The Ethiopian Journal of Social Sciences Volume 7, Number 1, May 2021

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

The Ethiopian Journal of Social Sciences Volume 7, Number 1, May 2021

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,

The Ethiopian Journal of Social Sciences Volume 7, Number 1, May 2021

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 3° 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

C. The corresponding amounts of maximum and minimum temperatures are 26.83

and 20.4

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

The Ethiopian Journal of Social Sciences Volume 7, Number 1, May 2021

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

The Ethiopian Journal of Social Sciences Volume 7, Number 1, May 2021

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:







Where SPI

is the SPI of the i

month at the j

timescale, X

is rainfall total for the i

month at

the j

timescale, μ

and a

are the long-term mean and standard deviation associated with the i

month at the j

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)

The Ethiopian Journal of Social Sciences Volume 7, Number 1, May 2021

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.

∑





∑





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



The Ethiopian Journal of Social Sciences Volume 7, Number 1, May 2021

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:

⎩

⎨

⎧



√



(



)

 >0

0  =0



√



(



)

 <0

The presence of a statistically significant trend is evaluated using the Z

value. In a two-sided

test for trend, the null hypothesis (H

) should be accepted at a given level of significance. Z1-α/2

is the critical value of Z

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:

=



ẋ

The Ethiopian Journal of Social Sciences Volume 7, Number 1, May 2021

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).

ESULTS

ISCUSSION

3.1.

AGNITUDE

REQUENCIES

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

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

The Ethiopian Journal of Social Sciences Volume 7, Number 1, May 2021

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

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

The Ethiopian Journal of Social Sciences Volume 7, Number 1, May 2021

Figure 4: Two-month timescale SPI-2 at El-Woye Station

Figure 5: Two-month timescale SPI-2 at Yabello Station

3.1.3. Annual drought

The total number of drought years at 12-month timescale (January-December) was found 15 at

Yabello and 16 at El-Woye, which constitutes 50% and 53.3% of the total number of drought

incidents in the study period, respectively (Figures 6 and 7). At this timescale, the most severe

droughts were recorded at Yabello in 2006 and 1999 with SPI values of -2.01 and -2.04,

respectively, whereas in El-Woye an extreme severe drought was recorded in 2006 with SPI

value of -2.14. This implies that in 2006 both areas underwent extreme severe drought events,

which had large effects on the pastoralists and agro-pastoralists. This is supported by other

-2

-1

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 2 El-weye

-3

-2

-1

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 2 Yabello

The Ethiopian Journal of Social Sciences Volume 7, Number 1, May 2021

studies (Temesgen et al., 2009; Getachew, 2018) who stated in their studies that 7.4 million

people were affected; 247,000 heads of cattle died in Borana lowlands, Tigray, Amhara, Afar

and Somali regions in the 2006 drought.

Figure 6: Twelve-month (annual) timescale SPI at Yabello Station

Figure 7: Twelve-month timescale SPI at El-Woye Station

The analysis of the 12-month timescale drought was recorded across the study areas in 1987,

1988, 1991, 1992, 1994, 1998, 1999, 2001, 2006, 2014 and 2015 although there was variation in

the degree of severity. The total number of moderate droughts at 12-month timescale was 13

months at El-Woye and 9 months at Yabello. Generally, the rainfall pattern in the studied

stations exhibited certain characteristics that a drought year is followed by another two or three

dry years vis-à-vis the wet years. These findings are in agreement with that of Girma et al.

(2013) and Girma et al. (2016). However, it is commented by Kumar et al. (2009) that the SPI

values are overestimated for low rainfall levels and underestimated for high rainfall levels in

-3

-2

-1

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 12 Yabello

-3

-2

-1

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 12 El-weye

The Ethiopian Journal of Social Sciences Volume 7, Number 1, May 2021

some districts in India, particularly in the low rainfall district. Hence, using SPI as a standalone

indicator needs to be interpreted with caution, for drought intensity assessment particularly in

low rainfall districts are more vulnerable to droughts. Despite this fact, the sensitivity of SPI for

the quality and reliability of data used to fit the distribution 30 to 50 years data is recommended

by the USA (Keyantash and Dracup 2002) that makes the findings of this study acceptable as it

used the data within the range of the recommendation.

3.2.

RENDS

ROUGHT

CCURRENCES

Trends of drought occurrences for 2-month (Short rainy season), 3-month (Main rainy season)

and 12-month (annual) timescales were shown in Figures 8 and 9. The trends of all of the SPI

values seem similar with no significant variations among them. However, the trends of the two

stations are slightly not in agreement with each other. As stated by Lin et al. (2020), the linear

trend can be stated by its upward and/or downward positions compared to the mean.

