An Ungulate Winter Range (UWR) is defined as an area that contains habitat that is necessary to meet the winter habitat requirements of an ungulate species. UWRs are based on current understanding of ungulate habitat requirements in winter, as interpreted by FLNR regional staff from current scientific and management literature, local knowledge, and other expertise from the region. UWRs within this set of data are currently in the review and consultation stage of the area designation process. As UWRs are designated they will be moved into the [Approved Ungulate Winter Range](http://catalogue.data.gov.bc.ca/dataset/ungulate-winter-range) dataset.

This catalog contains annual 30 m spatial resolution snow dynamics metrics for each snow-year from 2018-2019 to 2023-2024 for all of Canada. We gather all Landsat and Sentinel-2 images collected over Canada and identify the status of each pixel observation on the image collection date: snow (and ice), non-snow (i.e., land, water), unclear (i.e., clouds, shadows). We built an algorithm to calculate snow cover metrics for each pixel during each winter: start date of the first (and biggest) snow period [startF, startB], end date of the last (and biggest) snow period [endL, endB], number of days with snow cover in total (or in the biggest snow period) [lengthT, lengthB], number of snow periods (i.e., separated times with multiple confirmed snow observations) [periods], and a status classification (e.g., continuous snow, snow free) [status]. We do not obtain a clear observation every day because of satellite orbit frequencies and clouds. This means that timing-based metrics are identified by the middle date between two clear observations, with uncertainty quantified as half the length of the gap (i.e., ± days) [startF_u, startB_u, endL_u, endB_u, lengthT_u, lengthB_u].

Designated play areas for snowmobiles in Tweedsmuir and Itcha-Ilgachuz Provincial Parks. [__Tweedsmuir Provincial Park Master Plan__](https://bcparks.ca/planning/mgmtplns/tweedsmuir/tweeds_mp.pdf?v=1618329316506) [__Itcha Ilgachuz Provincial Park Management Plan__](https://bcparks.ca/planning/mgmtplns/itcha_il/itcha_ilgachuz_mp.pdf?v=1618329451240) [__Itcha Ilgachuz Provincial Park Winter Recreation Areas Map (PDF)__](https://bcparks.ca/planning/mgmtplns/itcha_il/rec_winmap.PDF)

This polygon layer shows the spatial distribution of forecasted accumulated precipitation across watershed sub‑basins using data derived from the Regional Ensemble Prediction System (REPS). In other words, it aggregates precipitation amounts—computed from processed REPS forecast output (converted from GRIB2 files into raster [TIF] format)—over defined watershed boundaries to provide a detailed view of expected rainfall over a typical 72‑hour forecast period. This information supports regional hydrological forecasting, flood risk analysis, and water resource management.REPS forecast data are first processed to extract the accumulated precipitation field (APCP) and converted into high‑resolution raster images. These “REPS APCP rasters” represent the spatial distribution of forecast precipitation (in millimeters) over the region. Next, using pre‑defined watershed or sub‑basin boundaries, zonal statistics are applied to compute the average precipitation for each sub‑basin. The final layer displays these averaged values as polygon features, highlighting variations in forecasted rainfall across different drainage areas. This approach helps users pinpoint regions that may receive higher or lower rainfall, thereby enhancing hydrological assessments and emergency planning.

## Purpose The Ministry of Agriculture, Food and Agribusiness (OMAFA) is responsible for Ontario’s provincial soil maps and maintains and update them as necessary. Digital Soil Mapping (DSM) is a modern methodology using spatially explicit soils and environmental data to predict soil variation throughout a landscape at a high, consistent resolution. Digital soil maps are being rolled out throughout Ontario’s agricultural land base to update provincial soil maps. ## Reach Provincial soil maps are used in many decision-making processes including: * land use planning * land evaluation * farming practices * best management practices * ecological monitoring * land resource mapping Potential users of this data include: * farmers * certified crop advisors * conservation authorities * academic researchers * land use planners ## Potential impacts Digital soil maps provide more accurate and precise soils data and enables improved management of soil resources across multiple stakeholders. This allows for better decision making to maximize land use efficiency, improve economic efficiency of soil resources and promote soil health and soil conservation. ## Technical description Digital soil mapping combines geo-referenced soil observations with geo-referenced environmental layers to mathematically model soil variation as a function of environment variation. These models are based on well established, but often complex relationships, between soil properties and topography, biology, geology and hydrology.

This dataset shows the surficial geology points for the Province of Saskatchewan and is designed for the Resource Map of Saskatchewan. This dataset shows the surficial geology points for the Province of Saskatchewan and is designed for the Resource Map of Saskatchewan. Mapping in support of the dataset includes years of field observations which were then compiled and integrated into this dataset. The data was created as a file geodatabase feature class and output for public distribution. 1:1,000,000 scale surficial geology points map of Saskatchewan symbol layer.  **Please Note – All published Saskatchewan Geological Survey datasets, including those available through the Saskatchewan Mining and Petroleum GeoAtlas, are sourced from the Enterprise GIS Data Warehouse. They are therefore identical and share the same refresh schedule.

