Simulating seawater intrusion in a complex coastal karst aquifer using an improved variable-density flow and solute transport–conduit flow process model View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2019-01-02

AUTHORS

Zhongyuan Xu, Bill X. Hu, Zexuan Xu, Xiujie Wu

ABSTRACT

VDFST-CFP (variable-density flow and solute transport–conduit flow process) is a density-dependent discrete-continuum numerical model for simulating seawater intrusion in a dual-permeability coastal karst aquifer. A previous study (Xu and Hu 2017) simulates variable-density flow only in a single conduit, and studies the parameter sensitivities only in the horizontal case (2D domain as horizontal section) by the VDFST-CFP model. This paper focuses on the density-dependent vertical case (2D domain as vertical section) with two major improvements: 1) when implementing double-conduit networks in the domain, simulated intruded plumes in the porous medium are extended in the double-conduit scenario, compared to the single-conduit system; 2) by quantifying micro-textures on the conduit wall by the Goudar-Sonnad equation and considering macro-structures as local head loss. Sensitivity analysis shows that medium hydraulic conductivity, conduit diameter and effective porosity are important parameters for simulating seawater intrusion in the discrete-continuum system. On the other hand, rougher micro-structures and additional macro-structure components on the conduit wall would reduce the distance of seawater intrusion to the conduit system, but, rarely affect salinity distribution in the matrix. Compared to the equivalent mean roughness height, the new method (with more detailed description of structure) simulates seawater intrusion slightly landward in the conduit system. The macro-structure measured by local head loss is more reasonable but needs further study on conduit flow. Xu and Hu (2017) Development of a discrete-continuum VDFST-CFP numerical model for simulating seawater intrusion to a coastal karst aquifer with a conduit system. Water Resources Research: 53, 688-711. More... »

PAGES

1277-1289

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s10040-018-1903-2

DOI

http://dx.doi.org/10.1007/s10040-018-1903-2

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1111057597


Indexing Status Check whether this publication has been indexed by Scopus and Web Of Science using the SN Indexing Status Tool
Incoming Citations Browse incoming citations for this publication using opencitations.net

JSON-LD is the canonical representation for SciGraph data.

TIP: You can open this SciGraph record using an external JSON-LD service: JSON-LD Playground Google SDTT

