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  - 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  - 
TY  - GEN
A1  - Wutzler, Bianca
A1  - Hudson, Paul
A1  - Thieken, Annegret
T1  - Adaptation strategies of flood-damaged businesses in Germany
T2  - Zweitveröffentlichungen der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe
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.
T3  - Zweitveröffentlichungen der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe - 1304 
KW  - risk management
KW  - climate change adaptation
KW  - floods
KW  - disaster risk reduction
KW  - Germany
KW  - precaution
KW  - emergency management
Y1  - 2023
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-577350
SN  - 1866-8372
IS  - 1304
ER  - 
TY  - THES
A1  - Uhlemann, Steffi
T1  - Understanding trans-basin floods in Germany : data, information and knowledge
T1  - Flussgebietsübergreifende Hochwasserereignisse in Deutschland : Daten, Informationen und Wissen.
N2  - Large Central European flood events of the past have demonstrated that flooding can affect several river basins at the same time leading to catastrophic economic and humanitarian losses that can stretch emergency resources beyond planned levels of service. For Germany, the spatial coherence of flooding, the contributing processes and the role of trans-basin floods for a national risk assessment is largely unknown and analysis is limited by a lack of systematic data, information and knowledge on past events. This study investigates the frequency and intensity of trans-basin flood events in Germany. It evaluates the data and information basis on which knowledge about trans-basin floods can be generated in order to improve any future flood risk assessment. In particu-lar, the study assesses whether flood documentations and related reports can provide a valuable data source for understanding trans-basin floods. An adaptive algorithm was developed that systematically captures trans-basin floods using series of mean daily discharge at a large number of sites of even time series length (1952-2002). It identifies the simultaneous occurrence of flood peaks based on the exceedance of an initial threshold of a 10 year flood at one location and consecutively pools all causally related, spatially and temporally lagged peak recordings at the other locations. A weighted cumulative index was developed that accounts for the spatial extent and the individual flood magnitudes within an event and allows quantifying the overall event severity. The parameters of the method were tested in a sensitivity analysis. An intensive study on sources and ways of information dissemination of flood-relevant publications in Germany was conducted. Based on the method of systematic reviews a strategic search approach was developed to identify relevant documentations for each of the 40 strongest trans-basin flood events. A novel framework for assessing the quality of event specific flood reports from a user’s perspective was developed and validated by independent peers. The framework was designed to be generally applicable for any natural hazard type and assesses the quality of a document addressing accessibility as well as representational, contextual, and intrinsic dimensions of quality. The analysis of time-series of mean daily discharge resulted in the identification of 80 trans-basin flood events within the period 1952-2002 in Germany. The set is dominated by events that were recorded in the hydrological winter (64%); 36% occurred during the summer months. The occurrence of floods is characterised by a distinct clustering in time. Dividing the study period into two sub-periods, we find an increase in the percentage of winter events from 58% in the first to 70.5% in the second sub-period. Accordingly, we find a significant increase in the number of extreme trans-basin floods in the second sub-period. A large body of 186 flood relevant documentations was identified. For 87.5% of the 40 strongest trans-basin floods in Germany at least one report has been found and for the most severe floods a substantial amount of documentation could be obtained. 80% of the material can be considered grey literature (i.e. literature not controlled by commercial publishers). The results of the quality assessment show that the majority of flood event specific reports are of a good quality, i.e. they are well enough drafted, largely accurate and objective, and contain a substantial amount of information on the sources, pathways and receptors/consequences of the floods. The inclusion of this information in the process of knowledge building for flood risk assessment is recommended. Both the results as well as the data produced in this study are openly accessible and can be used for further research. The results of this study contribute to an improved spatial risk assessment in Germany. The identified set of trans-basin floods provides the basis for an assessment of the chance that flooding occurs simultaneously at a number of sites. The information obtained from flood event documentation can usefully supplement the analysis of the processes that govern flood risk.
N2  - Abschätzungen zum Hochwasserrisiko beschränken sich zumeist auf die Analyse innerhalb eines Einzugsgebietes bzw. eines bestimmten Ortes. Die Zusammenhänge in größeren Regionen und vor allem Korrelationen zwischen verschiedenen Einzugsgebieten werden nur selten betrachtet. Solche einzugsgebietsübergreifenden Analysen sind jedoch sowohl für die Versicherungswirtschaft, den Katastrophenschutz sowie für großräumige strategische Hochwasserplanungen notwendig. Allerdings stehen nur für eine geringe Auswahl historischer Ereignisse Daten zur Verfügung und systematische Ansätze zu ihrer Erfassung wurden für Deutschland bisher nicht entwickelt. Die vorliegende Studie untersucht zum ersten die Häufigkeit und Intensität von flussgebietsübergreifenden Hochwasserereignissen in Deutschland anhand von gemessenen Abflüssen an einer Vielzahl von Stationen. Es können insgesamt 80 Hochwasserereignisse in Deutschland im Zeitraum von 1952-2002 nachgewiesen werden. Davon treten die meisten Ereignissen im hydrologischen Winterhalbjahr auf (64%). Wir können nachweisen, dass die Häufigkeit des Auftretens in verschiedenen Perioden unterschiedlich ist, und dass im Untersuchungszeitraum eine signifikante Zunahme von Winterereignissen und damit vor allem von sehr schweren flussgebietsübergreifenden Hochwasserereignissen zu verzeichnen ist. Die Studie hatte des Weiteren zum Ziel, die verfügbare Daten- und Informationsgrundlage zur Hochwasseranalyse zu erkunden. Im speziellen wird untersucht, inwieweit Hochwasserereignisdokumentationen und verwandte Berichte als eine weitere Datenquelle für ein verbessertes Prozessverständnis genutzt werden können. Im Rahmen einer systematischen Suche konnten für die 40 größten Hochwasserereignisse in Deutschland 186 relevante Berichte identifiziert werden. 80% des Materials kann als Grauliteratur eingestuft werden, d.h. als Veröffentlichungen welche nicht durch kommerzielle Verleger publiziert wird. Die sich daraus ergebende Frage nach der Qualität der Dokumente und ihres Informationsgehaltes wurde durch die Entwicklung und Anwendung eines Qualitätsbewertungsschemas beantwortet. Die Ergebnisse der Qualitätsbewertung zeigen, dass die Mehrheit der ereignisspezifischen Hochwasserberichte von guter Qualität ist, d.h. die Berichte sind in ausreichender Qualität verfasst, größtenteils korrekt und objektiv und beinhalten eine substantielle Menge an Informationen zu den Ursachen, Verläufen, betroffenen Objekten und Schäden eines Ereignisses. Es wird empfohlen diese Informationen in die Wissenssynthese für die Hochwasserrisikobewertung einfließen zu lassen. Sowohl Ergebnisse als auch Daten dieser Studie sind so publiziert, dass sie öffentlich zugänglich sind und für weitere Forschungsfragen genutzt werden können.
