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.

A predictive model for Canadian carbonatite-hosted REE ± Nb deposits is presented herein. This model was developed by integrating diverse data layers derived from geophysical, geochronological, and geological sources. These layers represent the key components of carbonatite-hosted REE ± Nb mineral systems, including the source, transport mechanisms, geological traps, and preservation processes. Deep learning algorithms were employed to integrate these layers into a comprehensive predictive framework. Here is a link to the publication that describes this product: https://link.springer.com/article/10.1007/s11053-024-10369-7

Predictive ecosite map of Saskatchewan's provincial forests and adjacent parks within the boreal plain, boreal shield and taiga shield ecozones: version 01.Download: Here The Predictive Ecosite Map of Saskatchewan is based on the classifications of McLaughlan et al. (2010). This version (v01) is an interim proof-of-concept product and is not considered highly accurate or finalized. Ecosite prediction accuracy is anticipated to between 40% and 65%, depending on geographic location. For the boreal plain ecozone, ecosites were derived using a random forest imputation with the yaImpute package in R and mapped by L. Gelhorn (2014-07-24). Pixel values presented here are prefixed with a 2. Non-forest / non-water pixels are often assigned a BP28 ecosite code (228). For the boreal shield and taiga shield ecozones, ecosites were derived using a random forest imputation with the yaImpute package in R and mapped by S. Oldford (2019-11-20). Pixel values presented here are prefixed with a 3 and a 4, respectively. In the case of the taiga shield ecozone, there were limited sample plot data. To increase the sample size for modelling, the majority of taiga shield ecosites data are combined and modeled with boreal sheild ecosites using the ecozonal synonyms of McLaughlan et al. (2010). Ecosites TS01 (401), TS04 (404) and TS17 (417) were modeled as such because no ecozonal synonyms exist. This map is clipped to the Saskatchewan provincial forest and adjacent park boundaries. Water bodies are masked according to the 2015 Landcover Map of Canada of White et al. (2017) and have a pixel value of zero (0). References: McLaughlan, M.S., Wright, R.A. and Jiricka, R.D. (2010). Field guide to the ecosites of Saskatchewan’s provincial forests. Saskatchewan Ministry of Environment, Forest Service. Prince Albert, Saskatchewan. 343 pp. White, J.C., Wulder, M.A., Hermosilla, T., Coops, N.C. and 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. 192: 303-321.

This model is derived from geological and geophysical data, which is processed using deep learning and natural language processing techniques. Displayed is a Pan-Canadian probability map indicating the likelihood of discovering next-generation lithium-cesium-tantalum (LCT) pegmatites. This map was generated using known Canadian LCT pegmatites and their associated geospatial features, incorporating geological and geophysical data analyzed through deep learning and natural language processing techniques. Higher probability values highlight areas with an increased likelihood of hosting next-generation deposits, making this map a valuable tool for decision-making.

## 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 polygon layer represents accumulated precipitation forecasts from the Regional Ensemble Prediction System (REPS), a regional probabilistic model. It delivers ensemble‑based, short‑range precipitation forecasts—typically a 72‑hour accumulation—that aid in assessing the risk and spatial distribution of rainfall events, supporting hydrological analysis, flood forecasting, and water resource management.This polygon layer is produced by processing REPS GRIB2 files. The workflow involves extracting the precipitation field, converting it to a TIF raster, and then applying resampling, smoothing, and classification to create polygon features. These features represent forecasted rainfall totals over a 72‑hour period and are updated with each model run to maintain current predictive information. Source: Environment & Climate Change Canada

Seamounts have been identified as Ecologically or Biologically Significant Areas (EBSAs) due to their unique oceanography and ecology; they frequently serve as sites for fisheries and as habitat for a number of species of conservation concern. A mix of isolated seamounts and seamount complexes are distributed throughout Canada’s Pacific offshore waters, although only a subset of these are named. We used several pre-existing spatial databases and predictive models to map all named seamounts within Canada’s Exclusive Economic Zone (EEZ), all named seamounts fished by Canada in international waters, and any predicted (modelled) unnamed seamounts in the EEZ. These data are intended to inform marine planning initiatives in BC by providing collaborative, peer-reviewed scientific data at scales relevant to a BC coast-wide analysis.

CaLDAS-NSRPS was installed as an experimental system within the National Surface and River Prediction System (NSRPS) at Environment and Climate Change Canada's (ECCC) Canadian Centre for Meteorological and Environmental Prediction (CCMEP) in July 2019. CaLDAS-NSRPS is a continuous offline land-surface assimilation system, which provides analyses of the land surface every 3 h over the domain of the High-Resolution Deterministic Prediction System (HRDPS) at a 2.5 km grid spacing. The emphasis in CaLDAS-NSRPS is to focus upon the assimilation of satellite based remote sensing observations to provide the optimal initial conditions for the predictive components of the NSRPS, the High Resolution Deterministic/Ensemble Land Surface Prediction System (HRDLPS/HRELPS) and the Deterministic/Ensemble Hydrological Prediction Systems (DHPS/EHPS). CaLDAS-NSRPS is launched 4 times per day, at 0000, 0600, 1200, and 1800 UTC.

This series of projected future flood susceptibility maps were generated using an XGBoost machine learning model trained on major floods from 2005 to 2023. The trained model was applied to future climate scenarios for 2050, 2070, and 2100, under two SSP scenarios: 245 and 585. The model uses temperature and precipitation time series to estimate potential future flood susceptibility. These maps represent model projections and should be interpreted as indicators of potential flood susceptibility, not precise forecasts.This dataset forms part of a broader collection of flood susceptibility datasets, offering related information and analyses. The collection includes an overview page with associated publications, historic susceptibility values, temporal trends, and future projections.- [Collection – Flood Susceptibility Mapping]( https://open.canada.ca/data/en/dataset/1074f781-85d3-4c86-86cb-fd1c339197dc)- [Historic - Flood Susceptibility Mapping]( https://open.canada.ca/data/en/dataset/ea1384df-bf4a-4743-97bb-870dc43f8d77)- [Trends and Extremes – Flood Susceptibility Mapping]( https://open.canada.ca/data/en/dataset/3202e0a0-0afb-4120-b102-b0c41f0fb9eb)

This polygon layer displays ensemble-based, medium-range precipitation forecasts from the Global Ensemble Prediction System (GEPS), offering a probabilistic view of future rainfall or snowfall over a 16‑day horizon. It aids in uncertainty analysis, risk assessment, and strategic resource planning.Ensemble Approach: GEPS runs multiple perturbed members of ECCC’s GEM model, capturing a range of atmospheric evolutions and yielding probability distributions for precipitation. Global Domain: Similar coverage to the GDPS but focuses on ensemble mean, spreads, and probabilities rather than a single deterministic outcome. Longer-Range Outlook: Extends up to 16 days, supporting risk-based planning for potential floods, extended rainfall events, or dryness. Data Utility: Allows decision-makers to weigh confidence levels in precipitation scenarios, vital for water management, agriculture, and emergency contingency strategies.