Data Sources and Methodology

Climate Data

Geospatial data referencing monthly maximum 1-day precipitation, days with Tmax more than 30oC, freeze thaw days, hottest day, ice days, maximum number of consecutive dry days, and wet days greater than or equal to 20mm are sourced from ClimateData.ca.

The following sections describe the climate information available on Climate Insight. All information is for illustrative purposes. For a comprehensive review of climate data for your community, we encourage users to visit www.climatedata.ca.

Emissions Scenario: Fossil-fueled Development (SSP5-8.5)

Shared Socio-economic Pathway 5 (SSP5 - Fossil-fueled Development), represents a high-emissions future where society remains heavily reliant on fossil fuels, including coal, with additional future development of fossil-based energy sources. It is characterized by many challenges to mitigation and few challenges to adaptation, and built on specific assumptions such as population growth, economic development, and technological changes. This “high carbon” scenario projects the most global warming among all SSPs. For more information, visit the Understanding Shared Socio-economic Pathways (SSPs) learning topic on ClimateData.ca.

Only one SSP has been made available on Climate Insight to simplify the user experience and for illustrative purposes. When completing a climate risk assessment for a community it is best practice to consider multiple SSPs to better understand the range of possible future climate conditions. Practitioners should consider which SSPs are most appropriate for their specific context, considering their risk tolerance, project vulnerability, and project lifetime.

Time Horizon

A time horizon is a defined period over which climate model projections are presented.These horizons typically encompass a 20- to 30- year period to capture meaningful climate trends and average out year-to-year natural variability. For use on Climate Insight, three time horizons are available: 1971- 2000 (Baseline), 2041 - 2070 (Mid-century), and 2071 - 2100 (End-of-century).

The baseline period serves as a historical reference point, representing recent climate conditions against which future climate projections are compared to calculate changes, while mid-century and end-of-century periods represent near-term and long-term planning horizons, respectively. When comparing changes from a baseline period, different data sources may be based on different baseline periods (e.g., 1991-2020), leading to potential differences.

Percentiles

In climate science, an ensemble refers to a collection of climate model simulations. Since no single model can be considered “best”, it is standard practice to analyze multiple models together, often using ensemble percentiles, when studying projected changes. Here, percentiles refers to the ensemble percentile of the data being shown. For simplicity and illustrative purposes, data is shown for the 50th percentile, also known as the median value. This represents the value where half of the ensemble results are below, and half are above. For information on the climate model ensemble used, see Climate Insight Spatial Data Methodology.

While the ensemble median (50th percentile) provides a central estimate, other percentiles such as the 10th and 90th, are important when assessing risk and uncertainty. A range of percentiles should be considered for infrastructure planning and when conducting risk assessments.

Variable

The term “variable” is used to refer to a climate variable that can be measured directly in the field (eg. at meteorological stations) or calculated by climate models. All variables shown on Climate Insight represent average annual values. Seasonal variations can be significant, and annual values may not fully capture the range of seasonal extremes. When assessing climate risks, it is important to consider that seasonal patterns, such as extreme summer heat or winter precipitation, can differ significantly from annual averages. All variables provided to illustrate climate hazards. In some instances (eg. Days with Maximum Temperature warmer than 30°C) the variables may directly illustrate a certain hazard (eg. Extreme Heat); in other instances, the variables are provided as a proxy to other hazards, for example Maximum 1-Day Precipitation as a proxy for pluvial flooding.

The variables (with descriptions from ClimateData.ca) included on Climate Insight are:

• Days with Maximum Temperature warmer than 30°C
  Days with Tmax > 30°C describes the number of days where the daytime high temperature is warmer than 30°C. This index gives an indication of the number of hot days in the selected time period. High temperatures are important. They determine if plants and animals can thrive, they limit or enable outdoor activities, define how we design our buildings and vehicles, and shape our transportation and energy use. However, when temperatures are very hot, people – especially the elderly – are much more likely to suffer from heat exhaustion and heat stroke. Many outdoor activities become dangerous or impossible in very high temperatures.
• Maximum 1-Day Precipitation
  Maximum 1-Day Precipitation describes the largest amount of precipitation (rain and snow combined) that falls within a single 24-hour day for the selected time period. This index is commonly referred to as the wettest day of the year. Very high 1-day precipitation totals could be the result of intense, but short-lived precipitation events such as thunderstorms, or may be due to precipitation occurring steadily over the course of the day. Short duration, high intensity precipitation events may lead to flash flooding, particularly in urban areas where storm drains may be overwhelmed. Heavy snowfall events can cause damage to buildings and disrupt transportation services.
• Freeze-thaw Days
  Freeze-thaw Days is a simple count of the days when the air temperature fluctuates between freezing and non-freezing temperatures on the same day. Freeze-thaw cycles can have major impacts on infrastructure. Water expands when it freezes, so the freezing, melting, and re-freezing of water can, over time, cause significant damage to roads, sidewalks, and other outdoor structures.
• Hottest Day
  The Hottest Day describes the warmest daytime temperature in the selected time period. In general, the hottest day of the year occurs during the summer months. High temperatures are important. They determine if plants and animals can thrive, they limit or enable outdoor activities, define how we design our buildings and vehicles, and shape our transportation and energy use. However, when temperatures are very hot, people – especially the elderly – are much more likely to suffer from heat exhaustion and heat stroke. Many outdoor activities become dangerous or impossible in very high temperatures.
• Ice Days
  Ice Days describe the number of days where the warmest temperature of the day is not above 0°C. In other words, this index indicates the number of days when temperatures have remained below freezing for the entire 24-hour period. This index is an indicator of the length and severity of the winter season.
• Maximum Number of Consecutive Dry Days
  The Maximum Number of Consecutive Dry Days describes the longest spell of days where less than 1mm of precipitation falls daily. Periods of dry weather can impact agriculture, energy demands and water availability. Drought conditions may result when dry periods are long-lasting.
• Wet Days >= 20mm
  Wet Days >=20mm describes the number of days where at least 20 mm of precipitation (rain and snow combined) falls in the selected time period. Adequate precipitation is crucial to water availability, agriculture, electricity generation and wildfire suppression.

