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.

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.

A tidal resource assessment dataset for the Quatsino Sound region, British Columbia, was developed, including temporal maximum, mean, and minimum velocity magnitudes, standard deviations, and power density. The dataset was generated using a high-resolution 2D depth-averaged hydrodynamic model based on the Telemac-Mascaret solver, with Natural Neighbor interpolation applied for raster creation. This newly published dataset is the first in a series of regional tidal energy maps for Canada. Developed by CanmetENERGY Ottawa in collaboration with partners, these maps aim to support effective project planning and development by providing comprehensive tidal resource data across the country.Disclaimer:Potential errors in the model results may arise from inherent limitations in the topo-bathymetric data accuracy, assumptions in boundary conditions, approximations within the numerical methods, and the input data used in the numerical modeling. These factors introduce uncertainties that can affect the overall model outcomes. The model is subject to the following conditions:• Topo-bathymetric data: Obtained from electronic navigational charts and the Canadian Hydrographic Service’s (CHS) NONNA-10 Bathymetric Data packages, consolidating CHS-managed digital bathymetric sources with a maximum resolution of 10 m.• Tidal and current harmonic components: Used as boundary conditions from the TPXO9 global tidal model.• Model calibration and validation: Performed using data from Acoustic Doppler Current Profilers (ADCP), surface elevations recorded at CHS tidal stations, and Lagrangian drifter measurements.• Interpolation method: Dataset outputs were generated with Natural Neighbor interpolation, which assumes smoothly varying data and may not capture sharp local gradients or features.• Modeled estimates: All values for velocity magnitudes, velocity standard deviations, and power density are modeled estimates and not direct field measurements.This dataset is intended for preliminary assessment of tidal projects only. It should not be the sole basis for making critical decisions or investments. We strongly recommend further validation and in-depth analysis. Users are responsible for conducting their own due diligence and additional research to verify the data's accuracy and relevance for specific applications.By accessing and using this dataset, users acknowledge and accept these disclaimers. The providers of this dataset explicitly absolve themselves of any responsibility or liability for any consequences arising from the use, reliance upon, or interpretation of this dataset. Users are advised that their use of the dataset is at their own risk, and they assume full responsibility for any actions or decisions made based on the information contained therein. This disclaimer is in accordance with applicable laws and regulations, and by accessing or utilizing the dataset, users agree to release the providers of this dataset from any legal claims, damages, or liabilities that may arise from such use.

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.

11 tagged Blue whales (Balaenoptera musculus) were tracked during the daytime movements as well as the feeding behaviour in the St. Lawrence River estuary. Kernel density was applied to derminate the high density feeding areas of all individuals combined (30, 40, 50, 60, 75, 95 %).Doniol-Valcroze T, Lesage V, Giard J, Michaud R, 2012. Challenges in marine mammal habitat modelling: evidence of multiple foraging habitats from the identification of feeding events in blue whales. Endang Species Res, Vol. 17 : 255–268, doi : 10.3354/esr00427(English version only)

__The link: *Access the data directory* is available in the section*Dataset Description Sheets; Additional Information*__.**Slope classes** express the slope of the terrain in a relatively homogeneous area with a minimum area of 0.5 ha. They are generated from a numerical model that groups terrain slopes into six classes:+ 0-3% zero + Low by 4-8% + 9-15% soft + Moderate by 16-30% + 31-40% strong + Steep by 41% or moreThese data cover almost all of Quebec south of the 52nd parallel. This map is presented in two groups: grouping at the provincial level and grouping at the scale of 1/250,000. Each grouping represents an assembly of sheets produced on a scale of 1/20,000.The datasets were originally produced in 2005 and then updated in 2006. In 2010, they were enhanced with slope information for the northernmost part. No updates have been made since then.__For more information, please refer to the document: [Products presenting the concept of slope] (https://diffusion.mffp.gouv.qc.ca/Diffusion/DonneeGratuite/Foret/DONNEES_FOR_ECO_SUD/Classes_pente/01-Documentation/Produits_presentant_la_notion_de_pente.pdf)____ ⚠️ Notes: __ The numerical terrain model is prepared from the level curves and altitude points that come from the topographic map at a scale of 1/20,000 and 1/50,000 from the Quebec Topographic Database (BDTQ). This geographic database was produced in 2010 and has never been updated.__ 🕰️ Former names:__ Numerical slope classes, Slope class**This third party metadata element was translated using an automated translation tool (Amazon Translate).**

