THE UNIVERSITY OF NAIROBIcivil.uonbi.ac.ke/sites/default/files/cae/engineering/civil/NYABUTI...for all the priceless pieces of advice passed on to me as well as all engineering students,

THE UNIVERSITY OF NAIROBI

DEPARTMENT OF CIVIL AND CONSTRUCTION ENGINEERING

A HYDROLOGICAL STUDY OF THE RISING WATER LEVEL AT LAKE

NAKURU

DONE BY: F16/1309/2010

NYABUTI FRANCIS MOTURI

A project submitted as a partial fulfilment for the award of the degree of

BACHELOR OF SCIENCE IN CIVIL ENGINEERING

2015

Hydrological analysis of L.Nakuru. F16/1309/2010

i

ABSTRACT

Onwards from the year 2011, there has been an unprecedented lake level changes by a significant

proportion in the Eastern Rift Valley lakes in Kenya. Rainfall records obtained from the highland

areas in Kenya have a significant correlation with the observed increase in the Eastern Rift Valley

lakes that is partially attributed to the regular occurrence during the period after the July to

September short rains that result from the S.E. Trade Winds. Though not thoroughly examined,

the documentation of rising water levels in the four Ramsar sites has been done by use of

Geographic Information System (GIS) digital techniques and hence information has been extracted

and represented from Landsat satellite image data for some designated periods since the beginning

of the phenomenon.

This study shall encompass mainly the sources of water for Lake Nakuru. The study area was

selected due to the alarming rise in water levels in the lake, although there was little long term and

consistent rainfall and stream flow data. The Mau catchment that is the source of rivers Makalia

and Nderit, and the Njoro watershed are the main areas of interest. The changing profiles of the

rivers due to increased discharge shall be closely analysed.

There has been a growing interest in the study area also by the Kenya Water Towers Agency

(KWTA), Kenya Meteorological Department (KMD) and the Ministry of Water. Discharge data

was obtained from the Water Resources Management Authority (WRMA) offices at Nakuru while

the rainfall data was obtained from the Kenya Meteorological Department.


ii

DEDICATION

To my family for moral and financial support throughout the undertaking of this project. To all the

young scientists interested in the lacustrine changes of Lake Nakuru as well as residents of Nakuru

County, may this be a motivation and an eye-opener into the comprehension of the fluctuating

levels of Lake Nakuru.


iii

ACKNOWLEDGEMENT

I would like to deeply thank my supervisor Dr. S.O. Dulo for all the rigorous guidance throughout

my research process. He has been a perfect advisor and counsellor throughout the entire project

period. He has relentlessly sacrificed his time to be available for me and taught me a lot of things

in the field of Hydrology. I shall be forever grateful.

I must acknowledge all the lecturers for all the information gained throughout my campus life and

for all the priceless pieces of advice passed on to me as well as all engineering students, during

this period.

Gratitude to Mr Christopher Musundi Mwanzi of the Kenya Meteorological Department, Mrs

Regina Githua of the Water Resource Management Authority at Nakuru as well as the chief

research scientist at Kenya Wildlife Service offices at Nakuru for all the data and information

acquired as well as the corporation accorded during this exercise.

Most importantly I would like to thank my family, friends, relatives and all well-wishers for all

moral and financial support they always gave me.

I shall be forever grateful to all my classmates for the information shared, support, constructive

criticism and ideas as well as all the fun times we had.


iv

TABLE OF CONTENTS

ABSTRACT ....................................................................................................................................................... i

Dedication ..................................................................................................................................................... ii

Acknowledgement ....................................................................................................................................... iii

Table of Contents ......................................................................................................................................... iv

LIST OF TABLES ............................................................................................................................................. vi

LIST OF PLATES ............................................................................................................................................ vii

LIST OF GRAPHS ......................................................................................................................................... viii

CHAPTER ONE ............................................................................................................................................... 1

1.1 Introduction ........................................................................................................................................ 1

1.2 historical background .......................................................................................................................... 2

1.3 Objectives of the study ....................................................................................................................... 3

1.4 Problem statement ............................................................................................................................. 3

1.5 Project scope ....................................................................................................................................... 4

CHAPTER TWO .............................................................................................................................................. 5

2.0 LITERATURE REVIEW ........................................................................................................................... 5

2.1 Introduction ........................................................................................................................................ 5

2.3 Lake Nakuru Catchment Basin ............................................................................................................ 6

2.4 Human Activities ............................................................................................................................... 10

2.5 NJORO CATCHMENT DESCRIPTION ................................................................................................... 12

CHAPTER THREE .......................................................................................................................................... 14

3.0 METHODOLOGY ................................................................................................................................ 14

3.1 Research Approach ........................................................................................................................... 14

3.2 Data Processing ................................................................................................................................. 14

3.2.1 Rainfall Data Simulation ............................................................................................................. 15

3.2.2 Discharge data ........................................................................................................................... 20

3.3 Data collection .............................................................................................................................. 21

3.3.1 Observation ................................................................................................................................ 21

3.3.2 Interviews ................................................................................................................................... 22

3.3.3 Facilities...................................................................................................................................... 23

3.4 Rainfall and Discharge Data .............................................................................................................. 23

CHAPTER FOUR ........................................................................................................................................... 25

4.0 ANALYSIS AND DISCUSSION .............................................................................................................. 25


v

4.1 Discharge Analysis ............................................................................................................................. 25

4.2 Rainfall Trend .................................................................................................................................... 27

4.3 Determination of the correlation between rainfall and discharge data ........................................... 29

CHAPTER FIVE ............................................................................................................................................. 46

5.0 CONCLUSION ..................................................................................................................................... 46

5.1 Recommendation .............................................................................................................................. 47

APPENDIX .................................................................................................................................................... 49

LIST OF ACRONYMS ................................................................................................................................. 49

REFERENCES ................................................................................................................................................ 50


vi

LIST OF TABLES

Table 2.1: Long-term annual water balance for Lake Nakuru (in million cubic meters). ............. 9

Table 3.1: Table showing the filling in of missing data............................................................... 14

Table 3.2: Table showing the filling in of missing data ............................................................... 15

Table 3.3: Rainfall and River gauging stations used in the study ................................................ 24

Table 4.1: Monthly Maximum Discharges in m3/s ...................................................................... 25

Table 4.2: Monthly Minimum Discharges in m3/s ....................................................................... 26

Table 4.3: Monthly Average Discharges in m3/s ......................................................................... 26

Table 4.4: Moving average method of determining the annual rainfall trend ............................. 27

Table 4.3: Showing the relevant rainfall and cumulative rainfall data as well as the maximum,

minimum and average discharges as well as their cumulates. ...................................................... 29


vii

LIST OF PLATES

Plate 1.1 Historical fluctuation in maximum depth of Lake Nakuru (Vareschi, 1982). ................ 2

Plate 2.1: Drainage basin characteristics (Raghunath, 2006). ........................................................ 5

Plate 2.2: Expansion of Nakuru City between 1930 & 1998 (Nurmi & Laura, 2010). .................. 7

Plate 2.3: Changes in forest cover in Lake Nakuru Basin 1930-1998 (Nurmi & Laura, 2010). .. 11

Plate 2.4: Njoro River watershed (Ogendi, 2007) ........................................................................ 13

Plate 3.1: Image of the flooded acacia forest ............................................................................... 22

Plate 4.1: Current lake dimensions obtained from Google maps area calculator. ....................... 42

Plate 4.2: Lake Nakuru lowest level in January 2010 showing the familiar shape and biodiversity

of the lake (Onywere et al. 2013). ................................................................................................. 43

Plate 4.3: Lake Nakuru high level in September 2013. Point to note: Submerged infrastructure

(Onywere et al. 2013). .................................................................................................................. 44

Plate 4.4: Lake Nakuru time series extent (more than 20km2 is currently under flood waters)

(Onywere et al. 2013). .................................................................................................................. 45


vii

i

LIST OF GRAPHS

Graph 3.1: Graph showing the cumulative Nakuru Rainfall against the cumulative Njoro

Rainfall. ......................................................................................................................................... 20

Graph 4.1: Rainfall trend by the moving average method for the Nakuru Meteorological Station.

