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Climate change-driven coastal erosion modelling in temperate sandy beaches: Methods and uncertainty treatment

Climate change-driven coastal erosion modelling in temperate sandy beaches: Methods and uncertainty treatment
Climate change-driven coastal erosion modelling in temperate sandy beaches: Methods and uncertainty treatment
Developing future projections of shoreline change requires a good understanding of the driving coastal processes. These processes result primarily from the combination of mean sea level, waves, storm surges and tides, which are affected by global and regional climate change, and whose uncertainty increases with time. This paper reviews the current state of the art of methods used to model climate change-induced coastal erosion focusing on how climate change-related drivers and the associated uncertainty are considered. We identify research gaps, describe and analyse the key components of a comprehensive framework to derive future estimates of shoreline change and make suggestions for good practice. Within the scope of the review, we find that although significant progress has been made over the last decade, most of the studies limit uncertainty sampling to considering ranges of variation of forcing variables and ensembles of emissions scenarios, and applications with high level of probabilistic development remain few. Further research is necessary to fully (a) incorporate projected time series of coastal drivers into the erosion models, including bias correction; (b) sufficiently sample the uncertainty associated with each step of the top-down approach, including the consideration of different emission scenarios, inter- and intra-model variability, and multiple runs of erosion models or model ensembles; and (c) reduce uncertainty in shoreline change estimates by developing better datasets and model parameterisations, and progressing in detection and attribution.
0012-8252
Toimil, A.
632f908a-bf1f-49f5-a073-95347b19d8f6
Camus, P.
66625386-9051-4ea8-a0fa-956751534796
Losada, I.j.
2ea31ffe-966e-40f9-b742-aefae8363ad3
Le Cozannet, G.
ea4b270c-e656-47e8-9016-5696f790e261
Nicholls, R.j.
903cd62d-0d6d-4150-8e73-35dfe20f1aaf
Idier, D.
a4f4ba0f-1973-47dc-a8b1-a73fe2a1703a
Maspataud, A.
dd15a242-7851-4214-b468-511c038b20bd
Toimil, A.
632f908a-bf1f-49f5-a073-95347b19d8f6
Camus, P.
66625386-9051-4ea8-a0fa-956751534796
Losada, I.j.
2ea31ffe-966e-40f9-b742-aefae8363ad3
Le Cozannet, G.
ea4b270c-e656-47e8-9016-5696f790e261
Nicholls, R.j.
903cd62d-0d6d-4150-8e73-35dfe20f1aaf
Idier, D.
a4f4ba0f-1973-47dc-a8b1-a73fe2a1703a
Maspataud, A.
dd15a242-7851-4214-b468-511c038b20bd

Toimil, A., Camus, P., Losada, I.j., Le Cozannet, G., Nicholls, R.j., Idier, D. and Maspataud, A. (2020) Climate change-driven coastal erosion modelling in temperate sandy beaches: Methods and uncertainty treatment. Earth-Science Reviews, 202, [103110]. (doi:10.1016/j.earscirev.2020.103110).

Record type: Article

Abstract

Developing future projections of shoreline change requires a good understanding of the driving coastal processes. These processes result primarily from the combination of mean sea level, waves, storm surges and tides, which are affected by global and regional climate change, and whose uncertainty increases with time. This paper reviews the current state of the art of methods used to model climate change-induced coastal erosion focusing on how climate change-related drivers and the associated uncertainty are considered. We identify research gaps, describe and analyse the key components of a comprehensive framework to derive future estimates of shoreline change and make suggestions for good practice. Within the scope of the review, we find that although significant progress has been made over the last decade, most of the studies limit uncertainty sampling to considering ranges of variation of forcing variables and ensembles of emissions scenarios, and applications with high level of probabilistic development remain few. Further research is necessary to fully (a) incorporate projected time series of coastal drivers into the erosion models, including bias correction; (b) sufficiently sample the uncertainty associated with each step of the top-down approach, including the consideration of different emission scenarios, inter- and intra-model variability, and multiple runs of erosion models or model ensembles; and (c) reduce uncertainty in shoreline change estimates by developing better datasets and model parameterisations, and progressing in detection and attribution.

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More information

Accepted/In Press date: 29 January 2020
e-pub ahead of print date: 30 January 2020
Published date: 1 March 2020

Identifiers

Local EPrints ID: 447778
URI: http://eprints.soton.ac.uk/id/eprint/447778
ISSN: 0012-8252
PURE UUID: 59e33836-7b38-4062-87c3-7601954666bb

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Date deposited: 19 Mar 2021 17:37
Last modified: 25 Nov 2021 18:42

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Contributors

Author: A. Toimil
Author: P. Camus
Author: I.j. Losada
Author: G. Le Cozannet
Author: R.j. Nicholls
Author: D. Idier
Author: A. Maspataud

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