Wind storm loss estimations in the Canton of Vaud   (Western Switzerland)

Etienne, Christophe; Beniston, Martin

doi:10.5194/nhess-12-3789-2012

Cited by 8 publications

(9 citation statements)

References 30 publications

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“…The vulnerability assumed in this study and the corresponding hazard intensity only considers the maximum gust speeds during an event and not the duration of high wind gusts within a windstorm event, which can however have a major impact on the damage to be expected. Taking the windstorm duration into account (e.g., Etienne and Beniston, 2012) could improve our damage modelling, and it is planned to implement this in a future version of GVZ's damage model. Furthermore, it is not considered that buildings are partially adapted to local wind conditions (e.g., multi-storey buildings or exposed buildings located on mountain tops).…”

Section: Discussionmentioning

confidence: 99%

Comparing an insurer's perspective on building damages with modelled damages from pan-European winter windstorm event sets: a case study from Zurich, Switzerland

Welker¹,

Röösli²,

Bresch³

2020

Preprint

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Abstract. With access to claims, insurers have a long tradition of being knowledge leaders on damages caused by e.g. windstorms. However, new opportunities have arisen to better assess the risks of winter windstorms in Europe through the availability of historic footprints provided by the Windstorm Information Service (Copernicus WISC). In this study, we compare how modelling of building damages complements claims-based risk assessment. We describe and use two windstorm risk models: the insurer's proprietary model and the open source CLIMADA platform. Both use the historic WISC dataset and a purposefully-built, probabilistic hazard event set of winter windstorms across Europe to model building damages in the canton of Zurich, Switzerland. These approaches project a considerably lower estimate for the annual average damage (CHF 1.4 million), compared to claims (CHF 2.3 million), which originates mainly from a different assessment of the return period of the most damaging historic event Lothar/Martin. Additionally, the probabilistic modelling approach allows assessing rare events, such as a 250-year return period windstorm causing CHF 75 million damages. Our study emphasises the importance of complementing a claims-based perspective with a probabilistic risk modelling approach to better understand windstorm risks. The presented open source model provides a straightforward entry point for small insurance companies.

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Section: Discussionmentioning

confidence: 99%

Comparing an insurer's perspective on building damages with modelled damages from pan-European winter windstorm event sets: a case study from Zurich, Switzerland

Welker¹,

Röösli²,

Bresch³

2020

Preprint

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“…When comparing wind speed types for both storms, maximum sustained wind speeds produced lower CVSEP values. This alludes to the increased variability and complexity of higher wind speeds (Etienne & Beniston 2012;Roberts et al 2014). Increased wind variability between adjacent stations may have led to over-or underestimation of variability in some areas.…”

Section: Interpolation Accuracy and Meteorological Considerationsmentioning

confidence: 92%

“…Etienne and Beniston (2012) examined extreme station data (i.e. top 10% of wind speeds) for wind storms in Europe using 'basic' kriging.…”

Section: Introductionmentioning

confidence: 99%

Isotropic and anisotropic kriging approaches for interpolating surface-level wind speeds across large, geographically diverse regions

Friedland

Joyner

Massarra

et al. 2016

Geomatics, Natural Hazards and Risk

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Windstorms result in significant damage and economic loss and are a major recurring threat in many countries. Estimating surface-level wind speeds resulting from windstorms is a complicated problem, but geostatistical spatial interpolation methods present a potential solution. Maximum sustained and peak gust weather station data from two historic windstorms in Europe were analyzed to predict surface-level wind speed surfaces across a large and topographically varied landscape. Disjunctively sampled maximum sustained wind speeds were adjusted to represent equivalent continuously sampled 10-minute wind speeds and missing peak gust station data were estimated by applying a gust factor to the recorded maximum sustained wind speeds. Wind surfaces were estimated based on anisotropic and isotropic kriging interpolation methodologies. The study found that anisotropic kriging is well-suited for interpolating wind speeds in meso-and macro-scale areas because it accounts for wind direction and trends in wind speeds across a large, heterogeneous surface, and resulted in interpolation surface improvement in most models evaluated. Statistical testing of interpolation error for stations stratified by geographic classification revealed that stations in coastal and/or mountainous locations had significantly higher prediction errors when compared with stations in non-coastal/non-mountainous locations. These results may assist in mitigating losses to structures due to excessive wind events.

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“…The approach has since been applied to other European locations (e.g. Leckebusch et al, 2007;Etienne and Beniston, 2012;Cusack, 2013) and was recently refined to the scale of German districts by Donat et al (2011b).…”

Section: Cubic Excess-over-threshold Damage Function [K]mentioning

confidence: 99%

Comparison of storm damage functions and their performance

Prahl

Rybski

Burghoff

et al. 2015

Nat. Hazards Earth Syst. Sci.

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Abstract. Winter storms are the most costly natural hazard for European residential property. We compare four distinct storm damage functions with respect to their forecast accuracy and variability, with particular regard to the most severe winter storms. The analysis focuses on daily loss estimates under differing spatial aggregation, ranging from district to country level. We discuss the broad and heavily skewed distribution of insured losses posing difficulties for both the calibration and the evaluation of damage functions. From theoretical considerations, we provide a synthesis between the frequently discussed cubic wind-damage relationship and recent studies that report much steeper damage functions for European winter storms. The performance of the storm loss models is evaluated for two sources of wind gust data, direct observations by the German Weather Service and ERAInterim reanalysis data. While the choice of gust data has little impact on the evaluation of German storm loss, spatially resolved coefficients of variation reveal dependence between model and data choice. The comparison shows that the probabilistic models by Heneka et al. (2006) and Prahl et al. (2012) both provide accurate loss predictions for moderate to extreme losses, with generally small coefficients of variation. We favour the latter model in terms of model applicability. Application of the versatile deterministic model by Klawa and Ulbrich (2003) should be restricted to extreme loss, for which it shows the least bias and errors comparable to the probabilistic model by Prahl et al. (2012).

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Wind storm loss estimations in the Canton of Vaud (Western Switzerland)

Cited by 8 publications

References 30 publications

Comparing an insurer's perspective on building damages with modelled damages from pan-European winter windstorm event sets: a case study from Zurich, Switzerland

Comparing an insurer's perspective on building damages with modelled damages from pan-European winter windstorm event sets: a case study from Zurich, Switzerland

Isotropic and anisotropic kriging approaches for interpolating surface-level wind speeds across large, geographically diverse regions

Comparison of storm damage functions and their performance

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