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966 results for “Snow”
Lake snow removal experiment snow, ice, and Secchi depth, 2019-2021
Although it is a historically understudied season, winter is now recognized as a time of biological activity and relevant to the annual cycle of north-temperate lakes. Emerging research points to a future of reduced ice cover duration and changing snow conditions that will impact aquatic ecosystems. The aim of the study was to explore how altered snow and ice conditions, and subsequent changes to under-ice light environment, might impact ecosystem dynamics in a north, temperate bog lake in northern Wisconsin, USA. This dataset resulted from a snow removal experiment that spanned the periods of ice cover on South Sparkling Bog during the winters of 2019, 2020, and 2021. During the winters 2020 and 2021, snow was removed from the surface of South Sparkling Bog using an ARGO ATV with a snow plow attached. The 2019 season served as a reference year, and snow was not removed from the lake. This dataset represents the snow depths, black and white ice thickness, and Secchi depths during the period of ice cover each winter.
Lake snow removal experiment buoy, light, and chlorophyll data, 2019-2021
Although it is a historically understudied season, winter is now recognized as a time of biological activity and relevant to the annual cycle of north-temperate lakes. Emerging research points to a future of reduced ice cover duration and changing snow conditions that will impact aquatic ecosystems. The aim of the study was to explore how altered snow and ice conditions, and subsequent changes to under-ice light environment, might impact ecosystem dynamics in a north, temperate bog lake in northern Wisconsin, USA. This dataset resulted from a snow removal experiment that spanned the periods of ice cover on South Sparkling Bog during the winters of 2019, 2020, and 2021. During the winters 2020 and 2021, snow was removed from the surface of South Sparkling Bog using an ARGO ATV with a snow plow attached. The 2019 season served as a reference year, and snow was not removed from the lake. This dataset represents chlorophyll, light, and high frequency buoy data collected from this project. Related datasets are: https://doi.org/10.6073/pasta/962fa57959ff9828eb6f1cbda79b82c0 https://doi.org/10.6073/pasta/f6e271634a04819e25bc7c913cd67155 https://doi.org/10.6073/pasta/9a26e819522152e878d802df76cf90d7
Warming and snow experiment plant reproductive and growth data for Saddle, 1993 - 1996.
The International Tundra Experiment (ITEX) is a consortium of research sites seeking to understand the response of tundra plant populations to changes in growing season temperatures through a simple temperature manipulation and transplant experiment. The research goal is to examine the phenologic and reproductive responses of a set of species to experimentally-induced warming at a network of sites. The ITEX design is hierarchical, with sites participating at whatever level they are able. At the minimum, participation in ITEX requires climate monitoring (using the LTER MSR standards), a temperature manipulation using one of three possible designs, and monitoring phenologic and reproductive variables for at least one designated ITEX species or two other species. The temperature manipulation is achieved through use of conical or hexagonal open-top chambers of solar fiberglass, which have been shown to increase the air temperature at the surface approximately 3 degrees C. ITEX studies at Niwot Ridge, a logical outgrowth of the long-term phenology studies there, uses a factorial design based around the long-term snowfence experiment. Twenty cones are placed behind the snowfence, distributed at 10, 25, 45, and 75 m from the fence; each cone is paired with an adjacent plot. Beginning with the 1995 season, 24 additional plots were implemented outside of the snowfence influence. Twelve cones are distributed beyond both the north and south edges of the snowfence area, at 10, 25, 45, and 75 m behind the line of the snowfence; each cone is paired with an adjacent plot. This results in the following treatments: increased winter snow, increased summer temperature, increased snow and increased temperature, and control. Key phenologic, growth, and reproductive traits are being followed on marked individuals of Acomastylis (Geum) rossii and Bistorta (Polygonum) bistortoides, and complete species composition is being monitored.
Snow depth sensor measurement data for Alpine site, 2010 - 2015
Effects of infrared heaters on snow accumulation, snowmelt, and snow–atmosphere energy exchange were examined at Niwot Ridge, Colorado (CO). These .zip data files contains hourly snow depth measurements collected using Judd snow depth sensors for water year 2010-2015 (1 October 2009 – 30 September 2015) at the Alpine site, located just southwest of the Tundra Lab in the Niwot Ridge Long-Term Ecological Research (NWTLTER) project area. The file contains both level 0 and level 1 (see details in “Process Description” below) hourly snow depth data measured in centimeters, and an accompanying metadata file.
