Comparison of storm damage functions and their performance

Abstract

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).

Seamless Estimation of Hydrometeorological Risk Across Spatial Scales

“…Most damage models provide point estimates that do not capture the uncertainties related to the damage process. For a consistent consideration of uncertainties, we suggest applying models that yield damage distributions instead (Figure 1e), as it is done in a few studies, for example, for the hazards of flooding (Egorova et al, 2008), wind storms (Prahl et al, 2015), or hurricanes (Wang et al, 2017). The combination of k objects in a specific area within the same sampling step n forms an object set.…”

Developments in large-scale coastal flood hazard mapping

“…Inundation maps are typically combined with socioeconomic exposure maps to assess coastal impacts or planning scenarios Boettle et al, 2016;Prahl et al, 2015). Given that the number of people affected (NPA) is a parameter commonly considered and even used as a direct or indirect proxy of coastal impacts (Brown et al, 2013;Hinkel et al, 2010;Lloyd et al, 2015), the sensitivity of the estimated total NPA to the applied inundation approach was assessed.…”

Cyclones, windstorms and the IMILAST project

“…Firstly the damage caused by the wind tends to vary with the cube of the speed (e.g. Lamb, 1991), or even speed raised to a power higher than 3 (Prahl et al, 2015). Secondly, damage thresholds, related for example to building regulations, would have been much more readily breached by Ulli's gusts (which probably relates to the aforementioned Prahl et al result).…”