Evidence map›Paper›PMID 41911339›Full record

ArticlePloS one2026

Filling the gaps between tide gauges: Demonstrating high-resolution seasonal high tide flooding predictions using NOAA's Coastal Ocean Reanalysis.

Matthew P Conlin, Gregory Dusek, John Ratcliff, John A Callahan, Karen E Kavanaugh, William Brooks, Blake Waring, Analise Keeney, William Sweet, Matthew J Widlansky

Abstract read
In one paragraph

Article in PloS one, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

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PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Matthew P ConlinNational Ocean Service, National Oceanic and Atmospheric Administration (NOAA), Silver Spring, Maryland, United States of America.ORCID https://orcid.org/0000-0002-6495-436X
Gregory DusekNational Ocean Service, National Oceanic and Atmospheric Administration (NOAA), Silver Spring, Maryland, United States of America.ORCID https://orcid.org/0000-0003-0289-1356
John RatcliffNational Ocean Service, National Oceanic and Atmospheric Administration (NOAA), Silver Spring, Maryland, United States of America.
John A CallahanNational Ocean Service, National Oceanic and Atmospheric Administration (NOAA), Silver Spring, Maryland, United States of America.ORCID https://orcid.org/0000-0002-6299-9388
Karen E KavanaughNational Ocean Service, National Oceanic and Atmospheric Administration (NOAA), Silver Spring, Maryland, United States of America.ORCID https://orcid.org/0009-0006-6785-6470
William BrooksNational Ocean Service, National Oceanic and Atmospheric Administration (NOAA), Silver Spring, Maryland, United States of America.ORCID https://orcid.org/0000-0002-0859-0581
Blake WaringNational Ocean Service, National Oceanic and Atmospheric Administration (NOAA), Silver Spring, Maryland, United States of America.ORCID https://orcid.org/0009-0007-5139-7510
Analise KeeneyNational Ocean Service, National Oceanic and Atmospheric Administration (NOAA), Silver Spring, Maryland, United States of America.
William SweetNational Ocean Service, National Oceanic and Atmospheric Administration (NOAA), Silver Spring, Maryland, United States of America.
Matthew J WidlanskyCooperative Institute for Marine and Atmospheric Research, School of Ocean and Earth Science and Technology, University of Hawai'i at Mãnoa, Honolulu, Hawai'i, United States of America.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

High Tide Flooding (HTF) is a present and increasing hazard for coastal communities across the United States. NOAA provides HTF outlooks at U.S. tide gauges, however, many coastal communities lie relatively far from a tide gauge and therefore currently lack localized HTF guidance. In this study, we demonstrate an approach to generate spatially-continuous daily predictions of HTF at 400-500 m resolution out to a year into the future, by combining NOAA's monthly HTF outlook framework with the newly-released Coastal Ocean Reanalysis (CORA). Using CORA to derive daily HTF predictions at tide gauges, as compared to using gauge observations, results in average HTF model skill reduction of ≤5% using three different statistical metrics at one month lead time. Further, stations which obtain statistically skillful HTF predictions using gauge data also do so using CORA for 94% of cases. The results suggest that CORA could enable skillful HTF predictions away from tide gauges, supporting the possibility of providing high resolution HTF outlooks for much of the U.S. coastline. The potential value of these spatially continuous HTF predictions is illustrated by identifying communities near Charleston S.C. with different CORA-derived local HTF risk than that provided by the closest tide gauge. Finally, we describe outstanding questions and needs for the scaling of these results to an operational national-scale monthly HTF outlook.

Indexed as

Environmental MonitoringFloodsTidal WavesOceans and SeasSeasonsUnited States

Identifiers

PMID41911339
PMCPMC13035166

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.