Characterizing the impact of roughness and connectivity features of aquifer conductivity using Bayesian inversion
•We assess the impact of roughness and connectivity features of hydraulic conductivity.•Several different data types are used for this assessment.•In general, roughness has only a limited impact on flow as well as transport data.•Connectivity has likewise only a limited impact on flow data.•Transpor...
Gespeichert in:
Veröffentlicht in: | Journal of hydrology (Amsterdam) 2015-12, Vol.531, p.73-87 |
---|---|
Hauptverfasser: | , , , , , |
Format: | Artikel |
Sprache: | eng |
Schlagworte: | |
Online-Zugang: | Volltext |
Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
container_end_page | 87 |
---|---|
container_issue | |
container_start_page | 73 |
container_title | Journal of hydrology (Amsterdam) |
container_volume | 531 |
creator | Heße, Falk Savoy, Heather Osorio-Murillo, Carlos A. Sege, Jon Attinger, Sabine Rubin, Yoram |
description | •We assess the impact of roughness and connectivity features of hydraulic conductivity.•Several different data types are used for this assessment.•In general, roughness has only a limited impact on flow as well as transport data.•Connectivity has likewise only a limited impact on flow data.•Transport data is more strongly affected by connectivity depending on the type.
The conductivity of an aquifer is usually difficult to represent in a precise manner due to having both a high degree of spatial variability combined with a scarcity of information. As a result, the conductivity is commonly modeled as a spatial random field, defined by its expected value and a covariance function. This covariance is usually parameterized by fitting a model function to experimental data derived from point measurements of the conductivity.
In this study, we investigated the two features that are often difficult to discern when using such classic characterization schemes: roughness and connectivity. These two features both share the fact that, based on point measurements of the conductivity alone, they are difficult to discern. It therefore stands to reason that the use of additional data could alleviate this problem.
To that end, we used the Method of Anchored Distributions (MAD), which is a novel Bayesian tool for the inverse characterization of spatial random fields. MAD is versatile with respect to the used data, it has a modular structure and it does not assume any formal relationship between the target variable, i.e. the log hydraulic conductivity, and the data used for the inversion process, e.g. head measurements, draw-down from pumping tests or breakthrough curves. With respect to the characterization of the aforementioned features, we investigated the impact of several factors, such as different data types on the characterization process.
Our findings suggest that both roughness and connectivity have only a limited impact on flow predictions, suggesting that the choice of such features is of lesser relevance if one is mainly interested in flow simulations. In case of roughness, this limited sensitivity was also seen for transport predictions if the solute had already travelled some distance. Connectivity however, can be a decisive factor for such simulations. |
doi_str_mv | 10.1016/j.jhydrol.2015.09.067 |
format | Article |
fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_miscellaneous_1778015822</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><els_id>S0022169415007507</els_id><sourcerecordid>1751207421</sourcerecordid><originalsourceid>FETCH-LOGICAL-a398t-9b4a7807d0c523fb6ea030b48eb941da6222d3ce7e64dbfb62ad25f58d71a3b93</originalsourceid><addsrcrecordid>eNqNkU1LxDAQhoMouK7-BKFHL61J-pH2JLr4BQte9BzSZLpN6Sa7SbtQf70pu551LgMzz_vCzIvQLcEJwaS475KunZSzfUIxyRNcJbhgZ2hBSlbFlGF2jhYYUxqTosou0ZX3HQ6VptkC7VetcEIO4PS3NptoaCHS212YRLaJnB03rQHvI2FUJK0xIAd90MMUNSCG0YGfMbEfdQNuBtT4C4x-9nsSE3gtTKTNAZzX1lyji0b0Hm5OfYm-Xp4_V2_x-uP1ffW4jkValUNc1ZlgJWYKy5ymTV2AwCmusxLqKiNKFJRSlUpgUGSqDnsqFM2bvFSMiLSu0iW6O_runN2P4Ae-1V5C3wsDdvScsGBP8pLSf6A5oZhllAQ0P6LSWe8dNHzn9Fa4iRPM5zR4x09p8DkNjise0gi6h6MOwskHDY57qcFIUNqFn3Jl9R8OP6qTmSU</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>1751207421</pqid></control><display><type>article</type><title>Characterizing the impact of roughness and connectivity features of aquifer conductivity using Bayesian inversion</title><source>Elsevier ScienceDirect Journals Complete</source><creator>Heße, Falk ; Savoy, Heather ; Osorio-Murillo, Carlos A. ; Sege, Jon ; Attinger, Sabine ; Rubin, Yoram</creator><creatorcontrib>Heße, Falk ; Savoy, Heather ; Osorio-Murillo, Carlos A. ; Sege, Jon ; Attinger, Sabine ; Rubin, Yoram</creatorcontrib><description>•We assess the impact of roughness and connectivity features of hydraulic conductivity.•Several different data types are used for this assessment.•In general, roughness has only a limited impact on flow as well as transport data.•Connectivity has likewise only a limited impact on flow data.•Transport data is more strongly affected by connectivity depending on the type.
