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16 results for “snow melt”
AIRBORNE SPECTROMETER MEASUREMENTS FROM BOREAL AND TUNDRA SITE DURING SPRING SNOW MELT
<p>The dataset contains 10 meter resolution reflectance data from boreal and tundra sites during spring snow melt. The purpose of the airborne measurements was to investigate the effect of forest canopy and snow melting on optical remote sensing signals at the very end of melting period. The hyperspectral airborne data was acquired with an AisaDUAL imaging spectrometer on 5 May 2011 in Sodankylä and in Saariselkä, Finland. Saariselkä is a fell region and partly represents open tundra. The image swath was 240 meters and flight lines were several kilometers long. The original spatial resolution of the data is 80 cm x 80 cm, but it was resampled to pixel size of 10 m x 10 m. Snow depth was between 0 cm and 30 cm at the Sodankylä site and between 0 cm and 60 cm at the Saariselkä site implying that the spring melt was clearly more advanced in Sodankylä. Additionally, more snow-free pixels were found at Sodankylä than Saariselkä. During the measurements the sky was cloudless in Sodankylä (cloud cover 0/8) and cloudy (cloud cover 7/8) in Saariselkä. The data contains mosaics of the flight lines for the bands 555 nm, 645 nm, 858.5 nm and 1640 nm for both study sites.</p>
Contamination pattern and risk assessment of polar compounds in snow melt: an integrative proxy of road runoffs
<p><strong>Abstract</strong></p> <p>To assess the contamination and potential risk of snow melt with polar compounds, road and background snow was sampled during a melting event at 23 sites at the city of Leipzig and screened for more than 500 chemicals using LC-HRMS. Additionally, six 24 h composite samples were taken from the influent and effluent of the Leipzig WWTP during the snow melt event. 207 compounds were at least detected once (concentrations between 0.80 ng/L and 75 µg/L). A toxic unit-based assessment was performed to investigate the risk of adverse environmental effects in the receiving water.</p> <p><strong>Description of the dataset</strong></p> <p>The dataset contains the list of sampling points, the target compounds, the chemical findings, the results of the toxic unit assessment, the underlying ecotoxicity data, and the estimated compound removal rates in WWTP. The data is provided in xlsx and ods formats.</p>
UAV observations of the NDVI, snow depth and melt out date, retreived ar the Izas Experimental Catchment in 2020 and 2021
<p>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.</p>
Sea ice, snow and melt pond example data from MOSAiC transect observations
<p>This data set contains in-situ observation of sea ice, snow and melt pond properties from two days in winter (January 23, 2020) and summer (July 7, 2020) during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition. It originates from two sensors:</p> <ol> <li>Broad-band electromagnetic induction sensor (Geophex GEM-2) measuring the combined thickness of the sea ice and snow layers</li> <li>A GPS snow depth probe (Snow-Hydro MagnaProbe) measuring the thickness of the snow layer and melt ponds depth during summer</li> </ol> <p>Both sensors were operated coincidently along transect loops while different loops were used in both days. This data set is a subset of similar weekly activities between October 2019 and September 2020. This publication intends to provide a preview of the data properties and approximate changes between the winter and summer periods. It also has to be noted, that the final data of the EM induction sensor might differ from this release, which is based on a quick-look processing directly after data acquisition.