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| Titel |
The impact of uncertain precipitation data on insurance loss estimates using a flood catastrophe model |
| VerfasserIn |
C. C. Sampson, T. J. Fewtrell, F. O'Loughlin, F. Pappenberger, P. B. Bates, J. E. Freer, H. L. Cloke |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1027-5606
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| Digitales Dokument |
URL |
| Erschienen |
In: Hydrology and Earth System Sciences ; 18, no. 6 ; Nr. 18, no. 6 (2014-06-23), S.2305-2324 |
| Datensatznummer |
250120391
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| Publikation (Nr.) |
 copernicus.org/hess-18-2305-2014.pdf |
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| Zusammenfassung |
| 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. |
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