The DOI matches its Crossref record; 33 of the 33 references it cites were found in a registry or an index.
Continental U.S. Hurricane Landfall Frequency and Associated Damage: Observations and Future Risks
Abstract
Continental United States (CONUS) hurricane-related inflation-adjusted damage has increased significantly since 1900. However, since 1900 neither observed CONUS landfalling hurricane frequency nor intensity shows significant trends, including the devastating 2017 season. Two large-scale climate modes that have been noted in prior research to significantly impact CONUS landfalling hurricane activity are El Niño–Southern Oscillation on interannual time scales and the Atlantic multidecadal oscillation on multidecadal time scales. La Niña seasons tend to be characterized by more CONUS hurricane landfalls than El Niño seasons, and positive Atlantic multidecadal oscillation phases tend to have more CONUS hurricane landfalls than negative phases. Growth in coastal population and regional wealth are the overwhelming drivers of observed increases in hurricane-related damage. As the population and wealth of the United States has increased in coastal locations, it has invariably led to the growth in exposure and vulnerability of coastal property along the U.S. Gulf and East Coasts. Unfortunately, the risks associated with more people and vulnerable exposure came to fruition in Texas and Florida during the 2017 season following the landfalls of Hurricanes Harvey and Irma. Total economic damage from those two storms exceeded $125 billion. Growth in coastal population and exposure is likely to continue in the future, and when hurricane landfalls do occur, this will likely lead to greater damage costs than previously seen. Such a statement is made recognizing that the vast scope of damage from hurricanes often highlights the effectiveness (or lack thereof) of building codes, flood maps, infrastructure, and insurance in at-risk communities.
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Citations
Cites
Tropical cyclones and climate change
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Self-citationAtlantic Hurricane Database Uncertainty and Presentation of a New Database Format
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Cited by
Knutson, T., Camargo, S. J., Chan, J. C. L., Emanuel, K., Ho, C.-H., Kossin, J., Mohapatra, M., Satoh, M., Sugi, M., Walsh, K., et al. (2020) · Bulletin of the American Meteorological Society · doi:10.1175/bams-d-18-0194.1 · Cited by 1,356
A review of Earth Artificial Intelligence
Sun, Z., Sandoval, L., Crystal-Ornelas, R., Mousavi, S. M., Wang, J., Lin, C., Cristea, N., Tong, D., Carande, W. H., Ma, X., et al. (2022) · Computers & Geosciences · doi:10.1016/j.cageo.2022.105034 · Cited by 271
Lomborg, B. (2020) · Technological Forecasting and Social Change · doi:10.1016/j.techfore.2020.119981 · Cited by 134
Ahmadalipour, A., & Moradkhani, H. (2019) · Journal of Hydrology · doi:10.1016/j.jhydrol.2019.124106 · Cited by 133
The impact of climate model sea surface temperature biases on tropical cyclone simulations.
Hsu, W.-C., Patricola, C. M., & Chang, P. (2019) · Climate Dynamics · doi:10.1007/s00382-018-4577-5 · Cited by 51
Metadata
- Authors
- Klotzbach, P. J., Bowen, S. G., Pielke, R., & Bell, M.
- Venue
- Bulletin of the American Meteorological Society
- Publisher
- American Meteorological Society
- Published
- 2018-07
- Volume
- 99
- Issue
- 7
- Pages
- 1359-1376
- ISSN
- 0003-0007 (ISSN-L)
- 1520-0477
- OpenAlex
- W2786932459
- Cited by
- 267
- References
- 33
- FWCI
- 13.31
- Type
- journal article
- Language
- en
- Open access
- closed
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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