@article{VogelWeiseSchroeteretal.2018,
  author    = {Vogel, Kristin and Weise, Laura and Schr{\"o}ter, Kai and Thieken, Annegret},
  title     = {Identifying Driving Factors in Flood-Damaging Processes Using Graphical Models},
  series = {Water resources research},
  volume    = {54},
  journal   = {Water resources research},
  number    = {11},
  publisher = {American Geophysical Union},
  address   = {Washington},
  issn      = {0043-1397},
  doi       = {10.1029/2018WR022858},
  pages     = {8864 -- 8889},
  year      = {2018},
  abstract  = {Flood damage estimation is a core task in flood risk assessments and requires reliable flood loss models. Identifying the driving factors of flood loss at residential buildings and gaining insight into their relations is important to improve our understanding of flood damage processes. For that purpose, we learn probabilistic graphical models, which capture and illustrate (in-)dependencies between the considered variables. The models are learned based on postevent surveys with flood-affected residents after six flood events, which occurred in Germany between 2002 and 2013. Besides the sustained building damage, the survey data contain information about flooding parameters, early warning and emergency measures, property-level mitigation measures and preparedness, socioeconomic characteristics of the household, and building characteristics. The analysis considers the entire data set with a total of 4,468 cases as well as subsets of the data set partitioned into single flood events and flood types: river floods, levee breaches, surface water flooding, and groundwater floods, to reveal differences in the damaging processes. The learned networks suggest that the flood loss ratio of residential buildings is directly influenced by hydrological and hydraulic aspects as well as by building characteristics and property-level mitigation measures. The study demonstrates also that for different flood events and process types the building damage is influenced by varying factors. This suggests that flood damage models need to be capable of reproducing these differences for spatial and temporal model transfers.},
  language  = {en}
}
@article{MetinNguyenVietDungSchroeteretal.2018,
  author    = {Metin, Ayse Duha and Nguyen Viet Dung, and Schr{\"o}ter, Kai and Guse, Bj{\"o}rn and Apel, Heiko and Kreibich, Heidi and Vorogushyn, Sergiy and Merz, Bruno},
  title     = {How do changes along the risk chain affect flood risk?},
  series = {Natural hazards and earth system sciences},
  volume    = {18},
  journal   = {Natural hazards and earth system sciences},
  number    = {11},
  publisher = {Copernicus},
  address   = {G{\"o}ttingen},
  issn      = {1561-8633},
  doi       = {10.5194/nhess-18-3089-2018},
  pages     = {3089 -- 3108},
  year      = {2018},
  abstract  = {Flood risk is impacted by a range of physical and socio-economic processes. Hence, the quantification of flood risk ideally considers the complete flood risk chain, from atmospheric processes through catchment and river system processes to damage mechanisms in the affected areas. Although it is generally accepted that a multitude of changes along the risk chain can occur and impact flood risk, there is a lack of knowledge of how and to what extent changes in influencing factors propagate through the chain and finally affect flood risk. To fill this gap, we present a comprehensive sensitivity analysis which considers changes in all risk components, i.e. changes in climate, catchment, river system, land use, assets, and vulnerability. The application of this framework to the mesoscale Mulde catchment in Germany shows that flood risk can vary dramatically as a consequence of plausible change scenarios. It further reveals that components that have not received much attention, such as changes in dike systems or in vulnerability, may outweigh changes in often investigated components, such as climate. Although the specific results are conditional on the case study area and the selected assumptions, they emphasize the need for a broader consideration of potential drivers of change in a comprehensive way. Hence, our approach contributes to a better understanding of how the different risk components influence the overall flood risk.},
  language  = {en}
}
@article{MerzApelDungNguyenetal.2018,
  author    = {Merz, Bruno and Apel, Heiko and Dung Nguyen, Viet-Dung and Falter, Daniela and Guse, Bj{\"o}rn and Hundecha, Yeshewatesfa and Kreibich, Heidi and Schr{\"o}ter, Kai and Vorogushyn, Sergiy},
  title     = {From precipitation to damage},
  series = {Global flood hazard : applications in modeling, mapping and forecasting},
  volume    = {233},
  journal   = {Global flood hazard : applications in modeling, mapping and forecasting},
  publisher = {American Geophysical Union},
  address   = {Washington},
  isbn      = {978-1-119-21788-6},
  issn      = {0065-8448},
  doi       = {10.1002/9781119217886.ch10},
  pages     = {169 -- 183},
  year      = {2018},