Snow depth sensor measurement data for Lower Sub Alpine site, 2010 - 2015
Effects of infrared heaters on snow accumulation, snowmelt, and snow–atmosphere energy exchange were examined at Niwot Ridge, Colorado (CO). These .zip data files contains hourly snow depth measurements collected using Judd snow depth sensors for water year 2010-2015 (1 October 2009 – 30 September 2015) at the Lower Sub Alpine site, located southeast of the Tundra Lab, below treeline in the Niwot Ridge Long-Term Ecological Research (NWTLTER) project area. The file contains both level 0 and level 1 (see details in “Process Description” below) hourly snow depth data measured in centimeters, and an accompanying metadata file.
Snow depth data for saddle grid, 1982 - 1990.
The depth of snow was measured at 88 points on the Saddle grid. Depths were measured form fixed extendable poles on the western or accumulation portion of the 350 x 500 m grid, while depths were measured by probing on the eastern portion. Measurements during winter months varied from every 2 weeks to about monthly depending on weather conditions. Measurements during the summer were approximately weekly until all snow disappeared or snow accumulation began for the next winter. Meltout date can be determined from the last date snow was present at a grid point. The area of the study was 19.7 ha.
Warming and snow experiment plant phenology data for Saddle snowfence, 1993 - 1996.
The International Tundra Experiment (ITEX) is a consortium of research sites seeking to understand the response of tundra plant populations to changes in growing season temperatures through a simple temperature manipulation and transplant experiment. The research goal is to examine the phenologic and reproductive responses of a set of species to experimentally-induced warming at a network of sites. The ITEX design is hierarchical, with sites participating at whatever level they are able. At the minimum, participation in ITEX requires climate monitoring (using the LTER MSR standards), a temperature manipulation using one of three possible designs, and monitoring phenologic and reproductive variables for at least one designated ITEX species or two other species. The temperature manipulation is achieved through use of conical or hexagonal open-top chambers of solar fiberglass, which have been shown to increase the air temperature at the surface approximately 3 degrees C. ITEX studies at Niwot Ridge, a logical outgrowth of the long-term phenology studies there, uses a factorial design based around the long-term snowfence experiment. Twenty cones are placed behind the snowfence, distributed at 10, 25, 45, and 75 m from the fence; each cone is paired with an adjacent plot. Beginning with the 1995 season, 24 additional plots were implemented outside of the snowfence influence. Twelve cones are distributed beyond both the north and south edges of the snowfence area, at 10, 25, 45, and 75 m behind the line of the snowfence; each cone is paired with an adjacent plot. This results in the following treatments: increased winter snow, increased summer temperature, increased snow and increased temperature, and control. Key phenologic, growth, and reproductive traits are being followed on marked individuals of Acomastylis (Geum) rossii and Bistorta (Polygonum) bistortoides, and complete species composition is being monitored.
Snow horizon chemistry data for Niwot Ridge and Green Lakes Valley, 1993 - ongoing.
Snow pits were excavated at various locations on Niwot Ridge and within the Green Lakes Valley. Temperature and snow density were measured at various depths throughout the snow cover profiles in order to characterize the temperature and snow water equivalent (SWE) of the snowpack throughout the year. Snow density was measured at 10-cm intervals using a 1000-ml cutter. Data on snow grain qualities were collected beginning in the 1994-95 snow season. Snow samples were collected and analyzed for cations and anions at the Mountain Research Station's Arikaree (formerly Kiowa) Laboratory.
