TY - GEN A1 - Kuhlicke, Christian A1 - Masson, Torsten A1 - Kienzler, Sarah A1 - Sieg, Tobias A1 - Thieken, Annegret A1 - Kreibich, Heidi T1 - Multiple flood experiences and social resilience BT - Findings from three surveys on households and companies exposed to the 2013 flood in Germany T2 - Zweitveröffentlichungen der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe N2 - Previous studies have explored the consequences of flood events for exposed households and companies by focusing on single flood events. Less is known about the consequences of experiencing repeated flood events for the resilience of households and companies. In this paper, we therefore explore how multiple floods experience affects the resilience of exposed households and companies. Resilience was made operational through individual appraisals of households and companies' ability to withstand and recover from material as well as health and psychological impacts of the 2013 flood in Germany. The paper is based on three different datasets including more than 2000 households and 300 companies that were affected by the 2013 flood. The surveys revealed that the resilience of households seems to increase, but only with regard to their subjectively appraised ability to withstand impacts on mobile goods and equipment (e.g., cars, TV, and radios). In regard to the ability of households to withstand overall financial consequences of repetitive floods, evidence for nonlinear (quadratic) trends can be found. With regard to psychological and health-related consequences, the findings are mixed but provide tentative evidence for eroding resilience among households. Companies' resilience increased with respect to material assets but appears to decrease with respect to ability to recover. We conclude by arguing that clear and operational definitions of resilience are required so that evidence-based resilience baselines can be established to assess whether resilience is eroding or improving over time. T3 - Zweitveröffentlichungen der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe - 1398 KW - social science KW - Europe Y1 - 2020 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-516500 SN - 1866-8372 IS - 1 ER - TY - JOUR A1 - Kuhlicke, Christian A1 - Masson, Torsten A1 - Kienzler, Sarah A1 - Sieg, Tobias A1 - Thieken, Annegret A1 - Kreibich, Heidi T1 - Multiple flood experiences and social resilience BT - Findings from three surveys on households and companies exposed to the 2013 flood in Germany JF - Weather, Climate, and Society N2 - Previous studies have explored the consequences of flood events for exposed households and companies by focusing on single flood events. Less is known about the consequences of experiencing repeated flood events for the resilience of households and companies. In this paper, we therefore explore how multiple floods experience affects the resilience of exposed households and companies. Resilience was made operational through individual appraisals of households and companies' ability to withstand and recover from material as well as health and psychological impacts of the 2013 flood in Germany. The paper is based on three different datasets including more than 2000 households and 300 companies that were affected by the 2013 flood. The surveys revealed that the resilience of households seems to increase, but only with regard to their subjectively appraised ability to withstand impacts on mobile goods and equipment (e.g., cars, TV, and radios). In regard to the ability of households to withstand overall financial consequences of repetitive floods, evidence for nonlinear (quadratic) trends can be found. With regard to psychological and health-related consequences, the findings are mixed but provide tentative evidence for eroding resilience among households. Companies' resilience increased with respect to material assets but appears to decrease with respect to ability to recover. We conclude by arguing that clear and operational definitions of resilience are required so that evidence-based resilience baselines can be established to assess whether resilience is eroding or improving over time. KW - social science KW - Europe Y1 - 2020 U6 - https://doi.org/10.1175/WCAS-D-18-0069.1 SN - 1948-8327 SN - 1948-8335 VL - 12 IS - 1 SP - 63 EP - 88 PB - American Meteorological Society CY - Boston ER - TY - GEN A1 - Kuhlicke, Christian A1 - Seebauer, Sebastian A1 - Hudson, Paul A1 - Begg, Chloe A1 - Bubeck, Philip A1 - Dittmer, Cordula A1 - Grothmann, Torsten A1 - Heidenreich, Anna A1 - Kreibich, Heidi A1 - Lorenz, Daniel F. A1 - Masson, Torsten A1 - Reiter, Jessica A1 - Thaler, Thomas A1 - Thieken, Annegret A1 - Bamberg, Sebastian T1 - The behavioral turn in flood risk management, its assumptions and potential implications T2 - Zweitveröffentlichungen der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe N2 - Recent policy changes highlight the need for citizens to take adaptive actions to reduce flood-related impacts. Here, we argue that these changes represent a wider behavioral turn in flood risk management (FRM). The behavioral turn is based on three fundamental assumptions: first, that the motivations of citizens to take adaptive actions can be