Winter storm risk of residential structures – model development and  application to the German state of Baden-Württemberg

Heneka, Patrick; Hofherr, T.; Ruck, Β.; Kottmeier, C.

doi:10.5194/nhess-6-721-2006

Cited by 47 publications

(66 citation statements)

References 27 publications

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“…The median values of DAM OOB are always lowest for mixed forests (between DAM OOB,30yr = 0.15 and DAM OOB,5˚= 0.24) and highest for conifers (between DAM OOB,30yr = 0.26 and DAM OOB,5* = 0.47). This effect might be due to higher drag of evergreen conifers in winter when most high-impact storms occur in the study area [55] while broad-leaved tree species are leafless [46,56,57]. [46,56,57].…”

Section: /23mentioning

confidence: 99%

The Role of Highly-Resolved Gust Speed in Simulations of Storm Damage in Forests at the Landscape Scale: A Case Study from Southwest Germany

et al. 2016

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Abstract:Routinely collected booking records of salvaged timber from the period 1979-2008 were used to empirically model the (1) storm damage probability; (2) proportions of storm-damaged timber and (3) endemic storm damage risk in the forest area of the German federal state of Baden-Wuerttemberg by applying random forests. Results from cross-validated predictor importance evaluation demonstrate that the relative impact of modeled gust speed fields on the predictive accuracy of the random forests models was greatest compared to the impact of forest and soil features. Forest areas prone to storm damage occurring within a period of five years were mainly located in mountainous upland regions where maximum gust speed exceeds 31 m/s in a five-year return period and conifers dominate the tree species composition. While mean storm damage probability continuously increased with increasing statistical gust speed proportions of storm-damaged timber peaked at a statistical maximum gust speed value of 29 m/s occurring in a five-year return period. Combining the statistical gust speed field with daily gust speed fields of two exceptional winter storms improved model accuracy and considerably increased the explained variance. Endemic storm damage risk was calculated from endemic storm damage probability and proportions of endemically storm-damaged timber. In combination with knowledge of local experts the storm damage risk modeled in a 50 mˆ50 m resolution raster dataset can easily be used to identify areas prone to storm damage and to adapt silvicultural management regimes to make forests more windfirm.

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Section: /23mentioning

confidence: 99%

The Role of Highly-Resolved Gust Speed in Simulations of Storm Damage in Forests at the Landscape Scale: A Case Study from Southwest Germany

et al. 2016

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“…A continuous probability density function is employed to describe the probability of critical gust speeds within the overall building stock. For modelling purposes, Heneka et al (2006) assumed a Gaussian distribution for critical gust speeds, which is non-physical in a sense as it yields finite probability for negative wind gust speeds. The claim ratio follows naturally as the cumulative distribution function of critical gust speeds, describing the fraction of buildings for which wind gust speed exceeds the critical threshold.…”

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

confidence: 99%

“…Heneka et al (2006) (see also Heneka and Ruck, 2008;Heneka and Hofherr, 2011) argue that the square term of their microscale damage relationship,…”

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

confidence: 99%

“…Their model was applied first to the federal state of Baden-Württemberg and subsequently to Germany (Heneka and Hofherr, 2011). Maintaining the key assumptions made by Heneka et al (2006) as far as possible, the intercomparison was based on the following considerations for model design and calibration.…”

Section: A4 Probabilistic Claim-based Damage Function [H ]mentioning

confidence: 99%

“…Prahl et al (2012) demonstrate that wind gust observations from neighbouring meteorological stations provide sufficient information for the calibration of a storm damage function. Higher precision may be attained via the use of mesoscale climate models for the computation of detailed and physically valid wind fields from reanalysis or observational data (Heneka et al, 2006;Huttenlau and Stötter, 2011). As this is clearly beyond the scope of our work, we limit ourselves to a simple inverse-distance interpolation scheme applied to both DWD and ERA-Interim data sources.…”

Section: Wind Gust Datamentioning

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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The return period of wind storms over Europe

Della-Marta

Mathis

Frei

et al. 2008

Intl Journal of Climatology

137

116

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Accurate assessment of the magnitude and frequency of extreme wind speed is of fundamental importance for many safety, engineering and reinsurance applications. We utilize the spatial and temporal consistency of the European Centre for Medium Range Forecasts ERA-40 reanalysis data to determine the frequency of extreme winds associated with wind storms over the eastern North Atlantic and Europe. Two parameters are investigated: 10-m wind gust and 10-m wind speed. The analysis follows two different view-points: In a spatially distributed view, wind-storm statistics are determined individually at each grid-point. In an integral, more generalized view, the wind-storm statistics are determined from extreme wind indices (EWI) that summarize storm magnitude and spatial extent. We apply classical peak over threshold (POT) extreme value analysis techniques (EVA) to the EWI and grid-point wind data. As a reference, a catalogue of the 200 most prominent European storms has been compiled based on available literature. The EWI-based return periods (RP) estimates of catalogue wind storms range from approximately 0.1 to 300 years, whereas grid-point-based RP estimates range from 0.1 to 500+ years. EWIs sensitive to the absolute magnitude of wind speed rank the RP of wind storms in the 1989/1990 and 1999/2000 extended winter season similarly to the RP derived from the distributed approach. The RP estimates derived from EWIs are generally higher when calculated using only land grid-points compared to RPs derived using whole domain. Both the uncertainties in EWIs and grid-point-based RPs show a greater dependence on the wind parameter used than on the uncertainty associated with the EVA for RPs less than 10 years, whereas for RPs greater than 10 years the effect of the different datasets is lower. The EWIs share up to approximately 50% of the variability of the local grid-point RPs.

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Winter storm risk of residential structures – model development and application to the German state of Baden-Württemberg

Cited by 47 publications

References 27 publications

The Role of Highly-Resolved Gust Speed in Simulations of Storm Damage in Forests at the Landscape Scale: A Case Study from Southwest Germany

The Role of Highly-Resolved Gust Speed in Simulations of Storm Damage in Forests at the Landscape Scale: A Case Study from Southwest Germany

Comparison of storm damage functions and their performance

The return period of wind storms over Europe

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