TY - JOUR A1 - Ozturk, Ugur A1 - Wendi, Dadiyorto A1 - Crisologo, Irene A1 - Riemer, Adrian A1 - Agarwal, Ankit A1 - Vogel, Kristin A1 - Andres Lopez-Tarazon, Jose A1 - Korup, Oliver T1 - Rare flash floods and debris flows in southern Germany JF - The science of the total environment : an international journal for scientific research into the environment and its relationship with man N2 - Flash floods and debris flows are iconic hazards inmountainous regions with steep relief, high rainfall intensities, rapid snowmelt events, and abundant sediments. The cuesta landscapes of southern Germany hardly come to mind when dealing with such hazards. A series of heavy rainstorms dumping up to 140mm in 2 h caused destructive flash floods and debris flows in May 2016. The most severe damage occurred in the Braunsbach municipality, which was partly buried by 42,000 m(3) of boulders, gravel, mud, and anthropogenic debris from the small catchment of Orlacher Bach (similar to 6 km(2)). We analysed this event by combining rainfall patterns, geological conditions, and geomorphic impacts to estimate an average sediment yield of 14,000 t/km(2) that mostly (similar to 95%) came from some 50 riparian landslides and channel-bed incision of similar to 2 m. This specific sediment yield ranks among the top 20% globally, while the intensity-duration curve of the rainstormis similarly in the upper percentile range of storms that had triggered landslides. Compared to similar-sized catchments in the greater region hit by the rainstorms, we find that the Orlacher Bach is above the 95th percentile in terms of steepness, storm-rainfall intensity, and topographic curvatures. The flash flood transported a sediment volume equal to as much as 20-40% of the Pleistocene sediment volume stored in the Orlacher Bach fan, andmay have had several predecessors in the Holocene. River control structures from 1903 and records of a debris flow in the 1920s in a nearby catchment indicate that the local inhabitants may have been aware of the debris-flow hazards earlier. Such recurring and destructive events elude flood-hazard appraisals in humid landscapes of gentle relief, and broaden mechanistic views of how landslides and debris flows contribute to shaping small and deeply cut tributaries in the southern Germany cuesta landscape. KW - Flash flood KW - Debris flow KW - Rainfall-triggered landslide KW - Hazard KW - Germany Y1 - 2018 U6 - https://doi.org/10.1016/j.scitotenv.2018.01.172 SN - 0048-9697 SN - 1879-1026 VL - 626 SP - 941 EP - 952 PB - Elsevier CY - Amsterdam ER - TY - JOUR A1 - Vogel, Kristin A1 - Weise, Laura A1 - Schröter, Kai A1 - Thieken, Annegret T1 - Identifying Driving Factors in Flood-Damaging Processes Using Graphical Models JF - Water resources research N2 - 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. KW - flood loss KW - Bayesian Network KW - Markov Blanket KW - vulnerability KW - Germany Y1 - 2018 U6 - https://doi.org/10.1029/2018WR022858 SN - 0043-1397 SN - 1944-7973 VL - 54 IS - 11 SP - 8864 EP - 8889 PB - American Geophysical Union CY - Washington ER -