Download Analysis el nino

yes no Was this document useful for you?
   Thank you for your participation!

* Your assessment is very important for improving the workof artificial intelligence, which forms the content of this project

Document related concepts
no text concepts found
Climate Services 23 (2021) 100254
Contents lists available at ScienceDirect
Climate Services
journal homepage:
Analysis of El Niño Southern Oscillation and its impact on rainfall
distribution and productivity of selected cereal crops in Kembata Alaba
Tembaro zone
Bereket Tesfaye Haile a, Tadesse Terefe Zeleke b, Kassahun Ture Beketie a, *, Desalegn
Yayeh Ayal c, Gudina Legese Feyisa a
Center for Environmental Science, College of Natural and Computational Science, Addis Ababa University, Ethiopia
Institutes of Geophysics, Space Science and Astronomy, Addis Ababa University, Ethiopia
Centers for Food Security Studies, College of Development Studies, Addis Ababa University, Ethiopia
Crop yield
Rainfall variability
Sea surface temperature
El Niño Southern Oscillation could likely distort the hydro climatological processes and adversely affect agri­
cultural production at various magnitudes. This study explored the manifestations of ENSO-induced rainfall
variability and its impact on selected cereal crops in Kembata Alaba Tembaro Zones of southern Ethiopia.
Accordingly, precipitation, temperature, crop, and Sea Surface Temperature (SST) data were collected from the
National Meteorology Agency of Ethiopia, and the National Oceanic and Atmospheric Administration (NOAA).
Rainfall variability was analyzed using the Coefficient of Variation and various anomaly indices. Spatial and
temporal relationships between SST and yield of selected crops were established using the person correlation
method. Mann-Kendal trend test was also used for trend analysis. The results revealed a statically significant (P
< 0.05) decreasing trend and highly variable spring and summer rainfall. Global SSTs strongly influence both
summer and spring rainfalls. El Niño and La Niña events were shown to influence the local rainfall distribution
and crop production at varying magnitude over different spaces and times. The yield reduction due to ENSO
increases from wheat, barley, maize to Sorghum, respectively, while Enset (ventricosum) was found to be less
influenced by ENSO-caused rainfall variability. This implies that sorghum and maize crops are more sensitive to
El Niño and La Niña events in the study area compared to the other crops considered in this study. The con­
formity of Enset yield with rainfall variability could be attributed to its tolerance to moisture stress. From the
results, one can conclude that the overall cereal crop productivity was adversely, but differentially, affected by
ENSO-induced climate variability.
Practical implication
The adverse impacts of ENSO are usually manifested in the forms
of drought and flood at meso and micro levels. ENSO-induced
hazards such as drought, flood, and torrential rainfall directly
and negatively affect crop production worldwide and conse­
quently food security in many developing countries. ENSO also
create favorable condition for the proliferation and prevalence of a
number pest and diseases that substantially influence food pro­
duction and livelihoods of the farming community. The Ethiopian
economy and individual households’ agricultural production and
food security have been seriously affecting particularly by ENSO-
induced drought. In Ethiopia, the drought events linked with
ENSO were responsible for substantial crop failure (Abdi et al.,
2016; Changnon 1999). The most recent 2015/16 was translated
into a remarkable reduction of crop and livestock production,
death of livestock, decline of the national economy. This study is
uniquely designed to examine the local-level influence of ENSO on
rainfall distributions and its impacts on the production of a
selected cereal crop. The findings from this study would be of
important contributions towards making informed decisions and
helping adaptation to climate impacts and enhancing reliance in
the context of smallholder subsistence farming systems in Ethiopia
and other areas with similar environmental and socio-economic
* Corresponding author.
E-mail address: [email protected] (K.T. Beketie).
Received 31 December 2020; Received in revised form 18 August 2021; Accepted 2 September 2021
Available online 13 September 2021
2405-8807/© 2021 The Author(s).
Published by Elsevier B.V. This is an open
B.T. Haile et al.
Climate Services 23 (2021) 100254
decline of 22.7% cattle herd in the lowland and 10% crop yield in the
highland (Koo et al., 2019). It caused widespread crop failure and left
more than 10 million people to food aid and acute food insecurity;
furthermore, the direct and indirect impacts of drought also reduce the
national GDP, agricultural GDP by 1.6%, and 3.6% respectively, even­
tually increasing the national poverty rate from 30 to 31.2% (Koo et al.,
2019). However, the pattern of impacts of ENSO-induced excrement
events is not uniform across the country. Moreover, the magnitude of
influence of the ENSO phases on different crops is not well understood.
Therefore, the local-level impacts of ENSO need to be investigated if an
effective and context-specific adaptation strategy has to be devised.
Hence, his study aims at exploring the effects of the El Niño shock on
major crops in Kembata Alaba Tembaro Zones of south-central Ethiopia,
where the smallholder farmers and subsistent agriculture are the main
livelihood strategy dominates and commonly subject to ENSO -induced
The El Niño Southern Oscillation event leads to large-scale changes
in sea-level pressures, Sea Surface Temperature (SST), precipitation, and
winds in the tropics. The consequences appear in many parts of the
world and are characterized by a varying shift between El Niño, La Niña,
and Neutral phases. EI Niño and La Niña are physical processes of the
entire equatorial zone of the central and eastern Pacific Ocean of the
Peruvian coast surface water warming, and cooling takes place every
two to seven years and lasts from 12 to 18 months (Kiladis and Diaz,
1989; Trenberth and Hoar, 1997). El Niño occurs when the central and
eastern equatorial Pacific SST is warmer than normal and vice versa
(Trenberth, 1997; Yeh et al., 2009). Research showed that Niño3.4
(20 –170 W), which is a region between Niño3 and Niño4, is best
correlated to many global teleconnections (Bamston et al., 1997; Van
Oldenborgh et al., 2021). The Niño 3.4 region provides a good measure
of Sea Surface Temperature Anomaly (SSTA) gradients that result in
changing the pattern of deep tropical convection and atmospheric cir­
culation (Lau et al., 1997).
The criteria that are often used to classify El Niño episodes are five
consecutive 3-months mean SSTA that exceed defined threshold values.
