Non-parametric Mann-Kendall Test Statistics for Rainfall Trend Analysis in Some Selected States within the Coastal Region of Nigeria

Ihimekpen Ngozi Isioma; Ilaboya Idowu Rudolph; Awah Lauretta Omena

doi:doi:10.11648/j.jccee.20180301.14

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Non-parametric Mann-Kendall Test Statistics for Rainfall Trend Analysis in Some Selected States within the Coastal Region of Nigeria

Ihimekpen Ngozi Isioma, Ilaboya Idowu Rudolph, Awah Lauretta Omena

Published in Journal of Civil, Construction and Environmental Engineering (Volume 3, Issue 1)

Received: 16 January 2018 Accepted: 5 February 2018 Published: 15 March 2018

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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.

Published in	Journal of Civil, Construction and Environmental Engineering (Volume 3, Issue 1)
DOI	10.11648/j.jccee.20180301.14
Page(s)	17-28
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2018. Published by Science Publishing Group

Keywords

Expectation Maximization Algorithm, Normality Test, Outlier Detection, Mann-Kendall Test, Non-parametric Analysis

References

[1]	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.
[2]	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.
[3]	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.
[4]	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.
[5]	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.
[6]	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
[7]	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.
[8]	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.
[9]	Turgay, P and Ercan, K (2006), Trend analysis in Turkish precipitation data, Hydrological Processes Journal, vol. 20, pp: 2011–2026.
[10]	Webb, B. W., (1996): Trends in stream and river temperature. Hydrological Processes, vol. 10 (2), pp: 205–226.
[11]	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.

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Ihimekpen Ngozi Isioma, Ilaboya Idowu Rudolph, Awah Lauretta Omena. (2018). 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, 3(1), 17-28. https://doi.org/10.11648/j.jccee.20180301.14

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ACS Style

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. J. Civ. Constr. Environ. Eng. 2018, 3(1), 17-28. doi: 10.11648/j.jccee.20180301.14

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AMA Style

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. J Civ Constr Environ Eng. 2018;3(1):17-28. doi: 10.11648/j.jccee.20180301.14

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@article{10.11648/j.jccee.20180301.14,
  author = {Ihimekpen Ngozi Isioma and Ilaboya Idowu Rudolph and Awah Lauretta Omena},
  title = {Non-parametric Mann-Kendall Test Statistics for Rainfall Trend Analysis in Some Selected States within the Coastal Region of Nigeria},
  journal = {Journal of Civil, Construction and Environmental Engineering},
  volume = {3},
  number = {1},
  pages = {17-28},
  doi = {10.11648/j.jccee.20180301.14},
  url = {https://doi.org/10.11648/j.jccee.20180301.14},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.jccee.20180301.14},
  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.},
 year = {2018}
}

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TY  - JOUR
T1  - Non-parametric Mann-Kendall Test Statistics for Rainfall Trend Analysis in Some Selected States within the Coastal Region of Nigeria
AU  - Ihimekpen Ngozi Isioma
AU  - Ilaboya Idowu Rudolph
AU  - Awah Lauretta Omena
Y1  - 2018/03/15
PY  - 2018
N1  - https://doi.org/10.11648/j.jccee.20180301.14
DO  - 10.11648/j.jccee.20180301.14
T2  - Journal of Civil, Construction and Environmental Engineering
JF  - Journal of Civil, Construction and Environmental Engineering
JO  - Journal of Civil, Construction and Environmental Engineering
SP  - 17
EP  - 28
PB  - Science Publishing Group
SN  - 2637-3890
UR  - https://doi.org/10.11648/j.jccee.20180301.14
AB  - 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.
VL  - 3
IS  - 1
ER  -

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Author Information

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

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Plain Text BibTeX RIS

APA Style

Ihimekpen Ngozi Isioma, Ilaboya Idowu Rudolph, Awah Lauretta Omena. (2018). 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, 3(1), 17-28. https://doi.org/10.11648/j.jccee.20180301.14

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ACS Style

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. J. Civ. Constr. Environ. Eng. 2018, 3(1), 17-28. doi: 10.11648/j.jccee.20180301.14

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AMA Style

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. J Civ Constr Environ Eng. 2018;3(1):17-28. doi: 10.11648/j.jccee.20180301.14

Copy | Download

@article{10.11648/j.jccee.20180301.14,
  author = {Ihimekpen Ngozi Isioma and Ilaboya Idowu Rudolph and Awah Lauretta Omena},
  title = {Non-parametric Mann-Kendall Test Statistics for Rainfall Trend Analysis in Some Selected States within the Coastal Region of Nigeria},
  journal = {Journal of Civil, Construction and Environmental Engineering},
  volume = {3},
  number = {1},
  pages = {17-28},
  doi = {10.11648/j.jccee.20180301.14},
  url = {https://doi.org/10.11648/j.jccee.20180301.14},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.jccee.20180301.14},
  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.},
 year = {2018}
}

Copy | Download

TY  - JOUR
T1  - Non-parametric Mann-Kendall Test Statistics for Rainfall Trend Analysis in Some Selected States within the Coastal Region of Nigeria
AU  - Ihimekpen Ngozi Isioma
AU  - Ilaboya Idowu Rudolph
AU  - Awah Lauretta Omena
Y1  - 2018/03/15
PY  - 2018
N1  - https://doi.org/10.11648/j.jccee.20180301.14
DO  - 10.11648/j.jccee.20180301.14
T2  - Journal of Civil, Construction and Environmental Engineering
JF  - Journal of Civil, Construction and Environmental Engineering
JO  - Journal of Civil, Construction and Environmental Engineering
SP  - 17
EP  - 28
PB  - Science Publishing Group
SN  - 2637-3890
UR  - https://doi.org/10.11648/j.jccee.20180301.14
AB  - 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.
VL  - 3
IS  - 1
ER  -

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