TY  - THES
A1  - Rözer, Viktor
T1  - Pluvial flood loss to private households
T1  - Schäden durch urbane Sturzfluten in Privathaushalten
N2  - Today, more than half of the world’s population lives in urban areas. With a high density of population and assets, urban areas are not only the economic, cultural and social hubs of every society, they are also highly susceptible to natural disasters. As a consequence of rising sea levels and an expected increase in extreme weather events caused by a changing climate in combination with growing cities, flooding is an increasing threat to many urban agglomerations around the globe.
To mitigate the destructive consequences of flooding, appropriate risk management and adaptation strategies are required. So far, flood risk management in urban areas is almost exclusively focused on managing river and coastal flooding. Often overlooked is the risk from small-scale rainfall-triggered flooding, where the rainfall intensity of rainstorms exceeds the capacity of urban drainage systems, leading to immediate flooding. Referred to as pluvial flooding, this flood type exclusive to urban areas has caused severe losses in cities around the world. Without further intervention, losses from pluvial flooding are expected to increase in many urban areas due to an increase of impervious surfaces compounded with an aging drainage infrastructure and a projected increase in heavy precipitation events. While this requires the integration of pluvial flood risk into risk management plans, so far little is known about the adverse consequences of pluvial flooding due to a lack of both detailed data sets and studies on pluvial flood impacts. As a consequence, methods for reliably estimating pluvial flood losses, needed for pluvial flood risk assessment, are still missing.
Therefore, this thesis investigates how pluvial flood losses to private households can be reliably estimated, based on an improved understanding of the drivers of pluvial flood loss. For this purpose, detailed data from pluvial flood-affected households was collected through structured telephone- and web-surveys following pluvial flood events in Germany and the Netherlands.
Pluvial flood losses to households are the result of complex interactions between impact characteristics such as the water depth and a household’s resistance as determined by its risk awareness, preparedness, emergency response, building properties and other influencing factors. Both exploratory analysis and machine-learning approaches were used to analyze differences in resistance and impacts between households and their effects on the resulting losses. The comparison of case studies showed that the awareness around pluvial flooding among private households is quite low. Low awareness not only challenges the effective dissemination of early warnings, but was also found to influence the implementation of private precautionary measures. The latter were predominately implemented by households with previous experience of pluvial flooding. Even cases where previous flood events affected a different part of the same city did not lead to an increase in preparedness of the surveyed households, highlighting the need to account for small-scale variability in both impact and resistance parameters when assessing pluvial flood risk.
While it was concluded that the combination of low awareness, ineffective early warning and the fact that only a minority of buildings were adapted to pluvial flooding impaired the coping capacities of private households, the often low water levels still enabled households to mitigate or even prevent losses through a timely and effective emergency response.
These findings were confirmed by the detection of loss-influencing variables, showing that cases in which households were able to prevent any loss to the building structure are predominately explained by resistance variables such as the household’s risk awareness, while the degree of loss is mainly explained by impact variables.
Based on the important loss-influencing variables detected, different flood loss models were developed. Similar to flood loss models for river floods, the empirical data from the preceding data collection was used to train flood loss models describing the relationship between impact and resistance parameters and the resulting loss to building structures. Different approaches were adapted from river flood loss models using both models with the water depth as only predictor for building structure loss and models incorporating additional variables from the preceding variable detection routine.
The high predictive errors of all compared models showed that point predictions are not suitable for estimating losses on the building level, as they severely impair the reliability of the estimates. For that reason, a new probabilistic framework based on Bayesian inference was introduced that is able to provide predictive distributions instead of single loss estimates. These distributions not only give a range of probable losses, they also provide information on how likely a specific loss value is, representing the uncertainty in the loss estimate.
Using probabilistic loss models, it was found that the certainty and reliability of a loss estimate on the building level is not only determined by the use of additional predictors as shown in previous studies, but also by the choice of response distribution defining the shape of the predictive distribution. Here, a mix between a beta and a Bernoulli distribution to account for households that are able to prevent losses to their building’s structure was found to provide significantly more certain and reliable estimates than previous approaches using Gaussian or non-parametric response distributions.
The successful model transfer and post-event application to estimate building structure loss in Houston, TX, caused by pluvial flooding during Hurricane Harvey confirmed previous findings, and demonstrated the potential of the newly developed multi-variable beta model for future risk assessments. The highly detailed input data set constructed from openly available data sources containing over 304,000 affected buildings in Harris County further showed the potential of data-driven, building-level loss models for pluvial flood risk assessment.
In conclusion, pluvial flood losses to private households are the result of complex interactions between impact and resistance variables, which should be represented in loss models. The local occurrence of pluvial floods requires loss estimates on high spatial resolutions, i.e. on the building level, where losses are variable and uncertainties are high.
