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72
Meteorological Droughts from 1987-2017 in Yabello and El-Woye Areas of Borana,
Oromia Region, Ethiopia
Gelgelo Wako Duba, Solomon Tekalign Demissie, Tegegne Sishaw Emiru
[1]
Abstract
Droughts originate from deficiency in precipitation over extended periods of time and affect
approximately 60% of the world‘s population. They are the major obstacle to viable rain-fed
agriculture. The study was undertaken to investigate the magnitude, frequency and trends of
drought incidents in lowlands of the Borana Zone, southern Ethiopia from 1987 to 2017.
Coefficient of variation, standard precipitation index, Mann–Kendal test, and Drought Index
Calculator were used to analyse the rainfall data. The SPI for the main rainy season, short rainy
season and annual period were computed. Accordingly, 1998, 2002, 2003, 2006, 2015 and 2016
were drought periods in the study area. During the 1987-2017 period, almost 50% of the period
faced drought and the year 2006 saw the most severe and extreme drought episode in the study
area with SPI value of -2.14 at El-Woye and -2.01 at Yabello. Except for the annual rainfall CV
at Yabello, which is 21.2% medium variability, the short and main rainfall seasons CV of both
Yabello and El-Woye as well as the annual rainfall of El-Woye showed high rainfall variability
as the CV value is over 30%. However, all timescales, except the two-month timescale at El-
Woye, were statistically insignificant (p<0.05). Tendencies of drought during the main rainy
season were observed to increase while for that of the short rainy season the annual scale of the
Borana area showed a decreasing trend. Therefore, stakeholders at local, regional and national
levels are required to take proper adaptive and mitigating measures and forecasting systems to
advance warning and proactive actions in favor of the communities and the environment and the
region in particular and elsewhere at large. Continuous and persistent drought monitoring is
essential to determine when droughts begin and end. Further studies in relation to using SPI as a
standalone method of drought analysis and interpretation are recommended for further
conformity to the scenario of the environment. Such studies are important to refine the existing
knowledge for proper representation of the study area.
Keywords: Borana, drought, Drought Index Calculator, Mann–Kendall, Standardized
Precipitation Index (SPI), trends
[1]
Corresponding author, e-mail address: tegegnesishaw@gmail.com
(Haramaya University, Ethiopia)
1. Introduction
Drought is a deficiency of precipitation from expected or normal conditions that extends over a
season or longer periods of time and where water is thus insufficient to meet the needs of human
activities (NRC, 2007). Drought is generally characterised as a short-term meteorological
occurrence, which stems from a shortage of rainfall for a long period of time compared with its
average and normal conditions (Mondol et al., 2015). Furthermore, Wilhite (2002) described
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73
drought as a normal, recurring phenomenon of climate that practically occurs in all regions of the
world.
Droughts, which originate from deficiency in precipitation over extended periods of time and
affect approximately 60 per cent of the world‘s population, are the major constraints to viable
rain-fed agriculture particularly in the ASALs (Huho and Mugalavai, 2010). The available
estimates on drought impacts suggest that, during the period 1998–2017 following the flood,
which affected a further 1.5 billion resulting in a huge toll to humanity, killed about 21,563
people (EM-DAT, 2018). There are four types of drought, namely meteorological, agricultural,
hydrological and socio-economic droughts (Wilhite and Glantz, 1985; WMO, 2006).
Meteorological drought is deficiency of rainfall which can be observed immediately (Panu and
Sharma, 2002). Meteorological drought is based on the degree of dryness or rainfall deficit and
the length of the dry period (NOAA, 2019). According to Swain et al. (2018), meteorological
drought occurs when there is a prolonged time with less than average precipitation. The
magnitude and severity of meteorological drought impacting social and economic systems of any
human society will be dependent on the underlying vulnerability and are exposed to the event as
well as climate and weather patterns that determine the frequency and severity of the event
(Wilhite et al., 2007).
In the Borana Lowland, prolonged and recurrent drought is the most typical event of climate
change. Remarkably, drought cycles have been shortened that increase their risks (Oxfam, 2011).
As a result, reproductive performance of livestock was reduced due to the fact that livestock
mortality is increasing (Herrero et al., 2010). In Borana Zone, drought occurs every 1–2 years,
compared to every 6–8 years in the past (Riche et al., 2009). This threatens the livestock
