pith. sign in

arxiv: 2605.23053 · v1 · pith:T6HZBFZOnew · submitted 2026-05-21 · 💰 econ.EM

A Comparative Multi-Hazard Risk Assessment of the US High-Voltage Transmission Network

D. Bor , E. J. Oughton , R. S. Weigel , R. Yang , T. Clower , A. Newman , A. R. Valle This is my paper

Pith reviewed 2026-05-25 04:57 UTC · model grok-4.3

classification 💰 econ.EM
keywords multi-hazard risktransmission networknatural hazardseconomic lossespower gridtropical cyclonestornadoesgeomagnetic storms
0
0 comments X

The pith

A consistent multi-hazard framework finds tropical cyclone winds cause the highest direct damage to the US high-voltage transmission network at $137 million per day.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper creates an integrated framework that connects hazard data, equipment fragility, and economic ripple effects to assess risks to the US power grid from nine different natural hazards plus one combined event. It applies this approach uniformly to a network of over 13,000 lines and 10,000 substations using national datasets. This produces comparable estimates of daily damage, affected people, and lost economic output for each hazard. A reader would care because the results highlight which hazards pose the biggest threats to critical infrastructure and where resilience efforts might focus.

Core claim

Applying the framework across hazards shows tropical cyclone wind produces the largest expected daily damage at $137 M/day, followed by lightning at $87 M/day. Tornado yields the largest downstream output loss at $4.93 B/day, with flood and earthquake next. A major geomagnetic storm causes $2.07 B/day in losses. The compound freezing rain and wind gust scenario affects 237.4 million people and produces $85.16 B/day in output losses, serving as an upper-bound stress test.

What carries the argument

The integrated framework that links hazard characterization, fragility modeling, and macroeconomic impact propagation applied to the national transmission network model.

If this is right

  • Tropical cyclone wind is the leading individual hazard for direct infrastructure damage.
  • Tornadoes generate the highest downstream economic losses among single hazards.
  • Geomagnetic storms produce losses comparable to major earth-based hazards like floods and earthquakes.
  • The compound freezing rain and wind scenario causes far larger disruption than any single hazard.
  • The results provide a baseline for comparing and prioritizing resilience investments across hazards.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • If the uniform modeling assumptions hold, the approach could be adapted to evaluate grid risks in other countries with similar data.
  • Climate-driven changes in hazard frequency might increase the relative importance of compound events.
  • Targeting resilience to multiple hazards simultaneously may yield better returns than single-hazard focus.
  • Further validation against historical event data could refine the economic multipliers used.

Load-bearing premise

Fragility curves, damage-to-population mappings, and macroeconomic multipliers apply uniformly without material bias across all hazards using the same national datasets and network model.

What would settle it

If observed damages and economic losses from historical events for several hazards deviate substantially from the model's predictions when using the same parameters, the uniform application would be called into question.

Figures

Figures reproduced from arXiv: 2605.23053 by A. Newman, A. R. Valle, D. Bor, E. J. Oughton, R. S. Weigel, R. Yang, T. Clower.

Figure 1
Figure 1. Figure 1: Multi-hazard power transmission risk framework. [PITH_FULL_IMAGE:figures/full_fig_p010_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Transmission network assets across the contiguous United States. [PITH_FULL_IMAGE:figures/full_fig_p011_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Per-hazard exposure of the US high-voltage power network. [PITH_FULL_IMAGE:figures/full_fig_p014_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Fragility curves for HV substations (>345 kV) across the eight parametric-fragility hazards. Panels (a)–(d) show multi-state lognormal curves from the literature synthesis. Panels (e)–(h) show single-state scenario curves based on physical reason￾ing. Dashed lines indicate median capacity (θ). FZG (discrete tier lookup) and geomagnetic disturbance (asset-specific thermal fragility) are not parametric logno… view at source ↗
Figure 5
Figure 5. Figure 5: Expected daily damage to the US bulk transmission network by hazard. [PITH_FULL_IMAGE:figures/full_fig_p026_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Spatial distribution of expected daily damage per asset across ten haz [PITH_FULL_IMAGE:figures/full_fig_p028_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Population exposure within affected substation service areas. [PITH_FULL_IMAGE:figures/full_fig_p030_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Sectoral economic output loss from transmission network disruption. [PITH_FULL_IMAGE:figures/full_fig_p032_8.png] view at source ↗
read the original abstract

