A data-driven exploratory study of extreme events based on joint probability analysis

基于联合概率分析的数据驱动的极端事件探索性研究

• 批准号: NE/F001118/1
• 负责人: Cecilia Svensson
• 金额: $ 27.5万
• 依托单位: Lancaster University
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2007
• 资助国家: 英国
• 起止时间: 2007 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=NE%2FF001118%2F1
• 关键词: data; driven; exploratory; study; extreme

The aim of this project is to examine records of past moderate and extreme flood events, and of associated environmental variables related to the causes of flooding, and to undertake an exploratory data analysis of these. This would avoid relying immediately on the assumptions built into existing methodology and provide an independent check on these. It would also allow the construction of a statistical methodology tailored both to the observed properties of the datasets and to the estimation of the relevant properties of extreme events that need to be extracted from the data. Joint probability analysis would be one of the main statistical approaches being used. As well as providing useful insight into the occurrence of flooding, this has the potential to lead to more statistically efficient estimation of floods. Further insights into flooding problems will be sought by directly considering the seasonality of flood events in all the analyses. The datasets available can realistically be expected to provide good estimates of floods with return periods of 10-20 years, but the statistical models used can be employed to extrapolate to return periods of 50, 100 or even 1000 years. For such extrapolation the uncertainties inherent in this estimation are likely to be large and an important aim of the project will be to provide a useful assessment of this uncertainty. Hourly datasets already held at CEH will be used for the project

该项目的目的是检查过去中度和极端洪水事件的记录，以及与洪水原因有关的相关环境变量，并对这些进行探索性数据分析。这将避免立即依赖现有方法中的假设，并对这些假设进行独立检查。它还将有助于建立一种统计方法，既适合于数据集的观测特性，又适合于估计需要从数据中提取的极端事件的相关特性。联合概率分析将是所使用的主要统计方法之一。除了对洪水的发生提供有用的见解外，这有可能导致对洪水进行更有效的统计估计。洪水问题的进一步见解将寻求直接考虑洪水事件的季节性在所有的分析。实际上，可以预期现有的数据集能够提供重现期为10-20年的洪水的良好估计，但所使用的统计模型可用于推断重现期为50年、100年甚至1 000年。就这种外推而言，这一估计所固有的不确定性可能很大，本项目的一个重要目标是对这种不确定性进行有用的评估。CEH已经拥有的每小时数据集将用于该项目

项目成果

Threshold models for river flow extremes

• DOI: 10.1002/env.2138
• 发表时间: 2012-06
• 期刊: Environmetrics
• 影响因子: 1.7
• 作者: O. Grigg;J. Tawn