On 7 Feb 2021, a catastrophic mass flow descended the Ronti Gad, Rishiganga, and Dhauliganga valleys in Chamoli, Uttarakhand, India, causing widespread devastation and severely damaging two hydropower projects. Over 200 people were killed or are missing. Our analysis of satellite imagery, seismic records, numerical model results, and eyewitness videos reveals that ~27x106 m3 of rock and glacier ice collapsed from the steep north face of Ronti Peak. The rock and ice avalanche rapidly transformed into an extraordinarily large and mobile debris flow that transported boulders >20 m in diameter, and scoured the valley walls up to 220 m above the valley floor. The intersection of the hazard cascade with downvalley infrastructure resulted in a disaster, which highlights key questions about adequate monitoring and sustainable development in the Himalaya as well as other remote, high-mountain environments.

Abstract. Accurate knowledge of snow depth distributions in mountain catchments is critical for applications in hydrology and ecology. Recently, a method was proposed to map snow depth at meter-scale resolution from very-high-resolution stereo satellite imagery (e.g., Pléiades) with an accuracy close to 0.5 m. However, the validation was limited to probe measurements and unmanned aircraft vehicle (UAV) photogrammetry, which sampled a limited fraction of the topographic and snow depth variability. We improve upon this evaluation using accurate maps of the snow depth derived from Airborne Snow Observatory laser-scanning measurements in the Tuolumne river basin, USA. We find a good agreement between both datasets over a snow-covered area of 138 km2 on a 3 m grid, with a positive bias for a Pléiades snow depth of 0.08 m, a root mean square error of 0.80 m and a normalized median absolute deviation (NMAD) of 0.69 m. Satellite data capture the relationship between snow depth and elevation at the catchment scale and also small-scale features like snow drifts and avalanche deposits at a typical scale of tens of meters. The random error at the pixel level is lower in snow-free areas than in snow-covered areas, but it is reduced by a factor of 2 (NMAD of approximately 0.40 m for snow depth) when averaged to a 36 m grid. We conclude that satellite photogrammetry stands out as a convenient method to estimate the spatial distribution of snow depth in high mountain catchments.

No abstract

In this study, we combine remote sensing, in situ and model-derived datasets from 1966 to 2014 to calculate the mass-balance components of Kronebreen, a fast-flowing tidewater glacier in Svalbard. For the well-surveyed period 2009–2014, we are able to close the glacier mass budget within the prescribed errors. During these 5 years, the glacier geodetic mass balance was −0.69 ± 0.12 m w.e. a−1, while the mass budget method led to a total mass balance of −0.92 ± 0.16 m w.e. a−1, as a consequence of a strong frontal ablation (−0.78 ± 0.11 m w.e. a−1), and a slightly negative climatic mass balance (−0.14 ± 0.11 m w.e. a−1). The trend towards more negative climatic mass balance between 1966–1990 (+0.20 ± 0.05 m w.e. a−1) and 2009–2014 is not reflected in the geodetic mass balance trend. Therefore, we suspect a reduction in ice-discharge in the most recent period. Yet, these multidecadal changes in ice-discharge cannot be measured from the available observations and thus are only estimated with relatively large errors as a residual of the mass continuity equation. Our study presents the multidecadal evolution of the dynamics and mass balance of a tidewater glacier and illustrates the errors introduced by inferring one unmeasured mass-balance component from the others.

The glacier evolution from two valleys located near Vicdessos, French Pyrenees, is deciphered from 17 in situ cosmic ray exposure (CRE) dating. In the Picot valley, Late Glacial glacier advances were documented during Heinrich Stadial 1 (HS1), while a rock glacier developed or was reactivated during the mid-Holocene. In the upper Médécourbe valley, the largest visible ice extent occurred during the Younger Dryas (YD) or earlier. At least two moraines formed during the early Holocene were dated, while an undated moraine located close to the head of the catchment may have been formed either during the Late Holocene or the Little Ice Age. A mass balance model suggests that a temperature about 5.1 °C cooler than today, without precipitation change, would be necessary to form a moraine at the base of the Picot catchment during HS1 at 2000 m a.s.l. A temperature about 3.9 °C cooler than today is necessary to explain a moraine formed during the Late Glacial-YD transition at Médécourbe at 2200 m. Comparing CRE dating from Picot and Médécourbe with those in the Pyrenees and the Alps highlights original glacial patterns. In the Picot catchment whose summit is below the current regional ELA, the absence of YD and Late Holocene moraines is consistent with the general low-altitude deglaciation trend documented in the northern and southern slope of the Pyrenees, but differs from high-altitude Pyrenean and Alpine records. However, due to specific geomorphological conditions, the glacial evolution of the Médécourbe valley took place differently compared to other low-altitude catchments in the Pyrenees. Article Highlights • CRE dating revealed moraines formed during the Heinrich Stadial 1(HS1) and the Late Glacial-Younger Dryas (YD) transition in Ariège valley (French Pyrenees). • About 5.1 °C and 3.9 °C cooler than today without change in precipitation would explain the HS1 and Late Glacial-YD transition moraines formation. • The evolution of the glacier fluctuation from the deglaciation may depend on the size of the accumulation area.