Modern economies depend critically on high-voltage power transmission networks. Yet this infrastructure is routinely disrupted by natural hazards ranging from earthquakes and floods to tornadoes and geomagnetic storms. Risk assessments have historically addressed hazards in isolation, leaving no common basis for comparing economic impacts across the full hazard portfolio. This study addresses this gap by developing an integrated framework linking hazard characterization, fragility modeling, and macroeconomic impact propagation. The framework is applied consistently across nine primary hazards and one compound freezing rain and wind gust hazard. Using national hazard datasets and a US high-voltage transmission network of over 13,000 line segments and 10,000 substations, we derive failure probabilities, expected damage, affected population, and downstream economic output losses. Among individual hazards, tropical cyclone wind produces the largest expected daily damage at $137 M/day, followed by lightning at $87 M/day, earthquake at $47 M/day, flood at $46 M/day, tornado at $42 M/day, and landslide at $34 M/day. Downstream economic output losses are largest for tornado at $4.93 B/day, followed by flood at $3.59 B/day and earthquake at $3.02 B/day. A 250-year geomagnetic storm produces $2.07 B/day, placing space weather within the range of major terrestrial hazards. The compound freezing rain and wind gust scenario produces the largest stress-test disruption, affecting 237.4 M people and yielding a modeled downstream output loss of $85.16 B/day. These results should be interpreted as first-order bounding estimates, with the compound scenario representing an upper-bound stress test. Overall, the framework establishes a consistent baseline for prioritizing investments in transmission network resilience.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

3 major / 2 minor

Summary. The paper develops an integrated multi-hazard risk assessment framework that links hazard characterization, fragility modeling, and macroeconomic propagation, then applies it uniformly to nine primary hazards plus one compound freezing-rain/wind-gust scenario on a US high-voltage transmission network model (13k line segments, 10k substations). Using national hazard datasets it reports expected daily damage (tropical cyclone wind highest at $137 M/day), downstream output losses (tornado highest at $4.93 B/day), and stress-test impacts (compound scenario affecting 237.4 M people and $85.16 B/day loss), positioning the results as first-order bounding estimates.

Significance. If the modeling steps are shown to be robust, the work supplies a rare consistent baseline for ranking hazards and guiding resilience investments across the full portfolio of threats to critical infrastructure.

major comments (3)
  1. [Methods] Methods (framework application paragraph): the headline rankings rest on the same fragility curves, population-damage mappings, and macroeconomic multipliers being transferred without material bias across nine physically distinct hazards; no per-hazard calibration, cross-validation against observed outages, or sensitivity tests for transferability are described, which directly undermines the comparative claims.
  2. [Results] Results (reported figures for tropical cyclone wind, tornado, and compound scenario): the quantitative values ($137 M/day, $4.93 B/day, $85.16 B/day) are presented without error bars, derivation details, or validation of the underlying fragility and multiplier steps, leaving the ordering vulnerable to systematic bias in any single transferred component.
  3. [Abstract] Abstract and stress-test section: the compound scenario is described as an 'upper-bound stress test' yet the same uniform national datasets and network model are used; without explicit checks that the fragility and propagation assumptions remain comparable under simultaneous hazards, the $85.16 B/day figure cannot be reliably distinguished from an artifact of the modeling choices.
minor comments (2)
  1. [Abstract] The abstract states numerical results but supplies no derivation details, validation of fragility models, or sensitivity checks.
  2. [Results] Notation for 'expected daily damage' versus 'downstream economic output losses' should be defined explicitly on first use to avoid ambiguity in the comparative tables.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for their constructive comments on the robustness of the multi-hazard framework. We address each major point below and indicate where revisions will be made to improve transparency and discussion of assumptions.

read point-by-point responses

  1. Referee: [Methods] Methods (framework application paragraph): the headline rankings rest on the same fragility curves, population-damage mappings, and macroeconomic multipliers being transferred without material bias across nine physically distinct hazards; no per-hazard calibration, cross-validation against observed outages, or sensitivity tests for transferability are described, which directly undermines the comparative claims.

