@misc{MiechielsenSchmidt2024,
  author    = {Miechielsen, Milena and Schmidt, Katja},
  title     = {Leitfaden f{\"u}r die Erstellung von kommunalen Aktionspl{\"a}nen zur Steigerung der urbanen Klimaresilienz},
  doi       = {10.25932/publishup-63057},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-630578},
  pages     = {85},
  year      = {2024},
  abstract  = {Die durch Klimaver{\"a}nderungen hervorgerufenen Auswirkungen auf Menschen und Umwelt werden immer offensichtlicher: Neben der gesundheitlichen Gef{\"a}hrdung durch Hitzewellen, die deutschlandweit seit einigen Jahren eine steigende Rate an Todes- und Krankheitsf{\"a}llen zur Folge hat sind in den letzten Jahren zunehmend Starkniederschl{\"a}ge und daraus resultierenden {\"U}berschwemmungen bzw. Sturzfluten aufgetreten. Diese ziehen zum Teil immensen wirtschaftlichen Sch{\"a}den, aber auch Beeintr{\"a}chtigungen f{\"u}r die menschliche Gesundheit - sowohl physisch als auch psychisch - sowie gar Todesopfer nach sich. Es ist davon auszugehen, dass diese Extremwetterereignisse zuk{\"u}nftiger noch h{\"a}ufiger auftreten werden. Um die Bev{\"o}lkerung besser vor den Folgen dieser Wetterextreme zu sch{\"u}tzen, sind neben Klimaschutzmaßnahmen auch Vorsorge- und Anpassungsmaßnahmen zur Steigerung der kommunalen Klimaresilienz dringend notwendig. Dazu bedarf es einerseits einer Auseinandersetzung mit den eigenen kommunalen Risiken und daraus resultierenden Handlungsbedarfen, und andererseits eines interdisziplin{\"a}ren, querschnittsorientierten und prozessorientierten Planens und Handelns. Aktionspl{\"a}ne sollen diese beiden Aspekte b{\"u}ndeln. In den letzten Jahren sind einige kommunale und kommunen{\"u}bergreifende (Hitze-) aufgestellt worden. Diese unterscheiden sich jedoch in ihrem Inhalt und Umfang zum Teil erheblich. Mit dem vorliegenden Leitfaden soll eine effektive Hilfestellung geschaffen werden, um Kommunen bzw. die kommunale Verwaltung auf dem Weg zum eigenen Aktionsplan zu unterst{\"u}tzt. Dabei fokussiert der Leitfaden auf die Herausforderungen, die sich durch vermehrte Hitze- und Starkregenereignisse ergeben. Er st{\"u}tzt sich auf schon vorhandene Arbeitshilfen, Handlungsempfehlungen, Leitf{\"a}den und weitere Hinweise und verweist an vielen Stellen auch darauf. So soll ein praxistauglicher Leitfaden entstehen, der flexibel anwendbar ist. Mit Hilfe des vorliegenden Leitfadens k{\"o}nnen Kommunen ihre Aktivit{\"a}ten auf Hitze oder Starkregen fokussieren oder einen umfassenden Aktionsplan f{\"u}r beide Themenbereiche erstellen.},
  language  = {de}
}
@article{RoezerKreibichSchroeteretal.2019,
  author    = {R{\"o}zer, Viktor and Kreibich, Heidi and Schr{\"o}ter, Kai and M{\"u}ller, Meike and Sairam, Nivedita and Doss-Gollin, James and Lall, Upmanu and Merz, Bruno},
  title     = {Probabilistic Models Significantly Reduce Uncertainty in Hurricane Harvey Pluvial Flood Loss Estimates},
  series = {Earths future},
  volume    = {7},
  journal   = {Earths future},
  number    = {4},
  publisher = {American Geophysical Union},
  address   = {Washington},
  issn      = {2328-4277},
  doi       = {10.1029/2018EF001074},
  pages     = {384 -- 394},
  year      = {2019},
  abstract  = {Pluvial flood risk is mostly excluded in urban flood risk assessment. However, the risk of pluvial flooding is a growing challenge with a projected increase of extreme rainstorms compounding with an ongoing global urbanization. Considered as a flood type with minimal impacts when rainfall rates exceed the capacity of urban drainage systems, the aftermath of rainfall-triggered flooding during Hurricane Harvey and other events show the urgent need to assess the risk of pluvial flooding. Due to the local extent and small-scale variations, the quantification of pluvial flood risk requires risk assessments on high spatial resolutions. While flood hazard and exposure information is becoming increasingly accurate, the estimation of losses is still a poorly understood component of pluvial flood risk quantification. We use a new probabilistic