ABSTRACT
Inadequate meteorological data for accurate designing, operation and planning of water resources against extreme rainfall event is a major challenge confronting engineers. Thirty two (32) years (1983-2014) daily rainfall data were collected for Port Harcourt, Calabar, Umuahia, Owerri, Benin, Warri, Enugu, Onitsha, Ibadan and Lagos from Nigeria Meteorological Agency (NIMET), Oshodi, Lagos State for the study. The method of annual maximum series was used to select data sets for rainfall analysis. Rainfall anomaly index and trend lines were plotted for each location to study the pattern of rainfall. Gumbel and Log Pearson Type 111 distributions were used to compute the observed rainfall intensity values at durations of 10, 15, 20, 30, 60,120,180,240, 300 and 360 minutes for return periods of 2, 5, 10,20,50 and 100 years. To obtain parameters for the IDF models for each location, the computed rainfall intensities were subjected to non-linear regression analysis using Microsoft Excel Optimization Technique Solver wizard for the respective durations and return periods. From the standardized anomaly index graphs, Calabar, Owerri, Warri, Onitsha, Benin, Enugu, Lagos, Ibadan, Port Harcourt and Umuahia had the highest positive rainfall anomaly index above average in the years 2010, 1996, 2008, 1995, 2011, 1997, 1988, 2014, 2006 and 1997, respectively, while their lowest negative rainfall anomaly index below average were in the years 1996, 1983, 1987, 1983, 2005, 2000,2008,1983,1983 and 1983, respectively. It is quite evident from the results, that the year 1983 had minimum amount of rainfall in most study stations. The trend analysis result showed that there is fluctuating rainfall pattern across the observed years. Though, each station had different trend. Port Harcourt had an annual rainfall trend that indicated a relatively steady increase. Owerri and Umuahia trend movement showed a decrease in annual rainfall and Lagos trend showed a relative steady decrease. All other stations showed a trend movement of annual rainfall increase. The inter-annual rainfall variability in all the study areas is due to climatic change. The IDF Models developed are for Enugu
𝐼= The performance of the models were analyzed by determining the chi-square(χ2 ), coefficient of determination(R2 ) and Root Mean Square Error(RMSE) of the fitted distributions. From the analysis, the R2 values ranged from 0.76-1.00 and the RMSE from 0.04-15.92 for the Gumbel distribution for the studied locations. Also for the Log Pearson Type 111 distribution, R2 values ranged from 0.98-1.00 with RMSE of 0.01-15.33 for the studied locations. Log Pearson Type 111 distribution ranked first with respect to R2 and RMSE for the IDF models, but no significant difference amongst the predicted intensities of the various IDF models. Log Pearson Type 111 Model is recommended for the prediction of rainfall intensities for the studied locations. The Intensity Duration Frequency (IDF) Models (results) developed will serve as tools for the Engineers and Hydrologists in estimating storm water runoff from a watershed for the design of drainage systems, reservoir management and planning of water resources development.. This will mitigate flooding and its consequences. Also the findings when applied as demonstration tools in teaching land drainage courses to Engineering students, will enhance their understanding and appreciation of the course.
TABLE OF CONTENTS
Title Page i
Declaration
ii
Certification
iii
Dedication
iv
Acknowledgements
v
Table of contents
vii
List of tables
x
List of figures
xii
List of Plates
xiv
Abstracts xv
1.0
CHAPTER 1: INTRODUCTION 1
1.1Background of the Study 1
1.2Statement of Problem
4
1.3 Objectives
5
1.3.1 General objective
5
1.3.2 Specific objectives
5
1.4 Justification
6
1.5 Scope of Work
10
2.0 CHAPTER 2: LITERATURE REVIEW
11
2.1 Intensity –duration-frequency (IDF)
theory
11
2.1.1 Intensity
11
2.1.2 Duration
11
2.1.3 Frequency of rainfall
12
2.2 Frequency Levels of Hydrologic
Design
13
2.2.1 Minor structures
13
2.2.2 Major structures
15
2.2.2.1 Probable maximum precipitation
(PMP)
15
2.2.2.2 Standard project storm (SPS)
18
2.3 Flooding in Nigeria- Current and
Past
19
2.3.1 Causes of flooding in Nigeria 23
2.4 Flood Risk Management ( FRM) and
Spatial Planning in Nigeria
25
2.5 Sustainable Development and the
