Comparison between gradient based UCODE_2005 and the ensemble Kalman Filter for transient groundwater flow inverse modeling View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2017-05

AUTHORS

JuXiu Tong, Bill X. Hu, JinZhong Yang

ABSTRACT

Gradient based UCODE_2005 and data assimilation based on the Ensemble Kalman Filter (EnKF) are two different inverse methods. A synthetic two-dimensional flow case with four no-flow boundaries is used to compare the UCODE_2005 with the Ensemble Kalman Filter (EnKF) for their efficiency to inversely calculate and calibrate a hydraulic conductivity field based on hydraulic head data. A zonal, random heterogeneous conductivity field is calibrated by assimilating the time series of heads observed in monitoring wells. The study results indicate that the two inverse methods, UCODE_2005 and EnKF, could be used to calibrate the hydraulic conductivity field to a certain degree. More available observations and information about the conductivity field, more accurate inverse results will be obtained for the UCODE_2005. On the other hand, for a realistic zonal heterogeneous hydraulic conductivity field, EnKF can only efficiently determine the hydraulic conductivity field at the first several assimilated time steps. The results obtained by the UCODE_2005 look better than those by the EnKF. This is possibly due to the fact that the UCODE_2005 uses observed head data at every time step, while EnKF can only use observed heads at first several steps due to the filter divergence problem. More... »

