Trend in rainfall data have a great impact on the hydrological cycle and thus involve both the character and quantity of water resources. Analysis of trend in rainfall data also aids to see the result of rainfall variability on the occurrence of drought and flood. The aim of this research is to detect and estimate the magnitude of trend associated with rainfall data from Akure and Calabar which are located within the coastal region of Nigeria using non-parametric Mann-Kendall test statistics. Monthly data for thirty six (36) years spanning from 1980 to 2016 was used as input parameters for the analysis. Infilling of the missing records was done with the aid of expectation maximization algorithm which is unarguably one of the best missing value analysis techniques. Preprocessing of the rainfall data was done by conducting numerous time series validation test such as test of homogeneity, test of normality and outlier detection. Homogeneity test was aimed at testing the assumption of same population distribution; outlier detection was to detect the presence of bias in the data while test of normality was done to validate the claim that climatic data are not always normally distributed. In addition to testing the normality assumption of the data, normality test was also employed to select the most suitable trend detection and estimation technique. Results of the analysis revealed that the rainfall data from Akure and Calabar are statistically homogeneous. The records did not contain outliers and they are not normally distributed as expected for most climatic variables. The non-parametric trend detection and estimation analysis revealed that the rainfall data from Akure shows statistical significant evidence of a decreasing trend with a computed M-K trend value of -129. Although, the rainfall records from Calabar do not have sufficient statistical evidence of a significant trend, the computed M-K trend value was +50 which is; evidence of an increasing trend.
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