Study area description
This study was conducted in Borana zone which is one of the 21 administrative zones of Oromia regional state, Southern Ethiopia. Borana zone is located in the southern part of the country (Fig. 1) bordered by Kenya in the South, West Guji zone in the North, Somali region and Guji zone in the East and South Nations and Nationalities Peoples’ Region (SNNPR) in the West. Astronomically, the study area stretches from 3°30’ N to 5°25’ N latitude and 36°40’ E to 39°45’ E longitude. Yabelo is the capital town of Borana zone and located at about 570 km South of Addis Ababa. The zone covers almost 48,360 km2 out of which more than 75% is a lowland.
The study area exhibits four seasons namely Bega the long dry period from December to February, Belg the long rainy period from March to May, Kiremt the short dry spell from June to August and Meher the short rainy period from September to November. The rainfall pattern of the region is different from most parts of the country. It is during Belg and Meher seasons that Borana zone receives most of its rain. The season naming ‘Bona’, ‘Ganna’, ‘Adolessa’ and ‘Hagayya’ are known at the community level and are equivalent respective names at national level Bega, Belg, Kiremt and Meher (Riche et al. 2009). Borana zone receives an average annual rainfall ranging from 350 mm to about 900 mm which is distributed through the two rainy seasons from March to May and September to October (Debela et al. 2019). Rainfall is highly variable across the zone and it highly erratic resulted in the frequent occurrence of drought in many parts of Borana. Rainfall has bimodal pattern of distribution with increasing unpredictability which necessitates adaptation and risk management as suggested by (Korecha and Barnston 2007). The mean annual temperature is about 19 °C in the Borana zone, where the mean maximum and minimum temperatures are 24.6 °C and 12.96 °C respectively. In general, the warmest period in the year is from March to May, while the lowest annual minimum temperatures occur between the months of November and January (National Meteorological Agency (NMA) of Ethiopia 2007).
The region has a semi-arid savannah landscape, marked by gently sloping lowlands and flood plains vegetated predominantly with grass and bush land. The geology is composed of a crystalline basement with overlying sedimentary and volcanic deposits (Gemedo-Dalle et al. 2006; Lasage et al. 2010). People are predominantly involved in small-scale subsistence agriculture production and mainly on livestock husbandry. These sectors are climate-sensitive and frequently hit by climate related hazard, which is of course drought. Small-scale farming is not widely practiced mainly due to the aridity that prevails over the study area and hence government introduced the farming practices as means of income diversification and to support the family .
Data types and sources
Gridded (4 km × 4 km spatial resolution) data for daily precipitation, daily Tmax and Tmin for all the points lying within the study area boundary for the period 1981 to 2018 were collected from National Meteorological Agency (NMA) of Ethoiopia. Therefore, a total of 2702 data points (Fig. 2) were considered as inputs and the mean values were used to analyze the variability and trends of rainfall as well as temperature at multiple timescales including monthly, seasonal, annual and decadal time periods. Therefore, the data generated were prepared for use in R software package to test trend and variability analysis. We prefer to use gridded data for a number of advantages including its accessibility and completeness. On the other hand, due to the remoteness of the location, meteorological stations are sparsely distributed in the study area with serious missing values in the dataset.
Furthermore, the gridded climate used in this study is a product of Enhancing National Climate Services (ENACTS) initiative which has developed and implemented a tool for quality-control of rainfall and temperature observations by national weather stations and then blends these observations with freely available global products. Therefore, with this aim, Columbia University’s International Research Institute for Climate and Society (IRI), in close collaboration with the local partners, launched the ENACTS initiative in 2012. The initiative was accomplished using IRI’s Climate Data Tool (CDT), which is installed at each meteorological station (https://iri.columbia.edu/resources/enacts/). The dataset is not freely accessible and for the purpose of this study, it is obtained from national Meteorological Agency (NMA) of Ethiopia.
Trend and statistical analysis
Serial correlation
One of the challenges in detecting and interpreting trend in timeseries data is the existence of serial correlation (autocorrelation), is where error terms in a time series transfer from one period to another (Yue et al. 2002; Birara et al. 2018). In the other words, the error for one time period ‘a’ is correlated with the error of a subsequent time period ‘b’. Autocorrelation is tested in this paper through calculating the autocorrelation coefficient at lag-1 and plotting the correlogram. It is said that, there is significant autocorrelation when the value for correlation coefficient, r, falls outside the range at 95% confidence interval. Therefore, we took a remedial measure to remove the effect of significant autocorrelation in the timeseries through the ‘pre-whitening’ procedure (Von Storch and Navarra1995; Kulkarni and Von Storch 1995). Pre-whitening (PW) efficiently removes the finding of significant trend in the MK test when actually there is no trend (Bayazit and Onoz 2007). Based on this, for the data points (x1, x2, x3, …, xn), the ‘pre-whitened’ time series was obtained through (x2—rx1, x3—rx2, …, xn—rxn-1, where r is the correlation coefficient between the two consecutive data points in the time series) procedure before applying Mann–Kendall trend test.