....................................................................................................................................................... 28

Graph4.2: Cumulative rainfall against cumulative maximum discharge, 2007 .......................... 35

Graph 4.3: Cumulative rainfall against cumulative minimum discharge, 2007 .......................... 36

Graph4.4: Cumulative rainfall against cumulative average discharge, 2007 .............................. 36

Graph4.5: Cumulative rainfall against cumulative maximum discharge, 2009 .......................... 37

Graph4.6: Cumulative rainfall against cumulative minimum discharge, 2009 ........................... 38

Graph 4.7: Cumulative rainfall against cumulative average discharge, 2009 ............................. 38

Graph 4.8: Cumulative rainfall against cumulative maximum discharge, 2012 ......................... 39

Graph 4.9: Cumulative rainfall against cumulative minimum discharge, 2012 .......................... 40

Graph 4.10: Cumulative rainfall against cumulative average discharge, 2012 ........................... 40


1

CHAPTER ONE

1.1 INTRODUCTION

Lake Nakuru is one of the few lakes that occur in the Rift Valley of Eastern Africa. It lies at a

height of 1754m above the seal level, south of Nakuru County. It is relatively small, shallow and

saline in nature, as it occurs in a closed basin with no outlets. With a surface area of about 44km2,

the lake is fed by one permanent river (Ngosur) and four seasonal rivers (Njoro, Nderit, Makalia

and Larmudiak). Lake Nakuru National Park is completely fenced and with an area of about

90km2, it protects the lake as well as a number of endangered species.

The lake has an incredible bird fauna of about 495 species, notably the flock of the lesser flamingos

Phoeniconaias minor. As such, it is primarily known as a tourist destination, and is under the

protection of Lake Nakuru National Park. The park also has waterbucks, impalas and hippos. It’s

boarded by the town of Nakuru to the North. The vast flamingos that are a famous lining of its

shore are as a result of the lake’s abundance of algae.

The lake Nakuru catchment is approximately 1800 km2. Part of the lake catchment is, the

Menengai Crater to the North, Eburu Crater to the South, Bahati hills to the East and Mau

escarpment to the West. The Mau Forest Complex is the main source of water for the lake. Water

that is obtained from the Bahati hills ends up infiltrating into the ground hence not contributing to

the recharge into the lake.

Since the 90s, the lake has been designated as a Ramsar wetland of international importance.

Despite this, it is threatened by inflows from a number of pollutants and the water levels in the

lake have also been fluctuating hence affecting the flamingo population in the lake (DFID, 2006).


2

Over the years, the lake has had a level of about three metres, albeit variable. Towards the year

2009, the lake was almost dry, but the levels have consistently risen by over two metres by

September 2013, submerging a considerable portion of the park. This includes the acacia forest to

the south, the KWS building at the park’s entrance and most of the northern route.

1.2 HISTORICAL BACKGROUND

According to Onywere et al. 2013, undocumented records in the history of the Rift Valley lakes

indicate there has been a flooded lakes regime in 1901 and in 1963. This implies that the

phenomenon being experienced currently could have resulted from a fifty year cycle event. More

undocumented information also shows that in February – April 1994 as well as in January 2010

periods, the lake nearly dried up on account of no recharge to the lake (Onywere et al. 2013).

Flamingoes often migrate to other lakes due to frequent fluctuations and drying up of the lake area

of between 30-50 Km2, in response to climatic variability (Bennun and Njoroge, 1999).

Plate 1.1 Historical fluctuation in maximum depth of Lake Nakuru (Vareschi, 1982).


3

1.3 OBJECTIVES OF THE STUDY

The main objective of the study is to investigate the reason behind the alarming rise in

water levels in the lake in recent times.

To investigate the total inflow characteristics of rivers that drain into the lake.

To investigate the catchment characteristics of the source of the rivers that drain into the

lake.

Analysis of rainfall data in the river catchments through graphical methods and other

various methods.

1.4 PROBLEM STATEMENT

Lake Nakuru is one of the Rift Valley lakes that have experienced a spectacular elevation in their

levels in recent times. It showed an increase in its flooded area from a low area of 31.8km2 in

January 2010 to a high of 54.7km2 in September 2013 (KWSTI, 2013). Consequently, various

changes have taken place notably: Reduced salinity in the lake discouraging algae growth;

migration of the lesser flamingos to Lake Bogoria; inhibited the production of the species of

Tilapia that exists in the lake that is food to birds of prey and, destruction of its shoreline which

acts as a major breeding site for a number of birds.

This increase in area by around 72% has affected transport and infrastructure in the park hence

tourism (Onywere et al. 2013). It has also led to the submergence of most of the Lake Nakuru

National Park hence threatening the survival of the Acacia forest and affecting the stability of the

buildings. This submergence has led to habitat constriction as it will force the relocation of the

buffaloes whose habitat has been flooded. Tourists as well as leopards which are hugely territorial,

have been restricted to limited space.


4

Severe ecological implications are at stake with this phenomenon as this may lead to economic

implications. The main speculation on the attribution to this fluctuation is the seasonal variation in

rainfall, partially as a result of climate change. There is also the possibility of a mysterious

groundwater inlet being discovered.

1.5 PROJECT SCOPE

The study involves the analysis of all the rainfall data in recent times as well as the stream flow

characteristics for rivers that drain into the lake. Also, important to note is the potential availability

of ground water inflow that might have swelled in order to increase the level of water beyond

heights that have never been realised in the past half century.


5

CHAPTER TWO

2.0 LITERATURE REVIEW

2.1 INTRODUCTION

The entire area of a river basin with the surface runoff generated from a storm drains into a river

or stream in the basin is taken as a hydraulic unit and is referred to as a drainage basin, catchment

area or watershed of the flowing river (Raghunath, 2006). A drainage divide is the boundary line

along a topographic ridge separating two drainage basins.

Plate 2.1: Drainage basin characteristics (Raghunath, 2006).

All the surface drainage from a basin converges or concentrates at a single point as outflow from

the basin in the stream channel. This point is called the concentration point or measuring point. It

is the point at which stream outflow is normally measured (Raghunath, 2006). Concentration time


6

is the time it takes for the rainfall falling from the furthest point in a drainage area, to reach the

concentration point. The drainage net may be physically described by the following components;

The number of streams

Length of streams

Stream density (it’s the number of streams per square kilometer in a basin.)

Drainage density (is the total length of all stream channels per unit area of the basin.)

Infiltration is the process of water penetrating the ground surface into the soil. The infiltration

capacity is the maximum rate at which water can enter the soil. Porosity is the ratio of the volume

of the voids to the total volume of the soil. The liquid water volume in the soil is the soil moisture

content. The voids in the soil are either filled by water or air. Infiltration is affected by

precipitation, soil types, water contents of the soils, vegetation cover and the ground slope. A very

sandy soil may allow water to infiltrate through it very fast as compared to soil with small pores

which are not interconnected such as clay (Han, 2010).

Rainfall in the hydrologic cycle is first intercepted by leaves of trees and stems of vegetation as

interception storage. On reaching the ground, it infiltrates until the infiltration capacity is exceeded

whereby surface runoff occurs.

2.3 LAKE NAKURU CATCHMENT BASIN

It is a closed drainage system of 1800km2. At its sump is the insulated Lake Nakuru National Park

(McClanahan et al, 1996). Having expanded considerably over the past 40yrs, Nakuru city has has

reached the LNNP boundary on the north side, and the Njoro River on the west as demonstrated

below (Nurmi & Laura, 2010).


7

Plate 2.2: Expansion of Nakuru City between 1930 & 1998 (Nurmi & Laura, 2010).

Precipitation being a part of the atmosphere is derived from water vapor. Atmospheric water

generally exists as vapor, but sometimes it becomes liquid (rainfall and cloud water droplets) or

as solid (snowfall, cloud ice crystals and hails). The atmosphere water originates from water vapor

which results from solar radiation from land and ocean (Han, 2010). Through the specific latent

heat for water vaporization, water is turned to vapor which is lighter than air.


8

It is vital to have accurate and consistent rainfall information in a catchment for any hydrological

assessment. Despite this, rainfall varies in space and hence the economics of finances and time do

not allow the installation of a dense rain gauge network to completely cover all corners of the

available catchments. Owing to this fact, only a selected number of gauges are installed leaving

considerable gaps in between them. Rainfall in a catchment can only be assessed by determining

the mean rainfall over the catchment and thereafter estimating the total amount of rainfall in a

catchment (Han, 2010).

There are two main forest biodiversity zones in the Lake Nakuru catchment namely the LNNP in

the middle and forest that cover the upper reaches of the catchment. Between these two zones are

human settlements with less biodiversity value. Being highly dependent on the ecological benefits

from the biodiversity zones, humans have impacted the ecological stability both directly and

indirectly. There has been considerable reduction in forest cover over the past few years hence

having serious effects on the environment and ecology of the lake (Nurmi & Laura, 2010).

