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Expanding shrubs in the Arctic trap blowing snow, increasing snow height and accelerating permafrost warming. Topography also affects snow height as snow accumulates in hollows. The respective roles of topography and erect vegetation in snow accumulation were investigated using a UAV-borne lidar at two nearby contrasted sites in northern Quebec, Canada. The North site featured tall vegetation up to 2.5 m high, moderate snow height, and smooth topography. The South site featured lower vegetation, greater snow height, and rougher topography. There was little correlation between topography and vegetation height at both sites. Vegetation lower than snow height had very little effect on snow height. When vegetation protruded above the snow, snow height was well correlated with vegetation height. The topographic position index (TPI) was well correlated with snow height when it was not masked by the effect of protruding vegetation. The North site with taller vegetation therefore showed a good correlation between vegetation height and snow height, R2 = 0.37, versus R2 = 0.04 at the South site. Regarding topography, the reverse was observed between TPI and snow height, with R2 = 0.29 at the North site and R2 = 0.67 at the South site. The combination of vegetation height and TPI improved the prediction of snow height at the North site (R2 = 0.59) but not at the South site because vegetation height has little influence there. Vegetation was therefore the main factor determining snow height when it protruded above the snow. When it did not protrude, snow height was mostly determined by topography. Significance Statement: Wind-induced snow drifting is a major snow redistribution process in the Arctic. Shrubs trap drifting snow, and drifting snow accumulates in hollows. Determining the respective roles of both these processes in snow accumulation is required to predict permafrost temperature and its emission of greenhouse gases, because thicker snow limits permafrost winter cooling. Using a UAV-borne lidar, we have determined snow height distribution over two contrasted sites in the Canadian low Arctic, with varied vegetation height and topography. When snow height exceeds vegetation height, topography is a good predictor of snow height, with negligible effect of buried vegetation. When vegetation protrudes above the snow, combining both topography and vegetation height is required for a good prediction of snow height. [ABSTRACT FROM AUTHOR]

Understanding and characterizing the spatial distribution of snow are critical to represent the energy balance and runoff production in mountain environments. In this study, we investigate the interannual and seasonal variability in snow depth scaling behavior at the Izas experimental catchment of the Spanish Pyrenees (2,000 to 2,300 m above sea level). We conduct variogram analyses of 24 snow depth maps derived from terrestrial light detection and ranging scans, acquired during six consecutive snow seasons (2011–2017) that span a range of hydroclimatic conditions. We complement our analyses with bare ground topography data and wind speed and direction measurements. Our results show temporal consistency in the spatial variability of snow depth, with short‐range fractal behavior and scale break lengths that are similar to the optimal search distance (25 m) previously reported for the topographic position index, a terrain‐based predictor of snow depth. Beyond the 25‐m scale break, there is little to no fractal structure. We report a long‐range scale break of the order of 185–300 m for most dates—aligned with the dominant wind direction—and patterns between anisotropies in scale break lengths of shallow snow cover and directional terrain scaling behavior. The temporal consistency of snow depth scaling patterns suggests that, in addition to guiding the spatial configuration of physically based models, fractal analysis could be used to inform the design of independent variables for statistical models used to predict snow depth and its variability. Key Points: Consistent short‐range fractal behavior and scale breaks in snow depth were detected for six consecutive seasons in a subalpine catchmentScale break anisotropies in shallow snowpacks during melt periods can be explained by bare‐earth terrain scaling patternsVariogram analysis can inform statistical and dynamical model decisions to best simulate snow distribution [ABSTRACT FROM AUTHOR]

The presence of a seasonal snowpack determines the hydrology, geomorphology and ecology of wide parts of the Iberian Peninsula, with strong implications for the economy, transport and risk management. Thus, reliable information on snow is necessary from a scientific and operational point of view. This is the case of the Iberian Peninsula where, lack of observation has impeded proper analysis of snowpack duration, magnitude and interannual variability. In this study, we present the first snow climatology of the entire Iberian Peninsula. The scarcity of in situ observations has been overcome, using a newly developed remote sensing snow database from MODIS satellite sensors for the period 2000–2014 and a physically based snow model (Factorial Snow Model—FSM), driven by a regional atmospheric model (Weather Research and Forecast model—WRF) over the Iberian Peninsula for the period 1980–2014. The snowpack of the main mountain areas (Pyrenees, Cantabrian, Central, Iberian range and Sierra Nevada) are described, estimated from the generated databases. The information has been processed using a k‐means cluster algorithm, looking for similarities in snow indices at different elevation bands. Results show four different types of snowpack in terms of depth, duration and interannual variability, lying over different elevation bands in the different ranges, proving the variability of the snowpack over Iberia. Analyses reveal areas characterized by ephemeral snowpacks, while in some sectors snowpack lasts, on average, 198 days per year with 3.02 m of peak snow depth. The coefficient of variation of interannual peak snow depth oscillated between 35.2 and 162.4%. All the analysed indices show that at common elevations the Cantabrian range and the Pyrenees host the deepest and longest lasting snowpacks, followed by the Central and Iberian ranges. The Sierra Nevada exhibits the shortest, shallowest snowpack and more year‐to‐year variability. [ABSTRACT FROM AUTHOR]

