1. IDENTIFICATION OF RAINFALL TRENDS,VARIABILITYAND DISTRIBUTION
OVER NAIROBI METROPOLITAN REGION
By
LAWRENCE SHIVAIRO MISANGO
I10/1283/2013
PROJECT WORK SUBMITTED IN PARTIAL FULFILMENT OF THE AWARD FOR
THE DEGREE OF BACHELOR OF SCIENCE IN METEOROLOGY
UNIVERSITY OF NAIROBI
16TH
DECEMBER 2016.
2. DECLARATION
I hereby declare that this research project is carried out and presented for examination by,
Signature……………………….. Date………………….
LAWRENCE SHIVAIRO MISANGO
Supervised by;
PROF ININDA, J.M.
Signature………………………… Date………………....
DR GITAU, W.
Signature………………………… Date…………………
4. ACKNOWLEDGEMENT
Glory to Yahweh for His Mighty hand has seen me through these four years in campus and
indeed for the past 20 years in my quest for knowledge. He has been faithful in my life and I am
carefully and humbly returning all glory to Him, Elohim. My deep and sincere gratitude to my
parents for their exponential effort and sacrifice in ensuring I reach these far. Yahweh will surely
bless you bountifully. I also thank my siblings for their persistent continued support.
Sincere gratitude to my supervisors Prof Ininda, J.M. and Dr Gitau, W. for their guidance,
courage and technical advice in my project. Thank you also to the staff for the knowledge
imparted during the four years in this precious institution and particularly those in the
Department of Meteorology. Deep hearted Thank you to my classmates for their support and
encouragement you guys are the best.
5. ABSTRACT.
Rainfall is a type of precipitation in form of water droplets. It is formed as a result of various
processes such as evaporation at the earth’s surface, advection, convection and condensation in
the troposphere. Rainfall is the most important weather element in the tropics Kenya included.
Together with temperature, it affects the day to day changes of weather in the tropical region.
Most of the countries in the tropical region are developing countries with the backbone of their
economies being dependent on rain-fed agriculture. Recently, most of these countries have
witnessed a sharp rise in urbanization due to rural-urban migration as the people continue to
search for better services and amenities as the developed countries provide for their population.
These two reasons call for an in depth understanding of rainfall through research and study. It is
for these reason I undertook these study. The study area is Nairobi metropolitan region, Kenya,
Africa.
The aim of the study was to identify rainfall trends and variability over Nairobi metropolitan
region. The main objective was to identify rainfall trends, variability and distribution over the
Nairobi metropolitan region. Time series analysis was used to identify the trends while principle
component analysis was used to identify the rainfall zones over the study area and 3-d maps were
used to identify how this rainfall varied and its distribution over the study area. There was an
increasing trend of rainfall and this was attributed to increase in built up area.
The data used was obtained from the Kenya Meteorological Department Headquarters from the
period of 2000 to 2014. It consists of monthly rainfall data of seven meteorological stations that
are within the study area.
The increasing trend of rainfall could have been attributed to the increase in built up area that is
expansion of the city leading to enhanced urban heat island effect thus causing a destabilizing
effect on the flow leading to enhanced convection resulting in increased rainfall over the study
area.
Rainfall was almost evenly distributed and showed an increased trend as one moves westwards
of the study area because of the interaction between large scale easterly flow with topography
and the urban-heat island effect.
The MAM distribution was opposite to that of OND because the OND has a positive correlation
with ENSO and interaction with orographic features such as Mt. Kilimanjaro, Mt.Kenya, Ngong
6. hills and the great rift valley may modulate the large scale ENSO and other coupled ocean
atmosphere signals in the region thus as you move upwards from the southern metro rainfall
tends to decrease and is minimum in the central parts since most local forcing features are
located on the central part of the study area. MAM is not affected by ENSO directly like OND.
Its variation is dependent on the inter tropical convergence zone (ITCZ) and the monsoonal flow
and convergence.
