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dc.date.accessioned2019-12-05T19:41:43Z
dc.date.available2019-12-05T19:41:43Z
dc.date.created2018-08-03T10:52:53Z
dc.date.issued2018
dc.identifier.citationXiong, Bijing Xiong, Lihua Chen, Jie Xu, Chong-Yu Li, Lingqi . Multiple causes of nonstationarity in the Weihe annual low-flow series. Hydrology and Earth System Sciences. 2018, 22(2), 1525-1542
dc.identifier.urihttp://hdl.handle.net/10852/71216
dc.description.abstractUnder the background of global climate change and local anthropogenic activities, multiple driving forces have introduced various nonstationary components into low-flow series. This has led to a high demand on low-flow frequency analysis that considers nonstationary conditions for modeling. In this study, through a nonstationary frequency analysis framework with the generalized linear model (GLM) to consider time-varying distribution parameters, the multiple explanatory variables were incorporated to explain the variation in low-flow distribution parameters. These variables are comprised of the three indices of human activities (HAs; i.e., population, POP; irrigation area, IAR; and gross domestic product, GDP) and the eight measuring indices of the climate and catchment conditions (i.e., total precipitation P, mean frequency of precipitation events λ, temperature T, potential evapotranspiration (EP), climate aridity index AIEP, base-flow index (BFI), recession constant K and the recession-related aridity index AIK). This framework was applied to model the annual minimum flow series of both Huaxian and Xianyang gauging stations in the Weihe River, China (also known as the Wei He River). The results from stepwise regression for the optimal explanatory variables show that the variables related to irrigation, recession, temperature and precipitation play an important role in modeling. Specifically, analysis of annual minimum 30-day flow in Huaxian shows that the nonstationary distribution model with any one of all explanatory variables is better than the one without explanatory variables, the nonstationary gamma distribution model with four optimal variables is the best model and AIK is of the highest relative importance among these four variables, followed by IAR, BFI and AIEP. We conclude that the incorporation of multiple indices related to low-flow generation permits tracing various driving forces. The established link in nonstationary analysis will be beneficial to analyze future occurrences of low-flow extremes in similar areas.
dc.languageEN
dc.publisherCopernicus
dc.rightsAttribution 3.0 Unported
dc.rights.urihttps://creativecommons.org/licenses/by/3.0/
dc.titleMultiple causes of nonstationarity in the Weihe annual low-flow series
dc.typeJournal article
dc.creator.authorXiong, Bijing
dc.creator.authorXiong, Lihua
dc.creator.authorChen, Jie
dc.creator.authorXu, Chong-Yu
dc.creator.authorLi, Lingqi
cristin.unitcode185,15,22,60
cristin.unitnameSeksjon for naturgeografi og hydrologi
cristin.ispublishedtrue
cristin.fulltextoriginal
cristin.qualitycode2
dc.identifier.cristin1599600
dc.identifier.bibliographiccitationinfo:ofi/fmt:kev:mtx:ctx&ctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.jtitle=Hydrology and Earth System Sciences&rft.volume=22&rft.spage=1525&rft.date=2018
dc.identifier.jtitleHydrology and Earth System Sciences
dc.identifier.volume22
dc.identifier.issue2
dc.identifier.startpage1525
dc.identifier.endpage1542
dc.identifier.doihttps://doi.org/10.5194/hess-22-1525-2018
dc.identifier.urnURN:NBN:no-74353
dc.type.documentTidsskriftartikkel
dc.type.peerreviewedPeer reviewed
dc.source.issn1027-5606
dc.identifier.fulltextFulltext https://www.duo.uio.no/bitstream/handle/10852/71216/1/hess-22-1525-2018.pdf
dc.type.versionPublishedVersion


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