THE DEVELOPMENT OF INTENSITY-DURATION-FREQUENCY CURVES FOR
PAHANG
NUR ASMA’ BINTI SUHAIMEE
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
UNIVERSITI MALAYSIA PAHANG
JUNE 2015
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ABSTRACT
In past few years, Pahang was experienced flooding in certain area and it getting worse
by year with heavy rainfall especially at low level area cause of clog drain, design
failing, wrong estimation and other natural causes. IDF curve use to design rainfall data
by estimate peak discharge from IDF curve for engineering design such drainage, flood
elevation, and other hydraulic design. This study purpose to develop intensity-duration-
frequency (IDF) curve for all 12 district in Pahang based on this past year data and
compare it with Log-Person Type III method than Gumbel method that use in Urban
Storm Water Management (MSMA) to compare the differences value of IDF curve
using both method. IDF curve requires rainfall data from the Department of Irrigation
and Drainage (DID) for period of 5 minutes to 72 hour starting on year 1990 to year
2014. To designing IDF curve, process involved are find the missing data from nearest
station, mean, standard deviation, frequency factor, and intensity value for 2, 5, 10 ,20,
50 and 100 years return period for both methods Gumbel and Log-Pearson Type III.
Both methods, Gumbel and Log-Pearson Type III is compared to see the different value
given by both methods. To test the accuracy of both methods, Komogorov Smirnov
(KS) was constructed for fitting distribution. Based on the result, Gumbel method give
clear trend than Log-Pearson Type III with 26.92% test accepted from 78 tests while
Log-Pearson Type III almost all test was rejected.
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ABSTRAK
Dalam tahun-tahun kebelakangan ini, Pahang telah mengalami banjir di kawasan
tertentu dan ia semakin teruk setiap tahun dengan hujan lebat terutama di kawasan
rendah menyebabkan tahap longkang tersumbat, reka bentuk gagal, anggaran yang salah
dan lain-lain sebab-sebab semula jadi. IDF menggunakan keluk untuk mereka bentuk
data hujan dengan anggaran pelepasan puncak dari lengkung IDF untuk reka bentuk
kejuruteraan saliran, ketinggian banjir, dan reka bentuk hidraulik lain. Tujuan kajian ini
adalah membangunkan keamatan-tempoh-frekuensi (IDF) lengkung untuk semua 12
daerah di Pahang ini berdasarkan data tahun-tahun lepas dan membandingkannya
dengan kaedah Log-Pearson Jenis III dengan kaedah Gumbel yang digunakan oleh
Pengurusan Air Ribut Bandar (MSMA) dengan membandingkan nilai perbezaan
lengkung IDF menggunakan kedua-dua kaedah tersebut. Lengkung IDF memerlukan
data hujan daripada Jabatan Pengairan dan Saliran (JPS) untuk tempoh 5 minit hingga
72 jam bermula pada tahun 1990 hingga tahun 2014. Untuk mereka bentuk lengkung
IDF, proses yang terlibat adalah mencari data yang hilang dari stesen terdekat, min,
standard penyimpangan, faktor kekerapan dan nilai keamatan untuk 2, 5, 10, 20, 50 dan
100 tahun kembali tempoh untuk kedua-dua kaedah Gumbel dan Log Pearson Jenis III.
Kedua-dua kaedah, Gumbel dan Log-Pearson Type III dibandingkan dengan melihat
nilai yang berbeza yang diberikan oleh kedua-dua kaedah. Untuk menguji ketepatan
kedua-dua kaedah, Komogorov Smirnov (KS) telah dibina untuk diagihkan sesuai.
Berdasarkan keputusan, kaedah Gumbel memberikan trend jelas daripada Log-Pearson
Type III dengan ujian 26,92% diterima daripada 78 ujian manakala Log-Pearson jenis
III hampir semua ujian telah ditolak.
