TY - THES A1 - Schoppa, Lukas T1 - Dynamics in the flood vulnerability of companies T1 - Dynamik der Hochwasservulnerabilität von Unternehmen N2 - River flooding is a constant peril for societies, causing direct economic losses in the order of $100 billion worldwide each year. Under global change, the prolonged concentration of people and assets in floodplains is accompanied by an emerging intensification of flood extremes due to anthropogenic global warming, ultimately exacerbating flood risk in many regions of the world. Flood adaptation plays a key role in the mitigation of impacts, but poor understanding of vulnerability and its dynamics limits the validity of predominant risk assessment methods and impedes effective adaptation strategies. Therefore, this thesis investigates new methods for flood risk assessment that embrace the complexity of flood vulnerability, using the understudied commercial sector as an application example. Despite its importance for accurate risk evaluation, flood loss modeling has been based on univariable and deterministic stage-damage functions for a long time. However, such simplistic methods only insufficiently describe the large variation in damage processes, which initiated the development of multivariable and probabilistic loss estimation techniques. The first study of this thesis developed flood loss models for companies that are based on emerging statistical and machine learning approaches (i.e., random forest, Bayesian network, Bayesian regression). In a benchmarking experiment on basis of object-level loss survey data, the study showed that all proposed models reproduced the heterogeneity in damage processes and outperformed conventional stage-damage functions with respect to predictive accuracy. Another advantage of the novel methods is that they convey probabilistic information in predictions, which communicates the large remaining uncertainties transparently and, hence, supports well-informed risk assessment. Flood risk assessment combines vulnerability assessment (e.g., loss estimation) with hazard and exposure analyses. Although all of the three risk drivers interact and change over time, such dependencies and dynamics are usually not explicitly included in flood risk models. Recently, systemic risk assessment that dissolves the isolated consideration of risk drivers has gained traction, but the move to holistic risk assessment comes with limited thoroughness in terms of loss estimation and data limitations. In the second study, I augmented a socio-hydrological system dynamics model for companies in Dresden, Germany, with the multivariable Bayesian regression loss model from the first study. The additional process-detail and calibration data improved the loss estimation in the systemic risk assessment framework and contributed to more accurate and reliable simulations. The model uses Bayesian inference to quantify uncertainty and learn the model parameters from a combination of prior knowledge and diverse data. The third study demonstrates the potential of the socio-hydrological flood risk model for continuous, long-term risk assessment and management. Using hydroclimatic ad socioeconomic forcing data, I projected a wide range of possible risk trajectories until the end of the century, taking into account the adaptive behavior of companies. The study results underline the necessity of increased adaptation efforts to counteract the expected intensification of flood risk due to climate change. A sensitivity analysis of the effectiveness of different adaptation measures and strategies revealed that optimized adaptation has the potential to mitigate flood risk by up to 60%, particularly when combining structural and non-structural measures. Additionally, the application shows that systemic risk assessment is capable of capturing adverse long-term feedbacks in the human-flood system such as the levee effect. Overall, this thesis advances the representation of vulnerability in flood risk modeling by offering modeling solutions that embrace the complexity of human-flood interactions and quantify uncertainties consistently using probabilistic modeling. The studies show how scarce information in data and previous experiments can be integrated in the inference process to provide model predictions and simulations that are reliable and rich in information. Finally, the focus on the flood vulnerability of companies provides new insights into the heterogeneous damage processes and distinct flood coping of this sector. N2 - Flussüberschwemmungen sind eine ständige Gefahr für die Gesellschaft und verursachen jedes Jahr weltweit wirtschaftliche Schäden in der Größenordnung von 100 Milliarden US-Dollar. Im Zuge des globalen Wandels erhöht sich die Konzentration von Menschen und Vermögenswerten in Überschwemmungsgebieten kontinuierlich, während der menschengemachte Klimawandel Hochwasserextreme verstärkt. Die Überlagerung dieser Prozesse führt zu einer Verschärfung des Hochwasserrisikos in vielen Weltregionen. Der Hochwasseranapassung kommt dabei eine Schlüsselrolle bei der Abschwächung von Schäden zu. Allerdings ist das Verständnis von Hochwasservulnerabilität (d.h., Anfälligkeit gegenüber Schäden) und damit verbundener Dynamiken noch sehr begrenzt, was die Risikoabschätzung und die Entwicklung von Anpassungsstrategien erschwert. In dieser kumulativen Dissertation werden anhand von drei Studien neue Methoden zur Hochwasserrisikoabschätzung für den gewerblichen Sektor vorgestellt, der in der Vergangenheit wenig untersucht wurde. Die erste Studie präsentiert Hochwasserschadensmodelle die auf statistischen Methoden und maschinellem Lernen basieren und eine Vielzahl von Einflussfaktoren berücksichtigen. In Verbindung