The Oceanic Niño Index (ONI) SSTA anomaly index values defined by
the National Oceanic and Atmospheric Administration (NOAA) are
widely used as indicators of different ENSO phases. El Niño: ≥ 0.5 ◦ C;
Neutral: − 0.5 ◦ C to 0.5 ◦ C; and La Niña: ≤ − 0.5 ◦ C (Pendergrass and
Deser, 2017; Trenberth and Stepaniak, 2001). Previous studies had
shown that ENSO has a significant influence on the inter-annual vari­
ability of Ethiopian rains (Alhamshry et al., 2020; Camberlin et al.,
2001; Gissila et al., 2004). ENSO and local rainfall are highly correlated
(Silva et al., 2019). Conditions at sea directly affect precipitation at the
local and regional levels, which is evidenced by “Teleconnections” that
affect local rainfall patterns across the globe (Gissila, 2001; Glantz et al.,
1991). El Niño leads to heavy rains in some parts of the world, and se­
vere droughts in other areas of the world, and have long been a major
climate-induced challenge to farming communities worldwide (Cirino
et al., 2015).
The adverse impact of ENSO on regional and local crop production
and food security is well understood (Gutierrez and Braunstein, 2017;
Ouyang et al., 2014; Panda et al., 2019; Yuan and Yamagata, 2015). The
extreme events associated with ENSO often lead to disruption of agri­
cultural activities, crop damage, and yield reduction of varying magni­
tude. It could compromise the livestock sector and disrupt agricultural
activity through climate variability and extreme events like drought,
flood, and torrential winds. Horn of Africa is among the hot-spot areas at
a global level that could be seriously affected during the ENSO phase
(Glantz, 1994; Hansen et al., 1998; Park et al., 2020).
In Ethiopia, rainfall is the most important climatic parameter in
influencing agricultural activities and production. The majority of the
farming community is reliant on rain-fed agriculture. Nearly 85% of the
people’s survival depends on rain-fed agriculture, which significantly
relies on the amount and spatiotemporal distribution of rainfall (Diro
et al., 2011; Tolossa and Books, 2015). The agricultural sector of the
country has been frequently affected by El Niño events (Funk et al.,
2018; Wolde-Georgis, 1997). For instance, during El Niño events during
the years 1986–2010, 1987, 1991, 1997, 2003, 2004, 2006, and 2009,
the first crop season was affected by drought. During the 2002 El Niño
event, both the first and second crop seasons were affected by drought
(Rojas et al., 2014). For instance, Ethiopian drought years: 1982, 1997,
1998, 2015/16 were linked to the El Niño phase of the ENSO; the
anomalously excessive rain events during the years 1988, 1989, 1999,
2000, 2007, 2008, 2011 were associated with LA Niña events (Gizaw
and Gan, 2017; Gleixner et al., 2017; Zaroug et al., 2014). El Niño /La
Niña lead droughts resulted in 50–90% crop failure in Ethiopia (Abtew
et al., 2009; Zaroug et al., 2014).
The recent 2015/16 El Niño event caused both severe drought and
flood-imposed negative impacts on the agricultural sector. It led to the
Method and materials
Study area description
The study site is located in the Southern Nations, Nationalities, and
People’s (SNNP) Regional State of Ethiopia (Fig. 1). The study area
encompasses Kembata and Alaba Zones. The Zones are endowed with
diverse landscapes like mountains, plateaus, and plains. Various Land­
scapes commonly range from steep mountains to plane land (Fig. 1). The
total population of the study area is 1,080,837 and covers a total area of
1,555.89 km2 (Comenetz and Caviedes, 2002). The study area receives
dominant rainfall during the summer season. The land use of the area is
mainly rainfed cereal crop production and fragmented homestead
agroforestry systems. The farming system is mainly mixed farming (crop
and livestock production. The area is sub-divided into three main agroecological zones of Ethiopia: Dega (temperate), Woinadega (sub-trop­
ical), and Kola (tropical). The study area receives bimodal rainfall pat­
terns: short rainy season (Belg/spring- February to May) and the main
rainy season (Kiremt/Summer- June to September). Bega/winter
(October to January) is the dry season of the area (Fig. 2). Summer,
spring, and winter seasons contribute 52%, 35%, and 13% to the annual
rainfall respectively.
Data collection
Monthly mean rainfall data of six stations (Angecha, Durame,
Kedida, Hadero, Mudulla, and Alaba) (Fig. 1), for the years 1981 to
2017, were obtained from the National Meteorological Agencies (NMA)
of Ethiopia. Annual yield data of selected crops (wheat, barley, maize to
Sorghum, and Enset) for the years 2000 to 2017 were acquired from the
zone and district agricultural offices. The SST data over Nino regions
were accessed from
Fig. 1. Topographic map of the study area. The red color at the center of the
map on the right indicates the location of the study area on top of the topo­
graphic map of Ethiopia. (For interpretation of the references to color in this
figure legend, the reader is referred to the web version of this article.)
B.T. Haile et al.
Climate Services 23 (2021) 100254
⎨ 1if (xj = x)i > 0
0if ( xj− xi =
sign(θ) =
) 0
− 1if xj − xi < 0
V(S) =
[n(n − 1)2n + 5 ) −
tk (tk − 1)(2tk + 5)]
V(S) =
S− 1
⎪ √̅̅̅̅̅̅̅̅̅̅̅̅̅ , S > 0
− 1)2n + 5 ) −
O, S = 0
tk (tk − 1)(2tk + 5)]Zs =
⎪ S+1
⎩ √̅̅̅̅̅̅̅̅̅̅̅̅̅ , S < 0
Data analysis
Coefficient of variation
The seasonal and inter-seasonal rainfall variability was analyzed
using Eqs. (1)–(3) and rainfall anomaly classification was performed
based on the value shown in Table 1.
Sen’s slope estimator
Sen’s Slope estimators (Sen, 1968) were used to establish the nonparametric technique for assessing the slope of trend in the sample of
N pairs using Eq. (8).