    Authors: The study applies a single integrated framework uniformly across hazards precisely to enable consistent comparison using the best available national-scale datasets. Per-hazard calibration and outage validation data do not exist in comparable form for all nine hazards, which is why the results are explicitly framed as first-order bounding estimates. We will add a new subsection on model assumptions and limitations, plus sensitivity tests on key transferred parameters (fragility thresholds and multipliers), to better qualify the comparative rankings. revision: partial

  2. Referee: [Results] Results (reported figures for tropical cyclone wind, tornado, and compound scenario): the quantitative values ($137 M/day, $4.93 B/day, $85.16 B/day) are presented without error bars, derivation details, or validation of the underlying fragility and multiplier steps, leaving the ordering vulnerable to systematic bias in any single transferred component.

    Authors: The figures are direct outputs of the sequential hazard-fragility-propagation steps detailed in the methods. We will expand the results section and add supplementary material with explicit derivation steps for the three highlighted values. Error bars are omitted because the estimates are constructed as bounding values rather than statistical forecasts; we will instead add a dedicated uncertainty discussion covering the main sources of potential bias in the transferred components. revision: partial

  3. Referee: [Abstract] Abstract and stress-test section: the compound scenario is described as an 'upper-bound stress test' yet the same uniform national datasets and network model are used; without explicit checks that the fragility and propagation assumptions remain comparable under simultaneous hazards, the $85.16 B/day figure cannot be reliably distinguished from an artifact of the modeling choices.

    Authors: The compound scenario applies the individual fragility functions concurrently to the same network model, which is the basis for labeling it an upper-bound stress test. We agree that interactions between simultaneous hazards are not explicitly validated. We will revise the abstract and stress-test section to clarify the additive application of fragility curves and add a short discussion of this modeling choice and its limitations. revision: yes

Circularity Check

0 steps flagged

No circularity: results propagate from external hazard datasets and network model

full rationale

The derivation applies an integrated framework to external national hazard datasets and a fixed 13k-line/10k-substation network model to compute failure probabilities, damage, and output losses. No step reduces a claimed prediction or ranking to a fitted parameter, self-citation, or input by construction. The uniform application of fragility curves and multipliers is an assumption about transferability, not a definitional loop. The paper is self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

2 free parameters · 2 axioms · 0 invented entities

Only the abstract is available, so the ledger is inferred from standard requirements of multi-hazard infrastructure risk models: multiple fitted fragility and economic parameters plus domain assumptions about data quality and model transferability.

free parameters (2)
  • hazard-specific fragility curve parameters
    Used to convert hazard intensity into component failure probabilities; must be calibrated to historical or engineering data for each of the nine hazards.
  • economic output loss multipliers
    Translate physical damage and affected population into daily macroeconomic output losses; calibrated to input-output tables or similar.
axioms (2)
  • domain assumption National hazard datasets accurately represent exposure and intensity distributions across the contiguous US.
    Framework relies on these datasets for all probability calculations without stated validation steps in the abstract.
  • domain assumption Fragility and propagation models remain unbiased when applied uniformly across dissimilar hazards.
    Central to the claim that results are directly comparable.

pith-pipeline@v0.9.0 · 5865 in / 1554 out tokens · 28075 ms · 2026-05-25T04:57:18.906692+00:00 · methodology

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Reference graph

Works this paper leans on

79 extracted references · 62 canonical work pages

  1. [1]

    Modeling and

    Afzal, Suhail and Mokhlis, Hazlie and Mansor, Nurulafiqah Nadzirah and Illias, Hazlee Azil and Jamian, Jasrul Jamani and Sarmin, Mohd Khairun Nizam Mohd , address =. Modeling and. 2024. 2024 , isbn =. doi:10.1109/ICPEA60617.2024.10498988 , url =

    work page doi:10.1109/icpea60617.2024.10498988 2024

  2. [2]

