Stephan Peter Galos

THERMAL REGIME OF ROCK AND ITS RELATION TO SNOW COVER IN STEEP ALPINE ROCK WALLS: GEMSSTOCK, CENTRAL SWISS ALPS

Abstract Snow cover influences the thermal regime and stability of frozen rock walls. In this study, we investigate and model the impact of the spatially variable snow cover on the thermal regime of steep permafrost rock walls. This is necessary for a more detailed understanding of the thermal and mechanical processes causing changes in rock temperature and in the ice and water contents of frozen rock, which possibly lead to rock wall instability. To assess the temporal and spatial evolution and influence of the snow, detailed measurements have been carried out at two selected points in steep north‐ and south‐facing rock walls since 2012. In parallel, the one‐dimensional energy balance model SNOWPACK is used to simulate the effects of snow cover on the thermal regime of the rock walls. For this, a multi‐method approach with high temporal resolution is applied, combining meteorological, borehole rock temperature and terrain parameter measurements. To validate the results obtained for the ground thermal regime and the seasonally varying snowpack, the model output is compared with near‐surface rock temperature measurements and remote snow cover observations. No decrease of snow depth at slope angles up to 70° was observed in rough terrain due to micro‐topographic structures. Strong contrasts in rock temperatures between north‐ and south‐facing slopes are due to differences in solar radiation, slope angle and the timing and depth of the snow cover. SNOWPACK proved to be useful for modelling snow cover–rock interactions in smooth, homogenous rock slopes.

Preliminary results of mass-balance observations of Yala Glacier and analysis of temperature and precipitation gradients in Langtang Valley, Nepal

Abstract Monitoring the glacier mass balance of summer-accumulation-type Himalayan glaciers is critical to not only assess the impact of climate change on the volume of such glaciers but also predict the downstream water availability and the global sea-level change in future. To better understand the change in meteorological parameters related to glacier mass balance and runoff in a glacierized basin and to assess the highly heterogeneous glacier responses to climate change in the Nepal Himalaya and nearby ranges, the Cryosphere Monitoring Project (CMP) carries out meteorological observations in Langtang Valley and mass-balance measurements on Yala Glacier, a debris-free glacier in the same valley. A negative annual mass balance of –0.89m w.e. and the rising equilibrium-line altitude of Yala Glacier indicate a continuation of a secular trend toward more negative mass balances. Lower temperature lapse rate during the monsoon, the effect of convective precipitation associated with mesoscale thermal circulation in the local precipitation and the occurrence of distinct diurnal cycles of temperature and precipitation at different stations in the valley are other conclusions of this comprehensive scientific study initiated by CMP which aims to yield multi-year glaciological, hydrological and meteorological observations in the glacierized Langtang River basin.

Common climatic signal from glaciers in the European Alps over the last 50 years

Conventional glacier-wide mass balances are commonly used to study the effect of climate forcing on glacier melt. Unfortunately, the glacier-wide mass balances are also influenced by the glaciers dynamic response. Investigations on the effects of climate forcing on glaciers can be largely improved by analyzing point mass balances. Using a statistical model, we have found that 52% of the year-to-year deviations in the point mass balances of six glaciers distributed across the entire European Alps can be attributed to a common variability. Point mass balance changes reveal remarkable regional consistencies reaching 80% for glaciers less than 10 km apart. Compared to the steady state conditions of the 1962–1982 period, the surface mass balance changes are −0.85 m water equivalent (w.e.) a−1 for 1983–2002 and −1.63 m w.e. a−1 for 2003–2013. This indicates a clear and regionally consistent acceleration of mass loss over recent decades over the entire European Alps.

A multitemporal probabilistic error correction approach to SVM classification of alpine glacier exploiting sentinel-1 images (Conference Presentation)

In mountain regions and their forelands, glaciers are key source of melt water during the middle and late ablation season, when most of the winter snow has already melted. Furthermore, alpine glaciers are recognized as sensitive indicators of climatic fluctuations. Monitoring glacier extent changes and glacier surface characteristics (i.e. snow, firn and bare ice coverage) is therefore important for both hydrological applications and climate change studies. Satellite remote sensing data have been widely employed for glacier surface classification. Many approaches exploit optical data, such as from Landsat. Despite the intuitive visual interpretation of optical images and the demonstrated capability to discriminate glacial surface thanks to the combination of different bands, one of the main disadvantages of available high-resolution optical sensors is their dependence on cloud conditions and low revisit time frequency. Therefore, operational monitoring strategies relying only on optical data have serious limitations. Since SAR data are insensitive to clouds, they are potentially a valid alternative to optical data for glacier monitoring. Compared to past SAR missions, the new Sentinel-1 mission provides much higher revisit time frequency (two acquisitions each 12 days) over the entire European Alps, and this number will be doubled once the Sentinel1-b will be in orbit (April 2016). In this work we present a method for glacier surface classification by exploiting dual polarimetric Sentinel-1 data. The method consists of a supervised approach based on Support Vector Machine (SVM). In addition to the VV and VH signals, we tested the contribution of local incidence angle, extracted from a digital elevation model and orbital information, as auxiliary input feature in order to account for the topographic effects. By exploiting impossible temporal transition between different classes (e.g. if at a given date one pixel is classified as rock it cannot be classified as glacier ice in a following date) we here propose an innovative post classification correction based on SVM classification probabilities. Optical data, i.e. Landsat-8 and Sentinel-2, have been employed, when available, for training sample collection. Detailed field observations from two glaciers located in the Ortles-Cevedale massif (Eastern Italian Alps) have been employed for validation.