This is a map of current snow basin indices across British Columbia. Snow basin index mapping is based on analysis from the River Forecast Centre, and is meant to accompany the Snow Conditions and Water Supply Bulletins that are published for data from January 1st, February 1st, March 1st, April 1st, May 1st, May 15th, June 1st and June 15th each year. Snow data collection is managed by the Ministry of Environment and Climate Change Strategy's Snow Program.

Data tracking historical truck travel speeds from the road network performance project. The travel speeds, Travel Time Index (TTI), Delay Index (DI) and Buffer Time Index (BTI) were calculated with GPS data collected by GPS fleet tracking units. This data is used by the Ministry of Transportation to monitor truck speed and performance on major roadways within Ontario. *[GPS]: Global Positioning System

The Canadian indexes of social resilience and vulnerability were created to provide area-based information on resilience and vulnerability to natural hazards and disasters across Canada. Specifically, the Canadian Index of Social Resilience (CISR) aims to reflect a community’s ability to respond to and recover from natural hazards. In contrast, the Canadian Index of Social Vulnerability (CISV) aims to reflect the social vulnerability of an area based on factors that have the potential to amplify the impact of disasters on populations.Before the CISR and CISV were built, indicator frameworks were developed for social resilience and social vulnerability, respectively. Indicators were selected because of their demonstrated association with social resilience or social vulnerability. The selection was informed by the theoretical and research literature, existing indexes, availability of relevant data and engagement with subject-matter experts.The CISR and the CISV were created using data from Dissemination areas (DAs) across the country. The selected indicators were included in a principal component analysis, which is a statistical technique that allows a large number of indicators to be collapsed into a smaller number of interpretable components. Based on the results of the principal component analysis, DA-level scores were calculated for each index. Higher CISR scores correspond to DAs that are more resilient and higher CISV scores correspond to DAs that are more vulnerable.These indexes can be used to better understand areas which may experience the largest disproportional social impacts from natural hazards.

Environment and Climate Change Canada’s (ECCC) CMIP6 statistically downscaled agroclimatic indices are an updated version of the CMIP5 agroclimatic indices dataset making use of two different sets of downscaled scenarios created by the Pacific Climate Impacts Consortium (PCIC): 1. Canadian Downscaled Climate Scenarios–Univariate method from CMIP6 (CanDCS-U6), and 2. Canadian Downscaled Climate Scenarios–Multivariate method from CMIP6 (CanDCS-M6). To address the needs of different user groups in Canada, 49 indices, including agroclimatic indices, were proposed by the Canadian adaptation community through a series of consultations. Please see the definition list for the equations of each index. In 2025, PCIC expanded the CMIP6 agroclimatic indices, by adding CanDCS-M6, which includes SSP3-7.0 for most GCMs. Additionally, PCIC introduced 18 new indices to the previous 49. The 67 indices are available for both the CanDCS-U6 and CanDCS-M6.The range of impact-relevant climate indices available for download includes, indices representing counts of the number of days when temperature or precipitation exceeds (or is below) a threshold value; the episode length when a particular weather/climate condition occurs; and indices that accumulate temperature departures above or below a fixed threshold. The statistically downscaled climate indices are available for individual models and ensembles, historical simulations (1951-2014) and three emissions scenarios called “Shared Socioeconomic Pathways” (SSPs), SSP1-2.6, SSP2-4.5, and SSP5-8.5 (2015-2100), at a 10 x 10 km degree grid resolution. The CanDCS-M6 agroclimatic indices dataset also includes SSP3-7.0 results.Note: projected future changes by statistically downscaled products are not necessarily more credible than those by the underlying climate model outputs. In many cases, especially for absolute threshold-based indices, projections based on downscaled data have a smaller spread because of the removal of model biases. However, this is not the case for all indices. Downscaling from GCM resolution to the fine resolution needed for impact assessment increases the level of spatial detail and temporal variability to better match observations. Since these adjustments are GCM dependent, the resulting indices could have a wider spread when computed from downscaled data as compared to those directly computed from GCM output. In the latter case, it is not the downscaling procedure that makes future projection more uncertain; rather, it is indicative of higher variability associated with a finer spatial scale.Individual model datasets and all related derived products are subject to the terms of use (https://pcmdi.llnl.gov/CMIP6/TermsOfUse/TermsOfUse6-1.html) of the source organization.