Sustainable Development Goals (SDG) 28
2.6 Rainfall Trends (Variations) 29
2.6.1 Factors causing rainfall trends
(variation) and patterns 30
2.6.2 Impacts of rainfall trends
(variations) 30
2.6.3 Practical applications of rainfall
trend and pattern in the field of
agriculture
31
2.6.4 Importance of analysis of rainfall
trends and patterns 31
2.7 Rainfall Data Selection Approach
33
2.8 Method for Estimating Extreme Rainfall
Amounts of Various Return
Periods
34
2.8.1 Statistical approach
34
2.8.2 Physical approach
35
2.8.3 Empirical approach
35
2.8.4 Graphical approach 35
2.9 Mathematical formulation of IDF
relationship
37
3.0
CHAPTER 3: METHODOLOGY 38
3.1 Description of the Study Area
38
3.2 Climate
38
3.3 Data Requirement and Collection for
the Study 41
3.4 Model Selection for Breakdown of Daily
Rainfall Data
42
3.5 Data Analysis
43
3.5.1 Analyzing trends of rainfall
43
3.5.2 Mean yearly (annual) rainfall
43
3.5.3Yearly rainfall standard
deviation
44
3.5.4Standard coefficient of skewness
44
3.5.5Standard coefficient of kurtosis 45
3.5.6 Standard coefficient of
variation
45
3.5.7 Standard anomaly index (SAI) 46
3.5.8 Graphical plots
46
3.6 Development of IDF Curves
46
3.6.1 Gumbel theory of distribution
47
3.6.2 Log person type 111
distribution
48
3.7 Intensity-Duration-Frequency (IDF)
Model Development
50
3.7.1 Application of excel solver
optimization technique to estimate IDF
parameters
51
3.7.2 Calibration of the Sherman (1932)
model
51
3.8 Model Performance Analysis
52
4.0
CHAPTER 4: RESULTS AND DISCUSSION
54
4.1 Descriptive statistics of annual
rainfall
54
4.1.1 Discussion on Descriptive statistics
of annual rainfall for PortHarcourt
54
4.1.2Discussion on descriptive statistics
of annual rainfall for Calabar
55
4.1.3Discussion on descriptive statistics
of annual rainfall for Owerri
55
4.1.4Discussion on descriptive statistics
of annual rainfall for Warri 56
4.1.5Discussion on descriptive statistics
of annual rainfall for Onitsha
56
4.1.6 Discussion on descriptive statistics
of annual rainfall for Benin
57
4.1.7Discussion on descriptive statistics
of annual rainfall for Enugu
57
4.1.8Discussion on descriptive statistics
of annual rainfall for Lagos 57
4.1.9Discussion on descriptive statistics
of annual rainfall for Ibadan 58
4.1.10 Discussion on descriptive
statistics of annual rainfall for Umuahia 58
4.2 Standardized Anomaly Index
59
4.2.1 Standardized
anomaly index for PortHarcourt 59
4.2.2 Standardized
anomaly index for Calabar 60
4.2.3 Standardized
anomaly index for Owerri 61
4.2.4 Standardized anomaly index for
Warri
62
4.2 .5Standardized anomaly index for
Onitsha
63
4.2 .6 Standardized anomaly index for
Benin
64
4.2 .7 Standardized anomaly index for
Enugu 65
4.2 .8 Standardized anomaly index for
Lagos
66
4.2 .9 Standardized anomaly index for
Ibadan
67
4.2 .10 Standardized anomaly for
Umuahia
69
4.3 Trend Plots of Annual Rainfall
69
4.3.1 Trend plots of annual rainfall for
PortHarcourt 70
4.3.2 Trend plots of annual rainfall for
Calabar
71
4.3.3Trend plots of annual rainfall for
Owerri
72
4.3 .4 Trend plots of annual rainfall for
Warri
73
4.3.5 Trend plots of annual rainfall for
Onitsha 74
4.3.6 Trend plots of annual rainfall for
Benin
75
4.3.7 Trend plots of annual rainfall for
Enugu
76
4.3 .8 Trend plots of annual rainfall for
Lagos
77
4.3 .9 Trend plots of annual rainfall for
Ibadan
78
4.3 .10 Trend plots of annual rainfall for
Umuahia
79
4.4 Intensity-Duration-Frequency Curves by
Gumbel and
Log Pearson Type (LPT) 111
79
4.4 .1Intensity-duration-frequency curves
by Gumbel and
Log Pearson Type (LPT) 111 for PortHarcourt
81
4.4 .2 Intensity-duration-frequency curves
by Gumbel and
Log Pearson Type (LPT) 111 for
Calabar
82
4.4.3Intensity-duration-frequency curves
by Gumbel and
Log Pearson Type (LPT) 111 for Owerri
84
4.4.4Intensity-duration-frequency curves
by Gumbel and
Log Pearson Type (LPT) 111 for Warri 85
4.4.5Intensity-duration-frequency curves by
Gumbel and
Log Pearson Type (LPT) 111 for
Onitsha
87
4.4 .6Intensity-duration-frequency curves
by Gumbel and
Log Pearson Type (LPT) 111 for Benin
88
4.4.7Intensity-duration-frequency curves
by Gumbel and
Log Pearson Type (LPT) 111 for Enugu
89
4.4.8 Results of
intensity-duration-frequency curves by Gumbel and
Log Pearson Type (LPT) 111 for Lagos
90
4.4 .9Intensity-duration-frequency curves
by Gumbel and