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TABLE OF CONTENT
Page
SUPERVISOR'S DECLARATION i
STUDENT'S DECLARATION ii
DEDICATION iii
ACKNOLEWDGEMENT iv
ABSTRACT v
ABSTRAK vi
TABLE OF CONTENTS vii
LIST OF TABLES xi
LIST OF FIGURES xiii
LIST OF SYMBOLS xvi
LIST OF ABBREVIATION xviii
CHAPTER 1 INTRODUCTION
1.1 Background 1
1.2 Problem Statement 2
1.3 Objectives 3
1.4 Scope of Study 3
1.5 Significant of Study 3
CHAPTER 2 LITERATURE RIVIEW
2.1 Introduction 5
2.2 Intensity-Duration-Frequency (IDF) Curve 6
2.3 Mass Curve 9
2.4 Missing Data Method 12
2.4.1 Normal Ratio Method 12
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2.4.2 Arithmetic Mean Method 12
2.5 Distribution for IDF Curve Development 13
2.5.1 Log-Normal Distribution 13
2.5.2 Normal Distribution 16
2.5.3 Gamma Distribution 17
2.5.4 Gumbel Distribution 18
2.5.5 Log-Pearson Type III Distribution 20
2.6 Goodness of Fit 21
2.6.1 Kolmogorov-Smirnov test (KS test) 21
2.6.2 Chi-square test 22
2.6.3 Anderson-Darling test 23
CHAPTER 3 METHODOLOGY
3.1 Introduction 25
3.2 Flow Chart 26
3.3 Study Area 27
3.4 Data Collection 27
3.5 Analysis Method 29
3.5.1 Missing Data 29
3.5.2 Frequent Factor 30
3.5.3 Mean and Standard Deviation 31
3.5.4 Gumbel Distribution 31
3.5.4.1 Frequency Factor 31
3.5.4.2 Flood of Specific Probability 32
3.5.5 Log-Pearson Type III Distribution 32
3.5.5.1 Frequency Factor 33
3.5.5.2 Flood of Specific Probability 35
3.6 Graphical Method 35
3.7 Confidence Limits 38
3.8 Percentage of Differences 39
CHAPTER 4 RESULT AND DISCUSSION
4.1 Introduction 40
ix
4.2 Missing Data 42
4.3 Mean and Standard Deviation 43
4.3.1 Gumbel Distribution 43
4.3.2 Log-Pearson Type III Distribution 44
4.4 Frequency Factor 44
4.4.1 Gumbel Distribution 44
4.4.2 Log-Pearson Type III Distribution 45
4.5 Intensity 46
4.5.1 Gumbel Distribution 46
4.5.2 Log-Pearson Type III Distribution 48
4.6 IDF Curve for Gumbel Distribution 50
4.6.1 Bentong 50
4.6.2 Bera 54
4.6.3 Cameron Highland 57
4.6.4 Jerantut 59
4.6.5 Kuala Lipis 62
4.6.6 Kuantan 66
4.6.7 Maran 68
4.6.8 Pulau Tioman 70
4.6.9 Pekan 71
4.6.10 Raub 74
4.6.11 Rompin 75
4.6.12 Temerloh 79
4.6.13 Conclusion 80
4.7 IDF Curve for Log-Pearson Type III Distribution 81
4.7.1 Bentong 81
4.7.2 Bera 85
4.7.3 Cameron Highland 88
4.7.4 Jerantut 89
4.7.5 Kuala Lipis 93
4.7.6 Kuantan 98
4.7.7 Maran 100
4.7.8 Pulau Tioman 102
4.7.9 Pekan 103
4.7.10 Raub 106
4.7.11 Rompin 107
4.7.12 Temerloh 111
4.7.13 Conclusion 112
4.8 Comparison between Constructed IDF Curve and
Existing IDF Curve in MSMA
113
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4.9 Comparison between IDF Curve for Gumbel
Distribution and Log-Pearson Type III Distribution
119
4.10 Comparison of Kolmogorov-Smirnov for Gumbel and
Log-Pearson Type III Distribution
131
4.10.1 Emperical Calculation for Gumbel Distribution 131
4.10.2 Emperical Calculation for Log-Pearson Type
III Distribution
136
4.10.3 Comparison of Probability Value between both
Method
139
4.11 Summary 141
CHAPTER 5 CONCLUSION
5.1 Background 143
5.2 Conclusion 143
5.3 Recommendation 145
REFERENCE 147
APPENDICES 150
A Rainfall Data in mm 151
B Rainfall Depth in mm 177
C Rainfall Intensity for Log-Pearson Type III Distribution in
mm/hr 191
