This catalog contains annual 30 m spatial resolution snow dynamics metrics for each snow-year from 2018-2019 to 2023-2024 for all of Canada. We gather all Landsat and Sentinel-2 images collected over Canada and identify the status of each pixel observation on the image collection date: snow (and ice), non-snow (i.e., land, water), unclear (i.e., clouds, shadows). We built an algorithm to calculate snow cover metrics for each pixel during each winter: start date of the first (and biggest) snow period [startF, startB], end date of the last (and biggest) snow period [endL, endB], number of days with snow cover in total (or in the biggest snow period) [lengthT, lengthB], number of snow periods (i.e., separated times with multiple confirmed snow observations) [periods], and a status classification (e.g., continuous snow, snow free) [status]. We do not obtain a clear observation every day because of satellite orbit frequencies and clouds. This means that timing-based metrics are identified by the middle date between two clear observations, with uncertainty quantified as half the length of the gap (i.e., ± days) [startF_u, startB_u, endL_u, endB_u, lengthT_u, lengthB_u].

Multi-model ensembles of snow depth based on projections from twenty-eight Coupled Model Intercomparison Project Phase 5 (CMIP5) global climate models are available for 1900-2100. Specifically, the 5th, 25th, 50th, 75th and 95th percentiles of the monthly, seasonal and annual ensembles of snow depth (m) are available for the historical time period, 1900-2005, and for emission scenarios, RCP2.6, RCP4.5 and RCP8.5, for 2006-2100. Note: Projections among climate models can vary because of differences in their underlying representation of earth system processes. Thus, the use of a multi-model ensemble approach has been demonstrated in recent scientific literature to likely provide better projected climate change information.

Manual snow survey (active & inactive) locations as part of the BC Snow Survey program.

A research survey on snow crab (Chionoecetes opilio) was conducted from July 7 to July 26, 2019 in the Estuary St. Lawrence River between Forestville, Baie-Comeau and Matane. The main objective of this survey was to assess the abundance of snow crab and benthic species associated with snow crab habitat. Only data for benthic species associated with snow crab habitat are presented in this dataset.Data were collected according to a fixed station sampling design consisting of 66 stations, between 31 and 279 meters depth. Specimens were collected using a beam trawl with a total width of 2.8 meters and a total height of 0.76 meters. The codend was lined with a 16 millimeter stretched mesh net in order to harvest the small individuals. The hauls were made at a target speed of 2 knots and a target duration of 15 minutes. Start and end positions were recorded to calculate the distance traveled on each tow using the geosphere library in R. The average tow distance was approximately 25 m. The area covered at each tow was the product of the trawl opening and the distance traveled.The two files provided (DarwinCore format) are complementary and are linked by the "eventID" key. The "Activity_Information" file includes generic activity information, including date and location. The "occurrence_taxon" file includes the taxonomy of the species observed, identified to the species or lowest possible taxonomic level. To obtain the abundance and biomass assessment, contact Cedric Juillet (cedric.juillet@dfo-mpo.gc.ca).For quality controls, all taxonomic names were checked against the World Register of Marine Species (WoRMS) to match recognized standards. The WoRMS match was placed in the "ScientificnameID" field of the occurrence file. Data quality checks were performed using the R obistools and worrms libraries. All sampling locations were spatially validated.

The Annual Minimum Snow and Ice (MSI) Extent of the Atlas of Canada National Scale Data, are data sets compiled containing annual data from 2000 to present. The data sets were derived from research published by the Canada Centre for Remote Sensing which classified satellite imagery over Canada and neighbouring regions for the continued presence or absence of snow and ice from April 1 to September 20 each year. The Atlas of Canada MSI products consist of a vector dataset and a raster time-series animation application.VECTOR DATASETThe vector dataset has been generalized to display at the scale of 1:1,000,000.TIME-SERIES ANIMATION APPLICATIONThe time-series animation application has not been generalized from its original scale (250 m pixels).The application is disseminated through the Data Cube Platform, implemented by the Canada Centre for Mapping and Earth Observation, Natural Resources Canada using geospatial big data management techniques. These technologies enable the rapid and efficient visualization of high-resolution geospatial data and allow for the rapid generation of dynamically derived products. The time-series is also available as a Web Map Service (WMS) and Web Coverage Service (WCS).CREDITSource data provided by Alexander P. Trishchenko, Canada Centre for Remote Sensing, Natural Resources Canada Metadata record: https://open.canada.ca/data/en/dataset/808b84a1-6356-4103-a8e9-db46d5c20fcf