    2026 , issn =

    Seismic fragility estimation of electrical substations accounting for component damage and short circuit faults , volume =. 2026 , issn =. doi:10.1016/j.ress.2025.111671 , journal =

    work page doi:10.1016/j.ress.2025.111671 2026

  3. [3]

    and Wei, Yu , shorttitle =

    Arab, Ali and Khodaei, Amin and Eskandarpour, Rozhin and Thompson, Matthew P. and Wei, Yu , shorttitle =. Three. 2021 , issn =. doi:10.1109/ACCESS.2021.3074477 , journal =

    work page doi:10.1109/access.2021.3074477 2021

  4. [4]

    2025 , issn =

    A probabilistic modeling and simulation framework for power grid flood risk assessment , volume =. 2025 , issn =. doi:10.1016/j.ijdrr.2025.105353 , journal =

    work page doi:10.1016/j.ijdrr.2025.105353 2025

  5. [5]

    2025 , issn =

    A comprehensive review on resilience definitions, frameworks, metrics, and enhancement strategies in electrical distribution systems , volume =. 2025 , issn =. doi:10.1016/j.apenergy.2025.126141 , journal =

    work page doi:10.1016/j.apenergy.2025.126141 2025

  6. [6]

    Bian, Haifeng and Zhang, Jun and Xie, Guanglong and Zhang, Chen , address =. Risk. 2024 9th. 2024 , isbn =. doi:10.1109/ACPEE60788.2024.10532389 , url =

    work page doi:10.1109/acpee60788.2024.10532389 2024

  7. [7]

    Science Advances , publisher =

    A globally consistent local-scale assessment of future tropical cyclone risk , volume =. Science Advances , publisher =. 2022 , doi =

    2022

  8. [8]

    2019 , issn =

    Risk-. 2019 , issn =. doi:10.1061/(ASCE)AS.1943-5525.0001029 , journal =

    work page doi:10.1061/(asce)as.1943-5525.0001029 2019

  9. [9]

    Census.gov , author =

    2020. Census.gov , author =. 2020 , url =

    2020

  10. [10]

    Census.gov , author =

    Statistics of. Census.gov , author =. 2023 , url =

    2023

  11. [11]

    2020 , issn =

    First. 2020 , issn =. doi:10.1029/2019SW002260 , journal =

    work page doi:10.1029/2019sw002260 2020

  12. [12]

    Environmental Research Letters , volume=

    Quantifying the compound hazard of freezing rain and wind gusts across CONUS , author=. Environmental Research Letters , volume=. 2024 , publisher=

    2024

  13. [13]

    and Barthelmie, Rebecca J

    Coburn, Jacob and Pryor, Sara C. and Barthelmie, Rebecca J. , title =. doi:10.5281/zenodo.10080809 , url =

    work page doi:10.5281/zenodo.10080809

  14. [14]

    and Oughton, Edward J

    Bor, Dennies K. and Oughton, Edward J. and Weigel, Robert S. and Gaunt, C. Trevor , year =. C-. doi:10.5281/zenodo.20089397 , url =

    work page doi:10.5281/zenodo.20089397

  15. [15]

    2023 , doi =

    Wildfire. 2023 , doi =

    2023

  16. [16]

    2016 , issn =

    Multi-phase assessment and adaptation of power systems resilience to natural hazards , volume =. 2016 , issn =. doi:10.1016/j.epsr.2016.03.019 , journal =

    work page doi:10.1016/j.epsr.2016.03.019 2016

  17. [17]

    2022 , month = jul, url =

    Hazus Earthquake Model Technical Manual, Hazus 5.1 , institution =. 2022 , month = jul, url =

    2022

  18. [18]

    2025 , issn =

    Probabilistic analysis of wind-induced failures of transmission tower-line systems , volume =. 2025 , issn =. doi:10.1080/15732479.2025.2554724 , journal =

    work page doi:10.1080/15732479.2025.2554724 2025

  19. [19]

