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148 results for “Snow cover”
Energy feedbacks of northern high-latitude ecosystems to the climate system due to reduced snow cover during 20th century warming-V
This data file contains data for changes in snow melt, snow return, and total snow cover duration as modeled with the Terrestrial Ecosystem Model for the area north of 50 degrees north latitude around the entire globe for the years 1970-2000. See Euskirchen et al. (2007) for full study details.
Time series of snow cover area products over the Kananaskis Country
<p>This dataset contains maps of the snow cover area over the Kananaskis Country (Canada) from 01 September 2017 to 31 August 2018. The products were derived from Sentinel-2 observations. All available Sentinel-2 level 1C products were processed to level 2A (surface reflectance and cloud mask) using the <a href="https://github.com/CNES/Start-MAJA">MAJA software</a>. Then, level 2A products were processed using the <a href="https://gitlab.orfeo-toolbox.org/remote_modules/let-it-snow/">LIS software</a> to generate the snow cover maps.</p> <p>The area is covered by four tiles: T11UPT (81 dates), T11UPS (116 dates), T11UNT (114 dates), T11UNS (87 dates). The data are provided as GeoTIFF images coded as follows:</p> <ul> <li>0: No-snow</li> <li>100: Snow</li> <li>205: Cloud including cloud shadow</li> <li>255: No data</li> </ul> <p>Read more about these products in <a href="https://labo.obs-mip.fr/multitemp/snow-cover-duration-in-the-canadian-rockies-from-sentinel-2-observations/">this blog post</a>.</p>
Data for "Decreasing snow cover alters functional composition and diversity of Arctic tundra"
<p>This page links to the data and code associated with the publication Niittynen et al. (2020) "<strong>Decreasing snow cover alters functional composition and diversity of Arctic tundra</strong>" published in PNAS ( <a href="https://doi.org/10.1073/pnas.2001254117">https://doi.org/10.1073/pnas.2001254117</a> ). The species data contain cover values of 200 vascular plant species recorded in 1325 study sites in Rastigaisa area in Northern Norway. The trait data includes species level median trait values for the 200 species calculated from records extracted from public databases for seven plant functional traits and community weighted means of these traits calculated for all the 1325 study sites. The environmental data contain six spatially continuous predictors used in the species level models. The data and analyses are described in the linked publication.</p>
Deepened snow cover mitigates soil carbon loss from intensive land use in a semi-arid temperate grassland
<p>Carbon (C) loss due to soil erosion is a major issue in semi-arid grasslands. The extent of soil erosion is determined by soil properties and vegetation structure, especially during the non-growing season. In many Inner Mongolian grasslands, intensive land use, such as overgrazing and mowing, has severely reduced plant cover and damaged soil structure, which has exacerbated soil C loss by erosion. At the same time, increasing winter snowfall due to climate change is stimulating plant growth and altering plant composition. However, we do not know how changes in winter snow cover interact with land-use practices to regulate soil C loss due to erosion.</p> <p>Here, we conducted a six-year snow manipulation experiment under different land-use practices (control; moderately mowed, MM; heavily mowed, HM) to measure net changes in soil depth, soil C, plant biomass, and vegetation structure.</p> <p>After six years, soil C loss under ambient snow was three times greater in the MM and four times greater in the HM treatment compared with controls during non-growing season. However, deepened winter snow alleviated erosion-induced soil C loss by 14%, 47%, 16% in the controls, MM and HM treatments, respectively.</p> <p>The severity of soil C loss declined with increasing aboveground biomass (AGB), surface root biomass and vegetation structure. Vegetation structure and AGB explained more of the variation in soil C loss than surface root biomass, possibly because a complex canopy and plant cover increases overall surface roughness, thereby reducing soil C loss. Intensified land use reduced AGB, surface root biomass and vegetation structure, but deepened snow increased overall surface roughness by promoting AGB. Hence, our study demonstrates that deepened snow can alleviate soil C loss due to land use practices by promoting AGB.</p>
Snow cover simulations and avalanche dynamics simulations for the area of Davos, Switzerland (2011-2014)
<p>Alpine3D model simulations of the snow cover at 100m resolution for the area surrounding Davos for the period October 2010 to September 2014.</p> <p>Avalanche dynamics simulations using the RAMMS-Extended model of 169 selected avalanches in the same period, using initial conditions as simulated by the Alpine3D model.</p> <p>This dataset belongs to:</p> <p>Wever, N., Vera Valero, C., and Techel, F. (2018): <em>Coupled snow cover and avalanche dynamics simulations to evaluate wet snow avalanche activity</em>, J. Geophys. Res. Earth Surf., 123, 1772–1796 <a href="https://doi.org/10.1029/2017JF004515">doi: 10.1029/2017JF004515.</a></p>
