In permafrost dominated regions, a gap persists in our understanding of water resources, the influence of groundwater, and the impact of climate change at the regional scale. Regional scale modelling can help to advance the understanding of these impacts by integrating with regional climate models. For regional modelling to be tenable, ongoing development of modelling methods and conceptualizations is required. By developing a fully integrated numerical groundwater-surface water climate model using HydroGeoSphere (HGS) (Aquanty 2021) for a gauged basin within the discontinuous permafrost zone, this dataset allows the verification of existing numerical methods and the testing of various conceptualizations of integrated groundwater-surface water flow in permafrost regions at the regional scale. This work informs future modelling and forecasting of regional water resources in permafrost regimes.

Level curves with an equidistance of 1 m derived from a lidar survey conducted in 2024.attributes:ID - Unique IDSubtype - Master (1) or secondary (2) level curve SCORE - Elevation value (m) The High Resolution Digital Elevation Model (m) product The High Resolution Digital Elevation Model (HRDM) product is available on the Open Government website.**This third party metadata element was translated using an automated translation tool (Amazon Translate).**

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).**

A vector representation of stream networks is a crucial dataset for the modelling the surface water and groundwater components of the hydrologic cycle. For many usages a crucial attribute of the drainage network is a digital topology and hierarchal stream order attribute (e.g., Strahler stream order). In Canada jurisdictional stream networks are available for the provinces and territories and nationally for Canada in the National Hydrological Network (NHN) dataset. Unfortunately, the NHN data lacks the same topological and attribute information that is available for numerous provinces due to standardization for the entire country. For Canada1Water it was also necessary to have a harmonized dataset with the United States, for both the southern transboundary watersheds and the Alaskan watersheds. This report documents the processes completed to upgrade the topological and graph network support for NHN and provide continuous connectivity with US datasets. It also highlights and corrects a number of stream density and stream order issues that occur within Canada across provincial and territorial borders and NTS tiles. All vector processing was completed in RivEX software extension for ArcMap. Following complete topological correction stream classification was assigned and a table of the node graph network developed. Additional work was then completed to normalize stream density particularly amongst low-order streams between British Columbia and the Yukon and amongst local NTS tiles in Quebec and Ontario. Corrected NHN Strahler stream order assignment was validated against a number of provincial and watershed datasets, all of which already have Strahler stream order attributed. These datasets are the same underlying digitized vector data, so there are no differences in node or polyline positions. Strahler stream order assignment validation was only done by visual comparison as due to differences in vector segments a statistical comparison is complicated. The transboundary integrated C1W stream network with complete classification provides a seamless national dataset to support transdisciplinary studies (fisheries, wildlife, health, pesticide and nutrient issues, mining impact, ecosystem restoration, numeric modelling) that involve a knowledge of stream distribution and ranking.

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.

This deep water substrate bottom type model was created to aid in habitat modeling, and to complement the nearshore bottom patches. It was created from a combination of bathymetrically-derived layers in addition to bottom type observations. Using random forest classification, the relationship between observed substrates and bathymetric derivatives was estimated across the entire area of interest. The raster is categorized into: 1) Rock, 2) Mixed, 3) Sand, 4) Mud

Leaf area index (LAI) quantified the density of vegetation irrespective of land cover. LAI quantifies the total foliage surface area per groud surface area. LAI has been identified by the Global Climate Observing System as an essential climate variable required for ecosystem,weather and climate modelling and monitoring. This product consists of annual maps of the maximum LAI during a grownig season (June-July-August) at 100m resolution covering Canada's land mass.

Statistics Canada, in collaboration with the Public Health Agency of Canada and Natural Resources Canada, is presenting selected Census data to help inform Canadians on the public health risk of the COVID-19 pandemic and to be used for modelling analysis.The data provided here show the population counts and percentage distribution for various geographic levels by broad age groups, males, females and both sexes, from the 2016 Census.

This polygon layer displays 84-hour accumulated precipitation forecasts from the Regional Deterministic Prediction System (RDPS), aggregated at the sub-basin level. This layer helps hydrologists, water resource managers, and emergency responders identify watersheds with potentially higher rainfall or snowfall, facilitating short-term flood risk analysis and operational planning.Model & Domain: The RDPS is Environment and Climate Change Canada’s regional numerical weather prediction model, running at ~10 km resolution to capture mesoscale weather patterns over Canada and adjacent regions. Forecast Integration: It produces short-range forecasts (up to 84 hours), updated 4 times daily with boundary conditions from the global GEM model (GDPS). Sub-Basin Aggregation: This layer averages forecasted precipitation across each sub-basin polygon, providing a convenient snapshot of expected accumulations for hydrological modeling and water management. Key Applications:Flood Forecasting – Identifying basins at risk of heavy runoff. Resource Allocation – Positioning crews and equipment in vulnerable watersheds. Planning – Adapting reservoir release schedules, urban drainage controls, and agricultural activities