Search

Climate warming is changing the magnitude, timing, and spatial patterns of mountain snowpacks. A warmer atmosphere may also induce precipitation phase shifts, resulting in a decreased snowfall fraction (Sf). The combination of Sf and snowpack directly influences the frequency and intensity of rain-on-snow (ROS) events, a common cause of flash-flood events in snow-dominated regions. In this work, we investigate ROS patterns and their sensitivity to temperature and precipitation changes in the Pyrenees by modeling ROS through a physically based snow model. This model is forced with reanalysis climate data for elevations of 1500, 1800, and 2400 m perturbed using a range of temperature and precipitation values consistent with 21st century climate projections. ROS patterns are characterized by their frequency, rainfall quantity, and snow ablation. The highest ROS frequency for the historical climate period (1980–2019) is found in the 2400 m zones of the southwest Pyrenees (17 d yr−1). The maximum ROS rainfall amount is detected in 1800 m areas of the southeast (45 mm d−1, autumn), whereas the highest ROS ablation is found in the 2400 m zones of the northwest (−10 cm d−1, summer). When air temperature increases from 1 to 4 ∘C compared to the historical climate period, ROS rainfall amount and frequency increase at a constant rate during winter and early spring for all elevation zones. For the rest of the seasons, non-linear responses of ROS frequency and ablation to warming are found. Overall, ROS frequency decreases in the shoulders of the season across eastern low-elevation zones due to snow cover depletion. However, ROS increases in cold, high-elevation zones where long-lasting snow cover exists until late spring. Similarly, warming induces greater ROS ablation (+10 % ∘C−1) during the coldest months of the season, 2400 m elevations, and northern sectors, where the deepest snow depths are found. In contrast, small differences in ROS ablation are found for warm and margin

A marginal snowpack is a recent concept that refers to snow cover that is relatively shallow and transient but still has important environmental and socioeconomic functions. This class of snow cover influences environments over large areas of the world and is very common in the Mediterranean climates, but also in the transition zone between persistent seasonal and ephemeral snowpacks all over the world. However, a globally-accepted definition to identify these environments has not been reached yet. Most of the modelling and monitoring tools for snow have been developed in cold and alpine areas with persistent snowpacks. The study and modelling of shallower snowpacks in milder environments are complicated by their high temporal variability, frequently patchy nature and typical coexistence with shrubs and forest. However, in recent years new remote sensing tools and more complex modelling approaches and specific parametrizations are facilitating their study. This paper provides a definition for marginal snowpacks, identifying them as those with: (i) seasonal peak depths that only occasionally surpass 1.5 m in favorable deposition zones such as wind drifts, ii) having typical seasonal durations ranging from 60 to 120 days, and (iii) that are predominantly in an isothermal state (on >75% of days) with high densification rates and several cycles of accumulation-ablation during each snow season. We review the main existing research challenges to improve scientific capabilities for their study and we discuss their hydrological and environmental relevance at different spatial scales.

This dataset includes very high spatial resolution observations at 1 m spatial resolution observations of the snow depth, the NDVI and the melt-out date (DOY of year) acquired with an Unmanned Aerial Vehicle at a sub-alpine site in the Pyrenees, the Izas Experimental Catchment. During two snow seasons (2019-2020 and 2020-2021), 14 NDVI and 17 snow depth distributions were acquired over 48ha. From the snow depth observations the melt-out dates have been derived. Also information on the main topographic variables (elevation, aspect and slope) is included, with same spatial resolution, in this dataset.

This study extends knowledge of the evolution of glacier shrinkage in the Cocuy-Güican Mountains since the maximum glacier extent of the Little Ice Age (LIA). Mass balance data for the Ritacuba Glacier since 2009 were acquired and compared with available data for the Conejeras Glacier (Los Nevados National Park). This study also investigated the hydrological significance of Colombian glaciers, which is still largely unknown because the available information is very limited. Glaciers in the Cocuy-Güican Mountains covered 13.2 km2 in 2019 compared with 127.8 km2 during the maximum LIA, representing a shrinkage of 89.7%. Analysis of glacier cover observations made in 1955, 1994, 2010, and 2019 revealed that the rate of ice loss was greatest from 1994 to 2010 (0.59 km2 yr−1), and was then almost halved from 2010 to 2019 (0.34 km2 yr−1). This slowing of glacier retreat is consistent with the moderate negative mass balance measured for 2009–2019, and an accumulated loss of 1766 mm w.e. (mm water equivalent). The progressive confinement of glaciers to higher elevations, ice accumulation in topographic locations providing shelter from solar radiation, and an absence of recent marked climatic anomalies could explain why the Cocuy-Güican glaciers have temporally reached a near equilibrium state conditions. This contrasts with the Conejeras Glacier, where 47,000 mm w.e. has been lost in the same period. The available data on runoff and isotope tracers of streamflow and precipitation suggest that precipitation rather than glacier melt water exerts primary control over the hydrological variability at high elevation sites.