Mann–Kendall (MK) test
Mann–Kendall (MK) test, a popular non-parametric test is used in order to detect trend in climatic variables at 5% level of significance (Mann 1945; Kendall 1975). MK test was then proposed as the null hypothesis (H0), there is no trend in the time series and alternative hypothesis (H1), there is a monotonic trend which can either be an upward or a downward.
The MK test (Mann 1945; Kendall, 1975) was first carried out by computing S statistic as:
$${S= \sum }_{i=1}^{n-1}{\sum }_{j=i+1}^{n}Sgn\left(xj-xi\right)$$
(1)
where, n is the number of observations, and xj is the jth observation and Sgn denotes the sign function, defined as:
$$Sgn(xj-xi) = \left\{\begin{array}{c}+1;\quad xj> xi\\ 0; \quad xj = xi\\ -1; \quad xj < xi\end{array}\right.$$
(2)
and variance defined by:
$$Var (S) = \frac{n(n-1)(2n+5) - {\sum }_{k=1}^{m}tk(tk-1)(2tk + 5)}{18}$$
(3)
where, n is the number of data, m is the number of tied groups (a tied group is a set of sample data with the same value), and it is the number of data points in the kth group.
Finally, the statistics of this test, designated by Z, is computed as:
$$Z = \left\{\begin{array}{ll}\frac{S - 1}{\sqrt{Var (S)}} \qquad if \, S < 0\\ 0 \qquad \qquad \, if \, S = 0\\ \frac{S + 1}{\sqrt{Var (S)}} \qquad if \, S > 0\end{array}\right.$$
(4)
The test statistics Z is used as a measure of significance of trend. If the value of Z is positive, it indicates increasing trends, while negative values of Z show decreasing trends. A significance level of α = 0.05 (confidence level of 95%), is also utilized for testing either upward or downward monotonic trend (a two-tailed test) (Jhajharia et al 2012). If Z appears greater than Zα/2 where α depicts the significance level, then the trend is considered as significant.
Sen’s Slope Estimator (SEE)
If a linear trend is present in a time series, then the true slope (change per unit time) can be estimated by using a simple non-parametric procedure developed by Sen (1968). Sen’s slope estimation can be calculated in the form of:
$$ft\, = \,Qx\, + \,b$$
(5)
where Q is the slope and b constant. To obtain slope Q in Eq. 5, it is necessary to calculate the slope for all data with the equation:
$$Qi= \frac{xj-xk}{j-k}$$
(6)
where, xj and xk are considered as data values at time j and k (j > k) correspondingly. The median of N values of Qi is ranked from small to large, with an estimated Sen’s estimator of slope, given by:
$$Qi = \left\{\begin{array}{ll}Q\frac{(N+1)}{2} \qquad \qquad \, \, N \, is \, odd\\ \frac{1}{2}(Q\frac{N}{2}+Q\frac{(N+2)}{2} \quad N\, is\, even\end{array}\right.$$
(7)
To obtain estimates of b in Eq. 5, the values of n data from the difference (xi—Qti) are calculated. The median value is the estimate for b. Finally, Qmed is computed by a two-sided test at α = 0.05 (95% confidence interval) and then a true slope can be obtained and its value indicates the steepness of the trend.
Statistical data analysis
The study tried to capture the variability of precipitation, maximum and minimum temperature at temporal and spatial basis. Temporally, variability is seen at monthly, seasonally, annually and decadal scales. Descriptive statistics including mean, standard deviation and coefficient of variation were calculated at different scales for the parameters. Hare (2003) computed coefficient of variation (CV) using the following formula:
$$CV\, = \,\sigma /\mu \, * \, 100$$
(8)
where CV represents the coefficient of variation, σ is the population standard deviation, and µ is the population mean.
Apart from ArcGIS which is used for spatial data analysis and R-software package for statistical and trend tests while Origin software version 17 was used to plot various precipitation and temperature graphs in this study.
Analyzing spatial variation
The inverse distance weighted (IDW) interpolation technique (Shepard 1968; Hodam et al. 2017; Biazar et al. 2019) in ArcGIS was employed to generate surface data for precipitation, Tmax and Tmin from grid points at seasonal and annual scales. To do this, time series precipitation and temperature data from the grid points were analyzed by using ArcGIS 10.5 interface. Hence, spatial maps showing precipitation, maximum and minimum temperature variability across the semi-arid Borana zone were produced at seasonal and annual timescales.