Conditional models for spatial extremes

Extreme environmental events endanger human life and cause serious damage to property and infrastructure. For example, Storm Desmond (2015) caused approximately £500m of damage in Lancashire and Cumbria, UK from high winds and flooding, while Storm Britta (2006) damaged shipping vessels and offshore structures in the southern North Sea, and led to coastal flooding. Estimating the probability of the occurrence of such events is key in designing structures and infrastructure that are able to withstand their impacts. Due to the rarity of these events, extreme value theory techniques are used for inference. This thesis focusses on developing novel spatial extreme value methods motivated by applications to significant wave height in the North Sea and north Atlantic, and extreme precipitation for the Netherlands. We develop methodology for analysing the dependence structure of significant wave height by utilising spatial conditional extreme value methods. Since the dependence structure of extremes between locations is likely to be complicated, with contributing factors including distance and covariates, we model dependence flexibly; otherwise, the incorrect assumption on the dependence between sites may lead to inaccurate estimation of the probabilities of spatial extreme events occurring. Existing methods for spatial extremes typically assume a particular form of extremal dependence termed asymptotic dependence, and often have intractable forms for describing the dependence of joint events over large numbers of locations. The model developed here overcomes these deficiencies. Moreover, the estimation of joint probabilities across sites under both asymptotic independence and asymptotic dependence, the two limiting extremal dependence classes, is possible with our model; this is not the case with other methods. We propose a method for the estimation of marginal extreme precipitation quantiles, utilising a Bayesian spatio-temporal hierarchical model. Our model parameters incorporate an autoregressive prior distribution, and use spatial interpolation to pool information on model parameters across neighbouring sites

On spatial conditional extremes for ocean storm severity

We describe a model for the conditional dependence of a spatial process measured at one or more remote locations given extreme values of the process at a conditioning location, motivated by the conditional extremes methodology of Heffernan and Tawn. Compared to alternative descriptions in terms of max‐stable spatial processes, the model is advantageous because it is conceptually straightforward and admits different forms of extremal dependence (including asymptotic dependence and asymptotic independence). We use the model within a Bayesian framework to estimate the extremal dependence of ocean storm severity (quantified using significant wave height, HS) for locations on spatial transects with approximate east–west (E‐W) and north–south (N‐S) orientations in the northern North Sea (NNS) and central North Sea (CNS). For HS on the standard Laplace marginal scale, the conditional extremes “linear slope” parameter α decays approximately exponentially with distance for all transects. Furthermore, the decay of mean dependence with distance is found to be faster in CNS than NNS. The persistence of mean dependence is greatest for the E‐W transect in NNS, potentially because this transect is approximately aligned with the direction of propagation of the most severe storms in the region

A marginal modelling approach for predicting wildfire extremes across the contiguous United States

This paper details a methodology proposed for the EVA 2021 conference data challenge. The aim of this challenge was to predict the number and size of wildfires over the contiguous US between 1993 and 2015, with more importance placed on extreme events. In the data set provided, over 14\% of both wildfire count and burnt area observations are missing; the objective of the data challenge was to estimate a range of marginal probabilities from the distribution functions of these missing observations. To enable this prediction, we make the assumption that the marginal distribution of a missing observation can be informed using non-missing data from neighbouring locations. In our method, we select spatial neighbourhoods for each missing observation and fit marginal models to non-missing observations in these regions. For the wildfire counts, we assume the compiled data sets follow a zero-inflated negative binomial distribution, while for burnt area values, we model the bulk and tail of each compiled data set using non-parametric and parametric techniques, respectively. Cross validation is used to select tuning parameters, and the resulting predictions are shown to significantly outperform the benchmark method proposed in the challenge outline. We conclude with a discussion of our modelling framework, and evaluate ways in which it could be extended.Comment: 13 pages, 5 figure

