Evidence map›Paper›PMID 40881032›Full record

ReviewCambridge prisms. Coastal futures2025

From flood forecasts to rapid assessments of risk and impacts: Establishing operational modelling frameworks for coastal flood management at the German Baltic Sea coast.

Athanasios T Vafeidis, Leigh MacPherson, Sunna Kupfer, Claudia Wolff, Joshua Kiesel

Abstract readReview
In one paragraph

Review in Cambridge prisms. Coastal futures, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
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

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

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

Corrections and comments

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

5 authors.

Athanasios T VafeidisCoastal Risks and Sea-Level Rise Research Group, Institute of Geography, Christian-Albrechts University Kiel, Kiel, Germany.ORCID https://orcid.org/0000-0002-3906-5544
Leigh MacPhersonCoastal Risks and Sea-Level Rise Research Group, Institute of Geography, Christian-Albrechts University Kiel, Kiel, Germany.
Sunna KupferCoastal Risks and Sea-Level Rise Research Group, Institute of Geography, Christian-Albrechts University Kiel, Kiel, Germany.
Claudia WolffCoastal Risks and Sea-Level Rise Research Group, Institute of Geography, Christian-Albrechts University Kiel, Kiel, Germany.
Joshua KieselInstitute for Environmental Studies, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.ORCID https://orcid.org/0009-0005-6832-5738

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The record storm surge of October 2023, which hit the southwestern German Baltic Sea, not only resulted in significant damages to coastal communities and infrastructure but also demonstrated that the region was prepared and able to avoid loss of lives and other catastrophic impacts. Numerical modelling has been a key tool utilised for providing information to support coastal flood management, at different levels of planning, for such events. Based on recent research conducted in the Baltic coast region as well as on empirical evidence acquired during the event, we present an operational scheme that utilises modelling tools and frameworks for supporting coastal flood management in the region. In this context, we distinguish between three successive phases of an extreme surge event and propose specific actions for each of these phases, aiming towards the development of an operational framework for managing events of high magnitude for the German Baltic Sea region and beyond.

Indexed as

coastal adaptationflood managementnumerical modelling

Identifiers

PMID40881032
PMCPMC12337576

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

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.