mit probabilistischen Vorhersagen führt dies zu einer Verbesserung der Modellgenauigkeit und -verlässlichkeit. Anschließend wird in einer Pilotstudie für Dresden, Deutschland, eines der neuen Schadensmodelle in ein ganzheitliches systemdynamisches Modell integriert, um Veränderungen in Hochwasservulnerabilität und -risiko kontinuierlich zu simulieren. Die Methode integriert zusätzliche Prozessdetails und Kalibrierungsdaten in das Modell und verbessert so die Simulationsleistung. Schließlich werden mit dem systemdynamischen Modell in der dritten Studie langfristige Projektionsläufe durchgeführt, um die Entwicklung des Hochwasserrisikos bis zum Ende des Jahrhunderts abzuschätzen. Die Ergebnisse der Studie unterstreichen das Potential von Hochwasseranpassung - insbesondere in Zeiten des Klimawandels - und demonstrieren die Fähigkeit ganzheitlicher Modellierungsansätze, ungünstige Entwicklungen des Risikos frühzeitig aufzudecken. Insgesamt verbessert diese Arbeit die Darstellung der Vulnerabilität in der Hochwasserrisikoabschätzung, indem sie Modellierungslösungen anbietet, die der Komplexität der Wechselwirkungen zwischen Mensch und Hochwasser gerecht werden und Unsicherheiten konsequent quantifizieren. KW - fluvial flooding KW - risk analysis KW - vulnerability KW - probabilistic modeling KW - Loss modeling KW - socio-hydrology KW - commercial sector KW - Flusshochwasser KW - Risikoanalyse KW - Vulnerabilität KW - probabilistische Modellierung KW - Schadensmodellierung KW - Soziohydrologie KW - gewerblicher Sektor Y1 - 2023 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-592424 ER - TY - THES A1 - Sieg, Tobias T1 - Reliability of flood damage estimations across spatial scales T1 - Verlässlichkeit von Hochwasserschadensschätzungen über räumliche Skalen N2 - Extreme Naturereignisse sind ein integraler Bestandteil der Natur der Erde. Sie werden erst dann zu Gefahren für die Gesellschaft, wenn sie diesen Ereignissen ausgesetzt ist. Dann allerdings können Naturgefahren verheerende Folgen für die Gesellschaft haben. Besonders hydro-meteorologische Gefahren wie zum Beispiel Flusshochwasser, Starkregenereignisse, Winterstürme, Orkane oder Tornados haben ein hohes Schadenspotential und treten rund um den Globus auf. Einhergehend mit einer immer wärmer werdenden Welt, werden auch Extremwetterereignisse, welche potentiell Naturgefahren auslösen können, immer wahrscheinlicher. Allerdings trägt nicht nur eine sich verändernde Umwelt zur Erhöhung des Risikos von Naturgefahren bei, sondern auch eine sich verändernde Gesellschaft. Daher ist ein angemessenes Risikomanagement erforderlich um die Gesellschaft auf jeder räumlichen Ebene an diese Veränderungen anzupassen. Ein essentieller Bestandteil dieses Managements ist die Abschätzung der ökonomischen Auswirkungen der Naturgefahren. Bisher allerdings fehlen verlässliche Methoden um die Auswirkungen von hydro-meteorologischen Gefahren abzuschätzen. Ein Hauptbestandteil dieser Arbeit ist daher die Entwicklung und Anwendung einer neuen Methode, welche die Verlässlichkeit der Schadensschätzung verbessert. Die Methode wurde beispielhaft zur Schätzung der ökonomischen Auswirkungen eines Flusshochwassers auf einzelne Unternehmen bis hin zu den Auswirkungen auf das gesamte Wirtschaftssystem Deutschlands erfolgreich angewendet. Bestehende Methoden geben meist wenig Information über die Verlässlichkeit ihrer Schätzungen. Da diese Informationen Entscheidungen zur Anpassung an das Risiko erleichtern, wird die Verlässlichkeit der Schadensschätzungen mit der neuen Methode dargestellt. Die Verlässlichkeit bezieht sich dabei nicht nur auf die Schadensschätzung selber, sondern auch auf die Annahmen, die über betroffene Gebäude gemacht werden. Nach diesem Prinzip kann auch die Verlässlichkeit von Annahmen über die Zukunft dargestellt werden, dies ist ein wesentlicher Aspekt für Prognosen. Die Darstellung der Verlässlichkeit und die erfolgreiche Anwendung zeigt das Potential der Methode zur Verwendung von Analysen für gegenwärtige und zukünftige hydro-meteorologische Gefahren. N2 - Natural extreme events are an integral part of nature on planet earth. Usually these events are only considered hazardous to humans, in case they are exposed. In this case, however, natural hazards can have devastating impacts on human societies. Especially hydro-meteorological hazards have a high damage potential in form of e.g. riverine and pluvial floods, winter storms, hurricanes and tornadoes, which can occur all over the globe. Along with an increasingly warm climate also an increase in extreme weather which potentially triggers natural hazards can be expected. Yet, not only changing natural systems, but also changing societal systems contribute to an increasing risk associated with these hazards. These can comprise increasing exposure and possibly also increasing vulnerability to the impacts of natural events. Thus, appropriate risk management is required to adapt all parts of society to existing and upcoming risks at various spatial scales. One essential part of risk management is the risk assessment including the estimation of the economic impacts. However, reliable methods for the estimation of economic impacts due to hydro-meteorological hazards are still missing. Therefore, this thesis deals with the question of how the reliability of hazard damage estimates can be improved, represented and propagated across all spatial scales. This question is investigated using the specific example of economic impacts to companies as a result of riverine floods in Germany. Flood damage models aim to describe the damage processes during a given flood event. In other words they describe the vulnerability of a specific object to a flood. The