temporal rainfall variability in the lake victoria basin...

ORIGINAL PAPER

Temporal rainfall variability in the Lake Victoria Basinin East Africa during the twentieth century

Michael Kizza & Allan Rodhe & Chong-Yu Xu &

Henry K. Ntale & Sven Halldin

Received: 17 April 2008 /Accepted: 6 December 2008 /Published online: 20 January 2009# Springer-Verlag 2009

Abstract Water resources systems are designed and oper-ated on assumption of stationary hydrology. Existence oftrends and other changes in the data invalidates thisassumption, and detection of the changes in hydrologicaltime series should help us revise the approaches used inassessing, designing and operating our systems. In addition,trend and step change studies help us understand the impactof man’s activities (e.g. urbanisation, deforestation, damconstruction, agricultural activities, etc.) on the hydrologi-cal cycle. Trends and step changes in the seasonal andannual total rainfall for 20 stations in the Lake Victoriabasin were analysed. The seasonal rainfall for any station ina given year was defined in two ways: (1) fixed time periodwhere the rainy seasons were taken as occurring fromMarch–May (long rains) and from October–December(short rains); and (2) variable periods where the rainyseasons were taken as the three consecutive months withmaximum total rainfall covering the period of January–June(long rains) and July–December (short rains), to take intoaccount the fact that the onset of rainy seasons within thebasin varies from year to year and from one station to thenext. For each station, sub datasets were derived covering

different periods (all available data at the station, 1941–1980, 1961–1990, 1971–end of each station’s time series).The trends were analysed using the Mann-Kendall method,while the step changes were analysed using the WorsleyLikelihood method. The results show that positive trendspredominate, with most stations showing trend beinglocated in the northern part of the basin, though this patternis not conclusive. In all, 17% of the cases have trends, ofwhich 67% are positive. The 1960s represent a significantupward jump in the basin rainfall. Seasonal rainfall analysisshows that the short rains tend to have more trends than thelong rains. The impact of the varying month of onset of therainy season is that the results from analyzing the fixed-period and variable-period time series are rarely the same,meaning the two series have different characteristics. It maybe argued that the variable-period time series are morereliable as a basis for analysing trends and step changes,since these time series reflect more closely the actualvariability in rainy seasons from one year to the next. Thefixed-period analysis would, on the other hand, find morepractical use in planning.

1 Introduction

Lake Victoria basin in East Africa has an abundance ofnatural resources and provides services like fishing,transport, agriculture, domestic and industrial water supply,as well as hydropower (Ntiba et al. 2001). The lake basin isone of the most densely populated in Africa with more than30 million people living around it and drawing theirlivelihoods directly or indirectly from its resources. Thelake is also one of the main sources of the Nile River,which is a key lifeline for Sudan and Egypt who dependalmost entirely on the river for water supply (Sutcliffe and

Theor Appl Climatol (2009) 98:119–135DOI 10.1007/s00704-008-0093-6

M. Kizza (*) :H. K. NtaleFaculty of Technology, Makerere University,P.O. Box 7062, Kampala, Ugandae-mail: [email protected]

A. Rodhe : S. HalldinDepartment of Earth Sciences, Uppsala University,Villavägen 16,752 36 Uppsala, Sweden

C.-Y. XuDepartment of Geosciences, University of Oslo,P.O. Box 1047, Blindern, NO-0316 Oslo, Norway

Parks 1999). In addition, the lake and its basin have a richdiversity of flora and fauna that are dependent on it forsurvival. For example the wetlands surrounding the lakeserve as breeding grounds for fish and birds.

However, 80% of the input into the lake’s water balanceis rainfall over its surface, leading some researchers todescribe it as ‘atmosphere controlled’ (Flohn and Burkhardt1985; Yin and Nicholson 2002; Tate et al. 2004). Thisessentially means that the variability of rainfall over thelake plays a key role in the fluctuation of the lake levels.The levels have exhibited large and rapid changes inresponse to rainfall anomalies over the last century (Con-way 2002; Mistry and Conway 2003). Changes in the lakebasin rainfall regime have far-reaching ecological, environ-mental, hydrological and socio-economic effects. Reductionin lake water levels affects plant and animal habitats,impairs navigation, reduces hydropower generation, fishcatch and also reduces access to clean water.

Several studies have been aimed at assessing the spatialand temporal variability in the region (Rodhe and Virji1976; Ogallo 1979; Ogallo 1989; Nyenzi 1990). Rodheand Virji (1976) examined the existence of trends andperiodicities in East African annual rainfall data. Compu-tations by Rodhe and Virji revealed that apart fromstations in northern Kenya, most stations did not showany appreciable trends. Spectral analysis also showed a 5–6 years spectral peak for rainfall in the Lake Victoriaregion. Ogallo (1979) also noted that most of the annualrainfall series in the region indicated an oscillatorycharacteristic with no significant trend. Ogallo (1989)used monthly records from over 90 stations in East Africato study the dominant spatial and temporal modes ofseasonal variation using rotated principal componentanalysis for the period 1922–1983. He demonstrated thedominant effect of nearby large water bodies like LakeVictoria and the Indian Ocean on the seasonal rainfallpatterns in the region. In particular, he showed that theLake Victoria region has a distinctive rainfall regime inEast Africa as a whole.

Between 2002 and 2006, water levels in Lake Victoriadropped to pre-1961 values despite having remained athigher values for over 40 years (LVBC 2006). This hashad profound environmental and socio-economic impacton all activities that depend on the lake resourcesmentioned above. The reasons for this drastic drop arenot yet fully understood but a reduction of the total rainfallinput into the lake and its basin has been identified as oneof the possibilities. However, for effective planning andmanagement of the regional water resources, there is needto constantly update the knowledge of temporal variabilityof rainfall in the lake basin. A number of years havepassed since the temporal studies were carried out andmore data have become available. There is clearly a need

to carry out temporal analysis using updated datasets inorder to analyse the current trends in precipitation in theregion.

