First Fall Frost (-2 °C) is defined as the average day of the second half of the year with the first occurrence of the minimum temperature of a climate day which is at or below -2 °C. These values are calculated across Canada in 10x10 km cells.

PURPOSE: Used as an abundance index for use in stock assessment. DESCRIPTION: Since 1991, an annual fishery-independent acoustic survey of early fall (September-October) concentrations of Herring has been conducted in the southern Gulf of St. Lawrence (sGSL). The standard annual survey area occurs in the 4Tmno areas where both NAFO Div. 4T Herring spawning components aggregate in the fall. The survey uses a random stratified design of parallel transects within predefined strata. Surveys are conducted at night and use two vessels: an acoustic vessel to quantify the fish schools' biomass using a hull-mounted 120 KHz split-beam transducer, and a fishing vessel to sample aggregates of fish with a pelagic trawl (details in LeBlanc et al. 2015; see also LeBlanc and Dale 1996). Trawl samples are used to separate the estimated biomass by spawning component and age, determine species composition, and size distribution for the estimation of the target strength (LeBlanc and Dale 1996; LeBlanc et al. 2015). A standardized abundance index is generated from this acoustic survey. This index includes catch-at-age data since 1994. This survey also provides the age-disaggregated acoustic abundance index for ages 2 to 10 for spring spawners and fall spawners. PARAMETERS COLLECTED: Size and age measurement (biological); acoustic tracking (ecological); species counts (ecological) SAMPLING METHODS: Please consult the research documents listed in the supplementary citation list for sampling details. USE LIMITATION: To ensure scientific integrity and appropriate use of the data, we would encourage you to contact the data custodian.

Statistically downscaled multi-model ensembles of minimum temperature are available at a 10km spatial resolution for 1951-2100. Statistically downscaled ensembles are based on output from twenty-four Coupled Model Intercomparison Project Phase 5 (CMIP5) global climate models (GCM). Daily minimum temperature from GCM outputs were downscaled using the Bias Correction/Constructed Analogues with Quantile mapping version 2 (BCCAQv2). A historical gridded minimum temperature dataset of Canada (ANUSPLIN) was used as the downscaling target. The 5th, 25th, 50th, 75th and 95th percentiles of the monthly, seasonal and annual ensembles of downscaled minimum temperature (°C) are available for the historical time period, 1951-2005, 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.

Each pixel value corresponds to the quality control, cloud cover and snow fraction value for each pixel in the Best-Quality Max-NDVI product.

Statistically downscaled multi-model ensembles of mean temperature are available at a 10km spatial resolution for 1951-2100. Statistically downscaled ensembles are based on output from twenty-four Coupled Model Intercomparison Project Phase 5 (CMIP5) global climate models (GCM). Downscaled daily mean temperature was calculated by averaging downscaled daily minimum and maximum temperature. Daily minimum and maximum temperature from GCM outputs were downscaled using the Bias Correction/Constructed Analogues with Quantile mapping version 2 (BCCAQv2). Historical gridded minimum and maximum temperature datasets of Canada (ANUSPLIN) were used as the respective downscaling targets. The 5th, 25th, 50th, 75th and 95th percentiles of the monthly, seasonal and annual ensembles of downscaled mean temperature (°C) are available for the historical time period, 1951-2005, 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.

Statistically downscaled multi-model ensembles of projected change (also known as anomalies) in mean temperature (°C) are available at a 10km spatial resolution for 1951-2100. Statistically downscaled ensembles are based on output from twenty-four Coupled Model Intercomparison Project Phase 5 (CMIP5) global climate models (GCM). Downscaled daily mean temperature was calculated by averaging downscaled daily minimum and maximum temperature. Daily minimum and maximum temperature from GCM outputs were downscaled using the Bias Correction/Constructed Analogues with Quantile mapping version 2 (BCCAQv2). Historical gridded minimum and maximum temperature datasets of Canada (ANUSPLIN) were used as the respective downscaling targets. Projected change in mean temperature (°C) is with respect to the reference period of 1986-2005. Seasonal and annual averages of projected mean temperature change to 1986-2005 are provided. Specifically, the 5th, 25th, 50th, 75th and 95th percentiles of the downscaled ensembles of mean temperature change are available for the historical time period, 1901-2005, and for emission scenarios, RCP2.6, RCP4.5 and RCP8.5, for 2006-2100. Twenty-year average changes in statistically downscaled minimum mean temperature (°C) 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 downscaled 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.

The “Prairie Agricultural Landscapes (PAL)” datasets identify areas of the agricultural portions of the Canadian Prairies with similar land and water resources, land use and farming practices. They are represented by vector polygons. Based on selected attributes from the Soil Landscapes of Canada (SLC) and the 1996 Census of Agriculture, the Prairies were classified into 13 (thirteen) classes of Land Practices Group and five (5) Major Land Practices Groups. Typical attributes used to define the Land Practice Groups include: land in pasture, land in summerfallow, crop mixture, farm size and the level of chemical and fertilizer inputs. The five (5) Major Groups were devised to help better understand the relationships between the groups.

The national wetland layer contains wetland data compiled from the best available data from each region, classified by wetland type. Wetlands are mapped as polygons in geographic layers, which are integrated into a master geodatabase at the national scale.Information from each contributing dataset was classified based on the Canadian Wetland Classification System, which contains five main wetland classes (Bog, Fen, Marsh, Swamp, and Shallow Water) that represent the types of wetlands encountered in Canada. An additional category, “partially classified” was used to preserve boundary information for wetlands that could not be classified into the main categories with existing information.

These datasets show the areas where major crops can be expected within the agricultural regions of Canada. Results are provided as rasters with numerical values for each pixel indicating the level of spatial density calculated for a specific crop type in that location. Regions with higher spatial density for a certain crop have higher likelihood to have the same crop based on the previous years mapped crop inventories.

McElhanney Consulting Services Ltd (MCSL) has performed a LiDAR and Imagery survey in southern Saskatchewan. The purpose was to generate DEMs for hydraulic modeling of floodplain, digital terrain maps, and other products for portions of the Swift Current Creek valley and other miscellaneous tributaries and related water course valleys in and around the City of Swift Current. The acquisition was completed between the 16th and 25th of October, 2009. The survey consisted of approximately 790 square kilometers of coverage. While collecting the LiDAR data, we also acquired aerial photo in RGB and NIR modes consisting of 1649 frames each. In addition to the main area of interest, McElhanney has acquired some LiDAR and photo of low lying areas adjacent to the project area. This additional area was acquired on speculation that the data may be required in the future. The 3Dimensional laser returns (point cloud) were classified using Microstation (v8), Terrascan and TerraModeler. A series of algorithms based on topography were created to separate laser returns that hit the ground from the ones that hit objects above the ground. Steps taken are: Classified LiDAR surface as Bare earth, Classified other features as non-bare earth or default, Formatted to ASPRS .LAS V1.1 (Class 1 - Default (non-bare earth), Class 2 – Ground points (bare earth)), 239 tiles each 2km x2km generated for LiDAR data, File prefix FF – Classified (Non-Bare Earth and Bare Earth), File Prefix BE – Bare Earth only, Bare Earth Model Key Point (MKPts) surface files are thinned Bare earth LiDAR points. MKPts files generate a virtually identical surface without the large file size, MKPts file format is ASCII (Easting Northing Z-elevation) xyz and LAS format.