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53 results for “Snow Water Equivalent”
Snow depth and snow water equivalent measurements along a road course and historic snow course in the Andrews Experimental Forest, 1978 to present
With an increase in emphasis on monitoring climate change impacts and change in the form of precipitation at HJ Andrews Experimental Forest, snow data collection within our climate monitoring program, a snow course to document depths of snow was designed around a dispersed sampling scheme rather than a point intensive scheme as previously employed in the historic Reference Stand snow course. Primary objectives are to document the presence/absence of snow, snow depth, and time of melt-off. Snow depths are verified using stakes placed near the road to allow for routine and frequent observation. Stakes are placed at different locations, elevations and aspects in paired forested/open sites. Time-lapse cameras were deployed at all the stakes to allow for daily measurements beginning in fall 2014. Truthing of points with snow core sampling for snow moisture content (snow water equivalent) is done when possible, usually 1-2 times per year. Cameras are set to take 3 readings per day (09:00, 12:00, 15:00 PST). One snow depth and coverage is extracted from the images per stake per day.
Snow water equivalent 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 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. This dataset contains derived values of SWE from snow profile measurements.
Snow Water Equivalent Dataset for the South Fork of the San Joaquin River (2018/2021) and Senales (2019/2021)
<p>The dataset is related to: Premier, V., Marin, C., Bertoldi, G., Barella, R., Notarnicola, C., & Bruzzone, L. (2022). Exploring the Use of Multi-source High-Resolution Satellite Data for Snow Water Equivalent Reconstruction over Mountainous Catchments. <em>The Cryosphere Discussions</em>, 1-42.</p> <p>It contains three hydrological seasons - from 1st of October 2018 to 30th of September 2021 - of snow water equivalent (SWE) for the South Fork of the San Joaquin river in California (USA) and two hydrological seasons - from 1st of October 2019 to 30th of September 2021 - for the Schnals/Senales basin in South Tyrol (Italy). The product is daily and with a spatial resolution of 25 m. SWE values are in mm. Snow cover area (SCA) can be derived from the same by thresholding pixel containing SWE greater than 0 mm. Further information about the reference system is contained in the attributes of the netcdf files. Please, contact the authors for further questions. Information about the methodology and the input data for producing these time series is contained in the related article.</p>
Marcell Experimental Forest biweekly snow depth, frost depth, and snow water equivalent, 1962 - ongoing
This data table contains snowpack and frost data measured at the Marcell Experimental Forest from 1962–ongoing. The data came from five peatland/upland forest watersheds instrumented for hydrologic monitoring. Frost thickness and snowpack (snow water content, snowpack depth) are measured at 10 snowcourses that encompass three cover types (conifer, deciduous, open). The Marcell Experimental Forest in Itasca County, Minnesota, is operated and maintained by the USDA Forest Service, Northern Research Station, and was formally established in 1962 to study the ecology and hydrology of peatlands.
Snow depth, snow water equivalent, ice thickness in Fuglebekken and Revdalen catchments collected in the SnowPilot campaign in Spring 2022
<p>File SnowPilot_snowdepth_along_the_GPR_profile_2022 contains snow depth measurements taken along the GPR profile performed during the SIOS SnowPilot campaign in Spring 2022. File SnowPilot_snowdepth_swe_2022 contains depth, snow water equivalent and basal ice thickness. Snowpits were dug on GPR profile crossings in the Fuglebekken and Revdalen catchments in the Hornsund fiord, Spitsbergen catchment. Snow density was measured with an IG PAS snow tube, and snow depth and basal ice (ice forming on the ground surface) thickness were measured with an avalanche probe. Point locations measured. with handheld GPR reciever.</p>
Downscaled 8km March Snow Water Equivalent Estimates for the Western US, 1901-2010
<p>Downscaled estimates of March mean snow water equivalent at approximately 8km resolution across the western United States for the years 1901-2010. Data downscaled from the CERA-20c reanalysis using UA-SWE daily observations. Downscaled data using both the CERA-20c ensemble mean as well as each individual ensemble member as predictors are included. Units are in millimeters of snow water equivalent.</p>
Spatial distribution of snow water equivalent for the Niwot Ridge, 1996 - 2019
This dataset provides a daily estimation of snow water equivalent for the Niwot Ridge during snow melting period from 1997 to 2019 at 30-meter spatial resolution. The dataset includes two series of SWE data: 1) 1996-2007 daily SWE dataset is generated by Jepsen et al., (2012); 2) 2008-2019 daily SWE dataset is generated by Dr. Kehan Yang following the same method used by Jepsen et al., (2012). In brief, a physically based reconstruction model is used to calculate daily SWE backward from snow disappearance date to peak snow accumulation. The infilled hourly climate data set for C1, Saddle and D1 (data available at https://portal.edirepository.org/nis/mapbrowse?packageid=knb-lter-nwt.168.2) is interpolated and used as the meteorological forcing in the snow energy balance calculation of SWE reconstruction. The shortwave radiation is estimated by downscaling hourly product of the Geostationary Operational Environmental Satellite (GOES) using TOPORAD tool. The USGS Landsat Level-3 fractional snow-covered area product is used to proportion potential energy flux for snowmelt at the pixel scale. Please see detailed methods included with this data package for more details and references.
