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.

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.

Leaf area index (LAI) quantifies 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 time series of LAI observed between 2016 and 2025 over reclamation sites in Alberta, Canada May and October. The temporal frequency depends on cloud cover.

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

The mapping depicts the relative abundance of segregated ice in upper permafrost at a national scale. The mapping is based on modelling by O'Neill et al. (2019) (https://doi.org/10.5194/tc-13-753-2019). The mapping offers an improved depiction of ground ice in Canada at a broad scale, incorporating current knowledge on the associations between geological and environmental conditions and ground ice type and abundance. It provides a foundation for hypothesis testing related to broad-scale controls on ground ice formation, preservation, and melt.

The mapping depicts the relative abundance of wedge ice in upper permafrost at a national scale. The mapping is based on modelling by O'Neill et al. (2019) (https://doi.org/10.5194/tc-13-753-2019). The mapping offers an improved depiction of ground ice in Canada at a broad scale, incorporating current knowledge on the associations between geological and environmental conditions and ground ice type and abundance. It provides a foundation for hypothesis testing related to broad-scale controls on ground ice formation, preservation, and melt.

This dataset is a locational record of the meteorological input files publically available on Saskatchewan GeoHub that can be used with the Environmental Protection Agency approved Regulatory Model (AERMOD). Each file represents the meteorology over an area of the province while minimizing the influences of local terrain on air flow. Additional attribute information for each location includes coordinates and a link to download the AERMOD data as a zip file.The Air Quality Section of the Ministry of Environment uses air quality modelling to simulate how air pollutants disperse in the ambient atmosphere in order to help manage the air quality in the province. The models are used to estimate the impact of air pollutants emitted from emission sources, and are typically employed to determine whether existing or new proposed industrial facilities are or will be in compliance with the ambient air quality standards outlined in Table 20 of the province's Environmental Code, June 1, 2015 under The Environmental Management and Protection Act, 2010.  The information needed to run dispersion models consists primarily of emissions and meteorological data. Five years (2012-2016) of preprocessed meteorological datasets in an AERMOD ready format is publicly available. This file is contained in the downloadable zipped file. The zipped file contains five files: the SFC and PFL files are the AERMOD ready files required to run AERMOD (i.e., data, sensible heat flux, frictional velocity, potential temperature gradient, vertical velocity, mixing height, monin-obukhov length, surface roughness, Bowen ratio, albedo, scalar wind speed, wind direction, ambient temperature, precipitation, precipitation rate, relative humidity, surface pressure, and total cloud amounts); the DAT file contains the land use information (i.e., Surface roughness, Bowen ratio and albedo) chosen for each month in the SFC file; the KMZ file contains the wind rose for that location which can be used on Google Earth; and the PNG file contains various graphs of monthly or diurnal meteorological distribution (i.e., temperature, wind speed, daytime mixing heights and sensible heat flux, and stability) which can be used to help determine if that location is representative of the area proposed for modelling.  Please note: Since this data is newly developed, it is possible there may be issues with the data as it gets used in more applications. Ongoing changes, edits and updates may be made by the Air Quality Section of the Ministry of Environment. Is is recommended for any future modelling to download the latest version of the input files and not archive any input files on your own server for future use, unless this notification no longer exists. If there are any issues discovered with data in the zipped file, please contact Dennis Fudge at dennis.fudge@gov.sk.ca or at 306-519-7105. Your support will be greatly appreciated.  There may be times you feel that the input files are not representative of the proposed modelling domain due to the surrounding features (i.e., forest/agricultural or rural/urban) being different than those used to generate the input files. If that is the case, the modeler can generate the input modelling files themselves. The relevant files to generate these input files are available upon request. Please contact Dennis Fudge at dennis.fudge@gov.sk.ca or at 306-519-7105. 

Dominant Species Map 2015The data represent dominant tree species for British Columbia forests in 2015, are based upon Landsat data and modeling, with results mapped at 30 m spatial resolution. The map was generated with the Random Forests classifier that used predictor variables derived from Landsat time series including surface reflectance, land cover, forest disturbance, and forest structure, and ancillary variables describing the topography and position. Training and validation samples were derived from the Vegetation Resources Inventory (VRI), from a pool of polygons with homogeneous internal conditions and with low discrepancies with the remotely sensed predictions. Local models were applied over 100x100 km tiles that considered training samples from the 5x5 neighbouring tiles to avoid edge effects. An overall accuracy of 72% was found for the species which occupy 80% of the forested areas. Satellite data and modeling have demonstrated the capacity for up-to-date, wall-to-wall, forest attribute maps at sub-stand level for British Columbia, Canada.BC Species Likelihood 2015The tree species class membership likelihood distribution data included in this product focused on the province of British Columbia, based upon Landsat data and modeling, with results mapped at 30 m spatial resolution. The data represent tree species class membership likelihood in 2015. The map was generated with the Random Forests classifier that used predictor variables derived from Landsat time series including surface reflectance, land cover, forest disturbance, and forest structure, and ancillary variables describing the topography and position. Training and validation samples were derived from the Vegetation Resources Inventory (VRI) selecting from a stratified pool of polygons with homogeneous internal conditions and with low discrepancies when related to remotely sensed information. Local models were applied over 100x100 km tiles that, to avoid edge effects, considered training samples from the 5x5 neighbouring tiles. An overall accuracy of 72% was found for the species which occupy 80% of the forested areas. As an element of the mapping process, we also obtain the votes received for each class by the Random Forest models. The votes can be understood as analogous to class membership likelihoods, providing enriched information on land cover class uncertainty for use in modeling. Tree species class membership likelihoods lower than 5% have been masked and converted to zero.When using this data, please cite as: Shang, C., Coops, N.C., Wulder, M.A., White, J.C., Hermosilla, T., 2020. Update and spatial extension of strategic forest inventories using time series remote sensing and modeling. International Journal of Applied Earth Observation and Geoinformation 84, 101956. DOI: 10.1016/j.jag.2019.101956 ( Shang et al. 2020).