Theor Appl Climatol (2009) 98:119–135
DOI 10.1007/s00704-008-0093-6
ORIGINAL PAPER
Temporal rainfall variability in the Lake Victoria Basin
in 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 operated on assumption of stationary hydrology. Existence of
trends and other changes in the data invalidates this
assumption, and detection of the changes in hydrological
time series should help us revise the approaches used in
assessing, designing and operating our systems. In addition,
trend and step change studies help us understand the impact
of man’s activities (e.g. urbanisation, deforestation, dam
construction, agricultural activities, etc.) on the hydrological cycle. Trends and step changes in the seasonal and
annual total rainfall for 20 stations in the Lake Victoria
basin were analysed. The seasonal rainfall for any station in
a given year was defined in two ways: (1) fixed time period
where the rainy seasons were taken as occurring from
March–May (long rains) and from October–December
(short rains); and (2) variable periods where the rainy
seasons were taken as the three consecutive months with
maximum total rainfall covering the period of January–June
(long rains) and July–December (short rains), to take into
account the fact that the onset of rainy seasons within the
basin varies from year to year and from one station to the
next. 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 Worsley
Likelihood method. The results show that positive trends
predominate, with most stations showing trend being
located in the northern part of the basin, though this pattern
is not conclusive. In all, 17% of the cases have trends, of
which 67% are positive. The 1960s represent a significant
upward jump in the basin rainfall. Seasonal rainfall analysis
shows that the short rains tend to have more trends than the
long rains. The impact of the varying month of onset of the
rainy season is that the results from analyzing the fixedperiod and variable-period time series are rarely the same,
meaning the two series have different characteristics. It may
be argued that the variable-period time series are more
reliable as a basis for analysing trends and step changes,
since these time series reflect more closely the actual
variability in rainy seasons from one year to the next. The
fixed-period analysis would, on the other hand, find more
practical use in planning.
1 Introduction
M. Kizza (*) : H. K. Ntale
Faculty of Technology, Makerere University,
P.O. Box 7062, Kampala, Uganda
e-mail: michael.kizza@hyd.uu.se
A. Rodhe : S. Halldin
Department of Earth Sciences, Uppsala University,
Villavägen 16,
752 36 Uppsala, Sweden
C.-Y. Xu
Department of Geosciences, University of Oslo,
P.O. Box 1047, Blindern, NO-0316 Oslo, Norway
Lake Victoria basin in East Africa has an abundance of
natural resources and provides services like fishing,
transport, agriculture, domestic and industrial water supply,
as well as hydropower (Ntiba et al. 2001). The lake basin is
one of the most densely populated in Africa with more than
30 million people living around it and drawing their
livelihoods directly or indirectly from its resources. The
lake is also one of the main sources of the Nile River,
which is a key lifeline for Sudan and Egypt who depend
almost entirely on the river for water supply (Sutcliffe and
120
Parks 1999). In addition, the lake and its basin have a rich
diversity of flora and fauna that are dependent on it for
survival. For example the wetlands surrounding the lake
serve as breeding grounds for fish and birds.
However, 80% of the input into the lake’s water balance
is rainfall over its surface, leading some researchers to
describe it as ‘atmosphere controlled’ (Flohn and Burkhardt
1985; Yin and Nicholson 2002; Tate et al. 2004). This
essentially means that the variability of rainfall over the
lake plays a key role in the fluctuation of the lake levels.
The levels have exhibited large and rapid changes in
response to rainfall anomalies over the last century (Conway 2002; Mistry and Conway 2003). Changes in the lake
basin rainfall regime have far-reaching ecological, environmental, hydrological and socio-economic effects. Reduction
in lake water levels affects plant and animal habitats,
impairs navigation, reduces hydropower generation, fish
catch and also reduces access to clean water.
Several studies have been aimed at assessing the spatial
and temporal variability in the region (Rodhe and Virji
1976; Ogallo 1979; Ogallo 1989; Nyenzi 1990). Rodhe
and Virji (1976) examined the existence of trends and
periodicities in East African annual rainfall data. Computations by Rodhe and Virji revealed that apart from
stations in northern Kenya, most stations did not show
any appreciable trends. Spectral analysis also showed a 5–
6 years spectral peak for rainfall in the Lake Victoria
region. Ogallo (1979) also noted that most of the annual
rainfall series in the region indicated an oscillatory
characteristic with no significant trend. Ogallo (1989)
used monthly records from over 90 stations in East Africa
to study the dominant spatial and temporal modes of
seasonal variation using rotated principal component
analysis for the period 1922–1983. He demonstrated the
dominant effect of nearby large water bodies like Lake
Victoria and the Indian Ocean on the seasonal rainfall
patterns in the region. In particular, he showed that the
Lake Victoria region has a distinctive rainfall regime in
East Africa as a whole.
Between 2002 and 2006, water levels in Lake Victoria
dropped to pre-1961 values despite having remained at
higher values for over 40 years (LVBC 2006). This has
had profound environmental and socio-economic impact
on all activities that depend on the lake resources
mentioned above. The reasons for this drastic drop are
not yet fully understood but a reduction of the total rainfall
input into the lake and its basin has been identified as one
of the possibilities. However, for effective planning and
management of the regional water resources, there is need
to constantly update the knowledge of temporal variability
of rainfall in the lake basin. A number of years have
passed since the temporal studies were carried out and
more data have become available. There is clearly a need
M. Kizza et al.
to carry out temporal analysis using updated datasets in
order to analyse the current trends in precipitation in the
region.