Qi =
or more periods, then N < n(n−2 1) N<(n(n-1)/2 where N is the total
number of observations. The N values of Qi, are categorized from min­
imum to maximum and the median of slope or Sen’s slope estimator is
calculated using Equation
N is odd
Qmed = 1
⎩ (QN + Q(N+2) ) N is even
2 2
where Qmed is the median of the slope, N is the number of the calculated
slope, A positive value of Q indicates an increasing trend and a negative
value indicates a decreasing trend in the time series (Bhuyan et al., 2018;
Güner Bacanli, 2017). To define whether the median slope is statically
different from zero, one should acquire the confidence interval Qmed at a
precise probability. The reason why the odd or even formulas are used in
sense slope estimator is to consider the number of study periods. If there
are multiple observations, the N values of Q are ranked from smallest to
largest and the median of slop or Sen’s estimator is computed. The
confidence interval about the time slope can be calculated as shown in
Eq. 10.
Mann-Kendall test
Different types of tests are available to detect and estimate trends of
hydro-climatic variables. In this study, Mann-Kendall or nonparametric
test was applied to analyze the trends of the hydroclimatic variables. The
tests are distribution-free and tolerant to outliers of the data (Mann,
1945). The mathematical equations for calculating Mann-Kendall sta­
tistics (S) and standardized test statistics (Z) are shown in Eqs. (4)–(7).
xj − xk
j− k
Where xj and xk are the data values at times J and k (j > k), corre­
spondingly. If there is only one datum in each period, then N = n(n-1)/2,
where n is the number of times, if there are several observations in one
xi − x
where: SD is the standard deviation, x long year mean; N: the total
number of years, Xi: rainfall for each month or season, and Rainfall
Anomaly Index (RAI) of each year.
Higher values of annual and seasonal coefficient of variation (CV)
indicate high variability in rainfall causing extremes, whether flood or
drought. Based on the CV values, rainfall variability can be classified as
less variable (CV < 20), moderately variable (20 < CV < 30), and highly
variable (CV greater than 30) (Hare, 2003).
In these equations, N is the number of data points, assuming
xj − xi) = θ and the value
sign(θ) was computed as the sigma functioning which is the number
of data points, g is the number of tied groups (data having the same
value), and tk is the number of data points in the kth group. Z is the
standard statistic Z test. The significance level of the trend was evaluated
using Zs value. A positive Zs value indicates an increasing trend while a
negative Zs shows a decreasing trend. Then the null hypothesis H0
should be excluded if /Zs/ > Z (1-α /2) at α = 0.05 the level of significance
(Du et al., 2013).
Fig. 2. Classifications of seasonal rainfall and spatial variation of rainfall: a)
Summer (JJAS), b) Spring (FMAM), c) Winter (ONDJ) distribution across the
study site.
CV = ( )*100, where SD = (xi − x)2
sign(xj − xi )
Cα = Z1− α √̅̅̅̅̅̅̅̅
Table 1
Rainfall anomaly classification.
where var (S) is defined in Eq. (6) and Z1 – α/2Z1 − 2α is obtained from the
standard normal distribution table, in this study, the confidence interval
was computed at two significance levels (α = 0.01 and α = 0.05). Sen’s
slope estimator has been widely used in hydro-meteorological time se­
ries data set (Gocic et al., 2013).
RAI classification
Very dry
<− 4
− 4 to
− 2
− 2 to
0 to 2
2 to 4
Source: (De Freitas et al., 2005)
B.T. Haile et al.
Climate Services 23 (2021) 100254
Analysis of relationships between crop yield and ENSO
The relationship between the yield of selected cereal crops and
ENSO-induced rainfall variability was analyzed using the Pearson cor­
relation coefficient (r). Correlation, in its widest sense, is a measure of an
association between variables; the change in the magnitude of one
variable is linked with a change in the magnitude of another variable,
either in the same (positive) or in the reverse (negative) correlation. Eq.
(11) was used to calculate the Pearson correlation to show the degree of
the relationship between the yield of the crop and SST-induced rainfall
∑ ∑
n xi yi −
xi yi
rxy = √̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅
∑ 2
∑ 2
∑ 2̅
∑ ̅
n xi − (n xi n yi − ( yi ) − ( y)2
and other associated ecological impacts.
Unlike the spring season, the summer season rainfall does not show a
statistically significant trend (Fig. 4). However, the rainfall variability is
high, and out of 36 years of rainfall records, only five years were normal.
The rest of the years experienced either positive or negative anomalies.
However, there is no clear overall trend of rainfall anomaly during the
summer season. It can be noted, from Figs. 3 and 4, that there is a large
variation among the stations, implying large spatial variation in rainfall
anomaly over the study area.
The declining trend of rainfall in the spring season could remarkably
affect the livelihood of farmers. Explicitly, the decline of rainfall during
Belg/spring could affect the first crop season and cause crop failure and/
or yield reduction, with critical implications for household food security
as the farming systems in the area are predominantly subsistence
The situation could also create water and pasture scarcity to the
livestock, sequentially, reduce the livestock production, productivity,
and traction performance. One can infer that the far-reaching impact of
rainfall amount and timing change could seriously harm the farming
activity and crop production and hence, the livelihood of farmers could
be seriously impacted (Liu et al., 2014). In line with our investigation,
Koo et al. (2019) reported that ENSO-induced agricultural drought re­
duces and undermines the crop and livestock sectors and hence, worsens
the seasonal food insecurity of subsistence farming (Belayneh et al.,
2020; Hermans and Garbe, 2019).
rxy: Pearson r correlation coefficient between x and y
n = number of observations, xi: the value of x (for ith observation)
yi = value of y (for ith observation)
Result and discussion
Characteristics of seasonal rainfall
Like other parts of Ethiopia, the rainfall distribution of the study area
is highly linked with the weather conditions in the equatorial Pacific
Ocean. The weather system, which evolves on the equatorial Pacific, is
the major driving force to the rainfall and temperature distribution in
the study area. The study showed that seasonal anomalies of Ocean
basins are strongly linked with Ethiopian spring and summer rainfall via
large-scale circulation. A negative/positive SST over the equatorial East
Pacific alters the rain-producing large-scale circulation over Ethiopia,
which is significantly correlated with spring and Kiremt events (Zeleke
et al., 2013).
The results of trend analysis illustrated that in the study area, Belg/
spring season rainfall has shown a significant decreasing trend between
the years 1981 and 2017, except at Mudula. The Belg rainfall showed an
overall decreasing trend at all of the six stations, with the rate of −
0.046, − 0.034, − 0.045, − 0.042, − 0.031 mm/year at Alaba, Angacha,
Areka, Durame, Hosanna (at P ≤ 0.05) respectively, and no significant
trend was shown at Mudula (Fig. 3). The Belg/Spring season rainfall
anomaly was mostly positively and significantly anomalous between the
years 1981–1987 and interestingly, the negative anomaly was dominant
after the year 1987 (Fig. 3).
