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These geographic data are for illustrative purposes only; they show shellfish harvest area classifications that may be superseded at any time by regulatory orders issued by DFO, which place areas in Closed Status, due to conditions such as sewage overflows or elevated biotoxin levels.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.

These datasets show the areas where major crops can be expected within the agricultural regions of Canada. Results are provided as rasters with numerical values for each pixel indicating the level of spatial density calculated for a specific crop type in that location. Regions with higher spatial density for a certain crop have higher likelihood to have the same crop based on the previous years mapped crop inventories.

Winds can significantly influence crop growth and yield mainly due to mechanical damage of plant vegetative and reproductive organs, an imbalance of plant-soil-atmosphere water relationships such as evapotranspiration, and pest and disease distributions in agricultural fields. The maximum wind speed and the number of strong wind days over the forecast period represent short term and extended strong wind events 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.

This collection is a legacy product that is no longer maintained. It may not meet current government standards. Users of Atlas of Canada National Scale Data 1:5,000,000 (release of May 2017) should plan to make the transition towards the new CanVec product. The Atlas of Canada National Scale Data 1:5,000,000 Series consists of boundary, coast, island, place name, railway, river, road, road ferry and waterbody data sets that were compiled to be used for atlas medium scale (1:5,000,000 to 1:15,000,000) mapping. These data sets have been integrated so that their relative positions are cartographically correct. Any data outside of Canada included in the data sets is strictly to complete the context of the data.

Drought is a deficiency in precipitation over an extended period, usually a season or more, resulting in a water shortage that has adverse impacts on vegetation, animals and/or people. The Climate Moisture Index (CMI) was calculated as the difference between annual precipitation and potential evapotranspiration (PET) – the potential loss of water vapour from a landscape covered by vegetation. Positive CMI values indicate wet or moist conditions and show that precipitation is sufficient to sustain a closed-canopy forest. Negative CMI values indicate dry conditions that, at best, can support discontinuous parkland-type forests. The CMI is well suited to evaluating moisture conditions in dry regions such as the Prairie Provinces and has been used for other ecological studies. Mean annual potential evapotranspiration (PET) was estimated for 30-year periods using the modified Penman-Monteith formulation of Hogg (1997), based on monthly 10-km gridded temperature data. Data shown on maps are 30-year averages. Historical values of CMI (1981-2010) were created by averaging annual CMI calculated from interpolated monthly temperature and precipitation data produced from climate station records. Future values of CMI were projected from downscaled monthly values of temperature and precipitation simulated using the Canadian Earth System Model version 2 (CanESM2) for two different Representative Concentration Pathways (RCP). RCPs are different greenhouse gas concentration trajectories adopted by the Intergovernmental Panel on Climate Change (IPCC) for its fifth Assessment Report. RCP 2.6 (referred to as rapid emissions reductions) assumes that greenhouse gas concentrations peak between 2010-2020, with emissions declining thereafter. In the RCP 8.5 scenario (referred to as continued emissions increases) greenhouse gas concentrations continue to rise throughout the 21st century. Multiple layers are provided. First, the mean annual Climate Moisture Index is shown across Canada for a reference period (1981-2010). Projected mean annual Climate Moisture Index is available for the short- (2011-2040), medium- (2041-2070), and long-term (2071-2100) under the RCP 8.5 (continued emissions increases) and, for the long-term (2071-2100), under RCP 2.6 (rapid emissions reductions). Reference: Hogg, E.H. 1997. Temporal scaling of moisture and the forest-grassland boundary in western Canada. Agricultural and Forest Meteorology 84,115–122.

Water quality and ecosystem health data used to conduct a cumulative effects assessment of Canadian Great Lakes nearshore waters in support of the Great Lakes Water Quality Agreement are included in this dataset. The data was collected by various government and non-government agencies and organizations and integrated into this dataset to allow the assessment to be conducted. By conducting a regular, systematic assessment of cumulative effects in the nearshore waters of the Great Lakes Environment and Climate Change Canada (ECCC) is able to identify areas of high quality and areas under stress. Knowledge of ecological thresholds, other Great Lakes assessments, stressor information, indicators and local and traditional ecological knowledge will be used to aid in: 1) the identification and mapping of high quality nearshore areas and areas that are or may become subject to high stress and; 2) the determination of factors and cumulative effects that are causing stress or threats. Cumulative effects impacting the nearshore and future threats to areas of high ecological value will be better understood and the knowledge shared will assist in priority setting for science and management at a meaningful and practical spatial scale within each Great Lake and connecting channel.

Fisheries and Oceans Canada’s (DFO) Coastal Environmental Baseline Program supports the collection of ecological information on the current state of key coastal ecosystems across Canada. This initiative aims to acquire environmental baseline data (physical, chemical and biological) contributing to the characterization of important coastal areas and to support evidence-based assessments and management decisions for preserving marine ecosystems. From this page, you will find links to the data from projects undertaken from 2018-2022 at six coastal sites across Canada.

