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Ogwang et al., 2016. 8:45-53. Journal of Environmental and Agricultural Sciences (ISSN: 2313-8629)
(45)
Journal of Environmental & Agricultural Sciences (JEAS) . Volume 8
Characteristics and Changes in SON Rainfall over Uganda (1901-2013)
Bob Alex Ogwang1,2,*, Alex Nimusiima3, Teddy Tindamanyire1, Margaret N. Serwanga1,
Godwin Ayesiga1, Moses Ojara1, Fred Ssebabi1, Gordon Gugwa1, Yusuf Nsubuga1, Rhoda Atim1,
Robert Kibwika1, Joseph Kiwuwa Balikudembe1, Herbert Kikonyogo1, Abubakar Kalema1, Victor Ongoma2,4,
Aggrey Taire1, Anne Kiryhabwe1, Musa Semujju1, Felix Einyu1, Rashid Kituusa1, Lawrence Aribo1
1Uganda National Meteorological Authority, P. O. Box 7025, Kampala, Uganda
2College of Atmospheric Science, Nanjing University of Information Science and Technology, Nanjing, Jiangsu,
210044, P. R. China
3Department of Geography, Geo-Informatics and Climatic Sciences, Makerere University, P. O. Box 7062,
Kampala, Uganda
4South Eastern Kenya University, P. O. Box 170-90200, Kitui, Kenya
Article History
Received
March 17, 2016
Published Online
August 20, 2016
Keywords:
Rainfall variability,
IOD,
EOF,
Hydrometeorology,
Sea surface
temperature,
Uganda.
Abstract: This study investigated the characteristics and changes in September-November (SON)
rainfall over Uganda. The dominant mode of variability of SON rainfall was identified by performing
Empirical orthogonal functions (EOF) analysis, using rainfall data from Climate Research Unit (CRU)
for the period 1901 to 2013. Results indicate that the dominant mode of variability of SON rainfall
exhibits a unimodal pattern, explaining 50.2% of the total variance. Mann-Kendall analysis was
deployed to examine sudden changes in SON rainfall over the country. The findings show that the
abrupt change in SON rainfall occurred in 1994. Further analysis reveal that SON rainfall over Uganda
has a correlation pattern with the sea surface temperature (SST) over Indian, which depicts the positive
phase of the Indian Ocean Dipole (IOD). Positive correlation is exhibited in the western IOD sub-
region, while negative correlation is shown in the southeastern IOD sub-region. Further study of the
both driest and wettest years during the investigated time span indicate that throughout the wettest year,
there were positive anomalies in the western sub-region, contrary to the driest year, when same sub-
region observed distinct negative anomalies. This illustrates that the positive phase of IOD enhances
SON rainfall over Uganda, as opposed to the negative phase which inhibits SON rainfall. The evolution
of the IOD can therefore be monitored for the improvement of SON rainfall forecasts, especially over
Uganda so as to avoid the losses associated with weather extremes.
*Corresponding authors: Bob Alex Ogwang: bob_ogwang@yahoo.co.uk
Cite this article as: Ogwang, B.A., A. Nimusiima, T. Tindamanyire, M.N. Serwanga, G. Ayesiga, M. Ojara, F.
Ssebabi, G. Gugwa, Y. Nsubuga, R. Atim, R. Kibwika, J.K. Balikudembe, H. Kikonyogo, A. Kalema, V. Ongoma, A.
Taire, A. Kiryhabwe, M. Semujju, F. Einyu, R. Kituusa and L. Aribo. 2016.Characteristics and changes in SON
rainfall over Uganda (1901-2013).Journal of Environmental & Agricultural Sciences. 8: 45-53.
Copyright © Ogwang et al.,2016
This is an open access article distributed under the terms of the Creative Commons
Attribution License, which permits unrestricted use, distribution, and reproduction in
any medium provided the original author and source are properly cited and credited.
