsebaie, 2012). IDF curves are used in combination with runoff estimation

formulas such, as the rational method, in order to predict the peak runoff flow from

exact point of basin. These are also used in certain aspects of hydraulic structures design

such as size of pipes and culvert (Dupont & Allen, 2000).

Extreme environmental events, such as floods, droughts, rainstorms, and high

winds, have severe consequences for human society. Planning for weather-related

emergencies, design of engineering structures, reservoir management, pollution control,

and insurance risk calculations, all rely on knowledge of the frequency of these extreme

events (Hosking and Wallis, 1997). The assessment of extreme precipitation is an

important problem in hydrologic risk analysis and design. This is why the evaluation of

rainfall extremes, as embodied in the Intensity-Duration Frequency (IDF) relationship,

has been a major focus of both theoretical and applied hydrology (Andreas and

Veneziano, 2009). Dupont et al. (2000) defined rainfall IDF relationships as graphical

representations of the amount of water that falls within a given period of time.

The total storm rainfall depth at a point, for a given rainfall duration and ARI, is a

function of the local climate. Rainfall depths can be further processed and converted

into rainfall intensities (Intensity = depth/duration), which are then presented in IDF

curves. Such curves are particularly useful in storm water drainage design because

many computational procedures require rainfall input in the form of average rainfall

intensity.

Based on Koutyoyiannis (2003) the IDF curves is a mathematical relationship

between the duration, d the rainfall intensity and the return period. This is allow the

estimation of return period in rainfall event corresponding to amount of rainfall at given

period for different aggregation times. These graphs are used to determine when an area

will be flooded, and when a certain rainfall rate or a specific volume of flow will

reoccur in the future. Figure 2.2 show example of IDF curve from MSMA.

9

Figure 2.2: IDF curve

Source: MSMA2, 2012

The three variables, frequency, intensity and duration, are all related to each other.

The data are normally presented as curves displaying two of the variables, such as

intensity and duration, for a range of frequencies. These data are then used as the input

in most storm water design processes.

The IDF curve is commonly use in water resource engineering for designing and

operating of water resources project. These methods usually use to estimate runoff

during storm, Empirical method, Rational method, Unit-Hydrograph method and Flood

frequency studies. To use those methods need to match with the purpose of study and

depend to available data use based on importance of the project.

The use of IDF was widely use and being standard practice for many years in

designing sewerage system and other hydraulics structure. IDF give idea about

frequency and return period for mean and volume rainfall intensity that can be expected

in certain period of storm duration. In this situation, storm duration is parameter can be

10

compromise as part of rainfall event. Even now, IDF can provide lot information for

rainfall and can be used as base for determination of design storm (A.S.Wayal, 2014).

Hydrologic design of storm sewers, culverts, retention/detention basins and other

component of storm water management systems is typically based on a specified design

storm, which in turn is often based on rainfall IDF estimated and assumed temporal

distribution of rainfall. Since use of inappropriate data and design storms could lead

either to costly overdesign or else undue risk to infrastructure systems and possibly

human safety, it is very important that accurate IDF estimates be available for a wide

range of durations.

The curve not been revised since 1991. The pattern should be reviewed using

the additional data that is now available. The period of data from which the curves was

derived was very short, in some cases only seven years. Few of the station had more

than 20 years of data (MSMA2).This means that there is a large potential error in

extrapolating to long ARI such as 100 years. The lower limit of the duration’s analyses

was 15 minutes. Department Irrigation and Drainage should expedite the installation of

digital pluviometers to capture data from short storm bursts, down to five minutes

duration. The limits of rainfall ARI were between two years and 100 years. The curves

were not in a convenient form for use in modern computer models. There was no

guidance given for urban areas outside the 135 centres listed in MSMA2.

In Malaysia, frequency and intensity of rainfall in Malaysia is higher than the most

countries, especially those with temperate climates. IDF curve very important to be

developed, based on the suitable method with weather in Malaysia, to ensure the

hydrology design base on the IDF curve are functional.

2.4 DEPTH-AREA-DURATION RELATIONSHIP

Once the sufficient rainfall records for the region are collected the basic or raw

data can be analyzed and processed to produce useful information in the form of curves

or statistical values for use in the planning of water resources development projects.

Many hydrologic problems require an analysis of time as well as areal distribution of

11

storm rainfall. Depth-Area-Duration (DAD) analysis of a storm is done to determine the

maximum amounts of rainfall within various durations over areas of various sizes.

Figure 2.3 show example of DAD Curve.

Figure 2.3: DAD Curve

Source: Elementary Engineering Hydrology, 2009

Although most severe storm in the listed storms may not have occurred right over

the catchment under consideration there is possibility of such occurrence. So from DAD

curves 1 day, 2 day, 3 day rainfall depths for the catchment area of the proposed project

are read. These give the rainfall depths when the storms are centered over the

catchment.

In hydrology, frequency analysis of station rainfall data is done for use in design

of bridges and culverts on highways, design of storm drains etc. With the advancement

of science of hydrology rainfall frequency analysis is done using Gumbel’s extreme-

value distribution and annual series data.

12

Now the frequency analysis concept is applied on a seasonal basis and for areal

frequency. The rainfall records of deficient length have to be extended by station year

method. The results of frequency analysis are plotted on the log-log paper. The typical

intensity-duration frequency curves are given in Figure. 2.4.

Figure 2.4: Typical IDF Curves

Source: Hydrology and Hydroclimatology: Principles and Applications, 2013

2.5 MASS CURVE

During high flows, water flowing in river has to be stored so that a uniform supply

of water can be assured, for water resources utilization lake irrigation, water supply,

power generation during period of low flow rivers.

A mass curve is graphical representation of cumulative inflow or outflow of water

versus time which may be monthly or yearly. A mass curve shown in Figure 2.5 is

13

example of mass curve. The slope of the mass at any point is a measure of the inflow

rate at that time. Mass curve or double mass curve is a commonly used data analysis

approach for investigating the behavior of records made of rainfall data at a number of

locations. It is used to determine whether there is a need for corrections to the data to

account for changes in data collection procedures or other local conditions. Such

changes may result from a variety of things including changes in instrumentation,

changes in observation procedures, or changes in gauge location or surrounding

conditions.

Mass analysis use for checking consistency of a rainfall record is considered to be

an essential tool before taking it for analysis purpose. This method is based on the

hypothesis that each item of the recorded data of a precipitation consistency (H. M.

Raghunath, 2006).

Figure 2.5: Mass curve (Cumulative Rainfall of Stations in The Period 2002-2010)

Source: Hydro-meteorological data analysis using OLAP techniques, 2014

The graph of the cumulative data of one variable versus the cumulative data of a

related variable is a straight line so long as the relation between the variables is a fixed

14

ratio. Breaks in the double-mass curve of such variables are caused by changes in the

relation between the variables. These changes may be due to changes in the method of

data collection or to physical changes that affect the relation. The procedure to construct

such diagram is as follows (Klemes, 2000):

i. From the past records, determine the hourly demand for all 24 hours for typical

days (maximum, average and minimum).

ii. Calculate and plot the cumulative demand against time, and thus plot the mass

curve of demand.

iii. Read the storage required as the sum of the two maximum ordinates between

demand and supply line as shown in Figure 2.5.

iv. Repeat the procedure for all the typical days (maximum, average and minimum),

and determine the maximum storage required for the worst day.

The theory of the double-mass curve is based on the fact that a graph of the

cumulating of one quantity against the cummulation of another quantity during the same

period will plot as a straight line so long as the data are proportional; the slope of the

line will represent the constant of proportionality between the quantities (Jamesk.

Searcy, 1960).

The slope of the mass curve means that a change in the constant of proportionality

between the two variables has occurred or perhaps that the proportionality is not a

constant at all rates of cummulation. In Figure 2.5 the slope indicates the time at which

a change occurs in the relation between the two variables. To get best fitted line, new

line was developed by considering consistency between two variables (Clayton, 1960,

and Ebru et.al. 2012).

As in checking the consistency of precipitation records, enough stations should be

included to insure that the average is not seriously affected by an inconsistency in the

record for one of the stations. The number of stations that can be included in a pattern is

sometimes limited by the criterion that the area in which the stations are located should

be small enough to be influenced by the same general weather conditions. If less than 10

stations are used in the pattern, each record should be tested for consistency by plotting

15

it against the pattern, and those records that are inconsistent should be eliminated from

the pattern.

Spurious breaks in the double-mass curve that should be recognized as such are

caused by the inherent variability in hydrologic data. Most users recognize that the year-

to-year breaks are due to chance and, thus, ignore any break that persists for less than 5

years. Breaks that persist for longer than 5 years are more subtle in that they may be due

to chance or they may be due to a real change. Unless the time of the break coincides

with a logical reason for the break, statical methods should be used to evaluate the

significance of the break (Walter B. Langbein, l960).

2.6 RAINFALL

Rainfall is a component in the hydrologic cycle, which is a continuous process

that happens on the earth. Rainfall is the amount of water that falls on the land from

precipitation process. Precipitation occurs when the vapors in the atmosphere having a

condensation process and change into droplets that cannot be suspended in the air.

There a few factor for the occurrence of rainfall. Ground elevation, wind direction and

location within a continental mass would give a big impact for the precipitation to

occur. By knowing the nature of the rainfall, we can make a prediction on how its effect

the surface runoff, infiltration, evaporation and water yield (Patra, 2001).

2.6.1 Rainfall Intensity

A new general rainfall Intensity‐Duration‐Frequency formula is presented,

utilizing a method similar to, but more accurate than one previously developed. The

previously developed formula was based on the average depth‐duration ratio of about 40

percent and the mean depth‐frequency ratio of 1.48. It is shown that this formula is only

a particular form of the more general formulation. The earlier formula, however,

requires only the 10‐yr 1‐hr rainfall depth instead of the three rainfall depths (i.e., 10‐yr

1‐hr, 10‐yr 24‐hr, and 100‐yr 1‐hr). The ratios of 1‐hr to corresponding 24‐hr depth and

100‐yr to corresponding 10‐yr depth can be computed from the required three rainfall

16

depths in the method so that geographical variation of rainfall can be evaluated in terms

of both ratios.

Generalized accumulated rainfall patterns developed by USDA (United States

Department of Agriculture) Soil Conservation Service were matched with rainfall data

for the locations of study, and the advanced pattern had the best fit with the observed

characteristics and was used to break down recorded daily totals into shorter duration

rainfall data. The method of annual maxima series was used to select data sets for the

rainfall analysis. In the statistical method, the Type I extreme-value distribution

(Gumbel) was applied to the annual maximum series for each of the seven stations to

estimate the relevant parameters of the IDF model. The non-parametric Kolmogorov-

Smirnov test and the χ2 test were used to confirm the appropriateness of the fitted

distributions for the locations. IDF data developed from the graphical and statistical

methods applied were very close for the lower return periods of two to ten years, but

differed for higher return periods of 50 to 100 years. However, the difference is not

significant at 5% level. The data developed by either of the methods will facilitate

planning and design for water resources development (Okonkwo, 2010).

2.6.2 Storm Duration

Appropriately designed flood control infrastructure should provide for public

safety without wasteful over-design. The design-storm duration is a very significant

determinant of the computed peak discharge. Presently, most hydrologic design is based

on either the 24-h storm duration or duration equal to the time of concentration. The

professional literature, however, has not included a rational basis for using either of

these durations. Since the annual maximum discharges are the basis for flood frequency

analyses and ultimately flood risk estimates, it is reasonable that the rainfall duration

that causes the annual maximum discharge should provide insight into the most

appropriate duration for design storms. Use of the time of concentration suggests that

the duration should depend on drainage area. Therefore, a range of watershed areas

were used to determine whether or not storm duration depends on watershed size.

Analysis of annual maximum discharge data for six Maryland watersheds (1.97 ≤ A ≤

52.6 sq mi) for 1972–1990 show that the rainfall duration causing the annual maximum

17

discharge is slightly longer than 24 h, even for watershed areas as small as 2 sq mi; and

that storm duration increases only slightly with watershed area. The actual data also

suggests that center-loaded design storms are appropriate. Therefore, the Soil

Conservation Service Type II storm is an appropriate design hyetograph for prediction

of discharges comparable to annual maximum discharges (Levy, B. and McCuen, R.,

1999)

2.7 AVERAGE RECURRENCE INTERVAL (ARI)

Average Recurrence Interval or Annual Recurrence Interval is the average period

or expected value of period between exceedances of a given rainfall total accumulated

over a given duration. In this definition, periods between exceedances are random.

Average Recurrence Interval is also known as return period is an estimate of the interval

of time between events like an earthquake, flood or river discharge flow of a certain

intensity or size. Return period is the statistical measurement denoting the average

recurrence interval over an extended period of time, and is usually required to

dimension structures so that they are capable of withstanding an event of a certain return

period with its associate’s intensity.

Rainfall and subsequent discharge estimate is based on the selected value of

frequency or return period, termed as the Average Recurrence Interval (ARI) which is

used throughout this Manual. ARI is the average length of time between rain events that

exceeds the same magnitude, volume or duration (Chow, 1964), and is expressed as:

(2.1)

where,

Tr = Average Recurrence Interval, ARI (year) and

P = Annual Exceedance Probability, AEP (%).

18

As an example, 1% AEP of storm has an ARI of 100 years. According to the

definition, a 100 year ARI storm can occur in any year with a probability of 1/100 or

0.01.

The design ARI of a stormwater facility is selected on the basis of economy and

level of protection (risk) that the facility offers. ARIs to be used for the design of minor

and major stormwater quantity systems. It is assumed that the design flow of a given

ARI is produced by a design storm rainfall of the same ARI. Design rainfall intensity

(mm/hr) depends on duration (minute or hour) and ARI (month or year). It is strongly

recommended that performance of the designed drainage system must be examined for a

range of ARIs and storm durations to ensure that the system(s) will perform

satisfactorily.

2.8 MISSING DATA METHOD

Rainfalls missing data can be filled by using estimation technique depend on the

period of time. The length of period to fill the data depends on individual judgment.

Generally, missing data for rainfall is estimated either by using Normal Ratio Method or

Arithmetic Mean Method.

2.8.1 Normal Ratio Method

In this method, the rainfall, P, at certain station is estimated as function of normal

daily or annual rainfall of the station under study. Same for those neighbor stations, that

will be grouped as index station for the nth

normal annual precipitation value for the x

station. According to the Normal Ratio Method, the missing precipitation is given as:

∑

(2.2)

Where;

Pi = The rainfall at neighbor stations

Nx = Annual rainfall at missing data station

19

Ni = Annual rainfall at neighbor station

n = The number neighbor station whose data are used.

The use of station is depending on location being use. There no fix number of

station use. This method preferred when the normal annual rainfall on missing data,

give surrounding gauge different by 10 % (P.Jaya Rami Reddy, 2005).

2.8.2 Distance Power Method

The rainfall at a station is estimated as a weighted average of the observed

rainfall at the neighboring stations. The weights are equal to the reciprocal of the

distance or some power of the reciprocal of the distance of the estimator stations from

the estimated stations. Let Di be the distance of the estimator station from the estimated

station. If the weights are an inverse square of distance, the estimated rainfall at station

A is:

∑

∑

(2.3)

The weights go on reducing with distance and approach zero at large distances.

A major shortcoming of this method is that the orographic features and spatial

distribution of the variables are not considered. The extra information, if stations are

close to each other, is not properly used. The procedure for estimating the rainfall data

by this technique is illustrated through an example. If A, B, C, D are the location of

stations discussed in the example of the normal ratio method, the distance of each

estimator station (B, C, and D) from station (A) whose data is to be estimated is

computed with the help of the coordinates using the formula:

,( ) ( ) - (2.4)

where x and y are the coordinates of the station whose data is estimated and xi and yi

are the coordinates of stations whose data are used in estimation (Jain and Singh, 2003).

20

2.8.3 Arithmetic Mean Method

Arithmetic Mean Method is simplest method to calculate missing data by

considering the value of precipitation divided number of station surrounding missing

data station. If the rain gauge uniformly distributed over area, the result get using this

method will be quite satisfactory and not much different obtain based on other method.

This method is suitable to use for storm rainfall, monthly or annual rainfall average

consumption. According to the Arithmetic Mean Method, the missing precipitation is

given as:

( ) (2.5)

Where;

Px = The missing precipitation

Pn = The precipitation value at n station

n = Number of station

Number of station use in this method is not fixed. This method can be categories

simple method when normal precipitations at adjacent station around 10% from normal

rainfall of the station use (P.Jaya Rami Reddy, 2005).

21

2.9 DISTRIBUTION FOR IDF CURVE DEVELOPMENT

2.9.1 Normal Distribution

The normal distribution was first developed by de Moivre in 1753 and a century

later was used to explain error in astromical measurement and derive mostly likely

values from a number of observations.

This distribution is widely used in hydrology, as well as in other civil engineering

application such as survey measurement errors. In general, the normal distribution is

applicable where the observed value are the sum of the effect of a large number of

independent process which has only a small effect on the total (Chow, 1964).

This distribution is symmetrical about mean and suitable only for data where the

skewness coefficient g is equal or closes to zero. It is use to examine the minima of data

set or when generate synthetic data. Figure 2.6 show example of normal distribution.

Figure 2.6: Normal Distribution of Precipitation

Source: J.G. Grijsen, 2013

22

A random variable x is said to have a normal distribution with parameter µ

(mean) and 𝜎2 (variance) (Patra, 2003). When probability density function (PDF) is

given by

( )

√ 0

( ) 1 (2.6)

Cumulative distribution function (CPF) for the Normal Distribution

( )

√ ∫ 0

( ) 1

(2.7)

Where;

𝜎 = Standard Deviation

µ = Mean


Log-Normal (LN) distribution is frequently used in hydrologic analysis of

extreme seasonal flow volumes, duration curves for daily stream flow, rainfall intensity-

duration soil water retention, etc. It is also popular in synthetic stream flow generation.

Properties of this distribution are discussed by Vijay P. Singh (1998).

The distribution is convenient to use because of the ease with its quantities can be

determined using normal tables. When generating synthetic stream flow sequences, the

log normal distribution is particularly convenient model of annual or seasonal flows

because of the ease with observed flow can be transformed to normally distributed

random variable and generated normal random variables can be converted to synthetic

flows (J .R. Stedinger, 1980) .

The Log-Normal distribution is important in the description of natural

phenomena. The reason is that for many natural processes of growth, growth rate is

independent of size. This is also known as Gibrat's law, after Robert Gibrat (1904-1980)

23

who formulated it for companies. In hydrology, the Log-Normal distribution is used to

analyze extreme values of such variables as monthly and annual maximum values of

daily rainfall and river discharge volumes. Figure 2.7 show the example of Log-Normal

Distribution for rainfall.

Figure 2.7: Cumulative Log-Normal Distribution of Rainfall

Source: Andale ,2015

Here, KT is the frequency factor and is equal to z for log-normal and normal

distribution. z is calculated by:

(2.8)

Here, 𝑤 is given as:

𝑤 , .

/- (2.9)

In the above equation is the probability of occurrence in a specified return period.

And p is given as:

(2.10)

For the case of p > 0.5, p in Eq. (2.8) is substituted by 1- p and z gives a negative

value

24

2.9.3 Gamma Distribution

It has gradually been recognized that complex hydrological events such as floods

and storms always appear to be multivariate events that are characterized by a few

correlated random variables. Single-variable hydrological frequency analysis can only

provide limited assessment of these events and it is not sufficient to represent multiple

episodic hydrological phenomena. A thorough understanding of multivariate

hydrological events requires the study of the joint probabilistic behavior of two or more

correlated random variables that characterize the events. Some meaningful attempts

have been made to address this topic (Ashkar, 1980).

Gamma distribution constructed from specified gamma marginal may be of

usefulness to hydrological engineers in evaluating multivariate hydrological events. In

the past, some researchers have investigated a few bivariate gamma distributions with

special gamma marginal for hydrological frequency analysis.

For the sake of consistency, the common form of the distribution factor of a

univariate gamma distribution with two parameters is presented by

( )

( ) (2.11)

Where:

= The scale

= Shape parameters of the gamma distribution.

Then the gamma distributed random variables X and Y can be obtained by

replacing z with x and y in Eq. 2.9, respectively. The corresponding cumulative

distribution functions (CDFs) of X and Y can be obtained by numerically integrating

Eq. 2.10 as follows:

( ) ∫ ( )

( ) (2.12)

25


Gumbel distribution was widely used for IDF analysis to perform flood

probability analysis. This is due to its suitability for modeling maxima which is simple

and use only extreme event that is peak rainfall or maximum values. Gumble method

use 2, 5, 10, 25, 50 and 100 year return intervals for each duration period and requires

several calculations (Lamia Abdul Jaleel, 2012).

Frequency precipitation, PT (in mm) for each duration with a specified return

period T (in year) is given by the following equation.

(2.13)

√

0 0 0

111 (2.14)

∑ (2.15)

Where;

PT = The frequency precipitation

K = Gumbel frequency

S = Standard deviation of P value

Pave = The average of the maximum precipitation in a specific duration

Pi = The individual extreme value of rainfall

n = The number of events or years of record

The standard deviation is calculated by using this relation:

0

∑ ( )

1

(2.16)

Where;

26

S = the standard deviation of P data

The frequency factor (K), which is a function of the return period and sample size,

when multiplied by the standard deviation gives the departure of a desired return period

rainfall from the average. Then the rainfall intensity, I (in mm/h) for return period T is

obtained from:

(2.17)

Where:

Td = Duration in hours.

2.9.5 Log-Pearson Type III Distribution

This type of method usually use in Vietnam to calculate the rainfall intensity at

different duration and return period to form IDF curve for each station. Logarithms were

involving in calculation of measured values such mean and standard deviation to

transform the data. It’s same as Gumbel distribution to obtain the frequency

precipitation. The simplify equation is given as follow:

( ) (2.18)

(2.19)

∑ (2.20)

0

∑ (

) 1

(2.21)

Where;

P* = Logarithm of precipitation

27

P*T = The frequency precipitation

K = Gumbel frequency

S* = Standard deviation of P* value

P*ave = The average of the maximum precipitation in a specific duration

n = The number of events or years of record.

KT = The Pearson frequency factor which depends on return period

(T) and skewness coefficient (Cs).

The skewness coefficient, Cs, is required to compute the frequency factor for

this distribution.

∑ (

)

( )( )( ) (2.22)

KT values can be obtained from tables in many hydrology references for

example reference Chow, 1988. By knowing the skewness coefficient and the

recurrence interval, the frequency factor, KT for the LPT III distribution can be

extracted.

2.9.6 Generalized Extreme Value (GEV) Distribution

The GEV distribution is a family of continuous probability distributions

developed within extreme value theory. Extreme value theory provides the statistical

framework to make inferences about the probability of very rare or extreme events. The

GEV distribution unites the Gumbel, Fréchet and Weibull distributions into a single

family to allow a continuous range of possible shapes. These three distributions are also

known as type I, II and III extreme value distributions. The GEV distribution is

parameterized with a shape parameter, location parameter and scale parameter. The

GEV Is equivalent to the type I, II and III, respectively, when a shape parameter is equal

to 0, greater than 0, and lower than 0. Based on the extreme value theorem the GEV

distribution is the limit distribution of properly normalized maxima of a sequence of

independent and identically distributed random variables. Thus, the GEV distribution is

http://en.wikipedia.org/wiki/Extreme_value_theory

28

used as an approximation to model the maxima of long (finite) sequences of random

variables (Wolfgang et al., Dietmar at al., and Michael et al., 2013).

The Generalized Extreme Value (GEV) distribution is a flexible three-parameter

model that combines the Gumbel, Fréchet, and Weibull maximum extreme value

distributions. It has the following

( )

𝜎 ( ( )

)( )

( )

( ( )) (2.23)

where z=(x-μ)/σ, and k, σ, μ are the shape, scale, and location parameters respectively.

The scale must be positive (sigma>0), the shape and location can take on any real value.

The range of definition of the GEV distribution depends on k:

( ) ( )

(2.24)

Various values of the shape parameter yield the extreme value type I, II, and III

distributions. Specifically, the three cases k=0, k>0, and k<0 correspond to the Gumbel,

Fréchet, and "reversed" Weibull distributions. The reversed Weibull distribution is a

quite rarely used model bounded on the upper side. For example, for k=−0.5, the GEV

PDF graph has the form in Figure 2.8.

29

Figure 2.8: Probability Density Function

Source: www.mathwave.com

When fitting the GEV distribution to sample data, the sign of the shape

parameter k will usually indicate which one of the three models best describes the

random process you are dealing with.

2.9.7 Generalized Poreto

The probability density function for the generalized Pareto distribution with

shape parameter k ≠ 0, scale parameter σ, and threshold parameter θ, is

y = f (x|k,σ,θ)= (1σ)(1+k(x−θ)σ)−1−1k (2.25)

For θ < x, when k > 0, or for θ < x < θ – σ/k when k < 0.

For k = 0, the density is

y = f (x|0,σ,θ)= (1σ)e−(x−θ)σ (2.26)

For θ < x.

30

If k = 0 and θ = 0, the generalized Pareto distribution is equivalent to the

exponential distribution. If k > 0 and θ = σ/k, the generalized Pareto distribution is

equivalent to the Pareto distribution with a scale parameter equal to σ/k and a shape

parameter equal to 1/k. Figure 2.8 show the generalized Pareto and Figure 2.10 show

the Pareto distribution.

Figure 2.9: Generalized Pareto

Source: www.mathworks.com

Figure 2.10: Pareto distribution

Source: www.mathworks.com

31

2.10 GOODNESS OF FIT

The integrity of fit test is one approach to focus the theoretical distribution

provide for certain case is adequate description of the data. This test is substantial for

dismissing insufficient model, not to prove that model is correct or not. Three types of

test applicable to widely used for rainfall data test; these are Kolmogorov-Smirnov, Chi-

square and Anderson-Darling.

2.10.1 Kolmogorov -Smirnoov Test (KS Test)

The Kolmogorov-Smirnov test (KS test) is a nonparametric test for the equality of

continuous, one-dimensional probability distributions that can be used to compare a

sample with a reference probability distribution (one-sample KS test), or to compare

two samples K-S test. The Kolmogorov-Smirnov statistic quantifies a distance between

the empirical distribution function of the sample and the cumulative distribution

function of the reference distribution, or between the empirical distribution functions of

two samples. The null distribution of this statistic is calculated under the null hypothesis

that the samples are drawn from the same distribution or that the sample is drawn from

the reference distribution In each case, the distributions considered under the null

hypothesis are continuous distributions but are otherwise unrestricted (Marco and Luigi

et al., 2013).

The Kolmogorov-Smirnov test can be modified to serve as a goodness of fit test.

In the special case of testing for normality of the distribution, samples are standardized

and compared with a standard normal distribution. This is equivalent to setting the mean

and variance of the reference distribution equal to the sample estimates, and it is known

that using these to define the specific reference distribution changes the null distribution

of the test statistic:

The test is conducted as follows

i. The data is arranged in descending order of magnitude.

32

ii. The cumulative probability P(xi) for each of the observations is calculated using

the Weibull’s formula.

iii. The theoretical cumulative probability F(xi) for each of the observation is

obtained using the assumed distribution. The absolute difference of P(xi) and

F(xi) is calculated.

iv. The Kolmogorov-Smirnov test statistic ∆ is the maximum of this absolute

difference.

∆ = maximum |P (xi) — F (xi)| (2.27)

The critical value of Kolmogorov-Smirnov statistic ∆o is obtained from the table

for a given significance level α.

v. If ∆< ∆o, accept the hypothesis that the assumed distribution is a good fit at

significance level α.

2.10.2 Chi-square Test

Chi-square (χ2) test is one of technique to check if specific distribution of certain

distribution event’s frequency sample is suitable for that sample or not. This test simply

compare how well empirical distribution (PDF) in test (Balakrishnan, Vassilly et.al.,and

M.S Nikulin, 2013).

The first step in Chi-square test is to arrange the number of observation N into k

cell (class interval) then calculated using statistical formula as:

∑( )

(2.28)

Where;

Oi = Observed frequency in the ith

cell

Ei = Expected frequency in the same cell

K = Number of interval

33

The expected frequency can be computed by:

(2.29)

Where;

N = Total number of observation

Pi = Probability distribution being test

The degree of freedom can be computed using:

(2.30)

Where;

v = Degree of freedom

s = Number of parameter using fitting distribution

The hypothesis of Chi-square test is no difference between observed and

estimated value. The test in tested distribution is describing the observed data, while the

alternate hypothesis tested distribution not describes the observed data. The hypothesis

will be rejected if the value of χ2 is greater than the Chi-square percent point function, v

and significant level of α which expressed as 1-α confident level.

2.10.3 Anderson-Darling Test

Another method to test goodness of fit is by using Anderson-Darling test where

it calculated the weighed square difference between hypothesized distribution and

empirical (PDF). Anderson-Darling statistic (A2) is defined as (Bryan Dodson, 2006):

∑ ( ) * ( ) , ( )-+ (2.31)

34

Where;

A2 = Anderson-Darling statistic

Fn(x(i)) = Empirical Distribution (PDF)

x(i) = The ordered data

Anderson-Darling test use to compare the fit of observed cumulative distribution

function to an expected cumulative distribution function. Thus it gives more weight to

tail than Kolmogorov-Smirnov test (Stephan, 1974).

CHAPTER 3

METHODOLOGY

3.1 INTRODUCTION

In developing Intensity Duration Frequency (IDF) curve, three parameter need to

be considered which are duration for x-axis, intensity for y-axis and return period as

third parameter . By fixing the return period two, five, 10, 20, 50 and 100 years or other

periods, a particular curve between intensity and duration can be obtained for the area

(Patra, 2001). The procedure was repeat for both method, Gumbel and Log-Normal

Distribution.

By following same procedure, IDF curve was developed for 135 towns in

Malaysia for MSMA2 to use as to find peak rainfall intensity for design. The curve

develop in MSMA2 is valid between 5 minutes to 72 hours. To extrapolate between this

limit is not recommended, it cause possible error for the result. The error is likely to be

highest for the durations shorter than 30 minutes and longer than 15 hours, and for ARI

longer than 50 years. For particular critical applications it may be appropriate to

conduct sensitivity tests for the effects of design rainfall errors (MSMA, 2000).

36

Title Selection

Set objective and literature review

Selection of Location

Districts in Klang Valley

Data Collection

AMS rainfall data for all district in Klang Valley

Data collection from 12 years until 45 years, start 1971 until

2015

AMS- 5,10,15,30,60,180,360,720,1440 min until 5 days

Provided by Department of Irrigation and Drainage (DID)

Result and Analysis Data

- Using Gumbel Equation & Log- Normal Distribution

- ARI – 2, 5, 10, 20, 50, 100 years

- Goodness of Fit - Kolmogorov Smirnov (KS Test)

- Compare the rainfall intensity values between MSMA2

Conclusion &

Recommendation

3.2 Flow Chart

Figure 3.1 shows the flow chart to produce IDF curve using frequency analysis.

Figure 3.1: Flow Chart to Produce IDF Curve

37

3.3 STUDY AREA

Data of rainfall intensity will be obtained from JPS (Jabatan Pengairan dan

Saliran). Klang Valley have 2,843 square kilometres. Figure 3.2 shown the Peninsular

Malaysia Mapping. Figure 3.3 shown the location of the District in Klang Valley.

Figure 3.2: Peninsular Malaysia Mapping

Sources:

http://www.etawau.com/HTML/KualaLumpur/KualaLumpurMap/MalaysiaMaps.htm

Area of Study

38

Figure 3.3: Location of the District in Klang Valley

Sources: http://www.dromoz.com/directory/place

3.4 DATA COLLECTION

For this study, the data needed to analysis is rainfall data from the Department of

Irrigation and Drainage Malaysia. The data use start 1971 until 2015. Department of

Irrigation and Drainage Malaysia was extracted data for each storm that occurs using

Annual Maximum Series rainfall data for duration of 5 minutes, 10 minutes, 15

minutes, 30 minutes, one hours, three hours, six hours, 12 hours, 24 hours, 48 hours, 72

hours and 120 hours. The rainfall data for each duration was listed for each 12 month

period from 1 July to 30 June for every year (MSMA2, 2000).

http://www.dromoz.com/directory/place

39

Based on Department of Irrigation and Drainage Malaysia (DID), in Klang

Valley, total rainfall stations that have the availability data are 18 stations. From those

stations, the data of rainfall was collected every year.

Station involves to get the rainfall data are state in Table 3.1:

Table 3.1: Selected Rainfall Station

Location Station

ID Station

Wilayah

Persekutuan &

Gombak

3116006 Ldg. Edinburgh Site 2

3216001 Kg. Sg. Tua

3216004 SMJK Kepong (Pindah ke Taman Sri Murni)

3217001 Ibu Bekalan Km. 16, Gombak

3217002 Empangan Genting Klang

3217003 Ibu Bekalan Km. 11, Gombak

3217004 Kg. Kuala Seleh

3217005 Kg. Kerdas (This station shifted from Gombak

Damsite) (SMART)

3317001 Air Terjun Sg. Batu

3317004 Genting Sempah

Hulu Langat

2818110 Sek. Men. Bandar Tasik Kesuma

2917001 RTM Kajang

3118102 Sek. Keb. Kg. Sg. Lui

Klang

2913001 Pintu Kawalan P/S Telok Gong

2913122 JPS Pulau Lumut

3113087 Ldg. Sg. Kapar

Petalig 3114085 Setia Alam

3115079 Pusat Penyelidikan Getah Sg. Buloh

40

Figure 3.4 shown the location of Selangor Rainfall Station

Figure 3.4: Selangor Rainfall Station

Source: DID, 2012

41

3.5 ANALYSIS METHOD


Gumbel distribution methodology was selected to perform the flood probability

analysis because using maximum flood peaks. The Gumbel method calculates the two,

five, 10, 20, 50 and 100 years return interval for each duration period and requires

several calculations.


Define the frequency factor, or K value, that correlates with the number of years

(T) of available data. The K values are calculated values and are derived from the

following equation:

√

2 0 .