Rainfall anomalies threaten agricultural systems in many ways (Lesk
et al., 2016) and disproportionately impact the developing world (Hall
et al., 2014). Repeated dry anomalies may lead to an increase in crop­
land expansion at the expense of important natural ecosystems such as
forests in many developing countries including Ethiopia (Desbureaux
and Damania, 2018; Zaveri et al., 2020). This may in turn aggravate
vulnerability to climate impacts through exacerbating land degradation
Seasonal and inter-annual rainfall variability
The coefficient of variation result shows that the study area experi­
enced high seasonal and annual rainfall variability for the last three
decades. The seasonal and annual rainfall distribution varies across
seasons. The variability of Kiremt/summer rainfall ranges from 32 to
49%, that of Belg/spring ranging from 42 to 49%, and Bega/winter 28 to
81%. The annual rainfall variability ranged from 28 to 36%. The highest
rainfall distribution fluctuation was observed in Belg/spring, followed
by Kiremt/summer seasons. The highest (49%) rainfall variability was
recorded at the Hadero station. The rainfall distribution during both
short and main rainy seasons was highly variable, hence unreliable for
rainfed agriculture in the area (Table 2). It was noted that at all stations,
the CV is larger than 30%, implying that these areas are vulnerable to
hydro-meteorological catastrophes (Zarch et al., 2015). Hence, it seems
clear that in Kembata Alaba Tembaro Zone the rainfed cereal crop
production was under substantial climate impact during the period
considered in this study.
The threshold of El Niño is further classified into weak (with a 0.5 to
0.9 SST anomaly), moderate (1.0 to 1.4), strong (1.5 to 1.9), and very
strong (≥2.0) events (Takahashi and Dewitte, 2016). In the study area,
Fig. 3. Spring rainfall and its anomalous trend: a) Alaba, Angacha, and Areka,
b) Durame, Hosanna, and Mudulla stations.
Fig. 4. Standardized Anomaly of summer rainfall distribution across the sta­
tions: a) Alaba Angacha, Areka b) Durame, Hosanna, and Mudulla.
B.T. Haile et al.
Climate Services 23 (2021) 100254
ENSO regions: Nino3 Nino4, and Nino3.4. The Transpolar Index (TPI) is
also positively related to rainfall in the study area. Normalized Pressure
Darwin (NPD), and NPI (Fig. 5) do not affect some of the districts
particularly Alaba and Parts of Kedida-Gamella (Northeastern/Eastern
Tips of the study area).
Previous studies have shown that Kiremt/summer rainfall deficits
were observed during the El Niño years, and the conditions were
strongly influenced by upper-level circulation changes (Gleixner et al.,
2017). The distribution of annual rainfall during the El Niño years has
shown greater variability (Degefu et al., 2017; Diro et al., 2011; Wel­
degerima et al., 2018). The rainfall condition in the first decades was
better than the last. In some of the stations, the fluctuations and com­
plete absences in rainfall distribution were noticed in the study area.
From the findings, one can infer that El Niño years and local rainfall
were highly correlated and the El Niño distorted the distributions of
local Rainfall (Result not shown).
One of the most important index contributions to the precipitations
of the aforementioned area is DMI (Dipole Model Index), which is
denoted in Fig. 5 by “h” IOD, is represented by anomalous SST gradient
between the western equatorial Indian Ocean (50E-70E and 10S-10 N)
and the southeastern equatorial Indian Ocean (90E-110E and 10S-0 N).
Our investigation shows that there is a weak correlation between rainfall
in the study area and DMI (Fig. 5).
The results show that local rainfall and SST within the tropics and
some parts of the eastern Indian Ocean and Pacific shows a strong cor­
relation with the seasonal rainfalls of Ethiopia. This is related to the
variability in opposite modes of temperatures within the oceans like El
Niño Southern Oscillation of the Pacific and the Indian Ocean Dipole
(IOD) of the Indian Ocean. The opposite SST anomalies cause positive
and negative rainfall anomalies in Ethiopia (Dubache et al., 2019;
Fetene et al., 2019). In many parts of Ethiopia, the warm ENSO periods
(El Niño years) are typically related to lower precipitation and drought
years, while cold periods (La-Niña-years) are related to higher precipi­
tation quantities (Seleshi and Camberlin, 2006; Weldegerima et al.,
2018). Diro et al. (2011) found out that there is some correlation be­
tween SSTs over the southern Atlantic Ocean and the Gulf of Guinea and
Ethiopian summer/Kiremit rainfall.
The complex spatiotemporal variability in rainfall over Ethiopia is
attributed to the large variations in altitude and (SSTs) over the Indian,
Pacific, and Atlantic Oceans (Gleixner et al., 2017). The Pacific Ocean
SST affected the average rainfall amount in different parts of Ethiopia.
This situation has reportedly aggravated the occurrence of droughts and
is associated with ENSO events like most other parts of Africa (Bayable
et al., 2021).
Summer and spring rainfall show contrasting links with global SST.
For instance, a strong positive link with Southern Oscillation Index
(SOI), Normalized Pressure Tahiti (NPT), and Pacific Decadal Oscilla­
tions (PDO) covering a large part of the study area was observed during
the summer season (Fig. 6). Compared with the spring season, the
summer rainfall was significantly affected by global SST indices. The
northeastern and central parts of the study areas receive better rainfall
in spring than the rest of the areas, due likely to their leeward side and
orographic nature of the rainfall.
Table 2
Inter-seasonal to seasonal rainfall coefficient of variation (CV %).
Belg (FMAM)
Kiremit (JJAS)
Bega (ONDJ)
very strong, strong, and moderate El Niño events were detected. Among
the El Niño years, four of them were very strong: these are 1982, 1983,
1997, 2015. The years 1986, 1991, 1992, 2002, 2009, 2010 were strong
El Niño years, while 1994 and 2004 were weak El Niño years. The years
1988, 1989, 1999, 2000, 2007, 2008, 2011 experienced strong La Niña
events, which is in agreement with the findings of Gissila (2001) and
Korecha and Barnston (2007). The incidence of drier conditions (El
Niño) is more frequent than the La Niña.