Log Pearson Type (LPT) 111 for Ibadan
92
4.4.10Intensity-duration-frequency curves
by Gumbel and
Log Pearson Type (LPT) 111 for
Umuahia
93
4.5Rainfall Intensity –Duration –
Frequency Models and their
Parameter Values
94
4.6 Model performance/Validation
98
4.6 .1Model performance/validation of
PortHarcourt IDF Model 99
4.6 .2Model performance/validation of
Calabar IDF Model 100
4.6 .3Model performance/validation of
Owerri IDF Model 102
4.6 .4Model performance/validation of
Warri IDF Model 103
4.6.5 Model performance/validation of
Onitsha IDF Model 105
4.6.6 Model performance/validation of
Benin IDF Model 107
4.6 .7 Model performance/validation of
Enugu IDF Model 108
4.6 .8Model performance/validation for
Lagos IDF Model 110
4.6 .9Model performance/validation for
Ibadan IDF Model 111
4.6 .10 Model performance/validation for
Umuahia IDF Model
113
4.7 Comparison of Observed and Predicted
Rainfall Intensities 114
4.8 Comparison of IDF Curves Obtained with
Published IDF Curves
117
5.0
CHAPTER FIVE: CONCLUSION AND RECOMMENDATION 119
5.1 Conclusion 119
5.1.1 Engineering Implications of
Results
119
5.1.2 Contributions to Knowledge
120
5.2 Recommendation
121
5.3Suggestions for further Studies 122
REFERENCES
123
APPENDICES 141
Appendix 1: Annual Series of Rainfall from
1983-2014
141
Appendix 2a: Computed Rainfall Intensities
for Different Durations and
Return Periods using Gumbel Distribution
for PortHarcourt 142
Appendix 2b: Computed Rainfall Intensities
for Different Durations and
Return Periods using LPT 111 Distribution
for PortHarcourt 142
Appendix 3a: Computed Rainfall Intensities
for Different Durations and
Return Periods using Gumbel Distribution
for Calabar
143
Appendix 3b: Computed Rainfall Intensities
for Different Durations and
Return Periods using LPT 111 Distribution
for Calabar
143
Appendix 4a: Computed Rainfall Intensities
for Different Durations and
Return Periods using Gumbel Distribution
for Owerri
144
Appendix 4b: Computed Rainfall Intensities
for Different Durations and
Return Periods using LPT 111 Distribution
for Owerri
144
Appendix 5a: Computed Rainfall Intensities
for Different Durations and
Return Periods using Gumbel Distribution
for Warri
145
Appendix 5b: Computed Rainfall Intensities
for Different Durations and
Return Periods using LPT 111 Distribution
for Warri
145
Appendix 6a: Computed Rainfall Intensities
for Different Durations and
Return Periods using Gumbel Distribution
for Onitsha 146
Appendix 6b: Computed Rainfall Intensities
for Different Durations and
Return Periods using LPT 111Distribution
for Onitsha
146
Appendix 7a:Computed Rainfall Intensities
for Different Durations and
Return Periods using Gumbel Distribution
for Benin
147
Appendix 7b: Computed Rainfall Intensities
for Different Durations and
Return Periods using LPT111Distribution
for Benin
147
Appendix 8a: Computed Rainfall Intensities
for Different Durations and
Return Periods using Gumbel Distribution
for Enugu
148
Appendix 8b: Computed Rainfall Intensities
for Different Durations and
Return Periods using LPT 111 Distribution
for Enugu
148
Appendix 9a: Computed Rainfall Intensities
for Different Durations and
Return Periods using Gumbel Distribution
for Lagos
149
Appendix 9b: Computed Rainfall Intensities
for Different Durations and
Return Periods using LPT 111 Distribution
for Lagos 149
Appendix 10a: Computed Rainfall
Intensities for Different Durations and
Return Periods using Gumbel Distribution
for Ibadan
150
Appendix 10b: Computed Rainfall
Intensities for Different Durations and
Return Periods using LPT 111Distribution
for Ibadan
150
Appendix 11a: Computed Rainfall
Intensities for Different Durations and
Return Periods using Gumbel Distribution
for Umuahia 151
Appendix 11b: Computed Rainfall
Intensities for Different Durations and
Return Periods using Gumbel Distribution
for Umuahia 151
Appendix 12a: Predicted Rainfall Intensity
Duration Frequencies
for Different Return Periods by Gumbel at
PortHarcourt
152
Appendix 12b: Predicted Rainfall Intensity
Duration Frequencies
for Different Return Periods by LPT 111 at
PortHarcourt 152
Appendix 13a: Predicted Rainfall Intensity
Duration Frequencies for
Different Return Periods by Gumbel at
Calabar
153
Appendix 13b: Predicted Rainfall Intensity
Duration Frequencies for
Different Return Periods by LPT 111 at
Calabar
153