/13 (3.1)

Which:

KT = Frequency Factor

T = Return period


Use available data to calculate for standard deviation. The equation as follow:

i. Mean

∑ (3.2)

42

ii. Standard deviation

𝜎 √

∑ ( ) (3.3)

Which:

µ = The arithmetic mean

Xi = The variance i.e. record used in the computation

N = The total number of record

3.5.1.3 Flood of Specific Probability.

The calculation values for the mean and the standard deviation, as well as the K

value are then used in the following equation to produce the magnitude of a particular

flood (Benadette et al., 2000).

𝜎 (3.4)

Which:

XT = Intensity

µ = Mean

KT = Frequency factor

σ = Standard Deviation

Because intensity in unit mm/hour the value of particular flood need to multiple

with time.

43


Log-Normal (LN) distribution is frequently used in hydrologic analysis of

extreme seasonal flow volumes, duration curves for daily stream flow, rainfall intensity-

duration soil water retention, etc. It is also popular in synthetic stream flow generation.

Properties of this distribution are discussed by Vijay P. Singh (1998).

The distribution is convenient to use because of the ease with its quantities can be

determined using normal tables. When generating synthetic stream flow sequences, the

log normal distribution is particularly convenient model of annual or seasonal flows

because of the ease with observed flow can be transformed to normally distributed

random variable and generated normal random variables can be converted to synthetic

flows (J .R. Stedinger, 1980)

The Log-Normal distribution is important in the description of natural

phenomena. The reason is that for many natural processes of growth, growth rate is

independent of size. This is also known as Gibrat's law, after Robert Gibrat (1904-1980)

who formulated it for companies. In hydrology, the Log-Normal distribution is used to

analyze extreme values of such variables as monthly and annual maximum values of

daily rainfall and river discharge volumes. Figure 3.5 show the example of Log-Normal

Distribution for rainfall.

Figure 3.5: Cumulative Log-Normal Distribution of Rainfall

Source: Andale ,2015

44




(3.5)


𝑤 , .

/- (3.6)

In the above equation is the probability of occurrence in a specified return period.

And p is given as:

(3.7)

For the case of p > 0.5, p in Eq (3.7) is substituted by 1- p and z gives a negative value


The equation as follow:

0

1∑

(3.8)

Which:

µ = The Arithmetic mean

Xi = The variate i.e. record used in the computation

N = The total number of record

45

and

𝜎 √

∑ ( ) (3.9)

For the unbiased estimated, it is expressed in the form

𝜎 √

∑ ( ) (3.10)

Which:

𝜎 = Standard deviation of the data

N = number of score

= Mean of the sample

Depending on where the sample length used for the parameter estimated is more

or less than 30. It has the same dimension as the variate. The higher is the value of the

standard derivation, the larger is the spread of data from the mean.

3.5.2.3 Flood of Specific Probability.

The calculation values for the mean and the standard deviation, as well as the K

value are then used in the following equation to produce the magnitude of a particular

flood (Benadette et al., 2000).

(3.11)

Which:

* = Mean

S* = Standard deviation

= Frequency factor

46

Because intensity in unit mm/hour the value of particular flood need to multiple

with time.

3.6 GOODNEES OF FIT

The goodness of fit test is one way to determine the assumed theoretical

distribution provide for certain case is adequate description of the data. This test is valid

for rejecting inadequate model, not to prove that model is correct or not. Kolmogorov-

Smirnov test applicable to widely used for rainfall data test.

3.6.1 Kolmogorov-Smirnov (KS Test)

The Kolmogorov-Smirnov test (KS test) is a nonparametric test for the equality of

continuous, one-dimensional probability distributions that can be used to compare a

sample with a reference probability distribution (one-sample KS test), or to compare

two samples K-S test. The Kolmogorov-Smirnov statistic quantifies a distance between

the empirical distribution function of the sample and the cumulative distribution

function of the reference distribution, or between the empirical distribution functions of

two samples. The null distribution of this statistic is calculated under the null hypothesis

that the samples are drawn from the same distribution or that the sample is drawn from

the reference distribution In each case, the distributions considered under the null

hypothesis are continuous distributions but are otherwise unrestricted.

The Kolmogorov-Smirnov test can be modified to serve as a goodness of fit test.

In the special case of testing for normality of the distribution, samples are standardized

and compared with a standard normal distribution. This is equivalent to setting the mean

and variance of the reference distribution equal to the sample estimates, and it is known

that using these to define the specific reference distribution changes the null distribution

of the test statistic:

The test is conducted as follows

i. The data is arranged in descending order of magnitude.

47

ii. The cumulative probability P(xi) for each of the observations is calculated

using the Weibull’s formula.

iii. The theoretical cumulative probability F(xi) for each of the observation is

obtained using the assumed distribution. The absolute difference of P(xi) and

F(xi) is calculated.

iv. The Kolmogorov-Smirnov test statistic ∆ is the maximum of this absolute

difference.

∆ = maximum |P (xi) — F (xi)| (3.12)

The critical value of Kolmogorov-Smirnov statistic ∆o is obtained from the table

for a given significance level α.

vi. If ∆< ∆o, accept the hypothesis that the assumed distribution is a good fit at

significance level α.

3.6.2 Graphical Method

Base on the K.C Patra (2003), the basic idea of graphical method is to develop a

linear relationship between the recurrence interval T (or probability) and the event

magnitudes. For such the recurrence interval is taken on abscissa and the event

magnitudes as ordinate. The ordinate may be either ordinary scale or logarithmic scale.

The scales are so selected that the observed data should plot close to a straight line.

A linear relationship do developed helps to extrapolate or interpolate the

relation between the event magnitudes and recurrence intervals. General procedures of

plotting the observed data on a probability paper are outlined:

i. Collect the required hydrological data.

ii. Prepare a table in which the first column contains the year and the second

columns contain the rank of the data m. The position of the data in

decreasing order.

iii. The third column contains corresponding to hydrologic data.

48

iv. In the next column, arrange the data of column 3 in increasing order of

magnitude.

v. Probability P of the event being equaled or exceeded is calculated by any

of the plotting formula given in Table 3.2.

Table 3.2: Various Plotting Positions Formula

No Formula Name Probability P of the event

1 California (1923) m/N

2 Hazen (1914) (m - 0.5)/n

3 Weibull (1939) m/(N+1)

4 Beard (1943) (m - 0.31)/(n+0.38)

5 Chegodayev (1955) (m - 0.3)/(n+0.4)

6 Blom (1958) (m - 3/8)/(n + 1/4)

7 Tukey (1962) (3m - 1)/(3n + 1)

8 Gringorten (1978) (m-0.44)/(n + 0.12)

9 Cunnane (1978) (m-0.4)/ (n + 0.2)

10 Adamowski (m - 1/4)/(n + 1/2)

vi. Recurrence interval or the return period is entered in the next column.

vii. A suitable probability paper is chosen. This depends on the types of data

and the experience of the analysis.

viii. Plot the point on the probability paper by taking T in abscissa and the

magnitude of the event as ordinate. Ordinate of some probability papers

have logarithmic scale. The abscissa represents probability. The aim of

the plotting the data on suitable paper is that the distribution plots a

straight line.

ix. A straight line is fitted, which helps to extrapolate the event for any given

recurrence interval.

Graphical methods are not suitable for larger extrapolations, the reason being, the

errors in sampling may be magnified giving wrong results. For large extrapolations,

graphical distributions should be used with caution. Graphical plotting rather should be

49

used as a check to know the suitability of the probability distribution than for large

extrapolations.

In hydrology, the most common plotting position is the Weibull formula, however

the venerable Weibull formula has been criticized because it does not provide an

estimate of the CDF F such that E (F) equals the theoretical value for the mth

largest out

of n total samples for any underlying distribution other than the uniform, thus excluding

all of the distributions commonly employed for flood frequency and other hydrologic

analysis (Cunnane, 1978). Instead it widely use in hydrological calculation because it

simple and easy to use thus Weibull (1939) in calculation (Philip et al., 2002):

(3.13)

(3.14)

(3.15)

Which:

n = Sample

m = Rank

3.6.3 Confidence Limit

Confident limits are control curves plotted on either side, of the fitted CDF, with

the property that, if the data belong to the fitted distribution, a known percentage of the

data points should fall between the two curves. Benjamin and Cornell (1970)

demonstrated used Kolmogorov-Smirnov (KS). KS goodness of fit statistic to plot

confidence serves as an approximate procedure for other distributions as well. Let FP (x)

is the predicted value of the CDF. Then a confidence interval on the CDF can be

constructed such that

50

Prob (F ≤ FU ) = Prob ((F ≤ Fp+ KS) = 1 - α (3.16)

Prob (F ≥FL ) = Prob ((F ≥Fp- KS) = 1 - α (3.17)

Which:

KS = Kolmogorov Smirnov Statistic

Α = Confidence Level

U = Mean Upper

L = Mean Lower

Table 3.3 listed a function of α and the sample size n for KS. The (1 - 2α)

percentage confidence limits may be formed on F(x) by

Prob (FP - KS ≤ F ≤ Fp+ KS) = 1 - 2α (3.18)

Table 3.3: Value of Kolmogorov Smirnov, ∆0

Size Sample

(N)

∆

10% 5% 1%

5 0.51 0.56 0.67

10 0.37 0.41 0.49

15 0.30 0.34 0.40

20 0.26 0.29 0.36

25 0.24 0.27 0.32

30 0.22 0.24 0.29

35 0.20 0.23 0.27

40 0.19 0.21 0.25

45 0.18 0.20 0.24

50 0.17 0.19 0.23

N > 50 1.22 / √N 1.36 / √N 1.63 / √N

51

A probability 0f 0.05 means that there is only 5% chance that the two distribution

are independent each other. Therefore a probability of 0.05 or smaller means can be

least 95% certain that your two distribution do not differ significantly (Saunders, 2007).

3.8 PERCENTAGE OF DIFFERENCES

In this study, the comparison between actual intensity from MASMA and

intensity from calculation were done to determine either intensity of the rainfall increase

or decrease. The calculation percentage of differences using Equation 3.17.

(3.19)

When the value percentage of difference was negative, the intensity was decrease.

If the percentage of difference was positive, the intensity was increase.

CHAPTER 4

RESULT AND DISCUSSION

4.1 INTRODUCTION

The main objective of this research is to develop IDF curve in Klang Valley area

for ARI curve using AMS rainfall data for 2, 5, 10, 20, 50 and 100 years ARI. There

were two frequency analysis used intensity were obtained used such as Gumbel

Distribution and Log-Normal Distribution equation to get value of the intensity. The

steps to get value of the frequency factor and intensity shown in this chapter.

Data from Department of Irrigation and Drainage come with raw data where

theres no specific time and location of station in system. All rainfall data recorded

directly from station to DID. System will show the rainfall data in average value for

specific interval such 5 minutes, 10 minutes and 1 hours per year.

The Kolmogorov-Smirnov (KS) test was done to ensure the data in confidence

intervals. The Kolmogorov-Smirnov goodness of fit test is used to evaluate the accuracy

of the fitting of a distribution. Besides, this study also did comparison on percentage of

error between constructed IDF curve and existing IDF curve in MSMA2.

Before start calculation, rainfall data for every 5 minutes,10 minutes, 15

minutes, 30 minutes, 3 hour, 6 hour, 12 hour, 24 hour, 48 hour, 72 hour and 120 hour

were separated, base on the district in Klang Valley.

53

4.2 RAINFALL DATA

Rainfall data for Kg. Kuala Seleh (Stn 3217004) shown in Table 4.1. It arranged

starting 1980 until 2015. The rest of the data shown in Appendix A for Gumbel Method

and Log-Normal Method.

Table 4.1: Rainfall Data for Kg. Kuala Seleh (Stn 3217004)

5 10 15 30 60 180 360 720 1440 2880 4320 7200

1980 18.4 26.2 29.9 46.0 54.7 79.8 82.0 92.0 105.0 111.5 116.4 143.5

1981 17.4 24.2 31.7 49.5 62.1 63.0 67.0 99.0 133.0 144.0 144.0 144.0

1982 14.1 26.0 32.7 52.5 76.4 88.0 88.0 89.5 119.6 180.0 217.0 217.0

1983 12.5 25.0 26.0 40.1 63.3 77.6 85.5 85.5 85.5 112.5 120.0 193.5

1984 22.5 22.5 28.3 39.9 64.7 95.0 96.0 96.5 103.0 120.0 123.5 161.0

1985 9.5 16.3 23.7 37.7 55.5 96.0 104.0 104.0 133.0 148.5 159.5 202.5

1986 24.5 24.5 36.0 42.1 62.5 71.5 71.5 71.5 83.5 109.3 152.0 152.0

1987 15.2 28.6 38.3 60.4 74.6 89.5 97.5 101.0 145.0 154.4 199.5 244.0

1988 30.6 38.4 46.9 61.4 80.2 97.0 97.0 97.0 135.5 174.5 188.0 188.0

1989 13.2 15.4 23.1 37.9 62.6 71.5 71.5 72.5 75.5 88.0 88.5 131.8

1991 30.0 30.5 31.8 43.2 55.4 64.5 78.8 83.5 97.0 142.0 150.2 175.5

1992 11.9 23.8 28.7 41.1 56.9 58.0 58.0 58.5 68.0 100.5 132.5 146.9

1993 8.5 15.0 21.0 33.5 56.5 109.5 115.0 155.5 133.0 163.5 182.0 208.5

1994 14.0 23.7 28.2 41.5 58.5 65.0 65.0 78.5 87.5 123.5 168.5 180.5

1995 10.5 18.0 25.0 39.0 60.5 96.5 97.5 97.5 116.0 127.5 131.5 140.5

1996 15.5 23.5 32.5 47.5 66.0 89.0 93.0 115.0 115.0 186.0 188.0 213.0

1997 18.9 31.6 34.0 51.5 82.5 91.0 91.0 91.0 104.5 144.5 178.0 220.0

1998 25.9 26.9 31.5 57.0 77.5 87.5 87.5 87.5 91.0 114.0 177.5 216.0

1999 16.5 25.6 34.0 50.5 67.6 77.0 77.0 81.0 126.5 157.5 158.0 204.0

2000 15.0 26.5 34.0 52.0 78.5 158.5 177.5 182.0 188.0 196.5 227.5 247.5

2001 25.8 38.3 40.8 51.0 66.5 79.5 79.5 79.5 117.7 134.0 142.5 235.5

2002 15.0 27.0 36.0 55.5 79.5 85.5 87.0 95.0 98.5 120.0 133.0 176.5

2003 13.0 25.0 35.5 46.5 53.0 66.0 66.5 82.0 83.0 114.5 121.0 157.5

2004 14.5 25.5 34.0 55.5 72.0 84.0 96.5 98.0 121.5 165.5 183.5 253.5

2005 13.0 23.0 33.0 52.5 69.5 75.5 99.0 100.0 132.0 132.0 171.0 175.0

2006 12.0 21.5 30.0 50.0 54.0 80.5 81.0 82.0 83.0 129.0 134.0 143.5

2007 13.5 24.0 33.0 53.5 77.0 89.0 98.0 98.0 101.0 108.0 123.5 150.5

2008 15.0 23.0 32.0 55.0 80.0 105.5 107.0 107.5 120.0 129.5 145.5 192.0

2009 16.0 27.0 39.5 62.0 81.0 85.0 85.0 85.0 91.0 105.5 144.5 161.0

2010 16.5 20.5 29.5 52.5 74.5 94.0 99.5 99.5 103.0 131.5 145.5 192.0

2011 58.5 71.0 71.0 91.5 107.0 107.0 107.0 107.0 107.0 128.5 179.0 227.0

2012 14.3 24.0 34.0 57.5 75.8 117.0 117.3 117.7 123.4 161.3 180.5 218.8

2013 12.4 21.9 30.6 50.2 69.0 95.0 95.1 95.2 95.5 156.6 178.1 223.1

2014 13.7 25.8 35.0 52.7 66.6 89.0 89.3 89.7 113.8 138.2 162.9 184.9

2015 28.8 34.5 34.5 54.5 81.5 122.5 122.5 122.5 196.5 196.5 197.5 264.0

YEARANNUAL MAXIMUM RAINFALL (mm) DATA FOR VARIOUS DURATIONS (minutes)

54

4.3 MEAN AND STANDARD DEVIATION


Using Microsoft Office Excel rainfall data for Kg. Kuala Seleh (Stn 3217004)

were count to get value of the mean and standard deviation. Table 4.2 shown the

descriptive statistic for Kg. Kuala Seleh (Stn 3217004). After all the value were

obtained, the data easy to analyze.

Table 4.2: Descriptive Statistic for Kg. Kuala Seleh (Stn 3217004) for

Gumbel Distribution

Duration Mean

Statistic

Std.

Deviation

Statistic

5 min 17.9 9.1

10 min 26.4 9.4

15 min 33.3 8.3

30 min 50.4 10.2

60 min 69.2 11.4

180 min 88.6 19.4

360 min 92.3 21.2

720 min 97.1 22.5

1440 min 112.3 27.6

2880 min 138.5 27.6

4320 min 158.4 30.9

7200 min 191 36.5

Table 4.2 shown the mean statistic, and standard deviation each of the 5 minutes,

10 minutes, 15 minutes, 30 minutes, 60 minutes, 3 hours, 6 hours, 12 hours, 24 hours, 2

days, 3 days, and 5 days.

55


Using Microsoft Office Excel rainfall data for Kg. Kuala Seleh (Stn 3217004)

were count to get value of the mean and standard deviation. Table 4.3 shown the

descriptive statistic for Kg. Kuala Seleh (Stn 3217004). After all the value were

obtained, the data easy to analyze.

Table 4.3: Descriptive Statistic for Kg. Kuala Seleh (Stn 3217004) for Log-Normal

Distribution

Duration Mean

Statistic

Std.

Deviation

Statistic

5 min 1.2 0.2

10 min 1.4 0.1

15 min 1.5 0.1

30 min 1.7 0.1

60 min 1.8 0.1

180 min 1.9 0.1

360 min 2.0 0.1

720 min 2.0 0.1

1440 min 2.0 0.1

2880 min 2.1 0.1

4320 min 2.2 0.1

7200 min 2.3 0.1

Table 4.3 shown the mean statistic, and standard deviation each of the 5 minutes,

10 minutes, 15 minutes, 30 minutes, 60 minutes, 3 hours, 12 hours, 24 hours, 2 days, 3

days, and 5 days.

56

4.4 FREQUENCY FACTOR



of available data. The K values are calculated values and are derived.

Example of the calculation, T = 2 ;

√

{ [

( )] }

K = -0.1643

Table 4.4 shown the value for the K for every return period two, fifth, 10, 20, 50,

and 100 year.

Table 4.4: Frequency Factor based on Return Period

ARI

(Year) K

2 -0.1643

5 0.7195

10 1.3046

20 1.8658

50 2.5923

100 3.1367



(T) of available data.

57



=1.2817


, (

)-

= 2.1460

In the above equation is the probability of occurrence in a specified return period. And

p is given as:

For the case of p > 0.5, p in Eq. (2.8) is substituted by 1- p and z gives a negative value

Table 4.5: Frequency Factor based on Return Period

ARI

(Year) K

2 0

5 0.8415

10 1.2817

20 1.6452

50 2.0542

100 2.3268

Table 4.5 shown the value for the K for every return period two, fifth, 10, 20, 50,

and 100 year.

58

4.5 INTENSITY


To calculate intensity, Eq. 3.2 and Eq. 3.3 in Chapter 3 were used. Frequency

factor (K) were substitute into Eq. 3.2 and Eq. 3.3.

Value of the mean and standard deviation will be applied to the equation 3.4.

Example for duration 30 minutes T = 2. The value of the mean and standard deviation

were taken from Table 4.2.

xT = μ + Kσ

xT = 50.4+( -0.1643) (10.2)

xT = 48.7 mm

Step was repeated using another duration and return period using the result shown

in Table 4.2. Because unit of the intensity in mm/hr , value of the of rainfall need to

converted from mm to mm/hr. Table 4.6 shown the rainfall depth for Kg. Kuala Seleh

(Stn 3217004) in mm and Table 4.7 shown the intensity for Kg. Kuala Seleh (Stn

3217004) in mm/hr.

xT = 48.7 x 60/30

xT = 97.5mm/hr

Table 4.6: Rainfall Depth for Kg. Kuala Seleh (Stn 3217004) in mm

ARI

(Year) 5 10 15 30 60 180 360 720 1440 2880 4320 7200

2 16.4 24.9 31.9 48.7 67.4 85.4 88.8 93.4 107.8 134.0 153.3 185.0

5 24.5 33.1 39.3 57.8 77.4 102.5 107.5 113.2 132.2 158.4 180.6 217.2

10 29.8 38.6 44.1 63.7 84.1 113.8 119.9 126.4 148.4 174.5 198.7 238.6

20 34.9 43.9 48.8 69.5 90.5 124.7 131.8 139.0 163.9 190.0 216.0 259.0

50 41.5 50.7 54.9 76.9 98.8 138.7 147.1 155.3 183.9 210.0 238.4 285.5

100 46.5 55.8 59.4 82.4 105.0 149.3 158.6 167.5 199.0 225.0 255.2 305.4

Design Rainfall (mm) Data for Various Storm Duration (minute)

59

Table 4.7 : Intensity for Kg. Kuala Seleh (Stn 3217004) in mm/hr

Depth of the rainfall and intensity of the rainfall data for another station shown

in Appendix B and Appendix C. IDF curve was plotted after value of the intensity

abstract from the rainfall data. Using the logarithm graph, X-axis was plotted by the

value of the duration 5 minutes, 10 minutes, 15 minutes, 30 minutes, 1 hour, 3 hour, 6

hour, 12 hour, 24 hour, 48 hour, 72 hour and 120 hour. Whereas Y-axis was plotted by

the value of the intensity of the rainfall follow by the duration period two, fifth, 10, 20,

50, and 100 years.


To calculate intensity, Eq. 3.11 in Chapter 3 were used. Frequency factor (K)

were substitute into Eq. 3.11.

Value of the mean and standard deviation will be applied to the equation 3.11.

Example for duration 15 minutes T = 5. The value of the mean and standard deviation

were taken from Table 4.3.

= 1.5122+( 0.8415) (0.0918)

= 1.5895 mm

Step was repeated using another duration and return period using the result

shown in Table 4.3. Because value of the intensity in log unit, it need to anti- log first

before calculate intensity. Table 4.8 show value of depth before anti-log then Table 4.9

ARI

(Year) 5 10 15 30 60 180 360 720 1440 2880 4320 7200

2 196.9 149.2 127.8 97.5 67.4 28.5 14.8 7.8 4.5 2.8 2.1 1.5

5 293.4 198.8 157.1 115.5 77.4 34.2 17.9 9.4 5.5 3.3 2.5 1.8

10 357.4 231.7 176.6 127.5 84.1 37.9 20.0 10.5 6.2 3.6 2.8 2.0

20 418.7 263.2 195.3 138.9 90.5 41.6 22.0 11.6 6.8 4.0 3.0 2.2

50 498.1 304.0 219.4 153.7 98.8 46.2 24.5 12.9 7.7 4.4 3.3 2.4

100 557.5 334.6 237.5 164.9 105.0 49.8 26.4 14.0 8.3 4.7 3.5 2.5


60

show value of depth after anti-log. Because value of intensity in mm/hr , value of the of

rainfall need to converted from mm to mm/hr. Refer Table 4.10.

xT = 38.8563 x 60/15

xT = 155.4251 mm/hr

Table 4.8: Rainfall Depth for Kg. Kuala Seleh (Stn 3217004) in mm before anti-log

Table 4.9: Rainfall Depth for Kg. Kuala Seleh (Stn 3217004) in mm after anti-log

Table 4.10: Intensity for Kg. Kuala Seleh (Stn 3217004) in mm/hr for Log-Normal

Depth of the rainfall and intensity of the rainfall data for another district shown

in Appendix B and Appendix C. IDF curve was plotted after value of the intensity

abstract from the rainfall data. Using the logarithm graph, X-axis was plotted by the

ARI

(Year) 5 10 15 30 60 180 360 720 1440 2880 4320 7200

2 1.2 1.4 1.5 1.7 1.8 1.9 2.0 2.0 2.0 2.1 2.2 2.3

5 1.4 1.5 1.6 1.8 1.9 2.0 2.0 2.1 2.1 2.2 2.3 2.3

10 1.4 1.6 1.6 1.8 1.9 2.1 2.1 2.1 2.2 2.2 2.3 2.4

20 1.5 1.6 1.7 1.8 1.9 2.1 2.1 2.1 2.2 2.3 2.3 2.4

50 1.6 1.6 1.7 1.9 2.0 2.1 2.1 2.2 2.2 2.3 2.4 2.4

100 1.6 1.7 1.7 1.9 2.0 2.1 2.2 2.2 2.3 2.3 2.4 2.5

Design Rainfall (mm) Data for Various Storm duration (minute)

ARI

(Year) 5 10 15 30 60 180 360 720 1440 2880 4320 7200

2 16.5 25.3 32.5 49.5 68.4 86.7 90.3 94.9 109.4 135.9 155.4 187.6

5 22.7 31.9 38.9 58.0 78.2 103.0 107.7 112.9 132.9 160.5 184.1 220.7

10 26.9 36.1 42.6 63.0 83.9 112.8 118.1 123.7 147.1 175.0 201.2 240.3

20 31.0 39.9 46.1 67.4 88.9 121.5 127.4 133.3 160.0 188.1 216.5 257.8

50 36.3 44.7 50.2 72.8 94.8 132.1 138.8 145.0 175.8 203.9 235.1 279.0

100 40.3 48.2 53.2 76.6 99.1 139.6 147.0 153.4 187.3 215.1 248.4 294.1


ARI

(Year) 5 10 15 30 60 180 360 720 1440 2880 4320 7200

2 197.6 151.9 130.1 99.1 68.4 28.9 15.0 7.9 4.6 2.8 2.2 1.6

5 273.0 191.6 155.4 116.0 78.2 34.3 17.9 9.4 5.5 3.3 2.6 1.8

10 323.3 216.4 170.6 126.0 83.9 37.6 19.7 10.3 6.1 3.6 2.8 2.0

20 371.8 239.3 184.2 134.9 88.9 40.5 21.2 11.1 6.7 3.9 3.0 2.1

50 435.1 267.9 200.8 145.6 94.8 44.0 23.1 12.1 7.3 4.2 3.3 2.3

100 483.1 288.9 212.8 153.3 99.1 46.5 24.5 12.8 7.8 4.5 3.4 2.5


61

value of the duration 5 minutes, 10 minutes, 15 minutes, 30 minutes,1 hour, 3 hour, 6

hour, 12 hour, 24 hour, 48 hour, 72 hour and 120 hour. Whereas Y-axis was plotted by

the value of the intensity of the rainfall follow by the duration period two, fifth, 10, 20,

50, and 100 years.

4.6 INTENSITY-DURATION-FREQUENCY (IDF) CURVE FOR GUMBEL

DISTRIBUTION


Figure 4.1 until Figure 4.10 shown the IDF curve for every district Wilayah

Persekutuan in Klang Valley that cover from station Ldg. Edinburgh Site 2 (Stn.

3116006), Kg. Sg. Tua (Stn. 3216001), SMJK Kepong (Pindah ke Taman Sri Murni)

(Stn. 3216004), Ibu Bekalan Km. 16, Gombak (Stn. 3217001), Empangan Genting

Klang (Stn. 3217002), Ibu Bekalan Km. 11, Gombak (Stn. 3217003), Kg. Kuala Seleh

(Stn. 3217004), Kg. Kerdas (This station shifted from Gombak Damsite)(SMART) (Stn.

3217005), Air Terjun Sg. Batu (Stn. 3317001), Genting Sempah (Stn. 3317004). The

intensity in IDF curve, represent by return period of two year followed by fifth year, ten

years, 15 year, 20 year, 50 years and 100 years.

62

Figure 4.1: IDF curve for Ldg Edinburgh Site 2 (Stn. 3116006)

Based on the Figure 4.1, the duration at 5 minutes, had shown 212.5 mm/hr,

312.8 mm/hr, 379.2 mm/hr, 442.9 mm/hr, 525.4 mm/hr, and 587.2 mm/hr intensity at

return period two years, fifth years, 10 years, 20 years, 50 years and 100 years

respectively for Ldg Edinburgh Site 2 (Stn. 3116006). The value of intensity decrease

when the duration increase, but at minutes 30, the intensity has stop reduce sharply from

minutes 15. The value of intensity at duration two years, fifth years, 10 years, 20 years,

50 years, and 100 years were 105.8 mm/hr, 124.8 mm/hr, 137.4 mm/hr, 149.5 mm/hr,

165.1 mm/hr and 176.8 mm/hr at minutes 30. The lowest value of the intensity were 1.5

mm/hr, 1.9 mm/hr, 2.1 mm/hr, 2.3 mm/hr, 2.6 mm/hr and 2.8 mm/hr with return period

two years, fifth years, 10 years, 20 years, 50 years, 100 years.

1

10

100

1000

1 10 100 1000 10000

INT

EN

SIT

Y (

mm

/hr)

DURATION (minutes)

IDF CURVE FOR LDG. EDINBURGH SITE 2

2-Year

ARI

5-Year

ARI

10-Year

ARI

20-Year

ARI

50-Year

ARI

100-

Year

ARI

63

Figure 4.2: IDF curve for Kg. Sg. Tua (Stn. 3216001)




respectively for Kg. Sg. Tua (Stn. 3216001). The value of intensity decrease when the

duration increase, but at minutes 30 to 60, the intensity has stop reduce sharply from

minutes 15, and 10. The value of intensity at duration two years, fifth years, 10 years,

20 years, 50 years, and 100 years were 63.0 mm/hr, 77.0 mm/hr, 86.2 mm/hr, 95.1

mm/hr, 106.6 mm/hr, and 115.2 mm/hr at minutes 60. The lowest value of the intensity

were 1.5 mm/hr, 1.7 mm/hr, 1.9 mm/hr, 2.1 mm/hr, 2.3 mm/hr and 2.5 mm/hr with

return period two years, fifth years, 10 years, 20 years, 50 years, 100 years.

1

10

100

1000

1 10 100 1000 10000

INT

EN

SIT

Y (

mm

/hr)

DURATION (minutes)

IDF CURVE FOR KG. SG. TUA

2-Year

ARI

5-Year

ARI

10-Year

ARI

20-Year

ARI

50-Year

ARI

100-Year

ARI

64

Figure 4.3: IDF curve for SMJK Kepong (Pindah ke Taman Sri Murni) (Stn. 3216004)

Based on the Figure 4.2, the at duration 5 minutes, had shown 191.1 mm/hr,



respectively for SMJK Kepong (Pindah ke Taman Sri Murni) (Stn. 3216004). The value

of intensity decrease when the duration increase, but at minutes 30, the intensity has

stop reduce sharply from minutes 15 and 10. The value of intensity at duration two

years, fifth years, 10 years, 20 years, 50 years, and 100 years were 96.9 mm/hr, 117.1

mm/hr, 130.4 mm/hr, 143.2 mm/hr, 159.8 mm/hr, and 172.2 mm/hr at minutes 30. The

lowest value of the intensity were 1.4 mm/hr, 1.8 mm/hr, 2.0 mm/hr, 2.2 mm/hr, 2.5

mm/hr and 2.7 mm/hr with return period two years, fifth years, 10 years, 20 years, 50

years, 100 years.

1

10

100

1000

1 10 100 1000 10000

INT

EN

SIT

Y (

mm

/hr)

DURATION (minutes)

IDF CURVE FOR SMJK KEPONG

2-Year

ARI

5-Year

ARI

10-Year

ARI

20-Year

ARI

50-Year

ARI

100-

Year

ARI

65

Figure 4.4: IDF curve for Ibu Bekalan Km. 16, Gombak (Stn. 3217001)




respectively for Ibu Bekalan Km. 16, Gombak (Stn. 3217001). The value of intensity

decrease when the duration increase, but at minutes 30, the intensity has stop reduce

sharply from minutes 15. The value of intensity at duration two years, fifth years, 10

years, 20 years, 50 years, and 100 years were 94.6 mm/hr, 117.3 mm/hr, 132.2 mm/hr,

146.6 mm/hr, 165.3 mm/hr, and 179.2 mm/hr at minutes 30. The lowest value of the

intensity were 1.5 mm/hr, 1.7 mm/hr, 1.9 mm/hr, 2.1 mm/hr, 2.3 mm/hr and 2.4 mm/hr

with return period two years, fifth years, 10 years, 20 years, 50 years, 100 years.

1

10

100

1000

1 10 100 1000 10000

INT

EN

SIT

Y (

mm

/hr)

DURATION (minutes)

IDF CURVE FOR IBU BEKALAN KM.16, GOMBAK

2-Year

ARI

5-Year

ARI

10-Year

ARI

20-Year

ARI

50-Year

ARI

100-Year

ARI

66

Figure 4.5: IDF curve for Empangan Genting Klang (Stn. 3217002)




respectively for Empangan Genting Klang (Stn. 3217002). The value of intensity


sharply from minutes 15 and 10. The value of intensity at duration two years, fifth

years, 10 years, 20 years, 50 years, and 100 years were 96.4 mm/hr, 118.5 mm/hr, 133.2

mm/hr, 147.2 mm/hr, 165.4 mm/hr, and 179.0 mm/hr at minutes 30. The lowest value of

the intensity were 1.5 mm/hr, 1.8 mm/hr, 2.0 mm/hr, 2.2 mm/hr, 2.4 mm/hr and 2.6

mm/hr with return period two years, fifth years, 10 years, 20 years, 50 years, 100 years.

