Insights · Climate-Informed Asset Management · Part 2

From historical analysis to climate adaptation

Design codes assume the climate is stationary — that the past predicts the future. Climate projections break that assumption, and the gap between historical and projected return levels is unpriced risk sitting in today's infrastructure.

2025-12-26 · 13 min read

This continues from Part 1, where we learned how the GEV distribution models extreme events and how return levels translate statistics into engineering design.

Where We Left Off

In Part 1, we discovered that extreme weather follows predictable statistical patterns (the GEV distribution), and we can calculate return levels - the intensity of events expected once every T years. This is what design codes like CSA C22.3 use when specifying "design for 50-year wind."

But here's the problem: All of this assumes the climate stays the same.

What if it doesn't?

The Critical Question

"If I design a pole today using historical weather data, will it still be adequate in 2075 when the pole reaches end-of-life?"

This question leads us to a three-part framework for climate risk:

Three-part climate risk framework diagram showing Historical Risk, Projected Risk and the Adaptation Gap between them

  1. Historical Risk — What the past tells us (what current design standards are based on; although design guidelines will have updates based on new weather data, they may not necessarily cover the extreme weather cases based on long-term climate forecasting. Engineers and designers need to fully understand the concepts behind it to make sure it fully addresses the risks)
  2. Projected Risk — What climate models predict for the future
  3. Adaptation Gap — The difference between the two (unpriced risk in current designs)

Let's walk through each part.

Step 6: Understanding "Tail Behavior" in Practice

Before we add climate projections, we need to understand something crucial: not all extremes behave the same way.

Infographic comparing GEV tail behavior with probability density and log-scale survival curves plus a table of Frechet, Gumbel and Weibull scenarios

The question: We learned that the shape parameter ξ controls the "tail" of the distribution. But what does this actually mean for utility design?

The answer: It determines how much worse rare events can get compared to common ones.

Looking at the GEV Comparison Plot:

Four-panel GEV comparison with probability density functions, log-scale tail behavior, return level plot and design wind speed implications for the three scenarios

The Three Scenarios Explained:

Table of three GEV scenarios with shape parameter, real-world example and tail behavior for Frechet, Gumbel and Weibull

Note: IEC 60826:2017 Table D.1 confirms Gumbel distribution typically fits wind analysis well in most temperate regions.

How to Read the PDFs Plot:

The probability density shows how likely different annual maximum wind speeds are:

Probability density functions of annual maximum wind speed for the Frechet, Gumbel and Weibull scenarios

  • The peak of each curve is the "most likely" annual maximum
  • The right tail shows how likely very extreme events are
  • The Fréchet curve extends much further right—meaning very extreme events are more probable

The Key Insight from the Log-Scale Survival Plot:

Log-scale tail behavior comparison showing annual exceedance probability versus wind speed with 20, 50 and 100-year return period lines

A survival plot shows exceedance probability on a log scale, with horizontal lines marking return periods:

  • At the 50-year line (1/50 = 0.02 probability), read across to see the corresponding wind speed for each scenario
  • The Fréchet curve hits this line at a much higher wind speed than Weibull
  • This is why regions with heavy-tailed wind distributions need stronger infrastructure

Step 7: From Statistics to Pole Selection

The question: How does tail behavior translate into actual pole class decisions?

The answer: Let's work through the numbers.

Assumptions for This Analysis:

ASSUMPTIONS — READ CAREFULLY

Assumptions notice stating the parameters are illustrative examples, with the assumed GEV parameters table for the Frechet, Gumbel and Weibull scenarios

Calculated Design Wind Speeds:

Using the return level formula from Part 1:

GEV return level formula giving the design wind speed z T as a function of mu, sigma, xi and return period T

Table of calculated design wind speeds by return period for the Frechet, Gumbel and Weibull scenarios

The Critical Ratio: How Much Worse Can It Get?

Calculate: (100-year wind) ÷ (20-year wind)

Table of the critical ratio of 100-year to 20-year wind for each scenario with interpretation of how much stronger the rare event is

Why this matters:

In a Fréchet region (heavy tail), you can't just design for "typical" storms - the rare events are dramatically worse. In a Weibull region (bounded tail), there's less difference between common and rare extremes.

