TY - JOUR A1 - Bubeck, Philip A1 - Berghäuser, Lisa A1 - Hudson, Paul A1 - Thieken, Annegret T1 - Using panel data to understand the dynamics of human behavior in response to flooding JF - Risk analysis : an international journal N2 - 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. KW - adaptation behavior KW - floods KW - individual recovery KW - LCGA KW - panel data Y1 - 2020 U6 - https://doi.org/10.1111/risa.13548 SN - 0272-4332 SN - 1539-6924 VL - 40 IS - 11 SP - 2340 EP - 2359 PB - Wiley CY - Hoboken 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: 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 - 2019 VL - 56 IS - 5 PB - John Wiley & Sons, Inc. CY - New Jersey ER - TY - JOUR A1 - Sieg, Tobias A1 - Thieken, Annegret T1 - Improving flood impact estimations JF - Environmental research letters N2 - A reliable estimation of flood impacts enables meaningful flood risk management and rapid assessments of flood impacts shortly after a flood. The flood in 2021 in Central Europe and the analysis of its impacts revealed that these estimations are still inadequate. Therefore, we investigate the influence of different data sets and methods aiming to improve flood impact estimates. We estimated economic flood impacts to private households and companies for a flood event in 2013 in Germany using (a) two different flood maps, (b) two approaches to map exposed objects based on OpenStreetMap and the Basic European Asset Map, (c) two different approaches to estimate asset values, and (d) tree-based models and Stage-Damage-Functions to describe the vulnerability. At the macro scale, water masks lead to reasonable impact estimations. At the micro and meso-scale, the identification of affected objects by means of water masks is insufficient leading to unreliable estimations. The choice of exposure data sets is most influential on the estimations. We find that reliable impact estimations are feasible with reported numbers of flood-affected objects from the municipalities. We conclude that more effort should be put in the investigation of different exposure data sets and the estimation of asset values. Furthermore, we recommend the establishment of a reporting system in the municipalities for a fast identification of flood-affected objects shortly after an event. KW - rapid impact assessment KW - floods KW - OpenStreetMap KW - flood risk management KW - natural hazards Y1 - 2022 U6 - https://doi.org/10.1088/1748-9326/ac6d6c SN - 1748-9326 VL - 17 IS - 6 PB - IOP Publ. Ltd. CY - Bristol ER - 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 reduction KW - Germany KW - precaution KW - emergency management Y1 - 2022 U6 - https://doi.org/10.3389/frwa.2022.932061 SN - 2624-9375 PB - Frontiers Media SA CY - Lausanne, Schweiz ER -