Insights
Thinking, in writing.
Series 01
Climate-Informed Asset Management
How utilities can integrate climate science into infrastructure planning — from pole strength fundamentals and extreme value statistics to climate projections, compound hazards, and exposure analysis. Engineering practice and climate data, connected end to end.
Part 0Understanding pole strength: the foundation
Before climate projections can inform asset decisions, you need the engineering chain that connects fiber stress, wind pressure, and code load factors to real replacement thresholds — and it ends with an uncomfortable question about designing for a climate that no longer exists.
Part 1Understanding extreme weather events
Extreme events are rare by definition, so averages tell you nothing about the storm that breaks your system. Extreme value theory — GEV distributions, return levels, and hazard curves — is the mathematical bridge from sparse weather records to defensible design decisions.
Part 2From 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.
Part 3Compound hazards and fragility curves
Treating wind and ice as independent turns a 130-year event into a 2,500-year one — a 20x error. Copulas model the dependence between hazards, and fragility curves translate the compound load into probability of damage.
Part 4Exposure analysis: which assets are actually in harm's way
Hazard models are useless until you know which of your hundred thousand poles actually face each hazard. Exposure analysis — spatial overlay, the E(x) response function, and Expected Annual Exposure — is the bridge between hazard statistics and vulnerability assessment.
Part 4 · AppendixExposure analysis appendix: North American climate and hazard data sources
A validated reference of official US and Canadian data sources — wind loading, climate normals, wildfire, and flood — for utility climate vulnerability assessments, with GIS integration and data-quality notes.
Part 4 · AppendixExposure analysis appendix: EAE, climate PoF, and the CNAIM framework
CNAIM's probability of failure has exposure baked in — but statically, generically, and backward-looking. Reconciling it with the IPCC climate risk framework shows exactly where forward-looking, probabilistic climate hazard modeling plugs into traditional asset risk.