The rainfall variability and trends over North Shewa investigated using gauge as well as gridded rainfall data from 1985 to 2018. The variability of rainfall in both annual and seasonal scales were evaluated using coefficient of variation (CV), standardized rainfall anomaly, precipitation concentration index (PCI), and standardized precipitation index. Mann-Kendall test and Sen’s slope estimator were used to assess the rainfall trends. The rainfall in North Shewa was found to be highly variable both in space and time; i.e., irregular rainfall distribution was observed (PCI = 20%). The coefficient of variation showed moderate variation in both annual and Kiremt (June-September) rainfall as compared to the rainfall in Belg (February-May) and Bega (October-January) seasons. Mann-Kendall test resulted a decreasing trend in Belg and Bega seasons and an increasing trend for annual and Kiremt rainfall. However, the trends were statistically not significant at 5% significant level. The onset and cessation dates showed a non-significant decreasing and increasing trends, respectively. Whereas, the length of growing period showed a significant increasing trend. Overall, in North Shewa, the wet season (Kirmet) has been wetter while the dry and small rainy seasons (Bega and Belg) have been drier; North Shewa has been vulnerable to drought during Belg season (CV > 30%). Due to the high contribution of Kiremt season for annual rainfall amount of about 75%, the annual rainfall has also showed an increasing trend.
Rainfall is one of the most important climate elements for agricultural production throughout the world . It is also the most important climate element for rainfed agriculture and the general socio-economic development of Ethiopia . Rainfall variability affects water resources sustainability which includes the availability, management, and utilization of water resources. This, in turn, may affect ecosystems, land productivity, agriculture, food security, water quantity and human health . When the uneven distribution of rainfall results in a mismatch between water availability and demand, irrigation structures are required to redistribute water concerning the requirements of a specific region . Hence, for ecosystem resilience and sustainable agricultural activities, accurate estimation of the spatial and temporal distribution of rainfall is crucial, particularly for rain-fed agriculture [5,6].
Various trend analysis of rainfall at different spatial (e.g., regional and national) and temporal (e.g., annual, seasonal, and monthly) scales have been studied which indicated changes in the spatial and temporal variability and trends. For example, according to Gamachu  rainfall in Ethiopia has shown large variations across time and space, due to the complex topography and varying latitude of the country . Spatially, the amount, seasonal cycle, onset and cessation times of rainfall as well as the length of growing period, have shown variability across the country [7,8]. Temporally, it varies from days to decades, with the magnitude and direction of historic rainfall trends varying from region to region and season to season 9-12]. This complex Spatio-temporal variability of rainfall over Ethiopia is attributed to the large variations in altitude, variations in sea surface temperatures (SSTs) over the Indian, Pacific and Atlantic Oceans and the inter- seasonal and interannual variation of the strength of the monsoon
over the Arabian Peninsula [7,8,13-15].
Although various studies indicated changes in the variability
and trends of rainfall over Ethiopia, they are not consistent and
clear. Because the climate of Ethiopia is geographically quite
diverse, due to its equatorial positioning and varied topography
(Enyew & Steeneveld, 2014). Also, the national (even the regional)
rainfall variability studies mask zonal scale variabilities as
Ethiopia is a large country in size; more than three times bigger
than Germany. Therefore, investigation of rainfall variability and
trends at a local level (at a smaller area with higher resolution)
has enormous advantage for a country like Ethiopia where the
economy is mainly dependent on rain fed agriculture; it helps
the decision-makers to take appropriate measures. In view of
that, Fitsum et al.  are probably the first to analyze rainfall
variability and trends at Bale zone (southeastern part of Ethiopia).
In contrast to the study by Fitsum et al. , in this study, the
spatial variability and the temporal trend of rainfall using gauge
as well as gridded rainfall data are investigated in a more detailed
way for North Shewa (central part of Ethiopia). Consideration of
the analysis of both the spatial variability and the temporal trend
of the number of rainy days, onset, ending, and length of growing
period (LGP) for the main rainy season (Kiremt) makes this
study unique. On top of that, consideration of the precipitation
concentration index as well as consideration of the analysis
meteorological drought indices such as the probability of dry
spell occurrence, standardized rainfall anomaly, and standardized
precipitation index for Belg and Kiremt seasons makes this study
unique. Note also that the study sites are located in different
climate regions . For example, North Shewa received the
highest rainfall amount during Kiremt season (75% of the annual
rainfall); whereas, Bale zone received it during Belg season
(42.5% of the annual rainfall); Bale zone received only 33.3 % of
the annual rainfall during Kiremt season [16,17].