    Modeling and

    Ghorani, Rahim and Fattaheian-Dehkordi, Sajjad and Farrokhi, Mahdi and Fotuhi-Firuzabad, Mahmud and Lehtonen, Matti , shorttitle =. Modeling and. 2021 , issn =. doi:10.1109/ACCESS.2021.3084368 , journal =

    work page doi:10.1109/access.2021.3084368 2021

  20. [20]

    2024 , doi =

    Slope-. 2024 , doi =

    2024

  21. [21]

    2020 , issn =

    Earthquake failure mode and collapse fragility of a 1000. 2020 , issn =. doi:10.1016/j.istruc.2020.06.018 , journal =

    work page doi:10.1016/j.istruc.2020.06.018 2020

  22. [22]

    2025 , issn =

    Multi-hazard failure analysis and performance evaluation of transmission towers utilizing data-driven probabilistic models subject to combined wind and earthquake , volume =. 2025 , issn =. doi:10.1016/j.istruc.2025.109196 , journal =

    work page doi:10.1016/j.istruc.2025.109196 2025

  23. [23]

    2023 , url =

    Transmission. 2023 , url =

    2023

  24. [24]

    2022 , issn =

    Review of failure risk and outage prediction in power system under wind hazards , volume =. 2022 , issn =. doi:10.1016/j.epsr.2022.108098 , journal =

    work page doi:10.1016/j.epsr.2022.108098 2022

  25. [25]

    Resilience-

    Huang, Liping and Cun, Xin and Wang, Yifei and Lai, Chun Sing and Lai, Loi Lei and Tang, Junxi and Zhong, Bang , series =. Resilience-. 2018 , issn =. doi:10.1016/j.ifacol.2018.11.744 , journal =

    work page doi:10.1016/j.ifacol.2018.11.744 2018

  26. [26]

    1998 , issn =

    Seismic fragility analysis of electric substation equipment and structures , volume =. 1998 , issn =. doi:10.1016/S0266-8920(97)00017-9 , journal =

    work page doi:10.1016/s0266-8920(97)00017-9 1998

  27. [27]

    and Sharma, Arjun and Skolfield, J

    Jackson, Nicole D. and Sharma, Arjun and Skolfield, J. Kyle , address =. Spatio-. 2025. 2025 , isbn =. doi:10.1109/RWS66711.2025.11304397 , url =

    work page doi:10.1109/rws66711.2025.11304397 2025

  28. [28]

    and Shafieezadeh, Abdollah and Hur, Jieun and Ha, Jeong‐Gon and Hahm, Daegi and Kim, Min‐Kyu , shorttitle =

    Jeddi, Ashkan B. and Shafieezadeh, Abdollah and Hur, Jieun and Ha, Jeong‐Gon and Hahm, Daegi and Kim, Min‐Kyu , shorttitle =. Multi‐hazard typhoon and earthquake collapse fragility models for transmission towers:. 2022 , issn =. doi:10.1002/eqe.3735 , journal =

    work page doi:10.1002/eqe.3735 2022

  29. [29]

    and Weimar, Mark R

    Kabre, Wilfried W. and Weimar, Mark R. , shorttitle =. Fragility. 2022 , doi =

    2022

  30. [30]

    2025 , issn =

    Fragility. 2025 , issn =. doi:10.1002/wcc.930 , journal =

    work page doi:10.1002/wcc.930 2025

  31. [31]

    EUR-Scientific and Technical Research Reports

    Climate Change and Critical Infrastructure–floods , year =. EUR-Scientific and Technical Research Reports. Luxembourg: Publications Office of the European Union , author =

  32. [32]

    and Schultz, Adam , year =

    Kelbert, Anna and Egbert, Gary D. and Schultz, Adam , year =. doi:10.17611/DP/EMTF.1 , url =

    work page doi:10.17611/dp/emtf.1

  33. [33]