Translation to Pole Class:

Pole class selection reference showing wind loading zones from GEV analysis and the Frechet coastal scenario calculation with recommended pole classes

Example pole class calculations for the Gumbel Southern Ontario scenario and the Weibull shielded valley scenario with key insights

For the Gumbel scenario (temperate region):

  • 50-year design wind: ~33 m/s → Wind pressure: ~670 Pa
  • This corresponds to Class 3-4 poles for standard distribution
  • Standard design criteria work well here

For the Fréchet scenario (hurricane coast):

  • 50-year design wind: ~49 m/s → Wind pressure: ~1,450 Pa
  • This requires Class 1 or H-class poles
  • Standard classes are inadequate—you need premium structures

For the Weibull scenario (shielded valley):

  • 50-year design wind: ~29 m/s → Wind pressure: ~490 Pa
  • Class 5 poles may be sufficient
  • Potential for cost optimization (but verify the bounded assumption!)

Recap:

Recap infographic translating tail behavior into design decisions with the critical ratio table and the wind speed to pole class table

Step 8: The Stationarity Assumption

The question: What's the hidden assumption in everything we've done so far?

The answer: We've assumed stationarity - that the climate statistics don't change over time.

Infographic on the hidden stationarity assumption that past climate statistics will represent the future, an assumption climate science says is no longer valid

In other words: past = future.

The problem: Climate science tells us this assumption is increasingly wrong.

What Current Design Codes Do:

Design codes like CSA C22.3 specify design wind speeds based on historical weather data. When they say "50-year return period," they mean:

"Based on the past 30-50 years of weather records, this wind speed should be exceeded on average once every 50 years."

This worked fine when climate was relatively stable. But if climate is changing, the historical 50-year wind may become a 30-year wind (more frequent) or even a 20-year wind.

As climate adaptation guidance increasingly recognizes:

"Due to the non-linear and highly variable models used to project climates based on a variety of scenarios, the direct application or trending of historical values is not sufficient."

Recall this from Part 0:

Infographic showing wind pressure equals one half rho V squared and how a small wind speed increase produces a much larger increase in force on the structure

The Three-Part Framework:

This leads us to separate climate risk into three components:

Step 9: Climate Projections and SSP Scenarios

The question: How do climate scientists project future extremes?

The answer: They use global climate models (GCMs) run under different emission scenarios called Shared Socioeconomic Pathways (SSPs).

The Three Main Scenarios:

Table of shared socioeconomic pathway scenarios SSP1-2.6, SSP2-4.5 and SSP5-8.5 with their names, assumptions and planning uses

Note on RCP/SSP Correspondence: Older studies use RCP (Representative Concentration Pathway) nomenclature. The approximate correspondence is:

  • RCP 4.5 ≈ SSP2-4.5 (both reach 4.5 W/m² radiative forcing by 2100)
  • RCP 8.5 ≈ SSP5-8.5 (both reach 8.5 W/m² radiative forcing by 2100)

According to the IPCC AR6 Summary for Policymakers:

"Global warming of 2°C would extremely likely be exceeded in the intermediate GHG emissions scenario (SSP2-4.5)."

How Projections Affect GEV Parameters:

Climate change doesn't just shift the average—it can change all three GEV parameters.

Table showing how climate change increases the GEV location, scale and shape parameters and the design implication of each

CRITICAL UNCERTAINTY NOTE

Wind speed projections have significantly lower confidence compared to temperature projections. Government climate reports often classify design wind pressure as a "Tier 3" variable (lowest confidence tier). The projections in this document should be used for sensitivity analysis and risk awareness, not as definitive design values. Consider the possibility of both higher AND lower wind speeds than projected.

