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RI_623

222 record(s)
 
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  • Historical earthquakes recorded by Earthquakes Canada. This serie is composed of 4 earthquake datasets. Each dataset contains the earthquakes grouped by decade; 1980-1989, 1990-1999, 2000-2009, 2010-2019. However, the National Earthquake Database makes available seismic bulletin data from 1985 and onward. For a complete listing of current and historical earthquakes, visit http://www.earthquakescanada.nrcan.gc.ca/.

  • The “Soil Landscapes of Canada (SLC) Version 3.2” dataset series provides a set of geo-referenced soil areas (polygons) that are linked to attribute data found in the associated Component Table (CMP), Component Rating Table (CRT), Soil Names Table (SNT), Soil Layer Table (SLT), Landscape Segmentation Table (LST), Landform Extent Table (LET), Landform Definition Table and Ecological Framework Table (EFT). Together, these datasets describe the spatial distribution of soils and associated landscapes for the agricultural areas of Canada. However, some provinces (Alberta, Nova Scotia, and Prince Edward Island) contain CMP, SNT and SLT data for the entire province (that is, beyond the agricultural areas). This version is complemented by the previous SLC version 2.2, which covers the entire country.

  • Here is a selection of web services displaying the geographic boundaries of the most common administrative and statistical areas published by Statistics Canada. Administrative areas are defined, with a few exceptions, by federal and provincial statutes and are adopted by Statistics Canada to support the collection and dissemination of data. Administrative areas supported by Statistics Canada include: Province and territory (PR) Federal electoral district (FED) Census division (CD) Census subdivision (CSD) Designated place (DPL) Statistical areas are defined by Statistics Canada to support the dissemination of data. They are created according to a set of rules based on geographic attributes and one or more characteristics of the resident population. Some statistical areas maintained by Statistics Canada include: Census agricultural region (CAR) Economic region (ER) Census consolidated subdivision (CSS) Census metropolitan area and census agglomeration (CMA/CA) Census tract (CT) Aggregate Dissemination Areas (ADA) Dissemination area (DA) Dissemination block (DB) To have a better understanding of the relationships between these areas, refer to the "Hierarchy of standard geographic areas for dissemination" diagram in the Data Resources below. NOTE: Services may not all be listed in the Related Products section below as they are added individually only once available for publication.

  • An archive of 2D regional seismic and long period magnetotelluric data collected during 20 years of work under the LITHOPROBE project. Data are primarily onshore and cover widespread regions of Canada. Available data types include raw digital data, processed sections, and images of final sections, as well as auxiliary information required for analysis of the data.

  • An archive of 2D regional seismic and long period magnetotelluric data collected during 20 years of work under the LITHOPROBE project. Data are primarily onshore and cover widespread regions of Canada. Available data types include raw digital data, processed sections, and images of final sections, as well as auxiliary information required for analysis of the data.

  • This release makes available the West Canadian Coast part of the results of an ongoing effort to scan and convert all our inventory of analog marine survey field records (seismic, sidescan and sounder) to digital format. These records have been scanned at 300 dpi and were converted into JPEG2000 format. Typically each of these files were from 1 to 2 gbyte in size before compression, and were compressed by a factor of 10:1. Empirical tests with a number of data sets suggest that there is minimal visual distortion of the scanned data at this level of compression. In this KML file, scanned data are available in a reduced-scale thumbnail format and a compressed full-resolultion JPEG2000 format.

  • Species At Risk Act (SARA) describes Critical Habitat (CH) as the habitat that is necessary for the survival or recovery of a listed wildlife species (schedule 1), and that is identified as the species’ critical habitat in a recovery strategy or in an action plan for the species. CH spatial data exists for 116 of the 469 Environment Canada – Species At Risk (EC SAR) of interest, which includes draft, candidate, proposed and final CH spatial data that were provided by CWS regional offices. In order to protect sensitive CH information, or for some sharing data issues, CH sites were generalized using a 10km x 10km national grid. As mentioned before, each region provides NCR-CWS with their CH spatial data. After the generalization process, all results were merged to constitute the national view.

  • The Canadian Environmental Sustainability Indicators (CESI) program provides data and information to track Canada's performance on key environmental sustainability issues. The Air quality indicators track ambient concentrations of fine particulate matter, ground-level ozone, sulphur dioxide, nitrogen dioxide, and volatile organic compounds at the national, regional and urban levels and at local monitoring stations. The national and regional indicators are presented with their corresponding Canadian Ambient Air Quality Standard when available. Canadians are exposed to air pollutants on a daily basis, and this exposure can cause adverse health and environmental effects. Information is provided to Canadians in a number of formats including: static and interactive maps, charts and graphs, HTML and CSV data tables and downloadable reports. See the supplementary documentation for the data sources and details on how the data were collected and how the indicator was calculated.

  • Seasonal and annual multi-model ensembles of projected change (also known as anomalies) in sea ice concentration based on an ensemble of twenty-eight Coupled Model Intercomparison Project Phase 5 (CMIP5) global climate models are available for 1900-2100. Sea ice concentration is represented as the percentage (%) of grid cell area. Therefore, projected change in sea ice concentration 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 ensembles of sea ice concentration 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 sea ice concentration (%) 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.

  • 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.