The Standardized Precipitation Index (SPI) is widely regarded as one of the most accessible tools for quantifying and reporting meteorological drought. At shorter timescales, SPI values are highly correlated with soil moisture, while at extended timescales, they reflect variations in groundwater and reservoir storage. The SPI model derives probability distributions from historical precipitation records, which are then normalized using an incomplete gamma function across multiple timescales. SPI values indicate the number of standard deviations the observed anomaly departs from the long-term period of record, with positive values corresponding to wetter-than-average conditions. Time periods calculated for monthly precipitation percentiles are 1, 2, 3, 6, 9, 12, 18, 24 months. Each ISO week is numbered from 1 to 52 (sometimes 53) within a year. An ISO week starts on Monday and ends on Sunday. Historical record goes back to 1980 Indices values were calculated using the xclim python package The National Agroclimate Series of Derived Indicators (NASDI) products provide a collection of comprehensive and regularly updated datasets on key agroclimatic variables, including accumulated precipitation, standardized precipitation index, and difference from normal temperature, among others. These datasets incorporate both real-time and historical climate information, offering enhanced insight into conditions and trends across Canada’s diverse agricultural regions.

The Wastewater Systems Effluent Regulations (WSER), developed under the Fisheries Act, came into force in 2012 to manage wastewater releases by systems that collect an average daily influent volume of 100 cubic metres or more. The WSER also does not apply to any wastewater system located in the Northwest Territories, Nunavut and north of the 54th parallel in the provinces of Quebec and Newfoundland and Labrador. The WSER set national baseline effluent quality standards that are achievable through secondary wastewater treatment. The province of Quebec provided some combined sewer overflow data for 2020, which includes information on whether a discharge occurred at a combined sewer overflow point during the year. The map below shows the number of CSO points with at least one overflow event within each wastewater system. The map is available in both ESRI REST (to use with ARC GIS) and WMS (open source) formats. For more information about the individual reporting wastewater systems, datasets are available in either CSV or XLS formats. More information on the wastewater sector including the regulations, agreements, contacts and resource documents is available at: https://www.canada.ca/wastewater

Multi-model ensembles of surface wind speed based on projections from twenty-nine Coupled Model Intercomparison Project Phase 5 (CMIP5) global climate models are available for 1900-2100. Specifically, the 5th, 25th, 50th, 75th and 95th percentiles of the monthly, seasonal and annual ensembles of surface wind speed (m/s) are available for the historical time period, 1900-2005, 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 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”.

Each pixel value corresponds to the difference (anomaly) between the mean “Best-Quality” Max-NDVI of the week specified (e.g. Week 18, 2000-2014) and the “Best-Quality” Max-NDVI of the same week in a specific year (e.g. Week 18, 2015). Max-NDVI anomalies < 0 indicate where weekly Max-NDVI is lower than normal. Anomalies > 0 indicate where weekly Max-NDVI is higher than normal. Anomalies close to 0 indicate where weekly Max-NDVI is similar to normal.

The health of individual amphibians, amphibian populations, and their wetland habitats are monitored in the oil sands region and at reference locations. Contaminants assessments are done at all sites. Amphibians developing near oil sands activities may be exposed to concentrations of oil sands-related contaminants, through air emissions as well as water contamination. The focus of field investigations is to evaluate the health of wild amphibian populations at varying distances from oil sands operations. Wood frog (Lithobates sylvaticus) populations are being studied in Alberta, Saskatchewan and the Northwest Territories in order to examine the relationship of proximity to oil sands activities and to prevalence of infectious diseases, malformation rates, endocrine and stress responses, genotoxicity, and concentrations of heavy metals, naphthenic acids and polycyclic aromatic hydrocarbons.

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.

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 “Soil Landscapes of Canada (SLC) Version 2.2” dataset series provides a set of geo-referenced soil areas (polygons) that are linked to attribute data found in the associated Component Table (CMP), Landscape Table (LAT), Carbon Layer Table (CLYR), and Dom/Sub File (DOM_SUB). Together, these datasets describe the spatial distribution of soils and associated landscapes for Canada.

Climatic variability plays a critical role in shaping Canada’s agricultural sector, influencing essential factors such as water availability, soil health, and both the yield and quality of crops. Shifts in temperature, distribution of rainfall, and increases in extreme weather events can severely disrupt agricultural operations, making continual environmental monitoring crucial. Canada’s vast and diverse agricultural regions require specialized, localized data to enable effective preparedness and resilience planning for farmers, policymakers, and rural communities; particularly in anticipating and managing drought and other climate-related risks. To address this need, National Agroclimate Series of Derived Indicators (NASDI) products provide a collection of comprehensive and regularly updated datasets on key agroclimatic variables, including accumulated precipitation, standardized precipitation index, and difference from normal temperature, among others. These datasets incorporate both real-time and historical climate information, offering enhanced insight into conditions and trends across Canada’s diverse agricultural regions.