SIMULATING FLOOD EVENT SETS USING EXTREMAL PRINCIPAL COMPONENTS

• 成果类型: Article
• 署名作者: Rohrbeck, Christian; Cooley, Daniel
• 署名单位: University of Bath; Colorado State University System; Colorado State University Fort Collins
• 刊物名称: ANNALS OF APPLIED STATISTICS
• ISSN/ISSBN: 1932-6157
• DOI: 10.1214/22-AOAS1672
• 发表日期: 2023
• 页码: 1333-1352
• 关键词: models; generation; dependence

摘要

Hazard event sets, a collection of synthetic extreme events over a given period, are important for catastrophe modelling. This paper addresses the is-sue of generating event sets of extreme river flow for northern England and southern Scotland, a region which has been particularly affected by severe flooding over the past 20 years. We start by analysing historical extreme river flow across 45 gauges, using methods from extreme value analysis, includ-ing the concept of extremal principal components. Our analysis reveals in-teresting connections between the extremal dependence structure and the re-gion's topography/climate. We then introduce a framework which is based on modelling the distribution of the extremal principal components in order to generate synthetic events of extreme river flow. The generative framework is dimension-reducing in that it distinctly handles the principal components based on their contribution to describing the nature of extreme river flow across the study region. We also detail a data-driven approach to select the optimal dimension. Synthetic flood events are subsequently generated effi-ciently by sampling from the fitted distribution. Our results indicate good agreement between the observed and simulated extreme river flow dynam-ics and, therefore, illustrate the usefulness of our approach to practitioners. For the considered application, we also find that our approach outperforms existing statistical approaches for generating hazard event sets.