Polyline feature layer of winter trails in Manitoba provincial parks. This feature layer contains polylines showing locations of winter trails in Manitoba provincial parks. Winter trails are those trails designated for certain winter activities, such as cross-country skiing, skijoring, skate-skiing, fat biking, and dog sledding. Most of these trails are groomed by Manitoba Parks staff throughout the season. This layer is used in the interactive map Manitoba Parks - Winter Activity Trails Map and the web app Manitoba Parks - Winter Trails and Recreation Areas. The dataset includes the following fields (Alias (Name): Description) Trail Name (Trail_Name): Name of the trail Park Name (Park_Name): Name of the provincial park where the trail is located Winter Use (Winter_Use): Indicates whether or not the trail is designated for winter use (Note: All features in this layer have a value of 'Yes') For more information about winter activities, visit the Manitoba Parks website.

This is the 5 year investment plan for Manitoba Highway and Infrastructure Capital Projects. This dataset will show all the proposed infrastructure projects for the next 5 years. This Feature layer is similar to the "Manitoba Capital Plans Project Status" and will be updated once a year.This dataset contains points showing the locations of the capital projects such as asphalt surface treatment, grading, bituminous rehabilitation, concrete reconstruction, culvert jacking, intersection improvement, structures improvement, traffic signal improvements, and traffic safety improvements. This dataset was created by digitizing high-resolution imagery of Manitoba road network, bridge sites, and culvert locations. The details of the capital projects are available as attributes of the dataset.Fields included (Alias (Field Name): Field description.)Hwy (Hwy): The highway number where the project is located.  Location Description (Location_Description): A detailed description of the project location. Project Distance in km (km): The physical length of the project Project Type (Project_Type): A particular category assigned to the project based on the nature of work. Investment Category : (Investment_Category): Category of future investment for the project. (Ex: Renewal, Economic Development, Connectivity and Innovation, Climate Resiliency)  Municipality Name (MUNI_NAME): The name of the municipality where the project is located. Department Program (Department_Program): The name of the MI department responsible for the project.This dataset is used in Manitoba Infrastructure Projects an interactive map that shows Manitoba infrastructure's new, approved, and continuing capital project locations.

Application showing the locations of sites included in the Contaminated Sites Registry This application shows the locations of sites included in the Contaminated Sites Registry. There are three types of sites in the registry: Designated Contaminated, Designated Impacted, and Not Designated.  The data for this map are provided via a web csv file that is maintained by Environment, Climate and Parks staff. For more information visit Environment, Climate and Parks website.

Polygon feature layer of Manitoba's economic regions This feature layer depicts the boundaries of economic regions in Manitoba. There are eight regions in Manitoba, defined by Statistics Canada. Fields included [Alias (Field Name): Field description] Economic Region (Economic_Region): The name of the economic region For more information, see Regional Economic Profiles on the Manitoba  government  website.FF

Map showing the locations of sites included in the Contaminated Sites Registry This map shows the locations of sites included in the Contaminated Sites Registry. There are three types of sites in the registry: Designated Contaminated, Designated Impacted, and Not Designated.  The data for this map are provided via a web csv file that is maintained by Environment, Climate and Parks staff. For more information visit Environment, Climate and Parks website.

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.

Feature layer of solid waste sites and waste transfer stations registered with the Waste Reduction and Recycling Support (WRARS) Program as well as First Nations landfills. Feature point layer of solid waste sites and waste transfer stations registered with the Waste Reduction and Recycling Support (WRARS) Program as well as First Nations landfills. First Nations data was sourced from Indigenous and Northern Affairs Canada (INAC).Fields included: Database_I INAC Database ID WRARS_Regi WRARS Registration Number Facility_T Facility Type Facility_N Facility Name Class Class (only usable with WRARS landfills) Owner Owner Name Owner_Type Owner Type Website Website of Facility Operating_ Operating Type Weigh_Scal Weigh Scale Present? Private_y Private Facility? Open_Close Open or Closed Status Population_Using_the_Facility Population number using the Facility Associatio Association using the Facility Users User areas Latitude Latitude (decimal degrees) Longitude Longitude (decimal degrees) Physical_L Physical Location Description Data_type Source of Data

