DOI: 10.5194/hess-18-2305-2014
Scopus记录号: 2-s2.0-84903639598
论文题名: The impact of uncertain precipitation data on insurance loss estimates using a flood catastrophe model
作者: Sampson C ; C ; , Fewtrell T ; J ; , O'Loughlin F ; , Pappenberger F ; , Bates P ; B ; , Freer J ; E ; , Cloke H ; L
刊名: Hydrology and Earth System Sciences
ISSN: 10275606
出版年: 2014
卷: 18, 期: 6 起始页码: 2305
结束页码: 2324
语种: 英语
Scopus关键词: Disaster prevention
; Disasters
; Experiments
; Floods
; Gages
; Hazards
; Losses
; Meteorological radar
; Model structures
; Rain
; Risk assessment
; Soil moisture
; Stochastic models
; Stochastic systems
; Weather forecasting
; Climate prediction centers
; Commercial products
; Computing resource
; European centre for medium-range weather forecasts
; Insurance industry
; Satellite rainfalls
; Stochastic rainfalls
; Synthetic database
; Uncertainty analysis
; catastrophic event
; data set
; database
; decision making
; flood forecasting
; flooding
; hazard assessment
; insurance industry
; rainfall
; risk assessment
; uncertainty analysis
; vulnerability
; weather forecasting
; County Dublin
; Dublin [County Dublin]
; Ireland
; Leinster
英文摘要: Catastrophe risk models used by the insurance industry are likely subject to significant uncertainty, but due to their proprietary nature and strict licensing conditions they are not available for experimentation. In addition, even if such experiments were conducted, these would not be repeatable by other researchers because commercial confidentiality issues prevent the details of proprietary catastrophe model structures from being described in public domain documents. However, such experimentation is urgently required to improve decision making in both insurance and reinsurance markets. In this paper we therefore construct our own catastrophe risk model for flooding in Dublin, Ireland, in order to assess the impact of typical precipitation data uncertainty on loss predictions. As we consider only a city region rather than a whole territory and have access to detailed data and computing resources typically unavailable to industry modellers, our model is significantly more detailed than most commercial products. The model consists of four components, a stochastic rainfall module, a hydrological and hydraulic flood hazard module, a vulnerability module, and a financial loss module. Using these we undertake a series of simulations to test the impact of driving the stochastic event generator with four different rainfall data sets: ground gauge data, gauge-corrected rainfall radar, meteorological reanalysis data (European Centre for Medium-Range Weather Forecasts Reanalysis-Interim; ERA-Interim) and a satellite rainfall product (The Climate Prediction Center morphing method; CMORPH). Catastrophe models are unusual because they use the upper three components of the modelling chain to generate a large synthetic database of unobserved and severe loss-driving events for which estimated losses are calculated. We find the loss estimates to be more sensitive to uncertainties propagated from the driving precipitation data sets than to other uncertainties in the hazard and vulnerability modules, suggesting that the range of uncertainty within catastrophe model structures may be greater than commonly believed. © Author(s) 2014. CC Attribution 3.0 License. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/78212
Appears in Collections: 气候变化事实与影响
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作者单位: School of Geographical Sciences, University of Bristol, BS8 1SS, United Kingdom; Willis Global Analytics, Willis Group, 51 Lime Street, London, EC3M 7DQ, United Kingdom; European Centre for Medium-Range Weather Forecasts, Reading, RG2 9AX, United Kingdom; Department of Geography and Environmental Science, University of Reading, RG6 6DW, United Kingdom
Recommended Citation:
Sampson C,C,, Fewtrell T,et al. The impact of uncertain precipitation data on insurance loss estimates using a flood catastrophe model[J]. Hydrology and Earth System Sciences,2014-01-01,18(6)