scholarly journals Copula-based drought severity-area-frequency curve and its uncertainty, a case study of Heihe River basin, China

2020 ◽  
Vol 51 (5) ◽  
pp. 867-881 ◽  
Author(s):  
Zhanling Li ◽  
Quanxi Shao ◽  
Qingyun Tian ◽  
Louie Zhang

Abstract Copulas are appropriate tools in drought frequency analysis. However, uncertainties originating from copulas in such frequency analysis have not received significant consideration. This study aims to develop a drought severity-areal extent-frequency (SAF) curve with copula theory and to evaluate the uncertainties in the curve. Three uncertainty sources are considered: different copula functions, copula parameter estimations, and copula input data. A case study in Heihe River basin in China is used as an example to illustrate the proposed approach. Results show that: (1) the dependence structure of drought severity and areal extent can be modeled well by Gumbel; Clayton and Frank depart the most from Gumbel in estimating drought SAF curves; (2) both copula parameter estimation and copula input data contribute to the uncertainties of SAF curves; uncertainty ranges associated with copula input data present wider than those associated with parameter estimations; (3) with the conditional probability decreasing, the differences in the curves derived from different copulas are increasing, and uncertainty ranges of the curves caused by copula parameter estimation and copula input data are also increasing. These results highlight the importance of uncertainty analysis of copula application, given that most studies in hydrology and climatology use copulas for extreme analysis.

Author(s):  
Qingyun Tian ◽  
Zhanling Li ◽  
Xueli Sun

Abstract The stationary assumption for the traditional frequency analysis of precipitation extremes has been challenged due to natural climate variability or human intervention. To overcome this challenge, this paper, taking Heihe River basin as the case study, performed the frequency analysis by developing a nonstationary GEV model for those seasonal maximum daily precipitation (SMP) time series with nonstationary characteristics by employing the GEV conditional density estimation network. In addition, the confidence intervals (CIs) of estimated return levels were also investigated by using the residual bootstrap technique. Results showed that, 7 of 12 SMP series were nonstationary. The parameters in the nonstationary model were specified as functions of time varying or correlated climate indices varying covariates. The frequency analysis showed that the return levels varied linearly or nonlinearly with covariates. Precipitation extremes with the same magnitude in the study area were found to be occurring more frequently in the future. The CIs of such return levels increased with time passing, especially those from the more complex GEV11 model, embedding a nonlinear increasing trend in model scale parameters. It implied that the increase of model complexity is likely to result in the increase of uncertainty in estimates.


2012 ◽  
Vol 20 (8) ◽  
pp. 1105-1112
Author(s):  
Juan WANG ◽  
Pu-Te WU ◽  
Yu-Bao WANG ◽  
Xi-Ning ZHAO ◽  
Jian-Feng SONG ◽  
...  

2020 ◽  
Vol 56 (8) ◽  
Author(s):  
Bin Xiong ◽  
Lihua Xiong ◽  
Shenglian Guo ◽  
Chong‐Yu Xu ◽  
Jun Xia ◽  
...  

1997 ◽  
Vol 30 (6) ◽  
pp. 1563-1568
Author(s):  
Vincent G. Ryckaert ◽  
Jan F. Van Impe
Keyword(s):  

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