Drought Analysis Using Copulas

Chen, Lu; Singh, Vijay P.; Guo, Shenglian; Mishra, Ashok Kumar; Guo, Jing

doi:10.1061/(asce)he.1943-5584.0000697

Cited by 135 publications

(33 citation statements)

References 45 publications

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“…Given that the drought characteristics may have different marginal distribution functions, the most suitable tool for achieving the joint behaviour of the drought characteristics is multivariate copula functions. Copula functions as a multivariate analysis method are widely used to analyse the functional structure of drought characteristics (Mirakbari et al, 2010;Chen et al, 2013;Dodangeh et al, 2017;Hao et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Meteorological drought analysis using copula theory and drought indicators under climate change scenarios (RCP)

Mesbahzadeh

Mirakbari

Saravi

et al. 2019

Meteorological Applications

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The study was carried out to assess meteorological drought on the basis of the standardized precipitation index (SPI) and standardized precipitation evapotranspiration index (SPEI) evaluated in future climate scenarios. Yazd province, located in an arid region in the centre of Iran, was chosen for analysis. The study area has just one synoptic station with a long‐term record (56 years). The impact of climate change on future drought was examined by using the CanESM2 of the CMIP5 model under three scenarios, that is, representative concentration pathways RCP2.6, RCP4.5 and RCP8.5. Given that a drought is defined by several dependent variables, the evaluation of this phenomenon should be based on a multivariate analysis. For this purpose, two main characteristics of drought (severity and duration) were extracted by run theory in a past (1961–2016) and future (2017–2100) period based on the SPI and SPEI, and studied using copula theory. Three functions, that is, Frank, Gaussian and Gumbel copula, were selected to fit with drought severity and duration. The results of the bivariate analysis using copula showed that, according to both indicators, the study area will experience droughts with greater severity and duration in future as compared with the historical period, and the drought represented by the SPEI is more severe than that associated with the SPI. Also, drought simulated using the RCP8.5 scenario was more severe than when using the other two scenarios. Finally, droughts with a longer return period will become more frequent in future.

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Section: Introductionmentioning

confidence: 99%

Meteorological drought analysis using copula theory and drought indicators under climate change scenarios (RCP)

Mesbahzadeh

Mirakbari

Saravi

et al. 2019

Meteorological Applications

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“…Copulas are often used for bivariate frequency analysis of hydrological extremes including droughts (Shiau, ; Chen et al , ; Masud et al , ; Xu et al , ; Hao et al , ). For an n ‐dimensional random vector X = ( x 1 , x 2 , x 3 , …, x n ) with continuous marginal CDFs F 1 ( x 1 ), F 2 ( x 2 ), F 3 ( x 3 ), …, F n ( x n ) according to Sklar's theorem (Sklar, ), the multivariate CDF of X can be expressed as:

H ((),,,,, x_{1}, x_{2}, x_{3}, \dots, x_{n}) = C ({},,,,, F_{1} ((), x_{1}), F_{2} ((), x_{2}), F_{3} ((), x_{3}), \dots, F_{n} ((), x_{n}))

where C is the copula function that represents dependences between random variables and marginal cumulative distributions.…”

Section: Methodsmentioning

confidence: 99%

“…Copulas are often used for bivariate frequency analysis of hydrological extremes including droughts (Shiau, 2006;Chen et al, 2012;Masud et al, 2015;Xu et al, 2015b;Hao et al, 2017). For an n-dimensional random vector X = (x 1 , x 2 , x 3 , …, x n ) with continuous marginal CDFs F 1 (x 1 ), F 2 (x 2 ), F 3 (x 3 ), …, F n (x n ) according to Sklar's theorem (Sklar, 1959), the multivariate CDF of X can be expressed as:…”

Section: Marginal and Joint Probability Distributionsmentioning

confidence: 99%

Characterization of meteorological droughts across South Australia

Rashid

Beecham

2019

Meteorological Applications

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An understanding of drought characteristics is vital for sustainable management of water resources. This is particularly important for dry regions such as Australia where large‐scale water supply infrastructure has to be designed to withstand multiyear variability in rainfall anomalies. This study considers the Standardized Precipitation Index (SPI) at time scales of 3, 6, 12 and 24 months to assess both short‐term and long‐term droughts. Trends in the SPI at 46 selected rainfall stations across South Australia (SA) are investigated. Furthermore, various drought parameters such as frequency, severity and duration are estimated, and their spatial distributions are presented. Drought risks are estimated in terms of copula based bivariate joint return periods and compared with traditional univariate return periods. Finally, the dominant climatic modes of sustained drought and wet anomalies are identified using correlation analysis. Results show that 23 out of the 46 selected stations had a significantly negative trend for the 24 month SPI whereas the count is four for the 3 month SPI. This reveals that droughts have intensified over 1960–2010 and this intensification is more prominent for long‐term drought. Strong spatial variability is evident in drought parameters across SA. The results also highlight the suitability of a bivariate joint return period over a univariate return period for drought risk assessment because drought is a multi‐faceted phenomenon. Thus, drought risk estimated for the univariate return period was found to be different from the drought risk estimated by the bivariate return period. The dominant climatic modes identified in this study are Niño3.4 and the dipole mode index (DMI), which are able to represent the SPI across SA at the selected time scales. The outcomes of this study are useful for water managers and policy makers involved in sustainable water resource management.

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“…The dependency of the univariate marginal distribution can be calculated from Copula. Many researchers in hydrology and climatology are now widely applying copula in calculated complex hydrological events such as storms, droughts, and floods [1][2][3][4][5][6]. These research events usually have several random variables, where the variables are generally not independent.…”

Section: Introductionmentioning

confidence: 99%

Bivariate copula in Johor rainfall data

Yee

Suhaila²,

Yusof³

et al. 2016

AIP Conference Proceedings

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Abstract. Copula is a probability distribution that allows a joint distribution function build from different univariate marginal distribution function. The climate in Malaysia is very humid, which cause the rainfall data is usually skewed. Gumbel, Clayton and skew t copula are distributions that good in analyze data that is extreme. Five rain gauge stations in Johor will be used in this study. The most suitable copula function that best suit the bivariate relation among the five stations will be studied. The Akaike information criterion and Bayesian information criterion will be the used as the moderators to decide the best suit copula function. Gumbel copula is the best suit copula function among the five rain gauge stations.

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Drought Analysis Using Copulas

Cited by 135 publications

References 45 publications

Meteorological drought analysis using copula theory and drought indicators under climate change scenarios (RCP)

Meteorological drought analysis using copula theory and drought indicators under climate change scenarios (RCP)

Characterization of meteorological droughts across South Australia

Bivariate copula in Johor rainfall data

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