The impact of uncertain precipitation data on insurance loss estimates using a flood catastrophe model

Sampson, C. C., Fewtrell, T. J., O'Loughlin, F., Pappenberger, F., Bates, P., Freer, J. E. and Cloke, H. L. ORCID: https://orcid.org/0000-0002-1472-868X (2014) The impact of uncertain precipitation data on insurance loss estimates using a flood catastrophe model. Hydrology and Earth System Sciences, 18. pp. 2305-2324. ISSN 1027-5606 doi: 10.5194/hess-18-2305-2014

Abstract/Summary

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.

Item Type	Article
URI	https://reading-clone.eprints-hosting.org/id/eprint/38774
Item Type	Article
Refereed	Yes
Divisions	Interdisciplinary Research Centres (IDRCs) > Walker Institute Science > School of Archaeology, Geography and Environmental Science > Earth Systems Science Science > School of Archaeology, Geography and Environmental Science > Department of Geography and Environmental Science Interdisciplinary centres and themes > Soil Research Centre Science > School of Mathematical, Physical and Computational Sciences > Department of Meteorology
Publisher	Copernicus
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