Conservative transport upscaling based on information of connectivity
Connected structures in highly heterogeneous hydraulic conductivity fields lead to channels and preferential pathways for the main fluid flux and fastest solute particles. Their spatial complement is zones of slow advection, where solutes are delayed, causing tailing of solute breakthrough curves. T...
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Veröffentlicht in: | Water resources research 2016-09, Vol.52 (9), p.6867-6880 |
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description | Connected structures in highly heterogeneous hydraulic conductivity fields lead to channels and preferential pathways for the main fluid flux and fastest solute particles. Their spatial complement is zones of slow advection, where solutes are delayed, causing tailing of solute breakthrough curves. These delays depend on the inclusion's size and the hydraulic conductivity contrast between inclusion and channel. The interplay between channels and small‐scale low conductivity inclusions leads to anomalous transport at larger scales. We test whether a simple separation of transport processes between channels and inclusions could be used to parameterize an effective transport model accounting for anomalous transport. Effective transport is represented by a multirate mass transfer model (MRMT): fast channel transport is controlled by parameters of the mobile zone, while slow advective delays are controlled by parameters of the mobile‐immobile exchange. We delineate the connected channels and analyze their connectivity followed by characterizing the low conductivity inclusions. We parameterize a MRMT model using connectivity and the statistics of the low permeable inclusions. Finally, we compare the parameterized MRMT with detailed numerical simulations in heterogeneous hydraulic conductivity fields with a clear separation between connected channel network and inclusions. In intermediately connected hydraulic conductivity fields only the cut‐off time of the tails is represented while early and intermediate time behavior is not reproduced. We suggest that an effective model for the latter case should account for additional processes like variability in advective velocity.
Key Points
Using connectivity to upscale anomalous transport caused by slow advection
Quantifying connectivity allows separating fast and slow transport processes
The parameterization of the immobile zones is based on the statistics of the low permeable inclusions |
doi_str_mv | 10.1002/2015WR018331 |
format | Article |
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Key Points
Using connectivity to upscale anomalous transport caused by slow advection
Quantifying connectivity allows separating fast and slow transport processes
The parameterization of the immobile zones is based on the statistics of the low permeable inclusions</description><identifier>ISSN: 0043-1397</identifier><identifier>EISSN: 1944-7973</identifier><identifier>DOI: 10.1002/2015WR018331</identifier><language>eng</language><publisher>Washington: John Wiley & Sons, Inc</publisher><subject>Advection ; anomalous transport ; Channels ; Complement ; Computer simulation ; Conductivity ; Connectivity ; Cut-off ; Exchanging ; Fields ; Flux ; Hydraulic conductivity ; Hydraulics ; Inclusions ; Low conductivity ; Mass transfer ; Mathematical models ; multirate mass transfer ; Numerical simulations ; Parameters ; Permeability ; Separation ; slow advection ; Solutes ; Statistical methods ; Statistics ; Structures ; Tails ; Transport ; Transport processes ; upscaling transport ; Variability ; Velocity</subject><ispartof>Water resources research, 2016-09, Vol.52 (9), p.6867-6880</ispartof><rights>2016. American Geophysical Union. All Rights Reserved.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-a3910-f3410235551adac6dee8da8f77a80179977b8f2f8d16394893be3759e37180153</citedby><cites>FETCH-LOGICAL-a3910-f3410235551adac6dee8da8f77a80179977b8f2f8d16394893be3759e37180153</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://onlinelibrary.wiley.com/doi/pdf/10.1002%2F2015WR018331$$EPDF$$P50$$Gwiley$$H</linktopdf><linktohtml>$$Uhttps://onlinelibrary.wiley.com/doi/full/10.1002%2F2015WR018331$$EHTML$$P50$$Gwiley$$H</linktohtml><link.rule.ids>314,776,780,1411,11495,27903,27904,45553,45554,46446,46870</link.rule.ids></links><search><creatorcontrib>Tyukhova, Alina R.</creatorcontrib><creatorcontrib>Willmann, Matthias</creatorcontrib><title>Conservative transport upscaling based on information of connectivity</title><title>Water resources research</title><description>Connected structures in highly heterogeneous hydraulic conductivity fields lead to channels and preferential pathways for the main fluid flux and fastest solute particles. Their spatial complement is zones of slow advection, where solutes are delayed, causing tailing of solute breakthrough curves. These delays depend on the inclusion's size and the hydraulic conductivity contrast between inclusion and channel. The interplay between channels and small‐scale low conductivity inclusions leads to anomalous transport at larger scales. We test whether a simple separation of transport processes between channels and inclusions could be used to parameterize an effective transport model accounting for anomalous transport. Effective transport is represented by a multirate mass transfer model (MRMT): fast channel transport is controlled by parameters of the mobile zone, while slow advective delays are controlled by parameters of the mobile‐immobile exchange. We delineate the connected channels and analyze their connectivity followed by characterizing the low conductivity inclusions. We parameterize a MRMT model using connectivity and the statistics of the low permeable inclusions. Finally, we compare the parameterized MRMT with detailed numerical simulations in heterogeneous hydraulic conductivity fields with a clear separation between connected channel network and inclusions. In intermediately connected hydraulic conductivity fields only the cut‐off time of the tails is represented while early and intermediate time behavior is not reproduced. We suggest that an effective model for the latter case should account for additional processes like variability in advective velocity.