  abstract  = {Flood risk assessments for large river basins often involve piecing together smaller-scale assessments leading to erroneous risk statements. We describe a coupled model chain for quantifying flood risk at the scale of 100,000 km(2). It consists of a catchment model, a 1D-2D river network model, and a loss model. We introduce the model chain and present two applications. The first application for the Elbe River basin with an area of 66,000 km(2) demonstrates that it is feasible to simulate the complete risk chain for large river basins in a continuous simulation mode with high temporal and spatial resolution. In the second application, RFM is coupled to a multisite weather generator and applied to the Mulde catchment with an area of 6,000 km(2). This approach is able to provide a very long time series of spatially heterogeneous patterns of precipitation, discharge, inundation, and damage. These patterns respect the spatial correlation of the different processes and are suitable to derive large-scale risk estimates. We discuss how the RFM approach can be transferred to the continental scale.},
  language  = {en}
}
@article{MerzNguyenApeletal.2018,
  author    = {Merz, Bruno and Nguyen, Viet Dung and Apel, Heiko and Gerlitz, Lars and Schr{\"o}ter, Kai and Steirou, Eva Styliani and Vorogushyn, Sergiy},
  title     = {Spatial coherence of flood-rich and flood-poor periods across Germany},
  series = {Journal of hydrology},
  volume    = {559},
  journal   = {Journal of hydrology},
  publisher = {Elsevier},
  address   = {Amsterdam},
  issn      = {0022-1694},
  doi       = {10.1016/j.jhydrol.2018.02.082},
  pages     = {813 -- 826},
  year      = {2018},
  abstract  = {Despite its societal relevance, the question whether fluctuations in flood occurrence or magnitude are coherent in space has hardly been addressed in quantitative terms. We investigate this question for Germany by analysing fluctuations in annual maximum series (AMS) values at 68 discharge gauges for the common time period 1932-2005. We find remarkable spatial coherence across Germany given its different flood regimes. For example, there is a tendency that flood-rich/-poor years in sub-catchments of the Rhine basin, which are dominated by winter floods, coincide with flood-rich/-poor years in the southern sub-catchments of the Danube basin, which have their dominant flood season in summer. Our findings indicate that coherence is caused rather by persistence in catchment wetness than by persistent periods of higher/lower event precipitation. Further, we propose to differentiate between event-type and non-event-type coherence. There are quite a number of hydrological years with considerable nonevent-type coherence, i.e. AMS values of the 68 gauges are spread out through the year but in the same magnitude range. Years with extreme flooding tend to be of event-type and non-coherent, i.e. there is at least one precipitation event that affects many catchments to various degree. Although spatial coherence is a remarkable phenomenon, and large-scale flooding across Germany can lead to severe situations, extreme magnitudes across the whole country within one event or within one year were not observed in the investigated period. (C) 2018 Elsevier B.V. All rights reserved.},
  language  = {en}
}
@misc{MetinDungSchroeteretal.2018,
  author    = {Metin, Ayse Duha and Dung, Nguyen Viet and Schr{\"o}ter, Kai and Guse, Bj{\"o}rn and Apel, Heiko and Kreibich, Heidi and Vorogushyn, Sergiy and Merz, Bruno},
  title     = {How do changes along the risk chain affect flood risk?},
  series = {Postprints der Universit{\"a}t Potsdam : Mathematisch-Naturwissenschaftliche Reihe},
  journal   = {Postprints der Universit{\"a}t Potsdam : Mathematisch-Naturwissenschaftliche Reihe},
  number    = {1067},
  issn      = {1866-8372},
  doi       = {10.25932/publishup-46879},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-468790},
  pages     = {22},
  year      = {2018},
  abstract  = {Flood risk is impacted by a range of physical and socio-economic processes. Hence, the quantification of flood risk ideally considers the complete flood risk chain, from atmospheric processes through catchment and river system processes to damage mechanisms in the affected areas. Although it is generally accepted that a multitude of changes along the risk chain can occur and impact flood risk, there is a lack of knowledge of how and to what extent changes in influencing factors propagate through the chain and finally affect flood risk. To fill this gap, we present a comprehensive sensitivity analysis which considers changes in all risk components, i.e. changes in climate, catchment, river system, land use, assets, and vulnerability. The application of this framework to the mesoscale Mulde catchment in Germany shows that flood risk can vary dramatically as a consequence of plausible change scenarios. It further reveals that components that have not received much attention, such as changes in dike systems or in vulnerability, may outweigh changes in often investigated components, such as climate. Although the specific results are conditional on the case study area and the selected assumptions, they emphasize the need for a broader consideration of potential drivers of change in a comprehensive way. Hence, our approach contributes to a better understanding of how the different risk components influence the overall flood risk.},
  language  = {en}
}