Appendix 14a: Predicted Rainfall Intensity
Duration Frequencies
for Different Return Periods by Gumbel at Owerri
154
Appendix 14b: Predicted Rainfall Intensity Duration Frequencies
for Different Return Periods by LPT 111 at Owerri
154
Appendix 15a: Predicted Rainfall Intensity
Duration Frequencies
for Different Return Periods by LPT 111 at Warri
155
Appendix 15b: Predicted Rainfall Intensity Duration Frequencies
for Different Return Periods by LPT 111 at Warri
155
Appendix 16a: Predicted Rainfall Intensity Duration Frequencies
for Different Return Periods by Gumbel at Onitsha 156
Appendix 16b: Predicted Rainfall Intensity Duration Frequencies
for Different Return Periods by LPT 111 at Onitsha
156
Appendix 17a: Predicted Rainfall Intensity Duration Frequencies
for Different Return Periods by Gumbel at Benin
157
Appendix 17b: Predicted Rainfall Intensity Duration Frequencies
for Different Return Periods by LPT 111 at Benin 157
Appendix
18a: Predicted Rainfall Intensity Duration Frequencies
for Different Return Periods by Gumbel at Enugu
158
Appendix 18b: Predicted Rainfall Intensity Duration Frequencies
for Different Return Periods by LPT 111 at Enugu
158
Appendix
19a: Predicted Rainfall Intensity Duration Frequencies
for Different Return Periods by Gumbel at Lagos
159
Appendix 19b: Predicted Rainfall Intensity Duration Frequencies
for Different Return Periods by LPT 111 at Lagos 159
Appendix
20a: Predicted Rainfall Intensity Duration Frequencies
for Different Return Periods by Gumbel at Ibadan
160
Appendix 20b: Predicted Rainfall Intensity Duration Frequencies
for Different Return Periods by LPT 111 at Ibadan
160
Appendix
21a: Predicted Rainfall Intensity Duration Frequencies
for Different Return Periods by Gumbel at Umuahia 161
Appendix 21b: Predicted Rainfall Intensity Duration Frequencies
for Different Return Periods by LPT 111 at Umuahia
162
LIST OF TABLES
Table
Page
2.1 Minor Structures Design
Frequencies
14
2.2 Partial Duration Frequency Factor
34
3.1Characteristics of the Meteorological
Stations of the Study Cities
39
3.2 Ratio of Six Hourly Total Rainfalls To
Accumulated Amount 42
4.1 Descriptive Statistics of Annual
Rainfall of Study Areas 54
4.2 Parameters Values Used in Deriving
Models for Rainfall Intensities
at Different Locations 95
4.3a Model Performance/Validation for
PortHarcourt IDF Model
obtained by Gumbel method
99
4.3b Model Performance/Validation for
PortHarcourt IDF Model
obtained by LPT 111 method
99
4.4a Model Performance/Validation for
Calabar IDF Model
obtained by Gumbel method
101
4.4b Model Performance/Validation for
Calabar IDF Model
obtained by LPT 111 method
101
4.5a Model Performance/Validation for
Owerri IDF Model
obtained by Gumbel method
102
4.5b Model Performance/Validation for
Owerri IDF Model
obtained by LPT 111 method
103
4.6a Model Performance/Validation for
Warri IDF Model
obtained by Gumbel method
104
4.6b Model Performance/Validation for
Warri IDF Model
obtained by LPT 111 method
104
4.7a Model Performance/Validation for
Onitsha IDF Model
obtained by Gumbel method 105
4.7b Model Performance/Validation for
Onitsha IDF Model
obtained by LPT 111 method
106
4.8a Model Performance/Validation for
Benin IDF Model
obtained by Gumbel method
107
4.8b Model Performance/Validation for
Benin IDF Model
obtained by LPT 111 method 107
4.9a Model Performance/Validation for
Enugu IDF Model
obtained by Gumbel method
108
4.9b Model Performance/Validation for
Enugu IDF Model
obtained by LPT 111 method
109
4.10a Model Performance/Validation for
Lagos IDF Model
obtained by Gumbel method 110
4.10b Model Performance/Validation for
Lagos IDF Model
obtained by LPT 111 method
110
4.11a Model Performance/Validation for
Ibadan IDF Model
obtained by Gumbel method
112
4.11b Model Performance/Validation for
Ibadan IDF Model
obtained by LPT 111 method
112
4.12a Model Performance/Validation for
Umuahia IDF Model
obtained by Gumbel method
113
4.12b Model Performance/Validation for
Umuahia IDF Model
obtained by LPT 111 method
113
Table 4.13: Comparison of selected index
values for predicted
intensities (mm/hr) for short, medium and higher durations 115
Table 4.14 : Comparison of Mbajiorgu and Okonkwo (2010)
estimated intensities with intensities
predicted by Gumbel distributions
117
LIST OF FIGURES
Figure Page
3.1: Map showing part of Nigeria and the
sampling locations 40
