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Flood risk management in Germany follows an integrative approach in which both private households and businesses can make an important contribution to reducing flood damage by implementing property-level adaptation measures. While the flood adaptation behavior of private households has already been widely researched, comparatively less attention has been paid to the adaptation strategies of businesses. However, their ability to cope with flood risk plays an important role in the social and economic development of a flood-prone region. Therefore, using quantitative survey data, this study aims to identify different strategies and adaptation drivers of 557 businesses damaged by a riverine flood in 2013 and 104 businesses damaged by pluvial or flash floods between 2014 and 2017. Our results indicate that a low perceived self-efficacy may be an important factor that can reduce the motivation of businesses to adapt to flood risk. Furthermore, property-owners tended to act more proactively than tenants. In addition, high experience with previous flood events and low perceived response costs could strengthen proactive adaptation behavior. These findings should be considered in business-tailored risk communication.
Flood damage estimation is a core task in flood risk assessments and requires reliable flood loss models. Identifying the driving factors of flood loss at residential buildings and gaining insight into their relations is important to improve our understanding of flood damage processes. For that purpose, we learn probabilistic graphical models, which capture and illustrate (in-)dependencies between the considered variables. The models are learned based on postevent surveys with flood-affected residents after six flood events, which occurred in Germany between 2002 and 2013. Besides the sustained building damage, the survey data contain information about flooding parameters, early warning and emergency measures, property-level mitigation measures and preparedness, socioeconomic characteristics of the household, and building characteristics. The analysis considers the entire data set with a total of 4,468 cases as well as subsets of the data set partitioned into single flood events and flood types: river floods, levee breaches, surface water flooding, and groundwater floods, to reveal differences in the damaging processes. The learned networks suggest that the flood loss ratio of residential buildings is directly influenced by hydrological and hydraulic aspects as well as by building characteristics and property-level mitigation measures. The study demonstrates also that for different flood events and process types the building damage is influenced by varying factors. This suggests that flood damage models need to be capable of reproducing these differences for spatial and temporal model transfers.
Weltweit verursachen Städte etwa 70 % der Treibhausgasemissionen und sind daher wichtige Akteure im Klimaschutz bzw. eine wichtige Zielgruppe von Klimapolitiken. Gleichzeitig sind Städte besonders stark von möglichen Auswirkungen des Klimawandels betroffen: Insbesondere extreme Wetterereignisse wie Hitzewellen oder Starkregenereignisse mit Überflutungen verursachen in Städten hohe Sachschäden und wirken sich negativ auf die Gesundheit der städtischen Bevölkerung aus. Daher verfolgt das Projekt ExTrass das Ziel, die städtische Resilienz gegenüber extremen Wetterereignissen in enger Zusammenarbeit mit Stadtverwaltungen, Strukturen des Bevölkerungsschutzes und der Zivilgesellschaft zu stärken. Im Fokus stehen dabei (kreisfreie) Groß- und Mittelstädte mit 50.000 bis 500.000 Einwohnern, insbesondere die Fallstudienstädte Potsdam, Remscheid und Würzburg.
Der vorliegende Bericht beinhaltet die Ergebnisse der 14-monatigen Definitionsphase von ExTrass, in der vor allem die Abstimmung eines Arbeitsprogramms im Mittelpunkt stand, das in einem nachfolgenden dreijährigen Forschungsprojekt (F+E-Phase) gemeinsam von Wissenschaft und Praxispartnern umgesetzt werden soll. Begleitend wurde eine Bestandsaufnahme von Klimaanpassungs- und Klimaschutzstrategien/-plänen in 99 deutschen Groß- und Mittelstädten vorgenommen. Zudem wurden für Potsdam und Würzburg Pfadanalysen für die Klimapolitik durchgeführt. Darin wird insbesondere die Bedeutung von Schlüsselakteuren deutlich. Weiterhin wurden im Rahmen von Stakeholder-Workshops Anpassungsherausforderungen und aktuelle Handlungsbedarfe in den Fallstudienstädten identifiziert und Lösungsansätze erarbeitet, die in der F+E-Phase entwickelt und getestet werden sollen. Neben Maßnahmen auf gesamtstädtischer Ebene und auf Stadtteilebene wurden Maßnahmen angestrebt, die die Risikowahrnehmung, Vorsorge und Selbsthilfefähigkeit von Unternehmen und Bevölkerung stärken können. Daher wurde der Stand der Risikokommunikation in Deutschland für das Projekt aufgearbeitet und eine erste Evaluation von Risikokommunikationswerkzeugen durchgeführt. Der Bericht endet mit einer Kurzfassung des Arbeitsprogramms 2018-2021.
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.
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.
As one of the 195 member countries of the United Nations, Germany signed the Sendai Framework for Disaster Risk Reduction 2015-2030 (SFDRR). Among other targets, the SFDRR aims at reducing direct economic losses caused by natural hazards by 2030. The United Nations Office for Disaster Risk Reduction (UNISDR) has hence proposed a methodology for estimating direct economic losses per event and country, based on experiences from developing countries. Since its usability in industrialized countries is unknown, this study presents the first implementation and validation of this approach in Germany. The methodology was tested for the three costliest natural hazard types in Germany, i.e. floods, wind and hail storms, considering 12 case studies between 1984 and 2016. Although the event-specific input data requirements are restricted to the number of damaged or destroyed units per sector, incomplete event documentations did not allow a full validation of all sectors necessary to describe the total direct economic loss. New modules (cars, forestry, paved roads, housing contents and overall costs of urban infrastructure) were developed to better adapt this methodology to German conditions. Whereas the original UNISDR methodology both over-and underestimates the losses of the tested events by a wide margin, the adapted methodology is able to calculate losses accounting well for all event types except for flash floods. Hence, this approach serves as a good starting point for macro-scale loss estimations. By implementing this approach into damage and event documentation and reporting standards, a consistent monitoring of the SFDRR could be achieved.