TableofContents
DEDICATION..................................................................................................................................... iii
ACKNOWLEDGEMENT........................................................................................................................iv
ABSTRACT..........................................................................................................................................v
List of figures...................................................................................................................................viii
ACRONYMS........................................................................................................................................x
CHAPTER ONE..................................................................................................................................11
1.1 INTRODUCTION...........................................................................................................................11
1.2 PROBLEMSTATEMENT ................................................................................................................12
1.3 OBJECTIVE OF STUDY...................................................................................................................12
1.4 JUSTIFICATION OF THE STUDY......................................................................................................13
1.5 AREA OF STUDY ..........................................................................................................................13
CHAPTER TWO .................................................................................................................................15
2.0 LITERATURE REVIEW ...................................................................................................................15
CHAPTER THREE ...............................................................................................................................16
3.0 DATA AND METHODOLOGY .........................................................................................................16
3.1 DATA..........................................................................................................................................16
3.2 Data quality control.....................................................................................................................17
3.2.2 Data homogeneity....................................................................................................................17
3.3 Data methodology ......................................................................................................................17
3.3.1 Trend Analysis..........................................................................................................................17
3.3.2 ROLE OF PRINCIPAL COMPONENT ANALYSIS..............................................................................18
3.3.3 ROLE OF 2-D MAPS...................................................................................................................18
CHAPTER FOUR.................................................................................................................................19
4.0 RESULTS AND DISCUSSION...........................................................................................................19
4.1 RAINFALL MASS CURVESANALYSIS...............................................................................................19
4.2 RAINFALL TIME SERIES DISCUUSSION...........................................................................................22
Fig 4.1 Rainfall time series for Dagoretti.............................................................................................22
7. Fig 4.2 Rainfall time series for J.K.I.A..................................................................................................23
Fig 4.3 Rainfall time series for Kabete ................................................................................................23
Fig 4.4 Rainfall time series for Eastleigh .............................................................................................24
Fig 4.7 Rainfall time series for Kajiado................................................................................................25
4.3 PRINCIPAL COMPONENT ANALYSIS DISCUSSION ...........................................................................26
4.3.1 MAMPCA RESULTS AND DISCUSSION........................................................................................26
Table 1.............................................................................................................................................27
Fig 4.8 screen plotfor Eigen values of MAMdata................................................................................28
4.3.2 OND PCA RESULTS AND DISCUSSION.........................................................................................28
Table 2.............................................................................................................................................29
Fig 4.9 screen plotfor Eigen values of OND data.................................................................................30
4.4 RESULTS OF THE 2-D MAPS..........................................................................................................30
FIG 4.11: OND RAINFALL DISTRIBUTION.............................................................................................32
DISCUSSION ON THE 2-D MAPS.........................................................................................................33
CHAPTER FIVE ..................................................................................................................................33
5.0 SUMMARY, CONCLUSION AND RECOMMENDATIONS....................................................................33
5.1 SUMMARY..................................................................................................................................33
5.2 CONCLUSIONS ............................................................................................................................34
5.3 RECOMMENDATIONS..................................................................................................................35
REFERENCE…………………………………………………………………………………………………………………………………………….36
8. List of figures
Fig 1.1 MAP OF NAIROBI METROPOLITAN REGION..............................................................................14
Fig3.1 Rainfall single masscurve forKajiado………………………………………………………………………………………..19
Fig3.2 Rainfall single masscurve forJ.K.I.A…………………………………………………………………………………………..20
Fig3.3 Rainfall single masscurve forThika……………………………………………………………………………………………22
Fig4.1 Rainfall time seriesforDagoretti………………………………………………………………………………………………22
Fig 4.2 Rainfall time series for J.K.I.A..................................................................................................23
Fig 4.3 Rainfall time series for Kabete ................................................................................................23
Fig 4.4 Rainfall time series for Eastleigh .............................................................................................24
Fig 4.7 Rainfall time series for Kajiado................................................................................................25
Table 1.............................................................................................................................................27
Fig 4.8 screen plotfor Eigen values of MAMdata................................................................................28
Table 2.............................................................................................................................................29
Fig 4.9 screen plotfor Eigen values of OND data.................................................................................30
Fig4.10 MAM RAINFALLDISTRIBUTION……………………………………………………………………….………………………..32
FIG 4.11: OND RAINFALLDISTRIBUTION…………………………………………………………………………………………………32
9.