The general vegetation in the Lake basin comprises of montane forests in the upper catchment,

and grasslands and scrublands in the lower parts of the basin. This is composed of yellow acacia

along the lakeshore and floodplains. There is also riverine vegetation along the various river

courses (Olang & Kundu, 2011). There is dry upland forest in slopes of the highlands. The forested

areas of the catchment basin consist of the Eastern Mau, the Eburru and Dondori forests.

The Eastern Mau is the largest of the forested areas, comprising 65000 ha compared to Eburru,

8736ha, and Dondori, 6956ha. The main plantation in the Eastern Mau is the indigenous forest

which has been gradually excised over the past decade in order to pave way mainly for human

settlement. The main forest remains are restricted to the crest of the escarpment. Major tree species


9

in the forests include; bamboo thickets, Olea Capensis, Prunus Africana, Albizia Gummifera and

Podocarpus Latifolius.

According to McClanahan, 1996, the soil in the catchment is hugely of volcanic origin hence has

got high porosity, permeability and has a loose structure. These factors promote erosion, land

subsidence as well as fractures when heavy rain falls.

Variation in climate patterns within the basin are a direct product of altitude and topography. Mean

temperature ranges between 10-29oC. Climate varies from cold to humid and arid to semi-arid.

The annual rainfall averages at around 1000mm with peaks in the period between November to

December and April to May (State of Environment Report, 2009). Mean annual evaporation is

1800mm.

The following table gives a summary of the long-term water balance of Lake Nakuru based on

long-term average monthly hydro-meteorological data (Ayenew & Becht 2007).

Table 2.1: Long-term annual water balance for Lake Nakuru (in million cubic meters).

WATER BUDGET LAKE NAKURU

Precipitation 31.8

Stream inflow 16.6

Ground water inflow 24

Total inflow 72.4

Lake evaporation 72.1

Total outflow 72.1

Residual 0.3

(Groundwater inflow/total inflow)*100 33.1


10

With an acute water shortage of about 1000m3/day, population increase is putting pressure on

Nakuru’s water supply, which comes from boreholes hence may have a constraining effect on

underground water supply that feeds Lake Nakuru (McClanahan, 1996).

2.4 HUMAN ACTIVITIES

The region is mainly a tourist site with most of the revenue coming from tourism. The main tourism

attraction of the lake is the flamingos, apart from other animals found in the Lake Nakuru National

Park. However, the lake is situated close to Nakuru town which is an urban centre, tipped to be a

city in the near future. Economic activities in the urban centre include businesses among others.

The catchment is mainly used for mixed farming which leads to siltation and pollution. There is

continued abstraction of water from rivers and sand harvesting. This has an effect on the level of

the lake. Deforestation experienced in the lake is due to land for cultivation, construction and

charcoal burning.

Nakuru is an endorheic system since it has no outflow. This implies that all rainfall is expelled

through evapotranspiration. Being a temporary storage, the lake forms a bridge between the wet

and dry seasons (DFID, 2006). To date, no consistent water balance system has been employed in

the catchment hence there is a high likelihood that overdependence of the population on the waters

that feed the lake might have adverse effects on the lake (DFID, 2006).


11

Plate 2.3: Changes in forest cover in Lake Nakuru Basin 1930-1998 (Nurmi & Laura, 2010).

There is massive waste production from the humans, domestic and industrial sectors. This is

mainly due to the commercial and industrial sector growth rate of 10%. The other hindering factor

is that the handling and treatment facilities that are in place are not able to keep pace with the

production of the waste hence leading to environmental pollution.


12

2.5 NJORO CATCHMENT DESCRIPTION

General: The Njoro River spans a distance of about 60km from the forests of the Eastern Mau

Escarpment and terminates at the Lake Nakuru in the Rift Valley floor. That is from an elevation

of around 2700-3000m to 1759m above the sea level. The catchment covers approximately 280

km2 of the L.Nakuru basin and has a population of about 300,000 people. 39% of L.Nakuru run-

off originates from the catchment, hence is the main source of water for the lake.

Rainfall: Long-term mean annual rainfall varies between 1200mm in the upper reaches and

800mm at L. Nakuru. The rainfall is tri-modally distributed with highest rainfall around April, the

second highest around August and the lowest around November. The dry season is around January

to March. Potential evapotranspiration at the catchment stands at 1150mm (Ogendi, 2007).

Profile Characteristics: studies have shown that the river becomes influent as it approaches its

terminus at the Lake boundary. It is thought to loose most of its flow in the porous fissured zones

on the floor of the rift valley. This is a contributory factor to the water table around the lake.

Land-use: Population pressure has led to the reduction of forested cover at the watershed by over

50%. This is almost directly proportional to the increase in the agricultural land by around 100%

from the year 1970 to 1987. Livestock growth has paralleled the human population growth and

further pressure is introduced by the nomadic pastoralists who own rights to public lands in the

upper part of the catchment. Most of the population thriving along the river is composed of

subsistent farmers who obtain their water from communal boreholes or by collection and

transportation by hand from the river. They mainly practice horticulture. The percentage of

cultivated land in the upper catchment has increased from 13% to 70% over the past 50 years. This

has gone hand in hand with the reduction of woodland and grassland cover from 87% to a mere

30% which has transformed the Njoro River from being a permanent to a seasonal river (DFID,


13

2006). A good percentage of the boreholes have dried up too. Another consequence is the reduction

in rainfall by about 10% below the preceding decades.

Runoff is the process by which precipitation flows off from a catchment area through channels that

drain into a stream. Overland flow is the excess precipitation that moves over the earth ending up

in streams or small channels. Interflow or subsurface runoff is rain water that infiltrates the soil

surface and may move laterally through the upper soil layers and later resurfaces at some location.

Plate 2.4: Njoro River watershed (Ogendi, 2007)


14

CHAPTER THREE

3.0 METHODOLOGY

This section shall be split into the methods of data analysis and the methods of data collection.

3.1 RESEARCH APPROACH

The nature of the data in this project is point data, for both rainfall and discharge data. Statistical

analysis has been applied to estimate the maximum, minimum and average rainfall and discharge

for the area from the given stations.

3.2 DATA PROCESSING

There was a wide presence of gaps in the data provided hence visual scrutiny was very beneficial

in picking the most relevant data used in the study.

Data on some of the key stations for the study was not available. This is owing to the fact that

some stations were abandoned ages back. The main reason though is that stations next to the lake

were submerged by the flooding lake. Some of the data collected from the relevant authorities had

extreme gaps and inconsistencies hence the excel software had to be employed to aid in the filling

of missing data, through interpolation as demonstrated below;

Table 3.1: Table showing the filling in of missing data

NAKURU

METEOROLOGICAL

STATION - NEW

Precipitation;

daily total

2003 54.2 1.5 83.7

NAKURU

METEOROLOGICAL

STATION - NEW

Precipitation;

daily total

2004 36.75 10 82.9

NAKURU

METEOROLOGICAL

STATION - NEW

Precipitation;

daily total

2005 19.3 18.5 82.1


15

(54.2+19.3)/2

=36.75

The average of the preceding and subsequent years was used due to the seasonality of rainfall

within the catchment basin.

3.2.1 RAINFALL DATA SIMULATION

The raw data obtained from the Nakuru Meteorological Station was the principle data applied for

simulation of other data. This is because it was the most relevant, up to date data with the least

gaps of missing data.

Cumulative graphs were plotted for the Nakuru Meteorological station against the cumulative for

the Plant Breeding Research Centre – Njoro in order to extrapolate and obtain the rainfall estimates

for the subsequent years for the station at Njoro. The importance of this data shall be discussed in

subsequent sections of the report;

