Abstract
Monitoring changes in groundwater quality over time helps identify time-dependent factors influencing water safety and supports the development of effective management strategies. This study investigates the spatiotemporal evolution of groundwater chemistry in the Debrecen area, Hungary, from 2019 to 2024, using indexing, machine learning, and multivariate statistical techniques. These techniques include self-organizing maps (SOM), hierarchical cluster analysis (HCA), principal component analysis (PCA), and groundwater quality indexing (GWQI). The hydrochemical analysis revealed that Ca-Mg-HCO₃ is the dominant water type, with a temporal shift toward Na-HCO₃, reflecting increased salinity driven by ongoing rock-water interactions. SOM analysis showed a transition from heterogeneous to more uniform groundwater chemistry over time, suggesting greater stability in the aquifer system. Elevated salinity zones shifted spatially due to changes in groundwater recharge and flow patterns, while hardness intensified and expanded, indicating continued carbonate dissolution. HCA highlighted temporal shifts in groundwater composition, with six clusters identified in 2019 and five clusters in 2024, reflecting a gradual homogenization of water quality. PCA further confirmed this trend, linking it to underlying hydrochemical processes, such as water-rock interactions, with limited contributions from anthropogenic influences. The GWQI analysis indicated a general improvement in groundwater quality over time, with most regions meeting drinking water standards. However, specific areas exhibited signs of localized contamination, requiring targeted management. These findings underscore the importance of continuous groundwater quality monitoring to detect emerging trends and guide resource management. The study highlights the need for sustainable practices to safeguard water resources and ensure long-term water security in the Debrecen area.