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Physical Geography

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Local level rainfall and temperature variability indrought-prone districts of rural Sidama, central riftvalley region of Ethiopia

Tafesse Matewos & Tewodros Tefera

To cite this article: Tafesse Matewos & Tewodros Tefera (2019): Local level rainfall andtemperature variability in drought-prone districts of rural Sidama, central rift valley region ofEthiopia, Physical Geography, DOI: 10.1080/02723646.2019.1625850

To link to this article: https://doi.org/10.1080/02723646.2019.1625850

Published online: 07 Jun 2019.

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ARTICLE

Local level rainfall and temperature variability indrought-prone districts of rural Sidama, central rift valleyregion of EthiopiaTafesse Matewosa and Tewodros Teferab

aInstitute of Policy and Development Research (IPDR), Hawassa University Ethiopia, and NMBU, Norway;bSchool of Environment, Gender and Development Studies (SEGDS), Hawassa University, Hawassa, Ethiopia

ABSTRACTThe purpose of this study is to examine local level spatiotemporalrainfall and temperature variability in drought-prone districts of ruralSidama, Central Rift Valley region of Ethiopia. The study used 129gridded monthly rainfall and temperature data of 32 years(1983–2014). The gridded rainfall and temperature records wereencoded into GIS software and evaluated through different statisticaland geospatial techniques. Mann-Kendal rank test and F distributiontests were used to test temporal and spatial statistical significance,respectively, of the data. The analysis revealed that Belg and Kiremt arethe main rainfall seasons, constituting 81% of the annual rainfall.Although annual, Kiremt, and Belg rainfall amounts appear to havedecreased over time, the decreasing trend is statistically significantonly for Belg rainfall records. On the other hand, rainfall standardanomaly results indicated seven droughts of different magnitudes:one extreme, two severe, and four moderate. The study also revealedincreasing temperature trends over the years under consideration thatare statistically significant. The findings of this study on rainfall contra-dict other findings obtained around the study area. Thus, climatechange adaptations need to focus on location-specific climate dataanalysis so that the intended adaptive interventions can be successful.

ARTICLE HISTORYReceived 14 November 2017Accepted 28 May 2019

KEYWORDSRainfall; temperature;drought-prone districts; ruralsidama; central rift valleyregion; Ethiopia

Introduction

Studies of climate change over Ethiopia indicate that the country has been experiencingclimate change and variability for a long period of time (Adem et al., 2016a, 2016b;Bewket, 2012; Bewket & Conway, 2007; Bewket, Radeny, & Mungai, 2015; Kidemu &Rao, 2016; Kiros, Shetty, & Nandagiri, 2016; Seleshi & Demaree, 1995; Wodaje, et al.,2016; Worqlul et al., 2018). Moreover, General Circulation Model (GCM) forecasts overEthiopia also predict rainfall and temperature variability and change in the comingdecades (Adem et al., 2014; Jury & Funk, 2013; Kassie et al., 2014). The models haveindicated that the average annual temperature over Ethiopia will increase in the rangesof 0.9–1.1°C, 1.7–2.1°C, and 2.7–3.4°C by 2030, 2050, and 2080, respectively (Adem &Bewket, 2011; NMA & MoWR, 2011). High intra-annual and inter-annual rainfallvariability are also projected over the country (Adem & Bewket, 2011; Kidemu &

CONTACT Tafesse Matewos tafessemk@gmail.com

PHYSICAL GEOGRAPHYhttps://doi.org/10.1080/02723646.2019.1625850

© 2019 Informa UK Limited, trading as Taylor & Francis Group

Rao, 2016; Kiros et al., 2016; NMA & MoWR, 2011; Viste, Korecha, & Sorteberg, 2013;Wodaje et al., 2016). These changes and fluctuations in rainfall and temperature willhave adverse impacts on the country’s climate-sensitive sectors (Alemayehu & Bewket,2017; Bewket et al., 2015; Emireta, 2013). Besides rainfall variability in the growingseason, its onset, offset, and duration have multiple impacts on Ethiopian rain-fedagriculture. In addition to effects on the amount and quality of the yield, climatechange, and climatic extremes (such as droughts and flooding) have economic andsocial consequences on farmers’ livelihoods and on the performance of the nationaleconomy (Bewket et al., 2015; Brown et al., 2017; World Bank, 2007; Deressa, Hassan, &Ringler, 2008; Evangelista, Young, & Burnett, 2013; Morton, 2007).

