Groundwater vulnerability and pollution risk assessment in the Achankovil river Basin, Western Ghats, Southern India: integration of GIS-based DRASTIC-LC model, hydrochemical indicators and multivariate statistical analysis

The Achankovil River Basin (ARB) is a tropical region of the Western Ghats in Kerala, southern India, facing critical issues with groundwater sources from natural and human-induced activities. This study aims to identify pollution vulnerability to groundwater using a GIS-based DRASTIC-LC model and hydrochemical analysis. Land cover (LC) change (2000 – 2025) was incorporated to evaluate its impacts on groundwater vulnerability. The DRASTIC-LC map indicates a vulnerability index ranging from 74 to 235, classified into five zones such as very low (13%), low (52.2%), moderate (16.6%), high (16.8%), and very high (1.4%). A higher vulnerability rate has been noted in the western coastal and lateritic midlands due to the occurrence of unconfined aquifers with shallow groundwater (< 3 m bgl), characterized by high seepage flow (> 2000 L min⁻1), formed by coastal sandy alluvium under both natural and human-induced pressures. Wherein the eastern highlands with hard rock formations exhibit a lower vulnerability rate, due to deeper confined aquifers (> 12 m bgl), and lower permeability (< 500 L min⁻1). In the middle plateaus, the laterite formations were noticed with moderate vulnerability conditions. Model reliability was validated through the strong spatial agreement between high DRASTIC-LC index zones and elevated hydrochemical indicators (EC, TDS, Na⁺, Cl⁻, SO₄2⁻, and NO₃⁻). Multivariate analyses (PCA and HCA) further confirmed that depth to water table, recharge, vadose zone characteristics, hydraulic conductivity, and land cover are the dominant controls on groundwater vulnerability. Overall, groundwater vulnerability in the ARB is influenced by urban discharge, agricultural residues, sewage and industrial infiltration, LULC change, the expansion of impervious surfaces, and reduced recharge. The consistency between DRASTIC-LC indices, hydrochemical indicators (EC, TDS, Na⁺, Cl⁻, SO₄2⁻, and NO₃⁻), and multivariate statistical results demonstrates the effectiveness of this approach in identifying aquifers prone to pollution. It supports groundwater protection measures aligned with the United Nations SDGs 6, 13, and 15.