Principal component analysis of time series for identifying indicator variables for riverine groundwater extraction management

Abstract

Summary Although alluvial aquifers connected to rivers can be a rich source of drinking water, they are susceptible to contamination by infiltrating river water. The processes governing river–groundwater interaction are variable in time and space. Natural filtration mechanisms are often not sufficient during high discharge events in the river. To capture the dynamics of river–groundwater interaction, indicator parameters that can serve as proxies for river water infiltration need to be derived. Principal component analysis of continuously measured time series was used to identify indicator wells and derive indicator parameters for a study area in NW Switzerland. The results showed different sources of variation in the parameters, including river stage fluctuations. The multivariate approach highlighted differences between observation wells based on the response of the measured parameters to effects of damping and delay of the input signals. Three observation wells were shown to be representative of river–groundwater interaction dynamics in the study area. Of the three parameters analysed, groundwater head and electrical conductivity are recommended as a combined proxy for river water infiltration in the study area. In contrast, temperature proved not to be a reliable indicator.