[
  {
    "@context": "https://springernature.github.io/scigraph/jsonld/sgcontext.json", 
    "about": [
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/09", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Engineering", 
        "type": "DefinedTerm"
      }, 
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/0915", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Interdisciplinary Engineering", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "College of Environment & Civil Engineering, Chengdu University of Technology, 610059, Chengdu, China", 
          "id": "http://www.grid.ac/institutes/grid.411288.6", 
          "name": [
            "College of Earth, Ocean, and Environment, University of Delaware, 19716, Newark, DE, USA", 
            "College of Environment & Civil Engineering, Chengdu University of Technology, 610059, Chengdu, China"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Xu", 
        "givenName": "Zhongyuan", 
        "id": "sg:person.016353753117.12", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.016353753117.12"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Institute of Groundwater and Earth Sciences, The Jinan University, 510632, Guangzhou, Guangdong Province, People\u2019s Republic of China", 
          "id": "http://www.grid.ac/institutes/grid.258164.c", 
          "name": [
            "School of Water Resources and Environment, China University of Geosciences (Beijing), 100083, Beijing, People\u2019s Republic of China", 
            "Institute of Groundwater and Earth Sciences, The Jinan University, 510632, Guangzhou, Guangdong Province, People\u2019s Republic of China"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Hu", 
        "givenName": "Bill X.", 
        "id": "sg:person.012556140447.41", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.012556140447.41"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Climate and Ecosystem Sciences Division, Lawrence Berkeley National Laboratory, 94720, Berkeley, CA, USA", 
          "id": "http://www.grid.ac/institutes/grid.184769.5", 
          "name": [
            "Climate and Ecosystem Sciences Division, Lawrence Berkeley National Laboratory, 94720, Berkeley, CA, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Xu", 
        "givenName": "Zexuan", 
        "id": "sg:person.011204357255.88", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.011204357255.88"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "College of Environment & Civil Engineering, Chengdu University of Technology, 610059, Chengdu, China", 
          "id": "http://www.grid.ac/institutes/grid.411288.6", 
          "name": [
            "College of Environment & Civil Engineering, Chengdu University of Technology, 610059, Chengdu, China"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Wu", 
        "givenName": "Xiujie", 
        "id": "sg:person.010365617047.27", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010365617047.27"
        ], 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "sg:pub.10.1007/s11242-012-0061-6", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1042232486", 
          "https://doi.org/10.1007/s11242-012-0061-6"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s12665-018-7660-7", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1105133033", 
          "https://doi.org/10.1007/s12665-018-7660-7"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/srep32235", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1021994585", 
          "https://doi.org/10.1038/srep32235"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2019-01-02", 
    "datePublishedReg": "2019-01-02", 
    "description": "VDFST-CFP (variable-density flow and solute transport\u2013conduit flow process) is a density-dependent discrete-continuum numerical model for simulating seawater intrusion in a dual-permeability coastal karst aquifer. A previous study (Xu and Hu 2017) simulates variable-density flow only in a single conduit, and studies the parameter sensitivities only in the horizontal case (2D domain as horizontal section) by the VDFST-CFP model. This paper focuses on the density-dependent vertical case (2D domain as vertical section) with two major improvements: 1) when implementing double-conduit networks in the domain, simulated intruded plumes in the porous medium are extended in the double-conduit scenario, compared to the single-conduit system; 2) by quantifying micro-textures on the conduit wall by the Goudar-Sonnad equation and considering macro-structures as local head loss. Sensitivity analysis shows that medium hydraulic conductivity, conduit diameter and effective porosity are important parameters for simulating seawater intrusion in the discrete-continuum system. On the other hand, rougher micro-structures and additional macro-structure components on the conduit wall would reduce the distance of seawater intrusion to the conduit system, but, rarely affect salinity distribution in the matrix. Compared to the equivalent mean roughness height, the new method (with more detailed description of structure) simulates seawater intrusion slightly landward in the conduit system. The macro-structure measured by local head loss is more reasonable but needs further study on conduit flow. Xu and Hu (2017) Development of a discrete-continuum VDFST-CFP numerical model for simulating seawater intrusion to a coastal karst aquifer with a conduit system. Water Resources Research: 53, 688-711.", 
    "genre": "article", 
    "id": "sg:pub.10.1007/s10040-018-1903-2", 
    "inLanguage": "en", 
    "isAccessibleForFree": true, 
    "isFundedItemOf": [
      {
        "id": "sg:grant.8231944", 
        "type": "MonetaryGrant"
      }
    ], 
    "isPartOf": [
      {
        "id": "sg:journal.1047968", 
        "issn": [
          "1431-2174", 
          "1435-0157"
        ], 
        "name": "Hydrogeology Journal", 
        "publisher": "Springer Nature", 
        "type": "Periodical"
      }, 
      {
        "issueNumber": "4", 
        "type": "PublicationIssue"
      }, 
      {
        "type": "PublicationVolume", 
        "volumeNumber": "27"
      }
    ], 
    "keywords": [
      "variable-density flow", 
      "local head loss", 
      "coastal karst aquifer", 
      "numerical model", 
      "head loss", 
      "mean roughness height", 
      "medium hydraulic conductivity", 
      "conduit walls", 
      "karst aquifers", 
      "roughness height", 
      "seawater intrusion", 
      "porous media", 
      "conduit system", 
      "horizontal case", 
      "effective porosity", 
      "vertical case", 
      "hydraulic conductivity", 
      "salinity distribution", 
      "important parameters", 
      "conduit flow", 
      "flow", 
      "aquifer", 
      "sensitivity analysis", 
      "new method", 
      "porosity", 
      "rougher", 
      "process model", 
      "conductivity", 
      "wall", 
      "parameters", 
      "system", 
      "single conduit", 
      "plume", 
      "conduit diameter", 
      "model", 
      "major improvements", 
      "diameter", 
      "matrix", 
      "equations", 
      "height", 
      "intrusion", 
      "loss", 
      "method", 
      "scenarios", 
      "distribution", 
      "components", 
      "improvement", 
      "conduit", 
      "network", 
      "distance", 
      "medium", 
      "analysis", 
      "cases", 
      "study", 
      "hand", 
      "development", 
      "previous studies", 
      "domain", 
      "Xu", 
      "Further studies", 
      "paper"
    ], 
    "name": "Simulating seawater intrusion in a complex coastal karst aquifer using an improved variable-density flow and solute transport\u2013conduit flow process model", 
    "pagination": "1277-1289", 
    "productId": [
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1111057597"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/s10040-018-1903-2"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1007/s10040-018-1903-2", 
      "https://app.dimensions.ai/details/publication/pub.1111057597"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2022-05-10T10:23", 
    "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
    "sdPublisher": {
      "name": "Springer Nature - SN SciGraph project", 
      "type": "Organization"
    }, 
    "sdSource": "s3://com-springernature-scigraph/baseset/20220509/entities/gbq_results/article/article_798.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "https://doi.org/10.1007/s10040-018-1903-2"
  }
]
 

Download the RDF metadata as:  json-ld nt turtle xml License info

HOW TO GET THIS DATA PROGRAMMATICALLY:

JSON-LD is a popular format for linked data which is fully compatible with JSON.

curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1007/s10040-018-1903-2'

N-Triples is a line-based linked data format ideal for batch operations.

