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Hydrogeochemical Studies by Multivariate Statistical Analysis in Upper Thirumanimuthar Sub-Basin, Cauvery River, Tamil Nadu, India


Affiliations
1 Geographic Information Technology Lab, Department of Earth Sciences, Annamalai University, Annamalai Nagar, Tamil Nadu, India
2 Centre for Geographic Information Technologies, Bharathidasan University, Trichy, Tamil Nadu, India
3 Department of Ocean Studies, Pondicherry University, Andaman, India
 

In the present paper deals with the study of hydrogeochemistry of groundwater by multivariate statistical techniques such as factor and cluster analyses. The upper Thirumanimuthar sub-basin, Cauvery River, hard rock terrain in Salem District covering an area of about 346.40 km2 has been selected for the study. Fifty one samples were collected during premonsoon season 2007 and analysed for various water quality parameters like pH, EC, TDS, Ca, Mg, Na, K, HCO3, CO3, SO4, Cl and TH. Hydrogeochemical data of 51 groundwater samples were subjected to Q- and R- mode factor and cluster analysis. R-mode analysis reveals the interrelations among the variables studied and the Q-mode analysis reveals the interrelations among the samples studied. The R-mode factor analysis shows that Na and CI with HCO3 account for most of the electrical conductivity and total dissolved solids of the groundwater. The 'single dominance' nature of the majority of the factors in the R-mode analysis indicates non-mixing or partial mixing of different types of groundwaters. Both Q-mode factor and R-mode cluster analyses show that there is an exchange between the river water and adjacent groundwater. Cluster classification map reveals that 97.79% of the study area comes under cluster I classification.

Keywords

Groundwater, Hydrogeochemical Studies, Multivariate Statistical Analysis, Factor Analysis, Cluster Analysis.
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  • Hydrogeochemical Studies by Multivariate Statistical Analysis in Upper Thirumanimuthar Sub-Basin, Cauvery River, Tamil Nadu, India

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Authors

M. Suresh
Geographic Information Technology Lab, Department of Earth Sciences, Annamalai University, Annamalai Nagar, Tamil Nadu, India
B. Gurugnanam
Geographic Information Technology Lab, Department of Earth Sciences, Annamalai University, Annamalai Nagar, Tamil Nadu, India
S. Vasudevan
Centre for Geographic Information Technologies, Bharathidasan University, Trichy, Tamil Nadu, India
B. Rajeshkanna
Centre for Geographic Information Technologies, Bharathidasan University, Trichy, Tamil Nadu, India
K. Dharanirajan
Department of Ocean Studies, Pondicherry University, Andaman, India
N. Prabhakaran
Geographic Information Technology Lab, Department of Earth Sciences, Annamalai University, Annamalai Nagar, Tamil Nadu, India

Abstract


In the present paper deals with the study of hydrogeochemistry of groundwater by multivariate statistical techniques such as factor and cluster analyses. The upper Thirumanimuthar sub-basin, Cauvery River, hard rock terrain in Salem District covering an area of about 346.40 km2 has been selected for the study. Fifty one samples were collected during premonsoon season 2007 and analysed for various water quality parameters like pH, EC, TDS, Ca, Mg, Na, K, HCO3, CO3, SO4, Cl and TH. Hydrogeochemical data of 51 groundwater samples were subjected to Q- and R- mode factor and cluster analysis. R-mode analysis reveals the interrelations among the variables studied and the Q-mode analysis reveals the interrelations among the samples studied. The R-mode factor analysis shows that Na and CI with HCO3 account for most of the electrical conductivity and total dissolved solids of the groundwater. The 'single dominance' nature of the majority of the factors in the R-mode analysis indicates non-mixing or partial mixing of different types of groundwaters. Both Q-mode factor and R-mode cluster analyses show that there is an exchange between the river water and adjacent groundwater. Cluster classification map reveals that 97.79% of the study area comes under cluster I classification.

Keywords


Groundwater, Hydrogeochemical Studies, Multivariate Statistical Analysis, Factor Analysis, Cluster Analysis.