Table 3.2: Table showing the filling in of missing data

RAINFAL

L

CUMULATIV

E

9036261 9035021 9036261 9035021

2001 JANUARY 60.0 90.2 60.0 90.2

FEBRUARY 18.2 44.7 78.2 134.9

MARCH 83.1 99.2 161.3 234.1

APRIL 203.3 127.2 364.6 361.3

MAY 47.9 35.0 412.5 396.3

JUNE 128.5 97.9 541.0 494.2

JULY 146.4 84.7 687.4 578.9

AUGUST 164.9 97.5 852.3 676.4

SEPTEMBER 82.0 83.3 934.3 759.7

OCTOBER 88.1 94.9 1022.4 854.6

NOVEMBER 98.0 110.7 1120.4 965.3

DECEMBER 9.4 23.7 1129.8 989.0

2002 JANUARY 40.1 28.8 1169.9 1017.8


16

FEBRUARY 17.5 18.7 1187.4 1036.5

MARCH 117.6 135.5 1305.0 1172.0

APRIL 188.6 126.1 1493.6 1298.1

MAY 107.3 154.4 1600.9 1452.5

JUNE 73.7 83.8 1674.6 1536.3

JULY 78.0 38.3 1752.6 1574.6

AUGUST 66.1 54.5 1818.7 1629.1

SEPTEMBER 11.1 19.0 1829.8 1648.1

OCTOBER 124.4 43.6 1954.2 1691.7

NOVEMBER 91.3 30.0 2045.5 1721.7

DECEMBER 168.0 201.3 2213.5 1923.0

2003 JANUARY 54.2 24.6 2267.7 1947.6

FEBRUARY 1.5 12.2 2269.2 1959.8

MARCH 83.7 129.6 2352.9 2089.4

APRIL 113.2 186.0 2466.1 2275.4

MAY 266.7 199.3 2732.8 2474.7

JUNE 95.2 86.0 2828.0 2560.7

JULY 78.6 85.1 2906.6 2645.8

AUGUST 211.9 213.7 3118.5 2859.5

SEPTEMBER 57.5 0.0 3176.0 2859.5

OCTOBER 92.9 78.2 3268.9 2937.7

NOVEMBER 52.2 79.2 3321.1 3016.9

DECEMBER 28.4 27.4 3349.5 3028.3

2004 JANUARY 36.8 7.0 3386.3 3035.7

FEBRUARY 10.0 9.0 3396.3 3044.7

MARCH 82.9 74.6 3479.2 3119.3

APRIL 113.4 102.0 3592.6 3221.3

MAY 203.1 182.7 3795.7 3404.0

JUNE 86.7 78.0 3882.3 3482.0

JULY 76.9 69.1 3959.2 3551.1

AUGUST 158.6 142.7 4117.8 3693.8

SEPTEMBER 54.1 48.7 4171.9 3742.5

OCTOBER 79.0 71.1 4250.9 3813.6

NOVEMBER 38.1 34.3 4289.0 3847.9

DECEMBER 24.0 21.5 4312.9 3869.4

2005 JANUARY 19.3 17.4 4332.2 3886.8

FEBRUARY 18.5 16.6 4350.7 3903.4

MARCH 82.1 73.9 4432.8 3977.3

APRIL 113.6 102.2 4546.4 4079.5

MAY 139.5 125.5 4685.9 4205.0

JUNE 78.1 70.3 4764.0 4275.3


17

JULY 75.1 67.6 4839.1 4342.8

AUGUST 105.3 94.7 4944.4 4437.6

SEPTEMBER 154.4 138.9 5098.8 4576.5

OCTOBER 65.1 58.6 5163.9 4635.1

NOVEMBER 24.0 21.6 5187.9 4656.6

DECEMBER 19.5 17.5 5207.4 4674.2

2006 JANUARY 8.7 7.8 5216.1 4682.0

FEBRUARY 13.0 11.7 5229.1 4693.7

MARCH 85.7 77.1 5314.8 4770.8

APRIL 113.9 102.5 5428.7 4873.3

MAY 131.9 118.7 5560.6 4992.0

JUNE 66.8 60.1 5627.4 5052.1

JULY 73.0 65.7 5700.4 5117.7

AUGUST 89.9 80.9 5790.3 5198.6

SEPTEMBER 11.7 10.5 5802.0 5209.2

OCTOBER 57.0 51.3 5859.0 5260.4

NOVEMBER 188.0 169.1 6047.0 5429.6

DECEMBER 106.6 95.9 6153.6 5525.5

2007 JANUARY 57.7 51.9 6211.3 5577.4

FEBRUARY 128.6 115.7 6339.9 5693.1

MARCH 28.3 25.5 6368.2 5718.6

APRIL 157.0 141.3 6525.2 5859.8

MAY 134.8 121.3 6660.0 5981.1

JUNE 104.3 93.8 6764.3 6074.9

JULY 163.2 146.8 6927.5 6221.8

AUGUST 145.0 130.5 7072.5 6352.2

SEPTEMBER 148.7 133.8 7221.2 6486.0

OCTOBER 92.1 82.9 7313.3 6568.9

NOVEMBER 46.2 41.6 7359.5 6610.4

DECEMBER 9.6 8.6 7369.1 6619.1

2008 JANUARY 15.7 14.1 7384.8 6633.2

FEBRUARY 6.5 5.8 7391.3 6639.0

MARCH 86.1 77.5 7477.4 6716.5

APRIL 48.7 43.8 7526.1 6760.3

MAY 43.2 38.9 7569.3 6799.2

JUNE 36.5 32.8 7605.8 6832.0

JULY 72.4 65.1 7678.2 6897.2

AUGUST 99.3 89.3 7777.5 6986.5

SEPTEMBER 106.1 95.5 7883.6 7082.0

OCTOBER 175.7 158.1 8059.3 7240.0


18

NOVEMBER 120.0 108.0 8179.3 7348.0

DECEMBER 15.3 13.8 8194.6 7361.8

2009 JANUARY 15.7 14.1 8210.3 7375.9

FEBRUARY 7.3 6.6 8217.6 7382.5

MARCH 11.6 10.4 8229.2 7392.9

APRIL 191.4 172.2 8420.6 7565.1

MAY 184.3 165.8 8604.9 7730.9

JUNE 15.3 13.8 8620.2 7744.7

JULY 19.0 17.1 8639.2 7761.8

AUGUST 37.4 33.6 8676.6 7795.4

SEPTEMBER 47.0 42.3 8723.6 7837.7

OCTOBER 62.3 56.1 8785.9 7893.8

NOVEMBER 67.3 60.5 8853.2 7954.3

DECEMBER 137.8 124.0 8991.0 8078.3

2010

JANUARY 15.7 14.1 9006.7 8092.4

FEBRUARY 151.4 136.2 9158.1 8228.6

MARCH 225.4 202.8 9383.5 8431.4

APRIL 157.2 141.4 9540.7 8572.9

MAY 200.1 180.0 9740.8 8752.9

JUNE 36.4 32.7 9777.2 8785.6

JULY 111.8 100.6 9889.0 8886.2

AUGUST 169.4 152.4 10058.4 9038.6

SEPTEMBER 161.1 144.9 10219.5 9183.6

OCTOBER 147.4 132.6 10366.9 9316.2

NOVEMBER 45.6 41.0 10412.5 9357.2

DECEMBER 13.9 12.5 10426.4 9369.7

2011 JANUARY 1.1 1.0 10427.5 9370.7

FEBRUARY 0.1 0.1 10427.6 9370.8

MARCH 104.5 94.0 10532.1 9464.8

APRIL 58.4 52.5 10590.5 9517.4

MAY 111.4 100.2 10701.9 9617.6

JUNE 109.0 98.1 10810.9 9715.7

JULY 177.8 160.0 10988.7 9875.6

AUGUST 123.9 111.5 11112.6 9987.1

SEPTEMBER 146.4 131.7 11259.0 10118.8

OCTOBER 114.1 102.7 11373.1 10221.5

NOVEMBER 126.7 114.0 11499.8 10335.5

DECEMBER 37.7 33.9 11537.5 10369.4

2012 JANUARY 0.0 0.0 11537.5 10369.4

FEBRUARY 34.8 31.3 11572.3 10400.7


19

MARCH 8.0 7.2 11580.3 10407.9

APRIL 274.3 246.8 11854.6 10654.7

MAY 170.4 153.3 12025.0 10808.0

JUNE 79.4 71.4 12104.4 10879.4

JULY 110.0 99.0 12214.4 10978.4

AUGUST 102.7 92.4 12317.1 11070.8

SEPTEMBER 116.3 104.6 12433.4 11175.4

OCTOBER 116.1 104.5 12549.5 11279.9

NOVEMBER 68.4 61.5 12617.9 11341.4

DECEMBER 65.1 58.6 12683.0 11400.0

2013 JANUARY 27.0 24.3 12710.0 11424.3

FEBRUARY 0.8 0.7 12710.8 11425.0

MARCH 74.8 67.3 12785.6 11492.3

APRIL 251.3 226.1 13036.9 11718.4

MAY 59.3 53.4 13096.2 11771.7

JUNE 165.7 149.1 13261.9 11920.8

JULY 161.3 145.1 13423.2 12065.9

AUGUST 112.7 101.4 13535.9 12167.3

SEPTEMBER 144.1 129.6 13680.0 12297.0

The table represents the data simulated for the Plant Breeding Research Centre – Njoro. 9036261

represents the Nakuru Meteorological Station, while 9035021 represents the Plant Breeding

Research Centre – Njoro. All the values in RED color are the averages of the preceding and

subsequent years that were initially gaps. All values in GREEN color are the simulated values for

the Plant Breeding Research Centre – Njoro.


20

Graph 3.1: Graph showing the cumulative Nakuru Rainfall against the cumulative Njoro Rainfall.

