Ontology type: schema:ScholarlyArticle
2019-02
AUTHORSFarshad Fathian, Ahmad Fakheri-Fard, T. B. M. J. Ouarda, Yagob Dinpashoh, S. Saeid Mousavi Nadoushani
ABSTRACTMultivariate time series modeling approaches are known as valuable methods for simulating and forecasting the temporal evolution of hydroclimatic variables. These approaches are also useful for modeling the temporal dependence and cross-dependence between variables and sites. Although multiple linear time series approaches, such as vector autoregressive (VAR) and multiple generalized autoregressive conditional heteroscedasticity (MGARCH) approaches are ordinarily applied in finance and econometrics, these methods have not been broadly applied in hydrology science. The present research employs the VAR and VAR–MGARCH methods to model the mean and conditional variance (heteroscedasticity) of daily streamflow data in the Zarrineh Rood dam watershed, in northwestern Iran. The bivariate diagonal vectorization heteroscedasticity (DVECH) model, as one of the key MGARCH models, demonstrates how the conditional variance, covariance, and correlation structures change in time between the residual time series from VAR model. In this regards, in the present study, five experiments which present different combinations of twofold streamflows (including both upstream and downstream stations) are conducted. The VAR approach is fitted to the twofold daily time series in each of the experiments with different orders. The Portmanteau test, as a formal test for demonstrating time-varying variance (or so-called ARCH effect), indicates the existence of conditional heteroscedastic behavior in the twofold residual time series obtained from the VAR models fitted to the twofold streamflows. Thus, the VAR–DVECH approach is suggested to capture the inherent heteroscedasticity in daily streamflow series. The bivariate DVECH approach indicates short-term and long-term persistency in the conditional variance–covariance structure of the twofold residuals of streamflows. Results show also that the use of the nonlinear bivariate DVECH model improves streamflow modeling efficiency by capturing the heteroscedasticity in the twofold residuals obtained from the VAR model for all experiments. The assessment criteria indicate also that the VAR–DVECH approach leads to a better performance than the VAR model. More... »
PAGES1-19
http://scigraph.springernature.com/pub.10.1007/s00477-019-01651-9
DOIhttp://dx.doi.org/10.1007/s00477-019-01651-9
DIMENSIONShttps://app.dimensions.ai/details/publication/pub.1111912343
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/2008/1403",
"inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/",
"name": "Econometrics",
"type": "DefinedTerm"
},
{
"id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/14",
"inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/",
"name": "Economics",
"type": "DefinedTerm"
}
],
"author": [
{
"affiliation": {
"alternateName": "University of Tabriz",
"id": "https://www.grid.ac/institutes/grid.412831.d",
"name": [
"Department of Water Science and Engineering, Faculty of Agriculture, Vali-e-Asr University of Rafsanjan, P.O. Box 77188-97111, Rafsanjan, Iran",
"Department of Water Engineering, Faculty of Agriculture, University of Tabriz, P.O. Box 51666-16471, Tabriz, Iran"
],
"type": "Organization"
},
"familyName": "Fathian",
"givenName": "Farshad",
"type": "Person"
},
{
"affiliation": {
"alternateName": "University of Tabriz",
"id": "https://www.grid.ac/institutes/grid.412831.d",
"name": [
"Department of Water Engineering, Faculty of Agriculture, University of Tabriz, P.O. Box 51666-16471, Tabriz, Iran"
],
"type": "Organization"
},
"familyName": "Fakheri-Fard",
"givenName": "Ahmad",
"type": "Person"
},
{
"affiliation": {
"alternateName": "Institut National de la Recherche Scientifique",
"id": "https://www.grid.ac/institutes/grid.418084.1",
"name": [
"National Institute for Scientific Research, INRS-ETE, 490 De La Couronne, G1K 9A9, Qu\u00e9bec, QC, Canada"
],
"type": "Organization"
},
"familyName": "Ouarda",
"givenName": "T. B. M. J.",
"type": "Person"
},
{
"affiliation": {
"alternateName": "University of Tabriz",
"id": "https://www.grid.ac/institutes/grid.412831.d",
"name": [
"Department of Water Engineering, Faculty of Agriculture, University of Tabriz, P.O. Box 51666-16471, Tabriz, Iran"
],
"type": "Organization"
},
"familyName": "Dinpashoh",
"givenName": "Yagob",
"type": "Person"
},
{
"affiliation": {
"alternateName": "Shahid Beheshti University of Medical Sciences",
"id": "https://www.grid.ac/institutes/grid.411600.2",
"name": [
"Department of Water Resources Engineering, Faculty of Civil, Water and Environmental Engineering, Abbaspour School of Engineering, Shahid Beheshti University, P.O. Box 16589-53571, Tehran, Iran"
],
"type": "Organization"
},
"familyName": "Mousavi Nadoushani",
"givenName": "S. Saeid",
"type": "Person"
}
],
"citation": [
{
"id": "https://doi.org/10.1080/02626667.2012.743662",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1002332503"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/s00477-006-0077-y",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1003052349",