10. ACRONYMS
Km²- kilometers square
MAB – Moi Air base, Eastleigh
J.K.I.A – Jomo Kenyatta International airport
K.M.D – Kenya Meteorological department
P.C.A – Principal component analysis
MAM: March- April- May
OND: October- November- December
11. CHAPTERONE
1.1 INTRODUCTION
Rainfall plays a very significant role in the life of a human being especially at the level of
decision making be it individually, communally, nationally and internationally. Proof of this is
the rapid industrialization that took place in Europe in the 19
th
and 20
th
century as more
advances were made in understanding precipitation and the information was used for better
planning leading to rapid industrialization.
Most of the countries in the tropical region are developing countries with the backbone of their
economies being dependent on rain-fed agriculture. Recently, most of these countries have
witnessed a sharp rise in urbanization due to rural-urban migration as the people continue to
search for better services and amenities as the developed countries provide for their population.
These two reasons call for an in depth understanding of rainfall through research and study. It is
for these reason I undertook these study. The study area is Nairobi metropolitan region, Kenya,
Africa.
The key in understanding rainfall is to identify the rainfall regimes over the specific area you
want to study so that you can analyze the causes of the variability in rainfall identified.
Identification of this rainfall regime is a useful tool for decision making in various socio-
economic activities such as transport, agriculture, land use and management, water use and
management, extreme weather events monitoring and disaster preparedness, construction
industry among other key sectors of the economy including health and education. The main
objective of this study is to identify rainfall trends, variability over Nairobi metropolitan region.
12. 1.2 PROBLEM STATEMENT
While research and various studies have been done on the rainfall trend and variability of
Nairobi city, there lacks refined information on the newly formed Nairobi metropolitan region.
The lack of information on rainfall trend and variability in this new region may lead if it has not
yet to poor decision making about the development policies and management of this region by
the government and other stakeholders in charge leading to poor productivity.
1.3 OBJECTIVE OFSTUDY
The main objective was to identify rainfall trends, patterns and variability over Nairobi
metropolitan region.
The specific objectives were;-
i. To determine rainfall trends over the study area.
ii. To determine rainfall variability and distribution over the study area.
13. 1.4 JUSTIFICATIONOF THE STUDY
To give refined rainfall information over the study area which may be of use to the government
in charge of the area, investors, conservationists, environmentalists, real estate developers,
farmers among others.
Use of this information will lead to proper and adequate planning reducing pressure on the little
available resources and increased industrial and human productivity over the study area.
1.5 AREAOF STUDY
Nairobi metropolitan region comprises of Nairobi county, Kiambu county, kajiado county and
Machakos county. The region is divided into 4 sub regions as explained below:-
Core metro- includes the city of Nairobi and the surrounding areas forming Nairobi County and
covers an area of 695km² with a population of 3.1 million people it is the most highly populated
sub region in the study area. (Statistical abstract Kenya national bureau of statistics, 2013)
Northern metro- covers the major agricultural town centers surrounding the fertile areas of
Kiambu County that are less than 40km from the central business district. These towns include
Thika, Ruiru, Limuru and Kikuyu. (Ministry of Nairobi metropolitan development 2009 page
38).
Southern metro- includes the major towns’ centers of Kajiado County that is Kitengela, Ol
kejuado, Ngong, Kiserian and bulbul.
Eastern metro- includes the major towns of Machakos County that include Machakos, kangundo,
Athi River, Mlolongo and syokimau.
In totality Nairobi metropolitan region covers an area of 32,000km² with a population of 5.6
million people meaning approximately an 1/8 of the country’s population is found in this region.
(Ministry of Nairobi metropolitan development, 2008, page 19)
The climate of this region is characterized by two annual rainy seasons. Long rains from March
to May and the short rains from October to December. Temperatures are generally warm
throughout the year with an exception of the “African winter” period from June to august when
temperatures are cool. The dominant winds in this region are the easterlies. The region is
generally on a flat plain but starts to rise as you move northwards to Kiambu County which is
characterized by small numerous hills the highest point being 1700metres above sea level. The
14. main physical features are the great Embakasi plains and the Ngong hills to the south of the
study area. Nairobi is situated on the Athi River basin and the main rivers passing through this
region include Rivers Ngong, Mathare, Nairobi and Rui ruaka.
The study area has a fairly good network of weather recording stations i.e. Moi air base, Wilson
airport, Jomo Kenyatta international airport, Dagoretti corner, Kabete, Thika, Machakos,
Katumani and Ngong stations.