PAGES

899-909

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s11430-015-0235-1

DOI

http://dx.doi.org/10.1007/s11430-015-0235-1

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1084031548


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/2008/0104", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Statistics", 
        "type": "DefinedTerm"
      }, 
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/01", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Mathematical Sciences", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "China University of Geosciences", 
          "id": "https://www.grid.ac/institutes/grid.162107.3", 
          "name": [
            "Key Laboratory of Groundwater Cycle and Environment Evolution (China University of Geosciences), Ministry of Education, 100083, Beijing, China", 
            "School of Water Resources and Environment, China University of Geosciences, 100083, Beijing, China"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Tong", 
        "givenName": "JuXiu", 
        "id": "sg:person.010314752347.24", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010314752347.24"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Florida State University", 
          "id": "https://www.grid.ac/institutes/grid.255986.5", 
          "name": [
            "Key Laboratory of Groundwater Cycle and Environment Evolution (China University of Geosciences), Ministry of Education, 100083, Beijing, China", 
            "School of Water Resources and Environment, China University of Geosciences, 100083, Beijing, China", 
            "Department of Earth, Ocean and Atmospheric Science/Geological Sciences, Florida State University, 108 Carraway Building, 909 Antarctic Way, 32306, Tallahassee, Florida, USA"
          ], 
          "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": "Wuhan University", 
          "id": "https://www.grid.ac/institutes/grid.49470.3e", 
          "name": [
            "State Key Laboratory of Water Resources and Hydropower Engineering Science, Wuhan University, 430072, Wuhan, China"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Yang", 
        "givenName": "JinZhong", 
        "id": "sg:person.014731165631.01", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.014731165631.01"
        ], 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "https://doi.org/10.1016/j.jconhyd.2009.06.001", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1001470628"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1029/94jc00572", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1006005755"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1029/95wr01395", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1006980961"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.jmarsys.2005.06.009", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1007952984"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.jhydrol.2008.11.033", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1009885398"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s11430-010-4160-3", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1010605385", 
          "https://doi.org/10.1007/s11430-010-4160-3"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s11242-006-0011-2", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1011330695", 
          "https://doi.org/10.1007/s11242-006-0011-2"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1175/1520-0493(1998)126<1719:asitek>2.0.co;2", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1012328057"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1175/1520-0493(1998)126<0796:dauaek>2.0.co;2", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1013190770"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1029/2000wr900118", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1021264363"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s00477-010-0392-1", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1029308599", 
          "https://doi.org/10.1007/s00477-010-0392-1"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s00477-010-0392-1", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1029308599", 
          "https://doi.org/10.1007/s00477-010-0392-1"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s00477-008-0289-4", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1029417657", 
          "https://doi.org/10.1007/s00477-008-0289-4"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s00477-008-0289-4", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1029417657", 
          "https://doi.org/10.1007/s00477-008-0289-4"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.advwatres.2005.09.007", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1030821237"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1111/j.1745-6584.2005.00117.x", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1034097384"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1111/j.1745-6584.2005.00117.x", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1034097384"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.rse.2007.06.026", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1035614225"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1029/2008wr006959", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1036959105"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s11430-014-4964-7", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1038848989", 
          "https://doi.org/10.1007/s11430-014-4964-7"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1029/2003wr002253", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1049098568"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/hyp.9523", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1050955721"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.jhydrol.2010.11.018", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1052186805"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s10236-003-0036-9", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1053511041", 
          "https://doi.org/10.1007/s10236-003-0036-9"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/s0309-1708(03)00003-4", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1053608950"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1115/1.3662552", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1062137462"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.4310/cms.2010.v8.n1.a3", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1072458950"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.2151/jmsj1965.75.1b_257", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1084936175"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/0470041080", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1098661443"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/0470041080", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1098661443"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2017-05", 
    "datePublishedReg": "2017-05-01", 
    "description": "Gradient based UCODE_2005 and data assimilation based on the Ensemble Kalman Filter (EnKF) are two different inverse methods. A synthetic two-dimensional flow case with four no-flow boundaries is used to compare the UCODE_2005 with the Ensemble Kalman Filter (EnKF) for their efficiency to inversely calculate and calibrate a hydraulic conductivity field based on hydraulic head data. A zonal, random heterogeneous conductivity field is calibrated by assimilating the time series of heads observed in monitoring wells. The study results indicate that the two inverse methods, UCODE_2005 and EnKF, could be used to calibrate the hydraulic conductivity field to a certain degree. More available observations and information about the conductivity field, more accurate inverse results will be obtained for the UCODE_2005. On the other hand, for a realistic zonal heterogeneous hydraulic conductivity field, EnKF can only efficiently determine the hydraulic conductivity field at the first several assimilated time steps. The results obtained by the UCODE_2005 look better than those by the EnKF. This is possibly due to the fact that the UCODE_2005 uses observed head data at every time step, while EnKF can only use observed heads at first several steps due to the filter divergence problem.", 
    "genre": "research_article", 
    "id": "sg:pub.10.1007/s11430-015-0235-1", 
    "inLanguage": [
      "en"
    ], 
    "isAccessibleForFree": false, 
    "isFundedItemOf": [
      {
        "id": "sg:grant.6978231", 
        "type": "MonetaryGrant"
      }, 
      {
        "id": "sg:grant.7006875", 
        "type": "MonetaryGrant"
      }
    ], 
    "isPartOf": [
      {
        "id": "sg:journal.1312375", 
        "issn": [
          "1006-9267", 
          "1006-9313"
        ], 
        "name": "Science China Earth Sciences", 
        "type": "Periodical"
      }, 
      {
        "issueNumber": "5", 
        "type": "PublicationIssue"
      }, 
      {
        "type": "PublicationVolume", 
        "volumeNumber": "60"
      }
    ], 
    "name": "Comparison between gradient based UCODE_2005 and the ensemble Kalman Filter for transient groundwater flow inverse modeling", 
    "pagination": "899-909", 
    "productId": [
      {
        "name": "readcube_id", 
        "type": "PropertyValue", 
        "value": [
          "c33b8f309d33819ce72c1724fc613d86abbb7df7a8cacf3ca689eaf0c93bd504"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/s11430-015-0235-1"
        ]
      }, 
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1084031548"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1007/s11430-015-0235-1", 
      "https://app.dimensions.ai/details/publication/pub.1084031548"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2019-04-11T10:16", 
    "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
    "sdPublisher": {
      "name": "Springer Nature - SN SciGraph project", 
      "type": "Organization"
    }, 
    "sdSource": "s3://com-uberresearch-data-dimensions-target-20181106-alternative/cleanup/v134/2549eaecd7973599484d7c17b260dba0a4ecb94b/merge/v9/a6c9fde33151104705d4d7ff012ea9563521a3ce/jats-lookup/v90/0000000348_0000000348/records_54302_00000001.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "https://link.springer.com/10.1007%2Fs11430-015-0235-1"
  }
]
 

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/s11430-015-0235-1'

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/s11430-015-0235-1'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s11430-015-0235-1'

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

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s11430-015-0235-1'


 

