TY - JOUR A1 - Wutzler, Bianca A1 - Hudson, Paul A1 - Thieken, Annegret T1 - Adaptation strategies of flood-damaged businesses in Germany JF - Frontiers in water N2 - 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. KW - risk management KW - climate change adaptation KW - floods KW - disaster risk KW - reduction KW - Germany KW - precaution KW - emergency management Y1 - 2022 U6 - https://doi.org/10.3389/frwa.2022.932061 SN - 2624-9375 VL - 4 PB - Frontiers Media CY - Lausanne ER - TY - JOUR A1 - Schroeter, Kai A1 - Kreibich, Heidi A1 - Vogel, Kristin A1 - Riggelsen, Carsten A1 - Scherbaum, Frank A1 - Merz, Bruno T1 - How useful are complex flood damage models? JF - Water resources research N2 - We investigate the usefulness of complex flood damage models for predicting relative damage to residential buildings in a spatial and temporal transfer context. We apply eight different flood damage models to predict relative building damage for five historic flood events in two different regions of Germany. Model complexity is measured in terms of the number of explanatory variables which varies from 1 variable up to 10 variables which are singled out from 28 candidate variables. Model validation is based on empirical damage data, whereas observation uncertainty is taken into consideration. The comparison of model predictive performance shows that additional explanatory variables besides the water depth improve the predictive capability in a spatial and temporal transfer context, i.e., when the models are transferred to different regions and different flood events. Concerning the trade-off between predictive capability and reliability the model structure seem more important than the number of explanatory variables. Among the models considered, the reliability of Bayesian network-based predictions in space-time transfer is larger than for the remaining models, and the uncertainties associated with damage predictions are reflected more completely. KW - floods KW - damage KW - model validation KW - Bayesian networks KW - regression tree Y1 - 2014 U6 - https://doi.org/10.1002/2013WR014396 SN - 0043-1397 SN - 1944-7973 VL - 50 IS - 4 SP - 3378 EP - 3395 PB - American Geophysical Union CY - Washington ER - TY - JOUR A1 - Schmidt, Lennart A1 - Hesse, Falk A1 - Attinger, Sabine A1 - Kumar, Rohini T1 - Challenges in applying machine learning models for hydrological inference BT - a case study for flooding events across Germany JF - Water resources research N2 - Machine learning (ML) algorithms are being increasingly used in Earth and Environmental modeling studies owing to the ever-increasing availability of diverse data sets and computational resources as well as advancement in ML algorithms. Despite advances in their predictive accuracy, the usefulness of ML algorithms for inference remains elusive. In this study, we employ two popular ML algorithms, artificial neural networks and random forest, to analyze a large data set of flood events across Germany with the goals to analyze their predictive accuracy and their usability to provide insights to hydrologic system functioning. The results of the ML algorithms are contrasted against a parametric approach based on multiple linear regression. For analysis, we employ a model-agnostic framework named Permuted Feature Importance to derive the influence of models' predictors. This allows us to compare the results of different algorithms for the first time in the context of hydrology. Our main findings are that (1) the ML models achieve higher prediction accuracy than linear regression, (2) the results reflect basic hydrological principles, but (3) further inference is hindered by the heterogeneity of results across algorithms. Thus, we conclude that the problem of equifinality as known from classical hydrological modeling also exists for ML and severely hampers its potential for inference. To account for the observed problems, we propose that when employing ML for inference, this should be made by using multiple algorithms and multiple methods, of which the latter should be embedded in a cross-validation routine. KW - machine learning KW - inference KW - floods Y1 - 2020 U6 - https://doi.org/10.1029/2019WR025924 SN - 0043-1397 SN - 1944-7973 VL - 56 IS - 5 PB - American Geophysical Union CY - Washington ER - TY - JOUR A1 - Macdonald, Elena A1 - Merz, Bruno A1 - Guse, Björn A1 - Wietzke, Luzie A1 - Ullrich, Sophie A1 - Kemter, Matthias A1 - Ahrens, Bodo A1 - Vorogushyn, Sergiy T1 - Event and catchment controls of heavy tail behavior of floods JF - Water resources research N2 - In some catchments, the distribution of annual maximum streamflow shows heavy tail behavior, meaning the occurrence probability of extreme events is higher than if the upper tail decayed exponentially. Neglecting heavy tail behavior can lead to an underestimation of the likelihood of extreme floods and the associated risk. Partly contradictory results regarding the controls of heavy tail behavior exist in the literature and the knowledge is still very dispersed and limited. To better understand the drivers, we analyze the upper tail behavior and its controls for 480 catchments in Germany and Austria over a period of more than 50 years. The catchments span from quickly reacting mountain catchments to large lowland catchments, allowing for general conclusions. We compile a wide range of event and catchment characteristics and investigate their association with an indicator of the tail heaviness of flood distributions, namely the shape parameter of the GEV distribution. Following univariate analyses of these characteristics, along with an evaluation of different aggregations of event characteristics, multiple linear regression models, as well as random forests, are constructed. A novel slope indicator, which represents the relation between the return period of flood peaks and event characteristics, captures the controls of heavy tails best. Variables describing the catchment response are found to dominate the heavy tail behavior, followed by event precipitation, flood seasonality, and catchment size. The pre-event moisture state in a catchment has no relevant impact on the tail heaviness even though it does influence flood magnitudes. KW - heavy tail behavior KW - floods KW - event characteristics KW - catchment KW - characteristics KW - catchment response Y1 - 2022 U6 - https://doi.org/10.1029/2021WR031260 SN - 0043-1397 SN - 1944-7973 VL - 58 IS - 6 PB - American Geophysical Union CY - Washington ER - TY - JOUR A1 - Do Thi Chinh, A1 - Bubeck, Philip A1 - Nguyen Viet Dung, A1 - Kreibich, Heidi T1 - The 2011 flood event in the Mekong Delta: preparedness, response, damage and recovery of private households and small businesses JF - Disasters : the journal of disaster studies, policy and management N2 - Floods frequently cause substantial economic and human losses, particularly in developing countries. For the development of sound flood risk management schemes that reduce flood consequences, detailed insights into the different components of the flood risk management cycle, such as preparedness, response, flood impact analyses and recovery, are needed. However, such detailed insights are often lacking: commonly, only (aggregated) data on direct flood damage are available. Other damage categories such as losses owing to the disruption of production processes are usually not considered, resulting in incomplete risk assessments and possibly inappropriate recommendations for risk management. In this paper, data from 858 face-to-face interviews among flood-prone households and small businesses in Can Tho city in the Vietnamese Mekong Delta are presented to gain better insights into the damage caused by the 2011 flood event and its management by households and businesses. KW - Can Tho KW - floods KW - flood damage KW - flood loss KW - flood risk management KW - Mekong Delta KW - Vietnam Y1 - 2016 U6 - https://doi.org/10.1111/disa.12171 SN - 0361-3666 SN - 1467-7717 VL - 40 SP - 753 EP - 778 PB - Wiley-Blackwell CY - Hoboken ER - TY - JOUR A1 - Bubeck, Philip A1 - Botzen, W. J. Wouter A1 - Laudan, Jonas A1 - Aerts, Jeroen C. J. H. A1 - Thieken, Annegret T1 - Insights into flood-coping appraisals of protection motivation theory BT - Empirical evidence from Germany and France JF - Risk analysis N2 - 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. KW - Coping appraisal KW - floods KW - protection motivation theory (PMT) KW - risk communication KW - social vulnerability Y1 - 2018 U6 - https://doi.org/10.1111/risa.12938 SN - 0272-4332 SN - 1539-6924 VL - 38 IS - 6 SP - 1239 EP - 1257 PB - Wiley CY - Hoboken ER -