3.2: Map showing the agro-ecological zones
of selected locations 41
3.3: Generalized Accumulated rainfall curves
for A (advanced), B(intermediate)
and C (retarded) types of storm
43
4.1: Standardized anomaly index for annual
total rainfall at Portharcourt 59
4.2: Standardized anomaly index for annual
total rainfall at Calabar
69
4.3: Standardized anomaly index for annual
total rainfall at Owerri
61
4.4: Standardized anomaly index for annual
total rainfall at Warri 62
4.5: Standardized anomaly index for annual
total rainfall at Onitsha 63
4.6: Standardized anomaly index for annual
total rainfall at Benin 64
4.7: Standardized anomaly index for annual
total rainfall at Enugu
65
4.8: Standardized anomaly index for annual
total rainfall at Lagos 66
4.9: Standardized anomaly index for annual
total rainfall at Ibadan 68
4.10: Standardized anomaly index for annual
total rainfall at Umuahia
69
4.11: Trend plots of annual rainfall in
Portharcourt 70
4.12: Trend plots of annual rainfall in
Calabar
71
4.13: Trend plots of annual rainfall in
Owerri 72
4.14: Trend plots of annual rainfall in
Warri
73
4.15: Trend plots of annual rainfall in
Onitsha 74
4.16: Trend plots of annual rainfall in
Benin
75
4.17: Trend plots of annual rainfall in
Enugu 76
4.18: Trend plots of annual rainfall in
Lagos
77
4.19: Trend plots of annual rainfall in
Ibadan 78
4.20: Trend plots of annual rainfall in
Umuahia
79
4.21a: IDF curves by Gumbel method at
Portharcourt 81
4.21b: IDF curves by LPT 111 Method at
PortHarcourt 81
4.22a: IDF curves by Gumbel method at
Calabar
82
4.22b: IDF curves by LPT 111 method at
Calabar
83
4.23a: IDF curves by Gumbel method at
Owerri
84
4.23b: IDF curves by LPT 111 method at
Owerri 84
4.24a: IDF curves by Gumbel method at
Warri
85
4.24b: IDF curves by LPT 111 method at
Warri
86
4.25a: IDF curves by Gumbel method at
Onitsha
86
4.25b: IDF curves by LPT 111 method at
Onitsha
87
4.26a: IDF curves by Gumbel method at
Benin
88
4.26b: IDF curves by LPT 111 method at
Benin 88
4.27a: IDF curves by Gumbel method at
Enugu
89
4.27b: IDF curves by LPT 111 method at
Enugu
89
4.28a: IDF curves by Gumbel method at
Lagos
90
4.28b: IDF curves by LPT 111 method at
Lagos 91
4.29a: IDF curves by Gumbel method at Ibadan
92
4.29b: IDF curves by LPT 111 method at
Ibadan
92
4.30a: IDF curves by Gumbel method at
Umuahia
93
4.30b: IDF curves by LPT 111method at
Umuahia
94
LIST OF PLATES
Plate Page
1: Flood occurrence in Aba South in Aba
urban area of Abia State
20
2: Flood occurrence in Aba South in Aba
urban area of Abia State
20
3: Farm land in Anambra East and West LGAs
submerged by flood in 2012 21
4: Flood forced farmers in Anambra east
and west LGAs to harvest
premature to save their yield
21
5: Former Governor of Anambra State, Peter
Obi and Anambra
Government officials inspecting flooded
areas in the state in the year 2012
22
6: An area view of submerged communities
and farmland in Rivers State
during the visit of president Good luck
Jonathan to the flooded areas in the State
in 2012
22
CHAPTER 1
INTRODUCTION
1.1 BACKGROUND
OF THE STUDY
Extreme rainfall events cause pollution of the
quality of water, destruction of assets and loss of lives due to flooding (Mohammed
and Inyathulla, 2019). Due to rapid population concentration, climate change
and changing of natural landscape into impervious surfaces, urban flooding has
become a global challenge (Zhang et al.,
2018). As a result, urban areas are constantly confronted with socioeconomic
impacts from flooding due to catastrophic events. Rainfall is an important
component in the hydrologic cycle. Akpen et
al. (2018) posits that rainfall frequency analyses are needed in the
development and designing of various water resources projects, this includes
storm sewers, culverts and other hydraulic structures. To design flood
protection structures involving hydrologic flows, rainfall events statistic
(that is, in relations to intensity, duration and period of return) are
required (Prodanovic and Simonovic, 2007). Graphically the quantity of
precipitation that falls within a catchment area in a given period of time are
represented by Rainfall – Intensity - Duration-Frequency (IDF) curves
(Elsebaie, 2012). IDF curves are important tool for the engineers when
designing urban drainage works. It is expected that global warming will adjust
rainfall extreme occurrence events.