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/s10040-018-1903-2'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s10040-018-1903-2'

RDF/XML is a standard XML format for linked data.

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s10040-018-1903-2'


 

This table displays all metadata directly associated to this object as RDF triples.

162 TRIPLES      22 PREDICATES      89 URIs      78 LITERALS      6 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1007/s10040-018-1903-2 schema:about anzsrc-for:09
2 anzsrc-for:0915
3 schema:author N0a70972345084760be234dbc38363056
4 schema:citation sg:pub.10.1007/s11242-012-0061-6
5 sg:pub.10.1007/s12665-018-7660-7
6 sg:pub.10.1038/srep32235
7 schema:datePublished 2019-01-02
8 schema:datePublishedReg 2019-01-02
9 schema:description VDFST-CFP (variable-density flow and solute transport–conduit flow process) is a density-dependent discrete-continuum numerical model for simulating seawater intrusion in a dual-permeability coastal karst aquifer. A previous study (Xu and Hu 2017) simulates variable-density flow only in a single conduit, and studies the parameter sensitivities only in the horizontal case (2D domain as horizontal section) by the VDFST-CFP model. This paper focuses on the density-dependent vertical case (2D domain as vertical section) with two major improvements: 1) when implementing double-conduit networks in the domain, simulated intruded plumes in the porous medium are extended in the double-conduit scenario, compared to the single-conduit system; 2) by quantifying micro-textures on the conduit wall by the Goudar-Sonnad equation and considering macro-structures as local head loss. Sensitivity analysis shows that medium hydraulic conductivity, conduit diameter and effective porosity are important parameters for simulating seawater intrusion in the discrete-continuum system. On the other hand, rougher micro-structures and additional macro-structure components on the conduit wall would reduce the distance of seawater intrusion to the conduit system, but, rarely affect salinity distribution in the matrix. Compared to the equivalent mean roughness height, the new method (with more detailed description of structure) simulates seawater intrusion slightly landward in the conduit system. The macro-structure measured by local head loss is more reasonable but needs further study on conduit flow. Xu and Hu (2017) Development of a discrete-continuum VDFST-CFP numerical model for simulating seawater intrusion to a coastal karst aquifer with a conduit system. Water Resources Research: 53, 688-711.
10 schema:genre article
11 schema:inLanguage en
12 schema:isAccessibleForFree true
13 schema:isPartOf N3bdc3aef393f46d1be21eacc4678a742
14 Nac3fe37d8f17471ba1c283338f6cb233
15 sg:journal.1047968
16 schema:keywords Further studies
17 Xu
18 analysis
19 aquifer
20 cases
21 coastal karst aquifer
22 components
23 conductivity
24 conduit
25 conduit diameter
26 conduit flow
27 conduit system
28 conduit walls
29 development
30 diameter
31 distance
32 distribution
33 domain
34 effective porosity
35 equations
36 flow
37 hand
38 head loss
39 height
40 horizontal case
41 hydraulic conductivity
42 important parameters
43 improvement
44 intrusion
45 karst aquifers
46 local head loss
47 loss
48 major improvements
49 matrix
50 mean roughness height
51 medium
52 medium hydraulic conductivity
53 method
54 model
55 network
56 new method
57 numerical model
58 paper
59 parameters
60 plume
61 porosity
62 porous media
63 previous studies
64 process model
65 rougher
66 roughness height
67 salinity distribution
68 scenarios
69 seawater intrusion
70 sensitivity analysis
71 single conduit
72 study
73 system
74 variable-density flow
75 vertical case
76 wall
77 schema:name Simulating seawater intrusion in a complex coastal karst aquifer using an improved variable-density flow and solute transport–conduit flow process model
78 schema:pagination 1277-1289
79 schema:productId N0f94325e233942c4a3397dc9a8dd8a99
80 Nee79d51248074e829f043f1f60ee7bd6
81 schema:sameAs https://app.dimensions.ai/details/publication/pub.1111057597
82 https://doi.org/10.1007/s10040-018-1903-2
83 schema:sdDatePublished 2022-05-10T10:23
84 schema:sdLicense https://scigraph.springernature.com/explorer/license/
85 schema:sdPublisher Na9b1f1f37dff4aac98b504aeeb5c99fa
86 schema:url https://doi.org/10.1007/s10040-018-1903-2
87 sgo:license sg:explorer/license/