D Rainfall Intensity for Gumbel Distribution in mm/hr 198
E Gumbel’s Distribution with 90% Confidence Intervals 205
xi
LIST OF TABLES
Table No. Title Page
3.1 Selected Rainfall Station 28
3.2 Frequency Factors K for Gamma and log-Pearson Type III Distributions 33
3.3 Various Plotting Positions Formula 36
3.4 Value of Kolmogorov Smirnov, ∆0 39
4.1 Rainfall Data for Rompin (Stn 3228174) 41
4.2 Rainfall Data for Pulau Tioman (Stn. 2841001) 42
4.3 Descriptive Statistic for Rompin (Stn. 3228174) for Gumbel Distribution 43
4.4 Descriptive Statistic for Rompin (Stn. 3228174) for Log-Pearson Type
III Distribution 44
4.5 Frequency Factor based on Return Period 45
4.6 Value Cs for Rompin (Stn 3228174) 46
4.7 Frequency Factor based on Return Period 46
4.8 Rainfall Depth for Rompin (Stn 3228174) in mm 47
4.9 Intensity for Rompin (Stn 3228174) in mm/hr 48
4.10 Rainfall Depth for Rompin (Stn 3228174) in mm before anti-log 49
4.11 Rainfall Depth for Rompin (Stn 3228174) in mm after anti-log 49
4.12 Intensity for Rompin (Stn 3228174) in mm/hr for Log-Pearson Type III 50
4.13 Coefficient IDF equation for Pahang in MSMA 114
4.14 Intensity from Coefficient in MSMA 115
4.15 Percentage of Difference for Rompin, Bera, Temerloh, Maran, Raub,
Jerantut, Kuantan and Cameron Highland 117
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4.16 Intensity of District in Pahang using Gumbel Distribution 119
4.17 Intensity of District in Pahang using Log-Pearson Type III Distribution 123
4.18 Percentage different between Gumbel and Log-Pearson Type III 127
4.19 Summary Calculation for Plotting Position Formula for Gumbel
Distribution 132
4.20 Summary Calculation for Plotting Position Formula for Log-Pearson
Type III Distribution 137
4.21 Maximum Probability Different between Gumbel and Log-Pearson Type
III Distribution 139
5.1 Maximum Value of the Intensity 144
5.2 Minimum Value of the Intensity 144
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LIST OF FIGURES
Table No. Title Page
2.1 IDF curve 7
2.2 Mass curve (Cumulative Rainfall of Stations in The Period 2002-2010) 10
2.3 Cumulative Log-Normal Distribution of Rainfall 14
2.4 Normal Distribution of Precipitation 16
3.1 Flow Chart of Study 26
3.2 Maps of District at Pahang 27
3.3 Pahang Rainfall Station 29
4.1 IDF curve for Bentong (Stn. 3122142) 51
4.2 IDF curve for Bentong (Stn. 3318127) 52
4.3 IDF curve for Bentong (Stn. 3420131) 53
4.4 IDF curve for Bera (Stn. 3026156) 54
4.5 IDF curve for Bera (Stn. 3324083) 55
4.6 IDF curve for Bera (Stn. 3325086) 56
4.7 IDF curve for Cameron Higland (Stn.. 4513033) 58
4.8 IDF curve for Jerantut (Stn. 3924072) 59
4.9 IDF curve for Jerantut (Stn. 4023001) 60
4.10 IDF curve for Jerantut (Stn. 4324001) 61
4.11 IDF curve for Kuala Lipis (Stn. 4019001) 62
4.12 IDF curve for Kuala Lipis (Stn. 4019063) 63
4.13 IDF curve for Kuala Lipis (Stn. 4620045) 64
4.14 IDF curve for Kuala Lipis (Stn. 4120064) 65
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4.15 IDF curve for Kuantan (Stn. 3833002) 66
4.16 IDF curve for Kuantan (Stn. 3930012) 67
4.17 IDF curve for Maran (Stn. 3628001) 69
4.18 IDF curve for Pulau Tioman (Stn. 2841001) 70
4.19 IDF curve for Pekan (Stn.3034168) 71
4.20 IDF curve for Pekan (Stn. 3330109) 72
