Accurate snow depth observations are critical to assess water resources. More than a billion people rely on water from snow, most of which originates in the Northern Hemisphere mountain ranges. Yet, remote sensing observations of mountain snow depth are still lacking at the large scale. Here, we show the ability of Sentinel-1 to map snow depth in the Northern Hemisphere mountains at 1 km² resolution using an empirical change detection approach. An evaluation with measurements from ~4000 sites and reanalysis data demonstrates that the Sentinel-1 retrievals capture the spatial variability between and within mountain ranges, as well as their inter-annual differences. This is showcased with the contrasting snow depths between 2017 and 2018 in the US Sierra Nevada and European Alps. With Sentinel-1 continuity ensured until 2030 and likely beyond, these findings lay a foundation for quantifying the long-term vulnerability of mountain snow-water resources to climate change.

The characteristics of an aboveground cosmic‐ray neutron sensor (CRNS) are evaluated for monitoring a mountain snowpack in the Austrian Alps from March 2014 to June 2016. Neutron counts were compared to continuous point‐scale snow depth (SD) and snow‐water‐equivalent (SWE) measurements from an automatic weather station with a maximum SWE of 600 mm (April 2014). Several spatially distributed Terrestrial Laser Scanning (TLS)‐based SD and SWE maps were additionally used. A strong nonlinear correlation is found for both SD and SWE. The representative footprint of the CRNS is in the range of 230–270 m. In contrast to previous studies suggesting signal saturation at around 100 mm of SWE, no complete signal saturation was observed. These results imply that CRNS could be transferred into an unprecedented method for continuous detection of spatially averaged SD and SWE for alpine snowpacks, though with sensitivity decreasing with increasing SWE. While initially different functions were found for accumulation and melting season conditions, this could be resolved by accounting for a limited measurement depth. This depth limit is in the range of 200 mm of SWE for dense snowpacks with high liquid water contents and associated snow density values around 450 kg m−3 and above. In contrast to prior studies with shallow snowpacks, interannual transferability of the results is very high regardless of presnowfall soil moisture conditions. This underlines the unexpectedly high potential of CRNS to close the gap between point‐scale measurements, hydrological models, and remote sensing of the cryosphere in alpine terrain.

Knowledge of the spatial snow distribution and its interannual persistence is of interest for a broad spectrum of issues in cryospheric sciences. In this study, snow depths derived from airborne laser scanning are analyzed for interannual persistence of the seasonal snow cover in a partly glacierized mountain area (~36 km2). At the end of five accumulation periods, the snow-covered area varied by 16% of its temporal mean. Mean snow depth of the total area ranged by a factor of two (1.31–2.58 m), with a standard deviation of 0.42 m. Interannual correlation coefficients of snow depth distribution were in the range 0.68–0.84. Of the investigated area, 75% was found to be interannually persistent. The remaining area showed variable snow cover from year to year, caused by occasional avalanches and changes in surface topography as a result of glacier retreat. Snow cover underwent a change from a homogeneous distribution on the former glacier surface to a more heterogeneous snow cover in the recently deglaciated terrain. A geostatistical analysis shows interannual persistence in scaling behavior of snow depth in ice-free terrain with scale break distances at 20 m. Scale-invariant behavior of snow depth is indicated over >100 m on smooth glacier surfaces.

No abstract

Abstract. The spatial distribution of snow accumulation substantially affects the seasonal course of water storage and runoff generation in high mountain catchments. Whereas the areal extent of snow cover can be recorded by satellite data, spatial distribution of snow depth and hence snow water equivalent (SWE) is difficult to measure on catchment scale. In this study we present the application of airborne LiDAR (Light Detecting And Ranging) data to extract snow depths and accumulation distribution in an alpine catchment.Airborne LiDAR measurements were performed in a glacierized catchment in theÖtztal Alps at the beginning and the end of three accumulation seasons. The resulting digital elevation models (DEMs) were used to calculate surface elevation changes throughout the winter season. These surface elevation changes were primarily referred to as snow depths and are discussed concerning measured precipitation and the spatial characteristics of the accumulation distribution in glacierized and unglacierized areas. To determine the redistribution of catchment precipitation, snow depths were converted into SWE using a simple regression model. Snow accumulation gradients and snow redistribution were evaluated for 100 m elevation bands.Mean surface elevation changes of the whole catchment ranges from 1.97 m to 2.65 m within the analyzed accumulation seasons. By analyzing the distribution of the snow depths, elevation dependent patterns were obtained as a function of the topography in terms of aspect and slope. The high resolution DEMs show clearly the higher variation of snow depths in rough unglacierized areas compared to snow depths on smooth glacier surfaces. Mean snow depths in glacierized areas are higher than in unglacierized areas. Maximum mean snow depths of 100 m elevation bands are found between 2900 m and 3000 m a.s.l. in unglacierized areas and between 2800 m and 2900 m a.s.l. in glacierized areas, respectively. Calculated accumulation gradients range from 8 % to 13 % per 100 m elevation band in the observed catchment. Elevation distribution of accumulation calculated by applying these seasonal gradients in comparison to elevation distribution of SWE obtained from airborne laser scanning (ALS) data show the total redistribution of snow from higher to lower elevation bands.Revealing both, information about the spatial distribution of snow depths and hence the volume of the snow pack, ALS data are an important source for extensive snow accumulation measurements in high alpine catchments. These information about the spatial characteristics of snow distribution are crucial for calibrating hydrological models in order to realistically compute temporal runoff generation by snow melt.