Statistically downscaled multi-model ensembles of projected change (also known as anomalies) in total precipitation are available at a 10km spatial resolution for 1951-2100. Statistically downscaled ensembles are based on output from twenty-four Coupled Model Intercomparison Project Phase 5 (CMIP5) global climate models (GCM). Daily precipitation (mm/day) from GCM outputs were downscaled using the Bias Correction/Constructed Analogues with Quantile mapping version 2 (BCCAQv2). A historical gridded precipitation dataset of Canada (ANUSPLIN) was used as the downscaling target. Projected relative change in total precipitation is with respect to the reference period of 1986-2005 and expressed as a percentage (%). Seasonal and annual averages of projected precipitation change to 1986-2005 are provided. Specifically, the 5th, 25th, 50th, 75th and 95th percentiles of the downscaled ensembles of projected 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 statistically downscaled total 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 downscaled 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.

Canada's National Forest Inventory (NFI) sampling program is designed to support reporting on forests at the national scale. On the other hand, continuous maps of forest attributes are required to support strategic analyses of regional policy and management issues. We have therefore produced maps covering 4.03 × 106 km2 of inventoried forest area for the 2001 base year using standardised observations from the NFI photo plots (PP) as reference data. We used the k nearest neighbours (kNN) method with 26 geospatial data layers including MODIS spectral data and climatic and topographic variables to produce maps of 127 forest attributes at a 250 × 250 m resolution. The stand-level attributes include land cover, structure, and tree species relative abundance. In this article, we report only on total live aboveground tree biomass, with all other attributes covered in the supplementary data (http://nrcresearchpress.com/doi/suppl/10.1139/cjfr-2013-0401). In general, deviations in predicted pixel-level values from those in a PP validation set are greater in mountainous regions and in areas with either low biomass or sparse PP sampling. Predicted pixel-level values are overestimated at small observed values and underestimated at large ones. Accuracy measures are improved through the spatial aggregation of pixels to 1 km2 and beyond. Overall, these new products provide unique baseline information for strategic-level analyses of forests (https://nfi.nfis.org) Related Products (16): - **[Poplars, Aspens and Cottonwoods (Genus Populus) in Canada 2006](https://ouvert.canada.ca/data/en/dataset/08620b3f-0bda-46f2-968d-e47d5a6032de)** - **[Birches (Genus Betula) in Canada 2006](https://ouvert.canada.ca/data/en/dataset/1410d784-ffde-43c8-a816-14cdfa0aa9c4)** - **[Treed land in Canada 2006](https://ouvert.canada.ca/data/en/dataset/1f1806b9-3927-496c-8c91-8789809f4472)** - **[Merchantable forest volume in Canada 2006](https://ouvert.canada.ca/data/en/dataset/2b3569c6-ff95-40a5-a958-dc68e3aa558b)** - **[Needle-leaved species in Canada 2006](https://ouvert.canada.ca/data/en/dataset/39ffee48-f89b-4b65-af03-58a706bac7a1)** - **[Forest height in Canada 2006](https://ouvert.canada.ca/data/en/dataset/3b860e37-32e6-4f47-a423-a7519ffa4429)** - **[Total live above-ground biomass in Canada 2006](https://ouvert.canada.ca/data/en/dataset/53af4b0e-015b-405e-8de8-e7eb4498eda1)** - **[Total forest volume in Canada 2006](https://ouvert.canada.ca/data/en/dataset/5b6b60d5-8299-45d4-8bd2-c274e75bc115)** - **[Spruces (Genus Picea) in Canada 2006](https://ouvert.canada.ca/data/en/dataset/72af5640-bb51-4c7a-8f41-6b71227a598f)** - **[Tree Crown Closure in Canada 2006](https://ouvert.canada.ca/data/en/dataset/a1510fe3-8ef0-4130-9ee4-8a7ea1f9a22d)** - **[Forest Composition across Canada 2006](https://ouvert.canada.ca/data/en/dataset/a42bd5d6-83a7-4fb7-a257-389dcf7ea48d)** - **[True Firs (Genus Abies) in Canada 2006](https://ouvert.canada.ca/data/en/dataset/d845f357-e2b5-494c-821f-064dba664427)** - **[Hemlocks (Genus Tsuga) in Canada 2006](https://ouvert.canada.ca/data/en/dataset/e676e5ab-b709-46ba-b471-4e982dce0c07)** - **[Broad-leaved species in Canada 2006](https://ouvert.canada.ca/data/en/dataset/e7b9b34a-70f8-47c6-9498-94361b9febbf)** - **[Maples (Genus Acer) in Canada 2006](https://ouvert.canada.ca/data/en/dataset/ed296d14-b222-45e7-9dcb-0ca8015207ad)** - **[Cedars (Genus Thuja) in Canada 2006](https://ouvert.canada.ca/data/en/dataset/fc8bb212-9ffe-447f-9152-e26baff7a735)**