The graph represents cumulative Nakuru rainfall against cumulative Njoro rainfall. It extends to

the year 2003 where the Njoro rainfall data terminates. Extrapolation of values was done beyond

this point where the formula generated in the graph, i.e. y = 0.8997x - 10.907, was applied to obtain

the respective cumulative Njoro rainfall data. The respective Njoro data for a specific month was

obtained by subtracting the previous cumulative Njoro data obtained by the above formula from

the cumulative of the specific month. The simulated values are represented in GREEN color. This

data would then be used in the analysis section.

3.2.2 DISCHARGE DATA

Reliable discharge data was obtained for the station 2FC19 given in m3/s. This data too had a wide

range of gaps and the method of averages had to be applied to fill in some of the missing data.

y = 0.8997x - 10.907R² = 0.9969

0.0

500.0

1000.0

1500.0

2000.0

2500.0

3000.0

3500.0

0.0 1000.0 2000.0 3000.0 4000.0

Cu

mu

lati

ve N

joro

rai

nfa

ll

Cumulative Nakuru rainfall

CUMULATIVE NAKURU VS CUMULATIVE NJORO RAINFALL

Series1

Linear (Series1)


21

Since the discharge data available is in form of daily data, it was processed into monthly data by

getting the maximum, minimum and average discharges for each month. This information would

later be used in the analysis section.

3.3 DATA COLLECTION

In this study, all the available data was obtained from the relevant authorities. Observations were

also made at the lake to show the extent of the flooding.

3.3.1 OBSERVATION

The observation technique is primarily used to describe a setting, activities that occurred, the

people present at the occurrence and deduce meaning to what was seen. It usually involves direct

contact with the target under research but can also be done by use of photography, video recordings

and/or audio tapes. The means of observation chosen, hugely depends on the purpose of the

research or study and the information sought (The International Development Research Centre,

2010).

In this case, observation by direct contact as well as photography was used to visualize the extent

of the damage cause by the flooding lake as well as the levels to which it had attained after the

upsurge of water. This would hence be resourceful in giving a deeper meaning to the issues at hand

and aid in the determination of the cause of this phenomenon.


22

Plate 3.1: Image of the flooded acacia forest

3.3.2 INTERVIEWS

Interviews are done to help the researcher gather people’s opinions, values and experiences. They

come in various forms, durations, and have different purposes. The main idea is to ask a select

number of questions on a particular topic (The International Development Research Centre, 2010).

In this case, informal conversations and semi-structures interviews were applied. Informal

conversation is a flexible type of method and it had no strict subject order in this study. Questions

as well as answers resulted from natural flow of information. The main disadvantage of this method


23

is that conversation can take an unwanted direction hence tampering with the accuracy of the

information sought.

The semi-structured interviews are a stricter method based on a straight list of questions and topics

that act as a guide for the interview. These was administered to the chief research scientist at the

KWS offices in Nakuru.

3.3.3 FACILITIES

During the study, facilities were used. Mobile phone cameras were essential in the study as the

photographs taken aided in the visualization of the extent of the flooding that had occurred at the

lake since the year 2010. Mobile phones were used to make contact with the relevant authorities

and the study supervisor. Motor bikes were extremely helpful in accessing the areas where matatus

do not traverse. A simple notebook was used during interviews in jotting down notes.

3.4 RAINFALL AND DISCHARGE DATA

The rainfall data was obtained from the Kenya Meteorological Department. The study uses two

rainfall stations (9036261 & 9035021). Although data was obtained from five rainfall gauging

stations, the chosen two contained the most relevant information for the study and had the least

number of gaps. This is because they provided what was closest to what was required for the study.

Discharge data was obtained from the Water Resources Management Authority (WRMA) offices

at Nakuru. The basis of choice of the stations was purely on the proximity to the lake and the

availability of the most reliable information that could be applied for the study. This is due to the

fact that most of the key stations were submerged in the flooding and many others had too much

missing data. Although there was no luxury of having a number of stations to choose from, the

available data provided tangible data according to the World Meteorological Organization (WMO)


24

standard, it is not recommended to fill more than 10% of missing data. Also, the Njoro discharges

available were due to the massive extent that the Njoro covers as compared to the Makalia and

Enderit rivers.

Table 3.3: Rainfall and River gauging stations used in the study

Station

number

Station

ID.

Station Name Data type

1. 9036261 NAKURU METEOROLOGICAL

STATION – NEW

Rainfall

2. 9035021 PLANT BREEDING RESEARCH

CENTRE – NJORO

Rainfall

3. 2FC19 Discharge


25

CHAPTER FOUR

4.0 ANALYSIS AND DISCUSSION

This section deals with the comparison of both rainfall and discharge data by use of graphs and

areas in order to determine the correlation between the rainfall and discharge data with respect to

the increasing volume of water in the lake. Since the discharge data available was only from the

station at Njoro, the rainfall data to be used has to be from Njoro. But since the available rainfall

data at Njoro wasn’t adequate, the missing data was processed as shown in the previous section.

4.1 DISCHARGE ANALYSIS

The daily discharge data was processed into monthly maximum, minimum and average as shown;

Table 4.1: Monthly Maximum Discharges in m3/s

JAN FEB MAR APR MAY JUN JUL AUG SEPT OCT NOV DEC TOTAL

2005 0.039 0.105 0.342 0.216 0.094 36.455 14.752 0.373 0.026 0.014 52.416

2006 0.014 0.010 0.039 0.066 0.030 0.026 2.891 0.342 0.130 0.012 5.978 30.588 40.125

2007 30.588 8.751 0.049 0.070 0.066 1.642 5.687 6.920 4.884 1.750 0.161 0.058 60.626

2008 0.030 0.012 0.058 0.074 0.026 0.010 0.238 0.407 0.313 0.890 4.401 0.261 6.719

2009 0.019 0.012 0.014 0.026 0.051 0.016 0.006 0.014 0.006 0.022 0.185

2010 0.000

2011 0.000

2012 1.026 6.279 5.687 29.511 4.174 5.409 25.502 77.589

2013 1.539 1.539

2014 5.687 4.174 1.179 1.642 1.983 3.549 18.215


26

Table 4.2: Monthly Minimum Discharges in m3/s

JAN FEB MAR APR MAY JUN JUL AUG SEPT OCT NOV DEC

2005 0.008 0.008 0.074 0.030 0.019 0.051 0.407 0.030 0.016 0.006

2006 0.004 0.004 0.003 0.003 0.004 0.003 0.231 0.006 0.003 0.003 0.007 0.130

2007 0.145 0.196 0.007 0.002 0.010 0.010 0.442 3.358 0.161 0.161 0.030 0.026

2008 0.012 0.012 0.012 0.001 0.007 0.007 0.007 0.058 0.058 0.030 0.286 0.019

2009 0.010 0.012 0.012 0.014 0.012 0.006 0.003 0.003 0.004 0.005

2010

2011

2012 0.768 1.864 2.524 4.401 2.524 1.642 0.956

2013 0.956

2014 0.956 0.660 0.611 0.564 0.890 0.956

Table 4.3: Monthly Average Discharges in m3/s

JAN FEB MAR APR MAY JUN JUL AUG SEPT OCT NOV DEC TOTAL

2005 0.014 0.018 0.179 0.094 0.044 4.328 4.957 0.126 0.023 0.009 9.793

2006 0.006 0.006 0.008 0.017 0.011 0.005 0.952 0.032 0.015 0.006 0.641 4.512 6.211

2007 3.252 2.190 0.013 0.023 0.025 0.387 1.859 5.078 1.133 0.500 0.091 0.032 14.584

2008 0.019 0.012 0.019 0.029 0.012 0.007 0.031 0.179 0.132 0.322 1.672 0.093 2.526

2009 0.013 0.012 0.012 0.018 0.019 0.010 0.004 0.005 0.005 0.021 0.011 0.011 0.140

2010 0.000

2011 0.000

2012 0.870 3.425 4.146 9.877 3.030 3.105 3.327 27.781

2013 1.162 1.162

2014 2.742 1.011 0.928 1.007 1.284 1.491 8.464


27

By visual inspection, it can be deduced that generally the year 2012 had the heaviest discharge

while the year 2009 had the least. This implies that while the lake was driest during the year 2009,

the level of water started rising onwards to the level it is today. This data shall be used in further

analysis of the lake.