Difference from Average Precipitation represents the accumulated precipitation value for a location, subtracted by the long-term average value. A negative value indicates that the location has received less than the normal amount of precipitation (mm) for that timeframe. A positive value indicates that the location has received more than the normal amount of precipitation (mm).Time periods calculated for difference from average precipitation are 1, 2, 3, 6, 9, 12, 18, 24 months.Long-term average is 1991-2020. Each ISO week is numbered from 1 to 52 (sometimes 53) within a year. An ISO week starts on Monday and ends on Sunday.The National Agroclimate Series of Derived Indicators (NASDI) products provide a collection of comprehensive and regularly updated datasets on key agroclimatic variables, including accumulated precipitation, standardized precipitation index, and difference from normal temperature, among others. These datasets incorporate both real-time and historical climate information, offering enhanced insight into conditions and trends across Canada’s diverse agricultural regions.

This polygon layer showcases ultra-fine (2.5 km) short-range precipitation forecasts from the High Resolution Deterministic Prediction System (HRDPS), a convection-permitting model by Environment and Climate Change Canada. It identifies local-scale rainfall or snowfall patterns up to 48 hours, supporting urban flood forecasting, severe weather response, and detailed water resource planning.Convection-Permitting: The HRDPS can explicitly resolve thunderstorms and other small-scale weather events by running at ~2.5 km. Short-Range Focus: Typically provides forecasts out to 36–48 hours, updated several times daily. Local Impact: Valuable for pinpointing high-impact precipitation in complex terrain or urban environments, aiding emergency managers and hydrologists in short-lead-time decisions. Nested Model: Receives lateral boundary conditions from RDPS, maintaining consistency with regional forecasts while refining detail in local domains.

This dataset represents the digital compilation of the Mineral Assessment Airborne surveys of the Province of Saskatchewan.This dataset represents the digital compilation of the Mineral Assessment Airborne surveys of the Province of Saskatchewan.  This data shows Mineral Assessment Work Catalogue: Outlines of areas and descriptions of work submitted for mineral assessment credits (Airborne surveys). This is an ongoing assemblage of mineral assessment airborne surveys. The data was created as a file geodatabase feature class and output for public distribution.**Please Note – All published Saskatchewan Geological Survey datasets, including those available through the Saskatchewan Mining and Petroleum GeoAtlas, are sourced from the Enterprise GIS Data Warehouse.  They are therefore identical and share the same refresh schedule.

The Forest Change Type data described here is an update to previously posted open data. The date range for this data is 2012 to 2015. The Forest Change Type data for the prior period from 1985 to 2011 can be found here: https://opendata.nfis.org/mapserver/nfis-change_eng.html or https://gcgeo.gc.ca/geonetwork/search/eng search for “Forest Change” but you must be logged in to see the data. It is developed within the framework of Canada’s National Terrestrial Ecosystem Monitoring System (NTEMS).The forest change data included in this product is national in scope (entire forested ecosystem) and represents the first wall-to-wall characterization of wildfire and harvest in Canada at a spatial resolution commensurate with human impacts. The information outcomes represent 25 years of stand replacing change in Canada’s forests, derived from a single, consistent spatially-explicit data source, derived in a fully automated manner. This demonstrated capacity to characterize forests at a resolution that captures human impacts is key to establishing a baseline for detailed monitoring of forested ecosystems from management and science perspectives. Time series of Landsat data were used to characterize national trends in stand replacing forest disturbances caused by wildfire and harvest for the period 1985–2010 for Canada's 650 million hectare forested ecosystems (https://authors.elsevier.com/sd/article/S0034425717301360). Landsat data has a 30m spatial resolution, so the change information is highly detailed and is commensurate with that of human impacts. These data represent annual stand replacing forest changes. The stand replacing disturbances types labeled are wildfire and harvest, with lower confidence wildfire and harvest, also shared. The distinction and sharing of lower class membership likelihoods is to indicate to users that some change events were more difficult to allocate to a change type, but are generally found to be in the correct category. For an overview on the data, image processing, and time series change detection methods applied, as well as information on independent accuracy assessment of the data, see Hermosilla et al. (2016; http://www.tandfonline.com/doi/full/10.1080/17538947.2016.1187673).The data available is, 1. a binary change/no-change; 2. Change year; and, 3. Change type. When using this data, please cite as: Hermosilla, T.,Wulder, M. A.,White, J. C.,Coops, N. C.,Hobart, G. W., (2017). Updating Landsat time series of surface-reflectance composites and forest change products with new observations. International Journal of Applied Earth Observation and Geoinformation. 63: 104-111. DOI: 10.1016/j.jag.2017.07.013 White, J.C., M.A. Wulder, T. Hermosilla, N.C. Coops, and G. Hobart. (2017). A nationwide annual characterization of 25 years of forest disturbance and recovery for Canada using Landsat time series. Remote Sensing of Environment. 192: 303-321. DOI: 10.1016/j.rse.2017.03.035.