Trend Analysis of the Mean Annual Temperature in Rwanda during the Last Fifty Two Years

Abstract

Climate change and global warming are widely recognized as the most significant environmental dilemma the world is experiencing today. Recent studies have shown that the Earth's surface air temperature has increased by 0.6˚C -0.8˚C during the 20 th century, along with changes in the hydrological cycle. This has alerted the international community and brought great interest to climate scientists leading to several studies on climate trend detection at various scales. This paper examines the long-term modification of the near surface air temperature in Rwanda. Time series of near surface air temperature data for the period ranging from 1958 to 2010 for five weather observatories were collected from the Rwanda National Meteorological Service. Variations and trends of annual mean temperature time series were examined. The cumulative sum charts (CUSUM) and bootstrapping and the sequential version of the Mann Kendall Rank Statistic were used for the detection of abrupt changes. Regression analysis was performed for the trends and the Mann-Kendall Rank Statistic Test was used for the examination of their significance. Statistically significant abrupt changes and trends have been detected. The major change point in the annual mean temperature occurred around [1977][1978][1979]. The analysis of the annual mean temperature showed for all observatories a not very significant cooling trend during the period ranging from 1958 to 1977-1979 while a significant warming trend was furthermore observed for the period after the [1977][1978][1979] where Kigali, the Capital of Rwanda, presented the highest values of the slope (0.0455/year) with high value of coefficient of determination (R 2 = 0.6798), the Kendall's tau statistic (M-K = 0.62), the Kendall Score (S = 328) with a two-sided p-value far less than the confidence level α of 5%. This is most likely explained by the growing population and increasing urbanization and industrialization the country has experienced, especially the Capital City Kigali, during the last decades.

Detection of Approximate Potential Trend Turning Points in Temperature Time Series (1941-2010) for Asansol Weather Observation Station, West Bengal, India

“…In case of the month of January, an apparently decreasing trend from 1957 to 2010 is observed. Though the curves intersect each other at 1973 but it cannot be recognized as a significant turning point as the associated probability value is much higher than the accepted level of significance (p < 0.05) in Table 1 Figure 1(c), intersect each other for several times which indicates that there is no recognizable trend in the March temperature data set [11]. Sequential Mann-Kendall plots for April temperature has been given in Figure 1(d).…”

“…During the last 100 years China has experienced a warming by 0.79˚C at the rate of 0.08˚C per decade with local level disparities in warming [5][6][7]. Large volume of literature has been produced on surface air temperature variation that includes research works to confirm global warming by use of a variety of methods [8][9][10][11]. It has been argued that daily minimum temperature exhibits more associations with global warming phenomenon rather than daily maximum [10].…”

Seroprevalence of Rift Valley fever in cattle along the Akagera-Nyabarongo rivers, Rwanda

“…Temperatures of less than 17 °C are recorded for the Congo ridges, which stretch up to the high volcanoes (Safari 2012). The country's annual rainfall varies, and the differences range from more than 1200 mm in the elevated zone of the north-west to less than 900 mm in the eastern zone (REMA 2011;Safari 2012).…”

Adapting to the impacts of climate change in the sub-humid zone of Burkina Faso, West Africa: Perceptions of agro-pastoralists

“…For this study, the statistically significant trend was reported at the 95 % confidence level (two-tailed test). Mann-Kendall's test is a non-parametric method used to test climatological data (Ly et al 2013;Safari 2012), and studies have shown that it is the most appropriate statistical tool for the analysis of changes in climatological time series or for the detection of abrupt climate change (Klein Tank et al 2009;Safari 2012). The MK test examines if a random variable monotonically increases or decreases with time or not (Jain and Kumar 2012;Karabulut et al 2008;Safari 2012) and the data used may or may not be normally distributed, which makes it particularly appropriate for use with climate data (Karabulut et al 2008).…”