@article{SchoppaSiegVogeletal.2020,
  author    = {Schoppa, Lukas and Sieg, Tobias and Vogel, Kristin and Z{\"o}ller, Gert and Kreibich, Heidi},
  title     = {Probabilistic flood loss models for companies},
  series = {Water resources research},
  volume    = {56},
  journal   = {Water resources research},
  number    = {9},
  publisher = {American Geophysical Union},
  address   = {Washington},
  issn      = {0043-1397},
  doi       = {10.1029/2020WR027649},
  pages     = {19},
  year      = {2020},
  abstract  = {Flood loss modeling is a central component of flood risk analysis. Conventionally, this involves univariable and deterministic stage-damage functions. Recent advancements in the field promote the use of multivariable and probabilistic loss models, which consider variables beyond inundation depth and account for prediction uncertainty. Although companies contribute significantly to total loss figures, novel modeling approaches for companies are lacking. Scarce data and the heterogeneity among companies impede the development of company flood loss models. We present three multivariable flood loss models for companies from the manufacturing, commercial, financial, and service sector that intrinsically quantify prediction uncertainty. Based on object-level loss data (n = 1,306), we comparatively evaluate the predictive capacity of Bayesian networks, Bayesian regression, and random forest in relation to deterministic and probabilistic stage-damage functions, serving as benchmarks. The company loss data stem from four postevent surveys in Germany between 2002 and 2013 and include information on flood intensity, company characteristics, emergency response, private precaution, and resulting loss to building, equipment, and goods and stock. We find that the multivariable probabilistic models successfully identify and reproduce essential relationships of flood damage processes in the data. The assessment of model skill focuses on the precision of the probabilistic predictions and reveals that the candidate models outperform the stage-damage functions, while differences among the proposed models are negligible. Although the combination of multivariable and probabilistic loss estimation improves predictive accuracy over the entire data set, wide predictive distributions stress the necessity for the quantification of uncertainty.},
  language  = {en}
}
@techreport{BerghaeuserSchoppaUlrichetal.2021,
  author    = {Bergh{\"a}user, Lisa and Schoppa, Lukas and Ulrich, Jana and Dillenardt, Lisa and Jurado, Oscar E. and Passow, Christian and Samprogna Mohor, Guilherme and Seleem, Omar and Petrow, Theresia and Thieken, Annegret},
  title     = {Starkregen in Berlin},
  doi       = {10.25932/publishup-50056},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-500560},
  pages     = {44},
  year      = {2021},
  abstract  = {In den Sommern der Jahre 2017 und 2019 kam es in Berlin an mehreren Orten zu {\"U}berschwemmungen in Folge von Starkregenereignissen. In beiden Jahren f{\"u}hrte dies zu erheblichen Beeintr{\"a}chtigungen im Alltag der Berliner:innen sowie zu hohen Sachsch{\"a}den. Eine interdisziplin{\"a}re Taskforce des DFG-Graduiertenkollegs NatRiskChange untersuchte (1) die meteorologischen Eigenschaften zweier besonders eindr{\"u}cklicher Unwetter, sowie (2) die Vulnerabilit{\"a}t der Berliner Bev{\"o}lkerung gegen{\"u}ber Starkregen. Eine vergleichende meteorologische Rekonstruktion der Starkregenereignisse von 2017 und 2019 ergab deutliche Unterschiede in der Entstehung und den {\"U}berschreitungswahrscheinlichkeiten der beiden Unwetter. So war das Ereignis von 2017 mit einer relativ großen r{\"a}umlichen Ausdehnung und langer Dauer ein untypisches Starkregenereignis, w{\"a}hrend es sich bei dem Unwetter von 2019 um ein typisches, kurzzeitiges Starkregenereignis mit ausgepr{\"a}gter r{\"a}umlicher Heterogenit{\"a}t handelte. Eine anschließende statistische Analyse zeigte, dass das Ereignis von 2017 f{\"u}r l{\"a}ngere Niederschlagsdauern (>=24 h) als großfl{\"a}chiges Extremereignis mit {\"U}berschreitungswahrscheinlichkeiten von unter 1 \% einzuordnen ist (d.h. Wiederkehrperioden >=100 Jahre). Im Jahr 2019 wurden dagegen {\"a}hnliche {\"U}berschreitungswahrscheinlichkeiten nur lokal und f{\"u}r k{\"u}rzere Zeitr{\"a}ume (1-2 h) berechnet. Die Vulnerabilit{\"a}tsanalyse basiert auf einer von April bis Juni 2020 in Berlin durchgef{\"u}hrten Onlinebefragung. Diese richtete sich an Personen, die bereits von vergangenen Starkregenereignissen betroffen waren und thematisierte das Schadensereignis selbst, daraus entstandene Beeintr{\"a}chtigungen und Sch{\"a}den, Risikowahrnehmung sowie Notfall- und Vorsorgemaßnahmen. Die erhobenen Umfragedaten (n=102) beziehen sich vornehmlich auf die Ereignisse von 2017 und 2019 und zeigen, dass die Berliner Bev{\"o}lkerung sowohl im Alltag (z.B. bei der Beschaffung von Lebensmitteln) als auch im eigenen Haushalt (z.B. durch {\"U}berschwemmungssch{\"a}den) von den Unwettern beeintr{\"a}chtigt war. Zudem deuteten die Antworten der Betroffenen auf M{\"o}glichkeiten hin, die Vulnerabilit{\"a}t der Gesellschaft gegen{\"u}ber Starkregen weiter zu reduzieren - etwa durch die Unterst{\"u}tzung besonders betroffener Gruppen (z.B. Pflegende), durch gezielte Informationskampagnen zum Schutz vor Starkregen oder durch die Erh{\"o}hung der Reichweite von Unwetterwarnungen. Eine statistische Analyse zur Effektivit{\"a}t privater Notfall- und Vorsorgemaßnahmen auf Grundlage der Umfragedaten best{\"a}tigte vorherige Studienergebnisse. So gab es Anhaltspunkte daf{\"u}r, dass durch das Umsetzen von Vorsorgemaßnahmen wie beispielsweise das Installieren von R{\"u}ckstauklappen, Barriere-Systemen oder Pumpen Starkregensch{\"a}den reduziert werden k{\"o}nnen. Die Ergebnisse dieses Berichts unterstreichen die Notwendigkeit f{\"u}r ein integriertes Starkregenrisikomanagment, das die Risikokomponenten Gef{\"a}hrdung, Vulnerabilit{\"a}t und Exposition ganzheitlich und auf mehreren Ebenen (z.B. staatlich, kommunal, privat) betrachtet.},
  language  = {de}
}
@phdthesis{Schoppa2023,
  author    = {Schoppa, Lukas},
  title     = {Dynamics in the flood vulnerability of companies},
  doi       = {10.25932/publishup-59242},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-592424},
  school      = {Universit{\"a}t Potsdam},
  pages     = {X, 165},
  year      = {2023},
  abstract  = {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.},
  language  = {en}
}