Studies of spatiotemporal rainfall and temperature variability and change over Ethiopialack consistency, as increasing, decreasing, and unclear trends have been reported fordifferent parts of the country. For instance, studies of rainfall trends over the northernhighlands of Ethiopia (1983–2013) and the Upper Blue Nile River Basin of Ethiopia(1981–2010) have shown increasing trends in annual and summer rainfall, although theresults are statistically insignificant (Alemayehu & Bewket, 2017; Mengistu, Bewket, & Lal,2014). The studies revealed a decreasing Belg season rainfall trend over the northernEthiopian highlands and Upper Blue Nile River Basin of Ethiopia. The findings alsoindicated an increasing temperature trend of >1°C in the last four decades. The samestudy reported a faster increase in minimum than maximum temperature (Mengistu et al.,2014). On the other hand, another study over northern, northwestern, and western parts ofEthiopia reported decreasing annual and seasonal rainfall trends (Wagesho, Goel, & Jain,2013). It also showed an increasing rainfall trend over some parts of eastern Ethiopia. Anincreasing annual rainfall trend was also reported by Degefu and Bewket (2014) for thesouthwestern parts of Ethiopia. One drawback of the above study is the limitation related tothe number of meteorological stations used to cover an extensive area considered in thisstudy (six stations were used to represent 78,200 km2).

A few studies have been done on the characteristics of rainfall in the Central RiftValley Region (CRVR) of Ethiopia over the past four decades. These studies hadmethodological limitations and came up with contradicting results. A study byMuluneh, Bewket, Keesstra, and Stroosnijder (2017) revealed an increasing annualand summer rainfall trend over the CRVR lowlands (1400–1700 m a.m.s.l.) over thefour decades (1970–2009) considered. The 18 meteorological stations used in the studywere very far from each other for generalizing areal rainfall of an extensive area(16,352 km2). Another study, by Kassie et al. (2014) in the Rift Valley region ofEthiopia, indicated a decreasing annual rainfall over time (1977–2007), although theresult was not statistically significant. Their study used 16 meteorological stations, alsofar apart from each other, to represent areal rainfall of the extensive area (10,000 km2).

In general, the findings on spatiotemporal rainfall variability over Ethiopia and theCRVR have been inconsistent. Thus, it has become very difficult to conclude rainfallvariability and trends across Ethiopia and the CRVR. This necessitates the location-specific study of spatiotemporal characteristics of rainfall and temperature for planningclimate change adaptation interventions. Furthermore, no study has been done ontemperature and rainfall variability in the current study area, where climate variabilityand change-induced impacts have been recurrent. Therefore, in line with the aboveresearch gaps, this study is aimed at examining local-level spatiotemporal rainfall and

2 T. MATEWOS AND T. TEFERA

temperature variability in drought-prone districts of rural Sidama, CRVR of Ethiopia,with an ultimate aim of providing a scientific base for planning and implementingclimate change adaptive responses against adverse climate-change-induced impacts.

Materials and methods

Study area description

The study area is located in Sidama Administrative Zone (SAZ), the most populous zone ofthe Southern Nations Nationalities and Peoples Regional State (SNNPRS), with a totalpopulation of 3,677,370 persons (SNNPRS, BoFED, 2015). The SNNPRS is one of the nineregional states of the Federal Democratic Republic of Ethiopia (FDRE). Of the 19 districts ofSAZ, this study focused on three purposely selected drought-prone districts where climatechange and variability-induced impacts have been severe and recurrent. The districts liebetween 6.43°–7.14° N and 38.01°–38.42° E (Figure 1). The three districts are situated in theheart of the Great East African Rift Valley (GEARV) and are characterized by poorinfrastructure, erratic rainfall, and higher temperature compared with other districts ofthe SAZ. Population density in these districts is relatively lower because of the arid andsemi-arid nature of the climate. A total of 576,865 persons and 113,285 households reside inthe three districts of the SAZ, of which about 97% reside in rural areas (SNNPRS, BoFED,2015).