Data and code for: Precipitation Biases and Snow Physics Limitations Drive the Uncertainties in Macroscale Modeled Snow Water Equivalent
<p>Code and data to reproduce figures in manuscript entitled "Precipitation Biases and Snow Physics Limitations Drive the Uncertainties in Macroscale Modeled Snow Water Equivalent" published in Hydrology and Earth System Sciences (https://hess.copernicus.org/preprints/hess-2022-136/).</p> <p>The contents include three folders, "Codes", "Data", and "Figures". In "Codes" folder, R scripts are listed in the order needed to reproduce the figures. All code is written in R version 4.2.0. Data sets needed to reproduce figures are provided in "Data" folder (Rdata format). The pdf files in "Figures" folder are outputs generated from the corresponding R scripts. Note that final figures in the article were produced by combining multiple figures using a vector graphics software (Inkscape) or PowerPoint. Please contact Eunsang Cho (<a href="mailto:eunsang.cho@nasa.gov">eunsang.cho@nasa.gov</a>) with any questions. </p> <p>Preferred citation: Cho, E., Vuyovich, C. M., Kumar, S. V., Wrzesien, M. L., Kim, R. S., and Jacobs, J. M. (2022). Precipitation Biases and Snow Physics Limitations Drive the Uncertainties in Macroscale Modeled Snow Water Equivalent, Hydrol. Earth Syst. Sci., https://doi.org/10.5194/hess-2022-136.</p> <p>Corresponding author: Eunsang Cho (<a href="mailto:eunsang.cho@nasa.gov">eunsang.cho@nasa.gov</a>; <a href="mailto:escho@umd.edu">escho@umd.edu</a>)</p>
Manual in-situ measurements of snow depth and snow water equivalent at the Polish Polar Station Hornsund - winter seasons 2021/2022and 2022/2023
<p>The dataset presents manual measurements of snow depth and snow water equivalent collected at the Polish Polar Station Hornsund in Svalbard during the winter seasons of 2021/2022 and 2022/2023.</p> <p>Snow depth measurements have been conducted at the same location by the Station's overwintering personnel since August 1982. Snow depth is calculated from a mean of three snow stakes to avoid the effects of the drifting snow. Measurements are taken manualy, on a daily basis. </p> <p>Snow water equivalent measurements have also been carried out at the same points by the Station's overwintering crew since October 1982. These measurements are performed every five days using a VS-43 snow tube. However, measurements are not taken when the snow depth is less than 5 cm.</p>
Dataset for the publication "Superconducting gravimeter observations show that satellite-derived snow depth image improves the simulation of the snow water equivalent evolution in a high alpine site"
<p>This datasset contains data to reproduce the following figures of the paper <em>Superconducting gravimeter observations show that satellite-derived snow depth image improves the simulation of the snow water equivalent evolution in a high alpine site</em>:</p> <ul> <li> <p>Time series data of Figures 1c and 2</p> </li> <li> <p>Data (*.asc) used for plotting Figures 1d and 1e (as well as Figure S3 and S4)</p> </li> <li>Pléiades snow depth map (Figure S1)</li> <li> <p>Data used for plotting Figure S2</p> </li> </ul> <p> </p>
NH-SWE: Northern Hemisphere Snow Water Equivalent dataset based on in-situ snow depth time series and the regionalisation of the ΔSNOW model