1. Introduction
Rainfall is an imperative weather parameter in
Uganda, just like in many other developing nations
whose economy is based on rainfed agriculture
(Okoola, 1999; Funk and Brown, 2009; Ogwang et al,
2012). Occurrence of extreme weather events
adversely affects socio-economic activities in the
country (Kodamura, 1994; Nicholson, 1996;
Tumwesigye and Musiitwa, 2004; UNFCCC, 2006;
Otieno and Awange, 2006; IPCC, 2007).The
understanding of weather and climate of the country;
Uganda and east Africa region at large is thus
essential for the planning purpose to minimize huge
socio-economic losses associated with extreme
weather events. The common extreme weather events
in the region are floods and drought (Lyon and
DeWitt, 2012).
Uganda lies along the equator, in east
Africa(Figure 1).The region generally experiences
rainfall that has high variability at spatiotemporal
scales (Indeje et al., 2001). The mean rainfall over
Uganda is bimodal, with 'long rains' experienced from
March to May (MAM) whereas 'short rains' occur
during September to November (SON) (Yang et al.,
2015). The bimodal pattern in most parts of the
country is influenced by the movement of the Inter-
tropical Convergence Zone (ITCZ) following the
overhead sun (Okoola, 1996; Mutemi, 2003;
Basalirwa, 1995).
Research Article Open Access
Ogwang et al., 2016. 8:45-53. Journal of Environmental and Agricultural Sciences (ISSN: 2313-8629)
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Journal of Environmental & Agricultural Sciences (JEAS). Volume 8
Two distinct dry spells, first from June to August
and second December to February separates the wet
seasons. However, in sections of western and
northwestern Uganda tri-modal regimes exists,
mainly due to significant rainfall during July to
September. Mid tropospheric moist westerly flow
penetration from Atlantic Ocean and tropical Congo
Rainforest air mass were attributed to the third rainy
season (Davies et al., 1985; Mutai et al., 1998;
Ogwang et al., 2016; Owiti and Zhu 2012).
Numerous studies have highlighted linkage
between East African rainfall and El Niño Southern
Oscillation (ENSO) (Indeje, 2000; Jonawiak, 1988;
Nicholson, 1996; Ogallo, 1988; Ropelewski and
Halpert, 1987). Strong relationship between
evolutionary phases of ENSO and rainfall in East
Africa has reported earlier, highlighting significant
role of ENSO in defining the monthly and seasonal
patterns of rainfall in the region (Mutemi, 2003).
During El-Niño events incident of rainfall generally
enhanced in the region as opposed to suppressed
rainfall, which have a tendency to occur during La
Nina events.
It has been noted that most of the wettest periods
in the region of east Africa have been linked to the
Indian Ocean Dipole (IOD) and the coupled IOD-
ENSO events (Owiti et al., 2008; Bowden and
Semazzi, 2007; Black et al., 2003). Anomalous SST
gradient between the south eastern and western
equatorial Indian Ocean is termed as Dipole Mode
Index (DMI), which describe the intensity of IOD.
Positive IOD leads to positive DMI and vice versa
(Saji et al., 1999; Webster et al., 1998; Webster et al.,
1999).
To predict future climatic conditions under
changing climate, it is imperative to recognize and
understand climate characteristics and associated
changes from the past and present dataset. This is
crucial for decision making about adaptation and
mitigation measures of coping with climate change,
particularly in a least developed country like Uganda.
Furthermore, most of the previous studies done over
the East African region made use of the regional
indices, which may not necessarily capture the
climate events over Uganda, especially due to the
influence of the local factors.
Current study was established to investigate the
characteristics and changes in September to
November (SON) rainfall over Uganda. This is
expected to enhance knowledge on the climate of
Uganda and provide a basis for climate prediction and
projection over the region.
2. Data and Methodology
The datasets consisting of precipitation,
temperature and sea surface temperatures (SST) are
used in this study. The precipitation and temperature
data were obtained from the University of East
Anglia Climatic Research Unit (CRU TS3.22), which
is available (1901-2013) at global scale and a
resolution of 0.5° x 0.5° (Harris et al., 2013). In this
study, the mean SON rainfall (precipitation) over
Uganda was computed from the area average
precipitation over longitudes 29ºE - 36ºE and
latitudes 1.5ºS - 4.5º for the period 1901-2013.
Figure 1: Map showing the area of study and the elevation in meters (a) the position of Uganda in the African
continent; the red rectangle (b) Map of Uganda indicating that most areas are 1000 m above sea level.