Figure 1. The distribution of gridded meteorological points.

PHYSICAL GEOGRAPHY 3

Diverse climatic conditions characterize drought-prone districts of the SAZ. Rain-fedcrop production and animal husbandry are the main sources of livelihoods. Major cropsgrown include maize, sweet potato, Khat, and fruits, all of which are climate sensitive.Altitude ranges from 1100 to 2100 m.a.s.l., with average annual rainfall in the range of820–1300 mm. There are three rainfall seasons in Ethiopia and in the study area: Kiremt(summer), also called the main rain season (June, July, August, and September); Belg(Spring) season, also called the short rainy season (February, March, April, and May);and the Bega (dry) season (October, November, December and January) (Alemayehu &Bewket, 2017). The amount of rainfall received in the three seasons varies significantly.While Kiremt is the main rainy season for cultivation of most crops, Belg season rainfallis equally important for growing short season crops and is also a source of moisture forgrowing livestock pasture. The rainfall pattern is bimodal, coming in Kiremt and Belg.Belg rainfall usually begins in February and lasts until the end of May, whereas Kiremtrainfall lasts from June to September. The rainfall characteristic is irregular andunpredictable. As a result, there have been frequent losses of harvests and cattle sincethere are no other sources of water. The western part of the study area is drained by theperennial Bilate River, which dissects the Wolayita and Sidama Zones. In the easternpart, there is no perennial river, and households largely depend on water collected fromartificial ponds and shallow hand-dug wells for domestic and other purposes. Pondsusually become dry during the dry period (December-February), and the water supplyproblem becomes more severe.

Data and methods

This study is based on 29 gridded monthly rainfall and temperature data records of 32years (1983–2014) at a resolution of 4 × 4 km. Data were obtained from the NationalMeteorological Agency (NMA) of Ethiopia. Documentation of the gridded data is basedon weather satellite observations and meteorological station records. The documenta-tion process is done by the NMA in collaboration with World Meteorological Agency(WMO). The process involves various data quality controlling mechanisms, includingcalibration TAMSAT (Tropical Applications of Meteorology using Satellite) data, andcomparison of the results with ground-based records. Studies conducted to examine thequality of gridded satellite data have shown that gridded data have good quality forstudying climate change and variability in parts of the world where it is difficult to getcontinuous weather data for longer period of time (Dinku et al., 2014). Anotheradvantage of using gridded data lies in its ability to represent areal RF and temperaturebecause a large number of gridded points are involved in the analysis.

Rainfall and temperature data of the 29 gridded points were encoded ina Geographic Information System (GIS) software package (Arc Map 10.3.1) (Figure2). Using the software, point data were converted to raster format through rasterinterpolation techniques. Further raster processes were used to clip, classify, andanalyze attribute data over the study area districts. The study used the inverse-distance weighting (IDW) method, a standard spatial interpolation method, preferablefor interpolating highly variable climate data (Akkala, Devabhaktuni, & Kumar, 2010;Lu & Wong, 2008). The F distribution test was used to test the statistical significance ofthe areal variability of the data.

4 T. MATEWOS AND T. TEFERA

Rainfall and temperature data were analyzed by using different statistical methodsincluding the following:

Total annual rainfall (TAR) is computed by adding rainfall records of each month:

TAR ¼X12i¼1

Ri (1)

Mean annual temperature (MAT) is computed by dividing the sum of mean monthlymaximum and mean monthly minimum temperature by two:

MATi ¼ TMax þ TMin

2(2)

Average annual temperature (AAT) is computed by dividing the sum of the meanannual temperatures of each year by the number of years under consideration (n):

AAT ¼P12n¼1

t

n(3)

Standard deviation (δ) is a deviation of the data from the average values where xi is RFof a given year, µ is mean rainfall, and n is the number of months/years underconsideration:

Figure 2. Study area location.

