Solving inverse problems of unknown contaminant source in groundwater-river integrated systems using a surrogate transport model based optimization

The paper presents a new approach to identify the unknown characteristics (release history and location) of contaminant sources in groundwater, starting from a few concentration observations at monitoring points. An inverse method that combines the forward model and an optimization algorithm is presented. To speed up the computation, the transfer function theory is applied to create a surrogate transport forward model. The performance of the developed approach is evaluated on two case studies (literature and a new one) under different scenarios and measurement error conditions. The literature case study regards a heterogeneous confined aquifer, while the proposed case study was never investigated before, it involves an aquifer-river integrated flow and transport system. In this case, the groundwater contaminant originated from a damaged tank, migrates to a river through the aquifer. The approach, starting from few concentration observations monitored at a downstream river cross-section, accurately estimates the release history at a groundwater contaminant source, even in presence of noise on observations. Moreover, the results show that the methodology is very fast, and can solve the inverse problem in much less computation time in comparison with other existing approaches.