Groundwater contaminant source identification based on QS-ILUES
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...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Groundwater Science and Engineering Limited
2021-03-01
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| Series: | Journal of Groundwater Science and Engineering |
| Subjects: | |
| Online Access: | https://www.sciopen.com/article/10.19637/j.cnki.2305-7068.2021.01.007 |
| _version_ | 1797840421157601280 |
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| author | Jin-bing LIU Si-min JIANG Nian-qing ZHOU Yi CAI Lu CHENG Zhi-yuan WANG |
| author_facet | Jin-bing LIU Si-min JIANG Nian-qing ZHOU Yi CAI Lu CHENG Zhi-yuan WANG |
| author_sort | Jin-bing LIU |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-09T16:14:50Z |
| format | Article |
| id | doaj.art-918c89627b744a239d27d5f2132562a8 |
| institution | Directory Open Access Journal |
| issn | 2305-7068 |
| language | English |
| last_indexed | 2024-04-09T16:14:50Z |
| publishDate | 2021-03-01 |
| publisher | Groundwater Science and Engineering Limited |
| record_format | Article |
| series | Journal of Groundwater Science and Engineering |
| spelling | doaj.art-918c89627b744a239d27d5f2132562a82023-04-24T05:59:23ZengGroundwater Science and Engineering LimitedJournal of Groundwater Science and Engineering2305-70682021-03-0191738210.19637/j.cnki.2305-7068.2021.01.007Groundwater contaminant source identification based on QS-ILUESJin-bing LIU0Si-min JIANG1Nian-qing ZHOU2Yi CAI3Lu CHENG4Zhi-yuan WANG5Department of Hydraulic Engineering, College of Civil Engineering, Tongji University, Shanghai 200092, ChinaDepartment of Hydraulic Engineering, College of Civil Engineering, Tongji University, Shanghai 200092, ChinaDepartment of Hydraulic Engineering, College of Civil Engineering, Tongji University, Shanghai 200092, ChinaDepartment of Hydraulic Engineering, College of Civil Engineering, Tongji University, Shanghai 200092, ChinaState Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Nanjing Hydraulic Research Institute, Nanjing 210029, ChinaState Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Nanjing Hydraulic Research Institute, Nanjing 210029, ChinaWhen 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.https://www.sciopen.com/article/10.19637/j.cnki.2305-7068.2021.01.007inverse groundwater problemdata assimilationmulti-point geostatisticsquick samplingnon-gaussian hydraulic conductivity field |
| spellingShingle | Jin-bing LIU Si-min JIANG Nian-qing ZHOU Yi CAI Lu CHENG Zhi-yuan WANG Groundwater contaminant source identification based on QS-ILUES Journal of Groundwater Science and Engineering inverse groundwater problem data assimilation multi-point geostatistics quick sampling non-gaussian hydraulic conductivity field |
| title | Groundwater contaminant source identification based on QS-ILUES |
| title_full | Groundwater contaminant source identification based on QS-ILUES |
| title_fullStr | Groundwater contaminant source identification based on QS-ILUES |
| title_full_unstemmed | Groundwater contaminant source identification based on QS-ILUES |
| title_short | Groundwater contaminant source identification based on QS-ILUES |
| title_sort | groundwater contaminant source identification based on qs ilues |
| topic | inverse groundwater problem data assimilation multi-point geostatistics quick sampling non-gaussian hydraulic conductivity field |
| url | https://www.sciopen.com/article/10.19637/j.cnki.2305-7068.2021.01.007 |
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