Accordingly, the linear trends in the late 1980s, late 1990s, and after 2007 showed opposite

direction of movement compared to the mean in the case of both Yabello and El-Woye areas

though the overall linear trend of SPI-2, SPI-3 and SPI-12 did not change significantly (Figure 7

and Figure 8).

Figure 8: Trends of SPI values (at 2-, 3- and 12-month) at Yabello Station

The Ethiopian Journal of Social Sciences Volume 7, Number 1, May 2021

Figure 9: Trends of SPI values (at 2-, 3- and 12-month) at El-Woye Station

The MK test showed decreasing changes in SPI values in Yabello, which implies an increasing

tendency of drought incidents at main rainy season, while El-Woye showed an increasing trend

that implies declining of the tendency of drought incidents (Table 2). On the other hand, SPI-2

and SPI-12 values showed increasing trends at both stations. The linear trend analysis showed no

statistical significance (p < 0.05) of any positive or negative trend of meteorological drought

severity and frequency for both stations except at SPI-2 at El-Woye Station. It was slightly

negative for the SPI-3 value when it comes to the Yabello area. Generally, detection of linear

trends using nonparametric methods showed increasing tendencies of drought during the main

rainy season and decreasing tendencies of drought during the short rainy season and annual scale

in the study region. The area is frequently affected by drought as is also confirmed by Habtamu

(2014).

Table 2: SPI MK trends for Yabello and El-Woye stations

Stations

SPI- 2 months SPI-3 months SPI- 12 months

Trend P-value Sen Slope Trend P-value Sen’s Slope Trend P-value Sen’s Slope

Yabello

0.05 0.98 0.008 -0.10 0.4 -0.02 0.01 0.94 0.002

El-Woye

0.28

0.03 0.048 0.12 0.32 0.03 0.23 0.07 0.04

*=significant at p< 0.05

3.3. Rainfall Variability

The coefficient of variation of the seasonal and annual rainfall of the stations is presented in

Table 19. The result indicated that rainfall variability at Yabello was CV=21.2%, while El-Woye

The Ethiopian Journal of Social Sciences Volume 7, Number 1, May 2021

station had CV=53%. The region has moderate and very high rainfall variability respectively.

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, and the short rainy

season (September and October) also contributed 21.4% and 24.7% at Yabello and El-Woye

stations, respectively. This result agreed with the findings of Koricha et al. (2012) and Eshetu et

al. (2016), who reported that main seasons made the highest contribution to annual rainfall.

The coefficient of variation range of the main rainfall season was 32.6% at Yabello Station

which shows high variability, while it was 71.2% at El-Woye Station which implies very high

variability in the study area (Table 2). This shows that during the main rainy season, the rainfall

is highly variable, and it is in line with other studies in Ethiopia (Wassie and Fikadu 2014). The

analysis of coefficient of variation for the short season (September and October) at Yabello

Station was 48.3% which shows very high variability and 94.4% at El-Woye Station which is

extremely variable. This shows that variability in both areas is higher than the main seasonal

rainfall which agreed with the findings of many other studies (Gebre et al., 2013; Belay, 2014;

Girma, et al., 2016). The short rainy season rainfall at both stations shows the highest variation

of rainfall distribution with the highest coefficient of variation, followed by the main rainy

season and annual rainfall, respectively.

This finding is consistent with Desalegn et al. (2018) where greater rainfall variability is

experienced during the small rainy season than the main rainy season and annual rainfall.

Generally, the study site experiences moderate to extremely high inter-annual rainfall variability.

As reported by Woldeamlak (2007) and Desalegn (2014), extreme high and low rainfall values

within the study period could influence the rainfall trend. So both seasonal and annual rainfall

distribution variability negatively affect the socio economic activities of the pastoralist and agro-

pastoralist communities that mostly depend on rain-fed agriculture.

Table 1: Annual, main, and short season rainfall variability for Yabello and El-Woye Stations

Station

Annual Main Rainfall Season Short Rainfall Season

Mean SD CV Mean SD % CV Mean SD % CV

Yabello

El-Woye

597 126.94 21.2

514.3 272.7 53.0

311.6 101.6 52.2 32.6

247.2 176.1 48.3 71.2

128 62.13 21.4 48.3

127.6 120.5 24.7 94.4

The Ethiopian Journal of Social Sciences Volume 7, Number 1, May 2021

4. Conclusion

This study investigated the frequency, magnitude and trends of droughts over lowlands of

Borana during the short and main rainy seasons and annual rainfall for the period 1987-2017.

