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While summer 2016 air temperatures were above long term average over the entire Greenland ice Sheet (GrIS), melt in summer 2017 was considered as significantly below average, which may lead to an even positive surface mass balance in 2017 for the GrIS. However, apart from surficial extent of melt, only very little is known about effects of melt induced changes for snow and firn such as liquid water content, percolation depth and mass fluxes. To overcome this deficit, we installed an upward-looking radar systems (upGPR) 3.5 m below the snow surface in May 2016 close to Camp Raven (66.4779N/ 46.2856W) at 2120 m a.s.l. within the deep percolation zone of the GrIS. The radar is capable to monitor quasi-continuously changes in snow and firn stratigraphy, which occur above the antennas. For summer 2016, we observed four major melt events, which routed liquid water into various depths. The last event in mid-August resulted in the deepest percolation down to about 2.5 m beneath the surface. For the subsequent summer season in 2017, liquid water percolation barely reached the previous summer horizon until 15 August. In consequence, seasonal mass flux into underlying firn was strongly different for summer 2016 and 2017 at the site. While until mid-August 2016, melt events transferred a cumulative mass of almost 60 kg m−2 from the surface into firn, in 2017, for the same time period, no mass flux beneath the previous summer horizon has been observed. Comparisons with results predicted by the regional climate model MAR are in very good agreement in terms of specific surface accumulation, while neither the temporal evolution of density, nor bulk liquid water contents nor percolation depths agree with upGPR data. Such inaccuracies bias simulations of changes in snow and firn and limit our understanding of effects of water percolation as well as water retention in firn. A multi-yearsummer monitoring with upGPR may lead to a valuable data base for melt effects in perennial firn. At the current stage, we have continuous observations for a very strong melt season and a below average melt in 2017. We are looking forward to monitor even more extreme events to provide temporally continuous in-situ data for a large variety of melt years in perennial firn within the percolation zone of the GrIS.

Warnsignal Klima: Das Eis der Erde

Snow stratigraphy is a key contributing factor for assessing avalanche danger, but so far only destructive methods can provide this kind of information. Furthermore, continuous monitoring of the temporal evolution of the snowpack is not possible with destructive methods. Radar technology provides information on the snowpack nondestructively and allows deriving internal snow properties from its signal response. In our previous work, we demonstrated that it is feasible to quantitatively derive snowpack properties relevant for avalanche formation and monitor their evolution in time using an upward-looking ground penetrating radar system (upGPR) that was buried in a wooden box underneath the snow. Reliable results could only be obtained for the time when the snow cover was dry. In addition, to determine some properties, we still needed additional information such as independently measured snow height or modeled snow density. Hence, the system was not yet able to provide information from avalanche starting zones, since this type of information is generally not available in avalanche-prone terrain. To fully exploit the information content of upGPR data, and thus to at least partially compensate for the lack of information, we applied full-waveform inversion (FWI) techniques. We refined the model of the snowpack by repeated forward modeling the waveforms and updating the model parameters to match it with recorded data. The forward model took into account both the effect of the snow density on the velocity of the electromagnetic wave, as well as the influence of snow wetness on the attenuation. This allowed the density and the liquid water content for each layer in the snowpack to be determined. As we conducted a measurement every 3 hours (every 30 minutes as soon as the snowpack became wet), we could also simulate the temporal evolution of the density and the liquid water profiles. The method worked without assumptions or external measurements, even when the snow cover was wet.

Snow stratigraphy and water percolation are key contributing factors to avalanche formation. So far, only destructive methods can provide this kind of information. Radar technology allows continuous, non-destructive scanning of the snowpack so that the temporal evolution of internal properties can be followed. We installed an upward-looking ground-penetrating radar system (upGPR) at the Weissfluhjoch study site (Davos, Switzerland). During two winter seasons (2010/11 and 2011/12) we recorded data with the aim of quantitatively determining snowpack properties and their temporal evolution. We automatically derived the snow height with an accuracy of about ± 5 cm, tracked the settlement of internal layers (± 7 cm) and measured the amount of new snow (± 10 cm). Using external snow height measurements, we determined the bulk density with a mean error of 4.3% compared to manual measurements. Radar-derived snow water equivalent deviated from manual measurements by 5%. Furthermore, we tracked the location of the dry-to-wet transition in the snowpack until water percolated to the ground. Based on the transition and an independent snow height measurement it was possible to estimate the volumetric liquid water content and its temporal evolution. Even though we need additional information to derive some of the snow properties, our results show that it is possible to quantitatively derive snow properties with upGPR. ISSN:0022-1430 ISSN:1727-5652