The objective of this paper was to investigate thetemporal distribution of rainfall in the Lake Victoria basinon seasonal to annual time scales. The aim was to use anupdated dataset with records covering the period 1903–2006 to test the presence of significant trends in the rainfalldata. The approach was to test for trends in the seasonal andannual rainfall data for selected stations in the lake basin.The study attempted to address issues of dependence oftrend test results on the period of study by dividing theprimary rainfall series for each station into different sub-series, carrying out trend tests on them and then makingcomparisons between the results. In this study, we alsocarried out temporal analysis of total rainfall for the twoperiods that correspond to the rainy seasons in East Africa;the ‘long rains’ and the ‘short rains’. The seasons weredefined in two ways. One was the fixed-time period wherethe long rain season occurs in March to May and the shortrain season was assumed to occur from October toDecember. The second was the maximum 3-month totalrainfall with the first covering the period January to June(for the long rains) and the second covering the period Julyto December (for the short rains).

2 Study region and data

2.1 Study area

Lake Victoria is the largest lake in Africa and the secondlargest lake in the world. The lake is located betweenlatitudes 0o20’N–3oS and longitudes 31o40’E–34o53’E(Fig. 1). The lake basin area is 194,000 km2 and the lakesurface area is about 68,800 km2 or 35% of the basin. Thelake surface is shared between Kenya (6%), Uganda (43%)and Tanzania (51%) while its basin includes parts ofBurundi and Rwanda. It is located in a continental sagbetween the two arms of the Great Rift Valley system, withhigh mountains ranges on the east and west (Kilimanjaro,Kenya and Rwenzori). The altitude of the lake surface isabout 1,135 m amsl while the basin is made of a series ofstepped plateaus with an average elevation of 2,700 m butrising to 4,000 m or more in the highland areas.

The general climate of the lake basin ranges from amodified equatorial type with substantial rainfall occurringthroughout the year, especially over the lake and its vicinityto a semiarid type characterised by intermittent droughtsover some areas located even within short distances fromthe lake shore. Climate variability at different time scales inthe lake basin is influenced by both large-scale and meso-scale circulations.

120 M. Kizza et al.

2.2 General circulation and rainfall variability

The diurnal, seasonal and inter-annual variability of LakeVictoria (and East Africa generally) climate results from acomplex interaction between the inter-tropical convergencezone (ITCZ), El Nino/Southern Oscillation (ENSO), Quasi-biennial Oscillation (QBO), large-scale monsoonal winds,meso-scale circulations and extra-tropical weather systems(Ogallo 1988; Mutai et al. 1998; Nicholson and Yin 2002).The wind and pressure patterns that govern the region’sclimate include three principal air streams and threeconvergence zones namely; the Congo airstream with awesterly and southwesterly flow, the southeast monsoonand the northeast monsoon (Trewartha 1981; Nicholson1996). The monsoons are thermally stable, and associatedwith subsiding air and are, therefore, relatively dry whichpartly accounts for the relatively arid conditions in much ofthe area. The Congo air mass is humid, thermally unstableand, therefore associated with rainfall. The Congo air masssignificantly boosts convection and overall rainfall amountsreceived, especially over the western and northwestern partsof the Lake (Nicholson 1996). The three airstreams areseparated by two convergence zones; the ITCZ whichseparates the monsoons and the Congo air boundary whichseparates the Indian Ocean easterlies and Atlantic Oceanwesterlies (Trewartha 1981). A third convergence zonealoft separates the dry, stable northerly flow from Saharaand the moister southerly flow.

The seasonal climate patterns follow the seasonal N–Smovement of the ITCZ which lags the seasonal migrationof the sun and results in a bimodal rainfall distribution; theMarch–May rainfall period (long rains) and the October–December rainfall period (short rains). The northeast (NE)and southeast (SE) monsoon winds also modify theseasonal climate of East Africa (Mukabana and Piekle1996). The NE monsoon air stream occurs during theSouthern Hemisphere summer and, after traversing overEgypt and Sudan, is warm and dry. On the other hand, theSE monsoon air stream occurs when the sun is north of theequator. It is cool and moist after picking up maritimemoisture from the Indian Ocean and is responsible forlarge-scale precipitation over the lake basin. The QBO is aquasi-periodic oscillation of the equatorial zonal windbetween easterlies and westerlies in the tropical stratospherewith a mean period of 28–29 months (Indeje et al. 2000).Inter-annual variability corresponds to the ENSO variabil-ity. El Niño years are usually associated with above normalrainfall amounts in the short rainfall season in most of theregion (Indeje et al. 2000). However, arguments remainwith regard to the relative importance of Indian Oceanversus Pacific Ocean forcing of East African rainfall(Mistry and Conway 2003; Latif et al. 1999). Meso-scalecirculations due to orography, lake surface temperature andother factors have also been shown to influence rainfallvariability in the Lake Victoria basin (Mukabana and Piekle1996; Nicholson and Yin 2002; Anyah et al. 2006)

Fig. 1 Lake Victoria basin andits location in Africa (inset) andthe rainfall stations used in thecurrent study