The objective of this paper was to investigate the
temporal distribution of rainfall in the Lake Victoria basin
on seasonal to annual time scales. The aim was to use an
updated dataset with records covering the period 1903–
2006 to test the presence of significant trends in the rainfall
data. The approach was to test for trends in the seasonal and
annual rainfall data for selected stations in the lake basin.
The study attempted to address issues of dependence of
trend test results on the period of study by dividing the
primary rainfall series for each station into different subseries, carrying out trend tests on them and then making
comparisons between the results. In this study, we also
carried out temporal analysis of total rainfall for the two
periods that correspond to the rainy seasons in East Africa;
the ‘long rains’ and the ‘short rains’. The seasons were
defined in two ways. One was the fixed-time period where
the long rain season occurs in March to May and the short
rain season was assumed to occur from October to
December. The second was the maximum 3-month total
rainfall with the first covering the period January to June
(for the long rains) and the second covering the period July
to December (for the short rains).
2 Study region and data
2.1 Study area
Lake Victoria is the largest lake in Africa and the second
largest lake in the world. The lake is located between
latitudes 0o20’N–3oS and longitudes 31o40’E–34o53’E
(Fig. 1). The lake basin area is 194,000 km2 and the lake
surface area is about 68,800 km2 or 35% of the basin. The
lake surface is shared between Kenya (6%), Uganda (43%)
and Tanzania (51%) while its basin includes parts of
Burundi and Rwanda. It is located in a continental sag
between the two arms of the Great Rift Valley system, with
high mountains ranges on the east and west (Kilimanjaro,
Kenya and Rwenzori). The altitude of the lake surface is
about 1,135 m amsl while the basin is made of a series of
stepped plateaus with an average elevation of 2,700 m but
rising to 4,000 m or more in the highland areas.
The general climate of the lake basin ranges from a
modified equatorial type with substantial rainfall occurring
throughout the year, especially over the lake and its vicinity
to a semiarid type characterised by intermittent droughts
over some areas located even within short distances from
the lake shore. Climate variability at different time scales in
the lake basin is influenced by both large-scale and mesoscale circulations.
Temporal rainfall variability in the Lake Victoria Basin
121
Fig. 1 Lake Victoria basin and
its location in Africa (inset) and
the rainfall stations used in the
current study
2.2 General circulation and rainfall variability
The diurnal, seasonal and inter-annual variability of Lake
Victoria (and East Africa generally) climate results from a
complex interaction between the inter-tropical convergence
zone (ITCZ), El Nino/Southern Oscillation (ENSO), Quasibiennial 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’s
climate include three principal air streams and three
convergence zones namely; the Congo airstream with a
westerly and southwesterly flow, the southeast monsoon
and the northeast monsoon (Trewartha 1981; Nicholson
1996). The monsoons are thermally stable, and associated
with subsiding air and are, therefore, relatively dry which
partly accounts for the relatively arid conditions in much of
the area. The Congo air mass is humid, thermally unstable
and, therefore associated with rainfall. The Congo air mass
significantly boosts convection and overall rainfall amounts
received, especially over the western and northwestern parts
of the Lake (Nicholson 1996). The three airstreams are
separated by two convergence zones; the ITCZ which
separates the monsoons and the Congo air boundary which
separates the Indian Ocean easterlies and Atlantic Ocean
westerlies (Trewartha 1981). A third convergence zone
aloft separates the dry, stable northerly flow from Sahara
and the moister southerly flow.
The seasonal climate patterns follow the seasonal N–S
movement of the ITCZ which lags the seasonal migration
of the sun and results in a bimodal rainfall distribution; the
March–May rainfall period (long rains) and the October–
December rainfall period (short rains). The northeast (NE)
and southeast (SE) monsoon winds also modify the
seasonal climate of East Africa (Mukabana and Piekle
1996). The NE monsoon air stream occurs during the
Southern Hemisphere summer and, after traversing over
Egypt and Sudan, is warm and dry. On the other hand, the
SE monsoon air stream occurs when the sun is north of the
equator. It is cool and moist after picking up maritime
moisture from the Indian Ocean and is responsible for
large-scale precipitation over the lake basin. The QBO is a
quasi-periodic oscillation of the equatorial zonal wind
between easterlies and westerlies in the tropical stratosphere
with a mean period of 28–29 months (Indeje et al. 2000).
