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Im Graduiertenkolleg NatRiskChange der Universität Potsdam und anderen Forschungseinrichtungen werden beobachtete sowie zukünftig mögliche Veränderungen von Naturgefahren untersucht. Teil des strukturierten Doktorandenprogramms sind sogenannte Task-Force-Einsätze, bei denen die Promovierende zeitlich begrenzt ein aktuelles Ereignis auswerten. Im Zuge dieser Aktivität wurde die Sturzflut vom 29.05.2016 in Braunsbach (Baden-Württemberg) untersucht.
In diesem Bericht werden erste Auswertungen zur Einordnung der Niederschläge, zu den hydrologischen und geomorphologischen Prozessen im Einzugsgebiet des Orlacher Bachs sowie zu den verursachten Schäden beleuchtet.
Die Region war Zentrum extremer Regenfälle in der Größenordnung von 100 mm innerhalb von 2 Stunden. Das 6 km² kleine Einzugsgebiet hat eine sehr schnelle Reaktionszeit, zumal bei vorgesättigtem Boden. Im steilen Bachtal haben mehrere kleinere und größere Hangrutschungen über 8000 m³ Geröll, Schutt und Schwemmholz in das Gewässer eingetragen und möglicherweise kurzzeitige Aufstauungen und Durchbrüche verursacht. Neben den großen Wassermengen mit einer Abflussspitze in einer Größenordnung von 100 m³/s hat gerade die Geschiebefracht zu großen Schäden an den Gebäuden entlang des Bachlaufs in Braunsbach geführt.
Starkregen in Berlin
(2021)
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.
The literature on the costing of mitigation measures for reducing impacts of natural hazards is rather fragmented. This paper provides a concise overview of the current state of knowledge in Europe on the costing of mitigation measures for the reduction of natural hazard risks (droughts, floods, storms and induced coastal hazards as well as alpine hazards) and identifies knowledge gaps and related research recommendations. Furthermore, it provides a taxonomy of related mitigation options, classifying them into nine categories: (1) management plans, land-use planning, and climate adaptation; (2) hazard modification; (3) infrastructure; (4) mitigation measures (stricto sensu); (5) communication in advance of events; (6) monitoring and early warning systems; (7) emergency response and evacuation; (8) financial incentives; and (9) risk transfer (including insurance). It is found that the costing of mitigation measures in European and in other countries has almost exclusively focused on estimating direct costs. A cost assessment framework that addresses a range of costs, possibly informed by multiple stakeholders, would provide more accurate estimates and could provide better guidance to decision makers. (C) 2014 American Society of Civil Engineers.
Insights into the dynamics of human behavior in response to flooding are urgently needed for the development of effective integrated flood risk management strategies, and for integrating human behavior in flood risk modeling. However, our understanding of the dynamics of risk perceptions, attitudes, individual recovery processes, as well as adaptive (i.e., risk reducing) intention and behavior are currently limited because of the predominant use of cross-sectional surveys in the flood risk domain. Here, we present the results from one of the first panel surveys in the flood risk domain covering a relatively long period of time (i.e., four years after a damaging event), three survey waves, and a wide range of topics relevant to the role of citizens in integrated flood risk management. The panel data, consisting of 227 individuals affected by the 2013 flood in Germany, were analyzed using repeated-measures ANOVA and latent class growth analysis (LCGA) to utilize the unique temporal dimension of the data set. Results show that attitudes, such as the respondents' perceived responsibility within flood risk management, remain fairly stable over time. Changes are observed partly for risk perceptions and mainly for individual recovery and intentions to undertake risk-reducing measures. LCGA reveal heterogeneous recovery and adaptation trajectories that need to be taken into account in policies supporting individual recovery and stimulating societal preparedness. More panel studies in the flood risk domain are needed to gain better insights into the dynamics of individual recovery, risk-reducing behavior, and associated risk and protective factors.
Protection motivation theory (PMT) has become a popular theory to explain the risk-reducing behavior of residents against natural hazards. PMT captures the two main cognitive processes that individuals undergo when faced with a threat, namely, threat appraisal and coping appraisal. The latter describes the evaluation of possible response measures that may reduce or avert the perceived threat. Although the coping appraisal component of PMT was found to be a better predictor of protective intentions and behavior, little is known about the factors that influence individuals’ coping appraisals of natural hazards. More insight into flood-coping appraisals of PMT, therefore, are needed to better understand the decision-making process of individuals and to develop effective risk communication strategies. This study presents the results of two surveys among more than 1,600 flood-prone households in Germany and France. Five hypotheses were tested using multivariate statistics regarding factors related to flood-coping appraisals, which were derived from the PMT framework, related literature, and the literature on social vulnerability. We found that socioeconomic characteristics alone are not sufficient to explain flood-coping appraisals. Particularly, observational learning from the social environment, such as friends and neighbors, is positively related to flood-coping appraisals. This suggests that social norms and networks play an important role in flood-preparedness decisions. Providing risk and coping information can also have a positive effect. Given the strong positive influence of the social environment on flood-coping appraisals, future research should investigate how risk communication can be enhanced by making use of the observed social norms and network effects.
For effective disaster risk management and adaptation planning, a good understanding of current and projected flood risk is required. Recent advances in quantifying flood risk at the regional and global scale have largely neglected critical infrastructure, or addressed this important sector with insufficient detail. Here, we present the first European-wide assessment of current and future flood risk to railway tracks for different global warming scenarios using an infrastructure-specific damage model. We find that the present risk, measured as expected annual damage, to railway networks in Europe is approx. (sic)581 million per year, with the highest risk relative to the length of the network in North Macedonia, Croatia, Norway, Portugal, and Germany. Based on an ensemble of climate projections for RCP8.5, we show that current risk to railway networks is projected to increase by 255% under a 1.5 degrees C, by 281% under a 2 degrees C, and by 310% under a 3 degrees C warming scenario. The largest increases in risk under a 3 degrees C scenario are projected for Slovakia, Austria, Slovenia, and Belgium. Our advances in the projection of flood risk to railway infrastructure are important given their criticality, and because losses to public infrastructure are usually not insured or even uninsurable in the private market. To cover the risk increase due to climate change, European member states would need to increase expenditure in transport by (sic)1.22 billion annually under a 3 degrees C warming scenario without further adaptation. Limiting global warming to the 1.5 degrees C goal of the Paris Agreement would result in avoided losses of (sic)317 million annually.
The number of people exposed to natural hazards has grown steadily over recent decades, mainly due to increasing exposure in hazard-prone areas. In the future, climate change could further enhance this trend. Still, empirical and comprehensive insights into individual recovery from natural hazards are largely lacking, hampering efforts to increase societal resilience. Drawing from a sample of 710 residents affected by flooding across Germany in June 2013, we empirically explore a wide range of variables possibly influencing self-reported recovery, including flood-event characteristics, the circumstances of the recovery process, socio-economic characteristics, and psychological factors, using multivariate statistics. We found that the amount of damage and other flood-event characteristics such as inundation depth are less important than socio-economic characteristics (e.g., sex or health status) and psychological factors (e.g., risk aversion and emotions). Our results indicate that uniform recovery efforts focusing on areas that were the most affected in terms of physical damage are insufficient to account for the heterogeneity in individual recovery results. To increase societal resilience, aid and recovery efforts should better address the long-term psychological effects of floods.
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.