Joint identification of contaminant source characteristics and hydraulic conductivity in a tide-influenced coastal aquifer
Coastal aquifers are a vital water source for the more than one billion people living in coastal regions around the globe. Due to the intensity of economic activities and density of population, these aquifers are highly susceptible not only to seawater intrusion, but also to anthropogenic contamination, which may contaminate the aquifer and submarine groundwater discharge. Identification and localization of contaminant source characteristics are needed to reduce contamination. The techniques of contaminant source identification are based on numerical models that require the knowledge of the hydrodynamic properties of aquifers. Thus, the challenging topic of contaminant source and aquifer characterization (CSAC) is widely developed in the literature. However, most of the existing studies are concerned with inland aquifers with relatively uniform groundwater flow. Coastal aquifers are influenced by density-driven seawater intrusion, tidal forces, and water injection and abstraction wells. These phenomena create complex flow and transport patterns, which render the CSAC especially challenging and may explain why CSAC has never been addressed in coastal settings. The presented study aims to provide an efficient methodology for the simultaneous identification of contaminant source characteristics and aquifer hydraulic conductivity in coastal aquifers. For this purpose, the study employs numerical modeling of density-dependent flow and multiple-species solute transport, to develop trained and validated artificial neural network metamodels, and then employs these metamodels in a version of the ensemble Kalman filter (EnKF) termed the 'constrained restart dual EnKF (CRD-EnKF)' algorithm. We show that this variant of the EnKF can be successfully applied to CSAC in the complex setting of coastal aquifers. Furthermore, the study analyzes the influence of common issues in CSAC monitoring, such as the effect of non-ideal monitoring network distributions, measurement errors, and multi-level vs. single level monitoring wells.
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Format: | artículo biblioteca |
Language: | English |
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Elsevier
2022-05
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Subjects: | Hydraulic conductivity, Coastal aquifer, Contaminant source identification, Ensemble Kalman filter, Ensure availability and sustainable management of water and sanitation for all, |
Online Access: | http://hdl.handle.net/10261/309381 https://api.elsevier.com/content/abstract/scopus_id/85125504892 |
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dig-idaea-es-10261-3093812023-05-22T20:35:26Z Joint identification of contaminant source characteristics and hydraulic conductivity in a tide-influenced coastal aquifer Dodangeh, Arezou Rajabi, Mohammad Mahdi Carrera, Jesús Fahs, Marwan Hydraulic conductivity Coastal aquifer Contaminant source identification Ensemble Kalman filter Ensure availability and sustainable management of water and sanitation for all Coastal aquifers are a vital water source for the more than one billion people living in coastal regions around the globe. Due to the intensity of economic activities and density of population, these aquifers are highly susceptible not only to seawater intrusion, but also to anthropogenic contamination, which may contaminate the aquifer and submarine groundwater discharge. Identification and localization of contaminant source characteristics are needed to reduce contamination. The techniques of contaminant source identification are based on numerical models that require the knowledge of the hydrodynamic properties of aquifers. Thus, the challenging topic of contaminant source and aquifer characterization (CSAC) is widely developed in the literature. However, most of the existing studies are concerned with inland aquifers with relatively uniform groundwater flow. Coastal aquifers are influenced by density-driven seawater intrusion, tidal forces, and water injection and abstraction wells. These phenomena create complex flow and transport patterns, which render the CSAC especially challenging and may explain why CSAC has never been addressed in coastal settings. The presented study aims to provide an efficient methodology for the simultaneous identification of contaminant source characteristics and aquifer hydraulic conductivity in coastal aquifers. For this purpose, the study employs numerical modeling of density-dependent flow and multiple-species solute transport, to develop trained and validated artificial neural network metamodels, and then employs these metamodels in a version of the ensemble Kalman filter (EnKF) termed the 'constrained restart dual EnKF (CRD-EnKF)' algorithm. We show that this variant of the EnKF can be successfully applied to CSAC in the complex setting of coastal aquifers. Furthermore, the study analyzes the influence of common issues in CSAC monitoring, such as the effect of non-ideal monitoring network distributions, measurement errors, and multi-level vs. single level monitoring wells. Peer reviewed 2023-05-22T10:29:55Z 2023-05-22T10:29:55Z 2022-05 artículo Journal of Contaminant Hydrology 247: 103980 (2023) 01697722 http://hdl.handle.net/10261/309381 10.1016/j.jconhyd.2022.103980 35245819 2-s2.0-85125504892 https://api.elsevier.com/content/abstract/scopus_id/85125504892 en Journal of contaminant hydrology Postprint https://doi.org/10.1016/j.jconhyd.2022.103980 Sí open Elsevier |