All the stations, which were examined in this study, have had a re­
cord of a surplus of precipitations during La Niña. The result was
corroborated by a report from the National Meteorology Agency, Zonal
Agricultural bureaus, and the local communities, La Niña events affected
lives seriously by producing extra rainfall (result not shown).
The spatial correlations between global SST with the local rainfall
The result ascertains the presence of a strong correlation between the
SST and the distributions of local rainfall in the study area. The analysis
of correlations between spring rainfall and global SST revealed that the
rainfall is negatively and significantly correlated with the three of the
major indices (North Pacific Index (NPI), Normalized Pressure Tahiti
(NPT), and Southern Oscillation Index (SOI). A relatively stronger
negative correlation was shown between spring rainfall and NPT. The
correlation between rainfall and DMI was the weakest (Fig. 5). The
spatial analysis portrayed that the correlation between the rainfall and
the indices considerably varied across the landscape (Fig. 5).
Spring rainfall is passively correlated with the three of the major
The relation between crop yield and ENSO phases
The influence of ENSO Phases on rainfall and cereal crop produc­
tivity was strongly linked with the global SSTA (Table 3 and Fig. 7).
SSTA of Nino 1.2, Nino 3, and Nino 3.4 has strongly correlated with the
reduction of local rainfall. NDP moderately and negatively correlated
with the rainfall, whereas SOI moderately and positively related with
the rainfall (Table 3). As Fig. 7 shows, the yield of crops and the rainfall
are strongly and positively correlated. Maize and Barley yields were
shown to decline during these SSTAs. The yield of sorghum was
moderately reduced, while Enset was almost not affected; a slight
increment of Enset was also observed in Tahiti in the same phase. The
Fig. 5. Correlations of spring rainfall and Global SST: a) Normalized Darwin
Pressure(NPD) vs. rainfall, b) Normalized Pressure Tihat (NPT) vs. rainfall, c)
Southern Oscillation (SOI) vs. Rainfall, d) ENSO/Nino regions vs. rainfall d-f, g)
Trans Polar index(TPI) vs. Rainfall, h) Dipole Model Index (DMI vs. rainfall, and
i) North Pacific Index vs. rainfall.
B.T. Haile et al.
Climate Services 23 (2021) 100254
Fig. 7. Bar graphs showing standardized anomalies in rainfall, and crop yield,
SST indices: a) seasonal areal standardized anomaly of rainfall and crop type, b)
seasonal standardized anomaly of global oscillation, c) seasonal standardized
anomaly of Southern Oscillation indices.
Fig. 6. Correlations between summer rainfall and Global SST: a) Normalized
Darwin Pressure (NDP) vs. rainfall), b) Normalized pressure Tihat (NPT) vs.
rainfall, c) Southern Oscillation (SOI) vs. Rainfall, d-f) ENSO/Nino regions vs.
rainfall, g) Trans Polar index (TPI) vs. Rainfall, h) Pacific Decadal Oscillation
(PDOI) vs. rainfall, i) Dipole Model Index rainfall.
regions. It seems Enset is less vulnerable to short-period moisture fluc­
tuation. Enset is a perennial crop in the Northwestern part of the study
area. The entire crop under consideration was not homogeneously
affected by the ENSO event. For instance, the yield decrease is very high
for wheat, barley, Maize, and Sorghum respectively, while Enset is
resilient to the change (Table 3), this is probably is because Enset pre­
dominantly grows in highland agro-climatic regions deep-rooted and it
has water holding capacity, which is often less impacted by climate
stress. Enset tolerates impacts of ENSO extreme events, and it has a
unique adaptation in conserving water in its biomass (Ambaw et al.,
2019; Birmeta, 2004; Kilavi et al., 2018).
Selvaraju (2003) showed that in the cold phase of ENSO, crop yield
increased from its normal, and both wheat and Sorghum were the major
crops that were seriously impacted by ENSO extremes and vulnerable to
the adverse impact on the productivity of the crops. A study by Iizumi
et al. (2014) indicated that El Niño likely improves the global-mean of
some crops such as soybean, but appears to change the yields of maize,
rice, and wheat by up to − 4.3%. Their study showed that the globalmean yields of the four crops they studied (soybean, maize, rice, and
wheat) during the La Niña years tended to be below normal. Our find­
ings highlight the importance of ENSO to global crop production. In
agreement with this study, our findings also implied the differential
magnitude of impacts of ENSO on important crops in the study area. In
support of this study, research confirmed that during El Niño, the SST
over the equatorial Pacific Ocean is anomalously high and the SOI value
is negative while during La Niña, the SST over the equatorial Pacific are
low and the SOI value is positive (Zebiak, 1999). The anomalously
excessive rain during the La Niña phase of ENSO often causes floods in
the lower areas of the agricultural landscapes and may lead to loss of
In general, rainfall distribution and crop productivity have shown a
varying correlation in the Kembata Alaba Tembaro Zone, and the
magnitudes of the relationship were found to be markedly different. As
indicated in Fig. 7, during the El Niño years, the yield of all crops has
shown a reduction in productivity. El Niño is one of the driving factors
that seriously affect the local rainfall distribution and affecting the yield
of crops. When the El Niño event begins to occur in the equatorial pa­
cific, the local rainfall begins to decline. When both summer and spring
rain failed, crop production completely failed and farmers become
dependent on aid-providing agencies. Commonly, El Niño years were
not always followed by an immediate La Niña, which often leads to
Table 3
Correlation coefficient (r) showing relationships among SST, rainfall, and crops
(Darwin Pressure, Tahiti Pressure, SOI, and Nino1-4 SST regions. Values in bold
indicate significant correlation (p-value <= 0.05).
Nino 1.2
Nino 3
Nino 3.4
Nino 4
− 0.206
− 0.186
− 0.145
− 0.300
− 0.094
− 0.123
− 0.026
− 0.231
− 0.319
− 0.188
− 0.425
− 0.408
− 0.380
− 0.491
− 0.424
− 0.231
increment of rainfall and cereal crops yield was recorded in 2001, 2002,
2006, 2007, and 2010 harvesting years. In these years, Barley and
Sorghum yield increased more than other crops during this phase. While
in the years 2011, 2012, 2013, 2014, and 2015, the average yield of
crops is highly reduced with the decline in the rainfall anomaly.