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

172 TRIPLES      21 PREDICATES      53 URIs      19 LITERALS      7 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1007/s11430-015-0235-1 schema:about anzsrc-for:01
2 anzsrc-for:0104
3 schema:author N51b3a1cfa1f3410f90a412d9571a3988
4 schema:citation sg:pub.10.1007/s00477-008-0289-4
5 sg:pub.10.1007/s00477-010-0392-1
6 sg:pub.10.1007/s10236-003-0036-9
7 sg:pub.10.1007/s11242-006-0011-2
8 sg:pub.10.1007/s11430-010-4160-3
9 sg:pub.10.1007/s11430-014-4964-7
10 https://doi.org/10.1002/0470041080
11 https://doi.org/10.1002/hyp.9523
12 https://doi.org/10.1016/j.advwatres.2005.09.007
13 https://doi.org/10.1016/j.jconhyd.2009.06.001
14 https://doi.org/10.1016/j.jhydrol.2008.11.033
15 https://doi.org/10.1016/j.jhydrol.2010.11.018
16 https://doi.org/10.1016/j.jmarsys.2005.06.009
17 https://doi.org/10.1016/j.rse.2007.06.026
18 https://doi.org/10.1016/s0309-1708(03)00003-4
19 https://doi.org/10.1029/2000wr900118
20 https://doi.org/10.1029/2003wr002253
21 https://doi.org/10.1029/2008wr006959
22 https://doi.org/10.1029/94jc00572
23 https://doi.org/10.1029/95wr01395
24 https://doi.org/10.1111/j.1745-6584.2005.00117.x
25 https://doi.org/10.1115/1.3662552
26 https://doi.org/10.1175/1520-0493(1998)126<0796:dauaek>2.0.co;2
27 https://doi.org/10.1175/1520-0493(1998)126<1719:asitek>2.0.co;2
28 https://doi.org/10.2151/jmsj1965.75.1b_257
29 https://doi.org/10.4310/cms.2010.v8.n1.a3
30 schema:datePublished 2017-05
31 schema:datePublishedReg 2017-05-01
32 schema:description Gradient based UCODE_2005 and data assimilation based on the Ensemble Kalman Filter (EnKF) are two different inverse methods. A synthetic two-dimensional flow case with four no-flow boundaries is used to compare the UCODE_2005 with the Ensemble Kalman Filter (EnKF) for their efficiency to inversely calculate and calibrate a hydraulic conductivity field based on hydraulic head data. A zonal, random heterogeneous conductivity field is calibrated by assimilating the time series of heads observed in monitoring wells. The study results indicate that the two inverse methods, UCODE_2005 and EnKF, could be used to calibrate the hydraulic conductivity field to a certain degree. More available observations and information about the conductivity field, more accurate inverse results will be obtained for the UCODE_2005. On the other hand, for a realistic zonal heterogeneous hydraulic conductivity field, EnKF can only efficiently determine the hydraulic conductivity field at the first several assimilated time steps. The results obtained by the UCODE_2005 look better than those by the EnKF. This is possibly due to the fact that the UCODE_2005 uses observed head data at every time step, while EnKF can only use observed heads at first several steps due to the filter divergence problem.
33 schema:genre research_article
34 schema:inLanguage en
35 schema:isAccessibleForFree false
36 schema:isPartOf N449b7cebad044cceb678e61e2ee48c65
37 Nc98998950281421e904f82e6b92052fc
38 sg:journal.1312375
39 schema:name Comparison between gradient based UCODE_2005 and the ensemble Kalman Filter for transient groundwater flow inverse modeling
40 schema:pagination 899-909
41 schema:productId N2399e4394a3a43919b33f7cc767bc200
42 N78bdcccfeca84f7cb89d6c73c76a42b9
43 Na31919d609a14dc1ae252aeb6c725e5f
44 schema:sameAs https://app.dimensions.ai/details/publication/pub.1084031548
45 https://doi.org/10.1007/s11430-015-0235-1
46 schema:sdDatePublished 2019-04-11T10:16
47 schema:sdLicense https://scigraph.springernature.com/explorer/license/
48 schema:sdPublisher N9c1701f818aa4a97ade7b93f394c12f1
49 schema:url https://link.springer.com/10.1007%2Fs11430-015-0235-1
50 sgo:license sg:explorer/license/
51 sgo:sdDataset articles
52 rdf:type schema:ScholarlyArticle
53 N2399e4394a3a43919b33f7cc767bc200 schema:name dimensions_id
54 schema:value pub.1084031548
55 rdf:type schema:PropertyValue