    2022 , issn =

    Lifetime multi-hazard fragility analysis of transmission towers under earthquake and wind considering wind-induced fatigue effect , volume =. 2022 , issn =. doi:10.1016/j.strusafe.2022.102266 , journal =

    work page doi:10.1016/j.strusafe.2022.102266 2022

  34. [34]

    2026 , issn =

    A multi-model probabilistic framework for seismic risk assessment and retrofit planning of electric power networks , volume =. 2026 , issn =. doi:10.1016/j.ress.2025.112001 , journal =

    work page doi:10.1016/j.ress.2025.112001 2026

  35. [35]

    2023 , issn =

    Seismic resilience assessment and improvement framework for electrical substations , volume =. 2023 , issn =. doi:10.1002/eqe.3800 , journal =

    work page doi:10.1002/eqe.3800 2023

  36. [36]

    2020 , issn =

    Risk assessment framework of geomagnetic storm disaster in long-distance. 2020 , issn =. doi:10.1088/1755-1315/510/2/022015 , journal =

    work page doi:10.1088/1755-1315/510/2/022015 2020

  37. [37]

    2021 , issn =

    Quantifying the seismic risk for electric power distribution systems , volume =. 2021 , issn =. doi:10.1080/15732479.2020.1734030 , journal =

    work page doi:10.1080/15732479.2020.1734030 2021

  38. [38]

    2020 , issn =

    A 100-year. 2020 , issn =. doi:10.1029/2019SW002329 , journal =

    work page doi:10.1029/2019sw002329 2020

  39. [39]

    2021 , issn =

    Component-based fragility analysis of transmission towers subjected to hurricane wind load , volume =. 2021 , issn =. doi:10.1016/j.engstruct.2021.112586 , journal =

    work page doi:10.1016/j.engstruct.2021.112586 2021

  40. [40]

    Mac Manus, D. H. and Rodger, C. J. and Renton, A. and Lo, V. and Malone-Leigh, J. and Petersen, T. and Copland, M. and Hendry, A. T. and Clilverd, M. A. and Richardson, G. S. , shorttitle =. Implementing. 2025 , issn =. doi:10.1029/2025SW004388 , journal =

    work page doi:10.1029/2025sw004388 2025

  41. [41]

    Assessing

    Macheri, Ramya and Yu, Samson and Islam, Shama and Trinh, Hieu , address =. Assessing. 2025. 2025 , isbn =. doi:10.1109/ETFG61999.2025.11401278 , url =

    work page doi:10.1109/etfg61999.2025.11401278 2025

  42. [42]

    2024 , url =

    Transmission. 2024 , url =

    2024

  43. [43]

    and Hagen, Scott C

    Movahednia, Mohadese and Kargarian, Amin and Ozdemir, Celalettin E. and Hagen, Scott C. , shorttitle =. Power. 2022 , issn =. doi:10.1109/TII.2021.3100079 , journal =

    work page doi:10.1109/tii.2021.3100079 2022

  44. [44]

    and Blanchard, Richard E

    Mujjuni, Francis and Betts, Thomas R. and Blanchard, Richard E. , shorttitle =. Evaluation of. 2023 , issn =. doi:10.1109/ACCESS.2023.3304643 , journal =

    work page doi:10.1109/access.2023.3304643 2023

  45. [45]

    2018 , issn =

    A multi-hazard approach to assess severe weather-induced major power outage risks in the. 2018 , issn =. doi:10.1016/j.ress.2018.03.015 , journal =

    work page doi:10.1016/j.ress.2018.03.015 2018

  46. [46]

    2017 , issn =

    Quantifying the daily economic impact of extreme space weather due to failure in electricity transmission infrastructure , volume =. 2017 , issn =. doi:10.1002/2016SW001491 , journal =

    work page doi:10.1002/2016sw001491 2017

  47. [47]

    and Hapgood, Mike and Richardson, Gemma S

    Oughton, Edward J. and Hapgood, Mike and Richardson, Gemma S. and Beggan, Ciarán D. and Thomson, Alan W. P. and Gibbs, Mark and Burnett, Catherine and Gaunt, C. Trevor and Trichas, Markos and Dada, Rabia and Horne, Richard B. , shorttitle =. A. 2019 , issn =. doi:10.1111/risa.13229 , journal =

    work page doi:10.1111/risa.13229 2019

  48. [48]