Temporal rainfall variability in the Lake Victoria Basin 121

2.3 Rainfall data availability

Rainfall in the lake basin has been recorded since the startof the twentieth century using manual rain gauges and,more recently, some automatic recording gauges. Therainfall data for the current study were collected fromvarious sources including the hydro-meteorological data-base of the World Meteorological Organisation (WMO1982), meteorological departments in Kenya, Tanzania andUganda as well as from our correspondence with variousresearchers in the region. The data format was either as theraw daily values or aggregated monthly values. Anassessment of the number of stations in the basin with time(Fig. 2) shows that from just a few stations at the turn of thetwentieth century, the number grew to over 400 at the peakin the 1970s. Most of the stations are concentrated inKenya, Tanzania and Uganda. The southwest of the basinhas very few stations, which are mainly located in Rwandaand Burundi where political problems have resulted in fewcurrent data being available. Similarly, the records from thenorthwest of the basin, which is mainly part of Uganda,were interrupted for long periods in the late 1970s and1980s. There has been a general decline in the rain gaugenetwork coverage since the 1970s. The drop in networkcoverage is an familiar pattern especially in developingcountries where insufficient funding, inadequate institution-al frameworks, a lack of appreciation of the worth of long-term data and sometimes political turmoil over the recentyears have resulted in a marked decline of national hydro-meteorological gauging network coverage (Sene andFarquharson 1998; Sawunyama and Hughes 2008).

2.4 Dataset and data properties

For this study, monthly rainfall records have been compiledfor 20 stations. The main factor in selecting stations forinclusion in the temporal analysis was the length of records,

which was set to 50 years or more whenever possible. Thisnumber of stations was considered representative of thelarge basin area because of the strongly coherent patterns ofvariability throughout the region (Yin and Nicholson 1998).Almost all stations had some periods of missing dataranging from a few days to several years, whose gaps werefilled using linear regression with nearby stations that havehighly correlated rainfall records. However, many of thestations still had other constraints that made them unsuit-able for the analysis. The first constraint was the length ofthe number of missing records, which was set to 5 monthsor less in order to minimise the uncertainty related toestimating the missing values. In cases where the stationhas several nearby stations with available records for use inestimation of missing values at a given station, thisconstraint was relaxed on the assumption that using manystations reduces the uncertainty in the estimated value.Other constraints included availability of recent records forassessment of the rainfall trends in recent years (since2000) and how they fit into the overall pattern as well asensuring a sufficient spread of stations around the basin.The locations of the stations that were used in the currentstudy are shown in Fig. 1, and Fig. 3 is a chronogramdetailing the data availability for each station with time.

The summary of the key statistics of the dataset used inthe study is shown in Table 1. It is seen that the meanannual rainfall varies between 2,037 mm for Bukoba and847 mm for Musoma. In general, stations on the north tonorth eastern part of the basin receive more rainfall thanthose in the southern part. For the yearly rainfall, thestandard deviation varies between 339 mm and 168 mm(for Bungoma and Mbarara respectively) while the coeffi-cient of variation (CV) varies within a range of 0.24 and0.13 for Ngudu and Bukoba respectively. The average CVis 0.19, indicating that the rainfall varies considerably fromone year to the next. On average the dataset contains65 years of records. Jinja has the longest records with96 years while Rulenge has the shortest with 28 years. Themaximum annual rainfall amount of 2,736 mm wasobserved in Bukoba while the minimum of 400 mm wasobserved in Ngudu. The maximum annual rainfall for fivestations (Jinja, Bukoba, Biharamulo, Mwanza and Ngudu)occurred in 1961 while that of Buvuma occurred in 1963.The minimum annual rainfall for five stations has occurredin years after 2000. The range in the annual varies between1,561 and 872 mm. Figure 4 shows a boxplot showing thevariation of the annual data at each of the stations.

A visual inspection of the annual time series from 12selected stations from the study (Fig. 5) reinforces thecommonly held view that the amount of rainfall received inthe 1960s was above average. Other periods with aboveaverage rainfall conditions include the late 1970s to early1980s as well as the late 1990s.

1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 20000

50

100

150

200

250

300

350

400

450

Years

Num

ber

of s

tatio

ns w

ith d

ata

Fig. 2 Variation of number of rainfall stations in the Lake Victoriabasin with time

122 M. Kizza et al.

2.5 Temporal distribution of the rainfall records

A key feature of the dataset is that the number of years withdata available for analysis varies with time period. Jinja hasthe time series with earliest available rainfall recordsstarting in 1903 while the Kahunda records are onlyavailable since 1971. Half of the stations used have recordsfrom before 1930; 13 stations have more than 50 years ofrecord and only one station has less than 40 years of record.A total of 17 of the 20 stations have data for the period after2000 making it possible to assess the recent rainfall trends

in the basin. Kericho (ending in 1986), Kitale (ending in1988) and Biharamulo (ending in 1995) are the onlystations with records ending before the year 2000.

3 Methodology

Change in a data series can occur in various ways, e.g.steadily (a trend), abruptly (a step change) or in a morecomplex form. The change may affect the mean, median,variance or any other aspect of the time series. There are

1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000

Jinja (1)Buvuma (2)

Bungoma (3)Kitale (4)

Eldoret (5)Kericho (6)

Sotik (7)Shirati (8)

Musoma (9)Mugumu (10)

Ngudu (11)Mwanza (12)

Kahunda (13)Biharamulo (14)

Rulenge (15)Bukoba (16)Kabale (17)

Mbarara (18)Kamenyamigo (19)

Entebbe (20)

Sta

tion

Year

Fig. 3 Chronograms showingyears with complete monthlydata, i.e. for which 12 monthlyvalues are available

Table 1 Properties of the annual rainfall data

Station name (number inFig. 1)

WMOnumber

Annual mean(mm)

CV Skewness Maximum(mm)

Minimum(mm)