The conductivity of an aquifer is usually difficult to represent in a precise manner due to having both a high degree of spatial variability combined with a scarcity of information. As a result, the conductivity is commonly modeled as a spatial random field, defined by its expected value and a covariance function. This covariance is usually parameterized by fitting a model function to experimental data derived from point measurements of the conductivity.
In this study, we investigated the two features that are often difficult to discern when using such classic characterization schemes: roughness and connectivity. These two features both share the fact that, based on point measurements of the conductivity alone, they are difficult to discern. It therefore stands to reason that the use of additional data could alleviate this problem.
To that end, we used the Method of Anchored Distributions (MAD), which is a novel Bayesian tool for the inverse characterization of spatial random fields. MAD is versatile with respect to the used data, it has a modular structure and it does not assume any formal relationship between the target variable, i.e. the log hydraulic conductivity, and the data used for the inversion process, e.g. head measurements, draw-down from pumping tests or breakthrough curves. With respect to the characterization of the aforementioned features, we investigated the impact of several factors, such as different data types on the characterization process.
Our findings suggest that both roughness and connectivity have only a limited impact on flow predictions, suggesting that the choice of such features is of lesser relevance if one is mainly interested in flow simulations. In case of roughness, this limited sensitivity was also seen for transport predictions if the solute had already travelled some distance. Connectivity however, can be a decisive factor for such simulations.</description><identifier>ISSN: 0022-1694</identifier><identifier>EISSN: 1879-2707</identifier><identifier>DOI: 10.1016/j.jhydrol.2015.09.067</identifier><language>eng</language><publisher>Elsevier B.V</publisher><subject>Aquifer characterization ; Aquifers ; Bayesian analysis ; Bayesian inversion ; Connectivity ; Covariance ; Fields (mathematics) ; Hydrology ; Inversions ; Mathematical models ; Method of Anchored Distributions ; Roughness ; Spatial random fields</subject><ispartof>Journal of hydrology (Amsterdam), 2015-12, Vol.531, p.73-87</ispartof><rights>2015 Elsevier B.V.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-a398t-9b4a7807d0c523fb6ea030b48eb941da6222d3ce7e64dbfb62ad25f58d71a3b93</citedby><cites>FETCH-LOGICAL-a398t-9b4a7807d0c523fb6ea030b48eb941da6222d3ce7e64dbfb62ad25f58d71a3b93</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://dx.doi.org/10.1016/j.jhydrol.2015.09.067$$EHTML$$P50$$Gelsevier$$H</linktohtml><link.rule.ids>314,780,784,3550,27924,27925,45995</link.rule.ids></links><search><creatorcontrib>Heße, Falk</creatorcontrib><creatorcontrib>Savoy, Heather</creatorcontrib><creatorcontrib>Osorio-Murillo, Carlos A.</creatorcontrib><creatorcontrib>Sege, Jon</creatorcontrib><creatorcontrib>Attinger, Sabine</creatorcontrib><creatorcontrib>Rubin, Yoram</creatorcontrib><title>Characterizing the impact of roughness and connectivity features of aquifer conductivity using Bayesian inversion</title><title>Journal of hydrology (Amsterdam)</title><description>•We assess the impact of roughness and connectivity features of hydraulic conductivity.•Several different data types are used for this assessment.•In general, roughness has only a limited impact on flow as well as transport data.•Connectivity has likewise only a limited impact on flow data.•Transport data is more strongly affected by connectivity depending on the type.
The conductivity of an aquifer is usually difficult to represent in a precise manner due to having both a high degree of spatial variability combined with a scarcity of information. As a result, the conductivity is commonly modeled as a spatial random field, defined by its expected value and a covariance function. This covariance is usually parameterized by fitting a model function to experimental data derived from point measurements of the conductivity.
In this study, we investigated the two features that are often difficult to discern when using such classic characterization schemes: roughness and connectivity. These two features both share the fact that, based on point measurements of the conductivity alone, they are difficult to discern. It therefore stands to reason that the use of additional data could alleviate this problem.