Inter-annual variability corresponds to the ENSO variability. El Niño years are usually associated with above normal
rainfall amounts in the short rainfall season in most of the
region (Indeje et al. 2000). However, arguments remain
with regard to the relative importance of Indian Ocean
versus Pacific Ocean forcing of East African rainfall
(Mistry and Conway 2003; Latif et al. 1999). Meso-scale
circulations due to orography, lake surface temperature and
other factors have also been shown to influence rainfall
variability in the Lake Victoria basin (Mukabana and Piekle
1996; Nicholson and Yin 2002; Anyah et al. 2006)
122
M. Kizza et al.
2.3 Rainfall data availability
Rainfall in the lake basin has been recorded since the start
of the twentieth century using manual rain gauges and,
more recently, some automatic recording gauges. The
rainfall data for the current study were collected from
various sources including the hydro-meteorological database of the World Meteorological Organisation (WMO
1982), meteorological departments in Kenya, Tanzania and
Uganda as well as from our correspondence with various
researchers in the region. The data format was either as the
raw daily values or aggregated monthly values. An
assessment of the number of stations in the basin with time
(Fig. 2) shows that from just a few stations at the turn of the
twentieth century, the number grew to over 400 at the peak
in the 1970s. Most of the stations are concentrated in
Kenya, Tanzania and Uganda. The southwest of the basin
has very few stations, which are mainly located in Rwanda
and Burundi where political problems have resulted in few
current data being available. Similarly, the records from the
northwest of the basin, which is mainly part of Uganda,
were interrupted for long periods in the late 1970s and
1980s. There has been a general decline in the rain gauge
network coverage since the 1970s. The drop in network
coverage is an familiar pattern especially in developing
countries where insufficient funding, inadequate institutional frameworks, a lack of appreciation of the worth of longterm data and sometimes political turmoil over the recent
years have resulted in a marked decline of national hydrometeorological gauging network coverage (Sene and
Farquharson 1998; Sawunyama and Hughes 2008).
2.4 Dataset and data properties
For this study, monthly rainfall records have been compiled
for 20 stations. The main factor in selecting stations for
inclusion in the temporal analysis was the length of records,
450
Number of stations with data
400
350
300
250
200
150
100
50
0
1900
1910
1920
1930
1940
1950 1960
Years
1970
1980
1990
2000
Fig. 2 Variation of number of rainfall stations in the Lake Victoria
basin with time
which was set to 50 years or more whenever possible. This
number of stations was considered representative of the
large basin area because of the strongly coherent patterns of
variability throughout the region (Yin and Nicholson 1998).
Almost all stations had some periods of missing data
ranging from a few days to several years, whose gaps were
filled using linear regression with nearby stations that have
highly correlated rainfall records. However, many of the
stations still had other constraints that made them unsuitable for the analysis. The first constraint was the length of
the number of missing records, which was set to 5 months
or less in order to minimise the uncertainty related to
estimating the missing values. In cases where the station
has several nearby stations with available records for use in
estimation of missing values at a given station, this
constraint was relaxed on the assumption that using many
stations reduces the uncertainty in the estimated value.
Other constraints included availability of recent records for
assessment of the rainfall trends in recent years (since
2000) and how they fit into the overall pattern as well as
ensuring a sufficient spread of stations around the basin.
The locations of the stations that were used in the current
study are shown in Fig. 1, and Fig. 3 is a chronogram
detailing the data availability for each station with time.
The summary of the key statistics of the dataset used in
the study is shown in Table 1. It is seen that the mean
annual rainfall varies between 2,037 mm for Bukoba and
847 mm for Musoma. In general, stations on the north to
north eastern part of the basin receive more rainfall than
those in the southern part. For the yearly rainfall, the
standard deviation varies between 339 mm and 168 mm
(for Bungoma and Mbarara respectively) while the coefficient of variation (CV) varies within a range of 0.24 and
0.13 for Ngudu and Bukoba respectively. The average CV
is 0.19, indicating that the rainfall varies considerably from
one year to the next. On average the dataset contains
65 years of records. Jinja has the longest records with
96 years while Rulenge has the shortest with 28 years. The
maximum annual rainfall amount of 2,736 mm was
observed in Bukoba while the minimum of 400 mm was
observed in Ngudu. The maximum annual rainfall for five
stations (Jinja, Bukoba, Biharamulo, Mwanza and Ngudu)
occurred in 1961 while that of Buvuma occurred in 1963.
The minimum annual rainfall for five stations has occurred
in years after 2000. The range in the annual varies between
1,561 and 872 mm. Figure 4 shows a boxplot showing the
variation of the annual data at each of the stations.
A visual inspection of the annual time series from 12
selected stations from the study (Fig. 5) reinforces the
commonly held view that the amount of rainfall received in
the 1960s was above average. Other periods with above
average rainfall conditions include the late 1970s to early
1980s as well as the late 1990s.
Temporal rainfall variability in the Lake Victoria Basin
Station
Fig. 3 Chronograms showing
years with complete monthly
data, i.e. for which 12 monthly
values are available
123
Entebbe (20)
Kamenyamigo (19)
Mbarara (18)
Kabale (17)
Bukoba (16)
Rulenge (15)
Biharamulo (14)
Kahunda (13)
Mwanza (12)
Ngudu (11)
Mugumu (10)
Musoma (9)
Shirati (8)
Sotik (7)
Kericho (6)
Eldoret (5)
Kitale (4)
Bungoma (3)
Buvuma (2)
Jinja (1)
1900
1910
1920
1930
1940
1950
1960
1970
1980
1990
2000
Year
2.5 Temporal distribution of the rainfall records
A key feature of the dataset is that the number of years with
data available for analysis varies with time period. Jinja has
the time series with earliest available rainfall records
starting in 1903 while the Kahunda records are only
available since 1971. Half of the stations used have records
from before 1930; 13 stations have more than 50 years of
record and only one station has less than 40 years of record.