Startyear

No. ofyears

Jinja(1) 8933043 1,170 0.17 0.22 1,731 726 1903 96Buvuma(2) 8933005 1,584 0.16 −0.29 2,131 913 1930 68Bungoma (3) 8934134 1,515 0.22 −0.35 2,130 725 1963 41Kitale (4) 8934008 1,305 0.17 −0.18 1,740 861 1929 59Eldoret(5) 8935133 1,073 0.21 0.60 1,716 590 1957 47Kericho(6) 9035003 1,826 0.16 0.44 2,485 1251 1927 59Sotik(7) 9035013 1,365 0.16 0.15 2,021 751 1926 78Shirati(8) 9133002 901 0.23 0.56 1,424 536 1944 57Musoma(9) 9133000 847 0.22 0.13 1,390 421 1922 84Mugumu(10) 9134033 1,100 0.24 −0.71 1,545 324 1966 33Ngudu(11) 9233005 868 0.24 0.43 1,444 400 1930 70Mwanza(12) 9232009 1,083 0.20 0.04 1,543 671 1950 56Kahunda(13 9232027 1,175 0.20 0.40 1,777 707 1971 29Biharamulo(14 9231000 986 0.17 0.61 1,599 624 1922 69Rulenge(15 923001 994 0.15 0.74 1,421 769 1971 28Bukoba(16) 9131002 2,037 0.13 0.83 2,736 1523 1922 82Kabale(17) 9129000 1,012 0.14 0.06 1,282 727 1944 57Mbarara(18 9030003 923 0.19 0.40 1,520 529 1903 89Kamenyamigo(19 9031026 998 0.23 0.00 1,450 556 1952 41Entebbe(20 8932066 1,617 0.19 0.90 2,679 1117 1944 58


many approaches that can be used to detect trends and otherforms of non-stationarity in time series data. The methodsare broadly classified as parametric and non-parametricprocedures. Non-parametric approaches find wide use inhydrological studies because there is no requirement ofmaking assumptions on the distribution form. An importantissue to deal with, when we attempt to test for existence oftrends in a series is the inherent variability of hydromete-orological data (WMO 2000; Burn and Hag Elnur 2002). Ifthe data series is sufficiently long so that natural cyclescancel out each other, then variability is not an importantissue. However, the length of the records is usually notsufficiently long to support this assumption. We thereforehave to develop a rigorous procedure for detection oftrends. A systematic procedure was adopted that involvesthree related stages.

– The first stage was to select the stations to be studied(see section 2.3).

– The second step was to test for presence of trends in therainfall data. Two trend methods (Mann-Kendall andlinear regression tests) and one step-jump (Worselylikelihood ratio test) method were used. On applicationof the procedure, it was discovered that, for the currentdataset, the Mann-Kendall and linear regression testshave similar power and give very similar results.Therefore, for the test for trend, only the results fromthe Mann-Kendal test are presented in this paper. Thenull hypothesis for the Mann-Kendall test is that thereis no trend in the data while that for Worsely likelihood

ratio test is that there is no change in mean of the dataseries for two different periods. The two methods aredescribed in detail in sections 3.1 and 3.2.

– The third step was to determine the significance of thedetected trends. This was achieved by carrying outresampling analysis using bootstrapping which helps toavoid the need for strict adherence of the data to testassumptions. The bootstrap resampling technique isdescribed in section 3.3.

3.1 Mann-Kendall test

This is a rank based method which is non-parametric and isbased on an alternative measure of correlation calledKendall’s τ. Mann (1945) originally used this test andKendall (1975) subsequently derived the test statisticdistribution. It is robust to the effect of extremes (forexample highly skewed data) and to deviation from a linearrelationship. It has been used by other researchers in similarapplications (Hamed and Rao 1998; Burn and Hag Elnur2002; Helsel and Hirsch 2002; Xu et al. 2003).

Helsel and Hirsch (2002) give a procedure for carryingout the Mann-Kendall test which involves computation ofthe standardised test statistic S given by

S ¼Xn�1

i¼1

Xnj¼iþ1

sgn Xj � Xi

� � ð1Þ

where Xi and Xj are sequential data values, n is the datasetrecord length and sgn(Xj–Xi) is +1, 0 and −1 for Xj–Xi

500

1000

1500

2000

2500

Rai

nfal

l (m

m)

Jinja

Buvum

a

Bungo

ma

Kitale

Eldore

t

Kerich

oSot

ik

Shirat

i

Mus

oma

Mug

umu

Ngudu

Mwan

za

Kahun

da

Bihara

mulo

Ruleng

e

Bukob

a

Kabale

Mba

rara

Kamen

yam

igo

Enteb

be

Fig. 4 Box-plot for the annualdata for the study rainfall sta-tions. Each box plot shows themedian, lower and upper quar-tiles in the main box indicatingthe main variation in the data.The whiskers show the fullrange of the data while the‘crosses’ represent data thatmight be considered as outliers

124 M. Kizza et al.

greater than, equal to or less than 0 respectively. Thesignificance level, which indicates the strength of the trend,was determined by resampling analysis while the Kendall’scorrelation coefficient (Hirsch et al. 1982), a measure of thestrength of the correlation, was calculated as

t ¼ 2S= n n� 1ð Þð Þ ð2Þ

A positive value of τ indicates increasing trend and viceversa.

3.2 Worsely likelihood ratio test

This method tests whether the means in two parts of arecord are different. It also estimates the most likely time ofchange (in case the null hypothesis is rejected). The testassumes that the data are normally distributed and the

purpose of the test is to determine the mean of a time seriesafter m observations (Worsley 1979).

E xið Þ ¼ m i ¼ 1; 2; . . . ;m ð3Þ

E xið Þ ¼ m þΔ i ¼ mþ 1;mþ 2; . . . ; n ð4Þ

where μ is the mean prior to the change and Δ is thechange in mean.