1

10

100

1000

1 10 100 1000 10000

INT

EN

SIT

Y (

mm

/hr)

DURATION (minutes)

IDF CURVE FOR EMPANGAN GENTING KLANG

2-Year

ARI

5-Year

ARI

10-Year

ARI

20-Year

ARI

50-Year

ARI

100-

Year

ARI

67

Figure 4.6: IDF curve for Ibu Bekalan Km. 11, Gombak (Stn. 3217003)




respectively for Ibu Bekalan Km. 11, Gombak (Stn. 3217003). The value of intensity







1

10

100

1000

1 10 100 1000 10000

INT

EN

SIT

Y (

mm

/hr)

DURATION (minutes)


2-Year

ARI

5-Year

ARI

10-Year

ARI

20-Year

ARI

50-Year

ARI

100-Year

ARI

68

Figure 4.7: IDF curve for Kg. Kuala Seleh (Stn. 3217004)




respectively for Kg. Kuala Seleh (Stn. 3217004). The value of intensity decrease when

the duration increase, but at minutes 30, the intensity has stop reduce sharply from

minutes 15 and 10. The value of intensity at duration two years, fifth years, 10 years, 20

years, 50 years, and 100 years were 97.5 mm/hr, 115.5 mm/hr, 127.5 mm/hr, 138.9




1

10

100

1000

1 10 100 1000 10000

INT

EN

SIT

Y (

mm

/hr)

DURATION (minutes)

IDF CURVE FOR KG.KUALA SELEH

2-Year

ARI

5-Year

ARI

10-Year

ARI

20-Year

ARI

50-Year

ARI

100-Year

ARI

69

Figure 4.8: IDF curve for Kg. Kerdas (Stn. 3217005)




respectively for Kg. Kerdas (Stn. 3217005). The value of intensity decrease when the

duration increase, but at minutes 60, the intensity has stop reduce sharply from minutes

30 and 15. The value of intensity at duration two years, fifth years, 10 years, 20 years,

50 years, and 100 years were 66.5 mm/hr, 83.8 mm/hr, 95.2 mm/hr, 106.2 mm/hr, 120.4

mm/hr, and 131.0 mm/hr at minutes 60. The lowest value of the intensity were 1.3



1

10

100

1000

1 10 100 1000 10000

INT

EN

SIT

Y (

mm

/hr)

DURATION (minutes)

IDF CURVE FOR KG. KERDAS

2-Year

ARI

5-Year

ARI

10-Year

ARI

20-Year

ARI

50-Year

ARI

100-

Year

ARI

70

Figure 4.9: IDF curve for Air Terjun Sg. Batu (Stn. 3317001)




respectively for Air Terjun Sg. Batu (Stn. 3317001). The value of intensity decrease







1

10

100

1000

1 10 100 1000 10000

INT

EN

SIT

Y (

mm

/hr)

DURATION (minutes)

IDF CURVE FOR AIR TERJUN SG. BATU

2-Year

ARI

5-Year

ARI

10-Year

ARI

20-Year

ARI

50-Year

ARI

100-Year

ARI

71

Figure 4.10: IDF curve for Genting Sempah (Stn. 3317004)




respectively for Genting Sempah (Stn. 3317004). The value of intensity decrease when







Based on Figure 4.1 to 4.10, it can be seen that station Kg. Kerdas (Stn.

3217005) has the highest intensity value than the rest at ARI 100 years which is 765.2

1

10

100

1000

1 10 100 1000 10000

INT

EN

SIT

Y (

mm

/hr)

DURATION (minutes)

IDF CURVE FOR GENTING SEMPAH

2-Year

ARI

5-Year

ARI

10-Year

ARI

20-Year

ARI

50-Year

ARI

100-

Year

ARI

72

mm/hr at 5 minutes interval. While the lowest intensity at station Kg. Kerdas (Stn.

3217005) at ARI 2 year which is 1.3 mm/hr at 7200 minutes interval.

From the Figure 4.1 to 4.10, it can be seen that the shape of the IDF slighty

different from shape that usually seen in MSMA2. Also as shown in all figure that line

of intensity from ARI 2 years was far from value of intensity for ARIs 5 years, 10 years,

20 years, 50 years and 100 years. This is due to factor of data involve and coefficient

that may affect the value of intensity for all period of time. As for ARI 2 years, it may

from calculation or the missing data that affect the result. Calculation for missing data

also affect the result may different from actual data that cause the curve was developed

as above.

For IDF curve at Figure 4.9, the shape of curve almost same as in usual IDF

curve shape but there have slightly different at interval 20 minutes that effect the shape

of the graph. The value of intensity at that point slightly different in value within period

of time that causes the point located far away from each other. This is probably affected

from value of raw data or calculation of missing data at that interval. After the value of

data calculated with coefficient, the intensity value turns that way.

From the result, it can be seen that the shape of the IDF curve for Figure 4.10

was different at line of intensity from ARI 2 year was far from value of intensity for

ARI 5 year, 10 year, 20 year, 50 year and 100 year. Also same to other period where it

start from interval 5 minutes until interval 360 minutes. This is due to factor of data

involve and coefficient that may affect the value of intensity for all period of time. As

for ARI 2 years, it may from calculation or the missing data that affect the result.

Calculation for missing data also affect the result may different from actual data that

cause the curve was developed as above.

4.6.2 Hulu Langat

Figure 4.11 until Figure 4.13 shown the IDF curve for every district Hulu Langat

in Klang Valley that cover from station Sek. Men. Bandar Tasik Kesuma (Stn.

2818110), RTM Kajang (Stn. 2917001), and Sek. Keb. Kg. Sg. Lui (Stn. 3118102). The

73


years, 20 year, 50 years and 100 years.

Figure 4.11: IDF curve for S.M. Bandar Tasik Kesuma (Stn. 2818110)




respectively for S.M. Bandar Tasik Kesuma (Stn. 2818110). The value of intensity



years, 20 years, 50 years, and 100 years were 2.4 mm/hr, 3.2 mm/hr, 3.8 mm/hr, 4.3




1

10

100

1000

1 10 100 1000 10000

INT

EN

SIT

Y (

mm

/hr)

DURATION (minutes)

IDF CURVE FOR S.M. BANDAR TASIK KESUMA

2-Year

ARI

5-Year

ARI

10-Year

ARI

20-Year

ARI

50-Year

ARI

100-

Year

ARI

74

Figure 4.12: IDF curve for Rtm Kajang (Stn. 2917001)




respectively for RTM Kajang (Stn. 2917001). The value of intensity decrease when the




161.9 mm/hr, and 175.7 mm/hr at minutes 30. The lowest value of the intensity were

1.4 mm/hr, 1.8 mm/hr, 2.0 mm/hr, 2.2 mm/hr, 2.5 mm/hr and 2.7 mm/hr with return

period two years, fifth years, 10 years, 20 years, 50 years, 100 years.

1

10

100

1000

1 10 100 1000 10000

INT

EN

SIT

Y (

mm

/hr)

DURATION (minutes)

IDF CURVE FOR RTM KAJANG

2-Year

ARI

5-Year

ARI

10-Year

ARI

20-Year

ARI

50-Year

ARI

100-Year

ARI

75

Figure 4.13: IDF curve for S.K. Kg. Sg. Lui (Stn. 3118102)




respectively for S.K. Kg. Sg. Lui (Stn. 3118102). The value of intensity decrease when







Based on Figure 4.11 to 4.13, it can be seen that station S.K. Kg. Sg. Lui (Stn.


1

10

100

1000

1 10 100 1000 10000

INT

EN

SIT

Y (

mm

/hr)

DURATION (minutes)

IDF CURVE FOR S.K. KG. SG. LUI

2-Year

ARI

5-Year

ARI

10-Year

ARI

20-Year

ARI

50-Year

ARI

100-

Year

ARI

76

mm/hr at 5 minutes interval. While the lowest intensity at station Sek. Men. Bandar

Tasik Kesuma (Stn. 2818110) at ARI 2 years which is 1.2 mm/hr at 7200 minutes

interval.

From the result, it can be seen that the shape of the IDF slightly different from

shape that usually seen in MSMA2. Also as shown in Figure 4.11 and 4.13 above, line

of intensity from ARI 2 year was far from value of intensity for ARIs 5 years, 10 years,





as above.

As Figure 4.11, the curve almost similar and smooth like IDF curve in MSMA2

but slightly different start from interval 2880 minutes which cause the shape bit

different and not declined smoothly. That may affect from the data calculated or

coefficient that cause the value of intensity turns that way.

4.6.3 Klang

Figure 4.14 until Figure 4.16 shown the IDF curve for every district Klang in

Klang Valley that cover from station Pintu Kawalan P/S Telok Gong (Stn. 2913001),

JPS Pulau Lumut (Stn. 2913122) and Ldg. Sg. Kapar (Stn. 3113087). The intensity in

IDF curve, represent by return period of two year followed by fifth year, ten years, 20

year, 50 years and 100 years.

77

Figure 4.14: IDF curve Pintu Kawalan P/S Telok Gong (Stn. 2913001)




respectively for Pintu Kawalan P/S Telok Gong (Stn. 2913001). The value of intensity


sharply from minutes 15 and 10. The value of intensity at duration two years, fifth


mm/hr, 144.7 mm/hr, 166.3 mm/hr, and 182.4 mm/hr at minutes 30. The lowest value of



1

10

100

1000

1 10 100 1000 10000

INT

EN

SIT

Y (

mm

/hr)

DURATION (minutes)

IDF CURVE FOR PINTU KAWALAN P/S TELOK GONG

2-Year

ARI

5-Year

ARI

10-Year

ARI

20-Year

ARI

50-Year

ARI

100-

Year

ARI

78

Figure 4.15: IDF curve for Jps Pulau Lumut (Stn. 2913122)




respectively for JPS Pulau Lumut (Stn. 2913122). The value of intensity decrease when







1

10

100

1000

1 10 100 1000 10000

INT

EN

SIT

Y (

mm

/hr)

DURATION (minutes)

IDF CURVE FOR JPS PULAU LUMUT

2-Year

ARI

5-Year

ARI

10-Year

ARI

20-Year

ARI

50-Year

ARI

100-Year

ARI

79

Figure 4.16: IDF curve for Ldg. Sg. Kapar (Stn. 3113087)




respectively for Ldg. Sg. Kapar (Stn. 3113087). The value of intensity decrease when







Based on Figure 4.14 to 4.16, it can be seen that station Pintu Kawalan P/S

Telok Gong (Stn. 2913001) has the highest intensity value than the rest at ARI 100

1

10

100

1000

1 10 100 1000 10000

INT

EN

SIT

Y (

mm

/hr)

DURATION (minutes)

IDF CURVE FOR LDG. SG. KAPAR

2-Year

ARI

5-Year

ARI

10-Year

ARI

20-Year

ARI

50-Year

ARI

100-

Year

ARI

80

years which is 610.4 mm/hr at 5 minutes interval. While the lowest intensity at station

Pintu Kawalan P/S Telok Gong (Stn. 2913001) at ARI 2 years which is 1.3 mm/hr at

7200 minutes interval.

From the result, it can be seen that the shape of the IDF different from shape that

usually seen in MSMA2. Also as shown in Figure 4.14 and 4.16 above, line of intensity

from ARI 2 year was far from value of intensity for ARIs 5 years, 10 years, 20 years, 50

years and 100 years. This is due to factor of data involve and coefficient that may affect

the value of intensity for all period of time. As for ARI 2 years, it may from calculation

or the missing data that affect the result. Calculation for missing data also affect the

result may different from actual data that cause the curve was developed as above.

As Figure 4.15, the curve almost same as IDF curve but slightly different start

from interval 10 minutes which cause the shape bit different and not declined smoothly.

That may affect from the data calculated or coefficient that cause the value of intensity

turns that way.

4.6.4 Petaling

Figure 4.17 until Figure 4.18 shown the IDF curve for every district Petaling in

Klang Valley that cover from station Setia Alam (Stn. 3114085) and Pusat Penyelidikan

Getah Sg.Buloh (Stn. 3115079).The intensity in IDF curve, represent by return period

of two year followed by fifth years, ten years, 20 years, 50 years and 100 years.

81

Figure 4.17: IDF curve for Setia Alam (Stn. 3114085)




respectively for Setia Alam (Stn. 3114085). The value of intensity at duration two years,

fifth years, 10 years, 20 years, 50 years, and 100 years were 113.7 mm/hr, 149.0 mm/hr,

172.3 mm/hr, 194.7 mm/hr, 223.7 mm/hr, and 245.5 mm/hr at minutes 30. The lowest

value of the intensity were 1.6 mm/hr, 1.8 mm/hr, 2.0 mm/hr, 2.1 mm/hr, 2.3 mm/hr and

2.5 mm/hr with return period two years, fifth years, 10 years, 20 years, 50 years, 100

years.

1

10

100

1000

1 10 100 1000 10000

INT

EN

SIT

Y (

mm

/hr)

DURATION (minutes)

IDF CURVE FOR SETIA ALAM

2-Year

ARI

5-Year

ARI

10-Year

ARI

20-Year

ARI

50-Year

ARI

100-Year

ARI

82

Figure 4.18: IDF curve for Pusat Penyelidikan Getah Sg. Buloh (Stn. 3115079)




respectively for Pusat Penyelidikan Getah Sg. Buloh (Stn. 3115079). The value of

intensity decrease when the duration increase, but at minutes 60, the intensity has stop

reduce sharply from minutes 30 and 15. The value of intensity at duration two years,


89.7 mm/hr, 96.6 mm/hr, 105.6 mm/hr, and 112.3 mm/hr at minutes 60. After that

value less decrease at 1440 to 2880 minutes. The value intensity at two year, fifth years,

and 10 years, 20 years, 50 years and 100 years were 2.6 mm/hr, 2.9 mm/hr, 3.1 mm/hr,

3.3 mm/hr, 3.6 mm/hr and 3.8 mm/hr at 2880 minutes. The lowest value of the intensity



1

10

100

1000

1 10 100 1000 10000

INT

EN

SIT

Y (

mm

/hr)

DURATION (minutes)

IDF CURVE PUSAT PENYELIDIKAN GETAH SG. BULOH

2-Year

ARI

5-Year

ARI

10-Year

ARI

20-Year

ARI

50-Year

ARI

100-Year

ARI

83

Based on Figure 4.17 to 4.18, it can be seen that station Setia Alam (Stn.


mm/hr at 5 minutes interval. While the lowest intensity at station and Pusat

Penyelidikan Getah Sg.Buloh (Stn. 3115079) at ARI 2 year which is 1.4 mm/hr at 7200

minutes interval.

For IDF curve at Figure 4.17, the value of intensity decrease when the duration

increase, but at ARI 2 year which is have big gap in value intensity from other ARI. As

can be seen its not close to other period of time which may cause from value of data and

effect from calculation with coefficient use for ARI 2 year, the intensity has stop reduce

sharply from minutes 5 and 30.

As Figure 4.18, the curve almost same as IDF curve but slightly different start

from interval 15 minutes for ARI 100 year which cause the shape bit different and not

declined smoothly. That may affect from the data calculated or coefficient that cause the

value of intensity turns that way.

4.6.5 Summary

Intensity will be decrease when the minutes are decrease. In minutes 5, Rtm

Kajang (Stn. 2917001) show the high of intensity for duration two year with 251.9

mm/hr, and Sek. Men. Tasik Kesuma (Stn. 2818110) show the minimum of intensity

with 1.2 mm/hr.

For ARI 5 years, Setia Alam (Stn. 3114085) show the maximum value of the

intensity with 481.7 mm/hr and Pusat Kawalan Telok Gong (Stn. 2913001) show the

lowers intensity with 1.5 mm/hr. Setia Alam (Stn. 3114085) show the highest intensity

with 653.5 mm/hr at duration 10 years and the minimum is Pusat Kawalan Telok Gong

(Stn. 2913001) with 1.7 mm/hr.

For ARI 20 years, 50 years and 100 years, Setia Alam (Stn. 3114085) shows the

maximum with 818.4 mm/hr, 1031.8 mm/hr, and 1191.7 mm/hr. While, Ldg. Sg. Kapar

(Stn. 3113087) shows the lowers intensity for 20 years and 50 years with 1.9 mm/hr, 2.1

84

mm/hr. Pusat Penyelidikan Getah Sg. Buloh (Stn. 3115079) shows the lowers intensity

for 100 years with 2.2 mm/hr.

As result mostly graph with slightly shape come from data which is come DID

without missing data. Graph with missing data still have same pattern as previous study.

It may problem from the data itself or station tool that collect the value of precipitation.

4.7 IDF CURVE FOR LOG-NORMAL DISTRIBUTION


Figure 4.19 until Figure 4.28 shown the IDF curve for every district Wilayah

Persekutuan in Klang Valley that cover from station Ldg. Edinburgh Site 2 (Stn.

3116006), Kg. Sg. Tua (Stn. 3216001), SMJK Kepong (Pindah ke Taman Sri Murni)

(Stn. 3216004), Ibu Bekalan Km. 16, Gombak (Stn. 3217001), Empangan Genting

Klang (Stn. 3217002), Ibu Bekalan Km. 11, Gombak (Stn. 3217003), Kg. Kuala Seleh

(Stn. 3217004), Kg. Kerdas (This station shifted from Gombak Damsite)(SMART) (Stn.

3217005), Air Terjun Sg. Batu (Stn. 3317001), Genting Sempah (Stn. 3317004). The


years, 15 year, 20 year, 50 years and 100 years.

85

Figure 4.19: IDF curve for Ldg.Edinburgh Site 2 (Stn. 3216006)




respectively for Ldg.Edinburgh Site 2 (Stn. 3216006). The value of intensity decrease







1

10

100

1000

1 10 100 1000 10000

INT

EN

SIT

Y (

mm

/hr)

DURATION (minutes)

IDF CURVE FOR LDG. EDINBURGH SITE 2

2-Year

ARI

5-Year

ARI

10-Year

ARI

20-Year

ARI

50-Year

ARI

100-Year

ARI

86

Figure 4.20: IDF curve for Smjk Kepong (Stn. 3216004)

Based on the Figure 4.20, the at duration 5 minutes, had shown 192.2 mm/hr,



respectively for Smjk Kepong (Stn. 3216004). The value of intensity decrease when the







1.0

10.0

100.0

1000.0

1 10 100 1000 10000

INT

EN

SIT

Y (

mm

/hr)

DURATION (minutes)

2-Year

ARI

5-Year

ARI

10-Year

ARI

20-Year

ARI

50-Year

ARI

100-Year

ARI

IDF CURVE FOR SMJK KEPONG

87

Figure 4.21: IDF curve for Ibu Bekalan Km.16, Gombak (Stn. 3217001)




respectively for Ibu Bekalan Km.16, Gombak (Stn. 3217001). The value of intensity



years, 20 years, 50 years, and 100 years were 95.7mm/hr, 119.0 mm/hr, 133.3 mm/hr,




1

10

100

1000

1 10 100 1000 10000

INT

EN

SIT

Y (

mm

/hr)

DURATION (minutes)


2-Year

ARI

5-Year

ARI

10-Year

ARI

20-Year

ARI

50-Year

ARI

100-Year

ARI

88

Figure 4.22: IDF curve for Genting Klang (Stn. 3217002)




respectively for Genting Klang (Stn. 3217002). The value of intensity decrease when







1

10

100

1000

1 10 100 1000 10000

INT

EN

SIT

Y (

mm

/hr)

DURATION (minutes)

IDF CURVE FOR EMPANGAN GENTING KLANG

2-Year

ARI

5-Year

ARI

10-Year

ARI

20-Year

ARI

50-Year

ARI

100-Year

ARI

89

Figure 4.23: IDF curve for Ibu Bekalan Km.11, Gombak (Stn. 3217003)




respectively for Ibu Bekalan Km.11, Gombak (Stn. 3217003). The value of intensity







1

10

100

1000

1 10 100 1000 10000

INT

EN

SIT

Y (

mm

/hr)

DURATION (minutes)


2-Year

ARI

5-Year

ARI

10-Year

ARI

20-Year

ARI

50-Year

ARI

100-Year

ARI

90

Figure 4.24: IDF curve for Kg. Kuala Seleh (Stn. 3217004)




respectively for Kg. Kuala Seleh (Stn. 3217004). The value of intensity decrease when







1

10

100

1000

1 10 100 1000 10000

INT

EN

SIT

Y (

mm

/hr)

DURATION (minutes)

IDF CURVE FOR KG.KUALA SELEH

2-Year

ARI

5-Year

ARI

10-Year

ARI

20-Year

ARI

50-Year

ARI

100-Year

ARI

91

Figure 4.25: IDF curve for Kg. Sg. Tua

(Stn. 3216001)




respectively. The value of intensity decrease when the duration increase, but at minutes

30 to 60, the intensity has stop reduce sharply from minutes 15, and 10. The value of

intensity at duration two years, fifth years, 10 years, 20 years, 50 years, and 100 years

were 63.6 mm/hr, 78.9 mm/hr, 88.3 mm/hr, 96.9 mm/hr, 107.5 mm/hr, and 115.3 mm/hr

at minutes 60. The lowest value of the intensity were 1.5 mm/hr, 1.8 mm/hr, 1.9 mm/hr,

2.1 mm/hr, 2.2 mm/hr and 2.4 mm/hr with return period two years, fifth years, 10 years,

20 years, 50 years, 100 years. Figure 4.26 shown the IDF curve for Kg. Kerdas (Stn.

3217005).

Figure 4.25: IDF curve for Kg. Sg. Tua (Stn. 3216001)




respectively for Kg. Sg. Tua (Stn. 3216001). The value of intensity decrease when the







1

10

100

1000

1 10 100 1000 10000

INT

EN

SIT

Y (

mm

/hr)

DURATION (minutes)

IDF CURVE FOR KG. SG. TUA

2-Year

ARI

5-Year

ARI

10-Year

ARI

20-Year

ARI

50-Year

ARI

100-Year

ARI

92

Figure 4.26: IDF curve for Kg. Kerdas (Stn. 3217005)




respectively for Kg. Kerdas (Stn. 3217005). The value of intensity decrease when the







1

10

100

1000

1 10 100 1000 10000

INT

EN

SIT

Y (

mm

/hr)

DURATION (minutes)

IDF CURVE FOR KG. KERDAS

2-Year

ARI

5-Year

ARI

10-Year

ARI

20-Year

ARI

50-Year

ARI

100-Year

ARI

93

Figure 4.27: IDF curve for Air Terjun Sg. Batu 2 (Stn. 3217001)




respectively for Air Terjun Sg. Batu 2 (Stn. 3217001). The value of intensity decrease







1

10

100

1000

1 10 100 1000 10000

INT

EN

SIT

Y (

mm

/hr)

DURATION (minutes)

IDF CURVE FOR AIR TERJUN SG. BATU2

2-Year

ARI

5-Year

ARI

10-Year

ARI

20-Year

ARI

50-Year

ARI

100-Year

ARI

94

Figure 4.28: IDF curve for Genting Sempah (Stn. 3217004)




respectively for Genting Sempah (Stn. 3217004). The value of intensity decrease when


minutes 1440. The value of intensity at duration two years, fifth years, 10 years, 20

years, 50 years, and 100 years were 2.4 mm/hr, 2.8 mm/hr, 3.1 mm/hr, 3.4 mm/hr, 3.7




1

10

100

1000

1 10 100 1000 10000

INT

EN

SIT

Y (

mm

/hr)

DURATION (minutes)

IDF CURVE FOR GENTING SEMPAH

2-Year

ARI

5-Year

ARI

10-Year

ARI

20-Year

ARI

50-Year

ARI

100-Year

ARI

95

Based on Figure 4.19 to 4.28, it can be seen that station Kg. Kerdas (Stn.


mm/hr at 5 minutes interval. While the lowest intensity at station Kg. Kerdas (Stn.

3217005) at ARI 2 year which is 1.2 mm/hr at 7200 minutes interval.

From the Figure 4.19 to 4.28, it can be seen that the shape of the IDF different

from shape that usually seen in MSMA2. Also as shown in all figure that line of

intensity from ARI 2 years was far from value of intensity for ARIs 5 years, 10 years,





as above.

For IDF curve at Figure 4.27, the shape of curve almost same as in usual IDF

curve shape but there have slightly different at interval 15 minutes that effect the shape

of the graph. The value of intensity at that point has a bit big different in value within

period of time that cause the point located far away from each other. This is probably

affected from value of raw data or calculation of missing data at that interval. After the

value of data calculated with coefficient, the intensity value turns that way.

As for the Figure 4.28 above it can be seen that the shape of the IDF different at

line of intensity from ARI 2 year was far from value of intensity for ARI 5 year, 10

year, 20 year, 50 year and 100 year. This is due to factor of data involve and coefficient




as above.

4.7.2 Hulu Langat

Figure 4.29 until Figure 4.31 shown the IDF curve for every district Hulu Langat

in Klang Valley that cover from station Sek. Men. Bandar Tasik Kesuma (Stn.

96

2818110), RTM Kajang (Stn. 2917001), and Sek. Keb. Kg. Sg. Lui (Stn. 3118102). The


years, 20 year, 50 years and 100 years.

Figure 4.29: IDF curve for S.M. Bandar Tasik Kesuma (Stn. 2818110)




respectively for S.M. Bandar Tasik Kesuma (Stn. 2818110). The value of intensity





1

10

100

1000

1 10 100 1000 10000

INT

EN

SIT

Y (

mm

/hr)

DURATION (minutes)

IDF CURVE FOR S.M. BANDAR TASIK KESUMA

2-Year

ARI

5-Year

ARI

10-Year

ARI

20-Year

ARI

50-Year

ARI

100-Year

ARI

97



Figure 4.30: IDF curve for Rtm Kajang (Stn. 2917001)




respectively for RTM Kajang (Stn. 2917001). The value of intensity decrease when the

duration increase, but at minutes 4320, the intensity has stop reduce sharply from






1

10

100

1000

1 10 100 1000 10000

INT

EN

SIT

Y (

mm

/hr)

DURATION (minutes)

IDF CURVE FOR RTM KAJANG

2-Year

ARI

5-Year

ARI

10-Year

ARI

20-Year

ARI

50-Year

ARI

100-

Year

ARI

98

Figure 4.31: IDF curve for S.K Kg. Sg. Lui (Stn. 3118102)




respectively for S.K Kg. Sg. Lui (Stn. 3118102). The value of intensity decrease when







Based on Figure 4.29 to 4.31, it can be seen that station S.K. Kg. Sg. Lui (Stn.


1

10

100

1000

1 10 100 1000 10000

INT

EN

SIT

Y (

mm

/hr)

DURATION (minutes)

IDF CURVE FOR S.K. KG. SG. LUI

2-Year

ARI

5-Year

ARI

10-Year

ARI

20-Year

ARI

50-Year

ARI

100-

Year

ARI

99

mm/hr at 5 minutes interval. While the lowest intensity at station Sek. Men. Bandar

Tasik Kesuma (Stn. 2818110) at ARI 2 years which is 1.3 mm/hr at7200 minutes

interval.


usually seen in MSMA2. Also as shown in figure 4.29 and 4.31 above, line of intensity

from ARI 2 year was far from value of intensity for ARIs 5 years, 10 years, 20 years, 50





As Figure 4.30, the figure almost same as IDF curve but slightly different start

from interval 15 minutes which cause the shape bit different and not declined smoothly.

That may affect from the data calculated or coefficient that cause the value of intensity

turns that way.

4.7.3 Klang

Figure 4.32 until Figure 4.34 shown the IDF curve for every district Klang in

Klang Valley that cover from station Pintu Kawalan P/S Telok Gong (Stn. 2913001),

JPS Pulau Lumut (Stn. 2913122) and Ldg. Sg. Kapar (Stn. 3113087). The intensity in

IDF curve, represent by return period of two year followed by fifth year, ten years, 20

year, 50 years and 100 years.

100

Figure 4.32: IDF curve for Pintu Kawalan P/S Telok Gadong (Stn. 2913001)




respectively for Pintu Kawalan P/S Telok Gadong (Stn. 2913001). The value of


reduce sharply from minutes 30. The value of intensity at duration two years, fifth


mm/hr 88.0 mm/hr, 97.3 mm/hr, and 103.9 mm/hr at minutes 30. The lowest value of



1

10

100

1000

1 10 100 1000 10000

INT

EN

SIT

Y (

mm

/hr)

DURATION (minutes)

IDF CURVE FOR PINTU KAWALAN P/S TELOK GADONG

2-Year

ARI

5-Year

ARI

10-Year

ARI

20-Year

ARI

50-Year

ARI

100-

Year

ARI

101

Figure 4.33: IDF curve for Jps Pulau Lumut (Stn. 2913122)




respectively for JPS Pulau Lumut (Stn. 2913122). The value of intensity decrease when







1

10

100

1000

1 10 100 1000 10000

INT

EN

SIT

Y (

mm

/hr)

DURATION (minutes)

IDF CURVE FOR JPS PULAU LUMUT

2-Year

ARI

5-Year

ARI

10-Year

ARI

20-Year

ARI

50-Year

ARI

100-

Year

ARI

102

Figure 4.34: IDF curve for Ldg. Sg. Kapar (Stn. 3113087)




respectively for Ldg. Sg. Kapar (Stn. 3113087). The value of intensity decrease when







Based on Figure 4.32 to 4.34, it can be seen that station Pintu Kawalan P/S

Telok Gong (Stn. 2913001) has the highest intensity value than the rest at ARI 100

1

10

100

1000

1 10 100 1000 10000

INT

EN

SIT

Y (

mm

/hr)

DURATION (minutes)

IDF CURVE FOR LDG. SG. KAPAR

2-Year

ARI

5-Year

ARI

10-Year

ARI

20-Year

ARI

50-Year

ARI

100-Year

ARI

103

years which is 733.5 mm/hr at 5 minutes interval. While the lowest intensity at station

Pintu Kawalan P/S Telok Gong (Stn. 2913001) at ARI 2 years which is 1.3 mm/hr at

7200 minutes interval.


usually seen in MSMA2. Also as shown in Fiqure 4.34 above, line of intensity from

ARI 2 year was far from value of intensity for ARIs 5 years, 10 years, 20 years, 50





As Figure 4.32 and Figure 4.33, the curve almost same as IDF curve but slightly

different start from interval 15 minutes which cause the shape bit different and not



4.7.4 Petaling

Figure 4.35 until Figure 4.36 shown the IDF curve for every district Petaling in

Klang Valley that cover from station Setia Alam (Stn. 3114085) and Pusat Penyelidikan

Getah Sg.Buloh (Stn. 3115079).The intensity in IDF curve, represent by return period

of two year followed by fifth years, ten years, 20 years, 50 years and 100 years.

104

Figure 4.35: IDF curve for Setia Alam (Stn. 3114085)




respectively for Setia Alam (Stn. 3114085). The value of intensity at duration two years,


100.5 mm/hr, 109.3 mm/hr, 120.2 mm/hr, and 128.0 mm/hr at minutes 60. The lowest

value of the intensity were 1.6 mm/hr, 1.8 mm/hr, 2.0 mm/hr, 2.1 mm/hr, 2.3 mm/hr and

2.4 mm/hr with return period two years, fifth years, 10 years, 20 years, 50 years, 100

years.

1

10

100

1000

1 10 100 1000 10000

INT

EN

SIT

Y (

mm

/hr)

DURATION (minutes)

IDF CURVE FOR SETIA ALAM

2-Year

ARI

5-Year

ARI

10-Year

ARI

20-Year

ARI

50-Year

ARI

100-Year

ARI

105

Figure 4.36: IDF curve for Pusat Penyelidikan Getah Sg. Buloh (Stn. 3115079)




respectively for Pusat Penyelidikan Getah Sg. Buloh (Stn. 3115079). The value of


reduce sharply from minutes 30 and 15. The value of intensity at duration two years,


90.2 mm/hr, 95.9 mm/hr, 102.8 mm/hr, and 107.7 mm/hr at minutes 60. After that

value less decrease at 1440 to 2880 minutes. The value intensity at two year, fifth years,

and 10 years, 20 years, 50 years and 100 years were 2.6 mm/hr, 2.9 mm/hr, 3.1 mm/hr,

3.3 mm/hr, 3.5 mm/hr and 3.6 mm/hr at 2880 minutes. The lowest value of the intensity



1

10

100

1000

1 10 100 1000 10000

INT

EN

SIT

Y (

mm

/hr)

DURATION (minutes)

IDF CURVE PUSAT PENYELIDIKAN GETAH SG. BULOH

2-Year

ARI

5-Year

ARI

10-Year

ARI

20-Year

ARI

50-Year

ARI

100-

Year

ARI

106

Based on Figure 4.35 to 4.36, it can be seen that station Setia Alam (Stn.


mm/hr at 5 minutes interval. While the lowest intensity at station and Pusat

Penyelidikan Getah Sg.Buloh (Stn. 3115079) at ARI 2 year which is 1.5 mm/hr at 7200

minutes interval.

For IDF curve at Figure 4.35, the value of intensity decrease when the duration

increase, but at ARI 2 year which is have big gap in value intensity from other ARI. As

can be seen it’s not close to other period of time which may cause from value of data

and effect from calculation with coefficient use for ARI 2 year, the intensity has stop

reduce sharply from minutes 5 and 30.

As Figure 4.36, the figure almost same as IDF curve but slightly different start

from interval 15 minutes for ARI 100 year which cause the shape bit different and not



4.7.5 Summary

Intensity will be decrease when the minutes are decrease. In minutes 5, RTM

Kajang (Stn. 2917001) show the high of intensity for duration two year with 251.6

mm/hr, and Kg. Kerdas (Stn. 3217005) show the minimum of intensity with 1.2 mm/hr.

For ARI 5 years, 10 years, 20 years, 50 years and 100 years Setia Alam (Stn.

3114085) show the maximum value of the intensity with 349.3 mm/hr, 454.8 mm/hr,

565.6 mm/hr, 722.8 mm/hr and 851.2 mm/hr. While for 10 years, Pusat Kawalan P/S

Telok Gong (Stn. 2913001) sho the minimum of intensity with 1.6 mm/hr and Ldg. Sg.

Kapar (Stn. 3113087) show the lowers intensity with 1.7 mm/hr, 1.8 mm/hr, 2.0 mm/hr,

2.1 mm/hr.

As result mostly graph with slightly shape come from data which is come DID

without missing data. Graph with missing data still have same pattern as previous study.

It may problem from the data itself or station tool that collect the value of precipitation.

107

4.8 COMPARISON OF KOLMOGOROV-SMIRNOV FOR GUMBEL AND

LOG-NORMAL DISTRIBUTION.

4.8.1 Emperical Calculation for Gumbel Distribution

In statistic, a visual inspection of the fit of the frequency distribution was probably

the best aid in determining the individual distribution fits a set of data. In this study, to

searching the goodness of fit statistic to plot confident limits for Gumbel Distribution

used KS. To plot the flood data, we used Empirical formula for Gumbel Distribution.

