| Groundwater contaminant source identification based on QS-ILUES |
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| 作者姓名： | LIU Jin-bing JIANG Si-min ZHOU Nian-qing CAI Yi CHENG Lu WANG Zhi-yuan |
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| 作者单位： | Department of Hydraulic Engineering;State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering |
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| 基金项目： | This work was supported by the Belt and Road Special Foundation of the State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering(No.2019nkzd01);National Natural Science Foundation of China(42077176). |
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| 摘 要： | When groundwater pollution occurs,to come up with an efficient remediation plan,it is particularly important to collect information of contaminant source(location and source strength)and hydraulic conductivity field of the site accurately and quickly.However,the information can not be obtained by direct observation,and can only be derived from limited measurement data.Data assimilation of observations such as head and concentration is often used to estimate parameters of contaminant source.As for hydraulic conductivity field,especially for complex non-Gaussian field,it can be directly estimated by geostatistics method based on limited hard data,while the accuracy is often not high.Better estimation of hydraulic conductivity can be achieved by solving inverse groundwater problem.Therefore,in this study,the multi-point geostatistics method Quick Sampling(QS)is proposed and introduced for the first time and combined with the iterative local updating ensemble smoother(ILUES)to develop a new data assimilation framework QS-ILUES.It helps to solve the contaminant source parameters and non-Gaussian hydraulic conductivity field simultaneously by assimilating hydraulic head and pollutant concentration data.While the pilot points are utilized to reduce the dimension of hydraulic conductivity field,the influence of pilot points’layout and the ensemble size of ILUES algorithm on the inverse simulation results are further explored.
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| 关 键 词： | Inverse groundwater problem Data assimilation Multi-point Geostatistics Quick Sampling Non-Gaussian hydraulic conductivity field |
Groundwater contaminant source identification based on QS-ILUES |
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| Authors: | LIU Jin-bing JIANG Si-min ZHOU Nian-qing CAI Yi CHENG Lu WANG Zhi-yuan |
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| Affiliation: | 1.Department of Hydraulic Engineering, College of Civil Engineering, Tongji University, Shanghai 200092, China2.State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Nanjing Hydraulic Research Institute, Nanjing 210029, China |
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| Abstract: | When groundwater pollution occurs, to come up with an efficient remediation plan, it is particularly important to collect information of contaminant source(location and source strength) and hydraulic conductivity field of the site accurately and quickly. However, the information can not be obtained by direct observation, and can only be derived from limited measurement data. Data assimilation of observations such as head and concentration is often used to estimate parameters of contaminant source. As for hydraulic conductivity field, especially for complex non-Gaussian field, it can be directly estimated by geostatistics method based on limited hard data, while the accuracy is often not high. Better estimation of hydraulic conductivity can be achieved by solving inverse groundwater problem. Therefore, in this study, the multi-point geostatistics method Quick Sampling(QS) is proposed and introduced for the first time and combined with the iterative local updating ensemble smoother(ILUES) to develop a new data assimilation framework QS-ILUES. It helps to solve the contaminant source parameters and non-Gaussian hydraulic conductivity field simultaneously by assimilating hydraulic head and pollutant concentration data. While the pilot points are utilized to reduce the dimension of hydraulic conductivity field, the influence of pilot points' layout and the ensemble size of ILUES algorithm on the inverse simulation results are further explored. |
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| Keywords: | Inverse groundwater problem Data assimilation Multi-point Geostatistics Quick Sampling Non-Gaussian hydraulic conductivity field |
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