Fig 1.1 MAPOF NAIROBI METROPOLITANREGION
15. CHAPTERTWO
2.0 LITERATURE REVIEW
In the Geo journal volume 61, Opijah F.J and Mukabana J.A 2004 (pp 121-129) noted that
although local systems, synoptic flow pattern and the inter tropical convergence zone mainly
dictate the observed distribution of rainfall over the study area, forests and the metropolitan area
in general enhance the rainfall received downwind.
The urban heat island has a destabilizing effect on the flow leading to enhanced convection
resulting in increased rainfall downwind of the urban area. Further growth and expansion of
Nairobi city would increase area and amount of rainfall received. Deforestation would decrease
rainfall amounts while massive reforestation would increase the observed rainfall. They
concluded that further growth and development of Nairobi would modify the water budget
significantly.
The intensity of wet events is higher than dry events and the number of rainy days is decreasing
while the total rainfall is increasing over the study area. (Ongoma V, Otieno A and Onyango A.O,
Rainfall variability over Nairobi city, Kenya)
(Adebayo Y.R, 1992) noted that when rainfall occurs, the road surfaces become wet reducing
friction between the road and the vehicle tires.
A large section of Nairobi city population live in low income residential estates and slums and do
not have access to piped or treated water thus they release waste discharges from their homes to
the only available drainage systems I.e. streams and rivers. Heat and humidity accelerate the
decomposition of refuse giving rise to foul smell and ideal sites for disease vectors. The main
contaminants in the rivers over the study area are organic in origin and result from point sources
16. such as waste disposal sites and or urban run-off elements. (Muthoka J.M and Ndegwa C.M,
volume 5, 2014).
Rodhe, H and Virji, H. (1976 pp 307-315) concluded that the peaks and frequencies of wet
events occur within a cyclical period of 2-5 years.
Analysis of self-organizing maps suggests that circulation systems that promote precipitation
have decreased over the last 40 years due to change in precipitation characteristics of the
synoptic-scale circulation features, rather than to their frequency of occurrence. Hewisten B.C,
Crane R.G (pp 13-26 2002).
Michelangeli P.A, Vautard R, Legras B (1995) said that recurrent flows have a systematic slow
evolution.
Interannual variability of daily rainfall frequency is shown to depend substantially on the
frequency of occurrence weather types specific to the beginning and end of the season. The
fraction of seasonal rainfall variability related to weather-type frequency is found to have a
strong relationship with Elnino event. Moron V, Robertson A.W, Ward M.N, Ndiaye O (2008a)
C.C. Mutai and M.Neil Ward,(2000), said that the East African short rains (OND) have a
positive correlation with ENSO. Interaction with orographic features may modulate the large-
scale ENSO and other coupled ocean-atmosphere signals in the region.
CHAPTERTHREE
3.0 DATAAND METHODOLOGY
This involved the type of data used in the study and the various methods employed on the data to
obtain the results.
3.1 DATA
The data used consisted of monthly rainfall data from the period 2000 to 2013. The data was
obtained from the Kenya meteorological department and consisted of data from the following
stations; Dagoretti corner, Kabete, Jomo Kenyatta international airport, Thika, Moi air base,
Kajiado and Machakos.
17. 3.2 Data qualitycontrol
The quality of the data depends on the accuracy of measurements and completeness. Quality
control ensures that meteorological data acquired meets certain standards. It will involve
estimating missing data and testing for data homogeneity or consistency.
3.2.1 Estimation of missing data
Missing data points were estimated by the arithmetic mean method given by the formula shown
below
𝑥̅ =
1
𝑛
∑ 𝑥 𝑖
𝑛
𝑖=1
𝑥̅= mean of the variable x
n= total number of observations
xᵢ= individual observations
3.2.2 Data homogeneity
The single mass curve method was used to check for data consistency and homogeneity by
plotting the cumulative annual rainfall data for each station against the years.
A resulting straight line graph was considered to indicate that the data was homogenous. Also, R-
squared was equal to 99% for all stations used in the area of study thus implying homogeneity
and the data could therefore be used directly for analysis.
3.3 Data methodology
3.3.1 TrendAnalysis.
In time series, trend is a long term movement that shows whether the series is increasing or not.