PHYSICAL GEOGRAPHY 5

δ ¼ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiP

xii � μð Þ2n

s(4)

Standard rainfall anomaly (SRA) is an index to measure drought incidence using rainfalldata, where Pi and Pµ represent rainfall of a given year and mean rainfall, respectively:

SRA ¼ Pi � Pμδ

(5)

For the coefficient of variability (CVT), µ and δ represent the mean and standarddeviation of temperature records under consideration:

CVT ¼ δ

μx100 (6)

Linear regression analysis was carried out to examine changes of temperature andrainfall over the years under consideration. In the regression, y is the explanatoryvariable, m is the slope, x is an independent variable, and b is the intercept:

Y ¼ mxþ b (7)

The Mann–Kendal test was used to test the statistical significance of temporal variabilityof the data.

Results and discussion

Intra-annual, inter-annual, and areal rainfall variability

Intra-annual rainfall variability refers to seasonal variations of rainfall within a given yearfrom its seasonal average amount whereas inter-annual variability refers to rainfallfluctuations from annual average amount over the years under consideration. Intra-annual, inter-annual and areal variability characterize the rainfall in the study area.

Intra-annual/seasonal/variabilityMean annual rainfall of the study area over the years under consideration (1983–2014)is found to be 1101.27 mm. The rainfall is bimodal, with Belg (February, March, andApril) and Kiremt (June, July, August, and September) being the main rainy seasons(Figure 3). Bega (October, November, December, and January) is the driest season.

About 81% of the rainfall comes during Belg andKiremt seasons, with 40% and 41% of thetotal annual rainfall, respectively. The remaining 19%of rainfall comes in theBega season. Therainfall data analysis also showed that December is the driest month, with mean rainfall of22.9 mm, followed by January (26.4 mm) and November (35.7 mm), whereas April andMayare identified as the wettest months, withmeanmonthly rainfalls of 157.6mm and 156.2mm,respectively. However, Belg is not the wettest season because average rainfall amounts forFebruary and March are <100 mm. The Kiremt season is the wettest and the longest rainfallseason in the study area since it provides >100mm rainfall for four consecutive months. Begais the driest and the harvesting season in the study area, but its slightly increasing rainfall trendhas a negative consequence on agricultural productivity.

6 T. MATEWOS AND T. TEFERA

The analysis of annual and seasonal rainfall variability coefficients indicated seasonalvariability. The annual rainfall coefficient of variability (CV) was 13% and the CV valuefor Belg and Kiremt rainfall was 23%. The Bega rainfall showed the highest variability,with a CV value of 35%. The higher seasonal rainfall variability has serious implicationin agricultural practices in the study area. This is because the onset, duration, and offsetof rainfall determine the amount and quality of agricultural produce.

Inter-annual rainfall variabilityAnnual and seasonal rainfall has shown variability over the years under consideration(1983–2014). Bega rainfall shows a slightly increasing trend from its average of 207.67 mm.On the other hand, annual, Belg, and Kiremt rainfalls depict decreasing trends during thethree decades under consideration. The years 1997, 2006, and 1993 were years of wetterBega seasons, with average rainfalls of 450 mm, 288 mm, and 287.9 mm, respectively. Onthe contrary, the years 1994, 2012, and 1985 had relatively drier Bega seasons, with averagerainfalls of 112 mm, 119 mm, and 121 mm, respectively (Figure 4). The increasing trend ofBega rainfall, however, is not statistically significant (Mann-Kendall test results; Table 1).

Kiremt, the main rainy season in the study area, has average rainfall of 454.34 mm. It isthe season when most crop production activities are carried out. It constitutes more than41% of the annual rainfall that lasts for four months (June to September). Its onset,duration, and offset determine crop productivity, in addition to its total amount. The

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Figure 3. Mean monthly rainfall (RF) distribution.

y = 1.0002x + 191.17

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Figure 4. Bega rainfall (RF) (1983–2014).

PHYSICAL GEOGRAPHY 7

years 2009, 1993, and 1987 were years of dry Kiremt, with seasonal rainfalls of 277 mm,303 mm, and 304 mm, respectively, whereas 1996, 1983, and 2007 were years of higherrainfall compared with the other years under consideration (Figure 5). The decreasingtrend for Kiremt rainfall over these years is not statistically significant, as indicated byMann–Kendall test result (Table 1). Also, the analysis of Kiremt SRA revealed that the years1987, 1993, and 2009were years of insufficient rainfall, with SRA values of −1.45, −1.47, and−1.72, respectively (Figure 6).