KW  - Hochwasser
KW  - Deutschland
KW  - Grauliteratur
KW  - Ereignisdokumentation
KW  - Qualitätsbewertung
KW  - floods
KW  - Germany
KW  - grey literature
KW  - event documentation
KW  - quality assessment framework
Y1  - 2013
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus-68868
ER  - 
TY  - GEN
A1  - Sultana, Zakia
A1  - Sieg, Tobias
A1  - Kellermann, Patric
A1  - Müller, Meike
A1  - Kreibich, Heidi
T1  - Assessment of business interruption of flood-affected companies using random forests
T2  - Postprints der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe
N2  - Losses due to floods have dramatically increased over the past decades, and losses of companies, comprising direct and indirect losses, have a large share of the total economic losses. Thus, there is an urgent need to gain more quantitative knowledge about flood losses, particularly losses caused by business interruption, in order to mitigate the economic loss of companies. However, business interruption caused by floods is rarely assessed because of a lack of sufficiently detailed data. A survey was undertaken to explore processes influencing business interruption, which collected information on 557 companies affected by the severe flood in June 2013 in Germany. Based on this data set, the study aims to assess the business interruption of directly affected companies by means of a Random Forests model. Variables that influence the duration and costs of business interruption were identified by the variable importance measures of Random Forests. Additionally, Random Forest-based models were developed and tested for their capacity to estimate business interruption duration and associated costs. The water level was found to be the most important variable influencing the duration of business interruption. Other important variables, relating to the estimation of business interruption duration, are the warning time, perceived danger of flood recurrence and inundation duration. In contrast, the amount of business interruption costs is strongly influenced by the size of the company, as assessed by the number of employees, emergency measures undertaken by the company and the fraction of customers within a 50 km radius. These results provide useful information and methods for companies to mitigate their losses from business interruption. However, the heterogeneity of companies is relatively high, and sector-specific analyses were not possible due to the small sample size. Therefore, further sector-specific analyses on the basis of more flood loss data of companies are recommended.
T3  - Zweitveröffentlichungen der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe - 939 
KW  - business interruption
KW  - floods
KW  - Random Forests
KW  - companies
KW  - variable importance
Y1  - 2020
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-459778
SN  - 1866-8372
IS  - 939
ER  - 
TY  - JOUR
A1  - Sultana, Zakia
A1  - Sieg, Tobias
A1  - Kellermann, Patric
A1  - Müller, Meike
A1  - Kreibich, Heidi
T1  - Assessment of business interruption of flood-affected companies using random forests
JF  - Water
N2  - Losses due to floods have dramatically increased over the past decades, and losses of companies, comprising direct and indirect losses, have a large share of the total economic losses. Thus, there is an urgent need to gain more quantitative knowledge about flood losses, particularly losses caused by business interruption, in order to mitigate the economic loss of companies. However, business interruption caused by floods is rarely assessed because of a lack of sufficiently detailed data. A survey was undertaken to explore processes influencing business interruption, which collected information on 557 companies affected by the severe flood in June 2013 in Germany. Based on this data set, the study aims to assess the business interruption of directly affected companies by means of a Random Forests model. Variables that influence the duration and costs of business interruption were identified by the variable importance measures of Random Forests. Additionally, Random Forest-based models were developed and tested for their capacity to estimate business interruption duration and associated costs. The water level was found to be the most important variable influencing the duration of business interruption. Other important variables, relating to the estimation of business interruption duration, are the warning time, perceived danger of flood recurrence and inundation duration. In contrast, the amount of business interruption costs is strongly influenced by the size of the company, as assessed by the number of employees, emergency measures undertaken by the company and the fraction of customers within a 50 km radius. These results provide useful information and methods for companies to mitigate their losses from business interruption. However, the heterogeneity of companies is relatively high, and sector-specific analyses were not possible due to the small sample size. Therefore, further sector-specific analyses on the basis of more flood loss data of companies are recommended.
KW  - business interruption
KW  - floods
KW  - Random Forests
KW  - companies
KW  - variable importance
Y1  - 2018
U6  - https://doi.org/10.3390/w10081049
SN  - 2073-4441
VL  - 10
IS  - 8
PB  - MDPI
CY  - Basel
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  - 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  - GEN
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
T2  - Postprints der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe
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.
T3  - Zweitveröffentlichungen der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe - 1193 
KW  - machine learning
KW  - inference
KW  - floods
Y1  - 2019
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-523843
SN  - 1866-8372
IS  - 5
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  -