The annual maximum and minimum daily data are the maximum and minimum daily mean values for a given year.

Zero Emission Vehicle Infrastructure Program (ZEVIP) and Electric Vehicle and Alternative Fuel Infrastructure Deployment Initiative (EVAFIDI) qualitative, quantitative, and geographic data set derived from the program database. This data defines the project number, the number of chargers, the name of the promoter, the type of connector, the address, the city, the province, the postal code, the geographical coordinates, the status, the opening date, and the type of contribution agreement for each project funded by the program.The Canada Infrastructure Bank’s (CIB) Charging and Hydrogen Refuelling Infrastructure Initiative (CHRI) aims to reduce transportation sector greenhouse gas emissions by accelerating the private sector’s rollout of large-scale ZEV chargers and hydrogen refuelling stations, helping to spur the market for private investment.Through this initiative, the CIB has dedicated a minimum of $500 million to support the federal government’s goals as part of Canada’s 2030 Emissions Reduction Plan. Technology funding current to 30 June 2025.

Level curves with an equidistance of 1 m derived from a lidar survey conducted in 2015.attributes:ID - Unique identifierSubtype - Master (1) or secondary (2) level curve SCORE - Elevation value (m) The product High Resolution Digital Elevation Model (MNEHR) is available on the Open Government website.**This third party metadata element was translated using an automated translation tool (Amazon Translate).**

The Coastal Infrastructure Vulnerability Index (CIVI) was jointly developed by DFO Science Branch, Small Craft Harbours (SCH) Program and the Economic Analysis and Statistics Directorate. The CIVI was designed with the intent of developing a climate change adaptation tool that would support management decisions regarding the long-term infrastructure planning for SCH sites.The CIVI provides a numerical indication of the relative vulnerability of small craft harbour sites to the effects of climate change and was designed with three component sub-indices: Environmental Exposure (natural forces), Infrastructure, and Socio-economic. The spatial component for the coastline was derived from the CanVec 1:50,000 hydrographic layer (https://open.canada.ca/data/en/dataset/9d96e8c9-22fe-4ad2-b5e8-94a6991b744b).This layer combines the 1:50,000 CanVec coastline of Canada with the following CIVI environmental exposure variables:- projected sea level rise (for the decades 2030, 2040,...2100) in meters- wave height (metres) and wind speed (metres/second)- change in sea ice coverage in Atlantic Canada from the 1970s to the 2000sSea level change:Data for relative sea level change (SLC) were derived from the Intergovernmental Panel on Climate Change Fifth Assessment Report (IPCC 2014, AR5). The projected relative sea level change under the high emission scenario (RCP8.5) was calculated for all years between 2006 and 2100. Sea level change for the years 2030, 2040, 2050, 2060, 2070, 2080, 2090, and 2100 were used.Wind Speed and Wave HeightModelled hindcasts of yearly maximum wind speed (1990 - 2012) and wave height (1990- 2014) were used. This dataset was generated from IFREMER wave hindcasts using the WAVEWATCH III model with wind data from NCEP Climate Forecast System Reanalysis (CFSR) (Saha et al. 2010). Two high resolution (10 minute) grids of Atlantic and Pacific maximum modeled wind speeds and maximum significant wave height were used for southern Canadian coastal areas while a coarser (30 minute) worldwide grid was used for the Arctic areas. From these datasets the mean annual maximum wind speed over 23 years and the mean maximum significant wave height over 25 years were calculated.Change in sea ice coverage:Sea ice data from the Canadian Ice Service were acquired for Atlantic and Arctic Canada, representing percent ice coverage for each week over four decades (1970s, 1980s, 1990, 2000s). For each decade a single dataset was calculated to represent the sum of all weeks with ice coverage in excess of 50%, with a maximum possible score of 52 weeks for each decade. To measure change in ice duration, the summary mapsheet from the 2000s was subtracted from the 1970s summary mapsheet. The final dataset represents the change between the 1970s and 2000s in the number of weeks with ice concentrations greater than 50%. A positive number indicates a reduction in weeks of ice coverage, a negative number an increase in ice coverage.The data for individual small craft harbours included here contains predicted sea level change for the decades between 2030 and 2100, wave height, windspeed, change in sea ice coverage, population, and the final environmental exposure sub-index value (ESI). The population for each harbour is derived from the 2016 Census of Canada data for the Census subdivision (CSD) geographic unit.Reference:Relative sea-level projections for Canada based on the IPCC Fifth Assessment Report and the NAD83v70VG national crustal velocity modelhttps://geoscan.nrcan.gc.ca/starweb/geoscan/servlet.starweb?path=geoscan/fulle.web&search1=R=327878IPCC, 2014. Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Field, C.B., V.R. Barros, D.J. Dokken, K.J. Mach, M.D. Mastrandrea, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken, P.R. Mastrandrea, and L.L. White (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 1132 pp.Cite this data as: Greenan B. and Greyson P. Coastal Environmental Exposure Layer. Published March 2022. Ocean Ecosystem Science Division, Fisheries and Oceans Canada, Dartmouth, N.S.