Belg, the second rainy season in the study area had a seasonal mean of 439.4 mm for theyears under consideration. This season is important not only for short growing-season crops,but also it is a source of water for livestock and pasture growth. Problems on the onset,duration, and offset of Belg rainfall have serious consequences on food security and human

Table 1. Mann–Kendall’s rank test result for inter-annual rainfall (RF).Year Bega RF Belg RF Kiremt RF Annual RF

Correlation Coefficient 1.000 0.117 −0.270* −0.020 −0.161Sig. (2-tailed) 0.347 0.030 0.871 0.195N 32 32 32 32 32

Correlation is significant at the 0.05 level (2-tailed).

y = -1.3324x + 476.32

R² = 0.015

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Kiremt Linear (Kiremt)

Figure 5. Kiremt rainfall (RF) trend (1983–2014).

y = -3.977x + 504.87

R² = 0.142

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Belg Total Linear (Belg Total)

Figure 6. Kiremt, Belg and annual RSA characteristics (1984–2014).

8 T. MATEWOS AND T. TEFERA

wellbeing in the study area. However, despite its immense role for livelihood security, Belgseason rainfall declined in the last three decades. The declining trend was statisticallysignificant at the p = 0.05 level (Table 1). The years 1997, 1990, 1993, 1996, 2006, and 2010received higher Belg rainfall (Figure 7). On the other hand, Belg SRA results showed that theyears 2000, 2008, 2009, and 2012 were years of severe drought, with SRA values of −1.34,−1.57, −1.61, and −1.64, respectively (Figure 6).

Annual rainfall also fluctuated over the years under consideration from its averageamount of 1101.27 mm. The year 2009 was the driest year, with an annual rainfall of796.37 mm. The years 1999, 2004, and 2012 were also drier, with an annual rainfall of870.1 mm, 925.1 and 891.2 mm, respectively, whereas 1984 and 1996 were the wettestyears, with an annual rainfall of 1420.4 mm, and 1368.4 mm, respectively (Figure 8).However, annual rainfall data should be interpreted with care, for years of normalannual rainfall do not necessarily mean positive years for farmers. For example, exceptfor 2004 and 2009, which were drier years with summer SRA values of −1.17 and −1.72,the remaining years of lower summer rainfall (for instance 1987 with RSA value of−1.47 and 1993 with RSA values of −1.47) were not consistent with lower annualrainfall in the same years. Hence, years of normal annual rainfall do not necessarilymean years of a good harvest for farmers unless seasonal rainfall amounts are normallydistributed over the two growing seasons (Belg and Kiremt).

The trend of annual rainfall has shown results that contradict those of previousstudies done in the CRVR of Ethiopia. A study by Muluneh et al. (2016) indicatedincreasing an annual rainfall trend in the Central Rift Valley escarpment of Ethiopiawith an altitudinal range of 1400–1700 m.a.s.l. The current study, however, revealedthat except the Bega rainfall, which showed an increasing trend, Kiremt, Belg, andannual rainfall trends are decreasing in the study area.

Rainfall Standard Anomalies (RSA) is used to determine the magnitude of droughtbased on rainfall data (Janowiak, Ropelewski, & Halpert, 1986). Analysis of rainfalldata revealed drought incidents over the three decades under consideration. Droughtfrequency and magnitude had increased since 1999 (Figure 6). The year 2009, with anannual SRA value of −2.11, was the driest year, with extreme drought condition(Figure 6). The above year coincides with the 2009/10 extreme drought all over thecountry. Further, 1999 and 2012 were also years of severe drought in the study area,

y = -4.3091x + 1172.4

R² = 0.078

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Total RF Linear (Total RF)

Figure 7. Belg rainfall (RF) in the study area (1983–2014).

