Identifying a contaminant time-varying release history is an ill-posed problem but crucial for groundwater contamination issues. A precise inversed release history offers a promising estimation of contaminant movement and is of great importance for environmental monitoring and further management. In this paper, a recent emerging data assimilation method, the ensemble smoother with multiple data assimilation (ES-MDA) is employed to handle this conundrum. The study starts with some synthetic cases in which several factors are analyzed, such as the observation data frequency, covariance inflation schemes, iteration numbers used in the ES-MDA for the purpose of identifying a time-varying contaminant injection event with different precision. The results show that the ES-MDA performs well in recovering the release history when the injection is discretized into 50 or 100-time steps but encounters fluctuation problems in the cases with 300-time steps. Further comparison reveals that the observation data frequency is a very influential factor, while the number of iterations or the kind of covariance inflation used has a lesser effect. Nevertheless, this is a first test in a non-synthetic environment, in which the ES-MDA has proven its ability to recover the release history in two close-to-reality sandbox experiments. The outcome shows that the ES-MDA with Rafiee's inflation scheme has the ability to capture the main pattern of the release history. But in order to move one more step to field cases, a more detailed description of uncertainties or elaborated parameterization of the time functions is paramount.
Keywords: Data assimilation; Inflation factor; Inverse modeling; Sandbox; Source identification.
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