Ogwang et al., 2016. 8:45-53. Journal of Environmental and Agricultural Sciences (ISSN: 2313-8629)
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Journal of Environmental & Agricultural Sciences (JEAS). Volume 8
Extended Reconstructed Sea Surface Temperature
(ERSST) Version-4 SST data acquired from the
National Oceanic and Atmospheric Administration
(NOAA)/National Climatic Data Center (NCDC) was
used (Smith et al., 2008). Analysis of the mean SON
SST in this study is done over longitudes 32E - 125E
and latitudes 32S - 25N.
Empirical Orthogonal Functions (EOF) analysis
was used to examine the dominant modes in SON
rainfall variability over Uganda. In order to prevent
dominating the eigenvectors from spatiotemporal
maximum variance dataset used was normalized
following Walsh and Mostek (1980).
Z
(Standardized rainfall anomaly) was calculated
as described in equation [1]
d
SXX
Z
[1]
where
X
denotes observed SON rainfall, 𝑋
long
term mean SON rainfall and
d
S
standard deviation in
the SON rainfall. The value of
Z
provides
instantaneous information about the deviation from 𝑋
.
In this study, anomaly (ANO) is computed as the
observed value of the variable (VAR) at a given time
minus the long term mean of the variable (VARLTM).
This is applied to both mean SON rainfall and mean
SON sea surface temperature (SST) as described in
equation [2];
ANO = VAR- VARLTM [2]
Non-parametric Mann-Kendall (MK) test was
utilized to detect trend, whereas the test statistic
distribution, was used to detect of abrupt change and
the significance of the changing trend in SON rainfall
over Uganda (Mann, 1945; Kendall, 1975). Mann-
Kendall-Sneyers test was utilized with forward and
backward sequential statistic from the progressive
analysis to investigate temporal change in the trend
(Sneyers, 1990; Jones et al., 2015).
Instead of direct comparison of the data values,
test performs relative comparison, whereas
standardized variable i.e., U(F) is characterized with
zero mean and unit standard deviation. In the plot of
sequential Mann-Kendall-Sneyers test, the confidence
limits of the standard normal
Z
values are at α = 5%,
with the upper (+1.96) and lower (-1.96) confidence
limits.
In this study MK test applied to detect abrupt
changes in the mean SON rainfall over Uganda as
well as examine the significance of the changing
trend of rainfall over the period 1901-2013.
Figure 2: The mean annual cycle of rainfall over the
period 1901-2013. The lines represent the mean rainfall
over Uganda (red), southern sector of Uganda (black)
and the northern sector of Uganda (blue).
3. Results and Discussion
3.1 Annual cycle
Mean annual rainfall cycle over the period 19011-
2013was analyzed to understand the annual rainfall
characteristics over Uganda (Fig. 2). Two main
precipitation peaks i.e., MAM season (March to May)
and SON season (September to November) were
observed. The northern sector exhibits a single
rainfall season, from March to November, whereas
the southern region experiences two distinct rainfall
peaks or rainfall seasons; MAM and SON (Ogwang
et al., 2014). The rainfall pattern in the northern
Uganda is expected since the area borders the areas of
the northern hemisphere that receives unimodal
rainfall in the months of June-August (JJA);
coinciding with northern hemisphere summer.
3.2Interannual variability
In order to understand the SON rainfall variability
over Uganda, the standardized anomaly of SON
rainfall (Fig. 3) was examined. Results show that the
highest mean SON rainfall over the period 1901-2013
was observed in 1961 (extremely wet), while the least
amount was noted in 1943 (extremely dry). However,
based on the standard deviation of the mean SON
rainfall of ≥1 (for wet years) and ≤(for dry years) as
used by Ogwang et al.(2015), eighteen wet years
were observed over the study period, while for dry
years, sixteen cases were noted.
More wet years (~16% of the total years) were
observed compared to dry years , which covered ~14%
of the total years in the study period. Further analysis
(Table 1) reveals that SON (MAM) season
contributes 29% (33%) of the mean annual rainfall
over Uganda and exhibits a higher (lower) standard
deviation of 24.5 mm (17.5 mm), implying a greater
(lower) interannual variability compared to MAM
(SON) season.
