Flood frequency analysis for annual maximum streamflow using a non-stationary GEV model

Under changing environment, the streamflow series in the Yangtze River have undergone great changes and it has raised widespread concerns. In this study, the annual maximum flow (AMF) series at the Yichang station were used for flood frequency analysis, in which a time varying model was constructed...

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Main Authors: Jiang Shangwen, Kang Ling
Format: Article
Language:English
Published: EDP Sciences 2019-01-01
Series:E3S Web of Conferences
Online Access:https://doi.org/10.1051/e3sconf/20197903022
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spelling doaj-5df2ec3a05d643af8c1f35b624e4bba52021-02-02T04:11:45ZengEDP SciencesE3S Web of Conferences2267-12422019-01-01790302210.1051/e3sconf/20197903022e3sconf_arfee2018_03022Flood frequency analysis for annual maximum streamflow using a non-stationary GEV modelJiang Shangwen0Kang Ling1School of Hydropower and Information Engineering, Huazhong University of Science and TechnologySchool of Hydropower and Information Engineering, Huazhong University of Science and TechnologyUnder changing environment, the streamflow series in the Yangtze River have undergone great changes and it has raised widespread concerns. In this study, the annual maximum flow (AMF) series at the Yichang station were used for flood frequency analysis, in which a time varying model was constructed to account for non-stationarity. The generalized extreme value (GEV) distribution was adopted to fit the AMF series, and the Generalized Additive Models for Location, Scale and Shape (GAMLSS) framework was applied for parameter estimation. The non-stationary return period and risk of failure were calculated and compared for flood risk assessment between stationary and non-stationary models. The results demonstrated that the flow regime at the Yichang station has changed over time and a decreasing trend was detected in the AMF series. The design flood peak given a return period decreased in the non-stationary model, and the risk of failure is also smaller given a design life, which indicated a safer flood condition in the future compared with the stationary model. The conclusions in this study may contribute to long-term decision making in the Yangtze River basin under non-stationary conditions.https://doi.org/10.1051/e3sconf/20197903022
collection DOAJ
language English
format Article
sources DOAJ
author Jiang Shangwen
Kang Ling
spellingShingle Jiang Shangwen
Kang Ling
Flood frequency analysis for annual maximum streamflow using a non-stationary GEV model
E3S Web of Conferences
author_facet Jiang Shangwen
Kang Ling
author_sort Jiang Shangwen
title Flood frequency analysis for annual maximum streamflow using a non-stationary GEV model
title_short Flood frequency analysis for annual maximum streamflow using a non-stationary GEV model
title_full Flood frequency analysis for annual maximum streamflow using a non-stationary GEV model
title_fullStr Flood frequency analysis for annual maximum streamflow using a non-stationary GEV model
title_full_unstemmed Flood frequency analysis for annual maximum streamflow using a non-stationary GEV model
title_sort flood frequency analysis for annual maximum streamflow using a non-stationary gev model
publisher EDP Sciences
series E3S Web of Conferences
issn 2267-1242
publishDate 2019-01-01
description Under changing environment, the streamflow series in the Yangtze River have undergone great changes and it has raised widespread concerns. In this study, the annual maximum flow (AMF) series at the Yichang station were used for flood frequency analysis, in which a time varying model was constructed to account for non-stationarity. The generalized extreme value (GEV) distribution was adopted to fit the AMF series, and the Generalized Additive Models for Location, Scale and Shape (GAMLSS) framework was applied for parameter estimation. The non-stationary return period and risk of failure were calculated and compared for flood risk assessment between stationary and non-stationary models. The results demonstrated that the flow regime at the Yichang station has changed over time and a decreasing trend was detected in the AMF series. The design flood peak given a return period decreased in the non-stationary model, and the risk of failure is also smaller given a design life, which indicated a safer flood condition in the future compared with the stationary model. The conclusions in this study may contribute to long-term decision making in the Yangtze River basin under non-stationary conditions.
url https://doi.org/10.1051/e3sconf/20197903022
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AT kangling floodfrequencyanalysisforannualmaximumstreamflowusinganonstationarygevmodel
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