Asian Journal of Chemical Sciences

Groundwater resources in South-Eastern Nigeria are increasingly threatened by accelerated gully erosion, intense seasonal rainfall, rapid urbanisation, and unsustainable land-use practices, all of which increase aquifer susceptibility to contamination. This study assessed groundwater vulnerability in selected gully erosion-prone communities of South-Eastern Nigeria using an integrated Geographic Information System (GIS) and DRASTIC modelling approach. Hydrogeological, geological, topographic, soil, rainfall, groundwater-depth, recharge, vadose-zone, hydraulic-conductivity, land-use, drainage-density, and lineament-density datasets were integrated within a GIS environment to generate groundwater vulnerability maps. Weighted overlay analysis and DRASTIC index modelling were used to classify groundwater vulnerability, and model performance was evaluated using the coefficient of determination (R²), root mean square error (RMSE), and prediction accuracy. The DRASTIC vulnerability index ranged from 156 to 192, with a mean of 175.4 ± 11.39, indicating predominantly moderate to high groundwater vulnerability. Approximately 50% of the sampled locations exhibited high vulnerability, whereas 40% were classified as very highly vulnerable, leaving only 10% within the moderate vulnerability class. GIS spatial analysis further showed that 801.9 km² (33.1%) of the study area falls within the high vulnerability zone, while 529.5 km² (21.8%) occurs within the very high vulnerability zone, representing a combined 54.9% of the total area. Conversely, only 16.9% of the area was classified as low to very low vulnerability. Depth to groundwater (23.8%) and groundwater recharge (19.6%) were identified as the dominant factors controlling groundwater vulnerability. The integrated GIS model demonstrated strong predictive performance, with an R² of 0.93, an RMSE of 0.31, and an overall prediction accuracy of 95.4%. The study demonstrates that integrating GIS, hydrogeological parameters, and vulnerability modelling provides a robust framework for identifying groundwater contamination risk in erosion-prone terrains. The generated vulnerability maps provide valuable information for groundwater protection, sustainable land-use planning, erosion mitigation, environmental monitoring, and water-resource management in South-Eastern Nigeria.