<p>Time series of daily Snow Water Equivalent (SWE) and Snow Density over the Northern Hemisphere, based on in-situ station observations of snow depth converted to SWE using the ΔSNOW model (Winkler et al., 2021) and regionalised parameters. </p> <p>An extensive description of the dataset and the method to generate it can be found in the data descriptor manuscript published in the journal Earth System Science Data: <a href="https://essd.copernicus.org/preprints/essd-2023-31/">https://essd.copernicus.org/articles/15/2577/2023/essd-15-2577-2023</a> </p> <p><strong>Dataset:</strong> A total of 11,0071 time series of modelled SWE and estimated snow density at the point scale, spanning 1950-2022, at daily resolution.<em> "NH-SWE_dataset_MAP.png"</em> shows a Northern Hemisphere map with the location of all stations in the NH-SWE dataset and their elevation in meters. </p> <p><strong>Files: </strong>The dataset is provided in two different formats:</p> <ol> <li>Individual <em>.csv</em> files for each station in the NH-SWE dataset at <em>"NH_SWE_dataset_vector_files.zip"</em></li> <li>Full-dataset <em>.csv </em>matrices with dates as rows and NH-SWE stations as columns at <em>"NH_SWE_dataset_matrix_files.zip"</em></li> </ol> <p><strong>Metadata:<em> </em></strong><em>"NH_SWE_METADATA.csv"</em> Includes information on NH-SWE stations location (ID, country, station name, coordinates, elevation), data source, length of time series, model parameters and the climate variables used to estimate them, and average snow climatology such as average maximum snow depth, average peak SWE and average maximum snow cover duration. More details and units in the <em>"README_fileformats.txt"</em> file. </p> <p><strong>ΔSNOW model parameter regionalisation: </strong>The code to obtain the ΔSNOW model parameters based on climate variables for all the stations in the NH-SWE dataset is shared in<em><strong> </strong>"DeltaSNOW_parameter_regionalisation.zip"</em>. The method is extensively described in the data descriptor manuscript by Fontrodona-Bach et al., (2023) submitted to Earth System Science Data. More details in the <em>"README_regionalisation.txt"</em> file. </p> <p><strong>Data use: </strong>Free, provided adequate citation of both the data descriptor manuscript and the zenodo record. See <em>"README_datausage.txt"</em></p> <p><strong>Version history:</strong><br>v1: Initial upload. The ΔSNOW model regionalisation was missing.<br>v2: Manuscript submission version. Updated dataset and includes the ΔSNOW model regionalisation code.</p> <p><strong>Reported errors:</strong><br>The dataset accidentally contains one station from the Southern Hemisphere (NH-SWE ID 500001), located in Antarctica (Country code AY). <br>The longitude of a few stations exceeds +180 decimal degrees. To obtain the correct value within the [-180,180] decimal degree longitude bounds, the value exceeding +180 needs to be added to -180 degrees (e.g. +181.0 degrees is actually -179.0 degrees).<br>Swedish stations have two different country codes, SE for the ECA&D stations, and SW for the GHCNd stations. <br>Japan country code is "JA" in the metadata, although the official country code should be JP. </p>
Long-term reconstruction of satellite-based precipitation, soil moisture, and snow water equivalent in China
<p>A daily 0.1<sup>°</sup> dataset of precipitation (<em>P</em>), soil moisture (SM), and snow water equivalent (SWE) in 1981-2017 across China.</p>
Daily gridded datasets of snow depth and snow water equivalent for the Iberian Peninsula from 1980 to 2014
