ALBERT

Description: Estimation of the total amount of water stored as snow in a catchment area during the winter season is a major driver for successful modeling and managing of water resources as well as for accurate predictions of mass balances and changes thereof on glaciated areas. As a comprehensive measurement of the entire catchment is usually impossible, the main difficulty is to link scales. Point measurements of snow depth and density must be combined to estimate the distribution of snow water equivalent (SWE) in a slope, and various slopes are combined to estimate in the average amount of SWE in a catchment. However, especially in mountainous areas, wind redistribution in combination with variable precipitation and complex surface topography, reduce the representativeness of single point data of SWE to sometimes less than a few meters. Therefore, the estimated variability pattern will highly depend on the applied measurement grid and its spatial resolution. For the present study, we employed radar technology to increase the resolution of measurement points to tens of centimeters and less. These radar measurements were performed at three different locations: (i) a relatively low slope, high Alpine glacier in Tirol, Austria, (ii) a non glaciated, high Alpine site in SW Colorado, USA and (iii) a highly wind influenced middle elevation site in Idaho, USA. A regular grid of circles subdivides the respective measurement area in several parts. The variability patterns of the two-way travel time (TWT) of the radar signal are analyzed for each circle separately utilizing geostatistical methods. These patterns are compared with the results using different spatial resolutions and to the results of the respective probings in the circles. At site (i) the observed snow depths were very homogeneous on a scale of hundreds of meters, and the variability patterns of the radar data stay fairly constant and correspond well with the probings. Site (ii) and (iii), however, are characterized by high variabilities in snow depth on a relatively small spatial scale. Therefore, the variability pattern changed significantly with varying spatial resolutions and the probings don't correspond to the radar measurements.

Description: Evaluating and improving snow models and outflow predictions for hydrological applications is hindered by the lack of continuous data on bulk volumetric liquid water content (θw) and storage capacity of the melting snowpack. The combination of upward looking ground-penetrating radar and conventional snow height sensors enable continuous, nondestructive determinations of θw in natural snow covers from first surficial wetting until shortly before melt out. We analyze diurnal and seasonal cycles of θw for 4 years in a flat study site and for three melt seasons on slopes and evaluate model simulations for two different water transport schemes in the snow cover model SNOWPACK. Observed maximum increases in θw during a day are below 1.7 vol % (90th percentile) at the flat site. Concerning seasonal characteristics of θw, less than 10% of recorded data exceed 5 vol % at the flat site and 3.5 vol % at slopes. Both water transport schemes in SNOWPACK underestimate maximum θw at the flat site systematically for all observed melt seasons, while simulated θw maxima on slopes are accurate. Implementing observed changes in θw per day in outflow predictions increases model performance toward higher agreement with lysimeter measurements. Hence, continuously monitoring θw improves our understanding of liquid water percolation and retention in snow, which is highly relevant for several aspects of the cryosphere such as avalanche formation, catchment hydrology, and ice sheet mass balances.

Description: One missing link in ground truth observations for remote sensing data of snow is continuous snowpack monitoring over the course of a season. While conventional snow pits represent only snapshots in time, which not necessarily coincident with satellite overpasses, continuous observations at short time intervals allow for direct relation of current snowpack conditions with recorded remote sensing data. Furthermore, such monitoring enables tracking of changes in adjacently recorded satellite signals to settling or disappearance/ appearance of specific snow layers or liquid water occurrences. The combination of upward-looking ground-penetrating radar (upGPR) and automatic weather station (AWS) allows for continuous monitoring of changes in snowpack stratigraphy, snow water equivalent (SWE) and bulk volumetric liquid water content (Theta_w) within the snowpack. Results thereof are not biased through spatial variability of pit locations, since upGPR is a non-destructive monitoring technique. Other non-destructive instruments recording snow parameters are usually measuring from above the snow surface. Above snow installations, however, are not capable in monitoring layer specific settling and depth of liquid water infiltrations into the snowpack. Even surface wetting cannot be clearly identified by above snow instrumentation. In addition, to monitor changes in Theta_w in snow, only non-destructive methods will produce reliable data. Here, we present upGPR data recorded over three consecutive winter season at the test site Weissfluhjoch, Davos Switzerland together with data from a slope sites above Davos, and a test site above Boise, Idaho, USA. We can show that upGPR continuously monitors major changes in snowpack stratigraphy, liquid water appearance/ disappearance and SWE within the snowpack. SWE determinations by radar were always within or close to a 5% range in comparison to manual measurements. While comparing estimated diurnal liquid water outflow with lysimeter records at the test site WFJ, the knowledge of the variations in residual water content from one day to another from the radar reduces deviations between measured and modeled outflow remarkably. The installation of upGPR systems in sheltered, spatially homogenous high Alpine areas will allow for continuous calibration and/or validation of snow retrieval algorithms for remote sensing (satellite and airborne) data. Data thereof can be used to assimilate model outputs and may help to improve and consolidate remote sensing retrieval algorithms for various kinds of snowpack conditions.

Description: A temporal observation of the stratigraphy of seasonal snowpacks is only possible with noninvasive methods. Electromagnetic waves, specifically radar waves, proved to be the most appropriate technique to estimate internal snow parameters and media transitions non-destructively. Thereby, it is possible to estimate quantitatively snowpack stratigraphy and observe the snowpack evolution with time. Radar systems work as an active wave transmitter, which records reflection intensities with travel-time. Either the system modulates the signal on a defined frequency range, such as frequency modulated continuous wave systems (FMCW) or a short impulse is radiated at a center frequency and bandwidth. The stratigraphic resolution and the penetration depth of both systems depends on the system parameters. The frequency determines the penetration depth and sensitivity and the bandwidth determines the vertical resolution. In previous studies FMCW X- and Ku-band frequencies failed to penetrate a moist snowpack, butprovided convincing results in resolving the snowpack stratigraphy. Pulsed 900 MHz antennas, as well as L- and C-band FMCW systems penetrated a wet snowpack up to one meter and measured adequate gradients in snow density. Current research in pulsed and modulated systems show that electromagnetic wave systems are convincing methods to quantitatively measure snow stratigraphy non-destructively.