Climate change and trend analysis of temperature in Addis Ababa, Ethiopia

Background: This paper presents the trend analysis of temperature in Addis Ababa City and the 24 effect of climate change in the study area. The analysis is based on the temperature variation in the 25 capital city of Addis Ababa over two stations - Bole and Entoto. 26 Methods: The Mann-Kendall trend test and Sen’s slope estimate were employed to find the nature 27 of the temperature trend and significance level in Addis Ababa city. 28 Results: It was found that the Mann-Kendall statistic (Z) value for minimum, maximum and 29 average temperatures for Bole station are 5.99, 3.32, and 6.14 respectively. The positive Kendall’s 30 Z value shows an upward trend and implies an increasing trend over time. This indicates that there 31 is a significant increase in the trend at a 5% level of significance since the p-value is less than the 32 significant level (p-values (0.0001) < 0.05). Whereas for Entoto station, the Mann-Kendall statistic 33 (Z) results are 1.64 for minimum, 0.85 for the maximum, and 1.40 for average temperature, and 34 this positive value show an indicator of an increasing trend. However, the increase is not significant 35 at 5% significant level since the p-value is greater than the significant level (p-value (0.219) > 36 0.05). 37 Conclusions: From both the Mann-Kendall trend test result and Sen’s Slope estimator, it can be 38 concluded that there is the tendency of temperature increments in Bole station. This could be due 39 to the influence of climate change and can lead to weather extremes in the capital city. Therefore, 40 the study recommend that the variability of temperature needs further monitoring technique, and 41 there is a need to consider the increasing temperature trend to minimize its effects on human health.


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Climate change has become of the most important issue in the domain of sustainable development, 47 and its impacts (e.g. rising of sea levels, melting of polar ice caps, wild bush fire, intense drought, Addis Ababa, could be signals of climate change (Birhanu et al. 2016). Also, the city temperature 69 is mostly affected by anthropogenic activities along with climate change.

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The non-parametric Mann-Kendall (MK) test is commonly employed to detect monotonic trends 71 in a series of environmental data, climate data, or hydrological data. The null hypothesis for this 72 test is that there is no trend, and the alternative hypothesis is that there is a trend in the two-sided 73 test or that there is an upward trend (or downward trend) in the one-sided test (Pohlert T. 2020). 74 Sen's slope estimator is another non-parametric method for the trend analysis of the hydroclimate 75 data set. It is used to detect the magnitude of the trend. Hence, this test computes both the slope 76 (i.e. linear rate of change) as well as the intercept according to Sen's method (Sen 1968 indicates an upward trend and also implies an increasing trend over time (Alhaji et al. 2018   The current study is conducted based on the temperature variation in the capital city of Addis 116 Ababa over two stations at Bole and Entoto. The historic temperature used from Bole station is 117 from 1983 to 2016 and the Entoto station from 1989 to 2016. The overall purpose of this study is 118 to analyze the trend of temperature in Addis Ababa City by using the Mann-Kendall trend test as 119 well as to look at the effect of climate change in the study area. MK test is a non-parametric 120 (distribution-free) test. Additionally, it is also a powerful trend test and chosen for the analysis 121 since its measurement does not follow the normal distribution. It is also used to analyze time-series 122 data for consistently monotonic trends. In terms of contribution, this study introduces one of the 123 earliest case study in this subject matter for Ethiopia and the findings will be useful in mitigating 124 the adverse impacts of climate change in the country. Also, the analytical framework presented 125 here can be employed by other researchers to study temperature variations in other regions of the 126 world. While this paper is crafted with a local case study, the results will also be useful for 127 international literature.

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The rest of this paper is structured thus: Section 2 explains the research methods used in the study 129 which incorporates the study area, Mann-Kendall Test, Sen's Slope estimator, data collection, and 130 processing, as well as data analysis tools. Section 3 describes the results and brief discussion, while 131 the general study conclusions are presented in section 4.  for these tests is that there is no trend in the series. The alternative hypothesis, Ha, is that the data 154 follow a monotonic trend (i.e. negative, non-null, or positive trend). MK test is commonly 155 employed to detect monotonic trends in a series of environmental data, climate data, or 156 hydrological data. There are two benefits of using this test. First, it does not require the data to be 157 normally distributed since the test is non-parametric (distribution-free test) and second, the test has 158 low sensitivity to abrupt breaks due to inhomogeneous time series. The data values are evaluated 159 as an order time series. Each data value is compared to all subsequent data values. The time series 160 x1, x2, x3… xn represents n data points.

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The Mann-Kendall test statistic (S) is calculated according to:

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A normal approximation test that could be used for datasets with more than 10 values was overall research data for this study were collected based on secondary data sources to address the 220 goals of the study. The data was used to analyses the temperature trend of Addis Ababa city.    From the Mann-Kendall test result, it was found that Z value for minimum, maximum and average 239 temperatures for Bole station are 5.99, 3.32, and 6.14 respectively as stated in (Table 1). The 240 positive Kendall's Z value shows an upward trend and also implies an increasing trend over time.

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This indicates that there is a significant increase in the trend at a 5% level of significance since the 1.40 respectively as displayed in (Table 1) and thus the positive value indicates an increasing trend 245 but not significant at 5% significant level since the p-value is greater than the significant level (p-246 value (0.219) > 0.05).

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The result obtained in this study agrees with the findings of an earlier study by Getachew (2018), 248 whose results revealed that the maximum temperature increasing trend analysis is found to be

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The Sen's slope estimates as shown in Table 1 and