Glaciers and ice masses for the province, derived from aerial imagery flown in the late 1980s and early 1990s. Please refer to the [Glaciers](https://catalogue.data.gov.bc.ca/dataset/glaciers) dataset for recent glacier extents in British Columbia, and [Historical Glaciers](https://catalogue.data.gov.bc.ca/dataset/historical-glaciers) for a comparable historic view.

All bank edges (of rivers, lakes, and wetlands), delimiter edges, glacier edges, and administrative boundary edges. These are the linear features that makeup the polygonal waterbodies

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

Snow and ice are important hydrological resources. Their minimum spatial extent here referred to as annual minimum snow/ice (MSI) cover, plays a very important role as an indicator of long-term changes and baseline capacity for surface water storage. The MSI probability is derived from sequence of seventeen 10-day clear-sky composites corresponding to April, 1 to September, 20 warm period for each year since 2000. Data from Moderate Resolution Imaging Spectroradiometer (MODIS) on Terra satellite for the period since 2000 have been processed with the special technology developed at the Canada Centre for Remote Sensing (CCRS) as described in Trishchenko, 2016; Trishchenko et al., 2016; 2009, 2006, Trishchenko and Ungureanu, 2021, Khlopenkov and Trishchenko, 2008, Luo et al., 2008. The presence of snow or ice is determined for each pixel of the image based on snow/ice scene identification procedure and the probability if computed for the entire warm season as a ratio of number of snow/ice flags to the total number of pixels available (less or equal to 17). The minimum snow and ice extent can be derived from the probability map by applying a certain threshold. New data version V5.0 replaces previous version V4.0 for all data available since 2000. All MSI files were reprocessed for all MODIS input data based on collection 6.1. The output format has not changed since previous version. It is described in Trishchenko (2024). The impact of input data change is small and can be detected only for time interval 2000-2015. Data starting 2016 has been already derived using MODIS collection 6.1 input.The differences between the MSI data based on MODIS Collection 5 (i.e. MSI V4) versus MODIS Collection 6.1 (i.e. MSI V5), on average, are quite small. The region-wide relative difference in the MSI extent varies from -3.97% to +1.75%. The mean value is -0.14%, the median value is 0.18% and standard deviation is 1.83%. As such, we do not expect any sizeable impact of the version change on our previous conclusions regarding trends and climate variations, except for refining the relative values of statistical parameters within the range of a few percents. References:TRISHCHENKO, A.P., 2024: Probability maps of the annual minimum snow and ice (MSI) presence over April,1 to September, 20 period since 2000 derived from MODIS 250m imagery over Canada and neighbouring regions. Data format description. CCRS, NRCan. 4pp.

The mapping depicts the relative abundance of relict (buried glacier) ice preserved in upper permafrost at a national scale. The mapping is updated and based on modelling by O'Neill et al. (2019) (https://doi.org/10.5194/tc-13-753-2019). The mapping offers an improved depiction of ground ice in Canada at a broad scale, incorporating current knowledge on the associations between geological and environmental conditions and ground ice type and abundance. It provides a foundation for hypothesis testing related to broad-scale controls on ground ice formation, preservation, and melt.

“Glacier National Park – Total GHG Emissions” datasets consist of estimates of GHG emissions (carbon dioxide (CO2), methane (CH4), and nitrous oxide (N2O)) in carbon dioxide equivalents (CO2e) from forested ecosystems in Glacier National Park from 1990 to 2020 (tonnes carbon dioxide equivalent per hectare).Total GHG emissions for 31 national parks were estimated using the Generic Carbon Budget Model (GCBM), a spatially explicit carbon budget model developed by Canadian Forest Service which uses forest inventory, disturbance, and mean annual temperature data along with yield data to estimate growth and merchantable volume for dominant tree species. Species- and Ecozone-specific equations are then used to convert merchantable volume to aboveground and belowground biomass carbon. The GCBM simulates carbon dynamics to produce spatially explicit estimations of carbon stocks and fluxes. The model simulates and tracks carbon stocks, transfers between Intergovernmental Panel on Climate Change (IPCC)-defined pools, and other metrics including net ecosystem production, net biome production, and emissions of carbon dioxide (CO2), methane (CH4), and nitrous oxide (N2O) in annual time steps. The stocks and fluxes are also tracked by disturbance event (e.g., forest fires).Total GHG emissions include those from natural processes like respiration and decomposition and those due to natural and anthropogenic disturbances, including wildfires, prescribed burns, and insect outbreaks. These were calculated as the sum of CO2, CH4, and N2O emission estimates in tonnes carbon (tonnes C) generated by the GCBM. Emissions estimates were then converted to carbon dioxide equivalents (CO2e) using the 100-year Global Warming Potential (IPCC Fourth Assessment Report) factors for CH4 (25) and N2O (298).These products have a spatial resolution of 30m.This information is part of the Parks Canada Carbon Atlas Series. To obtain a copy of this report, please contact changementclimatique-climatechange@pc.gc.ca.When using this data, please cite as follows:Sharma, T., Kurz, W.A., Fellows, M., MacDonald, A.L., Richards, J., Chisholm, C., Seutin, G., Richardson, K., Keenleyside, K. (2023). Parks Canada Carbon Atlas Series: Carbon Dynamics in the Forests of Canada’s National Parks. Scientific Report. Parks Canada Agency, Gatineau, QC, Canada, 104 p.

Icefields captured at 1:1,000,000 scale from Digital Chart of the World data for the Yukon and surrounding area.Distributed from [GeoYukon](https://yukon.ca/geoyukon) by the [Government of Yukon](https://yukon.ca/maps) . Discover more digital map data and interactive maps from Yukon's digital map data collection.For more information: [geomatics.help@yukon.ca](mailto:geomatics.help@yukon.ca)

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.

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].

Wildlife Key Areas (WKA) are locations used by wildlife for critical, seasonal life functions. WKAs are identified by interpreting observed locations of wildlife at key times of year, not through intensive habitat assessment. Polygons derived from interviews with locals and from GIS interpretation of wildlife/habitat surveys. GIS interpretation follows criteria specific for taxon and/or populations of taxon. Key Areas are based on observed locations of wildlife at key times of year, not on habitat assessment. With new information, boundaries and designations of Key Areas can change and additional Key Areas can be identified. Furthermore, Key Areas are not the only sites important for wildlife. Other information sources can identify other sites important for wildlife for reasons outside the scope of the WKA Inventory Program. Updates to Key Areas occur only periodically. For the most current information, please consult with the Regional Biologist for your area of interest. If you have questions or would like to contribute to the WKA database, please contact the WKA Inventory Program ( [wka@yukon.ca](mailto:wka@yukon.ca) ).Distributed from [GeoYukon](https://mapservices.gov.yk.ca/GeoYukon/) by the [Government of Yukon](https://yukon.ca/) . Discover more digital map data and interactive maps from Yukon's digital map data collection.For more information: [geomatics.help@yukon.ca](mailto:geomatics.help@yukon.ca)