The examples of calculation for Gumbel distribution shown in Table 4.11 below.

108

Table 4.11: Summary Calculation for Plotting Position Formula for Gumbel Distribution

1 2 3 4 5 6 7 8 9 10 11 12 13 14

Year/

Time Rank 15 min

Empirical

Return

Period

Emperical

CDF α u y

Fitted

CDF Differrent

Upper

confident

limit

Empirical

Return

Period

Lower

confident

Limit

Empirical

Return

Period

2011 1 78.5 26.00 0.96 11.13 30.08 4.35 0.9872 0.0257 1.26 -3.89 0.72 3.54

1998 2 68.5 13.00 0.92 11.13 30.08 3.45 0.97 0.0458 1.24 -4.19 0.70 3.32

1994 3 60.8 8.67 0.88 11.13 30.08 2.76 0.94 0.0541 1.21 -4.79 0.67 3.02

1997 4 45.6 6.50 0.85 11.13 30.08 1.39 0.78 0.0657 1.05 -19.83 0.51 2.04

1983 5 41.4 5.20 0.81 11.13 30.08 1.02 0.70 0.1111 0.97 29.91 0.43 1.74

1999 6 40.2 4.33 0.77 11.13 30.08 0.91 0.67 0.1008 0.94 16.25 0.40 1.66

1992 7 38.6 3.71 0.73 11.13 30.08 0.77 0.63 0.1027 0.90 9.81 0.36 1.56

2015 8 37.5 3.25 0.69 11.13 30.08 0.67 0.60 0.0938 0.87 7.60 0.33 1.49

1995 9 36.0 2.89 0.65 11.13 30.08 0.53 0.56 0.0981 0.83 5.74 0.29 1.40

1990 10 35.1 2.60 0.62 11.13 30.08 0.45 0.53 0.0865 0.80 4.97 0.26 1.35

1996 11 35.0 2.36 0.58 11.13 30.08 0.44 0.53 0.0511 0.80 4.90 0.26 1.34

2009 12 35.0 2.17 0.54 11.13 30.08 0.44 0.53 0.0126 0.80 4.90 0.26 1.34

2010 13 34.0 2.00 0.50 11.13 30.08 0.35 0.50 0.0050 0.77 4.26 0.23 1.29

2012 14 33.0 1.86 0.46 11.13 30.08 0.26 0.46 0.0018 0.73 3.75 0.19 1.24

2013 15 33.0 1.73 0.42 11.13 30.08 0.26 0.46 0.0403 0.73 3.75 0.19 1.24

1986 16 30.5 1.63 0.38 11.13 30.08 0.04 0.38 0.0029 0.65 2.87 0.11 1.13

2014 17 30.2 1.53 0.35 11.13 30.08 0.01 0.37 0.0256 0.64 2.79 0.10 1.11

2000 18 30.0 1.44 0.31 11.13 30.08 -0.01 0.37 0.0575 0.64 2.74 0.10 1.11

1984 19 29.9 1.37 0.27 11.13 30.08 -0.02 0.36 0.0926 0.63 2.72 0.09 1.10

1993 20 29.6 1.30 0.23 11.13 30.08 -0.04 0.35 0.1212 0.62 2.65 0.08 1.09

1991 21 27.5 1.24 0.19 11.13 30.08 -0.23 0.28 0.0910 0.55 2.24 0.01 1.01

1989 22 25.3 1.18 0.15 11.13 30.08 -0.43 0.22 0.0612 0.49 1.94 -0.05 0.95

1988 23 22.4 1.13 0.12 11.13 30.08 -0.69 0.14 0.0207 0.41 1.68 -0.13 0.88

1985 24 19.0 1.08 0.08 11.13 30.08 -1.00 0.07 0.0102 0.34 1.51 -0.20 0.83

2001 25 16.0 1.04 0.04 11.13 30.08 -1.27 0.03 0.0096 0.30 1.43 -0.24 0.81

109

For the column 4, refer to the Equation 3.12 in chapter three to compute return

period T.

Take first column, N = 25, m = 1 (for Gumbel) Weibull

= 26.00

For column five, refer Equation 3.13 in chapter 3 to compute empirical estimate

of frequency F.

F = 0.9615

For the column six to eight, using this formula

(√ )

U = μ - 0.5772 α

110

y = 4.35

Column nine, for fitted Cummulative Density Function, CDF for Gumbel

F(x) = exp [−exp (-y)]

F(x) = exp [−exp (-4.35)]

= 0.9872

For row 11 and 13, the values of the fitted CDF need to add value in Table 3.3,

to get Upper confident limit. The value of the Fitted CDF minus with value in Table 3.3

to get Lower Confident Limit. Table 4.11 shown summary calculations for plotting

method.

The KS statistic was weak in as much as it is independent of the actual

distribution being plotted. It is also a constant and does not reflect the additional

uncertainty in predicted values of F at the extremes of the plotted point. Finally, it

cannot be used to compute Fu when Fp + KS > 1.0 or the compute Fl when FP - KS < 0.

The best procedure is to use the method appropriate to each distribution (Philip et al,

2002). Figure 4.37 shown plotting Gumbel (15 minutes) in station Kg. Kerdas

(Stn.3217005) for 95% Confident Limit.

Refer to the Appendix D and for graphical method using KS for station us in

each district in Klang Valley. All district in confidence interval. A few reasons for

weird plotting because it has has less data if compare to other district and intensity was

effected by method to obtained rainfall data.

111

Figure 14.37: Plotting Gumbel (15 minutes) in station Kg. Kerdas (Stn.3217005) for 95% Confidence Intervals.

112

4.8.2 Emperical Calculation for Log-Normal Distribution

In statistic, a visual inspection of the fit of the frequency distribution was probably

the best aid in determining the individual distribution fits a set of data. In this study, to

searching the goodness of fit statistic to find value of fitted CDF for Log-Normal used

KS. We used Empirical formula for Log-Normal Distribution to calculate. The

examples of calculation for Log-Normal distribution shown in Table 4.12.

Table 4.12: Summary Calculation for Plotting Position Formula for Log-Normal Distribution

1 2 3 4 5 6 7 8 9 10 11 12 13 14

Year

/

Time

Ran

k

15

min

Empirica

l Return

Period

Emperica

l CDF z-score Sn(x) F(x)

Fitted

CDF

Differr

ent

Upper

confide

nt limit

Empirica

l Return

Period

Lower

confide

nt

Limit

Empiric

al

Return

Period

2011 1 1.9 26.00 0.9615 2.3416 0.8413 0.9904 0.6897 0.1491 0.9597 24.8436 0.4197 1.7234

1998 2 1.8 13.00 0.9231 1.9564 0.6001 0.9748 0.6857 0.3747 0.9557 22.5886 0.4157 1.7115

1994 3 1.8 8.67 0.8846 1.6193 0.4283 0.9473 0.6786 0.5191 0.9486 19.4403 0.4086 1.6908

1997 4 1.7 6.50 0.8462 0.8062 0.5523 0.7899 0.6352 0.2377 0.9052 10.5442 0.3652 1.5752

1983 5 1.6 5.20 0.8077 0.5330 0.0421 0.7030 0.6095 0.6609 0.8795 8.2995 0.3395 1.5140

1999 6 1.6 4.33 0.7692 0.4499 0.6419 0.6736 0.6006 0.0317 0.8706 7.7266 0.3306 1.4938

1992 7 1.6 3.71 0.7308 0.3351 0.3434 0.6312 0.5875 0.2878 0.8575 7.0158 0.3175 1.4651

2015 8 1.6 3.25 0.6923 0.2534 1.0000 0.6000 0.5776 0.4000 0.8476 6.5634 0.3076 1.4443

1995 9 1.6 2.89 0.6538 0.1380 0.4688 0.5549 0.5632 0.0861 0.8332 5.9948 0.2932 1.4148

1990 10 1.5 2.60 0.6154 0.0664 0.2646 0.5265 0.5540 0.2619 0.8240 5.6803 0.2840 1.3966

1996 11 1.5 2.36 0.5769 0.0584 0.5090 0.5233 0.5529 0.0142 0.8229 5.6465 0.2829 1.3945

2009 12 1.5 2.17 0.5385 0.0584 0.7520 0.5233 0.5529 0.2287 0.8229 5.6465 0.2829 1.3945

2010 13 1.5 2.00 0.5000 -0.0236 0.7919 0.4906 0.5421 0.3013 0.8121 5.3227 0.2721 1.3739

2012 14 1.5 1.86 0.4615 -0.1079 0.8809 0.4570 0.5309 0.4238 0.8009 5.0228 0.2609 1.3530

2013 15 1.5 1.73 0.4231 -0.1079 0.9204 0.4570 0.5309 0.4634 0.8009 5.0228 0.2609 1.3530

1986 16 1.5 1.63 0.3846 -0.3306 0.1526 0.3705 0.5014 0.2179 0.7714 4.3738 0.2314 1.3010

2014 17 1.5 1.53 0.3462 -0.3586 0.9590 0.3600 0.4977 0.5990 0.7677 4.3053 0.2277 1.2949

2000 18 1.5 1.44 0.3077 -0.3774 0.6804 0.3530 0.4953 0.3274 0.7653 4.2606 0.2253 1.2908

1984 19 1.5 1.37 0.2692 -0.3868 0.0806 0.3495 0.4941 0.2689 0.7641 4.2386 0.2241 1.2888

1993 20 1.5 1.30 0.2308 -0.4153 0.3818 0.3390 0.4904 0.0428 0.7604 4.1738 0.2204 1.2827

1991 21 1.4 1.24 0.1923 -0.6233 0.3021 0.2665 0.4649 0.0356 0.7349 3.7716 0.1949 1.2420

1989 22 1.4 1.18 0.1538 -0.8590 0.2243 0.1952 0.4392 0.0292 0.7092 3.4394 0.1692 1.2037

1988 23 1.4 1.13 0.1154 -1.2031 0.1878 0.1145 0.4099 0.0733 0.6799 3.1240 0.1399 1.1627

1985 24 1.3 1.08 0.0769 -1.6684 0.1139 0.0476 0.3854 0.0663 0.6554 2.9018 0.1154 1.1304

2001 25 1.2 1.04 0.0385 -2.1542 0.7118 0.0156 0.3736 0.6962 0.6436 2.8060 0.1036 1.1156

113

For column 6 to 16 use this formula,

(4.1)

( μ, 𝜎) (4.2)

( ) ∑ (4.3)

( ) ( ) (4.4)

Where,

µ*= Mean value for Log-Normal

𝜎*= Standard deviation for Log-Normal

For column 10 contains the differences between the values in columns 7 and 6.

Different = ABS[F(x) – S(x)]. (4.5)

If the original data is normally distributed these differences will be zero.

For row 11 and 13, the values of the fitted CDF need to add value in Table 3.3, to

get Upper confident limit. The value of the Fitted CDF minus with value in Table 3.3 to

get Lower Confident Limit. Table 4.11 shown summary calculations for plotting method.

The KS statistic was weak in as much as it is independent of the actual distribution

being plotted. It is also a constant and does not reflect the additional uncertainty in

predicted values of F at the extremes of the plotted point. Finally, it cannot be used to

compute Fu when Fp + KS > 1.0 or the compute Fl when FP - KS < 0. The best procedure is

to use the method appropriate to each distribution (Philip et al, 2002). Figure 4.38 shown

plotting Log-Normal (15 minutes) in station Kg. Kerdas (Stn.3217005) for 95% Confident

Limit. Refer to the Appendix E and for graphical method using KS for station us in each

district in Klang Valley.

114

Figure 14.38: Plotting Log-Normal ( 15 minutes) in station Kg. Kerdas (Stn.3217005) for 95% Confidence Intervals.

115

4.8.3 Comparison of Probability Value between Both Method

To see the decide which curve in IDF curve close to straight line and more

fitting distribution between Gumbel method and Log-Normal, probability method was

calculated. Tables 4.13 show the maximum value for probability different between

empirical CDF and fitted CDF for both methods.

116

117

Table 4.13: Maximum Probability Different Between Gumbel and Log-Normal

Distribution

15 60 1400 15 60 1400

0.222 0.222 0.243 0.736 0.942 0.881

0.102 0.369 0.370 1.000 1.000 1.000

< a

33%

0.132 0.124 0.093 0.167 0.327 0.818

< a < a < a < a

100%

0.100 0.058 0.096 0.605 0.021 0.053

< a < a < a < a

100% 67%

0.128 0.079 0.131 0.760 0.886 0.715

< a < a < a

100%

0.144 0.099 0.070 0.887 0.769 0.856

< a < a < a

100%

0.199 0.250 0.210 0.702 0.673 0.743

< a < a

67%

0.121 0.159 0.142 0.631 0.672 0.737

< a < a < a

100%

0.121 0.081 0.086 0.6849954 0.610 0.664

< a < a < a

100%

0.271 0.117 0.051 0.61 0.65 0.82

< a < a

67%

0.123 0.084 0.124 0.789 0.850 0.973

< a < a < a

100%

0.509 0.122 0.075 0.624 0.812 0.804

< a < a

67%

0.138 0.107 0.150 0.785 0.721 0.772

< a < a < a

100%

0.121 0.110 0.058 0.704 0.697 0.835

< a < a < a

100%

0.164 0.160 0.104 0.820 0.775 0.716

< a < a < a

100%

0.162 0.143 0.110 0.842 0.658 0.802

< a < a < a

100%

0.262 0.151 0.177 0.829 0.667 0.748

< a < a < a

100%

0.168 0.092 0.109 0.552 0.716 0.753

< a < a < a

100%

85% 6%

Klang

Petaling

LOG-NORMAL Intensity (mm/hr) Intensity (mm/hr)

AVERAGE

0%

0%

0%

0%

0%

0%

P.K. P/S Telok Gong

0%

0.00%0.382

0.38212Ldg. Sg. Kapar

JPS Pulau Lumut 12 0.382

39 0.214

3115079

3114085

3113087

2913122

2913001

3118102

2917001

0.20244Sek. Keb. Kg. Sg. Lui

0.36813P.P Getah Sg. Buloh

Setia Alam 12

0.21439RTM Kajang

ID Station Year ∂

3217001

Air Terjun Sg. Batu

Kg. Kerdas

Kg. Kuala Seleh

Ibu Bekalan km.11

SMJK Kepong

Ldg. Edinburgh site 2

25

38

43

3317001

3217005

3217004

95% confident interval

0%

GUMBEL

0.21833%

0.195Ibu Bekalan Km.16

Empangan Genting Klang

0.21040

0.24030

0.27025

0.23035

Genting Sempah

Location

0%

Hulu Langat

< a

0%

0.21040

0%

0%

43 0.195

0.20045S.M.Bandar Kesuma2818110

0%

0%

W.P

3216001 Kg. Sg. Tua 44 0.2020%

0.270

0%

3317004

3217003

3217002

3216004

3116006

118

From the Table 4.13, value of probability difference was compared with value in

Table 3.3 with size of sample of 25 and value of Kolmogorov Smirnov, ∆ = 0.27. From

table above Gumbel Distribution showed to fit the graph than Log-Normal by not

rejected the value above 85% than 54 test involve for both method.

The Log-Normal showed to have worst fit as it rejected most of test by

Kolmogorov-Smirnov, 1 times than Gumbel Distribution. This is due to value of

parameter and assumption value for parameter z, S(x), and F(x). Different version of

fitted CDF for Log-Normal formulae also effect the value for fitted CDF may cause

most of value different probability close to 1.

Goodness of fit result is one way to determine the method should be considered

which there have clear trend in the result by analyzing the value of probability different.

These test not to use as clean answer whether that method can be use or not but as one

analyzing method to accept or reject those method in given clear trend. Based on table

above, most of test in Log-Normal give higher value than value of Kolmogorov

Smirnov, Log-Normal was rejected in given clear trend. Thus Gumbel distribution is

better method use for develop IDF curve for district in Pahang than Log-Normal.

4.9 COMPARISON BETWEEN CONSTRUCTED IDF CURVE AND

EXISTING IDF CURVE IN MSMA

Comparison intensity between new developed IDF curve and existing IDF curve

in MSMA2 are done to looking the percentage of differences in Gumbel distribution for

new developed IDF curve and existing IDF. Gumbel distribution is chosen because

showed to fit the graph than Log-Normal by not rejected the value above 85% than 54

tests involve for both method. Coefficient for the IDF equation from MSMA2 are taken

and calculated for value of intensity in MSMA2. Table 4.14 below shown the

coefficient for IDF equation in MSMA2 that have same station new develop IDF curve.

The intensity will be produce and base on the data, the percentage of differences are

determine.

119

Table 4.14: Intensity of Coefficient and Percentage of Difference in MSMA2

Rainfall Intensity of District in Klang Valley using Gumbel Distribution

ARI

(Years) 15 60 1440 15 60 1400 15 60 1440

2 130.00 60.00 8.90 121.42 62.99 4.22 6.60 4.98 52.63

5 150.00 69.00 8.00 146.68 76.97 5.14 2.21 11.55 35.76

10 166.00 75.00 6.90 163.41 86.23 5.75 1.56 14.97 16.66

20 178.00 85.00 6.20 179.45 95.10 6.34 0.82 11.89 2.20

50 223.00 95.00 5.60 200.22 106.60 7.10 10.22 12.21 26.71

100 245.00 110.00 4.90 215.78 115.21 7.66 11.93 4.74 56.41

2 136.00 64.00 5.10 128.30 65.44 4.11 5.66 2.24 19.43

5 154.00 75.00 5.90 153.44 79.90 5.17 0.36 6.53 12.41

10 172.00 85.00 6.80 170.08 89.48 5.87 1.12 5.27 13.69

20 184.00 95.00 7.50 186.04 98.66 6.54 1.11 3.85 12.78

50 230.00 115.00 8.50 206.70 110.55 7.41 10.13 3.87 12.81

100 260.00 130.00 9.50 222.18 119.46 8.06 14.54 8.11 15.12

2 136.00 60.00 5.00 139.34 69.79 4.47 2.45 16.32 10.68

5 151.00 68.00 5.80 165.44 83.62 5.74 9.56 22.96 1.09

10 169.00 75.00 6.40 182.72 92.77 6.58 8.12 23.69 2.78

20 181.00 85.00 7.00 199.29 101.55 7.38 10.11 19.47 5.50

50 220.00 95.00 8.00 220.75 112.91 8.43 0.34 18.86 5.37

100 250.00 110.00 8.90 236.82 121.43 9.21 5.27 10.39 3.50

2 142.00 62.00 4.80 124.47 65.01 4.31 12.34 4.86 10.23

5 157.00 70.00 5.40 156.45 77.12 5.03 0.35 10.18 6.79

10 175.00 78.00 6.00 177.63 85.14 5.51 1.50 9.16 8.11

20 187.00 86.00 6.80 197.93 92.83 5.97 5.85 7.95 12.16

50 228.00 98.00 7.50 224.22 102.79 6.57 1.66 4.89 12.42

100 250.00 110.00 8.50 243.92 110.25 7.02 2.43 0.23 17.47

2 140.00 64.00 5.20 129.09 64.63 4.38 7.79 0.99 15.73

5 160.00 75.00 6.00 160.64 78.89 5.35 0.40 5.19 10.89

10 175.00 82.00 6.80 181.52 88.34 5.99 3.73 7.73 11.98

20 187.00 92.00 7.50 201.55 97.39 6.60 7.78 5.86 12.03

50 235.00 115.00 8.80 227.49 109.11 7.39 3.20 5.12 16.02

100 260.00 125.00 9.80 246.92 117.90 7.98 5.03 5.68 18.53

2 133.00 92.00 5.20 122.75 66.40 4.60 7.70 27.82 11.52

5 153.00 110.00 6.20 150.66 81.77 5.83 1.53 25.67 5.89

10 169.00 130.00 6.90 169.13 91.94 6.65 0.08 29.28 3.60

20 180.00 140.00 7.80 186.85 101.69 7.44 3.81 27.36 4.68

50 220.00 160.00 8.80 209.79 114.32 8.45 4.64 28.55 3.99

100 245.00 170.00 9.80 226.98 123.78 9.21 7.36 27.19 6.03

2 142.00 60.00 4.80 127.76 67.37 4.49 10.03 12.28 6.42

5 160.00 68.00 5.20 157.15 77.45 5.51 1.78 13.89 5.94

10 172.00 75.00 5.90 176.60 84.12 6.18 2.67 12.16 4.78

20 181.00 82.00 6.50 195.26 90.52 6.83 7.88 10.39 5.04

50 220.00 92.00 7.50 219.41 98.80 7.66 0.27 7.40 2.18

100 245.00 100.00 8.20 237.51 105.01 8.29 3.06 5.01 1.10

2 170.00 62.00 4.60 136.64 66.54 3.95 19.62 7.33 14.20

5 190.00 75.00 5.40 187.07 83.80 5.02 1.54 11.74 7.03

10 210.00 84.00 6.00 220.45 95.23 5.73 4.98 13.37 4.48

20 240.00 92.00 6.90 252.47 106.19 6.41 5.19 15.42 7.06

50 280.00 110.00 8.00 293.92 120.38 7.30 4.97 9.43 8.81

100 300.00 130.00 8.90 324.98 131.01 7.96 8.33 0.77 10.60

2 180.00 71.00 5.10 127.86 65.59 4.46 28.97 7.63 12.61

5 200.00 82.00 5.90 144.47 78.56 5.57 27.76 4.20 5.61

10 230.00 95.00 6.70 155.47 87.14 6.31 32.40 8.27 5.89

20 255.00 110.00 7.50 166.02 95.38 7.01 34.89 13.29 6.51

50 295.00 120.00 8.50 179.68 106.04 7.93 39.09 11.63 6.76

100 330.00 150.00 9.50 189.92 114.03 8.61 42.45 23.98 9.36

2 120.00 55.00 4.20 117.34 57.04 3.83 2.22 3.71 8.70

5 140.00 62.00 5.00 201.70 76.50 4.85 44.07 23.39 3.07

10 150.00 70.00 5.50 257.54 89.38 5.52 71.70 27.69 0.30

20 160.00 78.00 6.20 311.11 101.74 6.16 94.44 30.44 0.66

50 195.00 90.00 7.10 380.46 117.74 6.99 95.11 30.82 1.54

100 220.00 100.00 9.00 432.42 129.73 7.61 96.55 29.73 15.40

Kg. Kuala Seleh

Genting Sempah

Air Terjun

Kg. Kerdas

Intensity (mm/hr) % Intensity (mm/hr)

GUMBEL PERCENTAGE

3217001

3216004

3216001

Ldg. Edinburgh

Kg. Sg. Tua

SMJK Kepong

StationID

W.P

Ibu Bekalan km.11

3116006

3317004

Location

MSMA2

3317001

3217005

3217004

3217003

Empangan

Ibu Bekalan km16

3217002

120

The new and existing value of intensity was substitute to the Equation 3.17 in

chapter three.

For example, station for Kg.Kerdas with ARI = 2 year for 15 minutes

= 19.62 %

2 155.00 60.00 5.10 118.12 60.25 3.83 23.79 0.41 24.95

5 180.00 70.00 5.80 161.02 74.31 4.86 10.54 6.16 16.28

10 195.00 79.00 6.50 189.42 83.62 5.54 2.86 5.85 14.83

20 220.00 89.00 7.20 216.67 92.55 6.19 1.52 3.99 14.04

50 260.00 100.00 8.20 251.93 104.11 7.03 3.10 4.11 14.22

100 290.00 125.00 9.00 278.36 112.77 7.67 4.01 9.78 14.81

2 155.00 60.00 5.10 134.51 62.81 4.48 13.22 4.68 12.09

5 180.00 70.00 5.80 168.78 76.15 5.63 6.23 8.78 2.98

10 195.00 79.00 6.50 191.46 84.98 6.38 1.81 7.57 1.78

20 220.00 89.00 7.20 213.22 93.45 7.11 3.08 5.00 1.25

50 260.00 100.00 8.20 241.39 104.42 8.05 7.16 4.42 1.83

100 290.00 125.00 9.00 262.49 112.64 8.75 9.49 9.89 2.73

2 175.00 65.00 4.90 133.82 65.35 4.25 23.53 0.54 13.23

5 195.00 76.00 5.80 194.53 81.12 5.47 0.24 6.74 5.74

10 220.00 88.00 6.50 234.72 91.57 6.27 6.69 4.05 3.51

20 255.00 98.00 7.40 273.27 101.58 7.04 7.17 3.65 4.81

50 295.00 120.00 8.60 323.18 114.55 8.04 9.55 4.54 6.48

100 330.00 145.00 9.80 360.57 124.26 8.79 9.26 14.30 10.29

2 155.00 60.00 5.10 119.33 58.06 4.19 23.01 3.23 17.78

5 180.00 70.00 5.80 176.45 72.83 5.24 1.97 4.05 9.59

10 195.00 79.00 6.50 214.27 82.61 5.94 9.88 4.57 8.63

20 220.00 89.00 7.20 250.54 91.99 6.61 13.88 3.36 8.25

50 260.00 100.00 8.20 297.49 104.13 7.47 14.42 4.13 8.91

100 290.00 125.00 9.00 332.68 113.22 8.12 14.72 9.42 9.82

2 155.00 60.00 5.10 132.30 67.27 4.68 14.65 12.12 8.27

5 180.00 70.00 5.80 147.90 78.28 5.57 17.83 11.83 4.01

10 195.00 79.00 6.50 158.23 85.57 6.16 18.85 8.32 5.29

20 220.00 89.00 7.20 168.14 92.56 6.72 23.57 4.00 6.65

50 260.00 100.00 8.20 180.97 101.61 7.45 30.40 1.61 9.12

100 290.00 125.00 9.00 190.58 108.39 8.00 34.28 13.28 11.11

2 140.00 60.00 4.90 130.90 71.57 4.26 6.50 19.28 12.97

5 160.00 70.00 5.60 149.74 83.09 5.01 6.41 18.70 10.48

10 185.00 80.00 6.50 162.21 90.72 5.51 12.32 13.39 15.25

20 210.00 90.00 7.20 174.17 98.03 5.98 17.06 8.92 16.88

50 250.00 110.00 8.50 189.66 107.50 6.60 24.14 2.27 22.35

100 280.00 130.00 9.50 201.27 114.60 7.06 28.12 11.85 25.67

2 140.00 60.00 4.90 147.29 73.48 4.44 5.21 22.47 9.47

5 160.00 70.00 5.60 214.93 91.43 5.65 34.33 30.61 0.89

10 185.00 80.00 6.50 259.71 103.31 6.45 40.39 29.14 0.72

20 210.00 90.00 7.20 302.66 114.70 7.22 44.13 27.45 0.33

50 250.00 110.00 8.50 358.27 129.46 8.22 43.31 17.69 3.27

100 280.00 130.00 9.50 399.93 140.51 8.97 42.83 8.09 5.59

2 140.00 60.00 4.90 148.59 71.53 4.33 6.13 19.22 11.64

5 160.00 70.00 5.60 169.77 82.45 5.21 6.10 17.79 6.92

10 185.00 80.00 6.50 183.79 89.68 5.80 0.65 12.10 10.82

20 210.00 90.00 7.20 197.24 96.61 6.36 6.08 7.35 11.71

50 250.00 110.00 8.50 214.65 105.59 7.08 14.14 4.01 16.68

100 280.00 130.00 9.50 227.70 112.32 7.63 18.68 13.60 19.73

Klang

Petalig

2913001

2913122

3113087

3114085

3115079

Pintu Kawalan

JPS Pulau Lumut

Ldg. Sg. Kapar

Setia Alam

Pusat Penyelidikan Getah

S.K.Bandar Tasik Kesuma

Hulu Langat

3118102

2917001

S.K. Kg. Sg. Lui

RTM Kajang

2818110

121

Value of intensity at 15 minutes, 60 minutes and 1440 minutes was choosen to

compare the percentage different because those time suitable to determine the depth of

rainfall. 5 minutes not to consider because it too short to measure rainfall depth and

1440 minutes was average the longest time for rainfall. While 60 minutes was middle

average for rainfall happen.

Wilayah Persekutuan at station Genting Sempah (Stn. 3317004) shows the higher

percentage of differences with 96.55%, at minutes 5 with duration 100 years. Intensity

for this station was increase because, the percentage of differences shown in positive

value.

Wilayah Persekutuan at station Kg.Sungai Tua (Stn. 3216001) had shown the

lower percentage of differences with 52.63%, at minutes 1440 with duration 100 years.

The intensity was decrease if compare the new intensity with existing intensity because

the percentage shown if negative value.

Most of the percentage of differences shown the negative value because base on

the Meteorology, in peninsular Malaysia, reduction in rainfall is recorded from 1975 to

2005 and intense as compare to these of those of 1951 to 1975. Most of the El Nino

events as of 1970 have resulted in severely dry years for peninsular Malaysia. The

Three Driest years for Peninsular Malaysia (1963, 1997, and 2002) have been recorded

during El Nino events. However, most La Nina events have resulted in wet years for

Peninsular Malaysia with the exception of 1998 and 1955. The change of weather for

the past few years with flood event on place that not usually have also effect the change

of percentage difference in intensity.

122

4.10 SUMMARY

Mean, median and standard deviation very important to abstract from rainfall

data. Base on the data, missing data was calculated from nearest station. The intensity

was produce from Gumbel Distribution and Log-Normal Distribution. 36 IDF curve

were produce for all district in Klang Valley from the data. Rtm Kajang (Stn. 2917001)

and Setia Alam (Stn. 3114085) has high of intensity where, Kg. Kerdas (Stn. 3217005),

Pusat Kawalan Telok Gong (Stn. 2913001), and Ladang Sg. Kapar (Stn. 3113087)

shown the lowest intensity for Log-Normal Distribution. For Gumbel Distribution, Rtm

Kajang (Stn. 2917001) and Setia Alam (Stn. 3114085) show the high of intensity

where, Sek. Men Tasik Kesuma (Stn. 2818110), Pusat Kawalan Telok Gong (Stn.

2913001), and Ladang Sg. Kapar (Stn. 3113087) and Pusat Penyelidikan Sg. Buloh

show the minimum of intensity.

While comparing value of Probability Difference for both method show mostly

Log-Normal test was rejected than Gumbel. Gumbel Distribution showed to fit the

graph than Log-Normal by not rejected the value above 85% than 54 tests involve for

both method. Thus Gumbel Disribution is better method use for develop IDF curve for

district in Klang Valley than Log-Normal.All district in Klang Valley in confidence

interval when KS test was done. Probability Difference for both methods shows mostly

Log-Normal test was rejected than Gumbel. Raw data from DID may effect the result. It

may come from the rain gauges problem and it will effected measurement of the rainfall

depth. As a suggestion, district with lot missing data can use intensity for to nearest

station to design a drainage and stormwater management, because the intensity

represents the value for that area.

New IDF curve was developed at few station in MSMA2, thus new value of

intensity was compared with value of intensity in MSMA2 based on return period of

IDF curve. From the comparison, station Genting Sempah (Stn. 3317004) at Wilayah

Persekutuan show the higher percentage differences with 96.55% at minutes 5 with

duration 100 years ARI. Wilayah Persekutuan at station Kg.Sungai Tua (Stn. 3216001)

had shown percentage of differences with - 52.63% which is the value of small intensity

at minutes 1440 with duration 100 years ARI.

CHAPTER 5

CONCLUSION

5.1 BACKGROUND

The hydrology design standard for urban drainage systems are commonly based

on the frequency of occurrence of heavy rainfall events. Observation of recent climate

history indicate that the frequency of occurrence of heavy rainfall events in increasing.

This increasing trend will likely continue in the future due to global warming (Yiping,

2006).

This study was conducted in Klang Valley area using Annual Maximum Series

(AMS) rainfall data to develop IDF curves. The duration of IDF curves from 5, 10, 15,

30, 60, 180, 360, 720, 1440, 2880, 4320 and 7200 minutes and the ARI including 2, 5,

10, 20, 50 and 100 years. The data collections are from Department of Irrigation and

Drainage (DID).

There were two frequency analysis used intensity were obtained used such as

Gumbel Distribution and Log-Normal Distribution. Based on this methods values of the

intensity were obtained. Few stations were selected to represent each district. Besides,

this study also did comparison on percentage of error between constructed IDF curve

and existing IDF curve in MSMA2. To ensure the data in confidence intervals, the

Kolmogorov-Smirnov (KS) test was done. The Kolmogorov-Smirnov goodness of fit

test is used to evaluate the accuracy of the fitting of a distrubution.

124

5.2 CONCLUSION

From this study, all missing data at each station data at 5 districts was calculated

by using value of precipitation from nearest station within 100 km radius from station

calculated.

In this study, 36 IDF curves for 18 stations in 5 districts of Klang Valley were

produced for both method, Gumbel and Log-Normal method. Table 5.1 and 5.2 shown

the maximum and minimum value of the intensity for both methods at interval 5

minutes.

Table 5.1: Maximum Value of the Intensity

Return

Period

(year)

Maximum Intensity and Location

Gumbel

Method

(mm/hr)

Location

Log-

Normal

(mm/hr)

Location

2 251.9 RTM Kajang

(Stn.2917001) 251.6

RTM Kajang

(Stn.2917001)

5 481.7 Setia Alam

(Stn. 3114085) 349.3

Setia Alam

(Stn. 3114085)

10 653.5 Setia Alam

(Stn. 3114085) 456.7

Setia Alam

(Stn. 3114085)

20 818.4 Setia Alam

(Stn. 3114085) 577.7

Setia Alam

(Stn. 3114085)

50 1031.8 Setia Alam

(Stn. 3114085) 752.7

Setia Alam

(Stn. 3114085)

100 1191.7 Setia Alam

(Stn. 3114085) 897.8

Setia Alam

(Stn. 3114085)

125

Table 5.2: Minimum Value of the Intensity

Return

Period

(year)

Minimum Intensity and Location

Gumbel

Method

(mm/hr)

Location

Log-

Normal

(mm/hr)

Location

2 1.2 S.M. Tasik Kesuma

(Stn. 2818110) 1.2

Kg. Kerdas

(Stn. 3217005)

5 1.5 P.K. Telok Gong

(Stn. 2913001) 1.6

P.P Getah Sg.

