Description:Data on recreational boating are needed for marine spatial planning initiatives in British Columbia (BC). Vessel traffic data are typically obtained by analyzing automatic identification system (AIS) vessel tracking data, but recreational vessels are often omitted or underrepresented in AIS data because they are not required to carry AIS tracking devices. Transport Canada’s National Aerial Surveillance Program (NASP) conducted aerial surveys to collect information on recreational vessels along several sections of the BC coast between 2018 and 2022. Recreational vessel sightings were modeled against predictor variables (e.g., distance to shore, water depth, distance to, and density of marinas) to predict the number of recreational vessels along coastal waters of BC.The files included here are:--A Geodatabase (‘Recreational_Boating_Data_Model’), which includes: (1) recreational vessel sightings data collected by NASP in BC and used in the recreational vessel traffic model (‘Recreational_Vessels_PointData_BC’); (2) aerial survey effort (or number of aerial surveys) raster dataset (‘surveyeffort’); and (3) a vector grid dataset (2.5 km resolution) containing the predicted number of recreational vessels per cell and predictor variables (‘Recreational_Boating_Model_Results_BC).--Scripts folder which includes R Markdown file with R code to run the modelling analysis (‘Recreational_Boating_Model_R_Script’) and data used to run the code.Methods:Data on recreational vessels were collected by NASP during planned aerial surveys along pre-determined routes along the BC coast from 2018 to 2022. Data on non-AIS recreational vessels were collected using video cameras onboard the aircraft, and data on AIS recreational vessels using an AIS receiver also onboard the aircraft. Recreational boating predictors explored were: water depth, distance to shore, distance to marinas, density of marinas, latitude, and longitude. Recreational vessel traffic models were fitted using Generalized Linear Models (GLM) R packages and libraries used here include: AED (Roman Lustrik, 2021) and MASS (Venables, W. N., Ripley, 2002), pscl package (Zeileis, Kleiber, and Jackman, 2008) for zeroinfl() and hurdle() function. Final model was selected based on the Akaike’s information criterion (AIC) and the Bayes’ information criterion (BIC). An R Markdown file with code use to run this analysis is included in the data package in a folder called Script. Spatial Predictive Model: The selected model, ZINB, consist of two parts: one with a binomial process that predicts the probability of encountering a recreational vessel, and a second part that predicts the number of recreational vessels via a count model. The closer to shore and to marinas, and the higher the density of marinas, the higher the predicted number of recreational vessels. The probability of encountering recreational vessels is driven by water depth and distance to shore. For more information on methodology, consult metadata pdf available with the Open Data record.References:Serra-Sogas, N. et al. 2021. Using aerial surveys to fill gaps in AIS vessel traffic data to inform threat assessments, vessel management and planning. Marine Policy 133: 104765. https://doi.org/10.1016/j.marpol.2021.104765Data Sources:Recreational vessel sightings and survey effort: Data collected by NASP and analyzed by Norma Serra to extract vessel information and survey effort (more information on how this data was analyzed see SerraSogas et al, 2021). Bathymetry data for the whole BC coast and only waters within the Canadian EEZ was provided by DFO – Science (Selina Agbayani). The data layer was presented as a raster file of 100 meters resolution. Coastline dataset used to estimate distance to shore and to clip grid was provided by DFO – Science (Selina Agbayani), created by David Williams and Yuriko Hashimoto (DFO – Oceans). Marinas dataset was provided by DFO – Science (Selina Agbayani), created by Josie Iacarella (DFO – Science). This dataset includes large and medium size marinas and fishing lodges. The data can be downloaded from here: Floating Structures in the Pacific Northwest - Open Government Portal (https://open.canada.ca/data/en/dataset/049770ef-6cb3-44ee-afc8-5d77d6200a12)Uncertainties:Model results are based on recreational vessels sighted by NASP and their related predictor variables and not always might reflect real-world vessel distributions. Any biases caused by the opportunistic nature of the NASP surveys were minimized by using survey effort as an offset variable.

Fish stocking data for recreational fishing purposes. Contains: * stocking data for the past 10 years for species where the purpose of the stocking event was to promote recreational fishing. This is a subset of data from the fish stocking information system. This data can also be obtained through the [Fish ON-Line application](http://www.ontario.ca/fishonline). For information on where stocking occurs for research or rehabilitation purposes, contact a local Ministry of Natural Resources and Forestry district office. Related datasets: * [Ontario waterbody location identifer](https://data.ontario.ca/dataset/ontario-waterbody-location-identifier)

Water Stewardship Division, Water Management administrative areas. Water Precincts are a jurisdictional area within a Water District

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 

Province-wide SDE spatial layer displaying consumptive water licence points of diversion for drinking water systems. In the context of this layer, Drinking Water Systems means two or more water licences for domestic purposes at a single POD; and/or a water licence(s) for any other purpose indicating a water diversion and distribution system supplying water directly to residences and/or buildings for human consumption. This layer is an instantiation of the spatial view WLS_BC_POD_DRINKING_SOURCES_SVW

This product provides the ratio of surface freshwater intake to water yield for August 2013, with the exception of drainage regions 7, 8, 16, 17 and 18, which use the ratio of August intake to the long-term minimum monthly water yield. Surface freshwater intake aggregates data from the Survey of Drinking Water Plants, 2013 and the Industrial Water Use Survey, 2013 with estimates of agricultural water use for 2013 based on the Agricultural Water Use Survey and the Alberta Irrigation Information report. Data for water use by the oil and gas industry and households not supplied by a public water provider are also excluded.

Province wide spatial view showing aquifers designated as a Water Reservation. These Reserves set aside water in an aquifer specifically for future treaty obligations, and are formally established through Orders in Council issued by the Lieutenant Governor in Council, as authorized under Sections 39–41 of the Water Sustainability Act.

Point features showing the location of intakes and/or wellheads for drinking water systems. A drinking water system is any domestic system servicing anything other than a single-family dwelling, as defined in the Drinking Water Protection Act. These locations are updated as more information is received and is therefore continually being updated and may or may not match the corresponding Point of Diversion locations available through the Water Rights dataset and application. This is one of three Drinking Water Sources datasets (along with the DWS - Source Areas polygons dataset, and DWS - Protection Zones polygons dataset). Note: the polygons all have unique polygon IDs used to relate them to these points.

Under the Yukon Waters Act, the Yukon Water Board issues water use licences for the use of water and/or the deposit of waste to water. To see the applications, and their reports, please visit Waterline, our online registry at [https://www.yukonwaterboard.ca/](https://www.yukonwaterboard.ca/) .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)

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).**