</p> <p><em>GEM-2 data files</em></p> <p>The file format of GEM-2 data is a text file with comma-separated values. Notable parameters are:</p> <ul> <li>‘time’: UTC time</li> <li>‘longitude’: Longitude in degrees east (fill value: 0.0)</li> <li>‘latitude’: Latitude in degrees north (fill value: 0.0)</li> <li>‘`f{frequency}Hz_hcp_{i:Inphase|q:Quadrature}`: total (ice + snow) thickness of the sea ice and snow layers in meter for different channels*</li> </ul> <p>*The channels correspond to the real (Inphase) or imaginary (Quadrature) part of the EM signal at a given frequency. The variable name in the csv file is to be read as `f{frequency}Hz_hcp_{i:Inphase|q:Quadrature}`. It is recommended to use the Inphase component of the 18.325 kHz frequency (variable `f18325Hz_hcp_i`) for analysis.</p> <p><em>MagnaProbe data files</em></p> <p>The file format of MagnaProbe data is a text file with comma-separated values. Notable parameters are:</p> <ul> <li>`timestamp`: Timestamp</li> <li>`longitude_a`: longitude degree in degrees east</li> <li>`longitude_b`: longitude minute</li> <li>`latitude_a`: latitude degree in degrees north</li> <li>`latitude_b`: latitude minute</li> <li>`depthCm`: Snow thickness or melt ponds depth in cm</li> <li>`flag`: flag value indicating the type or measurement*</li> </ul> <p>Flag values are:</p> <ul> <li>-1 : melt pond</li> <li>1 : snow or surface scattering layer depth,</li> <li>2 : mixed surface type when pond water pools at the base of a melting</li> </ul> <p>The filenames follow the naming convention of <sensor>-mosaic-transect-<date>-<device-operations-id>.csv with the device operation id as a unique identifier of the sensor raw data within the MOSAiC project.</p>
Snow melt onset date estimates derived from CLARA-A2 SAL surface albedo dataset
<p>Snow melt onset date estimates for the Northern Hemisphere, 1982-2015. Derived from the CLARA-A2 SAL surface albedo dataset. Version for manuscript review.</p>
McMurdo Dry Valleys Major Ion Concentrations for Glacier Ice, Snow, and Melt Water Samples 1993-1997
The chemistry of various glaciers (Canada, Commonwealth, Howard, Suess, Taylor) in Taylor Valley was measured for the following analytes between 1993 and 1997: Alkalinity, Ca, Cl, F, K, Mg, Na, NO3, Si, and SO4.
Supplement to : Accelerated Snow Melt in the Russian Caucasus Mountains After the Saharan Dust Outbreak in March 2018
<p>These datasets contains all the data used in Accelerated Snow Melt in the Russian Caucasus Mountains After the Saharan Dust Outbreak in March 2018 by Dumont et al., in Journal of Geophysical Research.<br> This dataset includes : Sentinel-2 cloud masks, snow depth measurements, snow surface impurity content estimated from Sentinel-2, Sentinel-2 surface reflectances and digital elevation models.</p> <p>Dumont, M., Tuzet, F., Gascoin, S., Picard, G., Kutuzov, S., Lafaysse, M., et al. (2020). Accelerated snow melt in the Russian Caucasus mountains after the Saharan dust outbreak in March 2018. Journal of Geophysical Research: Earth Surface, 125, e2020JF005641. <a href="https://doi.org/10.1029/2020JF005641">https://doi.org/10.1029/2020JF005641</a></p>
Nesokia is sister to Bandicota and are nested in Rattus phylogenetically, making Rat- tus paraphyletic. Tarsomys, Limnomys, and Diplothrix are also phylogenetically in Rat- tus, and the clade is in need of focused re- vision at the generic level. Nesokia bunnui was originally described as a separate ge-nus, Erythronesokia, because it is morphologically very distinctive from N. indica. Type specimen was destroyed during the Iraq War, and a neotype was recently designated to replace it. Monotypic. Distribution. Tigris and Euphrates river valleys, SE Iraq. Descriptive notes. Head—body 230-260 mm, tail 205-270 mm, ear 18-21 mm, hindfoot 49-58 mm; weight 519 g. The Long-tailed Bandicoot Rat is larger than the Short-tailed Bandicoot Rat (N. indica). Pelage is soft and woolly, interspersed with harsher coarse hair and long black hairs near mid-back. Dorsum is fawn to ocherous red, washed with purple or chestnuton darker individuals. Hairs are basally slate-gray and distally rufous, occasionally with whitish or black tips. Muzzle is drab. Sides arefawn, with gray edge toward venter. Venteris whitish, extending onto cheeks where the same pattern from gray to fawn to dorsal