Major Space Weather Risks Identified via Coupled Physics-Engineering-Economic Modeling

TL;DR

The paper tackles quantifying socio-economic risks from space weather by bridging physics, grid engineering, and macroeconomic modeling. It presents a modular, transferable framework that maps geophysical drivers to geoelectric fields, GIC exposure, transformer reliability, and economy-wide losses using Monte Carlo risk propagation. Validation against the TVA May 2024 storm and Horton benchmark shows the approach can reproduce key GIC behavior and provide uncertainty bounds, applying to the U.S. case yields daily losses on the order of 0.8–2.1 billion USD for extreme events and millions affected. The framework is designed to be transferable to other countries, enabling risk-informed resilience investments and policy decisions.

Abstract

Space weather poses an important but under-quantified threat to critical infrastructure, the economy, and society. While extreme geomagnetic storms are recognized as potential global catastrophes, their socio-economic impacts remain poorly quantified. Here we present a novel physics-engineering-economic framework that links geophysical drivers of geomagnetic storms to power grid geoelectric fields, transformer vulnerability, and macroeconomic consequences. Using the United States as an example, we estimate daily economic losses from transformer thermal heating of 1.37 billion USD (95 percent confidence interval: 1.16 to 1.58 billion USD) for a 100-year geomagnetic storm, with power outages affecting 4.1 million people and 101,000 businesses. A 250-year event could raise losses to 2.09 billion USD per day (95 percent confidence interval: 1.84 to 2.34 billion USD), disrupting power for more than 6 million people and 155,000 businesses. Crucially, the framework is scalable and transferable, offering a template for assessing space weather risk to critical infrastructure in other countries. This integrative approach provides the first end-to-end quantification of space weather socio-economic impacts, bridging space physics through to policy-relevant metrics. Our results demonstrate that coupled socio-economic modeling of space weather is both feasible and essential, enabling evidence-based decision making in infrastructure protection and global risk management.

Figures (17)

• Figure 1: Graphical overview of the physics--engineering--economic coupling framework. The modules and data flows illustrated show how geophysical drivers are connected to socio-economic impacts through hazard characterization, engineering grid modeling, reliability assessment, and economic impact evaluation.
• Figure 2: Geospatial EHV network model constructed from OpenStreetMap and HIFLD data. Extra-high-voltage transmission network ($\geq$161 kV) for the contiguous United States showing substations and transmission lines used in the risk modeling framework outlined in Figure \ref{['fig:framework']}.
• Figure 3: Extreme-value geoelectric field maps and induced transmission-line voltages for different return periods. Spatial distribution of peak geoelectric fields and corresponding induced voltages across the contiguous United States for 100-year, 150-year, and 250-year return period scenarios, with comparison to the recent severe "Gannon" storm for reference.
• Figure 4: Estimated substation probability of failure under modeled GIC scenarios. Figures a--d show the spatial distribution of transformer failure probability across the contiguous United States for different geomagnetic storm return periods. Failure probabilities are computed using the fragility-based reliability model described in the Supplementary Materials (see Method \ref{['method:reliability-math-app']}). Vulnerability hotspots (locations exceeding 50% failure probability) are concentrated in regions with high ground conductivity and complex network topology, particularly in Wisconsin, Minnesota, the Upper Midwest, and coastal areas of the Northeast.
• Figure 5: Socio-economic impacts versus return period: affected businesses, population, direct losses, and total losses. Results show systematic increases across all impact metrics with storm severity.