The establishment of IDF relationships was
started as early as 1932 (David et al.,
2019). Since then, many sets of relationships have been constructed for several
parts of the globe. Data availability is more at daily scale compared to sub
daily resolutions in developed countries. This causes uncertainty of IDF
estimates for sub daily durations (Giuseppe, 2020). Urbanization occurs at high
rates and IDF curves are most needed in developing countries, but the problem
of data is high as rainfall data are available at sparse locations/daily
resolution (Liew et al., 2014).
Aysar
(2016) and Koutsoyiannis (2003) opined that the IDF relationship is a
mathematical relationship between the rainfall intensity (i), the duration (d),
and the return period (t) (or, equivalently, the annual frequency of exceedance
f, typically referred to as ‘frequency’ only). Indeed the IDF-curves allow for
the estimation of the return period of an observed rainfall event or conversely
of the rainfall amount corresponding to a given return period for different
aggregation times.
In Kentucky, for example, IDF curves are
used in conjunction with runoff estimation formulae, such as the Rational
Method, to predict the peak runoff rates from a particular watershed. The
information from the curves is then used in hydraulic design to size culverts and
pipes (Dupont and Allen, 2000).
Various
authors have attempted to relate IDF relationship to the synoptic
meteorological conditions in the area of the hydrometric stations (Dupont and
Allen, 2006; Mohymont et al., 2004)).
Ayad (2014) derived IDF empirical formula for Karbala province and compared
different statistical distributions. He concluded that the Log Pearson Type III
was the best of other methods.
Al-Khalaf (1997) conducted a study for
predicting short-duration, high-intensity rainfall in Saudi Arabia. The results
showed that the short duration/high intensity rainfall was far from the
universal relationship suggested by other researchers and concluded that a
relation for each region has to be obtained to act as a useful tool in
estimating rainfall intensities for different durations and return periods
ranging between 2 and 100 years.
With the recent technology of remote
sensing and satellite data analysis, Awadallah et al. (2011) conducted a study for developing IDF curves in scarce
data region using regional analysis and Satellite data. Accordingly, Awadallah et al. (2011) presented a methodology to overcome the lack of
ground stations rainfall by the joint use of available ground data with Tropical
Rainfall Measuring Mission (TRMM) satellite data to develop IDF curves and also
a method to develop ratios between 24-hr rainfall depth and shorter duration
depths.
AlHassoun (2011) developed an empirical
formula to estimate the rainfall intensity in Riyadh region and found that
there is not much difference in the results of rainfall analysis of IDF curves
in Riyadh area between Gumbel and LPT III methods. This was attributed to the
fact that Riyadh region has semi-arid climate and flat topography where
variations of precipitation are not significant.
In Nigeria, the development of IDF models
is still in its growth path and is limited to the extent of available data
(Nwaogazie et al., 2019). In Humid
Forest Zones of Nigeria, recent studies on rainfall IDF development have been
done in Southern Nigeria. Akpan and Okoro (2013), Nwaogazie and Duru(2002)
and Nwoke and Okoro(2012) developed
Rainfall Intensity Frequency Models based on statistical method of least
squares. Also Okonkwo and Mbajiorgu (2010) Ologhadien and Nwaogazie (2014)
developed IDF curves of extreme rainfall for South Eastern Nigeria based on
generalized accumulated rainfall. Akpen et
al. (2016) studied rainfall events for Makurdi metropolis. The IDF curves
developed were in accord with IDF theory of shorter recurrence periods of 2 to
10 years. Oyegoke et al. (2017) and
Ogarekpe (2014) analyzed rainfall intensity for Southern Nigeria. Their work
suffered the limitations of old and short records of rainfall data.
1.2 Statement of the Problem
A major challenge hydrologists and
engineers encounter in the planning and design of water resources structure is
that of unavailability of required long-term rainfall data. The development of
rainfall models requires long-term rainfall records with durations. Only a few
meteorological stations in developing countries like Nigeria can boast of
consistent 30 years rainfall data ; some of these stations are in Lagos,
Calabar, Benin, Port-harcourt, Kano, Owerri and with missing data in-between
and some without the duration of the rainfall events. The remaining stations
nation-wide have very short records of rainfall data (Nwaogazie and Duru,
2002).