As a final point, using gauge rainfall data is more reliable than
satellite data because satellite rainfall values are just estimates
which have various sources of uncertainty . Also, the gaugebased
data sets have generally provided long-term records of
precipitation, which are suitable for climate studies whereas the
satellite-related data sets have the limitations of their short length
of record .
The study was conducted in North Shewa which is one of
the 11 administrative zones of Amhara National Regional State,
Ethiopia (see Table 1 and Figure 1). It is located in between 9º -
11º N latitude, and 38º -40 º E longitudes with an area of about
15,936km2. According to the Central Statistical Agency of Ethiopia
(2007), North Shewa had a total population of 1,837,490; 928,694
men and 908,796 women.
The topography comprises uneven and rugged mountainous
highlands in the northern and central parts of the zone, extensive
plains and also deep gorges and cliffs in the periphery . The
topographic feature of the administration is lower in the south,
west and east peripheries and higher in the central part of the zone
. The zone has four agro-ecological zones; namely, lowland:
500-1500m a.s.l, mid-latitude: 1500-2300m a.s.l, highland: 2300-
3200m a.s.l, and ‘Wurch’: above 3200m a.s.l.
North Shewa is characterized by three distinct seasons with
four months each, classified based on the climatology of rainfall
and temperature. These seasons are locally known as ‘Bega’
(ONDJ), ‘Belg’ (FMAM), and ‘Kiremt’ (JJAS) (Mekonnen et al. 2018).
Kiremt and Belg are the main and small rain seasons, respectively
while Bega is the dry season of the zone. The rain seasons have
been inconsistent with respect to the onset, ending, distribution,
and amount of rainfall due to the seasonal movement of the ITCZ
to north in July and south in January, the atmospheric circulation
associated with ITCZ, and the complex topography with a marked
contrast in elevation. As a result of climate change, currently, the
rain seasons (Kiremt and Belg) are becoming more inconsistent
According to current studies, on average North Shewa receives
a mean annual rainfall ranging between 790.3-1765.1mm. At the
seasonal scale, it receives 633.2-1071.2, 121.1-483.5, and 25.3-
209.1 millimeters in Kiremt, Belg and Bega seasons, respectively.
Very good quality of gauge or station as well as gridded daily
rainfall data was obtained from the National Meteorological
Agency of Ethiopia (NMA) from 1985 to 2018 for the selected 18
Detected outliers were removed using the Turkey fence approach
. The data series was also examined for homogeneity and
no heterogeneity was detected. Missing data in the time series
was filled with data from neighboring stations using statistical
regression techniques as described in  and applied in various
studies [9,25]. Additionally, the missing data were filled with
gridded data. The gridded data are constructed data series based
on records of gauge stations and satellite observations. This data
is very useful because weather stations are limited in number
and unevenly distributed and have sometimes a short period of
In this study, we employed INSTAT, Genstat, XLSTAT, R
(RStudio), and MS Excel spreadsheet tools to analyses our data
set. Graphs were mapped using ArcGIS software; inverse distance
weighting was used for spatial interpolation [26,27].
Variability of rainfall has been computed using coefficient of
variation (CV) (see Hare 1983), standardized rainfall anomaly
(SRA) (see Agnew & Chappel 1999), precipitation concentration
index (PCI) , and standardized precipitation index (SPI) .
Contribution of seasonal rainfall to the total annual rainfall in
percent (CT) for each station is also computed.
For analysis, the monthly rainfall of all the stations was used
to calculate an areal average rainfall for North Shewa using the
equation of Nicholson , i.e.,where Rj is a real
integrated rainfall for year j; Xij is rainfall at station i for year j and
Ij is the number of stations available for year j.
To estimate the sign and slope of long-term rainfall trends for
the selected study sites, Mann-Kendall’s trend test [31,32] and
Sen’s slope estimation method  were used.