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 FROM AUTHOR]

The aim of this work is to understand aerosol transfers to the snowpack in the Spanish Pyrenees (Southern Europe) by determining their episodic mass-loading and composition, and to retrieve their regional impacts regarding optical properties and modification of snow melting. Regular aerosol monitoring has been performed during three consecutive years. Complementarily, short campaigns have been carried out to collect dust-rich snow samples. Atmospheric samples have been chemically characterized in terms of elemental composition and, in some cases, regarding their mineralogy. Snow albedo has been determined in different seasons along the campaign, and temporal variations of snow-depth from different observatories have been related to concentration of impurities in the snow surface. Our results noticed that aerosol flux in the Central Pyrenees during cold seasons (from November to May, up to 12–13 g m−2 of insoluble particles overall accumulated) is much higher than the observed during the warm period (from June to October, typically around 2.1–3.3 g m−2). Such high values observed during cold seasons were driven by the impact of severe African dust episodes. In absence of such extreme episodes, aerosol loadings in cold and warm season appeared comparable. Our study reveals that mineral dust particles from North Africa are a major driver of the aerosol loading in the snowpack in the southern side of the Central Pyrenees. Field data revealed that the heterogeneous spatial distribution of impurities on the snow surface led to differences close to 0.2 on the measured snow albedo within very short distances. Such impacts have clear implications for modelling distributed energy balance of snow and predicting snow melting from mountain headwaters. [ABSTRACT FROM AUTHOR]

The North Atlantic Oscillation (NAO) is considered to be the main atmospheric factor explaining the winter climate and snow evolution over much of the Northern Hemisphere. However, the absence of long-term snow data in mountain regions has prevented full assessment of the impact of the NAO at the regional scales, where data are limited. In this study, we assessed the relationship between the NAO of the winter months (DJFM-NAO) and the snowpack of the Iberian Peninsula. We simulated temperature, precipitation, and snow data for the period 1979–2014 by dynamic downscaling of ERA-Interim reanalysis data, and correlated this with the DJFM-NAO for the five main mountain ranges of the Iberian Peninsula (Cantabrian Range, Central Range, Iberian Range, the Pyrenees, and the Sierra Nevada). The results confirmed that negative DJFM-NAO values generally occur during wet and mild conditions over most of the Iberian Peninsula. Due to the direction of the wet air masses, the NAO has a large influence on snow duration and the annual peak snow water equivalent (peak SWE) in most of the mountain ranges in the study, mostly on the slopes south of the main axis of the ranges. In contrast, the impact of NAO variability is limited on north-facing slopes. Negative (positive) DJFM-NAO values were associated with longer (shorter) duration and higher (lower) peak SWEs in all mountains analyzed in the study. We found marked variability in correlations of the DJFM-NAO with snow indices within each mountain range, even when only the south-facing slopes were considered. The correlations were stronger for higher elevations in the mountain ranges, but geographical longitude also explained the intra-range variability in the majority of the studied mountains. [ABSTRACT FROM AUTHOR]

Understanding and characterizing the spatial variability of snow depth can help to inform the configuration of distributed snowmelt models. The past decades have seen outstanding advances on this topic by using LiDAR (Light Detection and Ranging) technology, which has been critical to identify fractal behavior in many mountain regions worldwide. In this study, we investigate the inter- and intra-annual variability in snow depth scaling behavior at the Izas experimental catchment, located on the southern side of the Spanish Pyrenees (2000 to 2300 m above sea level). To this end, we conduct variogram analysis for 24 snow depth maps derived from terrestrial LiDAR scans, acquired during six consecutive snow seasons (2011-2017). We complement our analyses with bare ground topography and wind speed and direction measurements. Our results show inter-annual consistency in snow depth accumulation patterns, with similar scale break lengths to the best searching distance (25 m) previously reported for the Topographic Position Index (TPI), a terrain-based predictor for snow depth. On the other hand, no scale breaks are observed for bare earth topography. Scale breaks (long-range fractal dimensions) perpendicular (parallel) to prevailing NW-SE winds are larger than those obtained from directional variograms parallel (perpendicular) to dominant winds. Finally, our results suggest that basin-scale snow depth statistics contain useful information to characterize the spatial structure of snow depth. [ABSTRACT FROM AUTHOR]