In
Humid Forest Zones of Nigeria, IDF curves and Models are not readily available
(Okonkwo and Mbajiorgu, 2010). The few available IDF curves for some parts of
the country are very costly and plotting of the curves were done manually ( i.e
fitting of lines were done by eye to the points). This manual method of
developing IDF curves is prone to error. Also, the number of years of data used
to develop a few IDF curves for the region found in the literature was rather
short (Oyebande, 1983 and Metibaiye, 1990). The methods employed were also
simplistic and lacking rigorous analyses. Akpan and Okoro (2013); Nwoke and
Okoro (2012) developed Rainfall Intensity Duration Frequency Models based on
rainfall data of 10 and 15 years for
Calabar and Warri cities, respectively.
Any change in climate produces modifications
in extreme weather events, such as heavy rainfall, heat and cold waves, in
addition to prolonged drought occurrences (Almazroui et al., 2012). Lack of IDF curves makes it difficult 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.
The Lack of accurate determinations of
peak runoff amount results in under or over design of culvet, channels and
pipes. This leads to Urban flooding which occur in towns located on flat or low
lying areas especially where little or no provision has been made for surface
drainage, or where existing drainage has been blocked with municipal waste,
refuse and eroded soil sediments. Extensive urban flooding is a phenomenon of
every rainy session in Nigeria.
1.3 OBJECTIVES OF THE STUDY
1.3.1 General Objective:
The general objective of this study is to
develop Rainfall Intensity- Duration –Frequency (IDF) curves for some selected
locations in Humid forest Zones of Nigeria.
1.3.2 Specific objectives:
The specific objectives of the study are
to:
1)
Analyze the rainfall trends in the selected locations in Humid Forest
Zones of Nigeria
2)
Develop IDF curves for Port-Harcourt, Calabar, Owerri, Onitsha, Enugu,
Umuahia, Warri, Ibadan, Lagos and Benin located in Humid Forest Zones of
Nigeria using two statistical methods, namely Gumbel distribution and Log
Pearson Type 111 distribution.
3)
Derive empirical IDF formulae to estimate rainfall intensity for various
return periods and rainfall durations for the selected locations.
4)
Compare the results produced by the two methods with each other, as well
as with existing IDF curves.
1.4 JUSTIFICATION OF THE STUDY
Extreme
environmental events, such
as floods, drought,
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 (Akpen et al., 2018). 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).
Rainfall Intensity Duration Frequency (IDF) curves provides
information on the likelihood of heavy rainfall events of various amounts and
durations. Flood has been known and declared all over the world as highly
destructive. Uncontrolled floodwaters are one of the most powerful and
destructive forces of nature (Manta and Ahaneku, 2009). IDF values are critical
to determining the appropriate design standards and management for rainwater
infrastructure to mitigate flooding. Since rainfall characteristics are often
used to design hydraulic structures, reviewing and updating rainfall
characteristics (i.e., Intensity–Duration–Frequency
(IDF) curves) for future climate scenarios is necessary (Mirhosseini et al., 2013).
Access to Umuahia, the capital of Abia State is a tough task. It is
an exercise that has deleterious effect on commuters as well as takes a heavy
toll on vehicles. Whether you are coming from Port Harcourt/ Aba axis or the
Okigwe section, it is the same story. The expressway is filled with gullies and
craters created by flood water. Also, roads in residential areas in Umuahia,
such as World Bank, Agbama and Low Cost Estates are dirty, narrow, flood-
ravaged and begging for attention (Henry, 2014).
The industrialized center of Abia State is
Aba which is bounded by oil wells that separate it from the Port Harcourt city.
A pipeline of 30 kilometres powers Aba with gas from the Imo River natural gas
source (Hoiberg, 2010). Its major economic contributions are textiles and palm
oil along with pharmaceuticals, plastics, shoes, cement, and cosmetics which
made the Ariaria international market to become the largest market in West
Africa followed by the Onitsha main market.
There is also a brewery and distillery within the city. Finally, it is
famous for its handicrafts (Hoiberg, 2010). However, despite the numerous
industries in Aba, there is no good road network and the roads are always
flooded after extreme rainfall events due to under design of drainage systems.
Flooding in Aba causes traffic jam, damage to properties and health hazards.
Umudike houses vital Federal Institutions, namely Michael Okpara
University of Agriculture (MOUA), Umudike, National Root Crop Research
Institute (NRCRI), Alvan Ikoku College of Education, Government College Campus,
Abia State University (Umudike Campus), Federal Ministry of Agriculture and
Land Resources, Federal Ministry of Environment and Forest Research Management
among others. Despite all the vital Institutions in Umudike, there is no IDF
Curve for Umudike. As such, most of the culverts, pipes and other flood control
structures are under designed. This results in flooding of most areas in
Umudike after heavy rainfall events. For instance, the heavy rainstorm in the
month of June, 2013 that lasted several hours resulted in total flooding of the
premises of the College of Engineering and Engineering Technology in Michael
Okpara University of Agriculture, Umudike and Umudike - IkotEkpene road.
