Abstract. Knowing the timing and the evolution of the snow melting process is very important, since it allows the prediction of (i) the snowmelt onset, (ii) the snow gliding and wet-snow avalanches, (iii) the release of snow contaminants, and (iv) the runoff onset. The snowmelt can be monitored by jointly measuring snowpack parameters such as the snow water equivalent (SWE) or the amount of free liquid water content (LWC). However, continuous measurements of SWE and LWC are rare and difficult to obtain. On the other hand, active microwave sensors such as the synthetic aperture radar (SAR) mounted on board satellites are highly sensitive to LWC of the snowpack and can provide spatially distributed information with a high resolution. Moreover, with the introduction of Sentinel-1, SAR images are regularly acquired every 6 d over several places in the world. In this paper we analyze the correlation between the multitemporal SAR backscattering and the snowmelt dynamics. We compared Sentinel-1 backscattering with snow properties derived from in situ observations and process-based snow modeling simulations for five alpine test sites in Italy, Germany and Switzerland considering 2 hydrological years. We found that the multitemporal SAR measurements allow the identification of the three melting phases that characterize the melting process, i.e., moistening, ripening and runoff. In particular, we found that the C-band SAR backscattering decreases as soon as the snow starts containing water and that the backscattering increases as soon as SWE starts decreasing, which corresponds to the release of meltwater from the snowpack. We discuss the possible reasons of this increase, which are not directly correlated to the SWE decrease but to the different snow conditions, which change the backscattering mechanisms. Finally, we show a spatially distributed application of the identification of the runoff onset from SAR images for a mountain catchment, i.e., the Zugspitze catchment in Germany. Results allow us to better understand the spatial and temporal evolution of melting dynamics in mountain regions. The presented investigation could have relevant applications for monitoring and predicting the snowmelt progress over large regions.

Abstract. Knowing the timing and the evolution of the snow melting process is very important, since it allows the prediction of: i) the snow melt onset; ii) the snow gliding and wet-snow avalanches; iii) the release of snow contaminants and iv) the runoff onset. The snowmelt can be monitored by jointly measuring snowpack parameters such as the snow water equivalent (SWE) or the amount of free liquid water content (LWC). However, continuous measurements of SWE and LWC are rare and difficult to be obtained. On the other hand, active microwave sensors such as the Synthetic Aperture Radar (SAR) mounted on board of satellites, are highly sensitive to LWC of the snowpack and can provide spatially distributed information with a high resolution. Moreover, with the introduction of Sentinel-1, SAR images are regularly acquired every 6 days over several places in the world. In this paper we analyze the correlation between the multi-temporal SAR backscattering and the snowmelt dynamics. We compared Sentinel-1 backscattering with snow properties derived from in situ observations and process-based snow modeling simulations for five alpine test sites in Italy, Germany and Switzerland considering two hydrological years. We found that the multi-temporal SAR measurements allow the identification of the three melting phases that characterize the melting process i.e., moistening, ripening and runoff. In detail, we found that the C-band SAR backscattering decreases as soon as the snow starts containing water, and that the backscattering increases as soon as SWE starts decreasing, which corresponds to the release of meltwater from the snowpack. We discuss the possible reasons of this increase, which are not directly correlated to the SWE decrease, but to the different snow conditions, which change the backscattering mechanisms. Finally, we show a spatially-distributed application of the identification of the runoff onset from SAR images for a mountain catchment, i.e., the Zugspitze catchment in Germany. Results allow to better understand the spatial and temporal evolution of melting dynamics in mountain regions. The presented investigation could have relevant applications for monitoring and predicting the snowmelt progress over large regions.

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Abstract. Detailed characterization and classification of precipitation is an important task in atmospheric research. Line scanning 2-D video disdrometer devices are well established for rain observations. The two orthogonal views taken of each hydrometeor passing the sensitive area of the instrument qualify these devices especially for detailed characterization of nonsymmetric solid hydrometeors. However, in case of solid precipitation, problems related to the matching algorithm have to be considered and the user must be aware of the limited spatial resolution when size and shape descriptors are analyzed. Clarifying the potential of 2-D video disdrometers in deriving size, velocity and shape parameters from single recorded pictures is the aim of this work. The need of implementing a matching algorithm suitable for mixed-and solidphase precipitation is highlighted as an essential step in data evaluation. For this purpose simple reproducible experiments with solid steel spheres and irregularly shaped Styrofoam particles are conducted. Self-consistency of shape parameter measurements is tested in 38 cases of real snowfall. As a result, it was found that reliable size and shape characterization with a relative standard deviation of less than 5 % is only possible for particles larger than 1 mm. For particles between 0.5 and 1.0 mm the relative standard deviation can grow up to 22 % for the volume, 17 % for size parameters and 14 % for shape descriptors. Testing the adapted matching algorithm with a reproducible experiment with Styrofoam particles, a mismatch probability of less than 3 % was found. For shape parameter measurements in case of real solid-phase precipitation, the 2-DVD shows self-consistent behavior.

High amounts of precipitation are temporarily stored in high-alpine snow covers and play an important role for the hydrological balance. Stable isotopes of hydrogen (δ 2 H) and oxygen (δ 18 O) in water samples have been proven to be useful for tracing transport processes in snow and meltwater since their isotopic ratio alters due to fractionation. In 18 snow profiles of two snowfall seasons, the temporal and spatial variation of isotopic composition was analysed on Mt.Zugspitze. The δ 18 O and δ 2 H ranged between -26.7‰ to -9. 3‰ and -193.4‰ to -62.5‰ in 2014/2015 and between -26.5‰ to -10.5‰ and -205.0‰ to -68.0‰ in 2015/2016, respectively. Depth-integrated samples of entire 10 cm layers and point measurements in the same layers showed comparable isotopic compositions. Isotopic composition of the snowpack at the same sampling time in spatially distributed snow profiles was isotopically more similar than that analysed at the same place at different times. Melting and refreezing were clearly identified as processes causing isotope fractionation in surficial, initial base or refrozen snow layers. For the future, a higher sampling frequency with detailed isotopic composition measurements during melt periods are recommended to improve the understanding of mass transport associated with snowmelt.