This dataset contains the modelled and observed data used in the publication "Fjord circulation permits persistent subsurface water mass in a long, deep mid-latitude inlet" by Laura Bianucci et al., DFO Ocean Sciences Division, Pacific Region (published in the journal Ocean Science in 2024). An application of the Finite Volume Community Ocean Model (FVCOM v4.1) was run from May 24 to June 27, 2019 in the Discovery Islands region of British Columbia, Canada. Observed temperature and salinity profiles available in this area during this time period are included in the dataset, along with the modelled values at the same times and locations.

In 2019, the Earth Observation Team of the Science and Technology Branch (STB) at Agriculture and Agri-Food Canada (AAFC) repeated the process of generating annual crop inventory digital maps using satellite imagery to for all of Canada, in support of a national crop inventory. A Decision Tree (DT) based methodology was applied using optical (Landsat-8, Sentinel-2) and radar (RADARSAT-2) based satellite images, and having a final spatial resolution of 30m. In conjunction with satellite acquisitions, ground-truth information was provided by: provincial crop insurance companies in Alberta, Saskatchewan, Manitoba, & Quebec; point observations from the PEI Department of Environment, Water and Climate Change and data collection supported by our regional AAFC Research and Development Centres in St. John’s, Kentville, Charlottetown, Fredericton, and Guelph.

Digitization of long-term water chemistry data collected between 1920's - 1990's from lakes across Saskatchewan by the Saskatchewan Fisheries Research Laboratory. Samples were collected using methods from the Standard Methods for the Examination of Water and Wastewater (APHA, AWWA and WPCF). This data serves as a baseline for water quality.This dataset is a digitization from paper records of water chemistry data across Saskatchewan collected by the Saskatchewan Fisheries Research Laboratory. Data ranges from the 1920's to the 1990's and were sampled using methodes from the Standard Methods for the Examination of Water and Wastewater (American Public Health Association, American Water Works Association and Water Pollution Control Facility) This long-term water chemistry data serves as a baseline for water quality. Different variables of water chemistry are organized into individual fields. The units of measurement appear at the end of each field name. Due to the historical nature of the data some uncertainty exist in values.  Additonal notes on data: ND: no detection Trace: trace amounts Nil: zero NA: no data 

This dataset provides the results obtained by Health Canada’s Radiological Monitoring Network (CRMN) for the tritium activity concentration in drinking water originating from the water treatment plants in Ottawa, ON. More information about the CRMN network can be found on the Health Canada website (see link below). The results provided are the tritium activity concentration in units of becquerels per litre (Bq/L). Although ensuring water quality is a matter of provincial jurisdiction, the CRMN, in collaboration with the city of Ottawa, has been conducting a targeted program to monitor the radiological content of drinking water from two water treatment plants in Ottawa, ON.The Guidelines for Canadian Drinking Water Quality recommend a Maximum Acceptable Concentration (MAC) for tritium in drinking water of 7000 Bq/L. The measured activity concentrations of tritium in drinking water are well below this guideline value.The map shows the approximate sampling location for each monitoring station. Stations are found within the associated location range.

A wide range of different territorial, federal, and first nation governments have a role in monitoring water. The water monitoring sites data set compiles these varied sources into a single source with the intent to provide a 'one-stop shop' when searching for water monitoring data. Information contained includes the 'who, what, when, and where' of water monitoring by providing contact information, sample type, sample period, and location.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@yukon.ca](mailto:geomatics.help@yukon.ca)

In 2022, the Earth Observation Team of the Science and Technology Branch (STB) at Agriculture and Agri-Food Canada (AAFC) repeated the process of generating annual crop inventory digital maps using satellite imagery for all Canadian provinces, in support of a national crop inventory. New this year, a map of the agricultural regions in the Yukon Territory was also produced. A Decision Tree (DT) based methodology was applied using optical (Landsat-8, Landsat-9,Sentinel-2), and radar (RCM) based satellite images, and having a final spatial resolution of 30m. In conjunction with satellite acquisitions, ground-truth information was provided by: provincialcrop insurance companies in Alberta, Saskatchewan, Manitoba, & Quebec; point observations from the PEI Department of Environment, Water and Climate Change; Ontario Ministry of Agriculture, Food and Rural Affairs; University of Guelph - Ridgetown campus; British Columbia Ministry of Agriculture; and data collection supported by our regional AAFC Research and Development Centres in St. John's, Kentville, Fredericton, Guelph, Summerland and Whitehorse.