well understood so that these motivations can be targeted in the practice of FRM; second, that private adaptive measures and actions are effective in reducing flood risk; and third, that individuals have the capacities to implement such measures. We assess the extent to which the assumptions can be supported by empirical evidence. We do this by engaging with three intellectual catchments. We turn to research by psychologists and other behavioral scientists which focus on the sociopsychological factors which influence individual motivations (Assumption 1). We engage with economists, engineers, and quantitative risk analysts who explore the extent to which individuals can reduce flood related impacts by quantifying the effectiveness and efficiency of household-level adaptive measures (Assumption 2). We converse with human geographers and sociologists who explore the types of capacities households require to adapt to and cope with threatening events (Assumption 3). We believe that an investigation of the behavioral turn is important because if the outlined assumptions do not hold, there is a risk of creating and strengthening inequalities in FRM. Therefore, we outline the current intellectual and empirical knowledge as well as future research needs. Generally, we argue that more collaboration across intellectual catchments is needed, that future research should be more theoretically grounded and become methodologically more rigorous and at the same time focus more explicitly on the normative underpinnings of the behavioral turn. T3 - Zweitveröffentlichungen der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe - 1440 KW - capacities KW - effectiveness KW - motivation KW - resources KW - risk governance KW - vulnerability Y1 - 2020 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-517696 SN - 1866-8372 IS - 3 ER - TY - JOUR A1 - Kuhlicke, Christian A1 - Seebauer, Sebastian A1 - Hudson, Paul A1 - Begg, Chloe A1 - Bubeck, Philip A1 - Dittmer, Cordula A1 - Grothmann, Torsten A1 - Heidenreich, Anna A1 - Kreibich, Heidi A1 - Lorenz, Daniel F. A1 - Masson, Torsten A1 - Reiter, Jessica A1 - Thaler, Thomas A1 - Thieken, Annegret A1 - Bamberg, Sebastian T1 - The behavioral turn in flood risk management, its assumptions and potential implications JF - WIREs Water N2 - Recent policy changes highlight the need for citizens to take adaptive actions to reduce flood-related impacts. Here, we argue that these changes represent a wider behavioral turn in flood risk management (FRM). The behavioral turn is based on three fundamental assumptions: first, that the motivations of citizens to take adaptive actions can be well understood so that these motivations can be targeted in the practice of FRM; second, that private adaptive measures and actions are effective in reducing flood risk; and third, that individuals have the capacities to implement such measures. We assess the extent to which the assumptions can be supported by empirical evidence. We do this by engaging with three intellectual catchments. We turn to research by psychologists and other behavioral scientists which focus on the sociopsychological factors which influence individual motivations (Assumption 1). We engage with economists, engineers, and quantitative risk analysts who explore the extent to which individuals can reduce flood related impacts by quantifying the effectiveness and efficiency of household-level adaptive measures (Assumption 2). We converse with human geographers and sociologists who explore the types of capacities households require to adapt to and cope with threatening events (Assumption 3). We believe that an investigation of the behavioral turn is important because if the outlined assumptions do not hold, there is a risk of creating and strengthening inequalities in FRM. Therefore, we outline the current intellectual and empirical knowledge as well as future research needs. Generally, we argue that more collaboration across intellectual catchments is needed, that future research should be more theoretically grounded and become methodologically more rigorous and at the same time focus more explicitly on the normative underpinnings of the behavioral turn. KW - capacities KW - effectiveness KW - motivation KW - resources KW - risk governance KW - vulnerability Y1 - 2020 U6 - https://doi.org/10.1002/wat2.1418 SN - 2049-1948 VL - 7 IS - 3 SP - 1 EP - 22 PB - Wiley-Blackwell CY - Hoboken ER - TY - JOUR A1 - Brill, Fabio Alexander A1 - Passuni Pineda, Silvia A1 - Espichan Cuya, Bruno A1 - Kreibich, Heidi T1 - A data-mining approach towards damage modelling for El Nino events in Peru JF - Geomatics, natural hazards and risk N2 - Compound natural hazards likeEl Ninoevents cause high damage to society, which to manage requires reliable risk assessments. Damage modelling is a prerequisite for quantitative risk estimations, yet many procedures still rely on expert knowledge, and empirical studies investigating damage from compound natural hazards hardly exist. A nationwide building survey in Peru after theEl Ninoevent 2017 - which caused intense rainfall, ponding water, flash floods and landslides - enables us to apply data-mining methods for statistical groundwork, using