Representation of the causes of flooding events in the form of multiple points. The point groupings correspond to the set of locations that were affected by the same event. The inventory of past flooding events was compiled from various public sources and standardized into a common data model. Sources used are included in the data. Event locations have been extensively revised to have one location per location reported as affected by the flood. Flood events for which no location was included in the sources used are positioned on the place name of the location affected by the flood. The event positions do not indicate where the flooding occurred. Flood events that affected more than one locality are represented by a multipoint.For each event after January 1, 1980 caused by a heavy rainfall or a coastal storm , a precipitation analysis document, a precipitation animation and the precipitation data are available. These documents are the result of a collaboration with Environment and Climate Change Canada.Disclaimer:It should be noted that no consultation was conducted with the various providers and stakeholders of the historic flood data. Disparities in content among the various sources result in an incomlete product. No warranty is given as to the accuracy or completeness of the information provided. The absence of information does not mean that no flooding has occurred.

The Innu Audio Index is an extract from the Canadian Geographical Names Data Base (CGNDB) of geographical names with associated audio. The shared audio with the Geographical Names Board of Canada (GNBC) is the intellectual property of the Innu Nation. The points represent official geographical names in Innu-aimun, the language of the Innu Nation. The CGNDB is the authoritative national database of Canada's geographical names. It contains geographical names and their attributes that have been approved by the GNBC, the national coordinating body responsible for standards and policies on place names.The GNBC is working to increase awareness of existing Indigenous place names and help promote the revitalization of Indigenous cultures and languages. The GNBC does not warrant or guarantee that the information is accurate, complete or current at all times. For more information, to report data errors, or to suggest improvements, please contact the GNBC Secretariat at Natural Resources Canada with questions or for more information.

The Global Ensemble Prediction System (GEPS) carries out physics calculations to arrive at probabilistic predictions of atmospheric elements from the current day out to 16 days into the future (up to 39 days twice a week on Mondays and Thursdays at 00UTC for calculating forecast anomalies). The GEPS produces different outlooks (scenarios) to estimate the forecast uncertainties due to the nonlinear (chaotic) behavior of the atmosphere. The probabilistic predictions are based on an ensemble of 20 scenarios that differ in their initial conditions, their physics parameters which are randomly perturbed by a Stochastic Parameter Perturbation (SPP) method, and the stochastic perturbations (kinetic energy). A control member that is not perturbed is also available. Weather elements include temperature, precipitation, cloud cover, wind speed and direction, humidity and others. This product contains raw numerical results of these calculations. Geographical coverage is global. Data is available on some fifteen vertical levels on a global latitude-longitude uniform grid with 0.5 degree horizontal resolution (about 39km). Predictions are performed twice a day.

Description:This dataset consists of three simulations from the Northeastern Pacific Canadian Ocean Ecosystem Model (NEP36-CanOE) which is a configuration of the Nucleus for European Modelling of the Ocean (NEMO) V3.6. The historical simulation is an estimate of the 1986-2005 mean climate. The future simulations project the 2046-2065 mean climate for representative concentration pathways (RCP) 4.5 (moderate mitigation scenario) and 8.5 (no mitigation scenario). Each simulation is forced by a climatology of atmospheric forcing fields calculated over these 20 year periods and the winds are augmented with high frequency variability, which introduces a small amount of interannual variability. Model outputs are averaged over 3 successive years of simulation (the last 3, following an equilibration period); standard deviation among the 3 years is available upon request. For each simulation, the dataset includes the air-sea carbon dioxide flux, monthly 3D fields for potential temperature, salinity, potential density, total alkalinity, dissolved inorganic carbon, nitrate, oxygen, pH, total chlorophyll, aragonite saturation state, total primary production, and monthly maximum and minimum values for oxygen, pH, and potential temperature. The data includes 50 vertical levels at a 1/36 degree spatial resolution and a mask is provided that indicates regions where these data should be used cautiously or not at all. For a more detailed description please refer to Holdsworth et al. 2021.The data available here are the outputs of NEP36-CanOE_RCP 8.5; a projection of the 2046-2065 climate for the no mitigation scenario RCP 8.5.Methods:This study uses a multi-stage downscaling approach to dynamically downscale global climate projections at a 1/36° (1.5 − 2.25 km) resolution. We chose to use the second-generation Canadian Earth System model (CanESM2) because high-resolution downscaled projections of the atmosphere over the region of interest are available from the Canadian Regional Climate Model version 4 (CanRCM4). We used anomalies from CanESM2 with a resolution of about 1° at the open boundaries, and the regional atmospheric model, CanRCM4 (Scinocca et al., 2016) for the surface boundary conditions. CanRCM4 is an atmosphere only model with a 0.22° resolution and was used to downscale climate projections from CanESM2 over North America and its adjacent oceans.The model used is computationally expensive. This is due to the relatively high number of points in the domain (715 × 1,021 × 50) and the relatively complex biogeochemical model (19 tracers). Therefore, rather than carrying out interannual simulations for the historical and future periods, we implemented a new method that uses atmospheric climatologies with augmented winds to force the ocean. We show that augmenting the winds with hourly anomalies allows for a more realistic representation of the surface freshwater distribution than using the climatologies alone.Section 2.1 describes the ocean model that is used to estimate the historical climate and project the ocean state under future climate scenarios. The time periods are somewhat arbitrary; 1986–2005 was chosen because the Coupled Model Intercomparison Project Phase 5 (CMIP5) historical simulations end in 2005 as no community-accepted estimates of emissions were available beyond that date (Taylor et al., 2009); 2046–2065 was chosen to be far enough in the future that changes in 20 year mean fields are unambiguously due to changing GHG forcing (as opposed to model internal variability) (e.g., Christian, 2014), but near enough to be considered relevant for management purposes.While it is true that 30 years rather than 20 is the canonical value for averaging over natural variability, in practice the difference between a 20 and a 30 year mean is small (e.g., if we average successive periods of an unforced control run, the variance among 20 year means will be only slightly larger than for 30 year means). Also, there is concern that longer averaging periods are inappropriate in a non-stationary climate (Livezey et al., 2007; Arguez and Vose, 2011). We chose 20 year periods because they are adequate to give a mean annual cycle with little influence from natural variability, while minimizing aliasing of the secular trend into the means. As the midpoints of the two time periods are separated by 60 years, the contribution of natural variability to the differences between the historical and future simulations is negligible e.g., (Hawkins and Sutton, 2009; Frölicher et al., 2016).Section 2.2 describes how climatologies derived from observations were used for the initialization and open boundary conditions for the historical simulations and pseudo-climatologies were used for the future scenarios. The limited availability of observations means that the years used for these climatologies differs somewhat from the historical and future periods. Section 2.3 details the atmospheric forcing fields and the method that we developed to generate winds with realistic high-frequency variability while preserving the daily climatological means from the CanRCM4 data. Section 2.4 shows the equilibration of key modeled variables to the forcing conditionsData Sources:Model outputUncertainties:These climate projections are downscaled from a single global climate model (CanESM2/CanRCM4) because the cost of ensembles is presently prohibitive. Our experimental design uses climatological forcing for each time period so the differences between them are almost entirely due to anthropogenic forcing with little effect of natural variability.

