RELATIONSHIP BETWEEN ELNINO SOUTHERN OSCILLATION (ENSO) AND THE
OCCURENCES OF FLOODS IN THE LOWER SHIRE
Mtilatira
L.M.
(Mrs),
Department
of
Meteorological
Services,
P.O.
Box
1808,
Blantyre,
E-mail:
lucyngombe@yahoo.com, Tel: 01 822 014/08 862 010
ABSTRACT
The world’s climate is the system of interaction among many components such as the
atmosphere, oceans, biosphere, ice and land. Climate has large natural variability on all time and
space scales. It is made of extreme events as well as periods of normal conditions. Extreme
events have been critical and destructive in nature. These extreme conditions include heavy rains
that result into floods; and prolonged dry spells that lead to drought. It is established that climate
variability can also be due to El Nino Southern Oscillation (ENSO). ENSO has two phases, the
warm phase is called El Nino and cold phase is La Nina. El Nino is a disruption of the oceanatmosphere system in the equatorial pacific, where the eastern pacific experiences warmer
oceans waters, while the western pacific becomes cooler. La Nina is actually the opposite of the
El Nino phenomenon. It has been established that El Nino is associated with reduced rainfall
conditions over Southern Africa and wet conditions over Eastern Africa while La Nina is
associated with wet conditions over Southern Africa and reduced rains over Eastern Africa.
Malawi is affected by floods each and every year. One of the areas highly affected is the Shire
Valley. This paper is analyzing the relationship between floods occurrences along the Shire
Valley in relation to ENSO. The analysis of occurrences and magnitude of floods is done with
respect to the episodes of La Nina and El Nino. Temperature and rainfall analysis in the Shire
River catchment is also done to find out if the trend of floods is due to change of climate or
climate variability due to ENSO.
The results show a high correlation between ENSO and December, January, February and March
(DJFM) total rainfall. Though there are other factors that influence rainfall over Malawi, ENSO
can be used as an indicator for predicting rainfall amounts over Malawi. These results can assist
in establishing mitigating and adaptation measures to reduce risk caused by floods in the lower
Shire Valley for the benefit of the community and river users such as marine and fishermen.
Future projections of floods in the region are also set.
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INTRODUCTION
Climate is the average condition of the atmosphere near the earth’s surface over a long period of
time. It has large natural variability on all time and space scales. Climate variability refers to
time scale ranging from months to decades, falling between the extremes of daily weather and
long term climate change. It is made of extreme events as well as periods of normal conditions.
Extreme events have been critical and destructive in nature. These extreme conditions include
heavy rains that result in floods; and prolonged dry spells that lead to drought. It is established
that climate variability can also be due to El Nino Southern Oscillation (ENSO).ENSO has two
phases, the warm phase is called El Nino and cold phase is La Nina. El Nino is a disruption of
the ocean-atmosphere systems in the equatorial Pacific, where the eastern Pacific experiences
warmer ocean waters, while the western Pacific cooler waters wells up. El Nino occurs every 2
to 7 years. Approximately 14 El Nino events affected the world between 1950 and 2003.
Amongst them was the 1997/98 event, by many measures the strongest thus far this century. La
Nina is actually the opposite of the El Nino phenomenon.
Figure 1a shows how temperatures and rainfall behave during a neural year along the equatorial
pacific comparison with an El Nino year in figure 1b.
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The changes in the Pacific Ocean are represented by the term “El Nino/La Nina”, while changes
in the atmosphere are known as the “Southern Oscillation” or El-Nino Southern Oscillation
(ENSO).
ENSO influences both rainfall and temperature pattern worldwide. El-Nino conditions are known
to cause less rainfall over southern Africa and south-central Africa, mainly Zambia, Zimbabwe,
Mozambique and Botswana. It was also established that El Nino brings increased longer rains
from March to May over Eastern Africa including Tanzania and Kenya. It has also been
established that not all El Nino episodes cause lower rainfall amounts over Southern Africa as
with during 1997/1998. This season some zones over southern Africa experienced good rains. La
Nina usually brings normal or above-normal rainfall over Southern Africa. South Africa is
divided into rainfall regions and has been found that each region has a different correlation with
ENSO. It was established that ENSO explains only approximately 30% of rainfall over South
Africa.