The presence of a statistically significant trend is evaluated
using the ZMK value [31,32]. In a two-sided trend test, the null
hypothesis Ho should be accepted if | | 1 / 2 MK Z < Z −α at a given
level of significance. Z1-α/2 is the critical value of ZMK from the
standard normal table. E.g. for 5% significant level, the value of
Z1-α/2 is 1.96. In this study, a 5% significant level is used. Note
also that the modified Mann-Kendall test  was not applied as
the data has no serial dependence.
Table 2 shows that the study area received annual rainfall
ranging from 790mm to 1765mm with a mean of 1029mm and
CV of 24%; CV varied from 13-40%. At the seasonal level, Kiremt
rainfall varied from 633-1071mm with mean of 757mm. For
Kiremt, the mean CV was 28%; CV revealed high (CV=42%) and
less (CV = 18%) variability. The mean total rainfall amount for
Belg and Bega were 209 and 75 millimeters; they varied from 121-
484mm and 25-209mm, respectively. Bega rainfall was extremely
variable (CV > 70%) for all stations. As compared to Kiremt
season, the Belg rainfall was more variable. For example, CV for
Belg was 53%; it ranged from 37-69%. This agrees with the study
by Woldeamlak & Conway (2007) . Based on Hare (1983)
classification, North Shewa has been vulnerable to drought during
Belg season (CV > 30%). Generally, the seasonal variability was
higher than the annual variability. This agrees with the findings of
previous studies conducted in Ethiopia .
CT for Kiremt was very high (CT = 75%); it ranged from 54-
83 %. This is supported by Bewket & Conway  and Ayalew
et al. . Belg rainfall also contributed a considerable amount
for the annual total rainfall (CT = 19%); it ranged from 13-31
%. For Bega rainfall the mean areal CT was 6%; it ranged from
2-15 %. The analysis of PCI showed that in all stations the rainfall
pattern was not uniformly distributed. Generally low or no
rainfall was received from October to February while intensive
rainfall was received between July and September. The maximum
and minimum Kiremt rainfall amount was 942.9mm (occurred
in 2007) and 361mm (occurred in 1987). Refer Figure 2 for the
corresponding values for Belg and Bega seasons.
Figure 3 shows the spatial distribution of rainfall for the
annual and seasonal time scales. Generally, the rainfall distribution
showed a general decrease in annual mean rainfall from south
to north. See Figure 3 for the corresponding seasonal rainfall
Figure 4 shows the annual and seasonal rainfall anomalies.
The result of SRA showed a 50% dry tendency and 50% wet
tendency over the study area on annual basis. For Kiremt season
47% showed weak to strong negative departure from the long
term mean rainfall and 53% recorded above the long-term average
rainfall. Likewise, SRA during Belg and Bega season showed 50%
and 59% dry tendency dominancy, respectively. According to the
drought assessment method by Agnew and Chappel (1999), seven
dry years: two extremes (1987 and 2018), two severe (1991 and
1992), and three moderate (1989, 1993 and 2015) dry years were
identified. In contrast, 2007 and 2012 had experienced severe wet
years; while that of 1998, 2003 and 2013 showed moderate wet
years during Kiremt season.
Figure 5 shows the average of 3-month SPI for a period of
three years (2016-2018) and ten years (2009-2018) for Belg
season. According to the 3-month SPI analysis, in both periods
North Shewa had experienced from extremely dry (SPI ≤ -2) to
extremely wet (SPI ≥ 2) conditions. Comparatively, the three years
(2016-2018) were a bit drier than the ten years (2009-2018). The
areal average 3-month SPI for 1985-2018, 2009-2018, and 2016-
2018 was 0.14, 0.13, and 0.09, respectively which is in agreement
with the trend of SRA for Belg season (see Figure 4).
The lowest, highest, and mean LGP was 55, 138, and 78 days,
respectively (see Table 3). In a similar study conducted in Tigray
region (northern Ethiopia) for the period 1980-2009, Hadgu et
al.  found the average LGP to vary from 66 to 85 days. The
coefficient of variation (CV) of LGP ranged from 14-87%. Higher
CV (> 13%) of LGP gives less confidence in crop selection based on
the maturity period.