4.2 RAINFALL TREND

By the use of the moving average method, the general rainfall trend was determined for the rainfall

gauging station at Nakuru as shown;

Table 4.4: Moving average method of determining the annual rainfall trend

MOVING AVERAGE METHOD OF DETERMINING THE ANNUAL RAINFALL TREND

NKU MET STATION RFL TOTAL

YEAR 2005 488.2

YEAR 2006 946.2 1434.4

YEAR 2007 1215.5 2161.7 3596.1 899.025

YEAR 2008 825.5 2041 4202.7 1050.675

YEAR 2009 796.4 1621.9 3662.9 915.725

YEAR 2010 1435.4 2231.8 3853.7 963.425

YEAR 2011 1111.1 2546.5 4778.3 1194.575

YEAR 2012 1145.5 2256.6 4803.1 1200.775

YEAR 2013 997 2142.5 4399.1 1099.775

The last column containing figures in bold was used to generate the following graph;


28

Graph 4.1: Rainfall trend by the moving average method for the Nakuru Meteorological Station.

It can be deduced that the rainfall has been on the rise leading to an increase in runoff most of

which subsequently ends up at the lake. This is due to the fact that the station in question is nearest

to the lake as compared to the plant breeding station at Njoro. Since the station at Nakuru observed

generally higher rainfall than that at Njoro, it can be deduced that rainfall was a major contributing

factor to the rising water levels in the lake.

0

200

400

600

800

1000

1200

1400

YEAR2007

YEAR2008

YEAR2009

YEAR2010

YEAR2011

YEAR2012

YEAR2013

RAINFALL TREND BY THE MOVING AVERAGE METHOD


29

4.3 DETERMINATION OF THE CORRELATION BETWEEN

RAINFALL AND DISCHARGE DATA

Table 4.3: Showing the relevant rainfall and cumulative rainfall data as well as the maximum,

minimum and average discharges as well as their cumulates.

