To support improved groundwater geoscience knowledge for southern Ontario, a regional 3-D model of the surficial geology of southern Ontario has been developed as a part of a collaboration between the Ontario Geological Survey and the Geological Survey of Canada. Covering approximately 66,870 km2 in area, the model is a synthesis of existing geological models, surficial geology mapping, and subsurface data. The model is a simplified 9-layer reclassification of numerous mapped local surficial sediment formations in places over 200 m thick with a total volume of approximately 2,455 km3. The model integrates 1:50,000 scale surficial geology mapping with 90 m bathymetrically corrected topographic digital elevation model (DEM) and 8 existing local 3-D models. Archival subsurface data include 10,237 geotechnical and stratigraphic boreholes, 3,312 picks from geophysical surveys, 15,902 field mapping sites and sections, 537 monitoring and water supply wells and 282,995 water well records. Roughly corresponding to regional aquifer and aquitard layers, primary model layers are (from oldest to youngest): Bedrock, Basal Aquifer, Lower Sediment, Regional Till, Post Regional Till Channel Fill, Glaciofluvial Sediment, Post Regional Till Mud, Glaciolacustrine Sand and Recent Sediment / Organics. Modelling was completed using an implicit modelling application (LeapFrog®) complemented by an expert knowledge approach to data classification and rules-based Expert System procedure for data interpretation and validation. An iterative cycle of automated data coding, intermediate model construction and manual data corrections, expert evaluations, and revisions lead to the final 3-D model. A semi-quantitative confidence assessment has been made for each model layer surface based on data quality, distribution and density. This surficial geology model completes the development of a series of regional 3-D geological and hydrogeological models for southern Ontario.

This model is derived from geological, geophysical and other forms of geodata. Feature extraction used deep learning. Predictive modelling made use of the deep ensemble method. Displayed is a Pan-Canadian probability map of mineral potential of graphite. This map was generated using known graphite deposits and occurrences and their associated features. Higher probability values highlight areas with an increased probability of graphite mineral systems.

Weather Elements on Grid (WEonG) based on the High Resolution Deterministic Prediction System (HRDPS) is a post-processing system designed to compute the weather elements required by different forecast programs (public, marine, aviation, air quality, etc.). This system amalgamates numerical and post-processed data using various diagnostic approaches. Hourly concepts are produced from different algorithms using outputs from the pan-Canadian High Resolution Deterministic Prediction System (HRDPS-NAT).

This polygon layer reflects short-range (up to 84 hours) accumulated precipitation forecasts from the Regional Deterministic Prediction System (RDPS), a high-resolution (~10 km) weather model developed by Environment and Climate Change Canada (ECCC). It supports flood forecasting, hydrological modeling, and operational planning by providing refined, near-real-time precipitation guidance for Canada and surrounding areas.Short-Range Forecasts: RDPS runs multiple times per day, offering precipitation outlooks for days 0–3.5 with updates every six hours. High Resolution: At ~10 km, RDPS captures critical mesoscale phenomena like localized downpours, lake-effect snow, and terrain-driven precipitation. Hydrological Utility: Especially valuable for sub-basin-level flood risk assessment and water resource management in near-term scenarios. Technical Basis: The RDPS is a limited-area configuration of the GEM model, using initial/boundary conditions from ECCC’s Global Deterministic Prediction System (GDPS).

__The link: *Access the data directory* is available in the section*Dataset Description Sheets; Additional Information*__.In 2020, the Forest Inventory Directorate of the Ministry of Natural Resources and Forests (MRNF) began the production of vector layers of riparian ecotones. This type of layer is produced using a series of geomatic tools that use the canopy height model (MHC) and the topographic humidity index (TWI) produced from aerial LiDAR, as well as data from the ecoforest map of southern Quebec. The development of the tools and their configuration were carried out in collaboration with the forest hydrology laboratory at Laval University.The layer thus produced aims to delimit riparian ecotones, which are defined as [_ “a transition zone between the aquatic environment and the forest, characterized by muscinal, herbaceous or shrubby vegetation in wetlands and sometimes including a few scattered trees” _] (https://mffp.gouv.qc.ca/RADF/guide/chapitre-i/article-1/).The layer produced defines the area where a significant change in tree heights is observed on the MHC LiDAR. However, if deforestation, agricultural fields, or natural disturbances are adjacent to a body of water, no change in tree heights is observed. In these cases, the tool instead uses potential water accumulation data from the Topographic Humidity Index (TWI), as well as data from the ecoforest map, to delineate riparian ecotones. On the other hand, the tool does not make it possible to filter all of these exceptional cases and we thus find a few “false coastal ecotones” in the layer. For example, deforested land along a body of water can sometimes be included in the ecotone layer. Before using these data, it is therefore recommended to plan validation work by photo-interpretation or by means of field visits. It should also be noted that ecotone data is primarily used to support forest mandates such as the delineation of buffer zones along watercourses. Therefore, they are not contours of wetlands within the meaning of the Act respecting the conservation of wetlands and bodies of water.**This third party metadata element was translated using an automated translation tool (Amazon Translate).**