IMPORTANT NUANCE: Types of Wind Events

Climate change affects different wind phenomena differently:

Table of projected changes and confidence levels for mean annual winds, thunderstorm gusts, synoptic storm winds and post-tropical cyclone remnants

ASSUMPTIONS — Climate Projection Deltas

The following changes to GEV parameters are illustrative based on general CMIP6 trends. They are NOT derived from any specific climate model study. Actual projections require:

  • Downloading specific model outputs from regional climate data portals
  • Applying bias correction (e.g., Quantile Delta Mapping)
  • Ensemble analysis across multiple GCMs

Table of assumed changes to the GEV parameters by SSP scenario and time period from the historical baseline

Step 10: Quantifying the Adaptation Gap

The question: How much stronger do we need to build if we account for climate change?

The answer: Let's calculate it for a representative temperate continental region.

Looking at the Climate Risk Framework:

Four-panel climate risk framework with historical versus 2050s return level comparison, risk gap by return period, percentage adaptation gap and the three-component framework summary

The Baseline (Historical):

Assumed Historical GEV for a Temperate Continental Region:

  • μ = 22.5 m/s (typical annual maximum ~81 km/h)
  • σ = 4.8 m/s (moderate variability)
  • ξ = 0.02 (near-Gumbel, slight heavy tail)

For site-specific analysis, obtain parameters from your regional meteorological service using at least 30 years of annual maximum wind speed data.

Using the return level formula:

Historical 50-year design wind = 42.0 m/s (151 km/h)

This is approximately what standard design codes would prescribe for this type of region.

The Projections (2050s):

Applying the climate deltas to the GEV parameters:

Table of projected GEV parameters and 50-year design winds by SSP scenario with percentage change from the historical baseline

Why Wind Pressure Matters More Than Wind Speed:

Structural loads scale with the square of wind speed:

Wind Pressure = ½ × ρ × V²

A small increase in wind speed means a larger increase in loading:

Table of projected 50-year wind speeds for the 2050s showing wind speed and wind pressure changes by SSP scenario, with a note that a structure designed today is already under-designed by 27 percent under SSP2-4.5

The Return Level Plot:

Return level plot comparing the historical baseline with 2050s SSP projections of design wind speed across return periods from 5 to 200 years

  • X-axis: Return period (5 to 200 years)
  • Y-axis: Design wind speed (m/s)
  • Curves: Historical baseline vs. three SSP scenarios

How to read it:

  1. Find the 50-year return period on the x-axis
  2. Draw a vertical line up to each curve
  3. Read the corresponding wind speed on the y-axis

The vertical gap between the historical curve and the SSP2-4.5 curve represents the adaptation gap—currently unaccounted risk.

The Percentage Gap Plot:

This shows the adaptation gap as a percentage increase over historical:

  • At 50-year return period, SSP2-4.5 shows ~13% additional risk
  • The gap grows for longer return periods
  • This is because climate change affects the shape parameter (ξ), making rare events proportionally worse

Step 11: Asset Life Alignment

The question: Does the climate when I install an asset match the climate when it fails?

The answer: Usually not - and this matters.

Looking at the Asset Life Alignment Plot:

Asset design life versus evolving climate risk plot showing rising 50-year return level winds under SSP scenarios against the constant historical design level, annotated with pole and transformer lifespans

The Timeline Problem:

Consider a pole installed in 2025 with a 50-year design life.

Timeline table for a pole installed in 2025 showing the climate condition at installation, at mid-life in 2050 and at end of life in 2075

The pole was designed for a climate that no longer exists by the time it fails.

Note on Wood Pole Service Life: The 50-year design life assumption is well-established in utility practice. As the North American Wood Pole Council notes: "Depending on the environment and other factors, wood poles can last on average for some 50 years. In some locations, wood poles have remained in service for as long as 70 years or more."

Reading the Asset Life Alignment Plot:

  • X-axis: Year (2025 to 2085)
  • Y-axis: 50-year return level wind speed
  • Horizontal dashed line: Historical design level (current code basis)
  • Rising curves: SSP projections showing how the 50-yr wind evolves
  • Annotated lifespans: Pole (50 years) and transformer (30 years) service periods

Key insight from the graph:

The historical design level (flat line at 42 m/s, assumed for illustration purposes) stays constant—it represents what current codes specify based on past data.