explanatory features generated from remote sensing products and open data. We separate regions of different dominant characteristics through unsupervised clustering, and investigate feature importance rankings for classifying damage via supervised machine learning. Besides the expected effect of precipitation, the classification algorithms select the topographic wetness index as most important feature, especially in low elevation areas. The slope length and steepness factor ranks high for mountains and canyons. Partial dependence plots further hint at amplified vulnerability in rural areas. An example of an empirical damage probability map, developed with a random forest model, is provided to demonstrate the technical feasibility. KW - Natural hazard KW - damage model KW - residential buildings KW - data-mining KW - remote KW - sensing KW - open data Y1 - 2020 U6 - https://doi.org/10.1080/19475705.2020.1818636 SN - 1947-5705 SN - 1947-5713 VL - 11 IS - 1 SP - 1966 EP - 1990 PB - Routledge, Taylor & Francis Group CY - Abingdon ER - TY - JOUR A1 - Wietzke, Luzie M. A1 - Merz, Bruno A1 - Gerlitz, Lars A1 - Kreibich, Heidi A1 - Guse, Björn A1 - Castellarin, Attilio A1 - Vorogushyn, Sergiy T1 - Comparative analysis of scalar upper tail indicators JF - Hydrological sciences journal = Journal des sciences hydrologiques N2 - Different upper tail indicators exist to characterize heavy tail phenomena, but no comparative study has been carried out so far. We evaluate the shape parameter (GEV), obesity index, Gini index and upper tail ratio (UTR) against a novel benchmark of tail heaviness - the surprise factor. Sensitivity analyses to sample size and changes in scale-to-location ratio are carried out in bootstrap experiments. The UTR replicates the surprise factor best but is most uncertain and only comparable between records of similar length. For samples with symmetric Lorenz curves, shape parameter, obesity and Gini indices provide consistent indications. For asymmetric Lorenz curves, however, the first two tend to overestimate, whereas Gini index tends to underestimate tail heaviness. We suggest the use of a combination of shape parameter, obesity and Gini index to characterize tail heaviness. These indicators should be supported with calculation of the Lorenz asymmetry coefficients and interpreted with caution. KW - upper tail behaviour KW - heavy-tailed distributions KW - extremes KW - diagnostics KW - surprise Y1 - 2020 U6 - https://doi.org/10.1080/02626667.2020.1769104 SN - 0262-6667 SN - 2150-3435 VL - 65 IS - 10 SP - 1625 EP - 1639 PB - Routledge, Taylor & Francis Group CY - Abingdon ER - TY - JOUR A1 - Kellermann, Patric A1 - Schröter, Kai A1 - Thieken, Annegret A1 - Haubrock, Sören-Nils A1 - Kreibich, Heidi T1 - The object-specific flood damage database HOWAS 21 JF - Natural hazards and earth system sciences N2 - The Flood Damage Database HOWAS 21 contains object-specific flood damage data resulting from fluvial, pluvial and groundwater flooding. The datasets incorporate various variables of flood hazard, exposure, vulnerability and direct tangible damage at properties from several economic sectors. The main purpose of development of HOWAS 21 was to support forensic flood analysis and the derivation of flood damage models. HOWAS 21 was first developed for Germany and currently almost exclusively contains datasets from Germany. However, its scope has recently been enlarged with the aim to serve as an international flood damage database; e.g. its web application is now available in German and English. This paper presents the recent advancements of HOWAS 21 and highlights exemplary analyses to demonstrate the use of HOWAS 21 flood damage data. The data applications indicate a large potential of the database for fostering a better understanding and estimation of the consequences of flooding. Y1 - 2020 U6 - https://doi.org/10.5194/nhess-20-2503-2020 SN - 1561-8633 SN - 1684-9981 VL - 20 IS - 9 SP - 2503 EP - 2519 PB - Copernicus CY - Göttingen ER - TY - JOUR A1 - Metin, Ayse Duha A1 - Dung, Nguyen Viet A1 - Schröter, Kai A1 - Vorogushyn, Sergiy A1 - Guse, Björn A1 - Kreibich, Heidi A1 - Merz, Bruno T1 - The role of spatial dependence for large-scale flood risk estimation JF - Natural hazards and earth system sciences N2 - Flood risk assessments are typically based on scenarios which assume homogeneous return periods of flood peaks throughout the catchment. This assumption is unrealistic for real flood events and may bias risk estimates for specific return periods. We investigate how three assumptions about the spatial dependence affect risk estimates: (i) spatially homogeneous scenarios (complete dependence), (ii) spatially heterogeneous scenarios (modelled dependence) and (iii) spatially heterogeneous but uncorrelated scenarios (complete independence). To this end, the model chain RFM (regional flood model) is applied to the Elbe catchment in Germany, accounting for the spatio-temporal dynamics of all flood generation processes, from the rainfall through catchment and river system processes to damage mechanisms. Different assumptions about the spatial dependence do not influence the expected annual damage (EAD); however, they bias the risk