Daily MODIS snow cover maps for the European Alps from 2002 onwards at 250m horizontal resolution along with a nearly cloud-free version
<p><strong>NOTE: We discovered some errors in the data for images after February 2019. They will be fixed in version >= 1.1.x, until then, usage of the data after Feb 2019 is not advised. The rest of the data is fine.</strong></p> <p> </p> <p>This is the data to the same-titled Data paper, which can be found at <a href="https://doi.org/10.3390/data5010001">https://doi.org/10.3390/data5010001</a>.</p> <p>Along with auxilary files for the <a href="https://gitlab.inf.unibz.it/earth_observation_public/modis_snow_cloud_removal">cloudremoval package</a>, and example scripts on how to access chunks of the data.</p> <p>The files contain:</p> <ol> <li><strong>python-cloudremoval-aux-data.tar.gz</strong> : auxilary data (altitude, aspect, ...) to run the cloudremoval module which can be found at <a href="https://gitlab.inf.unibz.it/earth_observation_public/modis_snow_cloud_removal">https://gitlab.inf.unibz.it/earth_observation_public/modis_snow_cloud_removal</a></li> <li><strong>python-example-data-access.html </strong>: Example script how to access parts of the data using python</li> <li><strong>R-example-data-access.html</strong> : Example script how to access parts of the data using R</li> <li><strong>zenodo_01_original.tar.gz</strong> : time series of snow cover maps, developed at the Institute for Earth Observation, Eurac Research, Bolzano, Italy. More information in same-title Data paper (<a href="https://doi.org/10.3390/data5010001">https://doi.org/10.3390/data5010001</a>), and for algorithm at <a href="https://doi.org/10.3390/rs5010110">https://doi.org/10.3390/rs5010110</a>.</li> <li><strong>zenodo_02_cloudremoval.tar.gz</strong> : time series of cloud filtered maps, based on 2. above, using code mentioned in 1. More information in same-titled Data paper.</li> </ol> <p> </p> <p>The maps are GeoTIFF with integer based values:</p> <p>0 = no data; 1 = snow; 2 = land; 3 = cloud; 4&5 = water bodies / nodata</p> <p> </p> <p>Version history:</p> <p>1.0.0 : initial upload<br> 1.0.1 : changes after revision of Data paper<br> 1.0.2 : added example scripts</p> <p> </p> <p> </p>
Terrestrial Laser Scanner observations of snow depth distribution at Col du Lautaret and Col du Lac Blanc mountain sites
<p>This dataset contains snow depth distribution observations obtained in two high mountain experimental sites, Col du Lac Blanc and Col du Lautaret, both located in French Alps. The snow depth distribution maps were generated using a Terrestrial Laser Scanner (TLS) for 10 acquisition dates. Observations obtained in Col du Lac Blanc were acquired in the 2014-15 snow season while Col du Lautaret observations were acquired in 2017-18 snow season. The snow depth maps have a grid cell size of 1x1m. The two study sites have extensions comprised between 17 and 31 ha with elevations ranging from 2000-2100 m a.s.l. (Col du Lautaret)and 2600-2800 m a.s.l. (Col du Lac Blanc)and show a patchy distribution of bare soil and alpine grass. The dataset allows a better understanding of snow related processes in mountain areas.</p>
FIG. 23 in A revision of New Caledonian Gossia N. Snow & Guymer (Myrtaceae)
FIG. 23. — Gossia vieillardii (Brongn. & Gris) N. Snow: A, branchlets in flower; B, mature (left) and immature (right) fruit and adaxial leaf surfaces. Vouchers: ©Hervé Vandrot, with permission; from forêt du Massif du Oua-Tilou (http://endemia.nc).
FIG. 21. — A-D in A revision of New Caledonian Gossia N. Snow & Guymer (Myrtaceae)
FIG. 21. — A-D, Gossia pancheri (Brongn. & Gris) N. Snow: A, flowering branch with details of abaxial leaf venation (offset on right); B, detail of flower prior to anthesis; C, longitudinal section of flower bud showing bilocular ovary with axile placentation; D, leaf and fruits; E-I, Gossia ramiflora N. Snow, sp. nov.: E, flowering branch; F, details of more or less rectangular flaking bark; G, flowers prior to anthesis; H, detail of flower bud; I, another view of leaf (with detail of sinuous margin) with young fruits. Vouchers: A-C, McPherson 3275 (WELTU); D, McPherson 5019 (NOU); E-H, MacKee 12272 (WELTU); I, Phillips 2105 (NOU). Scale bars: A, D, I, 2 cm; B, C, H, 3 mm; E, 2 mm; F, G, 5 mm.
FIG. 17 in A revision of New Caledonian Gossia N. Snow & Guymer (Myrtaceae)
FIG. 17. — Distribution maps of G. kaalaensis N. Snow, sp. nov., G. mandjeliaensis N. Snow, G. nigripes (Gullaumin) N. Snow, and G. ouazangouensis N. Snow, sp. nov.
FIG. 14 in A revision of New Caledonian Gossia N. Snow & Guymer (Myrtaceae)
FIG. 14. — Distribution maps of Gossia conspicua (Guillaumin ex Vieill.) N. Snow, comb. nov., G. katepahiensis N. Snow, sp. nov., and G. kuakuensis (Baker f.) N. Snow.
