US-CoastEX: Observation-based probabilistic reanalysis of storm surge and sea level extremes for the United States

Reliable estimates of storm surge and sea level extremes with proper uncertainty quantification are key for cost-effective risk/adaptation planning. However, observational estimates are often unavailable or uncertain along most coastlines owing to data scarcity. Here, we provide a fully observational-driven probabilistic dataset (US-CoastEX) of storm surge and sea level extremes for the U.S. coast (1950-2020). Non-stationary extreme storm surge distributions are generated for gauged and ungauged sites by applying Bayesian methods to the U.S. tide gauge network, complemented with additional storm data unavailable in commonly used tide gauge data. The distributions are combined with tidal peak data to estimate return periods and levels of extreme sea levels and their uncertainty. Ou results show that traditional site-by-site estimates based on existing model data, as well as regionally-aggregated analysis of standard tide gauge data, have underestimated 100-year extreme sea levels by 50% (on average) along much of the U.S. coast, especially in regions exposed to extreme storms. The data supports coastal managers to make decisions, especially in vulnerable areas where in-situ sea-level monitoring is limited.

Authors

Institutions

Publication Details

Journal
Scientific Data
Published
2025-08-09
DOI
https://doi.org/10.1038/s41597-025-05730-1
Citations
3
Primary Topic
Tropical and Extratropical Cyclones Research
Type
article
Field-Weighted Citation Impact
3.90

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

US-CoastEX: Observation-based probabilistic reanalysis of storm surge and sea level extremes for the United States

Thomas Wahl, Francisco M. Calafat, Joao Morim, Sönke Dangendorf et al.
3 citations
Scientific Data
Tropical and Extratropical Cyclones Research
3.90
article

US-CoastEX: Observation-based probabilistic reanalysis of storm surge and sea level extremes for the United States

Thomas Wahl, Francisco M. Calafat, Joao Morim, Sönke Dangendorf, D. J. Rasmussen, Robert E. Kopp, Michael Oppenheimer
article en
3 citations

Abstract

Reliable estimates of storm surge and sea level extremes with proper uncertainty quantification are key for cost-effective risk/adaptation planning. However, observational estimates are often unavailable or uncertain along most coastlines owing to data scarcity. Here, we provide a fully observational-driven probabilistic dataset (US-CoastEX) of storm surge and sea level extremes for the U.S. coast (1950-2020). Non-stationary extreme storm surge distributions are generated for gauged and ungauged sites by applying Bayesian methods to the U.S. tide gauge network, complemented with additional storm data unavailable in commonly used tide gauge data. The distributions are combined with tidal peak data to estimate return periods and levels of extreme sea levels and their uncertainty. Ou results show that traditional site-by-site estimates based on existing model data, as well as regionally-aggregated analysis of standard tide gauge data, have underestimated 100-year extreme sea levels by 50% (on average) along much of the U.S. coast, especially in regions exposed to extreme storms. The data supports coastal managers to make decisions, especially in vulnerable areas where in-situ sea-level monitoring is limited.

Scientific DataVol. 12(1)
Rutgers, The State University of New Jersey (US), University of Central Florida (US), Tulane University (US), National Oceanography Centre (GB), Princeton University (US), Princeton Public Schools (US), Universitat de les Illes Balears (ES)
National Science Foundation, National Aeronautics and Space Administration, National Academies of Sciences, Engineering, and Medicine, European Commission, Nuclear Safety and Security Commission, Gulf Research Program, Agencia Estatal de Investigación
Life below water
Openalex Percentile: Top 6%
Tropical and Extratropical Cyclones Research
3.90
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.