Contaminant source characterization in a coastal aquifer influenced by tidal forces and density-driven flow

Abstract

Contaminant source characterization (CSC) plays an important role in environmental forensics and pollution control. Past studies commonly solve the CSC problem in case studies where simple groundwater velocity fields (with e.g. parallel velocity vectors) prevail under steady state conditions. A few studies have addressed CSC in cases where the effect of heterogeneity or wells causes the velocity vectors to deviate from a parallel alignment. More complex transient velocity fields are yet to be addressed in groundwater CSC studies. One key example is coastal aquifers, where density-driven flow and the effect of oceanic forces (e.g. tides and waves) create complex transient velocity fields that lead to deformation of contaminant plumes and enhance mixing processes. The present paper aims to address this challenge by developing and validating a novel solution method for CSC in a coastal aquifer under the effect of tidal forces and density-driven flow. For this purpose, the study combines a numerical model of density-dependent flow and multiple-species solute transport, artificial neural networks, and a customized Kalman filtering technique, termed the ‘constrained restart dual ensemble Kalman filter’ (CRD-EnKF). In this approach, the time series of contaminant concentration downgradient of the source, and salinity concentrations in the transition zone between fresh and saline groundwater is employed for the estimation of contaminant source location and strength. We validated the proposed methodology by using a benchmark problem of a coastal aquifer with sloping beach and tide-induced pressure oscillation at the sea boundary.