Buloh

(Stn. 3115079)

10 1.7 Ldg. Sg. Kapar

(Stn. 3113087) 1.7

Ldg. Sg. Kapar

(Stn. 3113087)


(Stn. 3113087) 1.8

Ldg. Sg. Kapar

(Stn. 3113087))


(Stn. 3113087) 2.0

Ldg. Sg. Kapar

(Stn. 3113087)

100 2.2 P.P Getah Sg. Buloh

(Stn. 3115079) 2.1

Ldg. Sg. Kapar

(Stn. 3113087)

For graphical method, all the data in range of the confidence intervals and was

developed for Gumbel distribution and for Log-Normal. Probability to has error for the

data was high and make the calculation not accurate. A few station has been same

intensity between MSMA2 but not reliable and need to reviewed using additional data

and latest method.

Probability Difference for both methods shows mostly Log-Normal test was

rejected than Gumbel. Gumbel Distribution showed to fit the graph than Log-Normal by

not rejected the value above 85% than 54 tests involve for both method. Thus Gumbel

Disribution is better method use for develop IDF curve for district in Klang Valley than

Log-Normal. Log-Normal did not show good to fit. Goodness of fit result is one way to

determine the method should be considered which there have clear trend in the result by

analyzing the value of probability different.

From the comparison between Gumbel Distribution in MSMA2 and developed

IDF, station Genting Sempah (Stn. 3317004) at Wilayah Persekutuan show the higher

percentage differences with 96.55% at minutes 15 with duration 100 years ARI.

126

Wilayah Persekutuan at station Kg.Sungai Tua (Stn. 3216001) had shown percentage of

differences with -52.63% at minutes 1440 with duration 2 years ARI. When the value

percentage of difference was negative, the intensity was decrease while the percentage

of difference was positive, the intensity was increase.

5.3 RECOMMENDATION

A few recommendations suggested from this study are:

i. Further studies are recommended whenever there will be more data to

verify the results obtained or update the IDF curves

ii. The design standards and guidelines currently employed by the Klang

Valley should be reviewed and/or revised in light of the information

presented in this report.

iii. The climate change scenario recommended for use in the evaluation of

storm water management design standards (i.e., the wet scenario) reveals a

significant increase in rainfall magnitude (and intensity) for a range of

durations and return periods.

iv. Update the IDF curves contain data up to and including year 2016 and

should be regularly updated as new data becomes available to reflect

changes in the climate.

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APPENDICES

Appendix A Rainfall Data in mm

Appendix B Rainfall Depth in mm

Appendix C

Appendix D

Appendix E

Rainfall Intensity in mm/hr

Plotting Gumbel Distribution with 95% Confident

Intervals

Plotting Log-Normal Distribution with 95%

Confident Intervals

APPENDIX A

133

A.1 Rainfall Data for Gumbel and Log-Normal Method

Table A1.1: Annual Maximum Rainfall for Ldg. Edinburgh Site (Stn. 3116006)

5 10 15 30 60 180 360 720 1440 2880 4320 7200

1978 14.0 19.7 25.1 43.2 61.3 78.5 78.5 78.5 78.5 82.5 121.2 141.5

1979 11.6 17.6 21.0 40.1 59.2 62.5 63.0 63.0 79.0 79.0 120.5 132.0

1980 18.7 23.6 32.2 42.5 67.1 91.6 93.0 96.5 154.1 171.0 172.9 266.0

1981 18.2 29.3 40.4 45.8 66.6 83.0 104.0 104.0 123.5 149.5 180.3 225.5

1982 19.9 35.6 36.3 58.0 69.5 70.5 72.0 87.0 94.0 109.5 148.5 178.3

1983 17.0 21.2 31.8 51.7 54.5 57.5 68.5 69.0 69.5 83.0 100.5 108.0

1984 12.5 23.1 34.6 54.3 74.9 90.5 97.6 109.0 140.5 156.0 164.5 186.0

1985 32.5 33.6 43.7 45.1 61.1 76.0 76.0 76.0 92.0 108.5 119.8 156.5

1986 32.0 32.0 32.0 50.3 53.6 75.4 78.5 78.5 78.5 108.0 119.0 181.0

1987 31.5 31.5 31.5 41.7 51.7 58.8 61.0 61.0 95.5 108.0 119.5 147.0

1988 50.5 50.5 50.6 50.7 54.4 84.7 92.0 92.0 94.2 122.5 154.0 171.5

1989 27.5 27.5 27.5 39.3 45.0 66.0 66.5 66.5 66.5 82.5 89.0 108.3

1990 11.3 19.0 20.1 29.8 54.2 95.9 99.0 99.0 99.0 99.0 99.0 100.0

1991 9.5 19.0 28.5 50.2 53.6 77.5 83.5 84.0 101.0 116.2 156.0 191.5

1992 18.0 30.3 40.6 46.0 55.0 61.0 61.0 61.0 70.5 104.5 108.4 145.5

1993 11.0 19.5 28.0 52.0 55.5 87.0 88.5 90.0 93.3 109.0 115.0 183.0

1994 16.5 28.0 39.0 57.5 69.0 74.0 74.0 74.0 93.5 113.5 126.5 157.0

1995 27.6 30.5 42.0 71.0 94.4 105.5 106.0 118.5 137.0 158.0 228.5 267.5

1996 15.5 28.5 38.5 57.5 88.0 123.5 124.0 124.5 158.5 191.5 202.0 202.5

1997 18.3 25.5 33.0 47.0 75.5 76.0 76.0 76.0 95.0 115.0 127.5 167.0

1998 27.6 32.2 37.5 66.5 88.0 113.1 119.5 119.5 142.0 142.0 181.5 233.5

1999 17.5 24.5 36.0 61.5 77.0 113.5 114.0 118.8 205.0 212.5 214.0 237.0

2000 13.5 24.0 35.0 55.5 88.0 90.5 104.5 138.0 139.5 156.5 197.5 240.5

2001 13.5 25.0 37.5 52.5 75.5 94.5 95.5 95.5 95.5 118.0 137.0 169.0

2002 16.5 29.5 43.0 71.5 88.0 94.0 131.5 136.0 136.5 169.5 199.5 228.0

2003 16.5 28.0 41.0 71.5 79.5 96.5 97.5 97.5 109.5 138.5 162.0 215.5

2004 13.5 25.5 36.0 57.5 80.5 86.0 99.5 107.5 121.5 139.5 185.5 233.5

2005 13.0 24.5 31.5 52.5 77.0 117.0 125.5 126.5 129.0 129.5 180.5 193.5

2006 13.0 23.5 33.0 53.0 78.0 109.0 109.0 109.0 121.0 131.5 150.0 188.5

2007 14.5 27.0 37.5 51.5 61.0 72.0 88.5 92.5 94.5 131.5 141.5 188.0

2008 13.0 22.5 30.5 53.0 73.5 100.0 100.0 100.0 104.0 171.5 186.5 239.5

2009 16.0 27.0 38.0 73.0 99.0 100.0 100.0 100.5 105.5 147.5 151.5 204.5

2010 14.5 26.5 37.0 56.0 76.0 80.5 81.5 81.5 86.0 121.5 122.0 164.5

2011 14.5 27.0 38.5 52.5 72.0 81.5 81.5 82.0 114.0 117.0 128.0 198.0

2012 17.0 28.0 39.0 70.5 92.5 103.0 105.0 105.0 128.5 129.5 141.5 184.5

2013 18.5 34.4 43.8 72.4 113.4 141.4 150.1 150.4 226.2 254.6 255.5 338.3

2014 16.3 30.2 39.1 57.9 72.2 79.6 79.9 80.3 86.2 108.0 125.5 165.7

2015 49.6 59.5 59.5 74.5 94.5 116.0 116.0 116.0 130.5 158.5 178.5 214.9

38 732.1 1064.3 1369.8 2077.0 2749.7 3383.5 3561.6 3664.5 4288.5 5043.8 5810.6 7252.5

mean 19.3 28.0 36.0 54.7 72.4 89.0 93.7 96.4 112.9 132.7 152.9 190.9

std. dev. 9.5 7.9 7.4 10.8 15.6 19.3 20.8 22.5 34.5 36.6 38.6 48.0


134

Table A1.2: Annual Maximum Rainfall for Kg. Sg. Tua (Stn. 3216001)

5 10 15 30 60 180 360 720 1440 2880 4320 7200

1972 12.4 24.8 33.5 56.8 102.0 102.7 103.1 103.6 103.9 106.6 143.8 151.0

1973 17.2 22.6 29.3 44.7 65.5 81.6 93.5 94.5 127.3 138.5 151.0 159.4

1974 23.9 29.4 39.2 46.5 82.6 83.5 83.5 89.5 91.0 112.6 130.5 171.0

1975 24.9 27.7 30.5 40.9 60.5 77.5 77.5 79.0 79.5 111.0 127.5 157.0

1976 28.3 31.0 36.9 51.7 89.6 160.3 163.6 168.5 186.7 224.9 244.0 274.7

1977 22.6 31.4 42.1 69.0 85.8 101.5 101.5 102.5 104.0 117.5 155.5 169.0

1978 12.2 20.3 26.3 40.7 51.5 58.8 65.0 65.0 65.0 101.4 106.5 164.0

1979 15.6 21.6 25.5 46.3 82.7 92.3 129.9 133.0 133.5 161.0 176.5 186.0

1980 19.4 27.9 33.6 55.5 68.9 91.0 91.0 91.0 97.0 99.0 131.5 145.0

1981 9.7 18.3 22.6 34.3 58.2 78.5 83.9 142.6 173.5 180.5 190.0 295.5

1982 11.1 16.6 25.0 49.9 71.7 103.0 103.0 103.5 128.0 130.5 169.9 210.5

1983 45.6 46.0 46.5 49.5 57.3 83.0 83.0 83.0 111.0 120.5 149.0 165.0

1984 38.7 42.5 42.5 43.5 51.7 71.5 71.5 71.5 81.5 112.5 129.0 156.5

1985 38.1 41.0 41.0 41.5 43.8 60.8 67.4 72.5 76.0 116.5 150.5 176.0

1986 19.2 20.0 20.2 39.8 51.4 63.0 75.0 75.0 103.5 105.0 115.5 152.5

1987 11.1 17.1 25.5 39.3 64.0 70.0 74.5 90.5 90.5 117.0 117.0 133.0

1988 16.5 17.8 19.2 33.9 51.2 67.0 71.5 81.0 124.0 142.9 197.5 209.5

1989 27.0 27.0 29.7 30.6 45.1 63.3 73.0 82.5 90.0 103.7 141.0 207.0

1990 20.5 20.5 27.8 43.6 43.8 44.0 72.6 132.5 134.5 140.0 140.0 183.5

1991 35.1 35.5 35.9 37.0 44.9 81.9 93.0 93.5 93.5 120.0 122.5 164.2

1992 2.6 5.1 7.7 15.4 26.3 57.3 68.0 68.0 92.0 93.5 102.0 142.0

1993 9.0 16.0 21.5 36.5 57.1 74.0 133.7 141.5 141.5 147.5 148.5 179.5

1994 15.7 27.0 34.5 51.0 74.0 76.5 76.5 77.5 92.5 169.0 177.5 202.5

1995 13.0 21.5 31.0 48.0 53.5 79.0 112.5 128.0 134.5 148.5 153.0 199.5

1996 18.0 28.5 37.0 52.5 78.0 115.5 128.5 129.0 129.0 181.5 231.0 290.5

1997 17.5 27.5 32.5 42.5 66.0 71.0 87.5 88.0 88.0 121.0 124.5 133.0

1998 29.0 29.0 29.2 44.4 66.0 87.5 87.5 90.0 90.0 128.8 184.0 235.5

1999 28.5 31.5 34.5 43.6 61.7 87.2 92.0 92.0 98.0 101.5 121.0 192.9

2000 15.0 25.5 35.0 58.5 73.5 96.0 97.0 97.0 98.0 132.5 148.5 173.5

2001 15.9 21.0 27.5 36.0 59.5 78.5 79.0 79.0 88.5 120.5 124.1 175.5

2002 15.0 27.0 37.0 55.5 80.5 83.5 83.5 83.5 91.0 125.0 151.0 175.0

2003 15.0 24.0 29.0 42.5 54.5 87.5 87.5 87.5 87.5 94.5 102.5 124.5

2004 12.5 22.5 32.5 51.5 58.0 60.5 74.5 74.5 91.0 109.5 124.0 171.5

2005 14.4 25.0 33.0 48.5 59.5 63.9 64.2 64.5 89.8 90.0 98.9 113.0

2006 13.0 22.5 29.5 43.5 55.5 72.5 74.0 74.0 78.0 91.0 115.5 171.0

2007 17.0 28.5 41.5 63.5 78.0 86.0 99.5 100.5 107.5 139.0 139.0 176.5

2008 14.5 25.0 34.5 55.0 72.5 86.5 87.0 87.0 92.0 138.0 162.0 187.5

2009 13.5 26.0 37.5 65.5 105.5 117.0 119.0 120.5 120.5 132.5 137.5 178.5

2010 20.0 28.5 34.0 55.0 64.0 72.5 73.0 73.0 94.0 140.0 148.0 173.0

2011 13.0 24.0 33.5 53.0 65.0 68.0 68.5 68.5 83.0 116.0 118.0 161.5

2012 19.0 22.5 30.0 46.5 70.5 82.5 83.1 90.0 98.5 110.8 133.5 185.5

2013 13.8 22.3 30.7 56.4 83.6 90.5 91.2 91.5 106.5 124.7 133.2 236.9

2014 12.5 19.2 25.4 43.9 69.0 87.3 88.2 88.5 106.2 158.0 180.6 195.0

2015 30.0 36.0 36.0 60.5 82.0 88.0 88.0 110.5 142.0 142.0 143.5 170.5

44 836.5 1124.6 1387.3 2064.2 2885.9 3603.9 3920.4 4158.7 4633.4 5616.9 6390.0 7974.6

mean 19.0 25.6 31.5 46.9 65.6 81.9 89.1 94.5 105.3 127.7 145.2 181.2

std. dev. 8.8 7.4 7.1 10.0 15.8 19.3 20.7 23.6 25.1 27.1 31.4 38.6


135

Table A1.3: Annual Maximum Rainfall for SMJK Kepong (Stn. 3216004)

5 10 15 30 60 180 360 720 1440 2880 4320 7200

1983 9.8 19.6 24.8 48.9 79.8 99.5 100.0 105.5 105.5 106.0 123.5 123.5

1984 13.1 25.3 29.5 46.7 66.4 91.9 92.0 96.5 107.4 118.5 135.0 153.0

1985 17.0 29.4 37.1 58.5 82.1 97.5 99.0 99.5 99.5 100.0 143.0 167.0

1986 11.5 17.4 26.0 46.4 75.1 140.5 142.5 142.5 143.0 179.5 193.0 220.5

1987 20.1 28.1 31.6 44.6 60.5 76.8 94.8 101.0 101.5 151.5 151.5 184.5

1988 12.2 22.9 30.7 48.4 71.1 82.5 83.0 86.0 119.0 169.0 191.5 224.0

1989 12.4 24.8 30.0 46.9 73.6 82.5 86.5 86.5 86.5 105.0 129.0 134.5

1990 30.0 37.4 44.9 55.5 62.5 81.2 83.0 83.0 83.0 98.0 145.0 174.5

1991 28.8 34.5 40.2 55.3 75.7 87.3 107.0 108.0 109.0 144.1 206.5 272.0

1992 17.3 28.3 34.2 50.5 69.5 93.0 93.0 93.0 93.0 126.5 126.5 132.5

1993 10.0 18.2 27.1 49.6 70.1 95.5 100.5 100.5 120.5 132.5 132.5 171.0

1994 25.6 28.2 32.5 47.0 53.5 56.0 62.0 66.5 72.0 86.5 116.0 153.0

1995 15.5 28.0 39.0 59.5 76.5 81.5 87.5 109.5 132.0 145.0 176.5 202.5

1996 16.5 24.5 34.5 58.5 67.5 103.0 109.0 109.0 110.0 131.0 163.5 237.0

1997 15.0 23.0 31.5 48.5 65.0 71.5 72.0 73.0 93.0 112.5 125.5 186.0

1998 16.0 27.0 35.0 60.0 73.0 116.5 133.0 133.5 133.5 144.0 168.0 195.5

1999 29.4 33.7 42.0 68.5 92.0 106.0 106.0 106.5 126.7 128.5 138.0 232.0

2000 15.5 30.0 39.5 63.5 81.0 85.5 86.5 109.0 112.5 151.0 174.0 194.5

2001 19.7 29.0 38.5 51.5 58.7 79.5 80.5 81.0 92.0 122.0 122.0 137.5

2002 15.5 26.5 37.5 63.0 106.0 155.0 156.0 158.0 166.0 196.0 220.5 236.0

2003 19.0 31.5 45.5 64.0 77.5 100.0 103.5 103.5 130.5 134.0 156.0 206.0

2004 13.5 20.5 26.5 36.5 46.0 53.5 59.5 59.5 68.5 70.5 103.5 118.0

2007 15.0 24.0 33.5 41.0 51.0 72.0 86.0 86.0 89.0 105.5 122.0 151.5

2008 15.0 25.0 25.0 25.0 43.0 54.0 55.0 55.5 58.0 62.0 73.5 113.5

2011 9.0 12.5 14.5 20.5 26.0 31.0 32.0 32.0 32.0 42.5 47.0 54.0

25 422.4 649.3 831.1 1258.3 1703.1 2193.2 2309.8 2384.5 2583.6 3061.6 3583.0 4374.0

mean 16.9 26.0 33.2 50.3 68.1 87.7 92.4 95.4 103.3 122.5 143.3 175.0

std.dev. 5.9 5.6 7.1 11.4 16.4 26.3 26.7 27.0 28.8 35.6 39.2 49.2


136

Table A1.4: Annual Maximum Rainfall for Ibu Bekalan Km.16, Gombak

(Stn. 3217001)

5 10 15 30 60 180 360 720 1440 2880 4320 7200

1973 12.7 20.8 27.2 39.9 53.7 81.2 89.0 89.5 119.0 127.5 130.0 151.5

1974 18.2 32.6 39.7 48.0 69.8 108.9 112.0 112.0 130.1 163.7 172.5 181.0

1975 26.0 29.6 33.3 43.6 65.4 89.0 89.0 89.5 97.5 108.0 132.5 139.3

1976 30.1 33.4 36.8 47.4 51.9 61.5 63.1 66.0 76.0 96.0 125.0 166.5

1977 21.1 29.5 31.0 41.2 62.4 86.0 86.0 86.0 104.0 133.0 157.5 173.0

1978 18.8 24.3 29.9 49.3 70.2 77.9 81.0 81.5 91.0 140.0 160.0 232.2

1979 10.5 18.2 26.3 52.1 62.6 86.0 132.9 139.0 140.0 143.0 144.5 170.5

1980 15.0 24.2 31.6 43.9 53.8 65.4 73.1 100.2 104.8 114.1 137.1 156.3

1981 34.9 41.8 47.9 53.2 58.7 68.5 69.0 81.5 102.4 154.0 178.8 246.0

1982 15.5 30.9 45.7 54.1 70.9 74.5 74.5 74.5 84.0 137.0 191.0 191.5

1983 14.8 28.5 42.7 51.9 60.1 77.0 79.5 79.5 91.5 109.3 118.5 169.0

1984 29.9 30.7 31.6 52.3 57.0 81.0 81.0 81.5 84.0 118.0 154.5 215.0

1985 31.0 31.0 33.5 50.1 60.3 95.7 100.0 100.0 104.5 130.0 130.0 142.5

1986 50.2 51.5 52.7 56.4 63.0 101.0 101.0 101.5 114.0 122.0 122.5 159.0

1987 10.5 15.3 22.7 39.1 62.0 62.0 62.0 68.0 70.5 90.5 120.5 181.0

1988 9.5 13.0 19.5 28.2 51.2 74.2 81.5 81.5 82.0 112.5 116.0 133.5

1989 4.0 7.9 11.9 23.8 43.4 80.2 103.0 105.5 140.0 141.5 144.5 169.5

1990 4.5 8.9 13.4 26.7 45.3 79.4 86.5 86.5 91.5 111.0 125.0 169.0

1991 6.6 13.3 19.9 36.3 49.1 76.5 92.3 96.0 96.0 120.5 154.0 165.0

1992 11.5 23.0 25.9 32.1 52.8 105.7 107.5 107.5 116.0 141.5 159.0 182.0

1993 18.0 31.0 39.0 55.0 62.0 95.0 95.0 95.5 109.5 142.5 177.5 215.0

1994 17.8 25.0 33.5 63.0 106.0 156.5 157.5 158.0 159.0 223.0 242.0 275.5

1995 12.5 22.5 29.5 48.5 77.0 100.0 102.5 107.5 129.5 159.5 166.5 183.0

1996 17.7 21.0 27.0 42.0 69.0 78.5 82.8 85.5 92.0 128.5 148.5 190.5

1997 12.5 21.0 28.5 50.5 76.0 108.0 108.0 108.0 110.5 117.0 136.5 155.0

1998 50.0 50.0 50.0 99.5 99.5 99.5 99.5 100.0 116.0 131.0 167.0 224.0

1999 36.8 39.5 42.3 50.7 67.4 128.5 129.0 129.0 132.5 136.5 181.0 188.0

2000 17.8 31.8 33.4 45.8 59.1 80.0 80.5 81.5 99.5 107.5 121.5 149.0

2001 13.5 20.0 29.5 48.0 76.0 126.0 126.0 126.0 128.5 153.0 169.0 269.0

2002 15.5 25.5 33.5 52.5 71.0 81.5 82.0 82.0 83.0 120.5 150.0 186.5

2003 16.5 28.0 37.5 56.5 75.0 87.0 88.5 88.5 118.0 138.0 139.0 163.0

2004 8.0 14.5 19.5 34.0 56.5 93.0 108.5 110.5 129.5 134.0 179.0 239.0

2005 15.0 28.5 41.5 64.5 78.0 82.0 82.5 86.0 120.5 124.0 137.5 152.0

2006 16.5 27.0 33.5 44.5 63.0 72.5 72.5 78.5 81.0 98.5 118.5 126.5

2007 13.5 24.0 32.0 53.5 77.0 89.0 98.0 98.0 101.0 122.5 156.0 199.0

2008 22.0 22.0 30.0 52.0 72.5 83.5 83.5 83.5 83.5 133.0 134.0 161.0

2009 19.0 33.0 46.5 72.5 100.0 100.0 100.0 100.0 132.0 138.5 144.0 191.5

2010 13.0 22.0 31.0 49.5 66.0 74.5 74.5 75.5 96.5 121.0 129.0 163.0

2011 14.5 26.0 36.5 56.5 80.5 91.0 95.0 97.0 109.5 182.0 206.5 231.5

2012 12.0 22.0 28.0 47.0 67.0 95.5 104.5 104.5 105.0 152.0 152.5 166.5

2013 16.2 27.4 34.8 66.7 81.8 91.1 91.3 91.6 93.0 129.7 147.2 164.5

2014 15.2 28.3 36.8 58.1 82.9 89.6 90.1 90.6 105.9 174.0 189.9 219.5

2015 20.8 25.0 25.0 44.0 65.5 85.5 85.5 85.5 111.5 165.0 175.0 179.5

43 789.6 1123.4 1402.0 2124.4 2892.3 3819.3 4001.1 4089.9 4585.7 5744.3 6541.0 7885.3

mean 18.4 26.1 32.6 49.4 67.3 88.8 93.0 95.1 106.6 133.6 152.1 183.4

std.dev. 10.2 9.0 9.0 12.8 13.7 18.0 18.9 18.3 19.7 24.5 26.7 34.9


137

Table A1.5: Annual Maximum Rainfall for Empangan Genting Klang (Stn. 3217002)

5 10 15 30 60 180 360 720 1440 2880 4320 7200

1973 29.3 34.6 40.0 72.2 91.0 99.1 103.0 103.0 124.1 147.5 163.0 200.5

1974 27.3 35.7 41.7 49.6 86.6 125.0 125.5 125.5 125.5 156.2 161.5 178.5

1975 18.2 23.6 27.6 37.8 51.3 76.5 82.0 82.0 82.0 93.5 117.5 152.0

1976 26.1 31.2 40.6 45.4 55.8 68.5 68.5 75.0 75.0 91.5 130.0 160.5

1977 26.7 36.5 39.0 46.0 76.2 115.8 116.0 116.0 117.5 149.0 157.0 169.0

1978 13.7 25.7 27.3 32.2 41.8 74.7 81.5 83.0 90.5 147.0 185.5 208.5

1979 14.7 24.7 29.7 40.4 44.5 94.3 106.7 108.0 109.0 115.0 123.0 151.5

1980 15.6 28.0 34.0 51.1 69.8 71.0 77.3 87.4 88.0 123.0 127.5 148.7

1981 11.0 22.0 33.0 49.9 62.6 109.0 111.0 139.0 162.5 164.5 213.0 213.0

1982 13.5 16.6 20.0 40.0 49.3 66.0 75.0 75.0 105.5 139.5 155.5 184.3

1983 9.3 14.8 19.0 32.0 52.2 83.1 101.5 101.5 102.8 117.8 119.5 156.4

1984 28.0 36.8 43.0 47.3 55.9 58.0 58.0 58.0 76.3 107.5 131.5 147.0

1985 20.0 27.0 29.9 46.0 66.5 87.8 105.5 116.0 117.0 152.5 182.5 209.0

1986 25.5 25.5 28.5 44.6 56.0 80.5 83.0 84.0 87.0 116.0 130.4 223.0

1987 13.2 26.5 28.8 35.7 49.5 59.5 63.5 64.0 64.7 89.5 112.5 122.5

1988 13.6 27.3 31.2 35.5 59.4 95.0 111.5 111.5 141.5 182.0 215.5 270.5

1989 6.7 13.5 19.4 25.8 38.5 59.6 72.5 72.5 110.0 111.0 115.5 138.0

1990 10.4 14.1 21.1 36.6 44.8 54.0 54.0 54.0 65.5 70.0 75.0 98.0

1991 10.6 21.2 31.8 51.9 58.7 96.5 100.7 101.0 119.5 122.5 155.0 178.5

1992 6.7 13.4 20.1 40.2 55.2 69.0 69.0 69.0 70.0 80.5 98.0 112.0

1993 25.5 25.5 25.5 38.3 42.3 58.2 60.5 62.0 87.5 100.0 101.0 142.5

1994 13.3 19.5 26.5 43.0 72.0 102.0 103.0 103.0 126.5 176.0 185.0 212.0

1995 17.2 27.0 34.5 47.0 69.5 83.0 83.0 94.0 111.0 128.0 151.3 165.0

1996 24.5 29.0 43.0 62.0 68.5 72.0 81.0 81.5 84.5 125.0 134.5 183.0

1997 26.4 27.8 34.5 55.0 70.0 83.0 91.5 91.5 124.6 168.5 182.5 270.0

1998 31.2 35.5 39.7 54.5 81.6 102.5 125.5 125.5 143.0 165.5 205.0 266.0

1999 25.6 26.4 28.2 48.0 62.0 64.0 64.0 64.0 80.0 124.0 124.0 161.5

2000 28.4 33.8 39.2 55.5 77.3 104.5 120.0 123.5 123.5 173.5 179.5 182.5

2001 25.6 33.1 40.5 65.1 96.9 127.5 127.5 128.5 151.2 186.0 199.0 268.2

2002 13.5 26.0 36.5 59.0 76.0 80.5 105.0 120.5 121.0 130.5 137.0 183.0

2003 17.5 29.5 38.0 61.0 70.5 71.5 71.5 105.0 114.5 168.5 171.5 191.0

2004 18.5 32.0 40.5 55.0 64.5 70.5 74.0 74.5 90.0 131.0 166.0 215.5

2005 16.0 25.5 35.0 52.0 65.0 77.0 100.5 102.0 102.0 102.0 124.0 146.5

2006 14.0 27.5 40.0 70.0 87.5 116.5 116.5 116.5 116.5 125.5 167.5 168.0

2007 13.5 21.0 25.5 38.0 61.0 79.5 80.5 88.5 96.5 122.5 156.0 199.0

2008 14.0 26.0 35.0 53.5 72.5 105.5 107.0 107.5 110.5 135.0 167.0 210.0

2009 15.0 26.5 35.5 57.0 67.5 90.0 93.0 93.0 97.0 99.5 100.5 155.5

2010 29.0 33.5 35.0 53.5 74.5 105.5 107.0 107.5 110.5 131.5 166.0 207.0

2011 58.5 71.0 71.0 91.5 109.0 109.0 109.5 109.5 109.5 129.5 181.5 226.5

2012 14.3 24.0 34.0 57.0 75.8 111.5 111.8 112.2 117.9 156.1 166.5 206.8

2013 14.4 27.0 38.7 64.8 90.0 102.2 125.0 125.5 125.5 132.1 132.5 148.6

2014 14.1 23.6 35.3 63.4 85.3 99.5 99.8 100.1 139.9 140.0 164.9 188.9

2015 27.9 33.5 33.5 57.5 88.4 119.0 119.0 119.0 190.5 190.5 192.0 243.0

43 838.0 1182.9 1450.8 2161.8 2893.2 3777.3 4041.8 4180.2 190.5 5716.7 6523.6 7961.4

mean 19.5 27.5 33.7 50.3 67.3 87.8 94.0 97.2 109.5 132.9 151.7 185.1

std. dev. 9.2 9.2 8.9 12.5 16.1 20.1 21.1 21.7 26.2 29.4 33.3 41.6


138

Table A1.6: Annual Maximum Rainfall for Ibu Bekalan Km.11, Gombak

(Stn. 3217003)

5 10 15 30 60 180 360 720 1440 2880 4320 7200

1975 19.4 24.2 28.2 49.4 64.3 70.0 70.0 74.0 88.5 111.5 111.5 129.0

1976 34.1 41.6 49.1 62.1 86.4 96.0 96.0 96.0 158.5 161.5 168.5 217.0

1977 23.9 34.7 37.9 44.3 67.9 105.0 108.0 108.0 114.5 126.5 177.0 216.0

1978 16.8 22.2 27.6 43.9 64.1 73.0 75.5 75.5 75.5 98.5 112.0 113.0

1979 13.3 21.7 31.5 45.3 56.3 68.0 70.0 78.5 79.0 148.5 150.5 180.0

1980 15.5 23.1 32.1 49.7 71.2 104.2 126.6 155.9 250.0 268.0 294.2 334.2

1981 10.3 17.6 25.1 41.8 79.5 90.0 106.9 140.7 166.5 216.2 291.1 376.5

1982 24.5 24.5 24.5 47.8 72.7 99.0 99.4 130.0 143.5 148.5 210.0 237.5

1983 30.5 30.5 30.5 34.7 57.5 91.5 94.5 98.6 149.5 186.5 186.5 202.5

1984 16.0 32.0 40.1 58.7 96.1 114.5 114.5 115.0 158.7 198.0 205.5 212.5

1985 17.5 18.2 19.5 33.4 37.7 68.7 87.0 93.5 94.5 94.5 106.0 148.0

1986 8.1 16.3 24.4 48.8 55.3 72.5 72.6 74.5 80.5 81.0 102.3 116.2

1987 24.3 41.5 41.5 41.5 68.1 109.0 116.5 116.5 126.5 173.5 179.0 214.0

1988 14.0 14.0 19.6 39.1 57.8 82.2 95.5 95.5 130.1 153.0 188.0 201.0

1989 11.4 12.5 13.7 17.1 27.5 41.6 65.7 73.5 74.5 96.0 110.5 127.0

1990 3.4 6.9 10.3 20.6 41.2 76.5 76.5 88.0 93.5 112.0 130.0 130.5

1991 13.6 22.9 34.3 50.6 57.0 81.0 96.3 101.0 101.5 114.0 195.0 207.5

1993 11.5 20.5 28.0 32.0 40.0 62.5 62.5 62.5 73.5 110.0 124.0 153.0

1994 12.7 25.3 34.8 46.5 76.5 108.5 110.5 110.5 120.7 180.3 218.5 238.0

1995 17.0 30.5 40.0 57.5 60.5 76.0 76.5 80.5 125.0 134.0 145.5 185.0

1996 17.0 26.5 30.5 44.0 49.0 77.5 89.0 89.5 89.5 125.5 139.5 169.0

1997 16.0 29.5 40.0 57.5 71.0 74.0 74.0 74.0 88.5 107.5 137.0 157.5

1998 21.4 22.8 25.1 41.3 61.1 98.5 109.5 109.5 114.0 145.0 198.2 321.0

1999 32.0 33.9 35.8 50.5 86.5 94.0 104.0 104.0 105.0 116.0 134.5 185.0

2000 26.3 32.1 37.9 55.4 76.6 106.5 106.5 106.5 117.5 142.5 167.0 191.0

2001 18.4 28.5 34.5 57.0 65.5 105.5 124.0 124.0 132.5 172.5 185.5 256.5

2002 14.0 23.0 30.0 55.5 78.5 92.5 95.5 108.0 139.5 182.0 187.0 200.5

2003 12.5 22.0 30.0 54.0 77.5 124.0 129.5 129.5 137.5 177.0 182.5 230.5

2004 15.5 26.0 35.0 61.0 71.5 77.0 87.5 87.5 108.5 151.5 178.0 239.0

2005 13.0 23.5 32.5 58.0 77.5 79.0 79.0 79.5 79.5 84.5 117.5 127.5

2006 13.5 24.5 35.5 58.0 85.0 102.0 104.5 105.0 111.5 121.0 173.5 182.0

2007 15.5 29.0 40.0 63.5 88.0 106.0 131.5 132.0 146.5 146.5 158.5 190.0

2008 20.5 31.0 39.0 74.0 111.5 127.0 127.0 127.0 127.0 164.0 164.5 205.5

2009 26.5 30.6 36.5 59.0 83.5 97.5 97.5 97.5 113.0 135.5 155.5 212.0

2010 13.5 22.0 29.5 49.5 54.5 55.0 55.5 73.0 79.5 112.5 140.0 197.5

2011 16.0 26.0 36.5 54.5 69.0 91.0 94.0 94.0 107.5 177.0 201.5 225.5

2012 12.1 20.8 30.7 47.2 66.5 86.7 89.2 89.7 90.6 154.7 161.1 183.6

2013 17.3 31.3 44.7 76.3 95.2 96.8 97.2 100.2 127.2 143.2 151.0 177.9

2014 20.0 24.0 28.5 48.4 68.4 82.0 82.3 82.6 109.4 111.5 119.0 163.5

2015 28.8 34.5 34.5 65.0 96.5 108.5 108.5 108.5 108.5 126.0 134.0 161.1

40 707.6 1022.2 1279.4 1994.4 2770.4 3500.7 3806.7 3989.7 4637.2 5707.9 6590.9 7914.5

mean 17.7 25.6 32.0 49.9 69.3 89.3 95.2 99.7 115.9 142.7 164.8 197.9

std dev 6.7 7.2 7.9 12.1 17.4 18.5 19.5 21.2 33.5 38.3 43.4 55.8


139

Table A1.7: Annual Maximum Rainfall for Kg.Kuala Seleh (Stn.3217004)

