Major space weather risks identified via coupled physics–engineering–economic modeling

Edward J. Oughton; Dennies K. Bor; Robert S. Weigel; C. Trevor Gaunt; Ridvan Dogan; Liling Huang; Jeffrey J. Love; Michael Wiltberger

doi:10.1029/2026av002367

USGS · 70280638

Major space weather risks identified via coupled physics–engineering–economic modeling

Edward J. Oughton·Dennies K. Bor·Robert S. Weigel·C. Trevor Gaunt·Ridvan Dogan·Liling Huang·Jeffrey J. Love·Michael Wiltberger

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Abstract

Space weather poses an important but under-quantified threat to society. While severe geomagnetic storms are recognized as potential global catastrophes, their socio-economic impacts remain poorly quantified. We present a novel physics-engineering-economic framework that links geophysical drivers to power grid geoelectric fields, transformer vulnerability, and macroeconomic consequences. Using the United States as an example, we estimate daily economic losses for a 250-year geomagnetic storm from transformer thermal heating of 1.81 billion USD (95 percent confidence interval: 1.65 to 1.96 billion USD), disrupting power for approximately 5.1 million people and 135,000 businesses. These estimates are conservative lower bounds, reflecting only transformer thermal heating effects and excluding voltage collapse, cascading failures, and restoration costs. The true societal risk could be substantially higher. Nonetheless, this contribution provides the first nationwide end-to-end coupling from space physics to potential macroeconomic loss, with quantified uncertainties. Our results demonstrate that coupled socio-economic modeling of space weather is feasible and essential, and the framework is scalable and transferable, offering a template for assessing space weather risk to critical infrastructure in other countries.

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Edward J. Oughton, Dennies K. Bor, Robert S. Weigel, C. Trevor Gaunt, Ridvan Dogan, Liling Huang, Jeffrey J. Love, Michael Wiltberger. 2026-09-04. Major space weather risks identified via coupled physics–engineering–economic modeling. https://doi.org/10.1029/2026av002367

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