Abstract: Development and effective utilization of groundwater resources is essential especially in semi-arid region and in a region with abundant rainfall such as the study area for activities such as water supply and irrigation. The present study aims to analyse statistically the groundwater level, stream flow and rainfall data of 13years (2000-2012) collected from the Department of Minerals and Geosciences and Department of Irrigation and Drainage Terengganu for the seven stations: Besut, Dungun, Kemaman, Bukit, Paka, Cherul and Menerong of Terengganu Malaysia. The homogeneity test was made to make sure that all the series of data are homogenous. The regression analysis method was adapted to analyses the relationship of groundwater level variability with the rainfall distribution. The analysis indicated that the rainfall distribution has an influence on groundwater level in the study area due to positive relationship shown by regression analysis. Although in some stations the influence is not much significant, that is the groundwater levels depends on runoff and other factors rather than rainfall. Such stations are Site 4930401 SG. Berang at Menerong shows 14.7%, Site 4232401 SG. Kemaman shows 27.2 %, and Site 4732461 SG. Paka shows 35.2%. The station that shows great influence of rainfall in determining the groundwater level is Site 5229436 SG Nerus, which has 58.5%, while the remaining stations are moderate. Therefore Vector Error Correction Model were employed after the introducing of stream flow data to test and confirmed this relationship, and were found to be strong as indicated by the result of the analysis.
Keywords: Groundwater level, rainfall, stream flow, time series regression analysis, homogeneity test, correlation, vector error correction model.
Title: A Multivariate Co integration Analysis for Groundwater Pattern Recognition; Based on Rainfall Distribution, in Terengganu Malaysia
Author: Musa G. Abdullahi, Muhd Barzani Gasim, Iliyasu Garba, Sani Garba D/Iya
International Journal of Life Sciences Research
ISSN 2348-313X Print), ISSN 2348-3148 (online)
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