Copula statistical models for analyzing stochastic dependencies of systemic drought risk and potential adaptation strategies

Nguyen‐Huy, Thong; Deo, Ravinesh C.; Mushtaq, Shahbaz; Kath, Jarrod; Khan, Shahjahan

doi:10.1007/s00477-019-01662-6

Cited by 28 publications

(5 citation statements)

References 55 publications

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“…Vine copulas, among other copulas, can be used to achieve the utmost flexibility in constructing the JCDF and JPDF, given in Equations ( 8) and (9), respectively. Vine copulas have been applied in recent studies across various fields, such as weather and climate risk in agriculture [40,41], hydrology and water resources [27,[42][43][44][45][46][47][48], and finance and insurance [49][50][51]. The following section provides more details on vine copulas.…”

Section: Copula Analyticalmentioning

confidence: 99%

Copula-Probabilistic Flood Risk Analysis with an Hourly Flood Monitoring Index

Chand,

Nguyen-Huy,

Deo

et al. 2024

Water

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Floods are a common natural disaster whose severity in terms of duration, water resource volume, peak, and accumulated rainfall-based damage is likely to differ significantly for different geographical regions. In this paper, we first propose a novel hourly flood index (SWRI24−hr−S) derived from normalising the existing 24-hourly water resources index (WRI24−hr−S) in the literature to monitor flood risk on an hourly scale. The proposed SWRI24−hr−S is adopted to identify a flood situation and derive its characteristics, such as the duration (D), volume (V), and peak (Q). The comprehensive result analysis establishes the practical utility of SWRI24−hr−S in identifying flood situations at seven study sites in Fiji between 2014 and 2018 and deriving their characteristics (i.e., D, V, and Q). Secondly, this study develops a vine copula-probabilistic risk analysis system that models the joint distribution of flood characteristics (i.e., D, V, and Q) to extract their joint exceedance probability for the seven study sites in Fiji, enabling probabilistic flood risk assessment. The vine copula approach, particularly suited to Fiji’s study sites, introduces a novel probabilistic framework for flood risk assessment. The results show moderate differences in the spatial patterns of joint exceedance probability of flood characteristics in different combination scenarios generated by the proposed vine copula approach. In the worst-case scenario, the probability of any flood event occurring where the flood volume, peak, and duration are likely to exceed the 95th-quantile value (representing an extreme flood event) is found to be less than 5% for all study sites. The proposed hourly flood index and the vine copula approach can be feasible and cost-effective tools for flood risk monitoring and assessment. The methodologies proposed in this study can be applied to other data-scarce regions where only rainfall data are available, offering crucial information for flood risk monitoring and assessment and for the development of effective mitigation strategies.

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Section: Copula Analyticalmentioning

confidence: 99%

Copula-Probabilistic Flood Risk Analysis with an Hourly Flood Monitoring Index

Chand,

Nguyen-Huy,

Deo

et al. 2024

Water

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“…In the previous section, the determination of the joint distribution function is an important part of the RPRPC calculation. Copula functions have the ability to connect different marginal distribution functions together (Kao and Govindaraju 2010;Thong et al 2019). However, there are many kinds of copula functions, and different copula functions have different fitting effects.…”

Section: Copula Function Optimizationmentioning

confidence: 99%

Study on the early warning and forecasting of flash floods in small watersheds based on the rainfall pattern of risk probability combination

Yuan

et al. 2021

Stoch Environ Res Risk Assess

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“…The joint distribution function is a probability function, which describes the probability that the dependent variable occurs under the combined action of multiple independent variables (Nelsen, 1997;Hotta, 2006;Hu, 2006). Since the probability of a disaster is determined by multiple inducing factors, the construction of a joint distribution function describing the probability of a disaster through inducing factors is the basis to accurately evaluate the hazard (Hochrainer-Stigler et al, 2018;Nguyen-Huy et al, 2019).…”

Section: Construction Of Joint Distributionmentioning

confidence: 99%

Exploring the Highway Icing Risk: Considering the Dynamic Dependence of Icing-Inducing Factors

Liu¹,

Tang²,

Wang³

et al. 2021

Preprint

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In terms of the dynamic dependence between icing-inducing factors, this study is to explore the risk distribution of highways when icing events occur in the study area. A joint distribution considering the dynamic correlation of inducing factors was first constructed employing the Copula theory, which then yielded the possibility of icing events. Meanwhile, hazard zones and intensities of icing were proposed under different exceeding probabilities. After finishing the vulnerability analysis of highways, the risk matrix was used to conduct the icing risk for the highway, which was then applied to the construction of the risk zoning map. The results showed that there was an upper-tail dependence between extreme precipitation and temperature in the study area in winter, which could be well captured by the Gumbel Copula function. Indeed, the constructed joint distribution can express the possibility of icing under different intensities of precipitation and temperature. Besides, the highway with the tallest vulnerability in the study area was the Hegang-Yichun line. The case application showed that during March 2020, the traffic lines with a high icing risk were distributed around Fujin, Jiamusi, Hegang, and Qitaihe cities, and the Hegang section of the Hegang-Yichun line was at the highest icing risk. The low-risk lines were concentrated in the western part of the study area. This study is of great significance for the prevention and control of ice-snow disasters on the highway in cold regions.

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Copula statistical models for analyzing stochastic dependencies of systemic drought risk and potential adaptation strategies

Cited by 28 publications

References 55 publications

Copula-Probabilistic Flood Risk Analysis with an Hourly Flood Monitoring Index

Copula-Probabilistic Flood Risk Analysis with an Hourly Flood Monitoring Index

Study on the early warning and forecasting of flash floods in small watersheds based on the rainfall pattern of risk probability combination

Exploring the Highway Icing Risk: Considering the Dynamic Dependence of Icing-Inducing Factors

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