The study was able to identify the major drought years within the study period and their severity.

The assessment of meteorological drought showed the occurrence of slight to extremely severe

droughts in the historical drought episodes in the stations studied.

The meteorological data results revealed that greater rainfall variability is experienced during the

small rainy season followed by the main rainy season and annual rainfall in the Borana area,

which in turn calls for proper mitigating and adaptive strategies as well as integrated forecasting

and early warning systems to enhance the resilience of vulnerable communities. Continuous and

persistent drought monitoring is essential to determine when droughts begin and end. Further

studies are recommended for refinements of the results using advanced devices of drought

integrated with local community-based resilience to study the frequent and intense

meteorological droughts in the Borana area in particular and larger spatio-temporal dimensions.

5. Acknowledgements

The authors would like to thank the National Meteorological Agency and the employees working

at Yabello and El-Woye meteorological stations for the provision of meteorological data and for

their cooperation related to the study. The research participants and household heads are also

acknowledged for their kind cooperation and participation in generating the necessary

information and data. The anonymous peer reviewers and editors of this article are

acknowledged for their unreserved contribution in the refinement of the document.

5. References

Abera Bekele and Aklilu Amsalu. 2012. Household Response to Drought in Fentale Pastoral

Woreda of Oromia Regional State, Ethiopia, International Journal of Economic

development research and investment, Vol. 3, No. 2, August. 2012.

Argaw Ambelu., Zewdie Birhanu. and Negalign Berhanu. 2015. Rapid Appraisal of

Resilience to the Effects of Recurrent Drought in Borana Zone. Project; Resilience

study DOI;10.13140/RG.2.1.1873.9928

The Ethiopian Journal of Social Sciences Volume 7, Number 1, May 2021

Ayana Angassa. 2007. The Dynamics of Savanna Ecosystems and Management in

Borena, Southern Ethiopia, Doctoral Thesis in Norwegian University of Life

Sciences (UMB) Department of Environment and Development Studies,

Belay Tseganeh. 2014. Climate variability and change in Ethiopia: Exploring impacts and

adaptation options for cereal production. Production Ecology and Resource

Conservation, Wageningen University. Doctoral 16. Wageningen University.

BoZA (Borana Zone Administration Office). 2013. Yabello Woredas Annual Drought report

of 2012-2013.

Collins, M.J. 2009. “Evidence for changing flood risk in new England since the late 20th

century”, JAWRA-07-0187-P of the Journal of the American Water Resources

Association), Vol. 45 No. 2, pp. 279-290, doi: 10.1111/j.1752-1688.2008.

Desalegn Yayeh. 2014. Smallholder farmers’ vulnerability to climate variability in the

highland and lowland of Ethiopia: implications to adaptation strategies, Doctoral thesis,

University of South Africa, Geography Department.

Desalegn Yayeh., Maren Radeny., Solomon Desta. and Getachew Gebru. 2018. Climate

variability, perceptions of pastoralists and their adaptation strategies: Implications for

livestock system and diseases in Borana zone, International Journal of Climate

Change Strategies and Management, https://doi.org/10.1108/IJCCSM-06-2017-0143.

Dirriba Mengistu and Jema Haji. 2015. Factors Affecting the Choices of Coping Strategies

for Climate Extremes: The Case of Yabello District, Borana Zone, Oromia National

Regional State, Ethiopia. International Journal of Engineering Innovation &

Research 3 (4), 129-136. DOI: 10.11648/j.sr.20150304.11.

Dirriba Mengistu. 2016. Impacts of Drought and Conventional Coping Strategies of Borana

Community, Southern Ethiopia, Research on Humanities and Social Sciences ISSN

(Paper) 2224-5766 Vol.6, No.23.

Donald A. Wilhite and Glantz, M.H. 1985. Understanding the drought phenomenon: The role

of definition.Water International.Vol-10(3):111-120

Edwards, Daniel .C. and McKee, T.B. 1997. Characteristics of 20th century drought in the

United States at multiple time scales, Colorado State University: Fort Collins.

Climatology Report, 97-2.

The Ethiopian Journal of Social Sciences Volume 7, Number 1, May 2021

Elias Eyasu. 2009. Threats of climate change on pastoral production under restricted herd

mobility: A case study in Borena. A report for SOS Sahel Ethiopia.

Ellen Viste., Diriba Koricha and Sorteberg A. 2012. Recent drought and precipitation

tendencies in Ethiopia. Theory Appl. Climatol. 112:535- 551.

EM-DAT. 2018. The International Disaster Data Base http://www.emdat.be/disaster-list.2018

Gay, L.R., Mills, G.E. and Airasian, P. 2012. Educational research competencies for

analysis and applications. (10

ed) New Jersey: Pearson Education, Inc, USA.

