Abstract
A central factor in the modelling and analysis of the trend is the ability to establish whether a change or trend is present in the climatological record and to quantify this trend if it is present. The trend in a time series data can be expressed by a suitable linear (parametric) or nonlinear (non-parametric) model depending on the behaviour of the available data. The aim of this research is to detect and estimate the magnitude of trend associated with rainfall data from Warri and Benin City which are located within the coastal region of Nigeria using non-parametric Mann-Kendall test statistical approach. 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. 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 Warri and Benin City 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 Benin City shows statistical significant evidence of an increasing trend with a computed M-K trend value of +124. Although, the rainfall records from Warri do not have sufficient statistical evidence of a significant trend, the computed M-K trend value was -96 which is; evidence of a decreasing trend.
Highlights
Trend in rainfall data have a great impact on the hydrological cycle and involve both the character and quantity of water resources
The rainfall data collected from Warri and Benin City showing the missing values is presented in Figure 2 and Figure 3
Infilling of the missing records were based on the outcome of the correlation statistics which is critical to the fundamental assumptions of missing value analysis using expectation maximization algorithm
Summary
Trend in rainfall data have a great impact on the hydrological cycle and involve both the character and quantity of water resources. Analysis of trend in rainfall data aids to see the result of rainfall variability on the occurrence of drought and flood [3]. Numerous variables such as temperature, vegetation affects the hydrological cycle, precipitation remains the key climatic variable that governs the hydrologic cycle and the availability of water resources. Recent studies have suggested that analysis of hydro-climatic variables should be done at the local scale rather than at a large or global scale because the trends and their impacts may be different from one location to the other [6]. Trends and shifts in hydrologic time series are usually introduced due to natural or artificial
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