Generally, among the crops, wheat and maize, and sorghum were found
to negatively correlate with ENSO, while barley and Enset were less
affected by the ENSO-induced rainfall variability. While there were
variations in the reduction of crop yield among the different districts of
the study area, relatively wheat and maize crops experienced a reduc­
tion in almost all districts.
As we can see from Fig. 7, the seasonal pattern in the relationships
among anomalies of rainfall and that of different crops considerably
vary. Fig. 7 depicts that there are variations in the relationships at
different years and different phases of ENSO. For instance, relatively
stronger negative relationships among Nino 3 and Nino 3.4 regions, and
yield of maize, Barely and Sorghum were observed, whereas, these Nino
regions were weakly related to yield of Wheat (Table 3). Interestingly,
the yield of Enset was shown to have a very weak relationship with Nino
B.T. Haile et al.
Climate Services 23 (2021) 100254
severe food insecurity in low-income countries (Ubilava and Abdolra­
himi, 2019). Catley et al. (2016) showed that out of the four strong El
Niño, the El Niño years, those that occurred in the years 2015 and 2016
were very strong, extended for a longer period, and seriously affected
the local rainfall amount and yield of major crops commonly grown and
used by the people in Ethiopia.
Declaration of Competing Interest
The authors declare that they have no known competing financial
interests or personal relationships that could have appeared to influence
the work reported in this paper.
Conclusion and recommendation
Abdi, A.M., Vrieling, A., Yengoh, G.T., Anyamba, A., Seaquist, J.W., Ummenhofer, C.C.,
Ardö, J., 2016. The El Niño–La Niña cycle and recent trends in supply and demand of
net primary productivity in African drylands. Clim. Change 138 (1-2), 111–125.
Abtew, W., Melesse, A.M., Dessalegne, T., 2009. El Niño southern oscillation link to the
Blue Nile River basin hydrology. Hydrological Processes: An International Journal
23, 3653–3660.
Alhamshry, A., Fenta, A.A., Yasuda, H., Kimura, R., Shimizu, K., 2020. Seasonal rainfall
variability in Ethiopia and its long-term link to global sea surface temperatures.
Water 12 (1), 55.
Ambaw, G., Tadesse, M., and Recha, J.W. 2019. Activity Report: Implementation of the
CSA Monitoring framework in Doyogena Climate-Smart Landscape, Ethiopia.
Bamston, A.G., Chelliah, M., Goldenberg, S.B., 1997. Documentation of a highly ENSOrelated SST region in the equatorial Pacific: Research note. Atmos. Ocean 35 (3),
Bayable, G., Amare, G., Alemu, G., Gashaw, T., 2021. Spatiotemporal variability and
trends of rainfall and its association with Pacific Ocean Sea surface temperature in
West Harerge Zone, Eastern Ethiopia. Environ. Syst. Res. 10, 1–21.
Belayneh, M., Loha, E., Lindtjørn, B., 2021. Seasonal Variation of Household Food
Insecurity and Household Dietary Diversity on Wasting and Stunting among Young
Children in A Drought Prone Area in South Ethiopia: A Cohort Study. Ecol. Food.
Nutr. 60 (1), 44–69.
Bhuyan, M.D.I., Islam, M.M., Bhuiyan, M.E.K., 2018. A trend analysis of temperature and
rainfall to predict climate change for northwestern region of Bangladesh. Am. J.
Clim. Change 07 (02), 115–134.
Birmeta, G. 2004. Genetic variability and biotechnological studies for the conservation
and improvement of Ensete ventricosum.
Camberlin, P., Janicot, S., Poccard, I., 2001. Seasonality and atmospheric dynamics of
the teleconnection between African rainfall and tropical sea-surface temperature:
Atlantic vs. ENSO. International Journal of Climatology: A Journal of the Royal
Meteorological Society 21 (8), 973–1005.
Catley, A., Cullis, A., Abebe, D., 2016. El Niño in Ethiopia, 2015–2016: A Real-Time
Review of Impacts and Responses. USAID/Ethiopia Agriculture Knowledge,
Learning, Documentation and Policy Project.
Changnon, S.A., 1999. Impacts of 1997–98 EI Niño-Generated Weather in the United
States. Bull. Am. Meteorol. Soc. 80, 1819–1828.
Cirino, P.H., Féres, J.G., Braga, M.J., Reis, E., 2015. Assessing the impacts of ENSOrelated weather effects on the Brazilian agriculture. Procedia Economics and Finance
24, 146–155.
Comenetz, J., Caviedes, C., 2002. Climate variability, political crises, and historical
population displacements in Ethiopia. Global Environmental Change Part B:
Environmental Hazards 4 (4), 113–127.
De Freitas, C., Scott, D., and McBoyle, G. 2005. Specification and verification of a new
generation climate index for tourism.
Degefu, M.A., Rowell, D.P., Bewket, W., 2017. Teleconnections between Ethiopian
rainfall variability and global SSTs: observations and methods for model evaluation.
Meteorol. Atmos. Phys. 129 (2), 173–186.
Desbureaux, S., Damania, R., 2018. Rain, forests and farmers: Evidence of drought
induced deforestation in Madagascar and its consequences for biodiversity
conservation. Biol. Conserv. 221, 357–364.
Diro, G.T., Grimes, D.I.F., Black, E., 2011. Teleconnections between Ethiopian summer
rainfall and sea surface temperature: part I—observation and modelling. Clim. Dyn.
37 (1-2), 103–119.
Dubache, G., Ogwang, B.A., Ongoma, V., Towfiqul Islam, A.R.M., 2019. The effect of
Indian Ocean on Ethiopian seasonal rainfall. Meteorol. Atmos. Phys. 131 (6),
Fetene, Z.A., Zeleke, T.T., Zaitchik, B., Gashaw, A., and Beketie, K.T. 2019.
Spatiotemporal Variability of Rainfall in Connection with Ocean-Atmosphere
Coupling in the Lake Tana Basin.
