The development of a non-linear autoregressive model with exogenous input (NARX) to model climate-water clarity relationships: reconstructing a historical water ... View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2017-10

AUTHORS

Cameron C. Lee, Scott C. Sheridan, Brian B. Barnes, Chuanmin Hu, Douglas E. Pirhalla, Varis Ransibrahmanakul, Karsten Shein

ABSTRACT

The coastal waters of the southeastern USA contain important protected habitats and natural resources that are vulnerable to climate variability and singular weather events. Water clarity, strongly affected by atmospheric events, is linked to substantial environmental impacts throughout the region. To assess this relationship over the long-term, this study uses an artificial neural network-based time series modeling technique known as non-linear autoregressive models with exogenous input (NARX models) to explore the relationship between climate and a water clarity index (KDI) in this area and to reconstruct this index over a 66-year period. Results show that synoptic-scale circulation patterns, weather types, and precipitation all play roles in impacting water clarity to varying degrees in each region of the larger domain. In particular, turbid water is associated with transitional weather and cyclonic circulation in much of the study region. Overall, NARX model performance also varies—regionally, seasonally and interannually—with wintertime estimates of KDI along the West Florida Shelf correlating to the actual KDI at r > 0.70. Periods of extreme (high) KDI in this area coincide with notable El Niño events. An upward trend in extreme KDI events from 1948 to 2013 is also present across much of the Florida Gulf coast. More... »