Gebre Hadgu., Fantaye Tesfaye., Girma Mamo. and Belay Kassa., 2013. Trend and

variability of rainfall in Tigray, Northern Ethiopia: Analysis of meteorological data

and farmers’ perception. Academia Journal of Environmental Sciences, pp.1-13.

Getachew Alem. 2018. Drought and Its Impacts In Ethiopia, Elsevier Publication,

Weather and Climate Extremes 22 (2018) 24–35.

Girma Eshetu., Tino, J. and Wayessa Garedew. 2016. Rainfall trend and variability

analysis in Setema-Gatira area of Jimma, Southwestern Ethiopia.African journal of

Agricultural reasearch Vol. 11(32), pp. 3037-3045, 11 August, 2016

Guttman, N.B. 1999. Accepting the standardized precipitation index: a calculation

algorithm, Jawra Journal of the American Water Resources Association, Vol. 35 No.

2, pp. 311-322.

Hagos Fitsum., Awulachew, SB., Makombe, G. and Namara Regassa. 2009. Importance of

irrigated agriculture to the Ethiopian economy: Capturing the direct net benefits of

irrigation. Int. Water Manage. Inst. P 37(IWMI Research Report).

Herrero, M., C. Ringler, J., van de Steeg, P., Thornton, T., Zuo, E., Bryan, A., Omolo, J., Koo

and Notenbaert, A. 2010. Climate Variability and Climate Change and Their

Impacts On Kenya’s Agricultural Sector. Nairobi, Kenya. ILRI

Huho, Julius M. and Edward M. Mugalavai 2010. The Effects of Droughts on FoodSecurity

in Kenya. The International Journal of Climate Change: Impacts Resp. 2(2):61-72.

Kendall, M.G. 1975. Rank Correlation Methods; Charles Griffin: London, UK.

Mann HB, 1945. Non parametric tests again trend. Econometrica 13:245-259.

McKee TB., Doesken NJ. and Kleist, J. 1993. The relationship of drought frequency and

duration to time scales. In Proceedings of the 8

Conference on Applied Climatology,

Anaheim, Calif, January 17-22. American Meterological Society.

The Ethiopian Journal of Social Sciences Volume 7, Number 1, May 2021

Mekonnen Degefu and Weldeamlak Bewket. 2013. Trends and spatial patterns of drought

incidence in the Omo ghibe river basin, Ethiopia, Geografiska Annaler: Series A,

Physical Geography.volume 97, 2015-issue 2 doi: 10.1111/geoa.12080.

Mishra, A.K. and Singh, V. P. 2010. A review of drought concepts.J. Hydrol, 391(1) 202–

216.

Mondol, M.A.H., Das, S.C. and Islam, N. 2015, ‘Meteorological drought index at different

spatial unit of Bangladesh’, International Conference on Climate Change in Relation

to Water and Environment, pp. 197–204, Dhaka University of Engineering and

Technology, April 09–11, Gazipur, Bangladesh(Google Scholar).

NOAA, 2019 "U.S. Seasonal Drought Outlook", www.cpc.ncep.noaa.gov/ products

NRC (National Research Council). 2007. Tools and Methods for Estimating Population sat

Risk from Natural Disasters and Complex Humanitarian Crises. Case study

WashingtonD.C.,the National Academies Press.

Opiyo, F.E.O., O.V.Wasonga and M.M. Nyangito. 2014. Measuring household vulnerability

to climate-induced stresses in pastoral rangelands of Kenya: Implications for

resilience programming. Pastoralism: Research, Policy and Practice 4(10) 1–15.

Oxfam. 2011. Briefing on the Horn of Africa Drought: Climate change and future impacts on

food security, August 2011.

Panu, U. S. and Sharma, T.C. 2002. Challenges in drought research: some perspectives and

future directions. Hydrolog. Sci. J, 47(S1) S19–S30.

Partal, T. and Kahya, E. 2006. Trend Analysis in Turkish Precipitation Data. Hydrological

Processes. 20. 2011 - 2026. 10.1002/hyp.5993.

Paul, Lepenoi Lekapana. 2013. Socio economic Impact of Drought, their Coping Strategies

and Government Intervention in marsabit county, Kenya. A thesis submitted in partial

fulfilment of the requirements for the Master of Arts degree in Environmental Policy

Paulo, A. A., Rosa, R. D. and Pereira, L. S. 2012. Climate trends and behaviour of

drought indices based on precipitation and evapotranspiration in Portugal.