To that end, we used the Method of Anchored Distributions (MAD), which is a novel Bayesian tool for the inverse characterization of spatial random fields. MAD is versatile with respect to the used data, it has a modular structure and it does not assume any formal relationship between the target variable, i.e. the log hydraulic conductivity, and the data used for the inversion process, e.g. head measurements, draw-down from pumping tests or breakthrough curves. With respect to the characterization of the aforementioned features, we investigated the impact of several factors, such as different data types on the characterization process.
Our findings suggest that both roughness and connectivity have only a limited impact on flow predictions, suggesting that the choice of such features is of lesser relevance if one is mainly interested in flow simulations. In case of roughness, this limited sensitivity was also seen for transport predictions if the solute had already travelled some distance. Connectivity however, can be a decisive factor for such simulations.</description><subject>Aquifer characterization</subject><subject>Aquifers</subject><subject>Bayesian analysis</subject><subject>Bayesian inversion</subject><subject>Connectivity</subject><subject>Covariance</subject><subject>Fields (mathematics)</subject><subject>Hydrology</subject><subject>Inversions</subject><subject>Mathematical models</subject><subject>Method of Anchored Distributions</subject><subject>Roughness</subject><subject>Spatial random fields</subject><issn>0022-1694</issn><issn>1879-2707</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2015</creationdate><recordtype>article</recordtype><recordid>eNqNkU1LxDAQhoMouK7-BKFHL61J-pH2JLr4BQte9BzSZLpN6Sa7SbtQf70pu551LgMzz_vCzIvQLcEJwaS475KunZSzfUIxyRNcJbhgZ2hBSlbFlGF2jhYYUxqTosou0ZX3HQ6VptkC7VetcEIO4PS3NptoaCHS212YRLaJnB03rQHvI2FUJK0xIAd90MMUNSCG0YGfMbEfdQNuBtT4C4x-9nsSE3gtTKTNAZzX1lyji0b0Hm5OfYm-Xp4_V2_x-uP1ffW4jkValUNc1ZlgJWYKy5ymTV2AwCmusxLqKiNKFJRSlUpgUGSqDnsqFM2bvFSMiLSu0iW6O_runN2P4Ae-1V5C3wsDdvScsGBP8pLSf6A5oZhllAQ0P6LSWe8dNHzn9Fa4iRPM5zR4x09p8DkNjise0gi6h6MOwskHDY57qcFIUNqFn3Jl9R8OP6qTmSU</recordid><startdate>201512</startdate><enddate>201512</enddate><creator>Heße, Falk</creator><creator>Savoy, Heather</creator><creator>Osorio-Murillo, Carlos A.</creator><creator>Sege, Jon</creator><creator>Attinger, Sabine</creator><creator>Rubin, Yoram</creator><general>Elsevier B.V</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7QH</scope><scope>7ST</scope><scope>7TG</scope><scope>7UA</scope><scope>C1K</scope><scope>F1W</scope><scope>H96</scope><scope>KL.</scope><scope>L.G</scope><scope>SOI</scope><scope>8FD</scope><scope>FR3</scope><scope>KR7</scope></search><sort><creationdate>201512</creationdate><title>Characterizing the impact of roughness and connectivity features of aquifer conductivity using Bayesian inversion</title><author>Heße, Falk ; Savoy, Heather ; Osorio-Murillo, Carlos A. ; Sege, Jon ; Attinger, Sabine ; Rubin, Yoram</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-a398t-9b4a7807d0c523fb6ea030b48eb941da6222d3ce7e64dbfb62ad25f58d71a3b93</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2015</creationdate><topic>Aquifer characterization</topic><topic>Aquifers</topic><topic>Bayesian analysis</topic><topic>Bayesian inversion</topic><topic>Connectivity</topic><topic>Covariance</topic><topic>Fields (mathematics)</topic><topic>Hydrology</topic><topic>Inversions</topic><topic>Mathematical models</topic><topic>Method of Anchored Distributions</topic><topic>Roughness</topic><topic>Spatial random fields</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Heße, Falk</creatorcontrib><creatorcontrib>Savoy, Heather</creatorcontrib><creatorcontrib>Osorio-Murillo, Carlos A.</creatorcontrib><creatorcontrib>Sege, Jon</creatorcontrib><creatorcontrib>Attinger, Sabine</creatorcontrib><creatorcontrib>Rubin, Yoram</creatorcontrib><collection>CrossRef</collection><collection>Aqualine</collection><collection>Environment Abstracts</collection><collection>Meteorological & Geoastrophysical Abstracts</collection><collection>Water Resources Abstracts</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ASFA: Aquatic Sciences and Fisheries Abstracts</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) 2: Ocean Technology, Policy & Non-Living Resources</collection><collection>Meteorological & Geoastrophysical Abstracts - Academic</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) Professional</collection><collection>Environment Abstracts</collection><collection>Technology Research Database</collection><collection>Engineering Research Database</collection><collection>Civil Engineering Abstracts</collection><jtitle>Journal of hydrology (Amsterdam)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Heße, Falk</au><au>Savoy, Heather</au><au>Osorio-Murillo, Carlos A.