PAGES

557-569

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s00704-016-1906-7

DOI

http://dx.doi.org/10.1007/s00704-016-1906-7

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1004023540


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/2008/0401", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Atmospheric Sciences", 
        "type": "DefinedTerm"
      }, 
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/04", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Earth Sciences", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "Kent State University", 
          "id": "https://www.grid.ac/institutes/grid.258518.3", 
          "name": [
            "Department of Geography, Kent State University, 44242, Kent, OH, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Lee", 
        "givenName": "Cameron C.", 
        "id": "sg:person.01132761043.46", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01132761043.46"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Kent State University", 
          "id": "https://www.grid.ac/institutes/grid.258518.3", 
          "name": [
            "Department of Geography, Kent State University, 44242, Kent, OH, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Sheridan", 
        "givenName": "Scott C.", 
        "id": "sg:person.0577473663.28", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0577473663.28"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "University of South Florida", 
          "id": "https://www.grid.ac/institutes/grid.170693.a", 
          "name": [
            "College of Marine Science, University of South Florida, St. Petersburg, FL, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Barnes", 
        "givenName": "Brian B.", 
        "id": "sg:person.01247350344.09", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01247350344.09"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "University of South Florida", 
          "id": "https://www.grid.ac/institutes/grid.170693.a", 
          "name": [
            "College of Marine Science, University of South Florida, St. Petersburg, FL, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Hu", 
        "givenName": "Chuanmin", 
        "id": "sg:person.01015715373.37", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01015715373.37"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "National Ocean Service", 
          "id": "https://www.grid.ac/institutes/grid.423022.5", 
          "name": [
            "National Oceanic and Atmospheric Administration, National Ocean Service, Silver Spring, MD, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Pirhalla", 
        "givenName": "Douglas E.", 
        "id": "sg:person.014227526451.18", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.014227526451.18"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "National Ocean Service", 
          "id": "https://www.grid.ac/institutes/grid.423022.5", 
          "name": [
            "National Oceanic and Atmospheric Administration, National Ocean Service, Silver Spring, MD, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Ransibrahmanakul", 
        "givenName": "Varis", 
        "id": "sg:person.012760654051.52", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.012760654051.52"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "National Centers for Environmental Information", 
          "id": "https://www.grid.ac/institutes/grid.454206.1", 
          "name": [
            "NOAA National Centers for Environmental Information, Asheville, NC, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Shein", 
        "givenName": "Karsten", 
        "id": "sg:person.016461363337.98", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.016461363337.98"
        ], 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "https://doi.org/10.1029/2004jc002786", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1000095832"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1029/2004jc002786", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1000095832"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/s0278-4343(99)00012-6", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1000212338"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/s1364-8152(99)00007-9", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1002751584"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/joc.2394", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1011048456"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1175/1520-0477(1996)077<0437:tnyrp>2.0.co;2", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1011849757"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1175/jamc-d-12-0126.1", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1020313866"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s12237-014-9918-y", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1023190344", 
          "https://doi.org/10.1007/s12237-014-9918-y"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1029/2006gl028906", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1024051524"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.2307/1353203", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1031955502", 
          "https://doi.org/10.2307/1353203"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.rse.2013.09.020", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1033708762"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.5772/16004", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1034869803"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1201/9781420039412-38", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1040039153"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/joc.2126", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1040082461"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.rse.2013.03.016", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1042221467"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/s0304-3800(02)00064-9", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1042480730"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.ecss.2014.10.003", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1042697252"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1371/journal.pone.0023047", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1043973724"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/s0895-7177(00)00271-5", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1048792778"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s00338-015-1258-2", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1049419700", 
          "https://doi.org/10.1007/s00338-015-1258-2"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1029/2004jc002275", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1049513379"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/joc.709", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1049781706"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1029/2006gl028935", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1049796684"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1029/2006gl028935", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1049796684"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1017/s0376892900038212", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1053992576"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1017/s0376892900038212", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1053992576"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/72.329697", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061218516"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/tgrs.2014.2348713", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061613580"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.3368/er.29.1-2.82", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1071224226"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2017-10", 
    "datePublishedReg": "2017-10-01", 
    "description": "The coastal waters of the southeastern USA contain important protected habitats and natural resources that are vulnerable to climate variability and singular weather events. Water clarity, strongly affected by atmospheric events, is linked to substantial environmental impacts throughout the region. To assess this relationship over the long-term, this study uses an artificial neural network-based time series modeling technique known as non-linear autoregressive models with exogenous input (NARX models) to explore the relationship between climate and a water clarity index (KDI) in this area and to reconstruct this index over a 66-year period. Results show that synoptic-scale circulation patterns, weather types, and precipitation all play roles in impacting water clarity to varying degrees in each region of the larger domain. In particular, turbid water is associated with transitional weather and cyclonic circulation in much of the study region. Overall, NARX model performance also varies\u2014regionally, seasonally and interannually\u2014with wintertime estimates of KDI along the West Florida Shelf correlating to the actual KDI at r > 0.70. Periods of extreme (high) KDI in this area coincide with notable El Ni\u00f1o events. An upward trend in extreme KDI events from 1948 to 2013 is also present across much of the Florida Gulf coast.", 
    "genre": "research_article", 
    "id": "sg:pub.10.1007/s00704-016-1906-7", 
    "inLanguage": [
      "en"
    ], 
    "isAccessibleForFree": false, 
    "isFundedItemOf": [
      {
        "id": "sg:grant.3839510", 
        "type": "MonetaryGrant"
      }
    ], 
    "isPartOf": [
      {
        "id": "sg:journal.1086664", 
        "issn": [
          "0177-798X", 
          "1434-4483"
        ], 
        "name": "Theoretical and Applied Climatology", 
        "type": "Periodical"
      }, 
      {
        "issueNumber": "1-2", 
        "type": "PublicationIssue"
      }, 
      {
        "type": "PublicationVolume", 
        "volumeNumber": "130"
      }
    ], 
    "name": "The development of a non-linear autoregressive model with exogenous input (NARX) to model climate-water clarity relationships: reconstructing a historical water clarity index for the coastal waters of the southeastern USA", 
    "pagination": "557-569", 
    "productId": [
      {
        "name": "readcube_id", 
        "type": "PropertyValue", 
        "value": [
          "132b72caa8aa766b89778f57268c21f2a3cea31af3583411a1ea7db7c48e95c8"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/s00704-016-1906-7"
        ]
      }, 
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1004023540"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1007/s00704-016-1906-7", 
      "https://app.dimensions.ai/details/publication/pub.1004023540"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2019-04-11T12:23", 
    "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/v90/0000000362_0000000362/records_87091_00000000.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "https://link.springer.com/10.1007%2Fs00704-016-1906-7"
  }
]
 

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/s00704-016-1906-7'

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/s00704-016-1906-7'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s00704-016-1906-7'

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

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s00704-016-1906-7'


 