PLANT BREEDING RESEARCH CENTRE – NJORO

RAINFALL

CUMULATIVE

NJORO RIVER DISCHARGE

CUM NJORO RIVER

9035021

9035021 MAX MIN AVG MAX MIN AVG

2001 JANUARY 90.2 90.2

FEBRUARY

44.7 134.9

MARCH 99.2 234.1

APRIL 127.2 361.3

MAY 35.0 396.3

JUNE 97.9 494.2

JULY 84.7 578.9

AUGUST 97.5 676.4

SEPTEMBER

83.3 759.7

OCTOBER 94.9 854.6

NOVEMBER

110.7 965.3

DECEMBER

23.7 989.0

2002 JANUARY 28.8 1017.8

FEBRUARY

18.7 1036.5

MARCH 135.5 1172.0

APRIL 126.1 1298.1

MAY 154.4 1452.5

JUNE 83.8 1536.3

JULY 38.3 1574.6

AUGUST 54.5 1629.1

SEPTEMBER

19.0 1648.1

OCTOBER 43.6 1691.7

NOVEMBER

30.0 1721.7


30

DECEMBER

201.3 1923.0

2003 JANUARY 24.6 1947.6

FEBRUARY

12.2 1959.8

MARCH 129.6 2089.4

APRIL 186.0 2275.4

MAY 199.3 2474.7

JUNE 86.0 2560.7

JULY 85.1 2645.8

AUGUST 213.7 2859.5

SEPTEMBER

0.0 2859.5

OCTOBER 78.2 2937.7

NOVEMBER

79.2 3016.9

DECEMBER

27.4 3044.3

2004 JANUARY 7.0 3035.7

FEBRUARY

9.0 3044.7

MARCH 74.6 3119.3

APRIL 102.0 3221.3

MAY 182.7 3404.0

JUNE 78.0 3482.0

JULY 69.1 3551.1

AUGUST 142.7 3693.8

SEPTEMBER

48.7 3742.5

OCTOBER 71.1 3813.6

NOVEMBER

34.3 3847.9

DECEMBER

21.5 3869.4

2005 JANUARY 17.4 3886.8

FEBRUARY

16.6 3903.4

MARCH 73.9 3977.3 0.039439

0.008161

0.013756

0.039439

0.008161

0.013756

APRIL 102.2 4079.5 0.104948

0.008161

0.017716

0.144387

0.016322

0.031472

MAY 125.5 4205.0 0.342206

0.074329

0.179195

0.486593

0.090651

0.210667

JUNE 70.3 4275.3 0.216237

0.029871

0.09373

0.70283

0.120522

0.304397


31

JULY 67.6 4342.8 0.093794

0.019084

0.044319

0.796624

0.139606

0.348716

AUGUST 94.7 4437.6 36.45468

0.051314

4.327911

37.2513

0.19092

4.676627

SEPTEMBER

138.9 4576.5 14.75163

0.406605

4.957267

52.00293

0.597525

9.633894

OCTOBER 58.6 4635.1 0.373296

0.029871

0.126444

52.37623

0.627396

9.760338

NOVEMBER

21.6 4656.6 0.025841

0.016283

0.023123

52.40207

0.643679

9.783461

DECEMBER

17.5 4674.2 0.01382

0.005554

0.009318

52.41589

0.649233

9.792778

2006 JANUARY 7.8 4682.0 0.01382

0.003661

0.006393

52.42971

0.652894

9.799171

FEBRUARY

11.7 4693.7 0.009788

0.003661

0.006371

52.4395

0.656554

9.805542

MARCH 77.1 4770.8 0.039439

0.002931

0.008006

52.47894

0.659485

9.813547

APRIL 102.5 4873.3 0.065892

0.002931

0.01689

52.54483

0.662416

9.830437

MAY 118.7 4992.0 0.029871

0.003661

0.011426

52.5747

0.666077

9.841863

JUNE 60.1 5052.1 0.025841

0.002931

0.004848

52.60054

0.669008

9.846711

JULY 65.7 5117.7 2.890593

0.230668

0.951881

55.49114

0.899676

10.79859

AUGUST 80.9 5198.6 0.342206

0.005554

0.032095

55.83334

0.90523

10.83069

SEPTEMBER

10.5 5209.2 0.130438

0.002931

0.014594

55.96378

0.908161

10.84528

OCTOBER 51.3 5260.4 0.011665

0.002931

0.00561

55.97544

0.911092

10.85089

NOVEMBER

169.1 5429.6 5.977506

0.006757

0.640873

61.95295

0.917849

11.49176

DECEMBER

95.9 5525.5 30.58816

0.130438

4.512366

92.54111

1.048287

16.00413

2007 JANUARY 51.9 5577.4 30.58816

0.144917

3.252155

123.1293

1.193204

19.25628

FEBRUARY

115.7 5693.1 8.750759

0.196229

2.189948

131.88

1.389433

21.44623

MARCH 25.5 5718.6 0.048839

0.007298

0.013297

131.9289

1.396732

21.45953

APRIL 141.3 5859.8 0.070111

0.001869

0.023126

131.999

1.398601

21.48265

MAY 121.3 5981.1 0.065892

0.009788

0.024846

132.0649

1.408389

21.5075


32

JUNE 93.8 6074.9 1.642049

0.009788

0.387472

133.7069

1.418177

21.89497

JULY 146.8 6221.8 5.687392

0.442252

1.859443

139.3943

1.860429

23.75441

AUGUST 130.5 6352.2 6.919581

3.358348

5.077853

146.3139

5.218777

28.83227

SEPTEMBER

133.8 6486.0 4.883958

0.160655

1.132901

151.1979

5.379432

29.96517

OCTOBER 82.9 6568.9 1.750043

0.160655

0.499754

152.9479

5.540087

30.46492

NOVEMBER

41.6 6610.4 0.160655

0.029871

0.091419

153.1085

5.569958

30.55634

DECEMBER

8.6 6619.1 0.058239

0.025841

0.031772

153.1668

5.595799

30.58811

2008 JANUARY 14.1 6633.2 0.029871

0.011665

0.0193

153.1967

5.607464

30.60741

FEBRUARY

5.8 6639.0 0.011665

0.011665

0.011665

153.2083

5.61913

30.61908

MARCH 77.5 6716.5 0.058239

0.011665

0.018588

153.2666

5.630795

30.63767

APRIL 43.8 6760.3 0.074329

0.000808

0.029363

153.3409

5.631603

30.66703

MAY 38.9 6799.2 0.025841

0.006757

0.011579

153.3667

5.63836

30.67861

JUNE 32.8 6832.0 0.009788

0.006757

0.006952

153.3765

5.645118

30.68556

JULY 65.1 6897.2 0.237844

0.006757

0.03089

153.6144

5.651875

30.71645

AUGUST 89.3 6986.5 0.406605

0.058239

0.178651

154.021

5.710114

30.8951

SEPTEMBER

95.5 7082.0 0.313223

0.058239

0.132173

154.3342

5.768353

31.02727

OCTOBER 158.1 7240.0 0.889526

0.029871

0.322313

155.2237

5.798224

31.34959

NOVEMBER

108.0 7348.0 4.400795

0.286238

1.672125

159.6245

6.084462

33.02171

DECEMBER

13.8 7361.8 0.261145

0.019084

0.092798

159.8857

6.103545

33.11451

2009 JANUARY 14.1 7375.9 0.019084

0.009788

0.013263

159.9047

6.113334

33.12777

FEBRUARY

6.6 7382.5 0.011665

0.011665

0.011665

159.9164

6.124999

33.13944

MARCH 10.4 7392.9 0.01382

0.011665

0.011805

159.9302

6.136665

33.15124

APRIL 172.2 7565.1 0.025841

0.01382

0.017796

159.9561

6.150485

33.16904


33

MAY 165.8 7730.9 0.051314

0.011665

0.01943

160.0074

6.16215

33.18847

JUNE 13.8 7744.7 0.016283

0.005554

0.009524

160.0237

6.167705

33.19799

JULY 17.1 7761.8 0.005554

0.002931

0.003612

160.0292

6.170636

33.20161

AUGUST 33.6 7795.4 0.01382

0.002931

0.005286

160.043

6.173567

33.20689

SEPTEMBER

42.3 7837.7 0.005554

0.003661

0.004676

160.0486

6.177227

33.21157

OCTOBER 56.1 7893.8 0.018104

0.008187

0.010784

160.0667

6.185414

33.22235

NOVEMBER

60.5 7954.3 0.022258

0.004529

0.010778

160.089

6.189943

33.23313

DECEMBER

124.0 8078.3 0.018482

0.007854

0.010784

160.1074

6.197797

33.24391

2010

JANUARY 14.1 8092.4

FEBRUARY

136.2 8228.6

MARCH 202.8 8431.4

APRIL 141.4 8572.9

MAY 180.0 8752.9

JUNE 32.7 8785.6

JULY 100.6 8886.2

AUGUST 152.4 9038.6

SEPTEMBER

144.9 9183.6

OCTOBER 132.6 9316.2

NOVEMBER

41.0 9357.2

DECEMBER

12.5 9369.7

2011 JANUARY 1.0 9370.7

FEBRUARY

0.1 9370.8

MARCH 94.0 9464.8

APRIL 52.5 9517.4

MAY 100.2 9617.6

JUNE 98.1 9715.7

JULY 160.0 9875.6

AUGUST 111.5 9987.1

SEPTEMBER

131.7 10118.8

OCTOBER 102.7 10221.5


34

NOVEMBER

114.0 10335.5

DECEMBER

33.9 10369.4

2012 JANUARY 0.0 10369.4 1.026136

0.767869

0.870175

FEBRUARY

31.3 10400.7

MARCH 7.2 10407.9

APRIL 246.8 10654.7

MAY 153.3 10808.0

JUNE 71.4 10879.4

JULY 99.0 10978.4 6.27934

1.863645

3.425327

6.27934

1.863645

3.425327

AUGUST 92.4 11070.8 5.687392

2.523527

4.145678

11.96673

4.387172

7.571005

SEPTEMBER

104.6 11175.4 29.51122

4.400795

9.876897

41.47795

8.787967

17.4479

OCTOBER 104.5 11279.9 4.174 2.523527

3.030226

45.65195

11.31149

20.47813

NOVEMBER

61.5 11341.4 5.408654

1.642049

3.105398

51.06061

12.95354

23.58353

DECEMBER

58.6 11400.0 25.50246

0.95588

3.327156

76.56307

13.90942

26.91068

2013 JANUARY 24.3 11424.3

FEBRUARY

0.7 11425.0

MARCH 67.3 11492.3 1.539454

0.95588

1.161733

APRIL 226.1 11718.4

MAY 53.4 11771.7

JUNE 149.1 11920.8

JULY 145.1 12065.9

AUGUST 101.4 12167.3

SEPTEMBER

129.6 12297.0

Depending on the data and information available, and the main objective of the study, three main

years were selected for analysis (2007, 2009 and 2012). Monthly cumulative discharge data for


35

Njoro river from the station at Egerton University was computed for the purpose of simulation

with rainfall data. Due to the nature of the gaps in the discharge data, cumulative discharge was

obtained for the sections of interest. These were used to develop graphs for cumulative rainfall

against cumulative maximum, minimum and average discharge for the years of interest as follows.

Graph4.2: Cumulative rainfall against cumulative maximum discharge, 2007

y = 0.0271x - 26.737R² = 0.9389

0

20

40

60

80

100

120

140

160

180

5400.0 5600.0 5800.0 6000.0 6200.0 6400.0 6600.0 6800.0

Cu

mu

lati

ve D

isch

arge

m3

/s

Cumulative Rainfall in mm

CUMULATIVE RAINFALL AGAINST CUMULATIVE MAXIMUM DISCHARGE AT NJORO

Series1

Linear (Series1)


36

Graph 4.3: Cumulative rainfall against cumulative minimum discharge, 2007

Graph4.4: Cumulative rainfall against cumulative average discharge, 2007

y = 0.0049x - 27.287R² = 0.8269

0

1

2

3

4

5

6

5400.0 5600.0 5800.0 6000.0 6200.0 6400.0 6600.0 6800.0

Cu

mu

lati

ve D

isch

arge

m3

/s


CUMULATIVE RAINFALL AGAINST CUMULATIVE MINIMUM DISCHARGE AT NJORO

Series1

Linear (Series1)

y = 0.0113x - 44.152R² = 0.9064

0

5

10

15

20

25

30

35

5400.0 5600.0 5800.0 6000.0 6200.0 6400.0 6600.0 6800.0

Cu

mu

lati

ve D

isch

arge

m3

/s


CUMULATIVE RAINFALL AGAINST CUMULATIVE AVERAGE DISCHARGE AT NJORO

Series1

Linear (Series1)


37

Discussion: For the year 2007, it can be seen that the highest R2 value is 0.9389 which can be

translated as approximately 94% accuracy when obtaining the line of best fit. This shows an acute

relation between the maximum flows and the cumulative rainfall data.

Graph4.5: Cumulative rainfall against cumulative maximum discharge, 2009

y = 0.0003x + 157.77R² = 0.9863

159.85

159.9

159.95

160

160.05

160.1

160.15

7300.0 7400.0 7500.0 7600.0 7700.0 7800.0 7900.0 8000.0 8100.0 8200.0

Cu

mu

lati

ve D

isch

arge

m3

/s



Series1

Linear (Series1)


38

Graph4.6: Cumulative rainfall against cumulative minimum discharge, 2009

Graph 4.7: Cumulative rainfall against cumulative average discharge, 2009

y = 0.0001x + 5.3111R² = 0.9597

6.1

6.11

6.12

6.13

6.14

6.15

6.16

6.17

6.18

6.19

6.2

6.21

7300.0 7400.0 7500.0 7600.0 7700.0 7800.0 7900.0 8000.0 8100.0 8200.0

Cu

mu

lati

ve D

isch

arge

m3

/s



Series1

Linear (Series1)

y = 0.0002x + 31.977R² = 0.9795

33.12

33.14

33.16

33.18

33.2

33.22

33.24

33.26

7300.0 7400.0 7500.0 7600.0 7700.0 7800.0 7900.0 8000.0 8100.0 8200.0

Cu

mu

lati

ve D

isch

arge

m3

/s



Series1

Linear (Series1)


39


translated as approximately 99% accuracy when obtaining the line of best fit. This too shows an

acute relation between the maximum flows and the cumulative rainfall data.

Graph 4.8: Cumulative rainfall against cumulative maximum discharge, 2012

y = 0.1543x - 1691R² = 0.9257

0

10

20

30

40

50

60

70

80

90

10900.0 11000.0 11100.0 11200.0 11300.0 11400.0 11500.0

Cu

mu

lati

ve D

isch

arge

m3

/s



Series1

Linear (Series1)


40

Graph 4.9: Cumulative rainfall against cumulative minimum discharge, 2012

Graph 4.10: Cumulative rainfall against cumulative average discharge, 2012

y = 0.0296x - 322.39R² = 0.987

0

2

4

6

8

10

12

14

16

10900.0 11000.0 11100.0 11200.0 11300.0 11400.0 11500.0

Cu

mu

lati

ve D

isch

arge

m3

/s



Series1

Linear (Series1)

y = 0.0561x - 611.9R² = 0.9777

0

5

10

15

20

25

30

10900.0 11000.0 11100.0 11200.0 11300.0 11400.0 11500.0

Cu

mu

lati

ve D

isch

arge

m3

/s



Series1

Linear (Series1)


41


translated as approximately 99% accuracy when obtaining the line of best fit. This shows a relation

between the minimum flows and the cumulative rainfall data. Although in this case the trend

changes as it was initially the relation between maximum flows, it can be seen that all the

correlations are close to each other and the change can be attributed to the gaps of missing data

and the inaccuracies that may have resulted since the flooding started.

