TY - JOUR A1 - Blöthe, Jan Henrik A1 - Korup, Oliver T1 - Millennial lag times in the Himalayan sediment routing system JF - Earth & planetary science letters N2 - Any understanding of sediment routing from mountain belts to their forelands and offshore sinks remains incomplete without estimates of intermediate storage that decisively buffers sediment yields from erosion rates, attenuates water and sediment fluxes, and protects underlying bedrock from incision. We quantify for the first time the sediment stored in > 38000 mainly postglacial Himalayan valley fills, based on an empirical volume-area scaling of valley-fill outlines automatically extracted from digital topographic data. The estimated total volume of 690(+452/-242) km(3) is mostly contained in few large valley fills > 1 km(3), while catastrophic mass wasting adds another 177(31) km(3). Sediment storage volumes are highly disparate along the strike of the orogen. Much of the Himalaya's stock of sediment is sequestered in glacially scoured valleys that provide accommodation space for similar to 44% of the total volume upstream of the rapidly exhuming and incising syntaxes. Conversely, the step-like long-wave topography of the central Himalayas limits glacier extent, and thus any significant glacier-derived storage of sediment away from tectonic basins. We show that exclusive removal of Himalayan valley fills could nourish contemporary sediment flux from the Indus and Brahmaputra basins for > 1 kyr, though individual fills may attain residence times of > 100 kyr. These millennial lag times in the Himalayan sediment routing system may sufficiently buffer signals of short-term seismic as well as climatic disturbances, thus complicating simple correlation and interpretation of sedimentary archives from the Himalayan orogen, its foreland, and its submarine fan systems. (C) 2013 Elsevier B.V. All rights reserved. KW - sediment storage KW - Himalayas KW - sediment budget KW - tectonic geomorphology KW - geomorphometry Y1 - 2013 U6 - https://doi.org/10.1016/j.epsl.2013.08.044 SN - 0012-821X SN - 1385-013X VL - 382 IS - 20 SP - 38 EP - 46 PB - Elsevier CY - Amsterdam 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 - TY - JOUR A1 - Mey, Jürgen A1 - Scherler, Dirk A1 - Zeilinger, Gerold A1 - Strecker, Manfred T1 - Estimating the fill thickness and bedrock topography in intermontane valleys using artificial neural networks JF - Journal of geophysical research : Earth surface N2 - Thick sedimentary fills in intermontane valleys are common in formerly glaciated mountain ranges but difficult to quantify. Yet knowledge of the fill thickness distribution could help to estimate sediment budgets of mountain belts and to decipher the role of stored material in modulating sediment flux from the orogen to the foreland. Here we present a new approach to estimate valley fill thickness and bedrock topography based on the geometric properties of a landscape using artificial neural networks. We test the potential of this approach following a four-tiered procedure. First, experiments with synthetic, idealized landscapes show that increasing variability in surface slopes requires successively more complex network configurations. Second, in experiments with artificially filled natural landscapes, we find that fill volumes can be estimated with an error below 20%. Third, in natural examples with valley fill surfaces that have steeply inclined slopes, such as the Unteraar and the Rhone Glaciers in the Swiss Alps, for example, the average deviation of cross-sectional area between the measured and the modeled valley fill is 26% and 27%, respectively. Finally, application of the method to the Rhone Valley, an overdeepened glacial valley in the Swiss Alps, yields a total estimated sediment volume of 9711km(3) and an average deviation of cross-sectional area between measurements and model estimates of 21.5%. Our new method allows for rapid assessment of sediment volumes in intermontane valleys while eliminating most of the subjectivity that is typically inherent in other methods where bedrock reconstructions are based on digital elevation models. KW - sediment storage KW - sediment thickness KW - intermontane valleys KW - geomorphometry KW - artificial neural networks Y1 - 2015 U6 - https://doi.org/10.1002/2014JF003270 SN - 2169-9003 SN - 2169-9011 VL - 120 IS - 7 SP - 1301 EP - 1320 PB - American Geophysical Union CY - Washington ER - TY - GEN A1 - Schwanghart, Wolfgang A1 - Scherler, Dirk T1 - Bumps in river profiles BT - uncertainty assessment and smoothing using quantile regression techniques T2 - Postprints der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe N2 - The analysis of longitudinal river profiles is an important tool for studying landscape evolution. However, characterizing river profiles based on digital elevation models (DEMs) suffers from errors and artifacts that particularly prevail along valley bottoms. The aim of this study is to characterize uncertainties that arise from the analysis of river profiles derived from different, near-globally available DEMs. We devised new algorithms quantile carving and the CRS algorithm - that rely on quantile regression to enable hydrological correction and the uncertainty quantification of river profiles. We find that globally available DEMs commonly overestimate river elevations in steep topography. The distributions of elevation errors become increasingly wider and right skewed if adjacent hillslope gradients are steep. Our analysis indicates that the AW3D DEM has the highest precision and lowest bias for the analysis of river profiles in mountainous topography. The new 12m resolution TanDEM-X DEM has a very low precision, most likely due to the combined effect of steep valley walls and the presence of water surfaces in valley bottoms. Compared to the conventional approaches of carving and filling, we find that our new approach is able to reduce the elevation bias and errors in longitudinal river profiles. T3 - Zweitveröffentlichungen der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe - 624 KW - digital elevation model KW - drainage basins KW - DEM uncertainty KW - error KW - validation KW - SRTM KW - topography KW - resolution KW - terrain KW - geomorphometry Y1 - 2019 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-419077 SN - 1866-8372 IS - 624 ER -