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Hydraulic conductivity Coastal aquifer Contaminant source identification Ensemble Kalman filter Ensure availability and sustainable management of water and sanitation for all Hydraulic conductivity Coastal aquifer Contaminant source identification Ensemble Kalman filter Ensure availability and sustainable management of water and sanitation for all |
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Hydraulic conductivity Coastal aquifer Contaminant source identification Ensemble Kalman filter Ensure availability and sustainable management of water and sanitation for all Hydraulic conductivity Coastal aquifer Contaminant source identification Ensemble Kalman filter Ensure availability and sustainable management of water and sanitation for all Dodangeh, Arezou Rajabi, Mohammad Mahdi Carrera, Jesús Fahs, Marwan Joint identification of contaminant source characteristics and hydraulic conductivity in a tide-influenced coastal aquifer |
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Coastal aquifers are a vital water source for the more than one billion people living in coastal regions around the globe. Due to the intensity of economic activities and density of population, these aquifers are highly susceptible not only to seawater intrusion, but also to anthropogenic contamination, which may contaminate the aquifer and submarine groundwater discharge. Identification and localization of contaminant source characteristics are needed to reduce contamination. The techniques of contaminant source identification are based on numerical models that require the knowledge of the hydrodynamic properties of aquifers. Thus, the challenging topic of contaminant source and aquifer characterization (CSAC) is widely developed in the literature. However, most of the existing studies are concerned with inland aquifers with relatively uniform groundwater flow. Coastal aquifers are influenced by density-driven seawater intrusion, tidal forces, and water injection and abstraction wells. These phenomena create complex flow and transport patterns, which render the CSAC especially challenging and may explain why CSAC has never been addressed in coastal settings. The presented study aims to provide an efficient methodology for the simultaneous identification of contaminant source characteristics and aquifer hydraulic conductivity in coastal aquifers. For this purpose, the study employs numerical modeling of density-dependent flow and multiple-species solute transport, to develop trained and validated artificial neural network metamodels, and then employs these metamodels in a version of the ensemble Kalman filter (EnKF) termed the 'constrained restart dual EnKF (CRD-EnKF)' algorithm. We show that this variant of the EnKF can be successfully applied to CSAC in the complex setting of coastal aquifers. Furthermore, the study analyzes the influence of common issues in CSAC monitoring, such as the effect of non-ideal monitoring network distributions, measurement errors, and multi-level vs. single level monitoring wells. |
format |
artículo |
topic_facet |
Hydraulic conductivity Coastal aquifer Contaminant source identification Ensemble Kalman filter Ensure availability and sustainable management of water and sanitation for all |
author |
Dodangeh, Arezou Rajabi, Mohammad Mahdi Carrera, Jesús Fahs, Marwan |
author_facet |
Dodangeh, Arezou Rajabi, Mohammad Mahdi Carrera, Jesús Fahs, Marwan |
author_sort |
Dodangeh, Arezou |
title |
Joint identification of contaminant source characteristics and hydraulic conductivity in a tide-influenced coastal aquifer |
title_short |
Joint identification of contaminant source characteristics and hydraulic conductivity in a tide-influenced coastal aquifer |
title_full |
Joint identification of contaminant source characteristics and hydraulic conductivity in a tide-influenced coastal aquifer |
title_fullStr |
Joint identification of contaminant source characteristics and hydraulic conductivity in a tide-influenced coastal aquifer |
title_full_unstemmed |
Joint identification of contaminant source characteristics and hydraulic conductivity in a tide-influenced coastal aquifer |
title_sort |
joint identification of contaminant source characteristics and hydraulic conductivity in a tide-influenced coastal aquifer |
publisher |
Elsevier |
publishDate |
2022-05 |
url |
http://hdl.handle.net/10261/309381 https://api.elsevier.com/content/abstract/scopus_id/85125504892 |
work_keys_str_mv |
AT dodangeharezou jointidentificationofcontaminantsourcecharacteristicsandhydraulicconductivityinatideinfluencedcoastalaquifer AT rajabimohammadmahdi jointidentificationofcontaminantsourcecharacteristicsandhydraulicconductivityinatideinfluencedcoastalaquifer AT carrerajesus jointidentificationofcontaminantsourcecharacteristicsandhydraulicconductivityinatideinfluencedcoastalaquifer AT fahsmarwan jointidentificationofcontaminantsourcecharacteristicsandhydraulicconductivityinatideinfluencedcoastalaquifer |
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