Long-term hurricane risk assessment and expected damage to residential structures

Huang, Zhigang; Rosowsky, David V.; Sparks, P.R.

doi:10.1016/s0951-8320(01)00086-2

Cited by 121 publications

(61 citation statements)

References 7 publications

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“…Losses related to storm events can be assessed through empirical formulas that take into account variables describing the characteristics of a windstorm event, such as mean or maximum wind speeds (Dorland et al 1999;Unanwa et al 2000;Huang et al 2001) or storm duration (Schraft et al 1993). The most important parameter remains wind speed at the standard anemometer height of 10 m (maximum wind gusts) that is often the dominant factor leading to damage (Heneka and Ruck 2004).…”

Section: Introductionmentioning

confidence: 99%

Spatial Predictions of Extreme Wind Speeds over Switzerland Using Generalized Additive Models

Etienne

Lehmann

Goyette

et al. 2010

Journal of Applied Meteorology and Climatology

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The purpose of this work is to present a methodology aimed at predicting extreme wind speeds over Switzerland. Generalized additive models are used to regionalize wind statistics for Swiss weather stations using a number of variables that describe the main physiographical features of the country. This procedure enables one to present the results for Switzerland in the form of a map that provides the 98th percentiles of daily maximum wind speeds (W98) at a 10-m anemometer height for cells with a 50-m grid interval. This investigation comprises three major steps. First, meteorological data recorded by the weather stations was gathered to build local wind statistics at each station. Then, data describing the topographic and landscape characteristics of the country were prepared using geographic information systems (GIS). Third, appropriate regression models were selected to make spatially explicit predictions of extreme wind speeds in Switzerland. The predictions undertaken in this study provide realistic values of the W98. The effects of topography on the results are particularly conspicuous. Wind speeds increase with altitude and are greatest on mountain peaks in the Alps, as would be intuitively expected. Relative errors between observations and model results calculated for the meteorological stations do not exceed 30%, and only 12 out of 70 stations exhibit errors that exceed 20%. The combination of GIS techniques and statistical models used to predict a highly uncertain variable, such as extreme wind speed, yields interesting results that can be extended to other fields, such as the assessment of storm damage on infrastructures.

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Section: Introductionmentioning

confidence: 99%

Spatial Predictions of Extreme Wind Speeds over Switzerland Using Generalized Additive Models

Etienne

Lehmann

Goyette

et al. 2010

Journal of Applied Meteorology and Climatology

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“…However, recent literature provides evidence for a much stronger increase of insured storm loss with wind gust speed (Huang et al, 2001;Heneka and Ruck, 2008). For the insurance data set that we employ here, Prahl et al (2012) found a power law with regionally varying exponents that approximately range between 8 and 12.…”

Section: B F Prahl Et Al: Comparison Of Storm Damage Functionsmentioning

confidence: 83%

“…However, uncertainty estimates for extreme loss remain a concern and should be subject to further research. The assumption of an exponential damage relationship is not uncommon in the related literature (Huang et al, 2001;Prettenthaler et al, 2012;Murnane and Elsner, 2012) and such models are characterized by a steeper increase than comparable power law models.…”

Section: Discussion and Concluding Remarksmentioning

confidence: 99%

“…These damage functions are complemented by a simple exponential model inspired by recent US hurricane loss models (Huang et al, 2001;Murnane and Elsner, 2012), yielding four mathematically distinct modelling approaches. For simple referencing, we assign the acronyms X and K to the deterministic exponential model and the model by Klawa and Ulbrich (2003), respectively.…”

Section: B F Prahl Et Al: Comparison Of Storm Damage Functionsmentioning

confidence: 99%

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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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“…Consequently, as mentioned by, for example, Huang et al (2001) and Pinto et al (2007), storm losses can be normalised by these yearly total insured sums in order to avoid bias related to inflation and the growth of wealth in the calculations. Economic losses related to each individual storm were therefore divided by the corresponding year's total insured sum over the canton of Vaud to provide normalised storm losses (called loss ratios) that are used in this study.…”

Section: Insurance Datamentioning

confidence: 99%

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

Etienne

Beniston

2012

Nat. Hazards Earth Syst. Sci.

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Abstract. A storm loss model that was first developed forGermany is applied to the much smaller geographic area of the canton of Vaud, in Western Switzerland. 24 major wind storms that struck the region during the period 1990-2010 are analysed, and outputs are compared to loss observations provided by an insurance company. Model inputs include population data and daily maximum wind speeds from weather stations. These measured wind speeds are regionalised in the canton of Vaud following different methods, using either basic interpolation techniques from Geographic Information Systems (GIS), or by using an existing extreme wind speed map of Switzerland whose values are used as thresholds. A third method considers the wind power, integrating wind speeds temporally over storm duration to calculate losses. Outputs show that the model leads to similar results for all methods, with Pearson's correlation and Spearman's rank coefficients of roughly 0.7. Bootstrap techniques are applied to test the model's robustness. Impacts of population growth and possible changes in storminess under conditions of climate change shifts are also examined for this region, emphasizing high shifts in economic losses related to small increases of input wind speeds.

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Long-term hurricane risk assessment and expected damage to residential structures

Cited by 121 publications

References 7 publications

Spatial Predictions of Extreme Wind Speeds over Switzerland Using Generalized Additive Models

Spatial Predictions of Extreme Wind Speeds over Switzerland Using Generalized Additive Models

Comparison of storm damage functions and their performance

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

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