<p>We present snow observations and a validated daily gridded snowpack dataset that was simulated from downscaled reanalysis of data for the Iberian Peninsula. The Iberian Peninsula has long-lasting seasonal snowpacks in its different mountain ranges, and winter snowfalls occur in most of its area. However, there are only limited direct observations of snow depth (SD) and snow water equivalent (SWE), making it difficult to analyze snow dynamics and the spatiotemporal patterns of snowfall. We used meteorological data from downscaled reanalyses as input of a physically based snow energy balance model to simulate SWE and SD over the Iberian Peninsula from 1980 to 2014. More specifically, the ERA-Interim reanalysis was downscaled to 10 ×10 km resolution using the Weather Research and Forecasting (WRF) model. The WRF outputs were used directly, or as input to other submodels, to obtain data needed to drive the Factorial Snow Model (FSM). We used lapse-rate coefficients and hygrobarometric adjustments to simulate snow series at 100 m elevations bands for each 10 × 10 km grid cell in the Iberian Peninsula. The snow series were validated using data from MODIS satellite sensor and ground observations. The overall simulated snow series accurately reproduced the interannual variability of snowpack and the spatial variability of snow accumulation and melting, even in very complex topographic terrains. Thus, the presented dataset may be useful for many applications, including land management, hydrometeorological studies, phenology of flora and fauna, winter tourism and risk management .</p> <p> </p>
Daily snow water equivalent and snow depth data from the valley Wattental in the Tuxer Alpen, Tyrol, Austria [dataset]
<p>The herein published dataset contains daily snow depth (HS) and daily snow water equivalent (SWE) data from the catchment of the Lizumbach in the valley bottom of Wattental in the Tuxer Alpen, Tyrol, Austria (N47.16820, E11.63858). The measurements are obtained at the tree line in an altitude of 1995m a.s.l. Measurement data are available from January 11 2010 until September 30 of 2022. The repository will be updated during the coming years. </p> <p> </p>
Snow cover and snow water equivalent for: How do tradeoffs in satellite spatial and temporal resolution impact snow water equivalent reconstruction?
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SPIReS-MODIS-ParBal snow water equivalent reconstruction: Western USA, water years 2001–2024
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Snow water equivalent data for C1 Snotel, 1981 - 2010.
The U.S. Soil Conservation Service (SCS) operates hundreds of Snotel sites throughout the western United States. One of these is on Niwot Ridge at C-1, located approximately 1.6 km, by road, from the University of Colorado Mountain Research Station at an approximate elevation of 3000 meters. The Snotel installations operate on the principle that the snowpack exerts pressure on liquid-filled stainless steel "pillows" that rest on the ground. Pressure transducers output a digital signal that is sent via radio telemetry to one of two receiving stations. The transducer output is calibrated by measuring snowpack depth and density on or about the 25th of each month from December through April. A Mt. Rose snow sampler was used to determine the depth and density of the snowpack at each of the "pillow's" four corners. These four values were averaged to determine the monthly snow water equivalent (SWE) in inches. NOTE: The LTER data portal display does not display important maintenance/log information or other EML metadata features. Please be sure to view the EML file (a text file that contains XML tags) which is included in the zip archive (click on "Download zip archive") pertaining to each dataset. The EML file name will have the following format: knb-lter-nwt.[3 digit dataset number].[version number].xml. Most web browsers can parse the EML so it's easier to read.
