Multivariate statistical, hydrochemical, and indexing approaches for groundwater quality evaluation in a semi-arid region: case study of the Upper Mereb Catchment, Eritrea

Abstract Groundwater serves as a primary and often the most reliable source of water supply in arid and semi-arid regions, making the assessment and monitoring of its quality vital for sustainable drinking and agricultural use. This study provides a comprehensive evaluation of groundwater quality in the Upper Mereb Catchment of Eritrea using an integrated approach. The methodology includes graphical analysis, multivariate statistics, Groundwater Quality Index (GWQI) and irrigation indices. The hydrochemical analysis revealed that Ca-Mg-HCO₃ is the dominant groundwater type, primarily influenced by water-rock interactions. Multivariate statistical techniques, including Hierarchical Cluster Analysis (HCA) and Principal Component Analysis (PCA), indicated that groundwater contamination is localized and primarily caused by geogenic processes and anthropogenic agricultural activities. GWQI results indicated that 77.8% of the samples fall within the “good” category, while 16.3% are classified as “poor” for drinking purpose. Furthermore, Sodium adsorption ratio and sodium percent indices indicated that the groundwater is largely suitable for irrigation, falling within the “excellent” to “permissible” classes. Although overall water quality is favorable, some localized contamination highlights the need for ongoing monitoring and management. The study emphasizes the value of integrating multiple analytical methods for a more comprehensive understanding of groundwater quality in the Upper Mereb region.