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 - 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 -