Catastrophe loss modelling of storm-surge flood risk in eastern England

浪涌 风暴 洪水(心理学) 气候变化 地理
作者
Robert M. Wood,Michael Drayton,Agnete Berger,Paul J. Burgess,Timothy F. Wright
出处
期刊:Philosophical Transactions of the Royal Society A [The Royal Society]
卷期号:363 (1831): 1407-1422 被引量:40
标识
DOI:10.1098/rsta.2005.1575
摘要

Probabilistic catastrophe loss modelling techniques, comprising a large stochastic set of potential storm-surge flood events, each assigned an annual rate of occurrence, have been employed for quantifying risk in the coastal flood plain of eastern England. Based on the tracks of the causative extratropical cyclones, historical storm-surge events are categorized into three classes, with distinct windfields and surge geographies. Extreme combinations of ‘tide with surge’ are then generated for an extreme value distribution developed for each class. Fragility curves are used to determine the probability and magnitude of breaching relative to water levels and wave action for each section of sea defence. Based on the time-history of water levels in the surge, and the simulated configuration of breaching, flow is time-stepped through the defences and propagated into the flood plain using a 50 m horizontal-resolution digital elevation model. Based on the values and locations of the building stock in the flood plain, losses are calculated using vulnerability functions linking flood depth and flood velocity to measures of property loss. The outputs from this model for a UK insurance industry portfolio include ‘loss exceedence probabilities’ as well as ‘average annualized losses’, which can be employed for calculating coastal flood risk premiums in each postcode.

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
小蘑菇应助安静的兔子采纳,获得10
刚刚
千树海发布了新的文献求助10
刚刚
Owen应助Maestro_S采纳,获得10
1秒前
量子星尘发布了新的文献求助10
2秒前
huohuo完成签到,获得积分10
2秒前
2秒前
2秒前
velpro发布了新的文献求助10
2秒前
Jasper应助cmh采纳,获得10
3秒前
AN完成签到,获得积分0
4秒前
zhan完成签到,获得积分10
4秒前
4秒前
4秒前
4秒前
cao完成签到 ,获得积分10
4秒前
Wangyinan完成签到,获得积分10
4秒前
小咸鱼完成签到,获得积分10
5秒前
大模型应助carbonhan采纳,获得10
5秒前
NicotineZen完成签到,获得积分10
5秒前
MI完成签到,获得积分10
5秒前
旺仔发布了新的文献求助10
6秒前
6秒前
ssch197完成签到 ,获得积分10
6秒前
追风筝的少女完成签到 ,获得积分10
6秒前
科研努力版完成签到 ,获得积分10
7秒前
7秒前
biubiu完成签到,获得积分10
7秒前
DDDD发布了新的文献求助10
8秒前
8秒前
花间一壶酒完成签到,获得积分10
9秒前
共享精神应助Cody采纳,获得10
9秒前
852应助小宋同学采纳,获得10
9秒前
谦让晓晓发布了新的文献求助10
9秒前
9秒前
传奇3应助八荒来犬采纳,获得10
9秒前
www发布了新的文献求助10
9秒前
白嘉乐完成签到,获得积分10
10秒前
10秒前
量子星尘发布了新的文献求助10
10秒前
胡萝卜z完成签到 ,获得积分10
11秒前
高分求助中
(应助此贴封号)【重要!!请各用户(尤其是新用户)详细阅读】【科研通的精品贴汇总】 10000
The Cambridge History of China: Volume 4, Sui and T'ang China, 589–906 AD, Part Two 1000
The Composition and Relative Chronology of Dynasties 16 and 17 in Egypt 1000
Russian Foreign Policy: Change and Continuity 800
Real World Research, 5th Edition 800
Qualitative Data Analysis with NVivo By Jenine Beekhuyzen, Pat Bazeley · 2024 800
Superabsorbent Polymers 700
热门求助领域 (近24小时)
化学 材料科学 生物 医学 工程类 计算机科学 有机化学 物理 生物化学 纳米技术 复合材料 内科学 化学工程 人工智能 催化作用 遗传学 数学 基因 量子力学 物理化学
热门帖子
关注 科研通微信公众号,转发送积分 5708501
求助须知:如何正确求助?哪些是违规求助? 5188470
关于积分的说明 15254044
捐赠科研通 4861497
什么是DOI,文献DOI怎么找? 2609497
邀请新用户注册赠送积分活动 1560013
关于科研通互助平台的介绍 1517781