Northern Hemisphere historical in-situ Snow Water Equivalent dataset (NorSWE, 1979-2021)
<p><strong>Description</strong> (in English, French follows)</p> <p>The Northern Hemisphere historical in situ snow water equivalent dataset (NorSWE) includes snow water equivalent (SWE, or water equivalent of snow cover by WMO, 2018) observations from manual snow surveys, snow pillows, automated passive gamma radiation sensors (GMON), and from airborne passive gamma radiation surveys for the period 1979-2021 compiled from nine different sources covering North America, Russia, Finland, Norway and Switzerland. Exceptionally, to expand coverage over Europe we also include single point manual SWE observations (type_mes=1) from eleven sites in Switzerland. SWE is the primary variable of interest. Snow depth (SD) is included when available and derived bulk snow density is calculated from SD and SWE. NorSWE is described in detail in Mortimer and Vionnet (in prep). Sites intersecting the Global Mountain Biodiversity Assessment (GMBA) Mountain Inventory v2 (Snethlage et al., 2022; https://www.earthenv.org/mountains) with a 25 km buffer or a 2° slope mask derived from the GETASSE30 DEM are assigned a mountain mask flag of 1.</p> <p>Processing and quality control generally follows that described in Vionnet et al. (2021). Quality control involved range thresholding: ranges for SD, SWE and bulk density are 0-3 m (0-8 m where mmask = 1), 0-3000 kg m-2 (0-8000 kg m-2 where mmask =1), and 25-700 kg m-3. Where station elevation was not included in the original station metadata or it was deemed to be erroneous, elevation was taken from the United States Geological Survey’s National Elevation Dataset (Gesh et al., 2022). NorSWE was originally compiled to support evaluation of gridded SWE products over the modern satellite era (1979-2021) and focused on observations from snow course and airborne gamma SWE. In v2, we expanded the dataset to include automated data over North America to support hydrological modelling applications. In v3, we added data from Norway and Switzerland (Marty, 2020).</p> <p>NorSWE is provided as a netCDF (NorSWE-NorEEN_1979-2021_v3.nc, compressed into zip file) and following the conventions of the Canadian Snow Water Equivalent Dataset (CanSWE) described in Vionnet et al. (2021) with the addition of a mountain mask variable. The final dataset includes 10 153 locations spanning the years 1979 to 2021.</p> <p> </p> <p><strong>Description</strong> (Francais)</p> <p>Cet ensemble de données de l’Équivalent en Eau de la couverture Neigeuse (EEN, OMM, 2018) comprend des observations manuelles des lignes de neige, des mesures automatiques des coussins à neige et des capteurs gamma passif (GMON), et des estimations de l’EEN issues de mesures de radiation gamma aéroportée pour la période 1979-2021. Cette base de données compile des données issues de neuf sources couvrant l’Amérique du Nord, la Russie et la Finlande. L’EEN est la quantité d’intérêt principal. L’information sur la hauteur de neige (HN) est incluse lorsqu’elle est disponible et la masse volumique moyenne du manteau neigeux est calculée à partir de l’HN et de l’EEN. NorEEN est décrit en détail dans Mortimer and Vionnet (en préparation). Les sites montagneux sont indiqués par le code mmask (valeur = 1). Le masque de montagne combine le Global Mountain Biodiversity Assessment (GMBA) Mountain Inventory v2 (Snethlage et al., 2022; https://www.earthenv.org/mountains) (plus une zone tampon de 25 km) et un masque de topographie complexe (2°) calculée selon le modèle numérique de terrain (MNT) GETASSE30.</p> <p>Le traitement des données et le contrôle de qualité (CQ) suivent la méthodologie proposée par Vionnet et al. (2021). Pour le CQ, les observations en dehors de plages de valeurs prédéterminées ont été exclues : HN 0-3 m (0-8 m mmask = 1), EEN 0-3000 kg m-2 (0-8000 kg m-2 mmask = 1), et masse volumique moyenne du manteau neigeux 25-700 kg m-3. Si l’altitude de la station manquait ou était erronée, l’altitude du site a été extraite du fichier national d’élévation de la Commission Géologique des USA (Gesh et al., 2022). Originalement, la base de données décrite dans ce document a été mise en place pour évaluer de produits d’EEN sur grille d’échelle moyennes à large (4-50 km) couvrant la période moderne de la télédétection satellitaire (1979-2021). Pour cette raison, seules les observations des lignes de neige et les estimations de EEN dérivées de mesures de radiation gamma aéroportées avaient été incluses dans la version 1. Pour la version 2, les données historiques des stations automatiques couvrants l’Amérique du Nord ont été incluses en support des applications hydrologiques. Pour la version 3, les données historiques des stations de stations en Norvège et la Swisse ont été incluses (Marty, 2020).