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PRINTED FROM the OXFORD RESEARCH ENCYCLOPEDIA, NATURAL HAZARD SCIENCE (oxfordre.com/naturalhazardscience). (c) Oxford University Press USA, 2019. All Rights Reserved. Personal use only; commercial use is strictly prohibited (for details see Privacy Policy and Legal Notice).

date: 25 June 2019

Summary and Keywords

Natural disasters can have significant widespread impacts on society, and they often lead to loss of electric power for a large number of customers in the most heavily impacted areas. In the United States, severe weather and climate events have been the leading cause of major outages (i.e., more than 50,000 customers affected), leading to significant socioeconomic losses. Natural disaster impacts can be modeled and probabilistically predicted prior to the occurrence of the extreme event, although the accuracy of the predictive models will vary across different types of disasters. These predictions can help utilities plan for and respond to extreme weather and climate events, helping them better balance the costs of disaster responses with the need to restore power quickly. This, in turn, helps society recover from natural disasters such as storms, hurricanes, and earthquakes more efficiently. Modern Bayesian methods may provide an avenue to further improve the prediction of extreme event impacts by allowing first-principles structural reliability models to be integrated with field-observed failure data.

Climate change and climate nonstationarity pose challenges for natural hazards risk assessment, especially for hydrometeorological hazards such as tropical cyclones and floods, although the link between these types of hazards and climate change remains highly uncertain and the topic of many research efforts. A sensitivity-based approach can be taken to understand the potential impacts of climate change-induced alterations in natural hazards such as hurricanes. This approach gives an estimate of the impacts of different potential changes in hazard characteristics, such as hurricane frequency, intensity, and landfall location, on the power system, should they occur. Further research is needed to better understand and probabilistically characterize the relationship between climate change and hurricane intensity, frequency, and landfall location, and to extend the framework to other types of hydroclimatological events.

Underlying the reliability of power systems in the United States is a diverse set of regulations, policies, and rules governing electric power system reliability. An overview of these regulations and the challenges associated with current U.S. regulatory structure is provided. Specifically, high-impact, low-frequency events such as hurricanes are handled differently in the regulatory structure; there is a lack of consistency between bulk power and the distribution system in terms of how their reliability is regulated. Moreover, the definition of reliability used by the North American Reliability Corporation (NERC) is at odds with generally accepted definitions of reliability in the broader reliability engineering community. Improvements in the regulatory structure may have substantial benefit to power system customers, though changes are difficult to realize.

Overall, broader implications are raised for modeling other types of natural hazards. Some of the key takeaway messages are the following: (1) the impacts natural hazard on infrastructure can be modeled with reasonable accuracy given sufficient data and modern risk analysis methods; (2) there are substantial data on the impacts of some types of natural hazards on infrastructure; and (3) appropriate regulatory frameworks are needed to help translate modeling advances and insights into decreased impacts of natural hazards on infrastructure systems.

Keywords: natural hazards, climate change, resilience, power outage, infrastructure, data-driven models, Bayesian updating, predictive risk analytics, statistical learning

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