A total of 17 of the 20 stations have data for the period after
2000 making it possible to assess the recent rainfall trends
in the basin. Kericho (ending in 1986), Kitale (ending in
1988) and Biharamulo (ending in 1995) are the only
stations 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 more
complex form. The change may affect the mean, median,
variance or any other aspect of the time series. There are
Table 1 Properties of the annual rainfall data
Station name (number in
Fig. 1)
WMO
number
Annual mean
(mm)
CV
Skewness
Maximum
(mm)
Minimum
(mm)
Start
year
No. of
years
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
8933043
8933005
8934134
8934008
8935133
9035003
9035013
9133002
9133000
9134033
9233005
9232009
9232027
9231000
923001
9131002
9129000
9030003
9031026
8932066
1,170
1,584
1,515
1,305
1,073
1,826
1,365
901
847
1,100
868
1,083
1,175
986
994
2,037
1,012
923
998
1,617
0.17
0.16
0.22
0.17
0.21
0.16
0.16
0.23
0.22
0.24
0.24
0.20
0.20
0.17
0.15
0.13
0.14
0.19
0.23
0.19
0.22
−0.29
−0.35
−0.18
0.60
0.44
0.15
0.56
0.13
−0.71
0.43
0.04
0.40
0.61
0.74
0.83
0.06
0.40
0.00
0.90
1,731
2,131
2,130
1,740
1,716
2,485
2,021
1,424
1,390
1,545
1,444
1,543
1,777
1,599
1,421
2,736
1,282
1,520
1,450
2,679
726
913
725
861
590
1251
751
536
421
324
400
671
707
624
769
1523
727
529
556
1117
1903
1930
1963
1929
1957
1927
1926
1944
1922
1966
1930
1950
1971
1922
1971
1922
1944
1903
1952
1944
96
68
41
59
47
59
78
57
84
33
70
56
29
69
28
82
57
89
41
58
124
M. Kizza et al.
2500
2000
Rainfall (mm)
Fig. 4 Box-plot for the annual
data for the study rainfall stations. Each box plot shows the
median, lower and upper quartiles in the main box indicating
the main variation in the data.
The whiskers show the full
range of the data while the
‘crosses’ represent data that
might be considered as outliers
1500
1000
many approaches that can be used to detect trends and other
forms of non-stationarity in time series data. The methods
are broadly classified as parametric and non-parametric
procedures. Non-parametric approaches find wide use in
hydrological studies because there is no requirement of
making assumptions on the distribution form. An important
issue to deal with, when we attempt to test for existence of
trends in a series is the inherent variability of hydrometeorological data (WMO 2000; Burn and Hag Elnur 2002). If
the data series is sufficiently long so that natural cycles
cancel out each other, then variability is not an important
issue. However, the length of the records is usually not
sufficiently long to support this assumption. We therefore
have to develop a rigorous procedure for detection of
trends. A systematic procedure was adopted that involves
three 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 the
rainfall data. Two trend methods (Mann-Kendall and
linear regression tests) and one step-jump (Worsely
likelihood ratio test) method were used. On application
of the procedure, it was discovered that, for the current
dataset, the Mann-Kendall and linear regression tests
have similar power and give very similar results.
Therefore, for the test for trend, only the results from
the Mann-Kendal test are presented in this paper. The
null hypothesis for the Mann-Kendall test is that there
is no trend in the data while that for Worsely likelihood
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–
ratio test is that there is no change in mean of the data
series for two different periods. The two methods are
described in detail in sections 3.1 and 3.2.
The third step was to determine the significance of the
detected trends. This was achieved by carrying out
resampling analysis using bootstrapping which helps to
avoid the need for strict adherence of the data to test
assumptions. The bootstrap resampling technique is
described in section 3.3.
3.1 Mann-Kendall test
This is a rank based method which is non-parametric and is
based on an alternative measure of correlation called
Kendall’s τ. Mann (1945) originally used this test and
Kendall (1975) subsequently derived the test statistic
distribution. It is robust to the effect of extremes (for
example highly skewed data) and to deviation from a linear
relationship. It has been used by other researchers in similar
applications (Hamed and Rao 1998; Burn and Hag Elnur
2002; Helsel and Hirsch 2002; Xu et al. 2003).
Helsel and Hirsch (2002) give a procedure for carrying
out the Mann-Kendall test which involves computation of
the standardised test statistic S given by
S¼
n1 X
n
X
sgn Xj Xi
ð1Þ
i¼1 j¼iþ1
where Xi and Xj are sequential data values, n is the dataset
record length and sgn(Xj–Xi) is +1, 0 and −1 for Xj–Xi
Temporal rainfall variability in the Lake Victoria Basin
125
Fig. 5 Annual rainfall series of selected study stations
greater than, equal to or less than 0 respectively. The
significance level, which indicates the strength of the trend,
was determined by resampling analysis while the Kendall’s
correlation coefficient (Hirsch et al. 1982), a measure of the
strength of the correlation, was calculated as
purpose of the test is to determine the mean of a time series
after m observations (Worsley 1979).
i ¼ 1; 2; . . . ; m
ð3Þ
t ¼ 2S=ðnðn 1ÞÞ
E ðxi Þ ¼ m þ Δ i ¼ m þ 1; m þ 2; . . . ; n
ð4Þ
ð2Þ
A positive value of τ indicates increasing trend and vice
versa.