4.21 IDF curve for Pekan (Stn. 3334161) 73
4.22 IDF curve for Raub (Stn.3818054) 74
4.23 IDF curve for Rompin (Stn. 3228174) 76
4.24 IDF curve for Rompin (Stn. 2929001) 77
4.25 IDF curve for Rompin (Stn. 263001) 78
4.26 IDF curve for Rompin (Stn. 263001) 79
4.27 IDF curve for Bentong (Stn. 3122142) 82
4.28 IDF curve for Bentong (Stn. 3318127) 83
4.29 IDF curve for Bentong (Stn. 3420131) 84
4.30 IDF curve for Bera (Stn. 3026156) 85
4.31 IDF curve for Bera (Stn. 3324083) 86
4.32 IDF curve for Bera (Stn. 3325086) 87
4.33 IDF curve for Cameron Highland (Stn. 4513033) 88
4.34 IDF curve for Jerantut (Stn. 3924072) 90
4.35 IDF curve for Jerantut (Stn. 4023001) 91
4.36 IDF curve for Jerantut (Stn. 4324001) 92
4.37 IDF curve for Kuala Lipis (Stn. 4019001) 94
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4.38 IDF curve for Kuala Lipis (Stn. 4019063) 95
4.39 IDF curve for Kuala Lipis (Stn. 4620045) 96
4.40 IDF curve for Kuala Lipis (Stn. 4120064) 97
4.41 IDF curve for Kuantan (Stn. 3833002) 98
4.42 IDF curve for Kuantan (Stn. 39300012) 99
4.43 IDF curve for Maran (Stn. 368001) 101
4.44 IDF curve for Pulau Tioman (Stn. 2841001) 102
4.45 IDF curve for Pekan (Stn. 3330109) 103
4.46 IDF curve for Pekan (Stn. 3034168) 104
4.47 IDF curve for Pekan (Stn. 3334161) 105
4.48 IDF curve for Raub (Stn. 3818054) 106
4.49 IDF curve for Rompin (Stn. 3228174) 108
4.50 IDF curve for Rompin (Stn. 2929001) 109
4.51 IDF curve for Rompin (Stn. 2630001) 110
4.52 IDF curve for Temerloh (Stn. 3424081) 111
4.53 Plotting Gumbel ( 15 minutes) in Jerantut (Stn.4324001) for 95%
Confidence Intervals 135
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LIST OF SYMBOL
Pi The rainfall at neighbor stations
Nx Annual rainfall at missing data station
Ni Annual rainfall at neighbor station
n The number neighbor station whose data are used
Px The missing precipitation
Pn The precipitation value at n station
𝜎 Standard Deviation
µ Mean
Xi The variate i.e record used in the computation
N The total number of record
𝑥 𝑖 Mean of the sample
𝛼𝑧 The scale
𝜆𝑧 Shape parameters of the gamma distribution
PT The frequency precipitation
K Gumbel frequency
S Standard deviation of P value
Pave The average of the maximum precipitation in a specific duration
Td Duration in hours
P*T The frequency precipitation
S* Standard deviation of P* value
KT The Pearson frequency factor which depends on return period (T)
and skewness coefficient (Cs).
v Degree of freedom
xvii
s Number of parameter using fitting distribution
A2 Anderson-Darling statistic
Fn(x(i)) Empirical Distribution (PDF)
x(i) The ordered data
T Return period (years)
xviii
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
CHAPTER 1
INTRODUCTION
1.1 BACKGROUND
Malaysia is one of the countries located at Southeast Asia, near the equator which
is humid and hot all the year. The location of Malaysia at equator zone gives Malaysia
experience tropical climate with two type of monsoon season which are the northeast
and southwest through the year. Northeast occur during November to May bring
moisture and more rainfall. Where southwest give wind blowing monsoon within May
to September. These results give average rainfall in Malaysia in 2500mm with average
temperature 27oC a year.
