Analysis of Rainfall Trend in Selected States with in the Coastal Region of Nigeria using non-Parametric Mann-Kendall Test Statistics
Journal Title: Trends in Civil Engineering and its Architecture - Year 2018, Vol 2, Issue 3
Abstract
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. Adequate planning and management of water resources for sustainable development is an issue of concern and have developed into a widely studied area. Accurate knowledge of the past and present trend of climatic data can aid in visualization and characterization of the water resource situation both in the past and even presently [1,2]. The trend results will not only provide a general idea of any changes noticeable within the climatic data, it will also point out certain concerns for the area regarding extreme precipitation events such as drought and flooding. Climatic data trend results will likewise suffice as a comparison for extreme precipitation events predicted by generalized time series forecasting models such as least square regression and multivariate regression model [3]. 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 [4]. Although 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. Numerous studies have analyzed the changes in precipitation patterns in global as well as regional scale. Recent studies have also 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 [5]. There are many different ways in which changes in hydro-meteorological series can take place. A change can occur abruptly (step change) or gradually (trend) or may take more complex forms. A time series is pronounced to have trends, if there is a significant correlation (positive or minus) between the observations and time. Trends and shifts in hydrologic time series are usually introduced due to natural or artificial changes. Natural changes in hydrologic variables are usually gradual and are caused by a global or regional climate change, which would be a representative of changes occurring over the study area. Changes in monitoring variables that may not be able to be extrapolated over a study area could be caused by a gradual urbanization of the area surrounding the monitoring site, changes in the method of measurement at the monitoring site, or by moving the monitoring site even a short distance away. The artificial change is usually mentioned in the overall record at a monitoring site, but this information is not always shown in the sites’ data series. Thus, variables that appear to have a trend may actually just represent a change in climatological conditions near the monitoring site. In such a case, the affected climatological data should be changed so that the values are better represented of the study area as a whole [6].
Authors and Affiliations
Ihimekpen NI, Ilaboya IR, Awah LO
Keywords
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- EP ID EP610464
- DOI 10.32474/TCEIA.2018.02.000139
- Views 155
- Downloads 0
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