3.2 Worsely likelihood ratio test
This method tests whether the means in two parts of a
record are different. It also estimates the most likely time of
change (in case the null hypothesis is rejected). The test
assumes that the data are normally distributed and the
E ðxi Þ ¼ m
where μ is the mean prior to the change and Δ is the
change in mean.
The cumulative deviations from the mean Sk are
calculated as:
S0 ¼ 0
Sk ¼
k
X
i¼1
ðxi mÞ k ¼ 1; 2; . . . ; n
ð5Þ
126
M. Kizza et al.
The values of Sk are then weighted according to their
position in the time series.
h
i
k ðn k Þ0:5 Sk
ð6Þ
Zk ¼
s
where s is the sample standard deviation (assumed to be
equal for the two groups).
The test statistic W is:
n2 1=2
V ¼ maxZk ð7Þ
W ¼ V 1v
2
The critical values for different significance levels for
the test have been derived by Worsley (1979). A negative
value of W indicates that the latter part of the record has a
higher 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, independence) on the results, a bootstrap sampling strategy was
adopted to compute the significance levels for the two
test methods. In this case, the original data series is
sampled with replacement to give a new series that has
the same number of values as the original series but
may contain more than one of some values in the
original series but none of the other values (Davidson and
Hinkley 1997; WMO 2000). The rationale behind this
approach is that if there is no trend (using the Mann-Kendall
test) or step jump (using the Worsley Likelihood ratio test)
under the null hypothesis of no trend in the data, shuffling
the data should not change the gradient very much. The data
are shuffled many times and after each shuffle, the test
statistic of the generated series is recalculated. The test
statistic of the original series is then compared with that of
the generated data to determine the significance level.
Assuming that the test statistic of each of the generated series
is 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 or
equal to T0 under the null hypothesis is approximated as
p¼
k
N
ð8Þ
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 2 minðp; 1 pÞ%
ð9Þ
A 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 on
the degree of change seen in the data. Usually, a more
accurate estimate of the significance is achieved with more
samples. On the other hand, when using permutation
testing, 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 sufficient
and 1,000 samples were used for the current study.
3.4 Analysis framework
Several datasets were derived from the primary monthly
dataset for purposes of analysis of different aspects of the
rainfall time series. The annual rainfall total was used for
testing whether there have been trends in the overall
totals. Analysis of the seasonal rainfall trends was
divided into the short rains and long rains. The long
rains and short rains have been variously quoted by
researchers as occurring from March to May and October
to December respectively. The March-May (referred to as
MarMay) and October–December (referred to as OctDec)
rainfall totals were used as the second pair of variables in
the current study. On the other hand, it is also known
that the onset of the rainy season varies from year to
year and the actual rainy season may fall outside the
above months in some years. An additional pair of
variables where the maximum 3-month total rainfall for
each 6-month period (January–June for the long rains
and July–December for the short rains) was also
calculated. The January-June 3 month rainfall variable
is hereinafter referred to as JanJun3 while the JulyDecember rainfall variable is referred to as JulDec3.
Comparisons between the ‘fixed period’ seasonal rainfall
trends and the ‘variable period’ trends give a more clear
understanding of the inter-annual temporal variability as
well as giving some insight into the changes of the
seasons within the basin. The assumption that the two 6month periods represent a discontinuous break in the two
seasonal rainfall peaks is supported by plots of the longterm median monthly rainfall shown in Fig. 6.
Each of the five datasets (annual, MarMay, OctDec,
JanJun3, JulDec3) was further subdivided into different
sub-periods in order to test for trend in the different periods.
These are
1. Whole period of records available to test for general
trends in the data
2. The period 1941–1980 to test for the impact of the
heavy rains in the 1960s
3. The period 1961–1990 that is a WMO recommended
baseline period for climate studies
4. The years 1971 to the last year of record for a given
station. This was aimed at testing for the trends in the
recent years in relation to more recent data excluding
the 1961 rainfall event
Temporal rainfall variability in the Lake Victoria Basin
127
Fig. 6 Long term median monthly rainfall for selected stations in the study area
3.5 Serial correlation
4 Results
The existence of serial correlation in the data complicates the identification of trends. For example, a
positive serial correlation can increase the expected
number of false positive outcomes for the Mann-Kendall
test (Burn and Hag Elnur 2002). Serial correlation
coefficients for lag 1 and lag 2 years in the annual rainfall
series for each station used in this study were computed
and tested for their significance at the 5% level. The
assumption was that after a lag of 3 years, any correlation
in the data is not due to serial correlation especially if lag
1 and 2 correlations are not significant. The results
revealed that only one station showed a significant serial
correlation with a lag of 1 year. Therefore, no further
action was taken for the whole series as independence held
for the majority of the stations.
The results below are presented for annual and seasonal
(long and short rainy seasons) analyses. First we carry out
an assessment of the mean rainfall variation for all stations
in the basin to identify periods of significant departure from
the long-term mean (trend or step) including El Niño years.
We then present results of analyses at the individual stations
for 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 of
the total annual rains than the pattern of the OctDec rains
(Fig. 7). The mean rainfall for the annual total, MarMay
total and OctDec total are 1,202, 465 and 314 mm
respectively. On average, the MarMay and OctDec rainfall
128
M. Kizza et al.
totals account for 65% of the mean rainfall in the basin. If
the 3-month maximum rainfall (JanJun3 and JulDec3) is
used instead, the average contribution of the rainy season to
the total annual rainfall increases to 71%. The MarMay
rains contribute 39% of the total annual rainfall while the
OctDec rains contribute 26% though there are considerable
fluctuations (23%–50% for MarMay rains and 15%–46%
for OctDec rains).