This dataset contains location information for 2 of Ontario’s snow monitoring networks: * Surface Water Monitoring Centre (SWMC) * Snow Network for Ontario Wildlife (SNOW), administered by the Wildlife Research and Monitoring Section Snow course data is collected by: * conservation authorities * Ministry of Natural Resources (MNR) districts * Ontario Power Generation SWMC network data is collected twice a month from November 15 until May 15. SNOW network data is collected once a week from the first snowfall until snowmelt. The Surface Water Monitoring Centre uses the data to assess: * current snow cover * frozen ground conditions * snowpack * potential snowmelt * contributions to streamflow MNR’s Science and Research Branch use the data to: * help manage wildlife species including deer, moose, wild turkey, elk, wolves and coyotes * help ministry resource managers and scientists administer programs and conduct research * inform game management decisions such as white-tailed deer harvest quotas * support flight planning for the Moose Aerial Inventory program

This catalog contains annual 30 m spatial resolution snow dynamics metrics for each snow-year from 2018-2019 to 2023-2024 for all of Canada. We gather all Landsat and Sentinel-2 images collected over Canada and identify the status of each pixel observation on the image collection date: snow (and ice), non-snow (i.e., land, water), unclear (i.e., clouds, shadows). We built an algorithm to calculate snow cover metrics for each pixel during each winter: start date of the first (and biggest) snow period [startF, startB], end date of the last (and biggest) snow period [endL, endB], number of days with snow cover in total (or in the biggest snow period) [lengthT, lengthB], number of snow periods (i.e., separated times with multiple confirmed snow observations) [periods], and a status classification (e.g., continuous snow, snow free) [status]. We do not obtain a clear observation every day because of satellite orbit frequencies and clouds. This means that timing-based metrics are identified by the middle date between two clear observations, with uncertainty quantified as half the length of the gap (i.e., ± days) [startF_u, startB_u, endL_u, endB_u, lengthT_u, lengthB_u]. **This third party metadata element follows the Spatio Temporal Asset Catalog (STAC) specification.**

This catalog contains annual 30 m spatial resolution snow dynamics metrics for each snow-year from 2018-2019 to 2023-2024 for all of Canada. We gather all Landsat and Sentinel-2 images collected over Canada and identify the status of each pixel observation on the image collection date: snow (and ice), non-snow (i.e., land, water), unclear (i.e., clouds, shadows). We built an algorithm to calculate snow cover metrics for each pixel during each winter: start date of the first (and biggest) snow period [startF, startB], end date of the last (and biggest) snow period [endL, endB], number of days with snow cover in total (or in the biggest snow period) [lengthT, lengthB], number of snow periods (i.e., separated times with multiple confirmed snow observations) [periods], and a status classification (e.g., continuous snow, snow free) [status]. We do not obtain a clear observation every day because of satellite orbit frequencies and clouds. This means that timing-based metrics are identified by the middle date between two clear observations, with uncertainty quantified as half the length of the gap (i.e., ± days) [startF_u, startB_u, endL_u, endB_u, lengthT_u, lengthB_u]. **This third party metadata element follows the Spatio Temporal Asset Catalog (STAC) specification.**

This catalog contains annual 30 m spatial resolution snow dynamics metrics for each snow-year from 2018-2019 to 2023-2024 for all of Canada. We gather all Landsat and Sentinel-2 images collected over Canada and identify the status of each pixel observation on the image collection date: snow (and ice), non-snow (i.e., land, water), unclear (i.e., clouds, shadows). We built an algorithm to calculate snow cover metrics for each pixel during each winter: start date of the first (and biggest) snow period [startF, startB], end date of the last (and biggest) snow period [endL, endB], number of days with snow cover in total (or in the biggest snow period) [lengthT, lengthB], number of snow periods (i.e., separated times with multiple confirmed snow observations) [periods], and a status classification (e.g., continuous snow, snow free) [status]. We do not obtain a clear observation every day because of satellite orbit frequencies and clouds. This means that timing-based metrics are identified by the middle date between two clear observations, with uncertainty quantified as half the length of the gap (i.e., ± days) [startF_u, startB_u, endL_u, endB_u, lengthT_u, lengthB_u]. **This third party metadata element follows the Spatio Temporal Asset Catalog (STAC) specification.**

This catalog contains annual 30 m spatial resolution snow dynamics metrics for each snow-year from 2018-2019 to 2023-2024 for all of Canada. We gather all Landsat and Sentinel-2 images collected over Canada and identify the status of each pixel observation on the image collection date: snow (and ice), non-snow (i.e., land, water), unclear (i.e., clouds, shadows). We built an algorithm to calculate snow cover metrics for each pixel during each winter: start date of the first (and biggest) snow period [startF, startB], end date of the last (and biggest) snow period [endL, endB], number of days with snow cover in total (or in the biggest snow period) [lengthT, lengthB], number of snow periods (i.e., separated times with multiple confirmed snow observations) [periods], and a status classification (e.g., continuous snow, snow free) [status]. We do not obtain a clear observation every day because of satellite orbit frequencies and clouds. This means that timing-based metrics are identified by the middle date between two clear observations, with uncertainty quantified as half the length of the gap (i.e., ± days) [startF_u, startB_u, endL_u, endB_u, lengthT_u, lengthB_u]. **This third party metadata element follows the Spatio Temporal Asset Catalog (STAC) specification.**