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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 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.
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.
Ranking local climate policy
(2021)
Climate mitigation and climate adaptation are crucial tasks for urban areas and can involve synergies as well as trade-offs. However, few studies have examined how mitigation and adaptation efforts relate to each other in a large number of differently sized cities, and therefore we know little about whether forerunners in mitigation are also leading in adaptation or if cities tend to focus on just one policy field. This article develops an internationally applicable approach to rank cities on climate policy that incorporates multiple indicators related to (1) local commitments on mitigation and adaptation, (2) urban mitigation and adaptation plans and (3) climate adaptation and mitigation ambitions. We apply this method to rank 104 differently sized German cities and identify six clusters: climate policy leaders, climate adaptation leaders, climate mitigation leaders, climate policy followers, climate policy latecomers and climate policy laggards. The article seeks explanations for particular cities' positions and shows that coping with climate change in a balanced way on a high level depends on structural factors, in particular city size, the pathways of local climate policies since the 1990s and funding programmes for both climate mitigation and adaptation.
Cities can be severely affected by climate change. Hence, many of them have started to develop climate adaptation strategies or implement measures to help prepare for the challenges it will present. This study aims to provide an overview of climate adaptation in 104 German cities. While existing studies on adaptation tracking rely heavily on self-reported data or the mere existence of adaptation plans, we applied the broader concept of adaptation readiness, considering five factors and a total of twelve different indicators, when making our assessments. We clustered the cities depending on the contribution of these factors to the overall adaptation readiness index and grouped them according to their total score and cluster affiliations. This resulted in us identifying four groups of cities. First, a pioneering group comprises twelve (mainly big) cities with more than 500,000 inhabitants, which showed high scores for all five factors of adaptation readiness. Second, a set of 36 active cities, which follow different strategies on how to deal with climate adaptation. Third, a group of 28 cities showed considerably less activity toward climate adaptation, while a fourth set of 28 mostly small cities (with between 50,000 and 99,999 inhabitants) scored the lowest. We consider this final group to be pursuing a 'wait-and-see' approach. Since the city size correlates with the adaptation readiness index, we recommend policymakers introduce funding schemes that focus on supporting small cities, to help them prepare for the impact of a changing climate.
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.
River floods are among the most damaging natural hazards that frequently occur in Germany. Flooding causes high economic losses and impacts many residents. In 2016, several southern German municipalities were hit by flash floods after unexpectedly severe heavy rainfall, while in 2013 widespread river flooding had occurred. This study investigates and compares the psychological impacts of river floods and flash floods and potential consequences for precautionary behaviour. Data were collected using computer-aided telephone interviews that were conducted among flood-affected households around 9 months after each damaging event. This study applies Bayesian statistics and negative binomial regressions to test the suitability of psychological indicators to predict the precaution motivation of individuals. The results show that it is not the particular flood type but rather the severity and local impacts of the event that are crucial for the different, and potentially negative, impacts on mental health. According to the used data, however, predictions of the individual precaution motivation should not be based on the derived psychological indicators – i.e. coping appraisal, threat appraisal, burden and evasion – since their explanatory power was generally low and results are, for the most part, non-significant. Only burden reveals a significant positive relation to planned precaution regarding weak flash floods. In contrast to weak flash floods and river floods, the perceived threat of strong flash floods is significantly lower although feelings of burden and lower coping appraisals are more pronounced. Further research is needed to better include psychological assessment procedures and to focus on alternative data sources regarding floods and the connected precaution motivation of affected residents.