Years of anomalously high rainfall can be identified in all
plots in Fig. 7. For total annual rainfall, the years include
1937, 1941, 1947, 1951, 1961, 1963, 1977, 1989, 1997 and
2001. For MarMay rainfall, the years that have high rainfall
include 1931, 1942, 1951, 1963, 1970, 1981 and 2002, while
for the OctDec rainfall, the years include 1941, 1951, 1961,
1963, 1972, 1982, 1989 and 1997. The principal driving
mechanism of these extreme rainfall events has been
established as a dipole reversal in atmospheric circulation
and Indian Ocean sea surface temperatures (Conway 2002).
Hydrometeorological anomalies in the region (especially the
1961 and 1997 events) have received considerable research
attention in trying to understand their dynamics, spatial and
temporal nature as well as their hydrological impacts (Kite
1981; Flohn 1987; Latif et al. 1999; Webster et al. 1999;
Conway 2002). The 1961 rainfall event resulted in 2.5 m
increase in the water level of Lake Victoria which caused
widespread flooding. On the other hand, the 1997 rains
caused rise of only 1.7 m in the Lake water level but with
similar flooding effects.
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). Of
the stations with positive trend, 5 are located in the north to
north eastern part of the basin and only one (Ngudu) is
located in the south. Only one station (Bungoma) has a
negative trend. A similar pattern is followed by the step
change results with all stations that have trend in the annual
data also having step changes (Fig. 8, B-1). The years when
the step changes occurred are: 1993 (Jinja), 1999 (Bungoma), 2000 (Eldoret), 1962 (Sotik), 1949 (Musoma), 1959
(Ngudu), 1987 (Entebbe).
Total Annual Rainfall
Rainfall (mm)
1600
1200
800
800
Rainfall (mm)
March-May Total Rainfall
600
400
200
800
October-December Total Rainfall
600
400
200
0
20
10
1930
1940
1950
1960
1970
1980
1990
2000
No. of stations
Rainfall (mm)
Fig. 7 Total rainfall (annual,
March–May, and October–December) (continuous line) for
the study stations with the average (dash-dash line) and the 5
year moving average (dash-dot
line) superimposed. The lower
panel shows the number of
stations used to compute the
mean
A closer examination of the plots in Fig. 7 shows that a
large portion of the variability in the annual rainfall is
contributed by the OctDec rainfall. Spectral analysis using
the Fast Fourier Transform shows that the total annual
series has peaks at 2.4, 3.5, 5.2 and 6.5 years. The MarMay
series has peaks at 4.0, 5.2, and 6.5 years while the OctDec
series has peaks at 2.4, 3.0, 5.2, 6.5 years. In the annual
rainfall series, the dominant time scale of variability is
5.2 years which corresponds with the dominant time scale
for the ENSO phenomena (Nicholson 1996). The 2.4 year
peak can be associated with the quasibiennial oscillation
(Rodhe and Virji 1976).
Temporal rainfall variability in the Lake Victoria Basin
129
Fig. 8 Trend results (marked A)
and step change results (marked
B) for the annual time series for
each of the four cases studied
(all years, 1941–1980, 1961–
1990, 1971–end)
4.2.2 Case II (1941–1980)
There is no evidence of trends in the data apart from Ngudu
which has a positive trend (Fig. 8, A-2). This is also true for
the step change results with only Ngudu showing a positive
jump (Fig. 8, B-2)
4.2.3 Case III (1961–1990)
The series for Bukoba and Kamenyamigo show significant
negative trend (Fig. 8, A-3) with no trends detected at all
the other stations.
For step changes, four stations (Jinja, Musoma, Ngudu
and Kamenyamigo) show significant negative step jumps
occurring 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 for
Biharamulo and Bukoba show a negative trend
(Fig. 8, A-4). The stations showing positive trend are
all located in the northern part of the basin while the
stations showing negative trend are located to the south.
130
Annual rainfall for two stations (Entebbe and Eldoret)
show positive step changes (Fig. 8, B-4) while annual
data for three stations (Jinja, Bungoma and Sotik) show
negative step changes.
4.3 Long rainfall season results
4.3.1 Case I (all available data at each station)
The MarMay rains have two stations (Kericho and
Kahunda) with a positive trend and two stations (Buvuma
and Eldoret) with a negative trend (Fig. 9, A-1). The
JanJun3 rainfall total for three stations (Eldoret, Sotik and
Fig. 9 Trend results (marked A)
and step change results (marked
B) for the long rainfall season
time series for each of the four
cases studied (all years, 1941–
1980, 1961–1990, 1971–end)
M. Kizza et al.
Musoma) have a positive trend while two stations (Buvuma
and Bungoma) have a negative trend. For stations with a
negative trend, only Buvuma shows similar trends in the
two time series. The sign of the trend for Eldoret is reversed
from one time series to the next (positive for the MarMay
rains and negative for JanJun3 rainfall).