Funk, C., Davenport, F., Harrison, L., Magadzire, T., Galu, G., Artan, G.A., Shukla, S.,
Korecha, D., Indeje, M., and Pomposi, C. (Eds.) 2018. Anthropogenic enhancement
of moderate-to-strong el niño events likely contributed to drought and poor harvests
in southern africa during 2016. Bull. Amer. Meteor. Soc.
Gissila, T., 2001. Rainfall Variability and Teleconnections over Ethiopia. In: MSc thesis
(Meteorology). University of Reading, UK, p. pp109..
Gissila, T., Black, E., Grimes, D.I.F., Slingo, J.M., 2004. Seasonal forecasting of the
Ethiopian summer rains. International Journal of Climatology: A Journal of the
Royal Meteorological Society 24 (11), 1345–1358.
Gizaw, M.S., Gan, T.Y., 2017. Impact of climate change and El Niño episodes on droughts
in sub-Saharan Africa. Clim. Dyn. 49 (1-2), 665–682.
Glantz, M. 1994. Science’s Gift to the 21st Century’. In: Ecodecision.
Glantz, M., Katz, R., Nicholls, N., 1991. Teleconnections linking worldwide climate
anomalies. Cambridge University Press.
Gleixner, S., Keenlyside, N., Viste, E., Korecha, D., 2017. The El Niño effect on Ethiopian
summer rainfall. Clim. Dyn. 49 (5-6), 1865–1883.
From the investigation, it can be concluded that there is a spatial and
temporal variation in trends of rainfall across the Kembata Alaba Tem­
baro zone, with a varying magnitude and during ENSO in particular.
Crop production and rainfall pattern are highly interlinked, and ENSO
alters the weather conditions of the area. As we have noticed from the
analysis, both El Niño and La Niña phases affected crop productivity, but
different crop types responded differently and with varying magnitude.
Among the crops we studied, wheat, barley, and maize were more
seriously affected during El Niño and La Niña than sorghum and Enset.
Enset was found to be the most tolerant to the ENSO-induced rainfall
changes. Our investigation also showed considerable spatial variation in
rainfall anomalies over the studied area, though the spatial coverage is
relatively small spatial areas, implying different parts of global oscilla­
tions playing a pivotal role in regulating seasonal rainfall patterns at
local scales.
Hence, having the information on the ENSO phase in advance helps
the farming community and decision-makers in better adapting to
climate impacts through planning for tolerant crop types and varieties,
as well as adjusting cropping calendars in situations where ENSO may
induce early or late-onset/exit of rainfall. We recommend that agricul­
tural experts need be aware and informed of the ENSO cycles and help
the farming community in getting access to sufficient information about
the anomalies. Furthermore, the farmers may need to explore more
coping strategies and options for soil and water conservation with due
caution to combat moisture stress, and damages from excessive rain
events. Further studies focusing on extreme rainfall analysis and its re­
lationships with ENSO should be conducted in the areas to improve our
scientific understanding of how climate-induced natural calamities and
coping mechanisms of the local community. Agro-climatological studies
on physiological and anatomical mechanisms of different crops in
responding to extreme climate events could broaden our knowledge
about the links among multi-dimensional impacts of ENSO on the agri­
culture sector.
This particular study area is among areas in Ethiopia that are datascarce. There is limited access to dense ground-based weather stations
data. The level of details of crop yield data is also another limitation. The
data we were able to acquire were summarized into administrative
boundaries; hence spatially explicit and higher spatial resolution ground
data were not available. Despite these challenges, we were able to
explore the impacts of global processes in the climate systems at a local
scale and their differential influence on selected crops that are dominant
in the study area.
CRediT authorship contribution statement
Bereket Tesfaye Haile: Conceptualization, Methodology, Writing original draft, Investigation, Writing - review & editing. Tadesse Terefe
Zeleke: Conceptualization, Methodology, Writing - review & editing,
Project administration. Kassahun Ture Beketie: Conceptualization,
Writing - original draft, Investigation, Writing - review & editing, Project
administration. Desalegn Yayeh Ayal: Investigation, Writing - review
& editing, Project administration. Gudina Legese Feyisa: Writing original draft, Investigation, Writing - review & editing, Project
B.T. Haile et al.
Climate Services 23 (2021) 100254
Rojas, O., Li, Y., Cumani, R., 2014. Understanding the drought impact of El Niño on the
global agricultural areas: an assessment using FAO’s Agricultural Stress Index (ASI).
Food and Agriculture Organization of the United Nations (FAO).
Seleshi, Y., Camberlin, P., 2006. Recent changes in dry spell and extreme rainfall events
in Ethiopia. Theor. Appl. Climatol. 83 (1-4), 181–191.
Selvaraju, R., 2003. Impact of El Niño–southern oscillation on Indian foodgrain
production. International Journal of Climatology: A Journal of the Royal
Meteorological Society 23 (2), 187–206.
Sen, P.K., 1968. Estimates of the regression coefficient based on Kendall’s tau. J. Am.
Stat. Assoc. 63 (324), 1379–1389.
Silva M, T.D., and Hornberger, G.M. 2019. Identifying El Niño–Southern Oscillation
influences on rainfall with classification models: implications for water resource
management of Sri Lanka. Hydrology and Earth System Sciences, 23, 1905-1929.
Takahashi, K., Dewitte, B., 2016. Strong and moderate nonlinear El Niño regimes. Clim.
Dyn. 46 (5-6), 1627–1645.
Tolossa, A.A., Books, R., OER, R., SCARDA, R., and Tenders, R. 2015. Seasonal climate
prediction for rain-fed crop production planning in the upper Awash basin, Central
highland of Ethiopia.
Trenberth, K.E., 1997. The definition of el nino. Bull. Am. Meteorol. Soc. 78, 2771–2778.
Trenberth, K.E., Hoar, T.J., 1997. El Niño and climate change. Geophys. Res. Lett. 24
(23), 3057–3060.
Trenberth, K.E., Stepaniak, D.P., 2001. Indices of el niño evolution. J. Clim. 14 (8),
Ubilava, D., Abdolrahimi, M., 2019. The El Niño impact on maize yields is amplified in
lower income teleconnected countries. Environ. Res. Lett. 14 (5), 054008. https://
van Oldenborgh, G.J., Hendon, H., Stockdale, T., L’Heureux, M., Coughlan de Perez, E.,
Singh, R., van Aalst, M., 2021. Defining El Niño indices in a warming climate.