56 N449b7cebad044cceb678e61e2ee48c65 schema:issueNumber 5
57 rdf:type schema:PublicationIssue
58 N51b3a1cfa1f3410f90a412d9571a3988 rdf:first sg:person.010314752347.24
59 rdf:rest Nb17c8e26960740b1b3ba191911f8960a
60 N78bdcccfeca84f7cb89d6c73c76a42b9 schema:name doi
61 schema:value 10.1007/s11430-015-0235-1
62 rdf:type schema:PropertyValue
63 N8c95b4a1152a4dab894759dcdfa0d507 rdf:first sg:person.014731165631.01
64 rdf:rest rdf:nil
65 N9c1701f818aa4a97ade7b93f394c12f1 schema:name Springer Nature - SN SciGraph project
66 rdf:type schema:Organization
67 Na31919d609a14dc1ae252aeb6c725e5f schema:name readcube_id
68 schema:value c33b8f309d33819ce72c1724fc613d86abbb7df7a8cacf3ca689eaf0c93bd504
69 rdf:type schema:PropertyValue
70 Nb17c8e26960740b1b3ba191911f8960a rdf:first sg:person.012556140447.41
71 rdf:rest N8c95b4a1152a4dab894759dcdfa0d507
72 Nc98998950281421e904f82e6b92052fc schema:volumeNumber 60
73 rdf:type schema:PublicationVolume
74 anzsrc-for:01 schema:inDefinedTermSet anzsrc-for:
75 schema:name Mathematical Sciences
76 rdf:type schema:DefinedTerm
77 anzsrc-for:0104 schema:inDefinedTermSet anzsrc-for:
78 schema:name Statistics
79 rdf:type schema:DefinedTerm
80 sg:grant.6978231 http://pending.schema.org/fundedItem sg:pub.10.1007/s11430-015-0235-1
81 rdf:type schema:MonetaryGrant
82 sg:grant.7006875 http://pending.schema.org/fundedItem sg:pub.10.1007/s11430-015-0235-1
83 rdf:type schema:MonetaryGrant
84 sg:journal.1312375 schema:issn 1006-9267
85 1006-9313
86 schema:name Science China Earth Sciences
87 rdf:type schema:Periodical
88 sg:person.010314752347.24 schema:affiliation https://www.grid.ac/institutes/grid.162107.3
89 schema:familyName Tong
90 schema:givenName JuXiu
91 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010314752347.24
92 rdf:type schema:Person
93 sg:person.012556140447.41 schema:affiliation https://www.grid.ac/institutes/grid.255986.5
94 schema:familyName Hu
95 schema:givenName Bill X.
96 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.012556140447.41
97 rdf:type schema:Person
98 sg:person.014731165631.01 schema:affiliation https://www.grid.ac/institutes/grid.49470.3e
99 schema:familyName Yang
100 schema:givenName JinZhong
101 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.014731165631.01
102 rdf:type schema:Person
103 sg:pub.10.1007/s00477-008-0289-4 schema:sameAs https://app.dimensions.ai/details/publication/pub.1029417657
104 https://doi.org/10.1007/s00477-008-0289-4
105 rdf:type schema:CreativeWork
106 sg:pub.10.1007/s00477-010-0392-1 schema:sameAs https://app.dimensions.ai/details/publication/pub.1029308599
107 https://doi.org/10.1007/s00477-010-0392-1
108 rdf:type schema:CreativeWork
109 sg:pub.10.1007/s10236-003-0036-9 schema:sameAs https://app.dimensions.ai/details/publication/pub.1053511041
110 https://doi.org/10.1007/s10236-003-0036-9
111 rdf:type schema:CreativeWork
112 sg:pub.10.1007/s11242-006-0011-2 schema:sameAs https://app.dimensions.ai/details/publication/pub.1011330695
113 https://doi.org/10.1007/s11242-006-0011-2
114 rdf:type schema:CreativeWork
115 sg:pub.10.1007/s11430-010-4160-3 schema:sameAs https://app.dimensions.ai/details/publication/pub.1010605385
116 https://doi.org/10.1007/s11430-010-4160-3
117 rdf:type schema:CreativeWork
118 sg:pub.10.1007/s11430-014-4964-7 schema:sameAs https://app.dimensions.ai/details/publication/pub.1038848989
119 https://doi.org/10.1007/s11430-014-4964-7
120 rdf:type schema:CreativeWork
121 https://doi.org/10.1002/0470041080 schema:sameAs https://app.dimensions.ai/details/publication/pub.1098661443
122 rdf:type schema:CreativeWork
123 https://doi.org/10.1002/hyp.9523 schema:sameAs https://app.dimensions.ai/details/publication/pub.1050955721
124 rdf:type schema:CreativeWork
125 https://doi.org/10.1016/j.advwatres.2005.09.007 schema:sameAs https://app.dimensions.ai/details/publication/pub.1030821237
126 rdf:type schema:CreativeWork