This dataset contains the outputs for Bute Inlet from two simulations shown in the publication "Fjord circulation permits persistent subsurface water mass in a long, deep mid-latitude inlet" by Laura Bianucci et al., DFO Ocean Sciences Division, Pacific Region (published in the journal Ocean Science in 2024). The Finite Volume Community Ocean Model (FVCOM v4.1) was run with two different sets of initial conditions for the Discovery Islands region of British Columbia, Canada, from May 24 to June 27, 2019. The "Baseline" simulation used observed initial conditions, while the "Sensitivity" simulation removed the observed cold subsurface water mass from the initial profiles. Here in this dataset, we provide 29-day averages of the following variables in a transect along Bute Inlet: potential temperature, density, along-inlet velocity, and Brunt-Väisälä frequency (N^2). The averaging properly removes the tidal effects.

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).

The Global Deterministic Prediction System (GDPS) is a coupled atmosphere (GEM), ocean and sea ice (NEMO-CICE) deterministic numerical weather prediction model. Forecasts are carried out twice a day for 10 days lead time. The geographical coverage is global at 15 km horizontal resolution. Data is available on some thirty vertical levels and interpolated on a global latitude-longitude uniform grid with 0.15 degree horizontal resolution. Variables availability in number and time frequency is a function of forecast lead time.

A hydrogeological unit is defined as any soil or rock unit or zone that by virtue of its hydraulic properties has a distinct influence on the storage or movement of groundwater. It is considered the main dataset from the GGP point of view. Hydrogeological units are ranked into five levels (from largest to smallest): 1) hydrogeological region, 2) hydrogeological context, 3) aquifer system, 4) hydrostratigraphic unit, and 5) aquifer. Here are formal definitions for these different types of hydrogeologic units. - Hydrogeological region Hydrogeological regions are areas in which the properties of sub-surface water, or groundwater, are broadly similar in geology, climate and topography. There are 9 such regions identified in Canada (ref?). - Hydrogeological context Hydrogeological contexts are units of reporting, conceptually narrower than regions, and are additionally delineated by physiographic and hydrogeological aspects. - Aquifer system ""A heterogeneous body of intercalated permeable and poorly permeable material that functions regionally as a water-yielding hydraulic unit; it comprises two or more permeable beds [aquifers] separated at least locally by aquitards [confining units] that impede groundwater movement but do not greatly affect the regional hydraulic continuity of the system"" (Poland et al., 1972). - Hydrostratigraphic unit (HSU) ""Body of sediment and/or rock characterized by ground water flow that can be demonstrated to be distinct under both unstressed (natural) and stressed (pumping) conditions, and is distinguishable from flow in other HSUs"" (Noyes et al.) - Aquifer ""A formation, group of formations, or part of a formation that contains sufficient saturated permeable material to yield significant quantities of water to wells and springs"" (Lohman et al, 1972, p. 21). The rank attribute is used to specify the scope of the described unit. The general principle behind this specification is to allow the same data structure to apply to various types of hydrogeological units, from the local aquifer to the almost continental hydrogeological region. The dataset includes properties such as identification, physiography, geology, aquifer description and properties, water balance, groundwater use and risk. It features numerical values or a general description when no values are available. The description can also be used to add context to the numerical values. For each property, metadata identifying the source of the original data, links to similar data in GIN, and description of the processes, algorithms or methodology used to obtain these datasets will be available to complement the data. This dataset is designed to capture and represent a set of synthesized information pertaining to hydrogeological units through maps and succinct table reports. Some attributes (or properties) of the dataset are irrelevant depending of the rank of the unit. In general, this dataset is organised to include multiple properties associated with aquifers and larger hydrogeologic units. These properties are grouped into categories, which include identification, physiography, geology, aquifer description, water balance, groundwater use and risk. The numerical values associated with each of the properties can be used to create thematic maps; hence, the importance of using standardized units of measurement and definitions for these properties. When numerical values are not available, a general description may be supplied instead. The description can also be used to add context to the numerical values. Because this dataset is the cornerstone of the national view on groundwater, supplemental contextual information (metadata) must be part of the data. Thus, for each property, metadata identifying the source of the original data, links to similar data in GIN, and a description of the processes, algorithms or methodology used to obtain these datasets will be available to complement the data.