## 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.

Commercial and exploratory fisheries for Arctic Char, Salvelinus alpinus, provide significant economic opportunities for Nunavummiut in several Nunavut communities. Having an accurate understanding of the weight of the Arctic Char that are harvested is important for tracking harvest and for understanding how biological parameters may be changing over time as a result of exploitation and/or climactic and environmental changes. Unfortunately, most fish enter the processing plants as dressed (gills and viscera removed) and therefore conversion factors have to be applied to reconcile whole (round) weight from dressed weight. Here, we provide an updated conversion factor based on linear regression for Arctic Char from the Halokvik River (locally known as 30 Mile) near the community of Cambridge Bay. This conversion factor can be used moving forward as the Halokvik River continues to be commercially harvested.

Knowledge of the location of Earth’s surface water in time and space is critical to inform policy decisions on environment, wildlife, and human security. Dynamic surface water maps generally represent water occurrence, also referred to as inundation frequency, depicting the percentage of valid observations when water is detected at the surface. The location and duration of surface water varies from areas of permanent water with 100% inundation frequency where water is always observed, to areas of permanent land with 0% inundation where water never occurs. Between these two extremes are areas of ephemeral water that experience periodic flooding with inundation frequencies between 0-100%. National-scale dynamic surface water information was generated for years 1984-2023 at 30m spatial resolution from the historical Landsat archive over Canada. The complete dataset consists of annual water maps and derived products including inundation frequency and inter-annual wetting and drying trends calculated using per-pixel logistic regression. The complete dataset enables an assessment of the timing, duration, and trends towards wetting or drying at regional to national scales.Associated publication:https://www.sciencedirect.com/science/article/pii/S0034425722002358

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 data represents the dryness of the land surface based on vegetation conditions. The data is created weekly and uses weekly information on precipitation anomalies (namely the Standardized Precipitation Index or SPI) and satellite vegetation condition derived from Normalized Difference Vegetation Index (NDVI) from the MODIS Satellite. These dynamic data sets along with static data sets on land cover, soil water holding capacity, irrigation, ecozones and land surface elevation are used to model the drought severity, based on the Palmer Drought Severity Index (PDSI). The mapcubist model was trained on historical data and applied in real time to the dynamic inputs to produce drought severity ratings. The model is run at a 1km resolution and was developed by the AAFC, the United States Geological Survey and the United States Drought Monitor at the University of Nebraska Lincoln.

High resolution forest change for Canada (Binary Change/No-change) 1985-2011The 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 27 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 1985–2011 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: 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. https://authors.elsevier.com/sd/article/S0034425717301360Geographic extent: Canada's forested ecosystems (~ 650 Mha)Time period: 1985–2011

Statistically downscaled multi-model ensembles of projected change (also known as anomalies) in total precipitation are available at a 10km spatial resolution for 1951-2100. Statistically downscaled ensembles are based on output from twenty-four Coupled Model Intercomparison Project Phase 5 (CMIP5) global climate models (GCM). Daily precipitation (mm/day) from GCM outputs were downscaled using the Bias Correction/Constructed Analogues with Quantile mapping version 2 (BCCAQv2). A historical gridded precipitation dataset of Canada (ANUSPLIN) was used as the downscaling target. Projected relative change in total precipitation is with respect to the reference period of 1986-2005 and expressed as a percentage (%). Seasonal and annual averages of projected precipitation change to 1986-2005 are provided. Specifically, the 5th, 25th, 50th, 75th and 95th percentiles of the downscaled ensembles of projected precipitation change are available for the historical time period, 1901-2005, and for emission scenarios, RCP2.6, RCP4.5 and RCP8.5, for 2006-2100. Twenty-year average changes in statistically downscaled total precipitation (%) for four time periods (2021-2040; 2041-2060; 2061-2080; 2081-2100), with respect to the reference period of 1986-2005, for RCP2.6, RCP4.5 and RCP8.5 are also available in a range of formats. The median projected change across the ensemble of downscaled CMIP5 climate models is provided. Note: Projections among climate models can vary because of differences in their underlying representation of earth system processes. Thus, the use of a multi-model ensemble approach has been demonstrated in recent scientific literature to likely provide better projected climate change information.

High resolution forest change for Canada (Change Type) 1985-2011The 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 27 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 1985–2011 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: 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. https://authors.elsevier.com/sd/article/S0034425717301360Geographic extent: Canada's forested ecosystems (~ 650 Mha)Time period: 1985–2011

This polygon layer presents the spatial distribution of forecasted accumulated precipitation from the Global Forecast System (GFS) over watershed sub‑basins. GFS APCP raster data are overlaid with global watershed boundaries, and zonal statistics are computed to derive average precipitation per sub‑basin over a 7‑day (168‑hour) period. This product aids in global disaster preparedness and water management planning.GFS model output is processed into APCP rasters that capture accumulated precipitation over a 7‑day forecast period. These rasters are then combined with watershed boundary data, and zonal statistics are applied to compute average precipitation for each sub‑basin. The final polygon features provide a clear depiction of global rainfall and snowfall patterns, offering critical information for disaster risk management and international water resource planning.

The Standardized Precipitation Index (SPI) is widely regarded as one of the most accessible tools for quantifying and reporting meteorological drought. At shorter timescales, SPI values are highly correlated with soil moisture, while at extended timescales, they reflect variations in groundwater and reservoir storage. The SPI model derives probability distributions from historical precipitation records, which are then normalized using an incomplete gamma function across multiple timescales. SPI values indicate the number of standard deviations the observed anomaly departs from the long-term period of record, with positive values corresponding to wetter-than-average conditions.Time periods calculated for monthly precipitation percentiles are 1, 2, 3, 6, 9, 12, 18, 24 months. Each ISO week is numbered from 1 to 52 (sometimes 53) within a year. An ISO week starts on Monday and ends on Sunday. Historical record goes back to 1980Indices values were calculated using the xclim python package 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.