Groundwater contaminant source identification based on iterative local update ensemble smoother

YANG Ai-lin; JIANG Si-min; LIU Jin-bing; JIANG Qian-yun; ZHOU Ting; ZHANG Wen

doi:10.19637/j.cnki.2305-7068.2020.01.001

Volume 8 Issue 1

Mar. 2020

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YANG Ai-lin, JIANG Si-min, LIU Jin-bing, et al. 2020: Groundwater contaminant source identification based on iterative local update ensemble smoother. Journal of Groundwater Science and Engineering, 8(1): 1-9. doi: 10.19637/j.cnki.2305-7068.2020.01.001

Citation:

YANG Ai-lin, JIANG Si-min, LIU Jin-bing, et al. 2020: Groundwater contaminant source identification based on iterative local update ensemble smoother. Journal of Groundwater Science and Engineering, 8(1): 1-9. doi: 10.19637/j.cnki.2305-7068.2020.01.001

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Groundwater contaminant source identification based on iterative local update ensemble smoother

doi: 10.19637/j.cnki.2305-7068.2020.01.001

1Department of Hydraulic Engineering, College of Civil Engineering, Tongji University, Shanghai 200092, China.2Beijing Haidian

Funds:

JIANG Si-min

Publish Date: 2020-03-28

Abstract

Abstract

Identification of the location and intensity of groundwater pollution source contributes to the effect of pollution remediation, and is called groundwater contaminant source identifcation. This is a kind of typical groundwater inverse problem, and the solution is usually ill-posed. Especially considering the spatial variability of hydraulic conductivity field, the identification process is more challenging. In this paper, the solution framework of groundwater contaminant source identification is composed with groundwater pollutant transport model (MT3DMS) and a data assimilation method (Iterative local update ensemble smoother, ILUES). In addition, Karhunen-Loève expansion technique is adopted as a PCA method to realize dimension reduction. In practical problems, the geostatistical method is usually used to characterize the hydraulic conductivity feld, and only the contaminant source information is inversely calculated in the identifcation process. In this study, the identification of contaminant source information under Kriging K-field is compared with simultaneous identification of source information and K-field. The results indicate that it is necessary to carry out simultaneous identification under heterogeneous site, and ILUES has good performance in solving high-dimensional parameter inversion problems.
- Groundwater contamination,
- Groundwater inverse problem,
- Source identification,
- Ensemble smoother,
- Data assimilation

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References(21)

References

Sayeed M. 2005. Hybrid genetic algorithm-local search methods for solving groundwater source identifcation inverse problems. Journal of Water Resources Planning & Management,131(1): 45-57.

model for simulation of advection, dispersion, and chemical reactions of contaminants in groundwater systems; documentation and user's guide. Alabama University.

CHEN Yan, Oliver Dean S. 2013. LevenbergMarquardt forms of the iterative ensemble smoother for efficient history matching and uncertainty quantification. Computational Geosciences, 17(4): 689-703.

Mcdonald M G, Harbaugh A W. 1988. MODFLOW, A modular three-dimensional finite difference groundwater flow model, OpenFile Report 83-875, Chapter A1.

Snodgrass M F, Kitanidis P K. 1997. A geostatistical approach to contaminant source identification. Water Resources Research, 33(4): 329-335.

Prakash O, Datta B. 2015. Optimal characterization of pollutant sources in contaminated aquifers by integrating sequential-monitoringnetwork design and source identification: Methodology and an application in Australia. Hydrogeology J, 23(6): 1089-1107.

CHANG Hai-bin, LIAO Qin-zhuo, ZHANG Dongxiao. 2017. Surrogate model based iterative ensemble smoother for subsurface flow data assimilation. Advances in Water Resources, 100: 96-108.

Butera I, Tanda M G, Zanini A. 2013. Simultaneous identifcation of the pollutant release history and the source location in groundwater by means of a geostatistical approach. Stochastic Environmental Research and Risk Assessment, 27(5): 1269-1280.

Ayvaz M T. 2016. A hybrid simulation-optimization approach for solving the areal groundwater pollution source identification problems. Hydrology Journal, 538: 161-176.

ZHENG C, WANG P P. 1999. MT3DMS: A modular three-dimensional multispecies transport

Guneshwor L, Eldho T I, Kumar A V. 2018. Identification of groundwater contamination sources using meshfree RPCM simulation and particle swarm optimization. Water Resources Management, 32(4): 1517-1538.

Emerick A A, Reynolds A C. 2012. History matching time-lapse seismic data using the ensemble Kalman filter with multiple data assimilations. Computational Geosciences, 16(3): 639-665.

Srivastava D, Singh R M. 2014. Breakthrough curves characterization and identification of an unknown pollution source in groundwater system using an artificial neural network (ANN). Environmental Forensics, 15(2): 175-189.

JU Lei, ZHANG Jian-jiang, MENG Long, et al. 2018. An adaptive Gaussian processbased iterative ensemble smoother for data assimilation. Advances in Water Resources, 115: 125-135.

GU Wen-long, LU Wen-xi, ZHANG Yu, et al. 2016. Reconstructing the release history of groundwater contamination sources based on the Bayesian inference and improved MCMC method. Journal of Hydraulic Engineering, 47(6): 772-779.

LI Liang-ping, Stetler Larry, CAO Zhen-dan, et al. 2018. An iterative normal-score ensemble smoother for dealing with non-Gaussianity in data assimilation. Journal of Hydrology, 567: 759-766.

XU Teng, Jaime Gómez-Hernández J. 2018. Simultaneous identification of a contaminant source and hydraulic conductivity via the restart normal-score ensemble Kalman flter. Advances in Water Resources, 112: 106-123.

ZHANG Jian-jiang, LIN Guang, LI Wei-xuan, et al. 2018. An iterative local updating ensemble smoother for estimation and uncertainty assessment of hydrologic model parameters with multimodal distributions.Water Resources Research, 54: 1716-1733.

JIANG Si-min, ZHANG Ya-li, WANG Pei, et al. 2013. An almost-parameter-free harmony search algorithm for groundwater pollution source identification. Water Science and Technology, 68(11): 2359-2366.

ZHANG Dong-xiao, LU Zhi-ming. 2014. An efcient, high-order perturbation approach for ?ow in random porous media via KarhunenLoève and polynomial expansions. Journal of Computational Physics, 194(2): 773-794.

WU Jian-feng, ZHENG Chun-miao. 2004. Contaminant monitoring network design: Recent advances and future directions. Advances in Earth Science, 19(3): 429-436.

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