Academic work, movement of people and vehicles were delayed in these areas
until emergency outlets for runoff water were created that same day.
Since Aba, Umudike and Umuahia are in one geographical region, a
common IDF curve can be used for the design of effective flood control
structures in these locations.
Traveling on Federal roads in the South
East especially in the rainy season is a nightmare. However, the Federal
Government recently, awarded contracts for the rehabilitation of some federal
roads in the zone. To benefit from the exercise are the Enugu -Onitsha
expressway, Enugu -Port Harcourt Expressway and Abakaliki as well as Enugu
-Makurdi expressway. So far, some of the companies have started reconstruction
work on the road.
For the residents of Port Harcourt, particularly those living around
Rumuigbo, Mgbuoba, Rumuodumaya,
Rumuolumeni, Woji, D/line, Diobu, Aba Road, and Nkpolu areas, rainy
season is certainly not the best of times. The torrential rains that usually
characterize the rainy season in Rivers State, has often resulted in
devastating floods in the state capital. Though the rainy season hardly leaves
any trails of death, it, however, does not fail to render thousands of people
homeless and properties destroyed.
In the year 2012, the city experienced an unparalleled flooding, which
paralyzed economic activities and displaced some residents of the city. Economic
development has led to an explosion of poorly planned construction that has
strained Port Harcourt antiquated infrastructure to the limit in areas like
NTA/Choba Road, Mgbuoba that appears to be worst hit by perennial flooding. The
residents are desperate to see the government take steps to mitigate their
sufferings. Causes of flood in Port Harcourt vary. The annual rainfall in the
city varies between 2,000-3,000 mm because of its proximity to the Atlantic
Ocean. But inappropriate town planning, resulting in the construction of houses
on natural water channels, and hence causing obstruction of natural drainage
channels largely causes the flooding crisis in PortHarcourt. The fact that the
city has insufficient and poorly maintained drainage systems aggravates the
situation. Virtually all the drains and gutters are full of silt and clogged
with garbage, which are most times dumped there by the city's residents who
often display disdain for sanitation and decency.
The new IDF curves will
therefore contribute to the planning, design and management of water
infrastructures that can handle current and future rainfall events in and
around Umudike, Aba , Umuahia,
Portharcourt , Calabar, Owerri, Onitsha, Enugu, Lagos, Ibadan, Benin and Warri cities in humid rain forest zones of Nigeria. These curves when developed
will be 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. IDF estimates are important statistical summaries of precipitation
records used for hydrologic engineering design.
This work attempts to develop IDF data for
selected locations in Southern Nigeria that can be used in conjunction with
runoff estimation formula such as the rational method to predict peak runoff
rate from selected locations, namely Umuahia (Abia State), Enugu (Enugu State),
Port Harcourt (Rivers State), Onitsha (Anambra State), Owerri (Imo State),
Calabar (Cross River State), Warri (Delta State), Lagos (Lagos State), Ibadan (Oyo
State) and Benin (Edo State), which are important in the design of flood
control structures. With the recent devastations caused by flood in various
regions of Nigeria, this study becomes very necessary because most drainage
structures have been built without the required rainfall intensity data.
Also, the IDF curve will help in teaching
students in the Institutions located in these studied areas.
1.5 SCOPE OF THE WORK
The project concentrated on Port Harcourt,
Calabar, Owerri, Onitsha, Enugu, Umuahia, Warri, Ibadan, Lagos and Benin(10)
cities in Humid Rain Forest Zones of Nigeria. The major material that was used
for this work was rainfall data showing durations of rains and consequently
intensities of the rainfall in Port Harcourt, Calabar, Owerri, Onitsha, Enugu,
Umuahia, Warri, Ibadan, Lagos and Benin from 1983 to 2014. The data were obtained
from National Root Crop Research Institute (NRCRI), Umudike, and the Nigeria
Meteorological Agency (NIMET) in Lagos, respectively. The annual maximum
rainfall values for 10, 15, 20, 30, 60, 120, 180, 240, 300 and 360 minutes
durations were used in computations.
The relationship between the rainfall
intensity, storm duration and return periods from rainfall data for the cities
under study were determined using the two common frequency analysis techniques.
They are the Gumbel distribution and Log Pearson type 111 distribution
techniques. IDF curves were plotted for each of the frequency analysis
technique used. Also, IDF equation was derived for each of the locations.
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