Copula-based probabilistic characterization of precipitation extremes over North Sikkim Himalaya

Goswami, Uttam; Hazra, Budhaditya; Goyal, Manish Kumar

doi:10.1016/j.atmosres.2018.05.019

Cited by 57 publications

(27 citation statements)

References 46 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The Gumbel copula demonstrates asymmetric tail associations. Bivariate copula analysis was also used in forecasting the co-occurrence of extreme events (flood or drought) over the North Sikkim Himalayas using spatial datasets (Goswami et al 2018).…”

Section: Discussionmentioning

confidence: 99%

“…A second reason why we may expect understanding of complex dependence structures to benefit ecological research is that prior work has demonstrated, using copulas, complex stucture in the spatial dependence of environmental variables (Serinaldi 2008; Li et al 2013; Goswami et al 2018; She & Xia 2018). One may expect an environmental variable measured through time in two locations to show strong tail associations between the locations if intense meteorological events are also widespread in their effects, as seems frequently to be the case: extreme values are associated with intense events, so happen in both places at the same time, whereas moderate values of the environmental variable may be associated with local phenomena, which differ between the locations.…”

Section: Introductionmentioning

confidence: 99%

“…2, which can serve as a post hoc guide to the paper. Copula approaches have been used to great effect in neuroscience (Onken et al 2009), bioinformatics (Kim et al 2008), medical research (Emura & Chen 2016), direct study of environmental variables (Serinaldi 2008; Li et al 2013; Goswami et al 2018; She & Xia 2018), and finance (Li 2000), and have also been used very effectively, but rarely so far, in ecology (Valpine et al 2014; Anderson et al 2018; Popovic et al 2018). We argue that benefits of wider usage of complete copula descriptions of dependence in ecology will be substantial.…”

Section: Introductionmentioning

confidence: 99%

“…A second reason we may expect a copula approach to benefit ecological research is that prior work has demonstrated important copula stucture in the spatial dependence of environmental variables (Serinaldi 2008; Li et al 2013; Goswami et al 2018; She & Xia 2018). One may expect copula structure in environmental variables to take the form of strong tail dependence if intense meteorological events are also widespread in their effects, as seems frequently to be the case.…”

Section: Introductionmentioning

confidence: 99%

“…Copula approaches have been used to great effect in neuroscience (Onken et al 2009), bioinformatics (Kim et al 2008), medical research (Emura & Chen 2016), direct study of environmental variables (Serinaldi 2008; Li et al . 2013; Goswami et al . 2018; She & Xia 2018), and finance (Li 2000).…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Copulas and their potential for ecology

Ghosh

Sheppard

Holder

et al. 2019

Preprint

View full text Add to dashboard Cite

AbstractAll branches of ecology study relationships among and between environmental and biological variables. However, standard approaches to studying such relationships, based on correlation and regression, provide only a small slice of the complex information contained in the relationships. Other statistical approaches exist that provide a complete description of relationships between variables, based on the concept of the copula; they are applied in finance, neuroscience and other fields, but rarely in ecology. We here explore the concepts that underpin copulas and examine the potential for those concepts to improve our understanding of ecology. We find that informative copula structure in dependencies between variables is common across all the environmental, species-trait, phenological, population, community, and ecosystem functioning datasets we considered. Many datasets exhibited asymmetric tail associations, whereby two variables were more strongly related in their left compared to right tails, or vice versa. We describe mechanisms by which observed copula structure and asymmetric tail associations can arise in ecological data, including a Moran-like effect whereby dependence structures between environmental variables are inherited by ecological variables; and asymmetric or nonlinear influences of environments on ecological variables, such as under Liebig’s law of the minimum. We also describe consequences of copula structure for ecological phenomena, including impacts on extinction risk, Taylor’s law, and the stability through time of ecosystem services. By documenting the importance of a complete description of dependence between variables, advancing conceptual frameworks, and demonstrating a powerful approach, we aim to encourage widespread use of copulas in ecology, which we believe can benefit the discipline.

show abstract

Section: Discussionmentioning

confidence: 99%