Abstract

Drought is a major problem in Ethiopia and particularly affects the agricultural and water sectors. This paper aims to assess the spatial and temporal drought variability of central Ethiopia. For this purpose, archival rainfall data recorded from 1989 to 2017 and the Gurage zone topographic maps were used. The five stations’ Standardized Precipitation Index (SPI) were combined with the geographical information system (GIS) to analyze the spatial distribution of drought events. The results show that a total number of 41 drought events were recorded in the region. The number of drought events reaches its maximum value in the year 1992, whereas Bui and Koshe contain the most frequent drought events. The spatial analysis of droughts verifies that most of the frequent and extreme events are recorded in the eastern part of the region. The lowland part of Gurage zone is very prone to drought. The grounded spatio-temporal drought risk events analysis has shown a possible threat to the water and rain-fed farming that has a cascading effect on the livelihoods of farmers. Moreover, the drought condition of the region is unpredictable and recurrent. This study recommends further study containing remaining statistical drought indices such as reconnaissance drought and streamflow drought index.

HIGHLIGHTS

  • Some parts of the zone were found to be very sensitive and vulnerable to drought events.

  • Drought conditions in the area were found to be unpredictable and recurrent.

  • Areas vulnerable to drought were identified, which helps to point out adaptation options and spot early warning signs, establishing a climate research center and refugee camp.

  • The level of drought risk in the area was identified against the global risk measurement scale.

INTRODUCTION

A natural hazard is a natural occurrence that might have a negative effect on living organisms and the environment. Natural hazard events can be classified into two broad categories: geophysical and biological (Burton et al. 1993). Internal or external processes such as active diastrophism and climate changes are capable of changing landforms and activating natural hazards, which in some cases control human activities (Bathrellos et al. 2014). Among meteorological or climate hazards, drought and flood are common hazards for our ecosystem. Literally, drought is a period of below-average precipitation in a given region, resulting in lengthy shortages in the water supply, whether atmospheric, surface water, or groundwater. Scientists warn that global warming and climate change may result in more extensive droughts in the near future (Nagarajan 2010). These extensive droughts are likely to occur within the African continent due to its very low precipitation levels and high temperatures/pressure (Calow et al. 2010).

Droughts occur frequently in some parts of the world (Mishra & Singh 2011). A drought can last for years, or may be declared as drought after as few as 15 days, and if lasting for less than 15 days is declared a dry spell (Sivakumar 1992). Recurrent drought has a substantial impact on the ecosystem, agriculture and water sector of the affected region and harms the social, cultural, and economic life of the locality (Amsalu & Adem 2009). Extreme heat can significantly worsen drought conditions by hastening evaporation of soil and surface water and transpiration of plant leaf (Dai et al. 2018). Frequent drought is common in the tropics and significantly increases the chances of a famine, poverty, fragile ecosystem, and subsequent natural fires (Brando et al. 2019). Drought is one of the most devastating natural hazards, and has exerted negative impacts on industrial production, labor efficiency, agricultural production, electricity production, and groundwater potential (Omer 2008). In Ethiopia, recurrent drought has been observed in different time periods with diverse magnitude and dimensions since 1957 (Table 1).

Table 1

Chronological story of drought in Ethiopia from 1957 to 2017 (source: Menkir 2018; Mekonen et al. 2020)