For the MarMay period, five stations in the northeast of
the basin show step jumps (Fig. 9, B-1). Three of the jumps
are positive (Bungoma, Kitale, Kericho), while two are
negative (Buvuma, Eldoret). The step jump results show
that for the JanJun3 period, one station (Biharamulo) has a
positive jump while two stations (Bukoba and Bungoma)
have negative jumps.
Temporal rainfall variability in the Lake Victoria Basin
4.3.2 Case II (1941–1980)
Only one station (Kamenyamigo) has a positive trend in the
MarMay 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 the
MarMay series and the JanJun3 series (Fig. 9, B-2).
However, the signs are reversed with the MarMay series
having a positive jump while the JanJun3 rainfall total
series has a negative jump.
131
station (Bungoma) shows a negative trend for the OctDec
rains (Fig. 10, A-2). These stations are uniformly spread
within the basin with no clear spatial pattern. There is no
evidence of trend in the JulDec3 time series. None of the
OctDec time series shows any significant step changes
despite seven of them having significant trends (Fig. 10, B2). However, for the JulDec3 rainfall, two stations show
negative jumps (Kericho, Sotik) while Entebbe shows a
positive jump.
4.4.3 Case III (1961–1990)
4.3.3 Case III (1961–1990)
For the MarMay, Kamenyamigo has a positive trend and
Mwanza has a negative trend (Fig. 9, A-3). One station
(Kahunda) has a positive trend in the JanJun3 rainfall while
Bukoba has a negative trend in the same series. No step
jumps are detected in the MarMay series (Fig. 9, B-3). In
the JanJun3 rainfall, Bukoba has a positive jump, while
Buvuma and Ngudu have positive step jumps.
The OctDec rainfall totals for Kamenyaymigo and Kabale
show negative trends (Fig. 10, A-3) while there are no
trends detected in the JulDec3 rainfall series. The OctDec
rainfall series for 10 stations (Jinja, Kitale, Kericho, Shirati,
Musoma, Ngudu, Mwanza, Biharamulo, Kamenyamigo and
Entebbe) show negative jumps (Fig. 10, B-3). The JulDec3
rainfall for Entebbe, Jinja, Sotik and Mwanza show
negative jumps.
4.3.4 Case IV (1971–end of each station’s series)
4.4.4 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 for
Eldoret and Mwanza show negative trend. The JanJun3
rainfall for Jinja, Sotik, Musoma and Ngudu show positive
trend while those for Bungoma, Bukoba and Kabale have
negative trend (Fig. 9, A-4). Two stations (Kitale and
Kericho) have positive step jumps in their MarMay rains
while the series for Kericho has a negative jump. On the
other hand, step change results for the JanJun3 series show
that the jumps for Bungoma and Nugudu are negative and
positive respectively (Fig. 9, B-4).
The OctDec rains for Jinja and Eldoret both show a positive
trend (Fig. 10, A-4). The JulDec3 rainfall series for Sotik,
Musoma and Kabale have negative trends. The OctDec
rainfall 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 a
positive jump.
4.4 Short rainfall season results
4.4.1 Case I (all available data at each station)
The OctDec rains have eight stations with positive trend and
one with a negative trend (Fig. 10, A-1). On the other hand
the two stations from the JulDec3 show positive trend and
one station shows a negative trend.For the OctDec data, five
stations show positive step jumps and one station shows a
negative jump (Fig. 10, B-1). All the six stations with step
jumps also show trend apart from Eldoret. The JulDec3
rainfall 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
5 Discussion
The analysis for this study was based on data obtained from
different sources. In some cases, data from different sources
for a given station were combined to form a single time
series. Information on the type of instrument or any
instrument changes or changes in the station settings could
not be obtained and therefore we could not relate the results
to properties but we feel this is not necessary for the
validity of the current analysis. Data quality was checked
using visual inspection of rainfall plots with time to identify
clearly erroneous values and double mass plots to check for
non-homogeneity.
Twenty rainfall stations were included in the analysis to
test for the presence of significant trends and step changes
in the Lake Victoria basin. For each of the stations, five
time 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.
132
M. Kizza et al.
Fig. 10 Trend results (marked
A) and step change results
(marked B) for the short rainfall
season time series for each of
the four cases studied (all years,
1941–1980, 1961–1990, 1971–
end)
e. the ‘All years’ case, the 1940–1980 case, the 1961–1990
case and the 1971–end case. Therefore, a total of 400 cases
were analysed of which 65 (17%) had significant trends. Of
the stations showing significant trend, 43 cases (67%) are
positive trends and 22 (33%) are negative (Table 2), which
suggests that the positive trends predominate in the basin
over the twentieth century. For stations with significant
trend based on more than 60 years of recording the trend
represents an increase of 2–4 mm per year. This translates
to a rainfall increase of about 24% over the twentieth
century. Other studies have also found positive trends in the
Lake Victoria basin. Rodhe and Virji (1976) did not find
evidence of long-term trend in six gauges around the basin
which was probably due to the fact that the data used were
different from ours. However, Hulme et al. (2001)
computed a positive trend giving an increase of between
10–20% or more in the annual rainfall for Lake Victoria
basin over the period 1901–95.