In a study conducted in northern Ethiopia, Hadigu et al. 
found the start (onset) date of Kiremt growing areas to be between
the 1st and 3rd week of July. In contrast, in this study, the mean
onset date was varied from 159 DOY (June -6) to 189 DOY (July-
6). The areal mean onset date was 178 DOY (June -25) in the study
area. The observed variability of Kiremt onset was varied from
6-19 %. The onset date of Kiremt growing areas had experienced
dependable patterns across Gisherabel; while at Gudoberet the
patterns were not easily understood and consequently decisions
of crop plantation and related activities should be taken with
great care. Similarly, the mean cessation date ranged from 271
DOY (Sep-26) to 297 DOY (Oct-23) areas; the areal mean cessation
date was 281 DOY (Oct-7). At all the probability levels considered,
the end of Kiremt season was more extended at Mehalmeda
compared to other areas.
Figure 6 presents the spatial distributions of onset, cessation,
LGP, and number of rainy days. The first two graphs show the
mean spatial onset (the start) and the cessation (the end) of
Kiremt season where the numbers in the legend are the DOYs. For
example, DOY of 159.1 (~159) means the 6th of June and DOY of
188.8 (~189) means the 7th of July, and so forth. Accordingly, the
western part and some pocket areas in the northern, central and
southern parts of the study area had early onset of Kiremt rainfall
while late onset was observed in the northern and at some pocket
areas in the central parts of the study area. Similarly, the cessation
date of Kiremt season was early (27th of September) in a few
northern and western pocket areas; it was late (23rd of October) in
a few southern, northern and eastern pocket areas.
For Kiremt season, the number of rainy days varied from 40-
68 days with an areal mean of 61 days (see Table 3). The interannual
variability of the number of rainy days ranged from 13-
31% with an areal mean of 19.7 days.
The probability of dry spell occurrence for Belg (DOY = 32-
152) and Kiremt (DOY = 153-274) seasons for ten selected
stations is shown in Figure 7; dry spell lengths of 5,7,10, and 15
days were considered. Observations of the rainfall data illustrated
that the probability of dry spells occurring within the growing
seasons varied from month to month. During Belg season, the
probability of the occurrence of dry spells for 5, 7, 10, and 15 dry
spell days was above 40% in all stations. In the main rainy season
(Kiremt), the probability of 7, 10, and 15 days dry spell occurrence
in July and August was zero; whereas for 5 days dry spell it was
more than 30% at all stations.
Generally, the shorter dry spell events have a higher probability
of occurrence, compared to the longer ones. Also, Belg season had
a higher probability of dry spells than Kiremt season and is liable
to meteorological drought.
The challenges of the risk of the dry spell were more at Molale,
Gundomeskel and Enewari areas. This implies that, in these areas,
the risk of planting long cycle crops before June is above 65%.
The Mann–Kendall trend test showed a decreasing trend of
annual rainfall at Alemketema, Alyuamba, Rema and Shewarobit
areas (see Table 4). Only at Alyuamba station, the detected trends
were significant at 5% significant level. This agrees with the results
of Seleshi ; Cheung & McSweeney C ; Viste et al. ; NMA
; they reported statistically non-significant declining tendency
in annual rainfall across Ethiopia between 1960 and 2006. On the
contrary, the annual and Kiremt rainfall in North Shewa showed
a statistically non-significant increasing trend (increased by a
factor of 37 and 39mm per decade, respectively). This agrees with
the result of the study by Bewket & Conway ; they showed
that the annual and Kiremt rainfall at Dessie and Lalibela for the
period 1975-2003 had a significant increasing trend. Belg and
Bega rainfall had shown a non-significant decreasing trend.
The Mann-Kendall trend test on onset of Kiremt rainfall showed
a decreasing trend in all stations except Alyuamba and Meragna
stations (see Table 5). The observed trends were statistically
significant only at Gisherabel, Yigem and Gundomeskel stations
while in the remaining stations the trends were not significant.
The cessation date of Kiremt rainfall showed an increasing trend
in fourteen stations; in the four stations (Gudoberet, Mehalmeda,
Rema and Zemero), the trend was statistically significant.
Generally, the Mann-Kendal trend test showed that the onset
date had been decreasing non-significantly by 36 days per decade
while that of cessation date had been increasing non–significantly
by 2 days per decade in the study area.The length of the growing period (LGP) showed an increasing
trend in fourteen stations; in seven stations the trend was
statistically significant. In line with this Kelemu S  reported
decreasing trends of the length of the growing period at
Debretabor and Wereta stations in South Gonder zone for the
period 1985-2014. Generally, in North Shewa the test showed
statistically significant increasing trends of rainfall by 5 days per
decade over the last 34 years.