127 https://doi.org/10.1016/j.jconhyd.2009.06.001 schema:sameAs https://app.dimensions.ai/details/publication/pub.1001470628
128 rdf:type schema:CreativeWork
129 https://doi.org/10.1016/j.jhydrol.2008.11.033 schema:sameAs https://app.dimensions.ai/details/publication/pub.1009885398
130 rdf:type schema:CreativeWork
131 https://doi.org/10.1016/j.jhydrol.2010.11.018 schema:sameAs https://app.dimensions.ai/details/publication/pub.1052186805
132 rdf:type schema:CreativeWork
133 https://doi.org/10.1016/j.jmarsys.2005.06.009 schema:sameAs https://app.dimensions.ai/details/publication/pub.1007952984
134 rdf:type schema:CreativeWork
135 https://doi.org/10.1016/j.rse.2007.06.026 schema:sameAs https://app.dimensions.ai/details/publication/pub.1035614225
136 rdf:type schema:CreativeWork
137 https://doi.org/10.1016/s0309-1708(03)00003-4 schema:sameAs https://app.dimensions.ai/details/publication/pub.1053608950
138 rdf:type schema:CreativeWork
139 https://doi.org/10.1029/2000wr900118 schema:sameAs https://app.dimensions.ai/details/publication/pub.1021264363
140 rdf:type schema:CreativeWork
141 https://doi.org/10.1029/2003wr002253 schema:sameAs https://app.dimensions.ai/details/publication/pub.1049098568
142 rdf:type schema:CreativeWork
143 https://doi.org/10.1029/2008wr006959 schema:sameAs https://app.dimensions.ai/details/publication/pub.1036959105
144 rdf:type schema:CreativeWork
145 https://doi.org/10.1029/94jc00572 schema:sameAs https://app.dimensions.ai/details/publication/pub.1006005755
146 rdf:type schema:CreativeWork
147 https://doi.org/10.1029/95wr01395 schema:sameAs https://app.dimensions.ai/details/publication/pub.1006980961
148 rdf:type schema:CreativeWork
149 https://doi.org/10.1111/j.1745-6584.2005.00117.x schema:sameAs https://app.dimensions.ai/details/publication/pub.1034097384
150 rdf:type schema:CreativeWork
151 https://doi.org/10.1115/1.3662552 schema:sameAs https://app.dimensions.ai/details/publication/pub.1062137462
152 rdf:type schema:CreativeWork
153 https://doi.org/10.1175/1520-0493(1998)126<0796:dauaek>2.0.co;2 schema:sameAs https://app.dimensions.ai/details/publication/pub.1013190770
154 rdf:type schema:CreativeWork
155 https://doi.org/10.1175/1520-0493(1998)126<1719:asitek>2.0.co;2 schema:sameAs https://app.dimensions.ai/details/publication/pub.1012328057
156 rdf:type schema:CreativeWork
157 https://doi.org/10.2151/jmsj1965.75.1b_257 schema:sameAs https://app.dimensions.ai/details/publication/pub.1084936175
158 rdf:type schema:CreativeWork
159 https://doi.org/10.4310/cms.2010.v8.n1.a3 schema:sameAs https://app.dimensions.ai/details/publication/pub.1072458950
160 rdf:type schema:CreativeWork
161 https://www.grid.ac/institutes/grid.162107.3 schema:alternateName China University of Geosciences
162 schema:name Key Laboratory of Groundwater Cycle and Environment Evolution (China University of Geosciences), Ministry of Education, 100083, Beijing, China
163 School of Water Resources and Environment, China University of Geosciences, 100083, Beijing, China
164 rdf:type schema:Organization
165 https://www.grid.ac/institutes/grid.255986.5 schema:alternateName Florida State University
166 schema:name Department of Earth, Ocean and Atmospheric Science/Geological Sciences, Florida State University, 108 Carraway Building, 909 Antarctic Way, 32306, Tallahassee, Florida, USA
167 Key Laboratory of Groundwater Cycle and Environment Evolution (China University of Geosciences), Ministry of Education, 100083, Beijing, China
168 School of Water Resources and Environment, China University of Geosciences, 100083, Beijing, China
169 rdf:type schema:Organization
170 https://www.grid.ac/institutes/grid.49470.3e schema:alternateName Wuhan University
171 schema:name State Key Laboratory of Water Resources and Hydropower Engineering Science, Wuhan University, 430072, Wuhan, China
172 rdf:type schema:Organization
 




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


...