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

195 TRIPLES      21 PREDICATES      53 URIs      19 LITERALS      7 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1007/s00704-016-1906-7 schema:about anzsrc-for:04
2 anzsrc-for:0401
3 schema:author N1cc78657be5b4a68a4bd49bc910c6a3c
4 schema:citation sg:pub.10.1007/s00338-015-1258-2
5 sg:pub.10.1007/s12237-014-9918-y
6 sg:pub.10.2307/1353203
7 https://doi.org/10.1002/joc.2126
8 https://doi.org/10.1002/joc.2394
9 https://doi.org/10.1002/joc.709
10 https://doi.org/10.1016/j.ecss.2014.10.003
11 https://doi.org/10.1016/j.rse.2013.03.016
12 https://doi.org/10.1016/j.rse.2013.09.020
13 https://doi.org/10.1016/s0278-4343(99)00012-6
14 https://doi.org/10.1016/s0304-3800(02)00064-9
15 https://doi.org/10.1016/s0895-7177(00)00271-5
16 https://doi.org/10.1016/s1364-8152(99)00007-9
17 https://doi.org/10.1017/s0376892900038212
18 https://doi.org/10.1029/2004jc002275
19 https://doi.org/10.1029/2004jc002786
20 https://doi.org/10.1029/2006gl028906
21 https://doi.org/10.1029/2006gl028935
22 https://doi.org/10.1109/72.329697
23 https://doi.org/10.1109/tgrs.2014.2348713
24 https://doi.org/10.1175/1520-0477(1996)077<0437:tnyrp>2.0.co;2
25 https://doi.org/10.1175/jamc-d-12-0126.1
26 https://doi.org/10.1201/9781420039412-38
27 https://doi.org/10.1371/journal.pone.0023047
28 https://doi.org/10.3368/er.29.1-2.82
29 https://doi.org/10.5772/16004
30 schema:datePublished 2017-10
31 schema:datePublishedReg 2017-10-01
32 schema:description The coastal waters of the southeastern USA contain important protected habitats and natural resources that are vulnerable to climate variability and singular weather events. Water clarity, strongly affected by atmospheric events, is linked to substantial environmental impacts throughout the region. To assess this relationship over the long-term, this study uses an artificial neural network-based time series modeling technique known as non-linear autoregressive models with exogenous input (NARX models) to explore the relationship between climate and a water clarity index (KDI) in this area and to reconstruct this index over a 66-year period. Results show that synoptic-scale circulation patterns, weather types, and precipitation all play roles in impacting water clarity to varying degrees in each region of the larger domain. In particular, turbid water is associated with transitional weather and cyclonic circulation in much of the study region. Overall, NARX model performance also varies—regionally, seasonally and interannually—with wintertime estimates of KDI along the West Florida Shelf correlating to the actual KDI at r > 0.70. Periods of extreme (high) KDI in this area coincide with notable El Niño events. An upward trend in extreme KDI events from 1948 to 2013 is also present across much of the Florida Gulf coast.
33 schema:genre research_article
34 schema:inLanguage en
35 schema:isAccessibleForFree false
36 schema:isPartOf N0f12d8163b04440cbcbdada6bbde5fd0
37 Nb86111eeb7f042dcb529426bf168ca7f
38 sg:journal.1086664
39 schema:name The development of a non-linear autoregressive model with exogenous input (NARX) to model climate-water clarity relationships: reconstructing a historical water clarity index for the coastal waters of the southeastern USA
40 schema:pagination 557-569
41 schema:productId N2089f6414637442196e2b7c04ccb9ba9
42 N28e86c1e3ba24d8782532e3f83695d41
43 Na08de25394df47f1bd6d44b4b99420db
44 schema:sameAs https://app.dimensions.ai/details/publication/pub.1004023540
45 https://doi.org/10.1007/s00704-016-1906-7
46 schema:sdDatePublished 2019-04-11T12:23
47 schema:sdLicense https://scigraph.springernature.com/explorer/license/
48 schema:sdPublisher N03b60ee31f2740c4ad98368822ca130f
49 schema:url https://link.springer.com/10.1007%2Fs00704-016-1906-7
50 sgo:license sg:explorer/license/
51 sgo:sdDataset articles
52 rdf:type schema:ScholarlyArticle
53 N03b60ee31f2740c4ad98368822ca130f schema:name Springer Nature - SN SciGraph project
54 rdf:type schema:Organization
55 N0f12d8163b04440cbcbdada6bbde5fd0 schema:issueNumber 1-2
56 rdf:type schema:PublicationIssue
57 N0f73365cce0e466382ab8c709d8b18fa rdf:first sg:person.01247350344.09
58 rdf:rest Nec6362c74e044220aa04f91c58be17ec
