@phdthesis{Kellermann2017,
  author    = {Kellermann, Patric},
  title     = {Assessing natural risks for railway infrastructure and transportation in Austria},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-103877},
  school      = {Universit{\"a}t Potsdam},
  pages     = {x, 113},
  year      = {2017},
  abstract  = {Natural hazards can have serious societal and economic impacts. Worldwide, around one third of economic losses due to natural hazards are attributable to floods. The majority of natural hazards are triggered by weather-related extremes such as heavy precipitation, rapid snow melt, or extreme temperatures. Some of them, and in particular floods, are expected to further increase in terms of frequency and/or intensity in the coming decades due to the impacts of climate change. In this context, the European Alps areas are constantly disclosed as being particularly sensitive. In order to enhance the resilience of societies to natural hazards, risk assessments are substantial as they can deliver comprehensive risk information to be used as a basis for effective and sustainable decision-making in natural hazards management. So far, current assessment approaches mostly focus on single societal or economic sectors - e.g. flood damage models largely concentrate on private-sector housing - and other important sectors, such as the transport infrastructure sector, are widely neglected. However, transport infrastructure considerably contributes to economic and societal welfare, e.g. by ensuring mobility of people and goods. In Austria, for example, the national railway network is essential for the European transit of passengers and freights as well as for the development of the complex Alpine topography. Moreover, a number of recent experiences show that railway infrastructure and transportation is highly vulnerable to natural hazards. As a consequence, the Austrian Federal Railways had to cope with economic losses on the scale of several million euros as a result of flooding and other alpine hazards. The motivation of this thesis is to contribute to filling the gap of knowledge about damage to railway infrastructure caused by natural hazards by providing new risk information for actors and stakeholders involved in the risk management of railway transportation. Hence, in order to support the decision-making towards a more effective and sustainable risk management, the following two shortcomings in natural risks research are approached: i) the lack of dedicated models to estimate flood damage to railway infrastructure, and ii) the scarcity of insights into possible climate change impacts on the frequency of extreme weather events with focus on future implications for railway transportation in Austria. With regard to flood impacts to railway infrastructure, the empirically derived damage model Railway Infrastructure Loss (RAIL) proved expedient to reliably estimate both structural flood damage at exposed track sections of the Northern Railway and resulting repair cost. The results show that the RAIL model is capable of identifying flood risk hot spots along the railway network and, thus, facilitates the targeted planning and implementation of (technical) risk reduction measures. However, the findings of this study also show that the development and validation of flood damage models for railway infrastructure is generally constrained by the continuing lack of detailed event and damage data. In order to provide flood risk information on the large scale to support strategic flood risk management, the RAIL model was applied for the Austrian Mur River catchment using three different hydraulic scenarios as input as well as considering an increased risk aversion of the railway operator. Results indicate that the model is able to deliver comprehensive risk information also on the catchment level. It is furthermore demonstrated that the aspect of risk aversion can have marked influence on flood damage estimates for the study area and, hence, should be considered with regard to the development of risk management strategies. Looking at the results of the investigation on future frequencies of extreme weather events jeopardizing railway infrastructure and transportation in Austria, it appears that an increase in intense rainfall events and heat waves has to be expected, whereas heavy snowfall and cold days are likely to decrease. Furthermore, results indicate that frequencies of extremes are rather sensitive to changes of the underlying thresholds. It thus emphasizes the importance to carefully define, validate, and — if needed — to adapt the thresholds that are used to detect and forecast meteorological extremes. For this, continuous and standardized documentation of damaging events and near-misses is a prerequisite. Overall, the findings of the research presented in this thesis agree on the necessity to improve event and damage documentation procedures in order to enable the acquisition of comprehensive and reliable risk information via risk assessments and, thus, support strategic natural hazards management of railway infrastructure and transportation.},
  language  = {en}
}
@phdthesis{Roezer2018,
  author    = {R{\"o}zer, Viktor},
  title     = {Pluvial flood loss to private households},
  doi       = {10.25932/publishup-42991},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-429910},
  school      = {Universit{\"a}t Potsdam},
  pages     = {XXII, 109},
  year      = {2018},
  abstract  = {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.},
  language  = {en}
}
@phdthesis{Sieg2018,
  author    = {Sieg, Tobias},
  title     = {Reliability of flood damage estimations across spatial scales},
  doi       = {10.25932/publishup-42616},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-426161},
  school      = {Universit{\"a}t Potsdam},
  pages     = {XIII, 115},
  year      = {2018},
  abstract  = {Extreme Naturereignisse sind ein integraler Bestandteil der Natur der Erde. Sie werden erst dann zu Gefahren f{\"u}r die Gesellschaft, wenn sie diesen Ereignissen ausgesetzt ist. Dann allerdings k{\"o}nnen Naturgefahren verheerende Folgen f{\"u}r die Gesellschaft haben. Besonders hydro-meteorologische Gefahren wie zum Beispiel Flusshochwasser, Starkregenereignisse, Winterst{\"u}rme, Orkane oder Tornados haben ein hohes Schadenspotential und treten rund um den Globus auf. Einhergehend mit einer immer w{\"a}rmer werdenden Welt, werden auch Extremwetterereignisse, welche potentiell Naturgefahren ausl{\"o}sen k{\"o}nnen, immer wahrscheinlicher. Allerdings tr{\"a}gt nicht nur eine sich ver{\"a}ndernde Umwelt zur Erh{\"o}hung des Risikos von Naturgefahren bei, sondern auch eine sich ver{\"a}ndernde Gesellschaft. Daher ist ein angemessenes Risikomanagement erforderlich um die Gesellschaft auf jeder r{\"a}umlichen Ebene an diese Ver{\"a}nderungen anzupassen. Ein essentieller Bestandteil dieses Managements ist die Absch{\"a}tzung der {\"o}konomischen Auswirkungen der Naturgefahren. Bisher allerdings fehlen verl{\"a}ssliche Methoden um die Auswirkungen von hydro-meteorologischen Gefahren abzusch{\"a}tzen. Ein Hauptbestandteil dieser Arbeit ist daher die Entwicklung und Anwendung einer neuen Methode, welche die Verl{\"a}sslichkeit der Schadenssch{\"a}tzung verbessert. Die Methode wurde beispielhaft zur Sch{\"a}tzung der {\"o}konomischen Auswirkungen eines Flusshochwassers auf einzelne Unternehmen bis hin zu den Auswirkungen auf das gesamte Wirtschaftssystem Deutschlands erfolgreich angewendet. Bestehende Methoden geben meist wenig Information {\"u}ber die Verl{\"a}sslichkeit ihrer Sch{\"a}tzungen. Da diese Informationen Entscheidungen zur Anpassung an das Risiko erleichtern, wird die Verl{\"a}sslichkeit der Schadenssch{\"a}tzungen mit der neuen Methode dargestellt. Die Verl{\"a}sslichkeit bezieht sich dabei nicht nur auf die Schadenssch{\"a}tzung selber, sondern auch auf die Annahmen, die {\"u}ber betroffene Geb{\"a}ude gemacht werden. Nach diesem Prinzip kann auch die Verl{\"a}sslichkeit von Annahmen {\"u}ber die Zukunft dargestellt werden, dies ist ein wesentlicher Aspekt f{\"u}r Prognosen. Die Darstellung der Verl{\"a}sslichkeit und die erfolgreiche Anwendung zeigt das Potential der Methode zur Verwendung von Analysen f{\"u}r gegenw{\"a}rtige und zuk{\"u}nftige hydro-meteorologische Gefahren.},
  language  = {en}
}