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

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.

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.

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.

Description: Seasonal climatologies of the Canadian Pacific Exclusive Economic Zone (CPEEZ) were computed from a numerical simulation of the British Columbia continental margin (BCCM) model for the 1981 to 2010 period, which can be considered as a representation of the climatological state of the region. Methods: The BCCM model is an ocean circulation-biogeochemical model implementation of the Regional Ocean Modelling System (ROMS version 3.5). The horizontal resolution is eddy-resolving at 3 km and the vertical discretization is based on a terrain-following coordinate system with 42 depth levels of increasing resolution near the surface. A detailed description of the BCCM model is given in Peña et al. (2019). 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 climatology of temperature, salinity, current speed, nitrate, oxygen, total alkalinity, dissolved inorganic carbon, pH, aragonite saturation state, phytoplankton, and primary production. The data include 47 vertical levels (surface, bottom, and 45 more selected depths), except for total phytoplankton (surface values only) and primary production (depth-integrated values). A layer giving the bottom depth in metres at the centre of each grid point is also provided. Model grids were set to NaN values in regions where the model output is highly uncertain, such as inlets, nearshore areas, and the Strait of Georgia. Uncertainties: Model results have been compared against tide gauge data, altimetry, CTD and nutrient profiles, observed geostrophic currents, and seasonal temperature and salinity climatologies over the 1981 to 2010 period. The model is successful in reproducing the main features of the region including salient features of the seasonal cycle and the vertical structure of density and nutrients.

Water level and discharge data are available from Water Survey of Canada’s Hydrometric Network. The Water Survey of Canada (WSC) is the national authority responsible for the collection, interpretation and dissemination of standardized water resource data and information in Canada. In partnership with the provinces, territories and other agencies, WSC operates over 2500 active hydrometric gauges across the country, maintains an archive of historical information for over 7600 stations and provides access to near real-time (water level and stream flow) provisional data at over 1700 locations in Canada.

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.

Aquatic bird eggs are being collected for contaminants analysis. Egg collections in the Peace-Athabasca Delta area support Parks Canada’s activities at Wood Buffalo National Park and the multi-stakeholder Peace-Athabasca Ecosystem Monitoring Program. This monitoring activity employs repeated censuses of birds and builds on initial egg collections made in 2009 from Egg Island (Lake Athabasca) and Wood Buffalo National Park, with the goal of evaluating contaminant burdens, contaminant sources and changes in sources through time. Egg samples are collected from colonial waterbirds California Gulls (Larus californicus), Herring Gulls (Larus argentatus), Ring-billed Gulls (Larus delawarensis), Caspian Terns (Hydroprogne caspia) and Common Terns (Sterna hirundo) and insectivorous birds Bank Swallows (Riparia riparia), Cliff Swallows (Petrochelidon pyrrhonota) and Tree Swallows (Tachycineta bicolor) to monitor health and contaminant levels of aquatic and terrestrial birds in the oil sands region and in reference areas. The samples collected are analysed for oil sands-related contaminants including polycyclic aromatic hydrocarbons (PAHs) and metals such as mercury (Hg) and arsenic (As).

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.