In mountain areas, the phenology and productivity of grassland are closely related to snow dynamics. However, the influence that snow melt timing has on grassland growing still needs further attention for a full understanding, particularly at high spatial resolution. Aiming to reduce this knowledge gap, this work exploits 1 m resolution snow depth and Normalized Difference Vegetation Index observations acquired with an Unmanned Aerial Vehicle at a sub-alpine site in the Pyrenees. During two snow seasons (2019–2020 and 2020–2021), 14 NDVI and 17 snow depth distributions were acquired over 48 ha. Despite the snow dynamics being different in the two seasons, the response of grasslands greening to snow melt-out exhibited a very similar pattern in both. The NDVI temporal evolution in areas with distinct melt-out dates reveals that sectors where the melt-out date occurs in late April or early May (optimum melt-out) reach the maximum vegetation productivity. Zones with an earlier or a later melt-out rarely reach peak NDVI values. The results obtained in this study area, suggest that knowledge about snow depth distribution is not needed to understand NDVI grassland dynamics. The analysis did not reveal a clear link between the spatial variability in snow duration and the diversity and richness of grassland communities within the study area.

Study region: Peñalara catchment, in the mountains of the Central System in Spain, Study focus: For the first time, we investigated the streamflow and streamwater isotopes during the snow accumulation and melting periods and over subsequent months in two snow seasons. The aim is to better understand the hydrological processes linked to snowmelt; to describe the temporal evolution and the interannual differences in isotopic streamwater; and to improve the understanding about the hydrological functioning of snowmelt water across the catchment. New hydrological insights for the region: The isotopic signal of the streamwater progressively became isotopically depleted from the beginning of the melt period until the snow cover depletion of the catchment. Higher snowfall led to depleted isotopic values in the stream compared to a year with low snowfall. The interannual variability of the isotopic signature of streamwater during snowmelt may represent a difficulty to establishing reference values to be used in mixing models for hydrograph separation. The streamflow isotopic values had very limited sub-daily variation and showed slow temporal changes, suggesting a central role for alpine aquifers in explaining the hydrological functioning of the catchment, pointing to piston flow as a key process in streamflow generation.

The unprecedented precision of satellite laser altimetry data from the NASA ICESat-2 mission and the increasing availability of high-resolution elevation datasets open new opportunities to measure snow depth in mountains, a critical variable for ecosystem and water resource monitoring. We retrieved snow depth over the upper Tuolumne basin (California, USA) for 3 years by differencing ICESat-2 ATL06 snow-on elevations and various snow-off digital elevation models. Snow depth derived from ATL06 data only (snow-on and snow-off) offers a poor temporal and spatial coverage, limiting its potential utility. However, using a digital terrain model from airborne lidar surveys as the snow-off elevation source yielded a snow depth accuracy of ∼ 0.2 m (bias) and precision of ∼ 1 m (random error) across the basin, with an improved precision of 0.5 m for low slopes (< 10∘), compared to eight reference airborne lidar snow depth maps. Snow depths derived from ICESat-2 ATL06 and a satellite photogrammetry digital elevation model have a larger bias and reduced precision, partly induced by increased errors in forested areas. These various combinations of repeated ICESat-2 snow surface elevation measurements with satellite or airborne products will enable tailored approaches to map snow depth and estimate water resource availability in mountainous areas with limited snow depth observations.

