The surface water quality (WQ) program, as part of the Joint Canada/Alberta Implementation Plan, is designed to improve the ability to detect change and predict effects in relation to point and non‐point sources. A mass‐balance approach has been used for assessing the quantity, movement, and cycling of materials in the watershed. Applying this approach required a sampling program which included quantification of the sources, transport, flux, and fate of materials and contaminants. The Surface WQ monitoring sampling includes, in part, collection of; - event (freshet and rain) based WQ samples in tributaries ranging from daily to bi-weekly, - WQ samples in the Athabasca River using cross-channel transect methods at specified Phase 1 sites, - enhanced (additional parameters) WQ sampling at M9, M12, and M11A and at 5 new interconnecting channel stations within the Expanded Geographical Area (EGA), - ground water samples in specific high priority tributaries, and - auto-monitoring (near real-time) on a subset of parameters at sites in the EGA

This dataset provides geospatial polygon boundaries for marine bivalve shellfish harvest area classification in Canada (British Columbia, New Brunswick, Newfoundland and Labrador, Nova Scotia, Prince Edward Island and Quebec). These data represent the five classification categories of marine bivalve shellfish harvest areas (Approved; Conditionally Approved; Restricted; Conditionally Restricted; and Prohibited) under the Canadian Shellfish Sanitation Program (CSSP). Data are collected by Environment and Climate Change Canada (ECCC) for the purpose of making applicable classification recommendations based on pollution source assessment and water quality survey results. ECCC recommendations are reviewed and adopted by Regional Interdepartmental Shellfish Committees prior to regulatory implementation by Fisheries and Oceans Canada (DFO). These geographic data are for illustrative purposes only; they show shellfish harvest area classifications that may be superseded at any time by regulatory orders issued by DFO, which place areas in Closed Status, due to conditions such as sewage overflows or elevated biotoxin levels. For further information about the current status and boundary coordinates for areas under Prohibition Order, please contact your local DFO office.

Description: Seasonal mean temperature from the British Columbia continental margin model (BCCM) were averaged over the 1993 to 2020 period to create seasonal mean climatology of the Canadian Pacific Exclusive Economic Zone. Methods: Temperatures 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 temperature 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.

Minimum Temperature represents the lowest 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.

The objectives of the fish component of the integrated oil sands monitoring program are to provide the necessary data/information to address key questions related to both environmental health of fish populations and fish health issues that can be used to inform human use and consumption. The questions underlying the fish monitoring design are related to the status and health of wild fish populations in the Lower Athabasca River including and in an expanded geographical extent. Data is being collected to provide a baseline against which future changes in fish populations will be evaluated, and compared to data from historical studies to assess change over time to the current state. Data is also being collected in areas of new oil sands development, to develop baseline data for future site-specific comparisons, contribute to an expanded geographic basis of the overall monitoring plan, and contribute to an improved ability to examine cumulative effects.

The Blended Index (BI) is a model which employs multiple potential indicators of drought and excess moisture, such as the Palmer drought index, rolling precipitation amounts and soil moisture, and combines them into a weighted, normalized value between 0 and 100. The inputs and weights used in this model are subject to change periodically as it is optimized to best represent extent, duration and severity of impactful weather conditions. The blended index is deployed as two variations; short term (st) focusing on 1 to 3 months, and long term (lt) focusing on 6 months to 5 years.

Growing Degree Days (GDDs) are used to estimate the growth and development of plants and insects during the growing season. Insect and plant development are very dependent on temperature and the daily accumulation of heat. The amount of heat required to move a plant or pest to the next development stage remains constant from year to year. However, the actual amount of time (days) can vary considerably from year to year because of weather conditions. Base temperatures are a point below which development does not occur for the organism in question. Base 10 temperatures are commonly used for grasshoppers and beans. These values are calculated across Canada in 10x10 km cells.

Monthly 30-year Average Minimum Temperature represents the average monthly minimum temperature calculated at 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.

Seasonal and annual multi-model ensembles of projected relative change (also known as anomalies) in mean precipitation based on an ensemble of twenty-nine Coupled Model Intercomparison Project Phase 5 (CMIP5) global climate models are available for 1901-2100. Projected relative change in mean precipitation 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 mean precipitation 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 mean precipitation (%) 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.

Temperature is a key factor affecting the physiological development of field crops as well as crop yield and agricultural product quality achieved during the growing season. Crop responses to the temperature are characterized by three important cardinal temperature indices; the cardinal minimum temperature, maximum cardinal temperature, and optimum temperature for field crop production at which the plant growth and development can start, stop, and proceed at the maximum rate respectively. Agriculture is an important primary production sector in Canada. Agricultural production, profitability, sustainability and food security depend on many agrometeorological factors. Extreme weather events in Canada, such as drought, floods, heat waves, frosts and high intensity storms, have the ability to significantly impact field crop production. Agriculture and Agri-Food Canada (AAFC) and Environment and Climate Change Canada (ECCC) have together developed a suite of extreme agrometeorological indices based on four main categories of weather factors: temperature, precipitation, heat, and wind. The extreme weather indices are intended as short-term prediction tools and generated using ECCC’s medium range forecasts to create a weekly index product on a daily basis.