YearRegion affectedEffect and damage
1957/58 Tigray and Wollo Rain failure in 1957 and about 100,000 people died 
1962/63 Western Ethiopia Extreme drought 
1964–66 Tigray and Wollo About 1.5 million people affected 
1971–75 Ethiopia Rain failures; estimated about 250,000 dead; 50% of livestock lost in Tigray and Wollo 
1978/79 Southern Ethiopia Failure in Belg rain and 1.4 million people affected 
1982 Northern Ethiopia Late Meher rains and 2 million people affected 
1984/85 Ethiopia Rain failure; 8 million people affected 
1987/88 Ethiopia 7 million people affected 
1990–92 Northern, Eastern, and SW Ethiopia Rain failure, about 4 million people suffering food shortage 
1993/94 Tigray and Wollo Widespread food insecurity (7.6 million people were affected), but few deaths or cases of displacement were reported 
1997 Borena, Bale, Omo, Somali Almost 986,000 people affected 
1999 N. and S. Wollo, Wag, Himra; Tigray; B. Gumu, Gambela, Oromia, SNNPR, Somali Almost 5 million people affected 
2003/4 All regions Over 13 million people affected, but the response mitigated the worst potential outcomes 
2005 Somali, Oromia Almost 2.6 million drought disaster affected people 
2008/9 All regions Almost 12.6 million people affected 
2011 S and E. Oromia, Somali Severe food insecurity and 4 million people affected 
2015/16 N, E, and SW Ethiopia About 10.2 million people affected 
2017/2018  Southeastern Ethiopia Estimated a total of 7.88 million people affected 
YearRegion affectedEffect and damage
1957/58 Tigray and Wollo Rain failure in 1957 and about 100,000 people died 
1962/63 Western Ethiopia Extreme drought 
1964–66 Tigray and Wollo About 1.5 million people affected 
1971–75 Ethiopia Rain failures; estimated about 250,000 dead; 50% of livestock lost in Tigray and Wollo 
1978/79 Southern Ethiopia Failure in Belg rain and 1.4 million people affected 
1982 Northern Ethiopia Late Meher rains and 2 million people affected 
1984/85 Ethiopia Rain failure; 8 million people affected 
1987/88 Ethiopia 7 million people affected 
1990–92 Northern, Eastern, and SW Ethiopia Rain failure, about 4 million people suffering food shortage 
1993/94 Tigray and Wollo Widespread food insecurity (7.6 million people were affected), but few deaths or cases of displacement were reported 
1997 Borena, Bale, Omo, Somali Almost 986,000 people affected 
1999 N. and S. Wollo, Wag, Himra; Tigray; B. Gumu, Gambela, Oromia, SNNPR, Somali Almost 5 million people affected 
2003/4 All regions Over 13 million people affected, but the response mitigated the worst potential outcomes 
2005 Somali, Oromia Almost 2.6 million drought disaster affected people 
2008/9 All regions Almost 12.6 million people affected 
2011 S and E. Oromia, Somali Severe food insecurity and 4 million people affected 
2015/16 N, E, and SW Ethiopia About 10.2 million people affected 
2017/2018  Southeastern Ethiopia Estimated a total of 7.88 million people affected 

Drought occurrence can be assessed in space and time through a sound basis of scientific use of historical data (Tsakiris et al. 2007). Currently, there are many statistical-based drought indices, for example, the Reconnaissance Drought Index (RDI) and the Streamflow Drought Index (SDI). Also, the widely used Standardized Precipitation Index (SPI) and rainfall deciles can be used (Tigkas et al. 2015). The common characteristics of the SPI and rainfall deciles are that they require a relatively small amount of data for their analysis and the results can be easily interpreted and used in strategic planning and operational applications. Principally, SPI is usually used as a meteorological drought indicator but based on rainfall information alone, which is significantly both a strength and shortcoming (Temam et al. 2019). A study conducted by Shamshirband et al. (2020) verified that SPI is the drought index that delivers higher accuracy than other indices. Ali Ghorbani et al. (2018) stated that the possibility to estimate drought through evaporation rates using novel learning algorithms remains a vital task for agriculture and water resources management.

Drought is one of the most common climatic or meteorological hazards, and has significant impacts on the livelihoods and economy of Gurage zone. The use of long-term climate data can be employed to analyze the spatial and temporal drought characteristics, and the outcomes of such study would be helpful for better understanding drought behavior and for adaptation options. To fill the gap, SPI-12 and a topographic map were used to assess drought in Gurage zone, and the objective of this study is to assess spatio-temporal analysis of drought variability in central Ethiopia. This work will fill the gap in planning of water resource use, mitigation, and drought disaster prevention in the region.