The cumulative deviations from the mean S�k� �

arecalculated as:

S�0 ¼ 0 S�k ¼Xki¼1

xi � mð Þ k ¼ 1; 2; . . . ; n ð5Þ

Fig. 5 Annual rainfall series of selected study stations


The values of S�k are then weighted according to theirposition in the time series.

Z�k ¼

k n� kð Þ�0:5S�kh i

sð6Þ

where s is the sample standard deviation (assumed to beequal for the two groups).

The test statistic W is:

W ¼ V � n�21�v2

� �1=2 V ¼ max Z�k

�� ð7ÞThe critical values for different significance levels for

the test have been derived by Worsley (1979). A negativevalue of W indicates that the latter part of the record has ahigher mean than the earlier part and vice versa.

3.3 Estimation of the significance levels of the test statistics

In order minimize the effect of the test assumptions(like form and constancy of the distribution, indepen-dence) on the results, a bootstrap sampling strategy wasadopted to compute the significance levels for the twotest methods. In this case, the original data series issampled with replacement to give a new series that hasthe same number of values as the original series butmay contain more than one of some values in theoriginal series but none of the other values (Davidson andHinkley 1997; WMO 2000). The rationale behind thisapproach is that if there is no trend (using the Mann-Kendalltest) or step jump (using the Worsley Likelihood ratio test)under the null hypothesis of no trend in the data, shufflingthe data should not change the gradient very much. The dataare shuffled many times and after each shuffle, the teststatistic of the generated series is recalculated. The teststatistic of the original series is then compared with that ofthe generated data to determine the significance level.

Assuming that the test statistic of each of the generated seriesis estimated as Tk (which can be ordered as T1 ≤ T2 ≤…≤ TS),and assuming that the original test statistic is T0 and Tk ≤ T0 ≤Tk+1, then the probability of the test statistic being less orequal to T0 under the null hypothesis is approximated as

p ¼ k

Nð8Þ

where N is the number of times a series is resampled.If we assume that large values of T indicate departure from

the null hypothesis, the significance level is estimated from

100� 2min p; 1� pð Þ% ð9ÞA critical issue to address when using resampling

methods is the number of samples that should be generated,which depends on the level of significance required and onthe degree of change seen in the data. Usually, a more

accurate estimate of the significance is achieved with moresamples. On the other hand, when using permutationtesting, all permutations (n! where n is the series length)could be generated. These are typically too many. However,100–2,000 samples are usually recommended as sufficientand 1,000 samples were used for the current study.

3.4 Analysis framework

Several datasets were derived from the primary monthlydataset for purposes of analysis of different aspects of therainfall time series. The annual rainfall total was used fortesting whether there have been trends in the overalltotals. Analysis of the seasonal rainfall trends wasdivided into the short rains and long rains. The longrains and short rains have been variously quoted byresearchers as occurring from March to May and Octoberto December respectively. The March-May (referred to asMarMay) and October–December (referred to as OctDec)rainfall totals were used as the second pair of variables inthe current study. On the other hand, it is also knownthat the onset of the rainy season varies from year toyear and the actual rainy season may fall outside theabove months in some years. An additional pair ofvariables where the maximum 3-month total rainfall foreach 6-month period (January–June for the long rainsand July–December for the short rains) was alsocalculated. The January-June 3 month rainfall variableis hereinafter referred to as JanJun3 while the July-December rainfall variable is referred to as JulDec3.Comparisons between the ‘fixed period’ seasonal rainfalltrends and the ‘variable period’ trends give a more clearunderstanding of the inter-annual temporal variability aswell as giving some insight into the changes of theseasons within the basin. The assumption that the two 6-month periods represent a discontinuous break in the twoseasonal rainfall peaks is supported by plots of the long-term median monthly rainfall shown in Fig. 6.

Each of the five datasets (annual, MarMay, OctDec,JanJun3, JulDec3) was further subdivided into differentsub-periods in order to test for trend in the different periods.These are

1. Whole period of records available to test for generaltrends in the data

2. The period 1941–1980 to test for the impact of theheavy rains in the 1960s

3. The period 1961–1990 that is a WMO recommendedbaseline period for climate studies

4. The years 1971 to the last year of record for a givenstation. This was aimed at testing for the trends in therecent years in relation to more recent data excludingthe 1961 rainfall event

126 M. Kizza et al.

3.5 Serial correlation

The existence of serial correlation in the data compli-cates the identification of trends. For example, apositive serial correlation can increase the expectednumber of false positive outcomes for the Mann-Kendalltest (Burn and Hag Elnur 2002). Serial correlationcoefficients for lag 1 and lag 2 years in the annual rainfallseries for each station used in this study were computedand tested for their significance at the 5% level. Theassumption was that after a lag of 3 years, any correlationin the data is not due to serial correlation especially if lag1 and 2 correlations are not significant. The resultsrevealed that only one station showed a significant serialcorrelation with a lag of 1 year. Therefore, no furtheraction was taken for the whole series as independence heldfor the majority of the stations.

4 Results

The results below are presented for annual and seasonal(long and short rainy seasons) analyses. First we carry outan assessment of the mean rainfall variation for all stationsin the basin to identify periods of significant departure fromthe long-term mean (trend or step) including El Niño years.We then present results of analyses at the individual stationsfor the different cases that were introduced in section 3.4.

4.1 Annual and seasonal rainfall variation

The pattern of the MarMay rains is much closer to that ofthe total annual rains than the pattern of the OctDec rains(Fig. 7). The mean rainfall for the annual total, MarMaytotal and OctDec total are 1,202, 465 and 314 mmrespectively. On average, the MarMay and OctDec rainfall

Fig. 6 Long term median monthly rainfall for selected stations in the study area


totals account for 65% of the mean rainfall in the basin. Ifthe 3-month maximum rainfall (JanJun3 and JulDec3) isused instead, the average contribution of the rainy season tothe total annual rainfall increases to 71%. The MarMayrains contribute 39% of the total annual rainfall while theOctDec rains contribute 26% though there are considerablefluctuations (23%–50% for MarMay rains and 15%–46%for OctDec rains).