PHYSICAL GEOGRAPHY 9

with SRA vales of −1.6 and −1.45, respectively. This finding is consistent with resultsof other studies in southern Ethiopia by Viste et al. (2012) and Ayenew (2004). On theother hand, four moderate droughts had occurred in the three decades under con-sideration. These were in the years 1984 (SRA = −1.04), 2000 (SRA = −1.16), 2002(SRA = −0.98), and 2004 (SRA = −1.22). SRA results of the three decades revealed thatthe area has experienced seven droughts, of which one was extreme, two severe, andfour moderate (Figure 6). Most drought incidents identified in the study area areconsistent with national level drought records. At the national level, the countryexperienced eight major droughts since 1980. These were in (1984/85, 1987/88,1991, 1994, 1997, 2002/03, 2009/10, and 2015/16) (Gebrehiwot, van der Veen, &Maathuis, 2011; Seleshi & Zanke, 2004; Suryabhagavan, 2017)

Areal distribution of rainfallAnnual and seasonal rainfall also depictedmeaningful areal variability in the study areas. Theeastern parts receive relatively more annual and seasonal rainfall compared with the westernparts, which are drier throughout the year (Figure 9). The eastern part receives an annualrainfall of >1000 mm on average. In general, the three districts significantly vary in averageannual RF (F (2.13) = 10.59, p < 0.001) as well as across all seasons: Belg (F (2.126) = 11.16, p <0.001), Kiremt (F (2.126) = 15.53, p < 0.001), and Bega (F (2.126) = 37.87, p < 0.001).Specifically, the post-hoc means comparisons show significant differences between districtsin the amount of average annual and seasonal rainfall received. Scheffe’s post-hoc meancomparison result indicated significant rainfall variation between Loka Abaya and Boricha

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Figure 8. Annual rainfall (RF) in the study area (1983–2014).

10 T. MATEWOS AND T. TEFERA

districts. Besides, the mean comparison indicated significant statistical variability within andamong the three districts for Belg, Kiremt, and annual rainfall amounts (Table 2).

Temporal and areal temperature variability

Intra-annual temperature variabilityAnalysis of temperature data revealed seasonal temperature variability in the study area.The mean monthly minimum temperature in the study area, 14.2°C, oscillates between12.2°C and 16.6°C. The lowest minimum temperature is experienced during Begaseason, with 11.7°C, 11.3°C, and 11.6 °C during the months of November, December,and January, respectively (Figure 10). The highest minimum temperature is experiencedduring Belg seasons, with temperature records of 13.6°C, 13.6°C, and 13.6°C during themonths of April, May, and July, respectively. The minimum temperature is less variablecompared with maximum temperature, with a standard deviation of 0.87. Mean

Figure 9. Annual rainfall (RF) distribution.

PHYSICAL GEOGRAPHY 11

Table2.

Mean,

standard

deviation(SDs)andF-test

results

ofrainfall(RF)

distrib

ution(N

=129).

95%

CI

RFType

Districts

NMean

SDSE

Lower

Boun

dUpp

erBo

und

Min.

Max.

F(2,126)

PostHoc

Test

(scheff

e)

Annu

alLoka

Abaya(LA)

651109.94

165.39

20.51

1068.96

1150.92

828.12

1430.06

LA>B***

Borecha(B)

38982.48

115.71

18.77

944.44

1020.51

815.88

1172.34

10.59***

B<LA***

Haw

assa

Zuria(HZ)

261054.85

56.19

11.02

1032.15

1077.54

955.62

1147.25

Total

129

1061.29

145.66

12.82

1035.91

1086.67

815.88

1430.06

Belg

Loka

Abaya(LA)

65462.84

69.88

8.67

445.53

480.16

350.38

601.82

LA>B***

Borecha(B)

38413.35

44.39

7.2

398.76

427.94

343.92

478.74

11.16***

B<A***

Haw

assa

Zuria(HZ)

26419.26

29.32

5.75

407.41

431.1

362.52

464.22

HZ<

LA***

Total

129

439.48

61.17

5.39

428.82

450.14

343.92

601.82

Kiremt

Loka

Abaya(LA)

65459.7

64.38

7.99

443.75

475.66

340.73

573.33

LA>B***

Borecha(B)