pelage occurs. Feet are large and robust, being light brown and well-furred dorsally. Claws are amber on forefeet and dull brown on hindfeet; pollux is extremely small. Ears are moderately long and brownish, with no hair internally. Tail is ¢.82-104% of head-body length and deep brownish drab, interspersed with visible white hair. Skull is large and robust, similarly to the Short-tailed Bandicoot Rat. Habitat. Marsh and swamp land. Food and Feeding. No information. Breeding. No information. Activity patterns. The Long-tailed Bandicoot Rat is terrestrial, although it isfound in swampy and marshy areas and is probably amphibious. Movements, Home range and Social organization. No information. Status and Conservation. Classified as Endangered on The IUCN Red List. The Longtailed Bandicoot Rat is apparently rare and is known from very few specimens. Marsh and swamp habitats in which it is found were completely destroyed during the Iraq War by draining, war damage, and agricultural expansion. In recent years, flooding from Tigris and Euphrates rivers and high snow fall and melt haveresulted in partial restoration ofits native habitat, although restoration is not a complete. Populations are now probably highly fragmented. Bibliography. Al-Ansari et al. (2012), Al-Robaae & Felten (1990), Khajuria (1981), Krystufek et al. (2017), Musser & Carleton (2005), Richardson & Hussain (2006), Stuart (2008). in Muridae
Nesokia is sister to Bandicota and are nested in Rattus phylogenetically, making Rat- tus paraphyletic. Tarsomys, Limnomys, and Diplothrix are also phylogenetically in Rat- tus, and the clade is in need of focused re- vision at the generic level. Nesokia bunnui was originally described as a separate ge-nus, Erythronesokia, because it is morphologically very distinctive from N. indica. Type specimen was destroyed during the Iraq War, and a neotype was recently designated to replace it. Monotypic. Distribution. Tigris and Euphrates river valleys, SE Iraq. Descriptive notes. Head—body 230-260 mm, tail 205-270 mm, ear 18-21 mm, hindfoot 49-58 mm; weight 519 g. The Long-tailed Bandicoot Rat is larger than the Short-tailed Bandicoot Rat (N. indica). Pelage is soft and woolly, interspersed with harsher coarse hair and long black hairs near mid-back. Dorsum is fawn to ocherous red, washed with purple or chestnuton darker individuals. Hairs are basally slate-gray and distally rufous, occasionally with whitish or black tips. Muzzle is drab. Sides arefawn, with gray edge toward venter. Venteris whitish, extending onto cheeks where the same pattern from gray to fawn to dorsal pelage occurs. Feet are large and robust, being light brown and well-furred dorsally. Claws are amber on forefeet and dull brown on hindfeet; pollux is extremely small. Ears are moderately long and brownish, with no hair internally. Tail is ¢.82-104% of head-body length and deep brownish drab, interspersed with visible white hair. Skull is large and robust, similarly to the Short-tailed Bandicoot Rat. Habitat. Marsh and swamp land. Food and Feeding. No information. Breeding. No information. Activity patterns. The Long-tailed Bandicoot Rat is terrestrial, although it isfound in swampy and marshy areas and is probably amphibious. Movements, Home range and Social organization. No information. Status and Conservation. Classified as Endangered on The IUCN Red List. The Longtailed Bandicoot Rat is apparently rare and is known from very few specimens. Marsh and swamp habitats in which it is found were completely destroyed during the Iraq War by draining, war damage, and agricultural expansion. In recent years, flooding from Tigris and Euphrates rivers and high snow fall and melt haveresulted in partial restoration ofits native habitat, although restoration is not a complete. Populations are now probably highly fragmented. Bibliography. Al-Ansari et al. (2012), Al-Robaae & Felten (1990), Khajuria (1981), Krystufek et al. (2017), Musser & Carleton (2005), Richardson & Hussain (2006), Stuart (2008).