Years of anomalously high rainfall can be identified in allplots in Fig. 7. For total annual rainfall, the years include1937, 1941, 1947, 1951, 1961, 1963, 1977, 1989, 1997 and2001. For MarMay rainfall, the years that have high rainfallinclude 1931, 1942, 1951, 1963, 1970, 1981 and 2002, whilefor the OctDec rainfall, the years include 1941, 1951, 1961,1963, 1972, 1982, 1989 and 1997. The principal drivingmechanism of these extreme rainfall events has beenestablished as a dipole reversal in atmospheric circulationand Indian Ocean sea surface temperatures (Conway 2002).Hydrometeorological anomalies in the region (especially the1961 and 1997 events) have received considerable researchattention in trying to understand their dynamics, spatial andtemporal nature as well as their hydrological impacts (Kite1981; Flohn 1987; Latif et al. 1999; Webster et al. 1999;Conway 2002). The 1961 rainfall event resulted in 2.5 mincrease in the water level of Lake Victoria which causedwidespread flooding. On the other hand, the 1997 rainscaused rise of only 1.7 m in the Lake water level but withsimilar flooding effects.

A closer examination of the plots in Fig. 7 shows that alarge portion of the variability in the annual rainfall iscontributed by the OctDec rainfall. Spectral analysis usingthe Fast Fourier Transform shows that the total annualseries has peaks at 2.4, 3.5, 5.2 and 6.5 years. The MarMayseries has peaks at 4.0, 5.2, and 6.5 years while the OctDecseries has peaks at 2.4, 3.0, 5.2, 6.5 years. In the annualrainfall series, the dominant time scale of variability is5.2 years which corresponds with the dominant time scalefor the ENSO phenomena (Nicholson 1996). The 2.4 yearpeak can be associated with the quasibiennial oscillation(Rodhe and Virji 1976).

4.2 Annual time series results

4.2.1 Case I (all available data at each station)

Annual data for 6 stations (Jinja, Eldoret, Sotik, Musoma,Ngudu and Entebbe) show a positive trend (Fig. 8, A-1). Ofthe stations with positive trend, 5 are located in the north tonorth eastern part of the basin and only one (Ngudu) islocated in the south. Only one station (Bungoma) has anegative trend. A similar pattern is followed by the stepchange results with all stations that have trend in the annualdata also having step changes (Fig. 8, B-1). The years whenthe step changes occurred are: 1993 (Jinja), 1999 (Bun-goma), 2000 (Eldoret), 1962 (Sotik), 1949 (Musoma), 1959(Ngudu), 1987 (Entebbe).

800

1200

1600 Total Annual Rainfall

Rai

nfal

l (m

m)

200

400

600

800March-May Total Rainfall

Rai

nfal

l (m

m)

0

200

400

600

800October-December Total Rainfall

Rai

nfal

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m)

1930 1940 1950 1960 1970 1980 1990 2000

10

20

No

. of s

tatio

ns

Fig. 7 Total rainfall (annual,March–May, and October–De-cember) (continuous line) forthe study stations with the aver-age (dash-dash line) and the 5year moving average (dash-dotline) superimposed. The lowerpanel shows the number ofstations used to compute themean

128 M. Kizza et al.

4.2.2 Case II (1941–1980)

There is no evidence of trends in the data apart from Nguduwhich has a positive trend (Fig. 8, A-2). This is also true forthe step change results with only Ngudu showing a positivejump (Fig. 8, B-2)

4.2.3 Case III (1961–1990)

The series for Bukoba and Kamenyamigo show significantnegative trend (Fig. 8, A-3) with no trends detected at allthe other stations.

For step changes, four stations (Jinja, Musoma, Nguduand Kamenyamigo) show significant negative step jumpsoccurring in 1961 and 1962 (Fig. 8, B-3).

4.2.4 Case IV (1971–end of each station’s series)

The rainfall series for three stations (Entebbe, Jinja,and Eldoret) show positive trends while those forBiharamulo and Bukoba show a negative trend(Fig. 8, A-4). The stations showing positive trend areall located in the northern part of the basin while thestations showing negative trend are located to the south.

Fig. 8 Trend results (marked A)and step change results (markedB) for the annual time series foreach of the four cases studied(all years, 1941–1980, 1961–1990, 1971–end)


Annual rainfall for two stations (Entebbe and Eldoret)show positive step changes (Fig. 8, B-4) while annualdata for three stations (Jinja, Bungoma and Sotik) shownegative step changes.

4.3 Long rainfall season results


The MarMay rains have two stations (Kericho andKahunda) with a positive trend and two stations (Buvumaand Eldoret) with a negative trend (Fig. 9, A-1). TheJanJun3 rainfall total for three stations (Eldoret, Sotik and

Musoma) have a positive trend while two stations (Buvumaand Bungoma) have a negative trend. For stations with anegative trend, only Buvuma shows similar trends in thetwo time series. The sign of the trend for Eldoret is reversedfrom one time series to the next (positive for the MarMayrains and negative for JanJun3 rainfall).

For the MarMay period, five stations in the northeast ofthe basin show step jumps (Fig. 9, B-1). Three of the jumpsare positive (Bungoma, Kitale, Kericho), while two arenegative (Buvuma, Eldoret). The step jump results showthat for the JanJun3 period, one station (Biharamulo) has apositive jump while two stations (Bukoba and Bungoma)have negative jumps.