Le manteau neigeux en montagne est une ressource importante pour les écosystèmes et les activités humaines comme l'irrigation, l'approvisionnement en eau des populations, la production hydroélectrique et l'économie touristique. Il représente aussi un risque dans les zones exposées aux avalanches. L'étude et le suivi du manteau neigeux en montagne s'appuie souvent sur des réseaux de mesure, des observations par télédétection et de la modélisation. Les avancées récentes en photogrammétrie satellite offrent de nouvelles perspectives pour compléter les réseaux de mesures qui sont souvent insuffisants vis-à-vis de la forte variabilité spatiale du manteau neigeux. Une méthode de cartographie de la hauteur de neige à partir d'images stéréoscopiques Pléiades est présentée et appliquée sur plusieurs sites. La comparaison avec une carte de référence par lidar aéroporté fournit une estimation de l'erreur des produits de photogrammétrie satellite sur un bassin versant de Californie (États-Unis). A l'échelle d'un pixel de 3 m, l'erreur standard est de 0,7 m. L'erreur décroît à ~0,3 m lorsque les mesures sont moyennées sur des surfaces supérieures à 103 m². Avec cette précision, les cartes de hauteur de neige par photogrammétrie satellite permettent d'observer les processus modelant le manteau neigeux en montagne (transport par le vent, avalanche), de mesurer le volume de neige sur des zones de plus 100 km² et de décrire la variabilité spatiale du manteau. Une série de cartes de hauteur de neige est assimilée dans la chaine de modélisation SAFRAN-Crocus afin d'évaluer le potentiel de ces données pour améliorer la représentation spatiale des propriétés physiques du manteau neigeux. Un filtre particulaire est utilisé pour assimiler une carte de hauteur de neige par hiver pendant cinq hivers sur un bassin versant des Pyrénées. L'assimilation corrige des biais dans les précipitations initialement sous-estimées à haute altitude et introduit une variabilité spatiale autrement absente d, Mountain snowpack is a major resource for ecosystems and human activities. It supplies water for crop irrigation, human consumption, hydropower industries and the tourism sector. It is also a cause of damage in avalanche prone areas. The monitoring and study of mountain snowpack usually rely on field measurement networks, close range remote sensing and modeling. Recent improvements in satellite photogrammetry provide an alternative to measure the high spatial variability of the snowpack, which cannot be sampled by automatic networks. The results presented here, contribute to improve the mapping of snow-depth in mountains with satellite photogrammetry, a key variable for hydrology and risk assessment. Snow-depth maps from pairs and triplets of stereo images of the Pléiades satellite are calculated at several sites. The comparison with a reference snow-depth map measured with airborne lidar in California (USA), provides a robust estimation of the satellite products error. At the 3 m pixel scale, the standard error is about 0.7 m. The error decreases to 0.3 m when the snow-depth maps are averaged over areas greater than 103 m2. With this accuracy, Pléiades snow-depth maps allow the observation of the processes modeling mountain snowpack (wind transport, avalanche), the measurement of the snow volume over a 100 km2 area and the description of the spatial variability of the snowpack. The assimilation of such satellite snow-depth maps in the SAFRAN-Crocus snowpack model, resulted in promising outcomes for a mountainous catchment in the Pyrenees. A particle filter is used on a regular grid with 250 m spacing over five winters with one assimilation date per winter, near peak accumulation. The assimilation corrects an underestimation of the precipitation in the meteorological forcings. It also introduces spatial variability otherwise lacking in the forcings and the processes modeled. This innovative use of satellite products and complex spatial modeling, could help address t

Quantarctica (https://www.npolar.no/quantarctica) is a geospatial data package, analysis environment, and visualization platform for the Antarctic Continent, Southern Ocean (>40oS), and sub-Antarctic islands. Quantarctica works with the free, cross-platform Geographical Information System (GIS) software QGIS and can run without an Internet connection, making it a viable tool for fieldwork in remote areas. The data package includes basemaps, satellite imagery, terrain models, and scientific data in nine disciplines, including physical and biological sciences, environmental management, and social science. To provide a clear and responsive user experience, cartography and rendering settings are carefully prepared using colour sets that work well for typical data combinations and with consideration of users with common colour vision deficiencies. Metadata included in each dataset provides brief abstracts for non-specialists and references to the original data sources. Thus, Quantarctica provides an integrated environment to view and analyse multiple Antarctic datasets together conveniently and with a low entry barrier.

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.