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

• Published in: Earth-science reviews Vol. 202; p. 103110
• Main Authors: Toimil, A., Camus, P., Losada, I.J., Le Cozannet, G., Nicholls, R.J., Idier, D., Maspataud, A.
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.03.2020; Elsevier
• Subjects: Climate change; Coastal erosion modelling; Earth Sciences; Oceanography; Sandy beaches; Sciences of the Universe; Uncertainty treatment; Climate change; Coastal erosion modelling; Sandy beaches; Uncertainty treatment
• ISSN: 0012-8252; 1872-6828
• DOI: 10.1016/j.earscirev.2020.103110

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.