Timeline problem infographic showing the growing unpriced risk between the SSP2-4.5 wind projection and the constant historical design level over a 50-year pole lifespan

But the actual 50-year wind speed is rising (the SSP curves). By 2075:

  • SSP2-4.5 projects the 50-year wind at ~51 m/s
  • That's +21% wind speed and +46% wind pressure compared to design

The shaded area between the curves is the adaptation gap—the risk that's currently not priced into our infrastructure.

Step 12: What To Do About It

The question: If our standards are based on historical data, how do we account for climate change?

The answer: Several potential options:

The actual asset management strategy will need to be developed based on the utility's specific asset demography, geographical location, historical outage pattern with respect to weather and network model, and the overall grid modernization strategy.

Four actionable adaptation strategies with pros and cons of each, covering higher design return period, shorter replacement cycles, climate-adjusted design standards and a tiered risk-based approach

The Data Sources

The question: Where does this data actually come from?

The answer: Two main sources feed the framework.

Climate risk quantification framework flowchart from historical data and climate projections through stationary and non-stationary GEV fits to the adaptation gap

For Historical Risk

What you need:

  • Minimum 30 years of hourly wind data
  • Extract annual maximum for each year
  • Fit GEV using Maximum Likelihood or L-moments

Note on Wind Speed Measurement Type: Wind speeds in this document refer to 10-minute mean wind speeds at 10m height in open terrain, consistent with most international standards. For gust conversion, apply appropriate gust factors (3-second gusts are typically 1.3-1.5× higher than 10-minute sustained for the same event).

For Projected Risk

What you need:

  • Ensemble outputs for wind variables
  • Multiple GCMs (at least 5-10 models)
  • Multiple SSP scenarios for sensitivity

Note: All major climate data portals are publicly accessible without cost. Commercial alternatives may offer enhanced products for specific applications.

Summary: The Complete Climate Risk Framework

Key Takeaways

Key takeaways table summarizing the stationary assumption, shape parameter, SSP scenarios, adaptation gap, asset life alignment, wind pressure scaling and projection uncertainty

What's Next?

In Part 3, we'll explore:

  • Multi-hazard analysis: Combining wind, ice, flood, and wildfire risks
  • Fragility curves: How infrastructure damage probability relates to hazard intensity

Diagram showing extreme wind analysis as the foundation for multi-hazard analysis, fragility curves, risk quantification and investment prioritization

This framework provides the foundation for translating climate science into concrete engineering decisions about poles, lines, substations, and system hardening investments.

Appendix A: Formulas Reference

GEV Return Level (from Part 1):

z_T = μ − (σ/ξ)·[1 − y_p^(−ξ)]   for ξ ≠ 0
z_T = μ − σ·ln(y_p)              for ξ = 0

where y_p = −ln(1 − 1/T)

Wind Pressure:

Wind pressure formula P equals one half rho V squared where rho is approximately 1.2 kilograms per cubic meter

Adaptation Gap:

Adaptation gap formula as the percentage difference between the projected and historical return levels

Pressure Increase:

Pressure increase formula as the squared ratio of projected to historical wind speed minus one

Example: If wind increases by 12.7% (from 42.0 to 47.3 m/s):

Example pressure increase calculation of 1.127 squared minus 1 equals 27 percent

Appendix B: Suggested References

Foundational Texts

  • Coles, S. (2001). An Introduction to Statistical Modeling of Extreme Values. Springer.

Standards

  • CSA C22.3 No. 1 — Overhead systems
  • IEC 60826:2017 — Design criteria of overhead transmission lines
  • CSA S6 — Canadian Highway Bridge Design Code (includes climate adaptation provisions)

Climate Science

  • IPCC AR6 Working Group I — The Physical Science Basis, 2021
  • Regional climate data portals (varies by country)

Wind Studies

  • Hong, H.P., Li, S.H., & Mara, T.G. (2014). Basis for recommending an update of wind velocity pressures in Canadian design codes. Canadian Journal of Civil Engineering, 41(3), 206-221.
  • Cheng, C.S., Li, G., Li, Q., & Auld, H. (2012). Possible Impacts of Climate Change on Wind Gusts under Downscaled Future Climate Conditions. Journal of Climate, 25(9), 3390-3408.

Continue to Part 3: Compound Hazards and Fragility Curves.