Description: Seasonal mean total phytoplankton at the surface from the British Columbia continental margin model (BCCM) were averaged over the 1981 to 2010 period to create seasonal mean surface climatology of the Canadian Pacific Exclusive Economic Zone. Methods: Total phytoplankton is the sum of diatoms and flagellates concentration. Spring months were defined as April to June, summer months were defined as July to September, fall months were defined as October to December, and winter months were defined as January to March. The data available here contain a raster layer of seasonal surface phytoplankton climatology for the Canadian Pacific Exclusive Economic Zone at 3 km spatial resolution. Uncertainties: Model results have been extensively evaluated against observations (e.g. altimetry, CTD and nutrient profiles, observed geostrophic currents), which showed the model can reproduce with reasonable accuracy the main oceanographic features of the region including salient features of the seasonal cycle and the vertical and cross-shore gradient of water properties. However, the model resolution is too coarse to allow for an adequate representation of inlets, nearshore areas, and the Strait of Georgia.

Description: Seasonal mean nitrate concentration from the British Columbia continental margin model (BCCM) were averaged over the 1981 to 2010 period to create seasonal mean climatology of the Canadian Pacific Exclusive Economic Zone. Methods: Nitrate concentrations at up to forty-six linearly interpolated vertical levels from surface to 2400 m and at the sea bottom are included. Spring months were defined as April to June, summer months were defined as July to September, fall months were defined as October to December, and winter months were defined as January to March. The data available here contain raster layers of seasonal nitrate concentration climatology for the Canadian Pacific Exclusive Economic Zone at 3 km spatial resolution and 47 vertical levels. Uncertainties: Model results have been extensively evaluated against observations (e.g. altimetry, CTD and nutrient profiles, observed geostrophic currents), which showed the model can reproduce with reasonable accuracy the main oceanographic features of the region including salient features of the seasonal cycle and the vertical and cross-shore gradient of water properties. However, the model resolution is too coarse to allow for an adequate representation of inlets, nearshore areas, and the Strait of Georgia.

The ‘Land use allocation to Soils and Landforms by year’ dataset links agricultural land use activities to soils and landscapes within Soil Landscapes of Canada (SLC) polygons. The land use allocations to soils area datasets were generated on an annual time step (1971-2015). Agricultural land use is categorized and allocated based on the following general land use types: Annual cropland, Perennial cropland, Specialty Crops, Improved pasture and Unimproved Pasture.

30-year Average Number of Days with Temperature above 32 °C are defined as the count of the number of climate days during the month where the maximum daily temperature was greater than 32 °C. These values are calculated across Canada in 10x10 km cells.

Crop development stage in a numerical scale. All living organisms move from one stage of development to the next over time. For annual crops, it life cycle (growing season) completed within a year. Crop water use differs from one stage to another mostly due to the differences in the amount of green leaves, thus crop stage is closely related to its water consumption and water stress condition. Crop stages are mostly controlled by growing season heat accumulation and regulated by day-length crop some crops. The crop stages provided here are determined by a biometeorlogical time scale model (Robertson, 1968) for cool season crops (wheat, barley etc.) , and a Crop Heat Unit (Brown and Bootsma, 1993) algorithm for warm season crops (corn and soybean etc.).

The National Ecological Framework for Canada provides a consistent, national spatial framework that allows various ecosystems to be described, monitored and reported on. It provides standard ecological units that allow different jurisdictions and disciplines to use common communication and reporting, and a common ground to report on the state of the environment and the sustainability of ecosystems in Canada. The framework was developed between 1991 and 1999 by the Ecosystems Science Directorate, Environment Canada and the Center for Land and Biological Resources Research, Agriculture and Agri-Food Canada. Over 100 federal and provincial agencies, non-governmental organizations and private sector companies contributed to its development.

The National Earthquake Scenario Catalogue, presents the probable shaking, damage, loss and consequences from hypothetical earthquakes that could impact Canadians. It considers only damage to buildings, and their inhabitants, from earthquake shaking, and therefore does not include damage to critical infrastructure or vehicles. Losses from secondary hazards, such as aftershocks, liquefaction, landslides, or fire following are also not currently included. The information is provided at the approximate scale of Census dissemination areas, and is intended to support planning and emergency management activities in earthquake prone regions. This project is run by the Geological Survey of Canada's Public Safety Geoscience Program.

In 2015, a magnitude 4.7 earthquake occurred 60 km beneath Sidney, BC. This scenario visualizes the effects of that event if it had a magnitude of 7.1.

Last Spring Frost (0 °C) is defined as the average day, during the first half of the year, of the last occurrence of a minimum temperature at or below 0 °C. These values are calculated across Canada in 10x10 km cells.

Each pixel value corresponds to the mean historical “Best-quality” Max-NDVI value for a given week, as calculated from the previous 20 years in the MODIS historical record (i.e. does not include data from the current year). These data are also often referred to as “weekly baselines” or “weekly normals”.