Preprints
https://doi.org/10.5194/hess-2019-358
https://doi.org/10.5194/hess-2019-358

  14 Oct 2019

14 Oct 2019

Review status: this preprint was under review for the journal HESS but the revision was not accepted.

Time-varying copula and design life level-based nonstationary risk analysis of extreme rainfall events

Pengcheng Xu1, Dong Wang2, Vijay P. Singh3, Yuankun Wang2, Jichun Wu2, Huayu Lu1, Lachun Wang1, Jiufu Liu4, and Jianyun Zhang4 Pengcheng Xu et al.
  • 1School of Geographic and Oceanographic Science, Nanjing University, Nanjing, P.R. China
  • 2Key Laboratory of Surficial Geochemistry, Ministry of Education, Department of Hydrosciences, School of Earth Sciences and Engineering, State Key Laboratory of Pollution Control and Resource Reuse, Nanjing University, Nanjing, P.R. China
  • 3Department of Biological and Agricultural Engineering, Zachry Department of Civil Engineering, Texas A&M University, College Station, TX77843, USA; and National Water Center, UAE University, Al Ain, UAE
  • 4Nanjing Hydraulic Research Institute, Nanjing, P.R. China

Abstract. Due to global climate change and urbanization, more attention has been paid to decipher the nonstationary multivariate risk analysis from the perspective of probability distribution establishment. Because of the climate change, the exceedance probability belonging to a certain extreme rainfall event would not be time invariant any more, which impedes the widely-used return period method for the usual hydrological and hydraulic engineering practice, hence calling for a time dependent method. In this study, a multivariate nonstationary risk analysis of annual extreme rainfall events, extracted from daily precipitation data observed at six meteorological stations in Haihe River basin, China, was done in three phases: (1) Several statistical tests, such as Ljung-Box test, and univariate and multivariate Mann-Kendall and Pettist tests were applied to both the marginal distributions and the dependence structures to decipher different forms of nonstationarity; (2) Time-dependent Archimedean and elliptical copulas combined with the Generalized Extreme Value (GEV) distribution were adopted to model the distribution structure from marginal and dependence angles; (3) A design life level-based (DLL-based) risk analysis associated with Kendall's joint return period (JRPken)and AND's joint return period (JRPand) methods was done to compare stationary and nonstationary models. Results showed DLL-based risk analysis through the JRPken method exhibited more sensitivity to the nonstationarity of marginal and bivariate distribution models than that through the JRPand method.

Pengcheng Xu et al.

 
Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
Printer-friendly Version - Printer-friendly version Supplement - Supplement
 
Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
Printer-friendly Version - Printer-friendly version Supplement - Supplement

Pengcheng Xu et al.

Pengcheng Xu et al.

Viewed

Total article views: 730 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
530 184 16 730 26 30
  • HTML: 530
  • PDF: 184
  • XML: 16
  • Total: 730
  • BibTeX: 26
  • EndNote: 30
Views and downloads (calculated since 14 Oct 2019)
Cumulative views and downloads (calculated since 14 Oct 2019)

Viewed (geographical distribution)

Total article views: 632 (including HTML, PDF, and XML) Thereof 630 with geography defined and 2 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 24 Jul 2021
Download
Short summary
In this study, a multivariate nonstationary risk analysis of annual extreme rainfall events, extracted from daily precipitation data observed at six meteorological stations in Haihe River basin, China, was done in three phases: (1) Several statistical tests, were applied to both the marginal distributions and the dependence structures to decipher different forms of nonstationarity; (2) Time-dependent copulas were adopted to model the distribution structure.