Seasonal variation give effect on rainfall pattern depend on geography of
Malaysia that surrounded by mountain. This condition give two different climate which
is depend on highland and lowland region. As a result, both condition cause temperature
raging between 23oC to 32
oC through the year with humidity between 75% and 80%
and annually receive rainfall between 2000mm to 4000mm with 150 to 200 rainy days.
From this rainfall pattern, the data will be used to construct temporal pattern using
rainfall intensity-duration-frequency (IDF) curves. Rainfall intensity –duration-
frequency (IDF) should be up to date in line with the changes of rainfall pattern due to
global warming effect and temperature changes. Rainfall intensity-duration-frequency
(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
2
structure and flood elevation in urban and rural area (Le Minh Nhat,etc,2007). Failing in
implant the IDF estimation in design can cause public safety or fund at risk.
Those data from rainfall data was use in frequency analysis method to develop
intensity-duration-frequency (IDF) curve. To use this method, local history data was
needed to get maximum annual rainfall depth corresponding to various duration. Latest
duration data will be taken within period of 5 minutes to 120 hours with different return
periods 2, 5, 10, 20, 50 and 100 years. The IDF curve will show the infinite number of
rainfall event with different average intensity and duration with same return period. For
a specific return period, the average intensity will decrease as the duration increase. As
the result, for same duration, the average intensity is higher for longer return periods
than the shorter one (Akan et al., 2003).
1.2 PROBLEM STATEMENT
All rainwater design in Malaysia must refer to the Urban Storm Water
Management Manual 2nd
edition (MSMA 2) to follow standard. Based on observation in
MSMA 2, the data of IDF curve for Pahang was updated until 2009. The climate change
in Malaysia in storm rainfall intensity may affect the data by change of latest addition
data (MSMA, 2012).
To get more accurate analysis, it necessary to estimate reliable rainfall intensity
by comparing the IDF curve by using different theoretical distribution function in
developing rainfall intensity and return period from rainfall data. Method being use for
IDF graph in MSMA only Gumbel distribution using data until 2009 and never be
compared before. Gumbel may suitable for condition in Malaysia but not been approved
until it compared with other method. Beside data given in MSMA is updated to 2009,
while until 2014 lot of change happen during that period of time.
New MSMA 2 has covered limited location which provided the parameter for
intensity calculation. Average location provided in MSMA is 2 locations for each
district while rainfall station for each district average in 10 stations each district.
Department Irrigation and Drainage (DID) should provide more location area for value
3
of intensity. For district not covered in MSMA need to use nearest district intensity
parameter to calculate intensity for design drainage. That way may affect the value of
intensity should be use for that location area and design drainage for that location.
1.3 OBJECTIVES
The objectives of this study are;
i. To calculate missing data for each station.
ii. To develop IDF curve for every district in Pahang.
iii. To compare differences values of IDF curves using Gumbel distribution and
Log-Pearson Type III distribution.
1.4 SCOPE OF STUDY
This study was conducted in Pahang area using rainfall data from 1990-2014 to
develop IDF curves. The duration of IDF curve from 5, 15, 60, 180, 360, 720, 1440
minutes until 72 hours and the return period including 2, 5, 10, 20, 50 and 100 years.
The data collections are from Department of Irrigation and Drainage (DID).
