@misc{SamprognaMohorThiekenKorup2021,
  author    = {Samprogna Mohor, Guilherme and Thieken, Annegret and Korup, Oliver},
  title     = {Residential flood loss estimated from Bayesian multilevel models},
  series = {Postprints der Universit{\"a}t Potsdam : Mathematisch-Naturwissenschaftliche Reihe},
  journal   = {Postprints der Universit{\"a}t Potsdam : Mathematisch-Naturwissenschaftliche Reihe},
  issn      = {1866-8372},
  doi       = {10.25932/publishup-51774},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-517743},
  pages     = {1599 -- 1614},
  year      = {2021},
  abstract  = {Models for the predictions of monetary losses from floods mainly blend data deemed to represent a single flood type and region. Moreover, these approaches largely ignore indicators of preparedness and how predictors may vary between regions and events, challenging the transferability of flood loss models. We use a flood loss database of 1812 German flood-affected households to explore how Bayesian multilevel models can estimate normalised flood damage stratified by event, region, or flood process type. Multilevel models acknowledge natural groups in the data and allow each group to learn from others. We obtain posterior estimates that differ between flood types, with credibly varying influences of water depth, contamination, duration, implementation of property-level precautionary measures, insurance, and previous flood experience; these influences overlap across most events or regions, however. We infer that the underlying damaging processes of distinct flood types deserve further attention. Each reported flood loss and affected region involved mixed flood types, likely explaining the uncertainty in the coefficients. Our results emphasise the need to consider flood types as an important step towards applying flood loss models elsewhere. We argue that failing to do so may unduly generalise the model and systematically bias loss estimations from empirical data.},
  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{Huang2012,
  author    = {Huang, Shaochun},
  title     = {Modelling of environmental change impacts on water resources and hydrological extremes in Germany},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-59748},
  school      = {Universit{\"a}t Potsdam},
  year      = {2012},
  abstract  = {Water resources, in terms of quantity and quality, are significantly influenced by environmental changes, especially by climate and land use changes. The main objective of the present study is to project climate change impacts on the seasonal dynamics of water fluxes, spatial changes in water balance components as well as the future flood and low flow conditions in Germany. This study is based on the modeling results of the process-based eco-hydrological model SWIM (Soil and Water Integrated Model) driven by various regional climate scenarios on one hand. On the other hand, it is supported by statistical analysis on long-term trends of observed and simulated time series. In addition, this study evaluates the impacts of potential land use changes on water quality in terms of NO3-N load in selected sub-regions of the Elbe basin. In the context of climate change, the actual evapotransipration is likely to increase in most parts of Germany, while total runoff generation may decrease in south and east regions in the scenario period 2051-2060. Water discharge in all six studied large rivers (Ems, Weser, Saale, Danube, Main and Neckar) would be 8 - 30\% lower in summer and autumn compared to the reference period (1961 - 1990), and the strongest decline is expected for the Saale, Danube and Neckar. The 50-year low flow is likely to occur more frequently in western, southern and central Germany after 2061 as suggested by more than 80\% of the model runs. The current low flow period (from August to September) may be extended until the late autumn at the end of this century. Higher winter flow is expected in all of these rivers, and the increase is most significant for the Ems (about 18\%). No general pattern of changes in flood directions can be concluded according to the results driven by different RCMs, emission scenarios and multi-realizations. An optimal agricultural land use and management are essential for the reduction in nutrient loads and improvement of water quality. In the Weiße Elster and Unstrut sub-basins (Elbe), an increase of 10\% in the winter rape area can result in 12-19\% more NO3-N load in rivers. In contrast, another energy plant, maize, has a moderate effect on the water environment. Mineral fertilizers have a much stronger effect on the NO3-N load than organic fertilizers. Cover crops, which play an important role in the reduction of nitrate losses from fields, should be maintained on cropland. The uncertainty in estimating future high flows and, in particular, extreme floods remain high due to different RCM structures, emission scenarios and multi-realizations. In contrast, the projection of low flows under warmer climate conditions appears to be more pronounced and consistent. The largest source of uncertainty related to NO3-N modelling originates from the input data on the agricultural management.},
  language  = {en}
}