Prognose der Bauwerksschädigung unter Hochwassereinwirkung

Abstract. Flash floods are caused by intense rainfall events and represent an insufficiently understood phenomenon in Germany. As a result of higher precipitation intensities, flash floods might occur more frequently in future. In combination with changing land use patterns and urbanisation, damage mitigation, insurance and risk management in flash-floodprone regions are becoming increasingly important. However, a better understanding of damage caused by flash floods requires ex post collection of relevant but yet sparsely available information for research. At the end of May 2016, very high and concentrated rainfall intensities led to severe flash floods in several southern German municipalities. The small town of Braunsbach stood as a prime example of the devastating potential of such events. Eight to ten days after the flash flood event, damage assessment and data collection were conducted in Braunsbach by investigating all affected buildings and their surroundings. To record and store the data on site, the open-source software bundle KoBoCollect was used as an efficient and easy way to gather information. Since the damage driving factors of flash floods are expected to differ from those of riverine flooding, a post-hoc data analysis was performed, aiming to identify the influence of flood processes and building attributes on damage grades, which reflect the extent of structural damage. Data analyses include the application of random forest, a random general linear model and multinomial logistic regression as well as the construction of a local impact map to reveal influences on the damage grades. Further, a Spearman's Rho correlation matrix was calculated. The results reveal that the damage driving factors of flash floods differ from those of riverine floods to a certain extent. The exposition of a building in flow direction shows an especially strong correlation with the damage grade and has a high predictive power within the constructed damage models. Additionally, the results suggest that building materials as well as various building aspects, such as the existence of a shop window and the surroundings, might have an effect on the resulting damage. To verify and confirm the outcomes as well as to support future mitigation strategies, risk management and planning, more comprehensive and systematic data collection is necessary.

“…The type of recorded information is based on existing literature on flood damage surveys (e.g. Thieken et al, 2005;Schwarz and Maiwald, 2007;Merz et al, 2010;Molinari et al, 2014).…”

Section: Methodsmentioning

confidence: 99%

Section: Contents Of the Surveymentioning

confidence: 99%

Damage assessment in Braunsbach 2016: data collection and analysis for an improved understanding of damaging processes during flash floods

Laudan

Rözer

Sieg

et al. 2017

“…Due to the limited amount of damage data in dataset R1, the impact of flow velocity on the damage grade is studied without further differentiation and subdivision of the building types. Nevertheless, previous studies by Maiwald (2007) and Schwarz and Maiwald (2007) indicate that a more refined level of consideration is advantageous. Using a rank correlation (Spearman's rho) between impact parameters and damage grades, the following conclusions can be derived from dataset R1 (Table 6):…”

Section: Structural Damage Of Residential Buildingsmentioning

confidence: 99%

Is flow velocity a significant parameter in flood damage modelling?

Kreibich

Piroth²,

Seifert

et al. 2009

264

173

Abstract. Flow velocity is generally presumed to influence flood damage. However, this influence is hardly quantified and virtually no damage models take it into account. Therefore, the influences of flow velocity, water depth and combinations of these two impact parameters on various types of flood damage were investigated in five communities affected by the Elbe catchment flood in Germany in 2002. 2-D hydraulic models with high to medium spatial resolutions were used to calculate the impact parameters at the sites in which damage occurred. A significant influence of flow velocity on structural damage, particularly on roads, could be shown in contrast to a minor influence on monetary losses and business interruption. Forecasts of structural damage to road infrastructure should be based on flow velocity alone. The energy head is suggested as a suitable flood impact parameter for reliable forecasting of structural damage to residential buildings above a critical impact level of 2 m of energy head or water depth. However, general consideration of flow velocity in flood damage modelling, particularly for estimating monetary loss, cannot be recommended.

“…Currently, most damage models still use inundation depth as the main impact parameter (see e.g., Merz et al, 2010;Jongman et al, 2012 for an overview), but some models also integrate additional parameters like flow velocity (e.g., Schwarz and Maiwald, 2007;Pistrika and Jonkman, 2010), contamination (e.g., Kreibich and Thieken, 2008;Prettenthaler et al, 2010), the duration of flooding (e.g., Dutta et al, 2003;Penning-Rowsell et al, 2005) or the recurrence interval (e.g., Elmer et al, 2010). With regard to the consideration of different resistance parameters, the majority of damage models differentiates between the use or type of building (e.g., Oliveri and Santoro, 2000;Dutta et al, 2003;Kang et al, 2005;Büchele et al, 2006;Schwarz and Maiwald, 2007;Kreibich and Thieken, 2008;Thieken et al, 2008). Few models also take additional parameters, such as precautionary behavior (e.g., Büchele et al, 2006;Kreibich and Thieken, 2008;Thieken et al, 2008) or the early warning time (e.g., Penning-Rowsell et al, 2005), into account.…”

Section: Introductionmentioning

confidence: 99%

“…Since the majority of the stage-damage functions were derived for lowland regions with slowly rising floods, such as for the lower Rhine (MURL, 2000) or the German Lippe catchment (Hydrotec, 2002), their applicability is mainly directed towards static river flooding. In contrast, damage estimation studies that address the dynamical character of flooding by considering flood velocity, for example, are rare (e.g., Schwarz and Maiwald, 2007;Pistrika and Jonkman, 2010). Even if the general consideration of flow velocity in flood damage modeling on buildings cannot be necessarily recommended , the dynamic load of flooding seems to be fundamental for the damage pattern on buildings particularly for mountainous regions like the European Alps (e.g., Totschnig and Fuchs, 2013).…”

Section: Introductionmentioning

confidence: 99%

Adaptability and transferability of flood loss functions in residential areas

Cammerer

Thieken

Lammel

2013

130

144

Abstract. Flood loss modeling is an important component within flood risk assessments. Traditionally, stage-damage functions are used for the estimation of direct monetary damage to buildings. Although it is known that such functions are governed by large uncertainties, they are commonly applied -even in different geographical regions -without further validation, mainly due to the lack of real damage data. Until now, little research has been done to investigate the applicability and transferability of such damage models to other regions. In this study, the last severe flood event in the Austrian Lech Valley in 2005 was simulated to test the performance of various damage functions from different geographical regions in Central Europe for the residential sector. In addition to common stage-damage curves, new functions were derived from empirical flood loss data collected in the aftermath of recent flood events in neighboring Germany. Furthermore, a multi-parameter flood loss model for the residential sector was adapted to the study area and also evaluated with official damage data. The analysis reveals that flood loss functions derived from related and more similar regions perform considerably better than those from more heterogeneous data sets of different regions and flood events. While former loss functions estimate the observed damage well, the latter overestimate the reported loss clearly. To illustrate the effect of model choice on the resulting uncertainty of damage estimates, the current flood risk for residential areas was calculated. In the case of extreme events like the 300 yr flood, for example, the range of losses to residential buildings between the highest and the lowest estimates amounts to a factor of 18, in contrast to properly validated models with a factor of 2.3. Even if the risk analysis is only performed for residential areas, our results reveal evidently that a carefree model transfer in other geographical regions might be critical. Therefore, we conclude that loss models should at least be selected or derived from related regions with similar flood and building characteristics, as far as no model validation is possible. To further increase the general reliability of flood loss assessment in the future, more loss data and more comprehensive loss data for model development and validation are needed.