This GIS dataset depicts the drift thickness in the Edmonton-Calgary corridor based on water-well litholog data and bedrock outcrop locations. We used well data from an internal Edmonton-Calgary corridor geological mapping database. This thickness map was generated to assist in building a geological model for the region. The sediment thickness was generated by subtracting the bedrock topography surface from the 60 m Shuttle Radar Topography Mission v. 2 digital elevation model.

Yukon parks and protected areas captured using the National Topographic Base (NTDB) 1:250,000 scale dataset. Boundaries were generalized from survey information captured by Legal Survey Division of Natural Resources Canada (NRCan).Distributed from [GeoYukon](https://yukon.ca/geoyukon) by the [Government of Yukon](https://yukon.ca/maps) . Discover more digital map data and interactive maps from Yukon's digital map data collection.For more information: [geomatics.help@yukon.ca](mailto:geomatics.help@yukon.ca)

Data on recreational areas in Quebec are compiled from official technical plans and descriptions, and entered on topographic maps at a scale of 1/100,000. Data is generally updated once a year.Main components: * Community Wildlife Areas (CFAs);* Specific agreements with aboriginal people;* Small managed lakes (PLA);* Outfitters with exclusive rights;* Exclusive hunting territories;* Wildlife reserves;* Controlled exploitation areas (ZECS);* National parks of Quebec; and Canada;* Regional parks;* Marine parks;* Ecological reserves;* Wildlife refuges;* Migratory Bird Sanctuaries (MBS);* National Wildlife Areas;* Montmorency teaching and research forest;* Duchesnay forest station.**This third party metadata element was translated using an automated translation tool (Amazon Translate).**

Monthly mean currents from Bedford Institute of Oceanography North Atlantic Model (BNAM) results were averaged over 1990 to 2015 period to create monthly mean climatology for the Northwest Atlantic Ocean, which can be considered as a representation of the climatological state of the Northwest Atlantic Ocean. The BNAM model is eddy-resolving, NEMO-based ice-ocean coupled North Atlantic Ocean model developed at the Bedford Institute of Oceanography (BIO) to support DFO monitoring programs. The data available here is monthly climatology for eight selected depths (surface, 110 m, 156 m, 222 m, 318 m, 541 m, 1062 m, bottom) in 1/12 degree spatial resolution. The data for each month from 1990 until present for the entire model domain ( 8°–75°N latitude and 100°W–30°E longitude) and various depths is available upon request.The 1990-2017 model hindcast result is compared with observational data from surface drifter and satellite altimetry. The model demonstrates good skill in simulating surface currents, winter convection events in the Labrador Sea, and the Atlantic Meridional Overturning Circulation as observed at 26.5°N and 41°N. Model results have been used to interpret changes in the Labrador Current and observed warming events on the Scotian Shelf, and are reported through the annual AZMP Canadian Science Advisory Secretariat Process.When using data please cite following:Wang, Z., Lu, Y., Greenan, B., Brickman, D., and DeTracey, B., 2018. BNAM: An eddy resolving North Atlantic Ocean model to support ocean monitoring. Can. Tech. Rep. Hydrogr. Ocean. Sci. 327: vii + 18p

The numerical canopy model (MNC) is a representation of the altitude of the canopy. This 3D representation of the arboreal vegetation corresponds to the 2015 2D canopy.If necessary, the MNC can be coupled with the [Numerical Surface Model (MNS)] (/city-of-montreal/numeric-surface-model) 2015 in order to obtain more detailed coverage.**This third party metadata element was translated using an automated translation tool (Amazon Translate).**