If the trend is positive then the series is increasing but if the trend is negative then the series is
decreasing and if it’s neither decreasing nor increasing then it is negative.
Time series analysis was used to graphically depict the trends of rainfall over the study area.
18. 3.3.2 ROLE OF PRINCIPAL COMPONENT ANALYSIS
Principal component analysis was used to show how rainfall varied in the study area by
demarcating the study area into different rainfall zones. The zones were demarcated by
identifying significant Eigen values and factor loadings.
For an Eigen value to be significant, a station should explain a variance of 10% or more. For the
component loadings to be of value to the demarcated zone the total variance of all the stations
should be greater than 50%.
3.3.3 ROLE OF 2-D MAPS
The maps were used to show how the rainfall was distributed by plotting the standardized
anomalies using surfer mapping software.
19. CHAPTERFOUR
4.0 RESULTS AND DISCUSSION.
4.1 RAINFALLMASS CURVES ANALYSIS
Fig 3.1 Rainfall single mass curve for Kajiado
y = 687.76x - 1E+06
R² = 0.9975
0
1000
2000
3000
4000
5000
6000
7000
8000
9000
10000
1998 2000 2002 2004 2006 2008 2010 2012 2014
ACCUMM.RAINFALL(MM)
YEARS
KAJIADO RAINFALL MASS CURVE
KAJIADO
Linear (KAJIADO)
20. Fig 3.2 Rainfall singlemasscurveforJ.K.I.A
y = 726.07x - 1E+06
R² = 0.9954
0
2000
4000
6000
8000
10000
12000
1998 2000 2002 2004 2006 2008 2010 2012 2014 2016
ACCUMMULATEDRAINFALL(MM)
YEARS
J.K.I.A RAINFALL MASS CURVE
J.K.I.A
Linear (J.K.I.A)
21. Fig 3.3 Rainfall singlemasscurveforThika
The mass curves did not show inconsistency of more than five points meaning that the data was
persistent thus the data was of good quality. The R-squared for all stations were 99% meaning
the data was homogeneous.
y = 922.47x - 2E+06
R² = 0.9978
0
2000
4000
6000
8000
10000
12000
14000
16000
1998 2000 2002 2004 2006 2008 2010 2012 2014 2016
ACCUMMULATEDRAINALL(MM)
YEARS
THIKA RAINALL MASS CURVE
THIKA
Linear (THIKA)
22. 4.2 RAINFALLTIME SERIES DISCUUSSION
Fig 4.1 Rainfall time seriesforDagoretti
y = 86.304x + 542
0
500
1000
1500
2000
2500
3000
3500
Totalrainfall(mm)
Years
DAGORETTI
DAGORETTI
Linear (DAGORETTI)
23. Fig 4.2 Rainfall timeseriesforJ.K.I.A
Fig 4.3 Rainfall timeseriesforKabete
y = -5.8658x + 760.75
0.0
200.0
400.0
600.0
800.0
1000.0
1200.0
Totalrainfall(mm)
Years
J.K.I.A
J.K.I.A
Linear (J.K.I.A)
y = 119.72x + 390.87
0.0
500.0
1000.0
1500.0
2000.0
2500.0
3000.0
3500.0
4000.0
4500.0
Totalrainfall(mm)
Years
KABETE
KABETE
Linear (KABETE)
24. Fig 4.4 Rainfall timeseriesforEastleigh
Fig 4.5 Rainfall time series for Machakos
y = 8.4979x + 865.6
0.0
200.0
400.0
600.0
800.0
1000.0
1200.0
1400.0
Totalrainfall(mm)
Years
EASTLEIGH
EASTLEIGH
Linear (EASTLEIGH)
y = -0.6244x + 683.88
0.0
200.0
400.0
600.0
800.0
1000.0
1200.0
1400.0
Totalrainfall(mm)
Years
MACHAKOS
MACHAKOS
Linear (MACHAKOS)
25. Fig 4.6 Rainfall time series for Thika
Fig 4.7 Rainfall timeseriesforKajiado
y = 1.6378x + 894.11
0.0
200.0
400.0
600.0
800.0
1000.0
1200.0
1400.0
1600.0
Totalrainfall(mm)
Years
THIKA
THIKA
Linear (THIKA)
y = 8.1492x + 615.53
0.0
200.0
400.0
600.0
800.0
1000.0
1200.0
20002001200220032004200520062007200820092010201120122013
Totalrainfall(mm)
Years
KAJIADO
KAJIADO
Linear (KAJIADO)
26. All stations had an increasing trend except J.K.I.A and Machakos stations. The stations had the
following slopes in their trend lines; Dagoretti=86.304, J.K.IA=-5.8658, KABETE=119.72,
KAJIADO=8.1492, EASTLEIGH=8.4979, Machakos=-0.6244, Thika=1.6378.
