[1] The combination of glacier outlines with digital elevation models (DEMs) opens new dimensions for research on climate change impacts over entire mountain chains. Of particular interest is the modeling of glacier thickness distribution, where several new approaches were proposed recently. The tool applied herein, GlabTop (Glacier bed Topography) is a fast and robust approach to model thickness distribution and bed topography for large glacier samples using a Geographic Information System (GIS). The method is based on an empirical relation between average basal shear stress and elevation range of individual glaciers, calibrated with geometric information from paleoglaciers, and validated with radio echo soundings on contemporary glaciers. It represents an alternative and independent test possibility for approaches based on mass-conservation and flow. As an example for using GlabTop in entire mountain ranges, we here present the modeled ice thickness distribution and bed topography for all Swiss glaciers along with a geomorphometric analysis of glacier characteristics and the overdeepenings found in the modeled glacier bed. These overdeepenings can be seen as potential sites for future lake formation and are thus highly relevant in connection with hydropower production and natural hazards. The thickest ice of the largest glaciers rests on weakly inclined bedrock at comparably low elevations, resulting in a limited potential for a terminus retreat to higher elevations. The calculated total glacier volume for all Swiss glaciers is 75 AE 22 km 3 for 1973 and 65 AE 20 km 3 in 1999. Considering an uncertainty range of AE30%, these results are in good agreement with estimates from other approaches.Citation: Linsbauer, A., F. Paul, and W. Haeberli (2012), Modeling glacier thickness distribution and bed topography over entire mountain ranges with GlabTop: Application of a fast and robust approach,
Abstract. Ice volume estimates are crucial for assessing water reserves stored in glaciers. Due to its large glacier coverage, such estimates are of particular interest for the Himalayan-Karakoram (HK) region. In this study, different existing methodologies are used to estimate the ice reserves: three area-volume relations, one slope-dependent volume estimation method, and two ice-thickness distribution models are applied to a recent, detailed, and complete glacier inventory of the HK region, spanning over the period [2000][2001][2002][2003][2004][2005][2006][2007][2008][2009][2010] and revealing an ice coverage of 40 775 km 2 . An uncertainty and sensitivity assessment is performed to investigate the influence of the observed glacier area and important model parameters on the resulting total ice volume. Results of the two ice-thickness distribution models are validated with local icethickness measurements at six glaciers. The resulting ice volumes for the entire HK region range from 2955 to 4737 km 3 , depending on the approach. This range is lower than most previous estimates. Results from the ice thickness distribution models and the slope-dependent thickness estimations agree well with measured local ice thicknesses. However, total volume estimates from area-related relations are larger than those from other approaches. The study provides evidence on the significant effect of the selected method on results and underlines the importance of a careful and critical evaluation.
Abstract. Knowledge of the ice thickness distribution of glaciers and ice caps is an important prerequisite for many glaciological and hydrological investigations. A wealth of approaches has recently been presented for inferring ice thickness from characteristics of the surface. With the Ice Thickness Models Intercomparison eXperiment (ITMIX) we performed the first coordinated assessment quantifying individual model performance. A set of 17 different models showed that individual ice thickness estimates can differ considerably – locally by a spread comparable to the observed thickness. Averaging the results of multiple models, however, significantly improved the results: on average over the 21 considered test cases, comparison against direct ice thickness measurements revealed deviations on the order of 10 ± 24 % of the mean ice thickness (1σ estimate). Models relying on multiple data sets – such as surface ice velocity fields, surface mass balance, or rates of ice thickness change – showed high sensitivity to input data quality. Together with the requirement of being able to handle large regions in an automated fashion, the capacity of better accounting for uncertainties in the input data will be a key for an improved next generation of ice thickness estimation approaches.
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