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In den Sommern der Jahre 2017 und 2019 kam es in Berlin an mehreren Orten zu Überschwemmungen in Folge von Starkregenereignissen. In beiden Jahren führte dies zu erheblichen Beeinträchtigungen im Alltag der Berliner:innen sowie zu hohen Sachschäden. Eine interdisziplinäre Taskforce des DFG-Graduiertenkollegs NatRiskChange untersuchte (1) die meteorologischen Eigenschaften zweier besonders eindrücklicher Unwetter, sowie (2) die Vulnerabilität der Berliner Bevölkerung gegenüber Starkregen. Eine vergleichende meteorologische Rekonstruktion der Starkregenereignisse von 2017 und 2019 ergab deutliche Unterschiede in der Entstehung und den Überschreitungswahrscheinlichkeiten der beiden Unwetter. So war das Ereignis von 2017 mit einer relativ großen räumlichen Ausdehnung und langer Dauer ein untypisches Starkregenereignis, während es sich bei dem Unwetter von 2019 um ein typisches, kurzzeitiges Starkregenereignis mit ausgeprägter räumlicher Heterogenität handelte. Eine anschließende statistische Analyse zeigte, dass das Ereignis von 2017 für längere Niederschlagsdauern (>=24 h) als großflächiges Extremereignis mit Überschreitungswahrscheinlichkeiten von unter 1 % einzuordnen ist (d.h. Wiederkehrperioden >=100 Jahre). Im Jahr 2019 wurden dagegen ähnliche Überschreitungswahrscheinlichkeiten nur lokal und für kürzere Zeiträume (1-2 h) berechnet. Die Vulnerabilitätsanalyse basiert auf einer von April bis Juni 2020 in Berlin durchgeführten Onlinebefragung. Diese richtete sich an Personen, die bereits von vergangenen Starkregenereignissen betroffen waren und thematisierte das Schadensereignis selbst, daraus entstandene Beeinträchtigungen und Schäden, Risikowahrnehmung sowie Notfall- und Vorsorgemaßnahmen. Die erhobenen Umfragedaten (n=102) beziehen sich vornehmlich auf die Ereignisse von 2017 und 2019 und zeigen, dass die Berliner Bevölkerung sowohl im Alltag (z.B. bei der Beschaffung von Lebensmitteln) als auch im eigenen Haushalt (z.B. durch Überschwemmungsschäden) von den Unwettern beeinträchtigt war. Zudem deuteten die Antworten der Betroffenen auf Möglichkeiten hin, die Vulnerabilität der Gesellschaft gegenüber Starkregen weiter zu reduzieren - etwa durch die Unterstützung besonders betroffener Gruppen (z.B. Pflegende), durch gezielte Informationskampagnen zum Schutz vor Starkregen oder durch die Erhöhung der Reichweite von Unwetterwarnungen. Eine statistische Analyse zur Effektivität privater Notfall- und Vorsorgemaßnahmen auf Grundlage der Umfragedaten bestätigte vorherige Studienergebnisse. So gab es Anhaltspunkte dafür, dass durch das Umsetzen von Vorsorgemaßnahmen wie beispielsweise das Installieren von Rückstauklappen, Barriere-Systemen oder Pumpen Starkregenschäden reduziert werden können. Die Ergebnisse dieses Berichts unterstreichen die Notwendigkeit für ein integriertes Starkregenrisikomanagment, das die Risikokomponenten Gefährdung, Vulnerabilität und Exposition ganzheitlich und auf mehreren Ebenen (z.B. staatlich, kommunal, privat) betrachtet., In the summers of 2017 and 2019, the city of Berlin was hit by heavy rainfall leading to urban flooding in several locations. In both years, this led to considerable disruptions of the daily life and high property damage. With focus on two particularly impressive events a taskforce of the DFG Research Training Group NatRiskChange investigated (1) the meteorological characteristics of both events as well as (2) the vulnerability of the Berlin population to heavy rainfall. A comparative meteorological reconstruction of the 2017 and 2019 heavy rainfall events revealed fundamental differences between the two storms. The 2017 event was an atypical heavy rain event, as it was characterized by a relatively large spatial extent and long duration of rainfall, whereas the 2019 storm was a typical short duration heavy rain event with a distinct spatial heterogeneity. Subsequent statistical analysis indicated that the 2017 event should be classified as a large-scale extreme event with exceedance probabilities below 1 % for longer precipitation durations (i.e., return periods of over 100 years). In contrast, in 2019 similar exceedance probabilities were estimated only locally and for shorter durations (1-2 h). The vulnerability analysis of this taskforce was based on an online survey conducted in Berlin between April and June 2020. The survey was aimed at people who had experienced past heavy rainfall events in Berlin, and addressed the resulting impairments and damages, risk perceptions as well as emergency and preparedness measures. The survey data (n=102) primarily referred to the events of 2017 and 2019 and showed that the respondents were affected by the storms both in their daily lives (e.g., when purchasing food) and in their own households (e.g., due to flood damage). In addition, the analysis of the responses pointed to ways to further reduce society's vulnerability to heavy rain. That was, for example, by providing support to particularly affected groups (e.g., caregivers), through targeted information campaigns to protect against heavy rainfall or by improving the range of early warning systems. A statistical analysis of the efficacy of property-level emergency and preparedness measures based on the survey data confirmed previous study findings and provided evidence of reducing heavy rain damage through preparedness. The findings of the taskforce highlight the need for integrated heavy rainfall risk management that considers the risk components of hazard, vulnerability, and exposure holistically and at multiple levels (e.g., state, local and private households).