59 N1084338cce88455c9cf72ac23eb47b0b rdf:first sg:person.0577473663.28
60 rdf:rest N0f73365cce0e466382ab8c709d8b18fa
61 N1cc78657be5b4a68a4bd49bc910c6a3c rdf:first sg:person.01132761043.46
62 rdf:rest N1084338cce88455c9cf72ac23eb47b0b
63 N2089f6414637442196e2b7c04ccb9ba9 schema:name readcube_id
64 schema:value 132b72caa8aa766b89778f57268c21f2a3cea31af3583411a1ea7db7c48e95c8
65 rdf:type schema:PropertyValue
66 N28e86c1e3ba24d8782532e3f83695d41 schema:name dimensions_id
67 schema:value pub.1004023540
68 rdf:type schema:PropertyValue
69 N7e72865090d6402eac4ed3f6672b22da rdf:first sg:person.014227526451.18
70 rdf:rest Nfa788c668752487aaac043ff6763af00
71 Na029f85fddfd449b92fd9c0429b52cab rdf:first sg:person.016461363337.98
72 rdf:rest rdf:nil
73 Na08de25394df47f1bd6d44b4b99420db schema:name doi
74 schema:value 10.1007/s00704-016-1906-7
75 rdf:type schema:PropertyValue
76 Nb86111eeb7f042dcb529426bf168ca7f schema:volumeNumber 130
77 rdf:type schema:PublicationVolume
78 Nec6362c74e044220aa04f91c58be17ec rdf:first sg:person.01015715373.37
79 rdf:rest N7e72865090d6402eac4ed3f6672b22da
80 Nfa788c668752487aaac043ff6763af00 rdf:first sg:person.012760654051.52
81 rdf:rest Na029f85fddfd449b92fd9c0429b52cab
82 anzsrc-for:04 schema:inDefinedTermSet anzsrc-for:
83 schema:name Earth Sciences
84 rdf:type schema:DefinedTerm
85 anzsrc-for:0401 schema:inDefinedTermSet anzsrc-for:
86 schema:name Atmospheric Sciences
87 rdf:type schema:DefinedTerm
88 sg:grant.3839510 http://pending.schema.org/fundedItem sg:pub.10.1007/s00704-016-1906-7
89 rdf:type schema:MonetaryGrant
90 sg:journal.1086664 schema:issn 0177-798X
91 1434-4483
92 schema:name Theoretical and Applied Climatology
93 rdf:type schema:Periodical
94 sg:person.01015715373.37 schema:affiliation https://www.grid.ac/institutes/grid.170693.a
95 schema:familyName Hu
96 schema:givenName Chuanmin
97 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01015715373.37
98 rdf:type schema:Person
99 sg:person.01132761043.46 schema:affiliation https://www.grid.ac/institutes/grid.258518.3
100 schema:familyName Lee
101 schema:givenName Cameron C.
102 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01132761043.46
103 rdf:type schema:Person
104 sg:person.01247350344.09 schema:affiliation https://www.grid.ac/institutes/grid.170693.a
105 schema:familyName Barnes
106 schema:givenName Brian B.
107 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01247350344.09
108 rdf:type schema:Person
109 sg:person.012760654051.52 schema:affiliation https://www.grid.ac/institutes/grid.423022.5
110 schema:familyName Ransibrahmanakul
111 schema:givenName Varis
112 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.012760654051.52
113 rdf:type schema:Person
114 sg:person.014227526451.18 schema:affiliation https://www.grid.ac/institutes/grid.423022.5
115 schema:familyName Pirhalla
116 schema:givenName Douglas E.
117 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.014227526451.18
118 rdf:type schema:Person
119 sg:person.016461363337.98 schema:affiliation https://www.grid.ac/institutes/grid.454206.1
120 schema:familyName Shein
121 schema:givenName Karsten
122 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.016461363337.98
123 rdf:type schema:Person
124 sg:person.0577473663.28 schema:affiliation https://www.grid.ac/institutes/grid.258518.3
125 schema:familyName Sheridan
126 schema:givenName Scott C.
127 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0577473663.28
128 rdf:type schema:Person
129 sg:pub.10.1007/s00338-015-1258-2 schema:sameAs https://app.dimensions.ai/details/publication/pub.1049419700
130 https://doi.org/10.1007/s00338-015-1258-2
131 rdf:type schema:CreativeWork
132 sg:pub.10.1007/s12237-014-9918-y schema:sameAs https://app.dimensions.ai/details/publication/pub.1023190344
133 https://doi.org/10.1007/s12237-014-9918-y
134 rdf:type schema:CreativeWork
135 sg:pub.10.2307/1353203 schema:sameAs https://app.dimensions.ai/details/publication/pub.1031955502
136 https://doi.org/10.2307/1353203
137 rdf:type schema:CreativeWork
138 https://doi.org/10.1002/joc.2126 schema:sameAs https://app.dimensions.ai/details/publication/pub.1040082461
139 rdf:type schema:CreativeWork
140 https://doi.org/10.1002/joc.2394 schema:sameAs https://app.dimensions.ai/details/publication/pub.1011048456