In this study, arithmetic formula was used to find the missing data for each
station. The best station was selected based on number of missing data which the lesser
missing data, the best station was selected. Few stations were selected to represent each
district. To calculate missing data, station within area 100 km radiuses was considered
to use in formula arithmetic. To ensure data fit, the Komolgorov Smirnov (KS) was
done.
1.5 SIGNIFICANT OF STUDY
All the water rain design in Malaysia use MSMA as reference for engineer to
design. Method use in MSMA to determine the intensity of rainfall is Gumbel
distribution and never be compared with other method. 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 MSMA. By developing new IDF curve can plant awareness to
4
MSMA user about the changes in MSMA due to the climate change in Malaysia. With
this development the risk for damaging hydrology design can be reduced while using
MSMA for designing material.
By developing new IDF curve, 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 curve use in MSMA can be covered with new location.
Thus nearest design location can refer to new IDF curve in new location to predict
intensity value which more reliable than more nearest then provided in MSMA as now.
CHAPTER 2
LITERATURE RIVIEW
2.1 INTRODUCTION
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 (IDF)
relationship is one of the most commonly use basis for water resources planning and
development.
Break-point, short duration, rainfall data are not generally available in the
historical records at the locations. Generalized accumulated rainfall patterns developed
by Department of Irrigation and Drainage (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.
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2.2 INTENSITY-DURATIOM FREQUENCY (IDF) CURVE
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, 2006). Dupont et al. (2000) defined rainfall IDF relationships as graphical
representations of the amount of water that falls within a given period of time.
Intensity-Duration-Frequency is representing amount of water rainfall that fall in
certain period of time in catchment area. This relationship was done since 1932 and still
being constructed for other country but it may not accurately construct in many
developing country.
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. Below in Figure 2.1 show example of IDF curve for station at
Pulau Pinang.
7
Figure 2.1: IDF curve
Source: MSMA, 2011
According to Brian (2006), rainfall frequency analyses are used extensively in the
design of systems to handle storm runoff, including roads, culverts and drainage
systems. Smith (1993) states that the “the precipitation frequency analysis problem is to
compute the amount of precipitation y falling over a given area in a duration of x min
with a given probability of occurrence in any given year.” For engineering design
applications, it is necessary to specify the temporal distribution of rainfall for a given
frequency, or return interval. According to Stedinger (1993), IDF curves “allow
calculation of the average design rainfall intensity [or depth] for a given exceedance
probability over a range of durations” and is the result of the rainfall frequency analysis.
IDF estimates are important statistical summaries of precipitation records used for
hydrologic engineering design (Gerold and Watkins, 2005).
IDF curves received considerable attention in engineering hydrology over the past
decades. Approaches based on statistical analysis of data were developed,. Bell (1969)
and Chen (1983) derived the IDF formulae for the United States, Baghirathan and Shaw
(1978), Gert (1987) and Niemczynowicz (1982) developed IDF formulas for ungauged
8
sites, Sivapalan and Bloeschl (1998) proposed a method of constructing IDF curves
based on the spatial correlation structure of rainfall, Koutsoyiannis (1998) proposed a
new generalizing approach to the formulation of IDF curves using efficient
parameterization.
The first attempts to construct regional IDF curves were made by Dub (1950).
„Samaj and Valovi„c (1973) presented a comprehensive IDF study based on 68 stations
covering the area of Slovakia using data mostly from the period 11931-1960. Their
results were re-evaluated by Urcik'an and Horv'ath (in Urcik'an and Imri„ska, 1986);
however, the analysis mostly concentrated on the different formal presentations of IDF
curves. They also proposed a method for the spatial interpolation of IDF curves for sites
with no direct observations. These procedures were data and time demanding, therefore
it seemed advantageous to develop models which would describe rainfall characteristics
through a number of timescales including interpolation or extrapolation at time
resolutions that may not have been observed and start from there, IDF curve was widely
use in drainage system design.
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
compromise as part of rainfall event. Even now, IDF can provide a lot information for
rainfall and can be used as base for determination of design storm ( A.S.Wayal, 2014).
9
2.3 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 Fgure 2.2 is
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).