production system, which recurrently erodes the size of livestock before full recovery is achieved
(Ayana, 2007).
Over the past few decades there have been many studies with regard to drought which have been
carried out in different parts of pastoralist areas and in the study area (Abera and Aklilu, 2012;
Paul, 2013; Opiyo et al., 2014; Dirriba and Jema, 2015; Argaw et al., 2015; Dirriba, 2016). Most
of these studies have been devoted to analyzing the pastorals’ and agro-pastorals’ strategies for
coping with impacts of the droughts. Paul (2013) also studied socio- economic impacts of
droughts, coping strategies and government interventions in Marsabit County, Kenya. Moreover,
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74
Dirriba (2016) studied the impacts of droughts and conventional coping strategies of the Borana
Community in Yabello and Dirre districts, Ethiopia, where the survey results showed that
drought severely affected the livestock of the pastoralists. However, these studies failed to
identify the extent of annual rainfall variability in the short season and main season as well as
their trends and variations of drought in the study area for over three decades. Hence, the current
study focused on assessing meteorological drought for the period 1987-2017 by including short
and main rainy seasons as well as annual rainfall distribution using different indices and two
meteorological stations in the Borana Lowlands, Oromia Region, Ethiopia.
2. Materials and Methods
2.1. Description of Study Area
The study was conducted in Yabello and El-Woye districts of Borana zone in Oromia regional
state. Borana zone is located in southern Ethiopia between 36’-6° 38’ N and 36° 43’- 41° 40’
E, 570 kilometers away from Addis Ababa, the capital of Ethiopia (Figure 1). The study area
experiences a bi-modal monsoon rainfall type, where 60% of the 300-900mm annual rainfall
occurs during March to May (ganna
*
) and 40% between September and October (hagaya
**
)
(Zemenu, 2009). The same source further indicates that the average annual temperature of the
area is 24.5
0
C. The corresponding amounts of maximum and minimum temperatures are 26.83
0
C
and 20.4
0
C, respectively.
The livelihood of 60% of the population depends on pastoralism, while the livelihood of the
remaining 40% of the population relies on agro-pastoralism. The pastoralists and agro-
pastoralists in Borana are the owners of rich and respected cultural heritage and customary
institutions, in which they are involved in local administration, make rules and regulations of
social relationship and resource management. Nevertheless, the indigenous knowledge and
customary institutions to manage the resources have been adversely challenged by different
external political factors and natural phenomena like droughts (BoZA, 2013).
*
ganna= main rainy season;
**
hagaya= short rainy season
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Figure 1: Location map of the study area
2.2. Research Design, Data Sources, and Methods of Analysis
The study used mixed methods research, particularly the concurrent triangulation approach as
research design. The purpose of mixed methods research is to build on the synergy and strength
that exists between quantitative and qualitative research methods to understand a phenomenon
more fully than is possible using either quantitative or qualitative methods alone (Gay et al.,
2012).
Rrainfall data of Yabello and El-Woye stations of Borana area for the period 1987-2017 were
obtained from Ethiopian National Meteorological Agency (NMA) and used in the computations
related to meteorological drought. The data of the two stations of Borana area are used to
minimize or avoid the effects of spatio-temporal variations of rainfall due to gaps in the data as
well as for the complementation and maintenance of quality of the meteorological data in the
Borana area. Data were generated following the first order Markov chain model using INSTAT
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76
plus (v3. 6) Software (Stern et al., 2006) to fill the missing values. Drought Index Calculator
(DrinC) was used to analyze the SPI (Tigkas et al., 2014).
SPI Computation
The SPI is a z-score and represents the drought event departure from the mean, expressed in
standard deviation units. Standard Precipitation Index is used to identify the meteorological
drought or deficit of precipitation for multiple timescales in the stations studied (McKee et al.,
1993). The SPI value provides a comparison of the rainfall over a specific period with the
rainfall totals from the same period for all the years included in the historical record (Shahid,
2008; Yimer et al., 2017). To calculate the SPI, a long-term precipitation record at the desired
station is first fitted to a probability distribution (e.g. gamma distribution), which is then
transformed into a normal distribution so that the mean SPI is zero (McKee et al., 1993; Edwards
and McKee, 1997). It is expressed mathematically as follows:

ij
=


Where SPI
ij
is the SPI of the i
th
month at the j
th
timescale, X
ij
is rainfall total for the i
th
month at
the j
th
timescale, μ
ij
and a
ij
are the long-term mean and standard deviation associated with the i
th
month at the j
th
timescale, respectively.
Table 1: SPI based drought severity class
Index Value
Class SPI
Value
Drought severity class
SPI ≥2.0 Extremely wet
1.5≤SPI<2.0 Very wet Above 0 No drought
1.0≤SPI≤1.5 Moderately wet
-1.0<SPI<1.0 Nearly normal 0.0 to -0.99 Slight drought
-1.5<SPI≤-1.0 Moderate dry -1.0 to -1.49 Moderate drought
-2.0<SPI≤-1.5 Severely dry -1.5 to -1.99 Severe drought
SPI≤-2.0 Extremely dry -2 and less Extreme drought
Source: Adapted from Mondol et al. (2015) and McKee et al. (1993)
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Drought Index Calculator (DrinC) which was developed by the Laboratory of Reclamation
Works and Water Resources Management, National Technical University of Athens was used to
analyze Standard Precipitation Index (SPI) (Tigkas et al., 2014). McKee et al. (1993) first
defined the index and the criteria for drought classifications by SPI values. In the present
research, the modified classification of Mondol et al. (2015) is used (Table 1).
Although SPI can be calculated from 1 month up to 72 months, 1-24 months is the best practical
range of application (Guttman, 1999; WMO, 2012). The SPI values were computed at three
timescales including 2 months (SPI-2), 3 months (SPI-3) and 12 months or annual (SPI-12) as
used by Yimer et al. (2017) and also recommended in WMO (2012). SPI-3 was used by
Almedeij (2014) to assess drought characteristics in Kuwait.
Specifically, SPI-2, SPI-3 and SPI-12 were used to assess meteorological droughts and related
water shortages in the main rainy (ganna) season (March-May), short rainy (hagaya) season and
the annual, respectively, in Borana area. A drought occurs when the SPI is consecutively
negative and its value reaches an intensity of -1 or less and ends when the SPI becomes positive.
For each month of the calendar year, new data series were created, with the elements equal to the
corresponding rainfall moving sums (Degefu and Bewket, 2013).
Trend detection and analysis
The study employed Mann-Kendall’s (MK) test for rainfall trend analysis. Mann-Kendall’s test
checks the hypothesis of no trend versus the alternative hypothesis of an increasing or decreasing
trend (Collins, 2009). The study of Yue et al. (2002) and Koricha et al. (2012) noted that these
tests have to identify trends in time series.
S=



sgn (xj-xi)
Where, S is the Mann-Kendal’s test statistics; xi and xj are the sequential data values of the time
series in the years i and j (j >i) and N is the length of the time series.
Sign (xj − xi) =
+1,if (xj − xi) > 0
0,if (xj − xi) = 0
−1 if (xj − xi) <0
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78
The variance of S, for the situation where there may be ties (that is equal values) in the x values,
is given by:
Var(S)=

 
(
 −1
)(
2+ 5
)
− 
(
(
−1)(2
+ 5)
)

Where, m is the number of tied groups in the data set and ti is the number of data points in the ith
tied group. For n larger than 10, ZMK approximates the standard normal distribution (Partal and
Kahya, 2006; Yenigun et al., 2008) and computed as follows:
Z
MK
=