Floods are one of the weather and climate natural disasters that are caused by extreme weather
conditions. Flood distribution is affected by rainfall distribution, intensity and duration. It is also
affected by land surface such as watershed size, shape, topography, geology, land use
management etc.
Global data indicate that in the last decade natural hazards occurred more frequently than in the
past and were more destructive. Weather-related hazards continue to increase from annual
average of 200 per year between 1993 and 1997, to 331 per year between 1998 and 2002. The
number of people affected has also increased from 608 million people affected in 2002 compared
with the annual average of 200 million in the previous decade. Although compared to the 1990’s,
reported global deaths from the natural disasters had fallen to 24,500 people in 2002 against
yearly average of 62,000 in the previous decade. Events such as the recent Tsunami add another
dimension to the above figures.
Floods are affecting Malawi particularly Shire Valley each and every year. Statistics show that
from 1985/86 to 1994/95 only one flood episode was reported, but between 1995/96 to 2004/05
seven flood episodes were reported from Lower Shire.
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Floods like any other natural disasters take a direct toll on lives, health, livelihoods, assets and
infrastructure. Severe and repeated natural disaster can cause vulnerable households into
persistent poverty.
In Malawi floods are triggered by severe thunderstorms that are due to Inter-Tropical
Convergence Zone (ITCZ), Congo air mass and tropical cyclones that evolve in the Mozambique
Channel mainly during December, January, February, and March. Floods can occur anywhere
after heavy rain events, but floodplains are more vulnerable. Floods come in all sorts of forms
from small flash floods to sheets of water covering huge areas of land.
This paper looks at the relationship between ENSO and the occurrences of floods at Lower Shire.
Particularly it is trying to establish if there is any correlation between the occurrences of floods
and El Nino and La Nina episodes.
METHODOLOGY
Rainfall and temperature data from Bvumbwe, Chileka and Ngabu were used. Bvumbwe is
located within the Southern Highlands climate zone. Chileka is from middle Shire and Ngabu
lower Shire Valley basin. Both Chileka and Ngabu are within the Shire Climate zone.
Precipitation Analysis
Precipitation analysis was done during strong El Nino, strong La Nina and neutral years from
1969 to 2005. Table 1 below is showing the years used in this analysis.
Table 1 is showing strong El-Nino, strong Lanina and Neutral years.
Strong El Nino Years
Strong Lanina Years
Neutral years
1972/73,1977/78,
1973/74, 1975/76, 1988/89 and
1978/79, 1979/80, 1980/81, 1981/82
1982/83,1987/88,
1998/99
1983/84,
1993/94,1994/95 and 1997/98
1984/85,1985/86,
1986/87,
1989/90, 1990/91, 1995/96, 1996/97,
1999/00, 2001/02 and 2003/04.
The analysis was done to find out the amount and distribution of rainfall during these years and
their link to flood occurrences.
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Precipitation Return Period Analysis
Precipitation return period of 24 hour rainfall exceeding 80mm for the same three stations
Bvumbwe, Ngabu and Chileka were done. 80mm was chosen using the clay loam soil and also
putting into consideration that there are other factors that contribute to infiltration rate, such as
water storage capacity of soil, land management, vegetation, topography etc. The return period
was used to find the probability of occurrence of rainfall above 80 mm at these three stations.
Probability P is given as:
P=1/T, where T=return period
Correlation analysis between Southern Oscillation Index (SOI) and total rainfall of
December, January, February and March (DJFM)
This analysis was done to find out if there is any correlation between SOI and DJFM total
rainfall.
Temperature Analysis
Temperature analysis at the same three stations Bvumbwe, Chileka and Ngabu were also done to
establish if climate is really changing in Malawi and also to find out if the frequency of floods is
due to climate change or climate variability.
Flood Analysis
Flood analysis along Shire Valley from 1960 to 2004 was done to obtain the flood frequencies
and magnitudes in relation to Lanina, El Nino and neutral years. The magnitude was looked at in
terms of number of people affected.
