Backward Probability Model for Identifying Multiple Contaminant Source Zones Under Transient Variably Saturated Flow Conditions

Abstract

Groundwater contamination is one of the major concerns to public health and water resource management. Identification of contamination sources is essential for designing cost-effective groundwater remediation systems and minimizing risks of further contamination. Various numerical approaches have been applied to reconstruct spatiotemporal distributions of contaminant sources. Due to difficulties in obtaining source information, the backward probability model is computationally efficient; however, its applications have largely been limited to relatively simple problems such as two-dimensional or simplified three-dimensional steady-state saturated flow conditions with a single contaminant source. More importantly, the traditional backward probability model requires some a priori knowledge on the suspected source locations or source release times to quantify source area location probabilities. Here we improve the capability of the backward probability model for solving real-world problems by implementing it into a three-dimensional transient variably saturated flow model. To evaluate the applicability of the model, we apply it combined with the shift-average method to a field-scale problem for identifying multiple contaminant sources occurring in the vadose zone that release contaminants to the saturated zone through infiltration. Compared with the suspected potential sources reported through a site characterization study, the enhanced backward probability model reasonably captures most of the reported potential sources under various hydrologic/hydrogeological/dispersion conditions, which demonstrates that the approach developed here can overcome limitations of the backward probability theory previously reported in the literature.