The lake area has varied over time and the most accurate estimate on the area was obtained from

Google maps area calculator tool from the internet and the information is represented below;


42

Plate 4.1: Current lake dimensions obtained from Google maps area calculator.

The above was taken as a screen shot from the internet. It has to be noted that this estimate

represents the current lake area and historical areas were not obtainable from the same tool as it

doesn’t have such a provision. Of major interest is the lake area, and it can be seen to be 53.66

km2. Taking this area as the current lake area, it can be deduced that generally the lake levels have

started stabilizing since a maximum lake area of 54.7 km2 was obtained in September 2013

(Onywere et al. 2013).


43

The extent of the flooded area of the lake as well as its impact is illustrated in the below image

data and digitized maps.

Plate 4.2: Lake Nakuru lowest level in January 2010 showing the familiar shape and biodiversity

of the lake (Onywere et al. 2013).


44

Plate 4.3: Lake Nakuru high level in September 2013. Point to note: Submerged infrastructure



45

Plate 4.4: Lake Nakuru time series extent (more than 20km2 is currently under flood waters)


The above plates are a figural and pictorial representation of the changes that have occurred in

the lake in recent times. According to the graphs it can be seen that the rainfall has been on an

upward trend since the year 2010 and this may have greatly influenced the rising water levels in

the lake. The discharge data also seems to follow the same trend as it can be seen that with the

data available, both the annual total discharge and annual total rainfall were at their maximum in

the year 2012. According to Onywere et al. 2015, there has been increased recharge of the lake

from the Njoro, Makalia, Larmudiac and Enderit Rivers implying that the other rivers too have

played a significant role in the recharge at the lake.


46

CHAPTER FIVE

5.0 CONCLUSION

The hydrological study of the rising water levels at Lake Nakuru has led to various significant

revelations even though there were constraints of missing and inaccurate data. The objectives of

the study were achieved to an average extent. River Njoro is the largest contributor to Lake

Nakuru’s recharge. Hence taking it as a representative river for the study may lead to

approximately accurate and reliable findings. After the analysis of the discharge and rainfall data

as well as the correlation of the two, the following conclusions were drawn:

The total annual rainfall has been on an upward trend since the year 2007 towards 2013

with the maximum rainfall being experienced in the year 2012. This seems to be the most

significant factor in the increased recharge at the lake.

The rainfall-discharge correlation reveals a relationship between the rainfall and discharge

trend. This implies that the total inflow into the lake from the rivers is highly dependent on

the amount of rainfall that the catchment receives. The higher the rainfall, the higher the

discharge and hence the increased recharge at the lake.

Although most of the precipitation water may end up in rivers and further into the lake, an

indispensable portion of it may have fallen directly into the lake as convectional rain, and

the rest may have reached the lake as surface runoff.

From the comparison of rainfall data from the Nakuru Meteorological station and the Plant

Breeding Station at Njoro, it can be seen that the Nakuru station recorded higher rainfall

data than the station at Njoro which is placed on a higher ground. This implies that due to

the lesser distance travelled by the water either through rivers or surface runoff, the portion

of the catchment feeding the shorter seasonal rivers Enderit and Makalia, may have had


47

greater influence in the recharge than the Njoro catchment. Since River Larmudiac is an

underground source and though it was thought to be dry it has had continuous flow in the

recent past, it implies too that water from the Nakuru catchment that may have percolated

into the ground may have ended up in the lake.

5.1 RECOMMENDATION

Precipitation data can be used in various ways for water balance calculations. However, this input

is subject to a lot of uncertainty that result from measurement errors, systematic errors in the

interpolation method and stochastic error due to the random nature of rainfall. The magnitude and

nature of uncertainty is a governing factor in the determination of the techniques use for processing

of gauged data, and the adequacy of the conclusion from the final results (Buytaert, 2006).

The current drastic rise in water level offers scientists an opportunity to study ecological variations

that are a result of increased water volumes, and flooding of riparian areas of the lake. The need

for monitoring as well as documentation of a good percentage of the sources of water for the Lake

Nakuru catchment.

From historical documentation and studies, the raised levels can be attributed to the 50 year cycle

experienced in 1901 and 1963 (Onywere et al. 2013). Assessment of past and present climatic

records can reveal this possibility. The flood waters are prone to stay for a while hence the

challenges posed as a result, require attention and preparedness from a number of stakeholders

among them:

KWS (Kenya Wildlife Services - wildlife biodiversity and tourism, wildlife conflicts)

KMD (Kenya Meteorological Department - Rainfall and climatic impacts – rainfall data)


48

WRMA (Water Resources Management Authority - River discharge, water quality and

abstraction)

LNNP (Lake Nakuru National Park)


49

APPENDIX

LIST OF ACRONYMS

KWS - Kenya Wildlife Services

KMD - Kenya Meteorological Department

WRMA - Water Resources Management Authority

LNNP - Lake Nakuru National Park

KWTA - Kenya Water Towers Agency

DFID - Department for International Development


50

REFERENCES

Buytaert, W., R. Celleri, et al. (2006). “Spatial and temporal rainfall variability in mountaniuos

areas: A case study from the south Ecuadorian Andes.” Journal of Hydrology

Dawei Han, 2010. Concise Hydrology.

Department for International Development (DFID), 2006. European Tropical Forest Research

Network, Forests Water and Livelihoods.

Department of Environment, Pollution Control Section. 2009. State of Environment Report.

Toward better water quality management. Municipal council of Nakuru.

Dr. George M. Ogendi, Egerton University, Kenya & Arkansas State University, USA. 2007.

Cultures and Water Resource Use, Conservation & Degradation in the Njoro and Lamudiac River

Watersheds, Kenya.

H.M Raghunath, 2006. Hydrology Principles, Analysis and Design.

International Development Research Center, The Module 3: Quantitative research methods.

Referred 27.2.2010. http://www.idrrc.ca/en/ev-56615-201-1-DO_TOPIC.html

Kenya Wildlife Service Training Institute-Naivasha, 2013. The Soda Lakes Kenya: Their Current

Conservation Status and Management.

Leon A. Bennun, Peter Njoroge, National Museums of Kenya. Department of Ornithology. Nature

Kenya. The East Africa Natural History Society, 1999.

Luke Omondi Olang and Peter Musula Kundu (2011). Land Degradation of the Mau Forest

Complex in Eastern Africa: A Review for Management and Restoration Planning, Environmental

Monitoring, Dr Ema Ekundayo (Ed.), ISBN: 978-953-307-724-6, InTech, DOI: 10.5772/28532.

Available from: http://www.intechopen.com/books/environmental-monitoring/land-degradation-

of-the-mau-forest-complex-in-eastern-africa-a-review-for-management-and-restoration

McClanahan, T.R. & Young T.P. 1996. East African Ecosystems and their Conservation. Oxford

University Press. New York.

http://www.idrrc.ca/en/ev-56615-201-1-DO_TOPIC.html

http://www.intechopen.com/books/environmental-monitoring/land-degradation-of-the-mau-forest-complex-in-eastern-africa-a-review-for-management-and-restoration

http://www.intechopen.com/books/environmental-monitoring/land-degradation-of-the-mau-forest-complex-in-eastern-africa-a-review-for-management-and-restoration


51

Nurmi & Laura, 2010. Buffer Zone Plans for Lake Nakuru National Park and for Njoro River in

Kenya.

S.M. Onywere, C.A. Shisanya, J.A. Obando, A.O. Ndubi, D. Masiga, Z. Irura, N. Mariita and

H.O.Maragia, 2013. Geospatial Extent of 2011-2013 Flooding from the Eastern Mau Rift Valley

Lakes in Kenya and its Implication on the Ecosystems.

Tenalem Ayenew and Robert Becht, 2007. Comparative study of the hydrology of selected Ethio-

Kenyan rift lakes. Addis Ababa University, Department of Earth Sciences International Institute

for Geo-information Sciences and Earth Observation (ITC)

Vareschi, E. (1982). The ecology of Lake Nakuru (Kenya). III. Abiotic factors and primary

production.

Documents

THE UNIVERSITY OF NAIROBIcivil.uonbi.ac.ke/sites/default/files/cae/engineering/civil/NYABUTI...for all the priceless pieces of advice passed on to me as well as all engineering students,