Data-driven Discovery of Snow Cover Parameterization
<p>All data were derived from SNOTEL, Version 1, and were preprocessed for training symbolic regression models in convenience. Train and test data are range from water years of 2001-2009, and of 2010-2018. All NaN value were removed from data and shape as [Sample, feat]. The dataset contains preprocessed dimensonless features:</p> <p>The name and unit of each feature were listed in sequence as follows:</p> <p> 1. Snow depth (mm)<br> 2. Snow water equivalent (mm)<br> 3. Standard deviation of sub-grid topography (m)<br> 4. Air temperature (K)<br> 5. Precipitation (mm/day)<br> 6. 1/snow density (mm/mm)<br> 7. 1/Standard deviation of sub-grid topography (m^-1)</p> <p>The name and unit of target were listed as follows:</p> <p> 1. Snow cover fraction [%]</p> <p>Some own defined constant:</p> <ol> <li>surface roughness (0.1 m)</li> <li>0 degree of temperature (273.16 K)</li> <li>own defined std threshold (200 m)</li> <li>mean SWE (122.3 mm)</li> </ol>
From individual to population level: Temperature and snow cover modulate fledging success through breeding phenology in Greylag geese (Anser anser)
<p>Local weather conditions may be used as environmental cues by animals to optimize their breeding behaviour, and could be affected by climate change. We measured associations between climate, breeding phenology, and reproductive output in greylag geese (<i>Anser anser</i>) across 29 years (1990-2018). The birds are individually marked, which allows accurate long-term monitoring of life-history parameters for all pairs within the flock. We had three aims: (1) identify climate patterns at a local scale in Upper Austria, (2) measure the association between climate and greylag goose breeding phenology, and (3) measure the relationship between climate and both clutch size and fledging success. Ambient temperature increased 2°C across the 29-years study period, and higher winter temperature was associated with earlier onset of egg-laying. Using the hatch-fledge ratio, average annual temperature was the strongest predictor for the proportion of fledged goslings per season. There is evidence for an optimum time window for egg-laying (the earliest and latest eggs laid had the lowest fledging success). These findings broaden our understanding of environmental effects and population-level shifts which could be associated with increased ambient temperature and can thus inform future research about the ecological consequences of climate changes and reproductive output in avian systems.</p>
Snow cover from spectral mixture analysis algorithm SCAG: OLI and MODIS
<p>This data is snow cover fraction from the Snow Covered Area and Grain Size (SCAG) model for Landsat OLI and Terra MODIS. Terra MODIS data are gap filled to better represent on the ground snow. The data was used in the a publication for The Cyrosphere titled Landsat, MODIS, and VIIRS snow cover mapping algorithm performance as validated by airborne lidar datasets, doi.org/10.5194/tc-2022-159. Geotiffs and PNG files for Landsat 8 are self describing. The .mat files for Terra MODIS contain three variables:</p> <p>snow_fraction: the gap filled snow fraction stored as uint8 with 255 as the NoData value and valid values between and including 0 to 100.</p> <p>mstruct: projection structure describing the standard MODIS tile projection structure. The data represent data from tile h08v05 and h09v05</p> <p>RefMatrix: affine spatial referencing matrix for the snow_fraction grid with the projection described by mstruct</p>
Data from: Snow-cover seasonality in Kyrgyzstan: Variation and change over 20 years (2001-2021) as observed by the MODIS Terra snow product
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Deepened snow cover mitigates soil carbon loss from intensive land use in a semi-arid temperate grassland
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From individual to population level: Temperature and snow cover modulate fledging success through breeding phenology in Greylag geese (Anser anser)
Open the record for dataset details and reuse information.
Soil solution chemistry measurements at the snow cover experiment, Hubbard Brook Experimental Forest, 1997-1999
The effect of soil freezing on soil cation and anion losses was assessed using zero-tension lysimeters placed in plots of the snow removal experiment at the Hubbard Brook Expermental Forest. Four plots were used in which one subplot accumulated snow cover at natural rates throughout the winter (C), while the second subplot had snow removed from the first snowfall through early February (F). Replicate lysimeters were installed below the Oa and within the Bs soil horizons and soil solution collected weekly for two winters (1997 - 1998 and 1998 - 1999). This dataset includes solution chemistry measurements for individual lysimeters collected during that time period. These data were gathered as part of the Hubbard Brook Ecosystem Study (HBES). The HBES is a collaborative effort at the Hubbard Brook Experimental Forest, which is operated and maintained by the USDA Forest Service, Northern Research Station.
Snow depth map and land cover map from satellite photogrammetry (Pleiades) in Tuolumne, California.