FIG. 10 in A revision of New Caledonian Gossia N. Snow & Guymer (Myrtaceae)
FIG. 10. — Living specimens of Gossia clusioides: A, G. c. subsp. callmanderiana N. Snow, subsp. nov. vel aff.; B, G. c. subsp. bleuensis N. Snow, subsp. nov. vel aff.; C, G. c. subsp. ploumensis (Däniker) N. Snow, comb. et stat. nov.; D, G. c. subsp. ploumensis comb. et stat. nov. vel aff., but with larger and more prominently bullate leaves than normal and possibly representing an undescribed taxon. Vouchers: A, ©Hervé Vandrot, taken at Cascade la Pandanas-Koniambo in 2010 (http://endemia.nc); B, © Christian Létocart, taken at Yaté; C, ©Daniel & Irène Létocart, taken at Napoérédjeine in 2007; D, ©Jean-Louis Ruiz, taken at Pont des Japonais in 2005 (http://endemia.nc).
FIG. 9 in A revision of New Caledonian Gossia N. Snow & Guymer (Myrtaceae)
FIG. 9. — Gossia clusioides (Brongn. & Gris) N. Snow subsp. ploumensis (Däniker) N. Snow, comb. et stat. nov., showing peeling and cracking bark of branchlets. Voucher: Snow 9215, BISH (sheet 3 of 3). Scale bars: A, C, 2 cm; B, 1 cm.
Snow depth and land surface cover in Tuolumne basin (California) from Pléiades images
<p>This dataset contains products calculated from Pléiades images.</p> <p>Details about the products are available in https://doi.org/10.5194/tc-2020-15.</p> <p>These products were used in Figure 4.</p> <p>- pleiades_elevation_difference_raw_winter_minus_summer.tif : raw difference of digital elevation models (DEMs) calculated from Pléiades stereo images.</p> <p>- pleiades_snow_depth_winter.tif : difference of DEMs on snow terrain only (where pleiades_land_surface_cover_winter.tif==1 with morphological erosion)</p> <p>- pleiades_land_surface_cover_winter.tif : land cover surface in the winter images (1= snow, 2=forest, 3= stable terrain, 4=water)</p> <p>- pleiades_land_surface_cover_summer.tif : land cover surface in the summer images (1= snow, 2=forest, 3= stable terrain, 4=water) </p> <p>- elevation_difference_style.qml : qgis style used for elevation difference and snow depth.</p> <p>- land_surface_cover_style.qml : qgis style used for land cover surface.</p> <p> </p>
Annual Snow Timing Index Rasters for the Western US and Alaska, WY2001-2019
<p>Here, a collection of rasters describing annual snow onset (SO), snow cover duration (SCD), and day of snow disappearance (DSD) for WY2001-2019 over Alaska, Canada, and the Western United States (boundary box: -152W, -100W, 32N, 68N) are available. Units are in calendar Day of Year (DOY) for SO and DSD, and in days for SCD. Filenames follow the convention, "index_west_threshold_spatialsubdomain.tif". For example, "SCD_west_5-0000005376-0000005376.tif" indicates the file contains snow cover duration raster data obtained using a 5% threshold. Spatial subdomain naming convention is described below.</p> <p>These data accompany a manuscript entitled, "Investigating the Relationship Between Peak Snow-Water Equivalent and Snow Timing Indices in the Western U.S. and Alaska". A diagnostic model of peak SWE as a function of remotely sensed snow timing indices (the dataset provided here) was developed in the course of this study to address the following questions: 1) Are peak SWE and snow timing related?; 2) How does this relationship vary across the western United States?; and 3) What meteorological and topographical conditions affect this relationship?</p> <p>These rasters were created in the Google Earth Engine (GEE) from the MODIS MOD10A1 v006 product (MODIS/Terra Snow Cover Daily L3 Global 500m SIN Grid, Version 6). From the initial daily fSCA product, a moving median window (i.e., a low-pass filter) of filter length k=25 days was applied to obtain a binary snow cover series using fractional cover threshold of 1%, 5%, 10%, 20%, and 30% such that the timing indices may be extracted. The filter length smooths out small-scale short-lived snow deposition events that obscure snow timing. Pixels without seasonal snowpack (i.e., persistent year-round snow or little to no snow) were masked: within a given water year, pixels that 1) show no onset of snow between the 272nd day of year and end of calendar year (corresponding with the typical N. hemisphere timeframe for the start of snow accumulation), and/or 2) do not melt out between start of calendar year and the 272nd day of year (typical timeframe for the end of snowmelt) were masked. SCD was calculated as (DSD + 365 days - SO). Because of the large file size, rasters were split up spatially into multiple files by the GEE, and follow the naming convention for large file exports listed here: https://developers.google.com/earth-engine/guides/exporting. As stated, "the filename of each tile will be in the form baseFilename-yMin-xMin where xMin and yMin are the coordinates of each tile within the overall bounding box of the exported image." Each band represents a different water year and is labeled as such, e.g. "DSD_2001". </p>
Data from: Reduced snow cover increases wintertime nitrous oxide (N2O) emissions from an agricultural soil in the upper U.S. Midwest
Throughout most of the northern hemisphere, snow cover decreased in almost every winter month from 1967 to 2012. Because snow is an effective insulator, snow cover loss has likely enhanced soil freezing and the frequency of soil freeze–thaw cycles, which can disrupt soil nitrogen dynamics including the production of nitrous oxide (N2O). We used replicated automated gas flux chambers deployed in an annual cropping system in the upper Midwest US for three winters (December–March, 2011–2013) to examine the effects of snow removal and additions on N2O fluxes. Diminished snow cover resulted in increased N2O emissions each year; over the entire experiment, cumulative emissions in plots with snow removed were 69% higher than in ambient snow control plots and 95% higher than in plots that received additional snow (P < 0.001). Higher emissions coincided with a greater number of freeze–thaw cycles that broke up soil macroaggregates (250–8000 µm) and significantly increased soil inorganic nitrogen pools. We conclude that winters with less snow cover can be expected to accelerate N2O fluxes from agricultural soils subject to wintertime freezing.
