Evidence map›Paper›PMID 41675919›Full record

Articlenpj natural hazards2024

Stochastic coastal flood risk modelling for the east coast of Africa.

Irene Benito, Jeroen C J H Aerts, Dirk Eilander, Philip J Ward, Sanne Muis

Abstract read
In one paragraph

Article in npj natural hazards, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
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.

Irene BenitoInstitute for Environmental Studies (IVM), VU University Amsterdam, Amsterdam, The Netherlands.
Jeroen C J H AertsInstitute for Environmental Studies (IVM), VU University Amsterdam, Amsterdam, The Netherlands.
Dirk EilanderInstitute for Environmental Studies (IVM), VU University Amsterdam, Amsterdam, The Netherlands.
Philip J WardInstitute for Environmental Studies (IVM), VU University Amsterdam, Amsterdam, The Netherlands.
Sanne MuisInstitute for Environmental Studies (IVM), VU University Amsterdam, Amsterdam, The Netherlands.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Coastal flooding resulting from tropical cyclones can have large repercussions in many low-lying regions around the world. Accurate flood risk assessments are crucial for designing measures to reduce the societal impacts of coastal flooding. At continental to global scales, however, traditional flood risk assessments mostly use methods that do not capture the spatiotemporal dynamics of coastal flood risk patterns. In this study, we address these deficiencies by applying a novel modelling framework that dynamically simulates stochastic coastal flood risk for the east coast of Africa. Using 10,000 years of synthetic tropical cyclones and a cascade of hydrodynamic models to simulate storm tides and flooding, we calculate the damage of each individual tropical cyclone event and empirically derive the risk curve for each country. Results show that the largest aggregated annual losses in the region come from multiple events rather than from a single low-probability event. Results also reveal that events with the highest return periods in terms of storm surge residual levels and flood extents are not necessarily the most damaging events. Here, the 1 in 10,000-year damage event is associated with a 1 in 45-year event in terms of flood extent, showing that addressing exposure and vulnerability is essential in determining risk. Our modelling framework enables a high-resolution continental-scale risk analysis that takes the spatial dependencies of flood events into account.

Indexed as

Climate sciencesNatural hazards

Identifiers

PMID41675919
PMCPMC12885944

What OpenQuestion holds

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Registered trials

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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.