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

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 data contains location information for 1 of Ontario’s snow monitoring networks: * Surface Water Monitoring Centre (SWMC) Snow course data is collected by: * conservation authorities * Ontario Power Generation * Ministry of Natural Resources (MNR) districts Data is collected twice a month from November 15 until May 15. The Surface Water Monitoring Centre uses this data to assess: * current snow cover * frozen ground conditions * snowpack * potential snowmelt * contributions to streamflow The snow data is located in a corporate water and climate database. This data helps MNR and conservation authorities assess the potential for flood at the local and provincial scale.

Snow survey administrative basin areas, which are components of the BC snow survey network. Basin codes are used as basis of snow survey station names, and for some reporting purposes.

Locations of automated snow weather stations, active and inactive. Automated snow weather stations are components of the BC snow survey network.

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 dataset corresponds to daily snow cover percentage at 1km resolution grid over land areas of Canada from 2006-2010. The data are subsampled by 4km to reduce data volumes and considering the geolocation uncertainty of the input satellite imagery. The daily maps are generated by assimilation of daily cloud screened NOAA AVHRR satellite imagery and Canadian Meteorological Centre (CMC) snow depth analysis snow depth and density fields within an off-line version of the CMC daily snow depth model. The snow depth model is modified to include snowpack reflectance model and a surface radiative transfer scheme that relates vegetation and snowpack reflectance to top-of-canopy bi-directional reflectance. A logistic vegetation phenology model is used to parameterize temporal dynamics of canopy leaf area index. A per-pixel particle filter with a 30 day moving window is applied to assimilation observations corresponding to 1km resolution visible band directional reflectance and normalized difference vegetation index and 24km CMC daily snow depth and monthly snow density fields. The assimilation is forced using daily air temperature and precipitation fields. Validation of the datasets has been performed by comparison to MODIS snow cover maps and in-situ snow depth stations across Canada. Validation suggests similar accuracy to MODIS snow cover products over relatively flat terrain. Validation over mountainous regions is ongoing.

Point locations of water and weather monitoring stations used by the [Surface Water Monitoring Centre](http://www.ontario.ca/page/surface-water-monitoring-centre) to assess flood and drought conditions across Ontario. Monitoring station types include: * streamflow gauge stations * Environment and Climate Change Canada climate stations * Ministry of Transportation road weather stations * Ministry of Natural Resources (MNR) fire weather stations * MNR snow network stations (wildlife) * MNR snow survey stations (weather) * Ontario Power Generation snow survey stations (weather)

Seasonal and annual multi-model ensembles of projected change (also known as anomalies) in snow depth based on an ensemble of twenty-eight Coupled Model Intercomparison Project Phase 5 (CMIP5) global climate models are available for 1900-2100. Projected change in snow depth is with respect to the reference period of 1986-2005 and expressed as a percentage (%). The 5th, 25th, 50th, 75th and 95th percentiles of the ensemble of snow depth change are available for the historical time period, 1900-2005, and for emission scenarios, RCP2.6, RCP4.5 and RCP8.5, for 2006-2100. Twenty-year average changes in snow depth (%) for four time periods (2021-2040; 2041-2060; 2061-2080; 2081-2100), with respect to the reference period of 1986-2005, for RCP2.6, RCP4.5 and RCP8.5 are also available in a range of formats. The median projected change across the ensemble of CMIP5 climate models is provided. 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.

This project was completed by the Shellfish Section in the Newfoundland and Labrador Science Branch of Fisheries and Oceans Canada (DFO), in collaboration with industry partners. The Coastal Environmental Baseline program supported the Placentia Bay portion of project work for an ongoing industry-DFO collaborative post-season trap survey for Snow Crab that was initiated in 2003 and has occurred each year. This survey is conducted by Snow Crab harvesters accompanied by at-sea observers and takes place in NAFO Divisions 2J3KLNOP4R. Historically the survey focused on commercial fishing grounds but began transitioning to a partly random stratified design in 2017. Since 2018, approximately 50% of survey stations are randomly allocated while 50% remain fixed. At each station, six (for inshore stations) or ten (for offshore stations) commercial traps are set in a fleet. To gather data on non-commercial sized Snow Crab, including females, many fleets also include one small-mesh trap. The coverage of small-mesh traps has been expanding in recent years with the aim of one small-mesh trap for every station in the coming years. Biological sampling is undertaken on at least one commercial trap and the small-mesh trap at each station. The data from this survey is incorporated into the annual stock assessment for Snow Crab in the Newfoundland and Labrador region. This record contains trap locations for Placentia Bay, and information on the types of data collected. More detailed information can be found in Pantin et al. (2022).https://publications.gc.ca/collections/collection_2023/mpo-dfo/fs70-5/Fs70-5-2022-076-eng.pdf