Infrastructure Data - General

Geospatial layers showing infrastructure data, including public transit stops, bridges and tunnels, potable water, solid waste, wastewater and stormwater, and low carbon is sourced from Statistics Canada’s Open Database of Infrastructure (ODI). The ODI is part of the Linkable Open Data Environment. As noted on the ODI website, the data for the current version (version 2) was collected between October 2023 and June 2024 from government open data sites, version 1 was collected between November 2022 and March 2023. The ODI may not be an exhaustive dataset. For information on their methodology, visit:

https://www150.statcan.gc.ca/n1/pub/34-26-0003/342600032023001-eng.htm

The ODI has recently released version 2 of the infrastructure data set. Bridges and tunnels, potable water, solid waste, wastewater and stormwater, and low carbon infrastructure layers are from version 2. The public transit stops layer is legacy data from version 1 since it was not made available in version 2.

The ODI infrastructure layers currently have gaps in the included infrastructure and associated metadata, and are provided, as is, so they can be used in locations where data is included. Gaps should not be taken to imply that the related infrastructure does not exist.

Infrastructure Data - Pedestrian and Cycling

The geospatial layer showing pedestrian and cycling infrastructure is sourced from the OSM Can-BICS system version 2. As noted on the Can-BICS website, this version uses data from January 25, 2022. The Can-BICS may not be an exhaustive dataset. For information on their methodology, visit:

https://storymaps.arcgis.com/stories/a20689f708d940f1abd1492877f7b1e4

Data can be accessed by visiting:

https://www.arcgis.com/home/item.html?id=a91c3270b322473da8b3c81793e6902c

The Can-BICS infrastructure layers currently have gaps in the included infrastructure and associated metadata, and are provided, as is, so they can be used in locations where data is included. Gaps should not be taken to imply that the related infrastructure does not exist.

Infrastructure Data - Facilities

Geospatial layers showing facility infrastructure data are sourced from Statistics Canada’s Open Database of Healthcare Facilities (ODHF), Open Database of Educational Facilities (ODEF), and Open Database of Recreational and Sport Facilities (ODRSF). These data are part of the Linkable Open Data Environment. The most recent data has been used for each layer, version 1.1 (May to June 2020 data) for the ODHF, version 2.1 (August 2019 to March 2022 data), and version 1.0 (2020-2021 data) for the ODRSF. These facility infrastructure datasets may not be an exhaustive. For information on their methodology, visit:

ODHF: https://www.statcan.gc.ca/en/lode/databases/odhf

ODEF: https://www.statcan.gc.ca/en/lode/databases/odef

ODRSF: https://www.statcan.gc.ca/en/lode/databases/odrsf

The facility infrastructure layers currently have gaps in the included infrastructure and associated metadata, and are provided, as is, so they can be used in locations where data is included. Gaps should not be taken to imply that the related infrastructure does not exist.

Equity Data - Social Vulnerability Index

Geospatial layers labeled as the Social Vulnerability Index are representing the Canadian Index of Multiple Deprivation (CIMD). This index consists of variables such as income, newcomer status, age, home ownership, etc. Climate Insight uses this information, in tandem with climate data to illustrate areas with existing vulnerabilities which could be made worse by climate change. Please note that the SVI layers are static in time based on 2021 Canadian Census data; only climate layers change with time. For information on how this index was calculated, visit:

https://www150.statcan.gc.ca/n1/pub/45-20-0001/452000012023002-eng.htm

The Social Vulnerability Index layer, representing CIMD, currently has gaps and is provided, as is, so it can be used in locations where data exists. Gaps should not be taken to imply that no vulnerabilities exist in that area.

Equity Data - Dimensions of Vulnerability

Additional layers have been provided to refine the Social Vulnerability Index. The new layers are:

• Residential instability
• Economic dependency
• Ethno-cultural composition, and
• Situational vulnerability

These layers represent the dimensions of the CIMD and can be used to further refine understanding of underlying issues contributing to vulnerability in a specific community. Climate Insight uses this information, in tandem with climate data to illustrate areas with existing vulnerabilities which could be made worse by climate change.

Please note that the dimensions of vulnerability layers are static in time based on 2021 Canadian Census data; only climate layers change with time. For information on how this index was calculated, visit:

https://www150.statcan.gc.ca/n1/pub/45-20-0001/452000012023002-eng.htm

The dimensions of vulnerability index layers, representing the dimensions of the CIMD, currently have gaps and are provided, as is, so they can be used in locations where data exists. Gaps should not be taken to imply that no vulnerabilities exist in that area.

Combined Climate and Equity layers

Combining the equity data with climate data allows users to see what variables have high climate indicator values and high social vulnerability compared to the rest of the country. For information on how bivariate relationships are represented in ArcGIS, visit:

https://pro.arcgis.com/en/pro-app/latest/help/mapping/layer-properties/bivariate-colors.htm

Climate data on the bivariate layers is ranked low-high based on Canada-wide percentiles. These national percentiles provide a broad perspective but may not fully capture local or regional climate risk variations. Users should consider local context when completing a risk assessment.

For more detailed information on the Map data and methodology please access Climate Insight Spatial Data Methodology Climate Insight Spatial Data Methodology