5 10 15 30 60 180 360 720 1440 2880 4320 7200

1980 18.4 26.2 29.9 46.0 54.7 79.8 82.0 92.0 105.0 111.5 116.4 143.5

1981 17.4 24.2 31.7 49.5 62.1 63.0 67.0 99.0 133.0 144.0 144.0 144.0

1982 14.1 26.0 32.7 52.5 76.4 88.0 88.0 89.5 119.6 180.0 217.0 217.0

1983 12.5 25.0 26.0 40.1 63.3 77.6 85.5 85.5 85.5 112.5 120.0 193.5

1984 22.5 22.5 28.3 39.9 64.7 95.0 96.0 96.5 103.0 120.0 123.5 161.0

1985 9.5 16.3 23.7 37.7 55.5 96.0 104.0 104.0 133.0 148.5 159.5 202.5

1986 24.5 24.5 36.0 42.1 62.5 71.5 71.5 71.5 83.5 109.3 152.0 152.0

1987 15.2 28.6 38.3 60.4 74.6 89.5 97.5 101.0 145.0 154.4 199.5 244.0

1988 30.6 38.4 46.9 61.4 80.2 97.0 97.0 97.0 135.5 174.5 188.0 188.0

1989 13.2 15.4 23.1 37.9 62.6 71.5 71.5 72.5 75.5 88.0 88.5 131.8

1991 30.0 30.5 31.8 43.2 55.4 64.5 78.8 83.5 97.0 142.0 150.2 175.5

1992 11.9 23.8 28.7 41.1 56.9 58.0 58.0 58.5 68.0 100.5 132.5 146.9

1993 8.5 15.0 21.0 33.5 56.5 109.5 115.0 155.5 133.0 163.5 182.0 208.5

1994 14.0 23.7 28.2 41.5 58.5 65.0 65.0 78.5 87.5 123.5 168.5 180.5

1995 10.5 18.0 25.0 39.0 60.5 96.5 97.5 97.5 116.0 127.5 131.5 140.5

1996 15.5 23.5 32.5 47.5 66.0 89.0 93.0 115.0 115.0 186.0 188.0 213.0

1997 18.9 31.6 34.0 51.5 82.5 91.0 91.0 91.0 104.5 144.5 178.0 220.0

1998 25.9 26.9 31.5 57.0 77.5 87.5 87.5 87.5 91.0 114.0 177.5 216.0

1999 16.5 25.6 34.0 50.5 67.6 77.0 77.0 81.0 126.5 157.5 158.0 204.0

2000 15.0 26.5 34.0 52.0 78.5 158.5 177.5 182.0 188.0 196.5 227.5 247.5

2001 25.8 38.3 40.8 51.0 66.5 79.5 79.5 79.5 117.7 134.0 142.5 235.5

2002 15.0 27.0 36.0 55.5 79.5 85.5 87.0 95.0 98.5 120.0 133.0 176.5

2003 13.0 25.0 35.5 46.5 53.0 66.0 66.5 82.0 83.0 114.5 121.0 157.5

2004 14.5 25.5 34.0 55.5 72.0 84.0 96.5 98.0 121.5 165.5 183.5 253.5

2005 13.0 23.0 33.0 52.5 69.5 75.5 99.0 100.0 132.0 132.0 171.0 175.0

2006 12.0 21.5 30.0 50.0 54.0 80.5 81.0 82.0 83.0 129.0 134.0 143.5

2007 13.5 24.0 33.0 53.5 77.0 89.0 98.0 98.0 101.0 108.0 123.5 150.5

2008 15.0 23.0 32.0 55.0 80.0 105.5 107.0 107.5 120.0 129.5 145.5 192.0

2009 16.0 27.0 39.5 62.0 81.0 85.0 85.0 85.0 91.0 105.5 144.5 161.0

2010 16.5 20.5 29.5 52.5 74.5 94.0 99.5 99.5 103.0 131.5 145.5 192.0

2011 58.5 71.0 71.0 91.5 107.0 107.0 107.0 107.0 107.0 128.5 179.0 227.0

2012 14.3 24.0 34.0 57.5 75.8 117.0 117.3 117.7 123.4 161.3 180.5 218.8

2013 12.4 21.9 30.6 50.2 69.0 95.0 95.1 95.2 95.5 156.6 178.1 223.1

2014 13.7 25.8 35.0 52.7 66.6 89.0 89.3 89.7 113.8 138.2 162.9 184.9

2015 28.8 34.5 34.5 54.5 81.5 122.5 122.5 122.5 196.5 196.5 197.5 264.0

35 626.6 924.2 1165.7 1764.7 2423.4 3100.4 3230.5 3397.1 3932.0 4848.8 5544.1 6684.5

mean 17.9 26.4 33.3 50.4 69.2 88.6 92.3 97.1 112.3 138.5 158.4 191.0

std. dev. 9.1 9.4 8.3 10.2 11.4 19.4 21.2 22.5 27.6 27.6 30.9 36.5


140

Table A1.8: Annual Maximum Rainfall for Kg. Kerdas (Stn. 3217005)

5 10 15 30 60 180 360 720 1440 2880 4320 7200

1983 17.4 30.9 41.4 58.9 71.5 89.0 93.0 93.0 137.0 160.5 160.5 174.5

1984 11.9 23.7 29.9 46.1 69.5 95.0 95.5 95.5 110.7 173.5 202.5 217.0

1985 19.0 19.0 19.0 23.9 30.5 53.1 54.5 55.0 55.0 55.5 71.0 72.0

1986 11.1 22.2 30.5 49.8 51.0 51.5 51.5 51.5 52.0 52.5 52.5 65.0

1988 7.6 15.3 22.4 36.3 55.2 61.3 78.0 78.0 78.0 142.0 142.0 186.0

1989 8.6 17.1 25.3 40.1 55.4 67.5 67.5 68.0 96.5 99.0 111.0 121.0

1990 27.0 31.5 35.1 46.0 56.6 60.5 62.5 62.5 66.0 91.5 93.5 128.0

1991 25.0 26.3 27.5 39.3 55.8 65.5 67.0 67.0 75.5 80.5 90.5 114.5

1992 16.1 29.2 38.6 65.7 75.2 93.7 97.0 97.5 100.5 105.5 162.5 163.5

1993 14.5 26.0 29.6 46.7 73.8 80.0 80.0 85.1 126.5 136.0 136.0 168.5

1994 29.0 43.8 60.8 82.8 105.1 128.9 133.3 142.0 158.3 195.1 217.5 221.0

1995 18.8 25.0 36.0 64.5 85.5 86.5 89.5 112.0 142.5 152.5 169.0 192.5

1996 15.0 28.5 35.0 59.5 75.5 111.0 111.0 115.5 116.5 125.5 155.0 192.5

1997 30.4 40.8 45.6 54.5 68.5 77.5 77.5 78.0 98.0 116.5 139.0 143.5

1998 49.2 64.8 68.5 69.5 79.4 90.5 93.1 98.5 98.5 141.0 213.7 311.0

1999 29.0 34.6 40.2 58.8 65.5 72.0 73.0 73.5 76.0 123.5 123.5 165.6

2000 13.0 22.5 30.0 48.5 80.5 97.0 97.0 99.5 107.5 126.0 142.5 173.0

2001 11.5 14.5 16.0 18.0 21.0 34.0 35.5 36.0 41.0 47.0 47.0 57.0

2009 13.0 24.0 35.0 66.5 107.5 118.5 120.0 121.5 121.5 131.5 136.0 148.5

2010 15.0 25.5 34.0 56.0 63.5 72.0 74.0 74.0 80.0 118.5 132.0 166.0

2011 68.5 78.5 78.5 78.5 78.5 81.5 82.0 82.0 123.5 183.0 206.0 269.5

2012 13.0 25.0 33.0 51.0 70.5 88.0 89.0 90.0 99.5 118.0 125.5 14.0

2013 14.0 24.4 33.0 64.9 87.0 92.0 92.7 93.0 93.5 128.7 135.7 205.0

2014 14.1 22.8 30.2 51.9 70.3 98.6 100.2 100.5 119.7 163.6 188.6 203.1

2015 31.3 37.5 37.5 65.5 91.0 99.0 99.0 99.0 114.0 119.5 156.5 179.5

25 523.0 753.4 912.6 1343.2 1743.8 2064.1 2113.3 2168.1 2487.7 3086.4 3509.5 4051.7

mean 20.9 30.1 36.5 53.7 69.8 82.6 84.5 86.7 99.5 123.5 140.4 162.1

std. dev. 13.7 14.5 14.3 15.2 19.5 21.8 21.7 24.0 29.2 38.4 46.3 65.5

ANNUAL MAXIMUM RAINFALL (mm) DATA FOR VARIOUS DURATIONS (minutes)YEAR

141

Table A1.9: Annual Maximum Rainfall for Air Terjun Sg. Batu2 (Stn. 3317001)

5 10 15 30 60 180 360 720 1440 2880 4320 7200

1986 11.5 22.9 27.7 34.9 56.5 69.5 69.5 81.8 99.5 105.0 152.0 158.0

1987 18.1 26.8 29.7 38.5 63.3 80.5 80.5 82.5 82.5 98.5 111.4 142.5

1988 21.7 25.8 26.8 30.7 45.7 94.2 111.5 111.5 111.5 130.5 137.5 221.5

1989 26.3 28.0 29.7 35.4 52.3 61.2 65.8 66.5 89.0 108.0 132.0 179.5

1990 11.8 23.5 32.4 52.2 62.8 98.0 98.5 98.5 101.0 105.0 155.0 179.9

1991 26.7 29.5 32.0 38.2 55.3 98.6 111.0 111.0 111.0 139.0 139.0 196.4

1992 12.0 22.7 27.3 38.5 51.8 73.5 73.5 74.0 74.0 99.0 152.5 189.5

1993 20.8 29.3 35.2 50.3 62.2 82.0 86.5 94.5 129.0 144.5 177.0 213.5

1994 14.5 24.0 33.0 50.5 74.0 101.0 102.0 102.5 109.5 195.5 213.0 272.0

1995 20.5 32.1 34.7 60.0 89.5 101.0 127.5 142.0 145.5 175.5 193.0 248.0

1996 19.5 23.0 27.5 45.0 76.8 103.5 109.5 139.1 178.0 183.5 239.5 249.5

1997 18.9 26.5 33.9 45.0 60.0 66.0 72.0 79.0 79.0 89.5 127.5 171.5

1998 19.1 20.1 21.5 33.0 42.2 69.0 73.0 73.0 73.0 74.0 93.5 101.0

1999 14.5 25.5 32.5 43.5 54.0 64.0 64.0 64.0 73.5 93.0 138.0 166.5

2000 14.5 27.0 37.0 58.5 75.0 93.0 108.0 111.5 114.0 148.0 171.5 197.5

2001 22.1 27.5 32.9 61.3 74.5 93.5 95.5 95.5 95.5 134.5 158.0 185.2

2002 14.0 25.0 35.0 47.0 72.5 75.0 85.5 88.0 95.5 170.0 170.5 221.0

2003 13.5 24.5 29.5 43.0 57.5 68.0 73.0 79.0 108.5 135.0 135.0 158.0

2004 15.0 26.0 31.5 43.0 65.0 89.5 100.5 105.0 105.0 154.0 172.0 259.5

2005 44.0 44.0 44.0 58.0 71.5 98.0 98.5 98.5 100.5 128.0 136.5 160.0

2006 15.0 27.0 37.5 68.0 91.5 107.0 122.0 122.5 129.5 176.5 215.5 228.0

2007 14.5 24.0 33.0 52.0 63.0 87.5 93.0 93.0 93.0 114.5 126.5 210.0

2008 11.5 19.5 28.0 38.0 53.0 70.5 111.0 204.0 204.0 287.0 330.3 359.8

2009 17.5 33.0 41.5 53.0 89.5 113.5 113.5 113.5 113.5 164.5 230.5 310.0

2010 15.5 27.5 35.5 50.0 65.5 70.5 96.5 112.5 112.5 129.5 150.0 187.0

2011 13.0 24.0 33.5 47.5 66.5 74.0 80.0 87.5 87.5 113.5 138.0 158.0

2012 14.0 26.0 35.0 59.5 101.5 140.5 152.0 152.0 152.5 204.5 207.5 219.0

2013 14.1 23.1 29.3 50.7 77.7 92.6 96.5 96.5 142.7 168.9 192.0 221.0

2014 18.1 31.2 39.9 58.6 90.8 99.6 100.6 112.8 131.7 171.0 183.1 274.6

2015 18.3 24.6 35.1 56.2 78.5 93.8 94.1 94.5 116.0 118.0 160.0 207.3

30 530.5 793.6 982.1 1440.0 2039.9 2628.5 2865.0 3086.2 3357.9 4257.9 5037.8 6245.2

mean 17.7 26.5 32.7 48.0 68.0 87.6 95.5 102.9 111.9 141.9 167.9 208.2

std. dev. 6.4 4.6 4.7 9.6 14.7 17.7 20.1 28.6 30.2 43.6 46.6 52.8

ANNUAL MAXIMUM RAINFALL (mm) DATA FOR VARIOUS DURATIONS (minutes)YEAR

142

Table A1.10: Annual Maximum Rainfall for Genting Sempah (Stn. 3317004)

5 10 15 30 60 180 360 720 1440 2880 4320 7200

1975 10.2 18.0 25.1 29.6 37.0 55.9 59.0 59.5 71.9 77.0 86.5 114.5

1976 12.2 17.6 21.3 32.5 48.0 60.5 62.5 62.9 64.6 119.0 119.5 137.0

1977 15.8 23.0 25.7 35.9 62.5 65.0 70.5 70.5 70.5 99.0 114.0 138.5

1978 22.7 27.2 28.9 34.2 49.5 74.3 98.1 101.5 136.5 138.0 161.0 190.5

1979 14.6 20.4 27.9 39.4 46.9 67.0 67.0 68.0 76.0 97.0 125.0 165.7

1980 20.0 20.8 22.4 33.3 52.0 59.4 59.8 59.8 69.5 101.0 109.0 140.9

1981 15.6 23.3 27.0 43.7 60.6 78.0 78.0 78.0 126.0 127.5 133.5 148.0

1982 20.1 20.4 23.8 36.4 50.0 68.0 68.5 72.0 72.0 104.5 116.0 148.5

1983 11.7 23.4 31.3 37.5 39.0 48.5 51.5 51.5 51.5 78.5 90.0 126.0

1984 14.9 24.8 29.3 38.2 52.0 54.5 68.5 68.5 77.0 121.5 121.5 153.5

1985 11.6 18.3 27.5 54.0 85.0 103.5 121.0 130.5 130.5 131.5 144.0 245.5

1986 28.1 38.0 47.9 58.3 76.7 127.5 132.5 132.5 132.5 133.5 141.0 153.0

1987 24.1 28.3 32.6 45.6 61.4 97.4 105.5 116.0 117.5 120.5 120.5 235.0

1988 10.0 20.1 25.7 31.0 37.9 49.5 62.0 67.0 67.5 89.0 95.3 131.0

1989 9.4 18.9 25.2 30.1 44.6 53.0 53.0 53.5 95.0 99.5 145.0 148.5

1990 13.2 19.0 19.8 30.5 42.4 58.5 59.5 77.2 87.0 91.0 97.5 124.0

1991 10.1 20.3 25.8 45.4 71.6 80.5 84.0 84.5 86.5 102.0 181.5 182.0

1992 6.5 10.6 16.0 28.7 44.6 72.1 93.5 94.5 94.5 108.5 110.5 115.5

1993 10.0 17.2 25.1 37.0 53.0 79.5 79.5 79.5 97.5 99.0 120.0 160.5

1994 13.5 25.5 31.0 38.5 48.0 55.5 55.5 56.0 56.0 68.5 75.0 99.5

1995 21.8 28.0 34.1 43.4 73.0 97.0 97.0 97.0 165.5 166.5 168.0 241.5

1996 44.0 48.5 51.5 51.5 51.5 74.5 76.0 77.5 111.0 129.5 137.0 161.0

1997 17.7 27.4 37.0 49.7 64.5 66.5 67.0 67.0 106.5 128.5 133.5 177.5

1998 11.0 18.5 25.0 33.5 54.5 100.0 103.0 103.0 109.0 113.5 161.0 165.5

1999 75.0 150.0 174.5 174.5 174.5 174.5 174.5 174.5 175.0 220.0 229.5 252.0

2001 15.5 28.0 40.5 61.5 72.5 85.5 85.5 85.5 103.0 144.5 149.5 179.0

2002 11.5 20.0 28.0 49.5 65.0 70.5 71.0 71.5 85.5 118.5 148.5 200.5

2003 13.5 24.5 31.5 44.0 54.5 76.0 77.5 77.5 79.5 117.5 131.0 150.0

2004 12.0 20.5 29.0 44.5 48.0 55.0 55.0 66.5 68.0 110.0 126.0 194.0

2005 9.5 16.5 23.5 39.5 51.0 58.5 66.5 67.5 76.0 109.5 110.0 131.5

2006 13.5 25.0 35.0 53.0 72.7 86.4 87.4 95.0 105.5 138.7 152.9 161.5

2007 10.5 20.0 28.5 45.0 55.0 71.5 89.5 91.5 102.0 131.6 142.0 148.0

2008 31.0 31.0 31.0 38.0 61.0 79.5 86.0 87.0 102.0 130.0 150.0 201.5

2009 12.0 20.5 29.0 52.5 74.0 78.0 78.0 78.0 87.5 99.0 105.0 155.0

2010 12.5 24.5 34.5 41.5 53.0 72.0 81.5 82.5 85.5 91.0 102.0 120.5

2011 14.0 25.0 34.5 50.5 64.0 67.0 75.0 95.5 95.5 102.5 120.0 148.0

2012 14.3 24.0 34.0 58.0 80.5 121.3 122.3 122.7 128.4 144.1 151.7 189.5

2013 15.5 22.3 31.5 48.8 67.4 83.3 83.7 84.0 84.4 126.1 163.2 169.6

2014 14.3 23.5 29.3 44.1 65.0 89.3 89.6 107.0 115.6 117.0 117.5 147.1

2015 25.4 29.0 29.0 44.0 62.0 74.0 76.0 76.5 96.5 125.0 145.5 204.5

40 688.8 1061.8 1330.2 1826.8 2426.3 3088.4 3271.4 3390.6 3861.9 4669.0 5250.1 6555.3

mean 17.2 26.5 33.3 45.7 60.7 77.2 81.8 84.8 96.5 116.7 131.3 163.9

std. dev. 11.7 21.0 23.9 22.6 22.0 24.0 24.3 24.7 27.5 26.4 28.9 36.8


143

Table A1.11: Annual Maximum Rainfall for S.M. Bandar Tasik Kesuma

(Stn. 2818110)

5 10 15 30 60 180 360 720 1440 2880 4320 7200

1971 24.3 24.3 24.3 24.9 43.5 67.5 93.8 169.3 220.6 92.0 375.5 412.6

1972 17.8 26.2 28.1 33.8 45.1 78.3 85.1 85.1 94.0 133.0 113.1 126.5

1973 17.2 29.1 37.6 51.9 64.1 89.9 102.8 102.9 103.0 106.3 125.3 140.0

1974 5.9 11.7 17.6 35.2 70.5 79.5 79.5 80.0 88.5 96.5 173.5 185.0

1975 7.1 14.2 21.3 42.5 63.2 77.5 77.5 82.5 97.0 88.0 181.5 210.5

1976 5.6 11.2 16.8 33.5 40.0 48.5 53.5 54.0 66.5 127.0 103.5 119.5

1977 6.4 12.8 19.2 26.3 45.5 76.5 77.0 78.0 78.0 93.0 119.0 173.5

1978 6.3 12.5 17.2 33.0 39.5 74.5 81.0 81.0 81.0 139.0 92.5 107.0

1979 7.4 14.8 22.2 42.1 58.6 64.5 68.0 70.5 88.0 128.0 110.5 159.0

1980 7.9 15.8 23.7 38.5 64.3 96.0 103.0 106.5 122.3 92.5 133.5 141.0

1981 79.5 95.4 95.6 95.9 95.9 96.0 96.5 97.5 99.4 130.5 110.2 139.0

1982 35.0 42.0 48.4 77.4 77.4 77.4 77.4 77.4 77.4 97.0 120.3 143.0

1983 10.7 19.1 27.1 34.6 54.2 84.5 85.0 85.0 85.0 160.0 88.5 105.5

1984 11.5 22.9 29.3 46.7 70.4 84.5 84.5 84.5 99.0 89.5 137.0 167.5

1985 15.2 26.8 31.8 48.8 78.0 92.0 92.0 92.0 92.0 161.0 106.0 157.5

1986 13.7 25.9 28.8 44.3 67.5 85.5 89.5 90.0 93.5 95.6 144.5 218.8

1987 12.9 23.3 24.5 31.6 57.8 95.5 105.5 105.5 107.0 122.5 129.0 150.0

1988 16.4 26.3 28.2 34.9 50.9 56.0 58.7 68.0 81.5 121.5 129.0 157.0

1989 16.0 25.8 30.6 42.9 56.5 71.0 105.5 105.5 106.5 113.5 136.5 148.0

1990 23.1 25.0 35.2 50.0 76.0 77.8 78.0 80.0 81.5 330.3 110.0 129.0

1991 11.5 22.9 34.1 59.5 71.5 90.0 90.0 90.0 90.0 111.8 161.0 192.0

1992 11.8 21.3 25.3 31.4 52.7 68.0 68.5 69.0 82.5 110.4 103.5 117.0

1993 24.0 26.7 33.4 53.7 73.7 97.3 104.0 104.0 137.0 141.5 162.0 197.0

1994 22.0 28.1 35.1 49.2 53.3 58.0 93.0 95.5 95.5 173.5 146.5 147.3

1995 25.5 31.3 37.1 44.0 47.5 53.6 64.5 83.5 107.5 86.5 128.5 186.0

1996 14.3 28.1 40.5 71.4 90.7 109.0 114.0 120.0 120.0 110.5 140.0 148.5

1997 12.5 24.3 28.3 40.3 57.0 101.0 101.5 101.5 111.5 86.0 126.5 192.0

1998 26.7 28.9 31.0 37.3 54.3 67.1 68.0 68.2 69.0 105.5 105.5 134.5

1999 16.7 26.0 28.1 35.0 46.1 57.0 57.8 58.9 58.9 77.7 77.7 116.4

2000 17.7 26.8 34.3 47.4 62.4 62.4 62.4 63.5 86.2 112.7 157.1 191.3

2001 11.6 18.0 24.5 39.0 53.9 88.7 103.9 104.0 104.0 112.9 117.8 132.2

2002 12.0 22.0 28.5 47.0 63.0 76.0 135.5 146.0 146.0 211.5 212.0 221.0

2003 15.5 29.5 42.5 61.0 73.5 80.5 82.0 82.0 88.5 90.5 116.0 129.5

2004 15.5 26.0 35.5 51.0 79.0 88.0 88.0 97.5 111.5 173.5 190.5 199.5

2005 11.5 18.0 20.0 25.0 26.5 31.0 36.5 36.5 41.0 63.5 67.5 67.5

2006 16.5 27.8 35.4 56.6 64.0 75.5 75.7 75.9 82.2 122.2 137.3 162.4

2007 14.9 27.3 37.4 48.6 59.6 81.4 86.1 92.5 95.0 104.2 115.5 120.6

2008 16.0 26.9 36.2 57.4 72.9 109.1 116.6 116.8 117.0 200.0 212.0 221.5

2009 16.5 26.4 34.8 50.5 87.0 101.7 107.9 108.4 121.0 126.5 163.0 176.2

2010 16.3 29.6 38.7 69.9 85.6 87.5 88.2 88.5 104.8 119.9 142.5 169.6

2011 16.8 28.7 36.2 58.1 95.9 110.2 110.5 113.7 114.2 177.2 196.6 202.3

2012 16.9 28.8 38.5 71.7 86.9 87.6 87.8 88.1 88.5 121.0 128.4 189.4

2013 11.6 20.0 25.1 42.1 57.1 75.1 76.7 77.4 86.2 97.7 129.2 133.9

2014 13.4 22.4 27.2 38.3 49.6 52.0 52.3 53.7 58.1 79.0 110.1 126.2

2015 14.2 20.4 23.4 34.8 46.2 51.6 51.9 52.9 62.2 74.2 98.7 114.5

45 741.8 1141.3 1418.6 2089.0 2828.8 3532.2 3817.1 3983.2 4340.0 5506.6 6184.3 7278.2

mean 16.5 25.4 31.5 46.4 62.9 78.5 84.8 88.5 96.4 122.4 137.4 156.0

std. dev. 11.3 12.3 12.1 14.6 15.9 17.6 20.0 23.8 27.9 45.6 48.8 52.1


144

Table A1.12: Annual Maximum Rainfall for RTM Kajang (Stn. 2917001)

5 10 15 30 60 180 360 720 1440 2880 4320 7200

1976 21.6 27.1 32.9 52.2 70.5 72.5 72.5 72.5 98.5 107.5 121.4 180.0

1977 18.4 30.1 38.3 56.6 67.2 86.0 116.5 117.0 117.0 117.0 117.5 120.5

1978 13.0 16.2 20.6 35.3 46.2 64.0 66.6 73.0 100.5 121.5 125.0 178.5

1979 19.8 28.1 33.3 52.2 88.7 105.0 105.0 107.0 107.0 118.5 134.5 168.0

1980 14.0 14.0 14.9 29.0 45.2 58.0 58.7 58.7 87.2 163.2 223.5 225.5

1981 20.9 22.2 24.8 33.0 52.8 90.5 102.3 126.5 175.0 223.5 223.5 225.5

1982 20.6 26.9 33.2 42.0 63.5 71.4 73.0 73.0 86.5 117.0 117.0 125.5

1983 40.3 41.1 44.5 52.9 75.5 77.2 81.5 81.5 87.0 147.0 180.0 183.0

1984 10.5 15.5 18.0 24.0 36.2 68.5 77.0 77.0 93.5 117.0 127.0 165.5

1985 46.0 46.0 46.0 46.0 50.2 64.2 73.5 73.5 87.5 99.5 147.8 173.5

1986 29.4 38.5 38.5 38.5 51.1 71.0 71.0 71.0 105.0 133.0 134.0 156.0

1987 12.0 23.5 26.3 36.1 65.2 148.0 165.1 170.5 171.5 176.0 176.0 177.1

1988 36.3 47.8 53.7 71.3 82.0 88.5 88.5 106.6 110.5 143.0 203.5 210.5

1989 33.5 33.5 33.5 33.5 42.6 54.0 54.0 54.0 68.5 92.5 97.0 107.0

1990 23.0 27.0 38.0 38.0 58.8 90.8 100.7 120.4 136.0 136.0 142.0 183.0

1991 17.5 23.0 23.1 28.5 36.7 38.0 39.5 39.5 41.7 58.0 60.5 77.3

1992 30.7 50.4 51.4 54.4 60.5 83.6 87.7 95.5 113.5 128.9 147.7 239.0

1993 32.8 50.2 50.6 51.8 60.6 86.9 92.5 93.5 101.3 119.5 119.5 152.0

1994 17.2 18.8 21.9 31.7 51.1 83.0 98.4 105.0 105.0 116.0 146.0 149.5

1995 29.7 32.0 34.0 46.2 61.0 100.0 103.0 103.0 120.5 143.5 160.5 197.0

1997 54.0 54.0 54.0 54.0 54.0 75.0 75.0 75.0 75.0 81.0 99.5 121.0

1998 27.0 27.0 27.0 49.5 71.7 112.5 112.7 113.0 115.5 115.5 125.5 132.5

1999 29.5 29.5 29.8 33.0 62.4 91.3 91.3 91.3 97.7 135.1 165.5 191.9

2000 21.7 21.7 21.9 35.7 52.3 98.4 104.1 104.1 134.6 170.5 190.7 228.6

2001 20.2 23.5 32.5 53.0 62.5 97.1 128.0 128.7 128.7 146.6 148.6 225.4

2002 18.5 29.0 38.5 58.0 70.5 110.0 144.5 185.0 189.0 193.0 236.0 240.0

2003 15.5 27.5 37.0 53.0 63.5 74.5 74.5 75.0 119.0 135.0 139.0 159.0

2004 18.8 25.5 36.5 51.0 72.5 138.0 162.0 167.0 194.0 217.5 269.0 335.5

2005 20.4 25.0 31.5 45.0 72.5 93.0 103.5 127.4 126.5 159.0 185.0 209.5

2006 12.5 21.5 29.5 45.0 75.5 109.0 112.0 112.5 112.5 131.5 137.0 146.0

2007 15.5 29.5 37.0 50.5 57.0 66.0 83.0 87.0 107.5 111.5 147.5 185.0

2008 23.1 28.5 39.5 70.5 93.0 122.5 124.0 124.0 124.0 181.5 192.5 248.5

2009 18.3 33.9 45.8 77.7 96.6 97.1 97.4 97.7 111.0 111.0 118.3 156.5

2010 17.1 29.3 39.3 58.3 84.0 87.1 87.3 87.5 87.5 116.7 168.0 176.5

2011 17.5 34.1 42.7 53.1 90.1 116.3 117.9 121.2 121.9 128.5 159.6 167.0

2012 13.6 25.1 34.0 57.9 79.7 136.0 141.7 142.0 144.0 145.0 146.3 222.9

2013 17.0 26.1 34.3 51.5 70.7 101.1 101.2 101.5 115.5 125.1 127.3 142.6

2014 15.7 27.8 38.7 61.1 73.6 80.9 82.2 88.6 93.1 148.6 194.9 203.3

2015 17.3 33.9 46.6 67.8 78.5 80.1 80.3 84.5 85.3 122.7 138.0 165.5

39 880.4 1164.3 1373.6 1878.8 2546.2 3487.0 3749.6 3931.7 4395.5 5253.4 5992.1 7051.1

mean 22.6 29.9 35.2 48.2 65.3 89.4 96.1 100.8 112.7 134.7 153.6 180.8

std dev 9.6 9.6 9.7 12.6 15.1 23.4 27.6 31.2 31.1 33.3 41.5 47.0


145

Table A1.13: Annual Maximum Rainfall for S.K. Kg. Sg. Lui (Stn. 3118102)

