A multivariate cointegration analysis for groundwater pattern recognition; Based on rainfall distribution, in Terengganu Malaysia
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...
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my-unisza-ir.49692022-01-31T02:10:25Z http://eprints.unisza.edu.my/4969/ A multivariate cointegration analysis for groundwater pattern recognition; Based on rainfall distribution, in Terengganu Malaysia Musa, G. Abdullahi Muhd Barzani, Gasim Iliyasu, Garba Sani Garba, D/Iya G Geography (General) GE Environmental Sciences 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. 2015-08 Article PeerReviewed text en http://eprints.unisza.edu.my/4969/1/FH02-ESERI-15-03731.pdf Musa, G. Abdullahi and Muhd Barzani, Gasim and Iliyasu, Garba and Sani Garba, D/Iya (2015) A multivariate cointegration analysis for groundwater pattern recognition; Based on rainfall distribution, in Terengganu Malaysia. International Journal of Life Sciences Research, 3 (1). pp. 1-12. ISSN 2348-313X |
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G Geography (General) GE Environmental Sciences Musa, G. Abdullahi Muhd Barzani, Gasim Iliyasu, Garba Sani Garba, D/Iya A multivariate cointegration analysis for groundwater pattern recognition; Based on rainfall distribution, in Terengganu Malaysia |
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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. |
format |
Article |
author |
Musa, G. Abdullahi Muhd Barzani, Gasim Iliyasu, Garba Sani Garba, D/Iya |
author_facet |
Musa, G. Abdullahi Muhd Barzani, Gasim Iliyasu, Garba Sani Garba, D/Iya |
author_sort |
Musa, G. Abdullahi |
title |
A multivariate cointegration analysis for groundwater pattern recognition; Based on rainfall distribution, in Terengganu Malaysia |
title_short |
A multivariate cointegration analysis for groundwater pattern recognition; Based on rainfall distribution, in Terengganu Malaysia |
title_full |
A multivariate cointegration analysis for groundwater pattern recognition; Based on rainfall distribution, in Terengganu Malaysia |
title_fullStr |
A multivariate cointegration analysis for groundwater pattern recognition; Based on rainfall distribution, in Terengganu Malaysia |
title_full_unstemmed |
A multivariate cointegration analysis for groundwater pattern recognition; Based on rainfall distribution, in Terengganu Malaysia |
title_sort |
multivariate cointegration analysis for groundwater pattern recognition; based on rainfall distribution, in terengganu malaysia |
publishDate |
2015 |
url |
http://eprints.unisza.edu.my/4969/1/FH02-ESERI-15-03731.pdf http://eprints.unisza.edu.my/4969/ |
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