Contaminant Spill in a Sandbox with Non-Gaussian Conductivities: Simultaneous Identification by the Restart Normal-Score Ensemble Kalman Filter

Abstract

The joint identification of the parameters defining a contaminant source and the heterogeneous distribution of the hydraulic conductivities of the aquifer where the contamination took place is a difficult task. Previous studies have demonstrated the applicability of the restart normal-score ensemble Kalman filter (rNS-EnKF) in synthetic cases making use of sufficient hydraulic head and concentration data. This study shows an application of the same technique to a non-synthetic case under laboratory conditions and discusses the difficulties found on its application and the avenues taken to solve them. The method is first tested using a synthetic case that mimics the sandbox experiment to establish the minimum number of ensemble members and the best technique to prevent the filter collapsing. The synthetic case shows that among different techniques based on update damping and covariance inflation, the Bauser's covariance inflation method works best in preventing filter collapse. Its application to the sandbox data shows that the rNS-EnKF can benefit from Bauser's inflation to reduce the number of ensemble realizations substantially in comparison with a filter without inflation; yet, arriving

Joint identification of contaminant source characteristics and hydraulic conductivity in a tide-influenced coastal aquifer

“…Xu and Gómez-Hernández, 2018;Xu et al, 2021), and laboratory experiment (e.g. Chen et al, 2018Chen et al, , 2021. These authors show the great potential of EnKF methods in solving the groundwater contaminant source identification problem.…”

“…(2) In mathematical terms, the CSC inverse problem is ill-posed, meaning that the solution is often non-unique and unstable to data errors, and (3) in practice, data coverage is incomplete (Zhao et al, 2020;Jiang et al, 2021;Todaro et al, 2021). Hence, studies on groundwater CSC employ a variety of (in some cases unrealistic) simplifying assumptions, and as a result, there is a big gap between scientific research and professional practice (Chen et al, 2021;Gómez-Hernández and Xu, 2021). Most previous studies on groundwater CSC are based on two-dimensional (2D) conceptualizations (e.g.…”

“…Another major simplification of previous studies is that they frequently solve the CSC problem in hypothetical (and much less often, laboratory) case studies where simple groundwater flow patterns, often with near-parallel, steady state velocity fields, exist (e.g. Cupola et al, 2015;Xu and Gómez-Hernández, 2016;Chen et al, 2018Chen et al, , 2021Todaro et al, 2021;Chang et al, 2021). Only a few studies consider cases in which the effect of heterogeneity (in e.g., Xu and Gómez-Hernández, 2018;Essouayed et al, 2020) or water injection well (in e.g.…”

Contaminant source identification in groundwater by means of artificial neural network

“…As reported in Chen et al (2021), one of the most criticisms in literature, is the difficulty of applying these procedures in practice. Usually, the parameters of the aquifer are assumed to be known, when actually they are sparsely known and highly heterogeneous.…”

Assessment of Groundwater Quality Using APCS-MLR Model: A Case Study in the Pilot Promoter Region of Yangtze River Delta Integration Demonstration Zone, China

“…The stable isotope methods can provide precise apportionment of sources, but only for the isotope-related contaminant. As for the model-based numerical inversion methods, they are believed to be more suitable for local scale research [21][22][23][24]. Meanwhile, multivariate statistical approaches have gained popularity among researchers due to their convenience and efficiency for regional scale problems [25][26][27].…”