Hydraulic properties characterize a hydrogeological unit. The hydraulic properties considered for this dataset are the transmissivity, the hydraulic conductivity, the storage coefficient, the specific storage coefficient and the porosity. Hydraulic properties are estimated by performing aquifer tests (pumping tests, slug tests). The hydraulic tests and their duration are managed in this dataset. The methods used to create the dataset are described in the metadata associated with the dataset. The dataset exhibits a general description of hydraulic properties of the hydrogeological unit, including hydraulic test, total test duration, method and date. It includes numbers and/or ranges describing the aquifer tests results. Note that an alternate raster representation could be used in complement to the discrete point-based representation.

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.

This compilation sources data primarily from the Yukon Geological Survey’s (YGS) field projects and archival collections, and from the Geological Survey of Canada’s (GSC) Canadian rock physical property database (Enkin, 2014, 2018). The data distribution is far from uniform but covers a range of lithology classes and terranes. The compilation of data from archival samples is ongoing, and the dataset will continue to grow as new samples are collected for YGS projects and pass through the survey’s physical properties lab.The foundation of this dataset originates from samples collected for a mapping project in western Yukon led by Dirk Tempelman-Kluit from 1970 to 1972 (Tempelman-Kluit and Curie, 1978) and subsequently digitized by the Geological Survey of Canada and Mira Geoscience (2014). This dataset alone contributed 1642 records to the compilation. Map unit names from this original dataset were modernized for this compilation to reflect current understanding of Yukon geology.Systematic measurements of magnetic susceptibility and specific gravity have been incorporated into the workflow when archiving rock samples from YGS projects. Additional magnetic susceptibility measurements are also routinely collected in the field by YGS geologists. This will continue to expand the dataset. Rock samples are not collected specifically for the physical properties dataset but are well characterized and have sufficient metadata to be incorporated into the dataset. Samples are not uniform in size or weight.The Yukon Geological Survey’s HS Bostock Core Library houses >10 000 Yukon rock samples from previous and ongoing geological studies. Archived collections provide a great opportunity to further expand the dataset in specific regions or areas lacking representation.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)

Description:Seasonal climatologies for salinity of the Northeast Pacific Ocean were computed to cover the period 2001 to 2020. Historibal observations included all available conductivity-temperature_depth (CTD), bottle and profiling floats in the NODC World Ocean Database, Marine Environmental Data Services (MEDS), Institute of Ocean Sciences Water Properties website and the Canadian Integrated Ocean Observing System (CIOOS Pacific).Methods:Interpolation was carried out in up to fifty-two vertical levels from surface to 5000m. Data-Interpolating Variational Analysis (DIVA) was used for spatial interpolation for all years within each season and estimates projected onto a consistent grid. The average of the grid nodes was calculated to obtain the seasonal climatology. DIVA was used again on the final climatology followed by a median filter and a 5-point smoother. Spring months were defined as April to June, summer months were defined as July to September, fall months were defined as October to December, and winter months were defined as January to March. The data available here contain raster layers of seasonal salinity climatology for the Canadian Pacific Exclusive Economic Zone (EEZ), a subset of seasonal climatology of the Northeast Pacific Ocean, with high spatial resolution of 1/300 degree.Data Sources:NODC, MEDS, IOS and CIOOS Pacific Data.Uncertainties:Uncertainties are introduced when quality controlled observational data are spatially interpolated to varying distances from the observation point. Climatological averages are calculated from these interpolated values.

Water composition is defined by measuring the amounts of its various constituents; these are often expressed as milligrams of substance per litre of water (mg/L). Sampling methods vary according to the types of analysis. Dataset point: The dataset represents a general description of the sample, including name, ID, type of analysis and lab. It includes numbers describing the results of the analysis and physical properties of groundwater. Time series: The dataset represents a general description of the sample, including name, ID, type of analysis and lab. It includes series of numbers describing the results of the analysis and physical properties of groundwater with associated date. Dynamic values over time at the same sites provides temporal variation data of groundwater composition.

To show the locations of campsites on Parks Canada sites, campsites administered by Parks Canada, or other campsites of interest to Parks Canada. Data is not necessarily complete - updates will occur weekly.

Background:More than 80% of the heat produced in the Earth's crust comes from granitoid rocks. When granitoid rocks form they naturally concentrate radioactive elements such as U, Th, and K, and the radiogenic decay of these elements is an exothermic reaction. The radioactive decay of these elements within a granitoid body may generate local heat anomalies and elevated geothermal gradient at relatively shallow crustal levels. In combination with other local rock properties (e.g, porosity, permeability, thermal conductivity), radiogenic heat has the potential to generate a geothermal resource. The decay of radioactive elements converts mass into radiation energy, which in turn gets converted to heat. While all naturally radioactive isotopes generate some heat, significant heat generation only occurs from the decay of 238 U ,235 U ,232 Th and 40 K. Therefore, potential heat production is governed by the concentrations of U ,Th and K in the rock. In igneous rocks, radiogenic heat production is dependent on the bulk chemistry of the rock and decreases from acidic (e.g. granite) through basic to ultra basic rock types. Therefore, granites with anomalously high concentrations of U ,Th and K are targets for calculating potential radiogenic heat production. Potential radiogenic heat production (A)from plutonic rocks can be calculated using this equation:A (\\u03BCW/m 3 )=10 -5 \\u1D29 (9.52c u +2.56c K +3.48c Th )where "c" is the concentration of radioactive elements "U" and "Th" in ppm, and "K" in %; and "\\u1D29" is the rock density. Heat production constants of the natural radio-elements U, Th, K are 9.525x10 -5 , 2.561x10 -5 and 3.477x10 -9 W/kg, respectively.Data and Methods:Geochemical data from \~1760 samples of plutonic rocks from Yukon are used to calculate potential heat production. The calculated values for radiogenic heat production (A) are plotted over the mapped distribution of Paleozoic and younger plutonic rocks and major crustal faults are also shown for reference.

This publication contains thirteen (13) maps of different biogeochemical and soil properties of forest ecosystems of Canada’s managed forest. A scientific article gives additional details on the methodology: Paré, D., Manka, F., Barrette, J., Augustin, F., Beguin, J. 2021. Indicators of site sensitivity to the removal of forest harvest residues at the sub-continental scale: mapping, comparisons, and challenges. Ecol. Indicators. https://dx.doi.org/10.1016/j.ecolind.2021.107516

During the period of 1987 to 1994, Robin J. LeBrasseur and N. Brent Hargreaves lead a juvenile salmon predation research project in Alberni Inlet and Barkley Sound BC. This dataset contains the research survey catch data and individual fish examinations data.

Linear layer illustrating the lot divisions present in the graphic matrix in the case of properties containing several lots.**Collection context** Maintenance process in collaboration with the evaluation department.**Collection method** Computer-aided mapping.**Attributes*** `ID_MAT_DIV` (`integer`): Identifier* `DATE_CREATION` (`smalldatetime`): Created on* `DATE_MODIFICATION` (`smalldatetime`): Modified on* `USER_MODIFICATION` (`varchar`): Modified by* `MAT10` (`varchar`): Number* `ID_MAT_DIV_OLD` (`varchar`): Old idFor more information, consult the metadata on the Isogeo catalog (OpenCatalog link).**This third party metadata element was translated using an automated translation tool (Amazon Translate).**