Identifying key processes in the hydrochemistry of a basin through the combined use of factor and regression models

摘要

An innovative technique of measuring the intensities of major sources of variation in the hydrochemistry of (ground) water in a basin has been developed. This technique, which is based on the combination of R-mode factor and multiple regression analyses, can be used to measure the degrees of influence of the major sources of variation in the hydrochemistry without measuring the concentrations of the entire set of physico-chemical parameters which are often used to characterize water systems. R-mode factor analysis was applied to the data of 13 physico-chemical parameters and 50 samples in order to determine the major sources of variation in the hydrochemistry of some aquifers in the western region of Ghana. In this study, three sources of variation in the hydrochemistry were distinguished: the dissolution of chlorides and sulfates of the major cations, carbonate mineral dissolution, and silicate mineral weathering. Two key parameters were identified with each of the processes and multiple regression models were developed for each process. These models were tested and found to predict these processes quite accurately, and can be applied anywhere within the terrain. This technique can be reliably applied in areas where logistical constraints limit water sampling for whole basin hydrochemical characterization. Q-mode hierarchical cluster analysis (HCA) applied to the data revealed three major groundwater associations distinguished on the basis of the major causes of variation in the hydrochemistry. The three groundwater types represent Na–HCO3, Ca–HCO3, and Na–Cl groundwater types. Silicate stability diagrams suggest that all these groundwater types are mainly stable in the kaolinite and montmorillonite fields suggesting moderately restricted flow conditions.