Key Points
Using connectivity to upscale anomalous transport caused by slow advection
Quantifying connectivity allows separating fast and slow transport processes
The parameterization of the immobile zones is based on the statistics of the low permeable inclusions</description><subject>Advection</subject><subject>anomalous transport</subject><subject>Channels</subject><subject>Complement</subject><subject>Computer simulation</subject><subject>Conductivity</subject><subject>Connectivity</subject><subject>Cut-off</subject><subject>Exchanging</subject><subject>Fields</subject><subject>Flux</subject><subject>Hydraulic conductivity</subject><subject>Hydraulics</subject><subject>Inclusions</subject><subject>Low conductivity</subject><subject>Mass transfer</subject><subject>Mathematical models</subject><subject>multirate mass transfer</subject><subject>Numerical simulations</subject><subject>Parameters</subject><subject>Permeability</subject><subject>Separation</subject><subject>slow advection</subject><subject>Solutes</subject><subject>Statistical methods</subject><subject>Statistics</subject><subject>Structures</subject><subject>Tails</subject><subject>Transport</subject><subject>Transport processes</subject><subject>upscaling transport</subject><subject>Variability</subject><subject>Velocity</subject><issn>0043-1397</issn><issn>1944-7973</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2016</creationdate><recordtype>article</recordtype><recordid>eNp90M1LwzAUAPAgCs7pzT-g4MWD1ffy2iY5ypgfMBCGsmPJ2lQ6uqQm7WT_vZF5EA9e8nL4vU_GLhFuEYDfccB8tQSURHjEJqiyLBVK0DGbAGSUIilxys5C2ABglhdiwuYzZ4PxOz20O5MMXtvQOz8kYx8q3bX2PVnrYOrE2aS1jfPbCOPfNUnlrDVVTGuH_Tk7aXQXzMVPnLK3h_nr7CldvDw-z-4XqSaFkDaUIXDK8xx1rauiNkbWWjZCaAkolBJiLRveyBoLUplUtDYkchUfjCCnKbs-1O29-xhNGMptGyrTddoaN4Yybi6IK8iKSK_-0I0bvY3TlaggdgIo5L9KEkqFhaCobg6q8i4Eb5qy9-1W-32JUH5fvvx9-cjpwD_bzuz_teVqOVtyzgnoC-SMgog</recordid><startdate>201609</startdate><enddate>201609</enddate><creator>Tyukhova, Alina R.</creator><creator>Willmann, Matthias</creator><general>John Wiley & Sons, Inc</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7QH</scope><scope>7QL</scope><scope>7T7</scope><scope>7TG</scope><scope>7U9</scope><scope>7UA</scope><scope>8FD</scope><scope>C1K</scope><scope>F1W</scope><scope>FR3</scope><scope>H94</scope><scope>H96</scope><scope>KL.</scope><scope>KR7</scope><scope>L.G</scope><scope>M7N</scope><scope>P64</scope></search><sort><creationdate>201609</creationdate><title>Conservative transport upscaling based on information of connectivity</title><author>Tyukhova, Alina R. ; Willmann, Matthias</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-a3910-f3410235551adac6dee8da8f77a80179977b8f2f8d16394893be3759e37180153</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2016</creationdate><topic>Advection</topic><topic>anomalous transport</topic><topic>Channels</topic><topic>Complement</topic><topic>Computer simulation</topic><topic>Conductivity</topic><topic>Connectivity</topic><topic>Cut-off</topic><topic>Exchanging</topic><topic>Fields</topic><topic>Flux</topic><topic>Hydraulic conductivity</topic><topic>Hydraulics</topic><topic>Inclusions</topic><topic>Low conductivity</topic><topic>Mass transfer</topic><topic>Mathematical models</topic><topic>multirate mass transfer</topic><topic>Numerical simulations</topic><topic>Parameters</topic><topic>Permeability</topic><topic>Separation</topic><topic>slow advection</topic><topic>Solutes</topic><topic>Statistical methods</topic><topic>Statistics</topic><topic>Structures</topic><topic>Tails</topic><topic>Transport</topic><topic>Transport processes</topic><topic>upscaling transport</topic><topic>Variability</topic><topic>Velocity</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Tyukhova, Alina R.