RESULTS
DJFM rainfall performance during El-Nino ad Lanina years
Figure 2a, 2b and 2c are showing the performance of total DJFM rainfall during both strong El
Nino and strong Lanina years at Chileka, Bvumbwe and Ngabu in relation to normal DJFM
rainfall amount.
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Table 2 shows DJFM rainfall increase or decrease during strong La Nina and strong El-Nino
years
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Table 2 DJFM rainfall increase/decrease during strong Lanina and El-Nino years.
Station
DJFM total rainfall increase
DJFM rainfall increase/decrease (mm) during
(mm) during Lanino years
El-Nino years
Ngabu
210
19.88571
Chileka
213
-18.7291
Bvumbwe
364
-60.1645
Probability of the percentage of normal of total December, January, February and March (DJFM)
rainfall to be greater than normal during strong La-Nina years.
Table 3 Chances for total DJFM rainfall to be greater than normal
Probability of percentage of normal of total DJFM rainfall to be greater
than normal
Station
Strong El Nino years
Strong La Nina years
Ngabu
70%
100%
Chileka
70%
100%
Bvumbwe
70%
100%
Return period and probability for 24 hour rainfall to exceed 80 mm
Table 4: Return period and probability for 24 hours rainfall to exceed 80 mm
Station
Return period (years)
Probability of 24 hour rainfall to be
>80mm in a year
Ngabu
2.5
40%
Chileka
3.3
30%
Bvumbwe
1.8
50%
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Correlation analysis between SOI and total DJFN rainfall
Table 5 shows the correlation between SOI and Total DJFM rainfall. Figures 3a, b and c are the
scatter plot of SOI and total DJFM total rainfall at Chileka, Bvumbwe nad Ngabu respectively.
Table 5: Correlation between SOI and Total DJFM rainfall
Station
Correlation
Ngabu
0.3140
Chileka
0.3301
Bvumbwe
0.4280
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Fig 3c
Temperature Analysis
Figures 4a, b, and c below are temperature deviation during the month of December at
Bvumbwe, Chileka and Ngabu
Fig. 4a December temperature deviation at Bvumbwe. Fig. 4b December temperature deviation at Chileka
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Fig. 4c December temperature deviation at Ngebu
Flood Analysis
Table 6 is the probability of the occurrence of floods during Lanina, strong El Nino and neutral
years and figure 3.5 shows the number of people affected by floods in the Lower Shire.
Table 6 flood occurrence probability
Probability of flood occurrences
Strong El Nino years
75%
Strong La-Nina years
100%
Neutral years
80%
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Figure 5 Number of people affected by floods
DISCUSSION
The result in figures 2a, b and c show that during Lanina years total DJFM rainfall is always
higher than the normal at all the three stations used. On average the DJFM rainfall amount
increase during Lanina years is about 364 mm at Bvumbwe which is one of the upland stations
while at Ngabu and Chileka the increase is about 210 and 213 mm respectively. There is also
100% chance for DJFM total rainfall amounts to above normal at all the three stations as shown
in table 3 above. Strong El Nino years are not showing consistent trend because some years are
below normal and others are above normal rains as shown in figure 2a, b and c. This is clearly
observed at all three stations indicating that not all El Nino years have below normal rains. At
Bvumbwe, during El Nino years, the departure from normal is not much. It is seen that DJFM
total rainfall just oscillates around the normal. But at Chileka and Ngabu, there is a great
departure from normal. However on average, there is a slight decrease of total DJFM rainfall
amount during strong El Nino at Chileka and Bvumbwe as shown in table 2 and a very slight
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increase at Ngabu. Table 3 also shows that there is 70% chance during strong El Nino years for
total DJFM rains to be above normal at all the three stations.
There is a 50% chance every year for 24 hour rainfall to exceed 80mm at Bvumbwe. This
implies that the frequency of 24 hour rainfall exceeding 80mm is every 1.8 years as is shown in
table 4. While the return period for Ngabu and Chileka are 2.5 and 3.3 years respectively. This is
an indication that floods mostly occur due to rains from the higher grounds. Floods originating
from low lands range from 2.5 to 3.3 years.
