Multicomponent statistical analysis to identify flow and transport processes in a highly-complex environment

Abstract

A combined approach of multivariate statistical analysis, namely factor analysis (FA) and hierarchical cluster analysis (HCA), interpretation of geochemical processes, stable water isotope data and organic micropollutants enabling to assess spatial patterns of water types was performed for a study area in Switzerland, where drinking water production is close to different potential input pathways for contamination. To avoid drinking water contamination, artificial groundwater recharge with surface water into an aquifer is used to create a hydraulic barrier between potential intake pathways for contamination and drinking water extraction wells. Inter-aquifer mixing in the subsurface is identified, where a high amount of artificial infiltrated surface water is mixed with a lesser amount of water originating from the regional flow pathway in the vicinity of drinking water extraction wells. The spatial distribution of different water types can be estimated and a conceptual system understanding is developed. Results of the multivariate statistical analysis are comparable with gained information from isotopic data and organic micropollutants analyses. The integrated approach using different kinds of observations can be easily transferred to a variety of hydrological settings to synthesise and evaluate large hydrochemical datasets. The combination of additional data with different information content is conceivable and enabled effective interpretation of hydrological processes. Using the applied approach leads to more sound conceptual system understanding acting as the very basis to develop improved water resources management practices in a sustainable way.