</creatorcontrib><creatorcontrib>Willmann, Matthias</creatorcontrib><collection>CrossRef</collection><collection>Aqualine</collection><collection>Bacteriology Abstracts (Microbiology B)</collection><collection>Industrial and Applied Microbiology Abstracts (Microbiology A)</collection><collection>Meteorological & Geoastrophysical Abstracts</collection><collection>Virology and AIDS Abstracts</collection><collection>Water Resources Abstracts</collection><collection>Technology Research Database</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ASFA: Aquatic Sciences and Fisheries Abstracts</collection><collection>Engineering Research Database</collection><collection>AIDS and Cancer Research Abstracts</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) 2: Ocean Technology, Policy & Non-Living Resources</collection><collection>Meteorological & Geoastrophysical Abstracts - Academic</collection><collection>Civil Engineering Abstracts</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) Professional</collection><collection>Algology Mycology and Protozoology Abstracts (Microbiology C)</collection><collection>Biotechnology and BioEngineering Abstracts</collection><jtitle>Water resources research</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Tyukhova, Alina R.</au><au>Willmann, Matthias</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Conservative transport upscaling based on information of connectivity</atitle><jtitle>Water resources research</jtitle><date>2016-09</date><risdate>2016</risdate><volume>52</volume><issue>9</issue><spage>6867</spage><epage>6880</epage><pages>6867-6880</pages><issn>0043-1397</issn><eissn>1944-7973</eissn><abstract>Connected structures in highly heterogeneous hydraulic conductivity fields lead to channels and preferential pathways for the main fluid flux and fastest solute particles. Their spatial complement is zones of slow advection, where solutes are delayed, causing tailing of solute breakthrough curves. These delays depend on the inclusion's size and the hydraulic conductivity contrast between inclusion and channel. The interplay between channels and small‐scale low conductivity inclusions leads to anomalous transport at larger scales. We test whether a simple separation of transport processes between channels and inclusions could be used to parameterize an effective transport model accounting for anomalous transport. Effective transport is represented by a multirate mass transfer model (MRMT): fast channel transport is controlled by parameters of the mobile zone, while slow advective delays are controlled by parameters of the mobile‐immobile exchange. We delineate the connected channels and analyze their connectivity followed by characterizing the low conductivity inclusions. We parameterize a MRMT model using connectivity and the statistics of the low permeable inclusions. Finally, we compare the parameterized MRMT with detailed numerical simulations in heterogeneous hydraulic conductivity fields with a clear separation between connected channel network and inclusions. In intermediately connected hydraulic conductivity fields only the cut‐off time of the tails is represented while early and intermediate time behavior is not reproduced. We suggest that an effective model for the latter case should account for additional processes like variability in advective velocity.
Key Points
Using connectivity to upscale anomalous transport caused by slow advection
Quantifying connectivity allows separating fast and slow transport processes
The parameterization of the immobile zones is based on the statistics of the low permeable inclusions</abstract><cop>Washington</cop><pub>John Wiley & Sons, Inc</pub><doi>10.1002/2015WR018331</doi><tpages>14</tpages></addata></record> |
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subjects | Advection anomalous transport Channels Complement Computer simulation Conductivity Connectivity Cut-off Exchanging Fields Flux Hydraulic conductivity Hydraulics Inclusions Low conductivity Mass transfer Mathematical models multirate mass transfer Numerical simulations Parameters Permeability Separation slow advection Solutes Statistical methods Statistics Structures Tails Transport Transport processes upscaling transport Variability Velocity |
title | Conservative transport upscaling based on information of connectivity |
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