Therefore, probabilistic loss estimates describing the uncertainty of the estimate should be used instead of point predictions. While the performance of probabilistic models on the building level are mainly driven by the choice of response distribution, multi-variable models are recommended for two reasons:
First, additional resistance variables improve the detection of cases in which households were able to prevent structural losses.
Second, the added variability of additional predictors provides a better representation of the uncertainties when loss estimates from multiple buildings are aggregated.
This leads to the conclusion that data-driven probabilistic loss models on the building level allow for a reliable loss estimation at an unprecedented level of detail, with a consistent quantification of uncertainties on all aggregation levels. This makes the presented approach suitable for a wide range of applications, from decision support in spatial planning to impact- based early warning systems.
N2  - Über die Hälfte der Weltbevölkerung lebt heute in Städten. Mit einer hohen Dichte an Menschen, Gütern und Gebäuden sind Städte nicht nur die wirtschaftlichen, politischen und kulturellen Zentren einer Gesellschaft, sondern auch besonders anfällig gegenüber Naturkatastrophen. Insbesondere Hochwasser und Überflutungen stellen in Folge von steigenden Meeresspiegeln und einer erwarteten Zunahme von Extremwettereignissen eine wachsende Bedrohung in vielen Regionen dar.

Um die möglichen Folgen dieser Entwicklung zu vermeiden bzw. zu reduzieren, ist es notwendig sich der steigenden Gefahr durch geeignete Maßnahmen anzupassen. Bisher ist der Hochwasserschutz in Städten beinahe ausschließlich auf Überflutungen durch Flusshochwasser oder Sturmfluten fokussiert. Dabei werden sogenannte urbane Sturzfluten, die in den letzten Jahren vermehrt zu hohen Schäden in Städten geführt haben, nicht berücksichtigt. Bei urbanen Sturzfluten führen lokale Starkniederschläge mit hohen Regenmengen zu einer Überlastung des städtischen Abwassersystems und damit zu einer direkten, oft kleinräumigen Überflutung innerhalb eines bebauten Gebiets. Mit einer prognostizierten Zunahme von Starkniederschlägen, sowie einer baulichen Verdichtung und damit einhergehender Flächenversiegelung in vielen Städten, ist mit einer Zunahme von urbanen Sturzfluten zu rechnen. Dies verlangt die Einbindung des Risikos durch urbane Sturzfluten in bestehende Hochwasserschutzkonzepte. Bisher fehlen allerdings sowohl detaillierte Daten als auch Methoden um das Risiko durch urbane Sturzfluten und die dadurch verursachten Schäden, etwa an Wohngebäuden, zuverlässig abzuschätzen.  

Aus diesem Grund beschäftigt sich diese Arbeit hauptsächlich mit der Entwicklung von Verfahren und Modellen zur Abschätzung von Schäden an Privathaushalten durch urbane Sturzfluten. Dazu wurden detaillierte Daten durch Telefon- und Online-Umfragen nach urbanen Sturzflutereignissen in Deutschland und in den Niederlanden erhoben und ausgewertet. Die Erkenntnisse aus den detaillierten Analysen zu Vorsorge, Notmaßnahmen und Wiederherstellung, vor, während und nach urbanen Sturzflutereignissen, wurden genutzt um eine neue Methode zur Schätzung von Schäden an Wohngebäuden zu entwickeln. Dabei werden neben Angaben wie Dauer und Höhe der Überflutung, auch Eigenschaften von Haushalten, wie etwa deren Risikobewusstsein, in die Schätzung miteinbezogen. Nach lokaler Validierung wurde die neuentwickelte Methode beispielhaft zur Schätzung von Wohngebäudeschäden nach einem urbanen Sturzflutereignis im Großraum Houston (Texas, USA) erfolgreich angewendet. Anders als bei bisherigen Ansätzen wird der geschätzte Schaden eines Wohngebäudes nicht als einzelner Wert angegeben, sondern als Verteilung, welche die Bandbreite möglicher Schäden und deren Wahrscheinlichkeit angibt. Damit konnte die Zuverlässigkeit von Schadensschätzungen im Vergleich zu bisherigen Verfahren erheblich verbessert werden. Durch die erfolgreiche Anwendung sowohl auf der Ebene einzelner Gebäude als auch für gesamte Städte, ergibt sich ein breites Spektrum an Nutzungsmöglichkeiten, etwa als Entscheidungsunterstützung in der Stadtplanung oder für eine verbesserte Frühwarnung vor urbanen Sturzfluten.
KW  - Schadensmodellierung
KW  - Unsicherheiten
KW  - Starkregen
KW  - Privathaushalte
KW  - damage modeling
KW  - economic impacts
KW  - uncertainty
KW  - private households
KW  - probabilistic
KW  - pluvial flooding
Y1  - 2018
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-429910
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  -