</p> <p>La base de données est distribuée au format NetCDF (NorSWE-NorEEN_1979-2021_v3.nc, comprimée dans une archive zip) selon les conventions de la base de données historiques canadiennes d’Équivalent en Eau de la Neige (CanEEN, Vionnet et al., 2021) et comprenant une variable supplémentaire indiquant les sites montagneux. La base de données inclut des mesures issues de 10,153 sites uniques durant la période de 1979 à 2021.</p> <p><strong>References/Références</strong></p> <p>Beaudette, D., Skovlin, J., Roecker, S., and Brown, A.: soilDB: Soil Database Interface. R package version 2.8.5, [codebase] https://CRAN.R-project.org/package=soilDB, 2024.</p> <p>Carroll, T.R. Airborne Gamma Radiation Snow Survey Program: A user's guide, Version 5.0. National Operational Hydrologic Remote Sensing Center (NOHRSC), Chanhassen, 14, 2001. https://www.nohrsc.noaa.gov/special/tom/gamma50.pdf</p> <p>Gesch, D., Oimoen, M., Greenlee, S., Nelson, C., Steuck, M., and Tyler, D.: The National Elevation Dataset, Photogramm. Eng. Rem. S., 68, 5–32, 2002.</p> <p>Marty, C.: GCOS SWE data from 11 stations in Switzerland, EnviDat, [data Set], https://www.doi.org/10.16904/15, last updated 2024 (last access: February 2025), 2020.</p> <p>Snethlage, M.A., Geschke, J., Spehn, E.M., Ranipeta, A., Yoccoz, N. G., Körner, Ch., Jetz, W., Fischer, M., and Urbach, D.: A hierarchical inventory of the world’s mountains for global comparative mountain science, Sci. Data, 9, 149, https://doi.org/10.1038/s41597-022-01256-y, 2022.</p> <p>Snethlage, M.A., Geschke, J., Spehn, E.M., Ranipeta, A., Yoccoz, N. G., Körner, Ch., Jetz, W., Fischer, M., and Urbach, D.: GMBA Mountain Inventory v2 [data set], GMBA-EarthEnv., https://doi.org/10.48601/earthenv-t9k2-1407, 2022, accessed June 2023.</p> <p>Vionnet, V., Mortimer, C., Brady, M., Arnal, L., and Brown, R.: Canadian historical Snow Water Equivalent dataset (CanSWE, 1928–2020), Earth Syst. Sci. Data, 13, 4603–4619, https://doi.org/10.5194/essd-13-4603-2021, 2021.</p> <p>Vionnet, V., Mortimer C., Brady, M., Arnal, L., and Brown R.: Canadian historical Snow Water Equivalent dataset (CanSWE 1928-2022), Version 5, Zenodo, https://zenodo.org/records/7734616 , 2021, updated 13 March 2023.</p> <p>WMO (Ed.): Guide to instruments and methods of observation: Volume II - Measurement of Cryospheric Variables, 2018th ed., World Meteorological Organization, Geneva, WMO-No. 8, 52 pp., 2018. <strong><br></strong></p>
Global snow water equivalent product derived from machine learning model trained with in situ measurement data
<p>This dataset is a global snow water equivalent dataset using machine learning trained with in-situ measurements. The temporal resolution of the SWEML product is daily, and the spatial resolution is 0.25˚ (approximately 25km). It covers latitudes of 90S to 90N and longitudes of 180W to 180E with global scales, excluding Antarctica. The dataset is provided in NetCDF format, organized by year. Each year contains daily SWE data, including leap days in leap years.</p>
Canadian historical Snow Water Equivalent dataset (CanSWE, 1928-2024)
<p><strong>Description</strong> (in English, French follows)</p> <p>The Canadian historical Snow Water Equivalent dataset (CanSWE) includes manual and automated pan-Canadian observations of Snow Water Equivalent (SWE) collected by national, provincial and territorial agencies, hydropower companies and their partners, as well as academic institutions. Snow depth and derived bulk snow density are also included when available. A code describes the SWE measurement method for each site following World Meteorological Organization (WMO) standards (WMO, 2019). This new dataset supersedes the most recent update of the Canadian Historical Snow Survey (CHSSD) dataset published by Brown et al. (2019) and