Environment and Climate Change Canada’s (ECCC) Climate Research Division (CRD) and the Pacific Climate Impacts Consortium (PCIC) previously produced statistically downscaled climate scenarios based on simulations from climate models that participated in the Coupled Model Intercomparison Project phase 5 (CMIP5) in 2015. ECCC and PCIC have now updated the CMIP5-based downscaled scenarios with two new sets of downscaled scenarios based on the next generation of climate projections from the Coupled Model Intercomparison Project phase 6 (CMIP6). The scenarios are named Canadian Downscaled Climate Scenarios–Univariate method from CMIP6 (CanDCS-U6) and Canadian Downscaled Climate Scenarios–Multivariate method from CMIP6 (CanDCS-M6).CMIP6 climate projections are based on both updated global climate models and new emissions scenarios called “Shared Socioeconomic Pathways” (SSPs). Statistically downscaled datasets have been produced from 26 CMIP6 global climate models (GCMs) under three different emission scenarios (i.e., SSP1-2.6, SSP2-4.5, and SSP5-8.5), with PCIC later adding SSP3-7.0 to the CanDCS-M6 dataset. The CanDCS-U6 was downscaled using the Bias Correction/Constructed Analogues with Quantile mapping version 2 (BCCAQv2) procedure, and CanDCS-M6 was downscaled using the N-dimensional Multivariate Bias Correction (MBCn) method. The CanDCS-U6 dataset was produced using the same downscaling target data (NRCANmet) as the CMIP5-based downscaled scenarios, while the CanDCS-M6 dataset implements a new target dataset (ANUSPLIN and PNWNAmet blended dataset).Statistically downscaled individual model output and ensembles are available for download. Downscaled climate indices are available across Canada at 10km grid spatial resolution for the 1950-2014 historical period and for the 2015-2100 period following each of the three emission scenarios.Note: projected future changes by statistically downscaled products are not necessarily more credible than those by the underlying climate model outputs. In many cases, especially for absolute threshold-based indices, projections based on downscaled data have a smaller spread because of the removal of model biases. However, this is not the case for all indices. Downscaling from GCM resolution to the fine resolution needed for impacts assessment increases the level of spatial detail and temporal variability to better match observations. Since these adjustments are GCM dependent, the resulting indices could have a wider spread when computed from downscaled data as compared to those directly computed from GCM output. In the latter case, it is not the downscaling procedure that makes future projection more uncertain; rather, it is indicative of higher variability associated with finer spatial scale.Individual model datasets and all related derived products are subject to the terms of use (https://pcmdi.llnl.gov/CMIP6/TermsOfUse/TermsOfUse6-1.html) of the source organization.

This dataset includes five depth-attenuated relative wave exposure index layers in raster format. Relative Exposure Index (REI) values are calculated based on effective fetch (derived from fetch values) combined with modelled wind data. The output REI layers are attenuated by depth, resulting in greater values in shallow, nearshore areas (Bekkby et al. 2008). The cell values represent an estimate of wave exposure at bottom depth normalized between regions from 0 (protected) to 1 (exposed).The objective of this dataset is to provide an estimate of wave exposure at bottom depth, primarily for use in species distribution modelling. Each single-band raster corresponds to a marine region, which generally coincide with the following layers from the Species Distribution Modelling Boundaries (https://www.gis-hub.ca/dataset/sdm-boundaries) dataset: Nearshore_HG, Nearshore_NCC, Nearshore_QCS, Nearshore_QCS, and Shelf_SalishSea. These layers extend to 50 m depth and up to 5 km from shore.Tabular data (csv files) are also included as part of the data package. These data are the calculated Relative Exposure Index (REI) values with fields for position information. The fetch values from gridded nearshore fetch (https://gis-hub.ca/dataset/gridded-nearshore-fetch) are used as a source dataset and the locations in the REI are the same as the gridded fetch.

PURPOSE:Since 2003, a standardized otter trawl survey was conducted in August by commercial fishing vessels throughout the southern Gulf of St. Lawrence (NAFO Division 4T). The primary objective of this survey is to obtain abundance indices for the major commercial groundfish resources in the area.DESCRIPTION:Tow, catch, and length frequency for fish caught during the August sentinel surveys in the southern Gulf of St. Lawrence (NAFO Division 4T). Abundance indices and spatial distribution patterns of commercial groundfish.Note: Due to delays caused by logistic complexities and Covid, the project did not take place in 2020 PARAMETERS COLLECTED:Abundance estimates (ecological); distribution (ecological); species counts (ecological); gear (fishing); vessel information (fishing); point (spatial).NOTES ON QUALITY CONTROL:Scientific names listed in the survey species list have been mapped to recognized standards - marine taxa have been mapped to the World Register of Marine Species (WoRMS) using their online taxon match tool. All sampling locations were plotted on a map to perform a visual check confirming that the latitude and longitude coordinates were within the described sampling area.SAMPLING METHODS:For additional information on the sampling methods and supporting literature, please refer to the references providedUSE LIMITATION:To ensure scientific integrity and appropriate use of the data, we would encourage you to contact the data custodian.

This map service provides access to the Geophysical Survey Index datasets shown on the GeoAtlas application.**Please Note – All published Saskatchewan Geological Survey datasets, including those available through the Saskatchewan Mining and Petroleum GeoAtlas, are sourced from the Enterprise GIS Data Warehouse. They are therefore identical and share the same refresh schedule. This map service provides access to the Geophysical Survey Index datasets shown on the GeoAtlas application. It will contain data related to Lithoprobe Lines, NATGAM Spectrometer and Aeromagnetic Survey Indexes data.

This map service provides access to the Geophysical Survey Index datasets shown on the GeoAtlas application.**Please Note – All published Saskatchewan Geological Survey datasets, including those available through the Saskatchewan Mining and Petroleum GeoAtlas, are sourced from the Enterprise GIS Data Warehouse. They are therefore identical and share the same refresh schedule. This map service provides access to the Geophysical Survey Index datasets shown on the GeoAtlas application. It will contain data related to Lithoprobe Lines, NATGAM Spectrometer and Aeromagnetic Survey Indexes data.

PURPOSE:To track juvenile Atlantic salmon densities.DESCRIPTION:Indices of freshwater production are derived annually from electrofishing surveys in the Miramichi River of New Brunswick. Juvenile salmon abundances at sites, in terms of number of fish per habitat area sampled by age or size group (densities), are obtained using successive removal sampling or catch per unit effort sampling calibrated to densities. Sampling intensities vary among years and among rivers. PARAMETERS COLLECTED:Species counts (ecological); point (spatial).USE LIMITATION:To ensure scientific integrity and appropriate use of the data, we would encourage you to contact the data custodian.