In the hydrogeological unit, quantity of water that replenishes groundwater beneath the water table, expressed in mm/yr. Recharge is usually calculated using hydrology balance, integrating information from precipitation, hydrology data, drainage, soil properties, evapotranspiration, etc. The result is a raster dataset in which each cell has a given value for the recharge of the aquifer. It can be calculate using HELP software, developed by the US EPA. The methods used to create the dataset are described in the metadata associated with the dataset. The dataset represent a raster in which each cell has a mean value describing the global annual recharge of the hydrogeological unit.

The Zero Emission Vehicle Infrastructure Program (ZEVIP) aims at addressing the lack of charging infrastructure in Canada, one of the key barriers to zero emission vehicle adoption by increasing the availability of localized charging where Canadians live, work, travel and play. This Planning Map for Public EV Charging Infrastructure identifies priority areas and accounts for available charging infrastructure and expected charging needs with a focus on public corridor charging. To optimize web performance when using the map, it is recommended you zoom into the areas you are exploring. Priority areas are identified on a scale ranging from lowest to highest priority. Public Corridor Charging includes the EV charging needs of those travelling longer distances on highways and major roads. The objective is to ensure that EV drivers can travel over the majority of Canada’s road network connecting most communities in an EV without being limited on vehicle range. The map identifies priority locations within 1.6 kilometres of major roads based on criteria such as, traffic, expected EV adoption and distance between chargers

Charging station data set For more information consult the maphttps://lecircuitelectrique.com/fr/trouver-une-borne/**This third party metadata element was translated using an automated translation tool (Amazon Translate).**

This GIS dataset depicts an estimate of average annual groundwater recharge in the Edmonton-Calgary corridor based on hydrological water budget analysis using climate and hydrometric data from Environment Canada, the Water Survey of Canada, and Alberta Environment and Sustainable Resource Development. This average annual groundwater recharge grid was generated to assist in building steady-state numerical groundwater flow models for large regions within the Edmonton-Calgary Corridor.

Location of public charging stations for electric vehicles. This information can also be viewed on the [interactive map of the Electric Circuit] (https://lecircuitelectrique.com/fr/trouver-une-borne/).**This third party metadata element was translated using an automated translation tool (Amazon Translate).**

Zero Emission Vehicle Infrastructure Program (ZEVIP) and Electric Vehicle and Alternative Fuel Infrastructure Deployment Initiative (EVAFIDI) qualitative, quantitative, and geographic data set derived from the program database. This data defines the project number, the number of chargers, the name of the promoter, the type of connector, the address, the city, the province, the postal code, the geographical coordinates, the status, the opening date, and the type of contribution agreement for each project funded by the program.The Canada Infrastructure Bank’s (CIB) Charging and Hydrogen Refuelling Infrastructure Initiative (CHRI) aims to reduce transportation sector greenhouse gas emissions by accelerating the private sector’s rollout of large-scale ZEV chargers and hydrogen refuelling stations, helping to spur the market for private investment.Through this initiative, the CIB has dedicated a minimum of $500 million to support the federal government’s goals as part of Canada’s 2030 Emissions Reduction Plan. Technology funding current to April 1 2025.

The Grazing Rental Zones is comprised of two polygons which determine which zone a grazing disposition (GRL, FGL, GRP) is in. These zones are used to apply the rental rate that grazing leases (GRL), grazing licenses (FGL) and grazing permits (GRP) pay to the government of Alberta for use of public lands. The Public Lands Modernization (Grazing Lease and Obsolete Provisions) Amendment Act came into force January 1, 2020. Under the new rental rate framework (Ministerial Order 01/2020), there are now two grazing rental rate zones based on the transition of the boreal region of the province. The North Saskatchewan River is the dividing line between the south (Zone 1) and north (Zone 2).

The data represents the density of wetland habitat in the agricultural region of Alberta in 2002. Wetlands are depressional areas that are wet for a long enough period that the plant and animals living in them are adapted to, and often dependent on, living in wet conditions for at least part of their life cycle. In drier areas of the province, wetlands tend to be more intermittent, while in wetter areas, wetlands tend to be more persistent. Topography also affects the occurrence of wetlands. Hummocky landscapes allow for pooling of water in depressions, while landscapes with longer slopes (e.g. the foothills) generally have better defined surface drainage patterns. A wetland in influenced by the interaction between the wet area, the wetland margin and upland area.Wetlands provide important habitat for waterfowl and many other types of wildlife. Wetlands reduce the impact of flooding, provide erosion control, purify water by removing sediment and nutrients, and contribute to groundwater recharge. This resource was created using ArcGIS.

Groundwater flow is the movement of water in an aquifer or hydrogeological unit. The dataset shows groundwater flow rate and direction in the hydrogeological unit. Groundwater flow is establish from piezometric surface map. The method used to create the dataset is described in the metadata associated with the dataset. The dataset represents a description of the flow, including rate in m/d, direction, date and source. Typically, the data provided will not be in the form of a shapefile with linked properties but in the form of an image that sketches the groundwater flow. The image could also represent a cross section of the hydrogeologic units showing the regional trends of the groundwater flow.

Post-disturbance forest recovery data for Canada's forested ecosystems, representing a total area of ~650 million ha, captures the return of forests following wildfire and harvest that occurred between 1986 and 2012. It is developed within the framework of Canada’s National Terrestrial Ecosystem Monitoring System (NTEMS). These spatially-explicit outputs represent the rate of spectral recovery: the rate at which a pixel returns to 80% of its pre-disturbance value (White et al. 2017) within the observation period (1985-2017) using the Y2R or Years-to-Recovery metric derived from Landsat times series data. Baseline rates of spectral recovery (Y2R) were defined for each of Canada's 12 forested ecozones. These baselines were then used to identify spatial clusters of recovering pixels on the landscape where Y2R were either significantly faster or slower than their ecozonal baseline. Finally, areas that were disturbed by wildfire and harvest (1986-2012), but which had not recovered by the end of the observation period (2017) are also provided. Note that these areas are still recovering, but they had not yet recovered according to our metric of spectral recovery, by the end of the time series in 2017. For an overview of the methods, the validation of the Y2R metric, and interpretation of the derived trends, see White et al. (2022) and White et al. (2017).White, J.C., Hermosilla, T., Wulder, M.A., Coops, N.C., 2022. Mapping, validating, and interpreting spatio-temporal trends in post-disturbance forest recovery. Remote Sensing of Environment, 271, 112904. https://doi.org/10.1016/j.rse.2022.112904 ( White et al. 2022)White, J.C., Wulder, M.A., Hermosilla, T., Coops, N.C., Hobart, G.W. 2017. A nationwide annual characterization of 25 years of forest disturbance and recovery for Canada using Landsat time series. Remote Sensing of Environment, 194, pp. 303-321. DOI: https://doi.org/10.1016/j.rse.2017.03.035 .( White et al. 2017)