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Figure 3: The interannual variability of the mean SON rainfall over Uganda averaged between longitudes
29ºE -36ºE and latitudes 1.5ºS - 4.5º for the period 1901-2013.
3.3EOF analysis
Figure 4(a) shows the spatial component of the
first eigenvectors (EOF1) of rainfall in the SON
season, highlighting overall positive loadings
throughout the Uganda. Eastern part of the Uganda,
including Victoria and Kyogabasins Lakes, showed
the strongest loadings. Quasi-permanent trough due to
locally induced convection, land-lake thermal contrast
and orographic influence transformed rainfall pattern
over the Victoria Lake and hinterlands (Asnani, 1993).
Importantly this favored convection throughout the
year.
Weak loadings are exhibited in the southwestern
region. This may be attributed to weaker effects of
local systems in the annual cycle. Figure 4(b)
displays the first EOF time series (PC1)
corresponding to EOF1. It displays the variability
pattern (Figure 3) in the SON rainfall anomaly, with
the wettest and driest years captured as 1961 and
1943, respectively.
3.4Trend analysis
Results from Mann-Kendall analysis (Fig. 5)
indicated general decreasing trend in the mean SON
rainfall between 1901 and early 1940s. The observed
decrease became significant between 1933 and 1957.
After 1957, the mean rainfall exhibited an
insignificant increasing trend, until a sudden change
in rainfall was noted in the year 1994.
Table 1: Mean (Mn) and standard deviation (SD) of
rainfall for MAM and SON seasons, and their
respective contribution (CONT) to the mean annual
rainfall over Uganda based on CRU data for the period
1901-2013.
Mn (mm)
SD (mm)
CONT (%)
MAM
132.6
17.5
33%
SON
115.6
24.5
29%
Figure 4: (a) EOF’s first spatial mode, EOF1 (explains 50.2% of the total variance) of the mean SON rainfall
(b) its corresponding principal component (PC1).
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Journal of Environmental & Agricultural Sciences (JEAS). Volume 8
Table 2. The mean SON rainfall(RF) and temperature (TMP), showing the long term mean (LTM, 1901 - 2013),
the mean values (1901-1993) before the change in 1994, and those after the change (1995 - 2013).
RF (mm)
TMP (°C)
RFD(mm)
TMPD(°C)
LTM (1901-2013)
115.6
22.4
Before change (1901-1993)
111.9
22.2
After change (1995-2013)
132.6
23.3
20.7
1.1
Where RF is rainfall, LTM is long term mean, RFD and TMPD denote rainfall and temperature differences between the mean
values before and after the observed change in 1994.
Fig. 5: Mann-Kendall analysis of the mean SON
rainfall over Uganda for the period 1901-2013. The
dashed lines, above (A) and below (B) the line denoting
zero (dotted) represent critical values at 95% confidence
level. The sequential Mann-Kendall test statistic;
forward sequential statistic and back sequential statistic
are denoted by UF and UB, respectively.
After this abrupt change, the increasing rainfall
trend continued and became significant in 1999. This
trend persisted significantly and continued until 2013,
but with a temporary reduction in rainfall between
2002 and 2004.
Analysis of the mean rainfall recorded pre- and
post-1994 showed the 20.7 mm higher rainfall after
the 1994 (i.e., 1995-2013), when comparing with the
pre-1994 mean rainfall (1901-1993). Further analysis
showed ~1.1ºC warming trend in the region (Table 2)
after anomalous 1994 season, compared to the mean
value for the pre-1994 period (1901-1993).
3.5Case studies of 1943 and 1961
To understand the spatial distribution of rainfall
during the identified wettest (1961) and driest (1943)
years, as compared to the long term mean (LTM) of
SON rainfall, anomalies were computed for the both
years. The result from the LTM rainfall analysis
indicates that higher rainfall amounts are usually
received in the western Uganda (Fig. 6).
Fig. 6: (a) The long term mean (LTM) SON rainfall
(mm) over the period 1901-2013, (b) rainfall anomaly
(mm) in 1961 and (c) rainfall anomaly (mm) in 1943.
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Journal of Environmental & Agricultural Sciences (JEAS). Volume 8
Figure 7: The mean annual cycle of rainfall over Uganda for the wettest year (1961, blue line), the driest year
(1943, red line) and the long term mean (LTM, black line) over the period 1901-2013. The green-dotted line
depict SON season.
