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Modelling the effectiveness of vegetative nature-based solutions for coastal flood risk mitigation

Abstract:
Traditional grey solutions, such as seawalls, are increasingly recognised as being unsustainable for long-term coastal flood risk management, due to high costs and negative environmental impacts. In response, vegetative nature-based coastal solutions (NBCS), such as saltmarshes, are being increasingly proposed as a more sustainable alternative with wider environmental benefits. However, there is considerable uncertainty on the longevity of such solutions under sea-level rise. We, therefore, examine the effectiveness of vegetative NBCS for mitigating coastal flood risk through scenario modelling using a verified $ LISFLOOD\hbox{-} FP $ model for Absecon Island in New Jersey, USA. Specifically, we simulate various experimental vegetative NBCS scenarios, each designed to represent a saltmarsh system (young, mid-age, and old), under alternative sea-level conditions. Our results show that these solutions have a marginal influence on flood extent, depth, velocity, and timing under current and future projected sea-level conditions. These findings suggest that reliance on vegetative NBCS may not be sustainable for long-term coastal flood risk management, particularly under climate change. We discuss the wider implications of these findings and identify future research pathways towards improving and informing more robust coastal flood risk management decisions.
Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.1111/jfr3.70046

Authors

More by this author
Institution:
University of Oxford
Division:
SSD
Department:
SOGE
Sub department:
Environmental Change Institute
Role:
Author
More by this author
Institution:
University of Oxford
Division:
SSD
Department:
SOGE
Sub department:
Environmental Change Institute
Role:
Author


Publisher:
Wiley
Journal:
Journal of Flood Risk Management More from this journal
Volume:
18
Issue:
2
Article number:
e70046
Publication date:
2025-04-23
Acceptance date:
2025-01-27
DOI:
EISSN:
1753-318X


Language:
English
Keywords:
Pubs id:
2119639
Local pid:
pubs:2119639
Deposit date:
2025-04-24
ARK identifier:

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