curve, i.e. the cumulative distribution function of damage. The widespread assumption of complete dependence strongly overestimates flood damage of the order of 100% for return periods larger than approximately 200 years. On the other hand, for small and medium floods with return periods smaller than approximately 50 years, damage is underestimated. The overestimation aggravates when risk is estimated for larger areas. This study demonstrates the importance of representing the spatial dependence of flood peaks and damage for risk assessments. Y1 - 2020 U6 - https://doi.org/10.5194/nhess-20-967-2020 SN - 1561-8633 SN - 1684-9981 VL - 20 IS - 4 SP - 967 EP - 979 PB - European Geosciences Union (EGU) ; Copernicus CY - Göttingen ER - TY - JOUR A1 - Paprotny, Dominik A1 - Kreibich, Heidi A1 - Morales-Napoles, Oswaldo A1 - Wagenaar, Dennis A1 - Castellarin, Attilio A1 - Carisi, Francesca A1 - Bertin, Xavier A1 - Merz, Bruno A1 - Schröter, Kai T1 - A probabilistic approach to estimating residential losses from different flood types JF - Natural hazards : journal of the International Society for the Prevention and Mitigation of Natural Hazards N2 - Residential assets, comprising buildings and household contents, are a major source of direct flood losses. Existing damage models are mostly deterministic and limited to particular countries or flood types. Here, we compile building-level losses from Germany, Italy and the Netherlands covering a wide range of fluvial and pluvial flood events. Utilizing a Bayesian network (BN) for continuous variables, we find that relative losses (i.e. loss relative to exposure) to building structure and its contents could be estimated with five variables: water depth, flow velocity, event return period, building usable floor space area and regional disposable income per capita. The model's ability to predict flood losses is validated for the 11 flood events contained in the sample. Predictions for the German and Italian fluvial floods were better than for pluvial floods or the 1993 Meuse river flood. Further, a case study of a 2010 coastal flood in France is used to test the BN model's performance for a type of flood not included in the survey dataset. Overall, the BN model achieved better results than any of 10 alternative damage models for reproducing average losses for the 2010 flood. An additional case study of a 2013 fluvial flood has also shown good performance of the model. The study shows that data from many flood events can be combined to derive most important factors driving flood losses across regions and time, and that resulting damage models could be applied in an open data framework. KW - fluvial floods KW - coastal floods KW - pluvial floods KW - Bayesian networks KW - flood KW - damage surveys Y1 - 2020 U6 - https://doi.org/10.1007/s11069-020-04413-x SN - 0921-030X SN - 1573-0840 VL - 105 IS - 3 SP - 2569 EP - 2601 PB - Springer CY - New York ER - TY - JOUR A1 - Schoppa, Lukas A1 - Sieg, Tobias A1 - Vogel, Kristin A1 - Zöller, Gert A1 - Kreibich, Heidi T1 - Probabilistic flood loss models for companies JF - Water resources research N2 - Flood loss modeling is a central component of flood risk analysis. Conventionally, this involves univariable and deterministic stage-damage functions. Recent advancements in the field promote the use of multivariable and probabilistic loss models, which consider variables beyond inundation depth and account for prediction uncertainty. Although companies contribute significantly to total loss figures, novel modeling approaches for companies are lacking. Scarce data and the heterogeneity among companies impede the development of company flood loss models. We present three multivariable flood loss models for companies from the manufacturing, commercial, financial, and service sector that intrinsically quantify prediction uncertainty. Based on object-level loss data (n = 1,306), we comparatively evaluate the predictive capacity of Bayesian networks, Bayesian regression, and random forest in relation to deterministic and probabilistic stage-damage functions, serving as benchmarks. The company loss data stem from four postevent surveys in Germany between 2002 and 2013 and include information on flood intensity, company characteristics, emergency response, private precaution, and resulting loss to building, equipment, and goods and stock. We find that the multivariable probabilistic models successfully identify and reproduce essential relationships of flood damage processes in the data. The assessment of model skill focuses on the precision of the probabilistic predictions and reveals that the candidate models outperform the stage-damage functions, while differences among the proposed models are negligible. Although the combination of multivariable and probabilistic loss estimation improves predictive accuracy over the entire data set, wide predictive distributions stress the necessity for the quantification of uncertainty. KW - flood loss estimation KW - probabilistic modeling KW - companies KW - multivariable KW - models Y1 - 2020 U6 - https://doi.org/10.1029/2020WR027649 SN - 0043-1397 SN - 1944-7973 VL - 56 IS - 9 PB - American Geophysical Union CY - Washington ER -