On the other hand, the number of rainy days had shown
increasing trends in all areas except at Ginager, Rema, and
Shewarobit areas. Generally, the number of rainy days had shown
statistically significant increasing trends in the study area; it had
increased by 3.1 days per decade .
In this study, detailed analysis of the temporal and spatial
characteristics of rainfall using rainfall data obtained from the
National Meteorological Agency of Ethiopia for 34 years (1985-
2018) is presented. The data used is a combination of gauge
and gridded rainfall data which is believed to be more reliable
than using lonely satellite rainfall data. Rainfall is the major
climatic parameter that needs to be analyzed for its statistical
characteristics in order to conduct successful rain-fed agriculture
over central Ethiopia. Variation of rainfall in both time and
space has a significant effect in the performance of agricultural
productivity, particularly over central Ethiopia where agriculture
heavily relies on seasonal rainfall.
North Shewa is characterized by bimodal rainfall pattern
where much of the rainfall concentrated in the main rainy season
called Kiremt (June-September) and a small amount of rainfall
received in the second rainy season called Belg (February-May).
Bega (October-January) is a relatively dry season. The mean
annual rainfall amount was 1029mm while Kiremt, Belg, and
Bega received 757, 209, and 75 millimeters respectively. The
mean onset and cessation dates were June 25th and October 7th,
respectively while the mean duration of the Kiremt season was
The result showed that there was considerable temporal and
spatial variation of rainfall over North Shewa. The coefficient of
variation of the annual and Kiremt rainfall revealed moderate
inter-annual and seasonal variability. However, much larger
variation was observed during Belg and Bega season. On the other
hand, the result of coefficient of variation showed low variability
in the onset and cessation dates; whereas, the length of growing
period was highly variable. Spatially, a general decrease of mean
annual rainfall from south to north was observed. Moreover, the
rainfall was characterized by a sporadic fluctuation of wet and dry
years in a periodic pattern.
Trends of rainfall at annual and seasonal time scales for the
study period were analyzed using the Mann-Kendall test and Sen’s
slope estimator. The tests for annual and Kiremt rainfall resulted in
non-significant increasing trends; increased by 37 and 39mm per
decade, respectively. The result of the test showed non-significant
decreasing trends by 16 and 0.4mm per decade, during Belg and
Bega seasons, respectively. Also, the onset dates decreased nonsignificantly
by 36 days per decade; whereas, cessation dates
showed a non–significant increasing trend of 2 days per decade.
Likewise, the length of growing period also showed a statistically
significant increasing trend of 5 days per decade. On the other
hand, the number of rainy days increased significantly by 3.1 days
During Belg season, the probability of the occurrence of
dry spells for 5, 7, 10, and 15 dry spell days was above 40% in
all stations. The information on the length of dry spells can be
used as input in decision making concerning crop selection,
supplementary irrigation water demand scheduling, and in other
This study has offered useful information for a better
understanding of the temporal trends and spatial distribution
of rainfall in the study area, which is of great importance for
water and forest resources management particularly in securing
sustainable agricultural production. Moreover, this study can be
used as an input for a more comprehensive study that may include
the impact of the temporal rainfall trends and the spatial rainfall
variabilities on water, agriculture, and forests as well as on the
driving forces that caused the variabilities and trends.
All data used during the study were provided by a third party.
Direct requests for these materials may be made to the provider
as indicated in the Acknowledgements. All code used during the
study are available from the authors by request.
NSHSB (2007) North Shewa Highland Sheep and Barley, livelihood profile Amahra, Ethiopia.
Shefine BG (2018) Analysis of Meteorological Drought Using SPI and Large-Scale Climate Variability (ENSO) - A Case Study in North Shewa Zone, Amhara Regional State, Ethiopia. Hydrol Current Res 9: 307.
National Meteorological Agency (NMA) (2007) Climate change national adaptation program of action of Ethiopia. Technical report, United Nations Development Program (UNDP). Addis Ababa, Ethiopia.
Kelemu S (2016) Analysis of climate variability and its interconnection with major crops production in the South Gondar Zone,Amhara National Regional State, Ethiopia. MSc thesis submitted to Post Graduate Program Directorate, Haramaya University, Ethiopia.