Clustering of temporal profiles using a Bayesian logistic mixture model: Analyzing groundwater level data to understand the characteristics of urban groundwater recharge

Abstract

The hydrogeologic conditions of groundwater can be examined by carefully studying the patterns of fluctuations in groundwater levels. These fluctuations are spatially and temporally influenced by many complicated factors, including rainfall, topography, land use, and hydraulic properties of soils and bedrock (i.e., aquifers). In this article we report a methodology based on the Bayesian logistic mixture model to simultaneously cluster profiles of groundwater level changes over time and estimate the relationships between the characteristics of each cluster and environmental variables. We apply the proposed method to analyze groundwater level profiles from 37 monitoring wells in Seoul, South Korea, and we find four clusters of wells. Using the estimated relationship between the clusters and the environmental variables, we discern the hydrogeologic conditions of each cluster, thus gaining insight into the recharge and subsurface flow of bedrock groundwater in an urban setting and the vulnerability of groundwater to the inflow of potential pollutants from ground surface. This article has supplementary material online.