    2014 , issn =

    Multi-dimensional hurricane resilience assessment of electric power systems , volume =. 2014 , issn =. doi:10.1016/j.strusafe.2014.01.001 , journal =

    work page doi:10.1016/j.strusafe.2014.01.001 2014

  49. [49]

    2017 , issn =

    Metrics and. 2017 , issn =. doi:10.1109/TPWRS.2017.2664141 , journal =

    work page doi:10.1109/tpwrs.2017.2664141 2017

  50. [50]

    Panteli, Mathaios and Pickering, Cassandra and Wilkinson, Sean and Dawson, Richard and Mancarella, Pierluigi , shorttitle =. Power. 2017 , issn =. doi:10.1109/TPWRS.2016.2641463 , journal =

    work page doi:10.1109/tpwrs.2016.2641463 2017

  51. [51]

    doi:10.5194/egusphere-egu2020-19097 , url =

    Towards an Integrated Framework for Distributed, Modular Multi-risk Scenario Assessment , year =. doi:10.5194/egusphere-egu2020-19097 , url =

    work page doi:10.5194/egusphere-egu2020-19097

  52. [52]

    2024 , issn =

    High-resolution, open-source modeling of inland flooding impacts on the. 2024 , issn =. doi:10.1088/2753-3751/ad3558 , journal =

    work page doi:10.1088/2753-3751/ad3558 2024

  53. [53]

    2008 , issn =

    Statistics of extreme geomagnetically induced current events , volume =. 2008 , issn =. doi:10.1029/2008SW000388 , journal =

    work page doi:10.1029/2008sw000388 2008

  54. [54]

    2016 , issn =

    Multi-hazard system-level logit fragility functions , volume =. 2016 , issn =. doi:10.1016/j.engstruct.2016.05.006 , journal =

    work page doi:10.1016/j.engstruct.2016.05.006 2016

  55. [55]

    2013 , institution =

    Aqueduct Water Risk Framework , author =. 2013 , institution =

    2013

  56. [56]

    2024 , issn =

    Impact of. 2024 , issn =. doi:10.3390/atmos15111349 , journal =

    work page doi:10.3390/atmos15111349 2024

  57. [57]

    Rethinking

    Rokhideh, Maryam , shorttitle =. Rethinking. Oxford. 2025 , isbn =. doi:10.1093/acrefore/9780199389407.013.551 , url =

    work page doi:10.1093/acrefore/9780199389407.013.551 2025

  58. [58]

    Journal of Structural Engineering , publisher =

    Multihazard. Journal of Structural Engineering , publisher =. 2017 , doi =

    2017

  59. [59]

    2018 , issn =

    A probabilistic framework for multi-hazard risk mitigation for electric power transmission systems subjected to seismic and hurricane hazards , volume =. 2018 , issn =. doi:10.1080/15732479.2018.1459741 , journal =

    work page doi:10.1080/15732479.2018.1459741 2018

  60. [60]

    2020 , issn =

    Electrical. 2020 , issn =. doi:10.3390/su12041527 , journal =

    work page doi:10.3390/su12041527 2020

  61. [61]

    doi:10.1016/j.jclepro.2021.128793 , journal =

    2021 , issn =. doi:10.1016/j.jclepro.2021.128793 , journal =

    work page doi:10.1016/j.jclepro.2021.128793 2021

  62. [62]