It is clear that annual rainfall variability in the basin is
strongly influenced by variations in the ‘short rains’ which
generally occur from October to December. Most of the
stations whose annual rainfall data show trends also had
significant trends in their October to December rains. For
example, for the all-years-case, four of the stations whose
annual rainfall shows a positive trend (Fig. 8, A-1) also
have a positive trend in their OctDec rainfall (Fig. 10, A-1)
Temporal rainfall variability in the Lake Victoria Basin
133
Table 2 Number of stations with significant trend for all the periods
considered
Time series
Positive
Negative
Total
Annual
MarMay
OctDec
JanJun3
JulDec3
Total
10
8
16
7
2
43
5
4
4
5
4
22
15
12
20
12
6
65
suggesting a positive correlation between the two. A
scrutiny of Fig. 7 will show that the above-average mean
annual rainfall in the region since 1960 can be accounted
for by higher than average mean rainfall for the short rainy
period, with little or no trend observed in the rainfall for the
long rainy period. The total upward trend in the short rains
(OctDec) is about 30%. Several studies have shown that the
short rains in the region are strongly influenced by the
ENSO phenomenon (Mutai et al. 1998; Indeje et al. 2000).
Towards the end of the twentieth century, El Niño events
tended to be more frequent (WMO 2003), thereby explaining the upward trend in OctDec rainfall. However, other
factors like Indian Ocean sea surface temperatures have
also been proposed (Mutai et al. 1998; Camberlain et al.
2001; Mistry and Conway 2003).
There is a strong similarity between stations showing
significant trends and those showing significant step
changes in the annual and MarMay time series (Table 3).
This suggests that long-term changes in precipitation in the
study area are due to the presence of periods with increased
precipitation and are not purely monotonic in nature. The
OctDec step change results are strongly influenced by
results for the 1961–1990 time period, which show a
significant negative step change for most of the stations in
the basin which can be attributed to the anomalously heavy
rains in the early 1961 followed by relatively high rainfall
in 1962–1964 (Fig. 10, B-3). The extreme rains in 1961 and
1997 were studied by Conway (2002) who showed that the
two events were associated with a dipole-like reversal of
Indian Ocean sea surface temperatures. In addition, 1997
was a strong El Niño year. The 1961 and 1997 events were
Table 3 Number of stations with significant step jumps for all the
periods considered
Time series
Positive
Negative
Total
Annual
MarMay
OctDec
JanJun3
JulDec3
Total
9
6
6
3
4
28
8
3
11
6
9
37
17
9
17
9
13
65
similar in spatial and temporal characteristics and occurred
mainly in the short rains period (October–December). The
two events had far reaching hydrological impacts in the
regions (including record river flows and flooding) with
large 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 classified
by location of stations. Using this approach, 28 of the 43
cases with positive trends are located in the northern part of
the basin, while 15 are located in the southern part. On the
other hand, there are only weaker patterns in the distribution of the negative trends with 13 in the northern part and
9 in the southern part of the basin. The positive step jumps
are similarly distributed with 19 positive jumps in the
northern part and 9 positive jumps in the south. There are
also 26 negative jumps in the north and 11 negative jumps
in the south. Therefore, the trends and step jumps are more
likely to occur in the northern part of the basin than in the
south.
6 Conclusions
We have shown that the Lake Victoria basin experienced a
predominantly positive trend over the twentieth century.
The results are supported by other studies within the basin
and also within the East African region generally. This
means that assessments of future climate scenarios for the
basin should allow for wetter conditions. The magnitudes
and sign of the trends depend on the data period used in the
analysis and vary by station location with most of the
stations with positive trends being located in the northern to
north eastern part of the basin. However, the trends only
represent long-term conditions and short-term variability
may sometimes be more critical in assessing adaptation
mechanisms within the basin. The influence of short rains
on annual rainfall variability is discernible. Most of the
stations whose annual rainfall data had trend also had
significant trends in their October to December rains.
Step change results show a more balanced picture
between positive and negative changes within the basin.
The step-change test results show a clear similarity to the
trend test results, suggesting that the temporal rainfall
patterns are not entirely monotonic but step wise with
periods of dry years separated by wet years. The step
change results are dominated by two periods with anomalously high rainfall in 1961 and 1997.
The trend test results from analysing seasonal time series
are quite different when we consider fixed time periods
(March to May for the long rains and October to December
for the short rains) from when we consider variable time
periods representing the three consecutive months with
134
maximum rainfall totals in 6-month periods per year
(JanJun3 and JulDec3 series). This reflects the variability
of the rainfall seasons and may also reflect shifts in the
onset of the rainy season within the basin. Further studies
could shed more light on the pattern of such shifts.
The current study tested the existence of trends in only
the rainfall data. Additional work is needed to address the
issue of existence of trends in other hydrologic variables
like discharge and evapotranspiration in order to get a
clearer picture. Additional work is also needed to check
whether the observed trends are linked to climate change or
reflect natural variability.
Acknowledgements This work was performed within the doctoral
study programme of the first author at the Department of Earth
Sciences, Uppsala University, Sweden and Faculty of Technology,
Makerere University, Uganda. The study was funded by the Swedish
International Development Cooperation Agency (Sida) through the
Department for Research Cooperation (SAREC, Reference number
75007304). The authors are pleased to acknowledge this financial
support. Appreciation is also extended to the Departments of
Meteorology in Kenya, Tanzania and Uganda for granting accessibility to the rainfall data used in the study.
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