MATERIALS AND METHODS

Description of the study site

This study was conducted in Gurage zone, one of the administrative zones of Southern Nations, Nationalities, and People Region, central Ethiopia. It is about 155 km southwest of Addis Ababa, bordering the Awash River in the north, the Gibe River (a tributary of Omo River) to the southwest and Lake Ziway in the east. The total area of the zone is about 5,893.4 km2 and is geographically located between 7° 40′ 0″–8° 20′ 0″ and 38° 0′ 0″–39° 0′ 01″ (Figure 1). The zone is characterized by a bi-modal rainfall regime, locally known as Kiremt (main rainy season) and Belg (small rainy season) seasons. Based on the 2007 Census conducted by the Central Statistical Agency of Ethiopia (CSA), this zone has a total population of 1,279,646, of whom 622,078 are men and 657,568 women, and are frequently affected by drought.

Figure 1

Location map of the study area.

Figure 1

Location map of the study area.

Data sources and analysis techniques

Drought studies have received a great deal of attention from researchers worldwide. In the present study, historical rainfall data of Gurage zone stations were used. Daily archival rainfall data series recorded for 29 years (1989–2017) from five stations were collected from the Ethiopia National Meteorology Agency (NMA) (Table 2). Ethiopia Mapping Agency (EMA) shape file database and SPI-12 output data were used as input for geostatistical analysis (Empirical Bayesian kriging). Empirical Bayesian kriging is a geostatistical interpolation method that automates the most difficult aspects of building a valid kriging model. The primary benefits of the Empirical Bayesian kriging method are that standard errors of prediction are more accurate than other kriging methods, it requires minimal interactive modeling, allows accurate predictions of nonstationary data, and is more accurate than other kriging methods for small datasets. Moreover, other kriging methods require manual adjustment of some parameters to achieve accurate results, but Empirical Bayesian kriging automatically calculates these parameters through a process of sub-setting and simulations.

Table 2

Average annual rainfall and standard deviation (SD) in the study area

No.StationYearsMean rainfall (mm)SD (mm)Elev. (m)Long. (E)Lat. (N)
Imdibir 29 1,174.11 290.81 2,076 8.13 37.93 
Wolkite 29 1,191.89 389.36 2,000 8.29 37.78 
Butajira 29 984.04 402.45 2,000 8.12 38.37 
Bui 29 1,063.12 206.62 2,020 8.32 38.55 
Koshe 29 801.86 187.27 1,878 8.01 38.53 
No.StationYearsMean rainfall (mm)SD (mm)Elev. (m)Long. (E)Lat. (N)
Imdibir 29 1,174.11 290.81 2,076 8.13 37.93 
Wolkite 29 1,191.89 389.36 2,000 8.29 37.78 
Butajira 29 984.04 402.45 2,000 8.12 38.37 
Bui 29 1,063.12 206.62 2,020 8.32 38.55 
Koshe 29 801.86 187.27 1,878 8.01 38.53 

The daily rainfall database contains information such as the location of stations (elevation, xy coordinate, city, village, and special reference point) and daily rainfall records. The EMA database contains area boundaries, districts, village, and other details.

Instat + v3.37, DrinC and XLSTAT 2019 software were used to analyze temporal drought events, whereas the spatial event was interpolated using ArcGis 10.5.1. The primary benefits of DrinC are that it's a user-friendly tool, operates with small units of data and is suitable for meteorological, hydrological, and agricultural drought analysis.

Missing data and consistency check

Missing data may be due to the absence of an observer, short disturbances in observations due to breakage, malfunction, and calibration problem of instruments during a certain time period. Therefore, this needs to be solved before undertaking further analyses. The missing values were patched using a first-order Markov chain model of Instat version 3.37 software. The benefits of the first-order Markov chain model are simplicity and out of sample forecasting accuracy and simulation. Simple models, such as those used for first-order Markov chain model, are often better at making predictions than more complicated models. The consistency of the rainfall data set was checked by the double-mass curve method and a plot of average cumulative annual rainfall data (as ordinate) against the abscissa. The double-mass curve is used to check the consistency of many kinds of hydrologic data by comparing data for a single station with that of a pattern composed of the data from several other stations in the area. The double-mass curve can be used to adjust inconsistent precipitation data of more than two stations.