</au><au>Sege, Jon</au><au>Attinger, Sabine</au><au>Rubin, Yoram</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Characterizing the impact of roughness and connectivity features of aquifer conductivity using Bayesian inversion</atitle><jtitle>Journal of hydrology (Amsterdam)</jtitle><date>2015-12</date><risdate>2015</risdate><volume>531</volume><spage>73</spage><epage>87</epage><pages>73-87</pages><issn>0022-1694</issn><eissn>1879-2707</eissn><abstract>•We assess the impact of roughness and connectivity features of hydraulic conductivity.•Several different data types are used for this assessment.•In general, roughness has only a limited impact on flow as well as transport data.•Connectivity has likewise only a limited impact on flow data.•Transport data is more strongly affected by connectivity depending on the type.
The conductivity of an aquifer is usually difficult to represent in a precise manner due to having both a high degree of spatial variability combined with a scarcity of information. As a result, the conductivity is commonly modeled as a spatial random field, defined by its expected value and a covariance function. This covariance is usually parameterized by fitting a model function to experimental data derived from point measurements of the conductivity.
In this study, we investigated the two features that are often difficult to discern when using such classic characterization schemes: roughness and connectivity. These two features both share the fact that, based on point measurements of the conductivity alone, they are difficult to discern. It therefore stands to reason that the use of additional data could alleviate this problem.
To that end, we used the Method of Anchored Distributions (MAD), which is a novel Bayesian tool for the inverse characterization of spatial random fields. MAD is versatile with respect to the used data, it has a modular structure and it does not assume any formal relationship between the target variable, i.e. the log hydraulic conductivity, and the data used for the inversion process, e.g. head measurements, draw-down from pumping tests or breakthrough curves. With respect to the characterization of the aforementioned features, we investigated the impact of several factors, such as different data types on the characterization process.
Our findings suggest that both roughness and connectivity have only a limited impact on flow predictions, suggesting that the choice of such features is of lesser relevance if one is mainly interested in flow simulations. In case of roughness, this limited sensitivity was also seen for transport predictions if the solute had already travelled some distance. Connectivity however, can be a decisive factor for such simulations.</abstract><pub>Elsevier B.V</pub><doi>10.1016/j.jhydrol.2015.09.067</doi><tpages>15</tpages></addata></record> |
fulltext | fulltext |
identifier | ISSN: 0022-1694 |
ispartof | Journal of hydrology (Amsterdam), 2015-12, Vol.531, p.73-87 |
issn | 0022-1694 1879-2707 |
language | eng |
recordid | cdi_proquest_miscellaneous_1778015822 |
source | Elsevier ScienceDirect Journals Complete |
subjects | Aquifer characterization Aquifers Bayesian analysis Bayesian inversion Connectivity Covariance Fields (mathematics) Hydrology Inversions Mathematical models Method of Anchored Distributions Roughness Spatial random fields |
title | Characterizing the impact of roughness and connectivity features of aquifer conductivity using Bayesian inversion |
url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-02T05%3A49%3A31IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Characterizing%20the%20impact%20of%20roughness%20and%20connectivity%20features%20of%20aquifer%20conductivity%20using%20Bayesian%20inversion&rft.jtitle=Journal%20of%20hydrology%20(Amsterdam)&rft.au=He%C3%9Fe,%20Falk&rft.date=2015-12&rft.volume=531&rft.spage=73&rft.epage=87&rft.pages=73-87&rft.issn=0022-1694&rft.eissn=1879-2707&rft_id=info:doi/10.1016/j.jhydrol.2015.09.067&rft_dat=%3Cproquest_cross%3E1751207421%3C/proquest_cross%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=1751207421&rft_id=info:pmid/&rft_els_id=S0022169415007507&rfr_iscdi=true |