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

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.

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.

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

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.

Metallic and Industrial Minerals Agreement feature class contains provincial extent polygon features representing Metallic and Industrial Minerals applications, agreements, leases, and licences, with varying term dates and conditions. These applications and subsequent agreements give the holder the right to explore Metallic and Industrial Minerals.

Community Energy and Emissions Inventory (CEEI) Primary Indicators Total 2007 Reporting Year by Community

The Air Quality Health Index (AQHI) is a scale designed to help quantify the quality of the air in a certain region on a scale from 1 to 10. When the amount of air pollution is very high, the number is reported as 10+. It also includes a category that describes the health risk associated with the index reading (e.g. Low, Moderate, High, or Very High Health Risk). The AQHI is calculated based on the relative risks of a combination of common air pollutants that are known to harm human health, including ground-level ozone, particulate matter, and nitrogen dioxide. The AQHI formulation captures only the short term or acute health risk (exposure of hour or days at a maximum). The formulation of the AQHI may change over time to reflect new understanding associated with air pollution health effects. The AQHI is calculated from data observed in real time, without being verified (quality control).

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.

Community Energy and Emissions Inventory (CEEI) Primary Indicators Total 2007 Reporting Year by Regional District