Alberta Geological Survey created a coal and coalbed methane (CBM) database to capture and manage CBM data. The database compiles different sources and contains information on 7923 wells (15,200 formation picks, 37,357 coal picks, 495 coal analyses and 363 vitrinite reflectance measurements). From this parent dataset, individual coal zone was evaluated. This record describes the Mannville Coal Zone's calculated gas content.

Alberta Geological Survey created a coal and coalbed methane (CBM) database to capture and manage CBM data. The database compiles different sources and contains information on 7923 wells (15,200 formation picks, 37,357 coal picks, 495 coal analyses and 363 vitrinite reflectance measurements). From this parent dataset, individual coal zones were evaluated. This record describes the Mannville Coal Zone's thickness isopach.

The location of coastal British Columbia marine kayaking point features, such as destinations and overnight campsites. The Coastal BC datasets are circa 2004 and legacy in nature. Caution should be exercised when using this data, as it may not be accurate or complete. There are currently no plans to update.

Alberta Geological Survey created a coal and coalbed methane (CBM) database to capture and manage CBM data. The database compiles different sources and contains information on 7923 wells (15,200 formation picks, 37,357 coal picks, 495 coal analyses and 363 vitrinite reflectance measurements). From this parent dataset, individual coal zone was evaluated. This record describes the Carbon Thompson coal zone depth to top.

The datasets contain water surface evaporation (PET, in mm of H2O) over Canada's landmass at a spatial resolution of 10-km and temporal intervals of a month and a year over a 24-year period of 2000-2023. The PET was produced by the Land Surface Model EALCO (Ecological Assimilation of Land and Climate Observations) developed at Natural Resources Canada. The PET algorithm in EALCO integrates the dynamic surface evolutions of liquid water, ice, and snow-on-ice for a waterbody into the Penman Equation. The PET was simulated at a daily time step. The monthly (or annual) PET in the datasets is the sum of the daily PET values in a month (or a year). Dew and frost formations simulated by EALCO are included in the PET as negative values, so the PET represents the net water flux between water surface and the atmosphere. Details of the dataset and the EALCO PET modelling algorithms can be found in Li, Wang, and Li (2020, Spatial variations and long‑term trends of potential evaporation in Canada. Scientific Reports, 10: 22089, doi.org/10.1038/s41598-020-78994-9).

Polygon features showing various zones of protection around drinking water system intakes/wellheads. A drinking water system is any domestic system servicing anything other than a single-family dwelling, as defined in the Drinking Water Protection Act. Zone of protection is a broad term that represents a wide variety of areas as they have been delineated in their corresponding report. The methods and processes used to delineate these zones vary based on several factors including, but not limited to, population, vulnerability, hydrologic/hydrogeologic parameters, etc. For the use of this dataset, the term Protection Zone means that protection measures should be considered and/or implemented in these areas due to the importance of these areas supplying drinking water to their water system users. Protection Zone does not mean that protection measures have been implemented in these areas, nor that protection measures are not required beyond the boundary of these areas. This is one of three Drinking Water Sources datasets (along with the DWS - Intakes/Wellheads points dataset, and DWS - Source Areas polygons dataset). Note: these polygons have unique Protection Zone IDs used to relate them to the Intakes/Wellhead points.

Stream routes. Each stream channel is represented by a single line. Derived from the Stream Centreline Network Spatial layer and based on the 1:50,000 scale Canadian National Topographic Series of Maps.

Road centre-lines associated with oil and gas road activity and falling within the area representing the road right of way. This dataset contains line features for proposed applications collected through the BC Energy Regulator's Application Management System (AMS). This dataset is updated nightly.

This GIS dataset portrays the distribution of glacial landforms in Alberta, based on the compilation of existing government survey mapping and research literature, supplemented by new analysis of remote sensing data. It is the dataset that was used to create Alberta Geological Survey Map 604 (Glacial Landforms of Alberta) using an intermediate step of reducing the density of closely spaced line segments to make the features suitable for map production at 1:1 000 000 scale. This dataset contains the original line features without any cartographic generalization. The data were created in geodatabase format and output for public distribution in shapefile format.

The data represents the annual total precipitation in Alberta over the 30-year period from 1971 to 2000. A 30-year period is used to describe the present climate since it is enough time to filter our short-term fluctuations but is not dominated by any long-term trend in the climate. Annual total precipitation refers to rain, snow and other forms of moisture such as hail. Annual precipitation is greatest in the mountains and decreases at lower elevations. In the agricultural areas of the province, 50 to60 percent of annual precipitation generally occurs during the growing season, mostly as rain.Precipitation in any month can be extremely variable with the variability of precipitation being greater in southern Alberta than in the Peace River Region and central Alberta. However, long-term (30-year) data provides a reliable indication of what to expect in any given location. Climate information is used as a long-term planning tool, in selecting a location for a farm or planning a cropping program. Crop producers generally look at the most likely weather conditions rather than the extremes because the key inputs and decisions are made well in advance of achieving results. By combining knowledge of the agricultural operation with knowledge of what is likely to happen (climate), the producer can then decide on the acceptable level of risk due to adverse conditions. This resource was created using ArcGIS