"https://doi.org/10.1007/s00477-006-0077-y"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/s00477-006-0077-y",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1003052349",
"https://doi.org/10.1007/s00477-006-0077-y"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/0304-4076(86)90063-1",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1009802361"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1002/joc.3407",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1010835191"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/j.jhydrol.2013.06.044",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1013179558"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1029/wr003i004p00937",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1015105535"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/j.jhydrol.2006.05.017",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1015256387"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/s00382-014-2076-x",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1018555723",
"https://doi.org/10.1007/s00382-014-2076-x"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1002/2013wr013810",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1026650854"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/s40808-016-0253-0",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1027564079",
"https://doi.org/10.1007/s40808-016-0253-0"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/s40808-016-0253-0",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1027564079",
"https://doi.org/10.1007/s40808-016-0253-0"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1002/hyp.9452",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1028985974"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1080/09715010.2015.1103201",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1030776851"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/j.physa.2003.08.012",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1032152062"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/j.physa.2003.08.012",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1032152062"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/j.matcom.2010.07.004",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1032360755"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1029/wr007i006p01460",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1032558417"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/j.jhydrol.2007.10.050",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1032925728"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/s11269-011-9849-3",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1038718366",
"https://doi.org/10.1007/s11269-011-9849-3"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1111/1368-423x.t01-1-00088",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1038948619"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/j.jhydrol.2005.09.032",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1040275112"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.5194/npg-12-55-2005",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1043692612"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.5194/npg-12-55-2005",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1043692612"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1002/2015jd023192",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1045721412"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/j.envsoft.2006.06.008",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1046216062"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/s00484-013-0675-6",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1047323767",
"https://doi.org/10.1007/s00484-013-0675-6"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/s00704-014-1120-4",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1048763586",
"https://doi.org/10.1007/s00704-014-1120-4"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1017/s0266466600009063",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1054890699"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1017/s0266466600009063",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1054890699"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1086/261527",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1058575008"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1177/030913330102500104",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1063815162"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1177/030913330102500104",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1063815162"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1504/ijhst.2014.066437",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1067460877"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.2307/1912773",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1069640314"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/s00704-017-2186-6",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1085886301",