<p><strong>snow_depth_20170501_pleiades.tif</strong></p> <p>Snow depth from the difference of digital elevation models (DEMs) calculated from Pléiades images. The snow-on DEMs were acquired on 2017-05-01. The snow-off DEMs were acquired on 2017-08-08 and 2017-08-13.</p> <p> </p> <p><strong>land_cover_20170813-08_pleiades.tif land_cover_20170501_pleiades.tif</strong></p> <p>Land cover calculated from multi-spectral Pléiades images acquired on 2017-05-01 (snow-on) and 2017-08-08 and -13 (snow-off). Classes are snow (1), forest (2), bare rock/low vegetation (3), lake (4).</p>
Fractional Snow Covered Area at Ny-Ålesund (Svalbard, Norway) 2015-2019
<p>The produced dataset (in netCDF v4 format) contains the estimation of the Factional Snow Cover (FSC) in different sites located in Svalbard islands. We considered areas that complete the already available datasets and we focused the attention to the Ny-Ålesund area (Svalbard - Norway) (78.917° N, 11.933° E) where different facilities are available for supporting the use of terrestrial photography. One asset is the Zeppelin observatory, located on a panoramic spot where cameras are operating since 2000, and one is the Climate Change Tower (CCT) where we deployed a camera in 2018 (Figure 1). While the Zeppelin camera, operated by the Norwegian Polar Institute, offers a long time-series coupled with a pan-tilt-zoom device (4 different views daily), the CCT device was installed in order to cover the hidden side of the coastal plain not visible from the observatory. Furthermore, the CCT camera provided highly spatial and time resolved (hourly) images that were also below the cloud layer.</p>
Viewable Snow Covered Area Validation Masks over Rugged and Forested Terrain
<p>These data are maps of viewable snow cover generated from panchromatically sharpened cloud-free WorldView-2 and -3 data within 2 days of a Landsat 8 OLI acquisition or near-nadir MODIS acquisition. The spatial resolution of these validation data ranges from 0.34 m to 0.55 m, depending on the view angle of WorldView. ValKey.csv shows the list of validation dates and the corresponding Landsat 8 OLI and MODIS imagery. Validation images from December to June were selected to account for variability in illumination conditions, snow cover, and snow albedo. The imagery spans diverse locations across California’s Sierra Nevada that represent the heavily forested western slope, higher elevation regions, and drier eastern slopes. The WorldView images range from well illuminated alpine scenes above the tree line in June to heavily shadowed scenes below the tree line in December. The snow-covered WorldView pixels are assumed to be pure endmembers of 100% snow, which are then coarsened to Landsat or MODIS spatial resolutions and provided here as geotiffs. Neither WorldView, Landsat, nor MODIS can see through thick tree canopies, so the data is comprised of snow that an optical sensor identifies. Complete methods used to generate the dataset are available in the companion publication: tbd</p> <p>Binary snow cover maps at the native worldview resolution are unsigned 8 bit integers with fill pixels set to zero, snow pixels set to one, and snow free pixels set to two.</p> <p>Fill Pixels [0]</p> <p>Snow Covered Pixels [1]</p> <p>Snow Free Pixels [2]</p> <p>Fractional snow covered area geotiffs in the projections and at the spatial resolution of Landsat 8 and MODIS products are signed 16-bit integers with a fill value of -32768. The divisor and offset are 1000 & 0 respectively.</p> <p>Fractional snow covered area [0-1000)] (divisor of 1000 and offset of 0 for measurement range of 0-1)</p> <p>Fill Pixels [-32768]</p>
Spatial variation in early-winter snow cover determines local dynamics in a network of alpine butterfly populations
<p>Snow cover is an extremely variable but critical component of alpine environments. We use long term population data on multiple small populations of the alpine butterfly <i>Parnassius smintheus</i>, combined with high-resolution satellite imagery of meadows, to show a strong link between fine-scale spatial and temporal variation in early-winter snow cover and annual change in butterfly population size, accounting for up to 80 percent of the variation in annual population change. Snow cover in early winter for each meadow is the best predictor of annual adult population change, despite being estimated for a relatively short time-window in late November. We identify a means by which subpopulation response to a local, short-term weather variable can be assessed over a large spatial extent, but also at a resolution relevant to the biology and local dynamics of this alpine species.</p>
Data from: Short-term climate change manipulation effects do not scale up to long-term legacies: effects of an absent snow cover on boreal forest plants
1. Despite time lags and non-linearity in ecological processes, the majority of our knowledge about ecosystem responses to long-term changes in climate originates from relatively short-term experiments. 2. We utilized the longest ongoing snow removal experiment in the world and an additional set of new plots at the same location in northern Sweden to simultaneously measure the effects of long-term (11 winters) and short-term (1 winter) absence of snow cover on boreal forest understorey plants, including effects on root growth and phenology. 3. Short-term absence of snow reduced vascular plant cover in the understorey by 42%, reduced fine root biomass by 16%, reduced shoot growth by up to 53%, and induced tissue damage on two common dwarf shrubs. In the long-term manipulation, more substantial effects on understorey plant cover (92% reduced) and standing fine root biomass (39% reduced) were observed, whereas other response parameters, such as tissue damage, were observed less. Fine root growth was generally reduced, and its initiation delayed by c. 3 (short-term) to 6 weeks (long-term manipulation). 4. Synthesis We show that one extreme winter with a reduced snow cover can already induce ecologically significant alterations. We also show that long-term changes were smaller than suggested by an extrapolation of short-term manipulation results (using a constant proportional decline). In addition, some of those negative responses, such as frost damage and shoot growth, were even absolutely stronger in the short-term compared to the long-term manipulation. This suggests adaptation or survival of only those individuals that are able to cope with these extreme winter conditions, and that the short-term manipulation alone would over-predict long-term impacts. These results highlight both the ecological importance of snow cover in this boreal forest, and the value of combining short- and long-term experiments side by side in climate change research.
