The National Hydro Network (NHN) focuses on providing a quality geometric description and a set of basic attributes describing Canada's inland surface waters. It provides geospatial digital data compliant with the NHN Standard such as lakes, reservoirs, watercourses (rivers and streams), canals, islands, drainage linear network, toponyms or geographical names, constructions and obstacles related to surface waters, etc. The best available federal and provincial data are used for its production, which is done jointly by the federal and interested provincial and territorial partners. The NHN is created from existing data at the 1:50 000 scale or better. The NHN data have a great potential for analysis, cartographic representation and display and will serve as base data in many applications. The NHN Work Unit Limits were created based on Water Survey of Canada Sub-Sub-Drainage Area.

This series of historic flood susceptibility maps comes from an XGBboost machine learning model trained on major floods from 2005 to 2023. The trained model is then run for each year from 2000 to 2023, including unique temporal characteristics of temperature, precipitation, land use land cover and Normalized Difference Vegetation Index (NDVI), to predict the flood susceptibility of any given year.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)- [Trends and Extremes – Flood Susceptibility Mapping]( https://open.canada.ca/data/en/dataset/3202e0a0-0afb-4120-b102-b0c41f0fb9eb)- [Future - Flood Susceptibility Mapping]( https://open.canada.ca/data/en/dataset/c00f95a3-7bab-4d28-b9cc-b30f06b5afd2)

Get data on Lake Ontario tagged Atlantic salmon recaptures. This dataset represents tagged adult Atlantic salmon that were released and then recaptured by anglers. These fish were used as brood stock (breeding) in the Ministry of Natural Resources and Forestry’s fish culture program. All fish were tagged with a streamer tag near the dorsal fin. Each tag has a unique number and a phone number so anglers can call and let the ministry know about their catch. The data includes: * tagging date * recapture date * tag number/colour * location released * location recaptured * days since released * distance travelled

The Village dataset is comprised of all the polygons that represent Villages in Alberta. Village is a municipality type defined under the authority of the Municipal Government Act. The formation of a Village can occur when a majority of the buildings are on parcels of land smaller than 1850 square meters and there is a population of 300 or more.

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)

The purpose of this feature layer is to provide the 2009 overland flooding boundary in the Red River Valley.This dataset shows the extent of peak overland flooding in the Red River Valley in 20 09 . Data is based on RADARSAT – 1 satellite imagery. During processing, the raw data set was resampled to 12.5 meter pixel resolution, then classified using PCI Geomatica software which is a specialized software designed to manipulate space born imagery. The final output depicting the flooding boundary is available as a TIFF or Shapefile. Launched in November 1995, RADARSAT-1 was a Canadian-led project which provided useful information to both commercial and scientific users in such fields as disaster management, agriculture, cartography, hydrology, forestry, oceanography, ice studies and coastal monitoring. Equipped with a powerful synthetic aperture radar (SAR) instrument, it acquired images of the Earth day or night, in all weather and through cloud cover, smoke and haze. As of March 2013, the satellite was declared non-operational and is no longer collecting data. Many applications were developed to take advantage of RADARSAT-1 capacity for detecting the presence of water. These included monitoring flooding and the build-up of river ice, and mapping the melting of snow-covered areas. When used for flood monitoring, RADARSAT-1 data helped assess the impact of flooding, predicted the extent and duration of floodwaters, analyzed the environmental impact of water diversion projects, and developed flood mitigation measures. Fields Included:FID : Internal feature numberNAME : Flooded area nameAREA_SQKM : Size of flooded area

Toponymic Features entity is Named Feature (geographical name or toponym). CanVec is a digital cartographic reference product of Natural Resources Canada (NRCan). It originates from the best available data sources covering Canadian territory, offers quality topographical information in vector format, and complies with international geomatics standards. CanVec is a multi-source product coming mainly from the National Topographic Data Base (NTDB), the Mapping the North process conducted by the Canada Center for Mapping and Earth Observation (CCMEO), the Atlas of Canada data, the GeoBase initiative, and the data update using satellite imagery coverage (e.g. Landsat 7, Spot, Radarsat, etc.).Distributed from [GeoYukon](https://yukon.ca/geoyukon) by the [Government of Yukon](https://yukon.ca/maps) . Discover more digital map data and interactive maps from Yukon's digital map data collection.For more information: [geomatics.help@gov.yk. ca](mailto:geomatics.help@yukon.ca)

The Nova Scotia Hydrographic Network is an enhanced version of the Nova Scotia Topographic Database's Water Features theme. This dataset includes network spines for connectivity of water flow and various attribution for flow direction, priority of water flow and toponymic objects where applicable.

A feature is a representation of a real world object, such as a lake, stream, dam or rapid. There are three hydrographic feature classes:  points, lines and polys. All may impede or be hazardous to waterflow and/or navigation on a watercourse or waterbody. This data shows natural and manmade point features. Examples include: * waterfalls * rapids * rocks * Sea Lamprey barriers * shipwrecks This product requires the use of geographic information system (GIS) software. [Technical Bulletin: Data migrated to new Ontario Hydro Network (OHN) - Hydrographic Feature Data Classes (PDF)](https://geohub.lio.gov.on.ca/datasets/mnrf::ontario-hydro-network-ohn-hydrographic-point/)

The National Rail Network (NRWN) is a geometric and attributive description of the Canadian rail network.The NRWN product consists of the features classes: Track Segment, Railway Crossing, Railway Station, Marker Post, Junction and Railway Structure. Descriptive attributes include amongst others: Track Classification, Track Name, Track Operator, Track User, Track Owner, Subdivision Name, Junction Type, Crossing Type, Level of Crossing, Warning System, Transport Canada Identifier, Station Name, Station Type, Station User, Structure Type.