This data series was compiled by AAFC and Statistics Canada using a combination of agroclimate data and satellite-derived Normalized Difference Vegetation Index (NDVI) data for the current growing season. The forecast is made based on a statistical model using historical yield, climate and NDVI data.

Each pixel value corresponds to the actual number (count) of valid Best-quality Max-NDVI values used to calculate the mean weekly values for that pixel. Since 2020, the maximum number of possible observations used to create the Mean Best-Quality Max-NDVI for the 2000-2014 period is n=20. However, because data quality varies both temporally and geographically (e.g. cloud cover and snow cover in spring; cloud near large water bodies all year), the actual number (count) of observations used to create baselines can vary significantly for any given week and year.

This is a magnitude 5.0 earthquake scenario along the Burlington Toronto Structural Zone — a fault near Toronto and its surrounding region. This fault is not known to be active but demonstrates a plausible earthquake scenario for the Toronto region.

Funded through DFO's Strategic Program for Ecosystem-based Research and Advice (SPERA), this benthic survey covers several seabed areas adjacent to Deer Island and Campobello Island, the Wolves Islands, and Grand Manan (NB) over a two-year study period (2016-2017). One hundred and fifty drift camera transects were completed within the ~91 sq-km study region collecting continuous high-definition video with periodic 4K resolution video (provided by a downward facing Blackmagic Production Camera 4K equipped with video lights). A Nikon D800 36.1 megapixel digital still imagery camera (equipped with a studio strobe light) captured seafloor images at ~30s intervals over a maximum 25-minute drift survey period. The camera was triggered by lowering the camera frame within 1 m of the seabed, releasing tension on a trigger weight suspended below the frame. Camera location was tracked using an ultra-short baseline acoustic positioning system (Tracklink 1500HA transceiver with 1505B transponder on the camera frame). Species presence/absence, abundance, and bottom type was recorded manually using PhotoQuad v1.4 software. An average field of view of 0.7 x 0.5 m was determined from a subset of digital still images within which the 10 cm diameter trigger weight was fully in view. Thirty-eight key and common species were described using explicit taxonomic identifiers, while other species were recorded within broader general categories (e.g. unidentified Cnidaria). Identification was made to the lowest possible taxonomic level. Primary bottom-type was defined as the grain size with the most percent coverage for each image/video interval. Grain size limits were determined using the Wentworth scale. Cite this data as: Lawton P. Benthic Species Presence/Absence in the Lower Bay of Fundy Derived From High Resolution Video and Still Imagery. Published May 2022. Coastal Ecosystems Science Division, Fisheries and Oceans Canada, Dartmouth, N.S.

The Census of Agriculture is disseminated by Statistics Canada's standard geographic units (boundaries). Since these census units do not reflect or correspond with biophysical landscape units (such as ecological regions, soil landscapes or drainage areas), Agriculture and Agri-Food Canada in collaboration with Statistics Canada's Agriculture Division, have developed a process for interpolating (reallocating or proportioning) Census of Agriculture information from census polygon-based units to biophysical polygon-based units. In the “Interpolated census of agriculture”, suppression confidentiality procedures were applied by Statistics Canada to the custom tabulations to prevent the possibility of associating statistical data with any specific identifiable agricultural operation or individual. Confidentiality flags are denoted where "-1" appears in data cell. This indicates information has been suppressed by Statistics Canada to protect confidentiality. Null values/cells simply indicate no data is reported.

Maximum Temperature represents the highest recorded temperature value (°C) at each location for a given time period. Time periods include the previous 24 hours and the previous 7 days from the available date where a climate day starts at 0600UTC.

Monthly 30-year Average Maximum Temperature represents the average monthly maximum temperature calculated for a given location averaged across a 30 year period (1961-1991, 1971-2000, 1981-2010, 1991-2020). These values are calculated across Canada in 10x10 km cells.

The Ocean Data Inventory database is an inventory of all of the oceanographic time series data held by the Ocean Science Division at the Bedford Institute of Oceanography. The data archive includes about 5800 current meter and acoustic doppler time series, 4500 coastal temperature time series from thermographs, as well as a small number (200) of tide gauges. Many of the current meters also have temperature and salinity sensors. The area for which there are data is roughly defined as the North Atlantic and Arctic from 30° - 82° N, although there are some minor amounts of data from other parts of the world. The time period is from 1960 to present. The database is updated on a regular basis.

Difference from normal soil moisture is the modelled amount of plant available water (mm) in the root zone of the soil, minus the average amount that has historically been available on that day. This value is intended to provide users with a representation of conditions above or below normal and by the amount of water (mm). Values are computed using the Versatile Soil Moisture Budget (VSMB)

Multi-model ensembles of mean precipitation based on projections from twenty-nine Coupled Model Intercomparison Project Phase 5 (CMIP5) global climate models are available for 1901-2100. Specifically, the 5th, 25th, 50th, 75th and 95th percentiles of the monthly, seasonal and annual ensembles of mean precipitation (mm/day) are available for the historical time period, 1901-2005, 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.