Fig. 9 Trend results (marked A)and step change results (markedB) for the long rainfall seasontime series for each of the fourcases studied (all years, 1941–1980, 1961–1990, 1971–end)

130 M. Kizza et al.

4.3.2 Case II (1941–1980)

Only one station (Kamenyamigo) has a positive trend in theMarMay rains and one station (Kahunda) has a trend(positive) in the JanJun3 rainfall total series (Fig. 9, A-2).Only one station (Kitale) shows step jumps for both theMarMay series and the JanJun3 series (Fig. 9, B-2).However, the signs are reversed with the MarMay serieshaving a positive jump while the JanJun3 rainfall totalseries has a negative jump.

4.3.3 Case III (1961–1990)

For the MarMay, Kamenyamigo has a positive trend andMwanza has a negative trend (Fig. 9, A-3). One station(Kahunda) has a positive trend in the JanJun3 rainfall whileBukoba has a negative trend in the same series. No stepjumps are detected in the MarMay series (Fig. 9, B-3). Inthe JanJun3 rainfall, Bukoba has a positive jump, whileBuvuma and Ngudu have positive step jumps.

4.3.4 Case IV (1971–end of each station’s series)

The MarMay rains for Entebbe, Bungoma, Kitale, Kericho,Mugumu and Kahunda show positive trend while those forEldoret and Mwanza show negative trend. The JanJun3rainfall for Jinja, Sotik, Musoma and Ngudu show positivetrend while those for Bungoma, Bukoba and Kabale havenegative trend (Fig. 9, A-4). Two stations (Kitale andKericho) have positive step jumps in their MarMay rainswhile the series for Kericho has a negative jump. On theother hand, step change results for the JanJun3 series showthat the jumps for Bungoma and Nugudu are negative andpositive respectively (Fig. 9, B-4).

4.4 Short rainfall season results


The OctDec rains have eight stations with positive trend andone with a negative trend (Fig. 10, A-1). On the other handthe two stations from the JulDec3 show positive trend andone station shows a negative trend.For the OctDec data, fivestations show positive step jumps and one station shows anegative jump (Fig. 10, B-1). All the six stations with stepjumps also show trend apart from Eldoret. The JulDec3rainfall shows two stations with positive jumps (Sotik,Ngudu) and one with a negative jump (Bungoma).

4.4.2 Case II (1941–1980)

Six stations show evidence of positive trends (Buvuma,Sotik, Ngudu, Biharamulo, Mbarara, Entebbe) and one

station (Bungoma) shows a negative trend for the OctDecrains (Fig. 10, A-2). These stations are uniformly spreadwithin the basin with no clear spatial pattern. There is noevidence of trend in the JulDec3 time series. None of theOctDec time series shows any significant step changesdespite seven of them having significant trends (Fig. 10, B-2). However, for the JulDec3 rainfall, two stations shownegative jumps (Kericho, Sotik) while Entebbe shows apositive jump.

4.4.3 Case III (1961–1990)

The OctDec rainfall totals for Kamenyaymigo and Kabaleshow negative trends (Fig. 10, A-3) while there are notrends detected in the JulDec3 rainfall series. The OctDecrainfall series for 10 stations (Jinja, Kitale, Kericho, Shirati,Musoma, Ngudu, Mwanza, Biharamulo, Kamenyamigo andEntebbe) show negative jumps (Fig. 10, B-3). The JulDec3rainfall for Entebbe, Jinja, Sotik and Mwanza shownegative jumps.

4.4.4 Case IV (1971-end of each station’s series)

The OctDec rains for Jinja and Eldoret both show a positivetrend (Fig. 10, A-4). The JulDec3 rainfall series for Sotik,Musoma and Kabale have negative trends. The OctDecrainfall series for Eldoret has a positive trend (Fig. 10, A-4).For the JulDec3 rainfall, two stations (Sotik and Shirati)have negative jumps while one station (Bungoma) has apositive jump.

5 Discussion

The analysis for this study was based on data obtained fromdifferent sources. In some cases, data from different sourcesfor a given station were combined to form a single timeseries. Information on the type of instrument or anyinstrument changes or changes in the station settings couldnot be obtained and therefore we could not relate the resultsto properties but we feel this is not necessary for thevalidity of the current analysis. Data quality was checkedusing visual inspection of rainfall plots with time to identifyclearly erroneous values and double mass plots to check fornon-homogeneity.

Twenty rainfall stations were included in the analysis totest for the presence of significant trends and step changesin the Lake Victoria basin. For each of the stations, fivetime series were derived: annual rainfall totals, March–May(MarMay) rainfall totals, October–December (OctDec)rainfall totals, long rains 3-month maximum rainfall(JanJun3) and the short rains 3-month maximum rainfall(JulDec3). Analysis was carried out for four time periods, i.


e. the ‘All years’ case, the 1940–1980 case, the 1961–1990case and the 1971–end case. Therefore, a total of 400 caseswere analysed of which 65 (17%) had significant trends. Ofthe stations showing significant trend, 43 cases (67%) arepositive trends and 22 (33%) are negative (Table 2), whichsuggests that the positive trends predominate in the basinover the twentieth century. For stations with significanttrend based on more than 60 years of recording the trendrepresents an increase of 2–4 mm per year. This translatesto a rainfall increase of about 24% over the twentiethcentury. Other studies have also found positive trends in theLake Victoria basin. Rodhe and Virji (1976) did not findevidence of long-term trend in six gauges around the basin

which was probably due to the fact that the data used weredifferent from ours. However, Hulme et al. (2001)computed a positive trend giving an increase of between10–20% or more in the annual rainfall for Lake Victoriabasin over the period 1901–95.