Trend analysis and model specification

The Mann–Kendall test was used for analysis of the trend in rainfall for the time period 1989–2017. There are two benefits of using the Mann–Kendall test. First, it is a non-parametric test and does not require the data to be normally distributed. Second, the test has low sensitivity to abrupt breaks due to inhomogeneous time series. Each data value is likened to all subsequent data values. If a data value from a later time period is higher than a data value from an earlier time, the statistic S is increased by 1. On the other hand, if the data value from the later time period is lower than a data value sampled earlier, S is decreased by 1. The net result of all such increments and decrements is one that determines the final value of S. The Mann–Kendall S statistic is mathematically computed as follows:
formula
formula
where Tj and Ti are the annual values in years j and i, j > I, respectively.
A positive value of S indicates an increasing trend whereas a negative value indicates a declining trend in the data. At a certain probability level H0 is rejected in favor of H1 if the absolute value of S equals or exceeds a specified value Sα/2, where Sα/2 is the smallest S which has probability less than α/2 to appear in the case of no trend. For n ≥ 10, the statistic S is approximately normally distributed with the mean and variance as follows:
formula
The variance (σ2) for the S statistic is defined by:
formula
where ti denotes the number of ties to an extent i. The summation term in the numerator is used only if the data series contains tied values. The standard test statistic Zs is calculated as follows:
formula

The test statistic Zs is used as a measure of the significance of the trend. In fact, this test statistic is used to test the null hypothesis, Ho. If |Zs| is greater than Zα/2, where α represents the chosen significance level, then the null hypothesis is rejected, implying that the trend is significant.

Another statistic obtained on running the Mann–Kendall test is Kendall's tau, which is a measure of correlation and therefore measures the strength of the relationship between the two variables. In common with other extreme correlations, Kendall's tau will take values between ±1 and +1, with a positive correlation indicating that the ranks of both variables increase together while a negative correlation indicates that as the rank of one variable increases, the other decreases.

The SPI was determined as the difference between the annual totals of a particular year and the long-term average rainfall records divided by the standard deviation of the long-term data. This index is used to observe the nature of the trends and also enables determination of the extremely dry and wet years in the record. SPI was plotted against time (in years) to visualize and identify/select the extreme drought and flooding year in the time period for further analysis. In addition to this, McKee et al. (1993) designed a SPI for multiple time scales from 1 to 12 months which was used to identify the drought variability of an area and is mathematically computed as:
formula
where SPI-12 is Standardized Precipitation Index; X is the annual rainfall total of a particular year; μ is mean annual rainfall over a period of observation; and is the standard deviation of annual rainfall over the period of observation.

Nowadays, there are many drought indices used to analyze drought such as the RDI, SDI, SPI, and the rainfall deciles. For this study, the SPI drought index was used due to its advantages. The advantages of this index are that it requires a relatively small number of data for its analysis and the results can be easily interpreted and used in strategic planning and operational applications. SPI is usually used as a meteorological drought indicator, but based on rainfall information alone, which significantly shows both a strength and a shortcoming.

According to the classification scale for SPI values, a positive value of the SPI denotes that rainfall at the study area is higher than average whereas a negative value of the SPI indicates that rainfall in the area is lower than normal (Du et al. 2013; Pei et al. 2013). A region will be considered as ‘extreme wet’ if the SPI value of the area is greater than or equal to +2.00 and, oppositely, the region is considered as suffering drought if the SPI value of the area is less than −2.00 (Table 3).