Environ. Res. Lett. 16 (4), 044003.
Weldegerima, T.M., Zeleke, T.T., Birhanu, B.S., Zaitchik, B.F., Fetene, Z.A., 2018.
Analysis of rainfall trends and its relationship with SST signals in the Lake Tana
Basin, Ethiopia. Meteorol Atmos Physics 2018, 1–10.
Wolde-Georgis, T., 1997. El Nino and drought early warning in Ethiopia. Internet Journal
of African Studies.
Yeh, S.-W., Kug, J.-S., Dewitte, B., Kwon, M.-H., Kirtman, B.P., Jin, F.-F., 2009. El Niño in
a changing climate. Nature 461 (7263), 511–514.
Yuan, C., Yamagata, T., 2015. Impacts of IOD, ENSO and ENSO Modoki on the Australian
winter wheat yields in recent decades. Sci. Rep. 5, 1–8.
Asadi Zarch, M.A., Sivakumar, B., Sharma, A., 2015. Droughts in a warming climate: A
global assessment of Standardized precipitation index (SPI) and Reconnaissance
drought index (RDI). J. Hydrol. 526, 183–195.
Zaroug, M.A.H., Eltahir, E.A.B., Giorgi, F., 2014. Droughts and floods over the upper
catchment of the Blue Nile and their connections to the timing of El Niño and La
Niña events. Hydrol. Earth Syst. Sci. 18 (3), 1239–1249.
Zaveri, E., Russ, J., Damania, R., 2020. Rainfall anomalies are a significant driver of
cropland expansion. Proc. Natl. Acad. Sci. 117 (19), 10225–10233.
Zebiak, S., 1999. El Niño and the science of climate prediction. Consequences 5, 3–15.
Zeleke, T., Giorgi, F., Mengistu Tsidu, G., Diro, G.T., 2013. Spatial and temporal
variability of summer rainfall over Ethiopia from observations and a regional climate
model experiment. Theor. Appl. Climatol. 111 (3-4), 665–681.
Gocic, M., Trajkovic, S.J.G., and Change, P. 2013. Analysis of changes in meteorological
variables using Mann-Kendall and Sen’s slope estimator statistical tests in Serbia.
100, 172-182.
Güner Bacanli, Ü., 2017. Trend analysis of precipitation and drought in the A egean
region. T urkey. 24 (2), 239–249.
Gutierrez, L., Braunstein, L.A., 2017. Impacts of El Niño-Southern Oscillation on the
wheat market: A global dynamic analysis. PLoS ONE 12 (6), e0179086.
Hall, J.W., Grey, D., Garrick, D., Fung, F., Brown, C., Dadson, S.J., Sadoff, C.W., 2014.
Coping with the curse of freshwater variability. Science 346 (6208), 429–430.
Hansen, J.W., Hodges, A.W., Jones, J.W., 1998. ENSO influences on agriculture in the
southeastern United States. J. Clim. 11 (3), 404–411.
Hare, W. 2003. Assessment of knowledge on impacts of climate change-contribution to
the specification of art. 2 of the UNFCCC: Impacts on ecosystems, food production,
water and socio-economic systems.
Hermans, K., Garbe, L., 2019. Droughts, livelihoods, and human migration in northern
Ethiopia. Reg. Environ. Change 19 (4), 1101–1111.
Iizumi, T., Luo, J.-J., Challinor, A.J., Sakurai, G., Yokozawa, M., Sakuma, H., Brown, M.
E., Yamagata, T., 2014. Impacts of El Niño Southern Oscillation on the global yields
of major crops. Nat. Commun. 5, 1–7.
Kiladis, G.N., Diaz, H.F., 1989. Global climatic anomalies associated with extremes in the
Southern Oscillation. J. Clim. 2 (9), 1069–1090.
Kilavi, M., MacLeod, D., Ambani, M., Robbins, J., Dankers, R., Graham, R., Helen, T.,
Salih, A., Todd, M., 2018. Extreme rainfall and flooding over central Kenya including
Nairobi city during the long-rains season 2018: Causes, predictability, and potential
for early warning and actions. Atmosphere 9 (12), 472.
Koo, J., Thurlow, J., ElDidi, H., Ringler, C., De Pinto, A., 2019. Building resilience to
climate shocks in Ethiopia. IFPRI, Washington, DC.
Korecha, D., Barnston, A.G., 2007. Predictability of june–september rainfall in Ethiopia.
Mon. Weather Rev. 135, 628–650.
Lau, K.-M., Wu, H.-T., Bony, S., 1997. The role of large-scale atmospheric circulation in
the relationship between tropical convection and sea surface temperature. J. Clim.
10 (3), 381–392.
Lesk, C., Rowhani, P., Ramankutty, N., 2016. Influence of extreme weather disasters on
global crop production. Nature 529 (7584), 84–87.
Liu, Y., Yang, X., Wang, E., Xue, C., 2014. Climate and crop yields impacted by ENSO
episodes on the North China Plain: 1956–2006. Reg. Environ. Change 14 (1), 49–59.
Mann, H.B., 1945. Nonparametric tests against trend. Econometrica: Journal of the
econometric society 13 (3), 245.
Ouyang, R., Liu, W., Fu, G., Liu, C., Hu, L., Wang, H., 2014. Linkages between ENSO/PDO
signals and precipitation, streamflow in China during the last 100 years. Hydrol.
Earth Syst. Sci. 18 (9), 3651–3661.
Panda, A., Sahu, N., Behera, S., Sayama, T., Sahu, L., Avtar, R., Singh, R.B., Yamada, M.,
2019. Impact of Climate Variability on Crop Yield in Kalahandi, Bolangir, and
Koraput Districts of Odisha. India. Climate 7 (11), 126.
Park, S., Kang, D., Yoo, C., Im, J., Lee, M.-I., 2020. Recent ENSO influence on East African
drought during rainy seasons through the synergistic use of satellite and reanalysis
data. ISPRS J. Photogramm. Remote Sens. 162, 17–26.
Pendergrass, A.G., Deser, C., 2017. Climatological characteristics of typical daily
precipitation. J. Clim. 30 (15), 5985–6003.