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Article: Spatio-temporal variation and statistical characteristic of extreme dry spell in Yellow River Basin, China

TitleSpatio-temporal variation and statistical characteristic of extreme dry spell in Yellow River Basin, China
Authors
Issue Date2013
Citation
Theoretical and Applied Climatology, 2013, v. 112, n. 1-2, p. 201-213 How to Cite?
AbstractDrought is one of the most detrimental natural hazards in Yellow River Basin (YRB). In this research, spatio-temporal variation and statistical characteristic of drought in YRB is studied by using dry spell. Two extreme series, including annual maximum series (AMS) and partial duration series (PDS), are used and simulated with generalized extreme value (GEV), generalized Pareto (GP), and Pearson type III (PE3) distributions. The results show that the northern part is drier than the southern part of YRB. Besides, the maximum dry spell usually starts in October, November, and December. According to the trend analysis, mean maximum length of dry spell (MxDS) shows a negative trend in most stations. From the L-moments and Kolmogorov-Smirnov test method, it can be found that GEV model can better fit AMS while GP and PE3 can better fit PDS. Moreover, the quantiles from optimal model of AMS and PDS depict a similar distribution with values increases from south to north. The spatial distribution of scale and location parameters of GEV model for AMS shows a south-to-north gradient, while the distribution of shape parameter is a little irregularity. Furthermore, based on the linear correlation analysis, there is an evident linear relation between location and scale parameters with mean and standard variation of MxDS, respectively. © 2012 Springer-Verlag.
Persistent Identifierhttp://hdl.handle.net/10722/277622
ISSN
2023 Impact Factor: 2.8
2023 SCImago Journal Rankings: 0.803
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorShe, Dunxian-
dc.contributor.authorXia, Jun-
dc.contributor.authorSong, Jiyun-
dc.contributor.authorDu, Hong-
dc.contributor.authorChen, Junxu-
dc.contributor.authorWan, Long-
dc.date.accessioned2019-09-27T08:29:30Z-
dc.date.available2019-09-27T08:29:30Z-
dc.date.issued2013-
dc.identifier.citationTheoretical and Applied Climatology, 2013, v. 112, n. 1-2, p. 201-213-
dc.identifier.issn0177-798X-
dc.identifier.urihttp://hdl.handle.net/10722/277622-
dc.description.abstractDrought is one of the most detrimental natural hazards in Yellow River Basin (YRB). In this research, spatio-temporal variation and statistical characteristic of drought in YRB is studied by using dry spell. Two extreme series, including annual maximum series (AMS) and partial duration series (PDS), are used and simulated with generalized extreme value (GEV), generalized Pareto (GP), and Pearson type III (PE3) distributions. The results show that the northern part is drier than the southern part of YRB. Besides, the maximum dry spell usually starts in October, November, and December. According to the trend analysis, mean maximum length of dry spell (MxDS) shows a negative trend in most stations. From the L-moments and Kolmogorov-Smirnov test method, it can be found that GEV model can better fit AMS while GP and PE3 can better fit PDS. Moreover, the quantiles from optimal model of AMS and PDS depict a similar distribution with values increases from south to north. The spatial distribution of scale and location parameters of GEV model for AMS shows a south-to-north gradient, while the distribution of shape parameter is a little irregularity. Furthermore, based on the linear correlation analysis, there is an evident linear relation between location and scale parameters with mean and standard variation of MxDS, respectively. © 2012 Springer-Verlag.-
dc.languageeng-
dc.relation.ispartofTheoretical and Applied Climatology-
dc.titleSpatio-temporal variation and statistical characteristic of extreme dry spell in Yellow River Basin, China-
dc.typeArticle-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1007/s00704-012-0731-x-
dc.identifier.scopuseid_2-s2.0-84875380136-
dc.identifier.volume112-
dc.identifier.issue1-2-
dc.identifier.spage201-
dc.identifier.epage213-
dc.identifier.eissn1434-4483-
dc.identifier.isiWOS:000316574700015-
dc.identifier.issnl0177-798X-

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