Long-term deepened snow cover alters litter layer turnover rate in temperate steppes
<p>1. The turnover of litter layer is a biogeochemical process fundamental to carbon and nutrient cycling, influencing seed germination, species coexisting, and carbon storage. Winter snow depth is undergoing increasing trend in Northern China, which has been shown to alter litter decomposition rate of individual species. However, it remains unknown how changes in snow depth affect the turnover rate of the whole litter layer, and whether the responses vary between different steppes. Most current litter decomposition studies are site-based or short-term treated, limiting the exploration of the long-term response of litter layer turnover in regional pattern.</p> <p>2. In this study, we selected six long-term (11-13 years) snow fence sites in Inner Mongolia, with three in the dry steppe and another three in the wet steppe, and investigated the responses of community-weighted litter residence time (LRT) to long-term increased snow treatment.</p> <p>3. We found that LRT increased by 0.02 year for every 10 cm increase in snow depth in the wet steppe, but was not affected in the dry steppe. The lack of effect of deepened snow on LRT in the dry steppe was attributed to the offset between the positive effect of the increased plant community-weighted height possibly via inhibiting photodegradation and the enhanced litter recalcitrance by producing higher proportion of stem litter, and the negative effect of the increased soil moisture via accelerating microbial decomposition. The significantly positive effect of the increased snow depth on LRT in the wet steppe was mainly due to the positive effect of the increased grass biomass via decreasing litter quality.</p> <p>4. Overall, our findings indicated that deepened snow changed plant community, which altered environmental conditions and enhanced litter recalcitrance, thereby increasing LRT in temperate steppes. However, this effect was diminished by enhancing microbial decomposition in the dry but not wet steppe, resulting in different overall responses of LRT to deep-snow in the two steppes. The slow litter turnover rate in the wet steppe might result in greater litter accumulation under future increased snow depth, which could be unfavorable for seed germination and alter plant diversity.</p>
Winter soil temperature at the snow cover manipulation experiment in boreal forest
<p>The study was conducted in a spruce forest near Syktyvkar, taiga zone of northwestern Russia (N 61.650429, E50.731707). The mean annual air temperature is 0.5 C, with an annual precipitation of about 620 mm. Snow cover duration is averages 6 months (November-May). The stand is dominated by Norway spruce (Picea abies), but other species including Betula pubescens and Populus tremula are interspersed. There are sparse shrubs of rowan (Sorbus aucuparia) and dog rose (Rosa canina). The herbaceous layer is dominated by Oxalis acetosella and Vaccinium uliginosum. Less abundant herb species are Maianthemum bifolium, Pyrola rotundifolia, and mosses Hylocomium splendens, Pleurozium schreberi, Rhytidiadelphus triquetrus. In November 2018, three experimental plots (3 × 6 m) were established. The distance between the plots was at least 100 m. Each plot was divided into two sub-plots (3 × 3 m); each sub-plots corresponded to one option. The first option provided for the absence of snow cover in winter, which was achieved by the construction of sheds (a wooden frame covered with polyethylene film). The height of the sheds was 1 m. The fallen snow was regularly removed from the sheds to prevent their destruction. The second option was the control and did not involve any manipulations. The soil temperature was recorded eight time a day from November 2018 to May 2019 using a HOBO U12-008, ONSET, which was installed 5 cm below the soil surface at each sub-plot.</p>
Dataset related to the study "Black carbon and dust alter the response of mountain snow cover under climate change"
<p>This dataset contains the data of the manuscript "Black carbon and dust alter the response of mountain snow cover under climate change" under publication in Nature Communication. It is made of a dataset and information to reproduce the simulations presented in the study.</p>
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Allen Brain Atlas
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International Brain Laboratory public data
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OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.