Table 3

Classification of SPI values

SPIClassification
≥2.00 Extreme wet 
1.50 to 1.99 Severe wet 
1.00 to 1.49 Moderate wet 
0.50 to 0.99 Mild wet 
−0.49 to 0.49 Near normal 
−0.99 to −0.50 Mild drought 
−1.49 to −1.00 Moderate drought 
−1.99 to −1.50 Severe drought 
<− 2.00 Extreme drought 
SPIClassification
≥2.00 Extreme wet 
1.50 to 1.99 Severe wet 
1.00 to 1.49 Moderate wet 
0.50 to 0.99 Mild wet 
−0.49 to 0.49 Near normal 
−0.99 to −0.50 Mild drought 
−1.49 to −1.00 Moderate drought 
−1.99 to −1.50 Severe drought 
<− 2.00 Extreme drought 

RESULTS AND DISCUSSION

Missing data and consistency check

The consistency of the rainfall data set was checked by the double-mass curve method and a plot of average cumulative annual rainfall data (as ordinate) against the abscissa. As shown in Figure 2, the double-mass curve ensured that all stations’ data were consistent due to the fact that missed and outlier data were filled correctly.

Figure 2

Double-mass curve.

Figure 2

Double-mass curve.

Rainfall trend analysis

The annual rainfall in the four stations showed a decreasing trend by a factor of −0.2, −2.02, −8.8, and −3.21 mm per year at Bui, Butajira, Koshe, and Wolkite stations, respectively, but had an increasing trend at Imdibir station (Table 4). The result of the annual rainfall probability values showed a significant trend at Koshe and Wolkite but a non-significant trend was observed at the remaining stations, which might be associated with large inter-annual fluctuation. According to Hulme et al. (2001) and the IPCC (2001), East Africa rainfall shows an increasing trend. Negash & Eshetu (2016) reported a decreasing trend at Chida station (16.08 mm/year) and Butajira station (6.26 mm/year).

Table 4

Mann–Kendall trend statistics of annual rainfall of the region

StationYearsSen's slopeZ valueMk statistic (S)P-value
Imdibir 29 4.23 0.138 56 0.302 
Bui 29 − 0.2 − 0.02 − 6 0.922 
Butajira 29 − 2.02 − 0.24 − 97 0.072 
Koshe 29 − 8.8 − 0.3 − 112 0.04 
Wolkite 29 − 3.21 − 0.66 − 249 0.0001 
StationYearsSen's slopeZ valueMk statistic (S)P-value
Imdibir 29 4.23 0.138 56 0.302 
Bui 29 − 0.2 − 0.02 − 6 0.922 
Butajira 29 − 2.02 − 0.24 − 97 0.072 
Koshe 29 − 8.8 − 0.3 − 112 0.04 
Wolkite 29 − 3.21 − 0.66 − 249 0.0001 

Temporal trends of drought events

The year-to-year variation of drought in terms of normalized anomaly index (SPI-12) covering the period of 1989–2017 was examined. The study region had both wet and dry years over the study period. A mixture of dry and wet years have been observed. Of the observed period, 48.9% of rainfall was recorded above the normal average, however, below normal condition was recorded by 51.1%. Moreover, the largest negative deviation occurred in the years 1992, 2012, 2016, and 2017, while the highest positive anomalies occurred in the years 1993, 2005, and 2010 in the region (Figure 3). These findings conformed to the study by Kidane et al. (2010) on years of drought and floods in Ethiopia.

Figure 3

Temporal distribution of drought in the region.

Figure 3

Temporal distribution of drought in the region.

Spatial patterns of drought incidence

The general agriculture of the area predominantly depends on bimodal rainfall, Kiremt (main rainy season) and Belg (small rainy season), i.e., agricultural productivity heavily depends on rainfall characteristics such as onset and cessation of rainfall and length of growing period. Kiremt is the main rainy season in which about 85–95% of the agricultural crops of the region are cultivated. Kiremt, the period from June to September following the Belg rains, is associated with frequent rains and homogeneous temperatures, mainly in July and August. Due to their absolute dependency, a minor disturbance in Kiremt rainfall has a huge effect on the livelihood and economy of smallholder farmers of the region. The spatial analysis of droughts verifies that most of the frequent and extreme events are recorded in the eastern part of the region (Figure 4). The lowland part of Gurage zone is very prone to drought. The majority of the region has experienced severe and extreme (SPI ≤ −1.50) drought events. Specifically, Imdibir, Butajira, and Koshe experienced extreme drought with risk peak value of SPI ≤− 2) while Wolkite and Bui were affected by severe drought with risk peak value −1.8 to −1.63. The study indicated that the region is suspected of/experienced unpredictable drought events with different time scales. The observed spatio-temporal drought risk events indicate a potential hazard to the rain-oriented agriculture, hence steadily affecting the regular farming system, and water and food security.