The theme on physicochemical monitoring of rivers and rivers presents data from all stations in networks monitoring water quality in rivers in Quebec and the St. Lawrence River.The purpose of networks for monitoring general water quality is to characterize, using current physicochemical and bacteriological parameters, the quality of water in spatial terms and to monitor the evolution of this quality over time. For the regular monitoring of the general quality of river and river water, the parameters measured are: total phosphorus, total nitrogen, nitrites and nitrates, ammonia nitrogen, chlorophyll a, pheopigments, faecal coliforms, faecal coliforms, turbidity, suspended matter, pH, conductivity, dissolved organic carbon and temperature. This data is used to calculate the Bacteriological and Physicochemical Water Quality Index (IQBP), a water quality classification index.The data set on physicochemical monitoring of rivers and rivers also includes the drainage areas of some of the stations. The attribute table provides a compilation of land use by category for the last year available at the time the data was generated. Follow-up is carried out annually.**This third party metadata element was translated using an automated translation tool (Amazon Translate).**

Extreme water level along the marine coastline is a result of a combination of storm surge, tides, and ocean waves. Future projections of climate change in the marine environment indicate that rising sea level and declining sea ice will cause changes in extreme water levels, which will impact Canada's coastlines and the infrastructure in these areas. Understanding these changes is essential for developing adaptation strategies that can minimize the harmful effects that may result.CAN-EWLAT is a science-based planning tool for climate change adaptation of coastal infrastructure related to future water-level extremes and changes in wave climate. The tool includes two main components: 1) vertical allowance and 2) wave climate. CAN-EWLAT was developed primarily for DFO Small Craft Harbours (SCH) locations, but it should prove useful for coastal planners dealing with infrastructure along Canada’s ocean coastlines.Cite this data as: Greenan B. Canadian Extreme Water Level Adaptation Tool (CAN-EWLAT) Published June 2022. Oceans Ecosystems Science Division, Fisheries and Oceans Canada, Dartmouth, N.S.

In 2015, the Earth Observation Team of the Science and Technology Branch (STB) at Agriculture and Agri-Food Canada (AAFC) repeated the process of generating annual crop inventory digital maps using satellite imagery to for all of Canada, in support of a national crop inventory. A Decision Tree (DT) based methodology was applied using optical (Landsat-8) and radar (RADARSAT-2) based satellite images, and having a final spatial resolution of 30m. In conjunction with satellite acquisitions, ground-truth information was provided by provincial crop insurance companies and point observations from the BC Ministry of Agriculture and our regional AAFC colleagues.

In 2020, the Earth Observation Team of the Science and Technology Branch (STB) at Agriculture and Agri-Food Canada (AAFC) repeated the process of generating annual crop inventory digital maps using satellite imagery to for all of Canada, in support of a national crop inventory. A Decision Tree (DT) based methodology was applied using optical (Landsat-8, Sentinel-2) based satellite images, and having a final spatial resolution of 30m. In conjunction with satellite acquisitions, ground-truth information was provided by: provincial crop insurance companies in Alberta, Manitoba, & Quebec; point observations from the PEI Department of Environment, Water and Climate Change; the Ontario Ministry of Agriculture, Food and Rural Affairs; and data collection supported by our regional AAFC Research and Development Centres in St. John’s, Charlottetown, Fredericton, and Guelph. Due to COVID-19 travel restrictions, complete sampling coverages in NL, NS, NB and BC were not possible, as a result the general agriculture class (120) is found in these provinces in areas where there was no ground data collected.