Characterisation of Drought Properties with Bivariate Copula Analysis

Yusof, Fadhilah; Hui–Mean, Foo; Suhaila, Jamaludin; Yusof, Zulkifli Md.

doi:10.1007/s11269-013-0402-4

Cited by 77 publications

(35 citation statements)

References 22 publications

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“…However, Yusof et al (2013) show that drought severity and duration are best fit by the three-parameter lognormal distribution and the generalized Pareto distribution, respectively. Therefore, the representation of univariate marginal distributions is not only limited to distributions mentioned in previous studies.…”

Section: Marginal Distribution For Drought Severity and Durationmentioning

confidence: 95%

Copula-based risk assessment of drought in Yunnan province, China

et al. 2014

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Yunnan is one of the provinces which had been frequently and heavily affected by drought disasters in China. Recently, large severe droughts struck Yunnan, caused considerable social, economic and ecological losses. A risk assessment of meteorological drought for Yunnan province is provided in this study. Based on the daily meteorological data of 29 stations during 1960-2010, duration and severity as two major drought characteristics, defined by the runs and the composite meteorological drought index, are abstracted from the observed drought events. Three bivariate Archimedean copulas are employed to construct the joint distributions of the drought characteristics. Based on the error analysis and tail dependence coefficient, the Gumbel-Hougaard copula is selected to analyze spatial distributions of the joint return periods of drought. The results indicate that a high risk is observed in the middle parts and the northeast parts of Yunnan province, while a relative lower drought risk is observed in the northwest of Yunnan province. The probabilistic properties can provide useful information for water resources planning and management.

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Section: Marginal Distribution For Drought Severity and Durationmentioning

confidence: 95%

Copula-based risk assessment of drought in Yunnan province, China

et al. 2014

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“…is the cumulative standard normal distribution function. The standard procedure used to compute SPI is to first fit the precipitation data to the Gamma distribution using Maximum Likelihood Estimation (MLE) method (Yusof et al 2013), and then apply Eq. 8 to obtain the SPI values.…”

Section: Standard Precipitation Indexmentioning

confidence: 99%

“…8 to obtain the SPI values. The details SPI categories and how to monitor the rainfall characteristics (severity and duration) can be found in Yusof et al (2013) and Abdul Rauf and Zeephongsekul (2014).…”

Section: Standard Precipitation Indexmentioning

confidence: 99%

Analysis of Rainfall Severity and Duration in Victoria, Australia using Non-parametric Copulas and Marginal Distributions

Rauf

Zeephongsekul

2014

Water Resour Manage

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The analysis of joint probability distributions of rainfall characteristics such as severity and duration is important in water resources management. Deriving their distributions using standard statistical techniques are often problematical due to its complexity. Standard methods usually assume that the rainfall characteristics are independent or that their marginal distributions belong to the same family of distributions. The use of copulas based methodologies can circumvent these restrictions and are therefore increasingly popular. However, the copulas and marginal distributions that are commonly used belong to specific parametric families and their adoption could lead to spurious inferences if the underlying assumptions are violated. For this reason, we recommend a nonparametric or semiparametric approach to estimate the joint distribution of rainfall characteristics. In this paper, we introduce and compare several copula-based approaches, each involving a combination of parametric or nonparametric marginal distributions conjoined by a parametric or nonparametric copula. An empirical illustration of the different approaches using rainfall data collected from six stations in the state of Victoria, Australia, demonstrated that a nonparametric approach can often give better results than a purely parametric approach.

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“…Shiau (2006) took the Wushantou watershed (Taiwan) as the example to illustrate that the joint drought duration and severity was modeled satisfactorily with the two-dimensional copulas. A similar study was carried out in Peninsular Malaysia by Yusof et al (2013). Bekrizadeh et al (2013) analyzed the joint distribution of drainage area and discharge of 22 watersheds by employing the Weighted Clayton Copula in the Western Coastal Plain of Maryland and Virginia, USA.…”

Section: Introductionmentioning

confidence: 99%

Spatial Characterization and Bivariate Frequency Analysis of Precipitation and Runoff in the Upper Huai River Basin, China

Xing

Yan

Cheng

et al. 2015

Water Resour Manage

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The variability of the relationship between precipitation and runoff is of vital importance to study the characteristics of regional water cycle and water resource management and planning. In this study, in order to explore the relationship between precipitation and runoff of the different hydrological patterns, the analysis of bivariate precipitation and runoff distributions and the simultaneous occurrence probability was analyzed by employing the Archimedean copula, based on the monthly runoff and precipitation data during 1960~2000 in the upper Huai river basin, China. The results indicated that: (I) the study region could be classified into four hydrological patterns, namely the Huai river valley zone, Huaibei plain zone, Huainan mountain zone and Huaibei mountain zone, with Xixian, Zhuanqiao, Meishan and Zhaopingtai as the maximal loading subbasin respectively. (II) There were positive dependence structure between precipitation and runoff in the study area, and the bivariate frequency distributions could be fitted best by the Gumbel-Hougaard copula. (III) The simultaneous occurrence probability of bivariate drought events was remarkable higher than that of corresponding classification of bivariate flood events; besides, the simultaneous occurrence probability of bivariate extreme events was maximum in the Huainan mountain zone, followed by the Huai river valley zone and Huaibei mountain zone, while that was minimum in the Huaibei plain zone. These results would be of essential guiding significance for water resource management and planning, flood and drought control and layout optimization of water conservancy projects in the study region.Water Resour Manage

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Characterisation of Drought Properties with Bivariate Copula Analysis

Cited by 77 publications

References 22 publications

Copula-based risk assessment of drought in Yunnan province, China

Copula-based risk assessment of drought in Yunnan province, China

Analysis of Rainfall Severity and Duration in Victoria, Australia using Non-parametric Copulas and Marginal Distributions

Spatial Characterization and Bivariate Frequency Analysis of Precipitation and Runoff in the Upper Huai River Basin, China

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