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Coastal dunes are globally recognized as natural features that can enhance coastal resilience and protection from wave events, storm surges, coastal flooding, and longer- term sea level rise. As a result, dune restoration is being increasingly used along urban and natural coasts as an adaptation option for climate change. However, information on the performance of restored dunes in response to extreme events is limited. On urban beaches where management includes grooming, dunes are often degraded or absent, leaving coastal communities more vulnerable to flooding and erosion during storms and wave events. Following an extreme wave surge event in December 2023, we compared the performance of a small (1.2 hectare) pilot dune restoration on an intensively groomed urban beach in southern California to an adjacent mechanically groomed control site. We used total water level (wave setup, tide, wave runup) as a proxy for flooding potential. The average wave runup incursion distance was extended 13.6 m farther inland on the groomed control site compared to the dune restoration site. This result demonstrates the potential for restored dunes to enhance flood protection and the potential for increasing coastal resilience using nature-based solutions on urban beaches.
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Stochastic simulation of storm surge extremes along the contiguous United States coastlines using the max-stable process
Abstract Extreme sea levels impact coastal society, property, and the environment. Various mitigation measures are engineered to reduce these impacts, which require extreme event probabilities typically estimated site-by-site. The site-by-site estimates usually have high uncertainty, are conditionally independent, and do not provide estimates for ungauged locations. In contrast, the max-stable process explicitly incorporates the spatial dependence structure and produces more realistic event probabilities and spatial surfaces. We leverage the max-stable process to compute extreme event probabilities at gridded locations (gauged and ungauged) and derive their spatial surfaces along the contiguous United States coastlines by pooling annual maximum (AM) surges from selected long-record tide gauges. We also generate synthetic AM surges at the grid locations using the predicted distribution parameters and reordering them in the rank space to integrate the spatiotemporal variability. The results will support coastal planners, engineers, and stakeholders to make the most precise and confident decisions for coastal flood risk reduction.
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- PAR ID:
- 10554231
- Publisher / Repository:
- Nature Publishing Group
- Date Published:
- Journal Name:
- Communications Earth & Environment
- Volume:
- 5
- Issue:
- 1
- ISSN:
- 2662-4435
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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