TY - JOUR A1 - Vogel, Kristin A1 - Ozturk, Ugur A1 - Riemer, Adrian A1 - Laudan, Jonas A1 - Sieg, Tobias A1 - Wendi, Dadiyorto A1 - Agarwal, Ankit A1 - Roezer, Viktor A1 - Korup, Oliver A1 - Thieken, Annegret T1 - Die Sturzflut von Braunsbach am 29. Mai 2016 – Entstehung, Ablauf und Schäden eines „Jahrhundertereignisses“ T1 - The Braunsbach Flashflood of Mai 29th, 2016-Origin, Pathways and Impacts of an Extreme Hydro-Meteorological Event BT - Teil 2: Geomorphologische Prozesse und Schadensanalyse BT - Part 2: Geomorphological Processes and Damage Analysis JF - Hydrologie und Wasserbewirtschaftung N2 - Am Abend des 29. Mai 2016 wurde der Ort Braunsbach im Landkreis Schwäbisch-Hall (Baden-Württemberg) von einer Sturzflut getroffen, bei der mehrere Häuser stark beschädigt oder zerstört wurden. Die Sturzflut war eine der Unwetterfolgen, die im Frühsommer 2016 vom Tiefdruckgebiet Elvira ausgelöst wurden. Der vorliegende Bericht ist der zweite Teil einer Doppelveröffentlichung, welche die Ergebnisse zur Untersuchung des Sturzflutereignisses im Rahmen des DFG-Graduiertenkollegs “Naturgefahren und Risiken in einer sich verändernden Welt” (NatRiskChange, GRK 2043/1) der Universität Potsdam präsentiert. Während Teil 1 die meteorologischen und hydrologischen Ereignisse analysiert, fokussiert Teil 2 auf die geomorphologischen Prozesse und die verursachten Gebäudeschäden. Dazu wurden Ursprung und Ausmaß des während des Sturzflutereignisses mobilisierten und in den Ort getragenen Materials untersucht. Des Weiteren wurden zu 96 betroffenen Gebäuden Daten zum Schadensgrad sowie Prozess- und Gebäudecharakteristika aufgenommen und ausgewertet. Die Untersuchungen zeigen, dass bei der Betrachtung von Hochwassergefährdung die Berücksichtigung von Sturzfluten und ihrer speziellen Charakteristika, wie hoher Feststofftransport und sprunghaftes Verhalten insbesondere in bebautem Gelände, wesentlich ist, um effektive Schutzmaßnahmen ergreifen zu können. N2 - A severe flash flood event hit the town of Braunsbach (Baden-Wurttemberg, Germany) on the evening of May 29, 2016, heavily damaging and destroying several dozens of buildings. It was only one of several disastrous events in Central Europe caused by the low-pressure system "Elvira". The DFG Graduate School "Natural hazards and risks in a changing world" (NatRiskChange, GRK 2043/1) at the University of Potsdam investigated the Braunsbach flash flood as a recent showcase for catastrophic events triggered by severe weather. This contribution is part two of a back-to-back publication on the results of this storm event. While part 1 analyses the meteorological and hydrological situation, part 2 concentrates on the geomorphological aspects and damage to buildings. The study outlines the origin and amount of material that was mobilized and transported into the town by the flood, and analyses damage data collected for 96 affected buildings, describing the degree of impact, underlying processes, and building characteristics. Due to the potentially high sediment load of flash floods and their non-steady and non-uniform flow especially in built-up areas, the damaging processes differ from those of clear water floods. The results underline the need to consider flash floods and their specific behaviour in flood hazard assessments. KW - flash flood KW - flood risk KW - damaging processes KW - debris flow KW - erosion KW - landslides KW - Braunsbach KW - Sturzflut KW - Hochwassergefährdung KW - Schadensprozesse KW - Erosion KW - Hangrutschungen Y1 - 2017 U6 - https://doi.org/10.5675/HyWa_2017,3_2 SN - 1439-1783 VL - 61 IS - 3 SP - 163 EP - 175 PB - Bundesanst. für Gewässerkunde CY - Koblenz ER - TY - JOUR A1 - Stolle, Amelie A1 - Langer, Maria A1 - Blöthe, Jan Henrik A1 - Korup, Oliver T1 - On predicting debris flows in arid mountain belts JF - Global and planetary change N2 - The use of topographic metrics for estimating the susceptibility to, and reconstructing the characteristics of, debris flows has a long research tradition, although largely devoted to humid mountainous terrain. The exceptional 2010 monsoonal rainstorms in the high-altitude mountain desert of Ladakh and Zanskar, NW India, were a painful reminder of how susceptible arid regions are to rainfall-triggered flash floods, landslides, and debris flows. The rainstorms of August 4-6 triggered numerous debris flows, killing 182 people, devastating 607 houses, and more than 10 bridges around Ladakh's capital of Leh. The lessons from this disaster motivated us to revisit methods of predicting (a) flow parameters such as peak discharge and maximum velocity from field and remote sensing data, and (b) the susceptibility to debris flows from catchment morphometry. We focus on quantifying uncertainties tied to these approaches. Comparison of high-resolution satellite images pre- and post-dating the 2010 rainstorm reveals the extent of damage and catastrophic channel widening. Computations based on these geomorphic markers indicate maximum flow velocities of 1.6-6.7 m s(-1) with runout of up to similar to 10 km on several alluvial fans that sustain most of the region's settlements. We estimate median peak discharges of 310-610 m(3) s(-1), which are largely consistent with previous estimates. Monte Carlo-based error propagation for a single given flow-reconstruction method returns a variance in discharge similar to one derived from juxtaposing several different flow reconstruction methods. We further compare discriminant analysis, classification tree modelling, and Bayesian logistic regression to predict debris-flow susceptibility from morphometric variables of 171 catchments in the Ladakh Range. These methods distinguish between fluvial and debris flow-prone catchments at similar success rates, but Bayesian logistic regression allows quantifying uncertainties and relationships between potential predictors. We conclude that, in order to be robust and reliable, morphometric reconstruction of debris-flow properties and susceptibility requires careful assessment and reporting of errors and uncertainties. (C) 2015 Elsevier B.V. All rights reserved. KW - debris flow KW - peak discharge KW - channel geometry KW - geomorphometry KW - Bayesian logistic regression KW - Transhimalaya Y1 - 2015 U6 - https://doi.org/10.1016/j.gloplacha.2014.12.005 SN - 0921-8181 SN - 1872-6364 VL - 126 SP - 1 EP - 13 PB - Elsevier CY - Amsterdam ER -