38417.23

599.57

397.83

436.62

338

519.59

15.53***

B<LA***

Haw

assa

Zuria(HZ)

26496.52

23.27

4.56

487.12

505.92

434.88

525.51

HZ<

LA***

Total

129

454.61

635.55

443.63

465.59

338

573.33

Bega

Loka

Abaya(LA)

65230.28

38.51

4.78

220.74

239.82

168.36

311.55

LA>B***

Borecha(B)

38191.02

17.17

2.78

185.38

196.66

159.53

218.17

37.87***

Haw

assa

Zuria(HZ)

26176.62

18.63

3.65

169.1

184.15

140.89

206.78

AZ<LA***

Total

129

207.9

37.84

3.33

201.31

214.49

140.89

311.55

***p

<.001,d

f(1,126)

12 T. MATEWOS AND T. TEFERA

monthly maximum temperature in the study area, 26.7°C, fluctuates between 24°C and29°C. The highest and lowest maximum temperatures are experienced during themonths of February and July. Maximum temperature is higher during Bega seasonand lower during Kiremt. Maximum temperature is more variable than the minimumand average monthly temperatures, with a standard deviation of 1.79°C. The averagemonthly temperature of the study area is 19.7°C. The highest and lowest averagemonthly temperatures are experienced in the months of February and July, withtemperature records of 20.7°C and 18.7°C, respectively. Mean monthly temperature islower during Kiremt season and higher during Bega season. Mean monthly temperaturein the study area, with a standard deviation of 0.72°C, is less variable than monthlymaxima and monthly minima.

Inter-annual temperature variabilityRecords of minimum, maximum, and average annual temperatures have shownincreasing trends in the years under consideration. Of these three types of temperaturerecords, the maximum annual temperature has shown more variability, with a standarddeviation of 1.7°C. Annual minimum temperature is increasing faster than the annualmaximum temperature (Figure 11). This finding coincides with others from the CRVRof Ethiopia by Muluneh et al. (2017), whose research found minimum annual

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Figure 10. Monthly mean minimum, maximum, and average temperature fluctuations.

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Figure 11. The trend of mean minimum, mean maximum, and mean annual temperature (OC).

PHYSICAL GEOGRAPHY 13

temperature to increase faster than the maximum. Statistical tests of significance for allthree categories of temperature indicated statistically significant temperature increasesover time (Table 3).

Areal temperature variabilityMinimum, maximum, and average annual temperatures vary across the study area.The northeastern part of the study area experiences relatively lower temperaturesthan the southwestern parts. This areal variation in average annual temperaturerecords is statistically significant (F (2.126) = 28.82, p < 0.001). Besides, minimumand maximum temperature records also depict statistically significant spatial varia-bility (Minimum (F (2.126) = 23.89, p < 0.001), and Maximum (F (2.126) = 31.59,p < 0.001). Post-hoc means comparisons also indicated differences between districtsin the average annual, minimum, and maximum temperatures experienced. Scheffe’spost-hoc test indicated statistically significant variation in temperature between theLoka Abaya and Boricha districts. Almost all parts of Hawassa Zuria, nearly half ofBoricha, and northeastern parts of Loka Abaya districts experience relatively lowermean annual temperatures compared with other parts of the study area (Figure 12).Mean annual temperature is relatively less variable for Hawassa Zuria district, witha standard deviation of 0.34°C. It is more variable over Boricha district where itoscillates between 18.6°C and 23.5°C with a standard deviation of 1.30°C. Highermean annual temperature is experienced in the southwestern parts of Loka Abayadistrict where it reaches up to 23.7°C (Table 4).

