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

Abstract

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.

Identifying Driving Factors in Flood‐Damaging Processes Using Graphical Models

“…Besides changes in exposure and vulnerability, this might be due to differences in the flooding process. There is indication that different flood types result in different amounts and types of (structural) damage (see Kreibich & Thieken, for groundwater floods and Laudan et al, for flash floods). By including the flood type as predictor variable, we aim to analyze the need to distinguish damage models for different flood types.…”

Forensic analysis of flash flood response

“…While enabling a systematic approach, flash flood forensic analysis provides a consistent approach which facilitates cross-event learning. In recent years, forensic analysis of flash floods has been extended to include analysis of social response (Scolobig, De Marchi, & Borga, 2012, among others), economic impacts (Laudan, Rözer, Sieg, Vogel, & Thieken, 2017 and references therein) and disaster risk management (Keating et al, 2016). In this context, a group of authors (Creutin et al, 2013;Lutoff, Creutin, Ruin, & Borga, 2016;Papagiannaki, Kotroni, Lagouvardos, Ruin, & Bezes, 2017;Ruin et al, 2014) advanced the integrated post-flood survey of behavioral response to floods.…”

Empirical flash flood vulnerability functions for residential buildings

“…Vulnerability functions have been developed for transportation systems [12], agriculture [13] and buildings. Most of the literature focused on residential buildings damaged by riverine flooding [14], and there are few examples of empirical vulnerability analysis for flash flood or debris flows [15,16]. Laudan et al [16] adopted a random forest approach to determine the importance of contributing variables, e.g., flood depth, flow direction, year of construction, and correlated six qualitative damage grades to inundation depths.…”

“…Most of the literature focused on residential buildings damaged by riverine flooding [14], and there are few examples of empirical vulnerability analysis for flash flood or debris flows [15,16]. Laudan et al [16] adopted a random forest approach to determine the importance of contributing variables, e.g., flood depth, flow direction, year of construction, and correlated six qualitative damage grades to inundation depths. Fuchs et al [15] focused on the relationship between debris flow intensity and relative vulnerability in a mountainous catchment.…”

“…The digital cartography is used to identify residential buildings, their characteristics and their monetary exposure. With respect to other studies in the literature which focus on slow riverine floods, this work focuses on a number of observed losses related to a flash flood and offers insights on damage mechanisms rarely observed and studied [16].…”