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the slope, a resistive body with values higher than 25 kΩm and thickness of about 15 meters has been imaged by both apparent resistivity and inversed resistivity. It corresponds to the cold BTS temperatures (Fig. 1) and can be interpreted as a permafrost lens. In the middle of the ERT profile, the lower resistivities (
1 0 Symposium 6: Apply! Snow, ice and Permafrost Science + Geomorphology 6.16 Modelling of glacier bed topography from glacier outlines and DEM data in a GIS Linsbauer Andreas*, Paul Frank*, Hoelzle Martin* & Haeberli Wilfried* * Department of Geography, University of Zurich, Winterthurerstr. 190, CH-8057 Zürich (alinsbau@geo.uzh.ch) Due to the ongoing and expected future increase in global mean temperature, the Alpine environment will continue to get further away from equilibrium. This could have large environmental and societal impacts. Glaciers are a part of the highmountain cryosphere, and their changes are considered to be the best natural indicators of climatic changes. The observation of the high-mountain environment and its glaciers forms thus an important part in global climate related observing programs. The calculation and visualization of future glacier development is thus an important task of communicating climate change effects to a wider public (Paul et al., 2007). One of the most challenging topics in the assessment of climate change impacts on future glacier development is the unknown glacier bed and the related uncertainties in glacier volume estimations (Driedger and Kennard, 1986). In this respect, an estimated topography of the glacier bed would facilitate the calculation of glacier volume, the detection of local depressions, and the visualization of future ice-free grounds. While several methods exist to obtain a glacier bed from modelling combined with measurements (e.g. Huss et al., 2008), the goal of this study is the application of a simple but robust method which automatically approximates a glacier bed for a large sample of glaciers, using only few input data such as a digital elevation model (DEM), glacier outlines, and a set of flow lines for each glacier (Fig. 1). It is based on the calculation of the ice thickness along selected points of the flow line from the shallow ice approximation (SIA) following Haeberli and Hoelzle (1995) and subsequent spatial interpolation using a routine (topogrid) that is implemented in a geographic information system (GIS) and was developed by Hutchinson (1989). Sensitivity tests with simple geometric forms on flat and inclined surfaces helped to constrain the model parameters. This includes the specifications for topogrid as well as the rules for digitizing the flow lines. For Switzerland, they form the only part of the input data that have to be newly created. The model was tested with real data from the Bernina region which lead to some modifications of the flow line digitizing and interpolation process. Finally, the reconstructed beds are compared to glacier beds or thickness measurements for three glaciers (Morteratsch, Gorner and Zinal) which have been obtained in previous studies. The comparison revealed that the method has a large potential, but further improvements could be integrated as well. The general depth distribution of the reconstructed glacier beds is in a fairly good agreement with the results from the other studies and calculated glacier volumes or the location of local depressions do agree as well. However, there is a tendency to underestimate glacier thickness and locally some strong deviations exist. Currently, mean slope is the only (and thus a very sensitive) variable for the ice thickness calculation. Hence, it might be possible to improve the performance of the method by integrating some basic laws of glacier flow and further geomorphometry-dependent smoothing of the glacier surface (e. g. Huss et al., 2008). However, the method as a whole should stay as simple as possible to facilitate its automated and large scale application. The application of the (improved and locally calibrated) method to all glaciers in the Swiss Alps does only require that glacier flow lines are digitized according to the rules outlined in this study. In combination with further GIS-based models (e.g. Paul et al., 2007) the here presented method would allow to calculate and visualize impacts of climate change on glaciers and their role as a water resource for Switzerland. REFERENCES Driedger, C. & Kennard, P. 1986: Glacier volume estimation on cascade volcanos: an analysis and comparison with other methods. Annals of Glaciology, 8, 59–64. Haeberli, W. & Hoelzle, M. 1995: Application of inventory data for estimating characteristics of and regional climate-change effects on mountain glaciers: a pilot study with the European Alps. Annals of Glaciology, 21, 206–212. Huss, M., Farinotti, D., Bauder, A. & Funk, M. 2008: Modelling runoff from highly glacierized alpine drainage basins in a changing climate. Hydrological Processes, 22 (19), 3888–3902. Hutchinson, M. 1989: A new procedure for gridding elevation and stream line data with automatic removal of spurious pits. Journal of Hydrology, 106, 211–232. Paul, F., Maisch, M., Rothenbühler, C., Hoelzle, M. & Haeberli, W. 2007: Calculation and visualisation of future glacier extent in the Swiss Alps by means of hypsographic modelling. Global and Planetary Change, 55, 343–357.
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Abstract Volume 6 th Swiss Geoscien
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