Hazards Earth Syst. Sci. 12

Riche, B., Hachilek,E. and Cynthia, B.A. 2009. Climate-related vulnerability and adaptive

capacity in Ethiopia’s Borana and Somali communities, International Institute for

The Ethiopian Journal of Social Sciences Volume 7, Number 1, May 2021

Sustainable Development Final assessment report, Commissioned by SAVE the

Children UK and CARE Ethiopia, Unpublished.

Sen’s, PK. 1968. Estimates of the regression coefficient based on Kendall’s tau. J. Am. Stat.

Assoc. 39:1379–1389.

Shahid, S. 2008. Spatial and temporal characteristics of droughts in the western part of

Bangladesh, Hydrological Processes, Vol. 22 No. 13, pp. 2235-2247, doi:

10.1002/hyp.6820.

Stern R., Rijks D,D. and I, Knock J. 2006. INSTAT (interactive statistics) climate guide. pp.

330.

Swain, S., Patel. and Nandi, S. 2018. Application of SPI, EDI and PNPI using MSWEP

precipitation data over Marathwada, India. IEEE International Geoscience and

RemoteSensingSymposium(IGARSS. 2017:55055507. doi:10.1109/IGARSS.2017.81

28250.ISBN 978-1-5090-4951-6. Retrieved 15 June 2018.

Temesgen Deressa., Rashid Hassan.,ClaudiaRingler., Tekie Alemu, and Mohammed. Yusuf.

2009. Determinants of farmers’ choice of adaptation methods to climate change in

theNileBasinofEthiopia.GlobalEnvironmentalChangedoi:10.1016/j.gloenvcha.2009.0

1.002.

Tigkas, D., Vangelis, H. and Tsakiris, G. 2014. ‘DrinC: A software for drought analysis based

on drought indices’, Earth Science Informatics 8(3), 697–709. http://dx.doi.

org/10.1007/s12145-014-0178-y.

Wassie Berhanu and Fekadu Beyene. 2014. The impact of climate change on pastoral

production systems: A study of climate variability and household adaptation strategies

in southern Ethiopian rangelands, WIDER Working Paper, No. 2014/028, World

Institute For Development Economic Research.

Wilhite D. A.., 2002. Drought preparedness and Response in the context of sub-Saharan Africa.

Journal of Contingency and Crisis Management 8(2);81-92.

Wilhite, D. A. 2007. Drought. In J. Lidstone, L. M., Dechano., and J. P. Stoltman (Eds.),

International Perspectives on Natural Disasters: Occurrence, Mitigation, and

Consequences. Advances in natural and technologies Hazard research, vol 21,

Springer,Dordrecht.

The Ethiopian Journal of Social Sciences Volume 7, Number 1, May 2021

Wilhite, D.A.(Ed) 2000. Drought as a Natural Hazard: Concepts and Definitions (Chapter 1,

pp.3-18). In: Wilhite, D.A. (ed.) Drought: A Global Assessment (Volume 1),Routledge

Publishers, London, U.K.

WMO (World Metrological Agency). 2006. Drought Monitoring and Early Warning:

Concepts, Progress and Future Challenges.WMONo.1006 ISBN 92-63-11006-9 2006.

WMO (World Metrology Organization). 2012. Standardized Precipitation Index User Guide

WMO-No.10907 www.wmo.int/agm

Woldeamlak, Bewket. 2007. Rainfall variability and agricultural vulnerability in the Amhara

region, Ethiopia. Ethiopia. Journal of development Research. 29 (1), 1–34.

Yenigun Kasim., GumusVeysel and Bulut Hüsamettin. 2008. Trends in stream flow of the

Euphrates basin, Turkey. Proceedings of The Institution of Civil Engineers-water

Management-PROC INST CIVIL ENG-WATER MAN. 161. 189-198.

10.1680/wama.2008.161.4.189.

Yimer mohammed., Fantaw Yimer., Menfese Tadesse. and Kindie Tesfaye. 2017.

Meteorological drought assessment in north east highlands of Ethiopia. International

Journal of Climate Change Strategies and Management Vol. 10 No. 1, 2018 pp. 142-

160

Yue, S., Pilon, P. and Cavadias, G. 2002. Power of the Mann-Kendall and spearman’s rho

tests for detecting monotonic trends in hydrological series. Journal of Hydrology,

Vol. 259 Nos 1/4, pp. 254-271

Zemenu Mintesinot. 2009. Bush Encroachment Mapping Using Supervised Classification

and Spectral Mixture Analysis in Borana Rangelands: A Case Study in Yabello

Woreda. M.Sc Thesis, Addis Ababa University Addis Ababa.