models can be based on empirical data sets collected after flood events. In this thesis tree-based models trained with survey data are used for the estimation of direct economic flood impacts on the objects. It is found that these machine learning models, in conjunction with increasing sizes of data sets used to derive the models, outperform state-of-the-art damage models. However, despite the performance improvements induced by using multiple variables and more data points, large prediction errors remain at the object level. The occurrence of the high errors was explained by a further investigation using distributions derived from tree-based models. The investigation showed that direct economic impacts to individual objects cannot be modeled by a normal distribution. Yet, most state-of-the-art approaches assume a normal distribution and take mean values as point estimators. Subsequently, the predictions are unlikely values within the distributions resulting in high errors. At larger spatial scales more objects are considered for the damage estimation. This leads to a better fit of the damage estimates to a normal distribution. Consequently, also the performance of the point estimators get better, although large errors can still occur due to the variance of the normal distribution. It is recommended to use distributions instead of point estimates in order to represent the reliability of damage estimates. In addition current approaches also mostly ignore the uncertainty associated with the characteristics of the hazard and the exposed objects. For a given flood event e.g. the estimation of the water level at a certain building is prone to uncertainties. Current approaches define exposed objects mostly by the use of land use data sets. These data sets often show inconsistencies, which introduce additional uncertainties. Furthermore, state-of-the-art approaches also imply problems of missing consistency when predicting the damage at different spatial scales. This is due to the use of different types of exposure data sets for model derivation and application. In order to face these issues a novel object-based method was developed in this thesis. The method enables a seamless estimation of hydro-meteorological hazard damage across spatial scales including uncertainty quantification. The application and validation of the method resulted in plausible estimations at all spatial scales without overestimating the uncertainty. Mainly newly available data sets containing individual buildings make the application of the method possible as they allow for the identification of flood affected objects by overlaying the data sets with water masks. However, the identification of affected objects with two different water masks revealed huge differences in the number of identified objects. Thus, more effort is needed for their identification, since the number of objects affected determines the order of magnitude of the economic flood impacts to a large extent. In general the method represents the uncertainties associated with the three components of risk namely hazard, exposure and vulnerability, in form of probability distributions. The object-based approach enables a consistent propagation of these uncertainties in space. Aside from the propagation of damage estimates and their uncertainties across spatial scales, a propagation between models estimating direct and indirect economic impacts was demonstrated. This enables the inclusion of uncertainties associated with the direct economic impacts within the estimation of the indirect economic impacts. Consequently, the modeling procedure facilitates the representation of the reliability of estimated total economic impacts. The representation of the estimates' reliability prevents reasoning based on a false certainty, which might be attributed to point estimates. Therefore, the developed approach facilitates a meaningful flood risk management and adaptation planning. The successful post-event application and the representation of the uncertainties qualifies the method also for the use for future risk assessments. Thus, the developed method enables the representation of the assumptions made for the future risk assessments, which is crucial information for future risk management. This is an important step forward, since the representation of reliability associated with all components of risk is currently lacking in all state-of-the-art methods assessing future risk. In conclusion, the use of object-based methods giving results in the form of distributions instead of point estimations is recommended. The improvement of the model performance by the means of multi-variable models and additional data points is possible, but small. Uncertainties associated with all components of damage estimation should be included and represented within the results. Furthermore, the findings of the thesis suggest that, at larger scales, the influence of the uncertainty associated with the vulnerability is smaller than those associated with the hazard and exposure. This leads to the conclusion that for an increased reliability of flood damage estimations and risk assessments, the improvement and active inclusion of hazard and exposure, including their uncertainties, is needed in addition to the improvements of the models describing the vulnerability of the objects. KW - hydro-meteorological risk KW - damage modeling KW - uncertainty KW - probabilistic approach KW - economic impacts KW - OpenStreetMap KW - hydro-meteorologische Risiken KW - Schadensmodellierung KW - Unsicherheiten KW - probabilistischer Ansatz KW - ökonomische Auswirkungen KW - OpenStreetMap Y1 - 2018 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-426161 ER -