This polygon layer presents 7‑day and 10‑day accumulated precipitation forecasts from the Global Deterministic Prediction System (GDPS), aggregated by sub-basin. It is designed to help hydrologists, water resource managers, and emergency planners pinpoint watersheds facing higher rainfall or snowfall totals in the medium-to-long range, enabling proactive flood risk assessment, drought monitoring, and resource allocation. Developed by Environment and Climate Change Canada (ECCC), the GDPS is a global numerical weather prediction model running at approximately 15km resolution, updated twice daily (00Z and 12Z). This layer integrates 168-hour (7‑day) and 240-hour (10‑day) precipitation forecasts into sub-basin polygons, offering a comprehensive view of expected cumulative precipitation. By focusing on watershed boundaries, decision-makers can quickly gauge regional vulnerabilities to prolonged rainfall or snowfall events.Key highlights: Global Model Insight: Captures large-scale, multi-day weather systems (e.g., atmospheric rivers, persistent low-pressure systems). Sub-Basin Aggregation: Delivers averaged precip values per basin, simplifying hydrological analysis for flood or drought outlooks. Extended Outlook: Spanning from day 0 to day 10, covers both medium- and longer-term forecast horizons, essential for strategic planning and mitigation efforts. Typical Uses:Flood Forecasting – Identifying basins prone to heavy or prolonged precipitation. Water Resource Management – Adjusting reservoir release schedules or irrigation planning based on expected accumulations. Emergency Preparedness – Deploying resources or issuing advisories in vulnerable watersheds.

This is the web experience created using ArcGIS Web Experience Builder to portray the dynamic precipitation maps derived using various weather model data published by the Environment Canada, National Oceanic and Atmospheric Administration and European Centre for Medium-Range Weather Forecasts. It contains various precipitation layers for each of the models depicting various forecast periods / observation periods. The underlying data is updated regularly as the data gets published by ECCC/NOAA/ECMWF as per the publishing frequency. Following are the forecast weather models depicted in this Web Experience : HRDPS Model (High Resolution Deterministic Prediction System - Continental) for 24 and 48 hours of forecast periods. Regional Ensemble Prediction System (REPS) for 72 hours of forecast period hour. Regional Deterministic Prediction System (RDPS) for 84 hours of forecast period hour. Global Deterministic Prediction System (GDPS) for 168 and 240 hours of forecast periods. Global Forecast System (GFS) for 168 hours of forecast period. Global Ensemble Prediction System (GEPS) for 168 and 384 hours of forecast period. European Centre for Medium-Range Weather Forecasts for 168 hours of forecast periodAnd following are the observed weather models depicted in this Web Experience :High Resolution Deterministic Precipitation Analysis (HRDPA) with observation periods of the past 1 day, 3 days and 7 days.Special Thanks to Environment and Climate Change Canada, NOAA’s National Centers for Environmental Prediction, European Centre for Medium-Range Weather Forecasts

This polygon layer provides medium-range (up to 10 days) accumulated precipitation forecasts from the Global Deterministic Prediction System (GDPS), a worldwide numerical weather model run by Environment and Climate Change Canada. It addresses broad-scale weather systems and supplies boundary conditions for nested regional models. Global Scope: The GDPS covers the entire planet at ~15 km resolution, projecting large-scale atmospheric developments over a 240-hour window. Coupled Model: Integrates atmospheric and oceanic interactions, improving forecast accuracy for cyclones, frontal systems, and long-traveling storm patterns. Operational Backbone: Frequently used as a reference for regional or local models (e.g., RDPS) and for medium-range planning in water resource management or agriculture. Forecast Frequency: Runs twice daily, producing deterministic outputs that guide meteorologists, hydrologists, and emergency preparedness teams.