There is high correlation between SOI and DJFM total rainfall over the three stations used. For
example at Bvumbwe, the correlation is about 43%. Ngabu and Chileka, the stations from Shire
Valley Climatic region, have correlation of about 33% as shown in table 5. Therefore SOI can be
used as an indicator to predict approximate total DJFM rainfall using linear regression analysis in
figures 3a, b and c.
Figures 4a, b and c show that temperature is increasing. Greater changes are at Bvumbwe and
Chileka, but less increases at Ngabu. The mean position has slightly shifted to higher values.
The trend lines are also showing positive increase of temperature at all stations during all twelve
months, with fewer increases during the month of October.
There is 100% chance for floods to occur during Lanina years as indicated in table 6, and also
80% during neutral years and 75% during El Nino years.
Ther is 85% increase in the frequency of floods in the Lower Shire from 1994/95 to 2004/05.
There is also about 99% increase in the number of people affected by floods from 1994/95 to
2004/05. The significant damage is in 1996/97, where 100,000 people were affected.
CONCLUSION
This research has brought out several aspects of flooding in the Lower Shire. The results show
that, strong Lanina years bring increased total DJFM rainfall amounts. Very high increases in
rainfall amounts are at uplands, which is part of the catchment area for Shire Valley. Therefore
there are higher flood occurrences during Lanina and they can be caused highly due to rains that
fall at upper lands such as Bvumbwe. The results also show that, strong Lanina years means a
flood year. The probability of 24 hour rainfall that exceeds 80mm is also higher over highlands
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than low lying areas. It is also evident that not all flooding events occur during Lanina years. El
Nino and neutral years also have high chances of flood occurrences. Malawi has to be equally
prepared every year because the frequency of floods has increased a lot during the past decade
and is expected to continue.
The results also show that ENSO contributes to the performance of DJFM rainfall, so their
variations contribute to Malawi rainfall. That is why during Lanina years there is 100% chance
of flood occurrence. Then SOI can be used as indicators for total DJFM rainfall, but should bear
in mind that there are many conditions that influence rainfall performance.
The results indicate that climate change is also affecting Malawi; this is evident by the way
temperature is changing. All the three stations throughout all the 12 months show an increase in
temperature. So it can be concluded that as much as climate variability contributes to the
frequency of flooding, climate change also has contributed a lot by increasing extreme weather
and climate events.
RECOMMENDATIONS
The frequency of flooding has increased tremendously during the last decade. There are high
changes of flood occurrences along Shire Valley every year. So there is need to have a well
organized warning system that would benefit the community. Floods can be deadly – particularly
when they arrive without warning. Warning procedures have to be put in place with the
assistance from stakeholders. For instance ENSO prediction can act as indicator of chances of
flood occurrence. Warnings serve life and property, at the same time they help in decision
making by both the community and the government in mitigating the risk.
It has already been discovered that moving people out of the flood prone areas is a failure. This
is an indication that people benefit from these flood episodes. For example floods bring fertile
alluvial soil that is good for winter cropping. So if floods are managed well they can be more of a
benefit than a threat.
Flood mitigation and adaptation management call for multidisciplinary approach at all levels.
Stakeholders, together have to find ways of making life and development sustainable in the flood
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plains. Development activities have to be coordinated in such a way that they do not contribute
to increasing the intensity of the extent of floods.
Floods are also affecting many parts of the country including Karonga, Salima, Phalombe and
Nkhotakota, so it is my recommendation to do this kind of analysis to these areas as well.
The analysis looked at Bvumbwe only as one of the stations from upland, but there is need to
apply the same kind of analysis to some more stations from highlands, such as Mimosa.
References
Climate Change Information Kit, based on IPCC’s Climate Change: 2001, Published by UNEP
and UNFCCC. July 2002
En.wikipedia.org/wiki/El Nino
www.weathersa.co.za/References/elnino
Mathur V.K. and the WMO Secretariat, “Sustainable Development through Integrated Flood
Management “ in the journal of the world meteorological organization, volume
55(3), July 2006, pp 164
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