Analysis of the spatial circulation of the
anomalous rainfall patterns indicate that during the
wettest year; 1961 (Fig. 6b), the entire country of
Uganda experienced above normal rainfall, with the
highest rainfall recorded over the Victoria Lake basin,
the eastern and northwestern regions of Uganda
(yellow dashed lines). On the other hand, during the
driest year; 1943 (Fig. 6b), the whole country
received below normal rainfall, with the least rainfall
amounts recorded over the northwestern sector of
Uganda.
Analysis of rainfall temporal distribution during
1961 and 1943 (Fig. 7) showed rainfall amounts
received in 1943 from September through November
were far below the long term mean rainfall, as
opposed to that received in 1961, which exhibited far
above the long term mean values. The relationship
between the mean SON rainfall anomaly and the
mean SON sea surface temperature (SST) anomaly
over Indian Ocean was examined to understand the
possible linkage between SON rainfall and SST
anomalies over the IOD sub-regions.
Correlation results (Fig. 8) indicate that the
relationship between the mean SON rainfall anomaly
over Uganda and the mean SON SST anomaly over
Indian Ocean captures the IOD pattern, with
significant positive and negative correlations in the
western sector of Indian Ocean and south eastern
sector. This exhibits the positive phase of IOD,
during which above normal rainfall is recorded in the
region.
Further, results show that during the year 1961
(Fig. 9a), the SST anomalies depicted the positive
phase of IOD, where positive anomalies in SST are
shown in the western sub-region of IOD, as opposed
to the negative anomalies in SST of the southeastern
sub-region of IOD. The positive IOD phase is
connected with above average rainfall in the region,
hence wettest year. On the other hand, during the
driest year (Fig. 9b), the negative IOD phase is
exhibited over Indian Ocean, where positive
anomalies in SST were observed in the southeastern
sector, with negative SST anomalies noted in the
western sector of the Indian Ocean. The negative
phase of IOD results in below normal rainfall over
Uganda and the rest of East Africa (Saji et al, 1999).
Figure 8: The correlation between the mean SON
rainfall anomaly over Uganda and the mean SON
SST anomaly over Indian Ocean for the period 1901
- 2013.
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Journal of Environmental & Agricultural Sciences (JEAS). Volume 8
Figure 9: The mean SON SST anomaly during (a) the wettest year (1961) and (b) the driest year (1943).
4. Conclusion
Present study investigates the characteristics and
abrupt changes in September- November SON
rainfall over Uganda. Dominant modes of variability
of rainfall were identified by performing empirical
orthogonal function (EOF) analysis, using rainfall
data from Climate Research Unit for the period 1901
to 2013. Results indicate that the dominant mode of
variability of SON rainfall exhibits a unimodal
pattern, explaining 50.2% of the total variance.
During the study period, there were generally more
wet years than dry years. SON rainfall was shown to
exhibit higher interannual variability than March to
May rainfall. Mann-Kendall analysis was deployed to
examine sudden changes in SON rainfall over the
country. The findings show that the abrupt change in
SON rainfall occurred in 1994.
Further analysis reveal that the mean SON rainfall
over Uganda has a correlation pattern with sea surface
temperature (SST) over Indian Ocean, which depicts
the positive phase of the Indian Ocean Dipole (IOD),
exhibiting positive correlation in the western IOD
sub-region, whereas negative correlation for the
southeastern sub-region. This indicates that the
positive phase of IOD enhances SON rainfall over
Uganda, as opposed to the negative phase which
inhibits SON rainfall.
Case studies of the driest and wettest years in the
study period indicate that during the wettest year,
there were positive anomalies in the western sub-
region, contrary to the driest year, characterized with
the negative anomalies in the same sub-region. The
evolution of IOD can thus be monitored for the
improvement of SON rainfall forecasts, especially
over Uganda so as to avoid the losses associated with
weather extremes.
Acknowledgements: The authors are thankful to
Uganda National Meteorological Authority for the
financial support and the required facilities for data
analysis. Special gratitude to the sources of the datasets
used in the present study; NOAA and Climate Research
Unit.
Competing Interests: The authors declare that there is
no conflict of interest.
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