5 10 15 30 60 180 360 720 1440 2880 4320 7200

1972 12.0 17.1 22.1 33.3 52.2 67.8 70.1 70.1 91.2 90.0 106.2 147.5

1973 11.5 22.9 28.9 37.7 63.6 82.9 100.8 117.2 119.1 122.6 175.5 209.4

1974 5.7 11.3 17.0 34.0 54.3 89.0 101.5 102.0 102.0 142.5 146.0 175.5

1975 12.7 25.4 38.2 61.5 64.1 79.0 80.5 80.5 82.5 111.5 116.0 172.0

1976 4.7 9.5 14.2 24.2 48.5 71.5 71.5 71.5 107.0 107.0 133.0 143.0

1977 2.7 5.4 8.1 16.2 26.5 32.0 35.0 48.5 53.0 71.5 99.0 101.0

1978 20.5 41.0 49.5 61.1 73.5 74.0 74.0 81.0 81.0 98.0 115.5 145.0

1979 5.1 10.2 15.3 30.0 53.0 106.0 150.0 155.0 155.0 163.0 209.0 223.0

1980 64.5 77.4 77.4 77.5 77.8 78.4 80.3 84.8 84.9 115.0 127.6 156.0

1981 41.3 82.5 99.0 99.0 99.0 138.2 144.2 170.4 240.0 328.5 339.7 387.0

1982 41.3 49.5 49.6 50.0 64.5 67.5 69.5 72.0 80.4 109.8 152.0 205.4

1983 11.8 19.7 27.4 42.6 54.5 68.0 68.0 74.5 81.5 119.5 160.0 223.0

1984 19.7 23.8 33.4 38.8 48.5 67.9 72.0 72.0 83.5 96.5 136.0 175.0

1985 12.1 24.2 33.4 48.4 64.7 86.9 89.0 89.0 121.0 141.5 151.0 207.0

1986 15.3 26.9 32.2 48.1 65.5 73.5 75.5 75.5 94.3 102.5 138.5 154.5

1987 20.4 21.6 22.7 36.9 48.8 54.0 54.0 83.4 109.0 114.0 163.5 166.2

1988 10.5 20.2 26.3 36.2 51.6 66.5 85.5 88.6 122.5 154.5 160.0 204.5

1989 35.8 48.6 58.5 58.5 77.1 124.1 130.5 130.5 157.0 179.5 180.5 184.0

1990 13.4 23.5 28.5 48.0 76.3 91.0 91.5 91.5 94.5 135.0 177.5 181.0

1991 44.6 63.0 63.0 80.7 141.5 197.0 197.0 197.0 198.5 222.5 224.7 262.0

1992 9.7 19.5 27.5 44.1 66.6 77.6 81.5 81.5 83.5 84.5 112.5 154.0

1993 18.6 19.4 23.6 42.5 70.5 90.5 93.7 95.0 95.5 100.0 109.5 114.5

1994 43.0 44.1 45.2 50.0 74.3 80.5 80.5 86.3 96.5 113.0 130.0 164.0

1995 29.1 37.4 48.8 70.7 80.8 89.5 90.0 110.5 121.5 148.5 186.1 239.0

1996 17.6 25.5 33.5 59.0 90.0 91.5 91.5 92.5 109.5 161.5 161.5 168.5

1997 15.5 25.0 27.2 37.3 57.9 97.6 104.1 105.0 105.3 108.8 109.7 111.5

1998 24.0 26.6 29.8 37.1 50.6 55.0 55.5 55.5 86.0 95.5 95.5 117.5

1999 17.0 25.2 30.0 45.3 63.0 79.6 80.2 80.4 85.0 105.9 113.8 166.2

2000 16.9 25.4 29.8 41.3 68.1 79.0 79.2 79.4 79.9 101.7 102.9 156.4

2001 25.8 30.8 35.8 58.2 87.3 90.7 90.7 94.2 96.3 98.3 129.1 182.7

2002 19.2 34.3 47.7 61.4 85.6 96.9 97.1 113.3 115.5 136.7 148.9 164.5

2003 74.5 74.5 74.5 74.5 74.5 83.3 84.1 84.1 96.9 104.2 108.9 127.2

2004 17.3 27.7 36.4 62.9 76.0 90.9 91.7 92.0 98.3 142.8 179.0 250.4

2005 15.1 26.8 31.8 41.2 67.1 75.1 76.1 116.6 126.5 126.5 153.6 157.6

2006 14.0 23.9 32.4 55.8 78.0 83.2 83.6 83.9 84.0 103.1 105.5 144.6

2007 21.6 35.0 46.6 66.6 79.1 82.5 82.7 84.0 84.3 141.1 145.5 177.1

2008 15.7 25.3 32.7 49.0 56.7 75.1 76.2 76.7 88.8 111.0 137.5 156.8

2009 12.4 21.0 29.0 43.6 54.6 88.7 98.6 99.2 135.2 144.0 152.0 195.0

2010 16.1 23.9 27.9 43.1 56.4 66.0 94.1 94.5 107.7 121.8 133.0 167.8

2011 13.1 22.3 28.7 48.7 67.8 77.1 77.4 77.5 115.1 139.1 155.8 201.9

2012 12.5 23.5 33.7 58.6 88.8 116.8 135.7 137.8 156.4 232.3 232.5 275.5

2013 12.4 20.8 28.7 54.1 66.9 74.0 74.1 79.1 104.3 125.2 156.5 257.1

2014 14.9 26.8 33.4 43.8 71.6 95.5 95.7 98.5 98.5 98.5 113.2 148.5

2015 14.8 28.1 36.8 54.9 66.8 79.5 82.1 84.6 100.0 107.2 119.9 162.6

44 896.4 1316.5 1596.2 2206.4 3004.5 3731.3 3936.5 4157.1 4728.4 5676.1 6503.6 7982.9

mean 20.4 29.9 36.3 50.1 68.3 84.8 89.5 94.5 107.5 129.0 147.8 181.4

std. dev. 14.7 16.9 17.2 15.6 17.8 24.9 27.2 28.1 33.0 44.5 44.0 51.3


146

Table A1.14: Annual Maximum Rainfall for Pintu Kawalan P/S Telok Gadong

(Stn. 2913001)

5 10 15 30 60 180 360 720 1440 2880 4320 7200

1974 6.0 12.0 18.0 36.0 60.9 120.5 120.5 120.5 120.5 120.5 133.0 161.0

1975 7.1 14.2 21.3 34.0 48.6 67.9 109.0 110.5 110.5 128.5 136.0 141.5

1976 7.4 14.9 22.3 44.6 63.0 98.0 99.5 99.5 100.0 100.0 100.0 114.0

1977 5.2 10.4 15.6 31.2 44.5 73.8 113.0 120.6 120.6 130.3 131.8 172.3

1978 6.6 13.1 19.7 32.9 48.5 64.0 64.0 64.0 79.0 121.0 121.0 165.5

1979 5.3 10.6 15.9 31.9 55.5 93.5 105.5 105.5 105.5 155.5 174.5 198.0

1981 38.8 46.5 46.6 47.0 59.0 88.0 118.8 119.1 119.2 133.9 163.2 185.7

1982 17.9 21.5 21.5 34.7 67.9 72.0 72.0 72.0 72.5 76.0 80.5 116.0

1983 11.5 18.3 27.4 48.7 56.5 67.1 67.5 81.3 162.5 163.5 167.5 198.2

1984 13.7 27.4 41.2 47.9 61.6 86.6 105.0 105.5 105.5 106.5 192.0 199.0

1985 33.5 33.5 33.5 40.1 49.5 61.5 80.4 104.2 105.0 110.8 145.0 169.5

1986 6.0 10.5 14.0 28.1 53.1 95.2 96.5 96.5 103.5 160.0 167.0 171.0

1987 5.8 11.6 17.4 34.8 54.4 73.0 73.0 73.0 143.0 146.0 155.0 156.0

1988 17.5 20.6 23.7 33.0 46.6 65.0 81.5 85.0 100.5 141.0 141.0 195.5

1990 19.1 38.1 40.3 44.6 61.6 123.0 155.5 161.5 161.5 167.0 169.0 186.5

1991 26.5 43.5 48.4 63.0 90.5 110.0 111.9 112.5 112.5 117.0 155.5 191.0

1992 5.0 9.9 14.9 29.7 50.3 67.1 68.0 77.0 79.5 79.5 93.5 95.5

1993 25.0 25.0 25.0 41.1 51.5 64.5 64.5 64.5 68.7 71.0 79.0 113.5

1994 35.2 35.8 36.5 38.4 46.0 66.5 75.5 76.0 76.0 98.5 105.0 138.5

1995 27.6 27.7 29.0 36.5 47.5 75.8 113.5 113.5 130.5 145.5 155.0 215.5

1996 24.0 24.0 24.0 24.0 33.9 61.6 63.5 68.0 87.0 87.0 104.0 124.5

1997 13.4 26.9 40.3 44.4 52.1 83.2 90.0 90.0 91.0 93.0 93.0 98.0

1998 17.3 25.2 26.4 31.4 47.7 69.8 73.0 73.0 73.0 81.5 101.5 146.5

2000 29.0 33.4 37.7 50.8 75.0 97.3 112.8 114.1 114.1 141.1 138.2 180.0

2001 25.9 39.3 41.4 47.7 60.7 96.8 120.8 121.4 121.4 136.2 148.7 214.0

2002 26.2 28.6 42.7 46.8 54.9 87.4 92.7 92.7 92.7 92.7 142.4 149.8

2003 13.1 21.2 31.3 44.3 54.3 77.8 89.1 89.1 89.1 141.9 142.4 149.8

2004 16.4 29.5 40.1 59.7 69.6 78.7 81.5 83.2 88.4 94.2 104.6 118.8

2005 14.0 24.3 31.1 44.1 51.3 59.5 59.8 60.2 69.6 90.6 92.0 92.0

2006 13.9 26.6 36.4 60.1 100.2 172.3 182.6 183.6 184.0 184.5 195.5 203.5

2007 53.8 92.7 112.5 112.9 120.2 130.0 155.5 155.7 155.9 158.4 158.5 160.8

2008 12.8 20.3 29.0 37.9 53.6 55.7 56.0 62.0 65.5 107.5 123.8 136.5

2009 16.9 26.8 38.8 51.4 68.8 72.5 72.7 73.0 84.3 139.0 155.0 163.0

2010 14.7 24.3 35.7 52.6 82.5 118.8 119.6 119.8 122.0 137.0 140.5 140.5

2011 16.3 29.7 37.3 57.5 67.7 91.5 91.6 96.1 96.5 112.5 124.5 157.6

2012 15.4 25.0 36.0 56.7 89.5 117.2 119.0 133.8 134.3 202.9 214.2 247.0

2013 13.3 22.3 29.5 48.0 60.1 77.3 88.3 89.8 93.8 110.6 111.0 144.5

2014 14.1 26.0 35.0 53.8 60.5 64.6 64.7 68.6 77.0 105.5 109.5 158.0

2015 13.1 22.7 29.6 41.3 52.0 58.0 59.0 59.2 91.6 106.4 126.7 163.9

39 684.3 1013.9 1267.0 1743.6 2371.6 3303.0 3687.3 3795.5 4107.7 4794.5 5290.5 6232.4

mean 17.5 26.0 32.5 44.7 60.8 84.7 94.5 97.3 105.3 122.9 135.7 159.8

std. dev. 10.6 14.3 16.2 14.8 16.7 24.7 29.1 29.1 28.5 31.0 32.3 35.7


147

Table A1.15: Annual Maximum Rainfall for JPS Pulau Lumut (Stn. 2913122)

Table A1.16: Annual Maximum Rainfall for Ldg. Sg. Kapar (Stn. 3113087)

5 10 15 30 60 180 360 720 1440 2880 4320 7200

2004 20.3 27.6 39.8 67.7 73.2 76.4 77.2 88.3 97.0 97.0 112.0 112.0

2005 14.3 26.7 38.5 63.9 81.5 104.0 109.3 119.0 121.0 142.8 162.1 208.0

2006 15.5 26.6 33.9 52.7 86.9 121.2 133.8 134.6 134.9 140.3 201.0 225.5

2007 12.8 21.6 27.7 38.0 64.0 82.8 90.6 97.1 97.5 152.0 168.7 178.1

2008 11.6 21.4 30.2 56.2 86.1 92.0 104.1 131.6 160.5 170.8 207.4 212.3

2009 14.6 27.6 37.5 63.7 82.7 91.1 145.8 146.2 150.5 186.5 199.0 199.0

2010 15.2 27.0 38.9 57.8 63.0 102.2 110.6 111.0 111.0 117.7 138.0 157.0

2011 14.3 26.7 35.4 47.0 56.2 67.8 71.6 74.5 76.3 94.3 109.9 135.5

2012 11.6 21.0 27.9 38.1 52.5 70.2 76.9 97.1 107.4 119.2 153.8 179.0

2013 16.0 27.1 35.4 52.0 60.3 100.4 112.6 116.7 120.7 132.1 133.0 145.0

2014 11.9 22.4 30.7 42.8 56.3 94.6 95.1 95.2 95.5 98.2 100.0 150.4

2015 12.3 21.8 29.7 46.6 69.1 73.0 73.1 73.3 122.6 146.8 150.1 160.8

12 170.4 297.5 405.6 626.5 831.8 1075.7 1200.7 1284.6 1394.9 1597.7 1835.0 2062.6

mean 14.2 24.8 33.8 52.2 69.3 89.6 100.1 107.1 116.2 133.1 152.9 171.9

std. dev. 2.5 2.8 4.4 10.0 12.5 16.1 24.0 23.4 24.2 29.3 36.5 34.5


5 10 15 30 60 180 360 720 1440 2880 4320 7200

2003 17.4 30.5 42.3 62.8 80.9 107.7 112.5 112.7 112.9 114.5 129.6 131.1

2004 13.5 25.7 36.8 52.8 59.0 59.3 59.9 91.0 92.7 98.5 112.5 157.5

2005 13.1 25.3 37.1 64.7 92.2 93.2 94.1 94.4 107.0 113.9 123.0 171.5

2006 13.3 23.8 33.3 56.6 94.2 103.5 104.3 104.7 105.0 126.0 126.2 156.0

2007 14.9 26.0 35.2 51.8 83.8 126.9 135.9 141.1 154.8 178.9 191.2 191.5

2008 12.0 19.8 24.0 34.9 61.3 96.0 96.2 96.7 101.1 116.1 118.5 149.0

2009 11.7 22.3 28.1 33.2 55.3 66.0 68.2 68.7 87.8 100.0 117.6 140.1

2010 12.6 25.0 29.9 47.8 72.8 82.0 82.1 82.2 98.7 130.8 131.0 133.4

2012 11.6 21.0 28.6 46.4 69.1 74.4 83.3 84.8 98.7 132.1 132.9 164.0

2013 12.0 23.3 33.4 48.2 66.1 70.8 71.6 72.1 72.5 82.1 89.5 149.1

2014 14.4 25.0 33.8 47.2 65.9 89.3 91.2 97.7 113.7 116.7 130.8 191.5

2015 14.8 29.1 40.7 61.8 83.9 88.8 110.7 122.8 123.3 197.7 212.3 247.8

12 161.3 296.8 403.2 608.2 884.5 1057.9 1110.0 1168.9 1268.2 1507.3 1615.1 1982.5

mean 13.4 24.7 33.6 50.7 73.7 88.2 92.5 97.4 105.7 125.6 134.6 165.2

std. dev. 1.7 3.1 5.3 10.0 13.0 19.2 21.4 20.8 20.3 32.8 33.8 32.6


148

Table A1.17: Annual Maximum Rainfall for Setia Alam (Stn. 3114085)

Table A1.18: Annual Maximum Rainfall for Pusat Penyelidikan Getah Sg. Buloh

(Stn. 3115079)

5 10 15 30 60 180 360 720 1440 2880 4320 7200

2003 32.6 37.1 40.6 57.5 87.6 102.4 107.0 107.6 116.8 131.0 136.0 170.1

2004 14.7 24.9 37.4 52.2 63.0 63.2 63.4 98.1 107.5 137.0 181.0 232.5

2006 13.8 24.0 33.2 53.5 68.9 85.1 85.3 85.5 85.5 107.5 125.1 175.9

2007 98.3 99.0 99.5 120.8 129.9 149.7 167.1 167.4 203.0 203.5 208.1 221.5

2008 16.1 29.1 38.4 61.2 63.0 77.1 81.4 103.3 105.3 139.0 170.5 200.5

2009 13.7 26.2 35.8 67.8 98.6 105.0 112.1 112.5 112.5 112.8 146.4 168.2

2010 14.4 27.5 37.9 56.0 64.2 71.0 75.6 76.4 132.2 175.2 208.7 248.5

2011 13.9 24.8 31.6 46.6 56.3 64.7 71.9 72.3 78.7 115.5 124.3 146.2

2012 12.8 23.1 32.6 52.3 73.2 95.4 105.5 105.8 106.0 112.5 148.0 171.4

2013 14.3 25.4 35.4 55.2 73.7 75.6 75.8 76.0 76.0 110.0 140.0 168.3

2014 13.9 22.4 29.7 50.6 75.4 104.8 105.8 111.8 112.5 113.7 136.7 215.0

2015 11.8 19.6 27.5 47.9 68.0 86.1 97.4 106.1 106.5 186.5 201.5 220.0

12 270.3 383.1 479.6 721.6 921.8 1080.1 1148.3 1222.8 1342.5 1644.2 1926.3 2338.1

mean 22.5 31.9 40.0 60.1 76.8 90.0 95.7 101.9 111.9 137.0 160.5 194.8

std. dev. 24.5 21.6 19.1 20.0 20.3 24.0 27.7 25.3 33.0 33.3 32.0 32.2


5 10 15 30 60 180 360 720 1440 2880 4320 7200

2003 13.7 25.5 36.4 60.4 83.5 88.5 89.5 117.0 117.6 119.5 126.1 155.9

2004 16.0 29.5 40.8 54.8 64.4 83.9 92.0 95.6 115.7 123.4 143.5 183.6

2005 14.5 27.8 37.5 55.7 72.0 94.0 94.6 104.0 106.0 120.0 157.8 190.2

2006 13.3 23.1 29.6 48.4 57.5 59.0 59.2 63.9 70.7 124.0 138.4 161.0

2007 14.5 25.4 34.7 56.6 72.1 72.6 72.9 73.5 108.1 113.1 118.2 143.5

2008 23.9 26.7 37.2 55.6 69.8 72.0 86.4 88.8 89.3 119.1 140.3 187.5

2009 14.6 26.5 35.2 51.8 52.1 63.5 67.5 69.9 78.4 121.6 124.8 155.8

2010 16.0 27.2 36.9 55.7 80.9 99.1 99.3 104.1 110.3 113.0 160.4 163.2

2011 14.9 25.9 36.9 58.6 82.5 95.3 98.5 100.9 143.7 168.9 183.1 202.0

2012 21.8 39.0 55.5 67.1 100.5 113.1 113.3 113.5 134.6 142.6 199.5 233.2

2013 13.3 24.0 35.3 59.4 67.9 72.8 73.1 75.9 97.0 134.5 135.0 187.6

2014 15.7 29.5 41.8 71.0 77.7 83.5 83.7 84.4 84.5 95.1 111.8 142.2

2015 16.6 28.9 37.9 56.2 75.4 77.7 80.3 90.5 146.2 147.2 157.0 212.9

13 208.8 359.0 495.7 751.3 956.3 1075.0 1110.3 1182.0 1402.1 1642.0 1895.9 2318.6