(
)
>0
0 =0


(
)
<0
The presence of a statistically significant trend is evaluated using the Z
MK
value. In a two-sided
test for trend, the null hypothesis (H
o
) should be accepted at a given level of significance. Z1-α/2
is the critical value of Z
MK
from the standard normal table. For example, for 5% significance
level, the value of Z1-α/2 is 1.96.
The MK test, used by many researchers for trend detection due to its robustness for non-normally
distributed data, was applied in this study to assess trends in the time series data (Kendall, 1975;
Mann, 1945). The significance level of the slope was estimated using Sen’s method. Sen’s slope
(Q) estimates methods that account for seasonality of the precipitation data. This method uses a
simple non-parametric procedure developed by Sen (1968) to estimate the slope. The
nonparametric tests are used to detect trends but don’t quantify the size of the trend or change.
Hence, magnitude of the observed trend can be estimated with Sen’s slope estimator when
significant (Paulo et al., 2012).
Coefficient of variation
The rainfall variability for Yabello and El-Woye meteorological stations was calculated using the
CV to evaluate the inter-annual variability of seasonal and annual rainfall and is computed as:
=
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79
Where, CV= coefficient of variations, δ = standard deviation and ẋ= mean. The CV<20%
indicates low variability, CV between 20% and 30% indicate moderate rainfall variability,
CV>30% indicates high rainfall variability, CV>40% very high and CV>70% indicates
extremely high inter-annual variability of rainfall as used by (Hadju et al., 2013; Belay, 2014;
Eshetu, et al., 2016).
3.
R
ESULTS
A
ND
D
ISCUSSION
3.1.
M
AGNITUDE
A
ND
F
REQUENCIES
O
F
D
ROUGHT
3.1.1. Main rainy season (Ganna)
The total number of drought events with slight, moderate, severe and extreme severe computed
at 3-month timescale (March-May) in the main rainfall season accounted for 50% in Yabello and
53.3% in El-Woye stations (Figures 2 and 3). However, they had varied magnitude classes as can
be seen from the SPI results. Severe droughts were recorded in 1999, 2000, 2008 and 2011 in
Yabello station which ranges from -0.15 to -1.53, while severe droughts were recorded in El-
Woye in 1996 and 1992, with SPI values of -1.59 and -1.85, respectively. Extreme severe
drought was recorded in the year 2006 only in Yabello with SPI value -2.11, whereas it was not
observed at El-Woye (Figure 3). This is in line with the recent finding of Habtamu (2019).
Hence, continuous and persistent drought monitoring is essential to determine when droughts
begin and end as it is a good indicator for such climatic regions (WMO 2012).
Figure 2: Three-month timescale of SPI at Yabello Station
-3
-2
-1
0
1
2
3
1987-1988
1988-1999
1989-1990
1990-1991
1991-1992
1992-1993
1993-1994
1994-1995
1995-1996
1996-1997
1997-1998
1998-1999
1999-2000
2000-2001
2001-2002
2002-2003
2003-2004
2004-2005
2005-2006
2006-2007
2007-2008
2008-2009
2009-2010
2010-2011
2011-2012
2012-2013
2013-2014
2014-2015
2015-2016
2016-2017
SPI Value
SPI 3 Yabello
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80
Figure 3: Three-month timescale SPI at El-Woye Station
The analysis of 3-month timescale (March-May) showed that 1988, 1991, 1992, 1998, 2001,
2008, 2014, 2015, and 2016 were drought years in both studied areas. Generally, the drought
magnitude of the 3-month timescale varied from slight to extreme severe in the studied areas. As
the main source of water for the study area is rainfall, any change in rainfall amount and
distribution can lead to serious production deficit and undermine the delicate balance between
pasture and livestock on which pastoral and agro-pastoral livelihood depends (Elias, 2009;
Hagos et al., 2009). The main rainy season (March, April, May) rainfall contributed the highest
percentages of 52.2% and 48.3% of rainfall to annual rainfall at Yabello and El-Woye
respectively. So, failure of this season can make pastoral communities highly vulnerable to
drought impacts.
3.1.2. Short rainy season (Hagayyaa)
The total number of drought events at the two-month timescale (September and October) in the
entire period of analysis was found between 16 months at Yabello and 15 months at El-Woye
stations, respectively (Figures 4 and 5). Yabello had an extreme severe drought with SPI value -
2.28 at this timescale in the 2015 drought year. Severe droughts occurred in 1993, 2002 and 2006
whose values range from -1.55 to -1.76 at El-Woye station, whereas in Yabello station a severe
drought occurred in 1992 with SPI values of -1.68.
-3
-2
-1
0
1
2
3
1987-1988
1988-1999
1989-1990
1990-1991
1991-1992
1992-1993
1993-1994
1994-1995
1995-1996
1996-1997
1997-1998
1998-1999
1999-2000
2000-2001
2001-2002
2002-2003
2003-2004
2004-2005
2005-2006
2006-2007
2007-2008
2008-2009
2009-2010
2010-2011
2011-2012
2012-2013
2013-2014
2014-2015
2015-2016
2016-2017
SPI Value
SPI 3 El-weye