From the rainfall time series graphs, it clearly depicts that Kabete station recorded the highest
total amount of rainfall of `1900mm in 2014 while Kajiado recorded the least amount of rainfall
of about 200mm in 2000.
The following rainfall patterns were observed from the trends.
i. Dagoretti’s highest total rainfall was 1994.3mm in 2014 while its lowest
was 664.9mm in 2000.
ii. J.K.I.A’s highest total rainfall was 1123.6mm in 2006 while its lowest was
325.1mm in the year 2000.
iii. Kabete’s highest total rainfall was 1897.2mm in 2014 while its lowest was
640.9mm in the year 2003.
iv. East Leigh's highest total rainfall was 1301.6mm in 2009 while its lowest
was 464.7mm in the year 2000.
v. Machakos’s highest total rainfall was 1154.6mm in 2006 while its lowest
Was 483.8mm in the year 2000.
vi. Thika’s highest total rainfall was 1385.3mm in 2002 while its lowest was 357.3mm in the
year 2000.
Vii Kajiado’s highest total rainfall was 1022.5mm in 2012 while the lowest was 224.3mm.
4.3 PRINCIPALCOMPONENT ANALYSIS DISCUSSION
Here, the two annual rainfall seasons i.e. MAM and OND were analyzed respectively by
performing principal component analysis on each season data.
4.3.1 MAM PCARESULTS AND DISCUSSION
From the MAM analysis, the latent roots (Eigen values) have 2 significant values that are values
with one or more which explain a variance of more than 10% are 4.695 and 0.900 respectively.
27. Component loadings were derived from the first two Eigen values where two distinct rainfall
zones were identified over the study area.
J.K.IA, Eastleigh, Machakos and Thika were delineated in the same rainfall zone while
Dagoretti, Kabete and Kajiado with the highest communality were delineated in another rainfall
zone as shown in the factor loading plot.
The two significant components explained a total variance of 79.926% which is sufficient
enough to give farmers, entrepreneurs, conservationists, land developers among other stake
holders refined information of the rainfall variability as indicated by the two specific rainfall
zones over the study area during the MAM season.
Table1
STATION
NUMBER
1 2 3 4 5 6 7
EIGEN
VALUES
4.695 0.900 0.603 0.358 0.274 0.109 0.06
VARIANCE
EXPLAINED
BY ROTATED
COMPONENTS
2.132 1.203 1.111 1.053 1.241 0.097 0.164
PERCENT OF
TOTAL
VARIANCE
EXPLAINED
30.455 17.185 15.870 15.037 17.724 1.387 2.342
28. Fig 4.8 screenplotforEigenvaluesof MAM data.
4.3.2 OND PCARESULTS AND DISCUSSION
From the OND analysis, the latent roots (Eigen values) have three significant values that is
values with one or more we have 2.909, 2.167 and 0.996. These values explained a percentage
total variance of more than 10%. Component loadings were derived from the first three Eigen
values where three distinct rainfall zones were identified over the study area.
Dagoretti and Kabete were delineated in the same rainfall zone. Eastleigh, Machakos, Thika and
Kajiado were also delineated in the same rainfall zone and J.K.IA with the highest communality
was also delineated into a rainfall zone as shown in the factor loading plot.
Scree Plot
0 1 2 3 4 5 6 7 8
Number of Factors
0
1
2
3
4
5
Eigenvalue
29. The 3 significant components explained a total percentage variance of 86.747% which is more
than sufficient to give the stakeholders in the study area refined information of the three specific
rainfall zones over the study area during OND season.
