Gumbel–Hougaard Copula for Trivariate Rainfall Frequency Analysis

2007 ◽  
Vol 12 (4) ◽  
pp. 409-419 ◽  
Author(s):  
L. Zhang ◽  
Vijay P. Singh
Keyword(s):  
Frequency Analysis ◽  
Rainfall Frequency ◽  
Rainfall Frequency Analysis

Regional Rainfall Frequency Analysis for the State of Michigan

2005 ◽  
Vol 10 (6) ◽  
pp. 437-449 ◽  
Author(s):  
Christopher M. Trefry ◽  
David W. Watkins ◽  
Dennis Johnson
Keyword(s):  
Frequency Analysis ◽  
The State ◽  
Rainfall Frequency ◽  
Rainfall Frequency Analysis ◽  
Regional Rainfall

scholarly journals The Effect of Sample Size on Bivariate Rainfall Frequency Analysis of Extreme Precipitation

Proceedings ◽  
2018 ◽  
Vol 7 (1) ◽  
pp. 19 ◽  
Author(s):  
Nikoletta Stamatatou ◽  
Lampros Vasiliades ◽  
Athanasios Loukas
Keyword(s):  
Sample Size ◽  
Frequency Analysis ◽  
Extreme Precipitation ◽  
Goodness Of Fit ◽  
Return Periods ◽  
Goodness Of Fit Test ◽  
Maximum Rainfall ◽  
Rainfall Frequency ◽  
Rainfall Frequency Analysis ◽  
Pseudo Likelihood Estimation

The objective of this study is to compare univariate and joint bivariate return periods of extreme precipitation that all rely on different probability concepts in selected meteorological stations in Cyprus. Pairs of maximum rainfall depths with corresponding durations are estimated and compared using annual maximum series (AMS) for the complete period of the analysis and 30-year subsets for selected data periods. Marginal distributions of extreme precipitation are examined and used for the estimation of typical design periods. The dependence between extreme rainfall and duration is then assessed by an exploratory data analysis using K-plots and Chi-plots and the consistency of their relationship is quantified by Kendall’s correlation coefficient. Copulas from Archimedean, Elliptical, and Extreme Value families are fitted using a pseudo-likelihood estimation method, evaluated according to the corrected Akaike Information Criterion and verified using both graphical approaches and a goodness-of-fit test based on the Cramér-von Mises statistic. The selected copula functions and the corresponding conditional and joint return periods are calculated and the results are compared with the marginal univariate estimations of each variable. Results highlight the effect of sample size on univariate and bivariate rainfall frequency analysis for hydraulic engineering design practices.

scholarly journals Regional Rainfall Frequency Analysis by Multivariate Techniques

2008 ◽  
Vol 41 (5) ◽  
pp. 517-525 ◽  
Author(s):  
Woo-Sung Nam ◽  
Tae-Soon Kim ◽  
Ju-Young Shin ◽  
Jun-Haeng Heo
Keyword(s):  
Frequency Analysis ◽  
Multivariate Techniques ◽  
Rainfall Frequency ◽  
Rainfall Frequency Analysis ◽  
Regional Rainfall

scholarly journals Regional analysis of maximum rainfall using L-moment and LH-moment: A comparative case study for the northeast India

MAUSAM ◽  
2021 ◽  
Vol 68 (3) ◽  
pp. 451-462
Author(s):  
DHRUBA JYOTI BORA ◽  
MUNINDRA BORAH ◽  
ABHIJIT BHUYAN
Keyword(s):  
Frequency Analysis ◽  
Moment Method ◽  
Probability Distributions ◽  
Northeast India ◽  
Extreme Value ◽  
Generalized Extreme Value ◽  
Generalized Pareto ◽  
Rainfall Frequency ◽  
Best Fitting ◽  
Rainfall Frequency Analysis

Rainfall data of the northeast region of India has been considered for selecting best fit model for rainfall frequency analysis. The methods of L-moment has been employed for estimation of parameters five probability distributions, namely Generalized extreme value (GEV), Generalized Logistic(GLO), Pearson type 3 (PE3), 3 parameter Log normal (LN3) and Generalized Pareto (GPA) distributions. The methods of LH-moment of four orders (L1 L2, L3 & L4-moments) have also been used for estimating the parameters of three probability distributions namely Generalized extreme value (GEV), Generalized Logistic (GLO) and Generalized Pareto (GPA) distributions. PE3 distribution has been selected as the best fitting distribution using L-moment, GPA distribution using L1-moment and GLO distribution using L2, L3 & L4-moments. Relative root mean square error (RRMSE) and RBIAS are employed to compare between the results found from L-moment and LH-moment analysis. It is found that GPA distribution designated by L1-moment method is the most suitable and the best fitting distribution for rainfall frequency analysis of the northeast India. Also the L1-moment method is significantly more efficient than L-moment and other orders of LH-moment for rainfall frequency analysis of the northeast India.

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