It is clear that annual rainfall variability in the basin isstrongly influenced by variations in the ‘short rains’ whichgenerally occur from October to December. Most of thestations whose annual rainfall data show trends also hadsignificant trends in their October to December rains. Forexample, for the all-years-case, four of the stations whoseannual rainfall shows a positive trend (Fig. 8, A-1) alsohave a positive trend in their OctDec rainfall (Fig. 10, A-1)

Fig. 10 Trend results (markedA) and step change results(marked B) for the short rainfallseason time series for each ofthe four cases studied (all years,1941–1980, 1961–1990, 1971–end)

132 M. Kizza et al.

suggesting a positive correlation between the two. Ascrutiny of Fig. 7 will show that the above-average meanannual rainfall in the region since 1960 can be accountedfor by higher than average mean rainfall for the short rainyperiod, with little or no trend observed in the rainfall for thelong rainy period. The total upward trend in the short rains(OctDec) is about 30%. Several studies have shown that theshort rains in the region are strongly influenced by theENSO phenomenon (Mutai et al. 1998; Indeje et al. 2000).Towards the end of the twentieth century, El Niño eventstended to be more frequent (WMO 2003), thereby explain-ing the upward trend in OctDec rainfall. However, otherfactors like Indian Ocean sea surface temperatures havealso been proposed (Mutai et al. 1998; Camberlain et al.2001; Mistry and Conway 2003).

There is a strong similarity between stations showingsignificant trends and those showing significant stepchanges in the annual and MarMay time series (Table 3).This suggests that long-term changes in precipitation in thestudy area are due to the presence of periods with increasedprecipitation and are not purely monotonic in nature. TheOctDec step change results are strongly influenced byresults for the 1961–1990 time period, which show asignificant negative step change for most of the stations inthe basin which can be attributed to the anomalously heavyrains in the early 1961 followed by relatively high rainfallin 1962–1964 (Fig. 10, B-3). The extreme rains in 1961 and1997 were studied by Conway (2002) who showed that thetwo events were associated with a dipole-like reversal ofIndian Ocean sea surface temperatures. In addition, 1997was a strong El Niño year. The 1961 and 1997 events were

similar in spatial and temporal characteristics and occurredmainly in the short rains period (October–December). Thetwo events had far reaching hydrological impacts in theregions (including record river flows and flooding) withlarge socio-economic consequences (Conway et al. 2005).Other years with extreme rainfall include 1937, 1941, 1947,1951, 1961, 1963, 1977, 1989, 1997 and 2001.

The presence of trends in the data can also be classifiedby location of stations. Using this approach, 28 of the 43cases with positive trends are located in the northern part ofthe basin, while 15 are located in the southern part. On theother hand, there are only weaker patterns in the distribu-tion of the negative trends with 13 in the northern part and9 in the southern part of the basin. The positive step jumpsare similarly distributed with 19 positive jumps in thenorthern part and 9 positive jumps in the south. There arealso 26 negative jumps in the north and 11 negative jumpsin the south. Therefore, the trends and step jumps are morelikely to occur in the northern part of the basin than in thesouth.

6 Conclusions

We have shown that the Lake Victoria basin experienced apredominantly positive trend over the twentieth century.The results are supported by other studies within the basinand also within the East African region generally. Thismeans that assessments of future climate scenarios for thebasin should allow for wetter conditions. The magnitudesand sign of the trends depend on the data period used in theanalysis and vary by station location with most of thestations with positive trends being located in the northern tonorth eastern part of the basin. However, the trends onlyrepresent long-term conditions and short-term variabilitymay sometimes be more critical in assessing adaptationmechanisms within the basin. The influence of short rainson annual rainfall variability is discernible. Most of thestations whose annual rainfall data had trend also hadsignificant trends in their October to December rains.

Step change results show a more balanced picturebetween positive and negative changes within the basin.The step-change test results show a clear similarity to thetrend test results, suggesting that the temporal rainfallpatterns are not entirely monotonic but step wise withperiods of dry years separated by wet years. The stepchange results are dominated by two periods with anoma-lously high rainfall in 1961 and 1997.

The trend test results from analysing seasonal time seriesare quite different when we consider fixed time periods(March to May for the long rains and October to Decemberfor the short rains) from when we consider variable timeperiods representing the three consecutive months with

Table 2 Number of stations with significant trend for all the periodsconsidered

Time series Positive Negative Total

Annual 10 5 15MarMay 8 4 12OctDec 16 4 20JanJun3 7 5 12JulDec3 2 4 6Total 43 22 65

Table 3 Number of stations with significant step jumps for all theperiods considered

Time series Positive Negative Total

Annual 9 8 17MarMay 6 3 9OctDec 6 11 17JanJun3 3 6 9JulDec3 4 9 13Total 28 37 65


maximum rainfall totals in 6-month periods per year(JanJun3 and JulDec3 series). This reflects the variabilityof the rainfall seasons and may also reflect shifts in theonset of the rainy season within the basin. Further studiescould shed more light on the pattern of such shifts.

The current study tested the existence of trends in onlythe rainfall data. Additional work is needed to address theissue of existence of trends in other hydrologic variableslike discharge and evapotranspiration in order to get aclearer picture. Additional work is also needed to checkwhether the observed trends are linked to climate change orreflect natural variability.

Acknowledgements This work was performed within the doctoralstudy programme of the first author at the Department of EarthSciences, Uppsala University, Sweden and Faculty of Technology,Makerere University, Uganda. The study was funded by the SwedishInternational Development Cooperation Agency (Sida) through theDepartment for Research Cooperation (SAREC, Reference number75007304). The authors are pleased to acknowledge this financialsupport. Appreciation is also extended to the Departments ofMeteorology in Kenya, Tanzania and Uganda for granting accessibil-ity to the rainfall data used in the study.

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