Cloud-free snow cover area in the Pyrenees from MODIS
<p>This dataset contains the output of a gapfilling algorithm applied to MODIS snow products for the Pyrenees mountains as presented by Gascoin et al. (2015) and updated to the period 2000-Sep-01 to 2015-08-31 (15 hydrological years)</p> <ol> <li>Pirineos_gapfilled.tif: a multiband geotiff raster file in WGS84 UTM30N (EPSG:32630) at 500 m resolution with values 200 (snow) or 25 (no snow); <p>Corner Coordinates:<br> Upper Left ( 607750.000, 4789250.000) ( 1d40'21.72"W, 43d14'54.08"N)<br> Lower Left ( 607750.000, 4665250.000) ( 1d41'46.55"W, 42d 7'55.05"N)<br> Upper Right ( 973750.000, 4789250.000) ( 2d49'18.48"E, 43d 6'27.65"N)<br> Lower Right ( 973750.000, 4665250.000) ( 2d43' 8.76"E, 41d59'47.87"N)<br> Center ( 790750.000, 4727250.000) ( 0d32'47.24"E, 42d38'34.10"N)</p> </li> <li>Pirineos_gapfilled_dates.csv: a csv file indicating the date corresponding to each band (year, month, day)</li> <li>dem_Pirineos_UTM30_px500.tif: a geotiff raster of the elevation in WGS84 UTM30N (input of the gap-filling algorithm) with the same extent and resolution as 1.</li> <li>aspect_Pirineos_UTM30_px500.tif: a geotiff raster of the slope aspect in WGS84 UTM30N (input of the gap-filling algorithm) with the same extent and resolution as 1.</li> <li>Pirineos_gapfilled_probamap.png: a map of the mean annual number of snow days (snow cover duration) made from 1.</li> <li>Pirineos_gapfilled_scats.png: a plot of the timeseries of the daily snow cover area in km² over the Pyrenees mountain range from 2000-Sep-01 to 2015-08-31 made from 1.</li> </ol> <p><strong>Reference</strong></p> <p>Gascoin, S., Hagolle, O., Huc, M., Jarlan, L., Dejoux, J.-F., Szczypta, C., Marti, R., and Sánchez, R.: A snow cover climatology for the Pyrenees from MODIS snow products, Hydrol. Earth Syst. Sci., 19, 2337-2351, doi:10.5194/hess-19-2337-2015, 2015. http://doi.org/10.5194/hess-19-2337-2015</p> <p>Hall, D. K., V. V. Salomonson, and G. A. Riggs. 2006. MODIS/Terra Snow Cover Daily L3 Global 500m Grid, Version 5. Boulder, Colorado USA. NASA National Snow and Ice Data Center Distributed Active Archive Center. doi: http://dx.doi.org/10.5067/63NQASRDPDB0.</p> <p>Hall, D. K., V. V. Salomonson, and G. A. Riggs. 2006. MODIS/Aqua Snow Cover Daily L3 Global 500m Grid, Version 5. Boulder, Colorado USA. NASA National Snow and Ice Data Center Distributed Active Archive Center. doi: http://dx.doi.org/10.5067/ZFAEMQGSR4XD.</p>
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