141 rdf:type schema:CreativeWork
142 https://doi.org/10.1002/joc.709 schema:sameAs https://app.dimensions.ai/details/publication/pub.1049781706
143 rdf:type schema:CreativeWork
144 https://doi.org/10.1016/j.ecss.2014.10.003 schema:sameAs https://app.dimensions.ai/details/publication/pub.1042697252
145 rdf:type schema:CreativeWork
146 https://doi.org/10.1016/j.rse.2013.03.016 schema:sameAs https://app.dimensions.ai/details/publication/pub.1042221467
147 rdf:type schema:CreativeWork
148 https://doi.org/10.1016/j.rse.2013.09.020 schema:sameAs https://app.dimensions.ai/details/publication/pub.1033708762
149 rdf:type schema:CreativeWork
150 https://doi.org/10.1016/s0278-4343(99)00012-6 schema:sameAs https://app.dimensions.ai/details/publication/pub.1000212338
151 rdf:type schema:CreativeWork
152 https://doi.org/10.1016/s0304-3800(02)00064-9 schema:sameAs https://app.dimensions.ai/details/publication/pub.1042480730
153 rdf:type schema:CreativeWork
154 https://doi.org/10.1016/s0895-7177(00)00271-5 schema:sameAs https://app.dimensions.ai/details/publication/pub.1048792778
155 rdf:type schema:CreativeWork
156 https://doi.org/10.1016/s1364-8152(99)00007-9 schema:sameAs https://app.dimensions.ai/details/publication/pub.1002751584
157 rdf:type schema:CreativeWork
158 https://doi.org/10.1017/s0376892900038212 schema:sameAs https://app.dimensions.ai/details/publication/pub.1053992576
159 rdf:type schema:CreativeWork
160 https://doi.org/10.1029/2004jc002275 schema:sameAs https://app.dimensions.ai/details/publication/pub.1049513379
161 rdf:type schema:CreativeWork
162 https://doi.org/10.1029/2004jc002786 schema:sameAs https://app.dimensions.ai/details/publication/pub.1000095832
163 rdf:type schema:CreativeWork
164 https://doi.org/10.1029/2006gl028906 schema:sameAs https://app.dimensions.ai/details/publication/pub.1024051524
165 rdf:type schema:CreativeWork
166 https://doi.org/10.1029/2006gl028935 schema:sameAs https://app.dimensions.ai/details/publication/pub.1049796684
167 rdf:type schema:CreativeWork
168 https://doi.org/10.1109/72.329697 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061218516
169 rdf:type schema:CreativeWork
170 https://doi.org/10.1109/tgrs.2014.2348713 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061613580
171 rdf:type schema:CreativeWork
172 https://doi.org/10.1175/1520-0477(1996)077<0437:tnyrp>2.0.co;2 schema:sameAs https://app.dimensions.ai/details/publication/pub.1011849757
173 rdf:type schema:CreativeWork
174 https://doi.org/10.1175/jamc-d-12-0126.1 schema:sameAs https://app.dimensions.ai/details/publication/pub.1020313866
175 rdf:type schema:CreativeWork
176 https://doi.org/10.1201/9781420039412-38 schema:sameAs https://app.dimensions.ai/details/publication/pub.1040039153
177 rdf:type schema:CreativeWork
178 https://doi.org/10.1371/journal.pone.0023047 schema:sameAs https://app.dimensions.ai/details/publication/pub.1043973724
179 rdf:type schema:CreativeWork
180 https://doi.org/10.3368/er.29.1-2.82 schema:sameAs https://app.dimensions.ai/details/publication/pub.1071224226
181 rdf:type schema:CreativeWork
182 https://doi.org/10.5772/16004 schema:sameAs https://app.dimensions.ai/details/publication/pub.1034869803
183 rdf:type schema:CreativeWork
184 https://www.grid.ac/institutes/grid.170693.a schema:alternateName University of South Florida
185 schema:name College of Marine Science, University of South Florida, St. Petersburg, FL, USA
186 rdf:type schema:Organization
187 https://www.grid.ac/institutes/grid.258518.3 schema:alternateName Kent State University
188 schema:name Department of Geography, Kent State University, 44242, Kent, OH, USA
189 rdf:type schema:Organization
190 https://www.grid.ac/institutes/grid.423022.5 schema:alternateName National Ocean Service
191 schema:name National Oceanic and Atmospheric Administration, National Ocean Service, Silver Spring, MD, USA
192 rdf:type schema:Organization
193 https://www.grid.ac/institutes/grid.454206.1 schema:alternateName National Centers for Environmental Information
194 schema:name NOAA National Centers for Environmental Information, Asheville, NC, USA
195 rdf:type schema:Organization
 




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


...