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Single extreme storm sequence can offset decades of shoreline retreat projected to result from sea-level rise

Earth Sciences

Single extreme storm sequence can offset decades of shoreline retreat projected to result from sea-level rise

M. D. Harley, G. Masselink, et al.

This groundbreaking research challenges the notion that extreme storms solely harm coastal areas. Conducted by Mitchell D. Harley and colleagues, it uncovers how these storms can actively contribute to the nearshore sediment budget, potentially offsetting decades of shoreline retreat caused by sea-level rise. Discover the surprising impact of sediment movement in our changing climate!

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~3 min • Beginner • English
Abstract
Extreme storms cause extensive beach-dune erosion and are typically considered to enhance coastal erosion due to sea-level rise. However, extreme storms can also have a positive contribution to the nearshore sediment budget by exchanging sediment between the lower and upper shoreface and/or between adjacent headlands, potentially mitigating some adverse sea-level rise impacts. Here we use three high-resolution morphological datasets of extreme storm-recovery sequences from Australia, the UK and Mexico to quantify the nearshore sediment budget and relate these episodic volume changes to long-term coastal projections. We show that sediment gains over the upper shoreface were large (59–140 m³/m) and sufficient to theoretically offset decades of projected shoreline retreat due to sea-level rise, even for a high-end greenhouse gas emissions scenario (SSP5–8.5). We conclude that increased confidence in shoreline projections relies fundamentally on a robust quantitative understanding of the sediment budget, including any major short-term sediment contribution by extreme storms.
Publisher
Communications Earth & Environment
Published On
May 12, 2022
Authors
Mitchell D. Harley, Gerd Masselink, Amaia Ruiz de Alegría-Arzaburu, Nieves G. Valiente, Tim Scott
Tags
coastal erosion
sediment budget
sea-level rise
extreme storms
shoreline projections
morphological datasets
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