Basin-wide spatial conditional extremes for severe ocean storms

Physical considerations and previous studies suggest that extremal dependence between ocean storm severity at two locations exhibits near asymptotic dependence at short inter-location distances, leading to asymptotic independence and perfect independence with increasing distance. We present a spatial conditional extremes (SCE) model for storm severity, characterising extremal spatial dependence of severe storms by distance and direction. The model is an extension of Shooter et al. (2019) and Wadsworth and Tawn (2019), incorporating piecewise linear representations for SCE model parameters with distance and direction; model variants including parametric representations of some SCE model parameters are also considered. The SCE residual process is assumed to follow the delta-Laplace form marginally, with distance-dependent parameter. Residual dependence of remote locations given conditioning location is characterised by a conditional Gaussian covariance dependent on the distances between remote locations, and distances of remote locations to the conditioning location. We apply the model using Bayesian inference to estimates extremal spatial dependence of storm peak signicant wave height on a neighbourhood of 150 locations covering over 200,000 km2 in the North Sea

Modelling spatial extreme events with environmental applications

Spatial extreme value analysis has been an area of rapid growth in the last decade. The focus has been on modelling the spatial componentwise maxima by max-stable processes. Here, we will explain the limitations of these modelling approaches and show how spatial models can be developed that overcome these deficiencies by exploiting the flexible conditional multivariate extremes models of Heffernan and Tawn (2004). We illustrate the benefits of these new spatial models through applications to North Sea wave analysis and to widespread UK river flood risk analysis

Product Family Design Knowledge Representation, Aggregation, Reuse, and Analysis

A flexible information model for systematic development and deployment of product families during all phases of the product realization process is crucial for product-oriented organizations. In current practice, information captured while designing products in a family is often incomplete, unstructured, and is mostly proprietary in nature, making it difficult to index, search, refine, reuse, distribute, browse, aggregate, and analyze knowledge across heterogeneous organizational information systems. To this end, we propose a flexible knowledge management framework to capture, reorganize, and convert both linguistic and parametric product family design information into a unified network, which is called a networked bill of material (NBOM) using formal concept analysis (FCA); encode the NBOM as a cyclic, labeled graph using the Web Ontology Language (OWL) that designers can use to explore, search, and aggregate design information across different phases of product design as well as across multiple products in a product family; and analyze the set of products in a product family based on both linguistic and parametric information. As part of the knowledge management framework, a PostgreSQL database schema has been formulated to serve as a central design repository of product design knowledge, capable of housing the instances of the NBOM. Ontologies encoding the NBOM are utilized as a metalayer in the database schema to connect the design artifacts as part of a graph structure. Representing product families by preconceived common ontologies shows promise in promoting component sharing, and assisting designers search, explore, and analyze linguistic and parametric product family design information. An example involving a family of seven one-time-use cameras with different functions that satisfy a variety of customer needs is presented to demonstrate the implementation of the proposed framework

A Bayesian spatio-temporal model for precipitation extremes - STOR team contribution to the EVA2017 challenge

This paper concerns our approach to the EVA2017 challenge, the aim of which was to predict extreme precipitation quantiles across several sites in the Netherlands. Our approach uses a Bayesian hierarchical structure, which combines Gamma and generalised Pareto distributions. We impose a spatio-temporal structure in the model parameters via an autoregressive prior. Estimates are obtained using Markov chain Monte Carlo techniques and spatial interpolation. This approach has been successful in the context of the challenge, providing reasonable improvements over the benchmark

The Influence of Manga on the Graphic Novel

This material has been published in The Cambridge History of the Graphic Novel edited by Jan Baetens, Hugo Frey, Stephen E. Tabachnick. This version is free to view and download for personal use only. Not for re-distribution, re-sale or use in derivative works. © Cambridge University PressProviding a range of cogent examples, this chapter describes the influences of the Manga genre of comics strip on the Graphic Novel genre, over the last 35 years, considering the functions of domestication, foreignisation and transmedia on readers, markets and forms

Augmenting a Design Repository to Facilitate Product Family Planning

A Design Repository has been created in an effort to archive existing products and the components in each product. With this function-based archiving system, designers can retrieve design information on existing products to assist in a new design project. The use of product families has emerged as an approach to exploit commonality for more efficient product development. However, the Design Repository does not contain explicit design information on platforms and modules. This paper describes information for the design of a platform and proposes a new data structure that organizes the information for augmenting the Design Repository. An information flow model for the development of a single product is modified to describe the flow of information needed for product platform design. The information flow model and associated data structure has been shown to be effective in representing three common product families: the Black & Decker Firestorm tool set, Kodak single-use cameras, and the IceDozer family of ice scrapers. With this data structure implemented into the existing repository, designers can find useful information on how to create different products based on the a common platform