The combination of upward-looking ground-penetrating radar (upGPR), automatic weather station (AWS) and lysimeter allows for continuous monitoring of bulk volumetric liquid water content (�w) within the snowpack and direct comparison with measurements of the corresponding outflow. The AWS data can be utilized to calculate energy fluxes between atmosphere and snowpack at the location of the station. While combining all data sets, we were able to quantify diurnal and seasonal changes in residual water content and relate modeled energy fluxes to water outflow. Since upGPR is a non-destructive monitoring technique, it is possible to continuously observe the snowpack and results are not biased through spatial variability of pit locations. Data analysis conducted for three consecutive years at the flat test site Weissfluhjoch, Davos, Switzerland showed that diurnal �w variations never exceeded 2%. Without regard to days with new snow accumulation or refreezing, the diurnal patterns in �w were very similar, with always daily peaks in the late afternoon (at about 17:00h) at the site. Although �w values varied during a day up to 2%, the gradients during the season were very small. In 2012, for the whole melting period (>100 days), increases in �w from day to day were 0.4% liquid water content on average. After the snowpack has become isothermal, positive energy fluxes result in outflow and increase the residual water content (�r). Our data showed that as long as potential melt - calculated for the determined energy fluxes - was exceeding measured outflow, �r values were increasing but only until reaching a certain threshold. For all three years, the thresholds were similar at about �r=4–5%. Only shortly before full ablation, these thresholds were surpassed. In two sloped test sites (about 22 degree slope angle) in Boise, Idaho, USA and above Davos, we installed upGPR systems as well. AWS data and energy-flux calculations for both slopes were extrapolated for the respective aspect and slope angle. Our data showed that snow stratigraphy highly influences �r in slopes. As long as e.g. crusts ponded the vertical water flow, residual �w of the whole snowpack was fairly low (

The storage of water within the seasonal snow cover is a substantial source of runoff in high mountain catchments. Information about the spatial distribution of snow accumulation is necessary for calibration and validation of hydro-meteorological models. Generally, only a small number of precipitation measurements deliver precipitation input for modelling in mountain areas. The spatial interpolation and extrapolation of measurements of precipitation is still difficult. Multi-temporal application of lidar techniques from aircraft, so-called airborne laser scanning (ALS), provides surface elevations changes even in inaccessible terrain. These ALS surface elevation changes can be used to derive changes in snow depths of the mountain snow cover for seasonal or subseasonal time periods. However, since glacier surfaces are not static over time, ablation, densification of snow, densification of firn and ice flow contribute to surface elevation changes. ALS-derived surface elevation changes were compared to snow depths derived from 35.4 km of ground penetrating radar (GPR) profiles on four glaciers. With this combination of two different data acquisitions, it is possible to evaluate the effect of the summation of these processes on ALS-derived snow depth maps in the high alpine region of the Ötztal Alps (Austria). A Landsat 5 Thematic Mapper image was used to distinguish between snow covered area and bare ice areas of the glaciers at the end of the ablation season. In typical accumulation areas, ALS surface elevation changes differ from snow depths calculated from GPR measurements by 0.4 m on average with a mean standard deviation of 0.34 m. Differences between ALS surface elevation changes and GPR derived snow depths are small along the profiles conducted in areas of bare ice. In these areas, the mean absolute difference of ALS surface elevation changes and GPR snow depths is 0.004 m with a standard deviation of 0.27 m. This study presents a systematic approach to analyze deviations from ALS generated snow depth maps to ground truth measurements on four different glaciers. We could show that ALS can be an important and reliable data source for the spatial distribution of snow depths for most parts of the here investigated glaciers. However, within accumulation areas, just utilizing ALS data may lead to systematic underestimation of total snow depth distribution. K. Helfricht, M. Kuhn, M. Keuschnig, and A. Heilig

Snow stratigraphy and water percolation are key parameters in avalanche forecasting. It is, however, difficult to model or measure stratigraphy and water flow in a sloping snowpack. Numerical modeling results depend highly on the type and availability of input data and the parameterization of the physical processes. Furthermore, the sensors themselves may influence the snowpack or be destroyed due to snow gliding and avalanches. Radar technology allows non-destructive scanning of the snowpack and deducing internal snow properties. If the radar system is buried in the ground, it cannot be destroyed by avalanche impacts or snow creep. During the winter seasons 2010-2011 and 2011-2012 we recorded continuous data with upward-looking pulsed radar systems (upGPR) at two test sites. We demonstrate that it is possible to determine the snow height with an accuracy comparable to conventional snow depth measuring devices. We determined the bulk volumetric liquid water content and tracked the position of the first stable wetting front. Wet-snow avalanche activity increased, when melt water penetrated deeper into the snowpack.