Early snow melt and diverging thermal constraints control body size in arctic-alpine spiders
<p><span>To predict species' responses to a rapidly changing environment, it is necessary to detect current clines of life-history traits and understand their drivers. We studied body size variation, a key trait in evolutionary biology, of two arctic-alpine lycosid spiders and underlying mechanisms controlling this variation. We used long time-series data of body size sampled in Norway, augmented with museum data. Individuals of both species sampled in areas and years with longer snow-free periods grew larger than individuals in areas and years with shorter snow-free periods. </span><span>Interestingly, temperatures under 0° C led to a larger body size in Pardosa palustris, while temperatures above 0 °C led to a larger body size in Pardosa hyperborea. We assume that P. palustris, as the generally larger species, is less sensitive to environmental variability and cold temperatures, because it can retain more energy than a smaller species can and, therefore, can invest more resources in its offspring. With rising temperatures, both species might profit from a higher resource availability. In a rapidly changing arctic-alpine environment, alterations in the life-history traits and adaptation strategies of spiders are expected, which, regarding body size, seem to be highly influenced by early snowmelt and diverging thermal constraints.</span></p>
Figure 1 in Early snow melt and diverging thermal constraints control body size in arctic-alpine spiders
Figure 1. Pearson correlations between the significant explanatory variables and the body size of Pardosa hyperborea and P. palustris. CW = carapace width; p1dSF = first snow-free day in the year before sampling; pSFP = snow-free period in the year before sampling; pq2TTD5 = thermal threshold days> 5 °C in spring of the year before sampling; 2q2TTD5 = thermal threshold days> 5 °C in spring of both years; q2P = precipitation sum in spring in the year of sampling; yTTD0 = thermal threshold days> 0 °C for the whole year of sampling; pyTTD5 = thermal threshold days> 5 °C for the whole year before sampling; 2yTTD5 = thermal threshold days> 5 °C in the year of sampling and the previous year; 2q2TTD_2 = thermal threshold days ≤ –2 °C in spring of both years; 1DOY0 = first day of the year> 0 °C in the year of sampling; 1dSF = first snowfree day in the year of sampling; SFP = snow-free period in the year of sampling; q1SWE = snow-water equivalent in winter in the year of sampling; 2q1SWE = snow-water equivalent in winter of both years; q2SWE = snow-water equivalent in spring in the year of sampling; q2TTD5 = thermal threshold days> 5 °C in spring in the year of sampling; 2q2P = precipitation sum in spring of both years; pq4SWE = snow-water equivalent in autumn in the year before sampling; pq4P = precipitation sum in autumn in the year before sampling; ySWE = snow-water equivalent for the whole year of sampling; 2ySWE = snow-water equivalent of both years; q1TTD_2 = thermal threshold days ≤ –2 °C in winter in the year of sampling; 2q1TTD_2 = thermal threshold days ≤ –2 °C in winter of both years; q1TTD_0 = thermal threshold days ≤ 0 °C in winter in the year of sampling; 2q1TTD_0 = thermal threshold days ≤ 0 °C in winter of both years; q1TTD0 = thermal threshold days> 0 °C in winter in the year of sampling; 2q1TTD0 = thermal threshold days> 0 °C in winter of both years; q1P = precipitation sum in winter in the year of sampling; 2q1P = precipitation sum in winter of both years; 2q2SWE = snow-water equivalent in spring of both years;
Early snow melt and diverging thermal constraints control body size in arctic-alpine spiders
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Arctic sea ice snow melt onset dates from the Advanced Horizontal Range Algorithm, version 5 (1979 - 2022)
<h2>Data</h2><p>This data set includes one NetCDF (.nc) file containing the full set of annual Arctic sea ice melt onset dates and statistical summaries for the 1979 - 2022 period derived with the Advanced Horizontal Range Algorithm (AHRA) V5. The remaining .png files include browse images of each data layer contained within the primary NetCDF file.</p><p>A full description of the data provided herein can be found in the following publication: </p><p>Bliss, A. C. (submitted 2023), Passive microwave observations of Arctic sea ice melt onset from the Advanced Horizontal Range Algorithm 1979 – 2022, <i>Scientific Data</i>.</p><h2>Future updates</h2><p>This data set is distributed on an ongoing basis by the NASA Distributed Active Archive Center at the National Snow and Ice Data Center. Future updates to the AHRA V5 data set including annual updates of the data product will be available at the NSIDC archive below:</p><p>Bliss, A. C., M. Anderson, and S. Drobot. (2022). Snow Melt Onset Over Arctic Sea Ice from SMMR and SSM/I-SSMIS Brightness Temperatures, Version 5. Boulder, Colorado USA. NASA National Snow and Ice Data Center Distributed Active Archive Center. <a href="https://doi.org/10.5067/TRGWQ0ONTQG5">https://doi.org/10.5067/TRGWQ0ONTQG5</a>.</p>