Figure 4

Spatial distribution of drought in the region.

Figure 4

Spatial distribution of drought in the region.

CONCLUSIONS

This study presented space and time drought risk events through a sound basis of the scientific use of historical data in central Ethiopia using SPI-12. The results of the study prove that complex and localized spatio-temporal patterns of drought risk events were identified. This could help to recognize and characterize local-based drought conditions. The annual rainfall in four stations showed a decreasing trend whereas an increasing trend was observed at Imdibir station. There was a non-significant trend at Imdibir, Bui, and Butajira stations, but a significant trend was observed at Koshe and Wolkite stations.

During the period of study, the temporal analysis showed that there were times when the entire region experienced drought, and the largest negative deviation occurred in the years 1992, 2012, 2016, and 2017, while the highest positive anomalies occurred in the years 1993, 2005, and 2010. The number of drought events reached its maximum value in the year 1992.

The spatial analysis of droughts verifies that most of the frequent and extreme events are recorded in the eastern part of the region. The lowland part of Gurage zone is very prone to drought, and the area needs drought hazard assessment mapping. The majority of the region experienced severe and extreme (SPI ≤ −1.50) drought events. Specifically, Imdibir, Butajira, and Koshe experienced extreme drought with risk peak value of SPI ≤− 2) while Wolkite and Bui were affected by severe drought with risk peak value −1.8 to −1.63.

The study indicated that the region experienced unpredictable drought events at different time scales. The observed spatio-temporal drought risk events indicate a potential hazard to the rain-oriented agriculture, hence steadily affecting the regular farming system, and water and food security. The findings of this research could be important in strategic planning and operational applications like drought monitoring, platforms for early warning and preparedness, local-scale adaptation planning, and food security strategies and policy direction. The limitation of the study remains the lack of consistent, reliable, and recent years’ data for the case due to malfunctioning and relocation of some stations in the region. This study recommends further research on remaining statistical drought indices such as RDI and SDI. Supplementary irrigation is recommended as the best adaptation option throughout the drought period.

ACKNOWLEDGEMENTS

The author is very grateful to Wolkite University (WKU). I also express my sincere thanks to the Ethiopian National Meteorological Agency (NMA) and Mapping Agency (EMA) for readily providing me with the daily rainfall and shapefile data. The author declares no conflict of interests. The data will be provided on request to the journal.

DATA AVAILABILITY STATEMENT

All relevant data are included in the paper or its Supplementary Information.