Models for the predictions of monetary losses from floods mainly blend data deemed to represent a single flood type and region. Moreover, these approaches largely ignore indicators of preparedness and how predictors may vary between regions and events, challenging the transferability of flood loss models. We use a flood loss database of 1812 German flood-affected households to explore how Bayesian multilevel models can estimate normalised flood damage stratified by event, region, or flood process type. Multilevel models acknowledge natural groups in the data and allow each group to learn from others. We obtain posterior estimates that differ between flood types, with credibly varying influences of water depth, contamination, duration, implementation of property-level precautionary measures, insurance, and previous flood experience; these influences overlap across most events or regions, however. We infer that the underlying damaging processes of distinct flood types deserve further attention. Each reported flood loss and affected region involved mixed flood types, likely explaining the uncertainty in the coefficients. Our results emphasise the need to consider flood types as an important step towards applying flood loss models elsewhere. We argue that failing to do so may unduly generalise the model and systematically bias loss estimations from empirical data., Postprints der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe; 1148

The estimation of flood losses implies the use of vulnerability/exposure models for flood damage and risk assessment (Meyer et al. 2013; Zischg et al. 2018; Wagenaar et al. 2018; Molinari et al. 2019). According to Gerl et al. (2016), in central Europe there are 28 models with 652 functions to assess flood losses, whereas almost half of the functions refer to residential buildings. The main differences among damage models are: (i) spatial scale, (ii) metric (i.e. absolute or relative loss), (iii) exposure assessment (i.e. whole building or affected floors, market values or reconstruction-replacement costs), (iv) number of input variables, (v) calibration/validation with observed losses. These differences pose the issue of model transferability to other urban or environmental contexts. With a joint effort of eight research groups, the objective of this study is to test and compare damage models for residential buildings used or developed by each group, by applying them in a “blind exercise”, i.e. a common flood case study characterised by high availability of hazard and building data, with unknown information on observed losses in the implementation stage of the models. As the research groups use approaches representing many different types and characteristics of models (e.g. univariable – multivariable; absolute – relative; graduated – regression – machine learning), being calibrated based on empirical data stemming from different countries (Austria, France, Germany, Italy, Japan, Netherlands), with different landscapes, the blind exercise provides an extensive comparison of models and their transferability. The analysis of the differences in the ensemble of model outcomes aims at pointing out common patterns or divergent behaviours between model outcomes and with respect to observed losses.

Effective flood risk management requires a realistic estimation of flood losses. However, available flood damage estimates are still characterised by significant levels of uncertainty, questioning the capacity of flood damage models to depict real damages. With a joint effort of eight international research groups, the objective of this study was to compare the performances of different damage models for the estimation of the direct flood damage to the residential sector at the building level (i.e. micro scale) in a blind validation test. The test consisted in a common flood case study characterised by high availability of hazard and building data, but with undisclosed information on observed losses in the implementation stage of the models. The selected nine models were chosen in order to guarantee a good mastery of the models by the research teams, variety of the modelling approaches and heterogeneity of the original calibration context, in relation to both hazard and vulnerability features. By avoiding possible biases in model implementation, this blind comparison provided more objective insights on the transferability of the models and on the reliability of their estimations, especially regarding the potentials of local and multi-variable models. From another perspective, the exercise allowed to increase authors’ awareness on strengths and limits of flood damage modelling, which are summarised in the paper in the form of take-home messages from a modeller's perspective.