利用极值主成分模拟洪水事件集
Simulating flood event sets using extremal principal components
AI总结：
本文针对英格兰北部和苏格兰南部地区，提出基于极值主成分分布建模的降维生成框架，通过数据驱动选择最优维度并高效采样生成合成洪水事件集，实验表明该方法优于现有统计方法。
摘要：
灾害事件集是给定时期内合成极端事件的集合，对巨灾建模具有重要意义。本文针对英格兰北部和苏格兰南部地区极端河流流量的灾害事件集生成问题展开研究，该地区在过去20年中遭受了严重洪水的显著影响。我们首先利用极值分析方法（包括极值主成分概念）对研究区域内45个水文测站的历史极端河流流量进行分析。分析揭示了极端依赖结构与区域地形/气候之间的有趣联系。随后，我们引入一个基于对极值主成分分布进行建模的框架，以生成极端河流流量的合成事件。该生成框架具有降维特性，根据各主成分对描述研究区域极端河流流量性质的贡献程度分别进行处理。我们还详细介绍了一种数据驱动的方法来选择最优维度。随后，通过从拟合分布中采样，可以高效地生成合成洪水事件。我们生成灾害事件集的方法易于从业者实施，结果表明观测到的极端河流流量动态与模拟结果之间具有良好的一致性。在所考虑的应用中，我们还发现该方法优于现有的生成灾害事件集的统计方法。
英文摘要：
Hazard event sets, a collection of synthetic extreme events over a given period, are important for catastrophe modelling. This paper addresses the issue 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, located within the study region, using methods from extreme value analysis, including the concept of extremal principal components. Our analysis reveals interesting connections between the extremal dependence structure and the region'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 efficiently by sampling from the fitted distribution. Our approach for generating hazard event sets can be easily implemented by practitioners and our results indicate good agreement between the observed and simulated extreme river flow dynamics. For the considered application, we also find that our approach outperforms existing statistical approaches for generating hazard event sets.
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