The return level estimation is an essential topic in studying spatial extremes for environmental data. Recently, various models for spatial extremes have emerged, which generally yield different estimates for return levels, given the same data. In the meantime, several approaches that obtain confidence intervals (CIs) for return levels have arisen, and the results from different approaches can also largely disagree. These pose natural questions for assessing different return level estimation methods and different CI derivation approaches. In this article, we compare an array of popular models for spatial extremes in return level estimation, as well as three approaches in CI derivation, through extensive Monte Carlo simulations. Our results show that in general, max‐stable models yield return level estimates with similar mean squared error, and the spatial generalized extreme value model also provides comparable estimates. The bootstrap method is recommended for max‐stable models to compute the CI, and the profile likelihood CI works well for spatial generalized extreme value. We also evaluate the methods for return level interpolation at unknown spatial locations and find that kriging of marginal return level estimates performs as well as max‐stable models.
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.
more » « less- 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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