multivariable modeling approach to estimate pluvial flood losses of individual buildings, explicitly accounting for the associated uncertainties. Except for the water depth as the common most important predictor, we identified the drivers for having loss or not and for the degree of loss to be different. Applying this approach to estimate and validate building structure losses during Hurricane Harvey using a property level data set, we find that the reliability and dispersion of predictive loss distributions vary widely depending on the model and aggregation level of property level loss estimates. Our results show that the use of multivariable zero-inflated beta models reduce the 90\% prediction intervalsfor Hurricane Harvey building structure loss estimates on average by 78\% (totalling U.S.\$3.8 billion) compared to commonly used models.},
  language  = {en}
}
@techreport{ThiekenDierckDunstetal.2018,
  author    = {Thieken, Annegret and Dierck, Julia and Dunst, Lea and G{\"o}pfert, Christian and Heidenreich, Anna and Hetz, Karen and Kern, Julia and Kern, Kristine and Lipp, Torsten and Lippert, Cordine and Meves, Monika and Niederhafner, Stefan and Otto, Antje and Rohrbacher, Christian and Schmidt, Katja and Strate, Leander and Stumpp, Inga and Walz, Ariane},
  title     = {Urbane Resilienz gegen{\"u}ber extremen Wetterereignissen - Typologien und Transfer von Anpassungsstrategien in kleinen Großst{\"a}dten und Mittelst{\"a}dten (ExTrass)},
  organization    = {Leibniz-Institut f{\"u}r Raumbezogene Sozialforschung, adelphi research gGmbH},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-416067},
  pages     = {102},
  year      = {2018},
  abstract  = {Weltweit verursachen St{\"a}dte etwa 70 \% der Treibhausgasemissionen und sind daher wichtige Akteure im Klimaschutz bzw. eine wichtige Zielgruppe von Klimapolitiken. Gleichzeitig sind St{\"a}dte besonders stark von m{\"o}glichen Auswirkungen des Klimawandels betroffen: Insbesondere extreme Wetterereignisse wie Hitzewellen oder Starkregenereignisse mit {\"U}berflutungen verursachen in St{\"a}dten hohe Sachsch{\"a}den und wirken sich negativ auf die Gesundheit der st{\"a}dtischen Bev{\"o}lkerung aus. Daher verfolgt das Projekt ExTrass das Ziel, die st{\"a}dtische Resilienz gegen{\"u}ber extremen Wetterereignissen in enger Zusammenarbeit mit Stadtverwaltungen, Strukturen des Bev{\"o}lkerungsschutzes und der Zivilgesellschaft zu st{\"a}rken. Im Fokus stehen dabei (kreisfreie) Groß- und Mittelst{\"a}dte mit 50.000 bis 500.000 Einwohnern, insbesondere die Fallstudienst{\"a}dte Potsdam, Remscheid und W{\"u}rzburg. Der vorliegende Bericht beinhaltet die Ergebnisse der 14-monatigen Definitionsphase von ExTrass, in der vor allem die Abstimmung eines Arbeitsprogramms im Mittelpunkt stand, das in einem nachfolgenden dreij{\"a}hrigen Forschungsprojekt (F+E-Phase) gemeinsam von Wissenschaft und Praxispartnern umgesetzt werden soll. Begleitend wurde eine Bestandsaufnahme von Klimaanpassungs- und Klimaschutzstrategien/-pl{\"a}nen in 99 deutschen Groß- und Mittelst{\"a}dten vorgenommen. Zudem wurden f{\"u}r Potsdam und W{\"u}rzburg Pfadanalysen f{\"u}r die Klimapolitik durchgef{\"u}hrt. Darin wird insbesondere die Bedeutung von Schl{\"u}sselakteuren deutlich. Weiterhin wurden im Rahmen von Stakeholder-Workshops Anpassungsherausforderungen und aktuelle Handlungsbedarfe in den Fallstudienst{\"a}dten identifiziert und L{\"o}sungsans{\"a}tze erarbeitet, die in der F+E-Phase entwickelt und getestet werden sollen. Neben Maßnahmen auf gesamtst{\"a}dtischer Ebene und auf Stadtteilebene wurden Maßnahmen angestrebt, die die Risikowahrnehmung, Vorsorge und Selbsthilfef{\"a}higkeit von Unternehmen und Bev{\"o}lkerung st{\"a}rken k{\"o}nnen. Daher wurde der Stand der Risikokommunikation in Deutschland f{\"u}r das Projekt aufgearbeitet und eine erste Evaluation von Risikokommunikationswerkzeugen durchgef{\"u}hrt. Der Bericht endet mit einer Kurzfassung des Arbeitsprogramms 2018-2021.},
  language  = {de}
}
@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}
}