Identification of the long-term variations of groundwater and their governing factors based on hydrochemical and isotopic data in a river basin

Abstract

A clear identification of the hydrochemical processes in groundwater systems is critical for the management and protection of alternative water resources. The Dagu River Basin (DRB) has become one of the most concerning areas due to the typical geographical conditions and intensive human disturbance. In this study, the spatio-temporal variations of groundwater hydrochemical characteristics were discriminated based on the self-organizing maps (SOM) clustering results from 2001 to 2017. Simultaneously, their main governing factors were also identified according to the composite properties of hydrogen and oxygen isotopic evidence, hydrochemical facies evolution diagram (HFE-D), chloro-alkaline indices (CAI) values, and the enrichment of nitrogen. Analysis results indicated that the long-term evolution of hydrochemical signatures basically conformed to the trends of rising in the dry season and falling in the rainy season. Total dissolved solids (TDS) fluctuated from 187.90 mg/L to 2294.81 mg/L, with an average of 881.77 mg/L. Moreover, the nitrate concentrations in the river basin varied significantly from 0.70 mg/L to 605.32 mg/L. The unconfined aquifer beneath DRB was experiencing freshening gradually, while some other samples were still subjected to the interference of environmental pollution. Spatio-temporal variations in the respective clusters demonstrated that natural factors including precipitation infiltration, evaporation, and water–rock interactions played vital roles in the samples of Clusters I-III, V, and VIII, while the samples of Cluster VII were influenced by seawater intrusion; and those of Clusters IV and VI were mainly affected by nitrate and nitrite contamination. This study suggests that the SOM connected with stable isotope analysis, HFE-Diagram, CAI values, and correlation analysis could be successfully used to interpret nonlinear and high-dimensional multivariate systems and provide supplementary information about the dominant mechanisms controlling the regional groundwater evolution.