A bivariate pareto model for drought

Nadarajah, Saralees

doi:10.1007/s00477-008-0260-4

Cited by 35 publications

(13 citation statements)

References 35 publications

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“…However, in recent years, the bivariate probabilistic approaches, which take into account the relationship between drought properties, have been applied for modelling the multidisciplinary nature of drought events (El‐Jabi et al ., ; Nadarajah, ; Song and Singh, ; Wong et al ., ; Mishra and Singh, ). Shiau () first applied copula functions to bivariate frequency analysis of drought duration and severity.…”

Section: Introduction and Reviewmentioning

confidence: 99%

Data‐based analysis of bivariate copula tail dependence for drought duration and severity

Lee

Modarres

Ouarda

2012

Hydrological Processes

134

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In recent decades, copula functions have been applied in bivariate drought duration and severity frequency analysis. Among several potential copulas, Clayton has been mostly used in drought analysis. In this research, we studied the influence of the tail shape of various copula functions (i.e. Gumbel, Frank, Clayton and Gaussian) on drought bivariate frequency analysis. The appropriateness of Clayton copula for the characterization of drought characteristics is also investigated. Drought data are extracted from standardized precipitation index time series for four stations in Canada (La Tuque and Grande Prairie) and Iran (Anzali and Zahedan). Both duration and severity data sets are positively skewed. Different marginal distributions were first fitted to drought duration and severity data. The gamma and exponential distributions were selected for drought duration and severity, respectively, according to the positive skewness and Kolmogorov–Smirnov test. The results of copula modelling show that the Clayton copula function is not an appropriate choice for the used data sets in the current study and does not give more drought risk information than an independent model for which the duration and severity dependence is not significant. The reason is that the dependence of two variables in the upper tail of Clayton copula is very weak and similar to the independent case, whereas the observed data in the transformed domain of cumulative density function show high association in the upper tail. Instead, the Frank and Gumbel copula functions show better performance than Clayton function for drought bivariate frequency analysis. Copyright © 2012 John Wiley & Sons, Ltd.

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Section: Introduction and Reviewmentioning

confidence: 99%

Data‐based analysis of bivariate copula tail dependence for drought duration and severity

Lee

Modarres

Ouarda

2012

Hydrological Processes

134

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“…Droughts are one of the normal and recurrent climatic phenomena on earth. However, recurring prolonged droughts have caused far-reaching and diverse impacts because of water deficits (Nadarajah 2009). Droughts differ from other natural hazards, such as floods, tropical cyclones, and earth quakes in several ways.…”

Section: Introductionmentioning

confidence: 99%

The survey of climatic drought trend in Iran

Abarghouei

Zarch

Dastorani

et al. 2011

Stoch Environ Res Risk Assess

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Drought is one of the most important natural hazards in Iran. Therefore, drought monitoring has become a point of concern for most of the researchers. In the present study, the changes and trend of drought was surveyed, under the current global climate changes, by non parametric Mann-Kendall statistical test for 42 synoptic stations at different places of Iran. Standardized Precipitation Index (SPI) was calculated to recognize the drought condition at different time scales (3, 6, 9, 12, 18 and 24 months' time series) for analyzing the drought trend in the recent 30 years. The obtained results have indicated a significant negative trend of drought in many parts of Iran, especially the South-East, West and South-West regions of the country. According to the results, although some parts of Iran such as North (around the Caspian Sea) and Northeast show no significant trend but in other parts of country, the severity of drought has increased during the last 30 years.

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“…Factor models have been used in the modeling of data within hydrology (Nadarajah [26,27] 2006/2009, Nadarajah and Masoom [28] 2008), storm insurance (Lescourret and Robert, [22] 2006), soil erosion in crops (Todorovic and Gani [36] 1987, Alpuim and Athayde [2] 1990), reliability (Alpuim and Athayde [2] 1990, Kotz et al [19] 2000), economy (Arnold,[3] 1983) and finance (Ferreira and Canto e Castro, [13] 2010). Let X n = (X n1 , .…”

Section: Introductionmentioning

confidence: 99%

Extremes of scale mixtures of multivariate time series

Ferreira

Феррейра

2015

Journal of Multivariate Analysis

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a b s t r a c tFactor models have large potential in the modeling of several natural and human phenomena. In this paper we consider a multivariate time series Y n , n ≥ 1, rescaled through random factors T n , n ≥ 1, extending some scale mixture models in the literature. We analyze its extremal behavior by deriving the maximum domain of attraction and the multivariate extremal index, which leads to new ways to construct multivariate extreme value distributions. The computation of the multivariate extremal index and the characterization of the tail dependence show an interesting property of these models. More precisely, however much it is the dependence within and between factors T n , n ≥ 1, the extremal index of the model is unit whenever Y n , n ≥ 1, presents cross-sectional and sequential tail independence. We illustrate with examples of thinned multivariate time series and multivariate autoregressive processes with random coefficients. An application of these latter to financial data is presented at the end.

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A bivariate pareto model for drought

Cited by 35 publications

References 35 publications

Data‐based analysis of bivariate copula tail dependence for drought duration and severity

Data‐based analysis of bivariate copula tail dependence for drought duration and severity

The survey of climatic drought trend in Iran

Extremes of scale mixtures of multivariate time series

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