A Bayesian hierarchical model for spatial extremes with multiple durations

Wang, Yixin; So, Mike K. P.

doi:10.1016/j.csda.2015.09.001

Cited by 12 publications

(10 citation statements)

References 45 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Consequently, marginal parameters and related quantities such as return levels vary smoothly over space. Such models have been used to model hurricane wind speeds (Casson & Coles, ), ozone levels (Gilleland et al, ), precipitation (Cooley et al, ; Cooley & Sain, ; Jonathan et al, ; Sang & Gelfand, ; Wang & So, ), and wildfire (Mendes, de Zea Bermudez, Pereira, Turkman, & Vasconcelos, ; Turkman et al, ).…”

Section: Random Effects Models For Nonstationaritymentioning

confidence: 99%

“…In the statistical literature, random effects models for spatiotemporal environmental data have been implemented in a Bayesian hierarchical modelling framework (Casson & Coles, ; Cooley, Nychka, & Naveau, ; Jonathan, Favre, Blisle, & Angers, ; Turkman, Turkman, & Pereira, ; Wang & So, ). The random effects, also known as latent processes, are given a hyperprior that generates a spatiotemporal structure in the parameters of the marginal (site‐specific) model.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Nonstationarity in peaks‐over‐threshold river flows: A regional random effects model

Eastoe

2019

Environmetrics

View full text Add to dashboard Cite

Under the influence of local‐ and large‐scale climatological processes, extreme river flow events often show long‐term trends, seasonality, interyear variability, and other characteristics of temporal nonstationarity. Properly accounting for this nonstationarity is vital for making accurate predictions of future floods. In this paper, a regional model based on the generalised Pareto distribution is developed for peaks‐over‐threshold river flow data sets when the event sizes are nonstationary. If observations are nonstationary and covariates are available, then extreme‐value (semi)parametric regression models may be used. Unfortunately, the necessary covariates are rarely observed, and if they are, it is often not clear which process, or combination of processes, to include in the model. Within the statistical literature, latent process (or random effects) models are often used in such scenarios. We develop a regional time‐varying random effects model that allows identification of temporal nonstationarity in event sizes by pooling information across all sites in a spatially homogeneous region. The proposed model, which is an instance of a Bayesian hierarchical model, can be used to predict both unconditional extreme events such as the m‐year maximum and extreme events that condition on being in a given year. The estimated random effects may also tell us about likely candidates for the climatological processes that cause nonstationarity in the flood process. The model is applied to UK flood data from 817 stations spread across 81 hydrometric regions.

show abstract

Section: Random Effects Models For Nonstationaritymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Nonstationarity in peaks‐over‐threshold river flows: A regional random effects model

Eastoe

2019

Environmetrics

View full text Add to dashboard Cite

show abstract

“…Yan and Moradkhani [27] proposed a Bayesian hierarchical model for the analysis of frequent floods, which is an alternative to the traditional analysis because it captures the spatial dependence in its inner structure. Wang and So [28] proposed a Bayesian hierarchical model that addresses the problem of spatial extremes with multiple durations. e effect of these extreme durations is characterized by pooling the extremes with different durations and merging the duration into a latent spatial process structure as one of the covariates.…”

Section: Introductionmentioning

confidence: 99%

A Bayesian Hierarchical Model for the Spatial Analysis of Carbon Monoxide Pollution Extremes in Mexico City

Jiménez-Hernández

López-Cerino

Aguirre-Salado

2020

Mathematical Problems in Engineering

View full text Add to dashboard Cite

Air pollution by carbon monoxide is a serious problem that affects many cities around the world, and the theory of extreme values has played a crucial role in the study of this issue. In this paper, we proposed a Bayesian hierarchical spatial model of extreme values to evaluate the risk of extreme events of air pollution due to carbon monoxide in the metropolitan area of Mexico City. Spatial trends are modeled through of a Gaussian process for the generalized extreme value (GEV) distribution parameters, and prediction maps are produced for each of these. The results show a marginal spatial behavior for the location, scale, and shape parameters of GEV distribution, which indicate the existence of local variations that would not be possible to model using only stationary models. A return map of the maximum concentrations with a return period of one year is obtained. We found that the return levels for a one-year return period of CO concentration above 8 ppm in the Metropolitan Area of the Valley of Mexico are concentrated in the central part of this region, and the areas with the lowest estimates are distributed in the periphery. In addition, a quantile-quantile (QQ) plot between the theoretical and empirical quantiles was provided, which showed a very good fit of data to the proposed model.

show abstract

“…In this work, we use the model of Cooley and Sain (), which builds on the model of Sang and Gelfand () by additionally modeling the shape parameter using a latent spatial process. Other recent studies include those by Schliep, Cooley, Sain, and Hoeting (), Apputhurai and Stephenson (), Wang and So (), and Barlow, Rohrbeck, Sharkey, Shooter, and Simpson ().…”

Section: Introductionmentioning

confidence: 99%

A Bayesian spatial hierarchical model for extreme precipitation in Great Britain

Sharkey

Winter

2018

Environmetrics

View full text Add to dashboard Cite

Intense precipitation events are commonly known to be associated with an increased risk of flooding. As a result of the societal and infrastructural risks linked with flooding, extremes of precipitation require careful modeling. Extreme value analysis is typically used to model large precipitation events, although an independent analysis of neighboring sites can produce very different estimates of risk. In reality, one would expect neighboring locations to exhibit similar extremal behavior. A common method of inducing spatial similarity of extremal behavior is to define a spatial structure on the parameters of a generalized Pareto distribution in a Bayesian hierarchical modeling framework. These methods are often implemented under the assumption of conditional independence in time and space, with the consequence that standard errors of parameter estimates are too small. We present an approach for accounting for spatial and temporal dependence when quantifying the uncertainty in Bayesian hierarchical models when the misspecification of conditional independence has been made. We also present comparisons of performance between this class of models and alternative approaches, applied to precipitation data in Great Britain.

show abstract

A Bayesian hierarchical model for spatial extremes with multiple durations

Cited by 12 publications

References 45 publications

Nonstationarity in peaks‐over‐threshold river flows: A regional random effects model

Nonstationarity in peaks‐over‐threshold river flows: A regional random effects model

A Bayesian Hierarchical Model for the Spatial Analysis of Carbon Monoxide Pollution Extremes in Mexico City

A Bayesian spatial hierarchical model for extreme precipitation in Great Britain

Contact Info

Product

Resources

About