Sentinel-2 provides the opportunity to map the snow cover at unprecedented spatial and temporal resolutions on a global scale. Here we calibrate and evaluate a simple empirical function to estimate the fractional snow cover (FSC) in open terrains using the normalized difference snow index (NDSI) from 20 m resolution Sentinel-2 images. The NDSI is computed from flat surface reflectance after masking cloud and snow-free areas. The NDSI–FSC function is calibrated using Pléiades very high-resolution images and evaluated using independent datasets including SPOT 6/7 satellite images, time lapse camera photographs, terrestrial lidar scans and crowd-sourced in situ measurements. The calibration results show that the FSC can be represented with a sigmoid-shaped function 0.5 × tanh(a × NDSI + b) + 0.5, where a = 2.65 and b = −1.42, yielding a root mean square error (RMSE) of 25%. Similar RMSE are obtained with different evaluation datasets with a high topographic variability. With this function, we estimate that the confidence interval on the FSC retrievals is 38% at the 95% confidence level.

Abstract. An accurate knowledge of snow depth distribution in mountain catchments is critical for applications in hydrology and ecology. A recent new method was proposed to map the snow depth at meter-scale resolution from very-high resolution stereo satellite imagery (e.g., Pléiades) with an accuracy close to 0.50 m. However, the validation was mainly done using probe measurements which sampled a limited fraction of the topographic and snow depth variability. We deepen this evaluation using accurate maps of the snow depth derived from ASO airborne lidar measurements in the Tuolumne river basin, USA. We find a good agreement between both datasets over a snow-covered area of 137 km2 on a 3 m grid with a positive bias for Pléiades snow depth of 0.08 m, a root-mean-square error of 0.80 m and a normalized median absolute deviation of 0.69 m. Satellite data capture the relationship between snow depth and elevation at the catchment scale, and also small-scale features like snow drifts and avalanche deposits. The random error on snow depth can be reduced by a factor two (up to approximately 0.40 m) when the snow depth map is spatially averaged to a ~ 20 m grid. The random error at the pixel level is lower on snow-free areas than on snow-covered areas, but errors on both terrain type converge at coarser resolutions, which is important for further applications of the method in areas without snow depth reference data. We conclude that satellite photogrammetry stands out as an efficient method to estimate the spatial distribution of snow depth in high mountain catchments.

Abstract. The spatial distribution of snow in the mountains is significantly influenced through interactions of topography with wind, precipitation, shortwave and longwave radiation, and avalanches that may relocate the accumulated snow. One of the most crucial model parameters for various applications such as weather forecasts, climate predictions and in hydrological modeling is the fraction of the ground surface that is covered by snow, also called fractional snow-covered area (fSCA). While previous subgrid parameterizations for the spatial snow depth distribution and fSCA work well, performances were scale-dependent. Here, we were able to confirm a previously established empirical relationship of the peak of winter parameterization for the standard deviation of snow depth σ>sub>HS by evaluating it on 11 spatial snow depth data sets from 7 different geographic regions and snow climates with resolutions ranging from 0.1 m to 3 m. Enhanced performance (mean percentage errors (MPE) decreased by 25 %) across all spatial scales ≥ 200 m was achieved by recalibrating and introducing a scale-dependency in the dominant scaling variables. Scale-dependent MPEs vary between −7 % and 3 % for σ>sub>HS and between 0 % and 1 % for fSCA. A scale- as well as region-dependent evaluation revealed that for the majority of the regions the MPEs mostly lie between ±10 % for σ>sub>HS and between −1 % and 1.5 % for fSCA. This suggests that the new parameterizations perform similarly well in most geographical regions.