88 sgo:sdDataset articles
89 rdf:type schema:ScholarlyArticle
90 N0a70972345084760be234dbc38363056 rdf:first sg:person.016353753117.12
91 rdf:rest Ne855190148a44062b4fa85d29945d945
92 N0f94325e233942c4a3397dc9a8dd8a99 schema:name doi
93 schema:value 10.1007/s10040-018-1903-2
94 rdf:type schema:PropertyValue
95 N3bdc3aef393f46d1be21eacc4678a742 schema:volumeNumber 27
96 rdf:type schema:PublicationVolume
97 N54df3b3ef82947ccaf0a903920927497 rdf:first sg:person.011204357255.88
98 rdf:rest N7d4dc9e44f76466c83f4bdbdbbb18478
99 N7d4dc9e44f76466c83f4bdbdbbb18478 rdf:first sg:person.010365617047.27
100 rdf:rest rdf:nil
101 Na9b1f1f37dff4aac98b504aeeb5c99fa schema:name Springer Nature - SN SciGraph project
102 rdf:type schema:Organization
103 Nac3fe37d8f17471ba1c283338f6cb233 schema:issueNumber 4
104 rdf:type schema:PublicationIssue
105 Ne855190148a44062b4fa85d29945d945 rdf:first sg:person.012556140447.41
106 rdf:rest N54df3b3ef82947ccaf0a903920927497
107 Nee79d51248074e829f043f1f60ee7bd6 schema:name dimensions_id
108 schema:value pub.1111057597
109 rdf:type schema:PropertyValue
110 anzsrc-for:09 schema:inDefinedTermSet anzsrc-for:
111 schema:name Engineering
112 rdf:type schema:DefinedTerm
113 anzsrc-for:0915 schema:inDefinedTermSet anzsrc-for:
114 schema:name Interdisciplinary Engineering
115 rdf:type schema:DefinedTerm
116 sg:grant.8231944 http://pending.schema.org/fundedItem sg:pub.10.1007/s10040-018-1903-2
117 rdf:type schema:MonetaryGrant
118 sg:journal.1047968 schema:issn 1431-2174
119 1435-0157
120 schema:name Hydrogeology Journal
121 schema:publisher Springer Nature
122 rdf:type schema:Periodical
123 sg:person.010365617047.27 schema:affiliation grid-institutes:grid.411288.6
124 schema:familyName Wu
125 schema:givenName Xiujie
126 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010365617047.27
127 rdf:type schema:Person
128 sg:person.011204357255.88 schema:affiliation grid-institutes:grid.184769.5
129 schema:familyName Xu
130 schema:givenName Zexuan
131 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.011204357255.88
132 rdf:type schema:Person
133 sg:person.012556140447.41 schema:affiliation grid-institutes:grid.258164.c
134 schema:familyName Hu
135 schema:givenName Bill X.
136 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.012556140447.41
137 rdf:type schema:Person
138 sg:person.016353753117.12 schema:affiliation grid-institutes:grid.411288.6
139 schema:familyName Xu
140 schema:givenName Zhongyuan
141 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.016353753117.12
142 rdf:type schema:Person
143 sg:pub.10.1007/s11242-012-0061-6 schema:sameAs https://app.dimensions.ai/details/publication/pub.1042232486
144 https://doi.org/10.1007/s11242-012-0061-6
145 rdf:type schema:CreativeWork
146 sg:pub.10.1007/s12665-018-7660-7 schema:sameAs https://app.dimensions.ai/details/publication/pub.1105133033
147 https://doi.org/10.1007/s12665-018-7660-7
148 rdf:type schema:CreativeWork
149 sg:pub.10.1038/srep32235 schema:sameAs https://app.dimensions.ai/details/publication/pub.1021994585
150 https://doi.org/10.1038/srep32235
151 rdf:type schema:CreativeWork
152 grid-institutes:grid.184769.5 schema:alternateName Climate and Ecosystem Sciences Division, Lawrence Berkeley National Laboratory, 94720, Berkeley, CA, USA
153 schema:name Climate and Ecosystem Sciences Division, Lawrence Berkeley National Laboratory, 94720, Berkeley, CA, USA
154 rdf:type schema:Organization
155 grid-institutes:grid.258164.c schema:alternateName Institute of Groundwater and Earth Sciences, The Jinan University, 510632, Guangzhou, Guangdong Province, People’s Republic of China
156 schema:name Institute of Groundwater and Earth Sciences, The Jinan University, 510632, Guangzhou, Guangdong Province, People’s Republic of China
157 School of Water Resources and Environment, China University of Geosciences (Beijing), 100083, Beijing, People’s Republic of China
158 rdf:type schema:Organization
159 grid-institutes:grid.411288.6 schema:alternateName College of Environment & Civil Engineering, Chengdu University of Technology, 610059, Chengdu, China
160 schema:name College of Earth, Ocean, and Environment, University of Delaware, 19716, Newark, DE, USA
161 College of Environment & Civil Engineering, Chengdu University of Technology, 610059, Chengdu, China
162 rdf:type schema:Organization
 




Preview window. Press ESC to close (or click here)


...