CN Rail is one of North America’s largest railways with a network that spans the United States and Canada. Like all other major railways, they are exposed to many climate risks which can impact their operations, maintenance, and infrastructure. These impacts cause millions of dollars in damage and lost revenue every year. These include:

Extreme temperatures: High temperatures can cause "sun kinks" or buckling in the rails, leading to derailments and other safety issues. Low temperatures can cause rail and switch points to contract and crack, also leading to safety issues.

Flooding: This can wash away rail beds, damage bridges, and obstruct tracks, leading to service disruptions. Floods can also damage electrical systems and other infrastructure.

Sea-level rise and storm surges: Coastal railway infrastructure can be affected by sea-level rise and storm surges, causing erosion and flooding.

Landslides: Increased rainfall, particularly in hilly or mountainous regions, can lead to landslides that block tracks and damage infrastructure.

Extreme weather events: Hurricanes, tornadoes, heavy snowfall, and ice storms can all cause significant damage to tracks, overhead lines, signals, and rolling stock.

Drought: Drought conditions can make the ground beneath tracks unstable, leading to safety issues. In extreme cases, it can also increase the risk of wildfires that may damage railway infrastructure.

Heatwaves: Prolonged periods of high temperatures can affect the reliability and efficiency of locomotives and other equipment, and can also increase the risk of wildfires.

Permafrost thaw: In regions where railways are built on permafrost, warming temperatures can cause the ground to thaw and become unstable, leading to safety issues.

Considering the scope and scale of the various climate threats and their financial impacts, implementing early warning systems for extreme weather events is a key climate adaptation strategy for railways. 

As part of an innovation project, CN implemented SpatiaFi data to help detect wildfire risk, active wildfires and the predicted direction of wildfires in relation to their railways. Running every 10 minutes, the datasets allow CN to have greater visibility into threats to their operations. These datasets span a number of different time frames for different purposes. Historical data is used to understand the changing landscape over time. Near-real time remote sensing data is used to identify active fires and track how far away they are from critical assets. Fire weather forecasts are mapped to fuel loads to get a high-resolution view of where fire risks are along their routes. Finally, long term climate scenarios are used for planning.

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