REFERENCES

Ali Ghorbani
M.
Kazempour
R.
Chau
K. W.
Shamshirband
S.
Taherei Ghazvinei
P.
2018
Forecasting pan evaporation with an integrated artificial neural network quantum-behaved particle swarm optimization model: a case study in Talesh, Northern Iran
.
Engineering Applications of Computational Fluid Mechanics
12
(
1
),
724
737
.
Amsalu
A.
Adem
A.
2009
Assessment of Climate Change-Induced Hazards, Impacts and Responses in the Southern Lowlands of Ethiopia
.
Forum for Social Studies (FSS)
,
Ethiopia
.
Bathrellos
G. D.
Skilodimou
H. D.
Maroukian
H.
2014
The spatial distribution of Middle and Late Pleistocene cirques in Greece
.
Geografiska Annaler
96
,
323
338
.
Brando
P. M.
Paolucci
L.
Ummenhofer
C. C.
Ordway
E. M.
Hartmann
H.
Cattau
M. E.
Balch
J.
2019
Droughts, wildfires, and forest carbon cycling: a pantropical synthesis
.
Annual Review of Earth and Planetary Sciences
47
,
555
581
.
Burton
I.
Kates
R. W.
White
G. F.
1993
The Environment as Hazard
.
Guilford Press
,
New York
,
USA
.
ISBN 9780898621594
.
Calow
R. C.
MacDonald
A. M.
Nicol
A. L.
Robins
N. S.
2010
Ground water security and drought in Africa: linking availability, access, and demand
.
Groundwater
48
(
2
),
246
256
.
Dai
A.
Zhao
T.
Chen
J.
2018
Climate change and drought: a precipitation and evaporation perspective
.
Current Climate Change Reports
4
(
3
),
301
312
.
Hulme
M.
Doherty
R.
Ngara
T.
New
M.
Lister
D.
2001
African climate change: 1900–2100
.
Climate Research
17
,
145
168
.
IPCC
2001
Climate change 2001 the scientific basis
. In:
Contribution of Working Group I to the Third Assessment Report of the Intergovernmental Panel on Climate Change
(
Houghton
J. T.
Ding
Y.
Griggs
D. J.
Noguer
M.
van der Linden
P. J.
Dai
X.
Maskell
K.
Johnson
C. A.
, eds).
Cambridge University Press
,
Cambridge
,
UK
.
and New York, NY, USA
, p.
881
.
Kidane
G.
Alemneh
D.
Malo
M.
2010
Agricultural Based Livelihood Systems in Drylands in the Context of Climate Change. Inventory of Adaptation Practices and Technologies of Ethiopian Institute of Agricultural Research in Collaboration with Environmental Ssustainability
.
FAO
,
Rome
,
Italy
, p.
131
.
McKee
T. B.
Doesken
N. J.
Kleist
J.
1993
The relationship of drought frequency and duration to time scales
. In:
Proceedings of the 8th Conference on Applied Climatology
, Vol.
17
, No.
22
.
American Meteorological Society
,
Boston, MA
,
USA
, pp.
179
183
.
Mekonen
A. A.
Berlie
A. B.
Ferede
M. B.
2020
Spatial and temporal drought incidence analysis in the northeastern highlands of Ethiopia
.
Geoenvironmental Disasters
7
(
1
),
1
17
.
Menkir
S.
2018
Quality Management and Sustainable Service of International Civil Society Organizations in Ethiopia
.
Doctoral Dissertation
,
St. Mary's University
,
Addis Ababa
,
Ethiopia
.
Mishra
A. K.
Singh
V. P.
2011
Drought modeling–a review
.
Journal of Hydrology
403
(
1–2
),
157
175
.
Nagarajan
R.
2010
Drought Assessment
.
Springer Science & Business Media
.
Negash
W.
Eshetu
Y.
2016
Analysis of rainfall variability and farmers’ perception towards it in agrarian community of Southern Ethiopia
.
Journal of Environment and Earth Science
6
(
4
),
2016
.
Omer
A. M.
2008
Energy, environment and sustainable development
.
Renewable and Sustainable Energy Reviews
12
(
9
),
2265
2300
.
Pei
F.
Li
X.
Liu
X.
Lao
C.
2013
Assessing the impacts of droughts on net primary productivity in China
.
Journal of Environmental Management
114
,
362
371
.
Shamshirband
S.
Hashemi
S.
Salimi
H.
Samadianfard
S.
Asadi
E.
Shadkani
S.
Kargar
K.
Mosavi
A.
Nabipour
N.
Chau
K. W.
2020
Predicting standardized streamflow index for hydrological drought using machine learning models
.
Engineering Applications of Computational Fluid Mechanics
14
(
1
),
339
350
.
Temam
D.
Uddameri
V.
Mohammadi
G.
Hernandez
E. A.
Ekwaro-Osire
S.
2019
Long-term drought trends in Ethiopia with implications for dryland agriculture
.
Water
11
(
12
),
2571
.
Tigkas
D.
Vangelis
H.
Tsakiris
G.
2015
Drinc: a software for drought analysis based on drought indices
.
Earth Science Informatics
8
(
3
),
697
709
.
Tsakiris
G.
Pangalou
D.
Vangelis
H.
2007
Regional drought assessment based on the Reconnaissance Drought Index (RDI)
.
Water Resources Management
21
(
5
),
821
833
.
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