Evaluation of spatial and temporal dynamics of seawater intrusion in coastal aquifers of southeast India: insights from hydrochemical facies analysis.

This study aims to investigate and understand the temporal and spatial movement of seawater intrusion into the coastal aquifers. Groundwater salinity increase has affected the entire eastern part of the study area and is primarily influenced by direct and reverse ion exchange reactions associated with intrusion and freshwater influx phases, which alternate over monsoons. To gain insights into the spatiotemporal dynamics of the seawater intrusion process, hydrochemical facies analysis utilizing the HFE-Diagram was employed. Additionally, the study considered the major ionic changes during both the monsoons. The HFE-Diagram analysis of hydrochemical facies revealed distinctions in the behaviour of each coastal aquifer concerning seawater intrusion-induced salinization. In PRM 2020, the data shows that approximately 65% of the samples fall under the freshening phase, while the remaining 35% were categorized as intrusion phase. Within the freshening phase, seven different hydrochemical facies were identified, including Na-Cl, Na-MixCl, MixNa-MixCl, Na-MixHCO3/MixSO4, MixNa-MixSO4, Na-HCO3, and MixCa-HCO3. In contrast, the intrusion phase had four facies: MixCaMixHCO3, MixNa-Cl, Ca-Cl, and Na-Cl. Especially, the Na-Cl facies (f1) within the freshening phase attributed for the largest percentage, contributing 30% of the samples. In POM 2021, the distribution of samples shifted slightly, with approximately 72.5% belonging to the freshening phase and 27.5% to the intrusion phase. Within the freshening phase of POM 2021, five hydrochemical facies were identified: Na-Cl, Na-MixCl, Na-MixHCO3/MixSO4, MixNa-MixSO4, and Na-HCO3. The intrusion phase of POM 2021 had three facies: MixNa-Cl, Na-Cl, and MixCa-Cl. Similar to PRM 2020, the Na-Cl facies (f1) remained the most predominant in the freshening phase, comprising 30% of the samples. The relation between total dissolved solids (TDS) and various ionic ratios, such as HCO3-/Cl-, Na+/Cl-, Ca2+/Cl-, Mg2+/Cl-, K+/Cl-, and SO42-/Cl-, clearly demonstrates the presence of seawater influence within the coastal aquifers of the study area.

Assessment of groundwater vulnerability using water quality index and solute transport model in Poiney sub-basin of south India.

This paper demarcated the most vulnerable regions within the Poiney sub-basin (Tamil Nadu state in India) with respect to the groundwater quality. An index-based vulnerability assessment was carried out by measuring the physico-chemical variables such as pH, electrical conductivity, total dissolved solids, magnesium, sodium, chloride, sulphate, bicarbonate and fluoride in the pre-monsoon and post-monsoon samples. Water quality index varied across the sub-basin due to differences in the water quality induced by anthropogenic activities related to land use practices and presence of industries. The MT3D engine coupled with visual MODFLOW identified that sulphate released from tanneries and leather factories is the main effluent contaminating the groundwater. Model reveals that both the flow and contaminant transport is towards southeast with maximum and minimum calculated head of 201.82 mg/l and 265.92 mg/l and calculated sulphate concentration of 394.40 mg/l and 46.79 mg/l respectively.

Vulnerability mapping of the Paravanar sub-basin aquifer (Tamil Nadu, India) in SINTACS model for efficient land use planning.

Consideration of groundwater vulnerability as a planning parameter is imperative in the current context of depleting groundwater resources for the efficient land use planning. This study aims for groundwater vulnerability assessment by modifying SINTACS model and involve dynamics of land use change in a case study of Paravanar sub-basin in the Tamil Nadu state of south India. Thematic maps of land use generated from remote sensing data and associated field investigations were the input for the SINTACS model. These maps integrated in GIS helped to derive intrinsic vulnerability into very low, low, medium and high vulnerability categories. The strongest correlation (r = 0.74) between intrinsic vulnerability index and the water quality index, estimated from field observations, suggested better efficiency of this model compared to the conventional SINTACS index. Application of the modified SINTACS led to the conclusion that 12.2%, 28.7%, 45.9%, and 13.1% of the study area categorized very low vulnerability, low vulnerability, moderate vulnerability and high vulnerability, respectively and should be considered for efficient land use planning.