available at <a href="https://doi.org/10.18164/cf337b6b-9a87-4ffd-a8e5-41e6498b1474">https://doi.org/10.18164/cf337b6b-9a87-4ffd-a8e5-41e6498b1474</a>. The creation of CanSWE used the 2019 CHSSD update as a starting point and involved three main steps: (i) correction and cleaning of the 2019 CHSSD update (correction of metadata, removal of duplicates), (ii) update of this cleaned dataset until July 2020 and addition of snow data from new stations and agencies, and (iii) consistent quality control of the final dataset. The version 6 of CanSWE includes over one million SWE measurements from 2945 different locations across Canada over the snow seasons 1928 – 2024 where a snow season is defined as starting August 01 and ending July 31. CanSWE is described in detail in Vionnet et al. (2021).</p> <p>The data are distributed in 2 formats: a NetCDF file (CanSWE-CanEEN_1928-2024_v7.nc) and a zip file containing a csv version of CanSWE (CanSWE-CanEEN_1928-2024_v7.zip). More details about the dataset, the file format and the update made in CanSWEv7 are given in the files ReadMe_CanSWE_v7.pdf (in English) and LisezMoi_CanEEN_v7.pdf (in French).</p> <p><strong>Description</strong> (Francais)</p> <p>La base données historiques canadiennes d’Equivalent en Eau de la Neige (CanEEN) comprend des observations manuelles et automatiques de l’Equivalent en Eau de la Neige (EEN) à l’échelle du Canada collectées par des agences nationales, provinciales et territoriales, des compagnies productrices d’hydroélectricité et leurs partenaires ainsi que par des universités. Les informations sur la hauteur de neige et la masse volumique moyenne du manteau neigeux sont incluses lorsqu’elles sont disponibles. Un code qui suit les règles de l’Organisation Mondiale de la Météorologie (OMM, 2019) décrit la méthode de mesure de l’EEN pour chaque site. Cette nouvelle base de données remplace le jeu de données des Relevés Nivométriques Canadiens (RNC) publié par Brown et al. (2019) et disponible à l’adresse : <a href="https://doi.org/10.18164/cf337b6b-9a87-4ffd-a8e5-41e6498b1474">https://doi.org/10.18164/cf337b6b-9a87-4ffd-a8e5-41e6498b1474</a>. La création de CanEEN se base sur la version de 2019 des RNC et se décompose en 3 étapes principales : (i) correction et nettoyage de la version 2019 des RNC (correction des métadonnées, suppression des duplicata), (ii) mise à jour de ce jeu de données nettoyé avec des données disponibles jusqu’en Juillet 2020 et ajout de données historiques provenant de nouvelles stations et de nouveaux partenaires, (iii) contrôle qualité appliqué à l’ensemble du jeu de données. La version 7 de CanEEN inclut plus d’un million de mesures de l’EEN collectées dans 2945 stations à travers le Canada pour les années nivologiques 1928 à 2023 où une année nivologique est définie pour la période allant du 1 août au 31 juillet. CanEEN est décrit en détail dans Vionnet et al. (2021).</p> <p>Les données sont distribuées sous deux formats: un fichier au format NetCDF (CanSWE-CanEEN_1928-2024_v7.nc) et une archive zip contenant un fichier au format csv (CanSWE-CanEEN_1928-2024_v7.csv). Des informations complémentaires sur le jeu de données, leur format ainsi que les modifications apportées dans la version 6 sont fournies dans les fichiers ReadMe_CanSWE_v7.pdf (en Anglais) et LisezMoi_CanEEN_v7.pdf (en Francais).</p> <p><strong>References/Références: </strong></p> <p>Brown, R. D., Fang, B., and Mudryk, L.: Update of Canadian historical snow survey data and analysis of snow water equivalent trends, 1967–2016. Atmos. Ocean, 57, 149 156, <a href="https://doi.org/10.1080/07055900.2019.1598843">https://doi.org/10.1080/07055900.2019.1598843</a>, 2019</p> <p>Vionnet, V., Mortimer, C., Brady, M., Arnal, L., and Brown, R.: Canadian historical Snow Water Equivalent dataset (CanSWE, 1928–2020), Earth Syst. Sci. Data, 13, 4603–4619, https://doi.org/10.5194/essd-13-4603-2021, 2021.</p> <p>WMO (World Meteorological Organization): Global Cryosphere Watch: Improvements in the international reporting of Snow Depth, WIGOS Newsletter, 5, 3-4, <a href="https://community.wmo.int/wigos-newsletters-archive">https://community.wmo.int/wigos-newsletters-archive</a>, 2019</p>
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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.