The location of singular objects or layered transitions below the surface and properties thereof in the ground are a pivotal topic in geosciences. In mountainous regions is the investigation of objects and layer transitions specifically of interest for the seasonal snowpack, primarily to reduce the threat to humans and infrastructures by natural hazards. Snow avalanches are a major natural hazard causing numerous fatalities throughout the world and they are a direct consequence of snowpack conditions. The annual fatality numbers of avalanches are fairly constant for the last 30 years, while in other fields such as e.g. road traffic these numbers decreased significantly. It can be assumed that the permanent enhancements in active and passive safety systems in road traffic are the reason for the decrease in victim numbers. In the field of professional search and rescue operations or accident prevention in avalanches such as hazard forecast, enhancements of instrumentations are marginal for the last three decades. The present study describes two different assessments for the use of ground-penetrating radar (GPR) systems to improve the instrumentation for the location of buried avalanche victims and the prediction of avalanches. Consequently, it demonstrates the feasibility of radar systems for the detection of inhomogeneities in seasonal snowpacks. With regard to the improvement of current methods to search and locate buried avalanche victims, which are not equipped with a location device (e.g. avalanche beacon), the main objective is to shorten search time. The assessment of this thesis was therefore to use helicopter-borne non-invasive location methods. To simulate helicopter flights, test arrangements were designed to perform field tests from above the surface. I developed methods to measure from 6--12 m above the snow cover. To measure non-invasively, the arrangement is based on pulsed radar technology. To shorten search time and to minimize the influence of man-made error possibilities, an automatic location software was developed. The results of the field tests present the answers of the fundamental questions for an airborne location operation and enabled the development of a location algorithm. Measurements showed, that the sidewise detectable range of 3--5 m of an antenna set-up with one transmitter -- receiver pair is rather small for the given flight height of 6 to 12 m. Furthermore, the reflection amplitude of the snow surface decreases almost linearly with the flight height. Unfortunately, in wet snow avalanches a buried object in the snowpack does not appear as typical reflection pattern and is therefore not explicitly locatable. The developed software algorithm proved to be sufficient for all applied test arrangements in dry snow conditions. The algorithm is able to distinguish between buried victims in the snowpack and reflections caused by only air holes within the snow cover. Further implementations on helicopters can be achieved, based on these results, but more field tests are necessary to adapt the software to the rougher flight conditions in helicopters. Concerning the observation of stratigraphic inhomogeneities within a snowpack, this thesis showed that a record of specific snowpack conditions from beneath the snow cover is feasible with GPR. The assessment of the present work is to provide snowpack information in avalanche endangered slopes and to follow the temporal evolution of the snowpack over a whole season. Two different kinds of field measurements in dry and wet snow conditions were performed to ascertain the GPR set-up, which provides the best trade-off between penetration depth and layer resolution. On the one hand, temporally singular measurements at different locations, concerning altitude, snowpack conditions and climatic regions in the European Alps, enabled the determination of capable test arrangements. On the other hand, a temporal monitoring of the snow cover at a fixed position over several months, facilitated the record of the change of specific parameters in the snowpack. In terms of system parameters, antennas with a center frequency of about 800--900 MHz are able to penetrate and adequately record stratigraphic transitions in dry and wet snow conditions. The radar-measured snow height in dry snow using a mean wave speed value for the conversion of the two-way travel time was in a good agreement to the probed snow depth and arose in an uncertainty slightly higher than of ultrasonic sensors. In terms of snowpack parameters, the recorded signals of the various snow covers were in good agreement with the measured snow properties. For dry snow conditions, the appearance and the manner of reflections recorded in the snow cover corresponded to the size and the algebraic sign of the gradient in snow density. Moisture in the snowpack attenuates the radar signal significantly. This thesis presents encouraging results of the use of impulse radar technology for the location of inhomogeneities in seasonal snowpacks. Parts of the presented results and methodologies (e.g. the automatic location algorithm) are possibly easily adaptable in related areas of geoscientific research and could also provide advances in other, non-snow related fields.