Footprints for all imagery in the Government of Yukon [Aerial Imagery Service](https://open.yukon.ca/data/yukon-aerial-imagery-most-recent).Distributedfrom [GeoYukon](https://yukon.ca/geoyukon) by the [Government of Yukon](https://yukon.ca/maps). Discover more digital mapdata and interactive maps from Yukon's digital map data collection.For moreinformation: [geomatics.help@yukon.ca](mailto:geomatics.help@yukon.ca).

Results of the last hour available, in real time, of the [Air Quality Index (AQI)] (https://www.iqa.environnement.gouv.qc.ca/contenu/index.asp) for the stations of the [Quebec Air Quality Monitoring Network] (https://www.environnement.gouv.qc.ca/air/reseau-surveillance/Carte.asp). These results exclude those from stations located on [Montreal Island] (https://www.donneesquebec.ca/recherche/dataset/vmtl-rsqa-indice-qualite-air).The IQA is an information and awareness tool designed to inform the population about the quality of ambient air in Quebec.If you have any questions about this data, contact the Info-Air department:.**This third party metadata element was translated using an automated translation tool (Amazon Translate).**

The Homogenized Surface Pressure data consist of monthly, seasonal and annual means of hourly sea level and station pressure (hectopascals) for 626 locations in Canada. Homogenized climate data incorporate adjustments (derived from statistical procedures) to the original station data to account for discontinuities from non-climatic factors, such as instrument changes or station relocation. The time periods of the data vary by location, with the oldest data available from 1953 at some stations to the most recent update in 2014. Data availability over most of the Canadian Arctic is restricted to 1953 to present. The data will continue to be updated every few years (as time permits).

The Ontario Hydro Network (OHN) is a provincial medium scale originating from data with regional scales of 1: 10,000 in Southern Ontario, 1: 20,000 in Northern Ontario and 1: 50,000 in the Far North. The shoreline is taken from the OHN - Waterbody data class. This data is used for cartographic purposes and web mapping services. This product requires the use of geographic information system (GIS) software. [Ontario Hydro Network (OHN) User Guide (Word)](https://www.sdc.gov.on.ca/sites/MNRF-PublicDocs/EN/CMID/OHN%20-%20UserGuide.docx)

The Integrated Resource Plan - Local dataset is comprised of all the polygons that represent the Local Integrated Resource Plans (IRP) in Alberta. A Local IRP provides land resource management direction for a relatively smaller geographic planning area. A Local Plan is developed to provide more detailed land and resource use parameters than may be available in a Sub-Regional Plan. An IRP is a plan which identifies the values and associated land and resource management goals for the planning area in consideration of the maintenance of social, economic, and ecological values. An IRP provides direction regarding the type of land and resource management activity that would facilitate meeting the stated objectives in the planning area (e.g. recreation, grazing, industrial and commercial activities). The public was often involved in contributing input to the development of an IRP. IRPs were endorsed by the Government of Alberta in various periods.

JUNCTION_OFFICIAL is one of the important layers for Saskatchewan Upgraded Road Network (SURN) and National Road Network (NRN). The JUNCTION_OFFICIAL provides the information of road intersections to clients that require accurate, relatively up-to-date and detailed description of Saskatchewan Road Network.JUNCTION_OFFICIAL, A point has been generated at the intersection of three or more road segments, Ferry connection, National/Provincial/Territory boundary and at the dead end of the road. JUNCTION_OFFICIAL contains all the road intersection points in the Saskatchewan. JUNCTION_OFFICIAL is an important part of the Saskatchewan road network dataset. Each point geometry "JUNCTION" has unique Identifiers (NID). "NID" National Identifier is used to manage the updates between data producer and data users.