"https://doi.org/10.1007/s00704-017-2186-6"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/s00704-017-2186-6",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1085886301",
"https://doi.org/10.1007/s00704-017-2186-6"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1142/9789812702838_0165",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1088784620"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/s00477-017-1428-6",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1090387824",
"https://doi.org/10.1007/s00477-017-1428-6"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/s00477-017-1428-6",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1090387824",
"https://doi.org/10.1007/s00477-017-1428-6"
],
"type": "CreativeWork"
},
{
"id": "https://app.dimensions.ai/details/publication/pub.1106828679",
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1002/9781118619193",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1106828679"
],
"type": "CreativeWork"
},
{
"id": "https://app.dimensions.ai/details/publication/pub.1106891464",
"type": "CreativeWork"
}
],
"datePublished": "2019-02",
"datePublishedReg": "2019-02-01",
"description": "Multivariate time series modeling approaches are known as valuable methods for simulating and forecasting the temporal evolution of hydroclimatic variables. These approaches are also useful for modeling the temporal dependence and cross-dependence between variables and sites. Although multiple linear time series approaches, such as vector autoregressive (VAR) and multiple generalized autoregressive conditional heteroscedasticity (MGARCH) approaches are ordinarily applied in finance and econometrics, these methods have not been broadly applied in hydrology science. The present research employs the VAR and VAR\u2013MGARCH methods to model the mean and conditional variance (heteroscedasticity) of daily streamflow data in the Zarrineh Rood dam watershed, in northwestern Iran. The bivariate diagonal vectorization heteroscedasticity (DVECH) model, as one of the key MGARCH models, demonstrates how the conditional variance, covariance, and correlation structures change in time between the residual time series from VAR model. In this regards, in the present study, five experiments which present different combinations of twofold streamflows (including both upstream and downstream stations) are conducted. The VAR approach is fitted to the twofold daily time series in each of the experiments with different orders. The Portmanteau test, as a formal test for demonstrating time-varying variance (or so-called ARCH effect), indicates the existence of conditional heteroscedastic behavior in the twofold residual time series obtained from the VAR models fitted to the twofold streamflows. Thus, the VAR\u2013DVECH approach is suggested to capture the inherent heteroscedasticity in daily streamflow series. The bivariate DVECH approach indicates short-term and long-term persistency in the conditional variance\u2013covariance structure of the twofold residuals of streamflows. Results show also that the use of the nonlinear bivariate DVECH model improves streamflow modeling efficiency by capturing the heteroscedasticity in the twofold residuals obtained from the VAR model for all experiments. The assessment criteria indicate also that the VAR\u2013DVECH approach leads to a better performance than the VAR model.",
"genre": "research_article",
"id": "sg:pub.10.1007/s00477-019-01651-9",
"inLanguage": [
"en"
],
"isAccessibleForFree": false,
"isPartOf": [
{
"id": "sg:journal.1039987",
"issn": [
"1436-3240",
"1436-3259"
],
"name": "Stochastic Environmental Research and Risk Assessment",
"type": "Periodical"
},
{
"issueNumber": "2",
"type": "PublicationIssue"
},
{
"type": "PublicationVolume",
"volumeNumber": "33"
}
],
"name": "Multiple streamflow time series modeling using VAR\u2013MGARCH approach",
"pagination": "1-19",
"productId": [
{
"name": "readcube_id",
"type": "PropertyValue",
"value": [
"75529df0918738f3f0c23be8a02e411e2c5160360e134972af3dbae3c45d6cbd"
]
},
{
"name": "doi",
"type": "PropertyValue",
"value": [
"10.1007/s00477-019-01651-9"
]
},
{
"name": "dimensions_id",
"type": "PropertyValue",
"value": [
"pub.1111912343"
]
}
],
"sameAs": [
"https://doi.org/10.1007/s00477-019-01651-9",
"https://app.dimensions.ai/details/publication/pub.1111912343"
],
"sdDataset": "articles",
"sdDatePublished": "2019-04-11T13:53",
"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/v90/0000000371_0000000371/records_130805_00000006.jsonl",
"type": "ScholarlyArticle",
"url": "https://link.springer.com/10.1007%2Fs00477-019-01651-9"
}
]
Download the RDF metadata as: json-ld nt turtle xml License info
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/s00477-019-01651-9'
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/s00477-019-01651-9'
Turtle is a human-readable linked data format.
curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s00477-019-01651-9'
RDF/XML is a standard XML format for linked data.
curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s00477-019-01651-9'
This table displays all metadata directly associated to this object as RDF triples.
202 TRIPLES
21 PREDICATES
62 URIs
19 LITERALS
7 BLANK NODES