Table2
STATION
NUMBERS
1 2 3 4 5 6 7
EIGEN
VALUES
2.909 2.167 0.996 0.513 0.342 0.067 0.006
VARIANCE
EXPLAINED
BY
COMPONENTS
2.037 1.829 1.118 1.009 0.907 0.093 0.007
PERCENT OF
TOTAL
VARIANCE
EXPLAINED
29.100 26.133 15.970 14.416 12.958 1.325 0.098
33. DISCUSSIONON THE 2-D MAPS
For MAM the rainfall was normally distributed. The southern part of the study area received
below normal to near normal rainfall. The central area received normal rainfall. The north
western area received above normal rainfall while the northern area received near normal
rainfall.
For OND, the rainfall showed a distribution that was almost opposite to that of MAM. The
southern part received above normal rainfall, the central parts received normal to near normal
rainfall, the north western parts received near normal to below normal rainfall while the northern
parts received above normal rainfall.
CHAPTERFIVE
5.0 SUMMARY,CONCLUSIONAND RECOMMENDATIONS
5.1 SUMMARY
Generally, the stations showed an increasing trend in rainfall. Kabete and Dagoretti stations
recorded the highest total rainfall of above 1800mm in 2014 compared to other stations in the
study area while Kajiado recorded the least amount of total rainfall of below 500mm in 2000.
From the PCA analysis, two rainfall zones were identified during MAM and 3 rainfall zones
during OND. Kabete and Dagoretti were in the same rainfall zones for both seasons. Also,
Eastleigh and Thika were in the same rainfall zones for both seasons.
Rainfall was normally distributed. For MAM, the central areas received above normal rainfall
but vice versa for OND. This applied also to every part of the entire study area that is, the OND
distribution of rainfall was opposite to that of MAM.
34. 5.2 CONCLUSIONS
I. The increasing trend of rainfall could have been attributed to the increase in built up area
that is expansion of the city leading to enhanced urban heat island effect thus causing a
destabilizing effect on the flow leading to enhanced convection resulting in increased
rainfall over the study area. The decreasing trends at J.K.I.A and Machakos stations was
most likely due to their location in the study area i.e. they are located upwind of the study
area and they border the greater Yatta plateau which is associated with dry weather
conditions and below normal rainfall. Note that J.K.IA is located on the Embakasi plains
which are within the greater Yatta plateau. From the trends above, it was concluded that
2014 was the wettest year and 2000 the driest. This might be attributed to the ENSO
which explains about 50% East African rainfall variance. 2014 was an El Niño year thus
enhanced rainfall was received over the study area and 2000 was a la Niña year thus
causing below normal rainfall and dry conditions.
II. During MAM, Dagoretti, Kabete and Kajiado are in the same rainfall zone while J.K.I.A,
Eastleigh and Machakos in the second zone. This might be due to influence of
topography and vegetation. Dagoretti, Thika and Kabete are located on the slopes of hills
in Kiambu County while Kajiado is located on the slopes of Ngong hills. J.K.IA,
Eastleigh and Machakos are located on a flat terrain plain thus less influence of
topography. The OND is almost similar to MAM in rainfall zoning however the third
zone might be due to difference in the mean onset and cessation dates because the OND
is highly variable. The high rainfall variability suggests dominance of local factors rather
than large scale factors in the modulation of rainfall patterns.
III. Rainfall was almost evenly distributed and showed an increased trend as one moves
westwards of the study area because of the interaction between large scale easterly flow
with topography and the urban-heat island effect. The MAM distribution was opposite to
that of OND because the OND has a positive correlation with ENSO and interaction with
orographic features such as Mt. Kilimanjaro, Mt.Kenya, Ngong hills and the great rift
valley may modulate the large scale ENSO and other coupled ocean atmosphere signals
in the region thus as you move upwards from the southern metro rainfall tends to
decrease and is minimum in the central parts since most local forcing features are located
on the central part of the study area. MAM is not affected by ENSO directly like OND.
35. Its variation is dependent on the inter- tropical convergence zone (ITCZ) and the
monsoonal flow and convergence.
5.3 RECOMMENDATIONS
i. Unrotated principal component analysis should be incorporated in the future to identify
the rainfall zones and compared with the findings of this study which used rotated
principal component analysis.
ii. Future study of this area should incorporate climatological data of more than thirty years
to analyze the trend in the time series graphs.
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