The Standardized Precipitation Index (SPI) has been recognized as the most accessible index for quantifying and reporting meteorological drought. On short timescales, the SPI is closely related to soil moisture, while at longer timescales, the SPI can be related to groundwater and reservoir storage. The model uses observed historical precipitation amounts to compute probability distributions which are then normalized using an incomplete gamma function over a range of timescales. The values can be interpreted as the number of standard deviations by which the observed anomaly deviates from the long-term mean. where positive values (greater than zero) result from above average conditions.

Phytoplankton species abundance (cell/L) and oceanographic conditions (temperature, salinity, chlorophylle-a (mg/m³) for some years and nutrient content (mmol/m³)) at stations of the Harmful Algae Monitoring Programme (HAMP) from1994 to 2016.The layer presents the station positions of the HAMP. Two files are attached to each station: one containing the cell counts and the second the oceanographic conditions.PurposeThe summer growth of many toxic and harmful microalgae species poses a serious threat for the public health and commercial or recreational exploitation of some marine species.The Department of Fisheries and Oceans (DFO) initiated the Harmful Algae Monitoring Programme (HAMP) in 1989 in order to complete the monitoring program for paralytic shellfish poisoning (PSP). Under the responsibility of Maurice-Lamontagne Institute scientists, the HAMP is to monitor, by means of a coastal station network, the natural occurrence of toxic and harmful algae in the St. Lawrence in order to determine their spatio-temporal distribution and the environmental conditions leading to their bloom.The network is made up of 11 coastal stations which are sampled every week from April to November and which are established along Quebec eastern shores. It extends from Tadoussac to Tête-à-la-Baleine on the St. Lawrence north shore and from Sainte-Flavie to Carleton on the south shore along the Gaspé peninsula. Another station is located in Havre-Aux-Maisons, Magdalen Islands.The HAMP was discontinued in 2010 but opportunistic samplings are still done at some stations.Additional informationThe sampling and analysis protocol is described in details in the following publication apart from the fact that the number of identified and counted species significantly has been increasing with time. Phytoplankton samples are preserved in a lugol solution.Blasco D., M. Levasseur, R. Gélinas, R. Larocque, A.D. Cembella, B. Huppertz et E. Bonneau.1998. Monitorage du phytoplancton toxique et des toxines de type IPM dans les mollusques du Saint-Laurent: 1989 à 1994. Rapp. stat. can. hydrogr. sci. océan. 15 1 : x i-117 p.

The annual maximum and minimum instantaneous data are the maximum and minimum instantaneous values for a given year.

Maximum Temperature represents the highest recorded temperature value (°C) at each location for a given time period. Time periods include the previous 24 hours and the previous 7 days from the available date where a climate day starts at 0600UTC.

Statistically downscaled multi-model ensembles of maximum temperature are available at a 10km spatial resolution for 1951-2100. Statistically downscaled ensembles are based on output from twenty-four Coupled Model Intercomparison Project Phase 5 (CMIP5) global climate models (GCM). Daily maximum temperature from GCM outputs were downscaled using the Bias Correction/Constructed Analogues with Quantile mapping version 2 (BCCAQv2). A historical gridded maximum temperature dataset of Canada (ANUSPLIN) was used as the downscaling target. The 5th, 25th, 50th, 75th and 95th percentiles of the monthly, seasonal and annual ensembles of downscaled maximum temperature (°C) are available for the historical time period, 1951-2005, and for emission scenarios, RCP2.6, RCP4.5 and RCP8.5, for 2006-2100. Note: Projections among climate models can vary because of differences in their underlying representation of earth system processes. Thus, the use of a multi-model ensemble approach has been demonstrated in recent scientific literature to likely provide better projected climate change information.

Mean Temperature Difference From Normal values are computed by subtracting the normal monthly average temperature from the average monthly temperature of the month. The average monthly temperature is computed by obtaining the mean value of average daily temperatures for a month. If the month was colder than normal the value computed will be negative and if it was warmer the value will be positive.