    2023 , issn =

    A. 2023 , issn =. doi:10.1109/ACCESS.2023.3320579 , journal =

    work page doi:10.1109/access.2023.3320579 2023

  63. [63]

    doi:10.48438/JCHS.2023.0022 , journal =

    Vulnerability of power distribution utility poles to tsunami bore impacts , year =. doi:10.48438/JCHS.2023.0022 , journal =

    work page doi:10.48438/jchs.2023.0022 2023

  64. [64]

    and Jeddi, Ashkan B

    Darestani, Yousef M. and Jeddi, Ashkan B. and Shafieezadeh, Abdollah , editor =. Hurricane. Engineering for. 2022 , isbn =. doi:10.1007/978-3-030-85018-0_8 , url =

    work page doi:10.1007/978-3-030-85018-0_8 2022

  65. [65]

    2014 , issn =

    Assessing the hazard from geomagnetically induced currents to the entire high-voltage power network in. 2014 , issn =. doi:10.1186/1880-5981-66-87 , journal =

    work page doi:10.1186/1880-5981-66-87 2014

  66. [66]

    2022 , issn =

    Data‐driven spatio‐temporal analysis of wildfire risk to power systems operation , volume =. 2022 , issn =. doi:10.1049/gtd2.12463 , journal =

    work page doi:10.1049/gtd2.12463 2022

  67. [67]

    2020 , url =

    National. 2020 , url =

    2020

  68. [68]

    2025 , issn =

    Wildfire and power grid nexus in a changing climate , volume =. 2025 , issn =. doi:10.1038/s44287-025-00150-0 , journal =

    work page doi:10.1038/s44287-025-00150-0 2025

  69. [69]

    2023 , issn =

    Predicting wildfire ignition induced by dynamic conductor swaying under strong winds , volume =. 2023 , issn =. doi:10.1038/s41598-023-30802-w , journal =

    work page doi:10.1038/s41598-023-30802-w 2023

  70. [70]

    2020 , issn =

    Modeling. 2020 , issn =. doi:10.1109/TPWRS.2019.2942279 , journal =

    work page doi:10.1109/tpwrs.2019.2942279 2020

  71. [71]

    2026 , issn =

    Proposal of. 2026 , issn =. doi:10.1109/ACCESS.2026.3670018 , journal =

    work page doi:10.1109/access.2026.3670018 2026

  72. [72]

    and Forbes, Kevin F

    Worman, Stacey and Taylor, Susan and Onsager, Terrance and Adkins, Jeffery and Baker, Daniel N. and Forbes, Kevin F. , editor =. Chapter 29 -. Extreme. 2018 , isbn =. doi:10.1016/B978-0-12-812700-1.00029-7 , url =

    work page doi:10.1016/b978-0-12-812700-1.00029-7 2018

  73. [73]

    2020 , issn =

    Impact of transmission tower-line interaction to the bulk power system during hurricane , volume =. 2020 , issn =. doi:10.1016/j.ress.2020.107079 , journal =

    work page doi:10.1016/j.ress.2020.107079 2020

  74. [74]

    2022 , issn =

    Predicting electricity infrastructure induced wildfire risk in. 2022 , issn =. doi:10.1088/1748-9326/ac8d18 , journal =

    work page doi:10.1088/1748-9326/ac8d18 2022

  75. [75]

    , shorttitle =

    Zurbuchen, Thomas H. , shorttitle =. How likely is a space weather–induced. 2012 , issn =. doi:10.1029/2012SW000844 , journal =

    work page doi:10.1029/2012sw000844 2012

  76. [76]

    and Hariri-Ardebili, M

    Saouma, Victor E. and Hariri-Ardebili, M. Amin , title =. Aging, Shaking, and Cracking of Infrastructures: From Mechanics to Concrete Dams and Nuclear Structures , editor =. 2021 , pages =

    2021

  77. [77]

    Buildings , year =

    Kim, Seokjung and Kim, Jongkwan and Song, Homin and Yoo, Mintaek , title =. Buildings , year =

  78. [78]

    and Blair, Peter D

    Miller, Ronald E. and Blair, Peter D. , address =. Input-. 2009 , doi =

    2009

  79. [79]

    and Oughton, Edward J

    Bor, Dennies K. and Oughton, Edward J. and Weigel, Robert , title =. 2026 , publisher =. doi:10.5281/zenodo.20331026 , url =

    work page doi:10.5281/zenodo.20331026 2026