Please enter verification code
Non-parametric Mann-Kendall Test Statistics for Rainfall Trend Analysis in Some Selected States within the Coastal Region of Nigeria
Journal of Civil, Construction and Environmental Engineering
Volume 3, Issue 1, February 2018, Pages: 17-28
Received: Jan. 16, 2018; Accepted: Feb. 5, 2018; Published: Mar. 15, 2018
Views 1971      Downloads 207
Ihimekpen Ngozi Isioma, Department of Civil Engineering, University of Benin, Benin City, Nigeria
Ilaboya Idowu Rudolph, Department of Civil Engineering, University of Benin, Benin City, Nigeria
Awah Lauretta Omena, Department of Civil Engineering, University of Benin, Benin City, Nigeria
Article Tools
Follow on us
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.
Expectation Maximization Algorithm, Normality Test, Outlier Detection, Mann-Kendall Test, Non-parametric Analysis
To cite this article
Ihimekpen Ngozi Isioma, Ilaboya Idowu Rudolph, Awah Lauretta Omena, Non-parametric Mann-Kendall Test Statistics for Rainfall Trend Analysis in Some Selected States within the Coastal Region of Nigeria, Journal of Civil, Construction and Environmental Engineering. Vol. 3, No. 1, 2018, pp. 17-28. doi: 10.11648/j.jccee.20180301.14
Copyright © 2018 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License ( which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Alli, A. A.; Oguntunde, P. G.; Olufayo, A. A., and Fasinmirin, J. T (2012), Implications of Trends and Cycles of Rainfall on Agriculture and Water Resource in the Tropical Climate of Nigeria, Special Publication of the Nigerian Association of Hydrological Sciences, pp: 188–200.
Hameed, K. H. and Rao, A. R., (2008): A modified Mann- Kendall trend test for autocorrelated data, Journal of Hydrology, vol. 204, (1-4), pp: 182–196.
Ifabiyi, I. P.1 and Ojoye, S (2013), Rainfall Trends in the Sudano-Sahelian Ecological Zone of Nigeria, Earth Science Research; Vol. 2(2), pp: 194–202.
Levi, D. B.; Julie, E. K.; Olsen, J. R.; Pulwarty, R. S.; Raff, D. A.; Turnipseed, D. P.; Webb, R. S and Kathleen D. W (2009); Climate Change and Water Resources Management: A Federal Perspective, circular 1331, pp: 1–72.
Raes, D; Willens, P and Gbaguidi (2006), Rainbow – A software package for analyzing data and testing the homogeneity of historical data sets, vol. 1, pp: 1-15.
Shishutosh, B.; Nitin, M.; Ng, A. W. M and Perera, B. J. C (2013), Rainfall trend and its implications for water resource management within the Yarra River catchment, Australia, Hydrological Processes, Vol. 27, Issue 12, pp: 1727-1738
Solomon, S., Qin, D., Manning, M., Chen, Z., Marquis, M., Averyt, K. B., Tignor, M., and Miller, H. L., (2007), Intergovernmental Panel on Climate Change, Climate change 2007: The physical science basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change, eds.: Cambridge, United Kingdom, Cambridge University Press. Pp: 1-72.
Tayanç, M. And Toros, H., 1997: Urbanization effects on regional climate change in the case of four large cities of Turkey. Climatic Change, 35, 4, 501–524.
Turgay, P and Ercan, K (2006), Trend analysis in Turkish precipitation data, Hydrological Processes Journal, vol. 20, pp: 2011–2026.
Webb, B. W., (1996): Trends in stream and river temperature. Hydrological Processes, vol. 10 (2), pp: 205–226.
Xu, Z. X.; Takeuchi, K. and Ishidaira, H., (2003): Monotonic trend and step changes in Japanese precipitation. Journal of Hydrology, vol. 279 (1-4), pp: 144–150.
Science Publishing Group
1 Rockefeller Plaza,
10th and 11th Floors,
New York, NY 10020
Tel: (001)347-983-5186