Conclusion

The study examined local level spatiotemporal temperature and rainfall variability in drought-prone districts of rural Sidama, CRVRof Ethiopia. Analysis of the data revealed that rainfall inthe study area is bimodal, with almost equal amounts of rainfall experienced during Belg andKiremt seasons (40% and 41% of total annual rainfall, respectively). The remaining 19% isaccounted for by Bega season rainfall. Seasonal rainfall exhibitedmore variability than annualrainfall, with CV values of 23% (statistically significant at p = 0.01). Annual, Kiremt, and Belgrainfall amounts have shown decreasing trends over the years under consideration(1983–2014). However, the decreasing trend is statistically significant only for Belg rainfallat p = 0.05 level. Bega rainfall has shown a slightly increasing trend although the result is notstatistically significant. Further, RSA analysis revealed seven droughts of different magnitudeduring the three decades. The year 2009,with annual SRAvalue of−2.11, was the driest year inthe three decades. This coincides with the 2009–2010 extreme droughts all over the country.The years 1999 and 2012were also years of severe drought in the study area, with SRA vales of−1.6 and −1.45, respectively. The other four moderate drought years were in 1984 (SRA =

Table 3. Mann–Kendall’s rank test result for inter-annual rainfall.Year T_min T_max T_average

Correlation Coefficient 1.000 0.601** 0.657** 0.669**Sig. (2-tailed) 0.000 0.000 0.000N 32 32 32 32

**Correlation is significant at the 0.01 level (2-tailed)

14 T. MATEWOS AND T. TEFERA

Figure 12. Mean temperature distribution in the study area.

Table 4. ANOVA and F-distribution test result for temperature.95% CI

Temperaturetypes Districts N Mean SD SE

LowerBound

UpperBound Minimum Maximum

F(2,126)

Post HocTest (scheffe)

Minimum Loka Abaya 65 14.75 1.22 0.15 14.44 15.05 12.55 16.98 LA>HZ***Boricha 38 14.04 1.15 0.19 13.66 14.42 12.71 16.72 23.89 B < LA***Hawassa Zuria 26 13.04 0.26 0.05 12.93 13.14 12.2 13.56Total 129 14.19 1.26 0.11 13.97 14.41 12.2 16.98

Maximum Loka Abaya 65 27.67 1.6 0.2 27.27 28.07 24.64 30.41 LA> B***Boricha 38 26.12 1.54 0.25 25.61 26.62 24.27 30.25 31.59 B < A***Hawassa Zuria 26 25.24 0.54 0.11 25.02 25.46 23.79 26.01 HZ< LA***Total 129 26.72 1.74 0.15 26.42 27.02 23.79 30.41

Mean Loka Abaya 65 21.21 1.4 0.17 20.86 21.56 18.6 23.68 LA>B***Boricha 38 20.08 1.3 0.21 19.65 20.51 18.63 23.49 28.82 B< LA***Hawassa Zuria 26 19.14 0.34 0.07 19 19.28 18.21 19.63 HZ< LA***Total 129 20.46 1.48 0.13 20.2 20.72 18.21 23.68

***p < .001, df (1,126)

PHYSICAL GEOGRAPHY 15

−1.04), 2000 (SRA = −1.16), 2002 (SRA = −0.98), and 2004 (SRA = −1.22). Annual andseasonal rainfall also displayed meaningful spatial variability from the average amount of1101.27mm. Eastern parts receive relativelymore annual and seasonal rainfall comparedwiththe drier western parts. The study also showed spatiotemporal variability in temperaturerecords. Mean monthly temperature in the study area, 20.5°C, oscillates between 18.2°C and23.7°C. The lowest and highest minimum temperature are experienced during Bega and Belgseasons, respectively, whereas the lowest and highest maximum temperatures are witnessedduring of Kiremt and Bega seasons, respectively. Minimum annual temperature is increasingfaster than themaximumannual temperature. This finding coincideswith others in theCRVRof the country. Statistical tests of significance for trend lines of all three types of temperaturerecords indicated statistically significant temperature increase over time. The northeasternparts of the study area experience relatively lower temperature than the southwestern parts,which are characterized by higher temperatures. In general, the study has shown that rainfalland temperature variability are location specific and distinct from national and regionalcharacteristics. This necessitates location-specific climate data analysis for climate changeadaptation planning and implementation.

Acknowledgments

The authors are very grateful for the financial support of the NORHED DEG project of NORAD,and thank the Ethiopian National Meteorological Agency for providing rainfall and temperaturedata used in this study.

Disclosure statement

No potential conflict of interest was reported by the authors.

Funding

This work was supported by the NORHED DEG project of NORAD [1].

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