mean 16.1 27.6 38.1 57.8 73.6 82.7 85.4 90.9 107.9 126.3 145.8 178.4

std. dev. 3.2 3.9 6.0 5.9 12.4 15.2 14.8 16.8 24.0 18.4 25.3 27.6


APPENDIX B

150

B.1 Rainfall Depth for Gumbel Method

Table B1.1: Rainfall Depth for Ldg. Edinburgh Site in mm

Table B1.2: Rainfall Depth for Kg. Sg. Tua in mm

Table B1.3: Rainfall Depth for SMJK Kepong in mm

Table B1.4: Rainfall Depth for Ibu Bekalan Km. 16, Gombak in mm

ARI

(Year) 5 10 15 30 60 180 360 720 1440 2880 4320 7200

2 17.7 26.7 34.8 52.9 69.8 85.9 90.3 92.7 107.2 126.7 146.6 183.0

5 26.1 33.7 41.4 62.4 83.6 102.9 108.7 112.6 137.7 159.1 180.7 225.4

10 31.6 38.3 45.7 68.7 92.8 114.2 120.9 125.7 157.9 180.5 203.3 253.5

20 36.9 42.8 49.8 74.7 101.5 125.0 132.6 138.4 177.2 201.0 224.9 280.4

50 43.8 48.5 55.2 82.5 112.9 139.0 147.8 154.7 202.3 227.6 253.0 315.3

100 48.9 52.8 59.2 88.4 121.4 149.5 159.1 166.9 221.1 247.6 274.0 341.5


ARI

(Year) 5 10 15 30 60 180 360 720 1440 2880 4320 7200

2 16.7 24.6 31.1 47.3 65.0 85.9 89.9 92.1 103.4 129.6 147.7 177.6

5 25.7 32.6 39.1 58.6 77.1 101.8 106.7 108.3 120.8 151.2 171.3 208.5

10 31.6 37.9 44.4 66.1 85.1 112.3 117.8 119.0 132.3 165.6 186.9 228.9

20 37.3 43.0 49.5 73.3 92.8 122.4 128.4 129.2 143.4 179.4 201.9 248.5

50 44.7 49.6 56.1 82.6 102.8 135.5 142.1 142.5 157.7 197.2 221.2 273.8

100 50.3 54.5 61.0 89.6 110.3 145.3 152.4 152.5 168.4 210.6 235.8 292.8


ARI

(Year) 5 10 15 30 60 180 360 720 1440 2880 4320 7200

2 15.9 25.0 32.1 48.5 65.4 83.4 88.0 90.9 98.6 116.6 136.9 166.9

5 21.2 30.0 38.4 58.5 79.9 106.6 111.6 114.8 124.0 148.1 171.5 210.4

10 24.6 33.3 42.5 65.2 89.5 122.0 127.2 130.7 140.9 168.9 194.4 239.2

20 28.0 36.5 46.5 71.6 98.7 136.8 142.2 145.8 157.0 188.9 216.4 266.8

50 32.3 40.6 51.7 79.9 110.6 155.8 161.6 165.5 177.9 214.7 244.9 302.6

100 35.5 43.7 55.5 86.1 119.5 170.1 176.1 180.2 193.5 234.1 266.2 329.4


ARI

(Year) 5 10 15 30 60 180 360 720 1440 2880 4320 7200

2 18.0 26.0 32.3 48.2 64.6 84.5 90.5 93.6 105.2 128.1 146.2 178.3

5 26.1 34.1 40.2 59.3 78.9 102.3 109.2 112.9 128.3 154.1 175.6 215.1

10 31.5 39.5 45.4 66.6 88.3 114.1 121.5 125.6 143.6 171.4 195.1 239.4

20 36.7 44.6 50.4 73.6 97.4 125.4 133.3 137.8 158.3 187.9 213.8 262.8

50 43.4 51.3 56.9 82.7 109.1 140.0 148.6 153.6 177.4 209.3 238.0 293.0

100 48.5 56.3 61.7 89.5 117.9 150.9 160.1 165.4 191.6 225.3 256.1 315.6


151

Table B1.5: Rainfall Depth for Empangan Genting Klang in mm


Table B1.7: Rainfall Depth for Kg. Kuala Seleh in mm

Table B1.8: Rainfall Depth for Kg. Kerdas in mm

Table B1.9: Rainfall Depth for Air Terjun Sg. Batu in mm

ARI

(Year) 5 10 15 30 60 180 360 720 1440 2880 4320 7200

2 16.6 24.4 30.7 47.9 66.4 86.2 92.0 96.3 110.4 136.4 157.6 188.7

5 22.5 30.7 37.7 58.6 81.8 102.6 109.2 115.0 140.0 170.2 196.0 238.0

10 26.4 34.9 42.3 65.7 91.9 113.4 120.6 127.4 159.6 192.7 221.4 270.7

20 30.1 38.9 46.7 72.5 101.7 123.8 131.6 139.3 178.4 214.1 245.8 302.0

50 35.0 44.1 52.4 81.4 114.3 137.2 145.7 154.7 202.8 242.0 277.3 342.6

100 38.6 48.0 56.7 88.0 123.8 147.3 156.4 166.2 221.0 262.8 300.9 373.0


ARI

(Year) 5 10 15 30 60 180 360 720 1440 2880 4320 7200

2 16.4 24.9 31.9 48.7 67.4 85.4 88.8 93.4 107.8 134.0 153.3 185.0

5 24.5 33.1 39.3 57.8 77.4 102.5 107.5 113.2 132.2 158.4 180.6 217.2

10 29.8 38.6 44.1 63.7 84.1 113.8 119.9 126.4 148.4 174.5 198.7 238.6

20 34.9 43.9 48.8 69.5 90.5 124.7 131.8 139.0 163.9 190.0 216.0 259.0

50 41.5 50.7 54.9 76.9 98.8 138.7 147.1 155.3 183.9 210.0 238.4 285.5

100 46.5 55.8 59.4 82.4 105.0 149.3 158.6 167.5 199.0 225.0 255.2 305.4


ARI

(Year) 5 10 15 30 60 180 360 720 1440 2880 4320 7200

2 17.6 24.4 30.4 45.3 63.0 78.7 85.7 90.6 101.2 123.2 140.1 174.9

5 25.3 30.9 36.7 54.1 77.0 95.8 104.0 111.5 123.3 147.2 167.8 209.0

10 30.5 35.2 40.9 59.9 86.2 107.1 116.0 125.3 138.0 163.1 186.1 231.6

20 35.4 39.3 44.9 65.5 95.1 117.9 127.6 138.5 152.1 178.3 203.8 253.3

50 41.8 44.6 50.1 72.8 106.6 132.0 142.6 155.6 170.3 198.0 226.5 281.4

100 46.6 48.6 53.9 78.2 115.2 142.5 153.9 168.5 183.9 212.8 243.6 302.4


ARI

(Year) 5 10 15 30 60 180 360 720 1440 2880 4320 7200

2 18.7 27.7 34.2 51.2 66.5 79.0 81.0 82.8 94.7 117.2 132.8 151.3

5 30.7 40.6 46.8 64.7 83.8 98.2 100.2 104.0 120.5 151.1 173.7 209.2

10 38.7 49.1 55.1 73.6 95.2 111.0 112.9 118.1 137.5 173.5 200.8 247.5

20 46.4 57.3 63.1 82.2 106.2 123.2 125.1 131.6 153.9 195.0 226.8 284.3

50 56.3 67.8 73.5 93.2 120.4 139.1 140.8 149.0 175.1 222.9 260.4 331.9

100 63.8 75.7 81.2 101.5 131.0 150.9 152.7 162.1 191.0 243.8 285.6 367.5


ARI

(Year) 5 10 15 30 60 180 360 720 1440 2880 4320 7200

2 16.6 25.7 32.0 46.4 65.6 84.7 92.2 98.2 107.0 134.8 160.3 199.5

5 22.3 29.7 36.1 54.9 78.6 100.4 110.0 123.5 133.7 173.3 201.5 246.1

10 26.0 32.4 38.9 60.5 87.1 110.7 121.7 140.2 151.3 198.8 228.7 277.0

20 29.6 35.0 41.5 65.8 95.4 120.7 133.0 156.3 168.3 223.2 254.9 306.6

50 34.3 38.3 44.9 72.8 106.0 133.5 147.6 177.1 190.2 254.9 288.7 345.0

100 37.8 40.8 47.5 78.0 114.0 143.2 158.5 192.7 206.7 278.6 314.1 373.7


152

Table B1.10: Rainfall Depth for Genting Sempah in mm

Table B1.11: Rainfall Depth for Sek. Men. Bandar Tasik Kesuma in mm

Table B1.12: Rainfall Depth for RTM Kajang in mm

Table B1.13: Rainfall Depth for Sek. Keb. Kg. Sg. Lui in mm

Table B1.14: Rainfall Depth for Pintu Kawalan P/S Telok Gong in mm

ARI

(Year) 5 10 15 30 60 180 360 720 1440 2880 4320 7200

2 15.3 23.1 29.3 42.0 57.0 73.3 77.8 80.7 92.0 112.4 126.5 157.8

5 25.7 41.6 50.4 61.9 76.5 94.5 99.3 102.5 116.3 135.7 152.1 190.4

10 32.5 53.9 64.4 75.1 89.4 108.5 113.5 117.0 132.4 151.2 169.0 211.9

20 39.1 65.7 77.8 87.8 101.7 122.0 127.1 130.9 147.8 166.0 185.2 232.6

50 47.6 80.9 95.1 104.2 117.7 139.4 144.8 148.8 167.8 185.2 206.2 259.3

100 54.0 92.3 108.1 116.5 129.7 152.4 158.0 162.3 182.7 199.6 221.9 279.4


ARI

(Year) 5 10 15 30 60 180 360 720 1440 2880 4320 7200

2 14.6 23.3 29.5 44.0 60.2 75.6 81.5 84.6 91.9 114.9 129.4 147.5

5 24.6 34.2 40.3 57.0 74.3 91.2 99.2 105.6 116.5 155.2 172.5 193.5

10 31.2 41.4 47.4 65.5 83.6 101.4 110.9 119.6 132.9 181.9 201.0 224.0

20 37.6 48.3 54.2 73.8 92.5 111.3 122.1 132.9 148.5 207.5 228.4 253.2

50 45.8 57.2 63.0 84.4 104.1 124.1 136.7 150.2 168.8 240.7 263.8 291.0

100 52.0 63.9 69.6 92.4 112.8 133.7 147.5 163.2 184.0 265.5 290.3 319.4


ARI

(Year) 5 10 15 30 60 180 360 720 1440 2880 4320 7200

2 21.0 28.3 33.6 46.1 62.8 85.6 91.6 95.7 107.6 129.2 146.8 173.1

5 29.5 36.7 42.2 57.3 76.1 106.2 116.0 123.3 135.0 158.6 183.5 214.6

10 35.1 42.4 47.9 64.7 85.0 119.9 132.1 141.6 153.2 178.1 207.7 242.1

20 40.5 47.7 53.3 71.8 93.5 133.0 147.6 159.1 170.6 196.8 231.0 268.5

50 47.5 54.7 60.3 81.0 104.4 150.0 167.6 181.8 193.2 220.9 261.1 302.6

100 52.7 59.9 65.6 87.8 112.6 162.7 182.6 198.8 210.1 239.1 283.7 328.2


ARI

(Year) 5 10 15 30 60 180 360 720 1440 2880 4320 7200

2 18.0 27.1 33.5 47.6 65.4 80.7 85.0 89.9 102.0 121.7 140.6 173.0

5 31.0 42.1 48.6 61.4 81.1 102.7 109.0 114.7 131.2 161.0 179.4 218.3

10 39.6 52.0 58.7 70.5 91.6 117.3 125.0 131.2 150.5 187.1 205.2 248.3

20 47.9 61.5 68.3 79.3 101.6 131.2 140.3 147.0 169.0 212.1 229.9 277.1

50 58.6 73.8 80.8 90.6 114.5 149.3 160.0 167.4 193.0 244.4 261.8 314.4

100 66.6 83.0 90.1 99.1 124.3 162.9 174.8 182.8 211.0 268.7 285.7 342.3


ARI

(Year) 5 10 15 30 60 180 360 720 1440 2880 4320 7200

2 15.8 23.6 29.8 42.3 58.1 80.6 89.8 92.5 100.6 117.8 130.3 153.9

5 25.2 36.3 44.1 55.4 72.8 102.5 115.5 118.2 125.8 145.2 158.9 185.5

10 31.4 44.6 53.6 64.1 82.6 116.9 132.5 135.2 142.5 163.4 177.8 206.4

20 37.4 52.7 62.6 72.4 92.0 130.8 148.8 151.6 158.5 180.8 196.0 226.4

50 45.1 63.0 74.4 83.1 104.1 148.7 170.0 172.7 179.3 203.3 219.5 252.4

100 50.9 70.8 83.2 91.2 113.2 162.2 185.8 188.5 194.8 220.1 237.1 271.8


153

Table B1.15: Rainfall Depth for JPS Pulau Lumut in mm

Table B1.16: Rainfall Depth for Ldg. Sg. Kapar in mm

Table B1.17: Rainfall Depth for Setia Alam in mm

Table B1.18: Rainfall Depth for Pusat Penyelidikan Getah Sg. Buloh in mm

ARI

(Year) 5 10 15 30 60 180 360 720 1440 2880 4320 7200

2 13.8 24.3 33.1 50.6 67.3 87.0 96.1 103.2 112.3 128.3 146.9 166.2

5 16.0 26.8 37.0 59.4 78.3 101.2 117.3 123.9 133.6 154.2 179.2 196.7

10 17.4 28.5 39.6 65.3 85.6 110.7 131.3 137.6 147.7 171.3 200.6 216.9

20 18.8 30.0 42.0 70.9 92.6 119.7 144.7 150.8 161.3 187.7 221.1 236.2

50 20.6 32.1 45.2 78.2 101.6 131.5 162.1 167.8 178.8 209.0 247.7 261.3

100 22.0 33.6 47.6 83.6 108.4 140.2 175.2 180.5 192.0 224.9 267.5 280.0


ARI

(Year) 5 10 15 30 60 180 360 720 1440 2880 4320 7200

2 13.2 24.2 32.7 49.0 71.6 85.0 89.0 94.0 102.3 120.2 129.0 159.8

5 14.7 26.9 37.4 57.9 83.1 102.0 107.9 112.4 120.3 149.2 158.9 188.7

10 15.7 28.7 40.6 63.8 90.7 113.2 120.5 124.5 132.2 168.4 178.7 207.8

20 16.6 30.4 43.5 69.4 98.0 124.0 132.5 136.2 143.6 186.8 197.7 226.1

50 17.9 32.6 47.4 76.7 107.5 138.0 148.1 151.3 158.4 210.6 222.2 249.8

100 18.8 34.3 50.3 82.2 114.6 148.4 159.7 162.6 169.5 228.4 240.6 267.6


ARI

(Year) 5 10 15 30 60 180 360 720 1440 2880 4320 7200

2 18.5 28.4 36.8 56.9 73.5 86.1 91.1 97.7 106.5 131.6 155.3 189.6

5 40.1 47.4 53.7 74.5 91.4 107.3 115.6 120.1 135.6 160.9 183.6 218.0

10 54.5 60.0 64.9 86.2 103.3 121.3 131.8 134.9 154.9 180.4 202.3 236.8

20 68.2 72.1 75.7 97.4 114.7 134.7 147.3 149.1 173.4 199.1 220.3 254.9

50 86.0 87.8 89.6 111.9 129.5 152.2 167.4 167.5 197.3 223.2 243.6 278.2

100 99.3 99.5 100.0 122.7 140.5 165.2 182.5 181.3 215.3 241.3 261.0 295.7


ARI

(Year) 5 10 15 30 60 180 360 720 1440 2880 4320 7200

2 15.5 27.0 37.1 56.8 71.5 80.2 83.0 88.2 103.9 123.3 141.7 173.8

5 18.4 30.5 42.4 62.1 82.5 93.7 96.1 103.0 125.1 139.6 164.1 198.2

10 20.3 32.8 45.9 65.5 89.7 102.6 104.7 112.9 139.1 150.4 178.9 214.4

20 22.1 35.0 49.3 68.9 96.6 111.1 113.0 122.3 152.6 160.7 193.1 229.9

50 24.4 37.8 53.7 73.2 105.6 122.2 123.8 134.6 170.0 174.1 211.6 250.0

100 26.1 40.0 56.9 76.4 112.3 130.5 131.9 143.8 183.0 184.2 225.4 265.0


154

B.2 Rainfall Depth for Log-Normal Method

Table B2.1: Rainfall Depth for Ldg. Edinburgh Site in mm

Table B2.2: Rainfall Depth for Kg. Sg. Tua in mm

Table B2.3: Rainfall Depth for SMJK Kepong in mm


ARI

(Year) 5 10 15 30 60 180 360 720 1440 2880 4320 7200

2 17.7 27.1 35.3 53.6 70.7 87.1 91.5 93.9 108.4 128.3 148.3 185.0

5 24.6 33.3 42.1 63.5 84.9 104.3 110.3 114.4 137.2 159.8 182.9 229.7

10 29.2 37.1 46.2 69.5 93.3 114.7 121.6 126.8 155.2 179.2 204.1 257.2

20 33.7 40.5 49.9 74.7 101.0 124.0 131.8 138.0 171.8 197.0 223.4 282.5

50 39.5 44.7 54.4 81.2 110.3 135.4 144.3 151.9 192.7 219.1 247.4 313.8

100 44.0 47.7 57.6 85.8 117.0 143.5 153.3 161.9 207.9 235.3 264.8 336.6


ARI

(Year) 5 10 15 30 60 180 360 720 1440 2880 4320 7200

2 17.1 24.4 30.5 45.7 63.6 79.9 87.1 92.0 102.7 125.1 142.2 177.6

5 25.7 32.5 38.9 56.3 78.9 96.3 103.8 111.6 123.5 147.8 168.6 210.1

10 31.9 37.7 44.3 62.8 88.3 106.1 113.8 123.4 136.0 161.3 184.3 229.3

20 38.0 42.7 49.2 68.8 96.9 115.0 122.7 134.2 147.3 173.3 198.4 246.5

50 46.3 49.1 55.4 76.1 107.5 125.8 133.6 147.3 161.0 187.9 215.5 267.5

100 52.8 53.9 60.0 81.4 115.3 133.7 141.4 156.9 170.9 198.3 227.7 282.4


ARI

(Year) 5 10 15 30 60 180 360 720 1440 2880 4320 7200

2 16.0 25.3 32.4 48.8 65.9 83.7 88.3 91.1 98.7 116.6 137.1 167.0

5 21.1 31.0 39.8 61.5 83.5 110.3 115.9 120.0 130.9 155.3 180.4 221.4

10 24.4 34.4 44.2 69.5 94.4 127.4 133.7 138.6 151.7 180.3 208.2 256.7

20 27.5 37.6 48.3 76.9 104.6 143.5 150.3 156.2 171.4 204.1 234.3 290.0

50 31.5 41.4 53.4 86.1 117.3 164.1 171.6 178.5 196.6 234.6 267.7 332.7

100 34.4 44.2 57.0 92.8 126.7 179.5 187.4 195.2 215.4 257.4 292.6 364.5


ARI

(Year) 5 10 15 30 60 180 360 720 1440 2880 4320 7200

2 16.1 24.5 31.2 47.8 66.0 87.3 91.3 93.6 104.9 131.6 150.0 180.4

5 25.1 33.8 40.6 59.5 77.9 102.1 107.4 108.8 122.4 152.4 172.6 210.1

10 31.6 40.0 46.6 66.7 84.9 110.8 116.9 117.7 132.8 164.5 185.8 227.6

20 38.3 45.9 52.2 73.2 91.2 118.6 125.4 125.6 141.9 175.3 197.4 243.2

50 47.5 53.7 59.3 81.4 98.9 128.0 135.6 135.2 153.0 188.3 211.3 261.9

100 54.8 59.6 64.6 87.4 104.4 134.7 142.9 142.0 160.9 197.4 221.1 275.2


155

Table B2.5: Rainfall Depth for Empangan Genting Klang in mm


Table B2.7: Rainfall Depth for Kg. Kuala Seleh in mm

Table B2.8: Rainfall Depth for Kg. Kerdas in mm

Table B2.9: Rainfall Depth for Air Terjun Sg. Batu in mm

ARI

(Year) 5 10 15 30 60 180 360 720 1440 2880 4320 7200

2 17.7 26.3 32.7 48.8 65.4 85.6 91.5 94.6 106.5 129.6 147.9 180.5

5 25.7 33.9 40.6 60.1 80.3 104.2 111.8 115.8 130.2 157.6 180.0 219.1

10 31.1 38.8 45.4 67.1 89.5 115.5 124.2 128.7 144.7 174.5 199.5 242.4

20 36.6 43.3 49.9 73.4 97.8 125.8 135.4 140.5 157.9 189.9 217.2 263.5

50 43.8 49.1 55.4 81.3 108.1 138.4 149.2 155.0 174.1 208.8 239.0 289.5

100 49.4 53.3 59.4 87.0 115.6 147.6 159.2 165.5 185.8 222.4 254.7 308.2


ARI

(Year) 5 10 15 30 60 180 360 720 1440 2880 4320 7200

2 16.4 24.4 30.8 48.1 66.9 87.2 93.1 97.6 111.9 137.4 159.7 190.9

5 23.3 32.2 39.7 61.5 84.6 105.5 111.4 116.5 139.7 171.5 197.2 239.3

10 28.0 37.3 45.3 70.0 95.6 116.5 122.4 127.8 157.0 192.6 220.2 269.3

20 32.5 42.0 50.5 77.8 105.9 126.4 132.3 137.9 172.8 211.9 241.3 297.0

50 38.6 48.1 57.2 87.7 118.6 138.7 144.3 150.3 192.5 236.0 267.3 331.4

100 43.2 52.7 62.0 95.0 128.0 147.5 152.9 159.2 206.8 253.5 286.3 356.6


ARI

(Year) 5 10 15 30 60 180 360 720 1440 2880 4320 7200

2 16.5 25.3 32.5 49.5 68.4 86.7 90.3 94.9 109.4 135.9 155.4 187.6

5 22.7 31.9 38.9 58.0 78.2 103.0 107.7 112.9 132.9 160.5 184.1 220.7

10 26.9 36.1 42.6 63.0 83.9 112.8 118.1 123.7 147.1 175.0 201.2 240.3

20 31.0 39.9 46.1 67.4 88.9 121.5 127.4 133.3 160.0 188.1 216.5 257.8

50 36.3 44.7 50.2 72.8 94.8 132.1 138.8 145.0 175.8 203.9 235.1 279.0

100 40.3 48.2 53.2 76.6 99.1 139.6 147.0 153.4 187.3 215.1 248.4 294.1


ARI

(Year) 5 10 15 30 60 180 360 720 1440 2880 4320 7200

2 18.0 27.7 34.3 51.2 66.4 79.5 81.6 83.4 94.9 116.5 131.4 142.2

5 28.0 38.6 46.2 68.3 89.2 101.7 103.7 107.7 125.3 159.2 183.7 241.6

10 35.3 45.9 54.0 79.5 104.1 115.8 117.6 123.1 144.9 187.4 218.9 318.7

20 42.7 53.0 61.4 90.1 118.3 128.8 130.4 137.5 163.3 214.5 253.0 400.7

50 52.9 62.2 70.9 103.7 136.6 145.2 146.6 155.6 186.9 249.6 297.7 518.4

100 61.0 69.2 78.1 113.9 150.3 157.3 158.4 169.1 204.6 276.1 331.9 615.4


ARI

(Year) 5 10 15 30 60 180 360 720 1440 2880 4320 7200

2 16.9 26.1 32.4 47.1 66.5 86.0 93.5 99.6 108.4 136.1 162.5 201.9

5 21.6 29.8 36.7 55.9 79.7 101.5 111.5 123.1 133.9 173.9 201.3 249.7

10 24.6 31.9 39.1 61.2 87.6 110.8 122.2 137.5 149.6 197.7 225.2 279.1

20 27.3 33.8 41.2 65.9 94.8 119.0 131.9 150.6 163.8 219.8 247.0 305.9

50 30.8 36.0 43.8 71.7 103.5 129.1 143.7 166.9 181.5 247.7 274.1 339.2

100 33.4 37.6 45.5 75.8 109.8 136.2 152.1 178.8 194.4 268.1 293.8 363.3


156

Table B2.10: Rainfall Depth for Genting Sempah in mm

Table B2.11: Rainfall Depth for Sek. Men. Bandar Tasik Kesuma in mm

Table B2.12: Rainfall Depth for RTM Kajang in mm

Table B2.13: Rainfall Depth for Sek. Keb. Kg. Sg. Lui in mm

Table B2.14: Rainfall Depth for Pintu Kawalan P/S Telok Gong in mm

ARI

(Year) 5 10 15 30 60 180 360 720 1440 2880 4320 7200

2 15.2 23.7 30.3 43.0 58.1 74.3 78.9 81.8 93.0 114.1 128.3 160.1

5 22.3 32.9 40.9 55.6 73.2 93.1 98.5 102.1 117.2 136.6 154.0 192.2

10 27.2 39.1 47.8 63.5 82.7 104.8 110.6 114.6 132.3 150.1 169.5 211.5

20 32.1 45.0 54.5 71.0 91.3 115.5 121.7 126.2 146.3 162.3 183.4 228.9

50 38.7 52.8 63.0 80.4 102.2 128.9 135.6 140.5 163.7 177.2 200.5 250.2

100 43.8 58.6 69.5 87.3 110.2 138.6 145.7 151.0 176.4 187.8 212.7 265.5


ARI

(Year) 5 10 15 30 60 180 360 720 1440 2880 4320 7200

2 14.4 23.6 29.9 44.4 60.8 76.3 82.3 85.4 93.0 116.2 131.3 151.8

5 21.7 31.9 38.8 57.1 76.1 94.3 102.1 107.6 116.8 150.7 167.8 192.6

10 26.9 37.3 44.4 65.1 85.5 105.4 114.3 121.4 131.6 172.6 190.8 218.2

20 32.2 42.5 49.6 72.5 94.2 115.5 125.5 134.1 145.2 193.0 212.1 241.8

50 39.3 49.2 56.3 81.9 105.0 128.1 139.4 149.9 162.1 219.0 239.0 271.5

100 44.8 54.2 61.2 88.8 112.8 137.2 149.4 161.6 174.5 238.2 258.8 293.3


ARI

(Year) 5 10 15 30 60 180 360 720 1440 2880 4320 7200

2 21.0 28.5 33.8 46.5 63.5 86.4 92.3 96.2 108.6 130.7 148.2 174.8

5 28.8 37.0 43.5 58.6 77.9 108.5 118.1 125.4 137.7 161.6 187.0 219.0

10 34.0 42.4 49.7 66.2 86.6 122.2 134.4 144.0 155.9 180.5 211.2 246.4

20 39.1 47.5 55.4 73.2 94.6 134.8 149.5 161.4 172.7 197.8 233.5 271.6

50 45.6 54.0 62.7 82.0 104.5 150.6 168.5 183.6 193.8 219.3 261.5 303.0

100 50.5 58.8 68.0 88.4 111.6 162.1 182.5 200.1 209.3 234.9 281.9 325.9


ARI

(Year) 5 10 15 30 60 180 360 720 1440 2880 4320 7200

2 16.6 26.2 32.9 47.7 66.1 81.8 86.0 91.1 103.6 123.6 142.7 175.4

5 28.6 40.7 48.0 62.8 82.2 102.5 108.9 113.7 129.3 156.3 177.1 217.8

10 38.1 51.3 58.5 72.5 92.0 115.4 123.2 127.7 145.3 176.7 198.2 244.0

20 48.1 62.0 68.9 81.6 101.1 127.2 136.4 140.5 159.9 195.6 217.6 268.0

50 62.7 76.9 82.8 93.2 112.3 142.0 152.9 156.5 178.2 219.3 241.6 297.7

100 74.8 88.7 93.6 101.9 120.4 152.8 165.0 168.1 191.5 236.6 259.1 319.4

Design Rainfall (mm) Data for Various Storm Duration (minute

ARI

(Year) 5 10 15 30 60 180 360 720 1440 2880 4320 7200

2 14.7 23.2 29.8 42.9 59.0 81.7 90.6 93.5 101.9 119.2 131.8 155.7

5 24.7 34.6 41.9 54.3 72.4 101.9 115.9 118.8 126.7 147.6 162.0 189.7

10 32.3 42.6 50.1 61.5 80.6 114.3 131.8 134.6 141.9 165.0 180.5 210.3

20 32.3 42.6 50.1 61.5 80.6 114.3 131.8 134.6 141.9 165.0 180.5 210.3

50 51.7 61.3 68.4 76.4 97.3 139.9 165.1 167.7 173.4 200.8 218.0 251.9

100 61.1 69.7 76.4 82.5 103.9 150.3 178.8 181.2 186.0 215.1 233.1 268.5


157

Table B2.15: Rainfall Depth for JPS Pulau Lumut in mm

Table B2.16: Rainfall Depth for Ldg. Sg. Kapar in mm

Table B2.17: Rainfall Depth for Setia Alam in mm

Table B2.18: Rainfall Depth for Pusat Penyelidikan Getah Sg. Buloh in mm

ARI

(Year) 5 10 15 30 60 180 360 720 1440 2880 4320 7200

2 14.0 24.6 33.5 51.3 68.3 88.3 97.5 104.7 114.0 130.2 148.9 168.6

5 16.1 27.2 37.5 60.5 79.4 102.8 118.9 126.3 135.9 157.0 182.9 200.8

10 17.3 28.6 39.7 66.0 86.0 111.2 131.9 139.4 149.0 173.1 203.7 220.0

20 18.4 29.8 41.7 70.9 91.8 118.7 143.7 151.2 160.8 187.7 222.6 237.2

50 19.7 31.3 44.0 76.8 98.8 127.8 158.3 165.7 175.2 205.6 246.0 258.3

100 20.6 32.3 45.6 81.0 103.8 134.2 168.8 176.2 185.5 218.4 263.0 273.3


ARI

(Year) 5 10 15 30 60 180 360 720 1440 2880 4320 7200

2 13.3 24.6 33.2 49.7 72.7 86.3 90.2 95.5 104.0 122.1 131.2 162.6

5 14.8 27.2 38.1 59.4 84.3 103.7 109.8 113.8 121.6 149.8 159.0 189.3

10 15.6 28.8 40.9 65.2 91.2 114.2 121.7 124.7 132.0 166.7 175.8 205.0

20 16.3 30.1 43.4 70.4 97.2 123.6 132.5 134.6 141.3 182.0 191.0 218.9

50 17.1 31.6 46.4 76.7 104.5 135.2 145.8 146.5 152.4 201.0 209.6 235.7

100 17.7 32.7 48.5 81.3 109.7 143.5 155.4 155.1 160.4 214.7 223.1 247.7


ARI

(Year) 5 10 15 30 60 180 360 720 1440 2880 4320 7200

2 17.6 28.6 37.5 58.1 74.8 87.4 92.6 99.4 108.3 133.7 157.7 192.4

5 29.1 40.7 49.4 71.8 90.8 107.5 115.4 120.6 134.4 161.5 185.8 221.2

10 37.9 49.0 57.1 80.2 100.5 119.8 129.5 133.4 150.5 178.3 202.4 237.9

20 47.1 57.1 64.3 88.0 109.3 131.0 142.4 145.1 165.3 193.5 217.3 252.6

50 60.2 67.8 73.5 97.5 120.2 144.9 158.6 159.4 183.6 212.1 235.3 270.3

100 70.9 76.1 80.4 104.5 128.0 155.0 170.3 169.7 197.0 225.5 248.1 282.8


ARI

(Year) 5 10 15 30 60 180 360 720 1440 2880 4320 7200

2 15.8 27.4 37.8 57.5 72.6 81.4 84.2 89.4 105.4 125.1 143.9 176.4

5 18.4 30.5 42.6 62.6 83.7 95.1 97.8 105.0 127.5 141.0 165.8 200.7

10 19.9 32.3 45.3 65.4 90.2 103.1 105.8 114.2 140.9 150.1 178.5 214.6

20 21.2 33.9 47.7 67.8 95.9 110.3 112.9 122.4 153.0 158.0 189.8 226.9

50 22.8 35.7 50.6 70.6 102.8 119.0 121.4 132.3 167.9 167.5 203.3 241.5

100 23.9 37.0 52.6 72.5 107.7 125.1 127.5 139.4 178.6 174.1 212.8 251.8


APPENDIX C

159

C.1 Rainfall Intensity for Gumbel and Log-Normal Method

Table C1.1: Rainfall Depth for Ldg. Edinburgh Site in mm/hr

Table C1.2: Rainfall Depth for SMJK Kepong in mm/hr

Table C1.3: Rainfall Depth for Ibu Bekalan Km.16, Gombak in mm/hr

Table C1.4: Rainfall Depth for Empangan Genting Klang in mm/hr

ARI

(Year) 5 10 15 30 60 180 360 720 1440 2880 4320 7200

2 212.5 160.3 139.3 105.8 69.8 28.6 15.1 7.7 4.5 2.6 2.0 1.5

5 312.8 202.2 165.4 124.8 83.6 34.3 18.1 9.4 5.7 3.3 2.5 1.9

10 379.2 229.9 182.7 137.4 92.8 38.1 20.2 10.5 6.6 3.8 2.8 2.1

20 442.9 256.6 199.3 149.5 101.5 41.7 22.1 11.5 7.4 4.2 3.1 2.3

50 525.4 291.0 220.7 165.1 112.9 46.3 24.6 12.9 8.4 4.7 3.5 2.6

100 587.2 316.9 236.8 176.8 121.4 49.8 26.5 13.9 9.2 5.2 3.8 2.8


ARI

(Year) 5 10 15 30 60 180 360 720 1440 2880 4320 7200

2 191.1 150.3 128.3 96.9 65.4 27.8 14.7 7.6 4.1 2.4 1.9 1.4

5 253.9 180.2 153.4 117.1 79.9 35.5 18.6 9.6 5.2 3.1 2.4 1.8

10 295.6 200.0 170.1 130.4 89.5 40.7 21.2 10.9 5.9 3.5 2.7 2.0

20 335.5 219.0 186.0 143.2 98.7 45.6 23.7 12.2 6.5 3.9 3.0 2.2

50 387.2 243.6 206.7 159.8 110.6 51.9 26.9 13.8 7.4 4.5 3.4 2.5

100 425.9 262.0 222.2 172.2 119.5 56.7 29.4 15.0 8.1 4.9 3.7 2.7


ARI

(Year) 5 10 15 30 60 180 360 720 1440 2880 4320 7200

2 200.3 147.8 124.5 94.6 65.0 28.6 15.0 7.7 4.3 2.7 2.1 1.5

5 308.2 195.8 156.5 117.3 77.1 33.9 17.8 9.0 5.0 3.2 2.4 1.7

10 379.6 227.6 177.6 132.2 85.1 37.4 19.6 9.9 5.5 3.5 2.6 1.9

20 448.1 258.0 197.9 146.6 92.8 40.8 21.4 10.8 6.0 3.7 2.8 2.1

50 536.7 297.5 224.2 165.3 102.8 45.2 23.7 11.9 6.6 4.1 3.1 2.3

100 603.2 327.0 243.9 179.2 110.3 48.4 25.4 12.7 7.0 4.4 3.3 2.4


ARI

(Year) 5 10 15 30 60 180 360 720 1440 2880 4320 7200

2 215.6 156.0 129.1 96.4 64.6 28.2 15.1 7.8 4.4 2.7 2.0 1.5

5 313.7 204.6 160.6 118.5 78.9 34.1 18.2 9.4 5.3 3.2 2.4 1.8

10 378.6 236.8 181.5 133.2 88.3 38.0 20.2 10.5 6.0 3.6 2.7 2.0

20 440.8 267.7 201.6 147.2 97.4 41.8 22.2 11.5 6.6 3.9 3.0 2.2

50 521.4 307.6 227.5 165.4 109.1 46.7 24.8 12.8 7.4 4.4 3.3 2.4

100 581.7 337.6 246.9 179.0 117.9 50.3 26.7 13.8 8.0 4.7 3.6 2.6


160

Table C1.5: Rainfall Depth for Ibu Bekalan Km.11, Gombak in mm/hr

Table C1.6: Rainfall Intensity for Kg. Kuala Seleh in mm/hr

Table C1.7: Rainfall Intensity for Kg. Sg. Tua in mm/hr

Table C1.8: Rainfall Intensity for Kg. Kerdas in mm/hr

Table C1.9: Rainfall Intensity for Air Terjun Sg. Batu in mm/hr

ARI

(Year) 5 10 15 30 60 180 360 720 1440 2880 4320 7200

2 199.2 146.3 122.8 95.7 66.4 28.7 15.3 8.0 4.6 2.8 2.2 1.6

5 269.8 184.2 150.7 117.2 81.8 34.2 18.2 9.6 5.8 3.5 2.7 2.0

10 316.5 209.3 169.1 131.4 91.9 37.8 20.1 10.6 6.7 4.0 3.1 2.3

20 361.4 233.4 186.9 145.1 101.7 41.3 21.9 11.6 7.4 4.5 3.4 2.5

50 419.4 264.6 209.8 162.7 114.3 45.7 24.3 12.9 8.4 5.0 3.9 2.9

100 462.9 288.0 227.0 175.9 123.8 49.1 26.1 13.8 9.2 5.5 4.2 3.1


ARI

(Year) 5 10 15 30 60 180 360 720 1440 2880 4320 7200

2 196.9 149.2 127.8 97.5 67.4 28.5 14.8 7.8 4.5 2.8 2.1 1.5

5 293.4 198.8 157.1 115.5 77.4 34.2 17.9 9.4 5.5 3.3 2.5 1.8

10 357.4 231.7 176.6 127.5 84.1 37.9 20.0 10.5 6.2 3.6 2.8 2.0

20 418.7 263.2 195.3 138.9 90.5 41.6 22.0 11.6 6.8 4.0 3.0 2.2

50 498.1 304.0 219.4 153.7 98.8 46.2 24.5 12.9 7.7 4.4 3.3 2.4

100 557.5 334.6 237.5 164.9 105.0 49.8 26.4 14.0 8.3 4.7 3.5 2.5


ARI

(Year) 5 10 15 30 60 180 360 720 1440 2880 4320 7200

2 210.8 146.1 121.4 90.6 63.0 26.2 14.3 7.6 4.2 2.6 1.9 1.5

5 303.9 185.1 146.7 108.2 77.0 31.9 17.3 9.3 5.1 3.1 2.3 1.7

10 365.6 211.0 163.4 119.8 86.2 35.7 19.3 10.4 5.8 3.4 2.6 1.9

20 424.7 235.7 179.5 131.0 95.1 39.3 21.3 11.5 6.3 3.7 2.8 2.1

50 501.3 267.8 200.2 145.5 106.6 44.0 23.8 13.0 7.1 4.1 3.1 2.3

100 558.6 291.9 215.8 156.4 115.2 47.5 25.6 14.0 7.7 4.4 3.4 2.5


ARI

(Year) 5 10 15 30 60 180 360 720 1440 2880 4320 7200

2 224.1 166.5 136.6 102.5 66.5 26.3 13.5 6.9 3.9 2.4 1.8 1.3

5 369.0 243.6 187.1 129.4 83.8 32.7 16.7 8.7 5.0 3.1 2.4 1.7

10 464.9 294.6 220.4 147.2 95.2 37.0 18.8 9.8 5.7 3.6 2.8 2.1

20 556.9 343.6 252.5 164.3 106.2 41.1 20.8 11.0 6.4 4.1 3.1 2.4

50 676.0 406.9 293.9 186.4 120.4 46.4 23.5 12.4 7.3 4.6 3.6 2.8

100 765.2 454.4 325.0 203.0 131.0 50.3 25.4 13.5 8.0 5.1 4.0 3.1


ARI

(Year) 5 10 15 30 60 180 360 720 1440 2880 4320 7200

2 199.6 154.2 127.9 92.9 65.6 28.2 15.4 8.2 4.5 2.8 2.2 1.7

5 267.5 178.4 144.5 109.7 78.6 33.5 18.3 10.3 5.6 3.6 2.8 2.1

10 312.5 194.5 155.5 120.9 87.1 36.9 20.3 11.7 6.3 4.1 3.2 2.3

20 355.7 209.8 166.0 131.6 95.4 40.2 22.2 13.0 7.0 4.7 3.5 2.6

50 411.6 229.7 179.7 145.5 106.0 44.5 24.6 14.8 7.9 5.3 4.0 2.9

100 453.5 244.6 189.9 155.9 114.0 47.7 26.4 16.1 8.6 5.8 4.4 3.1


161

Table C1.10: Rainfall Intensity for Genting Sempah in mm/hr

Table C1.11: Rainfall Intensity for Sek. Men. Bandar Tasik Kesuma in mm/hr

Table C1.12: Rainfall Intensity for RTM Kajang in mm/hr

Table C1.13: Rainfall Intensity for Sek. Keb. Kg. Sg. Lui in mm/hr

Table C1.14: Rainfall Intensity for Pintu Kawalan P/S Telok Gong in mm/hr

ARI

(Year) 5 10 15 30 60 180 360 720 1440 2880 4320 7200

2 183.5 138.6 117.3 83.9 57.0 24.4 13.0 6.7 3.8 2.3 1.8 1.3

5 308.0 249.8 201.7 123.8 76.5 31.5 16.5 8.5 4.8 2.8 2.1 1.6

10 390.4 323.5 257.5 150.2 89.4 36.2 18.9 9.8 5.5 3.1 2.3 1.8

20 469.5 394.1 311.1 175.6 101.7 40.7 21.2 10.9 6.2 3.5 2.6 1.9

50 571.8 485.6 380.5 208.4 117.7 46.5 24.1 12.4 7.0 3.9 2.9 2.2

100 648.5 554.1 432.4 232.9 129.7 50.8 26.3 13.5 7.6 4.2 3.1 2.3


ARI

(Year) 5 10 15 30 60 180 360 720 1440 2880 4320 7200

2 175.5 140.1 118.1 88.0 60.2 25.2 13.6 7.1 3.8 2.4 1.8 1.2

5 295.5 205.2 161.0 113.9 74.3 30.4 16.5 8.8 4.9 3.2 2.4 1.6

10 375.0 248.3 189.4 131.1 83.6 33.8 18.5 10.0 5.5 3.8 2.8 1.9

20 451.2 289.6 216.7 147.5 92.5 37.1 20.4 11.1 6.2 4.3 3.2 2.1

50 549.9 343.1 251.9 168.8 104.1 41.4 22.8 12.5 7.0 5.0 3.7 2.4

100 623.8 383.3 278.4 184.7 112.8 44.6 24.6 13.6 7.7 5.5 4.0 2.7


ARI

(Year) 5 10 15 30 60 180 360 720 1440 2880 4320 7200

2 251.9 169.7 134.5 92.2 62.8 28.5 15.3 8.0 4.5 2.7 2.0 1.4

5 353.9 220.5 168.8 114.5 76.1 35.4 19.3 10.3 5.6 3.3 2.5 1.8

10 421.3 254.1 191.5 129.3 85.0 40.0 22.0 11.8 6.4 3.7 2.9 2.0

20 486.0 286.4 213.2 143.5 93.5 44.3 24.6 13.3 7.1 4.1 3.2 2.2

50 569.8 328.1 241.4 161.9 104.4 50.0 27.9 15.2 8.1 4.6 3.6 2.5

100 632.6 359.4 262.5 175.7 112.6 54.2 30.4 16.6 8.8 5.0 3.9 2.7


ARI

(Year) 5 10 15 30 60 180 360 720 1440 2880 4320 7200

2 215.4 162.8 133.8 95.2 65.4 26.9 14.2 7.5 4.3 2.5 2.0 1.4

5 371.7 252.6 194.5 122.8 81.1 34.2 18.2 9.6 5.5 3.4 2.5 1.8

10 475.1 312.1 234.7 141.1 91.6 39.1 20.8 10.9 6.3 3.9 2.8 2.1

20 574.3 369.1 273.3 158.6 101.6 43.7 23.4 12.2 7.0 4.4 3.2 2.3

50 702.8 443.0 323.2 181.3 114.5 49.8 26.7 14.0 8.0 5.1 3.6 2.6

100 799.0 498.3 360.6 198.3 124.3 54.3 29.1 15.2 8.8 5.6 4.0 2.9


ARI

(Year) 5 10 15 30 60 180 360 720 1440 2880 4320 7200

2 189.6 141.9 119.3 84.5 58.1 26.9 15.0 7.7 4.2 2.5 1.8 1.28

5 302.3 217.7 176.5 110.8 72.8 34.2 19.2 9.9 5.2 3.0 2.2 1.5

10 376.9 267.8 214.3 128.1 82.6 39.0 22.1 11.3 5.9 3.4 2.5 1.7

20 448.4 315.9 250.5 144.7 92.0 43.6 24.8 12.6 6.6 3.8 2.7 1.9

50 541.0 378.2 297.5 166.3 104.1 49.6 28.3 14.4 7.5 4.2 3.0 2.1

100 610.4 424.9 332.7 182.4 113.2 54.1 31.0 15.7 8.1 4.6 3.3 2.3


162

Table C1.15: Rainfall Intensity for JPS Pulau Lumut in mm/hr

Table C1.16: Rainfall Intensity for Ldg. Sg. Kapar in mm/hr

Table C1.17: Rainfall Intensity for Setia Alam in mm/hr

Table C1.18: Rainfall Intensity for Pusat Penyelidikan Getah Sg. Buloh in mm/hr

ARI

(Year) 5 10 15 30 60 180 360 720 1440 2880 4320 7200

2 165.5 146.0 132.3 101.1 67.3 29.0 16.0 8.6 4.7 2.7 2.0 1.4

5 191.8 160.9 147.9 118.8 78.3 33.7 19.5 10.3 5.6 3.2 2.5 1.6

10 209.2 170.8 158.2 130.6 85.6 36.9 21.9 11.5 6.2 3.6 2.8 1.8

20 225.9 180.3 168.1 141.8 92.6 39.9 24.1 12.6 6.7 3.9 3.1 2.0

50 247.6 192.6 181.0 156.4 101.6 43.8 27.0 14.0 7.5 4.4 3.4 2.2

100 263.8 201.8 190.6 167.3 108.4 46.7 29.2 15.0 8.0 4.7 3.7 2.3


ARI

(Year) 5 10 15 30 60 180 360 720 1440 2880 4320 7200

2 157.94 145.4 130.9 98.1 71.6 28.3 14.8 7.8 4.3 2.5 1.8 1.33

5 176.02 161.6 149.7 115.8 83.1 34.0 18.0 9.4 5.0 3.1 2.2 1.57

10 188.00 172.3 162.2 127.5 90.7 37.7 20.1 10.4 5.5 3.5 2.5 1.73

20 199.48 182.6 174.2 138.8 98.0 41.3 22.1 11.3 6.0 3.9 2.7 1.88

50 214.35 195.9 189.7 153.4 107.5 46.0 24.7 12.6 6.6 4.4 3.1 2.08

100 225.49 205.8 201.3 164.3 114.6 49.5 26.6 13.5 7.1 4.8 3.3 2.23


ARI

(Year) 5 10 15 30 60 180 360 720 1440 2880 4320 7200

2 222.0 170.3 147.3 113.7 73.5 28.7 15.2 8.1 4.4 2.7 2.2 1.6

5 481.7 284.6 214.9 149.0 91.4 35.8 19.3 10.0 5.6 3.4 2.5 1.8

10 653.5 360.3 259.7 172.3 103.3 40.4 22.0 11.2 6.5 3.8 2.8 2.0

20 818.4 432.9 302.7 194.7 114.7 44.9 24.6 12.4 7.2 4.1 3.1 2.1

50 1031.8 526.9 358.3 223.7 129.5 50.7 27.9 14.0 8.2 4.7 3.4 2.3

100 1191.7 597.3 399.9 245.5 140.5 55.1 30.4 15.1 9.0 5.0 3.6 2.5


ARI

(Year) 5 10 15 30 60 180 360 720 1440 2880 4320 7200

2 186.4 161.8 148.6 113.6 71.5 26.7 13.8 7.3 4.3 2.6 2.0 1.4

5 220.5 182.7 169.8 124.1 82.5 31.2 16.0 8.6 5.2 2.9 2.3 1.7

10 243.1 196.5 183.8 131.1 89.7 34.2 17.5 9.4 5.8 3.1 2.5 1.8

20 264.7 209.8 197.2 137.7 96.6 37.0 18.8 10.2 6.4 3.3 2.7 1.9

50 292.7 227.0 214.6 146.3 105.6 40.7 20.6 11.2 7.1 3.6 2.9 2.1

100 313.7 239.9 227.7 152.8 112.3 43.5 22.0 12.0 7.6 3.8 3.1 2.2


APPENDIX D

164

D.1 Gumbel’s Distribution (15 minutes) with 95% Confidence Intervals

Figure D1.1: Ldg. Edinburgh Site

Figure D1..2: Kg. Sg. Tua

Figure D1.3: SMJK Kepong

165

Figure D1.4: Ibu Bekalan Km.16, Gombak

Figure D1.5: Empangan Genting Klang


Figure D1.7: Kg. Kuala Seleh

166

Figure D1.8: Kg. Kerdas

Figure D1.9: Air Terjun Sg. Batu

Figure D1.10: Genting Sempah

Figure D1.11: Sek. Men. Bandar Tasik Kesuma

167

Figure D1.12: RTM Kajang

Figure D1.13: Sek. Keb. Kg. Sg. Lui

Figure D1.14: Pintu Kawalan P/S Telok Gong

Figure D1.15: JPS Pulau Lumut

168

Figure D1.16: Ldg.Sg.Kapar

Figure D1.17: Setia Alam

Figure D1.18: Pusat Penyelidikan Getah Sg. Buloh

169

D2 Gumbel’s Distribution (60 minutes) for 95% Confidence Intervals


Figure D2.2: Kg. Sg. Tua


170





171





172





173




174

D3 Gumbel’s Distribution (1440 minutes) for 95% Confident Intervals


Figure D3.2: Kg. Sg. Tua


175





176





177





178




APPENDIX E

180

E.1 Log-Normal Distribution (15 minutes) with 95% Confidence Intervals

Figure E1.1: Ldg. Edinburgh Site

Figure E1.2: Kg. Sg. Tua

Figure E1.3: SMJK Kepong

181

Figure E1.4: Ibu Bekalan Km.16, Gombak

Figure E1.5: Empangan Genting Klang


Figure E1.7: Kg. Kuala Seleh

182

Figure E1.8: Kg. Kerdas

Figure E1.9: Air Terjun Sg. Batu

Figure E1.10: Genting Sempah

Figure E1.11: Sek. Men. Bandar Tasik Kesuma

183

Figure E1.12: RTM Kajang

Figure E1.13: Sek. Keb. Kg. Sg. Lui

Figure E1.14: Pintu Kawalan P/S Telok Gong

Figure E1.15: JPS Pulau Lumut

184

Figure E1.16: Ldg.Sg.Kapar

Figure E1.17: Setia Alam

Figure E1.18: Pusat Penyelidikan Getah Sg. Buloh

185

E2 Log-Normal Distribution (60 minutes) for 95% Confidence Intervals





186




Figure E2.8 Kg. Kerdas

187





188


Figure E2.14 Pintu Kawalan P/S Telok Gong



189


Figure E2.18 Pusat Penyelidikan Getah Sg. Buloh

190

E3 Log-Normal Distribution (1440 minutes) for 95% Confident Intervals




191





192

Figure E3.8: Kg. Kerdas




193



Figure E3.14: Pintu Kawalan P/S Telok Gong


194



Figure E3.18: Pusat Penyelidikan Getah Sg. Buloh

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