Alpine plant species converge towards adopting elevation-specific resource-acquisition strategy in response to experimental early snow-melting
<p>An experimental to assess the effect of early snow-melting on alpine species at contrasting elevation was established in 2019 at Rohtang (32°22' N; 77°16' E) in western Himalaya. After completion of 02 year of experiment, sampling was performed on 08 alpine plant species. We have collected leaf functional trait and physiological traits during peak growing season (August 2020) from two elevation (3850 and 4150 m) and from control and treatment plots. The trait values of 15 replicates for each species were given here for leaf functional traits (viz. leaf area, specific leaf area, leaf dry matter content, leaf water content, plant height) and trait values of 05 replicated for each species were given for elemental (Nitrogen % and carbon/nitrogen ratio ) and physiological traits (leaf chlorophyll content, Carotenoid content, Malondialdehyde equivalents, Phenol content, Proline content, Total soluble Sugar content, Total protein content). We have provided trait values from both control and treatment plots. </p>
Snow Melt Onset Over Arctic Sea Ice from SMMR and SSM/I-SSMIS Brightness Temperatures, Version 5
This data set includes yearly snow melt onset dates over Arctic sea ice derived from Scanning Multichannel Microwave Radiometer (SMMR), Special Sensor Microwave/Imager (SSM/I), and the Special Sensor Microwave Imager/Sounder (SSMIS) brightness temperature measurements. The data are gridded to the 25 km Northern Hemisphere Polar Stereographic projection and available from 1979 through 2022. One browse image is available for each year. This data set also contains value-added statistics for each grid cell, including: mean melt onset date, latest (maximum) melt onset date, earliest (minimum) melt onset date, range of melt onset dates (the difference between maximum and minimum onset dates), and the standard deviation of melt onset dates. One browse image is also provided for each statistical field.
ABoVE: Passive Microwave-derived Annual Snow Melt Duration Date Maps, 1988-2018
This dataset provides the annual period of snowpack melting (i.e., snow melt duration, SMD) across northwest Canada; Alaska, U.S.; and parts of far eastern Russia at 6.25 km resolution for the period 1988-2018. SMD is the number of days between the main melt onset date (MMOD) and the last day of seasonal snow cover when the melting of snow is complete. These dates were derived from the Making Earth Science Data Records for Use in Research Environments (MEaSUREs) Calibrated Enhanced-Resolution Passive Microwave (PMW) EASE-Grid Brightness Temperature (Tb) Earth System Data Record (ESDR). This dataset documents variability in SMD across space and the 31-year temporal period. The data from 1988-2016 included a coastal mask removing coastal pixels due to potential water contamination from coarse brightness temperature observations (Dersken et al., 2012). There is not a coastal mask for the 2017-2018 data. The full data are included, and data users should be aware that coastal values can be adversely affected by adjacent water bodies.
Contribution to High Asia Runoff from Ice and Snow (CHARIS) Melt Model Output, 2001 - 2014, Version 1
This data set contains input and output data for temperature index (TI) model runs completed for the Contributions to High Asia Runoff from Ice and Snow (CHARIS) project at NSIDC in 2018 and 2019. The input data are the area of snow on land, snow on ice, and exposed glacier ice as well as surface air temperature. These inputs are used to model the volumes of melt runoff from the snow on land, snow on ice, and exposed glacier ice in certain areas of High Mountain Asia.
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
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.